(a(),u("div",Br,[p("div",{ref_key:"tablist",ref:i,class:"plugin-tabs--tab-list",role:"tablist",onKeydown:m},[(a(!0),u(M,null,E(r(t),S=>(a(),u("button",{id:`tab-${S}-${r(L)}`,ref_for:!0,ref_key:"buttonRefs",ref:v,key:S,role:"tab",class:"plugin-tabs--tab","aria-selected":S===r(s),"aria-controls":`panel-${S}-${r(L)}`,tabindex:S===r(s)?0:-1,onClick:()=>r(d)(S)},I(S),9,Hr))),128))],544),c(b.$slots,"default")]))}}),Dr=["id","aria-labelledby"],Fr=_({__name:"PluginTabsTab",props:{label:{}},setup(n){const{uid:e,selected:t}=Cr();return(s,o)=>r(t)===s.label?(a(),u("div",{key:0,id:`panel-${s.label}-${r(e)}`,class:"plugin-tabs--content",role:"tabpanel",tabindex:"0","aria-labelledby":`tab-${s.label}-${r(e)}`},[c(s.$slots,"default",{},void 0,!0)],8,Dr)):h("",!0)}}),Or=g(Fr,[["__scopeId","data-v-9b0d03d2"]]),jr=n=>{wr(n),n.component("PluginTabs",Er),n.component("PluginTabsTab",Or)},Gr={extends:Ve,Layout(){return ct(Ve.Layout,null,{})},enhanceApp({app:n,router:e,siteData:t}){jr(n)}};export{Gr as R,Xs as c,V as u};
diff --git a/dev/assets/documenter.js b/dev/assets/documenter.js
deleted file mode 100644
index b2bdd43..0000000
--- a/dev/assets/documenter.js
+++ /dev/null
@@ -1,1059 +0,0 @@
-// Generated by Documenter.jl
-requirejs.config({
- paths: {
- 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min',
- 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min',
- 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min',
- 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min',
- 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min',
- 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min',
- 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min',
- 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min',
- 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min',
- },
- shim: {
- "highlight-julia": {
- "deps": [
- "highlight"
- ]
- },
- "katex-auto-render": {
- "deps": [
- "katex"
- ]
- },
- "headroom-jquery": {
- "deps": [
- "jquery",
- "headroom"
- ]
- },
- "highlight-julia-repl": {
- "deps": [
- "highlight"
- ]
- }
-}
-});
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) {
-$(document).ready(function() {
- renderMathInElement(
- document.body,
- {
- "delimiters": [
- {
- "left": "$",
- "right": "$",
- "display": false
- },
- {
- "left": "$$",
- "right": "$$",
- "display": true
- },
- {
- "left": "\\[",
- "right": "\\]",
- "display": true
- }
- ]
-}
-
- );
-})
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) {
-$(document).ready(function() {
- hljs.highlightAll();
-})
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery'], function($) {
-
-let timer = 0;
-var isExpanded = true;
-
-$(document).on("click", ".docstring header", function () {
- let articleToggleTitle = "Expand docstring";
-
- debounce(() => {
- if ($(this).siblings("section").is(":visible")) {
- $(this)
- .find(".docstring-article-toggle-button")
- .removeClass("fa-chevron-down")
- .addClass("fa-chevron-right");
- } else {
- $(this)
- .find(".docstring-article-toggle-button")
- .removeClass("fa-chevron-right")
- .addClass("fa-chevron-down");
-
- articleToggleTitle = "Collapse docstring";
- }
-
- $(this)
- .find(".docstring-article-toggle-button")
- .prop("title", articleToggleTitle);
- $(this).siblings("section").slideToggle();
- });
-});
-
-$(document).on("click", ".docs-article-toggle-button", function (event) {
- let articleToggleTitle = "Expand docstring";
- let navArticleToggleTitle = "Expand all docstrings";
- let animationSpeed = event.noToggleAnimation ? 0 : 400;
-
- debounce(() => {
- if (isExpanded) {
- $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down");
- $(".docstring-article-toggle-button")
- .removeClass("fa-chevron-down")
- .addClass("fa-chevron-right");
-
- isExpanded = false;
-
- $(".docstring section").slideUp(animationSpeed);
- } else {
- $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up");
- $(".docstring-article-toggle-button")
- .removeClass("fa-chevron-right")
- .addClass("fa-chevron-down");
-
- isExpanded = true;
- articleToggleTitle = "Collapse docstring";
- navArticleToggleTitle = "Collapse all docstrings";
-
- $(".docstring section").slideDown(animationSpeed);
- }
-
- $(this).prop("title", navArticleToggleTitle);
- $(".docstring-article-toggle-button").prop("title", articleToggleTitle);
- });
-});
-
-function debounce(callback, timeout = 300) {
- if (Date.now() - timer > timeout) {
- callback();
- }
-
- clearTimeout(timer);
-
- timer = Date.now();
-}
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require([], function() {
-function addCopyButtonCallbacks() {
- for (const el of document.getElementsByTagName("pre")) {
- const button = document.createElement("button");
- button.classList.add("copy-button", "fa-solid", "fa-copy");
- button.setAttribute("aria-label", "Copy this code block");
- button.setAttribute("title", "Copy");
-
- el.appendChild(button);
-
- const success = function () {
- button.classList.add("success", "fa-check");
- button.classList.remove("fa-copy");
- };
-
- const failure = function () {
- button.classList.add("error", "fa-xmark");
- button.classList.remove("fa-copy");
- };
-
- button.addEventListener("click", function () {
- copyToClipboard(el.innerText).then(success, failure);
-
- setTimeout(function () {
- button.classList.add("fa-copy");
- button.classList.remove("success", "fa-check", "fa-xmark");
- }, 5000);
- });
- }
-}
-
-function copyToClipboard(text) {
- // clipboard API is only available in secure contexts
- if (window.navigator && window.navigator.clipboard) {
- return window.navigator.clipboard.writeText(text);
- } else {
- return new Promise(function (resolve, reject) {
- try {
- const el = document.createElement("textarea");
- el.textContent = text;
- el.style.position = "fixed";
- el.style.opacity = 0;
- document.body.appendChild(el);
- el.select();
- document.execCommand("copy");
-
- resolve();
- } catch (err) {
- reject(err);
- } finally {
- document.body.removeChild(el);
- }
- });
- }
-}
-
-if (document.readyState === "loading") {
- document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks);
-} else {
- addCopyButtonCallbacks();
-}
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) {
-
-// Manages the top navigation bar (hides it when the user starts scrolling down on the
-// mobile).
-window.Headroom = Headroom; // work around buggy module loading?
-$(document).ready(function () {
- $("#documenter .docs-navbar").headroom({
- tolerance: { up: 10, down: 10 },
- });
-});
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery'], function($) {
-
-$(document).ready(function () {
- let meta = $("div[data-docstringscollapsed]").data();
-
- if (meta?.docstringscollapsed) {
- $("#documenter-article-toggle-button").trigger({
- type: "click",
- noToggleAnimation: true,
- });
- }
-});
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery'], function($) {
-
-/*
-To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc
-
-PSEUDOCODE:
-
-Searching happens automatically as the user types or adjusts the selected filters.
-To preserve responsiveness, as much as possible of the slow parts of the search are done
-in a web worker. Searching and result generation are done in the worker, and filtering and
-DOM updates are done in the main thread. The filters are in the main thread as they should
-be very quick to apply. This lets filters be changed without re-searching with minisearch
-(which is possible even if filtering is on the worker thread) and also lets filters be
-changed _while_ the worker is searching and without message passing (neither of which are
-possible if filtering is on the worker thread)
-
-SEARCH WORKER:
-
-Import minisearch
-
-Build index
-
-On message from main thread
- run search
- find the first 200 unique results from each category, and compute their divs for display
- note that this is necessary and sufficient information for the main thread to find the
- first 200 unique results from any given filter set
- post results to main thread
-
-MAIN:
-
-Launch worker
-
-Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results)
-
-On text update
- if worker is not running, launch_search()
-
-launch_search
- set worker_is_running to true, set last_search_text to the search text
- post the search query to worker
-
-on message from worker
- if last_search_text is not the same as the text in the search field,
- the latest search result is not reflective of the latest search query, so update again
- launch_search()
- otherwise
- set worker_is_running to false
-
- regardless, display the new search results to the user
- save the unfiltered_results as a global
- update_search()
-
-on filter click
- adjust the filter selection
- update_search()
-
-update_search
- apply search filters by looping through the unfiltered_results and finding the first 200
- unique results that match the filters
-
- Update the DOM
-*/
-
-/////// SEARCH WORKER ///////
-
-function worker_function(documenterSearchIndex, documenterBaseURL, filters) {
- importScripts(
- "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js"
- );
-
- let data = documenterSearchIndex.map((x, key) => {
- x["id"] = key; // minisearch requires a unique for each object
- return x;
- });
-
- // list below is the lunr 2.1.3 list minus the intersect with names(Base)
- // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with)
- // ideally we'd just filter the original list but it's not available as a variable
- const stopWords = new Set([
- "a",
- "able",
- "about",
- "across",
- "after",
- "almost",
- "also",
- "am",
- "among",
- "an",
- "and",
- "are",
- "as",
- "at",
- "be",
- "because",
- "been",
- "but",
- "by",
- "can",
- "cannot",
- "could",
- "dear",
- "did",
- "does",
- "either",
- "ever",
- "every",
- "from",
- "got",
- "had",
- "has",
- "have",
- "he",
- "her",
- "hers",
- "him",
- "his",
- "how",
- "however",
- "i",
- "if",
- "into",
- "it",
- "its",
- "just",
- "least",
- "like",
- "likely",
- "may",
- "me",
- "might",
- "most",
- "must",
- "my",
- "neither",
- "no",
- "nor",
- "not",
- "of",
- "off",
- "often",
- "on",
- "or",
- "other",
- "our",
- "own",
- "rather",
- "said",
- "say",
- "says",
- "she",
- "should",
- "since",
- "so",
- "some",
- "than",
- "that",
- "the",
- "their",
- "them",
- "then",
- "there",
- "these",
- "they",
- "this",
- "tis",
- "to",
- "too",
- "twas",
- "us",
- "wants",
- "was",
- "we",
- "were",
- "what",
- "when",
- "who",
- "whom",
- "why",
- "will",
- "would",
- "yet",
- "you",
- "your",
- ]);
-
- let index = new MiniSearch({
- fields: ["title", "text"], // fields to index for full-text search
- storeFields: ["location", "title", "text", "category", "page"], // fields to return with results
- processTerm: (term) => {
- let word = stopWords.has(term) ? null : term;
- if (word) {
- // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names
- word = word
- .replace(/^[^a-zA-Z0-9@!]+/, "")
- .replace(/[^a-zA-Z0-9@!]+$/, "");
-
- word = word.toLowerCase();
- }
-
- return word ?? null;
- },
- // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not
- // find anything if searching for "add!", only for the entire qualification
- tokenize: (string) => string.split(/[\s\-\.]+/),
- // options which will be applied during the search
- searchOptions: {
- prefix: true,
- boost: { title: 100 },
- fuzzy: 2,
- },
- });
-
- index.addAll(data);
-
- /**
- * Used to map characters to HTML entities.
- * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts
- */
- const htmlEscapes = {
- "&": "&",
- "<": "<",
- ">": ">",
- '"': """,
- "'": "'",
- };
-
- /**
- * Used to match HTML entities and HTML characters.
- * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts
- */
- const reUnescapedHtml = /[&<>"']/g;
- const reHasUnescapedHtml = RegExp(reUnescapedHtml.source);
-
- /**
- * Escape function from lodash
- * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts
- */
- function escape(string) {
- return string && reHasUnescapedHtml.test(string)
- ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr])
- : string || "";
- }
-
- /**
- * RegX escape function from MDN
- * Refer: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
- function escapeRegExp(string) {
- return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
- }
-
- /**
- * Make the result component given a minisearch result data object and the value
- * of the search input as queryString. To view the result object structure, refer:
- * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult
- *
- * @param {object} result
- * @param {string} querystring
- * @returns string
- */
- function make_search_result(result, querystring) {
- let search_divider = `
`;
- let display_link =
- result.location.slice(Math.max(0), Math.min(50, result.location.length)) +
- (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div
-
- if (result.page !== "") {
- display_link += ` (${result.page})`;
- }
- searchstring = escapeRegExp(querystring);
- let textindex = new RegExp(`${searchstring}`, "i").exec(result.text);
- let text =
- textindex !== null
- ? result.text.slice(
- Math.max(textindex.index - 100, 0),
- Math.min(
- textindex.index + querystring.length + 100,
- result.text.length
- )
- )
- : ""; // cut-off text before and after from the match
-
- text = text.length ? escape(text) : "";
-
- let display_result = text.length
- ? "..." +
- text.replace(
- new RegExp(`${escape(searchstring)}`, "i"), // For first occurrence
- '$& '
- ) +
- "..."
- : ""; // highlights the match
-
- let in_code = false;
- if (!["page", "section"].includes(result.category.toLowerCase())) {
- in_code = true;
- }
-
- // We encode the full url to escape some special characters which can lead to broken links
- let result_div = `
-
-
-
${escape(result.title)}
-
${result.category}
-
- ${display_result}
-
-
-
-
- ${search_divider}
- `;
-
- return result_div;
- }
-
- self.onmessage = function (e) {
- let query = e.data;
- let results = index.search(query, {
- filter: (result) => {
- // Only return relevant results
- return result.score >= 1;
- },
- combineWith: "AND",
- });
-
- // Pre-filter to deduplicate and limit to 200 per category to the extent
- // possible without knowing what the filters are.
- let filtered_results = [];
- let counts = {};
- for (let filter of filters) {
- counts[filter] = 0;
- }
- let present = {};
-
- for (let result of results) {
- cat = result.category;
- cnt = counts[cat];
- if (cnt < 200) {
- id = cat + "---" + result.location;
- if (present[id]) {
- continue;
- }
- present[id] = true;
- filtered_results.push({
- location: result.location,
- category: cat,
- div: make_search_result(result, query),
- });
- }
- }
-
- postMessage(filtered_results);
- };
-}
-
-// `worker = Threads.@spawn worker_function(documenterSearchIndex)`, but in JavaScript!
-const filters = [
- ...new Set(documenterSearchIndex["docs"].map((x) => x.category)),
-];
-const worker_str =
- "(" +
- worker_function.toString() +
- ")(" +
- JSON.stringify(documenterSearchIndex["docs"]) +
- "," +
- JSON.stringify(documenterBaseURL) +
- "," +
- JSON.stringify(filters) +
- ")";
-const worker_blob = new Blob([worker_str], { type: "text/javascript" });
-const worker = new Worker(URL.createObjectURL(worker_blob));
-
-/////// SEARCH MAIN ///////
-
-// Whether the worker is currently handling a search. This is a boolean
-// as the worker only ever handles 1 or 0 searches at a time.
-var worker_is_running = false;
-
-// The last search text that was sent to the worker. This is used to determine
-// if the worker should be launched again when it reports back results.
-var last_search_text = "";
-
-// The results of the last search. This, in combination with the state of the filters
-// in the DOM, is used compute the results to display on calls to update_search.
-var unfiltered_results = [];
-
-// Which filter is currently selected
-var selected_filter = "";
-
-$(document).on("input", ".documenter-search-input", function (event) {
- if (!worker_is_running) {
- launch_search();
- }
-});
-
-function launch_search() {
- worker_is_running = true;
- last_search_text = $(".documenter-search-input").val();
- worker.postMessage(last_search_text);
-}
-
-worker.onmessage = function (e) {
- if (last_search_text !== $(".documenter-search-input").val()) {
- launch_search();
- } else {
- worker_is_running = false;
- }
-
- unfiltered_results = e.data;
- update_search();
-};
-
-$(document).on("click", ".search-filter", function () {
- if ($(this).hasClass("search-filter-selected")) {
- selected_filter = "";
- } else {
- selected_filter = $(this).text().toLowerCase();
- }
-
- // This updates search results and toggles classes for UI:
- update_search();
-});
-
-/**
- * Make/Update the search component
- */
-function update_search() {
- let querystring = $(".documenter-search-input").val();
-
- if (querystring.trim()) {
- if (selected_filter == "") {
- results = unfiltered_results;
- } else {
- results = unfiltered_results.filter((result) => {
- return selected_filter == result.category.toLowerCase();
- });
- }
-
- let search_result_container = ``;
- let modal_filters = make_modal_body_filters();
- let search_divider = `
`;
-
- if (results.length) {
- let links = [];
- let count = 0;
- let search_results = "";
-
- for (var i = 0, n = results.length; i < n && count < 200; ++i) {
- let result = results[i];
- if (result.location && !links.includes(result.location)) {
- search_results += result.div;
- count++;
- links.push(result.location);
- }
- }
-
- if (count == 1) {
- count_str = "1 result";
- } else if (count == 200) {
- count_str = "200+ results";
- } else {
- count_str = count + " results";
- }
- let result_count = `${count_str}
`;
-
- search_result_container = `
-
- ${modal_filters}
- ${search_divider}
- ${result_count}
-
- ${search_results}
-
-
- ${modal_filters}
- ${search_divider}
-
0 result(s)
-
No result found!
- `;
- }
-
- if ($(".search-modal-card-body").hasClass("is-justify-content-center")) {
- $(".search-modal-card-body").removeClass("is-justify-content-center");
- }
-
- $(".search-modal-card-body").html(search_result_container);
- } else {
- if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) {
- $(".search-modal-card-body").addClass("is-justify-content-center");
- }
-
- $(".search-modal-card-body").html(`
- Type something to get started!
- `);
- }
-}
-
-/**
- * Make the modal filter html
- *
- * @returns string
- */
-function make_modal_body_filters() {
- let str = filters
- .map((val) => {
- if (selected_filter == val.toLowerCase()) {
- return `${val} ${val}
- Filters:
- ${str}
-
`;
-}
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery'], function($) {
-
-// Modal settings dialog
-$(document).ready(function () {
- var settings = $("#documenter-settings");
- $("#documenter-settings-button").click(function () {
- settings.toggleClass("is-active");
- });
- // Close the dialog if X is clicked
- $("#documenter-settings button.delete").click(function () {
- settings.removeClass("is-active");
- });
- // Close dialog if ESC is pressed
- $(document).keyup(function (e) {
- if (e.keyCode == 27) settings.removeClass("is-active");
- });
-});
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery'], function($) {
-
-$(document).ready(function () {
- let search_modal_header = `
-
- `;
-
- let initial_search_body = `
- Type something to get started!
- `;
-
- let search_modal_footer = `
-
- `;
-
- $(document.body).append(
- `
-
-
-
- ${search_modal_header}
-
- ${initial_search_body}
-
- ${search_modal_footer}
-
-
Type something to get started!
- `;
-
- searchModal.classList.remove("is-active");
- document.querySelector(".documenter-search-input").blur();
-
- if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) {
- $(".search-modal-card-body").addClass("is-justify-content-center");
- }
-
- $(".documenter-search-input").val("");
- $(".search-modal-card-body").html(initial_search_body);
- }
-
- document
- .querySelector("#search-modal .modal-background")
- .addEventListener("click", () => {
- closeModal();
- });
-});
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery'], function($) {
-
-// Manages the showing and hiding of the sidebar.
-$(document).ready(function () {
- var sidebar = $("#documenter > .docs-sidebar");
- var sidebar_button = $("#documenter-sidebar-button");
- sidebar_button.click(function (ev) {
- ev.preventDefault();
- sidebar.toggleClass("visible");
- if (sidebar.hasClass("visible")) {
- // Makes sure that the current menu item is visible in the sidebar.
- $("#documenter .docs-menu a.is-active").focus();
- }
- });
- $("#documenter > .docs-main").bind("click", function (ev) {
- if ($(ev.target).is(sidebar_button)) {
- return;
- }
- if (sidebar.hasClass("visible")) {
- sidebar.removeClass("visible");
- }
- });
-});
-
-// Resizes the package name / sitename in the sidebar if it is too wide.
-// Inspired by: https://github.com/davatron5000/FitText.js
-$(document).ready(function () {
- e = $("#documenter .docs-autofit");
- function resize() {
- var L = parseInt(e.css("max-width"), 10);
- var L0 = e.width();
- if (L0 > L) {
- var h0 = parseInt(e.css("font-size"), 10);
- e.css("font-size", (L * h0) / L0);
- // TODO: make sure it survives resizes?
- }
- }
- // call once and then register events
- resize();
- $(window).resize(resize);
- $(window).on("orientationchange", resize);
-});
-
-// Scroll the navigation bar to the currently selected menu item
-$(document).ready(function () {
- var sidebar = $("#documenter .docs-menu").get(0);
- var active = $("#documenter .docs-menu .is-active").get(0);
- if (typeof active !== "undefined") {
- sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15;
- }
-});
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery'], function($) {
-
-// Theme picker setup
-$(document).ready(function () {
- // onchange callback
- $("#documenter-themepicker").change(function themepick_callback(ev) {
- var themename = $("#documenter-themepicker option:selected").attr("value");
- if (themename === "auto") {
- // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light');
- window.localStorage.removeItem("documenter-theme");
- } else {
- // set_theme(themename);
- window.localStorage.setItem("documenter-theme", themename);
- }
- // We re-use the global function from themeswap.js to actually do the swapping.
- set_theme_from_local_storage();
- });
-
- // Make sure that the themepicker displays the correct theme when the theme is retrieved
- // from localStorage
- if (typeof window.localStorage !== "undefined") {
- var theme = window.localStorage.getItem("documenter-theme");
- if (theme !== null) {
- $("#documenter-themepicker option").each(function (i, e) {
- e.selected = e.value === theme;
- });
- }
- }
-});
-
-})
-////////////////////////////////////////////////////////////////////////////////
-require(['jquery'], function($) {
-
-// update the version selector with info from the siteinfo.js and ../versions.js files
-$(document).ready(function () {
- // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the
- // siteinfo.js file, we just return immediately and not display the version selector.
