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<title>Addressing Inequalities in the World of Work</title>
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const clickEl = placeholderDescriptor.dismissOnClick
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elRect = undefined;
positionToggle();
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window.addEventListener("quarto-hrChanged", () => {
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const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
id: "quarto-toc-toggle",
titleSelector: "#toc-title",
dismissOnClick: true,
});
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dismissOnClick: false,
});
let tocLeftScrollVisibility;
if (leftTocEl) {
tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
id: "quarto-lefttoc-toggle",
titleSelector: "#toc-title",
dismissOnClick: true,
});
}
// Find the first element that uses formatting in special columns
const conflictingEls = window.document.body.querySelectorAll(
'[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
);
// Filter all the possibly conflicting elements into ones
// the do conflict on the left or ride side
const arrConflictingEls = Array.from(conflictingEls);
const leftSideConflictEls = arrConflictingEls.filter((el) => {
if (el.tagName === "ASIDE") {
return false;
}
return Array.from(el.classList).find((className) => {
return (
className !== "column-body" &&
className.startsWith("column-") &&
!className.endsWith("right") &&
!className.endsWith("container") &&
className !== "column-margin"
);
});
});
const rightSideConflictEls = arrConflictingEls.filter((el) => {
if (el.tagName === "ASIDE") {
return true;
}
const hasMarginCaption = Array.from(el.classList).find((className) => {
return className == "margin-caption";
});
if (hasMarginCaption) {
return true;
}
return Array.from(el.classList).find((className) => {
return (
className !== "column-body" &&
!className.endsWith("container") &&
className.startsWith("column-") &&
!className.endsWith("left")
);
});
});
const kOverlapPaddingSize = 10;
function toRegions(els) {
return els.map((el) => {
const boundRect = el.getBoundingClientRect();
const top =
boundRect.top +
document.documentElement.scrollTop -
kOverlapPaddingSize;
return {
top,
bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
};
});
}
let hasObserved = false;
const visibleItemObserver = (els) => {
let visibleElements = [...els];
const intersectionObserver = new IntersectionObserver(
(entries, _observer) => {
entries.forEach((entry) => {
if (entry.isIntersecting) {
if (visibleElements.indexOf(entry.target) === -1) {
visibleElements.push(entry.target);
}
} else {
visibleElements = visibleElements.filter((visibleEntry) => {
return visibleEntry !== entry;
});
}
});
if (!hasObserved) {
hideOverlappedSidebars();
}
hasObserved = true;
},
{}
);
els.forEach((el) => {
intersectionObserver.observe(el);
});
return {
getVisibleEntries: () => {
return visibleElements;
},
};
};
const rightElementObserver = visibleItemObserver(rightSideConflictEls);
const leftElementObserver = visibleItemObserver(leftSideConflictEls);
const hideOverlappedSidebars = () => {
marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
if (tocLeftScrollVisibility) {
tocLeftScrollVisibility(
toRegions(leftElementObserver.getVisibleEntries())
);
}
};
window.quartoToggleReader = () => {
// Applies a slow class (or removes it)
// to update the transition speed
const slowTransition = (slow) => {
const manageTransition = (id, slow) => {
const el = document.getElementById(id);
if (el) {
if (slow) {
el.classList.add("slow");
} else {
el.classList.remove("slow");
}
}
};
manageTransition("TOC", slow);
manageTransition("quarto-sidebar", slow);
};
const readerMode = !isReaderMode();
setReaderModeValue(readerMode);
// If we're entering reader mode, slow the transition
if (readerMode) {
slowTransition(readerMode);
}
highlightReaderToggle(readerMode);
hideOverlappedSidebars();
// If we're exiting reader mode, restore the non-slow transition
if (!readerMode) {
slowTransition(!readerMode);
}
};
const highlightReaderToggle = (readerMode) => {
const els = document.querySelectorAll(".quarto-reader-toggle");
if (els) {
els.forEach((el) => {
if (readerMode) {
el.classList.add("reader");
} else {
el.classList.remove("reader");
}
});
}
};
const setReaderModeValue = (val) => {
if (window.location.protocol !== "file:") {
window.localStorage.setItem("quarto-reader-mode", val);
} else {
localReaderMode = val;
}
};
const isReaderMode = () => {
if (window.location.protocol !== "file:") {
return window.localStorage.getItem("quarto-reader-mode") === "true";
} else {
return localReaderMode;
}
};
let localReaderMode = null;
const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
// Walk the TOC and collapse/expand nodes
// Nodes are expanded if:
// - they are top level
// - they have children that are 'active' links
// - they are directly below an link that is 'active'
const walk = (el, depth) => {
// Tick depth when we enter a UL
if (el.tagName === "UL") {
depth = depth + 1;
}
// It this is active link
let isActiveNode = false;
if (el.tagName === "A" && el.classList.contains("active")) {
isActiveNode = true;
}
// See if there is an active child to this element
let hasActiveChild = false;
for (child of el.children) {
hasActiveChild = walk(child, depth) || hasActiveChild;
}
// Process the collapse state if this is an UL
if (el.tagName === "UL") {
if (tocOpenDepth === -1 && depth > 1) {
el.classList.add("collapse");
} else if (
depth <= tocOpenDepth ||
hasActiveChild ||
prevSiblingIsActiveLink(el)
) {
el.classList.remove("collapse");
} else {
el.classList.add("collapse");
}
// untick depth when we leave a UL
depth = depth - 1;
}
return hasActiveChild || isActiveNode;
};
// walk the TOC and expand / collapse any items that should be shown
if (tocEl) {
walk(tocEl, 0);
updateActiveLink();
}
// Throttle the scroll event and walk peridiocally
window.document.addEventListener(
"scroll",
throttle(() => {
if (tocEl) {
updateActiveLink();
walk(tocEl, 0);
}
if (!isReaderMode()) {
hideOverlappedSidebars();
}
}, 5)
);
window.addEventListener(
"resize",
throttle(() => {
if (!isReaderMode()) {
hideOverlappedSidebars();
}
}, 10)
);
hideOverlappedSidebars();
highlightReaderToggle(isReaderMode());
});
// grouped tabsets
window.addEventListener("pageshow", (_event) => {
function getTabSettings() {
const data = localStorage.getItem("quarto-persistent-tabsets-data");
if (!data) {
localStorage.setItem("quarto-persistent-tabsets-data", "{}");
return {};
}
if (data) {
return JSON.parse(data);
}
}
function setTabSettings(data) {
localStorage.setItem(
"quarto-persistent-tabsets-data",
JSON.stringify(data)
);
}
function setTabState(groupName, groupValue) {
const data = getTabSettings();
data[groupName] = groupValue;
setTabSettings(data);
}
function toggleTab(tab, active) {
const tabPanelId = tab.getAttribute("aria-controls");
const tabPanel = document.getElementById(tabPanelId);
if (active) {
tab.classList.add("active");
tabPanel.classList.add("active");
} else {
tab.classList.remove("active");
tabPanel.classList.remove("active");
}
}
function toggleAll(selectedGroup, selectorsToSync) {
for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
const active = selectedGroup === thisGroup;
for (const tab of tabs) {
toggleTab(tab, active);
}
}
}
function findSelectorsToSyncByLanguage() {
const result = {};
const tabs = Array.from(
document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
);
for (const item of tabs) {
const div = item.parentElement.parentElement.parentElement;
const group = div.getAttribute("data-group");
if (!result[group]) {
result[group] = {};
}
const selectorsToSync = result[group];
const value = item.innerHTML;
if (!selectorsToSync[value]) {
selectorsToSync[value] = [];
}
selectorsToSync[value].push(item);
}
return result;
}
function setupSelectorSync() {
const selectorsToSync = findSelectorsToSyncByLanguage();
Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
Object.entries(tabSetsByValue).forEach(([value, items]) => {
items.forEach((item) => {
item.addEventListener("click", (_event) => {
setTabState(group, value);
toggleAll(value, selectorsToSync[group]);
});
});
});
});
return selectorsToSync;
}
const selectorsToSync = setupSelectorSync();
for (const [group, selectedName] of Object.entries(getTabSettings())) {
const selectors = selectorsToSync[group];
// it's possible that stale state gives us empty selections, so we explicitly check here.
if (selectors) {
toggleAll(selectedName, selectors);
}
}
});
function throttle(func, wait) {
let waiting = false;
return function () {
if (!waiting) {
func.apply(this, arguments);
waiting = true;
setTimeout(function () {
waiting = false;
}, wait);
}
};
}
function nexttick(func) {
return setTimeout(func, 0);
}
</script>
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// AnchorJS - v5.0.0 - 2023-01-18
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t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),"fixed"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,je,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new 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) format("woff");
}
.bi::before,
[class^="bi-"]::before,
[class*=" bi-"]::before {
display: inline-block;
font-family: bootstrap-icons !important;
font-style: normal;
font-weight: normal !important;
font-variant: normal;
text-transform: none;
line-height: 1;
vertical-align: -.125em;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.bi-123::before { content: "\f67f"; }
.bi-alarm-fill::before { content: "\f101"; }
.bi-alarm::before { content: "\f102"; }
.bi-align-bottom::before { content: "\f103"; }
.bi-align-center::before { content: "\f104"; }
.bi-align-end::before { content: "\f105"; }
.bi-align-middle::before { content: "\f106"; }
.bi-align-start::before { content: "\f107"; }
.bi-align-top::before { content: "\f108"; }
.bi-alt::before { content: "\f109"; }
.bi-app-indicator::before { content: "\f10a"; }
.bi-app::before { content: "\f10b"; }
.bi-archive-fill::before { content: "\f10c"; }
.bi-archive::before { content: "\f10d"; }
.bi-arrow-90deg-down::before { content: "\f10e"; }
.bi-arrow-90deg-left::before { content: "\f10f"; }
.bi-arrow-90deg-right::before { content: "\f110"; }
.bi-arrow-90deg-up::before { content: "\f111"; }
.bi-arrow-bar-down::before { content: "\f112"; }
.bi-arrow-bar-left::before { content: "\f113"; }
.bi-arrow-bar-right::before { content: "\f114"; }
.bi-arrow-bar-up::before { content: "\f115"; }
.bi-arrow-clockwise::before { content: "\f116"; }
.bi-arrow-counterclockwise::before { content: "\f117"; }
.bi-arrow-down-circle-fill::before { content: "\f118"; }
.bi-arrow-down-circle::before { content: "\f119"; }
.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
.bi-arrow-down-left-circle::before { content: "\f11b"; }
.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
.bi-arrow-down-left-square::before { content: "\f11d"; }
.bi-arrow-down-left::before { content: "\f11e"; }
.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
.bi-arrow-down-right-circle::before { content: "\f120"; }
.bi-arrow-down-right-square-fill::before { content: "\f121"; }
.bi-arrow-down-right-square::before { content: "\f122"; }
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.bi-floppy-fill::before { content: "\f7c5"; }
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.bi-send-arrow-up::before { content: "\f8d3"; }
.bi-sim-slash-fill::before { content: "\f8d4"; }
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.bi-backpack2-fill::before { content: "\f8e1"; }
.bi-backpack2::before { content: "\f8e2"; }
.bi-backpack3-fill::before { content: "\f8e3"; }
.bi-backpack3::before { content: "\f8e4"; }
.bi-backpack4-fill::before { content: "\f8e5"; }
.bi-backpack4::before { content: "\f8e6"; }
.bi-brilliance::before { content: "\f8e7"; }
.bi-cake-fill::before { content: "\f8e8"; }
.bi-cake2-fill::before { content: "\f8e9"; }
.bi-duffle-fill::before { content: "\f8ea"; }
.bi-duffle::before { content: "\f8eb"; }
.bi-exposure::before { content: "\f8ec"; }
.bi-gender-neuter::before { content: "\f8ed"; }
.bi-highlights::before { content: "\f8ee"; }
.bi-luggage-fill::before { content: "\f8ef"; }
.bi-luggage::before { content: "\f8f0"; }
.bi-mailbox-flag::before { content: "\f8f1"; }
.bi-mailbox2-flag::before { content: "\f8f2"; }
.bi-noise-reduction::before { content: "\f8f3"; }
.bi-passport-fill::before { content: "\f8f4"; }
.bi-passport::before { content: "\f8f5"; }
.bi-person-arms-up::before { content: "\f8f6"; }
.bi-person-raised-hand::before { content: "\f8f7"; }
.bi-person-standing-dress::before { content: "\f8f8"; }
.bi-person-standing::before { content: "\f8f9"; }
.bi-person-walking::before { content: "\f8fa"; }
.bi-person-wheelchair::before { content: "\f8fb"; }
.bi-shadows::before { content: "\f8fc"; }
.bi-suitcase-fill::before { content: "\f8fd"; }
.bi-suitcase-lg-fill::before { content: "\f8fe"; }
.bi-suitcase-lg::before { content: "\f8ff"; }
.bi-suitcase::before { content: "\f900"; }
.bi-suitcase2-fill::before { content: "\f901"; }
.bi-suitcase2::before { content: "\f902"; }
.bi-vignette::before { content: "\f903"; }
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${Na.lang.round(p)}%)`}},rW=nW,iW={colors:{aliceblue:"#f0f8ff",antiquewhite:"#faebd7",aqua:"#00ffff",aquamarine:"#7fffd4",azure:"#f0ffff",beige:"#f5f5dc",bisque:"#ffe4c4",black:"#000000",blanchedalmond:"#ffebcd",blue:"#0000ff",blueviolet:"#8a2be2",brown:"#a52a2a",burlywood:"#deb887",cadetblue:"#5f9ea0",chartreuse:"#7fff00",chocolate:"#d2691e",coral:"#ff7f50",cornflowerblue:"#6495ed",cornsilk:"#fff8dc",crimson:"#dc143c",cyanaqua:"#00ffff",darkblue:"#00008b",darkcyan:"#008b8b",darkgoldenrod:"#b8860b",darkgray:"#a9a9a9",darkgreen:"#006400",darkgrey:"#a9a9a9",darkkhaki:"#bdb76b",darkmagenta:"#8b008b",darkolivegreen:"#556b2f",darkorange:"#ff8c00",darkorchid:"#9932cc",darkred:"#8b0000",darksalmon:"#e9967a",darkseagreen:"#8fbc8f",darkslateblue:"#483d8b",darkslategray:"#2f4f4f",darkslategrey:"#2f4f4f",darkturquoise:"#00ced1",darkviolet:"#9400d3",deeppink:"#ff1493",deepskyblue:"#00bfff",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1e90ff",firebrick:"#b22222",floralwhite:"#fffaf0",forestgreen:"#228b22",fuchsia:"#ff00ff",gainsboro:"#dcdcdc",ghostwhite:"#f8f8ff",gold:"#ffd700",goldenrod:"#daa520",gray:"#808080",green:"#008000",greenyellow:"#adff2f",grey:"#808080",honeydew:"#f0fff0",hotpink:"#ff69b4",indianred:"#cd5c5c",indigo:"#4b0082",ivory:"#fffff0",khaki:"#f0e68c",lavender:"#e6e6fa",lavenderblush:"#fff0f5",lawngreen:"#7cfc00",lemonchiffon:"#fffacd",lightblue:"#add8e6",lightcoral:"#f08080",lightcyan:"#e0ffff",lightgoldenrodyellow:"#fafad2",lightgray:"#d3d3d3",lightgreen:"#90ee90",lightgrey:"#d3d3d3",lightpink:"#ffb6c1",lightsalmon:"#ffa07a",lightseagreen:"#20b2aa",lightskyblue:"#87cefa",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#b0c4de",lightyellow:"#ffffe0",lime:"#00ff00",limegreen:"#32cd32",linen:"#faf0e6",magenta:"#ff00ff",maroon:"#800000",mediumaquamarine:"#66cdaa",mediumblue:"#0000cd",mediumorchid:"#ba55d3",mediumpurple:"#9370db",mediumseagreen:"#3cb371",mediumslateblue:"#7b68ee",mediumspringgreen:"#00fa9a",mediumturquoise:"#48d1cc",mediumvioletred:"#c71585",midnightblue:"#191970",mintcream:"#f5fffa",mistyrose:"#ffe4e1",moccasin:"#ffe4b5",navajowhite:"#ffdead",navy:"#000080",oldlace:"#fdf5e6",olive:"#808000",olivedrab:"#6b8e23",orange:"#ffa500",orangered:"#ff4500",orchid:"#da70d6",palegoldenrod:"#eee8aa",palegreen:"#98fb98",paleturquoise:"#afeeee",palevioletred:"#db7093",papayawhip:"#ffefd5",peachpuff:"#ffdab9",peru:"#cd853f",pink:"#ffc0cb",plum:"#dda0dd",powderblue:"#b0e0e6",purple:"#800080",rebeccapurple:"#663399",red:"#ff0000",rosybrown:"#bc8f8f",royalblue:"#4169e1",saddlebrown:"#8b4513",salmon:"#fa8072",sandybrown:"#f4a460",seagreen:"#2e8b57",seashell:"#fff5ee",sienna:"#a0522d",silver:"#c0c0c0",skyblue:"#87ceeb",slateblue:"#6a5acd",slategray:"#708090",slategrey:"#708090",snow:"#fffafa",springgreen:"#00ff7f",tan:"#d2b48c",teal:"#008080",thistle:"#d8bfd8",transparent:"#00000000",turquoise:"#40e0d0",violet:"#ee82ee",wheat:"#f5deb3",white:"#ffffff",whitesmoke:"#f5f5f5",yellow:"#ffff00",yellowgreen:"#9acd32"},parse:i=>{i=i.toLowerCase();const a=iW.colors[i];if(a)return jN.parse(a)},stringify:i=>{const a=jN.stringify(i);for(const f in iW.colors)if(iW.colors[f]===a)return f}},$De=iW,HDe={re:/^rgba?\(\s*?(-?(?:\d+(?:\.\d+)?|(?:\.\d+))(?:e\d+)?(%?))\s*?(?:,|\s)\s*?(-?(?:\d+(?:\.\d+)?|(?:\.\d+))(?:e\d+)?(%?))\s*?(?:,|\s)\s*?(-?(?:\d+(?:\.\d+)?|(?:\.\d+))(?:e\d+)?(%?))(?:\s*?(?:,|\/)\s*?\+?(-?(?:\d+(?:\.\d+)?|(?:\.\d+))(?:e\d+)?(%?)))?\s*?\)$/i,parse:i=>{const a=i.charCodeAt(0);if(a!==114&&a!==82)return;const f=i.match(HDe.re);if(!f)return;const[,p,w,y,b,E,S,N,B]=f;return tW.set({r:Na.channel.clamp.r(w?parseFloat(p)*2.55:parseFloat(p)),g:Na.channel.clamp.g(b?parseFloat(y)*2.55:parseFloat(y)),b:Na.channel.clamp.b(S?parseFloat(E)*2.55:parseFloat(E)),a:N?Na.channel.clamp.a(B?parseFloat(N)/100:parseFloat(N)):1},i)},stringify:i=>{const{r:a,g:f,b:p,a:w}=i;return w<1?`rgba(${Na.lang.round(a)}, ${Na.lang.round(f)}, ${Na.lang.round(p)}, ${Na.lang.round(w)})`:`rgb(${Na.lang.round(a)}, ${Na.lang.round(f)}, ${Na.lang.round(p)})`}},sW=HDe,r3={format:{keyword:$De,hex:jN,rgb:sW,rgba:sW,hsl:rW,hsla:rW},parse:i=>{if(typeof i!="string")return i;const a=jN.parse(i)||sW.parse(i)||rW.parse(i)||$De.parse(i);if(a)return a;throw new Error(`Unsupported color format: "${i}"`)},stringify:i=>!i.changed&&i.color?i.color:i.type.is(y0.HSL)||i.data.r===void 0?rW.stringify(i):i.a<1||!Number.isInteger(i.r)||!Number.isInteger(i.g)||!Number.isInteger(i.b)?sW.stringify(i):jN.stringify(i)},zDe=(i,a)=>{const f=r3.parse(i);for(const p in a)f[p]=Na.channel.clamp[p](a[p]);return r3.stringify(f)},$N=(i,a,f=0,p=1)=>{if(typeof i!="number")return zDe(i,{a});const w=tW.set({r:Na.channel.clamp.r(i),g:Na.channel.clamp.g(a),b:Na.channel.clamp.b(f),a:Na.channel.clamp.a(p)});return r3.stringify(w)},o9t=i=>{const{r:a,g:f,b:p}=r3.parse(i),w=.2126*Na.channel.toLinear(a)+.7152*Na.channel.toLinear(f)+.0722*Na.channel.toLinear(p);return Na.lang.round(w)},c9t=i=>o9t(i)>=.5,GDe=i=>!c9t(i),qDe=(i,a,f)=>{const p=r3.parse(i),w=p[a],y=Na.channel.clamp[a](w+f);return w!==y&&(p[a]=y),r3.stringify(p)},Qs=(i,a)=>qDe(i,"l",a),ya=(i,a)=>qDe(i,"l",-a),Gn=(i,a)=>{const f=r3.parse(i),p={};for(const w in a)a[w]&&(p[w]=f[w]+a[w]);return zDe(i,p)},u9t=(i,a,f=50)=>{const{r:p,g:w,b:y,a:b}=r3.parse(i),{r:E,g:S,b:N,a:B}=r3.parse(a),R=f/100,j=R*2-1,$=b-B,Q=((j*$===-1?j:(j+$)/(1+j*$))+1)/2,oe=1-Q,ce=p*Q+E*oe,se=w*Q+S*oe,ge=y*Q+N*oe,ye=b*R+B*(1-R);return $N(ce,se,ge,ye)},Bi=(i,a=100)=>{const f=r3.parse(i);return f.r=255-f.r,f.g=255-f.g,f.b=255-f.b,u9t(f,i,a)},ig=(i,a)=>a?Gn(i,{s:-40,l:10}):Gn(i,{s:-40,l:-10}),aW="#ffffff",oW="#f2f2f2";let l9t=class{constructor(){this.background="#f4f4f4",this.primaryColor="#fff4dd",this.noteBkgColor="#fff5ad",this.noteTextColor="#333",this.THEME_COLOR_LIMIT=12,this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px"}updateColors(){if(this.primaryTextColor=this.primaryTextColor||(this.darkMode?"#eee":"#333"),this.secondaryColor=this.secondaryColor||Gn(this.primaryColor,{h:-120}),this.tertiaryColor=this.tertiaryColor||Gn(this.primaryColor,{h:180,l:5}),this.primaryBorderColor=this.primaryBorderColor||ig(this.primaryColor,this.darkMode),this.secondaryBorderColor=this.secondaryBorderColor||ig(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=this.tertiaryBorderColor||ig(this.tertiaryColor,this.darkMode),this.noteBorderColor=this.noteBorderColor||ig(this.noteBkgColor,this.darkMode),this.noteBkgColor=this.noteBkgColor||"#fff5ad",this.noteTextColor=this.noteTextColor||"#333",this.secondaryTextColor=this.secondaryTextColor||Bi(this.secondaryColor),this.tertiaryTextColor=this.tertiaryTextColor||Bi(this.tertiaryColor),this.lineColor=this.lineColor||Bi(this.background),this.arrowheadColor=this.arrowheadColor||Bi(this.background),this.textColor=this.textColor||this.primaryTextColor,this.border2=this.border2||this.tertiaryBorderColor,this.nodeBkg=this.nodeBkg||this.primaryColor,this.mainBkg=this.mainBkg||this.primaryColor,this.nodeBorder=this.nodeBorder||this.primaryBorderColor,this.clusterBkg=this.clusterBkg||this.tertiaryColor,this.clusterBorder=this.clusterBorder||this.tertiaryBorderColor,this.defaultLinkColor=this.defaultLinkColor||this.lineColor,this.titleColor=this.titleColor||this.tertiaryTextColor,this.edgeLabelBackground=this.edgeLabelBackground||(this.darkMode?ya(this.secondaryColor,30):this.secondaryColor),this.nodeTextColor=this.nodeTextColor||this.primaryTextColor,this.actorBorder=this.actorBorder||this.primaryBorderColor,this.actorBkg=this.actorBkg||this.mainBkg,this.actorTextColor=this.actorTextColor||this.primaryTextColor,this.actorLineColor=this.actorLineColor||"grey",this.labelBoxBkgColor=this.labelBoxBkgColor||this.actorBkg,this.signalColor=this.signalColor||this.textColor,this.signalTextColor=this.signalTextColor||this.textColor,this.labelBoxBorderColor=this.labelBoxBorderColor||this.actorBorder,this.labelTextColor=this.labelTextColor||this.actorTextColor,this.loopTextColor=this.loopTextColor||this.actorTextColor,this.activationBorderColor=this.activationBorderColor||ya(this.secondaryColor,10),this.activationBkgColor=this.activationBkgColor||this.secondaryColor,this.sequenceNumberColor=this.sequenceNumberColor||Bi(this.lineColor),this.sectionBkgColor=this.sectionBkgColor||this.tertiaryColor,this.altSectionBkgColor=this.altSectionBkgColor||"white",this.sectionBkgColor=this.sectionBkgColor||this.secondaryColor,this.sectionBkgColor2=this.sectionBkgColor2||this.primaryColor,this.excludeBkgColor=this.excludeBkgColor||"#eeeeee",this.taskBorderColor=this.taskBorderColor||this.primaryBorderColor,this.taskBkgColor=this.taskBkgColor||this.primaryColor,this.activeTaskBorderColor=this.activeTaskBorderColor||this.primaryColor,this.activeTaskBkgColor=this.activeTaskBkgColor||Qs(this.primaryColor,23),this.gridColor=this.gridColor||"lightgrey",this.doneTaskBkgColor=this.doneTaskBkgColor||"lightgrey",this.doneTaskBorderColor=this.doneTaskBorderColor||"grey",this.critBorderColor=this.critBorderColor||"#ff8888",this.critBkgColor=this.critBkgColor||"red",this.todayLineColor=this.todayLineColor||"red",this.taskTextColor=this.taskTextColor||this.textColor,this.taskTextOutsideColor=this.taskTextOutsideColor||this.textColor,this.taskTextLightColor=this.taskTextLightColor||this.textColor,this.taskTextColor=this.taskTextColor||this.primaryTextColor,this.taskTextDarkColor=this.taskTextDarkColor||this.textColor,this.taskTextClickableColor=this.taskTextClickableColor||"#003163",this.personBorder=this.personBorder||this.primaryBorderColor,this.personBkg=this.personBkg||this.mainBkg,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||this.tertiaryColor,this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.specialStateColor=this.lineColor,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Gn(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Gn(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Gn(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Gn(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Gn(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Gn(this.primaryColor,{h:210,l:150}),this.cScale9=this.cScale9||Gn(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Gn(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Gn(this.primaryColor,{h:330}),this.darkMode)for(let f=0;f<this.THEME_COLOR_LIMIT;f++)this["cScale"+f]=ya(this["cScale"+f],75);else for(let f=0;f<this.THEME_COLOR_LIMIT;f++)this["cScale"+f]=ya(this["cScale"+f],25);for(let f=0;f<this.THEME_COLOR_LIMIT;f++)this["cScaleInv"+f]=this["cScaleInv"+f]||Bi(this["cScale"+f]);for(let f=0;f<this.THEME_COLOR_LIMIT;f++)this.darkMode?this["cScalePeer"+f]=this["cScalePeer"+f]||Qs(this["cScale"+f],10):this["cScalePeer"+f]=this["cScalePeer"+f]||ya(this["cScale"+f],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let f=0;f<this.THEME_COLOR_LIMIT;f++)this["cScaleLabel"+f]=this["cScaleLabel"+f]||this.scaleLabelColor;const a=this.darkMode?-4:-1;for(let f=0;f<5;f++)this["surface"+f]=this["surface"+f]||Gn(this.mainBkg,{h:180,s:-15,l:a*(5+f*3)}),this["surfacePeer"+f]=this["surfacePeer"+f]||Gn(this.mainBkg,{h:180,s:-15,l:a*(8+f*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||this.primaryColor,this.fillType1=this.fillType1||this.secondaryColor,this.fillType2=this.fillType2||Gn(this.primaryColor,{h:64}),this.fillType3=this.fillType3||Gn(this.secondaryColor,{h:64}),this.fillType4=this.fillType4||Gn(this.primaryColor,{h:-64}),this.fillType5=this.fillType5||Gn(this.secondaryColor,{h:-64}),this.fillType6=this.fillType6||Gn(this.primaryColor,{h:128}),this.fillType7=this.fillType7||Gn(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||Gn(this.primaryColor,{l:-10}),this.pie5=this.pie5||Gn(this.secondaryColor,{l:-10}),this.pie6=this.pie6||Gn(this.tertiaryColor,{l:-10}),this.pie7=this.pie7||Gn(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||Gn(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||Gn(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||Gn(this.primaryColor,{h:60,l:-20}),this.pie11=this.pie11||Gn(this.primaryColor,{h:-60,l:-20}),this.pie12=this.pie12||Gn(this.primaryColor,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?ya(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||Gn(this.primaryColor,{h:-30}),this.git4=this.git4||Gn(this.primaryColor,{h:-60}),this.git5=this.git5||Gn(this.primaryColor,{h:-90}),this.git6=this.git6||Gn(this.primaryColor,{h:60}),this.git7=this.git7||Gn(this.primaryColor,{h:120}),this.darkMode?(this.git0=Qs(this.git0,25),this.git1=Qs(this.git1,25),this.git2=Qs(this.git2,25),this.git3=Qs(this.git3,25),this.git4=Qs(this.git4,25),this.git5=Qs(this.git5,25),this.git6=Qs(this.git6,25),this.git7=Qs(this.git7,25)):(this.git0=ya(this.git0,25),this.git1=ya(this.git1,25),this.git2=ya(this.git2,25),this.git3=ya(this.git3,25),this.git4=ya(this.git4,25),this.git5=ya(this.git5,25),this.git6=ya(this.git6,25),this.git7=ya(this.git7,25)),this.gitInv0=this.gitInv0||Bi(this.git0),this.gitInv1=this.gitInv1||Bi(this.git1),this.gitInv2=this.gitInv2||Bi(this.git2),this.gitInv3=this.gitInv3||Bi(this.git3),this.gitInv4=this.gitInv4||Bi(this.git4),this.gitInv5=this.gitInv5||Bi(this.git5),this.gitInv6=this.gitInv6||Bi(this.git6),this.gitInv7=this.gitInv7||Bi(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||aW,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||oW}calculate(a){if(typeof a!="object"){this.updateColors();return}const f=Object.keys(a);f.forEach(p=>{this[p]=a[p]}),this.updateColors(),f.forEach(p=>{this[p]=a[p]})}};const h9t=i=>{const a=new l9t;return a.calculate(i),a};let f9t=class{constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=Qs(this.primaryColor,16),this.tertiaryColor=Gn(this.primaryColor,{h:-160}),this.primaryBorderColor=Bi(this.background),this.secondaryBorderColor=ig(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=ig(this.tertiaryColor,this.darkMode),this.primaryTextColor=Bi(this.primaryColor),this.secondaryTextColor=Bi(this.secondaryColor),this.tertiaryTextColor=Bi(this.tertiaryColor),this.lineColor=Bi(this.background),this.textColor=Bi(this.background),this.mainBkg="#1f2020",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=Qs(Bi("#323D47"),10),this.lineColor="calculated",this.border1="#81B1DB",this.border2=$N(255,255,255,.25),this.arrowheadColor="calculated",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="#181818",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#F9FFFE",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="calculated",this.activationBkgColor="calculated",this.sequenceNumberColor="black",this.sectionBkgColor=ya("#EAE8D9",30),this.altSectionBkgColor="calculated",this.sectionBkgColor2="#EAE8D9",this.excludeBkgColor=ya(this.sectionBkgColor,10),this.taskBorderColor=$N(255,255,255,70),this.taskBkgColor="calculated",this.taskTextColor="calculated",this.taskTextLightColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor=$N(255,255,255,50),this.activeTaskBkgColor="#81B1DB",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="grey",this.critBorderColor="#E83737",this.critBkgColor="#E83737",this.taskTextDarkColor="calculated",this.todayLineColor="#DB5757",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.labelColor="calculated",this.errorBkgColor="#a44141",this.errorTextColor="#ddd"}updateColors(){this.secondBkg=Qs(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=Qs(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.mainContrastColor,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.secondaryBorderColor,this.noteBkgColor=this.secondBkg,this.noteTextColor=this.secondaryTextColor,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=Qs(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=this.darkTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#555",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#f4f4f4",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Gn(this.primaryColor,{h:64}),this.fillType3=Gn(this.secondaryColor,{h:64}),this.fillType4=Gn(this.primaryColor,{h:-64}),this.fillType5=Gn(this.secondaryColor,{h:-64}),this.fillType6=Gn(this.primaryColor,{h:128}),this.fillType7=Gn(this.secondaryColor,{h:128}),this.cScale1=this.cScale1||"#0b0000",this.cScale2=this.cScale2||"#4d1037",this.cScale3=this.cScale3||"#3f5258",this.cScale4=this.cScale4||"#4f2f1b",this.cScale5=this.cScale5||"#6e0a0a",this.cScale6=this.cScale6||"#3b0048",this.cScale7=this.cScale7||"#995a01",this.cScale8=this.cScale8||"#154706",this.cScale9=this.cScale9||"#161722",this.cScale10=this.cScale10||"#00296f",this.cScale11=this.cScale11||"#01629c",this.cScale12=this.cScale12||"#010029",this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Gn(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Gn(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Gn(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Gn(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Gn(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Gn(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Gn(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Gn(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Gn(this.primaryColor,{h:330});for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleInv"+a]=this["cScaleInv"+a]||Bi(this["cScale"+a]);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScalePeer"+a]=this["cScalePeer"+a]||Qs(this["cScale"+a],10);for(let a=0;a<5;a++)this["surface"+a]=this["surface"+a]||Gn(this.mainBkg,{h:30,s:-30,l:-(-10+a*4)}),this["surfacePeer"+a]=this["surfacePeer"+a]||Gn(this.mainBkg,{h:30,s:-30,l:-(-7+a*4)});this.scaleLabelColor=this.scaleLabelColor||(this.darkMode?"black":this.labelTextColor);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleLabel"+a]=this["cScaleLabel"+a]||this.scaleLabelColor;for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["pie"+a]=this["cScale"+a];this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.classText=this.primaryTextColor,this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?ya(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=Qs(this.secondaryColor,20),this.git1=Qs(this.pie2||this.secondaryColor,20),this.git2=Qs(this.pie3||this.tertiaryColor,20),this.git3=Qs(this.pie4||Gn(this.primaryColor,{h:-30}),20),this.git4=Qs(this.pie5||Gn(this.primaryColor,{h:-60}),20),this.git5=Qs(this.pie6||Gn(this.primaryColor,{h:-90}),10),this.git6=Qs(this.pie7||Gn(this.primaryColor,{h:60}),10),this.git7=Qs(this.pie8||Gn(this.primaryColor,{h:120}),20),this.gitInv0=this.gitInv0||Bi(this.git0),this.gitInv1=this.gitInv1||Bi(this.git1),this.gitInv2=this.gitInv2||Bi(this.git2),this.gitInv3=this.gitInv3||Bi(this.git3),this.gitInv4=this.gitInv4||Bi(this.git4),this.gitInv5=this.gitInv5||Bi(this.git5),this.gitInv6=this.gitInv6||Bi(this.git6),this.gitInv7=this.gitInv7||Bi(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||Bi(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||Bi(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||Qs(this.background,12),this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||Qs(this.background,2)}calculate(a){if(typeof a!="object"){this.updateColors();return}const f=Object.keys(a);f.forEach(p=>{this[p]=a[p]}),this.updateColors(),f.forEach(p=>{this[p]=a[p]})}};const d9t=i=>{const a=new f9t;return a.calculate(i),a};let g9t=class{constructor(){this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=Gn(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=Gn(this.primaryColor,{h:-160}),this.primaryBorderColor=ig(this.primaryColor,this.darkMode),this.secondaryBorderColor=ig(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=ig(this.tertiaryColor,this.darkMode),this.primaryTextColor=Bi(this.primaryColor),this.secondaryTextColor=Bi(this.secondaryColor),this.tertiaryTextColor=Bi(this.tertiaryColor),this.lineColor=Bi(this.background),this.textColor=Bi(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="#e8e8e8",this.textColor="#333",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="grey",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="calculated",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="calculated",this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor="calculated",this.taskTextOutsideColor=this.taskTextDarkColor,this.taskTextClickableColor="calculated",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBorderColor="calculated",this.critBkgColor="calculated",this.todayLineColor="calculated",this.sectionBkgColor=$N(102,102,255,.49),this.altSectionBkgColor="white",this.sectionBkgColor2="#fff400",this.taskBorderColor="#534fbc",this.taskBkgColor="#8a90dd",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="#534fbc",this.activeTaskBkgColor="#bfc7ff",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.updateColors()}updateColors(){this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Gn(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Gn(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Gn(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Gn(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Gn(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Gn(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Gn(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Gn(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Gn(this.primaryColor,{h:330}),this["cScalePeer1"]=this["cScalePeer1"]||ya(this.secondaryColor,45),this["cScalePeer2"]=this["cScalePeer2"]||ya(this.tertiaryColor,40);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScale"+a]=ya(this["cScale"+a],10),this["cScalePeer"+a]=this["cScalePeer"+a]||ya(this["cScale"+a],25);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleInv"+a]=this["cScaleInv"+a]||Gn(this["cScale"+a],{h:180});for(let a=0;a<5;a++)this["surface"+a]=this["surface"+a]||Gn(this.mainBkg,{h:30,l:-(5+a*5)}),this["surfacePeer"+a]=this["surfacePeer"+a]||Gn(this.mainBkg,{h:30,l:-(7+a*5)});if(this.scaleLabelColor=this.scaleLabelColor!=="calculated"&&this.scaleLabelColor?this.scaleLabelColor:this.labelTextColor,this.labelTextColor!=="calculated"){this.cScaleLabel0=this.cScaleLabel0||Bi(this.labelTextColor),this.cScaleLabel3=this.cScaleLabel3||Bi(this.labelTextColor);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleLabel"+a]=this["cScaleLabel"+a]||this.labelTextColor}this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.textColor,this.edgeLabelBackground=this.labelBackground,this.actorBorder=Qs(this.border1,23),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.signalColor=this.textColor,this.signalTextColor=this.textColor,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.specialStateColor=this.lineColor,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Gn(this.primaryColor,{h:64}),this.fillType3=Gn(this.secondaryColor,{h:64}),this.fillType4=Gn(this.primaryColor,{h:-64}),this.fillType5=Gn(this.secondaryColor,{h:-64}),this.fillType6=Gn(this.primaryColor,{h:128}),this.fillType7=Gn(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||Gn(this.tertiaryColor,{l:-40}),this.pie4=this.pie4||Gn(this.primaryColor,{l:-10}),this.pie5=this.pie5||Gn(this.secondaryColor,{l:-30}),this.pie6=this.pie6||Gn(this.tertiaryColor,{l:-20}),this.pie7=this.pie7||Gn(this.primaryColor,{h:60,l:-20}),this.pie8=this.pie8||Gn(this.primaryColor,{h:-60,l:-40}),this.pie9=this.pie9||Gn(this.primaryColor,{h:120,l:-40}),this.pie10=this.pie10||Gn(this.primaryColor,{h:60,l:-40}),this.pie11=this.pie11||Gn(this.primaryColor,{h:-90,l:-40}),this.pie12=this.pie12||Gn(this.primaryColor,{h:120,l:-30}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.labelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||Gn(this.primaryColor,{h:-30}),this.git4=this.git4||Gn(this.primaryColor,{h:-60}),this.git5=this.git5||Gn(this.primaryColor,{h:-90}),this.git6=this.git6||Gn(this.primaryColor,{h:60}),this.git7=this.git7||Gn(this.primaryColor,{h:120}),this.darkMode?(this.git0=Qs(this.git0,25),this.git1=Qs(this.git1,25),this.git2=Qs(this.git2,25),this.git3=Qs(this.git3,25),this.git4=Qs(this.git4,25),this.git5=Qs(this.git5,25),this.git6=Qs(this.git6,25),this.git7=Qs(this.git7,25)):(this.git0=ya(this.git0,25),this.git1=ya(this.git1,25),this.git2=ya(this.git2,25),this.git3=ya(this.git3,25),this.git4=ya(this.git4,25),this.git5=ya(this.git5,25),this.git6=ya(this.git6,25),this.git7=ya(this.git7,25)),this.gitInv0=this.gitInv0||ya(Bi(this.git0),25),this.gitInv1=this.gitInv1||Bi(this.git1),this.gitInv2=this.gitInv2||Bi(this.git2),this.gitInv3=this.gitInv3||Bi(this.git3),this.gitInv4=this.gitInv4||Bi(this.git4),this.gitInv5=this.gitInv5||Bi(this.git5),this.gitInv6=this.gitInv6||Bi(this.git6),this.gitInv7=this.gitInv7||Bi(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||Bi(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||Bi(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||aW,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||oW}calculate(a){if(typeof a!="object"){this.updateColors();return}const f=Object.keys(a);f.forEach(p=>{this[p]=a[p]}),this.updateColors(),f.forEach(p=>{this[p]=a[p]})}};const p9t=i=>{const a=new g9t;return a.calculate(i),a};let b9t=class{constructor(){this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.tertiaryColor=Qs("#cde498",10),this.primaryBorderColor=ig(this.primaryColor,this.darkMode),this.secondaryBorderColor=ig(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=ig(this.tertiaryColor,this.darkMode),this.primaryTextColor=Bi(this.primaryColor),this.secondaryTextColor=Bi(this.secondaryColor),this.tertiaryTextColor=Bi(this.primaryColor),this.lineColor=Bi(this.background),this.textColor=Bi(this.background),this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#333",this.edgeLabelBackground="#e8e8e8",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="grey",this.signalColor="#333",this.signalTextColor="#333",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="#326932",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="#6eaa49",this.altSectionBkgColor="white",this.sectionBkgColor2="#6eaa49",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="#487e3a",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.actorBorder=ya(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Gn(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Gn(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Gn(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Gn(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Gn(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Gn(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Gn(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Gn(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Gn(this.primaryColor,{h:330}),this["cScalePeer1"]=this["cScalePeer1"]||ya(this.secondaryColor,45),this["cScalePeer2"]=this["cScalePeer2"]||ya(this.tertiaryColor,40);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScale"+a]=ya(this["cScale"+a],10),this["cScalePeer"+a]=this["cScalePeer"+a]||ya(this["cScale"+a],25);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleInv"+a]=this["cScaleInv"+a]||Gn(this["cScale"+a],{h:180});this.scaleLabelColor=this.scaleLabelColor!=="calculated"&&this.scaleLabelColor?this.scaleLabelColor:this.labelTextColor;for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleLabel"+a]=this["cScaleLabel"+a]||this.scaleLabelColor;for(let a=0;a<5;a++)this["surface"+a]=this["surface"+a]||Gn(this.mainBkg,{h:30,s:-30,l:-(5+a*5)}),this["surfacePeer"+a]=this["surfacePeer"+a]||Gn(this.mainBkg,{h:30,s:-30,l:-(8+a*5)});this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.taskBorderColor=this.border1,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor=this.lineColor,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Gn(this.primaryColor,{h:64}),this.fillType3=Gn(this.secondaryColor,{h:64}),this.fillType4=Gn(this.primaryColor,{h:-64}),this.fillType5=Gn(this.secondaryColor,{h:-64}),this.fillType6=Gn(this.primaryColor,{h:128}),this.fillType7=Gn(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||Gn(this.primaryColor,{l:-30}),this.pie5=this.pie5||Gn(this.secondaryColor,{l:-30}),this.pie6=this.pie6||Gn(this.tertiaryColor,{h:40,l:-40}),this.pie7=this.pie7||Gn(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||Gn(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||Gn(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||Gn(this.primaryColor,{h:60,l:-50}),this.pie11=this.pie11||Gn(this.primaryColor,{h:-60,l:-50}),this.pie12=this.pie12||Gn(this.primaryColor,{h:120,l:-50}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.edgeLabelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||Gn(this.primaryColor,{h:-30}),this.git4=this.git4||Gn(this.primaryColor,{h:-60}),this.git5=this.git5||Gn(this.primaryColor,{h:-90}),this.git6=this.git6||Gn(this.primaryColor,{h:60}),this.git7=this.git7||Gn(this.primaryColor,{h:120}),this.darkMode?(this.git0=Qs(this.git0,25),this.git1=Qs(this.git1,25),this.git2=Qs(this.git2,25),this.git3=Qs(this.git3,25),this.git4=Qs(this.git4,25),this.git5=Qs(this.git5,25),this.git6=Qs(this.git6,25),this.git7=Qs(this.git7,25)):(this.git0=ya(this.git0,25),this.git1=ya(this.git1,25),this.git2=ya(this.git2,25),this.git3=ya(this.git3,25),this.git4=ya(this.git4,25),this.git5=ya(this.git5,25),this.git6=ya(this.git6,25),this.git7=ya(this.git7,25)),this.gitInv0=this.gitInv0||Bi(this.git0),this.gitInv1=this.gitInv1||Bi(this.git1),this.gitInv2=this.gitInv2||Bi(this.git2),this.gitInv3=this.gitInv3||Bi(this.git3),this.gitInv4=this.gitInv4||Bi(this.git4),this.gitInv5=this.gitInv5||Bi(this.git5),this.gitInv6=this.gitInv6||Bi(this.git6),this.gitInv7=this.gitInv7||Bi(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||Bi(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||Bi(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||aW,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||oW}calculate(a){if(typeof a!="object"){this.updateColors();return}const f=Object.keys(a);f.forEach(p=>{this[p]=a[p]}),this.updateColors(),f.forEach(p=>{this[p]=a[p]})}};const v9t=i=>{const a=new b9t;return a.calculate(i),a};class w9t{constructor(){this.primaryColor="#eee",this.contrast="#707070",this.secondaryColor=Qs(this.contrast,55),this.background="#ffffff",this.tertiaryColor=Gn(this.primaryColor,{h:-160}),this.primaryBorderColor=ig(this.primaryColor,this.darkMode),this.secondaryBorderColor=ig(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=ig(this.tertiaryColor,this.darkMode),this.primaryTextColor=Bi(this.primaryColor),this.secondaryTextColor=Bi(this.secondaryColor),this.tertiaryTextColor=Bi(this.tertiaryColor),this.lineColor=Bi(this.background),this.textColor=Bi(this.background),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="white",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="calculated",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="white",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBkgColor="calculated",this.critBorderColor="calculated",this.todayLineColor="calculated",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.secondBkg=Qs(this.contrast,55),this.border2=this.contrast,this.actorBorder=Qs(this.border1,23),this.actorBkg=this.mainBkg,this.actorTextColor=this.text,this.actorLineColor=this.lineColor,this.signalColor=this.text,this.signalTextColor=this.text,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.text,this.loopTextColor=this.text,this.noteBorderColor="#999",this.noteBkgColor="#666",this.noteTextColor="#fff",this.cScale0=this.cScale0||"#555",this.cScale1=this.cScale1||"#F4F4F4",this.cScale2=this.cScale2||"#555",this.cScale3=this.cScale3||"#BBB",this.cScale4=this.cScale4||"#777",this.cScale5=this.cScale5||"#999",this.cScale6=this.cScale6||"#DDD",this.cScale7=this.cScale7||"#FFF",this.cScale8=this.cScale8||"#DDD",this.cScale9=this.cScale9||"#BBB",this.cScale10=this.cScale10||"#999",this.cScale11=this.cScale11||"#777";for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleInv"+a]=this["cScaleInv"+a]||Bi(this["cScale"+a]);for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this.darkMode?this["cScalePeer"+a]=this["cScalePeer"+a]||Qs(this["cScale"+a],10):this["cScalePeer"+a]=this["cScalePeer"+a]||ya(this["cScale"+a],10);this.scaleLabelColor=this.scaleLabelColor||(this.darkMode?"black":this.labelTextColor),this.cScaleLabel0=this.cScaleLabel0||this.cScale1,this.cScaleLabel2=this.cScaleLabel2||this.cScale1;for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["cScaleLabel"+a]=this["cScaleLabel"+a]||this.scaleLabelColor;for(let a=0;a<5;a++)this["surface"+a]=this["surface"+a]||Gn(this.mainBkg,{l:-(5+a*5)}),this["surfacePeer"+a]=this["surfacePeer"+a]||Gn(this.mainBkg,{l:-(8+a*5)});this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.text,this.sectionBkgColor=Qs(this.contrast,30),this.sectionBkgColor2=Qs(this.contrast,30),this.taskBorderColor=ya(this.contrast,10),this.taskBkgColor=this.contrast,this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor=this.text,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.gridColor=Qs(this.border1,30),this.doneTaskBkgColor=this.done,this.doneTaskBorderColor=this.lineColor,this.critBkgColor=this.critical,this.critBorderColor=ya(this.critBkgColor,10),this.todayLineColor=this.critBkgColor,this.transitionColor=this.transitionColor||"#000",this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f4f4f4",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.stateBorder=this.stateBorder||"#000",this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#222",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Gn(this.primaryColor,{h:64}),this.fillType3=Gn(this.secondaryColor,{h:64}),this.fillType4=Gn(this.primaryColor,{h:-64}),this.fillType5=Gn(this.secondaryColor,{h:-64}),this.fillType6=Gn(this.primaryColor,{h:128}),this.fillType7=Gn(this.secondaryColor,{h:128});for(let a=0;a<this.THEME_COLOR_LIMIT;a++)this["pie"+a]=this["cScale"+a];this.pie12=this.pie0,this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.edgeLabelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=ya(this.pie1,25)||this.primaryColor,this.git1=this.pie2||this.secondaryColor,this.git2=this.pie3||this.tertiaryColor,this.git3=this.pie4||Gn(this.primaryColor,{h:-30}),this.git4=this.pie5||Gn(this.primaryColor,{h:-60}),this.git5=this.pie6||Gn(this.primaryColor,{h:-90}),this.git6=this.pie7||Gn(this.primaryColor,{h:60}),this.git7=this.pie8||Gn(this.primaryColor,{h:120}),this.gitInv0=this.gitInv0||Bi(this.git0),this.gitInv1=this.gitInv1||Bi(this.git1),this.gitInv2=this.gitInv2||Bi(this.git2),this.gitInv3=this.gitInv3||Bi(this.git3),this.gitInv4=this.gitInv4||Bi(this.git4),this.gitInv5=this.gitInv5||Bi(this.git5),this.gitInv6=this.gitInv6||Bi(this.git6),this.gitInv7=this.gitInv7||Bi(this.git7),this.branchLabelColor=this.branchLabelColor||this.labelTextColor,this.gitBranchLabel0=this.branchLabelColor,this.gitBranchLabel1="white",this.gitBranchLabel2=this.branchLabelColor,this.gitBranchLabel3="white",this.gitBranchLabel4=this.branchLabelColor,this.gitBranchLabel5=this.branchLabelColor,this.gitBranchLabel6=this.branchLabelColor,this.gitBranchLabel7=this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||aW,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||oW}calculate(a){if(typeof a!="object"){this.updateColors();return}const f=Object.keys(a);f.forEach(p=>{this[p]=a[p]}),this.updateColors(),f.forEach(p=>{this[p]=a[p]})}}const g5={base:{getThemeVariables:h9t},dark:{getThemeVariables:d9t},default:{getThemeVariables:p9t},forest:{getThemeVariables:v9t},neutral:{getThemeVariables:i=>{const a=new w9t;return a.calculate(i),a}}},Qk={theme:"default",themeVariables:g5.default.getThemeVariables(),themeCSS:void 0,maxTextSize:5e4,darkMode:!1,fontFamily:'"trebuchet ms", verdana, arial, sans-serif;',logLevel:5,securityLevel:"strict",startOnLoad:!0,arrowMarkerAbsolute:!1,secure:["secure","securityLevel","startOnLoad","maxTextSize"],deterministicIds:!1,deterministicIDSeed:void 0,flowchart:{titleTopMargin:25,diagramPadding:8,htmlLabels:!0,nodeSpacing:50,rankSpacing:50,curve:"basis",padding:15,useMaxWidth:!0,defaultRenderer:"dagre-wrapper",wrappingWidth:200},sequence:{hideUnusedParticipants:!1,activationWidth:10,diagramMarginX:50,diagramMarginY:10,actorMargin:50,width:150,height:65,boxMargin:10,boxTextMargin:5,noteMargin:10,messageMargin:35,messageAlign:"center",mirrorActors:!0,forceMenus:!1,bottomMarginAdj:1,useMaxWidth:!0,rightAngles:!1,showSequenceNumbers:!1,actorFontSize:14,actorFontFamily:'"Open Sans", sans-serif',actorFontWeight:400,noteFontSize:14,noteFontFamily:'"trebuchet ms", verdana, arial, sans-serif',noteFontWeight:400,noteAlign:"center",messageFontSize:16,messageFontFamily:'"trebuchet ms", verdana, arial, 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sans-serif',taskMargin:50,activationWidth:10,textPlacement:"fo",actorColours:["#8FBC8F","#7CFC00","#00FFFF","#20B2AA","#B0E0E6","#FFFFE0"],sectionFills:["#191970","#8B008B","#4B0082","#2F4F4F","#800000","#8B4513","#00008B"],sectionColours:["#fff"]},timeline:{diagramMarginX:50,diagramMarginY:10,leftMargin:150,width:150,height:50,boxMargin:10,boxTextMargin:5,noteMargin:10,messageMargin:35,messageAlign:"center",bottomMarginAdj:1,useMaxWidth:!0,rightAngles:!1,taskFontSize:14,taskFontFamily:'"Open Sans", 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83:this.$="";break}},table:[{3:1,4:2,5:3,6:4,7:a,8:f,9:p,10:w,11:5,12:10,18:y,22:b,25:E,26:S,27:N,28:B},{1:[3]},{1:[2,1]},{1:[2,2]},{3:17,4:2,5:3,6:4,7:a,8:f,9:p,10:w,11:5,12:10,18:y,22:b,25:E,26:S,27:N,28:B},{1:[2,8]},{1:[2,4]},{1:[2,5]},{1:[2,6]},{1:[2,7]},{13:18,19:[1,19]},{15:[1,20]},{15:[1,21]},{15:[1,22]},{15:[1,23]},{15:[1,24]},{19:[2,11]},{1:[2,3]},{14:25,16:[1,26],21:R},i([16,21],[2,12]),{23:28,29:29,30:30,31:31,32:j,33:$,34:V,36:Q,38:oe,39:58,40:70,42:71,44:ce,46:se,47:ge,48:ye,49:ke,50:Ae,51:de,53:32,54:ve,55:te,56:xe,57:De,58:he,59:Ie,60:ee,61:rt,62:me,63:gt,64:pe,65:Et,66:wt,67:jt,68:At,69:Bt,70:cn,71:Nn,72:Ot,73:oi,74:kt,75:Dt,76:vt,77:Nt,78:ze,79:Xe,80:Lt,81:Ge,82:Bn,83:Oe,84:Ri},{23:79,29:29,30:30,31:31,32:j,33:$,34:V,36:Q,38:oe,39:58,40:70,42:71,44:ce,46:se,47:ge,48:ye,49:ke,50:Ae,51:de,53:32,54:ve,55:te,56:xe,57:De,58:he,59:Ie,60:ee,61:rt,62:me,63:gt,64:pe,65:Et,66:wt,67:jt,68:At,69:Bt,70:cn,71:Nn,72:Ot,73:oi,74:kt,75:Dt,76:vt,77:Nt,78:ze,79:Xe,80:Lt,81:Ge,82:Bn,83:Oe,84:Ri},{23:80,29:29,30:30,31:31,32:j,33:$,34:V,36:Q,38:oe,39:58,40:70,42:71,44:ce,46:se,47:ge,48:ye,49:ke,50:Ae,51:de,53:32,54:ve,55:te,56:xe,57:De,58:he,59:Ie,60:ee,61:rt,62:me,63:gt,64:pe,65:Et,66:wt,67:jt,68:At,69:Bt,70:cn,71:Nn,72:Ot,73:oi,74:kt,75:Dt,76:vt,77:Nt,78:ze,79:Xe,80:Lt,81:Ge,82:Bn,83:Oe,84:Ri},{23:81,29:29,30:30,31:31,32:j,33:$,34:V,36:Q,38:oe,39:58,40:70,42:71,44:ce,46:se,47:ge,48:ye,49:ke,50:Ae,51:de,53:32,54:ve,55:te,56:xe,57:De,58:he,59:Ie,60:ee,61:rt,62:me,63:gt,64:pe,65:Et,66:wt,67:jt,68:At,69:Bt,70:cn,71:Nn,72:Ot,73:oi,74:kt,75:Dt,76:vt,77:Nt,78:ze,79:Xe,80:Lt,81:Ge,82:Bn,83:Oe,84:Ri},{23:82,29:29,30:30,31:31,32:j,33:$,34:V,36:Q,38:oe,39:58,40:70,42:71,44:ce,46:se,47:ge,48:ye,49:ke,50:Ae,51:de,53:32,54:ve,55:te,56:xe,57:De,58:he,59:Ie,60:ee,61:rt,62:me,63:gt,64:pe,65:Et,66:wt,67:jt,68:At,69:Bt,70:cn,71:Nn,72:Ot,73:oi,74:kt,75:Dt,76:vt,77:Nt,78:ze,79:Xe,80:Lt,81:Ge,82:Bn,83:Oe,84:Ri},{15:[1,83]},{17:84,20:[1,85]},{15:[2,14]},{24:[1,86]},i(tn,[2,20],{53:32,39:58,40:70,42:71,30:87,44:ce,46:se,47:ge,48:ye,49:ke,50:Ae,51:de,54:ve,55:te,56:xe,57:De,58:he,59:Ie,60:ee,61:rt,62:me,63:gt,64:pe,65:Et,66:wt,67:jt,68:At,69:Bt,70:cn,71:Nn,72:Ot,73:oi,74:kt,75:Dt,76:vt,77:Nt,78:ze,79:Xe,80:Lt,81:Ge,82:Bn,83:Oe,84:Ri}),i(tn,[2,21]),i(hi,[2,23],{15:[1,88]}),i(tn,[2,43],{15:[1,89]}),i(Sr,[2,26]),i(Sr,[2,27]),{35:[1,90]},{37:[1,91]},i(Sr,[2,30]),{45:92,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:98,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:99,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:100,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:101,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:102,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:103,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:104,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:105,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:106,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:107,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:108,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:109,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:110,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:111,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:112,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:113,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:114,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:115,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:116,85:93,86:Zn,87:Xn,89:ir,90:Hn},i(tr,[2,66]),{45:117,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:118,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:119,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:120,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:121,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:122,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:123,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:124,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:125,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:126,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:127,85:93,86:Zn,87:Xn,89:ir,90:Hn},{30:128,39:58,40:70,42:71,44:ce,46:se,47:ge,48:ye,49:ke,50:Ae,51:de,53:32,54:ve,55:te,56:xe,57:De,58:he,59:Ie,60:ee,61:rt,62:me,63:gt,64:pe,65:Et,66:wt,67:jt,68:At,69:Bt,70:cn,71:Nn,72:Ot,73:oi,74:kt,75:Dt,76:vt,77:Nt,78:ze,79:Xe,80:Lt,81:Ge,82:Bn,83:Oe,84:Ri},{15:[1,130],43:[1,129]},{45:131,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:132,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:133,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:134,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:135,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:136,85:93,86:Zn,87:Xn,89:ir,90:Hn},{45:137,85:93,86:Zn,87:Xn,89:ir,90:Hn},{24:[1,138]},{24:[1,139]},{24:[1,140]},{24:[1,141]},i(ha,[2,9]),{14:142,21:R},{21:[2,13]},{1:[2,15]},i(tn,[2,22]),i(hi,[2,24],{31:31,29:143,32:j,33:$,34:V,36:Q,38:oe}),i(tn,[2,44],{29:29,30:30,31:31,53:32,39:58,40:70,42:71,23:144,32:j,33:$,34:V,36:Q,38:oe,44:ce,46:se,47:ge,48:ye,49:ke,50:Ae,51:de,54:ve,55:te,56:xe,57:De,58:he,59:Ie,60:ee,61:rt,62:me,63:gt,64:pe,65:Et,66:wt,67:jt,68:At,69:Bt,70:cn,71:Nn,72:Ot,73:oi,74:kt,75:Dt,76:vt,77:Nt,78:ze,79:Xe,80:Lt,81:Ge,82:Bn,83:Oe,84:Ri}),i(Sr,[2,28]),i(Sr,[2,29]),i(tr,[2,46]),i(Zs,[2,78],{85:93,45:145,86:Zn,87:Xn,89:ir,90:Hn}),i(ns,[2,80]),{88:[1,146]},i(ns,[2,82]),i(ns,[2,83]),i(tr,[2,47]),i(tr,[2,48]),i(tr,[2,49]),i(tr,[2,50]),i(tr,[2,51]),i(tr,[2,52]),i(tr,[2,53]),i(tr,[2,54]),i(tr,[2,55]),i(tr,[2,56]),i(tr,[2,57]),i(tr,[2,58]),i(tr,[2,59]),i(tr,[2,60]),i(tr,[2,61]),i(tr,[2,62]),i(tr,[2,63]),i(tr,[2,64]),i(tr,[2,65]),i(tr,[2,67]),i(tr,[2,68]),i(tr,[2,69]),i(tr,[2,70]),i(tr,[2,71]),i(tr,[2,72]),i(tr,[2,73]),i(tr,[2,74]),i(tr,[2,75]),i(tr,[2,76]),i(tr,[2,77]),{41:147,52:[1,148]},{15:[1,149]},{43:[1,150]},i(Hi,[2,35]),i(Hi,[2,36]),i(Hi,[2,37]),i(Hi,[2,38]),i(Hi,[2,39]),i(Hi,[2,40]),i(Hi,[2,41]),{1:[2,16]},{1:[2,17]},{1:[2,18]},{1:[2,19]},{15:[1,151]},i(hi,[2,25]),i(tn,[2,45]),i(Zs,[2,79]),i(ns,[2,81]),i(tr,[2,31]),i(tr,[2,42]),i(Js,[2,32]),i(Js,[2,33],{15:[1,152]}),i(ha,[2,10]),i(Js,[2,34])],defaultActions:{2:[2,1],3:[2,2],5:[2,8],6:[2,4],7:[2,5],8:[2,6],9:[2,7],16:[2,11],17:[2,3],27:[2,14],85:[2,13],86:[2,15],138:[2,16],139:[2,17],140:[2,18],141:[2,19]},parseError:function(Er,br){if(br.recoverable)this.trace(Er);else{var Dr=new Error(Er);throw Dr.hash=br,Dr}},parse:function(Er){var br=this,Dr=[0],Vn=[],qi=[null],yn=[],Bc=this.table,jn="",Ms=0,Pa=0,Ta=2,_a=1,ka=yn.slice.call(arguments,1),Qi=Object.create(this.lexer),ea={yy:{}};for(var Ca in this.yy)Object.prototype.hasOwnProperty.call(this.yy,Ca)&&(ea.yy[Ca]=this.yy[Ca]);Qi.setInput(Er,ea.yy),ea.yy.lexer=Qi,ea.yy.parser=this,typeof Qi.yylloc>"u"&&(Qi.yylloc={});var Sa=Qi.yylloc;yn.push(Sa);var Ka=Qi.options&&Qi.options.ranges;typeof ea.yy.parseError=="function"?this.parseError=ea.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function cg(){var Ns;return Ns=Vn.pop()||Qi.lex()||_a,typeof Ns!="number"&&(Ns instanceof Array&&(Vn=Ns,Ns=Vn.pop()),Ns=br.symbols_[Ns]||Ns),Ns}for(var Gc,Dh,Es,lp,sd={},sh,Ai,nn,Tr;;){if(Dh=Dr[Dr.length-1],this.defaultActions[Dh]?Es=this.defaultActions[Dh]:((Gc===null||typeof Gc>"u")&&(Gc=cg()),Es=Bc[Dh]&&Bc[Dh][Gc]),typeof Es>"u"||!Es.length||!Es[0]){var ai="";Tr=[];for(sh in Bc[Dh])this.terminals_[sh]&&sh>Ta&&Tr.push("'"+this.terminals_[sh]+"'");Qi.showPosition?ai="Parse error on line "+(Ms+1)+`:
`+Qi.showPosition()+`
Expecting `+Tr.join(", ")+", got '"+(this.terminals_[Gc]||Gc)+"'":ai="Parse error on line "+(Ms+1)+": Unexpected "+(Gc==_a?"end of input":"'"+(this.terminals_[Gc]||Gc)+"'"),this.parseError(ai,{text:Qi.match,token:this.terminals_[Gc]||Gc,line:Qi.yylineno,loc:Sa,expected:Tr})}if(Es[0]instanceof Array&&Es.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Dh+", token: "+Gc);switch(Es[0]){case 1:Dr.push(Gc),qi.push(Qi.yytext),yn.push(Qi.yylloc),Dr.push(Es[1]),Gc=null,Pa=Qi.yyleng,jn=Qi.yytext,Ms=Qi.yylineno,Sa=Qi.yylloc;break;case 2:if(Ai=this.productions_[Es[1]][1],sd.$=qi[qi.length-Ai],sd._$={first_line:yn[yn.length-(Ai||1)].first_line,last_line:yn[yn.length-1].last_line,first_column:yn[yn.length-(Ai||1)].first_column,last_column:yn[yn.length-1].last_column},Ka&&(sd._$.range=[yn[yn.length-(Ai||1)].range[0],yn[yn.length-1].range[1]]),lp=this.performAction.apply(sd,[jn,Pa,Ms,ea.yy,Es[1],qi,yn].concat(ka)),typeof lp<"u")return lp;Ai&&(Dr=Dr.slice(0,-1*Ai*2),qi=qi.slice(0,-1*Ai),yn=yn.slice(0,-1*Ai)),Dr.push(this.productions_[Es[1]][0]),qi.push(sd.$),yn.push(sd._$),nn=Bc[Dr[Dr.length-2]][Dr[Dr.length-1]],Dr.push(nn);break;case 3:return!0}}return!0}},Ga=function(){var Oi={EOF:1,parseError:function(br,Dr){if(this.yy.parser)this.yy.parser.parseError(br,Dr);else throw new Error(br)},setInput:function(Er,br){return this.yy=br||this.yy||{},this._input=Er,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var Er=this._input[0];this.yytext+=Er,this.yyleng++,this.offset++,this.match+=Er,this.matched+=Er;var br=Er.match(/(?:\r\n?|\n).*/g);return br?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),Er},unput:function(Er){var br=Er.length,Dr=Er.split(/(?:\r\n?|\n)/g);this._input=Er+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-br),this.offset-=br;var Vn=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Dr.length-1&&(this.yylineno-=Dr.length-1);var qi=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Dr?(Dr.length===Vn.length?this.yylloc.first_column:0)+Vn[Vn.length-Dr.length].length-Dr[0].length:this.yylloc.first_column-br},this.options.ranges&&(this.yylloc.range=[qi[0],qi[0]+this.yyleng-br]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(Er){this.unput(this.match.slice(Er))},pastInput:function(){var Er=this.matched.substr(0,this.matched.length-this.match.length);return(Er.length>20?"...":"")+Er.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var Er=this.match;return Er.length<20&&(Er+=this._input.substr(0,20-Er.length)),(Er.substr(0,20)+(Er.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var Er=this.pastInput(),br=new Array(Er.length+1).join("-");return Er+this.upcomingInput()+`
`+br+"^"},test_match:function(Er,br){var Dr,Vn,qi;if(this.options.backtrack_lexer&&(qi={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(qi.yylloc.range=this.yylloc.range.slice(0))),Vn=Er[0].match(/(?:\r\n?|\n).*/g),Vn&&(this.yylineno+=Vn.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:Vn?Vn[Vn.length-1].length-Vn[Vn.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+Er[0].length},this.yytext+=Er[0],this.match+=Er[0],this.matches=Er,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(Er[0].length),this.matched+=Er[0],Dr=this.performAction.call(this,this.yy,this,br,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),Dr)return Dr;if(this._backtrack){for(var yn in qi)this[yn]=qi[yn];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var Er,br,Dr,Vn;this._more||(this.yytext="",this.match="");for(var qi=this._currentRules(),yn=0;yn<qi.length;yn++)if(Dr=this._input.match(this.rules[qi[yn]]),Dr&&(!br||Dr[0].length>br[0].length)){if(br=Dr,Vn=yn,this.options.backtrack_lexer){if(Er=this.test_match(Dr,qi[yn]),Er!==!1)return Er;if(this._backtrack){br=!1;continue}else return!1}else if(!this.options.flex)break}return br?(Er=this.test_match(br,qi[Vn]),Er!==!1?Er:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
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R=w,j=b.label.width;if(b.descr&&b.descr.text!==""){let $=ZN(xs,b.typeC4Shape.text);Jw("descr",b,S,$,N),b.descr.Y=w+20,w=b.descr.Y+b.descr.height,j=Math.max(b.label.width,b.descr.width),R=w-b.descr.textLines*5}j=j+xs.c4ShapePadding,b.width=Math.max(b.width||xs.width,j,xs.width),b.height=Math.max(b.height||xs.height,R,xs.height),b.margin=b.margin||xs.c4ShapeMargin,i.insert(b),c3.drawC4Shape(a,b,xs)}i.bumpLastMargin(xs.c4ShapeMargin)};let Kb=class{constructor(a,f){this.x=a,this.y=f}},qOe=function(i,a){let f=i.x,p=i.y,w=a.x,y=a.y,b=f+i.width/2,E=p+i.height/2,S=Math.abs(f-w),N=Math.abs(p-y),B=N/S,R=i.height/i.width,j=null;return p==y&&f<w?j=new Kb(f+i.width,E):p==y&&f>w?j=new Kb(f,E):f==w&&p<y?j=new Kb(b,p+i.height):f==w&&p>y&&(j=new Kb(b,p)),f>w&&p<y?R>=B?j=new Kb(f,E+B*i.width/2):j=new Kb(b-S/N*i.height/2,p+i.height):f<w&&p<y?R>=B?j=new Kb(f+i.width,E+B*i.width/2):j=new Kb(b+S/N*i.height/2,p+i.height):f<w&&p>y?R>=B?j=new Kb(f+i.width,E-B*i.width/2):j=new Kb(b+i.height/2*S/N,p):f>w&&p>y&&(R>=B?j=new Kb(f,E-i.width/2*B):j=new Kb(b-i.height/2*S/N,p)),j},tMt=function(i,a){let f={x:0,y:0};f.x=a.x+a.width/2,f.y=a.y+a.height/2;let p=qOe(i,f);f.x=i.x+i.width/2,f.y=i.y+i.height/2;let w=qOe(a,f);return{startPoint:p,endPoint:w}};const nMt=function(i,a,f,p){let w=0;for(let y of a){w=w+1;let b=y.wrap&&xs.wrap,E=eMt(xs);p.db.getC4Type()==="C4Dynamic"&&(y.label.text=w+": "+y.label.text);let N=s3(y.label.text,E);Jw("label",y,b,E,N),y.techn&&y.techn.text!==""&&(N=s3(y.techn.text,E),Jw("techn",y,b,E,N)),y.descr&&y.descr.text!==""&&(N=s3(y.descr.text,E),Jw("descr",y,b,E,N));let B=f(y.from),R=f(y.to),j=tMt(B,R);y.startPoint=j.startPoint,y.endPoint=j.endPoint}c3.drawRels(i,a,xs)};function VOe(i,a,f,p,w){let y=new HOe(w);y.data.widthLimit=f.data.widthLimit/Math.min(Ade,p.length);for(let[b,E]of p.entries()){let S=0;E.image={width:0,height:0,Y:0},E.sprite&&(E.image.width=48,E.image.height=48,E.image.Y=S,S=E.image.Y+E.image.height);let N=E.wrap&&xs.wrap,B=FW(xs);if(B.fontSize=B.fontSize+2,B.fontWeight="bold",Jw("label",E,N,B,y.data.widthLimit),E.label.Y=S+8,S=E.label.Y+E.label.height,E.type&&E.type.text!==""){E.type.text="["+E.type.text+"]";let V=FW(xs);Jw("type",E,N,V,y.data.widthLimit),E.type.Y=S+5,S=E.type.Y+E.type.height}if(E.descr&&E.descr.text!==""){let V=FW(xs);V.fontSize=V.fontSize-2,Jw("descr",E,N,V,y.data.widthLimit),E.descr.Y=S+20,S=E.descr.Y+E.descr.height}if(b==0||b%Ade===0){let V=f.data.startx+xs.diagramMarginX,Q=f.data.stopy+xs.diagramMarginY+S;y.setData(V,V,Q,Q)}else{let V=y.data.stopx!==y.data.startx?y.data.stopx+xs.diagramMarginX:y.data.startx,Q=y.data.starty;y.setData(V,V,Q,Q)}y.name=E.alias;let R=w.db.getC4ShapeArray(E.alias),j=w.db.getC4ShapeKeys(E.alias);j.length>0&&GOe(y,i,R,j),a=E.alias;let $=w.db.getBoundarys(a);$.length>0&&VOe(i,a,y,$,w),E.alias!=="global"&&zOe(i,E,y),f.data.stopy=Math.max(y.data.stopy+xs.c4ShapeMargin,f.data.stopy),f.data.stopx=Math.max(y.data.stopx+xs.c4ShapeMargin,f.data.stopx),BW=Math.max(BW,f.data.stopx),RW=Math.max(RW,f.data.stopy)}}const UOe={drawPersonOrSystemArray:GOe,drawBoundary:zOe,setConf:Lde,draw:function(i,a,f,p){xs=Tt().c4;const w=Tt().securityLevel;let y;w==="sandbox"&&(y=Cr("#i"+a));const b=Cr(w==="sandbox"?y.nodes()[0].contentDocument.body:"body");let E=p.db;p.db.setWrap(xs.wrap),$Oe=E.getC4ShapeInRow(),Ade=E.getC4BoundaryInRow(),Fe.debug(`C:${JSON.stringify(xs,null,2)}`);const S=w==="sandbox"?b.select(`[id="${a}"]`):Cr(`[id="${a}"]`);c3.insertComputerIcon(S),c3.insertDatabaseIcon(S),c3.insertClockIcon(S);let N=new HOe(p);N.setData(xs.diagramMarginX,xs.diagramMarginX,xs.diagramMarginY,xs.diagramMarginY),N.data.widthLimit=screen.availWidth,BW=xs.diagramMarginX,RW=xs.diagramMarginY;const B=p.db.getTitle();let R=p.db.getBoundarys("");VOe(S,"",N,R,p),c3.insertArrowHead(S),c3.insertArrowEnd(S),c3.insertArrowCrossHead(S),c3.insertArrowFilledHead(S),nMt(S,p.db.getRels(),p.db.getC4Shape,p),N.data.stopx=BW,N.data.stopy=RW;const j=N.data;let V=j.stopy-j.starty+2*xs.diagramMarginY;const oe=j.stopx-j.startx+2*xs.diagramMarginX;B&&S.append("text").text(B).attr("x",(j.stopx-j.startx)/2-4*xs.diagramMarginX).attr("y",j.starty+xs.diagramMarginY),Vw(S,V,oe,xs.useMaxWidth);const ce=B?60:0;S.attr("viewBox",j.startx-xs.diagramMarginX+" -"+(xs.diagramMarginY+ce)+" "+oe+" "+(V+ce)),Fe.debug("models:",j)}},rMt=Object.freeze(Object.defineProperty({__proto__:null,diagram:{parser:vLt,db:ROe,renderer:UOe,styles:i=>`.person {
stroke: ${i.personBorder};
fill: ${i.personBkg};
}
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GSTART",119:"direction_tb",120:"direction_bt",121:"direction_rl",122:"direction_lr",123:"PUNCTUATION",124:"UNICODE_TEXT",125:"PLUS",126:"EQUALS",127:"MULT",128:"UNDERSCORE",130:"ARROW_CROSS",131:"ARROW_POINT",132:"ARROW_CIRCLE",133:"ARROW_OPEN",134:"QUOTE"},productions_:[0,[3,1],[3,2],[5,4],[5,6],[6,1],[7,1],[11,1],[8,1],[4,2],[17,0],[17,2],[18,1],[18,1],[18,1],[18,1],[18,1],[16,2],[16,2],[16,2],[16,3],[28,2],[28,1],[29,1],[29,1],[29,1],[27,1],[27,1],[27,2],[31,2],[31,2],[31,1],[31,1],[30,2],[30,1],[19,2],[19,2],[19,2],[19,2],[19,2],[19,2],[19,9],[19,6],[19,4],[19,1],[19,2],[19,2],[19,1],[9,1],[9,1],[9,1],[32,3],[32,4],[32,2],[32,1],[50,1],[50,5],[50,3],[51,4],[51,4],[51,6],[51,4],[51,4],[51,4],[51,8],[51,4],[51,4],[51,4],[51,6],[51,4],[51,4],[51,4],[51,4],[51,4],[51,1],[49,2],[49,3],[49,3],[49,1],[49,3],[78,1],[79,3],[39,1],[39,2],[39,1],[39,1],[86,1],[86,1],[86,1],[86,1],[86,1],[86,1],[86,1],[86,1],[86,1],[86,1],[86,1],[94,1],[94,2],[35,5],[35,5],[36,5],[37,2],[37,4],[37,3],[37,5],[37,2],[37,4],[37,4],[37,6],[37,2],[37,4],[37,2],[37,4],[37,4],[37,6],[33,5],[33,5],[34,5],[34,5],[34,9],[34,9],[34,7],[34,7],[104,1],[104,3],[97,1],[97,3],[108,1],[108,2],[109,1],[109,1],[109,1],[109,1],[109,1],[109,1],[109,1],[109,1],[109,1],[109,1],[109,1],[83,1],[83,1],[83,1],[83,1],[83,1],[83,1],[95,1],[95,1],[95,1],[95,1],[54,1],[54,2],[98,1],[98,2],[118,1],[118,1],[118,1],[118,1],[43,1],[43,1],[43,1],[43,1],[116,1],[116,1],[116,1],[116,1],[116,1],[116,1],[116,1],[116,1],[116,1],[116,1],[116,1],[116,1],[116,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[117,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1],[129,1]],performAction:function(rs,Ps,Aa,pi,Rc,_t,hp){var Gt=_t.length-1;switch(Rc){case 5:pi.parseDirective("%%{","open_directive");break;case 6:pi.parseDirective(_t[Gt],"type_directive");break;case 7:_t[Gt]=_t[Gt].trim().replace(/'/g,'"'),pi.parseDirective(_t[Gt],"arg_directive");break;case 8:pi.parseDirective("}%%","close_directive","flowchart");break;case 10:this.$=[];break;case 11:(!Array.isArray(_t[Gt])||_t[Gt].length>0)&&_t[Gt-1].push(_t[Gt]),this.$=_t[Gt-1];break;case 12:case 97:case 153:case 155:case 156:this.$=_t[Gt];break;case 19:pi.setDirection("TB"),this.$="TB";break;case 20:pi.setDirection(_t[Gt-1]),this.$=_t[Gt-1];break;case 35:this.$=_t[Gt-1].nodes;break;case 36:case 37:case 38:case 39:case 40:this.$=[];break;case 41:this.$=pi.addSubGraph(_t[Gt-6],_t[Gt-1],_t[Gt-4]);break;case 42:this.$=pi.addSubGraph(_t[Gt-3],_t[Gt-1],_t[Gt-3]);break;case 43:this.$=pi.addSubGraph(void 0,_t[Gt-1],void 0);break;case 45:this.$=_t[Gt].trim(),pi.setAccTitle(this.$);break;case 46:case 47:this.$=_t[Gt].trim(),pi.setAccDescription(this.$);break;case 51:pi.addLink(_t[Gt-2].stmt,_t[Gt],_t[Gt-1]),this.$={stmt:_t[Gt],nodes:_t[Gt].concat(_t[Gt-2].nodes)};break;case 52:pi.addLink(_t[Gt-3].stmt,_t[Gt-1],_t[Gt-2]),this.$={stmt:_t[Gt-1],nodes:_t[Gt-1].concat(_t[Gt-3].nodes)};break;case 53:this.$={stmt:_t[Gt-1],nodes:_t[Gt-1]};break;case 54:this.$={stmt:_t[Gt],nodes:_t[Gt]};break;case 55:this.$=[_t[Gt]];break;case 56:this.$=_t[Gt-4].concat(_t[Gt]);break;case 57:this.$=[_t[Gt-2]],pi.setClass(_t[Gt-2],_t[Gt]);break;case 58:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"square");break;case 59:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"doublecircle");break;case 60:this.$=_t[Gt-5],pi.addVertex(_t[Gt-5],_t[Gt-2],"circle");break;case 61:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"ellipse");break;case 62:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"stadium");break;case 63:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"subroutine");break;case 64:this.$=_t[Gt-7],pi.addVertex(_t[Gt-7],_t[Gt-1],"rect",void 0,void 0,void 0,Object.fromEntries([[_t[Gt-5],_t[Gt-3]]]));break;case 65:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"cylinder");break;case 66:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"round");break;case 67:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"diamond");break;case 68:this.$=_t[Gt-5],pi.addVertex(_t[Gt-5],_t[Gt-2],"hexagon");break;case 69:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"odd");break;case 70:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"trapezoid");break;case 71:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"inv_trapezoid");break;case 72:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"lean_right");break;case 73:this.$=_t[Gt-3],pi.addVertex(_t[Gt-3],_t[Gt-1],"lean_left");break;case 74:this.$=_t[Gt],pi.addVertex(_t[Gt]);break;case 75:_t[Gt-1].text=_t[Gt],this.$=_t[Gt-1];break;case 76:case 77:_t[Gt-2].text=_t[Gt-1],this.$=_t[Gt-2];break;case 78:this.$=_t[Gt];break;case 79:var ff=pi.destructLink(_t[Gt],_t[Gt-2]);this.$={type:ff.type,stroke:ff.stroke,length:ff.length,text:_t[Gt-1]};break;case 80:var ff=pi.destructLink(_t[Gt]);this.$={type:ff.type,stroke:ff.stroke,length:ff.length};break;case 81:this.$=_t[Gt-1];break;case 82:this.$={text:_t[Gt],type:"text"};break;case 83:this.$={text:_t[Gt-1].text+""+_t[Gt],type:_t[Gt-1].type};break;case 84:this.$={text:_t[Gt],type:"text"};break;case 85:this.$={text:_t[Gt],type:"markdown"};break;case 98:case 154:this.$=_t[Gt-1]+""+_t[Gt];break;case 99:case 100:this.$=_t[Gt-4],pi.addClass(_t[Gt-2],_t[Gt]);break;case 101:this.$=_t[Gt-4],pi.setClass(_t[Gt-2],_t[Gt]);break;case 102:case 110:this.$=_t[Gt-1],pi.setClickEvent(_t[Gt-1],_t[Gt]);break;case 103:case 111:this.$=_t[Gt-3],pi.setClickEvent(_t[Gt-3],_t[Gt-2]),pi.setTooltip(_t[Gt-3],_t[Gt]);break;case 104:this.$=_t[Gt-2],pi.setClickEvent(_t[Gt-2],_t[Gt-1],_t[Gt]);break;case 105:this.$=_t[Gt-4],pi.setClickEvent(_t[Gt-4],_t[Gt-3],_t[Gt-2]),pi.setTooltip(_t[Gt-4],_t[Gt]);break;case 106:case 112:this.$=_t[Gt-1],pi.setLink(_t[Gt-1],_t[Gt]);break;case 107:case 113:this.$=_t[Gt-3],pi.setLink(_t[Gt-3],_t[Gt-2]),pi.setTooltip(_t[Gt-3],_t[Gt]);break;case 108:case 114:this.$=_t[Gt-3],pi.setLink(_t[Gt-3],_t[Gt-2],_t[Gt]);break;case 109:case 115:this.$=_t[Gt-5],pi.setLink(_t[Gt-5],_t[Gt-4],_t[Gt]),pi.setTooltip(_t[Gt-5],_t[Gt-2]);break;case 116:this.$=_t[Gt-4],pi.addVertex(_t[Gt-2],void 0,void 0,_t[Gt]);break;case 117:case 119:this.$=_t[Gt-4],pi.updateLink(_t[Gt-2],_t[Gt]);break;case 118:this.$=_t[Gt-4],pi.updateLink([_t[Gt-2]],_t[Gt]);break;case 120:this.$=_t[Gt-8],pi.updateLinkInterpolate([_t[Gt-6]],_t[Gt-2]),pi.updateLink([_t[Gt-6]],_t[Gt]);break;case 121:this.$=_t[Gt-8],pi.updateLinkInterpolate(_t[Gt-6],_t[Gt-2]),pi.updateLink(_t[Gt-6],_t[Gt]);break;case 122:this.$=_t[Gt-6],pi.updateLinkInterpolate([_t[Gt-4]],_t[Gt]);break;case 123:this.$=_t[Gt-6],pi.updateLinkInterpolate(_t[Gt-4],_t[Gt]);break;case 124:case 126:this.$=[_t[Gt]];break;case 125:case 127:_t[Gt-2].push(_t[Gt]),this.$=_t[Gt-2];break;case 129:this.$=_t[Gt-1]+_t[Gt];break;case 151:this.$=_t[Gt];break;case 152:this.$=_t[Gt-1]+""+_t[Gt];break;case 157:this.$="v";break;case 158:this.$="-";break;case 159:this.$={stmt:"dir",value:"TB"};break;case 160:this.$={stmt:"dir",value:"BT"};break;case 161:this.$={stmt:"dir",value:"RL"};break;case 162:this.$={stmt:"dir",value:"LR"};break}},table:[{3:1,4:2,5:3,6:5,12:a,16:4,21:f,22:p,24:w},{1:[3]},{1:[2,1]},{3:10,4:2,5:3,6:5,12:a,16:4,21:f,22:p,24:w},i(y,b,{17:11}),{7:12,13:[1,13]},{16:14,21:f,22:p,24:w},{16:15,21:f,22:p,24:w},{25:[1,16],26:[1,17]},{13:[2,5]},{1:[2,2]},{1:[2,9],18:18,19:19,20:E,21:S,22:N,23:B,32:24,33:25,34:26,35:27,36:28,37:29,38:R,43:31,44:j,46:$,48:V,50:35,51:45,52:Q,54:46,66:oe,67:ce,87:se,88:ge,89:ye,90:ke,91:Ae,92:de,96:ve,106:te,107:xe,110:De,112:he,113:Ie,117:47,119:ee,120:rt,121:me,122:gt,123:pe,124:Et,125:wt,126:jt,127:At,128:Bt},{8:64,10:[1,65],15:cn},i([10,15],[2,6]),i(y,[2,17]),i(y,[2,18]),i(y,[2,19]),{20:[1,68],21:[1,69],22:Nn,27:67,30:70},i(Ot,[2,11]),i(Ot,[2,12]),i(Ot,[2,13]),i(Ot,[2,14]),i(Ot,[2,15]),i(Ot,[2,16]),{9:72,20:oi,21:kt,23:Dt,49:73,78:77,81:[1,78],82:[1,79]},{9:80,20:oi,21:kt,23:Dt},{9:81,20:oi,21:kt,23:Dt},{9:82,20:oi,21:kt,23:Dt},{9:83,20:oi,21:kt,23:Dt},{9:84,20:oi,21:kt,23:Dt},{9:86,20:oi,21:kt,22:[1,85],23:Dt},i(Ot,[2,44]),{45:[1,87]},{47:[1,88]},i(Ot,[2,47]),i(vt,[2,54],{30:89,22:Nn}),{22:[1,90]},{22:[1,91]},{22:[1,92]},{22:[1,93]},{26:Nt,52:ze,66:Xe,67:Lt,84:[1,97],92:Ge,98:96,99:[1,94],101:[1,95],106:Bn,107:Oe,110:Ri,112:tn,113:hi,116:100,118:98,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},i(Ot,[2,159]),i(Ot,[2,160]),i(Ot,[2,161]),i(Ot,[2,162]),i(ha,[2,55],{53:[1,116]}),i(Zs,[2,74],{117:129,40:[1,117],52:Q,55:[1,118],57:[1,119],59:[1,120],61:[1,121],63:[1,122],65:[1,123],66:oe,67:ce,69:[1,124],71:[1,125],73:[1,126],74:[1,127],76:[1,128],92:de,96:ve,106:te,107:xe,110:De,112:he,113:Ie,123:pe,124:Et,125:wt,126:jt,127:At,128:Bt}),i(ns,[2,151]),i(ns,[2,176]),i(ns,[2,177]),i(ns,[2,178]),i(ns,[2,179]),i(ns,[2,180]),i(ns,[2,181]),i(ns,[2,182]),i(ns,[2,183]),i(ns,[2,184]),i(ns,[2,185]),i(ns,[2,186]),i(ns,[2,187]),i(ns,[2,188]),i(ns,[2,189]),i(ns,[2,190]),i(ns,[2,191]),{9:130,20:oi,21:kt,23:Dt},{11:131,14:[1,132]},i(Hi,[2,8]),i(y,[2,20]),i(y,[2,26]),i(y,[2,27]),{21:[1,133]},i(Js,[2,34],{30:134,22:Nn}),i(Ot,[2,35]),{50:135,51:45,52:Q,54:46,66:oe,67:ce,92:de,96:ve,106:te,107:xe,110:De,112:he,113:Ie,117:47,123:pe,124:Et,125:wt,126:jt,127:At,128:Bt},i(Pc,[2,48]),i(Pc,[2,49]),i(Pc,[2,50]),i(Ga,[2,78],{79:136,68:[1,138],80:[1,137]}),{22:ws,24:Oi,26:Er,38:br,39:139,42:Dr,52:ze,66:Xe,67:Lt,73:Vn,81:qi,83:140,84:yn,85:Bc,86:152,87:jn,88:Ms,89:Pa,90:Ta,91:_a,92:ka,93:Qi,95:143,96:ea,106:Bn,107:Oe,110:Ca,112:tn,113:hi,114:Sa,115:Ka,116:149,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},i([52,66,67,68,80,92,96,106,107,110,112,113,123,124,125,126,127,128],[2,80]),i(Ot,[2,36]),i(Ot,[2,37]),i(Ot,[2,38]),i(Ot,[2,39]),i(Ot,[2,40]),{22:ws,24:Oi,26:Er,38:br,39:164,42:Dr,52:ze,66:Xe,67:Lt,73:Vn,81:qi,83:140,84:yn,85:Bc,86:152,87:jn,88:Ms,89:Pa,90:Ta,91:_a,92:ka,93:Qi,95:143,96:ea,106:Bn,107:Oe,110:Ca,112:tn,113:hi,114:Sa,115:Ka,116:149,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},i(cg,b,{17:165}),i(Ot,[2,45]),i(Ot,[2,46]),i(vt,[2,53],{52:Gc}),{26:Nt,52:ze,66:Xe,67:Lt,92:Ge,98:167,103:[1,168],106:Bn,107:Oe,110:Ri,112:tn,113:hi,116:100,118:98,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},{96:[1,169],104:170,106:[1,171]},{26:Nt,52:ze,66:Xe,67:Lt,92:Ge,96:[1,172],98:173,106:Bn,107:Oe,110:Ri,112:tn,113:hi,116:100,118:98,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},{26:Nt,52:ze,66:Xe,67:Lt,92:Ge,98:174,106:Bn,107:Oe,110:Ri,112:tn,113:hi,116:100,118:98,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},i(Hi,[2,102],{22:[1,175],100:[1,176]}),i(Hi,[2,106],{22:[1,177]}),i(Hi,[2,110],{116:100,118:179,22:[1,178],26:Nt,52:ze,66:Xe,67:Lt,92:Ge,106:Bn,107:Oe,110:Ri,112:tn,113:hi,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr}),i(Hi,[2,112],{22:[1,180]}),i(Dh,[2,153]),i(Dh,[2,155]),i(Dh,[2,156]),i(Dh,[2,157]),i(Dh,[2,158]),i(Es,[2,163]),i(Es,[2,164]),i(Es,[2,165]),i(Es,[2,166]),i(Es,[2,167]),i(Es,[2,168]),i(Es,[2,169]),i(Es,[2,170]),i(Es,[2,171]),i(Es,[2,172]),i(Es,[2,173]),i(Es,[2,174]),i(Es,[2,175]),{52:Q,54:181,66:oe,67:ce,92:de,96:ve,106:te,107:xe,110:De,112:he,113:Ie,117:47,123:pe,124:Et,125:wt,126:jt,127:At,128:Bt},{22:ws,24:Oi,26:Er,38:br,39:182,42:Dr,52:ze,66:Xe,67:Lt,73:Vn,81:qi,83:140,84:yn,85:Bc,86:152,87:jn,88:Ms,89:Pa,90:Ta,91:_a,92:ka,93:Qi,95:143,96:ea,106:Bn,107:Oe,110:Ca,112:tn,113:hi,114:Sa,115:Ka,116:149,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},{22:ws,24:Oi,26:Er,38:br,39:183,42:Dr,52:ze,66:Xe,67:Lt,73:Vn,81:qi,83:140,84:yn,85:Bc,86:152,87:jn,88:Ms,89:Pa,90:Ta,91:_a,92:ka,93:Qi,95:143,96:ea,106:Bn,107:Oe,110:Ca,112:tn,113:hi,114:Sa,115:Ka,116:149,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},{22:ws,24:Oi,26:Er,38:br,39:185,42:Dr,52:ze,57:[1,184],66:Xe,67:Lt,73:Vn,81:qi,83:140,84:yn,85:Bc,86:152,87:jn,88:Ms,89:Pa,90:Ta,91:_a,92:ka,93:Qi,95:143,96:ea,106:Bn,107:Oe,110:Ca,112:tn,113:hi,114:Sa,115:Ka,116:149,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},{22:ws,24:Oi,26:Er,38:br,39:186,42:Dr,52:ze,66:Xe,67:Lt,73:Vn,81:qi,83:140,84:yn,85:Bc,86:152,87:jn,88:Ms,89:Pa,90:Ta,91:_a,92:ka,93:Qi,95:143,96:ea,106:Bn,107:Oe,110:Ca,112:tn,113:hi,114:Sa,115:Ka,116:149,123:Sr,124:Zn,125:Xn,126:ir,127:Hn,128:tr},{22:ws,24:O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Aa=new Error(rs);throw Aa.hash=Ps,Aa}},parse:function(rs){var Ps=this,Aa=[0],pi=[],Rc=[null],_t=[],hp=this.table,Gt="",ff=0,sm=0,m9=2,fp=1,N5=_t.slice.call(arguments,1),ah=Object.create(this.lexer),od={yy:{}};for(var P5 in this.yy)Object.prototype.hasOwnProperty.call(this.yy,P5)&&(od.yy[P5]=this.yy[P5]);ah.setInput(rs,od.yy),od.yy.lexer=ah,od.yy.parser=this,typeof ah.yylloc>"u"&&(ah.yylloc={});var B5=ah.yylloc;_t.push(B5);var y9=ah.options&&ah.options.ranges;typeof od.yy.parseError=="function"?this.parseError=od.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function vL(){var oh;return oh=pi.pop()||ah.lex()||fp,typeof oh!="number"&&(oh instanceof Array&&(pi=oh,oh=pi.pop()),oh=Ps.symbols_[oh]||oh),oh}for(var v1,dp,cd,am,ev={},om,yc,hx,Vo;;){if(dp=Aa[Aa.length-1],this.defaultActions[dp]?cd=this.defaultActions[dp]:((v1===null||typeof v1>"u")&&(v1=vL()),cd=hp[dp]&&hp[dp][v1]),typeof cd>"u"||!cd.length||!cd[0]){var fx="";Vo=[];for(om in hp[dp])this.terminals_[om]&&om>m9&&Vo.push("'"+this.terminals_[om]+"'");ah.showPosition?fx="Parse error on line "+(ff+1)+`:
`+ah.showPosition()+`
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`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(rs){this.unput(this.match.slice(rs))},pastInput:function(){var rs=this.matched.substr(0,this.matched.length-this.match.length);return(rs.length>20?"...":"")+rs.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var rs=this.match;return rs.length<20&&(rs+=this._input.substr(0,20-rs.length)),(rs.substr(0,20)+(rs.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var rs=this.pastInput(),Ps=new Array(rs.length+1).join("-");return rs+this.upcomingInput()+`
`+Ps+"^"},test_match:function(rs,Ps){var Aa,pi,Rc;if(this.options.backtrack_lexer&&(Rc={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Rc.yylloc.range=this.yylloc.range.slice(0))),pi=rs[0].match(/(?:\r\n?|\n).*/g),pi&&(this.yylineno+=pi.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:pi?pi[pi.length-1].length-pi[pi.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+rs[0].length},this.yytext+=rs[0],this.match+=rs[0],this.matches=rs,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(rs[0].length),this.matched+=rs[0],Aa=this.performAction.call(this,this.yy,this,Ps,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),Aa)return Aa;if(this._backtrack){for(var _t in Rc)this[_t]=Rc[_t];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var rs,Ps,Aa,pi;this._more||(this.yytext="",this.match="");for(var Rc=this._currentRules(),_t=0;_t<Rc.length;_t++)if(Aa=this._input.match(this.rules[Rc[_t]]),Aa&&(!Ps||Aa[0].length>Ps[0].length)){if(Ps=Aa,pi=_t,this.options.backtrack_lexer){if(rs=this.test_match(Aa,Rc[_t]),rs!==!1)return rs;if(this._backtrack){Ps=!1;continue}else return!1}else if(!this.options.flex)break}return Ps?(rs=this.test_match(Ps,Rc[pi]),rs!==!1?rs:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var Ps=this.next();return Ps||this.lex()},begin:function(Ps){this.conditionStack.push(Ps)},popState:function(){var Ps=this.conditionStack.length-1;return Ps>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(Ps){return Ps=this.conditionStack.length-1-Math.abs(Ps||0),Ps>=0?this.conditionStack[Ps]:"INITIAL"},pushState:function(Ps){this.begin(Ps)},stateStackSize:function(){return this.conditionStack.length},options:{},performAction:function(Ps,Aa,pi,Rc){switch(pi){case 0:return this.begin("open_directive"),12;case 1:return this.begin("type_directive"),13;case 2:return this.popState(),this.begin("arg_directive"),10;case 3:return this.popState(),this.popState(),15;case 4:return 14;case 5:return this.begin("acc_title"),44;case 6:return this.popState(),"acc_title_value";case 7:return this.begin("acc_descr"),46;case 8:return this.popState(),"acc_descr_value";case 9:this.begin("acc_descr_multiline");break;case 10:this.popState();break;case 11:return"acc_descr_multiline_value";case 12:this.begin("md_string");break;case 13:return"MD_STR";case 14:this.popState();break;case 15:this.begin("string");break;case 16:this.popState();break;case 17:return"STR";case 18:return 87;case 19:return 96;case 20:return 88;case 21:return 105;case 22:return 89;case 23:return 90;case 24:this.begin("href");break;case 25:this.popState();break;case 26:return 101;case 27:this.begin("callbackname");break;case 28:this.popState();break;case 29:this.popState(),this.begin("callbackargs");break;case 30:return 99;case 31:this.popState();break;case 32:return 100;case 33:this.begin("click");break;case 34:this.popState();break;case 35:return 91;case 36:return Ps.lex.firstGraph()&&this.begin("dir"),24;case 37:return Ps.lex.firstGraph()&&this.begin("dir"),24;case 38:return Ps.lex.firstGraph()&&this.begin("dir"),24;case 39:return 38;case 40:return 42;case 41:return 102;case 42:return 102;case 43:return 102;case 44:return 102;case 45:return this.popState(),25;case 46:return this.popState(),26;case 47:return this.popState(),26;case 48:return this.popState(),26;case 49:return this.popState(),26;case 50:return this.popState(),26;case 51:return this.popState(),26;case 52:return this.popState(),26;case 53:return this.popState(),26;case 54:return this.popState(),26;case 55:return this.popState(),26;case 56:return 119;case 57:return 120;case 58:return 121;case 59:return 122;case 60:return 106;case 61:return 112;case 62:return 53;case 63:return 67;case 64:return 52;case 65:return 20;case 66:return 107;case 67:return 127;case 68:return 82;case 69:return 82;case 70:return 82;case 71:return 82;case 72:return 81;case 73:return 81;case 74:return 81;case 75:return 59;case 76:return 60;case 77:return 61;case 78:return 62;case 79:return 63;case 80:return 64;case 81:return 65;case 82:return 69;case 83:return 70;case 84:return 55;case 85:return 56;case 86:return 110;case 87:return 113;case 88:return 128;case 89:return 125;case 90:return 114;case 91:return 126;case 92:return 126;case 93:return 115;case 94:return 73;case 95:return 93;case 96:return"SEP";case 97:return 92;case 98:return 66;case 99:return 75;case 100:return 74;case 101:return 77;case 102:return 76;case 103:return 123;case 104:return 124;case 105:return 68;case 106:return 57;case 107:return 58;case 108:return 40;case 109:return 41;case 110:return 71;case 111:return 72;case 112:return 134;case 113:return 21;case 114:return 22;case 115:return 23}},rules:[/^(?:%%\{)/,/^(?:((?:(?!\}%%)[^:.])*))/,/^(?::)/,/^(?:\}%%)/,/^(?:((?:(?!\}%%).|\n)*))/,/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:["][`])/,/^(?:[^`"]+)/,/^(?:[`]["])/,/^(?:["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:style\b)/,/^(?:default\b)/,/^(?:linkStyle\b)/,/^(?:interpolate\b)/,/^(?:classDef\b)/,/^(?:class\b)/,/^(?:href[\s]+["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:call[\s]+)/,/^(?:\([\s]*\))/,/^(?:\()/,/^(?:[^(]*)/,/^(?:\))/,/^(?:[^)]*)/,/^(?:click[\s]+)/,/^(?:[\s\n])/,/^(?:[^\s\n]*)/,/^(?:flowchart-elk\b)/,/^(?:graph\b)/,/^(?:flowchart\b)/,/^(?:subgraph\b)/,/^(?:end\b\s*)/,/^(?:_self\b)/,/^(?:_blank\b)/,/^(?:_parent\b)/,/^(?:_top\b)/,/^(?:(\r?\n)*\s*\n)/,/^(?:\s*LR\b)/,/^(?:\s*RL\b)/,/^(?:\s*TB\b)/,/^(?:\s*BT\b)/,/^(?:\s*TD\b)/,/^(?:\s*BR\b)/,/^(?:\s*<)/,/^(?:\s*>)/,/^(?:\s*\^)/,/^(?:\s*v\b)/,/^(?:.*direction\s+TB[^\n]*)/,/^(?:.*direction\s+BT[^\n]*)/,/^(?:.*direction\s+RL[^\n]*)/,/^(?:.*direction\s+LR[^\n]*)/,/^(?:[0-9]+)/,/^(?:#)/,/^(?::::)/,/^(?::)/,/^(?:&)/,/^(?:;)/,/^(?:,)/,/^(?:\*)/,/^(?:\s*[xo<]?--+[-xo>]\s*)/,/^(?:\s*[xo<]?==+[=xo>]\s*)/,/^(?:\s*[xo<]?-?\.+-[xo>]?\s*)/,/^(?:\s*~~[\~]+\s*)/,/^(?:\s*[xo<]?--\s*)/,/^(?:\s*[xo<]?==\s*)/,/^(?:\s*[xo<]?-\.\s*)/,/^(?:\(-)/,/^(?:-\))/,/^(?:\(\[)/,/^(?:\]\))/,/^(?:\[\[)/,/^(?:\]\])/,/^(?:\[\|)/,/^(?:\[\()/,/^(?:\)\])/,/^(?:\(\(\()/,/^(?:\)\)\))/,/^(?:-)/,/^(?:\.)/,/^(?:[\_])/,/^(?:\+)/,/^(?:%)/,/^(?:=)/,/^(?:=)/,/^(?:<)/,/^(?:>)/,/^(?:\^)/,/^(?:\\\|)/,/^(?:v\b)/,/^(?:[A-Za-z]+)/,/^(?:\\\])/,/^(?:\[\/)/,/^(?:\/\])/,/^(?:\[\\)/,/^(?:[!"#$%&'*+,-.`?\\_/])/,/^(?:[\u00AA\u00B5\u00BA\u00C0-\u00D6\u00D8-\u00F6]|[\u00F8-\u02C1\u02C6-\u02D1\u02E0-\u02E4\u02EC\u02EE\u0370-\u0374\u0376\u0377]|[\u037A-\u037D\u0386\u0388-\u038A\u038C\u038E-\u03A1\u03A3-\u03F5]|[\u03F7-\u0481\u048A-\u0527\u0531-\u0556\u0559\u0561-\u0587\u05D0-\u05EA]|[\u05F0-\u05F2\u0620-\u064A\u066E\u066F\u0671-\u06D3\u06D5\u06E5\u06E6\u06EE]|[\u06EF\u06FA-\u06FC\u06FF\u0710\u0712-\u072F\u074D-\u07A5\u07B1\u07CA-\u07EA]|[\u07F4\u07F5\u07FA\u0800-\u0815\u081A\u0824\u0828\u0840-\u0858\u08A0]|[\u08A2-\u08AC\u0904-\u0939\u093D\u0950\u0958-\u0961\u0971-\u0977]|[\u0979-\u097F\u0985-\u098C\u098F\u0990\u0993-\u09A8\u09AA-\u09B0\u09B2]|[\u09B6-\u09B9\u09BD\u09CE\u09DC\u09DD\u09DF-\u09E1\u09F0\u09F1\u0A05-\u0A0A]|[\u0A0F\u0A10\u0A13-\u0A28\u0A2A-\u0A30\u0A32\u0A33\u0A35\u0A36\u0A38\u0A39]|[\u0A59-\u0A5C\u0A5E\u0A72-\u0A74\u0A85-\u0A8D\u0A8F-\u0A91\u0A93-\u0AA8]|[\u0AAA-\u0AB0\u0AB2\u0AB3\u0AB5-\u0AB9\u0ABD\u0AD0\u0AE0\u0AE1\u0B05-\u0B0C]|[\u0B0F\u0B10\u0B13-\u0B28\u0B2A-\u0B30\u0B32\u0B33\u0B35-\u0B39\u0B3D\u0B5C]|[\u0B5D\u0B5F-\u0B61\u0B71\u0B83\u0B85-\u0B8A\u0B8E-\u0B90\u0B92-\u0B95\u0B99]|[\u0B9A\u0B9C\u0B9E\u0B9F\u0BA3\u0BA4\u0BA8-\u0BAA\u0BAE-\u0BB9\u0BD0]|[\u0C05-\u0C0C\u0C0E-\u0C10\u0C12-\u0C28\u0C2A-\u0C33\u0C35-\u0C39\u0C3D]|[\u0C58\u0C59\u0C60\u0C61\u0C85-\u0C8C\u0C8E-\u0C90\u0C92-\u0CA8\u0CAA-\u0CB3]|[\u0CB5-\u0CB9\u0CBD\u0CDE\u0CE0\u0CE1\u0CF1\u0CF2\u0D05-\u0D0C\u0D0E-\u0D10]|[\u0D12-\u0D3A\u0D3D\u0D4E\u0D60\u0D61\u0D7A-\u0D7F\u0D85-\u0D96\u0D9A-\u0DB1]|[\u0DB3-\u0DBB\u0DBD\u0DC0-\u0DC6\u0E01-\u0E30\u0E32\u0E33\u0E40-\u0E46\u0E81]|[\u0E82\u0E84\u0E87\u0E88\u0E8A\u0E8D\u0E94-\u0E97\u0E99-\u0E9F\u0EA1-\u0EA3]|[\u0EA5\u0EA7\u0EAA\u0EAB\u0EAD-\u0EB0\u0EB2\u0EB3\u0EBD\u0EC0-\u0EC4\u0EC6]|[\u0EDC-\u0EDF\u0F00\u0F40-\u0F47\u0F49-\u0F6C\u0F88-\u0F8C\u1000-\u102A]|[\u103F\u1050-\u1055\u105A-\u105D\u1061\u1065\u1066\u106E-\u1070\u1075-\u1081]|[\u108E\u10A0-\u10C5\u10C7\u10CD\u10D0-\u10FA\u10FC-\u1248\u124A-\u124D]|[\u1250-\u1256\u1258\u125A-\u125D\u1260-\u1288\u128A-\u128D\u1290-\u12B0]|[\u12B2-\u12B5\u12B8-\u12BE\u12C0\u12C2-\u12C5\u12C8-\u12D6\u12D8-\u1310]|[\u1312-\u1315\u1318-\u135A\u1380-\u138F\u13A0-\u13F4\u1401-\u166C]|[\u166F-\u167F\u1681-\u169A\u16A0-\u16EA\u1700-\u170C\u170E-\u1711]|[\u1720-\u1731\u1740-\u1751\u1760-\u176C\u176E-\u1770\u1780-\u17B3\u17D7]|[\u17DC\u1820-\u1877\u1880-\u18A8\u18AA\u18B0-\u18F5\u1900-\u191C]|[\u1950-\u196D\u1970-\u1974\u1980-\u19AB\u19C1-\u19C7\u1A00-\u1A16]|[\u1A20-\u1A54\u1AA7\u1B05-\u1B33\u1B45-\u1B4B\u1B83-\u1BA0\u1BAE\u1BAF]|[\u1BBA-\u1BE5\u1C00-\u1C23\u1C4D-\u1C4F\u1C5A-\u1C7D\u1CE9-\u1CEC]|[\u1CEE-\u1CF1\u1CF5\u1CF6\u1D00-\u1DBF\u1E00-\u1F15\u1F18-\u1F1D]|[\u1F20-\u1F45\u1F48-\u1F4D\u1F50-\u1F57\u1F59\u1F5B\u1F5D\u1F5F-\u1F7D]|[\u1F80-\u1FB4\u1FB6-\u1FBC\u1FBE\u1FC2-\u1FC4\u1FC6-\u1FCC\u1FD0-\u1FD3]|[\u1FD6-\u1FDB\u1FE0-\u1FEC\u1FF2-\u1FF4\u1FF6-\u1FFC\u2071\u207F]|[\u2090-\u209C\u2102\u2107\u210A-\u2113\u2115\u2119-\u211D\u2124\u2126\u2128]|[\u212A-\u212D\u212F-\u2139\u213C-\u213F\u2145-\u2149\u214E\u2183\u2184]|[\u2C00-\u2C2E\u2C30-\u2C5E\u2C60-\u2CE4\u2CEB-\u2CEE\u2CF2\u2CF3]|[\u2D00-\u2D25\u2D27\u2D2D\u2D30-\u2D67\u2D6F\u2D80-\u2D96\u2DA0-\u2DA6]|[\u2DA8-\u2DAE\u2DB0-\u2DB6\u2DB8-\u2DBE\u2DC0-\u2DC6\u2DC8-\u2DCE]|[\u2DD0-\u2DD6\u2DD8-\u2DDE\u2E2F\u3005\u3006\u3031-\u3035\u303B\u303C]|[\u3041-\u3096\u309D-\u309F\u30A1-\u30FA\u30FC-\u30FF\u3105-\u312D]|[\u3131-\u318E\u31A0-\u31BA\u31F0-\u31FF\u3400-\u4DB5\u4E00-\u9FCC]|[\uA000-\uA48C\uA4D0-\uA4FD\uA500-\uA60C\uA610-\uA61F\uA62A\uA62B]|[\uA640-\uA66E\uA67F-\uA697\uA6A0-\uA6E5\uA717-\uA71F\uA722-\uA788]|[\uA78B-\uA78E\uA790-\uA793\uA7A0-\uA7AA\uA7F8-\uA801\uA803-\uA805]|[\uA807-\uA80A\uA80C-\uA822\uA840-\uA873\uA882-\uA8B3\uA8F2-\uA8F7\uA8FB]|[\uA90A-\uA925\uA930-\uA946\uA960-\uA97C\uA984-\uA9B2\uA9CF\uAA00-\uAA28]|[\uAA40-\uAA42\uAA44-\uAA4B\uAA60-\uAA76\uAA7A\uAA80-\uAAAF\uAAB1\uAAB5]|[\uAAB6\uAAB9-\uAABD\uAAC0\uAAC2\uAADB-\uAADD\uAAE0-\uAAEA\uAAF2-\uAAF4]|[\uAB01-\uAB06\uAB09-\uAB0E\uAB11-\uAB16\uAB20-\uAB26\uAB28-\uAB2E]|[\uABC0-\uABE2\uAC00-\uD7A3\uD7B0-\uD7C6\uD7CB-\uD7FB\uF900-\uFA6D]|[\uFA70-\uFAD9\uFB00-\uFB06\uFB13-\uFB17\uFB1D\uFB1F-\uFB28\uFB2A-\uFB36]|[\uFB38-\uFB3C\uFB3E\uFB40\uFB41\uFB43\uFB44\uFB46-\uFBB1\uFBD3-\uFD3D]|[\uFD50-\uFD8F\uFD92-\uFDC7\uFDF0-\uFDFB\uFE70-\uFE74\uFE76-\uFEFC]|[\uFF21-\uFF3A\uFF41-\uFF5A\uFF66-\uFFBE\uFFC2-\uFFC7\uFFCA-\uFFCF]|[\uFFD2-\uFFD7\uFFDA-\uFFDC])/,/^(?:\|)/,/^(?:\()/,/^(?:\))/,/^(?:\[)/,/^(?:\])/,/^(?:\{)/,/^(?:\})/,/^(?:")/,/^(?:(\r?\n)+)/,/^(?:\s)/,/^(?:$)/],conditions:{close_directive:{rules:[],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},open_directive:{rules:[1],inclusive:!1},callbackargs:{rules:[31,32],inclusive:!1},callbackname:{rules:[28,29,30],inclusive:!1},href:{rules:[25,26],inclusive:!1},click:{rules:[34,35],inclusive:!1},vertex:{rules:[],inclusive:!1},dir:{rules:[45,46,47,48,49,50,51,52,53,54,55],inclusive:!1},acc_descr_multiline:{rules:[10,11],inclusive:!1},acc_descr:{rules:[8],inclusive:!1},acc_title:{rules:[6],inclusive:!1},md_string:{rules:[13,14],inclusive:!1},string:{rules:[16,17],inclusive:!1},INITIAL:{rules:[0,5,7,9,12,15,18,19,20,21,22,23,24,27,33,36,37,38,39,40,41,42,43,44,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115],inclusive:!0}}};return ug}();b3.lexer=bL;function im(){this.yy={}}return im.prototype=b3,b3.Parser=im,new im}();Mde.parser=Mde;const Dde=Mde,iMt="flowchart-";let WOe=0,Ide=Tt(),_l={},r9=[],YA={},m5=[],jW={},Ode={},$W=0,Nde=!0,Yb,HW,zW=[];const GW=i=>Wa.sanitizeText(i,Ide),KOe=function(i,a,f){rd.parseDirective(this,i,a,f)},JN=function(i){const a=Object.keys(_l);for(const f of a)if(_l[f].id===i)return _l[f].domId;return i},YOe=function(i,a,f,p,w,y,b={}){let E,S=i;S!==void 0&&S.trim().length!==0&&(_l[S]===void 0&&(_l[S]={id:S,labelType:"text",domId:iMt+S+"-"+WOe,styles:[],classes:[]}),WOe++,a!==void 0?(Ide=Tt(),E=GW(a.text.trim()),_l[S].labelType=a.type,E[0]==='"'&&E[E.length-1]==='"'&&(E=E.substring(1,E.length-1)),_l[S].text=E):_l[S].text===void 0&&(_l[S].text=i),f!==void 0&&(_l[S].type=f),p!=null&&p.forEach(function(N){_l[S].styles.push(N)}),w!=null&&w.forEach(function(N){_l[S].classes.push(N)}),y!==void 0&&(_l[S].dir=y),_l[S].props===void 0?_l[S].props=b:b!==void 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`+Ie+"^"},test_match:function(he,Ie){var ee,rt,me;if(this.options.backtrack_lexer&&(me={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(me.yylloc.range=this.yylloc.range.slice(0))),rt=he[0].match(/(?:\r\n?|\n).*/g),rt&&(this.yylineno+=rt.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:rt?rt[rt.length-1].length-rt[rt.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+he[0].length},this.yytext+=he[0],this.match+=he[0],this.matches=he,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(he[0].length),this.matched+=he[0],ee=this.performAction.call(this,this.yy,this,Ie,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),ee)return ee;if(this._backtrack){for(var gt in me)this[gt]=me[gt];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var he,Ie,ee,rt;this._more||(this.yytext="",this.match="");for(var me=this._currentRules(),gt=0;gt<me.length;gt++)if(ee=this._input.match(this.rules[me[gt]]),ee&&(!Ie||ee[0].length>Ie[0].length)){if(Ie=ee,rt=gt,this.options.backtrack_lexer){if(he=this.test_match(ee,me[gt]),he!==!1)return he;if(this._backtrack){Ie=!1;continue}else return!1}else if(!this.options.flex)break}return Ie?(he=this.test_match(Ie,me[rt]),he!==!1?he:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var Ie=this.next();return Ie||this.lex()},begin:function(Ie){this.conditionStack.push(Ie)},popState:function(){var Ie=this.conditionStack.length-1;return Ie>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(Ie){return Ie=this.conditionStack.length-1-Math.abs(Ie||0),Ie>=0?this.conditionStack[Ie]:"INITIAL"},pushState:function(Ie){this.begin(Ie)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(Ie,ee,rt,me){switch(rt){case 0:return this.begin("acc_title"),25;case 1:return this.popState(),"acc_title_value";case 2:return this.begin("acc_descr"),27;case 3:return this.popState(),"acc_descr_value";case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:return this.begin("open_directive"),51;case 8:return this.begin("type_directive"),52;case 9:return this.popState(),this.begin("arg_directive"),15;case 10:return this.popState(),this.popState(),54;case 11:return 53;case 12:return 11;case 13:break;case 14:return 9;case 15:return 31;case 16:return 50;case 17:return 4;case 18:return this.begin("block"),20;case 19:return 39;case 20:break;case 21:return 40;case 22:return 37;case 23:return 37;case 24:return 41;case 25:break;case 26:return this.popState(),22;case 27:return ee.yytext[0];case 28:return 44;case 29:return 46;case 30:return 46;case 31:return 46;case 32:return 44;case 33:return 44;case 34:return 45;case 35:return 45;case 36:return 45;case 37:return 45;case 38:return 45;case 39:return 46;case 40:return 45;case 41:return 46;case 42:return 47;case 43:return 47;case 44:return 47;case 45:return 47;case 46:return 44;case 47:return 45;case 48:return 46;case 49:return 48;case 50:return 49;case 51:return 49;case 52:return 48;case 53:return 48;case 54:return 48;case 55:return 30;case 56:return ee.yytext[0];case 57:return 6}},rules:[/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:[\s]+)/i,/^(?:"[^"%\r\n\v\b\\]+")/i,/^(?:"[^"]*")/i,/^(?:erDiagram\b)/i,/^(?:\{)/i,/^(?:,)/i,/^(?:\s+)/i,/^(?:\b((?:PK)|(?:FK)|(?:UK))\b)/i,/^(?:(.*?)[~](.*?)*[~])/i,/^(?:[A-Za-z_][A-Za-z0-9\-_\[\]\(\)]*)/i,/^(?:"[^"]*")/i,/^(?:[\n]+)/i,/^(?:\})/i,/^(?:.)/i,/^(?:one or zero\b)/i,/^(?:one or more\b)/i,/^(?:one or many\b)/i,/^(?:1\+)/i,/^(?:\|o\b)/i,/^(?:zero or one\b)/i,/^(?:zero or more\b)/i,/^(?:zero or many\b)/i,/^(?:0\+)/i,/^(?:\}o\b)/i,/^(?:many\(0\))/i,/^(?:many\(1\))/i,/^(?:many\b)/i,/^(?:\}\|)/i,/^(?:one\b)/i,/^(?:only one\b)/i,/^(?:1\b)/i,/^(?:\|\|)/i,/^(?:o\|)/i,/^(?:o\{)/i,/^(?:\|\{)/i,/^(?:\.\.)/i,/^(?:--)/i,/^(?:to\b)/i,/^(?:optionally to\b)/i,/^(?:\.-)/i,/^(?:-\.)/i,/^(?:[A-Za-z][A-Za-z0-9\-_]*)/i,/^(?:.)/i,/^(?:$)/i],conditions:{acc_descr_multiline:{rules:[5,6],inclusive:!1},acc_descr:{rules:[3],inclusive:!1},acc_title:{rules:[1],inclusive:!1},open_directive:{rules:[8],inclusive:!1},type_directive:{rules:[9,10],inclusive:!1},arg_directive:{rules:[10,11],inclusive:!1},block:{rules:[19,20,21,22,23,24,25,26,27],inclusive:!1},INITIAL:{rules:[0,2,4,7,12,13,14,15,16,17,18,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57],inclusive:!0}}};return De}();ve.lexer=te;function xe(){this.yy={}}return xe.prototype=ve,ve.Parser=xe,new xe}();A0e.parser=A0e;const EFt=A0e;let vP={},L0e=[];const TFt={ZERO_OR_ONE:"ZERO_OR_ONE",ZERO_OR_MORE:"ZERO_OR_MORE",ONE_OR_MORE:"ONE_OR_MORE",ONLY_ONE:"ONLY_ONE"},_Ft={NON_IDENTIFYING:"NON_IDENTIFYING",IDENTIFYING:"IDENTIFYING"},CFt=function(i,a,f){rd.parseDirective(this,i,a,f)},iRe=function(i){return vP[i]===void 0&&(vP[i]={attributes:[]},Fe.info("Added new entity :",i)),vP[i]},SFt={Cardinality:TFt,Identification:_Ft,parseDirective:CFt,getConfig:()=>Tt().er,addEntity:iRe,addAttributes:function(i,a){let f=iRe(i),p;for(p=a.length-1;p>=0;p--)f.attributes.push(a[p]),Fe.debug("Added attribute ",a[p].attributeName)},getEntities:()=>vP,addRelationship:function(i,a,f,p){let w={entityA:i,roleA:a,entityB:f,relSpec:p};L0e.push(w),Fe.debug("Added new relationship :",w)},getRelationships:()=>L0e,clear:function(){vP={},L0e=[],rp()},setAccTitle:ip,getAccTitle:L2,setAccDescription:M2,getAccDescription:D2,setDiagramTitle:Uw,getDiagramTitle:Ww},_5={ONLY_ONE_START:"ONLY_ONE_START",ONLY_ONE_END:"ONLY_ONE_END",ZERO_OR_ONE_START:"ZERO_OR_ONE_START",ZERO_OR_ONE_END:"ZERO_OR_ONE_END",ONE_OR_MORE_START:"ONE_OR_MORE_START",ONE_OR_MORE_END:"ONE_OR_MORE_END",ZERO_OR_MORE_START:"ZERO_OR_MORE_START",ZERO_OR_MORE_END:"ZERO_OR_MORE_END"},C5={ERMarkers:_5,insertMarkers:function(i,a){let f;i.append("defs").append("marker").attr("id",_5.ONLY_ONE_START).attr("refX",0).attr("refY",9).attr("markerWidth",18).attr("markerHeight",18).attr("orient","auto").append("path").attr("stroke",a.stroke).attr("fill","none").attr("d","M9,0 L9,18 M15,0 L15,18"),i.append("defs").append("marker").attr("id",_5.ONLY_ONE_END).attr("refX",18).attr("refY",9).attr("markerWidth",18).attr("markerHeight",18).attr("orient","auto").append("path").attr("stroke",a.stroke).attr("fill","none").attr("d","M3,0 L3,18 M9,0 L9,18"),f=i.append("defs").append("marker").attr("id",_5.ZERO_OR_ONE_START).attr("refX",0).attr("refY",9).attr("markerWidth",30).attr("markerHeight",18).attr("orient","auto"),f.append("circle").attr("stroke",a.stroke).attr("fill","white").attr("cx",21).attr("cy",9).attr("r",6),f.append("path").attr("stroke",a.stroke).attr("fill","none").attr("d","M9,0 L9,18"),f=i.append("defs").append("marker").attr("id",_5.ZERO_OR_ONE_END).attr("refX",30).attr("refY",9).attr("markerWidth",30).attr("markerHeight",18).attr("orient","auto"),f.append("circle").attr("stroke",a.stroke).attr("fill","white").attr("cx",9).attr("cy",9).attr("r",6),f.append("path").attr("stroke",a.stroke).attr("fill","none").attr("d","M21,0 L21,18"),i.append("defs").append("marker").attr("id",_5.ONE_OR_MORE_START).attr("refX",18).attr("refY",18).attr("markerWidth",45).attr("markerHeight",36).attr("orient","auto").append("path").attr("stroke",a.stroke).attr("fill","none").attr("d","M0,18 Q 18,0 36,18 Q 18,36 0,18 M42,9 L42,27"),i.append("defs").append("marker").attr("id",_5.ONE_OR_MORE_END).attr("refX",27).attr("refY",18).attr("markerWidth",45).attr("markerHeight",36).attr("orient","auto").append("path").attr("stroke",a.stroke).attr("fill","none").attr("d","M3,9 L3,27 M9,18 Q27,0 45,18 Q27,36 9,18"),f=i.append("defs").append("marker").attr("id",_5.ZERO_OR_MORE_START).attr("refX",18).attr("refY",18).attr("markerWidth",57).attr("markerHeight",36).attr("orient","auto"),f.append("circle").attr("stroke",a.stroke).attr("fill","white").attr("cx",48).attr("cy",18).attr("r",6),f.append("path").attr("stroke",a.stroke).attr("fill","none").attr("d","M0,18 Q18,0 36,18 Q18,36 0,18"),f=i.append("defs").append("marker").attr("id",_5.ZERO_OR_MORE_END).attr("refX",39).attr("refY",18).attr("markerWidth",57).attr("markerHeight",36).attr("orient","auto"),f.append("circle").attr("stroke",a.stroke).attr("fill","white").attr("cx",9).attr("cy",18).attr("r",6),f.append("path").attr("stroke",a.stroke).attr("fill","none").attr("d","M21,18 Q39,0 57,18 Q39,36 21,18")}},AFt=/^(?:[0-9a-f]{8}-[0-9a-f]{4}-[1-5][0-9a-f]{3}-[89ab][0-9a-f]{3}-[0-9a-f]{12}|00000000-0000-0000-0000-000000000000)$/i;function LFt(i){return typeof i=="string"&&AFt.test(i)}const Hd=[];for(let i=0;i<256;++i)Hd.push((i+256).toString(16).slice(1));function MFt(i,a=0){return(Hd[i[a+0]]+Hd[i[a+1]]+Hd[i[a+2]]+Hd[i[a+3]]+"-"+Hd[i[a+4]]+Hd[i[a+5]]+"-"+Hd[i[a+6]]+Hd[i[a+7]]+"-"+Hd[i[a+8]]+Hd[i[a+9]]+"-"+Hd[i[a+10]]+Hd[i[a+11]]+Hd[i[a+12]]+Hd[i[a+13]]+Hd[i[a+14]]+Hd[i[a+15]]).toLowerCase()}function DFt(i){if(!LFt(i))throw TypeError("Invalid UUID");let a;const f=new Uint8Array(16);return f[0]=(a=parseInt(i.slice(0,8),16))>>>24,f[1]=a>>>16&255,f[2]=a>>>8&255,f[3]=a&255,f[4]=(a=parseInt(i.slice(9,13),16))>>>8,f[5]=a&255,f[6]=(a=parseInt(i.slice(14,18),16))>>>8,f[7]=a&255,f[8]=(a=parseInt(i.slice(19,23),16))>>>8,f[9]=a&255,f[10]=(a=parseInt(i.slice(24,36),16))/1099511627776&255,f[11]=a/4294967296&255,f[12]=a>>>24&255,f[13]=a>>>16&255,f[14]=a>>>8&255,f[15]=a&255,f}function IFt(i){i=unescape(encodeURIComponent(i));const a=[];for(let f=0;f<i.length;++f)a.push(i.charCodeAt(f));return a}const OFt="6ba7b810-9dad-11d1-80b4-00c04fd430c8",NFt="6ba7b811-9dad-11d1-80b4-00c04fd430c8";function PFt(i,a,f){function p(w,y,b,E){var S;if(typeof w=="string"&&(w=IFt(w)),typeof y=="string"&&(y=DFt(y)),((S=y)===null||S===void 0?void 0:S.length)!==16)throw TypeError("Namespace must be array-like (16 iterable integer values, 0-255)");let N=new Uint8Array(16+w.length);if(N.set(y),N.set(w,y.length),N=f(N),N[6]=N[6]&15|a,N[8]=N[8]&63|128,b){E=E||0;for(let B=0;B<16;++B)b[E+B]=N[B];return b}return 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De=new Error(te);throw De.hash=xe,De}},parse:function(te){var xe=this,De=[0],he=[],Ie=[null],ee=[],rt=this.table,me="",gt=0,pe=0,Et=2,wt=1,jt=ee.slice.call(arguments,1),At=Object.create(this.lexer),Bt={yy:{}};for(var cn in this.yy)Object.prototype.hasOwnProperty.call(this.yy,cn)&&(Bt.yy[cn]=this.yy[cn]);At.setInput(te,Bt.yy),Bt.yy.lexer=At,Bt.yy.parser=this,typeof At.yylloc>"u"&&(At.yylloc={});var Nn=At.yylloc;ee.push(Nn);var Ot=At.options&&At.options.ranges;typeof Bt.yy.parseError=="function"?this.parseError=Bt.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function oi(){var Ri;return Ri=he.pop()||At.lex()||wt,typeof Ri!="number"&&(Ri instanceof Array&&(he=Ri,Ri=he.pop()),Ri=xe.symbols_[Ri]||Ri),Ri}for(var kt,Dt,vt,Nt,ze={},Xe,Lt,Ge,Bn;;){if(Dt=De[De.length-1],this.defaultActions[Dt]?vt=this.defaultActions[Dt]:((kt===null||typeof kt>"u")&&(kt=oi()),vt=rt[Dt]&&rt[Dt][kt]),typeof vt>"u"||!vt.length||!vt[0]){var Oe="";Bn=[];for(Xe in rt[Dt])this.terminals_[Xe]&&Xe>Et&&Bn.push("'"+this.terminals_[Xe]+"'");At.showPosition?Oe="Parse error on line "+(gt+1)+`:
`+At.showPosition()+`
Expecting `+Bn.join(", ")+", got '"+(this.terminals_[kt]||kt)+"'":Oe="Parse error on line "+(gt+1)+": Unexpected "+(kt==wt?"end of input":"'"+(this.terminals_[kt]||kt)+"'"),this.parseError(Oe,{text:At.match,token:this.terminals_[kt]||kt,line:At.yylineno,loc:Nn,expected:Bn})}if(vt[0]instanceof Array&&vt.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Dt+", token: "+kt);switch(vt[0]){case 1:De.push(kt),Ie.push(At.yytext),ee.push(At.yylloc),De.push(vt[1]),kt=null,pe=At.yyleng,me=At.yytext,gt=At.yylineno,Nn=At.yylloc;break;case 2:if(Lt=this.productions_[vt[1]][1],ze.$=Ie[Ie.length-Lt],ze._$={first_line:ee[ee.length-(Lt||1)].first_line,last_line:ee[ee.length-1].last_line,first_column:ee[ee.length-(Lt||1)].first_column,last_column:ee[ee.length-1].last_column},Ot&&(ze._$.range=[ee[ee.length-(Lt||1)].range[0],ee[ee.length-1].range[1]]),Nt=this.performAction.apply(ze,[me,pe,gt,Bt.yy,vt[1],Ie,ee].concat(jt)),typeof Nt<"u")return Nt;Lt&&(De=De.slice(0,-1*Lt*2),Ie=Ie.slice(0,-1*Lt),ee=ee.slice(0,-1*Lt)),De.push(this.productions_[vt[1]][0]),Ie.push(ze.$),ee.push(ze._$),Ge=rt[De[De.length-2]][De[De.length-1]],De.push(Ge);break;case 3:return!0}}return!0}},Ae=function(){var ve={EOF:1,parseError:function(xe,De){if(this.yy.parser)this.yy.parser.parseError(xe,De);else throw new Error(xe)},setInput:function(te,xe){return this.yy=xe||this.yy||{},this._input=te,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var te=this._input[0];this.yytext+=te,this.yyleng++,this.offset++,this.match+=te,this.matched+=te;var xe=te.match(/(?:\r\n?|\n).*/g);return xe?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),te},unput:function(te){var xe=te.length,De=te.split(/(?:\r\n?|\n)/g);this._input=te+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-xe),this.offset-=xe;var he=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),De.length-1&&(this.yylineno-=De.length-1);var Ie=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:De?(De.length===he.length?this.yylloc.first_column:0)+he[he.length-De.length].length-De[0].length:this.yylloc.first_column-xe},this.options.ranges&&(this.yylloc.range=[Ie[0],Ie[0]+this.yyleng-xe]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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wt.milestone?Q(wt.startTime)+De+.5*(Q(wt.endTime)-Q(wt.startTime))-.5*he:Q(wt.startTime)+De}).attr("y",function(wt,jt){return jt=wt.order,jt*te+xe}).attr("width",function(wt){return wt.milestone?he:Q(wt.renderEndTime||wt.endTime)-Q(wt.startTime)}).attr("height",he).attr("transform-origin",function(wt,jt){return jt=wt.order,(Q(wt.startTime)+De+.5*(Q(wt.endTime)-Q(wt.startTime))).toString()+"px "+(jt*te+xe+.5*he).toString()+"px"}).attr("class",function(wt){const jt="task";let At="";wt.classes.length>0&&(At=wt.classes.join(" "));let Bt=0;for(const[Nn,Ot]of R.entries())wt.type===Ot&&(Bt=Nn%w.numberSectionStyles);let cn="";return wt.active?wt.crit?cn+=" activeCrit":cn=" active":wt.done?wt.crit?cn=" doneCrit":cn=" done":wt.crit&&(cn+=" crit"),cn.length===0&&(cn=" task"),wt.milestone&&(cn=" milestone "+cn),cn+=Bt,cn+=" "+At,jt+cn}),gt.append("text").attr("id",function(wt){return wt.id+"-text"}).text(function(wt){return wt.task}).attr("font-size",w.fontSize).attr("x",function(wt){let 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taskText taskText"+Nn+" "+Ot+" width-"+Bt}),Tt().securityLevel==="sandbox"){let wt;wt=Cr("#i"+a);const jt=wt.nodes()[0].contentDocument;gt.filter(function(At){return pe[At.id]!==void 0}).each(function(At){var Bt=jt.querySelector("#"+At.id),cn=jt.querySelector("#"+At.id+"-text");const Nn=Bt.parentNode;var Ot=jt.createElement("a");Ot.setAttribute("xlink:href",pe[At.id]),Ot.setAttribute("target","_top"),Nn.appendChild(Ot),Ot.appendChild(Bt),Ot.appendChild(cn)})}}function ge(ve,te,xe,De,he,Ie,ee,rt){const me=Ie.reduce((Bt,{startTime:cn})=>Bt?Math.min(Bt,cn):cn,0),gt=Ie.reduce((Bt,{endTime:cn})=>Bt?Math.max(Bt,cn):cn,0),pe=p.db.getDateFormat();if(!me||!gt)return;const Et=[];let wt=null,jt=ru(me);for(;jt.valueOf()<=gt;)p.db.isInvalidDate(jt,pe,ee,rt)?wt?wt.end=jt:wt={start:jt,end:jt}:wt&&(Et.push(wt),wt=null),jt=jt.add(1,"d");V.append("g").selectAll("rect").data(Et).enter().append("rect").attr("id",function(Bt){return"exclude-"+Bt.start.format("YYYY-MM-DD")}).attr("x",function(Bt){return Q(Bt.start)+xe}).attr("y",w.gridLineStartPadding).attr("width",function(Bt){const cn=Bt.end.add(1,"day");return Q(cn)-Q(Bt.start)}).attr("height",he-te-w.gridLineStartPadding).attr("transform-origin",function(Bt,cn){return(Q(Bt.start)+xe+.5*(Q(Bt.end)-Q(Bt.start))).toString()+"px "+(cn*ve+.5*he).toString()+"px"}).attr("class","exclude-range")}function ye(ve,te,xe,De){let he=H5t(Q).tickSize(-De+te+w.gridLineStartPadding).tickFormat($U(p.db.getAxisFormat()||w.axisFormat||"%Y-%m-%d"));const ee=/^([1-9]\d*)(minute|hour|day|week|month)$/.exec(p.db.getTickInterval()||w.tickInterval);if(ee!==null){const rt=ee[1];switch(ee[2]){case"minute":he.ticks(PU.every(rt));break;case"hour":he.ticks(BU.every(rt));break;case"day":he.ticks(yA.every(rt));break;case"week":he.ticks(kA.every(rt));break;case"month":he.ticks(FU.every(rt));break}}if(V.append("g").attr("class","grid").attr("transform","translate("+ve+", "+(De-50)+")").call(he).selectAll("text").style("text-anchor","middle").attr("fill","#000").attr("stroke","none").attr("font-size",10).attr("dy","1em"),p.db.topAxisEnabled()||w.topAxis){let rt=$5t(Q).tickSize(-De+te+w.gridLineStartPadding).tickFormat($U(p.db.getAxisFormat()||w.axisFormat||"%Y-%m-%d"));if(ee!==null){const me=ee[1];switch(ee[2]){case"minute":rt.ticks(PU.every(me));break;case"hour":rt.ticks(BU.every(me));break;case"day":rt.ticks(yA.every(me));break;case"week":rt.ticks(kA.every(me));break;case"month":rt.ticks(FU.every(me));break}}V.append("g").attr("class","grid").attr("transform","translate("+ve+", "+te+")").call(rt).selectAll("text").style("text-anchor","middle").attr("fill","#000").attr("stroke","none").attr("font-size",10)}}function ke(ve,te){let xe=0;const De=Object.keys(j).map(he=>[he,j[he]]);V.append("g").selectAll("text").data(De).enter().append(function(he){const Ie=he[0].split(Wa.lineBreakRegex),ee=-(Ie.length-1)/2,rt=S.createElementNS("http://www.w3.org/2000/svg","text");rt.setAttribute("dy",ee+"em");for(const[me,gt]of Ie.entries()){const pe=S.createElementNS("http://www.w3.org/2000/svg","tspan");pe.setAttribute("alignment-baseline","central"),pe.setAttribute("x","10"),me>0&&pe.setAttribute("dy","1em"),pe.textContent=gt,rt.appendChild(pe)}return rt}).attr("x",10).attr("y",function(he,Ie){if(Ie>0)for(let ee=0;ee<Ie;ee++)return xe+=De[Ie-1][1],he[1]*ve/2+xe*ve+te;else return he[1]*ve/2+te}).attr("font-size",w.sectionFontSize).attr("class",function(he){for(const[Ie,ee]of R.entries())if(he[0]===ee)return"sectionTitle sectionTitle"+Ie%w.numberSectionStyles;return"sectionTitle"})}function Ae(ve,te,xe,De){const he=p.db.getTodayMarker();if(he==="off")return;const Ie=V.append("g").attr("class","today"),ee=new Date,rt=Ie.append("line");rt.attr("x1",Q(ee)+ve).attr("x2",Q(ee)+ve).attr("y1",w.titleTopMargin).attr("y2",De-w.titleTopMargin).attr("class","today"),he!==""&&rt.attr("style",he.replace(/,/g,";"))}function de(ve){const te={},xe=[];for(let De=0,he=ve.length;De<he;++De)Object.prototype.hasOwnProperty.call(te,ve[De])||(te[ve[De]]=!0,xe.push(ve[De]));return xe}}},styles:i=>`
.mermaid-main-font {
font-family: "trebuchet ms", verdana, arial, sans-serif;
font-family: var(--mermaid-font-family);
}
.exclude-range {
fill: ${i.excludeBkgColor};
}
.section {
stroke: none;
opacity: 0.2;
}
.section0 {
fill: ${i.sectionBkgColor};
}
.section2 {
fill: ${i.sectionBkgColor2};
}
.section1,
.section3 {
fill: ${i.altSectionBkgColor};
opacity: 0.2;
}
.sectionTitle0 {
fill: ${i.titleColor};
}
.sectionTitle1 {
fill: ${i.titleColor};
}
.sectionTitle2 {
fill: ${i.titleColor};
}
.sectionTitle3 {
fill: ${i.titleColor};
}
.sectionTitle {
text-anchor: start;
// font-size: ${i.ganttFontSize};
// text-height: 14px;
font-family: 'trebuchet ms', verdana, arial, sans-serif;
font-family: var(--mermaid-font-family);
}
/* Grid and axis */
.grid .tick {
stroke: ${i.gridColor};
opacity: 0.8;
shape-rendering: crispEdges;
text {
font-family: ${i.fontFamily};
fill: ${i.textColor};
}
}
.grid path {
stroke-width: 0;
}
/* Today line */
.today {
fill: none;
stroke: ${i.todayLineColor};
stroke-width: 2px;
}
/* Task styling */
/* Default task */
.task {
stroke-width: 2;
}
.taskText {
text-anchor: middle;
font-family: 'trebuchet ms', verdana, arial, sans-serif;
font-family: var(--mermaid-font-family);
}
// .taskText:not([font-size]) {
// font-size: ${i.ganttFontSize};
// }
.taskTextOutsideRight {
fill: ${i.taskTextDarkColor};
text-anchor: start;
// font-size: ${i.ganttFontSize};
font-family: 'trebuchet ms', verdana, arial, sans-serif;
font-family: var(--mermaid-font-family);
}
.taskTextOutsideLeft {
fill: ${i.taskTextDarkColor};
text-anchor: end;
// font-size: ${i.ganttFontSize};
}
/* Special case clickable */
.task.clickable {
cursor: pointer;
}
.taskText.clickable {
cursor: pointer;
fill: ${i.taskTextClickableColor} !important;
font-weight: bold;
}
.taskTextOutsideLeft.clickable {
cursor: pointer;
fill: ${i.taskTextClickableColor} !important;
font-weight: bold;
}
.taskTextOutsideRight.clickable {
cursor: pointer;
fill: ${i.taskTextClickableColor} !important;
font-weight: bold;
}
/* Specific task settings for the sections*/
.taskText0,
.taskText1,
.taskText2,
.taskText3 {
fill: ${i.taskTextColor};
}
.task0,
.task1,
.task2,
.task3 {
fill: ${i.taskBkgColor};
stroke: ${i.taskBorderColor};
}
.taskTextOutside0,
.taskTextOutside2
{
fill: ${i.taskTextOutsideColor};
}
.taskTextOutside1,
.taskTextOutside3 {
fill: ${i.taskTextOutsideColor};
}
/* Active task */
.active0,
.active1,
.active2,
.active3 {
fill: ${i.activeTaskBkgColor};
stroke: ${i.activeTaskBorderColor};
}
.activeText0,
.activeText1,
.activeText2,
.activeText3 {
fill: ${i.taskTextDarkColor} !important;
}
/* Completed task */
.done0,
.done1,
.done2,
.done3 {
stroke: ${i.doneTaskBorderColor};
fill: ${i.doneTaskBkgColor};
stroke-width: 2;
}
.doneText0,
.doneText1,
.doneText2,
.doneText3 {
fill: ${i.taskTextDarkColor} !important;
}
/* Tasks on the critical line */
.crit0,
.crit1,
.crit2,
.crit3 {
stroke: ${i.critBorderColor};
fill: ${i.critBkgColor};
stroke-width: 2;
}
.activeCrit0,
.activeCrit1,
.activeCrit2,
.activeCrit3 {
stroke: ${i.critBorderColor};
fill: ${i.activeTaskBkgColor};
stroke-width: 2;
}
.doneCrit0,
.doneCrit1,
.doneCrit2,
.doneCrit3 {
stroke: ${i.critBorderColor};
fill: ${i.doneTaskBkgColor};
stroke-width: 2;
cursor: pointer;
shape-rendering: crispEdges;
}
.milestone {
transform: rotate(45deg) scale(0.8,0.8);
}
.milestoneText {
font-style: italic;
}
.doneCritText0,
.doneCritText1,
.doneCritText2,
.doneCritText3 {
fill: ${i.taskTextDarkColor} !important;
}
.activeCritText0,
.activeCritText1,
.activeCritText2,
.activeCritText3 {
fill: ${i.taskTextDarkColor} !important;
}
.titleText {
text-anchor: middle;
font-size: 18px;
fill: ${i.textColor} ;
font-family: 'trebuchet ms', verdana, arial, sans-serif;
font-family: var(--mermaid-font-family);
}
`}},Symbol.toStringTag,{value:"Module"}));var W0e=function(){var i=function(y,b,E,S){for(E=E||{},S=y.length;S--;E[y[S]]=b);return E},a=[6,9,10],f={trace:function(){},yy:{},symbols_:{error:2,start:3,info:4,document:5,EOF:6,line:7,statement:8,NL:9,showInfo:10,$accept:0,$end:1},terminals_:{2:"error",4:"info",6:"EOF",9:"NL",10:"showInfo"},productions_:[0,[3,3],[5,0],[5,2],[7,1],[7,1],[8,1]],performAction:function(b,E,S,N,B,R,j){switch(R.length-1,B){case 1:return N;case 4:break;case 6:N.setInfo(!0);break}},table:[{3:1,4:[1,2]},{1:[3]},i(a,[2,2],{5:3}),{6:[1,4],7:5,8:6,9:[1,7],10:[1,8]},{1:[2,1]},i(a,[2,3]),i(a,[2,4]),i(a,[2,5]),i(a,[2,6])],defaultActions:{4:[2,1]},parseError:function(b,E){if(E.recoverable)this.trace(b);else{var S=new Error(b);throw S.hash=E,S}},parse:function(b){var E=this,S=[0],N=[],B=[null],R=[],j=this.table,$="",V=0,Q=0,oe=2,ce=1,se=R.slice.call(arguments,1),ge=Object.create(this.lexer),ye={yy:{}};for(var ke in this.yy)Object.prototype.hasOwnProperty.call(this.yy,ke)&&(ye.yy[ke]=this.yy[ke]);ge.setInput(b,ye.yy),ye.yy.lexer=ge,ye.yy.parser=this,typeof ge.yylloc>"u"&&(ge.yylloc={});var Ae=ge.yylloc;R.push(Ae);var de=ge.options&&ge.options.ranges;typeof ye.yy.parseError=="function"?this.parseError=ye.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function ve(){var Et;return Et=N.pop()||ge.lex()||ce,typeof Et!="number"&&(Et instanceof Array&&(N=Et,Et=N.pop()),Et=E.symbols_[Et]||Et),Et}for(var te,xe,De,he,Ie={},ee,rt,me,gt;;){if(xe=S[S.length-1],this.defaultActions[xe]?De=this.defaultActions[xe]:((te===null||typeof te>"u")&&(te=ve()),De=j[xe]&&j[xe][te]),typeof De>"u"||!De.length||!De[0]){var pe="";gt=[];for(ee in j[xe])this.terminals_[ee]&&ee>oe&&gt.push("'"+this.terminals_[ee]+"'");ge.showPosition?pe="Parse error on line "+(V+1)+`:
`+ge.showPosition()+`
Expecting `+gt.join(", ")+", got '"+(this.terminals_[te]||te)+"'":pe="Parse error on line "+(V+1)+": Unexpected "+(te==ce?"end of input":"'"+(this.terminals_[te]||te)+"'"),this.parseError(pe,{text:ge.match,token:this.terminals_[te]||te,line:ge.yylineno,loc:Ae,expected:gt})}if(De[0]instanceof Array&&De.length>1)throw new Error("Parse Error: multiple actions possible at state: "+xe+", token: "+te);switch(De[0]){case 1:S.push(te),B.push(ge.yytext),R.push(ge.yylloc),S.push(De[1]),te=null,Q=ge.yyleng,$=ge.yytext,V=ge.yylineno,Ae=ge.yylloc;break;case 2:if(rt=this.productions_[De[1]][1],Ie.$=B[B.length-rt],Ie._$={first_line:R[R.length-(rt||1)].first_line,last_line:R[R.length-1].last_line,first_column:R[R.length-(rt||1)].first_column,last_column:R[R.length-1].last_column},de&&(Ie._$.range=[R[R.length-(rt||1)].range[0],R[R.length-1].range[1]]),he=this.performAction.apply(Ie,[$,Q,V,ye.yy,De[1],B,R].concat(se)),typeof he<"u")return he;rt&&(S=S.slice(0,-1*rt*2),B=B.slice(0,-1*rt),R=R.slice(0,-1*rt)),S.push(this.productions_[De[1]][0]),B.push(Ie.$),R.push(Ie._$),me=j[S[S.length-2]][S[S.length-1]],S.push(me);break;case 3:return!0}}return!0}},p=function(){var y={EOF:1,parseError:function(E,S){if(this.yy.parser)this.yy.parser.parseError(E,S);else throw new Error(E)},setInput:function(b,E){return this.yy=E||this.yy||{},this._input=b,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var b=this._input[0];this.yytext+=b,this.yyleng++,this.offset++,this.match+=b,this.matched+=b;var E=b.match(/(?:\r\n?|\n).*/g);return E?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),b},unput:function(b){var E=b.length,S=b.split(/(?:\r\n?|\n)/g);this._input=b+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-E),this.offset-=E;var N=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),S.length-1&&(this.yylineno-=S.length-1);var B=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:S?(S.length===N.length?this.yylloc.first_column:0)+N[N.length-S.length].length-S[0].length:this.yylloc.first_column-E},this.options.ranges&&(this.yylloc.range=[B[0],B[0]+this.yyleng-E]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(b){this.unput(this.match.slice(b))},pastInput:function(){var b=this.matched.substr(0,this.matched.length-this.match.length);return(b.length>20?"...":"")+b.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var b=this.match;return b.length<20&&(b+=this._input.substr(0,20-b.length)),(b.substr(0,20)+(b.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var b=this.pastInput(),E=new Array(b.length+1).join("-");return b+this.upcomingInput()+`
`+E+"^"},test_match:function(b,E){var S,N,B;if(this.options.backtrack_lexer&&(B={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(B.yylloc.range=this.yylloc.range.slice(0))),N=b[0].match(/(?:\r\n?|\n).*/g),N&&(this.yylineno+=N.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:N?N[N.length-1].length-N[N.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+b[0].length},this.yytext+=b[0],this.match+=b[0],this.matches=b,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(b[0].length),this.matched+=b[0],S=this.performAction.call(this,this.yy,this,E,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),S)return S;if(this._backtrack){for(var R in B)this[R]=B[R];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var b,E,S,N;this._more||(this.yytext="",this.match="");for(var B=this._currentRules(),R=0;R<B.length;R++)if(S=this._input.match(this.rules[B[R]]),S&&(!E||S[0].length>E[0].length)){if(E=S,N=R,this.options.backtrack_lexer){if(b=this.test_match(S,B[R]),b!==!1)return b;if(this._backtrack){E=!1;continue}else return!1}else if(!this.options.flex)break}return E?(b=this.test_match(E,B[N]),b!==!1?b:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var E=this.next();return E||this.lex()},begin:function(E){this.conditionStack.push(E)},popState:function(){var E=this.conditionStack.length-1;return E>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(E){return E=this.conditionStack.length-1-Math.abs(E||0),E>=0?this.conditionStack[E]:"INITIAL"},pushState:function(E){this.begin(E)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(E,S,N,B){switch(N){case 0:return 4;case 1:return 9;case 2:return"space";case 3:return 10;case 4:return 6;case 5:return"TXT"}},rules:[/^(?:info\b)/i,/^(?:[\s\n\r]+)/i,/^(?:[\s]+)/i,/^(?:showInfo\b)/i,/^(?:$)/i,/^(?:.)/i],conditions:{INITIAL:{rules:[0,1,2,3,4,5],inclusive:!0}}};return y}();f.lexer=p;function w(){this.yy={}}return w.prototype=f,f.Parser=w,new w}();W0e.parser=W0e;const E$t=W0e;var ARe="",LRe=!1;const T$t=Object.freeze(Object.defineProperty({__proto__:null,diagram:{parser:E$t,db:{setMessage:i=>{Fe.debug("Setting message to: "+i),ARe=i},getMessage:()=>ARe,setInfo:i=>{LRe=i},getInfo:()=>LRe,clear:rp},renderer:{draw:(i,a,f)=>{try{Fe.debug(`Rendering info diagram
`+i);const p=Tt().securityLevel;let w;p==="sandbox"&&(w=Cr("#i"+a));const b=Cr(p==="sandbox"?w.nodes()[0].contentDocument.body:"body").select("#"+a);b.append("g").append("text").attr("x",100).attr("y",40).attr("class","version").attr("font-size","32px").style("text-anchor","middle").text("v "+f),b.attr("height",100),b.attr("width",400)}catch(p){Fe.error("Error while rendering info diagram"),Fe.error(p.message)}}},styles:()=>""}},Symbol.toStringTag,{value:"Module"}));var K0e=function(){var i=function(Ae,de,ve,te){for(ve=ve||{},te=Ae.length;te--;ve[Ae[te]]=de);return ve},a=[1,4],f=[1,5],p=[1,6],w=[1,7],y=[1,9],b=[1,11,13,15,17,19,20,26,27,28,29],E=[2,5],S=[1,6,11,13,15,17,19,20,26,27,28,29],N=[26,27,28],B=[2,8],R=[1,18],j=[1,19],$=[1,20],V=[1,21],Q=[1,22],oe=[1,23],ce=[1,28],se=[6,26,27,28,29],ge={trace:function(){},yy:{},symbols_:{error:2,start:3,eol:4,directive:5,PIE:6,document:7,showData:8,line:9,statement:10,txt:11,value:12,title:13,title_value:14,acc_title:15,acc_title_value:16,acc_descr:17,acc_descr_value:18,acc_descr_multiline_value:19,section:20,openDirective:21,typeDirective:22,closeDirective:23,":":24,argDirective:25,NEWLINE:26,";":27,EOF:28,open_directive:29,type_directive:30,arg_directive:31,close_directive:32,$accept:0,$end:1},terminals_:{2:"error",6:"PIE",8:"showData",11:"txt",12:"value",13:"title",14:"title_value",15:"acc_title",16:"acc_title_value",17:"acc_descr",18:"acc_descr_value",19:"acc_descr_multiline_value",20:"section",24:":",26:"NEWLINE",27:";",28:"EOF",29:"open_directive",30:"type_directive",31:"arg_directive",32:"close_directive"},productions_:[0,[3,2],[3,2],[3,2],[3,3],[7,0],[7,2],[9,2],[10,0],[10,2],[10,2],[10,2],[10,2],[10,1],[10,1],[10,1],[5,3],[5,5],[4,1],[4,1],[4,1],[21,1],[22,1],[25,1],[23,1]],performAction:function(de,ve,te,xe,De,he,Ie){var ee=he.length-1;switch(De){case 4:xe.setShowData(!0);break;case 7:this.$=he[ee-1];break;case 9:xe.addSection(he[ee-1],xe.cleanupValue(he[ee]));break;case 10:this.$=he[ee].trim(),xe.setDiagramTitle(this.$);break;case 11:this.$=he[ee].trim(),xe.setAccTitle(this.$);break;case 12:case 13:this.$=he[ee].trim(),xe.setAccDescription(this.$);break;case 14:xe.addSection(he[ee].substr(8)),this.$=he[ee].substr(8);break;case 21:xe.parseDirective("%%{","open_directive");break;case 22:xe.parseDirective(he[ee],"type_directive");break;case 23:he[ee]=he[ee].trim().replace(/'/g,'"'),xe.parseDirective(he[ee],"arg_directive");break;case 24:xe.parseDirective("}%%","close_directive","pie");break}},table:[{3:1,4:2,5:3,6:a,21:8,26:f,27:p,28:w,29:y},{1:[3]},{3:10,4:2,5:3,6:a,21:8,26:f,27:p,28:w,29:y},{3:11,4:2,5:3,6:a,21:8,26:f,27:p,28:w,29:y},i(b,E,{7:12,8:[1,13]}),i(S,[2,18]),i(S,[2,19]),i(S,[2,20]),{22:14,30:[1,15]},{30:[2,21]},{1:[2,1]},{1:[2,2]},i(N,B,{21:8,9:16,10:17,5:24,1:[2,3],11:R,13:j,15:$,17:V,19:Q,20:oe,29:y}),i(b,E,{7:25}),{23:26,24:[1,27],32:ce},i([24,32],[2,22]),i(b,[2,6]),{4:29,26:f,27:p,28:w},{12:[1,30]},{14:[1,31]},{16:[1,32]},{18:[1,33]},i(N,[2,13]),i(N,[2,14]),i(N,[2,15]),i(N,B,{21:8,9:16,10:17,5:24,1:[2,4],11:R,13:j,15:$,17:V,19:Q,20:oe,29:y}),i(se,[2,16]),{25:34,31:[1,35]},i(se,[2,24]),i(b,[2,7]),i(N,[2,9]),i(N,[2,10]),i(N,[2,11]),i(N,[2,12]),{23:36,32:ce},{32:[2,23]},i(se,[2,17])],defaultActions:{9:[2,21],10:[2,1],11:[2,2],35:[2,23]},parseError:function(de,ve){if(ve.recoverable)this.trace(de);else{var te=new Error(de);throw te.hash=ve,te}},parse:function(de){var ve=this,te=[0],xe=[],De=[null],he=[],Ie=this.table,ee="",rt=0,me=0,gt=2,pe=1,Et=he.slice.call(arguments,1),wt=Object.create(this.lexer),jt={yy:{}};for(var At in this.yy)Object.prototype.hasOwnProperty.call(this.yy,At)&&(jt.yy[At]=this.yy[At]);wt.setInput(de,jt.yy),jt.yy.lexer=wt,jt.yy.parser=this,typeof wt.yylloc>"u"&&(wt.yylloc={});var Bt=wt.yylloc;he.push(Bt);var cn=wt.options&&wt.options.ranges;typeof jt.yy.parseError=="function"?this.parseError=jt.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function Nn(){var Bn;return Bn=xe.pop()||wt.lex()||pe,typeof Bn!="number"&&(Bn instanceof Array&&(xe=Bn,Bn=xe.pop()),Bn=ve.symbols_[Bn]||Bn),Bn}for(var Ot,oi,kt,Dt,vt={},Nt,ze,Xe,Lt;;){if(oi=te[te.length-1],this.defaultActions[oi]?kt=this.defaultActions[oi]:((Ot===null||typeof Ot>"u")&&(Ot=Nn()),kt=Ie[oi]&&Ie[oi][Ot]),typeof kt>"u"||!kt.length||!kt[0]){var Ge="";Lt=[];for(Nt in Ie[oi])this.terminals_[Nt]&&Nt>gt&&Lt.push("'"+this.terminals_[Nt]+"'");wt.showPosition?Ge="Parse error on line "+(rt+1)+`:
`+wt.showPosition()+`
Expecting `+Lt.join(", ")+", got '"+(this.terminals_[Ot]||Ot)+"'":Ge="Parse error on line "+(rt+1)+": Unexpected "+(Ot==pe?"end of input":"'"+(this.terminals_[Ot]||Ot)+"'"),this.parseError(Ge,{text:wt.match,token:this.terminals_[Ot]||Ot,line:wt.yylineno,loc:Bt,expected:Lt})}if(kt[0]instanceof Array&&kt.length>1)throw new Error("Parse Error: multiple actions possible at state: "+oi+", token: "+Ot);switch(kt[0]){case 1:te.push(Ot),De.push(wt.yytext),he.push(wt.yylloc),te.push(kt[1]),Ot=null,me=wt.yyleng,ee=wt.yytext,rt=wt.yylineno,Bt=wt.yylloc;break;case 2:if(ze=this.productions_[kt[1]][1],vt.$=De[De.length-ze],vt._$={first_line:he[he.length-(ze||1)].first_line,last_line:he[he.length-1].last_line,first_column:he[he.length-(ze||1)].first_column,last_column:he[he.length-1].last_column},cn&&(vt._$.range=[he[he.length-(ze||1)].range[0],he[he.length-1].range[1]]),Dt=this.performAction.apply(vt,[ee,me,rt,jt.yy,kt[1],De,he].concat(Et)),typeof Dt<"u")return Dt;ze&&(te=te.slice(0,-1*ze*2),De=De.slice(0,-1*ze),he=he.slice(0,-1*ze)),te.push(this.productions_[kt[1]][0]),De.push(vt.$),he.push(vt._$),Xe=Ie[te[te.length-2]][te[te.length-1]],te.push(Xe);break;case 3:return!0}}return!0}},ye=function(){var Ae={EOF:1,parseError:function(ve,te){if(this.yy.parser)this.yy.parser.parseError(ve,te);else throw new Error(ve)},setInput:function(de,ve){return this.yy=ve||this.yy||{},this._input=de,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var de=this._input[0];this.yytext+=de,this.yyleng++,this.offset++,this.match+=de,this.matched+=de;var ve=de.match(/(?:\r\n?|\n).*/g);return ve?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),de},unput:function(de){var ve=de.length,te=de.split(/(?:\r\n?|\n)/g);this._input=de+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-ve),this.offset-=ve;var xe=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),te.length-1&&(this.yylineno-=te.length-1);var De=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:te?(te.length===xe.length?this.yylloc.first_column:0)+xe[xe.length-te.length].length-te[0].length:this.yylloc.first_column-ve},this.options.ranges&&(this.yylloc.range=[De[0],De[0]+this.yyleng-ve]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(de){this.unput(this.match.slice(de))},pastInput:function(){var de=this.matched.substr(0,this.matched.length-this.match.length);return(de.length>20?"...":"")+de.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var de=this.match;return de.length<20&&(de+=this._input.substr(0,20-de.length)),(de.substr(0,20)+(de.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var de=this.pastInput(),ve=new Array(de.length+1).join("-");return de+this.upcomingInput()+`
