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Qe=bi(_e("header"),{viewport$:we}),br=tt.pipe(m(()=>_e("main")),S(e=>yi(e,{viewport$:we,header$:Qe})),X(1)),Ws=A(...te("consent").map(e=>Yo(e,{target$:gt})),...te("dialog").map(e=>hi(e,{alert$:an})),...te("header").map(e=>vi(e,{viewport$:we,header$:Qe,main$:br})),...te("palette").map(e=>xi(e)),...te("search").map(e=>Hi(e,{index$:Bi,keyboard$:on})),...te("source").map(e=>Fi(e))),Vs=P(()=>A(...te("announce").map(e=>Qo(e)),...te("content").map(e=>di(e,{viewport$:we,target$:gt,print$:Qi})),...te("content").map(e=>Z("search.highlight")?Pi(e,{index$:Bi,location$:vr}):R),...te("header-title").map(e=>gi(e,{viewport$:we,header$:Qe})),...te("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Qr(Ki,()=>nn(e,{viewport$:we,header$:Qe,main$:br})):Qr(gr,()=>nn(e,{viewport$:we,header$:Qe,main$:br}))),...te("tabs").map(e=>Ui(e,{viewport$:we,header$:Qe})),...te("toc").map(e=>Di(e,{viewport$:we,header$:Qe,target$:gt})),...te("top").map(e=>Wi(e,{viewport$:we,header$:Qe,main$:br,target$:gt})))),Gi=tt.pipe(S(()=>Vs),et(Ws),X(1));Gi.subscribe();window.document$=tt;window.location$=vr;window.target$=gt;window.keyboard$=on;window.viewport$=we;window.tablet$=gr;window.screen$=Ki;window.print$=Qi;window.alert$=an;window.component$=Gi;})(); 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"src/assets/javascripts/components/search/suggest/index.ts", "src/assets/javascripts/components/search/_/index.ts", "src/assets/javascripts/components/search/highlight/index.ts", "src/assets/javascripts/components/sidebar/index.ts", "src/assets/javascripts/components/source/facts/github/index.ts", "src/assets/javascripts/components/source/facts/gitlab/index.ts", "src/assets/javascripts/components/source/facts/_/index.ts", "src/assets/javascripts/components/source/_/index.ts", "src/assets/javascripts/components/tabs/index.ts", "src/assets/javascripts/components/toc/index.ts", "src/assets/javascripts/components/top/index.ts", "src/assets/javascripts/patches/indeterminate/index.ts", "src/assets/javascripts/patches/scrollfix/index.ts", "src/assets/javascripts/patches/scrolllock/index.ts", "src/assets/javascripts/polyfills/index.ts"], + "sourceRoot": "../../../..", + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "(function(global) {\r\n /**\r\n * Polyfill URLSearchParams\r\n *\r\n * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n */\r\n\r\n var checkIfIteratorIsSupported = function() {\r\n try {\r\n return !!Symbol.iterator;\r\n } catch (error) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n var createIterator = function(items) {\r\n var iterator = {\r\n next: function() {\r\n var value = items.shift();\r\n return { done: value === void 0, value: value };\r\n }\r\n };\r\n\r\n if (iteratorSupported) {\r\n iterator[Symbol.iterator] = function() {\r\n return iterator;\r\n };\r\n }\r\n\r\n return iterator;\r\n };\r\n\r\n /**\r\n * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n */\r\n var serializeParam = function(value) {\r\n return encodeURIComponent(value).replace(/%20/g, '+');\r\n };\r\n\r\n var deserializeParam = function(value) {\r\n return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n };\r\n\r\n var polyfillURLSearchParams = function() {\r\n\r\n var URLSearchParams = function(searchString) {\r\n Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n var typeofSearchString = typeof searchString;\r\n\r\n if (typeofSearchString === 'undefined') {\r\n // do nothing\r\n } else if (typeofSearchString === 'string') {\r\n if (searchString !== '') {\r\n this._fromString(searchString);\r\n }\r\n } else if (searchString instanceof URLSearchParams) {\r\n var _this = this;\r\n searchString.forEach(function(value, name) {\r\n _this.append(name, value);\r\n });\r\n } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n for (var i = 0; i < searchString.length; i++) {\r\n var entry = searchString[i];\r\n if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n this.append(entry[0], entry[1]);\r\n } else {\r\n throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n }\r\n }\r\n } else {\r\n for (var key in searchString) {\r\n if (searchString.hasOwnProperty(key)) {\r\n this.append(key, searchString[key]);\r\n }\r\n }\r\n }\r\n } else {\r\n throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n }\r\n };\r\n\r\n var proto = URLSearchParams.prototype;\r\n\r\n proto.append = function(name, value) {\r\n if (name in this._entries) {\r\n this._entries[name].push(String(value));\r\n } else {\r\n this._entries[name] = [String(value)];\r\n }\r\n };\r\n\r\n proto.delete = function(name) {\r\n delete this._entries[name];\r\n };\r\n\r\n proto.get = function(name) {\r\n return (name in this._entries) ? this._entries[name][0] : null;\r\n };\r\n\r\n proto.getAll = function(name) {\r\n return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n };\r\n\r\n proto.has = function(name) {\r\n return (name in this._entries);\r\n };\r\n\r\n proto.set = function(name, value) {\r\n this._entries[name] = [String(value)];\r\n };\r\n\r\n proto.forEach = function(callback, thisArg) {\r\n var entries;\r\n for (var name in this._entries) {\r\n if (this._entries.hasOwnProperty(name)) {\r\n entries = this._entries[name];\r\n for (var i = 0; i < entries.length; i++) {\r\n callback.call(thisArg, entries[i], name, this);\r\n }\r\n }\r\n }\r\n };\r\n\r\n proto.keys = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push(name);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.values = function() {\r\n var items = [];\r\n this.forEach(function(value) {\r\n items.push(value);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.entries = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n if (iteratorSupported) {\r\n proto[Symbol.iterator] = proto.entries;\r\n }\r\n\r\n proto.toString = function() {\r\n var searchArray = [];\r\n this.forEach(function(value, name) {\r\n searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n });\r\n return searchArray.join('&');\r\n };\r\n\r\n\r\n global.URLSearchParams = URLSearchParams;\r\n };\r\n\r\n var checkIfURLSearchParamsSupported = function() {\r\n try {\r\n var URLSearchParams = global.URLSearchParams;\r\n\r\n return (\r\n (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n (typeof URLSearchParams.prototype.set === 'function') &&\r\n (typeof URLSearchParams.prototype.entries === 'function')\r\n );\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n if (!checkIfURLSearchParamsSupported()) {\r\n polyfillURLSearchParams();\r\n }\r\n\r\n var proto = global.URLSearchParams.prototype;\r\n\r\n if (typeof proto.sort !== 'function') {\r\n proto.sort = function() {\r\n var _this = this;\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n if (!_this._entries) {\r\n _this.delete(name);\r\n }\r\n });\r\n items.sort(function(a, b) {\r\n if (a[0] < b[0]) {\r\n return -1;\r\n } else if (a[0] > b[0]) {\r\n return +1;\r\n } else {\r\n return 0;\r\n }\r\n });\r\n if (_this._entries) { // force reset because IE keeps keys index\r\n _this._entries = {};\r\n }\r\n for (var i = 0; i < items.length; i++) {\r\n this.append(items[i][0], items[i][1]);\r\n }\r\n };\r\n }\r\n\r\n if (typeof proto._fromString !== 'function') {\r\n Object.defineProperty(proto, '_fromString', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function(searchString) {\r\n if (this._entries) {\r\n this._entries = {};\r\n } else {\r\n var keys = [];\r\n this.forEach(function(value, name) {\r\n keys.push(name);\r\n });\r\n for (var i = 0; i < keys.length; i++) {\r\n this.delete(keys[i]);\r\n }\r\n }\r\n\r\n searchString = searchString.replace(/^\\?/, '');\r\n var attributes = searchString.split('&');\r\n var attribute;\r\n for (var i = 0; i < attributes.length; i++) {\r\n attribute = attributes[i].split('=');\r\n this.append(\r\n deserializeParam(attribute[0]),\r\n (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n );\r\n }\r\n }\r\n });\r\n }\r\n\r\n // HTMLAnchorElement\r\n\r\n})(\r\n (typeof global !== 'undefined') ? global\r\n : ((typeof window !== 'undefined') ? window\r\n : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n /**\r\n * Polyfill URL\r\n *\r\n * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n */\r\n\r\n var checkIfURLIsSupported = function() {\r\n try {\r\n var u = new global.URL('b', 'http://a');\r\n u.pathname = 'c d';\r\n return (u.href === 'http://a/c%20d') && u.searchParams;\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var polyfillURL = function() {\r\n var _URL = global.URL;\r\n\r\n var URL = function(url, base) {\r\n if (typeof url !== 'string') url = String(url);\r\n if (base && typeof base !== 'string') base = String(base);\r\n\r\n // Only create another document if the base is different from current location.\r\n var doc = document, baseElement;\r\n if (base && (global.location === void 0 || base !== global.location.href)) {\r\n base = base.toLowerCase();\r\n doc = document.implementation.createHTMLDocument('');\r\n baseElement = doc.createElement('base');\r\n baseElement.href = base;\r\n doc.head.appendChild(baseElement);\r\n try {\r\n if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n } catch (err) {\r\n throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n }\r\n }\r\n\r\n var anchorElement = doc.createElement('a');\r\n anchorElement.href = url;\r\n if (baseElement) {\r\n doc.body.appendChild(anchorElement);\r\n anchorElement.href = anchorElement.href; // force href to refresh\r\n }\r\n\r\n var inputElement = doc.createElement('input');\r\n inputElement.type = 'url';\r\n inputElement.value = url;\r\n\r\n if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n throw new TypeError('Invalid URL');\r\n }\r\n\r\n Object.defineProperty(this, '_anchorElement', {\r\n value: anchorElement\r\n });\r\n\r\n\r\n // create a linked searchParams which reflect its changes on URL\r\n var searchParams = new global.URLSearchParams(this.search);\r\n var enableSearchUpdate = true;\r\n var enableSearchParamsUpdate = true;\r\n var _this = this;\r\n ['append', 'delete', 'set'].forEach(function(methodName) {\r\n var method = searchParams[methodName];\r\n searchParams[methodName] = function() {\r\n method.apply(searchParams, arguments);\r\n if (enableSearchUpdate) {\r\n enableSearchParamsUpdate = false;\r\n _this.search = searchParams.toString();\r\n enableSearchParamsUpdate = true;\r\n }\r\n };\r\n });\r\n\r\n Object.defineProperty(this, 'searchParams', {\r\n value: searchParams,\r\n enumerable: true\r\n });\r\n\r\n var search = void 0;\r\n Object.defineProperty(this, '_updateSearchParams', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function() {\r\n if (this.search !== search) {\r\n search = this.search;\r\n if (enableSearchParamsUpdate) {\r\n enableSearchUpdate = false;\r\n this.searchParams._fromString(this.search);\r\n enableSearchUpdate = true;\r\n }\r\n }\r\n }\r\n });\r\n };\r\n\r\n var proto = URL.prototype;\r\n\r\n var linkURLWithAnchorAttribute = function(attributeName) {\r\n Object.defineProperty(proto, attributeName, {\r\n get: function() {\r\n return this._anchorElement[attributeName];\r\n },\r\n set: function(value) {\r\n this._anchorElement[attributeName] = value;\r\n },\r\n enumerable: true\r\n });\r\n };\r\n\r\n ['hash', 'host', 'hostname', 'port', 'protocol']\r\n .forEach(function(attributeName) {\r\n linkURLWithAnchorAttribute(attributeName);\r\n });\r\n\r\n Object.defineProperty(proto, 'search', {\r\n get: function() {\r\n return this._anchorElement['search'];\r\n },\r\n set: function(value) {\r\n this._anchorElement['search'] = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n });\r\n\r\n Object.defineProperties(proto, {\r\n\r\n 'toString': {\r\n get: function() {\r\n var _this = this;\r\n return function() {\r\n return _this.href;\r\n };\r\n }\r\n },\r\n\r\n 'href': {\r\n get: function() {\r\n return this._anchorElement.href.replace(/\\?$/, '');\r\n },\r\n set: function(value) {\r\n this._anchorElement.href = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'pathname': {\r\n get: function() {\r\n return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n },\r\n set: function(value) {\r\n this._anchorElement.pathname = value;\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'origin': {\r\n get: function() {\r\n // get expected port from protocol\r\n var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n // add port to origin if, expected port is different than actual port\r\n // and it is not empty f.e http://foo:8080\r\n // 8080 != 80 && 8080 != ''\r\n var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n this._anchorElement.port !== '';\r\n\r\n return this._anchorElement.protocol +\r\n '//' +\r\n this._anchorElement.hostname +\r\n (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'password': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'username': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n });\r\n\r\n URL.createObjectURL = function(blob) {\r\n return _URL.createObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n URL.revokeObjectURL = function(url) {\r\n return _URL.revokeObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n global.URL = URL;\r\n\r\n };\r\n\r\n if (!checkIfURLIsSupported()) {\r\n polyfillURL();\r\n }\r\n\r\n if ((global.location !== void 0) && !('origin' in global.location)) {\r\n var getOrigin = function() {\r\n return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global global, define, System, Reflect, Promise */\r\nvar __extends;\r\nvar __assign;\r\nvar __rest;\r\nvar __decorate;\r\nvar __param;\r\nvar __metadata;\r\nvar __awaiter;\r\nvar __generator;\r\nvar __exportStar;\r\nvar __values;\r\nvar __read;\r\nvar __spread;\r\nvar __spreadArrays;\r\nvar __spreadArray;\r\nvar __await;\r\nvar __asyncGenerator;\r\nvar __asyncDelegator;\r\nvar __asyncValues;\r\nvar __makeTemplateObject;\r\nvar __importStar;\r\nvar __importDefault;\r\nvar __classPrivateFieldGet;\r\nvar __classPrivateFieldSet;\r\nvar __createBinding;\r\n(function (factory) {\r\n var root = typeof global === \"object\" ? global : typeof self === \"object\" ? self : typeof this === \"object\" ? this : {};\r\n if (typeof define === \"function\" && define.amd) {\r\n define(\"tslib\", [\"exports\"], function (exports) { factory(createExporter(root, createExporter(exports))); });\r\n }\r\n else if (typeof module === \"object\" && typeof module.exports === \"object\") {\r\n factory(createExporter(root, createExporter(module.exports)));\r\n }\r\n else {\r\n factory(createExporter(root));\r\n }\r\n function createExporter(exports, previous) {\r\n if (exports !== root) {\r\n if (typeof Object.create === \"function\") {\r\n Object.defineProperty(exports, \"__esModule\", { value: true });\r\n }\r\n else {\r\n exports.__esModule = true;\r\n }\r\n }\r\n return function (id, v) { return exports[id] = previous ? previous(id, v) : v; };\r\n }\r\n})\r\n(function (exporter) {\r\n var extendStatics = Object.setPrototypeOf ||\r\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n\r\n __extends = function (d, b) {\r\n if (typeof b !== \"function\" && b !== null)\r\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n extendStatics(d, b);\r\n function __() { this.constructor = d; }\r\n d.prototype = b === null ? 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'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n EMPTY,\n NEVER,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getOptionalElement,\n requestJSON,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantLoading,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? __search?.index || requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"[href][rel=prev]\")\n if (typeof prev !== \"undefined\")\n prev.click()\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"[href][rel=next]\")\n if (typeof next !== \"undefined\")\n next.click()\n break\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.component$ = component$ /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ReplaySubject,\n Subject,\n fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject {\n const document$ = new ReplaySubject(1)\n fromEvent(document, \"DOMContentLoaded\", { once: true })\n .subscribe(() => document$.next(document))\n\n /* Return document */\n return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements(\n selector: string, node?: ParentNode\n): T[]\n\nexport function getElements(\n selector: string, node: ParentNode = document\n): T[] {\n return Array.from(node.querySelectorAll(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement(\n selector: string, node?: ParentNode\n): T\n\nexport function getElement(\n selector: string, node: ParentNode = document\n): T {\n const el = getOptionalElement(selector, node)\n if (typeof el === \"undefined\")\n throw new ReferenceError(\n `Missing element: expected \"${selector}\" to be present`\n )\n\n /* Return element */\n return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement(\n selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement(\n selector: string, node: ParentNode = document\n): T | undefined {\n return node.querySelector(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n return document.activeElement instanceof HTMLElement\n ? document.activeElement || undefined\n : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n debounceTime,\n distinctUntilChanged,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(document.body, \"focusin\"),\n fromEvent(document.body, \"focusout\")\n )\n .pipe(\n debounceTime(1),\n map(() => {\n const active = getActiveElement()\n return typeof active !== \"undefined\"\n ? el.contains(active)\n : false\n }),\n startWith(el === getActiveElement()),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.offsetLeft,\n y: el.offsetTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(window, \"load\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementOffset(el)),\n startWith(getElementOffset(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.scrollLeft,\n y: el.scrollTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(el, \"scroll\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementContentOffset(el)),\n startWith(getElementContentOffset(el))\n )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n if (typeof Map !== 'undefined') {\r\n return Map;\r\n }\r\n /**\r\n * Returns index in provided array that matches the specified key.\r\n *\r\n * @param {Array} arr\r\n * @param {*} key\r\n * @returns {number}\r\n */\r\n function getIndex(arr, key) {\r\n var result = -1;\r\n arr.some(function (entry, index) {\r\n if (entry[0] === key) {\r\n result = index;\r\n return true;\r\n }\r\n return false;\r\n });\r\n return result;\r\n }\r\n return /** @class */ (function () {\r\n function class_1() {\r\n this.__entries__ = [];\r\n }\r\n Object.defineProperty(class_1.prototype, \"size\", {\r\n /**\r\n * @returns {boolean}\r\n */\r\n get: function () {\r\n return this.__entries__.length;\r\n },\r\n enumerable: true,\r\n configurable: true\r\n });\r\n /**\r\n * @param {*} key\r\n * @returns {*}\r\n */\r\n class_1.prototype.get = function (key) {\r\n var index = getIndex(this.__entries__, key);\r\n var entry = this.__entries__[index];\r\n return entry && entry[1];\r\n };\r\n /**\r\n * @param {*} key\r\n * @param {*} value\r\n * @returns {void}\r\n */\r\n class_1.prototype.set = function (key, value) {\r\n var index = getIndex(this.__entries__, key);\r\n if (~index) {\r\n this.__entries__[index][1] = value;\r\n }\r\n else {\r\n this.__entries__.push([key, value]);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.delete = function (key) {\r\n var entries = this.__entries__;\r\n var index = getIndex(entries, key);\r\n if (~index) {\r\n entries.splice(index, 1);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.has = function (key) {\r\n return !!~getIndex(this.__entries__, key);\r\n };\r\n /**\r\n * @returns {void}\r\n */\r\n class_1.prototype.clear = function () {\r\n this.__entries__.splice(0);\r\n };\r\n /**\r\n * @param {Function} callback\r\n * @param {*} [ctx=null]\r\n * @returns {void}\r\n */\r\n class_1.prototype.forEach = function (callback, ctx) {\r\n if (ctx === void 0) { ctx = null; }\r\n for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n var entry = _a[_i];\r\n callback.call(ctx, entry[1], entry[0]);\r\n }\r\n };\r\n return class_1;\r\n }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n if (typeof global !== 'undefined' && global.Math === Math) {\r\n return global;\r\n }\r\n if (typeof self !== 'undefined' && self.Math === Math) {\r\n return self;\r\n }\r\n if (typeof window !== 'undefined' && window.Math === Math) {\r\n return window;\r\n }\r\n // eslint-disable-next-line no-new-func\r\n return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n if (typeof requestAnimationFrame === 'function') {\r\n // It's required to use a bounded function because IE sometimes throws\r\n // an \"Invalid calling object\" error if rAF is invoked without the global\r\n // object on the left hand side.\r\n return requestAnimationFrame.bind(global$1);\r\n }\r\n return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n /**\r\n * Invokes the original callback function and schedules new invocation if\r\n * the \"proxy\" was called during current request.\r\n *\r\n * @returns {void}\r\n */\r\n function resolvePending() {\r\n if (leadingCall) {\r\n leadingCall = false;\r\n callback();\r\n }\r\n if (trailingCall) {\r\n proxy();\r\n }\r\n }\r\n /**\r\n * Callback invoked after the specified delay. It will further postpone\r\n * invocation of the original function delegating it to the\r\n * requestAnimationFrame.\r\n *\r\n * @returns {void}\r\n */\r\n function timeoutCallback() {\r\n requestAnimationFrame$1(resolvePending);\r\n }\r\n /**\r\n * Schedules invocation of the original function.\r\n *\r\n * @returns {void}\r\n */\r\n function proxy() {\r\n var timeStamp = Date.now();\r\n if (leadingCall) {\r\n // Reject immediately following calls.\r\n if (timeStamp - lastCallTime < trailingTimeout) {\r\n return;\r\n }\r\n // Schedule new call to be in invoked when the pending one is resolved.\r\n // This is important for \"transitions\" which never actually start\r\n // immediately so there is a chance that we might miss one if change\r\n // happens amids the pending invocation.\r\n trailingCall = true;\r\n }\r\n else {\r\n leadingCall = true;\r\n trailingCall = false;\r\n setTimeout(timeoutCallback, delay);\r\n }\r\n lastCallTime = timeStamp;\r\n }\r\n return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserverController.\r\n *\r\n * @private\r\n */\r\n function ResizeObserverController() {\r\n /**\r\n * Indicates whether DOM listeners have been added.\r\n *\r\n * @private {boolean}\r\n */\r\n this.connected_ = false;\r\n /**\r\n * Tells that controller has subscribed for Mutation Events.\r\n *\r\n * @private {boolean}\r\n */\r\n this.mutationEventsAdded_ = false;\r\n /**\r\n * Keeps reference to the instance of MutationObserver.\r\n *\r\n * @private {MutationObserver}\r\n */\r\n this.mutationsObserver_ = null;\r\n /**\r\n * A list of connected observers.\r\n *\r\n * @private {Array}\r\n */\r\n this.observers_ = [];\r\n this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n }\r\n /**\r\n * Adds observer to observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be added.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.addObserver = function (observer) {\r\n if (!~this.observers_.indexOf(observer)) {\r\n this.observers_.push(observer);\r\n }\r\n // Add listeners if they haven't been added yet.\r\n if (!this.connected_) {\r\n this.connect_();\r\n }\r\n };\r\n /**\r\n * Removes observer from observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.removeObserver = function (observer) {\r\n var observers = this.observers_;\r\n var index = observers.indexOf(observer);\r\n // Remove observer if it's present in registry.\r\n if (~index) {\r\n observers.splice(index, 1);\r\n }\r\n // Remove listeners if controller has no connected observers.\r\n if (!observers.length && this.connected_) {\r\n this.disconnect_();\r\n }\r\n };\r\n /**\r\n * Invokes the update of observers. It will continue running updates insofar\r\n * it detects changes.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.refresh = function () {\r\n var changesDetected = this.updateObservers_();\r\n // Continue running updates if changes have been detected as there might\r\n // be future ones caused by CSS transitions.\r\n if (changesDetected) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Updates every observer from observers list and notifies them of queued\r\n * entries.\r\n *\r\n * @private\r\n * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n * dimensions of it's elements.\r\n */\r\n ResizeObserverController.prototype.updateObservers_ = function () {\r\n // Collect observers that have active observations.\r\n var activeObservers = this.observers_.filter(function (observer) {\r\n return observer.gatherActive(), observer.hasActive();\r\n });\r\n // Deliver notifications in a separate cycle in order to avoid any\r\n // collisions between observers, e.g. when multiple instances of\r\n // ResizeObserver are tracking the same element and the callback of one\r\n // of them changes content dimensions of the observed target. Sometimes\r\n // this may result in notifications being blocked for the rest of observers.\r\n activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n return activeObservers.length > 0;\r\n };\r\n /**\r\n * Initializes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.connect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already added.\r\n if (!isBrowser || this.connected_) {\r\n return;\r\n }\r\n // Subscription to the \"Transitionend\" event is used as a workaround for\r\n // delayed transitions. This way it's possible to capture at least the\r\n // final state of an element.\r\n document.addEventListener('transitionend', this.onTransitionEnd_);\r\n window.addEventListener('resize', this.refresh);\r\n if (mutationObserverSupported) {\r\n this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n this.mutationsObserver_.observe(document, {\r\n attributes: true,\r\n childList: true,\r\n characterData: true,\r\n subtree: true\r\n });\r\n }\r\n else {\r\n document.addEventListener('DOMSubtreeModified', this.refresh);\r\n this.mutationEventsAdded_ = true;\r\n }\r\n this.connected_ = true;\r\n };\r\n /**\r\n * Removes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.disconnect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already removed.\r\n if (!isBrowser || !this.connected_) {\r\n return;\r\n }\r\n document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n window.removeEventListener('resize', this.refresh);\r\n if (this.mutationsObserver_) {\r\n this.mutationsObserver_.disconnect();\r\n }\r\n if (this.mutationEventsAdded_) {\r\n document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n }\r\n this.mutationsObserver_ = null;\r\n this.mutationEventsAdded_ = false;\r\n this.connected_ = false;\r\n };\r\n /**\r\n * \"Transitionend\" event handler.\r\n *\r\n * @private\r\n * @param {TransitionEvent} event\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n // Detect whether transition may affect dimensions of an element.\r\n var isReflowProperty = transitionKeys.some(function (key) {\r\n return !!~propertyName.indexOf(key);\r\n });\r\n if (isReflowProperty) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Returns instance of the ResizeObserverController.\r\n *\r\n * @returns {ResizeObserverController}\r\n */\r\n ResizeObserverController.getInstance = function () {\r\n if (!this.instance_) {\r\n this.instance_ = new ResizeObserverController();\r\n }\r\n return this.instance_;\r\n };\r\n /**\r\n * Holds reference to the controller's instance.\r\n *\r\n * @private {ResizeObserverController}\r\n */\r\n ResizeObserverController.instance_ = null;\r\n return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n var key = _a[_i];\r\n Object.defineProperty(target, key, {\r\n value: props[key],\r\n enumerable: false,\r\n writable: false,\r\n configurable: true\r\n });\r\n }\r\n return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n // Assume that the element is an instance of Node, which means that it\r\n // has the \"ownerDocument\" property from which we can retrieve a\r\n // corresponding global object.\r\n var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n // Return the local global object if it's not possible extract one from\r\n // provided element.\r\n return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n var positions = [];\r\n for (var _i = 1; _i < arguments.length; _i++) {\r\n positions[_i - 1] = arguments[_i];\r\n }\r\n return positions.reduce(function (size, position) {\r\n var value = styles['border-' + position + '-width'];\r\n return size + toFloat(value);\r\n }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n var positions = ['top', 'right', 'bottom', 'left'];\r\n var paddings = {};\r\n for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n var position = positions_1[_i];\r\n var value = styles['padding-' + position];\r\n paddings[position] = toFloat(value);\r\n }\r\n return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n * to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n var bbox = target.getBBox();\r\n return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n // Client width & height properties can't be\r\n // used exclusively as they provide rounded values.\r\n var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n // By this condition we can catch all non-replaced inline, hidden and\r\n // detached elements. Though elements with width & height properties less\r\n // than 0.5 will be discarded as well.\r\n //\r\n // Without it we would need to implement separate methods for each of\r\n // those cases and it's not possible to perform a precise and performance\r\n // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n // gives wrong results for elements with width & height less than 0.5.\r\n if (!clientWidth && !clientHeight) {\r\n return emptyRect;\r\n }\r\n var styles = getWindowOf(target).getComputedStyle(target);\r\n var paddings = getPaddings(styles);\r\n var horizPad = paddings.left + paddings.right;\r\n var vertPad = paddings.top + paddings.bottom;\r\n // Computed styles of width & height are being used because they are the\r\n // only dimensions available to JS that contain non-rounded values. It could\r\n // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n var width = toFloat(styles.width), height = toFloat(styles.height);\r\n // Width & height include paddings and borders when the 'border-box' box\r\n // model is applied (except for IE).\r\n if (styles.boxSizing === 'border-box') {\r\n // Following conditions are required to handle Internet Explorer which\r\n // doesn't include paddings and borders to computed CSS dimensions.\r\n //\r\n // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n // properties then it's either IE, and thus we don't need to subtract\r\n // anything, or an element merely doesn't have paddings/borders styles.\r\n if (Math.round(width + horizPad) !== clientWidth) {\r\n width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n }\r\n if (Math.round(height + vertPad) !== clientHeight) {\r\n height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n }\r\n }\r\n // Following steps can't be applied to the document's root element as its\r\n // client[Width/Height] properties represent viewport area of the window.\r\n // Besides, it's as well not necessary as the itself neither has\r\n // rendered scroll bars nor it can be clipped.\r\n if (!isDocumentElement(target)) {\r\n // In some browsers (only in Firefox, actually) CSS width & height\r\n // include scroll bars size which can be removed at this step as scroll\r\n // bars are the only difference between rounded dimensions + paddings\r\n // and \"client\" properties, though that is not always true in Chrome.\r\n var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n // Chrome has a rather weird rounding of \"client\" properties.\r\n // E.g. for an element with content width of 314.2px it sometimes gives\r\n // the client width of 315px and for the width of 314.7px it may give\r\n // 314px. And it doesn't happen all the time. So just ignore this delta\r\n // as a non-relevant.\r\n if (Math.abs(vertScrollbar) !== 1) {\r\n width -= vertScrollbar;\r\n }\r\n if (Math.abs(horizScrollbar) !== 1) {\r\n height -= horizScrollbar;\r\n }\r\n }\r\n return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n // interface.\r\n if (typeof SVGGraphicsElement !== 'undefined') {\r\n return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n }\r\n // If it's so, then check that element is at least an instance of the\r\n // SVGElement and that it has the \"getBBox\" method.\r\n // eslint-disable-next-line no-extra-parens\r\n return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element ().\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n if (!isBrowser) {\r\n return emptyRect;\r\n }\r\n if (isSVGGraphicsElement(target)) {\r\n return getSVGContentRect(target);\r\n }\r\n return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n var rect = Object.create(Constr.prototype);\r\n // Rectangle's properties are not writable and non-enumerable.\r\n defineConfigurable(rect, {\r\n x: x, y: y, width: width, height: height,\r\n top: y,\r\n right: x + width,\r\n bottom: height + y,\r\n left: x\r\n });\r\n return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObservation.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n */\r\n function ResizeObservation(target) {\r\n /**\r\n * Broadcasted width of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastWidth = 0;\r\n /**\r\n * Broadcasted height of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastHeight = 0;\r\n /**\r\n * Reference to the last observed content rectangle.\r\n *\r\n * @private {DOMRectInit}\r\n */\r\n this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n this.target = target;\r\n }\r\n /**\r\n * Updates content rectangle and tells whether it's width or height properties\r\n * have changed since the last broadcast.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObservation.prototype.isActive = function () {\r\n var rect = getContentRect(this.target);\r\n this.contentRect_ = rect;\r\n return (rect.width !== this.broadcastWidth ||\r\n rect.height !== this.broadcastHeight);\r\n };\r\n /**\r\n * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n * from the corresponding properties of the last observed content rectangle.\r\n *\r\n * @returns {DOMRectInit} Last observed content rectangle.\r\n */\r\n ResizeObservation.prototype.broadcastRect = function () {\r\n var rect = this.contentRect_;\r\n this.broadcastWidth = rect.width;\r\n this.broadcastHeight = rect.height;\r\n return rect;\r\n };\r\n return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObserverEntry.\r\n *\r\n * @param {Element} target - Element that is being observed.\r\n * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n */\r\n function ResizeObserverEntry(target, rectInit) {\r\n var contentRect = createReadOnlyRect(rectInit);\r\n // According to the specification following properties are not writable\r\n // and are also not enumerable in the native implementation.\r\n //\r\n // Property accessors are not being used as they'd require to define a\r\n // private WeakMap storage which may cause memory leaks in browsers that\r\n // don't support this type of collections.\r\n defineConfigurable(this, { target: target, contentRect: contentRect });\r\n }\r\n return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n * when one of the observed elements changes it's content dimensions.\r\n * @param {ResizeObserverController} controller - Controller instance which\r\n * is responsible for the updates of observer.\r\n * @param {ResizeObserver} callbackCtx - Reference to the public\r\n * ResizeObserver instance which will be passed to callback function.\r\n */\r\n function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n /**\r\n * Collection of resize observations that have detected changes in dimensions\r\n * of elements.\r\n *\r\n * @private {Array}\r\n */\r\n this.activeObservations_ = [];\r\n /**\r\n * Registry of the ResizeObservation instances.\r\n *\r\n * @private {Map}\r\n */\r\n this.observations_ = new MapShim();\r\n if (typeof callback !== 'function') {\r\n throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n }\r\n this.callback_ = callback;\r\n this.controller_ = controller;\r\n this.callbackCtx_ = callbackCtx;\r\n }\r\n /**\r\n * Starts observing provided element.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.observe = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is already being observed.\r\n if (observations.has(target)) {\r\n return;\r\n }\r\n observations.set(target, new ResizeObservation(target));\r\n this.controller_.addObserver(this);\r\n // Force the update of observations.\r\n this.controller_.refresh();\r\n };\r\n /**\r\n * Stops observing provided element.\r\n *\r\n * @param {Element} target - Element to stop observing.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.unobserve = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is not being observed.\r\n if (!observations.has(target)) {\r\n return;\r\n }\r\n observations.delete(target);\r\n if (!observations.size) {\r\n this.controller_.removeObserver(this);\r\n }\r\n };\r\n /**\r\n * Stops observing all elements.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.disconnect = function () {\r\n this.clearActive();\r\n this.observations_.clear();\r\n this.controller_.removeObserver(this);\r\n };\r\n /**\r\n * Collects observation instances the associated element of which has changed\r\n * it's content rectangle.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.gatherActive = function () {\r\n var _this = this;\r\n this.clearActive();\r\n this.observations_.forEach(function (observation) {\r\n if (observation.isActive()) {\r\n _this.activeObservations_.push(observation);\r\n }\r\n });\r\n };\r\n /**\r\n * Invokes initial callback function with a list of ResizeObserverEntry\r\n * instances collected from active resize observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.broadcastActive = function () {\r\n // Do nothing if observer doesn't have active observations.\r\n if (!this.hasActive()) {\r\n return;\r\n }\r\n var ctx = this.callbackCtx_;\r\n // Create ResizeObserverEntry instance for every active observation.\r\n var entries = this.activeObservations_.map(function (observation) {\r\n return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n });\r\n this.callback_.call(ctx, entries, ctx);\r\n this.clearActive();\r\n };\r\n /**\r\n * Clears the collection of active observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.clearActive = function () {\r\n this.activeObservations_.splice(0);\r\n };\r\n /**\r\n * Tells whether observer has active observations.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObserverSPI.prototype.hasActive = function () {\r\n return this.activeObservations_.length > 0;\r\n };\r\n return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n * dimensions of the observed elements change.\r\n */\r\n function ResizeObserver(callback) {\r\n if (!(this instanceof ResizeObserver)) {\r\n throw new TypeError('Cannot call a class as a function.');\r\n }\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n var controller = ResizeObserverController.getInstance();\r\n var observer = new ResizeObserverSPI(callback, controller, this);\r\n observers.set(this, observer);\r\n }\r\n return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n 'observe',\r\n 'unobserve',\r\n 'disconnect'\r\n].forEach(function (method) {\r\n ResizeObserver.prototype[method] = function () {\r\n var _a;\r\n return (_a = observers.get(this))[method].apply(_a, arguments);\r\n };\r\n});\n\nvar index = (function () {\r\n // Export existing implementation if available.\r\n if (typeof global$1.ResizeObserver !== 'undefined') {\r\n return global$1.ResizeObserver;\r\n }\r\n return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n startWith,\n switchMap,\n tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n width: number /* Element width */\n height: number /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new ResizeObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.offsetWidth,\n height: el.offsetHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(() => getElementSize(el))\n )\n ),\n startWith(getElementSize(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.scrollWidth,\n height: el.scrollHeight\n }\n}\n\n/**\n * Retrieve the overflowing container of an element, if any\n *\n * @param el - Element\n *\n * @returns Overflowing container or nothing\n */\nexport function getElementContainer(\n el: HTMLElement\n): HTMLElement | undefined {\n let parent = el.parentElement\n while (parent)\n if (\n el.scrollWidth <= parent.scrollWidth &&\n el.scrollHeight <= parent.scrollHeight\n )\n parent = (el = parent).parentElement\n else\n break\n\n /* Return overflowing container */\n return parent ? el : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport {\n getElementContentSize,\n getElementSize,\n watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new IntersectionObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n }, {\n threshold: 0\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(({ isIntersecting }) => isIntersecting)\n )\n )\n )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n el: HTMLElement, threshold = 16\n): Observable {\n return watchElementContentOffset(el)\n .pipe(\n map(({ y }) => {\n const visible = getElementSize(el)\n const content = getElementContentSize(el)\n return y >= (\n content.height - visible.height - threshold\n )\n }),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n | \"drawer\" /* Toggle for drawer */\n | \"search\" /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record = {\n drawer: getElement(\"[data-md-toggle=drawer]\"),\n search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n if (toggles[name].checked !== value)\n toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable {\n const el = toggles[name]\n return fromEvent(el, \"change\")\n .pipe(\n map(() => el.checked),\n startWith(el.checked)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n | \"global\" /* Global */\n | \"search\" /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n mode: KeyboardMode /* Keyboard mode */\n type: string /* Key type */\n claim(): void /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n el: HTMLElement, type: string\n): boolean {\n switch (el.constructor) {\n\n /* Input elements */\n case HTMLInputElement:\n /* @ts-expect-error - omit unnecessary type cast */\n if (el.type === \"radio\")\n return /^Arrow/.test(type)\n else\n return true\n\n /* Select element and textarea */\n case HTMLSelectElement:\n case HTMLTextAreaElement:\n return true\n\n /* Everything else */\n default:\n return el.isContentEditable\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable {\n return fromEvent(window, \"keydown\")\n .pipe(\n filter(ev => !(ev.metaKey || ev.ctrlKey)),\n map(ev => ({\n mode: getToggle(\"search\") ? \"search\" : \"global\",\n type: ev.key,\n claim() {\n ev.preventDefault()\n ev.stopPropagation()\n }\n } as Keyboard)),\n filter(({ mode, type }) => {\n if (mode === \"global\") {\n const active = getActiveElement()\n if (typeof active !== \"undefined\")\n return !isSusceptibleToKeyboard(active, type)\n }\n return true\n }),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject {\n return new Subject()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n & JSXInternal.HTMLAttributes\n & JSXInternal.SVGAttributes\n & Record\n\n/**\n * Child element\n */\ntype Child =\n | HTMLElement\n | Text\n | string\n | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n /* Handle primitive types (including raw HTML) */\n if (typeof child === \"string\" || typeof child === \"number\") {\n el.innerHTML += child.toString()\n\n /* Handle nodes */\n } else if (child instanceof Node) {\n el.appendChild(child)\n\n /* Handle nested children */\n } else if (Array.isArray(child)) {\n for (const node of child)\n appendChild(el, node)\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h(\n tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n const el = document.createElement(tag)\n\n /* Set attributes, if any */\n if (attributes)\n for (const attr of Object.keys(attributes)) {\n if (typeof attributes[attr] === \"undefined\")\n continue\n\n /* Set default attribute or boolean */\n if (typeof attributes[attr] !== \"boolean\")\n el.setAttribute(attr, attributes[attr])\n else\n el.setAttribute(attr, \"\")\n }\n\n /* Append child nodes */\n for (const child of children)\n appendChild(el, child)\n\n /* Return element */\n return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n namespace JSX {\n type Element = HTMLElement\n type IntrinsicElements = JSXInternal.IntrinsicElements\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n let i = n\n if (value.length > i) {\n while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n return `${value.substring(0, i)}...`\n }\n return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n if (value > 999) {\n const digits = +((value - 950) % 1000 > 99)\n return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n } else {\n return value.toString()\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n shareReplay,\n startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n const el = h(\"a\", { href: hash })\n el.addEventListener(\"click\", ev => ev.stopPropagation())\n el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable {\n return fromEvent(window, \"hashchange\")\n .pipe(\n map(getLocationHash),\n startWith(getLocationHash()),\n filter(hash => hash.length > 0),\n shareReplay(1)\n )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable {\n return watchLocationHash()\n .pipe(\n map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n filter(el => typeof el !== \"undefined\")\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n fromEvent,\n fromEventPattern,\n map,\n merge,\n startWith,\n switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable {\n const media = matchMedia(query)\n return fromEventPattern(next => (\n media.addListener(() => next(media.matches))\n ))\n .pipe(\n startWith(media.matches)\n )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable {\n const media = matchMedia(\"print\")\n return merge(\n fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n fromEvent(window, \"afterprint\").pipe(map(() => false))\n )\n .pipe(\n startWith(media.matches)\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at(\n query$: Observable, factory: () => Observable\n): Observable {\n return query$\n .pipe(\n switchMap(active => active ? factory() : EMPTY)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n from,\n map,\n of,\n shareReplay,\n switchMap,\n throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable {\n return from(fetch(`${url}`, options))\n .pipe(\n catchError(() => EMPTY),\n switchMap(res => res.status !== 200\n ? throwError(() => new Error(res.statusText))\n : of(res)\n )\n )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON(\n url: URL | string, options?: RequestInit\n): Observable {\n return request(url, options)\n .pipe(\n switchMap(res => res.json()),\n shareReplay(1)\n )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n url: URL | string, options?: RequestInit\n): Observable {\n const dom = new DOMParser()\n return request(url, options)\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/xml\")),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n switchMap,\n take,\n throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable {\n const script = h(\"script\", { src })\n return defer(() => {\n document.head.appendChild(script)\n return merge(\n fromEvent(script, \"load\"),\n fromEvent(script, \"error\")\n .pipe(\n switchMap(() => (\n throwError(() => new ReferenceError(`Invalid script: ${src}`))\n ))\n )\n )\n .pipe(\n map(() => undefined),\n finalize(() => document.head.removeChild(script)),\n take(1)\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n return {\n x: Math.max(0, scrollX),\n y: Math.max(0, scrollY)\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable {\n return merge(\n fromEvent(window, \"scroll\", { passive: true }),\n fromEvent(window, \"resize\", { passive: true })\n )\n .pipe(\n map(getViewportOffset),\n startWith(getViewportOffset())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n width: number /* Viewport width */\n height: number /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n return {\n width: innerWidth,\n height: innerHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable {\n return fromEvent(window, \"resize\", { passive: true })\n .pipe(\n map(getViewportSize),\n startWith(getViewportSize())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n map,\n shareReplay\n} from \"rxjs\"\n\nimport {\n ViewportOffset,\n watchViewportOffset\n} from \"../offset\"\nimport {\n ViewportSize,\n watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n offset: ViewportOffset /* Viewport offset */\n size: ViewportSize /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable {\n return combineLatest([\n watchViewportOffset(),\n watchViewportSize()\n ])\n .pipe(\n map(([offset, size]) => ({ offset, size })),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilKeyChanged,\n map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
/* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const size$ = viewport$\n .pipe(\n distinctUntilKeyChanged(\"size\")\n )\n\n /* Compute element offset */\n const offset$ = combineLatest([size$, header$])\n .pipe(\n map(() => getElementOffset(el))\n )\n\n /* Compute relative viewport, return hot observable */\n return combineLatest([header$, viewport$, offset$])\n .pipe(\n map(([{ height }, { offset, size }, { x, y }]) => ({\n offset: {\n x: offset.x - x,\n y: offset.y - y + height\n },\n size\n }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n fromEvent,\n map,\n share,\n switchMap,\n tap,\n throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n type: unknown /* Message type */\n data?: unknown /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n T extends WorkerMessage\n> {\n tx$: Subject /* Message transmission subject */\n rx$: Observable /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions {\n tx$: Observable /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker(\n worker: Worker, { tx$ }: WatchOptions\n): Observable {\n\n /* Intercept messages from worker-like objects */\n const rx$ = fromEvent(worker, \"message\")\n .pipe(\n map(({ data }) => data as T)\n )\n\n /* Send and receive messages, return hot observable */\n return tx$\n .pipe(\n throttle(() => rx$, { leading: true, trailing: true }),\n tap(message => worker.postMessage(message)),\n switchMap(() => rx$),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n | \"announce.dismiss\" /* Dismissable announcement bar */\n | \"content.code.annotate\" /* Code annotations */\n | \"content.lazy\" /* Lazy content elements */\n | \"content.tabs.link\" /* Link content tabs */\n | \"header.autohide\" /* Hide header */\n | \"navigation.expand\" /* Automatic expansion */\n | \"navigation.indexes\" /* Section pages */\n | \"navigation.instant\" /* Instant loading */\n | \"navigation.sections\" /* Section navigation */\n | \"navigation.tabs\" /* Tabs navigation */\n | \"navigation.tabs.sticky\" /* Tabs navigation (sticky) */\n | \"navigation.top\" /* Back-to-top button */\n | \"navigation.tracking\" /* Anchor tracking */\n | \"search.highlight\" /* Search highlighting */\n | \"search.share\" /* Search sharing */\n | \"search.suggest\" /* Search suggestions */\n | \"toc.follow\" /* Following table of contents */\n | \"toc.integrate\" /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n | \"clipboard.copy\" /* Copy to clipboard */\n | \"clipboard.copied\" /* Copied to clipboard */\n | \"search.config.lang\" /* Search language */\n | \"search.config.pipeline\" /* Search pipeline */\n | \"search.config.separator\" /* Search separator */\n | \"search.placeholder\" /* Search */\n | \"search.result.placeholder\" /* Type to start searching */\n | \"search.result.none\" /* No matching documents */\n | \"search.result.one\" /* 1 matching document */\n | \"search.result.other\" /* # matching documents */\n | \"search.result.more.one\" /* 1 more on this page */\n | \"search.result.more.other\" /* # more on this page */\n | \"search.result.term.missing\" /* Missing */\n | \"select.version.title\" /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n provider: \"mike\" /* Version provider */\n default?: string /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n base: string /* Base URL */\n features: Flag[] /* Feature flags */\n translations: Translations /* Translations */\n search: string /* Search worker URL */\n tags?: Record /* Tags mapping */\n version?: Versioning /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n key: Translation, value?: string | number\n): string {\n return typeof value !== \"undefined\"\n ? config.translations[key].replace(\"#\", value.toString())\n : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n | \"announce\" /* Announcement bar */\n | \"container\" /* Container */\n | \"consent\" /* Consent */\n | \"content\" /* Content */\n | \"dialog\" /* Dialog */\n | \"header\" /* Header */\n | \"header-title\" /* Header title */\n | \"header-topic\" /* Header topic */\n | \"main\" /* Main area */\n | \"outdated\" /* Version warning */\n | \"palette\" /* Color palette */\n | \"search\" /* Search */\n | \"search-query\" /* Search input */\n | \"search-result\" /* Search results */\n | \"search-share\" /* Search sharing */\n | \"search-suggest\" /* Search suggestions */\n | \"sidebar\" /* Sidebar */\n | \"skip\" /* Skip link */\n | \"source\" /* Repository information */\n | \"tabs\" /* Navigation tabs */\n | \"toc\" /* Table of contents */\n | \"top\" /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n T extends {} = {},\n U extends HTMLElement = HTMLElement\n> =\n T & {\n ref: U /* Component reference */\n }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n \"announce\": HTMLElement /* Announcement bar */\n \"container\": HTMLElement /* Container */\n \"consent\": HTMLElement /* Consent */\n \"content\": HTMLElement /* Content */\n \"dialog\": HTMLElement /* Dialog */\n \"header\": HTMLElement /* Header */\n \"header-title\": HTMLElement /* Header title */\n \"header-topic\": HTMLElement /* Header topic */\n \"main\": HTMLElement /* Main area */\n \"outdated\": HTMLElement /* Version warning */\n \"palette\": HTMLElement /* Color palette */\n \"search\": HTMLElement /* Search */\n \"search-query\": HTMLInputElement /* Search input */\n \"search-result\": HTMLElement /* Search results */\n \"search-share\": HTMLAnchorElement /* Search sharing */\n \"search-suggest\": HTMLElement /* Search suggestions */\n \"sidebar\": HTMLElement /* Sidebar */\n \"skip\": HTMLAnchorElement /* Skip link */\n \"source\": HTMLAnchorElement /* Repository information */\n \"tabs\": HTMLElement /* Navigation tabs */\n \"toc\": HTMLElement /* Table of contents */\n \"top\": HTMLAnchorElement /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n fromEvent,\n map,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n hash: number /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n el: HTMLElement\n): Observable {\n const button = getElement(\".md-typeset > :first-child\", el)\n return fromEvent(button, \"click\", { once: true })\n .pipe(\n map(() => getElement(\".md-typeset\", el)),\n map(content => ({ hash: __md_hash(content.innerHTML) }))\n )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n el: HTMLElement\n): Observable> {\n if (!feature(\"announce.dismiss\") || !el.childElementCount)\n return EMPTY\n\n /* Mount component on subscription */\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n startWith({ hash: __md_get(\"__announce\") })\n )\n .subscribe(({ hash }) => {\n if (hash && hash === (__md_get(\"__announce\") ?? hash)) {\n el.hidden = true\n\n /* Persist preference in local storage */\n __md_set(\"__announce\", hash)\n }\n })\n\n /* Create and return component */\n return watchAnnounce(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n hidden: boolean /* Consent is hidden */\n}\n\n/**\n * Consent defaults\n */\nexport interface ConsentDefaults {\n analytics?: boolean /* Consent for Analytics */\n github?: boolean /* Consent for GitHub */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n el: HTMLElement, { target$ }: WatchOptions\n): Observable {\n return target$\n .pipe(\n map(target => ({ hidden: target !== el }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const internal$ = new Subject()\n internal$.subscribe(({ hidden }) => {\n el.hidden = hidden\n })\n\n /* Create and return component */\n return watchConsent(el, options)\n .pipe(\n tap(state => internal$.next(state)),\n finalize(() => internal$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n map,\n mergeWith,\n switchMap,\n take,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElementContentSize,\n watchElementSize,\n watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n scrollable: boolean /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for code blocks\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n if (el.nextElementSibling) {\n const sibling = el.nextElementSibling as HTMLElement\n if (sibling.tagName === \"OL\")\n return sibling\n\n /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n else if (sibling.tagName === \"P\" && !sibling.children.length)\n return findCandidateList(sibling)\n }\n\n /* Everything else */\n return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n el: HTMLElement\n): Observable {\n return watchElementSize(el)\n .pipe(\n map(({ width }) => {\n const content = getElementContentSize(el)\n return {\n scrollable: content.width > width\n }\n }),\n distinctUntilKeyChanged(\"scrollable\")\n )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * Note that code blocks may be mounted eagerly or lazily. If they're mounted\n * lazily (on first visibility), code annotation anchor links will not work,\n * as they are evaluated on initial page load, and code annotations in general\n * might feel a little bumpier.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const { matches: hover } = matchMedia(\"(hover)\")\n\n /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n const factory$ = defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ scrollable }) => {\n if (scrollable && hover)\n el.setAttribute(\"tabindex\", \"0\")\n else\n el.removeAttribute(\"tabindex\")\n })\n\n /* Render button for Clipboard.js integration */\n if (ClipboardJS.isSupported()) {\n const parent = el.closest(\"pre\")!\n parent.id = `__code_${++sequence}`\n parent.insertBefore(\n renderClipboardButton(parent.id),\n el\n )\n }\n\n /* Handle code annotations */\n const container = el.closest(\".highlight\")\n if (container instanceof HTMLElement) {\n const list = findCandidateList(container)\n\n /* Mount code annotations, if enabled */\n if (typeof list !== \"undefined\" && (\n container.classList.contains(\"annotate\") ||\n feature(\"content.code.annotate\")\n )) {\n const annotations$ = mountAnnotationList(list, el, options)\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n mergeWith(\n watchElementSize(container)\n .pipe(\n map(({ width, height }) => width && height),\n distinctUntilChanged(),\n switchMap(active => active ? annotations$ : EMPTY)\n )\n )\n )\n }\n }\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n\n /* Mount code block lazily */\n if (feature(\"content.lazy\"))\n return watchElementVisibility(el)\n .pipe(\n filter(visible => visible),\n take(1),\n switchMap(() => factory$)\n )\n\n /* Mount code block */\n return factory$\n}\n", "/*\n * Copyright (c) 2016-2021 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a tooltip\n *\n * @param id - Tooltip identifier\n *\n * @returns Element\n */\nexport function renderTooltip(id?: string): HTMLElement {\n return (\n
\n
\n
\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\nimport { renderTooltip } from \"../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an annotation\n *\n * @param id - Annotation identifier\n * @param prefix - Tooltip identifier prefix\n *\n * @returns Element\n */\nexport function renderAnnotation(\n id: string | number, prefix?: string\n): HTMLElement {\n prefix = prefix ? `${prefix}_annotation_${id}` : undefined\n\n /* Render tooltip with anchor, if given */\n if (prefix) {\n const anchor = prefix ? `#${prefix}` : undefined\n return (\n \n )\n } else {\n return (\n \n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n return (\n code`}\n >\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport {\n SearchDocument,\n SearchMetadata,\n SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n TEASER = 1, /* Render teaser */\n PARENT = 2 /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n const parent = flag & Flag.PARENT\n const teaser = flag & Flag.TEASER\n\n /* Render missing query terms */\n const missing = Object.keys(document.terms)\n .filter(key => !document.terms[key])\n .reduce((list, key) => [\n ...list, {key}, \" \"\n ], [])\n .slice(0, -1)\n\n /* Assemble query string for highlighting */\n const url = new URL(document.location)\n if (feature(\"search.highlight\"))\n url.searchParams.set(\"h\", Object.entries(document.terms)\n .filter(([, match]) => match)\n .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n )\n\n /* Render article or section, depending on flags */\n const { tags } = configuration()\n return (\n \n \n {parent > 0 &&
}\n

