diff --git a/.buildinfo b/.buildinfo index c7c5ef0e..b452fc2e 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: f862bda0d340301d6c422c1388a7892a +config: 6b02d60495fa798a318565b52688c922 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_downloads/77043e17fc6d5e67b49e34783a35c12f/OECS_RSK.pdf b/_downloads/77043e17fc6d5e67b49e34783a35c12f/OECS_RSK.pdf new file mode 100644 index 00000000..dfb0b78f Binary files /dev/null and b/_downloads/77043e17fc6d5e67b49e34783a35c12f/OECS_RSK.pdf differ diff --git a/_sources/docs/intro-risk.md b/_sources/docs/intro-risk.md index e3dec5c6..81935a82 100644 --- a/_sources/docs/intro-risk.md +++ b/_sources/docs/intro-risk.md @@ -56,7 +56,7 @@ Due to requests from regional teams, a refined calculation of the integral for p --- align: center --- -The integral below the curve is calculated as the mean of lower and upper bound rectangles areas. +The integral below the curve is calculated as the mean of lower and upper bound rectangles areas ([Riemann sum](https://www.math.net/riemann-sum)). ``` ### Direct and 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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..8549469d --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,134 @@ +/* + * _sphinx_javascript_frameworks_compat.js + * ~~~~~~~~~~ + * + * Compatability shim for jQuery and underscores.js. + * + * WILL BE REMOVED IN Sphinx 6.0 + * xref RemovedInSphinx60Warning + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/_static/basic.css b/_static/basic.css index d54be806..a2b34275 100644 --- a/_static/basic.css +++ b/_static/basic.css @@ -222,7 +222,7 @@ table.modindextable td { /* -- general body styles --------------------------------------------------- */ div.body { - min-width: 450px; + min-width: 360px; max-width: 800px; } @@ -428,10 +428,6 @@ table.docutils td, table.docutils th { border-bottom: 1px solid #aaa; } -table.footnote td, table.footnote th { - border: 0 !important; -} - th { text-align: left; padding-right: 5px; @@ -615,6 +611,7 @@ ul.simple p { margin-bottom: 0; } +/* Docutils 0.17 and older (footnotes & citations) */ dl.footnote > dt, dl.citation > dt { float: left; @@ -632,6 +629,33 @@ dl.citation > dd:after { clear: both; } +/* Docutils 0.18+ (footnotes & citations) */ +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +/* Footnotes & citations ends */ + dl.field-list { display: grid; grid-template-columns: fit-content(30%) auto; diff --git a/_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css b/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css similarity index 56% rename from _static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css rename to _static/design-style.4045f2051d55cab465a707391d5b2007.min.css index 704c42ee..3225661c 100644 --- a/_static/design-style.b7bb847fb20b106c3d81b95245e65545.min.css +++ b/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css @@ -1 +1 @@ -.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) 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#d3d4d5;--sd-color-muted-highlight: #5c636a;--sd-color-dark-highlight: #1c1f23;--sd-color-black-highlight: black;--sd-color-white-highlight: #d9d9d9;--sd-color-primary-text: #fff;--sd-color-secondary-text: #fff;--sd-color-success-text: #fff;--sd-color-info-text: #fff;--sd-color-warning-text: #212529;--sd-color-danger-text: #fff;--sd-color-light-text: #212529;--sd-color-muted-text: #fff;--sd-color-dark-text: #fff;--sd-color-black-text: #fff;--sd-color-white-text: #212529;--sd-color-shadow: rgba(0, 0, 0, 0.15);--sd-color-card-border: rgba(0, 0, 0, 0.125);--sd-color-card-border-hover: hsla(231, 99%, 66%, 1);--sd-color-card-background: transparent;--sd-color-card-text: inherit;--sd-color-card-header: transparent;--sd-color-card-footer: transparent;--sd-color-tabs-label-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-hover: hsla(231, 99%, 66%, 1);--sd-color-tabs-label-inactive: hsl(0, 0%, 66%);--sd-color-tabs-underline-active: hsla(231, 99%, 66%, 1);--sd-color-tabs-underline-hover: rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/_static/doctools.js b/_static/doctools.js index e1bfd708..c3db08d1 100644 --- a/_static/doctools.js +++ b/_static/doctools.js @@ -2,357 +2,263 @@ * doctools.js * ~~~~~~~~~~~ * - * Sphinx JavaScript utilities for all documentation. + * Base JavaScript utilities for all Sphinx HTML documentation. * * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ +"use strict"; -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); } - return decodeURIComponent(x.replace(/\+/g, ' ')); }; /** - * small helper function to urlencode strings + * highlight a given string on a node by wrapping it in + * span elements with the given class name. */ -jQuery.urlencode = encodeURIComponent; +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); } } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; }; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; /** * Small JavaScript module for the documentation. */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - this.initOnKeyListeners(); +const Documentation = { + init: () => { + Documentation.highlightSearchWords(); + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); }, /** * i18n support */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } }, - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; }, - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; }, /** * highlight the search words provided in the url in the text */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('') - .appendTo($('#searchbox')); - } - }, + highlightSearchWords: () => { + const highlight = + new URLSearchParams(window.location.search).get("highlight") || ""; + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '" + ) + ); }, /** * helper function to hide the search marks again */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - var url = new URL(window.location); - url.searchParams.delete('highlight'); - window.history.replaceState({}, '', url); + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + const url = new URL(window.location); + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); }, - /** + /** * helper function to focus on search bar */ - focusSearchBar : function() { - $('input[name=q]').first().focus(); + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); }, /** - * make the url absolute + * Initialise the domain index toggle buttons */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); }, - initOnKeyListeners: function() { + initOnKeyListeners: () => { // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) - return; + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; - $(document).keydown(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box, textarea, dropdown or button - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && activeElementType !== 'BUTTON') { - if (event.altKey || event.ctrlKey || event.metaKey) - return; + const blacklistedElements = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", + ]); + document.addEventListener("keydown", (event) => { + if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements + if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys - if (!event.shiftKey) { - switch (event.key) { - case 'ArrowLeft': - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) - break; - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - break; - case 'ArrowRight': - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) - break; - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - break; - case 'Escape': - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) - break; - Documentation.hideSearchWords(); - return false; - } - } - - // some keyboard layouts may need Shift to get / + if (!event.shiftKey) { switch (event.key) { - case '/': - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) - break; - Documentation.focusSearchBar(); - return false; + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + case "Escape": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.hideSearchWords(); + event.preventDefault(); } } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } }); - } + }, }; // quick alias for translations -_ = Documentation.gettext; +const _ = Documentation.gettext; -$(document).ready(function() { - Documentation.init(); -}); +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js index 877e3c31..30637825 100644 --- a/_static/documentation_options.js +++ b/_static/documentation_options.js @@ -1,7 +1,7 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), VERSION: '', - LANGUAGE: 'None', + LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', FILE_SUFFIX: '.html', @@ -10,5 +10,5 @@ var DOCUMENTATION_OPTIONS = { SOURCELINK_SUFFIX: '', NAVIGATION_WITH_KEYS: true, SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: true, + ENABLE_SEARCH_SHORTCUTS: false, }; \ No newline at end of file diff --git a/_static/jquery-3.5.1.js b/_static/jquery-3.6.0.js similarity index 98% rename from _static/jquery-3.5.1.js rename to _static/jquery-3.6.0.js index 50937333..fc6c299b 100644 --- a/_static/jquery-3.5.1.js +++ b/_static/jquery-3.6.0.js @@ -1,15 +1,15 @@ /*! - * jQuery JavaScript Library v3.5.1 + * jQuery JavaScript Library v3.6.0 * https://jquery.com/ * * Includes Sizzle.js * https://sizzlejs.com/ * - * Copyright JS Foundation and other contributors + * Copyright OpenJS Foundation and other contributors * Released under the MIT license * https://jquery.org/license * - * Date: 2020-05-04T22:49Z + * Date: 2021-03-02T17:08Z */ ( function( global, factory ) { @@ -76,12 +76,16 @@ var support = {}; var isFunction = function isFunction( obj ) { - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - return typeof obj === "function" && typeof obj.nodeType !== "number"; - }; + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; var isWindow = function isWindow( obj ) { @@ -147,7 +151,7 @@ function toType( obj ) { var - version = "3.5.1", + version = "3.6.0", // Define a local copy of jQuery jQuery = function( selector, context ) { @@ -401,7 +405,7 @@ jQuery.extend( { if ( isArrayLike( Object( arr ) ) ) { jQuery.merge( ret, typeof arr === "string" ? - [ arr ] : arr + [ arr ] : arr ); } else { push.call( ret, arr ); @@ -496,9 +500,9 @@ if ( typeof Symbol === "function" ) { // Populate the class2type map jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); function isArrayLike( obj ) { @@ -518,14 +522,14 @@ function isArrayLike( obj ) { } var Sizzle = /*! - * Sizzle CSS Selector Engine v2.3.5 + * Sizzle CSS Selector Engine v2.3.6 * https://sizzlejs.com/ * * Copyright JS Foundation and other contributors * Released under the MIT license * https://js.foundation/ * - * Date: 2020-03-14 + * Date: 2021-02-16 */ ( function( window ) { var i, @@ -1108,8 +1112,8 @@ support = Sizzle.support = {}; * @returns {Boolean} True iff elem is a non-HTML XML node */ isXML = Sizzle.isXML = function( elem ) { - var namespace = elem.namespaceURI, - docElem = ( elem.ownerDocument || elem ).documentElement; + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; // Support: IE <=8 // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes @@ -3024,9 +3028,9 @@ var rneedsContext = jQuery.expr.match.needsContext; function nodeName( elem, name ) { - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); -}; +} var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); @@ -3997,8 +4001,8 @@ jQuery.extend( { resolveContexts = Array( i ), resolveValues = slice.call( arguments ), - // the master Deferred - master = jQuery.Deferred(), + // the primary Deferred + primary = jQuery.Deferred(), // subordinate callback factory updateFunc = function( i ) { @@ -4006,30 +4010,30 @@ jQuery.extend( { resolveContexts[ i ] = this; resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); + primary.resolveWith( resolveContexts, resolveValues ); } }; }; // Single- and empty arguments are adopted like Promise.resolve if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, !remaining ); // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || + if ( primary.state() === "pending" || isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - return master.then(); + return primary.then(); } } // Multiple arguments are aggregated like Promise.all array elements while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); } - return master.promise(); + return primary.promise(); } } ); @@ -4180,8 +4184,8 @@ var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { for ( ; i < len; i++ ) { fn( elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) ); } } @@ -5089,10 +5093,7 @@ function buildFragment( elems, context, scripts, selection, ignored ) { } -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; function returnTrue() { return true; @@ -5387,8 +5388,8 @@ jQuery.event = { event = jQuery.event.fix( nativeEvent ), handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], special = jQuery.event.special[ event.type ] || {}; // Use the fix-ed jQuery.Event rather than the (read-only) native event @@ -5512,12 +5513,12 @@ jQuery.event = { get: isFunction( hook ) ? function() { if ( this.originalEvent ) { - return hook( this.originalEvent ); + return hook( this.originalEvent ); } } : function() { if ( this.originalEvent ) { - return this.originalEvent[ name ]; + return this.originalEvent[ name ]; } }, @@ -5656,7 +5657,13 @@ function leverageNative( el, type, expectSync ) { // Cancel the outer synthetic event event.stopImmediatePropagation(); event.preventDefault(); - return result.value; + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; } // If this is an inner synthetic event for an event with a bubbling surrogate @@ -5821,34 +5828,7 @@ jQuery.each( { targetTouches: true, toElement: true, touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } + which: true }, jQuery.event.addProp ); jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { @@ -5874,6 +5854,12 @@ jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateTyp return true; }, + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + delegateType: delegateType }; } ); @@ -6541,6 +6527,10 @@ var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); // set in CSS while `offset*` properties report correct values. // Behavior in IE 9 is more subtle than in newer versions & it passes // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) reliableTrDimensions: function() { var table, tr, trChild, trStyle; if ( reliableTrDimensionsVal == null ) { @@ -6548,17 +6538,32 @@ var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); tr = document.createElement( "tr" ); trChild = document.createElement( "div" ); - table.style.cssText = "position:absolute;left:-11111px"; + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. tr.style.height = "1px"; trChild.style.height = "9px"; + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + documentElement .appendChild( table ) .appendChild( tr ) .appendChild( trChild ); trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; documentElement.removeChild( table ); } @@ -7022,10 +7027,10 @@ jQuery.each( [ "height", "width" ], function( _i, dimension ) { // Running getBoundingClientRect on a disconnected node // in IE throws an error. ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); } }, @@ -7084,7 +7089,7 @@ jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, swap( elem, { marginLeft: 0 }, function() { return elem.getBoundingClientRect().left; } ) - ) + "px"; + ) + "px"; } } ); @@ -7223,7 +7228,7 @@ Tween.propHooks = { if ( jQuery.fx.step[ tween.prop ] ) { jQuery.fx.step[ tween.prop ]( tween ); } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || + jQuery.cssHooks[ tween.prop ] || tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); } else { @@ -7468,7 +7473,7 @@ function defaultPrefilter( elem, props, opts ) { anim.done( function() { - /* eslint-enable no-loop-func */ + /* eslint-enable no-loop-func */ // The final step of a "hide" animation is actually hiding the element if ( !hidden ) { @@ -7588,7 +7593,7 @@ function Animation( elem, properties, options ) { tweens: [], createTween: function( prop, end ) { var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.opts.specialEasing[ prop ] || animation.opts.easing ); animation.tweens.push( tween ); return tween; }, @@ -7761,7 +7766,8 @@ jQuery.fn.extend( { anim.stop( true ); } }; - doAnimation.finish = doAnimation; + + doAnimation.finish = doAnimation; return empty || optall.queue === false ? this.each( doAnimation ) : @@ -8401,8 +8407,8 @@ jQuery.fn.extend( { if ( this.setAttribute ) { this.setAttribute( "class", className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" + "" : + dataPriv.get( this, "__className__" ) || "" ); } } @@ -8417,7 +8423,7 @@ jQuery.fn.extend( { while ( ( elem = this[ i++ ] ) ) { if ( elem.nodeType === 1 && ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; + return true; } } @@ -8707,9 +8713,7 @@ jQuery.extend( jQuery.event, { special.bindType || type; // jQuery handler - handle = ( - dataPriv.get( cur, "events" ) || Object.create( null ) - )[ event.type ] && + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && dataPriv.get( cur, "handle" ); if ( handle ) { handle.apply( cur, data ); @@ -8856,7 +8860,7 @@ var rquery = ( /\?/ ); // Cross-browser xml parsing jQuery.parseXML = function( data ) { - var xml; + var xml, parserErrorElem; if ( !data || typeof data !