`+ve+"^"},test_match:function(de,ve){var te,xe,De;if(this.options.backtrack_lexer&&(De={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(De.yylloc.range=this.yylloc.range.slice(0))),xe=de[0].match(/(?:\r\n?|\n).*/g),xe&&(this.yylineno+=xe.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:xe?xe[xe.length-1].length-xe[xe.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+de[0].length},this.yytext+=de[0],this.match+=de[0],this.matches=de,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(de[0].length),this.matched+=de[0],te=this.performAction.call(this,this.yy,this,ve,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),te)return te;if(this._backtrack){for(var he in De)this[he]=De[he];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var de,ve,te,xe;this._more||(this.yytext="",this.match="");for(var De=this._currentRules(),he=0;he<De.length;he++)if(te=this._input.match(this.rules[De[he]]),te&&(!ve||te[0].length>ve[0].length)){if(ve=te,xe=he,this.options.backtrack_lexer){if(de=this.test_match(te,De[he]),de!==!1)return de;if(this._backtrack){ve=!1;continue}else return!1}else if(!this.options.flex)break}return ve?(de=this.test_match(ve,De[xe]),de!==!1?de:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
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.pieCircle{
stroke: ${i.pieStrokeColor};
stroke-width : ${i.pieStrokeWidth};
opacity : ${i.pieOpacity};
}
.pieOuterCircle{
stroke: ${i.pieOuterStrokeColor};
stroke-width: ${i.pieOuterStrokeWidth};
fill: none;
}
.pieTitleText {
text-anchor: middle;
font-size: ${i.pieTitleTextSize};
fill: ${i.pieTitleTextColor};
font-family: ${i.fontFamily};
}
.slice {
font-family: ${i.fontFamily};
fill: ${i.pieSectionTextColor};
font-size:${i.pieSectionTextSize};
// fill: white;
}
.legend text {
fill: ${i.pieLegendTextColor};
font-family: ${i.fontFamily};
font-size: ${i.pieLegendTextSize};
}
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Dt},a=[1,3],f=[1,5],p=[1,6],w=[1,7],y=[1,8],b=[5,6,8,14,16,18,19,40,41,42,43,44,45,53,71,72],E=[1,22],S=[2,13],N=[1,26],B=[1,27],R=[1,28],j=[1,29],$=[1,30],V=[1,31],Q=[1,24],oe=[1,32],ce=[1,33],se=[1,36],ge=[71,72],ye=[5,8,14,16,18,19,40,41,42,43,44,45,53,60,62,71,72],ke=[1,56],Ae=[1,57],de=[1,58],ve=[1,59],te=[1,60],xe=[1,61],De=[1,62],he=[62,63],Ie=[1,74],ee=[1,70],rt=[1,71],me=[1,72],gt=[1,73],pe=[1,75],Et=[1,79],wt=[1,80],jt=[1,77],At=[1,78],Bt=[5,8,14,16,18,19,40,41,42,43,44,45,53,71,72],cn={trace:function(){},yy:{},symbols_:{error:2,start:3,directive:4,NEWLINE:5,RD:6,diagram:7,EOF:8,openDirective:9,typeDirective:10,closeDirective:11,":":12,argDirective:13,acc_title:14,acc_title_value:15,acc_descr:16,acc_descr_value:17,acc_descr_multiline_value:18,open_directive:19,type_directive:20,arg_directive:21,close_directive:22,requirementDef:23,elementDef:24,relationshipDef:25,requirementType:26,requirementName:27,STRUCT_START:28,requirementBody:29,ID:30,COLONSEP:31,id:32,TEXT:33,text:34,RISK:35,riskLevel:36,VERIFYMTHD:37,verifyType:38,STRUCT_STOP:39,REQUIREMENT:40,FUNCTIONAL_REQUIREMENT:41,INTERFACE_REQUIREMENT:42,PERFORMANCE_REQUIREMENT:43,PHYSICAL_REQUIREMENT:44,DESIGN_CONSTRAINT:45,LOW_RISK:46,MED_RISK:47,HIGH_RISK:48,VERIFY_ANALYSIS:49,VERIFY_DEMONSTRATION:50,VERIFY_INSPECTION:51,VERIFY_TEST:52,ELEMENT:53,elementName:54,elementBody:55,TYPE:56,type:57,DOCREF:58,ref:59,END_ARROW_L:60,relationship:61,LINE:62,END_ARROW_R:63,CONTAINS:64,COPIES:65,DERIVES:66,SATISFIES:67,VERIFIES:68,REFINES:69,TRACES:70,unqString:71,qString:72,$accept:0,$end:1},terminals_:{2:"error",5:"NEWLINE",6:"RD",8:"EOF",12:":",14:"acc_title",15:"acc_title_value",16:"acc_descr",17:"acc_descr_value",18:"acc_descr_multiline_value",19:"open_directive",20:"type_directive",21:"arg_directive",22:"close_directive",28:"STRUCT_START",30:"ID",31:"COLONSEP",33:"TEXT",35:"RISK",37:"VERIFYMTHD",39:"STRUCT_STOP",40:"REQUIREMENT",41:"FUNCTIONAL_REQUIREMENT",42:"INTERFACE_REQUIREMENT",43:"PERFORMANCE_REQUIREMENT",44:"PHYSICAL_REQUIREMENT",45:"DESIGN_CONSTRAINT",46:"LOW_RISK",47:"MED_RISK",48:"HIGH_RISK",49:"VERIFY_ANALYSIS",50:"VERIFY_DEMONSTRATION",51:"VERIFY_INSPECTION",52:"VERIFY_TEST",53:"ELEMENT",56:"TYPE",58:"DOCREF",60:"END_ARROW_L",62:"LINE",63:"END_ARROW_R",64:"CONTAINS",65:"COPIES",66:"DERIVES",67:"SATISFIES",68:"VERIFIES",69:"REFINES",70:"TRACES",71:"unqString",72:"qString"},productions_:[0,[3,3],[3,2],[3,4],[4,3],[4,5],[4,2],[4,2],[4,1],[9,1],[10,1],[13,1],[11,1],[7,0],[7,2],[7,2],[7,2],[7,2],[7,2],[23,5],[29,5],[29,5],[29,5],[29,5],[29,2],[29,1],[26,1],[26,1],[26,1],[26,1],[26,1],[26,1],[36,1],[36,1],[36,1],[38,1],[38,1],[38,1],[38,1],[24,5],[55,5],[55,5],[55,2],[55,1],[25,5],[25,5],[61,1],[61,1],[61,1],[61,1],[61,1],[61,1],[61,1],[27,1],[27,1],[32,1],[32,1],[34,1],[34,1],[54,1],[54,1],[57,1],[57,1],[59,1],[59,1]],performAction:function(kt,Dt,vt,Nt,ze,Xe,Lt){var 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31:this.$=Nt.RequirementType.DESIGN_CONSTRAINT;break;case 32:this.$=Nt.RiskLevel.LOW_RISK;break;case 33:this.$=Nt.RiskLevel.MED_RISK;break;case 34:this.$=Nt.RiskLevel.HIGH_RISK;break;case 35:this.$=Nt.VerifyType.VERIFY_ANALYSIS;break;case 36:this.$=Nt.VerifyType.VERIFY_DEMONSTRATION;break;case 37:this.$=Nt.VerifyType.VERIFY_INSPECTION;break;case 38:this.$=Nt.VerifyType.VERIFY_TEST;break;case 39:Nt.addElement(Xe[Ge-3]);break;case 40:Nt.setNewElementType(Xe[Ge-2]);break;case 41:Nt.setNewElementDocRef(Xe[Ge-2]);break;case 44:Nt.addRelationship(Xe[Ge-2],Xe[Ge],Xe[Ge-4]);break;case 45:Nt.addRelationship(Xe[Ge-2],Xe[Ge-4],Xe[Ge]);break;case 46:this.$=Nt.Relationships.CONTAINS;break;case 47:this.$=Nt.Relationships.COPIES;break;case 48:this.$=Nt.Relationships.DERIVES;break;case 49:this.$=Nt.Relationships.SATISFIES;break;case 50:this.$=Nt.Relationships.VERIFIES;break;case 51:this.$=Nt.Relationships.REFINES;break;case 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vt=new Error(kt);throw vt.hash=Dt,vt}},parse:function(kt){var Dt=this,vt=[0],Nt=[],ze=[null],Xe=[],Lt=this.table,Ge="",Bn=0,Oe=0,Ri=2,tn=1,hi=Xe.slice.call(arguments,1),Sr=Object.create(this.lexer),Zn={yy:{}};for(var Xn in this.yy)Object.prototype.hasOwnProperty.call(this.yy,Xn)&&(Zn.yy[Xn]=this.yy[Xn]);Sr.setInput(kt,Zn.yy),Zn.yy.lexer=Sr,Zn.yy.parser=this,typeof Sr.yylloc>"u"&&(Sr.yylloc={});var ir=Sr.yylloc;Xe.push(ir);var Hn=Sr.options&&Sr.options.ranges;typeof Zn.yy.parseError=="function"?this.parseError=Zn.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function tr(){var br;return br=Nt.pop()||Sr.lex()||tn,typeof br!="number"&&(br instanceof Array&&(Nt=br,br=Nt.pop()),br=Dt.symbols_[br]||br),br}for(var ha,Zs,ns,Hi,Js={},Pc,Ga,ws,Oi;;){if(Zs=vt[vt.length-1],this.defaultActions[Zs]?ns=this.defaultActions[Zs]:((ha===null||typeof ha>"u")&&(ha=tr()),ns=Lt[Zs]&&Lt[Zs][ha]),typeof ns>"u"||!ns.length||!ns[0]){var Er="";Oi=[];for(Pc in Lt[Zs])this.terminals_[Pc]&&Pc>Ri&&Oi.push("'"+this.terminals_[Pc]+"'");Sr.showPosition?Er="Parse error on line "+(Bn+1)+`:
`+Sr.showPosition()+`
Expecting `+Oi.join(", ")+", got '"+(this.terminals_[ha]||ha)+"'":Er="Parse error on line "+(Bn+1)+": Unexpected "+(ha==tn?"end of input":"'"+(this.terminals_[ha]||ha)+"'"),this.parseError(Er,{text:Sr.match,token:this.terminals_[ha]||ha,line:Sr.yylineno,loc:ir,expected:Oi})}if(ns[0]instanceof Array&&ns.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Zs+", token: "+ha);switch(ns[0]){case 1:vt.push(ha),ze.push(Sr.yytext),Xe.push(Sr.yylloc),vt.push(ns[1]),ha=null,Oe=Sr.yyleng,Ge=Sr.yytext,Bn=Sr.yylineno,ir=Sr.yylloc;break;case 2:if(Ga=this.productions_[ns[1]][1],Js.$=ze[ze.length-Ga],Js._$={first_line:Xe[Xe.length-(Ga||1)].first_line,last_line:Xe[Xe.length-1].last_line,first_column:Xe[Xe.length-(Ga||1)].first_column,last_column:Xe[Xe.length-1].last_column},Hn&&(Js._$.range=[Xe[Xe.length-(Ga||1)].range[0],Xe[Xe.length-1].range[1]]),Hi=this.performAction.apply(Js,[Ge,Oe,Bn,Zn.yy,ns[1],ze,Xe].concat(hi)),typeof Hi<"u")return Hi;Ga&&(vt=vt.slice(0,-1*Ga*2),ze=ze.slice(0,-1*Ga),Xe=Xe.slice(0,-1*Ga)),vt.push(this.productions_[ns[1]][0]),ze.push(Js.$),Xe.push(Js._$),ws=Lt[vt[vt.length-2]][vt[vt.length-1]],vt.push(ws);break;case 3:return!0}}return!0}},Nn=function(){var oi={EOF:1,parseError:function(Dt,vt){if(this.yy.parser)this.yy.parser.parseError(Dt,vt);else throw new Error(Dt)},setInput:function(kt,Dt){return this.yy=Dt||this.yy||{},this._input=kt,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var kt=this._input[0];this.yytext+=kt,this.yyleng++,this.offset++,this.match+=kt,this.matched+=kt;var Dt=kt.match(/(?:\r\n?|\n).*/g);return Dt?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),kt},unput:function(kt){var Dt=kt.length,vt=kt.split(/(?:\r\n?|\n)/g);this._input=kt+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-Dt),this.offset-=Dt;var Nt=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),vt.length-1&&(this.yylineno-=vt.length-1);var ze=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:vt?(vt.length===Nt.length?this.yylloc.first_column:0)+Nt[Nt.length-vt.length].length-vt[0].length:this.yylloc.first_column-Dt},this.options.ranges&&(this.yylloc.range=[ze[0],ze[0]+this.yyleng-Dt]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(kt){this.unput(this.match.slice(kt))},pastInput:function(){var kt=this.matched.substr(0,this.matched.length-this.match.length);return(kt.length>20?"...":"")+kt.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var kt=this.match;return kt.length<20&&(kt+=this._input.substr(0,20-kt.length)),(kt.substr(0,20)+(kt.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var kt=this.pastInput(),Dt=new Array(kt.length+1).join("-");return kt+this.upcomingInput()+`
`+Dt+"^"},test_match:function(kt,Dt){var vt,Nt,ze;if(this.options.backtrack_lexer&&(ze={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(ze.yylloc.range=this.yylloc.range.slice(0))),Nt=kt[0].match(/(?:\r\n?|\n).*/g),Nt&&(this.yylineno+=Nt.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:Nt?Nt[Nt.length-1].length-Nt[Nt.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+kt[0].length},this.yytext+=kt[0],this.match+=kt[0],this.matches=kt,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(kt[0].length),this.matched+=kt[0],vt=this.performAction.call(this,this.yy,this,Dt,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),vt)return vt;if(this._backtrack){for(var Xe in ze)this[Xe]=ze[Xe];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var kt,Dt,vt,Nt;this._more||(this.yytext="",this.match="");for(var ze=this._currentRules(),Xe=0;Xe<ze.length;Xe++)if(vt=this._input.match(this.rules[ze[Xe]]),vt&&(!Dt||vt[0].length>Dt[0].length)){if(Dt=vt,Nt=Xe,this.options.backtrack_lexer){if(kt=this.test_match(vt,ze[Xe]),kt!==!1)return kt;if(this._backtrack){Dt=!1;continue}else return!1}else if(!this.options.flex)break}return Dt?(kt=this.test_match(Dt,ze[Nt]),kt!==!1?kt:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
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this.popState(),this.popState(),22;case 4:return 21;case 5:return"title";case 6:return this.begin("acc_title"),14;case 7:return this.popState(),"acc_title_value";case 8:return this.begin("acc_descr"),16;case 9:return this.popState(),"acc_descr_value";case 10:this.begin("acc_descr_multiline");break;case 11:this.popState();break;case 12:return"acc_descr_multiline_value";case 13:return 5;case 14:break;case 15:break;case 16:break;case 17:return 8;case 18:return 6;case 19:return 28;case 20:return 39;case 21:return 31;case 22:return 30;case 23:return 33;case 24:return 35;case 25:return 37;case 26:return 40;case 27:return 41;case 28:return 42;case 29:return 43;case 30:return 44;case 31:return 45;case 32:return 46;case 33:return 47;case 34:return 48;case 35:return 49;case 36:return 50;case 37:return 51;case 38:return 52;case 39:return 53;case 40:return 64;case 41:return 65;case 42:return 66;case 43:return 67;case 44:return 68;case 45:return 69;case 46:return 70;case 47:return 56;case 48:return 58;case 49:return 60;case 50:return 63;case 51:return 62;case 52:this.begin("string");break;case 53:this.popState();break;case 54:return"qString";case 55:return vt.yytext=vt.yytext.trim(),71}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:title\s[^#\n;]+)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:(\r?\n)+)/i,/^(?:\s+)/i,/^(?:#[^\n]*)/i,/^(?:%[^\n]*)/i,/^(?:$)/i,/^(?:requirementDiagram\b)/i,/^(?:\{)/i,/^(?:\})/i,/^(?::)/i,/^(?:id\b)/i,/^(?:text\b)/i,/^(?:risk\b)/i,/^(?:verifyMethod\b)/i,/^(?:requirement\b)/i,/^(?:functionalRequirement\b)/i,/^(?:interfaceRequirement\b)/i,/^(?:performanceRequirement\b)/i,/^(?:physicalRequirement\b)/i,/^(?:designConstraint\b)/i,/^(?:low\b)/i,/^(?:medium\b)/i,/^(?:high\b)/i,/^(?:analysis\b)/i,/^(?:demonstration\b)/i,/^(?:inspection\b)/i,/^(?:test\b)/i,/^(?:element\b)/i,/^(?:contains\b)/i,/^(?:copies\b)/i,/^(?:derives\b)/i,/^(?:satisfies\b)/i,/^(?:verifies\b)/i,/^(?:refines\b)/i,/^(?:traces\b)/i,/^(?:type\b)/i,/^(?:docref\b)/i,/^(?:<-)/i,/^(?:->)/i,/^(?:-)/i,/^(?:["])/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:[\w][^\r\n\{\<\>\-\=]*)/i],conditions:{acc_descr_multiline:{rules:[11,12],inclusive:!1},acc_descr:{rules:[9],inclusive:!1},acc_title:{rules:[7],inclusive:!1},close_directive:{rules:[],inclusive:!1},arg_directive:{rules:[3,4],inclusive:!1},type_directive:{rules:[2,3],inclusive:!1},open_directive:{rules:[1],inclusive:!1},unqString:{rules:[],inclusive:!1},token:{rules:[],inclusive:!1},string:{rules:[53,54],inclusive:!1},INITIAL:{rules:[0,5,6,8,10,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,55],inclusive:!0}}};return oi}();cn.lexer=Nn;function Ot(){this.yy={}}return Ot.prototype=cn,cn.Parser=Ot,new Ot}();X0e.parser=X0e;const L$t=X0e;let Q0e=[],up={},SP={},ox={},AP={};const M$t={RequirementType:{REQUIREMENT:"Requirement",FUNCTIONAL_REQUIREMENT:"Functional Requirement",INTERFACE_REQUIREMENT:"Interface Requirement",PERFORMANCE_REQUIREMENT:"Performance Requirement",PHYSICAL_REQUIREMENT:"Physical Requirement",DESIGN_CONSTRAINT:"Design Constraint"},RiskLevel:{LOW_RISK:"Low",MED_RISK:"Medium",HIGH_RISK:"High"},VerifyType:{VERIFY_ANALYSIS:"Analysis",VERIFY_DEMONSTRATION:"Demonstration",VERIFY_INSPECTION:"Inspection",VERIFY_TEST:"Test"},Relationships:{CONTAINS:"contains",COPIES:"copies",DERIVES:"derives",SATISFIES:"satisfies",VERIFIES:"verifies",REFINES:"refines",TRACES:"traces"},parseDirective:function(i,a,f){rd.parseDirective(this,i,a,f)},getConfig:()=>Tt().req,addRequirement:(i,a)=>(SP[i]===void 0&&(SP[i]={name:i,type:a,id:up.id,text:up.text,risk:up.risk,verifyMethod:up.verifyMethod}),up={},SP[i]),getRequirements:()=>SP,setNewReqId:i=>{up!==void 0&&(up.id=i)},setNewReqText:i=>{up!==void 0&&(up.text=i)},setNewReqRisk:i=>{up!==void 0&&(up.risk=i)},setNewReqVerifyMethod:i=>{up!==void 0&&(up.verifyMethod=i)},setAccTitle:ip,getAccTitle:L2,setAccDescription:M2,getAccDescription:D2,addElement:i=>(AP[i]===void 0&&(AP[i]={name:i,type:ox.type,docRef:ox.docRef},Fe.info("Added new requirement: ",i)),ox={},AP[i]),getElements:()=>AP,setNewElementType:i=>{ox!==void 0&&(ox.type=i)},setNewElementDocRef:i=>{ox!==void 0&&(ox.docRef=i)},addRelationship:(i,a,f)=>{Q0e.push({type:i,src:a,dst:f})},getRelationships:()=>Q0e,clear:()=>{Q0e=[],up={},SP={},ox={},AP={},rp()}},D$t=i=>`
marker {
fill: ${i.relationColor};
stroke: ${i.relationColor};
}
marker.cross {
stroke: ${i.lineColor};
}
svg {
font-family: ${i.fontFamily};
font-size: ${i.fontSize};
}
.reqBox {
fill: ${i.requirementBackground};
fill-opacity: 1.0;
stroke: ${i.requirementBorderColor};
stroke-width: ${i.requirementBorderSize};
}
.reqTitle, .reqLabel{
fill: ${i.requirementTextColor};
}
.reqLabelBox {
fill: ${i.relationLabelBackground};
fill-opacity: 1.0;
}
.req-title-line {
stroke: ${i.requirementBorderColor};
stroke-width: ${i.requirementBorderSize};
}
.relationshipLine {
stroke: ${i.relationColor};
stroke-width: 1;
}
.relationshipLabel {
fill: ${i.relationLabelColor};
}
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66:this.$=[Ge[Oe-4],Ge[Oe-1],{type:"addMessage",from:Ge[Oe-4].actor,to:Ge[Oe-1].actor,signalType:Ge[Oe-3],msg:Ge[Oe]},{type:"activeStart",signalType:Xe.LINETYPE.ACTIVE_START,actor:Ge[Oe-1]}];break;case 67:this.$=[Ge[Oe-4],Ge[Oe-1],{type:"addMessage",from:Ge[Oe-4].actor,to:Ge[Oe-1].actor,signalType:Ge[Oe-3],msg:Ge[Oe]},{type:"activeEnd",signalType:Xe.LINETYPE.ACTIVE_END,actor:Ge[Oe-4]}];break;case 68:this.$=[Ge[Oe-3],Ge[Oe-1],{type:"addMessage",from:Ge[Oe-3].actor,to:Ge[Oe-1].actor,signalType:Ge[Oe-2],msg:Ge[Oe]}];break;case 69:this.$={type:"addParticipant",actor:Ge[Oe]};break;case 70:this.$=Xe.LINETYPE.SOLID_OPEN;break;case 71:this.$=Xe.LINETYPE.DOTTED_OPEN;break;case 72:this.$=Xe.LINETYPE.SOLID;break;case 73:this.$=Xe.LINETYPE.DOTTED;break;case 74:this.$=Xe.LINETYPE.SOLID_CROSS;break;case 75:this.$=Xe.LINETYPE.DOTTED_CROSS;break;case 76:this.$=Xe.LINETYPE.SOLID_POINT;break;case 77:this.$=Xe.LINETYPE.DOTTED_POINT;break;case 78:this.$=Xe.parseMessage(Ge[Oe].trim().substring(1));break;case 79:Xe.parseDirective("%%{","open_directive");break;case 80:Xe.parseDirective(Ge[Oe],"type_directive");break;case 81:Ge[Oe]=Ge[Oe].trim().replace(/'/g,'"'),Xe.parseDirective(Ge[Oe],"arg_directive");break;case 82:Xe.parseDirective("}%%","close_directive","sequence");break}},table:[{3:1,4:a,5:f,6:4,7:p,14:6,83:w},{1:[3]},{3:8,4:a,5:f,6:4,7:p,14:6,83:w},{3:9,4:a,5:f,6:4,7:p,14:6,83:w},{3:10,4:a,5:f,6:4,7:p,14:6,83:w},i([1,4,5,19,23,26,28,34,35,36,38,40,41,42,43,44,46,48,50,54,56,57,62,63,64,65,73,83],y,{8:11}),{15:12,84:[1,13]},{84:[2,79]},{1:[2,1]},{1:[2,2]},{1:[2,3]},{1:[2,4],4:b,5:E,6:41,9:14,10:16,13:18,14:6,19:S,22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},{16:51,17:[1,52],86:me},i([17,86],[2,80]),i(gt,[2,6]),{6:41,10:54,13:18,14:6,19:S,22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},i(gt,[2,8]),i(gt,[2,9]),i(gt,[2,17]),{20:[1,55]},{5:[1,56]},{5:[1,59],24:[1,57],25:[1,58]},{27:60,73:rt},{27:61,73:rt},{5:[1,62]},{5:[1,63]},{5:[1,64]},{5:[1,65]},{5:[1,66]},i(gt,[2,31]),i(gt,[2,32]),{37:[1,67]},{39:[1,68]},i(gt,[2,35]),{20:[1,69]},{20:[1,70]},{20:[1,71]},{20:[1,72]},{20:[1,73]},{20:[1,74]},{20:[1,75]},i(gt,[2,43]),{27:76,73:rt},{27:77,73:rt},{70:78,74:[1,79],75:[1,80],76:[1,81],77:[1,82],78:[1,83],79:[1,84],80:[1,85],81:[1,86]},{58:87,60:[1,88],68:[1,89],69:[1,90]},{27:91,73:rt},{27:92,73:rt},{27:93,73:rt},{27:94,73:rt},i([5,55,67,74,75,76,77,78,79,80,81,82],[2,69]),{5:[1,95]},{18:96,85:[1,97]},{5:[2,82]},i(gt,[2,7]),i(pe,[2,10],{11:98}),i(gt,[2,19]),{5:[1,100],24:[1,99]},{5:[1,101]},i(gt,[2,23]),{5:[1,102]},{5:[1,103]},i(gt,[2,26]),i(gt,[2,27]),i(gt,[2,28]),i(gt,[2,29]),i(gt,[2,30]),i(gt,[2,33]),i(gt,[2,34]),i(Et,y,{8:104}),i(Et,y,{8:105}),i(Et,y,{8:106}),i(wt,y,{45:107,8:108}),i(jt,y,{47:109,8:110}),i(At,y,{49:111,8:112}),i(Et,y,{8:113}),{5:[1,115],55:[1,114]},{5:[1,117],55:[1,116]},{27:120,71:[1,118],72:[1,119],73:rt},i(Bt,[2,70]),i(Bt,[2,71]),i(Bt,[2,72]),i(Bt,[2,73]),i(Bt,[2,74]),i(Bt,[2,75]),i(Bt,[2,76]),i(Bt,[2,77]),{27:121,73:rt},{27:123,61:122,73:rt},{73:[2,64]},{73:[2,65]},{59:124,82:cn},{59:126,82:cn},{59:127,82:cn},{59:128,82:cn},i(Nn,[2,15]),{16:129,86:me},{86:[2,81]},{4:[1,132],5:[1,134],12:131,13:133,21:[1,130],54:ve,56:te},{5:[1,135]},i(gt,[2,21]),i(gt,[2,22]),i(gt,[2,24]),i(gt,[2,25]),{4:b,5:E,6:41,9:14,10:16,13:18,14:6,19:S,21:[1,136],22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},{4:b,5:E,6:41,9:14,10:16,13:18,14:6,19:S,21:[1,137],22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},{4:b,5:E,6:41,9:14,10:16,13:18,14:6,19:S,21:[1,138],22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},{21:[1,139]},{4:b,5:E,6:41,9:14,10:16,13:18,14:6,19:S,21:[2,48],22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,53:[1,140],54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},{21:[1,141]},{4:b,5:E,6:41,9:14,10:16,13:18,14:6,19:S,21:[2,46],22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,52:[1,142],54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},{21:[1,143]},{4:b,5:E,6:41,9:14,10:16,13:18,14:6,19:S,21:[2,44],22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,51:[1,144],54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},{4:b,5:E,6:41,9:14,10:16,13:18,14:6,19:S,21:[1,145],22:20,23:N,26:B,27:44,28:R,29:24,30:25,31:26,32:27,33:28,34:j,35:$,36:V,38:Q,40:oe,41:ce,42:se,43:ge,44:ye,46:ke,48:Ae,50:de,54:ve,56:te,57:xe,62:De,63:he,64:Ie,65:ee,73:rt,83:w},{20:[1,146]},i(gt,[2,51]),{20:[1,147]},i(gt,[2,53]),{27:148,73:rt},{27:149,73:rt},{59:150,82:cn},{59:151,82:cn},{59:152,82:cn},{67:[1,153],82:[2,63]},{5:[2,56]},{5:[2,78]},{5:[2,57]},{5:[2,58]},{5:[2,59]},{5:[1,154]},i(gt,[2,18]),i(pe,[2,11]),{13:155,54:ve,56:te},i(pe,[2,13]),i(pe,[2,14]),i(gt,[2,20]),i(gt,[2,36]),i(gt,[2,37]),i(gt,[2,38]),i(gt,[2,39]),{20:[1,156]},i(gt,[2,40]),{20:[1,157]},i(gt,[2,41]),{20:[1,158]},i(gt,[2,42]),{5:[1,159]},{5:[1,160]},{59:161,82:cn},{59:162,82:cn},{5:[2,68]},{5:[2,54]},{5:[2,55]},{27:163,73:rt},i(Nn,[2,16]),i(pe,[2,12]),i(wt,y,{8:108,45:164}),i(jt,y,{8:110,47:165}),i(At,y,{8:112,49:166}),i(gt,[2,50]),i(gt,[2,52]),{5:[2,66]},{5:[2,67]},{82:[2,62]},{21:[2,49]},{21:[2,47]},{21:[2,45]}],defaultActions:{7:[2,79],8:[2,1],9:[2,2],10:[2,3],53:[2,82],89:[2,64],90:[2,65],97:[2,81],124:[2,56],125:[2,78],126:[2,57],127:[2,58],128:[2,59],150:[2,68],151:[2,54],152:[2,55],161:[2,66],162:[2,67],163:[2,62],164:[2,49],165:[2,47],166:[2,45]},parseError:function(vt,Nt){if(Nt.recoverable)this.trace(vt);else{var ze=new Error(vt);throw ze.hash=Nt,ze}},parse:function(vt){var Nt=this,ze=[0],Xe=[],Lt=[null],Ge=[],Bn=this.table,Oe="",Ri=0,tn=0,hi=2,Sr=1,Zn=Ge.slice.call(arguments,1),Xn=Object.create(this.lexer),ir={yy:{}};for(var Hn in this.yy)Object.prototype.hasOwnProperty.call(this.yy,Hn)&&(ir.yy[Hn]=this.yy[Hn]);Xn.setInput(vt,ir.yy),ir.yy.lexer=Xn,ir.yy.parser=this,typeof Xn.yylloc>"u"&&(Xn.yylloc={});var tr=Xn.yylloc;Ge.push(tr);var ha=Xn.options&&Xn.options.ranges;typeof ir.yy.parseError=="function"?this.parseError=ir.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function Zs(){var Vn;return Vn=Xe.pop()||Xn.lex()||Sr,typeof Vn!="number"&&(Vn instanceof Array&&(Xe=Vn,Vn=Xe.pop()),Vn=Nt.symbols_[Vn]||Vn),Vn}for(var ns,Hi,Js,Pc,Ga={},ws,Oi,Er,br;;){if(Hi=ze[ze.length-1],this.defaultActions[Hi]?Js=this.defaultActions[Hi]:((ns===null||typeof ns>"u")&&(ns=Zs()),Js=Bn[Hi]&&Bn[Hi][ns]),typeof Js>"u"||!Js.length||!Js[0]){var Dr="";br=[];for(ws in Bn[Hi])this.terminals_[ws]&&ws>hi&&br.push("'"+this.terminals_[ws]+"'");Xn.showPosition?Dr="Parse error on line "+(Ri+1)+`:
`+Xn.showPosition()+`
Expecting `+br.join(", ")+", got '"+(this.terminals_[ns]||ns)+"'":Dr="Parse error on line "+(Ri+1)+": Unexpected "+(ns==Sr?"end of input":"'"+(this.terminals_[ns]||ns)+"'"),this.parseError(Dr,{text:Xn.match,token:this.terminals_[ns]||ns,line:Xn.yylineno,loc:tr,expected:br})}if(Js[0]instanceof Array&&Js.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Hi+", token: "+ns);switch(Js[0]){case 1:ze.push(ns),Lt.push(Xn.yytext),Ge.push(Xn.yylloc),ze.push(Js[1]),ns=null,tn=Xn.yyleng,Oe=Xn.yytext,Ri=Xn.yylineno,tr=Xn.yylloc;break;case 2:if(Oi=this.productions_[Js[1]][1],Ga.$=Lt[Lt.length-Oi],Ga._$={first_line:Ge[Ge.length-(Oi||1)].first_line,last_line:Ge[Ge.length-1].last_line,first_column:Ge[Ge.length-(Oi||1)].first_column,last_column:Ge[Ge.length-1].last_column},ha&&(Ga._$.range=[Ge[Ge.length-(Oi||1)].range[0],Ge[Ge.length-1].range[1]]),Pc=this.performAction.apply(Ga,[Oe,tn,Ri,ir.yy,Js[1],Lt,Ge].concat(Zn)),typeof Pc<"u")return Pc;Oi&&(ze=ze.slice(0,-1*Oi*2),Lt=Lt.slice(0,-1*Oi),Ge=Ge.slice(0,-1*Oi)),ze.push(this.productions_[Js[1]][0]),Lt.push(Ga.$),Ge.push(Ga._$),Er=Bn[ze[ze.length-2]][ze[ze.length-1]],ze.push(Er);break;case 3:return!0}}return!0}},oi=function(){var Dt={EOF:1,parseError:function(Nt,ze){if(this.yy.parser)this.yy.parser.parseError(Nt,ze);else throw new Error(Nt)},setInput:function(vt,Nt){return this.yy=Nt||this.yy||{},this._input=vt,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var vt=this._input[0];this.yytext+=vt,this.yyleng++,this.offset++,this.match+=vt,this.matched+=vt;var Nt=vt.match(/(?:\r\n?|\n).*/g);return Nt?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),vt},unput:function(vt){var Nt=vt.length,ze=vt.split(/(?:\r\n?|\n)/g);this._input=vt+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-Nt),this.offset-=Nt;var Xe=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),ze.length-1&&(this.yylineno-=ze.length-1);var Lt=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:ze?(ze.length===Xe.length?this.yylloc.first_column:0)+Xe[Xe.length-ze.length].length-ze[0].length:this.yylloc.first_column-Nt},this.options.ranges&&(this.yylloc.range=[Lt[0],Lt[0]+this.yyleng-Nt]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(vt){this.unput(this.match.slice(vt))},pastInput:function(){var vt=this.matched.substr(0,this.matched.length-this.match.length);return(vt.length>20?"...":"")+vt.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var vt=this.match;return vt.length<20&&(vt+=this._input.substr(0,20-vt.length)),(vt.substr(0,20)+(vt.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var vt=this.pastInput(),Nt=new Array(vt.length+1).join("-");return vt+this.upcomingInput()+`
`+Nt+"^"},test_match:function(vt,Nt){var ze,Xe,Lt;if(this.options.backtrack_lexer&&(Lt={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Lt.yylloc.range=this.yylloc.range.slice(0))),Xe=vt[0].match(/(?:\r\n?|\n).*/g),Xe&&(this.yylineno+=Xe.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:Xe?Xe[Xe.length-1].length-Xe[Xe.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+vt[0].length},this.yytext+=vt[0],this.match+=vt[0],this.matches=vt,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(vt[0].length),this.matched+=vt[0],ze=this.performAction.call(this,this.yy,this,Nt,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),ze)return ze;if(this._backtrack){for(var Ge in Lt)this[Ge]=Lt[Ge];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var vt,Nt,ze,Xe;this._more||(this.yytext="",this.match="");for(var Lt=this._currentRules(),Ge=0;Ge<Lt.length;Ge++)if(ze=this._input.match(this.rules[Lt[Ge]]),ze&&(!Nt||ze[0].length>Nt[0].length)){if(Nt=ze,Xe=Ge,this.options.backtrack_lexer){if(vt=this.test_match(ze,Lt[Ge]),vt!==!1)return vt;if(this._backtrack){Nt=!1;continue}else return!1}else if(!this.options.flex)break}return Nt?(vt=this.test_match(Nt,Lt[Xe]),vt!==!1?vt:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var Nt=this.next();return Nt||this.lex()},begin:function(Nt){this.conditionStack.push(Nt)},popState:function(){var Nt=this.conditionStack.length-1;return Nt>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(Nt){return Nt=this.conditionStack.length-1-Math.abs(Nt||0),Nt>=0?this.conditionStack[Nt]:"INITIAL"},pushState:function(Nt){this.begin(Nt)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(Nt,ze,Xe,Lt){switch(Xe){case 0:return this.begin("open_directive"),83;case 1:return this.begin("type_directive"),84;case 2:return this.popState(),this.begin("arg_directive"),17;case 3:return this.popState(),this.popState(),86;case 4:return 85;case 5:return 5;case 6:break;case 7:break;case 8:break;case 9:break;case 10:break;case 11:return 24;case 12:return this.begin("LINE"),19;case 13:return this.begin("ID"),54;case 14:return this.begin("ID"),56;case 15:return ze.yytext=ze.yytext.trim(),this.begin("ALIAS"),73;case 16:return this.popState(),this.popState(),this.begin("LINE"),55;case 17:return this.popState(),this.popState(),5;case 18:return this.begin("LINE"),41;case 19:return this.begin("LINE"),42;case 20:return this.begin("LINE"),43;case 21:return this.begin("LINE"),44;case 22:return this.begin("LINE"),53;case 23:return this.begin("LINE"),46;case 24:return this.begin("LINE"),52;case 25:return this.begin("LINE"),48;case 26:return this.begin("LINE"),51;case 27:return this.begin("LINE"),50;case 28:return this.popState(),20;case 29:return 21;case 30:return 68;case 31:return 69;case 32:return 62;case 33:return 63;case 34:return 64;case 35:return 65;case 36:return 60;case 37:return 57;case 38:return this.begin("ID"),26;case 39:return this.begin("ID"),28;case 40:return 34;case 41:return 35;case 42:return this.begin("acc_title"),36;case 43:return this.popState(),"acc_title_value";case 44:return this.begin("acc_descr"),38;case 45:return this.popState(),"acc_descr_value";case 46:this.begin("acc_descr_multiline");break;case 47:this.popState();break;case 48:return"acc_descr_multiline_value";case 49:return 7;case 50:return 23;case 51:return 25;case 52:return 67;case 53:return 5;case 54:return ze.yytext=ze.yytext.trim(),73;case 55:return 76;case 56:return 77;case 57:return 74;case 58:return 75;case 59:return 78;case 60:return 79;case 61:return 80;case 62:return 81;case 63:return 82;case 64:return 71;case 65:return 72;case 66:return 5;case 67:return"INVALID"}},rules:[/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[0-9]+(?=[ \n]+))/i,/^(?:box\b)/i,/^(?:participant\b)/i,/^(?:actor\b)/i,/^(?:[^\->:\n,;]+?([\-]*[^\->:\n,;]+?)*?(?=((?!\n)\s)+as(?!\n)\s|[#\n;]|$))/i,/^(?:as\b)/i,/^(?:(?:))/i,/^(?:loop\b)/i,/^(?:rect\b)/i,/^(?:opt\b)/i,/^(?:alt\b)/i,/^(?:else\b)/i,/^(?:par\b)/i,/^(?:and\b)/i,/^(?:critical\b)/i,/^(?:option\b)/i,/^(?:break\b)/i,/^(?:(?:[:]?(?:no)?wrap)?[^#\n;]*)/i,/^(?:end\b)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:links\b)/i,/^(?:link\b)/i,/^(?:properties\b)/i,/^(?:details\b)/i,/^(?:over\b)/i,/^(?:note\b)/i,/^(?:activate\b)/i,/^(?:deactivate\b)/i,/^(?:title\s[^#\n;]+)/i,/^(?:title:\s[^#\n;]+)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:sequenceDiagram\b)/i,/^(?:autonumber\b)/i,/^(?:off\b)/i,/^(?:,)/i,/^(?:;)/i,/^(?:[^\+\->:\n,;]+((?!(-x|--x|-\)|--\)))[\-]*[^\+\->:\n,;]+)*)/i,/^(?:->>)/i,/^(?:-->>)/i,/^(?:->)/i,/^(?:-->)/i,/^(?:-[x])/i,/^(?:--[x])/i,/^(?:-[\)])/i,/^(?:--[\)])/i,/^(?::(?:(?:no)?wrap)?[^#\n;]+)/i,/^(?:\+)/i,/^(?:-)/i,/^(?:$)/i,/^(?:.)/i],conditions:{acc_descr_multiline:{rules:[47,48],inclusive:!1},acc_descr:{rules:[45],inclusive:!1},acc_title:{rules:[43],inclusive:!1},open_directive:{rules:[1,8],inclusive:!1},type_directive:{rules:[2,3,8],inclusive:!1},arg_directive:{rules:[3,4,8],inclusive:!1},ID:{rules:[7,8,15],inclusive:!1},ALIAS:{rules:[7,8,16,17],inclusive:!1},LINE:{rules:[7,8,28],inclusive:!1},INITIAL:{rules:[0,5,6,8,9,10,11,12,13,14,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,46,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67],inclusive:!0}}};return Dt}();Ot.lexer=oi;function kt(){this.yy={}}return kt.prototype=Ot,Ot.Parser=kt,new kt}();J0e.parser=J0e;const j$t=J0e;let LP,cx={},cL=[],rm=[],LK=!1,ege,L5;const $$t=function(i,a,f){rd.parseDirective(this,i,a,f)},H$t=function(i){cL.push({name:i.text,wrap:i.wrap===void 0&&d9()||!!i.wrap,fill:i.color,actorKeys:[]}),L5=cL.slice(-1)[0]},tge=function(i,a,f,p){let w=L5;const y=cx[i];if(y){if(L5&&y.box&&L5!==y.box)throw new Error("A same participant should only be defined in one Box: "+y.name+" can't be in '"+y.box.name+"' and in '"+L5.name+"' at the same time.");if(w=y.box?y.box:L5,y.box=w,y&&a===y.name&&f==null)return}(f==null||f.text==null)&&(f={text:a,wrap:null,type:p}),(p==null||f.text==null)&&(f={text:a,wrap:null,type:p}),cx[i]={box:w,name:a,description:f.text,wrap:f.wrap===void 0&&d9()||!!f.wrap,prevActor:LP,links:{},properties:{},actorCnt:null,rectData:null,type:p||"participant"},LP&&cx[LP]&&(cx[LP].nextActor=i),L5&&L5.actorKeys.push(i),LP=i},z$t=i=>{let 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Q="";ln.arrowMarkerAbsolute&&(Q=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search,Q=Q.replace(/\(/g,"\\("),Q=Q.replace(/\)/g,"\\)")),V.attr("stroke-width",2),V.attr("stroke","none"),V.style("fill","none"),(S===p.db.LINETYPE.SOLID||S===p.db.LINETYPE.DOTTED)&&V.attr("marker-end","url("+Q+"#arrowhead)"),(S===p.db.LINETYPE.SOLID_POINT||S===p.db.LINETYPE.DOTTED_POINT)&&V.attr("marker-end","url("+Q+"#filled-head)"),(S===p.db.LINETYPE.SOLID_CROSS||S===p.db.LINETYPE.DOTTED_CROSS)&&V.attr("marker-end","url("+Q+"#crosshead)"),(B||ln.showSequenceNumbers)&&(V.attr("marker-start","url("+Q+"#sequencenumber)"),i.append("text").attr("x",w).attr("y",f+4).attr("font-family","sans-serif").attr("font-size","12px").attr("text-anchor","middle").attr("class","sequenceNumber").text(N))},age=function(i,a,f,p,w,y,b){if(w.hideUnusedParticipants===!0){const R=new Set;y.forEach(j=>{R.add(j.from),R.add(j.to)}),f=f.filter(j=>R.has(j))}let E=0,S=0,N=0,B;for(const R of f){const j=a[R],$=j.box;B&&B!=$&&(b||si.models.addBox(B),S+=ln.boxMargin+B.margin),$&&$!=B&&(b||($.x=E+S,$.y=p),S+=$.margin),j.width=j.width||ln.width,j.height=Math.max(j.height||ln.height,ln.height),j.margin=j.margin||ln.actorMargin,j.x=E+S,j.y=si.getVerticalPos();const V=ih.drawActor(i,j,ln,b);N=Math.max(N,V),si.insert(j.x,p,j.x+j.width,j.height),E+=j.width+S,j.box&&(j.box.width=E+$.margin-j.box.x),S=j.margin,B=j.box,si.models.addActor(j)}B&&!b&&si.models.addBox(B),si.bumpVerticalPos(N)},WRe=function(i,a,f,p){let w=0,y=0;for(const b of f){const E=a[b],S=PHt(E),N=ih.drawPopup(i,E,S,ln,ln.forceMenus,p);N.height>w&&(w=N.height),N.width+E.x>y&&(y=N.width+E.x)}return{maxHeight:w,maxWidth:y}},KRe=function(i){nd(ln,i),i.fontFamily&&(ln.actorFontFamily=ln.noteFontFamily=ln.messageFontFamily=i.fontFamily),i.fontSize&&(ln.actorFontSize=ln.noteFontSize=ln.messageFontSize=i.fontSize),i.fontWeight&&(ln.actorFontWeight=ln.noteFontWeight=ln.messageFontWeight=i.fontWeight)},IK=function(i){return si.activations.filter(function(a){return a.actor===i})},YRe=function(i,a){const f=a[i],p=IK(i),w=p.reduce(function(b,E){return Math.min(b,E.startx)},f.x+f.width/2),y=p.reduce(function(b,E){return Math.max(b,E.stopx)},f.x+f.width/2);return[w,y]};function p3(i,a,f,p,w){si.bumpVerticalPos(f);let y=p;if(a.id&&a.message&&i[a.id]){const b=i[a.id].width,E=g9(ln);a.message=co.wrapLabel(`[${a.message}]`,b-2*ln.wrapPadding,E),a.width=b,a.wrap=!0;const S=co.calculateTextDimensions(a.message,E),N=Math.max(S.height,ln.labelBoxHeight);y=p+N,Fe.debug(`${N} - ${a.message}`)}w(a),si.bumpVerticalPos(y)}const OHt=function(i,a,f,p){const{securityLevel:w,sequence:y}=Tt();ln=y,p.db.clear(),p.parser.parse(i);let b;w==="sandbox"&&(b=Cr("#i"+a));const E=Cr(w==="sandbox"?b.nodes()[0].contentDocument.body:"body"),S=w==="sandbox"?b.nodes()[0].contentDocument:document;si.init(),Fe.debug(p.db);const N=w==="sandbox"?E.select(`[id="${a}"]`):Cr(`[id="${a}"]`),B=p.db.getActors(),R=p.db.getBoxes(),j=p.db.getActorKeys(),$=p.db.getMessages(),V=p.db.getDiagramTitle(),Q=p.db.hasAtLeastOneBox(),oe=p.db.hasAtLeastOneBoxWithTitle(),ce=NHt(B,$,p);ln.height=BHt(B,ce,R),ih.insertComputerIcon(N),ih.insertDatabaseIcon(N),ih.insertClockIcon(N),Q&&(si.bumpVerticalPos(ln.boxMargin),oe&&si.bumpVerticalPos(R[0].textMaxHeight)),age(N,B,j,0,ln,$,!1);const se=jHt($,B,ce,p);ih.insertArrowHead(N),ih.insertArrowCrossHead(N),ih.insertArrowFilledHead(N),ih.insertSequenceNumber(N);function ge(rt,me){const gt=si.endActivation(rt);gt.starty+18>me&&(gt.starty=me-6,me+=12),ih.drawActivation(N,gt,me,ln,IK(rt.from.actor).length),si.insert(gt.startx,me-10,gt.stopx,me)}let ye=1,ke=1;const Ae=[];$.forEach(function(rt){let me,gt,pe;switch(rt.type){case p.db.LINETYPE.NOTE:gt=rt.noteModel,MHt(N,gt);break;case p.db.LINETYPE.ACTIVE_START:si.newActivation(rt,N,B);break;case p.db.LINETYPE.ACTIVE_END:ge(rt,si.getVerticalPos());break;case p.db.LINETYPE.LOOP_START:p3(se,rt,ln.boxMargin,ln.boxMargin+ln.boxTextMargin,Et=>si.newLoop(Et));break;case p.db.LINETYPE.LOOP_END:me=si.endLoop(),ih.drawLoop(N,me,"loop",ln),si.bumpVerticalPos(me.stopy-si.getVerticalPos()),si.models.addLoop(me);break;case p.db.LINETYPE.RECT_START:p3(se,rt,ln.boxMargin,ln.boxMargin,Et=>si.newLoop(void 0,Et.message));break;case p.db.LINETYPE.RECT_END:me=si.endLoop(),ih.drawBackgroundRect(N,me),si.models.addLoop(me),si.bumpVerticalPos(me.stopy-si.getVerticalPos());break;case p.db.LINETYPE.OPT_START:p3(se,rt,ln.boxMargin,ln.boxMargin+ln.boxTextMargin,Et=>si.newLoop(Et));break;case p.db.LINETYPE.OPT_END:me=si.endLoop(),ih.drawLoop(N,me,"opt",ln),si.bumpVerticalPos(me.stopy-si.getVerticalPos()),si.models.addLoop(me);break;case p.db.LINETYPE.ALT_START:p3(se,rt,ln.boxMargin,ln.boxMargin+ln.boxTextMargin,Et=>si.newLoop(Et));break;case p.db.LINETYPE.ALT_ELSE:p3(se,rt,ln.boxMargin+ln.boxTextMargin,ln.boxMargin,Et=>si.addSectionToLoop(Et));break;case p.db.LINETYPE.ALT_END:me=si.endLoop(),ih.drawLoop(N,me,"alt",ln),si.bumpVerticalPos(me.stopy-si.getVerticalPos()),si.models.addLoop(me);break;case p.db.LINETYPE.PAR_START:p3(se,rt,ln.boxMargin,ln.boxMargin+ln.boxTextMargin,Et=>si.newLoop(Et));break;case p.db.LINETYPE.PAR_AND:p3(se,rt,ln.boxMargin+ln.boxTextMargin,ln.boxMargin,Et=>si.addSectionToLoop(Et));break;case p.db.LINETYPE.PAR_END:me=si.endLoop(),ih.drawLoop(N,me,"par",ln),si.bumpVerticalPos(me.stopy-si.getVerticalPos()),si.models.addLoop(me);break;case p.db.LINETYPE.AUTONUMBER:ye=rt.message.start||ye,ke=rt.message.step||ke,rt.message.visible?p.db.enableSequenceNumbers():p.db.disableSequenceNumbers();break;case p.db.LINETYPE.CRITICAL_START:p3(se,rt,ln.boxMargin,ln.boxMargin+ln.boxTextMargin,Et=>si.newLoop(Et));break;case p.db.LINETYPE.CRITICAL_OPTION:p3(se,rt,ln.boxMargin+ln.boxTextMargin,ln.boxMargin,Et=>si.addSectionToLoop(Et));break;case p.db.LINETYPE.CRITICAL_END:me=si.endLoop(),ih.drawLoop(N,me,"critical",ln),si.bumpVerticalPos(me.stopy-si.getVerticalPos()),si.models.addLoop(me);break;case p.db.LINETYPE.BREAK_START:p3(se,rt,ln.boxMargin,ln.boxMargin+ln.boxTextMargin,Et=>si.newLoop(Et));break;case p.db.LINETYPE.BREAK_END:me=si.endLoop(),ih.drawLoop(N,me,"break",ln),si.bumpVerticalPos(me.stopy-si.getVerticalPos()),si.models.addLoop(me);break;default:try{pe=rt.msgModel,pe.starty=si.getVerticalPos(),pe.sequenceIndex=ye,pe.sequenceVisible=p.db.showSequenceNumbers();const Et=DHt(N,pe);Ae.push({messageModel:pe,lineStartY:Et}),si.models.addMessage(pe)}catch(Et){Fe.error("error while drawing message",Et)}}[p.db.LINETYPE.SOLID_OPEN,p.db.LINETYPE.DOTTED_OPEN,p.db.LINETYPE.SOLID,p.db.LINETYPE.DOTTED,p.db.LINETYPE.SOLID_CROSS,p.db.LINETYPE.DOTTED_CROSS,p.db.LINETYPE.SOLID_POINT,p.db.LINETYPE.DOTTED_POINT].includes(rt.type)&&(ye=ye+ke)}),Ae.forEach(rt=>IHt(N,rt.messageModel,rt.lineStartY,p)),ln.mirrorActors&&(si.bumpVerticalPos(ln.boxMargin*2),age(N,B,j,si.getVerticalPos(),ln,$,!0),si.bumpVerticalPos(ln.boxMargin),VRe(N,si.getVerticalPos())),si.models.boxes.forEach(function(rt){rt.height=si.getVerticalPos()-rt.y,si.insert(rt.x,rt.y,rt.x+rt.width,rt.height),rt.startx=rt.x,rt.starty=rt.y,rt.stopx=rt.startx+rt.width,rt.stopy=rt.starty+rt.height,rt.stroke="rgb(0,0,0, 0.5)",ih.drawBox(N,rt,ln)}),Q&&si.bumpVerticalPos(ln.boxMargin);const de=WRe(N,B,j,S),{bounds:ve}=si.getBounds();Fe.debug("For line height fix Querying: #"+a+" .actor-line"),Jfe("#"+a+" .actor-line").attr("y2",ve.stopy);let xe=ve.stopy-ve.starty;xe<de.maxHeight&&(xe=de.maxHeight);let De=xe+2*ln.diagramMarginY;ln.mirrorActors&&(De=De-ln.boxMargin+ln.bottomMarginAdj);let he=ve.stopx-ve.startx;he<de.maxWidth&&(he=de.maxWidth);const Ie=he+2*ln.diagramMarginX;V&&N.append("text").text(V).attr("x",(ve.stopx-ve.startx)/2-2*ln.diagramMarginX).attr("y",-25),Vw(N,De,Ie,ln.useMaxWidth);const ee=V?40:0;N.attr("viewBox",ve.startx-ln.diagramMarginX+" -"+(ln.diagramMarginY+ee)+" "+Ie+" "+(De+ee)),Fe.debug("models:",si.models)};function NHt(i,a,f){const p={};return a.forEach(function(w){if(i[w.to]&&i[w.from]){const y=i[w.to];if(w.placement===f.db.PLACEMENT.LEFTOF&&!y.prevActor||w.placement===f.db.PLACEMENT.RIGHTOF&&!y.nextActor)return;const b=w.placement!==void 0,E=!b,S=b?lL(ln):g9(ln),N=w.wrap?co.wrapLabel(w.message,ln.width-2*ln.wrapPadding,S):w.message,R=co.calculateTextDimensions(N,S).width+2*ln.wrapPadding;E&&w.from===y.nextActor?p[w.to]=Math.max(p[w.to]||0,R):E&&w.from===y.prevActor?p[w.from]=Math.max(p[w.from]||0,R):E&&w.from===w.to?(p[w.from]=Math.max(p[w.from]||0,R/2),p[w.to]=Math.max(p[w.to]||0,R/2)):w.placement===f.db.PLACEMENT.RIGHTOF?p[w.from]=Math.max(p[w.from]||0,R):w.placement===f.db.PLACEMENT.LEFTOF?p[y.prevActor]=Math.max(p[y.prevActor]||0,R):w.placement===f.db.PLACEMENT.OVER&&(y.prevActor&&(p[y.prevActor]=Math.max(p[y.prevActor]||0,R/2)),y.nextActor&&(p[w.from]=Math.max(p[w.from]||0,R/2)))}}),Fe.debug("maxMessageWidthPerActor:",p),p}const PHt=function(i){let a=0;const f=sge(ln);for(const p in i.links){const y=co.calculateTextDimensions(p,f).width+2*ln.wrapPadding+2*ln.boxMargin;a<y&&(a=y)}return a};function BHt(i,a,f){let p=0;Object.keys(i).forEach(y=>{const b=i[y];b.wrap&&(b.description=co.wrapLabel(b.description,ln.width-2*ln.wrapPadding,sge(ln)));const E=co.calculateTextDimensions(b.description,sge(ln));b.width=b.wrap?ln.width:Math.max(ln.width,E.width+2*ln.wrapPadding),b.height=b.wrap?Math.max(E.height,ln.height):ln.height,p=Math.max(p,b.height)});for(const y in a){const b=i[y];if(!b)continue;const E=i[b.nextActor];if(!E){const R=a[y]+ln.actorMargin-b.width/2;b.margin=Math.max(R,ln.actorMargin);continue}const N=a[y]+ln.actorMargin-b.width/2-E.width/2;b.margin=Math.max(N,ln.actorMargin)}let w=0;return f.forEach(y=>{const b=g9(ln);let E=y.actorKeys.reduce((B,R)=>B+=i[R].width+(i[R].margin||0),0);E-=2*ln.boxTextMargin,y.wrap&&(y.name=co.wrapLabel(y.name,E-2*ln.wrapPadding,b));const S=co.calculateTextDimensions(y.name,b);w=Math.max(S.height,w);const N=Math.max(E,S.width+2*ln.wrapPadding);if(y.margin=ln.boxTextMargin,E<N){const B=(N-E)/2;y.margin+=B}}),f.forEach(y=>y.textMaxHeight=w),Math.max(p,ln.height)}const RHt=function(i,a,f){const p=a[i.from].x,w=a[i.to].x,y=i.wrap&&i.message;let b=co.calculateTextDimensions(y?co.wrapLabel(i.message,ln.width,lL(ln)):i.message,lL(ln));const E={width:y?ln.width:Math.max(ln.width,b.width+2*ln.noteMargin),height:0,startx:a[i.from].x,stopx:0,starty:0,stopy:0,message:i.message};return i.placement===f.db.PLACEMENT.RIGHTOF?(E.width=y?Math.max(ln.width,b.width):Math.max(a[i.from].width/2+a[i.to].width/2,b.width+2*ln.noteMargin),E.startx=p+(a[i.from].width+ln.actorMargin)/2):i.placement===f.db.PLACEMENT.LEFTOF?(E.width=Math.max(y?ln.width:a[i.from].width/2+a[i.to].width/2,b.width+2*ln.noteMargin),E.startx=p-E.width+(a[i.from].width-ln.actorMargin)/2):i.to===i.from?(b=co.calculateTextDimensions(y?co.wrapLabel(i.message,Math.max(ln.width,a[i.from].width),lL(ln)):i.message,lL(ln)),E.width=y?Math.max(ln.width,a[i.from].width):Math.max(a[i.from].width,ln.width,b.width+2*ln.noteMargin),E.startx=p+(a[i.from].width-E.width)/2):(E.width=Math.abs(p+a[i.from].width/2-(w+a[i.to].width/2))+ln.actorMargin,E.startx=p<w?p+a[i.from].width/2-ln.actorMargin/2:w+a[i.to].width/2-ln.actorMargin/2),y&&(E.message=co.wrapLabel(i.message,E.width-2*ln.wrapPadding,lL(ln))),Fe.debug(`NM:[${E.startx},${E.stopx},${E.starty},${E.stopy}:${E.width},${E.height}=${i.message}]`),E},FHt=function(i,a,f){let p=!1;if([f.db.LINETYPE.SOLID_OPEN,f.db.LINETYPE.DOTTED_OPEN,f.db.LINETYPE.SOLID,f.db.LINETYPE.DOTTED,f.db.LINETYPE.SOLID_CROSS,f.db.LINETYPE.DOTTED_CROSS,f.db.LINETYPE.SOLID_POINT,f.db.LINETYPE.DOTTED_POINT].includes(i.type)&&(p=!0),!p)return{};const w=YRe(i.from,a),y=YRe(i.to,a),b=w[0]<=y[0]?1:0,E=w[0]<y[0]?0:1,S=[...w,...y],N=Math.abs(y[E]-w[b]);i.wrap&&i.message&&(i.message=co.wrapLabel(i.message,Math.max(N+2*ln.wrapPadding,ln.width),g9(ln)));const B=co.calculateTextDimensions(i.message,g9(ln));return{width:Math.max(i.wrap?0:B.width+2*ln.wrapPadding,N+2*ln.wrapPadding,ln.width),height:0,startx:w[b],stopx:y[E],starty:0,stopy:0,message:i.message,type:i.type,wrap:i.wrap,fromBounds:Math.min.apply(null,S),toBounds:Math.max.apply(null,S)}},jHt=function(i,a,f,p){const w={},y=[];let b,E,S;return i.forEach(function(N){switch(N.id=co.random({length:10}),N.type){case p.db.LINETYPE.LOOP_START:case p.db.LINETYPE.ALT_START:case p.db.LINETYPE.OPT_START:case p.db.LINETYPE.PAR_START:case p.db.LINETYPE.CRITICAL_START:case p.db.LINETYPE.BREAK_START:y.push({id:N.id,msg:N.message,from:Number.MAX_SAFE_INTEGER,to:Number.MIN_SAFE_INTEGER,width:0});break;case p.db.LINETYPE.ALT_ELSE:case p.db.LINETYPE.PAR_AND:case p.db.LINETYPE.CRITICAL_OPTION:N.message&&(b=y.pop(),w[b.id]=b,w[N.id]=b,y.push(b));break;case p.db.LINETYPE.LOOP_END:case p.db.LINETYPE.ALT_END:case p.db.LINETYPE.OPT_END:case p.db.LINETYPE.PAR_END:case p.db.LINETYPE.CRITICAL_END:case p.db.LINETYPE.BREAK_END:b=y.pop(),w[b.id]=b;break;case p.db.LINETYPE.ACTIVE_START:{const R=a[N.from?N.from.actor:N.to.actor],j=IK(N.from?N.from.actor:N.to.actor).length,$=R.x+R.width/2+(j-1)*ln.activationWidth/2,V={startx:$,stopx:$+ln.activationWidth,actor:N.from.actor,enabled:!0};si.activations.push(V)}break;case p.db.LINETYPE.ACTIVE_END:{const R=si.activations.map(j=>j.actor).lastIndexOf(N.from.actor);delete si.activations.splice(R,1)[0]}break}N.placement!==void 0?(E=RHt(N,a,p),N.noteModel=E,y.forEach(R=>{b=R,b.from=Math.min(b.from,E.startx),b.to=Math.max(b.to,E.startx+E.width),b.width=Math.max(b.width,Math.abs(b.from-b.to))-ln.labelBoxWidth})):(S=FHt(N,a,p),N.msgModel=S,S.startx&&S.stopx&&y.length>0&&y.forEach(R=>{if(b=R,S.startx===S.stopx){const j=a[N.from],$=a[N.to];b.from=Math.min(j.x-S.width/2,j.x-j.width/2,b.from),b.to=Math.max($.x+S.width/2,$.x+j.width/2,b.to),b.width=Math.max(b.width,Math.abs(b.to-b.from))-ln.labelBoxWidth}else b.from=Math.min(S.startx,b.from),b.to=Math.max(S.stopx,b.to),b.width=Math.max(b.width,S.width)-ln.labelBoxWidth}))}),si.activations=[],Fe.debug("Loop type widths:",w),w},$Ht=Object.freeze(Object.defineProperty({__proto__:null,diagram:{parser:j$t,db:cHt,renderer:{bounds:si,drawActors:age,drawActorsPopup:WRe,setConf:KRe,draw:OHt},styles:uHt}},Symbol.toStringTag,{value:"Module"}));var oge=function(){var i=function(Nt,ze,Xe,Lt){for(Xe=Xe||{},Lt=Nt.length;Lt--;Xe[Nt[Lt]]=ze);return 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Ri[Pc])this.terminals_[Er]&&Er>Zn&&Vn.push("'"+this.terminals_[Er]+"'");Hn.showPosition?qi="Parse error on line "+(hi+1)+`:
`+Hn.showPosition()+`
Expecting `+Vn.join(", ")+", got '"+(this.terminals_[Js]||Js)+"'":qi="Parse error on line "+(hi+1)+": Unexpected "+(Js==Xn?"end of input":"'"+(this.terminals_[Js]||Js)+"'"),this.parseError(qi,{text:Hn.match,token:this.terminals_[Js]||Js,line:Hn.yylineno,loc:Zs,expected:Vn})}if(Ga[0]instanceof Array&&Ga.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Pc+", token: "+Js);switch(Ga[0]){case 1:Lt.push(Js),Bn.push(Hn.yytext),Oe.push(Hn.yylloc),Lt.push(Ga[1]),Js=null,Sr=Hn.yyleng,tn=Hn.yytext,hi=Hn.yylineno,Zs=Hn.yylloc;break;case 2:if(br=this.productions_[Ga[1]][1],Oi.$=Bn[Bn.length-br],Oi._$={first_line:Oe[Oe.length-(br||1)].first_line,last_line:Oe[Oe.length-1].last_line,first_column:Oe[Oe.length-(br||1)].first_column,last_column:Oe[Oe.length-1].last_column},ns&&(Oi._$.range=[Oe[Oe.length-(br||1)].range[0],Oe[Oe.length-1].range[1]]),ws=this.performAction.apply(Oi,[tn,Sr,hi,tr.yy,Ga[1],Bn,Oe].concat(ir)),typeof ws<"u")return ws;br&&(Lt=Lt.slice(0,-1*br*2),Bn=Bn.slice(0,-1*br),Oe=Oe.slice(0,-1*br)),Lt.push(this.productions_[Ga[1]][0]),Bn.push(Oi.$),Oe.push(Oi._$),Dr=Ri[Lt[Lt.length-2]][Lt[Lt.length-1]],Lt.push(Dr);break;case 3:return!0}}return!0}},Dt=function(){var Nt={EOF:1,parseError:function(Xe,Lt){if(this.yy.parser)this.yy.parser.parseError(Xe,Lt);else throw new Error(Xe)},setInput:function(ze,Xe){return this.yy=Xe||this.yy||{},this._input=ze,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var ze=this._input[0];this.yytext+=ze,this.yyleng++,this.offset++,this.match+=ze,this.matched+=ze;var Xe=ze.match(/(?:\r\n?|\n).*/g);return Xe?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),ze},unput:function(ze){var Xe=ze.length,Lt=ze.split(/(?:\r\n?|\n)/g);this._input=ze+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-Xe),this.offset-=Xe;var Ge=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Lt.length-1&&(this.yylineno-=Lt.length-1);var Bn=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Lt?(Lt.length===Ge.length?this.yylloc.first_column:0)+Ge[Ge.length-Lt.length].length-Lt[0].length:this.yylloc.first_column-Xe},this.options.ranges&&(this.yylloc.range=[Bn[0],Bn[0]+this.yyleng-Xe]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(ze){this.unput(this.match.slice(ze))},pastInput:function(){var ze=this.matched.substr(0,this.matched.length-this.match.length);return(ze.length>20?"...":"")+ze.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var ze=this.match;return ze.length<20&&(ze+=this._input.substr(0,20-ze.length)),(ze.substr(0,20)+(ze.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var ze=this.pastInput(),Xe=new Array(ze.length+1).join("-");return ze+this.upcomingInput()+`
`+Xe+"^"},test_match:function(ze,Xe){var Lt,Ge,Bn;if(this.options.backtrack_lexer&&(Bn={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Bn.yylloc.range=this.yylloc.range.slice(0))),Ge=ze[0].match(/(?:\r\n?|\n).*/g),Ge&&(this.yylineno+=Ge.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:Ge?Ge[Ge.length-1].length-Ge[Ge.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+ze[0].length},this.yytext+=ze[0],this.match+=ze[0],this.matches=ze,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(ze[0].length),this.matched+=ze[0],Lt=this.performAction.call(this,this.yy,this,Xe,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),Lt)return Lt;if(this._backtrack){for(var Oe in Bn)this[Oe]=Bn[Oe];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var ze,Xe,Lt,Ge;this._more||(this.yytext="",this.match="");for(var Bn=this._currentRules(),Oe=0;Oe<Bn.length;Oe++)if(Lt=this._input.match(this.rules[Bn[Oe]]),Lt&&(!Xe||Lt[0].length>Xe[0].length)){if(Xe=Lt,Ge=Oe,this.options.backtrack_lexer){if(ze=this.test_match(Lt,Bn[Oe]),ze!==!1)return ze;if(this._backtrack){Xe=!1;continue}else return!1}else if(!this.options.flex)break}return Xe?(ze=this.test_match(Xe,Bn[Ge]),ze!==!1?ze:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var Xe=this.next();return Xe||this.lex()},begin:function(Xe){this.conditionStack.push(Xe)},popState:function(){var Xe=this.conditionStack.length-1;return Xe>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(Xe){return Xe=this.conditionStack.length-1-Math.abs(Xe||0),Xe>=0?this.conditionStack[Xe]:"INITIAL"},pushState:function(Xe){this.begin(Xe)},stateStackSize:function(){return this.conditionStack.length},options:{},performAction:function(Xe,Lt,Ge,Bn){switch(Ge){case 0:return this.begin("open_directive"),19;case 1:return 8;case 2:return 9;case 3:return 10;case 4:return 11;case 5:return this.begin("type_directive"),20;case 6:return this.popState(),this.begin("arg_directive"),17;case 7:return this.popState(),this.popState(),22;case 8:return 21;case 9:break;case 10:break;case 11:return this.begin("acc_title"),42;case 12:return this.popState(),"acc_title_value";case 13:return this.begin("acc_descr"),44;case 14:return this.popState(),"acc_descr_value";case 15:this.begin("acc_descr_multiline");break;case 16:this.popState();break;case 17:return"acc_descr_multiline_value";case 18:return 16;case 19:break;case 20:return 23;case 21:return 23;case 22:return this.begin("struct"),49;case 23:return"EDGE_STATE";case 24:return"EOF_IN_STRUCT";case 25:return"OPEN_IN_STRUCT";case 26:return this.popState(),51;case 27:break;case 28:return"MEMBER";case 29:return 52;case 30:return 77;case 31:return 70;case 32:return 71;case 33:return 73;case 34:return 58;case 35:return 60;case 36:return 53;case 37:return 54;case 38:this.begin("generic");break;case 39:this.popState();break;case 40:return"GENERICTYPE";case 41:this.begin("string");break;case 42:this.popState();break;case 43:return"STR";case 44:this.begin("bqstring");break;case 45:this.popState();break;case 46:return"BQUOTE_STR";case 47:this.begin("href");break;case 48:this.popState();break;case 49:return 76;case 50:this.begin("callback_name");break;case 51:this.popState();break;case 52:this.popState(),this.begin("callback_args");break;case 53:return 74;case 54:this.popState();break;case 55:return 75;case 56:return 72;case 57:return 72;case 58:return 72;case 59:return 72;case 60:return 64;case 61:return 64;case 62:return 66;case 63:return 66;case 64:return 65;case 65:return 63;case 66:return 67;case 67:return 68;case 68:return 69;case 69:return 35;case 70:return 48;case 71:return 89;case 72:return"DOT";case 73:return"PLUS";case 74:return 86;case 75:return"EQUALS";case 76:return"EQUALS";case 77:return 93;case 78:return 27;case 79:return 29;case 80:return"PUNCTUATION";case 81:return 92;case 82:return 91;case 83:return 88;case 84:return 24}},rules:[/^(?:%%\{)/,/^(?:.*direction\s+TB[^\n]*)/,/^(?:.*direction\s+BT[^\n]*)/,/^(?:.*direction\s+RL[^\n]*)/,/^(?:.*direction\s+LR[^\n]*)/,/^(?:((?:(?!\}%%)[^:.])*))/,/^(?::)/,/^(?:\}%%)/,/^(?:((?:(?!\}%%).|\n)*))/,/^(?:%%(?!\{)*[^\n]*(\r?\n?)+)/,/^(?:%%[^\n]*(\r?\n)*)/,/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:\s*(\r?\n)+)/,/^(?:\s+)/,/^(?:classDiagram-v2\b)/,/^(?:classDiagram\b)/,/^(?:[{])/,/^(?:\[\*\])/,/^(?:$)/,/^(?:[{])/,/^(?:[}])/,/^(?:[\n])/,/^(?:[^{}\n]*)/,/^(?:class\b)/,/^(?:cssClass\b)/,/^(?:callback\b)/,/^(?:link\b)/,/^(?:click\b)/,/^(?:note for\b)/,/^(?:note\b)/,/^(?:<<)/,/^(?:>>)/,/^(?:[~])/,/^(?:[~])/,/^(?:[^~]*)/,/^(?:["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:[`])/,/^(?:[`])/,/^(?:[^`]+)/,/^(?:href[\s]+["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:call[\s]+)/,/^(?:\([\s]*\))/,/^(?:\()/,/^(?:[^(]*)/,/^(?:\))/,/^(?:[^)]*)/,/^(?:_self\b)/,/^(?:_blank\b)/,/^(?:_parent\b)/,/^(?:_top\b)/,/^(?:\s*<\|)/,/^(?:\s*\|>)/,/^(?:\s*>)/,/^(?:\s*<)/,/^(?:\s*\*)/,/^(?:\s*o\b)/,/^(?:\s*\(\))/,/^(?:--)/,/^(?:\.\.)/,/^(?::{1}[^:\n;]+)/,/^(?::{3})/,/^(?:-)/,/^(?:\.)/,/^(?:\+)/,/^(?:%)/,/^(?:=)/,/^(?:=)/,/^(?:\w+)/,/^(?:\[)/,/^(?:\])/,/^(?:[!"#$%&'*+,-.`?\\/])/,/^(?:[0-9]+)/,/^(?:[\u00AA\u00B5\u00BA\u00C0-\u00D6\u00D8-\u00F6]|[\u00F8-\u02C1\u02C6-\u02D1\u02E0-\u02E4\u02EC\u02EE\u0370-\u0374\u0376\u0377]|[\u037A-\u037D\u0386\u0388-\u038A\u038C\u038E-\u03A1\u03A3-\u03F5]|[\u03F7-\u0481\u048A-\u0527\u0531-\u0556\u0559\u0561-\u0587\u05D0-\u05EA]|[\u05F0-\u05F2\u0620-\u064A\u066E\u066F\u0671-\u06D3\u06D5\u06E5\u06E6\u06EE]|[\u06EF\u06FA-\u06FC\u06FF\u0710\u0712-\u072F\u074D-\u07A5\u07B1\u07CA-\u07EA]|[\u07F4\u07F5\u07FA\u0800-\u0815\u081A\u0824\u0828\u0840-\u0858\u08A0]|[\u08A2-\u08AC\u0904-\u0939\u093D\u0950\u0958-\u0961\u0971-\u0977]|[\u0979-\u097F\u0985-\u098C\u098F\u0990\u0993-\u09A8\u09AA-\u09B0\u09B2]|[\u09B6-\u09B9\u09BD\u09CE\u09DC\u09DD\u09DF-\u09E1\u09F0\u09F1\u0A05-\u0A0A]|[\u0A0F\u0A10\u0A13-\u0A28\u0A2A-\u0A30\u0A32\u0A33\u0A35\u0A36\u0A38\u0A39]|[\u0A59-\u0A5C\u0A5E\u0A72-\u0A74\u0A85-\u0A8D\u0A8F-\u0A91\u0A93-\u0AA8]|[\u0AAA-\u0AB0\u0AB2\u0AB3\u0AB5-\u0AB9\u0ABD\u0AD0\u0AE0\u0AE1\u0B05-\u0B0C]|[\u0B0F\u0B10\u0B13-\u0B28\u0B2A-\u0B30\u0B32\u0B33\u0B35-\u0B39\u0B3D\u0B5C]|[\u0B5D\u0B5F-\u0B61\u0B71\u0B83\u0B85-\u0B8A\u0B8E-\u0B90\u0B92-\u0B95\u0B99]|[\u0B9A\u0B9C\u0B9E\u0B9F\u0BA3\u0BA4\u0BA8-\u0BAA\u0BAE-\u0BB9\u0BD0]|[\u0C05-\u0C0C\u0C0E-\u0C10\u0C12-\u0C28\u0C2A-\u0C33\u0C35-\u0C39\u0C3D]|[\u0C58\u0C59\u0C60\u0C61\u0C85-\u0C8C\u0C8E-\u0C90\u0C92-\u0CA8\u0CAA-\u0CB3]|[\u0CB5-\u0CB9\u0CBD\u0CDE\u0CE0\u0CE1\u0CF1\u0CF2\u0D05-\u0D0C\u0D0E-\u0D10]|[\u0D12-\u0D3A\u0D3D\u0D4E\u0D60\u0D61\u0D7A-\u0D7F\u0D85-\u0D96\u0D9A-\u0DB1]|[\u0DB3-\u0DBB\u0DBD\u0DC0-\u0DC6\u0E01-\u0E30\u0E32\u0E33\u0E40-\u0E46\u0E81]|[\u0E82\u0E84\u0E87\u0E88\u0E8A\u0E8D\u0E94-\u0E97\u0E99-\u0E9F\u0EA1-\u0EA3]|[\u0EA5\u0EA7\u0EAA\u0EAB\u0EAD-\u0EB0\u0EB2\u0EB3\u0EBD\u0EC0-\u0EC4\u0EC6]|[\u0EDC-\u0EDF\u0F00\u0F40-\u0F47\u0F49-\u0F6C\u0F88-\u0F8C\u1000-\u102A]|[\u103F\u1050-\u1055\u105A-\u105D\u1061\u1065\u1066\u106E-\u1070\u1075-\u1081]|[\u108E\u10A0-\u10C5\u10C7\u10CD\u10D0-\u10FA\u10FC-\u1248\u124A-\u124D]|[\u1250-\u1256\u1258\u125A-\u125D\u1260-\u1288\u128A-\u128D\u1290-\u12B0]|[\u12B2-\u12B5\u12B8-\u12BE\u12C0\u12C2-\u12C5\u12C8-\u12D6\u12D8-\u1310]|[\u1312-\u1315\u1318-\u135A\u1380-\u138F\u13A0-\u13F4\u1401-\u166C]|[\u166F-\u167F\u1681-\u169A\u16A0-\u16EA\u1700-\u170C\u170E-\u1711]|[\u1720-\u1731\u1740-\u1751\u1760-\u176C\u176E-\u1770\u1780-\u17B3\u17D7]|[\u17DC\u1820-\u1877\u1880-\u18A8\u18AA\u18B0-\u18F5\u1900-\u191C]|[\u1950-\u196D\u1970-\u1974\u1980-\u19AB\u19C1-\u19C7\u1A00-\u1A16]|[\u1A20-\u1A54\u1AA7\u1B05-\u1B33\u1B45-\u1B4B\u1B83-\u1BA0\u1BAE\u1BAF]|[\u1BBA-\u1BE5\u1C00-\u1C23\u1C4D-\u1C4F\u1C5A-\u1C7D\u1CE9-\u1CEC]|[\u1CEE-\u1CF1\u1CF5\u1CF6\u1D00-\u1DBF\u1E00-\u1F15\u1F18-\u1F1D]|[\u1F20-\u1F45\u1F48-\u1F4D\u1F50-\u1F57\u1F59\u1F5B\u1F5D\u1F5F-\u1F7D]|[\u1F80-\u1FB4\u1FB6-\u1FBC\u1FBE\u1FC2-\u1FC4\u1FC6-\u1FCC\u1FD0-\u1FD3]|[\u1FD6-\u1FDB\u1FE0-\u1FEC\u1FF2-\u1FF4\u1FF6-\u1FFC\u2071\u207F]|[\u2090-\u209C\u2102\u2107\u210A-\u2113\u2115\u2119-\u211D\u2124\u2126\u2128]|[\u212A-\u212D\u212F-\u2139\u213C-\u213F\u2145-\u2149\u214E\u2183\u2184]|[\u2C00-\u2C2E\u2C30-\u2C5E\u2C60-\u2CE4\u2CEB-\u2CEE\u2CF2\u2CF3]|[\u2D00-\u2D25\u2D27\u2D2D\u2D30-\u2D67\u2D6F\u2D80-\u2D96\u2DA0-\u2DA6]|[\u2DA8-\u2DAE\u2DB0-\u2DB6\u2DB8-\u2DBE\u2DC0-\u2DC6\u2DC8-\u2DCE]|[\u2DD0-\u2DD6\u2DD8-\u2DDE\u2E2F\u3005\u3006\u3031-\u3035\u303B\u303C]|[\u3041-\u3096\u309D-\u309F\u30A1-\u30FA\u30FC-\u30FF\u3105-\u312D]|[\u3131-\u318E\u31A0-\u31BA\u31F0-\u31FF\u3400-\u4DB5\u4E00-\u9FCC]|[\uA000-\uA48C\uA4D0-\uA4FD\uA500-\uA60C\uA610-\uA61F\uA62A\uA62B]|[\uA640-\uA66E\uA67F-\uA697\uA6A0-\uA6E5\uA717-\uA71F\uA722-\uA788]|[\uA78B-\uA78E\uA790-\uA793\uA7A0-\uA7AA\uA7F8-\uA801\uA803-\uA805]|[\uA807-\uA80A\uA80C-\uA822\uA840-\uA873\uA882-\uA8B3\uA8F2-\uA8F7\uA8FB]|[\uA90A-\uA925\uA930-\uA946\uA960-\uA97C\uA984-\uA9B2\uA9CF\uAA00-\uAA28]|[\uAA40-\uAA42\uAA44-\uAA4B\uAA60-\uAA76\uAA7A\uAA80-\uAAAF\uAAB1\uAAB5]|[\uAAB6\uAAB9-\uAABD\uAAC0\uAAC2\uAADB-\uAADD\uAAE0-\uAAEA\uAAF2-\uAAF4]|[\uAB01-\uAB06\uAB09-\uAB0E\uAB11-\uAB16\uAB20-\uAB26\uAB28-\uAB2E]|[\uABC0-\uABE2\uAC00-\uD7A3\uD7B0-\uD7C6\uD7CB-\uD7FB\uF900-\uFA6D]|[\uFA70-\uFAD9\uFB00-\uFB06\uFB13-\uFB17\uFB1D\uFB1F-\uFB28\uFB2A-\uFB36]|[\uFB38-\uFB3C\uFB3E\uFB40\uFB41\uFB43\uFB44\uFB46-\uFBB1\uFBD3-\uFD3D]|[\uFD50-\uFD8F\uFD92-\uFDC7\uFDF0-\uFDFB\uFE70-\uFE74\uFE76-\uFEFC]|[\uFF21-\uFF3A\uFF41-\uFF5A\uFF66-\uFFBE\uFFC2-\uFFC7\uFFCA-\uFFCF]|[\uFFD2-\uFFD7\uFFDA-\uFFDC])/,/^(?:\s)/,/^(?:$)/],conditions:{acc_descr_multiline:{rules:[16,17],inclusive:!1},acc_descr:{rules:[14],inclusive:!1},acc_title:{rules:[12],inclusive:!1},arg_directive:{rules:[7,8],inclusive:!1},type_directive:{rules:[6,7],inclusive:!1},open_directive:{rules:[5],inclusive:!1},callback_args:{rules:[54,55],inclusive:!1},callback_name:{rules:[51,52,53],inclusive:!1},href:{rules:[48,49],inclusive:!1},struct:{rules:[23,24,25,26,27,28],inclusive:!1},generic:{rules:[39,40],inclusive:!1},bqstring:{rules:[45,46],inclusive:!1},string:{rules:[42,43],inclusive:!1},INITIAL:{rules:[0,1,2,3,4,9,10,11,13,15,18,19,20,21,22,23,29,30,31,32,33,34,35,36,37,38,41,44,47,50,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84],inclusive:!0}}};return 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fill: ${i.classText};
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}
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font-weight: bolder;
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stroke-width: 1;
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g.clickable {
cursor: pointer;
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g.classGroup rect {
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g.classGroup line {
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stroke: ${i.lineColor} !important;
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stroke: ${i.lineColor} !important;
stroke-width: 1;
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Expecting `+Zs.join(", ")+", got '"+(this.terminals_[hi]||hi)+"'":ns="Parse error on line "+(Dt+1)+": Unexpected "+(hi==ze?"end of input":"'"+(this.terminals_[hi]||hi)+"'"),this.parseError(ns,{text:Lt.match,token:this.terminals_[hi]||hi,line:Lt.yylineno,loc:Oe,expected:Zs})}if(Zn[0]instanceof Array&&Zn.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Sr+", token: "+hi);switch(Zn[0]){case 1:Bt.push(hi),Nn.push(Lt.yytext),Ot.push(Lt.yylloc),Bt.push(Zn[1]),hi=null,vt=Lt.yyleng,kt=Lt.yytext,Dt=Lt.yylineno,Oe=Lt.yylloc;break;case 2:if(tr=this.productions_[Zn[1]][1],ir.$=Nn[Nn.length-tr],ir._$={first_line:Ot[Ot.length-(tr||1)].first_line,last_line:Ot[Ot.length-1].last_line,first_column:Ot[Ot.length-(tr||1)].first_column,last_column:Ot[Ot.length-1].last_column},Ri&&(ir._$.range=[Ot[Ot.length-(tr||1)].range[0],Ot[Ot.length-1].range[1]]),Xn=this.performAction.apply(ir,[kt,vt,Dt,Ge.yy,Zn[1],Nn,Ot].concat(Xe)),typeof Xn<"u")return Xn;tr&&(Bt=Bt.slice(0,-1*tr*2),Nn=Nn.slice(0,-1*tr),Ot=Ot.slice(0,-1*tr)),Bt.push(this.productions_[Zn[1]][0]),Nn.push(ir.$),Ot.push(ir._$),ha=oi[Bt[Bt.length-2]][Bt[Bt.length-1]],Bt.push(ha);break;case 3:return!0}}return!0}},pe=function(){var wt={EOF:1,parseError:function(At,Bt){if(this.yy.parser)this.yy.parser.parseError(At,Bt);else throw new Error(At)},setInput:function(jt,At){return this.yy=At||this.yy||{},this._input=jt,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var jt=this._input[0];this.yytext+=jt,this.yyleng++,this.offset++,this.match+=jt,this.matched+=jt;var At=jt.match(/(?:\r\n?|\n).*/g);return At?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),jt},unput:function(jt){var At=jt.length,Bt=jt.split(/(?:\r\n?|\n)/g);this._input=jt+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-At),this.offset-=At;var cn=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Bt.length-1&&(this.yylineno-=Bt.length-1);var Nn=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Bt?(Bt.length===cn.length?this.yylloc.first_column:0)+cn[cn.length-Bt.length].length-Bt[0].length:this.yylloc.first_column-At},this.options.ranges&&(this.yylloc.range=[Nn[0],Nn[0]+this.yyleng-At]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(jt){this.unput(this.match.slice(jt))},pastInput:function(){var jt=this.matched.substr(0,this.matched.length-this.match.length);return(jt.length>20?"...":"")+jt.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var jt=this.match;return jt.length<20&&(jt+=this._input.substr(0,20-jt.length)),(jt.substr(0,20)+(jt.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var jt=this.pastInput(),At=new Array(jt.length+1).join("-");return jt+this.upcomingInput()+`
`+At+"^"},test_match:function(jt,At){var Bt,cn,Nn;if(this.options.backtrack_lexer&&(Nn={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Nn.yylloc.range=this.yylloc.range.slice(0))),cn=jt[0].match(/(?:\r\n?|\n).*/g),cn&&(this.yylineno+=cn.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:cn?cn[cn.length-1].length-cn[cn.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+jt[0].length},this.yytext+=jt[0],this.match+=jt[0],this.matches=jt,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(jt[0].length),this.matched+=jt[0],Bt=this.performAction.call(this,this.yy,this,At,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),Bt)return Bt;if(this._backtrack){for(var Ot in Nn)this[Ot]=Nn[Ot];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var jt,At,Bt,cn;this._more||(this.yytext="",this.match="");for(var Nn=this._currentRules(),Ot=0;Ot<Nn.length;Ot++)if(Bt=this._input.match(this.rules[Nn[Ot]]),Bt&&(!At||Bt[0].length>At[0].length)){if(At=Bt,cn=Ot,this.options.backtrack_lexer){if(jt=this.test_match(Bt,Nn[Ot]),jt!==!1)return jt;if(this._backtrack){At=!1;continue}else return!1}else if(!this.options.flex)break}return At?(jt=this.test_match(At,Nn[cn]),jt!==!1?jt:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var At=this.next();return At||this.lex()},begin:function(At){this.conditionStack.push(At)},popState:function(){var At=this.conditionStack.length-1;return At>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(At){return At=this.conditionStack.length-1-Math.abs(At||0),At>=0?this.conditionStack[At]:"INITIAL"},pushState:function(At){this.begin(At)},stateStackSize:function(){return this.conditionStack.length},options:{"case-insensitive":!0},performAction:function(At,Bt,cn,Nn){switch(cn){case 0:return 41;case 1:return 50;case 2:return 51;case 3:return 52;case 4:return 53;case 5:return this.begin("open_directive"),60;case 6:return this.begin("type_directive"),61;case 7:return this.popState(),this.begin("arg_directive"),48;case 8:return this.popState(),this.popState(),63;case 9:return 62;case 10:break;case 11:break;case 12:return 5;case 13:break;case 14:break;case 15:break;case 16:break;case 17:return this.pushState("SCALE"),17;case 18:return 18;case 19:this.popState();break;case 20:return this.begin("acc_title"),33;case 21:return this.popState(),"acc_title_value";case 22:return this.begin("acc_descr"),35;case 23:return this.popState(),"acc_descr_value";case 24:this.begin("acc_descr_multiline");break;case 25:this.popState();break;case 26:return"acc_descr_multiline_value";case 27:return this.pushState("CLASSDEF"),38;case 28:return this.popState(),this.pushState("CLASSDEFID"),"DEFAULT_CLASSDEF_ID";case 29:return this.popState(),this.pushState("CLASSDEFID"),39;case 30:return this.popState(),40;case 31:return this.pushState("CLASS"),42;case 32:return this.popState(),this.pushState("CLASS_STYLE"),43;case 33:return this.popState(),44;case 34:return this.pushState("SCALE"),17;case 35:return 18;case 36:this.popState();break;case 37:this.pushState("STATE");break;case 38:return this.popState(),Bt.yytext=Bt.yytext.slice(0,-8).trim(),25;case 39:return this.popState(),Bt.yytext=Bt.yytext.slice(0,-8).trim(),26;case 40:return this.popState(),Bt.yytext=Bt.yytext.slice(0,-10).trim(),27;case 41:return this.popState(),Bt.yytext=Bt.yytext.slice(0,-8).trim(),25;case 42:return this.popState(),Bt.yytext=Bt.yytext.slice(0,-8).trim(),26;case 43:return this.popState(),Bt.yytext=Bt.yytext.slice(0,-10).trim(),27;case 44:return 50;case 45:return 51;case 46:return 52;case 47:return 53;case 48:this.pushState("STATE_STRING");break;case 49:return this.pushState("STATE_ID"),"AS";case 50:return this.popState(),"ID";case 51:this.popState();break;case 52:return"STATE_DESCR";case 53:return 19;case 54:this.popState();break;case 55:return this.popState(),this.pushState("struct"),20;case 56:break;case 57:return this.popState(),21;case 58:break;case 59:return this.begin("NOTE"),29;case 60:return this.popState(),this.pushState("NOTE_ID"),58;case 61:return this.popState(),this.pushState("NOTE_ID"),59;case 62:this.popState(),this.pushState("FLOATING_NOTE");break;case 63:return this.popState(),this.pushState("FLOATING_NOTE_ID"),"AS";case 64:break;case 65:return"NOTE_TEXT";case 66:return this.popState(),"ID";case 67:return this.popState(),this.pushState("NOTE_TEXT"),24;case 68:return this.popState(),Bt.yytext=Bt.yytext.substr(2).trim(),31;case 69:return this.popState(),Bt.yytext=Bt.yytext.slice(0,-8).trim(),31;case 70:return 7;case 71:return 7;case 72:return 16;case 73:return 56;case 74:return 24;case 75:return Bt.yytext=Bt.yytext.trim(),14;case 76:return 15;case 77:return 28;case 78:return 57;case 79:return 5;case 80:return"INVALID"}},rules:[/^(?:default\b)/i,/^(?:.*direction\s+TB[^\n]*)/i,/^(?:.*direction\s+BT[^\n]*)/i,/^(?:.*direction\s+RL[^\n]*)/i,/^(?:.*direction\s+LR[^\n]*)/i,/^(?:%%\{)/i,/^(?:((?:(?!\}%%)[^:.])*))/i,/^(?::)/i,/^(?:\}%%)/i,/^(?:((?:(?!\}%%).|\n)*))/i,/^(?:%%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[\n]+)/i,/^(?:[\s]+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%[^\n]*)/i,/^(?:scale\s+)/i,/^(?:\d+)/i,/^(?:\s+width\b)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:classDef\s+)/i,/^(?:DEFAULT\s+)/i,/^(?:\w+\s+)/i,/^(?:[^\n]*)/i,/^(?:class\s+)/i,/^(?:(\w+)+((,\s*\w+)*))/i,/^(?:[^\n]*)/i,/^(?:scale\s+)/i,/^(?:\d+)/i,/^(?:\s+width\b)/i,/^(?:state\s+)/i,/^(?:.*<<fork>>)/i,/^(?:.*<<join>>)/i,/^(?:.*<<choice>>)/i,/^(?:.*\[\[fork\]\])/i,/^(?:.*\[\[join\]\])/i,/^(?:.*\[\[choice\]\])/i,/^(?:.*direction\s+TB[^\n]*)/i,/^(?:.*direction\s+BT[^\n]*)/i,/^(?:.*direction\s+RL[^\n]*)/i,/^(?:.*direction\s+LR[^\n]*)/i,/^(?:["])/i,/^(?:\s*as\s+)/i,/^(?:[^\n\{]*)/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:[^\n\s\{]+)/i,/^(?:\n)/i,/^(?:\{)/i,/^(?:%%(?!\{)[^\n]*)/i,/^(?:\})/i,/^(?:[\n])/i,/^(?:note\s+)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:")/i,/^(?:\s*as\s*)/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:[^\n]*)/i,/^(?:\s*[^:\n\s\-]+)/i,/^(?:\s*:[^:\n;]+)/i,/^(?:[\s\S]*?end note\b)/i,/^(?:stateDiagram\s+)/i,/^(?:stateDiagram-v2\s+)/i,/^(?:hide empty description\b)/i,/^(?:\[\*\])/i,/^(?:[^:\n\s\-\{]+)/i,/^(?:\s*:[^:\n;]+)/i,/^(?:-->)/i,/^(?:--)/i,/^(?::::)/i,/^(?:$)/i,/^(?:.)/i],conditions:{LINE:{rules:[14,15],inclusive:!1},close_directive:{rules:[14,15],inclusive:!1},arg_directive:{rules:[8,9,14,15],inclusive:!1},type_directive:{rules:[7,8,14,15],inclusive:!1},open_directive:{rules:[6,14,15],inclusive:!1},struct:{rules:[14,15,27,31,37,44,45,46,47,56,57,58,59,73,74,75,76,77],inclusive:!1},FLOATING_NOTE_ID:{rules:[66],inclusive:!1},FLOATING_NOTE:{rules:[63,64,65],inclusive:!1},NOTE_TEXT:{rules:[68,69],inclusive:!1},NOTE_ID:{rules:[67],inclusive:!1},NOTE:{rules:[60,61,62],inclusive:!1},CLASS_STYLE:{rules:[33],inclusive:!1},CLASS:{rules:[32],inclusive:!1},CLASSDEFID:{rules:[30],inclusive:!1},CLASSDEF:{rules:[28,29],inclusive:!1},acc_descr_multiline:{rules:[25,26],inclusive:!1},acc_descr:{rules:[23],inclusive:!1},acc_title:{rules:[21],inclusive:!1},SCALE:{rules:[18,19,35,36],inclusive:!1},ALIAS:{rules:[],inclusive:!1},STATE_ID:{rules:[50],inclusive:!1},STATE_STRING:{rules:[51,52],inclusive:!1},FORK_STATE:{rules:[],inclusive:!1},STATE:{rules:[14,15,38,39,40,41,42,43,48,49,53,54,55],inclusive:!1},ID:{rules:[14,15],inclusive:!1},INITIAL:{rules:[0,1,2,3,4,5,10,11,12,13,15,16,17,20,22,24,27,31,34,37,55,59,70,71,72,73,74,75,76,78,79,80],inclusive:!0}}};return wt}();gt.lexer=pe;function Et(){this.yy={}}return Et.prototype=gt,gt.Parser=Et,new Et}();dge.parser=dge;const sFe=dge,pzt="LR",bzt="TB",RK="state",gge="relation",vzt="classDef",wzt="applyClass",NP="default",aFe="divider",pge="[*]",oFe="start",cFe=pge,uFe="end",lFe="color",hFe="fill",mzt="bgFill",yzt=",";function fFe(){return{}}let dFe=pzt,FK=[],PP=fFe();const gFe=()=>({relations:[],states:{},documents:{}});let jK={root:gFe()},x0=jK.root,BP=0,pFe=0;const kzt={LINE:0,DOTTED_LINE:1},xzt={AGGREGATION:0,EXTENSION:1,COMPOSITION:2,DEPENDENCY:3},$K=i=>JSON.parse(JSON.stringify(i)),Ezt=function(i,a,f){rd.parseDirective(this,i,a,f)},Tzt=i=>{Fe.info("Setting root doc",i),FK=i},_zt=()=>FK,HK=(i,a,f)=>{if(a.stmt===gge)HK(i,a.state1,!0),HK(i,a.state2,!1);else if(a.stmt===RK&&(a.id==="[*]"?(a.id=f?i.id+"_start":i.id+"_end",a.start=f):a.id=a.id.trim()),a.doc){const p=[];let w=[],y;for(y=0;y<a.doc.length;y++)if(a.doc[y].type===aFe){const b=$K(a.doc[y]);b.doc=$K(w),p.push(b),w=[]}else w.push(a.doc[y]);if(p.length>0&&w.length>0){const b={stmt:RK,id:IIe(),type:"divider",doc:$K(w)};p.push($K(b)),a.doc=p}a.doc.forEach(b=>HK(a,b,!0))}},Czt=()=>(HK({id:"root"},{id:"root",doc:FK},!0),{id:"root",doc:FK}),Szt=i=>{let a;i.doc?a=i.doc:a=i,Fe.info(a),bFe(!0),Fe.info("Extract",a),a.forEach(f=>{switch(f.stmt){case RK:p9(f.id.trim(),f.type,f.doc,f.description,f.note,f.classes,f.styles,f.textStyles);break;case gge:vFe(f.state1,f.state2,f.description);break;case vzt:wFe(f.id.trim(),f.classes);break;case wzt:mge(f.id.trim(),f.styleClass);break}})},p9=function(i,a=NP,f=null,p=null,w=null,y=null,b=null,E=null){const S=i==null?void 0:i.trim();x0.states[S]===void 0?(Fe.info("Adding state ",S,p),x0.states[S]={id:S,descriptions:[],type:a,doc:f,note:w,classes:[],styles:[],textStyles:[]}):(x0.states[S].doc||(x0.states[S].doc=f),x0.states[S].type||(x0.states[S].type=a)),p&&(Fe.info("Setting state description",S,p),typeof p=="string"&&wge(S,p.trim()),typeof p=="object"&&p.forEach(N=>wge(S,N.trim()))),w&&(x0.states[S].note=w,x0.states[S].note.text=Wa.sanitizeText(x0.states[S].note.text,Tt())),y&&(Fe.info("Setting state classes",S,y),(typeof y=="string"?[y]:y).forEach(B=>mge(S,B.trim()))),b&&(Fe.info("Setting state styles",S,b),(typeof b=="string"?[b]:b).forEach(B=>Rzt(S,B.trim()))),E&&(Fe.info("Setting state styles",S,b),(typeof E=="string"?[E]:E).forEach(B=>Fzt(S,B.trim())))},bFe=function(i){jK={root:gFe()},x0=jK.root,BP=0,PP=fFe(),i||rp()},RP=function(i){return x0.states[i]},Azt=function(){return x0.states},Lzt=function(){Fe.info("Documents = ",jK)},Mzt=function(){return x0.relations};function bge(i=""){let a=i;return i===pge&&(BP++,a=`${oFe}${BP}`),a}function vge(i="",a=NP){return i===pge?oFe:a}function Dzt(i=""){let a=i;return i===cFe&&(BP++,a=`${uFe}${BP}`),a}function Izt(i="",a=NP){return i===cFe?uFe:a}function Ozt(i,a,f){let p=bge(i.id.trim()),w=vge(i.id.trim(),i.type),y=bge(a.id.trim()),b=vge(a.id.trim(),a.type);p9(p,w,i.doc,i.description,i.note,i.classes,i.styles,i.textStyles),p9(y,b,a.doc,a.description,a.note,a.classes,a.styles,a.textStyles),x0.relations.push({id1:p,id2:y,relationTitle:Wa.sanitizeText(f,Tt())})}const vFe=function(i,a,f){if(typeof i=="object")Ozt(i,a,f);else{const p=bge(i.trim()),w=vge(i),y=Dzt(a.trim()),b=Izt(a);p9(p,w),p9(y,b),x0.relations.push({id1:p,id2:y,title:Wa.sanitizeText(f,Tt())})}},wge=function(i,a){const f=x0.states[i],p=a.startsWith(":")?a.replace(":","").trim():a;f.descriptions.push(Wa.sanitizeText(p,Tt()))},Nzt=function(i){return i.substring(0,1)===":"?i.substr(2).trim():i.trim()},Pzt=()=>(pFe++,"divider-id-"+pFe),wFe=function(i,a=""){PP[i]===void 0&&(PP[i]={id:i,styles:[],textStyles:[]});const f=PP[i];a!=null&&a.split(yzt).forEach(p=>{const w=p.replace(/([^;]*);/,"$1").trim();if(p.match(lFe)){const b=w.replace(hFe,mzt).replace(lFe,hFe);f.textStyles.push(b)}f.styles.push(w)})},Bzt=function(){return PP},mge=function(i,a){i.split(",").forEach(function(f){let p=RP(f);if(p===void 0){const w=f.trim();p9(w),p=RP(w)}p.classes.push(a)})},Rzt=function(i,a){const f=RP(i);f!==void 0&&f.textStyles.push(a)},Fzt=function(i,a){const f=RP(i);f!==void 0&&f.textStyles.push(a)},D5={parseDirective:Ezt,getConfig:()=>Tt().state,addState:p9,clear:bFe,getState:RP,getStates:Azt,getRelations:Mzt,getClasses:Bzt,getDirection:()=>dFe,addRelation:vFe,getDividerId:Pzt,setDirection:i=>{dFe=i},cleanupLabel:Nzt,lineType:kzt,relationType:xzt,logDocuments:Lzt,getRootDoc:_zt,setRootDoc:Tzt,getRootDocV2:Czt,extract:Szt,trimColon:i=>i&&i[0]===":"?i.substr(1).trim():i.trim(),getAccTitle:L2,setAccTitle:ip,getAccDescription:D2,setAccDescription:M2,addStyleClass:wFe,setCssClass:mge,addDescription:wge,setDiagramTitle:Uw,getDiagramTitle:Ww},mFe=i=>`
defs #statediagram-barbEnd {
fill: ${i.transitionColor};
stroke: ${i.transitionColor};
}
g.stateGroup text {
fill: ${i.nodeBorder};
stroke: none;
font-size: 10px;
}
g.stateGroup text {
fill: ${i.textColor};
stroke: none;
font-size: 10px;
}
g.stateGroup .state-title {
font-weight: bolder;
fill: ${i.stateLabelColor};
}
g.stateGroup rect {
fill: ${i.mainBkg};
stroke: ${i.nodeBorder};
}
g.stateGroup line {
stroke: ${i.lineColor};
stroke-width: 1;
}
.transition {
stroke: ${i.transitionColor};
stroke-width: 1;
fill: none;
}
.stateGroup .composit {
fill: ${i.background};
border-bottom: 1px
}
.stateGroup .alt-composit {
fill: #e0e0e0;
border-bottom: 1px
}
.state-note {
stroke: ${i.noteBorderColor};
fill: ${i.noteBkgColor};
text {
fill: ${i.noteTextColor};
stroke: none;
font-size: 10px;
}
}
.stateLabel .box {
stroke: none;
stroke-width: 0;
fill: ${i.mainBkg};
opacity: 0.5;
}
.edgeLabel .label rect {
fill: ${i.labelBackgroundColor};
opacity: 0.5;
}
.edgeLabel .label text {
fill: ${i.transitionLabelColor||i.tertiaryTextColor};
}
.label div .edgeLabel {
color: ${i.transitionLabelColor||i.tertiaryTextColor};
}
.stateLabel text {
fill: ${i.stateLabelColor};
font-size: 10px;
font-weight: bold;
}
.node circle.state-start {
fill: ${i.specialStateColor};
stroke: ${i.specialStateColor};
}
.node .fork-join {
fill: ${i.specialStateColor};
stroke: ${i.specialStateColor};
}
.node circle.state-end {
fill: ${i.innerEndBackground};
stroke: ${i.background};
stroke-width: 1.5
}
.end-state-inner {
fill: ${i.compositeBackground||i.background};
// stroke: ${i.background};
stroke-width: 1.5
}
.node rect {
fill: ${i.stateBkg||i.mainBkg};
stroke: ${i.stateBorder||i.nodeBorder};
stroke-width: 1px;
}
.node polygon {
fill: ${i.mainBkg};
stroke: ${i.stateBorder||i.nodeBorder};;
stroke-width: 1px;
}
#statediagram-barbEnd {
fill: ${i.lineColor};
}
.statediagram-cluster rect {
fill: ${i.compositeTitleBackground};
stroke: ${i.stateBorder||i.nodeBorder};
stroke-width: 1px;
}
.cluster-label, .nodeLabel {
color: ${i.stateLabelColor};
}
.statediagram-cluster rect.outer {
rx: 5px;
ry: 5px;
}
.statediagram-state .divider {
stroke: ${i.stateBorder||i.nodeBorder};
}
.statediagram-state .title-state {
rx: 5px;
ry: 5px;
}
.statediagram-cluster.statediagram-cluster .inner {
fill: ${i.compositeBackground||i.background};
}
.statediagram-cluster.statediagram-cluster-alt .inner {
fill: ${i.altBackground?i.altBackground:"#efefef"};
}
.statediagram-cluster .inner {
rx:0;
ry:0;
}
.statediagram-state rect.basic {
rx: 5px;
ry: 5px;
}
.statediagram-state rect.divider {
stroke-dasharray: 10,10;
fill: ${i.altBackground?i.altBackground:"#efefef"};
}
.note-edge {
stroke-dasharray: 5;
}
.statediagram-note rect {
fill: ${i.noteBkgColor};
stroke: ${i.noteBorderColor};
stroke-width: 1px;
rx: 0;
ry: 0;
}
.statediagram-note rect {
fill: ${i.noteBkgColor};
stroke: ${i.noteBorderColor};
stroke-width: 1px;
rx: 0;
ry: 0;
}
.statediagram-note text {
fill: ${i.noteTextColor};
}
.statediagram-note .nodeLabel {
color: ${i.noteTextColor};
}
.statediagram .edgeLabel {
color: red; // ${i.noteTextColor};
}
#dependencyStart, #dependencyEnd {
fill: ${i.lineColor};
stroke: ${i.lineColor};
stroke-width: 1;
}
.statediagramTitleText {
text-anchor: middle;
font-size: 18px;
fill: ${i.textColor};
}
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i.insert("rect",":first-child").attr("x",j).attr("y",V).attr("class",f?"alt-composit":"composit").attr("width",R).attr("height",$.height+Tt().state.textHeight+Tt().state.titleShift+1).attr("rx","0"),S.attr("x",j+p),B<=b&&S.attr("x",E+(R-w)/2-B/2+p),i.insert("rect",":first-child").attr("x",j).attr("y",Tt().state.titleShift-Tt().state.textHeight-Tt().state.padding).attr("width",R).attr("height",Tt().state.textHeight*3).attr("rx",Tt().state.radius),i.insert("rect",":first-child").attr("x",j).attr("y",Tt().state.titleShift-Tt().state.textHeight-Tt().state.padding).attr("width",R).attr("height",$.height+3+2*Tt().state.textHeight).attr("rx",Tt().state.radius),i},Wzt=i=>(i.append("circle").attr("class","end-state-outer").attr("r",Tt().state.sizeUnit+Tt().state.miniPadding).attr("cx",Tt().state.padding+Tt().state.sizeUnit+Tt().state.miniPadding).attr("cy",Tt().state.padding+Tt().state.sizeUnit+Tt().state.miniPadding),i.append("circle").attr("class","end-state-inner").attr("r",Tt().state.sizeUnit).attr("cx",Tt().state.padding+Tt().state.sizeUnit+2).attr("cy",Tt().state.padding+Tt().state.sizeUnit+2)),Kzt=(i,a)=>{let 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f.attr("height",y+2*Tt().state.noteMargin),f.attr("width",w+Tt().state.noteMargin*2),f},kFe=function(i,a){const f=a.id,p={id:f,label:a.id,width:0,height:0},w=i.append("g").attr("id",f).attr("class","stateGroup");a.type==="start"&&zzt(w),a.type==="end"&&Wzt(w),(a.type==="fork"||a.type==="join")&&Kzt(w,a),a.type==="note"&&Xzt(a.note.text,w),a.type==="divider"&&Gzt(w),a.type==="default"&&a.descriptions.length===0&&qzt(w,a),a.type==="default"&&a.descriptions.length>0&&Vzt(w,a);const y=w.node().getBBox();return p.width=y.width+2*Tt().state.padding,p.height=y.height+2*Tt().state.padding,Hzt.set(f,p),p};let xFe=0;const Qzt=function(i,a,f){const p=function(S){switch(S){case D5.relationType.AGGREGATION:return"aggregation";case D5.relationType.EXTENSION:return"extension";case D5.relationType.COMPOSITION:return"composition";case D5.relationType.DEPENDENCY:return"dependency"}};a.points=a.points.filter(S=>!Number.isNaN(S.y));const w=a.points,y=WE().x(function(S){return S.x}).y(function(S){return S.y}).curve(SA),b=i.append("path").attr("d",y(w)).attr("id","edge"+xFe).attr("class","transition");let E="";if(Tt().state.arrowMarkerAbsolute&&(E=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search,E=E.replace(/\(/g,"\\("),E=E.replace(/\)/g,"\\)")),b.attr("marker-end","url("+E+"#"+p(D5.relationType.DEPENDENCY)+"End)"),f.title!==void 0){const S=i.append("g").attr("class","stateLabel"),{x:N,y:B}=co.calcLabelPosition(a.points),R=Wa.getRows(f.title);let j=0;const $=[];let V=0,Q=0;for(let se=0;se<=R.length;se++){const ge=S.append("text").attr("text-anchor","middle").text(R[se]).attr("x",N).attr("y",B+j),ye=ge.node().getBBox();V=Math.max(V,ye.width),Q=Math.min(Q,ye.x),Fe.info(ye.x,N,B+j),j===0&&(j=ge.node().getBBox().height,Fe.info("Title height",j,B)),$.push(ge)}let oe=j*R.length;if(R.length>1){const se=(R.length-1)*j*.5;$.forEach((ge,ye)=>ge.attr("y",B+ye*j-se)),oe=j*R.length}const ce=S.node().getBBox();S.insert("rect",":first-child").attr("class","box").attr("x",N-V/2-Tt().state.padding/2).attr("y",B-oe/2-Tt().state.padding/2-3.5).attr("width",V+Tt().state.padding).attr("height",oe+Tt().state.padding),Fe.info(ce)}xFe++};let B2;const kge={},Zzt=function(){},Jzt=function(i){i.append("defs").append("marker").attr("id","dependencyEnd").attr("refX",19).attr("refY",7).attr("markerWidth",20).attr("markerHeight",28).attr("orient","auto").append("path").attr("d","M 19,7 L9,13 L14,7 L9,1 Z")},eGt=function(i,a,f,p){B2=Tt().state;const w=Tt().securityLevel;let y;w==="sandbox"&&(y=Cr("#i"+a));const b=Cr(w==="sandbox"?y.nodes()[0].contentDocument.body:"body"),E=w==="sandbox"?y.nodes()[0].contentDocument:document;Fe.debug("Rendering diagram "+i);const S=b.select(`[id='${a}']`);Jzt(S),new zf({multigraph:!0,compound:!0,rankdir:"RL"}).setDefaultEdgeLabel(function(){return{}});const B=p.db.getRootDoc();EFe(B,S,void 0,!1,b,E,p);const R=B2.padding,j=S.node().getBBox(),$=j.width+R*2,V=j.height+R*2,Q=$*1.75;Vw(S,V,Q,B2.useMaxWidth),S.attr("viewBox",`${j.x-B2.padding} ${j.y-B2.padding} `+$+" "+V)},tGt=i=>i?i.length*B2.fontSizeFactor:1,EFe=(i,a,f,p,w,y,b)=>{const E=new zf({compound:!0,multigraph:!0});let S,N=!0;for(S=0;S<i.length;S++)if(i[S].stmt==="relation"){N=!1;break}f?E.setGraph({rankdir:"LR",multigraph:!0,compound:!0,ranker:"tight-tree",ranksep:N?1:B2.edgeLengthFactor,nodeSep:N?1:50,isMultiGraph:!0}):E.setGraph({rankdir:"TB",multigraph:!0,compound:!0,ranksep:N?1:B2.edgeLengthFactor,nodeSep:N?1:50,ranker:"tight-tree",isMultiGraph:!0}),E.setDefaultEdgeLabel(function(){return{}}),b.db.extract(i);const B=b.db.getStates(),R=b.db.getRelations(),j=Object.keys(B);for(const ce of j){const se=B[ce];f&&(se.parentId=f);let ge;if(se.doc){let ye=a.append("g").attr("id",se.id).attr("class","stateGroup");ge=EFe(se.doc,ye,se.id,!p,w,y,b);{ye=Uzt(ye,se,p);let 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rr(e);n.e!=n.i.gc();)if(t=u(pr(n),87),t.e||(!t.d&&(t.d=new Bs(Eo,t,1)),t.d).i!=0)return!0;return!1}function hun(e){var t,n,r;for(t=0,r=new C(e.c.a);r.a<r.c.c.length;)n=u(Y(r),10),t+=I0(new cr(fr(js(n).a.Kc(),new V)));return t/e.c.a.c.length}function pit(e){var t,n;for(e.c||mwn(e),n=new Gu,t=new C(e.a),Y(t);t.a<t.c.c.length;)ci(n,u(Y(t),407).a);return Qn(n.b!=0),bh(n,n.c.b),n}function ase(){ase=pe,ZCe=(Jse(),KCe),QCe=new kv(8),new fo((bi(),Fb),QCe),new fo(jb,8),Pyt=UCe,YCe=Syt,XCe=Ayt,Nyt=new fo(VO,(Mn(),!1))}function E3e(e,t,n,r){switch(t){case 7:return!e.e&&(e.e=new wn(ra,e,7,4)),e.e;case 8:return!e.d&&(e.d=new wn(ra,e,8,5)),e.d}return Gye(e,t,n,r)}function ose(e){var t;return e.a&&e.a.kh()&&(t=u(e.a,49),e.a=u(Up(e,t),138),e.a!=t&&e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,9,5,t,e.a))),e.a}function fb(e){return e<48||e>102?-1:e<=57?e-48:e<65?-1:e<=70?e-65+10:e<97?-1:e-97+10}function cse(e,t){if(e==null)throw J(new b6("null key in entry: null="+t));if(t==null)throw J(new b6("null value in entry: 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n,r,s,o,h,d;for(s=t.length-1,h=0,d=0,r=0;r<=s;r++)o=t[r],n=zfn(s,r)*Aye(1-e,s-r)*Aye(e,r),h+=o.a*n,d+=o.b*n;return new Pt(h,d)}function xit(e,t){var n,r,s,o,h;for(n=t.gc(),e.qi(e.i+n),o=t.Kc(),h=e.i,e.i+=n,r=h;r<e.i;++r)s=o.Pb(),n_(e,r,e.oi(r,s)),e.bi(r,s),e.ci();return n!=0}function Tun(e,t,n){var r,s,o;return e.ej()?(r=e.Vi(),o=e.fj(),++e.j,e.Hi(r,e.oi(r,t)),s=e.Zi(3,null,t,r,o),n?n.Ei(s):n=s):BUe(e,e.Vi(),t),n}function _un(e,t,n){var r,s,o;return r=u(Te(jc(e.a),t),87),o=(s=r.c,we(s,88)?u(s,26):(on(),sf)),(o.Db&64?Up(e.b,o):o)==n?dz(r):I8(r,n),o}function _3e(e,t,n,r,s,o,h,d){var v,x;r&&(v=r.a[0],v&&_3e(e,t,n,v,s,o,h,d),Jun(e,n,r.d,s,o,h,d)&&t.Fc(r),x=r.a[1],x&&_3e(e,t,n,x,s,o,h,d))}function Cun(e,t){var n;return e.a||(n=Me(pa,Ao,25,0,15,1),D3(e.b.a,new Je(n)),n.sort(Aet(he.prototype.te,he,[])),e.a=new gWe(n,e.d)),_$(e.a,t)}function i4(e,t,n){try{return mv(rie(e,t,n),1)}catch(r){throw r=ts(r),we(r,320)?J(new Do(koe+e.o+"*"+e.p+xoe+t+io+n+Eoe)):J(r)}}function Eit(e,t,n){try{return mv(rie(e,t,n),0)}catch(r){throw r=ts(r),we(r,320)?J(new Do(koe+e.o+"*"+e.p+xoe+t+io+n+Eoe)):J(r)}}function Tit(e,t,n){try{return mv(rie(e,t,n),2)}catch(r){throw r=ts(r),we(r,320)?J(new Do(koe+e.o+"*"+e.p+xoe+t+io+n+Eoe)):J(r)}}function _it(e,t){if(e.g==-1)throw J(new zu);e.mj();try{e.d._c(e.g,t),e.f=e.d.j}catch(n){throw n=ts(n),we(n,73)?J(new uh):J(n)}}function Sun(e,t,n){kr(n,"Linear segments node placement",1),e.b=u(K(t,(et(),G4)),304),Hyn(e,t),Tvn(e,t),$vn(e,t),yyn(e),e.a=null,e.b=null,ur(n)}function YD(e,t){var n,r,s,o;for(o=e.gc(),t.length<o&&(t=dF(new Array(o),t)),s=t,r=e.Kc(),n=0;n<o;++n)cs(s,n,r.Pb());return t.length>o&&cs(t,o,null),t}function Aun(e,t){var n,r;if(r=e.gc(),t==null){for(n=0;n<r;n++)if(e.Xb(n)==null)return n}else for(n=0;n<r;n++)if(Ci(t,e.Xb(n)))return n;return-1}function lse(e,t){var n,r,s;return n=t.cd(),s=t.dd(),r=e.xc(n),!(!(je(s)===je(r)||s!=null&&Ci(s,r))||r==null&&!e._b(n))}function Lun(e,t){var n,r,s;return t<=22?(n=e.l&(1<<t)-1,r=s=0):t<=44?(n=e.l,r=e.m&(1<<t-22)-1,s=0):(n=e.l,r=e.m,s=e.h&(1<<t-44)-1),fu(n,r,s)}function Mun(e,t){switch(t.g){case 1:return e.f.n.d+e.t;case 3:return e.f.n.a+e.t;case 2:return e.f.n.c+e.s;case 4:return e.f.n.b+e.s;default:return 0}}function Dun(e,t){var n,r;switch(r=t.c,n=t.a,e.b.g){case 0:n.d=e.e-r.a-r.d;break;case 1:n.d+=e.e;break;case 2:n.c=e.e-r.a-r.d;break;case 3:n.c=e.e+r.d}}function C3e(e,t,n,r){var s,o;this.a=t,this.c=r,s=e.a,Hge(this,new Pt(-s.c,-s.d)),Ni(this.b,n),o=r/2,t.a?BM(this.b,0,o):BM(this.b,o,0),it(e.c,this)}function jH(){jH=pe,Phe=new zF(U0,0),lCe=new zF(xft,1),hCe=new zF("EDGE_LENGTH_BY_POSITION",2),uCe=new zF("CROSSING_MINIMIZATION_BY_POSITION",3)}function hse(e,t){var n,r;if(n=u(K3(e.g,t),33),n)return n;if(r=u(K3(e.j,t),118),r)return r;throw J(new dd("Referenced shape does not exist: "+t))}function Iun(e,t){if(e.c==t)return e.d;if(e.d==t)return e.c;throw J(new Ln("Node 'one' must be either source or target of edge 'edge'."))}function Oun(e,t){if(e.c.i==t)return e.d.i;if(e.d.i==t)return e.c.i;throw J(new Ln("Node "+t+" is neither source nor target of edge "+e))}function Nun(e,t){var n;switch(t.g){case 2:case 4:n=e.a,e.c.d.n.b<n.d.n.b&&(n=e.c),kg(e,t,(R1(),ile),n);break;case 1:case 3:kg(e,t,(R1(),xk),null)}}function fse(e,t,n,r,s,o){var h,d,v,x,_;for(h=Rln(t,n,o),d=n==(ht(),An)||n==Dn?-1:1,x=e[n.g],_=0;_<x.length;_++)v=x[_],v>0&&(v+=s),x[_]=h,h+=d*(v+r)}function Cit(e){var t,n,r;for(r=e.f,e.n=Me(pa,Ao,25,r,15,1),e.d=Me(pa,Ao,25,r,15,1),t=0;t<r;t++)n=u(St(e.c.b,t),29),e.n[t]=nit(e,n),e.d[t]=Rot(e,n)}function dse(e,t){var n,r,s;for(s=0,r=2;r<t;r<<=1)e.Db&r&&++s;if(s==0){for(n=t<<=1;n<=128;n<<=1)if(e.Db&n)return 0;return-1}else return s}function Sit(e,t){var n,r,s,o,h;for(h=pu(e.e.Tg(),t),o=null,n=u(e.g,119),s=0;s<e.i;++s)r=n[s],h.rl(r.ak())&&(!o&&(o=new X5),Br(o,r));o&&jlt(e,o)}function Ait(e){var t,n,r;if(!e)return null;if(e.dc())return"";for(r=new bg,n=e.Kc();n.Ob();)t=n.Pb(),To(r,Hr(t)),r.a+=" ";return 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n=ie(re(pa,1),Ao,25,15,[y3e(e,(n1(),pc),t),y3e(e,lu,t),y3e(e,bc,t)]),e.f&&(n[0]=b.Math.max(n[0],n[2]),n[2]=n[0]),n}function Bun(e,t,n){var r;try{hz(e,t+e.j,n+e.k,!1,!0)}catch(s){throw s=ts(s),we(s,73)?(r=s,J(new Do(r.g+Pz+t+io+n+")."))):J(s)}}function Run(e,t,n){var r;try{hz(e,t+e.j,n+e.k,!0,!1)}catch(s){throw s=ts(s),we(s,73)?(r=s,J(new Do(r.g+Pz+t+io+n+")."))):J(s)}}function Iit(e){var t;ta(e,(pt(),Tw))&&(t=u(K(e,Tw),21),t.Hc((sy(),If))?(t.Mc(If),t.Fc(Of)):t.Hc(Of)&&(t.Mc(Of),t.Fc(If)))}function Oit(e){var t;ta(e,(pt(),Tw))&&(t=u(K(e,Tw),21),t.Hc((sy(),Pf))?(t.Mc(Pf),t.Fc(Zh)):t.Hc(Zh)&&(t.Mc(Zh),t.Fc(Pf)))}function Fun(e,t,n){kr(n,"Self-Loop ordering",1),ms(Cu(Vi(Vi(ic(new vn(null,new mn(t.b,16)),new rB),new VY),new UY),new WY),new mm(e)),ur(n)}function XD(e,t,n,r){var s,o;for(s=t;s<e.c.length;s++)if(o=(xn(s,e.c.length),u(e.c[s],11)),n.Mb(o))r.c[r.c.length]=o;else return s;return e.c.length}function gse(e,t,n,r){var s,o,h,d;return 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gln(e){switch(uj(),e.q.g){case 5:Oat(e,(ht(),An)),Oat(e,xr);break;case 4:Aot(e,(ht(),An)),Aot(e,xr);break;default:Alt(e,(ht(),An)),Alt(e,xr)}}function pln(e){switch(uj(),e.q.g){case 5:Kat(e,(ht(),$n)),Kat(e,Dn);break;case 4:sit(e,(ht(),$n)),sit(e,Dn);break;default:Llt(e,(ht(),$n)),Llt(e,Dn)}}function bln(e){var t,n;t=u(K(e,(a1(),dpt)),19),t?(n=t.a,n==0?Ye(e,(zp(),tq),new Fie):Ye(e,(zp(),tq),new m$(n))):Ye(e,(zp(),tq),new m$(1))}function vln(e,t){var n;switch(n=e.i,t.g){case 1:return-(e.n.b+e.o.b);case 2:return e.n.a-n.o.a;case 3:return e.n.b-n.o.b;case 4:return-(e.n.a+e.o.a)}return 0}function wln(e,t){switch(e.g){case 0:return t==(mh(),l2)?kq:xq;case 1:return t==(mh(),l2)?kq:bO;case 2:return t==(mh(),l2)?bO:xq;default:return bO}}function JD(e,t){var n,r,s;for(Au(e.a,t),e.e-=t.r+(e.a.c.length==0?0:e.c),s=Exe,r=new C(e.a);r.a<r.c.c.length;)n=u(Y(r),187),s=b.Math.max(s,n.d);e.b=s}function F3e(e,t){var n,r;return e.Db>>16==3?e.Cb.ih(e,12,hs,t):(r=go(u(gn((n=u(_n(e,16),26),n||(cu(),kAe)),e.Db>>16),18)),e.Cb.ih(e,r.n,r.f,t))}function j3e(e,t){var n,r;return e.Db>>16==11?e.Cb.ih(e,10,hs,t):(r=go(u(gn((n=u(_n(e,16),26),n||(cu(),EAe)),e.Db>>16),18)),e.Cb.ih(e,r.n,r.f,t))}function Xit(e,t){var n,r;return e.Db>>16==10?e.Cb.ih(e,11,tf,t):(r=go(u(gn((n=u(_n(e,16),26),n||(on(),Xg)),e.Db>>16),18)),e.Cb.ih(e,r.n,r.f,t))}function Qit(e,t){var n,r;return e.Db>>16==10?e.Cb.ih(e,12,nf,t):(r=go(u(gn((n=u(_n(e,16),26),n||(on(),Ky)),e.Db>>16),18)),e.Cb.ih(e,r.n,r.f,t))}function $h(e){var t;return!(e.Bb&1)&&e.r&&e.r.kh()&&(t=u(e.r,49),e.r=u(Up(e,t),138),e.r!=t&&e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,9,8,t,e.r))),e.r}function wse(e,t,n){var r;return r=ie(re(pa,1),Ao,25,15,[l4e(e,(n1(),pc),t,n),l4e(e,lu,t,n),l4e(e,bc,t,n)]),e.f&&(r[0]=b.Math.max(r[0],r[2]),r[2]=r[0]),r}function mln(e,t){var n,r,s;if(s=dun(e,t),s.c.length!=0)for(aa(s,new 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n,r,s,o,h,d,v,x;return v=e.h>>19,x=t.h>>19,v!=x?x-v:(s=e.h,d=t.h,s!=d?s-d:(r=e.m,h=t.m,r!=h?r-h:(n=e.l,o=t.l,n-o)))}function GH(){GH=pe,S7e=(uz(),Mue),C7e=new dn(A6e,S7e),_7e=(P$(),Lue),T7e=new dn(L6e,_7e),E7e=(LH(),Aue),x7e=new dn(M6e,E7e),k7e=new dn(D6e,(Mn(),!0))}function Z_(e,t,n){var r,s;r=t*n,we(e.g,145)?(s=j6(e),s.f.d?s.f.a||(e.d.a+=r+z1):(e.d.d-=r+z1,e.d.a+=r+z1)):we(e.g,10)&&(e.d.d-=r,e.d.a+=2*r)}function Zit(e,t,n){var r,s,o,h,d;for(s=e[n.g],d=new C(t.d);d.a<d.c.c.length;)h=u(Y(d),101),o=h.i,o&&o.i==n&&(r=h.d[n.g],s[r]=b.Math.max(s[r],o.j.b))}function Cln(e,t){var n,r,s,o,h;for(r=0,s=0,n=0,h=new C(t.d);h.a<h.c.c.length;)o=u(Y(h),443),$H(o),r=b.Math.max(r,o.b),s+=o.d+(n>0?e.g:0),++n;t.b=r,t.e=s}function Jit(e){var t,n,r;if(r=e.b,mGe(e.i,r.length)){for(n=r.length*2,e.b=Me(pue,AI,317,n,0,1),e.c=Me(pue,AI,317,n,0,1),e.f=n-1,e.i=0,t=e.a;t;t=t.c)aI(e,t,t);++e.g}}function Sln(e,t,n,r){var s,o,h,d;for(s=0;s<t.o;s++)for(o=s-t.j+n,h=0;h<t.p;h++)d=h-t.k+r,i4(t,s,h)?mun(e,o,d)||Bun(e,o,d):Tit(t,s,h)&&(yit(e,o,d)||Run(e,o,d))}function Aln(e,t,n){var r;r=t.c.i,r.k==(zn(),ca)?(Ye(e,(et(),l1),u(K(r,l1),11)),Ye(e,Yh,u(K(r,Yh),11))):(Ye(e,(et(),l1),t.c),Ye(e,Yh,n.d))}function c7(e,t,n){g4();var r,s,o,h,d,v;return h=t/2,o=n/2,r=b.Math.abs(e.a),s=b.Math.abs(e.b),d=1,v=1,r>h&&(d=h/r),s>o&&(v=o/s),bd(e,b.Math.min(d,v)),e}function Lln(){pz();var e,t;try{if(t=u(X3e((Ap(),rf),H7),2014),t)return t}catch(n){if(n=ts(n),we(n,102))e=n,Rve((jr(),e));else throw J(n)}return new Y5}function Mln(){zJe();var e,t;try{if(t=u(X3e((Ap(),rf),_b),2024),t)return t}catch(n){if(n=ts(n),we(n,102))e=n,Rve((jr(),e));else throw J(n)}return new bm}function Dln(){pz();var e,t;try{if(t=u(X3e((Ap(),rf),Uh),1941),t)return t}catch(n){if(n=ts(n),we(n,102))e=n,Rve((jr(),e));else throw J(n)}return new KZ}function Iln(e,t,n){var r,s;return s=e.e,e.e=t,e.Db&4&&!(e.Db&1)&&(r=new 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t,n,r,s,o;for(t=u(K(e,(et(),uS)),83),o=e.n,r=t.Cc().Kc();r.Ob();)n=u(r.Pb(),306),s=n.i,s.c+=o.a,s.d+=o.b,n.c?bct(n):vct(n);Ye(e,uS,null)}function Rln(e,t,n){var r,s;switch(s=e.b,r=s.d,t.g){case 1:return-r.d-n;case 2:return s.o.a+r.c+n;case 3:return s.o.b+r.a+n;case 4:return-r.b-n;default:return-1}}function Fln(e){var t,n,r,s,o;if(r=0,s=O7,e.b)for(t=0;t<360;t++)n=t*.017453292519943295,D5e(e,e.d,0,0,_4,n),o=e.b.ig(e.d),o<s&&(r=n,s=o);D5e(e,e.d,0,0,_4,r)}function jln(e,t){var n,r,s,o;for(o=new Mr,t.e=null,t.f=null,r=new C(t.i);r.a<r.c.c.length;)n=u(Y(r),65),s=u(er(e.g,n.a),46),n.a=jj(n.b),Si(o,n.a,s);e.g=o}function $ln(e,t,n){var r,s,o,h,d,v;for(s=t-e.e,o=s/e.d.c.length,h=0,v=new C(e.d);v.a<v.c.c.length;)d=u(Y(v),443),r=e.b-d.b+n,Srt(d,d.e+h*o,d.f),kun(d,o,r),++h}function tst(e){var t;if(e.f.qj(),e.b!=-1){if(++e.b,t=e.f.d[e.a],e.b<t.i)return;++e.a}for(;e.a<e.f.d.length;++e.a)if(t=e.f.d[e.a],t&&t.i!=0){e.b=0;return}e.b=-1}function Hln(e,t){var n,r,s;for(s=t.c.length,n=$1n(e,s==0?"":(xn(0,t.c.length),Hr(t.c[0]))),r=1;r<s&&n;++r)n=u(n,49).oh((xn(r,t.c.length),Hr(t.c[r])));return n}function nst(e,t){var n,r;for(r=new C(t);r.a<r.c.c.length;)n=u(Y(r),10),e.c[n.c.p][n.p].a=Lve(e.i),e.c[n.c.p][n.p].d=Ue(e.c[n.c.p][n.p].a),e.c[n.c.p][n.p].b=1}function zln(e,t){var n,r,s,o;for(o=0,r=new C(e);r.a<r.c.c.length;)n=u(Y(r),157),o+=b.Math.pow(Wu(n)*Dl(n)-t,2);return s=b.Math.sqrt(o/(e.c.length-1)),s}function rst(e,t,n,r){var s,o,h;return o=p2n(e,t,n,r),h=_5e(e,o),Ase(e,t,n,r),$re(e.b),hn(),aa(o,new Tje(e)),s=_5e(e,o),Ase(e,n,t,r),$re(e.b),new xa(ct(h),ct(s))}function Gln(e,t,n){var r,s;for(kr(n,"Interactive node placement",1),e.a=u(K(t,(et(),G4)),304),s=new C(t.b);s.a<s.c.c.length;)r=u(Y(s),29),Spn(e,r);ur(n)}function qln(e,t){var n;kr(t,"General Compactor",1),t.n&&e&&yf(t,kf(e),(Pl(),nh)),n=Ron(u(Ft(e,(Jm(),Nhe)),380)),n.hg(e),t.n&&e&&yf(t,kf(e),(Pl(),nh))}function Vln(e,t,n){var r,s;for(nj(e,e.j+t,e.k+n),s=new rr((!e.a&&(e.a=new 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t,n,r,s,o;if(mGe(e.f,e.b.length))for(r=Me(_0t,AI,330,e.b.length*2,0,1),e.b=r,s=r.length-1,n=e.a;n!=e;n=n.Rd())o=u(n,330),t=o.d&s,o.a=r[t],r[t]=o}function ist(e,t){var n,r,s,o;for(o=0,s=u(u(Ii(e.r,t),21),84).Kc();s.Ob();)r=u(s.Pb(),111),o=b.Math.max(o,r.e.a+r.b.rf().a);n=u(_o(e.b,t),124),n.n.b=0,n.a.a=o}function sst(e,t){var n,r,s,o;for(n=0,o=u(u(Ii(e.r,t),21),84).Kc();o.Ob();)s=u(o.Pb(),111),n=b.Math.max(n,s.e.b+s.b.rf().b);r=u(_o(e.b,t),124),r.n.d=0,r.a.b=n}function Kln(e){var t,n;return n=u(K(e,(et(),eu)),21),t=JF(Vwt),n.Hc((mo(),j4))&&Vl(t,Kwt),n.Hc(cS)&&Vl(t,Ywt),n.Hc(oE)&&Vl(t,Uwt),n.Hc(cE)&&Vl(t,Wwt),t}function Yln(e,t){var n;kr(t,"Delaunay triangulation",1),n=new st,Mu(e.i,new Uje(n)),It(Mt(K(e,(H8(),JC)))),e.e?no(e.e,qlt(n)):e.e=qlt(n),ur(t)}function V3e(e){if(e<0)throw J(new Ln("The input must be positive"));return e<iSe.length?Bv(iSe[e]):b.Math.sqrt(_4*e)*(Lan(e,e)/Aye(2.718281828459045,e))}function u7(e,t){var 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t,n,r,s,o,h,d;for(r=0,n=new C(e.b);n.a<n.c.c.length;)for(t=u(Y(n),29),o=new C(t.a);o.a<o.c.c.length;)for(s=u(Y(o),10),s.p=r++,d=new C(s.j);d.a<d.c.c.length;)h=u(Y(d),11),h.p=r++}function Sat(e,t,n,r,s){var o,h,d,v,x;if(t)for(d=t.Kc();d.Ob();)for(h=u(d.Pb(),10),x=E5e(h,(vo(),hu),n).Kc();x.Ob();)v=u(x.Pb(),11),o=u(hc($o(s.f,v)),112),o||(o=new AD(e.d),r.c[r.c.length]=o,uat(o,v,s))}function P4e(e,t){var n,r,s;if(s=v4((Yu(),Oa),e.Tg(),t),s)ho(),u(s,66).Oj()||(s=F6(Po(Oa,s))),r=(n=e.Yg(s),u(n>=0?e._g(n,!0,!0):tw(e,s,!0),153)),u(r,215).ol(t);else throw J(new Ln(i2+t.ne()+RC))}function B4e(e){var t,n;return e>-0x800000000000&&e<0x800000000000?e==0?0:(t=e<0,t&&(e=-e),n=_s(b.Math.floor(b.Math.log(e)/.6931471805599453)),(!t||e!=b.Math.pow(2,n))&&++n,n):Utt(Ou(e))}function tdn(e){var t,n,r,s,o,h,d;for(o=new C0,n=new C(e);n.a<n.c.c.length;)t=u(Y(n),129),h=t.a,d=t.b,!(o.a._b(h)||o.a._b(d))&&(s=h,r=d,h.e.b+h.j.b>2&&d.e.b+d.j.b<=2&&(s=d,r=h),o.a.zc(s,o),s.q=r);return o}function Aat(e,t){var n,r,s;return r=new H0(e),Ho(r,t),Ye(r,(et(),jq),t),Ye(r,(pt(),bs),(wa(),tu)),Ye(r,Bd,(t0(),MV)),T0(r,(zn(),Ls)),n=new $c,rc(n,r),Vs(n,(ht(),Dn)),s=new $c,rc(s,r),Vs(s,$n),r}function Lat(e){switch(e.g){case 0:return new ete((Gv(),IO));case 1:return new AR;case 2:return new ZJ;default:throw J(new Ln("No implementation is available for the crossing minimizer "+(e.f!=null?e.f:""+e.g)))}}function Mat(e,t){var n,r,s,o,h;for(e.c[t.p]=!0,it(e.a,t),h=new C(t.j);h.a<h.c.c.length;)for(o=u(Y(h),11),r=new N1(o.b);nc(r.a)||nc(r.b);)n=u(nc(r.a)?Y(r.a):Y(r.b),17),s=gcn(o,n).i,e.c[s.p]||Mat(e,s)}function Dat(e){var t,n,r,s,o,h,d;for(h=0,n=new rr((!e.a&&(e.a=new at(hs,e,10,11)),e.a));n.e!=n.i.gc();)t=u(pr(n),33),d=t.g,s=t.f,r=b.Math.sqrt(d*d+s*s),h=b.Math.max(r,h),o=Dat(t),h=b.Math.max(o,h);return h}function ol(){ol=pe,m2=new YT("OUTSIDE",0),Z0=new YT("INSIDE",1),JO=new YT("NEXT_TO_PORT_IF_POSSIBLE",2),GS=new YT("ALWAYS_SAME_SIDE",3),zS=new YT("ALWAYS_OTHER_SAME_SIDE",4),$k=new YT("SPACE_EFFICIENT",5)}function Iat(e,t,n){var r,s,o,h,d,v;return r=Otn(e,(s=(pv(),o=new kpe,o),n&&u5e(s,n),s),t),I_(r,D0(t,Id)),KH(t,r),w0n(t,r),I4e(t,r),h=t,d=Rv(h,"ports"),v=new Oqe(e,r),ign(v.a,v.b,d),pie(e,t,r),Ssn(e,t,r),r}function ndn(e){var t,n;return n=-e.a,t=ie(re(Sh,1),Td,25,15,[43,48,48,58,48,48]),n<0&&(t[0]=45,n=-n),t[1]=t[1]+((n/60|0)/10|0)&Ss,t[2]=t[2]+(n/60|0)%10&Ss,t[4]=t[4]+(n%60/10|0)&Ss,t[5]=t[5]+n%10&Ss,jh(t,0,t.length)}function rdn(e){var t;return t=ie(re(Sh,1),Td,25,15,[71,77,84,45,48,48,58,48,48]),e<=0&&(t[3]=43,e=-e),t[4]=t[4]+((e/60|0)/10|0)&Ss,t[5]=t[5]+(e/60|0)%10&Ss,t[7]=t[7]+(e%60/10|0)&Ss,t[8]=t[8]+e%10&Ss,jh(t,0,t.length)}function idn(e){var t,n,r,s,o;if(e==null)return Pu;for(o=new ib(io,"[","]"),n=e,r=0,s=n.length;r<s;++r)t=n[r],o.a?Yr(o.a,o.b):o.a=new Fl(o.d),ZT(o.a,""+d_(t));return o.a?o.e.length==0?o.a.a:o.a.a+(""+o.e):o.c}function R4e(e,t){var n,r,s;for(s=Ei,r=new C(t4(t));r.a<r.c.c.length;)n=u(Y(r),213),n.f&&!e.c[n.c]&&(e.c[n.c]=!0,s=b.Math.min(s,R4e(e,kH(n,t))));return e.i[t.d]=e.j,e.g[t.d]=b.Math.min(s,e.j++),e.g[t.d]}function Oat(e,t){var n,r,s;for(s=u(u(Ii(e.r,t),21),84).Kc();s.Ob();)r=u(s.Pb(),111),r.e.b=(n=r.b,n.Xe((bi(),kl))?n.Hf()==(ht(),An)?-n.rf().b-Ue(ft(n.We(kl))):Ue(ft(n.We(kl))):n.Hf()==(ht(),An)?-n.rf().b:0)}function sdn(e){var t,n,r,s,o,h,d;for(n=q2e(e.e),o=bd(BM(fc(G2e(e.e)),e.d*e.a,e.c*e.b),-.5),t=n.a-o.a,s=n.b-o.b,d=0;d<e.c;d++){for(r=t,h=0;h<e.d;h++)Aon(e.e,new fh(r,s,e.a,e.b))&&hz(e,h,d,!1,!0),r+=e.a;s+=e.b}}function adn(e){var t,n,r;if(It(Mt(Ft(e,(bi(),UO))))){for(r=new st,n=new cr(fr(z0(e).a.Kc(),new V));Vr(n);)t=u(Pr(n),79),ew(t)&&It(Mt(Ft(t,nfe)))&&(r.c[r.c.length]=t);return r}else return hn(),hn(),bo}function tk(e){var t,n;if(n=!1,we(e,204))return n=!0,u(e,204).a;if(!n&&we(e,258)&&(t=u(e,258).a%1==0,t))return n=!0,ct(JUt(u(e,258).a));throw J(new dd("Id must be a string or an integer: '"+e+"'."))}function odn(e,t){var n,r,s,o,h,d;for(o=null,s=new zKe((!e.a&&(e.a=new $ee(e)),e.a));z4e(s);)if(n=u(tz(s),56),r=(h=n.Tg(),d=(b4(h),h.o),!d||!n.mh(d)?null:Ube(lie(d),n.ah(d))),r!=null&&an(r,t)){o=n;break}return o}function Nat(e,t,n){var r,s,o,h,d;if(ql(n,"occurrences"),n==0)return d=u(Ym(B6(e.a),t),14),d?d.gc():0;if(h=u(Ym(B6(e.a),t),14),!h)return 0;if(o=h.gc(),n>=o)h.$b();else for(s=h.Kc(),r=0;r<n;r++)s.Pb(),s.Qb();return o}function cdn(e,t,n){var r,s,o,h;return ql(n,"oldCount"),ql(0,"newCount"),r=u(Ym(B6(e.a),t),14),(r?r.gc():0)==n?(ql(0,"count"),s=(o=u(Ym(B6(e.a),t),14),o?o.gc():0),h=-s,h>0?Npe():h<0&&Nat(e,t,-h),!0):!1}function oI(e){var t,n,r,s,o,h,d;if(d=0,e.b==0){for(h=bit(e,!0),t=0,r=h,s=0,o=r.length;s<o;++s)n=r[s],n>0&&(d+=n,++t);t>1&&(d+=e.c*(t-1))}else d=oze(ytt(Kj(Vi(Rne(e.a),new Ta),new _a)));return d>0?d+e.n.d+e.n.a:0}function cI(e){var t,n,r,s,o,h,d;if(d=0,e.b==0)d=oze(ytt(Kj(Vi(Rne(e.a),new Ms),new 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new YJ;case 3:return new LR;default:throw J(new Ln("No implementation is available for the layout phase "+(e.f!=null?e.f:""+e.g)))}}function F4e(e,t,n,r,s){var o;switch(o=0,s.g){case 1:o=b.Math.max(0,t.b+e.b-(n.b+r));break;case 3:o=b.Math.max(0,-e.b-r);break;case 2:o=b.Math.max(0,-e.a-r);break;case 4:o=b.Math.max(0,t.a+e.a-(n.a+r))}return o}function fdn(e,t,n){var r,s,o,h,d;if(n)for(s=n.a.length,r=new W2(s),d=(r.b-r.a)*r.c<0?(Lp(),C2):new Op(r);d.Ob();)h=u(d.Pb(),19),o=E8(n,h.a),Jxe in o.a||zce in o.a?E2n(e,o,t):Lyn(e,o,t),XWt(u(er(e.b,i7(o)),79))}function j4e(e){var t,n;switch(e.b){case-1:return!0;case 0:return n=e.t,n>1||n==-1?(e.b=-1,!0):(t=$h(e),t&&(ho(),t.Cj()==Idt)?(e.b=-1,!0):(e.b=1,!1));default:case 1:return!1}}function ddn(e,t){var n,r,s,o,h;for(r=(!t.s&&(t.s=new at(ju,t,21,17)),t.s),o=null,s=0,h=r.i;s<h;++s)switch(n=u(Te(r,s),170),Dv(Po(e,n))){case 2:case 3:!o&&(o=new st),o.c[o.c.length]=n}return o||(hn(),hn(),bo)}function $4e(e,t){var n,r,s,o;if(mi(e),e.c!=0||e.a!=123)throw 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n,r,s,o,h,d,v,x,_;for(v=Wd(e.c-e.b&e.a.length-1),x=null,_=null,o=new y_(e);o.a!=o.b;)s=u(gH(o),10),n=(d=u(K(s,(et(),l1)),11),d?d.i:null),r=(h=u(K(s,Yh),11),h?h.i:null),(x!=n||_!=r)&&(vot(v,t),x=n,_=r),v.c[v.c.length]=s;vot(v,t)}function Dot(e){var t,n,r,s,o,h,d;for(t=0,r=new C(e.a);r.a<r.c.c.length;)for(n=u(Y(r),10),o=new cr(fr(js(n).a.Kc(),new V));Vr(o);)s=u(Pr(o),17),e==s.d.i.c&&s.c.j==(ht(),Dn)&&(h=D1(s.c).b,d=D1(s.d).b,t=b.Math.max(t,b.Math.abs(d-h)));return t}function K0n(e,t,n){var r,s,o;kr(n,"Remove overlaps",1),n.n&&t&&yf(n,kf(t),(Pl(),nh)),r=u(Ft(t,(a_(),MS)),33),e.f=r,e.a=tse(u(Ft(t,(Jm(),BO)),293)),s=ft(Ft(t,(bi(),jb))),fT(e,(Sn(s),s)),o=oy(r),klt(e,t,o,n),n.n&&t&&yf(n,kf(t),(Pl(),nh))}function a5e(e,t,n){switch(n.g){case 1:return new Pt(t.a,b.Math.min(e.d.b,t.b));case 2:return new Pt(b.Math.max(e.c.a,t.a),t.b);case 3:return new Pt(t.a,b.Math.max(e.c.b,t.b));case 4:return new Pt(b.Math.min(t.a,e.d.a),t.b)}return new Pt(t.a,t.b)}function Y0n(e,t,n,r){var s,o,h,d,v,x,_,L,P;for(L=r?(ht(),Dn):(ht(),$n),s=!1,v=t[n],x=0,_=v.length;x<_;++x)d=v[x],!A6(u(K(d,(pt(),bs)),98))&&(h=d.e,P=!ac(d,L).dc()&&!!h,P&&(o=r4e(h),e.b=new i4e(o,r?0:o.length-1)),s=s|W2n(e,d,L,P));return s}function hI(e){var t,n,r;for(t=Wd(1+(!e.c&&(e.c=new at(xl,e,9,9)),e.c).i),it(t,(!e.d&&(e.d=new wn(ra,e,8,5)),e.d)),r=new rr((!e.c&&(e.c=new at(xl,e,9,9)),e.c));r.e!=r.i.gc();)n=u(pr(r),118),it(t,(!n.d&&(n.d=new wn(ra,n,8,5)),n.d));return Nr(t),new JT(t)}function z0(e){var t,n,r;for(t=Wd(1+(!e.c&&(e.c=new at(xl,e,9,9)),e.c).i),it(t,(!e.e&&(e.e=new wn(ra,e,7,4)),e.e)),r=new rr((!e.c&&(e.c=new at(xl,e,9,9)),e.c));r.e!=r.i.gc();)n=u(pr(r),118),it(t,(!n.e&&(n.e=new wn(ra,n,7,4)),n.e));return Nr(t),new JT(t)}function X0n(e){var t,n,r,s;if(e==null)return null;if(r=Xc(e,!0),s=tO.length,an(r.substr(r.length-s,s),tO)){if(n=r.length,n==4){if(t=(zr(0,r.length),r.charCodeAt(0)),t==43)return tLe;if(t==45)return R4t}else if(n==3)return tLe}return ry(r)}function Q0n(e){var t,n,r,s;for(t=0,n=0,s=new C(e.j);s.a<s.c.c.length;)if(r=u(Y(s),11),t=Or(Ua(t,zZe(Vi(new vn(null,new mn(r.e,16)),new wQ)))),n=Or(Ua(n,zZe(Vi(new vn(null,new mn(r.g,16)),new mQ)))),t>1||n>1)return 2;return t+n==1?2:0}function Iot(e,t,n){var r,s,o,h,d;for(kr(n,"ELK Force",1),It(Mt(Ft(t,(a1(),Q7e))))||i$((r=new sr((Tm(),new ym(t))),r)),d=cnt(t),bln(d),Ysn(e,u(K(d,X7e),424)),h=kut(e.a,d),o=h.Kc();o.Ob();)s=u(o.Pb(),231),D2n(e.b,s,Kc(n,1/h.gc()));d=Nlt(h),Dlt(d),ur(n)}function Z0n(e,t){var n,r,s,o,h;if(kr(t,"Breaking Point Processor",1),wmn(e),It(Mt(K(e,(pt(),i_e))))){for(s=new C(e.b);s.a<s.c.c.length;)for(r=u(Y(s),29),n=0,h=new C(r.a);h.a<h.c.c.length;)o=u(Y(h),10),o.p=n++;yvn(e),tct(e,!0),tct(e,!1)}ur(t)}function J0n(e,t,n){var r,s,o,h,d,v;for(d=e.c,h=(n.q?n.q:(hn(),hn(),l0)).vc().Kc();h.Ob();)o=u(h.Pb(),42),r=!LT(Vi(new vn(null,new mn(d,16)),new Ke(new gqe(t,o)))).sd((G2(),X7)),r&&(v=o.dd(),we(v,4)&&(s=l3e(v),s!=null&&(v=s)),t.Ye(u(o.cd(),146),v))}function nz(e,t){var n,r,s,o,h;if(t){for(o=we(e.Cb,88)||we(e.Cb,99),h=!o&&we(e.Cb,322),r=new rr((!t.a&&(t.a=new h_(t,Eo,t)),t.a));r.e!=r.i.gc();)if(n=u(pr(r),87),s=dz(n),o?we(s,88):h?we(s,148):s)return s;return o?(on(),sf):(on(),Z1)}else return null}function egn(e,t){var n,r,s,o,h,d;for(kr(t,"Constraints Postprocessor",1),h=0,o=new C(e.b);o.a<o.c.c.length;){for(s=u(Y(o),29),d=0,r=new C(s.a);r.a<r.c.c.length;)n=u(Y(r),10),n.k==(zn(),Hs)&&(Ye(n,(pt(),Yq),ct(h)),Ye(n,Vq,ct(d)),++d);++h}ur(t)}function tgn(e,t,n,r){var s,o,h,d,v,x,_;for(v=new Pt(n,r),da(v,u(K(t,(Tc(),SS)),8)),_=ii(t.b,0);_.b!=_.d.c;)x=u(ri(_),86),Ni(x.e,v),ci(e.b,x);for(d=ii(t.a,0);d.b!=d.d.c;){for(h=u(ri(d),188),o=ii(h.a,0);o.b!=o.d.c;)s=u(ri(o),8),Ni(s,v);ci(e.a,h)}}function o5e(e,t,n){var r,s,o;if(o=v4((Yu(),Oa),e.Tg(),t),o){if(ho(),!u(o,66).Oj()&&(o=F6(Po(Oa,o)),!o))throw J(new Ln(i2+t.ne()+RC));s=(r=e.Yg(o),u(r>=0?e._g(r,!0,!0):tw(e,o,!0),153)),u(s,215).ml(t,n)}else throw J(new Ln(i2+t.ne()+RC))}function ngn(e,t){var n,r,s,o,h;for(n=new st,s=ic(new vn(null,new mn(e,16)),new $Q),o=ic(new vn(null,new mn(e,16)),new HQ),h=Lrn(qnn(Kj(Pgn(ie(re(h3n,1),yt,833,0,[s,o])),new zQ))),r=1;r<h.length;r++)h[r]-h[r-1]>=2*t&&it(n,new vne(h[r-1]+t,h[r]-t));return n}function rgn(e,t,n){kr(n,"Eades radial",1),n.n&&t&&yf(n,kf(t),(Pl(),nh)),e.d=u(Ft(t,(a_(),MS)),33),e.c=Ue(ft(Ft(t,(Jm(),xV)))),e.e=tse(u(Ft(t,BO),293)),e.a=Bon(u(Ft(t,cCe),426)),e.b=ihn(u(Ft(t,oCe),340)),Fln(e),n.n&&t&&yf(n,kf(t),(Pl(),nh))}function ign(e,t,n){var r,s,o,h,d,v,x,_;if(n)for(o=n.a.length,r=new W2(o),d=(r.b-r.a)*r.c<0?(Lp(),C2):new Op(r);d.Ob();)h=u(d.Pb(),19),s=E8(n,h.a),s&&(v=nnn(e,(x=(pv(),_=new xpe,_),t&&c5e(x,t),x),s),I_(v,D0(s,Id)),KH(s,v),I4e(s,v),pie(e,s,v))}function rz(e){var t,n,r,s,o,h;if(!e.j){if(h=new H9,t=XS,o=t.a.zc(e,t),o==null){for(r=new rr(jo(e));r.e!=r.i.gc();)n=u(pr(r),26),s=rz(n),fs(h,s),Br(h,n);t.a.Bc(e)!=null}Km(h),e.j=new B3((u(Te(He((Rp(),En).o),11),18),h.i),h.g),dl(e).b&=-33}return e.j}function sgn(e){var t,n,r,s;if(e==null)return null;if(r=Xc(e,!0),s=tO.length,an(r.substr(r.length-s,s),tO)){if(n=r.length,n==4){if(t=(zr(0,r.length),r.charCodeAt(0)),t==43)return nLe;if(t==45)return F4t}else if(n==3)return nLe}return new hpe(r)}function agn(e){var t,n,r;return n=e.l,n&n-1||(r=e.m,r&r-1)||(t=e.h,t&t-1)||t==0&&r==0&&n==0?-1:t==0&&r==0&&n!=0?Dme(n):t==0&&r!=0&&n==0?Dme(r)+22:t!=0&&r==0&&n==0?Dme(t)+44:-1}function ogn(e,t){var n,r,s,o,h;for(kr(t,"Edge joining",1),n=It(Mt(K(e,(pt(),Jle)))),s=new C(e.b);s.a<s.c.c.length;)for(r=u(Y(s),29),h=new Ea(r.a,0);h.b<h.d.gc();)o=(Qn(h.b<h.d.gc()),u(h.d.Xb(h.c=h.b++),10)),o.k==(zn(),ca)&&(Eae(o,n),Ol(h));ur(t)}function cgn(e,t,n){var r,s;if(d$(e.b),Xd(e.b,(GD(),EV),(OT(),FO)),Xd(e.b,TV,t.g),Xd(e.b,_V,t.a),e.a=mz(e.b,t),kr(n,"Compaction by shrinking a tree",e.a.c.length),t.i.c.length>1)for(s=new C(e.a);s.a<s.c.c.length;)r=u(Y(s),51),r.pf(t,Kc(n,1));ur(n)}function h4(e,t){var n,r,s,o,h;for(s=t.a&e.f,o=null,r=e.b[s];;r=r.b){if(r==t){o?o.b=t.b:e.b[s]=t.b;break}o=r}for(h=t.f&e.f,o=null,n=e.c[h];;n=n.d){if(n==t){o?o.d=t.d:e.c[h]=t.d;break}o=n}t.e?t.e.c=t.c:e.a=t.c,t.c?t.c.e=t.e:e.e=t.e,--e.i,++e.g}function ugn(e){var t,n,r,s,o,h,d,v,x,_;for(n=e.o,t=e.p,h=Ei,s=$a,d=Ei,o=$a,x=0;x<n;++x)for(_=0;_<t;++_)i4(e,x,_)&&(h=b.Math.min(h,x),s=b.Math.max(s,x),d=b.Math.min(d,_),o=b.Math.max(o,_));return v=s-h+1,r=o-d+1,new IYe(ct(h),ct(d),ct(v),ct(r))}function Use(e,t){var n,r,s,o;for(o=new Ea(e,0),n=(Qn(o.b<o.d.gc()),u(o.d.Xb(o.c=o.b++),140));o.b<o.d.gc();)r=(Qn(o.b<o.d.gc()),u(o.d.Xb(o.c=o.b++),140)),s=new vve(r.c,n.d,t),Qn(o.b>0),o.a.Xb(o.c=--o.b),Dm(o,s),Qn(o.b<o.d.gc()),o.d.Xb(o.c=o.b++),s.a=!1,n=r}function Oot(e){var t,n,r,s,o,h;for(s=u(K(e,(et(),Bq)),11),h=new C(e.j);h.a<h.c.c.length;){for(o=u(Y(h),11),r=new C(o.g);r.a<r.c.c.length;)return t=u(Y(r),17),ba(t,s),o;for(n=new C(o.e);n.a<n.c.c.length;)return t=u(Y(n),17),Va(t,s),o}return null}function lgn(e,t,n){var r,s;r=Ou(n.q.getTime()),Mc(r,0)<0?(s=Pg-Or(VD(M8(r),Pg)),s==Pg&&(s=0)):s=Or(VD(r,Pg)),t==1?(s=b.Math.min((s+50)/100|0,9),Bp(e,48+s&Ss)):t==2?(s=b.Math.min((s+5)/10|0,99),e0(e,s,2)):(e0(e,s,3),t>3&&e0(e,0,t-3))}function hgn(e){var t,n,r,s;return je(K(e,(pt(),Iy)))===je((F0(),Wg))?!e.e&&je(K(e,_O))!==je((q8(),wO)):(r=u(K(e,Hle),292),s=It(Mt(K(e,zle)))||je(K(e,dS))===je((V6(),vO)),t=u(K(e,kTe),19).a,n=e.a.c.length,!s&&r!=(q8(),wO)&&(t==0||t>n))}function fgn(e){var t,n;for(n=0;n<e.c.length&&!(tKe((xn(n,e.c.length),u(e.c[n],113)))>0);n++);if(n>0&&n<e.c.length-1)return n;for(t=0;t<e.c.length&&!(tKe((xn(t,e.c.length),u(e.c[t],113)))>0);t++);return t>0&&n<e.c.length-1?t:e.c.length/2|0}function Not(e,t){var n,r;if(t!=e.Cb||e.Db>>16!=6&&t){if(o7(e,t))throw J(new Ln(FC+Zat(e)));r=null,e.Cb&&(r=(n=e.Db>>16,n>=0?N3e(e,r):e.Cb.ih(e,-1-n,null,r))),t&&(r=Q6(t,e,6,r)),r=Ibe(e,t,r),r&&r.Fi()}else e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,1,6,t,t))}function c5e(e,t){var n,r;if(t!=e.Cb||e.Db>>16!=9&&t){if(o7(e,t))throw J(new Ln(FC+Nct(e)));r=null,e.Cb&&(r=(n=e.Db>>16,n>=0?B3e(e,r):e.Cb.ih(e,-1-n,null,r))),t&&(r=Q6(t,e,9,r)),r=Obe(e,t,r),r&&r.Fi()}else e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,1,9,t,t))}function Wse(e,t){var n,r;if(t!=e.Cb||e.Db>>16!=3&&t){if(o7(e,t))throw J(new Ln(FC+Out(e)));r=null,e.Cb&&(r=(n=e.Db>>16,n>=0?F3e(e,r):e.Cb.ih(e,-1-n,null,r))),t&&(r=Q6(t,e,12,r)),r=Dbe(e,t,r),r&&r.Fi()}else e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,1,3,t,t))}function b7(e){var t,n,r,s,o;if(r=$h(e),o=e.j,o==null&&r)return e.$j()?null:r.zj();if(we(r,148)){if(n=r.Aj(),n&&(s=n.Nh(),s!=e.i)){if(t=u(r,148),t.Ej())try{e.g=s.Kh(t,o)}catch(h){if(h=ts(h),we(h,78))e.g=null;else throw J(h)}e.i=s}return e.g}return null}function Pot(e){var t;return t=new st,it(t,new y6(new Pt(e.c,e.d),new Pt(e.c+e.b,e.d))),it(t,new y6(new Pt(e.c,e.d),new Pt(e.c,e.d+e.a))),it(t,new y6(new Pt(e.c+e.b,e.d+e.a),new Pt(e.c+e.b,e.d))),it(t,new y6(new Pt(e.c+e.b,e.d+e.a),new Pt(e.c,e.d+e.a))),t}function Bot(e,t,n,r){var s,o,h;if(h=U3e(t,n),r.c[r.c.length]=t,e.j[h.p]==-1||e.j[h.p]==2||e.a[t.p])return r;for(e.j[h.p]=-1,o=new cr(fr(j0(h).a.Kc(),new V));Vr(o);)if(s=u(Pr(o),17),!(!(!to(s)&&!(!to(s)&&s.c.i.c==s.d.i.c))||s==t))return Bot(e,s,h,r);return r}function dgn(e,t,n){var r,s,o;for(o=t.a.ec().Kc();o.Ob();)s=u(o.Pb(),79),r=u(er(e.b,s),266),!r&&(us(n0(s))==us(Kp(s))?Apn(e,s,n):n0(s)==us(Kp(s))?er(e.c,s)==null&&er(e.b,Kp(s))!=null&&plt(e,s,n,!1):er(e.d,s)==null&&er(e.b,n0(s))!=null&&plt(e,s,n,!0))}function ggn(e,t){var n,r,s,o,h,d,v;for(s=e.Kc();s.Ob();)for(r=u(s.Pb(),10),d=new $c,rc(d,r),Vs(d,(ht(),$n)),Ye(d,(et(),$q),(Mn(),!0)),h=t.Kc();h.Ob();)o=u(h.Pb(),10),v=new $c,rc(v,o),Vs(v,Dn),Ye(v,$q,!0),n=new Iv,Ye(n,$q,!0),Va(n,d),ba(n,v)}function pgn(e,t,n,r){var s,o,h,d;s=Drt(e,t,n),o=Drt(e,n,t),h=u(er(e.c,t),112),d=u(er(e.c,n),112),s<o?new X2((Jf(),jy),h,d,o-s):o<s?new X2((Jf(),jy),d,h,s-o):(s!=0||!(!t.i||!n.i)&&r[t.i.c][n.i.c])&&(new X2((Jf(),jy),h,d,0),new X2(jy,d,h,0))}function Rot(e,t){var n,r,s,o,h,d,v;for(s=0,h=new C(t.a);h.a<h.c.c.length;)for(o=u(Y(h),10),s+=o.o.b+o.d.a+o.d.d+e.e,r=new cr(fr(Xo(o).a.Kc(),new V));Vr(r);)n=u(Pr(r),17),n.c.i.k==(zn(),Jc)&&(v=n.c.i,d=u(K(v,(et(),Mi)),10),s+=d.o.b+d.d.a+d.d.d);return s}function Fot(e,t,n){var r,s,o,h,d,v,x;for(o=new st,x=new as,h=new as,ivn(e,x,h,t),gmn(e,x,h,t,n),v=new C(e);v.a<v.c.c.length;)for(d=u(Y(v),112),s=new C(d.k);s.a<s.c.c.length;)r=u(Y(s),129),(!t||r.c==(Jf(),d2))&&d.g>r.b.g&&(o.c[o.c.length]=r);return o}function v7(){v7=pe,X4=new _M("CANDIDATE_POSITION_LAST_PLACED_RIGHT",0),Nk=new _M("CANDIDATE_POSITION_LAST_PLACED_BELOW",1),IS=new _M("CANDIDATE_POSITION_WHOLE_DRAWING_RIGHT",2),DS=new _M("CANDIDATE_POSITION_WHOLE_DRAWING_BELOW",3),OS=new _M("WHOLE_DRAWING",4)}function bgn(e,t){if(we(t,239))return Uan(e,u(t,33));if(we(t,186))return son(e,u(t,118));if(we(t,354))return wJt(e,u(t,137));if(we(t,352))return zbn(e,u(t,79));if(t)return null;throw J(new Ln(a8e+Yp(new Al(ie(re(Yn,1),yt,1,5,[t])))))}function vgn(e){var t,n,r,s,o,h,d;for(o=new as,s=new C(e.d.a);s.a<s.c.c.length;)r=u(Y(s),121),r.b.a.c.length==0&&ks(o,r,o.c.b,o.c);if(o.b>1)for(t=Ev((n=new z2,++e.b,n),e.d),d=ii(o,0);d.b!=d.d.c;)h=u(ri(d),121),Cf(bf(pf(vf(gf(new Nh,1),0),t),h))}function u5e(e,t){var n,r;if(t!=e.Cb||e.Db>>16!=11&&t){if(o7(e,t))throw J(new Ln(FC+S5e(e)));r=null,e.Cb&&(r=(n=e.Db>>16,n>=0?j3e(e,r):e.Cb.ih(e,-1-n,null,r))),t&&(r=Q6(t,e,10,r)),r=Hbe(e,t,r),r&&r.Fi()}else e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,1,11,t,t))}function wgn(e){var t,n,r,s;for(r=new ob(new dg(e.b).a);r.b;)n=$v(r),s=u(n.cd(),11),t=u(n.dd(),10),Ye(t,(et(),Mi),s),Ye(s,cl,t),Ye(s,kO,(Mn(),!0)),Vs(s,u(K(t,vc),61)),K(t,vc),Ye(s.i,(pt(),bs),(wa(),CE)),u(K(Ya(s.i),eu),21).Fc((mo(),uE))}function mgn(e,t,n){var r,s,o,h,d,v;if(o=0,h=0,e.c)for(v=new C(e.d.i.j);v.a<v.c.c.length;)d=u(Y(v),11),o+=d.e.c.length;else o=1;if(e.d)for(v=new C(e.c.i.j);v.a<v.c.c.length;)d=u(Y(v),11),h+=d.g.c.length;else h=1;return s=_s(WM(h-o)),r=(n+t)/2+(n-t)*(.4*s),r}function ygn(e){c4();var t,n;if(e.Hc((ht(),uc)))throw J(new Ln("Port sides must not contain UNDEFINED"));switch(e.gc()){case 1:return Tq;case 2:return t=e.Hc($n)&&e.Hc(Dn),n=e.Hc(An)&&e.Hc(xr),t||n?Sq:Cq;case 3:return _q;case 4:return Eq;default:return null}}function kgn(e,t,n){var r,s,o,h,d;for(kr(n,"Breaking Point Removing",1),e.a=u(K(t,(pt(),K0)),218),o=new C(t.b);o.a<o.c.c.length;)for(s=u(Y(o),29),d=new C(Pv(s.a));d.a<d.c.c.length;)h=u(Y(d),10),fet(h)&&(r=u(K(h,(et(),yw)),305),!r.d&&Plt(e,r));ur(n)}function Kse(e,t,n){return g4(),$8(e,t)&&$8(e,n)?!1:_ae(new Pt(e.c,e.d),new Pt(e.c+e.b,e.d),t,n)||_ae(new Pt(e.c+e.b,e.d),new Pt(e.c+e.b,e.d+e.a),t,n)||_ae(new Pt(e.c+e.b,e.d+e.a),new Pt(e.c,e.d+e.a),t,n)||_ae(new Pt(e.c,e.d+e.a),new Pt(e.c,e.d),t,n)}function l5e(e,t){var n,r,s,o;if(!e.dc()){for(n=0,r=e.gc();n<r;++n)if(o=Hr(e.Xb(n)),o==null?t==null:an(o.substr(0,3),"!##")?t!=null&&(s=t.length,!an(o.substr(o.length-s,s),t)||o.length!=t.length+3)&&!an(_b,t):an(o,tue)&&!an(_b,t)||an(o,t))return!0}return!1}function xgn(e,t,n,r){var s,o,h,d,v,x;for(h=e.j.c.length,v=Me(g3n,I6e,306,h,0,1),d=0;d<h;d++)o=u(St(e.j,d),11),o.p=d,v[d]=Adn(uot(o),n,r);for(Ugn(e,v,n,t,r),x=new Mr,s=0;s<v.length;s++)v[s]&&Si(x,u(St(e.j,s),11),v[s]);x.f.c+x.g.c!=0&&(Ye(e,(et(),uS),x),Xfn(e,v))}function Egn(e,t,n){var r,s,o;for(s=new C(e.a.b);s.a<s.c.c.length;)if(r=u(Y(s),57),o=U3(r),o&&o.k==(zn(),Ls))switch(u(K(o,(et(),vc)),61).g){case 4:o.n.a=t.a;break;case 2:o.n.a=n.a-(o.o.a+o.d.c);break;case 1:o.n.b=t.b;break;case 3:o.n.b=n.b-(o.o.b+o.d.a)}}function f4(){f4=pe,aV=new k6(U0,0),LO=new k6("NIKOLOV",1),MO=new k6("NIKOLOV_PIXEL",2),p_e=new k6("NIKOLOV_IMPROVED",3),b_e=new k6("NIKOLOV_IMPROVED_PIXEL",4),g_e=new k6("DUMMYNODE_PERCENTAGE",5),v_e=new k6("NODECOUNT_PERCENTAGE",6),oV=new k6("NO_BOUNDARY",7)}function Tgn(e,t,n){var r,s,o,h,d;return s=u(Ft(t,(XH(),fSe)),19),!s&&(s=ct(0)),o=u(Ft(n,fSe),19),!o&&(o=ct(0)),s.a>o.a?-1:s.a<o.a?1:e.a&&(r=Fs(t.j,n.j),r!=0||(r=Fs(t.i,n.i),r!=0))?r:(h=t.g*t.f,d=n.g*n.f,Fs(h,d))}function _gn(e,t){var n,r,s,o,h,d,v,x,_,L;if(++e.e,v=e.d==null?0:e.d.length,t>v){for(_=e.d,e.d=Me(SAe,p8e,63,2*v+4,0,1),o=0;o<v;++o)if(x=_[o],x)for(r=x.g,L=x.i,d=0;d<L;++d)s=u(r[d],133),h=Nbe(e,s.Sh()),n=e.d[h],!n&&(n=e.d[h]=e.uj()),n.Fc(s);return!0}else return!1}function Cgn(e,t,n){var r,s,o,h,d,v;if(s=n,o=s.ak(),G0(e.e,o)){if(o.hi()){for(r=u(e.g,119),h=0;h<e.i;++h)if(d=r[h],Ci(d,s)&&h!=t)throw J(new Ln(YI))}}else for(v=pu(e.e.Tg(),o),r=u(e.g,119),h=0;h<e.i;++h)if(d=r[h],v.rl(d.ak()))throw J(new Ln(eO));G_(e,t,n)}function jot(e,t){var n,r,s,o,h,d;for(n=u(K(t,(et(),Sy)),21),h=u(Ii((Aae(),$i),n),21),d=u(Ii(ei,n),21),o=h.Kc();o.Ob();)if(r=u(o.Pb(),21),!u(Ii(e.b,r),15).dc())return!1;for(s=d.Kc();s.Ob();)if(r=u(s.Pb(),21),!u(Ii(e.b,r),15).dc())return!1;return!0}function Sgn(e,t){var n,r,s,o,h,d;for(kr(t,"Partition postprocessing",1),r=new C(e.b);r.a<r.c.c.length;)for(n=u(Y(r),29),o=new C(n.a);o.a<o.c.c.length;)for(s=u(Y(o),10),d=new C(s.j);d.a<d.c.c.length;)h=u(Y(d),11),It(Mt(K(h,(et(),$q))))&&b_(d);ur(t)}function $ot(e,t){var n,r,s,o,h,d,v,x,_;if(e.a.c.length==1)return qst(u(St(e.a,0),187),t);for(h=Man(e),v=0,x=e.d,o=h,_=e.d,d=(x-o)/2+o;o+1<x;){for(v=0,r=new C(e.a);r.a<r.c.c.length;)n=u(Y(r),187),v+=(s=dC(n,d,!1),s.a);v<t?(_=d,x=d):o=d,d=(x-o)/2+o}return _}function Agn(e){var t,n,r,s,o;return isNaN(e)?(D8(),F8e):e<-9223372036854776e3?(D8(),D0t):e>=9223372036854776e3?(D8(),B8e):(s=!1,e<0&&(s=!0,e=-e),r=0,e>=vb&&(r=_s(e/vb),e-=r*vb),n=0,e>=ck&&(n=_s(e/ck),e-=n*ck),t=_s(e),o=fu(t,n,r),s&&gie(o),o)}function Lgn(e,t){var n,r,s,o;for(n=!t||!e.u.Hc((ol(),Z0)),o=0,s=new C(e.e.Cf());s.a<s.c.c.length;){if(r=u(Y(s),838),r.Hf()==(ht(),uc))throw J(new Ln("Label and node size calculator can only be used with ports that have port sides assigned."));r.vf(o++),San(e,r,n)}}function Mgn(e,t){var n,r,s,o,h;return s=t.Hh(e.a),s&&(r=(!s.b&&(s.b=new Ml((on(),oo),wc,s)),s.b),n=Hr(r1(r,Ha)),n!=null&&(o=n.lastIndexOf("#"),h=o==-1?Tbe(e,t.Aj(),n):o==0?gD(e,null,n.substr(1)):gD(e,n.substr(0,o),n.substr(o+1)),we(h,148)))?u(h,148):null}function Dgn(e,t){var n,r,s,o,h;return r=t.Hh(e.a),r&&(n=(!r.b&&(r.b=new Ml((on(),oo),wc,r)),r.b),o=Hr(r1(n,eue)),o!=null&&(s=o.lastIndexOf("#"),h=s==-1?Tbe(e,t.Aj(),o):s==0?gD(e,null,o.substr(1)):gD(e,o.substr(0,s),o.substr(s+1)),we(h,148)))?u(h,148):null}function h5e(e){var t,n,r,s,o;for(n=new C(e.a.a);n.a<n.c.c.length;){for(t=u(Y(n),307),t.j=null,o=t.a.a.ec().Kc();o.Ob();)r=u(o.Pb(),57),Zf(r.b),(!t.j||r.d.c<t.j.d.c)&&(t.j=r);for(s=t.a.a.ec().Kc();s.Ob();)r=u(s.Pb(),57),r.b.a=r.d.c-t.j.d.c,r.b.b=r.d.d-t.j.d.d}return e}function iz(e){var t,n,r,s,o;for(n=new C(e.a.a);n.a<n.c.c.length;){for(t=u(Y(n),189),t.f=null,o=t.a.a.ec().Kc();o.Ob();)r=u(o.Pb(),81),Zf(r.e),(!t.f||r.g.c<t.f.g.c)&&(t.f=r);for(s=t.a.a.ec().Kc();s.Ob();)r=u(s.Pb(),81),r.e.a=r.g.c-t.f.g.c,r.e.b=r.g.d-t.f.g.d}return e}function Ign(e){var t,n,r;return n=u(e.a,19).a,r=u(e.b,19).a,t=b.Math.max(b.Math.abs(n),b.Math.abs(r)),n<t&&r==-t?new xa(ct(n+1),ct(r)):n==t&&r<t?new xa(ct(n),ct(r+1)):n>=-t&&r==t?new xa(ct(n-1),ct(r)):new xa(ct(n),ct(r-1))}function Hot(){return po(),ie(re(m3n,1),tt,77,0,[MEe,SEe,tS,ele,YEe,uq,mq,eE,WEe,FEe,VEe,J7,KEe,PEe,XEe,kEe,dq,tle,oq,bq,ZEe,pq,xEe,UEe,JEe,vq,QEe,cq,IEe,GEe,zEe,yq,_Ee,aq,hq,TEe,Z7,$Ee,BEe,qEe,nS,AEe,CEe,HEe,REe,fq,wq,EEe,gq,jEe,lq,OEe,DEe,pO,sq,NEe,LEe])}function Ogn(e,t,n){e.d=0,e.b=0,t.k==(zn(),Jc)&&n.k==Jc&&u(K(t,(et(),Mi)),10)==u(K(n,Mi),10)&&(Hre(t).j==(ht(),An)?Cot(e,t,n):Cot(e,n,t)),t.k==Jc&&n.k==ca?Hre(t).j==(ht(),An)?e.d=1:e.b=1:n.k==Jc&&t.k==ca&&(Hre(n).j==(ht(),An)?e.b=1:e.d=1),yun(e,t,n)}function Ngn(e){var t,n,r,s,o,h,d,v,x,_,L;return L=c4e(e),t=e.a,v=t!=null,v&&f8(L,"category",e.a),s=hM(new vm(e.d)),h=!s,h&&(x=new hg,t1(L,"knownOptions",x),n=new S$e(x),Da(new vm(e.d),n)),o=hM(e.g),d=!o,d&&(_=new hg,t1(L,"supportedFeatures",_),r=new A$e(_),Da(e.g,r)),L}function Pgn(e){var t,n,r,s,o,h,d,v,x;for(r=!1,t=336,n=0,o=new HUe(e.length),d=e,v=0,x=d.length;v<x;++v)h=d[v],r=r|(ub(h),!1),s=(Fp(h),h.a),it(o.a,Nr(s)),t&=s.qd(),n=lan(n,s.rd());return u(u(WKe(new vn(null,Sse(new mn((Rm(),e3e(o.a)),16),new te,t,n)),new pee(e)),670),833)}function Bgn(e,t){var n;e.d&&(t.c!=e.e.c||Uin(e.e.b,t.b))&&(it(e.f,e.d),e.a=e.d.c+e.d.b,e.d=null,e.e=null),tWt(t.b)?e.c=t:e.b=t,(t.b==(V8(),vw)&&!t.a||t.b==Ty&&t.a||t.b==O4&&t.a||t.b==_y&&!t.a)&&e.c&&e.b&&(n=new fh(e.a,e.c.d,t.c-e.a,e.b.d-e.c.d),e.d=n,e.e=t)}function fI(e){var t;if(jHe.call(this),this.i=new bZ,this.g=e,this.f=u(e.e&&e.e(),9).length,this.f==0)throw J(new Ln("There must be at least one phase in the phase enumeration."));this.c=(t=u(Qf(this.g),9),new hh(t,u(wf(t,t.length),9),0)),this.a=new Xs,this.b=new Mr}function f5e(e,t){var n,r;if(t!=e.Cb||e.Db>>16!=7&&t){if(o7(e,t))throw J(new Ln(FC+Kst(e)));r=null,e.Cb&&(r=(n=e.Db>>16,n>=0?P3e(e,r):e.Cb.ih(e,-1-n,null,r))),t&&(r=u(t,49).gh(e,1,iN,r)),r=Ove(e,t,r),r&&r.Fi()}else e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,1,7,t,t))}function zot(e,t){var n,r;if(t!=e.Cb||e.Db>>16!=3&&t){if(o7(e,t))throw J(new Ln(FC+rrt(e)));r=null,e.Cb&&(r=(n=e.Db>>16,n>=0?R3e(e,r):e.Cb.ih(e,-1-n,null,r))),t&&(r=u(t,49).gh(e,0,aN,r)),r=Nve(e,t,r),r&&r.Fi()}else e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,1,3,t,t))}function Yse(e,t){d7();var n,r,s,o,h,d,v,x,_;return t.d>e.d&&(d=e,e=t,t=d),t.d<63?_pn(e,t):(h=(e.d&-2)<<4,x=Wwe(e,h),_=Wwe(t,h),r=hae(e,$6(x,h)),s=hae(t,$6(_,h)),v=Yse(x,_),n=Yse(r,s),o=Yse(hae(x,r),hae(s,_)),o=mae(mae(o,v),n),o=$6(o,h),v=$6(v,h<<1),mae(mae(v,o),n))}function Rgn(e,t,n){var r,s,o,h,d;for(h=H_(e,n),d=Me(h0,Bg,10,t.length,0,1),r=0,o=h.Kc();o.Ob();)s=u(o.Pb(),11),It(Mt(K(s,(et(),kO))))&&(d[r++]=u(K(s,cl),10));if(r<t.length)throw J(new Wo("Expected "+t.length+" hierarchical ports, but found only "+r+"."));return d}function Fgn(e,t){var n,r,s,o,h,d;if(!e.tb){for(o=(!e.rb&&(e.rb=new Pm(e,f1,e)),e.rb),d=new w6(o.i),s=new rr(o);s.e!=s.i.gc();)r=u(pr(s),138),h=r.ne(),n=u(h==null?gu(d.f,null,r):Wv(d.g,h,r),138),n&&(h==null?gu(d.f,null,n):Wv(d.g,h,n));e.tb=d}return u(Uc(e.tb,t),138)}function dI(e,t){var n,r,s,o,h;if((e.i==null&&xd(e),e.i).length,!e.p){for(h=new w6((3*e.g.i/2|0)+1),s=new C6(e.g);s.e!=s.i.gc();)r=u(Jie(s),170),o=r.ne(),n=u(o==null?gu(h.f,null,r):Wv(h.g,o,r),170),n&&(o==null?gu(h.f,null,n):Wv(h.g,o,n));e.p=h}return 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4:t=(ht(),An),n=xr}GR(e,u(Tv(Fj(u(Ii(e.k,t),15).Oc(),P4)),113)),zR(e,u(Tv(Rj(u(Ii(e.k,n),15).Oc(),P4)),113))}function $gn(e){var t,n,r,s,o,h;if(s=u(St(e.j,0),11),s.e.c.length+s.g.c.length==0)e.n.a=0;else{for(h=0,r=Pp(B1(ie(re(q1,1),yt,20,0,[new sa(s),new Ar(s)])));Vr(r);)n=u(Pr(r),11),h+=n.i.n.a+n.n.a+n.a.a;t=u(K(e,(pt(),Cw)),8),o=t?t.a:0,e.n.a=h/(s.e.c.length+s.g.c.length)-o}}function Got(e,t){var n,r,s;for(r=new C(t.a);r.a<r.c.c.length;)n=u(Y(r),221),Ane(u(n.b,65),da(fc(u(t.b,65).c),u(t.b,65).a)),s=aut(u(t.b,65).b,u(n.b,65).b),s>1&&(e.a=!0),JQt(u(n.b,65),Ni(fc(u(t.b,65).c),bd(da(fc(u(n.b,65).a),u(t.b,65).a),s))),FXe(e,t),Got(e,n)}function qot(e){var t,n,r,s,o,h,d;for(o=new C(e.a.a);o.a<o.c.c.length;)r=u(Y(o),189),r.e=0,r.d.a.$b();for(s=new C(e.a.a);s.a<s.c.c.length;)for(r=u(Y(s),189),n=r.a.a.ec().Kc();n.Ob();)for(t=u(n.Pb(),81),d=t.f.Kc();d.Ob();)h=u(d.Pb(),81),h.d!=r&&(Gs(r.d,h),++h.d.e)}function Hgn(e){var t,n,r,s,o,h,d,v;for(v=e.j.c.length,n=0,t=v,s=2*v,d=new C(e.j);d.a<d.c.c.length;)switch(h=u(Y(d),11),h.j.g){case 2:case 4:h.p=-1;break;case 1:case 3:r=h.e.c.length,o=h.g.c.length,r>0&&o>0?h.p=t++:r>0?h.p=n++:o>0?h.p=s++:h.p=n++}hn(),aa(e.j,new _L)}function zgn(e){var t,n;n=null,t=u(St(e.g,0),17);do{if(n=t.d.i,ta(n,(et(),Yh)))return u(K(n,Yh),11).i;if(n.k!=(zn(),Hs)&&Vr(new cr(fr(js(n).a.Kc(),new V))))t=u(Pr(new cr(fr(js(n).a.Kc(),new V))),17);else if(n.k!=Hs)return null}while(n&&n.k!=(zn(),Hs));return n}function Ggn(e,t){var n,r,s,o,h,d,v,x,_;for(d=t.j,h=t.g,v=u(St(d,d.c.length-1),113),_=(xn(0,d.c.length),u(d.c[0],113)),x=gse(e,h,v,_),o=1;o<d.c.length;o++)n=(xn(o-1,d.c.length),u(d.c[o-1],113)),s=(xn(o,d.c.length),u(d.c[o],113)),r=gse(e,h,n,s),r>x&&(v=n,_=s,x=r);t.a=_,t.c=v}function qgn(e,t){var n,r;if(r=KM(e.b,t.b),!r)throw J(new Wo("Invalid hitboxes for scanline constraint calculation."));(Qtt(t.b,u(qKt(e.b,t.b),57))||Qtt(t.b,u(GKt(e.b,t.b),57)))&&(Ud(),t.b+""),e.a[t.b.f]=u(hne(e.b,t.b),57),n=u(lne(e.b,t.b),57),n&&(e.a[n.f]=t.b)}function Cf(e){if(!e.a.d||!e.a.e)throw J(new Wo((S0(sgt),sgt.k+" must have a source and target "+(S0(R7e),R7e.k)+" specified.")));if(e.a.d==e.a.e)throw J(new Wo("Network simplex does not support self-loops: "+e.a+" "+e.a.d+" "+e.a.e));return lj(e.a.d.g,e.a),lj(e.a.e.b,e.a),e.a}function Vgn(e,t,n){var r,s,o,h,d,v,x;for(x=new Sp(new Cje(e)),h=ie(re(Upt,1),ift,11,0,[t,n]),d=0,v=h.length;d<v;++d)for(o=h[d],lH(x.a,o,(Mn(),Sb))==null,s=new N1(o.b);nc(s.a)||nc(s.b);)r=u(nc(s.a)?Y(s.a):Y(s.b),17),r.c==r.d||KM(x,o==r.c?r.d:r.c);return Nr(x),new Uu(x)}function Vot(e,t,n){var r,s,o,h,d,v;if(r=0,t.b!=0&&n.b!=0){o=ii(t,0),h=ii(n,0),d=Ue(ft(ri(o))),v=Ue(ft(ri(h))),s=!0;do{if(d>v-e.b&&d<v+e.b)return-1;d>v-e.a&&d<v+e.a&&++r,d<=v&&o.b!=o.d.c?d=Ue(ft(ri(o))):v<=d&&h.b!=h.d.c?v=Ue(ft(ri(h))):s=!1}while(s)}return r}function Ugn(e,t,n,r,s){var o,h,d,v;for(v=(o=u(Qf(ao),9),new hh(o,u(wf(o,o.length),9),0)),d=new C(e.j);d.a<d.c.c.length;)h=u(Y(d),11),t[h.p]&&(syn(h,t[h.p],r),Tf(v,h.j));s?(Ese(e,t,(ht(),$n),2*n,r),Ese(e,t,Dn,2*n,r)):(Ese(e,t,(ht(),An),2*n,r),Ese(e,t,xr,2*n,r))}function Wgn(e){var t,n,r,s,o;if(o=new st,Mu(e.b,new Kn(o)),e.b.c=Me(Yn,yt,1,0,5,1),o.c.length!=0){for(t=(xn(0,o.c.length),u(o.c[0],78)),n=1,r=o.c.length;n<r;++n)s=(xn(n,o.c.length),u(o.c[n],78)),s!=t&&sfn(t,s);if(we(t,60))throw J(u(t,60));if(we(t,289))throw J(u(t,289))}}function Kgn(e,t){var n,r,s,o;for(e=e==null?Pu:(Sn(e),e),n=new xm,o=0,r=0;r<t.length&&(s=e.indexOf("%s",o),s!=-1);)Yr(n,e.substr(o,s-o)),Ec(n,t[r++]),o=s+2;if(Yr(n,e.substr(o)),r<t.length){for(n.a+=" [",Ec(n,t[r++]);r<t.length;)n.a+=io,Ec(n,t[r++]);n.a+="]"}return n.a}function Ygn(e){var t,n,r,s;for(t=0,r=e.length,s=r-4,n=0;n<s;)t=(zr(n+3,e.length),e.charCodeAt(n+3)+(zr(n+2,e.length),31*(e.charCodeAt(n+2)+(zr(n+1,e.length),31*(e.charCodeAt(n+1)+(zr(n,e.length),31*(e.charCodeAt(n)+31*t))))))),t=t|0,n+=4;for(;n<r;)t=t*31+Ma(e,n++);return t=t|0,t}function 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t.$j()?!0:t.Zj()==-2?t==(nk(),Gk)||t==zk||t==Sfe||t==Afe?!0:(s=e.Tg(),Ji(s,t)>=0?!1:(n=v4((Yu(),Oa),s,t),n?(r=n.Zj(),(r>1||r==-1)&&Dv(Po(Oa,n))!=3):!0)):!1}function vpn(e,t,n,r){var s,o,h,d,v;return d=zo(u(Te((!t.b&&(t.b=new wn(mr,t,4,7)),t.b),0),82)),v=zo(u(Te((!t.c&&(t.c=new wn(mr,t,5,8)),t.c),0),82)),us(d)==us(v)||Vm(v,d)?null:(h=nD(t),h==n?r:(o=u(er(e.a,h),10),o&&(s=o.e,s)?s:null))}function wpn(e,t){var n;switch(n=u(K(e,(pt(),Wq)),276),kr(t,"Label side selection ("+n+")",1),n.g){case 0:fot(e,(Ul(),d0));break;case 1:fot(e,(Ul(),b2));break;case 2:Sut(e,(Ul(),d0));break;case 3:Sut(e,(Ul(),b2));break;case 4:cct(e,(Ul(),d0));break;case 5:cct(e,(Ul(),b2))}ur(t)}function b5e(e,t,n){var r,s,o,h,d,v;if(r=rUt(n,e.length),h=e[r],h[0].k==(zn(),Ls))for(o=lze(n,h.length),v=t.j,s=0;s<v.c.length;s++)d=(xn(s,v.c.length),u(v.c[s],11)),(n?d.j==(ht(),$n):d.j==(ht(),Dn))&&It(Mt(K(d,(et(),kO))))&&(gh(v,s,u(K(h[o],(et(),Mi)),11)),o+=n?1:-1)}function mpn(e,t){var n,r,s,o,h;h=new st,n=t;do 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n,r,s,o;for(o=ac(t,(ht(),xr)).Kc();o.Ob();)r=u(o.Pb(),11),n=u(K(r,(et(),cl)),10),n&&Cf(bf(pf(vf(gf(new Nh,0),.1),e.i[t.p].d),e.i[n.p].a));for(s=ac(t,An).Kc();s.Ob();)r=u(s.Pb(),11),n=u(K(r,(et(),cl)),10),n&&Cf(bf(pf(vf(gf(new Nh,0),.1),e.i[n.p].d),e.i[t.p].a))}function Qse(e){var t,n,r,s,o,h;if(!e.c){if(h=new Z5,t=XS,o=t.a.zc(e,t),o==null){for(r=new rr(jc(e));r.e!=r.i.gc();)n=u(pr(r),87),s=dz(n),we(s,88)&&fs(h,Qse(u(s,26))),Br(h,n);t.a.Bc(e)!=null,t.a.gc()==0}Uon(h),Km(h),e.c=new B3((u(Te(He((Rp(),En).o),15),18),h.i),h.g),dl(e).b&=-33}return e.c}function y5e(e){var t;if(e.c!=10)throw J(new $r(Ur((jr(),vG))));switch(t=e.a,t){case 110:t=10;break;case 114:t=13;break;case 116:t=9;break;case 92:case 124:case 46:case 94:case 45:case 63:case 42:case 43:case 123:case 125:case 40:case 41:case 91:case 93:break;default:throw J(new $r(Ur((jr(),xh))))}return t}function mct(e){var t,n,r,s,o;if(e.l==0&&e.m==0&&e.h==0)return"0";if(e.h==MI&&e.m==0&&e.l==0)return"-9223372036854775808";if(e.h>>19)return"-"+mct(U8(e));for(n=e,r="";!(n.l==0&&n.m==0&&n.h==0);){if(s=Mre(Lz),n=Q5e(n,s,!0),t=""+Lze(s2),!(n.l==0&&n.m==0&&n.h==0))for(o=9-t.length;o>0;o--)t="0"+t;r=t+r}return r}function Upn(){if(!Object.create||!Object.getOwnPropertyNames)return!1;var e="__proto__",t=Object.create(null);if(t[e]!==void 0)return!1;var n=Object.getOwnPropertyNames(t);return!(n.length!=0||(t[e]=42,t[e]!==42)||Object.getOwnPropertyNames(t).length==0)}function Wpn(e){var t,n,r,s,o,h,d;for(t=!1,n=0,s=new C(e.d.b);s.a<s.c.c.length;)for(r=u(Y(s),29),r.p=n++,h=new C(r.a);h.a<h.c.c.length;)o=u(Y(h),10),!t&&!T8(j0(o))&&(t=!0);d=Ui((wo(),f0),ie(re(RS,1),tt,103,0,[Xh,Df])),t||(Tf(d,X0),Tf(d,Y0)),e.a=new wet(d),sl(e.f),sl(e.b),sl(e.e),sl(e.g)}function Kpn(e,t,n){var r,s,o,h,d,v,x,_,L;for(r=n.c,s=n.d,d=D1(t.c),v=D1(t.d),r==t.c?(d=a5e(e,d,s),v=Cst(t.d)):(d=Cst(t.c),v=a5e(e,v,s)),x=new 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hexadecimal"))}}function Qpn(e,t,n){var r,s,o,h;for(kr(n,"Processor order nodes",2),e.a=Ue(ft(K(t,(nw(),V_e)))),s=new as,h=ii(t.b,0);h.b!=h.d.c;)o=u(ri(h),86),It(Mt(K(o,(Tc(),$y))))&&ks(s,o,s.c.b,s.c);r=(Qn(s.b!=0),u(s.a.a.c,86)),$ut(e,r),!n.b&&tie(n,1),C5e(e,r,0-Ue(ft(K(r,(Tc(),bV))))/2,0),!n.b&&tie(n,1),ur(n)}function uz(){uz=pe,P7e=new O3("SPIRAL",0),D7e=new O3("LINE_BY_LINE",1),I7e=new O3("MANHATTAN",2),M7e=new O3("JITTER",3),Mue=new O3("QUADRANTS_LINE_BY_LINE",4),N7e=new O3("QUADRANTS_MANHATTAN",5),O7e=new O3("QUADRANTS_JITTER",6),L7e=new O3("COMBINE_LINE_BY_LINE_MANHATTAN",7),A7e=new O3("COMBINE_JITTER_MANHATTAN",8)}function xct(e,t,n,r){var s,o,h,d,v,x;for(v=mse(e,n),x=mse(t,n),s=!1;v&&x&&(r||Wun(v,x,n));)h=mse(v,n),d=mse(x,n),bD(t),bD(e),o=v.c,Eae(v,!1),Eae(x,!1),n?