- if (
- typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" &&
- DOCUMENTER_VERSION_SELECTOR_DISABLED
- ) {
- return;
- }
-
- var version_selector = $("#documenter .docs-version-selector");
- var version_selector_select = $("#documenter .docs-version-selector select");
-
- version_selector_select.change(function (x) {
- target_href = version_selector_select
- .children("option:selected")
- .get(0).value;
- window.location.href = target_href;
- });
-
- // add the current version to the selector based on siteinfo.js, but only if the selector is empty
- if (
- typeof DOCUMENTER_CURRENT_VERSION !== "undefined" &&
- $("#version-selector > option").length == 0
- ) {
- var option = $(
- "" +
- DOCUMENTER_CURRENT_VERSION +
- " "
- );
- version_selector_select.append(option);
- }
-
- if (typeof DOC_VERSIONS !== "undefined") {
- var existing_versions = version_selector_select.children("option");
- var existing_versions_texts = existing_versions.map(function (i, x) {
- return x.text;
- });
- DOC_VERSIONS.forEach(function (each) {
- var version_url = documenterBaseURL + "/../" + each + "/";
- var existing_id = $.inArray(each, existing_versions_texts);
- // if not already in the version selector, add it as a new option,
- // otherwise update the old option with the URL and enable it
- if (existing_id == -1) {
- var option = $(
- "" + each + " "
- );
- version_selector_select.append(option);
- } else {
- var option = existing_versions[existing_id];
- option.value = version_url;
- option.disabled = false;
- }
- });
- }
-
- // only show the version selector if the selector has been populated
- if (version_selector_select.children("option").length > 0) {
- version_selector.toggleClass("visible");
- }
-});
-
-})
diff --git a/dev/assets/examples.md.qH_hEGoE.js b/dev/assets/examples.md.qH_hEGoE.js
new file mode 100644
index 0000000..1120f02
--- /dev/null
+++ b/dev/assets/examples.md.qH_hEGoE.js
@@ -0,0 +1 @@
+import{_ as e,c as s,o as t,a7 as i}from"./chunks/framework.CdnWR3-W.js";const m=JSON.parse('{"title":"Jet Reconstruction Examples","description":"","frontmatter":{},"headers":[],"relativePath":"examples.md","filePath":"examples.md","lastUpdated":null}'),a={name:"examples.md"},n=i('Jet Reconstruction Examples The Jet Reconstruction package has a number of example files that show how to usage.
Note: because of extra dependencies in these scripts, one must use the Project.toml file in the examples directory.
jetreco.jl This is a basic jet reconstruction example that shows how to call the package to perform a jet reconstruction, with different algorithms and (optionally) strategy, producing exclusive and inclusive jet selections.
sh julia --project=. examples/jetreco.jl --maxevents=100 --strategy=N2Plain test/data/events.hepmc3 ... julia --project=. examples/jetreco.jl --maxevents=100 --strategy=N2Tiled test/data/events.hepmc3 ... There are options to explicitly set the algorithm (use --help to see these).
instrumented-jetreco.jl This is a more sophisticated example that allows performance measurements to be made of the reconstruction, as well as profiling (flamegraphs and memory profiling). Use the --help option to see usage. e.g., to extract timing performance for the AntiKt algorithm using the tiled strategy:
sh julia --project instrumented-jetreco.jl -S N2Tiled -A AntiKt --nsamples 100 ../test/data/events.hepmc3 visualise-jets.jl This script will produce a PNG/PDF showing the results of a jet reconstruction. This is a 3D plot where all the initial energy deposits are visualised, with colours that indicate in which final cluster the deposit ended up in.
visualise-jets.ipynb Similar to visualise-jets.jl this notebook will produce a visualisation of jet reconstruction in the browser. This is a 3D plot where all the initial energy deposits are visualised, with colours that indicate in which final cluster the deposit ended up in.
animate-reconstruction.jl Performs jet reconstruction and then produces and animation of the process, showing how the jets merge from their different constituents.
',16),l=[n];function o(h,r,p,c,d,u){return t(),s("div",null,l)}const j=e(a,[["render",o]]);export{m as __pageData,j as default};
diff --git a/dev/assets/examples.md.qH_hEGoE.lean.js b/dev/assets/examples.md.qH_hEGoE.lean.js
new file mode 100644
index 0000000..cf3d8bd
--- /dev/null
+++ b/dev/assets/examples.md.qH_hEGoE.lean.js
@@ -0,0 +1 @@
+import{_ as e,c as s,o as t,a7 as i}from"./chunks/framework.CdnWR3-W.js";const m=JSON.parse('{"title":"Jet Reconstruction Examples","description":"","frontmatter":{},"headers":[],"relativePath":"examples.md","filePath":"examples.md","lastUpdated":null}'),a={name:"examples.md"},n=i("",16),l=[n];function o(h,r,p,c,d,u){return t(),s("div",null,l)}const j=e(a,[["render",o]]);export{m as __pageData,j as default};
diff --git a/dev/assets/illustration.jpeg b/dev/assets/illustration.jpeg
deleted file mode 100644
index 4aa85a2..0000000
Binary files a/dev/assets/illustration.jpeg and /dev/null differ
diff --git a/dev/assets/index.md.Cm9Ga1X8.js b/dev/assets/index.md.Cm9Ga1X8.js
new file mode 100644
index 0000000..e093522
--- /dev/null
+++ b/dev/assets/index.md.Cm9Ga1X8.js
@@ -0,0 +1,22 @@
+import{_ as n,c as e,j as t,a as s,a7 as a,o as i}from"./chunks/framework.CdnWR3-W.js";const l="/JetReconstruction.jl/dev/assets/jetvis.Bpiv-g6k.png",At=JSON.parse('{"title":"Jet Reconstruction","description":"","frontmatter":{"layout":"home","hero":{"name":"JetReconstruction.jl","tagline":"Jet reconstruction (reclustering) with Julia","image":{"src":"/logo.png","alt":"DocumenterVitepress"},"actions":[{"theme":"brand","text":"Examples","link":"/examples"},{"theme":"alt","text":"Public APIs","link":"/lib/public"}]}},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),o={name:"index.md"},h=t("h1",{id:"Jet-Reconstruction",tabindex:"-1"},[s("Jet Reconstruction "),t("a",{class:"header-anchor",href:"#Jet-Reconstruction","aria-label":'Permalink to "Jet Reconstruction {#Jet-Reconstruction}"'},"")],-1),r={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.276ex",height:"1.439ex",role:"img",focusable:"false",viewBox:"0 -442 1006 636","aria-hidden":"true"},d=a('Algorithms Algorithms used are based on the C++ FastJet package (https://fastjet.fr , hep-ph/0512210 , arXiv:1111.6097 ), reimplemented natively in Julia.
',2),x={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},_={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.339ex"},xmlns:"http://www.w3.org/2000/svg",width:"9.185ex",height:"1.91ex",role:"img",focusable:"false",viewBox:"0 -694 4060 844","aria-hidden":"true"},y=a('Reconstruction Interface The main interface for reconstruction is jet_reconstruct
julia jet_reconstruct (particles; p = - 1 , R = 1.0 ) Where particles is a collection of 4-vector objects to reconstruct.
The object returned is a ClusterSequence
Strategy Three strategies are available for the different algorithms:
',7),M={tabindex:"0"},j=t("thead",null,[t("tr",null,[t("th",{style:{"text-align":"right"}},"Strategy Name"),t("th",{style:{"text-align":"right"}},"Notes"),t("th",{style:{"text-align":"right"}},"Interface")])],-1),A=t("tr",null,[t("td",{style:{"text-align":"right"}},[t("code",null,"RecoStrategy.Best")]),t("td",{style:{"text-align":"right"}},"Dynamically switch strategy based on input particle density"),t("td",{style:{"text-align":"right"}},[t("code",null,"jet_reconstruct")])],-1),B=t("td",{style:{"text-align":"right"}},[t("code",null,"RecoStrategy.N2Plain")],-1),S={style:{"text-align":"right"}},L={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},D={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"2.009ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 888 683","aria-hidden":"true"},P=t("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[t("g",{"data-mml-node":"math"},[t("g",{"data-mml-node":"mi"},[t("path",{"data-c":"1D441",d:"M234 637Q231 637 226 637Q201 637 196 638T191 649Q191 676 202 682Q204 683 299 683Q376 683 387 683T401 677Q612 181 616 168L670 381Q723 592 723 606Q723 633 659 637Q635 637 635 648Q635 650 637 660Q641 676 643 679T653 683Q656 683 684 682T767 680Q817 680 843 681T873 682Q888 682 888 672Q888 650 880 642Q878 637 858 637Q787 633 769 597L620 7Q618 0 599 0Q585 0 582 2Q579 5 453 305L326 604L261 344Q196 88 196 79Q201 46 268 46H278Q284 41 284 38T282 19Q278 6 272 0H259Q228 2 151 2Q123 2 100 2T63 2T46 1Q31 1 31 10Q31 14 34 26T39 40Q41 46 62 46Q130 49 150 85Q154 91 221 362L289 634Q287 635 234 637Z",style:{"stroke-width":"3"}})])])],-1),R=[P],Z=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mi",null,"N")])],-1),J=t("td",{style:{"text-align":"right"}},[t("code",null,"plain_jet_reconstruct")],-1),I=t("td",{style:{"text-align":"right"}},[t("code",null,"RecoStrategy.N2Tiled")],-1),q={style:{"text-align":"right"}},N={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},G={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.048ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.717ex",height:"1.593ex",role:"img",focusable:"false",viewBox:"0 -683 759 704","aria-hidden":"true"},z=t("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[t("g",{"data-mml-node":"math"},[t("g",{"data-mml-node":"mi"},[t("path",{"data-c":"1D445",d:"M230 637Q203 637 198 638T193 649Q193 676 204 682Q206 683 378 683Q550 682 564 680Q620 672 658 652T712 606T733 563T739 529Q739 484 710 445T643 385T576 351T538 338L545 333Q612 295 612 223Q612 212 607 162T602 80V71Q602 53 603 43T614 25T640 16Q668 16 686 38T712 85Q717 99 720 102T735 105Q755 105 755 93Q755 75 731 36Q693 -21 641 -21H632Q571 -21 531 4T487 82Q487 109 502 166T517 239Q517 290 474 313Q459 320 449 321T378 323H309L277 193Q244 61 244 59Q244 55 245 54T252 50T269 48T302 46H333Q339 38 339 37T336 19Q332 6 326 0H311Q275 2 180 2Q146 2 117 2T71 2T50 1Q33 1 33 10Q33 12 36 24Q41 43 46 45Q50 46 61 46H67Q94 46 127 49Q141 52 146 61Q149 65 218 339T287 628Q287 635 230 637ZM630 554Q630 586 609 608T523 636Q521 636 500 636T462 637H440Q393 637 386 627Q385 624 352 494T319 361Q319 360 388 360Q466 361 492 367Q556 377 592 426Q608 449 619 486T630 554Z",style:{"stroke-width":"3"}})])])],-1),O=[z],X=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mi",null,"R")])],-1),$={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},K={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"2.009ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 888 683","aria-hidden":"true"},U=t("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[t("g",{"data-mml-node":"math"},[t("g",{"data-mml-node":"mi"},[t("path",{"data-c":"1D441",d:"M234 637Q231 637 226 637Q201 637 196 638T191 649Q191 676 202 682Q204 683 299 683Q376 683 387 683T401 677Q612 181 616 168L670 381Q723 592 723 606Q723 633 659 637Q635 637 635 648Q635 650 637 660Q641 676 643 679T653 683Q656 683 684 682T767 680Q817 680 843 681T873 682Q888 682 888 672Q888 650 880 642Q878 637 858 637Q787 633 769 597L620 7Q618 0 599 0Q585 0 582 2Q579 5 453 305L326 604L261 344Q196 88 196 79Q201 46 268 46H278Q284 41 284 38T282 19Q278 6 272 0H259Q228 2 151 2Q123 2 100 2T63 2T46 1Q31 1 31 10Q31 14 34 26T39 40Q41 46 62 46Q130 49 150 85Q154 91 221 362L289 634Q287 635 234 637Z",style:{"stroke-width":"3"}})])])],-1),W=[U],Y=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mi",null,"N")])],-1),tt=t("td",{style:{"text-align":"right"}},[t("code",null,"tiled_jet_reconstruct")],-1),st=a(`Generally one can use the jet_reconstruct interface, shown above, as the Best strategy safely as the overhead is extremely low. That interface supports a strategy option to switch to a different option.
Another option, if one wishes to use a specific strategy, is to call that strategy's interface directly, e.g.,
julia # For N2Plain strategy called directly plain_jet_reconstruct (particles :: Vector{T} ; p = - 1 , R = 1.0 , recombine = + ) Note that there is no strategy option in these interfaces.
Inclusive and Exclusive Selections `,5),et={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},it={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.452ex",height:"1.439ex",role:"img",focusable:"false",viewBox:"0 -442 1083.8 636","aria-hidden":"true"},at=a('julia sorted_jets = sort! ( inclusive_jets (cs :: ClusterSequence ; ptmin = 5.0 ), by = JetReconstruction . energy, rev = true ) Plotting and Animation
To visualise the clustered jets as a 3d bar plot (see illustration above) we now use Makie.jl. See the jetsplot function in ext/JetVisualisation.jl and its documentation for more. There are two worked examples in the examples directory.
The plotting code is a package extension and will load if the one of the Makie modules is loaded in the environment.
The animaterecoClusterSequence object. See the function documentation for the many options that can be customised.
Serialisation The package also provides methods such as loadjets, loadjets!, and savejets that one can use to save and load objects on/from disk easily in a very flexible format. See documentation for more.
Reference Although it has been developed further since the CHEP2023 conference, the CHEP conference proceedings, 10.1051/epjconf/202429505017 , should be cited if you use this package:
bibtex @article { refId0 , author = { {Stewart, Graeme Andrew} and {Gras, Philippe} and {Hegner, Benedikt} and {Krasnopolski, Atell} } , doi = { 10.1051/epjconf/202429505017 } , journal = { EPJ Web of Conf. } , pages = { 05017 } , title = { Polyglot Jet Finding } , url = { https://doi.org/10.1051/epjconf/202429505017 } , volume = 295 , year = 2024 , eprint = { 2309.17309 } , archivePrefix = { arXiv } , primaryClass = { hep-ex } } The original paper on arXiv is:
bibtex @misc { stewart2023polyglot , title = { Polyglot Jet Finding } , author = { Graeme Andrew Stewart and Philippe Gras and Benedikt Hegner and Atell Krasnopolski } , year = { 2023 } , eprint = { 2309.17309 } , archivePrefix = { arXiv } , primaryClass = { hep-ex } } Authors and Copyright Code in this package is authored by:
Atell Krasnopolski <delta_atell@protonmail.com>
Graeme A Stewart <graeme.andrew.stewart@cern.ch>
Philippe Gras <philippe.gras@cern.ch>
and is Copyright 2022-2024 The Authors, CERN.
The code is under the MIT License.
`,18);function ft(vt,Ft,Ct,Ht,Vt,Mt){return i(),e("div",null,[h,t("p",null,[s("This package implements sequential Jet Reconstruction (clustering) algorithms, which are used in high-energy physics as part of event reconstruction for "),t("mjx-container",r,[(i(),e("svg",p,c)),Q]),s(" and "),t("mjx-container",T,[(i(),e("svg",k,m)),u]),s(" colliders.")]),E,t("p",null,[s("The algorithms include "),t("mjx-container",x,[(i(),e("svg",_,w)),b]),s(", Cambridge/Aachen and inclusive "),t("mjx-container",f,[(i(),e("svg",v,C)),H]),s(".")]),V,t("table",M,[j,t("tbody",null,[A,t("tr",null,[B,t("td",S,[s("Global matching of particles at each interation (works well for low "),t("mjx-container",L,[(i(),e("svg",D,R)),Z]),s(")")]),J]),t("tr",null,[I,t("td",q,[s("Use tiles of radius "),t("mjx-container",N,[(i(),e("svg",G,O)),X]),s(" to limit search space (works well for higher "),t("mjx-container",$,[(i(),e("svg",K,W)),Y]),s(")")]),tt])])]),st,t("p",null,[s("To obtain final jets both inclusive ("),t("mjx-container",et,[(i(),e("svg",it,nt)),lt]),s(" cut) and exclusive ("),t("mjx-container",ot,[(i(),e("svg",ht,pt)),dt]),s(" or "),t("mjx-container",ct,[(i(),e("svg",Qt,kt)),gt]),s(" cut) selections are supported:")]),mt,ut,t("p",null,[s("Sorting vectors is trivial in Julia, no special sorting methods are provided. As an example, to sort exclusive jets of "),t("mjx-container",Et,[(i(),e("svg",xt,yt)),wt]),s(" (usually GeV, depending on your EDM) from highest energy to lowest:")]),bt])}const Bt=n(o,[["render",ft]]);export{At as __pageData,Bt as default};
diff --git a/dev/assets/index.md.Cm9Ga1X8.lean.js b/dev/assets/index.md.Cm9Ga1X8.lean.js
new file mode 100644
index 0000000..79fc0bf
--- /dev/null
+++ b/dev/assets/index.md.Cm9Ga1X8.lean.js
@@ -0,0 +1 @@
+import{_ as n,c as e,j as t,a as s,a7 as a,o as i}from"./chunks/framework.CdnWR3-W.js";const l="/JetReconstruction.jl/dev/assets/jetvis.Bpiv-g6k.png",At=JSON.parse('{"title":"Jet Reconstruction","description":"","frontmatter":{"layout":"home","hero":{"name":"JetReconstruction.jl","tagline":"Jet reconstruction (reclustering) with Julia","image":{"src":"/logo.png","alt":"DocumenterVitepress"},"actions":[{"theme":"brand","text":"Examples","link":"/examples"},{"theme":"alt","text":"Public APIs","link":"/lib/public"}]}},"headers":[],"relativePath":"index.md","filePath":"index.md","lastUpdated":null}'),o={name:"index.md"},h=t("h1",{id:"Jet-Reconstruction",tabindex:"-1"},[s("Jet Reconstruction "),t("a",{class:"header-anchor",href:"#Jet-Reconstruction","aria-label":'Permalink to "Jet Reconstruction {#Jet-Reconstruction}"'},"")],-1),r={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.276ex",height:"1.439ex",role:"img",focusable:"false",viewBox:"0 -442 1006 636","aria-hidden":"true"},d=a("",1),c=[d],Q=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mi",null,"p"),t("mi",null,"p")])],-1),T={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},k={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"4.973ex",height:"1.779ex",role:"img",focusable:"false",viewBox:"0 -775.2 2198.3 786.2","aria-hidden":"true"},g=a("",1),m=[g],u=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msup",null,[t("mi",null,"e"),t("mo",null,"+")]),t("msup",null,[t("mi",null,"e"),t("mo",null,"−")])])],-1),E=a("",2),x={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},_={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.339ex"},xmlns:"http://www.w3.org/2000/svg",width:"9.185ex",height:"1.91ex",role:"img",focusable:"false",viewBox:"0 -694 4060 844","aria-hidden":"true"},y=a("",1),w=[y],b=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mtext",null,"anti"),t("mo",null,"−"),t("msub",null,[t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",null,"k")]),t("mtext",null,"T")])])],-1),f={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},v={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.339ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.522ex",height:"1.91ex",role:"img",focusable:"false",viewBox:"0 -694 1114.5 844","aria-hidden":"true"},F=a("",1),C=[F],H=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msub",null,[t("mi",null,"k"),t("mtext",null,"T")])])],-1),V=a("",7),M={tabindex:"0"},j=t("thead",null,[t("tr",null,[t("th",{style:{"text-align":"right"}},"Strategy Name"),t("th",{style:{"text-align":"right"}},"Notes"),t("th",{style:{"text-align":"right"}},"Interface")])],-1),A=t("tr",null,[t("td",{style:{"text-align":"right"}},[t("code",null,"RecoStrategy.Best")]),t("td",{style:{"text-align":"right"}},"Dynamically switch strategy based on input particle density"),t("td",{style:{"text-align":"right"}},[t("code",null,"jet_reconstruct")])],-1),B=t("td",{style:{"text-align":"right"}},[t("code",null,"RecoStrategy.N2Plain")],-1),S={style:{"text-align":"right"}},L={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},D={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"2.009ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 888 683","aria-hidden":"true"},P=t("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[t("g",{"data-mml-node":"math"},[t("g",{"data-mml-node":"mi"},[t("path",{"data-c":"1D441",d:"M234 637Q231 637 226 637Q201 637 196 638T191 649Q191 676 202 682Q204 683 299 683Q376 683 387 683T401 677Q612 181 616 168L670 381Q723 592 723 606Q723 633 659 637Q635 637 635 648Q635 650 637 660Q641 676 643 679T653 683Q656 683 684 682T767 680Q817 680 843 681T873 682Q888 682 888 672Q888 650 880 642Q878 637 858 637Q787 633 769 597L620 7Q618 0 599 0Q585 0 582 2Q579 5 453 305L326 604L261 344Q196 88 196 79Q201 46 268 46H278Q284 41 284 38T282 19Q278 6 272 0H259Q228 2 151 2Q123 2 100 2T63 2T46 1Q31 1 31 10Q31 14 34 26T39 40Q41 46 62 46Q130 49 150 85Q154 91 221 362L289 634Q287 635 234 637Z",style:{"stroke-width":"3"}})])])],-1),R=[P],Z=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mi",null,"N")])],-1),J=t("td",{style:{"text-align":"right"}},[t("code",null,"plain_jet_reconstruct")],-1),I=t("td",{style:{"text-align":"right"}},[t("code",null,"RecoStrategy.N2Tiled")],-1),q={style:{"text-align":"right"}},N={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},G={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.048ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.717ex",height:"1.593ex",role:"img",focusable:"false",viewBox:"0 -683 759 704","aria-hidden":"true"},z=t("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[t("g",{"data-mml-node":"math"},[t("g",{"data-mml-node":"mi"},[t("path",{"data-c":"1D445",d:"M230 637Q203 637 198 638T193 649Q193 676 204 682Q206 683 378 683Q550 682 564 680Q620 672 658 652T712 606T733 563T739 529Q739 484 710 445T643 385T576 351T538 338L545 333Q612 295 612 223Q612 212 607 162T602 80V71Q602 53 603 43T614 25T640 16Q668 16 686 38T712 85Q717 99 720 102T735 105Q755 105 755 93Q755 75 731 36Q693 -21 641 -21H632Q571 -21 531 4T487 82Q487 109 502 166T517 239Q517 290 474 313Q459 320 449 321T378 323H309L277 193Q244 61 244 59Q244 55 245 54T252 50T269 48T302 46H333Q339 38 339 37T336 19Q332 6 326 0H311Q275 2 180 2Q146 2 117 2T71 2T50 1Q33 1 33 10Q33 12 36 24Q41 43 46 45Q50 46 61 46H67Q94 