== "string" ) { return null; } @@ -8865,12 +8869,17 @@ jQuery.parseXML = function( data ) { // IE throws on parseFromString with invalid input. try { xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } + } catch ( e ) {} - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); } return xml; }; @@ -8971,16 +8980,14 @@ jQuery.fn.extend( { // Can add propHook for "elements" to filter or add form elements var elements = jQuery.prop( this, "elements" ); return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { + } ).filter( function() { var type = this.type; // Use .is( ":disabled" ) so that fieldset[disabled] works return this.name && !jQuery( this ).is( ":disabled" ) && rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( _i, elem ) { + } ).map( function( _i, elem ) { var val = jQuery( this ).val(); if ( val == null ) { @@ -9033,7 +9040,8 @@ var // Anchor tag for parsing the document origin originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; + +originAnchor.href = location.href; // Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport function addToPrefiltersOrTransports( structure ) { @@ -9414,8 +9422,8 @@ jQuery.extend( { // Context for global events is callbackContext if it is a DOM node or jQuery collection globalEventContext = s.context && ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, + jQuery( callbackContext ) : + jQuery.event, // Deferreds deferred = jQuery.Deferred(), @@ -9727,8 +9735,10 @@ jQuery.extend( { response = ajaxHandleResponses( s, jqXHR, responses ); } - // Use a noop converter for missing script - if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { s.converters[ "text script" ] = function() {}; } @@ -10466,12 +10476,6 @@ jQuery.offset = { options.using.call( elem, props ); } else { - if ( typeof props.top === "number" ) { - props.top += "px"; - } - if ( typeof props.left === "number" ) { - props.left += "px"; - } curElem.css( props ); } } @@ -10640,8 +10644,11 @@ jQuery.each( [ "top", "left" ], function( _i, prop ) { // Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods jQuery.each( { Height: "height", Width: "width" }, function( name, type ) { - jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name }, - function( defaultExtra, funcName ) { + jQuery.each( { + padding: "inner" + name, + content: type, + "": "outer" + name + }, function( defaultExtra, funcName ) { // Margin is only for outerHeight, outerWidth jQuery.fn[ funcName ] = function( margin, value ) { @@ -10726,7 +10733,8 @@ jQuery.fn.extend( { } } ); -jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " + +jQuery.each( + ( "blur focus focusin focusout resize scroll click dblclick " + "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " + "change select submit keydown keypress keyup contextmenu" ).split( " " ), function( _i, name ) { @@ -10737,7 +10745,8 @@ jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " + this.on( name, null, data, fn ) : this.trigger( name ); }; - } ); + } +); diff --git a/_static/jquery.js b/_static/jquery.js index b0614034..c4c6022f 100644 --- a/_static/jquery.js +++ b/_static/jquery.js @@ -1,2 +1,2 @@ -/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */ -!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new 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+ border-left-width: medium; +} + +div.cell details.hide>summary>span { + opacity: var(--mystnb-hide-prompt-opacity); +} + +div.cell details.hide[open]>summary>span.collapsed { + display: none; +} + +div.cell details.hide:not([open])>summary>span.expanded { + display: none; +} + +@keyframes collapsed-fade-in { + 0% { + opacity: 0; + } + + 100% { + opacity: 1; + } +} +div.cell details.hide[open]>summary~* { + -moz-animation: collapsed-fade-in 0.3s ease-in-out; + -webkit-animation: collapsed-fade-in 0.3s ease-in-out; + animation: collapsed-fade-in 0.3s ease-in-out; } /* Math align to the left */ @@ -80,38 +161,47 @@ div.section div.jupyter_container { /* Pandas tables. 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right; + vertical-align: middle; + padding: 0.5em 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} + +div.cell_output th { + font-weight: bold; +} + +div.cell_output tbody tr:nth-child(odd) { + background: #f5f5f5; +} + +div.cell_output tbody tr:hover { + background: rgba(66, 165, 245, 0.2); +} + +/** source code line numbers **/ +span.linenos { + opacity: 0.5; +} /* Inline text from `paste` operation */ @@ -128,56 +218,75 @@ tbody span.pasted-inline img { } /* Font colors for translated ANSI escape sequences -Color values are adapted from share/jupyter/nbconvert/templates/classic/static/style.css +Color values are copied from Jupyter Notebook +https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21 +Background colors from +https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors */ div.highlight .-Color-Bold { font-weight: bold; } + div.highlight .-Color[class*=-Black] { - color :#3E424D + color: #3E424D } + div.highlight .-Color[class*=-Red] { color: #E75C58 } + div.highlight .-Color[class*=-Green] { color: #00A250 } + div.highlight .-Color[class*=-Yellow] { - color: yellow + color: #DDB62B } + div.highlight .-Color[class*=-Blue] { color: #208FFB } + div.highlight .-Color[class*=-Magenta] { color: #D160C4 } + div.highlight .-Color[class*=-Cyan] { color: #60C6C8 } + div.highlight .-Color[class*=-White] { color: #C5C1B4 } + div.highlight .-Color[class*=-BGBlack] { background-color: #3E424D } + div.highlight .-Color[class*=-BGRed] { background-color: #E75C58 } + div.highlight .-Color[class*=-BGGreen] { background-color: #00A250 } + div.highlight .-Color[class*=-BGYellow] { - background-color: yellow + background-color: #DDB62B } + div.highlight .-Color[class*=-BGBlue] { background-color: #208FFB } + div.highlight .-Color[class*=-BGMagenta] { background-color: #D160C4 } + div.highlight .-Color[class*=-BGCyan] { background-color: #60C6C8 } + div.highlight .-Color[class*=-BGWhite] { background-color: #C5C1B4 } @@ -187,1536 +296,2047 @@ div.highlight .-Color[class*=-BGWhite] { div.highlight .-Color[class*=-C0] { color: #000000 } + div.highlight .-Color[class*=-BGC0] { background-color: #000000 } + div.highlight .-Color[class*=-C1] { color: #800000 } + div.highlight .-Color[class*=-BGC1] { background-color: #800000 } + div.highlight .-Color[class*=-C2] { color: #008000 } + div.highlight .-Color[class*=-BGC2] { background-color: #008000 } + div.highlight .-Color[class*=-C3] { color: #808000 } + div.highlight .-Color[class*=-BGC3] { background-color: #808000 } + div.highlight .-Color[class*=-C4] { color: #000080 } + div.highlight .-Color[class*=-BGC4] { background-color: #000080 } + div.highlight .-Color[class*=-C5] { color: #800080 } + div.highlight .-Color[class*=-BGC5] { background-color: #800080 } + div.highlight .-Color[class*=-C6] { color: #008080 } + div.highlight .-Color[class*=-BGC6] { background-color: #008080 } + div.highlight .-Color[class*=-C7] { color: #C0C0C0 } + div.highlight .-Color[class*=-BGC7] { background-color: #C0C0C0 } + div.highlight .-Color[class*=-C8] { color: #808080 } + div.highlight .-Color[class*=-BGC8] { background-color: #808080 } + div.highlight .-Color[class*=-C9] { color: #FF0000 } + div.highlight .-Color[class*=-BGC9] { background-color: #FF0000 } + div.highlight .-Color[class*=-C10] { color: #00FF00 } + div.highlight .-Color[class*=-BGC10] { background-color: #00FF00 } + div.highlight .-Color[class*=-C11] { color: #FFFF00 } + div.highlight .-Color[class*=-BGC11] { background-color: #FFFF00 } + div.highlight .-Color[class*=-C12] { color: #0000FF } + div.highlight .-Color[class*=-BGC12] { background-color: #0000FF } + div.highlight .-Color[class*=-C13] { color: #FF00FF } + div.highlight .-Color[class*=-BGC13] { background-color: #FF00FF } + div.highlight .-Color[class*=-C14] { color: #00FFFF } + div.highlight .-Color[class*=-BGC14] { background-color: #00FFFF } + div.highlight .-Color[class*=-C15] { color: #FFFFFF } + div.highlight .-Color[class*=-BGC15] { background-color: #FFFFFF } + div.highlight .-Color[class*=-C16] { color: #000000 } + div.highlight .-Color[class*=-BGC16] { background-color: #000000 } + div.highlight .-Color[class*=-C17] { color: #00005F } + div.highlight .-Color[class*=-BGC17] { background-color: #00005F } + div.highlight .-Color[class*=-C18] { color: #000087 } + div.highlight .-Color[class*=-BGC18] { background-color: #000087 } + div.highlight .-Color[class*=-C19] { color: #0000AF } + div.highlight .-Color[class*=-BGC19] { background-color: #0000AF } + div.highlight .-Color[class*=-C20] { color: #0000D7 } + div.highlight .-Color[class*=-BGC20] { background-color: #0000D7 } + div.highlight .-Color[class*=-C21] { color: #0000FF } + div.highlight .-Color[class*=-BGC21] { background-color: #0000FF } + div.highlight .-Color[class*=-C22] { color: #005F00 } + div.highlight .-Color[class*=-BGC22] { background-color: #005F00 } + div.highlight .-Color[class*=-C23] { color: #005F5F } + div.highlight .-Color[class*=-BGC23] { background-color: #005F5F } + div.highlight .-Color[class*=-C24] { color: #005F87 } + div.highlight .-Color[class*=-BGC24] { background-color: #005F87 } + div.highlight .-Color[class*=-C25] { color: #005FAF } + div.highlight .-Color[class*=-BGC25] { background-color: #005FAF } + div.highlight .-Color[class*=-C26] { color: #005FD7 } + div.highlight .-Color[class*=-BGC26] { background-color: #005FD7 } + div.highlight .-Color[class*=-C27] { color: #005FFF } + div.highlight .-Color[class*=-BGC27] { background-color: #005FFF } + div.highlight .-Color[class*=-C28] { color: #008700 } + div.highlight .-Color[class*=-BGC28] { background-color: #008700 } + div.highlight .-Color[class*=-C29] { color: #00875F } + div.highlight .-Color[class*=-BGC29] { background-color: #00875F } + div.highlight .-Color[class*=-C30] { color: #008787 } + div.highlight .-Color[class*=-BGC30] { background-color: #008787 } + div.highlight .-Color[class*=-C31] { color: #0087AF } + div.highlight .-Color[class*=-BGC31] { background-color: #0087AF } + div.highlight .-Color[class*=-C32] { color: #0087D7 } + div.highlight .-Color[class*=-BGC32] { background-color: #0087D7 } + div.highlight .-Color[class*=-C33] { color: #0087FF } + div.highlight .-Color[class*=-BGC33] { background-color: #0087FF } + div.highlight .-Color[class*=-C34] { color: #00AF00 } + div.highlight .-Color[class*=-BGC34] { background-color: #00AF00 } + div.highlight .-Color[class*=-C35] { color: #00AF5F } + div.highlight .-Color[class*=-BGC35] { background-color: #00AF5F } + div.highlight .-Color[class*=-C36] { color: #00AF87 } + div.highlight .-Color[class*=-BGC36] { background-color: #00AF87 } + div.highlight .-Color[class*=-C37] { color: #00AFAF } + div.highlight .-Color[class*=-BGC37] { background-color: #00AFAF } + div.highlight .-Color[class*=-C38] { color: #00AFD7 } + div.highlight .-Color[class*=-BGC38] { background-color: #00AFD7 } + div.highlight .-Color[class*=-C39] { color: #00AFFF } + div.highlight .-Color[class*=-BGC39] { background-color: #00AFFF } + div.highlight .-Color[class*=-C40] { color: #00D700 } + div.highlight .-Color[class*=-BGC40] { background-color: #00D700 } + div.highlight .-Color[class*=-C41] { color: #00D75F } + div.highlight .-Color[class*=-BGC41] { background-color: #00D75F } + div.highlight .-Color[class*=-C42] { color: #00D787 } + div.highlight .-Color[class*=-BGC42] { background-color: #00D787 } + div.highlight .-Color[class*=-C43] { color: #00D7AF } + div.highlight .-Color[class*=-BGC43] { background-color: #00D7AF } + div.highlight .-Color[class*=-C44] { color: #00D7D7 } + div.highlight .-Color[class*=-BGC44] { background-color: #00D7D7 } + div.highlight .-Color[class*=-C45] { color: #00D7FF } + div.highlight .-Color[class*=-BGC45] { background-color: #00D7FF } + div.highlight .-Color[class*=-C46] { color: #00FF00 } + div.highlight .-Color[class*=-BGC46] { background-color: #00FF00 } + div.highlight .-Color[class*=-C47] { color: #00FF5F } + div.highlight .-Color[class*=-BGC47] { background-color: #00FF5F } + div.highlight .-Color[class*=-C48] { color: #00FF87 } + div.highlight .-Color[class*=-BGC48] { background-color: #00FF87 } + div.highlight .-Color[class*=-C49] { color: #00FFAF } + div.highlight .-Color[class*=-BGC49] { background-color: #00FFAF } + div.highlight .-Color[class*=-C50] { color: #00FFD7 } + div.highlight .-Color[class*=-BGC50] { background-color: #00FFD7 } + div.highlight .-Color[class*=-C51] { color: #00FFFF } + div.highlight .-Color[class*=-BGC51] { background-color: #00FFFF } + div.highlight .-Color[class*=-C52] { color: #5F0000 } + div.highlight .-Color[class*=-BGC52] { background-color: #5F0000 } + div.highlight .-Color[class*=-C53] { color: #5F005F } + div.highlight .-Color[class*=-BGC53] { background-color: #5F005F } + div.highlight .-Color[class*=-C54] { color: #5F0087 } + div.highlight .-Color[class*=-BGC54] { background-color: #5F0087 } + div.highlight .-Color[class*=-C55] { color: #5F00AF } + div.highlight .-Color[class*=-BGC55] { background-color: #5F00AF } + div.highlight .-Color[class*=-C56] { color: #5F00D7 } + div.highlight .-Color[class*=-BGC56] { background-color: #5F00D7 } + div.highlight .-Color[class*=-C57] { color: #5F00FF } + div.highlight .-Color[class*=-BGC57] { background-color: #5F00FF } + div.highlight .-Color[class*=-C58] { color: #5F5F00 } + div.highlight .-Color[class*=-BGC58] { background-color: #5F5F00 } + div.highlight .-Color[class*=-C59] { color: #5F5F5F } + div.highlight .-Color[class*=-BGC59] { background-color: #5F5F5F } + div.highlight .-Color[class*=-C60] { color: #5F5F87 } + div.highlight .-Color[class*=-BGC60] { background-color: #5F5F87 } + div.highlight .-Color[class*=-C61] { color: #5F5FAF } + div.highlight .-Color[class*=-BGC61] { background-color: #5F5FAF } + div.highlight .-Color[class*=-C62] { color: #5F5FD7 } + div.highlight .-Color[class*=-BGC62] { background-color: #5F5FD7 } + div.highlight .-Color[class*=-C63] { color: #5F5FFF } + div.highlight .-Color[class*=-BGC63] { background-color: #5F5FFF } + div.highlight .-Color[class*=-C64] { color: #5F8700 } + div.highlight .-Color[class*=-BGC64] { background-color: #5F8700 } + div.highlight .-Color[class*=-C65] { color: #5F875F } + div.highlight .-Color[class*=-BGC65] { background-color: #5F875F } + div.highlight .-Color[class*=-C66] { color: #5F8787 } + div.highlight .-Color[class*=-BGC66] { background-color: #5F8787 } + div.highlight .-Color[class*=-C67] { color: #5F87AF } + div.highlight .-Color[class*=-BGC67] { background-color: #5F87AF } + div.highlight .-Color[class*=-C68] { color: #5F87D7 } + div.highlight .-Color[class*=-BGC68] { background-color: #5F87D7 } + div.highlight .-Color[class*=-C69] { color: #5F87FF } + div.highlight .-Color[class*=-BGC69] { background-color: #5F87FF } + div.highlight .-Color[class*=-C70] { color: #5FAF00 } + div.highlight .-Color[class*=-BGC70] { background-color: #5FAF00 } + div.highlight .-Color[class*=-C71] { color: #5FAF5F } + div.highlight .-Color[class*=-BGC71] { background-color: #5FAF5F } + div.highlight .-Color[class*=-C72] { color: #5FAF87 } + div.highlight .-Color[class*=-BGC72] { background-color: #5FAF87 } + div.highlight .-Color[class*=-C73] { color: #5FAFAF } + div.highlight .-Color[class*=-BGC73] { background-color: #5FAFAF } + div.highlight .-Color[class*=-C74] { color: #5FAFD7 } + div.highlight .-Color[class*=-BGC74] { background-color: #5FAFD7 } + div.highlight .-Color[class*=-C75] { color: #5FAFFF } + div.highlight .-Color[class*=-BGC75] { background-color: #5FAFFF } + div.highlight .-Color[class*=-C76] { color: #5FD700 } + div.highlight .-Color[class*=-BGC76] { background-color: #5FD700 } + div.highlight .-Color[class*=-C77] { color: #5FD75F } + div.highlight .-Color[class*=-BGC77] { background-color: #5FD75F } + div.highlight .-Color[class*=-C78] { color: #5FD787 } + div.highlight .-Color[class*=-BGC78] { background-color: #5FD787 } + div.highlight .-Color[class*=-C79] { color: #5FD7AF } + div.highlight .-Color[class*=-BGC79] { background-color: #5FD7AF } + div.highlight .-Color[class*=-C80] { color: #5FD7D7 } + div.highlight .-Color[class*=-BGC80] { background-color: #5FD7D7 } + div.highlight .-Color[class*=-C81] { color: #5FD7FF } + div.highlight .-Color[class*=-BGC81] { background-color: #5FD7FF } + div.highlight .-Color[class*=-C82] { color: #5FFF00 } + div.highlight .-Color[class*=-BGC82] { background-color: #5FFF00 } + div.highlight .-Color[class*=-C83] { color: #5FFF5F } + div.highlight .-Color[class*=-BGC83] { background-color: #5FFF5F } + div.highlight .-Color[class*=-C84] { color: #5FFF87 } + div.highlight .-Color[class*=-BGC84] { background-color: #5FFF87 } + div.highlight .-Color[class*=-C85] { color: #5FFFAF } + div.highlight .-Color[class*=-BGC85] { background-color: #5FFFAF } + div.highlight .-Color[class*=-C86] { color: #5FFFD7 } + div.highlight .-Color[class*=-BGC86] { background-color: #5FFFD7 } + div.highlight .-Color[class*=-C87] { color: #5FFFFF } + div.highlight .-Color[class*=-BGC87] { background-color: #5FFFFF } + div.highlight .-Color[class*=-C88] { color: #870000 } + div.highlight .-Color[class*=-BGC88] { background-color: #870000 } + div.highlight .-Color[class*=-C89] { color: #87005F } + div.highlight .-Color[class*=-BGC89] { background-color: #87005F } + div.highlight .-Color[class*=-C90] { color: #870087 } + div.highlight .-Color[class*=-BGC90] { background-color: #870087 } + div.highlight .-Color[class*=-C91] { color: #8700AF } + div.highlight .-Color[class*=-BGC91] { background-color: #8700AF } + div.highlight .-Color[class*=-C92] { color: #8700D7 } + div.highlight .-Color[class*=-BGC92] { background-color: #8700D7 } + div.highlight .-Color[class*=-C93] { color: #8700FF } + div.highlight .-Color[class*=-BGC93] { background-color: #8700FF } + div.highlight .-Color[class*=-C94] { color: #875F00 } + div.highlight .-Color[class*=-BGC94] { background-color: #875F00 } + div.highlight .-Color[class*=-C95] { color: #875F5F } + div.highlight .-Color[class*=-BGC95] { background-color: #875F5F } + div.highlight .-Color[class*=-C96] { color: #875F87 } + div.highlight .