(ey(t,x.p,o),t.p=x.p,ey(e,v.p+1,o),e.p=v.p):(ey(e,v.p,o),e.p=v.p,ey(t,x.p+1,o),t.p=x.p),No(v,null),No(x,null),v=h,x=d,s=!0;return s}function Zpn(e,t,n,r){var s,o,h,d,v;for(s=!1,o=!1,d=new C(r.j);d.a<d.c.c.length;)h=u(Y(d),11),je(K(h,(et(),Mi)))===je(n)&&(h.g.c.length==0?h.e.c.length==0||(s=!0):o=!0);return v=0,s&&s^o?v=n.j==(ht(),An)?-e.e[r.c.p][r.p]:t-e.e[r.c.p][r.p]:o&&s^o?v=e.e[r.c.p][r.p]+1:s&&o&&(v=n.j==(ht(),An)?0:t/2),v}function Zse(e,t,n,r,s,o,h,d){var v,x,_;for(v=0,t!=null&&(v^=Ig(t.toLowerCase())),n!=null&&(v^=Ig(n)),r!=null&&(v^=Ig(r)),h!=null&&(v^=Ig(h)),d!=null&&(v^=Ig(d)),x=0,_=o.length;x<_;x++)v^=Ig(o[x]);e?v|=256:v&=-257,s?v|=16:v&=-17,this.f=v,this.i=t==null?null:(Sn(t),t),this.a=n,this.d=r,this.j=o,this.g=h,this.e=d}function E5e(e,t,n){var r,s;switch(s=null,t.g){case 1:s=(Ku(),bEe);break;case 2:s=(Ku(),wEe)}switch(r=null,n.g){case 1:r=(Ku(),vEe);break;case 2:r=(Ku(),pEe);break;case 3:r=(Ku(),mEe);break;case 4:r=(Ku(),yEe)}return s&&r?L6(e.j,new aT(new Al(ie(re(r3n,1),yt,169,0,[u(Nr(s),169),u(Nr(r),169)])))):(hn(),hn(),bo)}function Jpn(e){var t,n,r;switch(t=u(K(e,(pt(),fE)),8),Ye(e,fE,new Pt(t.b,t.a)),u(K(e,Bd),248).g){case 1:Ye(e,Bd,(t0(),DV));break;case 2:Ye(e,Bd,(t0(),LV));break;case 3:Ye(e,Bd,(t0(),jO));break;case 4:Ye(e,Bd,(t0(),$O))}(e.q?e.q:(hn(),hn(),l0))._b(By)&&(n=u(K(e,By),8),r=n.a,n.a=n.b,n.b=r)}function Ect(e,t,n,r,s,o){if(this.b=n,this.d=s,e>=t.length)throw J(new Do("Greedy SwitchDecider: Free layer not in graph."));this.c=t[e],this.e=new zM(r),sie(this.e,this.c,(ht(),Dn)),this.i=new zM(r),sie(this.i,this.c,$n),this.f=new wKe(this.c),this.a=!o&&s.i&&!s.s&&this.c[0].k==(zn(),Ls),this.a&&p1n(this,e,t.length)}function Tct(e,t){var n,r,s,o,h,d;o=!e.B.Hc((wl(),nN)),h=e.B.Hc(gfe),e.a=new mrt(h,o,e.c),e.n&&hwe(e.a.n,e.n),Zee(e.g,(n1(),lu),e.a),t||(r=new W_(1,o,e.c),r.n.a=e.k,M6(e.p,(ht(),An),r),s=new W_(1,o,e.c),s.n.d=e.k,M6(e.p,xr,s),d=new W_(0,o,e.c),d.n.c=e.k,M6(e.p,Dn,d),n=new W_(0,o,e.c),n.n.b=e.k,M6(e.p,$n,n))}function e2n(e){var t,n,r;switch(t=u(K(e.d,(pt(),K0)),218),t.g){case 2:n=Myn(e);break;case 3:n=(r=new st,ms(Vi(Cu(ic(ic(new vn(null,new mn(e.d.b,16)),new TX),new _X),new M9),new gX),new Nee(r)),r);break;default:throw J(new Wo("Compaction not supported for "+t+" edges."))}Zvn(e,n),Da(new vm(e.g),new Iee(e))}function t2n(e,t){var n;return n=new b3,t&&Ho(n,u(er(e.a,iN),94)),we(t,470)&&Ho(n,u(er(e.a,sN),94)),we(t,354)?(Ho(n,u(er(e.a,Jo),94)),n):(we(t,82)&&Ho(n,u(er(e.a,mr),94)),we(t,239)?(Ho(n,u(er(e.a,hs),94)),n):we(t,186)?(Ho(n,u(er(e.a,xl),94)),n):(we(t,352)&&Ho(n,u(er(e.a,ra),94)),n))}function a1(){a1=pe,Q7=new fo((bi(),BV),ct(1)),eq=new fo(jb,80),ppt=new fo(GSe,5),apt=new fo(Bk,N7),dpt=new fo(ufe,ct(1)),gpt=new fo(lfe,(Mn(),!0)),Z7e=new kv(50),hpt=new fo(Fb,Z7e),Y7e=OV,J7e=BS,opt=new fo(efe,!1),Q7e=KO,lpt=p2,upt=Rb,cpt=J4,fpt=Hy,X7e=(m4e(),Jgt),zue=rpt,JG=Zgt,Hue=ept,eEe=npt}function n2n(e){var t,n,r,s,o,h,d,v;for(v=new TZe,d=new C(e.a);d.a<d.c.c.length;)if(h=u(Y(d),10),h.k!=(zn(),Ls)){for(n0n(v,h,new Fa),o=new cr(fr(js(h).a.Kc(),new V));Vr(o);)if(s=u(Pr(o),17),!(s.c.i.k==Ls||s.d.i.k==Ls))for(r=ii(s.a,0);r.b!=r.d.c;)n=u(ri(r),8),t=n,eC(v,new r8(t.a,t.b))}return v}function Jse(){Jse=pe,KCe=new Zi(Ece),WCe=(OT(),FO),UCe=new dn(Cce,WCe),VCe=(OD(),AV),Oyt=new dn(Fxe,VCe),qCe=(rI(),zhe),Iyt=new dn(jxe,qCe),Lyt=new dn(Tce,null),GCe=(lD(),CV),Dyt=new dn(_ce,GCe),zCe=(_F(),jhe),_yt=new dn($xe,zCe),Cyt=new dn(Hxe,(Mn(),!1)),Syt=new dn(zxe,ct(64)),Ayt=new dn(Gxe,!0),Myt=Hhe}function _ct(e){var t,n,r,s,o,h;if(e.a==null)if(e.a=Me(El,c0,25,e.c.b.c.length,16,1),e.a[0]=!1,ta(e.c,(pt(),the)))for(r=u(K(e.c,the),15),n=r.Kc();n.Ob();)t=u(n.Pb(),19).a,t>0&&t<e.a.length&&(e.a[t]=!1);else for(h=new C(e.c.b),h.a<h.c.c.length&&Y(h),s=1;h.a<h.c.c.length;)o=u(Y(h),29),e.a[s++]=R0n(o)}function Cct(e,t){var n,r,s,o;switch(s=e.b,t){case 1:{e.b|=1,e.b|=4,e.b|=8;break}case 2:{e.b|=2,e.b|=4,e.b|=8;break}case 4:{e.b|=1,e.b|=2,e.b|=4,e.b|=8;break}case 3:{e.b|=16,e.b|=8;break}case 0:{e.b|=32,e.b|=16,e.b|=8,e.b|=1,e.b|=2,e.b|=4;break}}if(e.b!=s&&e.c)for(r=new rr(e.c);r.e!=r.i.gc();)o=u(pr(r),473),n=dl(o),iy(n,t)}function Sct(e,t,n,r){var s,o,h,d,v,x,_,L,P,z,q;for(s=!1,h=t,d=0,v=h.length;d<v;++d)o=h[d],It((Mn(),!!o.e))&&!u(St(e.b,o.e.p),214).s&&(s=s|(x=o.e,_=u(St(e.b,x.p),214),L=_.e,P=lze(n,L.length),z=L[P][0],z.k==(zn(),Ls)?L[P]=Rgn(o,L[P],n?(ht(),Dn):(ht(),$n)):_.c.Tf(L,n),q=gI(e,_,n,r),b5e(_.e,_.o,n),q));return s}function Act(e,t){var n,r,s,o,h;for(o=(!t.a&&(t.a=new at(hs,t,10,11)),t.a).i,s=new rr((!t.a&&(t.a=new at(hs,t,10,11)),t.a));s.e!=s.i.gc();)r=u(pr(s),33),je(Ft(r,(bi(),Q4)))!==je((F0(),jS))&&(h=u(Ft(t,xE),149),n=u(Ft(r,xE),149),(h==n||h&&Vwe(h,n))&&(!r.a&&(r.a=new at(hs,r,10,11)),r.a).i!=0&&(o+=Act(e,r)));return o}function r2n(e){var t,n,r,s,o,h,d;for(r=0,d=0,h=new C(e.d);h.a<h.c.c.length;)o=u(Y(h),101),s=u(zl(Vi(new vn(null,new mn(o.j,16)),new I9),eb(new vt,new Dt,new Hn,ie(re(yl,1),tt,132,0,[(F1(),Ql)]))),15),n=null,r<=d?(n=(ht(),An),r+=s.gc()):d<r&&(n=(ht(),xr),d+=s.gc()),t=n,ms(Cu(s.Oc(),new EB),new Ree(t))}function i2n(e){var t,n,r,s,o,h,d,v;for(e.b=new qat(new Al((ht(),ie(re(ao,1),Dc,61,0,[uc,An,$n,xr,Dn]))),new Al((qv(),ie(re(sle,1),tt,361,0,[mw,u2,ww])))),h=ie(re(ao,1),Dc,61,0,[uc,An,$n,xr,Dn]),d=0,v=h.length;d<v;++d)for(o=h[d],n=ie(re(sle,1),tt,361,0,[mw,u2,ww]),r=0,s=n.length;r<s;++r)t=n[r],shn(e.b,o,t,new st)}function Lct(e,t){var n,r,s,o,h,d,v,x,_,L;if(h=u(u(Ii(e.r,t),21),84),d=e.u.Hc((ol(),m2)),n=e.u.Hc(GS),r=e.u.Hc(zS),x=e.u.Hc($k),L=e.B.Hc((wl(),GV)),_=!n&&!r&&(x||h.gc()==2),spn(e,t),s=null,v=null,d){for(o=h.Kc(),s=u(o.Pb(),111),v=s;o.Ob();)v=u(o.Pb(),111);s.d.b=0,v.d.c=0,_&&!s.a&&(s.d.c=0)}L&&(rfn(h),d&&(s.d.b=0,v.d.c=0))}function Mct(e,t){var n,r,s,o,h,d,v,x,_,L;if(h=u(u(Ii(e.r,t),21),84),d=e.u.Hc((ol(),m2)),n=e.u.Hc(GS),r=e.u.Hc(zS),v=e.u.Hc($k),L=e.B.Hc((wl(),GV)),x=!n&&!r&&(v||h.gc()==2),ybn(e,t),_=null,s=null,d){for(o=h.Kc(),_=u(o.Pb(),111),s=_;o.Ob();)s=u(o.Pb(),111);_.d.d=0,s.d.a=0,x&&!_.a&&(_.d.a=0)}L&&(ifn(h),d&&(_.d.d=0,s.d.a=0))}function Dct(e,t,n){var r,s,o,h,d,v,x,_;if(s=t.k,t.p>=0)return!1;if(t.p=n.b,it(n.e,t),s==(zn(),ca)||s==Jc){for(h=new C(t.j);h.a<h.c.c.length;)for(o=u(Y(h),11),_=(r=new C(new Ar(o).a.g),new We(r));nc(_.a);)if(x=u(Y(_.a),17).d,d=x.i,v=d.k,t.c!=d.c&&(v==ca||v==Jc)&&Dct(e,d,n))return!0}return!0}function lz(e){var t;return e.Db&64?G4e(e):(t=new Ph(G4e(e)),t.a+=" (changeable: ",vg(t,(e.Bb&Sf)!=0),t.a+=", volatile: ",vg(t,(e.Bb&ky)!=0),t.a+=", transient: ",vg(t,(e.Bb&dy)!=0),t.a+=", defaultValueLiteral: ",To(t,e.j),t.a+=", unsettable: ",vg(t,(e.Bb&Zu)!=0),t.a+=", derived: ",vg(t,(e.Bb&Ed)!=0),t.a+=")",t.a)}function s2n(e){var t,n,r,s,o,h,d,v,x,_,L,P;for(s=ugn(e.d),h=u(K(e.b,(K_(),V7e)),116),d=h.b+h.c,v=h.d+h.a,_=s.d.a*e.e+d,x=s.b.a*e.f+v,zge(e.b,new Pt(_,x)),P=new C(e.g);P.a<P.c.c.length;)L=u(Y(P),562),t=L.g-s.a.a,n=L.i-s.c.a,r=Ni(mKt(new Pt(t,n),L.a,L.b),bd(BM(fc(G2e(L.e)),L.d*L.a,L.c*L.b),-.5)),o=q2e(L.e),YVt(L.e,da(r,o))}function a2n(e,t,n,r){var s,o,h,d,v;for(v=Me(pa,Qe,104,(ht(),ie(re(ao,1),Dc,61,0,[uc,An,$n,xr,Dn])).length,0,2),o=ie(re(ao,1),Dc,61,0,[uc,An,$n,xr,Dn]),h=0,d=o.length;h<d;++h)s=o[h],v[s.g]=Me(pa,Ao,25,e.c[s.g],15,1);return Zit(v,e,An),Zit(v,e,xr),fse(v,e,An,t,n,r),fse(v,e,$n,t,n,r),fse(v,e,xr,t,n,r),fse(v,e,Dn,t,n,r),v}function o2n(e,t,n){if(Il(e.a,t)){if(_0(u(er(e.a,t),53),n))return 1}else Si(e.a,t,new Ys);if(Il(e.a,n)){if(_0(u(er(e.a,n),53),t))return-1}else Si(e.a,n,new Ys);if(Il(e.b,t)){if(_0(u(er(e.b,t),53),n))return-1}else Si(e.b,t,new Ys);if(Il(e.b,n)){if(_0(u(er(e.b,n),53),t))return 1}else Si(e.b,n,new Ys);return 0}function T5e(e,t,n,r){var s,o,h,d,v,x;if(n==null){for(s=u(e.g,119),d=0;d<e.i;++d)if(h=s[d],h.ak()==t)return Xa(e,h,r)}return o=(ho(),u(t,66).Oj()?u(n,72):Jd(t,n)),Ll(e.e)?(x=!qD(e,t),r=ou(e,o,r),v=t.$j()?jp(e,3,t,null,n,y7(e,t,n,we(t,99)&&(u(t,18).Bb&so)!=0),x):jp(e,1,t,t.zj(),n,-1,x),r?r.Ei(v):r=v):r=ou(e,o,r),r}function c2n(e){var t,n,r,s,o,h;e.q==(wa(),p0)||e.q==tu||(s=e.f.n.d+Tj(u(_o(e.b,(ht(),An)),124))+e.c,t=e.f.n.a+Tj(u(_o(e.b,xr),124))+e.c,r=u(_o(e.b,$n),124),h=u(_o(e.b,Dn),124),o=b.Math.max(0,r.n.d-s),o=b.Math.max(o,h.n.d-s),n=b.Math.max(0,r.n.a-t),n=b.Math.max(n,h.n.a-t),r.n.d=o,h.n.d=o,r.n.a=n,h.n.a=n)}function u2n(e,t){var n,r,s,o,h,d,v,x,_,L,P;for(kr(t,"Restoring reversed edges",1),v=new C(e.b);v.a<v.c.c.length;)for(d=u(Y(v),29),_=new C(d.a);_.a<_.c.c.length;)for(x=u(Y(_),10),P=new C(x.j);P.a<P.c.c.length;)for(L=u(Y(P),11),h=kd(L.g),r=h,s=0,o=r.length;s<o;++s)n=r[s],It(Mt(K(n,(et(),W1))))&&iw(n,!1);ur(t)}function Ict(){this.b=new Z2,this.d=new Z2,this.e=new Z2,this.c=new Z2,this.a=new Mr,this.f=new Mr,V3(na,new yZ,new kZ),V3(sSe,new lc,new iu),V3(dEe,new Ki,new PL),V3(gEe,new DZ,new IZ),V3(O3t,new K5,new fm),V3(i3n,new xZ,new EZ),V3(o3n,new TZ,new _Z),V3(s3n,new CZ,new SZ),V3(a3n,new AZ,new Fc),V3(l3n,new LZ,new F9)}function Oct(e){var t,n,r,s,o,h;return o=0,t=$h(e),t.Bj()&&(o|=4),e.Bb&Zu&&(o|=2),we(e,99)?(n=u(e,18),s=go(n),n.Bb&_c&&(o|=32),s&&(Jn(Fm(s)),o|=8,h=s.t,(h>1||h==-1)&&(o|=16),s.Bb&_c&&(o|=64)),n.Bb&so&&(o|=ky),o|=Sf):we(t,457)?o|=512:(r=t.Bj(),r&&r.i&1&&(o|=256)),e.Bb&512&&(o|=128),o}function oC(e,t){var n,r,s,o,h;for(e=e==null?Pu:(Sn(e),e),s=0;s<t.length;s++)t[s]=q0n(t[s]);for(n=new xm,h=0,r=0;r<t.length&&(o=e.indexOf("%s",h),o!=-1);)n.a+=""+jl(e==null?Pu:(Sn(e),e),h,o),Ec(n,t[r++]),h=o+2;if(dQe(n,e,h,e.length),r<t.length){for(n.a+=" [",Ec(n,t[r++]);r<t.length;)n.a+=io,Ec(n,t[r++]);n.a+="]"}return n.a}function l2n(e){var t,n,r,s,o;for(o=new su(e.a.c.length),s=new C(e.a);s.a<s.c.c.length;){switch(r=u(Y(s),10),n=u(K(r,(pt(),vu)),163),t=null,n.g){case 1:case 2:t=(sb(),F4);break;case 3:case 4:t=(sb(),aE)}t?(Ye(r,(et(),Fq),(sb(),F4)),t==aE?sz(r,n,(vo(),ul)):t==F4&&sz(r,n,(vo(),hu))):o.c[o.c.length]=r}return o}function _5e(e,t){var n,r,s,o,h,d,v;for(n=0,v=new C(t);v.a<v.c.c.length;){for(d=u(Y(v),11),kye(e.b,e.d[d.p]),h=0,s=new N1(d.b);nc(s.a)||nc(s.b);)r=u(nc(s.a)?Y(s.a):Y(s.b),17),pYe(r)?(o=Wpe(e,d==r.c?r.d:r.c),o>e.d[d.p]&&(n+=Gwe(e.b,o),$p(e.a,ct(o)))):++h;for(n+=e.b.d*h;!TT(e.a);)yme(e.b,u(I6(e.a),19).a)}return n}function h2n(e,t){var n;return e.f==Cfe?(n=Dv(Po((Yu(),Oa),t)),e.e?n==4&&t!=(nk(),Gk)&&t!=(nk(),zk)&&t!=(nk(),Sfe)&&t!=(nk(),Afe):n==2):e.d&&(e.d.Hc(t)||e.d.Hc(F6(Po((Yu(),Oa),t)))||e.d.Hc(v4((Yu(),Oa),e.b,t)))?!0:e.f&&l5e((Yu(),e.f),QM(Po(Oa,t)))?(n=Dv(Po(Oa,t)),e.e?n==4:n==2):!1}function f2n(e,t,n,r){var s,o,h,d,v,x,_,L;return h=u(Ft(n,(bi(),Rk)),8),v=h.a,_=h.b+e,s=b.Math.atan2(_,v),s<0&&(s+=_4),s+=t,s>_4&&(s-=_4),d=u(Ft(r,Rk),8),x=d.a,L=d.b+e,o=b.Math.atan2(L,x),o<0&&(o+=_4),o+=t,o>_4&&(o-=_4),S1(),Ef(1e-10),b.Math.abs(s-o)<=1e-10||s==o||isNaN(s)&&isNaN(o)?0:s<o?-1:s>o?1:yv(isNaN(s),isNaN(o))}function eae(e){var t,n,r,s,o,h,d;for(d=new Mr,r=new C(e.a.b);r.a<r.c.c.length;)t=u(Y(r),57),Si(d,t,new st);for(s=new 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0:case 2:e.c+=s.rf().a+h+o.a+h;break;case 4:e.c-=h+o.a+h+t.rf().a;break;case 1:e.c+=s.rf().a+h,e.d-=h+o.b+h+t.rf().b;break;case 3:e.c+=s.rf().a+h,e.d+=s.rf().b+h+o.b+h}}function jct(e,t){var n,r;for(this.b=new st,this.e=new st,this.a=e,this.d=t,Ycn(this),icn(this),this.b.dc()?this.c=e.c.p:this.c=u(this.b.Xb(0),10).c.p,this.e.c.length==0?this.f=e.c.p:this.f=u(St(this.e,this.e.c.length-1),10).c.p,r=u(K(e,(et(),EO)),15).Kc();r.Ob();)if(n=u(r.Pb(),70),ta(n,(pt(),Uq))){this.d=u(K(n,Uq),227);break}}function w7(e,t,n){var r,s,o,h,d,v,x,_;for(r=u(er(e.a,t),53),o=u(er(e.a,n),53),s=u(er(e.e,t),53),h=u(er(e.e,n),53),r.a.zc(n,r),h.a.zc(t,h),_=o.a.ec().Kc();_.Ob();)x=u(_.Pb(),10),r.a.zc(x,r),Gs(u(er(e.e,x),53),t),no(u(er(e.e,x),53),s);for(v=s.a.ec().Kc();v.Ob();)d=u(v.Pb(),10),h.a.zc(d,h),Gs(u(er(e.a,d),53),n),no(u(er(e.a,d),53),o)}function bI(e,t,n){var r,s,o,h,d,v,x,_;for(r=u(er(e.a,t),53),o=u(er(e.a,n),53),s=u(er(e.b,t),53),h=u(er(e.b,n),53),r.a.zc(n,r),h.a.zc(t,h),_=o.a.ec().Kc();_.Ob();)x=u(_.Pb(),10),r.a.zc(x,r),Gs(u(er(e.b,x),53),t),no(u(er(e.b,x),53),s);for(v=s.a.ec().Kc();v.Ob();)d=u(v.Pb(),10),h.a.zc(d,h),Gs(u(er(e.a,d),53),n),no(u(er(e.a,d),53),o)}function x2n(e,t){var n,r,s;switch(kr(t,"Breaking Point Insertion",1),r=new X4e(e),u(K(e,(pt(),ehe)),337).g){case 2:s=new MB;case 0:s=new SB;break;default:s=new DB}if(n=s.Vf(e,r),It(Mt(K(e,r_e)))&&(n=dwn(e,n)),!s.Wf()&&ta(e,tV))switch(u(K(e,tV),338).g){case 2:n=Tat(r,n);break;case 1:n=Est(r,n)}if(n.dc()){ur(t);return}dyn(e,n),ur(t)}function E2n(e,t,n){var r,s,o,h,d,v,x,_,L,P;if(_=null,P=t,L=GQe(e,jQe(n),P),I_(L,D0(P,Id)),h=Rv(P,Jxe),r=new Tqe(e,L),X1n(r.a,r.b,h),d=Rv(P,zce),s=new _qe(e,L),Q1n(s.a,s.b,d),(!L.b&&(L.b=new wn(mr,L,4,7)),L.b).i==0||(!L.c&&(L.c=new wn(mr,L,5,8)),L.c).i==0)throw o=D0(P,Id),v=R1t+o,x=v+z7,J(new dd(x));return KH(P,L),myn(e,P,L),_=pie(e,P,L),_}function 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r,s,o,h,d,v,x,_;if(r=n.gc(),r==0)return!1;if(e.ej())if(x=e.fj(),Cse(e,t,n),h=r==1?e.Zi(3,null,n.Kc().Pb(),t,x):e.Zi(5,null,n,t,x),e.bj()){for(d=r<100?null:new _p(r),o=t+r,s=t;s<o;++s)_=e.g[s],d=e.cj(_,d),d=e.jj(_,d);d?(d.Ei(h),d.Fi()):e.$i(h)}else e.$i(h);else if(Cse(e,t,n),e.bj()){for(d=r<100?null:new _p(r),o=t+r,s=t;s<o;++s)v=e.g[s],d=e.cj(v,d);d&&d.Fi()}return!0}function H5e(e,t,n,r){var s,o,h,d,v;for(h=new C(e.k);h.a<h.c.c.length;)s=u(Y(h),129),(!r||s.c==(Jf(),d2))&&(v=s.b,v.g<0&&s.d>0&&(tM(v,v.d-s.d),s.c==(Jf(),d2)&&Wge(v,v.a-s.d),v.d<=0&&v.i>0&&ks(t,v,t.c.b,t.c)));for(o=new C(e.f);o.a<o.c.c.length;)s=u(Y(o),129),(!r||s.c==(Jf(),d2))&&(d=s.a,d.g<0&&s.d>0&&(hT(d,d.i-s.d),s.c==(Jf(),d2)&&sv(d,d.b-s.d),d.i<=0&&d.d>0&&ks(n,d,n.c.b,n.c)))}function Pbn(e,t,n){var r,s,o,h,d,v,x,_;for(kr(n,"Processor compute fanout",1),sl(e.b),sl(e.a),d=null,o=ii(t.b,0);!d&&o.b!=o.d.c;)x=u(ri(o),86),It(Mt(K(x,(Tc(),$y))))&&(d=x);for(v=new 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t,n;if(t=Hr(Ft(e,(bi(),PS))),!Ptt(t,e)&&!J2(e,xE)&&((!e.a&&(e.a=new at(hs,e,10,11)),e.a).i!=0||It(Mt(Ft(e,UO)))))if(t==null||ny(t).length==0){if(!Ptt(qn,e))throw n=Yr(Yr(new Fl("Unable to load default layout algorithm "),qn)," for unconfigured node "),yz(e,n),J(new M3(n.a))}else throw n=Yr(Yr(new Fl("Layout algorithm '"),t),"' not found for "),yz(e,n),J(new M3(n.a))}function uae(e){var t,n,r,s,o,h,d,v,x,_,L,P,z;if(n=e.i,t=e.n,e.b==0)for(z=n.c+t.b,P=n.b-t.b-t.c,h=e.a,v=0,_=h.length;v<_;++v)s=h[v],Nj(s,z,P);else r=vit(e,!1),Nj(e.a[0],n.c+t.b,r[0]),Nj(e.a[2],n.c+n.b-t.c-r[2],r[2]),L=n.b-t.b-t.c,r[0]>0&&(L-=r[0]+e.c,r[0]+=e.c),r[2]>0&&(L-=r[2]+e.c),r[1]=b.Math.max(r[1],L),Nj(e.a[1],n.c+t.b+r[0]-(r[1]-L)/2,r[1]);for(o=e.a,d=0,x=o.length;d<x;++d)s=o[d],we(s,326)&&u(s,326).Te()}function jbn(e){var t,n,r,s,o,h,d,v,x,_,L;for(L=new _Q,L.d=0,h=new C(e.b);h.a<h.c.c.length;)o=u(Y(h),29),L.d+=o.a.c.length;for(r=0,s=0,L.a=Me(Lr,Jr,25,e.b.c.length,15,1),x=0,_=0,L.e=Me(Lr,Jr,25,L.d,15,1),n=new 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t,n,r,s,o,h,d,v,x,_,L,P;if(d=e.d,L=u(K(e,(et(),Mk)),15),t=u(K(e,_k),15),!(!L&&!t)){if(o=Ue(ft(Qm(e,(pt(),Xle)))),h=Ue(ft(Qm(e,QTe))),P=0,L){for(x=0,s=L.Kc();s.Ob();)r=u(s.Pb(),10),x=b.Math.max(x,r.o.b),P+=r.o.a;P+=o*(L.gc()-1),d.d+=x+h}if(n=0,t){for(x=0,s=t.Kc();s.Ob();)r=u(s.Pb(),10),x=b.Math.max(x,r.o.b),n+=r.o.a;n+=o*(t.gc()-1),d.a+=x+h}v=b.Math.max(P,n),v>e.o.a&&(_=(v-e.o.a)/2,d.b=b.Math.max(d.b,_),d.c=b.Math.max(d.c,_))}}function mvn(e){var t,n,r,s,o,h,d,v;for(o=new uQe,rWt(o,(W6(),Fyt)),r=(s=aie(e,Me(mt,Qe,2,0,6,1)),new c6(new Al(new lte(e,s).b)));r.b<r.d.gc();)n=(Qn(r.b<r.d.gc()),Hr(r.d.Xb(r.c=r.b++))),h=e6e(x2,n),h&&(t=M0(e,n),t.je()?d=t.je().a:t.ge()?d=""+t.ge().a:t.he()?d=""+t.he().a:d=t.Ib(),v=Z5e(h,d),v!=null&&((Vu(h.j,(i1(),ua))||Vu(h.j,Fn))&&PD(Yre(o,hs),h,v),Vu(h.j,Fd)&&PD(Yre(o,ra),h,v),Vu(h.j,Bb)&&PD(Yre(o,xl),h,v),Vu(h.j,Vg)&&PD(Yre(o,Jo),h,v)));return o}function bz(e,t,n,r){var s,o,h,d,v,x;if(v=pu(e.e.Tg(),t),o=u(e.g,119),G0(e.e,t)){for(s=0,d=0;d<e.i;++d)if(h=o[d],v.rl(h.ak())){if(s==n)return ho(),u(t,66).Oj()?h:(x=h.dd(),x!=null&&r&&we(t,99)&&u(t,18).Bb&so&&(x=rC(e,t,d,s,x)),x);++s}throw J(new Do($C+n+Eb+s))}else{for(s=0,d=0;d<e.i;++d){if(h=o[d],v.rl(h.ak()))return ho(),u(t,66).Oj()?h:(x=h.dd(),x!=null&&r&&we(t,99)&&u(t,18).Bb&so&&(x=rC(e,t,d,s,x)),x);++s}return t.zj()}}function kI(e,t,n){var r,s,o,h,d,v,x,_;if(s=u(e.g,119),G0(e.e,t))return ho(),u(t,66).Oj()?new Cj(t,e):new DM(t,e);for(x=pu(e.e.Tg(),t),r=0,d=0;d<e.i;++d){if(o=s[d],h=o.ak(),x.rl(h)){if(ho(),u(t,66).Oj())return o;if(h==(nk(),Gk)||h==zk){for(v=new Fl(Qo(o.dd()));++d<e.i;)o=s[d],h=o.ak(),(h==Gk||h==zk)&&Yr(v,Qo(o.dd()));return Vbe(u(t.Yj(),148),v.a)}else return _=o.dd(),_!=null&&n&&we(t,99)&&u(t,18).Bb&so&&(_=rC(e,t,d,r,_)),_}++r}return t.zj()}function dC(e,t,n){var r,s,o,h,d,v,x,_,L,P;for(o=0,h=e.t,s=0,r=0,v=0,P=0,L=0,n&&(e.n.c=Me(Yn,yt,1,0,5,1),it(e.n,new o$(e.s,e.t,e.i))),d=0,_=new 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n,r,s,o,h;if(h=u(t,136),l4(e),l4(h),h.b!=null){if(e.c=!0,e.b==null){e.b=Me(Lr,Jr,25,h.b.length,15,1),Hc(h.b,0,e.b,0,h.b.length);return}for(o=Me(Lr,Jr,25,e.b.length+h.b.length,15,1),n=0,r=0,s=0;n<e.b.length||r<h.b.length;)n>=e.b.length?(o[s++]=h.b[r++],o[s++]=h.b[r++]):r>=h.b.length?(o[s++]=e.b[n++],o[s++]=e.b[n++]):h.b[r]<e.b[n]||h.b[r]===e.b[n]&&h.b[r+1]<e.b[n+1]?(o[s++]=h.b[r++],o[s++]=h.b[r++]):(o[s++]=e.b[n++],o[s++]=e.b[n++]);e.b=o}}function kvn(e,t){var n,r,s,o,h,d,v,x,_,L;return n=It(Mt(K(e,(et(),Ay)))),d=It(Mt(K(t,Ay))),r=u(K(e,l1),11),v=u(K(t,l1),11),s=u(K(e,Yh),11),x=u(K(t,Yh),11),_=!!r&&r==v,L=!!s&&s==x,!n&&!d?new nve(u(Y(new C(e.j)),11).p==u(Y(new C(t.j)),11).p,_,L):(o=(!It(Mt(K(e,Ay)))||It(Mt(K(e,xO))))&&(!It(Mt(K(t,Ay)))||It(Mt(K(t,xO)))),h=(!It(Mt(K(e,Ay)))||!It(Mt(K(e,xO))))&&(!It(Mt(K(t,Ay)))||!It(Mt(K(t,xO)))),new nve(_&&o||L&&h,_,L))}function xvn(e){var t,n,r,s,o,h,d,v;for(r=0,n=0,v=new as,t=0,d=new C(e.n);d.a<d.c.c.length;)h=u(Y(d),211),h.c.c.length==0?ks(v,h,v.c.b,v.c):(r=b.Math.max(r,h.d),n+=h.a+(t>0?e.i:0)),++t;for(Ion(e.n,v),e.d=n,e.r=r,e.g=0,e.f=0,e.e=0,e.o=gs,e.p=gs,o=new C(e.b);o.a<o.c.c.length;)s=u(Y(o),33),e.p=b.Math.min(e.p,s.g),e.g=b.Math.max(e.g,s.g),e.f=b.Math.max(e.f,s.f),e.o=b.Math.min(e.o,s.f),e.e+=s.f+e.i;e.a=e.e/e.b.c.length-e.i*((e.b.c.length-1)/e.b.c.length),A3e(e.j)}function Out(e){var t,n,r,s;return e.Db&64?Bie(e):(t=new Fl(Kxe),r=e.k,r?Yr(Yr((t.a+=' "',t),r),'"'):(!e.n&&(e.n=new at(Jo,e,1,7)),e.n.i>0&&(s=(!e.n&&(e.n=new at(Jo,e,1,7)),u(Te(e.n,0),137)).a,!s||Yr(Yr((t.a+=' "',t),s),'"'))),n=(!e.b&&(e.b=new wn(mr,e,4,7)),!(e.b.i<=1&&(!e.c&&(e.c=new wn(mr,e,5,8)),e.c.i<=1))),n?t.a+=" [":t.a+=" ",Yr(t,rbe(new ste,new rr(e.b))),n&&(t.a+="]"),t.a+=Ioe,n&&(t.a+="["),Yr(t,rbe(new ste,new rr(e.c))),n&&(t.a+="]"),t.a)}function bae(e,t){var n,r,s,o,h,d,v;if(e.a){if(d=e.a.ne(),v=null,d!=null?t.a+=""+d:(h=e.a.Dj(),h!=null&&(o=pd(h,Nu(91)),o!=-1?(v=h.substr(o),t.a+=""+jl(h==null?Pu:(Sn(h),h),0,o)):t.a+=""+h)),e.d&&e.d.i!=0){for(s=!0,t.a+="<",r=new rr(e.d);r.e!=r.i.gc();)n=u(pr(r),87),s?s=!1:t.a+=io,bae(n,t);t.a+=">"}v!=null&&(t.a+=""+v)}else e.e?(d=e.e.zb,d!=null&&(t.a+=""+d)):(t.a+="?",e.b?(t.a+=" super ",bae(e.b,t)):e.f&&(t.a+=" extends ",bae(e.f,t)))}function Evn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In;for(Ve=e.c,nt=t.c,n=Yo(Ve.a,e,0),r=Yo(nt.a,t,0),Ee=u(Yv(e,(vo(),ul)).Kc().Pb(),11),Ut=u(Yv(e,hu).Kc().Pb(),11),Ne=u(Yv(t,ul).Kc().Pb(),11),In=u(Yv(t,hu).Kc().Pb(),11),le=kd(Ee.e),bt=kd(Ut.g),Ce=kd(Ne.e),zt=kd(In.g),ey(e,r,nt),h=Ce,_=0,q=h.length;_<q;++_)s=h[_],ba(s,Ee);for(d=zt,L=0,W=d.length;L<W;++L)s=d[L],Va(s,Ut);for(ey(t,n,Ve),v=le,P=0,X=v.length;P<X;++P)s=v[P],ba(s,Ne);for(o=bt,x=0,z=o.length;x<z;++x)s=o[x],Va(s,In)}function Nut(e,t,n,r){var s,o,h,d,v,x,_;if(o=wH(r),d=It(Mt(K(r,(pt(),$Te)))),(d||It(Mt(K(e,Kq))))&&!R3(u(K(e,bs),98)))s=Y6(o),v=P5e(e,n,n==(vo(),hu)?s:zD(s));else switch(v=new $c,rc(v,e),t?(_=v.n,_.a=t.a-e.n.a,_.b=t.b-e.n.b,Vst(_,0,0,e.o.a,e.o.b),Vs(v,iut(v,o))):(s=Y6(o),Vs(v,n==(vo(),hu)?s:zD(s))),h=u(K(r,(et(),eu)),21),x=v.j,o.g){case 2:case 1:(x==(ht(),An)||x==xr)&&h.Fc((mo(),j4));break;case 4:case 3:(x==(ht(),$n)||x==Dn)&&h.Fc((mo(),j4))}return v}function K5e(e,t,n){var r,s,o,h,d,v,x,_;return b.Math.abs(t.s-t.c)<Sd||b.Math.abs(n.s-n.c)<Sd?0:(r=Vot(e,t.j,n.e),s=Vot(e,n.j,t.e),o=r==-1||s==-1,h=0,o?(r==-1&&(new X2((Jf(),d2),n,t,1),++h),s==-1&&(new X2((Jf(),d2),t,n,1),++h)):(d=e4(t.j,n.s,n.c),d+=e4(n.e,t.s,t.c),v=e4(n.j,t.s,t.c),v+=e4(t.e,n.s,n.c),x=r+16*d,_=s+16*v,x<_?new X2((Jf(),jy),t,n,_-x):x>_?new X2((Jf(),jy),n,t,x-_):x>0&&_>0&&(new X2((Jf(),jy),t,n,0),new X2(jy,n,t,0))),h)}function Put(e,t){var n,r,s,o,h,d;for(h=new ob(new dg(e.f.b).a);h.b;){if(o=$v(h),s=u(o.cd(),594),t==1){if(s.gf()!=(wo(),X0)&&s.gf()!=Y0)continue}else if(s.gf()!=(wo(),Xh)&&s.gf()!=Df)continue;switch(r=u(u(o.dd(),46).b,81),d=u(u(o.dd(),46).a,189),n=d.c,s.gf().g){case 2:r.g.c=e.e.a,r.g.b=b.Math.max(1,r.g.b+n);break;case 1:r.g.c=r.g.c+n,r.g.b=b.Math.max(1,r.g.b-n);break;case 4:r.g.d=e.e.b,r.g.a=b.Math.max(1,r.g.a+n);break;case 3:r.g.d=r.g.d+n,r.g.a=b.Math.max(1,r.g.a-n)}}}function Tvn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W;for(d=Me(Lr,Jr,25,t.b.c.length,15,1),x=Me(Zue,tt,267,t.b.c.length,0,1),v=Me(h0,Bg,10,t.b.c.length,0,1),L=e.a,P=0,z=L.length;P<z;++P){for(_=L[P],W=0,h=new C(_.e);h.a<h.c.c.length;)s=u(Y(h),10),r=F2e(s.c),++d[r],q=Ue(ft(K(t,(pt(),Mf)))),d[r]>0&&v[r]&&(q=j3(e.b,v[r],s)),W=b.Math.max(W,s.c.c.b+q);for(o=new C(_.e);o.a<o.c.c.length;)s=u(Y(o),10),s.n.b=W+s.d.d,n=s.c,n.c.b=W+s.d.d+s.o.b+s.d.a,x[Yo(n.b.b,n,0)]=s.k,v[Yo(n.b.b,n,0)]=s}}function But(e,t){var n,r,s,o,h,d,v,x,_,L,P;for(r=new cr(fr(z0(t).a.Kc(),new 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n,r,s,o,h,d,v,x,_;if(!(t.e.c.length<=1)){for(e.f=t,e.d=u(K(e.f,(ZD(),nEe)),379),e.g=u(K(e.f,aEe),19).a,e.e=Ue(ft(K(e.f,rEe))),e.c=Ue(ft(K(e.f,nq))),XKe(e.b),s=new C(e.f.c);s.a<s.c.c.length;)r=u(Y(s),282),A5e(e.b,r.c,r,null),A5e(e.b,r.d,r,null);for(d=e.f.e.c.length,e.a=U2(pa,[Qe,Ao],[104,25],15,[d,d],2),x=new C(e.f.e);x.a<x.c.c.length;)v=u(Y(x),144),rvn(e,v,e.a[v.b]);for(e.i=U2(pa,[Qe,Ao],[104,25],15,[d,d],2),o=0;o<d;++o)for(h=0;h<d;++h)n=e.a[o][h],_=1/(n*n),e.i[o][h]=_}}function gC(e){var t,n,r,s;if(!(e.b==null||e.b.length<=2)&&!e.a){for(t=0,s=0;s<e.b.length;){for(t!=s?(e.b[t]=e.b[s++],e.b[t+1]=e.b[s++]):s+=2,n=e.b[t+1];s<e.b.length&&!(n+1<e.b[s]);)if(n+1==e.b[s])e.b[t+1]=e.b[s+1],n=e.b[t+1],s+=2;else if(n>=e.b[s+1])s+=2;else if(n<e.b[s+1])e.b[t+1]=e.b[s+1],n=e.b[t+1],s+=2;else throw J(new tc("Token#compactRanges(): Internel Error: ["+e.b[t]+","+e.b[t+1]+"] ["+e.b[s]+","+e.b[s+1]+"]"));t+=2}t!=e.b.length&&(r=Me(Lr,Jr,25,t,15,1),Hc(e.b,0,r,0,t),e.b=r),e.a=!0}}function Bvn(e,t){var n,r,s,o,h,d,v;for(h=D6(e.a).Kc();h.Ob();){if(o=u(h.Pb(),17),o.b.c.length>0)for(r=new Uu(u(Ii(e.a,o),21)),hn(),aa(r,new Di(t)),s=new Ea(o.b,0);s.b<s.d.gc();){switch(n=(Qn(s.b<s.d.gc()),u(s.d.Xb(s.c=s.b++),70)),d=-1,u(K(n,(pt(),Rd)),272).g){case 1:d=r.c.length-1;break;case 0:d=ahn(r);break;case 2:d=0}d!=-1&&(v=(xn(d,r.c.length),u(r.c[d],243)),it(v.b.b,n),u(K(Ya(v.b.c.i),(et(),eu)),21).Fc((mo(),cE)),u(K(Ya(v.b.c.i),eu),21).Fc(oE),Ol(s),Ye(n,j9e,o))}Va(o,null),ba(o,null)}}function Rvn(e,t){var n,r,s,o;return n=new sh,r=u(zl(Cu(new vn(null,new mn(e.f,16)),n),zm(new Ri,new tn,new tr,new ha,ie(re(yl,1),tt,132,0,[(F1(),xy),Ql]))),21),s=r.gc(),s=s==2?1:0,s==1&&mv(VD(u(zl(Vi(r.Lc(),new Ai),Ntt(lb(0),new ir)),162).a,2),0)&&(s=0),r=u(zl(Cu(new vn(null,new mn(t.f,16)),n),zm(new Ri,new tn,new tr,new ha,ie(re(yl,1),tt,132,0,[xy,Ql]))),21),o=r.gc(),o=o==2?1:0,o==1&&mv(VD(u(zl(Vi(r.Lc(),new nn),Ntt(lb(0),new ir)),162).a,2),0)&&(o=0),s<o?-1:s==o?0:1}function Fvn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z;if(x=new st,!ta(e,(et(),Mle)))return x;for(r=u(K(e,Mle),15).Kc();r.Ob();)t=u(r.Pb(),10),Qvn(t,e),x.c[x.c.length]=t;for(o=new C(e.b);o.a<o.c.c.length;)for(s=u(Y(o),29),d=new C(s.a);d.a<d.c.c.length;)h=u(Y(d),10),h.k==(zn(),Ls)&&(v=u(K(h,jq),10),v&&(_=new $c,rc(_,h),L=u(K(h,vc),61),Vs(_,L),P=u(St(v.j,0),11),z=new Iv,Va(z,_),ba(z,P)));for(n=new C(x);n.a<n.c.c.length;)t=u(Y(n),10),No(t,u(St(e.b,e.b.c.length-1),29));return x}function Hut(e){var t,n,r,s,o,h,d,v,x,_,L,P;for(t=L1(e),o=It(Mt(Ft(t,(pt(),Oy)))),_=0,s=0,x=new rr((!e.e&&(e.e=new wn(ra,e,7,4)),e.e));x.e!=x.i.gc();)v=u(pr(x),79),d=ew(v),h=d&&o&&It(Mt(Ft(v,Db))),P=zo(u(Te((!v.c&&(v.c=new wn(mr,v,5,8)),v.c),0),82)),d&&h?++s:d&&!h?++_:us(P)==t||P==t?++s:++_;for(r=new rr((!e.d&&(e.d=new wn(ra,e,8,5)),e.d));r.e!=r.i.gc();)n=u(pr(r),79),d=ew(n),h=d&&o&&It(Mt(Ft(n,Db))),L=zo(u(Te((!n.b&&(n.b=new wn(mr,n,4,7)),n.b),0),82)),d&&h?++_:d&&!h?++s:us(L)==t||L==t?++_:++s;return _-s}function jvn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z;if(kr(t,"Edge 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n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt;r=Ue(ft(K(t,(pt(),GTe)))),Ve=u(K(t,vS),19).a,P=4,s=3,nt=20/Ve,z=!1,v=0,h=Ei;do{for(o=v!=1,L=v!=0,bt=0,X=e.a,Ce=0,Ne=X.length;Ce<Ne;++Ce)q=X[Ce],q.f=null,Smn(e,q,o,L,r),bt+=b.Math.abs(q.a);do d=wbn(e,t);while(d);for(W=e.a,le=0,Ee=W.length;le<Ee;++le)if(q=W[le],n=Gve(q).a,n!=0)for(_=new C(q.e);_.a<_.c.c.length;)x=u(Y(_),10),x.n.b+=n;v==0||v==1?(--P,P<=0&&(bt<h||-P>Ve)?(v=2,h=Ei):v==0?(v=1,h=bt):(v=0,h=bt)):(z=bt>=h||h-bt<nt,h=bt,z&&--s)}while(!(z&&s<=0))}function xae(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q;for(q=new Mr,o=e.a.ec().Kc();o.Ob();)r=u(o.Pb(),168),Si(q,r,n.Je(r));for(h=(Nr(e),e?new Uu(e):l8(e.a.ec().Kc())),aa(h,new Wn(q)),d=w$(h),v=new rj(t),z=new Mr,gu(z.f,t,v);d.a.gc()!=0;){for(x=null,_=null,L=null,s=d.a.ec().Kc();s.Ob();)if(r=u(s.Pb(),168),Ue(ft(hc($o(q.f,r))))<=gs){if(Il(z,r.a)&&!Il(z,r.b)){_=r.b,L=r.a,x=r;break}if(Il(z,r.b)&&!Il(z,r.a)){_=r.a,L=r.b,x=r;break}}if(!x)break;P=new rj(_),it(u(hc($o(z.f,L)),221).a,P),gu(z.f,_,P),d.a.Bc(x)!=null}return v}function Hvn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z;for(kr(n,"Depth-first cycle removal",1),L=t.a,_=L.c.length,e.c=new st,e.d=Me(El,c0,25,_,16,1),e.a=Me(El,c0,25,_,16,1),e.b=new st,h=0,x=new C(L);x.a<x.c.c.length;)v=u(Y(x),10),v.p=h,T8(Xo(v))&&it(e.c,v),++h;for(z=new C(e.c);z.a<z.c.c.length;)P=u(Y(z),10),S4e(e,P);for(o=0;o<_;o++)e.d[o]||(d=(xn(o,L.c.length),u(L.c[o],10)),S4e(e,d));for(s=new C(e.b);s.a<s.c.c.length;)r=u(Y(s),17),iw(r,!0),Ye(t,(et(),yO),(Mn(),!0));e.c=null,e.d=null,e.a=null,e.b=null,ur(n)}function zvn(e,t){var n,r,s,o,h,d,v;for(e.a.c=Me(Yn,yt,1,0,5,1),r=ii(t.b,0);r.b!=r.d.c;)n=u(ri(r),86),n.b.b==0&&(Ye(n,(Tc(),$y),(Mn(),!0)),it(e.a,n));switch(e.a.c.length){case 0:s=new vie(0,t,"DUMMY_ROOT"),Ye(s,(Tc(),$y),(Mn(),!0)),Ye(s,Ehe,!0),ci(t.b,s);break;case 1:break;default:for(o=new vie(0,t,"SUPER_ROOT"),d=new C(e.a);d.a<d.c.c.length;)h=u(Y(d),86),v=new 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n,r,s,o;o=e.F,t==null?(e.F=null,Y8(e,null)):(e.F=(Sn(t),t),r=pd(t,Nu(60)),r!=-1?(s=t.substr(0,r),pd(t,Nu(46))==-1&&!an(s,sk)&&!an(s,HC)&&!an(s,mG)&&!an(s,zC)&&!an(s,GC)&&!an(s,qC)&&!an(s,VC)&&!an(s,UC)&&(s=Odt),n=cj(t,Nu(62)),n!=-1&&(s+=""+t.substr(n+1)),Y8(e,s)):(s=t,pd(t,Nu(46))==-1&&(r=pd(t,Nu(91)),r!=-1&&(s=t.substr(0,r)),!an(s,sk)&&!an(s,HC)&&!an(s,mG)&&!an(s,zC)&&!an(s,GC)&&!an(s,qC)&&!an(s,VC)&&!an(s,UC)?(s=Odt,r!=-1&&(s+=""+t.substr(r))):s=t),Y8(e,s),s==t&&(e.F=e.D))),e.Db&4&&!(e.Db&1)&&_i(e,new oa(e,1,5,o,t))}function Twn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee;if(W=t.b.c.length,!(W<3)){for(z=Me(Lr,Jr,25,W,15,1),L=0,_=new C(t.b);_.a<_.c.c.length;)x=u(Y(_),29),z[L++]=x.a.c.length;for(P=new Ea(t.b,2),r=1;r<W-1;r++)for(n=(Qn(P.b<P.d.gc()),u(P.d.Xb(P.c=P.b++),29)),q=new 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n,r,s,o,h,d,v,x,_,L,P,z;if(n=u(_o(e.b,t),124),v=u(u(Ii(e.r,t),21),84),v.dc()){n.n.b=0,n.n.c=0;return}for(x=e.u.Hc((ol(),Z0)),h=0,d=v.Kc(),_=null,L=0,P=0;d.Ob();)r=u(d.Pb(),111),s=Ue(ft(r.b.We((uj(),WG)))),o=r.b.rf().a,e.A.Hc((Bl(),Hb))&&Lct(e,t),_?(z=P+_.d.c+e.w+r.d.b,h=b.Math.max(h,(S1(),Ef(z1),b.Math.abs(L-s)<=z1||L==s||isNaN(L)&&isNaN(s)?0:z/(s-L)))):e.C&&e.C.b>0&&(h=b.Math.max(h,Ett(e.C.b+r.d.b,s))),_=r,L=s,P=o;e.C&&e.C.c>0&&(z=P+e.C.c,x&&(z+=_.d.c),h=b.Math.max(h,(S1(),Ef(z1),b.Math.abs(L-1)<=z1||L==1||isNaN(L)&&isNaN(1)?0:z/(1-L)))),n.n.b=0,n.a.a=h}function Qut(e,t){var n,r,s,o,h,d,v,x,_,L,P,z;if(n=u(_o(e.b,t),124),v=u(u(Ii(e.r,t),21),84),v.dc()){n.n.d=0,n.n.a=0;return}for(x=e.u.Hc((ol(),Z0)),h=0,e.A.Hc((Bl(),Hb))&&Mct(e,t),d=v.Kc(),_=null,P=0,L=0;d.Ob();)r=u(d.Pb(),111),o=Ue(ft(r.b.We((uj(),WG)))),s=r.b.rf().b,_?(z=L+_.d.a+e.w+r.d.d,h=b.Math.max(h,(S1(),Ef(z1),b.Math.abs(P-o)<=z1||P==o||isNaN(P)&&isNaN(o)?0:z/(o-P)))):e.C&&e.C.d>0&&(h=b.Math.max(h,Ett(e.C.d+r.d.d,o))),_=r,P=o,L=s;e.C&&e.C.a>0&&(z=L+e.C.a,x&&(z+=_.d.a),h=b.Math.max(h,(S1(),Ef(z1),b.Math.abs(P-1)<=z1||P==1||isNaN(P)&&isNaN(1)?0:z/(1-P)))),n.n.d=0,n.a.b=h}function Zut(e,t,n){var r,s,o,h,d,v;for(this.g=e,d=t.d.length,v=n.d.length,this.d=Me(h0,Bg,10,d+v,0,1),h=0;h<d;h++)this.d[h]=t.d[h];for(o=0;o<v;o++)this.d[d+o]=n.d[o];if(t.e){if(this.e=VM(t.e),this.e.Mc(n),n.e)for(s=n.e.Kc();s.Ob();)r=u(s.Pb(),233),r!=t&&(this.e.Hc(r)?--r.c:this.e.Fc(r))}else n.e&&(this.e=VM(n.e),this.e.Mc(t));this.f=t.f+n.f,this.a=t.a+n.a,this.a>0?Xre(this,this.f/this.a):A1(t.g,t.d[0]).a!=null&&A1(n.g,n.d[0]).a!=null?Xre(this,(Ue(A1(t.g,t.d[0]).a)+Ue(A1(n.g,n.d[0]).a))/2):A1(t.g,t.d[0]).a!=null?Xre(this,A1(t.g,t.d[0]).a):A1(n.g,n.d[0]).a!=null&&Xre(this,A1(n.g,n.d[0]).a)}function Cwn(e,t){var n,r,s,o,h,d,v,x,_,L;for(e.a=new iYe(tsn(RS)),r=new C(t.a);r.a<r.c.c.length;){for(n=u(Y(r),841),d=new Wie(ie(re(Uue,1),yt,81,0,[])),it(e.a.a,d),x=new C(n.d);x.a<x.c.c.length;)v=u(Y(x),110),_=new fbe(e,v),n6e(_,u(K(n.c,(et(),Sy)),21)),Il(e.g,n)||(Si(e.g,n,new Pt(v.c,v.d)),Si(e.f,n,_)),it(e.a.b,_),yre(d,_);for(h=new C(n.b);h.a<h.c.c.length;)o=u(Y(h),594),_=new fbe(e,o.kf()),Si(e.b,o,new xa(d,_)),n6e(_,u(K(n.c,(et(),Sy)),21)),o.hf()&&(L=new d3e(e,o.hf(),1),n6e(L,u(K(n.c,Sy),21)),s=new Wie(ie(re(Uue,1),yt,81,0,[])),yre(s,L),sn(e.c,o.gf(),new xa(d,L)))}return e.a}function Jut(e){var t;this.a=e,t=(zn(),ie(re(Zue,1),tt,267,0,[Hs,ca,Ls,Jc,Rl,U1])).length,this.b=U2(Khe,[Qe,wxe],[593,146],0,[t,t],2),this.c=U2(Khe,[Qe,wxe],[593,146],0,[t,t],2),are(this,Hs,(pt(),Aw),U4),R_(this,Hs,ca,Sw,Nb),eD(this,Hs,Jc,Sw),eD(this,Hs,Ls,Sw),R_(this,Hs,Rl,Aw,U4),are(this,ca,Mf,V4),eD(this,ca,Jc,Mf),eD(this,ca,Ls,Mf),R_(this,ca,Rl,Sw,Nb),_Ve(this,Jc,Mf),eD(this,Jc,Ls,Mf),eD(this,Jc,Rl,Qle),_Ve(this,Ls,bS),R_(this,Ls,Rl,vE,bE),are(this,Rl,Mf,Mf),are(this,U1,Mf,V4),R_(this,U1,Hs,Sw,Nb),R_(this,U1,Rl,Sw,Nb),R_(this,U1,ca,Sw,Nb)}function Swn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X;if(h=n.ak(),we(h,99)&&u(h,18).Bb&so&&(P=u(n.dd(),49),W=Up(e.e,P),W!=P)){if(_=Jd(h,W),n_(e,t,Tse(e,t,_)),L=null,Ll(e.e)&&(r=v4((Yu(),Oa),e.e.Tg(),h),r!=gn(e.e.Tg(),e.c))){for(X=pu(e.e.Tg(),h),d=0,o=u(e.g,119),v=0;v<t;++v)s=o[v],X.rl(s.ak())&&++d;L=new _re(e.e,9,r,P,W,d,!1),L.Ei(new N0(e.e,9,e.c,n,_,t,!1))}return q=u(h,18),z=go(q),z?(L=P.ih(e.e,Ji(P.Tg(),z),null,L),L=u(W,49).gh(e.e,Ji(W.Tg(),z),null,L)):q.Bb&_c&&(x=-1-Ji(e.e.Tg(),q),L=P.ih(e.e,x,null,null),!u(W,49).eh()&&(L=u(W,49).gh(e.e,x,null,L))),L&&L.Fi(),_}return n}function Awn(e){var t,n,r,s,o,h,d,v;for(o=new C(e.a.b);o.a<o.c.c.length;)s=u(Y(o),81),s.b.c=s.g.c,s.b.d=s.g.d;for(v=new Pt(gs,gs),t=new Pt(Ds,Ds),r=new C(e.a.b);r.a<r.c.c.length;)n=u(Y(r),81),v.a=b.Math.min(v.a,n.g.c),v.b=b.Math.min(v.b,n.g.d),t.a=b.Math.max(t.a,n.g.c+n.g.b),t.b=b.Math.max(t.b,n.g.d+n.g.a);for(d=qj(e.c).a.nc();d.Ob();)h=u(d.Pb(),46),n=u(h.b,81),v.a=b.Math.min(v.a,n.g.c),v.b=b.Math.min(v.b,n.g.d),t.a=b.Math.max(t.a,n.g.c+n.g.b),t.b=b.Math.max(t.b,n.g.d+n.g.a);e.d=gj(new Pt(v.a,v.b)),e.e=da(new Pt(t.a,t.b),v),e.a.a.c=Me(Yn,yt,1,0,5,1),e.a.b.c=Me(Yn,yt,1,0,5,1)}function Lwn(e){var t,n,r;for(J3(x2,ie(re(I4,1),yt,130,0,[new wp])),n=new a6(e),r=0;r<n.a.length;++r)t=Gm(n,r).je().a,an(t,"layered")?J3(x2,ie(re(I4,1),yt,130,0,[new VJ])):an(t,"force")?J3(x2,ie(re(I4,1),yt,130,0,[new DJ])):an(t,"stress")?J3(x2,ie(re(I4,1),yt,130,0,[new IJ])):an(t,"mrtree")?J3(x2,ie(re(I4,1),yt,130,0,[new E3])):an(t,"radial")?J3(x2,ie(re(I4,1),yt,130,0,[new QJ])):an(t,"disco")?J3(x2,ie(re(I4,1),yt,130,0,[new VL,new MJ])):an(t,"sporeOverlap")||an(t,"sporeCompaction")?J3(x2,ie(re(I4,1),yt,130,0,[new IR])):an(t,"rectpacking")&&J3(x2,ie(re(I4,1),yt,130,0,[new NR]))}function elt(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le;if(P=new Io(e.o),le=t.a/P.a,d=t.b/P.b,W=t.a-P.a,o=t.b-P.b,n)for(s=je(K(e,(pt(),bs)))===je((wa(),tu)),q=new C(e.j);q.a<q.c.c.length;)switch(z=u(Y(q),11),z.j.g){case 1:s||(z.n.a*=le);break;case 2:z.n.a+=W,s||(z.n.b*=d);break;case 3:s||(z.n.a*=le),z.n.b+=o;break;case 4:s||(z.n.b*=d)}for(x=new C(e.b);x.a<x.c.c.length;)v=u(Y(x),70),_=v.n.a+v.o.a/2,L=v.n.b+v.o.b/2,X=_/P.a,h=L/P.b,X+h>=1&&(X-h>0&&L>=0?(v.n.a+=W,v.n.b+=o*h):X-h<0&&_>=0&&(v.n.a+=W*X,v.n.b+=o));e.o.a=t.a,e.o.b=t.b,Ye(e,(pt(),Ib),(Bl(),r=u(Qf(qS),9),new hh(r,u(wf(r,r.length),9),0)))}function Mwn(e,t,n,r,s,o){var h;if(!(t==null||!$ie(t,OAe,NAe)))throw J(new Ln("invalid scheme: "+t));if(!e&&!(n!=null&&pd(n,Nu(35))==-1&&n.length>0&&(zr(0,n.length),n.charCodeAt(0)!=47)))throw J(new Ln("invalid opaquePart: "+n));if(e&&!(t!=null&&gM(ZV,t.toLowerCase()))&&!(n==null||!$ie(n,KS,YS)))throw J(new Ln(ydt+n));if(e&&t!=null&&gM(ZV,t.toLowerCase())&&!Ehn(n))throw J(new Ln(ydt+n));if(!Son(r))throw J(new Ln("invalid device: "+r));if(!yan(s))throw h=s==null?"invalid segments: null":"invalid segment: "+ban(s),J(new Ln(h));if(!(o==null||pd(o,Nu(35))==-1))throw J(new Ln("invalid query: "+o))}function Dwn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le;for(kr(t,"Calculate Graph Size",1),t.n&&e&&yf(t,kf(e),(Pl(),nh)),d=O7,v=O7,o=Exe,h=Exe,L=new rr((!e.a&&(e.a=new at(hs,e,10,11)),e.a));L.e!=L.i.gc();)x=u(pr(L),33),q=x.i,W=x.j,le=x.g,r=x.f,s=u(Ft(x,(bi(),WO)),142),d=b.Math.min(d,q-s.b),v=b.Math.min(v,W-s.d),o=b.Math.max(o,q+le+s.c),h=b.Math.max(h,W+r+s.a);for(z=u(Ft(e,(bi(),Fb)),116),P=new Pt(d-z.b,v-z.d),_=new rr((!e.a&&(e.a=new at(hs,e,10,11)),e.a));_.e!=_.i.gc();)x=u(pr(_),33),Du(x,x.i-P.a),Iu(x,x.j-P.b);X=o-d+(z.b+z.c),n=h-v+(z.d+z.a),zv(e,X),Hv(e,n),t.n&&e&&yf(t,kf(e),(Pl(),nh))}function tlt(e){var t,n,r,s,o,h,d,v,x,_;for(r=new st,h=new C(e.e.a);h.a<h.c.c.length;){for(s=u(Y(h),121),_=0,s.k.c=Me(Yn,yt,1,0,5,1),n=new C(t4(s));n.a<n.c.c.length;)t=u(Y(n),213),t.f&&(it(s.k,t),++_);_==1&&(r.c[r.c.length]=s)}for(o=new C(r);o.a<o.c.c.length;)for(s=u(Y(o),121);s.k.c.length==1;){for(x=u(Y(new C(s.k)),213),e.b[x.c]=x.g,d=x.d,v=x.e,n=new C(t4(s));n.a<n.c.c.length;)t=u(Y(n),213),Ci(t,x)||(t.f?d==t.d||v==t.e?e.b[x.c]-=e.b[t.c]-t.g:e.b[x.c]+=e.b[t.c]-t.g:s==d?t.d==s?e.b[x.c]+=t.g:e.b[x.c]-=t.g:t.d==s?e.b[x.c]-=t.g:e.b[x.c]+=t.g);Au(d.k,x),Au(v.k,x),d==s?s=x.e:s=x.d}}function e6e(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q;if(t==null||t.length==0)return null;if(o=u(Uc(e.f,t),23),!o){for(s=(z=new E1(e.d).a.vc().Kc(),new T1(z));s.a.Ob();)if(n=(h=u(s.a.Pb(),42),u(h.dd(),23)),d=n.f,q=t.length,an(d.substr(d.length-q,q),t)&&(t.length==d.length||Ma(d,d.length-t.length-1)==46)){if(o)return null;o=n}if(!o){for(r=(P=new E1(e.d).a.vc().Kc(),new T1(P));r.a.Ob();)if(n=(h=u(r.a.Pb(),42),u(h.dd(),23)),L=n.g,L!=null){for(v=L,x=0,_=v.length;x<_;++x)if(d=v[x],q=t.length,an(d.substr(d.length-q,q),t)&&(t.length==d.length||Ma(d,d.length-t.length-1)==46)){if(o)return null;o=n}}}o&&Oo(e.f,t,o)}return o}function Iwn(e,t){var n,r,s,o,h;for(n=new xm,h=!1,o=0;o<t.length;o++){if(r=(zr(o,t.length),t.charCodeAt(o)),r==32){for(OH(e,n,0),n.a+=" ",OH(e,n,0);o+1<t.length&&(zr(o+1,t.length),t.charCodeAt(o+1)==32);)++o;continue}if(h){r==39?o+1<t.length&&(zr(o+1,t.length),t.charCodeAt(o+1)==39)?(n.a+=String.fromCharCode(r),++o):h=!1:n.a+=String.fromCharCode(r);continue}if(pd("GyMLdkHmsSEcDahKzZv",Nu(r))>0){OH(e,n,0),n.a+=String.fromCharCode(r),s=bcn(t,o),OH(e,n,s),o+=s-1;continue}r==39?o+1<t.length&&(zr(o+1,t.length),t.charCodeAt(o+1)==39)?(n.a+="'",++o):h=!0:n.a+=String.fromCharCode(r)}OH(e,n,0),Whn(e)}function Own(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le;if(kr(n,"Network simplex layering",1),e.b=t,le=u(K(t,(pt(),vS)),19).a*4,X=e.b.a,X.c.length<1){ur(n);return}for(o=O2n(e,X),W=null,s=ii(o,0);s.b!=s.d.c;){for(r=u(ri(s),15),d=le*_s(b.Math.sqrt(r.gc())),h=X2n(r),sae(Rpe(UVt(Fpe(bne(h),d),W),!0),Kc(n,1)),P=e.b.b,q=new C(h.a);q.a<q.c.c.length;){for(z=u(Y(q),121);P.c.length<=z.e;)Om(P,P.c.length,new Bh(e.b));_=u(z.f,10),No(_,u(St(P,z.e),29))}if(o.b>1)for(W=Me(Lr,Jr,25,e.b.b.c.length,15,1),L=0,x=new C(e.b.b);x.a<x.c.c.length;)v=u(Y(x),29),W[L++]=v.a.c.length}X.c=Me(Yn,yt,1,0,5,1),e.a=null,e.b=null,e.c=null,ur(n)}function nlt(e){var t,n,r,s,o,h,d;for(t=0,o=new C(e.b.a);o.a<o.c.c.length;)r=u(Y(o),189),r.b=0,r.c=0;for(Hst(e,0),Hie(e,e.g),iz(e.c),Ipe(e.c),n=(wo(),Xh),wI($te(w4(wI($te(w4(wI(w4(e.c,n)),art(n)))),n))),w4(e.c,Xh),Nie(e,e.g),Lst(e,0),Put(e,0),yot(e,1),Hst(e,1),Hie(e,e.d),iz(e.c),h=new C(e.b.a);h.a<h.c.c.length;)r=u(Y(h),189),t+=b.Math.abs(r.c);for(d=new C(e.b.a);d.a<d.c.c.length;)r=u(Y(d),189),r.b=0,r.c=0;for(n=X0,wI($te(w4(wI($te(w4(wI(Ipe(w4(e.c,n))),art(n)))),n))),w4(e.c,Xh),Nie(e,e.d),Lst(e,1),Put(e,1),yot(e,0),Ipe(e.c),s=new C(e.b.a);s.a<s.c.c.length;)r=u(Y(s),189),t+=b.Math.abs(r.c);return t}function rlt(e,t){var n,r,s,o,h,d,v,x,_;if(x=t,!(x.b==null||e.b==null)){for(l4(e),gC(e),l4(x),gC(x),n=Me(Lr,Jr,25,e.b.length+x.b.length,15,1),_=0,r=0,h=0;r<e.b.length&&h<x.b.length;)if(s=e.b[r],o=e.b[r+1],d=x.b[h],v=x.b[h+1],o<d)r+=2;else if(o>=d&&s<=v)d<=s&&o<=v?(n[_++]=s,n[_++]=o,r+=2):d<=s?(n[_++]=s,n[_++]=v,e.b[r]=v+1,h+=2):o<=v?(n[_++]=d,n[_++]=o,r+=2):(n[_++]=d,n[_++]=v,e.b[r]=v+1);else if(v<s)h+=2;else throw J(new tc("Token#intersectRanges(): Internal Error: ["+e.b[r]+","+e.b[r+1]+"] & ["+x.b[h]+","+x.b[h+1]+"]"));for(;r<e.b.length;)n[_++]=e.b[r++],n[_++]=e.b[r++];e.b=Me(Lr,Jr,25,_,15,1),Hc(n,0,e.b,0,_)}}function Nwn(e){var t,n,r,s,o,h,d;for(t=new st,e.g=new st,e.d=new st,h=new ob(new dg(e.f.b).a);h.b;)o=$v(h),it(t,u(u(o.dd(),46).b,81)),Dp(u(o.cd(),594).gf())?it(e.d,u(o.dd(),46)):it(e.g,u(o.dd(),46));for(Hie(e,e.d),Hie(e,e.g),e.c=new jat(e.b),KVt(e.c,(Qpe(),Dpt)),Nie(e,e.d),Nie(e,e.g),Rs(t,e.c.a.b),e.e=new Pt(gs,gs),e.a=new Pt(Ds,Ds),r=new C(t);r.a<r.c.c.length;)n=u(Y(r),81),e.e.a=b.Math.min(e.e.a,n.g.c),e.e.b=b.Math.min(e.e.b,n.g.d),e.a.a=b.Math.max(e.a.a,n.g.c+n.g.b),e.a.b=b.Math.max(e.a.b,n.g.d+n.g.a);$pe(e.c,new v1),d=0;do s=nlt(e),++d;while((d<2||s>Jp)&&d<10);$pe(e.c,new dp),nlt(e),yZt(e.c),Awn(e.f)}function Pwn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X;if(It(Mt(K(n,(pt(),Oy)))))for(d=new C(n.j);d.a<d.c.c.length;)for(h=u(Y(d),11),P=kd(h.g),x=P,_=0,L=x.length;_<L;++_)v=x[_],o=v.d.i==n,s=o&&It(Mt(K(v,Db))),s&&(q=v.c,z=u(er(e.b,q),10),z||(z=EI(q,(wa(),X1),q.j,-1,null,null,q.o,u(K(t,Zl),103),t),Ye(z,(et(),Mi),q),Si(e.b,q,z),it(t.a,z)),X=v.d,W=u(er(e.b,X),10),W||(W=EI(X,(wa(),X1),X.j,1,null,null,X.o,u(K(t,Zl),103),t),Ye(W,(et(),Mi),X),Si(e.b,X,W),it(t.a,W)),r=Dre(v),Va(r,u(St(z.j,0),11)),ba(r,u(St(W.j,0),11)),sn(e.a,v,new mj(r,t,(vo(),hu))),u(K(t,(et(),eu)),21).Fc((mo(),Th)))}function Bwn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q;for(kr(n,"Label dummy 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h.b+(t-h.a)/(d.a-h.a)*(d.b-h.b)}break;case 3:switch(o=u(K(u(St(e.j,0),11),(et(),Mi)),11),s=o.i,o.j.g){case 1:return s.n.b;case 3:return s.n.b+s.o.b}}return W3e(e).b}function Fwn(e){var t,n,r,s,o,h,d,v,x,_,L;for(h=new C(e.d.b);h.a<h.c.c.length;)for(o=u(Y(h),29),v=new C(o.a);v.a<v.c.c.length;){if(d=u(Y(v),10),It(Mt(K(d,(pt(),fS))))&&!T8(j0(d))){r=u(RJt(j0(d)),17),_=r.c.i,_==d&&(_=r.d.i),L=new xa(_,da(fc(d.n),_.n)),Si(e.b,d,L);continue}s=new fh(d.n.a-d.d.b,d.n.b-d.d.d,d.o.a+d.d.b+d.d.c,d.o.b+d.d.d+d.d.a),t=lUe(TGe(xGe(EGe(new bpe,d),s),s2t),e.a),uUe(kUt(Get(new ppe,ie(re(VG,1),yt,57,0,[t])),t),e.a),x=new Hee,Si(e.e,t,x),n=I0(new cr(fr(Xo(d).a.Kc(),new V)))-I0(new cr(fr(js(d).a.Kc(),new V))),n<0?ND(x,!0,(wo(),Xh)):n>0&&ND(x,!0,(wo(),Df)),d.k==(zn(),Ls)&&SYe(x),Si(e.f,d,t)}}function jwn(e,t,n){var r,s,o,h,d,v,x,_,L,P;switch(kr(n,"Node promotion heuristic",1),e.g=t,Vmn(e),e.q=u(K(t,(pt(),Wle)),260),_=u(K(e.g,FTe),19).a,o=new JP,e.q.g){case 2:case 1:k7(e,o);break;case 3:for(e.q=(f4(),oV),k7(e,o),v=0,d=new C(e.a);d.a<d.c.c.length;)h=u(Y(d),19),v=b.Math.max(v,h.a);v>e.j&&(e.q=LO,k7(e,o));break;case 4:for(e.q=(f4(),oV),k7(e,o),x=0,s=new C(e.b);s.a<s.c.c.length;)r=ft(Y(s)),x=b.Math.max(x,(Sn(r),r));x>e.k&&(e.q=MO,k7(e,o));break;case 6:P=_s(b.Math.ceil(e.f.length*_/100)),k7(e,new yp(P));break;case 5:L=_s(b.Math.ceil(e.d*_/100)),k7(e,new Xf(L));break;default:k7(e,o)}Ipn(e,t),ur(n)}function ilt(e,t,n){var r,s,o,h;this.j=e,this.e=r4e(e),this.o=this.j.e,this.i=!!this.o,this.p=this.i?u(St(n,Ya(this.o).p),214):null,s=u(K(e,(et(),eu)),21),this.g=s.Hc((mo(),Th)),this.b=new st,this.d=new Crt(this.e),h=u(K(this.j,Lk),230),this.q=vsn(t,h,this.e),this.k=new _Xe(this),o=O1(ie(re(o2t,1),yt,225,0,[this,this.d,this.k,this.q])),t==(Gv(),IO)&&!It(Mt(K(e,(pt(),My))))?(r=new s4e(this.e),o.c[o.c.length]=r,this.c=new Nwe(r,h,u(this.q,402))):t==IO&&It(Mt(K(e,(pt(),My))))?(r=new s4e(this.e),o.c[o.c.length]=r,this.c=new utt(r,h,u(this.q,402))):this.c=new sqe(t,this),it(o,this.c),jut(o,this.e),this.s=kyn(this.k)}function $wn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne;for(L=u(sj((h=ii(new Ep(t).a.d,0),new f6(h))),86),q=L?u(K(L,(Tc(),The)),86):null,s=1;L&&q;){for(v=0,Ne=0,n=L,r=q,d=0;d<s;d++)n=Fre(n),r=Fre(r),Ne+=Ue(ft(K(n,(Tc(),mE)))),v+=Ue(ft(K(r,mE)));if(Ee=Ue(ft(K(q,(Tc(),qg)))),Ce=Ue(ft(K(L,qg))),P=Uwe(L,q),z=Ee+v+e.a+P-Ce-Ne,0<z){for(x=t,_=0;x&&x!=r;)++_,x=u(K(x,pV),86);if(x)for(le=z/_,x=t;x!=r;)X=Ue(ft(K(x,qg)))+z,Ye(x,qg,X),W=Ue(ft(K(x,mE)))+z,Ye(x,mE,W),z-=le,x=u(K(x,pV),86);else return}++s,L.d.b==0?L=O5e(new Ep(t),s):L=u(sj((o=ii(new Ep(L).a.d,0),new f6(o))),86),q=L?u(K(L,The),86):null}}function slt(e,t){var n,r,s,o,h,d,v,x,_,L;for(v=!0,s=0,x=e.f[t.p],_=t.o.b+e.n,n=e.c[t.p][2],gh(e.a,x,ct(u(St(e.a,x),19).a-1+n)),gh(e.b,x,Ue(ft(St(e.b,x)))-_+n*e.e),++x,x>=e.i?(++e.i,it(e.a,ct(1)),it(e.b,_)):(r=e.c[t.p][1],gh(e.a,x,ct(u(St(e.a,x),19).a+1-r)),gh(e.b,x,Ue(ft(St(e.b,x)))+_-r*e.e)),(e.q==(f4(),LO)&&(u(St(e.a,x),19).a>e.j||u(St(e.a,x-1),19).a>e.j)||e.q==MO&&(Ue(ft(St(e.b,x)))>e.k||Ue(ft(St(e.b,x-1)))>e.k))&&(v=!1),h=new cr(fr(Xo(t).a.Kc(),new V));Vr(h);)o=u(Pr(h),17),d=o.c.i,e.f[d.p]==x&&(L=slt(e,d),s=s+u(L.a,19).a,v=v&&It(Mt(L.b)));return e.f[t.p]=x,s=s+e.c[t.p][0],new xa(ct(s),(Mn(),!!v))}function t6e(e,t,n,r,s){var o,h,d,v,x,_,L,P,z,q,W,X,le;for(L=new Mr,h=new st,Sat(e,n,e.d.fg(),h,L),Sat(e,r,e.d.gg(),h,L),e.b=.2*(W=bot(ic(new vn(null,new mn(h,16)),new GQ)),X=bot(ic(new vn(null,new mn(h,16)),new qQ)),b.Math.min(W,X)),o=0,d=0;d<h.c.length-1;d++)for(v=(xn(d,h.c.length),u(h.c[d],112)),q=d+1;q<h.c.length;q++)o+=K5e(e,v,(xn(q,h.c.length),u(h.c[q],112)));for(P=u(K(t,(et(),Lk)),230),o>=2&&(le=Fot(h,!0,P),!e.e&&(e.e=new $je(e)),wcn(e.e,le,h,e.b)),wst(h,P),nmn(h),z=-1,_=new C(h);_.a<_.c.c.length;)x=u(Y(_),112),!(b.Math.abs(x.s-x.c)<Sd)&&(z=b.Math.max(z,x.o),e.d.dg(x,s,e.c));return e.d.a.a.$b(),z+1}function alt(e,t){var n,r,s,o,h;n=Ue(ft(K(t,(pt(),Mf)))),n<2&&Ye(t,Mf,2),r=u(K(t,Zl),103),r==(wo(),f0)&&Ye(t,Zl,wH(t)),s=u(K(t,Uvt),19),s.a==0?Ye(t,(et(),Lk),new Fie):Ye(t,(et(),Lk),new m$(s.a)),o=Mt(K(t,gS)),o==null&&Ye(t,gS,(Mn(),je(K(t,K0))===je(($0(),_E)))),ms(new vn(null,new mn(t.a,16)),new Ts(e)),ms(ic(new vn(null,new mn(t.b,16)),new B5),new Yi(e)),h=new Jut(t),Ye(t,(et(),G4),h),d$(e.a),Xd(e.a,(ro(),Pd),u(K(t,Dy),246)),Xd(e.a,c2,u(K(t,jTe),246)),Xd(e.a,bu,u(K(t,dS),246)),Xd(e.a,Zc,u(K(t,Zq),246)),Xd(e.a,Go,gsn(u(K(t,K0),218))),pVe(e.a,Nyn(t)),Ye(t,Ple,mz(e.a,t))}function Hwn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt;return P=e.c[t],z=e.c[n],q=u(K(P,(et(),Sk)),15),!!q&&q.gc()!=0&&q.Hc(z)||(W=P.k!=(zn(),ca)&&z.k!=ca,X=u(K(P,xw),10),le=u(K(z,xw),10),Ce=X!=le,Ee=!!X&&X!=P||!!le&&le!=z,Ne=use(P,(ht(),An)),Ve=use(z,xr),Ee=Ee|(use(P,xr)||use(z,An)),nt=Ee&&Ce||Ne||Ve,W&&nt)||P.k==(zn(),Jc)&&z.k==Hs||z.k==(zn(),Jc)&&P.k==Hs?!1:(_=e.c[t],o=e.c[n],s=rst(e.e,_,o,(ht(),Dn)),v=rst(e.i,_,o,$n),Ogn(e.f,_,o),x=Cnt(e.b,_,o)+u(s.a,19).a+u(v.a,19).a+e.f.d,d=Cnt(e.b,o,_)+u(s.b,19).a+u(v.b,19).a+e.f.b,e.a&&(L=u(K(_,Mi),11),h=u(K(o,Mi),11),r=Hit(e.g,L,h),x+=u(r.a,19).a,d+=u(r.b,19).a),x>d)}function zwn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W;for(n=u(K(e,(pt(),bs)),98),h=e.f,o=e.d,d=h.a+o.b+o.c,v=0-o.d-e.c.b,_=h.b+o.d+o.a-e.c.b,x=new st,L=new st,s=new C(t);s.a<s.c.c.length;){switch(r=u(Y(s),10),n.g){case 1:case 2:case 3:$gn(r);break;case 4:P=u(K(r,Cw),8),z=P?P.a:0,r.n.a=d*Ue(ft(K(r,(et(),Ew))))-z,oH(r,!0,!1);break;case 5:q=u(K(r,Cw),8),W=q?q.a:0,r.n.a=Ue(ft(K(r,(et(),Ew))))-W,oH(r,!0,!1),h.a=b.Math.max(h.a,r.n.a+r.o.a/2)}switch(u(K(r,(et(),vc)),61).g){case 1:r.n.b=v,x.c[x.c.length]=r;break;case 3:r.n.b=_,L.c[L.c.length]=r}}switch(n.g){case 1:case 2:Qnt(x,e),Qnt(L,e);break;case 3:Znt(x,e),Znt(L,e)}}function Gwn(e,t){var n,r,s,o,h,d,v,x,_,L;for(_=new st,L=new L3,o=null,s=0,r=0;r<t.length;++r)switch(n=t[r],can(o,n)&&(s=a3e(e,L,_,uV,s)),ta(n,(et(),xw))&&(o=u(K(n,xw),10)),n.k.g){case 0:for(v=z2e(L6(ac(n,(ht(),An)),new IB));nie(v);)h=u(pme(v),11),e.d[h.p]=s++,_.c[_.c.length]=h;for(s=a3e(e,L,_,uV,s),x=z2e(L6(ac(n,xr),new IB));nie(x);)h=u(pme(x),11),e.d[h.p]=s++,_.c[_.c.length]=h;break;case 3:ac(n,N_e).dc()||(h=u(ac(n,N_e).Xb(0),11),e.d[h.p]=s++,_.c[_.c.length]=h),ac(n,uV).dc()||$p(L,n);break;case 1:for(d=ac(n,(ht(),Dn)).Kc();d.Ob();)h=u(d.Pb(),11),e.d[h.p]=s++,_.c[_.c.length]=h;ac(n,$n).Jc(new aqe(L,n))}return a3e(e,L,_,uV,s),_}function olt(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce;for(x=gs,_=gs,d=Ds,v=Ds,P=new C(t.i);P.a<P.c.c.length;)L=u(Y(P),65),s=u(u(er(e.g,L.a),46).b,33),C1(s,L.b.c,L.b.d),x=b.Math.min(x,s.i),_=b.Math.min(_,s.j),d=b.Math.max(d,s.i+s.g),v=b.Math.max(v,s.j+s.f);for(z=u(Ft(e.c,(nI(),xyt)),116),sw(e.c,d-x+(z.b+z.c),v-_+(z.d+z.a),!0,!0),f4e(e.c,-x+z.b,-_+z.d),r=new rr(NYe(e.c));r.e!=r.i.gc();)n=u(pr(r),79),h=d4(n,!0,!0),q=n0(n),X=Kp(n),W=new Pt(q.i+q.g/2,q.j+q.f/2),o=new Pt(X.i+X.g/2,X.j+X.f/2),le=da(new Pt(o.a,o.b),W),c7(le,q.g,q.f),Ni(W,le),Ce=da(new Pt(W.a,W.b),o),c7(Ce,X.g,X.f),Ni(o,Ce),nj(h,W.a,W.b),tj(h,o.a,o.b)}function qwn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q;if(e.c=e.d,q=Mt(K(t,(pt(),Wvt))),z=q==null||(Sn(q),q),o=u(K(t,(et(),eu)),21).Hc((mo(),Th)),s=u(K(t,bs),98),n=!(s==(wa(),$b)||s==p0||s==tu),z&&(n||!o)){for(L=new C(t.a);L.a<L.c.c.length;)x=u(Y(L),10),x.p=0;for(P=new st,_=new C(t.a);_.a<_.c.c.length;)if(x=u(Y(_),10),r=tut(e,x,null),r){for(v=new 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g6,s=(n1(),ie(re(Ey,1),tt,232,0,[pc,lu,bc])),h=0,v=s.length;h<v;++h)n=s[h],x=Qte(_,pc,n),x&&(P.d=b.Math.max(P.d,x.Re()));for(r=ie(re(Ey,1),tt,232,0,[pc,lu,bc]),o=0,d=r.length;o<d;++o)n=r[o],x=Qte(_,bc,n),x&&(P.a=b.Math.max(P.a,x.Re()));for(W=ie(re(Ey,1),tt,232,0,[pc,lu,bc]),le=0,Ee=W.length;le<Ee;++le)z=W[le],x=Qte(_,z,pc),x&&(P.b=b.Math.max(P.b,x.Se()));for(q=ie(re(Ey,1),tt,232,0,[pc,lu,bc]),X=0,Ce=q.length;X<Ce;++X)z=q[X],x=Qte(_,z,bc),x&&(P.c=b.Math.max(P.c,x.Se()));return P.d>0&&(P.d+=_.n.d,P.d+=_.d),P.a>0&&(P.a+=_.n.a,P.a+=_.d),P.b>0&&(P.b+=_.n.b,P.b+=_.d),P.c>0&&(P.c+=_.n.c,P.c+=_.d),P}function wlt(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q;for(P=n.d,L=n.c,o=new Pt(n.f.a+n.d.b+n.d.c,n.f.b+n.d.d+n.d.a),h=o.b,x=new C(e.a);x.a<x.c.c.length;)if(d=u(Y(x),10),d.k==(zn(),Ls)){switch(r=u(K(d,(et(),vc)),61),s=u(K(d,P9e),8),_=d.n,r.g){case 2:_.a=n.f.a+P.c-L.a;break;case 4:_.a=-L.a-P.b}switch(q=0,r.g){case 2:case 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Io(Ce.c.e),ks(t,bt,t.c.b,t.c),jst(le,u(s1(t,1),8),Ce.b.f),jst(bt,u(s1(t,t.b-2),8),Ce.c.f),n=d4(r,!0,!0),mI(t,n));zt=d-x+(q.b+q.c),h=v-_+(q.d+q.a),sw(s,zt,h,!1,!1)}function wmn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee;for(L=e.b,_=new Ea(L,0),Dm(_,new Bh(e)),Ce=!1,h=1;_.b<_.d.gc();){for(x=(Qn(_.b<_.d.gc()),u(_.d.Xb(_.c=_.b++),29)),W=(xn(h,L.c.length),u(L.c[h],29)),X=Pv(x.a),le=X.c.length,q=new C(X);q.a<q.c.c.length;)P=u(Y(q),10),No(P,W);if(Ce){for(z=_we(new lv(X),0);z.c.Sb();)for(P=u(xZe(z),10),o=new C(Pv(Xo(P)));o.a<o.c.c.length;)s=u(Y(o),17),iw(s,!0),Ye(e,(et(),yO),(Mn(),!0)),r=Yut(e,s,le),n=u(K(P,yw),305),Ee=u(St(r,r.c.length-1),17),n.k=Ee.c.i,n.n=Ee,n.b=s.d.i,n.c=s;Ce=!1}else X.c.length!=0&&(t=(xn(0,X.c.length),u(X.c[0],10)),t.k==(zn(),U1)&&(Ce=!0,h=-1));++h}for(d=new Ea(e.b,0);d.b<d.d.gc();)v=(Qn(d.b<d.d.gc()),u(d.d.Xb(d.c=d.b++),29)),v.a.c.length==0&&Ol(d)}function mmn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le;if(_=u(u(Ii(e.r,t),21),84),_.gc()<=2||t==(ht(),$n)||t==(ht(),Dn)){Flt(e,t);return}for(W=e.u.Hc((ol(),$k)),n=t==(ht(),An)?(Vv(),sO):(Vv(),iO),le=t==An?(Lu(),Lf):(Lu(),u1),r=jpe(rve(n),e.s),X=t==An?gs:Ds,x=_.Kc();x.Ob();)d=u(x.Pb(),111),!(!d.c||d.c.d.c.length<=0)&&(q=d.b.rf(),z=d.e,L=d.c,P=L.i,P.b=(o=L.n,L.e.a+o.b+o.c),P.a=(h=L.n,L.e.b+h.d+h.a),W?(P.c=z.a-(s=L.n,L.e.a+s.b+s.c)-e.s,W=!1):P.c=z.a+q.a+e.s,JM(le,O6e),L.f=le,e1(L,(al(),Af)),it(r.d,new Lne(P,Rye(r,P))),X=t==An?b.Math.min(X,z.b):b.Math.max(X,z.b+d.b.rf().b));for(X+=t==An?-e.t:e.t,Jye((r.e=X,r)),v=_.Kc();v.Ob();)d=u(v.Pb(),111),!(!d.c||d.c.d.c.length<=0)&&(P=d.c.i,P.c-=d.e.a,P.d-=d.e.b)}function ymn(e,t,n){var r;if(kr(n,"StretchWidth layering",1),t.a.c.length==0){ur(n);return}for(e.c=t,e.t=0,e.u=0,e.i=gs,e.g=Ds,e.d=Ue(ft(K(t,(pt(),Mf)))),Xcn(e),Zdn(e),Qdn(e),thn(e),rcn(e),e.i=b.Math.max(1,e.i),e.g=b.Math.max(1,e.g),e.d=e.d/e.i,e.f=e.g/e.i,e.s=hun(e),r=new Bh(e.c),it(e.c.b,r),e.r=Pv(e.p),e.n=s$(e.k,e.k.length);e.r.c.length!=0;)e.o=_an(e),!e.o||fnt(e)&&e.b.a.gc()!=0?(Chn(e,r),r=new Bh(e.c),it(e.c.b,r),no(e.a,e.b),e.b.a.$b(),e.t=e.u,e.u=0):fnt(e)?(e.c.b.c=Me(Yn,yt,1,0,5,1),r=new Bh(e.c),it(e.c.b,r),e.t=0,e.u=0,e.b.a.$b(),e.a.a.$b(),++e.f,e.r=Pv(e.p),e.n=s$(e.k,e.k.length)):(No(e.o,r),Au(e.r,e.o),Gs(e.b,e.o),e.t=e.t-e.k[e.o.p]*e.d+e.j[e.o.p],e.u+=e.e[e.o.p]*e.d);t.a.c=Me(Yn,yt,1,0,5,1),Nse(t.b),ur(n)}function kmn(e){var t,n,r,s;for(ms(Vi(new vn(null,new mn(e.a.b,16)),new bX),new vX),dhn(e),ms(Vi(new vn(null,new mn(e.a.b,16)),new wX),new mX),e.c==($0(),jk)&&(ms(Vi(ic(new vn(null,new mn(new vm(e.f),1)),new yX),new kX),new Dee(e)),ms(Vi(Cu(ic(ic(new vn(null,new mn(e.d.b,16)),new bB),new xX),new vB),new EX),new Oee(e))),s=new Pt(gs,gs),t=new Pt(Ds,Ds),r=new C(e.a.b);r.a<r.c.c.length;)n=u(Y(r),57),s.a=b.Math.min(s.a,n.d.c),s.b=b.Math.min(s.b,n.d.d),t.a=b.Math.max(t.a,n.d.c+n.d.b),t.b=b.Math.max(t.b,n.d.d+n.d.a);Ni(Zf(e.d.c),gj(new Pt(s.a,s.b))),Ni(Zf(e.d.f),da(new Pt(t.a,t.b),s)),Egn(e,s,t),sl(e.f),sl(e.b),sl(e.g),sl(e.e),e.a.a.c=Me(Yn,yt,1,0,5,1),e.a.b.c=Me(Yn,yt,1,0,5,1),e.a=null,e.d=null}function mlt(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee;for(s=new st,W=new C(t.a);W.a<W.c.c.length;)if(q=u(Y(W),10),z=q.e,z&&(r=mlt(e,z,q),Rs(s,r),Pwn(e,z,q),u(K(z,(et(),eu)),21).Hc((mo(),Th))))for(Ce=u(K(q,(pt(),bs)),98),P=u(K(q,Py),174).Hc((ol(),Z0)),le=new C(q.j);le.a<le.c.c.length;)for(X=u(Y(le),11),o=u(er(e.b,X),10),o||(o=EI(X,Ce,X.j,-(X.e.c.length-X.g.c.length),null,new Fa,X.o,u(K(z,Zl),103),z),Ye(o,Mi,X),Si(e.b,X,o),it(z.a,o)),h=u(St(o.j,0),11),_=new C(X.f);_.a<_.c.c.length;)x=u(Y(_),70),d=new bze,d.o.a=x.o.a,d.o.b=x.o.b,it(h.f,d),P||(Ee=X.j,L=0,g_(u(K(q,Py),21))&&(L=F4e(x.n,x.o,X.o,0,Ee)),Ce==(wa(),X1)||(ht(),wu).Hc(Ee)?d.o.a=L:d.o.b=L);return v=new st,Uwn(e,t,n,s,v),n&&cwn(e,t,n,v),v}function r6e(e,t,n){var r,s,o,h,d,v,x,_,L;if(!e.c[t.c.p][t.p].e){for(e.c[t.c.p][t.p].e=!0,e.c[t.c.p][t.p].b=0,e.c[t.c.p][t.p].d=0,e.c[t.c.p][t.p].a=null,_=new C(t.j);_.a<_.c.c.length;)for(x=u(Y(_),11),L=n?new sa(x):new Ar(x),v=L.Kc();v.Ob();)d=u(v.Pb(),11),h=d.i,h.c==t.c?h!=t&&(r6e(e,h,n),e.c[t.c.p][t.p].b+=e.c[h.c.p][h.p].b,e.c[t.c.p][t.p].d+=e.c[h.c.p][h.p].d):(e.c[t.c.p][t.p].d+=e.g[d.p],++e.c[t.c.p][t.p].b);if(o=u(K(t,(et(),D9e)),15),o)for(s=o.Kc();s.Ob();)r=u(s.Pb(),10),t.c==r.c&&(r6e(e,r,n),e.c[t.c.p][t.p].b+=e.c[r.c.p][r.p].b,e.c[t.c.p][t.p].d+=e.c[r.c.p][r.p].d);e.c[t.c.p][t.p].b>0&&(e.c[t.c.p][t.p].d+=vl(e.i,24)*NI*.07000000029802322-.03500000014901161,e.c[t.c.p][t.p].a=e.c[t.c.p][t.p].d/e.c[t.c.p][t.p].b)}}function xmn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X;for(q=new C(e);q.a<q.c.c.length;){for(z=u(Y(q),10),mg(z.n),mg(z.o),ime(z.f),Pct(z),Jpn(z),X=new C(z.j);X.a<X.c.c.length;){for(W=u(Y(X),11),mg(W.n),mg(W.a),mg(W.o),Vs(W,kit(W.j)),o=u(K(W,(pt(),zg)),19),o&&Ye(W,zg,ct(-o.a)),s=new C(W.g);s.a<s.c.c.length;){for(r=u(Y(s),17),n=ii(r.a,0);n.b!=n.d.c;)t=u(ri(n),8),mg(t);if(v=u(K(r,Fo),74),v)for(d=ii(v,0);d.b!=d.d.c;)h=u(ri(d),8),mg(h);for(L=new C(r.b);L.a<L.c.c.length;)x=u(Y(L),70),mg(x.n),mg(x.o)}for(P=new C(W.f);P.a<P.c.c.length;)x=u(Y(P),70),mg(x.n),mg(x.o)}for(z.k==(zn(),Ls)&&(Ye(z,(et(),vc),kit(u(K(z,vc),61))),upn(z)),_=new C(z.b);_.a<_.c.c.length;)x=u(Y(_),70),Pct(x),mg(x.o),mg(x.n)}}function Emn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt;for(e.e=t,d=Edn(t),nt=new st,r=new C(d);r.a<r.c.c.length;){for(n=u(Y(r),15),bt=new st,nt.c[nt.c.length]=bt,v=new Ys,q=n.Kc();q.Ob();){for(z=u(q.Pb(),33),o=pI(e,z,!0,0,0),bt.c[bt.c.length]=o,W=z.i,X=z.j,P=(!z.n&&(z.n=new at(Jo,z,1,7)),z.n),L=new rr(P);L.e!=L.i.gc();)x=u(pr(L),137),s=pI(e,x,!1,W,X),bt.c[bt.c.length]=s;for(Ve=(!z.c&&(z.c=new at(xl,z,9,9)),z.c),Ce=new rr(Ve);Ce.e!=Ce.i.gc();)for(le=u(pr(Ce),118),h=pI(e,le,!1,W,X),bt.c[bt.c.length]=h,Ee=le.i+W,Ne=le.j+X,P=(!le.n&&(le.n=new at(Jo,le,1,7)),le.n),_=new rr(P);_.e!=_.i.gc();)x=u(pr(_),137),s=pI(e,x,!1,Ee,Ne),bt.c[bt.c.length]=s;no(v,e$(B1(ie(re(q1,1),yt,20,0,[z0(z),hI(z)]))))}dgn(e,v,bt)}return e.f=new dze(nt),Ho(e.f,t),e.f}function Tmn(e,t,n,r,s){var o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr;In=er(e.e,r),In==null&&(In=new p6,z=u(In,183),Ce=t+"_s",Ee=Ce+s,P=new Bm(Ee),t1(z,Id,P)),Ut=u(In,183),O6(n,Ut),dr=new p6,Y2(dr,"x",r.j),Y2(dr,"y",r.k),t1(Ut,B1t,dr),bt=new p6,Y2(bt,"x",r.b),Y2(bt,"y",r.c),t1(Ut,"endPoint",bt),L=hM((!r.a&&(r.a=new Bs(ef,r,5)),r.a)),q=!L,q&&(nt=new hg,o=new m$e(nt),Da((!r.a&&(r.a=new Bs(ef,r,5)),r.a),o),t1(Ut,dG,nt)),v=nse(r),Ne=!!v,Ne&&h4e(e.a,Ut,n8e,Ose(e,nse(r))),le=rse(r),Ve=!!le,Ve&&h4e(e.a,Ut,t8e,Ose(e,rse(r))),x=(!r.e&&(r.e=new wn(os,r,10,9)),r.e).i==0,W=!x,W&&(zt=new hg,h=new jqe(e,zt),Da((!r.e&&(r.e=new wn(os,r,10,9)),r.e),h),t1(Ut,i8e,zt)),_=(!r.g&&(r.g=new wn(os,r,9,10)),r.g).i==0,X=!_,X&&(Rn=new hg,d=new $qe(e,Rn),Da((!r.g&&(r.g=new wn(os,r,9,10)),r.g),d),t1(Ut,r8e,Rn))}function _mn(e){Mm();var t,n,r,s,o,h,d;for(r=e.f.n,h=Zve(e.r).a.nc();h.Ob();){if(o=u(h.Pb(),111),s=0,o.b.Xe((bi(),kl))&&(s=Ue(ft(o.b.We(kl))),s<0))switch(o.b.Hf().g){case 1:r.d=b.Math.max(r.d,-s);break;case 3:r.a=b.Math.max(r.a,-s);break;case 2:r.c=b.Math.max(r.c,-s);break;case 4:r.b=b.Math.max(r.b,-s)}if(g_(e.u))switch(t=Yin(o.b,s),d=!u(e.e.We(p2),174).Hc((wl(),nN)),n=!1,o.b.Hf().g){case 1:n=t>r.d,r.d=b.Math.max(r.d,t),d&&n&&(r.d=b.Math.max(r.d,r.a),r.a=r.d+s);break;case 3:n=t>r.a,r.a=b.Math.max(r.a,t),d&&n&&(r.a=b.Math.max(r.a,r.d),r.d=r.a+s);break;case 2:n=t>r.c,r.c=b.Math.max(r.c,t),d&&n&&(r.c=b.Math.max(r.b,r.c),r.b=r.c+s);break;case 4:n=t>r.b,r.b=b.Math.max(r.b,t),d&&n&&(r.b=b.Math.max(r.b,r.c),r.c=r.b+s)}}}function Cmn(e){var t,n,r,s,o,h,d,v,x,_,L;for(x=new C(e);x.a<x.c.c.length;){switch(v=u(Y(x),10),h=u(K(v,(pt(),vu)),163),o=null,h.g){case 1:case 2:o=(sb(),F4);break;case 3:case 4:o=(sb(),aE)}if(o)Ye(v,(et(),Fq),(sb(),F4)),o==aE?sz(v,h,(vo(),ul)):o==F4&&sz(v,h,(vo(),hu));else if(R3(u(K(v,bs),98))&&v.j.c.length!=0){for(t=!0,L=new C(v.j);L.a<L.c.c.length;){if(_=u(Y(L),11),!(_.j==(ht(),$n)&&_.e.c.length-_.g.c.length>0||_.j==Dn&&_.e.c.length-_.g.c.length<0)){t=!1;break}for(s=new C(_.g);s.a<s.c.c.length;)if(n=u(Y(s),17),d=u(K(n.d.i,vu),163),d==(mh(),hS)||d==Ly){t=!1;break}for(r=new C(_.e);r.a<r.c.c.length;)if(n=u(Y(r),17),d=u(K(n.c.i,vu),163),d==(mh(),lS)||d==l2){t=!1;break}}t&&sz(v,h,(vo(),yS))}}}function Smn(e,t,n,r,s){var o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt;for(nt=0,z=0,L=new C(t.e);L.a<L.c.c.length;){for(_=u(Y(L),10),P=0,d=0,v=n?u(K(_,lV),19).a:$a,le=r?u(K(_,hV),19).a:$a,x=b.Math.max(v,le),Ee=new C(_.j);Ee.a<Ee.c.c.length;){if(Ce=u(Y(Ee),11),Ne=_.n.b+Ce.n.b+Ce.a.b,r)for(h=new C(Ce.g);h.a<h.c.c.length;)o=u(Y(h),17),W=o.d,q=W.i,t!=e.a[q.p]&&(X=b.Math.max(u(K(q,lV),19).a,u(K(q,hV),19).a),Ve=u(K(o,(pt(),Ik)),19).a,Ve>=x&&Ve>=X&&(P+=q.n.b+W.n.b+W.a.b-Ne,++d));if(n)for(h=new C(Ce.e);h.a<h.c.c.length;)o=u(Y(h),17),W=o.c,q=W.i,t!=e.a[q.p]&&(X=b.Math.max(u(K(q,lV),19).a,u(K(q,hV),19).a),Ve=u(K(o,(pt(),Ik)),19).a,Ve>=x&&Ve>=X&&(P+=q.n.b+W.n.b+W.a.b-Ne,++d))}d>0&&(nt+=P/d,++z)}z>0?(t.a=s*nt/z,t.g=z):(t.a=0,t.g=0)}function Amn(e,t){var n,r,s,o,h,d,v,x,_,L,P;for(s=new C(e.a.b);s.a<s.c.c.length;)for(n=u(Y(s),29),v=new C(n.a);v.a<v.c.c.length;)d=u(Y(v),10),t.j[d.p]=d,t.i[d.p]=t.o==(M1(),K1)?Ds:gs;for(sl(e.c),h=e.a.b,t.c==(yd(),Gg)&&(h=we(h,152)?H6(u(h,152)):we(h,131)?u(h,131).a:we(h,54)?new lv(h):new Em(h)),krn(e.e,t,e.b),mM(t.p,null),o=h.Kc();o.Ob();)for(n=u(o.Pb(),29),x=n.a,t.o==(M1(),K1)&&(x=we(x,152)?H6(u(x,152)):we(x,131)?u(x,131).a:we(x,54)?new lv(x):new Em(x)),P=x.Kc();P.Ob();)L=u(P.Pb(),10),t.g[L.p]==L&&$lt(e,L,t);for(vwn(e,t),r=h.Kc();r.Ob();)for(n=u(r.Pb(),29),P=new C(n.a);P.a<P.c.c.length;)L=u(Y(P),10),t.p[L.p]=t.p[t.g[L.p].p],L==t.g[L.p]&&(_=Ue(t.i[t.j[L.p].p]),(t.o==(M1(),K1)&&_>Ds||t.o==Pb&&_<gs)&&(t.p[L.p]=Ue(t.p[L.p])+_));e.e.cg()}function ylt(e,t,n,r){var s,o,h,d,v;return d=new i6e(t),Lgn(d,r),s=!0,e&&e.Xe((bi(),Mw))&&(o=u(e.We((bi(),Mw)),103),s=o==(wo(),f0)||o==Xh||o==Df),Tct(d,!1),Mu(d.e.wf(),new eve(d,!1,s)),ure(d,d.f,(n1(),pc),(ht(),An)),ure(d,d.f,bc,xr),ure(d,d.g,pc,Dn),ure(d,d.g,bc,$n),cit(d,An),cit(d,xr),_Ye(d,$n),_Ye(d,Dn),Mm(),h=d.A.Hc((Bl(),Gy))&&d.B.Hc((wl(),rN))?yrt(d):null,h&&XVt(d.a,h),_mn(d),Hun(d),zun(d),Xwn(d),L2n(d),gln(d),Kie(d,An),Kie(d,xr),c2n(d),Avn(d),n&&(Con(d),pln(d),Kie(d,$n),Kie(d,Dn),v=d.B.Hc((wl(),VS)),mat(d,v,An),mat(d,v,xr),yat(d,v,$n),yat(d,v,Dn),ms(new vn(null,new mn(new E1(d.i),0)),new ea),ms(Vi(new vn(null,Zve(d.r).a.oc()),new Ca),new Sa),Mhn(d),d.e.uf(d.o),ms(new vn(null,Zve(d.r).a.oc()),new cg)),d.o}function Lmn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W;for(x=gs,r=new C(e.a.b);r.a<r.c.c.length;)t=u(Y(r),81),x=b.Math.min(x,t.d.f.g.c+t.e.a);for(z=new as,h=new C(e.a.a);h.a<h.c.c.length;)o=u(Y(h),189),o.i=x,o.e==0&&ks(z,o,z.c.b,z.c);for(;z.b!=0;){for(o=u(z.b==0?null:(Qn(z.b!=0),bh(z,z.a.a)),189),s=o.f.g.c,P=o.a.a.ec().Kc();P.Ob();)_=u(P.Pb(),81),W=o.i+_.e.a,_.d.g||_.g.c<W?_.o=W:_.o=_.g.c;for(s-=o.f.o,o.b+=s,e.c==(wo(),Df)||e.c==Y0?o.c+=s:o.c-=s,L=o.a.a.ec().Kc();L.Ob();)for(_=u(L.Pb(),81),v=_.f.Kc();v.Ob();)d=u(v.Pb(),81),Dp(e.c)?q=e.f.ef(_,d):q=e.f.ff(_,d),d.d.i=b.Math.max(d.d.i,_.o+_.g.b+q-d.e.a),d.k||(d.d.i=b.Math.max(d.d.i,d.g.c-d.e.a)),--d.d.e,d.d.e==0&&ci(z,d.d)}for(n=new C(e.a.b);n.a<n.c.c.length;)t=u(Y(n),81),t.g.c=t.o}function Mmn(e){var t,n,r,s,o,h,d,v;switch(d=e.b,t=e.a,u(K(e,(GH(),T7e)),427).g){case 0:aa(d,new ue(new Ns));break;case 1:default:aa(d,new ue(new mc))}switch(u(K(e,x7e),428).g){case 1:aa(d,new ai),aa(d,new yu),aa(d,new 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dg(e.c).a);d.b;)h=$v(d),r=u(h.cd(),79),n=u(h.dd(),395).a,s=d4(r,!1,!1),L=mst(n0(r),iI(s),n),mI(L,s),Ee=Dst(r),Ee&&Yo(t,Ee,0)==-1&&(t.c[t.c.length]=Ee,PYe(Ee,(Qn(L.b!=0),u(L.a.a.c,8)),n));for(X=new ob(new dg(e.d).a);X.b;)W=$v(X),r=u(W.cd(),79),n=u(W.dd(),395).a,s=d4(r,!1,!1),L=mst(Kp(r),BD(iI(s)),n),L=BD(L),mI(L,s),Ee=Ist(r),Ee&&Yo(t,Ee,0)==-1&&(t.c[t.c.length]=Ee,PYe(Ee,(Qn(L.b!=0),u(L.c.b.c,8)),n))}function klt(e,t,n,r){var s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt;if(n.c.length!=0){for(z=new st,P=new C(n);P.a<P.c.c.length;)L=u(Y(P),33),it(z,new Pt(L.i,L.j));for(r.n&&t&&yf(r,kf(t),(Pl(),nh));p4e(e,n);)ez(e,n,!1);for(r.n&&t&&yf(r,kf(t),(Pl(),nh)),h=0,d=0,s=null,n.c.length!=0&&(s=(xn(0,n.c.length),u(n.c[0],33)),h=s.i-(xn(0,z.c.length),u(z.c[0],8)).a,d=s.j-(xn(0,z.c.length),u(z.c[0],8)).b),o=b.Math.sqrt(h*h+d*d),_=znt(n);_.a.gc()!=0;){for(x=_.a.ec().Kc();x.Ob();)v=u(x.Pb(),33),q=e.f,W=q.i+q.g/2,X=q.j+q.f/2,le=v.i+v.g/2,Ce=v.j+v.f/2,Ee=le-W,Ne=Ce-X,Ve=b.Math.sqrt(Ee*Ee+Ne*Ne),nt=Ee/Ve,bt=Ne/Ve,Du(v,v.i+nt*o),Iu(v,v.j+bt*o);r.n&&t&&yf(r,kf(t),(Pl(),nh)),_=znt(new Uu(_))}e.a&&e.a.lg(new Uu(_)),r.n&&t&&yf(r,kf(t),(Pl(),nh)),klt(e,t,new Uu(_),r)}}function Imn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le;if(W=e.n,X=e.o,P=e.d,L=Ue(ft(Qm(e,(pt(),Xle)))),t){for(_=L*(t.gc()-1),z=0,v=t.Kc();v.Ob();)h=u(v.Pb(),10),_+=h.o.a,z=b.Math.max(z,h.o.b);for(le=W.a-(_-X.a)/2,o=W.b-P.d+z,r=X.a/(t.gc()+1),s=r,d=t.Kc();d.Ob();)h=u(d.Pb(),10),h.n.a=le,h.n.b=o-h.o.b,le+=h.o.a+L,x=Oot(h),x.n.a=h.o.a/2-x.a.a,x.n.b=h.o.b,q=u(K(h,(et(),Bq)),11),q.e.c.length+q.g.c.length==1&&(q.n.a=s-q.a.a,q.n.b=0,rc(q,e)),s+=r}if(n){for(_=L*(n.gc()-1),z=0,v=n.Kc();v.Ob();)h=u(v.Pb(),10),_+=h.o.a,z=b.Math.max(z,h.o.b);for(le=W.a-(_-X.a)/2,o=W.b+X.b+P.a-z,r=X.a/(n.gc()+1),s=r,d=n.Kc();d.Ob();)h=u(d.Pb(),10),h.n.a=le,h.n.b=o,le+=h.o.a+L,x=Oot(h),x.n.a=h.o.a/2-x.a.a,x.n.b=0,q=u(K(h,(et(),Bq)),11),q.e.c.length+q.g.c.length==1&&(q.n.a=s-q.a.a,q.n.b=X.b,rc(q,e)),s+=r}}function Omn(e,t){var n,r,s,o,h,d;if(u(K(t,(et(),eu)),21).Hc((mo(),Th))){for(d=new C(t.a);d.a<d.c.c.length;)o=u(Y(d),10),o.k==(zn(),Hs)&&(s=u(K(o,(pt(),Xq)),142),e.c=b.Math.min(e.c,o.n.a-s.b),e.a=b.Math.max(e.a,o.n.a+o.o.a+s.c),e.d=b.Math.min(e.d,o.n.b-s.d),e.b=b.Math.max(e.b,o.n.b+o.o.b+s.a));for(h=new C(t.a);h.a<h.c.c.length;)if(o=u(Y(h),10),o.k!=(zn(),Hs))switch(o.k.g){case 2:if(r=u(K(o,(pt(),vu)),163),r==(mh(),l2)){o.n.a=e.c-10,qit(o,new AY).Jb(new lo(o));break}if(r==Ly){o.n.a=e.a+10,qit(o,new LY).Jb(new Sl(o));break}if(n=u(K(o,Lb),303),n==(P0(),$4)){Vut(o).Jb(new Yf(o)),o.n.b=e.d-10;break}if(n==Tk){Vut(o).Jb(new mp(o)),o.n.b=e.b+10;break}break;default:throw J(new Ln("The node type "+o.k+" is not supported by the "+w3n))}}}function Nmn(e,t,n,r){var s,o,h,d,v,x,_,L,P,z,q,W,X;for(v=new Pt(r.i+r.g/2,r.j+r.f/2),z=Hut(r),q=u(Ft(t,(pt(),bs)),98),X=u(Ft(r,gE),61),Kqe(tit(r),Ny)||(r.i==0&&r.j==0?W=0:W=ffn(r,X),So(r,Ny,W)),x=new Pt(t.g,t.f),s=EI(r,q,X,z,x,v,new Pt(r.g,r.f),u(K(n,Zl),103),n),Ye(s,(et(),Mi),r),o=u(St(s.j,0),11),mee(o,sbn(r)),Ye(s,Py,(ol(),rn(m2))),L=u(Ft(t,Py),174).Hc(Z0),d=new rr((!r.n&&(r.n=new at(Jo,r,1,7)),r.n));d.e!=d.i.gc();)if(h=u(pr(d),137),!It(Mt(Ft(h,Ob)))&&h.a&&(P=Mie(h),it(o.f,P),!L))switch(_=0,g_(u(Ft(t,Py),21))&&(_=F4e(new Pt(h.i,h.j),new Pt(h.g,h.f),new Pt(r.g,r.f),0,X)),X.g){case 2:case 4:P.o.a=_;break;case 1:case 3:P.o.b=_}Ye(s,bE,ft(Ft(us(t),bE))),Ye(s,vE,ft(Ft(us(t),vE))),Ye(s,Ry,ft(Ft(us(t),Ry))),it(n.a,s),Si(e.a,r,s)}function xlt(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve;for(kr(n,"Processor arrange level",1),_=0,hn(),Y3(t,new epe((Tc(),gV))),o=t.b,d=ii(t,t.b),x=!0;x&&d.b.b!=d.d.a;)le=u(mre(d),86),u(K(le,gV),19).a==0?--o:x=!1;if(Ve=new Zd(t,0,o),h=new dne(Ve),Ve=new Zd(t,o,t.b),v=new dne(Ve),h.b==0)for(q=ii(v,0);q.b!=q.d.c;)z=u(ri(q),86),Ye(z,vV,ct(_++));else for(L=h.b,Ne=ii(h,0);Ne.b!=Ne.d.c;){for(Ee=u(ri(Ne),86),Ye(Ee,vV,ct(_++)),r=eye(Ee),xlt(e,r,Kc(n,1/L|0)),Y3(r,owe(new epe(vV))),P=new as,Ce=ii(r,0);Ce.b!=Ce.d.c;)for(le=u(ri(Ce),86),X=ii(Ee.d,0);X.b!=X.d.c;)W=u(ri(X),188),W.c==le&&ks(P,W,P.c.b,P.c);for(Rh(Ee.d),no(Ee.d,P),d=ii(v,v.b),s=Ee.d.b,x=!0;0<s&&x&&d.b.b!=d.d.a;)le=u(mre(d),86),u(K(le,gV),19).a==0?(Ye(le,vV,ct(_++)),--s,F$(d)):x=!1}ur(n)}function Pmn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee;for(kr(t,"Inverted port preprocessing",1),_=e.b,x=new Ea(_,0),n=null,Ee=new st;x.b<x.d.gc();){for(Ce=n,n=(Qn(x.b<x.d.gc()),u(x.d.Xb(x.c=x.b++),29)),z=new C(Ee);z.a<z.c.c.length;)L=u(Y(z),10),No(L,Ce);for(Ee.c=Me(Yn,yt,1,0,5,1),q=new C(n.a);q.a<q.c.c.length;)if(L=u(Y(q),10),L.k==(zn(),Hs)&&R3(u(K(L,(pt(),bs)),98))){for(le=E5e(L,(vo(),ul),(ht(),$n)).Kc();le.Ob();)for(W=u(le.Pb(),11),v=W.e,d=u(j1(v,Me(Que,Ooe,17,v.c.length,0,1)),474),s=d,o=0,h=s.length;o<h;++o)r=s[o],avn(e,W,r,Ee);for(X=E5e(L,hu,Dn).Kc();X.Ob();)for(W=u(X.Pb(),11),v=W.g,d=u(j1(v,Me(Que,Ooe,17,v.c.length,0,1)),474),s=d,o=0,h=s.length;o<h;++o)r=s[o],svn(e,W,r,Ee)}}for(P=new C(Ee);P.a<P.c.c.length;)L=u(Y(P),10),No(L,n);ur(t)}function Bmn(e,t,n,r,s,o){var h,d,v,x,_,L;for(x=new $c,Ho(x,t),Vs(x,u(Ft(t,(pt(),gE)),61)),Ye(x,(et(),Mi),t),rc(x,n),L=x.o,L.a=t.g,L.b=t.f,_=x.n,_.a=t.i,_.b=t.j,Si(e.a,t,x),h=_8(Cu(ic(new vn(null,(!t.e&&(t.e=new wn(ra,t,7,4)),new mn(t.e,16))),new VP),new R5),new ar(t)),h||(h=_8(Cu(ic(new vn(null,(!t.d&&(t.d=new wn(ra,t,8,5)),new mn(t.d,16))),new dx),new x9),new or(t))),h||(h=_8(new vn(null,(!t.e&&(t.e=new wn(ra,t,7,4)),new mn(t.e,16))),new nY)),Ye(x,kO,(Mn(),!!h)),Jvn(x,o,s,u(Ft(t,Cw),8)),v=new rr((!t.n&&(t.n=new at(Jo,t,1,7)),t.n));v.e!=v.i.gc();)d=u(pr(v),137),!It(Mt(Ft(d,Ob)))&&d.a&&it(x.f,Mie(d));switch(s.g){case 2:case 1:(x.j==(ht(),An)||x.j==xr)&&r.Fc((mo(),j4));break;case 4:case 3:(x.j==(ht(),$n)||x.j==Dn)&&r.Fc((mo(),j4))}return x}function Cae(e,t,n,r,s,o,h){var d,v,x,_,L,P,z,q,W,X,le,Ce,Ee;for(P=null,r==(Ov(),g2)?P=t:r==Y4&&(P=n),W=P.a.ec().Kc();W.Ob();){for(q=u(W.Pb(),11),X=sc(ie(re(na,1),Qe,8,0,[q.i.n,q.n,q.a])).b,Ee=new Ys,d=new Ys,x=new N1(q.b);nc(x.a)||nc(x.b);)if(v=u(nc(x.a)?Y(x.a):Y(x.b),17),It(Mt(K(v,(et(),W1))))==s&&Yo(o,v,0)!=-1){if(v.d==q?le=v.c:le=v.d,Ce=sc(ie(re(na,1),Qe,8,0,[le.i.n,le.n,le.a])).b,b.Math.abs(Ce-X)<.2)continue;Ce<X?t.a._b(le)?Gs(Ee,new xa(g2,v)):Gs(Ee,new xa(Y4,v)):t.a._b(le)?Gs(d,new xa(g2,v)):Gs(d,new xa(Y4,v))}if(Ee.a.gc()>1)for(z=new Y5e(q,Ee,r),Da(Ee,new fqe(e,z)),h.c[h.c.length]=z,L=Ee.a.ec().Kc();L.Ob();)_=u(L.Pb(),46),Au(o,_.b);if(d.a.gc()>1)for(z=new Y5e(q,d,r),Da(d,new dqe(e,z)),h.c[h.c.length]=z,L=d.a.ec().Kc();L.Ob();)_=u(L.Pb(),46),Au(o,_.b)}}function Elt(e){wv(e,new gb(gF(gv(hv(dv(fv(new lg,Dd),"ELK Radial"),'A radial layout provider which is based on the algorithm of Peter Eades published in "Drawing free trees.", published by International Institute for Advanced Study of Social Information Science, Fujitsu Limited in 1991. 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cr(fr(z0(Ne).a.Kc(),new V));Vr(_);)x=u(Pr(_),79),iC(x)||Fyn(x,t,bt,nt);bt+=s.b+s.c,nt+=s.d+s.a,sw(e,bt,nt,!1,!0)}function xz(e){var t,n,r,s,o,h,d,v,x,_,L;if(e==null)throw J(new gd(Pu));if(x=e,o=e.length,v=!1,o>0&&(t=(zr(0,e.length),e.charCodeAt(0)),(t==45||t==43)&&(e=e.substr(1),--o,v=t==45)),o==0)throw J(new gd(cw+x+'"'));for(;e.length>0&&(zr(0,e.length),e.charCodeAt(0)==48);)e=e.substr(1),--o;if(o>(hut(),O0t)[10])throw J(new gd(cw+x+'"'));for(s=0;s<o;s++)if($rt((zr(s,e.length),e.charCodeAt(s)))==-1)throw J(new gd(cw+x+'"'));for(L=0,h=V8e[10],_=kue[10],d=M8(U8e[10]),n=!0,r=o%h,r>0&&(L=-parseInt(e.substr(0,r),10),e=e.substr(r),o-=r,n=!1);o>=h;){if(r=parseInt(e.substr(0,h),10),e=e.substr(h),o-=h,n)n=!1;else{if(Mc(L,d)<0)throw J(new gd(cw+x+'"'));L=ja(L,_)}L=Wp(L,r)}if(Mc(L,0)>0)throw J(new gd(cw+x+'"'));if(!v&&(L=M8(L),Mc(L,0)<0))throw J(new gd(cw+x+'"'));return L}function s6e(e,t){ZWe();var n,r,s,o,h,d,v;if(this.a=new Y2e(this),this.b=e,this.c=t,this.f=Bne(Po((Yu(),Oa),t)),this.f.dc())if((d=q3e(Oa,e))==t)for(this.e=!0,this.d=new st,this.f=new xx,this.f.Fc(_b),u(wz(wD(Oa,Gl(e)),""),26)==e&&this.f.Fc(f_(Oa,Gl(e))),s=iae(Oa,e).Kc();s.Ob();)switch(r=u(s.Pb(),170),Dv(Po(Oa,r))){case 4:{this.d.Fc(r);break}case 5:{this.f.Gc(Bne(Po(Oa,r)));break}}else if(ho(),u(t,66).Oj())for(this.e=!0,this.f=null,this.d=new st,h=0,v=(e.i==null&&xd(e),e.i).length;h<v;++h)for(r=(n=(e.i==null&&xd(e),e.i),h>=0&&h<n.length?n[h]:null),o=F6(Po(Oa,r));o;o=F6(Po(Oa,o)))o==t&&this.d.Fc(r);else Dv(Po(Oa,t))==1&&d?(this.f=null,this.d=(nk(),k4t)):(this.f=null,this.e=!0,this.d=(hn(),new D(t)));else this.e=Dv(Po(Oa,t))==5,this.f.Fb(Cfe)&&(this.f=Cfe)}function Tlt(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q;for(n=0,r=Mun(e,t),P=e.s,z=e.t,x=u(u(Ii(e.r,t),21),84).Kc();x.Ob();)if(v=u(x.Pb(),111),!(!v.c||v.c.d.c.length<=0)){switch(q=v.b.rf(),d=v.b.Xe((bi(),kl))?Ue(ft(v.b.We(kl))):0,_=v.c,L=_.i,L.b=(h=_.n,_.e.a+h.b+h.c),L.a=(o=_.n,_.e.b+o.d+o.a),t.g){case 1:L.c=v.a?(q.a-L.b)/2:q.a+P,L.d=q.b+d+r,e1(_,(al(),a2)),vd(_,(Lu(),u1));break;case 3:L.c=v.a?(q.a-L.b)/2:q.a+P,L.d=-d-r-L.a,e1(_,(al(),a2)),vd(_,(Lu(),Lf));break;case 2:L.c=-d-r-L.b,v.a?(s=e.v?L.a:u(St(_.d,0),181).rf().b,L.d=(q.b-s)/2):L.d=q.b+z,e1(_,(al(),Af)),vd(_,(Lu(),o2));break;case 4:L.c=q.a+d+r,v.a?(s=e.v?L.a:u(St(_.d,0),181).rf().b,L.d=(q.b-s)/2):L.d=q.b+z,e1(_,(al(),Nd)),vd(_,(Lu(),o2))}(t==(ht(),An)||t==xr)&&(n=b.Math.max(n,L.a))}n>0&&(u(_o(e.b,t),124).a.b=n)}function Fmn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le;for(kr(t,"Comment pre-processing",1),n=0,v=new C(e.a);v.a<v.c.c.length;)if(d=u(Y(v),10),It(Mt(K(d,(pt(),fS))))){for(++n,s=0,r=null,x=null,q=new C(d.j);q.a<q.c.c.length;)P=u(Y(q),11),s+=P.e.c.length+P.g.c.length,P.e.c.length==1&&(r=u(St(P.e,0),17),x=r.c),P.g.c.length==1&&(r=u(St(P.g,0),17),x=r.d);if(s==1&&x.e.c.length+x.g.c.length==1&&!It(Mt(K(x.i,fS))))fyn(d,r,x,x.i),b_(v);else{for(le=new st,z=new C(d.j);z.a<z.c.c.length;){for(P=u(Y(z),11),L=new C(P.g);L.a<L.c.c.length;)_=u(Y(L),17),_.d.g.c.length==0||(le.c[le.c.length]=_);for(h=new C(P.e);h.a<h.c.c.length;)o=u(Y(h),17),o.c.e.c.length==0||(le.c[le.c.length]=o)}for(X=new C(le);X.a<X.c.c.length;)W=u(Y(X),17),iw(W,!0)}}t.n&&V2(t,"Found "+n+" comment boxes"),ur(t)}function jmn(e,t,n,r){var s,o,h,d,v,x,_,L,P,z,q,W;if(P=Ue(ft(K(e,(pt(),bE)))),z=Ue(ft(K(e,vE))),L=Ue(ft(K(e,Ry))),d=e.o,o=u(St(e.j,0),11),h=o.n,W=b1n(o,L),!!W){if(t.Hc((ol(),Z0)))switch(u(K(e,(et(),vc)),61).g){case 1:W.c=(d.a-W.b)/2-h.a,W.d=z;break;case 3:W.c=(d.a-W.b)/2-h.a,W.d=-z-W.a;break;case 2:n&&o.e.c.length==0&&o.g.c.length==0?(_=r?W.a:u(St(o.f,0),70).o.b,W.d=(d.b-_)/2-h.b):W.d=d.b+z-h.b,W.c=-P-W.b;break;case 4:n&&o.e.c.length==0&&o.g.c.length==0?(_=r?W.a:u(St(o.f,0),70).o.b,W.d=(d.b-_)/2-h.b):W.d=d.b+z-h.b,W.c=P}else if(t.Hc(m2))switch(u(K(e,(et(),vc)),61).g){case 1:case 3:W.c=h.a+P;break;case 2:case 4:n&&!o.c?(_=r?W.a:u(St(o.f,0),70).o.b,W.d=(d.b-_)/2-h.b):W.d=h.b+z}for(s=W.d,x=new C(o.f);x.a<x.c.c.length;)v=u(Y(x),70),q=v.n,q.a=W.c,q.b=s,s+=v.o.b+L}}function $mn(){ui(hN,new vJ),ui(rU,new kJ),ui(fN,new Mx),ui(JAe,new CJ),ui(mt,new Ox),ui(re(el,1),new tT),ui(Us,new AJ),ui(mk,new n6),ui(mt,new w1),ui(mt,new dR),ui(mt,new gR),ui(ma,new pR),ui(mt,new bR),ui(Eh,new vR),ui(Eh,new wR),ui(mt,new Wf),ui(W7,new xu),ui(mt,new bJ),ui(mt,new wJ),ui(mt,new Eu),ui(mt,new Y9),ui(mt,new mR),ui(re(el,1),new yR),ui(mt,new t6),ui(mt,new mJ),ui(Eh,new Lx),ui(Eh,new yJ),ui(mt,new kR),ui(Za,new xJ),ui(mt,new X9),ui(pw,new xR),ui(mt,new $L),ui(mt,new Q9),ui(mt,new EJ),ui(mt,new TJ),ui(Eh,new HL),ui(Eh,new Z9),ui(mt,new m1),ui(mt,new _J),ui(mt,new Dx),ui(mt,new zL),ui(mt,new j2),ui(mt,new J9),ui(bw,new GL),ui(mt,new Kf),ui(mt,new eT),ui(mt,new y1),ui(bw,new k1),ui(pw,new SJ),ui(mt,new x3),ui(Za,new Ix)}function a6e(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee;if(P=t.length,P>0&&(v=(zr(0,t.length),t.charCodeAt(0)),v!=64)){if(v==37&&(L=t.lastIndexOf("%"),x=!1,L!=0&&(L==P-1||(x=(zr(L+1,t.length),t.charCodeAt(L+1)==46))))){if(h=t.substr(1,L-1),Ee=an("%",h)?null:o6e(h),r=0,x)try{r=Wl(t.substr(L+2),$a,Ei)}catch(Ne){throw Ne=ts(Ne),we(Ne,127)?(d=Ne,J(new D$(d))):J(Ne)}for(X=Hme(e.Wg());X.Ob();)if(q=aH(X),we(q,510)&&(s=u(q,590),Ce=s.d,(Ee==null?Ce==null:an(Ee,Ce))&&r--==0))return s;return null}if(_=t.lastIndexOf("."),z=_==-1?t:t.substr(0,_),n=0,_!=-1)try{n=Wl(t.substr(_+1),$a,Ei)}catch(Ne){if(Ne=ts(Ne),we(Ne,127))z=t;else throw J(Ne)}for(z=an("%",z)?null:o6e(z),W=Hme(e.Wg());W.Ob();)if(q=aH(W),we(q,191)&&(o=u(q,191),le=o.ne(),(z==null?le==null:an(z,le))&&n--==0))return o;return null}return Kut(e,t)}function Hmn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn;for(nt=new st,q=new C(e.b);q.a<q.c.c.length;)for(z=u(Y(q),29),le=new C(z.a);le.a<le.c.c.length;)if(W=u(Y(le),10),W.k==(zn(),Ls)&&ta(W,(et(),jq))){for(Ce=null,Ne=null,Ee=null,Ut=new C(W.j);Ut.a<Ut.c.c.length;)switch(zt=u(Y(Ut),11),zt.j.g){case 4:Ce=zt;break;case 2:Ne=zt;break;default:Ee=zt}for(Ve=u(St(Ee.g,0),17),_=new pF(Ve.a),x=new Io(Ee.n),Ni(x,W.n),L=ii(_,0),YM(L,x),bt=BD(Ve.a),P=new Io(Ee.n),Ni(P,W.n),ks(bt,P,bt.c.b,bt.c),In=u(K(W,jq),10),Rn=u(St(In.j,0),11),v=u(j1(Ce.e,Me(Que,Ooe,17,0,0,1)),474),r=v,o=0,d=r.length;o<d;++o)t=r[o],ba(t,Rn),_ie(t.a,t.a.b,_);for(v=kd(Ne.g),n=v,s=0,h=n.length;s<h;++s)t=n[s],Va(t,Rn),_ie(t.a,0,bt);Va(Ve,null),ba(Ve,null),nt.c[nt.c.length]=W}for(X=new C(nt);X.a<X.c.c.length;)W=u(Y(X),10),No(W,null)}function _lt(){_lt=pe;var e,t,n;for(new kD(1,0),new kD(10,0),new kD(0,0),N0t=Me(xue,Qe,240,11,0,1),Ab=Me(Sh,Td,25,100,15,1),X8e=ie(re(pa,1),Ao,25,15,[1,5,25,125,625,3125,15625,78125,390625,1953125,9765625,48828125,244140625,1220703125,6103515625,30517578125,152587890625,762939453125,3814697265625,19073486328125,95367431640625,476837158203125,0x878678326eac9]),Q8e=Me(Lr,Jr,25,X8e.length,15,1),Z8e=ie(re(pa,1),Ao,25,15,[1,10,100,Pg,1e4,ioe,1e6,1e7,1e8,Lz,1e10,1e11,1e12,1e13,1e14,1e15,1e16]),J8e=Me(Lr,Jr,25,Z8e.length,15,1),e7e=Me(xue,Qe,240,11,0,1),e=0;e<e7e.length;e++)N0t[e]=new kD(e,0),e7e[e]=new kD(0,e),Ab[e]=48;for(;e<Ab.length;e++)Ab[e]=48;for(n=0;n<Q8e.length;n++)Q8e[n]=B4e(X8e[n]);for(t=0;t<J8e.length;t++)J8e[t]=B4e(Z8e[t]);d7()}function zmn(){function e(){this.obj=this.createObject()}return e.prototype.createObject=function(t){return Object.create(null)},e.prototype.get=function(t){return this.obj[t]},e.prototype.set=function(t,n){this.obj[t]=n},e.prototype[coe]=function(t){delete this.obj[t]},e.prototype.keys=function(){return Object.getOwnPropertyNames(this.obj)},e.prototype.entries=function(){var t=this.keys(),n=this,r=0;return{next:function(){if(r>=t.length)return{done:!0};var s=t[r++];return{value:[s,n.get(s)],done:!1}}}},Upn()||(e.prototype.createObject=function(){return{}},e.prototype.get=function(t){return this.obj[":"+t]},e.prototype.set=function(t,n){this.obj[":"+t]=n},e.prototype[coe]=function(t){delete this.obj[":"+t]},e.prototype.keys=function(){var t=[];for(var n in this.obj)n.charCodeAt(0)==58&&t.push(n.substring(1));return t}),e}function Gmn(e){j5e();var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X;if(e==null)return null;if(L=e.length*8,L==0)return"";for(d=L%24,z=L/24|0,P=d!=0?z+1:z,o=null,o=Me(Sh,Td,25,P*4,15,1),x=0,_=0,t=0,n=0,r=0,h=0,s=0,v=0;v<z;v++)t=e[s++],n=e[s++],r=e[s++],_=(n&15)<<24>>24,x=(t&3)<<24>>24,q=t&-128?(t>>2^192)<<24>>24:t>>2<<24>>24,W=n&-128?(n>>4^240)<<24>>24:n>>4<<24>>24,X=r&-128?(r>>6^252)<<24>>24:r>>6<<24>>24,o[h++]=Zg[q],o[h++]=Zg[W|x<<4],o[h++]=Zg[_<<2|X],o[h++]=Zg[r&63];return 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C(e.a.a.b);r.a<r.c.c.length;)if(t=u(Y(r),57),x=U3(t),x)gu(_.f,x,t);else if(Ce=j6(t),Ce)for(o=new C(Ce.k);o.a<o.c.c.length;)s=u(Y(o),17),sn(v,s,t);for(n=new C(e.a.a.b);n.a<n.c.c.length;)if(t=u(Y(n),57),x=U3(t),x){for(d=new cr(fr(js(x).a.Kc(),new V));Vr(d);)if(h=u(Pr(d),17),!to(h)&&(q=h.c,le=h.d,!((ht(),Ru).Hc(h.c.j)&&Ru.Hc(h.d.j)))){if(W=u(er(_,h.d.i),57),Cf(bf(pf(vf(gf(new Nh,0),100),e.c[t.a.d]),e.c[W.a.d])),q.j==Dn&&PKe((Ku(),q))){for(P=u(Ii(v,h),21).Kc();P.Ob();)if(L=u(P.Pb(),57),L.d.c<t.d.c){if(z=e.c[L.a.d],X=e.c[t.a.d],z==X)continue;Cf(bf(pf(vf(gf(new Nh,1),100),z),X))}}if(le.j==$n&&NKe((Ku(),le))){for(P=u(Ii(v,h),21).Kc();P.Ob();)if(L=u(P.Pb(),57),L.d.c>t.d.c){if(z=e.c[t.a.d],X=e.c[L.a.d],z==X)continue;Cf(bf(pf(vf(gf(new Nh,1),100),z),X))}}}}}function o6e(e){kae();var t,n,r,s,o,h,d,v;if(e==null)return null;if(s=pd(e,Nu(37)),s<0)return e;for(v=new Fl(e.substr(0,s)),t=Me(el,A4,25,4,15,1),d=0,r=0,h=e.length;s<h;s++)if(zr(s,e.length),e.charCodeAt(s)==37&&e.length>s+2&&die((zr(s+1,e.length),e.charCodeAt(s+1)),DAe,IAe)&&die((zr(s+2,e.length),e.charCodeAt(s+2)),DAe,IAe))if(n=kQt((zr(s+1,e.length),e.charCodeAt(s+1)),(zr(s+2,e.length),e.charCodeAt(s+2))),s+=2,r>0?(n&192)==128?t[d++]=n<<24>>24:r=0:n>=128&&((n&224)==192?(t[d++]=n<<24>>24,r=2):(n&240)==224?(t[d++]=n<<24>>24,r=3):(n&248)==240&&(t[d++]=n<<24>>24,r=4)),r>0){if(d==r){switch(d){case 2:{Bp(v,((t[0]&31)<<6|t[1]&63)&Ss);break}case 3:{Bp(v,((t[0]&15)<<12|(t[1]&63)<<6|t[2]&63)&Ss);break}}d=0,r=0}}else{for(o=0;o<d;++o)Bp(v,t[o]&Ss);d=0,v.a+=String.fromCharCode(n)}else{for(o=0;o<d;++o)Bp(v,t[o]&Ss);d=0,Bp(v,(zr(s,e.length),e.charCodeAt(s)))}return v.a}function Ilt(e,t,n,r,s){var o,h,d;if(QJe(e,t),h=t[0],o=Ma(n.c,0),d=-1,dye(n))if(r>0){if(h+r>e.length)return!1;d=ZH(e.substr(0,h+r),t)}else d=ZH(e,t);switch(o){case 71:return d=u4(e,h,ie(re(mt,1),Qe,2,6,[fht,dht]),t),s.e=d,!0;case 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n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt;if(P=u(u(Ii(e.r,t),21),84),t==(ht(),$n)||t==Dn){Tlt(e,t);return}for(o=t==An?(Vv(),iO):(Vv(),sO),Ne=t==An?(Lu(),u1):(Lu(),Lf),n=u(_o(e.b,t),124),r=n.i,s=r.c+Q3(ie(re(pa,1),Ao,25,15,[n.n.b,e.C.b,e.k])),le=r.c+r.b-Q3(ie(re(pa,1),Ao,25,15,[n.n.c,e.C.c,e.k])),h=jpe(rve(o),e.t),Ce=t==An?Ds:gs,L=P.Kc();L.Ob();)x=u(L.Pb(),111),!(!x.c||x.c.d.c.length<=0)&&(X=x.b.rf(),W=x.e,z=x.c,q=z.i,q.b=(v=z.n,z.e.a+v.b+v.c),q.a=(d=z.n,z.e.b+d.d+d.a),JM(Ne,O6e),z.f=Ne,e1(z,(al(),Af)),q.c=W.a-(q.b-X.a)/2,Ve=b.Math.min(s,W.a),nt=b.Math.max(le,W.a+X.a),q.c<Ve?q.c=Ve:q.c+q.b>nt&&(q.c=nt-q.b),it(h.d,new Lne(q,Rye(h,q))),Ce=t==An?b.Math.max(Ce,W.b+x.b.rf().b):b.Math.min(Ce,W.b));for(Ce+=t==An?e.t:-e.t,Ee=Jye((h.e=Ce,h)),Ee>0&&(u(_o(e.b,t),124).a.b=Ee),_=P.Kc();_.Ob();)x=u(_.Pb(),111),!(!x.c||x.c.d.c.length<=0)&&(q=x.c.i,q.c-=x.e.a,q.d-=x.e.b)}function tyn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z;for(t=new Mr,v=new rr(e);v.e!=v.i.gc();){for(d=u(pr(v),33),n=new 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6:return"0.000000";default:return z=new Tp,t<0?z.a+="0E+":z.a+="0E",z.a+=t==$a?"2147483648":""+-t,z.a}_=18,L=Me(Sh,Td,25,_+1,15,1),n=_,W=e;do x=W,W=eI(W,10),L[--n]=Or(Ua(48,Wp(x,ja(W,10))))&Ss;while(Mc(W,0)!=0);if(s=Wp(Wp(Wp(_,n),t),1),t==0)return v&&(L[--n]=45),jh(L,n,_-n);if(t>0&&Mc(s,-6)>=0){if(Mc(s,0)>=0){for(o=n+Or(s),d=_-1;d>=o;d--)L[d+1]=L[d];return L[++o]=46,v&&(L[--n]=45),jh(L,n,_-n+1)}for(h=2;fte(h,Ua(M8(s),1));h++)L[--n]=48;return L[--n]=46,L[--n]=48,v&&(L[--n]=45),jh(L,n,_-n)}return q=n+1,r=_,P=new xm,v&&(P.a+="-"),r-q>=1?(Bp(P,L[n]),P.a+=".",P.a+=jh(L,n+1,_-n-1)):P.a+=jh(L,n,_-n),P.a+="E",Mc(s,0)>0&&(P.a+="+"),P.a+=""+d_(s),P.a}function ryn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z;if(e.e.a.$b(),e.f.a.$b(),e.c.c=Me(Yn,yt,1,0,5,1),e.i.c=Me(Yn,yt,1,0,5,1),e.g.a.$b(),t)for(h=new C(t.a);h.a<h.c.c.length;)for(o=u(Y(h),10),L=Og(o,(ht(),$n)).Kc();L.Ob();)for(_=u(L.Pb(),11),Gs(e.e,_),s=new 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at(xl,e,9,9)),e.c));Ce.e!=Ce.i.gc();)switch(le=u(pr(Ce),118),Ee=u(Ft(le,t5),61),Ee==(ht(),uc)&&(Ee=$5e(le,h),So(le,t5,Ee)),Ee.g){case 1:d||Du(le,le.i*nt);break;case 2:Du(le,le.i+Ne),d||Iu(le,le.j*_);break;case 3:d||Du(le,le.i*nt),Iu(le,le.j+v);break;case 4:d||Iu(le,le.j*_)}if(ej(e,W.a,W.b),s)for(P=new rr((!e.n&&(e.n=new at(Jo,e,1,7)),e.n));P.e!=P.i.gc();)L=u(pr(P),137),z=L.i+L.g/2,q=L.j+L.f/2,Ve=z/X.a,x=q/X.b,Ve+x>=1&&(Ve-x>0&&q>=0?(Du(L,L.i+Ne),Iu(L,L.j+v*x)):Ve-x<0&&z>=0&&(Du(L,L.i+Ne*Ve),Iu(L,L.j+v)));return So(e,(bi(),Rb),(Bl(),o=u(Qf(qS),9),new hh(o,u(wf(o,o.length),9),0))),new Pt(nt,_)}function Olt(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q;if(z=us(zo(u(Te((!e.b&&(e.b=new wn(mr,e,4,7)),e.b),0),82))),q=us(zo(u(Te((!e.c&&(e.c=new wn(mr,e,5,8)),e.c),0),82))),L=z==q,d=new Fa,t=u(Ft(e,(hH(),rAe)),74),t&&t.b>=2){if((!e.a&&(e.a=new at(os,e,6,6)),e.a).i==0)n=(pv(),s=new ld,s),Br((!e.a&&(e.a=new at(os,e,6,6)),e.a),n);else if((!e.a&&(e.a=new at(os,e,6,6)),e.a).i>1)for(P=new _6((!e.a&&(e.a=new at(os,e,6,6)),e.a));P.e!=P.i.gc();)J_(P);mI(t,u(Te((!e.a&&(e.a=new at(os,e,6,6)),e.a),0),202))}if(L)for(r=new rr((!e.a&&(e.a=new at(os,e,6,6)),e.a));r.e!=r.i.gc();)for(n=u(pr(r),202),x=new rr((!n.a&&(n.a=new Bs(ef,n,5)),n.a));x.e!=x.i.gc();)v=u(pr(x),469),d.a=b.Math.max(d.a,v.a),d.b=b.Math.max(d.b,v.b);for(h=new rr((!e.n&&(e.n=new at(Jo,e,1,7)),e.n));h.e!=h.i.gc();)o=u(pr(h),137),_=u(Ft(o,FS),8),_&&C1(o,_.a,_.b),L&&(d.a=b.Math.max(d.a,o.i+o.g),d.b=b.Math.max(d.b,o.j+o.f));return d}function iyn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt;for(Ee=t.c.length,s=new p4(e.a,n,null,null),zt=Me(pa,Ao,25,Ee,15,1),W=Me(pa,Ao,25,Ee,15,1),q=Me(pa,Ao,25,Ee,15,1),X=0,d=0;d<Ee;d++)W[d]=Ei,q[d]=$a;for(v=0;v<Ee;v++)for(r=(xn(v,t.c.length),u(t.c[v],180)),zt[v]=qse(r),zt[X]>zt[v]&&(X=v),L=new C(e.a.b);L.a<L.c.c.length;)for(_=u(Y(L),29),Ce=new C(_.a);Ce.a<Ce.c.c.length;)le=u(Y(Ce),10),nt=Ue(r.p[le.p])+Ue(r.d[le.p]),W[v]=b.Math.min(W[v],nt),q[v]=b.Math.max(q[v],nt+le.o.b);for(bt=Me(pa,Ao,25,Ee,15,1),x=0;x<Ee;x++)(xn(x,t.c.length),u(t.c[x],180)).o==(M1(),Pb)?bt[x]=W[X]-W[x]:bt[x]=q[X]-q[x];for(o=Me(pa,Ao,25,Ee,15,1),z=new C(e.a.b);z.a<z.c.c.length;)for(P=u(Y(z),29),Ve=new C(P.a);Ve.a<Ve.c.c.length;){for(Ne=u(Y(Ve),10),h=0;h<Ee;h++)o[h]=Ue((xn(h,t.c.length),u(t.c[h],180)).p[Ne.p])+Ue((xn(h,t.c.length),u(t.c[h],180)).d[Ne.p])+bt[h];o.sort(Aet(he.prototype.te,he,[])),s.p[Ne.p]=(o[1]+o[2])/2,s.d[Ne.p]=0}return s}function syn(e,t,n){var r,s,o,h,d;switch(r=t.i,o=e.i.o,s=e.i.d,d=e.n,h=sc(ie(re(na,1),Qe,8,0,[d,e.a])),e.j.g){case 1:vd(t,(Lu(),Lf)),r.d=-s.d-n-r.a,u(u(St(t.d,0),181).We((et(),Ak)),285)==(Ul(),d0)?(e1(t,(al(),Af)),r.c=h.a-Ue(ft(K(e,H4)))-n-r.b):(e1(t,(al(),Nd)),r.c=h.a+Ue(ft(K(e,H4)))+n);break;case 2:e1(t,(al(),Nd)),r.c=o.a+s.c+n,u(u(St(t.d,0),181).We((et(),Ak)),285)==(Ul(),d0)?(vd(t,(Lu(),Lf)),r.d=h.b-Ue(ft(K(e,H4)))-n-r.a):(vd(t,(Lu(),u1)),r.d=h.b+Ue(ft(K(e,H4)))+n);break;case 3:vd(t,(Lu(),u1)),r.d=o.b+s.a+n,u(u(St(t.d,0),181).We((et(),Ak)),285)==(Ul(),d0)?(e1(t,(al(),Af)),r.c=h.a-Ue(ft(K(e,H4)))-n-r.b):(e1(t,(al(),Nd)),r.c=h.a+Ue(ft(K(e,H4)))+n);break;case 4:e1(t,(al(),Af)),r.c=-s.b-n-r.b,u(u(St(t.d,0),181).We((et(),Ak)),285)==(Ul(),d0)?(vd(t,(Lu(),Lf)),r.d=h.b-Ue(ft(K(e,H4)))-n-r.a):(vd(t,(Lu(),u1)),r.d=h.b+Ue(ft(K(e,H4)))+n)}}function ayn(e,t,n,r,s,o,h){var d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr,ki,Ws;for(z=0,In=0,v=new C(e);v.a<v.c.c.length;)d=u(Y(v),33),blt(d),z=b.Math.max(z,d.g),In+=d.g*d.f;for(q=In/e.c.length,Ut=cln(e,q),In+=e.c.length*Ut,z=b.Math.max(z,b.Math.sqrt(In*h))+n.b,ki=n.b,Ws=n.d,P=0,_=n.b+n.c,zt=new as,ci(zt,ct(0)),nt=new as,x=new Ea(e,0);x.b<x.d.gc();)d=(Qn(x.b<x.d.gc()),u(x.d.Xb(x.c=x.b++),33)),dr=d.g,L=d.f,ki+dr>z&&(o&&(q2(nt,P),q2(zt,ct(x.b-1))),ki=n.b,Ws+=P+t,P=0,_=b.Math.max(_,n.b+n.c+dr)),Du(d,ki),Iu(d,Ws),_=b.Math.max(_,ki+dr+n.c),P=b.Math.max(P,L),ki+=dr+t;if(_=b.Math.max(_,r),Rn=Ws+P+n.a,Rn<s&&(P+=s-Rn,Rn=s),o)for(ki=n.b,x=new Ea(e,0),q2(zt,ct(e.c.length)),bt=ii(zt,0),le=u(ri(bt),19).a,q2(nt,P),Ve=ii(nt,0),Ne=0;x.b<x.d.gc();)x.b==le&&(ki=n.b,Ne=Ue(ft(ri(Ve))),le=u(ri(bt),19).a),d=(Qn(x.b<x.d.gc()),u(x.d.Xb(x.c=x.b++),33)),Ce=d.f,Hv(d,Ne),W=Ne,x.b==le&&(X=_-ki-n.c,Ee=d.g,zv(d,X),dat(d,new Pt(X,W),new Pt(Ee,Ce))),ki+=d.g+t;return new Pt(_,Rn)}function oyn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut;for(kr(t,"Compound graph postprocessor",1),n=It(Mt(K(e,(pt(),Jle)))),d=u(K(e,(et(),N9e)),224),_=new Ys,le=d.ec().Kc();le.Ob();){for(X=u(le.Pb(),17),h=new Uu(d.cc(X)),hn(),aa(h,new Di(e)),Ve=Gin((xn(0,h.c.length),u(h.c[0],243))),bt=xtt(u(St(h,h.c.length-1),243)),Ee=Ve.i,z8(bt.i,Ee)?Ce=Ee.e:Ce=Ya(Ee),L=Ucn(X,h),Rh(X.a),P=null,o=new C(h);o.a<o.c.c.length;)s=u(Y(o),243),W=new Fa,_4e(W,s.a,Ce),z=s.b,r=new Gu,_ie(r,0,z.a),Um(r,W),Ne=new Io(D1(z.c)),nt=new Io(D1(z.d)),Ni(Ne,W),Ni(nt,W),P&&(r.b==0?q=nt:q=(Qn(r.b!=0),u(r.a.a.c,8)),zt=b.Math.abs(P.a-q.a)>Sd,Ut=b.Math.abs(P.b-q.b)>Sd,(!n&&zt&&Ut||n&&(zt||Ut))&&ci(X.a,Ne)),no(X.a,r),r.b==0?P=Ne:P=(Qn(r.b!=0),u(r.c.b.c,8)),xsn(z,L,W),xtt(s)==bt&&(Ya(bt.i)!=s.a&&(W=new Fa,_4e(W,Ya(bt.i),Ce)),Ye(X,Rle,W)),Ufn(z,X,Ce),_.a.zc(z,_);Va(X,Ve),ba(X,bt)}for(x=_.a.ec().Kc();x.Ob();)v=u(x.Pb(),17),Va(v,null),ba(v,null);ur(t)}function Nlt(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne;if(e.gc()==1)return u(e.Xb(0),231);if(e.gc()<=0)return new k$;for(s=e.Kc();s.Ob();){for(n=u(s.Pb(),231),q=0,_=Ei,L=Ei,v=$a,x=$a,z=new C(n.e);z.a<z.c.c.length;)P=u(Y(z),144),q+=u(K(P,(a1(),Q7)),19).a,_=b.Math.min(_,P.d.a-P.e.a/2),L=b.Math.min(L,P.d.b-P.e.b/2),v=b.Math.max(v,P.d.a+P.e.a/2),x=b.Math.max(x,P.d.b+P.e.b/2);Ye(n,(a1(),Q7),ct(q)),Ye(n,(zp(),eS),new Pt(_,L)),Ye(n,fO,new Pt(v,x))}for(hn(),e.ad(new fp),W=new k$,Ho(W,u(e.Xb(0),94)),d=0,Ce=0,o=e.Kc();o.Ob();)n=u(o.Pb(),231),X=da(fc(u(K(n,(zp(),fO)),8)),u(K(n,eS),8)),d=b.Math.max(d,X.a),Ce+=X.a*X.b;for(d=b.Math.max(d,b.Math.sqrt(Ce)*Ue(ft(K(W,(a1(),apt))))),le=Ue(ft(K(W,eq))),Ee=0,Ne=0,h=0,t=le,r=e.Kc();r.Ob();)n=u(r.Pb(),231),X=da(fc(u(K(n,(zp(),fO)),8)),u(K(n,eS),8)),Ee+X.a>d&&(Ee=0,Ne+=h+le,h=0),q2n(W,n,Ee,Ne),t=b.Math.max(t,Ee+X.a),h=b.Math.max(h,X.b),Ee+=X.a+le;return W}function Plt(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q;switch(_=new Gu,e.a.g){case 3:P=u(K(t.e,(et(),Mb)),15),z=u(K(t.j,Mb),15),q=u(K(t.f,Mb),15),n=u(K(t.e,q4),15),r=u(K(t.j,q4),15),s=u(K(t.f,q4),15),h=new st,Rs(h,P),z.Jc(new XX),Rs(h,we(z,152)?H6(u(z,152)):we(z,131)?u(z,131).a:we(z,54)?new lv(z):new Em(z)),Rs(h,q),o=new st,Rs(o,n),Rs(o,we(r,152)?H6(u(r,152)):we(r,131)?u(r,131).a:we(r,54)?new lv(r):new Em(r)),Rs(o,s),Ye(t.f,Mb,h),Ye(t.f,q4,o),Ye(t.f,z9e,t.f),Ye(t.e,Mb,null),Ye(t.e,q4,null),Ye(t.j,Mb,null),Ye(t.j,q4,null);break;case 1:no(_,t.e.a),ci(_,t.i.n),no(_,nb(t.j.a)),ci(_,t.a.n),no(_,t.f.a);break;default:no(_,t.e.a),no(_,nb(t.j.a)),no(_,t.f.a)}Rh(t.f.a),no(t.f.a,_),Va(t.f,t.e.c),d=u(K(t.e,(pt(),Fo)),74),x=u(K(t.j,Fo),74),v=u(K(t.f,Fo),74),(d||x||v)&&(L=new Gu,Kve(L,v),Kve(L,x),Kve(L,d),Ye(t.f,Fo,L)),Va(t.j,null),ba(t.j,null),Va(t.e,null),ba(t.e,null),No(t.a,null),No(t.i,null),t.g&&Plt(e,t.g)}function cyn(e){j5e();var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X;if(e==null||(o=O$(e),q=Yan(o),q%4!=0))return null;if(W=q/4|0,W==0)return Me(el,A4,25,0,15,1);for(L=null,t=0,n=0,r=0,s=0,h=0,d=0,v=0,x=0,z=0,P=0,_=0,L=Me(el,A4,25,W*3,15,1);z<W-1;z++){if(!fM(h=o[_++])||!fM(d=o[_++])||!fM(v=o[_++])||!fM(x=o[_++]))return null;t=J1[h],n=J1[d],r=J1[v],s=J1[x],L[P++]=(t<<2|n>>4)<<24>>24,L[P++]=((n&15)<<4|r>>2&15)<<24>>24,L[P++]=(r<<6|s)<<24>>24}return!fM(h=o[_++])||!fM(d=o[_++])?null:(t=J1[h],n=J1[d],v=o[_++],x=o[_++],J1[v]==-1||J1[x]==-1?v==61&&x==61?n&15?null:(X=Me(el,A4,25,z*3+1,15,1),Hc(L,0,X,0,z*3),X[P]=(t<<2|n>>4)<<24>>24,X):v!=61&&x==61?(r=J1[v],r&3?null:(X=Me(el,A4,25,z*3+2,15,1),Hc(L,0,X,0,z*3),X[P++]=(t<<2|n>>4)<<24>>24,X[P]=((n&15)<<4|r>>2&15)<<24>>24,X)):null:(r=J1[v],s=J1[x],L[P++]=(t<<2|n>>4)<<24>>24,L[P++]=((n&15)<<4|r>>2&15)<<24>>24,L[P++]=(r<<6|s)<<24>>24,L))}function uyn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve;for(kr(t,yft,1),q=u(K(e,(pt(),K0)),218),s=new C(e.b);s.a<s.c.c.length;)for(r=u(Y(s),29),x=iD(r.a),h=x,d=0,v=h.length;d<v;++d)if(o=h[d],o.k==(zn(),Jc)){if(q==($0(),jk))for(L=new C(o.j);L.a<L.c.c.length;)_=u(Y(L),11),_.e.c.length==0||Acn(_),_.g.c.length==0||Lcn(_);else if(we(K(o,(et(),Mi)),17))X=u(K(o,Mi),17),le=u(Og(o,(ht(),Dn)).Kc().Pb(),11),Ce=u(Og(o,$n).Kc().Pb(),11),Ee=u(K(le,Mi),11),Ne=u(K(Ce,Mi),11),Va(X,Ne),ba(X,Ee),Ve=new Io(Ce.i.n),Ve.a=sc(ie(re(na,1),Qe,8,0,[Ne.i.n,Ne.n,Ne.a])).a,ci(X.a,Ve),Ve=new Io(le.i.n),Ve.a=sc(ie(re(na,1),Qe,8,0,[Ee.i.n,Ee.n,Ee.a])).a,ci(X.a,Ve);else{if(o.j.c.length>=2){for(W=!0,P=new C(o.j),n=u(Y(P),11),z=null;P.a<P.c.c.length;)if(z=n,n=u(Y(P),11),!Ci(K(z,Mi),K(n,Mi))){W=!1;break}}else W=!1;for(L=new C(o.j);L.a<L.c.c.length;)_=u(Y(L),11),_.e.c.length==0||npn(_,W),_.g.c.length==0||rpn(_,W)}No(o,null)}ur(t)}function Blt(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt;return Ee=e.c[(xn(0,t.c.length),u(t.c[0],17)).p],bt=e.c[(xn(1,t.c.length),u(t.c[1],17)).p],Ee.a.e.e-Ee.a.a-(Ee.b.e.e-Ee.b.a)==0&&bt.a.e.e-bt.a.a-(bt.b.e.e-bt.b.a)==0||(le=Ee.b.e.f,!we(le,10))?!1:(X=u(le,10),Ve=e.i[X.p],nt=X.c?Yo(X.c.a,X,0):-1,o=gs,nt>0&&(s=u(St(X.c.a,nt-1),10),h=e.i[s.p],zt=b.Math.ceil(j3(e.n,s,X)),o=Ve.a.e-X.d.d-(h.a.e+s.o.b+s.d.a)-zt),x=gs,nt<X.c.a.c.length-1&&(v=u(St(X.c.a,nt+1),10),_=e.i[v.p],zt=b.Math.ceil(j3(e.n,v,X)),x=_.a.e-v.d.d-(Ve.a.e+X.o.b+X.d.a)-zt),n&&(S1(),Ef(Md),b.Math.abs(o-x)<=Md||o==x||isNaN(o)&&isNaN(x))?!0:(r=$ne(Ee.a),d=-$ne(Ee.b),L=-$ne(bt.a),Ce=$ne(bt.b),W=Ee.a.e.e-Ee.a.a-(Ee.b.e.e-Ee.b.a)>0&&bt.a.e.e-bt.a.a-(bt.b.e.e-bt.b.a)<0,q=Ee.a.e.e-Ee.a.a-(Ee.b.e.e-Ee.b.a)<0&&bt.a.e.e-bt.a.a-(bt.b.e.e-bt.b.a)>0,z=Ee.a.e.e+Ee.b.a<bt.b.e.e+bt.a.a,P=Ee.a.e.e+Ee.b.a>bt.b.e.e+bt.a.a,Ne=0,!W&&!q&&(P?o+L>0?Ne=L:x-r>0&&(Ne=r):z&&(o+d>0?Ne=d:x-Ce>0&&(Ne=Ce))),Ve.a.e+=Ne,Ve.b&&(Ve.d.e+=Ne),!1))}function Rlt(e,t,n){var r,s,o,h,d,v,x,_,L,P;if(r=new fh(t.qf().a,t.qf().b,t.rf().a,t.rf().b),s=new T6,e.c)for(h=new C(t.wf());h.a<h.c.c.length;)o=u(Y(h),181),s.c=o.qf().a+t.qf().a,s.d=o.qf().b+t.qf().b,s.b=o.rf().a,s.a=o.rf().b,tC(r,s);for(x=new C(t.Cf());x.a<x.c.c.length;){if(v=u(Y(x),838),_=v.qf().a+t.qf().a,L=v.qf().b+t.qf().b,e.e&&(s.c=_,s.d=L,s.b=v.rf().a,s.a=v.rf().b,tC(r,s)),e.d)for(h=new C(v.wf());h.a<h.c.c.length;)o=u(Y(h),181),s.c=o.qf().a+_,s.d=o.qf().b+L,s.b=o.rf().a,s.a=o.rf().b,tC(r,s);if(e.b){if(P=new Pt(-n,-n),u(t.We((bi(),Hy)),174).Hc((ol(),m2)))for(h=new C(v.wf());h.a<h.c.c.length;)o=u(Y(h),181),P.a+=o.rf().a+n,P.b+=o.rf().b+n;P.a=b.Math.max(P.a,0),P.b=b.Math.max(P.b,0),sut(r,v.Bf(),v.zf(),t,v,P,n)}}e.b&&sut(r,t.Bf(),t.zf(),t,null,null,n),d=new pne(t.Af()),d.d=b.Math.max(0,t.qf().b-r.d),d.a=b.Math.max(0,r.d+r.a-(t.qf().b+t.rf().b)),d.b=b.Math.max(0,t.qf().a-r.c),d.c=b.Math.max(0,r.c+r.b-(t.qf().a+t.rf().a)),t.Ef(d)}function lyn(){var e=["\\u0000","\\u0001","\\u0002","\\u0003","\\u0004","\\u0005","\\u0006","\\u0007","\\b","\\t","\\n","\\u000B","\\f","\\r","\\u000E","\\u000F","\\u0010","\\u0011","\\u0012","\\u0013","\\u0014","\\u0015","\\u0016","\\u0017","\\u0018","\\u0019","\\u001A","\\u001B","\\u001C","\\u001D","\\u001E","\\u001F"];return e[34]='\\"',e[92]="\\\\",e[173]="\\u00ad",e[1536]="\\u0600",e[1537]="\\u0601",e[1538]="\\u0602",e[1539]="\\u0603",e[1757]="\\u06dd",e[1807]="\\u070f",e[6068]="\\u17b4",e[6069]="\\u17b5",e[8203]="\\u200b",e[8204]="\\u200c",e[8205]="\\u200d",e[8206]="\\u200e",e[8207]="\\u200f",e[8232]="\\u2028",e[8233]="\\u2029",e[8234]="\\u202a",e[8235]="\\u202b",e[8236]="\\u202c",e[8237]="\\u202d",e[8238]="\\u202e",e[8288]="\\u2060",e[8289]="\\u2061",e[8290]="\\u2062",e[8291]="\\u2063",e[8292]="\\u2064",e[8298]="\\u206a",e[8299]="\\u206b",e[8300]="\\u206c",e[8301]="\\u206d",e[8302]="\\u206e",e[8303]="\\u206f",e[65279]="\\ufeff",e[65529]="\\ufff9",e[65530]="\\ufffa",e[65531]="\\ufffb",e}function 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n,r,s,o,h,d,v,x,_,L,P;for(x=u(u(Ii(e.r,t),21),84),h=hfn(e,t),n=e.u.Hc((ol(),zS)),v=x.Kc();v.Ob();)if(d=u(v.Pb(),111),!(!d.c||d.c.d.c.length<=0)){switch(P=d.b.rf(),_=d.c,L=_.i,L.b=(o=_.n,_.e.a+o.b+o.c),L.a=(s=_.n,_.e.b+s.d+s.a),t.g){case 1:d.a?(L.c=(P.a-L.b)/2,e1(_,(al(),a2))):h||n?(L.c=-L.b-e.s,e1(_,(al(),Af))):(L.c=P.a+e.s,e1(_,(al(),Nd))),L.d=-L.a-e.t,vd(_,(Lu(),Lf));break;case 3:d.a?(L.c=(P.a-L.b)/2,e1(_,(al(),a2))):h||n?(L.c=-L.b-e.s,e1(_,(al(),Af))):(L.c=P.a+e.s,e1(_,(al(),Nd))),L.d=P.b+e.t,vd(_,(Lu(),u1));break;case 2:d.a?(r=e.v?L.a:u(St(_.d,0),181).rf().b,L.d=(P.b-r)/2,vd(_,(Lu(),o2))):h||n?(L.d=-L.a-e.t,vd(_,(Lu(),Lf))):(L.d=P.b+e.t,vd(_,(Lu(),u1))),L.c=P.a+e.s,e1(_,(al(),Nd));break;case 4:d.a?(r=e.v?L.a:u(St(_.d,0),181).rf().b,L.d=(P.b-r)/2,vd(_,(Lu(),o2))):h||n?(L.d=-L.a-e.t,vd(_,(Lu(),Lf))):(L.d=P.b+e.t,vd(_,(Lu(),u1))),L.c=-L.b-e.s,e1(_,(al(),Af))}h=!1}}function Zp(e,t){yi();var n,r,s,o,h,d,v,x,_,L,P,z,q;if(MT(ME)==0){for(L=Me(D3n,Qe,117,$4t.length,0,1),h=0;h<L.length;h++)L[h]=new Hl(4);for(r=new ST,o=0;o<rLe.length;o++){if(_=new Hl(4),o<84?(d=o*2,z=(zr(d,uue.length),uue.charCodeAt(d)),P=(zr(d+1,uue.length),uue.charCodeAt(d+1)),Yc(_,z,P)):(d=(o-84)*2,Yc(_,iLe[d],iLe[d+1])),v=rLe[o],an(v,"Specials")&&Yc(_,65520,65533),an(v,d0t)&&(Yc(_,983040,1048573),Yc(_,1048576,1114109)),Oo(ME,v,_),Oo(tA,v,uy(_)),x=r.a.length,0<x?r.a=r.a.substr(0,0):0>x&&(r.a+=$Ve(Me(Sh,Td,25,-x,15,1))),r.a+="Is",pd(v,Nu(32))>=0)for(s=0;s<v.length;s++)zr(s,v.length),v.charCodeAt(s)!=32&&b8(r,(zr(s,v.length),v.charCodeAt(s)));else r.a+=""+v;s3e(r.a,v,!0)}s3e(cue,"Cn",!1),s3e(A8e,"Cn",!0),n=new Hl(4),Yc(n,0,q7),Oo(ME,"ALL",n),Oo(tA,"ALL",uy(n)),!Yy&&(Yy=new Mr),Oo(Yy,cue,cue),!Yy&&(Yy=new Mr),Oo(Yy,A8e,A8e),!Yy&&(Yy=new Mr),Oo(Yy,"ALL","ALL")}return q=u(Uc(t?ME:tA,e),136),q}function fyn(e,t,n,r){var s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce;if(P=!1,L=!1,R3(u(K(r,(pt(),bs)),98))){h=!1,d=!1;e:for(q=new 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n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In;for(e.b=t,e.a=u(K(t,(pt(),MTe)),19).a,e.c=u(K(t,ITe),19).a,e.c==0&&(e.c=Ei),X=new Ea(t.b,0);X.b<X.d.gc();){for(W=(Qn(X.b<X.d.gc()),u(X.d.Xb(X.c=X.b++),29)),d=new st,_=-1,Ne=-1,Ee=new C(W.a);Ee.a<Ee.c.c.length;)Ce=u(Y(Ee),10),I0((GM(),new cr(fr(j0(Ce).a.Kc(),new V))))>=e.a&&(r=Zbn(e,Ce),_=b.Math.max(_,r.b),Ne=b.Math.max(Ne,r.d),it(d,new xa(Ce,r)));for(zt=new st,x=0;x<_;++x)Om(zt,0,(Qn(X.b>0),X.a.Xb(X.c=--X.b),Ut=new Bh(e.b),Dm(X,Ut),Qn(X.b<X.d.gc()),X.d.Xb(X.c=X.b++),Ut));for(h=new C(d);h.a<h.c.c.length;)if(s=u(Y(h),46),z=u(s.b,571).a,!!z)for(P=new C(z);P.a<P.c.c.length;)L=u(Y(P),10),O3e(e,L,rq,zt);for(n=new st,v=0;v<Ne;++v)it(n,(In=new Bh(e.b),Dm(X,In),In));for(o=new C(d);o.a<o.c.c.length;)if(s=u(Y(o),46),bt=u(s.b,571).c,!!bt)for(nt=new C(bt);nt.a<nt.c.c.length;)Ve=u(Y(nt),10),O3e(e,Ve,iq,n)}for(le=new Ea(t.b,0);le.b<le.d.gc();)q=(Qn(le.b<le.d.gc()),u(le.d.Xb(le.c=le.b++),29)),q.a.c.length==0&&Ol(le)}function vyn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr;if(kr(n,"Spline edge routing",1),t.b.c.length==0){t.f.a=0,ur(n);return}Ce=Ue(ft(K(t,(pt(),U4)))),d=Ue(ft(K(t,Nb))),h=Ue(ft(K(t,V4))),le=u(K(t,Vle),336),zt=le==(Q8(),kS),bt=Ue(ft(K(t,ATe))),e.d=t,e.j.c=Me(Yn,yt,1,0,5,1),e.a.c=Me(Yn,yt,1,0,5,1),sl(e.k),v=u(St(t.b,0),29),_=XF(v.a,(az(),OO)),q=u(St(t.b,t.b.c.length-1),29),L=XF(q.a,OO),W=new C(t.b),X=null,dr=0;do{for(Ee=W.a<W.c.c.length?u(Y(W),29):null,ryn(e,X,Ee),Dbn(e),Ut=jVt(zin(Yj(Vi(new vn(null,new mn(e.i,16)),new FQ),new RQ))),Rn=0,Ne=dr,P=!X||_&&X==v,z=!Ee||L&&Ee==q,Ut>0?(x=0,X&&(x+=d),x+=(Ut-1)*h,Ee&&(x+=d),zt&&Ee&&(x=b.Math.max(x,Qgn(Ee,h,Ce,bt))),x<Ce&&!P&&!z&&(Rn=(Ce-x)/2,x=Ce),Ne+=x):!P&&!z&&(Ne+=Ce),Ee&&X5e(Ee,Ne),nt=new C(e.i);nt.a<nt.c.c.length;)Ve=u(Y(nt),128),Ve.a.c=dr,Ve.a.b=Ne-dr,Ve.F=Rn,Ve.p=!X;Rs(e.a,e.i),dr=Ne,Ee&&(dr+=Ee.c.a),X=Ee,P=z}while(Ee);for(s=new C(e.j);s.a<s.c.c.length;)r=u(Y(s),17),o=dsn(e,r),Ye(r,(et(),q4),o),In=mpn(e,r),Ye(r,Mb,In);t.f.a=dr,e.d=null,ur(n)}function jlt(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne;if(W=e.i!=0,Ee=!1,le=null,Ll(e.e)){if(_=t.gc(),_>0){for(P=_<100?null:new _p(_),x=new Vme(t),q=x.g,le=Me(Lr,Jr,25,_,15,1),r=0,Ne=new jv(_),s=0;s<e.i;++s){d=e.g[s],z=d;e:for(Ce=0;Ce<2;++Ce){for(v=_;--v>=0;)if(z!=null?Ci(z,q[v]):je(z)===je(q[v])){le.length<=r&&(X=le,le=Me(Lr,Jr,25,2*le.length,15,1),Hc(X,0,le,0,r)),le[r++]=s,Br(Ne,q[v]);break e}if(z=z,je(z)===je(d))break}}if(x=Ne,q=Ne.g,_=r,r>le.length&&(X=le,le=Me(Lr,Jr,25,r,15,1),Hc(X,0,le,0,r)),r>0){for(Ee=!0,o=0;o<r;++o)z=q[o],P=pWe(e,u(z,72),P);for(h=r;--h>=0;)J6(e,le[h]);if(r!=_){for(s=_;--s>=r;)J6(x,s);X=le,le=Me(Lr,Jr,25,r,15,1),Hc(X,0,le,0,r)}t=x}}}else for(t=Yun(e,t),s=e.i;--s>=0;)t.Hc(e.g[s])&&(J6(e,s),Ee=!0);if(Ee){if(le!=null){for(n=t.gc(),L=n==1?x_(e,4,t.Kc().Pb(),null,le[0],W):x_(e,6,t,le,le[0],W),P=n<100?null:new _p(n),s=t.Kc();s.Ob();)z=s.Pb(),P=Ybe(e,u(z,72),P);P?(P.Ei(L),P.Fi()):_i(e.e,L)}else{for(P=cYt(t.gc()),s=t.Kc();s.Ob();)z=s.Pb(),P=Ybe(e,u(z,72),P);P&&P.Fi()}return!0}else return!1}function wyn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee;for(n=new hit(t),n.a||tbn(t),x=n2n(t),v=new Nv,X=new sct,W=new C(t.a);W.a<W.c.c.length;)for(q=u(Y(W),10),s=new cr(fr(js(q).a.Kc(),new V));Vr(s);)r=u(Pr(s),17),(r.c.i.k==(zn(),Ls)||r.d.i.k==Ls)&&(_=amn(e,r,x,X),sn(v,Qie(_.d),_.a));for(h=new st,Ee=u(K(n.c,(et(),Sy)),21).Kc();Ee.Ob();){switch(Ce=u(Ee.Pb(),61),z=X.c[Ce.g],P=X.b[Ce.g],d=X.a[Ce.g],o=null,le=null,Ce.g){case 4:o=new fh(e.d.a,z,x.b.a-e.d.a,P-z),le=new fh(e.d.a,z,d,P-z),_v(x,new Pt(o.c+o.b,o.d)),_v(x,new Pt(o.c+o.b,o.d+o.a));break;case 2:o=new fh(x.a.a,z,e.c.a-x.a.a,P-z),le=new fh(e.c.a-d,z,d,P-z),_v(x,new Pt(o.c,o.d)),_v(x,new Pt(o.c,o.d+o.a));break;case 1:o=new fh(z,e.d.b,P-z,x.b.b-e.d.b),le=new fh(z,e.d.b,P-z,d),_v(x,new Pt(o.c,o.d+o.a)),_v(x,new Pt(o.c+o.b,o.d+o.a));break;case 3:o=new fh(z,x.a.b,P-z,e.c.b-x.a.b),le=new fh(z,e.c.b-d,P-z,d),_v(x,new Pt(o.c,o.d)),_v(x,new Pt(o.c+o.b,o.d))}o&&(L=new lHe,L.d=Ce,L.b=o,L.c=le,L.a=e$(u(Ii(v,Qie(Ce)),21)),h.c[h.c.length]=L)}return Rs(n.b,h),n.d=xan(_wn(x)),n}function $lt(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W;if(n.p[t.p]==null){d=!0,n.p[t.p]=0,h=t,W=n.o==(M1(),Pb)?Ds:gs;do s=e.b.e[h.p],o=h.c.a.c.length,n.o==Pb&&s>0||n.o==K1&&s<o-1?(v=null,x=null,n.o==K1?v=u(St(h.c.a,s+1),10):v=u(St(h.c.a,s-1),10),x=n.g[v.p],$lt(e,x,n),W=e.e.bg(W,t,h),n.j[t.p]==t&&(n.j[t.p]=n.j[x.p]),n.j[t.p]==n.j[x.p]?(q=j3(e.d,h,v),n.o==K1?(r=Ue(n.p[t.p]),L=Ue(n.p[x.p])+Ue(n.d[v.p])-v.d.d-q-h.d.a-h.o.b-Ue(n.d[h.p]),d?(d=!1,n.p[t.p]=b.Math.min(L,W)):n.p[t.p]=b.Math.min(r,b.Math.min(L,W))):(r=Ue(n.p[t.p]),L=Ue(n.p[x.p])+Ue(n.d[v.p])+v.o.b+v.d.a+q+h.d.d-Ue(n.d[h.p]),d?(d=!1,n.p[t.p]=b.Math.max(L,W)):n.p[t.p]=b.Math.max(r,b.Math.max(L,W)))):(q=Ue(ft(K(e.a,(pt(),Aw)))),z=htt(e,n.j[t.p]),_=htt(e,n.j[x.p]),n.o==K1?(P=Ue(n.p[t.p])+Ue(n.d[h.p])+h.o.b+h.d.a+q-(Ue(n.p[x.p])+Ue(n.d[v.p])-v.d.d),WXe(z,_,P)):(P=Ue(n.p[t.p])+Ue(n.d[h.p])-h.d.d-Ue(n.p[x.p])-Ue(n.d[v.p])-v.o.b-v.d.a-q,WXe(z,_,P)))):W=e.e.bg(W,t,h),h=n.a[h.p];while(h!=t);bVt(e.e,t)}}function myn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr;for(Ee=t,Ce=new Nv,Ne=new Nv,_=Rv(Ee,e8e),r=new RYe(e,n,Ce,Ne),r1n(r.a,r.b,r.c,r.d,_),v=(bt=Ce.i,bt||(Ce.i=new H3(Ce,Ce.c))),Ut=v.Kc();Ut.Ob();)for(zt=u(Ut.Pb(),202),s=u(Ii(Ce,zt),21),W=s.Kc();W.Ob();)if(q=W.Pb(),Ve=u(K3(e.d,q),202),Ve)d=(!zt.e&&(zt.e=new wn(os,zt,10,9)),zt.e),Br(d,Ve);else throw h=D0(Ee,Id),P=F1t+q+j1t+h,z=P+z7,J(new dd(z));for(x=(nt=Ne.i,nt||(Ne.i=new H3(Ne,Ne.c))),Rn=x.Kc();Rn.Ob();)for(In=u(Rn.Pb(),202),o=u(Ii(Ne,In),21),le=o.Kc();le.Ob();)if(X=le.Pb(),Ve=u(K3(e.d,X),202),Ve)L=(!In.g&&(In.g=new wn(os,In,9,10)),In.g),Br(L,Ve);else throw h=D0(Ee,Id),P=F1t+X+j1t+h,z=P+z7,J(new dd(z));!n.b&&(n.b=new wn(mr,n,4,7)),n.b.i!=0&&(!n.c&&(n.c=new wn(mr,n,5,8)),n.c.i!=0)&&(!n.b&&(n.b=new wn(mr,n,4,7)),n.b.i<=1&&(!n.c&&(n.c=new wn(mr,n,5,8)),n.c.i<=1))&&(!n.a&&(n.a=new at(os,n,6,6)),n.a).i==1&&(dr=u(Te((!n.a&&(n.a=new at(os,n,6,6)),n.a),0),202),!nse(dr)&&!rse(dr)&&(W$(dr,u(Te((!n.b&&(n.b=new wn(mr,n,4,7)),n.b),0),82)),K$(dr,u(Te((!n.c&&(n.c=new wn(mr,n,5,8)),n.c),0),82))))}function yyn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In;for(Ee=e.a,Ne=0,Ve=Ee.length;Ne<Ve;++Ne){for(Ce=Ee[Ne],x=Ei,_=Ei,q=new C(Ce.e);q.a<q.c.c.length;)P=u(Y(q),10),h=P.c?Yo(P.c.a,P,0):-1,h>0?(L=u(St(P.c.a,h-1),10),zt=j3(e.b,P,L),X=P.n.b-P.d.d-(L.n.b+L.o.b+L.d.a+zt)):X=P.n.b-P.d.d,x=b.Math.min(X,x),h<P.c.a.c.length-1?(L=u(St(P.c.a,h+1),10),zt=j3(e.b,P,L),le=L.n.b-L.d.d-(P.n.b+P.o.b+P.d.a+zt)):le=2*P.n.b,_=b.Math.min(le,_);for(v=Ei,o=!1,s=u(St(Ce.e,0),10),In=new C(s.j);In.a<In.c.c.length;)for(Ut=u(Y(In),11),W=s.n.b+Ut.n.b+Ut.a.b,r=new C(Ut.e);r.a<r.c.c.length;)n=u(Y(r),17),nt=n.c,t=nt.i.n.b+nt.n.b+nt.a.b-W,b.Math.abs(t)<b.Math.abs(v)&&b.Math.abs(t)<(t<0?x:_)&&(v=t,o=!0);for(d=u(St(Ce.e,Ce.e.c.length-1),10),bt=new C(d.j);bt.a<bt.c.c.length;)for(nt=u(Y(bt),11),W=d.n.b+nt.n.b+nt.a.b,r=new C(nt.g);r.a<r.c.c.length;)n=u(Y(r),17),Ut=n.d,t=Ut.i.n.b+Ut.n.b+Ut.a.b-W,b.Math.abs(t)<b.Math.abs(v)&&b.Math.abs(t)<(t<0?x:_)&&(v=t,o=!0);if(o&&v!=0)for(z=new C(Ce.e);z.a<z.c.c.length;)P=u(Y(z),10),P.n.b+=v}}function Hlt(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X;if(Il(e.a,t)){if(_0(u(er(e.a,t),53),n))return 1}else Si(e.a,t,new Ys);if(Il(e.a,n)){if(_0(u(er(e.a,n),53),t))return-1}else Si(e.a,n,new Ys);if(Il(e.e,t)){if(_0(u(er(e.e,t),53),n))return-1}else Si(e.e,t,new Ys);if(Il(e.e,n)){if(_0(u(er(e.a,n),53),t))return 1}else Si(e.e,n,new Ys);if(e.c==(R0(),uhe)||!ta(t,(et(),Nc))||!ta(n,(et(),Nc))){if(v=u(hbe(ZQe(bie(Vi(new vn(null,new mn(t.j,16)),new ML)),new CB)),11),_=u(hbe(ZQe(bie(Vi(new vn(null,new mn(n.j,16)),new zX)),new GX)),11),v&&_){if(d=v.i,x=_.i,d&&d==x){for(P=new C(d.j);P.a<P.c.c.length;){if(L=u(Y(P),11),L==v)return w7(e,n,t),-1;if(L==_)return w7(e,t,n),1}return Tu(kse(e,t),kse(e,n))}for(q=e.d,W=0,X=q.length;W<X;++W){if(z=q[W],z==d)return w7(e,n,t),-1;if(z==x)return w7(e,t,n),1}}if(!ta(t,(et(),Nc))||!ta(n,Nc))return s=kse(e,t),h=kse(e,n),s>h?w7(e,t,n):w7(e,n,t),s<h?-1:s>h?1:0}return r=u(K(t,(et(),Nc)),19).a,o=u(K(n,Nc),19).a,r>o?w7(e,t,n):w7(e,n,t),r<o?-1:r>o?1:0}function c6e(e,t,n,r){var s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce;if(It(Mt(Ft(t,(bi(),PV)))))return hn(),hn(),bo;if(x=(!t.a&&(t.a=new at(hs,t,10,11)),t.a).i!=0,L=adn(t),_=!L.dc(),x||_){if(s=u(Ft(t,xE),149),!s)throw J(new M3("Resolved algorithm is not set; apply a LayoutAlgorithmResolver before computing layout."));if(Ce=D2e(s,(g7(),KV)),Urt(t),!x&&_&&!Ce)return hn(),hn(),bo;if(v=new st,je(Ft(t,Q4))===je((F0(),Wg))&&(D2e(s,UV)||D2e(s,VV)))for(z=Act(e,t),q=new as,no(q,(!t.a&&(t.a=new at(hs,t,10,11)),t.a));q.b!=0;)P=u(q.b==0?null:(Qn(q.b!=0),bh(q,q.a.a)),33),Urt(P),le=je(Ft(P,Q4))===je(jS),le||J2(P,PS)&&!Vwe(s,Ft(P,xE))?(d=c6e(e,P,n,r),Rs(v,d),So(P,Q4,jS),Vct(P)):no(q,(!P.a&&(P.a=new at(hs,P,10,11)),P.a));else for(z=(!t.a&&(t.a=new at(hs,t,10,11)),t.a).i,h=new rr((!t.a&&(t.a=new at(hs,t,10,11)),t.a));h.e!=h.i.gc();)o=u(pr(h),33),d=c6e(e,o,n,r),Rs(v,d),Vct(o);for(X=new C(v);X.a<X.c.c.length;)W=u(Y(X),79),So(W,PV,(Mn(),!0));return Pcn(t,s,Kc(r,z)),ebn(v),_&&Ce?L:(hn(),hn(),bo)}else return hn(),hn(),bo}function EI(e,t,n,r,s,o,h,d,v){var x,_,L,P,z,q,W;switch(z=n,_=new H0(v),T0(_,(zn(),Ls)),Ye(_,(et(),P9e),h),Ye(_,(pt(),bs),(wa(),tu)),W=Ue(ft(e.We(Ny))),Ye(_,Ny,W),L=new $c,rc(L,_),t!=X1&&t!=w2||(r>=0?z=Y6(d):z=zD(Y6(d)),e.Ye(gE,z)),x=new Fa,P=!1,e.Xe(Cw)?(ybe(x,u(e.We(Cw),8)),P=!0):WWt(x,h.a/2,h.b/2),z.g){case 4:Ye(_,vu,(mh(),l2)),Ye(_,Fq,(sb(),F4)),_.o.b=h.b,W<0&&(_.o.a=-W),Vs(L,(ht(),$n)),P||(x.a=h.a),x.a-=h.a;break;case 2:Ye(_,vu,(mh(),Ly)),Ye(_,Fq,(sb(),aE)),_.o.b=h.b,W<0&&(_.o.a=-W),Vs(L,(ht(),Dn)),P||(x.a=0);break;case 1:Ye(_,Lb,(P0(),$4)),_.o.a=h.a,W<0&&(_.o.b=-W),Vs(L,(ht(),xr)),P||(x.b=h.b),x.b-=h.b;break;case 3:Ye(_,Lb,(P0(),Tk)),_.o.a=h.a,W<0&&(_.o.b=-W),Vs(L,(ht(),An)),P||(x.b=0)}if(ybe(L.n,x),Ye(_,Cw,x),t==$b||t==p0||t==tu){if(q=0,t==$b&&e.Xe(zg))switch(z.g){case 1:case 2:q=u(e.We(zg),19).a;break;case 3:case 4:q=-u(e.We(zg),19).a}else switch(z.g){case 4:case 2:q=o.b,t==p0&&(q/=s.b);break;case 1:case 3:q=o.a,t==p0&&(q/=s.a)}Ye(_,Ew,q)}return Ye(_,vc,z),_}function kyn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut;if(n=Ue(ft(K(e.a.j,(pt(),xTe)))),n<-1||!e.a.i||A6(u(K(e.a.o,bs),98))||ac(e.a.o,(ht(),$n)).gc()<2&&ac(e.a.o,Dn).gc()<2)return!0;if(e.a.c.Rf())return!1;for(Ve=0,Ne=0,Ee=new st,v=e.a.e,x=0,_=v.length;x<_;++x){for(d=v[x],P=d,z=0,W=P.length;z<W;++z){if(L=P[z],L.k==(zn(),Jc)){Ee.c[Ee.c.length]=L;continue}for(r=e.b[L.c.p][L.p],L.k==Ls?(r.b=1,u(K(L,(et(),Mi)),11).j==(ht(),$n)&&(Ne+=r.a)):(Ut=ac(L,(ht(),Dn)),Ut.dc()||!Xte(Ut,new iQ)?r.c=1:(s=ac(L,$n),(s.dc()||!Xte(s,new rQ))&&(Ve+=r.a))),h=new cr(fr(js(L).a.Kc(),new V));Vr(h);)o=u(Pr(h),17),Ve+=r.c,Ne+=r.b,zt=o.d.i,cme(e,r,zt);for(le=B1(ie(re(q1,1),yt,20,0,[ac(L,(ht(),An)),ac(L,xr)])),bt=new cr(new nbe(le.a.length,le.a));Vr(bt);)nt=u(Pr(bt),11),Ce=u(K(nt,(et(),cl)),10),Ce&&(Ve+=r.c,Ne+=r.b,cme(e,r,Ce))}for(q=new C(Ee);q.a<q.c.c.length;)for(L=u(Y(q),10),r=e.b[L.c.p][L.p],h=new cr(fr(js(L).a.Kc(),new V));Vr(h);)o=u(Pr(h),17),Ve+=r.c,Ne+=r.b,zt=o.d.i,cme(e,r,zt);Ee.c=Me(Yn,yt,1,0,5,1)}return t=Ve+Ne,X=t==0?gs:(Ve-Ne)/t,X>=n}function xyn(){qpe();function e(r){var s=this;this.dispatch=function(o){var h=o.data;switch(h.cmd){case"algorithms":var d=Zye((hn(),new T(new E1(x2.b))));r.postMessage({id:h.id,data:d});break;case"categories":var v=Zye((hn(),new T(new E1(x2.c))));r.postMessage({id:h.id,data:v});break;case"options":var x=Zye((hn(),new T(new E1(x2.d))));r.postMessage({id:h.id,data:x});break;case"register":Lwn(h.algorithms),r.postMessage({id:h.id});break;case"layout":uvn(h.graph,h.layoutOptions||{},h.options||{}),r.postMessage({id:h.id,data:h.graph});break}},this.saveDispatch=function(o){try{s.dispatch(o)}catch(h){r.postMessage({id:o.data.id,error:h})}}}function t(r){var s=this;this.dispatcher=new 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L=Ibn(_u(e,(ht(),Q1)),t),q=s4(_u(e,Bf),t),Ne=s4(_u(e,eh),t),zt=HH(_u(e,Jh),t),P=HH(_u(e,_h),t),Ce=s4(_u(e,Rf),t),W=s4(_u(e,ll),t),nt=s4(_u(e,th),t),Ve=s4(_u(e,Ch),t),Ut=HH(_u(e,wu),t),le=s4(_u(e,Ru),t),Ee=s4(_u(e,hl),t),bt=s4(_u(e,Bu),t),In=HH(_u(e,Fu),t),z=HH(_u(e,Ju),t),X=s4(_u(e,nu),t),n=Q3(ie(re(pa,1),Ao,25,15,[Ce.a,zt.a,nt.a,In.a])),r=Q3(ie(re(pa,1),Ao,25,15,[q.a,L.a,Ne.a,X.a])),s=le.a,o=Q3(ie(re(pa,1),Ao,25,15,[W.a,P.a,Ve.a,z.a])),x=Q3(ie(re(pa,1),Ao,25,15,[Ce.b,q.b,W.b,Ee.b])),v=Q3(ie(re(pa,1),Ao,25,15,[zt.b,L.b,P.b,X.b])),_=Ut.b,d=Q3(ie(re(pa,1),Ao,25,15,[nt.b,Ne.b,Ve.b,bt.b])),_g(_u(e,Q1),n+s,x+_),_g(_u(e,nu),n+s,x+_),_g(_u(e,Bf),n+s,0),_g(_u(e,eh),n+s,x+_+v),_g(_u(e,Jh),0,x+_),_g(_u(e,_h),n+s+r,x+_),_g(_u(e,ll),n+s+r,0),_g(_u(e,th),0,x+_+v),_g(_u(e,Ch),n+s+r,x+_+v),_g(_u(e,wu),0,x),_g(_u(e,Ru),n,0),_g(_u(e,Bu),0,x+_+v),_g(_u(e,Ju),n+s+r,0),h=new Fa,h.a=Q3(ie(re(pa,1),Ao,25,15,[n+r+s+o,Ut.a,Ee.a,bt.a])),h.b=Q3(ie(re(pa,1),Ao,25,15,[x+v+_+d,le.b,In.b,z.b])),h}function Myn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X;for(W=new st,P=new C(e.d.b);P.a<P.c.c.length;)for(L=u(Y(P),29),q=new C(L.a);q.a<q.c.c.length;){for(z=u(Y(q),10),s=u(er(e.f,z),57),v=new cr(fr(js(z).a.Kc(),new V));Vr(v);)if(h=u(Pr(v),17),r=ii(h.a,0),x=!0,_=null,r.b!=r.d.c){for(t=u(ri(r),8),n=null,h.c.j==(ht(),An)&&(X=new hC(t,new Pt(t.a,s.d.d),s,h),X.f.a=!0,X.a=h.c,W.c[W.c.length]=X),h.c.j==xr&&(X=new hC(t,new Pt(t.a,s.d.d+s.d.a),s,h),X.f.d=!0,X.a=h.c,W.c[W.c.length]=X);r.b!=r.d.c;)n=u(ri(r),8),Eme(t.b,n.b)||(_=new hC(t,n,null,h),W.c[W.c.length]=_,x&&(x=!1,n.b<s.d.d?_.f.a=!0:n.b>s.d.d+s.d.a?_.f.d=!0:(_.f.d=!0,_.f.a=!0))),r.b!=r.d.c&&(t=n);_&&(o=u(er(e.f,h.d.i),57),t.b<o.d.d?_.f.a=!0:t.b>o.d.d+o.d.a?_.f.d=!0:(_.f.d=!0,_.f.a=!0))}for(d=new cr(fr(Xo(z).a.Kc(),new V));Vr(d);)h=u(Pr(d),17),h.a.b!=0&&(t=u(fj(h.a),8),h.d.j==(ht(),An)&&(X=new hC(t,new Pt(t.a,s.d.d),s,h),X.f.a=!0,X.a=h.d,W.c[W.c.length]=X),h.d.j==xr&&(X=new hC(t,new Pt(t.a,s.d.d+s.d.a),s,h),X.f.d=!0,X.a=h.d,W.c[W.c.length]=X))}return W}function Dyn(e,t,n){var r,s,o,h,d,v,x,_,L;if(kr(n,"Network simplex node placement",1),e.e=t,e.n=u(K(t,(et(),G4)),304),Kvn(e),Ahn(e),ms(ic(new vn(null,new mn(e.e.b,16)),new hQ),new Oje(e)),ms(Vi(ic(Vi(ic(new vn(null,new mn(e.e.b,16)),new N9),new kQ),new xQ),new EQ),new Ije(e)),It(Mt(K(e.e,(pt(),gS))))&&(h=Kc(n,1),kr(h,"Straight Edges Pre-Processing",1),dmn(e),ur(h)),uun(e.f),o=u(K(t,vS),19).a*e.f.a.c.length,sae(Rpe(Fpe(bne(e.f),o),!1),Kc(n,1)),e.d.a.gc()!=0){for(h=Kc(n,1),kr(h,"Flexible Where Space Processing",1),d=u(Tv(Fj(Cu(new vn(null,new mn(e.f.a,16)),new fQ),new sQ)),19).a,v=u(Tv(Rj(Cu(new vn(null,new mn(e.f.a,16)),new dQ),new aQ)),19).a,x=v-d,_=Ev(new z2,e.f),L=Ev(new z2,e.f),Cf(bf(pf(gf(vf(new Nh,2e4),x),_),L)),ms(Vi(Vi(Rne(e.i),new gQ),new pQ),new MYe(d,_,x,L)),s=e.d.a.ec().Kc();s.Ob();)r=u(s.Pb(),213),r.g=1;sae(Rpe(Fpe(bne(e.f),o),!1),Kc(h,1)),ur(h)}It(Mt(K(t,gS)))&&(h=Kc(n,1),kr(h,"Straight Edges Post-Processing",1),w1n(e),ur(h)),Qwn(e),e.e=null,e.f=null,e.i=null,e.c=null,sl(e.k),e.j=null,e.a=null,e.o=null,e.d.a.$b(),ur(n)}function Iyn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve;for(d=new C(e.a.b);d.a<d.c.c.length;)for(o=u(Y(d),29),Ee=new C(o.a);Ee.a<Ee.c.c.length;)Ce=u(Y(Ee),10),t.g[Ce.p]=Ce,t.a[Ce.p]=Ce,t.d[Ce.p]=0;for(v=e.a.b,t.c==(yd(),Gg)&&(v=we(v,152)?H6(u(v,152)):we(v,131)?u(v,131).a:we(v,54)?new lv(v):new Em(v)),h=v.Kc();h.Ob();)for(o=u(h.Pb(),29),z=-1,P=o.a,t.o==(M1(),K1)&&(z=Ei,P=we(P,152)?H6(u(P,152)):we(P,131)?u(P,131).a:we(P,54)?new lv(P):new Em(P)),Ve=P.Kc();Ve.Ob();)if(Ne=u(Ve.Pb(),10),L=null,t.c==Gg?L=u(St(e.b.f,Ne.p),15):L=u(St(e.b.b,Ne.p),15),L.gc()>0)if(r=L.gc(),x=_s(b.Math.floor((r+1)/2))-1,s=_s(b.Math.ceil((r+1)/2))-1,t.o==K1)for(_=s;_>=x;_--)t.a[Ne.p]==Ne&&(W=u(L.Xb(_),46),q=u(W.a,10),!_0(n,W.b)&&z>e.b.e[q.p]&&(t.a[q.p]=Ne,t.g[Ne.p]=t.g[q.p],t.a[Ne.p]=t.g[Ne.p],t.f[t.g[Ne.p].p]=(Mn(),!!(It(t.f[t.g[Ne.p].p])&Ne.k==(zn(),ca))),z=e.b.e[q.p]));else for(_=x;_<=s;_++)t.a[Ne.p]==Ne&&(le=u(L.Xb(_),46),X=u(le.a,10),!_0(n,le.b)&&z<e.b.e[X.p]&&(t.a[X.p]=Ne,t.g[Ne.p]=t.g[X.p],t.a[Ne.p]=t.g[Ne.p],t.f[t.g[Ne.p].p]=(Mn(),!!(It(t.f[t.g[Ne.p].p])&Ne.k==(zn(),ca))),z=e.b.e[X.p]))}function cu(){cu=pe,kze(),G3t=la.a,u(Te(He(la.a),0),18),H3t=la.f,u(Te(He(la.f),0),18),u(Te(He(la.f),1),34),z3t=la.n,u(Te(He(la.n),0),34),u(Te(He(la.n),1),34),u(Te(He(la.n),2),34),u(Te(He(la.n),3),34),xAe=la.g,u(Te(He(la.g),0),18),u(Te(He(la.g),1),34),$3t=la.c,u(Te(He(la.c),0),18),u(Te(He(la.c),1),18),EAe=la.i,u(Te(He(la.i),0),18),u(Te(He(la.i),1),18),u(Te(He(la.i),2),18),u(Te(He(la.i),3),18),u(Te(He(la.i),4),34),TAe=la.j,u(Te(He(la.j),0),18),kAe=la.d,u(Te(He(la.d),0),18),u(Te(He(la.d),1),18),u(Te(He(la.d),2),18),u(Te(He(la.d),3),18),u(Te(He(la.d),4),34),u(Te(He(la.d),5),34),u(Te(He(la.d),6),34),u(Te(He(la.d),7),34),j3t=la.b,u(Te(He(la.b),0),34),u(Te(He(la.b),1),34),qV=la.e,u(Te(He(la.e),0),34),u(Te(He(la.e),1),34),u(Te(He(la.e),2),34),u(Te(He(la.e),3),34),u(Te(He(la.e),4),18),u(Te(He(la.e),5),18),u(Te(He(la.e),6),18),u(Te(He(la.e),7),18),u(Te(He(la.e),8),18),u(Te(He(la.e),9),18),u(Te(He(la.e),10),34),k2=la.k,u(Te(He(la.k),0),34),u(Te(He(la.k),1),34)}function Oyn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn;for(Ut=new as,nt=new as,X=-1,v=new C(e);v.a<v.c.c.length;){for(h=u(Y(v),128),h.s=X--,_=0,Ee=0,o=new C(h.t);o.a<o.c.c.length;)r=u(Y(o),268),Ee+=r.c;for(s=new C(h.i);s.a<s.c.c.length;)r=u(Y(s),268),_+=r.c;h.n=_,h.u=Ee,Ee==0?ks(nt,h,nt.c.b,nt.c):_==0&&ks(Ut,h,Ut.c.b,Ut.c)}for(Rn=w$(e),L=e.c.length,W=L+1,le=L-1,z=new st;Rn.a.gc()!=0;){for(;nt.b!=0;)Ve=(Qn(nt.b!=0),u(bh(nt,nt.a.a),128)),Rn.a.Bc(Ve)!=null,Ve.s=le--,s5e(Ve,Ut,nt);for(;Ut.b!=0;)bt=(Qn(Ut.b!=0),u(bh(Ut,Ut.a.a),128)),Rn.a.Bc(bt)!=null,bt.s=W++,s5e(bt,Ut,nt);for(q=$a,x=Rn.a.ec().Kc();x.Ob();)h=u(x.Pb(),128),Ce=h.u-h.n,Ce>=q&&(Ce>q&&(z.c=Me(Yn,yt,1,0,5,1),q=Ce),z.c[z.c.length]=h);z.c.length!=0&&(P=u(St(z,BH(t,z.c.length)),128),Rn.a.Bc(P)!=null,P.s=W++,s5e(P,Ut,nt),z.c=Me(Yn,yt,1,0,5,1))}for(Ne=e.c.length+1,d=new C(e);d.a<d.c.c.length;)h=u(Y(d),128),h.s<L&&(h.s+=Ne);for(zt=new C(e);zt.a<zt.c.c.length;)for(bt=u(Y(zt),128),n=new Ea(bt.t,0);n.b<n.d.gc();)r=(Qn(n.b<n.d.gc()),u(n.d.Xb(n.c=n.b++),268)),In=r.b,bt.s>In.s&&(Ol(n),Au(In.i,r),r.c>0&&(r.a=In,it(In.t,r),r.b=bt,it(bt.i,r)))}function u6e(e){var t,n,r,s,o;switch(t=e.c,t){case 11:return e.Ml();case 12:return e.Ol();case 14:return e.Ql();case 15:return e.Tl();case 16:return e.Rl();case 17:return e.Ul();case 21:return mi(e),yi(),yi(),nA;case 10:switch(e.a){case 65:return e.yl();case 90:return e.Dl();case 122:return e.Kl();case 98:return e.El();case 66:return e.zl();case 60:return e.Jl();case 62:return e.Hl()}}switch(o=Syn(e),t=e.c,t){case 3:return e.Zl(o);case 4:return e.Xl(o);case 5:return e.Yl(o);case 0:if(e.a==123&&e.d<e.j){if(s=e.d,r=0,n=-1,(t=Ma(e.i,s++))>=48&&t<=57){for(r=t-48;s<e.j&&(t=Ma(e.i,s++))>=48&&t<=57;)if(r=r*10+t-48,r<0)throw J(new $r(Ur((jr(),d8e))))}else throw J(new $r(Ur((jr(),hdt))));if(n=r,t==44){if(s>=e.j)throw J(new $r(Ur((jr(),ddt))));if((t=Ma(e.i,s++))>=48&&t<=57){for(n=t-48;s<e.j&&(t=Ma(e.i,s++))>=48&&t<=57;)if(n=n*10+t-48,n<0)throw J(new $r(Ur((jr(),d8e))));if(r>n)throw J(new $r(Ur((jr(),gdt))))}else n=-1}if(t!=125)throw J(new $r(Ur((jr(),fdt))));e.sl(s)?(o=(yi(),yi(),new $m(9,o)),e.d=s+1):(o=(yi(),yi(),new $m(3,o)),e.d=s),o.dm(r),o.cm(n),mi(e)}}return o}function Glt(e,t,n,r,s){var o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn;for(W=new su(t.b),Ne=new su(t.b),P=new su(t.b),zt=new su(t.b),X=new su(t.b),bt=ii(t,0);bt.b!=bt.d.c;)for(Ve=u(ri(bt),11),d=new C(Ve.g);d.a<d.c.c.length;)if(o=u(Y(d),17),o.c.i==o.d.i){if(Ve.j==o.d.j){zt.c[zt.c.length]=o;continue}else if(Ve.j==(ht(),An)&&o.d.j==xr){X.c[X.c.length]=o;continue}}for(v=new C(X);v.a<v.c.c.length;)o=u(Y(v),17),gbn(e,o,n,r,(ht(),$n));for(h=new C(zt);h.a<h.c.c.length;)o=u(Y(h),17),Ut=new H0(e),T0(Ut,(zn(),Jc)),Ye(Ut,(pt(),bs),(wa(),tu)),Ye(Ut,(et(),Mi),o),In=new $c,Ye(In,Mi,o.d),Vs(In,(ht(),Dn)),rc(In,Ut),Rn=new $c,Ye(Rn,Mi,o.c),Vs(Rn,$n),rc(Rn,Ut),Ye(o.c,cl,Ut),Ye(o.d,cl,Ut),Va(o,null),ba(o,null),n.c[n.c.length]=Ut,Ye(Ut,Rq,ct(2));for(nt=ii(t,0);nt.b!=nt.d.c;)Ve=u(ri(nt),11),x=Ve.e.c.length>0,le=Ve.g.c.length>0,x&&le?P.c[P.c.length]=Ve:x?W.c[W.c.length]=Ve:le&&(Ne.c[Ne.c.length]=Ve);for(q=new C(W);q.a<q.c.c.length;)z=u(Y(q),11),it(s,V5e(e,z,null,n));for(Ee=new C(Ne);Ee.a<Ee.c.c.length;)Ce=u(Y(Ee),11),it(s,V5e(e,null,Ce,n));for(L=new C(P);L.a<L.c.c.length;)_=u(Y(L),11),it(s,V5e(e,_,_,n))}function qlt(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In;for(Ce=new Pt(gs,gs),t=new Pt(Ds,Ds),zt=new C(e);zt.a<zt.c.c.length;)bt=u(Y(zt),8),Ce.a=b.Math.min(Ce.a,bt.a),Ce.b=b.Math.min(Ce.b,bt.b),t.a=b.Math.max(t.a,bt.a),t.b=b.Math.max(t.b,bt.b);for(P=new Pt(t.a-Ce.a,t.b-Ce.b),x=new Pt(Ce.a-50,Ce.b-P.a-50),_=new Pt(Ce.a-50,t.b+P.a+50),L=new Pt(t.a+P.b/2+50,Ce.b+P.b/2),z=new z5e(x,_,L),nt=new Ys,o=new st,n=new st,nt.a.zc(z,nt),In=new C(e);In.a<In.c.c.length;){for(Ut=u(Y(In),8),o.c=Me(Yn,yt,1,0,5,1),Ve=nt.a.ec().Kc();Ve.Ob();)Ee=u(Ve.Pb(),308),r=Ee.d,Hp(r,Ee.a),Kv(Hp(Ee.d,Ut),Hp(Ee.d,Ee.a))<0&&(o.c[o.c.length]=Ee);for(n.c=Me(Yn,yt,1,0,5,1),Ne=new C(o);Ne.a<Ne.c.c.length;)for(Ee=u(Y(Ne),308),X=new C(Ee.e);X.a<X.c.c.length;){for(q=u(Y(X),168),h=!0,v=new C(o);v.a<v.c.c.length;)d=u(Y(v),308),d!=Ee&&(Vc(q,St(d.e,0))||Vc(q,St(d.e,1))||Vc(q,St(d.e,2)))&&(h=!1);h&&(n.c[n.c.length]=q)}for(_st(nt,o),Da(nt,new ws),W=new C(n);W.a<W.c.c.length;)q=u(Y(W),168),Gs(nt,new z5e(Ut,q.a,q.b))}for(le=new Ys,Da(nt,new kn(le)),s=le.a.ec().Kc();s.Ob();)q=u(s.Pb(),168),(I$(z,q.a)||I$(z,q.b))&&s.Qb();return Da(le,new Oi),le}function Nyn(e){var t,n,r,s,o;switch(n=u(K(e,(et(),eu)),21),t=JF(Spt),s=u(K(e,(pt(),Iy)),334),s==(F0(),Wg)&&Vl(t,Apt),It(Mt(K(e,Ule)))?xi(t,(ro(),Pd),(po(),tle)):xi(t,(ro(),bu),(po(),tle)),K(e,(T$(),NS))!=null&&Vl(t,Lpt),(It(Mt(K(e,OTe)))||It(Mt(K(e,LTe))))&&il(t,(ro(),Go),(po(),AEe)),u(K(e,Zl),103).g){case 2:case 3:case 4:il(xi(t,(ro(),Pd),(po(),MEe)),Go,LEe)}switch(n.Hc((mo(),Oq))&&il(xi(xi(t,(ro(),Pd),(po(),SEe)),Zc,_Ee),Go,CEe),je(K(e,Wle))!==je((f4(),aV))&&xi(t,(ro(),bu),(po(),VEe)),n.Hc(Pq)&&(xi(t,(ro(),Pd),(po(),YEe)),xi(t,c2,WEe),xi(t,bu,KEe)),je(K(e,Gq))!==je((h7(),aS))&&je(K(e,K0))!==je(($0(),YO))&&il(t,(ro(),Go),(po(),jEe)),It(Mt(K(e,DTe)))&&xi(t,(ro(),bu),(po(),FEe)),It(Mt(K(e,zle)))&&xi(t,(ro(),bu),(po(),XEe)),hgn(e)&&(je(K(e,Iy))===je(Wg)?r=u(K(e,_O),292):r=u(K(e,Hle),292),o=r==(q8(),Cle)?(po(),UEe):(po(),JEe),xi(t,(ro(),Zc),o)),u(K(e,s_e),377).g){case 1:xi(t,(ro(),Zc),(po(),QEe));break;case 2:il(xi(xi(t,(ro(),bu),(po(),kEe)),Zc,xEe),Go,EEe)}return je(K(e,h2))!==je((R0(),f2))&&xi(t,(ro(),bu),(po(),ZEe)),t}function Vlt(e){wv(e,new gb(gv(hv(dv(fv(new lg,Gh),"ELK Rectangle Packing"),"Algorithm for packing of unconnected boxes, i.e. graphs without edges. The given order of the boxes is always preserved and the main reading direction of the boxes is left to right. The algorithm is divided into two phases. One phase approximates the width in which the rectangles can be placed. The next phase places the rectangles in rows using the previously calculated width as bounding width and bundles rectangles with a similar height in blocks. A compaction step reduces the size of the drawing. Finally, the rectangles are expanded to fill their bounding box and eliminate empty unused spaces."),new sZ))),dt(e,Gh,fk,1.3),dt(e,Gh,uG,xt(xCe)),dt(e,Gh,uw,LCe),dt(e,Gh,py,15),dt(e,Gh,rG,xt(iyt)),dt(e,Gh,E4,xt(oyt)),dt(e,Gh,gk,xt(cyt)),dt(e,Gh,dk,xt(uyt)),dt(e,Gh,P7,xt(ayt)),dt(e,Gh,CC,xt(CCe)),dt(e,Gh,B7,xt(hyt)),dt(e,Gh,Dxe,xt(ACe)),dt(e,Gh,Ixe,xt(_Ce)),dt(e,Gh,Pxe,xt(SCe)),dt(e,Gh,Bxe,xt(MCe)),dt(e,Gh,xce,xt(ECe)),dt(e,Gh,jI,xt(TCe)),dt(e,Gh,fce,xt(syt)),dt(e,Gh,Nxe,xt(RO)),dt(e,Gh,Oxe,xt(kCe)),dt(e,Gh,Rxe,xt(DCe))}function aw(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X;if(n==null)return null;if(e.a!=t.Aj())throw J(new Ln($7+t.ne()+dw));if(we(t,457)){if(X=Hpn(u(t,671),n),!X)throw J(new Ln(Nce+n+"' is not a valid enumerator of '"+t.ne()+"'"));return X}switch(Ag((Yu(),Oa),t).cl()){case 2:{n=Xc(n,!1);break}case 3:{n=Xc(n,!0);break}}if(r=Ag(Oa,t).$k(),r)return r.Aj().Nh().Kh(r,n);if(P=Ag(Oa,t).al(),P){for(X=new st,x=mie(n),_=0,L=x.length;_<L;++_)v=x[_],it(X,P.Aj().Nh().Kh(P,v));return X}if(W=Ag(Oa,t).bl(),!W.dc()){for(q=W.Kc();q.Ob();){z=u(q.Pb(),148);try{if(X=z.Aj().Nh().Kh(z,n),X!=null)return X}catch(le){if(le=ts(le),!we(le,60))throw J(le)}}throw J(new Ln(Nce+n+"' does not match any member types of the union datatype '"+t.ne()+"'"))}if(u(t,834).Fj(),s=$an(t.Bj()),!s)return null;if(s==XC){h=0;try{h=Wl(n,$a,Ei)&Ss}catch(le){if(le=ts(le),we(le,127))o=O$(n),h=o[0];else throw J(le)}return DD(h)}if(s==FG){for(d=0;d<WS.length;++d)try{return IGe(WS[d],n)}catch(le){if(le=ts(le),!we(le,32))throw J(le)}throw J(new Ln(Nce+n+"' is not a date formatted string of the form yyyy-MM-dd'T'HH:mm:ss'.'SSSZ or a valid subset thereof"))}throw J(new Ln(Nce+n+"' is invalid. "))}function Pyn(e,t){var n,r,s,o,h,d,v,x;if(n=0,h=0,o=t.length,d=null,x=new xm,h<o&&(zr(h,t.length),t.charCodeAt(h)==43)&&(++h,++n,h<o&&(zr(h,t.length),t.charCodeAt(h)==43||(zr(h,t.length),t.charCodeAt(h)==45))))throw J(new gd(cw+t+'"'));for(;h<o&&(zr(h,t.length),t.charCodeAt(h)!=46)&&(zr(h,t.length),t.charCodeAt(h)!=101)&&(zr(h,t.length),t.charCodeAt(h)!=69);)++h;if(x.a+=""+jl(t==null?Pu:(Sn(t),t),n,h),h<o&&(zr(h,t.length),t.charCodeAt(h)==46)){for(++h,n=h;h<o&&(zr(h,t.length),t.charCodeAt(h)!=101)&&(zr(h,t.length),t.charCodeAt(h)!=69);)++h;e.e=h-n,x.a+=""+jl(t==null?Pu:(Sn(t),t),n,h)}else e.e=0;if(h<o&&(zr(h,t.length),t.charCodeAt(h)==101||(zr(h,t.length),t.charCodeAt(h)==69))&&(++h,n=h,h<o&&(zr(h,t.length),t.charCodeAt(h)==43)&&(++h,h<o&&(zr(h,t.length),t.charCodeAt(h)!=45)&&++n),d=t.substr(n,o-n),e.e=e.e-Wl(d,$a,Ei),e.e!=_s(e.e)))throw J(new gd("Scale out of range."));if(v=x.a,v.length<16){if(e.f=(t7e==null&&(t7e=new RegExp("^[+-]?\\d*$","i")),t7e.test(v)?parseInt(v,10):NaN),isNaN(e.f))throw J(new gd(cw+t+'"'));e.a=B4e(e.f)}else ncn(e,new Ip(v));for(e.d=x.a.length,s=0;s<x.a.length&&(r=Ma(x.a,s),!(r!=45&&r!=48));++s)--e.d;e.d==0&&(e.d=1)}function Aae(){Aae=pe,$i=new Nv,sn($i,(ht(),Q1),nu),sn($i,Jh,nu),sn($i,Jh,Fu),sn($i,_h,Ju),sn($i,_h,nu),sn($i,Bf,nu),sn($i,Bf,hl),sn($i,eh,Bu),sn($i,eh,nu),sn($i,Ru,wu),sn($i,Ru,nu),sn($i,Ru,hl),sn($i,Ru,Bu),sn($i,wu,Ru),sn($i,wu,Fu),sn($i,wu,Ju),sn($i,wu,nu),sn($i,Rf,Rf),sn($i,Rf,hl),sn($i,Rf,Fu),sn($i,ll,ll),sn($i,ll,hl),sn($i,ll,Ju),sn($i,th,th),sn($i,th,Bu),sn($i,th,Fu),sn($i,Ch,Ch),sn($i,Ch,Bu),sn($i,Ch,Ju),sn($i,hl,Bf),sn($i,hl,Ru),sn($i,hl,Rf),sn($i,hl,ll),sn($i,hl,nu),sn($i,hl,hl),sn($i,hl,Fu),sn($i,hl,Ju),sn($i,Bu,eh),sn($i,Bu,Ru),sn($i,Bu,th),sn($i,Bu,Ch),sn($i,Bu,Bu),sn($i,Bu,Fu),sn($i,Bu,Ju),sn($i,Bu,nu),sn($i,Fu,Jh),sn($i,Fu,wu),sn($i,Fu,Rf),sn($i,Fu,th),sn($i,Fu,hl),sn($i,Fu,Bu),sn($i,Fu,Fu),sn($i,Fu,nu),sn($i,Ju,_h),sn($i,Ju,wu),sn($i,Ju,ll),sn($i,Ju,Ch),sn($i,Ju,hl),sn($i,Ju,Bu),sn($i,Ju,Ju),sn($i,Ju,nu),sn($i,nu,Q1),sn($i,nu,Jh),sn($i,nu,_h),sn($i,nu,Bf),sn($i,nu,eh),sn($i,nu,Ru),sn($i,nu,wu),sn($i,nu,hl),sn($i,nu,Bu),sn($i,nu,Fu),sn($i,nu,Ju),sn($i,nu,nu)}function l6e(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt;for(e.d=new Pt(gs,gs),e.c=new Pt(Ds,Ds),P=t.Kc();P.Ob();)for(_=u(P.Pb(),37),Ee=new C(_.a);Ee.a<Ee.c.c.length;)Ce=u(Y(Ee),10),e.d.a=b.Math.min(e.d.a,Ce.n.a-Ce.d.b),e.d.b=b.Math.min(e.d.b,Ce.n.b-Ce.d.d),e.c.a=b.Math.max(e.c.a,Ce.n.a+Ce.o.a+Ce.d.c),e.c.b=b.Math.max(e.c.b,Ce.n.b+Ce.o.b+Ce.d.a);for(d=new oHe,L=t.Kc();L.Ob();)_=u(L.Pb(),37),r=wyn(e,_),it(d.a,r),r.a=r.a|!u(K(r.c,(et(),Sy)),21).dc();for(e.b=(Aie(),zt=new vL,zt.f=new Yet(n),zt.b=Cwn(zt.f,d),zt),Nwn((q=e.b,new Ux,q)),e.e=new Fa,e.a=e.b.f.e,h=new C(d.a);h.a<h.c.c.length;)for(s=u(Y(h),841),Ne=wen(e.b,s),y2n(s.c,Ne.a,Ne.b),X=new C(s.c.a);X.a<X.c.c.length;)W=u(Y(X),10),W.k==(zn(),Ls)&&(le=a5e(e,W.n,u(K(W,(et(),vc)),61)),Ni(Zf(W.n),le));for(o=new C(d.a);o.a<o.c.c.length;)for(s=u(Y(o),841),x=new C(Xan(s));x.a<x.c.c.length;)for(v=u(Y(x),17),bt=new pF(v.a),c8(bt,0,D1(v.c)),ci(bt,D1(v.d)),z=null,nt=ii(bt,0);nt.b!=nt.d.c;){if(Ve=u(ri(nt),8),!z){z=Ve;continue}Tme(z.a,Ve.a)?(e.e.a=b.Math.min(e.e.a,z.a),e.a.a=b.Math.max(e.a.a,z.a)):Tme(z.b,Ve.b)&&(e.e.b=b.Math.min(e.e.b,z.b),e.a.b=b.Math.max(e.a.b,z.b)),z=Ve}gj(e.e),Ni(e.a,e.e)}function Byn(e){Rr(e.b,Uh,ie(re(mt,1),Qe,2,6,[gw,"ConsistentTransient"])),Rr(e.a,Uh,ie(re(mt,1),Qe,2,6,[gw,"WellFormedSourceURI"])),Rr(e.o,Uh,ie(re(mt,1),Qe,2,6,[gw,"InterfaceIsAbstract AtMostOneID UniqueFeatureNames UniqueOperationSignatures NoCircularSuperTypes WellFormedMapEntryClass ConsistentSuperTypes DisjointFeatureAndOperationSignatures"])),Rr(e.p,Uh,ie(re(mt,1),Qe,2,6,[gw,"WellFormedInstanceTypeName UniqueTypeParameterNames"])),Rr(e.v,Uh,ie(re(mt,1),Qe,2,6,[gw,"UniqueEnumeratorNames UniqueEnumeratorLiterals"])),Rr(e.R,Uh,ie(re(mt,1),Qe,2,6,[gw,"WellFormedName"])),Rr(e.T,Uh,ie(re(mt,1),Qe,2,6,[gw,"UniqueParameterNames UniqueTypeParameterNames NoRepeatingVoid"])),Rr(e.U,Uh,ie(re(mt,1),Qe,2,6,[gw,"WellFormedNsURI WellFormedNsPrefix UniqueSubpackageNames UniqueClassifierNames UniqueNsURIs"])),Rr(e.W,Uh,ie(re(mt,1),Qe,2,6,[gw,"ConsistentOpposite SingleContainer ConsistentKeys ConsistentUnique ConsistentContainer"])),Rr(e.bb,Uh,ie(re(mt,1),Qe,2,6,[gw,"ValidDefaultValueLiteral"])),Rr(e.eb,Uh,ie(re(mt,1),Qe,2,6,[gw,"ValidLowerBound ValidUpperBound ConsistentBounds ValidType"])),Rr(e.H,Uh,ie(re(mt,1),Qe,2,6,[gw,"ConsistentType ConsistentBounds ConsistentArguments"]))}function Ryn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut;if(!t.dc()){if(s=new Gu,d=n||u(t.Xb(0),17),q=d.c,aC(),P=q.i.k,!(P==(zn(),Hs)||P==Jc||P==Ls||P==U1))throw J(new Ln("The target node of the edge must be a normal node or a northSouthPort."));for(q2(s,sc(ie(re(na,1),Qe,8,0,[q.i.n,q.n,q.a]))),(ht(),Ru).Hc(q.j)&&(X=Ue(ft(K(q,(et(),hE)))),L=new Pt(sc(ie(re(na,1),Qe,8,0,[q.i.n,q.n,q.a])).a,X),ks(s,L,s.c.b,s.c)),_=null,r=!1,v=t.Kc();v.Ob();)h=u(v.Pb(),17),o=h.a,o.b!=0&&(r?(x=bd(Ni(_,(Qn(o.b!=0),u(o.a.a.c,8))),.5),ks(s,x,s.c.b,s.c),r=!1):r=!0,_=fc((Qn(o.b!=0),u(o.c.b.c,8))),no(s,o),Rh(o));W=d.d,Ru.Hc(W.j)&&(X=Ue(ft(K(W,(et(),hE)))),L=new Pt(sc(ie(re(na,1),Qe,8,0,[W.i.n,W.n,W.a])).a,X),ks(s,L,s.c.b,s.c)),q2(s,sc(ie(re(na,1),Qe,8,0,[W.i.n,W.n,W.a]))),e.d==(Q8(),ghe)&&(le=(Qn(s.b!=0),u(s.a.a.c,8)),Ce=u(s1(s,1),8),Ee=new dre(pye(q.j)),Ee.a*=5,Ee.b*=5,Ne=da(new Pt(Ce.a,Ce.b),le),Ve=new Pt(lre(Ee.a,Ne.a),lre(Ee.b,Ne.b)),Ni(Ve,le),nt=ii(s,1),YM(nt,Ve),bt=(Qn(s.b!=0),u(s.c.b.c,8)),zt=u(s1(s,s.b-2),8),Ee=new dre(pye(W.j)),Ee.a*=5,Ee.b*=5,Ne=da(new Pt(zt.a,zt.b),bt),Ut=new Pt(lre(Ee.a,Ne.a),lre(Ee.b,Ne.b)),Ni(Ut,bt),c8(s,s.b-1,Ut)),z=new G5e(s),no(d.a,pit(z))}}function Fyn(e,t,n,r){var s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr,ki,Ws,rh,af,ed,aU,pN,rA,bN;if(Ee=u(Te((!e.b&&(e.b=new wn(mr,e,4,7)),e.b),0),82),Ve=Ee.Dg(),nt=Ee.Eg(),Ne=Ee.Cg()/2,W=Ee.Bg()/2,we(Ee,186)&&(Ce=u(Ee,118),Ve+=L1(Ce).i,Ve+=L1(Ce).i),Ve+=Ne,nt+=W,Rn=u(Te((!e.b&&(e.b=new wn(mr,e,4,7)),e.b),0),82),ki=Rn.Dg(),Ws=Rn.Eg(),dr=Rn.Cg()/2,bt=Rn.Bg()/2,we(Rn,186)&&(In=u(Rn,118),ki+=L1(In).i,ki+=L1(In).i),ki+=dr,Ws+=bt,(!e.a&&(e.a=new at(os,e,6,6)),e.a).i==0)d=(pv(),x=new ld,x),Br((!e.a&&(e.a=new at(os,e,6,6)),e.a),d);else if((!e.a&&(e.a=new at(os,e,6,6)),e.a).i>1)for(q=new _6((!e.a&&(e.a=new at(os,e,6,6)),e.a));q.e!=q.i.gc();)J_(q);for(h=u(Te((!e.a&&(e.a=new at(os,e,6,6)),e.a),0),202),X=ki,ki>Ve+Ne?X=Ve+Ne:ki<Ve-Ne&&(X=Ve-Ne),le=Ws,Ws>nt+W?le=nt+W:Ws<nt-W&&(le=nt-W),X>Ve-Ne&&X<Ve+Ne&&le>nt-W&&le<nt+W&&(X=Ve+Ne),N8(h,X),B8(h,le),zt=Ve,Ve>ki+dr?zt=ki+dr:Ve<ki-dr&&(zt=ki-dr),Ut=nt,nt>Ws+bt?Ut=Ws+bt:nt<Ws-bt&&(Ut=Ws-bt),zt>ki-dr&&zt<ki+dr&&Ut>Ws-bt&&Ut<Ws+bt&&(Ut=Ws+bt),O8(h,zt),P8(h,Ut),_r((!h.a&&(h.a=new Bs(ef,h,5)),h.a)),o=BH(t,5),Ee==Rn&&++o,ed=zt-X,rA=Ut-le,rh=b.Math.sqrt(ed*ed+rA*rA),L=rh*.20000000298023224,aU=ed/(o+1),bN=rA/(o+1),af=X,pN=le,_=0;_<o;_++)af+=aU,pN+=bN,P=af+vl(t,24)*NI*L-L/2,P<0?P=1:P>n&&(P=n-1),z=pN+vl(t,24)*NI*L-L/2,z<0?z=1:z>r&&(z=r-1),s=(pv(),v=new pp,v),z$(s,P),G$(s,z),Br((!h.a&&(h.a=new Bs(ef,h,5)),h.a),s)}function pt(){pt=pe,Xle=(bi(),c3t),QTe=u3t,SO=zSe,Mf=l3t,Ok=GSe,Sw=h3t,Ry=qSe,bE=VSe,vE=USe,Qle=RV,Aw=jb,Zle=f3t,bS=YSe,eV=Fk,CO=(f6e(),svt),V4=avt,Nb=ovt,U4=cvt,Vvt=new fo(BV,ct(0)),pE=nvt,XTe=rvt,Ik=ivt,s_e=Mvt,ZTe=hvt,JTe=gvt,ehe=kvt,e_e=vvt,t_e=mvt,tV=Nvt,the=Dvt,r_e=Cvt,n_e=Tvt,i_e=Avt,_w=Xbt,pS=Qbt,Vle=gbt,ATe=bbt,VTe=new kv(12),qTe=new fo(Fb,VTe),_Te=($0(),_E),K0=new fo(wSe,_Te),Ny=new fo(kl,0),Uvt=new fo(ufe,ct(1)),Hq=new fo(Bk,N7),Ob=PV,bs=BS,gE=t5,Fvt=VO,Bd=Jyt,Iy=Q4,Wvt=new fo(lfe,(Mn(),!0)),Oy=UO,Db=nfe,Ib=Rb,Jq=p2,Yle=NV,TTe=(wo(),f0),Zl=new fo(Mw,TTe),Tw=J4,Qq=SSe,Py=Hy,qvt=cfe,KTe=$Se,WTe=(n4(),ZO),new fo(PSe,WTe),Hvt=ife,zvt=sfe,Gvt=afe,$vt=rfe,Jle=lvt,jTe=Fbt,Wle=Rbt,vS=uvt,vu=Mbt,Dy=abt,dS=sbt,My=U2t,kTe=W2t,Hle=Q2t,_O=K2t,zle=rbt,$Te=jbt,HTe=$bt,NTe=Tbt,Zq=evt,Kle=Gbt,Ule=mbt,GTe=Kbt,STe=fbt,qle=dbt,$le=IV,zTe=Hbt,Gq=j2t,wTe=F2t,zq=R2t,DTe=xbt,MTe=kbt,ITe=Ebt,fE=e5,Fo=Z4,Hg=ySe,Rd=tfe,Gle=efe,xTe=J2t,zg=ofe,fS=n3t,Kq=r3t,Cw=RSe,UTe=i3t,dE=s3t,BTe=Ibt,RTe=Nbt,By=Rk,Fle=B2t,FTe=Bbt,Wq=ubt,Uq=cbt,Xq=WO,PTe=Sbt,gS=Vbt,AO=WSe,ETe=obt,YTe=tvt,CTe=lbt,jvt=Lbt,Rvt=tbt,OTe=TSe,Yq=Dbt,Vq=nbt,h2=V2t,yTe=G2t,qq=H2t,mTe=z2t,jle=q2t,Dk=$2t,LTe=ybt}function Lae(e,t){pae();var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr,ki;if(zt=e.e,q=e.d,s=e.a,zt==0)switch(t){case 0:return"0";case 1:return L7;case 2:return"0.00";case 3:return"0.000";case 4:return"0.0000";case 5:return"0.00000";case 6:return"0.000000";default:return nt=new Tp,t<0?nt.a+="0E+":nt.a+="0E",nt.a+=-t,nt.a}if(Ee=q*10+1+7,Ne=Me(Sh,Td,25,Ee+1,15,1),n=Ee,q==1)if(d=s[0],d<0){ki=qs(d,yo);do W=ki,ki=eI(ki,10),Ne[--n]=48+Or(Wp(W,ja(ki,10)))&Ss;while(Mc(ki,0)!=0)}else{ki=d;do W=ki,ki=ki/10|0,Ne[--n]=48+(W-ki*10)&Ss;while(ki!=0)}else{In=Me(Lr,Jr,25,q,15,1),dr=q,Hc(s,0,In,0,dr);e:for(;;){for(bt=0,x=dr-1;x>=0;x--)Rn=Ua(A0(bt,32),qs(In[x],yo)),le=Ghn(Rn),In[x]=Or(le),bt=Or(Np(le,32));Ce=Or(bt),X=n;do Ne[--n]=48+Ce%10&Ss;while((Ce=Ce/10|0)!=0&&n!=0);for(r=9-X+n,v=0;v<r&&n>0;v++)Ne[--n]=48;for(L=dr-1;In[L]==0;L--)if(L==0)break e;dr=L+1}for(;Ne[n]==48;)++n}if(z=zt<0,h=Ee-n-t-1,t==0)return z&&(Ne[--n]=45),jh(Ne,n,Ee-n);if(t>0&&h>=-6){if(h>=0){for(_=n+h,P=Ee-1;P>=_;P--)Ne[P+1]=Ne[P];return Ne[++_]=46,z&&(Ne[--n]=45),jh(Ne,n,Ee-n+1)}for(L=2;L<-h+1;L++)Ne[--n]=48;return Ne[--n]=46,Ne[--n]=48,z&&(Ne[--n]=45),jh(Ne,n,Ee-n)}return Ut=n+1,o=Ee,Ve=new xm,z&&(Ve.a+="-"),o-Ut>=1?(Bp(Ve,Ne[n]),Ve.a+=".",Ve.a+=jh(Ne,n+1,Ee-n-1)):Ve.a+=jh(Ne,n,Ee-n),Ve.a+="E",h>0&&(Ve.a+="+"),Ve.a+=""+h,Ve.a}function Ult(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt;switch(e.c=t,e.g=new Mr,n=(Tm(),new 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Pt(e.c.g,e.c.f),s.a*=.5,s.b*=.5,Lm(s,e.c.i,e.c.j),o=gs,le=new C(e.d.i);le.a<le.c.c.length;)X=u(Y(le),65),x=Hp(X.a,s),x<o&&(o=x,e.d.d=X);break;default:throw J(new Ln(lG+(Ne.f!=null?Ne.f:""+Ne.g)))}return e.d}function Wlt(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt;for(Ve=u(Te((!e.a&&(e.a=new at(os,e,6,6)),e.a),0),202),_=new Gu,Ne=new Mr,nt=uut(Ve),gu(Ne.f,Ve,nt),P=new Mr,r=new as,q=Pp(B1(ie(re(q1,1),yt,20,0,[(!t.d&&(t.d=new wn(ra,t,8,5)),t.d),(!t.e&&(t.e=new wn(ra,t,7,4)),t.e)])));Vr(q);){if(z=u(Pr(q),79),(!e.a&&(e.a=new at(os,e,6,6)),e.a).i!=1)throw J(new Ln(y1t+(!e.a&&(e.a=new at(os,e,6,6)),e.a).i));z!=e&&(X=u(Te((!z.a&&(z.a=new at(os,z,6,6)),z.a),0),202),ks(r,X,r.c.b,r.c),W=u(hc($o(Ne.f,X)),12),W||(W=uut(X),gu(Ne.f,X,W)),L=n?da(new Io(u(St(nt,nt.c.length-1),8)),u(St(W,W.c.length-1),8)):da(new Io((xn(0,nt.c.length),u(nt.c[0],8))),(xn(0,W.c.length),u(W.c[0],8))),gu(P.f,X,L))}if(r.b!=0)for(le=u(St(nt,n?nt.c.length-1:0),8),x=1;x<nt.c.length;x++){for(Ce=u(St(nt,n?nt.c.length-1-x:x),8),s=ii(r,0);s.b!=s.d.c;)X=u(ri(s),202),W=u(hc($o(Ne.f,X)),12),W.c.length<=x?F$(s):(Ee=Ni(new Io(u(St(W,n?W.c.length-1-x:x),8)),u(hc($o(P.f,X)),8)),(Ce.a!=Ee.a||Ce.b!=Ee.b)&&(o=Ce.a-le.a,d=Ce.b-le.b,h=Ee.a-le.a,v=Ee.b-le.b,h*d==v*o&&(o==0||isNaN(o)?o:o<0?-1:1)==(h==0||isNaN(h)?h:h<0?-1:1)&&(d==0||isNaN(d)?d:d<0?-1:1)==(v==0||isNaN(v)?v:v<0?-1:1)?(b.Math.abs(o)<b.Math.abs(h)||b.Math.abs(d)<b.Math.abs(v))&&ks(_,Ce,_.c.b,_.c):x>1&&ks(_,le,_.c.b,_.c),F$(s)));le=Ce}return _}function jyn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr,ki,Ws,rh,af,ed;for(kr(n,"Greedy cycle removal",1),Ee=t.a,ed=Ee.c.length,e.a=Me(Lr,Jr,25,ed,15,1),e.c=Me(Lr,Jr,25,ed,15,1),e.b=Me(Lr,Jr,25,ed,15,1),x=0,le=new C(Ee);le.a<le.c.c.length;){for(W=u(Y(le),10),W.p=x,Ut=new C(W.j);Ut.a<Ut.c.c.length;){for(nt=u(Y(Ut),11),d=new C(nt.e);d.a<d.c.c.length;)r=u(Y(d),17),r.c.i!=W&&(dr=u(K(r,(pt(),pE)),19).a,e.a[x]+=dr>0?dr+1:1);for(h=new C(nt.g);h.a<h.c.c.length;)r=u(Y(h),17),r.d.i!=W&&(dr=u(K(r,(pt(),pE)),19).a,e.c[x]+=dr>0?dr+1:1)}e.c[x]==0?ci(e.e,W):e.a[x]==0&&ci(e.f,W),++x}for(q=-1,z=1,L=new st,e.d=u(K(t,(et(),Lk)),230);ed>0;){for(;e.e.b!=0;)Ws=u(mne(e.e),10),e.b[Ws.p]=q--,I5e(e,Ws),--ed;for(;e.f.b!