{document.title}

\n {teaser > 0 && document.text.length > 0 &&\n

\n {truncate(document.text, 320)}\n

\n }\n {document.tags && (\n
\n {document.tags.map(tag => {\n const id = tag.replace(/<[^>]+>/g, \"\")\n const type = tags\n ? id in tags\n ? `md-tag-icon md-tag-icon--${tags[id]}`\n : \"md-tag-icon\"\n : \"\"\n return (\n {tag}\n )\n })}\n
\n )}\n {teaser > 0 && missing.length > 0 &&\n

\n {translation(\"search.result.term.missing\")}: {...missing}\n

\n }\n \n \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n result: SearchResultItem\n): HTMLElement {\n const threshold = result[0].score\n const docs = [...result]\n\n /* Find and extract parent article */\n const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n const [article] = docs.splice(parent, 1)\n\n /* Determine last index above threshold */\n let index = docs.findIndex(doc => doc.score < threshold)\n if (index === -1)\n index = docs.length\n\n /* Partition sections */\n const best = docs.slice(0, index)\n const more = docs.slice(index)\n\n /* Render children */\n const children = [\n renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n ...more.length ? [\n
\n \n {more.length > 0 && more.length === 1\n ? translation(\"search.result.more.one\")\n : translation(\"search.result.more.other\", more.length)\n }\n \n {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n
\n ] : []\n ]\n\n /* Render search result */\n return (\n
  • \n {children}\n
    • \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n return (\n
      \n {Object.entries(facts).map(([key, value]) => (\n
    • \n {typeof value === \"number\" ? round(value) : value}\n
      • \n ))}\n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n | \"prev\"\n | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n type: TabbedControlType\n): HTMLElement {\n const classes = `tabbed-control tabbed-control--${type}`\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n return (\n
    \n
    \n {table}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n version: string /* Version identifier */\n title: string /* Version title */\n aliases: string[] /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n const config = configuration()\n\n /* Ensure trailing slash - see https://bit.ly/3rL5u3f */\n const url = new URL(`../${version.version}/`, config.base)\n return (\n
  • \n \n {version.title}\n \n
    • \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n versions: Version[], active: Version\n): HTMLElement {\n return (\n
    \n \n {active.title}\n \n
      \n {versions.map(renderVersion)}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n auditTime,\n combineLatest,\n debounceTime,\n defer,\n delay,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap,\n throttleTime,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n ElementOffset,\n getActiveElement,\n getElementSize,\n watchElementContentOffset,\n watchElementFocus,\n watchElementOffset,\n watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n active: boolean /* Annotation is active */\n offset: ElementOffset /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable {\n const offset$ = defer(() => combineLatest([\n watchElementOffset(el),\n watchElementContentOffset(container)\n ]))\n .pipe(\n map(([{ x, y }, scroll]): ElementOffset => {\n const { width, height } = getElementSize(el)\n return ({\n x: x - scroll.x + width / 2,\n y: y - scroll.y + height / 2\n })\n })\n )\n\n /* Actively watch annotation on focus */\n return watchElementFocus(el)\n .pipe(\n switchMap(active => offset$\n .pipe(\n map(offset => ({ active, offset })),\n take(+!active || Infinity)\n )\n )\n )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n el: HTMLElement, container: HTMLElement, { target$ }: MountOptions\n): Observable> {\n const [tooltip, index] = Array.from(el.children)\n\n /* Mount component on subscription */\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe({\n\n /* Handle emission */\n next({ offset }) {\n el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-x\")\n el.style.removeProperty(\"--md-tooltip-y\")\n }\n })\n\n /* Start animation only when annotation is visible */\n watchElementVisibility(el)\n .pipe(\n takeUntil(done$)\n )\n .subscribe(visible => {\n el.toggleAttribute(\"data-md-visible\", visible)\n })\n\n /* Toggle tooltip presence to mitigate empty lines when copying */\n merge(\n push$.pipe(filter(({ active }) => active)),\n push$.pipe(debounceTime(250), filter(({ active }) => !active))\n )\n .subscribe({\n\n /* Handle emission */\n next({ active }) {\n if (active)\n el.prepend(tooltip)\n else\n tooltip.remove()\n },\n\n /* Handle complete */\n complete() {\n el.prepend(tooltip)\n }\n })\n\n /* Toggle tooltip visibility */\n push$\n .pipe(\n auditTime(16, animationFrameScheduler)\n )\n .subscribe(({ active }) => {\n tooltip.classList.toggle(\"md-tooltip--active\", active)\n })\n\n /* Track relative origin of tooltip */\n push$\n .pipe(\n throttleTime(125, animationFrameScheduler),\n filter(() => !!el.offsetParent),\n map(() => el.offsetParent!.getBoundingClientRect()),\n map(({ x }) => x)\n )\n .subscribe({\n\n /* Handle emission */\n next(origin) {\n if (origin)\n el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n else\n el.style.removeProperty(\"--md-tooltip-0\")\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-0\")\n }\n })\n\n /* Allow to copy link without scrolling to anchor */\n fromEvent(index, \"click\")\n .pipe(\n takeUntil(done$),\n filter(ev => !(ev.metaKey || ev.ctrlKey))\n )\n .subscribe(ev => ev.preventDefault())\n\n /* Allow to open link in new tab or blur on close */\n fromEvent(index, \"mousedown\")\n .pipe(\n takeUntil(done$),\n withLatestFrom(push$)\n )\n .subscribe(([ev, { active }]) => {\n\n /* Open in new tab */\n if (ev.button !== 0 || ev.metaKey || ev.ctrlKey) {\n ev.preventDefault()\n\n /* Close annotation */\n } else if (active) {\n ev.preventDefault()\n\n /* Focus parent annotation, if any */\n const parent = el.parentElement!.closest(\".md-annotation\")\n if (parent instanceof HTMLElement)\n parent.focus()\n else\n getActiveElement()?.blur()\n }\n })\n\n /* Open and focus annotation on location target */\n target$\n .pipe(\n takeUntil(done$),\n filter(target => target === tooltip),\n delay(125)\n )\n .subscribe(() => el.focus())\n\n /* Create and return component */\n return watchAnnotation(el, container)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n merge,\n share,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport {\n getElement,\n getElements,\n getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n const markers: Text[] = []\n for (const el of getElements(\".c, .c1, .cm\", container)) {\n const nodes: Text[] = []\n\n /* Find all text nodes in current element */\n const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n for (let node = it.nextNode(); node; node = it.nextNode())\n nodes.push(node as Text)\n\n /* Find all markers in each text node */\n for (let text of nodes) {\n let match: RegExpExecArray | null\n\n /* Split text at marker and add to list */\n while ((match = /(\\(\\d+\\))(!)?/.exec(text.textContent!))) {\n const [, id, force] = match\n if (typeof force === \"undefined\") {\n const marker = text.splitText(match.index)\n text = marker.splitText(id.length)\n markers.push(marker)\n\n /* Replace entire text with marker */\n } else {\n text.textContent = id\n markers.push(text)\n break\n }\n }\n }\n }\n return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n el: HTMLElement, container: HTMLElement, { target$, print$ }: MountOptions\n): Observable> {\n\n /* Compute prefix for tooltip anchors */\n const parent = container.closest(\"[id]\")\n const prefix = parent?.id\n\n /* Find and replace all markers with empty annotations */\n const annotations = new Map()\n for (const marker of findAnnotationMarkers(container)) {\n const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n if (getOptionalElement(`li:nth-child(${id})`, el)) {\n annotations.set(id, renderAnnotation(id, prefix))\n marker.replaceWith(annotations.get(id)!)\n }\n }\n\n /* Keep list if there are no annotations to render */\n if (annotations.size === 0)\n return EMPTY\n\n /* Mount component on subscription */\n return defer(() => {\n const done$ = new Subject()\n\n /* Retrieve container pairs for swapping */\n const pairs: [HTMLElement, HTMLElement][] = []\n for (const [id, annotation] of annotations)\n pairs.push([\n getElement(\".md-typeset\", annotation),\n getElement(`li:nth-child(${id})`, el)\n ])\n\n /* Handle print mode - see https://bit.ly/3rgPdpt */\n print$\n .pipe(\n takeUntil(done$.pipe(takeLast(1)))\n )\n .subscribe(active => {\n el.hidden = !active\n\n /* Show annotations in code block or list (print) */\n for (const [inner, child] of pairs)\n if (!active)\n swap(child, inner)\n else\n swap(inner, child)\n })\n\n /* Create and return component */\n return merge(...[...annotations]\n .map(([, annotation]) => (\n mountAnnotation(annotation, container, { target$ })\n ))\n )\n .pipe(\n finalize(() => done$.complete()),\n share()\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable {\n return typeof mermaid === \"undefined\" || mermaid instanceof Element\n ? watchScript(\"https://unpkg.com/mermaid@9.1.7/dist/mermaid.min.js\")\n : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n el: HTMLElement\n): Observable> {\n el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n mermaid$ ||= fetchScripts()\n .pipe(\n tap(() => mermaid.initialize({\n startOnLoad: false,\n themeCSS,\n sequence: {\n actorFontSize: \"16px\", // Hack: mitigate https://bit.ly/3y0NEi3\n messageFontSize: \"16px\",\n noteFontSize: \"16px\"\n }\n })),\n map(() => undefined),\n shareReplay(1)\n )\n\n /* Render diagram */\n mermaid$.subscribe(() => {\n el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n const id = `__mermaid_${sequence++}`\n const host = h(\"div\", { class: \"mermaid\" })\n mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n /* Create a shadow root and inject diagram */\n const shadow = host.attachShadow({ mode: \"closed\" })\n shadow.innerHTML = svg\n\n /* Replace code block with diagram */\n el.replaceWith(host)\n })\n })\n\n /* Create and return component */\n return mermaid$\n .pipe(\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n action: \"open\" | \"close\" /* Details state */\n reveal?: boolean /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable
    {\n let open = true\n return merge(\n\n /* Open and focus details on location target */\n target$\n .pipe(\n map(target => target.closest(\"details:not([open])\")!),\n filter(details => el === details),\n map(() => ({\n action: \"open\", reveal: true\n }) as Details)\n ),\n\n /* Open details on print and close afterwards */\n print$\n .pipe(\n filter(active => active || !open),\n tap(() => open = el.open),\n map(active => ({\n action: active ? \"open\" : \"close\"\n }) as Details)\n )\n )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n el: HTMLDetailsElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n push$.subscribe(({ action, reveal }) => {\n el.toggleAttribute(\"open\", action === \"open\")\n if (reveal)\n el.scrollIntoView()\n })\n\n /* Create and return component */\n return watchDetails(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n el: HTMLElement\n): Observable> {\n el.replaceWith(sentinel)\n sentinel.replaceWith(renderTable(el))\n\n /* Create and return component */\n return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n asyncScheduler,\n auditTime,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n skip,\n startWith,\n subscribeOn,\n takeLast,\n takeUntil,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n getElement,\n getElementContentOffset,\n getElementContentSize,\n getElementOffset,\n getElementSize,\n getElements,\n watchElementContentOffset,\n watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n active: HTMLLabelElement /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n el: HTMLElement\n): Observable {\n const inputs = getElements(\":scope > input\", el)\n const initial = inputs.find(input => input.checked) || inputs[0]\n return merge(...inputs.map(input => fromEvent(input, \"change\")\n .pipe(\n map(() => getElement(`label[for=\"${input.id}\"]`))\n )\n ))\n .pipe(\n startWith(getElement(`label[for=\"${initial.id}\"]`)),\n map(active => ({ active }))\n )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n * @param options - Options\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n el: HTMLElement, { viewport$ }: MountOptions\n): Observable> {\n\n /* Render content tab previous button for pagination */\n const prev = renderTabbedControl(\"prev\")\n el.append(prev)\n\n /* Render content tab next button for pagination */\n const next = renderTabbedControl(\"next\")\n el.append(next)\n\n /* Mount component on subscription */\n const container = getElement(\".tabbed-labels\", el)\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n combineLatest([push$, watchElementSize(el)])\n .pipe(\n auditTime(1, animationFrameScheduler),\n takeUntil(done$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ active }, size]) {\n const offset = getElementOffset(active)\n const { width } = getElementSize(active)\n\n /* Set tab indicator offset and width */\n el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n /* Scroll container to active content tab */\n const content = getElementContentOffset(container)\n if (\n offset.x < content.x ||\n offset.x + width > content.x + size.width\n )\n container.scrollTo({\n left: Math.max(0, offset.x - 16),\n behavior: \"smooth\"\n })\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-indicator-x\")\n el.style.removeProperty(\"--md-indicator-width\")\n }\n })\n\n /* Hide content tab buttons on borders */\n combineLatest([\n watchElementContentOffset(container),\n watchElementSize(container)\n ])\n .pipe(\n takeUntil(done$)\n )\n .subscribe(([offset, size]) => {\n const content = getElementContentSize(container)\n prev.hidden = offset.x < 16\n next.hidden = offset.x > content.width - size.width - 16\n })\n\n /* Paginate content tab container on click */\n merge(\n fromEvent(prev, \"click\").pipe(map(() => -1)),\n fromEvent(next, \"click\").pipe(map(() => +1))\n )\n .pipe(\n takeUntil(done$)\n )\n .subscribe(direction => {\n const { width } = getElementSize(container)\n container.scrollBy({\n left: width * direction,\n behavior: \"smooth\"\n })\n })\n\n /* Set up linking of content tabs, if enabled */\n if (feature(\"content.tabs.link\"))\n push$.pipe(\n skip(1),\n withLatestFrom(viewport$)\n )\n .subscribe(([{ active }, { offset }]) => {\n const tab = active.innerText.trim()\n if (active.hasAttribute(\"data-md-switching\")) {\n active.removeAttribute(\"data-md-switching\")\n\n /* Determine viewport offset of active tab */\n } else {\n const y = el.offsetTop - offset.y\n\n /* Passively activate other tabs */\n for (const set of getElements(\"[data-tabs]\"))\n for (const input of getElements(\n \":scope > input\", set\n )) {\n const label = getElement(`label[for=\"${input.id}\"]`)\n if (\n label !== active &&\n label.innerText.trim() === tab\n ) {\n label.setAttribute(\"data-md-switching\", \"\")\n input.click()\n break\n }\n }\n\n /* Bring active tab into view */\n window.scrollTo({\n top: el.offsetTop - y\n })\n\n /* Persist active tabs in local storage */\n const tabs = __md_get(\"__tabs\") || []\n __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n }\n })\n\n /* Create and return component */\n return watchContentTabs(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n .pipe(\n subscribeOn(asyncScheduler)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { Viewport, getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n CodeBlock,\n Mermaid,\n mountCodeBlock,\n mountMermaid\n} from \"../code\"\nimport {\n Details,\n mountDetails\n} from \"../details\"\nimport {\n DataTable,\n mountDataTable\n} from \"../table\"\nimport {\n ContentTabs,\n mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n | Annotation\n | ContentTabs\n | CodeBlock\n | Mermaid\n | DataTable\n | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n el: HTMLElement, { viewport$, target$, print$ }: MountOptions\n): Observable> {\n return merge(\n\n /* Code blocks */\n ...getElements(\"pre:not(.mermaid) > code\", el)\n .map(child => mountCodeBlock(child, { target$, print$ })),\n\n /* Mermaid diagrams */\n ...getElements(\"pre.mermaid\", el)\n .map(child => mountMermaid(child)),\n\n /* Data tables */\n ...getElements(\"table:not([class])\", el)\n .map(child => mountDataTable(child)),\n\n /* Details */\n ...getElements(\"details\", el)\n .map(child => mountDetails(child, { target$, print$ })),\n\n /* Content tabs */\n ...getElements(\"[data-tabs]\", el)\n .map(child => mountContentTabs(child, { viewport$ }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n delay,\n finalize,\n map,\n merge,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n message: string /* Dialog message */\n active: boolean /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n alert$: Subject /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n _el: HTMLElement, { alert$ }: WatchOptions\n): Observable {\n return alert$\n .pipe(\n switchMap(message => merge(\n of(true),\n of(false).pipe(delay(2000))\n )\n .pipe(\n map(active => ({ message, active }))\n )\n )\n )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const inner = getElement(\".md-typeset\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ message, active }) => {\n el.classList.toggle(\"md-dialog--active\", active)\n inner.textContent = message\n })\n\n /* Create and return component */\n return watchDialog(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n combineLatestWith,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n map,\n of,\n shareReplay,\n startWith,\n switchMap,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n height: number /* Header visible height */\n hidden: boolean /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable {\n if (!feature(\"header.autohide\"))\n return of(false)\n\n /* Compute direction and turning point */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => [a < b, b] as const),\n distinctUntilKeyChanged(0)\n )\n\n /* Compute whether header should be hidden */\n const hidden$ = combineLatest([viewport$, direction$])\n .pipe(\n filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n map(([, [direction]]) => direction),\n distinctUntilChanged()\n )\n\n /* Compute threshold for hiding */\n const search$ = watchToggle(\"search\")\n return combineLatest([viewport$, search$])\n .pipe(\n map(([{ offset }, search]) => offset.y > 400 && !search),\n distinctUntilChanged(),\n switchMap(active => active ? hidden$ : of(false)),\n startWith(false)\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n el: HTMLElement, options: WatchOptions\n): Observable
    {\n return defer(() => combineLatest([\n watchElementSize(el),\n isHidden(options)\n ]))\n .pipe(\n map(([{ height }, hidden]) => ({\n height,\n hidden\n })),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.hidden === b.hidden\n )),\n shareReplay(1)\n )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n el: HTMLElement, { header$, main$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n const done$ = push$.pipe(takeLast(1))\n push$\n .pipe(\n distinctUntilKeyChanged(\"active\"),\n combineLatestWith(header$)\n )\n .subscribe(([{ active }, { hidden }]) => {\n el.classList.toggle(\"md-header--shadow\", active && !hidden)\n el.hidden = hidden\n })\n\n /* Link to main area */\n main$.subscribe(push$)\n\n /* Create and return component */\n return header$\n .pipe(\n takeUntil(done$),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElementSize,\n getOptionalElement,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n active: boolean /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchViewportAt(el, { viewport$, header$ })\n .pipe(\n map(({ offset: { y } }) => {\n const { height } = getElementSize(el)\n return {\n active: y >= height\n }\n }),\n distinctUntilKeyChanged(\"active\")\n )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ active }) => {\n el.classList.toggle(\"md-header__title--active\", active)\n })\n\n /* Obtain headline, if any */\n const heading = getOptionalElement(\"article h1\")\n if (typeof heading === \"undefined\")\n return EMPTY\n\n /* Create and return component */\n return watchHeaderTitle(heading, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n map,\n switchMap\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n offset: number /* Main area top offset */\n height: number /* Main area visible height */\n active: boolean /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable
    {\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n map(({ height }) => height),\n distinctUntilChanged()\n )\n\n /* Compute the main area's top and bottom borders */\n const border$ = adjust$\n .pipe(\n switchMap(() => watchElementSize(el)\n .pipe(\n map(({ height }) => ({\n top: el.offsetTop,\n bottom: el.offsetTop + height\n })),\n distinctUntilKeyChanged(\"bottom\")\n )\n )\n )\n\n /* Compute the main area's offset, visible height and if we scrolled past */\n return combineLatest([adjust$, border$, viewport$])\n .pipe(\n map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n height = Math.max(0, height\n - Math.max(0, top - y, header)\n - Math.max(0, height + y - bottom)\n )\n return {\n offset: top - header,\n height,\n active: top - header <= y\n }\n }),\n distinctUntilChanged((a, b) => (\n a.offset === b.offset &&\n a.height === b.height &&\n a.active === b.active\n ))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n defer,\n finalize,\n fromEvent,\n map,\n mergeMap,\n observeOn,\n of,\n shareReplay,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n scheme?: string /* Color scheme */\n primary?: string /* Primary color */\n accent?: string /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n index: number /* Palette index */\n color: PaletteColor /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n inputs: HTMLInputElement[]\n): Observable {\n const current = __md_get(\"__palette\") || {\n index: inputs.findIndex(input => matchMedia(\n input.getAttribute(\"data-md-color-media\")!\n ).matches)\n }\n\n /* Emit changes in color palette */\n return of(...inputs)\n .pipe(\n mergeMap(input => fromEvent(input, \"change\")\n .pipe(\n map(() => input)\n )\n ),\n startWith(inputs[Math.max(0, current.index)]),\n map(input => ({\n index: inputs.indexOf(input),\n color: {\n scheme: input.getAttribute(\"data-md-color-scheme\"),\n primary: input.getAttribute(\"data-md-color-primary\"),\n accent: input.getAttribute(\"data-md-color-accent\")\n }\n } as Palette)),\n shareReplay(1)\n )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n el: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(palette => {\n document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n /* Set color palette */\n for (const [key, value] of Object.entries(palette.color))\n document.body.setAttribute(`data-md-color-${key}`, value)\n\n /* Toggle visibility */\n for (let index = 0; index < inputs.length; index++) {\n const label = inputs[index].nextElementSibling\n if (label instanceof HTMLElement)\n label.hidden = palette.index !== index\n }\n\n /* Persist preference in local storage */\n __md_set(\"__palette\", palette)\n })\n\n /* Revert transition durations after color switch */\n push$.pipe(observeOn(asyncScheduler))\n .subscribe(() => {\n document.body.removeAttribute(\"data-md-color-switching\")\n })\n\n /* Create and return component */\n const inputs = getElements(\"input\", el)\n return watchPalette(inputs)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n Observable,\n Subject,\n map,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n el.setAttribute(\"data-md-copying\", \"\")\n const text = el.innerText\n el.removeAttribute(\"data-md-copying\")\n return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n { alert$ }: SetupOptions\n): void {\n if (ClipboardJS.isSupported()) {\n new Observable(subscriber => {\n new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n text: el => (\n el.getAttribute(\"data-clipboard-text\")! ||\n extract(getElement(\n el.getAttribute(\"data-clipboard-target\")!\n ))\n )\n })\n .on(\"success\", ev => subscriber.next(ev))\n })\n .pipe(\n tap(ev => {\n const trigger = ev.trigger as HTMLElement\n trigger.focus()\n }),\n map(() => translation(\"clipboard.copied\"))\n )\n .subscribe(alert$)\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n of,\n tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n if (urls.length < 2)\n return [\"\"]\n\n /* Take the first two URLs and remove everything after the last slash */\n const [root, next] = [...urls]\n .sort((a, b) => a.length - b.length)\n .map(url => url.replace(/[^/]+$/, \"\"))\n\n /* Compute common prefix */\n let index = 0\n if (root === next)\n index = root.length\n else\n while (root.charCodeAt(index) === next.charCodeAt(index))\n index++\n\n /* Remove common prefix and return in original order */\n return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable {\n const cached = __md_get(\"__sitemap\", sessionStorage, base)\n if (cached) {\n return of(cached)\n } else {\n const config = configuration()\n return requestXML(new URL(\"sitemap.xml\", base || config.base))\n .pipe(\n map(sitemap => preprocess(getElements(\"loc\", sitemap)\n .map(node => node.textContent!)\n )),\n catchError(() => EMPTY), // @todo refactor instant loading\n defaultIfEmpty([]),\n tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n bufferCount,\n catchError,\n concatMap,\n debounceTime,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n fromEvent,\n map,\n merge,\n of,\n sample,\n share,\n skip,\n skipUntil,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n Viewport,\n ViewportOffset,\n getElements,\n getOptionalElement,\n request,\n setLocation,\n setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n url: URL /* State URL */\n offset?: ViewportOffset /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n location$: Subject /* Location subject */\n viewport$: Observable /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n { document$, location$, viewport$ }: SetupOptions\n): void {\n const config = configuration()\n if (location.protocol === \"file:\")\n return\n\n /* Disable automatic scroll restoration */\n if (\"scrollRestoration\" in history) {\n history.scrollRestoration = \"manual\"\n\n /* Hack: ensure that reloads restore viewport offset */\n fromEvent(window, \"beforeunload\")\n .subscribe(() => {\n history.scrollRestoration = \"auto\"\n })\n }\n\n /* Hack: ensure absolute favicon link to omit 404s when switching */\n const favicon = getOptionalElement(\"link[rel=icon]\")\n if (typeof favicon !== \"undefined\")\n favicon.href = favicon.href\n\n /* Intercept internal navigation */\n const push$ = fetchSitemap()\n .pipe(\n map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n switchMap(ev => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target) {\n const url = new URL(el.href)\n\n /* Canonicalize URL */\n url.search = \"\"\n url.hash = \"\"\n\n /* Check if URL should be intercepted */\n if (\n url.pathname !== location.pathname &&\n urls.includes(url.toString())\n ) {\n ev.preventDefault()\n return of({\n url: new URL(el.href)\n })\n }\n }\n }\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Intercept history back and forward */\n const pop$ = fromEvent(window, \"popstate\")\n .pipe(\n filter(ev => ev.state !== null),\n map(ev => ({\n url: new URL(location.href),\n offset: ev.state\n })),\n share()\n )\n\n /* Emit location change */\n merge(push$, pop$)\n .pipe(\n distinctUntilChanged((a, b) => a.url.href === b.url.href),\n map(({ url }) => url)\n )\n .subscribe(location$)\n\n /* Fetch document via `XMLHTTPRequest` */\n const response$ = location$\n .pipe(\n distinctUntilKeyChanged(\"pathname\"),\n switchMap(url => request(url.href)\n .pipe(\n catchError(() => {\n setLocation(url)\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Set new location via `history.pushState` */\n push$\n .pipe(\n sample(response$)\n )\n .subscribe(({ url }) => {\n history.pushState({}, \"\", `${url}`)\n })\n\n /* Parse and emit fetched document */\n const dom = new DOMParser()\n response$\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/html\"))\n )\n .subscribe(document$)\n\n /* Replace meta tags and components */\n document$\n .pipe(\n skip(1)\n )\n .subscribe(replacement => {\n for (const selector of [\n\n /* Meta tags */\n \"title\",\n \"link[rel=canonical]\",\n \"meta[name=author]\",\n \"meta[name=description]\",\n\n /* Components */\n \"[data-md-component=announce]\",\n \"[data-md-component=container]\",\n \"[data-md-component=header-topic]\",\n \"[data-md-component=outdated]\",\n \"[data-md-component=logo]\",\n \"[data-md-component=skip]\",\n ...feature(\"navigation.tabs.sticky\")\n ? [\"[data-md-component=tabs]\"]\n : []\n ]) {\n const source = getOptionalElement(selector)\n const target = getOptionalElement(selector, replacement)\n if (\n typeof source !== \"undefined\" &&\n typeof target !== \"undefined\"\n ) {\n source.replaceWith(target)\n }\n }\n })\n\n /* Re-evaluate scripts */\n document$\n .pipe(\n skip(1),\n map(() => getComponentElement(\"container\")),\n switchMap(el => getElements(\"script\", el)),\n concatMap(el => {\n const script = h(\"script\")\n if (el.src) {\n for (const name of el.getAttributeNames())\n script.setAttribute(name, el.getAttribute(name)!)\n el.replaceWith(script)\n\n /* Complete when script is loaded */\n return new Observable(observer => {\n script.onload = () => observer.complete()\n })\n\n /* Complete immediately */\n } else {\n script.textContent = el.textContent\n el.replaceWith(script)\n return EMPTY\n }\n })\n )\n .subscribe()\n\n /* Emit history state change */\n merge(push$, pop$)\n .pipe(\n sample(document$)\n )\n .subscribe(({ url, offset }) => {\n if (url.hash && !offset) {\n setLocationHash(url.hash)\n } else {\n window.scrollTo(0, offset?.y || 0)\n }\n })\n\n /* Debounce update of viewport offset */\n viewport$\n .pipe(\n skipUntil(push$),\n debounceTime(250),\n distinctUntilKeyChanged(\"offset\")\n )\n .subscribe(({ offset }) => {\n history.replaceState(offset, \"\")\n })\n\n /* Set viewport offset from history */\n merge(push$, pop$)\n .pipe(\n bufferCount(2, 1),\n filter(([a, b]) => a.url.pathname === b.url.pathname),\n map(([, state]) => state)\n )\n .subscribe(({ offset }) => {\n window.scrollTo(0, offset?.y || 0)\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n * that the resulting document must contain all terms, converting the query\n * to an `AND` query (as opposed to the default `OR` behavior). While users\n * may expect terms enclosed in quotation marks to map to span queries, i.e.\n * for which order is important, Lunr.js doesn't support them, so the best\n * we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n * query or preceded by white space, or are not followed by a non-whitespace\n * character or are at the end of the query string. Furthermore, filter\n * unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n return query\n .split(/\"([^\"]+)\"/g) /* => 1 */\n .map((terms, index) => index & 1\n ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n : terms\n )\n .join(\"\")\n .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n .trim() /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n SETUP, /* Search index setup */\n READY, /* Search index ready */\n QUERY, /* Search query */\n RESULT /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n type: SearchMessageType.SETUP /* Message type */\n data: SearchIndex /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n type: SearchMessageType.READY /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n type: SearchMessageType.QUERY /* Message type */\n data: string /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n type: SearchMessageType.RESULT /* Message type */\n data: SearchResult /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n | SearchSetupMessage\n | SearchReadyMessage\n | SearchQueryMessage\n | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n message: SearchMessage\n): message is SearchSetupMessage {\n return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n message: SearchMessage\n): message is SearchReadyMessage {\n return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n message: SearchMessage\n): message is SearchQueryMessage {\n return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n message: SearchMessage\n): message is SearchResultMessage {\n return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ObservableInput,\n Subject,\n from,\n map,\n share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n SearchOptions,\n SearchPipeline\n} from \"../../options\"\nimport {\n SearchMessage,\n SearchMessageType,\n SearchSetupMessage,\n isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n /* Override default language with value from translation */\n if (config.lang.length === 1 && config.lang[0] === \"en\")\n config.lang = [\n translation(\"search.config.lang\")\n ]\n\n /* Override default separator with value from translation */\n if (config.separator === \"[\\\\s\\\\-]+\")\n config.separator = translation(\"search.config.separator\")\n\n /* Set pipeline from translation */\n const pipeline = translation(\"search.config.pipeline\")\n .split(/\\s*,\\s*/)\n .filter(Boolean) as SearchPipeline\n\n /* Determine search options */\n const options: SearchOptions = {\n pipeline,\n suggestions: feature(\"search.suggest\")\n }\n\n /* Return search index after defaulting */\n return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n url: string, index: ObservableInput\n): SearchWorker {\n const config = configuration()\n const worker = new Worker(url)\n\n /* Create communication channels and resolve relative links */\n const tx$ = new Subject()\n const rx$ = watchWorker(worker, { tx$ })\n .pipe(\n map(message => {\n if (isSearchResultMessage(message)) {\n for (const result of message.data.items)\n for (const document of result)\n document.location = `${new URL(document.location, config.base)}`\n }\n return message\n }),\n share()\n )\n\n /* Set up search index */\n from(index)\n .pipe(\n map(data => ({\n type: SearchMessageType.SETUP,\n data: setupSearchIndex(data)\n } as SearchSetupMessage))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Return search worker */\n return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Subject,\n catchError,\n combineLatest,\n filter,\n fromEvent,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n getElement,\n getLocation,\n requestJSON,\n setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n Version,\n renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n { document$ }: SetupOptions\n): void {\n const config = configuration()\n const versions$ = requestJSON(\n new URL(\"../versions.json\", config.base)\n )\n .pipe(\n catchError(() => EMPTY) // @todo refactor instant loading\n )\n\n /* Determine current version */\n const current$ = versions$\n .pipe(\n map(versions => {\n const [, current] = config.base.match(/([^/]+)\\/?$/)!\n return versions.find(({ version, aliases }) => (\n version === current || aliases.includes(current)\n )) || versions[0]\n })\n )\n\n /* Intercept inter-version navigation */\n versions$\n .pipe(\n map(versions => new Map(versions.map(version => [\n `${new URL(`../${version.version}/`, config.base)}`,\n version\n ]))),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n withLatestFrom(current$),\n switchMap(([ev, current]) => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target && urls.has(el.href)) {\n const url = el.href\n // This is a temporary hack to detect if a version inside the\n // version selector or on another part of the site was clicked.\n // If we're inside the version selector, we definitely want to\n // find the same page, as we might have different deployments\n // due to aliases. However, if we're outside the version\n // selector, we must abort here, because we might otherwise\n // interfere with instant loading. We need to refactor this\n // at some point together with instant loading.\n //\n // See https://github.com/squidfunk/mkdocs-material/issues/4012\n if (!ev.target.closest(\".md-version\")) {\n const version = urls.get(url)!\n if (version === current)\n return EMPTY\n }\n ev.preventDefault()\n return of(url)\n }\n }\n return EMPTY\n }),\n switchMap(url => {\n const { version } = urls.get(url)!\n return fetchSitemap(new URL(url))\n .pipe(\n map(sitemap => {\n const location = getLocation()\n const path = location.href.replace(config.base, \"\")\n return sitemap.includes(path.split(\"#\")[0])\n ? new URL(`../${version}/${path}`, config.base)\n : new URL(url)\n })\n )\n })\n )\n )\n )\n .subscribe(url => setLocation(url))\n\n /* Render version selector and warning */\n combineLatest([versions$, current$])\n .subscribe(([versions, current]) => {\n const topic = getElement(\".md-header__topic\")\n topic.appendChild(renderVersionSelector(versions, current))\n })\n\n /* Integrate outdated version banner with instant loading */\n document$.pipe(switchMap(() => current$))\n .subscribe(current => {\n\n /* Check if version state was already determined */\n let outdated = __md_get(\"__outdated\", sessionStorage)\n if (outdated === null) {\n const latest = config.version?.default || \"latest\"\n outdated = !current.aliases.includes(latest)\n\n /* Persist version state in session storage */\n __md_set(\"__outdated\", outdated, sessionStorage)\n }\n\n /* Unhide outdated version banner */\n if (outdated)\n for (const warning of getComponentElements(\"outdated\"))\n warning.hidden = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n combineLatest,\n delay,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n share,\n shareReplay,\n startWith,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getLocation,\n setToggle,\n watchElementFocus,\n watchToggle\n} from \"~/browser\"\nimport {\n SearchMessageType,\n SearchQueryMessage,\n SearchWorker,\n defaultTransform,\n isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n value: string /* Query value */\n focus: boolean /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable {\n const fn = __search?.transform || defaultTransform\n\n /* Immediately show search dialog */\n const { searchParams } = getLocation()\n if (searchParams.has(\"q\"))\n setToggle(\"search\", true)\n\n /* Intercept query parameter (deep link) */\n const param$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1),\n map(() => searchParams.get(\"q\") || \"\")\n )\n\n /* Remove query parameter when search is closed */\n watchToggle(\"search\")\n .pipe(\n filter(active => !active),\n take(1)\n )\n .subscribe(() => {\n const url = new URL(location.href)\n url.searchParams.delete(\"q\")\n history.replaceState({}, \"\", `${url}`)\n })\n\n /* Set query from parameter */\n param$.subscribe(value => { // TODO: not ideal - find a better way\n if (value) {\n el.value = value\n el.focus()\n }\n })\n\n /* Intercept focus and input events */\n const focus$ = watchElementFocus(el)\n const value$ = merge(\n fromEvent(el, \"keyup\"),\n fromEvent(el, \"focus\").pipe(delay(1)),\n param$\n )\n .pipe(\n map(() => fn(el.value)),\n startWith(\"\"),\n distinctUntilChanged(),\n )\n\n /* Combine into single observable */\n return combineLatest([value$, focus$])\n .pipe(\n map(([value, focus]) => ({ value, focus })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n\n /* Handle value changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"value\"),\n map(({ value }): SearchQueryMessage => ({\n type: SearchMessageType.QUERY,\n data: value\n }))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Handle focus changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"focus\")\n )\n .subscribe(({ focus }) => {\n if (focus) {\n setToggle(\"search\", focus)\n el.placeholder = \"\"\n } else {\n el.placeholder = translation(\"search.placeholder\")\n }\n })\n\n /* Handle reset */\n fromEvent(el.form!, \"reset\")\n .pipe(\n takeUntil(done$)\n )\n .subscribe(() => el.focus())\n\n /* Create and return component */\n return watchSearchQuery(el, { tx$, rx$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n filter,\n finalize,\n map,\n merge,\n of,\n skipUntil,\n switchMap,\n take,\n tap,\n withLatestFrom,\n zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getElement,\n watchElementBoundary\n} from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchReadyMessage,\n isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const boundary$ = watchElementBoundary(el.parentElement!)\n .pipe(\n filter(Boolean)\n )\n\n /* Retrieve nested components */\n const meta = getElement(\":scope > :first-child\", el)\n const list = getElement(\":scope > :last-child\", el)\n\n /* Wait until search is ready */\n const ready$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1)\n )\n\n /* Update search result metadata */\n push$\n .pipe(\n withLatestFrom(query$),\n skipUntil(ready$)\n )\n .subscribe(([{ items }, { value }]) => {\n if (value) {\n switch (items.length) {\n\n /* No results */\n case 0:\n meta.textContent = translation(\"search.result.none\")\n break\n\n /* One result */\n case 1:\n meta.textContent = translation(\"search.result.one\")\n break\n\n /* Multiple result */\n default:\n meta.textContent = translation(\n \"search.result.other\",\n round(items.length)\n )\n }\n } else {\n meta.textContent = translation(\"search.result.placeholder\")\n }\n })\n\n /* Update search result list */\n push$\n .pipe(\n tap(() => list.innerHTML = \"\"),\n switchMap(({ items }) => merge(\n of(...items.slice(0, 10)),\n of(...items.slice(10))\n .pipe(\n bufferCount(4),\n zipWith(boundary$),\n switchMap(([chunk]) => chunk)\n )\n ))\n )\n .subscribe(result => list.appendChild(\n renderSearchResultItem(result)\n ))\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n fromEvent,\n map,\n tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n url: URL /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n query$: Observable /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n _el: HTMLElement, { query$ }: WatchOptions\n): Observable {\n return query$\n .pipe(\n map(({ value }) => {\n const url = getLocation()\n url.hash = \"\"\n url.searchParams.delete(\"h\")\n url.searchParams.set(\"q\", value)\n return { url }\n })\n )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n el: HTMLAnchorElement, options: MountOptions\n): Observable> {\n const push$ = new Subject()\n push$.subscribe(({ url }) => {\n el.setAttribute(\"data-clipboard-text\", el.href)\n el.href = `${url}`\n })\n\n /* Prevent following of link */\n fromEvent(el, \"click\")\n .subscribe(ev => ev.preventDefault())\n\n /* Create and return component */\n return watchSearchShare(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n combineLatestWith,\n distinctUntilChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n observeOn,\n tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n\n /* Retrieve query component and track all changes */\n const query = getComponentElement(\"search-query\")\n const query$ = merge(\n fromEvent(query, \"keydown\"),\n fromEvent(query, \"focus\")\n )\n .pipe(\n observeOn(asyncScheduler),\n map(() => query.value),\n distinctUntilChanged(),\n )\n\n /* Update search suggestions */\n push$\n .pipe(\n combineLatestWith(query$),\n map(([{ suggestions }, value]) => {\n const words = value.split(/([\\s-]+)/)\n if (suggestions?.length && words[words.length - 1]) {\n const last = suggestions[suggestions.length - 1]\n if (last.startsWith(words[words.length - 1]))\n words[words.length - 1] = last\n } else {\n words.length = 0\n }\n return words\n })\n )\n .subscribe(words => el.innerHTML = words\n .join(\"\")\n .replace(/\\s/g, \" \")\n )\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Right arrow: accept current suggestion */\n case \"ArrowRight\":\n if (\n el.innerText.length &&\n query.selectionStart === query.value.length\n )\n query.value = el.innerText\n break\n }\n })\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n ObservableInput,\n filter,\n merge,\n mergeWith,\n sample,\n take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n Keyboard,\n getActiveElement,\n getElements,\n setToggle\n} from \"~/browser\"\nimport {\n SearchIndex,\n SearchResult,\n isSearchQueryMessage,\n isSearchReadyMessage,\n setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n Component,\n getComponentElement,\n getComponentElements\n} from \"../../_\"\nimport {\n SearchQuery,\n mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n SearchShare,\n mountSearchShare\n} from \"../share\"\nimport {\n SearchSuggest,\n mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n | SearchQuery\n | SearchResult\n | SearchShare\n | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable> {\n const config = configuration()\n try {\n const url = __search?.worker || config.search\n const worker = setupSearchWorker(url, index$)\n\n /* Retrieve query and result components */\n const query = getComponentElement(\"search-query\", el)\n const result = getComponentElement(\"search-result\", el)\n\n /* Re-emit query when search is ready */\n const { tx$, rx$ } = worker\n tx$\n .pipe(\n filter(isSearchQueryMessage),\n sample(rx$.pipe(filter(isSearchReadyMessage))),\n take(1)\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n const active = getActiveElement()\n switch (key.type) {\n\n /* Enter: go to first (best) result */\n case \"Enter\":\n if (active === query) {\n const anchors = new Map()\n for (const anchor of getElements(\n \":first-child [href]\", result\n )) {\n const article = anchor.firstElementChild!\n anchors.set(anchor, parseFloat(\n article.getAttribute(\"data-md-score\")!\n ))\n }\n\n /* Go to result with highest score, if any */\n if (anchors.size) {\n const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n best.click()\n }\n\n /* Otherwise omit form submission */\n key.claim()\n }\n break\n\n /* Escape or Tab: close search */\n case \"Escape\":\n case \"Tab\":\n setToggle(\"search\", false)\n query.blur()\n break\n\n /* Vertical arrows: select previous or next search result */\n case \"ArrowUp\":\n case \"ArrowDown\":\n if (typeof active === \"undefined\") {\n query.focus()\n } else {\n const els = [query, ...getElements(\n \":not(details) > [href], summary, details[open] [href]\",\n result\n )]\n const i = Math.max(0, (\n Math.max(0, els.indexOf(active)) + els.length + (\n key.type === \"ArrowUp\" ? -1 : +1\n )\n ) % els.length)\n els[i].focus()\n }\n\n /* Prevent scrolling of page */\n key.claim()\n break\n\n /* All other keys: hand to search query */\n default:\n if (query !== getActiveElement())\n query.focus()\n }\n })\n\n /* Set up global keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\"),\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Open search and select query */\n case \"f\":\n case \"s\":\n case \"/\":\n query.focus()\n query.select()\n\n /* Prevent scrolling of page */\n key.claim()\n break\n }\n })\n\n /* Create and return component */\n const query$ = mountSearchQuery(query, worker)\n const result$ = mountSearchResult(result, worker, { query$ })\n return merge(query$, result$)\n .pipe(\n mergeWith(\n\n /* Search sharing */\n ...getComponentElements(\"search-share\", el)\n .map(child => mountSearchShare(child, { query$ })),\n\n /* Search suggestions */\n ...getComponentElements(\"search-suggest\", el)\n .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n )\n )\n\n /* Gracefully handle broken search */\n } catch (err) {\n el.hidden = true\n return NEVER\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n ObservableInput,\n combineLatest,\n filter,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n SearchIndex,\n setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n nodes: Map /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n location$: Observable /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n el: HTMLElement, { index$, location$ }: MountOptions\n): Observable> {\n return combineLatest([\n index$,\n location$\n .pipe(\n startWith(getLocation()),\n filter(url => !!url.searchParams.get(\"h\"))\n )\n ])\n .pipe(\n map(([index, url]) => setupSearchHighlighter(index.config, true)(\n url.searchParams.get(\"h\")!\n )),\n map(fn => {\n const nodes = new Map()\n\n /* Traverse text nodes and collect matches */\n const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n for (let node = it.nextNode(); node; node = it.nextNode()) {\n if (node.parentElement?.offsetHeight) {\n const original = node.textContent!\n const replaced = fn(original)\n if (replaced.length > original.length)\n nodes.set(node as ChildNode, replaced)\n }\n }\n\n /* Replace original nodes with matches */\n for (const [node, text] of nodes) {\n const { childNodes } = h(\"span\", null, text)\n node.replaceWith(...Array.from(childNodes))\n }\n\n /* Return component */\n return { ref: el, nodes }\n })\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n auditTime,\n combineLatest,\n defer,\n distinctUntilChanged,\n finalize,\n map,\n observeOn,\n take,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElement,\n getElementContainer,\n getElementOffset,\n getElementSize,\n getElements\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n height: number /* Sidebar height */\n locked: boolean /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable {\n const parent = el.parentElement!\n const adjust =\n parent.offsetTop -\n parent.parentElement!.offsetTop\n\n /* Compute the sidebar's available height and if it should be locked */\n return combineLatest([main$, viewport$])\n .pipe(\n map(([{ offset, height }, { offset: { y } }]) => {\n height = height\n + Math.min(adjust, Math.max(0, y - offset))\n - adjust\n return {\n height,\n locked: y >= offset + adjust\n }\n }),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.locked === b.locked\n ))\n )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n el: HTMLElement, { header$, ...options }: MountOptions\n): Observable> {\n const inner = getElement(\".md-sidebar__scrollwrap\", el)\n const { y } = getElementOffset(inner)\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n auditTime(0, animationFrameScheduler),\n withLatestFrom(header$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ height }, { height: offset }]) {\n inner.style.height = `${height - 2 * y}px`\n el.style.top = `${offset}px`\n },\n\n /* Handle complete */\n complete() {\n inner.style.height = \"\"\n el.style.top = \"\"\n }\n })\n\n /* Bring active item into view on initial load */\n push$\n .pipe(\n observeOn(animationFrameScheduler),\n take(1)\n )\n .subscribe(() => {\n for (const item of getElements(\".md-nav__link--active[href]\", el)) {\n const container = getElementContainer(item)\n if (typeof container !== \"undefined\") {\n const offset = item.offsetTop - container.offsetTop\n const { height } = getElementSize(container)\n container.scrollTo({\n top: offset - height / 2\n })\n }\n }\n })\n\n /* Create and return component */\n return watchSidebar(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n tag_name: string /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n user: string, repo?: string\n): Observable {\n if (typeof repo !