-Color[class*=-BGC96] { background-color: #875F87 } + div.highlight .-Color[class*=-C97] { color: #875FAF } + div.highlight .-Color[class*=-BGC97] { background-color: #875FAF } + div.highlight .-Color[class*=-C98] { color: #875FD7 } + div.highlight .-Color[class*=-BGC98] { background-color: #875FD7 } + div.highlight .-Color[class*=-C99] { color: #875FFF } + div.highlight .-Color[class*=-BGC99] { background-color: #875FFF } + div.highlight .-Color[class*=-C100] { color: #878700 } + div.highlight .-Color[class*=-BGC100] { background-color: #878700 } + div.highlight .-Color[class*=-C101] { color: #87875F } + div.highlight .-Color[class*=-BGC101] { background-color: #87875F } + div.highlight .-Color[class*=-C102] { color: #878787 } + div.highlight .-Color[class*=-BGC102] { background-color: #878787 } + div.highlight .-Color[class*=-C103] { color: #8787AF } + div.highlight .-Color[class*=-BGC103] { background-color: #8787AF } + div.highlight .-Color[class*=-C104] { color: #8787D7 } + div.highlight .-Color[class*=-BGC104] { background-color: #8787D7 } + div.highlight .-Color[class*=-C105] { color: #8787FF } + div.highlight .-Color[class*=-BGC105] { background-color: #8787FF } + div.highlight .-Color[class*=-C106] { color: #87AF00 } + div.highlight .-Color[class*=-BGC106] { background-color: #87AF00 } + div.highlight .-Color[class*=-C107] { color: #87AF5F } + div.highlight .-Color[class*=-BGC107] { background-color: #87AF5F } + div.highlight .-Color[class*=-C108] { color: #87AF87 } + div.highlight .-Color[class*=-BGC108] { background-color: #87AF87 } + div.highlight .-Color[class*=-C109] { color: #87AFAF } + div.highlight .-Color[class*=-BGC109] { background-color: #87AFAF } + div.highlight .-Color[class*=-C110] { color: #87AFD7 } + div.highlight .-Color[class*=-BGC110] { background-color: #87AFD7 } + div.highlight .-Color[class*=-C111] { color: #87AFFF } + div.highlight .-Color[class*=-BGC111] { background-color: #87AFFF } + div.highlight .-Color[class*=-C112] { color: #87D700 } + div.highlight .-Color[class*=-BGC112] { background-color: #87D700 } + div.highlight .-Color[class*=-C113] { color: #87D75F } + div.highlight .-Color[class*=-BGC113] { background-color: #87D75F } + div.highlight .-Color[class*=-C114] { color: #87D787 } + div.highlight .-Color[class*=-BGC114] { background-color: #87D787 } + div.highlight .-Color[class*=-C115] { color: #87D7AF } + div.highlight .-Color[class*=-BGC115] { background-color: #87D7AF } + div.highlight .-Color[class*=-C116] { color: #87D7D7 } + div.highlight .-Color[class*=-BGC116] { background-color: #87D7D7 } + div.highlight .-Color[class*=-C117] { color: #87D7FF } + div.highlight .-Color[class*=-BGC117] { background-color: #87D7FF } + div.highlight .-Color[class*=-C118] { color: #87FF00 } + div.highlight .-Color[class*=-BGC118] { background-color: #87FF00 } + div.highlight .-Color[class*=-C119] { color: #87FF5F } + div.highlight .-Color[class*=-BGC119] { background-color: #87FF5F } + div.highlight .-Color[class*=-C120] { color: #87FF87 } + div.highlight .-Color[class*=-BGC120] { background-color: #87FF87 } + div.highlight .-Color[class*=-C121] { color: #87FFAF } + div.highlight .-Color[class*=-BGC121] { background-color: #87FFAF } + div.highlight .-Color[class*=-C122] { color: #87FFD7 } + div.highlight .-Color[class*=-BGC122] { background-color: #87FFD7 } + div.highlight .-Color[class*=-C123] { color: #87FFFF } + div.highlight .-Color[class*=-BGC123] { background-color: #87FFFF } + div.highlight .-Color[class*=-C124] { color: #AF0000 } + div.highlight .-Color[class*=-BGC124] { background-color: #AF0000 } + div.highlight .-Color[class*=-C125] { color: #AF005F } + div.highlight .-Color[class*=-BGC125] { background-color: #AF005F } + div.highlight .-Color[class*=-C126] { color: #AF0087 } + div.highlight .-Color[class*=-BGC126] { background-color: #AF0087 } + div.highlight .-Color[class*=-C127] { color: #AF00AF } + div.highlight .-Color[class*=-BGC127] { background-color: #AF00AF } + div.highlight .-Color[class*=-C128] { color: #AF00D7 } + div.highlight .-Color[class*=-BGC128] { background-color: #AF00D7 } + div.highlight .-Color[class*=-C129] { color: #AF00FF } + div.highlight .-Color[class*=-BGC129] { background-color: #AF00FF } + div.highlight .-Color[class*=-C130] { color: #AF5F00 } + div.highlight .-Color[class*=-BGC130] { background-color: #AF5F00 } + div.highlight .-Color[class*=-C131] { color: #AF5F5F } + div.highlight .-Color[class*=-BGC131] { background-color: #AF5F5F } + div.highlight .-Color[class*=-C132] { color: #AF5F87 } + div.highlight .-Color[class*=-BGC132] { background-color: #AF5F87 } + div.highlight .-Color[class*=-C133] { color: #AF5FAF } + div.highlight .-Color[class*=-BGC133] { background-color: #AF5FAF } + div.highlight 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t=this;o.default(document).off("focusin.bs.modal").on("focusin.bs.modal",(function(e){document!==e.target&&t._element!==e.target&&0===o.default(t._element).has(e.target).length&&t._element.focus()}))},e._setEscapeEvent=function(){var t=this;this._isShown?o.default(this._element).on("keydown.dismiss.bs.modal",(function(e){t._config.keyboard&&27===e.which?(e.preventDefault(),t.hide()):t._config.keyboard||27!==e.which||t._triggerBackdropTransition()})):this._isShown||o.default(this._element).off("keydown.dismiss.bs.modal")},e._setResizeEvent=function(){var t=this;this._isShown?o.default(window).on("resize.bs.modal",(function(e){return t.handleUpdate(e)})):o.default(window).off("resize.bs.modal")},e._hideModal=function(){var t=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._showBackdrop((function(){o.default(document.body).removeClass("modal-open"),t._resetAdjustments(),t._resetScrollbar(),o.default(t._element).trigger("hidden.bs.modal")}))},e._removeBackdrop=function(){this._backdrop&&(o.default(this._backdrop).remove(),this._backdrop=null)},e._showBackdrop=function(t){var e=this,n=o.default(this._element).hasClass("fade")?"fade":"";if(this._isShown&&this._config.backdrop){if(this._backdrop=document.createElement("div"),this._backdrop.className="modal-backdrop",n&&this._backdrop.classList.add(n),o.default(this._backdrop).appendTo(document.body),o.default(this._element).on("click.dismiss.bs.modal",(function(t){e._ignoreBackdropClick?e._ignoreBackdropClick=!1:t.target===t.currentTarget&&("static"===e._config.backdrop?e._triggerBackdropTransition():e.hide())})),n&&d.reflow(this._backdrop),o.default(this._backdrop).addClass("show"),!t)return;if(!n)return void t();var i=d.getTransitionDurationFromElement(this._backdrop);o.default(this._backdrop).one(d.TRANSITION_END,t).emulateTransitionEnd(i)}else if(!this._isShown&&this._backdrop){o.default(this._backdrop).removeClass("show");var r=function(){e._removeBackdrop(),t&&t()};if(o.default(this._element).hasClass("fade")){var 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a=this.getTipElement(),s=d.getUID(this.constructor.NAME);a.setAttribute("id",s),this.element.setAttribute("aria-describedby",s),this.setContent(),this.config.animation&&o.default(a).addClass("fade");var l="function"==typeof this.config.placement?this.config.placement.call(this,a,this.element):this.config.placement,u=this._getAttachment(l);this.addAttachmentClass(u);var f=this._getContainer();o.default(a).data(this.constructor.DATA_KEY,this),o.default.contains(this.element.ownerDocument.documentElement,this.tip)||o.default(a).appendTo(f),o.default(this.element).trigger(this.constructor.Event.INSERTED),this._popper=new r.default(this.element,a,this._getPopperConfig(u)),o.default(a).addClass("show"),o.default(a).addClass(this.config.customClass),"ontouchstart"in document.documentElement&&o.default(document.body).children().on("mouseover",null,o.default.noop);var c=function(){t.config.animation&&t._fixTransition();var 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t=this.element.getAttribute("data-original-title");return t||(t="function"==typeof this.config.title?this.config.title.call(this.element):this.config.title),t},e._getPopperConfig=function(t){var e=this;return l({},{placement:t,modifiers:{offset:this._getOffset(),flip:{behavior:this.config.fallbackPlacement},arrow:{element:".arrow"},preventOverflow:{boundariesElement:this.config.boundary}},onCreate:function(t){t.originalPlacement!==t.placement&&e._handlePopperPlacementChange(t)},onUpdate:function(t){return e._handlePopperPlacementChange(t)}},this.config.popperConfig)},e._getOffset=function(){var t=this,e={};return"function"==typeof this.config.offset?e.fn=function(e){return e.offsets=l({},e.offsets,t.config.offset(e.offsets,t.element)),e}:e.offset=this.config.offset,e},e._getContainer=function(){return!1===this.config.container?document.body:d.isElement(this.config.container)?o.default(this.config.container):o.default(document).find(this.config.container)},e._getAttachment=function(t){return z[t.toUpperCase()]},e._setListeners=function(){var t=this;this.config.trigger.split(" ").forEach((function(e){if("click"===e)o.default(t.element).on(t.constructor.Event.CLICK,t.config.selector,(function(e){return t.toggle(e)}));else if("manual"!==e){var n="hover"===e?t.constructor.Event.MOUSEENTER:t.constructor.Event.FOCUSIN,i="hover"===e?t.constructor.Event.MOUSELEAVE:t.constructor.Event.FOCUSOUT;o.default(t.element).on(n,t.config.selector,(function(e){return t._enter(e)})).on(i,t.config.selector,(function(e){return 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t=this;o.default(document).off("focusin.bs.modal").on("focusin.bs.modal",(function(e){document!==e.target&&t._element!==e.target&&0===o.default(t._element).has(e.target).length&&t._element.focus()}))},e._setEscapeEvent=function(){var t=this;this._isShown?o.default(this._element).on("keydown.dismiss.bs.modal",(function(e){t._config.keyboard&&27===e.which?(e.preventDefault(),t.hide()):t._config.keyboard||27!==e.which||t._triggerBackdropTransition()})):this._isShown||o.default(this._element).off("keydown.dismiss.bs.modal")},e._setResizeEvent=function(){var t=this;this._isShown?o.default(window).on("resize.bs.modal",(function(e){return t.handleUpdate(e)})):o.default(window).off("resize.bs.modal")},e._hideModal=function(){var t=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._showBackdrop((function(){o.default(document.body).removeClass("modal-open"),t._resetAdjustments(),t._resetScrollbar(),o.default(t._element).trigger("hidden.bs.modal")}))},e._removeBackdrop=function(){this._backdrop&&(o.default(this._backdrop).remove(),this._backdrop=null)},e._showBackdrop=function(t){var e=this,n=o.default(this._element).hasClass("fade")?"fade":"";if(this._isShown&&this._config.backdrop){if(this._backdrop=document.createElement("div"),this._backdrop.className="modal-backdrop",n&&this._backdrop.classList.add(n),o.default(this._backdrop).appendTo(document.body),o.default(this._element).on("click.dismiss.bs.modal",(function(t){e._ignoreBackdropClick?e._ignoreBackdropClick=!1:t.target===t.currentTarget&&("static"===e._config.backdrop?e._triggerBackdropTransition():e.hide())})),n&&d.reflow(this._backdrop),o.default(this._backdrop).addClass("show"),!t)return;if(!n)return void t();var i=d.getTransitionDurationFromElement(this._backdrop);o.default(this._backdrop).one(d.TRANSITION_END,t).emulateTransitionEnd(i)}else if(!this._isShown&&this._backdrop){o.default(this._backdrop).removeClass("show");var r=function(){e._removeBackdrop(),t&&t()};if(o.default(this._element).hasClass("fade")){var 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a=this.getTipElement(),s=d.getUID(this.constructor.NAME);a.setAttribute("id",s),this.element.setAttribute("aria-describedby",s),this.setContent(),this.config.animation&&o.default(a).addClass("fade");var l="function"==typeof this.config.placement?this.config.placement.call(this,a,this.element):this.config.placement,u=this._getAttachment(l);this.addAttachmentClass(u);var f=this._getContainer();o.default(a).data(this.constructor.DATA_KEY,this),o.default.contains(this.element.ownerDocument.documentElement,this.tip)||o.default(a).appendTo(f),o.default(this.element).trigger(this.constructor.Event.INSERTED),this._popper=new r.default(this.element,a,this._getPopperConfig(u)),o.default(a).addClass("show"),o.default(a).addClass(this.config.customClass),"ontouchstart"in document.documentElement&&o.default(document.body).children().on("mouseover",null,o.default.noop);var c=function(){t.config.animation&&t._fixTransition();var 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t=this.element.getAttribute("data-original-title");return t||(t="function"==typeof this.config.title?this.config.title.call(this.element):this.config.title),t},e._getPopperConfig=function(t){var e=this;return l({},{placement:t,modifiers:{offset:this._getOffset(),flip:{behavior:this.config.fallbackPlacement},arrow:{element:".arrow"},preventOverflow:{boundariesElement:this.config.boundary}},onCreate:function(t){t.originalPlacement!==t.placement&&e._handlePopperPlacementChange(t)},onUpdate:function(t){return e._handlePopperPlacementChange(t)}},this.config.popperConfig)},e._getOffset=function(){var t=this,e={};return"function"==typeof this.config.offset?e.fn=function(e){return 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-if (!Scorer) { - /** - * Simple result scoring code. - */ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { var Scorer = { // Implement the following function to further tweak the score for each result - // The function takes a result array [filename, title, anchor, descr, score] + // The function takes a result array [docname, title, anchor, descr, score, filename] // and returns the new score. /* - score: function(result) { - return result[4]; + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score }, */ @@ -28,9 +30,11 @@ if (!Scorer) { // or matches in the last dotted part of the object name objPartialMatch: 6, // Additive scores depending on the priority of the object - objPrio: {0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5}, // used to be unimportantResults + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, // Used when the priority is not in the mapping. objPrioDefault: 0, @@ -39,452 +43,455 @@ if (!Scorer) { partialTitle: 7, // query found in terms term: 5, - partialTerm: 2 + partialTerm: 2, }; } -if (!splitQuery) { - function splitQuery(query) { - return query.split(/\s+/); +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, highlightTerms, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + const params = new URLSearchParams(); + params.set("highlight", [...highlightTerms].join(" ")); + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + "?" + params.toString() + anchor; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerText = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, highlightTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + highlightTerms, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), highlightTerms, searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, highlightTerms, searchTerms), + 5 + ); } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings } /** * Search Module */ -var Search = { - - _index : null, - _queued_query : null, - _pulse_status : -1, - - htmlToText : function(htmlString) { - var virtualDocument = document.implementation.createHTMLDocument('virtual'); - var htmlElement = $(htmlString, virtualDocument); - htmlElement.find('.headerlink').remove(); - docContent = htmlElement.find('[role=main]')[0]; - if(docContent === undefined) { - console.warn("Content block not found. Sphinx search tries to obtain it " + - "via '[role=main]'. Could you check your theme or template."); - return ""; - } - return docContent.textContent || docContent.innerText; +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = document + .createRange() + .createContextualFragment(htmlString); + _removeChildren(htmlElement.querySelectorAll(".headerlink")); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; }, - init : function() { - var params = $.getQueryParameters(); - if (params.q) { - var query = params.q[0]; - $('input[name="q"]')[0].value = query; - this.performSearch(query); - } + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); }, - loadIndex : function(url) { - $.ajax({type: "GET", url: url, data: null, - dataType: "script", cache: true, - complete: function(jqxhr, textstatus) { - if (textstatus != "success") { - document.getElementById("searchindexloader").src = url; - } - }}); - }, + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), - setIndex : function(index) { - var q; - this._index = index; - if ((q = this._queued_query) !== null) { - this._queued_query = null; - Search.query(q); + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); } }, - hasIndex : function() { - return this._index !== null; - }, + hasIndex: () => Search._index !== null, - deferQuery : function(query) { - this._queued_query = query; - }, + deferQuery: (query) => (Search._queued_query = query), - stopPulse : function() { - this._pulse_status = 0; - }, + stopPulse: () => (Search._pulse_status = -1), - startPulse : function() { - if (this._pulse_status >= 0) - return; - function pulse() { - var i; + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { Search._pulse_status = (Search._pulse_status + 1) % 4; - var dotString = ''; - for (i = 0; i < Search._pulse_status; i++) - dotString += '.'; - Search.dots.text(dotString); - if (Search._pulse_status > -1) - window.setTimeout(pulse, 500); - } + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; pulse(); }, /** * perform a search for something (or wait until index is loaded) */ - performSearch : function(query) { + performSearch: (query) => { // create the required interface elements - this.out = $('#search-results'); - this.title = $('