=0;)rh=u(mne(e.f),10),e.b[rh.p]=z++,I5e(e,rh),--ed;if(ed>0){for(P=$a,Ce=new C(Ee);Ce.a<Ce.c.c.length;)W=u(Y(Ce),10),e.b[W.p]==0&&(Ne=e.c[W.p]-e.a[W.p],Ne>=P&&(Ne>P&&(L.c=Me(Yn,yt,1,0,5,1),P=Ne),L.c[L.c.length]=W));_=e.Zf(L),e.b[_.p]=z++,I5e(e,_),--ed}}for(ki=Ee.c.length+1,x=0;x<Ee.c.length;x++)e.b[x]<0&&(e.b[x]+=ki);for(X=new C(Ee);X.a<X.c.c.length;)for(W=u(Y(X),10),Rn=TQe(W.j),bt=Rn,zt=0,In=bt.length;zt<In;++zt)for(nt=bt[zt],Ve=kd(nt.g),s=Ve,o=0,v=s.length;o<v;++o)r=s[o],af=r.d.i.p,e.b[W.p]>e.b[af]&&(iw(r,!0),Ye(t,yO,(Mn(),!0)));e.a=null,e.c=null,e.b=null,Rh(e.f),Rh(e.e),ur(n)}function Klt(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le;for(r=new st,d=new st,X=t/2,z=e.gc(),s=u(e.Xb(0),8),le=u(e.Xb(1),8),q=Gse(s.a,s.b,le.a,le.b,X),it(r,(xn(0,q.c.length),u(q.c[0],8))),it(d,(xn(1,q.c.length),u(q.c[1],8))),x=2;x<z;x++)W=s,s=le,le=u(e.Xb(x),8),q=Gse(s.a,s.b,W.a,W.b,X),it(r,(xn(1,q.c.length),u(q.c[1],8))),it(d,(xn(0,q.c.length),u(q.c[0],8))),q=Gse(s.a,s.b,le.a,le.b,X),it(r,(xn(0,q.c.length),u(q.c[0],8))),it(d,(xn(1,q.c.length),u(q.c[1],8)));for(q=Gse(le.a,le.b,s.a,s.b,X),it(r,(xn(1,q.c.length),u(q.c[1],8))),it(d,(xn(0,q.c.length),u(q.c[0],8))),n=new Gu,h=new st,ci(n,(xn(0,r.c.length),u(r.c[0],8))),_=1;_<r.c.length-2;_+=2)o=(xn(_,r.c.length),u(r.c[_],8)),P=oat((xn(_-1,r.c.length),u(r.c[_-1],8)),o,(xn(_+1,r.c.length),u(r.c[_+1],8)),(xn(_+2,r.c.length),u(r.c[_+2],8))),!isFinite(P.a)||!isFinite(P.b)?ks(n,o,n.c.b,n.c):ks(n,P,n.c.b,n.c);for(ci(n,u(St(r,r.c.length-1),8)),it(h,(xn(0,d.c.length),u(d.c[0],8))),L=1;L<d.c.length-2;L+=2)o=(xn(L,d.c.length),u(d.c[L],8)),P=oat((xn(L-1,d.c.length),u(d.c[L-1],8)),o,(xn(L+1,d.c.length),u(d.c[L+1],8)),(xn(L+2,d.c.length),u(d.c[L+2],8))),!isFinite(P.a)||!isFinite(P.b)?h.c[h.c.length]=o:h.c[h.c.length]=P;for(it(h,u(St(d,d.c.length-1),8)),v=h.c.length-1;v>=0;v--)ci(n,(xn(v,h.c.length),u(h.c[v],8)));return n}function $yn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z;if(h=!0,L=null,r=null,s=null,t=!1,z=e4t,x=null,o=null,d=0,v=Yie(e,d,OAe,NAe),v<e.length&&(zr(v,e.length),e.charCodeAt(v)==58)&&(L=e.substr(d,v-d),d=v+1),n=L!=null&&gM(ZV,L.toLowerCase()),n){if(v=e.lastIndexOf("!/"),v==-1)throw J(new Ln("no archive separator"));h=!0,r=jl(e,d,++v),d=v}else d>=0&&an(e.substr(d,2),"//")?(d+=2,v=Yie(e,d,KS,YS),r=e.substr(d,v-d),d=v):L!=null&&(d==e.length||(zr(d,e.length),e.charCodeAt(d)!=47))&&(h=!1,v=lbe(e,Nu(35),d),v==-1&&(v=e.length),r=e.substr(d,v-d),d=v);if(!n&&d<e.length&&(zr(d,e.length),e.charCodeAt(d)==47)&&(v=Yie(e,d+1,KS,YS),_=e.substr(d+1,v-(d+1)),_.length>0&&Ma(_,_.length-1)==58&&(s=_,d=v)),d<e.length&&(zr(d,e.length),e.charCodeAt(d)==47)&&(++d,t=!0),d<e.length&&(zr(d,e.length),e.charCodeAt(d)!=63)&&(zr(d,e.length),e.charCodeAt(d)!=35)){for(P=new st;d<e.length&&(zr(d,e.length),e.charCodeAt(d)!=63)&&(zr(d,e.length),e.charCodeAt(d)!=35);)v=Yie(e,d,KS,YS),it(P,e.substr(d,v-d)),d=v,d<e.length&&(zr(d,e.length),e.charCodeAt(d)==47)&&(Aan(e,++d)||(P.c[P.c.length]=""));z=Me(mt,Qe,2,P.c.length,6,1),j1(P,z)}return d<e.length&&(zr(d,e.length),e.charCodeAt(d)==63)&&(v=o8(e,35,++d),v==-1&&(v=e.length),x=e.substr(d,v-d),d=v),d<e.length&&(o=IM(e,++d)),Mwn(h,L,r,s,z,x),new Zse(h,L,r,s,t,z,x,o)}function Hyn(e,t){var n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr,ki,Ws,rh,af;for(Ws=new st,q=new C(t.b);q.a<q.c.c.length;)for(P=u(Y(q),29),Ve=new C(P.a);Ve.a<Ve.c.c.length;){for(Ne=u(Y(Ve),10),Ne.p=-1,L=$a,zt=$a,In=new C(Ne.j);In.a<In.c.c.length;){for(Ut=u(Y(In),11),s=new C(Ut.e);s.a<s.c.c.length;)n=u(Y(s),17),Rn=u(K(n,(pt(),Ik)),19).a,L=b.Math.max(L,Rn);for(r=new C(Ut.g);r.a<r.c.c.length;)n=u(Y(r),17),Rn=u(K(n,(pt(),Ik)),19).a,zt=b.Math.max(zt,Rn)}Ye(Ne,lV,ct(L)),Ye(Ne,hV,ct(zt))}for(le=0,z=new C(t.b);z.a<z.c.c.length;)for(P=u(Y(z),29),Ve=new C(P.a);Ve.a<Ve.c.c.length;)Ne=u(Y(Ve),10),Ne.p<0&&(ki=new wpe,ki.b=le++,Dct(e,Ne,ki),Ws.c[Ws.c.length]=ki);for(bt=Wd(Ws.c.length),_=Wd(Ws.c.length),h=0;h<Ws.c.length;h++)it(bt,new st),it(_,ct(0));for(lmn(t,Ws,bt,_),rh=u(j1(Ws,Me(Mwt,Rft,257,Ws.c.length,0,1)),840),nt=u(j1(bt,Me(Eh,gy,15,bt.c.length,0,1)),192),x=Me(Lr,Jr,25,_.c.length,15,1),d=0;d<x.length;d++)x[d]=(xn(d,_.c.length),u(_.c[d],19)).a;for(Ce=0,Ee=new st,v=0;v<rh.length;v++)x[v]==0&&it(Ee,rh[v]);for(X=Me(Lr,Jr,25,rh.length,15,1);Ee.c.length!=0;)for(ki=u(Eg(Ee,0),257),X[ki.b]=Ce++;!nt[ki.b].dc();)af=u(nt[ki.b].$c(0),257),--x[af.b],x[af.b]==0&&(Ee.c[Ee.c.length]=af);for(e.a=Me(Mwt,Rft,257,rh.length,0,1),o=0;o<rh.length;o++)for(W=rh[o],dr=X[o],e.a[dr]=W,W.b=dr,Ve=new C(W.e);Ve.a<Ve.c.c.length;)Ne=u(Y(Ve),10),Ne.p=dr;return e.a}function mi(e){var t,n,r;if(e.d>=e.j){e.a=-1,e.c=1;return}if(t=Ma(e.i,e.d++),e.a=t,e.b==1){switch(t){case 92:if(r=10,e.d>=e.j)throw J(new $r(Ur((jr(),vG))));e.a=Ma(e.i,e.d++);break;case 45:(e.e&512)==512&&e.d<e.j&&Ma(e.i,e.d)==91?(++e.d,r=24):r=0;break;case 91:if((e.e&512)!=512&&e.d<e.j&&Ma(e.i,e.d)==58){++e.d,r=20;break}default:(t&64512)==II&&e.d<e.j&&(n=Ma(e.i,e.d),(n&64512)==56320&&(e.a=so+(t-II<<10)+n-56320,++e.d)),r=0}e.c=r;return}switch(t){case 124:r=2;break;case 42:r=3;break;case 43:r=4;break;case 63:r=5;break;case 41:r=7;break;case 46:r=8;break;case 91:r=9;break;case 94:r=11;break;case 36:r=12;break;case 40:if(r=6,e.d>=e.j||Ma(e.i,e.d)!=63)break;if(++e.d>=e.j)throw J(new $r(Ur((jr(),Wce))));switch(t=Ma(e.i,e.d++),t){case 58:r=13;break;case 61:r=14;break;case 33:r=15;break;case 91:r=19;break;case 62:r=18;break;case 60:if(e.d>=e.j)throw J(new $r(Ur((jr(),Wce))));if(t=Ma(e.i,e.d++),t==61)r=16;else if(t==33)r=17;else throw J(new $r(Ur((jr(),W1t))));break;case 35:for(;e.d<e.j&&(t=Ma(e.i,e.d++),t!=41););if(t!=41)throw J(new $r(Ur((jr(),K1t))));r=21;break;default:if(t==45||97<=t&&t<=122||65<=t&&t<=90){--e.d,r=22;break}else if(t==40){r=23;break}throw J(new $r(Ur((jr(),Wce))))}break;case 92:if(r=10,e.d>=e.j)throw J(new $r(Ur((jr(),vG))));e.a=Ma(e.i,e.d++);break;default:r=0}e.c=r}function zyn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr;if(bt=u(K(e,(pt(),bs)),98),bt!=(wa(),X1)&&bt!=w2){for(q=e.b,z=q.c.length,_=new su((ql(z+2,Nae),R$(Ua(Ua(5,z+2),(z+2)/10|0)))),W=new su((ql(z+2,Nae),R$(Ua(Ua(5,z+2),(z+2)/10|0)))),it(_,new Mr),it(_,new Mr),it(W,new st),it(W,new st),nt=new st,t=0;t<z;t++)for(n=(xn(t,q.c.length),u(q.c[t],29)),zt=(xn(t,_.c.length),u(_.c[t],83)),X=new Mr,_.c[_.c.length]=X,In=(xn(t,W.c.length),u(W.c[t],15)),Ce=new st,W.c[W.c.length]=Ce,s=new C(n.a);s.a<s.c.c.length;){if(r=u(Y(s),10),Bye(r)){nt.c[nt.c.length]=r;continue}for(x=new cr(fr(Xo(r).a.Kc(),new V));Vr(x);)d=u(Pr(x),17),Rn=d.c.i,Bye(Rn)&&(Ut=u(zt.xc(K(Rn,(et(),Mi))),10),Ut||(Ut=Aat(e,Rn),zt.zc(K(Rn,Mi),Ut),In.Fc(Ut)),Va(d,u(St(Ut.j,1),11)));for(v=new cr(fr(js(r).a.Kc(),new V));Vr(v);)d=u(Pr(v),17),dr=d.d.i,Bye(dr)&&(le=u(er(X,K(dr,(et(),Mi))),10),le||(le=Aat(e,dr),Si(X,K(dr,Mi),le),Ce.c[Ce.c.length]=le),ba(d,u(St(le.j,0),11)))}for(L=0;L<W.c.length;L++)if(Ee=(xn(L,W.c.length),u(W.c[L],15)),!Ee.dc())for(P=null,L==0?(P=new Bh(e),jm(0,q.c.length),RT(q.c,0,P)):L==_.c.length-1?(P=new Bh(e),q.c[q.c.length]=P):P=(xn(L-1,q.c.length),u(q.c[L-1],29)),h=Ee.Kc();h.Ob();)o=u(h.Pb(),10),No(o,P);for(Ve=new C(nt);Ve.a<Ve.c.c.length;)Ne=u(Y(Ve),10),No(Ne,null);Ye(e,(et(),Mle),nt)}}function Gyn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve;if(kr(n,"Coffman-Graham Layering",1),t.a.c.length==0){ur(n);return}for(Ve=u(K(t,(pt(),PTe)),19).a,v=0,h=0,P=new C(t.a);P.a<P.c.c.length;)for(L=u(Y(P),10),L.p=v++,o=new cr(fr(js(L).a.Kc(),new V));Vr(o);)s=u(Pr(o),17),s.p=h++;for(e.d=Me(El,c0,25,v,16,1),e.a=Me(El,c0,25,h,16,1),e.b=Me(Lr,Jr,25,v,15,1),e.e=Me(Lr,Jr,25,v,15,1),e.f=Me(Lr,Jr,25,v,15,1),U$(e.c),uhn(e,t),q=new $j(new JR(e)),Ne=new C(t.a);Ne.a<Ne.c.c.length;){for(Ce=u(Y(Ne),10),o=new cr(fr(Xo(Ce).a.Kc(),new V));Vr(o);)s=u(Pr(o),17),e.a[s.p]||++e.b[Ce.p];e.b[Ce.p]==0&&S8(l7(q,Ce))}for(d=0;q.b.c.length!=0;)for(Ce=u(Kre(q),10),e.f[Ce.p]=d++,o=new cr(fr(js(Ce).a.Kc(),new V));Vr(o);)s=u(Pr(o),17),!e.a[s.p]&&(X=s.d.i,--e.b[X.p],sn(e.c,X,ct(e.f[Ce.p])),e.b[X.p]==0&&S8(l7(q,X)));for(z=new $j(new Fee(e)),Ee=new C(t.a);Ee.a<Ee.c.c.length;){for(Ce=u(Y(Ee),10),o=new cr(fr(js(Ce).a.Kc(),new V));Vr(o);)s=u(Pr(o),17),e.a[s.p]||++e.e[Ce.p];e.e[Ce.p]==0&&S8(l7(z,Ce))}for(_=new st,r=HXe(t,_);z.b.c.length!=0;)for(le=u(Kre(z),10),(r.a.c.length>=Ve||!ucn(le,r))&&(r=HXe(t,_)),No(le,r),o=new cr(fr(Xo(le).a.Kc(),new V));Vr(o);)s=u(Pr(o),17),!e.a[s.p]&&(W=s.c.i,--e.e[W.p],e.e[W.p]==0&&S8(l7(z,W)));for(x=_.c.length-1;x>=0;--x)it(t.b,(xn(x,_.c.length),u(_.c[x],29)));t.a.c=Me(Yn,yt,1,0,5,1),ur(n)}function Ylt(e){var t,n,r,s,o,h,d,v,x;for(e.b=1,mi(e),t=null,e.c==0&&e.a==94?(mi(e),t=(yi(),yi(),new Hl(4)),Yc(t,0,q7),d=new Hl(4)):d=(yi(),yi(),new Hl(4)),s=!0;(x=e.c)!=1;){if(x==0&&e.a==93&&!s){t&&(bC(t,d),d=t);break}if(n=e.a,r=!1,x==10)switch(n){case 100:case 68:case 119:case 87:case 115:case 83:ly(d,m7(n)),r=!0;break;case 105:case 73:case 99:case 67:n=(ly(d,m7(n)),-1),n<0&&(r=!0);break;case 112:case 80:if(v=$4e(e,n),!v)throw J(new $r(Ur((jr(),Kce))));ly(d,v),r=!0;break;default:n=y5e(e)}else if(x==24&&!s){if(t&&(bC(t,d),d=t),o=Ylt(e),bC(d,o),e.c!=0||e.a!=93)throw J(new $r(Ur((jr(),rdt))));break}if(mi(e),!r){if(x==0){if(n==91)throw J(new $r(Ur((jr(),h8e))));if(n==93)throw J(new $r(Ur((jr(),f8e))));if(n==45&&!s&&e.a!=93)throw J(new $r(Ur((jr(),Yce))))}if(e.c!=0||e.a!=45||n==45&&s)Yc(d,n,n);else{if(mi(e),(x=e.c)==1)throw J(new $r(Ur((jr(),wG))));if(x==0&&e.a==93)Yc(d,n,n),Yc(d,45,45);else{if(x==0&&e.a==93||x==24)throw J(new $r(Ur((jr(),Yce))));if(h=e.a,x==0){if(h==91)throw J(new $r(Ur((jr(),h8e))));if(h==93)throw J(new $r(Ur((jr(),f8e))));if(h==45)throw J(new $r(Ur((jr(),Yce))))}else x==10&&(h=y5e(e));if(mi(e),n>h)throw J(new $r(Ur((jr(),adt))));Yc(d,n,h)}}}s=!1}if(e.c==1)throw J(new $r(Ur((jr(),wG))));return l4(d),gC(d),e.b=0,mi(e),d}function qyn(e){Rr(e.c,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#decimal"])),Rr(e.d,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#integer"])),Rr(e.e,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#boolean"])),Rr(e.f,Zr,ie(re(mt,1),Qe,2,6,[Ha,"EBoolean",gi,"EBoolean:Object"])),Rr(e.i,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#byte"])),Rr(e.g,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#hexBinary"])),Rr(e.j,Zr,ie(re(mt,1),Qe,2,6,[Ha,"EByte",gi,"EByte:Object"])),Rr(e.n,Zr,ie(re(mt,1),Qe,2,6,[Ha,"EChar",gi,"EChar:Object"])),Rr(e.t,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#double"])),Rr(e.u,Zr,ie(re(mt,1),Qe,2,6,[Ha,"EDouble",gi,"EDouble:Object"])),Rr(e.F,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#float"])),Rr(e.G,Zr,ie(re(mt,1),Qe,2,6,[Ha,"EFloat",gi,"EFloat:Object"])),Rr(e.I,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#int"])),Rr(e.J,Zr,ie(re(mt,1),Qe,2,6,[Ha,"EInt",gi,"EInt:Object"])),Rr(e.N,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#long"])),Rr(e.O,Zr,ie(re(mt,1),Qe,2,6,[Ha,"ELong",gi,"ELong:Object"])),Rr(e.Z,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#short"])),Rr(e.$,Zr,ie(re(mt,1),Qe,2,6,[Ha,"EShort",gi,"EShort:Object"])),Rr(e._,Zr,ie(re(mt,1),Qe,2,6,[Ha,"http://www.w3.org/2001/XMLSchema#string"]))}function Vyn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr;if(e.c.length==1)return xn(0,e.c.length),u(e.c[0],135);if(e.c.length<=0)return new E$;for(v=new C(e);v.a<v.c.c.length;){for(h=u(Y(v),135),Ce=0,q=Ei,W=Ei,P=$a,z=$a,le=ii(h.b,0);le.b!=le.d.c;)X=u(ri(le),86),Ce+=u(K(X,(nw(),wV)),19).a,q=b.Math.min(q,X.e.a),W=b.Math.min(W,X.e.b),P=b.Math.max(P,X.e.a+X.f.a),z=b.Math.max(z,X.e.b+X.f.b);Ye(h,(nw(),wV),ct(Ce)),Ye(h,(Tc(),SS),new Pt(q,W)),Ye(h,PO,new Pt(P,z))}for(hn(),aa(e,new NQ),Ve=new E$,Ho(Ve,(xn(0,e.c.length),u(e.c[0],94))),L=0,In=0,x=new C(e);x.a<x.c.c.length;)h=u(Y(x),135),nt=da(fc(u(K(h,(Tc(),PO)),8)),u(K(h,SS),8)),L=b.Math.max(L,nt.a),In+=nt.a*nt.b;for(L=b.Math.max(L,b.Math.sqrt(In)*Ue(ft(K(Ve,(nw(),fmt))))),bt=Ue(ft(K(Ve,V_e))),Rn=0,dr=0,_=0,t=bt,d=new C(e);d.a<d.c.c.length;)h=u(Y(d),135),nt=da(fc(u(K(h,(Tc(),PO)),8)),u(K(h,SS),8)),Rn+nt.a>L&&(Rn=0,dr+=_+bt,_=0),tgn(Ve,h,Rn,dr),t=b.Math.max(t,Rn+nt.a),_=b.Math.max(_,nt.b),Rn+=nt.a+bt;for(Ne=new Mr,n=new Mr,Ut=new C(e);Ut.a<Ut.c.c.length;)for(zt=u(Y(Ut),135),r=It(Mt(K(zt,(bi(),VO)))),Ee=zt.q?zt.q:l0,o=Ee.vc().Kc();o.Ob();)s=u(o.Pb(),42),Il(Ne,s.cd())?je(u(s.cd(),146).wg())!==je(s.dd())&&(r&&Il(n,s.cd())?(Ud(),""+u(s.cd(),146).tg()):(Si(Ne,u(s.cd(),146),s.dd()),Ye(Ve,u(s.cd(),146),s.dd()),r&&Si(n,u(s.cd(),146),s.dd()))):(Si(Ne,u(s.cd(),146),s.dd()),Ye(Ve,u(s.cd(),146),s.dd()));return Ve}function Xlt(){Xlt=pe,Aae(),ei=new Nv,sn(ei,(ht(),Bf),Q1),sn(ei,Jh,Q1),sn(ei,ll,Q1),sn(ei,Rf,Q1),sn(ei,Fu,Q1),sn(ei,hl,Q1),sn(ei,Rf,Bf),sn(ei,Q1,_h),sn(ei,Bf,_h),sn(ei,Jh,_h),sn(ei,ll,_h),sn(ei,Ru,_h),sn(ei,Rf,_h),sn(ei,Fu,_h),sn(ei,hl,_h),sn(ei,wu,_h),sn(ei,Q1,eh),sn(ei,Bf,eh),sn(ei,_h,eh),sn(ei,Jh,eh),sn(ei,ll,eh),sn(ei,Ru,eh),sn(ei,Rf,eh),sn(ei,wu,eh),sn(ei,th,eh),sn(ei,Fu,eh),sn(ei,Ju,eh),sn(ei,hl,eh),sn(ei,Bf,Jh),sn(ei,ll,Jh),sn(ei,Rf,Jh),sn(ei,hl,Jh),sn(ei,Bf,ll),sn(ei,Jh,ll),sn(ei,Rf,ll),sn(ei,ll,ll),sn(ei,Fu,ll),sn(ei,Q1,Ch),sn(ei,Bf,Ch),sn(ei,_h,Ch),sn(ei,eh,Ch),sn(ei,Jh,Ch),sn(ei,ll,Ch),sn(ei,Ru,Ch),sn(ei,Rf,Ch),sn(ei,th,Ch),sn(ei,wu,Ch),sn(ei,hl,Ch),sn(ei,Fu,Ch),sn(ei,nu,Ch),sn(ei,Q1,th),sn(ei,Bf,th),sn(ei,_h,th),sn(ei,Jh,th),sn(ei,ll,th),sn(ei,Ru,th),sn(ei,Rf,th),sn(ei,wu,th),sn(ei,hl,th),sn(ei,Ju,th),sn(ei,nu,th),sn(ei,Bf,wu),sn(ei,Jh,wu),sn(ei,ll,wu),sn(ei,Rf,wu),sn(ei,th,wu),sn(ei,hl,wu),sn(ei,Fu,wu),sn(ei,Q1,Bu),sn(ei,Bf,Bu),sn(ei,_h,Bu),sn(ei,Jh,Bu),sn(ei,ll,Bu),sn(ei,Ru,Bu),sn(ei,Rf,Bu),sn(ei,wu,Bu),sn(ei,hl,Bu),sn(ei,Bf,Fu),sn(ei,_h,Fu),sn(ei,eh,Fu),sn(ei,ll,Fu),sn(ei,Q1,Ju),sn(ei,Bf,Ju),sn(ei,eh,Ju),sn(ei,Jh,Ju),sn(ei,ll,Ju),sn(ei,Ru,Ju),sn(ei,Rf,Ju),sn(ei,Rf,nu),sn(ei,ll,nu),sn(ei,wu,Q1),sn(ei,wu,Jh),sn(ei,wu,_h),sn(ei,Ru,Q1),sn(ei,Ru,Bf),sn(ei,Ru,eh)}function Ez(e,t){switch(e.e){case 0:case 2:case 4:case 6:case 42:case 44:case 46:case 48:case 8:case 10:case 12:case 14:case 16:case 18:case 20:case 22:case 24:case 26:case 28:case 30:case 32:case 34:case 36:case 38:return new YYe(e.b,e.a,t,e.c);case 1:return new aj(e.a,t,Ji(t.Tg(),e.c));case 43:return new VVe(e.a,t,Ji(t.Tg(),e.c));case 3:return new Bs(e.a,t,Ji(t.Tg(),e.c));case 45:return new qu(e.a,t,Ji(t.Tg(),e.c));case 41:return new Nl(u($h(e.c),26),e.a,t,Ji(t.Tg(),e.c));case 50:return new Xet(u($h(e.c),26),e.a,t,Ji(t.Tg(),e.c));case 5:return new zbe(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 47:return new nWe(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 7:return new at(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 49:return new S6(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 9:return new WVe(e.a,t,Ji(t.Tg(),e.c));case 11:return new UVe(e.a,t,Ji(t.Tg(),e.c));case 13:return new ebe(e.a,t,Ji(t.Tg(),e.c));case 15:return new xj(e.a,t,Ji(t.Tg(),e.c));case 17:return new KVe(e.a,t,Ji(t.Tg(),e.c));case 19:return new $3(e.a,t,Ji(t.Tg(),e.c));case 21:return new J2e(e.a,t,Ji(t.Tg(),e.c));case 23:return new UM(e.a,t,Ji(t.Tg(),e.c));case 25:return new sWe(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 27:return new wn(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 29:return new iWe(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 31:return new rWe(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 33:return new qbe(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 35:return new Gbe(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 37:return new une(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 39:return new Hj(e.a,t,Ji(t.Tg(),e.c),e.d.n);case 40:return new ds(t,Ji(t.Tg(),e.c));default:throw J(new tc("Unknown feature style: "+e.e))}}function Uyn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt;switch(kr(n,"Brandes & Koepf node placement",1),e.a=t,e.c=jbn(t),r=u(K(t,(pt(),Kle)),274),z=It(Mt(K(t,gS))),e.d=r==(tI(),Iq)&&!z||r==Tle,Twn(e,t),Ve=null,nt=null,le=null,Ce=null,X=(ql(4,fy),new su(4)),u(K(t,Kle),274).g){case 3:le=new p4(t,e.c.d,(M1(),Pb),(yd(),Gg)),X.c[X.c.length]=le;break;case 1:Ce=new p4(t,e.c.d,(M1(),K1),(yd(),Gg)),X.c[X.c.length]=Ce;break;case 4:Ve=new p4(t,e.c.d,(M1(),Pb),(yd(),Lw)),X.c[X.c.length]=Ve;break;case 2:nt=new p4(t,e.c.d,(M1(),K1),(yd(),Lw)),X.c[X.c.length]=nt;break;default:le=new p4(t,e.c.d,(M1(),Pb),(yd(),Gg)),Ce=new p4(t,e.c.d,K1,Gg),Ve=new p4(t,e.c.d,Pb,Lw),nt=new p4(t,e.c.d,K1,Lw),X.c[X.c.length]=Ve,X.c[X.c.length]=nt,X.c[X.c.length]=le,X.c[X.c.length]=Ce}for(s=new lqe(t,e.c),d=new C(X);d.a<d.c.c.length;)o=u(Y(d),180),Iyn(s,o,e.b),pvn(o);for(P=new Vit(t,e.c),v=new C(X);v.a<v.c.c.length;)o=u(Y(v),180),Amn(P,o);if(n.n)for(x=new C(X);x.a<x.c.c.length;)o=u(Y(x),180),V2(n,o+" size is "+qse(o));if(L=null,e.d&&(_=iyn(e,X,e.c.d),Dut(t,_,n)&&(L=_)),!L)for(x=new C(X);x.a<x.c.c.length;)o=u(Y(x),180),Dut(t,o,n)&&(!L||qse(L)>qse(o))&&(L=o);for(!L&&(L=(xn(0,X.c.length),u(X.c[0],180))),W=new C(t.b);W.a<W.c.c.length;)for(q=u(Y(W),29),Ne=new C(q.a);Ne.a<Ne.c.c.length;)Ee=u(Y(Ne),10),Ee.n.b=Ue(L.p[Ee.p])+Ue(L.d[Ee.p]);for(n.n&&(V2(n,"Chosen node placement: "+L),V2(n,"Blocks: "+Qat(L)),V2(n,"Classes: "+a0n(L,n)),V2(n,"Marked edges: "+e.b)),h=new C(X);h.a<h.c.c.length;)o=u(Y(h),180),o.g=null,o.b=null,o.a=null,o.d=null,o.j=null,o.i=null,o.p=null;Rin(e.c),e.b.a.$b(),ur(n)}function Wyn(e,t,n){var r,s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn;for(h=new as,Ve=u(K(n,(pt(),Zl)),103),q=0,no(h,(!t.a&&(t.a=new at(hs,t,10,11)),t.a));h.b!=0;)x=u(h.b==0?null:(Qn(h.b!=0),bh(h,h.a.a)),33),(je(Ft(t,h2))!==je((R0(),f2))||je(Ft(t,Dy))===je((db(),sS))||je(Ft(t,Dy))===je((db(),iS))||It(Mt(Ft(t,My)))||je(Ft(t,Dk))!==je((Uv(),N4)))&&!It(Mt(Ft(x,jle)))&&So(x,(et(),Nc),ct(q++)),X=!It(Mt(Ft(x,Ob))),X&&(L=(!x.a&&(x.a=new at(hs,x,10,11)),x.a).i!=0,z=Xhn(x),P=je(Ft(x,Iy))===je((F0(),Wg)),Rn=!J2(x,(bi(),PS))||an(Hr(Ft(x,PS)),qn),Ee=null,Rn&&P&&(L||z)&&(Ee=wut(x),Ye(Ee,Zl,Ve),ta(Ee,CO)&&WHe(new Kye(Ue(ft(K(Ee,CO)))),Ee),u(Ft(x,Ib),174).gc()!=0&&(_=Ee,ms(new vn(null,(!x.c&&(x.c=new at(xl,x,9,9)),new mn(x.c,16))),new Ir(_)),lct(x,Ee))),nt=n,bt=u(er(e.a,us(x)),10),bt&&(nt=bt.e),Ce=vlt(e,x,nt),Ee&&(Ce.e=Ee,Ee.e=Ce,no(h,(!x.a&&(x.a=new at(hs,x,10,11)),x.a))));for(q=0,ks(h,t,h.c.b,h.c);h.b!=0;){for(o=u(h.b==0?null:(Qn(h.b!=0),bh(h,h.a.a)),33),v=new rr((!o.b&&(o.b=new at(ra,o,12,3)),o.b));v.e!=v.i.gc();)d=u(pr(v),79),Bct(d),(je(Ft(t,h2))!==je((R0(),f2))||je(Ft(t,Dy))===je((db(),sS))||je(Ft(t,Dy))===je((db(),iS))||It(Mt(Ft(t,My)))||je(Ft(t,Dk))!==je((Uv(),N4)))&&So(d,(et(),Nc),ct(q++)),Ut=zo(u(Te((!d.b&&(d.b=new wn(mr,d,4,7)),d.b),0),82)),In=zo(u(Te((!d.c&&(d.c=new wn(mr,d,5,8)),d.c),0),82)),!(It(Mt(Ft(d,Ob)))||It(Mt(Ft(Ut,Ob)))||It(Mt(Ft(In,Ob))))&&(W=ew(d)&&It(Mt(Ft(Ut,Oy)))&&It(Mt(Ft(d,Db))),Ne=o,W||Vm(In,Ut)?Ne=Ut:Vm(Ut,In)&&(Ne=In),nt=n,bt=u(er(e.a,Ne),10),bt&&(nt=bt.e),le=h6e(e,d,Ne,nt),Ye(le,(et(),O9e),vpn(e,d,t,n)));if(P=je(Ft(o,Iy))===je((F0(),Wg)),P)for(s=new rr((!o.a&&(o.a=new at(hs,o,10,11)),o.a));s.e!=s.i.gc();)r=u(pr(s),33),Rn=!J2(r,(bi(),PS))||an(Hr(Ft(r,PS)),qn),zt=je(Ft(r,Iy))===je(Wg),Rn&&zt&&ks(h,r,h.c.b,h.c)}}function Kyn(e,t,n,r,s,o){var h,d,v,x,_,L,P,z,q,W,X,le;switch(t){case 71:d=r.q.getFullYear()-e2>=-1900?1:0,n>=4?Yr(e,ie(re(mt,1),Qe,2,6,[fht,dht])[d]):Yr(e,ie(re(mt,1),Qe,2,6,["BC","AD"])[d]);break;case 121:jcn(e,n,r);break;case 77:G2n(e,n,r);break;case 107:v=s.q.getHours(),v==0?e0(e,24,n):e0(e,v,n);break;case 83:lgn(e,n,s);break;case 69:_=r.q.getDay(),n==5?Yr(e,ie(re(mt,1),Qe,2,6,["S","M","T","W","T","F","S"])[_]):n==4?Yr(e,ie(re(mt,1),Qe,2,6,[Kae,Yae,Xae,Qae,Zae,Jae,eoe])[_]):Yr(e,ie(re(mt,1),Qe,2,6,["Sun","Mon","Tue","Wed","Thu","Fri","Sat"])[_]);break;case 97:s.q.getHours()>=12&&s.q.getHours()<24?Yr(e,ie(re(mt,1),Qe,2,6,["AM","PM"])[1]):Yr(e,ie(re(mt,1),Qe,2,6,["AM","PM"])[0]);break;case 104:L=s.q.getHours()%12,L==0?e0(e,12,n):e0(e,L,n);break;case 75:P=s.q.getHours()%12,e0(e,P,n);break;case 72:z=s.q.getHours(),e0(e,z,n);break;case 99:q=r.q.getDay(),n==5?Yr(e,ie(re(mt,1),Qe,2,6,["S","M","T","W","T","F","S"])[q]):n==4?Yr(e,ie(re(mt,1),Qe,2,6,[Kae,Yae,Xae,Qae,Zae,Jae,eoe])[q]):n==3?Yr(e,ie(re(mt,1),Qe,2,6,["Sun","Mon","Tue","Wed","Thu","Fri","Sat"])[q]):e0(e,q,1);break;case 76:W=r.q.getMonth(),n==5?Yr(e,ie(re(mt,1),Qe,2,6,["J","F","M","A","M","J","J","A","S","O","N","D"])[W]):n==4?Yr(e,ie(re(mt,1),Qe,2,6,[Rae,Fae,jae,$ae,ak,Hae,zae,Gae,qae,Vae,Uae,Wae])[W]):n==3?Yr(e,ie(re(mt,1),Qe,2,6,["Jan","Feb","Mar","Apr",ak,"Jun","Jul","Aug","Sep","Oct","Nov","Dec"])[W]):e0(e,W+1,n);break;case 81:X=r.q.getMonth()/3|0,n<4?Yr(e,ie(re(mt,1),Qe,2,6,["Q1","Q2","Q3","Q4"])[X]):Yr(e,ie(re(mt,1),Qe,2,6,["1st quarter","2nd quarter","3rd quarter","4th quarter"])[X]);break;case 100:le=r.q.getDate(),e0(e,le,n);break;case 109:x=s.q.getMinutes(),e0(e,x,n);break;case 115:h=s.q.getSeconds(),e0(e,h,n);break;case 122:n<4?Yr(e,o.c[0]):Yr(e,o.c[1]);break;case 118:Yr(e,o.b);break;case 90:n<3?Yr(e,J1n(o)):n==3?Yr(e,ndn(o)):Yr(e,rdn(o.a));break;default:return!1}return!0}function h6e(e,t,n,r){var s,o,h,d,v,x,_,L,P,z,q,W,X,le,Ce,Ee,Ne,Ve,nt,bt,zt,Ut,In,Rn,dr,ki;if(Bct(t),v=u(Te((!t.b&&(t.b=new wn(mr,t,4,7)),t.b),0),82),_=u(Te((!t.c&&(t.c=new wn(mr,t,5,8)),t.c),0),82),d=zo(v),x=zo(_),h=(!t.a&&(t.a=new at(os,t,6,6)),t.a).i==0?null:u(Te((!t.a&&(t.a=new at(os,t,6,6)),t.a),0),202),bt=u(er(e.a,d),10),Rn=u(er(e.a,x),10),zt=null,dr=null,we(v,186)&&(nt=u(er(e.a,v),299),we(nt,11)?zt=u(nt,11):we(nt,10)&&(bt=u(nt,10),zt=u(St(bt.j,0),11))),we(_,186)&&(In=u(er(e.a,_),299),we(In,11)?dr=u(In,11):we(In,10)&&(Rn=u(In,10),dr=u(St(Rn.j,0),11))),!bt||!Rn)throw J(new CT("The source or the target of edge "+t+" could not be found. This usually happens when an edge connects a node laid out by ELK Layered to a node in another level of hierarchy laid out by either another instance of ELK Layered or another layout algorithm alltogether. The former can be solved by setting the hierarchyHandling option to INCLUDE_CHILDREN."));for(W=new Iv,Ho(W,t),Ye(W,(et(),Mi),t),Ye(W,(pt(),Fo),null),z=u(K(r,eu),21),bt==Rn&&z.Fc((mo(),cS)),zt||(Ve=(vo(),hu),Ut=null,h&&R3(u(K(bt,bs),98))&&(Ut=new Pt(h.j,h.k),qQe(Ut,nD(t)),yZe(Ut,n),Vm(x,d)&&(Ve=ul,Ni(Ut,bt.n))),zt=Nut(bt,Ut,Ve,r)),dr||(Ve=(vo(),ul),ki=null,h&&R3(u(K(Rn,bs),98))&&(ki=new Pt(h.b,h.c),qQe(ki,nD(t)),yZe(ki,n)),dr=Nut(Rn,ki,Ve,Ya(Rn))),Va(W,zt),ba(W,dr),(zt.e.c.length>1||zt.g.c.length>1||dr.e.c.length>1||dr.g.c.length>1)&&z.Fc((mo(),oS)),P=new rr((!t.n&&(t.n=new at(Jo,t,1,7)),t.n));P.e!=P.i.gc();)if(L=u(pr(P),137),!It(Mt(Ft(L,Ob)))&&L.a)switch(X=Mie(L),it(W.b,X),u(K(X,Rd),272).g){case 1:case 2:z.Fc((mo(),cE));break;case 0:z.Fc((mo(),oE)),Ye(X,Rd,(P1(),EE))}if(o=u(K(r,dS),314),le=u(K(r,Zq),315),s=o==(V6(),vO)||le==(X_(),ohe),h&&(!h.a&&(h.a=new Bs(ef,h,5)),h.a).i!=0&&s){for(Ce=iI(h),q=new Gu,Ne=ii(Ce,0);Ne.b!=Ne.d.c;)Ee=u(ri(Ne),8),ci(q,new Io(Ee));Ye(W,R9e,q)}return W}function Yyn(e){e.gb||(e.gb=!0,e.b=gc(e,0),ls(e.b,18),zi(e.b,19),e.a=gc(e,1),ls(e.a,1),zi(e.a,2),zi(e.a,3),zi(e.a,4),zi(e.a,5),e.o=gc(e,2),ls(e.o,8),ls(e.o,9),zi(e.o,10),zi(e.o,11),zi(e.o,12),zi(e.o,13),zi(e.o,14),zi(e.o,15),zi(e.o,16),zi(e.o,17),zi(e.o,18),zi(e.o,19),zi(e.o,20),zi(e.o,21),zi(e.o,22),zi(e.o,23),Bo(e.o),Bo(e.o),Bo(e.o),Bo(e.o),Bo(e.o),Bo(e.o),Bo(e.o),Bo(e.o),Bo(e.o),Bo(e.o),e.p=gc(e,3),ls(e.p,2),ls(e.p,3),ls(e.p,4),ls(e.p,5),zi(e.p,6),zi(e.p,7),Bo(e.p),Bo(e.p),e.q=gc(e,4),ls(e.q,8),e.v=gc(e,5),zi(e.v,9),Bo(e.v),Bo(e.v),Bo(e.v),e.w=gc(e,6),ls(e.w,2),ls(e.w,3),ls(e.w,4),zi(e.w,5),e.B=gc(e,7),zi(e.B,1),Bo(e.B),Bo(e.B),Bo(e.B),e.Q=gc(e,8),zi(e.Q,0),Bo(e.Q),e.R=gc(e,9),ls(e.R,1),e.S=gc(e,10),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),Bo(e.S),e.T=gc(e,11),zi(e.T,10),zi(e.T,11),zi(e.T,12),zi(e.T,13),zi(e.T,14),Bo(e.T),Bo(e.T),e.U=gc(e,12),ls(e.U,2),ls(e.U,3),zi(e.U,4),zi(e.U,5),zi(e.U,6),zi(e.U,7),Bo(e.U),e.V=gc(e,13),zi(e.V,10),e.W=gc(e,14),ls(e.W,18),ls(e.W,19),ls(e.W,20),zi(e.W,21),zi(e.W,22),zi(e.W,23),e.bb=gc(e,15),ls(e.bb,10),ls(e.bb,11),ls(e.bb,12),ls(e.bb,13),ls(e.bb,14),ls(e.bb,15),ls(e.bb,16),zi(e.bb,17),Bo(e.bb),Bo(e.bb),e.eb=gc(e,16),ls(e.eb,2),ls(e.eb,3),ls(e.eb,4),ls(e.eb,5),ls(e.eb,6),ls(e.eb,7),zi(e.eb,8),zi(e.eb,9),e.ab=gc(e,17),ls(e.ab,0),ls(e.ab,1),e.H=gc(e,18),zi(e.H,0),zi(e.H,1),zi(e.H,2),zi(e.H,3),zi(e.H,4),zi(e.H,5),Bo(e.H),e.db=gc(e,19),zi(e.db,2),e.c=di(e,20),e.d=di(e,21),e.e=di(e,22),e.f=di(e,23),e.i=di(e,24),e.g=di(e,25),e.j=di(e,26),e.k=di(e,27),e.n=di(e,28),e.r=di(e,29),e.s=di(e,30),e.t=di(e,31),e.u=di(e,32),e.fb=di(e,33),e.A=di(e,34),e.C=di(e,35),e.D=di(e,36),e.F=di(e,37),e.G=di(e,38),e.I=di(e,39),e.J=di(e,40),e.L=di(e,41),e.M=di(e,42),e.N=di(e,43),e.O=di(e,44),e.P=di(e,45),e.X=di(e,46),e.Y=di(e,47),e.Z=di(e,48),e.$=di(e,49),e._=di(e,50),e.cb=di(e,51),e.K=di(e,52))}function bi(){bi=pe;var e,t;PS=new Zi(n1t),xE=new Zi(r1t),dSe=(t0(),Yhe),Jyt=new dn(Zke,dSe),Bk=new dn(fk,null),e3t=new Zi(Uxe),pSe=(ty(),Ui(Zhe,ie(re(Jhe,1),tt,291,0,[Qhe]))),IV=new dn(rG,pSe),VO=new dn(WI,(Mn(),!1)),bSe=(wo(),f0),Mw=new dn(txe,bSe),mSe=($0(),hfe),wSe=new dn(VI,mSe),xSe=new dn(uG,!1),ESe=(F0(),FV),Q4=new dn(nG,ESe),OSe=new kv(12),Fb=new dn(uw,OSe),OV=new dn(jI,!1),TSe=new dn(fce,!1),KO=new dn(CC,!1),FSe=(wa(),w2),BS=new dn(Doe,FSe),Rk=new Zi(iG),BV=new Zi(FI),ufe=new Zi(Fz),lfe=new Zi(_C),_Se=new Gu,Z4=new dn(lxe,_Se),n3t=new dn(dxe,!1),r3t=new dn(gxe,!1),CSe=new yT,WO=new dn(bxe,CSe),PV=new dn(Xke,!1),o3t=new dn(i1t,1),new dn(s1t,!0),ct(0),new dn(a1t,ct(100)),new dn(o1t,!1),ct(0),new dn(c1t,ct(4e3)),ct(0),new dn(u1t,ct(400)),new dn(l1t,!1),new dn(h1t,!1),new dn(f1t,!0),new dn(d1t,!1),gSe=(EH(),pfe),t3t=new dn(Vxe,gSe),c3t=new dn(Fke,10),u3t=new dn(jke,10),zSe=new dn(_oe,20),l3t=new dn($ke,10),GSe=new dn(Moe,2),h3t=new dn(Hke,10),qSe=new dn(zke,0),RV=new dn(Vke,5),VSe=new dn(Gke,1),USe=new dn(qke,1),jb=new dn(py,20),f3t=new dn(Uke,10),YSe=new dn(Wke,10),Fk=new Zi(Kke),KSe=new bVe,WSe=new dn(vxe,KSe),s3t=new Zi(hce),NSe=!1,i3t=new dn(lce,NSe),ASe=new kv(5),SSe=new dn(nxe,ASe),LSe=(sy(),t=u(Qf(xo),9),new hh(t,u(wf(t,t.length),9),0)),J4=new dn(P7,LSe),BSe=(n4(),v2),PSe=new dn(sxe,BSe),ife=new Zi(axe),sfe=new Zi(oxe),afe=new Zi(cxe),rfe=new Zi(uxe),MSe=(e=u(Qf(qS),9),new hh(e,u(wf(e,e.length),9),0)),Rb=new dn(E4,MSe),ISe=rn((wl(),SE)),p2=new dn(dk,ISe),DSe=new Pt(0,0),e5=new dn(gk,DSe),NV=new dn(uce,!1),vSe=(P1(),EE),tfe=new dn(hxe,vSe),efe=new dn(jz,!1),ct(1),new dn(g1t,null),RSe=new Zi(pxe),ofe=new Zi(fxe),HSe=(ht(),uc),t5=new dn(Qke,HSe),kl=new Zi(Yke),jSe=(ol(),rn(m2)),Hy=new dn(B7,jSe),cfe=new dn(rxe,!1),$Se=new dn(ixe,!0),UO=new dn(Jke,!1),nfe=new dn(exe,!1),ySe=new dn(Coe,1),kSe=(YH(),dfe),new dn(p1t,kSe),a3t=!0}function et(){et=pe;var e,t;Mi=new Zi(K6e),O9e=new Zi("coordinateOrigin"),Ple=new Zi("processors"),I9e=new zs("compoundNode",(Mn(),!1)),kO=new zs("insideConnections",!1),R9e=new Zi("originalBendpoints"),F9e=new Zi("originalDummyNodePosition"),j9e=new Zi("originalLabelEdge"),EO=new Zi("representedLabels"),uS=new Zi("endLabels"),Ck=new Zi("endLabel.origin"),Ak=new zs("labelSide",(Ul(),QO)),H4=new zs("maxEdgeThickness",0),W1=new zs("reversed",!1),Lk=new Zi(Qht),l1=new zs("longEdgeSource",null),Yh=new zs("longEdgeTarget",null),Ay=new zs("longEdgeHasLabelDummies",!1),xO=new zs("longEdgeBeforeLabelDummy",!1),Fq=new zs("edgeConstraint",(sb(),wle)),xw=new Zi("inLayerLayoutUnit"),Lb=new zs("inLayerConstraint",(P0(),mO)),Sk=new zs("inLayerSuccessorConstraint",new st),B9e=new zs("inLayerSuccessorConstraintBetweenNonDummies",!1),cl=new Zi("portDummy"),Rq=new zs("crossingHint",ct(0)),eu=new zs("graphProperties",(t=u(Qf(_le),9),new hh(t,u(wf(t,t.length),9),0))),vc=new zs("externalPortSide",(ht(),uc)),P9e=new zs("externalPortSize",new Fa),Mle=new Zi("externalPortReplacedDummies"),jq=new 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Cs("BREAKING_POINT_INSERTER",15),dq=new Cs("LONG_EDGE_SPLITTER",16),tle=new Cs("PORT_SIDE_PROCESSOR",17),oq=new Cs("INVERTED_PORT_PROCESSOR",18),bq=new Cs("PORT_LIST_SORTER",19),ZEe=new Cs("SORT_BY_INPUT_ORDER_OF_MODEL",20),pq=new Cs("NORTH_SOUTH_PORT_PREPROCESSOR",21),xEe=new Cs("BREAKING_POINT_PROCESSOR",22),UEe=new Cs(bft,23),JEe=new Cs(vft,24),vq=new Cs("SELF_LOOP_PORT_RESTORER",25),QEe=new Cs("SINGLE_EDGE_GRAPH_WRAPPER",26),cq=new Cs("IN_LAYER_CONSTRAINT_PROCESSOR",27),IEe=new Cs("END_NODE_PORT_LABEL_MANAGEMENT_PROCESSOR",28),GEe=new Cs("LABEL_AND_NODE_SIZE_PROCESSOR",29),zEe=new Cs("INNERMOST_NODE_MARGIN_CALCULATOR",30),yq=new Cs("SELF_LOOP_ROUTER",31),_Ee=new Cs("COMMENT_NODE_MARGIN_CALCULATOR",32),aq=new Cs("END_LABEL_PREPROCESSOR",33),hq=new Cs("LABEL_DUMMY_SWITCHER",34),TEe=new Cs("CENTER_LABEL_MANAGEMENT_PROCESSOR",35),Z7=new Cs("LABEL_SIDE_SELECTOR",36),$Ee=new Cs("HYPEREDGE_DUMMY_MERGER",37),BEe=new Cs("HIERARCHICAL_PORT_DUMMY_SIZE_PROCESSOR",38),qEe=new 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Arranges as many edges as possible into one direction by placing nodes into subsequent layers. This implementation supports different routing styles (straight, orthogonal, splines); if orthogonal routing is selected, arbitrary port constraints are respected, thus enabling the layout of block diagrams such as actor-oriented models or circuit schematics. 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\r\r `),Oo(s5,sue,r),Oo(LE,sue,uy(r)),r=new Hl(4),UD(r,h0t),Oo(s5,KC,r),Oo(LE,KC,uy(r)),r=new Hl(4),UD(r,h0t),Oo(s5,KC,r),Oo(LE,KC,uy(r)),r=new Hl(4),UD(r,f0t),ly(r,u(Uc(s5,KC),117)),Oo(s5,iue,r),Oo(LE,iue,uy(r)),r=new Hl(4),UD(r,"-.0:AZ__az··ÀÖØöøıĴľŁňŊžƀǃǍǰǴǵǺȗɐʨʻˁːˑ̀͠͡ͅΆΊΌΌΎΡΣώϐϖϚϚϜϜϞϞϠϠϢϳЁЌЎяёќўҁ҃҆ҐӄӇӈӋӌӐӫӮӵӸӹԱՖՙՙաֆֹֻֽֿֿׁׂ֑֣֡ׄׄאתװײءغـْ٠٩ٰڷںھۀێېۓە۪ۭۨ۰۹ँःअह़्॑॔क़ॣ०९ঁঃঅঌএঐওনপরললশহ়়াৄেৈো্ৗৗড়ঢ়য়ৣ০ৱਂਂਅਊਏਐਓਨਪਰਲਲ਼ਵਸ਼ਸਹ਼਼ਾੂੇੈੋ੍ਖ਼ੜਫ਼ਫ਼੦ੴઁઃઅઋઍઍએઑઓનપરલળવહ઼ૅેૉો્ૠૠ૦૯ଁଃଅଌଏଐଓନପରଲଳଶହ଼ୃେୈୋ୍ୖୗଡ଼ଢ଼ୟୡ୦୯ஂஃஅஊஎஐஒகஙசஜஜஞடணதநபமவஷஹாூெைொ்ௗௗ௧௯ఁఃఅఌఎఐఒనపళవహాౄెైొ్ౕౖౠౡ౦౯ಂಃಅಌಎಐಒನಪಳವಹಾೄೆೈೊ್ೕೖೞೞೠೡ೦೯ംഃഅഌഎഐഒനപഹാൃെൈൊ്ൗൗൠൡ൦൯กฮะฺเ๎๐๙ກຂຄຄງຈຊຊຍຍດທນຟມຣລລວວສຫອຮະູົຽເໄໆໆ່ໍ໐໙༘༙༠༩༹༹༵༵༷༷༾ཇཉཀྵ྄ཱ྆ྋྐྕྗྗྙྭྱྷྐྵྐྵႠჅაჶᄀᄀᄂᄃᄅᄇᄉᄉᄋᄌᄎᄒᄼᄼᄾᄾᅀᅀᅌᅌᅎᅎᅐᅐᅔᅕᅙᅙᅟᅡᅣᅣᅥᅥᅧᅧᅩᅩᅭᅮᅲᅳᅵᅵᆞᆞᆨᆨᆫᆫᆮᆯᆷᆸᆺᆺᆼᇂᇫᇫᇰᇰᇹᇹḀẛẠỹἀἕἘἝἠὅὈὍὐὗὙὙὛὛὝὝὟώᾀᾴᾶᾼιιῂῄῆῌῐΐῖΊῠῬῲῴῶῼ⃐⃜⃡⃡ΩΩKÅ℮℮ↀↂ々々〇〇〡〯〱〵ぁゔ゙゚ゝゞァヺーヾㄅㄬ一龥가힣"),Oo(s5,aue,r),Oo(LE,aue,uy(r)),r=new Hl(4),UD(r,f0t),Yc(r,95,95),Yc(r,58,58),Oo(s5,oue,r),Oo(LE,oue,uy(r))),n=u(Uc(t?s5:LE,e),136),n}function 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A quantity value overflow.":an("_UI_PackageRegistry_extensionpoint",e)?"Ecore Package Registry for Generated Packages":an("_UI_DynamicPackageRegistry_extensionpoint",e)?"Ecore Package Registry for Dynamic Packages":an("_UI_FactoryRegistry_extensionpoint",e)?"Ecore Factory Override Registry":an("_UI_URIExtensionParserRegistry_extensionpoint",e)?"URI Extension Parser Registry":an("_UI_URIProtocolParserRegistry_extensionpoint",e)?"URI Protocol Parser Registry":an("_UI_URIContentParserRegistry_extensionpoint",e)?"URI Content Parser Registry":an("_UI_ContentHandlerRegistry_extensionpoint",e)?"Content Handler Registry":an("_UI_URIMappingRegistry_extensionpoint",e)?"URI Converter Mapping Registry":an("_UI_PackageRegistryImplementation_extensionpoint",e)?"Ecore Package Registry Implementation":an("_UI_ValidationDelegateRegistry_extensionpoint",e)?"Validation Delegate Registry":an("_UI_SettingDelegateRegistry_extensionpoint",e)?"Feature Setting Delegate Factory Registry":an("_UI_InvocationDelegateRegistry_extensionpoint",e)?"Operation Invocation Delegate Factory Registry":an("_UI_EClassInterfaceNotAbstract_diagnostic",e)?"A class that is an interface must also be abstract":an("_UI_EClassNoCircularSuperTypes_diagnostic",e)?"A class may not be a super type of itself":an("_UI_EClassNotWellFormedMapEntryNoInstanceClassName_diagnostic",e)?"A class that inherits from a map entry class must have instance class name 'java.util.Map$Entry'":an("_UI_EReferenceOppositeOfOppositeInconsistent_diagnostic",e)?"The opposite of the opposite may not be a reference different from this one":an("_UI_EReferenceOppositeNotFeatureOfType_diagnostic",e)?"The opposite must be a feature of the reference's type":an("_UI_EReferenceTransientOppositeNotTransient_diagnostic",e)?"The opposite of a transient reference must be transient if it is proxy resolving":an("_UI_EReferenceOppositeBothContainment_diagnostic",e)?"The opposite of a containment reference must not be a containment reference":an("_UI_EReferenceConsistentUnique_diagnostic",e)?"A containment or bidirectional reference must be unique if its upper bound is different from 1":an("_UI_ETypedElementNoType_diagnostic",e)?"The typed element must have a type":an("_UI_EAttributeNoDataType_diagnostic",e)?"The generic attribute type must not refer to a class":an("_UI_EReferenceNoClass_diagnostic",e)?"The generic reference type must not refer to a data type":an("_UI_EGenericTypeNoTypeParameterAndClassifier_diagnostic",e)?"A generic type can't refer to both a type parameter and a classifier":an("_UI_EGenericTypeNoClass_diagnostic",e)?"A generic super type must refer to a class":an("_UI_EGenericTypeNoTypeParameterOrClassifier_diagnostic",e)?"A generic type in this context must refer to a classifier or a type parameter":an("_UI_EGenericTypeBoundsOnlyForTypeArgument_diagnostic",e)?"A generic type may have bounds only when used as a type argument":an("_UI_EGenericTypeNoUpperAndLowerBound_diagnostic",e)?"A generic type must not have both a lower and an upper bound":an("_UI_EGenericTypeNoTypeParameterOrClassifierAndBound_diagnostic",e)?"A generic type with bounds must not also refer to a type parameter or classifier":an("_UI_EGenericTypeNoArguments_diagnostic",e)?"A generic type may have arguments only if it refers to a classifier":an("_UI_EGenericTypeOutOfScopeTypeParameter_diagnostic",e)?"A generic type may only refer to a type parameter that is in scope":e}function Jyn(e){var t,n,r,s,o,h,d,v,x,_,L,P,z,q,W;e.r||(e.r=!0,au(e,"graph"),eH(e,"graph"),tH(e,H7),LD(e.o,"T"),Br(jo(e.a),e.p),Br(jo(e.f),e.a),Br(jo(e.n),e.f),Br(jo(e.g),e.n),Br(jo(e.c),e.n),Br(jo(e.i),e.c),Br(jo(e.j),e.c),Br(jo(e.d),e.f),Br(jo(e.e),e.a),dc(e.p,p3n,qht,!0,!0,!1),q=Z3(e.p,e.p,"setProperty"),W=Jtt(q),x=L0(e.o),_=(n=(r=new uv,r),n),Br((!x.d&&(x.d=new 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uv,t),v),Br((!x.d&&(x.d=new Bs(Eo,x,1)),x.d),_),Jat(u(Te(He(e.k),0),34),x,"key",Cb,!1,!1,!0,!1),ys(u(Te(He(e.k),1),34),e.s,jC,null,0,1,Cb,!1,!1,!0,!1,!0,!1),fi(e.o,Khe,"IProperty",!0),fi(e.s,Yn,"PropertyValue",!0),Cye(e,H7))}function Zlt(){Zlt=pe,fe=Me(el,A4,25,so,15,1),fe[9]=35,fe[10]=19,fe[13]=19,fe[32]=51,fe[33]=49,fe[34]=33,Pe(fe,35,38,49),fe[38]=1,Pe(fe,39,45,49),Pe(fe,45,47,-71),fe[47]=49,Pe(fe,48,58,-71),fe[58]=61,fe[59]=49,fe[60]=1,fe[61]=49,fe[62]=33,Pe(fe,63,65,49),Pe(fe,65,91,-3),Pe(fe,91,93,33),fe[93]=1,fe[94]=33,fe[95]=-3,fe[96]=33,Pe(fe,97,123,-3),Pe(fe,123,183,33),fe[183]=-87,Pe(fe,184,192,33),Pe(fe,192,215,-19),fe[215]=33,Pe(fe,216,247,-19),fe[247]=33,Pe(fe,248,306,-19),Pe(fe,306,308,33),Pe(fe,308,319,-19),Pe(fe,319,321,33),Pe(fe,321,329,-19),fe[329]=33,Pe(fe,330,383,-19),fe[383]=33,Pe(fe,384,452,-19),Pe(fe,452,461,33),Pe(fe,461,497,-19),Pe(fe,497,500,33),Pe(fe,500,502,-19),Pe(fe,502,506,33),Pe(fe,506,536,-19),Pe(fe,536,592,33),Pe(fe,592,681,-19),Pe(fe,681,699,33),Pe(fe,699,706,-19),Pe(fe,706,720,33),Pe(fe,720,722,-87),Pe(fe,722,768,33),Pe(fe,768,838,-87),Pe(fe,838,864,33),Pe(fe,864,866,-87),Pe(fe,866,902,33),fe[902]=-19,fe[903]=-87,Pe(fe,904,907,-19),fe[907]=33,fe[908]=-19,fe[909]=33,Pe(fe,910,930,-19),fe[930]=33,Pe(fe,931,975,-19),fe[975]=33,Pe(fe,976,983,-19),Pe(fe,983,986,33),fe[986]=-19,fe[987]=33,fe[988]=-19,fe[989]=33,fe[990]=-19,fe[991]=33,fe[992]=-19,fe[993]=33,Pe(fe,994,1012,-19),Pe(fe,1012,1025,33),Pe(fe,1025,1037,-19),fe[1037]=33,Pe(fe,1038,1104,-19),fe[1104]=33,Pe(fe,1105,1117,-19),fe[1117]=33,Pe(fe,1118,1154,-19),fe[1154]=33,Pe(fe,1155,1159,-87),Pe(fe,1159,1168,33),Pe(fe,1168,1221,-19),Pe(fe,1221,1223,33),Pe(fe,1223,1225,-19),Pe(fe,1225,1227,33),Pe(fe,1227,1229,-19),Pe(fe,1229,1232,33),Pe(fe,1232,1260,-19),Pe(fe,1260,1262,33),Pe(fe,1262,1270,-19),Pe(fe,1270,1272,33),Pe(fe,1272,1274,-19),Pe(fe,1274,1329,33),Pe(fe,1329,1367,-19),Pe(fe,1367,1369,33),fe[1369]=-19,Pe(fe,1370,1377,33),Pe(fe,1377,1415,-19),Pe(fe,1415,1425,33),Pe(fe,1425,1442,-87),fe[1442]=33,Pe(fe,1443,1466,-87),fe[1466]=33,Pe(fe,1467,1470,-87),fe[1470]=33,fe[1471]=-87,fe[1472]=33,Pe(fe,1473,1475,-87),fe[1475]=33,fe[1476]=-87,Pe(fe,1477,1488,33),Pe(fe,1488,1515,-19),Pe(fe,1515,1520,33),Pe(fe,1520,1523,-19),Pe(fe,1523,1569,33),Pe(fe,1569,1595,-19),Pe(fe,1595,1600,33),fe[1600]=-87,Pe(fe,1601,1611,-19),Pe(fe,1611,1619,-87),Pe(fe,1619,1632,33),Pe(fe,1632,1642,-87),Pe(fe,1642,1648,33),fe[1648]=-87,Pe(fe,1649,1720,-19),Pe(fe,1720,1722,33),Pe(fe,1722,1727,-19),fe[1727]=33,Pe(fe,1728,1743,-19),fe[1743]=33,Pe(fe,1744,1748,-19),fe[1748]=33,fe[1749]=-19,Pe(fe,1750,1765,-87),Pe(fe,1765,1767,-19),Pe(fe,1767,1769,-87),fe[1769]=33,Pe(fe,1770,1774,-87),Pe(fe,1774,1776,33),Pe(fe,1776,1786,-87),Pe(fe,1786,2305,33),Pe(fe,2305,2308,-87),fe[2308]=33,Pe(fe,2309,2362,-19),Pe(fe,2362,2364,33),fe[2364]=-87,fe[2365]=-19,Pe(fe,2366,2382,-87),Pe(fe,2382,2385,33),Pe(fe,2385,2389,-87),Pe(fe,2389,2392,33),Pe(fe,2392,2402,-19),Pe(fe,2402,2404,-87),Pe(fe,2404,2406,33),Pe(fe,2406,2416,-87),Pe(fe,2416,2433,33),Pe(fe,2433,2436,-87),fe[2436]=33,Pe(fe,2437,2445,-19),Pe(fe,2445,2447,33),Pe(fe,2447,2449,-19),Pe(fe,2449,2451,33),Pe(fe,2451,2473,-19),fe[2473]=33,Pe(fe,2474,2481,-19),fe[2481]=33,fe[2482]=-19,Pe(fe,2483,2486,33),Pe(fe,2486,2490,-19),Pe(fe,2490,2492,33),fe[2492]=-87,fe[2493]=33,Pe(fe,2494,2501,-87),Pe(fe,2501,2503,33),Pe(fe,2503,2505,-87),Pe(fe,2505,2507,33),Pe(fe,2507,2510,-87),Pe(fe,2510,2519,33),fe[2519]=-87,Pe(fe,2520,2524,33),Pe(fe,2524,2526,-19),fe[2526]=33,Pe(fe,2527,2530,-19),Pe(fe,2530,2532,-87),Pe(fe,2532,2534,33),Pe(fe,2534,2544,-87),Pe(fe,2544,2546,-19),Pe(fe,2546,2562,33),fe[2562]=-87,Pe(fe,2563,2565,33),Pe(fe,2565,2571,-19),Pe(fe,2571,2575,33),Pe(fe,2575,2577,-19),Pe(fe,2577,2579,33),Pe(fe,2579,2601,-19),fe[2601]=33,Pe(fe,2602,2609,-19),fe[2609]=33,Pe(fe,2610,2612,-19),fe[2612]=33,Pe(fe,2613,2615,-19),fe[2615]=33,Pe(fe,2616,2618,-19),Pe(fe,2618,2620,33),fe[2620]=-87,fe[2621]=33,Pe(fe,2622,2627,-87),Pe(fe,2627,2631,33),Pe(fe,2631,2633,-87),Pe(fe,2633,2635,33),Pe(fe,2635,2638,-87),Pe(fe,2638,2649,33),Pe(fe,2649,2653,-19),fe[2653]=33,fe[2654]=-19,Pe(fe,2655,2662,33),Pe(fe,2662,2674,-87),Pe(fe,2674,2677,-19),Pe(fe,2677,2689,33),Pe(fe,2689,2692,-87),fe[2692]=33,Pe(fe,2693,2700,-19),fe[2700]=33,fe[2701]=-19,fe[2702]=33,Pe(fe,2703,2706,-19),fe[2706]=33,Pe(fe,2707,2729,-19),fe[2729]=33,Pe(fe,2730,2737,-19),fe[2737]=33,Pe(fe,2738,2740,-19),fe[2740]=33,Pe(fe,2741,2746,-19),Pe(fe,2746,2748,33),fe[2748]=-87,fe[2749]=-19,Pe(fe,2750,2758,-87),fe[2758]=33,Pe(fe,2759,2762,-87),fe[2762]=33,Pe(fe,2763,2766,-87),Pe(fe,2766,2784,33),fe[2784]=-19,Pe(fe,2785,2790,33),Pe(fe,2790,2800,-87),Pe(fe,2800,2817,33),Pe(fe,2817,2820,-87),fe[2820]=33,Pe(fe,2821,2829,-19),Pe(fe,2829,2831,33),Pe(fe,2831,2833,-19),Pe(fe,2833,2835,33),Pe(fe,2835,2857,-19),fe[2857]=33,Pe(fe,2858,2865,-19),fe[2865]=33,Pe(fe,2866,2868,-19),Pe(fe,2868,2870,33),Pe(fe,2870,2874,-19),Pe(fe,2874,2876,33),fe[2876]=-87,fe[2877]=-19,Pe(fe,2878,2884,-87),Pe(fe,2884,2887,33),Pe(fe,2887,2889,-87),Pe(fe,2889,2891,33),Pe(fe,2891,2894,-87),Pe(fe,2894,2902,33),Pe(fe,2902,2904,-87),Pe(fe,2904,2908,33),Pe(fe,2908,2910,-19),fe[2910]=33,Pe(fe,2911,2914,-19),Pe(fe,2914,2918,33),Pe(fe,2918,2928,-87),Pe(fe,2928,2946,33),Pe(fe,2946,2948,-87),fe[2948]=33,Pe(fe,2949,2955,-19),Pe(fe,2955,2958,33),Pe(fe,2958,2961,-19),fe[2961]=33,Pe(fe,2962,2966,-19),Pe(fe,2966,2969,33),Pe(fe,2969,2971,-19),fe[2971]=33,fe[2972]=-19,fe[2973]=33,Pe(fe,2974,2976,-19),Pe(fe,2976,2979,33),Pe(fe,2979,2981,-19),Pe(fe,2981,2984,33),Pe(fe,2984,2987,-19),Pe(fe,2987,2990,33),Pe(fe,2990,2998,-19),fe[2998]=33,Pe(fe,2999,3002,-19),Pe(fe,3002,3006,33),Pe(fe,3006,3011,-87),Pe(fe,3011,3014,33),Pe(fe,3014,3017,-87),fe[3017]=33,Pe(fe,3018,3022,-87),Pe(fe,3022,3031,33),fe[3031]=-87,Pe(fe,3032,3047,33),Pe(fe,3047,3056,-87),Pe(fe,3056,3073,33),Pe(fe,3073,3076,-87),fe[3076]=33,Pe(fe,3077,3085,-19),fe[3085]=33,Pe(fe,3086,3089,-19),fe[3089]=33,Pe(fe,3090,3113,-19),fe[3113]=33,Pe(fe,3114,3124,-19),fe[3124]=33,Pe(fe,3125,3130,-19),Pe(fe,3130,3134,33),Pe(fe,3134,3141,-87),fe[3141]=33,Pe(fe,3142,3145,-87),fe[3145]=33,Pe(fe,3146,3150,-87),Pe(fe,3150,3157,33),Pe(fe,3157,3159,-87),Pe(fe,3159,3168,33),Pe(fe,3168,3170,-19),Pe(fe,3170,3174,33),Pe(fe,3174,3184,-87),Pe(fe,3184,3202,33),Pe(fe,3202,3204,-87),fe[3204]=33,Pe(fe,3205,3213,-19),fe[3213]=33,Pe(fe,3214,3217,-19),fe[3217]=33,Pe(fe,3218,3241,-19),fe[3241]=33,Pe(fe,3242,3252,-19),fe[3252]=33,Pe(fe,3253,3258,-19),Pe(fe,3258,3262,33),Pe(fe,3262,3269,-87),fe[3269]=33,Pe(fe,3270,3273,-87),fe[3273]=33,Pe(fe,3274,3278,-87),Pe(fe,3278,3285,33),Pe(fe,3285,3287,-87),Pe(fe,3287,3294,33),fe[3294]=-19,fe[3295]=33,Pe(fe,3296,3298,-19),Pe(fe,3298,3302,33),Pe(fe,3302,3312,-87),Pe(fe,3312,3330,33),Pe(fe,3330,3332,-87),fe[3332]=33,Pe(fe,3333,3341,-19),fe[3341]=33,Pe(fe,3342,3345,-19),fe[3345]=33,Pe(fe,3346,3369,-19),fe[3369]=33,Pe(fe,3370,3386,-19),Pe(fe,3386,3390,33),Pe(fe,3390,3396,-87),Pe(fe,3396,3398,33),Pe(fe,3398,3401,-87),fe[3401]=33,Pe(fe,3402,3406,-87),Pe(fe,3406,3415,33),fe[3415]=-87,Pe(fe,3416,3424,33),Pe(fe,3424,3426,-19),Pe(fe,3426,3430,33),Pe(fe,3430,3440,-87),Pe(fe,3440,3585,33),Pe(fe,3585,3631,-19),fe[3631]=33,fe[3632]=-19,fe[3633]=-87,Pe(fe,3634,3636,-19),Pe(fe,3636,3643,-87),Pe(fe,3643,3648,33),Pe(fe,3648,3654,-19),Pe(fe,3654,3663,-87),fe[3663]=33,Pe(fe,3664,3674,-87),Pe(fe,3674,3713,33),Pe(fe,3713,3715,-19),fe[3715]=33,fe[3716]=-19,Pe(fe,3717,3719,33),Pe(fe,3719,3721,-19),fe[3721]=33,fe[3722]=-19,Pe(fe,3723,3725,33),fe[3725]=-19,Pe(fe,3726,3732,33),Pe(fe,3732,3736,-19),fe[3736]=33,Pe(fe,3737,3744,-19),fe[3744]=33,Pe(fe,3745,3748,-19),fe[3748]=33,fe[3749]=-19,fe[3750]=33,fe[3751]=-19,Pe(fe,3752,3754,33),Pe(fe,3754,3756,-19),fe[3756]=33,Pe(fe,3757,3759,-19),fe[3759]=33,fe[3760]=-19,fe[3761]=-87,Pe(fe,3762,3764,-19),Pe(fe,3764,3770,-87),fe[3770]=33,Pe(fe,3771,3773,-87),fe[3773]=-19,Pe(fe,3774,3776,33),Pe(fe,3776,3781,-19),fe[3781]=33,fe[3782]=-87,fe[3783]=33,Pe(fe,3784,3790,-87),Pe(fe,3790,3792,33),Pe(fe,3792,3802,-87),Pe(fe,3802,3864,33),Pe(fe,3864,3866,-87),Pe(fe,3866,3872,33),Pe(fe,3872,3882,-87),Pe(fe,3882,3893,33),fe[3893]=-87,fe[3894]=33,fe[3895]=-87,fe[3896]=33,fe[3897]=-87,Pe(fe,3898,3902,33),Pe(fe,3902,3904,-87),Pe(fe,3904,3912,-19),fe[3912]=33,Pe(fe,3913,3946,-19),Pe(fe,3946,3953,33),Pe(fe,3953,3973,-87),fe[3973]=33,Pe(fe,3974,3980,-87),Pe(fe,3980,3984,33),Pe(fe,3984,3990,-87),fe[3990]=33,fe[3991]=-87,fe[3992]=33,Pe(fe,3993,4014,-87),Pe(fe,4014,4017,33),Pe(fe,4017,4024,-87),fe[4024]=33,fe[4025]=-87,Pe(fe,4026,4256,33),Pe(fe,4256,4294,-19),Pe(fe,4294,4304,33),Pe(fe,4304,4343,-19),Pe(fe,4343,4352,33),fe[4352]=-19,fe[4353]=33,Pe(fe,4354,4356,-19),fe[4356]=33,Pe(fe,4357,4360,-19),fe[4360]=33,fe[4361]=-19,fe[4362]=33,Pe(fe,4363,4365,-19),fe[4365]=33,Pe(fe,4366,4371,-19),Pe(fe,4371,4412,33),fe[4412]=-19,fe[4413]=33,fe[4414]=-19,fe[4415]=33,fe[4416]=-19,Pe(fe,4417,4428,33),fe[4428]=-19,fe[4429]=33,fe[4430]=-19,fe[4431]=33,fe[4432]=-19,Pe(fe,4433,4436,33),Pe(fe,4436,4438,-19),Pe(fe,4438,4441,33),fe[4441]=-19,Pe(fe,4442,4447,33),Pe(fe,4447,4450,-19),fe[4450]=33,fe[4451]=-19,fe[4452]=33,fe[4453]=-19,fe[4454]=33,fe[4455]=-19,fe[4456]=33,fe[4457]=-19,Pe(fe,4458,4461,33),Pe(fe,4461,4463,-19),Pe(fe,4463,4466,33),Pe(fe,4466,4468,-19),fe[4468]=33,fe[4469]=-19,Pe(fe,4470,4510,33),fe[4510]=-19,Pe(fe,4511,4520,33),fe[4520]=-19,Pe(fe,4521,4523,33),fe[4523]=-19,Pe(fe,4524,4526,33),Pe(fe,4526,4528,-19),Pe(fe,4528,4535,33),Pe(fe,4535,4537,-19),fe[4537]=33,fe[4538]=-19,fe[4539]=33,Pe(fe,4540,4547,-19),Pe(fe,4547,4587,33),fe[4587]=-19,Pe(fe,4588,4592,33),fe[4592]=-19,Pe(fe,4593,4601,33),fe[4601]=-19,Pe(fe,4602,7680,33),Pe(fe,7680,7836,-19),Pe(fe,7836,7840,33),Pe(fe,7840,7930,-19),Pe(fe,7930,7936,33),Pe(fe,7936,7958,-19),Pe(fe,7958,7960,33),Pe(fe,7960,7966,-19),Pe(fe,7966,7968,33),Pe(fe,7968,8006,-19),Pe(fe,8006,8008,33),Pe(fe,8008,8014,-19),Pe(fe,8014,8016,33),Pe(fe,8016,8024,-19),fe[8024]=33,fe[8025]=-19,fe[8026]=33,fe[8027]=-19,fe[8028]=33,fe[8029]=-19,fe[8030]=33,Pe(fe,8031,8062,-19),Pe(fe,8062,8064,33),Pe(fe,8064,8117,-19),fe[8117]=33,Pe(fe,8118,8125,-19),fe[8125]=33,fe[8126]=-19,Pe(fe,8127,8130,33),Pe(fe,8130,8133,-19),fe[8133]=33,Pe(fe,8134,8141,-19),Pe(fe,8141,8144,33),Pe(fe,8144,8148,-19),Pe(fe,8148,8150,33),Pe(fe,8150,8156,-19),Pe(fe,8156,8160,33),P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ce="org.eclipse.elk.layered.considerModelOrder.crossingCounterPortInfluence",oce="layering",_ft="layering.minWidth",Cft="layering.nodePromotion",qI="crossingMinimization",nG="org.eclipse.elk.hierarchyHandling",Sft="crossingMinimization.greedySwitch",Aft="nodePlacement",Lft="nodePlacement.bk",Mft="edgeRouting",VI="org.eclipse.elk.edgeRouting",G1="spacing",Oke="priority",Nke="compaction",Dft="compaction.postCompaction",Ift="Specifies whether and how post-process compaction is applied.",Pke="highDegreeNodes",Bke="wrapping",Oft="wrapping.cutting",Nft="wrapping.validify",Rke="wrapping.multiEdge",cce="edgeLabels",UI="considerModelOrder",Fke="org.eclipse.elk.spacing.commentComment",jke="org.eclipse.elk.spacing.commentNode",$ke="org.eclipse.elk.spacing.edgeEdge",Hke="org.eclipse.elk.spacing.edgeNode",zke="org.eclipse.elk.spacing.labelLabel",Gke="org.eclipse.elk.spacing.labelPortHorizontal",qke="org.eclipse.elk.spacing.labelPortVertical",Vke="org.eclipse.elk.spacing.labelNode",Uke="org.eclipse.elk.spacing.nodeSelfLoop",Wke="org.eclipse.elk.spacing.portPort",Kke="org.eclipse.elk.spacing.individual",Yke="org.eclipse.elk.port.borderOffset",Xke="org.eclipse.elk.noLayout",Qke="org.eclipse.elk.port.side",WI="org.eclipse.elk.debugMode",Zke="org.eclipse.elk.alignment",Jke="org.eclipse.elk.insideSelfLoops.activate",exe="org.eclipse.elk.insideSelfLoops.yo",uce="org.eclipse.elk.nodeSize.fixedGraphSize",txe="org.eclipse.elk.direction",nxe="org.eclipse.elk.nodeLabels.padding",rxe="org.eclipse.elk.portLabels.nextToPortIfPossible",ixe="org.eclipse.elk.portLabels.treatAsGroup",sxe="org.eclipse.elk.portAlignment.default",axe="org.eclipse.elk.portAlignment.north",oxe="org.eclipse.elk.portAlignment.south",cxe="org.eclipse.elk.portAlignment.west",uxe="org.eclipse.elk.portAlignment.east",rG="org.eclipse.elk.contentAlignment",lxe="org.eclipse.elk.junctionPoints",hxe="org.eclipse.elk.edgeLabels.placement",fxe="org.eclipse.elk.port.index",dxe="org.eclipse.elk.commentBox",gxe="org.eclipse.elk.hypernode",pxe="org.eclipse.elk.port.anchor",lce="org.eclipse.elk.partitioning.activate",hce="org.eclipse.elk.partitioning.partition",iG="org.eclipse.elk.position",bxe="org.eclipse.elk.margins",vxe="org.eclipse.elk.spacing.portsSurrounding",fce="org.eclipse.elk.interactiveLayout",Oc="org.eclipse.elk.core.util",wxe={3:1,4:1,5:1,593:1},Pft="NETWORK_SIMPLEX",Qc={123:1,51:1},sG="org.eclipse.elk.alg.layered.p1cycles",by="org.eclipse.elk.alg.layered.p2layers",mxe={402:1,225:1},Bft={832:1,3:1,4:1},Qu="org.eclipse.elk.alg.layered.p3order",ko="org.eclipse.elk.alg.layered.p4nodes",Rft={3:1,4:1,5:1,840:1},Md=1e-5,r2="org.eclipse.elk.alg.layered.p4nodes.bk",dce="org.eclipse.elk.alg.layered.p5edges",o1="org.eclipse.elk.alg.layered.p5edges.orthogonal",gce="org.eclipse.elk.alg.layered.p5edges.orthogonal.direction",pce=1e-6,vy="org.eclipse.elk.alg.layered.p5edges.splines",bce=.09999999999999998,aG=1e-8,Fft=4.71238898038469,jft=3.141592653589793,IC="org.eclipse.elk.alg.mrtree",OC="org.eclipse.elk.alg.mrtree.graph",vk="org.eclipse.elk.alg.mrtree.intermediate",$ft="Set neighbors in level",Hft="DESCENDANTS",yxe="org.eclipse.elk.mrtree.weighting",kxe="org.eclipse.elk.mrtree.searchOrder",oG="org.eclipse.elk.alg.mrtree.options",Fg="org.eclipse.elk.mrtree",zft="org.eclipse.elk.tree",xxe="org.eclipse.elk.alg.radial",_4=6.283185307179586,Exe=5e-324,Gft="org.eclipse.elk.alg.radial.intermediate",vce="org.eclipse.elk.alg.radial.intermediate.compaction",qft={3:1,4:1,5:1,106:1},Txe="org.eclipse.elk.alg.radial.intermediate.optimization",wce="No implementation is available for the layout option ",NC="org.eclipse.elk.alg.radial.options",_xe="org.eclipse.elk.radial.orderId",Cxe="org.eclipse.elk.radial.radius",mce="org.eclipse.elk.radial.compactor",yce="org.eclipse.elk.radial.compactionStepSize",Sxe="org.eclipse.elk.radial.sorter",Axe="org.eclipse.elk.radial.wedgeCriteria",Lxe="org.eclipse.elk.radial.optimizationCriteria",Dd="org.eclipse.elk.radial",Vft="org.eclipse.elk.alg.radial.p1position.wedge",Mxe="org.eclipse.elk.alg.radial.sorting",Uft=5.497787143782138,Wft=3.9269908169872414,Kft=2.356194490192345,Yft="org.eclipse.elk.alg.rectpacking",cG="org.eclipse.elk.alg.rectpacking.firstiteration",kce="org.eclipse.elk.alg.rectpacking.options",Dxe="org.eclipse.elk.rectpacking.optimizationGoal",Ixe="org.eclipse.elk.rectpacking.lastPlaceShift",Oxe="org.eclipse.elk.rectpacking.currentPosition",Nxe="org.eclipse.elk.rectpacking.desiredPosition",Pxe="org.eclipse.elk.rectpacking.onlyFirstIteration",Bxe="org.eclipse.elk.rectpacking.rowCompaction",xce="org.eclipse.elk.rectpacking.expandToAspectRatio",Rxe="org.eclipse.elk.rectpacking.targetWidth",uG="org.eclipse.elk.expandNodes",Gh="org.eclipse.elk.rectpacking",KI="org.eclipse.elk.alg.rectpacking.util",lG="No implementation available for ",wy="org.eclipse.elk.alg.spore",my="org.eclipse.elk.alg.spore.options",lw="org.eclipse.elk.sporeCompaction",Ece="org.eclipse.elk.underlyingLayoutAlgorithm",Fxe="org.eclipse.elk.processingOrder.treeConstruction",jxe="org.eclipse.elk.processingOrder.spanningTreeCostFunction",Tce="org.eclipse.elk.processingOrder.preferredRoot",_ce="org.eclipse.elk.processingOrder.rootSelection",Cce="org.eclipse.elk.structure.structureExtractionStrategy",$xe="org.eclipse.elk.compaction.compactionStrategy",Hxe="org.eclipse.elk.compaction.orthogonal",zxe="org.eclipse.elk.overlapRemoval.maxIterations",Gxe="org.eclipse.elk.overlapRemoval.runScanline",Sce="processingOrder",Xft="overlapRemoval",R7="org.eclipse.elk.sporeOverlap",Qft="org.eclipse.elk.alg.spore.p1structure",Ace="org.eclipse.elk.alg.spore.p2processingorder",Lce="org.eclipse.elk.alg.spore.p3execution",Zft="Invalid index: ",F7="org.eclipse.elk.core.alg",C4={331:1},yy={288:1},Jft="Make sure its type is registered with the ",qxe=" utility class.",j7="true",Mce="false",e1t="Couldn't clone property '",hw=.05,qh="org.eclipse.elk.core.options",t1t=1.2999999523162842,fw="org.eclipse.elk.box",Vxe="org.eclipse.elk.box.packingMode",n1t="org.eclipse.elk.algorithm",r1t="org.eclipse.elk.resolvedAlgorithm",Uxe="org.eclipse.elk.bendPoints",t3n="org.eclipse.elk.labelManager",i1t="org.eclipse.elk.scaleFactor",s1t="org.eclipse.elk.animate",a1t="org.eclipse.elk.animTimeFactor",o1t="org.eclipse.elk.layoutAncestors",c1t="org.eclipse.elk.maxAnimTime",u1t="org.eclipse.elk.minAnimTime",l1t="org.eclipse.elk.progressBar",h1t="org.eclipse.elk.validateGraph",f1t="org.eclipse.elk.validateOptions",d1t="org.eclipse.elk.zoomToFit",n3n="org.eclipse.elk.font.name",g1t="org.eclipse.elk.font.size",p1t="org.eclipse.elk.edge.type",b1t="partitioning",v1t="nodeLabels",hG="portAlignment",Dce="nodeSize",Ice="port",Wxe="portLabels",w1t="insideSelfLoops",PC="org.eclipse.elk.fixed",fG="org.eclipse.elk.random",m1t="port must have a parent node to calculate the port side",y1t="The edge needs to have exactly one edge section. 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size=",Vce="sourceIndex=",Od={3:1,4:1,20:1,28:1,52:1,14:1,15:1,54:1,67:1,63:1,58:1},Uce={3:1,4:1,20:1,28:1,52:1,14:1,47:1,15:1,54:1,67:1,63:1,58:1,588:1},bG="logging",q1t="measureExecutionTime",V1t="parser.parse.1",U1t="parser.parse.2",vG="parser.next.1",Wce="parser.next.2",W1t="parser.next.3",K1t="parser.next.4",Tb="parser.factor.1",c8e="parser.factor.2",Y1t="parser.factor.3",X1t="parser.factor.4",Q1t="parser.factor.5",Z1t="parser.factor.6",J1t="parser.atom.1",edt="parser.atom.2",tdt="parser.atom.3",u8e="parser.atom.4",Kce="parser.atom.5",l8e="parser.cc.1",wG="parser.cc.2",ndt="parser.cc.3",rdt="parser.cc.5",h8e="parser.cc.6",f8e="parser.cc.7",Yce="parser.cc.8",idt="parser.ope.1",sdt="parser.ope.2",adt="parser.ope.3",jg="parser.descape.1",odt="parser.descape.2",cdt="parser.descape.3",udt="parser.descape.4",ldt="parser.descape.5",xh="parser.process.1",hdt="parser.quantifier.1",fdt="parser.quantifier.2",ddt="parser.quantifier.3",gdt="parser.quantifier.4",d8e="parser.quantifier.5",pdt="org.eclipse.emf.common.notify",g8e={415:1,672:1},bdt={3:1,4:1,20:1,28:1,52:1,14:1,15:1,67:1,58:1},XI={366:1,143:1},$C="index=",Xce={3:1,4:1,5:1,126:1},vdt={3:1,4:1,20:1,28:1,52:1,14:1,15:1,54:1,67:1,58:1},p8e={3:1,6:1,4:1,5:1,192:1},wdt={3:1,4:1,5:1,165:1,367:1},mdt=";/?:@&=+$,",ydt="invalid authority: ",kdt="EAnnotation",xdt="ETypedElement",Edt="EStructuralFeature",Tdt="EAttribute",_dt="EClassifier",Cdt="EEnumLiteral",Sdt="EGenericType",Adt="EOperation",Ldt="EParameter",Mdt="EReference",Ddt="ETypeParameter",Wi="org.eclipse.emf.ecore.util",Qce={76:1},b8e={3:1,20:1,14:1,15:1,58:1,589:1,76:1,69:1,95:1},Idt="org.eclipse.emf.ecore.util.FeatureMap$Entry",Zu=8192,ky=2048,HC="byte",mG="char",zC="double",GC="float",qC="int",VC="long",UC="short",Odt="java.lang.Object",L4={3:1,4:1,5:1,247:1},v8e={3:1,4:1,5:1,673:1},Ndt={3:1,4:1,20:1,28:1,52:1,14:1,15:1,54:1,67:1,63:1,58:1,69:1},Zo={3:1,4:1,20:1,28:1,52:1,14:1,15:1,54:1,67:1,63:1,58:1,76:1,69:1,95:1},QI="mixed",Zr="http:///org/eclipse/emf/ecore/util/ExtendedMetaData",Vh="kind",Pdt={3:1,4:1,5:1,674:1},w8e={3:1,4:1,20:1,28:1,52:1,14:1,15:1,67:1,58:1,76:1,69:1,95:1},yG={20:1,28:1,52:1,14:1,15:1,58:1,69:1},kG={47:1,125:1,279:1},xG={72:1,332:1},EG="The value of type '",TG="' must be of type '",M4=1316,Uh="http://www.eclipse.org/emf/2002/Ecore",_G=-32768,gw="constraints",Ha="baseType",Bdt="getEStructuralFeature",Rdt="getFeatureID",WC="feature",Fdt="getOperationID",m8e="operation",jdt="defaultValue",$dt="eTypeParameters",Hdt="isInstance",zdt="getEEnumLiteral",Gdt="eContainingClass",li={55:1},qdt={3:1,4:1,5:1,119:1},Vdt="org.eclipse.emf.ecore.resource",Udt={92:1,90:1,591:1,1935:1},Zce="org.eclipse.emf.ecore.resource.impl",y8e="unspecified",ZI="simple",CG="attribute",Wdt="attributeWildcard",SG="element",Jce="elementWildcard",c1="collapse",eue="itemType",AG="namespace",JI="##targetNamespace",Wh="whiteSpace",k8e="wildcards",_b="http://www.eclipse.org/emf/2003/XMLType",tue="##any",G7="uninitialized",eO="The multiplicity constraint is 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Tk,mO,$4,I2t=Gr(Ic,"InLayerConstraint",303,Wr,qen,QYt),O2t;M(420,22,{3:1,35:1,22:1,420:1},v2e);var Lle,L9e,M9e=Gr(Ic,"InteractiveReferencePoint",420,Wr,YJt,ZYt),N2t,D9e,_k,yw,Bq,I9e,O9e,Rq,N9e,yO,Fq,uS,Ck,Sy,Mle,jq,vc,P9e,kw,eu,Dle,Ile,kO,Lb,xw,Sk,B9e,Ak,xO,Ay,l1,Yh,Ole,H4,Nc,Mi,R9e,F9e,j9e,$9e,H9e,Nle,$q,cl,Ew,Ple,Lk,EO,W1,z4,lE,G4,q4,hE,Mb,z9e,Ble,Rle,Mk;M(163,22,{3:1,35:1,22:1,163:1},TM);var lS,l2,hS,Ly,TO,G9e=Gr(Ic,"LayerConstraint",163,Wr,Onn,JYt),P2t;M(848,1,zh,VJ),l.Qe=function(t){en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,nke),""),"Direction Congruency"),"Specifies how drawings of the same graph with different layout directions compare to each other: either a natural reading direction is preserved or the drawings are rotated versions of each other."),J9e),(Ng(),vs)),g9e),rn((i1(),Fn))))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,rke),""),"Feedback Edges"),"Whether feedback edges should be highlighted by routing around the nodes."),(Mn(),!1)),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Gz),""),"Interactive Reference Point"),"Determines which point of a node is considered by interactive layout phases."),sTe),vs),M9e),rn(Fn)))),va(t,Gz,Roe,Cbt),va(t,Gz,MC,_bt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,ike),""),"Merge Edges"),"Edges that have no ports are merged so they touch the connected nodes at the same points. When this option is disabled, one port is created for each edge directly connected to a node. When it is enabled, all such incoming edges share an input port, and all outgoing edges share an output port."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,ske),""),"Merge Hierarchy-Crossing Edges"),"If hierarchical layout is active, hierarchy-crossing edges use as few hierarchical ports as possible. They are broken by the algorithm, with hierarchical ports inserted as required. Usually, one such port is created for each edge at each hierarchy crossing point. With this option set to true, we try to create as few hierarchical ports as possible in the process. In particular, all edges that form a hyperedge can share a port."),!0),za),Us),rn(Fn)))),en(t,new Vt(sUt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,ake),""),"Allow Non-Flow Ports To Switch Sides"),"Specifies whether non-flow ports may switch sides if their node's port constraints are either FIXED_SIDE or FIXED_ORDER. A non-flow port is a port on a side that is not part of the currently configured layout flow. For instance, given a left-to-right layout direction, north and south ports would be considered non-flow ports. Further note that the underlying criterium whether to switch sides or not solely relies on the minimization of edge crossings. Hence, edge length and other aesthetics criteria are not addressed."),!1),za),Us),rn(Bb)),ie(re(mt,1),Qe,2,6,["org.eclipse.elk.layered.northOrSouthPort"])))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,oke),""),"Port Sorting Strategy"),"Only relevant for nodes with FIXED_SIDE port constraints. Determines the way a node's ports are distributed on the sides of a node if their order is not prescribed. The option is set on parent nodes."),dTe),vs),k_e),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,cke),""),"Thoroughness"),"How much effort should be spent to produce a nice layout."),ct(7)),Cc),Za),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,uke),""),"Add Unnecessary Bendpoints"),"Adds bend points even if an edge does not change direction. If true, each long edge dummy will contribute a bend point to its edges and hierarchy-crossing edges will always get a bend point where they cross hierarchy boundaries. By default, bend points are only added where an edge changes direction."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,lke),""),"Generate Position and Layer IDs"),"If enabled position id and layer id are generated, which are usually only used internally when setting the interactiveLayout option. This option should be specified on the root node."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Roe),"cycleBreaking"),"Cycle Breaking Strategy"),"Strategy for cycle breaking. Cycle breaking looks for cycles in the graph and determines which edges to reverse to break the cycles. Reversed edges will end up pointing to the opposite direction of regular edges (that is, reversed edges will point left if edges usually point right)."),Z9e),vs),f9e),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,GI),oce),"Node Layering Strategy"),"Strategy for node layering."),cTe),vs),u_e),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,hke),oce),"Layer Constraint"),"Determines a constraint on the placement of the node regarding the layering."),aTe),vs),G9e),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,fke),oce),"Layer Choice Constraint"),"Allows to set a constraint regarding the layer placement of a node. Let i be the value of teh constraint. Assumed the drawing has n layers and i < n. If set to i, it expresses that the node should be placed in i-th layer. Should i>=n be true then the node is placed in the last layer of the drawing. Note that this option is not part of any of ELK Layered's default configurations but is only evaluated as part of the `InteractiveLayeredGraphVisitor`, which must be applied manually or used via the `DiagramLayoutEngine."),ct(-1)),Cc),Za),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,dke),oce),"Layer ID"),"Layer identifier that was calculated by ELK Layered for a node. This is only generated if interactiveLayot or generatePositionAndLayerIds is set."),ct(-1)),Cc),Za),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Foe),_ft),"Upper Bound On Width [MinWidth Layerer]"),"Defines a loose upper bound on the width of the MinWidth layerer. If set to '-1' multiple values are tested and the best result is selected."),ct(4)),Cc),Za),rn(Fn)))),va(t,Foe,GI,Obt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,joe),_ft),"Upper Layer Estimation Scaling Factor [MinWidth Layerer]"),"Multiplied with Upper Bound On Width for defining an upper bound on the width of layers which haven't been determined yet, but whose maximum width had been (roughly) estimated by the MinWidth algorithm. Compensates for too high estimations. If set to '-1' multiple values are tested and the best result is selected."),ct(2)),Cc),Za),rn(Fn)))),va(t,joe,GI,Pbt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,$oe),Cft),"Node Promotion Strategy"),"Reduces number of dummy nodes after layering phase (if possible)."),oTe),vs),w_e),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Hoe),Cft),"Max Node Promotion Iterations"),"Limits the number of iterations for node promotion."),ct(0)),Cc),Za),rn(Fn)))),va(t,Hoe,$oe,null),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,zoe),"layering.coffmanGraham"),"Layer Bound"),"The maximum number of nodes allowed per layer."),ct(Ei)),Cc),Za),rn(Fn)))),va(t,zoe,GI,Abt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,MC),qI),"Crossing Minimization Strategy"),"Strategy for crossing minimization."),Q9e),vs),c9e),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,gke),qI),"Force Node Model Order"),"The node order given by the model does not change to produce a better layout. E.g. if node A is before node B in the model this is not changed during crossing minimization. This assumes that the node model order is already respected before crossing minimization. This can be achieved by setting considerModelOrder.strategy to NODES_AND_EDGES."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Goe),qI),"Hierarchical Sweepiness"),"How likely it is to use cross-hierarchy (1) vs bottom-up (-1)."),.1),qo),ma),rn(Fn)))),va(t,Goe,nG,ebt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,qoe),qI),"Semi-Interactive Crossing Minimization"),"Preserves the order of nodes within a layer but still minimizes crossings between edges connecting long edge dummies. Derives the desired order from positions specified by the 'org.eclipse.elk.position' layout option. Requires a crossing minimization strategy that is able to process 'in-layer' constraints."),!1),za),Us),rn(Fn)))),va(t,qoe,MC,ibt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,pke),qI),"Position Choice Constraint"),"Allows to set a constraint regarding the position placement of a node in a layer. Assumed the layer in which the node placed includes n other nodes and i < n. If set to i, it expresses that the node should be placed at the i-th position. Should i>=n be true then the node is placed at the last position in the layer. Note that this option is not part of any of ELK Layered's default configurations but is only evaluated as part of the `InteractiveLayeredGraphVisitor`, which must be applied manually or used via the `DiagramLayoutEngine."),ct(-1)),Cc),Za),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,bke),qI),"Position ID"),"Position within a layer that was determined by ELK Layered for a node. This is only generated if interactiveLayot or generatePositionAndLayerIds is set."),ct(-1)),Cc),Za),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,vke),Sft),"Greedy Switch Activation Threshold"),"By default it is decided automatically if the greedy switch is activated or not. The decision is based on whether the size of the input graph (without dummy nodes) is smaller than the value of this option. A '0' enforces the activation."),ct(40)),Cc),Za),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Voe),Sft),"Greedy Switch Crossing Minimization"),"Greedy Switch strategy for crossing minimization. The greedy switch heuristic is executed after the regular crossing minimization as a post-processor. Note that if 'hierarchyHandling' is set to 'INCLUDE_CHILDREN', the 'greedySwitchHierarchical.type' option must be used."),X9e),vs),Ale),rn(Fn)))),va(t,Voe,MC,Z2t),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,qz),"crossingMinimization.greedySwitchHierarchical"),"Greedy Switch Crossing Minimization (hierarchical)"),"Activates the greedy switch heuristic in case hierarchical layout is used. The differences to the non-hierarchical case (see 'greedySwitch.type') are: 1) greedy switch is inactive by default, 3) only the option value set on the node at which hierarchical layout starts is relevant, and 2) if it's activated by the user, it properly addresses hierarchy-crossing edges."),Y9e),vs),Ale),rn(Fn)))),va(t,qz,MC,Y2t),va(t,qz,nG,X2t),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,T4),Aft),"Node Placement Strategy"),"Strategy for node placement."),fTe),vs),d_e),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,Vz),Aft),"Favor Straight Edges Over Balancing"),"Favor straight edges over a balanced node placement. The default behavior is determined automatically based on the used 'edgeRouting'. For an orthogonal style it is set to true, for all other styles to false."),za),Us),rn(Fn)))),va(t,Vz,T4,Ubt),va(t,Vz,T4,Wbt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Uoe),Lft),"BK Edge Straightening"),"Specifies whether the Brandes Koepf node placer tries to increase the number of straight edges at the expense of diagram size. There is a subtle difference to the 'favorStraightEdges' option, which decides whether a balanced placement of the nodes is desired, or not. In bk terms this means combining the four alignments into a single balanced one, or not. This option on the other hand tries to straighten additional edges during the creation of each of the four alignments."),uTe),vs),v9e),rn(Fn)))),va(t,Uoe,T4,zbt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Woe),Lft),"BK Fixed Alignment"),"Tells the BK node placer to use a certain alignment (out of its four) instead of the one producing the smallest height, or the combination of all four."),lTe),vs),x9e),rn(Fn)))),va(t,Woe,T4,qbt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Koe),"nodePlacement.linearSegments"),"Linear Segments Deflection Dampening"),"Dampens the movement of nodes to keep the diagram from getting too large."),.3),qo),ma),rn(Fn)))),va(t,Koe,T4,Ybt),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,Yoe),"nodePlacement.networkSimplex"),"Node Flexibility"),"Aims at shorter and straighter edges. Two configurations are possible: (a) allow ports to move freely on the side they are assigned to (the order is always defined beforehand), (b) additionally allow to enlarge a node wherever it helps. If this option is not configured for a node, the 'nodeFlexibility.default' value is used, which is specified for the node's parent."),vs),ahe),rn(ua)))),va(t,Yoe,T4,Jbt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Xoe),"nodePlacement.networkSimplex.nodeFlexibility"),"Node Flexibility Default"),"Default value of the 'nodeFlexibility' option for the children of a hierarchical node."),hTe),vs),ahe),rn(Fn)))),va(t,Xoe,T4,Zbt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,wke),Mft),"Self-Loop Distribution"),"Alter the distribution of the loops around the node. It only takes effect for PortConstraints.FREE."),nTe),vs),T_e),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,mke),Mft),"Self-Loop Ordering"),"Alter the ordering of the loops they can either be stacked or sequenced. It only takes effect for PortConstraints.FREE."),rTe),vs),__e),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Uz),"edgeRouting.splines"),"Spline Routing Mode"),"Specifies the way control points are assembled for each individual edge. CONSERVATIVE ensures that edges are properly routed around the nodes but feels rather orthogonal at times. SLOPPY uses fewer control points to obtain curvier edge routes but may result in edges overlapping nodes."),iTe),vs),S_e),rn(Fn)))),va(t,Uz,VI,pbt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Wz),"edgeRouting.splines.sloppy"),"Sloppy Spline Layer Spacing Factor"),"Spacing factor for routing area between layers when using sloppy spline routing."),.2),qo),ma),rn(Fn)))),va(t,Wz,VI,vbt),va(t,Wz,Uz,wbt),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Qoe),"edgeRouting.polyline"),"Sloped Edge Zone Width"),"Width of the strip to the left and to the right of each layer where the polyline edge router is allowed to refrain from ensuring that edges are routed horizontally. This prevents awkward bend points for nodes that extent almost to the edge of their layer."),2),qo),ma),rn(Fn)))),va(t,Qoe,VI,hbt),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,yke),G1),"Spacing Base Value"),"An optional base value for all other layout options of the 'spacing' group. It can be used to conveniently alter the overall 'spaciousness' of the drawing. Whenever an explicit value is set for the other layout options, this base value will have no effect. The base value is not inherited, i.e. it must be set for each hierarchical node."),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,kke),G1),"Edge Node Between Layers Spacing"),"The spacing to be preserved between nodes and edges that are routed next to the node's layer. For the spacing between nodes and edges that cross the node's layer 'spacing.edgeNode' is used."),10),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,xke),G1),"Edge Edge Between Layer Spacing"),"Spacing to be preserved between pairs of edges that are routed between the same pair of layers. 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This is used by the 'Fixed Layout' algorithm to specify a pre-defined routing for an edge. The vector chain must include the source point, any bend points, and the target point, so it must have at least two points."),Y1),sSe),rn(Fd)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,rG),""),"Content Alignment"),"Specifies how the content of a node are aligned. Each node can individually control the alignment of its contents. I.e. if a node should be aligned top left in its parent node, the parent node should specify that option."),pSe),Pk),Jhe),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,WI),""),"Debug Mode"),"Whether additional debug information shall be generated."),(Mn(),!1)),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,txe),""),P6e),"Overall direction of edges: horizontal (right / left) or vertical (down / up)."),bSe),vs),RS),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,VI),""),"Edge Routing"),"What kind of edge routing style should be applied for the content of a parent node. Algorithms may also set this option to single edges in order to mark them as splines. The bend point list of edges with this option set to SPLINES must be interpreted as control points for a piecewise cubic spline."),mSe),vs),ffe),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,uG),""),"Expand Nodes"),"If active, nodes are expanded to fill the area of their parent."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,nG),""),"Hierarchy Handling"),"Determines whether separate layout runs are triggered for different compound nodes in a hierarchical graph. Setting a node's hierarchy handling to `INCLUDE_CHILDREN` will lay out that node and all of its descendants in a single layout run, until a descendant is encountered which has its hierarchy handling set to `SEPARATE_CHILDREN`. In general, `SEPARATE_CHILDREN` will ensure that a new layout run is triggered for a node with that setting. Including multiple levels of hierarchy in a single layout run may allow cross-hierarchical edges to be laid out properly. If the root node is set to `INHERIT` (or not set at all), the default behavior is `SEPARATE_CHILDREN`."),ESe),vs),cAe),Ui(Fn,ie(re(Ug,1),tt,175,0,[ua]))))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,uw),""),"Padding"),"The padding to be left to a parent element's border when placing child elements. This can also serve as an output option of a layout algorithm if node size calculation is setup appropriately."),OSe),Y1),gEe),Ui(Fn,ie(re(Ug,1),tt,175,0,[ua]))))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,jI),""),"Interactive"),"Whether the algorithm should be run in interactive mode for the content of a parent node. What this means exactly depends on how the specific algorithm interprets this option. Usually in the interactive mode algorithms try to modify the current layout as little as possible."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,fce),""),"interactive Layout"),"Whether the graph should be changeable interactively and by setting constraints"),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,CC),""),"Omit Node Micro Layout"),"Node micro layout comprises the computation of node dimensions (if requested), the placement of ports and their labels, and the placement of node labels. The functionality is implemented independent of any specific layout algorithm and shouldn't have any negative impact on the layout algorithm's performance itself. Yet, if any unforeseen behavior occurs, this option allows to deactivate the micro layout."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Doe),""),"Port Constraints"),"Defines constraints of the position of the ports of a node."),FSe),vs),hAe),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,iG),""),"Position"),"The position of a node, port, or label. This is used by the 'Fixed Layout' algorithm to specify a pre-defined position."),Y1),na),Ui(ua,ie(re(Ug,1),tt,175,0,[Bb,Vg]))))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,FI),""),"Priority"),"Defines the priority of an object; its meaning depends on the specific layout algorithm and the context where it is used."),Cc),Za),Ui(ua,ie(re(Ug,1),tt,175,0,[Fd]))))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,Fz),""),"Randomization Seed"),"Seed used for pseudo-random number generators to control the layout algorithm. If the value is 0, the seed shall be determined pseudo-randomly (e.g. from the system time)."),Cc),Za),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,_C),""),"Separate Connected Components"),"Whether each connected component should be processed separately."),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,lxe),""),"Junction Points"),"This option is not used as option, but as output of the layout algorithms. It is attached to edges and determines the points where junction symbols should be drawn in order to represent hyperedges with orthogonal routing. Whether such points are computed depends on the chosen layout algorithm and edge routing style. The points are put into the vector chain with no specific order."),_Se),Y1),sSe),rn(Fd)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,dxe),""),"Comment Box"),"Whether the node should be regarded as a comment box instead of a regular node. In that case its placement should be similar to how labels are handled. Any edges incident to a comment box specify to which graph elements the comment is related."),!1),za),Us),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,gxe),""),"Hypernode"),"Whether the node should be handled as a hypernode."),!1),za),Us),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,t3n),""),"Label Manager"),"Label managers can shorten labels upon a layout algorithm's request."),Y1),I3n),Ui(Fn,ie(re(Ug,1),tt,175,0,[Vg]))))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,bxe),""),"Margins"),"Margins define additional space around the actual bounds of a graph element. For instance, ports or labels being placed on the outside of a node's border might introduce such a margin. The margin is used to guarantee non-overlap of other graph elements with those ports or labels."),CSe),Y1),dEe),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Xke),""),"No Layout"),"No layout is done for the associated element. This is used to mark parts of a diagram to avoid their inclusion in the layout graph, or to mark parts of the layout graph to prevent layout engines from processing them. If you wish to exclude the contents of a compound node from automatic layout, while the node itself is still considered on its own layer, use the 'Fixed Layout' algorithm for that node."),!1),za),Us),Ui(ua,ie(re(Ug,1),tt,175,0,[Fd,Bb,Vg]))))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,i1t),""),"Scale Factor"),"The scaling factor to be applied to the corresponding node in recursive layout. It causes the corresponding node's size to be adjusted, and its ports and labels to be sized and placed accordingly after the layout of that node has been determined (and before the node itself and its siblings are arranged). The scaling is not reverted afterwards, so the resulting layout graph contains the adjusted size and position data. This option is currently not supported if 'Layout Hierarchy' is set."),1),qo),ma),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,s1t),""),"Animate"),"Whether the shift from the old layout to the new computed layout shall be animated."),!0),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,a1t),""),"Animation Time Factor"),"Factor for computation of animation time. The higher the value, the longer the animation time. If the value is 0, the resulting time is always equal to the minimum defined by 'Minimal Animation Time'."),ct(100)),Cc),Za),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,o1t),""),"Layout Ancestors"),"Whether the hierarchy levels on the path from the selected element to the root of the diagram shall be included in the layout process."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,c1t),""),"Maximal Animation Time"),"The maximal time for animations, in milliseconds."),ct(4e3)),Cc),Za),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,u1t),""),"Minimal Animation Time"),"The minimal time for animations, in milliseconds."),ct(400)),Cc),Za),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,l1t),""),"Progress Bar"),"Whether a progress bar shall be displayed during layout computations."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,h1t),""),"Validate Graph"),"Whether the graph shall be validated before any layout algorithm is applied. If this option is enabled and at least one error is found, the layout process is aborted and a message is shown to the user."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,f1t),""),"Validate Options"),"Whether layout options shall be validated before any layout algorithm is applied. If this option is enabled and at least one error is found, the layout process is aborted and a message is shown to the user."),!0),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,d1t),""),"Zoom to Fit"),"Whether the zoom level shall be set to view the whole diagram after layout."),!1),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Vxe),"box"),"Box Layout Mode"),"Configures the packing mode used by the {@link BoxLayoutProvider}. If SIMPLE is not required (neither priorities are used nor the interactive mode), GROUP_DEC can improve the packing and decrease the area. GROUP_MIXED and GROUP_INC may, in very specific scenarios, work better."),gSe),vs),vAe),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Fke),G1),"Comment Comment Spacing"),"Spacing to be preserved between a comment box and other comment boxes connected to the same node. The space left between comment boxes of different nodes is controlled by the node-node spacing."),10),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,jke),G1),"Comment Node Spacing"),"Spacing to be preserved between a node and its connected comment boxes. The space left between a node and the comments of another node is controlled by the node-node spacing."),10),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,_oe),G1),"Components Spacing"),"Spacing to be preserved between pairs of connected components. This option is only relevant if 'separateConnectedComponents' is activated."),20),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,$ke),G1),"Edge Spacing"),"Spacing to be preserved between any two edges. Note that while this can somewhat easily be satisfied for the segments of orthogonally drawn edges, it is harder for general polylines or splines."),10),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Moe),G1),"Edge Label Spacing"),"The minimal distance to be preserved between a label and the edge it is associated with. Note that the placement of a label is influenced by the 'edgelabels.placement' option."),2),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Hke),G1),"Edge Node Spacing"),"Spacing to be preserved between nodes and edges."),10),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,zke),G1),"Label Spacing"),"Determines the amount of space to be left between two labels of the same graph element."),0),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Vke),G1),"Label Node Spacing"),"Spacing to be preserved between labels and the border of node they are associated with. Note that the placement of a label is influenced by the 'nodelabels.placement' option."),5),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Gke),G1),"Horizontal spacing between Label and Port"),"Horizontal spacing to be preserved between labels and the ports they are associated with. Note that the placement of a label is influenced by the 'portlabels.placement' option."),1),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,qke),G1),"Vertical spacing between Label and Port"),"Vertical spacing to be preserved between labels and the ports they are associated with. Note that the placement of a label is influenced by the 'portlabels.placement' option."),1),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,py),G1),"Node Spacing"),"The minimal distance to be preserved between each two nodes."),20),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Uke),G1),"Node Self Loop Spacing"),"Spacing to be preserved between a node and its self loops."),10),qo),ma),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,Wke),G1),"Port Spacing"),"Spacing between pairs of ports of the same node."),10),qo),ma),Ui(Fn,ie(re(Ug,1),tt,175,0,[ua]))))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,Kke),G1),"Individual Spacing"),"Allows to specify individual spacing values for graph elements that shall be different from the value specified for the element's parent."),Y1),O3t),Ui(ua,ie(re(Ug,1),tt,175,0,[Fd,Bb,Vg]))))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,vxe),G1),"Additional Port Space"),"Additional space around the sets of ports on each node side. For each side of a node, this option can reserve additional space before and after the ports on each side. For example, a top spacing of 20 makes sure that the first port on the western and eastern side is 20 units away from the northern border."),KSe),Y1),dEe),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,hce),b1t),"Layout Partition"),"Partition to which the node belongs. This requires Layout Partitioning to be active. Nodes with lower partition IDs will appear to the left of nodes with higher partition IDs (assuming a left-to-right layout direction)."),Cc),Za),Ui(Fn,ie(re(Ug,1),tt,175,0,[ua]))))),va(t,hce,lce,a3t),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,lce),b1t),"Layout Partitioning"),"Whether to activate partitioned layout. This will allow to group nodes through the Layout Partition option. a pair of nodes with different partition indices is then placed such that the node with lower index is placed to the left of the other node (with left-to-right layout direction). Depending on the layout algorithm, this may only be guaranteed to work if all nodes have a layout partition configured, or at least if edges that cross partitions are not part of a partition-crossing cycle."),NSe),za),Us),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,nxe),v1t),"Node Label Padding"),"Define padding for node labels that are placed inside of a node."),ASe),Y1),gEe),rn(Fn)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,P7),v1t),"Node Label Placement"),"Hints for where node labels are to be placed; if empty, the node label's position is not modified."),LSe),Pk),xo),Ui(ua,ie(re(Ug,1),tt,175,0,[Vg]))))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,sxe),hG),"Port Alignment"),"Defines the default port distribution for a node. May be overridden for each side individually."),BSe),vs),HS),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,axe),hG),"Port Alignment (North)"),"Defines how ports on the northern side are placed, overriding the node's general port alignment."),vs),HS),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,oxe),hG),"Port Alignment (South)"),"Defines how ports on the southern side are placed, overriding the node's general port alignment."),vs),HS),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,cxe),hG),"Port Alignment (West)"),"Defines how ports on the western side are placed, overriding the node's general port alignment."),vs),HS),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(Wt(Xt(Kt(Yt(new Ht,uxe),hG),"Port Alignment (East)"),"Defines how ports on the eastern side are placed, overriding the node's general port alignment."),vs),HS),rn(ua)))),en(t,new Vt(Zt(Qt(Jt(pn(Wt(Xt(Kt(Yt(new Ht,E4),Dce),"Node Size Constraints"),"What should be taken into account when calculating a node's size. 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11:return Mn(),!!(this.Bb&ky);case 12:return Mn(),!!(this.Bb&dy);case 13:return this.j;case 14:return b7(this);case 15:return Mn(),!!(this.Bb&Zu);case 16:return Mn(),!!(this.Bb&Ed);case 17:return Fm(this)}return ph(this,t-Jn(this.zh()),gn((s=u(_n(this,16),26),s||this.zh()),t),n,r)},l.hh=function(t,n,r){var s,o,h;switch(n){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),ou(this.Ab,t,r);case 17:return this.Cb&&(r=(o=this.Db>>16,o>=0?jit(this,r):this.Cb.ih(this,-1-o,null,r))),Kl(this,t,17,r)}return h=u(gn((s=u(_n(this,16),26),s||this.zh()),n),66),h.Nj().Qj(this,du(this),n-Jn(this.zh()),t,r)},l.jh=function(t,n,r){var s,o;switch(n){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),Xa(this.Ab,t,r);case 9:return zne(this,r);case 17:return Kl(this,null,17,r)}return o=u(gn((s=u(_n(this,16),26),s||this.zh()),n),66),o.Nj().Rj(this,du(this),n-Jn(this.zh()),t,r)},l.lh=function(t){var n,r;switch(t){case 0:return!!this.Ab&&this.Ab.i!=0;case 1:return this.zb!=null;case 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10:J8(this,It(Mt(n)));return;case 11:n7(this,It(Mt(n)));return;case 12:e7(this,It(Mt(n)));return;case 13:N2e(this,Hr(n));return;case 15:t7(this,It(Mt(n)));return;case 16:r7(this,It(Mt(n)));return}yh(this,t-Jn(this.zh()),gn((r=u(_n(this,16),26),r||this.zh()),t),n)},l.zh=function(){return on(),h4t},l.Bh=function(t){var n,r;switch(t){case 0:!this.Ab&&(this.Ab=new at(ti,this,0,3)),_r(this.Ab);return;case 1:we(this.Cb,88)&&iy(dl(u(this.Cb,88)),4),au(this,null);return;case 2:Lg(this,!0);return;case 3:Mg(this,!0);return;case 4:Cg(this,0);return;case 5:this.ok(1);return;case 8:cb(this,null);return;case 9:r=$1(this,null,null),r&&r.Fi();return;case 10:J8(this,!0);return;case 11:n7(this,!1);return;case 12:e7(this,!1);return;case 13:this.i=null,X$(this,null);return;case 15:t7(this,!1);return;case 16:r7(this,!1);return}wh(this,t-Jn(this.zh()),gn((n=u(_n(this,16),26),n||this.zh()),t))},l.Gh=function(){m8(Po((Yu(),Oa),this)),$h(this),this.Bb|=1},l.Gj=function(){return this.f},l.zj=function(){return b7(this)},l.Hj=function(){return Fm(this)},l.Lj=function(){return null},l.pk=function(){return this.k},l.aj=function(){return this.n},l.Mj=function(){return UH(this)},l.Nj=function(){var t,n,r,s,o,h,d,v,x;return this.p||(r=Fm(this),(r.i==null&&xd(r),r.i).length,s=this.Lj(),s&&Jn(Fm(s)),o=$h(this),d=o.Bj(),t=d?d.i&1?d==El?Us:d==Lr?Za:d==Xy?W7:d==pa?ma:d==S2?pw:d==a5?bw:d==el?mk:XC:d:null,n=b7(this),v=o.zj(),gon(this),this.Bb&Ed&&((h=q3e((Yu(),Oa),r))&&h!=this||(h=F6(Po(Oa,this))))?this.p=new Gqe(this,h):this.$j()?this.rk()?s?this.Bb&Zu?t?this.sk()?this.p=new K2(47,t,this,s):this.p=new K2(5,t,this,s):this.sk()?this.p=new Q2(46,this,s):this.p=new Q2(4,this,s):t?this.sk()?this.p=new K2(49,t,this,s):this.p=new K2(7,t,this,s):this.sk()?this.p=new Q2(48,this,s):this.p=new Q2(6,this,s):this.Bb&Zu?t?t==Cb?this.p=new yg(50,W3t,this):this.sk()?this.p=new yg(43,t,this):this.p=new yg(1,t,this):this.sk()?this.p=new xg(42,this):this.p=new xg(0,this):t?t==Cb?this.p=new yg(41,W3t,this):this.sk()?this.p=new yg(45,t,this):this.p=new yg(3,t,this):this.sk()?this.p=new xg(44,this):this.p=new xg(2,this):we(o,148)?t==eU?this.p=new xg(40,this):this.Bb&512?this.Bb&Zu?t?this.p=new yg(9,t,this):this.p=new xg(8,this):t?this.p=new yg(11,t,this):this.p=new xg(10,this):this.Bb&Zu?t?this.p=new yg(13,t,this):this.p=new xg(12,this):t?this.p=new yg(15,t,this):this.p=new xg(14,this):s?(x=s.t,x>1||x==-1?this.sk()?this.Bb&Zu?t?this.p=new K2(25,t,this,s):this.p=new Q2(24,this,s):t?this.p=new K2(27,t,this,s):this.p=new Q2(26,this,s):this.Bb&Zu?t?this.p=new K2(29,t,this,s):this.p=new Q2(28,this,s):t?this.p=new K2(31,t,this,s):this.p=new Q2(30,this,s):this.sk()?this.Bb&Zu?t?this.p=new K2(33,t,this,s):this.p=new Q2(32,this,s):t?this.p=new K2(35,t,this,s):this.p=new Q2(34,this,s):this.Bb&Zu?t?this.p=new K2(37,t,this,s):this.p=new Q2(36,this,s):t?this.p=new K2(39,t,this,s):this.p=new Q2(38,this,s)):this.sk()?this.Bb&Zu?t?this.p=new yg(17,t,this):this.p=new xg(16,this):t?this.p=new yg(19,t,this):this.p=new xg(18,this):this.Bb&Zu?t?this.p=new yg(21,t,this):this.p=new xg(20,this):t?this.p=new yg(23,t,this):this.p=new xg(22,this):this.qk()?this.sk()?this.p=new MWe(u(o,26),this,s):this.p=new vwe(u(o,26),this,s):we(o,148)?t==eU?this.p=new xg(40,this):this.Bb&Zu?t?this.p=new AKe(n,v,this,(Gie(),d==Lr?qAe:d==El?jAe:d==S2?VAe:d==Xy?GAe:d==pa?zAe:d==a5?UAe:d==el?$Ae:d==Sh?HAe:_fe)):this.p=new $Ye(u(o,148),n,v,this):t?this.p=new SKe(n,v,this,(Gie(),d==Lr?qAe:d==El?jAe:d==S2?VAe:d==Xy?GAe:d==pa?zAe:d==a5?UAe:d==el?$Ae:d==Sh?HAe:_fe)):this.p=new jYe(u(o,148),n,v,this):this.rk()?s?this.Bb&Zu?this.sk()?this.p=new IWe(u(o,26),this,s):this.p=new cve(u(o,26),this,s):this.sk()?this.p=new DWe(u(o,26),this,s):this.p=new wne(u(o,26),this,s):this.Bb&Zu?this.sk()?this.p=new SUe(u(o,26),this):this.p=new xbe(u(o,26),this):this.sk()?this.p=new CUe(u(o,26),this):this.p=new ine(u(o,26),this):this.sk()?s?this.Bb&Zu?this.p=new OWe(u(o,26),this,s):this.p=new ave(u(o,26),this,s):this.Bb&Zu?this.p=new AUe(u(o,26),this):this.p=new Ebe(u(o,26),this):s?this.Bb&Zu?this.p=new NWe(u(o,26),this,s):this.p=new ove(u(o,26),this,s):this.Bb&Zu?this.p=new LUe(u(o,26),this):this.p=new Mj(u(o,26),this)),this.p},l.Ij=function(){return(this.Bb&Sf)!=0},l.qk=function(){return!1},l.rk=function(){return!1},l.Jj=function(){return(this.Bb&Ed)!=0},l.Oj=function(){return Ure(this)},l.sk=function(){return!1},l.Kj=function(){return(this.Bb&Zu)!=0},l.tk=function(t){this.k=t},l.Lh=function(t){bre(this,t)},l.Ib=function(){return lz(this)},l.e=!1,l.n=0,O(Tn,"EStructuralFeatureImpl",449),M(322,449,{105:1,92:1,90:1,34:1,147:1,191:1,56:1,170:1,66:1,108:1,472:1,49:1,97:1,322:1,150:1,449:1,284:1,114:1,115:1,677:1},Vee),l._g=function(t,n,r){var s,o;switch(t){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),this.Ab;case 1:return this.zb;case 2:return Mn(),!!(this.Bb&256);case 3:return Mn(),!!(this.Bb&512);case 4:return ct(this.s);case 5:return ct(this.t);case 6:return Mn(),!!j4e(this);case 7:return Mn(),o=this.s,o>=1;case 8:return n?$h(this):this.r;case 9:return this.q;case 10:return Mn(),!!(this.Bb&Sf);case 11:return Mn(),!!(this.Bb&ky);case 12:return Mn(),!!(this.Bb&dy);case 13:return this.j;case 14:return b7(this);case 15:return Mn(),!!(this.Bb&Zu);case 16:return Mn(),!!(this.Bb&Ed);case 17:return Fm(this);case 18:return Mn(),!!(this.Bb&_c);case 19:return n?lie(this):$Ze(this)}return ph(this,t-Jn((on(),Wy)),gn((s=u(_n(this,16),26),s||Wy),t),n,r)},l.lh=function(t){var n,r;switch(t){case 0:return!!this.Ab&&this.Ab.i!=0;case 1:return this.zb!=null;case 2:return(this.Bb&256)==0;case 3:return(this.Bb&512)==0;case 4:return this.s!=0;case 5:return this.t!=1;case 6:return j4e(this);case 7:return r=this.s,r>=1;case 8:return!!this.r&&!this.q.e&&Mv(this.q).i==0;case 9:return!!this.q&&!(this.r&&!this.q.e&&Mv(this.q).i==0);case 10:return(this.Bb&Sf)==0;case 11:return(this.Bb&ky)!=0;case 12:return(this.Bb&dy)!=0;case 13:return this.j!=null;case 14:return b7(this)!=null;case 15:return(this.Bb&Zu)!=0;case 16:return(this.Bb&Ed)!=0;case 17:return!!Fm(this);case 18:return(this.Bb&_c)!=0;case 19:return!!$Ze(this)}return dh(this,t-Jn((on(),Wy)),gn((n=u(_n(this,16),26),n||Wy),t))},l.sh=function(t,n){var r,s;switch(t){case 0:!this.Ab&&(this.Ab=new at(ti,this,0,3)),_r(this.Ab),!this.Ab&&(this.Ab=new at(ti,this,0,3)),fs(this.Ab,u(n,14));return;case 1:bre(this,Hr(n));return;case 2:Lg(this,It(Mt(n)));return;case 3:Mg(this,It(Mt(n)));return;case 4:Cg(this,u(n,19).a);return;case 5:wze(this,u(n,19).a);return;case 8:cb(this,u(n,138));return;case 9:s=$1(this,u(n,87),null),s&&s.Fi();return;case 10:J8(this,It(Mt(n)));return;case 11:n7(this,It(Mt(n)));return;case 12:e7(this,It(Mt(n)));return;case 13:N2e(this,Hr(n));return;case 15:t7(this,It(Mt(n)));return;case 16:r7(this,It(Mt(n)));return;case 18:Pie(this,It(Mt(n)));return}yh(this,t-Jn((on(),Wy)),gn((r=u(_n(this,16),26),r||Wy),t),n)},l.zh=function(){return on(),Wy},l.Bh=function(t){var n,r;switch(t){case 0:!this.Ab&&(this.Ab=new at(ti,this,0,3)),_r(this.Ab);return;case 1:we(this.Cb,88)&&iy(dl(u(this.Cb,88)),4),au(this,null);return;case 2:Lg(this,!0);return;case 3:Mg(this,!0);return;case 4:Cg(this,0);return;case 5:this.b=0,Wm(this,1);return;case 8:cb(this,null);return;case 9:r=$1(this,null,null),r&&r.Fi();return;case 10:J8(this,!0);return;case 11:n7(this,!1);return;case 12:e7(this,!1);return;case 13:this.i=null,X$(this,null);return;case 15:t7(this,!1);return;case 16:r7(this,!1);return;case 18:Pie(this,!1);return}wh(this,t-Jn((on(),Wy)),gn((n=u(_n(this,16),26),n||Wy),t))},l.Gh=function(){lie(this),m8(Po((Yu(),Oa),this)),$h(this),this.Bb|=1},l.$j=function(){return j4e(this)},l.nk=function(t,n){return this.b=0,this.a=null,Iye(this,t,n)},l.ok=function(t){wze(this,t)},l.Ib=function(){var t;return this.Db&64?lz(this):(t=new Ph(lz(this)),t.a+=" (iD: ",vg(t,(this.Bb&_c)!=0),t.a+=")",t.a)},l.b=0,O(Tn,"EAttributeImpl",322),M(351,438,{105:1,92:1,90:1,138:1,147:1,191:1,56:1,108:1,49:1,97:1,351:1,150:1,114:1,115:1,676:1}),l.uk=function(t){return t.Tg()==this},l.Qg=function(t){return pse(this,t)},l.Rg=function(t,n){this.w=null,this.Db=n<<16|this.Db&255,this.Cb=t},l._g=function(t,n,r){var s;switch(t){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),this.Ab;case 1:return this.zb;case 2:return this.D!=null?this.D:this.B;case 3:return Jv(this);case 4:return this.zj();case 5:return this.F;case 6:return n?Gl(this):x8(this);case 7:return!this.A&&(this.A=new qu(mu,this,7)),this.A}return ph(this,t-Jn(this.zh()),gn((s=u(_n(this,16),26),s||this.zh()),t),n,r)},l.hh=function(t,n,r){var s,o,h;switch(n){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),ou(this.Ab,t,r);case 6:return this.Cb&&(r=(o=this.Db>>16,o>=0?pse(this,r):this.Cb.ih(this,-1-o,null,r))),Kl(this,t,6,r)}return h=u(gn((s=u(_n(this,16),26),s||this.zh()),n),66),h.Nj().Qj(this,du(this),n-Jn(this.zh()),t,r)},l.jh=function(t,n,r){var s,o;switch(n){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),Xa(this.Ab,t,r);case 6:return Kl(this,null,6,r);case 7:return!this.A&&(this.A=new qu(mu,this,7)),Xa(this.A,t,r)}return o=u(gn((s=u(_n(this,16),26),s||this.zh()),n),66),o.Nj().Rj(this,du(this),n-Jn(this.zh()),t,r)},l.lh=function(t){var n;switch(t){case 0:return!!this.Ab&&this.Ab.i!=0;case 1:return this.zb!=null;case 2:return this.D!=null&&this.D==this.F;case 3:return!!Jv(this);case 4:return this.zj()!=null;case 5:return this.F!=null&&this.F!=this.D&&this.F!=this.B;case 6:return!!x8(this);case 7:return!!this.A&&this.A.i!=0}return dh(this,t-Jn(this.zh()),gn((n=u(_n(this,16),26),n||this.zh()),t))},l.sh=function(t,n){var r;switch(t){case 0:!this.Ab&&(this.Ab=new at(ti,this,0,3)),_r(this.Ab),!this.Ab&&(this.Ab=new at(ti,this,0,3)),fs(this.Ab,u(n,14));return;case 1:l$(this,Hr(n));return;case 2:Vte(this,Hr(n));return;case 5:x7(this,Hr(n));return;case 7:!this.A&&(this.A=new qu(mu,this,7)),_r(this.A),!this.A&&(this.A=new qu(mu,this,7)),fs(this.A,u(n,14));return}yh(this,t-Jn(this.zh()),gn((r=u(_n(this,16),26),r||this.zh()),t),n)},l.zh=function(){return on(),s4t},l.Bh=function(t){var n;switch(t){case 0:!this.Ab&&(this.Ab=new at(ti,this,0,3)),_r(this.Ab);return;case 1:we(this.Cb,179)&&(u(this.Cb,179).tb=null),au(this,null);return;case 2:Y8(this,null),R8(this,this.D);return;case 5:x7(this,null);return;case 7:!this.A&&(this.A=new qu(mu,this,7)),_r(this.A);return}wh(this,t-Jn(this.zh()),gn((n=u(_n(this,16),26),n||this.zh()),t))},l.yj=function(){var t;return this.G==-1&&(this.G=(t=Gl(this),t?Dg(t.Mh(),this):-1)),this.G},l.zj=function(){return null},l.Aj=function(){return Gl(this)},l.vk=function(){return this.v},l.Bj=function(){return Jv(this)},l.Cj=function(){return this.D!=null?this.D:this.B},l.Dj=function(){return this.F},l.wj=function(t){return oae(this,t)},l.wk=function(t){this.v=t},l.xk=function(t){wtt(this,t)},l.yk=function(t){this.C=t},l.Lh=function(t){l$(this,t)},l.Ib=function(){return xH(this)},l.C=null,l.D=null,l.G=-1,O(Tn,"EClassifierImpl",351),M(88,351,{105:1,92:1,90:1,26:1,138:1,147:1,191:1,56:1,108:1,49:1,97:1,88:1,351:1,150:1,473:1,114:1,115:1,676:1},YL),l.uk=function(t){return XKt(this,t.Tg())},l._g=function(t,n,r){var s;switch(t){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),this.Ab;case 1:return this.zb;case 2:return this.D!=null?this.D:this.B;case 3:return Jv(this);case 4:return null;case 5:return this.F;case 6:return n?Gl(this):x8(this);case 7:return!this.A&&(this.A=new qu(mu,this,7)),this.A;case 8:return Mn(),!!(this.Bb&256);case 9:return Mn(),!!(this.Bb&512);case 10:return jo(this);case 11:return!this.q&&(this.q=new at(nf,this,11,10)),this.q;case 12:return b4(this);case 13:return fC(this);case 14:return fC(this),this.r;case 15:return b4(this),this.k;case 16:return L4e(this);case 17:return 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o=this.p?this.p.Pb():this.j?this.j.pi(this.n++):this.k.Xb(this.n++),this.f?(t=u(o,72),t.ak(),r=t.dd(),this.i=r):(r=o,this.i=r),this.g=3,!0}else if(h!=null)return this.k=null,this.p=null,r=h,this.i=r,this.g=2,!0}return this.k=null,this.p=null,this.f=!1,this.g=1,!1}else return o=this.p?this.p.Pb():this.j?this.j.pi(this.n++):this.k.Xb(this.n++),this.f?(t=u(o,72),t.ak(),r=t.dd(),this.i=r):(r=o,this.i=r),this.g=3,!0}},l.Sb=function(){var t,n,r,s,o,h;switch(this.g){case-3:case-2:return!0;case-1:return!1;case 3:this.p?this.p.Ub():--this.n;default:if(!this.k||(this.p?!rat(this,this.p):!pat(this))){for(;this.d>0;)if(n=this.c[--this.d],(!this.e||n.Gj()!=AE||n.aj()!=0)&&(!this.Mk()||this.b.mh(n))){if(h=this.b.bh(n,this.Lk()),this.f=(ho(),u(n,66).Oj()),this.f||n.$j()){if(this.Lk()?(s=u(h,15),this.k=s):(s=u(h,69),this.k=this.j=s),we(this.k,54)?(this.o=this.k.gc(),this.n=this.o):this.p=this.j?this.j._h(this.k.gc()):this.k.Zc(this.k.gc()),this.p?rat(this,this.p):pat(this))return o=this.p?this.p.Ub():this.j?this.j.pi(--this.n):this.k.Xb(--this.n),this.f?(t=u(o,72),t.ak(),r=t.dd(),this.i=r):(r=o,this.i=r),this.g=-3,!0}else if(h!=null)return this.k=null,this.p=null,r=h,this.i=r,this.g=-2,!0}return this.k=null,this.p=null,this.g=-1,!1}else return o=this.p?this.p.Ub():this.j?this.j.pi(--this.n):this.k.Xb(--this.n),this.f?(t=u(o,72),t.ak(),r=t.dd(),this.i=r):(r=o,this.i=r),this.g=-3,!0}},l.Pb=function(){return aH(this)},l.Tb=function(){return this.a},l.Ub=function(){var t;if(this.g<-1||this.Sb())return--this.a,this.g=0,t=this.i,this.Sb(),t;throw J(new xc)},l.Vb=function(){return this.a-1},l.Qb=function(){throw J(new Fr)},l.Lk=function(){return!1},l.Wb=function(t){throw J(new Fr)},l.Mk=function(){return!0},l.a=0,l.d=0,l.f=!1,l.g=0,l.n=0,l.o=0;var lN;O(Wi,"EContentsEList/FeatureIteratorImpl",279),M(697,279,kG,kbe),l.Lk=function(){return!0},O(Wi,"EContentsEList/ResolvingFeatureIteratorImpl",697),M(1157,697,kG,_Ue),l.Mk=function(){return!1},O(Tn,"ENamedElementImpl/1/1",1157),M(1158,279,kG,TUe),l.Mk=function(){return!1},O(Tn,"ENamedElementImpl/1/2",1158),M(36,143,XI,Hm,Tre,oa,jre,N0,xf,_me,JXe,Cme,eQe,Xwe,tQe,Lme,nQe,Qwe,rQe,Sme,iQe,p_,dD,nre,Ame,sQe,Zwe,aQe),l._i=function(){return ume(this)},l.gj=function(){var t;return t=ume(this),t?t.zj():null},l.yi=function(t){return this.b==-1&&this.a&&(this.b=this.c.Xg(this.a.aj(),this.a.Gj())),this.c.Og(this.b,t)},l.Ai=function(){return this.c},l.hj=function(){var t;return t=ume(this),t?t.Kj():!1},l.b=-1,O(Tn,"ENotificationImpl",36),M(399,284,{105:1,92:1,90:1,147:1,191:1,56:1,59:1,108:1,472:1,49:1,97:1,150:1,399:1,284:1,114:1,115:1},Uee),l.Qg=function(t){return Xit(this,t)},l._g=function(t,n,r){var s,o,h;switch(t){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),this.Ab;case 1:return this.zb;case 2:return Mn(),!!(this.Bb&256);case 3:return Mn(),!!(this.Bb&512);case 4:return ct(this.s);case 5:return ct(this.t);case 6:return Mn(),h=this.t,h>1||h==-1;case 7:return Mn(),o=this.s,o>=1;case 8:return n?$h(this):this.r;case 9:return this.q;case 10:return this.Db>>16==10?u(this.Cb,26):null;case 11:return!this.d&&(this.d=new qu(mu,this,11)),this.d;case 12:return!this.c&&(this.c=new at(Iw,this,12,10)),this.c;case 13:return!this.a&&(this.a=new jM(this,this)),this.a;case 14:return gl(this)}return ph(this,t-Jn((on(),Xg)),gn((s=u(_n(this,16),26),s||Xg),t),n,r)},l.hh=function(t,n,r){var s,o,h;switch(n){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),ou(this.Ab,t,r);case 10:return this.Cb&&(r=(o=this.Db>>16,o>=0?Xit(this,r):this.Cb.ih(this,-1-o,null,r))),Kl(this,t,10,r);case 12:return!this.c&&(this.c=new at(Iw,this,12,10)),ou(this.c,t,r)}return h=u(gn((s=u(_n(this,16),26),s||(on(),Xg)),n),66),h.Nj().Qj(this,du(this),n-Jn((on(),Xg)),t,r)},l.jh=function(t,n,r){var s,o;switch(n){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),Xa(this.Ab,t,r);case 9:return zne(this,r);case 10:return Kl(this,null,10,r);case 11:return!this.d&&(this.d=new qu(mu,this,11)),Xa(this.d,t,r);case 12:return!this.c&&(this.c=new at(Iw,this,12,10)),Xa(this.c,t,r);case 14:return Xa(gl(this),t,r)}return o=u(gn((s=u(_n(this,16),26),s||(on(),Xg)),n),66),o.Nj().Rj(this,du(this),n-Jn((on(),Xg)),t,r)},l.lh=function(t){var n,r,s;switch(t){case 0:return!!this.Ab&&this.Ab.i!=0;case 1:return this.zb!=null;case 2:return(this.Bb&256)==0;case 3:return(this.Bb&512)==0;case 4:return this.s!=0;case 5:return this.t!=1;case 6:return s=this.t,s>1||s==-1;case 7:return r=this.s,r>=1;case 8:return!!this.r&&!this.q.e&&Mv(this.q).i==0;case 9:return!!this.q&&!(this.r&&!this.q.e&&Mv(this.q).i==0);case 10:return!!(this.Db>>16==10&&u(this.Cb,26));case 11:return!!this.d&&this.d.i!=0;case 12:return!!this.c&&this.c.i!=0;case 13:return!!this.a&&gl(this.a.a).i!=0&&!(this.b&&sse(this.b));case 14:return!!this.b&&sse(this.b)}return dh(this,t-Jn((on(),Xg)),gn((n=u(_n(this,16),26),n||Xg),t))},l.sh=function(t,n){var r,s;switch(t){case 0:!this.Ab&&(this.Ab=new at(ti,this,0,3)),_r(this.Ab),!this.Ab&&(this.Ab=new at(ti,this,0,3)),fs(this.Ab,u(n,14));return;case 1:au(this,Hr(n));return;case 2:Lg(this,It(Mt(n)));return;case 3:Mg(this,It(Mt(n)));return;case 4:Cg(this,u(n,19).a);return;case 5:Wm(this,u(n,19).a);return;case 8:cb(this,u(n,138));return;case 9:s=$1(this,u(n,87),null),s&&s.Fi();return;case 11:!this.d&&(this.d=new qu(mu,this,11)),_r(this.d),!this.d&&(this.d=new qu(mu,this,11)),fs(this.d,u(n,14));return;case 12:!this.c&&(this.c=new at(Iw,this,12,10)),_r(this.c),!this.c&&(this.c=new at(Iw,this,12,10)),fs(this.c,u(n,14));return;case 13:!this.a&&(this.a=new jM(this,this)),pC(this.a),!this.a&&(this.a=new jM(this,this)),fs(this.a,u(n,14));return;case 14:_r(gl(this)),fs(gl(this),u(n,14));return}yh(this,t-Jn((on(),Xg)),gn((r=u(_n(this,16),26),r||Xg),t),n)},l.zh=function(){return on(),Xg},l.Bh=function(t){var n,r;switch(t){case 0:!this.Ab&&(this.Ab=new at(ti,this,0,3)),_r(this.Ab);return;case 1:au(this,null);return;case 2:Lg(this,!0);return;case 3:Mg(this,!0);return;case 4:Cg(this,0);return;case 5:Wm(this,1);return;case 8:cb(this,null);return;case 9:r=$1(this,null,null),r&&r.Fi();return;case 11:!this.d&&(this.d=new qu(mu,this,11)),_r(this.d);return;case 12:!this.c&&(this.c=new at(Iw,this,12,10)),_r(this.c);return;case 13:this.a&&pC(this.a);return;case 14:this.b&&_r(this.b);return}wh(this,t-Jn((on(),Xg)),gn((n=u(_n(this,16),26),n||Xg),t))},l.Gh=function(){var t,n;if(this.c)for(t=0,n=this.c.i;t<n;++t)PM(Te(this.c,t));$h(this),this.Bb|=1},O(Tn,"EOperationImpl",399),M(505,742,w8e,jM),l.Hi=function(t,n){EKt(this,t,u(n,138))},l.Ii=function(t){mWt(this,u(t,138))},l.Oi=function(t){var n,r;return n=u(Te(gl(this.a),t),87),r=n.c,r||(on(),Z1)},l.Ti=function(t){var n,r;return n=u(ay(gl(this.a),t),87),r=n.c,r||(on(),Z1)},l.Ui=function(t,n){return ycn(this,t,u(n,138))},l.ai=function(){return!1},l.Zi=function(t,n,r,s,o){return null},l.Ji=function(){return new $$e(this)},l.Ki=function(){_r(gl(this.a))},l.Li=function(t){return irt(this,t)},l.Mi=function(t){var n,r;for(r=t.Kc();r.Ob();)if(n=r.Pb(),!irt(this,n))return!1;return!0},l.Ni=function(t){var n,r,s;if(we(t,15)&&(s=u(t,15),s.gc()==gl(this.a).i)){for(n=s.Kc(),r=new rr(this);n.Ob();)if(je(n.Pb())!==je(pr(r)))return!1;return!0}return!1},l.Pi=function(){var t,n,r,s,o;for(r=1,n=new rr(gl(this.a));n.e!=n.i.gc();)t=u(pr(n),87),s=(o=t.c,o||(on(),Z1)),r=31*r+(s?Xi(s):0);return r},l.Qi=function(t){var n,r,s,o;for(s=0,r=new rr(gl(this.a));r.e!=r.i.gc();){if(n=u(pr(r),87),je(t)===je((o=n.c,o||(on(),Z1))))return s;++s}return-1},l.Ri=function(){return gl(this.a).i==0},l.Si=function(){return null},l.Vi=function(){return gl(this.a).i},l.Wi=function(){var t,n,r,s,o,h;for(h=gl(this.a).i,o=Me(Yn,yt,1,h,5,1),r=0,n=new rr(gl(this.a));n.e!=n.i.gc();)t=u(pr(n),87),o[r++]=(s=t.c,s||(on(),Z1));return o},l.Xi=function(t){var n,r,s,o,h,d,v;for(v=gl(this.a).i,t.length<v&&(o=TD(pl(t).c,v),t=o),t.length>v&&cs(t,v,null),s=0,r=new rr(gl(this.a));r.e!=r.i.gc();)n=u(pr(r),87),h=(d=n.c,d||(on(),Z1)),cs(t,s++,h);return t},l.Yi=function(){var t,n,r,s,o;for(o=new bg,o.a+="[",t=gl(this.a),n=0,s=gl(this.a).i;n<s;)To(o,r_((r=u(Te(t,n),87).c,r||(on(),Z1)))),++n<s&&(o.a+=io);return o.a+="]",o.a},l.$i=function(t){},l.aj=function(){return 13},l.Bk=function(){return!0},l.bj=function(){return!1},l.Ck=function(){return!1},l.Dk=function(){return!1},l.Ek=function(){return!0},l.rk=function(){return!1},l.Fk=function(){return!0},l.wj=function(t){return we(t,138)},l.fj=function(){return Etn(this.a)},l.hi=function(){return!0},l.ni=function(){return!0},O(Tn,"EOperationImpl/1",505),M(1340,1964,A7,$$e),l.Zc=function(t){return _D(this.a,t)},l.gc=function(){return gl(this.a.a).i},O(Tn,"EOperationImpl/1/1",1340),M(1341,545,Zo,CKe),l.ii=function(t,n){var r,s;return r=u(q_(this,t,n),87),Ll(this.e)&&Vx(this,new dD(this.a,7,(on(),l4t),ct(n),(s=r.c,s||Z1),t)),r},l.jj=function(t,n){return Ban(this,u(t,87),n)},l.kj=function(t,n){return Ran(this,u(t,87),n)},l.lj=function(t,n,r){return Bcn(this,u(t,87),u(n,87),r)},l.Zi=function(t,n,r,s,o){switch(t){case 3:return x_(this,t,n,r,s,this.i>1);case 5:return x_(this,t,n,r,s,this.i-u(r,15).gc()>0);default:return new N0(this.e,t,this.c,n,r,s,!0)}},l.ij=function(){return!0},l.fj=function(){return sse(this)},l.Xj=function(){_r(this)},O(Tn,"EOperationImpl/2",1341),M(498,1,{1938:1,498:1},zqe),O(Tn,"EPackageImpl/1",498),M(16,85,Zo,at),l.zk=function(){return this.d},l.Ak=function(){return this.b},l.Dk=function(){return!0},l.b=0,O(Wi,"EObjectContainmentWithInverseEList",16),M(353,16,Zo,S6),l.Ek=function(){return!0},l.li=function(t,n){return rk(this,t,u(n,56))},O(Wi,"EObjectContainmentWithInverseEList/Resolving",353),M(298,353,Zo,Pm),l.ci=function(){this.a.tb=null},O(Tn,"EPackageImpl/2",298),M(1228,1,{},YB),O(Tn,"EPackageImpl/3",1228),M(718,43,y4,Epe),l._b=function(t){return fa(t)?rre(this,t):!!$o(this.f,t)},O(Tn,"EPackageRegistryImpl",718),M(509,284,{105:1,92:1,90:1,147:1,191:1,56:1,2017:1,108:1,472:1,49:1,97:1,150:1,509:1,284:1,114:1,115:1},Wee),l.Qg=function(t){return Qit(this,t)},l._g=function(t,n,r){var s,o,h;switch(t){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),this.Ab;case 1:return this.zb;case 2:return Mn(),!!(this.Bb&256);case 3:return Mn(),!!(this.Bb&512);case 4:return ct(this.s);case 5:return ct(this.t);case 6:return Mn(),h=this.t,h>1||h==-1;case 7:return Mn(),o=this.s,o>=1;case 8:return n?$h(this):this.r;case 9:return this.q;case 10:return this.Db>>16==10?u(this.Cb,59):null}return ph(this,t-Jn((on(),Ky)),gn((s=u(_n(this,16),26),s||Ky),t),n,r)},l.hh=function(t,n,r){var s,o,h;switch(n){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),ou(this.Ab,t,r);case 10:return this.Cb&&(r=(o=this.Db>>16,o>=0?Qit(this,r):this.Cb.ih(this,-1-o,null,r))),Kl(this,t,10,r)}return h=u(gn((s=u(_n(this,16),26),s||(on(),Ky)),n),66),h.Nj().Qj(this,du(this),n-Jn((on(),Ky)),t,r)},l.jh=function(t,n,r){var s,o;switch(n){case 0:return!this.Ab&&(this.Ab=new at(ti,this,0,3)),Xa(this.Ab,t,r);case 9:return zne(this,r);case 10:return Kl(this,null,10,r)}return o=u(gn((s=u(_n(this,16),26),s||(on(),Ky)),n),66),o.Nj().Rj(this,du(this),n-Jn((on(),Ky)),t,r)},l.lh=function(t){var n,r,s;switch(t){case 0:return!!this.Ab&&this.Ab.i!=0;case 1:return this.zb!=null;case 2:return(this.Bb&256)==0;case 3:return(this.Bb&512)==0;case 4:return this.s!=0;case 5:return this.t!=1;case 6:return s=this.t,s>1||s==-1;case 7:return r=this.s,r>=1;case 8:return!!this.r&&!this.q.e&&Mv(this.q).i==0;case 9:return!!this.q&&!(this.r&&!this.q.e&&Mv(this.q).i==0);case 10:return!!(this.Db>>16==10&&u(this.Cb,59))}return 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ds(this,0)),Hr(kI(this.c,(Fi(),QS),!0));case 4:return Vbe(this.a,(!this.c&&(this.c=new ds(this,0)),Hr(kI(this.c,(Fi(),QS),!0))));case 5:return this.a}return ph(this,t-Jn((Fi(),i5)),gn(this.j&2?(!this.k&&(this.k=new ch),this.k).ck():i5,t),n,r)},l.lh=function(t){switch(t){case 0:return!!this.c&&this.c.i!=0;case 1:return!(!this.c&&(this.c=new ds(this,0)),u(Wc(this.c,(Fi(),zb)),153)).dc();case 2:return!!this.b&&this.b.i!=0;case 3:return!this.c&&(this.c=new ds(this,0)),Hr(kI(this.c,(Fi(),QS),!0))!=null;case 4:return Vbe(this.a,(!this.c&&(this.c=new ds(this,0)),Hr(kI(this.c,(Fi(),QS),!0))))!=null;case 5:return!!this.a}return dh(this,t-Jn((Fi(),i5)),gn(this.j&2?(!this.k&&(this.k=new ch),this.k).ck():i5,t))},l.sh=function(t,n){switch(t){case 0:!this.c&&(this.c=new ds(this,0)),XM(this.c,n);return;case 1:(!this.c&&(this.c=new ds(this,0)),u(u(Wc(this.c,(Fi(),zb)),153),215)).Wb(n);return;case 2:!this.b&&(this.b=new ds(this,2)),XM(this.b,n);return;case 3:Mwe(this,Hr(n));return;case 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r?(!this.c&&(this.c=new Nl((on(),oo),wc,this,2)),this.c):(!this.c&&(this.c=new Nl((on(),oo),wc,this,2)),hD(this.c));case 3:return!this.a&&(this.a=new ds(this,0)),Wc(this.a,(Fi(),dN));case 4:return!this.a&&(this.a=new ds(this,0)),Wc(this.a,(Fi(),gN));case 5:return!this.a&&(this.a=new ds(this,0)),Wc(this.a,(Fi(),ZS));case 6:return!this.a&&(this.a=new ds(this,0)),Wc(this.a,(Fi(),JS))}return ph(this,t-Jn((Fi(),Pw)),gn(this.j&2?(!this.k&&(this.k=new ch),this.k).ck():Pw,t),n,r)},l.jh=function(t,n,r){var s;switch(n){case 0:return!this.a&&(this.a=new ds(this,0)),vI(this.a,t,r);case 1:return!this.b&&(this.b=new Nl((on(),oo),wc,this,1)),vj(this.b,t,r);case 2:return!this.c&&(this.c=new Nl((on(),oo),wc,this,2)),vj(this.c,t,r);case 5:return!this.a&&(this.a=new ds(this,0)),FUe(Wc(this.a,(Fi(),ZS)),t,r)}return s=u(gn(this.j&2?(!this.k&&(this.k=new ch),this.k).ck():(Fi(),Pw),n),66),s.Nj().Rj(this,vme(this),n-Jn((Fi(),Pw)),t,r)},l.lh=function(t){switch(t){case 0:return!!this.a&&this.a.i!=0;case 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ds(this,0)),d6(Wc(this.a,(Fi(),ZS))),!this.a&&(this.a=new ds(this,0)),t_(Wc(this.a,ZS),u(n,14));return;case 6:!this.a&&(this.a=new ds(this,0)),d6(Wc(this.a,(Fi(),JS))),!this.a&&(this.a=new ds(this,0)),t_(Wc(this.a,JS),u(n,14));return}yh(this,t-Jn((Fi(),Pw)),gn(this.j&2?(!this.k&&(this.k=new ch),this.k).ck():Pw,t),n)},l.zh=function(){return Fi(),Pw},l.Bh=function(t){switch(t){case 0:!this.a&&(this.a=new ds(this,0)),_r(this.a);return;case 1:!this.b&&(this.b=new Nl((on(),oo),wc,this,1)),this.b.c.$b();return;case 2:!this.c&&(this.c=new Nl((on(),oo),wc,this,2)),this.c.c.$b();return;case 3:!this.a&&(this.a=new ds(this,0)),d6(Wc(this.a,(Fi(),dN)));return;case 4:!this.a&&(this.a=new ds(this,0)),d6(Wc(this.a,(Fi(),gN)));return;case 5:!this.a&&(this.a=new ds(this,0)),d6(Wc(this.a,(Fi(),ZS)));return;case 6:!this.a&&(this.a=new ds(this,0)),d6(Wc(this.a,(Fi(),JS)));return}wh(this,t-Jn((Fi(),Pw)),gn(this.j&2?(!this.k&&(this.k=new ch),this.k).ck():Pw,t))},l.Ib=function(){var t;return 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color: ${i.nodeTextColor||i.textColor};
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stroke: ${i.nodeBorder};
stroke-width: 1px;
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.arrowheadPath {
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}
.edgePath .path {
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stroke-width: 2.0px;
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fill: none;
}
.edgeLabel {
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rect {
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background-color: ${i.edgeLabelBackground};
fill: ${i.edgeLabelBackground};
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text-align: center;
}
.cluster rect {
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stroke: ${i.clusterBorder};
stroke-width: 1px;
}
.cluster text {
fill: ${i.titleColor};
}
.cluster span {
color: ${i.titleColor};
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text-align: center;
max-width: 200px;
padding: 2px;
font-family: ${i.fontFamily};
font-size: 12px;
background: ${i.tertiaryColor};
border: 1px solid ${i.border2};
border-radius: 2px;
pointer-events: none;
z-index: 100;
}
.flowchartTitleText {
text-anchor: middle;
font-size: 18px;
fill: ${i.textColor};
}
.subgraph {
stroke-width:2;
rx:3;
}
// .subgraph-lvl-1 {
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// // stroke:black;
// }
.flowchart-label text {
text-anchor: middle;
}
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b=y.append("g"),E=y.append("g"),N=E.append("text").text(a.descr).attr("dy","1em").attr("alignment-baseline","middle").attr("dominant-baseline","middle").attr("text-anchor","middle").call(aje,a.width).node().getBBox(),B=p.fontSize&&p.fontSize.replace?p.fontSize.replace("px",""):p.fontSize;return a.height=N.height+B*1.1*.5+a.padding,a.height=Math.max(a.height,a.maxHeight),a.width=a.width+2*a.padding,E.attr("transform","translate("+a.width/2+", "+a.padding/2+")"),wqt(b,a,w),a},vqt=function(i,a,f){const p=i.append("g"),y=p.append("text").text(a.descr).attr("dy","1em").attr("alignment-baseline","middle").attr("dominant-baseline","middle").attr("text-anchor","middle").call(aje,a.width).node().getBBox(),b=f.fontSize&&f.fontSize.replace?f.fontSize.replace("px",""):f.fontSize;return p.remove(),y.height+b*1.1*.5+a.padding},wqt=function(i,a,f){i.append("path").attr("id","node-"+a.id).attr("class","node-bkg node-"+a.type).attr("d",`M0 ${a.height-5} v${-a.height+2*5} q0,-5 5,-5 h${a.width-2*5} q5,0 5,5 v${a.height-5} H0 Z`),i.append("line").attr("class","node-line-"+f).attr("x1",0).attr("y1",a.height).attr("x2",a.width).attr("y2",a.height)},v9={drawRect:KK,drawCircle:uqt,drawSection:hqt,drawText:rje,drawLabel:lqt,drawTask:fqt,drawBackgroundRect:dqt,getTextObj:gqt,getNoteRect:Oge,initGraphics:pqt,drawNode:bqt,getVirtualNodeHeight:vqt},mqt=function(i,a,f,p){var te,xe,De,he;const w=Tt(),y=w.leftMargin??50;(xe=(te=p.db).clear)==null||xe.call(te),p.parser.parse(i+`
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.section-${f-1} rect, .section-${f-1} path, .section-${f-1} circle, .section-${f-1} path {
fill: ${i["cScale"+f]};
}
.section-${f-1} text {
fill: ${i["cScaleLabel"+f]};
}
.node-icon-${f-1} {
font-size: 40px;
color: ${i["cScaleLabel"+f]};
}
.section-edge-${f-1}{
stroke: ${i["cScale"+f]};
}
.edge-depth-${f-1}{
stroke-width: ${p};
}
.section-${f-1} line {
stroke: ${i["cScaleInv"+f]} ;
stroke-width: 3;
}
.lineWrapper line{
stroke: ${i["cScaleLabel"+f]} ;
}
.disabled, .disabled circle, .disabled text {
fill: lightgray;
}
.disabled text {
fill: #efefef;
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`}return a},Eqt=Object.freeze(Object.defineProperty({__proto__:null,diagram:{db:aqt,renderer:kqt,parser:sqt,styles:i=>`
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stroke-width: 3;
}
${xqt(i)}
.section-root rect, .section-root path, .section-root circle {
fill: ${i.git0};
}
.section-root text {
fill: ${i.gitBranchLabel0};
}
.icon-container {
height:100%;
display: flex;
justify-content: center;
align-items: center;
}
.edge {
fill: none;
}
.eventWrapper {
filter: brightness(120%);
}
`}},Symbol.toStringTag,{value:"Module"}));var Nge=function(){var i=function(ye,ke,Ae,de){for(Ae=Ae||{},de=ye.length;de--;Ae[ye[de]]=ke);return Ae},a=[1,4],f=[1,13],p=[1,12],w=[1,15],y=[1,16],b=[1,20],E=[1,19],S=[6,7,8],N=[1,26],B=[1,24],R=[1,25],j=[6,7,11],$=[1,6,13,15,16,19,22],V=[1,33],Q=[1,34],oe=[1,6,7,11,13,15,16,19,22],ce={trace:function(){},yy:{},symbols_:{error:2,start:3,mindMap:4,spaceLines:5,SPACELINE:6,NL:7,MINDMAP:8,document:9,stop:10,EOF:11,statement:12,SPACELIST:13,node:14,ICON:15,CLASS:16,nodeWithId:17,nodeWithoutId:18,NODE_DSTART:19,NODE_DESCR:20,NODE_DEND:21,NODE_ID:22,$accept:0,$end:1},terminals_:{2:"error",6:"SPACELINE",7:"NL",8:"MINDMAP",11:"EOF",13:"SPACELIST",15:"ICON",16:"CLASS",19:"NODE_DSTART",20:"NODE_DESCR",21:"NODE_DEND",22:"NODE_ID"},productions_:[0,[3,1],[3,2],[5,1],[5,2],[5,2],[4,2],[4,3],[10,1],[10,1],[10,1],[10,2],[10,2],[9,3],[9,2],[12,2],[12,2],[12,2],[12,1],[12,1],[12,1],[12,1],[12,1],[14,1],[14,1],[18,3],[17,1],[17,4]],performAction:function(ke,Ae,de,ve,te,xe,De){var he=xe.length-1;switch(te){case 6:case 7:return ve;case 8:ve.getLogger().trace("Stop NL ");break;case 9:ve.getLogger().trace("Stop EOF ");break;case 11:ve.getLogger().trace("Stop NL2 ");break;case 12:ve.getLogger().trace("Stop EOF2 ");break;case 15:ve.getLogger().info("Node: ",xe[he].id),ve.addNode(xe[he-1].length,xe[he].id,xe[he].descr,xe[he].type);break;case 16:ve.getLogger().trace("Icon: ",xe[he]),ve.decorateNode({icon:xe[he]});break;case 17:case 21:ve.decorateNode({class:xe[he]});break;case 18:ve.getLogger().trace("SPACELIST");break;case 19:ve.getLogger().trace("Node: ",xe[he].id),ve.addNode(0,xe[he].id,xe[he].descr,xe[he].type);break;case 20:ve.decorateNode({icon:xe[he]});break;case 25:ve.getLogger().trace("node found ..",xe[he-2]),this.$={id:xe[he-1],descr:xe[he-1],type:ve.getType(xe[he-2],xe[he])};break;case 26:this.$={id:xe[he],descr:xe[he],type:ve.nodeType.DEFAULT};break;case 27:ve.getLogger().trace("node found ..",xe[he-3]),this.$={id:xe[he-3],descr:xe[he-1],type:ve.getType(xe[he-2],xe[he])};break}},table:[{3:1,4:2,5:3,6:[1,5],8:a},{1:[3]},{1:[2,1]},{4:6,6:[1,7],7:[1,8],8:a},{6:f,7:[1,10],9:9,12:11,13:p,14:14,15:w,16:y,17:17,18:18,19:b,22:E},i(S,[2,3]),{1:[2,2]},i(S,[2,4]),i(S,[2,5]),{1:[2,6],6:f,12:21,13:p,14:14,15:w,16:y,17:17,18:18,19:b,22:E},{6:f,9:22,12:11,13:p,14:14,15:w,16:y,17:17,18:18,19:b,22:E},{6:N,7:B,10:23,11:R},i(j,[2,22],{17:17,18:18,14:27,15:[1,28],16:[1,29],19:b,22:E}),i(j,[2,18]),i(j,[2,19]),i(j,[2,20]),i(j,[2,21]),i(j,[2,23]),i(j,[2,24]),i(j,[2,26],{19:[1,30]}),{20:[1,31]},{6:N,7:B,10:32,11:R},{1:[2,7],6:f,12:21,13:p,14:14,15:w,16:y,17:17,18:18,19:b,22:E},i($,[2,14],{7:V,11:Q}),i(oe,[2,8]),i(oe,[2,9]),i(oe,[2,10]),i(j,[2,15]),i(j,[2,16]),i(j,[2,17]),{20:[1,35]},{21:[1,36]},i($,[2,13],{7:V,11:Q}),i(oe,[2,11]),i(oe,[2,12]),{21:[1,37]},i(j,[2,25]),i(j,[2,27])],defaultActions:{2:[2,1],6:[2,2]},parseError:function(ke,Ae){if(Ae.recoverable)this.trace(ke);else{var de=new Error(ke);throw de.hash=Ae,de}},parse:function(ke){var Ae=this,de=[0],ve=[],te=[null],xe=[],De=this.table,he="",Ie=0,ee=0,rt=2,me=1,gt=xe.slice.call(arguments,1),pe=Object.create(this.lexer),Et={yy:{}};for(var wt in this.yy)Object.prototype.hasOwnProperty.call(this.yy,wt)&&(Et.yy[wt]=this.yy[wt]);pe.setInput(ke,Et.yy),Et.yy.lexer=pe,Et.yy.parser=this,typeof pe.yylloc>"u"&&(pe.yylloc={});var jt=pe.yylloc;xe.push(jt);var At=pe.options&&pe.options.ranges;typeof Et.yy.parseError=="function"?this.parseError=Et.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function Bt(){var Lt;return Lt=ve.pop()||pe.lex()||me,typeof Lt!="number"&&(Lt instanceof Array&&(ve=Lt,Lt=ve.pop()),Lt=Ae.symbols_[Lt]||Lt),Lt}for(var cn,Nn,Ot,oi,kt={},Dt,vt,Nt,ze;;){if(Nn=de[de.length-1],this.defaultActions[Nn]?Ot=this.defaultActions[Nn]:((cn===null||typeof cn>"u")&&(cn=Bt()),Ot=De[Nn]&&De[Nn][cn]),typeof Ot>"u"||!Ot.length||!Ot[0]){var Xe="";ze=[];for(Dt in De[Nn])this.terminals_[Dt]&&Dt>rt&&ze.push("'"+this.terminals_[Dt]+"'");pe.showPosition?Xe="Parse error on line "+(Ie+1)+`:
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E0=function(g){return g===1/0||g===-1/0?0:g},vp=function(g,k,T,A,D){A-k===0||D-T===0||k==null||T==null||A==null||D==null||(g.x1=k<g.x1?k:g.x1,g.x2=A>g.x2?A:g.x2,g.y1=T<g.y1?T:g.y1,g.y2=D>g.y2?D:g.y2,g.w=g.x2-g.x1,g.h=g.y2-g.y1)},k3=function(g,k){return k==null?g:vp(g,k.x1,k.y1,k.x2,k.y2)},Tx=function(g,k,T){return gp(g,k,T)},H9=function(g,k,T){if(!k.cy().headless()){var A=k._private,D=A.rstyle,I=D.arrowWidth/2,F=k.pstyle(T+"-arrow-shape").value,H,C;if(F!=="none"){T==="source"?(H=D.srcX,C=D.srcY):T==="target"?(H=D.tgtX,C=D.tgtY):(H=D.midX,C=D.midY);var G=A.arrowBounds=A.arrowBounds||{},U=G[T]=G[T]||{};U.x1=H-I,U.y1=C-I,U.x2=H+I,U.y2=C+I,U.w=U.x2-U.x1,U.h=U.y2-U.y1,_9(U,1),vp(g,U.x1,U.y1,U.x2,U.y2)}}},YB=function(g,k,T){if(!k.cy().headless()){var A;T?A=T+"-":A="";var D=k._private,I=D.rstyle,F=k.pstyle(A+"label").strValue;if(F){var H=k.pstyle("text-halign"),C=k.pstyle("text-valign"),G=Tx(I,"labelWidth",T),U=Tx(I,"labelHeight",T),Z=Tx(I,"labelX",T),ne=Tx(I,"labelY",T),ae=k.pstyle(A+"text-margin-x").pfValue,ue=k.pstyle(A+"text-margin-y").pfValue,_e=k.isEdge(),be=k.pstyle(A+"text-rotation"),Se=k.pstyle("text-outline-width").pfValue,Le=k.pstyle("text-border-width").pfValue,Be=Le/2,Ke=k.pstyle("text-background-padding").pfValue,qe=2,Re=U,Ze=G,$e=Ze/2,ot=Re/2,ut,Je,Ct,lt;if(_e)ut=Z-$e,Je=Z+$e,Ct=ne-ot,lt=ne+ot;else{switch(H.value){case"left":ut=Z-Ze,Je=Z;break;case"center":ut=Z-$e,Je=Z+$e;break;case"right":ut=Z,Je=Z+Ze;break}switch(C.value){case"top":Ct=ne-Re,lt=ne;break;case"center":Ct=ne-ot,lt=ne+ot;break;case"bottom":Ct=ne,lt=ne+Re;break}}ut+=ae-Math.max(Se,Be)-Ke-qe,Je+=ae+Math.max(Se,Be)+Ke+qe,Ct+=ue-Math.max(Se,Be)-Ke-qe,lt+=ue+Math.max(Se,Be)+Ke+qe;var un=T||"main",Rt=D.labelBounds,$t=Rt[un]=Rt[un]||{};$t.x1=ut,$t.y1=Ct,$t.x2=Je,$t.y2=lt,$t.w=Je-ut,$t.h=lt-Ct;var bn=_e&&be.strValue==="autorotate",Cn=be.pfValue!=null&&be.pfValue!==0;if(bn||Cn){var Kn=bn?Tx(D.rstyle,"labelAngle",T):be.pfValue,kn=Math.cos(Kn),Wn=Math.sin(Kn),sr=(ut+Je)/2,yr=(Ct+lt)/2;if(!_e){switch(H.value){case"left":sr=Je;break;case"right":sr=ut;break}switch(C.value){case"top":yr=lt;break;case"bottom":yr=Ct;break}}var hr=function(Ba,Li){return Ba=Ba-sr,Li=Li-yr,{x:Ba*kn-Li*Wn+sr,y:Ba*Wn+Li*kn+yr}},nr=hr(ut,Ct),fn=hr(ut,lt),vr=hr(Je,Ct),gr=hr(Je,lt);ut=Math.min(nr.x,fn.x,vr.x,gr.x),Je=Math.max(nr.x,fn.x,vr.x,gr.x),Ct=Math.min(nr.y,fn.y,vr.y,gr.y),lt=Math.max(nr.y,fn.y,vr.y,gr.y)}var ni=un+"Rot",Ti=Rt[ni]=Rt[ni]||{};Ti.x1=ut,Ti.y1=Ct,Ti.x2=Je,Ti.y2=lt,Ti.w=Je-ut,Ti.h=lt-Ct,vp(g,ut,Ct,Je,lt),vp(D.labelBounds.all,ut,Ct,Je,lt)}return g}},UZ=function(g,k){var T=g._private.cy,A=T.styleEnabled(),D=T.headless(),I=zd(),F=g._private,H=g.isNode(),C=g.isEdge(),G,U,Z,ne,ae,ue,_e=F.rstyle,be=H&&A?g.pstyle("bounds-expansion").pfValue:[0],Se=function(Ts){return 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Ba=F.overlayBounds=F.overlayBounds||{};KP(Ba,I),kL(Ba,be),_9(Ba,1);var Li=F.labelBounds=F.labelBounds||{};Li.all!=null?TY(Li.all):Li.all=zd(),A&&k.includeLabels&&(k.includeMainLabels&&YB(I,g,null),C&&(k.includeSourceLabels&&YB(I,g,"source"),k.includeTargetLabels&&YB(I,g,"target")))}return I.x1=E0(I.x1),I.y1=E0(I.y1),I.x2=E0(I.x2),I.y2=E0(I.y2),I.w=E0(I.x2-I.x1),I.h=E0(I.y2-I.y1),I.w>0&&I.h>0&&Le&&(kL(I,be),_9(I,1)),I},XB=function(g){var k=0,T=function(I){return(I?1:0)<<k++},A=0;return A+=T(g.incudeNodes),A+=T(g.includeEdges),A+=T(g.includeLabels),A+=T(g.includeMainLabels),A+=T(g.includeSourceLabels),A+=T(g.includeTargetLabels),A+=T(g.includeOverlays),A},QB=function(g){if(g.isEdge()){var k=g.source().position(),T=g.target().position(),A=function(I){return Math.round(I)};return ah([A(k.x),A(k.y),A(T.x),A(T.y)])}else return 0},ZB=function(g,k){var T=g._private,A,D=g.isEdge(),I=k==null?JB:XB(k),F=I===JB,H=QB(g),C=T.bbCachePosKey===H,G=k.useCache&&C,U=function(ue){return ue._private.bbCache==null||ue._private.styleDirty},Z=!G||U(g)||D&&U(g.source())||U(g.target());if(Z?(C||g.recalculateRenderedStyle(G),A=UZ(g,_x),T.bbCache=A,T.bbCachePosKey=H):A=T.bbCache,!F){var ne=g.isNode();A=zd(),(k.includeNodes&&ne||k.includeEdges&&!ne)&&(k.includeOverlays?k3(A,T.overlayBounds):k3(A,T.bodyBounds)),k.includeLabels&&(k.includeMainLabels&&(!D||k.includeSourceLabels&&k.includeTargetLabels)?k3(A,T.labelBounds.all):(k.includeMainLabels&&k3(A,T.labelBounds.mainRot),k.includeSourceLabels&&k3(A,T.labelBounds.sourceRot),k.includeTargetLabels&&k3(A,T.labelBounds.targetRot))),A.w=A.x2-A.x1,A.h=A.y2-A.y1}return A},_x={includeNodes:!0,includeEdges:!0,includeLabels:!0,includeMainLabels:!0,includeSourceLabels:!0,includeTargetLabels:!0,includeOverlays:!0,includeUnderlays:!0,useCache:!0},JB=XB(_x),eR=Vf(_x);F2.boundingBox=function(m){var g;if(this.length===1&&this[0]._private.bbCache!=null&&!this[0]._private.styleDirty&&(m===void 0||m.useCache===void 0||m.useCache===!0))m===void 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g=this.nodes(),k=this.cy(),T=k.hasCompoundNodes(),A=k.collection();if(T&&(A=g.filter(function(C){return C.isParent()}),g=g.not(A)),de(m)){var D=m;m=function(){return D}}var I=function(G,U){return G._private.bbAtOldPos=m(G,U)},F=function(G){return G._private.bbAtOldPos};k.startBatch(),g.forEach(I).silentPositions(m),T&&(A.dirtyCompoundBoundsCache(),A.dirtyBoundingBoxCache(),A.updateCompoundBounds(!0));var H=EY(this.boundingBox({useCache:!1}));return g.silentPositions(F),T&&(A.dirtyCompoundBoundsCache(),A.dirtyBoundingBoxCache(),A.updateCompoundBounds(!0)),k.endBatch(),H},Z5.boundingbox=Z5.bb=Z5.boundingBox,Z5.renderedBoundingbox=Z5.renderedBoundingBox;var WZ=F2,Cx,Sx;Cx=Sx={};var gm=function(g){g.uppercaseName=oi(g.name),g.autoName="auto"+g.uppercaseName,g.labelName="label"+g.uppercaseName,g.outerName="outer"+g.uppercaseName,g.uppercaseOuterName=oi(g.outerName),Cx[g.name]=function(){var T=this[0],A=T._private,D=A.cy,I=D._private.styleEnabled;if(T)if(I){if(T.isParent())return T.updateCompoundBounds(),A[g.autoName]||0;var F=T.pstyle(g.name);switch(F.strValue){case"label":return T.recalculateRenderedStyle(),A.rstyle[g.labelName]||0;default:return F.pfValue}}else return 1},Cx["outer"+g.uppercaseName]=function(){var T=this[0],A=T._private,D=A.cy,I=D._private.styleEnabled;if(T)if(I){var F=T[g.name](),H=T.pstyle("border-width").pfValue,C=2*T.padding();return F+H+C}else return 1},Cx["rendered"+g.uppercaseName]=function(){var T=this[0];if(T){var A=T[g.name]();return A*this.cy().zoom()}},Cx["rendered"+g.uppercaseOuterName]=function(){var T=this[0];if(T){var A=T[g.outerName]();return A*this.cy().zoom()}}};gm({name:"width"}),gm({name:"height"}),Sx.padding=function(){var m=this[0],g=m._private;return m.isParent()?(m.updateCompoundBounds(),g.autoPadding!==void 0?g.autoPadding:m.pstyle("padding").pfValue):m.pstyle("padding").pfValue},Sx.paddedHeight=function(){var m=this[0];return m.height()+2*m.padding()},Sx.paddedWidth=function(){var m=this[0];return m.width()+2*m.padding()};var RL=Sx,KZ=function(g,k){if(g.isEdge())return k(g)},YZ=function(g,k){if(g.isEdge()){var T=g.cy();return E9(k(g),T.zoom(),T.pan())}},XZ=function(g,k){if(g.isEdge()){var T=g.cy(),A=T.pan(),D=T.zoom();return k(g).map(function(I){return E9(I,D,A)})}},QZ=function(g){return g.renderer().getControlPoints(g)},ZZ=function(g){return g.renderer().getSegmentPoints(g)},JZ=function(g){return g.renderer().getSourceEndpoint(g)},eJ=function(g){return g.renderer().getTargetEndpoint(g)},tJ=function(g){return g.renderer().getEdgeMidpoint(g)},tR={controlPoints:{get:QZ,mult:!0},segmentPoints:{get:ZZ,mult:!0},sourceEndpoint:{get:JZ},targetEndpoint:{get:eJ},midpoint:{get:tJ}},nJ=function(g){return"rendered"+g[0].toUpperCase()+g.substr(1)},rJ=Object.keys(tR).reduce(function(m,g){var k=tR[g],T=nJ(g);return m[g]=function(){return KZ(this,k.get)},k.mult?m[T]=function(){return XZ(this,k.get)}:m[T]=function(){return YZ(this,k.get)},m},{}),iJ=Oe({},KB,WZ,RL,rJ);/*!