== \"undefined\") {\n const url = `https://api.github.com/repos/${user}/${repo}`\n return zip(\n\n /* Fetch version */\n requestJSON(`${url}/releases/latest`)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(release => ({\n version: release.tag_name\n })),\n defaultIfEmpty({})\n ),\n\n /* Fetch stars and forks */\n requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(info => ({\n stars: info.stargazers_count,\n forks: info.forks_count\n })),\n defaultIfEmpty({})\n )\n )\n .pipe(\n map(([release, info]) => ({ ...release, ...info }))\n )\n\n /* User or organization */\n } else {\n const url = `https://api.github.com/users/${user}`\n return requestJSON(url)\n .pipe(\n map(info => ({\n repositories: info.public_repos\n })),\n defaultIfEmpty({})\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n base: string, project: string\n): Observable {\n const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n return requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(({ star_count, forks_count }) => ({\n stars: star_count,\n forks: forks_count\n })),\n defaultIfEmpty({})\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n stars?: number /* Number of stars */\n forks?: number /* Number of forks */\n version?: string /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n repositories?: number /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n | RepositoryFacts\n | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n url: string\n): Observable {\n\n /* Try to match GitHub repository */\n let match = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)\n if (match) {\n const [, user, repo] = match\n return fetchSourceFactsFromGitHub(user, repo)\n }\n\n /* Try to match GitLab repository */\n match = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)\n if (match) {\n const [, base, slug] = match\n return fetchSourceFactsFromGitLab(base, slug)\n }\n\n /* Fallback */\n return EMPTY\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n catchError,\n defer,\n filter,\n finalize,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { ConsentDefaults } from \"~/components/consent\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport {\n Component,\n getComponentElements\n} from \"../../_\"\nimport {\n SourceFacts,\n fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n facts: SourceFacts /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n el: HTMLAnchorElement\n): Observable {\n return fetch$ ||= defer(() => {\n const cached = __md_get(\"__source\", sessionStorage)\n if (cached) {\n return of(cached)\n } else {\n\n /* Check if consent is configured and was given */\n const els = getComponentElements(\"consent\")\n if (els.length) {\n const consent = __md_get(\"__consent\")\n if (!(consent && consent.github))\n return EMPTY\n }\n\n /* Fetch repository facts */\n return fetchSourceFacts(el.href)\n .pipe(\n tap(facts => __md_set(\"__source\", facts, sessionStorage))\n )\n }\n })\n .pipe(\n catchError(() => EMPTY),\n filter(facts => Object.keys(facts).length > 0),\n map(facts => ({ facts })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n el: HTMLAnchorElement\n): Observable> {\n const inner = getElement(\":scope > :last-child\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ facts }) => {\n inner.appendChild(renderSourceFacts(facts))\n inner.classList.add(\"md-source__repository--active\")\n })\n\n /* Create and return component */\n return watchSource(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n hidden: boolean /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchElementSize(document.body)\n .pipe(\n switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n map(({ offset: { y } }) => {\n return {\n hidden: y >= 10\n }\n }),\n distinctUntilKeyChanged(\"hidden\")\n )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n },\n\n /* Handle complete */\n complete() {\n el.hidden = false\n }\n })\n\n /* Create and return component */\n return (\n feature(\"navigation.tabs.sticky\")\n ? of({ hidden: false })\n : watchTabs(el, options)\n )\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatestWith,\n debounceTime,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n map,\n merge,\n of,\n repeat,\n scan,\n share,\n skip,\n startWith,\n switchMap,\n takeLast,\n takeUntil,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n getElement,\n getElementContainer,\n getElementSize,\n getElements,\n getLocation,\n getOptionalElement,\n watchElementSize\n} from \"~/browser\"\n\nimport {\n Component,\n getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n prev: HTMLAnchorElement[][] /* Anchors (previous) */\n next: HTMLAnchorElement[][] /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const table = new Map()\n\n /* Compute anchor-to-target mapping */\n const anchors = getElements(\"[href^=\\\\#]\", el)\n for (const anchor of anchors) {\n const id = decodeURIComponent(anchor.hash.substring(1))\n const target = getOptionalElement(`[id=\"${id}\"]`)\n if (typeof target !== \"undefined\")\n table.set(anchor, target)\n }\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n map(({ height }) => {\n const main = getComponentElement(\"main\")\n const grid = getElement(\":scope > :first-child\", main)\n return height + 0.8 * (\n grid.offsetTop -\n main.offsetTop\n )\n }),\n share()\n )\n\n /* Compute partition of previous and next anchors */\n const partition$ = watchElementSize(document.body)\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n\n /* Build index to map anchor paths to vertical offsets */\n switchMap(body => defer(() => {\n let path: HTMLAnchorElement[] = []\n return of([...table].reduce((index, [anchor, target]) => {\n while (path.length) {\n const last = table.get(path[path.length - 1])!\n if (last.tagName >= target.tagName) {\n path.pop()\n } else {\n break\n }\n }\n\n /* If the current anchor is hidden, continue with its parent */\n let offset = target.offsetTop\n while (!offset && target.parentElement) {\n target = target.parentElement\n offset = target.offsetTop\n }\n\n /* Map reversed anchor path to vertical offset */\n return index.set(\n [...path = [...path, anchor]].reverse(),\n offset\n )\n }, new Map()))\n })\n .pipe(\n\n /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n combineLatestWith(adjust$),\n\n /* Re-compute partition when viewport offset changes */\n switchMap(([index, adjust]) => viewport$\n .pipe(\n scan(([prev, next], { offset: { y }, size }) => {\n const last = y + size.height >= Math.floor(body.height)\n\n /* Look forward */\n while (next.length) {\n const [, offset] = next[0]\n if (offset - adjust < y || last) {\n prev = [...prev, next.shift()!]\n } else {\n break\n }\n }\n\n /* Look backward */\n while (prev.length) {\n const [, offset] = prev[prev.length - 1]\n if (offset - adjust >= y && !last) {\n next = [prev.pop()!, ...next]\n } else {\n break\n }\n }\n\n /* Return partition */\n return [prev, next]\n }, [[], [...index]]),\n distinctUntilChanged((a, b) => (\n a[0] === b[0] &&\n a[1] === b[1]\n ))\n )\n )\n )\n )\n )\n\n /* Compute and return anchor list migrations */\n return partition$\n .pipe(\n map(([prev, next]) => ({\n prev: prev.map(([path]) => path),\n next: next.map(([path]) => path)\n })),\n\n /* Extract anchor list migrations */\n startWith({ prev: [], next: [] }),\n bufferCount(2, 1),\n map(([a, b]) => {\n\n /* Moving down */\n if (a.prev.length < b.prev.length) {\n return {\n prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n next: []\n }\n\n /* Moving up */\n } else {\n return {\n prev: b.prev.slice(-1),\n next: b.next.slice(0, b.next.length - a.next.length)\n }\n }\n })\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe(({ prev, next }) => {\n\n /* Look forward */\n for (const [anchor] of next) {\n anchor.classList.remove(\"md-nav__link--passed\")\n anchor.classList.remove(\"md-nav__link--active\")\n }\n\n /* Look backward */\n for (const [index, [anchor]] of prev.entries()) {\n anchor.classList.add(\"md-nav__link--passed\")\n anchor.classList.toggle(\n \"md-nav__link--active\",\n index === prev.length - 1\n )\n }\n })\n\n /* Set up following, if enabled */\n if (feature(\"toc.follow\")) {\n\n /* Toggle smooth scrolling only for anchor clicks */\n const smooth$ = merge(\n viewport$.pipe(debounceTime(1), map(() => undefined)),\n viewport$.pipe(debounceTime(250), map(() => \"smooth\" as const))\n )\n\n /* Bring active anchor into view */\n push$\n .pipe(\n filter(({ prev }) => prev.length > 0),\n withLatestFrom(smooth$)\n )\n .subscribe(([{ prev }, behavior]) => {\n const [anchor] = prev[prev.length - 1]\n if (anchor.offsetHeight) {\n\n /* Retrieve overflowing container and scroll */\n const container = getElementContainer(anchor)\n if (typeof container !== \"undefined\") {\n const offset = anchor.offsetTop - container.offsetTop\n const { height } = getElementSize(container)\n container.scrollTo({\n top: offset - height / 2,\n behavior\n })\n }\n }\n })\n }\n\n /* Set up anchor tracking, if enabled */\n if (feature(\"navigation.tracking\"))\n viewport$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"offset\"),\n debounceTime(250),\n skip(1),\n takeUntil(target$.pipe(skip(1))),\n repeat({ delay: 250 }),\n withLatestFrom(push$)\n )\n .subscribe(([, { prev }]) => {\n const url = getLocation()\n\n /* Set hash fragment to active anchor */\n const anchor = prev[prev.length - 1]\n if (anchor && anchor.length) {\n const [active] = anchor\n const { hash } = new URL(active.href)\n if (url.hash !== hash) {\n url.hash = hash\n history.replaceState({}, \"\", `${url}`)\n }\n\n /* Reset anchor when at the top */\n } else {\n url.hash = \"\"\n history.replaceState({}, \"\", `${url}`)\n }\n })\n\n /* Create and return component */\n return watchTableOfContents(el, { viewport$, header$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n endWith,\n finalize,\n map,\n repeat,\n skip,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n hidden: boolean /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable {\n\n /* Compute direction */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => a > b && b > 0),\n distinctUntilChanged()\n )\n\n /* Compute whether main area is active */\n const active$ = main$\n .pipe(\n map(({ active }) => active)\n )\n\n /* Compute threshold for hiding */\n return combineLatest([active$, direction$])\n .pipe(\n map(([active, direction]) => !(active && direction)),\n distinctUntilChanged(),\n takeUntil(target$.pipe(skip(1))),\n endWith(true),\n repeat({ delay: 250 }),\n map(hidden => ({ hidden }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n if (hidden) {\n el.setAttribute(\"tabindex\", \"-1\")\n el.blur()\n } else {\n el.removeAttribute(\"tabindex\")\n }\n },\n\n /* Handle complete */\n complete() {\n el.style.top = \"\"\n el.hidden = true\n el.removeAttribute(\"tabindex\")\n }\n })\n\n /* Watch header height */\n header$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"height\")\n )\n .subscribe(({ height }) => {\n el.style.top = `${height + 16}px`\n })\n\n /* Create and return component */\n return watchBackToTop(el, { viewport$, main$, target$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n takeWhile,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n { document$, tablet$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\n // @todo `data-md-state` is deprecated and removed in v9\n \".md-toggle--indeterminate, [data-md-state=indeterminate]\"\n )),\n tap(el => {\n el.indeterminate = true\n el.checked = false\n }),\n mergeMap(el => fromEvent(el, \"change\")\n .pipe(\n takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n map(() => el)\n )\n ),\n withLatestFrom(tablet$)\n )\n .subscribe(([el, tablet]) => {\n el.classList.remove(\"md-toggle--indeterminate\")\n if (tablet)\n el.checked = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n { document$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\"[data-md-scrollfix]\")),\n tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n filter(isAppleDevice),\n mergeMap(el => fromEvent(el, \"touchstart\")\n .pipe(\n map(() => el)\n )\n )\n )\n .subscribe(el => {\n const top = el.scrollTop\n\n /* We're at the top of the container */\n if (top === 0) {\n el.scrollTop = 1\n\n /* We're at the bottom of the container */\n } else if (top + el.offsetHeight === el.scrollHeight) {\n el.scrollTop = top - 1\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n delay,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n viewport$: Observable /* Viewport observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n { viewport$, tablet$ }: PatchOptions\n): void {\n combineLatest([watchToggle(\"search\"), tablet$])\n .pipe(\n map(([active, tablet]) => active && !tablet),\n switchMap(active => of(active)\n .pipe(\n delay(active ? 400 : 100)\n )\n ),\n withLatestFrom(viewport$)\n )\n .subscribe(([active, { offset: { y }}]) => {\n if (active) {\n document.body.setAttribute(\"data-md-scrolllock\", \"\")\n document.body.style.top = `-${y}px`\n } else {\n const value = -1 * parseInt(document.body.style.top, 10)\n document.body.removeAttribute(\"data-md-scrolllock\")\n document.body.style.top = \"\"\n if (value)\n window.scrollTo(0, value)\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n 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IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n ReplaySubject,\n delay,\n distinctUntilChanged,\n filter,\n finalize,\n fromEvent,\n interval,\n merge,\n mergeMap,\n of,\n repeat,\n switchMap,\n take,\n takeUntil,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Script\n * ------------------------------------------------------------------------- */\n\n/* Append container for instances */\nconst container = document.createElement(\"div\")\ndocument.body.appendChild(container)\n\n/* Append button next to palette toggle */\nconst header = document.querySelector(\".md-header__option\")\nif (header) {\n const button = document.createElement(\"button\")\n 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nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.de.min.js b/assets/javascripts/lunr/min/lunr.de.min.js new file mode 100644 index 0000000..f3b5c10 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.de.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `German` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 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andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.du.min.js b/assets/javascripts/lunr/min/lunr.du.min.js new file mode 100644 index 0000000..49a0f3f --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.du.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Dutch` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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b/assets/javascripts/lunr/min/lunr.es.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Spanish` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,s){"function"==typeof define&&define.amd?define(s):"object"==typeof exports?module.exports=s():s()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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languages, `Italian` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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avevo avrai avranno avrebbe avrebbero avrei avremmo avremo avreste avresti avrete avrà avrò avuta avute avuti avuto c che chi ci coi col come con contro cui da dagl dagli dai dal dall dalla dalle dallo degl degli dei del dell della delle dello di dov dove e ebbe ebbero ebbi ed era erano eravamo eravate eri ero essendo faccia facciamo facciano facciate faccio facemmo facendo facesse facessero facessi facessimo faceste facesti faceva facevamo facevano facevate facevi facevo fai fanno farai faranno farebbe farebbero farei faremmo faremo fareste faresti farete farà farò fece fecero feci fosse fossero fossi fossimo foste fosti fu fui fummo furono gli ha hai hanno ho i il in io l la le lei li lo loro lui ma mi mia mie miei mio ne negl negli nei nel nell nella nelle nello noi non nostra nostre nostri nostro o per perché più quale quanta quante quanti quanto quella quelle quelli quello questa queste questi questo sarai saranno sarebbe sarebbero sarei saremmo saremo sareste saresti sarete sarà sarò se sei si sia siamo siano siate siete sono sta stai stando stanno starai staranno starebbe starebbero starei staremmo staremo stareste staresti starete starà starò stava stavamo stavano stavate stavi stavo stemmo stesse stessero stessi stessimo steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua sue sugl sugli sui sul sull sulla sulle sullo suo suoi ti tra tu tua tue tuo tuoi tutti tutto un una uno vi voi vostra vostre vostri vostro è".split(" ")),e.Pipeline.registerFunction(e.it.stopWordFilter,"stopWordFilter-it")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.ja.min.js b/assets/javascripts/lunr/min/lunr.ja.min.js new file mode 100644 index 0000000..5f254eb --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.ja.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not 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t(){for(;!C.in_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}for(;!C.out_grouping(q,97,232);){if(C.cursor>=C.limit)return!0;C.cursor++}return!1}function s(){for(var r;;)if(C.bra=C.cursor,r=C.find_among(p,3))switch(C.ket=C.cursor,r){case 1:C.slice_from("y");break;case 2:C.slice_from("i");break;case 3:if(C.cursor>=C.limit)return;C.cursor++}}function u(){return _<=C.cursor}function c(){return d<=C.cursor}function a(){var r=C.limit-C.cursor;C.find_among_b(g,3)&&(C.cursor=C.limit-r,C.ket=C.cursor,C.cursor>C.limit_backward&&(C.cursor--,C.bra=C.cursor,C.slice_del()))}function l(){var r;w=!1,C.ket=C.cursor,C.eq_s_b(1,"e")&&(C.bra=C.cursor,u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.slice_del(),w=!0,a())))}function m(){var r;u()&&(r=C.limit-C.cursor,C.out_grouping_b(q,97,232)&&(C.cursor=C.limit-r,C.eq_s_b(3,"gem")||(C.cursor=C.limit-r,C.slice_del(),a())))}function f(){var 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i;this.setCurrent=function(r){C.setCurrent(r)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var e=C.cursor;return r(),C.cursor=e,o(),C.limit_backward=e,C.cursor=C.limit,f(),C.cursor=C.limit_backward,s(),!0}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.nl.stemmer,"stemmer-nl"),r.nl.stopWordFilter=r.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" 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dă ea ei el ele eram este eu eşti face fata fi fie fiecare fii fim fiu fiţi frumos fără graţie halbă iar ieri la le li lor lui lângă lîngă mai mea mei mele mereu meu mi mie mine mult multă mulţi mulţumesc mâine mîine mă ne nevoie nici nicăieri nimeni nimeri nimic nişte noastre noastră noi noroc nostru nouă noştri nu opt ori oricare orice oricine oricum oricând oricât oricînd oricît oriunde patra patru patrulea pe pentru peste pic poate pot prea prima primul prin puţin puţina puţină până pînă rog sa sale sau se spate spre sub sunt suntem sunteţi sută sînt sîntem sînteţi să săi său ta tale te timp tine toate toată tot totuşi toţi trei treia treilea tu tăi tău un una unde undeva unei uneia unele uneori unii unor unora unu unui unuia unul vi voastre voastră voi vostru vouă voştri vreme vreo vreun vă zece zero zi zice îi îl îmi împotriva în înainte înaintea încotro încât încît între întrucât întrucît îţi ăla ălea ăsta ăstea ăştia şapte şase şi ştiu ţi ţie".split(" ")),e.Pipeline.registerFunction(e.ro.stopWordFilter,"stopWordFilter-ro")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.ru.min.js b/assets/javascripts/lunr/min/lunr.ru.min.js new file mode 100644 index 0000000..186cc48 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.ru.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Russian` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,n){"function"==typeof define&&define.amd?define(n):"object"==typeof exports?module.exports=n():n()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.sv.min.js b/assets/javascripts/lunr/min/lunr.sv.min.js new file mode 100644 index 0000000..3e5eb64 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.sv.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Swedish` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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r,i=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,nr=!0,B()&&(Dr.cursor=Dr.limit-i,D()&&(Dr.cursor=Dr.limit-i,G()&&(Dr.cursor=Dr.limit-i,H()&&(Dr.cursor=Dr.limit-i,I()))))){if(Dr.cursor=Dr.limit-i,!x())return;Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,r=Dr.limit-Dr.cursor,S()||(Dr.cursor=Dr.limit-r,W()||(Dr.cursor=Dr.limit-r,C()||(Dr.cursor=Dr.limit-r,P()||(Dr.cursor=Dr.limit-r,F()||(Dr.cursor=Dr.limit-r))))),T()||(Dr.cursor=Dr.limit-r)}Dr.bra=Dr.cursor,Dr.slice_del()}function K(){var r,i,e,n;if(Dr.ket=Dr.cursor,h()){if(r=Dr.limit-Dr.cursor,p())return 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i=Dr.limit-Dr.cursor;if(d())Dr.bra=Dr.cursor,Dr.slice_del();else if(Dr.cursor=Dr.limit-i,m())Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K());else if(Dr.cursor=Dr.limit-i,!K())return!1;return!0}function N(r){if(Dr.ket=Dr.cursor,!z()&&(Dr.cursor=Dr.limit-r,!b()))return!1;var i=Dr.limit-Dr.cursor;return!(!m()&&(Dr.cursor=Dr.limit-i,!d()))&&(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K()),!0)}function O(){var r,i=Dr.limit-Dr.cursor;return Dr.ket=Dr.cursor,!(!w()&&(Dr.cursor=Dr.limit-i,!v()))&&(Dr.bra=Dr.cursor,Dr.slice_del(),r=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,!(!W()||(Dr.bra=Dr.cursor,Dr.slice_del(),!K()))||(Dr.cursor=Dr.limit-r,Dr.ket=Dr.cursor,!(a()||(Dr.cursor=Dr.limit-r,m()||(Dr.cursor=Dr.limit-r,K())))||(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K()),!0)))}function Q(){var r,i,e=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,!p()&&(Dr.cursor=Dr.limit-e,!f()&&(Dr.cursor=Dr.limit-e,!_())))return!1;if(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,r=Dr.limit-Dr.cursor,a())Dr.bra=Dr.cursor,Dr.slice_del(),i=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,W()||(Dr.cursor=Dr.limit-i);else if(Dr.cursor=Dr.limit-r,!W())return!0;return Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,K(),!0}function R(){var r,i,e=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,W())return Dr.bra=Dr.cursor,Dr.slice_del(),void K();if(Dr.cursor=Dr.limit-e,Dr.ket=Dr.cursor,q())if(Dr.bra=Dr.cursor,Dr.slice_del(),r=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,d())Dr.bra=Dr.cursor,Dr.slice_del();else{if(Dr.cursor=Dr.limit-r,Dr.ket=Dr.cursor,!a()&&(Dr.cursor=Dr.limit-r,!m())){if(Dr.cursor=Dr.limit-r,Dr.ket=Dr.cursor,!W())return;if(Dr.bra=Dr.cursor,Dr.slice_del(),!K())return}Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K())}else if(Dr.cursor=Dr.limit-e,!M(e)&&(Dr.cursor=Dr.limit-e,!N(e))){if(Dr.cursor=Dr.limit-e,Dr.ket=Dr.cursor,y())return Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,i=Dr.limit-Dr.cursor,void(a()?(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K())):(Dr.cursor=Dr.limit-i,W()?(Dr.bra=Dr.cursor,Dr.slice_del(),K()):(Dr.cursor=Dr.limit-i,K())));if(Dr.cursor=Dr.limit-e,!O()){if(Dr.cursor=Dr.limit-e,d())return Dr.bra=Dr.cursor,void Dr.slice_del();Dr.cursor=Dr.limit-e,K()||(Dr.cursor=Dr.limit-e,Q()||(Dr.cursor=Dr.limit-e,Dr.ket=Dr.cursor,(a()||(Dr.cursor=Dr.limit-e,m()))&&(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K()))))}}}function U(){var r;if(Dr.ket=Dr.cursor,r=Dr.find_among_b(Sr,4))switch(Dr.bra=Dr.cursor,r){case 1:Dr.slice_from("p");break;case 2:Dr.slice_from("ç");break;case 3:Dr.slice_from("t");break;case 4:Dr.slice_from("k")}}function V(){for(;;){var r=Dr.limit-Dr.cursor;if(Dr.in_grouping_b(Wr,97,305)){Dr.cursor=Dr.limit-r;break}if(Dr.cursor=Dr.limit-r,Dr.cursor<=Dr.limit_backward)return!1;Dr.cursor--}return!0}function X(r,i,e){if(Dr.cursor=Dr.limit-r,V()){var n=Dr.limit-Dr.cursor;if(!Dr.eq_s_b(1,i)&&(Dr.cursor=Dr.limit-n,!Dr.eq_s_b(1,e)))return!0;Dr.cursor=Dr.limit-r;var t=Dr.cursor;return Dr.insert(Dr.cursor,Dr.cursor,e),Dr.cursor=t,!1}return!0}function Y(){var r=Dr.limit-Dr.cursor;(Dr.eq_s_b(1,"d")||(Dr.cursor=Dr.limit-r,Dr.eq_s_b(1,"g")))&&X(r,"a","ı")&&X(r,"e","i")&&X(r,"o","u")&&X(r,"ö","ü")}function $(){for(var r,i=Dr.cursor,e=2;;){for(r=Dr.cursor;!Dr.in_grouping(Wr,97,305);){if(Dr.cursor>=Dr.limit)return Dr.cursor=r,!(e>0)&&(Dr.cursor=i,!0);Dr.cursor++}e--}}function rr(r,i,e){for(;!Dr.eq_s(i,e);){if(Dr.cursor>=Dr.limit)return!0;Dr.cursor++}return(tr=i)!=Dr.limit||(Dr.cursor=r,!1)}function ir(){var r=Dr.cursor;return!rr(r,2,"ad")||(Dr.cursor=r,!rr(r,5,"soyad"))}function er(){var r=Dr.cursor;return!ir()&&(Dr.limit_backward=r,Dr.cursor=Dr.limit,Y(),Dr.cursor=Dr.limit,U(),!0)}var nr,tr,ur=[new i("m",-1,-1),new i("n",-1,-1),new i("miz",-1,-1),new i("niz",-1,-1),new i("muz",-1,-1),new i("nuz",-1,-1),new i("müz",-1,-1),new i("nüz",-1,-1),new i("mız",-1,-1),new i("nız",-1,-1)],or=[new i("leri",-1,-1),new i("ları",-1,-1)],sr=[new i("ni",-1,-1),new i("nu",-1,-1),new i("nü",-1,-1),new i("nı",-1,-1)],cr=[new i("in",-1,-1),new i("un",-1,-1),new i("ün",-1,-1),new i("ın",-1,-1)],lr=[new i("a",-1,-1),new i("e",-1,-1)],ar=[new i("na",-1,-1),new i("ne",-1,-1)],mr=[new i("da",-1,-1),new i("ta",-1,-1),new i("de",-1,-1),new i("te",-1,-1)],dr=[new i("nda",-1,-1),new i("nde",-1,-1)],fr=[new i("dan",-1,-1),new i("tan",-1,-1),new i("den",-1,-1),new i("ten",-1,-1)],br=[new i("ndan",-1,-1),new i("nden",-1,-1)],wr=[new i("la",-1,-1),new i("le",-1,-1)],_r=[new i("ca",-1,-1),new i("ce",-1,-1)],kr=[new i("im",-1,-1),new i("um",-1,-1),new i("üm",-1,-1),new i("ım",-1,-1)],pr=[new i("sin",-1,-1),new i("sun",-1,-1),new i("sün",-1,-1),new i("sın",-1,-1)],gr=[new i("iz",-1,-1),new i("uz",-1,-1),new i("üz",-1,-1),new i("ız",-1,-1)],yr=[new i("siniz",-1,-1),new i("sunuz",-1,-1),new i("sünüz",-1,-1),new i("sınız",-1,-1)],zr=[new i("lar",-1,-1),new i("ler",-1,-1)],vr=[new i("niz",-1,-1),new i("nuz",-1,-1),new i("nüz",-1,-1),new i("nız",-1,-1)],hr=[new i("dir",-1,-1),new i("tir",-1,-1),new i("dur",-1,-1),new i("tur",-1,-1),new i("dür",-1,-1),new i("tür",-1,-1),new i("dır",-1,-1),new i("tır",-1,-1)],qr=[new i("casına",-1,-1),new i("cesine",-1,-1)],Cr=[new i("di",-1,-1),new i("ti",-1,-1),new i("dik",-1,-1),new i("tik",-1,-1),new i("duk",-1,-1),new i("tuk",-1,-1),new i("dük",-1,-1),new i("tük",-1,-1),new i("dık",-1,-1),new i("tık",-1,-1),new i("dim",-1,-1),new i("tim",-1,-1),new i("dum",-1,-1),new i("tum",-1,-1),new i("düm",-1,-1),new i("tüm",-1,-1),new i("dım",-1,-1),new i("tım",-1,-1),new i("din",-1,-1),new i("tin",-1,-1),new i("dun",-1,-1),new i("tun",-1,-1),new i("dün",-1,-1),new i("tün",-1,-1),new i("dın",-1,-1),new i("tın",-1,-1),new i("du",-1,-1),new i("tu",-1,-1),new i("dü",-1,-1),new i("tü",-1,-1),new i("dı",-1,-1),new i("tı",-1,-1)],Pr=[new i("sa",-1,-1),new i("se",-1,-1),new i("sak",-1,-1),new i("sek",-1,-1),new i("sam",-1,-1),new i("sem",-1,-1),new i("san",-1,-1),new i("sen",-1,-1)],Fr=[new i("miş",-1,-1),new i("muş",-1,-1),new i("müş",-1,-1),new i("mış",-1,-1)],Sr=[new i("b",-1,1),new i("c",-1,2),new i("d",-1,3),new i("ğ",-1,4)],Wr=[17,65,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32,8,0,0,0,0,0,0,1],Lr=[1,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1],xr=[1,64,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],Ar=[17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,130],Er=[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],jr=[17],Tr=[65],Zr=[65],Br=[["a",xr,97,305],["e",Ar,101,252],["ı",Er,97,305],["i",jr,101,105],["o",Tr,111,117],["ö",Zr,246,252],["u",Tr,111,117]],Dr=new e;this.setCurrent=function(r){Dr.setCurrent(r)},this.getCurrent=function(){return Dr.getCurrent()},this.stem=function(){return!!($()&&(Dr.limit_backward=Dr.cursor,Dr.cursor=Dr.limit,J(),Dr.cursor=Dr.limit,nr&&(R(),Dr.cursor=Dr.limit_backward,er())))}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.tr.stemmer,"stemmer-tr"),r.tr.stopWordFilter=r.generateStopWordFilter("acaba altmış altı ama ancak arada aslında ayrıca bana bazı belki ben benden beni benim beri beş bile bin bir biri birkaç birkez birçok birşey birşeyi biz bizden bize bizi bizim bu buna bunda bundan bunlar bunları bunların bunu bunun burada böyle böylece da daha dahi de defa değil diye diğer doksan dokuz dolayı dolayısıyla dört edecek eden ederek edilecek ediliyor edilmesi ediyor elli en etmesi etti ettiği ettiğini eğer gibi göre halen hangi hatta hem henüz hep hepsi her herhangi herkesin hiç hiçbir iki ile ilgili ise itibaren itibariyle için işte kadar karşın katrilyon kendi kendilerine kendini kendisi kendisine kendisini kez ki kim kimden kime kimi kimse kırk milyar milyon mu mü mı nasıl ne neden nedenle nerde nerede nereye niye niçin o olan olarak oldu olduklarını olduğu olduğunu olmadı olmadığı olmak olması olmayan olmaz olsa olsun olup olur olursa oluyor on ona ondan onlar onlardan onları onların onu onun otuz oysa pek rağmen sadece sanki sekiz seksen sen senden seni senin siz sizden sizi sizin tarafından trilyon tüm var vardı ve veya ya yani yapacak yapmak yaptı yaptıkları yaptığı yaptığını yapılan yapılması yapıyor yedi yerine yetmiş yine yirmi yoksa yüz zaten çok çünkü öyle üzere üç şey şeyden şeyi şeyler şu şuna şunda şundan şunları şunu şöyle".split(" ")),r.Pipeline.registerFunction(r.tr.stopWordFilter,"stopWordFilter-tr")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.vi.min.js b/assets/javascripts/lunr/min/lunr.vi.min.js new file mode 100644 index 0000000..22aed28 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.vi.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.vi=function(){this.pipeline.reset(),this.pipeline.add(e.vi.stopWordFilter,e.vi.trimmer)},e.vi.wordCharacters="[A-Za-ẓ̀͐́͑̉̃̓ÂâÊêÔôĂ-ăĐ-đƠ-ơƯ-ư]",e.vi.trimmer=e.trimmerSupport.generateTrimmer(e.vi.wordCharacters),e.Pipeline.registerFunction(e.vi.trimmer,"trimmer-vi"),e.vi.stopWordFilter=e.generateStopWordFilter("là cái nhưng mà".split(" "))}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.zh.min.js b/assets/javascripts/lunr/min/lunr.zh.min.js new file mode 100644 index 0000000..9838ef9 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.zh.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 以 于 上 他 而 后 之 来 及 了 因 下 可 到 由 这 与 也 此 但 并 个 其 已 无 小 我 们 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 从 到 得 打 凡 儿 尔 该 各 给 跟 和 何 还 即 几 既 看 据 距 靠 啦 了 另 么 每 们 嘛 拿 哪 那 您 凭 且 却 让 仍 啥 如 若 使 谁 虽 随 同 所 她 哇 嗡 往 哪 些 向 沿 哟 用 于 咱 则 怎 曾 至 致 着 诸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/tinyseg.js b/assets/javascripts/lunr/tinyseg.js new file mode 100644 index 0000000..167fa6d --- /dev/null +++ b/assets/javascripts/lunr/tinyseg.js @@ -0,0 +1,206 @@ +/** + * export the module via AMD, CommonJS or as a browser global + * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js + */ +;(function (root, factory) { + if (typeof define === 'function' && define.amd) { + // AMD. Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + factory()(root.lunr); + } +}(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + + return function(lunr) { + // TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript + // (c) 2008 Taku Kudo + // TinySegmenter is freely distributable under the terms of a new BSD licence. + // For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt + + function TinySegmenter() { + var patterns = { + "[一二三四五六七八九十百千万億兆]":"M", + "[一-龠々〆ヵヶ]":"H", + "[ぁ-ん]":"I", + "[ァ-ヴーｱ-ﾝﾞｰ]":"K", + "[a-zA-Zａ-ｚＡ-Ｚ]":"A", + "[0-9０-９]":"N" + } + this.chartype_ = []; + for (var i in patterns) { + var regexp = new RegExp(i); + this.chartype_.push([regexp, patterns[i]]); + } + + this.BIAS__ = -332 + this.BC1__ = {"HH":6,"II":2461,"KH":406,"OH":-1378}; + this.BC2__ = {"AA":-3267,"AI":2744,"AN":-878,"HH":-4070,"HM":-1711,"HN":4012,"HO":3761,"IA":1327,"IH":-1184,"II":-1332,"IK":1721,"IO":5492,"KI":3831,"KK":-8741,"MH":-3132,"MK":3334,"OO":-2920}; + this.BC3__ = {"HH":996,"HI":626,"HK":-721,"HN":-1307,"HO":-836,"IH":-301,"KK":2762,"MK":1079,"MM":4034,"OA":-1652,"OH":266}; + this.BP1__ = {"BB":295,"OB":304,"OO":-125,"UB":352}; + this.BP2__ = {"BO":60,"OO":-1762}; + this.BQ1__ = {"BHH":1150,"BHM":1521,"BII":-1158,"BIM":886,"BMH":1208,"BNH":449,"BOH":-91,"BOO":-2597,"OHI":451,"OIH":-296,"OKA":1851,"OKH":-1020,"OKK":904,"OOO":2965}; + this.BQ2__ = {"BHH":118,"BHI":-1159,"BHM":466,"BIH":-919,"BKK":-1720,"BKO":864,"OHH":-1139,"OHM":-181,"OIH":153,"UHI":-1146}; + this.BQ3__ = {"BHH":-792,"BHI":2664,"BII":-299,"BKI":419,"BMH":937,"BMM":8335,"BNN":998,"BOH":775,"OHH":2174,"OHM":439,"OII":280,"OKH":1798,"OKI":-793,"OKO":-2242,"OMH":-2402,"OOO":11699}; + this.BQ4__ = {"BHH":-3895,"BIH":3761,"BII":-4654,"BIK":1348,"BKK":-1806,"BMI":-3385,"BOO":-12396,"OAH":926,"OHH":266,"OHK":-2036,"ONN":-973}; 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+ + return this; + } + TinySegmenter.prototype.ctype_ = function(str) { + for (var i in this.chartype_) { + if (str.match(this.chartype_[i][0])) { + return this.chartype_[i][1]; + } + } + return "O"; + } + + TinySegmenter.prototype.ts_ = function(v) { + if (v) { return v; } + return 0; + } + + TinySegmenter.prototype.segment = function(input) { + if (input == null || input == undefined || input == "") { + return []; + } + var result = []; + var seg = ["B3","B2","B1"]; + var ctype = ["O","O","O"]; + var o = input.split(""); + for (i = 0; i < o.length; ++i) { + seg.push(o[i]); + ctype.push(this.ctype_(o[i])) + } + seg.push("E1"); + seg.push("E2"); + seg.push("E3"); + ctype.push("O"); + ctype.push("O"); + ctype.push("O"); + var word = seg[3]; + var p1 = "U"; + var p2 = "U"; + var p3 = "U"; + for (var i = 4; i < seg.length - 3; ++i) { + var score = this.BIAS__; + var w1 = seg[i-3]; + var w2 = seg[i-2]; + var w3 = seg[i-1]; + var w4 = seg[i]; + var w5 = seg[i+1]; + var w6 = seg[i+2]; + var c1 = ctype[i-3]; + var c2 = ctype[i-2]; + var c3 = ctype[i-1]; + var c4 = ctype[i]; + var c5 = ctype[i+1]; + var c6 = ctype[i+2]; + score += this.ts_(this.UP1__[p1]); + score += this.ts_(this.UP2__[p2]); + score += this.ts_(this.UP3__[p3]); + score += this.ts_(this.BP1__[p1 + p2]); + score += this.ts_(this.BP2__[p2 + p3]); + score += this.ts_(this.UW1__[w1]); + score += this.ts_(this.UW2__[w2]); + score += this.ts_(this.UW3__[w3]); + score += this.ts_(this.UW4__[w4]); + score += this.ts_(this.UW5__[w5]); + score += this.ts_(this.UW6__[w6]); + score += this.ts_(this.BW1__[w2 + w3]); + score += this.ts_(this.BW2__[w3 + w4]); + score += this.ts_(this.BW3__[w4 + w5]); + score += this.ts_(this.TW1__[w1 + w2 + w3]); + score += this.ts_(this.TW2__[w2 + w3 + w4]); + score += this.ts_(this.TW3__[w3 + w4 + w5]); + score += this.ts_(this.TW4__[w4 + w5 + w6]); + score += this.ts_(this.UC1__[c1]); + score += this.ts_(this.UC2__[c2]); + score += this.ts_(this.UC3__[c3]); + score += this.ts_(this.UC4__[c4]); + score += this.ts_(this.UC5__[c5]); + score += this.ts_(this.UC6__[c6]); + score += this.ts_(this.BC1__[c2 + c3]); + score += this.ts_(this.BC2__[c3 + c4]); + score += this.ts_(this.BC3__[c4 + c5]); + score += this.ts_(this.TC1__[c1 + c2 + c3]); + score += this.ts_(this.TC2__[c2 + c3 + c4]); + score += this.ts_(this.TC3__[c3 + c4 + c5]); + score += this.ts_(this.TC4__[c4 + c5 + c6]); + // score += this.ts_(this.TC5__[c4 + c5 + c6]); + score += this.ts_(this.UQ1__[p1 + c1]); + score += this.ts_(this.UQ2__[p2 + c2]); + score += this.ts_(this.UQ3__[p3 + c3]); + score += this.ts_(this.BQ1__[p2 + c2 + c3]); + score += this.ts_(this.BQ2__[p2 + c3 + c4]); + score += this.ts_(this.BQ3__[p3 + c2 + c3]); + score += this.ts_(this.BQ4__[p3 + c3 + c4]); + score += this.ts_(this.TQ1__[p2 + c1 + c2 + c3]); + score += this.ts_(this.TQ2__[p2 + c2 + c3 + c4]); + score += this.ts_(this.TQ3__[p3 + c1 + c2 + c3]); + score += this.ts_(this.TQ4__[p3 + c2 + c3 + c4]); + var p = "O"; + if (score > 0) { + result.push(word); + word = ""; + p = "B"; + } + p1 = p2; + p2 = p3; + p3 = p; + word += seg[i]; + } + result.push(word); + + return result; + } + + lunr.TinySegmenter = TinySegmenter; + }; + +})); \ No newline at end of file diff --git a/assets/javascripts/lunr/wordcut.js b/assets/javascripts/lunr/wordcut.js new file mode 100644 index 0000000..146f4b4 --- /dev/null +++ b/assets/javascripts/lunr/wordcut.js @@ -0,0 +1,6708 @@ +(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}(g.lunr || (g.lunr = {})).wordcut = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o 1; + }) + this.addWords(words, false) + } + if(finalize){ + this.finalizeDict(); + } + }, + + dictSeek: function (l, r, ch, strOffset, pos) { + var ans = null; + while (l <= r) { + var m = Math.floor((l + r) / 2), + dict_item = this.dict[m], + len = dict_item.length; + if (len <= strOffset) { + l = m + 1; + } else { + var ch_ = dict_item[strOffset]; + if (ch_ < ch) { + l = m + 1; + } else if (ch_ > ch) { + r = m - 1; + } else { + ans = m; + if (pos == LEFT) { + r = m - 1; + } else { + l = m + 1; + } + } + } + } + return ans; + }, + + isFinal: function (acceptor) { + return this.dict[acceptor.l].length == acceptor.strOffset; + }, + + createAcceptor: function () { + return { + l: 0, + r: this.dict.length - 1, + strOffset: 0, + isFinal: false, + dict: this, + transit: function (ch) { + return this.dict.transit(this, ch); + }, + isError: false, + tag: "DICT", + w: 1, + type: "DICT" + }; + }, + + transit: function (acceptor, ch) { + var l = this.dictSeek(acceptor.l, + acceptor.r, + ch, + acceptor.strOffset, + LEFT); + if (l !== null) { + var r = this.dictSeek(l, + acceptor.r, + ch, + acceptor.strOffset, + RIGHT); + acceptor.l = l; + acceptor.r = r; + acceptor.strOffset++; + acceptor.isFinal = this.isFinal(acceptor); + } else { + acceptor.isError = true; + } + return acceptor; + }, + + sortuniq: function(a){ + return a.sort().filter(function(item, pos, arr){ + return !pos || item != arr[pos - 1]; + }) + }, + + flatten: function(a){ + //[[1,2],[3]] -> [1,2,3] + return [].concat.apply([], a); + } +}; +module.exports = WordcutDict; + +}).call(this,"/dist/tmp") +},{"glob":16,"path":22}],3:[function(require,module,exports){ +var WordRule = { + createAcceptor: function(tag) { + if (tag["WORD_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + var lch = ch.toLowerCase(); + if (lch >= "a" && lch <= "z") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "WORD_RULE", + type: "WORD_RULE", + w: 1}; + } +}; + +var NumberRule = { + createAcceptor: function(tag) { + if (tag["NUMBER_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch >= "0" && ch <= "9") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "NUMBER_RULE", + type: "NUMBER_RULE", + w: 1}; + } +}; + +var SpaceRule = { + tag: "SPACE_RULE", + createAcceptor: function(tag) { + + if (tag["SPACE_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch == " " || ch == "\t" || ch == "\r" || ch == "\n" || + ch == "\u00A0" || ch=="\u2003"//nbsp and emsp + ) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: SpaceRule.tag, + w: 1, + type: "SPACE_RULE"}; + } +} + +var SingleSymbolRule = { + tag: "SINSYM", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (this.strOffset == 0 && ch.match(/^[\@\(\)\/\,\-\."`]$/)) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "SINSYM", + w: 1, + type: "SINSYM"}; + } +} + + +var LatinRules = [WordRule, SpaceRule, SingleSymbolRule, NumberRule]; + +module.exports = LatinRules; + +},{}],4:[function(require,module,exports){ +var _ = require("underscore") + , WordcutCore = require("./wordcut_core"); +var PathInfoBuilder = { + + /* + buildByPartAcceptors: function(path, acceptors, i) { + var + var genInfos = partAcceptors.reduce(function(genInfos, acceptor) { + + }, []); + + return genInfos; + } + */ + + buildByAcceptors: function(path, finalAcceptors, i) { + var self = this; + var infos = finalAcceptors.map(function(acceptor) { + var p = i - acceptor.strOffset + 1 + , _info = path[p]; + + var info = {p: p, + mw: _info.mw + (acceptor.mw === undefined ? 0 : acceptor.mw), + w: acceptor.w + _info.w, + unk: (acceptor.unk ? acceptor.unk : 0) + _info.unk, + type: acceptor.type}; + + if (acceptor.type == "PART") { + for(var j = p + 1; j <= i; j++) { + path[j].merge = p; + } + info.merge = p; + } + + return info; + }); + return infos.filter(function(info) { return info; }); + }, + + fallback: function(path, leftBoundary, text, i) { + var _info = path[leftBoundary]; + if (text[i].match(/[\u0E48-\u0E4E]/)) { + if (leftBoundary != 0) + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + mw: 0, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; +/* } else if(leftBoundary > 0 && path[leftBoundary].type !== "UNK") { + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; */ + } else { + return {p: leftBoundary, + mw: _info.mw, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; + } + }, + + build: function(path, finalAcceptors, i, leftBoundary, text) { + var basicPathInfos = this.buildByAcceptors(path, finalAcceptors, i); + if (basicPathInfos.length > 0) { + return basicPathInfos; + } else { + return [this.fallback(path, leftBoundary, text, i)]; + } + } +}; + +module.exports = function() { + return _.clone(PathInfoBuilder); +} + +},{"./wordcut_core":8,"underscore":25}],5:[function(require,module,exports){ +var _ = require("underscore"); + + +var PathSelector = { + selectPath: function(paths) { + var path = paths.reduce(function(selectedPath, path) { + if (selectedPath == null) { + return path; + } else { + if (path.unk < selectedPath.unk) + return path; + if (path.unk == selectedPath.unk) { + if (path.mw < selectedPath.mw) + return path + if (path.mw == selectedPath.mw) { + if (path.w < selectedPath.w) + return path; + } + } + return selectedPath; + } + }, null); + return path; + }, + + createPath: function() { + return [{p:null, w:0, unk:0, type: "INIT", mw:0}]; + } +}; + +module.exports = function() { + return _.clone(PathSelector); +}; + +},{"underscore":25}],6:[function(require,module,exports){ +function isMatch(pat, offset, ch) { + if (pat.length <= offset) + return false; + var _ch = pat[offset]; + return _ch == ch || + (_ch.match(/[กข]/) && ch.match(/[ก-ฮ]/)) || + (_ch.match(/[มบ]/) && ch.match(/[ก-ฮ]/)) || + (_ch.match(/\u0E49/) && ch.match(/[\u0E48-\u0E4B]/)); +} + +var Rule0 = { + pat: "เหก็ม", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (isMatch(Rule0.pat, this.strOffset,ch)) { + this.isFinal = (this.strOffset + 1 == Rule0.pat.length); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "THAI_RULE", + type: "THAI_RULE", + w: 1}; + } +}; + +var PartRule = { + createAcceptor: function(tag) { + return {strOffset: 0, + patterns: [ + "แก", "เก", "ก้", "กก์", "กา", "กี", "กิ", "กืก" + ], + isFinal: false, + transit: function(ch) { + var offset = this.strOffset; + this.patterns = this.patterns.filter(function(pat) { + return isMatch(pat, offset, ch); + }); + + if (this.patterns.length > 0) { + var len = 1 + offset; + this.isFinal = this.patterns.some(function(pat) { + return pat.length == len; + }); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "PART", + type: "PART", + unk: 1, + w: 1}; + } +}; + +var ThaiRules = [Rule0, PartRule]; + +module.exports = ThaiRules; + +},{}],7:[function(require,module,exports){ +var sys = require("sys") + , WordcutDict = require("./dict") + , WordcutCore = require("./wordcut_core") + , PathInfoBuilder = require("./path_info_builder") + , PathSelector = require("./path_selector") + , Acceptors = require("./acceptors") + , latinRules = require("./latin_rules") + , thaiRules = require("./thai_rules") + , _ = require("underscore"); + + +var Wordcut = Object.create(WordcutCore); +Wordcut.defaultPathInfoBuilder = PathInfoBuilder; +Wordcut.defaultPathSelector = PathSelector; +Wordcut.defaultAcceptors = Acceptors; +Wordcut.defaultLatinRules = latinRules; +Wordcut.defaultThaiRules = thaiRules; +Wordcut.defaultDict = WordcutDict; + + +Wordcut.initNoDict = function(dict_path) { + var self = this; + self.pathInfoBuilder = new self.defaultPathInfoBuilder; + self.pathSelector = new self.defaultPathSelector; + self.acceptors = new self.defaultAcceptors; + self.defaultLatinRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); + self.defaultThaiRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); +}; + +Wordcut.init = function(dict_path, withDefault, additionalWords) { + withDefault = withDefault || false; + this.initNoDict(); + var dict = _.clone(this.defaultDict); + dict.init(dict_path, withDefault, additionalWords); + this.acceptors.creators.push(dict); +}; + +module.exports = Wordcut; + +},{"./acceptors":1,"./dict":2,"./latin_rules":3,"./path_info_builder":4,"./path_selector":5,"./thai_rules":6,"./wordcut_core":8,"sys":28,"underscore":25}],8:[function(require,module,exports){ +var WordcutCore = { + + buildPath: function(text) { + var self = this + , path = self.pathSelector.createPath() + , leftBoundary = 0; + self.acceptors.reset(); + for (var i = 0; i < text.length; i++) { + var ch = text[i]; + self.acceptors.transit(ch); + + var possiblePathInfos = self + .pathInfoBuilder + .build(path, + self.acceptors.getFinalAcceptors(), + i, + leftBoundary, + text); + var selectedPath = self.pathSelector.selectPath(possiblePathInfos) + + path.push(selectedPath); + if (selectedPath.type !== "UNK") { + leftBoundary = i; + } + } + return path; + }, + + pathToRanges: function(path) { + var e = path.length - 1 + , ranges = []; + + while (e > 0) { + var info = path[e] + , s = info.p; + + if (info.merge !== undefined && ranges.length > 0) { + var r = ranges[ranges.length - 1]; + r.s = info.merge; + s = r.s; + } else { + ranges.push({s:s, e:e}); + } + e = s; + } + return ranges.reverse(); + }, + + rangesToText: function(text, ranges, delimiter) { + return ranges.map(function(r) { + return text.substring(r.s, r.e); + }).join(delimiter); + }, + + cut: function(text, delimiter) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + return this + .rangesToText(text, ranges, + (delimiter === undefined ? "|" : delimiter)); + }, + + cutIntoRanges: function(text, noText) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + if (!noText) { + ranges.forEach(function(r) { + r.text = text.substring(r.s, r.e); + }); + } + return ranges; + }, + + cutIntoArray: function(text) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + return ranges.map(function(r) { + return text.substring(r.s, r.e) + }); + } +}; + +module.exports = WordcutCore; + +},{}],9:[function(require,module,exports){ +// http://wiki.commonjs.org/wiki/Unit_Testing/1.0 +// +// THIS IS NOT TESTED NOR LIKELY TO WORK OUTSIDE V8! +// +// Originally from narwhal.js (http://narwhaljs.org) +// Copyright (c) 2009 Thomas Robinson <280north.com> +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the 'Software'), to +// deal in the Software without restriction, including without limitation the +// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +// sell copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +// WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +// when used in node, this will actually load the util module we depend on +// versus loading the builtin util module as happens otherwise +// this is a bug in node module loading as far as I am concerned +var util = require('util/'); + +var pSlice = Array.prototype.slice; +var hasOwn = Object.prototype.hasOwnProperty; + +// 1. The assert module provides functions that throw +// AssertionError's when particular conditions are not met. The +// assert module must conform to the following interface. + +var assert = module.exports = ok; + +// 2. The AssertionError is defined in assert. +// new assert.AssertionError({ message: message, +// actual: actual, +// expected: expected }) + +assert.AssertionError = function AssertionError(options) { + this.name = 'AssertionError'; + this.actual = options.actual; + this.expected = options.expected; + this.operator = options.operator; + if (options.message) { + this.message = options.message; + this.generatedMessage = false; + } else { + this.message = getMessage(this); + this.generatedMessage = true; + } + var stackStartFunction = options.stackStartFunction || fail; + + if (Error.captureStackTrace) { + Error.captureStackTrace(this, stackStartFunction); + } + else { + // non v8 browsers so we can have a stacktrace + var err = new Error(); + if (err.stack) { + var out = err.stack; + + // try to strip useless frames + var fn_name = stackStartFunction.name; + var idx = out.indexOf('\n' + fn_name); + if (idx >= 0) { + // once we have located the function frame + // we need to strip out everything before it (and its line) + var next_line = out.indexOf('\n', idx + 1); + out = out.substring(next_line + 1); + } + + this.stack = out; + } + } +}; + +// assert.AssertionError instanceof Error +util.inherits(assert.AssertionError, Error); + +function replacer(key, value) { + if (util.isUndefined(value)) { + return '' + value; + } + if (util.isNumber(value) && !isFinite(value)) { + return value.toString(); + } + if (util.isFunction(value) || util.isRegExp(value)) { + return value.toString(); + } + return value; +} + +function truncate(s, n) { + if (util.isString(s)) { + return s.length < n ? s : s.slice(0, n); + } else { + return s; + } +} + +function getMessage(self) { + return truncate(JSON.stringify(self.actual, replacer), 128) + ' ' + + self.operator + ' ' + + truncate(JSON.stringify(self.expected, replacer), 128); +} + +// At present only the three keys mentioned above are used and +// understood by the spec. Implementations or sub modules can pass +// other keys to the AssertionError's constructor - they will be +// ignored. + +// 3. All of the following functions must throw an AssertionError +// when a corresponding condition is not met, with a message that +// may be undefined if not provided. All assertion methods provide +// both the actual and expected values to the assertion error for +// display purposes. + +function fail(actual, expected, message, operator, stackStartFunction) { + throw new assert.AssertionError({ + message: message, + actual: actual, + expected: expected, + operator: operator, + stackStartFunction: stackStartFunction + }); +} + +// EXTENSION! allows for well behaved errors defined elsewhere. +assert.fail = fail; + +// 4. Pure assertion tests whether a value is truthy, as determined +// by !!guard. +// assert.ok(guard, message_opt); +// This statement is equivalent to assert.equal(true, !!guard, +// message_opt);. To test strictly for the value true, use +// assert.strictEqual(true, guard, message_opt);. + +function ok(value, message) { + if (!value) fail(value, true, message, '==', assert.ok); +} +assert.ok = ok; + +// 5. The equality assertion tests shallow, coercive equality with +// ==. +// assert.equal(actual, expected, message_opt); + +assert.equal = function equal(actual, expected, message) { + if (actual != expected) fail(actual, expected, message, '==', assert.equal); +}; + +// 6. The non-equality assertion tests for whether two objects are not equal +// with != assert.notEqual(actual, expected, message_opt); + +assert.notEqual = function notEqual(actual, expected, message) { + if (actual == expected) { + fail(actual, expected, message, '!=', assert.notEqual); + } +}; + +// 7. The equivalence assertion tests a deep equality relation. +// assert.deepEqual(actual, expected, message_opt); + +assert.deepEqual = function deepEqual(actual, expected, message) { + if (!_deepEqual(actual, expected)) { + fail(actual, expected, message, 'deepEqual', assert.deepEqual); + } +}; + +function _deepEqual(actual, expected) { + // 7.1. All identical values are equivalent, as determined by ===. + if (actual === expected) { + return true; + + } else if (util.isBuffer(actual) && util.isBuffer(expected)) { + if (actual.length != expected.length) return false; + + for (var i = 0; i < actual.length; i++) { + if (actual[i] !== expected[i]) return false; + } + + return true; + + // 7.2. If the expected value is a Date object, the actual value is + // equivalent if it is also a Date object that refers to the same time. + } else if (util.isDate(actual) && util.isDate(expected)) { + return actual.getTime() === expected.getTime(); + + // 7.3 If the expected value is a RegExp object, the actual value is + // equivalent if it is also a RegExp object with the same source and + // properties (`global`, `multiline`, `lastIndex`, `ignoreCase`). + } else if (util.isRegExp(actual) && util.isRegExp(expected)) { + return actual.source === expected.source && + actual.global === expected.global && + actual.multiline === expected.multiline && + actual.lastIndex === expected.lastIndex && + actual.ignoreCase === expected.ignoreCase; + + // 7.4. Other pairs that do not both pass typeof value == 'object', + // equivalence is determined by ==. + } else if (!util.isObject(actual) && !util.isObject(expected)) { + return actual == expected; + + // 7.5 For all other Object pairs, including Array objects, equivalence is + // determined by having the same number of owned properties (as verified + // with Object.prototype.hasOwnProperty.call), the same set of keys + // (although not necessarily the same order), equivalent values for every + // corresponding key, and an identical 'prototype' property. Note: this + // accounts for both named and indexed properties on Arrays. + } else { + return objEquiv(actual, expected); + } +} + +function isArguments(object) { + return Object.prototype.toString.call(object) == '[object Arguments]'; +} + +function objEquiv(a, b) { + if (util.isNullOrUndefined(a) || util.isNullOrUndefined(b)) + return false; + // an identical 'prototype' property. + if (a.prototype !