46 127 49Q141 52 146 61Q149 65 218 339T287 628Q287 635 230 637ZM630 554Q630 586 609 608T523 636Q521 636 500 636T462 637H440Q393 637 386 627Q385 624 352 494T319 361Q319 360 388 360Q466 361 492 367Q556 377 592 426Q608 449 619 486T630 554Z",style:{"stroke-width":"3"}})])])],-1),O=[z],X=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mi",null,"R")])],-1),$={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},K={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"2.009ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 888 683","aria-hidden":"true"},U=t("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[t("g",{"data-mml-node":"math"},[t("g",{"data-mml-node":"mi"},[t("path",{"data-c":"1D441",d:"M234 637Q231 637 226 637Q201 637 196 638T191 649Q191 676 202 682Q204 683 299 683Q376 683 387 683T401 677Q612 181 616 168L670 381Q723 592 723 606Q723 633 659 637Q635 637 635 648Q635 650 637 660Q641 676 643 679T653 683Q656 683 684 682T767 680Q817 680 843 681T873 682Q888 682 888 672Q888 650 880 642Q878 637 858 637Q787 633 769 597L620 7Q618 0 599 0Q585 0 582 2Q579 5 453 305L326 604L261 344Q196 88 196 79Q201 46 268 46H278Q284 41 284 38T282 19Q278 6 272 0H259Q228 2 151 2Q123 2 100 2T63 2T46 1Q31 1 31 10Q31 14 34 26T39 40Q41 46 62 46Q130 49 150 85Q154 91 221 362L289 634Q287 635 234 637Z",style:{"stroke-width":"3"}})])])],-1),W=[U],Y=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mi",null,"N")])],-1),tt=t("td",{style:{"text-align":"right"}},[t("code",null,"tiled_jet_reconstruct")],-1),st=a("",5),et={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},it={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.452ex",height:"1.439ex",role:"img",focusable:"false",viewBox:"0 -442 1083.8 636","aria-hidden":"true"},at=a("",1),nt=[at],lt=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msub",null,[t("mi",null,"p"),t("mi",null,"T")])])],-1),ot={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},ht={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.666ex"},xmlns:"http://www.w3.org/2000/svg",width:"4.278ex",height:"1.666ex",role:"img",focusable:"false",viewBox:"0 -442 1890.7 736.2","aria-hidden":"true"},rt=a("",1),pt=[rt],dt=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msub",null,[t("mi",null,"n"),t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",null,"j"),t("mi",null,"e"),t("mi",null,"t"),t("mi",null,"s")])])])],-1),ct={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},Qt={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.666ex"},xmlns:"http://www.w3.org/2000/svg",width:"2.575ex",height:"2.236ex",role:"img",focusable:"false",viewBox:"0 -694 1138.3 988.2","aria-hidden":"true"},Tt=a("",1),kt=[Tt],gt=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("msub",null,[t("mi",null,"d"),t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",null,"i"),t("mi",null,"j")])])])],-1),mt=t("ul",null,[t("li",null,[t("p",null,[t("a",{href:"/JetReconstruction.jl/dev/lib/public#JetReconstruction.inclusive_jets"},"inclusive_jets(clusterseq::ClusterSequence, ptmin = 0.0)")])]),t("li",null,[t("p",null,[t("a",{href:"/JetReconstruction.jl/dev/lib/public#JetReconstruction.exclusive_jets-Tuple{ClusterSequence}"},"exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing)")])])],-1),ut=t("h3",{id:"sorting",tabindex:"-1"},[s("Sorting "),t("a",{class:"header-anchor",href:"#sorting","aria-label":'Permalink to "Sorting"'},"")],-1),Et={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},xt={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.09ex"},xmlns:"http://www.w3.org/2000/svg",width:"5.28ex",height:"1.597ex",role:"img",focusable:"false",viewBox:"0 -666 2333.8 706","aria-hidden":"true"},_t=a("",1),yt=[_t],wt=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mo",null,">"),t("mn",null,"5.0")])],-1),bt=a("",18);function ft(vt,Ft,Ct,Ht,Vt,Mt){return i(),e("div",null,[h,t("p",null,[s("This package implements sequential Jet Reconstruction (clustering) algorithms, which are used in high-energy physics as part of event reconstruction for "),t("mjx-container",r,[(i(),e("svg",p,c)),Q]),s(" and "),t("mjx-container",T,[(i(),e("svg",k,m)),u]),s(" colliders.")]),E,t("p",null,[s("The algorithms include "),t("mjx-container",x,[(i(),e("svg",_,w)),b]),s(", Cambridge/Aachen and inclusive "),t("mjx-container",f,[(i(),e("svg",v,C)),H]),s(".")]),V,t("table",M,[j,t("tbody",null,[A,t("tr",null,[B,t("td",S,[s("Global matching of particles at each interation (works well for low "),t("mjx-container",L,[(i(),e("svg",D,R)),Z]),s(")")]),J]),t("tr",null,[I,t("td",q,[s("Use tiles of radius "),t("mjx-container",N,[(i(),e("svg",G,O)),X]),s(" to limit search space (works well for higher "),t("mjx-container",$,[(i(),e("svg",K,W)),Y]),s(")")]),tt])])]),st,t("p",null,[s("To obtain final jets both inclusive ("),t("mjx-container",et,[(i(),e("svg",it,nt)),lt]),s(" cut) and exclusive ("),t("mjx-container",ot,[(i(),e("svg",ht,pt)),dt]),s(" or "),t("mjx-container",ct,[(i(),e("svg",Qt,kt)),gt]),s(" cut) selections are supported:")]),mt,ut,t("p",null,[s("Sorting vectors is trivial in Julia, no special sorting methods are provided. As an example, to sort exclusive jets of "),t("mjx-container",Et,[(i(),e("svg",xt,yt)),wt]),s(" (usually GeV, depending on your EDM) from highest energy to lowest:")]),bt])}const Bt=n(o,[["render",ft]]);export{At as __pageData,Bt as default};
diff --git a/dev/assets/inter-italic-cyrillic-ext.r48I6akx.woff2 b/dev/assets/inter-italic-cyrillic-ext.r48I6akx.woff2
new file mode 100644
index 0000000..b6b603d
Binary files /dev/null and b/dev/assets/inter-italic-cyrillic-ext.r48I6akx.woff2 differ
diff --git a/dev/assets/inter-italic-cyrillic.By2_1cv3.woff2 b/dev/assets/inter-italic-cyrillic.By2_1cv3.woff2
new file mode 100644
index 0000000..def40a4
Binary files /dev/null and b/dev/assets/inter-italic-cyrillic.By2_1cv3.woff2 differ
diff --git a/dev/assets/inter-italic-greek-ext.1u6EdAuj.woff2 b/dev/assets/inter-italic-greek-ext.1u6EdAuj.woff2
new file mode 100644
index 0000000..e070c3d
Binary files /dev/null and b/dev/assets/inter-italic-greek-ext.1u6EdAuj.woff2 differ
diff --git a/dev/assets/inter-italic-greek.DJ8dCoTZ.woff2 b/dev/assets/inter-italic-greek.DJ8dCoTZ.woff2
new file mode 100644
index 0000000..a3c16ca
Binary files /dev/null and b/dev/assets/inter-italic-greek.DJ8dCoTZ.woff2 differ
diff --git a/dev/assets/inter-italic-latin-ext.CN1xVJS-.woff2 b/dev/assets/inter-italic-latin-ext.CN1xVJS-.woff2
new file mode 100644
index 0000000..2210a89
Binary files /dev/null and b/dev/assets/inter-italic-latin-ext.CN1xVJS-.woff2 differ
diff --git a/dev/assets/inter-italic-latin.C2AdPX0b.woff2 b/dev/assets/inter-italic-latin.C2AdPX0b.woff2
new file mode 100644
index 0000000..790d62d
Binary files /dev/null and b/dev/assets/inter-italic-latin.C2AdPX0b.woff2 differ
diff --git a/dev/assets/inter-italic-vietnamese.BSbpV94h.woff2 b/dev/assets/inter-italic-vietnamese.BSbpV94h.woff2
new file mode 100644
index 0000000..1eec077
Binary files /dev/null and b/dev/assets/inter-italic-vietnamese.BSbpV94h.woff2 differ
diff --git a/dev/assets/inter-roman-cyrillic-ext.BBPuwvHQ.woff2 b/dev/assets/inter-roman-cyrillic-ext.BBPuwvHQ.woff2
new file mode 100644
index 0000000..2cfe615
Binary files /dev/null and b/dev/assets/inter-roman-cyrillic-ext.BBPuwvHQ.woff2 differ
diff --git a/dev/assets/inter-roman-cyrillic.C5lxZ8CY.woff2 b/dev/assets/inter-roman-cyrillic.C5lxZ8CY.woff2
new file mode 100644
index 0000000..e3886dd
Binary files /dev/null and b/dev/assets/inter-roman-cyrillic.C5lxZ8CY.woff2 differ
diff --git a/dev/assets/inter-roman-greek-ext.CqjqNYQ-.woff2 b/dev/assets/inter-roman-greek-ext.CqjqNYQ-.woff2
new file mode 100644
index 0000000..36d6748
Binary files /dev/null and b/dev/assets/inter-roman-greek-ext.CqjqNYQ-.woff2 differ
diff --git a/dev/assets/inter-roman-greek.BBVDIX6e.woff2 b/dev/assets/inter-roman-greek.BBVDIX6e.woff2
new file mode 100644
index 0000000..2bed1e8
Binary files /dev/null and b/dev/assets/inter-roman-greek.BBVDIX6e.woff2 differ
diff --git a/dev/assets/inter-roman-latin-ext.4ZJIpNVo.woff2 b/dev/assets/inter-roman-latin-ext.4ZJIpNVo.woff2
new file mode 100644
index 0000000..9a8d1e2
Binary files /dev/null and b/dev/assets/inter-roman-latin-ext.4ZJIpNVo.woff2 differ
diff --git a/dev/assets/inter-roman-latin.Di8DUHzh.woff2 b/dev/assets/inter-roman-latin.Di8DUHzh.woff2
new file mode 100644
index 0000000..07d3c53
Binary files /dev/null and b/dev/assets/inter-roman-latin.Di8DUHzh.woff2 differ
diff --git a/dev/assets/inter-roman-vietnamese.BjW4sHH5.woff2 b/dev/assets/inter-roman-vietnamese.BjW4sHH5.woff2
new file mode 100644
index 0000000..57bdc22
Binary files /dev/null and b/dev/assets/inter-roman-vietnamese.BjW4sHH5.woff2 differ
diff --git a/dev/assets/jetvis.png b/dev/assets/jetvis.Bpiv-g6k.png
similarity index 100%
rename from dev/assets/jetvis.png
rename to dev/assets/jetvis.Bpiv-g6k.png
diff --git a/dev/assets/lib_internal.md.BRkM1dic.js b/dev/assets/lib_internal.md.BRkM1dic.js
new file mode 100644
index 0000000..1370827
--- /dev/null
+++ b/dev/assets/lib_internal.md.BRkM1dic.js
@@ -0,0 +1,6 @@
+import{_ as e,c as t,o as i,a7 as s}from"./chunks/framework.CdnWR3-W.js";const g=JSON.parse('{"title":"Jet Reconstruction Internal Documentation","description":"","frontmatter":{},"headers":[],"relativePath":"lib/internal.md","filePath":"lib/internal.md","lastUpdated":null}'),o={name:"lib/internal.md"},n=s(`Jet Reconstruction Internal Documentation Documentation for JetReconstruction.jl's internal methods and types.
N.B. no guarantee is made of stability of these interfaces or types.
Index Public Interface # Base.:+ —
Method .
julia + (j1 :: PseudoJet , j2 :: PseudoJet ) Addition operator for PseudoJet objects.
Arguments
Returns
A new PseudoJet object with the sum of the momenta and energy of j1 and j2.
source
# Base.copy —
Method .
Create a copy of a TiledJet object.
Arguments
j::TiledJet: The TiledJet object to be copied.Returns
A new TiledJet object with the same attributes as the input object.
source
# Base.iterate —
Function .
julia Base . iterate (t :: neighbour_tiles , state = 1 ) Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.
source
# Base.iterate —
Function .
julia Base . iterate (t :: rightmost_tiles , state = 1 ) Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.
source
# Base.iterate —
Method .
julia Base . iterate (tj :: TiledJet ) Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).
Arguments
tj::TiledJet: The TiledJet object to start to iterate over.source
# Base.show —
Method .
julia show (io :: IO , jet :: PseudoJet ) Print a PseudoJet object to the specified IO stream.
Arguments
source
# Base.tryparse —
Method .
julia Base . tryparse (E :: Type{<:Enum} , str :: String ) Parser that converts a string to an enum value if it exists, otherwise returns nothing.
source
# JetReconstruction.CosTheta —
Method .
julia CosTheta (p :: PseudoJet ) Compute the cosine of the angle between the momentum vector p and the z-axis.
Arguments
p::PseudoJet: The PseudoJet object representing the momentum vector.Returns
The cosine of the angle between p and the z-axis. source
# JetReconstruction._ensure_valid_rap_phi —
Method .
julia _ensure_valid_rap_phi (p :: PseudoJet ) Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.source
# JetReconstruction._plain_jet_reconstruct —
Method .
julia _plain_jet_reconstruct (; particles :: Vector{PseudoJet} , p = - 1 , R = 1.0 , recombine = + ) This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt.
Arguments
particles: A vector of PseudoJet objects representing the input particles.
p=-1: The power to which the transverse momentum (pt) of each particle is raised.
R=1.0: The jet radius parameter.
recombine: The recombination function used to merge two jets. Default is + (additive recombination).
Returns
clusterseq: The resulting ClusterSequence object representing the reconstructed jets.source
# JetReconstruction._set_rap_phi! —
Method .
_set_rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source
# JetReconstruction._tj_diJ —
Method .
Compute the dij metric value for a given jet.
Arguments
Returns
The dij value for the jet. Example
source
# JetReconstruction._tj_dist —
Method .
julia _tj_dist (jetA, jetB) Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
jetA: The first jet.
jetB: The second jet.
Returns
The squared distance between jetA and jetB.
Examples
source
# JetReconstruction.add_step_to_history! —
Method .
julia add_step_to_history! (clusterseq :: ClusterSequence , parent1, parent2, jetp_index, dij) Add a new jet's history into the recombination sequence.
Arguments:
clusterseq::ClusterSequence: The cluster sequence object.
parent1: The index of the first parent.
parent2: The index of the second parent.
jetp_index: The index of the jet.
dij: The dij value.
This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source
# JetReconstruction.add_untagged_neighbours_to_tile_union —
Method .
julia add_untagged_neighbours_to_tile_union (center_index, tile_union, n_near_tiles, tiling) Adds to the vector tile_union the tiles that are in the neighbourhood of the specified center_index, including itself and whose tagged status are false - start adding from position n_near_tiles-1, and increase n_near_tiles. When a neighbour is added its tagged status is set to true.
Arguments
center_index: The index of the center tile.
tile_union: An array to store the indices of neighbouring tiles.
n_near_tiles: The number of neighbouring tiles.
tiling: The tiling object containing the tile tags.
Returns
The updated number of near tiles.
source
# JetReconstruction.detach! —
Method .
julia detach! (jet :: TiledJet ) Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
jet::TiledJet: The TiledJet object to detach.source
# JetReconstruction.determine_rapidity_extent —
Method .
julia determine_rapidity_extent (eta :: Vector{T} ) where T <: AbstractFloat Calculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
eta::Vector{T}: A vector of rapidity values.Returns
source
# JetReconstruction.dij —
Method .
julia dij (i, kt2_array, nn, nndist) Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
i: The index of the element.
kt2_array: An array of kt2 values.
nn: An array of nearest neighbors.
nndist: An array of nearest neighbor distances.
Returns
source
# JetReconstruction.dist —
Method .
julia dist (i, j, rapidity_array, phi_array) Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).
j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).
rapidity_array::Vector{Float64}: Array of rapidity coordinates.
phi_array::Vector{Float64}: Array of phi coordinates.
Returns
distance::Float64: The distance between the two points.source
# JetReconstruction.do_iB_recombination_step! —
Method .
julia do_iB_recombination_step! (clusterseq :: ClusterSequence , jet_i, diB) Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
source
# JetReconstruction.do_ij_recombination_step! —
Function .
julia do_ij_recombination_step! (clusterseq :: ClusterSequence , jet_i, jet_j, dij, recombine =+ ) Perform the bookkeeping associated with the step of recombining jet_i and jet_j (assuming a distance dij).
Arguments
clusterseq::ClusterSequence: The cluster sequence object.
jet_i: The index of the first jet to be recombined.
jet_j: The index of the second jet to be recombined.
dij: The distance between the two jets.
recombine=+: The recombination function to be used. Default is addition.
Returns
newjet_k: The index of the newly created jet.Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source
# JetReconstruction.energy —
Method .
julia energy (p :: PseudoJet ) Return the energy of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The energy of the PseudoJet. source
# JetReconstruction.eta —
Method .
Compute the pseudorapidity (η) of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.Returns
The pseudorapidity (η) of the PseudoJet. source
# JetReconstruction.fast_findmin —
Method .
julia fast_findmin (dij, n) Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
Returns
source
# JetReconstruction.find_tile_neighbours! —
Method .
julia find_tile_neighbours! (tile_union, jetA, jetB, oldB, tiling) Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
tile_union: The tile union to which the neighbouring tiles will be added.
jetA: The first jet.
jetB: The second jet.
oldB: The old second jet.
tiling: The tiling information.
Returns
The number of neighbouring tiles added to the tile_union.
source
# JetReconstruction.geometric_distance —
Method .
julia geometric_distance (eta1 :: AbstractFloat , phi1 :: AbstractFloat , eta2 :: AbstractFloat , phi2 :: AbstractFloat ) Compute the geometric distance between two points in the rap-phi plane.
Arguments
eta1::AbstractFloat: The eta coordinate of the first point.
phi1::AbstractFloat: The phi coordinate of the first point.
eta2::AbstractFloat: The eta coordinate of the second point.
phi2::AbstractFloat: The phi coordinate of the second point.
Returns
distance::Float64: The geometric distance between the two points.source
# JetReconstruction.get_all_ancestors —
Method .
julia get_all_ancestors (idx, cs :: ClusterSequence ) Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
Returns
An array of indices representing the ancestors of the given jet.
source
# JetReconstruction.get_dij_dist —
Method .
julia get_dij_dist (nn_dist, kt2_1, kt2_2, R2) Compute the dij metric distance between two jets.
Arguments
nn_dist: The nearest-neighbor distance between two jets.
kt2_1: The squared momentum metric value of the first jet.
kt2_2: The squared momentum metric value of the second jet.
R2: The jet radius parameter squared.
Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source
# JetReconstruction.get_tile —
Method .
julia get_tile (tiling_setup :: TilingDef , eta :: AbstractFloat , phi :: AbstractFloat ) Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
tiling_setup: A TilingDef object that contains the tiling setup parameters.
eta: The eta value for which to calculate the tile index.
phi: The phi value for which to calculate the tile index.
Returns
source
# JetReconstruction.get_tile_cartesian_indices —
Method .
julia get_tile_linear_index (tiling_setup :: TilingDef , i_η :: Int , i_ϕ :: Int ) Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.
i_η::Int: The index of the tile in the η dimension.
i_ϕ::Int: The index of the tile in the ϕ dimension.
Returns
The linear index of the tile. source
# JetReconstruction.initial_history —
Method .
julia initial_history (particles) Create an initial history for the given particles.
Arguments
particles: The initial vector of stable particles.Returns
source
# JetReconstruction.insert! —
Method .
julia insert! (nextjet :: TiledJet , jettomove :: TiledJet ) Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
Example
source
# JetReconstruction.isvalid —
Method .
julia isvalid (t :: TiledJet ) Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
t::TiledJet: The TiledJet object to check.Returns
Bool: true if the TiledJet object is valid, false otherwise.source
# JetReconstruction.jet_ranks —
Method .
julia jet_ranks (clusterseq :: ClusterSequence ; compare_fn = JetReconstruction . pt) Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.
compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.
Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source
# JetReconstruction.m —
Method .
Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the invariant mass.Returns
The invariant mass of the PseudoJet object.
source
# JetReconstruction.m2 —
Method .
Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.Returns
The invariant mass squared (m^2) of the PseudoJet. source
# JetReconstruction.mag —
Method .
Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the magnitude.Returns
The magnitude of the PseudoJet object.
source
# JetReconstruction.mass —
Method .
Compute the invariant mass (alias for m(p)).
Arguments
p::PseudoJet: The PseudoJet object for which to compute the mass.Returns
The mass of the PseudoJet. source
# JetReconstruction.mass2 —
Function .
Alias for m2 function
source
# JetReconstruction.merge_steps —
Method .
julia merge_steps (clusterseq :: ClusterSequence ) Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
clusterseq::ClusterSequence: The cluster sequence object.Returns
merge_steps::Int: The number of merge steps.source
# JetReconstruction.phi —
Method .
Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.Returns
The azimuthal angle of p in the range [0, 2π). source
# JetReconstruction.phi_02pi —
Method .
julia phi_02pi (p :: PseudoJet ) Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.Returns
The azimuthal angle of p in the range [0, 2π). source
# JetReconstruction.pt —
Method .
Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.Returns
The transverse momentum (pt) of the PseudoJet. source
# JetReconstruction.pt2 —
Method .
Get the squared transverse momentum of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The squared transverse momentum of the PseudoJet. source
# JetReconstruction.px —
Method .
Return the x-component of the momentum of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The x-component of the momentum of the PseudoJet. source
# JetReconstruction.py —
Method .