Event object based on jQuery events, MIT license
https://jquery.org/license/
https://tldrlegal.com/license/mit-license
https://github.com/jquery/jquery/blob/master/src/event.js
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A=pi(),D=function(){RJ(T,g,m),m.fit===!0&&g.fit(m.padding)},I=function(Z){return!(k.stopped||Z>=m.numIter||(FJ(T,m),T.temperature=T.temperature*m.coolingFactor,T.temperature<m.minTemp))},F=function(){if(m.animate===!0||m.animate===!1)D(),k.one("layoutstop",m.stop),k.emit({type:"layoutstop",layout:k});else{var Z=m.eles.nodes(),ne=_R(T,m,Z);Z.layoutPositions(k,m,ne)}},H=0,C=!0;if(m.animate===!0){var G=function U(){for(var Z=0;C&&Z<m.refresh;)C=I(H),H++,Z++;if(!C)SR(T,m),F();else{var ne=pi();ne-A>=m.animationThreshold&&D(),Aa(U)}};G()}else{for(;C;)C=I(H),H++;SR(T,m),F()}return this},rT.prototype.stop=function(){return this.stopped=!0,this.thread&&this.thread.stop(),this.emit("layoutstop"),this},rT.prototype.destroy=function(){return this.thread&&this.thread.stop(),this};var IJ=function(g,k,T){for(var A=T.eles.edges(),D=T.eles.nodes(),I={isCompound:g.hasCompoundNodes(),layoutNodes:[],idToIndex:{},nodeSize:D.size(),graphSet:[],indexToGraph:[],layoutEdges:[],edgeSize:A.size(),temperature:T.initialTemp,clientWidth:g.width(),clientHeight:g.width(),boundingBox:zd(T.boundingBox?T.boundingBox:{x1:0,y1:0,w:g.width(),h:g.height()})},F=T.eles.components(),H={},C=0;C<F.length;C++)for(var G=F[C],U=0;U<G.length;U++){var Z=G[U];H[Z.id()]=C}for(var C=0;C<I.nodeSize;C++){var ne=D[C],ae=ne.layoutDimensions(T),ue={};ue.isLocked=ne.locked(),ue.id=ne.data("id"),ue.parentId=ne.data("parent"),ue.cmptId=H[ne.id()],ue.children=[],ue.positionX=ne.position("x"),ue.positionY=ne.position("y"),ue.offsetX=0,ue.offsetY=0,ue.height=ae.w,ue.width=ae.h,ue.maxX=ue.positionX+ue.width/2,ue.minX=ue.positionX-ue.width/2,ue.maxY=ue.positionY+ue.height/2,ue.minY=ue.positionY-ue.height/2,ue.padLeft=parseFloat(ne.style("padding")),ue.padRight=parseFloat(ne.style("padding")),ue.padTop=parseFloat(ne.style("padding")),ue.padBottom=parseFloat(ne.style("padding")),ue.nodeRepulsion=ke(T.nodeRepulsion)?T.nodeRepulsion(ne):T.nodeRepulsion,I.layoutNodes.push(ue),I.idToIndex[ue.id]=C}for(var _e=[],be=0,Se=-1,Le=[],C=0;C<I.nodeSize;C++){var ne=I.layoutNodes[C],Be=ne.parentId;Be!=null?I.layoutNodes[I.idToIndex[Be]].children.push(ne.id):(_e[++Se]=ne.id,Le.push(ne.id))}for(I.graphSet.push(Le);be<=Se;){var Ke=_e[be++],qe=I.idToIndex[Ke],Z=I.layoutNodes[qe],Re=Z.children;if(Re.length>0){I.graphSet.push(Re);for(var C=0;C<Re.length;C++)_e[++Se]=Re[C]}}for(var C=0;C<I.graphSet.length;C++)for(var Ze=I.graphSet[C],U=0;U<Ze.length;U++){var $e=I.idToIndex[Ze[U]];I.indexToGraph[$e]=C}for(var C=0;C<I.edgeSize;C++){var ot=A[C],ut={};ut.id=ot.data("id"),ut.sourceId=ot.data("source"),ut.targetId=ot.data("target");var Je=ke(T.idealEdgeLength)?T.idealEdgeLength(ot):T.idealEdgeLength,Ct=ke(T.edgeElasticity)?T.edgeElasticity(ot):T.edgeElasticity,lt=I.idToIndex[ut.sourceId],un=I.idToIndex[ut.targetId],Rt=I.indexToGraph[lt],$t=I.indexToGraph[un];if(Rt!=$t){for(var bn=OJ(ut.sourceId,ut.targetId,I),Cn=I.graphSet[bn],Kn=0,ue=I.layoutNodes[lt];Cn.indexOf(ue.id)===-1;)ue=I.layoutNodes[I.idToIndex[ue.parentId]],Kn++;for(ue=I.layoutNodes[un];Cn.indexOf(ue.id)===-1;)ue=I.layoutNodes[I.idToIndex[ue.parentId]],Kn++;Je*=Kn*T.nestingFactor}ut.idealLength=Je,ut.elasticity=Ct,I.layoutEdges.push(ut)}return 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Rt=$e.allpts,$t=0;$t+3<Rt.length;$t+=2)if(AY(m,g,Rt[$t],Rt[$t+1],Rt[$t+2],Rt[$t+3],lt)&&Ct>(un=OY(m,g,Rt[$t],Rt[$t+1],Rt[$t+2],Rt[$t+3])))return _e(Re,un),!0}else if($e.edgeType==="bezier"||$e.edgeType==="multibezier"||$e.edgeType==="self"||$e.edgeType==="compound"){for(var Rt=$e.allpts,$t=0;$t+5<$e.allpts.length;$t+=4)if(LY(m,g,Rt[$t],Rt[$t+1],Rt[$t+2],Rt[$t+3],Rt[$t+4],Rt[$t+5],lt)&&Ct>(un=IY(m,g,Rt[$t],Rt[$t+1],Rt[$t+2],Rt[$t+3],Rt[$t+4],Rt[$t+5])))return _e(Re,un),!0}for(var bn=bn||Ze.source,Cn=Cn||Ze.target,Kn=A.getArrowWidth(ot,ut),kn=[{name:"source",x:$e.arrowStartX,y:$e.arrowStartY,angle:$e.srcArrowAngle},{name:"target",x:$e.arrowEndX,y:$e.arrowEndY,angle:$e.tgtArrowAngle},{name:"mid-source",x:$e.midX,y:$e.midY,angle:$e.midsrcArrowAngle},{name:"mid-target",x:$e.midX,y:$e.midY,angle:$e.midtgtArrowAngle}],$t=0;$t<kn.length;$t++){var Wn=kn[$t],sr=D.arrowShapes[Re.pstyle(Wn.name+"-arrow-shape").value],yr=Re.pstyle("width").pfValue;if(sr.roughCollide(m,g,Kn,Wn.angle,{x:Wn.x,y:Wn.y},yr,G)&&sr.collide(m,g,Kn,Wn.angle,{x:Wn.x,y:Wn.y},yr,G))return _e(Re),!0}C&&F.length>0&&(be(bn),be(Cn))}function Le(Re,Ze,$e){return gp(Re,Ze,$e)}function Be(Re,Ze){var $e=Re._private,ot=Z,ut;Ze?ut=Ze+"-":ut="",Re.boundingBox();var Je=$e.labelBounds[Ze||"main"],Ct=Re.pstyle(ut+"label").value,lt=Re.pstyle("text-events").strValue==="yes";if(!(!lt||!Ct)){var un=Le($e.rscratch,"labelX",Ze),Rt=Le($e.rscratch,"labelY",Ze),$t=Le($e.rscratch,"labelAngle",Ze),bn=Re.pstyle(ut+"text-margin-x").pfValue,Cn=Re.pstyle(ut+"text-margin-y").pfValue,Kn=Je.x1-ot-bn,kn=Je.x2+ot-bn,Wn=Je.y1-ot-Cn,sr=Je.y2+ot-Cn;if($t){var yr=Math.cos($t),hr=Math.sin($t),nr=function(Ba,Li){return Ba=Ba-un,Li=Li-Rt,{x:Ba*yr-Li*hr+un,y:Ba*hr+Li*yr+Rt}},fn=nr(Kn,Wn),vr=nr(Kn,sr),gr=nr(kn,Wn),ni=nr(kn,sr),Ti=[fn.x+bn,fn.y+Cn,gr.x+bn,gr.y+Cn,ni.x+bn,ni.y+Cn,vr.x+bn,vr.y+Cn];if(Gd(m,g,Ti))return _e(Re),!0}else if($5(Je,m,g))return _e(Re),!0}}for(var Ke=I.length-1;Ke>=0;Ke--){var qe=I[Ke];qe.isNode()?be(qe)||Be(qe):Se(qe)||Be(qe)||Be(qe,"source")||Be(qe,"target")}return F},E3.getAllInBox=function(m,g,k,T){var A=this.getCachedZSortedEles().interactive,D=[],I=Math.min(m,k),F=Math.max(m,k),H=Math.min(g,T),C=Math.max(g,T);m=I,k=F,g=H,T=C;for(var G=zd({x1:m,y1:g,x2:k,y2:T}),U=0;U<A.length;U++){var Z=A[U];if(Z.isNode()){var ne=Z,ae=ne.boundingBox({includeNodes:!0,includeEdges:!1,includeLabels:!1});xL(G,ae)&&!YP(ae,G)&&D.push(ne)}else{var ue=Z,_e=ue._private,be=_e.rscratch;if(be.startX!=null&&be.startY!=null&&!$5(G,be.startX,be.startY)||be.endX!=null&&be.endY!=null&&!$5(G,be.endX,be.endY))continue;if(be.edgeType==="bezier"||be.edgeType==="multibezier"||be.edgeType==="self"||be.edgeType==="compound"||be.edgeType==="segments"||be.edgeType==="haystack"){for(var Se=_e.rstyle.bezierPts||_e.rstyle.linePts||_e.rstyle.haystackPts,Le=!0,Be=0;Be<Se.length;Be++)if(!SY(G,Se[Be])){Le=!1;break}Le&&D.push(ue)}else(be.edgeType==="haystack"||be.edgeType==="straight")&&D.push(ue)}}return D};var sT={};sT.calculateArrowAngles=function(m){var g=m._private.rscratch,k=g.edgeType==="haystack",T=g.edgeType==="bezier",A=g.edgeType==="multibezier",D=g.edgeType==="segments",I=g.edgeType==="compound",F=g.edgeType==="self",H,C,G,U,Z,ne,be,Se;if(k?(G=g.haystackPts[0],U=g.haystackPts[1],Z=g.haystackPts[2],ne=g.haystackPts[3]):(G=g.arrowStartX,U=g.arrowStartY,Z=g.arrowEndX,ne=g.arrowEndY),be=g.midX,Se=g.midY,D)H=G-g.segpts[0],C=U-g.segpts[1];else if(A||I||F||T){var ae=g.allpts,ue=df(ae[0],ae[2],ae[4],.1),_e=df(ae[1],ae[3],ae[5],.1);H=G-ue,C=U-_e}else H=G-be,C=U-Se;g.srcArrowAngle=T9(H,C);var be=g.midX,Se=g.midY;if(k&&(be=(G+Z)/2,Se=(U+ne)/2),H=Z-G,C=ne-U,D){var ae=g.allpts;if(ae.length/2%2===0){var Le=ae.length/2,Be=Le-2;H=ae[Le]-ae[Be],C=ae[Le+1]-ae[Be+1]}else{var Le=ae.length/2-1,Be=Le-2,Ke=Le+2;H=ae[Le]-ae[Be],C=ae[Le+1]-ae[Be+1]}}else if(A||I||F){var ae=g.allpts,qe=g.ctrlpts,Re,Ze,$e,ot;if(qe.length/2%2===0){var ut=ae.length/2-1,Je=ut+2,Ct=Je+2;Re=df(ae[ut],ae[Je],ae[Ct],0),Ze=df(ae[ut+1],ae[Je+1],ae[Ct+1],0),$e=df(ae[ut],ae[Je],ae[Ct],1e-4),ot=df(ae[ut+1],ae[Je+1],ae[Ct+1],1e-4)}else{var Je=ae.length/2-1,ut=Je-2,Ct=Je+2;Re=df(ae[ut],ae[Je],ae[Ct],.4999),Ze=df(ae[ut+1],ae[Je+1],ae[Ct+1],.4999),$e=df(ae[ut],ae[Je],ae[Ct],.5),ot=df(ae[ut+1],ae[Je+1],ae[Ct+1],.5)}H=$e-Re,C=ot-Ze}if(g.midtgtArrowAngle=T9(H,C),g.midDispX=H,g.midDispY=C,H*=-1,C*=-1,D){var ae=g.allpts;if(ae.length/2%2!==0){var Le=ae.length/2-1,Ke=Le+2;H=-(ae[Ke]-ae[Le]),C=-(ae[Ke+1]-ae[Le+1])}}if(g.midsrcArrowAngle=T9(H,C),D)H=Z-g.segpts[g.segpts.length-2],C=ne-g.segpts[g.segpts.length-1];else if(A||I||F||T){var ae=g.allpts,lt=ae.length,ue=df(ae[lt-6],ae[lt-4],ae[lt-2],.9),_e=df(ae[lt-5],ae[lt-3],ae[lt-1],.9);H=Z-ue,C=ne-_e}else H=Z-be,C=ne-Se;g.tgtArrowAngle=T9(H,C)},sT.getArrowWidth=sT.getArrowHeight=function(m,g){var k=this.arrowWidthCache=this.arrowWidthCache||{},T=k[m+", "+g];return T||(T=Math.max(Math.pow(m*13.37,.9),29)*g,k[m+", "+g]=T,T)};var hd={};hd.findHaystackPoints=function(m){for(var g=0;g<m.length;g++){var k=m[g],T=k._private,A=T.rscratch;if(!A.haystack){var D=Math.random()*2*Math.PI;A.source={x:Math.cos(D),y:Math.sin(D)},D=Math.random()*2*Math.PI,A.target={x:Math.cos(D),y:Math.sin(D)}}var I=T.source,F=T.target,H=I.position(),C=F.position(),G=I.width(),U=F.width(),Z=I.height(),ne=F.height(),ae=k.pstyle("haystack-radius").value,ue=ae/2;A.haystackPts=A.allpts=[A.source.x*G*ue+H.x,A.source.y*Z*ue+H.y,A.target.x*U*ue+C.x,A.target.y*ne*ue+C.y],A.midX=(A.allpts[0]+A.allpts[2])/2,A.midY=(A.allpts[1]+A.allpts[3])/2,A.edgeType="haystack",A.haystack=!0,this.storeEdgeProjections(k),this.calculateArrowAngles(k),this.recalculateEdgeLabelProjections(k),this.calculateLabelAngles(k)}},hd.findSegmentsPoints=function(m,g){var k=m._private.rscratch,T=g.posPts,A=g.intersectionPts,D=g.vectorNormInverse,I=m.pstyle("edge-distances").value,F=m.pstyle("segment-weights"),H=m.pstyle("segment-distances"),C=Math.min(F.pfValue.length,H.pfValue.length);k.edgeType="segments",k.segpts=[];for(var G=0;G<C;G++){var U=F.pfValue[G],Z=H.pfValue[G],ne=1-U,ae=U,ue=I==="node-position"?T:A,_e={x:ue.x1*ne+ue.x2*ae,y:ue.y1*ne+ue.y2*ae};k.segpts.push(_e.x+D.x*Z,_e.y+D.y*Z)}},hd.findLoopPoints=function(m,g,k,T){var A=m._private.rscratch,D=g.dirCounts,I=g.srcPos,F=m.pstyle("control-point-distances"),H=F?F.pfValue[0]:void 0,C=m.pstyle("loop-direction").pfValue,G=m.pstyle("loop-sweep").pfValue,U=m.pstyle("control-point-step-size").pfValue;A.edgeType="self";var Z=k,ne=U;T&&(Z=0,ne=H);var ae=C-Math.PI/2,ue=ae-G/2,_e=ae+G/2,be=C+"_"+G;Z=D[be]===void 0?D[be]=0:++D[be],A.ctrlpts=[I.x+Math.cos(ue)*1.4*ne*(Z/3+1),I.y+Math.sin(ue)*1.4*ne*(Z/3+1),I.x+Math.cos(_e)*1.4*ne*(Z/3+1),I.y+Math.sin(_e)*1.4*ne*(Z/3+1)]},hd.findCompoundLoopPoints=function(m,g,k,T){var A=m._private.rscratch;A.edgeType="compound";var D=g.srcPos,I=g.tgtPos,F=g.srcW,H=g.srcH,C=g.tgtW,G=g.tgtH,U=m.pstyle("control-point-step-size").pfValue,Z=m.pstyle("control-point-distances"),ne=Z?Z.pfValue[0]:void 0,ae=k,ue=U;T&&(ae=0,ue=ne);var _e=50,be={x:D.x-F/2,y:D.y-H/2},Se={x:I.x-C/2,y:I.y-G/2},Le={x:Math.min(be.x,Se.x),y:Math.min(be.y,Se.y)},Be=.5,Ke=Math.max(Be,Math.log(F*.01)),qe=Math.max(Be,Math.log(C*.01));A.ctrlpts=[Le.x,Le.y-(1+Math.pow(_e,1.12)/100)*ue*(ae/3+1)*Ke,Le.x-(1+Math.pow(_e,1.12)/100)*ue*(ae/3+1)*qe,Le.y]},hd.findStraightEdgePoints=function(m){m._private.rscratch.edgeType="straight"},hd.findBezierPoints=function(m,g,k,T,A){var D=m._private.rscratch,I=g.vectorNormInverse,F=g.posPts,H=g.intersectionPts,C=m.pstyle("edge-distances").value,G=m.pstyle("control-point-step-size").pfValue,U=m.pstyle("control-point-distances"),Z=m.pstyle("control-point-weights"),ne=U&&Z?Math.min(U.value.length,Z.value.length):1,ae=U?U.pfValue[0]:void 0,ue=Z.value[0],_e=T;D.edgeType=_e?"multibezier":"bezier",D.ctrlpts=[];for(var be=0;be<ne;be++){var Se=(.5-g.eles.length/2+k)*G*(A?-1:1),Le=void 0,Be=WP(Se);_e&&(ae=U?U.pfValue[be]:G,ue=Z.value[be]),T?Le=ae:Le=ae!==void 0?Be*ae:void 0;var Ke=Le!==void 0?Le:Se,qe=1-ue,Re=ue,Ze=C==="node-position"?F:H,$e={x:Ze.x1*qe+Ze.x2*Re,y:Ze.y1*qe+Ze.y2*Re};D.ctrlpts.push($e.x+I.x*Ke,$e.y+I.y*Ke)}},hd.findTaxiPoints=function(m,g){var k=m._private.rscratch;k.edgeType="segments";var T="vertical",A="horizontal",D="leftward",I="rightward",F="downward",H="upward",C="auto",G=g.posPts,U=g.srcW,Z=g.srcH,ne=g.tgtW,ae=g.tgtH,ue=m.pstyle("edge-distances").value,_e=ue!=="node-position",be=m.pstyle("taxi-direction").value,Se=be,Le=m.pstyle("taxi-turn"),Be=Le.units==="%",Ke=Le.pfValue,qe=Ke<0,Re=m.pstyle("taxi-turn-min-distance").pfValue,Ze=_e?(U+ne)/2:0,$e=_e?(Z+ae)/2:0,ot=G.x2-G.x1,ut=G.y2-G.y1,Je=function(is,Uo){return is>0?Math.max(is-Uo,0):Math.min(is+Uo,0)},Ct=Je(ot,Ze),lt=Je(ut,$e),un=!1;Se===C?be=Math.abs(Ct)>Math.abs(lt)?A:T:Se===H||Se===F?(be=T,un=!0):(Se===D||Se===I)&&(be=A,un=!0);var Rt=be===T,$t=Rt?lt:Ct,bn=Rt?ut:ot,Cn=WP(bn),Kn=!1;!(un&&(Be||qe))&&(Se===F&&bn<0||Se===H&&bn>0||Se===D&&bn>0||Se===I&&bn<0)&&(Cn*=-1,$t=Cn*Math.abs($t),Kn=!0);var kn;if(Be){var Wn=Ke<0?1+Ke:Ke;kn=Wn*$t}else{var sr=Ke<0?$t:0;kn=sr+Ke*Cn}var yr=function(is){return Math.abs(is)<Re||Math.abs(is)>=Math.abs($t)},hr=yr(kn),nr=yr(Math.abs($t)-Math.abs(kn)),fn=hr||nr;if(fn&&!Kn)if(Rt){var vr=Math.abs(bn)<=Z/2,gr=Math.abs(ot)<=ne/2;if(vr){var ni=(G.x1+G.x2)/2,Ti=G.y1,ia=G.y2;k.segpts=[ni,Ti,ni,ia]}else if(gr){var Ba=(G.y1+G.y2)/2,Li=G.x1,wi=G.x2;k.segpts=[Li,Ba,wi,Ba]}else k.segpts=[G.x1,G.y2]}else{var Ts=Math.abs(bn)<=U/2,Yi=Math.abs(ut)<=ae/2;if(Ts){var Di=(G.y1+G.y2)/2,es=G.x1,eo=G.x2;k.segpts=[es,Di,eo,Di]}else if(Yi){var sa=(G.x1+G.x2)/2,Ks=G.y1,Ar=G.y2;k.segpts=[sa,Ks,sa,Ar]}else k.segpts=[G.x2,G.y1]}else if(Rt){var We=G.y1+kn+(_e?Z/2*Cn:0),On=G.x1,Ir=G.x2;k.segpts=[On,We,Ir,We]}else{var ar=G.x1+kn+(_e?U/2*Cn:0),or=G.y1,qa=G.y2;k.segpts=[ar,or,ar,qa]}},hd.tryToCorrectInvalidPoints=function(m,g){var k=m._private.rscratch;if(k.edgeType==="bezier"){var T=g.srcPos,A=g.tgtPos,D=g.srcW,I=g.srcH,F=g.tgtW,H=g.tgtH,C=g.srcShape,G=g.tgtShape,U=!te(k.startX)||!te(k.startY),Z=!te(k.arrowStartX)||!te(k.arrowStartY),ne=!te(k.endX)||!te(k.endY),ae=!te(k.arrowEndX)||!te(k.arrowEndY),ue=3,_e=this.getArrowWidth(m.pstyle("width").pfValue,m.pstyle("arrow-scale").value)*this.arrowShapeWidth,be=ue*_e,Se=v3({x:k.ctrlpts[0],y:k.ctrlpts[1]},{x:k.startX,y:k.startY}),Le=Se<be,Be=v3({x:k.ctrlpts[0],y:k.ctrlpts[1]},{x:k.endX,y:k.endY}),Ke=Be<be,qe=!1;if(U||Z||Le){qe=!0;var Re={x:k.ctrlpts[0]-T.x,y:k.ctrlpts[1]-T.y},Ze=Math.sqrt(Re.x*Re.x+Re.y*Re.y),$e={x:Re.x/Ze,y:Re.y/Ze},ot=Math.max(D,I),ut={x:k.ctrlpts[0]+$e.x*2*ot,y:k.ctrlpts[1]+$e.y*2*ot},Je=C.intersectLine(T.x,T.y,D,I,ut.x,ut.y,0);Le?(k.ctrlpts[0]=k.ctrlpts[0]+$e.x*(be-Se),k.ctrlpts[1]=k.ctrlpts[1]+$e.y*(be-Se)):(k.ctrlpts[0]=Je[0]+$e.x*be,k.ctrlpts[1]=Je[1]+$e.y*be)}if(ne||ae||Ke){qe=!0;var Ct={x:k.ctrlpts[0]-A.x,y:k.ctrlpts[1]-A.y},lt=Math.sqrt(Ct.x*Ct.x+Ct.y*Ct.y),un={x:Ct.x/lt,y:Ct.y/lt},Rt=Math.max(D,I),$t={x:k.ctrlpts[0]+un.x*2*Rt,y:k.ctrlpts[1]+un.y*2*Rt},bn=G.intersectLine(A.x,A.y,F,H,$t.x,$t.y,0);Ke?(k.ctrlpts[0]=k.ctrlpts[0]+un.x*(be-Be),k.ctrlpts[1]=k.ctrlpts[1]+un.y*(be-Be)):(k.ctrlpts[0]=bn[0]+un.x*be,k.ctrlpts[1]=bn[1]+un.y*be)}qe&&this.findEndpoints(m)}},hd.storeAllpts=function(m){var g=m._private.rscratch;if(g.edgeType==="multibezier"||g.edgeType==="bezier"||g.edgeType==="self"||g.edgeType==="compound"){g.allpts=[],g.allpts.push(g.startX,g.startY);for(var k=0;k+1<g.ctrlpts.length;k+=2)g.allpts.push(g.ctrlpts[k],g.ctrlpts[k+1]),k+3<g.ctrlpts.length&&g.allpts.push((g.ctrlpts[k]+g.ctrlpts[k+2])/2,(g.ctrlpts[k+1]+g.ctrlpts[k+3])/2);g.allpts.push(g.endX,g.endY);var T,A;g.ctrlpts.length/2%2===0?(T=g.allpts.length/2-1,g.midX=g.allpts[T],g.midY=g.allpts[T+1]):(T=g.allpts.length/2-3,A=.5,g.midX=df(g.allpts[T],g.allpts[T+2],g.allpts[T+4],A),g.midY=df(g.allpts[T+1],g.allpts[T+3],g.allpts[T+5],A))}else if(g.edgeType==="straight")g.allpts=[g.startX,g.startY,g.endX,g.endY],g.midX=(g.startX+g.endX+g.arrowStartX+g.arrowEndX)/4,g.midY=(g.startY+g.endY+g.arrowStartY+g.arrowEndY)/4;else if(g.edgeType==="segments")if(g.allpts=[],g.allpts.push(g.startX,g.startY),g.allpts.push.apply(g.allpts,g.segpts),g.allpts.push(g.endX,g.endY),g.segpts.length%4===0){var D=g.segpts.length/2,I=D-2;g.midX=(g.segpts[I]+g.segpts[D])/2,g.midY=(g.segpts[I+1]+g.segpts[D+1])/2}else{var F=g.segpts.length/2-1;g.midX=g.segpts[F],g.midY=g.segpts[F+1]}},hd.checkForInvalidEdgeWarning=function(m){var g=m[0]._private.rscratch;g.nodesOverlap||te(g.startX)&&te(g.startY)&&te(g.endX)&&te(g.endY)?g.loggedErr=!1:g.loggedErr||(g.loggedErr=!0,Vo("Edge `"+m.id()+"` has invalid endpoints and so it is impossible to draw. Adjust your edge style (e.g. control points) accordingly or use an alternative edge type. This is expected behaviour when the source node and the target node overlap."))},hd.findEdgeControlPoints=function(m){var g=this;if(!(!m||m.length===0)){for(var k=this,T=k.cy,A=T.hasCompoundNodes(),D={map:new R2,get:function(Re){var Ze=this.map.get(Re[0]);return Ze!=null?Ze.get(Re[1]):null},set:function(Re,Ze){var $e=this.map.get(Re[0]);$e==null&&($e=new R2,this.map.set(Re[0],$e)),$e.set(Re[1],Ze)}},I=[],F=[],H=0;H<m.length;H++){var C=m[H],G=C._private,U=C.pstyle("curve-style").value;if(!(C.removed()||!C.takesUpSpace())){if(U==="haystack"){F.push(C);continue}var Z=U==="unbundled-bezier"||U==="segments"||U==="straight"||U==="straight-triangle"||U==="taxi",ne=U==="unbundled-bezier"||U==="bezier",ae=G.source,ue=G.target,_e=ae.poolIndex(),be=ue.poolIndex(),Se=[_e,be].sort(),Le=D.get(Se);Le==null&&(Le={eles:[]},D.set(Se,Le),I.push(Se)),Le.eles.push(C),Z&&(Le.hasUnbundled=!0),ne&&(Le.hasBezier=!0)}}for(var Be=function(Re){var Ze=I[Re],$e=D.get(Ze),ot=void 0;if(!$e.hasUnbundled){var ut=$e.eles[0].parallelEdges().filter(function(Ar){return Ar.isBundledBezier()});wL($e.eles),ut.forEach(function(Ar){return $e.eles.push(Ar)}),$e.eles.sort(function(Ar,We){return Ar.poolIndex()-We.poolIndex()})}var Je=$e.eles[0],Ct=Je.source(),lt=Je.target();if(Ct.poolIndex()>lt.poolIndex()){var un=Ct;Ct=lt,lt=un}var Rt=$e.srcPos=Ct.position(),$t=$e.tgtPos=lt.position(),bn=$e.srcW=Ct.outerWidth(),Cn=$e.srcH=Ct.outerHeight(),Kn=$e.tgtW=lt.outerWidth(),kn=$e.tgtH=lt.outerHeight(),Wn=$e.srcShape=k.nodeShapes[g.getNodeShape(Ct)],sr=$e.tgtShape=k.nodeShapes[g.getNodeShape(lt)];$e.dirCounts={north:0,west:0,south:0,east:0,northwest:0,southwest:0,northeast:0,southeast:0};for(var yr=0;yr<$e.eles.length;yr++){var hr=$e.eles[yr],nr=hr[0]._private.rscratch,fn=hr.pstyle("curve-style").value,vr=fn==="unbundled-bezier"||fn==="segments"||fn==="taxi",gr=!Ct.same(hr.source());if(!$e.calculatedIntersection&&Ct!==lt&&($e.hasBezier||$e.hasUnbundled)){$e.calculatedIntersection=!0;var ni=Wn.intersectLine(Rt.x,Rt.y,bn,Cn,$t.x,$t.y,0),Ti=$e.srcIntn=ni,ia=sr.intersectLine($t.x,$t.y,Kn,kn,Rt.x,Rt.y,0),Ba=$e.tgtIntn=ia,Li=$e.intersectionPts={x1:ni[0],x2:ia[0],y1:ni[1],y2:ia[1]},wi=$e.posPts={x1:Rt.x,x2:$t.x,y1:Rt.y,y2:$t.y},Ts=ia[1]-ni[1],Yi=ia[0]-ni[0],Di=Math.sqrt(Yi*Yi+Ts*Ts),es=$e.vector={x:Yi,y:Ts},eo=$e.vectorNorm={x:es.x/Di,y:es.y/Di},sa={x:-eo.y,y:eo.x};$e.nodesOverlap=!te(Di)||sr.checkPoint(ni[0],ni[1],0,Kn,kn,$t.x,$t.y)||Wn.checkPoint(ia[0],ia[1],0,bn,Cn,Rt.x,Rt.y),$e.vectorNormInverse=sa,ot={nodesOverlap:$e.nodesOverlap,dirCounts:$e.dirCounts,calculatedIntersection:!0,hasBezier:$e.hasBezier,hasUnbundled:$e.hasUnbundled,eles:$e.eles,srcPos:$t,tgtPos:Rt,srcW:Kn,srcH:kn,tgtW:bn,tgtH:Cn,srcIntn:Ba,tgtIntn:Ti,srcShape:sr,tgtShape:Wn,posPts:{x1:wi.x2,y1:wi.y2,x2:wi.x1,y2:wi.y1},intersectionPts:{x1:Li.x2,y1:Li.y2,x2:Li.x1,y2:Li.y1},vector:{x:-es.x,y:-es.y},vectorNorm:{x:-eo.x,y:-eo.y},vectorNormInverse:{x:-sa.x,y:-sa.y}}}var Ks=gr?ot:$e;nr.nodesOverlap=Ks.nodesOverlap,nr.srcIntn=Ks.srcIntn,nr.tgtIntn=Ks.tgtIntn,A&&(Ct.isParent()||Ct.isChild()||lt.isParent()||lt.isChild())&&(Ct.parents().anySame(lt)||lt.parents().anySame(Ct)||Ct.same(lt)&&Ct.isParent())?g.findCompoundLoopPoints(hr,Ks,yr,vr):Ct===lt?g.findLoopPoints(hr,Ks,yr,vr):fn==="segments"?g.findSegmentsPoints(hr,Ks):fn==="taxi"?g.findTaxiPoints(hr,Ks):fn==="straight"||!vr&&$e.eles.length%2===1&&yr===Math.floor($e.eles.length/2)?g.findStraightEdgePoints(hr):g.findBezierPoints(hr,Ks,yr,vr,gr),g.findEndpoints(hr),g.tryToCorrectInvalidPoints(hr,Ks),g.checkForInvalidEdgeWarning(hr),g.storeAllpts(hr),g.storeEdgeProjections(hr),g.calculateArrowAngles(hr),g.recalculateEdgeLabelProjections(hr),g.calculateLabelAngles(hr)}},Ke=0;Ke<I.length;Ke++)Be(Ke);this.findHaystackPoints(F)}};function NR(m){var g=[];if(m!=null){for(var k=0;k<m.length;k+=2){var T=m[k],A=m[k+1];g.push({x:T,y:A})}return g}}hd.getSegmentPoints=function(m){var g=m[0]._private.rscratch,k=g.edgeType;if(k==="segments")return this.recalculateRenderedStyle(m),NR(g.segpts)},hd.getControlPoints=function(m){var g=m[0]._private.rscratch,k=g.edgeType;if(k==="bezier"||k==="multibezier"||k==="self"||k==="compound")return this.recalculateRenderedStyle(m),NR(g.ctrlpts)},hd.getEdgeMidpoint=function(m){var g=m[0]._private.rscratch;return this.recalculateRenderedStyle(m),{x:g.midX,y:g.midY}};var Nx={};Nx.manualEndptToPx=function(m,g){var k=this,T=m.position(),A=m.outerWidth(),D=m.outerHeight();if(g.value.length===2){var I=[g.pfValue[0],g.pfValue[1]];return g.units[0]==="%"&&(I[0]=I[0]*A),g.units[1]==="%"&&(I[1]=I[1]*D),I[0]+=T.x,I[1]+=T.y,I}else{var F=g.pfValue[0];F=-Math.PI/2+F;var H=2*Math.max(A,D),C=[T.x+Math.cos(F)*H,T.y+Math.sin(F)*H];return k.nodeShapes[this.getNodeShape(m)].intersectLine(T.x,T.y,A,D,C[0],C[1],0)}},Nx.findEndpoints=function(m){var g=this,k,T=m.source()[0],A=m.target()[0],D=T.position(),I=A.position(),F=m.pstyle("target-arrow-shape").value,H=m.pstyle("source-arrow-shape").value,C=m.pstyle("target-distance-from-node").pfValue,G=m.pstyle("source-distance-from-node").pfValue,U=m.pstyle("curve-style").value,Z=m._private.rscratch,ne=Z.edgeType,ae=U==="taxi",ue=ne==="self"||ne==="compound",_e=ne==="bezier"||ne==="multibezier"||ue,be=ne!=="bezier",Se=ne==="straight"||ne==="segments",Le=ne==="segments",Be=_e||be||Se,Ke=ue||ae,qe=m.pstyle("source-endpoint"),Re=Ke?"outside-to-node":qe.value,Ze=m.pstyle("target-endpoint"),$e=Ke?"outside-to-node":Ze.value;Z.srcManEndpt=qe,Z.tgtManEndpt=Ze;var ot,ut,Je,Ct;if(_e){var lt=[Z.ctrlpts[0],Z.ctrlpts[1]],un=be?[Z.ctrlpts[Z.ctrlpts.length-2],Z.ctrlpts[Z.ctrlpts.length-1]]:lt;ot=un,ut=lt}else if(Se){var Rt=Le?Z.segpts.slice(0,2):[I.x,I.y],$t=Le?Z.segpts.slice(Z.segpts.length-2):[D.x,D.y];ot=$t,ut=Rt}if($e==="inside-to-node")k=[I.x,I.y];else if(Ze.units)k=this.manualEndptToPx(A,Ze);else if($e==="outside-to-line")k=Z.tgtIntn;else if($e==="outside-to-node"||$e==="outside-to-node-or-label"?Je=ot:($e==="outside-to-line"||$e==="outside-to-line-or-label")&&(Je=[D.x,D.y]),k=g.nodeShapes[this.getNodeShape(A)].intersectLine(I.x,I.y,A.outerWidth(),A.outerHeight(),Je[0],Je[1],0),$e==="outside-to-node-or-label"||$e==="outside-to-line-or-label"){var bn=A._private.rscratch,Cn=bn.labelWidth,Kn=bn.labelHeight,kn=bn.labelX,Wn=bn.labelY,sr=Cn/2,yr=Kn/2,hr=A.pstyle("text-valign").value;hr==="top"?Wn-=yr:hr==="bottom"&&(Wn+=yr);var nr=A.pstyle("text-halign").value;nr==="left"?kn-=sr:nr==="right"&&(kn+=sr);var fn=vx(Je[0],Je[1],[kn-sr,Wn-yr,kn+sr,Wn-yr,kn+sr,Wn+yr,kn-sr,Wn+yr],I.x,I.y);if(fn.length>0){var vr=D,gr=w3(vr,F5(k)),ni=w3(vr,F5(fn)),Ti=gr;if(ni<gr&&(k=fn,Ti=ni),fn.length>2){var ia=w3(vr,{x:fn[2],y:fn[3]});ia<Ti&&(k=[fn[2],fn[3]])}}}var Ba=C9(k,ot,g.arrowShapes[F].spacing(m)+C),Li=C9(k,ot,g.arrowShapes[F].gap(m)+C);if(Z.endX=Li[0],Z.endY=Li[1],Z.arrowEndX=Ba[0],Z.arrowEndY=Ba[1],Re==="inside-to-node")k=[D.x,D.y];else if(qe.units)k=this.manualEndptToPx(T,qe);else if(Re==="outside-to-line")k=Z.srcIntn;else if(Re==="outside-to-node"||Re==="outside-to-node-or-label"?Ct=ut:(Re==="outside-to-line"||Re==="outside-to-line-or-label")&&(Ct=[I.x,I.y]),k=g.nodeShapes[this.getNodeShape(T)].intersectLine(D.x,D.y,T.outerWidth(),T.outerHeight(),Ct[0],Ct[1],0),Re==="outside-to-node-or-label"||Re==="outside-to-line-or-label"){var wi=T._private.rscratch,Ts=wi.labelWidth,Yi=wi.labelHeight,Di=wi.labelX,es=wi.labelY,eo=Ts/2,sa=Yi/2,Ks=T.pstyle("text-valign").value;Ks==="top"?es-=sa:Ks==="bottom"&&(es+=sa);var Ar=T.pstyle("text-halign").value;Ar==="left"?Di-=eo:Ar==="right"&&(Di+=eo);var We=vx(Ct[0],Ct[1],[Di-eo,es-sa,Di+eo,es-sa,Di+eo,es+sa,Di-eo,es+sa],D.x,D.y);if(We.length>0){var On=I,Ir=w3(On,F5(k)),ar=w3(On,F5(We)),or=Ir;if(ar<Ir&&(k=[We[0],We[1]],or=ar),We.length>2){var qa=w3(On,{x:We[2],y:We[3]});qa<or&&(k=[We[2],We[3]])}}}var qr=C9(k,ut,g.arrowShapes[H].spacing(m)+G),is=C9(k,ut,g.arrowShapes[H].gap(m)+G);Z.startX=is[0],Z.startY=is[1],Z.arrowStartX=qr[0],Z.arrowStartY=qr[1],Be&&(!te(Z.startX)||!te(Z.startY)||!te(Z.endX)||!te(Z.endY)?Z.badLine=!0:Z.badLine=!1)},Nx.getSourceEndpoint=function(m){var g=m[0]._private.rscratch;switch(this.recalculateRenderedStyle(m),g.edgeType){case"haystack":return{x:g.haystackPts[0],y:g.haystackPts[1]};default:return{x:g.arrowStartX,y:g.arrowStartY}}},Nx.getTargetEndpoint=function(m){var g=m[0]._private.rscratch;switch(this.recalculateRenderedStyle(m),g.edgeType){case"haystack":return{x:g.haystackPts[2],y:g.haystackPts[3]};default:return{x:g.arrowEndX,y:g.arrowEndY}}};var KL={};function JJ(m,g,k){for(var T=function(C,G,U,Z){return df(C,G,U,Z)},A=g._private,D=A.rstyle.bezierPts,I=0;I<m.bezierProjPcts.length;I++){var F=m.bezierProjPcts[I];D.push({x:T(k[0],k[2],k[4],F),y:T(k[1],k[3],k[5],F)})}}KL.storeEdgeProjections=function(m){var g=m._private,k=g.rscratch,T=k.edgeType;if(g.rstyle.bezierPts=null,g.rstyle.linePts=null,g.rstyle.haystackPts=null,T==="multibezier"||T==="bezier"||T==="self"||T==="compound"){g.rstyle.bezierPts=[];for(var A=0;A+5<k.allpts.length;A+=4)JJ(this,m,k.allpts.slice(A,A+6))}else if(T==="segments")for(var D=g.rstyle.linePts=[],A=0;A+1<k.allpts.length;A+=2)D.push({x:k.allpts[A],y:k.allpts[A+1]});else if(T==="haystack"){var I=k.haystackPts;g.rstyle.haystackPts=[{x:I[0],y:I[1]},{x:I[2],y:I[3]}]}g.rstyle.arrowWidth=this.getArrowWidth(m.pstyle("width").pfValue,m.pstyle("arrow-scale").value)*this.arrowShapeWidth},KL.recalculateEdgeProjections=function(m){this.findEdgeControlPoints(m)};var wp={};wp.recalculateNodeLabelProjection=function(m){var g=m.pstyle("label").strValue;if(!pe(g)){var k,T,A=m._private,D=m.width(),I=m.height(),F=m.padding(),H=m.position(),C=m.pstyle("text-halign").strValue,G=m.pstyle("text-valign").strValue,U=A.rscratch,Z=A.rstyle;switch(C){case"left":k=H.x-D/2-F;break;case"right":k=H.x+D/2+F;break;default:k=H.x}switch(G){case"top":T=H.y-I/2-F;break;case"bottom":T=H.y+I/2+F;break;default:T=H.y}U.labelX=k,U.labelY=T,Z.labelX=k,Z.labelY=T,this.calculateLabelAngles(m),this.applyLabelDimensions(m)}};var PR=function(g,k){var T=Math.atan(k/g);return g===0&&T<0&&(T=T*-1),T},BR=function(g,k){var T=k.x-g.x,A=k.y-g.y;return PR(T,A)},RR=function(g,k,T,A){var D=px(0,A-.001,1),I=px(0,A+.001,1),F=j5(g,k,T,D),H=j5(g,k,T,I);return BR(F,H)};wp.recalculateEdgeLabelProjections=function(m){var g,k=m._private,T=k.rscratch,A=this,D={mid:m.pstyle("label").strValue,source:m.pstyle("source-label").strValue,target:m.pstyle("target-label").strValue};if(D.mid||D.source||D.target){g={x:T.midX,y:T.midY};var I=function(U,Z,ne){um(k.rscratch,U,Z,ne),um(k.rstyle,U,Z,ne)};I("labelX",null,g.x),I("labelY",null,g.y);var F=PR(T.midDispX,T.midDispY);I("labelAutoAngle",null,F);var H=function G(){if(G.cache)return G.cache;for(var U=[],Z=0;Z+5<T.allpts.length;Z+=4){var ne={x:T.allpts[Z],y:T.allpts[Z+1]},ae={x:T.allpts[Z+2],y:T.allpts[Z+3]},ue={x:T.allpts[Z+4],y:T.allpts[Z+5]};U.push({p0:ne,p1:ae,p2:ue,startDist:0,length:0,segments:[]})}var _e=k.rstyle.bezierPts,be=A.bezierProjPcts.length;function Se(Re,Ze,$e,ot,ut){var Je=v3(Ze,$e),Ct=Re.segments[Re.segments.length-1],lt={p0:Ze,p1:$e,t0:ot,t1:ut,startDist:Ct?Ct.startDist+Ct.length:0,length:Je};Re.segments.push(lt),Re.length+=Je}for(var Le=0;Le<U.length;Le++){var Be=U[Le],Ke=U[Le-1];Ke&&(Be.startDist=Ke.startDist+Ke.length),Se(Be,Be.p0,_e[Le*be],0,A.bezierProjPcts[0]);for(var qe=0;qe<be-1;qe++)Se(Be,_e[Le*be+qe],_e[Le*be+qe+1],A.bezierProjPcts[qe],A.bezierProjPcts[qe+1]);Se(Be,_e[Le*be+be-1],Be.p2,A.bezierProjPcts[be-1],1)}return G.cache=U},C=function(U){var Z,ne=U==="source";if(D[U]){var ae=m.pstyle(U+"-text-offset").pfValue;switch(T.edgeType){case"self":case"compound":case"bezier":case"multibezier":{for(var ue=H(),_e,be=0,Se=0,Le=0;Le<ue.length;Le++){for(var Be=ue[ne?Le:ue.length-1-Le],Ke=0;Ke<Be.segments.length;Ke++){var qe=Be.segments[ne?Ke:Be.segments.length-1-Ke],Re=Le===ue.length-1&&Ke===Be.segments.length-1;if(be=Se,Se+=qe.length,Se>=ae||Re){_e={cp:Be,segment:qe};break}}if(_e)break}var Ze=_e.cp,$e=_e.segment,ot=(ae-be)/$e.length,ut=$e.t1-$e.t0,Je=ne?$e.t0+ut*ot:$e.t1-ut*ot;Je=px(0,Je,1),g=j5(Ze.p0,Ze.p1,Ze.p2,Je),Z=RR(Ze.p0,Ze.p1,Ze.p2,Je);break}case"straight":case"segments":case"haystack":{for(var Ct=0,lt,un,Rt,$t,bn=T.allpts.length,Cn=0;Cn+3<bn&&(ne?(Rt={x:T.allpts[Cn],y:T.allpts[Cn+1]},$t={x:T.allpts[Cn+2],y:T.allpts[Cn+3]}):(Rt={x:T.allpts[bn-2-Cn],y:T.allpts[bn-1-Cn]},$t={x:T.allpts[bn-4-Cn],y:T.allpts[bn-3-Cn]}),lt=v3(Rt,$t),un=Ct,Ct+=lt,!(Ct>=ae));Cn+=2);var Kn=ae-un,kn=Kn/lt;kn=px(0,kn,1),g=xY(Rt,$t,kn),Z=BR(Rt,$t);break}}I("labelX",U,g.x),I("labelY",U,g.y),I("labelAutoAngle",U,Z)}};C("source"),C("target"),this.applyLabelDimensions(m)}},wp.applyLabelDimensions=function(m){this.applyPrefixedLabelDimensions(m),m.isEdge()&&(this.applyPrefixedLabelDimensions(m,"source"),this.applyPrefixedLabelDimensions(m,"target"))},wp.applyPrefixedLabelDimensions=function(m,g){var k=m._private,T=this.getLabelText(m,g),A=this.calculateLabelDimensions(m,T),D=m.pstyle("line-height").pfValue,I=m.pstyle("text-wrap").strValue,F=gp(k.rscratch,"labelWrapCachedLines",g)||[],H=I!=="wrap"?1:Math.max(F.length,1),C=A.height/H,G=C*D,U=A.width,Z=A.height+(H-1)*(D-1)*C;um(k.rstyle,"labelWidth",g,U),um(k.rscratch,"labelWidth",g,U),um(k.rstyle,"labelHeight",g,Z),um(k.rscratch,"labelHeight",g,Z),um(k.rscratch,"labelLineHeight",g,G)},wp.getLabelText=function(m,g){var k=m._private,T=g?g+"-":"",A=m.pstyle(T+"label").strValue,D=m.pstyle("text-transform").value,I=function(Kn,kn){return kn?(um(k.rscratch,Kn,g,kn),kn):gp(k.rscratch,Kn,g)};if(!A)return"";D=="none"||(D=="uppercase"?A=A.toUpperCase():D=="lowercase"&&(A=A.toLowerCase()));var F=m.pstyle("text-wrap").value;if(F==="wrap"){var H=I("labelKey");if(H!=null&&I("labelWrapKey")===H)return I("labelWrapCachedText");for(var C="​",G=A.split(`
`),U=m.pstyle("text-max-width").pfValue,Z=m.pstyle("text-overflow-wrap").value,ne=Z==="anywhere",ae=[],ue=/[\s\u200b]+/,_e=ne?"":" ",be=0;be<G.length;be++){var Se=G[be],Le=this.calculateLabelDimensions(m,Se),Be=Le.width;if(ne){var Ke=Se.split("").join(C);Se=Ke}if(Be>U){for(var qe=Se.split(ue),Re="",Ze=0;Ze<qe.length;Ze++){var $e=qe[Ze],ot=Re.length===0?$e:Re+_e+$e,ut=this.calculateLabelDimensions(m,ot),Je=ut.width;Je<=U?Re+=$e+_e:(Re&&ae.push(Re),Re=$e+_e)}Re.match(/^[\s\u200b]+$/)||ae.push(Re)}else ae.push(Se)}I("labelWrapCachedLines",ae),A=I("labelWrapCachedText",ae.join(`
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Lc=Ir.collection(m.getAllInBox(or[0],or[1],or[2],or[3]));m.redrawHint("select",!0),Lc.length>0&&m.redrawHint("eles",!0),Ir.emit({type:"boxend",originalEvent:We,position:{x:ar[0],y:ar[1]}});var Ra=function(Yf){return Yf.selectable()&&!Yf.selected()};Ir.selectionType()==="additive"||Uo||Ir.$(g).unmerge(Lc).unselect(),Lc.emit("box").stdFilter(Ra).select().emit("boxselect"),m.redraw()}if(m.hoverData.dragging&&(m.hoverData.dragging=!1,m.redrawHint("select",!0),m.redrawHint("eles",!0),m.redraw()),!or[4]){m.redrawHint("drag",!0),m.redrawHint("eles",!0);var lo=is&&is.grabbed();ue(qr),lo&&(is.emit("freeon"),qr.emit("free"),m.dragData.didDrag&&(is.emit("dragfreeon"),qr.emit("dragfree")))}}or[4]=0,m.hoverData.down=null,m.hoverData.cxtStarted=!1,m.hoverData.draggingEles=!1,m.hoverData.selecting=!1,m.hoverData.isOverThresholdDrag=!1,m.dragData.didDrag=!1,m.hoverData.dragged=!1,m.hoverData.dragDelta=[],m.hoverData.mdownPos=null,m.hoverData.mdownGPos=null}},!1);var Je=function(We){if(!m.scrollingPage){var On=m.cy,Ir=On.zoom(),ar=On.pan(),or=m.projectIntoViewport(We.clientX,We.clientY),qa=[or[0]*Ir+ar.x,or[1]*Ir+ar.y];if(m.hoverData.draggingEles||m.hoverData.dragging||m.hoverData.cxtStarted||Re()){We.preventDefault();return}if(On.panningEnabled()&&On.userPanningEnabled()&&On.zoomingEnabled()&&On.userZoomingEnabled()){We.preventDefault(),m.data.wheelZooming=!0,clearTimeout(m.data.wheelTimeout),m.data.wheelTimeout=setTimeout(function(){m.data.wheelZooming=!1,m.redrawHint("eles",!0),m.redraw()},150);var qr;We.deltaY!=null?qr=We.deltaY/-250:We.wheelDeltaY!=null?qr=We.wheelDeltaY/1e3:qr=We.wheelDelta/1e3,qr=qr*m.wheelSensitivity;var is=We.deltaMode===1;is&&(qr*=33);var Uo=On.zoom()*Math.pow(10,qr);We.type==="gesturechange"&&(Uo=m.gestureStartZoom*We.scale),On.zoom({level:Uo,renderedPosition:{x:qa[0],y:qa[1]}}),On.emit(We.type==="gesturechange"?"pinchzoom":"scrollzoom")}}};m.registerBinding(m.container,"wheel",Je,!0),m.registerBinding(window,"scroll",function(We){m.scrollingPage=!0,clearTimeout(m.scrollingPageTimeout),m.scrollingPageTimeout=setTimeout(function(){m.scrollingPage=!1},250)},!0),m.registerBinding(m.container,"gesturestart",function(We){m.gestureStartZoom=m.cy.zoom(),m.hasTouchStarted||We.preventDefault()},!0),m.registerBinding(m.container,"gesturechange",function(Ar){m.hasTouchStarted||Je(Ar)},!0),m.registerBinding(m.container,"mouseout",function(We){var On=m.projectIntoViewport(We.clientX,We.clientY);m.cy.emit({originalEvent:We,type:"mouseout",position:{x:On[0],y:On[1]}})},!1),m.registerBinding(m.container,"mouseover",function(We){var On=m.projectIntoViewport(We.clientX,We.clientY);m.cy.emit({originalEvent:We,type:"mouseover",position:{x:On[0],y:On[1]}})},!1);var Ct,lt,un,Rt,$t,bn,Cn,Kn,kn,Wn,sr,yr,hr,nr=function(We,On,Ir,ar){return Math.sqrt((Ir-We)*(Ir-We)+(ar-On)*(ar-On))},fn=function(We,On,Ir,ar){return(Ir-We)*(Ir-We)+(ar-On)*(ar-On)},vr;m.registerBinding(m.container,"touchstart",vr=function(We){if(m.hasTouchStarted=!0,!!Ze(We)){be(),m.touchData.capture=!0,m.data.bgActivePosistion=void 0;var On=m.cy,Ir=m.touchData.now,ar=m.touchData.earlier;if(We.touches[0]){var or=m.projectIntoViewport(We.touches[0].clientX,We.touches[0].clientY);Ir[0]=or[0],Ir[1]=or[1]}if(We.touches[1]){var or=m.projectIntoViewport(We.touches[1].clientX,We.touches[1].clientY);Ir[2]=or[0],Ir[3]=or[1]}if(We.touches[2]){var or=m.projectIntoViewport(We.touches[2].clientX,We.touches[2].clientY);Ir[4]=or[0],Ir[5]=or[1]}if(We.touches[1]){m.touchData.singleTouchMoved=!0,ue(m.dragData.touchDragEles);var qa=m.findContainerClientCoords();kn=qa[0],Wn=qa[1],sr=qa[2],yr=qa[3],Ct=We.touches[0].clientX-kn,lt=We.touches[0].clientY-Wn,un=We.touches[1].clientX-kn,Rt=We.touches[1].clientY-Wn,hr=0<=Ct&&Ct<=sr&&0<=un&&un<=sr&&0<=lt&&lt<=yr&&0<=Rt&&Rt<=yr;var qr=On.pan(),is=On.zoom();$t=nr(Ct,lt,un,Rt),bn=fn(Ct,lt,un,Rt),Cn=[(Ct+un)/2,(lt+Rt)/2],Kn=[(Cn[0]-qr.x)/is,(Cn[1]-qr.y)/is];var Uo=200,qc=Uo*Uo;if(bn<qc&&!We.touches[2]){var uo=m.findNearestElement(Ir[0],Ir[1],!0,!0),Lc=m.findNearestElement(Ir[2],Ir[3],!0,!0);uo&&uo.isNode()?(uo.activate().emit({originalEvent:We,type:"cxttapstart",position:{x:Ir[0],y:Ir[1]}}),m.touchData.start=uo):Lc&&Lc.isNode()?(Lc.activate().emit({originalEvent:We,type:"cxttapstart",position:{x:Ir[0],y:Ir[1]}}),m.touchData.start=Lc):On.emit({originalEvent:We,type:"cxttapstart",position:{x:Ir[0],y:Ir[1]}}),m.touchData.start&&(m.touchData.start._private.grabbed=!1),m.touchData.cxt=!0,m.touchData.cxtDragged=!1,m.data.bgActivePosistion=void 0,m.redraw();return}}if(We.touches[2])On.boxSelectionEnabled()&&We.preventDefault();else if(!We.touches[1]){if(We.touches[0]){var Ra=m.findNearestElements(Ir[0],Ir[1],!0,!0),lo=Ra[0];if(lo!=null&&(lo.activate(),m.touchData.start=lo,m.touchData.starts=Ra,m.nodeIsGrabbable(lo))){var Sl=m.dragData.touchDragEles=On.collection(),Yf=null;m.redrawHint("eles",!0),m.redrawHint("drag",!0),lo.selected()?(Yf=On.$(function(fd){return fd.selected()&&m.nodeIsGrabbable(fd)}),ne(Yf,{addToList:Sl})):ae(lo,{addToList:Sl}),C(lo);var mp=function(ov){return{originalEvent:We,type:ov,position:{x:Ir[0],y:Ir[1]}}};lo.emit(mp("grabon")),Yf?Yf.forEach(function(fd){fd.emit(mp("grab"))}):lo.emit(mp("grab"))}k(lo,["touchstart","tapstart","vmousedown"],We,{x:Ir[0],y:Ir[1]}),lo==null&&(m.data.bgActivePosistion={x:or[0],y:or[1]},m.redrawHint("select",!0),m.redraw()),m.touchData.singleTouchMoved=!1,m.touchData.singleTouchStartTime=+new Date,clearTimeout(m.touchData.tapholdTimeout),m.touchData.tapholdTimeout=setTimeout(function(){m.touchData.singleTouchMoved===!1&&!m.pinching&&!m.touchData.selecting&&k(m.touchData.start,["taphold"],We,{x:Ir[0],y:Ir[1]})},m.tapholdDuration)}}if(We.touches.length>=1){for(var yp=m.touchData.startPosition=[],Xf=0;Xf<Ir.length;Xf++)yp[Xf]=ar[Xf]=Ir[Xf];var gg=We.touches[0];m.touchData.startGPosition=[gg.clientX,gg.clientY]}}},!1);var gr;m.registerBinding(window,"touchmove",gr=function(We){var On=m.touchData.capture;if(!(!On&&!Ze(We))){var Ir=m.selection,ar=m.cy,or=m.touchData.now,qa=m.touchData.earlier,qr=ar.zoom();if(We.touches[0]){var is=m.projectIntoViewport(We.touches[0].clientX,We.touches[0].clientY);or[0]=is[0],or[1]=is[1]}if(We.touches[1]){var is=m.projectIntoViewport(We.touches[1].clientX,We.touches[1].clientY);or[2]=is[0],or[3]=is[1]}if(We.touches[2]){var is=m.projectIntoViewport(We.touches[2].clientX,We.touches[2].clientY);or[4]=is[0],or[5]=is[1]}var Uo=m.touchData.startGPosition,qc;if(On&&We.touches[0]&&Uo){for(var uo=[],Lc=0;Lc<or.length;Lc++)uo[Lc]=or[Lc]-qa[Lc];var Ra=We.touches[0].clientX-Uo[0],lo=Ra*Ra,Sl=We.touches[0].clientY-Uo[1],Yf=Sl*Sl,mp=lo+Yf;qc=mp>=m.touchTapThreshold2}if(On&&m.touchData.cxt){We.preventDefault();var yp=We.touches[0].clientX-kn,Xf=We.touches[0].clientY-Wn,gg=We.touches[1].clientX-kn,fd=We.touches[1].clientY-Wn,ov=fn(yp,Xf,gg,fd),mm=ov/bn,l6=150,S3=l6*l6,h6=1.5,gT=h6*h6;if(mm>=gT||ov>=S3){m.touchData.cxt=!1,m.data.bgActivePosistion=void 0,m.redrawHint("select",!0);var A3={originalEvent:We,type:"cxttapend",position:{x:or[0],y:or[1]}};m.touchData.start?(m.touchData.start.unactivate().emit(A3),m.touchData.start=null):ar.emit(A3)}}if(On&&m.touchData.cxt){var A3={originalEvent:We,type:"cxtdrag",position:{x:or[0],y:or[1]}};m.data.bgActivePosistion=void 0,m.redrawHint("select",!0),m.touchData.start?m.touchData.start.emit(A3):ar.emit(A3),m.touchData.start&&(m.touchData.start._private.grabbed=!1),m.touchData.cxtDragged=!0;var qd=m.findNearestElement(or[0],or[1],!0,!0);(!m.touchData.cxtOver||qd!==m.touchData.cxtOver)&&(m.touchData.cxtOver&&m.touchData.cxtOver.emit({originalEvent:We,type:"cxtdragout",position:{x:or[0],y:or[1]}}),m.touchData.cxtOver=qd,qd&&qd.emit({originalEvent:We,type:"cxtdragover",position:{x:or[0],y:or[1]}}))}else if(On&&We.touches[2]&&ar.boxSelectionEnabled())We.preventDefault(),m.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,m.touchData.selecting||ar.emit({originalEvent:We,type:"boxstart",position:{x:or[0],y:or[1]}}),m.touchData.selecting=!0,m.touchData.didSelect=!0,Ir[4]=1,!Ir||Ir.length===0||Ir[0]===void 0?(Ir[0]=(or[0]+or[2]+or[4])/3,Ir[1]=(or[1]+or[3]+or[5])/3,Ir[2]=(or[0]+or[2]+or[4])/3+1,Ir[3]=(or[1]+or[3]+or[5])/3+1):(Ir[2]=(or[0]+or[2]+or[4])/3,Ir[3]=(or[1]+or[3]+or[5])/3),m.redrawHint("select",!0),m.redraw();else if(On&&We.touches[1]&&!m.touchData.didSelect&&ar.zoomingEnabled()&&ar.panningEnabled()&&ar.userZoomingEnabled()&&ar.userPanningEnabled()){We.preventDefault(),m.data.bgActivePosistion=void 0,m.redrawHint("select",!0);var _1=m.dragData.touchDragEles;if(_1){m.redrawHint("drag",!0);for(var Vd=0;Vd<_1.length;Vd++){var pT=_1[Vd]._private;pT.grabbed=!1,pT.rscratch.inDragLayer=!1}}var $2=m.touchData.start,yp=We.touches[0].clientX-kn,Xf=We.touches[0].clientY-Wn,gg=We.touches[1].clientX-kn,fd=We.touches[1].clientY-Wn,QR=nr(yp,Xf,gg,fd),Dee=QR/$t;if(hr){var Iee=yp-Ct,Oee=Xf-lt,Nee=gg-un,Pee=fd-Rt,Bee=(Iee+Nee)/2,Ree=(Oee+Pee)/2,Gx=ar.zoom(),iM=Gx*Dee,bT=ar.pan(),ZR=Kn[0]*Gx+bT.x,JR=Kn[1]*Gx+bT.y,Fee={x:-iM/Gx*(ZR-bT.x-Bee)+ZR,y:-iM/Gx*(JR-bT.y-Ree)+JR};if($2&&$2.active()){var _1=m.dragData.touchDragEles;ue(_1),m.redrawHint("drag",!0),m.redrawHint("eles",!0),$2.unactivate().emit("freeon"),_1.emit("free"),m.dragData.didDrag&&($2.emit("dragfreeon"),_1.emit("dragfree"))}ar.viewport({zoom:iM,pan:Fee,cancelOnFailedZoom:!0}),ar.emit("pinchzoom"),$t=QR,Ct=yp,lt=Xf,un=gg,Rt=fd,m.pinching=!0}if(We.touches[0]){var is=m.projectIntoViewport(We.touches[0].clientX,We.touches[0].clientY);or[0]=is[0],or[1]=is[1]}if(We.touches[1]){var is=m.projectIntoViewport(We.touches[1].clientX,We.touches[1].clientY);or[2]=is[0],or[3]=is[1]}if(We.touches[2]){var is=m.projectIntoViewport(We.touches[2].clientX,We.touches[2].clientY);or[4]=is[0],or[5]=is[1]}}else if(We.touches[0]&&!m.touchData.didSelect){var kp=m.touchData.start,sM=m.touchData.last,qd;if(!m.hoverData.draggingEles&&!m.swipePanning&&(qd=m.findNearestElement(or[0],or[1],!0,!0)),On&&kp!=null&&We.preventDefault(),On&&kp!=null&&m.nodeIsDraggable(kp))if(qc){var _1=m.dragData.touchDragEles,eF=!m.dragData.didDrag;eF&&ne(_1,{inDragLayer:!0}),m.dragData.didDrag=!0;var qx={x:0,y:0};if(te(uo[0])&&te(uo[1])&&(qx.x+=uo[0],qx.y+=uo[1],eF)){m.redrawHint("eles",!0);var xp=m.touchData.dragDelta;xp&&te(xp[0])&&te(xp[1])&&(qx.x+=xp[0],qx.y+=xp[1])}m.hoverData.draggingEles=!0,_1.silentShift(qx).emit("position drag"),m.redrawHint("drag",!0),m.touchData.startPosition[0]==qa[0]&&m.touchData.startPosition[1]==qa[1]&&m.redrawHint("eles",!0),m.redraw()}else{var xp=m.touchData.dragDelta=m.touchData.dragDelta||[];xp.length===0?(xp.push(uo[0]),xp.push(uo[1])):(xp[0]+=uo[0],xp[1]+=uo[1])}if(k(kp||qd,["touchmove","tapdrag","vmousemove"],We,{x:or[0],y:or[1]}),(!kp||!kp.grabbed())&&qd!=sM&&(sM&&sM.emit({originalEvent:We,type:"tapdragout",position:{x:or[0],y:or[1]}}),qd&&qd.emit({originalEvent:We,type:"tapdragover",position:{x:or[0],y:or[1]}})),m.touchData.last=qd,On)for(var Vd=0;Vd<or.length;Vd++)or[Vd]&&m.touchData.startPosition[Vd]&&qc&&(m.touchData.singleTouchMoved=!0);if(On&&(kp==null||kp.pannable())&&ar.panningEnabled()&&ar.userPanningEnabled()){var jee=A(kp,m.touchData.starts);jee&&(We.preventDefault(),m.data.bgActivePosistion||(m.data.bgActivePosistion=F5(m.touchData.startPosition)),m.swipePanning?(ar.panBy({x:uo[0]*qr,y:uo[1]*qr}),ar.emit("dragpan")):qc&&(m.swipePanning=!0,ar.panBy({x:Ra*qr,y:Sl*qr}),ar.emit("dragpan"),kp&&(kp.unactivate(),m.redrawHint("select",!0),m.touchData.start=null)));var is=m.projectIntoViewport(We.touches[0].clientX,We.touches[0].clientY);or[0]=is[0],or[1]=is[1]}}for(var Lc=0;Lc<or.length;Lc++)qa[Lc]=or[Lc];On&&We.touches.length>0&&!m.hoverData.draggingEles&&!m.swipePanning&&m.data.bgActivePosistion!=null&&(m.data.bgActivePosistion=void 0,m.redrawHint("select",!0),m.redraw())}},!1);var ni;m.registerBinding(window,"touchcancel",ni=function(We){var On=m.touchData.start;m.touchData.capture=!1,On&&On.unactivate()});var Ti,ia,Ba,Li;if(m.registerBinding(window,"touchend",Ti=function(We){var On=m.touchData.start,Ir=m.touchData.capture;if(Ir)We.touches.length===0&&(m.touchData.capture=!1),We.preventDefault();else return;var ar=m.selection;m.swipePanning=!1,m.hoverData.draggingEles=!1;var or=m.cy,qa=or.zoom(),qr=m.touchData.now,is=m.touchData.earlier;if(We.touches[0]){var Uo=m.projectIntoViewport(We.touches[0].clientX,We.touches[0].clientY);qr[0]=Uo[0],qr[1]=Uo[1]}if(We.touches[1]){var Uo=m.projectIntoViewport(We.touches[1].clientX,We.touches[1].clientY);qr[2]=Uo[0],qr[3]=Uo[1]}if(We.touches[2]){var Uo=m.projectIntoViewport(We.touches[2].clientX,We.touches[2].clientY);qr[4]=Uo[0],qr[5]=Uo[1]}On&&On.unactivate();var qc;if(m.touchData.cxt){if(qc={originalEvent:We,type:"cxttapend",position:{x:qr[0],y:qr[1]}},On?On.emit(qc):or.emit(qc),!m.touchData.cxtDragged){var uo={originalEvent:We,type:"cxttap",position:{x:qr[0],y:qr[1]}};On?On.emit(uo):or.emit(uo)}m.touchData.start&&(m.touchData.start._private.grabbed=!1),m.touchData.cxt=!1,m.touchData.start=null,m.redraw();return}if(!We.touches[2]&&or.boxSelectionEnabled()&&m.touchData.selecting){m.touchData.selecting=!1;var Lc=or.collection(m.getAllInBox(ar[0],ar[1],ar[2],ar[3]));ar[0]=void 0,ar[1]=void 0,ar[2]=void 0,ar[3]=void 0,ar[4]=0,m.redrawHint("select",!0),or.emit({type:"boxend",originalEvent:We,position:{x:qr[0],y:qr[1]}});var Ra=function(S3){return S3.selectable()&&!S3.selected()};Lc.emit("box").stdFilter(Ra).select().emit("boxselect"),Lc.nonempty()&&m.redrawHint("eles",!0),m.redraw()}if(On!=null&&On.unactivate(),We.touches[2])m.data.bgActivePosistion=void 0,m.redrawHint("select",!0);else if(!We.touches[1]){if(!We.touches[0]){if(!We.touches[0]){m.data.bgActivePosistion=void 0,m.redrawHint("select",!0);var lo=m.dragData.touchDragEles;if(On!=null){var Sl=On._private.grabbed;ue(lo),m.redrawHint("drag",!0),m.redrawHint("eles",!0),Sl&&(On.emit("freeon"),lo.emit("free"),m.dragData.didDrag&&(On.emit("dragfreeon"),lo.emit("dragfree"))),k(On,["touchend","tapend","vmouseup","tapdragout"],We,{x:qr[0],y:qr[1]}),On.unactivate(),m.touchData.start=null}else{var Yf=m.findNearestElement(qr[0],qr[1],!0,!0);k(Yf,["touchend","tapend","vmouseup","tapdragout"],We,{x:qr[0],y:qr[1]})}var mp=m.touchData.startPosition[0]-qr[0],yp=mp*mp,Xf=m.touchData.startPosition[1]-qr[1],gg=Xf*Xf,fd=yp+gg,ov=fd*qa*qa;m.touchData.singleTouchMoved||(On||or.$(":selected").unselect(["tapunselect"]),k(On,["tap","vclick"],We,{x:qr[0],y:qr[1]}),ia=!1,We.timeStamp-Li<=or.multiClickDebounceTime()?(Ba&&clearTimeout(Ba),ia=!0,Li=null,k(On,["dbltap","vdblclick"],We,{x:qr[0],y:qr[1]})):(Ba=setTimeout(function(){ia||k(On,["onetap","voneclick"],We,{x:qr[0],y:qr[1]})},or.multiClickDebounceTime()),Li=We.timeStamp)),On!=null&&!m.dragData.didDrag&&On._private.selectable&&ov<m.touchTapThreshold2&&!m.pinching&&(or.selectionType()==="single"?(or.$(g).unmerge(On).unselect(["tapunselect"]),On.select(["tapselect"])):On.selected()?On.unselect(["tapunselect"]):On.select(["tapselect"]),m.redrawHint("eles",!0)),m.touchData.singleTouchMoved=!0}}}for(var mm=0;mm<qr.length;mm++)is[mm]=qr[mm];m.dragData.didDrag=!1,We.touches.length===0&&(m.touchData.dragDelta=[],m.touchData.startPosition=null,m.touchData.startGPosition=null,m.touchData.didSelect=!1),We.touches.length<2&&(We.touches.length===1&&(m.touchData.startGPosition=[We.touches[0].clientX,We.touches[0].clientY]),m.pinching=!1,m.redrawHint("eles",!0),m.redraw())},!1),typeof TouchEvent>"u"){var wi=[],Ts=function(We){return{clientX:We.clientX,clientY:We.clientY,force:1,identifier:We.pointerId,pageX:We.pageX,pageY:We.pageY,radiusX:We.width/2,radiusY:We.height/2,screenX:We.screenX,screenY:We.screenY,target:We.target}},Yi=function(We){return{event:We,touch:Ts(We)}},Di=function(We){wi.push(Yi(We))},es=function(We){for(var On=0;On<wi.length;On++){var Ir=wi[On];if(Ir.event.pointerId===We.pointerId){wi.splice(On,1);return}}},eo=function(We){var On=wi.filter(function(Ir){return Ir.event.pointerId===We.pointerId})[0];On.event=We,On.touch=Ts(We)},sa=function(We){We.touches=wi.map(function(On){return On.touch})},Ks=function(We){return We.pointerType==="mouse"||We.pointerType===4};m.registerBinding(m.container,"pointerdown",function(Ar){Ks(Ar)||(Ar.preventDefault(),Di(Ar),sa(Ar),vr(Ar))}),m.registerBinding(m.container,"pointerup",function(Ar){Ks(Ar)||(es(Ar),sa(Ar),Ti(Ar))}),m.registerBinding(m.container,"pointercancel",function(Ar){Ks(Ar)||(es(Ar),sa(Ar),ni(Ar))}),m.registerBinding(m.container,"pointermove",function(Ar){Ks(Ar)||(Ar.preventDefault(),eo(Ar),sa(Ar),gr(Ar))})}};var iv={};iv.generatePolygon=function(m,g){return this.nodeShapes[m]={renderer:this,name:m,points:g,draw:function(T,A,D,I,F){this.renderer.nodeShapeImpl("polygon",T,A,D,I,F,this.points)},intersectLine:function(T,A,D,I,F,H,C){return vx(F,H,this.points,T,A,D/2,I/2,C)},checkPoint:function(T,A,D,I,F,H,C){return tv(T,A,this.points,H,C,I,F,[0,-1],D)}}},iv.generateEllipse=function(){return this.nodeShapes.ellipse={renderer:this,name:"ellipse",draw:function(g,k,T,A,D){this.renderer.nodeShapeImpl(this.name,g,k,T,A,D)},intersectLine:function(g,k,T,A,D,I,F){return PY(D,I,g,k,T/2+F,A/2+F)},checkPoint:function(g,k,T,A,D,I,F){return m3(g,k,A,D,I,F,T)}}},iv.generateRoundPolygon=function(m,g){for(var k=new Array(g.length*2),T=0;T<g.length/2;T++){var A=T*2,D=void 0;T<g.length/2-1?D=(T+1)*2:D=0,k[T*4]=g[A],k[T*4+1]=g[A+1];var I=g[D]-g[A],F=g[D+1]-g[A+1],H=Math.sqrt(I*I+F*F);k[T*4+2]=I/H,k[T*4+3]=F/H}return this.nodeShapes[m]={renderer:this,name:m,points:k,draw:function(G,U,Z,ne,ae){this.renderer.nodeShapeImpl("round-polygon",G,U,Z,ne,ae,this.points)},intersectLine:function(G,U,Z,ne,ae,ue,_e){return BY(ae,ue,this.points,G,U,Z,ne)},checkPoint:function(G,U,Z,ne,ae,ue,_e){return NY(G,U,this.points,ue,_e,ne,ae)}}},iv.generateRoundRectangle=function(){return this.nodeShapes["round-rectangle"]=this.nodeShapes.roundrectangle={renderer:this,name:"round-rectangle",points:ud(4,0),draw:function(g,k,T,A,D){this.renderer.nodeShapeImpl(this.name,g,k,T,A,D)},intersectLine:function(g,k,T,A,D,I,F){return XP(D,I,g,k,T,A,F)},checkPoint:function(g,k,T,A,D,I,F){var H=wx(A,D),C=H*2;return!!(tv(g,k,this.points,I,F,A,D-C,[0,-1],T)||tv(g,k,this.points,I,F,A-C,D,[0,-1],T)||m3(g,k,C,C,I-A/2+H,F-D/2+H,T)||m3(g,k,C,C,I+A/2-H,F-D/2+H,T)||m3(g,k,C,C,I+A/2-H,F+D/2-H,T)||m3(g,k,C,C,I-A/2+H,F+D/2-H,T))}}},iv.generateCutRectangle=function(){return this.nodeShapes["cut-rectangle"]=this.nodeShapes.cutrectangle={renderer:this,name:"cut-rectangle",cornerLength:eB(),points:ud(4,0),draw:function(g,k,T,A,D){this.renderer.nodeShapeImpl(this.name,g,k,T,A,D)},generateCutTrianglePts:function(g,k,T,A){var D=this.cornerLength,I=k/2,F=g/2,H=T-F,C=T+F,G=A-I,U=A+I;return{topLeft:[H,G+D,H+D,G,H+D,G+D],topRight:[C-D,G,C,G+D,C-D,G+D],bottomRight:[C,U-D,C-D,U,C-D,U-D],bottomLeft:[H+D,U,H,U-D,H+D,U-D]}},intersectLine:function(g,k,T,A,D,I,F){var H=this.generateCutTrianglePts(T+2*F,A+2*F,g,k),C=[].concat.apply([],[H.topLeft.splice(0,4),H.topRight.splice(0,4),H.bottomRight.splice(0,4),H.bottomLeft.splice(0,4)]);return vx(D,I,C,g,k)},checkPoint:function(g,k,T,A,D,I,F){if(tv(g,k,this.points,I,F,A,D-2*this.cornerLength,[0,-1],T)||tv(g,k,this.points,I,F,A-2*this.cornerLength,D,[0,-1],T))return!0;var H=this.generateCutTrianglePts(A,D,I,F);return Gd(g,k,H.topLeft)||Gd(g,k,H.topRight)||Gd(g,k,H.bottomRight)||Gd(g,k,H.bottomLeft)}}},iv.generateBarrel=function(){return this.nodeShapes.barrel={renderer:this,name:"barrel",points:ud(4,0),draw:function(g,k,T,A,D){this.renderer.nodeShapeImpl(this.name,g,k,T,A,D)},intersectLine:function(g,k,T,A,D,I,F){var H=.15,C=.5,G=.85,U=this.generateBarrelBezierPts(T+2*F,A+2*F,g,k),Z=function(ue){var _e=j5({x:ue[0],y:ue[1]},{x:ue[2],y:ue[3]},{x:ue[4],y:ue[5]},H),be=j5({x:ue[0],y:ue[1]},{x:ue[2],y:ue[3]},{x:ue[4],y:ue[5]},C),Se=j5({x:ue[0],y:ue[1]},{x:ue[2],y:ue[3]},{x:ue[4],y:ue[5]},G);return[ue[0],ue[1],_e.x,_e.y,be.x,be.y,Se.x,Se.y,ue[4],ue[5]]},ne=[].concat(Z(U.topLeft),Z(U.topRight),Z(U.bottomRight),Z(U.bottomLeft));return vx(D,I,ne,g,k)},generateBarrelBezierPts:function(g,k,T,A){var D=k/2,I=g/2,F=T-I,H=T+I,C=A-D,G=A+D,U=CL(g,k),Z=U.heightOffset,ne=U.widthOffset,ae=U.ctrlPtOffsetPct*g,ue={topLeft:[F,C+Z,F+ae,C,F+ne,C],topRight:[H-ne,C,H-ae,C,H,C+Z],bottomRight:[H,G-Z,H-ae,G,H-ne,G],bottomLeft:[F+ne,G,F+ae,G,F,G-Z]};return ue.topLeft.isTop=!0,ue.topRight.isTop=!0,ue.bottomLeft.isBottom=!0,ue.bottomRight.isBottom=!0,ue},checkPoint:function(g,k,T,A,D,I,F){var H=CL(A,D),C=H.heightOffset,G=H.widthOffset;if(tv(g,k,this.points,I,F,A,D-2*C,[0,-1],T)||tv(g,k,this.points,I,F,A-2*G,D,[0,-1],T))return!0;for(var U=this.generateBarrelBezierPts(A,D,I,F),Z=function(Re,Ze,$e){var ot=$e[4],ut=$e[2],Je=$e[0],Ct=$e[5],lt=$e[1],un=Math.min(ot,Je),Rt=Math.max(ot,Je),$t=Math.min(Ct,lt),bn=Math.max(Ct,lt);if(un<=Re&&Re<=Rt&&$t<=Ze&&Ze<=bn){var Cn=RY(ot,ut,Je),Kn=MY(Cn[0],Cn[1],Cn[2],Re),kn=Kn.filter(function(Wn){return 0<=Wn&&Wn<=1});if(kn.length>0)return kn[0]}return null},ne=Object.keys(U),ae=0;ae<ne.length;ae++){var ue=ne[ae],_e=U[ue],be=Z(g,k,_e);if(be!=null){var Se=_e[5],Le=_e[3],Be=_e[1],Ke=df(Se,Le,Be,be);if(_e.isTop&&Ke<=k||_e.isBottom&&k<=Ke)return!0}}return!1}}},iv.generateBottomRoundrectangle=function(){return this.nodeShapes["bottom-round-rectangle"]=this.nodeShapes.bottomroundrectangle={renderer:this,name:"bottom-round-rectangle",points:ud(4,0),draw:function(g,k,T,A,D){this.renderer.nodeShapeImpl(this.name,g,k,T,A,D)},intersectLine:function(g,k,T,A,D,I,F){var H=g-(T/2+F),C=k-(A/2+F),G=C,U=g+(T/2+F),Z=lm(D,I,g,k,H,C,U,G,!1);return Z.length>0?Z:XP(D,I,g,k,T,A,F)},checkPoint:function(g,k,T,A,D,I,F){var H=wx(A,D),C=2*H;if(tv(g,k,this.points,I,F,A,D-C,[0,-1],T)||tv(g,k,this.points,I,F,A-C,D,[0,-1],T))return!0;var G=A/2+2*T,U=D/2+2*T,Z=[I-G,F-U,I-G,F,I+G,F,I+G,F-U];return!!(Gd(g,k,Z)||m3(g,k,C,C,I+A/2-H,F+D/2-H,T)||m3(g,k,C,C,I-A/2+H,F+D/2-H,T))}}},iv.registerNodeShapes=function(){var m=this.nodeShapes={},g=this;this.generateEllipse(),this.generatePolygon("triangle",ud(3,0)),this.generateRoundPolygon("round-triangle",ud(3,0)),this.generatePolygon("rectangle",ud(4,0)),m.square=m.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();{var k=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",k),this.generateRoundPolygon("round-diamond",k)}this.generatePolygon("pentagon",ud(5,0)),this.generateRoundPolygon("round-pentagon",ud(5,0)),this.generatePolygon("hexagon",ud(6,0)),this.generateRoundPolygon("round-hexagon",ud(6,0)),this.generatePolygon("heptagon",ud(7,0)),this.generateRoundPolygon("round-heptagon",ud(7,0)),this.generatePolygon("octagon",ud(8,0)),this.generateRoundPolygon("round-octagon",ud(8,0));var T=new Array(20);{var A=TL(5,0),D=TL(5,Math.PI/5),I=.5*(3-Math.sqrt(5));I*=1.57;for(var F=0;F<D.length/2;F++)D[F*2]*=I,D[F*2+1]*=I;for(var F=0;F<20/4;F++)T[F*4]=A[F*2],T[F*4+1]=A[F*2+1],T[F*4+2]=D[F*2],T[F*4+3]=D[F*2+1]}T=JP(T),this.generatePolygon("star",T),this.generatePolygon("vee",[-1,-1,0,-.333,1,-1,0,1]),this.generatePolygon("rhomboid",[-1,-1,.333,-1,1,1,-.333,1]),this.nodeShapes.concavehexagon=this.generatePolygon("concave-hexagon",[-1,-.95,-.75,0,-1,.95,1,.95,.75,0,1,-.95]);{var H=[-1,-1,.25,-1,1,0,.25,1,-1,1];this.generatePolygon("tag",H),this.generateRoundPolygon("round-tag",H)}m.makePolygon=function(C){var G=C.join("$"),U="polygon-"+G,Z;return(Z=this[U])?Z:g.generatePolygon(U,C)}};var Rx={};Rx.timeToRender=function(){return this.redrawTotalTime/this.redrawCount},Rx.redraw=function(m){m=m||GP();var g=this;g.averageRedrawTime===void 0&&(g.averageRedrawTime=0),g.lastRedrawTime===void 0&&(g.lastRedrawTime=0),g.lastDrawTime===void 0&&(g.lastDrawTime=0),g.requestedFrame=!0,g.renderOptions=m},Rx.beforeRender=function(m,g){if(!this.destroyed){g==null&&yc("Priority is not optional for beforeRender");var k=this.beforeRenderCallbacks;k.push({fn:m,priority:g}),k.sort(function(T,A){return A.priority-T.priority})}};var $R=function(g,k,T){for(var A=g.beforeRenderCallbacks,D=0;D<A.length;D++)A[D].fn(k,T)};Rx.startRenderLoop=function(){var m=this,g=m.cy;if(!m.renderLoopStarted){m.renderLoopStarted=!0;var k=function T(A){if(!m.destroyed){if(!g.batching())if(m.requestedFrame&&!m.skipFrame){$R(m,!0,A);var D=pi();m.render(m.renderOptions);var I=m.lastDrawTime=pi();m.averageRedrawTime===void 0&&(m.averageRedrawTime=I-D),m.redrawCount===void 0&&(m.redrawCount=0),m.redrawCount++,m.redrawTotalTime===void 0&&(m.redrawTotalTime=0);var F=I-D;m.redrawTotalTime+=F,m.lastRedrawTime=F,m.averageRedrawTime=m.averageRedrawTime/2+F/2,m.requestedFrame=!1}else $R(m,!1,A);m.skipFrame=!1,Aa(T)}};Aa(k)}};var eee=function(g){this.init(g)},XL=eee,T3=XL.prototype;T3.clientFunctions=["redrawHint","render","renderTo","matchCanvasSize","nodeShapeImpl","arrowShapeImpl"],T3.init=function(m){var g=this;g.options=m,g.cy=m.cy;var k=g.container=m.cy.container();if($){var T=$.document,A=T.head,D="__________cytoscape_stylesheet",I="__________cytoscape_container",F=T.getElementById(D)!=null;if(k.className.indexOf(I)<0&&(k.className=(k.className||"")+" "+I),!F){var H=T.createElement("style");H.id=D,H.innerHTML="."+I+" { position: relative; }",A.insertBefore(H,A.children[0])}var C=$.getComputedStyle(k),G=C.getPropertyValue("position");G==="static"&&Vo("A Cytoscape container has style position:static and so can not use UI extensions properly")}g.selection=[void 0,void 0,void 0,void 0,0],g.bezierProjPcts=[.05,.225,.4,.5,.6,.775,.95],g.hoverData={down:null,last:null,downTime:null,triggerMode:null,dragging:!1,initialPan:[null,null],capture:!1},g.dragData={possibleDragElements:[]},g.touchData={start:null,capture:!1,startPosition:[null,null,null,null,null,null],singleTouchStartTime:null,singleTouchMoved:!0,now:[null,null,null,null,null,null],earlier:[null,null,null,null,null,null]},g.redraws=0,g.showFps=m.showFps,g.debug=m.debug,g.hideEdgesOnViewport=m.hideEdgesOnViewport,g.textureOnViewport=m.textureOnViewport,g.wheelSensitivity=m.wheelSensitivity,g.motionBlurEnabled=m.motionBlur,g.forcedPixelRatio=te(m.pixelRatio)?m.pixelRatio:null,g.motionBlur=m.motionBlur,g.motionBlurOpacity=m.motionBlurOpacity,g.motionBlurTransparency=1-g.motionBlurOpacity,g.motionBlurPxRatio=1,g.mbPxRBlurry=1,g.minMbLowQualFrames=4,g.fullQualityMb=!1,g.clearedForMotionBlur=[],g.desktopTapThreshold=m.desktopTapThreshold,g.desktopTapThreshold2=m.desktopTapThreshold*m.desktopTapThreshold,g.touchTapThreshold=m.touchTapThreshold,g.touchTapThreshold2=m.touchTapThreshold*m.touchTapThreshold,g.tapholdDuration=500,g.bindings=[],g.beforeRenderCallbacks=[],g.beforeRenderPriorities={animations:400,eleCalcs:300,eleTxrDeq:200,lyrTxrDeq:150,lyrTxrSkip:100},g.registerNodeShapes(),g.registerArrowShapes(),g.registerCalculationListeners()},T3.notify=function(m,g){var k=this,T=k.cy;if(!this.destroyed){if(m==="init"){k.load();return}if(m==="destroy"){k.destroy();return}(m==="add"||m==="remove"||m==="move"&&T.hasCompoundNodes()||m==="load"||m==="zorder"||m==="mount")&&k.invalidateCachedZSortedEles(),m==="viewport"&&k.redrawHint("select",!0),(m==="load"||m==="resize"||m==="mount")&&(k.invalidateContainerClientCoordsCache(),k.matchCanvasSize(k.container)),k.redrawHint("eles",!0),k.redrawHint("drag",!0),this.startRenderLoop(),this.redraw()}},T3.destroy=function(){var m=this;m.destroyed=!0,m.cy.stopAnimationLoop();for(var g=0;g<m.bindings.length;g++){var k=m.bindings[g],T=k,A=T.target;(A.off||A.removeEventListener).apply(A,T.args)}if(m.bindings=[],m.beforeRenderCallbacks=[],m.onUpdateEleCalcsFns=[],m.removeObserver&&m.removeObserver.disconnect(),m.styleObserver&&m.styleObserver.disconnect(),m.resizeObserver&&m.resizeObserver.disconnect(),m.labelCalcDiv)try{document.body.removeChild(m.labelCalcDiv)}catch{}},T3.isHeadless=function(){return!1},[WL,Bx,jR,r6,iv,Rx].forEach(function(m){Oe(T3,m)});var QL=1e3/60,oT={setupDequeueing:function(g){return function(){var T=this,A=this.renderer;if(!T.dequeueingSetup){T.dequeueingSetup=!0;var D=im(function(){A.redrawHint("eles",!0),A.redrawHint("drag",!0),A.redraw()},g.deqRedrawThreshold),I=function(C,G){var U=pi(),Z=A.averageRedrawTime,ne=A.lastRedrawTime,ae=[],ue=A.cy.extent(),_e=A.getPixelRatio();for(C||A.flushRenderedStyleQueue();;){var be=pi(),Se=be-U,Le=be-G;if(ne<QL){var Be=QL-(C?Z:0);if(Le>=g.deqFastCost*Be)break}else if(C){if(Se>=g.deqCost*ne||Se>=g.deqAvgCost*Z)break}else if(Le>=g.deqNoDrawCost*QL)break;var Ke=g.deq(T,_e,ue);if(Ke.length>0)for(var qe=0;qe<Ke.length;qe++)ae.push(Ke[qe]);else break}ae.length>0&&(g.onDeqd(T,ae),!C&&g.shouldRedraw(T,ae,_e,ue)&&D())},F=g.priority||om;A.beforeRender(I,F(T))}}}},tee=function(){function m(g){var k=arguments.length>1&&arguments[1]!==void 0?arguments[1]:am;p(this,m),this.idsByKey=new R2,this.keyForId=new R2,this.cachesByLvl=new R2,this.lvls=[],this.getKey=g,this.doesEleInvalidateKey=k}return y(m,[{key:"getIdsFor",value:function(k){k==null&&yc("Can not get id list for null key");var T=this.idsByKey,A=this.idsByKey.get(k);return A||(A=new R5,T.set(k,A)),A}},{key:"addIdForKey",value:function(k,T){k!=null&&this.getIdsFor(k).add(T)}},{key:"deleteIdForKey",value:function(k,T){k!=null&&this.getIdsFor(k).delete(T)}},{key:"getNumberOfIdsForKey",value:function(k){return k==null?0:this.getIdsFor(k).size}},{key:"updateKeyMappingFor",value:function(k){var T=k.id(),A=this.keyForId.get(T),D=this.getKey(k);this.deleteIdForKey(A,T),this.addIdForKey(D,T),this.keyForId.set(T,D)}},{key:"deleteKeyMappingFor",value:function(k){var T=k.id(),A=this.keyForId.get(T);this.deleteIdForKey(A,T),this.keyForId.delete(T)}},{key:"keyHasChangedFor",value:function(k){var T=k.id(),A=this.keyForId.get(T),D=this.getKey(k);return A!==D}},{key:"isInvalid",value:function(k){return this.keyHasChangedFor(k)||this.doesEleInvalidateKey(k)}},{key:"getCachesAt",value:function(k){var T=this.cachesByLvl,A=this.lvls,D=T.get(k);return D||(D=new R2,T.set(k,D),A.push(k)),D}},{key:"getCache",value:function(k,T){return this.getCachesAt(T).get(k)}},{key:"get",value:function(k,T){var A=this.getKey(k),D=this.getCache(A,T);return D!=null&&this.updateKeyMappingFor(k),D}},{key:"getForCachedKey",value:function(k,T){var A=this.keyForId.get(k.id()),D=this.getCache(A,T);return D}},{key:"hasCache",value:function(k,T){return this.getCachesAt(T).has(k)}},{key:"has",value:function(k,T){var A=this.getKey(k);return this.hasCache(A,T)}},{key:"setCache",value:function(k,T,A){A.key=k,this.getCachesAt(T).set(k,A)}},{key:"set",value:function(k,T,A){var D=this.getKey(k);this.setCache(D,T,A),this.updateKeyMappingFor(k)}},{key:"deleteCache",value:function(k,T){this.getCachesAt(T).delete(k)}},{key:"delete",value:function(k,T){var A=this.getKey(k);this.deleteCache(A,T)}},{key:"invalidateKey",value:function(k){var T=this;this.lvls.forEach(function(A){return T.deleteCache(k,A)})}},{key:"invalidate",value:function(k){var T=k.id(),A=this.keyForId.get(T);this.deleteKeyMappingFor(k);var D=this.doesEleInvalidateKey(k);return 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T.reqs-k.reqs});return g},Oh.getElementKeyToQueue=function(){var m=this,g=m.eleKeyToCacheQueue=m.eleKeyToCacheQueue||{};return g},Oh.getElement=function(m,g,k,T,A){var D=this,I=this.renderer,F=I.cy.zoom(),H=this.lookup;if(!g||g.w===0||g.h===0||isNaN(g.w)||isNaN(g.h)||!m.visible()||m.removed()||!D.allowEdgeTxrCaching&&m.isEdge()||!D.allowParentTxrCaching&&m.isParent())return null;if(T==null&&(T=Math.ceil(yL(F*k))),T<i6)T=i6;else if(F>=JL||T>ZL)return null;var C=Math.pow(2,T),G=g.h*C,U=g.w*C,Z=I.eleTextBiggerThanMin(m,C);if(!this.isVisible(m,Z))return null;var ne=H.get(m,T);if(ne&&ne.invalidated&&(ne.invalidated=!1,ne.texture.invalidatedWidth-=ne.width),ne)return ne;var ae;if(G<=cT?ae=cT:G<=uT?ae=uT:ae=Math.ceil(G/uT)*uT,G>HR||U>iee)return null;var ue=D.getTextureQueue(ae),_e=ue[ue.length-2],be=function(){return D.recycleTexture(ae,U)||D.addTexture(ae,U)};_e||(_e=ue[ue.length-1]),_e||(_e=be()),_e.width-_e.usedWidth<U&&(_e=be());for(var Se=function(Rt){return 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this.rect.y+this.rect.height/2},R.prototype.getCenter=function(){return new B(this.rect.x+this.rect.width/2,this.rect.y+this.rect.height/2)},R.prototype.getLocation=function(){return new B(this.rect.x,this.rect.y)},R.prototype.getRect=function(){return this.rect},R.prototype.getDiagonal=function(){return Math.sqrt(this.rect.width*this.rect.width+this.rect.height*this.rect.height)},R.prototype.getHalfTheDiagonal=function(){return Math.sqrt(this.rect.height*this.rect.height+this.rect.width*this.rect.width)/2},R.prototype.setRect=function($,V){this.rect.x=$.x,this.rect.y=$.y,this.rect.width=V.width,this.rect.height=V.height},R.prototype.setCenter=function($,V){this.rect.x=$-this.rect.width/2,this.rect.y=V-this.rect.height/2},R.prototype.setLocation=function($,V){this.rect.x=$,this.rect.y=V},R.prototype.moveBy=function($,V){this.rect.x+=$,this.rect.y+=V},R.prototype.getEdgeListToNode=function($){var V=[],Q=this;return Q.edges.forEach(function(oe){if(oe.target==$){if(oe.source!=Q)throw"Incorrect edge source!";V.push(oe)}}),V},R.prototype.getEdgesBetween=function($){var V=[],Q=this;return Q.edges.forEach(function(oe){if(!(oe.source==Q||oe.target==Q))throw"Incorrect edge source and/or target";(oe.target==$||oe.source==$)&&V.push(oe)}),V},R.prototype.getNeighborsList=function(){var $=new Set,V=this;return V.edges.forEach(function(Q){if(Q.source==V)$.add(Q.target);else{if(Q.target!=V)throw"Incorrect incidency!";$.add(Q.source)}}),$},R.prototype.withChildren=function(){var $=new Set,V,Q;if($.add(this),this.child!=null)for(var oe=this.child.getNodes(),ce=0;ce<oe.length;ce++)V=oe[ce],Q=V.withChildren(),Q.forEach(function(se){$.add(se)});return $},R.prototype.getNoOfChildren=function(){var $=0,V;if(this.child==null)$=1;else for(var Q=this.child.getNodes(),oe=0;oe<Q.length;oe++)V=Q[oe],$+=V.getNoOfChildren();return $==0&&($=1),$},R.prototype.getEstimatedSize=function(){if(this.estimatedSize==b.MIN_VALUE)throw"assert failed";return this.estimatedSize},R.prototype.calcEstimatedSize=function(){return this.child==null?this.estimatedSize=(this.rect.width+this.rect.height)/2:(this.estimatedSize=this.child.calcEstimatedSize(),this.rect.width=this.estimatedSize,this.rect.height=this.estimatedSize,this.estimatedSize)},R.prototype.scatter=function(){var $,V,Q=-S.INITIAL_WORLD_BOUNDARY,oe=S.INITIAL_WORLD_BOUNDARY;$=S.WORLD_CENTER_X+N.nextDouble()*(oe-Q)+Q;var ce=-S.INITIAL_WORLD_BOUNDARY,se=S.INITIAL_WORLD_BOUNDARY;V=S.WORLD_CENTER_Y+N.nextDouble()*(se-ce)+ce,this.rect.x=$,this.rect.y=V},R.prototype.updateBounds=function(){if(this.getChild()==null)throw"assert failed";if(this.getChild().getNodes().length!=0){var $=this.getChild();if($.updateBounds(!0),this.rect.x=$.getLeft(),this.rect.y=$.getTop(),this.setWidth($.getRight()-$.getLeft()),this.setHeight($.getBottom()-$.getTop()),S.NODE_DIMENSIONS_INCLUDE_LABELS){var V=$.getRight()-$.getLeft(),Q=$.getBottom()-$.getTop();this.labelWidth>V&&(this.rect.x-=(this.labelWidth-V)/2,this.setWidth(this.labelWidth)),this.labelHeight>Q&&(this.labelPos=="center"?this.rect.y-=(this.labelHeight-Q)/2:this.labelPos=="top"&&(this.rect.y-=this.labelHeight-Q),this.setHeight(this.labelHeight))}}},R.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==b.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},R.prototype.transform=function($){var V=this.rect.x;V>S.WORLD_BOUNDARY?V=S.WORLD_BOUNDARY:V<-S.WORLD_BOUNDARY&&(V=-S.WORLD_BOUNDARY);var Q=this.rect.y;Q>S.WORLD_BOUNDARY?Q=S.WORLD_BOUNDARY:Q<-S.WORLD_BOUNDARY&&(Q=-S.WORLD_BOUNDARY);var oe=new B(V,Q),ce=$.inverseTransformPoint(oe);this.setLocation(ce.x,ce.y)},R.prototype.getLeft=function(){return this.rect.x},R.prototype.getRight=function(){return this.rect.x+this.rect.width},R.prototype.getTop=function(){return this.rect.y},R.prototype.getBottom=function(){return this.rect.y+this.rect.height},R.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},f.exports=R},function(f,p,w){function y(b,E){b==null&&E==null?(this.x=0,this.y=0):(this.x=b,this.y=E)}y.prototype.getX=function(){return this.x},y.prototype.getY=function(){return this.y},y.prototype.setX=function(b){this.x=b},y.prototype.setY=function(b){this.y=b},y.prototype.getDifference=function(b){return new DimensionD(this.x-b.x,this.y-b.y)},y.prototype.getCopy=function(){return new y(this.x,this.y)},y.prototype.translate=function(b){return this.x+=b.width,this.y+=b.height,this},f.exports=y},function(f,p,w){var y=w(2),b=w(10),E=w(0),S=w(6),N=w(3),B=w(1),R=w(13),j=w(12),$=w(11);function V(oe,ce,se){y.call(this,se),this.estimatedSize=b.MIN_VALUE,this.margin=E.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=oe,ce!=null&&ce instanceof S?this.graphManager=ce:ce!=null&&ce instanceof Layout&&(this.graphManager=ce.graphManager)}V.prototype=Object.create(y.prototype);for(var Q in y)V[Q]=y[Q];V.prototype.getNodes=function(){return this.nodes},V.prototype.getEdges=function(){return this.edges},V.prototype.getGraphManager=function(){return this.graphManager},V.prototype.getParent=function(){return this.parent},V.prototype.getLeft=function(){return this.left},V.prototype.getRight=function(){return this.right},V.prototype.getTop=function(){return this.top},V.prototype.getBottom=function(){return this.bottom},V.prototype.isConnected=function(){return this.isConnected},V.prototype.add=function(oe,ce,se){if(ce==null&&se==null){var ge=oe;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(ge)>-1)throw"Node already in graph!";return ge.owner=this,this.getNodes().push(ge),ge}else{var ye=oe;if(!(this.getNodes().indexOf(ce)>-1&&this.getNodes().indexOf(se)>-1))throw"Source or target not in graph!";if(!(ce.owner==se.owner&&ce.owner==this))throw"Both owners must be this graph!";return ce.owner!=se.owner?null:(ye.source=ce,ye.target=se,ye.isInterGraph=!1,this.getEdges().push(ye),ce.edges.push(ye),se!=ce&&se.edges.push(ye),ye)}},V.prototype.remove=function(oe){var ce=oe;if(oe instanceof N){if(ce==null)throw"Node is null!";if(!(ce.owner!=null&&ce.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var se=ce.edges.slice(),ge,ye=se.length,ke=0;ke<ye;ke++)ge=se[ke],ge.isInterGraph?this.graphManager.remove(ge):ge.source.owner.remove(ge);var Ae=this.nodes.indexOf(ce);if(Ae==-1)throw"Node not in owner node list!";this.nodes.splice(Ae,1)}else if(oe instanceof B){var ge=oe;if(ge==null)throw"Edge is null!";if(!(ge.source!=null&&ge.target!=null))throw"Source and/or target is null!";if(!(ge.source.owner!=null&&ge.target.owner!=null&&ge.source.owner==this&&ge.target.owner==this))throw"Source and/or target owner is invalid!";var de=ge.source.edges.indexOf(ge),ve=ge.target.edges.indexOf(ge);if(!(de>-1&&ve>-1))throw"Source and/or target doesn't know this edge!";ge.source.edges.splice(de,1),ge.target!=ge.source&&ge.target.edges.splice(ve,1);var Ae=ge.source.owner.getEdges().indexOf(ge);if(Ae==-1)throw"Not in owner's edge list!";ge.source.owner.getEdges().splice(Ae,1)}},V.prototype.updateLeftTop=function(){for(var oe=b.MAX_VALUE,ce=b.MAX_VALUE,se,ge,ye,ke=this.getNodes(),Ae=ke.length,de=0;de<Ae;de++){var ve=ke[de];se=ve.getTop(),ge=ve.getLeft(),oe>se&&(oe=se),ce>ge&&(ce=ge)}return oe==b.MAX_VALUE?null:(ke[0].getParent().paddingLeft!=null?ye=ke[0].getParent().paddingLeft:ye=this.margin,this.left=ce-ye,this.top=oe-ye,new j(this.left,this.top))},V.prototype.updateBounds=function(oe){for(var ce=b.MAX_VALUE,se=-b.MAX_VALUE,ge=b.MAX_VALUE,ye=-b.MAX_VALUE,ke,Ae,de,ve,te,xe=this.nodes,De=xe.length,he=0;he<De;he++){var Ie=xe[he];oe&&Ie.child!=null&&Ie.updateBounds(),ke=Ie.getLeft(),Ae=Ie.getRight(),de=Ie.getTop(),ve=Ie.getBottom(),ce>ke&&(ce=ke),se<Ae&&(se=Ae),ge>de&&(ge=de),ye<ve&&(ye=ve)}var ee=new R(ce,ge,se-ce,ye-ge);ce==b.MAX_VALUE&&(this.left=this.parent.getLeft(),this.right=this.parent.getRight(),this.top=this.parent.getTop(),this.bottom=this.parent.getBottom()),xe[0].getParent().paddingLeft!=null?te=xe[0].getParent().paddingLeft:te=this.margin,this.left=ee.x-te,this.right=ee.x+ee.width+te,this.top=ee.y-te,this.bottom=ee.y+ee.height+te},V.calculateBounds=function(oe){for(var ce=b.MAX_VALUE,se=-b.MAX_VALUE,ge=b.MAX_VALUE,ye=-b.MAX_VALUE,ke,Ae,de,ve,te=oe.length,xe=0;xe<te;xe++){var De=oe[xe];ke=De.getLeft(),Ae=De.getRight(),de=De.getTop(),ve=De.getBottom(),ce>ke&&(ce=ke),se<Ae&&(se=Ae),ge>de&&(ge=de),ye<ve&&(ye=ve)}var he=new R(ce,ge,se-ce,ye-ge);return he},V.prototype.getInclusionTreeDepth=function(){return this==this.graphManager.getRoot()?1:this.parent.getInclusionTreeDepth()},V.prototype.getEstimatedSize=function(){if(this.estimatedSize==b.MIN_VALUE)throw"assert failed";return this.estimatedSize},V.prototype.calcEstimatedSize=function(){for(var oe=0,ce=this.nodes,se=ce.length,ge=0;ge<se;ge++){var ye=ce[ge];oe+=ye.calcEstimatedSize()}return oe==0?this.estimatedSize=E.EMPTY_COMPOUND_NODE_SIZE:this.estimatedSize=oe/Math.sqrt(this.nodes.length),this.estimatedSize},V.prototype.updateConnected=function(){var oe=this;if(this.nodes.length==0){this.isConnected=!0;return}var ce=new $,se=new Set,ge=this.nodes[0],ye,ke,Ae=ge.withChildren();for(Ae.forEach(function(he){ce.push(he),se.add(he)});ce.length!==0;){ge=ce.shift(),ye=ge.getEdges();for(var de=ye.length,ve=0;ve<de;ve++){var te=ye[ve];if(ke=te.getOtherEndInGraph(ge,this),ke!=null&&!se.has(ke)){var xe=ke.withChildren();xe.forEach(function(he){ce.push(he),se.add(he)})}}}if(this.isConnected=!1,se.size>=this.nodes.length){var De=0;se.forEach(function(he){he.owner==oe&&De++}),De==this.nodes.length&&(this.isConnected=!0)}},f.exports=V},function(f,p,w){var y,b=w(1);function E(S){y=w(5),this.layout=S,this.graphs=[],this.edges=[]}E.prototype.addRoot=function(){var S=this.layout.newGraph(),N=this.layout.newNode(null),B=this.add(S,N);return this.setRootGraph(B),this.rootGraph},E.prototype.add=function(S,N,B,R,j){if(B==null&&R==null&&j==null){if(S==null)throw"Graph is null!";if(N==null)throw"Parent node is null!";if(this.graphs.indexOf(S)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(S),S.parent!=null)throw"Already has a parent!";if(N.child!=null)throw"Already has a child!";return S.parent=N,N.child=S,S}else{j=B,R=N,B=S;var $=R.getOwner(),V=j.getOwner();if(!($!=null&&$.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(V!=null&&V.getGraphManager()==this))throw"Target not in this graph mgr!";if($==V)return B.isInterGraph=!1,$.add(B,R,j);if(B.isInterGraph=!0,B.source=R,B.target=j,this.edges.indexOf(B)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(B),!(B.source!=null&&B.target!=null))throw"Edge source and/or target is null!";if(!(B.source.edges.indexOf(B)==-1&&B.target.edges.indexOf(B)==-1))throw"Edge already in source and/or target incidency list!";return B.source.edges.push(B),B.target.edges.push(B),B}},E.prototype.remove=function(S){if(S instanceof y){var N=S;if(N.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(N==this.rootGraph||N.parent!=null&&N.parent.graphManager==this))throw"Invalid parent node!";var B=[];B=B.concat(N.getEdges());for(var R,j=B.length,$=0;$<j;$++)R=B[$],N.remove(R);var V=[];V=V.concat(N.getNodes());var Q;j=V.length;for(var $=0;$<j;$++)Q=V[$],N.remove(Q);N==this.rootGraph&&this.setRootGraph(null);var oe=this.graphs.indexOf(N);this.graphs.splice(oe,1),N.parent=null}else if(S instanceof b){if(R=S,R==null)throw"Edge is null!";if(!R.isInterGraph)throw"Not an inter-graph edge!";if(!(R.source!=null&&R.target!=null))throw"Source and/or target is null!";if(!(R.source.edges.indexOf(R)!=-1&&R.target.edges.indexOf(R)!=-1))throw"Source and/or target doesn't know this edge!";var oe=R.source.edges.indexOf(R);if(R.source.edges.splice(oe,1),oe=R.target.edges.indexOf(R),R.target.edges.splice(oe,1),!(R.source.owner!=null&&R.source.owner.getGraphManager()!=null))throw"Edge owner graph or owner graph manager is null!";if(R.source.owner.getGraphManager().edges.indexOf(R)==-1)throw"Not in owner graph manager's edge list!";var oe=R.source.owner.getGraphManager().edges.indexOf(R);R.source.owner.getGraphManager().edges.splice(oe,1)}},E.prototype.updateBounds=function(){this.rootGraph.updateBounds(!0)},E.prototype.getGraphs=function(){return this.graphs},E.prototype.getAllNodes=function(){if(this.allNodes==null){for(var S=[],N=this.getGraphs(),B=N.length,R=0;R<B;R++)S=S.concat(N[R].getNodes());this.allNodes=S}return this.allNodes},E.prototype.resetAllNodes=function(){this.allNodes=null},E.prototype.resetAllEdges=function(){this.allEdges=null},E.prototype.resetAllNodesToApplyGravitation=function(){this.allNodesToApplyGravitation=null},E.prototype.getAllEdges=function(){if(this.allEdges==null){var S=[],N=this.getGraphs();N.length;for(var B=0;B<N.length;B++)S=S.concat(N[B].getEdges());S=S.concat(this.edges),this.allEdges=S}return this.allEdges},E.prototype.getAllNodesToApplyGravitation=function(){return this.allNodesToApplyGravitation},E.prototype.setAllNodesToApplyGravitation=function(S){if(this.allNodesToApplyGravitation!=null)throw"assert failed";this.allNodesToApplyGravitation=S},E.prototype.getRoot=function(){return this.rootGraph},E.prototype.setRootGraph=function(S){if(S.getGraphManager()!=this)throw"Root not in this graph mgr!";this.rootGraph=S,S.parent==null&&(S.parent=this.layout.newNode("Root node"))},E.prototype.getLayout=function(){return this.layout},E.prototype.isOneAncestorOfOther=function(S,N){if(!(S!=null&&N!=null))throw"assert failed";if(S==N)return!0;var B=S.getOwner(),R;do{if(R=B.getParent(),R==null)break;if(R==N)return!0;if(B=R.getOwner(),B==null)break}while(!0);B=N.getOwner();do{if(R=B.getParent(),R==null)break;if(R==S)return!0;if(B=R.getOwner(),B==null)break}while(!0);return!1},E.prototype.calcLowestCommonAncestors=function(){for(var S,N,B,R,j,$=this.getAllEdges(),V=$.length,Q=0;Q<V;Q++){if(S=$[Q],N=S.source,B=S.target,S.lca=null,S.sourceInLca=N,S.targetInLca=B,N==B){S.lca=N.getOwner();continue}for(R=N.getOwner();S.lca==null;){for(S.targetInLca=B,j=B.getOwner();S.lca==null;){if(j==R){S.lca=j;break}if(j==this.rootGraph)break;if(S.lca!=null)throw"assert failed";S.targetInLca=j.getParent(),j=S.targetInLca.getOwner()}if(R==this.rootGraph)break;S.lca==null&&(S.sourceInLca=R.getParent(),R=S.sourceInLca.getOwner())}if(S.lca==null)throw"assert failed"}},E.prototype.calcLowestCommonAncestor=function(S,N){if(S==N)return S.getOwner();var B=S.getOwner();do{if(B==null)break;var R=N.getOwner();do{if(R==null)break;if(R==B)return R;R=R.getParent().getOwner()}while(!0);B=B.getParent().getOwner()}while(!0);return B},E.prototype.calcInclusionTreeDepths=function(S,N){S==null&&N==null&&(S=this.rootGraph,N=1);for(var B,R=S.getNodes(),j=R.length,$=0;$<j;$++)B=R[$],B.inclusionTreeDepth=N,B.child!=null&&this.calcInclusionTreeDepths(B.child,N+1)},E.prototype.includesInvalidEdge=function(){for(var S,N=this.edges.length,B=0;B<N;B++)if(S=this.edges[B],this.isOneAncestorOfOther(S.source,S.target))return!0;return!1},f.exports=E},function(f,p,w){var y=w(0);function b(){}for(var E in y)b[E]=y[E];b.MAX_ITERATIONS=2500,b.DEFAULT_EDGE_LENGTH=50,b.DEFAULT_SPRING_STRENGTH=.45,b.DEFAULT_REPULSION_STRENGTH=4500,b.DEFAULT_GRAVITY_STRENGTH=.4,b.DEFAULT_COMPOUND_GRAVITY_STRENGTH=1,b.DEFAULT_GRAVITY_RANGE_FACTOR=3.8,b.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=1.5,b.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION=!0,b.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION=!0,b.DEFAULT_COOLING_FACTOR_INCREMENTAL=.3,b.COOLING_ADAPTATION_FACTOR=.33,b.ADAPTATION_LOWER_NODE_LIMIT=1e3,b.ADAPTATION_UPPER_NODE_LIMIT=5e3,b.MAX_NODE_DISPLACEMENT_INCREMENTAL=100,b.MAX_NODE_DISPLACEMENT=b.MAX_NODE_DISPLACEMENT_INCREMENTAL*3,b.MIN_REPULSION_DIST=b.DEFAULT_EDGE_LENGTH/10,b.CONVERGENCE_CHECK_PERIOD=100,b.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=.1,b.MIN_EDGE_LENGTH=1,b.GRID_CALCULATION_CHECK_PERIOD=10,f.exports=b},function(f,p,w){var y=w(12);function b(){}b.calcSeparationAmount=function(E,S,N,B){if(!E.intersects(S))throw"assert failed";var R=new Array(2);this.decideDirectionsForOverlappingNodes(E,S,R),N[0]=Math.min(E.getRight(),S.getRight())-Math.max(E.x,S.x),N[1]=Math.min(E.getBottom(),S.getBottom())-Math.max(E.y,S.y),E.getX()<=S.getX()&&E.getRight()>=S.getRight()?N[0]+=Math.min(S.getX()-E.getX(),E.getRight()-S.getRight()):S.getX()<=E.getX()&&S.getRight()>=E.getRight()&&(N[0]+=Math.min(E.getX()-S.getX(),S.getRight()-E.getRight())),E.getY()<=S.getY()&&E.getBottom()>=S.getBottom()?N[1]+=Math.min(S.getY()-E.getY(),E.getBottom()-S.getBottom()):S.getY()<=E.getY()&&S.getBottom()>=E.getBottom()&&(N[1]+=Math.min(E.getY()-S.getY(),S.getBottom()-E.getBottom()));var j=Math.abs((S.getCenterY()-E.getCenterY())/(S.getCenterX()-E.getCenterX()));S.getCenterY()===E.getCenterY()&&S.getCenterX()===E.getCenterX()&&(j=1);var 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b)R[j]=b[j];R.prototype.initParameters=function(){b.prototype.initParameters.call(this,arguments),this.totalIterations=0,this.notAnimatedIterations=0,this.useFRGridVariant=E.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION,this.grid=[]},R.prototype.calcIdealEdgeLengths=function(){for(var $,V,Q,oe,ce,se,ge=this.getGraphManager().getAllEdges(),ye=0;ye<ge.length;ye++)$=ge[ye],$.idealLength=this.idealEdgeLength,$.isInterGraph&&(Q=$.getSource(),oe=$.getTarget(),ce=$.getSourceInLca().getEstimatedSize(),se=$.getTargetInLca().getEstimatedSize(),this.useSmartIdealEdgeLengthCalculation&&($.idealLength+=ce+se-2*S.SIMPLE_NODE_SIZE),V=$.getLca().getInclusionTreeDepth(),$.idealLength+=E.DEFAULT_EDGE_LENGTH*E.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR*(Q.getInclusionTreeDepth()+oe.getInclusionTreeDepth()-2*V))},R.prototype.initSpringEmbedder=function(){var $=this.getAllNodes().length;this.incremental?($>E.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*E.COOLING_ADAPTATION_FACTOR,this.coolingFactor-($-E.ADAPTATION_LOWER_NODE_LIMIT)/(E.ADAPTATION_UPPER_NODE_LIMIT-E.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-E.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=E.MAX_NODE_DISPLACEMENT_INCREMENTAL):($>E.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(E.COOLING_ADAPTATION_FACTOR,1-($-E.ADAPTATION_LOWER_NODE_LIMIT)/(E.ADAPTATION_UPPER_NODE_LIMIT-E.ADAPTATION_LOWER_NODE_LIMIT)*(1-E.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=E.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},R.prototype.calcSpringForces=function(){for(var $=this.getAllEdges(),V,Q=0;Q<$.length;Q++)V=$[Q],this.calcSpringForce(V,V.idealLength)},R.prototype.calcRepulsionForces=function(){var $=arguments.length>0&&arguments[0]!==void 0?arguments[0]:!0,V=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,Q,oe,ce,se,ge=this.getAllNodes(),ye;if(this.useFRGridVariant)for(this.totalIterations%E.GRID_CALCULATION_CHECK_PERIOD==1&&$&&this.updateGrid(),ye=new Set,Q=0;Q<ge.length;Q++)ce=ge[Q],this.calculateRepulsionForceOfANode(ce,ye,$,V),ye.add(ce);else for(Q=0;Q<ge.length;Q++)for(ce=ge[Q],oe=Q+1;oe<ge.length;oe++)se=ge[oe],ce.getOwner()==se.getOwner()&&this.calcRepulsionForce(ce,se)},R.prototype.calcGravitationalForces=function(){for(var $,V=this.getAllNodesToApplyGravitation(),Q=0;Q<V.length;Q++)$=V[Q],this.calcGravitationalForce($)},R.prototype.moveNodes=function(){for(var $=this.getAllNodes(),V,Q=0;Q<$.length;Q++)V=$[Q],V.move()},R.prototype.calcSpringForce=function($,V){var Q=$.getSource(),oe=$.getTarget(),ce,se,ge,ye;if(this.uniformLeafNodeSizes&&Q.getChild()==null&&oe.getChild()==null)$.updateLengthSimple();else if($.updateLength(),$.isOverlapingSourceAndTarget)return;ce=$.getLength(),ce!=0&&(se=this.springConstant*(ce-V),ge=se*($.lengthX/ce),ye=se*($.lengthY/ce),Q.springForceX+=ge,Q.springForceY+=ye,oe.springForceX-=ge,oe.springForceY-=ye)},R.prototype.calcRepulsionForce=function($,V){var Q=$.getRect(),oe=V.getRect(),ce=new Array(2),se=new Array(4),ge,ye,ke,Ae,de,ve,te;if(Q.intersects(oe)){N.calcSeparationAmount(Q,oe,ce,E.DEFAULT_EDGE_LENGTH/2),ve=2*ce[0],te=2*ce[1];var xe=$.noOfChildren*V.noOfChildren/($.noOfChildren+V.noOfChildren);$.repulsionForceX-=xe*ve,$.repulsionForceY-=xe*te,V.repulsionForceX+=xe*ve,V.repulsionForceY+=xe*te}else this.uniformLeafNodeSizes&&$.getChild()==null&&V.getChild()==null?(ge=oe.getCenterX()-Q.getCenterX(),ye=oe.getCenterY()-Q.getCenterY()):(N.getIntersection(Q,oe,se),ge=se[2]-se[0],ye=se[3]-se[1]),Math.abs(ge)<E.MIN_REPULSION_DIST&&(ge=B.sign(ge)*E.MIN_REPULSION_DIST),Math.abs(ye)<E.MIN_REPULSION_DIST&&(ye=B.sign(ye)*E.MIN_REPULSION_DIST),ke=ge*ge+ye*ye,Ae=Math.sqrt(ke),de=this.repulsionConstant*$.noOfChildren*V.noOfChildren/ke,ve=de*ge/Ae,te=de*ye/Ae,$.repulsionForceX-=ve,$.repulsionForceY-=te,V.repulsionForceX+=ve,V.repulsionForceY+=te},R.prototype.calcGravitationalForce=function($){var V,Q,oe,ce,se,ge,ye,ke;V=$.getOwner(),Q=(V.getRight()+V.getLeft())/2,oe=(V.getTop()+V.getBottom())/2,ce=$.getCenterX()-Q,se=$.getCenterY()-oe,ge=Math.abs(ce)+$.getWidth()/2,ye=Math.abs(se)+$.getHeight()/2,$.getOwner()==this.graphManager.getRoot()?(ke=V.getEstimatedSize()*this.gravityRangeFactor,(ge>ke||ye>ke)&&($.gravitationForceX=-this.gravityConstant*ce,$.gravitationForceY=-this.gravityConstant*se)):(ke=V.getEstimatedSize()*this.compoundGravityRangeFactor,(ge>ke||ye>ke)&&($.gravitationForceX=-this.gravityConstant*ce*this.compoundGravityConstant,$.gravitationForceY=-this.gravityConstant*se*this.compoundGravityConstant))},R.prototype.isConverged=function(){var $,V=!1;return this.totalIterations>this.maxIterations/3&&(V=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),$=this.totalDisplacement<this.totalDisplacementThreshold,this.oldTotalDisplacement=this.totalDisplacement,$||V},R.prototype.animate=function(){this.animationDuringLayout&&!this.isSubLayout&&(this.notAnimatedIterations==this.animationPeriod?(this.update(),this.notAnimatedIterations=0):this.notAnimatedIterations++)},R.prototype.calcNoOfChildrenForAllNodes=function(){for(var $,V=this.graphManager.getAllNodes(),Q=0;Q<V.length;Q++)$=V[Q],$.noOfChildren=$.getNoOfChildren()},R.prototype.calcGrid=function($){var V=0,Q=0;V=parseInt(Math.ceil(($.getRight()-$.getLeft())/this.repulsionRange)),Q=parseInt(Math.ceil(($.getBottom()-$.getTop())/this.repulsionRange));for(var oe=new Array(V),ce=0;ce<V;ce++)oe[ce]=new Array(Q);for(var ce=0;ce<V;ce++)for(var se=0;se<Q;se++)oe[ce][se]=new Array;return oe},R.prototype.addNodeToGrid=function($,V,Q){var oe=0,ce=0,se=0,ge=0;oe=parseInt(Math.floor(($.getRect().x-V)/this.repulsionRange)),ce=parseInt(Math.floor(($.getRect().width+$.getRect().x-V)/this.repulsionRange)),se=parseInt(Math.floor(($.getRect().y-Q)/this.repulsionRange)),ge=parseInt(Math.floor(($.getRect().height+$.getRect().y-Q)/this.repulsionRange));for(var ye=oe;ye<=ce;ye++)for(var ke=se;ke<=ge;ke++)this.grid[ye][ke].push($),$.setGridCoordinates(oe,ce,se,ge)},R.prototype.updateGrid=function(){var $,V,Q=this.getAllNodes();for(this.grid=this.calcGrid(this.graphManager.getRoot()),$=0;$<Q.length;$++)V=Q[$],this.addNodeToGrid(V,this.graphManager.getRoot().getLeft(),this.graphManager.getRoot().getTop())},R.prototype.calculateRepulsionForceOfANode=function($,V,Q,oe){if(this.totalIterations%E.GRID_CALCULATION_CHECK_PERIOD==1&&Q||oe){var ce=new Set;$.surrounding=new Array;for(var se,ge=this.grid,ye=$.startX-1;ye<$.finishX+2;ye++)for(var ke=$.startY-1;ke<$.finishY+2;ke++)if(!(ye<0||ke<0||ye>=ge.length||ke>=ge[0].length)){for(var Ae=0;Ae<ge[ye][ke].length;Ae++)if(se=ge[ye][ke][Ae],!($.getOwner()!=se.getOwner()||$==se)&&!V.has(se)&&!ce.has(se)){var de=Math.abs($.getCenterX()-se.getCenterX())-($.getWidth()/2+se.getWidth()/2),ve=Math.abs($.getCenterY()-se.getCenterY())-($.getHeight()/2+se.getHeight()/2);de<=this.repulsionRange&&ve<=this.repulsionRange&&ce.add(se)}}$.surrounding=[].concat(y(ce))}for(ye=0;ye<$.surrounding.length;ye++)this.calcRepulsionForce($,$.surrounding[ye])},R.prototype.calcRepulsionRange=function(){return 0},f.exports=R},function(f,p,w){var y=w(1),b=w(7);function E(N,B,R){y.call(this,N,B,R),this.idealLength=b.DEFAULT_EDGE_LENGTH}E.prototype=Object.create(y.prototype);for(var S in y)E[S]=y[S];f.exports=E},function(f,p,w){var y=w(3);function b(S,N,B,R){y.call(this,S,N,B,R),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0,this.startX=0,this.finishX=0,this.startY=0,this.finishY=0,this.surrounding=[]}b.prototype=Object.create(y.prototype);for(var E in y)b[E]=y[E];b.prototype.setGridCoordinates=function(S,N,B,R){this.startX=S,this.finishX=N,this.startY=B,this.finishY=R},f.exports=b},function(f,p,w){function y(b,E){this.width=0,this.height=0,b!==null&&E!==null&&(this.height=E,this.width=b)}y.prototype.getWidth=function(){return this.width},y.prototype.setWidth=function(b){this.width=b},y.prototype.getHeight=function(){return this.height},y.prototype.setHeight=function(b){this.height=b},f.exports=y},function(f,p,w){var y=w(14);function b(){this.map={},this.keys=[]}b.prototype.put=function(E,S){var N=y.createID(E);this.contains(N)||(this.map[N]=S,this.keys.push(E))},b.prototype.contains=function(E){return y.createID(E),this.map[E]!=null},b.prototype.get=function(E){var S=y.createID(E);return this.map[S]},b.prototype.keySet=function(){return this.keys},f.exports=b},function(f,p,w){var y=w(14);function b(){this.set={}}b.prototype.add=function(E){var S=y.createID(E);this.contains(S)||(this.set[S]=E)},b.prototype.remove=function(E){delete this.set[y.createID(E)]},b.prototype.clear=function(){this.set={}},b.prototype.contains=function(E){return this.set[y.createID(E)]==E},b.prototype.isEmpty=function(){return this.size()===0},b.prototype.size=function(){return Object.keys(this.set).length},b.prototype.addAllTo=function(E){for(var S=Object.keys(this.set),N=S.length,B=0;B<N;B++)E.push(this.set[S[B]])},b.prototype.size=function(){return Object.keys(this.set).length},b.prototype.addAll=function(E){for(var S=E.length,N=0;N<S;N++){var B=E[N];this.add(B)}},f.exports=b},function(f,p,w){var y=function(){function N(B,R){for(var j=0;j<R.length;j++){var $=R[j];$.enumerable=$.enumerable||!1,$.configurable=!0,"value"in $&&($.writable=!0),Object.defineProperty(B,$.key,$)}}return function(B,R,j){return R&&N(B.prototype,R),j&&N(B,j),B}}();function b(N,B){if(!(N instanceof B))throw new TypeError("Cannot call a class as a function")}var E=w(11),S=function(){function N(B,R){b(this,N),(R!==null||R!==void 0)&&(this.compareFunction=this._defaultCompareFunction);var j=void 0;B instanceof E?j=B.size():j=B.length,this._quicksort(B,0,j-1)}return y(N,[{key:"_quicksort",value:function(R,j,$){if(j<$){var V=this._partition(R,j,$);this._quicksort(R,j,V),this._quicksort(R,V+1,$)}}},{key:"_partition",value:function(R,j,$){for(var V=this._get(R,j),Q=j,oe=$;;){for(;this.compareFunction(V,this._get(R,oe));)oe--;for(;this.compareFunction(this._get(R,Q),V);)Q++;if(Q<oe)this._swap(R,Q,oe),Q++,oe--;else return oe}}},{key:"_get",value:function(R,j){return R instanceof E?R.get_object_at(j):R[j]}},{key:"_set",value:function(R,j,$){R instanceof E?R.set_object_at(j,$):R[j]=$}},{key:"_swap",value:function(R,j,$){var V=this._get(R,j);this._set(R,j,this._get(R,$)),this._set(R,$,V)}},{key:"_defaultCompareFunction",value:function(R,j){return j>R}}]),N}();f.exports=S},function(f,p,w){var y=function(){function S(N,B){for(var R=0;R<B.length;R++){var j=B[R];j.enumerable=j.enumerable||!1,j.configurable=!0,"value"in j&&(j.writable=!0),Object.defineProperty(N,j.key,j)}}return function(N,B,R){return B&&S(N.prototype,B),R&&S(N,R),N}}();function b(S,N){if(!(S instanceof N))throw new TypeError("Cannot call a class as a function")}var E=function(){function S(N,B){var R=arguments.length>2&&arguments[2]!==void 0?arguments[2]:1,j=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,$=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;b(this,S),this.sequence1=N,this.sequence2=B,this.match_score=R,this.mismatch_penalty=j,this.gap_penalty=$,this.iMax=N.length+1,this.jMax=B.length+1,this.grid=new Array(this.iMax);for(var V=0;V<this.iMax;V++){this.grid[V]=new Array(this.jMax);for(var Q=0;Q<this.jMax;Q++)this.grid[V][Q]=0}this.tracebackGrid=new Array(this.iMax);for(var oe=0;oe<this.iMax;oe++){this.tracebackGrid[oe]=new Array(this.jMax);for(var ce=0;ce<this.jMax;ce++)this.tracebackGrid[oe][ce]=[null,null,null]}this.alignments=[],this.score=-1,this.computeGrids()}return y(S,[{key:"getScore",value:function(){return this.score}},{key:"getAlignments",value:function(){return this.alignments}},{key:"computeGrids",value:function(){for(var B=1;B<this.jMax;B++)this.grid[0][B]=this.grid[0][B-1]+this.gap_penalty,this.tracebackGrid[0][B]=[!1,!1,!0];for(var R=1;R<this.iMax;R++)this.grid[R][0]=this.grid[R-1][0]+this.gap_penalty,this.tracebackGrid[R][0]=[!1,!0,!1];for(var j=1;j<this.iMax;j++)for(var $=1;$<this.jMax;$++){var V=void 0;this.sequence1[j-1]===this.sequence2[$-1]?V=this.grid[j-1][$-1]+this.match_score:V=this.grid[j-1][$-1]+this.mismatch_penalty;var Q=this.grid[j-1][$]+this.gap_penalty,oe=this.grid[j][$-1]+this.gap_penalty,ce=[V,Q,oe],se=this.arrayAllMaxIndexes(ce);this.grid[j][$]=ce[se[0]],this.tracebackGrid[j][$]=[se.includes(0),se.includes(1),se.includes(2)]}this.score=this.grid[this.iMax-1][this.jMax-1]}},{key:"alignmentTraceback",value:function(){var B=[];for(B.push({pos:[this.sequence1.length,this.sequence2.length],seq1:"",seq2:""});B[0];){var R=B[0],j=this.tracebackGrid[R.pos[0]][R.pos[1]];j[0]&&B.push({pos:[R.pos[0]-1,R.pos[1]-1],seq1:this.sequence1[R.pos[0]-1]+R.seq1,seq2:this.sequence2[R.pos[1]-1]+R.seq2}),j[1]&&B.push({pos:[R.pos[0]-1,R.pos[1]],seq1:this.sequence1[R.pos[0]-1]+R.seq1,seq2:"-"+R.seq2}),j[2]&&B.push({pos:[R.pos[0],R.pos[1]-1],seq1:"-"+R.seq1,seq2:this.sequence2[R.pos[1]-1]+R.seq2}),R.pos[0]===0&&R.pos[1]===0&&this.alignments.push({sequence1:R.seq1,sequence2:R.seq2}),B.shift()}return this.alignments}},{key:"getAllIndexes",value:function(B,R){for(var j=[],$=-1;($=B.indexOf(R,$+1))!==-1;)j.push($);return j}},{key:"arrayAllMaxIndexes",value:function(B){return this.getAllIndexes(B,Math.max.apply(null,B))}}]),S}();f.exports=E},function(f,p,w){var y=function(){};y.FDLayout=w(18),y.FDLayoutConstants=w(7),y.FDLayoutEdge=w(19),y.FDLayoutNode=w(20),y.DimensionD=w(21),y.HashMap=w(22),y.HashSet=w(23),y.IGeometry=w(8),y.IMath=w(9),y.Integer=w(10),y.Point=w(12),y.PointD=w(4),y.RandomSeed=w(16),y.RectangleD=w(13),y.Transform=w(17),y.UniqueIDGeneretor=w(14),y.Quicksort=w(24),y.LinkedList=w(11),y.LGraphObject=w(2),y.LGraph=w(5),y.LEdge=w(1),y.LGraphManager=w(6),y.LNode=w(3),y.Layout=w(15),y.LayoutConstants=w(0),y.NeedlemanWunsch=w(25),f.exports=y},function(f,p,w){function y(){this.listeners=[]}var b=y.prototype;b.addListener=function(E,S){this.listeners.push({event:E,callback:S})},b.removeListener=function(E,S){for(var N=this.listeners.length;N>=0;N--){var B=this.listeners[N];B.event===E&&B.callback===S&&this.listeners.splice(N,1)}},b.emit=function(E,S){for(var N=0;N<this.listeners.length;N++){var B=this.listeners[N];E===B.event&&B.callback(S)}},f.exports=y}])})}(Uqt)),XK}var pje;function Kqt(){return pje||(pje=1,function(i,a){(function(p,w){i.exports=w(Wqt())})(Wk,function(f){return function(p){var w={};function y(b){if(w[b])return w[b].exports;var E=w[b]={i:b,l:!1,exports:{}};return p[b].call(E.exports,E,E.exports,y),E.l=!0,E.exports}return y.m=p,y.c=w,y.i=function(b){return b},y.d=function(b,E,S){y.o(b,E)||Object.defineProperty(b,E,{configurable:!1,enumerable:!0,get:S})},y.n=function(b){var E=b&&b.__esModule?function(){return b.default}:function(){return b};return y.d(E,"a",E),E},y.o=function(b,E){return Object.prototype.hasOwnProperty.call(b,E)},y.p="",y(y.s=7)}([function(p,w){p.exports=f},function(p,w,y){var b=y(0).FDLayoutConstants;function E(){}for(var S in b)E[S]=b[S];E.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,E.DEFAULT_RADIAL_SEPARATION=b.DEFAULT_EDGE_LENGTH,E.DEFAULT_COMPONENT_SEPERATION=60,E.TILE=!0,E.TILING_PADDING_VERTICAL=10,E.TILING_PADDING_HORIZONTAL=10,E.TREE_REDUCTION_ON_INCREMENTAL=!1,p.exports=E},function(p,w,y){var b=y(0).FDLayoutEdge;function E(N,B,R){b.call(this,N,B,R)}E.prototype=Object.create(b.prototype);for(var S in b)E[S]=b[S];p.exports=E},function(p,w,y){var b=y(0).LGraph;function E(N,B,R){b.call(this,N,B,R)}E.prototype=Object.create(b.prototype);for(var S in b)E[S]=b[S];p.exports=E},function(p,w,y){var b=y(0).LGraphManager;function E(N){b.call(this,N)}E.prototype=Object.create(b.prototype);for(var S in b)E[S]=b[S];p.exports=E},function(p,w,y){var b=y(0).FDLayoutNode,E=y(0).IMath;function S(B,R,j,$){b.call(this,B,R,j,$)}S.prototype=Object.create(b.prototype);for(var N in b)S[N]=b[N];S.prototype.move=function(){var B=this.graphManager.getLayout();this.displacementX=B.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY=B.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren,Math.abs(this.displacementX)>B.coolingFactor*B.maxNodeDisplacement&&(this.displacementX=B.coolingFactor*B.maxNodeDisplacement*E.sign(this.displacementX)),Math.abs(this.displacementY)>B.coolingFactor*B.maxNodeDisplacement&&(this.displacementY=B.coolingFactor*B.maxNodeDisplacement*E.sign(this.displacementY)),this.child==null?this.moveBy(this.displacementX,this.displacementY):this.child.getNodes().length==0?this.moveBy(this.displacementX,this.displacementY):this.propogateDisplacementToChildren(this.displacementX,this.displacementY),B.totalDisplacement+=Math.abs(this.displacementX)+Math.abs(this.displacementY),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0},S.prototype.propogateDisplacementToChildren=function(B,R){for(var j=this.getChild().getNodes(),$,V=0;V<j.length;V++)$=j[V],$.getChild()==null?($.moveBy(B,R),$.displacementX+=B,$.displacementY+=R):$.propogateDisplacementToChildren(B,R)},S.prototype.setPred1=function(B){this.pred1=B},S.prototype.getPred1=function(){return pred1},S.prototype.getPred2=function(){return pred2},S.prototype.setNext=function(B){this.next=B},S.prototype.getNext=function(){return next},S.prototype.setProcessed=function(B){this.processed=B},S.prototype.isProcessed=function(){return processed},p.exports=S},function(p,w,y){var b=y(0).FDLayout,E=y(4),S=y(3),N=y(5),B=y(2),R=y(1),j=y(0).FDLayoutConstants,$=y(0).LayoutConstants,V=y(0).Point,Q=y(0).PointD,oe=y(0).Layout,ce=y(0).Integer,se=y(0).IGeometry,ge=y(0).LGraph,ye=y(0).Transform;function ke(){b.call(this),this.toBeTiled={}}ke.prototype=Object.create(b.prototype);for(var Ae in b)ke[Ae]=b[Ae];ke.prototype.newGraphManager=function(){var de=new E(this);return this.graphManager=de,de},ke.prototype.newGraph=function(de){return new S(null,this.graphManager,de)},ke.prototype.newNode=function(de){return new N(this.graphManager,de)},ke.prototype.newEdge=function(de){return new B(null,null,de)},ke.prototype.initParameters=function(){b.prototype.initParameters.call(this,arguments),this.isSubLayout||(R.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=R.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=R.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.springConstant=j.DEFAULT_SPRING_STRENGTH,this.repulsionConstant=j.DEFAULT_REPULSION_STRENGTH,this.gravityConstant=j.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=j.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=j.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=j.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1,this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/j.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=j.CONVERGENCE_CHECK_PERIOD/this.maxIterations,this.coolingAdjuster=1)},ke.prototype.layout=function(){var de=$.DEFAULT_CREATE_BENDS_AS_NEEDED;return de&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},ke.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(R.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var ve=new Set(this.getAllNodes()),te=this.nodesWithGravity.filter(function(he){return ve.has(he)});this.graphManager.setAllNodesToApplyGravitation(te)}}else{var de=this.getFlatForest();if(de.length>0)this.positionNodesRadially(de);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var ve=new Set(this.getAllNodes()),te=this.nodesWithGravity.filter(function(xe){return ve.has(xe)});this.graphManager.setAllNodesToApplyGravitation(te),this.positionNodesRandomly()}}return this.initSpringEmbedder(),this.runSpringEmbedder(),!0},ke.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%j.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var de=new Set(this.getAllNodes()),ve=this.nodesWithGravity.filter(function(De){return de.has(De)});this.graphManager.setAllNodesToApplyGravitation(ve),this.graphManager.updateBounds(),this.updateGrid(),this.coolingFactor=j.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),this.coolingFactor=j.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var te=!this.isTreeGrowing&&!this.isGrowthFinished,xe=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(te,xe),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},ke.prototype.getPositionsData=function(){for(var de=this.graphManager.getAllNodes(),ve={},te=0;te<de.length;te++){var xe=de[te].rect,De=de[te].id;ve[De]={id:De,x:xe.getCenterX(),y:xe.getCenterY(),w:xe.width,h:xe.height}}return ve},ke.prototype.runSpringEmbedder=function(){this.initialAnimationPeriod=25,this.animationPeriod=this.initialAnimationPeriod;var de=!1;if(j.ANIMATE==="during")this.emit("layoutstarted");else{for(;!de;)de=this.tick();this.graphManager.updateBounds()}},ke.prototype.calculateNodesToApplyGravitationTo=function(){var de=[],ve,te=this.graphManager.getGraphs(),xe=te.length,De;for(De=0;De<xe;De++)ve=te[De],ve.updateConnected(),ve.isConnected||(de=de.concat(ve.getNodes()));return de},ke.prototype.createBendpoints=function(){var de=[];de=de.concat(this.graphManager.getAllEdges());var ve=new Set,te;for(te=0;te<de.length;te++){var xe=de[te];if(!ve.has(xe)){var De=xe.getSource(),he=xe.getTarget();if(De==he)xe.getBendpoints().push(new Q),xe.getBendpoints().push(new Q),this.createDummyNodesForBendpoints(xe),ve.add(xe);else{var Ie=[];if(Ie=Ie.concat(De.getEdgeListToNode(he)),Ie=Ie.concat(he.getEdgeListToNode(De)),!ve.has(Ie[0])){if(Ie.length>1){var ee;for(ee=0;ee<Ie.length;ee++){var rt=Ie[ee];rt.getBendpoints().push(new Q),this.createDummyNodesForBendpoints(rt)}}Ie.forEach(function(me){ve.add(me)})}}}if(ve.size==de.length)break}},ke.prototype.positionNodesRadially=function(de){for(var ve=new V(0,0),te=Math.ceil(Math.sqrt(de.length)),xe=0,De=0,he=0,Ie=new Q(0,0),ee=0;ee<de.length;ee++){ee%te==0&&(he=0,De=xe,ee!=0&&(De+=R.DEFAULT_COMPONENT_SEPERATION),xe=0);var rt=de[ee],me=oe.findCenterOfTree(rt);ve.x=he,ve.y=De,Ie=ke.radialLayout(rt,me,ve),Ie.y>xe&&(xe=Math.floor(Ie.y)),he=Math.floor(Ie.x+R.DEFAULT_COMPONENT_SEPERATION)}this.transform(new Q($.WORLD_CENTER_X-Ie.x/2,$.WORLD_CENTER_Y-Ie.y/2))},ke.radialLayout=function(de,ve,te){var xe=Math.max(this.maxDiagonalInTree(de),R.DEFAULT_RADIAL_SEPARATION);ke.branchRadialLayout(ve,null,0,359,0,xe);var De=ge.calculateBounds(de),he=new ye;he.setDeviceOrgX(De.getMinX()),he.setDeviceOrgY(De.getMinY()),he.setWorldOrgX(te.x),he.setWorldOrgY(te.y);for(var Ie=0;Ie<de.length;Ie++){var ee=de[Ie];ee.transform(he)}var rt=new Q(De.getMaxX(),De.getMaxY());return he.inverseTransformPoint(rt)},ke.branchRadialLayout=function(de,ve,te,xe,De,he){var Ie=(xe-te+1)/2;Ie<0&&(Ie+=180);var ee=(Ie+te)%360,rt=ee*se.TWO_PI/360,me=De*Math.cos(rt),gt=De*Math.sin(rt);de.setCenter(me,gt);var pe=[];pe=pe.concat(de.getEdges());var Et=pe.length;ve!=null&&Et--;for(var wt=0,jt=pe.length,At,Bt=de.getEdgesBetween(ve);Bt.length>1;){var cn=Bt[0];Bt.splice(0,1);var Nn=pe.indexOf(cn);Nn>=0&&pe.splice(Nn,1),jt--,Et--}ve!=null?At=(pe.indexOf(Bt[0])+1)%jt:At=0;for(var Ot=Math.abs(xe-te)/Et,oi=At;wt!=Et;oi=++oi%jt){var kt=pe[oi].getOtherEnd(de);if(kt!=ve){var Dt=(te+wt*Ot)%360,vt=(Dt+Ot)%360;ke.branchRadialLayout(kt,de,Dt,vt,De+he,he),wt++}}},ke.maxDiagonalInTree=function(de){for(var ve=ce.MIN_VALUE,te=0;te<de.length;te++){var xe=de[te],De=xe.getDiagonal();De>ve&&(ve=De)}return ve},ke.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},ke.prototype.groupZeroDegreeMembers=function(){var de=this,ve={};this.memberGroups={},this.idToDummyNode={};for(var te=[],xe=this.graphManager.getAllNodes(),De=0;De<xe.length;De++){var he=xe[De],Ie=he.getParent();this.getNodeDegreeWithChildren(he)===0&&(Ie.id==null||!this.getToBeTiled(Ie))&&te.push(he)}for(var De=0;De<te.length;De++){var he=te[De],ee=he.getParent().id;typeof ve[ee]>"u"&&(ve[ee]=[]),ve[ee]=ve[ee].concat(he)}Object.keys(ve).forEach(function(rt){if(ve[rt].length>1){var me="DummyCompound_"+rt;de.memberGroups[me]=ve[rt];var gt=ve[rt][0].getParent(),pe=new N(de.graphManager);pe.id=me,pe.paddingLeft=gt.paddingLeft||0,pe.paddingRight=gt.paddingRight||0,pe.paddingBottom=gt.paddingBottom||0,pe.paddingTop=gt.paddingTop||0,de.idToDummyNode[me]=pe;var Et=de.getGraphManager().add(de.newGraph(),pe),wt=gt.getChild();wt.add(pe);for(var jt=0;jt<ve[rt].length;jt++){var At=ve[rt][jt];wt.remove(At),Et.add(At)}}})},ke.prototype.clearCompounds=function(){var de={},ve={};this.performDFSOnCompounds();for(var te=0;te<this.compoundOrder.length;te++)ve[this.compoundOrder[te].id]=this.compoundOrder[te],de[this.compoundOrder[te].id]=[].concat(this.compoundOrder[te].getChild().getNodes()),this.graphManager.remove(this.compoundOrder[te].getChild()),this.compoundOrder[te].child=null;this.graphManager.resetAllNodes(),this.tileCompoundMembers(de,ve)},ke.prototype.clearZeroDegreeMembers=function(){var de=this,ve=this.tiledZeroDegreePack=[];Object.keys(this.memberGroups).forEach(function(te){var xe=de.idToDummyNode[te];ve[te]=de.tileNodes(de.memberGroups[te],xe.paddingLeft+xe.paddingRight),xe.rect.width=ve[te].width,xe.rect.height=ve[te].height})},ke.prototype.repopulateCompounds=function(){for(var de=this.compoundOrder.length-1;de>=0;de--){var ve=this.compoundOrder[de],te=ve.id,xe=ve.paddingLeft,De=ve.paddingTop;this.adjustLocations(this.tiledMemberPack[te],ve.rect.x,ve.rect.y,xe,De)}},ke.prototype.repopulateZeroDegreeMembers=function(){var de=this,ve=this.tiledZeroDegreePack;Object.keys(ve).forEach(function(te){var xe=de.idToDummyNode[te],De=xe.paddingLeft,he=xe.paddingTop;de.adjustLocations(ve[te],xe.rect.x,xe.rect.y,De,he)})},ke.prototype.getToBeTiled=function(de){var ve=de.id;if(this.toBeTiled[ve]!=null)return this.toBeTiled[ve];var te=de.getChild();if(te==null)return this.toBeTiled[ve]=!1,!1;for(var xe=te.getNodes(),De=0;De<xe.length;De++){var he=xe[De];if(this.getNodeDegree(he)>0)return this.toBeTiled[ve]=!1,!1;if(he.getChild()==null){this.toBeTiled[he.id]=!1;continue}if(!this.getToBeTiled(he))return this.toBeTiled[ve]=!1,!1}return this.toBeTiled[ve]=!0,!0},ke.prototype.getNodeDegree=function(de){de.id;for(var ve=de.getEdges(),te=0,xe=0;xe<ve.length;xe++){var De=ve[xe];De.getSource().id!==De.getTarget().id&&(te=te+1)}return te},ke.prototype.getNodeDegreeWithChildren=function(de){var ve=this.getNodeDegree(de);if(de.getChild()==null)return ve;for(var te=de.getChild().getNodes(),xe=0;xe<te.length;xe++){var De=te[xe];ve+=this.getNodeDegreeWithChildren(De)}return ve},ke.prototype.performDFSOnCompounds=function(){this.compoundOrder=[],this.fillCompexOrderByDFS(this.graphManager.getRoot().getNodes())},ke.prototype.fillCompexOrderByDFS=function(de){for(var ve=0;ve<de.length;ve++){var te=de[ve];te.getChild()!=null&&this.fillCompexOrderByDFS(te.getChild().getNodes()),this.getToBeTiled(te)&&this.compoundOrder.push(te)}},ke.prototype.adjustLocations=function(de,ve,te,xe,De){ve+=xe,te+=De;for(var he=ve,Ie=0;Ie<de.rows.length;Ie++){var ee=de.rows[Ie];ve=he;for(var rt=0,me=0;me<ee.length;me++){var gt=ee[me];gt.rect.x=ve,gt.rect.y=te,ve+=gt.rect.width+de.horizontalPadding,gt.rect.height>rt&&(rt=gt.rect.height)}te+=rt+de.verticalPadding}},ke.prototype.tileCompoundMembers=function(de,ve){var te=this;this.tiledMemberPack=[],Object.keys(de).forEach(function(xe){var De=ve[xe];te.tiledMemberPack[xe]=te.tileNodes(de[xe],De.paddingLeft+De.paddingRight),De.rect.width=te.tiledMemberPack[xe].width,De.rect.height=te.tiledMemberPack[xe].height})},ke.prototype.tileNodes=function(de,ve){var te=R.TILING_PADDING_VERTICAL,xe=R.TILING_PADDING_HORIZONTAL,De={rows:[],rowWidth:[],rowHeight:[],width:0,height:ve,verticalPadding:te,horizontalPadding:xe};de.sort(function(ee,rt){return ee.rect.width*ee.rect.height>rt.rect.width*rt.rect.height?-1:ee.rect.width*ee.rect.height<rt.rect.width*rt.rect.height?1:0});for(var he=0;he<de.length;he++){var Ie=de[he];De.rows.length==0?this.insertNodeToRow(De,Ie,0,ve):this.canAddHorizontal(De,Ie.rect.width,Ie.rect.height)?this.insertNodeToRow(De,Ie,this.getShortestRowIndex(De),ve):this.insertNodeToRow(De,Ie,De.rows.length,ve),this.shiftToLastRow(De)}return De},ke.prototype.insertNodeToRow=function(de,ve,te,xe){var De=xe;if(te==de.rows.length){var he=[];de.rows.push(he),de.rowWidth.push(De),de.rowHeight.push(0)}var Ie=de.rowWidth[te]+ve.rect.width;de.rows[te].length>0&&(Ie+=de.horizontalPadding),de.rowWidth[te]=Ie,de.width<Ie&&(de.width=Ie);var ee=ve.rect.height;te>0&&(ee+=de.verticalPadding);var rt=0;ee>de.rowHeight[te]&&(rt=de.rowHeight[te],de.rowHeight[te]=ee,rt=de.rowHeight[te]-rt),de.height+=rt,de.rows[te].push(ve)},ke.prototype.getShortestRowIndex=function(de){for(var ve=-1,te=Number.MAX_VALUE,xe=0;xe<de.rows.length;xe++)de.rowWidth[xe]<te&&(ve=xe,te=de.rowWidth[xe]);return ve},ke.prototype.getLongestRowIndex=function(de){for(var ve=-1,te=Number.MIN_VALUE,xe=0;xe<de.rows.length;xe++)de.rowWidth[xe]>te&&(ve=xe,te=de.rowWidth[xe]);return ve},ke.prototype.canAddHorizontal=function(de,ve,te){var xe=this.getShortestRowIndex(de);if(xe<0)return!0;var De=de.rowWidth[xe];if(De+de.horizontalPadding+ve<=de.width)return!0;var he=0;de.rowHeight[xe]<te&&xe>0&&(he=te+de.verticalPadding-de.rowHeight[xe]);var Ie;de.width-De>=ve+de.horizontalPadding?Ie=(de.height+he)/(De+ve+de.horizontalPadding):Ie=(de.height+he)/de.width,he=te+de.verticalPadding;var ee;return de.width<ve?ee=(de.height+he)/ve:ee=(de.height+he)/de.width,ee<1&&(ee=1/ee),Ie<1&&(Ie=1/Ie),Ie<ee},ke.prototype.shiftToLastRow=function(de){var ve=this.getLongestRowIndex(de),te=de.rowWidth.length-1,xe=de.rows[ve],De=xe[xe.length-1],he=De.width+de.horizontalPadding;if(de.width-de.rowWidth[te]>he&&ve!=te){xe.splice(-1,1),de.rows[te].push(De),de.rowWidth[ve]=de.rowWidth[ve]-he,de.rowWidth[te]=de.rowWidth[te]+he,de.width=de.rowWidth[instance.getLongestRowIndex(de)];for(var Ie=Number.MIN_VALUE,ee=0;ee<xe.length;ee++)xe[ee].height>Ie&&(Ie=xe[ee].height);ve>0&&(Ie+=de.verticalPadding);var rt=de.rowHeight[ve]+de.rowHeight[te];de.rowHeight[ve]=Ie,de.rowHeight[te]<De.height+de.verticalPadding&&(de.rowHeight[te]=De.height+de.verticalPadding);var me=de.rowHeight[ve]+de.rowHeight[te];de.height+=me-rt,this.shiftToLastRow(de)}},ke.prototype.tilingPreLayout=function(){R.TILE&&(this.groupZeroDegreeMembers(),this.clearCompounds(),this.clearZeroDegreeMembers())},ke.prototype.tilingPostLayout=function(){R.TILE&&(this.repopulateZeroDegreeMembers(),this.repopulateCompounds())},ke.prototype.reduceTrees=function(){for(var de=[],ve=!0,te;ve;){var xe=this.graphManager.getAllNodes(),De=[];ve=!1;for(var he=0;he<xe.length;he++)te=xe[he],te.getEdges().length==1&&!te.getEdges()[0].isInterGraph&&te.getChild()==null&&(De.push([te,te.getEdges()[0],te.getOwner()]),ve=!0);if(ve==!0){for(var Ie=[],ee=0;ee<De.length;ee++)De[ee][0].getEdges().length==1&&(Ie.push(De[ee]),De[ee][0].getOwner().remove(De[ee][0]));de.push(Ie),this.graphManager.resetAllNodes(),this.graphManager.resetAllEdges()}}this.prunedNodesAll=de},ke.prototype.growTree=function(de){for(var ve=de.length,te=de[ve-1],xe,De=0;De<te.length;De++)xe=te[De],this.findPlaceforPrunedNode(xe),xe[2].add(xe[0]),xe[2].add(xe[1],xe[1].source,xe[1].target);de.splice(de.length-1,1),this.graphManager.resetAllNodes(),this.graphManager.resetAllEdges()},ke.prototype.findPlaceforPrunedNode=function(de){var ve,te,xe=de[0];xe==de[1].source?te=de[1].target:te=de[1].source;var De=te.startX,he=te.finishX,Ie=te.startY,ee=te.finishY,rt=0,me=0,gt=0,pe=0,Et=[rt,gt,me,pe];if(Ie>0)for(var wt=De;wt<=he;wt++)Et[0]+=this.grid[wt][Ie-1].length+this.grid[wt][Ie].length-1;if(he<this.grid.length-1)for(var wt=Ie;wt<=ee;wt++)Et[1]+=this.grid[he+1][wt].length+this.grid[he][wt].length-1;if(ee<this.grid[0].length-1)for(var wt=De;wt<=he;wt++)Et[2]+=this.grid[wt][ee+1].length+this.grid[wt][ee].length-1;if(De>0)for(var wt=Ie;wt<=ee;wt++)Et[3]+=this.grid[De-1][wt].length+this.grid[De][wt].length-1;for(var jt=ce.MAX_VALUE,At,Bt,cn=0;cn<Et.length;cn++)Et[cn]<jt?(jt=Et[cn],At=1,Bt=cn):Et[cn]==jt&&At++;if(At==3&&jt==0)Et[0]==0&&Et[1]==0&&Et[2]==0?ve=1:Et[0]==0&&Et[1]==0&&Et[3]==0?ve=0:Et[0]==0&&Et[2]==0&&Et[3]==0?ve=3:Et[1]==0&&Et[2]==0&&Et[3]==0&&(ve=2);else if(At==2&&jt==0){var Nn=Math.floor(Math.random()*2);Et[0]==0&&Et[1]==0?Nn==0?ve=0:ve=1:Et[0]==0&&Et[2]==0?Nn==0?ve=0:ve=2:Et[0]==0&&Et[3]==0?Nn==0?ve=0:ve=3:Et[1]==0&&Et[2]==0?Nn==0?ve=1:ve=2:Et[1]==0&&Et[3]==0?Nn==0?ve=1:ve=3:Nn==0?ve=2:ve=3}else if(At==4&&jt==0){var Nn=Math.floor(Math.random()*4);ve=Nn}else ve=Bt;ve==0?xe.setCenter(te.getCenterX(),te.getCenterY()-te.getHeight()/2-j.DEFAULT_EDGE_LENGTH-xe.getHeight()/2):ve==1?xe.setCenter(te.getCenterX()+te.getWidth()/2+j.DEFAULT_EDGE_LENGTH+xe.getWidth()/2,te.getCenterY()):ve==2?xe.setCenter(te.getCenterX(),te.getCenterY()+te.getHeight()/2+j.DEFAULT_EDGE_LENGTH+xe.getHeight()/2):xe.setCenter(te.getCenterX()-te.getWidth()/2-j.DEFAULT_EDGE_LENGTH-xe.getWidth()/2,te.getCenterY())},p.exports=ke},function(p,w,y){var b={};b.layoutBase=y(0),b.CoSEConstants=y(1),b.CoSEEdge=y(2),b.CoSEGraph=y(3),b.CoSEGraphManager=y(4),b.CoSELayout=y(6),b.CoSENode=y(5),p.exports=b}])})}(Vqt)),YK}(function(i,a){(function(p,w){i.exports=w(Kqt())})(Wk,function(f){return function(p){var w={};function y(b){if(w[b])return w[b].exports;var E=w[b]={i:b,l:!1,exports:{}};return p[b].call(E.exports,E,E.exports,y),E.l=!0,E.exports}return y.m=p,y.c=w,y.i=function(b){return b},y.d=function(b,E,S){y.o(b,E)||Object.defineProperty(b,E,{configurable:!1,enumerable:!0,get:S})},y.n=function(b){var E=b&&b.__esModule?function(){return b.default}:function(){return b};return y.d(E,"a",E),E},y.o=function(b,E){return Object.prototype.hasOwnProperty.call(b,E)},y.p="",y(y.s=1)}([function(p,w){p.exports=f},function(p,w,y){var b=y(0).layoutBase.LayoutConstants,E=y(0).layoutBase.FDLayoutConstants,S=y(0).CoSEConstants,N=y(0).CoSELayout,B=y(0).CoSENode,R=y(0).layoutBase.PointD,j=y(0).layoutBase.DimensionD,$={ready:function(){},stop:function(){},quality:"default",nodeDimensionsIncludeLabels:!1,refresh:30,fit:!0,padding:10,randomize:!0,nodeRepulsion:4500,idealEdgeLength:50,edgeElasticity:.45,nestingFactor:.1,gravity:.25,numIter:2500,tile:!0,animate:"end",animationDuration:500,tilingPaddingVertical:10,tilingPaddingHorizontal:10,gravityRangeCompound:1.5,gravityCompound:1,gravityRange:3.8,initialEnergyOnIncremental:.5};function V(se,ge){var ye={};for(var ke in se)ye[ke]=se[ke];for(var ke in ge)ye[ke]=ge[ke];return ye}function Q(se){this.options=V($,se),oe(this.options)}var oe=function(ge){ge.nodeRepulsion!=null&&(S.DEFAULT_REPULSION_STRENGTH=E.DEFAULT_REPULSION_STRENGTH=ge.nodeRepulsion),ge.idealEdgeLength!=null&&(S.DEFAULT_EDGE_LENGTH=E.DEFAULT_EDGE_LENGTH=ge.idealEdgeLength),ge.edgeElasticity!=null&&(S.DEFAULT_SPRING_STRENGTH=E.DEFAULT_SPRING_STRENGTH=ge.edgeElasticity),ge.nestingFactor!=null&&(S.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=E.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=ge.nestingFactor),ge.gravity!=null&&(S.DEFAULT_GRAVITY_STRENGTH=E.DEFAULT_GRAVITY_STRENGTH=ge.gravity),ge.numIter!=null&&(S.MAX_ITERATIONS=E.MAX_ITERATIONS=ge.numIter),ge.gravityRange!=null&&(S.DEFAULT_GRAVITY_RANGE_FACTOR=E.DEFAULT_GRAVITY_RANGE_FACTOR=ge.gravityRange),ge.gravityCompound!=null&&(S.DEFAULT_COMPOUND_GRAVITY_STRENGTH=E.DEFAULT_COMPOUND_GRAVITY_STRENGTH=ge.gravityCompound),ge.gravityRangeCompound!=null&&(S.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=E.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=ge.gravityRangeCompound),ge.initialEnergyOnIncremental!=null&&(S.DEFAULT_COOLING_FACTOR_INCREMENTAL=E.DEFAULT_COOLING_FACTOR_INCREMENTAL=ge.initialEnergyOnIncremental),ge.quality=="draft"?b.QUALITY=0:ge.quality=="proof"?b.QUALITY=2:b.QUALITY=1,S.NODE_DIMENSIONS_INCLUDE_LABELS=E.NODE_DIMENSIONS_INCLUDE_LABELS=b.NODE_DIMENSIONS_INCLUDE_LABELS=ge.nodeDimensionsIncludeLabels,S.DEFAULT_INCREMENTAL=E.DEFAULT_INCREMENTAL=b.DEFAULT_INCREMENTAL=!ge.randomize,S.ANIMATE=E.ANIMATE=b.ANIMATE=ge.animate,S.TILE=ge.tile,S.TILING_PADDING_VERTICAL=typeof 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gt(){for(var pe=function(){ye.fit&&ye.cy.fit(ye.eles,ye.padding),se||(se=!0,Ae.cy.one("layoutready",ye.ready),Ae.cy.trigger({type:"layoutready",layout:Ae}))},Et=Ae.options.refresh,wt,jt=0;jt<Et&&!wt;jt++)wt=Ae.stopped||Ae.layout.tick();if(wt){ke.checkLayoutSuccess()&&!ke.isSubLayout&&ke.doPostLayout(),ke.tilingPostLayout&&ke.tilingPostLayout(),ke.isLayoutFinished=!0,Ae.options.eles.nodes().positions(rt),pe(),Ae.cy.one("layoutstop",Ae.options.stop),Ae.cy.trigger({type:"layoutstop",layout:Ae}),ge&&cancelAnimationFrame(ge),se=!1;return}var At=Ae.layout.getPositionsData();ye.eles.nodes().positions(function(Bt,cn){if(typeof Bt=="number"&&(Bt=cn),!Bt.isParent()){for(var Nn=Bt.id(),Ot=At[Nn],oi=Bt;Ot==null&&(Ot=At[oi.data("parent")]||At["DummyCompound_"+oi.data("parent")],At[Nn]=Ot,oi=oi.parent()[0],oi!=null););return Ot!=null?{x:Ot.x,y:Ot.y}:{x:Bt.position("x"),y:Bt.position("y")}}}),pe(),ge=requestAnimationFrame(gt)};return ke.addListener("layoutstarted",function(){Ae.options.animate==="during"&&(ge=requestAnimationFrame(me))}),ke.runLayout(),this.options.animate!=="during"&&(Ae.options.eles.nodes().not(":parent").layoutPositions(Ae,Ae.options,rt),se=!1),this},Q.prototype.getTopMostNodes=function(se){for(var ge={},ye=0;ye<se.length;ye++)ge[se[ye].id()]=!0;var ke=se.filter(function(Ae,de){typeof Ae=="number"&&(Ae=de);for(var ve=Ae.parent()[0];ve!=null;){if(ge[ve.id()])return!1;ve=ve.parent()[0]}return!0});return ke},Q.prototype.processChildrenList=function(se,ge,ye){for(var ke=ge.length,Ae=0;Ae<ke;Ae++){var de=ge[Ae],ve=de.children(),te,xe=de.layoutDimensions({nodeDimensionsIncludeLabels:this.options.nodeDimensionsIncludeLabels});if(de.outerWidth()!=null&&de.outerHeight()!=null?te=se.add(new B(ye.graphManager,new R(de.position("x")-xe.w/2,de.position("y")-xe.h/2),new j(parseFloat(xe.w),parseFloat(xe.h)))):te=se.add(new B(this.graphManager)),te.id=de.data("id"),te.paddingLeft=parseInt(de.css("padding")),te.paddingTop=parseInt(de.css("padding")),te.paddingRight=parseInt(de.css("padding")),te.paddingBottom=parseInt(de.css("padding")),this.options.nodeDimensionsIncludeLabels&&de.isParent()){var De=de.boundingBox({includeLabels:!0,includeNodes:!1}).w,he=de.boundingBox({includeLabels:!0,includeNodes:!1}).h,Ie=de.css("text-halign");te.labelWidth=De,te.labelHeight=he,te.labelPos=Ie}if(this.idToLNode[de.data("id")]=te,isNaN(te.rect.x)&&(te.rect.x=0),isNaN(te.rect.y)&&(te.rect.y=0),ve!=null&&ve.length>0){var ee;ee=ye.getGraphManager().add(ye.newGraph(),te),this.processChildrenList(ee,ve,ye)}}},Q.prototype.stop=function(){return this.stopped=!0,this};var ce=function(ge){ge("layout","cose-bilkent",Q)};typeof cytoscape<"u"&&ce(cytoscape),p.exports=ce}])})})(qqt);const Yqt=xLe(Fge);dje.use(Yqt);function bje(i,a,f,p){fje.drawNode(i,a,f,p),a.children&&a.children.forEach((w,y)=>{bje(i,w,f<0?y:f,p)})}function Xqt(i,a){a.edges().map((f,p)=>{const w=f.data();if(f[0]._private.bodyBounds){const y=f[0]._private.rscratch;Fe.trace("Edge: ",p,w),i.insert("path").attr("d",`M ${y.startX},${y.startY} L ${y.midX},${y.midY} L${y.endX},${y.endY} `).attr("class","edge section-edge-"+w.section+" edge-depth-"+w.depth)}})}function vje(i,a,f,p){a.add({group:"nodes",data:{id:i.id,labelText:i.descr,height:i.height,width:i.width,level:p,nodeId:i.id,padding:i.padding,type:i.type},position:{x:i.x,y:i.y}}),i.children&&i.children.forEach(w=>{vje(w,a,f,p+1),a.add({group:"edges",data:{id:`${i.id}_${w.id}`,source:i.id,target:w.id,depth:p,section:w.section}})})}function Qqt(i,a){return new Promise(f=>{const p=Cr("body").append("div").attr("id","cy").attr("style","display:none"),w=dje({container:document.getElementById("cy"),style:[{selector:"edge",style:{"curve-style":"bezier"}}]});p.remove(),vje(i,w,a,0),w.nodes().forEach(function(y){y.layoutDimensions=()=>{const b=y.data();return{w:b.width,h:b.height}}}),w.layout({name:"cose-bilkent",quality:"proof",styleEnabled:!1,animate:!1}).run(),w.ready(y=>{Fe.info("Ready",y),f(w)})})}function Zqt(i){i.nodes().map((a,f)=>{const p=a.data();p.x=a.position().x,p.y=a.position().y,fje.positionNode(p);const w=Bge(p.nodeId);Fe.info("Id:",f,"Position: (",a.position().x,", ",a.position().y,")",p),w.attr("transform",`translate(${a.position().x-p.width/2}, ${a.position().y-p.height/2})`),w.attr("attr",`apa-${f})`)})}const Jqt={draw:async(i,a,f,p)=>{const w=Tt();w.htmlLabels=!1,p.db.clear(),p.parser.parse(i),Fe.debug(`Rendering mindmap diagram
`+i,p.parser);const y=Tt().securityLevel;let b;y==="sandbox"&&(b=Cr("#i"+a));const S=Cr(y==="sandbox"?b.nodes()[0].contentDocument.body:"body").select("#"+a);S.append("g");const N=p.db.getMindmap(),B=S.append("g");B.attr("class","mindmap-edges");const R=S.append("g");R.attr("class","mindmap-nodes"),bje(R,N,-1,w);const j=await Qqt(N,w);Xqt(B,j),Zqt(j),YE(void 0,S,w.mindmap.padding,w.mindmap.useMaxWidth)}},eVt=i=>{let a="";for(let f=0;f<i.THEME_COLOR_LIMIT;f++)i["lineColor"+f]=i["lineColor"+f]||i["cScaleInv"+f],GDe(i["lineColor"+f])?i["lineColor"+f]=Qs(i["lineColor"+f],20):i["lineColor"+f]=ya(i["lineColor"+f],20);for(let f=0;f<i.THEME_COLOR_LIMIT;f++){const p=""+(17-3*f);a+=`
.section-${f-1} rect, .section-${f-1} path, .section-${f-1} circle, .section-${f-1} polygon, .section-${f-1} path {
fill: ${i["cScale"+f]};
}
.section-${f-1} text {
fill: ${i["cScaleLabel"+f]};
}
.node-icon-${f-1} {
font-size: 40px;
color: ${i["cScaleLabel"+f]};
}
.section-edge-${f-1}{
stroke: ${i["cScale"+f]};
}
.edge-depth-${f-1}{
stroke-width: ${p};
}
.section-${f-1} line {
stroke: ${i["cScaleInv"+f]} ;
stroke-width: 3;
}
.disabled, .disabled circle, .disabled text {
fill: lightgray;
}
.disabled text {
fill: #efefef;
}
`}return a},tVt=Object.freeze(Object.defineProperty({__proto__:null,diagram:{db:Nqt,renderer:Jqt,parser:Tqt,styles:i=>`
.edge {
stroke-width: 3;
}
${eVt(i)}
.section-root rect, .section-root path, .section-root circle, .section-root polygon {
fill: ${i.git0};
}
.section-root text {
fill: ${i.gitBranchLabel0};
}
.icon-container {
height:100%;
display: flex;
justify-content: center;
align-items: center;
}
.edge {
fill: none;
}
.mindmap-node-label {
dy: 1em;
alignment-baseline: middle;
text-anchor: middle;
dominant-baseline: middle;
text-align: center;
}
`}},Symbol.toStringTag,{value:"Module"}));return O2});
</script>
<script>// mermaid-init.js
// Initializes the quarto-mermaid JS runtime
//
// Copyright (C) 2022 Posit Software, PBC
/**
* String.prototype.replaceAll() polyfill
* https://gomakethings.com/how-to-replace-a-section-of-a-string-with-another-one-with-vanilla-js/
* @author Chris Ferdinandi
* @license MIT
*/
if (!String.prototype.replaceAll) {
String.prototype.replaceAll = function (str, newStr) {
// If a regex pattern
if (
Object.prototype.toString.call(str).toLowerCase() === "[object regexp]"
) {
return this.replace(str, newStr);
}
// If a string
return this.replace(new RegExp(str, "g"), newStr);
};
}
const mermaidOpts = {
startOnLoad: false,
};
// this CSS is adapted from
// mkdocs-material
// Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>
const defaultCSS =
'.label text {fill: var(--mermaid-fg-color);}.node circle, .node ellipse, .node path, .node polygon, .node rect {fill: var(--mermaid-node-bg-color);stroke: var(--mermaid-node-fg-color);}marker {fill: var(--mermaid-edge-color) !important;}.edgeLabel .label rect {fill: #0000;}.label {color: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.label foreignObject {line-height: normal;overflow: visible;}.label div .edgeLabel {color: var(--mermaid-label-fg-color);}.edgeLabel, .edgeLabel rect, .label div .edgeLabel {background-color: var(--mermaid-label-bg-color);}.edgeLabel, .edgeLabel rect {fill: var(--mermaid-label-bg-color);color: var(--mermaid-edge-color);}.edgePath .path, .flowchart-link {stroke: var(--mermaid-edge-color);}.edgePath .arrowheadPath {fill: var(--mermaid-edge-color);stroke: none;}.cluster rect {fill: var(--mermaid-fg-color--lightest);stroke: var(--mermaid-fg-color--lighter);}.cluster span {color: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}defs #flowchart-circleEnd, defs #flowchart-circleStart, defs #flowchart-crossEnd, defs #flowchart-crossStart, defs #flowchart-pointEnd, defs #flowchart-pointStart {stroke: none;}g.classGroup line, g.classGroup rect {fill: var(--mermaid-node-bg-color);stroke: var(--mermaid-node-fg-color);}g.classGroup text {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.classLabel .box {fill: var(--mermaid-label-bg-color);background-color: var(--mermaid-label-bg-color);opacity: 1;}.classLabel .label {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.node .divider {stroke: var(--mermaid-node-fg-color);}.relation {stroke: var(--mermaid-edge-color);}.cardinality {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.cardinality text {fill: inherit !important;}defs #classDiagram-compositionEnd, defs #classDiagram-compositionStart, defs #classDiagram-dependencyEnd, defs #classDiagram-dependencyStart, defs #classDiagram-extensionEnd, defs #classDiagram-extensionStart {fill: var(--mermaid-edge-color) !important;stroke: var(--mermaid-edge-color) !important;}defs #classDiagram-aggregationEnd, defs #classDiagram-aggregationStart {fill: var(--mermaid-label-bg-color) !important;stroke: var(--mermaid-edge-color) !important;}g.stateGroup rect {fill: var(--mermaid-node-bg-color);stroke: var(--mermaid-node-fg-color);}g.stateGroup .state-title {fill: var(--mermaid-label-fg-color) !important;font-family: var(--mermaid-font-family);}g.stateGroup .composit {fill: var(--mermaid-label-bg-color);}.nodeLabel {color: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.node circle.state-end, .node circle.state-start, .start-state {fill: var(--mermaid-edge-color);stroke: none;}.end-state-inner, .end-state-outer {fill: var(--mermaid-edge-color);}.end-state-inner, .node circle.state-end {stroke: var(--mermaid-label-bg-color);}.transition {stroke: var(--mermaid-edge-color);}[id^="state-fork"] rect, [id^="state-join"] rect {fill: var(--mermaid-edge-color) !important;stroke: none !important;}.statediagram-cluster.statediagram-cluster .inner {fill: var(--mermaid-bg-color);}.statediagram-cluster rect {fill: var(--mermaid-node-bg-color);stroke: var(--mermaid-node-fg-color);}.statediagram-state rect.divider {fill: var(--mermaid-fg-color--lightest);stroke: var(--mermaid-fg-color--lighter);}defs #statediagram-barbEnd {stroke: var(--mermaid-edge-color);}.entityBox {fill: var(--mermaid-label-bg-color);stroke: var(--mermaid-node-fg-color);}.entityLabel {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}.relationshipLabelBox {fill: var(--mermaid-label-bg-color);fill-opacity: 1;background-color: var(--mermaid-label-bg-color);opacity: 1;}.relationshipLabel {fill: var(--mermaid-label-fg-color);}.relationshipLine {stroke: var(--mermaid-edge-color);}defs #ONE_OR_MORE_END *, defs #ONE_OR_MORE_START *, defs #ONLY_ONE_END *, defs #ONLY_ONE_START *, defs #ZERO_OR_MORE_END *, defs #ZERO_OR_MORE_START *, defs #ZERO_OR_ONE_END *, defs #ZERO_OR_ONE_START * {stroke: var(--mermaid-edge-color) !important;}.actor, defs #ZERO_OR_MORE_END circle, defs #ZERO_OR_MORE_START circle {fill: var(--mermaid-label-bg-color);}.actor {stroke: var(--mermaid-node-fg-color);}text.actor > tspan {fill: var(--mermaid-label-fg-color);font-family: var(--mermaid-font-family);}line {stroke: var(--mermaid-fg-color--lighter);}.messageLine0, .messageLine1 {stroke: var(--mermaid-edge-color);}.loopText > tspan, .messageText, .noteText > tspan {fill: var(--mermaid-edge-color);stroke: none;font-family: var(--mermaid-font-family) !important;}.noteText > tspan {fill: #000;}#arrowhead path {fill: var(--mermaid-edge-color);stroke: none;}.loopLine {stroke: var(--mermaid-node-fg-color);}.labelBox, .loopLine {fill: var(--mermaid-node-bg-color);}.labelBox {stroke: none;}.labelText, .labelText > span {fill: var(--mermaid-node-fg-color);font-family: var(--mermaid-font-family);}';
const mermaidThemeEl = document.querySelector('meta[name="mermaid-theme"]');
if (mermaidThemeEl) {
mermaidOpts.theme = mermaidThemeEl.content;
} else {
mermaidOpts.themeCSS = defaultCSS;
}
mermaid.initialize(mermaidOpts);
const _quartoMermaid = {
// NB: there's effectively a copy of this function
// in `core/svg.ts`.