== b.prototype) return false; + // if one is a primitive, the other must be same + if (util.isPrimitive(a) || util.isPrimitive(b)) { + return a === b; + } + var aIsArgs = isArguments(a), + bIsArgs = isArguments(b); + if ((aIsArgs && !bIsArgs) || (!aIsArgs && bIsArgs)) + return false; + if (aIsArgs) { + a = pSlice.call(a); + b = pSlice.call(b); + return _deepEqual(a, b); + } + var ka = objectKeys(a), + kb = objectKeys(b), + key, i; + // having the same number of owned properties (keys incorporates + // hasOwnProperty) + if (ka.length != kb.length) + return false; + //the same set of keys (although not necessarily the same order), + ka.sort(); + kb.sort(); + //~~~cheap key test + for (i = ka.length - 1; i >= 0; i--) { + if (ka[i] != kb[i]) + return false; + } + //equivalent values for every corresponding key, and + //~~~possibly expensive deep test + for (i = ka.length - 1; i >= 0; i--) { + key = ka[i]; + if (!_deepEqual(a[key], b[key])) return false; + } + return true; +} + +// 8. The non-equivalence assertion tests for any deep inequality. +// assert.notDeepEqual(actual, expected, message_opt); + +assert.notDeepEqual = function notDeepEqual(actual, expected, message) { + if (_deepEqual(actual, expected)) { + fail(actual, expected, message, 'notDeepEqual', assert.notDeepEqual); + } +}; + +// 9. The strict equality assertion tests strict equality, as determined by ===. +// assert.strictEqual(actual, expected, message_opt); + +assert.strictEqual = function strictEqual(actual, expected, message) { + if (actual !== expected) { + fail(actual, expected, message, '===', assert.strictEqual); + } +}; + +// 10. The strict non-equality assertion tests for strict inequality, as +// determined by !==. assert.notStrictEqual(actual, expected, message_opt); + +assert.notStrictEqual = function notStrictEqual(actual, expected, message) { + if (actual === expected) { + fail(actual, expected, message, '!==', assert.notStrictEqual); + } +}; + +function expectedException(actual, expected) { + if (!actual || !expected) { + return false; + } + + if (Object.prototype.toString.call(expected) == '[object RegExp]') { + return expected.test(actual); + } else if (actual instanceof expected) { + return true; + } else if (expected.call({}, actual) === true) { + return true; + } + + return false; +} + +function _throws(shouldThrow, block, expected, message) { + var actual; + + if (util.isString(expected)) { + message = expected; + expected = null; + } + + try { + block(); + } catch (e) { + actual = e; + } + + message = (expected && expected.name ? ' (' + expected.name + ').' : '.') + + (message ? ' ' + message : '.'); + + if (shouldThrow && !actual) { + fail(actual, expected, 'Missing expected exception' + message); + } + + if (!shouldThrow && expectedException(actual, expected)) { + fail(actual, expected, 'Got unwanted exception' + message); + } + + if ((shouldThrow && actual && expected && + !expectedException(actual, expected)) || (!shouldThrow && actual)) { + throw actual; + } +} + +// 11. Expected to throw an error: +// assert.throws(block, Error_opt, message_opt); + +assert.throws = function(block, /*optional*/error, /*optional*/message) { + _throws.apply(this, [true].concat(pSlice.call(arguments))); +}; + +// EXTENSION! This is annoying to write outside this module. +assert.doesNotThrow = function(block, /*optional*/message) { + _throws.apply(this, [false].concat(pSlice.call(arguments))); +}; + +assert.ifError = function(err) { if (err) {throw err;}}; + +var objectKeys = Object.keys || function (obj) { + var keys = []; + for (var key in obj) { + if (hasOwn.call(obj, key)) keys.push(key); + } + return keys; +}; + +},{"util/":28}],10:[function(require,module,exports){ +'use strict'; +module.exports = balanced; +function balanced(a, b, str) { + if (a instanceof RegExp) a = maybeMatch(a, str); + if (b instanceof RegExp) b = maybeMatch(b, str); + + var r = range(a, b, str); + + return r && { + start: r[0], + end: r[1], + pre: str.slice(0, r[0]), + body: str.slice(r[0] + a.length, r[1]), + post: str.slice(r[1] + b.length) + }; +} + +function maybeMatch(reg, str) { + var m = str.match(reg); + return m ? m[0] : null; +} + +balanced.range = range; +function range(a, b, str) { + var begs, beg, left, right, result; + var ai = str.indexOf(a); + var bi = str.indexOf(b, ai + 1); + var i = ai; + + if (ai >= 0 && bi > 0) { + begs = []; + left = str.length; + + while (i >= 0 && !result) { + if (i == ai) { + begs.push(i); + ai = str.indexOf(a, i + 1); + } else if (begs.length == 1) { + result = [ begs.pop(), bi ]; + } else { + beg = begs.pop(); + if (beg < left) { + left = beg; + right = bi; + } + + bi = str.indexOf(b, i + 1); + } + + i = ai < bi && ai >= 0 ? ai : bi; + } + + if (begs.length) { + result = [ left, right ]; + } + } + + return result; +} + +},{}],11:[function(require,module,exports){ +var concatMap = require('concat-map'); +var balanced = require('balanced-match'); + +module.exports = expandTop; + +var escSlash = '\0SLASH'+Math.random()+'\0'; +var escOpen = '\0OPEN'+Math.random()+'\0'; +var escClose = '\0CLOSE'+Math.random()+'\0'; +var escComma = '\0COMMA'+Math.random()+'\0'; +var escPeriod = '\0PERIOD'+Math.random()+'\0'; + +function numeric(str) { + return parseInt(str, 10) == str + ? parseInt(str, 10) + : str.charCodeAt(0); +} + +function escapeBraces(str) { + return str.split('\\\\').join(escSlash) + .split('\\{').join(escOpen) + .split('\\}').join(escClose) + .split('\\,').join(escComma) + .split('\\.').join(escPeriod); +} + +function unescapeBraces(str) { + return str.split(escSlash).join('\\') + .split(escOpen).join('{') + .split(escClose).join('}') + .split(escComma).join(',') + .split(escPeriod).join('.'); +} + + +// Basically just str.split(","), but handling cases +// where we have nested braced sections, which should be +// treated as individual members, like {a,{b,c},d} +function parseCommaParts(str) { + if (!str) + return ['']; + + var parts = []; + var m = balanced('{', '}', str); + + if (!m) + return str.split(','); + + var pre = m.pre; + var body = m.body; + var post = m.post; + var p = pre.split(','); + + p[p.length-1] += '{' + body + '}'; + var postParts = parseCommaParts(post); + if (post.length) { + p[p.length-1] += postParts.shift(); + p.push.apply(p, postParts); + } + + parts.push.apply(parts, p); + + return parts; +} + +function expandTop(str) { + if (!str) + return []; + + // I don't know why Bash 4.3 does this, but it does. + // Anything starting with {} will have the first two bytes preserved + // but *only* at the top level, so {},a}b will not expand to anything, + // but a{},b}c will be expanded to [a}c,abc]. + // One could argue that this is a bug in Bash, but since the goal of + // this module is to match Bash's rules, we escape a leading {} + if (str.substr(0, 2) === '{}') { + str = '\\{\\}' + str.substr(2); + } + + return expand(escapeBraces(str), true).map(unescapeBraces); +} + +function identity(e) { + return e; +} + +function embrace(str) { + return '{' + str + '}'; +} +function isPadded(el) { + return /^-?0\d/.test(el); +} + +function lte(i, y) { + return i <= y; +} +function gte(i, y) { + return i >= y; +} + +function expand(str, isTop) { + var expansions = []; + + var m = balanced('{', '}', str); + if (!m || /\$$/.test(m.pre)) return [str]; + + var isNumericSequence = /^-?\d+\.\.-?\d+(?:\.\.-?\d+)?$/.test(m.body); + var isAlphaSequence = /^[a-zA-Z]\.\.[a-zA-Z](?:\.\.-?\d+)?$/.test(m.body); + var isSequence = isNumericSequence || isAlphaSequence; + var isOptions = m.body.indexOf(',') >= 0; + if (!isSequence && !isOptions) { + // {a},b} + if (m.post.match(/,.*\}/)) { + str = m.pre + '{' + m.body + escClose + m.post; + return expand(str); + } + return [str]; + } + + var n; + if (isSequence) { + n = m.body.split(/\.\./); + } else { + n = parseCommaParts(m.body); + if (n.length === 1) { + // x{{a,b}}y ==> x{a}y x{b}y + n = expand(n[0], false).map(embrace); + if (n.length === 1) { + var post = m.post.length + ? expand(m.post, false) + : ['']; + return post.map(function(p) { + return m.pre + n[0] + p; + }); + } + } + } + + // at this point, n is the parts, and we know it's not a comma set + // with a single entry. + + // no need to expand pre, since it is guaranteed to be free of brace-sets + var pre = m.pre; + var post = m.post.length + ? expand(m.post, false) + : ['']; + + var N; + + if (isSequence) { + var x = numeric(n[0]); + var y = numeric(n[1]); + var width = Math.max(n[0].length, n[1].length) + var incr = n.length == 3 + ? Math.abs(numeric(n[2])) + : 1; + var test = lte; + var reverse = y < x; + if (reverse) { + incr *= -1; + test = gte; + } + var pad = n.some(isPadded); + + N = []; + + for (var i = x; test(i, y); i += incr) { + var c; + if (isAlphaSequence) { + c = String.fromCharCode(i); + if (c === '\\') + c = ''; + } else { + c = String(i); + if (pad) { + var need = width - c.length; + if (need > 0) { + var z = new Array(need + 1).join('0'); + if (i < 0) + c = '-' + z + c.slice(1); + else + c = z + c; + } + } + } + N.push(c); + } + } else { + N = concatMap(n, function(el) { return expand(el, false) }); + } + + for (var j = 0; j < N.length; j++) { + for (var k = 0; k < post.length; k++) { + var expansion = pre + N[j] + post[k]; + if (!isTop || isSequence || expansion) + expansions.push(expansion); + } + } + + return expansions; +} + + +},{"balanced-match":10,"concat-map":13}],12:[function(require,module,exports){ + +},{}],13:[function(require,module,exports){ +module.exports = function (xs, fn) { + var res = []; + for (var i = 0; i < xs.length; i++) { + var x = fn(xs[i], i); + if (isArray(x)) res.push.apply(res, x); + else res.push(x); + } + return res; +}; + +var isArray = Array.isArray || function (xs) { + return Object.prototype.toString.call(xs) === '[object Array]'; +}; + +},{}],14:[function(require,module,exports){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +function EventEmitter() { + this._events = this._events || {}; + this._maxListeners = this._maxListeners || undefined; +} +module.exports = EventEmitter; + +// Backwards-compat with node 0.10.x +EventEmitter.EventEmitter = EventEmitter; + +EventEmitter.prototype._events = undefined; +EventEmitter.prototype._maxListeners = undefined; + +// By default EventEmitters will print a warning if more than 10 listeners are +// added to it. This is a useful default which helps finding memory leaks. +EventEmitter.defaultMaxListeners = 10; + +// Obviously not all Emitters should be limited to 10. This function allows +// that to be increased. Set to zero for unlimited. +EventEmitter.prototype.setMaxListeners = function(n) { + if (!isNumber(n) || n < 0 || isNaN(n)) + throw TypeError('n must be a positive number'); + this._maxListeners = n; + return this; +}; + +EventEmitter.prototype.emit = function(type) { + var er, handler, len, args, i, listeners; + + if (!this._events) + this._events = {}; + + // If there is no 'error' event listener then throw. + if (type === 'error') { + if (!this._events.error || + (isObject(this._events.error) && !this._events.error.length)) { + er = arguments[1]; + if (er instanceof Error) { + throw er; // Unhandled 'error' event + } + throw TypeError('Uncaught, unspecified "error" event.'); + } + } + + handler = this._events[type]; + + if (isUndefined(handler)) + return false; + + if (isFunction(handler)) { + switch (arguments.length) { + // fast cases + case 1: + handler.call(this); + break; + case 2: + handler.call(this, arguments[1]); + break; + case 3: + handler.call(this, arguments[1], arguments[2]); + break; + // slower + default: + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + handler.apply(this, args); + } + } else if (isObject(handler)) { + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + + listeners = handler.slice(); + len = listeners.length; + for (i = 0; i < len; i++) + listeners[i].apply(this, args); + } + + return true; +}; + +EventEmitter.prototype.addListener = function(type, listener) { + var m; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events) + this._events = {}; + + // To avoid recursion in the case that type === "newListener"! Before + // adding it to the listeners, first emit "newListener". + if (this._events.newListener) + this.emit('newListener', type, + isFunction(listener.listener) ? + listener.listener : listener); + + if (!this._events[type]) + // Optimize the case of one listener. Don't need the extra array object. + this._events[type] = listener; + else if (isObject(this._events[type])) + // If we've already got an array, just append. + this._events[type].push(listener); + else + // Adding the second element, need to change to array. + this._events[type] = [this._events[type], listener]; + + // Check for listener leak + if (isObject(this._events[type]) && !this._events[type].warned) { + var m; + if (!isUndefined(this._maxListeners)) { + m = this._maxListeners; + } else { + m = EventEmitter.defaultMaxListeners; + } + + if (m && m > 0 && this._events[type].length > m) { + this._events[type].warned = true; + console.error('(node) warning: possible EventEmitter memory ' + + 'leak detected. %d listeners added. ' + + 'Use emitter.setMaxListeners() to increase limit.', + this._events[type].length); + if (typeof console.trace === 'function') { + // not supported in IE 10 + console.trace(); + } + } + } + + return this; +}; + +EventEmitter.prototype.on = EventEmitter.prototype.addListener; + +EventEmitter.prototype.once = function(type, listener) { + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + var fired = false; + + function g() { + this.removeListener(type, g); + + if (!fired) { + fired = true; + listener.apply(this, arguments); + } + } + + g.listener = listener; + this.on(type, g); + + return this; +}; + +// emits a 'removeListener' event iff the listener was removed +EventEmitter.prototype.removeListener = function(type, listener) { + var list, position, length, i; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events || !this._events[type]) + return this; + + list = this._events[type]; + length = list.length; + position = -1; + + if (list === listener || + (isFunction(list.listener) && list.listener === listener)) { + delete this._events[type]; + if (this._events.removeListener) + this.emit('removeListener', type, listener); + + } else if (isObject(list)) { + for (i = length; i-- > 0;) { + if (list[i] === listener || + (list[i].listener && list[i].listener === listener)) { + position = i; + break; + } + } + + if (position < 0) + return this; + + if (list.length === 1) { + list.length = 0; + delete this._events[type]; + } else { + list.splice(position, 1); + } + + if (this._events.removeListener) + this.emit('removeListener', type, listener); + } + + return this; +}; + +EventEmitter.prototype.removeAllListeners = function(type) { + var key, listeners; + + if (!this._events) + return this; + + // not listening for removeListener, no need to emit + if (!this._events.removeListener) { + if (arguments.length === 0) + this._events = {}; + else if (this._events[type]) + delete this._events[type]; + return this; + } + + // emit removeListener for all listeners on all events + if (arguments.length === 0) { + for (key in this._events) { + if (key === 'removeListener') continue; + this.removeAllListeners(key); + } + this.removeAllListeners('removeListener'); + this._events = {}; + return this; + } + + listeners = this._events[type]; + + if (isFunction(listeners)) { + this.removeListener(type, listeners); + } else { + // LIFO order + while (listeners.length) + this.removeListener(type, listeners[listeners.length - 1]); + } + delete this._events[type]; + + return this; +}; + +EventEmitter.prototype.listeners = function(type) { + var ret; + if (!this._events || !this._events[type]) + ret = []; + else if (isFunction(this._events[type])) + ret = [this._events[type]]; + else + ret = this._events[type].slice(); + return ret; +}; + +EventEmitter.listenerCount = function(emitter, type) { + var ret; + if (!emitter._events || !emitter._events[type]) + ret = 0; + else if (isFunction(emitter._events[type])) + ret = 1; + else + ret = emitter._events[type].length; + return ret; +}; + +function isFunction(arg) { + return typeof arg === 'function'; +} + +function isNumber(arg) { + return typeof arg === 'number'; +} + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} + +function isUndefined(arg) { + return arg === void 0; +} + +},{}],15:[function(require,module,exports){ +(function (process){ +exports.alphasort = alphasort +exports.alphasorti = alphasorti +exports.setopts = setopts +exports.ownProp = ownProp +exports.makeAbs = makeAbs +exports.finish = finish +exports.mark = mark +exports.isIgnored = isIgnored +exports.childrenIgnored = childrenIgnored + +function ownProp (obj, field) { + return Object.prototype.hasOwnProperty.call(obj, field) +} + +var path = require("path") +var minimatch = require("minimatch") +var isAbsolute = require("path-is-absolute") +var Minimatch = minimatch.Minimatch + +function alphasorti (a, b) { + return a.toLowerCase().localeCompare(b.toLowerCase()) +} + +function alphasort (a, b) { + return a.localeCompare(b) +} + +function setupIgnores (self, options) { + self.ignore = options.ignore || [] + + if (!Array.isArray(self.ignore)) + self.ignore = [self.ignore] + + if (self.ignore.length) { + self.ignore = self.ignore.map(ignoreMap) + } +} + +function ignoreMap (pattern) { + var gmatcher = null + if (pattern.slice(-3) === '/**') { + var gpattern = pattern.replace(/(\/\*\*)+$/, '') + gmatcher = new Minimatch(gpattern) + } + + return { + matcher: new Minimatch(pattern), + gmatcher: gmatcher + } +} + +function setopts (self, pattern, options) { + if (!options) + options = {} + + // base-matching: just use globstar for that. + if (options.matchBase && -1 === pattern.indexOf("/")) { + if (options.noglobstar) { + throw new Error("base matching requires globstar") + } + pattern = "**/" + pattern + } + + self.silent = !!options.silent + self.pattern = pattern + self.strict = options.strict !== false + self.realpath = !!options.realpath + self.realpathCache = options.realpathCache || Object.create(null) + self.follow = !!options.follow + self.dot = !!options.dot + self.mark = !!options.mark + self.nodir = !!options.nodir + if (self.nodir) + self.mark = true + self.sync = !!options.sync + self.nounique = !!options.nounique + self.nonull = !!options.nonull + self.nosort = !!options.nosort + self.nocase = !!options.nocase + self.stat = !!options.stat + self.noprocess = !!options.noprocess + + self.maxLength = options.maxLength || Infinity + self.cache = options.cache || Object.create(null) + self.statCache = options.statCache || Object.create(null) + self.symlinks = options.symlinks || Object.create(null) + + setupIgnores(self, options) + + self.changedCwd = false + var cwd = process.cwd() + if (!ownProp(options, "cwd")) + self.cwd = cwd + else { + self.cwd = options.cwd + self.changedCwd = path.resolve(options.cwd) !== cwd + } + + self.root = options.root || path.resolve(self.cwd, "/") + self.root = path.resolve(self.root) + if (process.platform === "win32") + self.root = self.root.replace(/\\/g, "/") + + self.nomount = !!options.nomount + + // disable comments and negation unless the user explicitly + // passes in false as the option. + options.nonegate = options.nonegate === false ? false : true + options.nocomment = options.nocomment === false ? false : true + deprecationWarning(options) + + self.minimatch = new Minimatch(pattern, options) + self.options = self.minimatch.options +} + +// TODO(isaacs): remove entirely in v6 +// exported to reset in tests +exports.deprecationWarned +function deprecationWarning(options) { + if (!options.nonegate || !options.nocomment) { + if (process.noDeprecation !== true && !exports.deprecationWarned) { + var msg = 'glob WARNING: comments and negation will be disabled in v6' + if (process.throwDeprecation) + throw new Error(msg) + else if (process.traceDeprecation) + console.trace(msg) + else + console.error(msg) + + exports.deprecationWarned = true + } + } +} + +function finish (self) { + var nou = self.nounique + var all = nou ? [] : Object.create(null) + + for (var i = 0, l = self.matches.length; i < l; i ++) { + var matches = self.matches[i] + if (!matches || Object.keys(matches).length === 0) { + if (self.nonull) { + // do like the shell, and spit out the literal glob + var literal = self.minimatch.globSet[i] + if (nou) + all.push(literal) + else + all[literal] = true + } + } else { + // had matches + var m = Object.keys(matches) + if (nou) + all.push.apply(all, m) + else + m.forEach(function (m) { + all[m] = true + }) + } + } + + if (!nou) + all = Object.keys(all) + + if (!self.nosort) + all = all.sort(self.nocase ? alphasorti : alphasort) + + // at *some* point we statted all of these + if (self.mark) { + for (var i = 0; i < all.length; i++) { + all[i] = self._mark(all[i]) + } + if (self.nodir) { + all = all.filter(function (e) { + return !(/\/$/.test(e)) + }) + } + } + + if (self.ignore.length) + all = all.filter(function(m) { + return !isIgnored(self, m) + }) + + self.found = all +} + +function mark (self, p) { + var abs = makeAbs(self, p) + var c = self.cache[abs] + var m = p + if (c) { + var isDir = c === 'DIR' || Array.isArray(c) + var slash = p.slice(-1) === '/' + + if (isDir && !slash) + m += '/' + else if (!isDir && slash) + m = m.slice(0, -1) + + if (m !== p) { + var mabs = makeAbs(self, m) + self.statCache[mabs] = self.statCache[abs] + self.cache[mabs] = self.cache[abs] + } + } + + return m +} + +// lotta situps... +function makeAbs (self, f) { + var abs = f + if (f.charAt(0) === '/') { + abs = path.join(self.root, f) + } else if (isAbsolute(f) || f === '') { + abs = f + } else if (self.changedCwd) { + abs = path.resolve(self.cwd, f) + } else { + abs = path.resolve(f) + } + return abs +} + + +// Return true, if pattern ends with globstar '**', for the accompanying parent directory. +// Ex:- If node_modules/** is the pattern, add 'node_modules' to ignore list along with it's contents +function isIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return item.matcher.match(path) || !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +function childrenIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +}).call(this,require('_process')) +},{"_process":24,"minimatch":20,"path":22,"path-is-absolute":23}],16:[function(require,module,exports){ +(function (process){ +// Approach: +// +// 1. Get the minimatch set +// 2. For each pattern in the set, PROCESS(pattern, false) +// 3. Store matches per-set, then uniq them +// +// PROCESS(pattern, inGlobStar) +// Get the first [n] items from pattern that are all strings +// Join these together. This is PREFIX. +// If there is no more remaining, then stat(PREFIX) and +// add to matches if it succeeds. END. +// +// If inGlobStar and PREFIX is symlink and points to dir +// set ENTRIES = [] +// else readdir(PREFIX) as ENTRIES +// If fail, END +// +// with ENTRIES +// If pattern[n] is GLOBSTAR +// // handle the case where the globstar match is empty +// // by pruning it out, and testing the resulting pattern +// PROCESS(pattern[0..n] + pattern[n+1 .. $], false) +// // handle other cases. +// for ENTRY in ENTRIES (not dotfiles) +// // attach globstar + tail onto the entry +// // Mark that this entry is a globstar match +// PROCESS(pattern[0..n] + ENTRY + pattern[n .. $], true) +// +// else // not globstar +// for ENTRY in ENTRIES (not dotfiles, unless pattern[n] is dot) +// Test ENTRY against pattern[n] +// If fails, continue +// If passes, PROCESS(pattern[0..n] + item + pattern[n+1 .. $]) +// +// Caveat: +// Cache all stats and readdirs results to minimize syscall. Since all +// we ever care about is existence and directory-ness, we can just keep +// `true` for files, and [children,...] for directories, or `false` for +// things that don't exist. + +module.exports = glob + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var inherits = require('inherits') +var EE = require('events').EventEmitter +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var globSync = require('./sync.js') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var inflight = require('inflight') +var util = require('util') +var childrenIgnored = common.childrenIgnored +var isIgnored = common.isIgnored + +var once = require('once') + +function glob (pattern, options, cb) { + if (typeof options === 'function') cb = options, options = {} + if (!options) options = {} + + if (options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return globSync(pattern, options) + } + + return new Glob(pattern, options, cb) +} + +glob.sync = globSync +var GlobSync = glob.GlobSync = globSync.GlobSync + +// old api surface +glob.glob = glob + +glob.hasMagic = function (pattern, options_) { + var options = util._extend({}, options_) + options.noprocess = true + + var g = new Glob(pattern, options) + var set = g.minimatch.set + if (set.length > 1) + return true + + for (var j = 0; j < set[0].length; j++) { + if (typeof set[0][j] !== 'string') + return true + } + + return false +} + +glob.Glob = Glob +inherits(Glob, EE) +function Glob (pattern, options, cb) { + if (typeof options === 'function') { + cb = options + options = null + } + + if (options && options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return new GlobSync(pattern, options) + } + + if (!(this instanceof Glob)) + return new Glob(pattern, options, cb) + + setopts(this, pattern, options) + this._didRealPath = false + + // process each pattern in the minimatch set + var n = this.minimatch.set.length + + // The matches are stored as {: true,...} so that + // duplicates are automagically pruned. + // Later, we do an Object.keys() on these. + // Keep them as a list so we can fill in when nonull is set. + this.matches = new Array(n) + + if (typeof cb === 'function') { + cb = once(cb) + this.on('error', cb) + this.on('end', function (matches) { + cb(null, matches) + }) + } + + var self = this + var n = this.minimatch.set.length + this._processing = 0 + this.matches = new Array(n) + + this._emitQueue = [] + this._processQueue = [] + this.paused = false + + if (this.noprocess) + return this + + if (n === 0) + return done() + + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false, done) + } + + function done () { + --self._processing + if (self._processing <= 0) + self._finish() + } +} + +Glob.prototype._finish = function () { + assert(this instanceof Glob) + if (this.aborted) + return + + if (this.realpath && !this._didRealpath) + return this._realpath() + + common.finish(this) + this.emit('end', this.found) +} + +Glob.prototype._realpath = function () { + if (this._didRealpath) + return + + this._didRealpath = true + + var n = this.matches.length + if (n === 0) + return this._finish() + + var self = this + for (var i = 0; i < this.matches.length; i++) + this._realpathSet(i, next) + + function next () { + if (--n === 0) + self._finish() + } +} + +Glob.prototype._realpathSet = function (index, cb) { + var matchset = this.matches[index] + if (!matchset) + return cb() + + var found = Object.keys(matchset) + var self = this + var n = found.length + + if (n === 0) + return cb() + + var set = this.matches[index] = Object.create(null) + found.forEach(function (p, i) { + // If there's a problem with the stat, then it means that + // one or more of the links in the realpath couldn't be + // resolved. just return the abs value in that case. + p = self._makeAbs(p) + fs.realpath(p, self.realpathCache, function (er, real) { + if (!er) + set[real] = true + else if (er.syscall === 'stat') + set[p] = true + else + self.emit('error', er) // srsly wtf right here + + if (--n === 0) { + self.matches[index] = set + cb() + } + }) + }) +} + +Glob.prototype._mark = function (p) { + return common.mark(this, p) +} + +Glob.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +Glob.prototype.abort = function () { + this.aborted = true + this.emit('abort') +} + +Glob.prototype.pause = function () { + if (!this.paused) { + this.paused = true + this.emit('pause') + } +} + +Glob.prototype.resume = function () { + if (this.paused) { + this.emit('resume') + this.paused = false + if (this._emitQueue.length) { + var eq = this._emitQueue.slice(0) + this._emitQueue.length = 0 + for (var i = 0; i < eq.length; i ++) { + var e = eq[i] + this._emitMatch(e[0], e[1]) + } + } + if (this._processQueue.length) { + var pq = this._processQueue.slice(0) + this._processQueue.length = 0 + for (var i = 0; i < pq.length; i ++) { + var p = pq[i] + this._processing-- + this._process(p[0], p[1], p[2], p[3]) + } + } + } +} + +Glob.prototype._process = function (pattern, index, inGlobStar, cb) { + assert(this instanceof Glob) + assert(typeof cb === 'function') + + if (this.aborted) + return + + this._processing++ + if (this.paused) { + this._processQueue.push([pattern, index, inGlobStar, cb]) + return + } + + //console.error('PROCESS %d', this._processing, pattern) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // see if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index, cb) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip _processing + if (childrenIgnored(this, read)) + return cb() + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar, cb) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar, cb) +} + +Glob.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + return self._processReaddir2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + +Glob.prototype._processReaddir2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return cb() + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + //console.error('prd2', prefix, entries, remain[0]._glob, matchedEntries) + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return cb() + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this._emitMatch(index, e) + } + // This was the last one, and no stats were needed + return cb() + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + this._process([e].concat(remain), index, inGlobStar, cb) + } + cb() +} + +Glob.prototype._emitMatch = function (index, e) { + if (this.aborted) + return + + if (this.matches[index][e]) + return + + if (isIgnored(this, e)) + return + + if (this.paused) { + this._emitQueue.push([index, e]) + return + } + + var abs = this._makeAbs(e) + + if (this.nodir) { + var c = this.cache[abs] + if (c === 'DIR' || Array.isArray(c)) + return + } + + if (this.mark) + e = this._mark(e) + + this.matches[index][e] = true + + var st = this.statCache[abs] + if (st) + this.emit('stat', e, st) + + this.emit('match', e) +} + +Glob.prototype._readdirInGlobStar = function (abs, cb) { + if (this.aborted) + return + + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false, cb) + + var lstatkey = 'lstat\0' + abs + var self = this + var lstatcb = inflight(lstatkey, lstatcb_) + + if (lstatcb) + fs.lstat(abs, lstatcb) + + function lstatcb_ (er, lstat) { + if (er) + return cb() + + var isSym = lstat.isSymbolicLink() + self.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) { + self.cache[abs] = 'FILE' + cb() + } else + self._readdir(abs, false, cb) + } +} + +Glob.prototype._readdir = function (abs, inGlobStar, cb) { + if (this.aborted) + return + + cb = inflight('readdir\0'+abs+'\0'+inGlobStar, cb) + if (!cb) + return + + //console.error('RD %j %j', +inGlobStar, abs) + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs, cb) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return cb() + + if (Array.isArray(c)) + return cb(null, c) + } + + var self = this + fs.readdir(abs, readdirCb(this, abs, cb)) +} + +function readdirCb (self, abs, cb) { + return function (er, entries) { + if (er) + self._readdirError(abs, er, cb) + else + self._readdirEntries(abs, entries, cb) + } +} + +Glob.prototype._readdirEntries = function (abs, entries, cb) { + if (this.aborted) + return + + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + return cb(null, entries) +} + +Glob.prototype._readdirError = function (f, er, cb) { + if (this.aborted) + return + + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) { + this.emit('error', er) + // If the error is handled, then we abort + // if not, we threw out of here + this.abort() + } + if (!this.silent) + console.error('glob error', er) + break + } + + return cb() +} + +Glob.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + self._processGlobStar2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + + +Glob.prototype._processGlobStar2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + //console.error('pgs2', prefix, remain[0], entries) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return cb() + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false, cb) + + var isSym = this.symlinks[abs] + var len = entries.length + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return cb() + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true, cb) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true, cb) + } + + cb() +} + +Glob.prototype._processSimple = function (prefix, index, cb) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var self = this + this._stat(prefix, function (er, exists) { + self._processSimple2(prefix, index, er, exists, cb) + }) +} +Glob.prototype._processSimple2 = function (prefix, index, er, exists, cb) { + + //console.error('ps2', prefix, exists) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return cb() + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this._emitMatch(index, prefix) + cb() +} + +// Returns either 'DIR', 'FILE', or false +Glob.prototype._stat = function (f, cb) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return cb() + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return cb(null, c) + + if (needDir && c === 'FILE') + return cb() + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (stat !== undefined) { + if (stat === false) + return cb(null, stat) + else { + var type = stat.isDirectory() ? 'DIR' : 'FILE' + if (needDir && type === 'FILE') + return cb() + else + return cb(null, type, stat) + } + } + + var self = this + var statcb = inflight('stat\0' + abs, lstatcb_) + if (statcb) + fs.lstat(abs, statcb) + + function lstatcb_ (er, lstat) { + if (lstat && lstat.isSymbolicLink()) { + // If it's a symlink, then treat it as the target, unless + // the target does not exist, then treat it as a file. + return fs.stat(abs, function (er, stat) { + if (er) + self._stat2(f, abs, null, lstat, cb) + else + self._stat2(f, abs, er, stat, cb) + }) + } else { + self._stat2(f, abs, er, lstat, cb) + } + } +} + +Glob.prototype._stat2 = function (f, abs, er, stat, cb) { + if (er) { + this.statCache[abs] = false + return cb() + } + + var needDir = f.slice(-1) === '/' + this.statCache[abs] = stat + + if (abs.slice(-1) === '/' && !stat.isDirectory()) + return cb(null, false, stat) + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return cb() + + return cb(null, c, stat) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./sync.js":17,"_process":24,"assert":9,"events":14,"fs":12,"inflight":18,"inherits":19,"minimatch":20,"once":21,"path":22,"path-is-absolute":23,"util":28}],17:[function(require,module,exports){ +(function (process){ +module.exports = globSync +globSync.GlobSync = GlobSync + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var Glob = require('./glob.js').Glob +var util = require('util') +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var childrenIgnored = common.childrenIgnored + +function globSync (pattern, options) { + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + return new GlobSync(pattern, options).found +} + +function GlobSync (pattern, options) { + if (!pattern) + throw new Error('must provide pattern') + + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + if (!(this instanceof GlobSync)) + return new GlobSync(pattern, options) + + setopts(this, pattern, options) + + if (this.noprocess) + return this + + var n = this.minimatch.set.length + this.matches = new Array(n) + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false) + } + this._finish() +} + +GlobSync.prototype._finish = function () { + assert(this instanceof GlobSync) + if (this.realpath) { + var self = this + this.matches.forEach(function (matchset, index) { + var set = self.matches[index] = Object.create(null) + for (var p in matchset) { + try { + p = self._makeAbs(p) + var real = fs.realpathSync(p, self.realpathCache) + set[real] = true + } catch (er) { + if (er.syscall === 'stat') + set[self._makeAbs(p)] = true + else + throw er + } + } + }) + } + common.finish(this) +} + + +GlobSync.prototype._process = function (pattern, index, inGlobStar) { + assert(this instanceof GlobSync) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // See if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip processing + if (childrenIgnored(this, read)) + return + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar) +} + + +GlobSync.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar) { + var entries = this._readdir(abs, inGlobStar) + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix.slice(-1) !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this.matches[index][e] = true + } + // This was the last one, and no stats were needed + return + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) + newPattern = [prefix, e] + else + newPattern = [e] + this._process(newPattern.concat(remain), index, inGlobStar) + } +} + + +GlobSync.prototype._emitMatch = function (index, e) { + var abs = this._makeAbs(e) + if (this.mark) + e = this._mark(e) + + if (this.matches[index][e]) + return + + if (this.nodir) { + var c = this.cache[this._makeAbs(e)] + if (c === 'DIR' || Array.isArray(c)) + return + } + + this.matches[index][e] = true + if (this.stat) + this._stat(e) +} + + +GlobSync.prototype._readdirInGlobStar = function (abs) { + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false) + + var entries + var lstat + var stat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + // lstat failed, doesn't exist + return null + } + + var isSym = lstat.isSymbolicLink() + this.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) + this.cache[abs] = 'FILE' + else + entries = this._readdir(abs, false) + + return entries +} + +GlobSync.prototype._readdir = function (abs, inGlobStar) { + var entries + + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return null + + if (Array.isArray(c)) + return c + } + + try { + return this._readdirEntries(abs, fs.readdirSync(abs)) + } catch (er) { + this._readdirError(abs, er) + return null + } +} + +GlobSync.prototype._readdirEntries = function (abs, entries) { + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + + // mark and cache dir-ness + return entries +} + +GlobSync.prototype._readdirError = function (f, er) { + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) + throw er + if (!this.silent) + console.error('glob error', er) + break + } +} + +GlobSync.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar) { + + var entries = this._readdir(abs, inGlobStar) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false) + + var len = entries.length + var isSym = this.symlinks[abs] + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true) + } +} + +GlobSync.prototype._processSimple = function (prefix, index) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var exists = this._stat(prefix) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this.matches[index][prefix] = true +} + +// Returns either 'DIR', 'FILE', or false +GlobSync.prototype._stat = function (f) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return false + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return c + + if (needDir && c === 'FILE') + return false + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (!stat) { + var lstat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + return false + } + + if (lstat.isSymbolicLink()) { + try { + stat = fs.statSync(abs) + } catch (er) { + stat = lstat + } + } else { + stat = lstat + } + } + + this.statCache[abs] = stat + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return false + + return c +} + +GlobSync.prototype._mark = function (p) { + return common.mark(this, p) +} + +GlobSync.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./glob.js":16,"_process":24,"assert":9,"fs":12,"minimatch":20,"path":22,"path-is-absolute":23,"util":28}],18:[function(require,module,exports){ +(function (process){ +var wrappy = require('wrappy') +var reqs = Object.create(null) +var once = require('once') + +module.exports = wrappy(inflight) + +function inflight (key, cb) { + if (reqs[key]) { + reqs[key].push(cb) + return null + } else { + reqs[key] = [cb] + return makeres(key) + } +} + +function makeres (key) { + return once(function RES () { + var cbs = reqs[key] + var len = cbs.length + var args = slice(arguments) + + // XXX It's somewhat ambiguous whether a new callback added in this + // pass should be queued for later execution if something in the + // list of callbacks throws, or if it should just be discarded. + // However, it's such an edge case that it hardly matters, and either + // choice is likely as surprising as the other. + // As it happens, we do go ahead and schedule it for later execution. + try { + for (var i = 0; i < len; i++) { + cbs[i].apply(null, args) + } + } finally { + if (cbs.length > len) { + // added more in the interim. + // de-zalgo, just in case, but don't call again. + cbs.splice(0, len) + process.nextTick(function () { + RES.apply(null, args) + }) + } else { + delete reqs[key] + } + } + }) +} + +function slice (args) { + var length = args.length + var array = [] + + for (var i = 0; i < length; i++) array[i] = args[i] + return array +} + +}).call(this,require('_process')) +},{"_process":24,"once":21,"wrappy":29}],19:[function(require,module,exports){ +if (typeof Object.create === 'function') { + // implementation from standard node.js 'util' module + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + ctor.prototype = Object.create(superCtor.prototype, { + constructor: { + value: ctor, + enumerable: false, + writable: true, + configurable: true + } + }); + }; +} else { + // old school shim for old browsers + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + var TempCtor = function () {} + TempCtor.prototype = superCtor.prototype + ctor.prototype = new TempCtor() + ctor.prototype.constructor = ctor + } +} + +},{}],20:[function(require,module,exports){ +module.exports = minimatch +minimatch.Minimatch = Minimatch + +var path = { sep: '/' } +try { + path = require('path') +} catch (er) {} + +var GLOBSTAR = minimatch.GLOBSTAR = Minimatch.GLOBSTAR = {} +var expand = require('brace-expansion') + +var plTypes = { + '!': { open: '(?:(?!(?:', close: '))[^/]*?)'}, + '?': { open: '(?:', close: ')?' }, + '+': { open: '(?:', close: ')+' }, + '*': { open: '(?:', close: ')*' }, + '@': { open: '(?:', close: ')' } +} + +// any single thing other than / +// don't need to escape / when using new RegExp() +var qmark = '[^/]' + +// * => any number of characters +var star = qmark + '*?' + +// ** when dots are allowed. Anything goes, except .. and . +// not (^ or / followed by one or two dots followed by $ or /), +// followed by anything, any number of times. +var twoStarDot = '(?:(?!(?:\\\/|^)(?:\\.{1,2})($|\\\/)).)*?' + +// not a ^ or / followed by a dot, +// followed by anything, any number of times. +var twoStarNoDot = '(?:(?!(?:\\\/|^)\\.).)*?' + +// characters that need to be escaped in RegExp. +var reSpecials = charSet('().*{}+?[]^$\\!') + +// "abc" -> { a:true, b:true, c:true } +function charSet (s) { + return s.split('').reduce(function (set, c) { + set[c] = true + return set + }, {}) +} + +// normalizes slashes. +var slashSplit = /\/+/ + +minimatch.filter = filter +function filter (pattern, options) { + options = options || {} + return function (p, i, list) { + return minimatch(p, pattern, options) + } +} + +function ext (a, b) { + a = a || {} + b = b || {} + var t = {} + Object.keys(b).forEach(function (k) { + t[k] = b[k] + }) + Object.keys(a).forEach(function (k) { + t[k] = a[k] + }) + return t +} + +minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return minimatch + + var orig = minimatch + + var m = function minimatch (p, pattern, options) { + return orig.minimatch(p, pattern, ext(def, options)) + } + + m.Minimatch = function Minimatch (pattern, options) { + return new orig.Minimatch(pattern, ext(def, options)) + } + + return m +} + +Minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return Minimatch + return minimatch.defaults(def).Minimatch +} + +function minimatch (p, pattern, options) { + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + + // shortcut: comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + return false + } + + // "" only matches "" + if (pattern.trim() === '') return p === '' + + return new Minimatch(pattern, options).match(p) +} + +function Minimatch (pattern, options) { + if (!(this instanceof Minimatch)) { + return new Minimatch(pattern, options) + } + + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + pattern = pattern.trim() + + // windows support: need to use /, not \ + if (path.sep !== '/') { + pattern = pattern.split(path.sep).join('/') + } + + this.options = options + this.set = [] + this.pattern = pattern + this.regexp = null + this.negate = false + this.comment = false + this.empty = false + + // make the set of regexps etc. + this.make() +} + +Minimatch.prototype.debug = function () {} + +Minimatch.prototype.make = make +function make () { + // don't do it more than once. + if (this._made) return + + var pattern = this.pattern + var options = this.options + + // empty patterns and comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + this.comment = true + return + } + if (!pattern) { + this.empty = true + return + } + + // step 1: figure out negation, etc. + this.parseNegate() + + // step 2: expand braces + var set = this.globSet = this.braceExpand() + + if (options.debug) this.debug = console.error + + this.debug(this.pattern, set) + + // step 3: now we have a set, so turn each one into a series of path-portion + // matching patterns. + // These will be regexps, except in the case of "**", which is + // set to the GLOBSTAR object for globstar behavior, + // and will not contain any / characters + set = this.globParts = set.map(function (s) { + return s.split(slashSplit) + }) + + this.debug(this.pattern, set) + + // glob --> regexps + set = set.map(function (s, si, set) { + return s.map(this.parse, this) + }, this) + + this.debug(this.pattern, set) + + // filter out everything that didn't compile properly. + set = set.filter(function (s) { + return s.indexOf(false) === -1 + }) + + this.debug(this.pattern, set) + + this.set = set +} + +Minimatch.prototype.parseNegate = parseNegate +function parseNegate () { + var pattern = this.pattern + var negate = false + var options = this.options + var negateOffset = 0 + + if (options.nonegate) return + + for (var i = 0, l = pattern.length + ; i < l && pattern.charAt(i) === '!' + ; i++) { + negate = !negate + negateOffset++ + } + + if (negateOffset) this.pattern = pattern.substr(negateOffset) + this.negate = negate +} + +// Brace expansion: +// a{b,c}d -> abd acd +// a{b,}c -> abc ac +// a{0..3}d -> a0d a1d a2d a3d +// a{b,c{d,e}f}g -> abg acdfg acefg +// a{b,c}d{e,f}g -> abdeg acdeg abdeg abdfg +// +// Invalid sets are not expanded. +// a{2..}b -> a{2..}b +// a{b}c -> a{b}c +minimatch.braceExpand = function (pattern, options) { + return braceExpand(pattern, options) +} + +Minimatch.prototype.braceExpand = braceExpand + +function braceExpand (pattern, options) { + if (!options) { + if (this instanceof Minimatch) { + options = this.options + } else { + options = {} + } + } + + pattern = typeof pattern === 'undefined' + ? this.pattern : pattern + + if (typeof pattern === 'undefined') { + throw new TypeError('undefined pattern') + } + + if (options.nobrace || + !pattern.match(/\{.*\}/)) { + // shortcut. no need to expand. + return [pattern] + } + + return expand(pattern) +} + +// parse a component of the expanded set. +// At this point, no pattern may contain "/" in it +// so we're going to return a 2d array, where each entry is the full +// pattern, split on '/', and then turned into a regular expression. +// A regexp is made at the end which joins each array with an +// escaped /, and another full one which joins each regexp with |. +// +// Following the lead of Bash 4.1, note that "**" only has special meaning +// when it is the *only* thing in a path portion. Otherwise, any series +// of * is equivalent to a single *. Globstar behavior is enabled by +// default, and can be disabled by setting options.noglobstar. +Minimatch.prototype.parse = parse +var SUBPARSE = {} +function parse (pattern, isSub) { + if (pattern.length > 1024 * 64) { + throw new TypeError('pattern is too long') + } + + var options = this.options + + // shortcuts + if (!options.noglobstar && pattern === '**') return GLOBSTAR + if (pattern === '') return '' + + var re = '' + var hasMagic = !!options.nocase + var escaping = false + // ? => one single character + var patternListStack = [] + var negativeLists = [] + var stateChar + var inClass = false + var reClassStart = -1 + var classStart = -1 + // . and .. never match anything that doesn't start with ., + // even when options.dot is set. + var patternStart = pattern.charAt(0) === '.' ? '' // anything + // not (start or / followed by . or .. followed by / or end) + : options.dot ? '(?!(?:^|\\\/)\\.{1,2}(?:$|\\\/))' + : '(?!\\.)' + var self = this + + function clearStateChar () { + if (stateChar) { + // we had some state-tracking character + // that wasn't consumed by this pass. + switch (stateChar) { + case '*': + re += star + hasMagic = true + break + case '?': + re += qmark + hasMagic = true + break + default: + re += '\\' + stateChar + break + } + self.debug('clearStateChar %j %j', stateChar, re) + stateChar = false + } + } + + for (var i = 0, len = pattern.length, c + ; (i < len) && (c = pattern.charAt(i)) + ; i++) { + this.debug('%s\t%s %s %j', pattern, i, re, c) + + // skip over any that are escaped. + if (escaping && reSpecials[c]) { + re += '\\' + c + escaping = false + continue + } + + switch (c) { + case '/': + // completely not allowed, even escaped. + // Should already be path-split by now. + return false + + case '\\': + clearStateChar() + escaping = true + continue + + // the various stateChar values + // for the "extglob" stuff. + case '?': + case '*': + case '+': + case '@': + case '!': + this.debug('%s\t%s %s %j <-- stateChar', pattern, i, re, c) + + // all of those are literals inside a class, except that + // the glob [!a] means [^a] in regexp + if (inClass) { + this.debug(' in class') + if (c === '!' && i === classStart + 1) c = '^' + re += c + continue + } + + // if we already have a stateChar, then it means + // that there was something like ** or +? in there. + // Handle the stateChar, then proceed with this one. + self.debug('call clearStateChar %j', stateChar) + clearStateChar() + stateChar = c + // if extglob is disabled, then +(asdf|foo) isn't a thing. + // just clear the statechar *now*, rather than even diving into + // the patternList stuff. + if (options.noext) clearStateChar() + continue + + case '(': + if (inClass) { + re += '(' + continue + } + + if (!stateChar) { + re += '\\(' + continue + } + + patternListStack.push({ + type: stateChar, + start: i - 1, + reStart: re.length, + open: plTypes[stateChar].open, + close: plTypes[stateChar].close + }) + // negation is (?:(?!js)[^/]*) + re += stateChar === '!' ? '(?:(?!