Return the y-component of the momentum of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The y-component of the momentum of the PseudoJet. source
# JetReconstruction.pz —
Method .
Return the z-component of the momentum of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The z-component of the momentum of the PseudoJet. source
# JetReconstruction.rapidity —
Method .
julia rapidity (p :: PseudoJet ) Compute the rapidity of a PseudoJet object.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the rapidity.Returns
The rapidity of the PseudoJet object.
source
# JetReconstruction.reco_state —
Method .
julia reco_state (cs :: ClusterSequence , pt_ranks; iteration = 0 ) This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
cs::ClusterSequence: The ClusterSequence object to update.
ranks: The ranks of the original clusters, that are inherited by peudojets
during the reconstruction process.
iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).
ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).
Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source
# JetReconstruction.rightneighbours —
Method .
julia rightneighbours (center :: Int , tiling :: Tiling ) Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
Returns
Surrounding: An object containing the indices of the right neighbors.source
# JetReconstruction.set_momentum! —
Method .
julia set_momentum! (j :: PseudoJet , px, py, pz, E) Set the momentum components and energy of a PseudoJet object.
Arguments
j::PseudoJet: The PseudoJet object to set the momentum for.
px: The x-component of the momentum.
py: The y-component of the momentum.
pz: The z-component of the momentum.
E: The energy of the particle.
source
# JetReconstruction.set_nearest_neighbours! —
Method .
julia set_nearest_neighbours! (clusterseq :: ClusterSequence , tiling :: Tiling , tiledjets :: Vector{TiledJet} ) This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
clusterseq::ClusterSequence: The cluster sequence object.
tiling::Tiling: The tiling object.
tiledjets::Vector{TiledJet}: The vector of tiled jets.
Returns
The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source
# JetReconstruction.setup_tiling —
Method .
julia setup_tiling (eta :: Vector{T} , Rparam :: AbstractFloat ) where T <: AbstractFloat This function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
Returns
tiling_setup: A TilingDef object containing the tiling setup parameters.Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source
# JetReconstruction.surrounding —
Method .
julia surrounding (center :: Int , tiling :: Tiling ) Compute the surrounding indices of a given center index in a tiling.
Arguments
Returns
Surrounding: An object containing the surrounding indices.source
# JetReconstruction.tile_index —
Method .
julia tile_index (tiling_setup, eta :: Float64 , phi :: Float64 ) Compute the tile index for a given (eta, phi) coordinate.
Arguments
tiling_setup: The tiling setup object containing the tile size and number of tiles.
eta::Float64: The eta coordinate.
phi::Float64: The phi coordinate.
Returns
The tile index corresponding to the (eta, phi) coordinate.
source
# JetReconstruction.tiledjet_remove_from_tiles! —
Method .
julia tiledjet_remove_from_tiles! (tiling, jet) Remove a jet from the given tiling structure.
Arguments
Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source
# JetReconstruction.tiledjet_set_jetinfo! —
Method .
julia tiledjet_set_jetinfo! (jet :: TiledJet , clusterseq :: ClusterSequence , tiling :: Tiling , jets_index, R2, p) Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
jet::TiledJet: The TiledJet object to set the information for.
clusterseq::ClusterSequence: The ClusterSequence object containing the jets.
tiling::Tiling: The Tiling object containing the tile information.
jets_index: The index of the jet in the ClusterSequence.
R2: The jet radius parameter squared.
p: The power to raise the pt2 value to.
This function sets the eta, phi, kt2, jets_index, NN_dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
source
# JetReconstruction.upd_nn_crosscheck! —
Method .
julia upd_nn_crosscheck! (i, from, to, rapidity_array, phi_array, R2, nndist, nn) Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck ).
Arguments
i::Int: The index of the particle to update and check against.
from::Int: The starting index of the range of particles to check against.
to::Int: The ending index of the range of particles to check against.
rapidity_array: An array containing the rapidity values of all particles.
phi_array: An array containing the phi values of the all particles.
R2: The squared jet distance threshold for considering a particle as a neighbour.
nndist: The array that stores the nearest neighbor distances.
nn: The array that stores the nearest neighbor indices.
source
# JetReconstruction.upd_nn_nocross! —
Method .
julia upd_nn_nocross! (i, from, to, rapidity_array, phi_array, R2, nndist, nn) Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross ).
Arguments
i::Int: The index of the particle to update and check against.
from::Int: The starting index of the range of particles to check against.
to::Int: The ending index of the range of particles to check against.
rapidity_array: An array containing the rapidity values of all particles.
phi_array: An array containing the phi values of the all particles.
R2: The squared jet distance threshold for considering a particle as a neighbour.
nndist: The array that stores the nearest neighbor distances.
nn: The array that stores the nearest neighbor indices.
source
# JetReconstruction.upd_nn_step! —
Method .
julia upd_nn_step! (i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij) Update the nearest neighbor information after a jet merge step.
Arguments:
i: Index of the first particle in the last merge step.
j: Index of the second particle in the last merge step.
k: Index of the current particle for which the nearest neighbour will be updated.
N: Total number of particles (currently vaild array indexes are [1:N]).
Nn: Number of nearest neighbors to consider.
kt2_array: Array of transverse momentum squared values.
rapidity_array: Array of rapidity values.
phi_array: Array of azimuthal angle values.
R2: Distance threshold squared for nearest neighbors.
nndist: Array of nearest neighbor geometric distances.
nn: Array of nearest neighbor indices.
nndij: Array of metric distances between particles.
This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source
# JetReconstruction.η —
Function .
Alias for the pseudorapidity function, eta.
source
# JetReconstruction.FourMomentum —
Type .
Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.
source
# JetReconstruction.HistoryElement —
Type .
julia struct HistoryElement A struct holding a record of jet mergers and finalisations
Fields:
parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)
parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)
child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.
jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.
dij: The distance corresponding to the recombination at this stage of the clustering.
max_dij_so_far: The largest recombination distance seen so far in the clustering history.
source
# JetReconstruction.HistoryElement —
Method .
julia HistoryElement (jetp_index) Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
jetp_index: The index of the jetp.Returns
A HistoryElement object.
source
# JetReconstruction.JetWithAncestors —
Type .
julia struct JetWithAncestors A struct representing a jet with its origin ancestors.
Fields
self::PseudoJet: The PseudoJet object for this jet.
jetp_index::Int: The index of the jet in the corresponding cluster sequence.
ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).
jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors
Note
This structure needs its associated cluster sequence origin to be useful.
source
# JetReconstruction.Surrounding —
Type .
julia struct Surrounding{N} Structure used for iterating over neighbour tiles.
Fields
indices::NTuple{N, Int}: A tuple of N integers representing the indices.source
# JetReconstruction.TiledJet —
Type .
TiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
id::Int: The ID of the jet.
eta::Float64: The rapidity of the jet.
phi::Float64: The azimuthal angle of the jet.
kt2::Float64: The transverse momentum squared of the jet.
NN_dist::Float64: The distance to the nearest neighbor.
jets_index::Int: The index of the jet in the jet array.
tile_index::Int: The index of the tile in the tile array.
dij_posn::Int: The position of this jet in the dij compact array.
NN::TiledJet: The nearest neighbor.
previous::TiledJet: The previous jet.
next::TiledJet: The next jet.
source
# JetReconstruction.TiledJet —
Method .
Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
id: The ID of the TiledJet object.Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source
# JetReconstruction.Tiling —
Type .
The Tiling struct represents a tiling configuration for jet reconstruction.
Fields
setup::TilingDef: The tiling definition used for the configuration.
tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).
positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.
tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).
source
# JetReconstruction.Tiling —
Method .
julia Tiling (setup :: TilingDef ) Constructs a intial Tiling object based on the provided setup parameters.
Arguments
setup::TilingDef: The setup parameters for the tiling.Returns
A Tiling object.
source
# JetReconstruction.TilingDef —
Type .
A struct representing the definition of a spcific tiling scheme.
Fields
_tiles_eta_min::Float64: The minimum rapidity of the tiles.
_tiles_eta_max::Float64: The maximum rapidity of the tiles.
_tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).
_tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).
_n_tiles_eta::Int: The number of tiles across rapidity.
_n_tiles_phi::Int: The number of tiles across phi.
_n_tiles::Int: The total number of tiles.
_tiles_ieta_min::Int: The minimum rapidity tile index.
_tiles_ieta_max::Int: The maximum rapidity tile index.
Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi, _n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max) Constructs a TilingDef object with the given parameters.
source
# JetReconstruction.neighbour_tiles —
Type .
julia struct neighbour_tiles A struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
n_η::Int: Number of η tiles
n_ϕ::Int: Number of ϕ tiles
start_η::Int: Centre η tile coordinate
start_ϕ::Int: Centre ϕ tile coordinate
source
# JetReconstruction.rightmost_tiles —
Type .
julia struct rightmost_tiles A struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
Note, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
n_η::Int: Number of η tiles
n_ϕ::Int: Number of ϕ tiles
start_η::Int: Centre η tile coordinate
start_ϕ::Int: Centre ϕ tile coordinate
source
Jet Reconstruction Public Documentation Documentation for JetReconstruction.jl's public interfaces.
Index Public Methods and Types # JetReconstruction.exclusive_jets —
Method .
julia exclusive_jets (clusterseq :: ClusterSequence ; dcut = nothing , njets = nothing ) Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.
dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.
njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.
Note : Either dcut or njets must be provided (but not both).
Returns
An array of LorentzVectorCyl objects representing the exclusive jets. Exceptions
ArgumentError: If neither dcut nor njets is provided.
ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.
ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.
Examples
julia exclusive_jets (clusterseq, dcut = 20.0 ) exclusive_jets (clusterseq, njets = 3 ) source
# JetReconstruction.final_jets —
Function .
Specialisation for final jets from LorentzVectors (TODO: merge into more general function)
source
# JetReconstruction.final_jets —
Function .
Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)
source
# JetReconstruction.final_jets —
Function .
julia final_jets (jets :: Vector{PseudoJet} , ptmin :: AbstractFloat = 0.0 ) This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.
ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.
Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source
# JetReconstruction.inclusive_jets —
Function .
julia inclusive_jets (clusterseq :: ClusterSequence , ptmin = 0.0 ) Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
Returns
An array of LorentzVectorCyl objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Example
julia inclusive_jets (clusterseq, ptmin = 10.0 ) source
# JetReconstruction.jet_reconstruct —
Method .
julia jet_reconstruct (particles; p = - 1 , R = 1.0 , recombine = + , strategy = RecoStrategy . Best) Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
particles: A collection of particles used for jet reconstruction.
p=-1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).
R=1.0: The jet radius parameter.
recombine=+: The recombination scheme used for combining particles.
strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.
Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E -scheme.
Example
julia jet_reconstruct (particles; p = - 1 , R = 0.4 ) source
# JetReconstruction.loadjets! —
Method .
julia loadjets! (filename, jets; splitby = isspace, constructor = (px,py,pz,E) -> LorentzVectorHEP (E,px,py,pz), dtype = Float64) Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
julia # Load jets from two files into one array jets = [] loadjets! ( "myjets1.dat" , jets) loadjets! ( "myjets2.dat" , jets) source
# JetReconstruction.loadjets —
Method .
julia loadjets (filename; splitby = isspace, constructor = (px,py,pz,E) -> LorentzVectorHEP (E,px,py,pz), VT = LorentzVector) Load jets from a file.
Arguments
filename: The name of the file to load jets from.
splitby: The delimiter used to split the data in the file. Default is isspace.
constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).
VT: The type of the vector used to store the jet data. Default is LorentzVector.
Returns
A vector of VT objects representing the loaded jets. source
# JetReconstruction.n_exclusive_jets —
Method .
julia n_exclusive_jets (clusterseq :: ClusterSequence ; dcut :: AbstractFloat ) Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
Returns
The number of exclusive jets in the ClusterSequence object.
Example
julia n_exclusive_jets (clusterseq, dcut = 20.0 ) source
# JetReconstruction.plain_jet_reconstruct —
Method .
julia plain_jet_reconstruct (particles :: Vector{T} ; p = - 1 , R = 1.0 , recombine = + ) where T Perform jet reconstruction using the plain algorithm.
Arguments
particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.
p::Int=-1: The integer value used for jet reconstruction.
R::Float64=1.0: The radius parameter used for jet reconstruction.
recombine::Function=+: The recombination function used for jet reconstruction.
Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
Returns
Vector{PseudoJet}: A vector of reconstructed jets.Example
julia jets = plain_jet_reconstruct (particles; p = - 1 , R = 1.0 ) source
# JetReconstruction.read_final_state_particles —
Method .
julia read_final_state_particles (fname; maxevents = - 1 , skipevents = 0 , T = PseudoJet) Reads final state particles from a file and returns them as a vector of type T.
Arguments
fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.
maxevents=-1: The maximum number of events to read. -1 means all events will be read.
skipevents=0: The number of events to skip before an event is included.
T=PseudoJet: The type of object to contruct and return.
Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source
# JetReconstruction.savejets —
Method .
julia savejets (filename, jets; format = "px py pz E" ) Save jet data to a file.
Arguments
filename: The name of the file to save the jet data to.
jets: An array of jet objects to save.
format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".
Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
"E" or "energy": Jet energy
"px": Momentum along the x-axis
"py": Momentum along the y-axis
"pz": Momentum along the z-axis
"pt2": Square of the transverse momentum
"phi": Azimuth angle
"rapidity": Rapidity
Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source
# JetReconstruction.tiled_jet_reconstruct —
Method .
julia tiled_jet_reconstruct (particles :: Vector{PseudoJet} ; p = - 1 , R = 1.0 , recombine = + ) where {T} Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for the prefered internal jet type, PseudoJet.
Arguments
particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.
p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
recombine::Function = +: The recombination function used for combining pseudojets.
Returns
Vector{PseudoJet}: A vector of reconstructed jets.Example
julia tiled_jet_reconstruct (particles :: Vector{PseudoJet} ; p = 1 , R = 1.0 , recombine = + ) source
# JetReconstruction.tiled_jet_reconstruct —
Method .
julia tiled_jet_reconstruct (particles :: Vector{T} ; p = - 1 , R = 1.0 , recombine = + ) where {T} Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
Arguments
particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)
p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.
R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.
recombine::Function = +: The recombination function used for combining pseudojets.
Returns
Vector{PseudoJet}: A vector of reconstructed jets.Example
julia tiled_jet_reconstruct (particles :: Vector{LorentzVectorHEP} ; p = - 1 , R = 0.4 , recombine = + ) source
# JetReconstruction.ClusterSequence —
Type .
julia struct ClusterSequence A struct holding the full history of a jet clustering sequence, including the final jets.
Fields
algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.
strategy::RecoStrategy.Strategy: The strategy used for clustering.
jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.
n_initial_jets::Int: The initial number of particles used for exclusive jets.
history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.
Qtot::Any: The total energy of the event.
source
# JetReconstruction.ClusterSequence —
Method .
julia ClusterSequence (p :: Int , strategy :: RecoStrategy.Strategy , jets, history, Qtot) Constructs a ClusterSequence object with a specific power value mapped to a pp reconstruction algorithm.
Arguments
p::Int: The power value for the algorithm.
strategy::RecoStrategy.Strategy: The reconstruction strategy.
jets: The jets to be clustered.
history: The length of the jets.
Qtot: The total energy of the jets.
Returns
A ClusterSequence object.
source
# JetReconstruction.ClusterSequence —
Method .
julia ClusterSequence (alg :: JetAlgorithm.Algorithm , strategy :: RecoStrategy.Strategy , jets, history, Qtot) Constructs a ClusterSequence object with a specific algorithm spcified.
Arguments
alg::JetAlgorithm.Algorithm: The algorithm.
strategy::RecoStrategy.Strategy: The reconstruction strategy.
jets: The jets to be clustered.
history: The length of the jets.
Qtot: The total energy of the jets.
Returns
A ClusterSequence object.
source
# JetReconstruction.FinalJet —
Type .
A struct representing the final properties of a jet, used for JSON serialisation.
Fields
rap::Float64: The rapidity of the jet.
phi::Float64: The azimuthal angle of the jet.
pt::Float64: The transverse momentum of the jet.
source
# JetReconstruction.FinalJets —
Type .
A struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
source
# JetReconstruction.PseudoJet —
Type .
julia mutable struct PseudoJet <: FourMomentum The PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
px::Float64: The x-component of the momentum.
py::Float64: The y-component of the momentum.
pz::Float64: The z-component of the momentum.
E::Float64: The energy component of the momentum.
_cluster_hist_index::Int: The index of the cluster history.
_pt2::Float64: The squared transverse momentum.
_inv_pt2::Float64: The inverse squared transverse momentum.
_rap::Float64: The rapidity.
_phi::Float64: The azimuthal angle.
source
# JetReconstruction.PseudoJet —
Method .
julia PseudoJet (px :: Float64 , py :: Float64 , pz :: Float64 , E :: Float64 ) Constructs a PseudoJet object with the given momentum components and energy.
Arguments
px::Float64: The x-component of the momentum.
py::Float64: The y-component of the momentum.
pz::Float64: The z-component of the momentum.
E::Float64: The energy.
Returns
A PseudoJet object.
source
# JetReconstruction.PseudoJet —
Method .
julia PseudoJet (px :: Float64 , py :: Float64 , pz :: Float64 , E :: Float64 , _cluster_hist_index :: Int , pt2 :: Float64 ) Constructs a PseudoJet object with the given momentum components and energy and history index.
Arguments
px::Float64: The x-component of the momentum.
py::Float64: The y-component of the momentum.
pz::Float64: The z-component of the momentum.
E::Float64: The energy.
_cluster_hist_index::Int: The cluster history index.
pt2::Float64: The transverse momentum squared.
Returns
A PseudoJet object.
source
Jet Visualisation Documentation Documentation for visualisation interfaces extension module.
Index Jet Visualisation Public Interfaces # JetReconstruction.animatereco —
Method .
julia animatereco (cs :: ClusterSequence , filename; barsize_phi = 0.1 , barsize_y = 0.1 , colormap = :glasbey_category10_n256 , perspective = 0.5 , azimuth = 2.7 , elevation = 0.5 , framerate = 5 , ancestors = false , Module = Makie) Animate the jet reconstruction process and save it as a video file.
Arguments
Optional Arguments
barsize_phi=0.1: The size of the bars in the phi direction.
barsize_y=0.1: The size of the bars in the y direction.
colormap=:glasbey_category10_n256: The colormap to use for coloring the jets.
perspective=0.5: The perspective of the plot.
azimuth=2.7: The azimuth angle of the plot.
elevation=0.5: The elevation angle of the plot.
framerate=5: The framerate of the output video.
end_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.
title=nothing: The title to add to the plot.
ancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.
Module: The plotting module to use. Default is Makie.
For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.
Returns
fig: The figure object representing the final frame.source
# JetReconstruction.jetsplot —
Method .
jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example using CairoMakie # use any other Makie that you have here jetsplot([object1, object2, object3], [[1], [2, 3]]) The example above plots object1 as a separate jet in one colour and object2 and object3 together in another colour.
This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:
import CairoMakie jetsplot(my_objects, my_colour_arrays, Module=CairoMakie) import GLMakie jetsplot(my_objects, my_colour_arrays, Module=GLMakie) using WGLMakie jetsplot(my_objects, my_colour_arrays, Module=Main) #default source
# JetReconstruction.jetsplot —
Method .
julia jetsplot (objects, cs :: ClusterSequence ; barsize_phi = 0.1 , barsize_eta = 0.1 , colormap = :glasbey_hv_n256 , Module = Main) Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example using CairoMakie # use any other Makie that you have here jetsplot([object1, object2, object3], cluster_sequence_I_got_from_jet_reconstruct; Module=CairoMakie) This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:
import CairoMakie jetsplot(my_objects, cs, Module=CairoMakie) import GLMakie jetsplot(my_objects, cs, Module=GLMakie) using WGLMakie jetsplot(my_objects, cs, Module=Main) #default source
- Maintainer: @highlightjs/core-team
- Website: https://highlightjs.org/
- License: see project LICENSE
- Touched: 2021
-*/pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}.hljs{background:#F3F3F3;color:#444}.hljs-comment{color:#697070}.hljs-tag,.hljs-punctuation{color:#444a}.hljs-tag .hljs-name,.hljs-tag .hljs-attr{color:#444}.hljs-keyword,.hljs-attribute,.hljs-selector-tag,.hljs-meta .hljs-keyword,.hljs-doctag,.hljs-name{font-weight:bold}.hljs-type,.hljs-string,.hljs-number,.hljs-selector-id,.hljs-selector-class,.hljs-quote,.hljs-template-tag,.hljs-deletion{color:#880000}.hljs-title,.hljs-section{color:#880000;font-weight:bold}.hljs-regexp,.hljs-symbol,.hljs-variable,.hljs-template-variable,.hljs-link,.hljs-selector-attr,.hljs-operator,.hljs-selector-pseudo{color:#ab5656}.hljs-literal{color:#695}.hljs-built_in,.hljs-bullet,.hljs-code,.hljs-addition{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta .hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem}
diff --git a/dev/assets/themeswap.js b/dev/assets/themeswap.js
deleted file mode 100644
index 9f5eebe..0000000
--- a/dev/assets/themeswap.js
+++ /dev/null
@@ -1,84 +0,0 @@
-// Small function to quickly swap out themes. Gets put into the tag..
-function set_theme_from_local_storage() {
- // Initialize the theme to null, which means default
- var theme = null;
- // If the browser supports the localstorage and is not disabled then try to get the
- // documenter theme
- if (window.localStorage != null) {
- // Get the user-picked theme from localStorage. May be `null`, which means the default
- // theme.
- theme = window.localStorage.getItem("documenter-theme");
- }
- // Check if the users preference is for dark color scheme
- var darkPreference =
- window.matchMedia("(prefers-color-scheme: dark)").matches === true;
- // Initialize a few variables for the loop:
- //
- // - active: will contain the index of the theme that should be active. Note that there
- // is no guarantee that localStorage contains sane values. If `active` stays `null`
- // we either could not find the theme or it is the default (primary) theme anyway.
- // Either way, we then need to stick to the primary theme.