' + _('Searching') + '

').appendTo(this.out); - this.dots = $('').appendTo(this.title); - this.status = $('

 

').appendTo(this.out); - this.output = $(' - - - + + + + - -
-
- Contents -
- +
-
- -
- -
- +
+
-

Expected Annual Exposure (EAE)#

+

Expected Annual Exposure (EAE)#

This analytical approach applies to probabilistic hazard scenarios (multiple layers by Return Period) which cannot be coupled with a proper physical vulnerability model, but can be ranked in terms of hazard intensity thresholds. Thus the hazard intensity layer is translated into discrete classes and, for each one, the total exposure for the selected category is calculated. For example, flood hazard over agriculture is measured in terms of hectars of land falling within different intervals of water depth.

@@ -585,7 +856,7 @@

Expected Annual Exposure (EAE) -

SCRIPT OVERVIEW#

+

SCRIPT OVERVIEW#

The python notebooks performs combination of hazard and exposure geodata from global datasets according to user input and settings, and returns a risk score in the form of Expected Annual Exposure (EAE) for baseline (reference period).

Note

@@ -598,7 +869,7 @@

SCRIPT OVERVIEW -

DATA MANAGEMENT#

+

DATA MANAGEMENT#

  • Download country boundaries for multiple administrative levels sourced from HDX or Geoboundaries. Note that oftern there are several versions for the same country, so be sure to use the most updated from official agencies (eg. United Nations). Verify that shapes, names and codes are consistent across different levels.

  • Download exposure data.

  • @@ -606,7 +877,7 @@

    DATA MANAGEMENT -

    SETUP THE NOTEBOOK#

    +

    SETUP THE NOTEBOOK#

    • Create environment and folder structure as explained in tool setup

    • Move verified input data into the tools folders

    • @@ -614,9 +885,9 @@

      SETUP THE NOTEBOOK -

      PROCESSING#

      +

      PROCESSING#

      -

      LOOP over each hazard RPi layers:#

      +

      LOOP over each hazard RPi layers:#

      • Classify hazard layer RPi according to settings: number and size of classes: RPi -> RPi_Cj (multiband raster)

      • Each class Cj of RPi is used to mask the Exposure layer -> RPi_Cj_Exp (multiband raster)

      • @@ -625,71 +896,71 @@

        LOOP over each hazard RPi layers: -

        Calculate EAE#

        +

        Calculate EAE#

        • Calculate the exceedance frequency for each RPi -> RPi_ef = (1/RPi - 1/RPi+1) where RPi+1 means the next RP in the serie. Example using 3 RP scenarios: RP 10, 100, and 1000 years. Then: RP10_ef = (1/10 - 1/100) = 0.09

        • Multiply exposure for each scenario i and class j (RPi_Cj_Exp) with its exceedence frequency (RPi_ef) -> RPi_Cj_EAE

        • Sum RPi_Cj_Exp_EAE across multiple RPi for the same class Cj -> table [ADMi;Cj_Exp_EAE]
          Example using 4 classes (C1-4): table [ADMi;C1_EAE;C2_EAE;C3_EAE;C4_EAE]

          - +
          - + - - - - + + + + - - - + + + - + - - - - + + + + - - - + + + - + - - - - + + + + - - - + + + - + - - - - + + + + - - - + + + - + - - - - + + + + - - - + + +

          RP

          RP

          Exc. frequency

          C1_exp

          C2_exp

          C3_exp

          C4_exp

          C1_exp

          C2_exp

          C3_exp

          C4_exp

          C1_EAE

          C2_EAE

          C3_EAE

          C4_EAE

          C2_EAE

          C3_EAE

          C4_EAE

          10

          10

          0.09

          4,036

          1,535

          2,111

          1,967

          4,036

          1,535

          2,111

          1,967

          363

          138

          190

          177

          138

          190

          177

          100

          100

          0.009

          8,212

          5,766

          5,007

          13,282

          8,212

          5,766

          5,007

          13,282

          739

          519

          451

          1,195

          519

          451

          1,195

          1000

          1000

          0.0009

          8,399

          5,134

          4,371

          25,989

          8,399

          5,134

          4,371

          25,989

          756

          462

          393

          2,339

          462

          393

          2,339

          Total

          Total

          1,858

          1,119

          1,034

          3,711

          1,119

          1,034

          3,711

          @@ -698,7 +969,7 @@

          Calculate EAE -

          Present results#

          +

          Present results#

          +
+ + + + + + +
+ + + + + +
- - + + + + + + + + - + + + + - - + + \ No newline at end of file diff --git a/docs/EAI.html b/docs/EAI.html index bb8639fd..f8487810 100644 --- a/docs/EAI.html +++ b/docs/EAI.html @@ -1,49 +1,57 @@ - + - + Expected Annual Impact (EAI) — Climate & Disaster Risk Screening Tools - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + + - + - + + - - - - - - + + - - - - - - - - -
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- +
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- +
+
-

Expected Annual Impact (EAI)#

+

Expected Annual Impact (EAI)#

This analytical approach applies to probabilistic hazard scenarios (multiple layers by Return Period) and aims to produce a mean estimate of Expected Annual Impact (EAI) over exposed categories for the historical baseline, as explained in the risk concepts. In order to calculate impacts, we need some probabilistic hazards datasets depticting hazard intensity and aligned physical vulnerability model to translate the intensity into impact over exposed categories. In relation to the global datasets currently available, this is possible for:

    @@ -572,7 +848,7 @@

    Expected Annual Impact (EAI)../_images/EAI.png
    -

    SCRIPT OVERVIEW#

    +

    SCRIPT OVERVIEW#

    The notebooks for EAI calculation performs combination of hazard and exposure geodata from global datasets according to user input and settings, and returns a risk score in the form of Expected Annual Impact (EAI) for baseline (reference period).