// if you change something here, you must keep it consistent there as well.
setSvgSize(svg) {
const { widthInPoints, heightInPoints, explicitHeight, explicitWidth } =
this.resolveSize(svg);
if (explicitWidth && explicitHeight) {
svg.setAttribute("width", widthInPoints);
svg.setAttribute("height", heightInPoints);
svg.style.maxWidth = null; // remove mermaid's default max-width
} else {
if (explicitWidth) {
svg.style.maxWidth = `${widthInPoints}px`;
}
if (explicitHeight) {
svg.style.maxHeight = `${heightInPoints}px`;
}
}
},
// NB: there's effectively a copy of this function
// in `core/svg.ts`.
// if you change something here, you must keep it consistent there as well.
makeResponsive(svg) {
const width = svg.getAttribute("width");
if (width === null) {
throw new Error("Couldn't find SVG width");
}
const numWidth = Number(width.slice(0, -2));
if (numWidth > 650) {
changed = true;
svg.setAttribute("width", "100%");
svg.removeAttribute("height");
}
},
// NB: there's effectively a copy of this function
// in `core/svg.ts`.
// if you change something here, you must keep it consistent there as well.
fixupAlignment(svg, align) {
let style = svg.getAttribute("style") || "";
switch (align) {
case "left":
style = `${style}; display: block; margin: auto auto auto 0`;
break;
case "right":
style = `${style}; display: block; margin: auto 0 auto auto`;
break;
case "center":
style = `${style}; display: block; margin: auto auto auto auto`;
break;
}
svg.setAttribute("style", style);
},
resolveOptions(svgEl) {
return svgEl.parentElement.parentElement.parentElement.parentElement
.dataset;
},
// NB: there's effectively a copy of this function
// in our mermaid runtime in `core/svg.ts`.
// if you change something here, you must keep it consistent there as well.
resolveSize(svgEl) {
const inInches = (size) => {
if (size.endsWith("in")) {
return Number(size.slice(0, -2));
}
if (size.endsWith("pt") || size.endsWith("px")) {
// assume 96 dpi for now
return Number(size.slice(0, -2)) / 96;
}
return Number(size);
};
// these are figWidth and figHeight on purpose,
// because data attributes are translated to camelCase by the DOM API
const kFigWidth = "figWidth",
kFigHeight = "figHeight";
const options = this.resolveOptions(svgEl);
let width = svgEl.getAttribute("width");
let height = svgEl.getAttribute("height");
const getViewBox = () => {
const vb = svgEl.attributes.getNamedItem("viewBox").value; // do it the roundabout way so that viewBox isn't dropped by deno_dom and text/html
if (!vb) return undefined;
const lst = vb.trim().split(" ").map(Number);
if (lst.length !== 4) return undefined;
if (lst.some(isNaN)) return undefined;
return lst;
};
if (!width || !height) {
// attempt to resolve figure dimensions via viewBox
const viewBox = getViewBox();
if (viewBox !== undefined) {
const [_mx, _my, vbWidth, vbHeight] = viewBox;
width = `${vbWidth}px`;
height = `${vbHeight}px`;
} else {
throw new Error(
"Mermaid generated an SVG without a viewbox attribute. Without knowing the diagram dimensions, quarto cannot convert it to a PNG"
);
}
}
let svgWidthInInches, svgHeightInInches;
if (
(width.slice(0, -2) === "pt" && height.slice(0, -2) === "pt") ||
(width.slice(0, -2) === "px" && height.slice(0, -2) === "px") ||
(!isNaN(Number(width)) && !isNaN(Number(height)))
) {
// we assume 96 dpi which is generally what seems to be used.
svgWidthInInches = Number(width.slice(0, -2)) / 96;
svgHeightInInches = Number(height.slice(0, -2)) / 96;
}
const viewBox = getViewBox();
if (viewBox !== undefined) {
// assume width and height come from viewbox.
const [_mx, _my, vbWidth, vbHeight] = viewBox;
svgWidthInInches = vbWidth / 96;
svgHeightInInches = vbHeight / 96;
} else {
throw new Error(
"Internal Error: Couldn't resolve width and height of SVG"
);
}
const svgWidthOverHeight = svgWidthInInches / svgHeightInInches;
let widthInInches, heightInInches;
if (options[kFigWidth] && options[kFigHeight]) {
// both were prescribed, so just go with them
widthInInches = inInches(String(options[kFigWidth]));
heightInInches = inInches(String(options[kFigHeight]));
} else if (options[kFigWidth]) {
// we were only given width, use that and adjust height based on aspect ratio;
widthInInches = inInches(String(options[kFigWidth]));
heightInInches = widthInInches / svgWidthOverHeight;
} else if (options[kFigHeight]) {
// we were only given height, use that and adjust width based on aspect ratio;
heightInInches = inInches(String(options[kFigHeight]));
widthInInches = heightInInches * svgWidthOverHeight;
} else {
// we were not given either, use svg's prescribed height
heightInInches = svgHeightInInches;
widthInInches = svgWidthInInches;
}
return {
widthInInches,
heightInInches,
widthInPoints: Math.round(widthInInches * 96),
heightInPoints: Math.round(heightInInches * 96),
explicitWidth: options?.[kFigWidth] !== undefined,
explicitHeight: options?.[kFigHeight] !== undefined,
};
},
postProcess(svg) {
const options = this.resolveOptions(svg);
if (
options.responsive &&
options["figWidth"] === undefined &&
options["figHeight"] === undefined
) {
this.makeResponsive(svg);
} else {
this.setSvgSize(svg);
}
if (options["reveal"]) {
this.fixupAlignment(svg, options["figAlign"] || "center");
}
// forward align attributes to the correct parent dif
// so that the svg figure is aligned correctly
const div = svg.parentElement.parentElement.parentElement;
const align = div.parentElement.parentElement.dataset.layoutAlign;
if (align) {
div.classList.remove("quarto-figure-left");
div.classList.remove("quarto-figure-center");
div.classList.remove("quarto-figure-right");
div.classList.add(`quarto-figure-${align}`);
}
},
};
// deno-lint-ignore no-window-prefix
window.addEventListener(
"load",
async function () {
let i = 0;
// we need pre because of whitespace preservation
for (const el of Array.from(document.querySelectorAll("pre.mermaid-js"))) {
// &nbsp; doesn't appear to be treated as whitespace by mermaid
// so we replace it with a space.
const text = el.textContent.replaceAll("&nbsp;", " ");
const { svg: output } = await mermaid.mermaidAPI.render(
`mermaid-${++i}`,
text,
el
);
el.innerHTML = output;
if (el.dataset.label) {
// patch mermaid's emitted style
const svg = el.firstChild;
const style = svg.querySelector("style");
style.innerHTML = style.innerHTML.replaceAll(
`#${svg.id}`,
`#${el.dataset.label}-mermaid`
);
svg.id = el.dataset.label + "-mermaid";
delete el.dataset.label;
}
const svg = el.querySelector("svg");
const parent = el.parentElement;
parent.removeChild(el);
parent.appendChild(svg);
svg.classList.add("mermaid-js");
}
for (const svgEl of Array.from(
document.querySelectorAll("svg.mermaid-js")
)) {
_quartoMermaid.postProcess(svgEl);
}
},
false
);
</script>
<style type="text/css">.mermaidTooltip {
position: absolute;
text-align: center;
max-width: 200px;
padding: 2px;
font-family: "trebuchet ms", verdana, arial;
font-size: 12px;
background: #ffffde;
border: 1px solid #aaaa33;
border-radius: 2px;
pointer-events: none;
z-index: 1000;
}
</style>
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<li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction">Introduction</a></li>
<li><a href="#the-world-of-work" id="toc-the-world-of-work" class="nav-link" data-scroll-target="#the-world-of-work">The world of work</a>
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<li><a href="#inequalities-in-the-world-of-work" id="toc-inequalities-in-the-world-of-work" class="nav-link" data-scroll-target="#inequalities-in-the-world-of-work">Inequalities in the world of work</a></li>
<li><a href="#existing-reviews-alternative-approaches" id="toc-existing-reviews-alternative-approaches" class="nav-link" data-scroll-target="#existing-reviews-alternative-approaches">Existing reviews: alternative approaches</a></li>
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<li><a href="#the-search-protocol" id="toc-the-search-protocol" class="nav-link" data-scroll-target="#the-search-protocol">The search protocol</a></li>
<li><a href="#data" id="toc-data" class="nav-link" data-scroll-target="#data">Data</a></li>
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<li><a href="#synthesis-a-multitude-of-lenses" id="toc-synthesis-a-multitude-of-lenses" class="nav-link" data-scroll-target="#synthesis-a-multitude-of-lenses">Synthesis: A multitude of lenses</a>
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<li><a href="#institutional" id="toc-institutional" class="nav-link" data-scroll-target="#institutional">Institutional</a>
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<li><a href="#labour-laws-and-regulatory-systems" id="toc-labour-laws-and-regulatory-systems" class="nav-link" data-scroll-target="#labour-laws-and-regulatory-systems">Labour laws and regulatory systems</a></li>
<li><a href="#minimum-wage-laws" id="toc-minimum-wage-laws" class="nav-link" data-scroll-target="#minimum-wage-laws">Minimum wage laws</a></li>
<li><a href="#collective-bargaining" id="toc-collective-bargaining" class="nav-link" data-scroll-target="#collective-bargaining">Collective bargaining</a></li>
<li><a href="#workfare-programmes" id="toc-workfare-programmes" class="nav-link" data-scroll-target="#workfare-programmes">Workfare programmes</a></li>
<li><a href="#social-protection" id="toc-social-protection" class="nav-link" data-scroll-target="#social-protection">Social protection</a></li>
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<li><a href="#fiscal-growth-and-trade-liberalisation" id="toc-fiscal-growth-and-trade-liberalisation" class="nav-link" data-scroll-target="#fiscal-growth-and-trade-liberalisation">Fiscal growth and trade liberalisation</a></li>
<li><a href="#automation-and-technological-change" id="toc-automation-and-technological-change" class="nav-link" data-scroll-target="#automation-and-technological-change">Automation and technological change</a></li>
<li><a href="#infrastructure" id="toc-infrastructure" class="nav-link" data-scroll-target="#infrastructure">Infrastructure</a></li>
<li><a href="#education-access" id="toc-education-access" class="nav-link" data-scroll-target="#education-access">Education access</a></li>
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<li><a href="#agency" id="toc-agency" class="nav-link" data-scroll-target="#agency">Agency</a>
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<li><a href="#occupational-segregation-and-social-exclusion" id="toc-occupational-segregation-and-social-exclusion" class="nav-link" data-scroll-target="#occupational-segregation-and-social-exclusion">Occupational segregation and social exclusion</a></li>
<li><a href="#unconscious-bias-and-discriminatory-norms" id="toc-unconscious-bias-and-discriminatory-norms" class="nav-link" data-scroll-target="#unconscious-bias-and-discriminatory-norms">Unconscious bias and discriminatory norms</a></li>
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<li><a href="#discussion-and-policy-implications" id="toc-discussion-and-policy-implications" class="nav-link" data-scroll-target="#discussion-and-policy-implications">Discussion and policy implications</a>
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<li><a href="#robustness-of-evidence" id="toc-robustness-of-evidence" class="nav-link" data-scroll-target="#robustness-of-evidence">Robustness of evidence</a>
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<li><a href="#regional-spread" id="toc-regional-spread" class="nav-link" data-scroll-target="#regional-spread">Regional spread</a></li>
<li><a href="#internal-and-external-validity" id="toc-internal-and-external-validity" class="nav-link" data-scroll-target="#internal-and-external-validity">Internal and external validity</a></li>
<li><a href="#inequality-types-analysed" id="toc-inequality-types-analysed" class="nav-link" data-scroll-target="#inequality-types-analysed">Inequality types analysed</a></li>
</ul></li>
<li><a href="#gender-inequalities" id="toc-gender-inequalities" class="nav-link" data-scroll-target="#gender-inequalities">Gender inequalities</a></li>
<li><a href="#spatial-inequalities" id="toc-spatial-inequalities" class="nav-link" data-scroll-target="#spatial-inequalities">Spatial inequalities</a></li>
<li><a href="#disability-inequalities" id="toc-disability-inequalities" class="nav-link" data-scroll-target="#disability-inequalities">Disability inequalities</a></li>
<li><a href="#migration-and-ethnic-inequalities" id="toc-migration-and-ethnic-inequalities" class="nav-link" data-scroll-target="#migration-and-ethnic-inequalities">Migration and ethnic inequalities</a></li>
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<li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion">Conclusion</a></li>
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<div class="quarto-title-block"><div><h1 class="title">Addressing Inequalities in the World of Work</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
<p class="subtitle lead">Scoping Review on ‘What Works’</p>
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<p> </p>
<section id="introduction" class="level1">
<h1>Introduction</h1>
<p>This study presents a systematic scoping review of the current literature concerning inequalities in the world of work. It attempts to trace the main mechanisms and channels of the interventions employed in the global world of work to reduce its inequalities, while simultaneously investigating the methodologies and indicators used in evidence-based research on them to systematically elaborate the current state of the art on inequalities in the world of work.</p>
<p>The following section presents a typology of policies that directly or indirectly tackle inequalities in the world of work both within the labour market and outside this domain (e.g. education policy). The section also makes an attempt to clearly identify the theoretical mechanisms and channels through which policies are expected to impact inequalities in forms of work and ultimate labour market outcomes.</p>
<p>The ILO has a policy approach to reducing inequalities in the world of work segmented into five major focus areas: employment creation, access to education, labour rights protection, formalization, gender equality and diversity, and social protection <span class="citation" data-cites="ILO2022b">(<a href="#ref-ILO2022b" role="doc-biblioref">ILO, 2022b</a>)</span>. Each of these areas in turn rests on a variety of more specific emphases which further describe the potential implemented policy measures.</p>
<p>The rest of the study is structured as follows: Section 2 will introduce the world of work, as well as the ILO’s approach to inequalities within it, and provide a variety of other recent approaches to make sense of inequalities in the world of work. Section 3 will then introduce the method applied in the scoping review of this study, before introducing the initial identified literature as a coherent sample. Section 4 will synthesize findings on a variety of intervention found in the literature, organized by general policy area of intervention pursued. Section 5 will then provide a brief discussion on these findings from the perspective of individual inequalities, the interventions found to reduce them, and resulting policy implications, before Section 6 briefly concludes.</p>
</section>
<section id="the-world-of-work" class="level1">
<h1>The world of work</h1>
<p>The policy areas and their respective focus perspectives are based in the conceptual understanding of the world of work, following the definition of work being activities performed by persons of any sex and age producing goods or providing services for “economic units [which] can be allocated mutually exclusively to one of the following sectors:” the formal sector, the informal sector, or the community and household own-use sector <span class="citation" data-cites="ILO2023c">(<a href="#ref-ILO2023c" role="doc-biblioref">ILO, 2023b, p. 6</a>)</span>. This is the broader understanding of work which specifically separates itself from a more narrow conception of those in employment who are engaging in “production for pay or profit”, whether for the informal or the formal market economy <span class="citation" data-cites="ILO2023c">(see especially <a href="#ref-ILO2023c" role="doc-biblioref">ILO, 2023b</a>, Point 18ff)</span>. The key differentiations for these concepts are founded on an understanding of the production of goods or provision of services, as well as the distinctions between use by others for ultimate own-use and that of working for pay and/or profit – that is, as part of a market transaction in exchange for remuneration or in the form of profits derived from the goods or services.</p>
<p>Whether these services or goods are produced in what is defined as the informal economy, the formal economy or under informal employment outside the informal sector is, for the general encapsulation of no importance – they occur in the world of work. Here, conceptually, it should be captured under one of the five mutually exclusive forms of work <span class="citation" data-cites="ILO2023">(<a href="#ref-ILO2023" role="doc-biblioref">ILO, 2023d, p. 4</a>, Point 7c)</span> to be understood as: own-use production work, performing “any activity to produce goods or provide services for own final use” <span class="citation" data-cites="ILO2013">(<a href="#ref-ILO2013" role="doc-biblioref">ILO, 2013, p. 5</a>)</span>; employment work comprising those performing work for others in exchange for pay or profit introduced above; unpaid trainee work, performing “any unpaid activity to produce goods or provide services […] to acquire workplace experience or skills” <span class="citation" data-cites="ILO2013">(<a href="#ref-ILO2013" role="doc-biblioref">ILO, 2013, p. 7</a>)</span>; and volunteer work, that being “any unpaid, non-compulsory activity to produce goods or provide services for others” <span class="citation" data-cites="ILO2013">(<a href="#ref-ILO2013" role="doc-biblioref">ILO, 2013, p. 8</a>)</span>.</p>
<p>Any activity falling under work as defined above on the one hand, but not under any of these forms of work on the other, is instead designated as other work activities in the following considerations. The key concepts between these categories come down to a varying intensity of participation, the distinction of working for pay and/or profit mentioned above, whether it is for ultimate own-use or the use by others, and its compulsory nature.</p>
<p>The ILO has a policy approach to reducing inequalities in the world of work segmented into five major focus areas: employment creation, access to education, labour rights protection, formalization, gender equality and diversity, and social protection. Each of these areas in turn rests on a variety of more specific emphases which further describe the potential implemented policy measures. An exemplary typology of general policy area, related specified policy focus and related focus if any can be found in <a href="#tbl-policy-areas" class="quarto-xref">Table 1</a>.</p>
<div id="tbl-policy-areas" class="quarto-float anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-policy-areas-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 1: ILO focus areas for inequality reduction
</figcaption>
<div aria-describedby="tbl-policy-areas-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<table class="table">
<colgroup>
<col style="width: 23%">
<col style="width: 46%">
<col style="width: 30%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: right;">area of policy</th>
<th>focus</th>
<th>related</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: right;">employment creation</td>
<td>pro-employment framework</td>
<td></td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>gender-transformative framework</td>
<td></td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>promotion of business sustainability</td>
<td>productivity increases</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td></td>
<td>reduction in productivity gaps</td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>promotion of digital infrastructure</td>
<td>technology for decent work</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td></td>
<td>reducing digital divide</td>
</tr>
<tr class="odd">
<td style="text-align: right;">access to education</td>
<td>quality of education/training/skills development</td>
<td>green transition</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>relevance of education/training/skills development</td>
<td>digital transition</td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>gender-transformative career guidance</td>
<td></td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>improvements of public services/social protection</td>
<td></td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>work-life balance</td>
<td>juggle paid work and family care</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>targeted support for disadvantaged groups</td>
<td>targeted education</td>
</tr>
<tr class="odd">
<td style="text-align: right;">labour rights protection</td>
<td>promotion of rights for all workers</td>
<td>collective bargaining systems</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>minimum wage</td>
<td></td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>inclusive labour market institutions</td>
<td></td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>equal pay for work of equal value</td>
<td></td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>wage transparency</td>
<td></td>
</tr>
<tr class="even">
<td style="text-align: right;">formalization</td>
<td>equality-driven approach to formalization</td>
<td>gender-responsive</td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>increase decent work in formal economy</td>
<td>country-tailored</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>absorb informal workers / economic units</td>
<td>comprehensive</td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td></td>
<td>non-discriminatory</td>
</tr>
<tr class="even">
<td style="text-align: right;">gender equality</td>
<td>removal of discriminatory practice</td>
<td>removal of stereotypes</td>
</tr>
<tr class="odd">
<td style="text-align: right;">diversity</td>
<td>promotion of equality of treatment</td>
<td>removal of discriminatory law</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>promotion of equality of opportunity</td>
<td></td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>data collection improvements</td>
<td>gender-focus</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>occupational gender segregation</td>
<td>age-focus</td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>unequal pay for work of equal value</td>
<td>disability-focus</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>gender-based violence</td>
<td>race-focus</td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>gender-based harassment</td>
<td>ethnicity-focus</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>gender unequal division of unpaid care work</td>
<td>migrant status-focus</td>
</tr>
<tr class="odd">
<td style="text-align: right;">social protection</td>
<td>extend reach of social protection schemes</td>
<td></td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td>reach those not adequately protected</td>
<td></td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td>ensure access to social protection</td>
<td>comprehensive social protection</td>
</tr>
<tr class="even">
<td style="text-align: right;"></td>
<td></td>
<td>adequate social protection</td>
</tr>
<tr class="odd">
<td style="text-align: right;"></td>
<td></td>
<td>sustainable social protection</td>
</tr>
</tbody>
</table>
<p>Source: Authors’ elaboration based on ILO <span class="citation" data-cites="ILO2022b">(<a href="#ref-ILO2022b" role="doc-biblioref">2022b</a>)</span>.</p>
</div>
</figure>
</div>
<section id="inequalities-in-the-world-of-work" class="level2">
<h2 class="anchored" data-anchor-id="inequalities-in-the-world-of-work">Inequalities in the world of work</h2>
<p>Inequalities in the world of work have to be fundamentally conceptualized along two axes: On the one hand, vertical inequality captures the “income inequality between all households in a country” <span class="citation" data-cites="ILO2021">(<a href="#ref-ILO2021" role="doc-biblioref">ILO, 2021b</a>)</span>. Measurements of vertical inequalities is a perspective which focuses primarily on incomes as data, with debate of top income percentiles versus the remaining body of people often posing the primary area of debate <span class="citation" data-cites="ILO2021a">(<a href="#ref-ILO2021a" role="doc-biblioref">ILO, 2021a</a>)</span>. Horizontal inequalities, on the other hand, occur when “some groups within the population find themselves disadvantaged and discriminated against on the basis of their visible identity, for example their gender, colour or beliefs, among others” <span class="citation" data-cites="ILO2021a">(<a href="#ref-ILO2021a" role="doc-biblioref">ILO, 2021a</a>)</span>.</p>
<p>Importantly, these inequalities do not act in a vacuum but create an interplay through overlaps and accumulations which take on their own driving dynamics for people belonging to multiple disadvantaged groups, captured in the idea of inequality’s intersectionality <span class="citation" data-cites="ILO2022b">(<a href="#ref-ILO2022b" role="doc-biblioref">ILO, 2022b</a>)</span>. Here, especially horizontal inequalities may be hard to disentangle for impact finding, an important aspect of effective rigorous analysis in quantitative studies.</p>
<p>Thus, for a study on inequalities, or in turn a study on policies aimed at reducing inequalities in the world of work to be one of rigorous analysis, it must clearly define the type of policy taken as its object of analysis (its independent variable) as well as the types of inequalities targeted for reduction through the respective policy and measured as channels of impact. Ultimately, then, the individual outcome measures need to be clearly specified and disentangled, most clearly reflecting in labour market outcome measures (dependent variables). Only then can the targeted inequality be delineated as a clear channel.</p>
<p>In targeting an increase in equality, there are then two approaches to take: either levelling the playing field so that characteristics beyond an individual’s control can not influence their future perspectives, nor limit the potential of the powers they possess, through achieving equality of opportunity; or strive for an equality of outcomes, in factual observed resulting (in-)equalities. As the ILO established, such a focus on equality of outcomes can be of great importance since “high levels of inequality today tend to reduce social mobility tomorrow” <span class="citation" data-cites="ILO2021a">(<a href="#ref-ILO2021a" role="doc-biblioref">ILO, 2021a</a>)</span>, making it that much more difficult to ultimately ensure equality of opportunity for following generation. The key concepts here are thus the distinction of within-group and between-group inequalities, their overlapping characteristics, as well as policies enabling an equality of opportunity or of outcome.</p>
<p>Income inequality is still the primary lens of inequality that many approaches target, as well as the main focus point of many inequality measurements such as the Gini coefficient or ratios such as the Palma ratio <span class="citation" data-cites="DFI2023">(<a href="#ref-DFI2023" role="doc-biblioref">DFI, 2023</a>)</span>. Following the ILO, “labour income is the main source of income for most households in the world [thus] unequal access to work and working poverty are major drivers of inequalities” <span class="citation" data-cites="ILO2021">(<a href="#ref-ILO2021" role="doc-biblioref">ILO, 2021b</a>)</span>. Income inequality, here, can be affected by a wide set of factors: status in employment, forms of work, the sector of activity, the respective occupation, type of enterprise, type of contract for those in waged work, and the status of formality among others <span class="citation" data-cites="ILO2019">(<a href="#ref-ILO2019" role="doc-biblioref">ILO, 2019</a>)</span>. Income inequality should also not be seen as separate from, nor standing above, other inequalities, but closely linked to other inequalities. As the ILO states, “income inequality, inequality of employment outcomes more generally and inequality of opportunities are intimately related” <span class="citation" data-cites="ILO2022b">(<a href="#ref-ILO2022b" role="doc-biblioref">ILO, 2022b</a>)</span>. At the same time the exact linkages of these factors remain under-analysed, which is the reason why the channels of inequalities and the policies to reduce them will pose a fruitful space of analysis for this research.</p>
<p>While income inequality holds a primary position of importance for many analyses from a perspective of quantity, it should not be understood as carrying more importance qualitatively for itself compared to other inequalities but rather be understood “like a prism, which reveals many other forms of inequality, including those generated in the world of work” <span class="citation" data-cites="ILO2021a">(<a href="#ref-ILO2021a" role="doc-biblioref">ILO, 2021a, p. 13</a>)</span>. It is the primary measure of vertical inequality, however, with other inequalities describing primarily the concept of horizontal inequality.</p>
<p>Here, of primary focus for the ILO, and many studies on inequality in the world of work, is gender inequality. It describes the inequalities that arise because of an individual’s gender. Generally, while the type and extend of other inequalities does vary substantially by global location and country, “gender inequalities, despite some progress over the past decades, remain persistent and pervasive” <span class="citation" data-cites="ILO2021">(<a href="#ref-ILO2021" role="doc-biblioref">ILO, 2021b</a>)</span>.</p>
<p>Following a report on the gendered make-up of work globally, women are making up a larger part of those in underemployment, they primarily make up the service sector – a rising trend – while suffering a persistently substantial wage gap, tend to work shorter hours in employment but in turn have longer working days when including unpaid work, as well as contributing disproportionally to family work <span class="citation" data-cites="ILO2016">(<a href="#ref-ILO2016" role="doc-biblioref">ILO, 2016</a>)</span>. The domestic area of work is also dominated by women, who make up 76.2 per cent of it, in addition to domestic work being overwhelmingly informal labour globally <span class="citation" data-cites="ILO2023a">(<a href="#ref-ILO2023a" role="doc-biblioref">ILO, 2023c</a>)</span>.</p>
<p>These inequalities in the world of work in turn also reflect in women being hindered in accessing adequate education, training, as well as the possibility for lifelong learning, and furthermore access to quality jobs, housing, mobility, capital, land, and adequate social protection – disparities which, on the basis of deeply rooted inequalities of gender roles, education and places of residence remain largely static if not on the rise. These channels and outcomes, viewed intersectionally, must thus represent the primary lens through which to disentangle the gender inequality in the world of work today.</p>
<p>There are additional socio-demographic inequalities beyond gender which are based on the innate, most often visible, identification of a person. These are made up of, though not limited to, ethnic and racial inequalities, those based on religion and beliefs, based on a person’s status as a migrant, a person’s age, sex, or disabilities <span class="citation" data-cites="ILO2021a">(<a href="#ref-ILO2021a" role="doc-biblioref">ILO, 2021a</a>)</span>. For example, young people generally fare significantly worse in labour markets shown through outcomes such as a higher incidence of temporary employment throughout youth and the young labour force <span class="citation" data-cites="ILO2023b ILO2019">(<a href="#ref-ILO2019" role="doc-biblioref">ILO, 2019</a>, <a href="#ref-ILO2023b" role="doc-biblioref">2023a</a>)</span>.</p>
<p>As a report on the global conditions of work established, over “7% of workers felt they had been discriminated against in the 12 months prior to the survey on grounds of sex, race, religion, age, nationality, disability or sexual orientation” <span class="citation" data-cites="ILO2019">(<a href="#ref-ILO2019" role="doc-biblioref">ILO, 2019</a>)</span> in the EU alone, making socio-demographic inequalities a prevalent and important to tackle angle of horizontal inequality. Here, it will be especially important to correctly disentangle individual sources or contributing characteristics from each other in finding their linkages to relevant outcomes.</p>
<p>Another form of inequality are spatial inequalities, those that arise because of an individual’s location relative to other. These inequalities exist primarily between different regions of a country: those between urbanity and rurality or more peripheral areas, but also between richer and poorer regions and, as the ILO established, can even lead to a ‘growing sense of fractured societies’ <span class="citation" data-cites="ILO2021">(<a href="#ref-ILO2021" role="doc-biblioref">ILO, 2021b</a>)</span>.</p>
<p>One of the channels this can manifest itself is through an unequal access to decent work opportunities or economic opportunities more generally, an unequal access to financial resources, quality public services or even overall access to an essential social service infrastructure and digital infrastructure, as well as quality access to education or relevant training. For spatial inequalities it will be especially important to take note of locally bound differences versus more generalizable results, with the dimensions and contributing factors to its outcomes potentially varying widely between different geographies and national contexts.</p>
<p>In mentioning unequal access to quality education or public infrastructure another important dimension of inequalities becomes highlighted: the dimension of pre-existing inequalities, that is, inequalities which exist prior to an individual’s interaction with the labour market and, though closely intertwined with socio-demographic inequalities, will prove useful to analytically differentiate between. A differentiation which becomes especially important with a view on the inter-generational effects of inequality, highlighted in recognizing the difference between equality of opportunity and outcome. The level of education, an individual’s poverty, productivity on the labour market and similar inequalities in opportunities are often the result of long-running pre-existing inequalities such as unequal access to health services, education, lacking property rights or clear ownership of assets, the lack of formal recognition as an individual, no access to formal banking <span class="citation" data-cites="ILO2021a">(<a href="#ref-ILO2021a" role="doc-biblioref">ILO, 2021a</a>)</span>. Understanding such channels becomes difficult if not taking pre-existing inequalities into account as a separate category of inequality and long-term impacting channel.</p>
<p>Addressing these inequalities, in turn, is just as important to reducing inequalities within the labour market (as well as beyond) since they do play such a role for intergenerational social mobility and their impacts can be seen, once again, reflecting in the prism of subsequent income inequality. For pre-existing inequalities, it will be especially important to understand the often delayed and more opaque nature of the roots of many outcomes, with channel being more difficult to identify and clearly label – especially in an intersectional context. These five dimensions of inequalities — income inequality, gender inequality, socio-demographic inequality, spatial inequality and pre-existing inequalities — will thus provide the categorical anchors along which the reviewed studies will be analysed for their policy effects, each with a slightly different focus in linkages between inequality, policy and outcome.</p>
</section>
<section id="existing-reviews-alternative-approaches" class="level2">
<h2 class="anchored" data-anchor-id="existing-reviews-alternative-approaches">Existing reviews: alternative approaches</h2>
<p>Aside from the general typology by the ILO introduced above, there are a variety of differing approaches to the interplay of inequalities and outcomes, outlined in the following section.</p>
<!-- gender/pre-existing -->
<p>Chaudhuri et al. <span class="citation" data-cites="Chaudhuri2021">(<a href="#ref-Chaudhuri2021" role="doc-biblioref">2021</a>)</span> conduct a systematic review to look at coping strategies and the effects of food insecurity, often through poverty, on social and health outcomes for women and children. They find that one of the primary non-food coping strategies for women is to look for outdoor employment, mostly farm work, which can in turn lead to what the authors argue as <em>time</em> poverty when their time for childcare or personal nutrition is now cut short. This in turn can, in combination with food-based coping strategies such as food rationing (in size or frequency), nutritional switches or food sharing, lead to negative health outcomes for children including disrupted socio-cognitive development as well as coping through dropping out of school, thereby furthering the rift of pre-existing inequalities.</p>
<!-- gender -->
<p>Finlay <span class="citation" data-cites="Finlay2021">(<a href="#ref-Finlay2021" role="doc-biblioref">2021</a>)</span> looks at the effects of female women’s reproductive health on female labour force participation, especially career advancement, job quality and hours worked, to find a variety of responses differing between low-income, middle-income and high-income countries. The main findings are that in low-income countries because of the prevalence of informal work, women are forced to adopt individual strategies of balancing child rearing and labour force participation through job type selection, reliance on other women in the household for child care, or birth spacing. In middle-income countries, women have to juggle child rearing and labour force participation with an overall income inequality; here, early childbearing or lone motherhood especially can perpetuate poverty. In high-income countries, social protection policies can assist in balancing child rearing and work but many underlying issues of gender inequality remain. Throughout all countries, childbearing significantly interrupts career advancement.</p>
<!-- gender -->
<p>Chang et al. <span class="citation" data-cites="Chang2021">(<a href="#ref-Chang2021" role="doc-biblioref">2021</a>)</span> use a qualitative systematic review to look at the linkages of breast-feeding and returning to paid employment for women and identify multiple barriers provided through inequalities discouraging continued breast-feeding after return to employment — an experience often experienced as physically and emotionally difficult and potentially providing a barrier to full labour force participation. Aside from individual motivation and support from employers, colleagues, and family members, women highlighted the importance of having workplace legislation in place to facilitate breast-feeding during employment, as well as access to convenient child care. The review concludes indicating remaining gender and employment inequalities in accessing and receiving the support needed: gender role expectations viewing women as responsible for domestic work or childcare, with shorter maternity leave further discouraging breast-feeding especially of women not in managerial roles.</p>
<!-- policy interv -->
<p>Looking strictly at the impact of basic income interventions on labour market, health, educational, housing and other outcomes, Pinto et al. <span class="citation" data-cites="Pinto2021">(<a href="#ref-Pinto2021" role="doc-biblioref">2021</a>)</span> find that, while workforce participation is the primary outcome in most studies, the evaluations have shifted over time to include a wider array of outcomes, perhaps reflecting an understanding of lower health and social care spending offsetting some of the basic income investments. Most of the studies investigating basic income perspectives focus on advanced economies such as the US.</p>
<!-- disability -->
<p>Undertaking a systematic review to find the effects of brain tumours in individuals on their labour market outcomes, Silvaggi <span class="citation" data-cites="Silvaggi2020">(<a href="#ref-Silvaggi2020" role="doc-biblioref">2020</a>)</span> find an impact of neuropsychological functioning on work productivity, issues for their process of returning to work, and often an exit from employment (job loss) for long-term survivors of brain tumours While the channels are primarily viewed as stemming from the high short-term mortality and depressive symptoms or cognitive deficits, environmental barriers are identified as one channel as well, with the review ending in the policy recommendation of increased vocational rehabilitation for affected persons.</p>
<!-- basic income -->
<p>De Paz-Banez et al. <span class="citation" data-cites="dePaz-Banez2020">(<a href="#ref-dePaz-Banez2020" role="doc-biblioref">2020</a>)</span> use a systematic review of empirical studies to look at the effects of universal basic income on labour supply to find that, with no evidence of significant reductions in labour supply, instead the labour supply would increase globally among adults, men, women, young and old. The insignificant reductions they found they assumed functional, since they were in the categories of: children, elderly, sick, people with disabilities, women with young children, young people continuing their studies and were offset by the otherwise increased supply.</p>
<!-- disabilities, gender -->
<p>Looking at the impact of gender on the employment outcomes for young disabled adults, Lindsay et al. <span class="citation" data-cites="Lindsay2018">(<a href="#ref-Lindsay2018" role="doc-biblioref">2018</a>)</span> find that while youth with disabilities are half as likely to be employed, gender inequalities may play a compounding role with men being more likely to be in employment than women, working longer hours and having higher wages. The identified channels here are different social supports, gender role expectations, as well as women’s lower job expectations and overprotection from parents or guardians discouraging their independence.</p>
<!-- gender -->
<p>Kumari <span class="citation" data-cites="Kumari2018">(<a href="#ref-Kumari2018" role="doc-biblioref">2018</a>)</span> looks at the relationship of both economic growth and gender disparity on the labour supply in investigating their effects on female work participation. <!-- TODO explain U-shape --> They see a U-shaped participation rate and some evidence of cross-sector gender pay disparity which is affected by demographic factors such as migration, marriage, child care and fertility, as well as economic factors such as per capita income, unemployment, infrastructure and the prevalence of non-farm jobs. Ultimately, they argue that the labour supply inequalities are based on inequality between the sexes and, while regulatory measures such as adequate family and childcare policies, tax regimes and the presence of subsidized healthcare help, changes to the female labour force participation fundamentally require the replacement of such a traditional value system itself.</p>
<!-- income -->
<p>While undertaking a systematic review concerning the effects of adopting technology on employment in LICs or LMICs, Ugur and Mitra <span class="citation" data-cites="Ugur2017">(<a href="#ref-Ugur2017" role="doc-biblioref">2017</a>)</span> find when adoption favours product innovation positive effects are somewhat likely. They also find, however, that existing income inequalities can make the possible positive effects of its adoption more ambiguous and may in turn widen the rift of demand for skilled versus unskilled labour. Lastly, policies favouring green transition technologies may in turn reduce income inequality, providing another possible linkage.</p>
<!-- disability -->
<p>Lettieri and Diez Villoria <span class="citation" data-cites="Lettieri2017">(<a href="#ref-Lettieri2017" role="doc-biblioref">2017</a>)</span> find that hiding mental illness is one of the primary strategies for improved employment outcomes in a meta-review looking at barriers to labour market inclusion for people mental disabilities. This act of concealment of identity and self-stigmatization can seem necessary, they argue, due to the channels of workplace prejudices, perceiving them missing skills, as dangerous or unpredictable, or seeing the act of their hiring as charity due to expectations of lower productivity; but also due to discriminatory hiring practices and pre-existing inequalities leading to them being lower-skilled individuals due to prior discrimination, cultural and social barriers to training and work inclusion. Here, relevant policies include interventions of supported employment (removing an environmental barrier), cognitive behavioural or computer-assisted therapies (cognitive barrier) or vocational rehabilitation programmes (human capital).</p>
<!-- gender -->
<p>Taukobong et al. <span class="citation" data-cites="Taukobong2016">(<a href="#ref-Taukobong2016" role="doc-biblioref">2016</a>)</span> review various dimensions of female empowerment and their effects on a variety of health and development outcomes, including the access and use of financial services for the poor. They find that, aside from gender inequalities being both highly contextual and intersectional, especially the channels of control over one’s income, assets, resources, having decision-making power and individual education affected these outcomes across all dimensions, reflecting their position as channels of gender inequality. Additionally, personal mobility, safety and equitable interpersonal relationships are associated with some health and family planning outcomes. Ultimately, the review shows that due to the contextual nature, interventions need to identify the variations of inequality at their start, see where inequalities exist, overlap and work as barriers for an effective implementation.</p>
<!-- disability -->
<p>Ruhindwa et al. <span class="citation" data-cites="Ruhindwa2016">(<a href="#ref-Ruhindwa2016" role="doc-biblioref">2016</a>)</span> review a variety of barriers to adequate workforce inclusion for people with disabilities, proposing an inclusive approach in which the individual is given space to take ownership of the solutions addressing challenges experienced in the employment sector. Similarly, they view hiring discrimination and workplace stigmatization as the largest channels through which inequalities of disability manifest themselves. They see especially employment support practices, with focus on enabling this, as relevant policy strategies, as well as national campaigns to ease disclosing one’s disability in the labour market.</p>
<!-- disability, gender, age -->
<p>In looking at the various dimensions affecting the labour market outcomes of supported employment interventions for people with disabilities, Kirsh <span class="citation" data-cites="Kirsh2016">(<a href="#ref-Kirsh2016" role="doc-biblioref">2016</a>)</span> finds that most literature still only regards the overall efficacy of the interventions without taking into account compounding intersectional characteristics. They find that generally men are more likely to find employment through the intervention, possibly resting on current programmes focus on manual labour, as well as younger people generally finding better employment. This highlights the intersectional nature of inequalities between disability, gender and age. One relevant policy they see is that of vocational rehabilitation.</p>
<!-- disability -->
<p>Hastbacka et al. <span class="citation" data-cites="Hastbacka2016">(<a href="#ref-Hastbacka2016" role="doc-biblioref">2016</a>)</span> undertake a scoping review to find the linkages between societal participation and people with disabilities, looking at specific interventions for the identity of participants, types of participation analysed, and channels of effect. They see most literature focusing on labour market participation and viewing disabled people as coherent group instead of intersectional. The main channels of inequality providing barriers they identify are financial factors, attitudes of discrimination, health issues and unemployment, while the main driving mechanisms identified are legislation and disability policies, as well as support from people in close contact with disabled people and attitudes in society and the hiring process.</p>
<!-- disability -->
<p>In a systematic review looking at the effectiveness of workplace accommodations on employment and return to work, Nevala et al. <span class="citation" data-cites="Nevala2015">(<a href="#ref-Nevala2015" role="doc-biblioref">2015</a>)</span> find few studies with rigorous design leading to conclusive evidence. They do find moderate evidence that employment in disability can be increased through workplace accommodations such as vocational counselling or guidance, education, self-advocacy, positive perception and help by others. There is also low evidence for return to work being increased by education, work aids and techniques and cooperation between employers and other professionals (such as occupational health care, or service providers).</p>
</section>
</section>
<section id="methodology-and-data" class="level1">
<h1>Methodology and data</h1>
<p>This section will discuss the systematic scoping review methodology that is proposed to conduct the review of the literature on policy interventions that are expected to address inequalities in forms of work and labour market outcomes. This study follows the principles of a systematic review framework, to systematically assess the impact of an array of policies on inequalities in the world of work. It strives to follow the clear and reproducible method of identification prior to synthesis of relevant research, while limiting “bias by the systematic assembly, critical appraisal and synthesis” through applying scientific strategies to the review itself <span class="citation" data-cites="Cook1995">(<a href="#ref-Cook1995" role="doc-biblioref">Cook et al., 1995</a>)</span>. It thereby attempts to provide an improved basis for comparative analysis between studies through the rigorous application of systematic criteria and thus to avoid the potential bias of narrative reviews.</p>
<p>Unlike purely systematic reviews which typically focus on specific policy questions and interventions, systematic scoping reviews focus on a wider spectrum of policies, where different study designs and research questions can be investigated. Since scoping reviews allow both broad and in-depth analyses, they are the most appropriate rigorous method to make a synthesis of the current evidence in this area <span class="citation" data-cites="Arksey2005">(<a href="#ref-Arksey2005" role="doc-biblioref"><strong>Arksey2005?</strong></a>)</span>.</p>
<p>The scoping review allows broad focus to be given to a subject for which no unified path with clear edges has been laid out yet by prior reviews, as remains the case with policies targeting inequalities in the world of work. It does so through a breadth-first approach through a search protocol which favours working through a large body of literature to subsequently move toward a depth-favouring approach once the literature has been sufficiently delimited. Its purpose, clearly mapping a body of literature on a (broad) topic area, is thereby useful on its own or in combination with a systematic approach <span class="citation" data-cites="Arksey2005">(<a href="#ref-Arksey2005" role="doc-biblioref"><strong>Arksey2005?</strong></a>)</span>. With an increasingly adopted approach in recent years, with rigorous dichotomy of inclusion and exclusion criteria it provides a way of charting the relevance of literature related to its overall body that strives to be free of influencing biases which could affect the skew of the resulting literature sample <span class="citation" data-cites="Pham2014">(<a href="#ref-Pham2014" role="doc-biblioref">Pham et al., 2014</a>)</span>.</p>
<section id="the-search-protocol" class="level2">
<h2 class="anchored" data-anchor-id="the-search-protocol">The search protocol</h2>
<!-- TODO need correct above definitions -->
<p>The search protocol was carried out based on the introduced areas of policies as well as the possible combination of definitions and outcomes in the world of work. For each dimension of definitions, a cluster containing possible utilized terms will be created, that is for: definitions of work and labour, forms of work, definitions of inequality, forms of vertical and forms of horizontal inequalities, labour market outcomes, and definitions of policy. Each of the clusters contains synonymous terms as well as term-adjacent phrase combinations which are in turn used to refine or broaden the search scope to best encapsulate each respective cluster, based on the above definitions.</p>
<!-- TODO Why WOS database? -->
<p>The search protocol then follows a three-staged process of execution: identification, screening and extraction. First, in identification, the relevant policy, inequality and world of work related dimensions are combined through Boolean operators to conduct a search through the database repository Web of Science and supplemental searches via Google Scholar to supply potential gray literature. While the resulting study pools could be screened for in multiple languages, the search queries themselves are passed to the databases in English-language only. Relevant results are then complemented through the adoption of a ‘snowballing’ technique, in which an array of identified adjacent published reviews is analysed for their reference lists to find cross-references of potentially missing literature and in turn add those to the pool of studies.</p>
<p>To identify potential studies and create an initial sample, relevant terms for the clusters of world of work, inequality and policy interventions have been extracted from the existing reviews as well as the ILO definitions. Identified terms comprising the world of work can be found in <a href="#tbl-wow-terms" class="quarto-xref">Table 2</a>, with the search query requiring a term from the general column and one other column.</p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>terms_wow <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_wow.csv&quot;</span>)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_wow.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-wow-terms" class="quarto-float anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-wow-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 2: World of work term cluster
</figcaption>
<div aria-describedby="tbl-wow-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell" data-execution_count="2">
<div id="tbl-wow-terms" class="cell quarto-float anchored" data-execution_count="2">
<figure class="quarto-float quarto-subfloat-tbl figure">
<figcaption class="quarto-float-caption-top quarto-subfloat-caption quarto-subfloat-tbl" id="tbl-wow-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
(a) World of work term cluster
</figcaption>
<div aria-describedby="tbl-wow-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output cell-output-display cell-output-markdown" data-execution_count="2">
<table class="do-not-create-environment cell table table-sm table-striped small">
<colgroup>
<col style="width: 26%">
<col style="width: 26%">
<col style="width: 46%">
</colgroup>
<thead>
<tr class="header">
<th>General</th>
<th>Forms of work</th>
<th>Labour market outcomes</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>work</td>
<td>own-use</td>
<td>employment outcomes</td>
</tr>
<tr class="even">
<td>labour</td>
<td>employment</td>
<td>labour rights</td>
</tr>
<tr class="odd">
<td>production of goods</td>
<td>unpaid trainee</td>
<td>equality of opportunity</td>
</tr>
<tr class="even">
<td>provision of services</td>
<td>volunteer</td>
<td>equality of outcome</td>
</tr>
<tr class="odd">
<td>own-use</td>
<td>other work activities</td>
<td>labour force participation <span class="citation" data-cites="Pinto2021">(<a href="#ref-Pinto2021" role="doc-biblioref">Pinto et al., 2021</a>)</span></td>
</tr>
<tr class="even">
<td>use by others</td>
<td>wage-employed</td>
<td>labour force exit <span class="citation" data-cites="Silvaggi2020">(<a href="#ref-Silvaggi2020" role="doc-biblioref">Silvaggi et al., 2020</a>)</span></td>
</tr>
<tr class="odd">
<td>of working age</td>
<td>self-employed</td>
<td>job quality <span class="citation" data-cites="Finlay2021">(<a href="#ref-Finlay2021" role="doc-biblioref">Finlay, 2021</a>)</span></td>
</tr>
<tr class="even">
<td>for pay</td>
<td>formal work</td>
<td>career advancement <span class="citation" data-cites="Finlay2021">(<a href="#ref-Finlay2021" role="doc-biblioref">Finlay, 2021</a>)</span></td>
</tr>
<tr class="odd">
<td>for profit</td>
<td>informal work</td>
<td>hours worked <span class="citation" data-cites="Finlay2021">(<a href="#ref-Finlay2021" role="doc-biblioref">Finlay, 2021</a>)</span></td>
</tr>
<tr class="even">
<td>remuneration</td>
<td>domestic work</td>
<td>wage</td>
</tr>
<tr class="odd">
<td>market transactions</td>
<td>care work</td>
<td>salary</td>
</tr>
<tr class="even">
<td></td>
<td>unpaid work</td>
<td>return to work <span class="citation" data-cites="Silvaggi2020">(<a href="#ref-Silvaggi2020" role="doc-biblioref">Silvaggi et al., 2020</a>)</span></td>
</tr>
</tbody>
</table>
</div>
</div>
</figure>
</div>
</div>
</div>
</figure>
</div>
<p>The world of work cluster, like the inequality and policy intervention clusters below, is made up of a general signifier (such as “work”, “inequality” or “intervention”) which has to be labelled in a study to form part of the sample, as well as any additional terms looking into one or multiple specific dimensions or categories of these signifiers (such as “domestic” work, “gender” inequality, “maternity leave” intervention). At least one general term and at least one additional term have to be mentioned by a study to be identified for the initial sample pool.</p>
<p>For the policy intervention cluster, a variety of terms have been identified both from the ILO policy areas and guidelines as well as existing reviews, as can be seen in <a href="#tbl-intervention-terms" class="quarto-xref">Table 3</a>. Where terms have been identified from previous reviews outside the introduced ILO policy guidelines, there source has been included in the table. For the database query, a single term from the general category is required to be included in addition to one term from <em>any</em> of the remaining categories.</p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>terms_policy <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_policy.csv&quot;</span>)</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="co"># different headers to include &#39;social norms&#39;</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>headers <span class="op">=</span> [<span class="st">&quot;General&quot;</span>, <span class="st">&quot;Institutional&quot;</span>, <span class="st">&quot;Structural&quot;</span>, <span class="st">&quot;Agency &amp; social norms&quot;</span>]</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_policy.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>headers, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-intervention-terms" class="quarto-float anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-intervention-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 3: Policy intervention term cluster
</figcaption>
<div aria-describedby="tbl-intervention-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell" data-execution_count="3">
<div id="tbl-intervention-terms" class="cell quarto-float anchored" data-execution_count="3">
<figure class="quarto-float quarto-subfloat-tbl figure">
<figcaption class="quarto-float-caption-top quarto-subfloat-caption quarto-subfloat-tbl" id="tbl-intervention-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
(a) Intervention term cluster
</figcaption>
<div aria-describedby="tbl-intervention-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output cell-output-display cell-output-markdown" data-execution_count="3">
<table class="do-not-create-environment cell table table-sm table-striped small">
<colgroup>
<col style="width: 12%">
<col style="width: 23%">
<col style="width: 34%">
<col style="width: 28%">
</colgroup>
<thead>
<tr class="header">
<th>General</th>
<th>Institutional</th>
<th>Structural</th>
<th>Agency &amp; social norms</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>intervention</td>
<td>support for childcare <span class="citation" data-cites="Perez2022">(<a href="#ref-Perez2022" role="doc-biblioref">Perez et al., 2022</a>)</span></td>
<td>cash benefits</td>
<td>credit programs <span class="citation" data-cites="Perez2022">(<a href="#ref-Perez2022" role="doc-biblioref">Perez et al., 2022</a>)</span></td>
</tr>
<tr class="even">
<td>policy</td>
<td>labour rights</td>
<td>services in kind</td>
<td>career guidance</td>
</tr>
<tr class="odd">
<td>participation</td>
<td>minimum wage</td>
<td>green transition</td>
<td>vocational guidance <span class="citation" data-cites="Nevala2015">(<a href="#ref-Nevala2015" role="doc-biblioref">Nevala et al., 2015</a>)</span></td>
</tr>
<tr class="even">
<td>targeting/ targeted</td>
<td>collective bargaining</td>
<td>infrastructure</td>
<td>vocational counselling <span class="citation" data-cites="Nevala2015">(<a href="#ref-Nevala2015" role="doc-biblioref">Nevala et al., 2015</a>)</span></td>
</tr>
<tr class="odd">
<td>distributive</td>
<td>business sustainability promotion</td>
<td>digital infrastructure</td>
<td>counteracting of stereotypes</td>
</tr>
<tr class="even">
<td>redistributive</td>
<td>work-life balance promotion</td>
<td>quality of education</td>
<td>commuting subsidies <span class="citation" data-cites="Perez2022">(<a href="#ref-Perez2022" role="doc-biblioref">Perez et al., 2022</a>)</span></td>
</tr>
<tr class="odd">
<td></td>
<td>equal pay for work of equal value</td>
<td>public service improvement</td>
<td>housing mobility programs <span class="citation" data-cites="Perez2022">(<a href="#ref-Perez2022" role="doc-biblioref">Perez et al., 2022</a>)</span></td>
</tr>
<tr class="even">
<td></td>
<td>removal of (discriminatory) law</td>
<td>lowering of gender segregation</td>
<td>encouraging re-situation/migration <span class="citation" data-cites="Perez2022">(<a href="#ref-Perez2022" role="doc-biblioref">Perez et al., 2022</a>)</span></td>
</tr>
<tr class="odd">
<td></td>
<td>law reformation</td>
<td>price stability intervention</td>
<td>encouraging self-advocacy <span class="citation" data-cites="Nevala2015">(<a href="#ref-Nevala2015" role="doc-biblioref">Nevala et al., 2015</a>)</span></td>
</tr>
<tr class="even">
<td></td>
<td>social dialogue</td>
<td>extended social protection scheme</td>
<td>cognitive behavioural therapy <span class="citation" data-cites="Lettieri2017">(<a href="#ref-Lettieri2017" role="doc-biblioref">Lettieri &amp; Diez Villoria, 2017</a>)</span></td>
</tr>
<tr class="odd">
<td></td>
<td>guaranteed income <span class="citation" data-cites="Perez2022">(<a href="#ref-Perez2022" role="doc-biblioref">Perez et al., 2022</a>)</span></td>
<td>comprehensive social protection</td>
<td>computer-assisted therapy <span class="citation" data-cites="Lettieri2017">(<a href="#ref-Lettieri2017" role="doc-biblioref">Lettieri &amp; Diez Villoria, 2017</a>)</span></td>
</tr>
<tr class="even">
<td></td>
<td>universal basic income <span class="citation" data-cites="Perez2022">(<a href="#ref-Perez2022" role="doc-biblioref">Perez et al., 2022</a>)</span></td>
<td>sustainable social protection</td>
<td>work organization <span class="citation" data-cites="Nevala2015">(<a href="#ref-Nevala2015" role="doc-biblioref">Nevala et al., 2015</a>)</span></td>
</tr>
<tr class="odd">
<td></td>
<td>provision of living wage <span class="citation" data-cites="Perez2022">(<a href="#ref-Perez2022" role="doc-biblioref">Perez et al., 2022</a>)</span></td>
<td>supported employment <span class="citation" data-cites="Lettieri2017">(<a href="#ref-Lettieri2017" role="doc-biblioref">Lettieri &amp; Diez Villoria, 2017</a>)</span></td>
<td>special transportation <span class="citation" data-cites="Nevala2015">(<a href="#ref-Nevala2015" role="doc-biblioref">Nevala et al., 2015</a>)</span></td>
</tr>
<tr class="even">
<td></td>
<td>maternity leave <span class="citation" data-cites="Chang2021">(<a href="#ref-Chang2021" role="doc-biblioref">Chang et al., 2021</a>)</span></td>
<td>vocational rehabilitation <span class="citation" data-cites="Silvaggi2020">Lettieri &amp; Diez Villoria (<a href="#ref-Lettieri2017" role="doc-biblioref">2017</a>)</span></td>
<td>collective action</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>unionization</td>
<td></td>
</tr>
</tbody>
</table>
</div>
</div>
</figure>
</div>
</div>
</div>
</figure>
</div>
<p>Lastly, the inequality cluster is once again made up of a general term describing inequality which has to form part of the query results, as well as at least one term describing a specific vertical or horizontal inequality, as seen in <a href="#tbl-inequality-terms" class="quarto-xref">Table 4</a>.</p>
<div class="cell" data-execution_count="4">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>terms_inequality <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_inequality.csv&quot;</span>)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_inequality.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-inequality-terms" class="cell quarto-float anchored" data-execution_count="4">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-inequality-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 4: Inequality term cluster
</figcaption>
<div aria-describedby="tbl-inequality-terms-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output cell-output-display cell-output-markdown" data-execution_count="4">
<table class="do-not-create-environment cell table table-sm table-striped small">
<colgroup>
<col style="width: 32%">
<col style="width: 40%">
<col style="width: 26%">
</colgroup>
<thead>
<tr class="header">
<th>General</th>
<th>Vertical</th>
<th>Horizontal</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>inequality</td>
<td>income</td>
<td>identity</td>
</tr>
<tr class="even">
<td>barrier</td>
<td>Palma ratio <span class="citation" data-cites="DFI2023">(<a href="#ref-DFI2023" role="doc-biblioref">DFI, 2023</a>)</span></td>
<td>demographic</td>
</tr>
<tr class="odd">
<td>advantaged</td>
<td>Gini coefficient <span class="citation" data-cites="DFI2023">(<a href="#ref-DFI2023" role="doc-biblioref">DFI, 2023</a>)</span></td>
<td>gender</td>
</tr>
<tr class="even">
<td>disadvantaged</td>
<td>Log deviation</td>
<td>colour</td>
</tr>
<tr class="odd">
<td>discriminated</td>
<td>Theil</td>
<td>beliefs</td>
</tr>
<tr class="even">
<td>disparity</td>
<td>Atkinson</td>
<td>racial</td>
</tr>
<tr class="odd">
<td>horizontal inequality</td>
<td>class <span class="citation" data-cites="Kalasa2021">(<a href="#ref-Kalasa2021" role="doc-biblioref">Kalasa et al., 2021</a>)</span></td>
<td>ethnic</td>
</tr>
<tr class="even">
<td>vertical inequality</td>
<td>fertility <span class="citation" data-cites="Kalasa2021">(<a href="#ref-Kalasa2021" role="doc-biblioref">Kalasa et al., 2021</a>)</span></td>
<td>migrant</td>
</tr>
<tr class="odd">
<td></td>
<td>bottom percentile</td>
<td>spatial</td>
</tr>
<tr class="even">
<td></td>
<td>top percentile</td>
<td>rural</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>urban</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>mega-cities</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>small cities</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>peripheral cities</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>age</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>nationality</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>ethnicity</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>health status</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td>disability</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td>characteristics</td>
</tr>
</tbody>
</table>
</div>
</div>
</figure>
</div>
</div>
<p>A general as well as category-specific term from each cluster will be required, using a intersection merge (Boolean ‘AND’), as well as in turn a single of those from each of the three clusters using an intersection merge. The resulting sample pool will thus include a term and specific dimension of inequality and of policy intervention within the world of work.</p>
<p>Second, in screening, duplicate results are removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on: language, title, abstract, full text and literature supersession through newer publications. Properties in these characteristics are used to assess an individual study on its suitability for further review.</p>
<p>Narrowing criteria are applied to restrict the sample to studies looking at i) the effects of individual evidence-based policy measures or intervention initiatives ii) attempting to address a single or multiple of the defined inequalities in the world of work. iii) using appropriate quantitative methods to examine the links of intervention and impact on the given inequalities. The narrowing process makes use of the typology of inequalities, of forms of work, and of policy areas introduced above as its criteria.</p>
<p>An overview of the respective criteria used for inclusion or exclusion can be found in <a href="#tbl-inclusion-criteria" class="quarto-xref">Table 5</a>. It restricts studies to those that comprise primary research published after 2000, with a focus on the narrowing criteria specified in <a href="#tbl-inclusion-criteria" class="quarto-xref">Table 5</a>.</p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>inclusion_criteria <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/inclusion-criteria.tsv&quot;</span>, sep<span class="op">=</span><span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>md(tabulate(inclusion_criteria, showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-inclusion-criteria" class="quarto-float anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-inclusion-criteria-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 5: Study inclusion and exclusion scoping criteria
</figcaption>
<div aria-describedby="tbl-inclusion-criteria-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div id="tbl-inclusion-criteria" class="cell anchored" data-execution_count="5">
<div class="cell-output cell-output-display cell-output-markdown" data-execution_count="5">
<table class="table table-sm table-striped small">
<colgroup>
<col style="width: 9%">
<col style="width: 44%">
<col style="width: 44%">
</colgroup>
<thead>
<tr class="header">
<th>Parameter</th>
<th>Inclusion criteria</th>
<th>Exclusion criteria</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Time frame</td>
<td>study published in or after 2000</td>
<td>study published before 2000</td>
</tr>
<tr class="even">
<td>Study type</td>
<td>primary research</td>
<td>opinion piece, editorial, commentary, news article, literature review</td>
</tr>
<tr class="odd">
<td>Study recency</td>
<td>most recent publication of study</td>
<td>gray literature superseded by white literature publication</td>
</tr>
<tr class="even">
<td>Study data</td>
<td>evidence-based study or based on empirical approach</td>
<td>no empirical approach or not clearly based on evidential data</td>
</tr>
<tr class="odd">
<td>Study focus</td>
<td>effects on inequality/equality as primary outcome (dependent variable)</td>
<td>neither inequality nor equality outcomes as dependent variable</td>
</tr>
<tr class="even">
<td></td>
<td>policy measure or strategy as primary intervention (independent variable)</td>
<td>no policy measure/strategy as intervention or relationship unclear</td>
</tr>
<tr class="odd">
<td></td>
<td>specifically relates to some dimension of world of work</td>
<td>exists outside world of work for both independent and dependent variables</td>
</tr>
<tr class="even">
<td></td>
<td>focus on dimension of inequality in analysis</td>
<td>no focus on mention of inequality in analysis</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>Source: Author’s elaboration</p>
</div>
</figure>
</div>
<p>To facilitate the screening process, with the help of ‘Zotero’ reference manager a system of keywords is used to tag individual studies in the sample with their reason for exclusion,such as ‘excluded::language’, ‘excluded::title’, ‘excluded::abstract’, and ‘excluded::superseded’. This keyword-based system is equally used to further categorize the sample studies that do not fall into exclusion criteria, based on primary country of analysis, world region, as well as income level classification. To that end, a ‘country::’, ‘region::’ and ‘income::’ are used to disambiguate between the respective characteristics, such as ‘region::LAC’ for Latin America and the Caribbean, ‘region::SSA’ for Sub-Saharan Africa; as well as for example ‘income::low-middle’, ‘income::upper-middle’ or ‘income::high’. These two delineations follow the ILO categorizations on world regions and the country income classifications based on World Bank income groupings <span class="citation" data-cites="ILO2022">(<a href="#ref-ILO2022" role="doc-biblioref">ILO, 2022a</a>)</span>.</p>
<p>Similarly, if a specific type of inequality, or a specific intervention, represents the focus of a study, these will be reflected in the same keyword system, through for example ‘inequality::income’ or ‘inequality::gender’. The complete process of identification and screening is undertaken with the help of the Zotero reference manager, ultimately leaving only publications which are relevant for final full-text review and analysis. Last, for extraction, studies are screened for their full-texts, irrelevant studies excluded with ‘excluded::full-text’ as explained above and relevant studies then ingested into the final sample pool.</p>
<p>Should any literature reviews be identified as relevant during this screening process, they will in turn be crawled for cited sources in a ‘snowballing’ process, and the sources will be added to the sample to undergo the same screening process explained above.</p>
<div id="calculate-scoping-flowchart" class="cell" data-execution_count="6">
<div class="cell" data-fig-width="6" data-layout-align="default">
<div class="cell-output-display">
<div id="fig-prisma" class="quarto-figure quarto-figure-center quarto-float anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-prisma-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div>
<pre class="mermaid mermaid-js" data-label="fig-prisma">flowchart TD;
search_db[&quot;Records identified through database searching (n=1749)&quot;] --&gt; starting_sample;
search_prev[&quot;Records identified through other sources (n=2240)&quot;] --&gt; starting_sample[&quot;Starting sample (n=3989)&quot;];
starting_sample -- &quot;Duplicate removal (267 removed) &quot;--&gt; dedup[&quot;Records after duplicates removed (n=3723)&quot;];
dedup -- &quot;Title screening (1779 excluded)&quot; --&gt; title_screened[&quot;Records after titles screened (n=1944)&quot;];
title_screened -- &quot;Abstract screening (1506 excluded)&quot;--&gt; abstract_screened[&quot;Records after abstracts screened (n=438)&quot;];
abstract_screened -- &quot; Language screening (2 excluded) &quot;--&gt; language_screened[&quot;Records after language screened (n=436)&quot;];
language_screened -- &quot; Full-text screening (383 excluded) &quot;--&gt; full-text_screened[&quot;Full-text articles assessed for eligibility (n=52)&quot;];
</pre>
</div>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-prisma-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 1: Sample sorting process through identification and screening
</figcaption>
</figure>
</div>
</div>
</div>
</div>
<p>All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix. The results to be identified in the matrix include a study’s: i) key outcome measures (dependent variables), ii) main findings, iii) main policy interventions (independent variables), iv) study design and sample size, v) dataset and methods of evaluation, vi) direction of relation and level of representativeness, vii) level of statistical significance, viii) main limitations.</p>
<p>Finally, following <span class="citation" data-cites="Maitrot2017">Maîtrot &amp; Niño-Zarazúa (<a href="#ref-Maitrot2017" role="doc-biblioref">2017</a>)</span>, the relevant studies are ranked for their validity. Here, a 2-dimensional approach is taken to separate the external validity from the internal validity of the studies. The ranking process then uses the representativeness of a study’s underlying dataset, from a non-representative survey sample, through a sub-nationally representative sample, a nationally representative and the use of census data, to arrive at a ranking between 2.0 and 5.0 respectively. Similarly, the studies are ranked for internal validity using the study design, with only quasi-experimental and experimental studies receiving similar rankings between 2.0 and 5.0 depending on the individually applied methods due to their quantifiability, while observational and qualitative studies go without an internal validity rank (0.0) due to the more contextual nature of their analyses. For a full list of validity ranks, see <a href="#apptbl-validity-external" class="quarto-xref">Table A1</a> and <a href="#apptbl-validity-internal" class="quarto-xref">Table A2</a>.</p>
</section>
<section id="data" class="level2">
<h2 class="anchored" data-anchor-id="data">Data</h2>
<p>The query execution results in an initial sample of 1749 potential studies identified from the database search as well as 2240 potential studies from other sources, leading to a total initial number of 3989. This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria. Of these, 244 have been identified as potentially relevant studies for the purposes of this scoping review, from which 52 have been extracted.</p>
<!-- {{++ FIXME: Update description for changing study pool ++}} -->
<p>The currently identified literature rises somewhat in volume over time, with first larger outputs identified from 2014, as can be seen in <a href="#fig-publications-per-year" class="quarto-xref">Figure 2</a>.</p>
<!-- {{++ FIXME: give full year scale ++}} -->
<p><!-- TODO Should this be sub-divided by region or subdivision later per-section? --></p>
<div id="cell-fig-publications-per-year" class="cell" data-execution_count="8">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>df_study_years <span class="op">=</span> (</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a> bib_df.groupby([<span class="st">&quot;author&quot;</span>, <span class="st">&quot;year&quot;</span>, <span class="st">&quot;title&quot;</span>])</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a> .first()</span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a> .reset_index()</span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a> .drop_duplicates()</span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a><span class="co"># plot by year </span><span class="al">TODO</span><span class="co"> decide if we want to distinguish by literature type/region/etc as hue</span></span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="co"># </span><span class="al">FIXME</span><span class="co"> should be timeseries plot so no years are missing</span></span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(df_study_years, x<span class="op">=</span><span class="st">&quot;year&quot;</span>)</span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>ax.tick_params(axis<span class="op">=</span><span class="st">&#39;x&#39;</span>, rotation<span class="op">=</span><span class="dv">45</span>)</span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a>df_study_years <span class="op">=</span> <span class="va">None</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
<div id="fig-publications-per-year" class="quarto-figure quarto-figure-center quarto-float anchored">
<figure class="quarto-float quarto-float-fig figure">
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<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-publications-per-year-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 2: Publications per year
</figcaption>
</figure>
</div>
</div>
</div>
<p>Anomalies such as the relatively significant dips in output in 2016 and 2012 become especially interesting against the strong later increase of output. While this can mean a decreased interest or different focus points within academia during those time spans, it may also point towards alternative term clusters that are newly arising, or a re-focus towards different interventions, and should thus be kept in mind for future scoping efforts.</p>
<p>The predominant amount of literature is based on white literature, with only a marginal amount solely published as gray literature. This represents a gap which seems reasonable and not surprising since the database query efforts were primarily aimed at finding the most current versions of white literature. Such a stark gap speaks to a well targeted identifaction procedure, with more up-to-date white literature correctly superseding potential previous publications.</p>
<p><a href="#fig-citations-per-year-avg" class="quarto-xref">Figure 3</a> shows the average number of citations for all studies published within an individual year. From the literature sample, several patterns emerge: First, in general, citation counts are slightly decreasing - as should generally be expected with newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.</p>
<div id="cell-fig-citations-per-year-avg" class="cell" data-execution_count="9">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>bib_df[<span class="st">&quot;zot_cited&quot;</span>] <span class="op">=</span> bib_df[<span class="st">&quot;zot_cited&quot;</span>].dropna().astype(<span class="st">&quot;int&quot;</span>)</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>grpd <span class="op">=</span> bib_df.groupby([<span class="st">&quot;year&quot;</span>], as_index<span class="op">=</span><span class="va">False</span>)[<span class="st">&quot;zot_cited&quot;</span>].mean()</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots()</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>ax.bar(grpd[<span class="st">&quot;year&quot;</span>], grpd[<span class="st">&quot;zot_cited&quot;</span>])</span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>sns.regplot(x<span class="op">=</span>grpd[<span class="st">&quot;year&quot;</span>], y<span class="op">=</span>grpd[<span class="st">&quot;zot_cited&quot;</span>], ax<span class="op">=</span>ax)</span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a><span class="co">#ax = sns.lmplot(data=grpd, x=&quot;year&quot;, y=&quot;zot_cited&quot;, fit_reg=True)</span></span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>ax.tick_params(axis<span class="op">=</span><span class="st">&#39;x&#39;</span>, rotation<span class="op">=</span><span class="dv">45</span>)</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
<div id="fig-citations-per-year-avg" class="quarto-figure quarto-figure-center quarto-float anchored">
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<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-citations-per-year-avg-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 3: Average citations per year
</figcaption>
</figure>
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</div>
<p>Second, while such a decrease is visible the changes between individual years are more erratic due to strong changes from year to year. This suggests, first, no overall decrease in academic interest in the topic over this period of time, and second, no linearly developing concentration or centralization of knowledge output and dissemination, though it also throws into question a clear-cut increase of <em>relevant</em> output over time.</p>
<p>Positive outlier years in citation amount can point to clusters of relevant literature feeding wider dissemination or cross-disciplinary interest, a possible sign of still somewhat unfocused research production which does not approach from a single coherent perspective yet. It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the review process, a possible sign of more focused knowledge production and thus valuable to more closely review during the screening process.</p>
<p>It may also suggest that clearly influential studies have been produced during those years, a possibility which may be more relevant during years of more singular releases (such as 2011 and 2013). This is because, as <a href="#fig-publications-per-year" class="quarto-xref">Figure 2</a> showed, the overall output was nowhere near as rich as in the following years, allowing single influential works to skew the visible means for those years.</p>
<p>In all of these cases, such outliers should provide clear points of interest during the screening process for eventual re-evaluation of utilized scoping term clusters and for future research focus. Should they point towards gaps (or over-optimization) of specific areas of interest during those time-frames or more generally, they may provide an impetus for tweaking future identification queries to better align with the prevailing literature output.</p>
<!-- {{++ TODO: Add breakdown by thematic area++}} -->
<!-- {{++ TODO: describe intervention types with complete dataset ++}} -->
</section>
</section>
<section id="synthesis-a-multitude-of-lenses" class="level1">
<h1>Synthesis: A multitude of lenses</h1>
<p>This section will present a synthesis of evidence from the scoping review, analysing the main findings per policy area, as well as underscore individual studies’ approaches and limitations.</p>
<div id="cell-fig-intervention-types" class="cell" data-execution_count="10">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>by_intervention <span class="op">=</span> (</span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a> bib_df</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a> .fillna(<span class="st">&quot;&quot;</span>)</span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a> .groupby([<span class="st">&quot;author&quot;</span>, <span class="st">&quot;year&quot;</span>, <span class="st">&quot;title&quot;</span>, <span class="st">&quot;design&quot;</span>, <span class="st">&quot;method&quot;</span>, <span class="st">&quot;representativeness&quot;</span>, <span class="st">&quot;citation&quot;</span>])</span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a> .agg(</span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> {</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;intervention&quot;</span>: <span class="kw">lambda</span> _col: <span class="st">&quot;; &quot;</span>.join(_col),</span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a> .reset_index()</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a> .drop_duplicates()</span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a> intervention<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;intervention&quot;</span>].<span class="bu">apply</span>(</span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a> <span class="kw">lambda</span> _cell: <span class="bu">set</span>([x.strip() <span class="cf">for</span> x <span class="kw">in</span> re.sub(<span class="vs">r&quot;\(.*\)&quot;</span>, <span class="st">&quot;&quot;</span>, _cell).split(<span class="st">&quot;;&quot;</span>)])</span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;intervention&quot;</span>)</span>
<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a>sort_order <span class="op">=</span> by_intervention[<span class="st">&quot;intervention&quot;</span>].value_counts().index</span>
<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> plt.figure()</span>
<span id="cb7-22"><a href="#cb7-22" aria-hidden="true" tabindex="-1"></a>fig.set_size_inches(<span class="dv">6</span>, <span class="dv">3</span>)</span>
<span id="cb7-23"><a href="#cb7-23" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(by_intervention, x<span class="op">=</span><span class="st">&quot;intervention&quot;</span>, order<span class="op">=</span>by_intervention[<span class="st">&quot;intervention&quot;</span>].value_counts().index)</span>
<span id="cb7-24"><a href="#cb7-24" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb7-25"><a href="#cb7-25" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb7-26"><a href="#cb7-26" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
<div id="fig-intervention-types" class="quarto-figure quarto-figure-center quarto-float anchored">
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<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-intervention-types-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 4: Available studies by primary type of intervention
</figcaption>
</figure>
</div>
</div>
</div>
<p><a href="#fig-intervention-types" class="quarto-xref">Figure 4</a> shows the predominant interventions contained in the reviewed literature. Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and collective bargaining, education and training. The entire spread of policies captures interventions aimed primarily at institutional and structural mechanisms, but also mechanisms focused on individual agency.</p>
<p>Since policies employed in the pursuit of increased equality can take a wide form of actors, strategy approaches and implementation details, the following synthesis will first categorise between the main thematic area and its associated interventions. Individual observations are then descriptively distinguished between for the primary outcome variables (inequalities) of interest. Thus, in the following synthesis each reviewed study will be analysed through the primary policies or mechanisms they use as independent variables to analyse the effects on a variety of inequalities.</p>
<p>One of the primary lenses of inequality in viewing policy interventions to reduce inequalities in the world of work is that of income, often measured for all people throughout a country (vertical inequality) or subsets thereof (horizontal inequality). At the same time, the primacy of income should not be overstated as disregarding the intersectional nature of inequalities could lead to diminished intervention outcomes through adverse targeting.</p>
<p>Each main thematic area will be preceded by a table presenting a summary of findings for the respective policies, their identified channels and an estimation of their strength of evidence base. Afterwards, the analytical lens will be inverted for the discussion (Section 5) and the reviewed studies discussed from a perspective of their analysed inequalities and limitations, to better identify areas of strong analytical lenses or areas of more limited analyses.</p>
<section id="institutional" class="level2">
<h2 class="anchored" data-anchor-id="institutional">Institutional</h2>
<p> </p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>study_strength_bins <span class="op">=</span> {</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> <span class="fl">0.0</span>: <span class="vs">r&quot;\-&quot;</span>,</span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> <span class="fl">5.0</span>: <span class="vs">r&quot;\+&quot;</span>,</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a> <span class="fl">10.0</span>: <span class="vs">r&quot;\++&quot;</span>,</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> strength_for(val):</span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="bu">list</span>(study_strength_bins.keys())[<span class="bu">list</span>(study_strength_bins.values()).index(val)]</span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>findings_institutional <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-institutional.csv&quot;</span>)</span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)</span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-findings-institutional" class="quarto-float anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-findings-institutional-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 6: Summary of main findings for institutional policies
</figcaption>
<div aria-describedby="tbl-findings-institutional-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div id="tbl-findings-institutional" class="cell anchored" data-execution_count="11">
<div class="cell-output cell-output-display cell-output-markdown" data-execution_count="11">
<table class="table table-sm table-striped small">
<colgroup>
<col style="width: 8%">
<col style="width: 7%">
<col style="width: 7%">
<col style="width: 34%">
<col style="width: 40%">
</colgroup>
<thead>
<tr class="header">
<th>area of policy</th>
<th>internal strength</th>
<th>external strength</th>
<th>main findings</th>
<th>channels</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>minimum wage</td>
<td>+</td>
<td>++</td>
<td>mixed evidence for short-/medium-term income inequality impacts</td>
<td>can lead to income compression at higher-earner ends</td>
</tr>
<tr class="even">
<td></td>
<td>+</td>
<td>++</td>
<td>some evidence for long-term inequality decrease</td>
<td>job loss offsets through higher wages</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>some spatial transfer from urban manufacturing sectors to rural agricultural sectors</td>
</tr>
<tr class="even">
<td></td>
<td>-</td>
<td>++</td>
<td>bad targeting can exacerbate existing inequalities</td>
<td>negative effect on women’s hours worked if strong household labour divisions</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>low-earners sometimes secondary high-income household earners while low-wage households have no earners at all</td>
</tr>
<tr class="even">
<td></td>
<td>-</td>
<td>++</td>
<td>potential impact larger for single parents, rural/disadvantaged locations</td>
<td>women more affected if they make up large share of low-wage earners</td>
</tr>
<tr class="odd">
<td>labour regulation</td>
<td>++</td>
<td>++</td>
<td>mixed evidence for effects of labour regulation on income inequality</td>
<td>with lacking institutional capabilities no effective targeting possible</td>
</tr>
<tr class="even">
<td>paid leave</td>
<td>+</td>
<td>++</td>
<td>evidence for significant increase in rtw after childbirth</td>
<td>esp. disadvantaged women benefit due to no prior employer-funded leave</td>
</tr>
<tr class="odd">
<td></td>
<td>+</td>
<td>++</td>
<td>some evidence for positive rtw effects to occur with medium-/long-term time delay</td>
<td>short-term exit but no long-term increase to hiring pattern discrimination</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>can exacerbate existing household labour division</td>
</tr>
<tr class="odd">
<td></td>
<td>-</td>
<td>+</td>
<td>mixed evidence for fixed-/short-term contracts counter-acting effect on rtw</td>
<td>fixed-term contracts often insufficiently covered by otherwise applicable labour regulation</td>
</tr>
<tr class="even">
<td>collective bargaining</td>
<td>-</td>
<td>+</td>
<td>evidence for decreased income inequality with strong unionisation</td>
<td>stronger collective political power vector enables more equal redistributive policies</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>increased probability for employment on formal, standard employment contract</td>
</tr>
<tr class="even">
<td></td>
<td>+</td>
<td>+</td>
<td>marginal evidence for increased income/representation of women/minorities in workforce/management</td>
<td>internal heterogeneity due to predominantly affecting median part of wage distribution</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>self-selection of people joining more unionised enterprises/organisations/sectors</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>depending on targeting of concurrent policies can bestow more benefits on men, increasing horizontal inequalities</td>
</tr>
<tr class="odd">
<td>workfare programmes</td>
<td>-</td>
<td>+</td>
<td>evidence for decrease of vertical inequality</td>
<td></td>
</tr>
<tr class="even">
<td></td>
<td>-</td>
<td>-</td>
<td>evidence for possibility of increased spatial inequalities</td>
<td>bad targeting increases deprivations for already job-deprived areas</td>
</tr>
<tr class="odd">
<td></td>
<td>-</td>
<td>+</td>
<td>evidence for effective outcomes dependent on on prior material equalities</td>
<td>prior inequalities such as land ownership can lead to political capture and less effective policies</td>
</tr>
<tr class="even">
<td>social protection</td>
<td>+</td>
<td>+</td>
<td>evidence for conditional cash transfers producing short- and long-term inequality reduction</td>
<td>production of short-term cash influx</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>conditioning on school attendance can decrease educational inequalities over long-term</td>
</tr>
<tr class="even">
<td></td>
<td>++</td>
<td>++</td>
<td>mixed evidence for childcare subsidies decreasing gender inequalities</td>
<td>lifting credit constraints greater effect on low-income households</td>
</tr>
<tr class="odd">
<td></td>
<td>-</td>
<td>-</td>
<td>evidence for stagnating income replacement rates exacerbating existing vertical inequalities</td>
<td>benefit levels unlinked from wages can widen division between income groups</td>
</tr>
<tr class="even">
<td></td>
<td>-</td>
<td>-</td>
<td>healthcare subsidy impacts strongly dependent on correct targeting</td>
<td>dependence on non-participation in labour market may generate benefit trap</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding. Validities are segmented to a weak (-) evidence base under a validity ranking of 5.0, evidential (+) from 5.0 and under 10.0 and strong evidence base (++) for 10.0 and above.</p>
</div>
</figure>
</div>
<p> </p>
<section id="labour-laws-and-regulatory-systems" class="level3">
<h3 class="anchored" data-anchor-id="labour-laws-and-regulatory-systems">Labour laws and regulatory systems</h3>
<p><span class="citation" data-cites="Adams2015">Adams &amp; Atsu (<a href="#ref-Adams2015" role="doc-biblioref">2015</a>)</span> study the effects of labour, business and credit regulations and looks at their long-term correlations to income inequality in developing countries from 1970 to 2012. Additionally, the study looks at the effects of FDI and school enrolment, which will be reviewed in their respective policy sections. They find that in MENA, SSA, LAC and to some extend AP increased labour and business regulations are actually negatively related to equitable income distribution, with market regulation not having significant effects. The authors identify developing countries lacking in institutional capability to accomplish regulatory policies optimized for benefits and see the need for policies requiring more specific targeting of inequality reduction as their agenda. Overall, the authors suggest that regulatory policy in developing countries needs to be built for their specific contexts and not exported from developed countries due to their different institutional capabilities and structural make-up. The study is limited in its design focus relying purely on the macro-level regional analyses and can thus, when finding correlations towards income inequality, not necessarily drill down into their qualitative root causes.</p>
<!-- maternity leave and benefits -->
<p><span class="citation" data-cites="Broadway2020">Broadway et al. (<a href="#ref-Broadway2020" role="doc-biblioref">2020</a>)</span> study the introduction of universal paid maternal leave in Australia, looking at its impacts on mothers returning to work and the conditions they return under. It finds that, while there is a short-term decrease of mothers returning to work since they make use of the introduced leave period, over the long-term (after six to nine months) there is a significant positive impact on return to work. Furthermore, there is a positive impact on returning to work in the same job and under the same conditions, the effects of which are stronger for more disadvantaged mothers (measured through income, education and access to employer-funded leave). This suggests that the intervention reduced the opportunity costs for delaying the return to work, and especially for those women that did not have employer-funded leave options, directly benefiting more disadvantaged mothers. Some potential biases of the study are its inability to account for child-care costs, as well as not being able to fully exclude selection bias into motherhood. There also remains the potential of results being biased through pre-birth labour supply effects or the results of the financial crisis, which may create a down-ward bias for either the short- or long-term effects.</p>
<p><span class="citation" data-cites="Dustmann2012">(<a href="#ref-Dustmann2012" role="doc-biblioref"><strong>Dustmann2012?</strong></a>)</span> analyse the long-run effects on children’s outcomes of increasing the period of paid leave for mothers in Germany. While the study focuses on the children’s outcomes, it also analyses the effects on the return to work rates and cumulative incomes of the policies within the first 40 months after childbirth. It finds that, while short-term increases of paid leave periods (up to 6 months) significantly increased incomes, over longer periods (10-36 months) the cumulative incomes in fact decreased significantly, marginally for low-wage mothers for 10 month periods, and across all wage segments for 36 month periods. For the share of mothers returning to work, it finds that there is a significant increase in the months away from work among all wage segments for all paid leave period increases, positively correlated with their length. Still similar numbers of mothers return once the leave period ends, though with significant decreases for leave periods from 18 to 36 months. For its analysis of long-term educational outcomes on children, however, it does not find any evidence for the expansions improving children’s outcomes, even suggesting a possible decrease of educational attainment for the paid leave extension to 36 months.<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a> Some limitations of the study include its sample being restricted to mothers who go on maternity leave and some control group identification restrictions possibly introducing some sampling bias.</p>
<p>In a study on the effects of introductions of a variety of maternity leave laws in Japan, <span class="citation" data-cites="Mun2018">Mun &amp; Jung (<a href="#ref-Mun2018" role="doc-biblioref">2018</a>)</span> look at the effects on employment numbers and job quality in managerial positions of women. Contrary to notions of demand-side mechanisms of the welfare state paradox, with women being less represented in high-authority employment positions due to hiring or workplace discrimination against them with increased maternity benefits, it finds that this is not the case for the Japanese labour market between 1992 and 2009. There were no increases in hiring discrimination against women, and either no significant change in promotions for firms not providing paid leave before the laws or instead a positive impact on promotions for firms that already provided paid leave. The authors suggest the additional promotions were primarily based on voluntary compliance of firms in order to maintain positive reputations, signalled through a larger positive response to incentive-based laws than for mandate-based ones. Additionally, the authors suggest that the welfare paradox may rather be due to supply-side mechanisms, based on individual career planning, as well as reinforced along existing gender divisions of household labour which may increase alongside the laws. Limitations of the study include foremost its limited generalizability due to the unique Japanese institutional labour market structure (with many employments, for example, being within a single firm until retirement), as well as no ability yet to measure the true causes and effects of adhering to the voluntary incentive-based labour policies, with lasting effects or done as symbolic compliance efforts and mere impression management.</p>
<p><span class="citation" data-cites="Davies2022">Davies et al. (<a href="#ref-Davies2022" role="doc-biblioref">2022</a>)</span> conduct a study on the return to work ratios for high-skill women workers in public academic universities in the United Kingdom, comparing the results for those in fixed-term contract work versus those in open-ended contracts. It finds that there is a significantly decreased return to work probability for those working under fixed-term contracts, and most universities providing policies with more limited access to maternity payment for fixed-contract staff. This is possibly due to provisions in the policies implicitly working against utilization under fixed-terms: there are strict policies on payments if a contract ends before the maternity leave period is over, and obligations on repayments if not staying in the position long enough after rtw. Additionally, most policies require long-term continuous service before qualifying for enhanced payments in the maternity policies. There is high internal heterogeneity between the universities, primarily due to the diverging maternity policy documents, only a small number of the overall dataset providing favourable conditions for fixed-term work within.</p>
</section>
<section id="minimum-wage-laws" class="level3">
<h3 class="anchored" data-anchor-id="minimum-wage-laws">Minimum wage laws</h3>
<p><span class="citation" data-cites="Chao2022">Chao et al. (<a href="#ref-Chao2022" role="doc-biblioref">2022</a>)</span>, in a study looking at the effects of minimum wage increases on a country’s income inequality, analyse the impacts in a sample of 43 countries, both LMIC and HIC. Using a general-equilibrium model, it finds that there are differences between the short-term and long-term effects of the increase: In the short term it leads to a reduction of the skilled-unskilled wage gap, however an increase in unemployment and welfare, while in the long term the results are an overall decrease in wage inequality as well as improved social welfare. It finds those results primarily stem from LMIC which experience significant effects driven by a long-term firm exit from the urban manufacturing sector thereby increasing available capital for the rural agricultural sector, while in HIC the results remain insignificant. The study uses the Gini coefficient for identifying a country’s inequality. Some limitations of the study include the necessity to omit short-term urban firm exit for the impact to be significant, as well as requiring the, reasonable but necessary, prior assumption of decreased inequality through increased rural agricultural capital.</p>
<p><span class="citation" data-cites="Alinaghi2020">Alinaghi et al. (<a href="#ref-Alinaghi2020" role="doc-biblioref">2020</a>)</span> conduct a study using a microsimulation to estimate the effects of a minimum wage increase in New Zealand on overall income inequality and further disaggregation along gender and poverty lines. It finds limited redistributional effects for the policy, with negligible impact on overall income inequality and the possibility of actually increasing inequalities among lower percentile income households. Additionally, while it finds a significant reduction in some poverty measures for sole parents that are in employment, when looking at sole parents overall the effects become insignificant again. The authors suggest this points to bad programme targeting, which at best has negligible positive impact on income equality and at worst worsens income inequality in lower income households, due to may low-wage earners being the secondary earners of higher-income households but low-wage households often having no wage earners at all. A pertinent limitation of the study includes its large sample weights possibly biasing the impacts on specific groups such as sole parents and thus being careful not to overestimate their significance.</p>
<p>In a study on the impacts of minimum wage increases in Ecuador <span class="citation" data-cites="Wong2019">Wong (<a href="#ref-Wong2019" role="doc-biblioref">2019</a>)</span> specifically looks at the income and hours worked of low-wage earners to analyse the policies effectiveness. The study finds that, generally, there was a significant increase on the income of low-wage earners and also a significant increase on wage workers hours worked which would reflect positively on a decrease in the country’s income inequality. At the same time, it finds some potential negative effects on the income of high earners, suggesting an income-compression effect as employers freeze or reduce high-earners wages to offset low-earners new floors. The findings hide internal heterogeneity, however: For income the effect is largest for agricultural workers while for women the effect is significantly smaller than overall affected workers. For hours worked there is a significant negative impact on women’s hours worked, a fact which may point to a decreased intensive margin for female workers and thus also affect their lower income increases. Limitations of the study include some sort-dependency in their panel data and only being able to account for effects during a period of economic growth. Thus, while overall income inequality seems well targeted in the intervention, it may exacerbate the gender gap that already existed at the same time.</p>
<!-- non-spatial policy but spatial effects -->
<p><span class="citation" data-cites="Gilbert2001">Gilbert et al. (<a href="#ref-Gilbert2001" role="doc-biblioref">2001</a>)</span> undertake a study looking at the distributional effects of introducing a minimum wage in Britain, with a specific spatial component. Overall it finds little effect on income inequality in the country. It finds that the effects on rural areas differ depending on their proximity to urban areas. While overall income inequality only decreases a small amount, the intervention results in effective targeting with remote rural households having around twice the reduction in inequality compared to others. Rural areas that are accessible to urban markets are less affected, with insignificant impacts to overall income inequality reduction. One limit of the study is that it has to assume no effects on employment after the enaction of the minimum wage for its results to hold.</p>
<p>In a study on the impacts of minimum wage and direct cash transfers in Brazil on the country’s income inequality, <span class="citation" data-cites="SilveiraNeto2011">Silveira Neto &amp; Azzoni (<a href="#ref-SilveiraNeto2011" role="doc-biblioref">2011</a>)</span> especially analyse the way the policies interact with spatial inequalities. It finds that incomes between regions have converged during the time frame and overall the cash transfers under the ‘Bolsa Familia’ programme and minimum wage were accounting for 26.2% of the effect. Minimum wage contributed 16.6% of the effect to overall Gini reduction between the regions while cash transfers accounted for 9.6% of the effect. The authors argue that this highlights the way even non-spatial policies can have a positive (or, with worse targeting or selection, negative) influence on spatial inequalities, as transfers occurring predominantly to poorer regions and minimum wages having larger impacts in those regions created quasi-regional effects without being explicitly addressed in the policies. Some limitations include limited underlying data only making it possible to estimate the cash transfer impacts for the analysis end-line, and minimum wage effects having to be constructed from the effects wages equal to minimum wage.</p>
<p><span class="citation" data-cites="Militaru2019">Militaru et al. (<a href="#ref-Militaru2019" role="doc-biblioref">2019</a>)</span> conduct an analysis of the effects of minimum wage increases on income inequality in Romania. They find that, generally, minimum wage increases correlate with small wage inequality decreases, but carry a larger impact for women. The channels for the policies effects are two-fold in that there is an inequality decrease as the number of wage earners in total number of employees increases, as well as the concentration of workers at the minimum level mattering — the probable channel for a larger impact on women since they make up larger parts of low-income and minimum wage households in Romania. Limitations to the study are some remaining unobservables for the final inequality outcomes (such as other wages or incomes), the sample over-representing employees and not being able to account for any tax evasion or behavioural changes in the model.</p>
<p><span class="citation" data-cites="Sotomayor2021">Sotomayor (<a href="#ref-Sotomayor2021" role="doc-biblioref">2021</a>)</span> conducts a study on the impact of subsequent minimum wage floor introductions on poverty and income inequality in Brazil. He finds that in the short-term (3 months) wage floor increases reduced poverty by 2.8% and reduced income inequality by 2.4%. Over the longer-term though these impacts decrease, the minimum wage increases only show diminishing returns when the legal minimum is already high in relation to median earnings. It suggests that additional unemployment costs, created through new job losses through the introduction, are offset by the increased benefits — the higher wages for workers. The authors also suggest an inelastic relationship between increases and poverty incidence. One limitation of the study is the limit of tracking individuals in the underlying data which can not account for people moving household to new locations. The data can only track individual dwellings — instead of the households and inhabitants within — and thus resembles repeated cross-sectional data more than actual panel data.</p>
</section>
<section id="collective-bargaining" class="level3">
<h3 class="anchored" data-anchor-id="collective-bargaining">Collective bargaining</h3>
<p><span class="citation" data-cites="Alexiou2023">Alexiou &amp; Trachanas (<a href="#ref-Alexiou2023" role="doc-biblioref">2023</a>)</span> study the effects of both political orientation of governments’ parties and a country’s trade unionisation on its income inequality. They find that, generally, strong unionisation is strongly related to decreasing income inequality, most likely through a redistribution of political power through collective mobilization in national contexts of stronger unions. It also suggests that in contexts of weaker unionisation, post-redistribution income inequality is higher, thus also fostering unequal redistributive policies. Lastly, it finds positive relations between right-wing orientation of a country’s government and its income inequality, with more mixed results for centrist governments pointing to potential fragmentations in their redistributive policy approaches. The study is mostly limited in not being able to account for individual drivers (or barriers) and can thus not disaggregate for the effects for example arbitration or collective bargaining.</p>
<p><span class="citation" data-cites="Dieckhoff2015">Dieckhoff et al. (<a href="#ref-Dieckhoff2015" role="doc-biblioref">2015</a>)</span> undertake a study on the effect of trade unionisation in European labour markets, with a specific emphasis on its effects on gender inequalities. It finds, first of all, that increased unionisation is related to the probability of being employed on a standard employment contract for both men and women. It also finds no evidence that men seem to carry increased benefits from increased unionisation alone, although in combination with temporary contract and family policy re-regulations, men can experience greater benefits than women. At the same time women’s employment under standard contracts does not decrease, such that there is no absolute detrimental effect for either gender. It does, however, leave open the question of the allocation of relative benefits between the genders through unionisation efforts. The study is limited in that, by averaging outcomes across European nations, it can not account for nation-specific labour market contexts or gender disaggregations.</p>
<p><span class="citation" data-cites="Cardinaleschi2019">Cardinaleschi et al. (<a href="#ref-Cardinaleschi2019" role="doc-biblioref">2019</a>)</span> study the wage gap in the Italian labour market, looking especially at the effects of collective negotiation practices. It finds that the Italian labour market’s wage gap exists primarily due to occupational segregation between the genders, with women often working in more ‘feminized’ industries, and not due to educational lag by women in Italy. It also finds that collective negotiation practices targeting especially managerial representation and wages do address the gender pay gap, but only marginally significantly. The primary channel for only marginal significance stems from internal heterogeneity in that only the median part of wage distributions is significantly affected by the measures. Instead, the authors recommend a stronger mix of policy approaches, also considering the human-capital aspects with for example active labour-market policies targeting it.</p>
<p><span class="citation" data-cites="Ferguson2015">Ferguson (<a href="#ref-Ferguson2015" role="doc-biblioref">2015</a>)</span> conducts a study on the effects of a more unionised workforce in the United States, on the representation of women and minorities in the management of enterprises. It finds that while stronger unionisation is associated both with more women and more minorities represented in the overall workforce and in management, this effect is only marginally significant. Additionally, there are drivers which may be based on unobservables and not a direct effect — it may be a selection effect of more unionised enterprises. It uses union elections as its base of analysis, and thus can not exclude self-selection effects of people joining more heavily unionised enterprises rather than unionisation increasing representation in its conclusions.</p>
<p><span class="citation" data-cites="Ahumada2023">Ahumada (<a href="#ref-Ahumada2023" role="doc-biblioref">2023</a>)</span> on the other hand create a study on the effects of unequal distributions of political power on the extent and provision of collective labour rights. It is a combination of quantitative global comparison with qualitative case studies for Argentina and Chile. It finds that, for societies in which power is more unequally distributed, collective bargaining possibilities are more limited and weaker. It suggests that, aside from a less entrenched trade unionisation in the country, the primary channel for its weakening are that existing collective labour rights are often either restricted or disregarded outright. Employers were restricted in their ability to effectively conduct lobbying, and made more vulnerable to what the authors suggest are ‘divide-and-conquer’ strategies by government with a strongly entrenched trade unionisation, due to being more separate and uncoordinated. A limit is the strong institutional context of the two countries which makes generalizable application of its underlying channels more difficult to the overarching quantitative analysis of inequality outcomes.</p>
</section>
<section id="workfare-programmes" class="level3">
<h3 class="anchored" data-anchor-id="workfare-programmes">Workfare programmes</h3>
<p><span class="citation" data-cites="Whitworth2021">Whitworth (<a href="#ref-Whitworth2021" role="doc-biblioref">2021</a>)</span> analyse the spatial consequences of a UK work programme on spatial factors of job deprivation or opportunity increases. The programme follows a quasi-marketized approach of rewarding employment-favourable results of transitions into employment and further sustained months in employment. The author argues, however, that the non-spatial implementation of the policy leads to spatial outcomes. Founded on the approach of social ‘creaming’ and ‘parking’ and applied to the spatial dimension, the study shows that already job-deprived areas indeed experience further deprivations under the programme, while non-deprived areas are correlated with positive impacts, thereby further deteriorating spatial inequality outcomes. This occurs because of providers in the programme de-prioritizing the already deprived areas (‘parking’) in favour prioritizing wealthier areas for improved within-programme results.</p>
<p><span class="citation" data-cites="Li2022">Li &amp; Sunder (<a href="#ref-Li2022" role="doc-biblioref">2022</a>)</span> conduct a study on the effects of previous inequalities on the outcomes of a work programme in India intended to provide job opportunity equality for already disadvantages population. It specifically looks at the NREGA programme in India, and takes the land-ownership inequality measured through the Gini coefficient as its preceding inequality.<a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a> The study finds that there is significantly negative relationship between the Gini coefficient and the provision of jobs through the work programme. In other words, the workfare policy implemented at least in part to reduce a district’s inequality seems to be less effective if there is a larger prior capital inequality. The authors see the primary channel to be the landlords’ opposition to broad workfare programme introduction since they are often followed by overall wage increases in the districts. They suggest that in more inequally distributed channels the landlords can use a more unequal power structure to lobby and effect political power decreasing the effectiveness of the programmes, in addition to often reduced collective bargaining power on the side of labour in these districts. The results show the same trends for measurement of land inequality using the share of land owned by the top 10 per cent largest holdings instead.</p>
</section>
<section id="social-protection" class="level3">
<h3 class="anchored" data-anchor-id="social-protection">Social protection</h3>
<!-- TODO Include part of Pi2016 on social security -->
<!-- social assistance benefits and wages -->
<p><span class="citation" data-cites="Wang2016">J. Wang &amp; Van Vliet (<a href="#ref-Wang2016" role="doc-biblioref">2016</a>)</span> undertake an observational study on the levels of social assistance benefits and wages in a national comparative study within 26 OECD countries. It finds that real minimum income benefit levels generally increased in most countries from 1990 to 2009, with only a few countries, mostly in Eastern European welfare states, showing decreases during the time frame. The majority of changes in real benefit levels are from deliberate policy changes and the study calculates them by a comparison of the changes in benefit levels to the changes in consumer prices. Secondly, it finds that changes for income replacement rates are more mixed, with rates decreasing even in some countries which have increasing real benefits levels. The study suggests this is because benefit levels are in most cases not linked to wages and policy changes also do not take changes in wages into account resulting in diverging benefit levels and wages, which may lead to exacerbating inequality gaps between income groups.</p>
<!-- conditional cash transfer -->
<p><span class="citation" data-cites="Debowicz2014">Debowicz &amp; Golan (<a href="#ref-Debowicz2014" role="doc-biblioref">2014</a>)</span> conduct a study looking at the impact of the cash transfer programme Oportunidades in Mexico, conditioned on a household’s children school attendance, on income inequality among others. It finds that a combination of effects raises the average income of the poorest households by 23 percent. The authors argue in the short run this benefits households through the direct cash influx itself, as well as generating a positive wage effect benefitting those who keep their children at work. For the estimation of income inequality it uses the Gini coefficient. Additionally, over the long-term for the children in the model there is a direct benefit for those whose human capital is increased due to the programme, but also an indirect benefit for those who did not increase their human capital, because of the increased scarcity of unskilled labor as a secondary effect. Due to the relatively low cost of the programme if correctly targeted, it seems to have a significantly positive effect on the Mexican economy and its income equality.</p>
<p>In a study on the labour force impacts for women <span class="citation" data-cites="Hardoy2015">Hardoy &amp; Schøne (<a href="#ref-Hardoy2015" role="doc-biblioref">2015</a>)</span> look at the effects of reducing overall child care costs in Norway through subsidies. It finds that overall the reductions in child care cost increased the female labour supply in the country (by about 5 per cent), while there were no significant impacts on mothers which already participated in the labour market. It also finds some internal heterogeneity, with the impact being strongest for low-education mothers and low-income households, a finding the authors expected due to day care expenditure representing a larger part of those households’ budgets thus creating a larger impact. Though it may alternatively also be generated by the lower average pre-intervention employment rate for those households. Interestingly when disaggregating by native and immigrant mothers there is only a significant impact on native mothers, though the authors do not form an inference on why this difference would be. A limitation of the study is that there was a simultaneous child care capacity increase in the country, which may bias the labour market results due to being affected by both the cost reduction and the capacity increase.</p>
<!-- health care -->
<p><span class="citation" data-cites="Carstens2018">Carstens &amp; Massatti (<a href="#ref-Carstens2018" role="doc-biblioref">2018</a>)</span> conduct an analysis of the potential factors influencing mentally ill individuals in the United States to participate in the labour force, using correlation between different programmes of Medicaid and labour force status. In trying to find labour force participation predictors it finds employment motivating factors in reduced depression and anxiety, increased responsibility and problem-solving and stress management being positive predictors. In turn increased stress, discrimination based on their mental, loss of free time, loss of government benefits and tests for illegal drugs were listed as barriers negatively associated with labour force participation. For the government benefits, it finds significant variations for the different varieties of Medicaid programmes, with the strongest negative labour force participation correlated to Medicaid ABD, a programme for which it has to be demonstrated that an individual cannot work due to their disability. The authors suggest this shows the primary channel of the programme becoming a benefit trap, with disability being determined by not working and benefits disappearing when participants enter the labour force, creating dependency to the programme as a primary barrier. Two limitations of the study are its small sample size due to a low response rate, and an over-representation of racial minorities, women and older persons in the sample mentioned as introducing possible downward bias for measured labour force participation rates.</p>
<!-- UBI -->
<!-- TODO! Add to main findings and possibly env protection/labour regulation -->
<!-- TODO Add quick sentence of Standing also looking into UBI -->
<p><span class="citation" data-cites="Cieplinski2021">Cieplinski et al. (<a href="#ref-Cieplinski2021" role="doc-biblioref">2021</a>)</span> undertake a simulation study on the income inequality effects of both a policy targeting a reduction in working time and the introduction of a UBI in Italy. It finds that while both decrease overall income inequality, measured through Gini coefficient, they do so through different channels. While provision of a UBI sustains aggregate demand, thereby spreading income in a more equitable manner, working time reductions significantly decrease aggregate demand through lower individual income but significantly increases labour force participation and thus employment. It also finds that through these channels of changing aggregate demand, the environmental outcomes are oppositional, with work time reduction decreasing and UBI increasing the overall ecological footprint. One limitation of the study is the modelling assumption that workers will have to accept both lower income and lower consumption levels under a policy of work time reduction through stable labour market entry for the results to hold.</p>
</section>
</section>
<section id="structural" class="level2">
<h2 class="anchored" data-anchor-id="structural">Structural</h2>
<p> </p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>findings_structural <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-structural.csv&quot;</span>)</span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)</span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-findings-structural" class="quarto-float anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-findings-structural-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 7: Summary of main findings for structural policies
</figcaption>
<div aria-describedby="tbl-findings-structural-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div id="tbl-findings-structural" class="cell anchored" data-execution_count="12">
<div class="cell-output cell-output-display cell-output-markdown" data-execution_count="12">
<table class="table table-sm table-striped small">
<colgroup>
<col style="width: 9%">
<col style="width: 9%">
<col style="width: 9%">
<col style="width: 32%">
<col style="width: 38%">
</colgroup>
<thead>
<tr class="header">
<th>area of policy</th>
<th>internal strength</th>
<th>external strength</th>
<th>main findings</th>
<th>channels</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>trade liberalisation</td>
<td>+</td>
<td>++</td>
<td>evidence for slightly negative effects on income equality</td>
<td>highly dependent on targeting/micro-economic factors</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>increase in sectorial wage differences</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>growing income gap if transfers to low-income households do not rise with liberalisation</td>
</tr>
<tr class="even">
<td></td>
<td>-</td>
<td>+</td>
<td>evidence for reduction of absolute poverty</td>
<td></td>
</tr>
<tr class="odd">
<td></td>
<td>+</td>
<td>+</td>
<td>mixed evidence for effect of FDI on long-term income equality</td>
<td>requires incentive structure to directly connect local business with outside economies</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>correctly targeted FDI can generate low-skill agricultural employment</td>
</tr>
<tr class="odd">
<td>fiscal policies</td>
<td>-</td>
<td>++</td>
<td>evidence for wage/firm subsidies increasing income equality</td>
<td>effective targeting crucial to reach disadvantaged sectors</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>wage subsidy increases formal employment but can lead to wage compression</td>
</tr>
<tr class="odd">
<td></td>
<td>-</td>
<td>-</td>
<td>evidence for wage/firm subsidies to reduce absolute poverty</td>
<td>lifting of credit constraints through income gains</td>
</tr>
<tr class="even">
<td>techn. change</td>
<td>-</td>
<td>-</td>
<td>evidence for legal contraceptive access increasing gender income equality</td>
<td>educational attainment, occupational upgrading and later labour market exit</td>
</tr>
<tr class="odd">
<td>infrastructure</td>
<td>-</td>
<td>-</td>
<td>evidence for increase in spatial equality</td>
<td>increased employment probability through large-scale rural energy projects</td>
</tr>
<tr class="even">
<td></td>
<td>-</td>
<td>+</td>
<td>mixed evidence for increase of existing inequalities</td>
<td>elite policy capture can exacerbate existing social exclusion &amp; disadvantages</td>
</tr>
<tr class="odd">
<td></td>
<td>+</td>
<td>+</td>
<td>mixed evidence for transport infrastructure effects on income inequality</td>
<td>deficit-/tariff-financing can exacerbate spatia inequality</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>transit-rich area creation alone not enough for employment gains</td>
</tr>
<tr class="odd">
<td>access to education</td>
<td>++</td>
<td>++</td>
<td>evidence for increasing income equality</td>
<td>human capital building</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>occupational upgrading and increased probability for formal employment</td>
</tr>
<tr class="odd">
<td></td>
<td>+</td>
<td>++</td>
<td>evidence for increasing gender and spatial income equality</td>
<td>gendered occupational upgrading can decrease gender pay gap</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>education alone necessary but not sufficient condition for increased FLFP</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>higher overall access but more inequal access can generate new inequalities</td>
</tr>
<tr class="even">
<td></td>
<td>++</td>
<td>++</td>
<td>evidence for increased employment equality for people with disabilities</td>
<td>increased employment probability and hours worked</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>strong remaining intersectional gender inequalities require effective targeting</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding. Validities are segmented to a weak (-) evidence base under a validity ranking of 5.0, evidential (+) from 5.0 and under 10.0 and strong evidence base (++) for 10.0 and above.</p>
</div>
</figure>
</div>
<p> </p>
<section id="fiscal-growth-and-trade-liberalisation" class="level3">
<h3 class="anchored" data-anchor-id="fiscal-growth-and-trade-liberalisation">Fiscal growth and trade liberalisation</h3>
<p>Complementing their research on institutional labour regulation, <span class="citation" data-cites="Adams2015">Adams &amp; Atsu (<a href="#ref-Adams2015" role="doc-biblioref">2015</a>)</span> study the effects of business and credit regulations and FDI on long-term income inequality in developing countries. While for them business regulations seemed to have mixed relationships with income inequality, they find that, FDI is positively related with income inequality and the authors suggest it is unlikely to generate general welfare effects in developing countries. This, they argue, is due to FDI often operating on the wrong targeting incentive structure and only able to generate more equity when correctly targeting the creation of connections from the local to surrounding economies. While a long-term study, its scale is purely on the macro-level without delving deeper into individual-level changes per country.</p>
<p><span class="citation" data-cites="Xu2021">Xu et al. (<a href="#ref-Xu2021" role="doc-biblioref">2021</a>)</span> study the effects of trade liberalization and FDI on income inequality in 38 countries in the Sub-Saharan region. It finds that increased FDI is negatively correlated with income inequality measured through the Gini coefficient, while trade liberalization is positively correlated with income inequality — as are corruption, political stability, rule of law and education, which contradicts some findings of the previous study. The authors argue this may be due to the difference in sample and variables used, and the periods under study. They suggest that FDI may primarily go to the agricultural sector which can employ low-skilled labour and thereby reduce inequalities, while trade openness in fact creates jobs in other countries through higher import than export rates. They do not clearly identify channels through which a higher overall education level positively correlates with inequality, though some possibilities are an unequal access to education (through excluding factors such as those based on spatial, gender or financial inequalities), as well as a differentiated quality of education. Limitations of the study are the region-wide level of analysis which may obscure context-dependent mechanisms within the different institutional-structural contexts of the countries and potential hidden unobservables which may bias the results.</p>
<p>A simulation study on the effects of trade liberalization through free trade agreements (FTA) by <span class="citation" data-cites="Khan2021">Khan et al. (<a href="#ref-Khan2021" role="doc-biblioref">2021</a>)</span> looks at income inequality in Pakistan between different households, measured through the Gini coefficient. It finds that there is no clear general direction for changes through FTA visible, with its impact primarily depending on micro-economic factors. Some large trade agreements are negatively correlated with the Gini while others are positively related, similar to regional and bilateral agreements. Generally, this is due to increases in the income of poor rural agricultural farm households being dependent on grain (which is the largest export good often rising under FTA), while livestock predominantly owned by poor rural households decreases in returns under FTA. The deciding channel can then be increases on the wages of farm workers (after among others grain export increases) increasing income equity, which, when they do not happen, can in turn lead to an overall decrease. Lastly, there are wage compression effects between urban and rural households, with richer urban households often decreasing processed food or service production. A greater mobility would dissipate all short-term gains and losses, as changes would get more evenly distributed across regions and households, while over the long term some positive aspects on income equality are visible if increased agricultural growth can be sustained. The study may have some limits to its generalizability due to the production factor reallocations for agricultural households being specific to the rural poor context in Pakistan.</p>
<p><span class="citation" data-cites="Liyanaarachchi2016">Liyanaarachchi et al. (<a href="#ref-Liyanaarachchi2016" role="doc-biblioref">2016</a>)</span> run a simulation model on the effects of trade liberalization in Sri Lanka on income inequality and absolute poverty. It finds that the complete elimination of tariffs results in an overall reduction in absolute poverty, while tariff elimination with resulting fiscal policy responses to balance the budget would result in more mixed results but still pointing to an absolute reduction in poverty. On the other hand, income inequality is seen to increase for most sectors over the short term and for all sectors over the long term. The primary channels for this change are increased wage differences — especially the increased wages for managers, professionals and technicians, as well as increased differences between urban workers — and low-income households being more dependent on private or government transfers, which do not increase with trade liberalization.</p>
<p><span class="citation" data-cites="Rendall2013">Rendall (<a href="#ref-Rendall2013" role="doc-biblioref">2013</a>)</span> undertake a cross-country analysis on the impacts of structural changes in Brazil, Mexico, Thailand and India from 1987 to 2008, and its effects on female labour market participation and the gender wage gap. Basing its analysis on the theory of capital displacing brawn in production for transition economies, it finds that all countries had reduced brawn requirements over time, though with large heterogeneity: Thailand lead the change with 15 percentage points while India had the smallest change with 0.2 percentage points. Following this, there was the largest steady labour market participation inequality in India, while there were mixed results for Mexico and Thailand, with Brazil having female employment shares changes similar to that of the United States. The channels here are seen as a reduced requirement for physical labour replaced by for example more service-oriented economies (‘brawn’ to ‘brain’). For female wage shares, in Brazil the wage gap closed most rapidly, though it began widening in 2005, while Thailand and India had converging but mixed changes. In Mexico, while the gap widened during the 1990s, it began closing again afterwards. The differences in wage gap effects compared to both other countries and the respective country’s physical labour market requirements show that contextual structural changes played a large role in each case: with erstwhile reduced returns on Brazilian returns for brain intensive occupations, the introduction of a female-lead manufacturing sector in Mexico in the 90s, and widely diverging basic labour market skill structures in Thailand and India necessitating subsistence-oriented participation; the results show impacts of structural changes, though limited through a variety of mediating factors influencing each case.</p>
<p><span class="citation" data-cites="Wang2020">C. Wang et al. (<a href="#ref-Wang2020" role="doc-biblioref">2020</a>)</span> conduct a simulation to examine the impact of terminating subsidies for the agricultural grain sectors in China, with a particular focus on analysing the effects on rural-urban income inequality. The findings indicate that the removal of grain subsidies would lead to gradual improvements in the industrial economic structure. However, in the short term, it is observed that rural-urban income inequality is exacerbated. Over an extended period, the decrease in real wages for rural workers would alleviate, suggesting an increase in the rural income ratio, yet the gap remains incompletely closed. The study attributes this outcome to the displacement of rural unskilled labour, resulting in an increased supply of unskilled labour that is challenging to absorb into the manufacturing or service sectors. Additionally, the low income and price elasticity of agricultural products contribute to an overall decline in rural incomes. Consequently, the authors identify a trade-off between long-term national economic output, adversely affected by the removal of subsidies, and the reduction in rural-urban income ratios facilitated by the subsidies, albeit with diminishing contributions over time. Limitations of the study include the need to assume static national employment and, notably, limited generalizability due to the simulation of specific Chinese structural economic characteristics in the model.</p>
<p><span class="citation" data-cites="Go2010">Go et al. (<a href="#ref-Go2010" role="doc-biblioref">2010</a>)</span> model the effects of a targeted wage subsidy aimed at low- and medium-skilled workers and provided to their employers as an incentive for new job creations, looking at its effects on poverty and income inequality in South Africa. The study finds that, using the Gini coefficient, the overall income inequality reduced by 0.5 percentage points, which provides an insignificant outcome. This primarily occurs because of an overall income redistribution and especially an increase in formal employment for low- and medium-skill workers. Using an absolute poverty headcount ratio, it finds that a significant 1.6 per cent of households move out of poverty, with similar changes observed across urban and rural spaces. They attribute this primarily to income gains for poorer households and the targeting benefiting the poorest households most by providing them greater income gains. Limitations of the study include the general equilibrium model approach being potentially restricted by its prior assumptions in validity and generalizability, as well as potentially not accounting for unobservables or exogenous shocks.</p>
<p>Due to the high number of studies on these policy areas being based on equilibrium modelling simulations, there are some potentially exacerbated blind-spots: they can possess a higher reliance on prior assumptions for their results to hold, which includes the effort to subsume all potentially relevant channels and mediators into the equilibrium models. They are generally more prone to disregarding exogenous factors which may provide shock effects into the system under analysis, and often can not cleanly account for longer-term dynamics. Lastly, they can not address practical implementation challenges which may be faced by those implementing such policies, the institutional context and political ability to pursue the results modelled therein. These limitations should be taken into consideration when evaluating their results.</p>
</section>
<section id="automation-and-technological-change" class="level3">
<h3 class="anchored" data-anchor-id="automation-and-technological-change">Automation and technological change</h3>
<p><span class="citation" data-cites="Bailey2012">Bailey et al. (<a href="#ref-Bailey2012" role="doc-biblioref">2012</a>)</span> undertake a study on the effects of the introduction of legal access to contraceptive measures for women in the United States, measuring the impacts on closing the gender gap through the gendered hourly working wage distribution. The study finds that of the closing gender pay gap from 1980 to 2000, legal access to ‘the pill’ as contraceptive from an early age contributed by nearly percent in the 1980s and over 30 percent in the 1990s. Thus, overall the authors estimate that nearly one third of total female wage gains during this time were attributable to legal access to contraception. The primary channels identified are greater educational attainment, occupational upgrading, and increased labour market experience made possible due to no early exit. The authors also argue that the pill spurred individual agency to invest in personal human capital and career. However, there are some limitations to the findings: The dataset cannot capture specific access to contraception beyond age 20, which makes the window of analysis more restricted and especially focused on the segment of women under 21. Additionally, the study can not control for social multiplier effects such as employers reacting with changed hiring or promotion patterns or expectations about marriage and childbearing, as well as the overall coinciding paradigmatic change in norms and ideas about women’s work and end of the national baby boom.</p>
<!-- ### Informal Economy -->
</section>
<section id="infrastructure" class="level3">
<h3 class="anchored" data-anchor-id="infrastructure">Infrastructure</h3>
<p><span class="citation" data-cites="Kuriyama2021">Kuriyama &amp; Abe (<a href="#ref-Kuriyama2021" role="doc-biblioref">2021</a>)</span> look at the effects of Japan’s move to decarbonise its energy sector on employment, especially rural employment. It finds that, while employment in general is positively affected, especially rural sectors benefit from additional employment probability. This is due to the renewable energy sector, while able to utilise urban areas for smaller scale power generation, being largely attached to rural areas for larger scale projects such as geothermal, wind power or large-scale solar generation. The study also suggests some possible inequality being created in between the different regions of Japan due to the Hokkaido region having limited transmission line capacity and locational imbalance between demand and potential supplies. Limitations include its design as a projection model with multiple having to make strong assumptions about initial employment numbers and their extrapolation into the future, as well as having to assume the amount of generated power to increase as a stable square function.</p>
<p>In an observational study looking at the inclusive or exclusionary effects of infrastructure development, <span class="citation" data-cites="Stock2021">Stock (<a href="#ref-Stock2021" role="doc-biblioref">2021</a>)</span> analyses the ‘gender inclusive’ development of a solar park in India which specifically aims to work towards micro-scale equality through regional uplifting. The project included a training and temporary employment to local unskilled/semi-skilled labour. It finds that the development instead impacted equality negatively, creating socio-economic exclusion and disproportionately negatively affected women of lower castes. While acquiring basic additional skills, none of the women participating in training remained connected to the operators of the solar park and none were hired. An insignificant amount of women from local villages were working at the solar park, of which most belonged to the dominant caste, and the redistributive potential was stymied through capture by village female elites. The author suggests this is an example of institutional design neglecting individual agency and structural power relations, especially intersectional inequalities between gender and caste. The study is limited in explanatory power through its observational design, not being able to make causal inferences.</p>
<!-- explicitly spatial policies -->
<p><span class="citation" data-cites="Blumenberg2014">Blumenberg &amp; Pierce (<a href="#ref-Blumenberg2014" role="doc-biblioref">2014</a>)</span> look at the effects of a housing mobility intervention in the United States on employment for disadvantaged households, and comparing its impacts to the ownership of a car for the same sample. It follows the ‘Moving to Opportunity’ programme which provided vouchers to randomized households for movement to a geographically unrestricted area or to specifically to a low-poverty area (treatment group), some of which are in areas with well-connected public transport opportunities. The sample for the study is made up predominantly of women (98%). No relationship between programme participation and increased employment probability could be established. However, a positive relationship exists between owning an auto-mobile and improved employment outcomes for low-income households, as well as including those households that are located in ‘transit-rich’ areas. Access to better transit itself is related to employment probability but not gains in employment - the authors suggest this reflects individuals’ strategic relocation to use public transit for their job. However, moving to a better transit area itself does not increase employment probability, perhaps pointing to a certain threshold required in transit extensiveness before it facilitates employment. Ultimately, the findings suggest the need to further individual access to auto-mobiles in disadvantaged households or for extensive transit network upgrade which have to cross an efficiency threshold. Some limitations of the study are its models low explanatory power for individual outcomes, more so among disadvantaged population groups, as well as some remaining possibility of endogeneity bias through unobserved factors such as individual motivation or ability.</p>
<p><span class="citation" data-cites="Adam2018">Adam et al. (<a href="#ref-Adam2018" role="doc-biblioref">2018</a>)</span> model the effects of transport infrastructure investments in Tanzania on rural income inequalities and household welfare inequalities, modelled through consumption indicators. Generally it finds that the results of public investment measures into transport infrastructure largely depend on the financing scheme used. Comparing four financing schemes when looking at the effects on rural households, it finds that they are generally worse off when the development is deficit-financed or paid through tariff revenues. On the other hand, rural households benefit through increased income from measures financed through consumption taxes, or by external aid. The general finding is that there is no Pareto optimum for any of the investment measures for all locations, and that much of the increases in welfare are based on movement of rural workers out of quasi-subsistence agriculture to other locations and other sectors. The study creates causal inferences but is limited in its modelling approach representing a limited subset of empirical possibility spaces, as well as having to make the assumption of no population growth for measures to hold.</p>
</section>
<section id="education-access" class="level3">
<h3 class="anchored" data-anchor-id="education-access">Education access</h3>
<p>In addition to the institutional effects of regulation above, <span class="citation" data-cites="Adams2015">Adams &amp; Atsu (<a href="#ref-Adams2015" role="doc-biblioref">2015</a>)</span> analyse the effects of school enrolment and on income inequality in developing countries between 1970 and 2012. Contrary to the regulatory policies, they find school enrolment and thus well-effected education-oriented policies to be positively related with an equitable income distribution. They suggest additional enrolment increases the capacity of public administration practitioners and in turn lead to more adapted policies specific to developing countries’ institutional contexts. Due to the often limited contexts of institutional capabilities such policies thus have a two-fold function: they increase human capital in the medium term, but may also function as capability-building measures long-term. It is important to keep in mind that the recommendations of the study should be understood as made from a macro-perspective, detached from the more micro-oriented contexts of individual countries or regions.</p>
<p><span class="citation" data-cites="Mukhopadhaya2003">Mukhopadhaya (<a href="#ref-Mukhopadhaya2003" role="doc-biblioref">2003</a>)</span> looks at the income inequality in Singapore and how national education policies impact this inequality, focusing especially on the ‘Yearly Awards’ scheme and the ‘Edusave Entrance Scholarship for Independent Schools’. It finds that, generally, income inequality for migrants in Singapore is relatively high, primarily due to generated between-occupational income inequalities and migration policies which further stimulate occupational segregation. Then, for the higher-education interventions, it identifies issues which may exacerbate the existing inequalities along these lines: Already-advantaged (high-income) households generally stem from non-migration households and are also reflected in higher representation of high-achievement education brackets. The education policies thus may exacerbate income inequality through their bad targeting when considering inter-generational academic achievements with high-education households remaining the primary beneficiaries of the policies, a finding which is more significant for the ‘Edusave Entrance Scholarship for Independent Schools’ than the ‘Yearly Awards’ scheme which has fewer benefit accruals to wealthier households. More generally, the study suggests that the system of financing for higher education in Singapore aiming for providing equal education opportunity for all, may in fact further disadvantage poorer, low-income households that have a low-education parental background.</p>
<!-- education -> gender economic empowerment -->
<p>Looking at the returns of the Tanzanian ‘Universal Primary Education’ programme on consumption and on rural labour market outcomes, <span class="citation" data-cites="Delesalle2021">Delesalle (<a href="#ref-Delesalle2021" role="doc-biblioref">2021</a>)</span>, finds outcomes that additionally differ along spatial and gender lines. The programme both attempted to increase access to schools but also changed curricula to contain more technical classes, judged relevant to increase equity in rural areas. Even though the programme aims to increase universal equality of access to education, the study finds that gender, geographical and income inequalities persist throughout, with individuals that complete primary education more likely to be male urban wage workers. The study measures returns purely on consumption of households to show the estimated effect on their productivity — here, it finds generally positive returns but greatest for non-agricultural work, self-employed or as wage work. Importantly, the introduction of more technical classes also changes employment sector choices, with men working less in agricultural work and more in non-farm wage sectors and an increased probability for rural women to both work in agriculture and to work formally. Limitations of the study include the inability to directly identify intervention compliers and having to construct returns for each household head only and a possibly unobserved ‘villagization’ effect by bringing people together in community villages for their education leading to other unobserved variable impacting the returns.</p>
<!-- increased education access for migrants -> wage inequality -->
<p><span class="citation" data-cites="Pi2016">Pi &amp; Zhang (<a href="#ref-Pi2016" role="doc-biblioref">2016</a>)</span> conduct a study on the impacts of allowing increased access to social welfare provisions and education to urban migrants in China, looking at the effects on wage inequality between skilled and unskilled sectors and workers. It uses skilled-unskilled inequality instead of rural-urban inequalities since the real wages of the rural sector are already much lower in China, making comparisons along the 90th to 10th decile ratios more difficult. The study finds that reforms to increase access to social security and education for urban migrants decreases wage inequality between the sectors if the skilled sector is more capital intensive than the unskilled sector, though it makes no specific identification of individual channels. There are several limitations to the study such as no disaggregation between the private and the (very important for the Chinese economy) public sector, job searching not being part of the model, and, most importantly, a severely restricted generalizability due to the reform characteristics being strongly bound to the institutional contexts of Chinese <em>hukou</em><a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a> systems.</p>
<p><span class="citation" data-cites="Suh2017">Suh (<a href="#ref-Suh2017" role="doc-biblioref">2017</a>)</span> studies the effects of structural changes on married women’s employment in South Korea, looking specifically at the impact of education and family structure. The study finds that educational interventions significantly increase the employment probability of married women, and it finds overall female labour force participation showing a negative correlation with income inequality. However, education alone is only a necessary not a sufficient condition for increased employment, with a married woman’s family size and family structure having an impact as well. Finally, education also has an intergenerational impact, with the female education also positively relating to daughters’ education levels.</p>
<p><span class="citation" data-cites="Coutinho2006">Coutinho et al. (<a href="#ref-Coutinho2006" role="doc-biblioref">2006</a>)</span> study the impacts of special education between young men and women on their relative employment probabilities and incomes. It finds that, overall, young women with disabilities were significantly less likely to be employed, earned less than males with disabilities, had lower likelihood of obtaining a high school diploma and were more likely to be a biological parent. For the employment outcomes, the primary channels identified were men with disabilities being in employment both more months in the preceding period and more hours per week on average than women with disabilities. Overall, more women were employed in clerical positions and substantially more men employed in technical or skilled positions for both special education and the control samples. Similarly, for income there was a gender-based difference for the whole sample, though with substantial internal heterogeneity showing only marginal differences between men and women in the high-achieving subsample and the largest differences in the low-achieving and special needs subsample. The suggestions include a strengthening of personal agency to remain in education longer and delay having children through self-advocacy and -determination transition services for young women to supplement structural education efforts. Some limitations include initial subsample selection based on parent-reporting possibly introducing selection bias and the special education sample not including students with more severe impairments due to the requirement of self-reporting.</p>
<p><span class="citation" data-cites="Shepherd-Banigan2021">Shepherd-Banigan et al. (<a href="#ref-Shepherd-Banigan2021" role="doc-biblioref">2021</a>)</span> undertake a qualitative study on the significance of vocational and educational training provided for disabled veterans in the United States. It finds that both the vocational and educational services help strengthen individual agency, autonomy and motivation but impacts can be dampened if the potential for disability payment loss due to the potential for job acquisition impedes skill development efforts. The primary barriers of return to work efforts identified are an individual’s health problems as well as various programmes not accommodating the needs of disabled veteran students, while the primary Facilitators identified are financial assistance provided for education as well as strengthened individual agency through motivation. Some limitations include a possible bias of accommodations required through the sample being restricted to veterans with a caregiver, which often signals more substantial impairments than for a larger training-participatory sample, as well as the data not being able to identify the impact of supported employment.</p>
<p>The studies thus not only reinforce recommendations for strength-based approaches, emphasising the benefits of work, but also highlight the targeting importance of subsidy programmes in general on the one hand, in the worst case reducing equity through bad targeting mechanisms, and their negative reinforcement effects widening existing inequalities of gender, age and racial discrimination through such targeting on the other.</p>
<p>With a similar focus on agency, <span class="citation" data-cites="Gates2000">Gates (<a href="#ref-Gates2000" role="doc-biblioref">2000</a>)</span> conducts a qualitative study on the mechanisms of workplace accommodation for people with mental health conditions to allow their successful return-to-work. The intervention is based on an accommodation which disaggregates the effects of social and technical components of the process and included a disclosure and psycho-educational plan. It finds that successful return-to-work through accommodation requires consideration of the social component (‘who is involved’), with unsuccessful accommodation often only relying on the functional aspect (‘what is involved’). The primary barrier identified for successful return-to-work are actually relationship issues not functional ones, with supervisors playing a key role for the success of the accommodation process. Additionally, it highlighted the necessity of strengthening the individual agency of the returnee, accomplished in the intervention through a concrete training plan with the worker but also with other key workplace players such as the supervisors. Additionally, providers must be willing to develop a disclosure plan with the employee and enter the workplace itself to adequately assist in the accommodation process. Limitations to the study include the limited generalizability of its findings with a small non-randomized sample size and restriction to mental health disability.</p>
<p>A study looking at the effects of vocational rehabilitation on employment probabilities, <span class="citation" data-cites="Poppen2017">Poppen et al. (<a href="#ref-Poppen2017" role="doc-biblioref">2017</a>)</span> look at the factors influencing successful employment for disabled people in the United States. It finds that the primary factors negatively correlated with successful employment were for women in the sample, for having mental illness or traumatic brain injury as the primary disability, having multiple disabilities, an interpersonal or self-care impediment and receiving social security benefits. On the other hand, having participated in a youth-transition training programme, as well as making use of more vocational rehabilitation services, are correlated with an increased employment probability. It thereby highlights the gendered dimension of employment probabilities and points to a necessity to focus training and rehabilitation efforts along multiple dimensions. Some limitations of the study include its limited generalizability, having a sample located in a single state, as well as a dataset intended for service provision not academic pursuits possibly introducing unreliability in its data and not measuring service quality.</p>
<p><span class="citation" data-cites="Thoresen2021">Thoresen et al. (<a href="#ref-Thoresen2021" role="doc-biblioref">2021</a>)</span> conduct a survey combined with qualitative interviews for the participants of a vocational training programme in Australia, looking at the effects on participants’ hours worked and incomes. It finds, foremost, that initially both the hours worked and the income of people with disabilities are lower on the Australian labour market in general and this reflects in the results for the disability group of participants, which have significantly lower weekly incomes and hours worked than the control group. Over time, hours worked increase for the disability group to no longer be significantly different but still lower than for the control group (from 3.1 hours to 1 hour difference per week), however there are large fluctuations in the control group. Similarly, the wages of the disability group are initially substantially lower than of the control group, which increases to be non-significant though still lower over time, more so for the earnings of female participants and participants which received a disability pension. Relevant limitations of the study include the use of a non-representative sample for the national representativeness, and the overall generalisability being low due to an increased labour force participation bias and attrition bias of the surveys, as well as only having access to a small control sample size. Thus, findings should be understood as guiding policy directions, while generalisations should be done with care as some of the larger changes may be due to those limitations, such as the increased survey response of those with positive wage outcomes.</p>
<p>An experimental study on the impacts of benefits and vocational training counselling for disabled veterans in the United States by <span class="citation" data-cites="Rosen2014">Rosen et al. (<a href="#ref-Rosen2014" role="doc-biblioref">2014</a>)</span> measures the effects on return to work through average hours worked. It identifies time worked through a timeline follow-back calendar, measuring the change in days worked in the 28 days preceding the final study measurement. Here, it finds the sessions having a significant increase on more waged days worked, with an additional three days for the 28 preceding days on average. One limitation is the inability of the study to locate an active ingredient: Though the intervention clearly aims at strengthening some aspect of individual agency, the exact mediators are not clear, with neither beliefs about work, beliefs about benefits, nor provided service use for mental health or substance abuse impacted significantly.</p>
</section>
</section>
<section id="agency" class="level2">
<h2 class="anchored" data-anchor-id="agency">Agency</h2>
<p> </p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>findings_agency <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-agency.csv&quot;</span>)</span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)</span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-findings-agency" class="quarto-float anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-findings-agency-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 8: Summary of main findings for agency-based policies
</figcaption>
<div aria-describedby="tbl-findings-agency-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div id="tbl-findings-agency" class="cell anchored" data-execution_count="13">
<div class="cell-output cell-output-display cell-output-markdown" data-execution_count="13">
<table class="table table-sm table-striped small">
<colgroup>
<col style="width: 13%">
<col style="width: 11%">
<col style="width: 11%">
<col style="width: 22%">
<col style="width: 40%">
</colgroup>
<thead>
<tr class="header">
<th>area of policy</th>
<th>internal strength</th>
<th>external strength</th>
<th>main findings</th>
<th>channels</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>direct transfers</td>
<td>++</td>
<td>++</td>
<td>evidence for increasing gender equality</td>
<td>lifted credit constraints and debt dependency increases employment probability</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>requires effective targeting to disadvantaged women</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>can counter negative rtw effects of childbirth</td>
</tr>
<tr class="even">
<td></td>
<td>-</td>
<td>+</td>
<td>evidence for reduction of absolute poverty</td>
<td>positive short-term effects but mixed evidence long-term</td>
</tr>
<tr class="odd">
<td>individual microfinance</td>
<td>+</td>
<td>+</td>
<td>evidence for increased gender equality</td>
<td>increased personal economic security and household decision-making long-term</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
<td>can decrease local discriminatory gender norms</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td>constrained by loan obtainment abilities through individual focus</td>
</tr>
</tbody>
</table>
</div>
</div>
<p>Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding. Validities are segmented to a weak (-) evidence base under a validity ranking of 5.0, evidential (+) from 5.0 and under 10.0 and strong evidence base (++) for 10.0 and above.</p>
</div>
</figure>
</div>
<p> </p>
<section id="occupational-segregation-and-social-exclusion" class="level3">
<h3 class="anchored" data-anchor-id="occupational-segregation-and-social-exclusion">Occupational segregation and social exclusion</h3>
<p><span class="citation" data-cites="Emigh2018">Emigh et al. (<a href="#ref-Emigh2018" role="doc-biblioref">2018</a>)</span> study the effects of direct state transfers to people in poverty in the post-socialist market transition countries of Hungary, Romania and Bulgaria. To do so, the study first looks at the correlations of socio-demographic characteristics with poverty to find that in each country there was an increased probability for poverty of low-education, larger and predominantly Roma households. It also found that poverty itself was most feminized Hungary, the country with the most advanced market transition in the study period, and least feminized in Bulgaria, the country with the least advanced market transition, and suggests that poverty may have feminized as the market transitions progressed. For the state transfers it found that while the level of payments may have been too small to eliminate longer-term adverse effects of the market transitions, in each country’s case the transfers to individuals reduced their poverty and were beneficial at least in the short term. The authors thus suggest that their findings may be compatible both with an institutionalist perspective seeing poverty-eliminating benefits in the short term and with an underclass perspective which contends that nonetheless the transfers do not eliminate the deprivations members of disadvantaged groups face, while providing little evidence for generating welfare dependency proposed in a more neoclassical perspective. However, due to no long-term panel data available to fully analyse the underclass and neoclassical arguments, these findings’ generalizability should be understood as more restricted.</p>
<p><span class="citation" data-cites="Bartha2020">Bartha &amp; Zentai (<a href="#ref-Bartha2020" role="doc-biblioref">2020</a>)</span> conduct an observational study on the effects of the policy trajectories of European countries concerning long-term care work, with a special focus on the impacts on gender equality. The trajectories for the study are mostly described through measures of social protection and social security such as pensions or the provision of residential or at-home care facilities, regulation and fiscal policies. Regarding the effects on the labour market it uses the full-time equivalent employment rate gap between men and women. It finds that few countries in Europe fit one of the ideal-type household their ranking predicted, between male bread-winner, unsupported double-earner and supported double-earner households. Only half of the countries clearly fall into one of the three ideal-types and no countries fall into the category of male bread-winner. While supported double-earner type is mostly prevalent in Western Europe and the Scandinavian countries, Southern and Eastern Europe are predominantly shaped by the unsupported double-earner type. Generally, more women will take on more unpaid care work in this model especially, though the prevalence exists in all models, which also explains the employment rate gap not decreasing significantly. Where it decreases, the ‘familialization’ of care work is often undergoing a process of being taken on as cash-for-care work by migrants in a rising work sector in the former countries, which in turn may slightly increase the overall female labour force participation. However, relying on this type of work may not be sustainable or provide decent work, as it often remains poorly regulated and low paid, and may in turn have negative consequences on gender inequality in migrant communities or home countries. Some limitations of the study include its scarce underlying data for comparable care work and care migration data, as well as the weak categorization possibilities perhaps obscuring incongruent patterns of policy effects.</p>
<p><span class="citation" data-cites="Shin2006">(<a href="#ref-Shin2006" role="doc-biblioref"><strong>Shin2006?</strong></a>)</span> look at the effects of providing relatively higher wages for teachers, as well as fertility differences, on labour market participation of young female teachers. They find that providing relatively higher wages for teaching professions as compared to non-teaching professions significantly increases female labour force participation for teachers, though the strongest determinant for it is possessing a college major in education, with overall education level being another determinant. The study also looks at the effects of the presence of a new-born baby and finds that it significantly decreases female labour force participation and is almost twice as large for women in the teaching profession as compared to non-teaching jobs, though it does not have an effect on the choice of job between teaching or non-teaching. The authors suggest this relatively higher exit from the labour market for women with new-born babies in teaching professions may once again be due to low wages: teachers leaving the labour market experience relatively lower temporary wage losses than in other professions, decreasing the exit-cost. A limitation of the study is its restricted focus on strictly female underlying panel data which does not allow for comparisons between genders within or across professions.</p>
<p>An experimental study of providing UBI for villages in India by <span class="citation" data-cites="Standing2015">Standing (<a href="#ref-Standing2015" role="doc-biblioref">2015</a>)</span> looks at the effects on absolute low-income household debts, utilizing a combination of qualitative and quantitative experimental research. It finds that the provision of UBI significantly reduced household debts, a finding generally agreeing with assumptions in the literature, but goes beyond this by investigating the qualitative causes going beyond purely monetary value into what the authors call ‘emancipatory value’. They find UBI reduces dependency risk - primarily to lenders with high associated fees by allowing the repayment of existing debt, not having to work for the lender directly or by providing them parts of their wages, avoiding taking on new debts and, if new debts have to be taken on, allows choosing less exploitative forms of borrowing (such as from relatives or friends). The last channel especially is a point of interest of the study: the intervention did not just reduce absolute debts through an individual possessing more money, it generally infused more money into the local contexts, reducing its scarcity and allowing others such as neighbours and friends to provide more collective risk spreading in the villages.. The intervention also significantly increased possibility of saving in treatment households, allowing for an increased economic security and empowerment, which was also influenced by household head education, landholding, the household’s caste and size. The main channel this is accomplished through is a shift to institutionalized saving, with provides increased resilience against shock events.</p>
<!-- childcare subsidy -->
<p><span class="citation" data-cites="Clark2019">Clark et al. (<a href="#ref-Clark2019" role="doc-biblioref">2019</a>)</span> undertake an experimental study on the impacts of providing vouchers for childcare to poor women in urban Kenya, estimating the impacts on their economic empowerment. The empowerment is measured through disaggregated analyses of maternal income, employment probability and hours worked. It finds that, for married mothers there was a significantly positive effect on employment probability and hours worked, suggesting their increased ability to work through lower childcare costs increasing personal agency. For single mothers, it finds a negative effect on hours worked, though with a stable income. The authors suggest this is due to single Kenyan mothers already working increased hours compared to married mothers, though the effect shows the ability of single mothers to shift to jobs with more regular hours, even if they are not compatible with childcare. Minor limitations of the study are its restriction to effects within a period of 1 year, and a somewhat significant attrition rate to the endline survey.</p>
<p><span class="citation" data-cites="Hojman2019">Hojman &amp; López Bóo (<a href="#ref-Hojman2019" role="doc-biblioref">2019</a>)</span>, in an experimental study looking at the effects of providing childcare vouchers for poor urban mothers in Nicaragua under the ‘Programo Urbano’, examine the effects on inequality for mothers and children. It finds that providing free childcare for young children of poor mothers significantly increases the employment probability of the mothers (14ppts) independently of the childcare quality. It also finds significantly positive impacts on the human capital of the children, though the results are also dependent on the quality of childcare facilities. This suggests childcare costs being removed through a quasi-transfer reducing the required childcare time burden on mothers, increasing parental agency and employment choices. Some limitations to the study include a relatively small overall sample size, as well as employment effects becoming insignificant when the effect is measured on randomization alone (without an additional instrumental variable).</p>
</section>
<section id="unconscious-bias-and-discriminatory-norms" class="level3">
<h3 class="anchored" data-anchor-id="unconscious-bias-and-discriminatory-norms">Unconscious bias and discriminatory norms</h3>
<!-- TODO include discussion of Gates2000 on social components of disability rtw -->
<!-- TODO include hiring / firing patterns of Mun2018? mat laws -->
<p><span class="citation" data-cites="Al-Mamun2014">Al-Mamun et al. (<a href="#ref-Al-Mamun2014" role="doc-biblioref">2014</a>)</span> conduct a study on the impacts of an urban micro-finance programme in Malaysia on the economic empowerment of women. The programme introduced the ability for low-income urban individuals to receive collateral-free credit. The study finds that the programme, though not specifically aimed at women, indeed increased women’s economic empowerment with an increase in household decision-making, as well as increased personal economic security. Primarily this is due to the increased access to finance, though it also functions thorugh an increase of collective agency established for the women in organised meetings and trainings. It also finds, however, that the empowerment outcomes are constrained by the inability for individuals to obtain loans, with the programme only disbursing group loans which are harder to achieve through obstacles to collective organisation by different racial and socio-demographic backgrounds in each dwelling. The study is somewhat limited in its explanatory power since even through its random sampling design it can not establish control for all factors required in experimental design.</p>
<p>In turn, <span class="citation" data-cites="Field2019">Field et al. (<a href="#ref-Field2019" role="doc-biblioref">2019</a>)</span> undertake an experimental study looking at the effects of granting women increased access to their own financial accounts and training, on their employment and hours worked, as well as more long-term economic empowerment. The background of the experiment was the rural Indian MGNREGS<a href="#fn4" class="footnote-ref" id="fnref4" role="doc-noteref"><sup>4</sup></a> programme which, despite ostensibly mandated gender wage parity, runs the risk of discouraging female workers and restricting their agency by depositing earned wages into a single household account — predominantly owned by the male head of household. To grant increased financial access, the treatment changed the deposits into newly opened individual accounts for the women workers, as well as providing additional training to some women. It found that, short-term, the deposits into women’s individual accounts in combination with provided training increased their labour supply, while longer-term there was an increased acceptance of female work in affected households and a significant increase in women’s hours worked. The impacts on increased hours worked were concentrated on those households where previously women worked relatively lower amounts and there were stronger norms against female work while less constrained households’ impacts dissipated over time. The authors suggest the primary channel is the newly increased bargaining power through having a greater control of one’s income, and that it in turn also reflects onto gender norms themselves.</p>
</section>
</section>
</section>
<section id="discussion-and-policy-implications" class="level1 page-columns page-full">
<h1>Discussion and policy implications</h1>
<section id="robustness-of-evidence" class="level2 page-columns page-full">
<h2 class="anchored" data-anchor-id="robustness-of-evidence">Robustness of evidence</h2>
<div id="prep-inequalities-crosstabs" class="cell" data-execution_count="14">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># dataframe containing each intervention inequality pair</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>df_inequality <span class="op">=</span> (</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> bib_df[[<span class="st">&quot;region&quot;</span>, <span class="st">&quot;intervention&quot;</span>, <span class="st">&quot;inequality&quot;</span>]]</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> Intervention <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;intervention&quot;</span>]</span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot;\(.+\)&quot;</span>, <span class="st">&quot;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a> inequality <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;inequality&quot;</span>]</span>
<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot;\(.+\)&quot;</span>, <span class="st">&quot;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb11-14"><a href="#cb11-14" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb11-15"><a href="#cb11-15" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb11-16"><a href="#cb11-16" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb11-17"><a href="#cb11-17" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb11-18"><a href="#cb11-18" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;Intervention&quot;</span>)</span>
<span id="cb11-19"><a href="#cb11-19" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb11-20"><a href="#cb11-20" aria-hidden="true" tabindex="-1"></a> .reset_index(drop<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb11-21"><a href="#cb11-21" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb11-22"><a href="#cb11-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb11-23"><a href="#cb11-23" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> crosstab_inequality(df, inequality:<span class="bu">str</span>, <span class="op">**</span>kwargs):</span>
<span id="cb11-24"><a href="#cb11-24" aria-hidden="true" tabindex="-1"></a> df_temp <span class="op">=</span> df.loc[(df[<span class="st">&quot;inequality&quot;</span>] <span class="op">==</span> inequality) <span class="op">|</span> (df[<span class="st">&quot;inequality&quot;</span>] <span class="op">==</span> <span class="st">&quot;income&quot;</span>)]</span>
<span id="cb11-25"><a href="#cb11-25" aria-hidden="true" tabindex="-1"></a> tab <span class="op">=</span> pd.crosstab(df_temp[<span class="st">&quot;Intervention&quot;</span>], df_temp[<span class="st">&quot;inequality&quot;</span>], <span class="op">**</span>kwargs)</span>
<span id="cb11-26"><a href="#cb11-26" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> tab.drop(tab[tab[inequality] <span class="op">==</span> <span class="dv">0</span>].index)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
<section id="regional-spread" class="level3">
<h3 class="anchored" data-anchor-id="regional-spread">Regional spread</h3>
<p>As can be seen in <a href="#fig-region-counts" class="quarto-xref">Figure 5</a>, taken by region for the overall study sample, the evidence base receives a relatively even split between the World Bank regional country groupings with the exception of the Middle East and North Africa (MENA) region, in which fewer studies have been identified.</p>
<div id="cell-fig-region-counts" class="cell" data-execution_count="15">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>by_region <span class="op">=</span> (</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a> bib_df[[<span class="st">&quot;region&quot;</span>]]</span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> region <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;region&quot;</span>]</span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;region&quot;</span>)</span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a> .reset_index(drop<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(by_region, x<span class="op">=</span><span class="st">&quot;region&quot;</span>, order<span class="op">=</span>by_region[<span class="st">&quot;region&quot;</span>].value_counts().index)</span>
<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> regions_for_inequality(df, inequality:<span class="bu">str</span>):</span>
<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a> df_temp <span class="op">=</span> df.loc[(df[<span class="st">&quot;inequality&quot;</span>] <span class="op">==</span> inequality)]</span>
<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> sns.countplot(df_temp, x<span class="op">=</span><span class="st">&quot;region&quot;</span>, order<span class="op">=</span>df_temp[<span class="st">&quot;region&quot;</span>].value_counts().index)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
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</g>
</g>
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<!-- North America -->
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<!-- Middle East &amp; North Africa -->
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<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-region-counts-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 5: Studies by regions analysed
</figcaption>
</figure>
</div>
</div>
</div>
<p>Most studies come from a context of East Asia and the Pacific, though with an almost equal amount analysing Europe and Central Asia. With slightly fewer studies, the contexts of North America, Sub-Saharan Africa follow for amount of anlalyses, and in turn Latin America and the Caribbean and South Asia with an equal amount of studies for each region.</p>
<p>The lower amount of studies stemming from a MENA context can point to a variety of underlying causes: First, it is possible that there is simply not as much evidence-based analysis undertaken for countries in the region as for other national or subnational contexts, with research either following a more theoretical trajectory, or missing the underlying data collection that is available for other regional contexts.</p>
<p>However, it cannot be ruled out that the search protocol itself did not capture the same depth of analytical material as for other contexts, with each region often having both a specific focus in policy-orientations and academically, and in some cases also differing underlying term bases. Such a contextual term differences may then not be captured adequately by the existing query terms and would point to a necessity to re-align it to the required specifics.</p>
<p>One reason for such a differentiation could be a larger amount of gray literature captured compared to other regions, which may be utilising less established terms than the majority of captured literature for policy implementations. Another reason could be the actual implementation of different policy programmes which are then equally not captured by existing term clusters.</p>
</section>
<section id="internal-and-external-validity" class="level3 page-columns page-full">
<h3 class="anchored" data-anchor-id="internal-and-external-validity">Internal and external validity</h3>
<p>Using the validity ranking separated into internal and external validity for each study, it is possible to identify the general make-up of the overall sample, the relationship between both dimensions and the distribution of studies within.</p>
<p>As can be seen in <a href="#fig-validity-relation" class="quarto-xref">Figure 6</a>, the relationship between the internal dimension and the external dimension of validity for the study pool follows a normal distribution. Generally, studies that have a lower internal validity, between 2.0 and 3.5, rank higher on their external validity, while studies with higher internal validity in turn do not reach as high on the external validity ranking.</p>
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>validities <span class="op">=</span> validity.calculate(by_intervention)</span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>validities[<span class="st">&quot;identifier&quot;</span>] <span class="op">=</span> validities[<span class="st">&quot;author&quot;</span>].<span class="bu">str</span>.replace(<span class="vs">r&#39;,.*$&#39;</span>, <span class="st">&#39;&#39;</span>, regex<span class="op">=</span><span class="va">True</span>) <span class="op">+</span> <span class="st">&quot; (&quot;</span> <span class="op">+</span> validities[<span class="st">&quot;year&quot;</span>].astype(<span class="bu">str</span>) <span class="op">+</span> <span class="st">&quot;)&quot;</span></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>validities <span class="op">=</span> validities.loc[(validities[<span class="st">&quot;design&quot;</span>] <span class="op">==</span> <span class="st">&quot;quasi-experimental&quot;</span>) <span class="op">|</span> (validities[<span class="st">&quot;design&quot;</span>] <span class="op">==</span> <span class="st">&quot;experimental&quot;</span>)]</span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a><span class="co">#validities[&quot;external_validity&quot;] = validities[&quot;external_validity&quot;].astype(&#39;category&#39;)</span></span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>validities[<span class="st">&quot;internal_validity&quot;</span>] <span class="op">=</span> validities[<span class="st">&quot;internal_validity&quot;</span>].astype(<span class="st">&#39;category&#39;</span>)</span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>plt.figure().set_figheight(<span class="dv">5</span>)</span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a>sns.violinplot(</span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a> data<span class="op">=</span>validities,</span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a> x<span class="op">=</span><span class="st">&quot;internal_validity&quot;</span>, y<span class="op">=</span><span class="st">&quot;external_validity&quot;</span>, hue<span class="op">=</span><span class="st">&quot;design&quot;</span>,</span>
<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a> cut<span class="op">=</span><span class="dv">0</span>, bw_method<span class="op">=</span><span class="st">&quot;scott&quot;</span>,</span>
<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a> orient<span class="op">=</span><span class="st">&quot;x&quot;</span></span>
<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a>sns.swarmplot(</span>
<span id="cb13-17"><a href="#cb13-17" aria-hidden="true" tabindex="-1"></a> data<span class="op">=</span>validities,</span>
<span id="cb13-18"><a href="#cb13-18" aria-hidden="true" tabindex="-1"></a> x<span class="op">=</span><span class="st">&quot;internal_validity&quot;</span>, y<span class="op">=</span><span class="st">&quot;external_validity&quot;</span>, legend<span class="op">=</span><span class="va">False</span>,</span>
<span id="cb13-19"><a href="#cb13-19" aria-hidden="true" tabindex="-1"></a> color<span class="op">=</span><span class="st">&quot;darkmagenta&quot;</span>,</span>
<span id="cb13-20"><a href="#cb13-20" aria-hidden="true" tabindex="-1"></a> s<span class="op">=</span><span class="dv">4</span></span>
<span id="cb13-21"><a href="#cb13-21" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb13-22"><a href="#cb13-22" aria-hidden="true" tabindex="-1"></a>sns.displot(</span>
<span id="cb13-23"><a href="#cb13-23" aria-hidden="true" tabindex="-1"></a> data<span class="op">=</span>validities,</span>
<span id="cb13-24"><a href="#cb13-24" aria-hidden="true" tabindex="-1"></a> x<span class="op">=</span><span class="st">&quot;external_validity&quot;</span>, hue<span class="op">=</span><span class="st">&quot;internal_validity&quot;</span>,</span>
<span id="cb13-25"><a href="#cb13-25" aria-hidden="true" tabindex="-1"></a> kind<span class="op">=</span><span class="st">&quot;kde&quot;</span>,</span>
<span id="cb13-26"><a href="#cb13-26" aria-hidden="true" tabindex="-1"></a> multiple<span class="op">=</span><span class="st">&quot;fill&quot;</span>, clip<span class="op">=</span>(<span class="dv">0</span>, <span class="va">None</span>),</span>
<span id="cb13-27"><a href="#cb13-27" aria-hidden="true" tabindex="-1"></a> palette<span class="op">=</span><span class="st">&quot;ch:rot=-0.5,hue=1.5,light=0.9&quot;</span>,</span>
<span id="cb13-28"><a href="#cb13-28" aria-hidden="true" tabindex="-1"></a> bw_adjust<span class="op">=</span><span class="fl">.65</span>, cut<span class="op">=</span><span class="dv">0</span>,</span>
<span id="cb13-29"><a href="#cb13-29" aria-hidden="true" tabindex="-1"></a> warn_singular <span class="op">=</span> <span class="va">False</span></span>
<span id="cb13-30"><a href="#cb13-30" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="column-body-outset quarto-layout-panel" data-layout-ncol="2">
<div class="quarto-layout-row">
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<div class="cell-output cell-output-display">
<div id="fig-validity-relation" class="quarto-figure quarto-figure-center quarto-float anchored">
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Figure 7: Distribution of internal validities
</figcaption>
</figure>
</div>
</div>
</div>
</div>
</div>
<p>Studies with an internal validity ranking of of 3.0 (primarily made up of difference-in-difference approaches) and an internal ranking of 5.0 (randomized control trials) have the same tight clustering around an external validity between 4.0 (national) and 5.0 (census-based), and 2.0 (local) and 3.0 (subnational), respectively. This clearly shows the expected overall relationship of studies with high internal validity generally ranking lower on their external validity.</p>
<p>The situation is less clear-cut with the internal rankings of 2.0 (primarily ordinary least squares) and 4.0 (primarily instrumental variable), which show a larger external validity spread. For 2.0-ranked studies, there is an overall larger spread with most using nationally representative data, while a significant amount makes use of census-based data and others in turn only being subnationally representative. Studies ranked 4.0 internally have a higher heterogeneity with the significant outlier of <span class="citation" data-cites="Thoresen2021">Thoresen et al. (<a href="#ref-Thoresen2021" role="doc-biblioref">2021</a>)</span>, which had the limitation of its underlying data being non-representative.</p>
<p>Looking at the overall density of studies along their external validity dimension, <a href="#fig-validity-distribution" class="quarto-xref">Figure 7</a> reiterates this overall relationship with internal validity. It additionally shows that studies with low internal validity make up the dominant number of nationally representative analyses and the slight majority of census-based analyses, while locally or non-representative samples are almost solely made up of internally highly valid (ranking 4.0 or above) analyses, again with the exception of <span class="citation" data-cites="Thoresen2021">Thoresen et al. (<a href="#ref-Thoresen2021" role="doc-biblioref">2021</a>)</span> already mentioned.</p>
<p>Looking at the data per region, census-based studies are primarily spread between Latin America and the Caribbean, as well as Europe and Central Asia. Meanwhile, studies using nationally, subnationally or non-representative data then to have a larger focus on North America, as well as East Asia and the Pacific. A slight trend towards studies focusing on evidence-based research in developing countries is visible, though with an overall rising output, as seen in <a href="#fig-publications-per-year" class="quarto-xref">Figure 2</a>, and the possibly a reliance on more recent datasets, this would be expected.</p>
</section>
<section id="inequality-types-analysed" class="level3">
<h3 class="anchored" data-anchor-id="inequality-types-analysed">Inequality types analysed</h3>
<p>Policy interventions undertaken either with the explicit aim of reducing one or multiple inequalities, or analysed under the lens of such an aim implicitly, appear in a wide array of variations to their approach and primary targeted inequality, as was highlighted in the previous section. To make further sense of the studies shining a light on such approaches, it makes sense to divide their attention not just by primary approach, but by individual or overlapping inequalities being targeted, as well as the region of their operation.</p>
<p>As can be seen in <a href="#fig-inequality-types" class="quarto-xref">Figure 8</a> which breaks down available studies by targeted inequalities, income inequality is the type of inequality traced in most of the relevant studies. This follows the identified multi-purpose lens income inequality can provide, through which to understand other inequalities — many studies use income measurements and changes in income or income inequality over time as indicators to understand a variety of other inequalities’ linkages through.</p>
<div id="inequality-targeting-implicit-explicit" class="cell" data-execution_count="18">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>targeting_majority <span class="op">=</span> bib_df[<span class="st">&quot;targeting&quot;</span>].value_counts().index.tolist()[<span class="dv">0</span>]</span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>targeting_minority <span class="op">=</span> bib_df[<span class="st">&quot;targeting&quot;</span>].value_counts().index.tolist()[<span class="op">-</span><span class="dv">1</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
<p>Often, however, income inequality is not the primary inequality being targeted, but used to measure the effects on other inequalities by seeing how the effects of respective inequality and income intersect, as will be discussed in the following section. The majority of policies under analysis had an implicit focus on all the inequalities analysed in the respective study, with only a minority of studies looking at policies with an explicit targeting on the inequalities itself.</p>
<div id="cell-fig-inequality-types" class="cell" data-execution_count="19">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>by_inequality <span class="op">=</span> (</span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a> bib_df[[<span class="st">&quot;inequality&quot;</span>]]</span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a> inequality <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;inequality&quot;</span>]</span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot;\(.+\)&quot;</span>, <span class="st">&quot;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a> .reset_index(drop<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> plt.figure()</span>
<span id="cb15-16"><a href="#cb15-16" aria-hidden="true" tabindex="-1"></a>fig.set_size_inches(<span class="dv">6</span>, <span class="dv">3</span>)</span>
<span id="cb15-17"><a href="#cb15-17" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(by_inequality, x<span class="op">=</span><span class="st">&quot;inequality&quot;</span>, order<span class="op">=</span>by_inequality[<span class="st">&quot;inequality&quot;</span>].value_counts().index)</span>
<span id="cb15-18"><a href="#cb15-18" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb15-19"><a href="#cb15-19" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb15-20"><a href="#cb15-20" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb15-21"><a href="#cb15-21" aria-hidden="true" tabindex="-1"></a>by_inequality <span class="op">=</span> <span class="va">None</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
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Figure 8: Types of inequality analysed
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<p>With income inequality on its own often describing vertical inequality within a national context, the remaining inequalities gathered from the data rather form horizontal lenses to view their contexts through. The second most analysed inequality is that of gender, followed by spatial inequalities, disabilities, ethnicities, age, inequalities of migration, education and intergenerational issues.</p>
<p>The following sections will dive deeper into the identified predominant inequality areas, discuss what the main interventions analysed in the literature are, and where overlaps between theoretical approaches and qualitative considerations are, as well as where gaps and limitations lie. Only a small amount of studies carried analysis of inequalities in the world of work surrounding migration, generational connections, age and education into the world of work.</p>
<p>Age-related inequalities prominently factor into studies as an intersection with disability, in focusing on the effects of older people with disabilities on the labour market <span class="citation" data-cites="Kirsh2016">(<a href="#ref-Kirsh2016" role="doc-biblioref">Kirsh, 2016</a>)</span>. Studies that solely or mainly target age-related inequalities themselves often do so with a stronger focus on the effects on seniors’ health outcomes and long-term activation measures, with some extending into the effects of differentiated pension systems.</p>
<p>While a pursuit both worthwhile in its own right and, by the nature of pensions, closely tied to labour markets, the studies ultimately focus on impacts which rarely intersect back into the world of work itself and are thus beyond the scope of this review.<a href="#fn5" class="footnote-ref" id="fnref5" role="doc-noteref"><sup>5</sup></a></p>
<p>Equally, for migration few studies strictly delineate it from racial inequalities or considerations of ethnicity. For the purposes of discussion, studies analysing both inequalities concerning ethnicity and migration will be discussed as part of one socio-demographic point of view, though results that do only speak to migration will be highlighted accordingly.</p>
<p>Surprisingly few studies focus on the eventual outcomes in the world of work of earlier education inequalities. The majority of studies analysing education-oriented policies focus on direct outcomes of child health and development, education accessibility itself or social outcomes.<a href="#fn6" class="footnote-ref" id="fnref6" role="doc-noteref"><sup>6</sup></a> Educational inequalities themselves were the outcome-focus of almost no studies, often analysed as a different dimension from the world of work and more focused on educations systems for children and youth, especially early childhood development. Similarly, rarely do studies delineate generational outcomes from income, gender or education issues enough to mark their own category of analysis.</p>
<!-- frameworks/qualitative discussion -->
<!-- explanatory framework; see data/processed/irrelevant/Eckardt2022 TODO connect with study results above -->
<p>The effects of automation on income inequality are more clearly put into focus by <span class="citation" data-cites="Eckardt2022">Eckardt (<a href="#ref-Eckardt2022" role="doc-biblioref">2022</a>)</span> by studying income inequality and under the effects of various kinds of automation and a minimum wage within the economy. He considers several types of automation, with automation on the extensive margin (automation of more tasks) leading to decreased wage inequality between low-skill and high-skill earners if it results in decreased overall outputs due to wage compression, and vice versa for increased total outputs. Automation on the intensive margin (increased productivity of automating existing tasks) has ambiguous effects on the employment share of low-skill workers (who are possibly displaced) and a higher minimum wage here decreases the inequality between low-skill wages and higher-skill wages.</p>
<p>However, it may also result in a ripple effect which results in the overall share of income of low-skill workers not increasing, if more machines or high-skill workers displace them. Then, while the wage differences may decrease, the low-skill workers share of national income is identified as non-increasing and the share of low-skill employment could decrease. The effects on low-skill income share under a system of minimum wage are thus primarily dependent on the amount of low-skill job displacement, as well as the effects of the minimum wage on overall economic output in the first place.</p>
<p>Ultimately, the author also suggests the institution of low-skill worker training programmes either targeting enhanced productivity for their existing tasks (‘deepening skills’) or enabling their capability for undertaking tasks previously only assigned to high-skill workers (‘expanding skills’) which would respectively counteract the negative automation effects on both margins.</p>
<p>Thus, for the current state of the literature on analyses of policy interventions through the lens of inequality reduction within the world of work, there are strong gaps of academic lenses for generational inequalities, age inequalities, educational inequalities and inequalities of non-ethnic migration processes when looking at the quantity of output. Care should be taken not to overestimate the decisiveness of merely quantified outputs — multiple studies with strong risk of bias may produce less reliable outcomes than fewer studies with stronger evidence bases — however, it does provide an overview of the size of evidence base in the first place.</p>
<p>The following sections will instead discuss in more depth the implications for individual inequalities, as well as providing a comparative view of the respective intersection with income inequality.</p>
</section>
</section>
<section id="gender-inequalities" class="level2">
<h2 class="anchored" data-anchor-id="gender-inequalities">Gender inequalities</h2>
<!--
findings - channels - policy recc
- persistent discrimination and cultural views (strenghtening female agency, vicious circle of low FLFP and education)
- supply-side effects, esp maternal (family planning; care work)
- organisational disadvantagement in new economy (networking needs, self-promotion, managerial discretions)
TODO include unionisation effects on gender
-->
<p>Due to its persistent characteristics, gender inequality is an often analysed horizontal dimension of workplace inequality in the study sample, with a variety of studies looking at it predominantly through the lens of female economic empowerment or through gender pay gaps. As <a href="#fig-gender-regions" class="quarto-xref">Figure 9</a> shows there is a somewhat higher output of research into this inequality in the Europe and Central Asian region, ahead of East Asia and the Pacific and North America, with the other regions trailing further behind in output.</p>
<div id="cell-fig-gender-regions" class="cell" data-execution_count="20">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>by_region_and_inequality <span class="op">=</span> (</span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a> bib_df[[<span class="st">&quot;inequality&quot;</span>, <span class="st">&quot;region&quot;</span>]]</span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a> region <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;region&quot;</span>]</span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a> inequality <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;inequality&quot;</span>]</span>
<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot;\(.+\)&quot;</span>, <span class="st">&quot;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;region&quot;</span>)</span>
<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a> .reset_index(drop<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb16-19"><a href="#cb16-19" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb16-20"><a href="#cb16-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-21"><a href="#cb16-21" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> regions_for_inequality(by_region_and_inequality, <span class="st">&quot;gender&quot;</span>)</span>
<span id="cb16-22"><a href="#cb16-22" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb16-23"><a href="#cb16-23" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb16-24"><a href="#cb16-24" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb16-25"><a href="#cb16-25" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb16-26"><a href="#cb16-26" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
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<!-- Sub-Saharan Africa -->
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<!-- Middle East &amp; North Africa -->
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<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-gender-regions-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 9: Regional distribution of studies analysing gender inequalities
</figcaption>
</figure>
</div>
</div>
</div>
<p>Looking into the prevalence of individual interventions within the gender dimension, <a href="#tbl-gender-crosstab" class="quarto-xref">Table 9</a> shows that paid leave, subsidies, collective bargaining, and education received the most attention. Thus there is a slight leaning towards institutional and structural interventions visible, though the dimension seems to be viewed from angles of strengthening individual agency just as well, with subsidies often seeking to nourish this approach, and training, and interventions towards financial agency being represented in the interventions.</p>
<div class="cell" data-execution_count="21">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>crosstab_inequality(df_inequality, <span class="st">&quot;gender&quot;</span>).sort_values(<span class="st">&quot;gender&quot;</span>, ascending<span class="op">=</span><span class="va">False</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-gender-crosstab" class="cell quarto-float anchored" data-execution_count="21">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-gender-crosstab-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 9: Interventions targeting gender inequalities
</figcaption>
<div aria-describedby="tbl-gender-crosstab-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output cell-output-display" data-execution_count="21">
<div>
<div>
<table class="dataframe do-not-create-environment cell table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th">inequality</th>
<th data-quarto-table-cell-role="th">gender</th>
<th data-quarto-table-cell-role="th">income</th>
</tr>
<tr class="odd">
<th data-quarto-table-cell-role="th">Intervention</th>
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td data-quarto-table-cell-role="th">paid leave</td>
<td>7</td>
<td>1</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">subsidy</td>
<td>5</td>
<td>4</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">collective action</td>
<td>4</td>
<td>3</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">education</td>
<td>4</td>
<td>6</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">minimum wage</td>
<td>3</td>
<td>9</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">training</td>
<td>3</td>
<td>3</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">infrastructure</td>
<td>2</td>
<td>2</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">trade liberalization</td>
<td>2</td>
<td>7</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">direct transfers</td>
<td>1</td>
<td>4</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">microcredit</td>
<td>1</td>
<td>1</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">regulation</td>
<td>1</td>
<td>2</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">social security</td>
<td>1</td>
<td>1</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">technological change</td>
<td>1</td>
<td>1</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</figure>
</div>
</div>
<!-- gender -->
<p>Approaches of paid leave, child care and education agree with the findings of Zeinali et al. <span class="citation" data-cites="Zeinali2021">(<a href="#ref-Zeinali2021" role="doc-biblioref">2021</a>)</span> on the main barriers at the intersection of gender and social identity: The main barriers limiting women’s access to career development resources can be reduced access to mentorship and sponsorship opportunities, as well as a reduced recognition, respect, and impression of value at work for women in leadership positions, with inequalities entrenching these barriers being an increased likelihood for women to take on the ‘dual burdens’ of professional work and childcare or domestic work, as well as biased views of the effectiveness of men’s over women’s leadership styles.</p>
<p>Whereas institutional programmes such as minimum wage and structural interventions such as education or the contextual trade liberalization are strongly viewed through the lens of income effects, with more studies targeting gender along income dimensions and the income dimension on its own, studies of agency-based interventions approach gender inequalities less through this dimension. Instead, they tend to rely on employment numbers or representation in absolute terms or as shares for their analyses.</p>
<p>As <span class="citation" data-cites="Grotti2016">Grotti &amp; Scherer (<a href="#ref-Grotti2016" role="doc-biblioref">2016</a>)</span> demonstrate, an increased gender equality does not engender an increase in overall economic inequality. Using the Theil index, they decompose a method to account for the different mediating effects of employment similarity and earnings similarity between the genders and find that neither correlated with an increased income inequality. In fact the opposite seems the case, at least in their analysis of developed nations, with increased female employment reducing the economic inequality, which they see rather generated by a polarisation between high-income and low-income households.</p>
<!-- maternal intersection, children -->
<p>A variety of studies also look at female economic empowerment outcomes through a more generational lens, focusing on the effects of interventions aimed at maternity support for the mother and/or children — childcare programmes, paid leave and maternity benefits. A reoccurring question is that of the reasons for inequality in female leadership positions, between institutional discrimination, self selection and family life trajectories. Like <span class="citation" data-cites="Mun2018">Mun &amp; Jung (<a href="#ref-Mun2018" role="doc-biblioref">2018</a>)</span> identified for Japan, while a complex interplay of a variety of factors, the primary channel seems to lie in a combination of the self-selection of women into different individual career plans, and reproductions of the existing gender divisions when confronted with the household responsibility for care labour. While focused more on the effects of education itself, <span class="citation" data-cites="Suh2017">Suh (<a href="#ref-Suh2017" role="doc-biblioref">2017</a>)</span> also agreed with this and sees family structure, alongside education, having a direct impact on labour market participation <span class="citation" data-cites="Ochsenfeld2012">(see also <a href="#ref-Ochsenfeld2012" role="doc-biblioref">Ochsenfeld, 2012</a>)</span>.</p>
<p>These findings of supply-side channels does not imply non-applicability of policy interventions, but points to a necessity to focus on supporting those causes directly, through parental leave policies, childcare subsidies and strengthening their return to work effect. Generally, a reduced cost of child care or expansion of the costs on both parents has been identified to increase mothers’ potential to participate in the labour force and pursue further career choices. On the other hand, currently the presence alone of a new-born child in a household has been identified to strongly negatively correlate with labour force participation, which can simultaneously foreclose further career choices or advancements.</p>
<!-- organisational structure -->
<p>At the same time, within organisations in the new economy’s logic of not being bound to a single employer, different focal points gain importance: team structures, career maps and networking receive more emphasis, and often reflect gendered organisational logics. In a quantitative study, <span class="citation" data-cites="Williams2012">Williams et al. (<a href="#ref-Williams2012" role="doc-biblioref">2012</a>)</span> identify the necessity of maintaining large networks, engage in self-promotion, and supervisory discretion as potentially prominent intra-organisational barriers to workplace gender equality, suggesting suitable policy efforts to focus on an increased managerial accountability, inclusive efforts regarding corporate-sponsored events as well as counter-acting more informally driven male-only events, and the general publication of co-workers salaries and individualised career development plans.</p>
<p>Finally, it is important to reiterate the cross-dimensional nature of such inequalities. While the changing face of the economy directly affects organisational processes and structural discrimination, it also has an impact on the work-family relations and thus, ultimately, the gender inequalities affected on the supply side <span class="citation" data-cites="Edgell2012">(<a href="#ref-Edgell2012" role="doc-biblioref">Edgell et al., 2012</a>)</span>. These inequalities surface particularly across the intersection of structural disadvantages and should thus provide the foundation for a holistic picture on inequality instead of one closed off between structural economic concerns and family and maternal decision-making.</p>
</section>
<section id="spatial-inequalities" class="level2">
<h2 class="anchored" data-anchor-id="spatial-inequalities">Spatial inequalities</h2>
<p>Spatial inequalities are less focused within European, Central Asian and North American regions, as <a href="#fig-spatial-regions" class="quarto-xref">Figure 10</a> shows. Instead, both Southern Asia and Sub-Saharan Africa are the primary areas of interest, with studies especially into Tanzania, India and Pakistan. In the European and North American context, the distribution of spatial inequality analyses is primarily conducted in the countries of the United States and the United Kingdom.</p>
<div id="cell-fig-spatial-regions" class="cell" data-execution_count="22">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> regions_for_inequality(by_region_and_inequality, <span class="st">&quot;spatial&quot;</span>)</span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
<div id="fig-spatial-regions" class="quarto-figure quarto-figure-center quarto-float anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-spatial-regions-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<svg id="svg_20844d0734c12d4b75d5" class="img-fluid figure-img" width="496.6pt" height="351.960311pt" viewBox="0 0 496.6 351.960311">
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<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-spatial-regions-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 10: Regional distribution of studies analysing spatial inequalities
</figcaption>
</figure>
</div>
</div>
</div>
<p>This spread may point to those countries’ large rural populations or wider inequality gaps between rural and urban populations. While large rural populations are a sign of a predominantly agrarian economy, widening gaps are argued to be specifically appearing between rural and urban locations in industrial and post-industrial societies: Under modes of financialization, a spatial redistribution of high- and low-income sectors and increasing occupational segregation, rural locations are often left behind economically and require structural-institutional interventions to be improved <span class="citation" data-cites="Crouch2019">(<a href="#ref-Crouch2019" role="doc-biblioref">Crouch, 2019</a>)</span>.</p>
<p>Interventions affecting spatial inequalities are often also viewed through indicators of income, as can be seen in <a href="#tbl-spatial-crosstab" class="quarto-xref">Table 10</a>. The primary intervention aiming at reduction of spatial inequalities is based on infrastructural changes, which aligns with expectations of the infrastructural schism between urban and rural regions.</p>
<div class="cell" data-execution_count="23">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>crosstab_inequality(df_inequality, <span class="st">&quot;spatial&quot;</span>).sort_values(<span class="st">&quot;spatial&quot;</span>, ascending<span class="op">=</span><span class="va">False</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-spatial-crosstab" class="cell quarto-float anchored" data-execution_count="23">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-spatial-crosstab-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 10: Interventions targeting spatial inequalities
</figcaption>
<div aria-describedby="tbl-spatial-crosstab-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output cell-output-display" data-execution_count="23">
<div>
<div>
<table class="dataframe do-not-create-environment cell table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th">inequality</th>
<th data-quarto-table-cell-role="th">income</th>
<th data-quarto-table-cell-role="th">spatial</th>
</tr>
<tr class="odd">
<th data-quarto-table-cell-role="th">Intervention</th>
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td data-quarto-table-cell-role="th">infrastructure</td>
<td>2</td>
<td>4</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">education</td>
<td>6</td>
<td>2</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">minimum wage</td>
<td>9</td>
<td>2</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">subsidy</td>
<td>4</td>
<td>2</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">work programme</td>
<td>1</td>
<td>2</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">direct transfers</td>
<td>4</td>
<td>1</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">trade liberalization</td>
<td>7</td>
<td>1</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">training</td>
<td>3</td>
<td>1</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</figure>
</div>
</div>
<p>Additionally, education interventions target spatial inequalities, with the effects of minimum wage, work programmes, interventions strengthening financial agency, trade liberalization and training also playing a role. Thus, structural interventions are the dominant approach to reducing spatial inequalities, with institutional and agency-driven interventions often less specifically targeted.</p>
<p>This can pose a problem, as even non-spatial policies will almost invariably have spatially divergent effects, be they positive — as is the case for higher positive income effects on rural households due to unintentional good targeting of minimum wage to lower-income households <span class="citation" data-cites="Gilbert2001">(<a href="#ref-Gilbert2001" role="doc-biblioref">Gilbert et al., 2001</a>)</span> — or negative: as seen in the further exclusion of already disadvantaged women from employment, infrastructure and training opportunities in India under bad targeting and elite capture <span class="citation" data-cites="Stock2021">(<a href="#ref-Stock2021" role="doc-biblioref">Stock, 2021</a>)</span>.</p>
<p>Policies, even those of an ostensibly non-spatial nature, must thus keep in mind possibly adverse targeting effects if not correctly adjusting for potential impacts on spatial inequalities. Rural communities relying on agricultural economies in particular may be vulnerable to exogenous structural shock events such as climate change, which may thus need to be a focal point for future structural interventions <span class="citation" data-cites="Salvati2014">(<a href="#ref-Salvati2014" role="doc-biblioref">Salvati, 2014</a>)</span>.</p>
<p>The measures used to investigate spatial effects of policy interventions follow an even split between relative inequality measured through indicators such as the Gini coefficient or urban-rural employment shares, and absolute measures such as the effects on rural employment. With the level of analysis mostly taking place at the household level, some individual horizontal inequalities such as intra-household gender roles and economic participation or racial intersections can be considered, however, analyses of spatial inequalities often remain solely focused on spatial employment and income effects.</p>
<!-- income, spatial, pre-existing -->
<p>Spatial inequalities move both ways, however, as also shown by <span class="citation" data-cites="Perez2022">Perez et al. (<a href="#ref-Perez2022" role="doc-biblioref">2022</a>)</span> in a multi-disciplinary systematic review of the association between a person’s income, their employment and poverty in an urban environment. They find, similarly to the rural-urban divide, that employment plays a significant role in the poverty of urban residents, though here the primary barriers are identified as lack of access to public transport, geographical segregation, labour informality and inadequate human capital. They also agree with the potential policy interventions identified to counteract these inequalities: credit programs, institutional support for childcare, guaranteed minimum income/universal basic income or the provision of living wages, commuting subsidies, and housing mobility programs, which largely map onto structural or institutional efforts identified by the studies. On the other hand, <span class="citation" data-cites="Hunt2004">Hunt &amp; Czerwinski (<a href="#ref-Hunt2004" role="doc-biblioref">2004</a>)</span> show that individual measures on their own such as commuting subsidies in this case, while having positive results, may not provide significantly lasting impact over the long term and thus may need to be undertaken in a more holistic approach, combining multiple policy packages.</p>
<p>Like the study pool shows, many of the highlighted barriers can be mapped onto channels of inequality: gender inequality’s impact, through traditional gender roles and lack of empowerment, a lack of childcare possibilities, or unequal proportions of domestic work; spatial inequality, through residential segregation or discrimination, lack of access to transportation, and a limited access to work; as well as pre-existing inequalities, here defined as the generational persistence of poverty, larger household sizes, and its possible negative impacts on human capital.</p>
</section>
<section id="disability-inequalities" class="level2">
<h2 class="anchored" data-anchor-id="disability-inequalities">Disability inequalities</h2>
<p>The dimension of disabilities in inequalities remains strongly focused on developed nations, primarily through analysis of effects on inequality in the world of work in a context of the United States labour market, as can be seen in <a href="#fig-disability-regions" class="quarto-xref">Figure 11</a>.</p>
<div id="cell-fig-disability-regions" class="cell" data-execution_count="24">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> regions_for_inequality(by_region_and_inequality, <span class="st">&quot;disability&quot;</span>)</span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
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<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-disability-regions-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 11: Regional distribution of studies analysing disability inequalities
</figcaption>
</figure>
</div>
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</div>
<p>Few studies approach disability inequalities primarily through the prism of income inequality, with more analyses focusing on other outcome measures as can be seen in <a href="#tbl-disability-crosstab" class="quarto-xref">Table 11</a>. The interventions targeting such inequalities in the world of work favour an approach to measuring inequalities through employment, by absolute amounts of hours worked, return to work numbers or employment rates instead. Only when looking at the intersection of disability and gender is income the more utilized indicator, through measuring female income ratios compared to those of males.</p>
<div class="cell" data-execution_count="25">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>crosstab_inequality(df_inequality, <span class="st">&quot;disability&quot;</span>).sort_values(<span class="st">&quot;disability&quot;</span>, ascending<span class="op">=</span><span class="va">False</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-disability-crosstab" class="cell quarto-float anchored" data-execution_count="25">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-disability-crosstab-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 11: Interventions targeting disability inequalities
</figcaption>
<div aria-describedby="tbl-disability-crosstab-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output cell-output-display" data-execution_count="25">
<div>
<div>
<table class="dataframe do-not-create-environment cell table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th">inequality</th>
<th data-quarto-table-cell-role="th">disability</th>
<th data-quarto-table-cell-role="th">income</th>
</tr>
<tr class="odd">
<th data-quarto-table-cell-role="th">Intervention</th>
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td data-quarto-table-cell-role="th">training</td>
<td>4</td>
<td>3</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">counseling</td>
<td>2</td>
<td>0</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">education</td>
<td>1</td>
<td>6</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">subsidy</td>
<td>1</td>
<td>4</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</figure>
</div>
</div>
<p>Studies into interventions within the dimension of disabilities are predominantly focused on agency-based perspectives, with counselling and training being the primary approaches. Structurally approached interventions are also pursued, looking at the overall effects of education, or subsidies in health care, though even here, the individual effects of activation play a role <span class="citation" data-cites="Carstens2018">(<a href="#ref-Carstens2018" role="doc-biblioref">Carstens &amp; Massatti, 2018</a>)</span>.</p>
<!-- frameworks/qualitative discussion -->
<p>The findings for a need toward agency-based interventions reflect in frameworks which put the organizational barriers into focus and simultaneously demand a more inclusive look into (re)integration of people with disabilities into the labour market and within the world of work <span class="citation" data-cites="Martin2020">(<a href="#ref-Martin2020" role="doc-biblioref">Martin &amp; Honig, 2020</a>)</span>. <span class="citation" data-cites="Kim2020">Kim et al. (<a href="#ref-Kim2020" role="doc-biblioref">2020</a>)</span> find the environmental factors in workplaces can significantly affect the individual job retention wishes of disabled employees, through the provided disability facilities influencing both work satisfaction and perceived workplace safety. Here, in addition to the predominantly used measures of employment and return to work rates, meaningful achievement and decent work should be measured from individual economic and social-psychological indicators, especially in view of the already predominantly agency-based variety of interventions.</p>
<p>Similarly, <span class="citation" data-cites="Lindsay2015">C. Lindsay et al. (<a href="#ref-Lindsay2015" role="doc-biblioref">2015</a>)</span> highlight a variety of barriers to activation such as limited network ties to working population, skills problems and lower levels of qualification for those receiving disability benefits, though also emphasising environmental factors of workplaces not facilitating integration measures or issues of spatial exclusion from labour markets through being located in areas of large-scale industrial restructuring and low geographic mobility. One framework which approaches the discussion from an almost entirely institutional-structural view is provided by the systems level theoretical grounding of <span class="citation" data-cites="Gruber2014">Gruber et al. (<a href="#ref-Gruber2014" role="doc-biblioref">2014</a>)</span>, separating into the exclusionary effects of disability into institutional factors at the macro level, at the meso level and factors influencing the micro level, and directly focusing on the separation or inclusion of education, recognition of eligibility for vocational rehabilitation and self-recognition as pre-condition for effective programme undertaking respectively.</p>
<p>These discussions reinforce the necessity of correct targeting, as <span class="citation" data-cites="Poppen2017">Poppen et al. (<a href="#ref-Poppen2017" role="doc-biblioref">2017</a>)</span> and <span class="citation" data-cites="Thoresen2021">Thoresen et al. (<a href="#ref-Thoresen2021" role="doc-biblioref">2021</a>)</span> highlighted in the fears of losing existing benefits, or negative relation between benefits and employment probabilities. The case seems not one of benefits on their own diminishing the readiness for work activation, but the monetary assistance often being provided instead of effective methods of activation, environmental support and agency-driven motivating factors to their respective recipients.</p>
<p>There is a clear bias in studies on disability interventions towards studies undertaken in developed countries and, more specifically, based on the Veteran Disability system in the United States which has been the object of analysis for a variety of studies, but simultaneously highlights gaps in research on the topic in other contexts and other regions. A recurring focus in all these discussions is their insistence on the intersectional nature of the issue, with gender, ethnicity, location, type and level of disability among others often creating more adverse conditions for disabled individuals. This constitutes a second gap which should provide stronger focus in empirical works, in attempts to disaggregate analyses beyond disability and control group to further understand factors of inequality at work.</p>
</section>
<section id="migration-and-ethnic-inequalities" class="level2">
<h2 class="anchored" data-anchor-id="migration-and-ethnic-inequalities">Migration and ethnic inequalities</h2>
<p>The effects of policy interventions targeting migratory and ethnic inequalities in the world of work are viewed primarily through the regions of North America, Europe, Central, South and East Asia, and the Pacific, as can be seen in <a href="#fig-ethnicity-regions" class="quarto-xref">Figure 12</a>. Especially the specifics regarding migration are reviewed in an Asian context, while studies in North America focus predominantly on aspects of ethnicity in their analyses, though both dimensions are deeply intertwined and hard to disentangle for most studies.</p>
<div id="cell-fig-ethnicity-regions" class="cell" data-execution_count="26">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>by_region_and_inequality.loc[by_region_and_inequality[<span class="st">&quot;inequality&quot;</span>] <span class="op">==</span> <span class="st">&quot;migration&quot;</span>, <span class="st">&quot;inequality&quot;</span>] <span class="op">=</span> <span class="st">&quot;ethnicity&quot;</span></span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> regions_for_inequality(by_region_and_inequality, <span class="st">&quot;ethnicity&quot;</span>)</span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-display">
<div id="fig-ethnicity-regions" class="quarto-figure quarto-figure-center quarto-float anchored">
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<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-ethnicity-regions-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 12: Regional distribution of studies analysing migration and ethnicity inequalities
</figcaption>
</figure>
</div>
</div>
</div>
<p>As seen in <a href="#tbl-ethnicity-crosstab" class="quarto-xref">Table 12</a>, in terms of primary interventions analysed for these dimensions, most focus on structural interventions such as education, fiscal policies, or infrastructure, as well as institutional contexts such as the possibility for collective bargaining and unionisation, or the effects of universal income on the world of work.</p>
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<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>crosstab_inequality(df_inequality, <span class="st">&quot;ethnicity&quot;</span>).sort_values(<span class="st">&quot;ethnicity&quot;</span>, ascending<span class="op">=</span><span class="va">False</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div id="tbl-ethnicity-crosstab" class="cell quarto-float anchored" data-execution_count="27">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-ethnicity-crosstab-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 12: Interventions targeting migration and ethnicity inequalities
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<div aria-describedby="tbl-ethnicity-crosstab-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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<div>
<table class="dataframe do-not-create-environment cell table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th">inequality</th>
<th data-quarto-table-cell-role="th">ethnicity</th>
<th data-quarto-table-cell-role="th">income</th>
</tr>
<tr class="odd">
<th data-quarto-table-cell-role="th">Intervention</th>
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th"></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td data-quarto-table-cell-role="th">education</td>
<td>2</td>
<td>6</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">ubi</td>
<td>2</td>
<td>3</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">collective action</td>
<td>1</td>
<td>3</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">direct transfers</td>
<td>1</td>
<td>4</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">infrastructure</td>
<td>1</td>
<td>2</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">social security</td>
<td>1</td>
<td>1</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">subsidy</td>
<td>1</td>
<td>4</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</figure>
</div>
</div>
<p>There is a mixed approach to using income-based indicators of inequality or other markers such as employment. At the same time, there is a somewhat stronger focus on absolute measures of inequality, such poverty, debt or savings, or hours worked in absolute terms. Relative indicators have a wider spread with the Gini coefficient, the Theil index, decile ratios or employment rates for sub-samples used. From an organisational perspective, the focus on structural effects is in agreement with perspectives which highlight the conceptualisation of workplace ethnicity as separate from the majority in many places as a structural power structure <span class="citation" data-cites="Samaluk2014">(<a href="#ref-Samaluk2014" role="doc-biblioref">Samaluk, 2014</a>)</span>.</p>
<p>At the same time in a broader context, job insecurities, both produced by the dis-embeddedness of migrants and the broader contemporary institutional work organisational context speak to the same institutional-structural focus required as is already pursued in the literature <span class="citation" data-cites="Landsbergis2014">(<a href="#ref-Landsbergis2014" role="doc-biblioref">Landsbergis et al., 2014</a>)</span>. With a focus on remittances of temporary migratory work, <span class="citation" data-cites="Rosewarne2012">Rosewarne (<a href="#ref-Rosewarne2012" role="doc-biblioref">2012</a>)</span> similarly argues for the necessity to allow for greater continuity of employment to counteract while cementing the workers’ bounds to their respective home countries, through circular labour migration being supported by formal embedding in employment contract through contract succession negotiations and shifting the focus to labour rights specifically for the temporary nature of such work.</p>
<p>While some frameworks do put agency-driven necessities to the foreground <span class="citation" data-cites="Siebers2015">(see <a href="#ref-Siebers2015" role="doc-biblioref">Siebers &amp; van Gastel, 2015</a>)</span>, the consensus seems a requirement for structural approaches enabling this agency and their institutional embedding before more agency-driven interventions alone increase their effectiveness.<a href="#fn7" class="footnote-ref" id="fnref7" role="doc-noteref"><sup>7</sup></a></p>
</section>
</section>
<section id="conclusion" class="level1">
<h1>Conclusion</h1>
<p>The preceding study undertook a systematic scoping review of the literature on inequalities in the world of work. It focused on the variety of approaches to policy interventions, from institutional to structural to more agency-driven programmes, and highlighted the inequalities targeted, analysed in subsequent study, their methods and limitations, to arrive at a picture of which lays out the strengths and weaknesses of current approaches.</p>
<p>Wide gaps exist between the research on existing topics within the areas and intersections of inequalities in the world of work. First, while regionally research on such inequalities seems relatively evenly distributed, focus prevalence on individual inequalities varies widely.</p>
<p>Research into interventions preventing income inequality are still the dominant form of measured outcomes, which makes sense for its prevailing usefulness through a variety of indicators and its use to investigate both vertical and horizontal inequalities. However, care should be taken not to over-emphasize the reliance on income inequality outcomes: they can obscure intersections with other inequalities, or diminish the perceived importance of tackling other inequalities themselves, if not directly measurable through income. Thus, while interventions attempt to approach the inequality from a variety of institutional, structural and agency-oriented approaches already, this could be further enhanced by putting a continuous focus on the closely intertwined intersectional nature of the issue.</p>
<p>Gender inequality is an almost equally considered dimension in the interventions, a reasonable conclusion due to the inequality’s global ubiquity and persistence. Most gender-oriented policy approaches tackle it directly alongside income inequality outcomes, especially viewed through gender pay gaps and economic (dis-)empowerment, approaching it from backgrounds of structural or agency-driven interventions. While both approaches seem fruitful in different contexts, few interventions strive to provide a holistic approach which combines the individual-level with macro-impacts, tackling both institutional-structural issues while driving concerns of agency simultaneously.</p>
<p>Spatial inequalities are primarily viewed through rural-urban divides, concerning welfare, opportunities and employment probabilities. Spatially focused interventions primarily tackle infrastructural issues which should be an effective avenue since most positive interventions are focused on the structural dimension of the inequality. However, too many interventions, especially focused on reducing income inequalities, still do not take spatial components fully into view, potentially leading to worse outcomes for inequalities along the spatial dimension.</p>
<p>Disabilities are rarely viewed through lenses other than employment opportunities. While most interventions already focus on dimensions of strengthening agency and improved integration or reintegration of individuals with disabilities into the world of work, a wider net needs to be cast with future research focusing on developing regions and the effects of more institutional-structural approaches before clearer recommendations can be given based on existing evidence.</p>
<p>Ethnicity and migration provide dimensions of inequalities which are, while more evenly distributed regionally, still equally underdeveloped in research on evidence-based intervention impacts. Currently, there is a strong focus on institutional-structural approaches, which seems to follow the literature in what is required for effective interventions. However, similarly to research on inequalities based on disability, there are clear gaps in research on ethnicity and especially migration, before clearer pictures of what works can develop.</p>
<p>The intertwined nature of inequalities, once recognized, requires intervention approaches which heed multi-dimensional issues and can flexibly intervene and subsequently correctly measure their relative effectiveness. To do so, perspectives need to shift and align towards a new, more intersectional approach which can incorporate both a wider array of methodological approaches between purely quantitative and qualitative research, while relying on indicators for measurement which are flexible yet overlapping enough to encompass such a broadened perspective.</p>
<div style="page-break-after: always;"></div>
</section>
<section id="references" class="level1">
<h1>References</h1>
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<div style="page-break-after: always;"></div>
</section>
<div id="quarto-appendix" class="default"><section id="appendix" class="level1 appendix unnumbered"><h2 class="anchored quarto-appendix-heading">Appendix</h2><div class="quarto-appendix-contents">
<section id="full-search-query" class="level2 unnumbered">
<h2 class="unnumbered anchored" data-anchor-id="full-search-query">Full search query</h2>
<div id="full-search-query" class="cell" data-execution_count="28">
<div class="sourceCode" id="cb24"><pre class="sourceCode sql code-with-copy"><code class="sourceCode sql"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>TS<span class="op">=</span></span>
<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>(</span>
<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a> <span class="kw">work</span> <span class="kw">OR</span></span>
<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a> labour <span class="kw">OR</span></span>
<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a> production <span class="kw">of</span> goods <span class="kw">OR</span></span>
<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a> provision <span class="kw">of</span> services <span class="kw">OR</span></span>
<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a> <span class="kw">own</span><span class="op">-</span><span class="kw">use</span> <span class="kw">OR</span></span>
<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a> <span class="kw">use</span> <span class="kw">by</span> others <span class="kw">OR</span></span>
<span id="cb24-10"><a href="#cb24-10" aria-hidden="true" tabindex="-1"></a> <span class="kw">of</span> working age <span class="kw">OR</span></span>
<span id="cb24-11"><a href="#cb24-11" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> pay <span class="kw">OR</span></span>
<span id="cb24-12"><a href="#cb24-12" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> profit <span class="kw">OR</span></span>
<span id="cb24-13"><a href="#cb24-13" aria-hidden="true" tabindex="-1"></a> remuneration <span class="kw">OR</span></span>
<span id="cb24-14"><a href="#cb24-14" aria-hidden="true" tabindex="-1"></a> market transactions</span>
<span id="cb24-15"><a href="#cb24-15" aria-hidden="true" tabindex="-1"></a> ) <span class="kw">AND</span></span>
<span id="cb24-16"><a href="#cb24-16" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-17"><a href="#cb24-17" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-18"><a href="#cb24-18" aria-hidden="true" tabindex="-1"></a> <span class="kw">own</span><span class="op">-</span><span class="kw">use</span> <span class="kw">OR</span></span>
<span id="cb24-19"><a href="#cb24-19" aria-hidden="true" tabindex="-1"></a> employment <span class="kw">OR</span></span>
<span id="cb24-20"><a href="#cb24-20" aria-hidden="true" tabindex="-1"></a> unpaid trainee <span class="kw">OR</span></span>
<span id="cb24-21"><a href="#cb24-21" aria-hidden="true" tabindex="-1"></a> volunteer <span class="kw">OR</span></span>
<span id="cb24-22"><a href="#cb24-22" aria-hidden="true" tabindex="-1"></a> other <span class="kw">work</span> activities <span class="kw">OR</span></span>
<span id="cb24-23"><a href="#cb24-23" aria-hidden="true" tabindex="-1"></a> wage<span class="op">-</span>employed <span class="kw">OR</span></span>
<span id="cb24-24"><a href="#cb24-24" aria-hidden="true" tabindex="-1"></a> self<span class="op">-</span>employed <span class="kw">OR</span></span>
<span id="cb24-25"><a href="#cb24-25" aria-hidden="true" tabindex="-1"></a> formal <span class="kw">work</span> <span class="kw">OR</span></span>
<span id="cb24-26"><a href="#cb24-26" aria-hidden="true" tabindex="-1"></a> informal <span class="kw">work</span> <span class="kw">OR</span></span>
<span id="cb24-27"><a href="#cb24-27" aria-hidden="true" tabindex="-1"></a> domestic <span class="kw">work</span> <span class="kw">OR</span></span>
<span id="cb24-28"><a href="#cb24-28" aria-hidden="true" tabindex="-1"></a> care <span class="kw">work</span> <span class="kw">OR</span></span>
<span id="cb24-29"><a href="#cb24-29" aria-hidden="true" tabindex="-1"></a> unpaid <span class="kw">work</span></span>
<span id="cb24-30"><a href="#cb24-30" aria-hidden="true" tabindex="-1"></a> ) <span class="kw">OR</span></span>
<span id="cb24-31"><a href="#cb24-31" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-32"><a href="#cb24-32" aria-hidden="true" tabindex="-1"></a> employment outcomes <span class="kw">OR</span></span>
<span id="cb24-33"><a href="#cb24-33" aria-hidden="true" tabindex="-1"></a> labour rights <span class="kw">OR</span></span>
<span id="cb24-34"><a href="#cb24-34" aria-hidden="true" tabindex="-1"></a> equality <span class="kw">of</span> oppoertunity <span class="kw">OR</span></span>
<span id="cb24-35"><a href="#cb24-35" aria-hidden="true" tabindex="-1"></a> equality <span class="kw">of</span> outcome <span class="kw">OR</span></span>
<span id="cb24-36"><a href="#cb24-36" aria-hidden="true" tabindex="-1"></a> labour <span class="kw">force</span> participationOR</span>
<span id="cb24-37"><a href="#cb24-37" aria-hidden="true" tabindex="-1"></a> labour <span class="kw">force</span> exit <span class="kw">OR</span></span>
<span id="cb24-38"><a href="#cb24-38" aria-hidden="true" tabindex="-1"></a> job quality <span class="kw">OR</span></span>
<span id="cb24-39"><a href="#cb24-39" aria-hidden="true" tabindex="-1"></a> career advancement <span class="kw">OR</span></span>
<span id="cb24-40"><a href="#cb24-40" aria-hidden="true" tabindex="-1"></a> hours worked <span class="kw">OR</span></span>
<span id="cb24-41"><a href="#cb24-41" aria-hidden="true" tabindex="-1"></a> wage <span class="kw">OR</span></span>
<span id="cb24-42"><a href="#cb24-42" aria-hidden="true" tabindex="-1"></a> salary <span class="kw">OR</span></span>
<span id="cb24-43"><a href="#cb24-43" aria-hidden="true" tabindex="-1"></a> <span class="kw">return</span> <span class="kw">to</span> <span class="kw">work</span></span>
<span id="cb24-44"><a href="#cb24-44" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-45"><a href="#cb24-45" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-46"><a href="#cb24-46" aria-hidden="true" tabindex="-1"></a>) <span class="kw">AND</span></span>
<span id="cb24-47"><a href="#cb24-47" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-48"><a href="#cb24-48" aria-hidden="true" tabindex="-1"></a>TS<span class="op">=</span></span>
<span id="cb24-49"><a href="#cb24-49" aria-hidden="true" tabindex="-1"></a>(</span>
<span id="cb24-50"><a href="#cb24-50" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-51"><a href="#cb24-51" aria-hidden="true" tabindex="-1"></a> intervention <span class="kw">OR</span></span>
<span id="cb24-52"><a href="#cb24-52" aria-hidden="true" tabindex="-1"></a> policy <span class="kw">OR</span></span>
<span id="cb24-53"><a href="#cb24-53" aria-hidden="true" tabindex="-1"></a> participation <span class="kw">OR</span></span>
<span id="cb24-54"><a href="#cb24-54" aria-hidden="true" tabindex="-1"></a> targeting<span class="op">/</span>targeted <span class="kw">OR</span></span>
<span id="cb24-55"><a href="#cb24-55" aria-hidden="true" tabindex="-1"></a> distributive <span class="kw">OR</span></span>
<span id="cb24-56"><a href="#cb24-56" aria-hidden="true" tabindex="-1"></a> redistributive</span>
<span id="cb24-57"><a href="#cb24-57" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-58"><a href="#cb24-58" aria-hidden="true" tabindex="-1"></a> <span class="kw">AND</span></span>
<span id="cb24-59"><a href="#cb24-59" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-60"><a href="#cb24-60" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-61"><a href="#cb24-61" aria-hidden="true" tabindex="-1"></a> support <span class="cf">for</span> childcare <span class="kw">OR</span></span>
<span id="cb24-62"><a href="#cb24-62" aria-hidden="true" tabindex="-1"></a> labour rights <span class="kw">OR</span></span>
<span id="cb24-63"><a href="#cb24-63" aria-hidden="true" tabindex="-1"></a> <span class="kw">minimum</span> wage <span class="kw">OR</span></span>
<span id="cb24-64"><a href="#cb24-64" aria-hidden="true" tabindex="-1"></a> collective bargaining <span class="kw">OR</span></span>
<span id="cb24-65"><a href="#cb24-65" aria-hidden="true" tabindex="-1"></a> business sustainability promotion <span class="kw">OR</span></span>
<span id="cb24-66"><a href="#cb24-66" aria-hidden="true" tabindex="-1"></a> <span class="kw">work</span><span class="op">-</span>life balance promotion <span class="kw">OR</span></span>
<span id="cb24-67"><a href="#cb24-67" aria-hidden="true" tabindex="-1"></a> equal pay <span class="cf">for</span> <span class="kw">work</span> <span class="kw">of</span> equal <span class="fu">value</span> <span class="kw">OR</span></span>
<span id="cb24-68"><a href="#cb24-68" aria-hidden="true" tabindex="-1"></a> removal <span class="kw">of</span> (discriminatory) law <span class="kw">OR</span></span>
<span id="cb24-69"><a href="#cb24-69" aria-hidden="true" tabindex="-1"></a> law reformation <span class="kw">OR</span></span>
<span id="cb24-70"><a href="#cb24-70" aria-hidden="true" tabindex="-1"></a> guaranteed income <span class="kw">OR</span></span>
<span id="cb24-71"><a href="#cb24-71" aria-hidden="true" tabindex="-1"></a> universal basic income <span class="kw">OR</span></span>
<span id="cb24-72"><a href="#cb24-72" aria-hidden="true" tabindex="-1"></a> provision <span class="kw">of</span> living wage <span class="kw">OR</span></span>
<span id="cb24-73"><a href="#cb24-73" aria-hidden="true" tabindex="-1"></a> maternity leave</span>
<span id="cb24-74"><a href="#cb24-74" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-75"><a href="#cb24-75" aria-hidden="true" tabindex="-1"></a> <span class="kw">OR</span></span>
<span id="cb24-76"><a href="#cb24-76" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-77"><a href="#cb24-77" aria-hidden="true" tabindex="-1"></a> cash benefits <span class="kw">OR</span></span>
<span id="cb24-78"><a href="#cb24-78" aria-hidden="true" tabindex="-1"></a> services <span class="kw">in</span> kind <span class="kw">OR</span></span>
<span id="cb24-79"><a href="#cb24-79" aria-hidden="true" tabindex="-1"></a> green transition <span class="kw">OR</span></span>
<span id="cb24-80"><a href="#cb24-80" aria-hidden="true" tabindex="-1"></a> infrastructure <span class="kw">OR</span></span>
<span id="cb24-81"><a href="#cb24-81" aria-hidden="true" tabindex="-1"></a> digital infrastructure <span class="kw">OR</span></span>
<span id="cb24-82"><a href="#cb24-82" aria-hidden="true" tabindex="-1"></a> quality <span class="kw">of</span> education <span class="kw">OR</span></span>
<span id="cb24-83"><a href="#cb24-83" aria-hidden="true" tabindex="-1"></a> <span class="kw">public</span> service improvement <span class="kw">OR</span></span>
<span id="cb24-84"><a href="#cb24-84" aria-hidden="true" tabindex="-1"></a> lowering <span class="kw">of</span> gender segregation <span class="kw">OR</span></span>
<span id="cb24-85"><a href="#cb24-85" aria-hidden="true" tabindex="-1"></a> price stability intervention <span class="kw">OR</span></span>
<span id="cb24-86"><a href="#cb24-86" aria-hidden="true" tabindex="-1"></a> extended social protection scheme <span class="kw">OR</span></span>
<span id="cb24-87"><a href="#cb24-87" aria-hidden="true" tabindex="-1"></a> comprehensive social protection <span class="kw">OR</span></span>
<span id="cb24-88"><a href="#cb24-88" aria-hidden="true" tabindex="-1"></a> sustainable social protection <span class="kw">OR</span></span>
<span id="cb24-89"><a href="#cb24-89" aria-hidden="true" tabindex="-1"></a> supported employment <span class="kw">OR</span></span>
<span id="cb24-90"><a href="#cb24-90" aria-hidden="true" tabindex="-1"></a> vocational rehabilitation</span>
<span id="cb24-91"><a href="#cb24-91" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-92"><a href="#cb24-92" aria-hidden="true" tabindex="-1"></a> <span class="kw">OR</span></span>
<span id="cb24-93"><a href="#cb24-93" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-94"><a href="#cb24-94" aria-hidden="true" tabindex="-1"></a> credit programs <span class="kw">OR</span></span>
<span id="cb24-95"><a href="#cb24-95" aria-hidden="true" tabindex="-1"></a> career guidance <span class="kw">OR</span></span>
<span id="cb24-96"><a href="#cb24-96" aria-hidden="true" tabindex="-1"></a> vocational guidance <span class="kw">OR</span></span>
<span id="cb24-97"><a href="#cb24-97" aria-hidden="true" tabindex="-1"></a> vocational counselling <span class="kw">OR</span></span>
<span id="cb24-98"><a href="#cb24-98" aria-hidden="true" tabindex="-1"></a> counteracting <span class="kw">of</span> stereotypes <span class="kw">OR</span></span>
<span id="cb24-99"><a href="#cb24-99" aria-hidden="true" tabindex="-1"></a> commuting subsidies <span class="kw">OR</span></span>
<span id="cb24-100"><a href="#cb24-100" aria-hidden="true" tabindex="-1"></a> housing mobility programs <span class="kw">OR</span></span>
<span id="cb24-101"><a href="#cb24-101" aria-hidden="true" tabindex="-1"></a> encouraging re<span class="op">-</span>situation<span class="op">/</span>migration <span class="kw">OR</span></span>
<span id="cb24-102"><a href="#cb24-102" aria-hidden="true" tabindex="-1"></a> encouraging self<span class="op">-</span>advocacy <span class="kw">OR</span></span>
<span id="cb24-103"><a href="#cb24-103" aria-hidden="true" tabindex="-1"></a> cognitive behavioural therapy <span class="kw">OR</span></span>
<span id="cb24-104"><a href="#cb24-104" aria-hidden="true" tabindex="-1"></a> computer<span class="op">-</span>assisted therapy <span class="kw">OR</span></span>
<span id="cb24-105"><a href="#cb24-105" aria-hidden="true" tabindex="-1"></a> <span class="kw">work</span> <span class="kw">organization</span> <span class="kw">OR</span></span>
<span id="cb24-106"><a href="#cb24-106" aria-hidden="true" tabindex="-1"></a> special transportation</span>
<span id="cb24-107"><a href="#cb24-107" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-108"><a href="#cb24-108" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-109"><a href="#cb24-109" aria-hidden="true" tabindex="-1"></a>) <span class="kw">AND</span></span>
<span id="cb24-110"><a href="#cb24-110" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-111"><a href="#cb24-111" aria-hidden="true" tabindex="-1"></a>TS<span class="op">=</span></span>
<span id="cb24-112"><a href="#cb24-112" aria-hidden="true" tabindex="-1"></a>(</span>
<span id="cb24-113"><a href="#cb24-113" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-114"><a href="#cb24-114" aria-hidden="true" tabindex="-1"></a> inequality <span class="kw">OR</span></span>
<span id="cb24-115"><a href="#cb24-115" aria-hidden="true" tabindex="-1"></a> inequalities <span class="kw">OR</span></span>
<span id="cb24-116"><a href="#cb24-116" aria-hidden="true" tabindex="-1"></a> barriers <span class="kw">OR</span></span>
<span id="cb24-117"><a href="#cb24-117" aria-hidden="true" tabindex="-1"></a> advantaged <span class="kw">OR</span></span>
<span id="cb24-118"><a href="#cb24-118" aria-hidden="true" tabindex="-1"></a> disadvantaged <span class="kw">OR</span></span>
<span id="cb24-119"><a href="#cb24-119" aria-hidden="true" tabindex="-1"></a> discriminated <span class="kw">OR</span></span>
<span id="cb24-120"><a href="#cb24-120" aria-hidden="true" tabindex="-1"></a> disparity <span class="kw">OR</span></span>
<span id="cb24-121"><a href="#cb24-121" aria-hidden="true" tabindex="-1"></a> disparities</span>
<span id="cb24-122"><a href="#cb24-122" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-123"><a href="#cb24-123" aria-hidden="true" tabindex="-1"></a> NEAR<span class="op">/</span><span class="dv">5</span></span>
<span id="cb24-124"><a href="#cb24-124" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-125"><a href="#cb24-125" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-126"><a href="#cb24-126" aria-hidden="true" tabindex="-1"></a> income <span class="kw">OR</span></span>
<span id="cb24-127"><a href="#cb24-127" aria-hidden="true" tabindex="-1"></a> <span class="ot">&quot;Palma ratio&quot;</span> <span class="kw">OR</span></span>
<span id="cb24-128"><a href="#cb24-128" aria-hidden="true" tabindex="-1"></a> <span class="ot">&quot;Gini coefficient&quot;</span> <span class="kw">OR</span></span>
<span id="cb24-129"><a href="#cb24-129" aria-hidden="true" tabindex="-1"></a> <span class="kw">class</span> <span class="kw">OR</span></span>
<span id="cb24-130"><a href="#cb24-130" aria-hidden="true" tabindex="-1"></a> fertility <span class="kw">OR</span></span>
<span id="cb24-131"><a href="#cb24-131" aria-hidden="true" tabindex="-1"></a> <span class="ot">&quot;bottom percentile&quot;</span> <span class="kw">OR</span></span>
<span id="cb24-132"><a href="#cb24-132" aria-hidden="true" tabindex="-1"></a> <span class="ot">&quot;top percentile&quot;</span></span>
<span id="cb24-133"><a href="#cb24-133" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-134"><a href="#cb24-134" aria-hidden="true" tabindex="-1"></a> <span class="kw">OR</span></span>
<span id="cb24-135"><a href="#cb24-135" aria-hidden="true" tabindex="-1"></a> (</span>
<span id="cb24-136"><a href="#cb24-136" aria-hidden="true" tabindex="-1"></a> identity <span class="kw">OR</span></span>
<span id="cb24-137"><a href="#cb24-137" aria-hidden="true" tabindex="-1"></a> demographic <span class="kw">OR</span></span>
<span id="cb24-138"><a href="#cb24-138" aria-hidden="true" tabindex="-1"></a> gender <span class="kw">OR</span></span>
<span id="cb24-139"><a href="#cb24-139" aria-hidden="true" tabindex="-1"></a> colour <span class="kw">OR</span></span>
<span id="cb24-140"><a href="#cb24-140" aria-hidden="true" tabindex="-1"></a> beliefs <span class="kw">OR</span></span>
<span id="cb24-141"><a href="#cb24-141" aria-hidden="true" tabindex="-1"></a> racial <span class="kw">OR</span></span>
<span id="cb24-142"><a href="#cb24-142" aria-hidden="true" tabindex="-1"></a> ethnic <span class="kw">OR</span></span>
<span id="cb24-143"><a href="#cb24-143" aria-hidden="true" tabindex="-1"></a> migrant <span class="kw">OR</span></span>
<span id="cb24-144"><a href="#cb24-144" aria-hidden="true" tabindex="-1"></a> spatial <span class="kw">OR</span></span>
<span id="cb24-145"><a href="#cb24-145" aria-hidden="true" tabindex="-1"></a> rural <span class="kw">OR</span></span>
<span id="cb24-146"><a href="#cb24-146" aria-hidden="true" tabindex="-1"></a> urban <span class="kw">OR</span></span>
<span id="cb24-147"><a href="#cb24-147" aria-hidden="true" tabindex="-1"></a> mega<span class="op">-</span>cities <span class="kw">OR</span></span>
<span id="cb24-148"><a href="#cb24-148" aria-hidden="true" tabindex="-1"></a> <span class="ot">&quot;small cities&quot;</span> <span class="kw">OR</span></span>
<span id="cb24-149"><a href="#cb24-149" aria-hidden="true" tabindex="-1"></a> <span class="ot">&quot;peripheral cities&quot;</span> <span class="kw">OR</span></span>
<span id="cb24-150"><a href="#cb24-150" aria-hidden="true" tabindex="-1"></a> age <span class="kw">OR</span></span>
<span id="cb24-151"><a href="#cb24-151" aria-hidden="true" tabindex="-1"></a> nationality <span class="kw">OR</span></span>
<span id="cb24-152"><a href="#cb24-152" aria-hidden="true" tabindex="-1"></a> ethnicity <span class="kw">OR</span></span>
<span id="cb24-153"><a href="#cb24-153" aria-hidden="true" tabindex="-1"></a> <span class="ot">&quot;health status&quot;</span> <span class="kw">OR</span></span>
<span id="cb24-154"><a href="#cb24-154" aria-hidden="true" tabindex="-1"></a> disability <span class="kw">OR</span></span>
<span id="cb24-155"><a href="#cb24-155" aria-hidden="true" tabindex="-1"></a> characteristics</span>
<span id="cb24-156"><a href="#cb24-156" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-157"><a href="#cb24-157" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb24-158"><a href="#cb24-158" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="sec-appendix-validity-rankings" class="level2 unnumbered">
<h2 class="unnumbered anchored" data-anchor-id="sec-appendix-validity-rankings">Validity rankings</h2>
<div id="apptbl-validity-external" class="quarto-float">
<figure class="quarto-float quarto-float-apptbl figure">
<div aria-describedby="apptbl-validity-external-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<table class="table">
<thead>
<tr class="header">
<th>Representativeness</th>
<th>Ranking</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>non-representative survey/dataset</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>subnationally representative survey/dataset</td>
<td>3.0</td>
</tr>
<tr class="odd">
<td>nationally representative survey/dataset</td>
<td>4.0</td>
</tr>
<tr class="even">
<td>census-based dataset</td>
<td>5.0</td>
</tr>
</tbody>
</table>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-apptbl" id="apptbl-validity-external-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table A1: External validity ranking. Adapted from <span class="citation" data-cites="Maitrot2017">Maîtrot &amp; Niño-Zarazúa (<a href="#ref-Maitrot2017" role="doc-biblioref">2017</a>)</span>.