- //
- // - disabled: style sheets that should be disabled (i.e. all the theme style sheets
- // that are not the currently active theme)
- var active = null;
- var disabled = [];
- var primaryLightTheme = null;
- var primaryDarkTheme = null;
- for (var i = 0; i < document.styleSheets.length; i++) {
- var ss = document.styleSheets[i];
- // The Click here to go to the documentation for the latest stable release. ';
- div.appendChild(closer);
- document.body.appendChild(div);
-}
-
-if (document.readyState === "loading") {
- document.addEventListener("DOMContentLoaded", maybeAddWarning);
-} else {
- maybeAddWarning();
-}
diff --git a/dev/examples.html b/dev/examples.html
new file mode 100644
index 0000000..ec1112f
--- /dev/null
+++ b/dev/examples.html
@@ -0,0 +1,27 @@
+
+
+
+ Jet Reconstruction Examples | JetReconstruction.jl
+ Examples · JetReconstruction.jl The Jet Reconstruction package has a number of example files that show how to usage.
Note: because of extra dependencies in these scripts, one must use the Project.toml file in the examples directory.
This is a basic jet reconstruction example that shows how to call the package to perform a jet reconstruction, with different algorithms and (optionally) strategy, producing exclusive and inclusive jet selections.
julia --project=. examples/jetreco.jl --maxevents=100 --strategy=N2Plain test/data/events.hepmc3
-...
-julia --project=. examples/jetreco.jl --maxevents=100 --strategy=N2Tiled test/data/events.hepmc3
-...There are options to explicitly set the algorithm (use --help to see these).
This is a more sophisticated example that allows performance measurements to be made of the reconstruction, as well as profiling (flamegraphs and memory profiling). Use the --help option to see usage. e.g., to extract timing performance for the AntiKt algorithm using the tiled strategy:
julia --project instrumented-jetreco.jl -S N2Tiled -A AntiKt --nsamples 100 ../test/data/events.hepmc3This script will produce a PNG/PDF showing the results of a jet reconstruction. This is a 3D plot where all the initial energy deposits are visualised, with colours that indicate in which final cluster the deposit ended up in.
Similar to visualise-jets.jl this notebook will produce a visualisation of jet reconstruction in the browser. This is a 3D plot where all the initial energy deposits are visualised, with colours that indicate in which final cluster the deposit ended up in.
Performs jet reconstruction and then produces and animation of the process, showing how the jets merge from their different constituents.
Theme
Automatic (OS) documenter-light documenter-dark catppuccin-latte catppuccin-frappe catppuccin-macchiato catppuccin-mocha
This document was generated with Documenter.jl version 1.5.0 on Tuesday 16 July 2024 . Using Julia version 1.10.4.
Home · JetReconstruction.jl This package implements sequential Jet Reconstruction (clustering) algorithms, which are used in high-energy physics as part of event reconstruction for $pp$ and $e^+e^-$ colliders.
Algorithms used are based on the C++ FastJet package (https://fastjet.fr , hep-ph/0512210 , arXiv:1111.6097 ), reimplemented natively in Julia.
The algorithms include $\text{anti}-{k}_\text{T}$ , Cambridge/Aachen and inclusive $k_\text{T}$ .
The main interface for reconstruction is jet_reconstruct
jet_reconstruct(particles; p = -1, R = 1.0)Where particles is a collection of 4-vector objects to reconstruct.
The object returned is a ClusterSequence
Three strategies are available for the different algorithms:
Strategy Name Notes Interface RecoStrategy.BestDynamically switch strategy based on input particle density jet_reconstructRecoStrategy.N2PlainGlobal matching of particles at each interation (works well for low $N$ ) plain_jet_reconstructRecoStrategy.N2TiledUse tiles of radius $R$ to limit search space (works well for higher $N$ ) tiled_jet_reconstruct
Generally one can use the jet_reconstruct interface, shown above, as the Best strategy safely as the overhead is extremely low. That interface supports a strategy option to switch to a different option.
Another option, if one wishes to use a specific strategy, is to call that strategy's interface directly, e.g.,
# For N2Plain strategy called directly
-plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +)Note that there is no strategy option in these interfaces.
To obtain final jets both inclusive ($p_T$ cut) and exclusive ($n_{jets}$ or $d_{ij}$ cut) selections are supported:
Sorting vectors is trivial in Julia, no special sorting methods are provided. As an example, to sort exclusive jets of $>5.0$ (usually GeV, depending on your EDM) from highest energy to lowest:
sorted_jets = sort!(inclusive_jets(cs::ClusterSequence; ptmin=5.0),
- by=JetReconstruction.energy, rev=true)
To visualise the clustered jets as a 3d bar plot (see illustration above) we now use Makie.jl. See the jetsplot function in ext/JetVisualisation.jl and its documentation for more. There are two worked examples in the examples directory.
The plotting code is a package extension and will load if the one of the Makie modules is loaded in the environment.
The animaterecoClusterSequence object. See the function documentation for the many options that can be customised.
The package also provides methods such as loadjets, loadjets!, and savejets that one can use to save and load objects on/from disk easily in a very flexible format. See documentation for more.
Although it has been developed further since the CHEP2023 conference, the CHEP conference proceedings, 10.1051/epjconf/202429505017 , should be cited if you use this package:
@article{refId0,
- author = {{Stewart, Graeme Andrew} and {Gras, Philippe} and {Hegner, Benedikt} and {Krasnopolski, Atell}},
- doi = {10.1051/epjconf/202429505017},
- journal = {EPJ Web of Conf.},
- pages = {05017},
- title = {Polyglot Jet Finding},
- url = {https://doi.org/10.1051/epjconf/202429505017},
- volume = 295,
- year = 2024,
- eprint={2309.17309},
- archivePrefix={arXiv},
- primaryClass={hep-ex}
-}The original paper on arXiv is:
@misc{stewart2023polyglot,
- title={Polyglot Jet Finding},
- author={Graeme Andrew Stewart and Philippe Gras and Benedikt Hegner and Atell Krasnopolski},
- year={2023},
- eprint={2309.17309},
- archivePrefix={arXiv},
- primaryClass={hep-ex}
-}Code in this package is authored by:
Atell Krasnopolski <delta_atell@protonmail.com> Graeme A Stewart <graeme.andrew.stewart@cern.ch> Philippe Gras <philippe.gras@cern.ch> and is Copyright 2022-2024 The Authors, CERN.
The code is under the MIT License.
Theme
Automatic (OS) documenter-light documenter-dark catppuccin-latte catppuccin-frappe catppuccin-macchiato catppuccin-mocha
This document was generated with Documenter.jl version 1.5.0 on Tuesday 16 July 2024 . Using Julia version 1.10.4.
Jet Reconstruction | JetReconstruction.jl
+ Skip to content JetReconstruction.jl Jet reconstruction (reclustering) with Julia
Jet Reconstruction This package implements sequential Jet Reconstruction (clustering) algorithms, which are used in high-energy physics as part of event reconstruction for p p e + e −
Algorithms Algorithms used are based on the C++ FastJet package (https://fastjet.fr , hep-ph/0512210 , arXiv:1111.6097 ), reimplemented natively in Julia.
The algorithms include anti − k T k T
Reconstruction Interface The main interface for reconstruction is jet_reconstruct
julia jet_reconstruct (particles; p = - 1 , R = 1.0 ) Where particles is a collection of 4-vector objects to reconstruct.
The object returned is a ClusterSequence
Strategy Three strategies are available for the different algorithms:
Strategy Name Notes Interface RecoStrategy.BestDynamically switch strategy based on input particle density jet_reconstructRecoStrategy.N2PlainGlobal matching of particles at each interation (works well for low N plain_jet_reconstructRecoStrategy.N2TiledUse tiles of radius R N tiled_jet_reconstruct
Generally one can use the jet_reconstruct interface, shown above, as the Best strategy safely as the overhead is extremely low. That interface supports a strategy option to switch to a different option.
Another option, if one wishes to use a specific strategy, is to call that strategy's interface directly, e.g.,
julia # For N2Plain strategy called directly plain_jet_reconstruct (particles :: Vector{T} ; p = - 1 , R = 1.0 , recombine = + ) Note that there is no strategy option in these interfaces.
Inclusive and Exclusive Selections To obtain final jets both inclusive (p T n j e t s d i j
Sorting Sorting vectors is trivial in Julia, no special sorting methods are provided. As an example, to sort exclusive jets of > 5.0
julia sorted_jets = sort! ( inclusive_jets (cs :: ClusterSequence ; ptmin = 5.0 ), by = JetReconstruction . energy, rev = true ) Plotting and Animation
To visualise the clustered jets as a 3d bar plot (see illustration above) we now use Makie.jl. See the jetsplot function in ext/JetVisualisation.jl and its documentation for more. There are two worked examples in the examples directory.
The plotting code is a package extension and will load if the one of the Makie modules is loaded in the environment.
The animaterecoClusterSequence object. See the function documentation for the many options that can be customised.
Serialisation The package also provides methods such as loadjets, loadjets!, and savejets that one can use to save and load objects on/from disk easily in a very flexible format. See documentation for more.
Reference Although it has been developed further since the CHEP2023 conference, the CHEP conference proceedings, 10.1051/epjconf/202429505017 , should be cited if you use this package:
bibtex @article { refId0 , author = { {Stewart, Graeme Andrew} and {Gras, Philippe} and {Hegner, Benedikt} and {Krasnopolski, Atell} } , doi = { 10.1051/epjconf/202429505017 } , journal = { EPJ Web of Conf. } , pages = { 05017 } , title = { Polyglot Jet Finding } , url = { https://doi.org/10.1051/epjconf/202429505017 } , volume = 295 , year = 2024 , eprint = { 2309.17309 } , archivePrefix = { arXiv } , primaryClass = { hep-ex } } The original paper on arXiv is:
bibtex @misc { stewart2023polyglot , title = { Polyglot Jet Finding } , author = { Graeme Andrew Stewart and Philippe Gras and Benedikt Hegner and Atell Krasnopolski } , year = { 2023 } , eprint = { 2309.17309 } , archivePrefix = { arXiv } , primaryClass = { hep-ex } } Authors and Copyright Code in this package is authored by:
Atell Krasnopolski <delta_atell@protonmail.com>
Graeme A Stewart <graeme.andrew.stewart@cern.ch>
Philippe Gras <philippe.gras@cern.ch>
and is Copyright 2022-2024 The Authors, CERN.
The code is under the MIT License.
Jet Reconstruction Internal Documentation | JetReconstruction.jl
+ Internal API · JetReconstruction.jl Documentation for JetReconstruction.jl's internal methods and types.
N.B. no guarantee is made of stability of these interfaces or types.
+(j1::PseudoJet, j2::PseudoJet)Addition operator for PseudoJet objects.
Arguments
j1::PseudoJet: The first PseudoJet object.j2::PseudoJet: The second PseudoJet object.Returns
A new PseudoJet object with the sum of the momenta and energy of j1 and j2.
source copy(j::TiledJet)Create a copy of a TiledJet object.
Arguments
j::TiledJet: The TiledJet object to be copied.Returns
A new TiledJet object with the same attributes as the input object.
source Base.iterate(t::rightmost_tiles, state=1)Iterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.
source Base.iterate(t::neighbour_tiles, state=1)Iterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.
source Base.iterate(tj::TiledJet)Iterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).
Arguments
tj::TiledJet: The TiledJet object to start to iterate over.source show(io::IO, jet::PseudoJet)Print a PseudoJet object to the specified IO stream.
Arguments
io::IO: The IO stream to which the information will be printed.jet::PseudoJet: The PseudoJet object whose information will be printed.source Base.tryparse(E::Type{<:Enum}, str::String)Parser that converts a string to an enum value if it exists, otherwise returns nothing.
source CosTheta(p::PseudoJet)Compute the cosine of the angle between the momentum vector p and the z-axis.
Arguments
p::PseudoJet: The PseudoJet object representing the momentum vector.Returns
The cosine of the angle between p and the z-axis. source _ensure_valid_rap_phi(p::PseudoJet)Ensure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.
Arguments
p::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.source _plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)This is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.
Users of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.
The power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt.
Arguments
particles: A vector of PseudoJet objects representing the input particles.p=-1: The power to which the transverse momentum (pt) of each particle is raised.R=1.0: The jet radius parameter.recombine: The recombination function used to merge two jets. Default is + (additive recombination).Returns
clusterseq: The resulting ClusterSequence object representing the reconstructed jets.source set rap_phi!(p::PseudoJet)
Set the rapidity and azimuthal angle of the PseudoJet p.
Arguments
p::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.Description
This function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.
Note - the ϕ angle is calculated in the range [0, 2π).
source _tj_diJ(jet)Compute the dij metric value for a given jet.
Arguments
Returns
The dij value for the jet. Example
source _tj_dist(jetA, jetB)Compute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.
Arguments
jetA: The first jet.jetB: The second jet.Returns
The squared distance between jetA and jetB.
Examples
source add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)Add a new jet's history into the recombination sequence.
Arguments:
clusterseq::ClusterSequence: The cluster sequence object.parent1: The index of the first parent.parent2: The index of the second parent.jetp_index: The index of the jet.dij: The dij value.This function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.
If the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.
source add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)Adds to the vector tileunion the tiles that are in the neighbourhood of the specified center index, including itself and whose tagged status are false - start adding from position nnear tiles-1, and increase nnear tiles. When a neighbour is added its tagged status is set to true.
Arguments
center_index: The index of the center tile.tile_union: An array to store the indices of neighbouring tiles.n_near_tiles: The number of neighbouring tiles.tiling: The tiling object containing the tile tags.Returns
The updated number of near tiles.
source detach!(jet::TiledJet)Detach a TiledJet from its linked list by updating the previous and next pointers.
Arguments
jet::TiledJet: The TiledJet object to detach.source determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloatCalculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.
This is the heuristic which is used by FastJet (inline comments are from FastJet).
Arguments
eta::Vector{T}: A vector of rapidity values.Returns
minrap::T: The minimum rapidity value.maxrap::T: The maximum rapidity value.source dij(i, kt2_array, nn, nndist)Compute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß
Arguments
i: The index of the element.kt2_array: An array of kt2 values.nn: An array of nearest neighbors.nndist: An array of nearest neighbor distances.Returns
source dist(i, j, rapidity_array, phi_array)Compute the distance between points in a 2D space defined by rapidity and phi coordinates.
Arguments
i::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).j::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).rapidity_array::Vector{Float64}: Array of rapidity coordinates.phi_array::Vector{Float64}: Array of phi coordinates.Returns
distance::Float64: The distance between the two points.source do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)Bookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.
Arguments
clusterseq::ClusterSequence: The cluster sequence object.jet_i: The index of the jet.diB: The diB value.source do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)Perform the bookkeeping associated with the step of recombining jeti and jet j (assuming a distance dij).
Arguments
clusterseq::ClusterSequence: The cluster sequence object.jet_i: The index of the first jet to be recombined.jet_j: The index of the second jet to be recombined.dij: The distance between the two jets.recombine=+: The recombination function to be used. Default is addition.Returns
newjet_k: The index of the newly created jet.Description
This function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.
source energy(p::PseudoJet)Return the energy of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The energy of the PseudoJet. source eta(p::PseudoJet)Compute the pseudorapidity (η) of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.Returns
The pseudorapidity (η) of the PseudoJet. source fast_findmin(dij, n)Find the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.
Arguments
dij: An array of values.n: The number of elements to consider in the dij array.Returns
dij_min: The minimum value in the first n elements of the dij array.best: The index of the minimum value in the dij array.source find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)Find the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run
Arguments
tile_union: The tile union to which the neighbouring tiles will be added.jetA: The first jet.jetB: The second jet.oldB: The old second jet.tiling: The tiling information.Returns
The number of neighbouring tiles added to the tile_union.
source geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)Compute the geometric distance between two points in the rap-phi plane.
Arguments
eta1::AbstractFloat: The eta coordinate of the first point.phi1::AbstractFloat: The phi coordinate of the first point.eta2::AbstractFloat: The eta coordinate of the second point.phi2::AbstractFloat: The phi coordinate of the second point.Returns
distance::Float64: The geometric distance between the two points.source get_all_ancestors(idx, cs::ClusterSequence)Recursively finds all ancestors of a given index in a ClusterSequence object.
Arguments
idx: The index of the jet for which to find ancestors.cs: The ClusterSequence object containing the jet history.Returns
An array of indices representing the ancestors of the given jet.
source get_dij_dist(nn_dist, kt2_1, kt2_2, R2)Compute the dij metric distance between two jets.
Arguments
nn_dist: The nearest-neighbor distance between two jets.kt2_1: The squared momentum metric value of the first jet.kt2_2: The squared momentum metric value of the second jet.R2: The jet radius parameter squared.Returns
The distance between the two jets.
If kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.
source get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)Given a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.
Arguments
tiling_setup: A TilingDef object that contains the tiling setup parameters.eta: The eta value for which to calculate the tile index.phi: The phi value for which to calculate the tile index.Returns
ieta: The tile index along the eta direction.iphi: The tile index along the phi direction.source get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)Compute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)
Arguments
tiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.i_η::Int: The index of the tile in the η dimension.i_ϕ::Int: The index of the tile in the ϕ dimension.Returns
The linear index of the tile. source initial_history(particles)Create an initial history for the given particles.
Arguments
particles: The initial vector of stable particles.Returns
history: An array of HistoryElement objects.Qtot: The total energy in the event.source insert!(nextjet::TiledJet, jettomove::TiledJet)Inserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list
Arguments
nextjet::TiledJet: The TiledJet object after which jettomove should be inserted.jettomove::TiledJet: The TiledJet object to be inserted.Example
source isvalid(t::TiledJet)Check if a TiledJet is valid, by seeing if it is not the noTiledJet object.
Arguments
t::TiledJet: The TiledJet object to check.Returns
Bool: true if the TiledJet object is valid, false otherwise.source jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)Compute the ranks of jets in a given ClusterSequence object based on a specified comparison function.
Arguments
clusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.compare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.Returns
A dictionary mapping each jet index to its rank.
Note
This is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent "rank" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.
source m(p::PseudoJet)Compute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the invariant mass.Returns
The invariant mass of the PseudoJet object.
source m2(p::PseudoJet)Calculate the invariant mass squared (m^2) of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.Returns
The invariant mass squared (m^2) of the PseudoJet. source mag(p::PseudoJet)Return the magnitude of the momentum of a PseudoJet, |p|.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the magnitude.Returns
The magnitude of the PseudoJet object.
source mass(p::PseudoJet)Compute the invariant mass (alias for m(p)).
Arguments
p::PseudoJet: The PseudoJet object for which to compute the mass.Returns
The mass of the PseudoJet. source merge_steps(clusterseq::ClusterSequence)Compute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).
Arguments
clusterseq::ClusterSequence: The cluster sequence object.Returns
merge_steps::Int: The number of merge steps.source phi(p::PseudoJet)Compute the ϕ angle of a PseudoJet object p.
Note this function is a wrapper for phi_02pi(p).
Arguments
p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.Returns
The azimuthal angle of p in the range [0, 2π). source phi_02pi(p::PseudoJet)Compute the azimuthal angle of a PseudoJet object p in the range [0, 2π).
Arguments
p::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.Returns
The azimuthal angle of p in the range [0, 2π). source pt(p::PseudoJet)Compute the scalar transverse momentum (pt) of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the transverse momentum.Returns
The transverse momentum (pt) of the PseudoJet. source pt2(p::PseudoJet)Get the squared transverse momentum of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The squared transverse momentum of the PseudoJet. source px(p::PseudoJet)Return the x-component of the momentum of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The x-component of the momentum of the PseudoJet. source py(p::PseudoJet)Return the y-component of the momentum of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The y-component of the momentum of the PseudoJet. source pz(p::PseudoJet)Return the z-component of the momentum of a PseudoJet.
Arguments
p::PseudoJet: The PseudoJet object.Returns
The z-component of the momentum of the PseudoJet. source rapidity(p::PseudoJet)Compute the rapidity of a PseudoJet object.
Arguments
p::PseudoJet: The PseudoJet object for which to compute the rapidity.Returns
The rapidity of the PseudoJet object.
source reco_state(cs::ClusterSequence, pt_ranks; iteration=0)This function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.
Arguments
cs::ClusterSequence: The ClusterSequence object to update.ranks: The ranks of the original clusters, that are inherited by peudojetsduring the reconstruction process.
iteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).ignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).Returns
A dictionary representing a snapshot of the reconstruction state.
Details
The function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.
source rightneighbours(center::Int, tiling::Tiling)Compute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.
Arguments
center::Int: The center index.tiling::Tiling: The tiling object.Returns
Surrounding: An object containing the indices of the right neighbors.source set_momentum!(j::PseudoJet, px, py, pz, E)Set the momentum components and energy of a PseudoJet object.
Arguments
j::PseudoJet: The PseudoJet object to set the momentum for.px: The x-component of the momentum.py: The y-component of the momentum.pz: The z-component of the momentum.E: The energy of the particle.source set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})This function sets the nearest neighbor information for all jets in the tiledjets vector.
Arguments
clusterseq::ClusterSequence: The cluster sequence object.tiling::Tiling: The tiling object.tiledjets::Vector{TiledJet}: The vector of tiled jets.Returns
NNs::Vector{TiledJet}: The vector of nearest neighbor jets.diJ::Vector{Float64}: The vector of diJ values.The function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.
Note: The diJ values are calculated as the kt distance multiplied by R^2.
source setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloatThis function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.
Arguments
eta::Vector{T}: A vector of rapidities.Rparam::AbstractFloat: The jet radius parameter.Returns
tiling_setup: A TilingDef object containing the tiling setup parameters.Description
The function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.
source surrounding(center::Int, tiling::Tiling)Compute the surrounding indices of a given center index in a tiling.
Arguments
center::Int: The center index.tiling::Tiling: The tiling object.Returns
Surrounding: An object containing the surrounding indices.source tile_index(tiling_setup, eta::Float64, phi::Float64)Compute the tile index for a given (eta, phi) coordinate.
Arguments
tiling_setup: The tiling setup object containing the tile size and number of tiles.eta::Float64: The eta coordinate.phi::Float64: The phi coordinate.Returns
The tile index corresponding to the (eta, phi) coordinate.
source tiledjet_remove_from_tiles!(tiling, jet)Remove a jet from the given tiling structure.