(?:' : '(?:' + this.debug('plType %j %j', stateChar, re) + stateChar = false + continue + + case ')': + if (inClass || !patternListStack.length) { + re += '\\)' + continue + } + + clearStateChar() + hasMagic = true + var pl = patternListStack.pop() + // negation is (?:(?!js)[^/]*) + // The others are (?:) + re += pl.close + if (pl.type === '!') { + negativeLists.push(pl) + } + pl.reEnd = re.length + continue + + case '|': + if (inClass || !patternListStack.length || escaping) { + re += '\\|' + escaping = false + continue + } + + clearStateChar() + re += '|' + continue + + // these are mostly the same in regexp and glob + case '[': + // swallow any state-tracking char before the [ + clearStateChar() + + if (inClass) { + re += '\\' + c + continue + } + + inClass = true + classStart = i + reClassStart = re.length + re += c + continue + + case ']': + // a right bracket shall lose its special + // meaning and represent itself in + // a bracket expression if it occurs + // first in the list. -- POSIX.2 2.8.3.2 + if (i === classStart + 1 || !inClass) { + re += '\\' + c + escaping = false + continue + } + + // handle the case where we left a class open. + // "[z-a]" is valid, equivalent to "\[z-a\]" + if (inClass) { + // split where the last [ was, make sure we don't have + // an invalid re. if so, re-walk the contents of the + // would-be class to re-translate any characters that + // were passed through as-is + // TODO: It would probably be faster to determine this + // without a try/catch and a new RegExp, but it's tricky + // to do safely. For now, this is safe and works. + var cs = pattern.substring(classStart + 1, i) + try { + RegExp('[' + cs + ']') + } catch (er) { + // not a valid class! + var sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + '\\]' + hasMagic = hasMagic || sp[1] + inClass = false + continue + } + } + + // finish up the class. + hasMagic = true + inClass = false + re += c + continue + + default: + // swallow any state char that wasn't consumed + clearStateChar() + + if (escaping) { + // no need + escaping = false + } else if (reSpecials[c] + && !(c === '^' && inClass)) { + re += '\\' + } + + re += c + + } // switch + } // for + + // handle the case where we left a class open. + // "[abc" is valid, equivalent to "\[abc" + if (inClass) { + // split where the last [ was, and escape it + // this is a huge pita. We now have to re-walk + // the contents of the would-be class to re-translate + // any characters that were passed through as-is + cs = pattern.substr(classStart + 1) + sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + hasMagic = hasMagic || sp[1] + } + + // handle the case where we had a +( thing at the *end* + // of the pattern. + // each pattern list stack adds 3 chars, and we need to go through + // and escape any | chars that were passed through as-is for the regexp. + // Go through and escape them, taking care not to double-escape any + // | chars that were already escaped. + for (pl = patternListStack.pop(); pl; pl = patternListStack.pop()) { + var tail = re.slice(pl.reStart + pl.open.length) + this.debug('setting tail', re, pl) + // maybe some even number of \, then maybe 1 \, followed by a | + tail = tail.replace(/((?:\\{2}){0,64})(\\?)\|/g, function (_, $1, $2) { + if (!$2) { + // the | isn't already escaped, so escape it. + $2 = '\\' + } + + // need to escape all those slashes *again*, without escaping the + // one that we need for escaping the | character. As it works out, + // escaping an even number of slashes can be done by simply repeating + // it exactly after itself. That's why this trick works. + // + // I am sorry that you have to see this. + return $1 + $1 + $2 + '|' + }) + + this.debug('tail=%j\n %s', tail, tail, pl, re) + var t = pl.type === '*' ? star + : pl.type === '?' ? qmark + : '\\' + pl.type + + hasMagic = true + re = re.slice(0, pl.reStart) + t + '\\(' + tail + } + + // handle trailing things that only matter at the very end. + clearStateChar() + if (escaping) { + // trailing \\ + re += '\\\\' + } + + // only need to apply the nodot start if the re starts with + // something that could conceivably capture a dot + var addPatternStart = false + switch (re.charAt(0)) { + case '.': + case '[': + case '(': addPatternStart = true + } + + // Hack to work around lack of negative lookbehind in JS + // A pattern like: *.!(x).!(y|z) needs to ensure that a name + // like 'a.xyz.yz' doesn't match. So, the first negative + // lookahead, has to look ALL the way ahead, to the end of + // the pattern. + for (var n = negativeLists.length - 1; n > -1; n--) { + var nl = negativeLists[n] + + var nlBefore = re.slice(0, nl.reStart) + var nlFirst = re.slice(nl.reStart, nl.reEnd - 8) + var nlLast = re.slice(nl.reEnd - 8, nl.reEnd) + var nlAfter = re.slice(nl.reEnd) + + nlLast += nlAfter + + // Handle nested stuff like *(*.js|!(*.json)), where open parens + // mean that we should *not* include the ) in the bit that is considered + // "after" the negated section. + var openParensBefore = nlBefore.split('(').length - 1 + var cleanAfter = nlAfter + for (i = 0; i < openParensBefore; i++) { + cleanAfter = cleanAfter.replace(/\)[+*?]?/, '') + } + nlAfter = cleanAfter + + var dollar = '' + if (nlAfter === '' && isSub !== SUBPARSE) { + dollar = '$' + } + var newRe = nlBefore + nlFirst + nlAfter + dollar + nlLast + re = newRe + } + + // if the re is not "" at this point, then we need to make sure + // it doesn't match against an empty path part. + // Otherwise a/* will match a/, which it should not. + if (re !== '' && hasMagic) { + re = '(?=.)' + re + } + + if (addPatternStart) { + re = patternStart + re + } + + // parsing just a piece of a larger pattern. + if (isSub === SUBPARSE) { + return [re, hasMagic] + } + + // skip the regexp for non-magical patterns + // unescape anything in it, though, so that it'll be + // an exact match against a file etc. + if (!hasMagic) { + return globUnescape(pattern) + } + + var flags = options.nocase ? 'i' : '' + try { + var regExp = new RegExp('^' + re + '$', flags) + } catch (er) { + // If it was an invalid regular expression, then it can't match + // anything. This trick looks for a character after the end of + // the string, which is of course impossible, except in multi-line + // mode, but it's not a /m regex. + return new RegExp('$.') + } + + regExp._glob = pattern + regExp._src = re + + return regExp +} + +minimatch.makeRe = function (pattern, options) { + return new Minimatch(pattern, options || {}).makeRe() +} + +Minimatch.prototype.makeRe = makeRe +function makeRe () { + if (this.regexp || this.regexp === false) return this.regexp + + // at this point, this.set is a 2d array of partial + // pattern strings, or "**". + // + // It's better to use .match(). This function shouldn't + // be used, really, but it's pretty convenient sometimes, + // when you just want to work with a regex. + var set = this.set + + if (!set.length) { + this.regexp = false + return this.regexp + } + var options = this.options + + var twoStar = options.noglobstar ? star + : options.dot ? twoStarDot + : twoStarNoDot + var flags = options.nocase ? 'i' : '' + + var re = set.map(function (pattern) { + return pattern.map(function (p) { + return (p === GLOBSTAR) ? twoStar + : (typeof p === 'string') ? regExpEscape(p) + : p._src + }).join('\\\/') + }).join('|') + + // must match entire pattern + // ending in a * or ** will make it less strict. + re = '^(?:' + re + ')$' + + // can match anything, as long as it's not this. + if (this.negate) re = '^(?!' + re + ').*$' + + try { + this.regexp = new RegExp(re, flags) + } catch (ex) { + this.regexp = false + } + return this.regexp +} + +minimatch.match = function (list, pattern, options) { + options = options || {} + var mm = new Minimatch(pattern, options) + list = list.filter(function (f) { + return mm.match(f) + }) + if (mm.options.nonull && !list.length) { + list.push(pattern) + } + return list +} + +Minimatch.prototype.match = match +function match (f, partial) { + this.debug('match', f, this.pattern) + // short-circuit in the case of busted things. + // comments, etc. + if (this.comment) return false + if (this.empty) return f === '' + + if (f === '/' && partial) return true + + var options = this.options + + // windows: need to use /, not \ + if (path.sep !== '/') { + f = f.split(path.sep).join('/') + } + + // treat the test path as a set of pathparts. + f = f.split(slashSplit) + this.debug(this.pattern, 'split', f) + + // just ONE of the pattern sets in this.set needs to match + // in order for it to be valid. If negating, then just one + // match means that we have failed. + // Either way, return on the first hit. + + var set = this.set + this.debug(this.pattern, 'set', set) + + // Find the basename of the path by looking for the last non-empty segment + var filename + var i + for (i = f.length - 1; i >= 0; i--) { + filename = f[i] + if (filename) break + } + + for (i = 0; i < set.length; i++) { + var pattern = set[i] + var file = f + if (options.matchBase && pattern.length === 1) { + file = [filename] + } + var hit = this.matchOne(file, pattern, partial) + if (hit) { + if (options.flipNegate) return true + return !this.negate + } + } + + // didn't get any hits. this is success if it's a negative + // pattern, failure otherwise. + if (options.flipNegate) return false + return this.negate +} + +// set partial to true to test if, for example, +// "/a/b" matches the start of "/*/b/*/d" +// Partial means, if you run out of file before you run +// out of pattern, then that's fine, as long as all +// the parts match. +Minimatch.prototype.matchOne = function (file, pattern, partial) { + var options = this.options + + this.debug('matchOne', + { 'this': this, file: file, pattern: pattern }) + + this.debug('matchOne', file.length, pattern.length) + + for (var fi = 0, + pi = 0, + fl = file.length, + pl = pattern.length + ; (fi < fl) && (pi < pl) + ; fi++, pi++) { + this.debug('matchOne loop') + var p = pattern[pi] + var f = file[fi] + + this.debug(pattern, p, f) + + // should be impossible. + // some invalid regexp stuff in the set. + if (p === false) return false + + if (p === GLOBSTAR) { + this.debug('GLOBSTAR', [pattern, p, f]) + + // "**" + // a/**/b/**/c would match the following: + // a/b/x/y/z/c + // a/x/y/z/b/c + // a/b/x/b/x/c + // a/b/c + // To do this, take the rest of the pattern after + // the **, and see if it would match the file remainder. + // If so, return success. + // If not, the ** "swallows" a segment, and try again. + // This is recursively awful. + // + // a/**/b/**/c matching a/b/x/y/z/c + // - a matches a + // - doublestar + // - matchOne(b/x/y/z/c, b/**/c) + // - b matches b + // - doublestar + // - matchOne(x/y/z/c, c) -> no + // - matchOne(y/z/c, c) -> no + // - matchOne(z/c, c) -> no + // - matchOne(c, c) yes, hit + var fr = fi + var pr = pi + 1 + if (pr === pl) { + this.debug('** at the end') + // a ** at the end will just swallow the rest. + // We have found a match. + // however, it will not swallow /.x, unless + // options.dot is set. + // . and .. are *never* matched by **, for explosively + // exponential reasons. + for (; fi < fl; fi++) { + if (file[fi] === '.' || file[fi] === '..' || + (!options.dot && file[fi].charAt(0) === '.')) return false + } + return true + } + + // ok, let's see if we can swallow whatever we can. + while (fr < fl) { + var swallowee = file[fr] + + this.debug('\nglobstar while', file, fr, pattern, pr, swallowee) + + // XXX remove this slice. Just pass the start index. + if (this.matchOne(file.slice(fr), pattern.slice(pr), partial)) { + this.debug('globstar found match!', fr, fl, swallowee) + // found a match. + return true + } else { + // can't swallow "." or ".." ever. + // can only swallow ".foo" when explicitly asked. + if (swallowee === '.' || swallowee === '..' || + (!options.dot && swallowee.charAt(0) === '.')) { + this.debug('dot detected!', file, fr, pattern, pr) + break + } + + // ** swallows a segment, and continue. + this.debug('globstar swallow a segment, and continue') + fr++ + } + } + + // no match was found. + // However, in partial mode, we can't say this is necessarily over. + // If there's more *pattern* left, then + if (partial) { + // ran out of file + this.debug('\n>>> no match, partial?', file, fr, pattern, pr) + if (fr === fl) return true + } + return false + } + + // something other than ** + // non-magic patterns just have to match exactly + // patterns with magic have been turned into regexps. + var hit + if (typeof p === 'string') { + if (options.nocase) { + hit = f.toLowerCase() === p.toLowerCase() + } else { + hit = f === p + } + this.debug('string match', p, f, hit) + } else { + hit = f.match(p) + this.debug('pattern match', p, f, hit) + } + + if (!hit) return false + } + + // Note: ending in / means that we'll get a final "" + // at the end of the pattern. This can only match a + // corresponding "" at the end of the file. + // If the file ends in /, then it can only match a + // a pattern that ends in /, unless the pattern just + // doesn't have any more for it. But, a/b/ should *not* + // match "a/b/*", even though "" matches against the + // [^/]*? pattern, except in partial mode, where it might + // simply not be reached yet. + // However, a/b/ should still satisfy a/* + + // now either we fell off the end of the pattern, or we're done. + if (fi === fl && pi === pl) { + // ran out of pattern and filename at the same time. + // an exact hit! + return true + } else if (fi === fl) { + // ran out of file, but still had pattern left. + // this is ok if we're doing the match as part of + // a glob fs traversal. + return partial + } else if (pi === pl) { + // ran out of pattern, still have file left. + // this is only acceptable if we're on the very last + // empty segment of a file with a trailing slash. + // a/* should match a/b/ + var emptyFileEnd = (fi === fl - 1) && (file[fi] === '') + return emptyFileEnd + } + + // should be unreachable. + throw new Error('wtf?') +} + +// replace stuff like \* with * +function globUnescape (s) { + return s.replace(/\\(.)/g, '$1') +} + +function regExpEscape (s) { + return s.replace(/[-[\]{}()*+?.,\\^$|#\s]/g, '\\$&') +} + +},{"brace-expansion":11,"path":22}],21:[function(require,module,exports){ +var wrappy = require('wrappy') +module.exports = wrappy(once) +module.exports.strict = wrappy(onceStrict) + +once.proto = once(function () { + Object.defineProperty(Function.prototype, 'once', { + value: function () { + return once(this) + }, + configurable: true + }) + + Object.defineProperty(Function.prototype, 'onceStrict', { + value: function () { + return onceStrict(this) + }, + configurable: true + }) +}) + +function once (fn) { + var f = function () { + if (f.called) return f.value + f.called = true + return f.value = fn.apply(this, arguments) + } + f.called = false + return f +} + +function onceStrict (fn) { + var f = function () { + if (f.called) + throw new Error(f.onceError) + f.called = true + return f.value = fn.apply(this, arguments) + } + var name = fn.name || 'Function wrapped with `once`' + f.onceError = name + " shouldn't be called more than once" + f.called = false + return f +} + +},{"wrappy":29}],22:[function(require,module,exports){ +(function (process){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +// resolves . and .. elements in a path array with directory names there +// must be no slashes, empty elements, or device names (c:\) in the array +// (so also no leading and trailing slashes - it does not distinguish +// relative and absolute paths) +function normalizeArray(parts, allowAboveRoot) { + // if the path tries to go above the root, `up` ends up > 0 + var up = 0; + for (var i = parts.length - 1; i >= 0; i--) { + var last = parts[i]; + if (last === '.') { + parts.splice(i, 1); + } else if (last === '..') { + parts.splice(i, 1); + up++; + } else if (up) { + parts.splice(i, 1); + up--; + } + } + + // if the path is allowed to go above the root, restore leading ..s + if (allowAboveRoot) { + for (; up--; up) { + parts.unshift('..'); + } + } + + return parts; +} + +// Split a filename into [root, dir, basename, ext], unix version +// 'root' is just a slash, or nothing. +var splitPathRe = + /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/; +var splitPath = function(filename) { + return splitPathRe.exec(filename).slice(1); +}; + +// path.resolve([from ...], to) +// posix version +exports.resolve = function() { + var resolvedPath = '', + resolvedAbsolute = false; + + for (var i = arguments.length - 1; i >= -1 && !resolvedAbsolute; i--) { + var path = (i >= 0) ? arguments[i] : process.cwd(); + + // Skip empty and invalid entries + if (typeof path !== 'string') { + throw new TypeError('Arguments to path.resolve must be strings'); + } else if (!path) { + continue; + } + + resolvedPath = path + '/' + resolvedPath; + resolvedAbsolute = path.charAt(0) === '/'; + } + + // At this point the path should be resolved to a full absolute path, but + // handle relative paths to be safe (might happen when process.cwd() fails) + + // Normalize the path + resolvedPath = normalizeArray(filter(resolvedPath.split('/'), function(p) { + return !!p; + }), !resolvedAbsolute).join('/'); + + return ((resolvedAbsolute ? '/' : '') + resolvedPath) || '.'; +}; + +// path.normalize(path) +// posix version +exports.normalize = function(path) { + var isAbsolute = exports.isAbsolute(path), + trailingSlash = substr(path, -1) === '/'; + + // Normalize the path + path = normalizeArray(filter(path.split('/'), function(p) { + return !!p; + }), !isAbsolute).join('/'); + + if (!path && !isAbsolute) { + path = '.'; + } + if (path && trailingSlash) { + path += '/'; + } + + return (isAbsolute ? '/' : '') + path; +}; + +// posix version +exports.isAbsolute = function(path) { + return path.charAt(0) === '/'; +}; + +// posix version +exports.join = function() { + var paths = Array.prototype.slice.call(arguments, 0); + return exports.normalize(filter(paths, function(p, index) { + if (typeof p !== 'string') { + throw new TypeError('Arguments to path.join must be strings'); + } + return p; + }).join('/')); +}; + + +// path.relative(from, to) +// posix version +exports.relative = function(from, to) { + from = exports.resolve(from).substr(1); + to = exports.resolve(to).substr(1); + + function trim(arr) { + var start = 0; + for (; start < arr.length; start++) { + if (arr[start] !== '') break; + } + + var end = arr.length - 1; + for (; end >= 0; end--) { + if (arr[end] !== '') break; + } + + if (start > end) return []; + return arr.slice(start, end - start + 1); + } + + var fromParts = trim(from.split('/')); + var toParts = trim(to.split('/')); + + var length = Math.min(fromParts.length, toParts.length); + var samePartsLength = length; + for (var i = 0; i < length; i++) { + if (fromParts[i] !== toParts[i]) { + samePartsLength = i; + break; + } + } + + var outputParts = []; + for (var i = samePartsLength; i < fromParts.length; i++) { + outputParts.push('..'); + } + + outputParts = outputParts.concat(toParts.slice(samePartsLength)); + + return outputParts.join('/'); +}; + +exports.sep = '/'; +exports.delimiter = ':'; + +exports.dirname = function(path) { + var result = splitPath(path), + root = result[0], + dir = result[1]; + + if (!root && !dir) { + // No dirname whatsoever + return '.'; + } + + if (dir) { + // It has a dirname, strip trailing slash + dir = dir.substr(0, dir.length - 1); + } + + return root + dir; +}; + + +exports.basename = function(path, ext) { + var f = splitPath(path)[2]; + // TODO: make this comparison case-insensitive on windows? + if (ext && f.substr(-1 * ext.length) === ext) { + f = f.substr(0, f.length - ext.length); + } + return f; +}; + + +exports.extname = function(path) { + return splitPath(path)[3]; +}; + +function filter (xs, f) { + if (xs.filter) return xs.filter(f); + var res = []; + for (var i = 0; i < xs.length; i++) { + if (f(xs[i], i, xs)) res.push(xs[i]); + } + return res; +} + +// String.prototype.substr - negative index don't work in IE8 +var substr = 'ab'.substr(-1) === 'b' + ? function (str, start, len) { return str.substr(start, len) } + : function (str, start, len) { + if (start < 0) start = str.length + start; + return str.substr(start, len); + } +; + +}).call(this,require('_process')) +},{"_process":24}],23:[function(require,module,exports){ +(function (process){ +'use strict'; + +function posix(path) { + return path.charAt(0) === '/'; +} + +function win32(path) { + // https://github.com/nodejs/node/blob/b3fcc245fb25539909ef1d5eaa01dbf92e168633/lib/path.js#L56 + var splitDeviceRe = /^([a-zA-Z]:|[\\\/]{2}[^\\\/]+[\\\/]+[^\\\/]+)?([\\\/])?([\s\S]*?)$/; + var result = splitDeviceRe.exec(path); + var device = result[1] || ''; + var isUnc = Boolean(device && device.charAt(1) !== ':'); + + // UNC paths are always absolute + return Boolean(result[2] || isUnc); +} + +module.exports = process.platform === 'win32' ? win32 : posix; +module.exports.posix = posix; +module.exports.win32 = win32; + +}).call(this,require('_process')) +},{"_process":24}],24:[function(require,module,exports){ +// shim for using process in browser +var process = module.exports = {}; + +// cached from whatever global is present so that test runners that stub it +// don't break things. But we need to wrap it in a try catch in case it is +// wrapped in strict mode code which doesn't define any globals. It's inside a +// function because try/catches deoptimize in certain engines. + +var cachedSetTimeout; +var cachedClearTimeout; + +function defaultSetTimout() { + throw new Error('setTimeout has not been defined'); +} +function defaultClearTimeout () { + throw new Error('clearTimeout has not been defined'); +} +(function () { + try { + if (typeof setTimeout === 'function') { + cachedSetTimeout = setTimeout; + } else { + cachedSetTimeout = defaultSetTimout; + } + } catch (e) { + cachedSetTimeout = defaultSetTimout; + } + try { + if (typeof clearTimeout === 'function') { + cachedClearTimeout = clearTimeout; + } else { + cachedClearTimeout = defaultClearTimeout; + } + } catch (e) { + cachedClearTimeout = defaultClearTimeout; + } +} ()) +function runTimeout(fun) { + if (cachedSetTimeout === setTimeout) { + //normal enviroments in sane situations + return setTimeout(fun, 0); + } + // if setTimeout wasn't available but was latter defined + if ((cachedSetTimeout === defaultSetTimout || !cachedSetTimeout) && setTimeout) { + cachedSetTimeout = setTimeout; + return setTimeout(fun, 0); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedSetTimeout(fun, 0); + } catch(e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedSetTimeout.call(null, fun, 0); + } catch(e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error + return cachedSetTimeout.call(this, fun, 0); + } + } + + +} +function runClearTimeout(marker) { + if (cachedClearTimeout === clearTimeout) { + //normal enviroments in sane situations + return clearTimeout(marker); + } + // if clearTimeout wasn't available but was latter defined + if ((cachedClearTimeout === defaultClearTimeout || !cachedClearTimeout) && clearTimeout) { + cachedClearTimeout = clearTimeout; + return clearTimeout(marker); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedClearTimeout(marker); + } catch (e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedClearTimeout.call(null, marker); + } catch (e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error. + // Some versions of I.E. have different rules for clearTimeout vs setTimeout + return cachedClearTimeout.call(this, marker); + } + } + + + +} +var queue = []; +var draining = false; +var currentQueue; +var queueIndex = -1; + +function cleanUpNextTick() { + if (!draining || !currentQueue) { + return; + } + draining = false; + if (currentQueue.length) { + queue = currentQueue.concat(queue); + } else { + queueIndex = -1; + } + if (queue.length) { + drainQueue(); + } +} + +function drainQueue() { + if (draining) { + return; + } + var timeout = runTimeout(cleanUpNextTick); + draining = true; + + var len = queue.length; + while(len) { + currentQueue = queue; + queue = []; + while (++queueIndex < len) { + if (currentQueue) { + currentQueue[queueIndex].run(); + } + } + queueIndex = -1; + len = queue.length; + } + currentQueue = null; + draining = false; + runClearTimeout(timeout); +} + +process.nextTick = function (fun) { + var args = new Array(arguments.length - 1); + if (arguments.length > 1) { + for (var i = 1; i < arguments.length; i++) { + args[i - 1] = arguments[i]; + } + } + queue.push(new Item(fun, args)); + if (queue.length === 1 && !draining) { + runTimeout(drainQueue); + } +}; + +// v8 likes predictible objects +function Item(fun, array) { + this.fun = fun; + this.array = array; +} +Item.prototype.run = function () { + this.fun.apply(null, this.array); +}; +process.title = 'browser'; +process.browser = true; +process.env = {}; +process.argv = []; +process.version = ''; // empty string to avoid regexp issues +process.versions = {}; + +function noop() {} + +process.on = noop; +process.addListener = noop; +process.once = noop; +process.off = noop; +process.removeListener = noop; +process.removeAllListeners = noop; +process.emit = noop; +process.prependListener = noop; +process.prependOnceListener = noop; + +process.listeners = function (name) { return [] } + +process.binding = function (name) { + throw new Error('process.binding is not supported'); +}; + +process.cwd = function () { return '/' }; +process.chdir = function (dir) { + throw new Error('process.chdir is not supported'); +}; +process.umask = function() { return 0; }; + +},{}],25:[function(require,module,exports){ +// Underscore.js 1.8.3 +// http://underscorejs.org +// (c) 2009-2015 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors +// Underscore may be freely distributed under the MIT license. + +(function() { + + // Baseline setup + // -------------- + + // Establish the root object, `window` in the browser, or `exports` on the server. + var root = this; + + // Save the previous value of the `_` variable. + var previousUnderscore = root._; + + // Save bytes in the minified (but not gzipped) version: + var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype; + + // Create quick reference variables for speed access to core prototypes. + var + push = ArrayProto.push, + slice = ArrayProto.slice, + toString = ObjProto.toString, + hasOwnProperty = ObjProto.hasOwnProperty; + + // All **ECMAScript 5** native function implementations that we hope to use + // are declared here. + var + nativeIsArray = Array.isArray, + nativeKeys = Object.keys, + nativeBind = FuncProto.bind, + nativeCreate = Object.create; + + // Naked function reference for surrogate-prototype-swapping. + var Ctor = function(){}; + + // Create a safe reference to the Underscore object for use below. + var _ = function(obj) { + if (obj instanceof _) return obj; + if (!(this instanceof _)) return new _(obj); + this._wrapped = obj; + }; + + // Export the Underscore object for **Node.js**, with + // backwards-compatibility for the old `require()` API. If we're in + // the browser, add `_` as a global object. + if (typeof exports !== 'undefined') { + if (typeof module !== 'undefined' && module.exports) { + exports = module.exports = _; + } + exports._ = _; + } else { + root._ = _; + } + + // Current version. + _.VERSION = '1.8.3'; + + // Internal function that returns an efficient (for current engines) version + // of the passed-in callback, to be repeatedly applied in other Underscore + // functions. + var optimizeCb = function(func, context, argCount) { + if (context === void 0) return func; + switch (argCount == null ? 3 : argCount) { + case 1: return function(value) { + return func.call(context, value); + }; + case 2: return function(value, other) { + return func.call(context, value, other); + }; + case 3: return function(value, index, collection) { + return func.call(context, value, index, collection); + }; + case 4: return function(accumulator, value, index, collection) { + return func.call(context, accumulator, value, index, collection); + }; + } + return function() { + return func.apply(context, arguments); + }; + }; + + // A mostly-internal function to generate callbacks that can be applied + // to each element in a collection, returning the desired result — either + // identity, an arbitrary callback, a property matcher, or a property accessor. + var cb = function(value, context, argCount) { + if (value == null) return _.identity; + if (_.isFunction(value)) return optimizeCb(value, context, argCount); + if (_.isObject(value)) return _.matcher(value); + return _.property(value); + }; + _.iteratee = function(value, context) { + return cb(value, context, Infinity); + }; + + // An internal function for creating assigner functions. + var createAssigner = function(keysFunc, undefinedOnly) { + return function(obj) { + var length = arguments.length; + if (length < 2 || obj == null) return obj; + for (var index = 1; index < length; index++) { + var source = arguments[index], + keys = keysFunc(source), + l = keys.length; + for (var i = 0; i < l; i++) { + var key = keys[i]; + if (!undefinedOnly || obj[key] === void 0) obj[key] = source[key]; + } + } + return obj; + }; + }; + + // An internal function for creating a new object that inherits from another. + var baseCreate = function(prototype) { + if (!_.isObject(prototype)) return {}; + if (nativeCreate) return nativeCreate(prototype); + Ctor.prototype = prototype; + var result = new Ctor; + Ctor.prototype = null; + return result; + }; + + var property = function(key) { + return function(obj) { + return obj == null ? void 0 : obj[key]; + }; + }; + + // Helper for collection methods to determine whether a collection + // should be iterated as an array or as an object + // Related: http://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength + // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094 + var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1; + var getLength = property('length'); + var isArrayLike = function(collection) { + var length = getLength(collection); + return typeof length == 'number' && length >= 0 && length <= MAX_ARRAY_INDEX; + }; + + // Collection Functions + // -------------------- + + // The cornerstone, an `each` implementation, aka `forEach`. + // Handles raw objects in addition to array-likes. Treats all + // sparse array-likes as if they were dense. + _.each = _.forEach = function(obj, iteratee, context) { + iteratee = optimizeCb(iteratee, context); + var i, length; + if (isArrayLike(obj)) { + for (i = 0, length = obj.length; i < length; i++) { + iteratee(obj[i], i, obj); + } + } else { + var keys = _.keys(obj); + for (i = 0, length = keys.length; i < length; i++) { + iteratee(obj[keys[i]], keys[i], obj); + } + } + return obj; + }; + + // Return the results of applying the iteratee to each element. + _.map = _.collect = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + results = Array(length); + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + results[index] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Create a reducing function iterating left or right. + function createReduce(dir) { + // Optimized iterator function as using arguments.length + // in the main function will deoptimize the, see #1991. + function iterator(obj, iteratee, memo, keys, index, length) { + for (; index >= 0 && index < length; index += dir) { + var currentKey = keys ? keys[index] : index; + memo = iteratee(memo, obj[currentKey], currentKey, obj); + } + return memo; + } + + return function(obj, iteratee, memo, context) { + iteratee = optimizeCb(iteratee, context, 4); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + index = dir > 0 ? 0 : length - 1; + // Determine the initial value if none is provided. + if (arguments.length < 3) { + memo = obj[keys ? keys[index] : index]; + index += dir; + } + return iterator(obj, iteratee, memo, keys, index, length); + }; + } + + // **Reduce** builds up a single result from a list of values, aka `inject`, + // or `foldl`. + _.reduce = _.foldl = _.inject = createReduce(1); + + // The right-associative version of reduce, also known as `foldr`. + _.reduceRight = _.foldr = createReduce(-1); + + // Return the first value which passes a truth test. Aliased as `detect`. + _.find = _.detect = function(obj, predicate, context) { + var key; + if (isArrayLike(obj)) { + key = _.findIndex(obj, predicate, context); + } else { + key = _.findKey(obj, predicate, context); + } + if (key !== void 0 && key !== -1) return obj[key]; + }; + + // Return all the elements that pass a truth test. + // Aliased as `select`. + _.filter = _.select = function(obj, predicate, context) { + var results = []; + predicate = cb(predicate, context); + _.each(obj, function(value, index, list) { + if (predicate(value, index, list)) results.push(value); + }); + return results; + }; + + // Return all the elements for which a truth test fails. + _.reject = function(obj, predicate, context) { + return _.filter(obj, _.negate(cb(predicate)), context); + }; + + // Determine whether all of the elements match a truth test. + // Aliased as `all`. + _.every = _.all = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (!predicate(obj[currentKey], currentKey, obj)) return false; + } + return true; + }; + + // Determine if at least one element in the object matches a truth test. + // Aliased as `any`. + _.some = _.any = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (predicate(obj[currentKey], currentKey, obj)) return true; + } + return false; + }; + + // Determine if the array or object contains a given item (using `===`). + // Aliased as `includes` and `include`. + _.contains = _.includes = _.include = function(obj, item, fromIndex, guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + if (typeof fromIndex != 'number' || guard) fromIndex = 0; + return _.indexOf(obj, item, fromIndex) >= 0; + }; + + // Invoke a method (with arguments) on every item in a collection. + _.invoke = function(obj, method) { + var args = slice.call(arguments, 2); + var isFunc = _.isFunction(method); + return _.map(obj, function(value) { + var func = isFunc ? method : value[method]; + return func == null ? func : func.apply(value, args); + }); + }; + + // Convenience version of a common use case of `map`: fetching a property. + _.pluck = function(obj, key) { + return _.map(obj, _.property(key)); + }; + + // Convenience version of a common use case of `filter`: selecting only objects + // containing specific `key:value` pairs. + _.where = function(obj, attrs) { + return _.filter(obj, _.matcher(attrs)); + }; + + // Convenience version of a common use case of `find`: getting the first object + // containing specific `key:value` pairs. + _.findWhere = function(obj, attrs) { + return _.find(obj, _.matcher(attrs)); + }; + + // Return the maximum element (or element-based computation). + _.max = function(obj, iteratee, context) { + var result = -Infinity, lastComputed = -Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value > result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed > lastComputed || computed === -Infinity && result === -Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Return the minimum element (or element-based computation). + _.min = function(obj, iteratee, context) { + var result = Infinity, lastComputed = Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value < result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed < lastComputed || computed === Infinity && result === Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Shuffle a collection, using the modern version of the + // [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher–Yates_shuffle). + _.shuffle = function(obj) { + var set = isArrayLike(obj) ? obj : _.values(obj); + var length = set.length; + var shuffled = Array(length); + for (var index = 0, rand; index < length; index++) { + rand = _.random(0, index); + if (rand !== index) shuffled[index] = shuffled[rand]; + shuffled[rand] = set[index]; + } + return shuffled; + }; + + // Sample **n** random values from a collection. + // If **n** is not specified, returns a single random element. + // The internal `guard` argument allows it to work with `map`. + _.sample = function(obj, n, guard) { + if (n == null || guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + return obj[_.random(obj.length - 1)]; + } + return _.shuffle(obj).slice(0, Math.max(0, n)); + }; + + // Sort the object's values by a criterion produced by an iteratee. + _.sortBy = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + return _.pluck(_.map(obj, function(value, index, list) { + return { + value: value, + index: index, + criteria: iteratee(value, index, list) + }; + }).sort(function(left, right) { + var a = left.criteria; + var b = right.criteria; + if (a !== b) { + if (a > b || a === void 0) return 1; + if (a < b || b === void 0) return -1; + } + return left.index - right.index; + }), 'value'); + }; + + // An internal function used for aggregate "group by" operations. + var group = function(behavior) { + return function(obj, iteratee, context) { + var result = {}; + iteratee = cb(iteratee, context); + _.each(obj, function(value, index) { + var key = iteratee(value, index, obj); + behavior(result, value, key); + }); + return result; + }; + }; + + // Groups the object's values by a criterion. Pass either a string attribute + // to group by, or a function that returns the criterion. + _.groupBy = group(function(result, value, key) { + if (_.has(result, key)) result[key].push(value); else result[key] = [value]; + }); + + // Indexes the object's values by a criterion, similar to `groupBy`, but for + // when you know that your index values will be unique. + _.indexBy = group(function(result, value, key) { + result[key] = value; + }); + + // Counts instances of an object that group by a certain criterion. Pass + // either a string attribute to count by, or a function that returns the + // criterion. + _.countBy = group(function(result, value, key) { + if (_.has(result, key)) result[key]++; else result[key] = 1; + }); + + // Safely create a real, live array from anything iterable. + _.toArray = function(obj) { + if (!obj) return []; + if (_.isArray(obj)) return slice.call(obj); + if (isArrayLike(obj)) return _.map(obj, _.identity); + return _.values(obj); + }; + + // Return the number of elements in an object. + _.size = function(obj) { + if (obj == null) return 0; + return isArrayLike(obj) ? obj.length : _.keys(obj).length; + }; + + // Split a collection into two arrays: one whose elements all satisfy the given + // predicate, and one whose elements all do not satisfy the predicate. + _.partition = function(obj, predicate, context) { + predicate = cb(predicate, context); + var pass = [], fail = []; + _.each(obj, function(value, key, obj) { + (predicate(value, key, obj) ? pass : fail).push(value); + }); + return [pass, fail]; + }; + + // Array Functions + // --------------- + + // Get the first element of an array. Passing **n** will return the first N + // values in the array. Aliased as `head` and `take`. The **guard** check + // allows it to work with `_.map`. + _.first = _.head = _.take = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[0]; + return _.initial(array, array.length - n); + }; + + // Returns everything but the last entry of the array. Especially useful on + // the arguments object. Passing **n** will return all the values in + // the array, excluding the last N. + _.initial = function(array, n, guard) { + return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n))); + }; + + // Get the last element of an array. Passing **n** will return the last N + // values in the array. + _.last = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[array.length - 1]; + return _.rest(array, Math.max(0, array.length - n)); + }; + + // Returns everything but the first entry of the array. Aliased as `tail` and `drop`. + // Especially useful on the arguments object. Passing an **n** will return + // the rest N values in the array. + _.rest = _.tail = _.drop = function(array, n, guard) { + return slice.call(array, n == null || guard ? 1 : n); + }; + + // Trim out all falsy values from an array. + _.compact = function(array) { + return _.filter(array, _.identity); + }; + + // Internal implementation of a recursive `flatten` function. + var flatten = function(input, shallow, strict, startIndex) { + var output = [], idx = 0; + for (var i = startIndex || 0, length = getLength(input); i < length; i++) { + var value = input[i]; + if (isArrayLike(value) && (_.isArray(value) || _.isArguments(value))) { + //flatten current level of array or arguments object + if (!shallow) value = flatten(value, shallow, strict); + var j = 0, len = value.length; + output.length += len; + while (j < len) { + output[idx++] = value[j++]; + } + } else if (!strict) { + output[idx++] = value; + } + } + return output; + }; + + // Flatten out an array, either recursively (by default), or just one level. + _.flatten = function(array, shallow) { + return flatten(array, shallow, false); + }; + + // Return a version of the array that does not contain the specified value(s). + _.without = function(array) { + return _.difference(array, slice.call(arguments, 1)); + }; + + // Produce a duplicate-free version of the array. If the array has already + // been sorted, you have the option of using a faster algorithm. + // Aliased as `unique`. + _.uniq = _.unique = function(array, isSorted, iteratee, context) { + if (!_.isBoolean(isSorted)) { + context = iteratee; + iteratee = isSorted; + isSorted = false; + } + if (iteratee != null) iteratee = cb(iteratee, context); + var result = []; + var seen = []; + for (var i = 0, length = getLength(array); i < length; i++) { + var value = array[i], + computed = iteratee ? iteratee(value, i, array) : value; + if (isSorted) { + if (!i || seen !== computed) result.push(value); + seen = computed; + } else if (iteratee) { + if (!_.contains(seen, computed)) { + seen.push(computed); + result.push(value); + } + } else if (!_.contains(result, value)) { + result.push(value); + } + } + return result; + }; + + // Produce an array that contains the union: each distinct element from all of + // the passed-in arrays. + _.union = function() { + return _.uniq(flatten(arguments, true, true)); + }; + + // Produce an array that contains every item shared between all the + // passed-in arrays. + _.intersection = function(array) { + var result = []; + var argsLength = arguments.length; + for (var i = 0, length = getLength(array); i < length; i++) { + var item = array[i]; + if (_.contains(result, item)) continue; + for (var j = 1; j < argsLength; j++) { + if (!_.contains(arguments[j], item)) break; + } + if (j === argsLength) result.push(item); + } + return result; + }; + + // Take the difference between one array and a number of other arrays. + // Only the elements present in just the first array will remain. + _.difference = function(array) { + var rest = flatten(arguments, true, true, 1); + return _.filter(array, function(value){ + return !_.contains(rest, value); + }); + }; + + // Zip together multiple lists into a single array -- elements that share + // an index go together. + _.zip = function() { + return _.unzip(arguments); + }; + + // Complement of _.zip. Unzip accepts an array of arrays and groups + // each array's elements on shared indices + _.unzip = function(array) { + var length = array && _.max(array, getLength).length || 0; + var result = Array(length); + + for (var index = 0; index < length; index++) { + result[index] = _.pluck(array, index); + } + return result; + }; + + // Converts lists into objects. Pass either a single array of `[key, value]` + // pairs, or two parallel arrays of the same length -- one of keys, and one of + // the corresponding values. + _.object = function(list, values) { + var result = {}; + for (var i = 0, length = getLength(list); i < length; i++) { + if (values) { + result[list[i]] = values[i]; + } else { + result[list[i][0]] = list[i][1]; + } + } + return result; + }; + + // Generator function to create the findIndex and findLastIndex functions + function createPredicateIndexFinder(dir) { + return function(array, predicate, context) { + predicate = cb(predicate, context); + var length = getLength(array); + var index = dir > 0 ? 0 : length - 1; + for (; index >= 0 && index < length; index += dir) { + if (predicate(array[index], index, array)) return index; + } + return -1; + }; + } + + // Returns the first index on an array-like that passes a predicate test + _.findIndex = createPredicateIndexFinder(1); + _.findLastIndex = createPredicateIndexFinder(-1); + + // Use a comparator function to figure out the smallest index at which + // an object should be inserted so as to maintain order. Uses binary search. + _.sortedIndex = function(array, obj, iteratee, context) { + iteratee = cb(iteratee, context, 1); + var value = iteratee(obj); + var low = 0, high = getLength(array); + while (low < high) { + var mid = Math.floor((low + high) / 2); + if (iteratee(array[mid]) < value) low = mid + 1; else high = mid; + } + return low; + }; + + // Generator function to create the indexOf and lastIndexOf functions + function createIndexFinder(dir, predicateFind, sortedIndex) { + return function(array, item, idx) { + var i = 0, length = getLength(array); + if (typeof idx == 'number') { + if (dir > 0) { + i = idx >= 0 ? idx : Math.max(idx + length, i); + } else { + length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1; + } + } else if (sortedIndex && idx && length) { + idx = sortedIndex(array, item); + return array[idx] === item ? idx : -1; + } + if (item !== item) { + idx = predicateFind(slice.call(array, i, length), _.isNaN); + return idx >= 0 ? idx + i : -1; + } + for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) { + if (array[idx] === item) return idx; + } + return -1; + }; + } + + // Return the position of the first occurrence of an item in an array, + // or -1 if the item is not included in the array. + // If the array is large and already in sort order, pass `true` + // for **isSorted** to use binary search. + _.indexOf = createIndexFinder(1, _.findIndex, _.sortedIndex); + _.lastIndexOf = createIndexFinder(-1, _.findLastIndex); + + // Generate an integer Array containing an arithmetic progression. A port of + // the native Python `range()` function. See + // [the Python documentation](http://docs.python.org/library/functions.html#range). + _.range = function(start, stop, step) { + if (stop == null) { + stop = start || 0; + start = 0; + } + step = step || 1; + + var length = Math.max(Math.ceil((stop - start) / step), 0); + var range = Array(length); + + for (var idx = 0; idx < length; idx++, start += step) { + range[idx] = start; + } + + return range; + }; + + // Function (ahem) Functions + // ------------------ + + // Determines whether to execute a function as a constructor + // or a normal function with the provided arguments + var executeBound = function(sourceFunc, boundFunc, context, callingContext, args) { + if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args); + var self = baseCreate(sourceFunc.prototype); + var result = sourceFunc.apply(self, args); + if (_.isObject(result)) return result; + return self; + }; + + // Create a function bound to a given object (assigning `this`, and arguments, + // optionally). Delegates to **ECMAScript 5**'s native `Function.bind` if + // available. + _.bind = function(func, context) { + if (nativeBind && func.bind === nativeBind) return nativeBind.apply(func, slice.call(arguments, 1)); + if (!_.isFunction(func)) throw new TypeError('Bind must be called on a function'); + var args = slice.call(arguments, 2); + var bound = function() { + return executeBound(func, bound, context, this, args.concat(slice.call(arguments))); + }; + return bound; + }; + + // Partially apply a function by creating a version that has had some of its + // arguments pre-filled, without changing its dynamic `this` context. _ acts + // as a placeholder, allowing any combination of arguments to be pre-filled. + _.partial = function(func) { + var boundArgs = slice.call(arguments, 1); + var bound = function() { + var position = 0, length = boundArgs.length; + var args = Array(length); + for (var i = 0; i < length; i++) { + args[i] = boundArgs[i] === _ ? arguments[position++] : boundArgs[i]; + } + while (position < arguments.length) args.push(arguments[position++]); + return executeBound(func, bound, this, this, args); + }; + return bound; + }; + + // Bind a number of an object's methods to that object. Remaining arguments + // are the method names to be bound. Useful for ensuring that all callbacks + // defined on an object belong to it. + _.bindAll = function(obj) { + var i, length = arguments.length, key; + if (length <= 1) throw new Error('bindAll must be passed function names'); + for (i = 1; i < length; i++) { + key = arguments[i]; + obj[key] = _.bind(obj[key], obj); + } + return obj; + }; + + // Memoize an expensive function by storing its results. + _.memoize = function(func, hasher) { + var memoize = function(key) { + var cache = memoize.cache; + var address = '' + (hasher ? hasher.apply(this, arguments) : key); + if (!_.has(cache, address)) cache[address] = func.apply(this, arguments); + return cache[address]; + }; + memoize.cache = {}; + return memoize; + }; + + // Delays a function for the given number of milliseconds, and then calls + // it with the arguments supplied. + _.delay = function(func, wait) { + var args = slice.call(arguments, 2); + return setTimeout(function(){ + return func.apply(null, args); + }, wait); + }; + + // Defers a function, scheduling it to run after the current call stack has + // cleared. + _.defer = _.partial(_.delay, _, 1); + + // Returns a function, that, when invoked, will only be triggered at most once + // during a given window of time. Normally, the throttled function will run + // as much as it can, without ever going more than once per `wait` duration; + // but if you'd like to disable the execution on the leading edge, pass + // `{leading: false}`. To disable execution on the trailing edge, ditto. + _.throttle = function(func, wait, options) { + var context, args, result; + var timeout = null; + var previous = 0; + if (!options) options = {}; + var later = function() { + previous = options.leading === false ? 0 : _.now(); + timeout = null; + result = func.apply(context, args); + if (!timeout) context = args = null; + }; + return function() { + var now = _.now(); + if (!previous && options.leading === false) previous = now; + var remaining = wait - (now - previous); + context = this; + args = arguments; + if (remaining <= 0 || remaining > wait) { + if (timeout) { + clearTimeout(timeout); + timeout = null; + } + previous = now; + result = func.apply(context, args); + if (!timeout) context = args = null; + } else if (!timeout && options.trailing !