</figcaption>
</figure>
</div>
<div id="apptbl-validity-internal" class="quarto-float">
<figure class="quarto-float quarto-float-apptbl figure">
<div aria-describedby="apptbl-validity-internal-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<table class="table">
<thead>
<tr class="header">
<th>Method</th>
<th>Ranking</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>ordinary least squares &amp; fixed-effects</td>
<td>2.0</td>
</tr>
<tr class="even">
<td>discontinuity matching</td>
<td>3.0</td>
</tr>
<tr class="odd">
<td>difference in difference (&amp; triple difference)</td>
<td>3.0</td>
</tr>
<tr class="even">
<td>propensity score matching</td>
<td>3.5</td>
</tr>
<tr class="odd">
<td>instrumental variable</td>
<td>4.0</td>
</tr>
<tr class="even">
<td>general method of moments</td>
<td>4.0</td>
</tr>
<tr class="odd">
<td>regression discontinuity</td>
<td>4.5</td>
</tr>
<tr class="even">
<td>randomised control trial</td>
<td>5.0</td>
</tr>
</tbody>
</table>
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-apptbl" id="apptbl-validity-internal-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table A2: Internal validity ranking. Adapted from <span class="citation" data-cites="Maitrot2017">Maîtrot &amp; Niño-Zarazúa (<a href="#ref-Maitrot2017" role="doc-biblioref">2017</a>)</span>.
</figcaption>
</figure>
</div>
</section>
<section id="extraction-matrix" class="level2 unnumbered">
<h2 class="unnumbered anchored" data-anchor-id="extraction-matrix">Extraction matrix</h2>
<div class="cell" data-execution_count="29">
<details class="code-fold">
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>bib_df</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="apptbl-extraction-matrix" class="cell-output cell-output-display" data-execution_count="29">
<div>
<div>
<table class="dataframe table table-sm table-striped small" data-quarto-postprocess="true" data-border="1">
<caption>Extraction matrix {#cell-apptbl-extraction-matrix}</caption>
<thead>
<tr class="header">
<th data-quarto-table-cell-role="th"></th>
<th data-quarto-table-cell-role="th">citation</th>
<th data-quarto-table-cell-role="th">author</th>
<th data-quarto-table-cell-role="th">year</th>
<th data-quarto-table-cell-role="th">title</th>
<th data-quarto-table-cell-role="th">publisher</th>
<th data-quarto-table-cell-role="th">uri</th>
<th data-quarto-table-cell-role="th">pubtype</th>
<th data-quarto-table-cell-role="th">discipline</th>
<th data-quarto-table-cell-role="th">country</th>
<th data-quarto-table-cell-role="th">period</th>
<th data-quarto-table-cell-role="th">...</th>
<th data-quarto-table-cell-role="th">channels</th>
<th data-quarto-table-cell-role="th">direction</th>
<th data-quarto-table-cell-role="th">significance</th>
<th data-quarto-table-cell-role="th">doi</th>
<th data-quarto-table-cell-role="th">date</th>
<th data-quarto-table-cell-role="th">zot_cited</th>
<th data-quarto-table-cell-role="th">zot_usage</th>
<th data-quarto-table-cell-role="th">zot_keywords</th>
<th data-quarto-table-cell-role="th">region</th>
<th data-quarto-table-cell-role="th">income_group</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td data-quarto-table-cell-role="th">0</td>
<td>Adam2018</td>
<td>Adam, C., Bevan, D., &amp; Gollin, D.</td>
<td>2018</td>
<td>Rural-urban linkages, public investment and tr...</td>
<td>World Development</td>
<td>https://doi.org/10.1016/j.worlddev.2016.08.013</td>
<td>article</td>
<td>development</td>
<td>Tanzania</td>
<td>2001</td>
<td>...</td>
<td>movement of rural workers out of quasi-subsist...</td>
<td>-1.0</td>
<td>2.0</td>
<td>10.1016/j.worlddev.2016.08.013</td>
<td>2018-01-01</td>
<td>13.0</td>
<td>None</td>
<td>country::Tanzania,done::extracted,inequality::...</td>
<td>Sub-Saharan Africa</td>
<td>Lower middle income</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">1</td>
<td>Rosen2014</td>
<td>Rosen, M. I., Ablondi, K., Black, A. C., Muell...</td>
<td>2014</td>
<td>Work outcomes after benefits counseling among ...</td>
<td>Psychiatric Services</td>
<td>https://doi.org/10.1176/appi.ps.201300478</td>
<td>article</td>
<td>health</td>
<td>United States</td>
<td>2008-2011</td>
<td>...</td>
<td>not clear, neither belief about work, benefits...</td>
<td>1.0</td>
<td>2.0</td>
<td>10.1176/appi.ps.201300478</td>
<td>2014-01-01</td>
<td>10.0</td>
<td>None</td>
<td>country::US,done::extracted,inequality::age,in...</td>
<td>North America</td>
<td>High income</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">2</td>
<td>Xu2021</td>
<td>Xu, C., Han, M., Dossou, T. A. M., &amp; Bekun, F. V.</td>
<td>2021</td>
<td>Trade openness, FDI, and income inequality: Ev...</td>
<td>African Development Review</td>
<td>https://doi.org/10.1111/1467-8268.12511</td>
<td>article</td>
<td>development</td>
<td>Angola; Benin; Botswana; Burkina Faso; Burundi...</td>
<td>2000-2015</td>
<td>...</td>
<td>primarily goes to agriculture which can employ...</td>
<td>-1.0</td>
<td>1.0</td>
<td>10.1111/1467-8268.12511</td>
<td>2021-01-01</td>
<td>42.0</td>
<td>None</td>
<td>direction::vertical,done::extracted,indicator:...</td>
<td>Sub-Saharan Africa</td>
<td>Upper middle income;Lower middle income;High i...</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">3</td>
<td>Xu2021</td>
<td>Xu, C., Han, M., Dossou, T. A. M., &amp; Bekun, F. V.</td>
<td>2021</td>
<td>Trade openness, FDI, and income inequality: Ev...</td>
<td>African Development Review</td>
<td>https://doi.org/10.1111/1467-8268.12511</td>
<td>article</td>
<td>development</td>
<td>Angola; Benin; Botswana; Burkina Faso; Burundi...</td>
<td>2000-2015</td>
<td>...</td>
<td>higher import than export, creating jobs in ot...</td>
<td>1.0</td>
<td>2.0</td>
<td>10.1111/1467-8268.12511</td>
<td>2021-01-01</td>
<td>42.0</td>
<td>None</td>
<td>direction::vertical,done::extracted,indicator:...</td>
<td>Sub-Saharan Africa</td>
<td>Upper middle income;Lower middle income;High i...</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">4</td>
<td>Xu2021</td>
<td>Xu, C., Han, M., Dossou, T. A. M., &amp; Bekun, F. V.</td>
<td>2021</td>
<td>Trade openness, FDI, and income inequality: Ev...</td>
<td>African Development Review</td>
<td>https://doi.org/10.1111/1467-8268.12511</td>
<td>article</td>
<td>development</td>
<td>Angola; Benin; Botswana; Burkina Faso; Burundi...</td>
<td>2000-2015</td>
<td>...</td>
<td>potentially inequal access to education throug...</td>
<td>1.0</td>
<td>2.0</td>
<td>10.1111/1467-8268.12511</td>
<td>2021-01-01</td>
<td>42.0</td>
<td>None</td>
<td>direction::vertical,done::extracted,indicator:...</td>
<td>Sub-Saharan Africa</td>
<td>Upper middle income;Lower middle income;High i...</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">65</td>
<td>Mun2018</td>
<td>Mun, E., &amp; Jung, J.</td>
<td>2018</td>
<td>Policy generosity, employer heterogeneity, and...</td>
<td>American Sociological Review</td>
<td>https://doi.org/10.1177/0003122418772857</td>
<td>article</td>
<td>sociology</td>
<td>Japan</td>
<td>1992-2009</td>
<td>...</td>
<td>decreases may be due to supply-side mechanisms...</td>
<td>0.0</td>
<td>0.0</td>
<td>10.1177/0003122418772857</td>
<td>2018-01-01</td>
<td>14.0</td>
<td>None</td>
<td>country::Japan,done::extracted,inequality::gen...</td>
<td>East Asia &amp; Pacific</td>
<td>High income</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">66</td>
<td>Thoresen2021</td>
<td>Thoresen, S. H., Cocks, E., &amp; Parsons, R.</td>
<td>2021</td>
<td>Three year longitudinal study of graduate empl...</td>
<td>International journal of disability developmen...</td>
<td>https://doi.org/10.1080/1034912X.2019.1699648</td>
<td>article</td>
<td>education</td>
<td>Australia</td>
<td>2011-204</td>
<td>...</td>
<td>significant but small overall increase (3.1 ho...</td>
<td>1.0</td>
<td>2.0</td>
<td>10.1080/1034912X.2019.1699648</td>
<td>2021-01-01</td>
<td>2.0</td>
<td>None</td>
<td>country::Australia,done::extracted,inequality:...</td>
<td>East Asia &amp; Pacific</td>
<td>High income</td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">67</td>
<td>Thoresen2021</td>
<td>Thoresen, S. H., Cocks, E., &amp; Parsons, R.</td>
<td>2021</td>
<td>Three year longitudinal study of graduate empl...</td>
<td>International journal of disability developmen...</td>
<td>https://doi.org/10.1080/1034912X.2019.1699648</td>
<td>article</td>
<td>education</td>
<td>Australia</td>
<td>2011-204</td>
<td>...</td>
<td>strong initial diff means disability group pot...</td>
<td>1.0</td>
<td>2.0</td>
<td>10.1080/1034912X.2019.1699648</td>
<td>2021-01-01</td>
<td>2.0</td>
<td>None</td>
<td>country::Australia,done::extracted,inequality:...</td>
<td>East Asia &amp; Pacific</td>
<td>High income</td>
</tr>
<tr class="even">
<td data-quarto-table-cell-role="th">68</td>
<td>Wang2016</td>
<td>Wang, J., &amp; Van Vliet, O.</td>
<td>2016</td>
<td>Social Assistance and Minimum Income Benefits:...</td>
<td>European Journal of Social Security</td>
<td>https://doi.org/10.1177/138826271601800401</td>
<td>article</td>
<td>economics</td>
<td>global</td>
<td>1990-2009</td>
<td>...</td>
<td>bulk of increases comes from deliberate policy...</td>
<td>1.0</td>
<td>NaN</td>
<td>10.1177/138826271601800401</td>
<td>2016-01-01</td>
<td>10.0</td>
<td>None</td>
<td>done::extracted,inequality::income,region::EU,...</td>
<td>Europe &amp; Central Asia;Sub-Saharan Africa;Latin...</td>
<td></td>
</tr>
<tr class="odd">
<td data-quarto-table-cell-role="th">69</td>
<td>Wang2020</td>
<td>Wang, C., Deng, M., &amp; Deng, J.</td>
<td>2020</td>
<td>Factor reallocation and structural transformat...</td>
<td>Journal of Asian Economics</td>
<td>https://doi.org/10.1016/j.asieco.2020.101248</td>
<td>article</td>
<td>economics</td>
<td>China</td>
<td>2007-2016</td>
<td>...</td>
<td>displacement of rural unskilled labour; unskil...</td>
<td>1.0</td>
<td>2.0</td>
<td>10.1016/j.asieco.2020.101248</td>
<td>2020-01-01</td>
<td>14.0</td>
<td>None</td>
<td>country::China,done::extracted,inequality::inc...</td>
<td>East Asia &amp; Pacific</td>
<td>Upper middle income</td>
</tr>
</tbody>
</table>
<p>70 rows × 43 columns</p>
</div>
</div>
</div>
</div>
<div style="page-break-after: always;"></div>
<!-- -->
</section>
</div></section><section id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"><h2 class="anchored quarto-appendix-heading">Footnotes</h2>
<ol>
<li id="fn1"><p>The authors suggest that the negative effect for children under the long-term paid leave program of 36 months may stem from the fact that children require more external stimuli (aside from the mother) before this period ends, as well as the negative long-term effects of the mother’s significantly reduced income for the long-term leave periods.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn2"><p>The National Rural Employment Guarantee Scheme (NREGA) is a workfare programme implemented in India, the largest of its kind, which seeks to provide 100 days of employment for each household per year. It was rolled out from 2005 over several phases until it reached all districts in India in 2008.<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn3"><p>The hukou system generally denotes a permission towards either rural land-ownership and agricultural subsidies for the rural hukou or social welfare benefits and employment possibilities for the urban hukou, and children of migrants often have to go back to their place of registered residence for their college entrance examination. This study looks at reforms undoing some of the restrictions under the sytem.<a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn4"><p>The Mahatma Gandhi National Rural Employment Guarantee Scheme, one of the largest redistribution programmes on the household level in the world, entitling each household to up to 100 days of work per year.<a href="#fnref4" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn5"><p>For an overview of how retirement and pensions reflect on health aspects in ageing, see <span class="citation" data-cites="VanDerHeide2013">Van Der Heide et al. (<a href="#ref-VanDerHeide2013" role="doc-biblioref">2013</a>)</span>, for a review of pensions intersecting with other possible inequalities and also health outcomes, see <span class="citation" data-cites="Zantinge2014">Zantinge et al. (<a href="#ref-Zantinge2014" role="doc-biblioref">2014</a>)</span>.<a href="#fnref5" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn6"><p>For gender inequalities within education paths themselves, see <span class="citation" data-cites="Stepanenko2021">Stepanenko et al. (<a href="#ref-Stepanenko2021" role="doc-biblioref">2021</a>)</span>. For possible ways to integrate gender-transformative interventions into professional education, see <span class="citation" data-cites="Newman2016">Newman et al. (<a href="#ref-Newman2016" role="doc-biblioref">2016</a>)</span>. For the effects of prior inequalities on taxation preferences, school enrolment and educational choices, see <span class="citation" data-cites="Gutierrez2009">Gutierrez &amp; Tanaka (<a href="#ref-Gutierrez2009" role="doc-biblioref">2009</a>)</span> and <span class="citation" data-cites="Zamfir2017">Zamfir (<a href="#ref-Zamfir2017" role="doc-biblioref">2017</a>)</span>. For interactions between policies for the knowledge translation of sexual education and their barriers, see <span class="citation" data-cites="Curran2022">Curran et al. (<a href="#ref-Curran2022" role="doc-biblioref">2022</a>)</span>.<a href="#fnref6" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
<li id="fn7"><p>See for structural necessities <span class="citation" data-cites="Do2020">Do et al. (<a href="#ref-Do2020" role="doc-biblioref">2020</a>)</span> and <span class="citation" data-cites="Goodburn2020">Goodburn (<a href="#ref-Goodburn2020" role="doc-biblioref">2020</a>)</span>. For institutional contexts see <span class="citation" data-cites="Clibborn2022">Clibborn &amp; Wright (<a href="#ref-Clibborn2022" role="doc-biblioref">2022</a>)</span>.<a href="#fnref7" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
</ol>
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</script><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class>
<div class="sourceCode" id="cb26" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a><span class="an">papersize:</span><span class="co"> A4</span></span>
<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a><span class="an">linestretch:</span><span class="co"> 1.5</span></span>
<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a><span class="an">fontfamily:</span><span class="co"> lmodern</span></span>
<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a><span class="an">fontsize:</span><span class="co"> &quot;12&quot;</span></span>
<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a><span class="an">geometry:</span></span>
<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a><span class="co"> - left=2.2cm</span></span>
<span id="cb26-8"><a href="#cb26-8" aria-hidden="true" tabindex="-1"></a><span class="co"> - right=3.5cm</span></span>
<span id="cb26-9"><a href="#cb26-9" aria-hidden="true" tabindex="-1"></a><span class="co"> - top=2.5cm</span></span>
<span id="cb26-10"><a href="#cb26-10" aria-hidden="true" tabindex="-1"></a><span class="co"> - bottom=2.5cm</span></span>
<span id="cb26-11"><a href="#cb26-11" aria-hidden="true" tabindex="-1"></a><span class="an">lang:</span><span class="co"> en</span></span>
<span id="cb26-12"><a href="#cb26-12" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> Addressing Inequalities in the World of Work</span></span>
<span id="cb26-13"><a href="#cb26-13" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> Scoping Review on &#39;What Works&#39;</span></span>
<span id="cb26-14"><a href="#cb26-14" aria-hidden="true" tabindex="-1"></a><span class="an">crossref:</span></span>
<span id="cb26-15"><a href="#cb26-15" aria-hidden="true" tabindex="-1"></a><span class="co"> custom:</span></span>
<span id="cb26-16"><a href="#cb26-16" aria-hidden="true" tabindex="-1"></a><span class="co"> - kind: float</span></span>
<span id="cb26-17"><a href="#cb26-17" aria-hidden="true" tabindex="-1"></a><span class="co"> key: apptbl</span></span>
<span id="cb26-18"><a href="#cb26-18" aria-hidden="true" tabindex="-1"></a><span class="co"> latex-env: apptbl</span></span>
<span id="cb26-19"><a href="#cb26-19" aria-hidden="true" tabindex="-1"></a><span class="co"> reference-prefix: Table A</span></span>
<span id="cb26-20"><a href="#cb26-20" aria-hidden="true" tabindex="-1"></a><span class="co"> space-before-numbering: false</span></span>
<span id="cb26-21"><a href="#cb26-21" aria-hidden="true" tabindex="-1"></a><span class="co"> latex-list-of-description: Appendix Table</span></span>
<span id="cb26-22"><a href="#cb26-22" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
<span id="cb26-23"><a href="#cb26-23" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-26"><a href="#cb26-26" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-27"><a href="#cb26-27" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: load-data</span></span>
<span id="cb26-28"><a href="#cb26-28" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb26-29"><a href="#cb26-29" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pathlib <span class="im">import</span> Path</span>
<span id="cb26-30"><a href="#cb26-30" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> re</span>
<span id="cb26-31"><a href="#cb26-31" aria-hidden="true" tabindex="-1"></a><span class="co">## standard imports</span></span>
<span id="cb26-32"><a href="#cb26-32" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> IPython.core.display <span class="im">import</span> Markdown <span class="im">as</span> md</span>
<span id="cb26-33"><a href="#cb26-33" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
<span id="cb26-34"><a href="#cb26-34" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb26-35"><a href="#cb26-35" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> matplotlib <span class="im">import</span> pyplot <span class="im">as</span> plt</span>
<span id="cb26-36"><a href="#cb26-36" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> seaborn <span class="im">as</span> sns</span>
<span id="cb26-37"><a href="#cb26-37" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tabulate <span class="im">import</span> tabulate</span>
<span id="cb26-38"><a href="#cb26-38" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> bibtexparser</span>
<span id="cb26-39"><a href="#cb26-39" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-40"><a href="#cb26-40" aria-hidden="true" tabindex="-1"></a>sns.set_style(<span class="st">&quot;whitegrid&quot;</span>)</span>
<span id="cb26-41"><a href="#cb26-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-42"><a href="#cb26-42" aria-hidden="true" tabindex="-1"></a>DATA_DIR<span class="op">=</span>Path(<span class="st">&quot;./data&quot;</span>)</span>
<span id="cb26-43"><a href="#cb26-43" aria-hidden="true" tabindex="-1"></a>RAW_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;raw&quot;</span>)</span>
<span id="cb26-44"><a href="#cb26-44" aria-hidden="true" tabindex="-1"></a>WORKING_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;intermediate&quot;</span>)</span>
<span id="cb26-45"><a href="#cb26-45" aria-hidden="true" tabindex="-1"></a>PROCESSED_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;processed&quot;</span>)</span>
<span id="cb26-46"><a href="#cb26-46" aria-hidden="true" tabindex="-1"></a>SUPPLEMENTARY_DATA<span class="op">=</span>DATA_DIR.joinpath(<span class="st">&quot;supplementary&quot;</span>)</span>
<span id="cb26-47"><a href="#cb26-47" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-48"><a href="#cb26-48" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src <span class="im">import</span> prep_data</span>
<span id="cb26-49"><a href="#cb26-49" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-50"><a href="#cb26-50" aria-hidden="true" tabindex="-1"></a><span class="co"># raw database-search results</span></span>
<span id="cb26-51"><a href="#cb26-51" aria-hidden="true" tabindex="-1"></a>bib_sample_raw_db <span class="op">=</span> prep_data.bib_library_from_dir(RAW_DATA)</span>
<span id="cb26-52"><a href="#cb26-52" aria-hidden="true" tabindex="-1"></a><span class="co"># the complete library of sampled (and working) literature</span></span>
<span id="cb26-53"><a href="#cb26-53" aria-hidden="true" tabindex="-1"></a>bib_sample <span class="op">=</span> prep_data.bib_library_from_dir(WORKING_DATA)</span>
<span id="cb26-54"><a href="#cb26-54" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-55"><a href="#cb26-55" aria-hidden="true" tabindex="-1"></a><span class="co"># load relevant studies</span></span>
<span id="cb26-56"><a href="#cb26-56" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src <span class="im">import</span> load_data</span>
<span id="cb26-57"><a href="#cb26-57" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-58"><a href="#cb26-58" aria-hidden="true" tabindex="-1"></a>bib_df <span class="op">=</span> prep_data.observations_with_metadata_df(</span>
<span id="cb26-59"><a href="#cb26-59" aria-hidden="true" tabindex="-1"></a> raw_observations <span class="op">=</span> load_data.from_yml(PROCESSED_DATA),</span>
<span id="cb26-60"><a href="#cb26-60" aria-hidden="true" tabindex="-1"></a> study_metadata <span class="op">=</span> prep_data.bib_metadata_df(bib_sample),</span>
<span id="cb26-61"><a href="#cb26-61" aria-hidden="true" tabindex="-1"></a> country_groups <span class="op">=</span> prep_data.country_groups_df(Path(<span class="ss">f&quot;</span><span class="sc">{</span>SUPPLEMENTARY_DATA<span class="sc">}</span><span class="ss">/wb-country-groupings.xlsx&quot;</span>)),</span>
<span id="cb26-62"><a href="#cb26-62" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-63"><a href="#cb26-63" aria-hidden="true" tabindex="-1"></a>raw_observations <span class="op">=</span> <span class="va">None</span></span>
<span id="cb26-64"><a href="#cb26-64" aria-hidden="true" tabindex="-1"></a>zot_df <span class="op">=</span> <span class="va">None</span></span>
<span id="cb26-65"><a href="#cb26-65" aria-hidden="true" tabindex="-1"></a>df_country_groups <span class="op">=</span> <span class="va">None</span></span>
<span id="cb26-66"><a href="#cb26-66" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-67"><a href="#cb26-67" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-68"><a href="#cb26-68" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- pagebreak to separate from TOC --&gt;</span></span>
<span id="cb26-69"><a href="#cb26-69" aria-hidden="true" tabindex="-1"></a>{{&lt; pagebreak &gt;}}</span>
<span id="cb26-70"><a href="#cb26-70" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-71"><a href="#cb26-71" aria-hidden="true" tabindex="-1"></a>{{&lt; portrait &gt;}}</span>
<span id="cb26-72"><a href="#cb26-72" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-73"><a href="#cb26-73" aria-hidden="true" tabindex="-1"></a><span class="fu"># Introduction</span></span>
<span id="cb26-74"><a href="#cb26-74" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-75"><a href="#cb26-75" aria-hidden="true" tabindex="-1"></a>This study presents a systematic scoping review of the current literature concerning inequalities in the world of work.</span>
<span id="cb26-76"><a href="#cb26-76" aria-hidden="true" tabindex="-1"></a>It attempts to trace the main mechanisms and channels of the interventions employed in the global world of work to reduce its inequalities,</span>
<span id="cb26-77"><a href="#cb26-77" aria-hidden="true" tabindex="-1"></a>while simultaneously investigating the methodologies and indicators used in evidence-based research on them to systematically elaborate the current state of the art on inequalities in the world of work.</span>
<span id="cb26-78"><a href="#cb26-78" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-79"><a href="#cb26-79" aria-hidden="true" tabindex="-1"></a>The following section presents a typology of policies that directly or indirectly tackle inequalities in the world of work both within the labour market and outside this domain (e.g. education policy).</span>
<span id="cb26-80"><a href="#cb26-80" aria-hidden="true" tabindex="-1"></a>The section also makes an attempt to clearly identify the theoretical mechanisms and channels through which policies are expected to impact inequalities in forms of work and ultimate labour market outcomes.</span>
<span id="cb26-81"><a href="#cb26-81" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-82"><a href="#cb26-82" aria-hidden="true" tabindex="-1"></a>The ILO has a policy approach to reducing inequalities in the world of work segmented into five major focus areas:</span>
<span id="cb26-83"><a href="#cb26-83" aria-hidden="true" tabindex="-1"></a>employment creation, access to education, labour rights protection, formalization, gender equality and diversity, and social protection <span class="co">[</span><span class="ot">@ILO2022b</span><span class="co">]</span>.</span>
<span id="cb26-84"><a href="#cb26-84" aria-hidden="true" tabindex="-1"></a>Each of these areas in turn rests on a variety of more specific emphases which further describe the potential implemented policy measures.</span>
<span id="cb26-85"><a href="#cb26-85" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-86"><a href="#cb26-86" aria-hidden="true" tabindex="-1"></a>The rest of the study is structured as follows:</span>
<span id="cb26-87"><a href="#cb26-87" aria-hidden="true" tabindex="-1"></a>Section 2 will introduce the world of work, as well as the ILO&#39;s approach to inequalities within it, and provide a variety of other recent approaches to make sense of inequalities in the world of work.</span>
<span id="cb26-88"><a href="#cb26-88" aria-hidden="true" tabindex="-1"></a>Section 3 will then introduce the method applied in the scoping review of this study, before introducing the initial identified literature as a coherent sample.</span>
<span id="cb26-89"><a href="#cb26-89" aria-hidden="true" tabindex="-1"></a>Section 4 will synthesize findings on a variety of intervention found in the literature, organized by general policy area of intervention pursued.</span>
<span id="cb26-90"><a href="#cb26-90" aria-hidden="true" tabindex="-1"></a>Section 5 will then provide a brief discussion on these findings from the perspective of individual inequalities, the interventions found to reduce them, and resulting policy implications, before Section 6 briefly concludes.</span>
<span id="cb26-91"><a href="#cb26-91" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-92"><a href="#cb26-92" aria-hidden="true" tabindex="-1"></a><span class="fu"># The world of work</span></span>
<span id="cb26-93"><a href="#cb26-93" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-94"><a href="#cb26-94" aria-hidden="true" tabindex="-1"></a>The policy areas and their respective focus perspectives are based in the conceptual understanding of the world of work, following the definition of work being activities performed by persons of any sex and age producing goods or providing services for &quot;economic units <span class="co">[</span><span class="ot">which</span><span class="co">]</span> can be allocated mutually exclusively to one of the following sectors:&quot; the formal sector, the informal sector, or the community and household own-use sector <span class="co">[</span><span class="ot">@ILO2023c, 6</span><span class="co">]</span>.</span>
<span id="cb26-95"><a href="#cb26-95" aria-hidden="true" tabindex="-1"></a>This is the broader understanding of work which specifically separates itself from a more narrow conception of those in employment who are engaging in &quot;production for pay or profit&quot;, whether for the informal or the formal market economy <span class="co">[</span><span class="ot">see especially @ILO2023c, Point 18ff</span><span class="co">]</span>.</span>
<span id="cb26-96"><a href="#cb26-96" aria-hidden="true" tabindex="-1"></a>The key differentiations for these concepts are founded on an understanding of the production of goods or provision of services, as well as the distinctions between use by others for ultimate own-use and that of working for pay and/or profit – that is, as part of a market transaction in exchange for remuneration or in the form of profits derived from the goods or services.</span>
<span id="cb26-97"><a href="#cb26-97" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-98"><a href="#cb26-98" aria-hidden="true" tabindex="-1"></a>Whether these services or goods are produced in what is defined as the informal economy, the formal economy or under informal employment outside the informal sector is, for the general encapsulation of no importance – they occur in the world of work.</span>
<span id="cb26-99"><a href="#cb26-99" aria-hidden="true" tabindex="-1"></a>Here, conceptually, it should be captured under one of the five mutually exclusive forms of work <span class="co">[</span><span class="ot">@ILO2023, p. 4, Point 7c</span><span class="co">]</span> to be understood as: own-use production work, performing &quot;any activity to produce goods or provide services for own final use&quot; <span class="co">[</span><span class="ot">@ILO2013, p.5</span><span class="co">]</span>; employment work comprising those performing work for others in exchange for pay or profit introduced above; unpaid trainee work, performing &quot;any unpaid activity to produce goods or provide services <span class="co">[</span><span class="ot">…</span><span class="co">]</span> to acquire workplace experience or skills&quot; <span class="co">[</span><span class="ot">@ILO2013, p.7</span><span class="co">]</span>; and volunteer work, that being &quot;any unpaid, non-compulsory activity to produce goods or provide services for others&quot; <span class="co">[</span><span class="ot">@ILO2013, p.8</span><span class="co">]</span>.</span>
<span id="cb26-100"><a href="#cb26-100" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-101"><a href="#cb26-101" aria-hidden="true" tabindex="-1"></a>Any activity falling under work as defined above on the one hand, but not under any of these forms of work on the other, is instead designated as other work activities in the following considerations. The key concepts between these categories come down to a varying intensity of participation, the distinction of working for pay and/or profit mentioned above, whether it is for ultimate own-use or the use by others, and its compulsory nature.</span>
<span id="cb26-102"><a href="#cb26-102" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-103"><a href="#cb26-103" aria-hidden="true" tabindex="-1"></a>The ILO has a policy approach to reducing inequalities in the world of work segmented into five major focus areas: employment creation, access to education, labour rights protection, formalization, gender equality and diversity, and social protection.</span>
<span id="cb26-104"><a href="#cb26-104" aria-hidden="true" tabindex="-1"></a>Each of these areas in turn rests on a variety of more specific emphases which further describe the potential implemented policy measures.</span>
<span id="cb26-105"><a href="#cb26-105" aria-hidden="true" tabindex="-1"></a>An exemplary typology of general policy area, related specified policy focus and related focus if any can be found in @tbl-policy-areas.</span>
<span id="cb26-106"><a href="#cb26-106" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-107"><a href="#cb26-107" aria-hidden="true" tabindex="-1"></a>::: {#tbl-policy-areas}</span>
<span id="cb26-108"><a href="#cb26-108" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-109"><a href="#cb26-109" aria-hidden="true" tabindex="-1"></a>| area of policy | focus | related |</span>
<span id="cb26-110"><a href="#cb26-110" aria-hidden="true" tabindex="-1"></a>|-------------------------:|----------------------------------------------------|----------------------------------|</span>
<span id="cb26-111"><a href="#cb26-111" aria-hidden="true" tabindex="-1"></a>| employment creation | pro-employment framework | |</span>
<span id="cb26-112"><a href="#cb26-112" aria-hidden="true" tabindex="-1"></a>| | gender-transformative framework | |</span>
<span id="cb26-113"><a href="#cb26-113" aria-hidden="true" tabindex="-1"></a>| | promotion of business sustainability | productivity increases |</span>
<span id="cb26-114"><a href="#cb26-114" aria-hidden="true" tabindex="-1"></a>| | | reduction in productivity gaps |</span>
<span id="cb26-115"><a href="#cb26-115" aria-hidden="true" tabindex="-1"></a>| | promotion of digital infrastructure | technology for decent work |</span>
<span id="cb26-116"><a href="#cb26-116" aria-hidden="true" tabindex="-1"></a>| | | reducing digital divide |</span>
<span id="cb26-117"><a href="#cb26-117" aria-hidden="true" tabindex="-1"></a>| access to education | quality of education/training/skills development | green transition |</span>
<span id="cb26-118"><a href="#cb26-118" aria-hidden="true" tabindex="-1"></a>| | relevance of education/training/skills development | digital transition |</span>
<span id="cb26-119"><a href="#cb26-119" aria-hidden="true" tabindex="-1"></a>| | gender-transformative career guidance | |</span>
<span id="cb26-120"><a href="#cb26-120" aria-hidden="true" tabindex="-1"></a>| | improvements of public services/social protection | |</span>
<span id="cb26-121"><a href="#cb26-121" aria-hidden="true" tabindex="-1"></a>| | work-life balance | juggle paid work and family care |</span>
<span id="cb26-122"><a href="#cb26-122" aria-hidden="true" tabindex="-1"></a>| | targeted support for disadvantaged groups | targeted education |</span>
<span id="cb26-123"><a href="#cb26-123" aria-hidden="true" tabindex="-1"></a>| labour rights protection | promotion of rights for all workers | collective bargaining systems |</span>
<span id="cb26-124"><a href="#cb26-124" aria-hidden="true" tabindex="-1"></a>| | minimum wage | |</span>
<span id="cb26-125"><a href="#cb26-125" aria-hidden="true" tabindex="-1"></a>| | inclusive labour market institutions | |</span>
<span id="cb26-126"><a href="#cb26-126" aria-hidden="true" tabindex="-1"></a>| | equal pay for work of equal value | |</span>
<span id="cb26-127"><a href="#cb26-127" aria-hidden="true" tabindex="-1"></a>| | wage transparency | |</span>
<span id="cb26-128"><a href="#cb26-128" aria-hidden="true" tabindex="-1"></a>| formalization | equality-driven approach to formalization | gender-responsive |</span>
<span id="cb26-129"><a href="#cb26-129" aria-hidden="true" tabindex="-1"></a>| | increase decent work in formal economy | country-tailored |</span>
<span id="cb26-130"><a href="#cb26-130" aria-hidden="true" tabindex="-1"></a>| | absorb informal workers / economic units | comprehensive |</span>
<span id="cb26-131"><a href="#cb26-131" aria-hidden="true" tabindex="-1"></a>| | | non-discriminatory |</span>
<span id="cb26-132"><a href="#cb26-132" aria-hidden="true" tabindex="-1"></a>| gender equality | removal of discriminatory practice | removal of stereotypes |</span>
<span id="cb26-133"><a href="#cb26-133" aria-hidden="true" tabindex="-1"></a>| diversity | promotion of equality of treatment | removal of discriminatory law |</span>
<span id="cb26-134"><a href="#cb26-134" aria-hidden="true" tabindex="-1"></a>| | promotion of equality of opportunity | |</span>
<span id="cb26-135"><a href="#cb26-135" aria-hidden="true" tabindex="-1"></a>| | data collection improvements | gender-focus |</span>
<span id="cb26-136"><a href="#cb26-136" aria-hidden="true" tabindex="-1"></a>| | occupational gender segregation | age-focus |</span>
<span id="cb26-137"><a href="#cb26-137" aria-hidden="true" tabindex="-1"></a>| | unequal pay for work of equal value | disability-focus |</span>
<span id="cb26-138"><a href="#cb26-138" aria-hidden="true" tabindex="-1"></a>| | gender-based violence | race-focus |</span>
<span id="cb26-139"><a href="#cb26-139" aria-hidden="true" tabindex="-1"></a>| | gender-based harassment | ethnicity-focus |</span>
<span id="cb26-140"><a href="#cb26-140" aria-hidden="true" tabindex="-1"></a>| | gender unequal division of unpaid care work | migrant status-focus |</span>
<span id="cb26-141"><a href="#cb26-141" aria-hidden="true" tabindex="-1"></a>| social protection | extend reach of social protection schemes | |</span>
<span id="cb26-142"><a href="#cb26-142" aria-hidden="true" tabindex="-1"></a>| | reach those not adequately protected | |</span>
<span id="cb26-143"><a href="#cb26-143" aria-hidden="true" tabindex="-1"></a>| | ensure access to social protection | comprehensive social protection |</span>
<span id="cb26-144"><a href="#cb26-144" aria-hidden="true" tabindex="-1"></a>| | | adequate social protection |</span>
<span id="cb26-145"><a href="#cb26-145" aria-hidden="true" tabindex="-1"></a>| | | sustainable social protection |</span>
<span id="cb26-146"><a href="#cb26-146" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-147"><a href="#cb26-147" aria-hidden="true" tabindex="-1"></a>Source: Authors&#39; elaboration based on ILO <span class="co">[</span><span class="ot">-@ILO2022b</span><span class="co">]</span>.</span>
<span id="cb26-148"><a href="#cb26-148" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-149"><a href="#cb26-149" aria-hidden="true" tabindex="-1"></a>ILO focus areas for inequality reduction</span>
<span id="cb26-150"><a href="#cb26-150" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-151"><a href="#cb26-151" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-152"><a href="#cb26-152" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-153"><a href="#cb26-153" aria-hidden="true" tabindex="-1"></a><span class="fu">## Inequalities in the world of work</span></span>
<span id="cb26-154"><a href="#cb26-154" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-155"><a href="#cb26-155" aria-hidden="true" tabindex="-1"></a>Inequalities in the world of work have to be fundamentally conceptualized along two axes: On the one hand, vertical inequality captures the &quot;income inequality between all households in a country&quot; <span class="co">[</span><span class="ot">@ILO2021</span><span class="co">]</span>.</span>
<span id="cb26-156"><a href="#cb26-156" aria-hidden="true" tabindex="-1"></a>Measurements of vertical inequalities is a perspective which focuses primarily on incomes as data, with debate of top income percentiles versus the remaining body of people often posing the primary area of debate <span class="co">[</span><span class="ot">@ILO2021a</span><span class="co">]</span>.</span>
<span id="cb26-157"><a href="#cb26-157" aria-hidden="true" tabindex="-1"></a>Horizontal inequalities, on the other hand, occur when &quot;some groups within the population find themselves disadvantaged and discriminated against on the basis of their visible identity, for example their gender, colour or beliefs, among others&quot; <span class="co">[</span><span class="ot">@ILO2021a</span><span class="co">]</span>.</span>
<span id="cb26-158"><a href="#cb26-158" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-159"><a href="#cb26-159" aria-hidden="true" tabindex="-1"></a>Importantly, these inequalities do not act in a vacuum but create an interplay through overlaps and accumulations which take on their own driving dynamics for people belonging to multiple disadvantaged groups, captured in the idea of inequality’s intersectionality <span class="co">[</span><span class="ot">@ILO2022b</span><span class="co">]</span>.</span>
<span id="cb26-160"><a href="#cb26-160" aria-hidden="true" tabindex="-1"></a>Here, especially horizontal inequalities may be hard to disentangle for impact finding, an important aspect of effective rigorous analysis in quantitative studies.</span>
<span id="cb26-161"><a href="#cb26-161" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-162"><a href="#cb26-162" aria-hidden="true" tabindex="-1"></a>Thus, for a study on inequalities, or in turn a study on policies aimed at reducing inequalities in the world of work to be one of rigorous analysis, it must clearly define the type of policy taken as its object of analysis (its independent variable) as well as the types of inequalities targeted for reduction through the respective policy and measured as channels of impact.</span>
<span id="cb26-163"><a href="#cb26-163" aria-hidden="true" tabindex="-1"></a>Ultimately, then, the individual outcome measures need to be clearly specified and disentangled, most clearly reflecting in labour market outcome measures (dependent variables).</span>
<span id="cb26-164"><a href="#cb26-164" aria-hidden="true" tabindex="-1"></a>Only then can the targeted inequality be delineated as a clear channel.</span>
<span id="cb26-165"><a href="#cb26-165" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-166"><a href="#cb26-166" aria-hidden="true" tabindex="-1"></a>In targeting an increase in equality, there are then two approaches to take: either levelling the playing field so that characteristics beyond an individual’s control can not influence their future perspectives, nor limit the potential of the powers they possess, through achieving equality of opportunity; or strive for an equality of outcomes, in factual observed resulting (in-)equalities.</span>
<span id="cb26-167"><a href="#cb26-167" aria-hidden="true" tabindex="-1"></a>As the ILO established, such a focus on equality of outcomes can be of great importance since &quot;high levels of inequality today tend to reduce social mobility tomorrow&quot; <span class="co">[</span><span class="ot">@ILO2021a</span><span class="co">]</span>, making it that much more difficult to ultimately ensure equality of opportunity for following generation.</span>
<span id="cb26-168"><a href="#cb26-168" aria-hidden="true" tabindex="-1"></a>The key concepts here are thus the distinction of within-group and between-group inequalities, their overlapping characteristics, as well as policies enabling an equality of opportunity or of outcome.</span>
<span id="cb26-169"><a href="#cb26-169" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-170"><a href="#cb26-170" aria-hidden="true" tabindex="-1"></a>Income inequality is still the primary lens of inequality that many approaches target, as well as the main focus point of many inequality measurements such as the Gini coefficient or ratios such as the Palma ratio <span class="co">[</span><span class="ot">@DFI2023</span><span class="co">]</span>.</span>
<span id="cb26-171"><a href="#cb26-171" aria-hidden="true" tabindex="-1"></a>Following the ILO, &quot;labour income is the main source of income for most households in the world <span class="co">[</span><span class="ot">thus</span><span class="co">]</span> unequal access to work and working poverty are major drivers of inequalities&quot; <span class="co">[</span><span class="ot">@ILO2021</span><span class="co">]</span>.</span>
<span id="cb26-172"><a href="#cb26-172" aria-hidden="true" tabindex="-1"></a>Income inequality, here, can be affected by a wide set of factors: status in employment, forms of work, the sector of activity, the respective occupation, type of enterprise, type of contract for those in waged work, and the status of formality among others <span class="co">[</span><span class="ot">@ILO2019</span><span class="co">]</span>.</span>
<span id="cb26-173"><a href="#cb26-173" aria-hidden="true" tabindex="-1"></a>Income inequality should also not be seen as separate from, nor standing above, other inequalities, but closely linked to other inequalities.</span>
<span id="cb26-174"><a href="#cb26-174" aria-hidden="true" tabindex="-1"></a>As the ILO states, &quot;income inequality, inequality of employment outcomes more generally and inequality of opportunities are intimately related&quot; <span class="co">[</span><span class="ot">@ILO2022b</span><span class="co">]</span>.</span>
<span id="cb26-175"><a href="#cb26-175" aria-hidden="true" tabindex="-1"></a>At the same time the exact linkages of these factors remain under-analysed, which is the reason why the channels of inequalities and the policies to reduce them will pose a fruitful space of analysis for this research.</span>
<span id="cb26-176"><a href="#cb26-176" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-177"><a href="#cb26-177" aria-hidden="true" tabindex="-1"></a>While income inequality holds a primary position of importance for many analyses from a perspective of quantity, it should not be understood as carrying more importance qualitatively for itself compared to other inequalities but rather be understood &quot;like a prism, which reveals many other forms of inequality, including those generated in the world of work&quot; <span class="co">[</span><span class="ot">@ILO2021a, p. 13</span><span class="co">]</span>.</span>
<span id="cb26-178"><a href="#cb26-178" aria-hidden="true" tabindex="-1"></a>It is the primary measure of vertical inequality, however, with other inequalities describing primarily the concept of horizontal inequality.</span>
<span id="cb26-179"><a href="#cb26-179" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-180"><a href="#cb26-180" aria-hidden="true" tabindex="-1"></a>Here, of primary focus for the ILO, and many studies on inequality in the world of work, is gender inequality.</span>
<span id="cb26-181"><a href="#cb26-181" aria-hidden="true" tabindex="-1"></a>It describes the inequalities that arise because of an individual’s gender.</span>
<span id="cb26-182"><a href="#cb26-182" aria-hidden="true" tabindex="-1"></a>Generally, while the type and extend of other inequalities does vary substantially by global location and country, &quot;gender inequalities, despite some progress over the past decades, remain persistent and pervasive&quot; <span class="co">[</span><span class="ot">@ILO2021</span><span class="co">]</span>.</span>
<span id="cb26-183"><a href="#cb26-183" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-184"><a href="#cb26-184" aria-hidden="true" tabindex="-1"></a>Following a report on the gendered make-up of work globally, women are making up a larger part of those in underemployment, they primarily make up the service sector – a rising trend – while suffering a persistently substantial wage gap, tend to work shorter hours in employment but in turn have longer working days when including unpaid work, as well as contributing disproportionally to family work <span class="co">[</span><span class="ot">@ILO2016</span><span class="co">]</span>.</span>
<span id="cb26-185"><a href="#cb26-185" aria-hidden="true" tabindex="-1"></a>The domestic area of work is also dominated by women, who make up 76.2 per cent of it, in addition to domestic work being overwhelmingly informal labour globally <span class="co">[</span><span class="ot">@ILO2023a</span><span class="co">]</span>.</span>
<span id="cb26-186"><a href="#cb26-186" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-187"><a href="#cb26-187" aria-hidden="true" tabindex="-1"></a>These inequalities in the world of work in turn also reflect in women being hindered in accessing adequate education, training, as well as the possibility for lifelong learning, and furthermore access to quality jobs, housing, mobility, capital, land, and adequate social protection – disparities which, on the basis of deeply rooted inequalities of gender roles, education and places of residence remain largely static if not on the rise.</span>
<span id="cb26-188"><a href="#cb26-188" aria-hidden="true" tabindex="-1"></a>These channels and outcomes, viewed intersectionally, must thus represent the primary lens through which to disentangle the gender inequality in the world of work today.</span>
<span id="cb26-189"><a href="#cb26-189" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-190"><a href="#cb26-190" aria-hidden="true" tabindex="-1"></a>There are additional socio-demographic inequalities beyond gender which are based on the innate, most often visible, identification of a person.</span>
<span id="cb26-191"><a href="#cb26-191" aria-hidden="true" tabindex="-1"></a>These are made up of, though not limited to, ethnic and racial inequalities, those based on religion and beliefs, based on a person’s status as a migrant, a person’s age, sex, or disabilities <span class="co">[</span><span class="ot">@ILO2021a</span><span class="co">]</span>.</span>
<span id="cb26-192"><a href="#cb26-192" aria-hidden="true" tabindex="-1"></a>For example, young people generally fare significantly worse in labour markets shown through outcomes such as a higher incidence of temporary employment throughout youth and the young labour force <span class="co">[</span><span class="ot">@ILO2023b; @ILO2019</span><span class="co">]</span>.</span>
<span id="cb26-193"><a href="#cb26-193" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-194"><a href="#cb26-194" aria-hidden="true" tabindex="-1"></a>As a report on the global conditions of work established, over &quot;7% of workers felt they had been discriminated against in the 12 months prior to the survey on grounds of sex, race, religion, age, nationality, disability or sexual orientation&quot; <span class="co">[</span><span class="ot">@ILO2019</span><span class="co">]</span> in the EU alone, making socio-demographic inequalities a prevalent and important to tackle angle of horizontal inequality.</span>
<span id="cb26-195"><a href="#cb26-195" aria-hidden="true" tabindex="-1"></a>Here, it will be especially important to correctly disentangle individual sources or contributing characteristics from each other in finding their linkages to relevant outcomes.</span>
<span id="cb26-196"><a href="#cb26-196" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-197"><a href="#cb26-197" aria-hidden="true" tabindex="-1"></a>Another form of inequality are spatial inequalities, those that arise because of an individual’s location relative to other.</span>
<span id="cb26-198"><a href="#cb26-198" aria-hidden="true" tabindex="-1"></a>These inequalities exist primarily between different regions of a country: those between urbanity and rurality or more peripheral areas, but also between richer and poorer regions and, as the ILO established, can even lead to a ‘growing sense of fractured societies’ <span class="co">[</span><span class="ot">@ILO2021</span><span class="co">]</span>.</span>
<span id="cb26-199"><a href="#cb26-199" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-200"><a href="#cb26-200" aria-hidden="true" tabindex="-1"></a>One of the channels this can manifest itself is through an unequal access to decent work opportunities or economic opportunities more generally, an unequal access to financial resources, quality public services or even overall access to an essential social service infrastructure and digital infrastructure, as well as quality access to education or relevant training.</span>
<span id="cb26-201"><a href="#cb26-201" aria-hidden="true" tabindex="-1"></a>For spatial inequalities it will be especially important to take note of locally bound differences versus more generalizable results, with the dimensions and contributing factors to its outcomes potentially varying widely between different geographies and national contexts.</span>
<span id="cb26-202"><a href="#cb26-202" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-203"><a href="#cb26-203" aria-hidden="true" tabindex="-1"></a>In mentioning unequal access to quality education or public infrastructure another important dimension of inequalities becomes highlighted: the dimension of pre-existing inequalities, that is, inequalities which exist prior to an individual’s interaction with the labour market and, though closely intertwined with socio-demographic inequalities, will prove useful to analytically differentiate between.</span>
<span id="cb26-204"><a href="#cb26-204" aria-hidden="true" tabindex="-1"></a>A differentiation which becomes especially important with a view on the inter-generational effects of inequality, highlighted in recognizing the difference between equality of opportunity and outcome.</span>
<span id="cb26-205"><a href="#cb26-205" aria-hidden="true" tabindex="-1"></a>The level of education, an individual’s poverty, productivity on the labour market and similar inequalities in opportunities are often the result of long-running pre-existing inequalities such as unequal access to health services, education, lacking property rights or clear ownership of assets, the lack of formal recognition as an individual, no access to formal banking <span class="co">[</span><span class="ot">@ILO2021a</span><span class="co">]</span>.</span>
<span id="cb26-206"><a href="#cb26-206" aria-hidden="true" tabindex="-1"></a>Understanding such channels becomes difficult if not taking pre-existing inequalities into account as a separate category of inequality and long-term impacting channel.</span>
<span id="cb26-207"><a href="#cb26-207" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-208"><a href="#cb26-208" aria-hidden="true" tabindex="-1"></a>Addressing these inequalities, in turn, is just as important to reducing inequalities within the labour market (as well as beyond) since they do play such a role for intergenerational social mobility and their impacts can be seen, once again, reflecting in the prism of subsequent income inequality.</span>
<span id="cb26-209"><a href="#cb26-209" aria-hidden="true" tabindex="-1"></a>For pre-existing inequalities, it will be especially important to understand the often delayed and more opaque nature of the roots of many outcomes, with channel being more difficult to identify and clearly label – especially in an intersectional context.</span>
<span id="cb26-210"><a href="#cb26-210" aria-hidden="true" tabindex="-1"></a>These five dimensions of inequalities --- income inequality, gender inequality, socio-demographic inequality, spatial inequality and pre-existing inequalities --- will thus provide the categorical anchors along which the reviewed studies will be analysed for their policy effects, each with a slightly different focus in linkages between inequality, policy and outcome.</span>
<span id="cb26-211"><a href="#cb26-211" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-212"><a href="#cb26-212" aria-hidden="true" tabindex="-1"></a><span class="fu">## Existing reviews: alternative approaches</span></span>
<span id="cb26-213"><a href="#cb26-213" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-214"><a href="#cb26-214" aria-hidden="true" tabindex="-1"></a>Aside from the general typology by the ILO introduced above, there are a variety of differing approaches to the interplay of inequalities and outcomes,</span>
<span id="cb26-215"><a href="#cb26-215" aria-hidden="true" tabindex="-1"></a>outlined in the following section.</span>
<span id="cb26-216"><a href="#cb26-216" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-217"><a href="#cb26-217" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- gender/pre-existing --&gt;</span></span>
<span id="cb26-218"><a href="#cb26-218" aria-hidden="true" tabindex="-1"></a>Chaudhuri et al. <span class="co">[</span><span class="ot">-@Chaudhuri2021</span><span class="co">]</span> conduct a systematic review to look at coping strategies and the effects of food insecurity, often through poverty, on social and health outcomes for women and children.</span>
<span id="cb26-219"><a href="#cb26-219" aria-hidden="true" tabindex="-1"></a>They find that one of the primary non-food coping strategies for women is to look for outdoor employment, mostly farm work, which can in turn lead to what the authors argue as *time* poverty when their time for childcare or personal nutrition is now cut short.</span>
<span id="cb26-220"><a href="#cb26-220" aria-hidden="true" tabindex="-1"></a>This in turn can, in combination with food-based coping strategies such as food rationing (in size or frequency), nutritional switches or food sharing, lead to negative health outcomes for children including disrupted socio-cognitive development as well as coping through dropping out of school, thereby furthering the rift of pre-existing inequalities.</span>
<span id="cb26-221"><a href="#cb26-221" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-222"><a href="#cb26-222" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- gender --&gt;</span></span>
<span id="cb26-223"><a href="#cb26-223" aria-hidden="true" tabindex="-1"></a>Finlay <span class="co">[</span><span class="ot">-@Finlay2021</span><span class="co">]</span> looks at the effects of female women&#39;s reproductive health on female labour force participation, especially career advancement, job quality and hours worked, to find a variety of responses differing between low-income, middle-income and high-income countries.</span>
<span id="cb26-224"><a href="#cb26-224" aria-hidden="true" tabindex="-1"></a>The main findings are that in low-income countries because of the prevalence of informal work, women are forced to adopt individual strategies of balancing child rearing and labour force participation through job type selection, reliance on other women in the household for child care, or birth spacing.</span>
<span id="cb26-225"><a href="#cb26-225" aria-hidden="true" tabindex="-1"></a>In middle-income countries, women have to juggle child rearing and labour force participation with an overall income inequality; here, early childbearing or lone motherhood especially can perpetuate poverty.</span>
<span id="cb26-226"><a href="#cb26-226" aria-hidden="true" tabindex="-1"></a>In high-income countries, social protection policies can assist in balancing child rearing and work but many underlying issues of gender inequality remain.</span>
<span id="cb26-227"><a href="#cb26-227" aria-hidden="true" tabindex="-1"></a>Throughout all countries, childbearing significantly interrupts career advancement.</span>
<span id="cb26-228"><a href="#cb26-228" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-229"><a href="#cb26-229" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- gender --&gt;</span></span>
<span id="cb26-230"><a href="#cb26-230" aria-hidden="true" tabindex="-1"></a>Chang et al. <span class="co">[</span><span class="ot">-@Chang2021</span><span class="co">]</span> use a qualitative systematic review to look at the linkages of breast-feeding and returning to paid employment for women and identify multiple barriers provided through inequalities discouraging continued breast-feeding after return to employment --- an experience often experienced as physically and emotionally difficult and potentially providing a barrier to full labour force participation.</span>
<span id="cb26-231"><a href="#cb26-231" aria-hidden="true" tabindex="-1"></a>Aside from individual motivation and support from employers, colleagues, and family members, women highlighted the importance of having workplace legislation in place to facilitate breast-feeding during employment, as well as access to convenient child care.</span>
<span id="cb26-232"><a href="#cb26-232" aria-hidden="true" tabindex="-1"></a>The review concludes indicating remaining gender and employment inequalities in accessing and receiving the support needed: gender role expectations viewing women as responsible for domestic work or childcare, with shorter maternity leave further discouraging breast-feeding especially of women not in managerial roles.</span>
<span id="cb26-233"><a href="#cb26-233" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-234"><a href="#cb26-234" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- policy interv --&gt;</span></span>
<span id="cb26-235"><a href="#cb26-235" aria-hidden="true" tabindex="-1"></a>Looking strictly at the impact of basic income interventions on labour market, health, educational, housing and other outcomes, Pinto et al. <span class="co">[</span><span class="ot">-@Pinto2021</span><span class="co">]</span> find that, while workforce participation is the primary outcome in most studies, the evaluations have shifted over time to include a wider array of outcomes, perhaps reflecting an understanding of lower health and social care spending offsetting some of the basic income investments.</span>
<span id="cb26-236"><a href="#cb26-236" aria-hidden="true" tabindex="-1"></a>Most of the studies investigating basic income perspectives focus on advanced economies such as the US.</span>
<span id="cb26-237"><a href="#cb26-237" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-238"><a href="#cb26-238" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- disability --&gt;</span></span>
<span id="cb26-239"><a href="#cb26-239" aria-hidden="true" tabindex="-1"></a>Undertaking a systematic review to find the effects of brain tumours in individuals on their labour market outcomes, Silvaggi <span class="co">[</span><span class="ot">-@Silvaggi2020</span><span class="co">]</span> find an impact of neuropsychological functioning on work productivity, issues for their process of returning to work, and often an exit from employment (job loss) for long-term survivors of brain tumours</span>
<span id="cb26-240"><a href="#cb26-240" aria-hidden="true" tabindex="-1"></a>While the channels are primarily viewed as stemming from the high short-term mortality and depressive symptoms or cognitive deficits, environmental barriers are identified as one channel as well, with the review ending in the policy recommendation of increased vocational rehabilitation for affected persons.</span>
<span id="cb26-241"><a href="#cb26-241" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-242"><a href="#cb26-242" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- basic income --&gt;</span></span>
<span id="cb26-243"><a href="#cb26-243" aria-hidden="true" tabindex="-1"></a>De Paz-Banez et al. <span class="co">[</span><span class="ot">-@dePaz-Banez2020</span><span class="co">]</span> use a systematic review of empirical studies to look at the effects of universal basic income on labour supply to find that, with no evidence of significant reductions in labour supply, instead the labour supply would increase globally among adults, men, women, young and old.</span>
<span id="cb26-244"><a href="#cb26-244" aria-hidden="true" tabindex="-1"></a>The insignificant reductions they found they assumed functional, since they were in the categories of: children, elderly, sick, people with disabilities, women with young children, young people continuing their studies and were offset by the otherwise increased supply.</span>
<span id="cb26-245"><a href="#cb26-245" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-246"><a href="#cb26-246" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- disabilities, gender --&gt;</span></span>
<span id="cb26-247"><a href="#cb26-247" aria-hidden="true" tabindex="-1"></a>Looking at the impact of gender on the employment outcomes for young disabled adults, Lindsay et al. <span class="co">[</span><span class="ot">-@Lindsay2018</span><span class="co">]</span> find that while youth with disabilities are half as likely to be employed, gender inequalities may play a compounding role with men being more likely to be in employment than women, working longer hours and having higher wages.</span>
<span id="cb26-248"><a href="#cb26-248" aria-hidden="true" tabindex="-1"></a>The identified channels here are different social supports, gender role expectations, as well as women&#39;s lower job expectations and overprotection from parents or guardians discouraging their independence.</span>
<span id="cb26-249"><a href="#cb26-249" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-250"><a href="#cb26-250" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- gender --&gt;</span></span>
<span id="cb26-251"><a href="#cb26-251" aria-hidden="true" tabindex="-1"></a>Kumari <span class="co">[</span><span class="ot">-@Kumari2018</span><span class="co">]</span> looks at the relationship of both economic growth and gender disparity on the labour supply in investigating their effects on female work participation.</span>
<span id="cb26-252"><a href="#cb26-252" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">TODO</span><span class="co"> explain U-shape --&gt;</span></span>
<span id="cb26-253"><a href="#cb26-253" aria-hidden="true" tabindex="-1"></a>They see a U-shaped participation rate and some evidence of cross-sector gender pay disparity which is affected by demographic factors such as migration, marriage, child care and fertility, as well as economic factors such as per capita income, unemployment, infrastructure and the prevalence of non-farm jobs.</span>
<span id="cb26-254"><a href="#cb26-254" aria-hidden="true" tabindex="-1"></a>Ultimately, they argue that the labour supply inequalities are based on inequality between the sexes and, while regulatory measures such as adequate family and childcare policies, tax regimes and the presence of subsidized healthcare help, changes to the female labour force participation fundamentally require the replacement of such a traditional value system itself.</span>
<span id="cb26-255"><a href="#cb26-255" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-256"><a href="#cb26-256" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- income --&gt;</span></span>
<span id="cb26-257"><a href="#cb26-257" aria-hidden="true" tabindex="-1"></a>While undertaking a systematic review concerning the effects of adopting technology on employment in LICs or LMICs, Ugur and Mitra <span class="co">[</span><span class="ot">-@Ugur2017</span><span class="co">]</span> find when adoption favours product innovation positive effects are somewhat likely.</span>
<span id="cb26-258"><a href="#cb26-258" aria-hidden="true" tabindex="-1"></a>They also find, however, that existing income inequalities can make the possible positive effects of its adoption more ambiguous and may in turn widen the rift of demand for skilled versus unskilled labour.</span>
<span id="cb26-259"><a href="#cb26-259" aria-hidden="true" tabindex="-1"></a>Lastly, policies favouring green transition technologies may in turn reduce income inequality, providing another possible linkage.</span>
<span id="cb26-260"><a href="#cb26-260" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-261"><a href="#cb26-261" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- disability --&gt;</span></span>
<span id="cb26-262"><a href="#cb26-262" aria-hidden="true" tabindex="-1"></a>Lettieri and Diez Villoria <span class="co">[</span><span class="ot">-@Lettieri2017</span><span class="co">]</span> find that hiding mental illness is one of the primary strategies for improved employment outcomes in a meta-review looking at barriers to labour market inclusion for people mental disabilities.</span>
<span id="cb26-263"><a href="#cb26-263" aria-hidden="true" tabindex="-1"></a>This act of concealment of identity and self-stigmatization can seem necessary, they argue, due to the channels of workplace prejudices, perceiving them missing skills, as dangerous or unpredictable, or seeing the act of their hiring as charity due to expectations of lower productivity; but also due to discriminatory hiring practices and pre-existing inequalities leading to them being lower-skilled individuals due to prior discrimination, cultural and social barriers to training and work inclusion.</span>
<span id="cb26-264"><a href="#cb26-264" aria-hidden="true" tabindex="-1"></a>Here, relevant policies include interventions of supported employment (removing an environmental barrier), cognitive behavioural or computer-assisted therapies (cognitive barrier) or vocational rehabilitation programmes (human capital).</span>
<span id="cb26-265"><a href="#cb26-265" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-266"><a href="#cb26-266" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- gender --&gt;</span></span>
<span id="cb26-267"><a href="#cb26-267" aria-hidden="true" tabindex="-1"></a>Taukobong et al. <span class="co">[</span><span class="ot">-@Taukobong2016</span><span class="co">]</span> review various dimensions of female empowerment and their effects on a variety of health and development outcomes, including the access and use of financial services for the poor.</span>
<span id="cb26-268"><a href="#cb26-268" aria-hidden="true" tabindex="-1"></a>They find that, aside from gender inequalities being both highly contextual and intersectional, especially the channels of control over one&#39;s income, assets, resources, having decision-making power and individual education affected these outcomes across all dimensions, reflecting their position as channels of gender inequality.</span>
<span id="cb26-269"><a href="#cb26-269" aria-hidden="true" tabindex="-1"></a>Additionally, personal mobility, safety and equitable interpersonal relationships are associated with some health and family planning outcomes.</span>
<span id="cb26-270"><a href="#cb26-270" aria-hidden="true" tabindex="-1"></a>Ultimately, the review shows that due to the contextual nature, interventions need to identify the variations of inequality at their start, see where inequalities exist, overlap and work as barriers for an effective implementation.</span>
<span id="cb26-271"><a href="#cb26-271" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-272"><a href="#cb26-272" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- disability --&gt;</span></span>
<span id="cb26-273"><a href="#cb26-273" aria-hidden="true" tabindex="-1"></a>Ruhindwa et al. <span class="co">[</span><span class="ot">-@Ruhindwa2016</span><span class="co">]</span> review a variety of barriers to adequate workforce inclusion for people with disabilities, proposing an inclusive approach in which the individual is given space to take ownership of the solutions addressing challenges experienced in the employment sector.</span>
<span id="cb26-274"><a href="#cb26-274" aria-hidden="true" tabindex="-1"></a>Similarly, they view hiring discrimination and workplace stigmatization as the largest channels through which inequalities of disability manifest themselves.</span>
<span id="cb26-275"><a href="#cb26-275" aria-hidden="true" tabindex="-1"></a>They see especially employment support practices, with focus on enabling this, as relevant policy strategies, as well as national campaigns to ease disclosing one&#39;s disability in the labour market.</span>
<span id="cb26-276"><a href="#cb26-276" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-277"><a href="#cb26-277" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- disability, gender, age --&gt;</span></span>
<span id="cb26-278"><a href="#cb26-278" aria-hidden="true" tabindex="-1"></a>In looking at the various dimensions affecting the labour market outcomes of supported employment interventions for people with disabilities, Kirsh <span class="co">[</span><span class="ot">-@Kirsh2016</span><span class="co">]</span> finds that most literature still only regards the overall efficacy of the interventions without taking into account compounding intersectional characteristics.</span>
<span id="cb26-279"><a href="#cb26-279" aria-hidden="true" tabindex="-1"></a>They find that generally men are more likely to find employment through the intervention, possibly resting on current programmes focus on manual labour, as well as younger people generally finding better employment.</span>
<span id="cb26-280"><a href="#cb26-280" aria-hidden="true" tabindex="-1"></a>This highlights the intersectional nature of inequalities between disability, gender and age.</span>
<span id="cb26-281"><a href="#cb26-281" aria-hidden="true" tabindex="-1"></a>One relevant policy they see is that of vocational rehabilitation.</span>
<span id="cb26-282"><a href="#cb26-282" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-283"><a href="#cb26-283" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- disability --&gt;</span></span>
<span id="cb26-284"><a href="#cb26-284" aria-hidden="true" tabindex="-1"></a>Hastbacka et al. <span class="co">[</span><span class="ot">-@Hastbacka2016</span><span class="co">]</span> undertake a scoping review to find the linkages between societal participation and people with disabilities, looking at specific interventions for the identity of participants, types of participation analysed, and channels of effect.</span>
<span id="cb26-285"><a href="#cb26-285" aria-hidden="true" tabindex="-1"></a>They see most literature focusing on labour market participation and viewing disabled people as coherent group instead of intersectional.</span>
<span id="cb26-286"><a href="#cb26-286" aria-hidden="true" tabindex="-1"></a>The main channels of inequality providing barriers they identify are financial factors, attitudes of discrimination, health issues and unemployment, while the main driving mechanisms identified are legislation and disability policies, as well as support from people in close contact with disabled people and attitudes in society and the hiring process.</span>
<span id="cb26-287"><a href="#cb26-287" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-288"><a href="#cb26-288" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- disability --&gt;</span></span>
<span id="cb26-289"><a href="#cb26-289" aria-hidden="true" tabindex="-1"></a>In a systematic review looking at the effectiveness of workplace accommodations on employment and return to work, Nevala et al. <span class="co">[</span><span class="ot">-@Nevala2015</span><span class="co">]</span> find few studies with rigorous design leading to conclusive evidence.</span>
<span id="cb26-290"><a href="#cb26-290" aria-hidden="true" tabindex="-1"></a>They do find moderate evidence that employment in disability can be increased through workplace accommodations such as vocational counselling or guidance, education, self-advocacy, positive perception and help by others.</span>
<span id="cb26-291"><a href="#cb26-291" aria-hidden="true" tabindex="-1"></a>There is also low evidence for return to work being increased by education, work aids and techniques and cooperation between employers and other professionals (such as occupational health care, or service providers).</span>
<span id="cb26-292"><a href="#cb26-292" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-293"><a href="#cb26-293" aria-hidden="true" tabindex="-1"></a><span class="fu"># Methodology and data</span></span>
<span id="cb26-294"><a href="#cb26-294" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-295"><a href="#cb26-295" aria-hidden="true" tabindex="-1"></a>This section will discuss the systematic scoping review methodology that is proposed to conduct the review of the literature on policy interventions that are expected to address inequalities in forms of work and labour market outcomes.</span>
<span id="cb26-296"><a href="#cb26-296" aria-hidden="true" tabindex="-1"></a>This study follows the principles of a systematic review framework, to systematically assess the impact of an array of policies on inequalities in the world of work.</span>
<span id="cb26-297"><a href="#cb26-297" aria-hidden="true" tabindex="-1"></a>It strives to follow the clear and reproducible method of identification prior to synthesis of relevant research,</span>
<span id="cb26-298"><a href="#cb26-298" aria-hidden="true" tabindex="-1"></a>while limiting &quot;bias by the systematic assembly, critical appraisal and synthesis&quot; through applying scientific strategies to the review itself <span class="co">[</span><span class="ot">@Cook1995</span><span class="co">]</span>.</span>
<span id="cb26-299"><a href="#cb26-299" aria-hidden="true" tabindex="-1"></a>It thereby attempts to provide an improved basis for comparative analysis between studies through the rigorous application of systematic criteria and thus to avoid the potential bias of narrative reviews.</span>
<span id="cb26-300"><a href="#cb26-300" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-301"><a href="#cb26-301" aria-hidden="true" tabindex="-1"></a>Unlike purely systematic reviews which typically focus on specific policy questions and interventions, systematic scoping reviews focus on a wider spectrum of policies, where different study designs and research questions can be investigated.</span>
<span id="cb26-302"><a href="#cb26-302" aria-hidden="true" tabindex="-1"></a>Since scoping reviews allow both broad and in-depth analyses, they are the most appropriate rigorous method to make a synthesis of the current evidence in this area <span class="co">[</span><span class="ot">@Arksey2005</span><span class="co">]</span>.</span>
<span id="cb26-303"><a href="#cb26-303" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-304"><a href="#cb26-304" aria-hidden="true" tabindex="-1"></a>The scoping review allows broad focus to be given to a subject for which no unified path with clear edges has been laid out yet by prior reviews, as remains the case with policies targeting inequalities in the world of work.</span>
<span id="cb26-305"><a href="#cb26-305" aria-hidden="true" tabindex="-1"></a>It does so through a breadth-first approach through a search protocol which favours working through a large body of literature to subsequently move toward a depth-favouring approach once the literature has been sufficiently delimited.</span>
<span id="cb26-306"><a href="#cb26-306" aria-hidden="true" tabindex="-1"></a>Its purpose, clearly mapping a body of literature on a (broad) topic area, is thereby useful on its own or in combination with a systematic approach <span class="co">[</span><span class="ot">@Arksey2005</span><span class="co">]</span>.</span>
<span id="cb26-307"><a href="#cb26-307" aria-hidden="true" tabindex="-1"></a>With an increasingly adopted approach in recent years, with rigorous dichotomy of inclusion and exclusion criteria it provides a way of charting the relevance of literature related to its overall body that strives to be free of influencing biases which could affect the skew of the resulting literature sample <span class="co">[</span><span class="ot">@Pham2014</span><span class="co">]</span>.</span>
<span id="cb26-308"><a href="#cb26-308" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-309"><a href="#cb26-309" aria-hidden="true" tabindex="-1"></a><span class="fu">## The search protocol</span></span>
<span id="cb26-310"><a href="#cb26-310" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-311"><a href="#cb26-311" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">TODO</span><span class="co"> need correct above definitions --&gt;</span></span>
<span id="cb26-312"><a href="#cb26-312" aria-hidden="true" tabindex="-1"></a>The search protocol was carried out based on the introduced areas of policies as well as the possible combination of definitions and outcomes in the world of work.</span>
<span id="cb26-313"><a href="#cb26-313" aria-hidden="true" tabindex="-1"></a>For each dimension of definitions, a cluster containing possible utilized terms will be created, that is for: definitions of work and labour, forms of work, definitions of inequality, forms of vertical and forms of horizontal inequalities, labour market outcomes, and definitions of policy.</span>
<span id="cb26-314"><a href="#cb26-314" aria-hidden="true" tabindex="-1"></a>Each of the clusters contains synonymous terms as well as term-adjacent phrase combinations which are in turn used to refine or broaden the search scope to best encapsulate each respective cluster, based on the above definitions.</span>
<span id="cb26-315"><a href="#cb26-315" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-316"><a href="#cb26-316" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">TODO</span><span class="co"> Why WOS database? --&gt;</span></span>
<span id="cb26-317"><a href="#cb26-317" aria-hidden="true" tabindex="-1"></a>The search protocol then follows a three-staged process of execution: identification, screening and extraction.</span>
<span id="cb26-318"><a href="#cb26-318" aria-hidden="true" tabindex="-1"></a>First, in identification, the relevant policy, inequality and world of work related dimensions are combined through Boolean operators to conduct a search through the database repository Web of Science and supplemental searches via Google Scholar to supply potential gray literature.</span>
<span id="cb26-319"><a href="#cb26-319" aria-hidden="true" tabindex="-1"></a>While the resulting study pools could be screened for in multiple languages, the search queries themselves are passed to the databases in English-language only.</span>
<span id="cb26-320"><a href="#cb26-320" aria-hidden="true" tabindex="-1"></a>Relevant results are then complemented through the adoption of a &#39;snowballing&#39; technique,</span>
<span id="cb26-321"><a href="#cb26-321" aria-hidden="true" tabindex="-1"></a>in which an array of identified adjacent published reviews is analysed for their reference lists to find cross-references of potentially missing literature and in turn add those to the pool of studies.</span>
<span id="cb26-322"><a href="#cb26-322" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-323"><a href="#cb26-323" aria-hidden="true" tabindex="-1"></a>To identify potential studies and create an initial sample, relevant terms for the clusters of world of work, inequality and policy interventions have been extracted from the existing reviews as well as the ILO definitions.</span>
<span id="cb26-324"><a href="#cb26-324" aria-hidden="true" tabindex="-1"></a>Identified terms comprising the world of work can be found in @tbl-wow-terms,</span>
<span id="cb26-325"><a href="#cb26-325" aria-hidden="true" tabindex="-1"></a>with the search query requiring a term from the general column and one other column.</span>
<span id="cb26-326"><a href="#cb26-326" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-327"><a href="#cb26-327" aria-hidden="true" tabindex="-1"></a>::: {#tbl-wow-terms}</span>
<span id="cb26-328"><a href="#cb26-328" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-331"><a href="#cb26-331" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-332"><a href="#cb26-332" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-wow-terms</span></span>
<span id="cb26-333"><a href="#cb26-333" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: World of work term cluster</span></span>
<span id="cb26-334"><a href="#cb26-334" aria-hidden="true" tabindex="-1"></a>terms_wow <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_wow.csv&quot;</span>)</span>
<span id="cb26-335"><a href="#cb26-335" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_wow.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-336"><a href="#cb26-336" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-337"><a href="#cb26-337" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-338"><a href="#cb26-338" aria-hidden="true" tabindex="-1"></a>World of work term cluster</span>
<span id="cb26-339"><a href="#cb26-339" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-340"><a href="#cb26-340" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-341"><a href="#cb26-341" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-342"><a href="#cb26-342" aria-hidden="true" tabindex="-1"></a>The world of work cluster, like the inequality and policy intervention clusters below, is made up of a general signifier (such as &quot;work&quot;, &quot;inequality&quot; or &quot;intervention&quot;) which has to be labelled in a study to form part of the sample,</span>
<span id="cb26-343"><a href="#cb26-343" aria-hidden="true" tabindex="-1"></a>as well as any additional terms looking into one or multiple specific dimensions or categories of these signifiers (such as &quot;domestic&quot; work, &quot;gender&quot; inequality, &quot;maternity leave&quot; intervention).</span>
<span id="cb26-344"><a href="#cb26-344" aria-hidden="true" tabindex="-1"></a>At least one general term and at least one additional term have to be mentioned by a study to be identified for the initial sample pool.</span>
<span id="cb26-345"><a href="#cb26-345" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-346"><a href="#cb26-346" aria-hidden="true" tabindex="-1"></a>For the policy intervention cluster, a variety of terms have been identified both from the ILO policy areas and guidelines as well as existing reviews, as can be seen in @tbl-intervention-terms.</span>
<span id="cb26-347"><a href="#cb26-347" aria-hidden="true" tabindex="-1"></a>Where terms have been identified from previous reviews outside the introduced ILO policy guidelines,</span>
<span id="cb26-348"><a href="#cb26-348" aria-hidden="true" tabindex="-1"></a>there source has been included in the table.</span>
<span id="cb26-349"><a href="#cb26-349" aria-hidden="true" tabindex="-1"></a>For the database query, a single term from the general category is required to be included in addition to one term from *any* of the remaining categories.</span>
<span id="cb26-350"><a href="#cb26-350" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-351"><a href="#cb26-351" aria-hidden="true" tabindex="-1"></a>::: {#tbl-intervention-terms}</span>
<span id="cb26-352"><a href="#cb26-352" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-355"><a href="#cb26-355" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-356"><a href="#cb26-356" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-intervention-terms</span></span>
<span id="cb26-357"><a href="#cb26-357" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Intervention term cluster</span></span>
<span id="cb26-358"><a href="#cb26-358" aria-hidden="true" tabindex="-1"></a>terms_policy <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_policy.csv&quot;</span>)</span>
<span id="cb26-359"><a href="#cb26-359" aria-hidden="true" tabindex="-1"></a><span class="co"># different headers to include &#39;social norms&#39;</span></span>
<span id="cb26-360"><a href="#cb26-360" aria-hidden="true" tabindex="-1"></a>headers <span class="op">=</span> [<span class="st">&quot;General&quot;</span>, <span class="st">&quot;Institutional&quot;</span>, <span class="st">&quot;Structural&quot;</span>, <span class="st">&quot;Agency &amp; social norms&quot;</span>]</span>
<span id="cb26-361"><a href="#cb26-361" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_policy.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>headers, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-362"><a href="#cb26-362" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-363"><a href="#cb26-363" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-364"><a href="#cb26-364" aria-hidden="true" tabindex="-1"></a>Policy intervention term cluster</span>
<span id="cb26-365"><a href="#cb26-365" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-366"><a href="#cb26-366" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-367"><a href="#cb26-367" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-368"><a href="#cb26-368" aria-hidden="true" tabindex="-1"></a>Lastly, the inequality cluster is once again made up of a general term describing inequality which has to form part of the query results, as well as at least one term describing a specific vertical or horizontal inequality,</span>
<span id="cb26-369"><a href="#cb26-369" aria-hidden="true" tabindex="-1"></a>as seen in @tbl-inequality-terms.</span>
<span id="cb26-370"><a href="#cb26-370" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-373"><a href="#cb26-373" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-374"><a href="#cb26-374" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-inequality-terms</span></span>
<span id="cb26-375"><a href="#cb26-375" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Inequality term cluster</span></span>
<span id="cb26-376"><a href="#cb26-376" aria-hidden="true" tabindex="-1"></a>terms_inequality <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/terms_inequality.csv&quot;</span>)</span>
<span id="cb26-377"><a href="#cb26-377" aria-hidden="true" tabindex="-1"></a>md(tabulate(terms_inequality.fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-378"><a href="#cb26-378" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-379"><a href="#cb26-379" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-380"><a href="#cb26-380" aria-hidden="true" tabindex="-1"></a>A general as well as category-specific term from each cluster will be required, using a intersection merge (Boolean &#39;AND&#39;),</span>
<span id="cb26-381"><a href="#cb26-381" aria-hidden="true" tabindex="-1"></a>as well as in turn a single of those from each of the three clusters using an intersection merge.</span>
<span id="cb26-382"><a href="#cb26-382" aria-hidden="true" tabindex="-1"></a>The resulting sample pool will thus include a term and specific dimension of inequality and of policy intervention within the world of work.</span>
<span id="cb26-383"><a href="#cb26-383" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-384"><a href="#cb26-384" aria-hidden="true" tabindex="-1"></a>Second, in screening, duplicate results are removed and the resulting literature sample is sorted based on a variety of excluding characteristics based on: language, title, abstract, full text and literature supersession through newer publications.</span>
<span id="cb26-385"><a href="#cb26-385" aria-hidden="true" tabindex="-1"></a>Properties in these characteristics are used to assess an individual study on its suitability for further review.</span>
<span id="cb26-386"><a href="#cb26-386" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-387"><a href="#cb26-387" aria-hidden="true" tabindex="-1"></a>Narrowing criteria are applied to restrict the sample to studies looking at i) the effects of individual evidence-based policy measures or intervention initiatives ii) attempting to address a single or multiple of the defined inequalities in the world of work.</span>
<span id="cb26-388"><a href="#cb26-388" aria-hidden="true" tabindex="-1"></a>iii) using appropriate quantitative methods to examine the links of intervention and impact on the given inequalities.</span>
<span id="cb26-389"><a href="#cb26-389" aria-hidden="true" tabindex="-1"></a>The narrowing process makes use of the typology of inequalities, of forms of work, and of policy areas introduced above as its criteria.</span>
<span id="cb26-390"><a href="#cb26-390" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-391"><a href="#cb26-391" aria-hidden="true" tabindex="-1"></a>An overview of the respective criteria used for inclusion or exclusion can be found in @tbl-inclusion-criteria.</span>
<span id="cb26-392"><a href="#cb26-392" aria-hidden="true" tabindex="-1"></a>It restricts studies to those that comprise primary research published after 2000,</span>
<span id="cb26-393"><a href="#cb26-393" aria-hidden="true" tabindex="-1"></a>with a focus on the narrowing criteria specified in @tbl-inclusion-criteria.</span>
<span id="cb26-394"><a href="#cb26-394" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-395"><a href="#cb26-395" aria-hidden="true" tabindex="-1"></a>::: {#tbl-inclusion-criteria}</span>
<span id="cb26-396"><a href="#cb26-396" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-399"><a href="#cb26-399" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-400"><a href="#cb26-400" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-inclusion-criteria</span></span>
<span id="cb26-401"><a href="#cb26-401" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-402"><a href="#cb26-402" aria-hidden="true" tabindex="-1"></a>inclusion_criteria <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/inclusion-criteria.tsv&quot;</span>, sep<span class="op">=</span><span class="st">&quot;</span><span class="ch">\t</span><span class="st">&quot;</span>)</span>
<span id="cb26-403"><a href="#cb26-403" aria-hidden="true" tabindex="-1"></a>md(tabulate(inclusion_criteria, showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span><span class="st">&quot;keys&quot;</span>, tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-404"><a href="#cb26-404" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-405"><a href="#cb26-405" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-406"><a href="#cb26-406" aria-hidden="true" tabindex="-1"></a>Source: Author&#39;s elaboration</span>
<span id="cb26-407"><a href="#cb26-407" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-408"><a href="#cb26-408" aria-hidden="true" tabindex="-1"></a>Study inclusion and exclusion scoping criteria</span>
<span id="cb26-409"><a href="#cb26-409" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-410"><a href="#cb26-410" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-411"><a href="#cb26-411" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-412"><a href="#cb26-412" aria-hidden="true" tabindex="-1"></a>To facilitate the screening process, with the help of &#39;Zotero&#39; reference manager a system of keywords is used to tag individual studies in the sample with their reason for exclusion,such as ‘excluded::language’, ‘excluded::title’, ‘excluded::abstract’, and ‘excluded::superseded’.</span>
<span id="cb26-413"><a href="#cb26-413" aria-hidden="true" tabindex="-1"></a>This keyword-based system is equally used to further categorize the sample studies that do not fall into exclusion criteria, based on primary country of analysis, world region, as well as income level classification.</span>
<span id="cb26-414"><a href="#cb26-414" aria-hidden="true" tabindex="-1"></a>To that end, a ‘country::’, ‘region::’ and ‘income::’ are used to disambiguate between the respective characteristics, such as ‘region::LAC’ for Latin America and the Caribbean, ‘region::SSA’ for Sub-Saharan Africa; as well as for example ‘income::low-middle’, ‘income::upper-middle’ or ‘income::high’.</span>
<span id="cb26-415"><a href="#cb26-415" aria-hidden="true" tabindex="-1"></a>These two delineations follow the ILO categorizations on world regions and the country income classifications based on World Bank income groupings <span class="co">[</span><span class="ot">@ILO2022</span><span class="co">]</span>.</span>
<span id="cb26-416"><a href="#cb26-416" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-417"><a href="#cb26-417" aria-hidden="true" tabindex="-1"></a>Similarly, if a specific type of inequality, or a specific intervention, represents the focus of a study, these will be reflected in the same keyword system, through for example ‘inequality::income’ or ‘inequality::gender’.</span>
<span id="cb26-418"><a href="#cb26-418" aria-hidden="true" tabindex="-1"></a>The complete process of identification and screening is undertaken with the help of the Zotero reference manager, ultimately leaving only publications which are relevant for final full-text review and analysis.</span>
<span id="cb26-419"><a href="#cb26-419" aria-hidden="true" tabindex="-1"></a>Last, for extraction, studies are screened for their full-texts, irrelevant studies excluded with ‘excluded::full-text’ as explained above and relevant studies then ingested into the final sample pool.</span>
<span id="cb26-420"><a href="#cb26-420" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-421"><a href="#cb26-421" aria-hidden="true" tabindex="-1"></a>Should any literature reviews be identified as relevant during this screening process,</span>
<span id="cb26-422"><a href="#cb26-422" aria-hidden="true" tabindex="-1"></a>they will in turn be crawled for cited sources in a &#39;snowballing&#39; process,</span>
<span id="cb26-423"><a href="#cb26-423" aria-hidden="true" tabindex="-1"></a>and the sources will be added to the sample to undergo the same screening process explained above.</span>
<span id="cb26-424"><a href="#cb26-424" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-427"><a href="#cb26-427" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-428"><a href="#cb26-428" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: calculate-scoping-flowchart</span></span>
<span id="cb26-429"><a href="#cb26-429" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb26-430"><a href="#cb26-430" aria-hidden="true" tabindex="-1"></a><span class="co">#| output: asis</span></span>
<span id="cb26-431"><a href="#cb26-431" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-432"><a href="#cb26-432" aria-hidden="true" tabindex="-1"></a>nr_database_query_raw <span class="op">=</span> <span class="bu">len</span>(bib_sample_raw_db.entries)</span>
<span id="cb26-433"><a href="#cb26-433" aria-hidden="true" tabindex="-1"></a>nr_snowballing_raw <span class="op">=</span> <span class="dv">2240</span></span>
<span id="cb26-434"><a href="#cb26-434" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-435"><a href="#cb26-435" aria-hidden="true" tabindex="-1"></a>all_keywords <span class="op">=</span> [entry[<span class="st">&quot;keywords&quot;</span>] <span class="cf">for</span> entry <span class="kw">in</span> bib_sample.entries <span class="cf">if</span> <span class="st">&quot;keywords&quot;</span> <span class="kw">in</span> entry.fields_dict.keys()]</span>
<span id="cb26-436"><a href="#cb26-436" aria-hidden="true" tabindex="-1"></a>nr_database_deduplicated <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;sample::database&quot;</span> <span class="kw">in</span> kw])</span>
<span id="cb26-437"><a href="#cb26-437" aria-hidden="true" tabindex="-1"></a>nr_snowballing_deduplicated <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;sample::snowballing&quot;</span> <span class="kw">in</span> kw])</span>
<span id="cb26-438"><a href="#cb26-438" aria-hidden="true" tabindex="-1"></a>nr_out_superseded <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::superseded&quot;</span> <span class="kw">in</span> kw])</span>
<span id="cb26-439"><a href="#cb26-439" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-440"><a href="#cb26-440" aria-hidden="true" tabindex="-1"></a>FULL_RAW_SAMPLE_NOTHING_REMOVED <span class="op">=</span> nr_database_query_raw <span class="op">+</span> nr_snowballing_raw</span>
<span id="cb26-441"><a href="#cb26-441" aria-hidden="true" tabindex="-1"></a>FULL_SAMPLE_DUPLICATES_REMOVED <span class="op">=</span> nr_database_deduplicated <span class="op">+</span> nr_snowballing_deduplicated <span class="op">+</span> nr_out_superseded</span>
<span id="cb26-442"><a href="#cb26-442" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-443"><a href="#cb26-443" aria-hidden="true" tabindex="-1"></a>NON_ZOTERO_CAPTURE_TITLE_REMOVAL <span class="op">=</span> <span class="dv">1150</span></span>
<span id="cb26-444"><a href="#cb26-444" aria-hidden="true" tabindex="-1"></a>NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL <span class="op">=</span> <span class="dv">727</span></span>
<span id="cb26-445"><a href="#cb26-445" aria-hidden="true" tabindex="-1"></a>NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL <span class="op">=</span> <span class="dv">348</span></span>
<span id="cb26-446"><a href="#cb26-446" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-447"><a href="#cb26-447" aria-hidden="true" tabindex="-1"></a>nr_out_duplicates <span class="op">=</span> FULL_RAW_SAMPLE_NOTHING_REMOVED <span class="op">-</span> FULL_SAMPLE_DUPLICATES_REMOVED</span>
<span id="cb26-448"><a href="#cb26-448" aria-hidden="true" tabindex="-1"></a>nr_out_title <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::title&quot;</span> <span class="kw">in</span> kw]) <span class="op">+</span> NON_ZOTERO_CAPTURE_TITLE_REMOVAL</span>
<span id="cb26-449"><a href="#cb26-449" aria-hidden="true" tabindex="-1"></a>nr_out_abstract <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::abstract&quot;</span> <span class="kw">in</span> kw]) <span class="op">+</span> NON_ZOTERO_CAPTURE_ABSTRACT_REMOVAL</span>
<span id="cb26-450"><a href="#cb26-450" aria-hidden="true" tabindex="-1"></a>nr_out_fulltext <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::full-text&quot;</span> <span class="kw">in</span> kw]) <span class="op">+</span> NON_ZOTERO_CAPTURE_FULLTEXT_REMOVAL</span>
<span id="cb26-451"><a href="#cb26-451" aria-hidden="true" tabindex="-1"></a>nr_out_language <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;out::language&quot;</span> <span class="kw">in</span> kw])</span>
<span id="cb26-452"><a href="#cb26-452" aria-hidden="true" tabindex="-1"></a>nr_extraction_done <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;done::extracted&quot;</span> <span class="kw">in</span> kw])</span>
<span id="cb26-453"><a href="#cb26-453" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-454"><a href="#cb26-454" aria-hidden="true" tabindex="-1"></a>t3 <span class="op">=</span> <span class="st">&quot;`&quot;</span> <span class="op">*</span> <span class="dv">3</span></span>
<span id="cb26-455"><a href="#cb26-455" aria-hidden="true" tabindex="-1"></a><span class="co"># </span><span class="al">FIXME</span><span class="co"> use data/supplementary undeduplciated counts to get database starting and snowballing counts</span></span>
<span id="cb26-456"><a href="#cb26-456" aria-hidden="true" tabindex="-1"></a><span class="co"># from: https://github.com/quarto-dev/quarto-cli/discussions/6508</span></span>
<span id="cb26-457"><a href="#cb26-457" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f&quot;&quot;&quot;</span></span>
<span id="cb26-458"><a href="#cb26-458" aria-hidden="true" tabindex="-1"></a><span class="ss">```</span><span class="sc">{</span>mermaid<span class="sc">}</span></span>
<span id="cb26-459"><a href="#cb26-459" aria-hidden="true" tabindex="-1"></a><span class="ss">%%| label: fig-prisma</span></span>
<span id="cb26-460"><a href="#cb26-460" aria-hidden="true" tabindex="-1"></a><span class="ss">%%| fig-cap: &quot;Sample sorting process through identification and screening&quot;</span></span>
<span id="cb26-461"><a href="#cb26-461" aria-hidden="true" tabindex="-1"></a><span class="ss">%%| fig-width: 6</span></span>
<span id="cb26-462"><a href="#cb26-462" aria-hidden="true" tabindex="-1"></a><span class="ss">flowchart TD;</span></span>
<span id="cb26-463"><a href="#cb26-463" aria-hidden="true" tabindex="-1"></a><span class="ss"> search_db[&quot;Records identified through database searching (n=</span><span class="sc">{</span>nr_database_query_raw<span class="sc">}</span><span class="ss">)&quot;] --&gt; starting_sample;</span></span>
<span id="cb26-464"><a href="#cb26-464" aria-hidden="true" tabindex="-1"></a><span class="ss"> search_prev[&quot;Records identified through other sources (n=</span><span class="sc">{</span>nr_snowballing_raw<span class="sc">}</span><span class="ss">)&quot;] --&gt; starting_sample[&quot;Starting sample (n=</span><span class="sc">{</span>FULL_RAW_SAMPLE_NOTHING_REMOVED<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb26-465"><a href="#cb26-465" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-466"><a href="#cb26-466" aria-hidden="true" tabindex="-1"></a><span class="ss"> starting_sample -- &quot;Duplicate removal (</span><span class="sc">{</span>nr_out_duplicates<span class="op">+</span>nr_out_superseded<span class="sc">}</span><span class="ss"> removed) &quot;--&gt; dedup[&quot;Records after duplicates removed (n=</span><span class="sc">{</span>FULL_SAMPLE_DUPLICATES_REMOVED<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb26-467"><a href="#cb26-467" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-468"><a href="#cb26-468" aria-hidden="true" tabindex="-1"></a><span class="ss"> dedup -- &quot;Title screening (</span><span class="sc">{</span>nr_out_title<span class="sc">}</span><span class="ss"> excluded)&quot; --&gt; title_screened[&quot;Records after titles screened (n=</span><span class="sc">{</span>FULL_SAMPLE_DUPLICATES_REMOVED <span class="op">-</span> nr_out_title<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb26-469"><a href="#cb26-469" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-470"><a href="#cb26-470" aria-hidden="true" tabindex="-1"></a><span class="ss"> title_screened -- &quot;Abstract screening (</span><span class="sc">{</span>nr_out_abstract<span class="sc">}</span><span class="ss"> excluded)&quot;--&gt; abstract_screened[&quot;Records after abstracts screened (n=</span><span class="sc">{</span>FULL_SAMPLE_DUPLICATES_REMOVED<span class="op">-</span>nr_out_title<span class="op">-</span>nr_out_abstract<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb26-471"><a href="#cb26-471" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-472"><a href="#cb26-472" aria-hidden="true" tabindex="-1"></a><span class="ss"> abstract_screened -- &quot; Language screening (</span><span class="sc">{</span>nr_out_language<span class="sc">}</span><span class="ss"> excluded) &quot;--&gt; language_screened[&quot;Records after language screened (n=</span><span class="sc">{</span>FULL_SAMPLE_DUPLICATES_REMOVED<span class="op">-</span>nr_out_title<span class="op">-</span>nr_out_abstract<span class="op">-</span>nr_out_language<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb26-473"><a href="#cb26-473" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-474"><a href="#cb26-474" aria-hidden="true" tabindex="-1"></a><span class="ss"> language_screened -- &quot; Full-text screening (</span><span class="sc">{</span>nr_out_fulltext<span class="sc">}</span><span class="ss"> excluded) &quot;--&gt; full-text_screened[&quot;Full-text articles assessed for eligibility (n=</span><span class="sc">{</span>nr_extraction_done<span class="sc">}</span><span class="ss">)&quot;];</span></span>
<span id="cb26-475"><a href="#cb26-475" aria-hidden="true" tabindex="-1"></a><span class="sc">{</span>t3<span class="sc">}</span></span>
<span id="cb26-476"><a href="#cb26-476" aria-hidden="true" tabindex="-1"></a><span class="ss">&quot;&quot;&quot;</span>)</span>
<span id="cb26-477"><a href="#cb26-477" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-478"><a href="#cb26-478" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-479"><a href="#cb26-479" aria-hidden="true" tabindex="-1"></a>All relevant data concerning both their major findings and statistical significance are then extracted from the individual studies into a collective results matrix.</span>
<span id="cb26-480"><a href="#cb26-480" aria-hidden="true" tabindex="-1"></a>The results to be identified in the matrix include a study’s: i) key outcome measures (dependent variables), ii) main findings, iii) main policy interventions (independent variables), iv) study design and sample size, v) dataset and methods of evaluation, vi) direction of relation and level of representativeness, vii) level of statistical significance, viii) main limitations.</span>
<span id="cb26-481"><a href="#cb26-481" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-482"><a href="#cb26-482" aria-hidden="true" tabindex="-1"></a>Finally, following @Maitrot2017, the relevant studies are ranked for their validity.</span>
<span id="cb26-483"><a href="#cb26-483" aria-hidden="true" tabindex="-1"></a>Here, a 2-dimensional approach is taken to separate the external validity from the internal validity of the studies.</span>
<span id="cb26-484"><a href="#cb26-484" aria-hidden="true" tabindex="-1"></a>The ranking process then uses the representativeness of a study&#39;s underlying dataset,</span>
<span id="cb26-485"><a href="#cb26-485" aria-hidden="true" tabindex="-1"></a>from a non-representative survey sample, through a sub-nationally representative sample, a nationally representative and the use of census data,</span>
<span id="cb26-486"><a href="#cb26-486" aria-hidden="true" tabindex="-1"></a>to arrive at a ranking between 2.0 and 5.0 respectively.</span>
<span id="cb26-487"><a href="#cb26-487" aria-hidden="true" tabindex="-1"></a>Similarly, the studies are ranked for internal validity using the study design,</span>
<span id="cb26-488"><a href="#cb26-488" aria-hidden="true" tabindex="-1"></a>with only quasi-experimental and experimental studies receiving similar rankings between 2.0 and 5.0 depending on the individually applied methods due to their quantifiability,</span>
<span id="cb26-489"><a href="#cb26-489" aria-hidden="true" tabindex="-1"></a>while observational and qualitative studies go without an internal validity rank (0.0) due to the more contextual nature of their analyses.</span>
<span id="cb26-490"><a href="#cb26-490" aria-hidden="true" tabindex="-1"></a>For a full list of validity ranks, see @apptbl-validity-external and @apptbl-validity-internal.</span>
<span id="cb26-491"><a href="#cb26-491" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-492"><a href="#cb26-492" aria-hidden="true" tabindex="-1"></a><span class="fu">## Data</span></span>
<span id="cb26-493"><a href="#cb26-493" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-496"><a href="#cb26-496" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-497"><a href="#cb26-497" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: calculate-relevant-studies</span></span>
<span id="cb26-498"><a href="#cb26-498" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb26-499"><a href="#cb26-499" aria-hidden="true" tabindex="-1"></a><span class="co"># </span><span class="al">TODO</span><span class="co"> Remove redundant &#39;relevant&#39; studies observation below once all studies are extracted.</span></span>
<span id="cb26-500"><a href="#cb26-500" aria-hidden="true" tabindex="-1"></a>nr_relevant <span class="op">=</span> <span class="bu">len</span>([<span class="dv">1</span> <span class="cf">for</span> kw <span class="kw">in</span> all_keywords <span class="cf">if</span> <span class="st">&quot;relevant&quot;</span> <span class="kw">in</span> kw])</span>
<span id="cb26-501"><a href="#cb26-501" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-502"><a href="#cb26-502" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-503"><a href="#cb26-503" aria-hidden="true" tabindex="-1"></a>The query execution results in an initial sample of <span class="in">`{python} nr_database_query_raw`</span> potential studies identified from the database search as well as <span class="in">`{python} nr_snowballing_raw`</span> potential studies from other sources, leading to a total initial number of <span class="in">`{python} FULL_RAW_SAMPLE_NOTHING_REMOVED`</span>.</span>
<span id="cb26-504"><a href="#cb26-504" aria-hidden="true" tabindex="-1"></a>This accounts for all identified studies without duplicate removal, without controlling for literature that has been superseded or applying any other screening criteria.</span>
<span id="cb26-505"><a href="#cb26-505" aria-hidden="true" tabindex="-1"></a>Of these, <span class="in">`{python} nr_relevant`</span> have been identified as potentially relevant studies for the purposes of this scoping review, from which <span class="in">`{python} nr_extraction_done`</span> have been extracted.</span>
<span id="cb26-506"><a href="#cb26-506" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-507"><a href="#cb26-507" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- {{++ </span><span class="al">FIXME</span><span class="co">: Update description for changing study pool ++}} --&gt;</span></span>
<span id="cb26-508"><a href="#cb26-508" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-509"><a href="#cb26-509" aria-hidden="true" tabindex="-1"></a>The currently identified literature rises somewhat in volume over time,</span>
<span id="cb26-510"><a href="#cb26-510" aria-hidden="true" tabindex="-1"></a>with first larger outputs identified from 2014,</span>
<span id="cb26-511"><a href="#cb26-511" aria-hidden="true" tabindex="-1"></a>as can be seen in @fig-publications-per-year.</span>
<span id="cb26-512"><a href="#cb26-512" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-513"><a href="#cb26-513" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- {{++ </span><span class="al">FIXME</span><span class="co">: give full year scale ++}} --&gt;</span></span>
<span id="cb26-514"><a href="#cb26-514" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-515"><a href="#cb26-515" aria-hidden="true" tabindex="-1"></a><span class="co"> &lt;!-- </span><span class="al">TODO</span><span class="co"> Should this be sub-divided by region or subdivision later per-section? --&gt;</span></span>
<span id="cb26-518"><a href="#cb26-518" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-519"><a href="#cb26-519" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-publications-per-year</span></span>
<span id="cb26-520"><a href="#cb26-520" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Publications per year</span></span>
<span id="cb26-521"><a href="#cb26-521" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-522"><a href="#cb26-522" aria-hidden="true" tabindex="-1"></a>df_study_years <span class="op">=</span> (</span>
<span id="cb26-523"><a href="#cb26-523" aria-hidden="true" tabindex="-1"></a> bib_df.groupby([<span class="st">&quot;author&quot;</span>, <span class="st">&quot;year&quot;</span>, <span class="st">&quot;title&quot;</span>])</span>
<span id="cb26-524"><a href="#cb26-524" aria-hidden="true" tabindex="-1"></a> .first()</span>
<span id="cb26-525"><a href="#cb26-525" aria-hidden="true" tabindex="-1"></a> .reset_index()</span>
<span id="cb26-526"><a href="#cb26-526" aria-hidden="true" tabindex="-1"></a> .drop_duplicates()</span>
<span id="cb26-527"><a href="#cb26-527" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-528"><a href="#cb26-528" aria-hidden="true" tabindex="-1"></a><span class="co"># plot by year </span><span class="al">TODO</span><span class="co"> decide if we want to distinguish by literature type/region/etc as hue</span></span>
<span id="cb26-529"><a href="#cb26-529" aria-hidden="true" tabindex="-1"></a><span class="co"># </span><span class="al">FIXME</span><span class="co"> should be timeseries plot so no years are missing</span></span>
<span id="cb26-530"><a href="#cb26-530" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(df_study_years, x<span class="op">=</span><span class="st">&quot;year&quot;</span>)</span>
<span id="cb26-531"><a href="#cb26-531" aria-hidden="true" tabindex="-1"></a>ax.tick_params(axis<span class="op">=</span><span class="st">&#39;x&#39;</span>, rotation<span class="op">=</span><span class="dv">45</span>)</span>
<span id="cb26-532"><a href="#cb26-532" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb26-533"><a href="#cb26-533" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb26-534"><a href="#cb26-534" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-535"><a href="#cb26-535" aria-hidden="true" tabindex="-1"></a>df_study_years <span class="op">=</span> <span class="va">None</span></span>
<span id="cb26-536"><a href="#cb26-536" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-537"><a href="#cb26-537" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-538"><a href="#cb26-538" aria-hidden="true" tabindex="-1"></a>Anomalies such as the relatively significant dips in output in 2016 and 2012 become especially interesting against the strong later increase of output.</span>
<span id="cb26-539"><a href="#cb26-539" aria-hidden="true" tabindex="-1"></a>While this can mean a decreased interest or different focus points within academia during those time spans,</span>
<span id="cb26-540"><a href="#cb26-540" aria-hidden="true" tabindex="-1"></a>it may also point towards alternative term clusters that are newly arising, or a re-focus towards different interventions,</span>
<span id="cb26-541"><a href="#cb26-541" aria-hidden="true" tabindex="-1"></a>and should thus be kept in mind for future scoping efforts.</span>
<span id="cb26-542"><a href="#cb26-542" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-543"><a href="#cb26-543" aria-hidden="true" tabindex="-1"></a>The predominant amount of literature is based on white literature, with only a marginal amount solely published as gray literature.</span>
<span id="cb26-544"><a href="#cb26-544" aria-hidden="true" tabindex="-1"></a>This represents a gap which seems reasonable and not surprising since the database query efforts were primarily aimed at finding the most current versions of white literature.</span>
<span id="cb26-545"><a href="#cb26-545" aria-hidden="true" tabindex="-1"></a>Such a stark gap speaks to a well targeted identifaction procedure, with more up-to-date white literature correctly superseding potential previous publications.</span>
<span id="cb26-546"><a href="#cb26-546" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-547"><a href="#cb26-547" aria-hidden="true" tabindex="-1"></a>@fig-citations-per-year-avg shows the average number of citations for all studies published within an individual year.</span>
<span id="cb26-548"><a href="#cb26-548" aria-hidden="true" tabindex="-1"></a>From the literature sample, several patterns emerge:</span>
<span id="cb26-549"><a href="#cb26-549" aria-hidden="true" tabindex="-1"></a>First, in general, citation counts are slightly decreasing - as should generally be expected with newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.</span>
<span id="cb26-550"><a href="#cb26-550" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-553"><a href="#cb26-553" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-554"><a href="#cb26-554" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-citations-per-year-avg</span></span>
<span id="cb26-555"><a href="#cb26-555" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Average citations per year</span></span>
<span id="cb26-556"><a href="#cb26-556" aria-hidden="true" tabindex="-1"></a>bib_df[<span class="st">&quot;zot_cited&quot;</span>] <span class="op">=</span> bib_df[<span class="st">&quot;zot_cited&quot;</span>].dropna().astype(<span class="st">&quot;int&quot;</span>)</span>
<span id="cb26-557"><a href="#cb26-557" aria-hidden="true" tabindex="-1"></a>grpd <span class="op">=</span> bib_df.groupby([<span class="st">&quot;year&quot;</span>], as_index<span class="op">=</span><span class="va">False</span>)[<span class="st">&quot;zot_cited&quot;</span>].mean()</span>
<span id="cb26-558"><a href="#cb26-558" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots()</span>
<span id="cb26-559"><a href="#cb26-559" aria-hidden="true" tabindex="-1"></a>ax.bar(grpd[<span class="st">&quot;year&quot;</span>], grpd[<span class="st">&quot;zot_cited&quot;</span>])</span>
<span id="cb26-560"><a href="#cb26-560" aria-hidden="true" tabindex="-1"></a>sns.regplot(x<span class="op">=</span>grpd[<span class="st">&quot;year&quot;</span>], y<span class="op">=</span>grpd[<span class="st">&quot;zot_cited&quot;</span>], ax<span class="op">=</span>ax)</span>
<span id="cb26-561"><a href="#cb26-561" aria-hidden="true" tabindex="-1"></a><span class="co">#ax = sns.lmplot(data=grpd, x=&quot;year&quot;, y=&quot;zot_cited&quot;, fit_reg=True)</span></span>
<span id="cb26-562"><a href="#cb26-562" aria-hidden="true" tabindex="-1"></a>ax.tick_params(axis<span class="op">=</span><span class="st">&#39;x&#39;</span>, rotation<span class="op">=</span><span class="dv">45</span>)</span>
<span id="cb26-563"><a href="#cb26-563" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb26-564"><a href="#cb26-564" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-565"><a href="#cb26-565" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-566"><a href="#cb26-566" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-567"><a href="#cb26-567" aria-hidden="true" tabindex="-1"></a>Second, while such a decrease is visible the changes between individual years are more erratic due to strong changes from year to year.</span>
<span id="cb26-568"><a href="#cb26-568" aria-hidden="true" tabindex="-1"></a>This suggests, first, no overall decrease in academic interest in the topic over this period of time,</span>
<span id="cb26-569"><a href="#cb26-569" aria-hidden="true" tabindex="-1"></a>and second, no linearly developing concentration or centralization of knowledge output and dissemination,</span>
<span id="cb26-570"><a href="#cb26-570" aria-hidden="true" tabindex="-1"></a>though it also throws into question a clear-cut increase of *relevant* output over time.</span>
<span id="cb26-571"><a href="#cb26-571" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-572"><a href="#cb26-572" aria-hidden="true" tabindex="-1"></a>Positive outlier years in citation amount can point to clusters of relevant literature feeding wider dissemination or cross-disciplinary interest, a possible sign of still somewhat unfocused research production which does not approach from a single coherent perspective yet.</span>
<span id="cb26-573"><a href="#cb26-573" aria-hidden="true" tabindex="-1"></a>It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the review process, a possible sign of more focused knowledge production and thus valuable to more closely review during the screening process.</span>
<span id="cb26-574"><a href="#cb26-574" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-575"><a href="#cb26-575" aria-hidden="true" tabindex="-1"></a>It may also suggest that clearly influential studies have been produced during those years, a possibility which may be more relevant during years of more singular releases (such as 2011 and 2013).</span>
<span id="cb26-576"><a href="#cb26-576" aria-hidden="true" tabindex="-1"></a>This is because, as @fig-publications-per-year showed, the overall output was nowhere near as rich as in the following years, allowing single influential works to skew the visible means for those years.</span>
<span id="cb26-577"><a href="#cb26-577" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-578"><a href="#cb26-578" aria-hidden="true" tabindex="-1"></a>In all of these cases, such outliers should provide clear points of interest during the screening process for eventual re-evaluation of utilized scoping term clusters and for future research focus.</span>
<span id="cb26-579"><a href="#cb26-579" aria-hidden="true" tabindex="-1"></a>Should they point towards gaps (or over-optimization) of specific areas of interest during those time-frames or more generally, they may provide an impetus for tweaking future identification queries to better align with the prevailing literature output.</span>
<span id="cb26-580"><a href="#cb26-580" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-581"><a href="#cb26-581" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- {{++ </span><span class="al">TODO</span><span class="co">: Add breakdown by thematic area++}} --&gt;</span></span>
<span id="cb26-582"><a href="#cb26-582" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-583"><a href="#cb26-583" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- {{++ </span><span class="al">TODO</span><span class="co">: describe intervention types with complete dataset ++}} --&gt;</span></span>
<span id="cb26-584"><a href="#cb26-584" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-585"><a href="#cb26-585" aria-hidden="true" tabindex="-1"></a><span class="fu"># Synthesis: A multitude of lenses</span></span>
<span id="cb26-586"><a href="#cb26-586" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-587"><a href="#cb26-587" aria-hidden="true" tabindex="-1"></a>This section will present a synthesis of evidence from the scoping review,</span>
<span id="cb26-588"><a href="#cb26-588" aria-hidden="true" tabindex="-1"></a>analysing the main findings per policy area, as well as underscore individual studies&#39; approaches and limitations.</span>
<span id="cb26-589"><a href="#cb26-589" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-592"><a href="#cb26-592" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-593"><a href="#cb26-593" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-intervention-types</span></span>
<span id="cb26-594"><a href="#cb26-594" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Available studies by primary type of intervention</span></span>
<span id="cb26-595"><a href="#cb26-595" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-596"><a href="#cb26-596" aria-hidden="true" tabindex="-1"></a>by_intervention <span class="op">=</span> (</span>
<span id="cb26-597"><a href="#cb26-597" aria-hidden="true" tabindex="-1"></a> bib_df</span>
<span id="cb26-598"><a href="#cb26-598" aria-hidden="true" tabindex="-1"></a> .fillna(<span class="st">&quot;&quot;</span>)</span>
<span id="cb26-599"><a href="#cb26-599" aria-hidden="true" tabindex="-1"></a> .groupby([<span class="st">&quot;author&quot;</span>, <span class="st">&quot;year&quot;</span>, <span class="st">&quot;title&quot;</span>, <span class="st">&quot;design&quot;</span>, <span class="st">&quot;method&quot;</span>, <span class="st">&quot;representativeness&quot;</span>, <span class="st">&quot;citation&quot;</span>])</span>
<span id="cb26-600"><a href="#cb26-600" aria-hidden="true" tabindex="-1"></a> .agg(</span>
<span id="cb26-601"><a href="#cb26-601" aria-hidden="true" tabindex="-1"></a> {</span>
<span id="cb26-602"><a href="#cb26-602" aria-hidden="true" tabindex="-1"></a> <span class="st">&quot;intervention&quot;</span>: <span class="kw">lambda</span> _col: <span class="st">&quot;; &quot;</span>.join(_col),</span>
<span id="cb26-603"><a href="#cb26-603" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb26-604"><a href="#cb26-604" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-605"><a href="#cb26-605" aria-hidden="true" tabindex="-1"></a> .reset_index()</span>
<span id="cb26-606"><a href="#cb26-606" aria-hidden="true" tabindex="-1"></a> .drop_duplicates()</span>
<span id="cb26-607"><a href="#cb26-607" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb26-608"><a href="#cb26-608" aria-hidden="true" tabindex="-1"></a> intervention<span class="op">=</span><span class="kw">lambda</span> _df: _df[<span class="st">&quot;intervention&quot;</span>].<span class="bu">apply</span>(</span>
<span id="cb26-609"><a href="#cb26-609" aria-hidden="true" tabindex="-1"></a> <span class="kw">lambda</span> _cell: <span class="bu">set</span>([x.strip() <span class="cf">for</span> x <span class="kw">in</span> re.sub(<span class="vs">r&quot;\(.*\)&quot;</span>, <span class="st">&quot;&quot;</span>, _cell).split(<span class="st">&quot;;&quot;</span>)])</span>
<span id="cb26-610"><a href="#cb26-610" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb26-611"><a href="#cb26-611" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-612"><a href="#cb26-612" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;intervention&quot;</span>)</span>
<span id="cb26-613"><a href="#cb26-613" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-614"><a href="#cb26-614" aria-hidden="true" tabindex="-1"></a>sort_order <span class="op">=</span> by_intervention[<span class="st">&quot;intervention&quot;</span>].value_counts().index</span>
<span id="cb26-615"><a href="#cb26-615" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-616"><a href="#cb26-616" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> plt.figure()</span>
<span id="cb26-617"><a href="#cb26-617" aria-hidden="true" tabindex="-1"></a>fig.set_size_inches(<span class="dv">6</span>, <span class="dv">3</span>)</span>
<span id="cb26-618"><a href="#cb26-618" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(by_intervention, x<span class="op">=</span><span class="st">&quot;intervention&quot;</span>, order<span class="op">=</span>by_intervention[<span class="st">&quot;intervention&quot;</span>].value_counts().index)</span>
<span id="cb26-619"><a href="#cb26-619" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb26-620"><a href="#cb26-620" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb26-621"><a href="#cb26-621" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-622"><a href="#cb26-622" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-623"><a href="#cb26-623" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-624"><a href="#cb26-624" aria-hidden="true" tabindex="-1"></a>@fig-intervention-types shows the predominant interventions contained in the reviewed literature.</span>
<span id="cb26-625"><a href="#cb26-625" aria-hidden="true" tabindex="-1"></a>Overall, there is a focus on measures of minimum wage, subsidisation, considerations of trade liberalisation and collective bargaining, education and training.</span>
<span id="cb26-626"><a href="#cb26-626" aria-hidden="true" tabindex="-1"></a>The entire spread of policies captures interventions aimed primarily at institutional and structural mechanisms, but also mechanisms focused on individual agency.</span>
<span id="cb26-627"><a href="#cb26-627" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-628"><a href="#cb26-628" aria-hidden="true" tabindex="-1"></a>Since policies employed in the pursuit of increased equality can take a wide form of actors, strategy approaches and implementation details,</span>
<span id="cb26-629"><a href="#cb26-629" aria-hidden="true" tabindex="-1"></a>the following synthesis will first categorise between the main thematic area and its associated interventions.</span>
<span id="cb26-630"><a href="#cb26-630" aria-hidden="true" tabindex="-1"></a>Individual observations are then descriptively distinguished between for the primary outcome variables (inequalities) of interest.</span>
<span id="cb26-631"><a href="#cb26-631" aria-hidden="true" tabindex="-1"></a>Thus, in the following synthesis each reviewed study will be analysed through the primary policies or mechanisms they use as independent variables to analyse the effects on a variety of inequalities.</span>
<span id="cb26-632"><a href="#cb26-632" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-633"><a href="#cb26-633" aria-hidden="true" tabindex="-1"></a>One of the primary lenses of inequality in viewing policy interventions to reduce inequalities in the world of work is that of income,</span>
<span id="cb26-634"><a href="#cb26-634" aria-hidden="true" tabindex="-1"></a>often measured for all people throughout a country (vertical inequality) or subsets thereof (horizontal inequality).</span>
<span id="cb26-635"><a href="#cb26-635" aria-hidden="true" tabindex="-1"></a>At the same time, the primacy of income should not be overstated as disregarding the intersectional nature of inequalities could lead to diminished intervention outcomes through adverse targeting.</span>
<span id="cb26-636"><a href="#cb26-636" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-637"><a href="#cb26-637" aria-hidden="true" tabindex="-1"></a>Each main thematic area will be preceded by a table presenting a summary of findings for the respective policies,</span>
<span id="cb26-638"><a href="#cb26-638" aria-hidden="true" tabindex="-1"></a>their identified channels and an estimation of their strength of evidence base.</span>
<span id="cb26-639"><a href="#cb26-639" aria-hidden="true" tabindex="-1"></a>Afterwards, the analytical lens will be inverted for the discussion (Section 5) and the reviewed studies discussed from a perspective of their analysed inequalities and limitations,</span>
<span id="cb26-640"><a href="#cb26-640" aria-hidden="true" tabindex="-1"></a>to better identify areas of strong analytical lenses or areas of more limited analyses.</span>
<span id="cb26-641"><a href="#cb26-641" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-642"><a href="#cb26-642" aria-hidden="true" tabindex="-1"></a><span class="fu">## Institutional</span></span>
<span id="cb26-643"><a href="#cb26-643" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-644"><a href="#cb26-644" aria-hidden="true" tabindex="-1"></a>{{&lt; portrait &gt;}}</span>
<span id="cb26-645"><a href="#cb26-645" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-646"><a href="#cb26-646" aria-hidden="true" tabindex="-1"></a>::: {#tbl-findings-institutional}</span>
<span id="cb26-647"><a href="#cb26-647" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-650"><a href="#cb26-650" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-651"><a href="#cb26-651" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-findings-institutional</span></span>
<span id="cb26-652"><a href="#cb26-652" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb26-653"><a href="#cb26-653" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-654"><a href="#cb26-654" aria-hidden="true" tabindex="-1"></a>study_strength_bins <span class="op">=</span> {</span>
<span id="cb26-655"><a href="#cb26-655" aria-hidden="true" tabindex="-1"></a> <span class="fl">0.0</span>: <span class="vs">r&quot;\-&quot;</span>,</span>
<span id="cb26-656"><a href="#cb26-656" aria-hidden="true" tabindex="-1"></a> <span class="fl">5.0</span>: <span class="vs">r&quot;\+&quot;</span>,</span>
<span id="cb26-657"><a href="#cb26-657" aria-hidden="true" tabindex="-1"></a> <span class="fl">10.0</span>: <span class="vs">r&quot;\++&quot;</span>,</span>
<span id="cb26-658"><a href="#cb26-658" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb26-659"><a href="#cb26-659" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> strength_for(val):</span>
<span id="cb26-660"><a href="#cb26-660" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="bu">list</span>(study_strength_bins.keys())[<span class="bu">list</span>(study_strength_bins.values()).index(val)]</span>
<span id="cb26-661"><a href="#cb26-661" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-662"><a href="#cb26-662" aria-hidden="true" tabindex="-1"></a>findings_institutional <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-institutional.csv&quot;</span>)</span>
<span id="cb26-663"><a href="#cb26-663" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_institutional, by_intervention, study_strength_bins)</span>
<span id="cb26-664"><a href="#cb26-664" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-665"><a href="#cb26-665" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-666"><a href="#cb26-666" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-667"><a href="#cb26-667" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-668"><a href="#cb26-668" aria-hidden="true" tabindex="-1"></a>Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.</span>
<span id="cb26-669"><a href="#cb26-669" aria-hidden="true" tabindex="-1"></a>Validities are segmented to a weak (-) evidence base under a validity ranking of <span class="in">`{python} strength_for(r&quot;\+&quot;)`</span>,</span>
<span id="cb26-670"><a href="#cb26-670" aria-hidden="true" tabindex="-1"></a>evidential (+) from <span class="in">`{python} strength_for(r&quot;\+&quot;)`</span> and under <span class="in">`{python} strength_for(r&quot;\++&quot;)`</span> and strong evidence base (++) for <span class="in">`{python} strength_for(r&quot;\++&quot;)`</span> and above.</span>
<span id="cb26-671"><a href="#cb26-671" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-672"><a href="#cb26-672" aria-hidden="true" tabindex="-1"></a>Summary of main findings for institutional policies</span>
<span id="cb26-673"><a href="#cb26-673" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-674"><a href="#cb26-674" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-675"><a href="#cb26-675" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-676"><a href="#cb26-676" aria-hidden="true" tabindex="-1"></a>{{&lt; landscape &gt;}}</span>
<span id="cb26-677"><a href="#cb26-677" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-678"><a href="#cb26-678" aria-hidden="true" tabindex="-1"></a><span class="fu">### Labour laws and regulatory systems</span></span>
<span id="cb26-679"><a href="#cb26-679" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-680"><a href="#cb26-680" aria-hidden="true" tabindex="-1"></a>@Adams2015 study the effects of labour, business and credit regulations and looks at their long-term correlations to income inequality in developing countries from 1970 to 2012.</span>
<span id="cb26-681"><a href="#cb26-681" aria-hidden="true" tabindex="-1"></a>Additionally, the study looks at the effects of FDI and school enrolment, which will be reviewed in their respective policy sections.</span>
<span id="cb26-682"><a href="#cb26-682" aria-hidden="true" tabindex="-1"></a>They find that in MENA, SSA, LAC and to some extend AP increased labour and business regulations are actually negatively related to equitable income distribution, with market regulation not having significant effects.</span>
<span id="cb26-683"><a href="#cb26-683" aria-hidden="true" tabindex="-1"></a>The authors identify developing countries lacking in institutional capability to accomplish regulatory policies optimized for benefits and see the need for policies requiring more specific targeting of inequality reduction as their agenda.</span>
<span id="cb26-684"><a href="#cb26-684" aria-hidden="true" tabindex="-1"></a>Overall, the authors suggest that regulatory policy in developing countries needs to be built for their specific contexts and not exported from developed countries due to their different institutional capabilities and structural make-up.</span>
<span id="cb26-685"><a href="#cb26-685" aria-hidden="true" tabindex="-1"></a>The study is limited in its design focus relying purely on the macro-level regional analyses and can thus,</span>
<span id="cb26-686"><a href="#cb26-686" aria-hidden="true" tabindex="-1"></a>when finding correlations towards income inequality, not necessarily drill down into their qualitative root causes.</span>
<span id="cb26-687"><a href="#cb26-687" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-688"><a href="#cb26-688" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- maternity leave and benefits --&gt;</span></span>
<span id="cb26-689"><a href="#cb26-689" aria-hidden="true" tabindex="-1"></a>@Broadway2020 study the introduction of universal paid maternal leave in Australia, looking at its impacts on mothers returning to work and the conditions they return under.</span>
<span id="cb26-690"><a href="#cb26-690" aria-hidden="true" tabindex="-1"></a>It finds that, while there is a short-term decrease of mothers returning to work since they make use of the introduced leave period, over the long-term (after six to nine months) there is a significant positive impact on return to work.</span>
<span id="cb26-691"><a href="#cb26-691" aria-hidden="true" tabindex="-1"></a>Furthermore, there is a positive impact on returning to work in the same job and under the same conditions,</span>
<span id="cb26-692"><a href="#cb26-692" aria-hidden="true" tabindex="-1"></a>the effects of which are stronger for more disadvantaged mothers (measured through income, education and access to employer-funded leave).</span>
<span id="cb26-693"><a href="#cb26-693" aria-hidden="true" tabindex="-1"></a>This suggests that the intervention reduced the opportunity costs for delaying the return to work, and especially for those women that did not have employer-funded leave options, directly benefiting more disadvantaged mothers.</span>
<span id="cb26-694"><a href="#cb26-694" aria-hidden="true" tabindex="-1"></a>Some potential biases of the study are its inability to account for child-care costs, as well as not being able to fully exclude selection bias into motherhood.</span>
<span id="cb26-695"><a href="#cb26-695" aria-hidden="true" tabindex="-1"></a>There also remains the potential of results being biased through pre-birth labour supply effects or the results of the financial crisis, which may create a down-ward bias for either the short- or long-term effects.</span>
<span id="cb26-696"><a href="#cb26-696" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-697"><a href="#cb26-697" aria-hidden="true" tabindex="-1"></a>@Dustmann2012 analyse the long-run effects on children&#39;s outcomes of increasing the period of paid leave for mothers in Germany.</span>
<span id="cb26-698"><a href="#cb26-698" aria-hidden="true" tabindex="-1"></a>While the study focuses on the children&#39;s outcomes, it also analyses the effects on the return to work rates and cumulative incomes of the policies within the first 40 months after childbirth.</span>
<span id="cb26-699"><a href="#cb26-699" aria-hidden="true" tabindex="-1"></a>It finds that, while short-term increases of paid leave periods (up to 6 months) significantly increased incomes, over longer periods (10-36 months) the cumulative incomes in fact decreased significantly,</span>
<span id="cb26-700"><a href="#cb26-700" aria-hidden="true" tabindex="-1"></a>marginally for low-wage mothers for 10 month periods, and across all wage segments for 36 month periods.</span>
<span id="cb26-701"><a href="#cb26-701" aria-hidden="true" tabindex="-1"></a>For the share of mothers returning to work, it finds that there is a significant increase in the months away from work among all wage segments for all paid leave period increases, positively correlated with their length.</span>
<span id="cb26-702"><a href="#cb26-702" aria-hidden="true" tabindex="-1"></a>Still similar numbers of mothers return once the leave period ends, though with significant decreases for leave periods from 18 to 36 months.</span>
<span id="cb26-703"><a href="#cb26-703" aria-hidden="true" tabindex="-1"></a>For its analysis of long-term educational outcomes on children, however, it does not find any evidence for the expansions improving children&#39;s outcomes, even suggesting a possible decrease of educational attainment for the paid leave extension to 36 months.<span class="ot">[^dustmann-childoutcomes]</span></span>
<span id="cb26-704"><a href="#cb26-704" aria-hidden="true" tabindex="-1"></a>Some limitations of the study include its sample being restricted to mothers who go on maternity leave and some control group identification restrictions possibly introducing some sampling bias.</span>
<span id="cb26-705"><a href="#cb26-705" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-706"><a href="#cb26-706" aria-hidden="true" tabindex="-1"></a><span class="ot">[^dustmann-childoutcomes]: </span>The authors suggest that the negative effect for children under the long-term paid leave program of 36 months may stem from the fact that children require more external stimuli (aside from the mother) before this period ends, as well as the negative long-term effects of the mother&#39;s significantly reduced income for the long-term leave periods.</span>
<span id="cb26-707"><a href="#cb26-707" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-708"><a href="#cb26-708" aria-hidden="true" tabindex="-1"></a>In a study on the effects of introductions of a variety of maternity leave laws in Japan, @Mun2018 look at the effects on employment numbers and job quality in managerial positions of women.</span>
<span id="cb26-709"><a href="#cb26-709" aria-hidden="true" tabindex="-1"></a>Contrary to notions of demand-side mechanisms of the welfare state paradox, with women being less represented in high-authority employment positions due to hiring or workplace discrimination against them with increased maternity benefits,</span>
<span id="cb26-710"><a href="#cb26-710" aria-hidden="true" tabindex="-1"></a>it finds that this is not the case for the Japanese labour market between 1992 and 2009.</span>
<span id="cb26-711"><a href="#cb26-711" aria-hidden="true" tabindex="-1"></a>There were no increases in hiring discrimination against women, and either no significant change in promotions for firms not providing paid leave before the laws or instead a positive impact on promotions for firms that already provided paid leave.</span>
<span id="cb26-712"><a href="#cb26-712" aria-hidden="true" tabindex="-1"></a>The authors suggest the additional promotions were primarily based on voluntary compliance of firms in order to maintain positive reputations,</span>
<span id="cb26-713"><a href="#cb26-713" aria-hidden="true" tabindex="-1"></a>signalled through a larger positive response to incentive-based laws than for mandate-based ones.</span>
<span id="cb26-714"><a href="#cb26-714" aria-hidden="true" tabindex="-1"></a>Additionally, the authors suggest that the welfare paradox may rather be due to supply-side mechanisms, based on individual career planning, as well as reinforced along existing gender divisions of household labour which may increase alongside the laws.</span>
<span id="cb26-715"><a href="#cb26-715" aria-hidden="true" tabindex="-1"></a>Limitations of the study include foremost its limited generalizability due to the unique Japanese institutional labour market structure (with many employments, for example, being within a single firm until retirement), as well as no ability yet to measure the true causes and effects of adhering to the voluntary incentive-based labour policies, with lasting effects or done as symbolic compliance efforts and mere impression management.</span>
<span id="cb26-716"><a href="#cb26-716" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-717"><a href="#cb26-717" aria-hidden="true" tabindex="-1"></a>@Davies2022 conduct a study on the return to work ratios for high-skill women workers in public academic universities in the United Kingdom, comparing the results for those in fixed-term contract work versus those in open-ended contracts.</span>
<span id="cb26-718"><a href="#cb26-718" aria-hidden="true" tabindex="-1"></a>It finds that there is a significantly decreased return to work probability for those working under fixed-term contracts, and most universities providing policies with more limited access to maternity payment for fixed-contract staff.</span>
<span id="cb26-719"><a href="#cb26-719" aria-hidden="true" tabindex="-1"></a>This is possibly due to provisions in the policies implicitly working against utilization under fixed-terms:</span>
<span id="cb26-720"><a href="#cb26-720" aria-hidden="true" tabindex="-1"></a>there are strict policies on payments if a contract ends before the maternity leave period is over, and obligations on repayments if not staying in the position long enough after rtw.</span>
<span id="cb26-721"><a href="#cb26-721" aria-hidden="true" tabindex="-1"></a>Additionally, most policies require long-term continuous service before qualifying for enhanced payments in the maternity policies.</span>
<span id="cb26-722"><a href="#cb26-722" aria-hidden="true" tabindex="-1"></a>There is high internal heterogeneity between the universities, primarily due to the diverging maternity policy documents, only a small number of the overall dataset providing favourable conditions for fixed-term work within.</span>
<span id="cb26-723"><a href="#cb26-723" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-724"><a href="#cb26-724" aria-hidden="true" tabindex="-1"></a><span class="fu">### Minimum wage laws</span></span>
<span id="cb26-725"><a href="#cb26-725" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-726"><a href="#cb26-726" aria-hidden="true" tabindex="-1"></a>@Chao2022, in a study looking at the effects of minimum wage increases on a country&#39;s income inequality, analyse the impacts in a sample of 43 countries, both LMIC and HIC.</span>
<span id="cb26-727"><a href="#cb26-727" aria-hidden="true" tabindex="-1"></a>Using a general-equilibrium model, it finds that there are differences between the short-term and long-term effects of the increase:</span>
<span id="cb26-728"><a href="#cb26-728" aria-hidden="true" tabindex="-1"></a>In the short term it leads to a reduction of the skilled-unskilled wage gap, however an increase in unemployment and welfare,</span>
<span id="cb26-729"><a href="#cb26-729" aria-hidden="true" tabindex="-1"></a>while in the long term the results are an overall decrease in wage inequality as well as improved social welfare.</span>
<span id="cb26-730"><a href="#cb26-730" aria-hidden="true" tabindex="-1"></a>It finds those results primarily stem from LMIC which experience significant effects driven by a long-term firm exit from the urban manufacturing sector thereby increasing available capital for the rural agricultural sector, while in HIC the results remain insignificant.</span>
<span id="cb26-731"><a href="#cb26-731" aria-hidden="true" tabindex="-1"></a>The study uses the Gini coefficient for identifying a country&#39;s inequality.</span>
<span id="cb26-732"><a href="#cb26-732" aria-hidden="true" tabindex="-1"></a>Some limitations of the study include the necessity to omit short-term urban firm exit for the impact to be significant, as well as requiring the, reasonable but necessary, prior assumption of decreased inequality through increased rural agricultural capital.</span>
<span id="cb26-733"><a href="#cb26-733" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-734"><a href="#cb26-734" aria-hidden="true" tabindex="-1"></a>@Alinaghi2020 conduct a study using a microsimulation to estimate the effects of a minimum wage increase in New Zealand on overall income inequality and further disaggregation along gender and poverty lines.</span>
<span id="cb26-735"><a href="#cb26-735" aria-hidden="true" tabindex="-1"></a>It finds limited redistributional effects for the policy, with negligible impact on overall income inequality and the possibility of actually increasing inequalities among lower percentile income households.</span>
<span id="cb26-736"><a href="#cb26-736" aria-hidden="true" tabindex="-1"></a>Additionally, while it finds a significant reduction in some poverty measures for sole parents that are in employment, when looking at sole parents overall the effects become insignificant again.</span>
<span id="cb26-737"><a href="#cb26-737" aria-hidden="true" tabindex="-1"></a>The authors suggest this points to bad programme targeting, which at best has negligible positive impact on income equality and at worst worsens income inequality in lower income households, due to may low-wage earners being the secondary earners of higher-income households but low-wage households often having no wage earners at all.</span>
<span id="cb26-738"><a href="#cb26-738" aria-hidden="true" tabindex="-1"></a>A pertinent limitation of the study includes its large sample weights possibly biasing the impacts on specific groups such as sole parents and thus being careful not to overestimate their significance.</span>
<span id="cb26-739"><a href="#cb26-739" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-740"><a href="#cb26-740" aria-hidden="true" tabindex="-1"></a>In a study on the impacts of minimum wage increases in Ecuador @Wong2019 specifically looks at the income and hours worked of low-wage earners to analyse the policies effectiveness.</span>
<span id="cb26-741"><a href="#cb26-741" aria-hidden="true" tabindex="-1"></a>The study finds that, generally, there was a significant increase on the income of low-wage earners and also a significant increase on wage workers hours worked which would reflect positively on a decrease in the country&#39;s income inequality.</span>
<span id="cb26-742"><a href="#cb26-742" aria-hidden="true" tabindex="-1"></a>At the same time, it finds some potential negative effects on the income of high earners, suggesting an income-compression effect as employers freeze or reduce high-earners wages to offset low-earners new floors.</span>
<span id="cb26-743"><a href="#cb26-743" aria-hidden="true" tabindex="-1"></a>The findings hide internal heterogeneity, however:</span>
<span id="cb26-744"><a href="#cb26-744" aria-hidden="true" tabindex="-1"></a>For income the effect is largest for agricultural workers while for women the effect is significantly smaller than overall affected workers.</span>
<span id="cb26-745"><a href="#cb26-745" aria-hidden="true" tabindex="-1"></a>For hours worked there is a significant negative impact on women&#39;s hours worked, a fact which may point to a decreased intensive margin for female workers and thus also affect their lower income increases.</span>
<span id="cb26-746"><a href="#cb26-746" aria-hidden="true" tabindex="-1"></a>Limitations of the study include some sort-dependency in their panel data and only being able to account for effects during a period of economic growth.</span>
<span id="cb26-747"><a href="#cb26-747" aria-hidden="true" tabindex="-1"></a>Thus, while overall income inequality seems well targeted in the intervention, it may exacerbate the gender gap that already existed at the same time.</span>
<span id="cb26-748"><a href="#cb26-748" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-749"><a href="#cb26-749" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- non-spatial policy but spatial effects --&gt;</span></span>
<span id="cb26-750"><a href="#cb26-750" aria-hidden="true" tabindex="-1"></a>@Gilbert2001 undertake a study looking at the distributional effects of introducing a minimum wage in Britain, with a specific spatial component.</span>
<span id="cb26-751"><a href="#cb26-751" aria-hidden="true" tabindex="-1"></a>Overall it finds little effect on income inequality in the country.</span>
<span id="cb26-752"><a href="#cb26-752" aria-hidden="true" tabindex="-1"></a>It finds that the effects on rural areas differ depending on their proximity to urban areas.</span>
<span id="cb26-753"><a href="#cb26-753" aria-hidden="true" tabindex="-1"></a>While overall income inequality only decreases a small amount, the intervention results in effective targeting with remote rural households having around twice the reduction in inequality compared to others.</span>
<span id="cb26-754"><a href="#cb26-754" aria-hidden="true" tabindex="-1"></a>Rural areas that are accessible to urban markets are less affected, with insignificant impacts to overall income inequality reduction.</span>
<span id="cb26-755"><a href="#cb26-755" aria-hidden="true" tabindex="-1"></a>One limit of the study is that it has to assume no effects on employment after the enaction of the minimum wage for its results to hold.</span>
<span id="cb26-756"><a href="#cb26-756" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-757"><a href="#cb26-757" aria-hidden="true" tabindex="-1"></a>In a study on the impacts of minimum wage and direct cash transfers in Brazil on the country&#39;s income inequality,</span>
<span id="cb26-758"><a href="#cb26-758" aria-hidden="true" tabindex="-1"></a>@SilveiraNeto2011 especially analyse the way the policies interact with spatial inequalities.</span>
<span id="cb26-759"><a href="#cb26-759" aria-hidden="true" tabindex="-1"></a>It finds that incomes between regions have converged during the time frame and overall the cash transfers under the &#39;Bolsa Familia&#39; programme and minimum wage were accounting for 26.2% of the effect.</span>
<span id="cb26-760"><a href="#cb26-760" aria-hidden="true" tabindex="-1"></a>Minimum wage contributed 16.6% of the effect to overall Gini reduction between the regions while cash transfers accounted for 9.6% of the effect.</span>
<span id="cb26-761"><a href="#cb26-761" aria-hidden="true" tabindex="-1"></a>The authors argue that this highlights the way even non-spatial policies can have a positive (or, with worse targeting or selection, negative) influence on spatial inequalities,</span>
<span id="cb26-762"><a href="#cb26-762" aria-hidden="true" tabindex="-1"></a>as transfers occurring predominantly to poorer regions and minimum wages having larger impacts in those regions created quasi-regional effects without being explicitly addressed in the policies.</span>
<span id="cb26-763"><a href="#cb26-763" aria-hidden="true" tabindex="-1"></a>Some limitations include limited underlying data only making it possible to estimate the cash transfer impacts for the analysis end-line,</span>
<span id="cb26-764"><a href="#cb26-764" aria-hidden="true" tabindex="-1"></a>and minimum wage effects having to be constructed from the effects wages equal to minimum wage.</span>
<span id="cb26-765"><a href="#cb26-765" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-766"><a href="#cb26-766" aria-hidden="true" tabindex="-1"></a>@Militaru2019 conduct an analysis of the effects of minimum wage increases on income inequality in Romania.</span>
<span id="cb26-767"><a href="#cb26-767" aria-hidden="true" tabindex="-1"></a>They find that, generally, minimum wage increases correlate with small wage inequality decreases, but carry a larger impact for women.</span>
<span id="cb26-768"><a href="#cb26-768" aria-hidden="true" tabindex="-1"></a>The channels for the policies effects are two-fold in that there is an inequality decrease as the number of wage earners in total number of employees increases,</span>
<span id="cb26-769"><a href="#cb26-769" aria-hidden="true" tabindex="-1"></a>as well as the concentration of workers at the minimum level mattering --- the probable channel for a larger impact on women since they make up larger parts of low-income and minimum wage households in Romania.</span>
<span id="cb26-770"><a href="#cb26-770" aria-hidden="true" tabindex="-1"></a>Limitations to the study are some remaining unobservables for the final inequality outcomes (such as other wages or incomes), the sample over-representing employees and not being able to account for any tax evasion or behavioural changes in the model.</span>
<span id="cb26-771"><a href="#cb26-771" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-772"><a href="#cb26-772" aria-hidden="true" tabindex="-1"></a>@Sotomayor2021 conducts a study on the impact of subsequent minimum wage floor introductions on poverty and income inequality in Brazil.</span>
<span id="cb26-773"><a href="#cb26-773" aria-hidden="true" tabindex="-1"></a>He finds that in the short-term (3 months) wage floor increases reduced poverty by 2.8% and reduced income inequality by 2.4%.</span>
<span id="cb26-774"><a href="#cb26-774" aria-hidden="true" tabindex="-1"></a>Over the longer-term though these impacts decrease,</span>
<span id="cb26-775"><a href="#cb26-775" aria-hidden="true" tabindex="-1"></a>the minimum wage increases only show diminishing returns when the legal minimum is already high in relation to median earnings.</span>
<span id="cb26-776"><a href="#cb26-776" aria-hidden="true" tabindex="-1"></a>It suggests that additional unemployment costs, created through new job losses through the introduction, are offset by the increased benefits --- the higher wages for workers.</span>
<span id="cb26-777"><a href="#cb26-777" aria-hidden="true" tabindex="-1"></a>The authors also suggest an inelastic relationship between increases and poverty incidence.</span>
<span id="cb26-778"><a href="#cb26-778" aria-hidden="true" tabindex="-1"></a>One limitation of the study is the limit of tracking individuals in the underlying data which can not account for people moving household to new locations.</span>
<span id="cb26-779"><a href="#cb26-779" aria-hidden="true" tabindex="-1"></a>The data can only track individual dwellings --- instead of the households and inhabitants within --- and thus resembles repeated cross-sectional data more than actual panel data.</span>
<span id="cb26-780"><a href="#cb26-780" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-781"><a href="#cb26-781" aria-hidden="true" tabindex="-1"></a><span class="fu">### Collective bargaining</span></span>
<span id="cb26-782"><a href="#cb26-782" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-783"><a href="#cb26-783" aria-hidden="true" tabindex="-1"></a>@Alexiou2023 study the effects of both political orientation of governments&#39; parties and a country&#39;s trade unionisation on its income inequality.</span>
<span id="cb26-784"><a href="#cb26-784" aria-hidden="true" tabindex="-1"></a>They find that, generally, strong unionisation is strongly related to decreasing income inequality, most likely through a redistribution of political power through collective mobilization in national contexts of stronger unions.</span>
<span id="cb26-785"><a href="#cb26-785" aria-hidden="true" tabindex="-1"></a>It also suggests that in contexts of weaker unionisation, post-redistribution income inequality is higher, thus also fostering unequal redistributive policies.</span>
<span id="cb26-786"><a href="#cb26-786" aria-hidden="true" tabindex="-1"></a>Lastly, it finds positive relations between right-wing orientation of a country&#39;s government and its income inequality, with more mixed results for centrist governments pointing to potential fragmentations in their redistributive policy approaches.</span>
<span id="cb26-787"><a href="#cb26-787" aria-hidden="true" tabindex="-1"></a>The study is mostly limited in not being able to account for individual drivers (or barriers) and can thus not disaggregate for the effects for example arbitration or collective bargaining.</span>
<span id="cb26-788"><a href="#cb26-788" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-789"><a href="#cb26-789" aria-hidden="true" tabindex="-1"></a>@Dieckhoff2015 undertake a study on the effect of trade unionisation in European labour markets, with a specific emphasis on its effects on gender inequalities.</span>
<span id="cb26-790"><a href="#cb26-790" aria-hidden="true" tabindex="-1"></a>It finds, first of all, that increased unionisation is related to the probability of being employed on a standard employment contract for both men and women.</span>
<span id="cb26-791"><a href="#cb26-791" aria-hidden="true" tabindex="-1"></a>It also finds no evidence that men seem to carry increased benefits from increased unionisation alone,</span>
<span id="cb26-792"><a href="#cb26-792" aria-hidden="true" tabindex="-1"></a>although in combination with temporary contract and family policy re-regulations, men can experience greater benefits than women.</span>
<span id="cb26-793"><a href="#cb26-793" aria-hidden="true" tabindex="-1"></a>At the same time women&#39;s employment under standard contracts does not decrease, such that there is no absolute detrimental effect for either gender.</span>
<span id="cb26-794"><a href="#cb26-794" aria-hidden="true" tabindex="-1"></a>It does, however, leave open the question of the allocation of relative benefits between the genders through unionisation efforts.</span>
<span id="cb26-795"><a href="#cb26-795" aria-hidden="true" tabindex="-1"></a>The study is limited in that, by averaging outcomes across European nations, it can not account for nation-specific labour market contexts or gender disaggregations.</span>
<span id="cb26-796"><a href="#cb26-796" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-797"><a href="#cb26-797" aria-hidden="true" tabindex="-1"></a>@Cardinaleschi2019 study the wage gap in the Italian labour market, looking especially at the effects of collective negotiation practices.</span>
<span id="cb26-798"><a href="#cb26-798" aria-hidden="true" tabindex="-1"></a>It finds that the Italian labour market&#39;s wage gap exists primarily due to occupational segregation between the genders, with women often working in more &#39;feminized&#39; industries, and not due to educational lag by women in Italy.</span>
<span id="cb26-799"><a href="#cb26-799" aria-hidden="true" tabindex="-1"></a>It also finds that collective negotiation practices targeting especially managerial representation and wages do address the gender pay gap, but only marginally significantly.</span>
<span id="cb26-800"><a href="#cb26-800" aria-hidden="true" tabindex="-1"></a>The primary channel for only marginal significance stems from internal heterogeneity in that only the median part of wage distributions is significantly affected by the measures.</span>
<span id="cb26-801"><a href="#cb26-801" aria-hidden="true" tabindex="-1"></a>Instead, the authors recommend a stronger mix of policy approaches, also considering the human-capital aspects with for example active labour-market policies targeting it.</span>
<span id="cb26-802"><a href="#cb26-802" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-803"><a href="#cb26-803" aria-hidden="true" tabindex="-1"></a>@Ferguson2015 conducts a study on the effects of a more unionised workforce in the United States, on the representation of women and minorities in the management of enterprises.</span>
<span id="cb26-804"><a href="#cb26-804" aria-hidden="true" tabindex="-1"></a>It finds that while stronger unionisation is associated both with more women and more minorities represented in the overall workforce and in management, this effect is only marginally significant.</span>
<span id="cb26-805"><a href="#cb26-805" aria-hidden="true" tabindex="-1"></a>Additionally, there are drivers which may be based on unobservables and not a direct effect ---</span>
<span id="cb26-806"><a href="#cb26-806" aria-hidden="true" tabindex="-1"></a>it may be a selection effect of more unionised enterprises.</span>
<span id="cb26-807"><a href="#cb26-807" aria-hidden="true" tabindex="-1"></a>It uses union elections as its base of analysis, and thus can not exclude self-selection effects of people joining more heavily unionised enterprises rather than unionisation increasing representation in its conclusions.</span>
<span id="cb26-808"><a href="#cb26-808" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-809"><a href="#cb26-809" aria-hidden="true" tabindex="-1"></a>@Ahumada2023 on the other hand create a study on the effects of unequal distributions of political power on the extent and provision of collective labour rights.</span>
<span id="cb26-810"><a href="#cb26-810" aria-hidden="true" tabindex="-1"></a>It is a combination of quantitative global comparison with qualitative case studies for Argentina and Chile.</span>
<span id="cb26-811"><a href="#cb26-811" aria-hidden="true" tabindex="-1"></a>It finds that, for societies in which power is more unequally distributed, collective bargaining possibilities are more limited and weaker.</span>
<span id="cb26-812"><a href="#cb26-812" aria-hidden="true" tabindex="-1"></a>It suggests that, aside from a less entrenched trade unionisation in the country, the primary channel for its weakening are that existing collective labour rights are often either restricted or disregarded outright.</span>
<span id="cb26-813"><a href="#cb26-813" aria-hidden="true" tabindex="-1"></a>Employers were restricted in their ability to effectively conduct lobbying, and made more vulnerable to what the authors suggest are &#39;divide-and-conquer&#39; strategies by government with a strongly entrenched trade unionisation, due to being more separate and uncoordinated.</span>
<span id="cb26-814"><a href="#cb26-814" aria-hidden="true" tabindex="-1"></a>A limit is the strong institutional context of the two countries which makes generalizable application of its underlying channels more difficult to the overarching quantitative analysis of inequality outcomes.</span>
<span id="cb26-815"><a href="#cb26-815" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-816"><a href="#cb26-816" aria-hidden="true" tabindex="-1"></a><span class="fu">### Workfare programmes</span></span>
<span id="cb26-817"><a href="#cb26-817" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-818"><a href="#cb26-818" aria-hidden="true" tabindex="-1"></a>@Whitworth2021 analyse the spatial consequences of a UK work programme on spatial factors of job deprivation or opportunity increases.</span>
<span id="cb26-819"><a href="#cb26-819" aria-hidden="true" tabindex="-1"></a>The programme follows a quasi-marketized approach of rewarding employment-favourable results of transitions into employment and further sustained months in employment.</span>
<span id="cb26-820"><a href="#cb26-820" aria-hidden="true" tabindex="-1"></a>The author argues, however, that the non-spatial implementation of the policy leads to spatial outcomes.</span>
<span id="cb26-821"><a href="#cb26-821" aria-hidden="true" tabindex="-1"></a>Founded on the approach of social &#39;creaming&#39; and &#39;parking&#39; and applied to the spatial dimension,</span>
<span id="cb26-822"><a href="#cb26-822" aria-hidden="true" tabindex="-1"></a>the study shows that already job-deprived areas indeed experience further deprivations under the programme,</span>
<span id="cb26-823"><a href="#cb26-823" aria-hidden="true" tabindex="-1"></a>while non-deprived areas are correlated with positive impacts, thereby further deteriorating spatial inequality outcomes.</span>
<span id="cb26-824"><a href="#cb26-824" aria-hidden="true" tabindex="-1"></a>This occurs because of providers in the programme de-prioritizing the already deprived areas (&#39;parking&#39;) in favour prioritizing wealthier areas for improved within-programme results.</span>
<span id="cb26-825"><a href="#cb26-825" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-826"><a href="#cb26-826" aria-hidden="true" tabindex="-1"></a>@Li2022 conduct a study on the effects of previous inequalities on the outcomes of a work programme in India intended to provide job opportunity equality for already disadvantages population.</span>
<span id="cb26-827"><a href="#cb26-827" aria-hidden="true" tabindex="-1"></a>It specifically looks at the NREGA programme in India, and takes the land-ownership inequality measured through the Gini coefficient as its preceding inequality.<span class="ot">[^nrega]</span></span>
<span id="cb26-828"><a href="#cb26-828" aria-hidden="true" tabindex="-1"></a>The study finds that there is significantly negative relationship between the Gini coefficient and the provision of jobs through the work programme.</span>
<span id="cb26-829"><a href="#cb26-829" aria-hidden="true" tabindex="-1"></a>In other words, the workfare policy implemented at least in part to reduce a district&#39;s inequality seems to be less effective if there is a larger prior capital inequality.</span>
<span id="cb26-830"><a href="#cb26-830" aria-hidden="true" tabindex="-1"></a>The authors see the primary channel to be the landlords&#39; opposition to broad workfare programme introduction since they are often followed by overall wage increases in the districts.</span>
<span id="cb26-831"><a href="#cb26-831" aria-hidden="true" tabindex="-1"></a>They suggest that in more inequally distributed channels the landlords can use a more unequal power structure to lobby and effect political power decreasing the effectiveness of the programmes,</span>
<span id="cb26-832"><a href="#cb26-832" aria-hidden="true" tabindex="-1"></a>in addition to often reduced collective bargaining power on the side of labour in these districts.</span>
<span id="cb26-833"><a href="#cb26-833" aria-hidden="true" tabindex="-1"></a>The results show the same trends for measurement of land inequality using the share of land owned by the top 10 per cent largest holdings instead.</span>
<span id="cb26-834"><a href="#cb26-834" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-835"><a href="#cb26-835" aria-hidden="true" tabindex="-1"></a><span class="ot">[^nrega]: </span>The National Rural Employment Guarantee Scheme (NREGA) is a workfare programme implemented in India, the largest of its kind, which seeks to provide 100 days of employment for each household per year. It was rolled out from 2005 over several phases until it reached all districts in India in 2008.</span>
<span id="cb26-836"><a href="#cb26-836" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-837"><a href="#cb26-837" aria-hidden="true" tabindex="-1"></a><span class="fu">### Social protection</span></span>
<span id="cb26-838"><a href="#cb26-838" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-839"><a href="#cb26-839" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">TODO</span><span class="co"> Include part of Pi2016 on social security --&gt;</span></span>
<span id="cb26-840"><a href="#cb26-840" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-841"><a href="#cb26-841" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- social assistance benefits and wages --&gt;</span></span>
<span id="cb26-842"><a href="#cb26-842" aria-hidden="true" tabindex="-1"></a>@Wang2016 undertake an observational study on the levels of social assistance benefits and wages in a national comparative study within 26 OECD countries.</span>
<span id="cb26-843"><a href="#cb26-843" aria-hidden="true" tabindex="-1"></a>It finds that real minimum income benefit levels generally increased in most countries from 1990 to 2009, with only a few countries, mostly in Eastern European welfare states, showing decreases during the time frame.</span>
<span id="cb26-844"><a href="#cb26-844" aria-hidden="true" tabindex="-1"></a>The majority of changes in real benefit levels are from deliberate policy changes and the study calculates them by a comparison of the changes in benefit levels to the changes in consumer prices.</span>
<span id="cb26-845"><a href="#cb26-845" aria-hidden="true" tabindex="-1"></a>Secondly, it finds that changes for income replacement rates are more mixed, with rates decreasing even in some countries which have increasing real benefits levels.</span>
<span id="cb26-846"><a href="#cb26-846" aria-hidden="true" tabindex="-1"></a>The study suggests this is because benefit levels are in most cases not linked to wages and policy changes also do not take changes in wages into account resulting in diverging benefit levels and wages, which may lead to exacerbating inequality gaps between income groups.</span>
<span id="cb26-847"><a href="#cb26-847" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-848"><a href="#cb26-848" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- conditional cash transfer --&gt;</span></span>
<span id="cb26-849"><a href="#cb26-849" aria-hidden="true" tabindex="-1"></a>@Debowicz2014 conduct a study looking at the impact of the cash transfer programme Oportunidades in Mexico, conditioned on a household&#39;s children school attendance, on income inequality among others.</span>
<span id="cb26-850"><a href="#cb26-850" aria-hidden="true" tabindex="-1"></a>It finds that a combination of effects raises the average income of the poorest households by 23 percent.</span>
<span id="cb26-851"><a href="#cb26-851" aria-hidden="true" tabindex="-1"></a>The authors argue in the short run this benefits households through the direct cash influx itself, as well as generating a positive wage effect benefitting those who keep their children at work.</span>
<span id="cb26-852"><a href="#cb26-852" aria-hidden="true" tabindex="-1"></a>For the estimation of income inequality it uses the Gini coefficient.</span>
<span id="cb26-853"><a href="#cb26-853" aria-hidden="true" tabindex="-1"></a>Additionally, over the long-term for the children in the model there is a direct benefit for those whose human capital is increased due to the programme, but also an indirect benefit for those who did not increase their human capital, because of the increased scarcity of unskilled labor as a secondary effect.</span>
<span id="cb26-854"><a href="#cb26-854" aria-hidden="true" tabindex="-1"></a>Due to the relatively low cost of the programme if correctly targeted, it seems to have a significantly positive effect on the Mexican economy and its income equality.</span>
<span id="cb26-855"><a href="#cb26-855" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-856"><a href="#cb26-856" aria-hidden="true" tabindex="-1"></a>In a study on the labour force impacts for women @Hardoy2015 look at the effects of reducing overall child care costs in Norway through subsidies.</span>
<span id="cb26-857"><a href="#cb26-857" aria-hidden="true" tabindex="-1"></a>It finds that overall the reductions in child care cost increased the female labour supply in the country (by about 5 per cent),</span>
<span id="cb26-858"><a href="#cb26-858" aria-hidden="true" tabindex="-1"></a>while there were no significant impacts on mothers which already participated in the labour market.</span>
<span id="cb26-859"><a href="#cb26-859" aria-hidden="true" tabindex="-1"></a>It also finds some internal heterogeneity, with the impact being strongest for low-education mothers and low-income households,</span>
<span id="cb26-860"><a href="#cb26-860" aria-hidden="true" tabindex="-1"></a>a finding the authors expected due to day care expenditure representing a larger part of those households&#39; budgets thus creating a larger impact.</span>
<span id="cb26-861"><a href="#cb26-861" aria-hidden="true" tabindex="-1"></a>Though it may alternatively also be generated by the lower average pre-intervention employment rate for those households.</span>
<span id="cb26-862"><a href="#cb26-862" aria-hidden="true" tabindex="-1"></a>Interestingly when disaggregating by native and immigrant mothers there is only a significant impact on native mothers,</span>
<span id="cb26-863"><a href="#cb26-863" aria-hidden="true" tabindex="-1"></a>though the authors do not form an inference on why this difference would be.</span>
<span id="cb26-864"><a href="#cb26-864" aria-hidden="true" tabindex="-1"></a>A limitation of the study is that there was a simultaneous child care capacity increase in the country,</span>
<span id="cb26-865"><a href="#cb26-865" aria-hidden="true" tabindex="-1"></a>which may bias the labour market results due to being affected by both the cost reduction and the capacity increase.</span>
<span id="cb26-866"><a href="#cb26-866" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-867"><a href="#cb26-867" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- health care --&gt;</span></span>
<span id="cb26-868"><a href="#cb26-868" aria-hidden="true" tabindex="-1"></a>@Carstens2018 conduct an analysis of the potential factors influencing mentally ill individuals in the United States to participate in the labour force, using correlation between different programmes of Medicaid and labour force status.</span>
<span id="cb26-869"><a href="#cb26-869" aria-hidden="true" tabindex="-1"></a>In trying to find labour force participation predictors it finds employment motivating factors in reduced depression and anxiety, increased responsibility and problem-solving and stress management being positive predictors.</span>
<span id="cb26-870"><a href="#cb26-870" aria-hidden="true" tabindex="-1"></a>In turn increased stress, discrimination based on their mental, loss of free time, loss of government benefits and tests for illegal drugs were listed as barriers negatively associated with labour force participation.</span>
<span id="cb26-871"><a href="#cb26-871" aria-hidden="true" tabindex="-1"></a>For the government benefits, it finds significant variations for the different varieties of Medicaid programmes, with the strongest negative labour force participation correlated to Medicaid ABD, a programme for which it has to be demonstrated that an individual cannot work due to their disability.</span>
<span id="cb26-872"><a href="#cb26-872" aria-hidden="true" tabindex="-1"></a>The authors suggest this shows the primary channel of the programme becoming a benefit trap, with disability being determined by not working and benefits disappearing when participants enter the labour force, creating dependency to the programme as a primary barrier.</span>
<span id="cb26-873"><a href="#cb26-873" aria-hidden="true" tabindex="-1"></a>Two limitations of the study are its small sample size due to a low response rate, and an over-representation of racial minorities, women and older persons in the sample mentioned as introducing possible downward bias for measured labour force participation rates.</span>
<span id="cb26-874"><a href="#cb26-874" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-875"><a href="#cb26-875" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- UBI --&gt;</span></span>
<span id="cb26-876"><a href="#cb26-876" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">TODO</span><span class="co">! Add to main findings and possibly env protection/labour regulation --&gt;</span></span>
<span id="cb26-877"><a href="#cb26-877" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">TODO</span><span class="co"> Add quick sentence of Standing also looking into UBI --&gt;</span></span>
<span id="cb26-878"><a href="#cb26-878" aria-hidden="true" tabindex="-1"></a>@Cieplinski2021 undertake a simulation study on the income inequality effects of both a policy targeting a reduction in working time and the introduction of a UBI in Italy.</span>
<span id="cb26-879"><a href="#cb26-879" aria-hidden="true" tabindex="-1"></a>It finds that while both decrease overall income inequality, measured through Gini coefficient, they do so through different channels.</span>
<span id="cb26-880"><a href="#cb26-880" aria-hidden="true" tabindex="-1"></a>While provision of a UBI sustains aggregate demand, thereby spreading income in a more equitable manner,</span>
<span id="cb26-881"><a href="#cb26-881" aria-hidden="true" tabindex="-1"></a>working time reductions significantly decrease aggregate demand through lower individual income but significantly increases labour force participation and thus employment.</span>