Arguments
tiling: The tiling structure from which the jet will be removed.jet: The jet to be removed from the tiling structure.Description
This function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.
source tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)Initialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)
Arguments:
jet::TiledJet: The TiledJet object to set the information for.clusterseq::ClusterSequence: The ClusterSequence object containing the jets.tiling::Tiling: The Tiling object containing the tile information.jets_index: The index of the jet in the ClusterSequence.R2: The jet radius parameter squared.p: The power to raise the pt2 value to.This function sets the eta, phi, kt2, jetsindex, NN dist, NN, tile_index, previous, and next fields of the TiledJet object.
Returns:
source upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck ).
Arguments
i::Int: The index of the particle to update and check against.from::Int: The starting index of the range of particles to check against.to::Int: The ending index of the range of particles to check against.rapidity_array: An array containing the rapidity values of all particles.phi_array: An array containing the phi values of the all particles.R2: The squared jet distance threshold for considering a particle as a neighbour.nndist: The array that stores the nearest neighbor distances.nn: The array that stores the nearest neighbor indices.source upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)Update the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross ).
Arguments
i::Int: The index of the particle to update and check against.from::Int: The starting index of the range of particles to check against.to::Int: The ending index of the range of particles to check against.rapidity_array: An array containing the rapidity values of all particles.phi_array: An array containing the phi values of the all particles.R2: The squared jet distance threshold for considering a particle as a neighbour.nndist: The array that stores the nearest neighbor distances.nn: The array that stores the nearest neighbor indices.source upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)Update the nearest neighbor information after a jet merge step.
Arguments:
i: Index of the first particle in the last merge step.j: Index of the second particle in the last merge step.k: Index of the current particle for which the nearest neighbour will be updated.N: Total number of particles (currently vaild array indexes are [1:N]).Nn: Number of nearest neighbors to consider.kt2_array: Array of transverse momentum squared values.rapidity_array: Array of rapidity values.phi_array: Array of azimuthal angle values.R2: Distance threshold squared for nearest neighbors.nndist: Array of nearest neighbor geometric distances.nn: Array of nearest neighbor indices.nndij: Array of metric distances between particles.This function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.
source const η = etaAlias for the pseudorapidity function, eta.
source Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.
source struct HistoryElementA struct holding a record of jet mergers and finalisations
Fields:
parent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)parent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)child: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.jetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.dij: The distance corresponding to the recombination at this stage of the clustering.max_dij_so_far: The largest recombination distance seen so far in the clustering history.source HistoryElement(jetp_index)Constructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.
Arguments
jetp_index: The index of the jetp.Returns
A HistoryElement object.
source struct JetWithAncestorsA struct representing a jet with its origin ancestors.
Fields
self::PseudoJet: The PseudoJet object for this jet.jetp_index::Int: The index of the jet in the corresponding cluster sequence.ancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).jet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestorsNote
This structure needs its associated cluster sequence origin to be useful.
source struct Surrounding{N}Structure used for iterating over neighbour tiles.
Fields
indices::NTuple{N, Int}: A tuple of N integers representing the indices.source struct TiledJetTiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.
Fields
id::Int: The ID of the jet.eta::Float64: The rapidity of the jet.phi::Float64: The azimuthal angle of the jet.kt2::Float64: The transverse momentum squared of the jet.NN_dist::Float64: The distance to the nearest neighbor.jets_index::Int: The index of the jet in the jet array.tile_index::Int: The index of the tile in the tile array.dij_posn::Int: The position of this jet in the dij compact array.NN::TiledJet: The nearest neighbor.previous::TiledJet: The previous jet.next::TiledJet: The next jet.source TiledJet(id)Constructs a TiledJet object with the given id and initializes its properties to zero.
Arguments
id: The ID of the TiledJet object.Returns
A TiledJet object with the specified id and values set to zero or noTiledJet.
source struct TilingThe Tiling struct represents a tiling configuration for jet reconstruction.
Fields
setup::TilingDef: The tiling definition used for the configuration.tiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).positions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.tags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).source Tiling(setup::TilingDef)Constructs a intial Tiling object based on the provided setup parameters.
Arguments
setup::TilingDef: The setup parameters for the tiling.Returns
A Tiling object.
source struct TilingDefA struct representing the definition of a spcific tiling scheme.
Fields
_tiles_eta_min::Float64: The minimum rapidity of the tiles._tiles_eta_max::Float64: The maximum rapidity of the tiles._tile_size_eta::Float64: The size of a tile in rapidity (usually R^2)._tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2)._n_tiles_eta::Int: The number of tiles across rapidity._n_tiles_phi::Int: The number of tiles across phi._n_tiles::Int: The total number of tiles._tiles_ieta_min::Int: The minimum rapidity tile index._tiles_ieta_max::Int: The maximum rapidity tile index.Constructor
TilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,
- _n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)Constructs a TilingDef object with the given parameters.
source struct neighbour_tilesA struct representing the neighbouring tiles.
A struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
-X.X
-XXXNote, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
n_η::Int: Number of η tilesn_ϕ::Int: Number of ϕ tilesstart_η::Int: Centre η tile coordinatestart_ϕ::Int: Centre ϕ tile coordinatesource struct rightmost_tilesA struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):
XXX
-O.X
-OOONote, rapidity coordinate must be in range, ϕ coordinate wraps
Fields
n_η::Int: Number of η tilesn_ϕ::Int: Number of ϕ tilesstart_η::Int: Centre η tile coordinatestart_ϕ::Int: Centre ϕ tile coordinatesource
Theme
Automatic (OS) documenter-light documenter-dark catppuccin-latte catppuccin-frappe catppuccin-macchiato catppuccin-mocha
This document was generated with Documenter.jl version 1.5.0 on Tuesday 16 July 2024 . Using Julia version 1.10.4.
diff --git a/dev/lib/public.html b/dev/lib/public.html
new file mode 100644
index 0000000..8e930f9
--- /dev/null
+++ b/dev/lib/public.html
@@ -0,0 +1,30 @@
+
+
+
+ Jet Reconstruction Public Documentation | JetReconstruction.jl
+ Public API · JetReconstruction.jl Documentation for JetReconstruction.jl's public interfaces.
exclusive_jets(clusterseq::ClusterSequence; dcut = nothing, njets = nothing)Return all exclusive jets of a ClusterSequence, with either a specific number of jets or a cut on the maximum distance parameter.
Arguments
clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.dcut::Union{Nothing, Real}: The distance parameter used to define the exclusive jets. If dcut is provided, the number of exclusive jets will be calculated based on this parameter.njets::Union{Nothing, Integer}: The number of exclusive jets to be calculated. If njets is provided, the distance parameter dcut will be calculated based on this number.Note : Either dcut or njets must be provided (but not both).
Returns
An array of LorentzVectorCyl objects representing the exclusive jets. Exceptions
ArgumentError: If neither dcut nor njets is provided.ArgumentError: If the algorithm used in the ClusterSequence object is not suitable for exclusive jets.ErrorException: If the cluster sequence is incomplete and exclusive jets are unavailable.Examples
exclusive_jets(clusterseq, dcut = 20.0)
-exclusive_jets(clusterseq, njets = 3)source Specialisation for final jets from LorentzVectors (TODO: merge into more general function)
source Specialisation for final jets from LorentzVectorCyl (TODO: merge into more general function)
source final_jets(jets::Vector{PseudoJet}, ptmin::AbstractFloat=0.0)This function takes a vector of PseudoJet objects and a minimum transverse momentum ptmin as input. It returns a vector of FinalJet objects that satisfy the transverse momentum condition.
Arguments
jets::Vector{PseudoJet}: A vector of PseudoJet objects representing the input jets.ptmin::AbstractFloat=0.0: The minimum transverse momentum required for a jet to be included in the final jets vector.Returns
A vector of FinalJet objects that satisfy the transverse momentum condition.
source inclusive_jets(clusterseq::ClusterSequence, ptmin = 0.0)Return all inclusive jets of a ClusterSequence with pt > ptmin.
Arguments
clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history and jets.ptmin::Float64 = 0.0: The minimum transverse momentum (pt) threshold for the inclusive jets.Returns
An array of LorentzVectorCyl objects representing the inclusive jets.
Description
This function computes the inclusive jets from a given ClusterSequence object. It iterates over the clustering history and checks the transverse momentum of each parent jet. If the transverse momentum is greater than or equal to ptmin, the jet is added to the array of inclusive jets.
Example
inclusive_jets(clusterseq, ptmin = 10.0)source jet_reconstruct(particles; p = -1, R = 1.0, recombine = +, strategy = RecoStrategy.Best)Reconstructs jets from a collection of particles using a specified algorithm and strategy
Arguments
particles: A collection of particles used for jet reconstruction. p=-1: The power value used for the distance measure for generalised k_T, which maps to a particular reconstruction algorithm (-1 = AntiKt, 0 = Cambridge/Aachen, 1 = Kt).R=1.0: The jet radius parameter.recombine=+: The recombination scheme used for combining particles.strategy=RecoStrategy.Best: The jet reconstruction strategy to use. RecoStrategy.Best makes a dynamic decision based on the number of starting particles.Returns
A cluster sequence object containing the reconstructed jets and the merging history.
Details
particles argument
Any type that supplies the methods pt2(), phi(), rapidity(), px(), py(), pz(), energy() (in the JetReconstruction namespace) can be used. This includes LorentzVector, LorentzVectorCyl, and PseudoJet, for which these methods are already predefined in the JetReconstruction namespace.
recombine argument
The recombine argument is the function used to merge pairs of particles. The default is simply +(jet1,jet2), i.e. 4-momenta addition or the E -scheme.
Example
jet_reconstruct(particles; p = -1, R = 0.4)source loadjets!(filename, jets; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), dtype=Float64)Loads the jets from a file. Ignores lines that start with '#'. Each line gets processed in the following way: the line is split using split(line, splitby) or simply split(line) by default. Every value in this line is then converted to the dtype (which is Float64 by default). These values are then used as arguments for the constructor function which should produce individual jets. By default, the constructor constructs Lorentz vectors.
Everything that was already in jets is not affected as we only use push! on it.
Example
# Load jets from two files into one array
-jets = []
-loadjets!("myjets1.dat", jets)
-loadjets!("myjets2.dat", jets)source loadjets(filename; splitby=isspace, constructor=(px,py,pz,E)->LorentzVectorHEP(E,px,py,pz), VT=LorentzVector)Load jets from a file.
Arguments
filename: The name of the file to load jets from.splitby: The delimiter used to split the data in the file. Default is isspace.constructor: A function that constructs a VT object from the jet data. Default is (px,py,pz,E)->LorentzVector(E,px,py,pz).VT: The type of the vector used to store the jet data. Default is LorentzVector.Returns
A vector of VT objects representing the loaded jets. source n_exclusive_jets(clusterseq::ClusterSequence; dcut::AbstractFloat)Return the number of exclusive jets of a ClusterSequence that are above a certain dcut value.
Arguments
clusterseq::ClusterSequence: The ClusterSequence object containing the clustering history.dcut::AbstractFloat: The maximum calue for the distance parameter in the reconstruction.Returns
The number of exclusive jets in the ClusterSequence object.
Example
n_exclusive_jets(clusterseq, dcut = 20.0)source plain_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where TPerform jet reconstruction using the plain algorithm.
Arguments
particles::Vector{T}: A vector of particles used for jet reconstruction, any array of particles, which supports suitable 4-vector methods, viz. pt2(), phi(), rapidity(), px(), py(), pz(), energy(), can be used. for each element.p::Int=-1: The integer value used for jet reconstruction.R::Float64=1.0: The radius parameter used for jet reconstruction.recombine::Function=+: The recombination function used for jet reconstruction.Note for the particles argument, the 4-vector methods need to exist in the JetReconstruction package namespace.
Returns
Vector{PseudoJet}: A vector of reconstructed jets.Example
jets = plain_jet_reconstruct(particles; p = -1, R = 1.0)source read_final_state_particles(fname; maxevents = -1, skipevents = 0, T=PseudoJet)Reads final state particles from a file and returns them as a vector of type T.
Arguments
fname: The name of the HepMC3 ASCII file to read particles from. If the file is gzipped, the function will automatically decompress it.maxevents=-1: The maximum number of events to read. -1 means all events will be read.skipevents=0: The number of events to skip before an event is included.T=PseudoJet: The type of object to contruct and return.Returns
A vector of vectors of T objects, where each inner vector represents all the particles of a particular event. In particular T can be PseudoJet or a LorentzVector type. Note, if T is not PseudoJet, the order of the arguments in the constructor must be (t, x, y, z).
source savejets(filename, jets; format="px py pz E")Save jet data to a file.
Arguments
filename: The name of the file to save the jet data to.jets: An array of jet objects to save.format="px py pz E": (optional) A string specifying the format of the jet data to save. The default format is "px py pz E".Details
This function saves jet data to a file in a specific format. Each line in the file represents a jet and contains the information about the jet in the specified format. The format string can include the following placeholders:
"E" or "energy": Jet energy "px": Momentum along the x-axis "py": Momentum along the y-axis "pz": Momentum along the z-axis "pt2": Square of the transverse momentum "phi": Azimuth angle "rapidity": Rapidity Lines starting with '#' are treated as comments and are ignored.
It is strongly NOT recommended to put something other than values and (possibly custom) separators in the format string.
source tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = -1, R = 1.0, recombine = +) where {T}Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for the prefered internal jet type, PseudoJet.
Arguments
particles::Vector{PseudoJet}: A vector of PseudoJet particles used as input for jet reconstruction.p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.recombine::Function = +: The recombination function used for combining pseudojets.Returns
Vector{PseudoJet}: A vector of reconstructed jets.Example
tiled_jet_reconstruct(particles::Vector{PseudoJet}; p = 1, R = 1.0, recombine = +)source tiled_jet_reconstruct(particles::Vector{T}; p = -1, R = 1.0, recombine = +) where {T}Main jet reconstruction algorithm entry point for reconstructing jets using the tiled stragegy for generic jet type T.
Note - if a non-standard recombination is used, it must be defined for JetReconstruction.PseudoJet, as this struct is used internally.
Arguments
particles::Vector{T}: A vector of particles used as input for jet reconstruction. T must support methods px, py, pz and energy (defined in the JetReconstruction namespace)p::Int = -1: The power parameter for the jet reconstruction algorithm, thus swiching between different algorithms.R::Float64 = 1.0: The jet radius parameter for the jet reconstruction algorithm.recombine::Function = +: The recombination function used for combining pseudojets.Returns
Vector{PseudoJet}: A vector of reconstructed jets.Example
tiled_jet_reconstruct(particles::Vector{LorentzVectorHEP}; p = -1, R = 0.4, recombine = +)source struct ClusterSequenceA struct holding the full history of a jet clustering sequence, including the final jets.
Fields
algorithm::JetAlgorithm.Algorithm: The algorithm used for clustering.strategy::RecoStrategy.Strategy: The strategy used for clustering.jets::Vector{PseudoJet}: The physical PseudoJets in the cluster sequence. Each PseudoJet corresponds to a position in the history.n_initial_jets::Int: The initial number of particles used for exclusive jets.history::Vector{HistoryElement}: The branching history of the cluster sequence. Each stage in the history indicates where to look in the jets vector to get the physical PseudoJet.Qtot::Any: The total energy of the event.source ClusterSequence(p::Int, strategy::RecoStrategy.Strategy, jets, history, Qtot)Constructs a ClusterSequence object with a specific power value mapped to a pp reconstruction algorithm.
Arguments
p::Int: The power value for the algorithm.strategy::RecoStrategy.Strategy: The reconstruction strategy.jets: The jets to be clustered.history: The length of the jets.Qtot: The total energy of the jets.Returns
A ClusterSequence object.
source ClusterSequence(alg::JetAlgorithm.Algorithm, strategy::RecoStrategy.Strategy, jets, history, Qtot)Constructs a ClusterSequence object with a specific algorithm spcified.
Arguments
alg::JetAlgorithm.Algorithm: The algorithm.strategy::RecoStrategy.Strategy: The reconstruction strategy.jets: The jets to be clustered.history: The length of the jets.Qtot: The total energy of the jets.Returns
A ClusterSequence object.
source struct FinalJetA struct representing the final properties of a jet, used for JSON serialisation.
Fields
rap::Float64: The rapidity of the jet.phi::Float64: The azimuthal angle of the jet.pt::Float64: The transverse momentum of the jet.source struct FinalJetsA struct with the vector of all jets for a certain jet identifier, used for JSON serialisation.
Fields
jetid::Int64: The ID of the jet.jets::Vector{FinalJet}: A vector of FinalJet objects representing the jets.source mutable struct PseudoJet <: FourMomentumThe PseudoJet struct represents a pseudojet, a four-momentum object used in jet reconstruction algorithms. Additonal information for the link back into the history of the clustering is stored in the _cluster_hist_index field. There is caching of the more expensive calculations for rapidity and azimuthal angle.
Fields
px::Float64: The x-component of the momentum.py::Float64: The y-component of the momentum.pz::Float64: The z-component of the momentum.E::Float64: The energy component of the momentum._cluster_hist_index::Int: The index of the cluster history._pt2::Float64: The squared transverse momentum._inv_pt2::Float64: The inverse squared transverse momentum._rap::Float64: The rapidity._phi::Float64: The azimuthal angle.source PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64)Constructs a PseudoJet object with the given momentum components and energy.
Arguments
px::Float64: The x-component of the momentum.py::Float64: The y-component of the momentum.pz::Float64: The z-component of the momentum.E::Float64: The energy.Returns
A PseudoJet object.
source PseudoJet(px::Float64, py::Float64, pz::Float64, E::Float64,
- _cluster_hist_index::Int,
- pt2::Float64)Constructs a PseudoJet object with the given momentum components and energy and history index.
Arguments
px::Float64: The x-component of the momentum.py::Float64: The y-component of the momentum.pz::Float64: The z-component of the momentum.E::Float64: The energy._cluster_hist_index::Int: The cluster history index.pt2::Float64: The transverse momentum squared.Returns
A PseudoJet object.
source
Theme
Automatic (OS) documenter-light documenter-dark catppuccin-latte catppuccin-frappe catppuccin-macchiato catppuccin-mocha
This document was generated with Documenter.jl version 1.5.0 on Tuesday 16 July 2024 . Using Julia version 1.10.4.
Jet Visualisation Documentation | JetReconstruction.jl
+ Visualisation API · JetReconstruction.jl Documentation for visualisation interfaces extension module.
animatereco(cs::ClusterSequence, filename;
- barsize_phi = 0.1,
- barsize_y = 0.1,
- colormap = :glasbey_category10_n256,
- perspective = 0.5,
- azimuth = 2.7,
- elevation = 0.5,
- framerate = 5,
- ancestors = false,
- Module = Makie)Animate the jet reconstruction process and save it as a video file.
Arguments
cs::ClusterSequence: The cluster sequence object containing the jets.filename: The name of the output video file.Optional Arguments
barsize_phi=0.1: The size of the bars in the phi direction.barsize_y=0.1: The size of the bars in the y direction.colormap=:glasbey_category10_n256: The colormap to use for coloring the jets.perspective=0.5: The perspective of the plot.azimuth=2.7: The azimuth angle of the plot.elevation=0.5: The elevation angle of the plot.framerate=5: The framerate of the output video.end_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.title=nothing: The title to add to the plot.ancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.Module: The plotting module to use. Default is Makie.For perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.
Returns
fig: The figure object representing the final frame.source jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)
Plots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
-using CairoMakie # use any other Makie that you have here
-
-jetsplot([object1, object2, object3], [[1], [2, 3]])The example above plots object1 as a separate jet in one colour and object2 and object3 together in another colour.
This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:
import CairoMakie
-jetsplot(my_objects, my_colour_arrays, Module=CairoMakie)
-
-import GLMakie
-jetsplot(my_objects, my_colour_arrays, Module=GLMakie)
-
-using WGLMakie
-jetsplot(my_objects, my_colour_arrays, Module=Main) #defaultsource jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)Plots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.
Optional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);
# example
-using CairoMakie # use any other Makie that you have here
-jetsplot([object1, object2, object3], cluster_sequence_I_got_from_jet_reconstruct; Module=CairoMakie)This function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:
import CairoMakie
-jetsplot(my_objects, cs, Module=CairoMakie)
-
-import GLMakie
-jetsplot(my_objects, cs, Module=GLMakie)
-
-using WGLMakie
-jetsplot(my_objects, cs, Module=Main) #defaultsource
Theme
Automatic (OS) documenter-light documenter-dark catppuccin-latte catppuccin-frappe catppuccin-macchiato catppuccin-mocha
This document was generated with Documenter.jl version 1.5.0 on Tuesday 16 July 2024 . Using Julia version 1.10.4.