== false) { + timeout = setTimeout(later, remaining); + } + return result; + }; + }; + + // Returns a function, that, as long as it continues to be invoked, will not + // be triggered. The function will be called after it stops being called for + // N milliseconds. If `immediate` is passed, trigger the function on the + // leading edge, instead of the trailing. + _.debounce = function(func, wait, immediate) { + var timeout, args, context, timestamp, result; + + var later = function() { + var last = _.now() - timestamp; + + if (last < wait && last >= 0) { + timeout = setTimeout(later, wait - last); + } else { + timeout = null; + if (!immediate) { + result = func.apply(context, args); + if (!timeout) context = args = null; + } + } + }; + + return function() { + context = this; + args = arguments; + timestamp = _.now(); + var callNow = immediate && !timeout; + if (!timeout) timeout = setTimeout(later, wait); + if (callNow) { + result = func.apply(context, args); + context = args = null; + } + + return result; + }; + }; + + // Returns the first function passed as an argument to the second, + // allowing you to adjust arguments, run code before and after, and + // conditionally execute the original function. + _.wrap = function(func, wrapper) { + return _.partial(wrapper, func); + }; + + // Returns a negated version of the passed-in predicate. + _.negate = function(predicate) { + return function() { + return !predicate.apply(this, arguments); + }; + }; + + // Returns a function that is the composition of a list of functions, each + // consuming the return value of the function that follows. + _.compose = function() { + var args = arguments; + var start = args.length - 1; + return function() { + var i = start; + var result = args[start].apply(this, arguments); + while (i--) result = args[i].call(this, result); + return result; + }; + }; + + // Returns a function that will only be executed on and after the Nth call. + _.after = function(times, func) { + return function() { + if (--times < 1) { + return func.apply(this, arguments); + } + }; + }; + + // Returns a function that will only be executed up to (but not including) the Nth call. + _.before = function(times, func) { + var memo; + return function() { + if (--times > 0) { + memo = func.apply(this, arguments); + } + if (times <= 1) func = null; + return memo; + }; + }; + + // Returns a function that will be executed at most one time, no matter how + // often you call it. Useful for lazy initialization. + _.once = _.partial(_.before, 2); + + // Object Functions + // ---------------- + + // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed. + var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString'); + var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString', + 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString']; + + function collectNonEnumProps(obj, keys) { + var nonEnumIdx = nonEnumerableProps.length; + var constructor = obj.constructor; + var proto = (_.isFunction(constructor) && constructor.prototype) || ObjProto; + + // Constructor is a special case. + var prop = 'constructor'; + if (_.has(obj, prop) && !_.contains(keys, prop)) keys.push(prop); + + while (nonEnumIdx--) { + prop = nonEnumerableProps[nonEnumIdx]; + if (prop in obj && obj[prop] !== proto[prop] && !_.contains(keys, prop)) { + keys.push(prop); + } + } + } + + // Retrieve the names of an object's own properties. + // Delegates to **ECMAScript 5**'s native `Object.keys` + _.keys = function(obj) { + if (!_.isObject(obj)) return []; + if (nativeKeys) return nativeKeys(obj); + var keys = []; + for (var key in obj) if (_.has(obj, key)) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve all the property names of an object. + _.allKeys = function(obj) { + if (!_.isObject(obj)) return []; + var keys = []; + for (var key in obj) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve the values of an object's properties. + _.values = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var values = Array(length); + for (var i = 0; i < length; i++) { + values[i] = obj[keys[i]]; + } + return values; + }; + + // Returns the results of applying the iteratee to each element of the object + // In contrast to _.map it returns an object + _.mapObject = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = _.keys(obj), + length = keys.length, + results = {}, + currentKey; + for (var index = 0; index < length; index++) { + currentKey = keys[index]; + results[currentKey] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Convert an object into a list of `[key, value]` pairs. + _.pairs = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var pairs = Array(length); + for (var i = 0; i < length; i++) { + pairs[i] = [keys[i], obj[keys[i]]]; + } + return pairs; + }; + + // Invert the keys and values of an object. The values must be serializable. + _.invert = function(obj) { + var result = {}; + var keys = _.keys(obj); + for (var i = 0, length = keys.length; i < length; i++) { + result[obj[keys[i]]] = keys[i]; + } + return result; + }; + + // Return a sorted list of the function names available on the object. + // Aliased as `methods` + _.functions = _.methods = function(obj) { + var names = []; + for (var key in obj) { + if (_.isFunction(obj[key])) names.push(key); + } + return names.sort(); + }; + + // Extend a given object with all the properties in passed-in object(s). + _.extend = createAssigner(_.allKeys); + + // Assigns a given object with all the own properties in the passed-in object(s) + // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign) + _.extendOwn = _.assign = createAssigner(_.keys); + + // Returns the first key on an object that passes a predicate test + _.findKey = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = _.keys(obj), key; + for (var i = 0, length = keys.length; i < length; i++) { + key = keys[i]; + if (predicate(obj[key], key, obj)) return key; + } + }; + + // Return a copy of the object only containing the whitelisted properties. + _.pick = function(object, oiteratee, context) { + var result = {}, obj = object, iteratee, keys; + if (obj == null) return result; + if (_.isFunction(oiteratee)) { + keys = _.allKeys(obj); + iteratee = optimizeCb(oiteratee, context); + } else { + keys = flatten(arguments, false, false, 1); + iteratee = function(value, key, obj) { return key in obj; }; + obj = Object(obj); + } + for (var i = 0, length = keys.length; i < length; i++) { + var key = keys[i]; + var value = obj[key]; + if (iteratee(value, key, obj)) result[key] = value; + } + return result; + }; + + // Return a copy of the object without the blacklisted properties. + _.omit = function(obj, iteratee, context) { + if (_.isFunction(iteratee)) { + iteratee = _.negate(iteratee); + } else { + var keys = _.map(flatten(arguments, false, false, 1), String); + iteratee = function(value, key) { + return !_.contains(keys, key); + }; + } + return _.pick(obj, iteratee, context); + }; + + // Fill in a given object with default properties. + _.defaults = createAssigner(_.allKeys, true); + + // Creates an object that inherits from the given prototype object. + // If additional properties are provided then they will be added to the + // created object. + _.create = function(prototype, props) { + var result = baseCreate(prototype); + if (props) _.extendOwn(result, props); + return result; + }; + + // Create a (shallow-cloned) duplicate of an object. + _.clone = function(obj) { + if (!_.isObject(obj)) return obj; + return _.isArray(obj) ? obj.slice() : _.extend({}, obj); + }; + + // Invokes interceptor with the obj, and then returns obj. + // The primary purpose of this method is to "tap into" a method chain, in + // order to perform operations on intermediate results within the chain. + _.tap = function(obj, interceptor) { + interceptor(obj); + return obj; + }; + + // Returns whether an object has a given set of `key:value` pairs. + _.isMatch = function(object, attrs) { + var keys = _.keys(attrs), length = keys.length; + if (object == null) return !length; + var obj = Object(object); + for (var i = 0; i < length; i++) { + var key = keys[i]; + if (attrs[key] !== obj[key] || !(key in obj)) return false; + } + return true; + }; + + + // Internal recursive comparison function for `isEqual`. + var eq = function(a, b, aStack, bStack) { + // Identical objects are equal. `0 === -0`, but they aren't identical. + // See the [Harmony `egal` proposal](http://wiki.ecmascript.org/doku.php?id=harmony:egal). + if (a === b) return a !== 0 || 1 / a === 1 / b; + // A strict comparison is necessary because `null == undefined`. + if (a == null || b == null) return a === b; + // Unwrap any wrapped objects. + if (a instanceof _) a = a._wrapped; + if (b instanceof _) b = b._wrapped; + // Compare `[[Class]]` names. + var className = toString.call(a); + if (className !== toString.call(b)) return false; + switch (className) { + // Strings, numbers, regular expressions, dates, and booleans are compared by value. + case '[object RegExp]': + // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i') + case '[object String]': + // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is + // equivalent to `new String("5")`. + return '' + a === '' + b; + case '[object Number]': + // `NaN`s are equivalent, but non-reflexive. + // Object(NaN) is equivalent to NaN + if (+a !== +a) return +b !== +b; + // An `egal` comparison is performed for other numeric values. + return +a === 0 ? 1 / +a === 1 / b : +a === +b; + case '[object Date]': + case '[object Boolean]': + // Coerce dates and booleans to numeric primitive values. Dates are compared by their + // millisecond representations. Note that invalid dates with millisecond representations + // of `NaN` are not equivalent. + return +a === +b; + } + + var areArrays = className === '[object Array]'; + if (!areArrays) { + if (typeof a != 'object' || typeof b != 'object') return false; + + // Objects with different constructors are not equivalent, but `Object`s or `Array`s + // from different frames are. + var aCtor = a.constructor, bCtor = b.constructor; + if (aCtor !== bCtor && !(_.isFunction(aCtor) && aCtor instanceof aCtor && + _.isFunction(bCtor) && bCtor instanceof bCtor) + && ('constructor' in a && 'constructor' in b)) { + return false; + } + } + // Assume equality for cyclic structures. The algorithm for detecting cyclic + // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`. + + // Initializing stack of traversed objects. + // It's done here since we only need them for objects and arrays comparison. + aStack = aStack || []; + bStack = bStack || []; + var length = aStack.length; + while (length--) { + // Linear search. Performance is inversely proportional to the number of + // unique nested structures. + if (aStack[length] === a) return bStack[length] === b; + } + + // Add the first object to the stack of traversed objects. + aStack.push(a); + bStack.push(b); + + // Recursively compare objects and arrays. + if (areArrays) { + // Compare array lengths to determine if a deep comparison is necessary. + length = a.length; + if (length !== b.length) return false; + // Deep compare the contents, ignoring non-numeric properties. + while (length--) { + if (!eq(a[length], b[length], aStack, bStack)) return false; + } + } else { + // Deep compare objects. + var keys = _.keys(a), key; + length = keys.length; + // Ensure that both objects contain the same number of properties before comparing deep equality. + if (_.keys(b).length !== length) return false; + while (length--) { + // Deep compare each member + key = keys[length]; + if (!(_.has(b, key) && eq(a[key], b[key], aStack, bStack))) return false; + } + } + // Remove the first object from the stack of traversed objects. + aStack.pop(); + bStack.pop(); + return true; + }; + + // Perform a deep comparison to check if two objects are equal. + _.isEqual = function(a, b) { + return eq(a, b); + }; + + // Is a given array, string, or object empty? + // An "empty" object has no enumerable own-properties. + _.isEmpty = function(obj) { + if (obj == null) return true; + if (isArrayLike(obj) && (_.isArray(obj) || _.isString(obj) || _.isArguments(obj))) return obj.length === 0; + return _.keys(obj).length === 0; + }; + + // Is a given value a DOM element? + _.isElement = function(obj) { + return !!(obj && obj.nodeType === 1); + }; + + // Is a given value an array? + // Delegates to ECMA5's native Array.isArray + _.isArray = nativeIsArray || function(obj) { + return toString.call(obj) === '[object Array]'; + }; + + // Is a given variable an object? + _.isObject = function(obj) { + var type = typeof obj; + return type === 'function' || type === 'object' && !!obj; + }; + + // Add some isType methods: isArguments, isFunction, isString, isNumber, isDate, isRegExp, isError. + _.each(['Arguments', 'Function', 'String', 'Number', 'Date', 'RegExp', 'Error'], function(name) { + _['is' + name] = function(obj) { + return toString.call(obj) === '[object ' + name + ']'; + }; + }); + + // Define a fallback version of the method in browsers (ahem, IE < 9), where + // there isn't any inspectable "Arguments" type. + if (!_.isArguments(arguments)) { + _.isArguments = function(obj) { + return _.has(obj, 'callee'); + }; + } + + // Optimize `isFunction` if appropriate. Work around some typeof bugs in old v8, + // IE 11 (#1621), and in Safari 8 (#1929). + if (typeof /./ != 'function' && typeof Int8Array != 'object') { + _.isFunction = function(obj) { + return typeof obj == 'function' || false; + }; + } + + // Is a given object a finite number? + _.isFinite = function(obj) { + return isFinite(obj) && !isNaN(parseFloat(obj)); + }; + + // Is the given value `NaN`? (NaN is the only number which does not equal itself). + _.isNaN = function(obj) { + return _.isNumber(obj) && obj !== +obj; + }; + + // Is a given value a boolean? + _.isBoolean = function(obj) { + return obj === true || obj === false || toString.call(obj) === '[object Boolean]'; + }; + + // Is a given value equal to null? + _.isNull = function(obj) { + return obj === null; + }; + + // Is a given variable undefined? + _.isUndefined = function(obj) { + return obj === void 0; + }; + + // Shortcut function for checking if an object has a given property directly + // on itself (in other words, not on a prototype). + _.has = function(obj, key) { + return obj != null && hasOwnProperty.call(obj, key); + }; + + // Utility Functions + // ----------------- + + // Run Underscore.js in *noConflict* mode, returning the `_` variable to its + // previous owner. Returns a reference to the Underscore object. + _.noConflict = function() { + root._ = previousUnderscore; + return this; + }; + + // Keep the identity function around for default iteratees. + _.identity = function(value) { + return value; + }; + + // Predicate-generating functions. Often useful outside of Underscore. + _.constant = function(value) { + return function() { + return value; + }; + }; + + _.noop = function(){}; + + _.property = property; + + // Generates a function for a given object that returns a given property. + _.propertyOf = function(obj) { + return obj == null ? function(){} : function(key) { + return obj[key]; + }; + }; + + // Returns a predicate for checking whether an object has a given set of + // `key:value` pairs. + _.matcher = _.matches = function(attrs) { + attrs = _.extendOwn({}, attrs); + return function(obj) { + return _.isMatch(obj, attrs); + }; + }; + + // Run a function **n** times. + _.times = function(n, iteratee, context) { + var accum = Array(Math.max(0, n)); + iteratee = optimizeCb(iteratee, context, 1); + for (var i = 0; i < n; i++) accum[i] = iteratee(i); + return accum; + }; + + // Return a random integer between min and max (inclusive). + _.random = function(min, max) { + if (max == null) { + max = min; + min = 0; + } + return min + Math.floor(Math.random() * (max - min + 1)); + }; + + // A (possibly faster) way to get the current timestamp as an integer. + _.now = Date.now || function() { + return new Date().getTime(); + }; + + // List of HTML entities for escaping. + var escapeMap = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + "'": ''', + '`': '`' + }; + var unescapeMap = _.invert(escapeMap); + + // Functions for escaping and unescaping strings to/from HTML interpolation. + var createEscaper = function(map) { + var escaper = function(match) { + return map[match]; + }; + // Regexes for identifying a key that needs to be escaped + var source = '(?:' + _.keys(map).join('|') + ')'; + var testRegexp = RegExp(source); + var replaceRegexp = RegExp(source, 'g'); + return function(string) { + string = string == null ? '' : '' + string; + return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string; + }; + }; + _.escape = createEscaper(escapeMap); + _.unescape = createEscaper(unescapeMap); + + // If the value of the named `property` is a function then invoke it with the + // `object` as context; otherwise, return it. + _.result = function(object, property, fallback) { + var value = object == null ? void 0 : object[property]; + if (value === void 0) { + value = fallback; + } + return _.isFunction(value) ? value.call(object) : value; + }; + + // Generate a unique integer id (unique within the entire client session). + // Useful for temporary DOM ids. + var idCounter = 0; + _.uniqueId = function(prefix) { + var id = ++idCounter + ''; + return prefix ? prefix + id : id; + }; + + // By default, Underscore uses ERB-style template delimiters, change the + // following template settings to use alternative delimiters. + _.templateSettings = { + evaluate : /<%([\s\S]+?)%>/g, + interpolate : /<%=([\s\S]+?)%>/g, + escape : /<%-([\s\S]+?)%>/g + }; + + // When customizing `templateSettings`, if you don't want to define an + // interpolation, evaluation or escaping regex, we need one that is + // guaranteed not to match. + var noMatch = /(.)^/; + + // Certain characters need to be escaped so that they can be put into a + // string literal. + var escapes = { + "'": "'", + '\\': '\\', + '\r': 'r', + '\n': 'n', + '\u2028': 'u2028', + '\u2029': 'u2029' + }; + + var escaper = /\\|'|\r|\n|\u2028|\u2029/g; + + var escapeChar = function(match) { + return '\\' + escapes[match]; + }; + + // JavaScript micro-templating, similar to John Resig's implementation. + // Underscore templating handles arbitrary delimiters, preserves whitespace, + // and correctly escapes quotes within interpolated code. + // NB: `oldSettings` only exists for backwards compatibility. + _.template = function(text, settings, oldSettings) { + if (!settings && oldSettings) settings = oldSettings; + settings = _.defaults({}, settings, _.templateSettings); + + // Combine delimiters into one regular expression via alternation. + var matcher = RegExp([ + (settings.escape || noMatch).source, + (settings.interpolate || noMatch).source, + (settings.evaluate || noMatch).source + ].join('|') + '|$', 'g'); + + // Compile the template source, escaping string literals appropriately. + var index = 0; + var source = "__p+='"; + text.replace(matcher, function(match, escape, interpolate, evaluate, offset) { + source += text.slice(index, offset).replace(escaper, escapeChar); + index = offset + match.length; + + if (escape) { + source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'"; + } else if (interpolate) { + source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'"; + } else if (evaluate) { + source += "';\n" + evaluate + "\n__p+='"; + } + + // Adobe VMs need the match returned to produce the correct offest. + return match; + }); + source += "';\n"; + + // If a variable is not specified, place data values in local scope. + if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n'; + + source = "var __t,__p='',__j=Array.prototype.join," + + "print=function(){__p+=__j.call(arguments,'');};\n" + + source + 'return __p;\n'; + + try { + var render = new Function(settings.variable || 'obj', '_', source); + } catch (e) { + e.source = source; + throw e; + } + + var template = function(data) { + return render.call(this, data, _); + }; + + // Provide the compiled source as a convenience for precompilation. + var argument = settings.variable || 'obj'; + template.source = 'function(' + argument + '){\n' + source + '}'; + + return template; + }; + + // Add a "chain" function. Start chaining a wrapped Underscore object. + _.chain = function(obj) { + var instance = _(obj); + instance._chain = true; + return instance; + }; + + // OOP + // --------------- + // If Underscore is called as a function, it returns a wrapped object that + // can be used OO-style. This wrapper holds altered versions of all the + // underscore functions. Wrapped objects may be chained. + + // Helper function to continue chaining intermediate results. + var result = function(instance, obj) { + return instance._chain ? _(obj).chain() : obj; + }; + + // Add your own custom functions to the Underscore object. + _.mixin = function(obj) { + _.each(_.functions(obj), function(name) { + var func = _[name] = obj[name]; + _.prototype[name] = function() { + var args = [this._wrapped]; + push.apply(args, arguments); + return result(this, func.apply(_, args)); + }; + }); + }; + + // Add all of the Underscore functions to the wrapper object. + _.mixin(_); + + // Add all mutator Array functions to the wrapper. + _.each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + var obj = this._wrapped; + method.apply(obj, arguments); + if ((name === 'shift' || name === 'splice') && obj.length === 0) delete obj[0]; + return result(this, obj); + }; + }); + + // Add all accessor Array functions to the wrapper. + _.each(['concat', 'join', 'slice'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + return result(this, method.apply(this._wrapped, arguments)); + }; + }); + + // Extracts the result from a wrapped and chained object. + _.prototype.value = function() { + return this._wrapped; + }; + + // Provide unwrapping proxy for some methods used in engine operations + // such as arithmetic and JSON stringification. + _.prototype.valueOf = _.prototype.toJSON = _.prototype.value; + + _.prototype.toString = function() { + return '' + this._wrapped; + }; + + // AMD registration happens at the end for compatibility with AMD loaders + // that may not enforce next-turn semantics on modules. Even though general + // practice for AMD registration is to be anonymous, underscore registers + // as a named module because, like jQuery, it is a base library that is + // popular enough to be bundled in a third party lib, but not be part of + // an AMD load request. Those cases could generate an error when an + // anonymous define() is called outside of a loader request. + if (typeof define === 'function' && define.amd) { + define('underscore', [], function() { + return _; + }); + } +}.call(this)); + +},{}],26:[function(require,module,exports){ +arguments[4][19][0].apply(exports,arguments) +},{"dup":19}],27:[function(require,module,exports){ +module.exports = function isBuffer(arg) { + return arg && typeof arg === 'object' + && typeof arg.copy === 'function' + && typeof arg.fill === 'function' + && typeof arg.readUInt8 === 'function'; +} +},{}],28:[function(require,module,exports){ +(function (process,global){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +var formatRegExp = /%[sdj%]/g; +exports.format = function(f) { + if (!isString(f)) { + var objects = []; + for (var i = 0; i < arguments.length; i++) { + objects.push(inspect(arguments[i])); + } + return objects.join(' '); + } + + var i = 1; + var args = arguments; + var len = args.length; + var str = String(f).replace(formatRegExp, function(x) { + if (x === '%%') return '%'; + if (i >= len) return x; + switch (x) { + case '%s': return String(args[i++]); + case '%d': return Number(args[i++]); + case '%j': + try { + return JSON.stringify(args[i++]); + } catch (_) { + return '[Circular]'; + } + default: + return x; + } + }); + for (var x = args[i]; i < len; x = args[++i]) { + if (isNull(x) || !isObject(x)) { + str += ' ' + x; + } else { + str += ' ' + inspect(x); + } + } + return str; +}; + + +// Mark that a method should not be used. +// Returns a modified function which warns once by default. +// If --no-deprecation is set, then it is a no-op. +exports.deprecate = function(fn, msg) { + // Allow for deprecating things in the process of starting up. + if (isUndefined(global.process)) { + return function() { + return exports.deprecate(fn, msg).apply(this, arguments); + }; + } + + if (process.noDeprecation === true) { + return fn; + } + + var warned = false; + function deprecated() { + if (!warned) { + if (process.throwDeprecation) { + throw new Error(msg); + } else if (process.traceDeprecation) { + console.trace(msg); + } else { + console.error(msg); + } + warned = true; + } + return fn.apply(this, arguments); + } + + return deprecated; +}; + + +var debugs = {}; +var debugEnviron; +exports.debuglog = function(set) { + if (isUndefined(debugEnviron)) + debugEnviron = process.env.NODE_DEBUG || ''; + set = set.toUpperCase(); + if (!debugs[set]) { + if (new RegExp('\\b' + set + '\\b', 'i').test(debugEnviron)) { + var pid = process.pid; + debugs[set] = function() { + var msg = exports.format.apply(exports, arguments); + console.error('%s %d: %s', set, pid, msg); + }; + } else { + debugs[set] = function() {}; + } + } + return debugs[set]; +}; + + +/** + * Echos the value of a value. Trys to print the value out + * in the best way possible given the different types. + * + * @param {Object} obj The object to print out. + * @param {Object} opts Optional options object that alters the output. + */ +/* legacy: obj, showHidden, depth, colors*/ +function inspect(obj, opts) { + // default options + var ctx = { + seen: [], + stylize: stylizeNoColor + }; + // legacy... + if (arguments.length >= 3) ctx.depth = arguments[2]; + if (arguments.length >= 4) ctx.colors = arguments[3]; + if (isBoolean(opts)) { + // legacy... + ctx.showHidden = opts; + } else if (opts) { + // got an "options" object + exports._extend(ctx, opts); + } + // set default options + if (isUndefined(ctx.showHidden)) ctx.showHidden = false; + if (isUndefined(ctx.depth)) ctx.depth = 2; + if (isUndefined(ctx.colors)) ctx.colors = false; + if (isUndefined(ctx.customInspect)) ctx.customInspect = true; + if (ctx.colors) ctx.stylize = stylizeWithColor; + return formatValue(ctx, obj, ctx.depth); +} +exports.inspect = inspect; + + +// http://en.wikipedia.org/wiki/ANSI_escape_code#graphics +inspect.colors = { + 'bold' : [1, 22], + 'italic' : [3, 23], + 'underline' : [4, 24], + 'inverse' : [7, 27], + 'white' : [37, 39], + 'grey' : [90, 39], + 'black' : [30, 39], + 'blue' : [34, 39], + 'cyan' : [36, 39], + 'green' : [32, 39], + 'magenta' : [35, 39], + 'red' : [31, 39], + 'yellow' : [33, 39] +}; + +// Don't use 'blue' not visible on cmd.exe +inspect.styles = { + 'special': 'cyan', + 'number': 'yellow', + 'boolean': 'yellow', + 'undefined': 'grey', + 'null': 'bold', + 'string': 'green', + 'date': 'magenta', + // "name": intentionally not styling + 'regexp': 'red' +}; + + +function stylizeWithColor(str, styleType) { + var style = inspect.styles[styleType]; + + if (style) { + return '\u001b[' + inspect.colors[style][0] + 'm' + str + + '\u001b[' + inspect.colors[style][1] + 'm'; + } else { + return str; + } +} + + +function stylizeNoColor(str, styleType) { + return str; +} + + +function arrayToHash(array) { + var hash = {}; + + array.forEach(function(val, idx) { + hash[val] = true; + }); + + return hash; +} + + +function formatValue(ctx, value, recurseTimes) { + // Provide a hook for user-specified inspect functions. + // Check that value is an object with an inspect function on it + if (ctx.customInspect && + value && + isFunction(value.inspect) && + // Filter out the util module, it's inspect function is special + value.inspect !== exports.inspect && + // Also filter out any prototype objects using the circular check. + !(value.constructor && value.constructor.prototype === value)) { + var ret = value.inspect(recurseTimes, ctx); + if (!isString(ret)) { + ret = formatValue(ctx, ret, recurseTimes); + } + return ret; + } + + // Primitive types cannot have properties + var primitive = formatPrimitive(ctx, value); + if (primitive) { + return primitive; + } + + // Look up the keys of the object. + var keys = Object.keys(value); + var visibleKeys = arrayToHash(keys); + + if (ctx.showHidden) { + keys = Object.getOwnPropertyNames(value); + } + + // IE doesn't make error fields non-enumerable + // http://msdn.microsoft.com/en-us/library/ie/dww52sbt(v=vs.94).aspx + if (isError(value) + && (keys.indexOf('message') >= 0 || keys.indexOf('description') >= 0)) { + return formatError(value); + } + + // Some type of object without properties can be shortcutted. + if (keys.length === 0) { + if (isFunction(value)) { + var name = value.name ? ': ' + value.name : ''; + return ctx.stylize('[Function' + name + ']', 'special'); + } + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } + if (isDate(value)) { + return ctx.stylize(Date.prototype.toString.call(value), 'date'); + } + if (isError(value)) { + return formatError(value); + } + } + + var base = '', array = false, braces = ['{', '}']; + + // Make Array say that they are Array + if (isArray(value)) { + array = true; + braces = ['[', ']']; + } + + // Make functions say that they are functions + if (isFunction(value)) { + var n = value.name ? ': ' + value.name : ''; + base = ' [Function' + n + ']'; + } + + // Make RegExps say that they are RegExps + if (isRegExp(value)) { + base = ' ' + RegExp.prototype.toString.call(value); + } + + // Make dates with properties first say the date + if (isDate(value)) { + base = ' ' + Date.prototype.toUTCString.call(value); + } + + // Make error with message first say the error + if (isError(value)) { + base = ' ' + formatError(value); + } + + if (keys.length === 0 && (!array || value.length == 0)) { + return braces[0] + base + braces[1]; + } + + if (recurseTimes < 0) { + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } else { + return ctx.stylize('[Object]', 'special'); + } + } + + ctx.seen.push(value); + + var output; + if (array) { + output = formatArray(ctx, value, recurseTimes, visibleKeys, keys); + } else { + output = keys.map(function(key) { + return formatProperty(ctx, value, recurseTimes, visibleKeys, key, array); + }); + } + + ctx.seen.pop(); + + return reduceToSingleString(output, base, braces); +} + + +function formatPrimitive(ctx, value) { + if (isUndefined(value)) + return ctx.stylize('undefined', 'undefined'); + if (isString(value)) { + var simple = '\'' + JSON.stringify(value).replace(/^"|"$/g, '') + .replace(/'/g, "\\'") + .replace(/\\"/g, '"') + '\''; + return ctx.stylize(simple, 'string'); + } + if (isNumber(value)) + return ctx.stylize('' + value, 'number'); + if (isBoolean(value)) + return ctx.stylize('' + value, 'boolean'); + // For some reason typeof null is "object", so special case here. + if (isNull(value)) + return ctx.stylize('null', 'null'); +} + + +function formatError(value) { + return '[' + Error.prototype.toString.call(value) + ']'; +} + + +function formatArray(ctx, value, recurseTimes, visibleKeys, keys) { + var output = []; + for (var i = 0, l = value.length; i < l; ++i) { + if (hasOwnProperty(value, String(i))) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + String(i), true)); + } else { + output.push(''); + } + } + keys.forEach(function(key) { + if (!key.match(/^\d+$/)) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + key, true)); + } + }); + return output; +} + + +function formatProperty(ctx, value, recurseTimes, visibleKeys, key, array) { + var name, str, desc; + desc = Object.getOwnPropertyDescriptor(value, key) || { value: value[key] }; + if (desc.get) { + if (desc.set) { + str = ctx.stylize('[Getter/Setter]', 'special'); + } else { + str = ctx.stylize('[Getter]', 'special'); + } + } else { + if (desc.set) { + str = ctx.stylize('[Setter]', 'special'); + } + } + if (!hasOwnProperty(visibleKeys, key)) { + name = '[' + key + ']'; + } + if (!str) { + if (ctx.seen.indexOf(desc.value) < 0) { + if (isNull(recurseTimes)) { + str = formatValue(ctx, desc.value, null); + } else { + str = formatValue(ctx, desc.value, recurseTimes - 1); + } + if (str.indexOf('\n') > -1) { + if (array) { + str = str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n').substr(2); + } else { + str = '\n' + str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n'); + } + } + } else { + str = ctx.stylize('[Circular]', 'special'); + } + } + if (isUndefined(name)) { + if (array && key.match(/^\d+$/)) { + return str; + } + name = JSON.stringify('' + key); + if (name.match(/^"([a-zA-Z_][a-zA-Z_0-9]*)"$/)) { + name = name.substr(1, name.length - 2); + name = ctx.stylize(name, 'name'); + } else { + name = name.replace(/'/g, "\\'") + .replace(/\\"/g, '"') + .replace(/(^"|"$)/g, "'"); + name = ctx.stylize(name, 'string'); + } + } + + return name + ': ' + str; +} + + +function reduceToSingleString(output, base, braces) { + var numLinesEst = 0; + var length = output.reduce(function(prev, cur) { + numLinesEst++; + if (cur.indexOf('\n') >= 0) numLinesEst++; + return prev + cur.replace(/\u001b\[\d\d?m/g, '').length + 1; + }, 0); + + if (length > 60) { + return braces[0] + + (base === '' ? '' : base + '\n ') + + ' ' + + output.join(',\n ') + + ' ' + + braces[1]; + } + + return braces[0] + base + ' ' + output.join(', ') + ' ' + braces[1]; +} + + +// NOTE: These type checking functions intentionally don't use `instanceof` +// because it is fragile and can be easily faked with `Object.create()`. +function isArray(ar) { + return Array.isArray(ar); +} +exports.isArray = isArray; + +function isBoolean(arg) { + return typeof arg === 'boolean'; +} +exports.isBoolean = isBoolean; + +function isNull(arg) { + return arg === null; +} +exports.isNull = isNull; + +function isNullOrUndefined(arg) { + return arg == null; +} +exports.isNullOrUndefined = isNullOrUndefined; + +function isNumber(arg) { + return typeof arg === 'number'; +} +exports.isNumber = isNumber; + +function isString(arg) { + return typeof arg === 'string'; +} +exports.isString = isString; + +function isSymbol(arg) { + return typeof arg === 'symbol'; +} +exports.isSymbol = isSymbol; + +function isUndefined(arg) { + return arg === void 0; +} +exports.isUndefined = isUndefined; + +function isRegExp(re) { + return isObject(re) && objectToString(re) === '[object RegExp]'; +} +exports.isRegExp = isRegExp; + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} +exports.isObject = isObject; + +function isDate(d) { + return isObject(d) && objectToString(d) === '[object Date]'; +} +exports.isDate = isDate; + +function isError(e) { + return isObject(e) && + (objectToString(e) === '[object Error]' || e instanceof Error); +} +exports.isError = isError; + +function isFunction(arg) { + return typeof arg === 'function'; +} +exports.isFunction = isFunction; + +function isPrimitive(arg) { + return arg === null || + typeof arg === 'boolean' || + typeof arg === 'number' || + typeof arg === 'string' || + typeof arg === 'symbol' || // ES6 symbol + typeof arg === 'undefined'; +} +exports.isPrimitive = isPrimitive; + +exports.isBuffer = require('./support/isBuffer'); + +function objectToString(o) { + return Object.prototype.toString.call(o); +} + + +function pad(n) { + return n < 10 ? '0' + n.toString(10) : n.toString(10); +} + + +var months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', + 'Oct', 'Nov', 'Dec']; + +// 26 Feb 16:19:34 +function timestamp() { + var d = new Date(); + var time = [pad(d.getHours()), + pad(d.getMinutes()), + pad(d.getSeconds())].join(':'); + return [d.getDate(), months[d.getMonth()], time].join(' '); +} + + +// log is just a thin wrapper to console.log that prepends a timestamp +exports.log = function() { + console.log('%s - %s', timestamp(), exports.format.apply(exports, arguments)); +}; + + +/** + * Inherit the prototype methods from one constructor into another. + * + * The Function.prototype.inherits from lang.js rewritten as a standalone + * function (not on Function.prototype). NOTE: If this file is to be loaded + * during bootstrapping this function needs to be rewritten using some native + * functions as prototype setup using normal JavaScript does not work as + * expected during bootstrapping (see mirror.js in r114903). + * + * @param {function} ctor Constructor function which needs to inherit the + * prototype. + * @param {function} superCtor Constructor function to inherit prototype from. + */ +exports.inherits = require('inherits'); + +exports._extend = function(origin, add) { + // Don't do anything if add isn't an object + if (!add || !isObject(add)) return origin; + + var keys = Object.keys(add); + var i = keys.length; + while (i--) { + origin[keys[i]] = add[keys[i]]; + } + return origin; +}; + +function hasOwnProperty(obj, prop) { + return Object.prototype.hasOwnProperty.call(obj, prop); +} + +}).call(this,require('_process'),typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {}) +},{"./support/isBuffer":27,"_process":24,"inherits":26}],29:[function(require,module,exports){ +// Returns a wrapper function that returns a wrapped callback +// The wrapper function should do some stuff, and return a +// presumably different callback function. +// This makes sure that own properties are retained, so that +// decorations and such are not lost along the way. +module.exports = wrappy +function wrappy (fn, cb) { + if (fn && cb) return wrappy(fn)(cb) + + if (typeof fn !== 'function') + throw new TypeError('need wrapper function') + + Object.keys(fn).forEach(function (k) { + wrapper[k] = fn[k] + }) + + return wrapper + + function wrapper() { + var args = new Array(arguments.length) + for (var i = 0; i < args.length; i++) { + args[i] = arguments[i] + } + var ret = fn.apply(this, args) + var cb = args[args.length-1] + if (typeof ret === 'function' && ret !== cb) { + Object.keys(cb).forEach(function (k) { + ret[k] = cb[k] + }) + } + return ret + } +} + +},{}]},{},[7])(7) +}); \ No newline at end of file diff --git a/assets/javascripts/workers/search.16e2a7d4.min.js b/assets/javascripts/workers/search.16e2a7d4.min.js new file mode 100644 index 0000000..e0dc159 --- /dev/null +++ b/assets/javascripts/workers/search.16e2a7d4.min.js @@ -0,0 +1,48 @@ +"use strict";(()=>{var ge=Object.create;var W=Object.defineProperty,ye=Object.defineProperties,me=Object.getOwnPropertyDescriptor,ve=Object.getOwnPropertyDescriptors,xe=Object.getOwnPropertyNames,G=Object.getOwnPropertySymbols,Se=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,Qe=Object.prototype.propertyIsEnumerable;var J=(t,e,r)=>e in t?W(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,M=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(G)for(var r of G(e))Qe.call(e,r)&&J(t,r,e[r]);return t},Z=(t,e)=>ye(t,ve(e));var K=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var be=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of xe(e))!X.call(t,i)&&i!==r&&W(t,i,{get:()=>e[i],enumerable:!(n=me(e,i))||n.enumerable});return t};var H=(t,e,r)=>(r=t!=null?ge(Se(t)):{},be(e||!t||!t.__esModule?W(r,"default",{value:t,enumerable:!0}):r,t));var z=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var re=K((ee,te)=>{/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9 + * Copyright (C) 2020 Oliver Nightingale + * @license MIT + */(function(){var t=function(e){var r=new t.Builder;return r.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),r.searchPipeline.add(t.stemmer),e.call(r,r),r.build()};t.version="2.3.9";/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */t.utils={},t.utils.warn=function(e){return function(r){e.console&&console.warn&&console.warn(r)}}(this),t.utils.asString=function(e){return e==null?"":e.toString()},t.utils.clone=function(e){if(e==null)return e;for(var r=Object.create(null),n=Object.keys(e),i=0;i0){var h=t.utils.clone(r)||{};h.position=[a,c],h.index=s.length,s.push(new t.Token(n.slice(a,o),h))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,r){r in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+r),e.label=r,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var r=e.label&&e.label in this.registeredFunctions;r||t.utils.warn(`Function is not registered with pipeline. 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this.pos1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof ee=="object"?te.exports=r():e.lunr=r()}(this,function(){return t})})()});var q=K((Re,ne)=>{"use strict";/*! + * escape-html + * Copyright(c) 2012-2013 TJ Holowaychuk + * Copyright(c) 2015 Andreas Lubbe + * Copyright(c) 2015 Tiancheng "Timothy" Gu + * MIT Licensed + */var Le=/["'&<>]/;ne.exports=we;function we(t){var e=""+t,r=Le.exec(e);if(!r)return e;var n,i="",s=0,o=0;for(s=r.index;s=0;r--){let n=t[r];typeof n=="string"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?e.insertBefore(this.previousSibling,n):e.replaceChild(n,this)}}}));var ie=H(q());function se(t){let e=new Map,r=new Set;for(let n of t){let[i,s]=n.location.split("#"),o=n.location,a=n.title,u=n.tags,c=(0,ie.default)(n.text).replace(/\s+(?=[,.:;!?])/g,"").replace(/\s+/g," ");if(s){let h=e.get(i);r.has(h)?e.set(o,{location:o,title:a,text:c,parent:h}):(h.title=n.title,h.text=c,r.add(h))}else e.set(o,M({location:o,title:a,text:c},u&&{tags:u}))}return e}var oe=H(q());function ae(t,e){let r=new RegExp(t.separator,"img"),n=(i,s,o)=>`${s}${o}`;return i=>{i=i.replace(/[\s*+\-:~^]+/g," ").trim();let s=new RegExp(`(^|${t.separator})(${i.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return o=>(e?(0,oe.default)(o):o).replace(s,n).replace(/<\/mark>(\s+)]*>/img,"$1")}}function ue(t){let e=new lunr.Query(["title","text"]);return new lunr.QueryParser(t,e).parse(),e.clauses}function ce(t,e){var i;let r=new Set(t),n={};for(let s=0;s!n.has(i)))]}var U=class{constructor({config:e,docs:r,options:n}){this.options=n,this.documents=se(r),this.highlight=ae(e,!1),lunr.tokenizer.separator=new RegExp(e.separator),this.index=lunr(function(){e.lang.length===1&&e.lang[0]!=="en"?this.use(lunr[e.lang[0]]):e.lang.length>1&&this.use(lunr.multiLanguage(...e.lang));let i=Ee(["trimmer","stopWordFilter","stemmer"],n.pipeline);for(let s of e.lang.map(o=>o==="en"?lunr:lunr[o]))for(let o of i)this.pipeline.remove(s[o]),this.searchPipeline.remove(s[o]);this.ref("location"),this.field("title",{boost:1e3}),this.field("text"),this.field("tags",{boost:1e6,extractor:s=>{let{tags:o=[]}=s;return o.reduce((a,u)=>[...a,...lunr.tokenizer(u)],[])}});for(let s of r)this.add(s,{boost:s.boost})})}search(e){if(e)try{let r=this.highlight(e),n=ue(e).filter(o=>o.presence!==lunr.Query.presence.PROHIBITED),i=this.index.search(`${e}*`).reduce((o,{ref:a,score:u,matchData:c})=>{let h=this.documents.get(a);if(typeof h!="undefined"){let{location:y,title:g,text:b,tags:m,parent:Q}=h,p=ce(n,Object.keys(c.metadata)),d=+!Q+ +Object.values(p).every(w=>w);o.push(Z(M({location:y,title:r(g),text:r(b)},m&&{tags:m.map(r)}),{score:u*(1+d),terms:p}))}return o},[]).sort((o,a)=>a.score-o.score).reduce((o,a)=>{let u=this.documents.get(a.location);if(typeof u!="undefined"){let c="parent"in u?u.parent.location:u.location;o.set(c,[...o.get(c)||[],a])}return o},new Map),s;if(this.options.suggestions){let o=this.index.query(a=>{for(let u of n)a.term(u.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});s=o.length?Object.keys(o[0].matchData.metadata):[]}return M({items:[...i.values()]},typeof s!="undefined"&&{suggestions:s})}catch(r){console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`)}return{items:[]}}};var Y;function ke(t){return z(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=document.querySelector("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Te(t){return z(this,null,function*(){switch(t.type){case 0:return yield ke(t.data.config),Y=new U(t.data),{type:1};case 2:return{type:3,data:Y?Y.search(t.data):{items:[]}};default:throw new TypeError("Invalid message type")}})}self.lunr=le.default;addEventListener("message",t=>z(void 0,null,function*(){postMessage(yield Te(t.data))}));})(); +//# sourceMappingURL=search.16e2a7d4.min.js.map + diff --git a/assets/javascripts/workers/search.16e2a7d4.min.js.map b/assets/javascripts/workers/search.16e2a7d4.min.js.map new file mode 100644 index 0000000..fa01f37 --- /dev/null +++ b/assets/javascripts/workers/search.16e2a7d4.min.js.map @@ -0,0 +1,8 @@ +{ + "version": 3, + "sources": ["node_modules/lunr/lunr.js", "node_modules/escape-html/index.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"], + "sourceRoot": "../../../..", + "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n * this.field('title')\n * this.field('body')\n * this.ref('id')\n *\n * documents.forEach(function (doc) {\n * this.add(doc)\n * }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n var builder = new lunr.Builder\n\n builder.pipeline.add(\n lunr.trimmer,\n lunr.stopWordFilter,\n lunr.stemmer\n )\n\n builder.searchPipeline.add(\n lunr.stemmer\n )\n\n config.call(builder, builder)\n return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n /* eslint-disable no-console */\n return function (message) {\n if (global.console && console.warn) {\n console.warn(message)\n }\n }\n /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n if (obj === void 0 || obj === null) {\n return \"\"\n } else {\n return obj.toString()\n }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n if (obj === null || obj === undefined) {\n return obj\n }\n\n var clone = Object.create(null),\n keys = Object.keys(obj)\n\n for (var i = 0; i < keys.length; i++) {\n var key = keys[i],\n val = obj[key]\n\n if (Array.isArray(val)) {\n clone[key] = val.slice()\n continue\n }\n\n if (typeof val === 'string' ||\n typeof val === 'number' ||\n typeof val === 'boolean') {\n clone[key] = val\n continue\n }\n\n throw new TypeError(\"clone is not deep and does not support nested objects\")\n }\n\n return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n this.docRef = docRef\n this.fieldName = fieldName\n this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n var n = s.indexOf(lunr.FieldRef.joiner)\n\n if (n === -1) {\n throw \"malformed field ref string\"\n }\n\n var fieldRef = s.slice(0, n),\n docRef = s.slice(n + 1)\n\n return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n if (this._stringValue == undefined) {\n this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n }\n\n return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n this.elements = Object.create(null)\n\n if (elements) {\n this.length = elements.length\n\n for (var i = 0; i < this.length; i++) {\n this.elements[elements[i]] = true\n }\n } else {\n this.length = 0\n }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n intersect: function (other) {\n return other\n },\n\n union: function () {\n return this\n },\n\n contains: function () {\n return true\n }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n intersect: function () {\n return this\n },\n\n union: function (other) {\n return other\n },\n\n contains: function () {\n return false\n }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n var a, b, elements, intersection = []\n\n if (other === lunr.Set.complete) {\n return this\n }\n\n if (other === lunr.Set.empty) {\n return other\n }\n\n if (this.length < other.length) {\n a = this\n b = other\n } else {\n a = other\n b = this\n }\n\n elements = Object.keys(a.elements)\n\n for (var i = 0; i < elements.length; i++) {\n var element = elements[i]\n if (element in b.elements) {\n intersection.push(element)\n }\n }\n\n return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n if (other === lunr.Set.complete) {\n return lunr.Set.complete\n }\n\n if (other === lunr.Set.empty) {\n return this\n }\n\n return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n var documentsWithTerm = 0\n\n for (var fieldName in posting) {\n if (fieldName == '_index') continue // Ignore the term index, its not a field\n documentsWithTerm += Object.keys(posting[fieldName]).length\n }\n\n var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n this.str = str || \"\"\n this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n * return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n this.str = fn(this.str, this.metadata)\n return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n fn = fn || function (s) { return s }\n return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n if (obj == null || obj == undefined) {\n return []\n }\n\n if (Array.isArray(obj)) {\n return obj.map(function (t) {\n return new lunr.Token(\n lunr.utils.asString(t).toLowerCase(),\n lunr.utils.clone(metadata)\n )\n })\n }\n\n var str = obj.toString().toLowerCase(),\n len = str.length,\n tokens = []\n\n for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n var char = str.charAt(sliceEnd),\n sliceLength = sliceEnd - sliceStart\n\n if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n if (sliceLength > 0) {\n var tokenMetadata = lunr.utils.clone(metadata) || {}\n tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n tokenMetadata[\"index\"] = tokens.length\n\n tokens.push(\n new lunr.Token (\n str.slice(sliceStart, sliceEnd),\n tokenMetadata\n )\n )\n }\n\n sliceStart = sliceEnd + 1\n }\n\n }\n\n return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n if (label in this.registeredFunctions) {\n lunr.utils.warn('Overwriting existing registered function: ' + label)\n }\n\n fn.label = label\n lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n if (!isRegistered) {\n lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n var pipeline = new lunr.Pipeline\n\n serialised.forEach(function (fnName) {\n var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n if (fn) {\n pipeline.add(fn)\n } else {\n throw new Error('Cannot load unregistered function: ' + fnName)\n }\n })\n\n return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n var fns = Array.prototype.slice.call(arguments)\n\n fns.forEach(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n this._stack.push(fn)\n }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n pos = pos + 1\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n var pos = this._stack.indexOf(fn)\n if (pos == -1) {\n return\n }\n\n this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n var stackLength = this._stack.length\n\n for (var i = 0; i < stackLength; i++) {\n var fn = this._stack[i]\n var memo = []\n\n for (var j = 0; j < tokens.length; j++) {\n var result = fn(tokens[j], j, tokens)\n\n if (result === null || result === void 0 || result === '') continue\n\n if (Array.isArray(result)) {\n for (var k = 0; k < result.length; k++) {\n memo.push(result[k])\n }\n } else {\n memo.push(result)\n }\n }\n\n tokens = memo\n }\n\n return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n var token = new lunr.Token (str, metadata)\n\n return this.run([token]).map(function (t) {\n return t.toString()\n })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n return this._stack.map(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n return fn.label\n })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n this._magnitude = 0\n this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n // For an empty vector the tuple can be inserted at the beginning\n if (this.elements.length == 0) {\n return 0\n }\n\n var start = 0,\n end = this.elements.length / 2,\n sliceLength = end - start,\n pivotPoint = Math.floor(sliceLength / 2),\n pivotIndex = this.elements[pivotPoint * 2]\n\n while (sliceLength > 1) {\n if (pivotIndex < index) {\n start = pivotPoint\n }\n\n if (pivotIndex > index) {\n end = pivotPoint\n }\n\n if (pivotIndex == index) {\n break\n }\n\n sliceLength = end - start\n pivotPoint = start + Math.floor(sliceLength / 2)\n pivotIndex = this.elements[pivotPoint * 2]\n }\n\n if (pivotIndex == index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex > index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex < index) {\n return (pivotPoint + 1) * 2\n }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n this.upsert(insertIdx, val, function () {\n throw \"duplicate index\"\n })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n this._magnitude = 0\n var position = this.positionForIndex(insertIdx)\n\n if (this.elements[position] == insertIdx) {\n this.elements[position + 1] = fn(this.elements[position + 1], val)\n } else {\n this.elements.splice(position, 0, insertIdx, val)\n }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n if (this._magnitude) return this._magnitude\n\n var sumOfSquares = 0,\n elementsLength = this.elements.length\n\n for (var i = 1; i < elementsLength; i += 2) {\n var val = this.elements[i]\n sumOfSquares += val * val\n }\n\n return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n var dotProduct = 0,\n a = this.elements, b = otherVector.elements,\n aLen = a.length, bLen = b.length,\n aVal = 0, bVal = 0,\n i = 0, j = 0\n\n while (i < aLen && j < bLen) {\n aVal = a[i], bVal = b[j]\n if (aVal < bVal) {\n i += 2\n } else if (aVal > bVal) {\n j += 2\n } else if (aVal == bVal) {\n dotProduct += a[i + 1] * b[j + 1]\n i += 2\n j += 2\n }\n }\n\n return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n var output = new Array (this.elements.length / 2)\n\n for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n output[j] = this.elements[i]\n }\n\n return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n var step2list = {\n \"ational\" : \"ate\",\n \"tional\" : \"tion\",\n \"enci\" : \"ence\",\n \"anci\" : \"ance\",\n \"izer\" : \"ize\",\n \"bli\" : \"ble\",\n \"alli\" : \"al\",\n \"entli\" : \"ent\",\n \"eli\" : \"e\",\n \"ousli\" : \"ous\",\n \"ization\" : \"ize\",\n \"ation\" : \"ate\",\n \"ator\" : \"ate\",\n \"alism\" : \"al\",\n \"iveness\" : \"ive\",\n \"fulness\" : \"ful\",\n \"ousness\" : \"ous\",\n \"aliti\" : \"al\",\n \"iviti\" : \"ive\",\n \"biliti\" : \"ble\",\n \"logi\" : \"log\"\n },\n\n step3list = {\n \"icate\" : \"ic\",\n \"ative\" : \"\",\n \"alize\" : \"al\",\n \"iciti\" : \"ic\",\n \"ical\" : \"ic\",\n \"ful\" : \"\",\n \"ness\" : \"\"\n },\n\n c = \"[^aeiou]\", // consonant\n v = \"[aeiouy]\", // vowel\n C = c + \"[^aeiouy]*\", // consonant sequence\n V = v + \"[aeiou]*\", // vowel sequence\n\n mgr0 = \"^(\" + C + \")?