    Note

    @@ -590,7 +866,7 @@

    SCRIPT OVERVIEW -

    SETUP THE NOTEBOOK#

    +

    SETUP THE NOTEBOOK#

    As explained in tool setup:

    • Create environment and folder structure

    • @@ -599,9 +875,9 @@

      SETUP THE NOTEBOOK -

      PROCESSING#

      +

      PROCESSING#

      -

      LOOP over each hazard RPi layers:#

      +

      LOOP over each hazard RPi layers:#

      • Filter hazard layer according to settings (min and max thresholds) for each RPi -> RPi_filtered

      • Transform hazard intensity value into impact factor using specific hazard impact function or table: RPi_filtered -> RP_IF

      • @@ -611,45 +887,45 @@

        LOOP over each hazard RPi layers: -

        Calculate Expected Annual Impact#

        +

        Calculate Expected Annual Impact#

        • Calculate the exceedance frequency for each RPi -> RPi_ef = (1/RPi - 1/RPj) where j is the next RP in the serie. Example using 3 scenarios: RP 10, 100, and 1000 years. Then: RP10_ef = (1/10 - 1/100) = 0.09

        • Multiply impact on exposure for each scenario (RPi_Exp_imp) with its exceedence frequency (RPi_ef) -> RPi_Exp_EAI

        • Sum all RPi_exp_EAI columns for each ADMi -> table [ADMi;Exp_EAI]

          - +
          - - - - - + + + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + +

          RP

          Frequency

          Exc. frequency

          Impact

          EAI

          RP

          Frequency

          Exc. frequency

          Impact

          EAI

          10

          0.100

          0.09

          193

          17

          10

          0.100

          0.09

          193

          17

          100

          0.010

          0.009

          1,210

          11

          100

          0.010

          0.009

          1,210

          11

          1000

          0.001

          0.001

          3,034

          3

          1000

          0.001

          0.001

          3,034

          3

          Total

          31

          Total

          31

          @@ -658,7 +934,7 @@

          Calculate Expected Annual Impact -

          Present results#

          +

          Present results#

          • Plot Exceedance Frequency Curve. Example considering ten RP scenarios:
            immagine

          • @@ -697,56 +973,132 @@

            Present results - - +

+ + + + + + +
+ + + + + +
-
- +
+ + + +
+ + + - +
+ + + - - + + \ No newline at end of file diff --git a/docs/climate-indices.html b/docs/climate-indices.html index cd115d6a..542b87ef 100644 --- a/docs/climate-indices.html +++ b/docs/climate-indices.html @@ -1,49 +1,57 @@ - + - + Climate indices — Climate & Disaster Risk Screening Tools - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + + - + - + + - - - - - - + + - - - - - - - - -
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Climate indices#

+

Climate indices#

The climate component offers an overview of climate indices related to hydro-meteorological hazards based on the most updated information (CMIP6). The challenge is to offer a tool that convey the complexity of climate models into statistics that are easily interpretable by non-climate experts, providign a well-rounded perspective for both space and time dimension. This is reflected on the technical side, by the challenge to reduce huge datasets into manageable pieces.

The climate component provides aggregated statistics at boundary level (country or subnational level) for a selection of:

-
    +
    1. climate-related hazards

    2. country

    3. time periods

    The table summarises the relevant climate indices and related time scale.

    - +
    - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + +

    Name

    Description

    Time-scale

    Name

    Description

    Time-scale

    R10mm

    Days with rainfall > 10 mm [days]

    Annual

    R10mm

    Days with rainfall > 10 mm [days]

    Annual

    Rx5day

    Maximum 5-day precipitation [mm]

    Monthly

    Rx5day

    Maximum 5-day precipitation [mm]

    Monthly

    R99p

    Extremely wet day precipitation [days]

    Monthly

    R99p

    Extremely wet day precipitation [days]

    Monthly

    CWD

    Consecutive Wet Days [days/month]

    Annual

    CWD

    Consecutive Wet Days [days/month]

    Annual

    CDD

    Consecutive Dry Days [days/month]

    Annual

    CDD

    Consecutive Dry Days [days/month]

    Annual

    slr

    Sea Level Rise [m]

    Annual

    slr

    Sea Level Rise [m]

    Annual

    SPEI

    Standard Precipitation-EvapoT Index [-]

    Annual

    SPEI

    Standard Precipitation-EvapoT Index [-]

    Annual

    Heat

    WBGT or UTCI [°C] - bias adjusted

    Daily

    Heat

    WBGT or UTCI [°C] - bias adjusted

    Daily

    tmean

    Mean surface temperature [°C]

    Monthly

    tmean

    Mean surface temperature [°C]

    Monthly

    @@ -623,7 +899,7 @@

    Climate indices -

    Input data#

    +

    Input data#

+ + + + + + +
+ + + +
-
- +
+ + + +
+ + + - +
+ + + - - + + \ No newline at end of file diff --git a/docs/external-data.html b/docs/external-data.html index ca2118b3..d9aacbcf 100644 --- a/docs/external-data.html +++ b/docs/external-data.html @@ -1,49 +1,57 @@ - + - + Additional data sources — Climate & Disaster Risk Screening Tools - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + + - + - + + - - - - - - + + - - - - - - - - -
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Additional data sources#

+

Additional data sources#

Additional sources of risk data (hazard, exposure, disaster losses).

- + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + @@ -533,56 +854,84 @@

Additional data sources -

- -
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External tools#

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External tools#

Additional tools to help collect, view and process risk data.

- +
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Name

License

Description

Purpose

Name

License

Description

Purpose

QGIS

GNU

GIS software

Open, plot, manipulate geodata

QGIS

GNU

GIS software

Open, plot, manipulate geodata

@@ -516,56 +837,84 @@

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+ + + - - + + \ No newline at end of file diff --git a/docs/global-exposure.html b/docs/global-exposure.html index 6bbc3f19..63a87939 100644 --- a/docs/global-exposure.html +++ b/docs/global-exposure.html @@ -1,49 +1,57 @@ - + - + Exposure datasets — Climate & Disaster Risk Screening Tools - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + + - + - + + - - - - - - + + - - - - - - - - -
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Exposure datasets#

+

Exposure datasets#

Exposure datasets refer to a variety of datasets that can be used to represent the value that is exposed to suffer damage and losses from natural hazards. This section presents some of the most common and recent type of data and indicators used for this purpose.

@@ -544,38 +832,38 @@

Exposure datasetsRisk Data Library Collection: EXPOSURE.

Below is a quick link to openly-licensed datasets that are used by the CCDR standard screening tool.

- +
- - - - - + + + + + - - - - - + + + + + - - - - - + + + + + - - - - - + + + + +

Name

Developer

Metric

Resolution

Update frequency

Name

Developer

Metric

Resolution

Update frequency

Global Human Settlement Layer

EU-JRC

Population count

100 m

Annual

Global Human Settlement Layer

EU-JRC

Population count

100 m

Annual

World Settlement Footprints

DLR

Presence of built-up

10 m

Annual

World Settlement Footprints

DLR

Presence of built-up

10 m

Annual

WorldCover

ESA

Land cover classes

10 m

Annual

WorldCover

ESA

Land cover classes

10 m

Annual

-

EU-JRC Global Human Settlement Layer#

+

EU-JRC Global Human Settlement Layer#

The Global Human Settlement population model [GHS-POP, Schiavina et al. 2022] offers 100 meters global population data projected over built-up land cover. It is similar to WorldPop in terms of approach, but the reliability is generally better, and the errors are contained. Note that high-resolution population mapping relies on census projections distributed in proportion to built-up density obtained from remote sensing data. This can induce model errors, particularly in mountainous and forest environments, resulting in an overestimation of natural hazard risk in those areas. The dataset is downloaded as regular tiles from the global map.

@@ -586,7 +874,7 @@

EU-JRC Global Human Settlement Layer -

DLR World Settlment Footprint#

+

DLR World Settlment Footprint#

Built-up assets include houses, commercial and industrial buildings, infrastructures, facilities, and others. Data from 2019 World Settlement Footprint (WSF) is used for this current analysis. This is a high-resolution (10m) remotely sensed dataset which indicates whether each cell is primarily built up, excluding roads.

../_images/DLR_WSF.jpg @@ -597,7 +885,7 @@

DLR World Settlment Footprint

Note

How to create the WSF19 layer and resample it to 100 m for faster analysis.

-
    +
    1. Download country tiles from WSF download page

    2. Load tiles in QGIS and build a virtual raster made of all tiles.

    3. Use GDAL Warp (reproject) on the vrt, set the resolution to original resolution x 10 (e.g. 8.983e-05 becomes 8.983e-04). Resampling method: “mean”. Advanced parameters > creation profile: “deflate”. This will return a resampled layer (c.a. 100m).

    4. @@ -606,7 +894,7 @@

      DLR World Settlment Footprint -

      ESA World Cover#

      +

      ESA World Cover#

      The 2020 WorldCover dataset at 10m resolution from the European Space Agency can be used to identify specific types, e.g. agricultural land.

      ../_images/ESA_WC.jpg @@ -616,29 +904,29 @@

      ESA World Cover -

      Additional datasets#

      +

      Additional datasets#

      For a more granular analysis of agricultural impacts, additional exposure datasets can be considered.

      - +
      - - - - - + + + + + - - - - - + + + + + - - - - - + + + + +

      Name

      Developer

      Metric

      Resolution

      Last update

      Name

      Developer

      Metric

      Resolution

      Last update

      Global Agro-Ecological Zones (GAEZ)

      FAO

      The GAEZ v4 spatial data cover six themes: (1) Land and Water Resources, (2) Agro-climatic Resources, (3) Agro-climatic Potential Yield, (4) Suitability and Attainable Yield, (5) Actual Yields and Production, and (6) Yield and Production Gaps

      1 km

      2010

      Global Agro-Ecological Zones (GAEZ)

      FAO

      The GAEZ v4 spatial data cover six themes: (1) Land and Water Resources, (2) Agro-climatic Resources, (3) Agro-climatic Potential Yield, (4) Suitability and Attainable Yield, (5) Actual Yields and Production, and (6) Yield and Production Gaps

      1 km

      2010

      Gridded Livestock of the World (GLW)

      FAO

      Global distributions of cattle, buffaloes, sheep, goats, horses, pigs, chickens and ducks

      10 km

      2015

      Gridded Livestock of the World (GLW)

      FAO

      Global distributions of cattle, buffaloes, sheep, goats, horses, pigs, chickens and ducks

      10 km

      2015

      @@ -665,56 +953,120 @@

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+ + + + + -
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Hazard datasets#

Hazard datasets refer to a variety of datasets that can be used to represent the value that is exposed to suffer damage and losses from natural hazards. This section presents some of the most common and recent type of data and indicators used for this purpose. Please note that CCDR focus only on hydro-meteorological hazards, as geopyhisical hazards occurrence and intensity are not affected by climate change.

See also

Hazard datasets developed by WB disaster risk projects have been placed in a special collection of the WB Development Data Hub: Risk Data Library Collection: HAZARD.

The most relevant datasets (updated, high resolution, scientific quality) representing extreme events and long-term hazards that were considered for inclusion in the CCDR and other risk-related activities across the Bank have been listed below for each hazard, explaining their pros and cons and providing suggestions for improvement. Most datasets only cover the historical period (baseline); while a few of the hydro-met hazards also offer some hazard projections.

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GEOPHYSICAL

HYDRO-METEOROLOGICAL

ENVIRONMENTAL FACTORS

GEOPHYSICAL

HYDRO-METEOROLOGICAL

ENVIRONMENTAL FACTORS

Earthquake

River floods

Air pollution

Earthquake

River floods

Air pollution

Tsunami

Landslides

Tsunami

Landslides

Volcanic activity

Coastal flood

Volcanic activity

Coastal flood

Tropical cyclones

Tropical cyclones

Drought

Drought

Heat stress

Heat stress

Wildfires

Wildfires

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Vulnerability models#

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Vulnerability models#

Vulnerability datasets refer to a variety of data that can be used to represent the value that is exposed to suffer damage and losses from natural hazards. Vulnerability data are usually the most difficult piece to collect: not all hazards are described appropriately by existing vulnerability studies; and many V models are propietary (e.g. part of complex catastrophy disaster models) instead of open data, and as such, they are often left unshared within a project output. This section presents some of the most common and recent models and datasets available.

Below is a quick link to openly-available vulnerability models and data that are used by the CCDR standard screening tool.

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Source

Hazard type

Exposure category

Model type

Scale

Name

Source

Hazard type

Exposure category

Model type

Scale

Global flood damage functions

EU-JRC

Floods

Built-up

Damage function

Regional

Global flood damage functions

EU-JRC

Floods

Built-up

Damage function

Regional

Global flood mortality function

Jonkman et al. 2018

Floods

Population

Mortality function

Global

Global flood mortality function

Jonkman et al. 2018

Floods

Population

Mortality function

Global

Regional tropical cyclone impact functions

Eberenz et al. 2021

Tropical cyclone (wind)

Built-up

Damage function

Regional

Regional tropical cyclone impact functions

Eberenz et al. 2021

Tropical cyclone (wind)

Built-up

Damage function

Regional

Heat stress index classification

Zare et al. 2019

Heat stress

Population

Impact classification

Global

Heat stress index classification

Zare et al. 2019

Heat stress

Population

Impact classification

Global

Global mortality rates from air pollution

Burnett et al. 2018

Air pollution [PM2.5]

Population

Mortality rate

Global

Global mortality rates from air pollution

Burnett et al. 2018

Air pollution [PM2.5]

Population

Mortality rate

Global

Relative Wealth Index

META

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Population

Socio-economic

National

Relative Wealth Index

META

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Population

Socio-economic

National

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Flood impact functions#

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Flood impact functions#

  • Damage over built-up: vulnerability functions translating water depth into relative damage (depth-damage curves) have been developed by JRC [Huizinga et al. 2017] based on survey damage data from national studies. The resulting models are calibrated by region (continent) and offer maximum damage values for a variety of exposure types: residential buildings, industrial buildings, commercial buildings, transport, roads and agriculture. This dataset can be used for consistent supra-national scale flood damage assessments, and guide assessment in countries where no damage model is currently available.