\nGraeme A Stewart \nPhilippe Gras ","category":"page"},{"location":"","page":"Home","title":"Home","text":"and is Copyright 2022-2024 The Authors, CERN.","category":"page"},{"location":"","page":"Home","title":"Home","text":"The code is under the MIT License.","category":"page"},{"location":"lib/internal/#Jet-Reconstruction-Internal-Documentation","page":"Internal API","title":"Jet Reconstruction Internal Documentation","text":"","category":"section"},{"location":"lib/internal/","page":"Internal API","title":"Internal API","text":"Documentation for JetReconstruction.jl's internal methods and types.","category":"page"},{"location":"lib/internal/","page":"Internal API","title":"Internal API","text":"N.B. no guarantee is made of stability of these interfaces or types.","category":"page"},{"location":"lib/internal/","page":"Internal API","title":"Internal API","text":"CollapsedDocStrings = true","category":"page"},{"location":"lib/internal/#Index","page":"Internal API","title":"Index","text":"","category":"section"},{"location":"lib/internal/","page":"Internal API","title":"Internal API","text":"Pages = [\"internal.md\"]","category":"page"},{"location":"lib/internal/#Public-Interface","page":"Internal API","title":"Public Interface","text":"","category":"section"},{"location":"lib/internal/","page":"Internal API","title":"Internal API","text":"Modules = [JetReconstruction]\nPublic = false\nOrder = [:function, :type]","category":"page"},{"location":"lib/internal/#Base.:+-Tuple{PseudoJet, PseudoJet}","page":"Internal API","title":"Base.:+","text":"+(j1::PseudoJet, j2::PseudoJet)\n\nAddition operator for PseudoJet objects.\n\nArguments\n\nj1::PseudoJet: The first PseudoJet object.\nj2::PseudoJet: The second PseudoJet object.\n\nReturns\n\nA new PseudoJet object with the sum of the momenta and energy of j1 and j2.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#Base.copy-Tuple{JetReconstruction.TiledJet}","page":"Internal API","title":"Base.copy","text":"copy(j::TiledJet)\n\nCreate a copy of a TiledJet object.\n\nArguments\n\nj::TiledJet: The TiledJet object to be copied.\n\nReturns\n\nA new TiledJet object with the same attributes as the input object.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#Base.iterate","page":"Internal API","title":"Base.iterate","text":"Base.iterate(t::rightmost_tiles, state=1)\n\nIterate over the rightmost_tiles object, returning all the rightmost tiles for a given Cartesian tile index.\n\n\n\n\n\n","category":"function"},{"location":"lib/internal/#Base.iterate-2","page":"Internal API","title":"Base.iterate","text":"Base.iterate(t::neighbour_tiles, state=1)\n\nIterate over the neighbour_tiles object, returning all the neighbour tiles for a given Cartesian tile index.\n\n\n\n\n\n","category":"function"},{"location":"lib/internal/#Base.iterate-Tuple{JetReconstruction.TiledJet}","page":"Internal API","title":"Base.iterate","text":"Base.iterate(tj::TiledJet)\n\nIterate over a TiledJet object's linked list, walking over all jets until the end (then the next jet is invalid).\n\nArguments\n\ntj::TiledJet: The TiledJet object to start to iterate over.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#Base.show-Tuple{IO, PseudoJet}","page":"Internal API","title":"Base.show","text":"show(io::IO, jet::PseudoJet)\n\nPrint a PseudoJet object to the specified IO stream.\n\nArguments\n\nio::IO: The IO stream to which the information will be printed.\njet::PseudoJet: The PseudoJet object whose information will be printed.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#Base.tryparse-Tuple{Type{<:Enum}, String}","page":"Internal API","title":"Base.tryparse","text":"Base.tryparse(E::Type{<:Enum}, str::String)\n\nParser that converts a string to an enum value if it exists, otherwise returns nothing.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.CosTheta-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.CosTheta","text":"CosTheta(p::PseudoJet)\n\nCompute the cosine of the angle between the momentum vector p and the z-axis.\n\nArguments\n\np::PseudoJet: The PseudoJet object representing the momentum vector.\n\nReturns\n\nThe cosine of the angle between p and the z-axis.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction._ensure_valid_rap_phi-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction._ensure_valid_rap_phi","text":"_ensure_valid_rap_phi(p::PseudoJet)\n\nEnsure that the rapidity and azimuthal angle of the PseudoJet p are valid. If the azimuthal angle is invalid (used as a proxy for both variables), they are set to a valid value using _set_rap_phi!.\n\nArguments\n\np::PseudoJet: The PseudoJet object to ensure valid rapidity and azimuthal angle for.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction._plain_jet_reconstruct-Tuple{}","page":"Internal API","title":"JetReconstruction._plain_jet_reconstruct","text":"_plain_jet_reconstruct(; particles::Vector{PseudoJet}, p = -1, R = 1.0, recombine = +)\n\nThis is the internal implementation of jet reconstruction using the plain algorithm. It takes a vector of particles representing the input particles and reconstructs jets based on the specified parameters. Here the particles must be of type PseudoJet.\n\nUsers of the package should use the plain_jet_reconstruct function as their entry point to this jet reconstruction.\n\nThe power value maps to specific pp jet reconstruction algorithms: -1 = AntiKt, 0 = Cambridge/Aachen, 1 = Inclusive Kt.\n\nArguments\n\nparticles: A vector of PseudoJet objects representing the input particles.\np=-1: The power to which the transverse momentum (pt) of each particle is raised.\nR=1.0: The jet radius parameter.\nrecombine: The recombination function used to merge two jets. Default is + (additive recombination).\n\nReturns\n\nclusterseq: The resulting ClusterSequence object representing the reconstructed jets.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction._set_rap_phi!-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction._set_rap_phi!","text":"setrap_phi!(p::PseudoJet)\n\nSet the rapidity and azimuthal angle of the PseudoJet p.\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to set the rapidity and azimuthal angle.\n\nDescription\n\nThis function calculates and sets the rapidity and azimuthal angle of the PseudoJet p based on its momentum components. The rapidity is calculated in a way that is insensitive to roundoff errors when the momentum components are large. If the PseudoJet represents a point with infinite rapidity, a large number is assigned to the rapidity in order to lift the degeneracy between different zero-pt momenta.\n\nNote - the ϕ angle is calculated in the range [0, 2π).\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction._tj_diJ-Tuple{Any}","page":"Internal API","title":"JetReconstruction._tj_diJ","text":"_tj_diJ(jet)\n\nCompute the dij metric value for a given jet.\n\nArguments\n\njet: The input jet.\n\nReturns\n\nThe dij value for the jet.\n\nExample\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction._tj_dist-Tuple{Any, Any}","page":"Internal API","title":"JetReconstruction._tj_dist","text":"_tj_dist(jetA, jetB)\n\nCompute the geometric distance in the (y, ϕ)-plane between two jets in the TiledAlgoLL module.\n\nArguments\n\njetA: The first jet.\njetB: The second jet.\n\nReturns\n\nThe squared distance between jetA and jetB.\n\nExamples\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.add_step_to_history!-Tuple{ClusterSequence, Vararg{Any, 4}}","page":"Internal API","title":"JetReconstruction.add_step_to_history!","text":"add_step_to_history!(clusterseq::ClusterSequence, parent1, parent2, jetp_index, dij)\n\nAdd a new jet's history into the recombination sequence.\n\nArguments:\n\nclusterseq::ClusterSequence: The cluster sequence object.\nparent1: The index of the first parent.\nparent2: The index of the second parent.\njetp_index: The index of the jet.\ndij: The dij value.\n\nThis function adds a new HistoryElement to the history vector of the clusterseq object. The HistoryElement contains information about the parents, child, jet index, dij value, and the maximum dij value so far. It also updates the child index of the parent elements.\n\nIf the parent1 or parent2 have already been recombined, an InternalError is thrown. The jetp_index is used to update the _cluster_hist_index of the corresponding PseudoJet object.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.add_untagged_neighbours_to_tile_union-NTuple{4, Any}","page":"Internal API","title":"JetReconstruction.add_untagged_neighbours_to_tile_union","text":"add_untagged_neighbours_to_tile_union(center_index, tile_union, n_near_tiles, tiling)\n\nAdds to the vector tileunion the tiles that are in the neighbourhood of the specified centerindex, including itself and whose tagged status are false - start adding from position nneartiles-1, and increase nneartiles. When a neighbour is added its tagged status is set to true.\n\nArguments\n\ncenter_index: The index of the center tile.\ntile_union: An array to store the indices of neighbouring tiles.\nn_near_tiles: The number of neighbouring tiles.\ntiling: The tiling object containing the tile tags.\n\nReturns\n\nThe updated number of near tiles.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.detach!-Tuple{JetReconstruction.TiledJet}","page":"Internal API","title":"JetReconstruction.detach!","text":"detach!(jet::TiledJet)\n\nDetach a TiledJet from its linked list by updating the previous and next pointers.\n\nArguments\n\njet::TiledJet: The TiledJet object to detach.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.determine_rapidity_extent-Union{Tuple{Vector{T}}, Tuple{T}} where T<:AbstractFloat","page":"Internal API","title":"JetReconstruction.determine_rapidity_extent","text":"determine_rapidity_extent(eta::Vector{T}) where T <: AbstractFloat\n\nCalculate the minimum and maximum rapidities based on the input vector eta. The function determines the rapidity extent by binning the multiplicities as a function of rapidity and finding the minimum and maximum rapidities such that the edge bins contain a certain fraction (~1/4) of the busiest bin and a minimum number of particles.\n\nThis is the heuristic which is used by FastJet (inline comments are from FastJet).\n\nArguments\n\neta::Vector{T}: A vector of rapidity values.\n\nReturns\n\nminrap::T: The minimum rapidity value.\nmaxrap::T: The maximum rapidity value.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.dij-NTuple{4, Any}","page":"Internal API","title":"JetReconstruction.dij","text":"dij(i, kt2_array, nn, nndist)\n\nCompute the dij value for a given index i to its nearest neighbor. The nearest neighbor is determined from nn[i], and the metric distance to the nearest neighbor is given by the distance nndist[i] applying the lower of the kt2_array values for the two particles.ßß\n\nArguments\n\ni: The index of the element.\nkt2_array: An array of kt2 values.\nnn: An array of nearest neighbors.\nnndist: An array of nearest neighbor distances.\n\nReturns\n\nThe computed dij value.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.dist-NTuple{4, Any}","page":"Internal API","title":"JetReconstruction.dist","text":"dist(i, j, rapidity_array, phi_array)\n\nCompute the distance between points in a 2D space defined by rapidity and phi coordinates.\n\nArguments\n\ni::Int: Index of the first point to consider (indexes into rapidity_array and phi_array).\nj::Int: Index of the second point to consider (indexes into rapidity_array and phi_array).\nrapidity_array::Vector{Float64}: Array of rapidity coordinates.\nphi_array::Vector{Float64}: Array of phi coordinates.\n\nReturns\n\ndistance::Float64: The distance between the two points.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.do_iB_recombination_step!-Tuple{ClusterSequence, Any, Any}","page":"Internal API","title":"JetReconstruction.do_iB_recombination_step!","text":"do_iB_recombination_step!(clusterseq::ClusterSequence, jet_i, diB)\n\nBookkeeping for recombining a jet with the beam (i.e., finalising the jet) by adding a step to the history of the cluster sequence.\n\nArguments\n\nclusterseq::ClusterSequence: The cluster sequence object.\njet_i: The index of the jet.\ndiB: The diB value.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.do_ij_recombination_step!","page":"Internal API","title":"JetReconstruction.do_ij_recombination_step!","text":"do_ij_recombination_step!(clusterseq::ClusterSequence, jet_i, jet_j, dij, recombine=+)\n\nPerform the bookkeeping associated with the step of recombining jeti and jetj (assuming a distance dij).\n\nArguments\n\nclusterseq::ClusterSequence: The cluster sequence object.\njet_i: The index of the first jet to be recombined.\njet_j: The index of the second jet to be recombined.\ndij: The distance between the two jets.\nrecombine=+: The recombination function to be used. Default is addition.\n\nReturns\n\nnewjet_k: The index of the newly created jet.\n\nDescription\n\nThis function performs the i-j recombination step in the cluster sequence. It creates a new jet by recombining the first two jets using the specified recombination function. The new jet is then added to the cluster sequence. The function also updates the indices and history information of the new jet and sorts out the history.\n\n\n\n\n\n","category":"function"},{"location":"lib/internal/#JetReconstruction.energy-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.energy","text":"energy(p::PseudoJet)\n\nReturn the energy of a PseudoJet.\n\nArguments\n\np::PseudoJet: The PseudoJet object.\n\nReturns\n\nThe energy of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.eta-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.eta","text":"eta(p::PseudoJet)\n\nCompute the pseudorapidity (η) of a PseudoJet.\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to compute the pseudorapidity.\n\nReturns\n\nThe pseudorapidity (η) of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.fast_findmin-Tuple{Any, Any}","page":"Internal API","title":"JetReconstruction.fast_findmin","text":"fast_findmin(dij, n)\n\nFind the minimum value and its index in the first n elements of the dij array. The use of @turbo macro gives a significiant performance boost.\n\nArguments\n\ndij: An array of values.\nn: The number of elements to consider in the dij array.\n\nReturns\n\ndij_min: The minimum value in the first n elements of the dij array.\nbest: The index of the minimum value in the dij array.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.find_tile_neighbours!-NTuple{5, Any}","page":"Internal API","title":"JetReconstruction.find_tile_neighbours!","text":"find_tile_neighbours!(tile_union, jetA, jetB, oldB, tiling)\n\nFind the union of neighbouring tiles of jetA, jetB, and oldB and add them to the tile_union. This established the set of tiles over which searches for updated and new nearest-neighbours must be run\n\nArguments\n\ntile_union: The tile union to which the neighbouring tiles will be added.\njetA: The first jet.\njetB: The second jet.\noldB: The old second jet.\ntiling: The tiling information.\n\nReturns\n\nThe number of neighbouring tiles added to the tile_union.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.geometric_distance-NTuple{4, AbstractFloat}","page":"Internal API","title":"JetReconstruction.geometric_distance","text":"geometric_distance(eta1::AbstractFloat, phi1::AbstractFloat, eta2::AbstractFloat, phi2::AbstractFloat)\n\nCompute the geometric distance between two points in the rap-phi plane.\n\nArguments\n\neta1::AbstractFloat: The eta coordinate of the first point.\nphi1::AbstractFloat: The phi coordinate of the first point.\neta2::AbstractFloat: The eta coordinate of the second point.\nphi2::AbstractFloat: The phi coordinate of the second point.\n\nReturns\n\ndistance::Float64: The geometric distance between the two points.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.get_all_ancestors-Tuple{Any, ClusterSequence}","page":"Internal API","title":"JetReconstruction.get_all_ancestors","text":"get_all_ancestors(idx, cs::ClusterSequence)\n\nRecursively finds all ancestors of a given index in a ClusterSequence object.\n\nArguments\n\nidx: The index of the jet for which to find ancestors.\ncs: The ClusterSequence object containing the jet history.\n\nReturns\n\nAn array of indices representing the ancestors of the given jet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.get_dij_dist-NTuple{4, Any}","page":"Internal API","title":"JetReconstruction.get_dij_dist","text":"get_dij_dist(nn_dist, kt2_1, kt2_2, R2)\n\nCompute the dij metric distance between two jets.\n\nArguments\n\nnn_dist: The nearest-neighbor distance between two jets.\nkt2_1: The squared momentum metric value of the first jet.\nkt2_2: The squared momentum metric value of the second jet.\nR2: The jet radius parameter squared.\n\nReturns\n\nThe distance between the two jets.\n\nIf kt2_2 is equal to 0.0, then the first jet doesn't actually have a valid neighbour, so it's treated as a single jet adjecent to the beam.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.get_tile-Tuple{JetReconstruction.TilingDef, AbstractFloat, AbstractFloat}","page":"Internal API","title":"JetReconstruction.get_tile","text":"get_tile(tiling_setup::TilingDef, eta::AbstractFloat, phi::AbstractFloat)\n\nGiven a tiling_setup object, eta and phi values, this function calculates the tile indices for the given eta and phi values.\n\nArguments\n\ntiling_setup: A TilingDef object that contains the tiling setup parameters.\neta: The eta value for which to calculate the tile index.\nphi: The phi value for which to calculate the tile index.\n\nReturns\n\nieta: The tile index along the eta direction.\niphi: The tile index along the phi direction.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.get_tile_cartesian_indices-Tuple{JetReconstruction.TilingDef, Int64}","page":"Internal API","title":"JetReconstruction.get_tile_cartesian_indices","text":"get_tile_linear_index(tiling_setup::TilingDef, i_η::Int, i_ϕ::Int)\n\nCompute the linear index of a tile in a tiled setup. This is much faster in this function than using the LinearIndices construct (like x100, which is bonkers, but there you go...)\n\nArguments\n\ntiling_setup::TilingDef: The tiling setup defining the number of tiles in each dimension.\ni_η::Int: The index of the tile in the η dimension.\ni_ϕ::Int: The index of the tile in the ϕ dimension.\n\nReturns\n\nThe linear index of the tile.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.initial_history-Tuple{Any}","page":"Internal API","title":"JetReconstruction.initial_history","text":"initial_history(particles)\n\nCreate an initial history for the given particles.\n\nArguments\n\nparticles: The initial vector of stable particles.\n\nReturns\n\nhistory: An array of HistoryElement objects.\nQtot: The total energy in the event.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.insert!-Tuple{JetReconstruction.TiledJet, JetReconstruction.TiledJet}","page":"Internal API","title":"JetReconstruction.insert!","text":"insert!(nextjet::TiledJet, jettomove::TiledJet)\n\nInserts a TiledJet object into the linked list of TiledJet objects, before the nextjet object. The jet to move can be an isolated jet, a jet from another list or a jet from the same list\n\nArguments\n\nnextjet::TiledJet: The TiledJet object after which jettomove should be inserted.\njettomove::TiledJet: The TiledJet object to be inserted.\n\nExample\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.isvalid-Tuple{JetReconstruction.TiledJet}","page":"Internal API","title":"JetReconstruction.isvalid","text":"isvalid(t::TiledJet)\n\nCheck if a TiledJet is valid, by seeing if it is not the noTiledJet object.\n\nArguments\n\nt::TiledJet: The TiledJet object to check.\n\nReturns\n\nBool: true if the TiledJet object is valid, false otherwise.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.jet_ranks-Tuple{ClusterSequence}","page":"Internal API","title":"JetReconstruction.jet_ranks","text":"jet_ranks(clusterseq::ClusterSequence; compare_fn = JetReconstruction.pt)\n\nCompute the ranks of jets in a given ClusterSequence object based on a specified comparison function.\n\nArguments\n\nclusterseq::ClusterSequence: The ClusterSequence object containing the jets to rank.\ncompare_fn = JetReconstruction.pt: The comparison function used to determine the order of the jets. Defaults to JetReconstruction.pt, which compares jets based on their transverse momentum.\n\nReturns\n\nA dictionary mapping each jet index to its rank.\n\nNote\n\nThis is a utility function that can be used to rank initial clusters based on a specified jet property. It can be used to assign a consistent \"rank\" to each reconstructed jet in the cluster sequence, which is useful for stable plotting of jet outputs.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.m-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.m","text":"m(p::PseudoJet)\n\nCompute the invariant mass of a PseudoJet object. By convention if m^2 < 0, then -sqrt{(-m^2)} is returned.\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to compute the invariant mass.\n\nReturns\n\nThe invariant mass of the PseudoJet object.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.m2-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.m2","text":"m2(p::PseudoJet)\n\nCalculate the invariant mass squared (m^2) of a PseudoJet.\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to calculate the invariant mass squared.\n\nReturns\n\nThe invariant mass squared (m^2) of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.mag-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.mag","text":"mag(p::PseudoJet)\n\nReturn the magnitude of the momentum of a PseudoJet, |p|.\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to compute the magnitude.\n\nReturns\n\nThe magnitude of the PseudoJet object.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.mass-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.mass","text":"mass(p::PseudoJet)\n\nCompute the invariant mass (alias for m(p)).\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to compute the mass.\n\nReturns\n\nThe mass of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.mass2","page":"Internal API","title":"JetReconstruction.mass2","text":"Alias for m2 function\n\n\n\n\n\n","category":"function"},{"location":"lib/internal/#JetReconstruction.merge_steps-Tuple{ClusterSequence}","page":"Internal API","title":"JetReconstruction.merge_steps","text":"merge_steps(clusterseq::ClusterSequence)\n\nCompute the number of jet-jet merge steps in a cluster sequence. This is useful to give the number of meaningful recombination steps in a jet reconstruction sequence (beam merge steps are not counted).\n\nArguments\n\nclusterseq::ClusterSequence: The cluster sequence object.\n\nReturns\n\nmerge_steps::Int: The number of merge steps.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.phi-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.phi","text":"phi(p::PseudoJet)\n\nCompute the ϕ angle of a PseudoJet object p.\n\nNote this function is a wrapper for phi_02pi(p).\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.\n\nReturns\n\nThe azimuthal angle of p in the range [0, 2π).\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.phi_02pi-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.phi_02pi","text":"phi_02pi(p::PseudoJet)\n\nCompute the azimuthal angle of a PseudoJet object p in the range [0, 2π).\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to compute the azimuthal angle.\n\nReturns\n\nThe azimuthal angle of p in the range [0, 2π).\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.pt-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.pt","text":"pt(p::PseudoJet)\n\nCompute the scalar transverse momentum (pt) of a PseudoJet.\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to compute the transverse momentum.\n\nReturns\n\nThe transverse momentum (pt) of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.pt2-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.pt2","text":"pt2(p::PseudoJet)\n\nGet the squared transverse momentum of a PseudoJet.\n\nArguments\n\np::PseudoJet: The PseudoJet object.\n\nReturns\n\nThe squared transverse momentum of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.px-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.px","text":"px(p::PseudoJet)\n\nReturn the x-component of the momentum of a PseudoJet.\n\nArguments\n\np::PseudoJet: The PseudoJet object.\n\nReturns\n\nThe x-component of the momentum of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.py-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.py","text":"py(p::PseudoJet)\n\nReturn the y-component of the momentum of a PseudoJet.