<span id="cb26-882"><a href="#cb26-882" aria-hidden="true" tabindex="-1"></a>It also finds that through these channels of changing aggregate demand, the environmental outcomes are oppositional, with work time reduction decreasing and UBI increasing the overall ecological footprint.</span>
<span id="cb26-883"><a href="#cb26-883" aria-hidden="true" tabindex="-1"></a>One limitation of the study is the modelling assumption that workers will have to accept both lower income and lower consumption levels under a policy of work time reduction through stable labour market entry for the results to hold.</span>
<span id="cb26-884"><a href="#cb26-884" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-885"><a href="#cb26-885" aria-hidden="true" tabindex="-1"></a><span class="fu">## Structural</span></span>
<span id="cb26-886"><a href="#cb26-886" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-887"><a href="#cb26-887" aria-hidden="true" tabindex="-1"></a>{{&lt; portrait &gt;}}</span>
<span id="cb26-888"><a href="#cb26-888" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-889"><a href="#cb26-889" aria-hidden="true" tabindex="-1"></a>::: {#tbl-findings-structural}</span>
<span id="cb26-890"><a href="#cb26-890" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-893"><a href="#cb26-893" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-894"><a href="#cb26-894" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-findings-structural</span></span>
<span id="cb26-895"><a href="#cb26-895" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb26-896"><a href="#cb26-896" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-897"><a href="#cb26-897" aria-hidden="true" tabindex="-1"></a>findings_structural <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-structural.csv&quot;</span>)</span>
<span id="cb26-898"><a href="#cb26-898" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_structural, by_intervention, study_strength_bins)</span>
<span id="cb26-899"><a href="#cb26-899" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-900"><a href="#cb26-900" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-901"><a href="#cb26-901" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-902"><a href="#cb26-902" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-903"><a href="#cb26-903" aria-hidden="true" tabindex="-1"></a>Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.</span>
<span id="cb26-904"><a href="#cb26-904" aria-hidden="true" tabindex="-1"></a>Validities are segmented to a weak (-) evidence base under a validity ranking of <span class="in">`{python} strength_for(r&quot;\+&quot;)`</span>,</span>
<span id="cb26-905"><a href="#cb26-905" aria-hidden="true" tabindex="-1"></a>evidential (+) from <span class="in">`{python} strength_for(r&quot;\+&quot;)`</span> and under <span class="in">`{python} strength_for(r&quot;\++&quot;)`</span> and strong evidence base (++) for <span class="in">`{python} strength_for(r&quot;\++&quot;)`</span> and above.</span>
<span id="cb26-906"><a href="#cb26-906" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-907"><a href="#cb26-907" aria-hidden="true" tabindex="-1"></a>Summary of main findings for structural policies</span>
<span id="cb26-908"><a href="#cb26-908" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-909"><a href="#cb26-909" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-910"><a href="#cb26-910" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-911"><a href="#cb26-911" aria-hidden="true" tabindex="-1"></a>{{&lt; landscape &gt;}}</span>
<span id="cb26-912"><a href="#cb26-912" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-913"><a href="#cb26-913" aria-hidden="true" tabindex="-1"></a><span class="fu">### Fiscal growth and trade liberalisation</span></span>
<span id="cb26-914"><a href="#cb26-914" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-915"><a href="#cb26-915" aria-hidden="true" tabindex="-1"></a>Complementing their research on institutional labour regulation,</span>
<span id="cb26-916"><a href="#cb26-916" aria-hidden="true" tabindex="-1"></a>@Adams2015 study the effects of business and credit regulations and FDI on long-term income inequality in developing countries.</span>
<span id="cb26-917"><a href="#cb26-917" aria-hidden="true" tabindex="-1"></a>While for them business regulations seemed to have mixed relationships with income inequality,</span>
<span id="cb26-918"><a href="#cb26-918" aria-hidden="true" tabindex="-1"></a>they find that, FDI is positively related with income inequality and the authors suggest it is unlikely to generate general welfare effects in developing countries.</span>
<span id="cb26-919"><a href="#cb26-919" aria-hidden="true" tabindex="-1"></a>This, they argue, is due to FDI often operating on the wrong targeting incentive structure and only able to generate more equity when correctly targeting the creation of connections from the local to surrounding economies.</span>
<span id="cb26-920"><a href="#cb26-920" aria-hidden="true" tabindex="-1"></a>While a long-term study, its scale is purely on the macro-level without delving deeper into individual-level changes per country.</span>
<span id="cb26-921"><a href="#cb26-921" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-922"><a href="#cb26-922" aria-hidden="true" tabindex="-1"></a>@Xu2021 study the effects of trade liberalization and FDI on income inequality in 38 countries in the Sub-Saharan region.</span>
<span id="cb26-923"><a href="#cb26-923" aria-hidden="true" tabindex="-1"></a>It finds that increased FDI is negatively correlated with income inequality measured through the Gini coefficient, while trade liberalization is positively correlated with income inequality ---</span>
<span id="cb26-924"><a href="#cb26-924" aria-hidden="true" tabindex="-1"></a>as are corruption, political stability, rule of law and education, which contradicts some findings of the previous study.</span>
<span id="cb26-925"><a href="#cb26-925" aria-hidden="true" tabindex="-1"></a>The authors argue this may be due to the difference in sample and variables used, and the periods under study.</span>
<span id="cb26-926"><a href="#cb26-926" aria-hidden="true" tabindex="-1"></a>They suggest that FDI may primarily go to the agricultural sector which can employ low-skilled labour and thereby reduce inequalities,</span>
<span id="cb26-927"><a href="#cb26-927" aria-hidden="true" tabindex="-1"></a>while trade openness in fact creates jobs in other countries through higher import than export rates.</span>
<span id="cb26-928"><a href="#cb26-928" aria-hidden="true" tabindex="-1"></a>They do not clearly identify channels through which a higher overall education level positively correlates with inequality,</span>
<span id="cb26-929"><a href="#cb26-929" aria-hidden="true" tabindex="-1"></a>though some possibilities are an unequal access to education (through excluding factors such as those based on spatial, gender or financial inequalities),</span>
<span id="cb26-930"><a href="#cb26-930" aria-hidden="true" tabindex="-1"></a>as well as a differentiated quality of education.</span>
<span id="cb26-931"><a href="#cb26-931" aria-hidden="true" tabindex="-1"></a>Limitations of the study are the region-wide level of analysis which may obscure context-dependent mechanisms within the different institutional-structural contexts of the countries and potential hidden unobservables which may bias the results.</span>
<span id="cb26-932"><a href="#cb26-932" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-933"><a href="#cb26-933" aria-hidden="true" tabindex="-1"></a>A simulation study on the effects of trade liberalization through free trade agreements (FTA) by @Khan2021 looks at income inequality in Pakistan between different households, measured through the Gini coefficient.</span>
<span id="cb26-934"><a href="#cb26-934" aria-hidden="true" tabindex="-1"></a>It finds that there is no clear general direction for changes through FTA visible, with its impact primarily depending on micro-economic factors.</span>
<span id="cb26-935"><a href="#cb26-935" aria-hidden="true" tabindex="-1"></a>Some large trade agreements are negatively correlated with the Gini while others are positively related, similar to regional and bilateral agreements.</span>
<span id="cb26-936"><a href="#cb26-936" aria-hidden="true" tabindex="-1"></a>Generally, this is due to increases in the income of poor rural agricultural farm households being dependent on grain (which is the largest export good often rising under FTA), while livestock predominantly owned by poor rural households decreases in returns under FTA.</span>
<span id="cb26-937"><a href="#cb26-937" aria-hidden="true" tabindex="-1"></a>The deciding channel can then be increases on the wages of farm workers (after among others grain export increases) increasing income equity, which, when they do not happen, can in turn lead to an overall decrease.</span>
<span id="cb26-938"><a href="#cb26-938" aria-hidden="true" tabindex="-1"></a>Lastly, there are wage compression effects between urban and rural households, with richer urban households often decreasing processed food or service production.</span>
<span id="cb26-939"><a href="#cb26-939" aria-hidden="true" tabindex="-1"></a>A greater mobility would dissipate all short-term gains and losses, as changes would get more evenly distributed across regions and households, while over the long term some positive aspects on income equality are visible if increased agricultural growth can be sustained.</span>
<span id="cb26-940"><a href="#cb26-940" aria-hidden="true" tabindex="-1"></a>The study may have some limits to its generalizability due to the production factor reallocations for agricultural households being specific to the rural poor context in Pakistan.</span>
<span id="cb26-941"><a href="#cb26-941" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-942"><a href="#cb26-942" aria-hidden="true" tabindex="-1"></a>@Liyanaarachchi2016 run a simulation model on the effects of trade liberalization in Sri Lanka on income inequality and absolute poverty.</span>
<span id="cb26-943"><a href="#cb26-943" aria-hidden="true" tabindex="-1"></a>It finds that the complete elimination of tariffs results in an overall reduction in absolute poverty, while tariff elimination with resulting fiscal policy responses to balance the budget would result in more mixed results but still pointing to an absolute reduction in poverty.</span>
<span id="cb26-944"><a href="#cb26-944" aria-hidden="true" tabindex="-1"></a>On the other hand, income inequality is seen to increase for most sectors over the short term and for all sectors over the long term.</span>
<span id="cb26-945"><a href="#cb26-945" aria-hidden="true" tabindex="-1"></a>The primary channels for this change are increased wage differences --- especially the increased wages for managers, professionals and technicians, as well as increased differences between urban workers ---</span>
<span id="cb26-946"><a href="#cb26-946" aria-hidden="true" tabindex="-1"></a>and low-income households being more dependent on private or government transfers, which do not increase with trade liberalization.</span>
<span id="cb26-947"><a href="#cb26-947" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-948"><a href="#cb26-948" aria-hidden="true" tabindex="-1"></a>@Rendall2013 undertake a cross-country analysis on the impacts of structural changes in Brazil, Mexico, Thailand and India from 1987 to 2008, and its effects on female labour market participation and the gender wage gap.</span>
<span id="cb26-949"><a href="#cb26-949" aria-hidden="true" tabindex="-1"></a>Basing its analysis on the theory of capital displacing brawn in production for transition economies, it finds that all countries had reduced brawn requirements over time, though with large heterogeneity: Thailand lead the change with 15 percentage points while India had the smallest change with 0.2 percentage points.</span>
<span id="cb26-950"><a href="#cb26-950" aria-hidden="true" tabindex="-1"></a>Following this, there was the largest steady labour market participation inequality in India, while there were mixed results for Mexico and Thailand, with Brazil having female employment shares changes similar to that of the United States.</span>
<span id="cb26-951"><a href="#cb26-951" aria-hidden="true" tabindex="-1"></a>The channels here are seen as a reduced requirement for physical labour replaced by for example more service-oriented economies (&#39;brawn&#39; to &#39;brain&#39;).</span>
<span id="cb26-952"><a href="#cb26-952" aria-hidden="true" tabindex="-1"></a>For female wage shares, in Brazil the wage gap closed most rapidly, though it began widening in 2005, while Thailand and India had converging but mixed changes.</span>
<span id="cb26-953"><a href="#cb26-953" aria-hidden="true" tabindex="-1"></a>In Mexico, while the gap widened during the 1990s, it began closing again afterwards.</span>
<span id="cb26-954"><a href="#cb26-954" aria-hidden="true" tabindex="-1"></a>The differences in wage gap effects compared to both other countries and the respective country&#39;s physical labour market requirements show that contextual structural changes played a large role in each case:</span>
<span id="cb26-955"><a href="#cb26-955" aria-hidden="true" tabindex="-1"></a>with erstwhile reduced returns on Brazilian returns for brain intensive occupations, the introduction of a female-lead manufacturing sector in Mexico in the 90s, and widely diverging basic labour market skill structures in Thailand and India necessitating subsistence-oriented participation; the results show impacts of structural changes, though limited through a variety of mediating factors influencing each case.</span>
<span id="cb26-956"><a href="#cb26-956" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-957"><a href="#cb26-957" aria-hidden="true" tabindex="-1"></a>@Wang2020 conduct a simulation to examine the impact of terminating subsidies for the agricultural grain sectors in China, with a particular focus on analysing the effects on rural-urban income inequality.</span>
<span id="cb26-958"><a href="#cb26-958" aria-hidden="true" tabindex="-1"></a>The findings indicate that the removal of grain subsidies would lead to gradual improvements in the industrial economic structure.</span>
<span id="cb26-959"><a href="#cb26-959" aria-hidden="true" tabindex="-1"></a>However, in the short term, it is observed that rural-urban income inequality is exacerbated.</span>
<span id="cb26-960"><a href="#cb26-960" aria-hidden="true" tabindex="-1"></a>Over an extended period, the decrease in real wages for rural workers would alleviate, suggesting an increase in the rural income ratio, yet the gap remains incompletely closed.</span>
<span id="cb26-961"><a href="#cb26-961" aria-hidden="true" tabindex="-1"></a>The study attributes this outcome to the displacement of rural unskilled labour, resulting in an increased supply of unskilled labour that is challenging to absorb into the manufacturing or service sectors.</span>
<span id="cb26-962"><a href="#cb26-962" aria-hidden="true" tabindex="-1"></a>Additionally, the low income and price elasticity of agricultural products contribute to an overall decline in rural incomes.</span>
<span id="cb26-963"><a href="#cb26-963" aria-hidden="true" tabindex="-1"></a>Consequently, the authors identify a trade-off between long-term national economic output, adversely affected by the removal of subsidies, and the reduction in rural-urban income ratios facilitated by the subsidies, albeit with diminishing contributions over time.</span>
<span id="cb26-964"><a href="#cb26-964" aria-hidden="true" tabindex="-1"></a>Limitations of the study include the need to assume static national employment and, notably, limited generalizability due to the simulation of specific Chinese structural economic characteristics in the model.</span>
<span id="cb26-965"><a href="#cb26-965" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-966"><a href="#cb26-966" aria-hidden="true" tabindex="-1"></a>@Go2010 model the effects of a targeted wage subsidy aimed at low- and medium-skilled workers and provided to their employers as an incentive for new job creations, looking at its effects on poverty and income inequality in South Africa.</span>
<span id="cb26-967"><a href="#cb26-967" aria-hidden="true" tabindex="-1"></a>The study finds that, using the Gini coefficient, the overall income inequality reduced by 0.5 percentage points, which provides an insignificant outcome.</span>
<span id="cb26-968"><a href="#cb26-968" aria-hidden="true" tabindex="-1"></a>This primarily occurs because of an overall income redistribution and especially an increase in formal employment for low- and medium-skill workers.</span>
<span id="cb26-969"><a href="#cb26-969" aria-hidden="true" tabindex="-1"></a>Using an absolute poverty headcount ratio, it finds that a significant 1.6 per cent of households move out of poverty, with similar changes observed across urban and rural spaces.</span>
<span id="cb26-970"><a href="#cb26-970" aria-hidden="true" tabindex="-1"></a>They attribute this primarily to income gains for poorer households and the targeting benefiting the poorest households most by providing them greater income gains.</span>
<span id="cb26-971"><a href="#cb26-971" aria-hidden="true" tabindex="-1"></a>Limitations of the study include the general equilibrium model approach being potentially restricted by its prior assumptions in validity and generalizability, as well as potentially not accounting for unobservables or exogenous shocks.</span>
<span id="cb26-972"><a href="#cb26-972" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-973"><a href="#cb26-973" aria-hidden="true" tabindex="-1"></a>Due to the high number of studies on these policy areas being based on equilibrium modelling simulations,</span>
<span id="cb26-974"><a href="#cb26-974" aria-hidden="true" tabindex="-1"></a>there are some potentially exacerbated blind-spots:</span>
<span id="cb26-975"><a href="#cb26-975" aria-hidden="true" tabindex="-1"></a>they can possess a higher reliance on prior assumptions for their results to hold,</span>
<span id="cb26-976"><a href="#cb26-976" aria-hidden="true" tabindex="-1"></a>which includes the effort to subsume all potentially relevant channels and mediators into the equilibrium models.</span>
<span id="cb26-977"><a href="#cb26-977" aria-hidden="true" tabindex="-1"></a>They are generally more prone to disregarding exogenous factors which may provide shock effects into the system under analysis, and often can not cleanly account for longer-term dynamics.</span>
<span id="cb26-978"><a href="#cb26-978" aria-hidden="true" tabindex="-1"></a>Lastly, they can not address practical implementation challenges which may be faced by those implementing such policies, the institutional context and political ability to pursue the results modelled therein.</span>
<span id="cb26-979"><a href="#cb26-979" aria-hidden="true" tabindex="-1"></a>These limitations should be taken into consideration when evaluating their results.</span>
<span id="cb26-980"><a href="#cb26-980" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-981"><a href="#cb26-981" aria-hidden="true" tabindex="-1"></a><span class="fu">### Automation and technological change</span></span>
<span id="cb26-982"><a href="#cb26-982" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-983"><a href="#cb26-983" aria-hidden="true" tabindex="-1"></a>@Bailey2012 undertake a study on the effects of the introduction of legal access to contraceptive measures for women in the United States, measuring the impacts on closing the gender gap through the gendered hourly working wage distribution.</span>
<span id="cb26-984"><a href="#cb26-984" aria-hidden="true" tabindex="-1"></a>The study finds that of the closing gender pay gap from 1980 to 2000, legal access to &#39;the pill&#39; as contraceptive from an early age contributed by nearly percent in the 1980s and over 30 percent in the 1990s.</span>
<span id="cb26-985"><a href="#cb26-985" aria-hidden="true" tabindex="-1"></a>Thus, overall the authors estimate that nearly one third of total female wage gains during this time were attributable to legal access to contraception.</span>
<span id="cb26-986"><a href="#cb26-986" aria-hidden="true" tabindex="-1"></a>The primary channels identified are greater educational attainment, occupational upgrading, and increased labour market experience made possible due to no early exit.</span>
<span id="cb26-987"><a href="#cb26-987" aria-hidden="true" tabindex="-1"></a>The authors also argue that the pill spurred individual agency to invest in personal human capital and career.</span>
<span id="cb26-988"><a href="#cb26-988" aria-hidden="true" tabindex="-1"></a>However, there are some limitations to the findings: The dataset cannot capture specific access to contraception beyond age 20, which makes the window of analysis more restricted and especially focused on the segment of women under 21.</span>
<span id="cb26-989"><a href="#cb26-989" aria-hidden="true" tabindex="-1"></a>Additionally, the study can not control for social multiplier effects such as employers reacting with changed hiring or promotion patterns or expectations about marriage and childbearing, as well as the overall coinciding paradigmatic change in norms and ideas about women&#39;s work and end of the national baby boom.</span>
<span id="cb26-990"><a href="#cb26-990" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-991"><a href="#cb26-991" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">###</span><span class="co"> Informal Economy --&gt;</span></span>
<span id="cb26-992"><a href="#cb26-992" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-993"><a href="#cb26-993" aria-hidden="true" tabindex="-1"></a><span class="fu">### Infrastructure</span></span>
<span id="cb26-994"><a href="#cb26-994" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-995"><a href="#cb26-995" aria-hidden="true" tabindex="-1"></a>@Kuriyama2021 look at the effects of Japan&#39;s move to decarbonise its energy sector on employment, especially rural employment.</span>
<span id="cb26-996"><a href="#cb26-996" aria-hidden="true" tabindex="-1"></a>It finds that, while employment in general is positively affected, especially rural sectors benefit from additional employment probability.</span>
<span id="cb26-997"><a href="#cb26-997" aria-hidden="true" tabindex="-1"></a>This is due to the renewable energy sector, while able to utilise urban areas for smaller scale power generation, being largely attached to rural areas for larger scale projects such as geothermal, wind power or large-scale solar generation.</span>
<span id="cb26-998"><a href="#cb26-998" aria-hidden="true" tabindex="-1"></a>The study also suggests some possible inequality being created in between the different regions of Japan due to the Hokkaido region having limited transmission line capacity and locational imbalance between demand and potential supplies.</span>
<span id="cb26-999"><a href="#cb26-999" aria-hidden="true" tabindex="-1"></a>Limitations include its design as a projection model with multiple having to make strong assumptions about initial employment numbers and their extrapolation into the future,</span>
<span id="cb26-1000"><a href="#cb26-1000" aria-hidden="true" tabindex="-1"></a>as well as having to assume the amount of generated power to increase as a stable square function.</span>
<span id="cb26-1001"><a href="#cb26-1001" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1002"><a href="#cb26-1002" aria-hidden="true" tabindex="-1"></a>In an observational study looking at the inclusive or exclusionary effects of infrastructure development, @Stock2021 analyses the &#39;gender inclusive&#39; development of a solar park in India which specifically aims to work towards micro-scale equality through regional uplifting.</span>
<span id="cb26-1003"><a href="#cb26-1003" aria-hidden="true" tabindex="-1"></a>The project included a training and temporary employment to local unskilled/semi-skilled labour.</span>
<span id="cb26-1004"><a href="#cb26-1004" aria-hidden="true" tabindex="-1"></a>It finds that the development instead impacted equality negatively, creating socio-economic exclusion and disproportionately negatively affected women of lower castes.</span>
<span id="cb26-1005"><a href="#cb26-1005" aria-hidden="true" tabindex="-1"></a>While acquiring basic additional skills, none of the women participating in training remained connected to the operators of the solar park and none were hired.</span>
<span id="cb26-1006"><a href="#cb26-1006" aria-hidden="true" tabindex="-1"></a>An insignificant amount of women from local villages were working at the solar park, of which most belonged to the dominant caste, and the redistributive potential was stymied through capture by village female elites.</span>
<span id="cb26-1007"><a href="#cb26-1007" aria-hidden="true" tabindex="-1"></a>The author suggests this is an example of institutional design neglecting individual agency and structural power relations, especially intersectional inequalities between gender and caste.</span>
<span id="cb26-1008"><a href="#cb26-1008" aria-hidden="true" tabindex="-1"></a>The study is limited in explanatory power through its observational design, not being able to make causal inferences.</span>
<span id="cb26-1009"><a href="#cb26-1009" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1010"><a href="#cb26-1010" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- explicitly spatial policies --&gt;</span></span>
<span id="cb26-1011"><a href="#cb26-1011" aria-hidden="true" tabindex="-1"></a>@Blumenberg2014 look at the effects of a housing mobility intervention in the United States on employment for disadvantaged households,</span>
<span id="cb26-1012"><a href="#cb26-1012" aria-hidden="true" tabindex="-1"></a>and comparing its impacts to the ownership of a car for the same sample.</span>
<span id="cb26-1013"><a href="#cb26-1013" aria-hidden="true" tabindex="-1"></a>It follows the &#39;Moving to Opportunity&#39; programme which provided vouchers to randomized households for movement to a geographically unrestricted area or to specifically to a low-poverty area (treatment group),</span>
<span id="cb26-1014"><a href="#cb26-1014" aria-hidden="true" tabindex="-1"></a>some of which are in areas with well-connected public transport opportunities.</span>
<span id="cb26-1015"><a href="#cb26-1015" aria-hidden="true" tabindex="-1"></a>The sample for the study is made up predominantly of women (98%).</span>
<span id="cb26-1016"><a href="#cb26-1016" aria-hidden="true" tabindex="-1"></a>No relationship between programme participation and increased employment probability could be established.</span>
<span id="cb26-1017"><a href="#cb26-1017" aria-hidden="true" tabindex="-1"></a>However, a positive relationship exists between owning an auto-mobile and improved employment outcomes for low-income households,</span>
<span id="cb26-1018"><a href="#cb26-1018" aria-hidden="true" tabindex="-1"></a>as well as including those households that are located in &#39;transit-rich&#39; areas.</span>
<span id="cb26-1019"><a href="#cb26-1019" aria-hidden="true" tabindex="-1"></a>Access to better transit itself is related to employment probability but not gains in employment -</span>
<span id="cb26-1020"><a href="#cb26-1020" aria-hidden="true" tabindex="-1"></a>the authors suggest this reflects individuals&#39; strategic relocation to use public transit for their job.</span>
<span id="cb26-1021"><a href="#cb26-1021" aria-hidden="true" tabindex="-1"></a>However, moving to a better transit area itself does not increase employment probability,</span>
<span id="cb26-1022"><a href="#cb26-1022" aria-hidden="true" tabindex="-1"></a>perhaps pointing to a certain threshold required in transit extensiveness before it facilitates employment.</span>
<span id="cb26-1023"><a href="#cb26-1023" aria-hidden="true" tabindex="-1"></a>Ultimately, the findings suggest the need to further individual access to auto-mobiles in disadvantaged households or for extensive transit network upgrade which have to cross an efficiency threshold.</span>
<span id="cb26-1024"><a href="#cb26-1024" aria-hidden="true" tabindex="-1"></a>Some limitations of the study are its models low explanatory power for individual outcomes, more so among disadvantaged population groups,</span>
<span id="cb26-1025"><a href="#cb26-1025" aria-hidden="true" tabindex="-1"></a>as well as some remaining possibility of endogeneity bias through unobserved factors such as individual motivation or ability.</span>
<span id="cb26-1026"><a href="#cb26-1026" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1027"><a href="#cb26-1027" aria-hidden="true" tabindex="-1"></a>@Adam2018 model the effects of transport infrastructure investments in Tanzania on rural income inequalities and household welfare inequalities, modelled through consumption indicators.</span>
<span id="cb26-1028"><a href="#cb26-1028" aria-hidden="true" tabindex="-1"></a>Generally it finds that the results of public investment measures into transport infrastructure largely depend on the financing scheme used.</span>
<span id="cb26-1029"><a href="#cb26-1029" aria-hidden="true" tabindex="-1"></a>Comparing four financing schemes when looking at the effects on rural households, it finds that they are generally worse off when the development is deficit-financed or paid through tariff revenues.</span>
<span id="cb26-1030"><a href="#cb26-1030" aria-hidden="true" tabindex="-1"></a>On the other hand, rural households benefit through increased income from measures financed through consumption taxes, or by external aid.</span>
<span id="cb26-1031"><a href="#cb26-1031" aria-hidden="true" tabindex="-1"></a>The general finding is that there is no Pareto optimum for any of the investment measures for all locations,</span>
<span id="cb26-1032"><a href="#cb26-1032" aria-hidden="true" tabindex="-1"></a>and that much of the increases in welfare are based on movement of rural workers out of quasi-subsistence agriculture to other locations and other sectors.</span>
<span id="cb26-1033"><a href="#cb26-1033" aria-hidden="true" tabindex="-1"></a>The study creates causal inferences but is limited in its modelling approach representing a limited subset of empirical possibility spaces,</span>
<span id="cb26-1034"><a href="#cb26-1034" aria-hidden="true" tabindex="-1"></a>as well as having to make the assumption of no population growth for measures to hold.</span>
<span id="cb26-1035"><a href="#cb26-1035" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1036"><a href="#cb26-1036" aria-hidden="true" tabindex="-1"></a><span class="fu">### Education access</span></span>
<span id="cb26-1037"><a href="#cb26-1037" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1038"><a href="#cb26-1038" aria-hidden="true" tabindex="-1"></a>In addition to the institutional effects of regulation above, @Adams2015 analyse the effects of school enrolment and on income inequality in developing countries between 1970 and 2012.</span>
<span id="cb26-1039"><a href="#cb26-1039" aria-hidden="true" tabindex="-1"></a>Contrary to the regulatory policies, they find school enrolment and thus well-effected education-oriented policies to be positively related with an equitable income distribution.</span>
<span id="cb26-1040"><a href="#cb26-1040" aria-hidden="true" tabindex="-1"></a>They suggest additional enrolment increases the capacity of public administration practitioners and in turn lead to more adapted policies specific to developing countries&#39; institutional contexts.</span>
<span id="cb26-1041"><a href="#cb26-1041" aria-hidden="true" tabindex="-1"></a>Due to the often limited contexts of institutional capabilities such policies thus have a two-fold function:</span>
<span id="cb26-1042"><a href="#cb26-1042" aria-hidden="true" tabindex="-1"></a>they increase human capital in the medium term, but may also function as capability-building measures long-term.</span>
<span id="cb26-1043"><a href="#cb26-1043" aria-hidden="true" tabindex="-1"></a>It is important to keep in mind that the recommendations of the study should be understood as made from a macro-perspective, detached from the more micro-oriented contexts of individual countries or regions.</span>
<span id="cb26-1044"><a href="#cb26-1044" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1045"><a href="#cb26-1045" aria-hidden="true" tabindex="-1"></a>@Mukhopadhaya2003 looks at the income inequality in Singapore and how national education policies impact this inequality, focusing especially on the &#39;Yearly Awards&#39; scheme and the &#39;Edusave Entrance Scholarship for Independent Schools&#39;.</span>
<span id="cb26-1046"><a href="#cb26-1046" aria-hidden="true" tabindex="-1"></a>It finds that, generally, income inequality for migrants in Singapore is relatively high, primarily due to generated between-occupational income inequalities and migration policies which further stimulate occupational segregation.</span>
<span id="cb26-1047"><a href="#cb26-1047" aria-hidden="true" tabindex="-1"></a>Then, for the higher-education interventions, it identifies issues which may exacerbate the existing inequalities along these lines:</span>
<span id="cb26-1048"><a href="#cb26-1048" aria-hidden="true" tabindex="-1"></a>Already-advantaged (high-income) households generally stem from non-migration households and are also reflected in higher representation of high-achievement education brackets.</span>
<span id="cb26-1049"><a href="#cb26-1049" aria-hidden="true" tabindex="-1"></a>The education policies thus may exacerbate income inequality through their bad targeting when considering inter-generational academic achievements with high-education households remaining the primary beneficiaries of the policies, a finding which is more significant for the &#39;Edusave Entrance Scholarship for Independent Schools&#39; than the &#39;Yearly Awards&#39; scheme which has fewer benefit accruals to wealthier households.</span>
<span id="cb26-1050"><a href="#cb26-1050" aria-hidden="true" tabindex="-1"></a>More generally, the study suggests that the system of financing for higher education in Singapore aiming for providing equal education opportunity for all, may in fact further disadvantage poorer, low-income households that have a low-education parental background.</span>
<span id="cb26-1051"><a href="#cb26-1051" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1052"><a href="#cb26-1052" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- education -&gt; gender economic empowerment --&gt;</span></span>
<span id="cb26-1053"><a href="#cb26-1053" aria-hidden="true" tabindex="-1"></a>Looking at the returns of the Tanzanian &#39;Universal Primary Education&#39; programme on consumption and on rural labour market outcomes, @Delesalle2021, finds outcomes that additionally differ along spatial and gender lines.</span>
<span id="cb26-1054"><a href="#cb26-1054" aria-hidden="true" tabindex="-1"></a>The programme both attempted to increase access to schools but also changed curricula to contain more technical classes, judged relevant to increase equity in rural areas.</span>
<span id="cb26-1055"><a href="#cb26-1055" aria-hidden="true" tabindex="-1"></a>Even though the programme aims to increase universal equality of access to education, the study finds that gender, geographical and income inequalities persist throughout, with individuals that complete primary education more likely to be male urban wage workers.</span>
<span id="cb26-1056"><a href="#cb26-1056" aria-hidden="true" tabindex="-1"></a>The study measures returns purely on consumption of households to show the estimated effect on their productivity ---</span>
<span id="cb26-1057"><a href="#cb26-1057" aria-hidden="true" tabindex="-1"></a>here, it finds generally positive returns but greatest for non-agricultural work, self-employed or as wage work.</span>
<span id="cb26-1058"><a href="#cb26-1058" aria-hidden="true" tabindex="-1"></a>Importantly, the introduction of more technical classes also changes employment sector choices, with men working less in agricultural work and more in non-farm wage sectors and an increased probability for rural women to both work in agriculture and to work formally.</span>
<span id="cb26-1059"><a href="#cb26-1059" aria-hidden="true" tabindex="-1"></a>Limitations of the study include the inability to directly identify intervention compliers and having to construct returns for each household head only and a possibly unobserved &#39;villagization&#39; effect by bringing people together in community villages for their education leading to other unobserved variable impacting the returns.</span>
<span id="cb26-1060"><a href="#cb26-1060" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1061"><a href="#cb26-1061" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- increased education access for migrants -&gt; wage inequality --&gt;</span></span>
<span id="cb26-1062"><a href="#cb26-1062" aria-hidden="true" tabindex="-1"></a>@Pi2016 conduct a study on the impacts of allowing increased access to social welfare provisions and education to urban migrants in China, looking at the effects on wage inequality between skilled and unskilled sectors and workers.</span>
<span id="cb26-1063"><a href="#cb26-1063" aria-hidden="true" tabindex="-1"></a>It uses skilled-unskilled inequality instead of rural-urban inequalities since the real wages of the rural sector are already much lower in China, making comparisons along the 90th to 10th decile ratios more difficult.</span>
<span id="cb26-1064"><a href="#cb26-1064" aria-hidden="true" tabindex="-1"></a>The study finds that reforms to increase access to social security and education for urban migrants decreases wage inequality between the sectors if the skilled sector is more capital intensive than the unskilled sector, though it makes no specific identification of individual channels.</span>
<span id="cb26-1065"><a href="#cb26-1065" aria-hidden="true" tabindex="-1"></a>There are several limitations to the study such as no disaggregation between the private and the (very important for the Chinese economy) public sector, job searching not being part of the model, and, most importantly, a severely restricted generalizability due to the reform characteristics being strongly bound to the institutional contexts of Chinese *hukou*<span class="ot">[^hukou]</span> systems.</span>
<span id="cb26-1066"><a href="#cb26-1066" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1067"><a href="#cb26-1067" aria-hidden="true" tabindex="-1"></a><span class="ot">[^hukou]: </span>The hukou system generally denotes a permission towards either rural land-ownership and agricultural subsidies for the rural hukou or social welfare benefits and employment possibilities for the urban hukou, and children of migrants often have to go back to their place of registered residence for their college entrance examination. This study looks at reforms undoing some of the restrictions under the sytem.</span>
<span id="cb26-1068"><a href="#cb26-1068" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1069"><a href="#cb26-1069" aria-hidden="true" tabindex="-1"></a>@Suh2017 studies the effects of structural changes on married women&#39;s employment in South Korea, looking specifically at the impact of education and family structure.</span>
<span id="cb26-1070"><a href="#cb26-1070" aria-hidden="true" tabindex="-1"></a>The study finds that educational interventions significantly increase the employment probability of married women, and it finds overall female labour force participation showing a negative correlation with income inequality.</span>
<span id="cb26-1071"><a href="#cb26-1071" aria-hidden="true" tabindex="-1"></a>However, education alone is only a necessary not a sufficient condition for increased employment, with a married woman&#39;s family size and family structure having an impact as well.</span>
<span id="cb26-1072"><a href="#cb26-1072" aria-hidden="true" tabindex="-1"></a>Finally, education also has an intergenerational impact, with the female education also positively relating to daughters&#39; education levels.</span>
<span id="cb26-1073"><a href="#cb26-1073" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1074"><a href="#cb26-1074" aria-hidden="true" tabindex="-1"></a>@Coutinho2006 study the impacts of special education between young men and women on their relative employment probabilities and incomes.</span>
<span id="cb26-1075"><a href="#cb26-1075" aria-hidden="true" tabindex="-1"></a>It finds that, overall, young women with disabilities were significantly less likely to be employed, earned less than males with disabilities, had lower likelihood of obtaining a high school diploma and were more likely to be a biological parent.</span>
<span id="cb26-1076"><a href="#cb26-1076" aria-hidden="true" tabindex="-1"></a>For the employment outcomes, the primary channels identified were men with disabilities being in employment both more months in the preceding period and more hours per week on average than women with disabilities.</span>
<span id="cb26-1077"><a href="#cb26-1077" aria-hidden="true" tabindex="-1"></a>Overall, more women were employed in clerical positions and substantially more men employed in technical or skilled positions for both special education and the control samples.</span>
<span id="cb26-1078"><a href="#cb26-1078" aria-hidden="true" tabindex="-1"></a>Similarly, for income there was a gender-based difference for the whole sample, though with substantial internal heterogeneity showing only marginal differences between men and women in the high-achieving subsample and the largest differences in the low-achieving and special needs subsample.</span>
<span id="cb26-1079"><a href="#cb26-1079" aria-hidden="true" tabindex="-1"></a>The suggestions include a strengthening of personal agency to remain in education longer and delay having children through self-advocacy and -determination transition services for young women to supplement structural education efforts.</span>
<span id="cb26-1080"><a href="#cb26-1080" aria-hidden="true" tabindex="-1"></a>Some limitations include initial subsample selection based on parent-reporting possibly introducing selection bias and the special education sample not including students with more severe impairments due to the requirement of self-reporting.</span>
<span id="cb26-1081"><a href="#cb26-1081" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1082"><a href="#cb26-1082" aria-hidden="true" tabindex="-1"></a>@Shepherd-Banigan2021 undertake a qualitative study on the significance of vocational and educational training provided for disabled veterans in the United States.</span>
<span id="cb26-1083"><a href="#cb26-1083" aria-hidden="true" tabindex="-1"></a>It finds that both the vocational and educational services help strengthen individual agency, autonomy and motivation but impacts can be dampened if the potential for disability payment loss due to the potential for job acquisition impedes skill development efforts.</span>
<span id="cb26-1084"><a href="#cb26-1084" aria-hidden="true" tabindex="-1"></a>The primary barriers of return to work efforts identified are an individual&#39;s health problems as well as various programmes not accommodating the needs of disabled veteran students,</span>
<span id="cb26-1085"><a href="#cb26-1085" aria-hidden="true" tabindex="-1"></a>while the primary Facilitators identified are financial assistance provided for education as well as strengthened individual agency through motivation.</span>
<span id="cb26-1086"><a href="#cb26-1086" aria-hidden="true" tabindex="-1"></a>Some limitations include a possible bias of accommodations required through the sample being restricted to veterans with a caregiver, which often signals more substantial impairments than for a larger training-participatory sample, as well as the data not being able to identify the impact of supported employment.</span>
<span id="cb26-1087"><a href="#cb26-1087" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1088"><a href="#cb26-1088" aria-hidden="true" tabindex="-1"></a>The studies thus not only reinforce recommendations for strength-based approaches, emphasising the benefits of work, but also highlight the targeting importance of subsidy programmes in general on the one hand,</span>
<span id="cb26-1089"><a href="#cb26-1089" aria-hidden="true" tabindex="-1"></a>in the worst case reducing equity through bad targeting mechanisms,</span>
<span id="cb26-1090"><a href="#cb26-1090" aria-hidden="true" tabindex="-1"></a>and their negative reinforcement effects widening existing inequalities of gender, age and racial discrimination through such targeting on the other.</span>
<span id="cb26-1091"><a href="#cb26-1091" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1092"><a href="#cb26-1092" aria-hidden="true" tabindex="-1"></a>With a similar focus on agency, @Gates2000 conducts a qualitative study on the mechanisms of workplace accommodation for people with mental health conditions to allow their successful return-to-work.</span>
<span id="cb26-1093"><a href="#cb26-1093" aria-hidden="true" tabindex="-1"></a>The intervention is based on an accommodation which disaggregates the effects of social and technical components of the process and included a disclosure and psycho-educational plan.</span>
<span id="cb26-1094"><a href="#cb26-1094" aria-hidden="true" tabindex="-1"></a>It finds that successful return-to-work through accommodation requires consideration of the social component (&#39;who is involved&#39;), with unsuccessful accommodation often only relying on the functional aspect (&#39;what is involved&#39;).</span>
<span id="cb26-1095"><a href="#cb26-1095" aria-hidden="true" tabindex="-1"></a>The primary barrier identified for successful return-to-work are actually relationship issues not functional ones, with supervisors playing a key role for the success of the accommodation process.</span>
<span id="cb26-1096"><a href="#cb26-1096" aria-hidden="true" tabindex="-1"></a>Additionally, it highlighted the necessity of strengthening the individual agency of the returnee, accomplished in the intervention through a concrete training plan with the worker but also with other key workplace players such as the supervisors.</span>
<span id="cb26-1097"><a href="#cb26-1097" aria-hidden="true" tabindex="-1"></a>Additionally, providers must be willing to develop a disclosure plan with the employee and enter the workplace itself to adequately assist in the accommodation process.</span>
<span id="cb26-1098"><a href="#cb26-1098" aria-hidden="true" tabindex="-1"></a>Limitations to the study include the limited generalizability of its findings with a small non-randomized sample size and restriction to mental health disability.</span>
<span id="cb26-1099"><a href="#cb26-1099" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1100"><a href="#cb26-1100" aria-hidden="true" tabindex="-1"></a>A study looking at the effects of vocational rehabilitation on employment probabilities, @Poppen2017 look at the factors influencing successful employment for disabled people in the United States.</span>
<span id="cb26-1101"><a href="#cb26-1101" aria-hidden="true" tabindex="-1"></a>It finds that the primary factors negatively correlated with successful employment were for women in the sample, for having mental illness or traumatic brain injury as the primary disability, having multiple disabilities, an interpersonal or self-care impediment and receiving social security benefits.</span>
<span id="cb26-1102"><a href="#cb26-1102" aria-hidden="true" tabindex="-1"></a>On the other hand, having participated in a youth-transition training programme, as well as making use of more vocational rehabilitation services, are correlated with an increased employment probability.</span>
<span id="cb26-1103"><a href="#cb26-1103" aria-hidden="true" tabindex="-1"></a>It thereby highlights the gendered dimension of employment probabilities and points to a necessity to focus training and rehabilitation efforts along multiple dimensions.</span>
<span id="cb26-1104"><a href="#cb26-1104" aria-hidden="true" tabindex="-1"></a>Some limitations of the study include its limited generalizability, having a sample located in a single state, as well as a dataset intended for service provision not academic pursuits possibly introducing unreliability in its data and not measuring service quality.</span>
<span id="cb26-1105"><a href="#cb26-1105" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1106"><a href="#cb26-1106" aria-hidden="true" tabindex="-1"></a>@Thoresen2021 conduct a survey combined with qualitative interviews for the participants of a vocational training programme in Australia, looking at the effects on participants&#39; hours worked and incomes.</span>
<span id="cb26-1107"><a href="#cb26-1107" aria-hidden="true" tabindex="-1"></a>It finds, foremost, that initially both the hours worked and the income of people with disabilities are lower on the Australian labour market in general and this reflects in the results for the disability group of participants, which have significantly lower weekly incomes and hours worked than the control group.</span>
<span id="cb26-1108"><a href="#cb26-1108" aria-hidden="true" tabindex="-1"></a>Over time, hours worked increase for the disability group to no longer be significantly different but still lower than for the control group (from 3.1 hours to 1 hour difference per week),</span>
<span id="cb26-1109"><a href="#cb26-1109" aria-hidden="true" tabindex="-1"></a>however there are large fluctuations in the control group.</span>
<span id="cb26-1110"><a href="#cb26-1110" aria-hidden="true" tabindex="-1"></a>Similarly, the wages of the disability group are initially substantially lower than of the control group,</span>
<span id="cb26-1111"><a href="#cb26-1111" aria-hidden="true" tabindex="-1"></a>which increases to be non-significant though still lower over time, more so for the earnings of female participants and participants which received a disability pension.</span>
<span id="cb26-1112"><a href="#cb26-1112" aria-hidden="true" tabindex="-1"></a>Relevant limitations of the study include the use of a non-representative sample for the national representativeness,</span>
<span id="cb26-1113"><a href="#cb26-1113" aria-hidden="true" tabindex="-1"></a>and the overall generalisability being low due to an increased labour force participation bias and attrition bias of the surveys,</span>
<span id="cb26-1114"><a href="#cb26-1114" aria-hidden="true" tabindex="-1"></a>as well as only having access to a small control sample size.</span>
<span id="cb26-1115"><a href="#cb26-1115" aria-hidden="true" tabindex="-1"></a>Thus, findings should be understood as guiding policy directions, while generalisations should be done with care as some of the larger changes may be due to those limitations,</span>
<span id="cb26-1116"><a href="#cb26-1116" aria-hidden="true" tabindex="-1"></a>such as the increased survey response of those with positive wage outcomes.</span>
<span id="cb26-1117"><a href="#cb26-1117" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1118"><a href="#cb26-1118" aria-hidden="true" tabindex="-1"></a>An experimental study on the impacts of benefits and vocational training counselling for disabled veterans in the United States by @Rosen2014 measures the effects on return to work through average hours worked.</span>
<span id="cb26-1119"><a href="#cb26-1119" aria-hidden="true" tabindex="-1"></a>It identifies time worked through a timeline follow-back calendar, measuring the change in days worked in the 28 days preceding the final study measurement.</span>
<span id="cb26-1120"><a href="#cb26-1120" aria-hidden="true" tabindex="-1"></a>Here, it finds the sessions having a significant increase on more waged days worked, with an additional three days for the 28 preceding days on average.</span>
<span id="cb26-1121"><a href="#cb26-1121" aria-hidden="true" tabindex="-1"></a>One limitation is the inability of the study to locate an active ingredient:</span>
<span id="cb26-1122"><a href="#cb26-1122" aria-hidden="true" tabindex="-1"></a>Though the intervention clearly aims at strengthening some aspect of individual agency, the exact mediators are not clear, with neither beliefs about work, beliefs about benefits, nor provided service use for mental health or substance abuse impacted significantly.</span>
<span id="cb26-1123"><a href="#cb26-1123" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1124"><a href="#cb26-1124" aria-hidden="true" tabindex="-1"></a><span class="fu">## Agency</span></span>
<span id="cb26-1125"><a href="#cb26-1125" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1126"><a href="#cb26-1126" aria-hidden="true" tabindex="-1"></a>{{&lt; portrait &gt;}}</span>
<span id="cb26-1127"><a href="#cb26-1127" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1128"><a href="#cb26-1128" aria-hidden="true" tabindex="-1"></a>::: {#tbl-findings-agency}</span>
<span id="cb26-1129"><a href="#cb26-1129" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1132"><a href="#cb26-1132" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1133"><a href="#cb26-1133" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-findings-agency</span></span>
<span id="cb26-1134"><a href="#cb26-1134" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb26-1135"><a href="#cb26-1135" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1136"><a href="#cb26-1136" aria-hidden="true" tabindex="-1"></a>findings_agency <span class="op">=</span> pd.read_csv(<span class="st">&quot;data/supplementary/findings-agency.csv&quot;</span>)</span>
<span id="cb26-1137"><a href="#cb26-1137" aria-hidden="true" tabindex="-1"></a>fd_df <span class="op">=</span> validity.add_to_findings(findings_agency, by_intervention, study_strength_bins)</span>
<span id="cb26-1138"><a href="#cb26-1138" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1139"><a href="#cb26-1139" aria-hidden="true" tabindex="-1"></a>md(tabulate(fd_df[[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal_validity&quot;</span>, <span class="st">&quot;external_validity&quot;</span>, <span class="st">&quot;findings&quot;</span>, <span class="st">&quot;channels&quot;</span>]].fillna(<span class="st">&quot;&quot;</span>), showindex<span class="op">=</span><span class="va">False</span>, headers<span class="op">=</span>[<span class="st">&quot;area of policy&quot;</span>, <span class="st">&quot;internal strength&quot;</span>, <span class="st">&quot;external strength&quot;</span>, <span class="st">&quot;main findings&quot;</span>, <span class="st">&quot;channels&quot;</span>], tablefmt<span class="op">=</span><span class="st">&quot;grid&quot;</span>))</span>
<span id="cb26-1140"><a href="#cb26-1140" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1141"><a href="#cb26-1141" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1142"><a href="#cb26-1142" aria-hidden="true" tabindex="-1"></a>Note: Each main finding is presented with an internal strength of evidence and an external strength of evidence which describe the combined validities of the evidence base for the respective finding.</span>
<span id="cb26-1143"><a href="#cb26-1143" aria-hidden="true" tabindex="-1"></a>Validities are segmented to a weak (-) evidence base under a validity ranking of <span class="in">`{python} strength_for(r&quot;\+&quot;)`</span>,</span>
<span id="cb26-1144"><a href="#cb26-1144" aria-hidden="true" tabindex="-1"></a>evidential (+) from <span class="in">`{python} strength_for(r&quot;\+&quot;)`</span> and under <span class="in">`{python} strength_for(r&quot;\++&quot;)`</span> and strong evidence base (++) for <span class="in">`{python} strength_for(r&quot;\++&quot;)`</span> and above.</span>
<span id="cb26-1145"><a href="#cb26-1145" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1146"><a href="#cb26-1146" aria-hidden="true" tabindex="-1"></a>Summary of main findings for agency-based policies</span>
<span id="cb26-1147"><a href="#cb26-1147" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1148"><a href="#cb26-1148" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-1149"><a href="#cb26-1149" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1150"><a href="#cb26-1150" aria-hidden="true" tabindex="-1"></a>{{&lt; landscape &gt;}}</span>
<span id="cb26-1151"><a href="#cb26-1151" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1152"><a href="#cb26-1152" aria-hidden="true" tabindex="-1"></a><span class="fu">### Occupational segregation and social exclusion</span></span>
<span id="cb26-1153"><a href="#cb26-1153" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1154"><a href="#cb26-1154" aria-hidden="true" tabindex="-1"></a>@Emigh2018 study the effects of direct state transfers to people in poverty in the post-socialist market transition countries of Hungary, Romania and Bulgaria.</span>
<span id="cb26-1155"><a href="#cb26-1155" aria-hidden="true" tabindex="-1"></a>To do so, the study first looks at the correlations of socio-demographic characteristics with poverty to find that in each country there was an increased probability for poverty of low-education, larger and predominantly Roma households.</span>
<span id="cb26-1156"><a href="#cb26-1156" aria-hidden="true" tabindex="-1"></a>It also found that poverty itself was most feminized Hungary, the country with the most advanced market transition in the study period, and least feminized in Bulgaria, the country with the least advanced market transition, and suggests that poverty may have feminized as the market transitions progressed.</span>
<span id="cb26-1157"><a href="#cb26-1157" aria-hidden="true" tabindex="-1"></a>For the state transfers it found that while the level of payments may have been too small to eliminate longer-term adverse effects of the market transitions,</span>
<span id="cb26-1158"><a href="#cb26-1158" aria-hidden="true" tabindex="-1"></a>in each country&#39;s case the transfers to individuals reduced their poverty and were beneficial at least in the short term.</span>
<span id="cb26-1159"><a href="#cb26-1159" aria-hidden="true" tabindex="-1"></a>The authors thus suggest that their findings may be compatible both with an institutionalist perspective seeing poverty-eliminating benefits in the short term and with an underclass perspective which contends that nonetheless the transfers do not eliminate the deprivations members of disadvantaged groups face, while providing little evidence for generating welfare dependency proposed in a more neoclassical perspective.</span>
<span id="cb26-1160"><a href="#cb26-1160" aria-hidden="true" tabindex="-1"></a>However, due to no long-term panel data available to fully analyse the underclass and neoclassical arguments, these findings&#39; generalizability should be understood as more restricted.</span>
<span id="cb26-1161"><a href="#cb26-1161" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1162"><a href="#cb26-1162" aria-hidden="true" tabindex="-1"></a>@Bartha2020 conduct an observational study on the effects of the policy trajectories of European countries concerning long-term care work, with a special focus on the impacts on gender equality.</span>
<span id="cb26-1163"><a href="#cb26-1163" aria-hidden="true" tabindex="-1"></a>The trajectories for the study are mostly described through measures of social protection and social security such as pensions or the provision of residential or at-home care facilities, regulation and fiscal policies.</span>
<span id="cb26-1164"><a href="#cb26-1164" aria-hidden="true" tabindex="-1"></a>Regarding the effects on the labour market it uses the full-time equivalent employment rate gap between men and women.</span>
<span id="cb26-1165"><a href="#cb26-1165" aria-hidden="true" tabindex="-1"></a>It finds that few countries in Europe fit one of the ideal-type household their ranking predicted, between male bread-winner, unsupported double-earner and supported double-earner households.</span>
<span id="cb26-1166"><a href="#cb26-1166" aria-hidden="true" tabindex="-1"></a>Only half of the countries clearly fall into one of the three ideal-types and no countries fall into the category of male bread-winner.</span>
<span id="cb26-1167"><a href="#cb26-1167" aria-hidden="true" tabindex="-1"></a>While supported double-earner type is mostly prevalent in Western Europe and the Scandinavian countries, Southern and Eastern Europe are predominantly shaped by the unsupported double-earner type.</span>
<span id="cb26-1168"><a href="#cb26-1168" aria-hidden="true" tabindex="-1"></a>Generally, more women will take on more unpaid care work in this model especially, though the prevalence exists in all models, which also explains the employment rate gap not decreasing significantly.</span>
<span id="cb26-1169"><a href="#cb26-1169" aria-hidden="true" tabindex="-1"></a>Where it decreases, the &#39;familialization&#39; of care work is often undergoing a process of being taken on as cash-for-care work by migrants in a rising work sector in the former countries, which in turn may slightly increase the overall female labour force participation.</span>
<span id="cb26-1170"><a href="#cb26-1170" aria-hidden="true" tabindex="-1"></a>However, relying on this type of work may not be sustainable or provide decent work, as it often remains poorly regulated and low paid, and may in turn have negative consequences on gender inequality in migrant communities or home countries.</span>
<span id="cb26-1171"><a href="#cb26-1171" aria-hidden="true" tabindex="-1"></a>Some limitations of the study include its scarce underlying data for comparable care work and care migration data, as well as the weak categorization possibilities perhaps obscuring incongruent patterns of policy effects.</span>
<span id="cb26-1172"><a href="#cb26-1172" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1173"><a href="#cb26-1173" aria-hidden="true" tabindex="-1"></a>@Shin2006 look at the effects of providing relatively higher wages for teachers, as well as fertility differences, on labour market participation of young female teachers.</span>
<span id="cb26-1174"><a href="#cb26-1174" aria-hidden="true" tabindex="-1"></a>They find that providing relatively higher wages for teaching professions as compared to non-teaching professions significantly increases female labour force participation for teachers, though the strongest determinant for it is possessing a college major in education, with overall education level being another determinant.</span>
<span id="cb26-1175"><a href="#cb26-1175" aria-hidden="true" tabindex="-1"></a>The study also looks at the effects of the presence of a new-born baby and finds that it significantly decreases female labour force participation and is almost twice as large for women in the teaching profession as compared to non-teaching jobs, though it does not have an effect on the choice of job between teaching or non-teaching.</span>
<span id="cb26-1176"><a href="#cb26-1176" aria-hidden="true" tabindex="-1"></a>The authors suggest this relatively higher exit from the labour market for women with new-born babies in teaching professions may once again be due to low wages: teachers leaving the labour market experience relatively lower temporary wage losses than in other professions, decreasing the exit-cost.</span>
<span id="cb26-1177"><a href="#cb26-1177" aria-hidden="true" tabindex="-1"></a>A limitation of the study is its restricted focus on strictly female underlying panel data which does not allow for comparisons between genders within or across professions.</span>
<span id="cb26-1178"><a href="#cb26-1178" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1179"><a href="#cb26-1179" aria-hidden="true" tabindex="-1"></a>An experimental study of providing UBI for villages in India by @Standing2015 looks at the effects on absolute low-income household debts, utilizing a combination of qualitative and quantitative experimental research.</span>
<span id="cb26-1180"><a href="#cb26-1180" aria-hidden="true" tabindex="-1"></a>It finds that the provision of UBI significantly reduced household debts,</span>
<span id="cb26-1181"><a href="#cb26-1181" aria-hidden="true" tabindex="-1"></a>a finding generally agreeing with assumptions in the literature,</span>
<span id="cb26-1182"><a href="#cb26-1182" aria-hidden="true" tabindex="-1"></a>but goes beyond this by investigating the qualitative causes going beyond purely monetary value into what the authors call &#39;emancipatory value&#39;.</span>
<span id="cb26-1183"><a href="#cb26-1183" aria-hidden="true" tabindex="-1"></a>They find UBI reduces dependency risk - primarily to lenders with high associated fees by allowing the repayment of existing debt, not having to work for the lender directly or by providing them parts of their wages, avoiding taking on new debts and, if new debts have to be taken on, allows choosing less exploitative forms of borrowing (such as from relatives or friends).</span>
<span id="cb26-1184"><a href="#cb26-1184" aria-hidden="true" tabindex="-1"></a>The last channel especially is a point of interest of the study: the intervention did not just reduce absolute debts through an individual possessing more money, it generally infused more money into the local contexts, reducing its scarcity and allowing others such as neighbours and friends to provide more collective risk spreading in the villages..</span>
<span id="cb26-1185"><a href="#cb26-1185" aria-hidden="true" tabindex="-1"></a>The intervention also significantly increased possibility of saving in treatment households, allowing for an increased economic security and empowerment, which was also influenced by household head education, landholding, the household&#39;s caste and size.</span>
<span id="cb26-1186"><a href="#cb26-1186" aria-hidden="true" tabindex="-1"></a>The main channel this is accomplished through is a shift to institutionalized saving, with provides increased resilience against shock events.</span>
<span id="cb26-1187"><a href="#cb26-1187" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1188"><a href="#cb26-1188" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- childcare subsidy --&gt;</span></span>
<span id="cb26-1189"><a href="#cb26-1189" aria-hidden="true" tabindex="-1"></a>@Clark2019 undertake an experimental study on the impacts of providing vouchers for childcare to poor women in urban Kenya, estimating the impacts on their economic empowerment.</span>
<span id="cb26-1190"><a href="#cb26-1190" aria-hidden="true" tabindex="-1"></a>The empowerment is measured through disaggregated analyses of maternal income, employment probability and hours worked.</span>
<span id="cb26-1191"><a href="#cb26-1191" aria-hidden="true" tabindex="-1"></a>It finds that, for married mothers there was a significantly positive effect on employment probability and hours worked, suggesting their increased ability to work through lower childcare costs increasing personal agency.</span>
<span id="cb26-1192"><a href="#cb26-1192" aria-hidden="true" tabindex="-1"></a>For single mothers, it finds a negative effect on hours worked, though with a stable income.</span>
<span id="cb26-1193"><a href="#cb26-1193" aria-hidden="true" tabindex="-1"></a>The authors suggest this is due to single Kenyan mothers already working increased hours compared to married mothers, though the effect shows the ability of single mothers to shift to jobs with more regular hours, even if they are not compatible with childcare.</span>
<span id="cb26-1194"><a href="#cb26-1194" aria-hidden="true" tabindex="-1"></a>Minor limitations of the study are its restriction to effects within a period of 1 year, and a somewhat significant attrition rate to the endline survey.</span>
<span id="cb26-1195"><a href="#cb26-1195" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1196"><a href="#cb26-1196" aria-hidden="true" tabindex="-1"></a>@Hojman2019, in an experimental study looking at the effects of providing childcare vouchers for poor urban mothers in Nicaragua under the &#39;Programo Urbano&#39;, examine the effects on inequality for mothers and children.</span>
<span id="cb26-1197"><a href="#cb26-1197" aria-hidden="true" tabindex="-1"></a>It finds that providing free childcare for young children of poor mothers significantly increases the employment probability of the mothers (14ppts) independently of the childcare quality.</span>
<span id="cb26-1198"><a href="#cb26-1198" aria-hidden="true" tabindex="-1"></a>It also finds significantly positive impacts on the human capital of the children, though the results are also dependent on the quality of childcare facilities.</span>
<span id="cb26-1199"><a href="#cb26-1199" aria-hidden="true" tabindex="-1"></a>This suggests childcare costs being removed through a quasi-transfer reducing the required childcare time burden on mothers, increasing parental agency and employment choices.</span>
<span id="cb26-1200"><a href="#cb26-1200" aria-hidden="true" tabindex="-1"></a>Some limitations to the study include a relatively small overall sample size, as well as employment effects becoming insignificant when the effect is measured on randomization alone (without an additional instrumental variable).</span>
<span id="cb26-1201"><a href="#cb26-1201" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1202"><a href="#cb26-1202" aria-hidden="true" tabindex="-1"></a><span class="fu">### Unconscious bias and discriminatory norms</span></span>
<span id="cb26-1203"><a href="#cb26-1203" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1204"><a href="#cb26-1204" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">TODO</span><span class="co"> include discussion of Gates2000 on social components of disability rtw --&gt;</span></span>
<span id="cb26-1205"><a href="#cb26-1205" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- </span><span class="al">TODO</span><span class="co"> include hiring / firing patterns of Mun2018? mat laws --&gt;</span></span>
<span id="cb26-1206"><a href="#cb26-1206" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1207"><a href="#cb26-1207" aria-hidden="true" tabindex="-1"></a>@Al-Mamun2014 conduct a study on the impacts of an urban micro-finance programme in Malaysia on the economic empowerment of women.</span>
<span id="cb26-1208"><a href="#cb26-1208" aria-hidden="true" tabindex="-1"></a>The programme introduced the ability for low-income urban individuals to receive collateral-free credit.</span>
<span id="cb26-1209"><a href="#cb26-1209" aria-hidden="true" tabindex="-1"></a>The study finds that the programme, though not specifically aimed at women, indeed increased women&#39;s economic empowerment with an increase in household decision-making, as well as increased personal economic security.</span>
<span id="cb26-1210"><a href="#cb26-1210" aria-hidden="true" tabindex="-1"></a>Primarily this is due to the increased access to finance, though it also functions thorugh an increase of collective agency established for the women in organised meetings and trainings.</span>
<span id="cb26-1211"><a href="#cb26-1211" aria-hidden="true" tabindex="-1"></a>It also finds, however, that the empowerment outcomes are constrained by the inability for individuals to obtain loans, with the programme only disbursing group loans which are harder to achieve through obstacles to collective organisation by different racial and socio-demographic backgrounds in each dwelling.</span>
<span id="cb26-1212"><a href="#cb26-1212" aria-hidden="true" tabindex="-1"></a>The study is somewhat limited in its explanatory power since even through its random sampling design it can not establish control for all factors required in experimental design.</span>
<span id="cb26-1213"><a href="#cb26-1213" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1214"><a href="#cb26-1214" aria-hidden="true" tabindex="-1"></a>In turn, @Field2019 undertake an experimental study looking at the effects of granting women increased access to their own financial accounts and training,</span>
<span id="cb26-1215"><a href="#cb26-1215" aria-hidden="true" tabindex="-1"></a>on their employment and hours worked, as well as more long-term economic empowerment.</span>
<span id="cb26-1216"><a href="#cb26-1216" aria-hidden="true" tabindex="-1"></a>The background of the experiment was the rural Indian MGNREGS<span class="ot">[^1]</span> programme which, despite ostensibly mandated gender wage parity, runs the risk of discouraging female workers and restricting their agency by depositing earned wages into a single household account --- predominantly owned by the male head of household.</span>
<span id="cb26-1217"><a href="#cb26-1217" aria-hidden="true" tabindex="-1"></a>To grant increased financial access, the treatment changed the deposits into newly opened individual accounts for the women workers, as well as providing additional training to some women.</span>
<span id="cb26-1218"><a href="#cb26-1218" aria-hidden="true" tabindex="-1"></a>It found that, short-term, the deposits into women&#39;s individual accounts in combination with provided training increased their labour supply, while longer-term there was an increased acceptance of female work in affected households and a significant increase in women&#39;s hours worked.</span>
<span id="cb26-1219"><a href="#cb26-1219" aria-hidden="true" tabindex="-1"></a>The impacts on increased hours worked were concentrated on those households where previously women worked relatively lower amounts and there were stronger norms against female work while less constrained households&#39; impacts dissipated over time.</span>
<span id="cb26-1220"><a href="#cb26-1220" aria-hidden="true" tabindex="-1"></a>The authors suggest the primary channel is the newly increased bargaining power through having a greater control of one&#39;s income, and that it in turn also reflects onto gender norms themselves.</span>
<span id="cb26-1221"><a href="#cb26-1221" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1222"><a href="#cb26-1222" aria-hidden="true" tabindex="-1"></a><span class="ot">[^1]: </span>The Mahatma Gandhi National Rural Employment Guarantee Scheme, one of the largest redistribution programmes on the household level in the world, entitling each household to up to 100 days of work per year.</span>
<span id="cb26-1223"><a href="#cb26-1223" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1224"><a href="#cb26-1224" aria-hidden="true" tabindex="-1"></a><span class="fu"># Discussion and policy implications</span></span>
<span id="cb26-1225"><a href="#cb26-1225" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1226"><a href="#cb26-1226" aria-hidden="true" tabindex="-1"></a><span class="fu">## Robustness of evidence</span></span>
<span id="cb26-1227"><a href="#cb26-1227" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1228"><a href="#cb26-1228" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1231"><a href="#cb26-1231" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1232"><a href="#cb26-1232" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: prep-inequalities-crosstabs</span></span>
<span id="cb26-1233"><a href="#cb26-1233" aria-hidden="true" tabindex="-1"></a><span class="co"># dataframe containing each intervention inequality pair</span></span>
<span id="cb26-1234"><a href="#cb26-1234" aria-hidden="true" tabindex="-1"></a>df_inequality <span class="op">=</span> (</span>
<span id="cb26-1235"><a href="#cb26-1235" aria-hidden="true" tabindex="-1"></a> bib_df[[<span class="st">&quot;region&quot;</span>, <span class="st">&quot;intervention&quot;</span>, <span class="st">&quot;inequality&quot;</span>]]</span>
<span id="cb26-1236"><a href="#cb26-1236" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb26-1237"><a href="#cb26-1237" aria-hidden="true" tabindex="-1"></a> Intervention <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;intervention&quot;</span>]</span>
<span id="cb26-1238"><a href="#cb26-1238" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot;\(.+\)&quot;</span>, <span class="st">&quot;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1239"><a href="#cb26-1239" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1240"><a href="#cb26-1240" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb26-1241"><a href="#cb26-1241" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb26-1242"><a href="#cb26-1242" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb26-1243"><a href="#cb26-1243" aria-hidden="true" tabindex="-1"></a> inequality <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;inequality&quot;</span>]</span>
<span id="cb26-1244"><a href="#cb26-1244" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot;\(.+\)&quot;</span>, <span class="st">&quot;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1245"><a href="#cb26-1245" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1246"><a href="#cb26-1246" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb26-1247"><a href="#cb26-1247" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb26-1248"><a href="#cb26-1248" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-1249"><a href="#cb26-1249" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-1250"><a href="#cb26-1250" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;Intervention&quot;</span>)</span>
<span id="cb26-1251"><a href="#cb26-1251" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb26-1252"><a href="#cb26-1252" aria-hidden="true" tabindex="-1"></a> .reset_index(drop<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1253"><a href="#cb26-1253" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-1254"><a href="#cb26-1254" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1255"><a href="#cb26-1255" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> crosstab_inequality(df, inequality:<span class="bu">str</span>, <span class="op">**</span>kwargs):</span>
<span id="cb26-1256"><a href="#cb26-1256" aria-hidden="true" tabindex="-1"></a> df_temp <span class="op">=</span> df.loc[(df[<span class="st">&quot;inequality&quot;</span>] <span class="op">==</span> inequality) <span class="op">|</span> (df[<span class="st">&quot;inequality&quot;</span>] <span class="op">==</span> <span class="st">&quot;income&quot;</span>)]</span>
<span id="cb26-1257"><a href="#cb26-1257" aria-hidden="true" tabindex="-1"></a> tab <span class="op">=</span> pd.crosstab(df_temp[<span class="st">&quot;Intervention&quot;</span>], df_temp[<span class="st">&quot;inequality&quot;</span>], <span class="op">**</span>kwargs)</span>
<span id="cb26-1258"><a href="#cb26-1258" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> tab.drop(tab[tab[inequality] <span class="op">==</span> <span class="dv">0</span>].index)</span>
<span id="cb26-1259"><a href="#cb26-1259" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1260"><a href="#cb26-1260" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1261"><a href="#cb26-1261" aria-hidden="true" tabindex="-1"></a><span class="fu">### Regional spread</span></span>
<span id="cb26-1262"><a href="#cb26-1262" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1263"><a href="#cb26-1263" aria-hidden="true" tabindex="-1"></a>As can be seen in @fig-region-counts, taken by region for the overall study sample,</span>
<span id="cb26-1264"><a href="#cb26-1264" aria-hidden="true" tabindex="-1"></a>the evidence base receives a relatively even split between the World Bank regional country groupings with the exception of the Middle East and North Africa (MENA) region,</span>
<span id="cb26-1265"><a href="#cb26-1265" aria-hidden="true" tabindex="-1"></a>in which fewer studies have been identified.</span>
<span id="cb26-1266"><a href="#cb26-1266" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1269"><a href="#cb26-1269" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1270"><a href="#cb26-1270" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-region-counts</span></span>
<span id="cb26-1271"><a href="#cb26-1271" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Studies by regions analysed</span></span>
<span id="cb26-1272"><a href="#cb26-1272" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1273"><a href="#cb26-1273" aria-hidden="true" tabindex="-1"></a>by_region <span class="op">=</span> (</span>
<span id="cb26-1274"><a href="#cb26-1274" aria-hidden="true" tabindex="-1"></a> bib_df[[<span class="st">&quot;region&quot;</span>]]</span>
<span id="cb26-1275"><a href="#cb26-1275" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb26-1276"><a href="#cb26-1276" aria-hidden="true" tabindex="-1"></a> region <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;region&quot;</span>]</span>
<span id="cb26-1277"><a href="#cb26-1277" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1278"><a href="#cb26-1278" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb26-1279"><a href="#cb26-1279" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb26-1280"><a href="#cb26-1280" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-1281"><a href="#cb26-1281" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-1282"><a href="#cb26-1282" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;region&quot;</span>)</span>
<span id="cb26-1283"><a href="#cb26-1283" aria-hidden="true" tabindex="-1"></a> .reset_index(drop<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1284"><a href="#cb26-1284" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-1285"><a href="#cb26-1285" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(by_region, x<span class="op">=</span><span class="st">&quot;region&quot;</span>, order<span class="op">=</span>by_region[<span class="st">&quot;region&quot;</span>].value_counts().index)</span>
<span id="cb26-1286"><a href="#cb26-1286" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb26-1287"><a href="#cb26-1287" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb26-1288"><a href="#cb26-1288" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-1289"><a href="#cb26-1289" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1290"><a href="#cb26-1290" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> regions_for_inequality(df, inequality:<span class="bu">str</span>):</span>
<span id="cb26-1291"><a href="#cb26-1291" aria-hidden="true" tabindex="-1"></a> df_temp <span class="op">=</span> df.loc[(df[<span class="st">&quot;inequality&quot;</span>] <span class="op">==</span> inequality)]</span>
<span id="cb26-1292"><a href="#cb26-1292" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> sns.countplot(df_temp, x<span class="op">=</span><span class="st">&quot;region&quot;</span>, order<span class="op">=</span>df_temp[<span class="st">&quot;region&quot;</span>].value_counts().index)</span>
<span id="cb26-1293"><a href="#cb26-1293" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1294"><a href="#cb26-1294" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1295"><a href="#cb26-1295" aria-hidden="true" tabindex="-1"></a>Most studies come from a context of East Asia and the Pacific, though with an almost equal amount analysing Europe and Central Asia.</span>
<span id="cb26-1296"><a href="#cb26-1296" aria-hidden="true" tabindex="-1"></a>With slightly fewer studies, the contexts of North America, Sub-Saharan Africa follow for amount of anlalyses,</span>
<span id="cb26-1297"><a href="#cb26-1297" aria-hidden="true" tabindex="-1"></a>and in turn Latin America and the Caribbean and South Asia with an equal amount of studies for each region.</span>
<span id="cb26-1298"><a href="#cb26-1298" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1299"><a href="#cb26-1299" aria-hidden="true" tabindex="-1"></a>The lower amount of studies stemming from a MENA context can point to a variety of underlying causes:</span>
<span id="cb26-1300"><a href="#cb26-1300" aria-hidden="true" tabindex="-1"></a>First, it is possible that there is simply not as much evidence-based analysis undertaken for countries in the region as for other national or subnational contexts,</span>
<span id="cb26-1301"><a href="#cb26-1301" aria-hidden="true" tabindex="-1"></a>with research either following a more theoretical trajectory, or missing the underlying data collection that is available for other regional contexts.</span>
<span id="cb26-1302"><a href="#cb26-1302" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1303"><a href="#cb26-1303" aria-hidden="true" tabindex="-1"></a>However, it cannot be ruled out that the search protocol itself did not capture the same depth of analytical material as for other contexts,</span>
<span id="cb26-1304"><a href="#cb26-1304" aria-hidden="true" tabindex="-1"></a>with each region often having both a specific focus in policy-orientations and academically,</span>
<span id="cb26-1305"><a href="#cb26-1305" aria-hidden="true" tabindex="-1"></a>and in some cases also differing underlying term bases.</span>
<span id="cb26-1306"><a href="#cb26-1306" aria-hidden="true" tabindex="-1"></a>Such a contextual term differences may then not be captured adequately by the existing query terms and would point to a necessity to re-align it to the required specifics.</span>
<span id="cb26-1307"><a href="#cb26-1307" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1308"><a href="#cb26-1308" aria-hidden="true" tabindex="-1"></a>One reason for such a differentiation could be a larger amount of gray literature captured compared to other regions,</span>
<span id="cb26-1309"><a href="#cb26-1309" aria-hidden="true" tabindex="-1"></a>which may be utilising less established terms than the majority of captured literature for policy implementations.</span>
<span id="cb26-1310"><a href="#cb26-1310" aria-hidden="true" tabindex="-1"></a>Another reason could be the actual implementation of different policy programmes which are then equally not captured by existing term clusters.</span>
<span id="cb26-1311"><a href="#cb26-1311" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1312"><a href="#cb26-1312" aria-hidden="true" tabindex="-1"></a><span class="fu">### Internal and external validity</span></span>
<span id="cb26-1313"><a href="#cb26-1313" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1314"><a href="#cb26-1314" aria-hidden="true" tabindex="-1"></a>Using the validity ranking separated into internal and external validity for each study,</span>
<span id="cb26-1315"><a href="#cb26-1315" aria-hidden="true" tabindex="-1"></a>it is possible to identify the general make-up of the overall sample,</span>
<span id="cb26-1316"><a href="#cb26-1316" aria-hidden="true" tabindex="-1"></a>the relationship between both dimensions and the distribution of studies within.</span>
<span id="cb26-1317"><a href="#cb26-1317" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1318"><a href="#cb26-1318" aria-hidden="true" tabindex="-1"></a>As can be seen in @fig-validity-relation, the relationship between the internal dimension and the external dimension of validity for the study pool follows a normal distribution.</span>
<span id="cb26-1319"><a href="#cb26-1319" aria-hidden="true" tabindex="-1"></a>Generally, studies that have a lower internal validity, between 2.0 and 3.5, rank higher on their external validity,</span>
<span id="cb26-1320"><a href="#cb26-1320" aria-hidden="true" tabindex="-1"></a>while studies with higher internal validity in turn do not reach as high on the external validity ranking.</span>
<span id="cb26-1321"><a href="#cb26-1321" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1322"><a href="#cb26-1322" aria-hidden="true" tabindex="-1"></a>::: {layout-ncol=2 .column-body-outset}</span>
<span id="cb26-1323"><a href="#cb26-1323" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1326"><a href="#cb26-1326" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1327"><a href="#cb26-1327" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-validity-relation</span></span>
<span id="cb26-1328"><a href="#cb26-1328" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: &quot;Relation between internal and external validity&quot;</span></span>
<span id="cb26-1329"><a href="#cb26-1329" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1330"><a href="#cb26-1330" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> src.model <span class="im">import</span> validity</span>
<span id="cb26-1331"><a href="#cb26-1331" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1332"><a href="#cb26-1332" aria-hidden="true" tabindex="-1"></a>validities <span class="op">=</span> validity.calculate(by_intervention)</span>
<span id="cb26-1333"><a href="#cb26-1333" aria-hidden="true" tabindex="-1"></a>validities[<span class="st">&quot;identifier&quot;</span>] <span class="op">=</span> validities[<span class="st">&quot;author&quot;</span>].<span class="bu">str</span>.replace(<span class="vs">r&#39;,.*$&#39;</span>, <span class="st">&#39;&#39;</span>, regex<span class="op">=</span><span class="va">True</span>) <span class="op">+</span> <span class="st">&quot; (&quot;</span> <span class="op">+</span> validities[<span class="st">&quot;year&quot;</span>].astype(<span class="bu">str</span>) <span class="op">+</span> <span class="st">&quot;)&quot;</span></span>
<span id="cb26-1334"><a href="#cb26-1334" aria-hidden="true" tabindex="-1"></a>validities <span class="op">=</span> validities.loc[(validities[<span class="st">&quot;design&quot;</span>] <span class="op">==</span> <span class="st">&quot;quasi-experimental&quot;</span>) <span class="op">|</span> (validities[<span class="st">&quot;design&quot;</span>] <span class="op">==</span> <span class="st">&quot;experimental&quot;</span>)]</span>
<span id="cb26-1335"><a href="#cb26-1335" aria-hidden="true" tabindex="-1"></a><span class="co">#validities[&quot;external_validity&quot;] = validities[&quot;external_validity&quot;].astype(&#39;category&#39;)</span></span>
<span id="cb26-1336"><a href="#cb26-1336" aria-hidden="true" tabindex="-1"></a>validities[<span class="st">&quot;internal_validity&quot;</span>] <span class="op">=</span> validities[<span class="st">&quot;internal_validity&quot;</span>].astype(<span class="st">&#39;category&#39;</span>)</span>
<span id="cb26-1337"><a href="#cb26-1337" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1338"><a href="#cb26-1338" aria-hidden="true" tabindex="-1"></a>plt.figure().set_figheight(<span class="dv">5</span>)</span>
<span id="cb26-1339"><a href="#cb26-1339" aria-hidden="true" tabindex="-1"></a>sns.violinplot(</span>
<span id="cb26-1340"><a href="#cb26-1340" aria-hidden="true" tabindex="-1"></a> data<span class="op">=</span>validities,</span>
<span id="cb26-1341"><a href="#cb26-1341" aria-hidden="true" tabindex="-1"></a> x<span class="op">=</span><span class="st">&quot;internal_validity&quot;</span>, y<span class="op">=</span><span class="st">&quot;external_validity&quot;</span>, hue<span class="op">=</span><span class="st">&quot;design&quot;</span>,</span>
<span id="cb26-1342"><a href="#cb26-1342" aria-hidden="true" tabindex="-1"></a> cut<span class="op">=</span><span class="dv">0</span>, bw_method<span class="op">=</span><span class="st">&quot;scott&quot;</span>,</span>
<span id="cb26-1343"><a href="#cb26-1343" aria-hidden="true" tabindex="-1"></a> orient<span class="op">=</span><span class="st">&quot;x&quot;</span></span>
<span id="cb26-1344"><a href="#cb26-1344" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-1345"><a href="#cb26-1345" aria-hidden="true" tabindex="-1"></a>sns.swarmplot(</span>
<span id="cb26-1346"><a href="#cb26-1346" aria-hidden="true" tabindex="-1"></a> data<span class="op">=</span>validities,</span>
<span id="cb26-1347"><a href="#cb26-1347" aria-hidden="true" tabindex="-1"></a> x<span class="op">=</span><span class="st">&quot;internal_validity&quot;</span>, y<span class="op">=</span><span class="st">&quot;external_validity&quot;</span>, legend<span class="op">=</span><span class="va">False</span>,</span>
<span id="cb26-1348"><a href="#cb26-1348" aria-hidden="true" tabindex="-1"></a> color<span class="op">=</span><span class="st">&quot;darkmagenta&quot;</span>,</span>
<span id="cb26-1349"><a href="#cb26-1349" aria-hidden="true" tabindex="-1"></a> s<span class="op">=</span><span class="dv">4</span></span>
<span id="cb26-1350"><a href="#cb26-1350" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-1351"><a href="#cb26-1351" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1352"><a href="#cb26-1352" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1355"><a href="#cb26-1355" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1356"><a href="#cb26-1356" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-validity-distribution</span></span>
<span id="cb26-1357"><a href="#cb26-1357" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: &quot;Distribution of internal validities&quot;</span></span>
<span id="cb26-1358"><a href="#cb26-1358" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1359"><a href="#cb26-1359" aria-hidden="true" tabindex="-1"></a>sns.displot(</span>
<span id="cb26-1360"><a href="#cb26-1360" aria-hidden="true" tabindex="-1"></a> data<span class="op">=</span>validities,</span>
<span id="cb26-1361"><a href="#cb26-1361" aria-hidden="true" tabindex="-1"></a> x<span class="op">=</span><span class="st">&quot;external_validity&quot;</span>, hue<span class="op">=</span><span class="st">&quot;internal_validity&quot;</span>,</span>
<span id="cb26-1362"><a href="#cb26-1362" aria-hidden="true" tabindex="-1"></a> kind<span class="op">=</span><span class="st">&quot;kde&quot;</span>,</span>
<span id="cb26-1363"><a href="#cb26-1363" aria-hidden="true" tabindex="-1"></a> multiple<span class="op">=</span><span class="st">&quot;fill&quot;</span>, clip<span class="op">=</span>(<span class="dv">0</span>, <span class="va">None</span>),</span>
<span id="cb26-1364"><a href="#cb26-1364" aria-hidden="true" tabindex="-1"></a> palette<span class="op">=</span><span class="st">&quot;ch:rot=-0.5,hue=1.5,light=0.9&quot;</span>,</span>
<span id="cb26-1365"><a href="#cb26-1365" aria-hidden="true" tabindex="-1"></a> bw_adjust<span class="op">=</span><span class="fl">.65</span>, cut<span class="op">=</span><span class="dv">0</span>,</span>
<span id="cb26-1366"><a href="#cb26-1366" aria-hidden="true" tabindex="-1"></a> warn_singular <span class="op">=</span> <span class="va">False</span></span>
<span id="cb26-1367"><a href="#cb26-1367" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-1368"><a href="#cb26-1368" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1369"><a href="#cb26-1369" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1370"><a href="#cb26-1370" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-1371"><a href="#cb26-1371" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1372"><a href="#cb26-1372" aria-hidden="true" tabindex="-1"></a>Studies with an internal validity ranking of of 3.0 (primarily made up of difference-in-difference approaches) and an internal ranking of 5.0 (randomized control trials) have the same tight clustering around an external validity between 4.0 (national) and 5.0 (census-based), and 2.0 (local) and 3.0 (subnational), respectively.</span>
<span id="cb26-1373"><a href="#cb26-1373" aria-hidden="true" tabindex="-1"></a>This clearly shows the expected overall relationship of studies with high internal validity generally ranking lower on their external validity.</span>
<span id="cb26-1374"><a href="#cb26-1374" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1375"><a href="#cb26-1375" aria-hidden="true" tabindex="-1"></a>The situation is less clear-cut with the internal rankings of 2.0 (primarily ordinary least squares) and 4.0 (primarily instrumental variable),</span>
<span id="cb26-1376"><a href="#cb26-1376" aria-hidden="true" tabindex="-1"></a>which show a larger external validity spread.</span>
<span id="cb26-1377"><a href="#cb26-1377" aria-hidden="true" tabindex="-1"></a>For 2.0-ranked studies, there is an overall larger spread with most using nationally representative data,</span>
<span id="cb26-1378"><a href="#cb26-1378" aria-hidden="true" tabindex="-1"></a>while a significant amount makes use of census-based data and others in turn only being subnationally representative.</span>
<span id="cb26-1379"><a href="#cb26-1379" aria-hidden="true" tabindex="-1"></a>Studies ranked 4.0 internally have a higher heterogeneity with the significant outlier of @Thoresen2021,</span>
<span id="cb26-1380"><a href="#cb26-1380" aria-hidden="true" tabindex="-1"></a>which had the limitation of its underlying data being non-representative.</span>
<span id="cb26-1381"><a href="#cb26-1381" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1382"><a href="#cb26-1382" aria-hidden="true" tabindex="-1"></a>Looking at the overall density of studies along their external validity dimension,</span>
<span id="cb26-1383"><a href="#cb26-1383" aria-hidden="true" tabindex="-1"></a>@fig-validity-distribution reiterates this overall relationship with internal validity.</span>
<span id="cb26-1384"><a href="#cb26-1384" aria-hidden="true" tabindex="-1"></a>It additionally shows that studies with low internal validity make up the dominant number of nationally representative analyses and the slight majority of census-based analyses,</span>
<span id="cb26-1385"><a href="#cb26-1385" aria-hidden="true" tabindex="-1"></a>while locally or non-representative samples are almost solely made up of internally highly valid (ranking 4.0 or above) analyses,</span>
<span id="cb26-1386"><a href="#cb26-1386" aria-hidden="true" tabindex="-1"></a>again with the exception of @Thoresen2021 already mentioned.</span>
<span id="cb26-1387"><a href="#cb26-1387" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1388"><a href="#cb26-1388" aria-hidden="true" tabindex="-1"></a>Looking at the data per region, census-based studies are primarily spread between Latin America and the Caribbean, as well as Europe and Central Asia.</span>
<span id="cb26-1389"><a href="#cb26-1389" aria-hidden="true" tabindex="-1"></a>Meanwhile, studies using nationally, subnationally or non-representative data then to have a larger focus on North America, as well as East Asia and the Pacific.</span>
<span id="cb26-1390"><a href="#cb26-1390" aria-hidden="true" tabindex="-1"></a>A slight trend towards studies focusing on evidence-based research in developing countries is visible, though with an overall rising output, as seen in @fig-publications-per-year,</span>
<span id="cb26-1391"><a href="#cb26-1391" aria-hidden="true" tabindex="-1"></a>and the possibly a reliance on more recent datasets, this would be expected.</span>
<span id="cb26-1392"><a href="#cb26-1392" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1393"><a href="#cb26-1393" aria-hidden="true" tabindex="-1"></a><span class="fu">### Inequality types analysed</span></span>
<span id="cb26-1394"><a href="#cb26-1394" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1395"><a href="#cb26-1395" aria-hidden="true" tabindex="-1"></a>Policy interventions undertaken either with the explicit aim of reducing one or multiple inequalities, or analysed under the lens of such an aim implicitly, appear in a wide array of variations to their approach and primary targeted inequality, as was highlighted in the previous section.</span>
<span id="cb26-1396"><a href="#cb26-1396" aria-hidden="true" tabindex="-1"></a>To make further sense of the studies shining a light on such approaches,</span>
<span id="cb26-1397"><a href="#cb26-1397" aria-hidden="true" tabindex="-1"></a>it makes sense to divide their attention not just by primary approach,</span>
<span id="cb26-1398"><a href="#cb26-1398" aria-hidden="true" tabindex="-1"></a>but by individual or overlapping inequalities being targeted,</span>
<span id="cb26-1399"><a href="#cb26-1399" aria-hidden="true" tabindex="-1"></a>as well as the region of their operation.</span>
<span id="cb26-1400"><a href="#cb26-1400" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1401"><a href="#cb26-1401" aria-hidden="true" tabindex="-1"></a>As can be seen in @fig-inequality-types which breaks down available studies by targeted inequalities,</span>
<span id="cb26-1402"><a href="#cb26-1402" aria-hidden="true" tabindex="-1"></a>income inequality is the type of inequality traced in most of the relevant studies.</span>
<span id="cb26-1403"><a href="#cb26-1403" aria-hidden="true" tabindex="-1"></a>This follows the identified multi-purpose lens income inequality can provide, through which to understand other inequalities ---</span>
<span id="cb26-1404"><a href="#cb26-1404" aria-hidden="true" tabindex="-1"></a>many studies use income measurements and changes in income or income inequality over time as indicators to understand a variety of other inequalities&#39; linkages through.</span>
<span id="cb26-1405"><a href="#cb26-1405" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1408"><a href="#cb26-1408" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1409"><a href="#cb26-1409" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: inequality-targeting-implicit-explicit</span></span>
<span id="cb26-1410"><a href="#cb26-1410" aria-hidden="true" tabindex="-1"></a>targeting_majority <span class="op">=</span> bib_df[<span class="st">&quot;targeting&quot;</span>].value_counts().index.tolist()[<span class="dv">0</span>]</span>
<span id="cb26-1411"><a href="#cb26-1411" aria-hidden="true" tabindex="-1"></a>targeting_minority <span class="op">=</span> bib_df[<span class="st">&quot;targeting&quot;</span>].value_counts().index.tolist()[<span class="op">-</span><span class="dv">1</span>]</span>
<span id="cb26-1412"><a href="#cb26-1412" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1413"><a href="#cb26-1413" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1414"><a href="#cb26-1414" aria-hidden="true" tabindex="-1"></a>Often, however, income inequality is not the primary inequality being targeted, but used to measure the effects on other inequalities by seeing how the effects of respective inequality and income intersect, as will be discussed in the following section.</span>
<span id="cb26-1415"><a href="#cb26-1415" aria-hidden="true" tabindex="-1"></a>The majority of policies under analysis had an <span class="in">`{python} targeting_majority`</span> focus on all the inequalities analysed in the respective study,</span>
<span id="cb26-1416"><a href="#cb26-1416" aria-hidden="true" tabindex="-1"></a>with only a minority of studies looking at policies with an <span class="in">`{python} targeting_minority`</span> targeting on the inequalities itself.</span>
<span id="cb26-1417"><a href="#cb26-1417" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1420"><a href="#cb26-1420" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1421"><a href="#cb26-1421" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-inequality-types</span></span>
<span id="cb26-1422"><a href="#cb26-1422" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Types of inequality analysed</span></span>
<span id="cb26-1423"><a href="#cb26-1423" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1424"><a href="#cb26-1424" aria-hidden="true" tabindex="-1"></a>by_inequality <span class="op">=</span> (</span>
<span id="cb26-1425"><a href="#cb26-1425" aria-hidden="true" tabindex="-1"></a> bib_df[[<span class="st">&quot;inequality&quot;</span>]]</span>
<span id="cb26-1426"><a href="#cb26-1426" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb26-1427"><a href="#cb26-1427" aria-hidden="true" tabindex="-1"></a> inequality <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;inequality&quot;</span>]</span>
<span id="cb26-1428"><a href="#cb26-1428" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot;\(.+\)&quot;</span>, <span class="st">&quot;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1429"><a href="#cb26-1429" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1430"><a href="#cb26-1430" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb26-1431"><a href="#cb26-1431" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb26-1432"><a href="#cb26-1432" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-1433"><a href="#cb26-1433" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-1434"><a href="#cb26-1434" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb26-1435"><a href="#cb26-1435" aria-hidden="true" tabindex="-1"></a> .reset_index(drop<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1436"><a href="#cb26-1436" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-1437"><a href="#cb26-1437" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1438"><a href="#cb26-1438" aria-hidden="true" tabindex="-1"></a>fig <span class="op">=</span> plt.figure()</span>
<span id="cb26-1439"><a href="#cb26-1439" aria-hidden="true" tabindex="-1"></a>fig.set_size_inches(<span class="dv">6</span>, <span class="dv">3</span>)</span>
<span id="cb26-1440"><a href="#cb26-1440" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> sns.countplot(by_inequality, x<span class="op">=</span><span class="st">&quot;inequality&quot;</span>, order<span class="op">=</span>by_inequality[<span class="st">&quot;inequality&quot;</span>].value_counts().index)</span>
<span id="cb26-1441"><a href="#cb26-1441" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb26-1442"><a href="#cb26-1442" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb26-1443"><a href="#cb26-1443" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-1444"><a href="#cb26-1444" aria-hidden="true" tabindex="-1"></a>by_inequality <span class="op">=</span> <span class="va">None</span></span>
<span id="cb26-1445"><a href="#cb26-1445" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1446"><a href="#cb26-1446" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1447"><a href="#cb26-1447" aria-hidden="true" tabindex="-1"></a>With income inequality on its own often describing vertical inequality within a national context,</span>
<span id="cb26-1448"><a href="#cb26-1448" aria-hidden="true" tabindex="-1"></a>the remaining inequalities gathered from the data rather form horizontal lenses to view their contexts through.</span>
<span id="cb26-1449"><a href="#cb26-1449" aria-hidden="true" tabindex="-1"></a>The second most analysed inequality is that of gender, followed by spatial inequalities, disabilities, ethnicities, age, inequalities of migration, education and intergenerational issues.</span>
<span id="cb26-1450"><a href="#cb26-1450" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1451"><a href="#cb26-1451" aria-hidden="true" tabindex="-1"></a>The following sections will dive deeper into the identified predominant inequality areas,</span>
<span id="cb26-1452"><a href="#cb26-1452" aria-hidden="true" tabindex="-1"></a>discuss what the main interventions analysed in the literature are,</span>
<span id="cb26-1453"><a href="#cb26-1453" aria-hidden="true" tabindex="-1"></a>and where overlaps between theoretical approaches and qualitative considerations are,</span>
<span id="cb26-1454"><a href="#cb26-1454" aria-hidden="true" tabindex="-1"></a>as well as where gaps and limitations lie.</span>
<span id="cb26-1455"><a href="#cb26-1455" aria-hidden="true" tabindex="-1"></a>Only a small amount of studies carried analysis of inequalities in the world of work surrounding migration, generational connections, age and education into the world of work.</span>
<span id="cb26-1456"><a href="#cb26-1456" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1457"><a href="#cb26-1457" aria-hidden="true" tabindex="-1"></a>Age-related inequalities prominently factor into studies as an intersection with disability,</span>
<span id="cb26-1458"><a href="#cb26-1458" aria-hidden="true" tabindex="-1"></a>in focusing on the effects of older people with disabilities on the labour market <span class="co">[</span><span class="ot">@Kirsh2016</span><span class="co">]</span>.</span>
<span id="cb26-1459"><a href="#cb26-1459" aria-hidden="true" tabindex="-1"></a>Studies that solely or mainly target age-related inequalities themselves often do so with a stronger focus on the effects on seniors&#39; health outcomes and long-term activation measures,</span>
<span id="cb26-1460"><a href="#cb26-1460" aria-hidden="true" tabindex="-1"></a>with some extending into the effects of differentiated pension systems.</span>
<span id="cb26-1461"><a href="#cb26-1461" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1462"><a href="#cb26-1462" aria-hidden="true" tabindex="-1"></a>While a pursuit both worthwhile in its own right and, by the nature of pensions, closely tied to labour markets, the studies ultimately focus on impacts which rarely intersect back into the world of work itself and are thus beyond the scope of this review.<span class="ot">[^pension-studies]</span></span>
<span id="cb26-1463"><a href="#cb26-1463" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1464"><a href="#cb26-1464" aria-hidden="true" tabindex="-1"></a><span class="ot">[^pension-studies]: </span>For an overview of how retirement and pensions reflect on health aspects in ageing, see @VanDerHeide2013, for a review of pensions intersecting with other possible inequalities and also health outcomes, see @Zantinge2014.</span>
<span id="cb26-1465"><a href="#cb26-1465" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1466"><a href="#cb26-1466" aria-hidden="true" tabindex="-1"></a>Equally, for migration few studies strictly delineate it from racial inequalities or considerations of ethnicity.</span>
<span id="cb26-1467"><a href="#cb26-1467" aria-hidden="true" tabindex="-1"></a>For the purposes of discussion, studies analysing both inequalities concerning ethnicity and migration will be discussed as part of one socio-demographic point of view,</span>
<span id="cb26-1468"><a href="#cb26-1468" aria-hidden="true" tabindex="-1"></a>though results that do only speak to migration will be highlighted accordingly.</span>
<span id="cb26-1469"><a href="#cb26-1469" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1470"><a href="#cb26-1470" aria-hidden="true" tabindex="-1"></a>Surprisingly few studies focus on the eventual outcomes in the world of work of earlier education inequalities.</span>
<span id="cb26-1471"><a href="#cb26-1471" aria-hidden="true" tabindex="-1"></a>The majority of studies analysing education-oriented policies focus on direct outcomes of child health and development, education accessibility itself or social outcomes.<span class="ot">[^education-studies]</span></span>
<span id="cb26-1472"><a href="#cb26-1472" aria-hidden="true" tabindex="-1"></a>Educational inequalities themselves were the outcome-focus of almost no studies,</span>
<span id="cb26-1473"><a href="#cb26-1473" aria-hidden="true" tabindex="-1"></a>often analysed as a different dimension from the world of work and more focused on educations systems for children and youth,</span>
<span id="cb26-1474"><a href="#cb26-1474" aria-hidden="true" tabindex="-1"></a>especially early childhood development.</span>
<span id="cb26-1475"><a href="#cb26-1475" aria-hidden="true" tabindex="-1"></a>Similarly, rarely do studies delineate generational outcomes from income, gender or education issues enough to mark their own category of analysis.</span>
<span id="cb26-1476"><a href="#cb26-1476" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1477"><a href="#cb26-1477" aria-hidden="true" tabindex="-1"></a><span class="ot">[^education-studies]: </span>For gender inequalities within education paths themselves, see @Stepanenko2021. For possible ways to integrate gender-transformative interventions into professional education, see @Newman2016. For the effects of prior inequalities on taxation preferences, school enrolment and educational choices, see @Gutierrez2009 and @Zamfir2017. For interactions between policies for the knowledge translation of sexual education and their barriers, see @Curran2022.</span>
<span id="cb26-1478"><a href="#cb26-1478" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1479"><a href="#cb26-1479" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- frameworks/qualitative discussion --&gt;</span></span>
<span id="cb26-1480"><a href="#cb26-1480" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- explanatory framework; see data/processed/irrelevant/Eckardt2022 </span><span class="al">TODO</span><span class="co"> connect with study results above --&gt;</span></span>
<span id="cb26-1481"><a href="#cb26-1481" aria-hidden="true" tabindex="-1"></a>The effects of automation on income inequality are more clearly put into focus by @Eckardt2022 by studying income inequality and under the effects of various kinds of automation and a minimum wage within the economy.</span>
<span id="cb26-1482"><a href="#cb26-1482" aria-hidden="true" tabindex="-1"></a>He considers several types of automation, with automation on the extensive margin (automation of more tasks) leading to decreased wage inequality between low-skill and high-skill earners if it results in decreased overall outputs due to wage compression, and vice versa for increased total outputs.</span>
<span id="cb26-1483"><a href="#cb26-1483" aria-hidden="true" tabindex="-1"></a>Automation on the intensive margin (increased productivity of automating existing tasks) has ambiguous effects on the employment share of low-skill workers (who are possibly displaced) and a higher minimum wage here decreases the inequality between low-skill wages and higher-skill wages.</span>
<span id="cb26-1484"><a href="#cb26-1484" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1485"><a href="#cb26-1485" aria-hidden="true" tabindex="-1"></a>However, it may also result in a ripple effect which results in the overall share of income of low-skill workers not increasing, if more machines or high-skill workers displace them.</span>
<span id="cb26-1486"><a href="#cb26-1486" aria-hidden="true" tabindex="-1"></a>Then, while the wage differences may decrease, the low-skill workers share of national income is identified as non-increasing and the share of low-skill employment could decrease.</span>
<span id="cb26-1487"><a href="#cb26-1487" aria-hidden="true" tabindex="-1"></a>The effects on low-skill income share under a system of minimum wage are thus primarily dependent on the amount of low-skill job displacement, as well as the effects of the minimum wage on overall economic output in the first place.</span>
<span id="cb26-1488"><a href="#cb26-1488" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1489"><a href="#cb26-1489" aria-hidden="true" tabindex="-1"></a>Ultimately, the author also suggests the institution of low-skill worker training programmes either targeting enhanced productivity for their existing tasks (&#39;deepening skills&#39;) or enabling their capability for undertaking tasks previously only assigned to high-skill workers (&#39;expanding skills&#39;) which would respectively counteract the negative automation effects on both margins.</span>
<span id="cb26-1490"><a href="#cb26-1490" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1491"><a href="#cb26-1491" aria-hidden="true" tabindex="-1"></a>Thus, for the current state of the literature on analyses of policy interventions through the lens of inequality reduction within the world of work, there are strong gaps of academic lenses for generational inequalities, age inequalities, educational inequalities and inequalities of non-ethnic migration processes when looking at the quantity of output.</span>
<span id="cb26-1492"><a href="#cb26-1492" aria-hidden="true" tabindex="-1"></a>Care should be taken not to overestimate the decisiveness of merely quantified outputs ---</span>
<span id="cb26-1493"><a href="#cb26-1493" aria-hidden="true" tabindex="-1"></a>multiple studies with strong risk of bias may produce less reliable outcomes than fewer studies with stronger evidence bases ---</span>
<span id="cb26-1494"><a href="#cb26-1494" aria-hidden="true" tabindex="-1"></a>however, it does provide an overview of the size of evidence base in the first place.</span>
<span id="cb26-1495"><a href="#cb26-1495" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1496"><a href="#cb26-1496" aria-hidden="true" tabindex="-1"></a>The following sections will instead discuss in more depth the implications for individual inequalities,</span>
<span id="cb26-1497"><a href="#cb26-1497" aria-hidden="true" tabindex="-1"></a>as well as providing a comparative view of the respective intersection with income inequality.</span>
<span id="cb26-1498"><a href="#cb26-1498" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1499"><a href="#cb26-1499" aria-hidden="true" tabindex="-1"></a><span class="fu">## Gender inequalities</span></span>
<span id="cb26-1500"><a href="#cb26-1500" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1501"><a href="#cb26-1501" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!--</span></span>
<span id="cb26-1502"><a href="#cb26-1502" aria-hidden="true" tabindex="-1"></a><span class="co">findings - channels - policy recc</span></span>
<span id="cb26-1503"><a href="#cb26-1503" aria-hidden="true" tabindex="-1"></a><span class="co">- persistent discrimination and cultural views (strenghtening female agency, vicious circle of low FLFP and education)</span></span>
<span id="cb26-1504"><a href="#cb26-1504" aria-hidden="true" tabindex="-1"></a><span class="co">- supply-side effects, esp maternal (family planning; care work)</span></span>
<span id="cb26-1505"><a href="#cb26-1505" aria-hidden="true" tabindex="-1"></a><span class="co">- organisational disadvantagement in new economy (networking needs, self-promotion, managerial discretions)</span></span>
<span id="cb26-1506"><a href="#cb26-1506" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1507"><a href="#cb26-1507" aria-hidden="true" tabindex="-1"></a><span class="al">TODO</span><span class="co"> include unionisation effects on gender</span></span>
<span id="cb26-1508"><a href="#cb26-1508" aria-hidden="true" tabindex="-1"></a><span class="co">--&gt;</span></span>
<span id="cb26-1509"><a href="#cb26-1509" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1510"><a href="#cb26-1510" aria-hidden="true" tabindex="-1"></a>Due to its persistent characteristics, gender inequality is an often analysed horizontal dimension of workplace inequality in the study sample,</span>
<span id="cb26-1511"><a href="#cb26-1511" aria-hidden="true" tabindex="-1"></a>with a variety of studies looking at it predominantly through the lens of female economic empowerment or through gender pay gaps.</span>
<span id="cb26-1512"><a href="#cb26-1512" aria-hidden="true" tabindex="-1"></a>As @fig-gender-regions shows there is a somewhat higher output of research into this inequality in the Europe and Central Asian region, ahead of East Asia and the Pacific and North America,</span>
<span id="cb26-1513"><a href="#cb26-1513" aria-hidden="true" tabindex="-1"></a>with the other regions trailing further behind in output.</span>
<span id="cb26-1514"><a href="#cb26-1514" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1517"><a href="#cb26-1517" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1518"><a href="#cb26-1518" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-gender-regions</span></span>
<span id="cb26-1519"><a href="#cb26-1519" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Regional distribution of studies analysing gender inequalities</span></span>
<span id="cb26-1520"><a href="#cb26-1520" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1521"><a href="#cb26-1521" aria-hidden="true" tabindex="-1"></a>by_region_and_inequality <span class="op">=</span> (</span>
<span id="cb26-1522"><a href="#cb26-1522" aria-hidden="true" tabindex="-1"></a> bib_df[[<span class="st">&quot;inequality&quot;</span>, <span class="st">&quot;region&quot;</span>]]</span>
<span id="cb26-1523"><a href="#cb26-1523" aria-hidden="true" tabindex="-1"></a> .assign(</span>
<span id="cb26-1524"><a href="#cb26-1524" aria-hidden="true" tabindex="-1"></a> region <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;region&quot;</span>]</span>
<span id="cb26-1525"><a href="#cb26-1525" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1526"><a href="#cb26-1526" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb26-1527"><a href="#cb26-1527" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb26-1528"><a href="#cb26-1528" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb26-1529"><a href="#cb26-1529" aria-hidden="true" tabindex="-1"></a> inequality <span class="op">=</span> <span class="kw">lambda</span> _df: (_df[<span class="st">&quot;inequality&quot;</span>]</span>
<span id="cb26-1530"><a href="#cb26-1530" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot;\(.+\)&quot;</span>, <span class="st">&quot;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1531"><a href="#cb26-1531" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.replace(<span class="vs">r&quot; ?; ?&quot;</span>, <span class="st">&quot;;&quot;</span>, regex<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1532"><a href="#cb26-1532" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.strip()</span>
<span id="cb26-1533"><a href="#cb26-1533" aria-hidden="true" tabindex="-1"></a> .<span class="bu">str</span>.split(<span class="st">&quot;;&quot;</span>)</span>
<span id="cb26-1534"><a href="#cb26-1534" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-1535"><a href="#cb26-1535" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb26-1536"><a href="#cb26-1536" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;inequality&quot;</span>)</span>
<span id="cb26-1537"><a href="#cb26-1537" aria-hidden="true" tabindex="-1"></a> .explode(<span class="st">&quot;region&quot;</span>)</span>
<span id="cb26-1538"><a href="#cb26-1538" aria-hidden="true" tabindex="-1"></a> .reset_index(drop<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb26-1539"><a href="#cb26-1539" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-1540"><a href="#cb26-1540" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1541"><a href="#cb26-1541" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> regions_for_inequality(by_region_and_inequality, <span class="st">&quot;gender&quot;</span>)</span>
<span id="cb26-1542"><a href="#cb26-1542" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb26-1543"><a href="#cb26-1543" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb26-1544"><a href="#cb26-1544" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb26-1545"><a href="#cb26-1545" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb26-1546"><a href="#cb26-1546" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-1547"><a href="#cb26-1547" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1548"><a href="#cb26-1548" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1549"><a href="#cb26-1549" aria-hidden="true" tabindex="-1"></a>Looking into the prevalence of individual interventions within the gender dimension,</span>
<span id="cb26-1550"><a href="#cb26-1550" aria-hidden="true" tabindex="-1"></a>@tbl-gender-crosstab shows that paid leave, subsidies, collective bargaining, and education received the most attention.</span>
<span id="cb26-1551"><a href="#cb26-1551" aria-hidden="true" tabindex="-1"></a>Thus there is a slight leaning towards institutional and structural interventions visible,</span>
<span id="cb26-1552"><a href="#cb26-1552" aria-hidden="true" tabindex="-1"></a>though the dimension seems to be viewed from angles of strengthening individual agency just as well,</span>
<span id="cb26-1553"><a href="#cb26-1553" aria-hidden="true" tabindex="-1"></a>with subsidies often seeking to nourish this approach, and training, and interventions towards financial agency being represented in the interventions.</span>
<span id="cb26-1554"><a href="#cb26-1554" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1557"><a href="#cb26-1557" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1558"><a href="#cb26-1558" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-gender-crosstab</span></span>
<span id="cb26-1559"><a href="#cb26-1559" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Interventions targeting gender inequalities</span></span>
<span id="cb26-1560"><a href="#cb26-1560" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1561"><a href="#cb26-1561" aria-hidden="true" tabindex="-1"></a>crosstab_inequality(df_inequality, <span class="st">&quot;gender&quot;</span>).sort_values(<span class="st">&quot;gender&quot;</span>, ascending<span class="op">=</span><span class="va">False</span>)</span>
<span id="cb26-1562"><a href="#cb26-1562" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1563"><a href="#cb26-1563" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1564"><a href="#cb26-1564" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- gender --&gt;</span></span>
<span id="cb26-1565"><a href="#cb26-1565" aria-hidden="true" tabindex="-1"></a>Approaches of paid leave, child care and education agree with the findings of Zeinali et al. <span class="co">[</span><span class="ot">-@Zeinali2021</span><span class="co">]</span> on the main barriers at the intersection of gender and social identity:</span>
<span id="cb26-1566"><a href="#cb26-1566" aria-hidden="true" tabindex="-1"></a>The main barriers limiting women&#39;s access to career development resources can be reduced access to mentorship and sponsorship opportunities, as well as a reduced recognition, respect, and impression of value at work for women in leadership positions, with inequalities entrenching these barriers being an increased likelihood for women to take on the &#39;dual burdens&#39; of professional work and childcare or domestic work, as well as biased views of the effectiveness of men&#39;s over women&#39;s leadership styles.</span>
<span id="cb26-1567"><a href="#cb26-1567" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1568"><a href="#cb26-1568" aria-hidden="true" tabindex="-1"></a>Whereas institutional programmes such as minimum wage and structural interventions such as education or the contextual trade liberalization are strongly viewed through the lens of income effects,</span>
<span id="cb26-1569"><a href="#cb26-1569" aria-hidden="true" tabindex="-1"></a>with more studies targeting gender along income dimensions and the income dimension on its own,</span>
<span id="cb26-1570"><a href="#cb26-1570" aria-hidden="true" tabindex="-1"></a>studies of agency-based interventions approach gender inequalities less through this dimension.</span>
<span id="cb26-1571"><a href="#cb26-1571" aria-hidden="true" tabindex="-1"></a>Instead, they tend to rely on employment numbers or representation in absolute terms or as shares for their analyses.</span>
<span id="cb26-1572"><a href="#cb26-1572" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1573"><a href="#cb26-1573" aria-hidden="true" tabindex="-1"></a>As @Grotti2016 demonstrate, an increased gender equality does not engender an increase in overall economic inequality.</span>
<span id="cb26-1574"><a href="#cb26-1574" aria-hidden="true" tabindex="-1"></a>Using the Theil index, they decompose a method to account for the different mediating effects of employment similarity and earnings similarity between the genders and find that neither correlated with an increased income inequality.</span>
<span id="cb26-1575"><a href="#cb26-1575" aria-hidden="true" tabindex="-1"></a>In fact the opposite seems the case, at least in their analysis of developed nations, with increased female employment reducing the economic inequality,</span>
<span id="cb26-1576"><a href="#cb26-1576" aria-hidden="true" tabindex="-1"></a>which they see rather generated by a polarisation between high-income and low-income households.</span>
<span id="cb26-1577"><a href="#cb26-1577" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1578"><a href="#cb26-1578" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- maternal intersection, children --&gt;</span></span>
<span id="cb26-1579"><a href="#cb26-1579" aria-hidden="true" tabindex="-1"></a>A variety of studies also look at female economic empowerment outcomes through a more generational lens,</span>
<span id="cb26-1580"><a href="#cb26-1580" aria-hidden="true" tabindex="-1"></a>focusing on the effects of interventions aimed at maternity support for the mother and/or children ---</span>
<span id="cb26-1581"><a href="#cb26-1581" aria-hidden="true" tabindex="-1"></a>childcare programmes, paid leave and maternity benefits.</span>
<span id="cb26-1582"><a href="#cb26-1582" aria-hidden="true" tabindex="-1"></a>A reoccurring question is that of the reasons for inequality in female leadership positions, between institutional discrimination, self selection and family life trajectories.</span>
<span id="cb26-1583"><a href="#cb26-1583" aria-hidden="true" tabindex="-1"></a>Like @Mun2018 identified for Japan, while a complex interplay of a variety of factors,</span>
<span id="cb26-1584"><a href="#cb26-1584" aria-hidden="true" tabindex="-1"></a>the primary channel seems to lie in a combination of the self-selection of women into different individual career plans,</span>
<span id="cb26-1585"><a href="#cb26-1585" aria-hidden="true" tabindex="-1"></a>and reproductions of the existing gender divisions when confronted with the household responsibility for care labour.</span>
<span id="cb26-1586"><a href="#cb26-1586" aria-hidden="true" tabindex="-1"></a>While focused more on the effects of education itself, @Suh2017 also agreed with this and sees family structure,</span>
<span id="cb26-1587"><a href="#cb26-1587" aria-hidden="true" tabindex="-1"></a>alongside education, having a direct impact on labour market participation <span class="co">[</span><span class="ot">see also @Ochsenfeld2012</span><span class="co">]</span>.</span>
<span id="cb26-1588"><a href="#cb26-1588" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1589"><a href="#cb26-1589" aria-hidden="true" tabindex="-1"></a>These findings of supply-side channels does not imply non-applicability of policy interventions,</span>
<span id="cb26-1590"><a href="#cb26-1590" aria-hidden="true" tabindex="-1"></a>but points to a necessity to focus on supporting those causes directly,</span>
<span id="cb26-1591"><a href="#cb26-1591" aria-hidden="true" tabindex="-1"></a>through parental leave policies, childcare subsidies and strengthening their return to work effect.</span>
<span id="cb26-1592"><a href="#cb26-1592" aria-hidden="true" tabindex="-1"></a>Generally, a reduced cost of child care or expansion of the costs on both parents has been identified to increase mothers&#39; potential to participate in the labour force and pursue further career choices.</span>
<span id="cb26-1593"><a href="#cb26-1593" aria-hidden="true" tabindex="-1"></a>On the other hand, currently the presence alone of a new-born child in a household has been identified to strongly negatively correlate with labour force participation,</span>
<span id="cb26-1594"><a href="#cb26-1594" aria-hidden="true" tabindex="-1"></a>which can simultaneously foreclose further career choices or advancements.</span>
<span id="cb26-1595"><a href="#cb26-1595" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1596"><a href="#cb26-1596" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- organisational structure --&gt;</span></span>
<span id="cb26-1597"><a href="#cb26-1597" aria-hidden="true" tabindex="-1"></a>At the same time, within organisations in the new economy&#39;s logic of not being bound to a single employer,</span>
<span id="cb26-1598"><a href="#cb26-1598" aria-hidden="true" tabindex="-1"></a>different focal points gain importance: team structures, career maps and networking receive more emphasis,</span>
<span id="cb26-1599"><a href="#cb26-1599" aria-hidden="true" tabindex="-1"></a>and often reflect gendered organisational logics.</span>
<span id="cb26-1600"><a href="#cb26-1600" aria-hidden="true" tabindex="-1"></a>In a quantitative study, @Williams2012 identify the necessity of maintaining large networks, engage in self-promotion, and supervisory discretion as potentially prominent intra-organisational barriers to workplace gender equality,</span>
<span id="cb26-1601"><a href="#cb26-1601" aria-hidden="true" tabindex="-1"></a>suggesting suitable policy efforts to focus on an increased managerial accountability,</span>
<span id="cb26-1602"><a href="#cb26-1602" aria-hidden="true" tabindex="-1"></a>inclusive efforts regarding corporate-sponsored events as well as counter-acting more informally driven male-only events,</span>
<span id="cb26-1603"><a href="#cb26-1603" aria-hidden="true" tabindex="-1"></a>and the general publication of co-workers salaries and individualised career development plans.</span>
<span id="cb26-1604"><a href="#cb26-1604" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1605"><a href="#cb26-1605" aria-hidden="true" tabindex="-1"></a>Finally, it is important to reiterate the cross-dimensional nature of such inequalities.</span>
<span id="cb26-1606"><a href="#cb26-1606" aria-hidden="true" tabindex="-1"></a>While the changing face of the economy directly affects organisational processes and structural discrimination,</span>
<span id="cb26-1607"><a href="#cb26-1607" aria-hidden="true" tabindex="-1"></a>it also has an impact on the work-family relations and thus, ultimately,</span>
<span id="cb26-1608"><a href="#cb26-1608" aria-hidden="true" tabindex="-1"></a>the gender inequalities affected on the supply side <span class="co">[</span><span class="ot">@Edgell2012</span><span class="co">]</span>.</span>
<span id="cb26-1609"><a href="#cb26-1609" aria-hidden="true" tabindex="-1"></a>These inequalities surface particularly across the intersection of structural disadvantages and should thus provide the foundation for a holistic picture on inequality instead of one closed off between structural economic concerns and family and maternal decision-making.</span>
<span id="cb26-1610"><a href="#cb26-1610" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1611"><a href="#cb26-1611" aria-hidden="true" tabindex="-1"></a><span class="fu">## Spatial inequalities</span></span>
<span id="cb26-1612"><a href="#cb26-1612" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1613"><a href="#cb26-1613" aria-hidden="true" tabindex="-1"></a>Spatial inequalities are less focused within European, Central Asian and North American regions,</span>
<span id="cb26-1614"><a href="#cb26-1614" aria-hidden="true" tabindex="-1"></a>as @fig-spatial-regions shows.</span>
<span id="cb26-1615"><a href="#cb26-1615" aria-hidden="true" tabindex="-1"></a>Instead, both Southern Asia and Sub-Saharan Africa are the primary areas of interest,</span>
<span id="cb26-1616"><a href="#cb26-1616" aria-hidden="true" tabindex="-1"></a>with studies especially into Tanzania, India and Pakistan.</span>
<span id="cb26-1617"><a href="#cb26-1617" aria-hidden="true" tabindex="-1"></a>In the European and North American context, the distribution of spatial inequality analyses is primarily conducted in the countries of the United States and the United Kingdom.</span>
<span id="cb26-1618"><a href="#cb26-1618" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1621"><a href="#cb26-1621" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1622"><a href="#cb26-1622" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-spatial-regions</span></span>
<span id="cb26-1623"><a href="#cb26-1623" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Regional distribution of studies analysing spatial inequalities</span></span>
<span id="cb26-1624"><a href="#cb26-1624" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1625"><a href="#cb26-1625" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> regions_for_inequality(by_region_and_inequality, <span class="st">&quot;spatial&quot;</span>)</span>
<span id="cb26-1626"><a href="#cb26-1626" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb26-1627"><a href="#cb26-1627" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb26-1628"><a href="#cb26-1628" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb26-1629"><a href="#cb26-1629" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb26-1630"><a href="#cb26-1630" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-1631"><a href="#cb26-1631" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1632"><a href="#cb26-1632" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1633"><a href="#cb26-1633" aria-hidden="true" tabindex="-1"></a>This spread may point to those countries&#39; large rural populations or wider inequality gaps between rural and urban populations.</span>
<span id="cb26-1634"><a href="#cb26-1634" aria-hidden="true" tabindex="-1"></a>While large rural populations are a sign of a predominantly agrarian economy,</span>
<span id="cb26-1635"><a href="#cb26-1635" aria-hidden="true" tabindex="-1"></a>widening gaps are argued to be specifically appearing between rural and urban locations in industrial and post-industrial societies:</span>
<span id="cb26-1636"><a href="#cb26-1636" aria-hidden="true" tabindex="-1"></a>Under modes of financialization, a spatial redistribution of high- and low-income sectors and increasing occupational segregation,</span>
<span id="cb26-1637"><a href="#cb26-1637" aria-hidden="true" tabindex="-1"></a>rural locations are often left behind economically and require structural-institutional interventions to be improved <span class="co">[</span><span class="ot">@Crouch2019</span><span class="co">]</span>.</span>
<span id="cb26-1638"><a href="#cb26-1638" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1639"><a href="#cb26-1639" aria-hidden="true" tabindex="-1"></a>Interventions affecting spatial inequalities are often also viewed through indicators of income,</span>
<span id="cb26-1640"><a href="#cb26-1640" aria-hidden="true" tabindex="-1"></a>as can be seen in @tbl-spatial-crosstab.</span>
<span id="cb26-1641"><a href="#cb26-1641" aria-hidden="true" tabindex="-1"></a>The primary intervention aiming at reduction of spatial inequalities is based on infrastructural changes,</span>
<span id="cb26-1642"><a href="#cb26-1642" aria-hidden="true" tabindex="-1"></a>which aligns with expectations of the infrastructural schism between urban and rural regions.</span>
<span id="cb26-1643"><a href="#cb26-1643" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1646"><a href="#cb26-1646" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1647"><a href="#cb26-1647" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-spatial-crosstab</span></span>
<span id="cb26-1648"><a href="#cb26-1648" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Interventions targeting spatial inequalities</span></span>
<span id="cb26-1649"><a href="#cb26-1649" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1650"><a href="#cb26-1650" aria-hidden="true" tabindex="-1"></a>crosstab_inequality(df_inequality, <span class="st">&quot;spatial&quot;</span>).sort_values(<span class="st">&quot;spatial&quot;</span>, ascending<span class="op">=</span><span class="va">False</span>)</span>
<span id="cb26-1651"><a href="#cb26-1651" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1652"><a href="#cb26-1652" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1653"><a href="#cb26-1653" aria-hidden="true" tabindex="-1"></a>Additionally, education interventions target spatial inequalities, with the effects of minimum wage, work programmes, interventions strengthening financial agency, trade liberalization and training also playing a role.</span>
<span id="cb26-1654"><a href="#cb26-1654" aria-hidden="true" tabindex="-1"></a>Thus, structural interventions are the dominant approach to reducing spatial inequalities,</span>
<span id="cb26-1655"><a href="#cb26-1655" aria-hidden="true" tabindex="-1"></a>with institutional and agency-driven interventions often less specifically targeted.</span>
<span id="cb26-1656"><a href="#cb26-1656" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1657"><a href="#cb26-1657" aria-hidden="true" tabindex="-1"></a>This can pose a problem, as even non-spatial policies will almost invariably have spatially divergent effects,</span>
<span id="cb26-1658"><a href="#cb26-1658" aria-hidden="true" tabindex="-1"></a>be they positive ---</span>
<span id="cb26-1659"><a href="#cb26-1659" aria-hidden="true" tabindex="-1"></a>as is the case for higher positive income effects on rural households due to unintentional good targeting of minimum wage to lower-income households <span class="co">[</span><span class="ot">@Gilbert2001</span><span class="co">]</span> ---</span>
<span id="cb26-1660"><a href="#cb26-1660" aria-hidden="true" tabindex="-1"></a>or negative: as seen in the further exclusion of already disadvantaged women from employment, infrastructure and training opportunities in India under bad targeting and elite capture <span class="co">[</span><span class="ot">@Stock2021</span><span class="co">]</span>.</span>
<span id="cb26-1661"><a href="#cb26-1661" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1662"><a href="#cb26-1662" aria-hidden="true" tabindex="-1"></a>Policies, even those of an ostensibly non-spatial nature, must thus keep in mind possibly adverse targeting effects if not correctly adjusting for potential impacts on spatial inequalities.</span>
<span id="cb26-1663"><a href="#cb26-1663" aria-hidden="true" tabindex="-1"></a>Rural communities relying on agricultural economies in particular may be vulnerable to exogenous structural shock events such as climate change, which may thus need to be a focal point for future structural interventions <span class="co">[</span><span class="ot">@Salvati2014</span><span class="co">]</span>.</span>
<span id="cb26-1664"><a href="#cb26-1664" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1665"><a href="#cb26-1665" aria-hidden="true" tabindex="-1"></a>The measures used to investigate spatial effects of policy interventions follow an even split between relative inequality measured through indicators such as the Gini coefficient or urban-rural employment shares, and absolute measures such as the effects on rural employment.</span>
<span id="cb26-1666"><a href="#cb26-1666" aria-hidden="true" tabindex="-1"></a>With the level of analysis mostly taking place at the household level, some individual horizontal inequalities such as intra-household gender roles and economic participation or racial intersections can be considered,</span>
<span id="cb26-1667"><a href="#cb26-1667" aria-hidden="true" tabindex="-1"></a>however, analyses of spatial inequalities often remain solely focused on spatial employment and income effects.</span>
<span id="cb26-1668"><a href="#cb26-1668" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1669"><a href="#cb26-1669" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- income, spatial, pre-existing --&gt;</span></span>
<span id="cb26-1670"><a href="#cb26-1670" aria-hidden="true" tabindex="-1"></a>Spatial inequalities move both ways, however, as also shown by @Perez2022 in a multi-disciplinary systematic review of the association between a person&#39;s income, their employment and poverty in an urban environment.</span>
<span id="cb26-1671"><a href="#cb26-1671" aria-hidden="true" tabindex="-1"></a>They find, similarly to the rural-urban divide, that employment plays a significant role in the poverty of urban residents, though here the primary barriers are identified as lack of access to public transport, geographical segregation, labour informality and inadequate human capital.</span>
<span id="cb26-1672"><a href="#cb26-1672" aria-hidden="true" tabindex="-1"></a>They also agree with the potential policy interventions identified to counteract these inequalities:</span>
<span id="cb26-1673"><a href="#cb26-1673" aria-hidden="true" tabindex="-1"></a>credit programs, institutional support for childcare, guaranteed minimum income/universal basic income or the provision of living wages, commuting subsidies, and housing mobility programs,</span>
<span id="cb26-1674"><a href="#cb26-1674" aria-hidden="true" tabindex="-1"></a>which largely map onto structural or institutional efforts identified by the studies.</span>
<span id="cb26-1675"><a href="#cb26-1675" aria-hidden="true" tabindex="-1"></a>On the other hand, @Hunt2004 show that individual measures on their own such as commuting subsidies in this case, while having positive results,</span>
<span id="cb26-1676"><a href="#cb26-1676" aria-hidden="true" tabindex="-1"></a>may not provide significantly lasting impact over the long term and thus may need to be undertaken in a more holistic approach, combining multiple policy packages.</span>
<span id="cb26-1677"><a href="#cb26-1677" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1678"><a href="#cb26-1678" aria-hidden="true" tabindex="-1"></a>Like the study pool shows, many of the highlighted barriers can be mapped onto channels of inequality:</span>
<span id="cb26-1679"><a href="#cb26-1679" aria-hidden="true" tabindex="-1"></a>gender inequality&#39;s impact, through traditional gender roles and lack of empowerment, a lack of childcare possibilities, or unequal proportions of domestic work;</span>
<span id="cb26-1680"><a href="#cb26-1680" aria-hidden="true" tabindex="-1"></a>spatial inequality, through residential segregation or discrimination, lack of access to transportation, and a limited access to work;</span>
<span id="cb26-1681"><a href="#cb26-1681" aria-hidden="true" tabindex="-1"></a>as well as pre-existing inequalities, here defined as the generational persistence of poverty, larger household sizes, and its possible negative impacts on human capital.</span>
<span id="cb26-1682"><a href="#cb26-1682" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1683"><a href="#cb26-1683" aria-hidden="true" tabindex="-1"></a><span class="fu">## Disability inequalities</span></span>
<span id="cb26-1684"><a href="#cb26-1684" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1685"><a href="#cb26-1685" aria-hidden="true" tabindex="-1"></a>The dimension of disabilities in inequalities remains strongly focused on developed nations,</span>
<span id="cb26-1686"><a href="#cb26-1686" aria-hidden="true" tabindex="-1"></a>primarily through analysis of effects on inequality in the world of work in a context of the United States labour market,</span>
<span id="cb26-1687"><a href="#cb26-1687" aria-hidden="true" tabindex="-1"></a>as can be seen in @fig-disability-regions.</span>
<span id="cb26-1688"><a href="#cb26-1688" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1691"><a href="#cb26-1691" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1692"><a href="#cb26-1692" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-disability-regions</span></span>
<span id="cb26-1693"><a href="#cb26-1693" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Regional distribution of studies analysing disability inequalities</span></span>
<span id="cb26-1694"><a href="#cb26-1694" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1695"><a href="#cb26-1695" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> regions_for_inequality(by_region_and_inequality, <span class="st">&quot;disability&quot;</span>)</span>
<span id="cb26-1696"><a href="#cb26-1696" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb26-1697"><a href="#cb26-1697" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb26-1698"><a href="#cb26-1698" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb26-1699"><a href="#cb26-1699" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb26-1700"><a href="#cb26-1700" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-1701"><a href="#cb26-1701" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1702"><a href="#cb26-1702" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1703"><a href="#cb26-1703" aria-hidden="true" tabindex="-1"></a>Few studies approach disability inequalities primarily through the prism of income inequality, with more analyses focusing on other outcome measures as can be seen in @tbl-disability-crosstab.</span>
<span id="cb26-1704"><a href="#cb26-1704" aria-hidden="true" tabindex="-1"></a>The interventions targeting such inequalities in the world of work favour an approach to measuring inequalities through employment, by absolute amounts of hours worked, return to work numbers or employment rates instead.</span>
<span id="cb26-1705"><a href="#cb26-1705" aria-hidden="true" tabindex="-1"></a>Only when looking at the intersection of disability and gender is income the more utilized indicator,</span>
<span id="cb26-1706"><a href="#cb26-1706" aria-hidden="true" tabindex="-1"></a>through measuring female income ratios compared to those of males.</span>
<span id="cb26-1707"><a href="#cb26-1707" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1710"><a href="#cb26-1710" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1711"><a href="#cb26-1711" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-disability-crosstab</span></span>
<span id="cb26-1712"><a href="#cb26-1712" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Interventions targeting disability inequalities</span></span>
<span id="cb26-1713"><a href="#cb26-1713" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1714"><a href="#cb26-1714" aria-hidden="true" tabindex="-1"></a>crosstab_inequality(df_inequality, <span class="st">&quot;disability&quot;</span>).sort_values(<span class="st">&quot;disability&quot;</span>, ascending<span class="op">=</span><span class="va">False</span>)</span>
<span id="cb26-1715"><a href="#cb26-1715" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1716"><a href="#cb26-1716" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1717"><a href="#cb26-1717" aria-hidden="true" tabindex="-1"></a>Studies into interventions within the dimension of disabilities are predominantly focused on agency-based perspectives, with counselling and training being the primary approaches.</span>
<span id="cb26-1718"><a href="#cb26-1718" aria-hidden="true" tabindex="-1"></a>Structurally approached interventions are also pursued, looking at the overall effects of education, or subsidies in health care, though even here,</span>
<span id="cb26-1719"><a href="#cb26-1719" aria-hidden="true" tabindex="-1"></a>the individual effects of activation play a role <span class="co">[</span><span class="ot">@Carstens2018</span><span class="co">]</span>.</span>
<span id="cb26-1720"><a href="#cb26-1720" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1721"><a href="#cb26-1721" aria-hidden="true" tabindex="-1"></a><span class="co">&lt;!-- frameworks/qualitative discussion --&gt;</span></span>
<span id="cb26-1722"><a href="#cb26-1722" aria-hidden="true" tabindex="-1"></a>The findings for a need toward agency-based interventions reflect in frameworks which put the organizational barriers into focus and simultaneously demand a more inclusive look into (re)integration of people with disabilities into the labour market and within the world of work <span class="co">[</span><span class="ot">@Martin2020</span><span class="co">]</span>.</span>
<span id="cb26-1723"><a href="#cb26-1723" aria-hidden="true" tabindex="-1"></a>@Kim2020 find the environmental factors in workplaces can significantly affect the individual job retention wishes of disabled employees, through the provided disability facilities influencing both work satisfaction and perceived workplace safety.</span>
<span id="cb26-1724"><a href="#cb26-1724" aria-hidden="true" tabindex="-1"></a>Here, in addition to the predominantly used measures of employment and return to work rates,</span>
<span id="cb26-1725"><a href="#cb26-1725" aria-hidden="true" tabindex="-1"></a>meaningful achievement and decent work should be measured from individual economic and social-psychological indicators, especially in view of the already predominantly agency-based variety of interventions.</span>
<span id="cb26-1726"><a href="#cb26-1726" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1727"><a href="#cb26-1727" aria-hidden="true" tabindex="-1"></a>Similarly, @Lindsay2015 highlight a variety of barriers to activation such as limited network ties to working population, skills problems and lower levels of qualification for those receiving disability benefits,</span>
<span id="cb26-1728"><a href="#cb26-1728" aria-hidden="true" tabindex="-1"></a>though also emphasising environmental factors of workplaces not facilitating integration measures or issues of spatial exclusion from labour markets through being located in areas of large-scale industrial restructuring and low geographic mobility.</span>
<span id="cb26-1729"><a href="#cb26-1729" aria-hidden="true" tabindex="-1"></a>One framework which approaches the discussion from an almost entirely institutional-structural view is provided by the systems level theoretical grounding of @Gruber2014,</span>
<span id="cb26-1730"><a href="#cb26-1730" aria-hidden="true" tabindex="-1"></a>separating into the exclusionary effects of disability into institutional factors at the macro level, at the meso level and factors influencing the micro level,</span>
<span id="cb26-1731"><a href="#cb26-1731" aria-hidden="true" tabindex="-1"></a>and directly focusing on the separation or inclusion of education, recognition of eligibility for vocational rehabilitation and self-recognition as pre-condition for effective programme undertaking respectively.</span>
<span id="cb26-1732"><a href="#cb26-1732" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1733"><a href="#cb26-1733" aria-hidden="true" tabindex="-1"></a>These discussions reinforce the necessity of correct targeting, as @Poppen2017 and @Thoresen2021 highlighted in the fears of losing existing benefits, or negative relation between benefits and employment probabilities.</span>
<span id="cb26-1734"><a href="#cb26-1734" aria-hidden="true" tabindex="-1"></a>The case seems not one of benefits on their own diminishing the readiness for work activation, but the monetary assistance often being provided instead of effective methods of activation, environmental support and agency-driven motivating factors to their respective recipients.</span>
<span id="cb26-1735"><a href="#cb26-1735" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1736"><a href="#cb26-1736" aria-hidden="true" tabindex="-1"></a>There is a clear bias in studies on disability interventions towards studies undertaken in developed countries and, more specifically,</span>
<span id="cb26-1737"><a href="#cb26-1737" aria-hidden="true" tabindex="-1"></a>based on the Veteran Disability system in the United States which has been the object of analysis for a variety of studies,</span>
<span id="cb26-1738"><a href="#cb26-1738" aria-hidden="true" tabindex="-1"></a>but simultaneously highlights gaps in research on the topic in other contexts and other regions.</span>
<span id="cb26-1739"><a href="#cb26-1739" aria-hidden="true" tabindex="-1"></a>A recurring focus in all these discussions is their insistence on the intersectional nature of the issue, with gender, ethnicity, location, type and level of disability among others often creating more adverse conditions for disabled individuals.</span>
<span id="cb26-1740"><a href="#cb26-1740" aria-hidden="true" tabindex="-1"></a>This constitutes a second gap which should provide stronger focus in empirical works, in attempts to disaggregate analyses beyond disability and control group to further understand factors of inequality at work.</span>
<span id="cb26-1741"><a href="#cb26-1741" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1742"><a href="#cb26-1742" aria-hidden="true" tabindex="-1"></a><span class="fu">## Migration and ethnic inequalities</span></span>
<span id="cb26-1743"><a href="#cb26-1743" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1744"><a href="#cb26-1744" aria-hidden="true" tabindex="-1"></a>The effects of policy interventions targeting migratory and ethnic inequalities in the world of work are viewed primarily through the regions of North America, Europe, Central, South and East Asia, and the Pacific,</span>
<span id="cb26-1745"><a href="#cb26-1745" aria-hidden="true" tabindex="-1"></a>as can be seen in @fig-ethnicity-regions.</span>
<span id="cb26-1746"><a href="#cb26-1746" aria-hidden="true" tabindex="-1"></a>Especially the specifics regarding migration are reviewed in an Asian context, while studies in North America focus predominantly on aspects of ethnicity in their analyses,</span>
<span id="cb26-1747"><a href="#cb26-1747" aria-hidden="true" tabindex="-1"></a>though both dimensions are deeply intertwined and hard to disentangle for most studies.</span>
<span id="cb26-1748"><a href="#cb26-1748" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1751"><a href="#cb26-1751" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1752"><a href="#cb26-1752" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: fig-ethnicity-regions</span></span>
<span id="cb26-1753"><a href="#cb26-1753" aria-hidden="true" tabindex="-1"></a><span class="co">#| fig-cap: Regional distribution of studies analysing migration and ethnicity inequalities</span></span>
<span id="cb26-1754"><a href="#cb26-1754" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1755"><a href="#cb26-1755" aria-hidden="true" tabindex="-1"></a>by_region_and_inequality.loc[by_region_and_inequality[<span class="st">&quot;inequality&quot;</span>] <span class="op">==</span> <span class="st">&quot;migration&quot;</span>, <span class="st">&quot;inequality&quot;</span>] <span class="op">=</span> <span class="st">&quot;ethnicity&quot;</span></span>
<span id="cb26-1756"><a href="#cb26-1756" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1757"><a href="#cb26-1757" aria-hidden="true" tabindex="-1"></a>ax <span class="op">=</span> regions_for_inequality(by_region_and_inequality, <span class="st">&quot;ethnicity&quot;</span>)</span>
<span id="cb26-1758"><a href="#cb26-1758" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">&quot;&quot;</span>)</span>
<span id="cb26-1759"><a href="#cb26-1759" aria-hidden="true" tabindex="-1"></a>plt.setp(ax.get_xticklabels(), rotation<span class="op">=</span><span class="dv">45</span>, ha<span class="op">=</span><span class="st">&quot;right&quot;</span>,</span>
<span id="cb26-1760"><a href="#cb26-1760" aria-hidden="true" tabindex="-1"></a> rotation_mode<span class="op">=</span><span class="st">&quot;anchor&quot;</span>)</span>
<span id="cb26-1761"><a href="#cb26-1761" aria-hidden="true" tabindex="-1"></a>plt.tight_layout()</span>
<span id="cb26-1762"><a href="#cb26-1762" aria-hidden="true" tabindex="-1"></a>plt.show()</span>
<span id="cb26-1763"><a href="#cb26-1763" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1764"><a href="#cb26-1764" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1765"><a href="#cb26-1765" aria-hidden="true" tabindex="-1"></a>As seen in @tbl-ethnicity-crosstab, in terms of primary interventions analysed for these dimensions, most focus on structural interventions such as education, fiscal policies, or infrastructure,</span>
<span id="cb26-1766"><a href="#cb26-1766" aria-hidden="true" tabindex="-1"></a>as well as institutional contexts such as the possibility for collective bargaining and unionisation, or the effects of universal income on the world of work.</span>
<span id="cb26-1767"><a href="#cb26-1767" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1770"><a href="#cb26-1770" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1771"><a href="#cb26-1771" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: tbl-ethnicity-crosstab</span></span>
<span id="cb26-1772"><a href="#cb26-1772" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Interventions targeting migration and ethnicity inequalities</span></span>
<span id="cb26-1773"><a href="#cb26-1773" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1774"><a href="#cb26-1774" aria-hidden="true" tabindex="-1"></a>crosstab_inequality(df_inequality, <span class="st">&quot;ethnicity&quot;</span>).sort_values(<span class="st">&quot;ethnicity&quot;</span>, ascending<span class="op">=</span><span class="va">False</span>)</span>
<span id="cb26-1775"><a href="#cb26-1775" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1776"><a href="#cb26-1776" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1777"><a href="#cb26-1777" aria-hidden="true" tabindex="-1"></a>There is a mixed approach to using income-based indicators of inequality or other markers such as employment.</span>
<span id="cb26-1778"><a href="#cb26-1778" aria-hidden="true" tabindex="-1"></a>At the same time, there is a somewhat stronger focus on absolute measures of inequality, such poverty, debt or savings, or hours worked in absolute terms.</span>
<span id="cb26-1779"><a href="#cb26-1779" aria-hidden="true" tabindex="-1"></a>Relative indicators have a wider spread with the Gini coefficient, the Theil index, decile ratios or employment rates for sub-samples used.</span>
<span id="cb26-1780"><a href="#cb26-1780" aria-hidden="true" tabindex="-1"></a>From an organisational perspective, the focus on structural effects is in agreement with perspectives which highlight the conceptualisation of workplace ethnicity as separate from the majority in many places as a structural power structure <span class="co">[</span><span class="ot">@Samaluk2014</span><span class="co">]</span>.</span>
<span id="cb26-1781"><a href="#cb26-1781" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1782"><a href="#cb26-1782" aria-hidden="true" tabindex="-1"></a>At the same time in a broader context, job insecurities, both produced by the dis-embeddedness of migrants and the broader contemporary institutional work organisational context speak to the same institutional-structural focus required as is already pursued in the literature <span class="co">[</span><span class="ot">@Landsbergis2014</span><span class="co">]</span>.</span>
<span id="cb26-1783"><a href="#cb26-1783" aria-hidden="true" tabindex="-1"></a>With a focus on remittances of temporary migratory work,</span>
<span id="cb26-1784"><a href="#cb26-1784" aria-hidden="true" tabindex="-1"></a>@Rosewarne2012 similarly argues for the necessity to allow for greater continuity of employment to counteract while cementing the workers&#39; bounds to their respective home countries,</span>
<span id="cb26-1785"><a href="#cb26-1785" aria-hidden="true" tabindex="-1"></a>through circular labour migration being supported by formal embedding in employment contract through contract succession negotiations and shifting the focus to labour rights specifically for the temporary nature of such work.</span>
<span id="cb26-1786"><a href="#cb26-1786" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1787"><a href="#cb26-1787" aria-hidden="true" tabindex="-1"></a>While some frameworks do put agency-driven necessities to the foreground <span class="co">[</span><span class="ot">see @Siebers2015</span><span class="co">]</span>,</span>
<span id="cb26-1788"><a href="#cb26-1788" aria-hidden="true" tabindex="-1"></a>the consensus seems a requirement for structural approaches enabling this agency and their institutional embedding before more agency-driven interventions alone increase their effectiveness.<span class="ot">[^structural-frameworks]</span></span>
<span id="cb26-1789"><a href="#cb26-1789" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1790"><a href="#cb26-1790" aria-hidden="true" tabindex="-1"></a><span class="ot">[^structural-frameworks]: </span>See for structural necessities @Do2020 and @Goodburn2020. For institutional contexts see @Clibborn2022.</span>
<span id="cb26-1791"><a href="#cb26-1791" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1792"><a href="#cb26-1792" aria-hidden="true" tabindex="-1"></a><span class="fu"># Conclusion</span></span>
<span id="cb26-1793"><a href="#cb26-1793" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1794"><a href="#cb26-1794" aria-hidden="true" tabindex="-1"></a>The preceding study undertook a systematic scoping review of the literature on inequalities in the world of work.</span>
<span id="cb26-1795"><a href="#cb26-1795" aria-hidden="true" tabindex="-1"></a>It focused on the variety of approaches to policy interventions, from institutional to structural to more agency-driven programmes,</span>
<span id="cb26-1796"><a href="#cb26-1796" aria-hidden="true" tabindex="-1"></a>and highlighted the inequalities targeted, analysed in subsequent study, their methods and limitations,</span>
<span id="cb26-1797"><a href="#cb26-1797" aria-hidden="true" tabindex="-1"></a>to arrive at a picture of which lays out the strengths and weaknesses of current approaches.</span>
<span id="cb26-1798"><a href="#cb26-1798" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1799"><a href="#cb26-1799" aria-hidden="true" tabindex="-1"></a>Wide gaps exist between the research on existing topics within the areas and intersections of inequalities in the world of work.</span>
<span id="cb26-1800"><a href="#cb26-1800" aria-hidden="true" tabindex="-1"></a>First, while regionally research on such inequalities seems relatively evenly distributed,</span>
<span id="cb26-1801"><a href="#cb26-1801" aria-hidden="true" tabindex="-1"></a>focus prevalence on individual inequalities varies widely.</span>
<span id="cb26-1802"><a href="#cb26-1802" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1803"><a href="#cb26-1803" aria-hidden="true" tabindex="-1"></a>Research into interventions preventing income inequality are still the dominant form of measured outcomes,</span>
<span id="cb26-1804"><a href="#cb26-1804" aria-hidden="true" tabindex="-1"></a>which makes sense for its prevailing usefulness through a variety of indicators and its use to investigate both vertical and horizontal inequalities.</span>
<span id="cb26-1805"><a href="#cb26-1805" aria-hidden="true" tabindex="-1"></a>However, care should be taken not to over-emphasize the reliance on income inequality outcomes:</span>
<span id="cb26-1806"><a href="#cb26-1806" aria-hidden="true" tabindex="-1"></a>they can obscure intersections with other inequalities,</span>
<span id="cb26-1807"><a href="#cb26-1807" aria-hidden="true" tabindex="-1"></a>or diminish the perceived importance of tackling other inequalities themselves, if not directly measurable through income.</span>
<span id="cb26-1808"><a href="#cb26-1808" aria-hidden="true" tabindex="-1"></a>Thus, while interventions attempt to approach the inequality from a variety of institutional, structural and agency-oriented approaches already,</span>
<span id="cb26-1809"><a href="#cb26-1809" aria-hidden="true" tabindex="-1"></a>this could be further enhanced by putting a continuous focus on the closely intertwined intersectional nature of the issue.</span>
<span id="cb26-1810"><a href="#cb26-1810" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1811"><a href="#cb26-1811" aria-hidden="true" tabindex="-1"></a>Gender inequality is an almost equally considered dimension in the interventions,</span>
<span id="cb26-1812"><a href="#cb26-1812" aria-hidden="true" tabindex="-1"></a>a reasonable conclusion due to the inequality&#39;s global ubiquity and persistence.</span>
<span id="cb26-1813"><a href="#cb26-1813" aria-hidden="true" tabindex="-1"></a>Most gender-oriented policy approaches tackle it directly alongside income inequality outcomes,</span>
<span id="cb26-1814"><a href="#cb26-1814" aria-hidden="true" tabindex="-1"></a>especially viewed through gender pay gaps and economic (dis-)empowerment,</span>
<span id="cb26-1815"><a href="#cb26-1815" aria-hidden="true" tabindex="-1"></a>approaching it from backgrounds of structural or agency-driven interventions.</span>
<span id="cb26-1816"><a href="#cb26-1816" aria-hidden="true" tabindex="-1"></a>While both approaches seem fruitful in different contexts, few interventions strive to provide a holistic approach which combines the individual-level with macro-impacts,</span>
<span id="cb26-1817"><a href="#cb26-1817" aria-hidden="true" tabindex="-1"></a>tackling both institutional-structural issues while driving concerns of agency simultaneously.</span>
<span id="cb26-1818"><a href="#cb26-1818" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1819"><a href="#cb26-1819" aria-hidden="true" tabindex="-1"></a>Spatial inequalities are primarily viewed through rural-urban divides,</span>
<span id="cb26-1820"><a href="#cb26-1820" aria-hidden="true" tabindex="-1"></a>concerning welfare, opportunities and employment probabilities.</span>
<span id="cb26-1821"><a href="#cb26-1821" aria-hidden="true" tabindex="-1"></a>Spatially focused interventions primarily tackle infrastructural issues which should be an effective avenue since most positive interventions are focused on the structural dimension of the inequality.</span>
<span id="cb26-1822"><a href="#cb26-1822" aria-hidden="true" tabindex="-1"></a>However, too many interventions, especially focused on reducing income inequalities,</span>
<span id="cb26-1823"><a href="#cb26-1823" aria-hidden="true" tabindex="-1"></a>still do not take spatial components fully into view,</span>
<span id="cb26-1824"><a href="#cb26-1824" aria-hidden="true" tabindex="-1"></a>potentially leading to worse outcomes for inequalities along the spatial dimension.</span>
<span id="cb26-1825"><a href="#cb26-1825" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1826"><a href="#cb26-1826" aria-hidden="true" tabindex="-1"></a>Disabilities are rarely viewed through lenses other than employment opportunities.</span>
<span id="cb26-1827"><a href="#cb26-1827" aria-hidden="true" tabindex="-1"></a>While most interventions already focus on dimensions of strengthening agency and improved integration or reintegration of individuals with disabilities into the world of work,</span>
<span id="cb26-1828"><a href="#cb26-1828" aria-hidden="true" tabindex="-1"></a>a wider net needs to be cast with future research focusing on developing regions and the effects of more institutional-structural approaches before clearer recommendations can be given based on existing evidence.</span>
<span id="cb26-1829"><a href="#cb26-1829" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1830"><a href="#cb26-1830" aria-hidden="true" tabindex="-1"></a>Ethnicity and migration provide dimensions of inequalities which are, while more evenly distributed regionally,</span>
<span id="cb26-1831"><a href="#cb26-1831" aria-hidden="true" tabindex="-1"></a>still equally underdeveloped in research on evidence-based intervention impacts.</span>
<span id="cb26-1832"><a href="#cb26-1832" aria-hidden="true" tabindex="-1"></a>Currently, there is a strong focus on institutional-structural approaches,</span>
<span id="cb26-1833"><a href="#cb26-1833" aria-hidden="true" tabindex="-1"></a>which seems to follow the literature in what is required for effective interventions.</span>
<span id="cb26-1834"><a href="#cb26-1834" aria-hidden="true" tabindex="-1"></a>However, similarly to research on inequalities based on disability, there are clear gaps in research</span>
<span id="cb26-1835"><a href="#cb26-1835" aria-hidden="true" tabindex="-1"></a>on ethnicity and especially migration, before clearer pictures of what works can develop.</span>
<span id="cb26-1836"><a href="#cb26-1836" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1837"><a href="#cb26-1837" aria-hidden="true" tabindex="-1"></a>The intertwined nature of inequalities, once recognized, requires intervention approaches which heed multi-dimensional issues and can flexibly intervene and subsequently correctly measure their relative effectiveness.</span>
<span id="cb26-1838"><a href="#cb26-1838" aria-hidden="true" tabindex="-1"></a>To do so, perspectives need to shift and align towards a new, more intersectional approach which can incorporate both a wider array of methodological approaches between purely quantitative and qualitative research,</span>
<span id="cb26-1839"><a href="#cb26-1839" aria-hidden="true" tabindex="-1"></a>while relying on indicators for measurement which are flexible yet overlapping enough to encompass such a broadened perspective.</span>
<span id="cb26-1840"><a href="#cb26-1840" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1841"><a href="#cb26-1841" aria-hidden="true" tabindex="-1"></a>{{&lt; pagebreak &gt;}}</span>
<span id="cb26-1842"><a href="#cb26-1842" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1843"><a href="#cb26-1843" aria-hidden="true" tabindex="-1"></a><span class="fu"># References</span></span>
<span id="cb26-1844"><a href="#cb26-1844" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1845"><a href="#cb26-1845" aria-hidden="true" tabindex="-1"></a>::: {#refs}</span>
<span id="cb26-1846"><a href="#cb26-1846" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-1847"><a href="#cb26-1847" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1848"><a href="#cb26-1848" aria-hidden="true" tabindex="-1"></a>{{&lt; pagebreak &gt;}}</span>
<span id="cb26-1849"><a href="#cb26-1849" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1850"><a href="#cb26-1850" aria-hidden="true" tabindex="-1"></a><span class="fu"># Appendix {.appendix .unnumbered}</span></span>
<span id="cb26-1851"><a href="#cb26-1851" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1852"><a href="#cb26-1852" aria-hidden="true" tabindex="-1"></a><span class="fu">## Full search query {.unnumbered}</span></span>
<span id="cb26-1853"><a href="#cb26-1853" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1856"><a href="#cb26-1856" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1857"><a href="#cb26-1857" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: full-search-query</span></span>
<span id="cb26-1858"><a href="#cb26-1858" aria-hidden="true" tabindex="-1"></a><span class="co">#| echo: false</span></span>
<span id="cb26-1859"><a href="#cb26-1859" aria-hidden="true" tabindex="-1"></a><span class="co">#| output: asis</span></span>
<span id="cb26-1860"><a href="#cb26-1860" aria-hidden="true" tabindex="-1"></a><span class="cf">with</span> <span class="bu">open</span>(<span class="ss">f&quot;</span><span class="sc">{</span>SUPPLEMENTARY_DATA<span class="sc">}</span><span class="ss">/query.txt&quot;</span>) <span class="im">as</span> f:</span>
<span id="cb26-1861"><a href="#cb26-1861" aria-hidden="true" tabindex="-1"></a> query <span class="op">=</span> f.read()</span>
<span id="cb26-1862"><a href="#cb26-1862" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1863"><a href="#cb26-1863" aria-hidden="true" tabindex="-1"></a>t3 <span class="op">=</span> <span class="st">&quot;`&quot;</span> <span class="op">*</span> <span class="dv">3</span></span>
<span id="cb26-1864"><a href="#cb26-1864" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f&quot;&quot;&quot;</span></span>
<span id="cb26-1865"><a href="#cb26-1865" aria-hidden="true" tabindex="-1"></a><span class="ss">```sql</span></span>
<span id="cb26-1866"><a href="#cb26-1866" aria-hidden="true" tabindex="-1"></a><span class="sc">{</span>query<span class="sc">}</span></span>
<span id="cb26-1867"><a href="#cb26-1867" aria-hidden="true" tabindex="-1"></a><span class="sc">{</span>t3<span class="sc">}</span></span>
<span id="cb26-1868"><a href="#cb26-1868" aria-hidden="true" tabindex="-1"></a><span class="ss">&quot;&quot;&quot;</span>)</span>
<span id="cb26-1869"><a href="#cb26-1869" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1870"><a href="#cb26-1870" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1871"><a href="#cb26-1871" aria-hidden="true" tabindex="-1"></a><span class="fu">## Validity rankings {#sec-appendix-validity-rankings .unnumbered}</span></span>
<span id="cb26-1872"><a href="#cb26-1872" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1873"><a href="#cb26-1873" aria-hidden="true" tabindex="-1"></a>::: {#apptbl-validity-external}</span>
<span id="cb26-1874"><a href="#cb26-1874" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1875"><a href="#cb26-1875" aria-hidden="true" tabindex="-1"></a>| Representativeness | Ranking |</span>
<span id="cb26-1876"><a href="#cb26-1876" aria-hidden="true" tabindex="-1"></a>| --- | --- |</span>
<span id="cb26-1877"><a href="#cb26-1877" aria-hidden="true" tabindex="-1"></a>| non-representative survey/dataset | 2.0 |</span>
<span id="cb26-1878"><a href="#cb26-1878" aria-hidden="true" tabindex="-1"></a>| subnationally representative survey/dataset | 3.0 |</span>
<span id="cb26-1879"><a href="#cb26-1879" aria-hidden="true" tabindex="-1"></a>| nationally representative survey/dataset | 4.0 |</span>
<span id="cb26-1880"><a href="#cb26-1880" aria-hidden="true" tabindex="-1"></a>| census-based dataset | 5.0 |</span>
<span id="cb26-1881"><a href="#cb26-1881" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1882"><a href="#cb26-1882" aria-hidden="true" tabindex="-1"></a>External validity ranking. Adapted from @Maitrot2017.</span>
<span id="cb26-1883"><a href="#cb26-1883" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1884"><a href="#cb26-1884" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-1885"><a href="#cb26-1885" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1886"><a href="#cb26-1886" aria-hidden="true" tabindex="-1"></a>::: {#apptbl-validity-internal}</span>
<span id="cb26-1887"><a href="#cb26-1887" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1888"><a href="#cb26-1888" aria-hidden="true" tabindex="-1"></a>| Method | Ranking |</span>
<span id="cb26-1889"><a href="#cb26-1889" aria-hidden="true" tabindex="-1"></a>| --- | --- |</span>
<span id="cb26-1890"><a href="#cb26-1890" aria-hidden="true" tabindex="-1"></a>| ordinary least squares &amp; fixed-effects | 2.0 |</span>
<span id="cb26-1891"><a href="#cb26-1891" aria-hidden="true" tabindex="-1"></a>| discontinuity matching | 3.0 |</span>
<span id="cb26-1892"><a href="#cb26-1892" aria-hidden="true" tabindex="-1"></a>| difference in difference (&amp; triple difference) | 3.0 |</span>
<span id="cb26-1893"><a href="#cb26-1893" aria-hidden="true" tabindex="-1"></a>| propensity score matching | 3.5 |</span>
<span id="cb26-1894"><a href="#cb26-1894" aria-hidden="true" tabindex="-1"></a>| instrumental variable | 4.0 |</span>
<span id="cb26-1895"><a href="#cb26-1895" aria-hidden="true" tabindex="-1"></a>| general method of moments | 4.0 |</span>
<span id="cb26-1896"><a href="#cb26-1896" aria-hidden="true" tabindex="-1"></a>| regression discontinuity | 4.5 |</span>
<span id="cb26-1897"><a href="#cb26-1897" aria-hidden="true" tabindex="-1"></a>| randomised control trial | 5.0 |</span>
<span id="cb26-1898"><a href="#cb26-1898" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1899"><a href="#cb26-1899" aria-hidden="true" tabindex="-1"></a>Internal validity ranking. Adapted from @Maitrot2017.</span>
<span id="cb26-1900"><a href="#cb26-1900" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1901"><a href="#cb26-1901" aria-hidden="true" tabindex="-1"></a>:::</span>
<span id="cb26-1902"><a href="#cb26-1902" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1903"><a href="#cb26-1903" aria-hidden="true" tabindex="-1"></a><span class="fu">## Extraction matrix {.unnumbered}</span></span>
<span id="cb26-1904"><a href="#cb26-1904" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1907"><a href="#cb26-1907" aria-hidden="true" tabindex="-1"></a><span class="in">```{python}</span></span>
<span id="cb26-1908"><a href="#cb26-1908" aria-hidden="true" tabindex="-1"></a><span class="co">#| label: apptbl-extraction-matrix</span></span>
<span id="cb26-1909"><a href="#cb26-1909" aria-hidden="true" tabindex="-1"></a><span class="co">#| tbl-cap: Extraction matrix</span></span>
<span id="cb26-1910"><a href="#cb26-1910" aria-hidden="true" tabindex="-1"></a>bib_df</span>
<span id="cb26-1911"><a href="#cb26-1911" aria-hidden="true" tabindex="-1"></a><span class="in">```</span></span>
<span id="cb26-1912"><a href="#cb26-1912" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-1913"><a href="#cb26-1913" aria-hidden="true" tabindex="-1"></a>{{&lt; pagebreak &gt;}}</span>
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