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    Flood impact functions

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Wind impact functions#

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Wind impact functions#

  • Damage over built-up: wind intensity is translated into damage over buildings (general) using a mathematical relationship [Emanuel 2011].

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    Wind impact functions

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Heat stress classification#

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Heat stress classification#

The following table from literature [Zare et al. 2019] provides classification of heat stress over population health according to two common indices, WBGT °C and UTCI °C.

../_images/hzd_hs_class.png @@ -677,7 +953,7 @@

Heat stress classificationMorabito et al 2021].

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Air pollution impact classification#

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Air pollution impact classification#

Life expectancy can be greatly impacted by air pollution, as long-term exposure enhances the risk of cardiovascular and respiratory diseases [Lelieveld et al. 2020]. There are a few studies proposing mortality impact functions from particulate matter. Burnett et al. [2019] proposes a general relationship between PM2.5 concentration and health hazard ratio, as an aggregation of individual health risks.

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Additional datasets#

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Additional datasets#

Additional sources of impact models are under review.

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Environmental factors#

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Environmental factors#

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Surface PM2.5

Name

Surface PM2.5

Developer

ACAG

Developer

ACAG

Hazard process

Air pollution

Hazard process

Air pollution

Resolution

1.1 km

Resolution

1.1 km

Analysis type

Deterministic

Analysis type

Deterministic

Frequency type

Annual mean

Frequency type

Annual mean

Time reference

Baseline (1998-2020)

Time reference

Baseline (1998-2020)

Intensity metric

PM2.5 [ug/m3]

Intensity metric

PM2.5 [ug/m3]

License

Opend ata

License

Opend ata

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Air pollution#

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Air pollution#

Air pollution can be measured by the mean annual surface-level concentrations of PM2.5, which is available from 1998 to 2020 [van Donkelaar et al, 2021] at 1.1km spatial resolution. This dataset combines data from different sources, including NASA MODIS, MISR, and SeaWIFS observations with the GEOS-Chem chemical transport model into a high-resolution map of air pollution. The dataset combines both human-induced PM2.5 emissions, emitted, for instance, by car engines, power plants, as well as fireplaces and biomass burning [NCRD 2014] and natural sources of PM2.5, which include forest fires and desert dust [McDuffie et al. 2021]. These fine particles, smaller than 2.5 micrometers in diameter, pose enormous health risks as they can lodge deeply into the lungs [WHO 2019].

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Geophysical hazards#

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Geophysical hazards#

Geophysical hazards refer to natural hazards that originate from processes occurring within the Earth’s interior or its immediate surroundings. These processes can include seismic activity (earthquakes), volcanic eruptions, and tsunamis. Geophysical hazards often have a sudden onset, meaning they can occur with little to no warning for preparedness or evacuation and cause widespread impacts, affecting large areas far from the source of the hazard. Geophysical hazards are not influenced by climatic conditions, but on the other side they can affect climate.

The National Oceanic and Atmospheric Administration (NOAA) offers a global map [Natural Hazard Viewer] monitoring geophisical hazards (Earthquakes, tsunamis, volcanic activity).

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Geophysical hazards
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Earthquake#

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Earthquake#

Earthquakes can strike any location at any time, but history shows they occur in the same general patterns year after year, principally in three large zones of the earth:

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    1. The world’s greatest earthquake belt, the circum-Pacific seismic belt (“Ring of Fire”), is found along the rim of the Pacific Ocean, where about 81% of global earthquakes occur. Earthquakes in these subduction zones are caused by slip between plates and rupture within plates. Earthquakes in the curcum-Pacific seismic belt include the M9.5 Bio-Bio earthquake in Chile (1960) and the M9.2 Great Alaska Earthquake (1964).

    2. The Alpide earthquake belt extends from Java to Sumatra through the Himalayas, the Mediterranean, and out into the Atlantic. This belt accounts for about 17% of the world’s largest earthquakes, including some of the most destructive, such as the 2005 M7.6 shock in Pakistan that killed over 80,000 and the 2004 M9.1 Indonesia earthquake, which generated a tsunami that killed over 230,000 people.

    3. The third prominent belt follows the mid-Atlantic Ridge running deep underwater and far from human development, with the exception of Iceland, which sits over the ridge.

    4. @@ -551,8 +844,8 @@

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      Tsunami#

      -

      The Global Tsunami Model (GTM) has been created by a group of tsunami experts around the world to provide coordinate action for tsunami hazard and risk assessment. It offers a global probabilistic tsunami hazard assessment from earthquake sources Davies et al. 2018.

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      Tsunami#

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      The Global Tsunami Model (GTM) has been created by a group of tsunami experts around the world to provide coordinate action for tsunami hazard and risk assessment. It offers a global probabilistic tsunami hazard assessment from earthquake sources Davies et al. 2018.

      ../_images/hzd_gtm.jpg
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      Tsunami -

      Volcanic activity#

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      Volcanic activity#

      Volcanic eruptions occur most frequently at plate boundaries, but some volcanoes (e.g. Hawaiian Islands) occur in the interior of plates, at areas called hot spots. The greatest number of the Earth’s volcanoes are hidden from view, occurring on the ocean floor along spreading ridges. Volcanic hazards can include various phenomena, such as lava flows, pyroclastic flows, ashfall, volcanic gases, lahars (mudflows), and volcanic landslides. In disaster risk management, understanding the types of volcanoes and volcanic eruptions is crucial for assessing the associated hazard and risk levels.

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Hydro-Meteorological hazards#

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Hydro-Meteorological hazards#

Process or phenomenon of atmospheric, hydrological or oceanographic nature that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage (UNISDR).


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River floods#

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River floods#

Flood hazard is commonly described in terms of flood frequency (multiple scenarios) and severity, which is measured in terms of water extent and related depth modelled over Digital Elevation Model (DEM). Inland flood events can be split into 2 categories:

  • Fluvial (or river) floods occur when intense precipitation or snow melt collects in a catchment, causing river(s) to exceed capacity, triggering the overflow, or breaching of barriers and causing the submersion of land, especially along the floodplains.

  • Pluvial (or surface water) floods are a consequence of heavy rainfall, but unrelated to the presence of water bodies. Fast accumulation of rainfall is due to reduced soil absorbing capacity or due to the saturation of the drainage infrastructures; meaning that the same event intensity can trigger very different risk outcomes depending on those parameters. For this reason, static hazard maps based on rainfall and DEM alone should be used with extreme caution.

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Name

Fathom flood hazard maps

Aqueduct flood hazard maps

Name

Fathom flood hazard maps

Aqueduct flood hazard maps

Developer

Fathom

WRI

Developer

Fathom

WRI

Hazard process

Fluvial flood, Pluvial flood

Fluvial flood

Hazard process

Fluvial flood, Pluvial flood

Fluvial flood

Resolution

90 m

900 m

Resolution

90 m

900 m

Analysis type

Probabilistic

Probabilistic

Analysis type

Probabilistic

Probabilistic

Frequency type

Return Period (11 scenarios)

Return Period (10 scenarios)

Frequency type

Return Period (11 scenarios)

Return Period (10 scenarios)

Time reference

Baseline (1989-2018)

Baseline (1960-1999); Projections – CMIP5 (2030-2050-2080)

Time reference

Baseline (1989-2018)

Baseline (1960-1999); Projections – CMIP5 (2030-2050-2080)

Intensity metric

Water depth [m]

Water depth [m]

Intensity metric

Water depth [m]

Water depth [m]

License

Commercial

Open data

License

Commercial

Open data

Other

Includes defended/undefended option

Other

Includes defended/undefended option

Notes

Standard for WB analysis

The only open flood dataset addressing future hazard scenarios

Notes

Standard for WB analysis

The only open flood dataset addressing future hazard scenarios

@@ -667,99 +933,99 @@

River floods -

Coastal floods (storm surge)#

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Coastal floods (storm surge)#

Coastal floods occur when the level in a water body (sea, estuary) rises to engulf otherwise dry land. This happens mainly due to storm surges, triggered by tropical cyclones and/or strong winds pushing surface water inland. Like for inland floods, hazard intensity is measured using the water extent and associated depth.

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Name

Aqueduct flood hazard maps

Global Flood map

Name

Aqueduct flood hazard maps

Global Flood map

Developer

WRI-Deltares

Deltares

Developer

WRI-Deltares

Deltares

Hazard process

Coastal flood

Coastal flood, SLR

Hazard process

Coastal flood

Coastal flood, SLR

Resolution

1 km

90 m, 1 km, 5 km

Resolution

1 km

90 m, 1 km, 5 km

Analysis type

Probabilistic

Analysis type

Probabilistic

Frequency type

Return Period (10 scenarios)

Return Period (6 scenarios)

Frequency type

Return Period (10 scenarios)

Return Period (6 scenarios)

Time reference

Baseline (1960–1999);
Projections – CMIP5 (2030-2050-2080)

Baseline (2018);
Projections – SLR (2050)

Time reference

Baseline (1960–1999);
Projections – CMIP5 (2030-2050-2080)

Baseline (2018);
Projections – SLR (2050)

Intensity metric

Water depth [m]

Water depth [m]

Intensity metric

Water depth [m]

Water depth [m]

License

Open data

Access requested

License

Open data

Access requested

Notes

Includes effect of local subsidence (2 datasets) and flood attenuation. Modelled future scenarios.

Essentially an evolution of the WRI

Notes

Includes effect of local subsidence (2 datasets) and flood attenuation. Modelled future scenarios.

Essentially an evolution of the WRI

The current availability of global dataset is poor, with WRI products (recently updated by Deltares) representing the best option in terms of resolution and time coverage (baseline + scenarios), and water routing, including inundation attenuation to generate more realistic flood extent. The latest version has a much better resolution of 90 m based on MeritDEM or NASADEM, overcoming WRI limitations for local-scale assessment. Note that the Fathom is working to include coastal floods and climate scenarios in the next version (3) of the dataset (coming sometime in 2023/24), which will likely become the best option for risk assessment in the next future.

Additional datasets that have been previously used in WB coastal flood analytics are:

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Name

Coastal flood hazard maps

Coastal risk screening

Name

Coastal flood hazard maps

Coastal risk screening

Developer

Muis et al. (2016, 2020)

Climate Central

Developer

Muis et al. (2016, 2020)

Climate Central

Hazard process

Coastal flood

Mean sea level

Hazard process

Coastal flood

Mean sea level

Resolution

1 km

Resolution

1 km

Analysis type

Probabilistic

Analysis type

Probabilistic

Frequency type

Return Period (10 scenarios)

One layer per period

Frequency type

Return Period (10 scenarios)

One layer per period

Time reference

Baseline (1979–2014)

Baseline; Projections

Time reference

Baseline (1979–2014)

Baseline; Projections

Intensity metric

Water depth [m]

Water extent

Intensity metric

Water depth [m]

Water extent

License

Open data

Licensed

License

Open data

Licensed

Notes

The update of Muis 2020 has been considered; however, the available data does include easily applicable land inundation, only extreme sea levels.

Does use simple bathtub distribution without flood attenuation – does not simulate extreme sea events.

Notes

The update of Muis 2020 has been considered; however, the available data does include easily applicable land inundation, only extreme sea levels.

Does use simple bathtub distribution without flood attenuation – does not simulate extreme sea events.

@@ -773,62 +1039,62 @@

Coastal floods (storm surge) -

Sea level rise#

+

Sea level rise#

Most of the listed models include flood hazard simulations that account for the effect of Sea Level Rise under climate change projections: RWI uses CMIP5 climate data, while Deltares and ClimateCentral dataset is based on CMIP6. In addition to increasing water volumes, sea level projections account for land movements (sinking or rising land) caused by tectonic activity, large-scale underground extraction, or glacial isostatic adjustment.

In additon to coastal flood projections, the NASA Sea Level Projection Tool allows users to visualize and download the sea level projection data from the IPCC 6th Assessment Report (AR6). The tool shows both global and regional sea level projections from 2020 to 2150, along with how these projections differ depending on future scenario or warming level. Data can be downloaded in multiple formats.


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Landslides#

+

Landslides#

Landslides (mass movements) are affected by geological features (rock type and structure) and geomorphological setting (slope gradient). Landslides can be split into two categories depending on their trigger:

  • Dry mass movement (rockfalls, debris flows) is driven by gravity and can be triggered by seismic events, but they can also be a consequence of soil erosion and environmental degradation.

  • Wet mass movement can be triggered by heavy precipitation and flooding and are strongly affected by geological features (e.g. soil type and structure) and geomorphological settings (e.g., slope gradient). They do not typically include avalanches.

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Name

Global landslide hazard layer

Global landslide susceptibility
(LHASA)

Name

Global landslide hazard layer

Global landslide susceptibility
(LHASA)

Developer

ARUP

NASA

Developer

ARUP

NASA

Hazard process

Dry (seismic) mass movement
Wet (rainfall) mass movement

none

Hazard process

Dry (seismic) mass movement
Wet (rainfall) mass movement

none

Resolution

1 km

1 km

Resolution

1 km

1 km

Analysis type

Deterministic

Deterministic

Analysis type

Deterministic

Deterministic

Frequency type

none

none

Frequency type

none

none

Time reference

Baseline (rainfall trigger) (1980-2018)

Time reference

Baseline (rainfall trigger) (1980-2018)

Intensity metric

Hazard frequency [-]

Susceptibility index [-]

Intensity metric

Hazard frequency [-]

Susceptibility index [-]

License

Open

License

Open

Notes

Based on NASA landslide susceptibility layer. Median and Mean layers provided.