\n\nArguments\n\np::PseudoJet: The PseudoJet object.\n\nReturns\n\nThe y-component of the momentum of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.pz-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.pz","text":"pz(p::PseudoJet)\n\nReturn the z-component of the momentum of a PseudoJet.\n\nArguments\n\np::PseudoJet: The PseudoJet object.\n\nReturns\n\nThe z-component of the momentum of the PseudoJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.rapidity-Tuple{PseudoJet}","page":"Internal API","title":"JetReconstruction.rapidity","text":"rapidity(p::PseudoJet)\n\nCompute the rapidity of a PseudoJet object.\n\nArguments\n\np::PseudoJet: The PseudoJet object for which to compute the rapidity.\n\nReturns\n\nThe rapidity of the PseudoJet object.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.reco_state-Tuple{ClusterSequence, Any}","page":"Internal API","title":"JetReconstruction.reco_state","text":"reco_state(cs::ClusterSequence, pt_ranks; iteration=0)\n\nThis function returns the reconstruction state of a ClusterSequence object based on a given iteration number in the reconstruction.\n\nArguments\n\ncs::ClusterSequence: The ClusterSequence object to update.\nranks: The ranks of the original clusters, that are inherited by peudojets\n\nduring the reconstruction process.\n\niteration=0: The iteration number to consider for updating the reconstruction state (0 represents the initial state).\nignore_beam_merge=true: Ignore beam merging steps in the reconstruction (which produce no change in status).\n\nReturns\n\nA dictionary representing a snapshot of the reconstruction state.\n\nDetails\n\nThe function starts by initializing the reconstruction state with the initial particles. Then, it walks over the iteration sequence and updates the reconstruction state based on the history of recombination and finalization/beam merger steps.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.rightneighbours-Tuple{Int64, JetReconstruction.Tiling}","page":"Internal API","title":"JetReconstruction.rightneighbours","text":"rightneighbours(center::Int, tiling::Tiling)\n\nCompute the indices of the right neighbors of a given center index in a tiling. This is used in the inital sweep to calculate the nearest neighbors, where the search between jets for the nearest neighbour is bi-directional, thus when a tile is considered only the right neighbours are needed to compare jet distances as the left-hand tiles have been done from that tile already.\n\nArguments\n\ncenter::Int: The center index.\ntiling::Tiling: The tiling object.\n\nReturns\n\nSurrounding: An object containing the indices of the right neighbors.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.set_momentum!-Tuple{PseudoJet, Vararg{Any, 4}}","page":"Internal API","title":"JetReconstruction.set_momentum!","text":"set_momentum!(j::PseudoJet, px, py, pz, E)\n\nSet the momentum components and energy of a PseudoJet object.\n\nArguments\n\nj::PseudoJet: The PseudoJet object to set the momentum for.\npx: The x-component of the momentum.\npy: The y-component of the momentum.\npz: The z-component of the momentum.\nE: The energy of the particle.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.set_nearest_neighbours!-Tuple{ClusterSequence, JetReconstruction.Tiling, Vector{JetReconstruction.TiledJet}}","page":"Internal API","title":"JetReconstruction.set_nearest_neighbours!","text":"set_nearest_neighbours!(clusterseq::ClusterSequence, tiling::Tiling, tiledjets::Vector{TiledJet})\n\nThis function sets the nearest neighbor information for all jets in the tiledjets vector.\n\nArguments\n\nclusterseq::ClusterSequence: The cluster sequence object.\ntiling::Tiling: The tiling object.\ntiledjets::Vector{TiledJet}: The vector of tiled jets.\n\nReturns\n\nNNs::Vector{TiledJet}: The vector of nearest neighbor jets.\ndiJ::Vector{Float64}: The vector of diJ values.\n\nThe function iterates over each tile in the tiling and sets the nearest neighbor information for each jet in the tile. It then looks for neighbor jets in the neighboring tiles and updates the nearest neighbor information accordingly. Finally, it creates the diJ table and returns the vectors of nearest neighbor jets and diJ values.\n\nNote: The diJ values are calculated as the kt distance multiplied by R^2.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.setup_tiling-Union{Tuple{T}, Tuple{Vector{T}, AbstractFloat}} where T<:AbstractFloat","page":"Internal API","title":"JetReconstruction.setup_tiling","text":"setup_tiling(eta::Vector{T}, Rparam::AbstractFloat) where T <: AbstractFloat\n\nThis function sets up the tiling parameters for a reconstruction given a vector of rapidities eta and a radius parameter Rparam.\n\nArguments\n\neta::Vector{T}: A vector of rapidities.\nRparam::AbstractFloat: The jet radius parameter.\n\nReturns\n\ntiling_setup: A TilingDef object containing the tiling setup parameters.\n\nDescription\n\nThe function first decides the tile sizes based on the Rparam value. It then determines the number of tiles in the phi direction (n_tiles_phi) based on the tile size. Next, it determines the rapidity extent of the input eta vector and adjusts the values accordingly. Finally, it creates a TilingDef object with the calculated tiling parameters and returns it.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.surrounding-Tuple{Int64, JetReconstruction.Tiling}","page":"Internal API","title":"JetReconstruction.surrounding","text":"surrounding(center::Int, tiling::Tiling)\n\nCompute the surrounding indices of a given center index in a tiling.\n\nArguments\n\ncenter::Int: The center index.\ntiling::Tiling: The tiling object.\n\nReturns\n\nSurrounding: An object containing the surrounding indices.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.tile_index-Tuple{Any, Float64, Float64}","page":"Internal API","title":"JetReconstruction.tile_index","text":"tile_index(tiling_setup, eta::Float64, phi::Float64)\n\nCompute the tile index for a given (eta, phi) coordinate.\n\nArguments\n\ntiling_setup: The tiling setup object containing the tile size and number of tiles.\neta::Float64: The eta coordinate.\nphi::Float64: The phi coordinate.\n\nReturns\n\nThe tile index corresponding to the (eta, phi) coordinate.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.tiledjet_remove_from_tiles!-Tuple{Any, Any}","page":"Internal API","title":"JetReconstruction.tiledjet_remove_from_tiles!","text":"tiledjet_remove_from_tiles!(tiling, jet)\n\nRemove a jet from the given tiling structure.\n\nArguments\n\ntiling: The tiling structure from which the jet will be removed.\njet: The jet to be removed from the tiling structure.\n\nDescription\n\nThis function removes a jet from the tiling structure. It adjusts the linked list to be consistent with the removal of the jet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.tiledjet_set_jetinfo!-Tuple{JetReconstruction.TiledJet, ClusterSequence, JetReconstruction.Tiling, Any, Any, Any}","page":"Internal API","title":"JetReconstruction.tiledjet_set_jetinfo!","text":"tiledjet_set_jetinfo!(jet::TiledJet, clusterseq::ClusterSequence, tiling::Tiling, jets_index, R2, p)\n\nInitialise a tiled jet from a PseudoJet (using an index into our ClusterSequence)\n\nArguments:\n\njet::TiledJet: The TiledJet object to set the information for.\nclusterseq::ClusterSequence: The ClusterSequence object containing the jets.\ntiling::Tiling: The Tiling object containing the tile information.\njets_index: The index of the jet in the ClusterSequence.\nR2: The jet radius parameter squared.\np: The power to raise the pt2 value to.\n\nThis function sets the eta, phi, kt2, jetsindex, NNdist, NN, tile_index, previous, and next fields of the TiledJet object.\n\nReturns:\n\nnothing\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.upd_nn_crosscheck!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}","page":"Internal API","title":"JetReconstruction.upd_nn_crosscheck!","text":"upd_nn_crosscheck!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)\n\nUpdate the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, both for particle i and the checked particles [from:to] (hence crosscheck).\n\nArguments\n\ni::Int: The index of the particle to update and check against.\nfrom::Int: The starting index of the range of particles to check against.\nto::Int: The ending index of the range of particles to check against.\nrapidity_array: An array containing the rapidity values of all particles.\nphi_array: An array containing the phi values of the all particles.\nR2: The squared jet distance threshold for considering a particle as a neighbour.\nnndist: The array that stores the nearest neighbor distances.\nnn: The array that stores the nearest neighbor indices.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.upd_nn_nocross!-Tuple{Int64, Int64, Int64, Vararg{Any, 5}}","page":"Internal API","title":"JetReconstruction.upd_nn_nocross!","text":"upd_nn_nocross!(i, from, to, rapidity_array, phi_array, R2, nndist, nn)\n\nUpdate the nearest neighbor information for a given particle index i against all particles in the range indexes from to to. The function updates the nndist and nn arrays with the nearest neighbor distance and index respectively, only for particle i (hence nocross).\n\nArguments\n\ni::Int: The index of the particle to update and check against.\nfrom::Int: The starting index of the range of particles to check against.\nto::Int: The ending index of the range of particles to check against.\nrapidity_array: An array containing the rapidity values of all particles.\nphi_array: An array containing the phi values of the all particles.\nR2: The squared jet distance threshold for considering a particle as a neighbour.\nnndist: The array that stores the nearest neighbor distances.\nnn: The array that stores the nearest neighbor indices.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.upd_nn_step!-NTuple{12, Any}","page":"Internal API","title":"JetReconstruction.upd_nn_step!","text":"upd_nn_step!(i, j, k, N, Nn, kt2_array, rapidity_array, phi_array, R2, nndist, nn, nndij)\n\nUpdate the nearest neighbor information after a jet merge step.\n\nArguments:\n\ni: Index of the first particle in the last merge step.\nj: Index of the second particle in the last merge step.\nk: Index of the current particle for which the nearest neighbour will be updated.\nN: Total number of particles (currently vaild array indexes are [1:N]).\nNn: Number of nearest neighbors to consider.\nkt2_array: Array of transverse momentum squared values.\nrapidity_array: Array of rapidity values.\nphi_array: Array of azimuthal angle values.\nR2: Distance threshold squared for nearest neighbors.\nnndist: Array of nearest neighbor geometric distances.\nnn: Array of nearest neighbor indices.\nnndij: Array of metric distances between particles.\n\nThis function updates the nearest neighbor information for the current particle k by considering the distances to particles i and j. It checks if the distance between k and i is smaller than the current nearest neighbor distance for k, and updates the nearest neighbor information accordingly. It also updates the nearest neighbor information for i if the distance between k and i is smaller than the current nearest neighbor distance for i. Finally, it checks if the nearest neighbor of k is the total number of particles Nn and updates it to j if necessary.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.η","page":"Internal API","title":"JetReconstruction.η","text":"const η = eta\n\nAlias for the pseudorapidity function, eta.\n\n\n\n\n\n","category":"function"},{"location":"lib/internal/#JetReconstruction.FourMomentum","page":"Internal API","title":"JetReconstruction.FourMomentum","text":"Interface for composite types that includes fields px, py, py, and E that represents the components of a four-momentum vector.\n\n\n\n\n\n","category":"type"},{"location":"lib/internal/#JetReconstruction.HistoryElement","page":"Internal API","title":"JetReconstruction.HistoryElement","text":"struct HistoryElement\n\nA struct holding a record of jet mergers and finalisations\n\nFields:\n\nparent1: Index in history where first parent of this jet was created (NonexistentParent if this jet is an original particle)\nparent2: Index in history where second parent of this jet was created (NonexistentParent if this jet is an original particle); BeamJet if this history entry just labels the fact that the jet has recombined with the beam)\nchild: Index in history where the current jet is recombined with another jet to form its child. It is Invalid if this jet does not further recombine.\njetp_index: Index in the jets vector where we will find the PseudoJet object corresponding to this jet (i.e. the jet created at this entry of the history). NB: if this element of the history corresponds to a beam recombination, then jetp_index=Invalid.\ndij: The distance corresponding to the recombination at this stage of the clustering.\nmax_dij_so_far: The largest recombination distance seen so far in the clustering history.\n\n\n\n\n\n","category":"type"},{"location":"lib/internal/#JetReconstruction.HistoryElement-Tuple{Any}","page":"Internal API","title":"JetReconstruction.HistoryElement","text":"HistoryElement(jetp_index)\n\nConstructs a HistoryElement object with the given jetp_index, used for initialising the history with original particles.\n\nArguments\n\njetp_index: The index of the jetp.\n\nReturns\n\nA HistoryElement object.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.JetWithAncestors","page":"Internal API","title":"JetReconstruction.JetWithAncestors","text":"struct JetWithAncestors\n\nA struct representing a jet with its origin ancestors.\n\nFields\n\nself::PseudoJet: The PseudoJet object for this jet.\njetp_index::Int: The index of the jet in the corresponding cluster sequence.\nancestors::Set{Int}: A set of indices representing the jetp_indexes of ancestors of the jet (in the cluster sequence).\njet_rank::Int: The rank of the jet based on a comparison of all of the jet's ancestors\n\nNote\n\nThis structure needs its associated cluster sequence origin to be useful.\n\n\n\n\n\n","category":"type"},{"location":"lib/internal/#JetReconstruction.Surrounding","page":"Internal API","title":"JetReconstruction.Surrounding","text":"struct Surrounding{N}\n\nStructure used for iterating over neighbour tiles.\n\nFields\n\nindices::NTuple{N, Int}: A tuple of N integers representing the indices.\n\n\n\n\n\n","category":"type"},{"location":"lib/internal/#JetReconstruction.TiledJet","page":"Internal API","title":"JetReconstruction.TiledJet","text":"struct TiledJet\n\nTiledJet represents a jet in a tiled algorithm for jet reconstruction, with additional information to track the jet's position in the tiled structures.\n\nFields\n\nid::Int: The ID of the jet.\neta::Float64: The rapidity of the jet.\nphi::Float64: The azimuthal angle of the jet.\nkt2::Float64: The transverse momentum squared of the jet.\nNN_dist::Float64: The distance to the nearest neighbor.\njets_index::Int: The index of the jet in the jet array.\ntile_index::Int: The index of the tile in the tile array.\ndij_posn::Int: The position of this jet in the dij compact array.\nNN::TiledJet: The nearest neighbor.\nprevious::TiledJet: The previous jet.\nnext::TiledJet: The next jet.\n\n\n\n\n\n","category":"type"},{"location":"lib/internal/#JetReconstruction.TiledJet-Tuple{Any}","page":"Internal API","title":"JetReconstruction.TiledJet","text":"TiledJet(id)\n\nConstructs a TiledJet object with the given id and initializes its properties to zero.\n\nArguments\n\nid: The ID of the TiledJet object.\n\nReturns\n\nA TiledJet object with the specified id and values set to zero or noTiledJet.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.Tiling","page":"Internal API","title":"JetReconstruction.Tiling","text":"struct Tiling\n\nThe Tiling struct represents a tiling configuration for jet reconstruction.\n\nFields\n\nsetup::TilingDef: The tiling definition used for the configuration.\ntiles::Matrix{TiledJet}: A matrix of tiled jets, containing the first jet in each tile (then the linked list of the first jet is followed to get access to all jets in this tile).\npositions::Matrix{Int}: Used to track tiles that are on the edge of ϕ array, where neighbours need to be wrapped around.\ntags::Matrix{Bool}: The matrix of tags indicating whether a tile is valid or not (set to false initially, then true when the tile has been setup properly).\n\n\n\n\n\n","category":"type"},{"location":"lib/internal/#JetReconstruction.Tiling-Tuple{JetReconstruction.TilingDef}","page":"Internal API","title":"JetReconstruction.Tiling","text":"Tiling(setup::TilingDef)\n\nConstructs a intial Tiling object based on the provided setup parameters.\n\nArguments\n\nsetup::TilingDef: The setup parameters for the tiling.\n\nReturns\n\nA Tiling object.\n\n\n\n\n\n","category":"method"},{"location":"lib/internal/#JetReconstruction.TilingDef","page":"Internal API","title":"JetReconstruction.TilingDef","text":"struct TilingDef\n\nA struct representing the definition of a spcific tiling scheme.\n\nFields\n\n_tiles_eta_min::Float64: The minimum rapidity of the tiles.\n_tiles_eta_max::Float64: The maximum rapidity of the tiles.\n_tile_size_eta::Float64: The size of a tile in rapidity (usually R^2).\n_tile_size_phi::Float64: The size of a tile in phi (usually a bit more than R^2).\n_n_tiles_eta::Int: The number of tiles across rapidity.\n_n_tiles_phi::Int: The number of tiles across phi.\n_n_tiles::Int: The total number of tiles.\n_tiles_ieta_min::Int: The minimum rapidity tile index.\n_tiles_ieta_max::Int: The maximum rapidity tile index.\n\nConstructor\n\nTilingDef(_tiles_eta_min, _tiles_eta_max, _tile_size_eta, _tile_size_phi,\n\t_n_tiles_eta, _n_tiles_phi, _tiles_ieta_min, _tiles_ieta_max)\n\nConstructs a TilingDef object with the given parameters.\n\n\n\n\n\n","category":"type"},{"location":"lib/internal/#JetReconstruction.neighbour_tiles","page":"Internal API","title":"JetReconstruction.neighbour_tiles","text":"struct neighbour_tiles\n\nA struct representing the neighbouring tiles.\n\nA struct for iterating over all neighbour tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):\n\nXXX\nX.X\nXXX\n\nNote, rapidity coordinate must be in range, ϕ coordinate wraps\n\nFields\n\nn_η::Int: Number of η tiles\nn_ϕ::Int: Number of ϕ tiles\nstart_η::Int: Centre η tile coordinate\nstart_ϕ::Int: Centre ϕ tile coordinate\n\n\n\n\n\n","category":"type"},{"location":"lib/internal/#JetReconstruction.rightmost_tiles","page":"Internal API","title":"JetReconstruction.rightmost_tiles","text":"struct rightmost_tiles\n\nA struct for iterating over rightmost tiles for a given Cartesian tile index. These are the tiles above and to the right of the given tile (X=included, O=not included):\n\nXXX\nO.X\nOOO\n\nNote, rapidity coordinate must be in range, ϕ coordinate wraps\n\nFields\n\nn_η::Int: Number of η tiles\nn_ϕ::Int: Number of ϕ tiles\nstart_η::Int: Centre η tile coordinate\nstart_ϕ::Int: Centre ϕ tile coordinate\n\n\n\n\n\n","category":"type"},{"location":"lib/visualisation/#Jet-Visualisation-Documentation","page":"Visualisation API","title":"Jet Visualisation Documentation","text":"","category":"section"},{"location":"lib/visualisation/","page":"Visualisation API","title":"Visualisation API","text":"Documentation for visualisation interfaces extension module.","category":"page"},{"location":"lib/visualisation/#Index","page":"Visualisation API","title":"Index","text":"","category":"section"},{"location":"lib/visualisation/","page":"Visualisation API","title":"Visualisation API","text":"Pages = [\"visualisation.md\"]","category":"page"},{"location":"lib/visualisation/#Jet-Visualisation-Public-Interfaces","page":"Visualisation API","title":"Jet Visualisation Public Interfaces","text":"","category":"section"},{"location":"lib/visualisation/","page":"Visualisation API","title":"Visualisation API","text":"Modules = [JetVisualisation]\nOrder = [:function]","category":"page"},{"location":"lib/visualisation/#JetReconstruction.animatereco-Tuple{ClusterSequence, Any}","page":"Visualisation API","title":"JetReconstruction.animatereco","text":"animatereco(cs::ClusterSequence, filename;\n barsize_phi = 0.1,\n barsize_y = 0.1,\n colormap = :glasbey_category10_n256,\n perspective = 0.5,\n azimuth = 2.7,\n elevation = 0.5,\n framerate = 5,\n ancestors = false,\n Module = Makie)\n\nAnimate the jet reconstruction process and save it as a video file.\n\nArguments\n\ncs::ClusterSequence: The cluster sequence object containing the jets.\nfilename: The name of the output video file.\n\nOptional Arguments\n\nbarsize_phi=0.1: The size of the bars in the phi direction.\nbarsize_y=0.1: The size of the bars in the y direction.\ncolormap=:glasbey_category10_n256: The colormap to use for coloring the jets.\nperspective=0.5: The perspective of the plot.\nazimuth=2.7: The azimuth angle of the plot.\nelevation=0.5: The elevation angle of the plot.\nframerate=5: The framerate of the output video.\nend_frames=0: The number of static frames to show at the end of the animation. This can be useful to show the final state of the jets for a longer time.\ntitle=nothing: The title to add to the plot.\nancestors=false: Whether to include ancestors of the jets in the animation. When true the ancestors of the jets will be plotted as well, as height zero bars, with the same colour as the jet they are ancestors of.\nModule: The plotting module to use. Default is Makie.\n\nFor perspective, azimuth, and elevation, a single value can be passed for a fixed viewpoint, or a tuple of two values for a changing viewpoint. The viewpoint will then change linearly between the two values over the course of the animation.\n\nReturns\n\nfig: The figure object representing the final frame.\n\n\n\n\n\n","category":"method"},{"location":"lib/visualisation/#JetReconstruction.jetsplot-Tuple{Any, Any}","page":"Visualisation API","title":"JetReconstruction.jetsplot","text":"jetsplot(objects, idx_arrays; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)\n\nPlots a 3d bar chart that represents jets. Takes an objects array of objects to display and idx_arrays, an array of arrays with indeces, where idx_arrays[i] gives indeces of objects that form the jet number i. This function's signature might not be the most practical for the current version of the JetReconstruction.jl package, as it has been written during the early stage of development. There is now an overload of it that takes a ClusterSequence object as its argument.\n\nOptional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);\n\n# example\nusing CairoMakie # use any other Makie that you have here\n\njetsplot([object1, object2, object3], [[1], [2, 3]])\n\nThe example above plots object1 as a separate jet in one colour and object2 and object3 together in another colour.\n\nThis function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:\n\nimport CairoMakie\njetsplot(my_objects, my_colour_arrays, Module=CairoMakie)\n\nimport GLMakie\njetsplot(my_objects, my_colour_arrays, Module=GLMakie)\n\nusing WGLMakie\njetsplot(my_objects, my_colour_arrays, Module=Main) #default\n\n\n\n\n\n","category":"method"},{"location":"lib/visualisation/#JetReconstruction.jetsplot-Tuple{Any, ClusterSequence}","page":"Visualisation API","title":"JetReconstruction.jetsplot","text":"jetsplot(objects, cs::ClusterSequence; barsize_phi=0.1, barsize_eta=0.1, colormap=:glasbey_hv_n256, Module=Main)\n\nPlots a 3d bar chart that represents jets. Takes objects, an array of objects to display (should be the same array you have passed to jet_reconstruct to get the cs::ClusterSequence), and the cs::ClusterSequence itself as arguments.\n\nOptional arguments: barsize_phi::Real — width of a bar along the ϕ axis; barsize_eta::Real — width of a bar along the η axis; colormap::Symbol — Makie colour map; Module — the module where you have your Makie (see below);\n\n# example\nusing CairoMakie # use any other Makie that you have here\njetsplot([object1, object2, object3], cluster_sequence_I_got_from_jet_reconstruct; Module=CairoMakie)\n\nThis function needs Makie.jl to work. You should install and import/use a specific backend yourself. jetsplot works with CairoMakie, WGLMakie, GLMakie, etc. Additionally, you can specify the module where you have your Makie explicitly:\n\nimport CairoMakie\njetsplot(my_objects, cs, Module=CairoMakie)\n\nimport GLMakie\njetsplot(my_objects, cs, Module=GLMakie)\n\nusing WGLMakie\njetsplot(my_objects, cs, Module=Main) #default\n\n\n\n\n\n","category":"method"}]
-}