\" + V + C, // [C]VC... is m>0\n meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\", // [C]VC[V] is m=1\n mgr1 = \"^(\" + C + \")?\" + V + C + V + C, // [C]VCVC... is m>1\n s_v = \"^(\" + C + \")?\" + v; // vowel in stem\n\n var re_mgr0 = new RegExp(mgr0);\n var re_mgr1 = new RegExp(mgr1);\n var re_meq1 = new RegExp(meq1);\n var re_s_v = new RegExp(s_v);\n\n var re_1a = /^(.+?)(ss|i)es$/;\n var re2_1a = /^(.+?)([^s])s$/;\n var re_1b = /^(.+?)eed$/;\n var re2_1b = /^(.+?)(ed|ing)$/;\n var re_1b_2 = /.$/;\n var re2_1b_2 = /(at|bl|iz)$/;\n var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var re_1c = /^(.+?[^aeiou])y$/;\n var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n var re_5 = /^(.+?)e$/;\n var re_5_1 = /ll$/;\n var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var porterStemmer = function porterStemmer(w) {\n var stem,\n suffix,\n firstch,\n re,\n re2,\n re3,\n re4;\n\n if (w.length < 3) { return w; }\n\n firstch = w.substr(0,1);\n if (firstch == \"y\") {\n w = firstch.toUpperCase() + w.substr(1);\n }\n\n // Step 1a\n re = re_1a\n re2 = re2_1a;\n\n if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n // Step 1b\n re = re_1b;\n re2 = re2_1b;\n if (re.test(w)) {\n var fp = re.exec(w);\n re = re_mgr0;\n if (re.test(fp[1])) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1];\n re2 = re_s_v;\n if (re2.test(stem)) {\n w = stem;\n re2 = re2_1b_2;\n re3 = re3_1b_2;\n re4 = re4_1b_2;\n if (re2.test(w)) { w = w + \"e\"; }\n else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n else if (re4.test(w)) { w = w + \"e\"; }\n }\n }\n\n // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n re = re_1c;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n w = stem + \"i\";\n }\n\n // Step 2\n re = re_2;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step2list[suffix];\n }\n }\n\n // Step 3\n re = re_3;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step3list[suffix];\n }\n }\n\n // Step 4\n re = re_4;\n re2 = re2_4;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n if (re.test(stem)) {\n w = stem;\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1] + fp[2];\n re2 = re_mgr1;\n if (re2.test(stem)) {\n w = stem;\n }\n }\n\n // Step 5\n re = re_5;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n re2 = re_meq1;\n re3 = re3_5;\n if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n w = stem;\n }\n }\n\n re = re_5_1;\n re2 = re_mgr1;\n if (re.test(w) && re2.test(w)) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n\n // and turn initial Y back to y\n\n if (firstch == \"y\") {\n w = firstch.toLowerCase() + w.substr(1);\n }\n\n return w;\n };\n\n return function (token) {\n return token.update(porterStemmer);\n }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n var words = stopWords.reduce(function (memo, stopWord) {\n memo[stopWord] = stopWord\n return memo\n }, {})\n\n return function (token) {\n if (token && words[token.toString()] !== token.toString()) return token\n }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n 'a',\n 'able',\n 'about',\n 'across',\n 'after',\n 'all',\n 'almost',\n 'also',\n 'am',\n 'among',\n 'an',\n 'and',\n 'any',\n 'are',\n 'as',\n 'at',\n 'be',\n 'because',\n 'been',\n 'but',\n 'by',\n 'can',\n 'cannot',\n 'could',\n 'dear',\n 'did',\n 'do',\n 'does',\n 'either',\n 'else',\n 'ever',\n 'every',\n 'for',\n 'from',\n 'get',\n 'got',\n 'had',\n 'has',\n 'have',\n 'he',\n 'her',\n 'hers',\n 'him',\n 'his',\n 'how',\n 'however',\n 'i',\n 'if',\n 'in',\n 'into',\n 'is',\n 'it',\n 'its',\n 'just',\n 'least',\n 'let',\n 'like',\n 'likely',\n 'may',\n 'me',\n 'might',\n 'most',\n 'must',\n 'my',\n 'neither',\n 'no',\n 'nor',\n 'not',\n 'of',\n 'off',\n 'often',\n 'on',\n 'only',\n 'or',\n 'other',\n 'our',\n 'own',\n 'rather',\n 'said',\n 'say',\n 'says',\n 'she',\n 'should',\n 'since',\n 'so',\n 'some',\n 'than',\n 'that',\n 'the',\n 'their',\n 'them',\n 'then',\n 'there',\n 'these',\n 'they',\n 'this',\n 'tis',\n 'to',\n 'too',\n 'twas',\n 'us',\n 'wants',\n 'was',\n 'we',\n 'were',\n 'what',\n 'when',\n 'where',\n 'which',\n 'while',\n 'who',\n 'whom',\n 'why',\n 'will',\n 'with',\n 'would',\n 'yet',\n 'you',\n 'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n return token.update(function (s) {\n return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n this.final = false\n this.edges = {}\n this.id = lunr.TokenSet._nextId\n lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n var builder = new lunr.TokenSet.Builder\n\n for (var i = 0, len = arr.length; i < len; i++) {\n builder.insert(arr[i])\n }\n\n builder.finish()\n return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n if ('editDistance' in clause) {\n return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n } else {\n return lunr.TokenSet.fromString(clause.term)\n }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n var root = new lunr.TokenSet\n\n var stack = [{\n node: root,\n editsRemaining: editDistance,\n str: str\n }]\n\n while (stack.length) {\n var frame = stack.pop()\n\n // no edit\n if (frame.str.length > 0) {\n var char = frame.str.charAt(0),\n noEditNode\n\n if (char in frame.node.edges) {\n noEditNode = frame.node.edges[char]\n } else {\n noEditNode = new lunr.TokenSet\n frame.node.edges[char] = noEditNode\n }\n\n if (frame.str.length == 1) {\n noEditNode.final = true\n }\n\n stack.push({\n node: noEditNode,\n editsRemaining: frame.editsRemaining,\n str: frame.str.slice(1)\n })\n }\n\n if (frame.editsRemaining == 0) {\n continue\n }\n\n // insertion\n if (\"*\" in frame.node.edges) {\n var insertionNode = frame.node.edges[\"*\"]\n } else {\n var insertionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = insertionNode\n }\n\n if (frame.str.length == 0) {\n insertionNode.final = true\n }\n\n stack.push({\n node: insertionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str\n })\n\n // deletion\n // can only do a deletion if we have enough edits remaining\n // and if there are characters left to delete in the string\n if (frame.str.length > 1) {\n stack.push({\n node: frame.node,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // deletion\n // just removing the last character from the str\n if (frame.str.length == 1) {\n frame.node.final = true\n }\n\n // substitution\n // can only do a substitution if we have enough edits remaining\n // and if there are characters left to substitute\n if (frame.str.length >= 1) {\n if (\"*\" in frame.node.edges) {\n var substitutionNode = frame.node.edges[\"*\"]\n } else {\n var substitutionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = substitutionNode\n }\n\n if (frame.str.length == 1) {\n substitutionNode.final = true\n }\n\n stack.push({\n node: substitutionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // transposition\n // can only do a transposition if there are edits remaining\n // and there are enough characters to transpose\n if (frame.str.length > 1) {\n var charA = frame.str.charAt(0),\n charB = frame.str.charAt(1),\n transposeNode\n\n if (charB in frame.node.edges) {\n transposeNode = frame.node.edges[charB]\n } else {\n transposeNode = new lunr.TokenSet\n frame.node.edges[charB] = transposeNode\n }\n\n if (frame.str.length == 1) {\n transposeNode.final = true\n }\n\n stack.push({\n node: transposeNode,\n editsRemaining: frame.editsRemaining - 1,\n str: charA + frame.str.slice(2)\n })\n }\n }\n\n return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n var node = new lunr.TokenSet,\n root = node\n\n /*\n * Iterates through all characters within the passed string\n * appending a node for each character.\n *\n * When a wildcard character is found then a self\n * referencing edge is introduced to continually match\n * any number of any characters.\n */\n for (var i = 0, len = str.length; i < len; i++) {\n var char = str[i],\n final = (i == len - 1)\n\n if (char == \"*\") {\n node.edges[char] = node\n node.final = final\n\n } else {\n var next = new lunr.TokenSet\n next.final = final\n\n node.edges[char] = next\n node = next\n }\n }\n\n return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n var words = []\n\n var stack = [{\n prefix: \"\",\n node: this\n }]\n\n while (stack.length) {\n var frame = stack.pop(),\n edges = Object.keys(frame.node.edges),\n len = edges.length\n\n if (frame.node.final) {\n /* In Safari, at this point the prefix is sometimes corrupted, see:\n * https://github.com/olivernn/lunr.js/issues/279 Calling any\n * String.prototype method forces Safari to \"cast\" this string to what\n * it's supposed to be, fixing the bug. */\n frame.prefix.charAt(0)\n words.push(frame.prefix)\n }\n\n for (var i = 0; i < len; i++) {\n var edge = edges[i]\n\n stack.push({\n prefix: frame.prefix.concat(edge),\n node: frame.node.edges[edge]\n })\n }\n }\n\n return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n // NOTE: Using Object.keys here as this.edges is very likely\n // to enter 'hash-mode' with many keys being added\n //\n // avoiding a for-in loop here as it leads to the function\n // being de-optimised (at least in V8). From some simple\n // benchmarks the performance is comparable, but allowing\n // V8 to optimize may mean easy performance wins in the future.\n\n if (this._str) {\n return this._str\n }\n\n var str = this.final ? '1' : '0',\n labels = Object.keys(this.edges).sort(),\n len = labels.length\n\n for (var i = 0; i < len; i++) {\n var label = labels[i],\n node = this.edges[label]\n\n str = str + label + node.id\n }\n\n return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n var output = new lunr.TokenSet,\n frame = undefined\n\n var stack = [{\n qNode: b,\n output: output,\n node: this\n }]\n\n while (stack.length) {\n frame = stack.pop()\n\n // NOTE: As with the #toString method, we are using\n // Object.keys and a for loop instead of a for-in loop\n // as both of these objects enter 'hash' mode, causing\n // the function to be de-optimised in V8\n var qEdges = Object.keys(frame.qNode.edges),\n qLen = qEdges.length,\n nEdges = Object.keys(frame.node.edges),\n nLen = nEdges.length\n\n for (var q = 0; q < qLen; q++) {\n var qEdge = qEdges[q]\n\n for (var n = 0; n < nLen; n++) {\n var nEdge = nEdges[n]\n\n if (nEdge == qEdge || qEdge == '*') {\n var node = frame.node.edges[nEdge],\n qNode = frame.qNode.edges[qEdge],\n final = node.final && qNode.final,\n next = undefined\n\n if (nEdge in frame.output.edges) {\n // an edge already exists for this character\n // no need to create a new node, just set the finality\n // bit unless this node is already final\n next = frame.output.edges[nEdge]\n next.final = next.final || final\n\n } else {\n // no edge exists yet, must create one\n // set the finality bit and insert it\n // into the output\n next = new lunr.TokenSet\n next.final = final\n frame.output.edges[nEdge] = next\n }\n\n stack.push({\n qNode: qNode,\n output: next,\n node: node\n })\n }\n }\n }\n }\n\n return output\n}\nlunr.TokenSet.Builder = function () {\n this.previousWord = \"\"\n this.root = new lunr.TokenSet\n this.uncheckedNodes = []\n this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n var node,\n commonPrefix = 0\n\n if (word < this.previousWord) {\n throw new Error (\"Out of order word insertion\")\n }\n\n for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n if (word[i] != this.previousWord[i]) break\n commonPrefix++\n }\n\n this.minimize(commonPrefix)\n\n if (this.uncheckedNodes.length == 0) {\n node = this.root\n } else {\n node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n }\n\n for (var i = commonPrefix; i < word.length; i++) {\n var nextNode = new lunr.TokenSet,\n char = word[i]\n\n node.edges[char] = nextNode\n\n this.uncheckedNodes.push({\n parent: node,\n char: char,\n child: nextNode\n })\n\n node = nextNode\n }\n\n node.final = true\n this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n var node = this.uncheckedNodes[i],\n childKey = node.child.toString()\n\n if (childKey in this.minimizedNodes) {\n node.parent.edges[node.char] = this.minimizedNodes[childKey]\n } else {\n // Cache the key for this node since\n // we know it can't change anymore\n node.child._str = childKey\n\n this.minimizedNodes[childKey] = node.child\n }\n\n this.uncheckedNodes.pop()\n }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n this.invertedIndex = attrs.invertedIndex\n this.fieldVectors = attrs.fieldVectors\n this.tokenSet = attrs.tokenSet\n this.fields = attrs.fields\n this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example Simple single term query\n * hello\n * @example Multiple term query\n * hello world\n * @example term scoped to a field\n * title:hello\n * @example term with a boost of 10\n * hello^10\n * @example term with an edit distance of 2\n * hello~2\n * @example terms with presence modifiers\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first. For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n return this.query(function (query) {\n var parser = new lunr.QueryParser(queryString, query)\n parser.parse()\n })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n // for each query clause\n // * process terms\n // * expand terms from token set\n // * find matching documents and metadata\n // * get document vectors\n // * score documents\n\n var query = new lunr.Query(this.fields),\n matchingFields = Object.create(null),\n queryVectors = Object.create(null),\n termFieldCache = Object.create(null),\n requiredMatches = Object.create(null),\n prohibitedMatches = Object.create(null)\n\n /*\n * To support field level boosts a query vector is created per\n * field. An empty vector is eagerly created to support negated\n * queries.\n */\n for (var i = 0; i < this.fields.length; i++) {\n queryVectors[this.fields[i]] = new lunr.Vector\n }\n\n fn.call(query, query)\n\n for (var i = 0; i < query.clauses.length; i++) {\n /*\n * Unless the pipeline has been disabled for this term, which is\n * the case for terms with wildcards, we need to pass the clause\n * term through the search pipeline. A pipeline returns an array\n * of processed terms. Pipeline functions may expand the passed\n * term, which means we may end up performing multiple index lookups\n * for a single query term.\n */\n var clause = query.clauses[i],\n terms = null,\n clauseMatches = lunr.Set.empty\n\n if (clause.usePipeline) {\n terms = this.pipeline.runString(clause.term, {\n fields: clause.fields\n })\n } else {\n terms = [clause.term]\n }\n\n for (var m = 0; m < terms.length; m++) {\n var term = terms[m]\n\n /*\n * Each term returned from the pipeline needs to use the same query\n * clause object, e.g. the same boost and or edit distance. The\n * simplest way to do this is to re-use the clause object but mutate\n * its term property.\n */\n clause.term = term\n\n /*\n * From the term in the clause we create a token set which will then\n * be used to intersect the indexes token set to get a list of terms\n * to lookup in the inverted index\n */\n var termTokenSet = lunr.TokenSet.fromClause(clause),\n expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n /*\n * If a term marked as required does not exist in the tokenSet it is\n * impossible for the search to return any matches. We set all the field\n * scoped required matches set to empty and stop examining any further\n * clauses.\n */\n if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = lunr.Set.empty\n }\n\n break\n }\n\n for (var j = 0; j < expandedTerms.length; j++) {\n /*\n * For each term get the posting and termIndex, this is required for\n * building the query vector.\n */\n var expandedTerm = expandedTerms[j],\n posting = this.invertedIndex[expandedTerm],\n termIndex = posting._index\n\n for (var k = 0; k < clause.fields.length; k++) {\n /*\n * For each field that this query term is scoped by (by default\n * all fields are in scope) we need to get all the document refs\n * that have this term in that field.\n *\n * The posting is the entry in the invertedIndex for the matching\n * term from above.\n */\n var field = clause.fields[k],\n fieldPosting = posting[field],\n matchingDocumentRefs = Object.keys(fieldPosting),\n termField = expandedTerm + \"/\" + field,\n matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n /*\n * if the presence of this term is required ensure that the matching\n * documents are added to the set of required matches for this clause.\n *\n */\n if (clause.presence == lunr.Query.presence.REQUIRED) {\n clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n if (requiredMatches[field] === undefined) {\n requiredMatches[field] = lunr.Set.complete\n }\n }\n\n /*\n * if the presence of this term is prohibited ensure that the matching\n * documents are added to the set of prohibited matches for this field,\n * creating that set if it does not yet exist.\n */\n if (clause.presence == lunr.Query.presence.PROHIBITED) {\n if (prohibitedMatches[field] === undefined) {\n prohibitedMatches[field] = lunr.Set.empty\n }\n\n prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n /*\n * Prohibited matches should not be part of the query vector used for\n * similarity scoring and no metadata should be extracted so we continue\n * to the next field\n */\n continue\n }\n\n /*\n * The query field vector is populated using the termIndex found for\n * the term and a unit value with the appropriate boost applied.\n * Using upsert because there could already be an entry in the vector\n * for the term we are working with. In that case we just add the scores\n * together.\n */\n queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n /**\n * If we've already seen this term, field combo then we've already collected\n * the matching documents and metadata, no need to go through all that again\n */\n if (termFieldCache[termField]) {\n continue\n }\n\n for (var l = 0; l < matchingDocumentRefs.length; l++) {\n /*\n * All metadata for this term/field/document triple\n * are then extracted and collected into an instance\n * of lunr.MatchData ready to be returned in the query\n * results\n */\n var matchingDocumentRef = matchingDocumentRefs[l],\n matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n metadata = fieldPosting[matchingDocumentRef],\n fieldMatch\n\n if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n } else {\n fieldMatch.add(expandedTerm, field, metadata)\n }\n\n }\n\n termFieldCache[termField] = true\n }\n }\n }\n\n /**\n * If the presence was required we need to update the requiredMatches field sets.\n * We do this after all fields for the term have collected their matches because\n * the clause terms presence is required in _any_ of the fields not _all_ of the\n * fields.\n */\n if (clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n }\n }\n }\n\n /**\n * Need to combine the field scoped required and prohibited\n * matching documents into a global set of required and prohibited\n * matches\n */\n var allRequiredMatches = lunr.Set.complete,\n allProhibitedMatches = lunr.Set.empty\n\n for (var i = 0; i < this.fields.length; i++) {\n var field = this.fields[i]\n\n if (requiredMatches[field]) {\n allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n }\n\n if (prohibitedMatches[field]) {\n allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n }\n }\n\n var matchingFieldRefs = Object.keys(matchingFields),\n results = [],\n matches = Object.create(null)\n\n /*\n * If the query is negated (contains only prohibited terms)\n * we need to get _all_ fieldRefs currently existing in the\n * index. This is only done when we know that the query is\n * entirely prohibited terms to avoid any cost of getting all\n * fieldRefs unnecessarily.\n *\n * Additionally, blank MatchData must be created to correctly\n * populate the results.\n */\n if (query.isNegated()) {\n matchingFieldRefs = Object.keys(this.fieldVectors)\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n var matchingFieldRef = matchingFieldRefs[i]\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n matchingFields[matchingFieldRef] = new lunr.MatchData\n }\n }\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n /*\n * Currently we have document fields that match the query, but we\n * need to return documents. The matchData and scores are combined\n * from multiple fields belonging to the same document.\n *\n * Scores are calculated by field, using the query vectors created\n * above, and combined into a final document score using addition.\n */\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n docRef = fieldRef.docRef\n\n if (!allRequiredMatches.contains(docRef)) {\n continue\n }\n\n if (allProhibitedMatches.contains(docRef)) {\n continue\n }\n\n var fieldVector = this.fieldVectors[fieldRef],\n score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n docMatch\n\n if ((docMatch = matches[docRef]) !== undefined) {\n docMatch.score += score\n docMatch.matchData.combine(matchingFields[fieldRef])\n } else {\n var match = {\n ref: docRef,\n score: score,\n matchData: matchingFields[fieldRef]\n }\n matches[docRef] = match\n results.push(match)\n }\n }\n\n /*\n * Sort the results objects by score, highest first.\n */\n return results.sort(function (a, b) {\n return b.score - a.score\n })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n var invertedIndex = Object.keys(this.invertedIndex)\n .sort()\n .map(function (term) {\n return [term, this.invertedIndex[term]]\n }, this)\n\n var fieldVectors = Object.keys(this.fieldVectors)\n .map(function (ref) {\n return [ref, this.fieldVectors[ref].toJSON()]\n }, this)\n\n return {\n version: lunr.version,\n fields: this.fields,\n fieldVectors: fieldVectors,\n invertedIndex: invertedIndex,\n pipeline: this.pipeline.toJSON()\n }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n var attrs = {},\n fieldVectors = {},\n serializedVectors = serializedIndex.fieldVectors,\n invertedIndex = Object.create(null),\n serializedInvertedIndex = serializedIndex.invertedIndex,\n tokenSetBuilder = new lunr.TokenSet.Builder,\n pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n if (serializedIndex.version != lunr.version) {\n lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n }\n\n for (var i = 0; i < serializedVectors.length; i++) {\n var tuple = serializedVectors[i],\n ref = tuple[0],\n elements = tuple[1]\n\n fieldVectors[ref] = new lunr.Vector(elements)\n }\n\n for (var i = 0; i < serializedInvertedIndex.length; i++) {\n var tuple = serializedInvertedIndex[i],\n term = tuple[0],\n posting = tuple[1]\n\n tokenSetBuilder.insert(term)\n invertedIndex[term] = posting\n }\n\n tokenSetBuilder.finish()\n\n attrs.fields = serializedIndex.fields\n\n attrs.fieldVectors = fieldVectors\n attrs.invertedIndex = invertedIndex\n attrs.tokenSet = tokenSetBuilder.root\n attrs.pipeline = pipeline\n\n return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n this._ref = \"id\"\n this._fields = Object.create(null)\n this._documents = Object.create(null)\n this.invertedIndex = Object.create(null)\n this.fieldTermFrequencies = {}\n this.fieldLengths = {}\n this.tokenizer = lunr.tokenizer\n this.pipeline = new lunr.Pipeline\n this.searchPipeline = new lunr.Pipeline\n this.documentCount = 0\n this._b = 0.75\n this._k1 = 1.2\n this.termIndex = 0\n this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example Extracting a nested field\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n if (/\\//.test(fieldName)) {\n throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n }\n\n this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n if (number < 0) {\n this._b = 0\n } else if (number > 1) {\n this._b = 1\n } else {\n this._b = number\n }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n var docRef = doc[this._ref],\n fields = Object.keys(this._fields)\n\n this._documents[docRef] = attributes || {}\n this.documentCount += 1\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i],\n extractor = this._fields[fieldName].extractor,\n field = extractor ? extractor(doc) : doc[fieldName],\n tokens = this.tokenizer(field, {\n fields: [fieldName]\n }),\n terms = this.pipeline.run(tokens),\n fieldRef = new lunr.FieldRef (docRef, fieldName),\n fieldTerms = Object.create(null)\n\n this.fieldTermFrequencies[fieldRef] = fieldTerms\n this.fieldLengths[fieldRef] = 0\n\n // store the length of this field for this document\n this.fieldLengths[fieldRef] += terms.length\n\n // calculate term frequencies for this field\n for (var j = 0; j < terms.length; j++) {\n var term = terms[j]\n\n if (fieldTerms[term] == undefined) {\n fieldTerms[term] = 0\n }\n\n fieldTerms[term] += 1\n\n // add to inverted index\n // create an initial posting if one doesn't exist\n if (this.invertedIndex[term] == undefined) {\n var posting = Object.create(null)\n posting[\"_index\"] = this.termIndex\n this.termIndex += 1\n\n for (var k = 0; k < fields.length; k++) {\n posting[fields[k]] = Object.create(null)\n }\n\n this.invertedIndex[term] = posting\n }\n\n // add an entry for this term/fieldName/docRef to the invertedIndex\n if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n }\n\n // store all whitelisted metadata about this token in the\n // inverted index\n for (var l = 0; l < this.metadataWhitelist.length; l++) {\n var metadataKey = this.metadataWhitelist[l],\n metadata = term.metadata[metadataKey]\n\n if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n }\n\n this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n }\n }\n\n }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n var fieldRefs = Object.keys(this.fieldLengths),\n numberOfFields = fieldRefs.length,\n accumulator = {},\n documentsWithField = {}\n\n for (var i = 0; i < numberOfFields; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n field = fieldRef.fieldName\n\n documentsWithField[field] || (documentsWithField[field] = 0)\n documentsWithField[field] += 1\n\n accumulator[field] || (accumulator[field] = 0)\n accumulator[field] += this.fieldLengths[fieldRef]\n }\n\n var fields = Object.keys(this._fields)\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i]\n accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n }\n\n this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n var fieldVectors = {},\n fieldRefs = Object.keys(this.fieldTermFrequencies),\n fieldRefsLength = fieldRefs.length,\n termIdfCache = Object.create(null)\n\n for (var i = 0; i < fieldRefsLength; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n fieldName = fieldRef.fieldName,\n fieldLength = this.fieldLengths[fieldRef],\n fieldVector = new lunr.Vector,\n termFrequencies = this.fieldTermFrequencies[fieldRef],\n terms = Object.keys(termFrequencies),\n termsLength = terms.length\n\n\n var fieldBoost = this._fields[fieldName].boost || 1,\n docBoost = this._documents[fieldRef.docRef].boost || 1\n\n for (var j = 0; j < termsLength; j++) {\n var term = terms[j],\n tf = termFrequencies[term],\n termIndex = this.invertedIndex[term]._index,\n idf, score, scoreWithPrecision\n\n if (termIdfCache[term] === undefined) {\n idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n termIdfCache[term] = idf\n } else {\n idf = termIdfCache[term]\n }\n\n score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n score *= fieldBoost\n score *= docBoost\n scoreWithPrecision = Math.round(score * 1000) / 1000\n // Converts 1.23456789 to 1.234.\n // Reducing the precision so that the vectors take up less\n // space when serialised. Doing it now so that they behave\n // the same before and after serialisation. Also, this is\n // the fastest approach to reducing a number's precision in\n // JavaScript.\n\n fieldVector.insert(termIndex, scoreWithPrecision)\n }\n\n fieldVectors[fieldRef] = fieldVector\n }\n\n this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n this.tokenSet = lunr.TokenSet.fromArray(\n Object.keys(this.invertedIndex).sort()\n )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n this.calculateAverageFieldLengths()\n this.createFieldVectors()\n this.createTokenSet()\n\n return new lunr.Index({\n invertedIndex: this.invertedIndex,\n fieldVectors: this.fieldVectors,\n tokenSet: this.tokenSet,\n fields: Object.keys(this._fields),\n pipeline: this.searchPipeline\n })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n var args = Array.prototype.slice.call(arguments, 1)\n args.unshift(this)\n fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n var clonedMetadata = Object.create(null),\n metadataKeys = Object.keys(metadata || {})\n\n // Cloning the metadata to prevent the original\n // being mutated during match data combination.\n // Metadata is kept in an array within the inverted\n // index so cloning the data can be done with\n // Array#slice\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n clonedMetadata[key] = metadata[key].slice()\n }\n\n this.metadata = Object.create(null)\n\n if (term !== undefined) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = clonedMetadata\n }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n var terms = Object.keys(otherMatchData.metadata)\n\n for (var i = 0; i < terms.length; i++) {\n var term = terms[i],\n fields = Object.keys(otherMatchData.metadata[term])\n\n if (this.metadata[term] == undefined) {\n this.metadata[term] = Object.create(null)\n }\n\n for (var j = 0; j < fields.length; j++) {\n var field = fields[j],\n keys = Object.keys(otherMatchData.metadata[term][field])\n\n if (this.metadata[term][field] == undefined) {\n this.metadata[term][field] = Object.create(null)\n }\n\n for (var k = 0; k < keys.length; k++) {\n var key = keys[k]\n\n if (this.metadata[term][field][key] == undefined) {\n this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n } else {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n }\n\n }\n }\n }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n if (!(term in this.metadata)) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = metadata\n return\n }\n\n if (!(field in this.metadata[term])) {\n this.metadata[term][field] = metadata\n return\n }\n\n var metadataKeys = Object.keys(metadata)\n\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n\n if (key in this.metadata[term][field]) {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n } else {\n this.metadata[term][field][key] = metadata[key]\n }\n }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n this.clauses = []\n this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with trailing wildcard\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example query term with leading and trailing wildcard\n * query.term('foo', {\n * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with required presence\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n /**\n * Term's presence in a document is optional, this is the default value.\n */\n OPTIONAL: 1,\n\n /**\n * Term's presence in a document is required, documents that do not contain\n * this term will not be returned.\n */\n REQUIRED: 2,\n\n /**\n * Term's presence in a document is prohibited, documents that do contain\n * this term will not be returned.\n */\n PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n if (!('fields' in clause)) {\n clause.fields = this.allFields\n }\n\n if (!('boost' in clause)) {\n clause.boost = 1\n }\n\n if (!('usePipeline' in clause)) {\n clause.usePipeline = true\n }\n\n if (!('wildcard' in clause)) {\n clause.wildcard = lunr.Query.wildcard.NONE\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n clause.term = \"*\" + clause.term\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n clause.term = \"\" + clause.term + \"*\"\n }\n\n if (!('presence' in clause)) {\n clause.presence = lunr.Query.presence.OPTIONAL\n }\n\n this.clauses.push(clause)\n\n return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n for (var i = 0; i < this.clauses.length; i++) {\n if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n return false\n }\n }\n\n return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example adding a single term to a query\n * query.term(\"foo\")\n * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard\n * query.term(\"foo\", {\n * fields: [\"title\"],\n * boost: 10,\n * wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example using lunr.tokenizer to convert a string to tokens before using them as terms\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n if (Array.isArray(term)) {\n term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n return this\n }\n\n var clause = options || {}\n clause.term = term.toString()\n\n this.clause(clause)\n\n return this\n}\nlunr.QueryParseError = function (message, start, end) {\n this.name = \"QueryParseError\"\n this.message = message\n this.start = start\n this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n this.lexemes = []\n this.str = str\n this.length = str.length\n this.pos = 0\n this.start = 0\n this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n var state = lunr.QueryLexer.lexText\n\n while (state) {\n state = state(this)\n }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n var subSlices = [],\n sliceStart = this.start,\n sliceEnd = this.pos\n\n for (var i = 0; i < this.escapeCharPositions.length; i++) {\n sliceEnd = this.escapeCharPositions[i]\n subSlices.push(this.str.slice(sliceStart, sliceEnd))\n sliceStart = sliceEnd + 1\n }\n\n subSlices.push(this.str.slice(sliceStart, this.pos))\n this.escapeCharPositions.length = 0\n\n return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n this.lexemes.push({\n type: type,\n str: this.sliceString(),\n start: this.start,\n end: this.pos\n })\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n this.escapeCharPositions.push(this.pos - 1)\n this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n if (this.pos >= this.length) {\n return lunr.QueryLexer.EOS\n }\n\n var char = this.str.charAt(this.pos)\n this.pos += 1\n return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n if (this.start == this.pos) {\n this.pos += 1\n }\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n var char, charCode\n\n do {\n char = this.next()\n charCode = char.charCodeAt(0)\n } while (charCode > 47 && charCode < 58)\n\n if (char != lunr.QueryLexer.EOS) {\n this.backup()\n }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.FIELD)\n lexer.ignore()\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n if (lexer.width() > 1) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.TERM)\n }\n\n lexer.ignore()\n\n if (lexer.more()) {\n return lunr.QueryLexer.lexText\n }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.BOOST)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n while (true) {\n var char = lexer.next()\n\n if (char == lunr.QueryLexer.EOS) {\n return lunr.QueryLexer.lexEOS\n }\n\n // Escape character is '\\'\n if (char.charCodeAt(0) == 92) {\n lexer.escapeCharacter()\n continue\n }\n\n if (char == \":\") {\n return lunr.QueryLexer.lexField\n }\n\n if (char == \"~\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexEditDistance\n }\n\n if (char == \"^\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexBoost\n }\n\n // \"+\" indicates term presence is required\n // checking for length to ensure that only\n // leading \"+\" are considered\n if (char == \"+\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n // \"-\" indicates term presence is prohibited\n // checking for length to ensure that only\n // leading \"-\" are considered\n if (char == \"-\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n if (char.match(lunr.QueryLexer.termSeparator)) {\n return lunr.QueryLexer.lexTerm\n }\n }\n}\n\nlunr.QueryParser = function (str, query) {\n this.lexer = new lunr.QueryLexer (str)\n this.query = query\n this.currentClause = {}\n this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n this.lexer.run()\n this.lexemes = this.lexer.lexemes\n\n var state = lunr.QueryParser.parseClause\n\n while (state) {\n state = state(this)\n }\n\n return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n var lexeme = this.peekLexeme()\n this.lexemeIdx += 1\n return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n var completedClause = this.currentClause\n this.query.clause(completedClause)\n this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n var lexeme = parser.peekLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.type) {\n case lunr.QueryLexer.PRESENCE:\n return lunr.QueryParser.parsePresence\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n if (lexeme.str.length >= 1) {\n errorMessage += \" with value '\" + lexeme.str + \"'\"\n }\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.str) {\n case \"-\":\n parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n break\n case \"+\":\n parser.currentClause.presence = lunr.Query.presence.REQUIRED\n break\n default:\n var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term or field, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.fields = [lexeme.str]\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n parser.currentClause.term = lexeme.str.toLowerCase()\n\n if (lexeme.str.indexOf(\"*\") != -1) {\n parser.currentClause.usePipeline = false\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var editDistance = parseInt(lexeme.str, 10)\n\n if (isNaN(editDistance)) {\n var errorMessage = \"edit distance must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.editDistance = editDistance\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var boost = parseInt(lexeme.str, 10)\n\n if (isNaN(boost)) {\n var errorMessage = \"boost must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.boost = boost\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\n /**\n * export the module via AMD, CommonJS or as a browser global\n * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n */\n ;(function (root, factory) {\n if (typeof define === 'function' && define.amd) {\n // AMD. Register as an anonymous module.\n define(factory)\n } else if (typeof exports === 'object') {\n /**\n * Node. Does not work with strict CommonJS, but\n * only CommonJS-like enviroments that support module.exports,\n * like Node.\n */\n module.exports = factory()\n } else {\n // Browser globals (root is window)\n root.lunr = factory()\n }\n }(this, function () {\n /**\n * Just return a value to define the module export.\n * This example returns an object, but the module\n * can return a function as the exported value.\n */\n return lunr\n }))\n})();\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport \"~/polyfills\"\n\nimport { Search, SearchIndexConfig } from \"../../_\"\nimport {\n SearchMessage,\n SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for usage with `iframe-worker` polyfill\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous polyfilled implementation. The\n * cool thing is that awaiting a non-Promise is a noop, so extending the type\n * definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n */\ndeclare global {\n function importScripts(...urls: string[]): Promise | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages, which are defined through the search index configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search index configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n config: SearchIndexConfig\n): Promise {\n let base = \"../lunr\"\n\n /* Detect `iframe-worker` and fix base URL */\n if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n const worker = document.querySelector(\"script[src]\")!\n const [path] = worker.src.split(\"/worker\")\n\n /* Prefix base with path */\n base = base.replace(\"..\", path)\n }\n\n /* Add scripts for languages */\n const scripts = []\n for (const lang of config.lang) {\n switch (lang) {\n\n /* Add segmenter for Japanese */\n case \"ja\":\n scripts.push(`${base}/tinyseg.js`)\n break\n\n /* Add segmenter for Hindi and Thai */\n case \"hi\":\n case \"th\":\n scripts.push(`${base}/wordcut.js`)\n break\n }\n\n /* Add language support */\n if (lang !== \"en\")\n scripts.push(`${base}/min/lunr.${lang}.min.js`)\n }\n\n /* Add multi-language support */\n if (config.lang.length > 1)\n scripts.push(`${base}/min/lunr.multi.min.js`)\n\n /* Load scripts synchronously */\n if (scripts.length)\n await importScripts(\n `${base}/min/lunr.stemmer.support.min.js`,\n ...scripts\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n message: SearchMessage\n): Promise {\n switch (message.type) {\n\n /* Search setup message */\n case SearchMessageType.SETUP:\n await setupSearchLanguages(message.data.config)\n index = new Search(message.data)\n return {\n type: SearchMessageType.READY\n }\n\n /* Search query message */\n case SearchMessageType.QUERY:\n return {\n type: SearchMessageType.RESULT,\n data: index ? index.search(message.data) : { items: [] }\n }\n\n /* All other messages */\n default:\n throw new TypeError(\"Invalid message type\")\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* @ts-expect-error - expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push(obj[key])\n\n /* Return values */\n return data\n }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n /* Polyfill `Element.scrollTo` */\n if (!Element.prototype.scrollTo)\n Element.prototype.scrollTo = function (\n x?: ScrollToOptions | number, y?: number\n ): void {\n if (typeof x === \"object\") {\n this.scrollLeft = x.left!\n this.scrollTop = x.top!\n } else {\n this.scrollLeft = x!\n this.scrollTop = y!\n }\n }\n\n /* Polyfill `Element.replaceWith` */\n if (!Element.prototype.replaceWith)\n Element.prototype.replaceWith = function (\n ...nodes: Array\n ): void {\n const parent = this.parentNode\n if (parent) {\n if (nodes.length === 0)\n parent.removeChild(this)\n\n /* Replace children and create text nodes */\n for (let i = nodes.length - 1; i >= 0; i--) {\n let node = nodes[i]\n if (typeof node === \"string\")\n node = document.createTextNode(node)\n else if (node.parentNode)\n node.parentNode.removeChild(node)\n\n /* Replace child or insert before previous sibling */\n if (!i)\n parent.replaceChild(node, this)\n else\n parent.insertBefore(this.previousSibling!, node)\n }\n }\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n presence: lunr.Query.presence /* Clause presence */\n term: string /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n value: string\n): SearchQueryClause[] {\n const query = new (lunr as any).Query([\"title\", \"text\"])\n const parser = new (lunr as any).QueryParser(value, query)\n\n /* Parse and return query clauses */\n parser.parse()\n return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n const clauses = new Set(query)\n\n /* Match query clauses against terms */\n const result: SearchQueryTerms = {}\n for (let t = 0; t < terms.length; t++)\n for (const clause of clauses)\n if (terms[t].startsWith(clause.term)) {\n result[clause.term] = true\n clauses.delete(clause)\n }\n\n /* Annotate unmatched non-stopword query clauses */\n for (const clause of clauses)\n if (lunr.stopWordFilter?.(clause.term as any))\n result[clause.term] = false\n\n /* Return query terms */\n return result\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n SearchDocument,\n SearchDocumentMap,\n setupSearchDocumentMap\n} from \"../document\"\nimport {\n SearchHighlightFactoryFn,\n setupSearchHighlighter\n} from \"../highlighter\"\nimport { SearchOptions } from \"../options\"\nimport {\n SearchQueryTerms,\n getSearchQueryTerms,\n parseSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index configuration\n */\nexport interface SearchIndexConfig {\n lang: string[] /* Search languages */\n separator: string /* Search separator */\n}\n\n/**\n * Search index document\n */\nexport interface SearchIndexDocument {\n location: string /* Document location */\n title: string /* Document title */\n text: string /* Document text */\n tags?: string[] /* Document tags */\n boost?: number /* Document boost */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n *\n * This interfaces describes the format of the `search_index.json` file which\n * is automatically built by the MkDocs search plugin.\n */\nexport interface SearchIndex {\n config: SearchIndexConfig /* Search index configuration */\n docs: SearchIndexDocument[] /* Search index documents */\n options: SearchOptions /* Search options */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search metadata\n */\nexport interface SearchMetadata {\n score: number /* Score (relevance) */\n terms: SearchQueryTerms /* Search query terms */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result document\n */\nexport type SearchResultDocument = SearchDocument & SearchMetadata\n\n/**\n * Search result item\n */\nexport type SearchResultItem = SearchResultDocument[]\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result\n */\nexport interface SearchResult {\n items: SearchResultItem[] /* Search result items */\n suggestions?: string[] /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n const [x, y] = [new Set(a), new Set(b)]\n return [\n ...new Set([...x].filter(value => !y.has(value)))\n ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n /**\n * Search document mapping\n *\n * A mapping of URLs (including hash fragments) to the actual articles and\n * sections of the documentation. The search document mapping must be created\n * regardless of whether the index was prebuilt or not, as Lunr.js itself\n * only stores the actual index.\n */\n protected documents: SearchDocumentMap\n\n /**\n * Search highlight factory function\n */\n protected highlight: SearchHighlightFactoryFn\n\n /**\n * The underlying Lunr.js search index\n */\n protected index: lunr.Index\n\n /**\n * Search options\n */\n protected options: SearchOptions\n\n /**\n * Create the search integration\n *\n * @param data - Search index\n */\n public constructor({ config, docs, options }: SearchIndex) {\n this.options = options\n\n /* Set up document map and highlighter factory */\n this.documents = setupSearchDocumentMap(docs)\n this.highlight = setupSearchHighlighter(config, false)\n\n /* Set separator for tokenizer */\n lunr.tokenizer.separator = new RegExp(config.separator)\n\n /* Create search index */\n this.index = lunr(function () {\n\n /* Set up multi-language support */\n if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n this.use((lunr as any)[config.lang[0]])\n } else if (config.lang.length > 1) {\n this.use((lunr as any).multiLanguage(...config.lang))\n }\n\n /* Compute functions to be removed from the pipeline */\n const fns = difference([\n \"trimmer\", \"stopWordFilter\", \"stemmer\"\n ], options.pipeline)\n\n /* Remove functions from the pipeline for registered languages */\n for (const lang of config.lang.map(language => (\n language === \"en\" ? lunr : (lunr as any)[language]\n ))) {\n for (const fn of fns) {\n this.pipeline.remove(lang[fn])\n this.searchPipeline.remove(lang[fn])\n }\n }\n\n /* Set up reference */\n this.ref(\"location\")\n\n /* Set up fields */\n this.field(\"title\", { boost: 1e3 })\n this.field(\"text\")\n this.field(\"tags\", { boost: 1e6, extractor: doc => {\n const { tags = [] } = doc as SearchDocument\n return tags.reduce((list, tag) => [\n ...list,\n ...lunr.tokenizer(tag)\n ], [] as lunr.Token[])\n } })\n\n /* Index documents */\n for (const doc of docs)\n this.add(doc, { boost: doc.boost })\n })\n }\n\n /**\n * Search for matching documents\n *\n * The search index which MkDocs provides is divided up into articles, which\n * contain the whole content of the individual pages, and sections, which only\n * contain the contents of the subsections obtained by breaking the individual\n * pages up at `h1` ... `h6`. As there may be many sections on different pages\n * with identical titles (for example within this very project, e.g. \"Usage\"\n * or \"Installation\"), they need to be put into the context of the containing\n * page. For this reason, section results are grouped within their respective\n * articles which are the top-level results that are returned.\n *\n * @param query - Query value\n *\n * @returns Search results\n */\n public search(query: string): SearchResult {\n if (query) {\n try {\n const highlight = this.highlight(query)\n\n /* Parse query to extract clauses for analysis */\n const clauses = parseSearchQuery(query)\n .filter(clause => (\n clause.presence !== lunr.Query.presence.PROHIBITED\n ))\n\n /* Perform search and post-process results */\n const groups = this.index.search(`${query}*`)\n\n /* Apply post-query boosts based on title and search query terms */\n .reduce((item, { ref, score, matchData }) => {\n const document = this.documents.get(ref)\n if (typeof document !== \"undefined\") {\n const { location, title, text, tags, parent } = document\n\n /* Compute and analyze search query terms */\n const terms = getSearchQueryTerms(\n clauses,\n Object.keys(matchData.metadata)\n )\n\n /* Highlight title and text and apply post-query boosts */\n const boost = +!parent + +Object.values(terms).every(t => t)\n item.push({\n location,\n title: highlight(title),\n text: highlight(text),\n ...tags && { tags: tags.map(highlight) },\n score: score * (1 + boost),\n terms\n })\n }\n return item\n }, [])\n\n /* Sort search results again after applying boosts */\n .sort((a, b) => b.score - a.score)\n\n /* Group search results by page */\n .reduce((items, result) => {\n const document = this.documents.get(result.location)\n if (typeof document !== \"undefined\") {\n const ref = \"parent\" in document\n ? document.parent!.location\n : document.location\n items.set(ref, [...items.get(ref) || [], result])\n }\n return items\n }, new Map())\n\n /* Generate search suggestions, if desired */\n let suggestions: string[] | undefined\n if (this.options.suggestions) {\n const titles = this.index.query(builder => {\n for (const clause of clauses)\n builder.term(clause.term, {\n fields: [\"title\"],\n presence: lunr.Query.presence.REQUIRED,\n wildcard: lunr.Query.wildcard.TRAILING\n })\n })\n\n /* Retrieve suggestions for best match */\n suggestions = titles.length\n ? Object.keys(titles[0].matchData.metadata)\n : []\n }\n\n /* Return items and suggestions */\n return {\n items: [...groups.values()],\n ...typeof suggestions !== \"undefined\" && { suggestions }\n }\n\n /* Log errors to console (for now) */\n } catch {\n console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n }\n }\n\n /* Return nothing in case of error or empty query */\n return { items: [] }\n }\n}\n"], + "mappings": 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\ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..173be2d --- /dev/null +++ b/index.html @@ -0,0 +1,586 @@ + + + + + + + + + + + + + + + + PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/javascripts/mathjax.js b/javascripts/mathjax.js new file mode 100644 index 0000000..1c35fc1 --- /dev/null +++ b/javascripts/mathjax.js @@ -0,0 +1,17 @@ +window.MathJax = { + tex: { + inlineMath: [["\\(", "\\)"]], + displayMath: [["\\[", "\\]"]], + processEscapes: true, + processEnvironments: true + }, + options: { + ignoreHtmlClass: ".*|", + processHtmlClass: "arithmatex" + } + }; + + document$.subscribe(() => { + MathJax.typesetPromise() + }) + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 0000000..e79aadc Binary files /dev/null and b/objects.inv differ diff --git a/reference/SUMMARY/index.html b/reference/SUMMARY/index.html new file mode 100644 index 0000000..b731fec --- /dev/null +++ b/reference/SUMMARY/index.html @@ -0,0 +1,588 @@ + + + + + + + + + + + + + + + + SUMMARY - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/index.html b/reference/prismtoolbox/index.html new file mode 100644 index 0000000..d32c0e9 --- /dev/null +++ b/reference/prismtoolbox/index.html @@ -0,0 +1,5748 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
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    + + + +
    +
    + + + + + + + + + +