Although not a hazard layer, it can be accounted for in addition to the ARUP layer.

Notes

Based on NASA landslide susceptibility layer. Median and Mean layers provided.

Although not a hazard layer, it can be accounted for in addition to the ARUP layer.

@@ -842,56 +1108,56 @@

Landslides -

Tropical cyclones#

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Tropical cyclones#

Tropical cyclones (including hurricanes, typhoons) are events that can trigger different hazard processes at once such as strong winds, intense rainfall, extreme waves, and storm surges. In this category, we consider only the wind component of cyclone hazard, while other components (floods, storm surge) are typically considered separately.

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GAR15-IBTrACS

IBTrACSv4

STORMv3

Name

GAR15-IBTrACS

IBTrACSv4

STORMv3

Developer

NOAA

NOAA

IVM

Developer

NOAA

NOAA

IVM

Hazard process

Strong winds

Strong winds

Strong winds

Hazard process

Strong winds

Strong winds

Strong winds

Resolution

30 km

10 km

10 km

Resolution

30 km

10 km

10 km

Analysis type

Probabilistic

Empirical

Empirical, Probabilistic

Analysis type

Probabilistic

Empirical

Empirical, Probabilistic

Frequency type

Return Period (5 scenarios)

Return periods (10 to 10,000 years)

Frequency type

Return Period (5 scenarios)

Return periods (10 to 10,000 years)

Time reference

Baseline (1989-2007)

Baseline (1980-2022)

Baseline (1984-2022);
Projections (2015-2050; SSP5/8.5)

Time reference

Baseline (1989-2007)

Baseline (1980-2022)

Baseline (1984-2022);
Projections (2015-2050; SSP5/8.5)

Intensity metric

Wind gust speed [5-sec m/s]

Many variables

Many variables

Intensity metric

Wind gust speed [5-sec m/s]

Many variables

Many variables

License

Open data

Open data

Open data

License

Open data

Open data

Open data

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Drought & Water scarcity#

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Drought & Water scarcity#

The Agricultural Stress Index (ASI) produced by FAO depicts the frequency of severe drought affecting crop areas by means of remote-sensed Vegetation Health Index (VHI). FAO provides annual drought frequency data split as the main crop season (S1) and secondary crop season (S2). For each season there are two indicators, according to two exposure intensity thresholds:

    @@ -931,64 +1197,64 @@

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    Heat stress#

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    Heat stress#

    Heat discomfort increases when hot temperatures are associated with high humidity [Coffel et al 2018]. Heat stress can cause long-term impairment and reduce labour productivity and incomes [Goodman et al 2018]. Extreme heat events lead to heat stress and can increase morbidity and mortality as well as losses of work productivity [Kjellstrom et al 2009, Singh et al 2015]. Not everyone reacts to the heat stresses in the same way, as individual responses are conditional on their medical condition, level of fitness, body weight, age, and economic situation [National Institute for Occupational Safety and Health 2016].

    Various definitions regarding magnitude and duration thresholds and heat metrics exist. There are several heat indices involving both temperature and relative humidity, here are listed the most common ones.

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    Global extreme temperatures (WBGT)

    Universal Thermal Climate Index (UTCI)

    Heat-humidity index

    Name

    Global extreme temperatures (WBGT)

    Universal Thermal Climate Index (UTCI)

    Heat-humidity index

    Developer

    VITO

    Copernicus

    CORDEX

    Developer

    VITO

    Copernicus

    CORDEX

    Hazard process

    Extreme heat stress

    Heat stress on human health

    Extreme heat and humidity

    Hazard process

    Extreme heat stress

    Heat stress on human health

    Extreme heat and humidity

    Resolution

    10 km

    30 km

    25 km

    Resolution

    10 km

    30 km

    25 km

    Analysis type

    Probabilistic

    Index

    Probabilistic

    Analysis type

    Probabilistic

    Index

    Probabilistic

    Frequency type

    Return Period (3 scenarios)

    None

    Frequency type

    Return Period (3 scenarios)

    None

    Time reference

    Baseline (1980-2009)

    Baseline (1979-2020)

    Baseline (1970-2000); Projections (2040-2070, 2070-2100)

    Time reference

    Baseline (1980-2009)

    Baseline (1979-2020)

    Baseline (1970-2000); Projections (2040-2070, 2070-2100)

    Intensity metric

    Wet Bulb Globe Temperature [°C]

    UTCI (°C)

    Heat Index, Humidex

    Intensity metric

    Wet Bulb Globe Temperature [°C]

    UTCI (°C)

    Heat Index, Humidex

    License

    Open data

    Open data

    Open data

    License

    Open data

    Open data

    Open data

    Notes

    Accounts for air temperature, humidity, wind speed, radiation, fatigue-heating. Includes intensity-impact classification.

    Accounts for air temperature, humidity. Includes intensity-impact classification.

    Notes

    Accounts for air temperature, humidity, wind speed, radiation, fatigue-heating. Includes intensity-impact classification.

    Accounts for air temperature, humidity. Includes intensity-impact classification.

    @@ -1006,7 +1272,7 @@

    Heat stress

    See also

    -

    UTCI data from ERA-5 climate reanalysis has been processed into a probabilistic analysis of extremes from the Copernicus CDS. +

    UTCI data from ERA-5 climate reanalysis has been processed into a probabilistic analysis of extremes from the Copernicus CDS. A collection of scenarios representing the frequency distribution of heat has been produced in form of multiple layers representing return periods.
    The objective is to facilitate the use of these data for heat risk analysis. The scenarios include ten return periods for mean, min and max daily UTCI (C°) for the period 1940-2020. Return Period scenarios: 5, 10, 20, 50, 75, 100, 200, 250, 500 and 1000 years.
    NOT RELEASED YET.

@@ -1014,11 +1280,11 @@

Heat stress ../_images/hzd_hs_class.png -

In terms of future projections, both UTCI and WBGT projections have been produced under CMIP6 scenarios and are available via Copernicus CDS. The indices are provided for historical and future climate projections (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) included in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and used in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). These have daily resolution and would allow to derive downscaled extreme temperature projections. These projections have not yet been processed into a frequency analysis, but that can be produced using the same approach.

+

In terms of future projections, both UTCI and WBGT projections have been produced under CMIP6 scenarios and are available via Copernicus CDS. The indices are provided for historical and future climate projections (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) included in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and used in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). These have daily resolution and would allow to derive downscaled extreme temperature projections. These projections have not yet been processed into a frequency analysis, but that can be produced using the same approach.


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Wildfires#

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Wildfires#

Content under development.

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Wildfires - - + + + + + + -

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Exposure#

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Exposure#

The concept of exposure relates to the receptors of risk, which are the physical, human and production capitals that could be loss due to natural hazards.

See also

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Natural hazards#

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Natural hazards#

Natural events (including extreme events and long term phenomena) are only termed hazards when they have the potential to harm people or cause property damage, social and economic disruption. The location of natural hazards primarily depends on natural processes, including the movement of tectonic plates, the influence of weather systems, and the morphology of the terrain (slopes, drainage, vegetation, …). Anthropic processes such as urbanization, environmental degradation and climate change can also influence the location, occurrence frequency and intensity of natural hazards. These processes are known as risk drivers.

Some of the hazards typically considered in risk screening are:

    @@ -541,7 +844,7 @@

    Natural hazards -

    Intensity and frequency#

    +

    Intensity and frequency#

    Hazard intensity is one of the key factors that determine the size of the impact over exposed elements.

    ../_images/hzd_intensity.jpg @@ -562,14 +865,14 @@

    Intensity and frequency ../_images/hzd_models.jpg

    -

    A probabilistic hazard representation is required in order to produce probabilistic risk mapping.

    +

    A probabilistic hazard representation is required in order to produce probabilistic risk mapping.

    Note

    Note that a return period of 1,000 years, while very unlikely, can occur anyday! Therefore it is important to consider all range of probabilities when assessing risk.

    RP 1,000 = change of occurring once every 1,000 years = 1/1,000 annual occurrence probability (0.001 or 0.1% any given year).

    Hazard models carry limitations related to their applicability. Their quality depend on scale, resolution, model quality, training period and related input data quality. As a rule of thumb, their fitness for application in the context of a risk screening or assessment exercise depends on the scale of the risk analysis, i.e. locally-sourced models are expeceted to be best fitted for local scale assessment (e.g. city level), while global models are best suited for national or sub-national estimates.

    -

    In the context of developing countries, however, a global model is often the only available source for a location. In those cases, the application of the global model must be taken with caution and correctly interpreted acknowledging the limitations. See uncertainty.

    +

    In the context of developing countries, however, a global model is often the only available source for a location. In those cases, the application of the global model must be taken with caution and correctly interpreted acknowledging the limitations. See uncertainty.

    Caution

    When it comes to natural hazards and risk, misinformation can be worse than no information at all.

    @@ -597,56 +900,105 @@

    Intensity and frequency -

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Impact and risk#

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Impact and risk#

In this framework, Risk (R) is calculated as a function of: the hazard occurrence probability and intensity (i.e., physical magnitude) in a particular location (H, for Hazard); the people and physical assets (infrastructure, buildings, crops, etc.) situated in that location and therefore exposed to the hazard (E, for Exposure); and the conditions determined by physical, social, and economic factors which increase the susceptibility of an exposed individual, community, asset or system to the impacts of hazards (V, for Vulnerability).

Risk = Hazard x Exposure x Vulnerability

@@ -580,7 +856,7 @@

Impact and risk -

Deterministic impact and probabilistic risk#

+

Deterministic impact and probabilistic risk#

While historical losses can explain the past, they do not necessarily provide a good guide to the future; most disasters that could happen have not happened yet. Probabilistic risk assessment simulates those future disasters which, based on scientific evidence, are likely to occur. As a result, these risk assessments resolve the problem posed by the limits of historical data. Probabilistic models therefore complement historical records by reproducing the physics and/or the statistics/statistical distribution of the phenomena and recreating the intensity of a large number of synthetic events.

Note

@@ -591,14 +867,14 @@

Deterministic impact and probabilistic risk -

Annual risk baseline#

-

Baseline refers to the historical period to which the data refer, as opposed to risk projections.

+

Annual risk baseline#

+

Baseline refers to the historical period to which the data refer, as opposed to risk projections.

  • The EAI is calculated by multiplying the impact from each scenario with its exceedance probability, and then summing up to obtain the mean annual risk considering the whole range of hazard occurrence probabilities. The exceedance frequency curve highlights the relationship between the return period of each hazard and the estimated impact: the area below the curve represents the total annual damage considering all individual scenario probabilities.

  • In lack of a proper vulnerability function, the EAE is calculated by multiplying the exposure to each hazard scenario with its exceedance probability, and then summing up to obtain the mean annual risk considering the whole range of hazard occurrence probabilities for a range of hazard thresholds. Risk (EAE) is then expressed as annual exposure to hazard over a certain threshold.

-

Lower and Upper bounds#

+

Lower and Upper bounds#

Originally, the calculation of EAI was performed using the customary approach, as exemplified here and here.

Due to requests from regional teams, a refined calculation of the integral for probabilistic EAI and EAE was included:

-

Direct and indirect losses#

+

Direct and indirect losses#

Direct disaster losses refer to directly quantifiable losses such as the number of people killed and the damage to buildings, infrastructure and natural resources.
Indirect disaster losses include declines in output or revenue, and impact on wellbeing of people, and generally arise from disruptions to the flow of goods and services as a result of a disaster [GFDRR 2014; UNDRR].

See also

@@ -624,7 +900,7 @@

Direct and indirect losses -

Supported Hazard and exposure combinations#

+

Supported Hazard and exposure combinations#

The following matrix show the combinations of hazard and exposure for which a vulnerability model is provided, and the type of model, allowing to express the risk either in form of impact (damage) or exposure to hazard classes.

../_images/rsk_combo.png @@ -634,7 +910,7 @@

Supported Hazard and exposure combinations -

Climate change and disaster risk#

+

Climate change and disaster risk#

Climate change can increase disaster risk in a variety of ways:

  • by altering the frequency, intensity and geographic distribution of weather-related hazards, which may lead to new patterns of risk.

  • @@ -672,56 +948,132 @@

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Vulnerability#

The vulnerability of people and asset which are exposed to hazard events determines how likely they are to suffer an impact.

See also

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Vulnerability -

Vulnerability data and models#

+

Vulnerability data and models#

Two main components of vulnerability are typically accounted:

  • Impact models: draw the relationship between the intensity of hazard and the predisposition of damage suffered by specific exposed categories into actual impact. They are called fragility functions when they describe a physical damage (e.g. flood depth/damage functions). Impact models can be quantitative, providing an absolute (i.e. USD) or relative estimate (i.e. ratio of total value) of the damage; or qualitative, classifying the impact in nominal categories.

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    Vulnerability data and models - - +

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Mapping & Geocoding#

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Mapping & Geocoding#

https://melaniewalsh.github.io/Intro-Cultural-Analytics/07-Mapping/01-Mapping.html

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Making Interactive Maps#

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Making Interactive Maps#

To map our geocoded coordinates, we’re going to use the Python library Folium. Folium is built on top of the popular JavaScript library Leaflet.

To install and import Folium, run the cells below:

@@ -594,7 +856,7 @@

Making Interactive Maps

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Base Map#

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Base Map#

First, we need to establish a base map. This is where we’ll map our geocoded Ithaca locations. To do so, we’re going to call folium.Map()and enter the general latitude/longitude coordinates of the Ithaca area at a particular zoom.

(To find latitude/longitude coordintes for a particular location, you can use Google Maps, as described here.)

@@ -609,7 +871,7 @@

Base Map -

Add a Marker#

+

Add a Marker#

Adding a marker to a map is easy with Folium! We’ll simply call folium.Marker() at a particular lat/lon, enter some text to display when the marker is clicked on, and then add it to our base map.

@@ -623,7 +885,7 @@

Add a Marker -

Choropleth Maps#

+

Choropleth Maps#

To create a chropleth map with Folium, we need to pair a “geo.json” file (which indicates which parts of the map to shade) with a CSV file (which includes the variable that we want to shade by).

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Results presentation#

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Maps#

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Maps#

The best way to depict spatial variability is through maps. For baseline risk mapping, there is no automation produced at the moment; QGIS is used to import the data and print into maps.

[quick tutorial]

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Figures#

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Figures#

  • Charts allow to give a quick snapshots of key output

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Figures -

Dashboard#

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Dashboard#

An open dashboard based on R-shiny has been developed in order to quickly disseminate results of risk screening and assessment. See the DEMO for Nigeria.

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Risk Data Library#

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Risk Data Library#

The Risk Data Library project led by GFDRR grew out of in-depth Disaster Risk Management community consultation on improving access to risk information. Its overarching purpose is to support disaster resilience work by making risk data easier and more effective to work with.

Building on GFDRR experience, extensive & intensive review of risk literature and case studies, and partnership with some of the main risk data actors (GEM, UCL EPICentre, BGS, OasisHUB and more), the RDL proposes a standard data and metadata schema to organise, format, describe, store and share risk data. @@ -499,56 +820,84 @@

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Risk and poverty#

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Risk and poverty#

The distributional impact of climate risks can be examined by overlaying risk maps (EAI or EAE) with poverty maps. The combination can be displayed using bivariate maps that show the locations where risk is most likely to translate into most severe impacts on the poorest and vulnerable households.

@@ -536,7 +829,7 @@

Risk and poverty -

Wealth indices#

+

Wealth indices#

The estimate of poverty distribution is usually done measuring either the income distribution (of the household’s dwellers), or expenditure (average monthly expenditure of the household). However, due to the difficulties in obtaining reliable income/consumption data in low- and middle-income countries, alternative ways to build the wealth index are often the best available approximation of relative socio-economic status.
The presence of physical assets in the household can be used to construct a wealth index (Filmer and Pritchett 2001). Relative wealth indices are an equally valid, but distinct measure of household socio-economic status from income and consumption measures (Poirier et al. 2019). Context-specific factors such as country development level may affect the concordance of health and educational outcomes with wealth indices and urban–rural disparities can be more pronounced using wealth indices compared to income or consumption.

@@ -544,24 +837,24 @@

Wealth indicesCaution

While the wealth quintiles are useful to understand relative wealth and equity within a country, they do not give one a sense of absolute wealth and neither comply with a predefined mathematical relationship; for instance, a tile with a wealth index value of 2 is richer than a tile with a value of 0.5, but there is no information on how much richer.
Someone deemed “rich” according to the wealth quintiles in developing countries might still have few resources for out-of-pocket expenditures.

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Name

Source

Model type

Scale

Name

Source

Model type

Scale

Relative Wealth Index

META

Wealth index

National

Relative Wealth Index

META

Wealth index

National

Demographic Health Survey

USAID

Wealth index

National

Demographic Health Survey

USAID

Wealth index

National

@@ -569,7 +862,7 @@

Wealth indices(Chi et al. 2022) are appealing due to the immediate and cost-effective estimates they can provide. The RWI is an index estimated by a machine learning model for 135 low and middle-income countries to provide micro-estimates (projections) of wealth and poverty at fine-grained 2.4 km resolution tiles. The model is trained on vast and heterogeneous datasets from satellites, mobile phone networks, topographic maps, as well as connectivity data from Facebook. The approach for creating the RWI map overcomes essential limitations of the traditional surveys, such as fine-grained coverage, and timely and cost-efficient data, while extending to countries where DHS does not operate. However, the application of RWI index to a real-world scenario would be sensitive to the socioeconomic particularities of the country, leading to significantly different estimates from the ones obtained by a traditional survey approach.

-

Index normalisation#

+

Index normalisation#

The DHS data is weighted using population density to render the survey demographically representative; while the RWI needs to be weighted in a similar fashon. The High-Resolution Settlement Layer (HRSL) is used to obtain the population estimates at 100 m resolution.

@@ -591,67 +884,67 @@

Index normalisation -

Bi-variate maps#

+

Bi-variate maps#

Poverty maps are combined with EAE/EAI maps to generate bi-variate maps. These maps provide ranks explained by a 3x3 matrix, resulting in 9 possible scores ranging from low-risk / low-poverty to high-risk / high poverty. The matrix is built by classifying poverty indicators into three quantiles and dividing risk indicators (EAE/EAI) of each hazard type into groups.

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Risk indicator

Unit

Metric

Risk Classification

Risk indicator

Unit

Metric

Risk Classification

Low

Medium

High

Low

Medium

High

Flood x Population

EAI [#]

Total count

0.01 - 100

100 – 1,000

> 1,000

Flood x Population

EAI [#]

Total count

0.01 - 100

100 – 1,000

> 1,000

Flood x Built-up

EAI [Ha]

Total count

0.01 – 1

1 – 10

> 10

Flood x Built-up

EAI [Ha]

Total count

0.01 – 1

1 – 10

> 10

Flood x Agri land

EAE [Ha]

Total count

0.01 - 100

100 – 1,000

> 1,000

Flood x Agri land

EAE [Ha]

Total count

0.01 - 100

100 – 1,000

> 1,000

Drought x Agri land

EAE [Ha]

Ratio

1% - 5%

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EAE [Ha]

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Baseline risk#

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Baseline risk#

Once required input layers have been created according to tool setup, the analysis can run.

  • Select CCDR.ipynb and chose the hazard to analyse.

  • @@ -532,7 +835,7 @@

    Baseline risk -

    Analytical approach: Expected Annual Impact#

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    Analytical approach: Expected Annual Impact#

    This approach uses a mathematical relationship to calculate impact over exposed categories (read more). For example, flood hazard impact over population is calculated using a mortality function, while the impact over built-up uses a damage function for buildings.

    The functions can be manually edited in the notebook, if a better model is available for the country of interest.

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Team and Acknowledgements

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Team and Acknowledgements#

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Team and Acknowledgements#

The core team behind the CCDR tools:

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Team and Acknowledgements - - + + + + + + + + + + + + - - + + + + + + + + - + + + + - - + + \ No newline at end of file diff --git a/docs/tool-setup.html b/docs/tool-setup.html index d94b42d5..f831eb92 100644 --- a/docs/tool-setup.html +++ b/docs/tool-setup.html @@ -1,49 +1,57 @@ - + - + TOOLS SETUP — Climate & Disaster Risk Screening Tools - + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + + - + - + + - - - - - - + + - - - - - - - - -
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TOOLS SETUP#

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TOOLS SETUP#

The analytical scripts can be downloaded as:

  • Jupyter notebooks: user-friendly script that run via browser interface. @@ -580,7 +851,7 @@

    TOOLS SETUP -

    Python environment#

    +

    Python environment#

    • Python 3 needs to be installed on your system. We suggest the latest Anaconda distribution. Mamba is also encouraged.

    • Create new CCDR-tools environment according to your operating system: win.yml or linux.yml. @@ -593,7 +864,7 @@

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      • Download the latest version of the notebooks or the the parallel code.

      • Create folder structure as:

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        Input data management
      • ADM0 layer

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Name

Name

Role

Team

Mattia Amadio

Mattia Amadio

Disasterologist - Project Lead and implementation; documentation

WB GFDRR

Arthur Hrast Essenfelder

Arthur Hrast Essenfelder

Analyst - Code optimisation and parallelisation; climate data processing; bi-variate maps

WB

Takuya Iwanaga

Takuya Iwanaga

Programmer - Development of original python code for spatial analysis and EAI calculation

WB GFDRR

+
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Input data management
  • ADM1 layer

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    Settings#

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    Settings#

    Edit the .env file inside the notebook directories to specify the working directory:

    # Environment variables for the CCDR Climate and Disasater Risk analysis notebooks
     
    @@ -767,7 +1038,7 @@ 

    Settings -

    Run Jupyter notebooks#

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    Run Jupyter notebooks#

    • Be sure to activate the correct environment

      activate CCDR-tools
      @@ -791,9 +1062,9 @@ 

      Run Jupyter notebooks

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    Parallel processing#

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    Parallel processing#

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    Setting parameters#

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    Setting parameters#

    Edit the main.py file to specify:

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    Validation and interpretation#

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    Validation and interpretation#

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    Validate input#

    It is always a good practice to spend some time to evaluate the quality and representativeness of input data before diving into the analytics. Input data can contain errors and artefacts; sometimes they are large and evident, sometimes they are small and hard to catch - but that doesn’t mean they don’t have an impact over the quality of results!

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    Hazard#

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    Hazard#

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    Correct values interpretation and outliers#

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    Correct values interpretation and outliers#

    • Check layer projection sysyem (CRS) and resolution

        @@ -687,7 +917,7 @@

        Correct values interpretation and outliers -

        Geographic correlation#

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        Geographic correlation#

        This is easier to check for hazards with strong dependency to the geomorphology, such as floods and landslides. An inspection of hazard layers against a reliable basemap (e.g. ESRI or Google) can help to spot inconsistencies between the representation of hazard distribution and its expected behaviour (rule of thumb) in relation to the basemap.

        Some examples:

          @@ -697,7 +927,7 @@

          Geographic correlation

    -

    Validation against empirical datasets#

    +

    Validation against empirical datasets#

    Probabilistic scenarios of hazard data can be compared with observed disaster events to corroborate the analysis, although this is often a difficult task due to lack of granular spatial data representing the events. Some hazards are better covered by observations than others - see the disaster records.

    ../_images/hzd_validate.jpg @@ -713,7 +943,7 @@

    Validation against empirical datasets -

    Cross-comparison between alternative datasets#

    +

    Cross-comparison between alternative datasets#

    Run a spatial and numerical comparison between models to estimate their similarity.

    ../_images/hzd_match_models.jpg @@ -730,9 +960,9 @@

    Cross-comparison between alternative datasets -

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    Exposure#

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    Correct values interpretation and outliers#

    • Check layer projection sysyem (CRS) and resolution

        @@ -764,7 +994,7 @@

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    Comparison against basemap#

    Identify artifacts in exposure data by sample inspection; if the errors are limited and a better source of truth is available for comparison, fix them manually; else, account for them in the results interpretation and uncertainty.

    ../_images/exp_wpop_sample.jpg @@ -774,7 +1004,7 @@

    Comparison against basemap -

    Cross-comparison between alternative models#

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    Cross-comparison between alternative models#

    Run a spatial and numerical comparison between models to estimate their similarity.

    ../_images/exp_wpop_sample_compare.jpg @@ -786,10 +1016,10 @@

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    Output interpretation#

    +

    Output interpretation#

    The analytical model produces numbers; then is to the ability of the risk analyst to interpret them correctly, spot errors, and build a narrative to make the results digestable for a non-expert audience.

    -

    Validation against empirical disaster data#

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    Validation against empirical disaster data#

    • First, check that results make sense in terms of metrics and units of measures.

        @@ -804,7 +1034,7 @@

        Validation against empirical disaster data -

        Uncertainty#

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        Uncertainty#

        In the development of risk models, many different data sets are used as input components. The level of uncertainty is directly linked to the quality of the input data. In addition, there is also random uncertainty that cannot be reduced. On many occasions during model development, expert judgment and proxies are used in the absence of historical data, and the results are very sensitive to most of these assumptions and variations in input data. As such, outputs of these models should be considered indicators of the order of magnitude of the risks, not as exact values. Better data quality and advances in science and modelling methodologies reduce the level of uncertainty, but it is crucial to interpret the results of any risk assessment against the backdrop of unavoidable uncertainty.

        A risk model can produce a very precise result—it may show, for example, that a 1-in-100-year flood will affect 388,123 people—but in reality the accuracy of the model and input data may provide only an order of magnitude estimate. Similarly, sharply delineated flood zones on a hazard map do not adequately reflect the uncertainty associated with the estimate and could lead to decisions such as locating critical facilities just outside the flood line, where the actual risk is the same as if the facility was located inside the flood zone.

        We should not be apprehensive of using information that is uncertain so long as any decisions and actions based upon the information are made with a full understanding of the associated uncertainty and its implications. It should be remembered that uncertainty will usually promote an analytical debate that should lead to robust decisions, which is a positive manifestation of uncertainty. Credible scientific results should also have associated uncertainty clearly presented.

        @@ -831,56 +1061,178 @@

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    Climate & Disaster Risk Screening Tools#

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    Climate & Disaster Risk Screening Tools#

    A collection of scripted tools developed to inform risk analytics for the World Bank’s Country Climate and Development Report risk screening activities (2022/2023).

    The tools collected in this repository allow to:

      -
    • Perform spatial analytics of disaster risk for the present period (baseline) based on global datasets

    • +
    • Perform spatial analytics of disaster risk for the present period (baseline) based on global datasets

    • Combine risk and poverty information into bi-variate maps

    • Produce climate risk outlook based on CMIP6 indices

    • -
    • Present results into an interactive dashboard

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    • Present results into an interactive dashboard

    Note

    @@ -522,11 +825,11 @@

    Climate & Disaster Risk Screening Tools
  • Cambodia

  • Senegal

  • -
  • OECS countries

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  • OECS countries

  • Countries that have been already covered by the risk analytics:

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    Climate & Disaster Risk Screening Tools_images/GFDRR_logo.png
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    Disaster risk framework#

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    Disaster risk framework#

    In the field of Disaster Risk Management (DRM), Risk (R) is typically calculated as a function of: the hazard occurrence probability and intensity (i.e., physical magnitude) in a particular location (H, for Hazard); the people and physical assets (infrastructure, buildings, crops, etc.) situated in that location and therefore exposed to the hazard (E, for Exposure); and the conditions determined by physical, social, and economic factors which increase the susceptibility of an exposed individual, community, asset or system to the impacts of hazards (V, for Vulnerability).

    _images/risk_framing.png @@ -602,49 +905,98 @@

    Disaster risk framework - - - - + - + + + + - - + + \ No newline at end of file diff --git a/search.html b/search.html index 3a1db3b8..828edf19 100644 --- a/search.html +++ b/search.html @@ -1,49 +1,55 @@ - + - - Search — Climate & Disaster Risk Screening Tools - + Search - Climate & Disaster Risk Screening Tools + + + + + - - + + + - - - - + + + + + - + - + - - + + - + + - + - + - + - - + + + + - + + + - - - + + - - + + + - - - - - - - - - -
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    South & South-East Asia

    West Africa & Sahel countries