    Index

    + +
    + + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + WSI(slide_path, engine='openslide') + +

    + + +
    + + +

    The WSI (Whole Slide Image) class is responsible for handling operations +related to whole slide images.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_path + str + +
    +

    The path to the slide image file.

    +
    +     		+ required +
    engine + str + +
    +

    The engine used to read the slide image.

    +
    +     		+ 'openslide' +
    + + + +

    Attributes:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescription
    slide_path + str + +
    +

    The path to the slide image file.

    +
    +
    engine + str + +
    +

    The engine used to read the slide image.

    +
    +
    slide_name + str + +
    +

    The name of the slide image file. +Retrieved from the slide path using +retrieve_slide_name_ext method.

    +
    +
    slide_ext + str + +
    +

    The extension of the slide image file. +Retrieved from the slide path using +retrieve_slide_name_ext method.

    +
    +
    slide + OpenSlide | TiffSlide + +
    +

    The wsi read from the file using engine.

    +
    +
    dimensions + list[tuple[int, int]] + +
    +

    The dimensions of the slide image. +Set by the set_slide_attributes method.

    +
    +
    level_dimensions + list[tuple[int, int]] + +
    +

    The dimensions of the different levels of the slide image. +Set by the set_slide_attributes method.

    +
    +
    level_downsamples + list[tuple[int, int]] + +
    +

    The downsampling factors of the different levels of +the slide image. Set by the set_slide_attributes +method.

    +
    +
    properties + dict + +
    +

    The properties of the slide image. +Set by the set_slide_attributes method.

    +
    +
    offset + tuple[int, int] + +
    +

    The offset of the slide image. +Set by the set_slide_attributes method.

    +
    +
    ROI + ndarray + +
    +

    The region of interest in the slide image. +Please use the set_roi method to set the ROI.

    +
    +
    ROI_width + int + +
    +

    The width of the region of interest. +Set by the set_roi method.

    +
    +
    ROI_height + int + +
    +

    The height of the region of interest. +Set by the set_roi method.

    +
    +
    tissue_contours + list[ndarray] + +
    +

    The contours of the tissue in the slide image. +Please use the detect_tissue +method to detect the tissue contours.

    +
    +
    coords + ndarray + +
    +

    The coordinates of patches extracted from slide image. +Please use the extract_patches +method to extract patches.

    +
    +
    coords_attrs + dict + +
    +

    The attributes of the coordinates. +Set by the extract_patches method.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
     48
+ 49
+ 50
+ 51
+ 52
+ 53
+ 54
+ 55
+ 56
+ 57
+ 58
+ 59
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+ 61
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+ 65
+ 66
+ 67
+ 68
+ 69
+ 70
+ 71
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+ 73
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+ 75
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+107
    def __init__(self, slide_path: str, engine: str = "openslide"):
+    """The WSI (Whole Slide Image) class is responsible for handling operations
+    related to whole slide images.
+
+    Args:
+        slide_path: The path to the slide image file.
+        engine: The engine used to read the slide image.
+
+    Attributes:
+        slide_path (str): The path to the slide image file.
+        engine (str): The engine used to read the slide image.
+        slide_name (str): The name of the slide image file.
+            Retrieved from the slide path using
+            [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method.
+        slide_ext (str): The extension of the slide image file.
+            Retrieved from the slide path using
+            [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method.
+        slide (OpenSlide | TiffSlide): The wsi read from the file using engine.
+        dimensions (list[tuple[int, int]]): The dimensions of the slide image.
+            Set by the set_slide_attributes method.
+        level_dimensions (list[tuple[int, int]]): The dimensions of the different levels of the slide image.
+            Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method.
+        level_downsamples (list[tuple[int, int]]): The downsampling factors of the different levels of
+            the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes]
+            method.
+        properties (dict): The properties of the slide image.
+            Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method.
+        offset (tuple[int, int]): The offset of the slide image.
+            Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method.
+        ROI (ndarray): The region of interest in the slide image.
+            Please use the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method to set the ROI.
+        ROI_width (int): The width of the region of interest.
+            Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method.
+        ROI_height (int): The height of the region of interest.
+            Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method.
+        tissue_contours (list[ndarray]): The contours of the tissue in the slide image.
+            Please use the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue]
+            method to detect the tissue contours.
+        coords (np.ndarray): The coordinates of patches extracted from slide image.
+            Please use the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches]
+            method to extract patches.
+        coords_attrs (dict): The attributes of the coordinates.
+            Set by the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method.
+    """
+    self.slide_path = slide_path
+    self.engine = engine
+    self.slide_name, self.slide_ext = self.retrieve_slide_name_ext(self.slide_path)
+    self.slide = self.read(slide_path, engine)
+    self.dimensions = None
+    self.level_dimensions = None
+    self.level_downsamples = None
+    self.properties = None
+    self.offset = (0, 0)
+    self.set_slide_attributes()
+    self.ROI = None
+    self.ROI_width = None
+    self.ROI_height = None
+    self.tissue_contours = None
+    self.coords = None
+    self.coords_attrs = None
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + apply_pathologist_annotations(path, class_name='annotation', column_to_select='objectType') + +

    + + +
    + +

    Apply pathologist annotations to the tissue contours by intersecting the annotations +with the tissue contours. Requires the tissue contours to be set for the slide beforehand +with the detect_tissue method.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    path + str + +
    +

    The path to the .geojson file containing the annotations extracted from QuPath.

    +
    +     		+ required +
    class_name + str + +
    +

    The class name to use for selecting the annotations to apply.

    +
    +     		+ 'annotation' +
    column_to_select + str + +
    +

    The column to select in the GeoDataFrame.

    +
    +     		+ 'objectType' +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    629
+630
+631
+632
+633
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    def apply_pathologist_annotations(
+    self,
+    path: str,
+    class_name: str = "annotation",
+    column_to_select: str = "objectType",
+) -> None:
+    """Apply pathologist annotations to the tissue contours by intersecting the annotations
+    with the tissue contours. Requires the tissue contours to be set for the slide beforehand
+    with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method.
+
+    Args:
+        path: The path to the .geojson file containing the annotations extracted from QuPath.
+        class_name: The class name to use for selecting the annotations to apply.
+        column_to_select: The column to select in the GeoDataFrame.
+    """
+    assert (
+        self.tissue_contours is not None
+    ), "No tissue contours found for the slide, please run the detect_tissue method first"
+    offset = (-self.offset[0], -self.offset[1])
+    pathologist_annotations = read_qupath_annotations(
+        path, offset=offset, class_name=class_name, column_to_select=column_to_select
+    )
+    polygons = contoursToPolygons(self.tissue_contours, make_valid=True)
+    intersection = intersectionPolygons(polygons, pathologist_annotations)
+    self.tissue_contours = PolygonsToContours(intersection)
+
    +
    +
    + +
    + + +
    + + + +

    + convert_micrometer_to_pixel(value, level, axis='x') + +

    + + +
    + +

    Convert a value from micrometer to pixel.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    value + float + +
    +

    The value to convert (in micrometer).

    +
    +     		+ required +
    level + int + +
    +

    The level at which the conversion should be performed.

    +
    +     		+ required +
    axis + str + +
    +

    The axis to use for getting the conversion factor (x or y).

    +
    +     		+ 'x' +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + int + +
    +

    The input value in pixel.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    234
+235
+236
+237
+238
+239
+240
+241
+242
+243
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+245
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+247
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+250
+251
+252
    def convert_micrometer_to_pixel(
+    self,
+    value: float,
+    level: int,
+    axis: str = "x",
+) -> int:
+    """Convert a value from micrometer to pixel.
+
+    Args:
+        value: The value to convert (in micrometer).
+        level: The level at which the conversion should be performed.
+        axis: The axis to use for getting the conversion factor (x or y).
+
+    Returns:
+        The input value in pixel.
+    """
+    return int(value / float(self.properties[f"{self.engine}.mpp-{axis}"])) // int(
+        self.level_downsamples[level]
+    )
+
    +
    +
    + +
    + + +
    + + + +

    + convert_pixel_to_micrometer(value, level, axis='x') + +

    + + +
    + +

    Convert a value from pixel to micrometer.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    value + float + +
    +

    The value to convert (in pixel).

    +
    +     		+ required +
    level + int + +
    +

    The level at which the conversion should be performed.

    +
    +     		+ required +
    axis + str + +
    +

    The axis to use for getting the conversion factor (x or y).

    +
    +     		+ 'x' +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + float + +
    +

    The input value in micrometer.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    254
+255
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+274
    def convert_pixel_to_micrometer(
+    self,
+    value: float,
+    level: int,
+    axis: str = "x",
+) -> float:
+    """Convert a value from pixel to micrometer.
+
+    Args:
+        value: The value to convert (in pixel).
+        level: The level at which the conversion should be performed.
+        axis: The axis to use for getting the conversion factor (x or y).
+
+    Returns:
+        The input value in micrometer.
+    """
+    return (
+        value
+        * float(self.properties[f"{self.engine}.mpp-{axis}"])
+        * self.level_downsamples[level]
+    )
+
    +
    +
    + +
    + + +
    + + + +

    + convert_units(value, level, from_unit, to_unit='px', axis='x') + +

    + + +
    + +

    Convert a value from one unit to another.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    value + float + +
    +

    The value to convert.

    +
    +     		+ required +
    level + int + +
    +

    The level at which the conversion should be performed.

    +
    +     		+ required +
    from_unit + str + +
    +

    The unit to convert from (px or micro).

    +
    +     		+ required +
    to_unit + str + +
    +

    The unit to convert to (px or micro).

    +
    +     		+ 'px' +
    axis + str + +
    +

    The axis to use for getting the conversion factor (x or y).

    +
    +     		+ 'x' +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + int + +
    +

    The input value converted in the desired unit.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    276
+277
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+280
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+300
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+303
+304
    def convert_units(
+    self,
+    value: float,
+    level: int,
+    from_unit: str,
+    to_unit: str = "px",
+    axis: str = "x",
+) -> int:
+    """Convert a value from one unit to another.
+
+    Args:
+        value: The value to convert.
+        level: The level at which the conversion should be performed.
+        from_unit: The unit to convert from (px or micro).
+        to_unit: The unit to convert to (px or micro).
+        axis: The axis to use for getting the conversion factor (x or y).
+
+    Returns:
+        The input value converted in the desired unit.
+    """
+    if from_unit == "micro" and to_unit == "px":
+        value = self.convert_micrometer_to_pixel(value, level, axis)
+    elif from_unit == "px" and to_unit == "micro":
+        value = self.convert_pixel_to_micrometer(value, level, axis)
+    elif from_unit == to_unit:
+        pass
+    else:
+        raise ValueError(f"Conversion from {from_unit} to {to_unit} not supported")
+    return value
+
    +
    +
    + +
    + + +
    + + + +

    + create_thumbnail(level, crop_roi=False) + +

    + + +
    + +

    Create a thumbnail of the slide at a given level.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    level + int + +
    +

    The level at which the thumbnail should be created.

    +
    +     		+ required +
    crop_roi + bool + +
    +

    A boolean to crop the thumbnail to the region of interest defined for the slide +(requires a ROI to be set for the slide beforehand with the +set_roi method)

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Image + +
    +

    A thumbnail of the slide as a PIL image.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    499
+500
+501
+502
+503
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+518
+519
+520
+521
+522
+523
+524
+525
+526
    def create_thumbnail(
+    self,
+    level: int,
+    crop_roi: bool = False,
+) -> Image.Image:
+    """Create a thumbnail of the slide at a given level.
+
+    Args:
+        level: The level at which the thumbnail should be created.
+        crop_roi: A boolean to crop the thumbnail to the region of interest defined for the slide
+            (requires a ROI to be set for the slide beforehand with the
+            [set_roi][prismtoolbox.wsicore.WSI.set_roi] method)
+
+    Returns:
+        A thumbnail of the slide as a PIL image.
+    """
+    assert self.ROI is not None if crop_roi else True, (
+        "No ROI provided, while crop_roi is set to True. Please set a ROI for "
+        "the slide or set crop_roi to False."
+    )
+    thumb = self.read_region((0, 0), level, self.level_dimensions[level]).convert(
+        "RGB"
+    )
+    if crop_roi:
+        log.info(f"Creating thumbnail with ROI {self.ROI}.")
+        coords_roi = (self.ROI / self.level_downsamples[level]).astype(int)
+        thumb = thumb.crop(coords_roi)
+    return thumb
+
    +
    +
    + +
    + + +
    + + + +

    + detect_tissue(seg_level, window_avg, window_eng, thresh, inside_roi=False, inv_thresh=False, area_min=200000.0, start=(0, 0)) + +

    + + +
    + +

    Segment the tissue on the slide based on a threshold on the Law's texture +energy spot map and floodfill algorithm to fill the holes in the mask. The +tissue contours are stored in the tissue_contours attribute.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    seg_level + int + +
    +

    The level at which the segmentation should be performed.

    +
    +     		+ required +
    window_avg + int + +
    +

    The size of the window for local averaging.

    +
    +     		+ required +
    window_eng + int + +
    +

    The size of the window for Law's texture energy computation.

    +
    +     		+ required +
    thresh + int + +
    +

    The threshold for binarization on the Law's texture energy spot map.

    +
    +     		+ required +
    inside_roi + bool + +
    +

    Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide +beforehand with the set_roi method).

    +
    +     		+ False +
    inv_thresh + bool + +
    +

    Set to True to invert the thresholding.

    +
    +     		+ False +
    area_min + float + +
    +

    The minimum area for a contour to be kept.

    +
    +     		+ 200000.0 +
    start + tuple[int, int] + +
    +

    The starting point for the floodfill algorithm (should be left at (0, 0) in most cases).

    +
    +     		+ (0, 0) +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    567
+568
+569
+570
+571
+572
+573
+574
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+622
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+624
+625
+626
+627
    def detect_tissue(
+    self,
+    seg_level: int,
+    window_avg: int,
+    window_eng: int,
+    thresh: int,
+    inside_roi: bool = False,
+    inv_thresh: bool = False,
+    area_min: float = 2e5,
+    start: tuple[int, int] = (0, 0),
+) -> None:
+    """Segment the tissue on the slide based on a threshold on the Law's texture
+    energy spot map and floodfill algorithm to fill the holes in the mask. The
+    tissue contours are stored in the tissue_contours attribute.
+
+    Args:
+        seg_level: The level at which the segmentation should be performed.
+        window_avg: The size of the window for local averaging.
+        window_eng: The size of the window for Law's texture energy computation.
+        thresh: The threshold for binarization on the Law's texture energy spot map.
+        inside_roi: Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide
+            beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method).
+        inv_thresh: Set to True to invert the thresholding.
+        area_min: The minimum area for a contour to be kept.
+        start: The starting point for the floodfill algorithm (should be left at (0, 0) in most cases).
+    """
+    final_contours = []
+    img = np.array(self.create_thumbnail(seg_level, inside_roi))
+    img_avg = local_average(np.asarray(img), window_avg)
+    law_feats = compute_law_feats(img_avg, window_eng)[0]
+    filterred_img = apply_bilateral_filter(np.clip(law_feats, 0, 255).astype("uint8"))
+    threshed_img = apply_binary_thresh(filterred_img, thresh, inv_thresh)
+    flooded_img = floodfill_img(np.pad(threshed_img, 1), start)
+    contours = contour_mask(flooded_img)
+    for contour in contours:
+        c = contour.copy()
+        area = cv2.contourArea(c)
+        if area > area_min:
+            final_contours.append(contour)
+    if len(final_contours) == 0:
+        self.tissue_contours = []
+        print(f"No contours found for slide {self.slide_name}.")
+        return
+    else:
+        scale = self.level_downsamples[seg_level]
+        offset = np.array(self.ROI[:2]) if self.ROI is not None else np.array([0, 0])
+        final_contours = self.scale_contours(final_contours, scale)
+        final_contours = [cont + offset for cont in final_contours]
+        # Sanity check to ensure that the contours are all within a ROI is provided
+        if self.ROI is not None:
+            assert all(
+                [
+                    np.all(cont >= self.ROI[:2]) and np.all(cont <= self.ROI[2:])
+                    for cont in final_contours
+                ]
+            )
+        self.tissue_contours = final_contours
+        print(
+            f"Identified {len(final_contours)} contours for slide {self.slide_name}."
+        )
+        return
+
    +
    +
    + +
    + + +
    + + + +

    + extract_patches(patch_size, patch_level, mode, step_size=None, overlap=None, units=('px', 'px'), use_padding=True, contours_mode=None, rgb_threshs=(2, 220), percentages=(0.6, 0.9)) + +

    + + +
    + +

    Extract valid patches from the slide with different extraction modes. A patch +is considered valid if it is not black or white and is within the region of +interest or the tissue contours if relevant. The extracted patches are stored as +coordinates in the coords attribute, and the attributes of the coordinates are +stored in the coords_attrs attribute.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    patch_size + float + +
    +

    The size of the patches to extract (assumed to be square).

    +
    +     		+ required +
    patch_level + int + +
    +

    The level at which the patches should be extracted.

    +
    +     		+ required +
    mode + str + +
    +

    The mode to use for the extraction:

    +
      +
    • "contours" mode extracts patches within the tissue contours (requires the tissue contours +to be set for the slide beforehand with the detect_tissue +method).
      • +
    • "roi" mode extracts patches within the region of interest (requires the ROI to be set +for the slide beforehand with the set_roi method).
      • +
    • "all" mode extracts patches from the entire slide
      • +
    +
    +     		+ required +
    step_size + float | None + +
    +

    The step size for the sliding window (if set to None, the step size will be computed based on the +overlap).

    +
    +     		+ None +
    overlap + float | None + +
    +

    The overlap between patches as an absolute value (must be provided if step_size is set to None).

    +
    +     		+ None +
    units + tuple[str, str] + +
    +

    The units for the patch size and step size/overlap values (pixels: px, micrometers: micro).

    +
    +     		+ ('px', 'px') +
    use_padding + bool + +
    +

    Set to True to use padding for the extraction.

    +
    +     		+ True +
    contours_mode + str | None + +
    +

    The mode to use for the contour checking function (must be provided if mode is +set to contours). See IsInContour for +more details.

    +
    +     		+ None +
    rgb_threshs + tuple[int, int] + +
    +

    The tuple of thresholds for the RGB channels (black threshold, white threshold).

    +
    +     		+ (2, 220) +
    percentages + tuple[float, float] + +
    +

    The tuple of percentages of pixels below/above the thresholds to consider the patch as +black/white.

    +
    +     		+ (0.6, 0.9) +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def extract_patches(
+    self,
+    patch_size: float,
+    patch_level: int,
+    mode: str,
+    step_size: float | None = None,
+    overlap: float | None = None,
+    units: tuple[str, str] = ("px", "px"),
+    use_padding: bool = True,
+    contours_mode: str | None = None,
+    rgb_threshs: tuple[int, int] = (2, 220),
+    percentages: tuple[float, float] = (0.6, 0.9),
+) -> None:
+    """Extract valid patches from the slide with different extraction modes. A patch
+    is considered valid if it is not black or white and is within the region of
+    interest or the tissue contours if relevant. The extracted patches are stored as
+    coordinates in the coords attribute, and the attributes of the coordinates are
+    stored in the coords_attrs attribute.
+
+    Args:
+        patch_size: The size of the patches to extract (assumed to be square).
+        patch_level: The level at which the patches should be extracted.
+        mode: The mode to use for the extraction:
+
+            - "contours" mode extracts patches within the tissue contours (requires the tissue contours
+            to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue]
+            method).
+            - "roi" mode extracts patches within the region of interest (requires the ROI to be set
+            for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method).
+            - "all" mode extracts patches from the entire slide
+
+        step_size: The step size for the sliding window (if set to None, the step size will be computed based on the
+            overlap).
+        overlap: The overlap between patches as an absolute value (must be provided if step_size is set to None).
+        units: The units for the patch size and step size/overlap values (pixels: px, micrometers: micro).
+        use_padding: Set to True to use padding for the extraction.
+        contours_mode: The mode to use for the contour checking function (must be provided if mode is
+            set to contours). See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for
+            more details.
+        rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold).
+        percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as
+            black/white.
+    """
+    assert (
+        step_size is not None or overlap is not None
+    ), "Either step_size or overlap must be provided"
+    assert all(
+        [unit in ["micro", "px"] for unit in units]
+    ), "Units must be either 'micro' or 'px'"
+
+    patch_size = self.convert_units(patch_size, patch_level, units[0])
+    if step_size is None:
+        step_size = patch_size - self.convert_units(overlap, patch_level, units[1])
+    else:
+        step_size = self.convert_units(step_size, patch_level, units[1])
+
+    log.info(
+        f"Extracting patches of size {patch_size} at level {patch_level} with step size {step_size}."
+    )
+
+    if mode == "contours":
+        log.info("Extracting patches with 'contours' mode.")
+        assert self.tissue_contours is not None
+        assert len(self.tissue_contours) > 0
+        assert contours_mode is not None
+        valid_coords = []
+        for cont in self.tissue_contours:
+            roi_dim = cv2.boundingRect(cont)
+            log.info(f"Processing ROI of dimensions: {roi_dim}")
+            valid_coords.extend(
+                self.extract_patches_roi(
+                    patch_level,
+                    patch_size,
+                    step_size,
+                    roi_dim,
+                    use_padding,
+                    cont,
+                    contours_mode,
+                    rgb_threshs=rgb_threshs,
+                    percentages=percentages,
+                )
+            )
+        valid_coords = np.array(valid_coords)
+    elif mode == "roi":
+        log.info("Extracting patches with 'roi' mode.")
+        assert self.ROI is not None
+        roi_dim = self.ROI[0], self.ROI[1], self.ROI_width, self.ROI_height
+        log.info("Processing ROI of dimensions:", roi_dim)
+        valid_coords = self.extract_patches_roi(
+            patch_level,
+            patch_size,
+            step_size,
+            roi_dim,
+            use_padding,
+            rgb_threshs=rgb_threshs,
+            percentages=percentages,
+        )
+    elif mode == "all":
+        roi_dim = 0, 0, self.level_dimensions[0][0], self.level_dimensions[0][1]
+        log.info("Processing ROI of dimensions:", roi_dim)
+        valid_coords = self.extract_patches_roi(
+            patch_level,
+            patch_size,
+            step_size,
+            roi_dim,
+            use_padding,
+            rgb_threshs=rgb_threshs,
+            percentages=percentages,
+        )
+    else:
+        raise ValueError(f"Mode {mode} not supported")
+
+    attr = {
+        "patch_size": patch_size,
+        "patch_level": patch_level,
+        "downsample": self.level_downsamples[patch_level],
+        "downsampled_level_dim": tuple(np.array(self.level_dimensions[patch_level])),
+        "level_dim": self.level_dimensions[patch_level],
+        "name": self.slide_name,
+    }
+
+    if len(valid_coords) == 0:
+        log.warning(f"No valid coordinates found for slide {self.slide_name}.")
+    else:
+        print(
+            f"Identified a total of {len(valid_coords)}  valid coordinates in the slide {self.slide_name}."
+        )
+        self.coords = valid_coords
+        self.coords_attrs = attr
+
    +
    +
    + +
    + + +
    + + + +

    + extract_patches_roi(patch_level, patch_size, step_size=None, roi_dim=None, use_padding=True, contour=None, contours_mode=None, coord_candidates=None, rgb_threshs=(2, 220), percentages=(0.6, 0.9), return_indices=False) + +

    + + +
    + +

    Extract valid patches from a region of interest, i.e if the patch is not +black or white and is within the region of interest/contours if relevant).

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    patch_level + int + +
    +

    The level at which the patches should be extracted.

    +
    +     		+ required +
    patch_size + int + +
    +

    The size of the patches to extract (assumed to be square).

    +
    +     		+ required +
    step_size + int | None + +
    +

    The step size to use for the sliding window.

    +
    +     		+ None +
    roi_dim + tuple[int, int, int, int] | None + +
    +

    The top-left corner coordinates and dimensions of the region of interest. Must be provided if +coord_candidates is set to None.

    +
    +     		+ None +
    use_padding + bool + +
    +

    Set to True to use padding for the extraction.

    +
    +     		+ True +
    contour + ndarray | None + +
    +

    The tissue contour to use for the extraction. If set to None, will not check if patches are within +a contour.

    +
    +     		+ None +
    contours_mode + str | None + +
    +

    The mode for the contour checking function. +See IsInContour for more details. Must be provided +if mode is set to contours. Otherwise, will not check if patches are within the contours.

    +
    +     		+ None +
    coord_candidates + ndarray | None + +
    +

    Precomputed candidate coordinates for the patches. If set to None, will compute the +candidate coordinates based on the ROI dimensions and the step size.

    +
    +     		+ None +
    rgb_threshs + tuple[int, int] + +
    +

    The tuple of thresholds for the RGB channels (black threshold, white threshold).

    +
    +     		+ (2, 220) +
    percentages + tuple[float, float] + +
    +

    The tuple of percentages of pixels below/above the thresholds to consider the patch as +black/white.

    +
    +     		+ (0.6, 0.9) +
    return_indices + bool + +
    +

    Set to True to return the indices of the valid coordinates.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches).

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def extract_patches_roi(
+    self,
+    patch_level: int,
+    patch_size: int,
+    step_size: int | None = None,
+    roi_dim: tuple[int, int, int, int] | None = None,
+    use_padding: bool = True,
+    contour: np.ndarray | None = None,
+    contours_mode: str | None = None,
+    coord_candidates: np.ndarray | None = None,
+    rgb_threshs: tuple[int, int] = (2, 220),
+    percentages: tuple[float, float] = (0.6, 0.9),
+    return_indices: bool = False,
+) -> np.ndarray:
+    """Extract valid patches from a region of interest, i.e if the patch is not
+    black or white and is within the region of interest/contours if relevant).
+
+    Args:
+        patch_level: The level at which the patches should be extracted.
+        patch_size: The size of the patches to extract (assumed to be square).
+        step_size: The step size to use for the sliding window.
+        roi_dim: The top-left corner coordinates and dimensions of the region of interest. Must be provided if
+            coord_candidates is set to None.
+        use_padding: Set to True to use padding for the extraction.
+        contour: The tissue contour to use for the extraction. If set to None, will not check if patches are within
+            a contour.
+        contours_mode: The mode for the contour checking function.
+            See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. Must be provided
+            if mode is set to contours. Otherwise, will not check if patches are within the contours.
+        coord_candidates: Precomputed candidate coordinates for the patches. If set to None, will compute the
+            candidate coordinates based on the ROI dimensions and the step size.
+        rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold).
+        percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as
+            black/white.
+        return_indices: Set to True to return the indices of the valid coordinates.
+
+    Returns:
+        An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches).
+    """
+    assert contours_mode is not None if contour is not None else True, (
+        "A contour mode must be provided if patch "
+        "extraction mode is set to contours"
+    )
+    assert (
+        roi_dim is not None and step_size is not None
+    ) or coord_candidates is not None, (
+        "Either (roi_dim and step_size) or coord_candidates must be provided"
+    )
+    patch_downsample = int(self.level_downsamples[patch_level])
+    ref_patch_size = patch_size * patch_downsample
+
+    if coord_candidates is None:
+        start_x, start_y, w, h = roi_dim
+
+        img_w, img_h = self.level_dimensions[0]
+
+        if use_padding:
+            stop_y = start_y + h
+            stop_x = start_x + w
+        else:
+            stop_y = min(start_y + h, img_h - ref_patch_size + 1)
+            stop_x = min(start_x + w, img_w - ref_patch_size + 1)
+
+        step_size = step_size * patch_downsample
+
+        x_range = np.arange(start_x, stop_x, step=step_size)
+        y_range = np.arange(start_y, stop_y, step=step_size)
+        x_coords, y_coords = np.meshgrid(x_range, y_range, indexing="ij")
+        coord_candidates = np.array(
+            [x_coords.flatten(), y_coords.flatten()]
+        ).transpose()
+
+    if contour is not None:
+        cont_check_fn = IsInContour(
+            contour, patch_size=ref_patch_size, center_shift=0.5, mode=contours_mode
+        )
+        log.info(
+            f"Extracting patches with contour checking function mode {contours_mode}."
+        )
+    else:
+        cont_check_fn = None
+
+    num_workers = mp.cpu_count()
+    pool = mp.Pool(
+        num_workers,
+        initializer=WSI.worker_init,
+        initargs=(
+            self.slide_path,
+            self.engine,
+        ),
+    )
+    iterable = [
+        (coord, cont_check_fn, patch_level, patch_size, rgb_threshs, percentages)
+        for coord in coord_candidates
+    ]
+    valid_coords = pool.starmap(WSI.process_coord_candidate, iterable)
+    pool.close()
+
+    valid_indices = [i for i, coord in enumerate(valid_coords) if coord is not None]
+    valid_coords = np.array([coord_candidates[i] for i in valid_indices])
+
+    log.info(
+        f"Identified {len(valid_coords)}  valid coordinates in the ROI {roi_dim}."
+    )
+
+    if return_indices:
+        return valid_coords, valid_indices
+    else:
+        return valid_coords
+
    +
    +
    + +
    + + +
    + + + +

    + is_black_white(patch, rgb_threshs=(2, 220), percentages=(0.6, 0.9)) + + + staticmethod + + +

    + + +
    + +

    Check if a patch is black or white.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    patch + Image + +
    +

    The input patch.

    +
    +     		+ required +
    rgb_threshs + tuple[int, int] + +
    +

    The tuple of thresholds for the RGB channels (black threshold, white threshold).

    +
    +     		+ (2, 220) +
    percentages + tuple[float, float] + +
    +

    The tuple of percentages of pixels below/above the thresholds to consider the patch +as black/white.

    +
    +     		+ (0.6, 0.9) +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + bool + +
    +

    True if the patch is black or white, False otherwise.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def is_black_white(
+    patch: Image.Image,
+    rgb_threshs: tuple[int, int] = (2, 220),
+    percentages: tuple[float, float] = (0.6, 0.9),
+) -> bool:
+    """Check if a patch is black or white.
+
+    Args:
+        patch: The input patch.
+        rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold).
+        percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch
+            as black/white.
+
+    Returns:
+        True if the patch is black or white, False otherwise.
+    """
+    return isBlackPatch(
+        patch, rgb_thresh=rgb_threshs[0], percentage=percentages[0]
+    ) or isWhitePatch(patch, rgb_thresh=rgb_threshs[1], percentage=percentages[1])
+
    +
    +
    + +
    + + +
    + + + +

    + load_patches(file_path) + +

    + + +
    + +

    Load the patches from a hdf5 file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    file_path + str + +
    +

    The path to the hdf5 file containing the patches.

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def load_patches(self, file_path: str) -> None:
+    """Load the patches from a hdf5 file.
+
+    Args:
+        file_path: The path to the hdf5 file containing the patches.
+    """
+    log.info(f"Loading patches for slide {self.slide_name} from {file_path}.")
+    self.coords, self.coords_attrs = read_h5_file(file_path, "coords")
+
    +
    +
    + +
    + + +
    + + + +

    + load_tissue_contours(file_path) + +

    + + +
    + +

    Load the tissue contours from a pickle file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    file_path + str + +
    +

    The path to the pickle file containing the tissue contours.

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def load_tissue_contours(self, file_path: str) -> None:
+    """Load the tissue contours from a pickle file.
+
+    Args:
+        file_path: The path to the pickle file containing the tissue contours.
+    """
+    log.info(f"Loading tissue contours for slide {self.slide_name} from {file_path}.")
+    self.tissue_contours = load_obj_with_pickle(file_path)
+
    +
    +
    + +
    + + +
    + + + +

    + process_coord_candidate(coord, cont_check_fn, patch_level, patch_size, rgb_threshs=(2, 220), percentages=(0.6, 0.9)) + + + staticmethod + + +

    + + +
    + +

    Determine if a candidate coordinate is valid based on a contour checking +function and/or black/white thresholding.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    coord + tuple[int, int] + +
    +

    The candidate coordinate.

    +
    +     		+ required +
    cont_check_fn + IsInContour | None + +
    +

    The contour checking function.

    +
    +     		+ required +
    patch_level + int + +
    +

    The level at which the patch should be extracted.

    +
    +     		+ required +
    patch_size + int + +
    +

    The size of the patch (assumed to be square).

    +
    +     		+ required +
    rgb_threshs + tuple[int, int] + +
    +

    The tuple of thresholds for the RGB channels (black threshold, white threshold).

    +
    +     		+ (2, 220) +
    percentages + tuple[float, float] + +
    +

    The tuple of percentages of pixels below/above the thresholds to consider the patch as +black/white.

    +
    +     		+ (0.6, 0.9) +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + tuple[int, int] | None + +
    +

    The coordinate if it is valid, None otherwise.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def process_coord_candidate(
+    coord: tuple[int, int],
+    cont_check_fn: IsInContour | None,
+    patch_level: int,
+    patch_size: int,
+    rgb_threshs: tuple[int, int] = (2, 220),
+    percentages: tuple[float, float] = (0.6, 0.9),
+) -> tuple[int, int] | None:
+    """Determine if a candidate coordinate is valid based on a contour checking
+    function and/or black/white thresholding.
+
+    Args:
+        coord: The candidate coordinate.
+        cont_check_fn: The contour checking function.
+        patch_level: The level at which the patch should be extracted.
+        patch_size: The size of the patch (assumed to be square).
+        rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold).
+        percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as
+            black/white.
+
+    Returns:
+        The coordinate if it is valid, None otherwise.
+    """
+    if cont_check_fn is None or cont_check_fn(coord):
+        patch = wsi.read_region(
+            tuple(coord), patch_level, (patch_size, patch_size)
+        ).convert("RGB")
+        if not WSI.is_black_white(patch, rgb_threshs, percentages):
+            return coord
+        else:
+            return None
+    else:
+        return None
+
    +
    +
    + +
    + + +
    + + + +

    + read(slide_path, engine) + + + staticmethod + + +

    + + +
    + +

    Read a slide with a given engine.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_path + str + +
    +

    The path to the slide.

    +
    +     		+ required +
    engine + str + +
    +

    The backend library to use for reading the slide +(currently only openslide and tiffslide are supported).

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + +
    +

    A slide object.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def read(slide_path: str, engine: str):
+    """Read a slide with a given engine.
+
+    Args:
+        slide_path: The path to the slide.
+        engine: The backend library to use for reading the slide
+            (currently only openslide and tiffslide are supported).
+
+    Returns:
+        A slide object.
+    """
+    if engine == "openslide":
+        import openslide
+
+        slide = openslide.OpenSlide(slide_path)
+    elif engine == "tiffslide":
+        import tiffslide
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=UserWarning)
+            slide = tiffslide.TiffSlide(slide_path)
+    else:
+        raise NotImplementedError(f"engine {engine} not supported")
+    return slide
+
    +
    +
    + +
    + + +
    + + + +

    + read_region(location, level, size) + +

    + + +
    + +

    Read a region from the slide for a given level and size.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    location + tuple[int, int] + +
    +

    The coordinates of the top left corner of the region (in pixels).

    +
    +     		+ required +
    level + int + +
    +

    The level at which the region should be read.

    +
    +     		+ required +
    size + tuple[int, int] + +
    +

    The size of the region (in pixels).

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Image + +
    +

    The desired region of the slide as a PIL image.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def read_region(
+    self,
+    location: tuple[int, int],
+    level: int,
+    size: tuple[int, int],
+) -> Image.Image:
+    """Read a region from the slide for a given level and size.
+
+    Args:
+        location: The coordinates of the top left corner of the region (in pixels).
+        level: The level at which the region should be read.
+        size: The size of the region (in pixels).
+
+    Returns:
+        The desired region of the slide as a PIL image.
+    """
+    return self.slide.read_region(location, level, size).convert("RGB")
+
    +
    +
    + +
    + + +
    + + + +

    + retrieve_slide_name_ext(slide_path) + + + staticmethod + + +

    + + +
    + +

    Retrieves slide name and slide extension from slide path.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_path + str + +
    +

    The path to the slide.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + tuple[str, str] + +
    +

    A tuple (slide name, slide ext).

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def retrieve_slide_name_ext(slide_path: str) -> tuple[str, str]:
+    """Retrieves slide name and slide extension from slide path.
+
+    Args:
+        slide_path: The path to the slide.
+
+    Returns:
+        A tuple (slide name, slide ext).
+    """
+    slide_ext = re.search(r"(?<=\.)[\w\.]+$", slide_path).group(0)
+    slide_name = re.search(r"([^/]+?)(?=\.[\w\.]+$)", slide_path).group(0)
+    return slide_name, slide_ext
+
    +
    +
    + +
    + + +
    + + + +

    + save_patches(save_dir, file_format='h5', selected_idx=None, merge=False, label=None, color=(255, 0, 0), append_to_existing_file=False) + +

    + + +
    + +

    Save the patches in a hdf5 or geojson file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    save_dir + str + +
    +

    The path to the directory where the patches will be saved.

    +
    +     		+ required +
    file_format + str + +
    +

    The format for the saving (h5 for python processing, geojson for QuPath processing).

    +
    +     		+ 'h5' +
    selected_idx + ndarray | None + +
    +

    An array of indices of the patches to save (if set to None, all the patches will be saved).

    +
    +     		+ None +
    merge + bool + +
    +

    Set to True to merge the patches into a single polygon (for geojson format only).

    +
    +     		+ False +
    label + str | None + +
    +

    An optional label to assign to the patches (for geojson format only).

    +
    +     		+ None +
    color + tuple[int, int, int] + +
    +

    An optional color to assign to the patches (for geojson format only).

    +
    +     		+ (255, 0, 0) +
    append_to_existing_file + bool + +
    +

    Set to True to append the patches to an existing geojson file.

    +
    +     		+ False +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def save_patches(
+    self,
+    save_dir: str,
+    file_format: str = "h5",
+    selected_idx: np.ndarray | None = None,
+    merge: bool = False,
+    label: str | None = None,
+    color: tuple[int, int, int] = (255, 0, 0),
+    append_to_existing_file: bool = False,
+) -> None:
+    """Save the patches in a hdf5 or geojson file.
+
+    Args:
+        save_dir: The path to the directory where the patches will be saved.
+        file_format: The format for the saving (h5 for python processing, geojson for QuPath processing).
+        selected_idx: An array of indices of the patches to save (if set to None, all the patches will be saved).
+        merge: Set to True to merge the patches into a single polygon (for geojson format only).
+        label: An optional label to assign to the patches (for geojson format only).
+        color: An optional color to assign to the patches (for geojson format only).
+        append_to_existing_file: Set to True to append the patches to an existing geojson file.
+    """
+    if selected_idx is not None:
+        coords = self.coords[selected_idx]
+    else:
+        coords = self.coords
+    if not os.path.isdir(save_dir):
+        log.warning(f"Folder {save_dir} does not exist, creating new folder...")
+        pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
+    if file_format == "h5":
+        asset_dict = {"coords": self.coords}
+        attr_dict = {"coords": self.coords_attrs}
+        file_path = os.path.join(save_dir, f"{self.slide_name}.h5")
+        log.info(
+            f"Saving patches for slide {self.slide_name} at {file_path} with hdf5."
+        )
+        save_patches_with_hdf5(file_path, asset_dict, attr_dict)
+    elif file_format == "geojson":
+        file_path = os.path.join(save_dir, f"{self.slide_name}.geojson")
+        patch_downsample = int(
+            self.level_downsamples[self.coords_attrs["patch_level"]]
+        )
+        polygons = patchesToPolygons(
+            coords, self.coords_attrs["patch_size"], patch_downsample, merge
+        )
+        log.info(
+            f"Saving {len(coords)} patches for slide {self.slide_name} at {file_path} with geojson."
+        )
+        export_polygons_to_qupath(
+            polygons,
+            file_path,
+            "annotation",
+            offset=self.offset,
+            label=label,
+            color=color,
+            append_to_existing_file=append_to_existing_file,
+        )
+    elif file_format == "jpg" or file_format == "png":
+        save_dir = os.path.join(save_dir, self.slide_name)
+        if not os.path.isdir(save_dir):
+            log.warning(f"Folder {save_dir} does not exist, creating new folder...")
+            pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
+        log.info(
+            f"Saving {len(coords)} patches for slide {self.slide_name} at {save_dir} with {file_format}."
+        )
+        for coord in coords:
+            patch = self.read_region(
+                coord,
+                self.coords_attrs["patch_level"],
+                (self.coords_attrs["patch_size"], self.coords_attrs["patch_size"]),
+            ).convert("RGB")
+            patch.save(os.path.join(save_dir, f"{coord[0]}_{coord[1]}.{file_format}"))
+    else:
+        raise ValueError(f"format {file_format} not supported")
+
    +
    +
    + +
    + + +
    + + + +

    + save_tissue_contours(save_dir, selected_idx=None, file_format='pickle', merge=False, label=None, color=(255, 0, 0), append_to_existing_file=False, make_valid=False) + +

    + + +
    + +

    Save the tissue contours in a pickle or geojson file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    save_dir + str + +
    +

    The path to the directory where the contours will be saved.

    +
    +     		+ required +
    selected_idx + ndarray | None + +
    +

    An array of indices of the contours to save +(if set to None, all the contours will be saved).

    +
    +     		+ None +
    file_format + str + +
    +

    The file format for saving the contours +(pickle for python processing, geojson for QuPath processing).

    +
    +     		+ 'pickle' +
    merge + bool + +
    +

    Set to True to merge the contours into a single polygon (for geojson format only).

    +
    +     		+ False +
    label + str | None + +
    +

    An optional label to assign to the tissue contours (for geojson format only).

    +
    +     		+ None +
    color + tuple[int, int, int] + +
    +

    An optional color to assign to the tissue contours (for geojson format only).

    +
    +     		+ (255, 0, 0) +
    append_to_existing_file + bool + +
    +

    Set to True to append the contours to an existing geojson file.

    +
    +     		+ False +
    make_valid + bool + +
    +

    Set to True to make the polygons valid (for geojson format only).

    +
    +     		+ False +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def save_tissue_contours(
+    self,
+    save_dir: str,
+    selected_idx: np.ndarray | None = None,
+    file_format: str = "pickle",
+    merge: bool = False,
+    label: str | None = None,
+    color: tuple[int, int, int] = (255, 0, 0),
+    append_to_existing_file: bool = False,
+    make_valid: bool = False,
+) -> None:
+    """Save the tissue contours in a pickle or geojson file.
+
+    Args:
+        save_dir: The path to the directory where the contours will be saved.
+        selected_idx: An array of indices of the contours to save
+            (if set to None, all the contours will be saved).
+        file_format: The file format for saving the contours
+            (pickle for python processing, geojson for QuPath processing).
+        merge: Set to True to merge the contours into a single polygon (for geojson format only).
+        label: An optional label to assign to the tissue contours (for geojson format only).
+        color: An optional color to assign to the tissue contours (for geojson format only).
+        append_to_existing_file: Set to True to append the contours to an existing geojson file.
+        make_valid: Set to True to make the polygons valid (for geojson format only).
+    """
+    assert self.tissue_contours is not None, (
+        "No tissue contours found for the slide, "
+        "please run the detect_tissue method first"
+    )
+    if selected_idx is not None:
+        tissue_contours = [self.tissue_contours[idx] for idx in selected_idx]
+    else:
+        tissue_contours = self.tissue_contours
+    if not os.path.isdir(save_dir):
+        log.warning(f"Folder {save_dir} does not exist, creating new folder...")
+        pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
+    if file_format == "pickle":
+        file_path = os.path.join(save_dir, f"{self.slide_name}.pkl")
+        log.info(
+            f"Saving tissue contours for slide {self.slide_name} at {file_path} with pickle."
+        )
+        save_obj_with_pickle(tissue_contours, file_path)
+    elif file_format == "geojson":
+        file_path = os.path.join(save_dir, f"{self.slide_name}.geojson")
+        log.info(
+            f"Saving {selected_idx} tissue contours for slide {self.slide_name} at {file_path} with geojson."
+        )
+        polygons = contoursToPolygons(tissue_contours, merge, make_valid)
+        export_polygons_to_qupath(
+            polygons,
+            file_path,
+            "annotation",
+            offset=self.offset,
+            label=label,
+            color=color,
+            append_to_existing_file=append_to_existing_file,
+        )
+    else:
+        raise ValueError(f"format {file_format} not supported")
+
    +
    +
    + +
    + + +
    + + + +

    + scale_contours(contours, scale) + + + staticmethod + + +

    + + +
    + +

    Scale the contours by a given factor.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    contours + list[ndarray] + +
    +

    The contours to scale.

    +
    +     		+ required +
    scale + int + +
    +

    The scale factor to apply.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + list[ndarray] + +
    +

    The input list with scaled contours.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def scale_contours(
+    contours: list[np.ndarray],
+    scale: int,
+) -> list[np.ndarray]:
+    """Scale the contours by a given factor.
+
+    Args:
+        contours: The contours to scale.
+        scale: The scale factor to apply.
+
+    Returns:
+         The input list with scaled contours.
+    """
+    scaled_contours = [np.array(cont * scale, dtype="int") for cont in contours]
+    return scaled_contours
+
    +
    +
    + +
    + + +
    + + + +

    + set_roi(roi=None, rois_df_path=None) + +

    + + +
    + +

    Set the region of interest for the slide. Can be set manually or by selecting +a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    roi + tuple[int, int, int, int] | None + +
    +

    Set the region of interest manually as a tuple (x1, y1, x2, y2).

    +
    +     		+ None +
    rois_df_path + str | None + +
    +

    The path to dataframe containing the ROIs with a slide_id column identifying the slide.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + None + +
    +

    The region of interest set for the slide as a tuple (x1, y1, x2, y2).

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def set_roi(
+    self,
+    roi: tuple[int, int, int, int] | None = None,
+    rois_df_path: str | None = None,
+) -> None:
+    """Set the region of interest for the slide. Can be set manually or by selecting
+    a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute.
+
+    Args:
+        roi: Set the region of interest manually as a tuple (x1, y1, x2, y2).
+        rois_df_path: The path to dataframe containing the ROIs with a slide_id column identifying the slide.
+
+    Returns:
+        The region of interest set for the slide as a tuple (x1, y1, x2, y2).
+    """
+    if roi is not None:
+        ROI = np.array(roi).astype(int)
+    elif rois_df_path is not None:
+        rois_df = pd.read_csv(rois_df_path)
+        ROI = rois_df[rois_df.slide_id == self.slide_name].values[0].astype(int)
+    else:
+        log.info("No ROI provided, prompting user to select one.")
+        level = input(
+            f"No ROI was provided, please select a level at which the ROI should be created (max level: {len(self.level_dimensions)-1}): "
+        )
+        if not level:
+            log.warning("No level provided, setting the ROI at the highest level.")
+            level = len(self.level_downsamples) - 1
+        else:
+            level = int(level)
+        img = np.array(self.create_thumbnail(level))
+        ROI = select_roi_on_thumbnail(img, int(self.level_downsamples[level]))
+        ROI = (ROI * self.level_downsamples[level]).astype(int)
+    self.ROI = ROI
+    self.ROI_width = ROI[2] - ROI[0]
+    self.ROI_height = ROI[3] - ROI[1]
+    print(f"ROI for slide {self.slide_name} has been set to {self.ROI}.")
+    return ROI
+
    +
    +
    + +
    + + +
    + + + +

    + set_slide_attributes() + +

    + + +
    + +

    Set the slide attributes.

    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def set_slide_attributes(self):
+    """Set the slide attributes."""
+    if self.engine == "openslide":
+        self.dimensions = self.slide.dimensions
+        self.level_dimensions = self.slide.level_dimensions
+        self.level_downsamples = self.slide.level_downsamples
+        self.properties = self.slide.properties
+    elif self.engine == "tiffslide":
+        self.dimensions = self.slide.dimensions
+        self.level_dimensions = self.slide.level_dimensions
+        self.level_downsamples = self.slide.level_downsamples
+        self.properties = self.slide.properties
+    else:
+        raise NotImplementedError(f"engine {self.engine} not supported")
+    if (
+        f"{self.engine}.bounds-x" in self.properties.keys()
+        and self.properties[f"{self.engine}.bounds-x"] is not None
+    ):
+        self.offset = (
+            -int(self.properties[f"{self.engine}.bounds-x"]),
+            -int(self.properties[f"{self.engine}.bounds-y"]),
+        )
+
    +
    +
    + +
    + + +
    + + + +

    + stitch(vis_level, selected_idx=None, colors=None, alpha=0.6, black_white=False, draw_grid=False, crop_roi=False, background_color=(0, 0, 0)) + +

    + + +
    + +

    Stitch the patches extracted on an image. The patches can be masked and +colored depending on the mask and colors provided. Requires the coordinates of +the patches to be set for the slide beforehand with the +extract_patches method.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    vis_level + int + +
    +

    The level at which the patches should be visualized.

    +
    +     		+ required +
    selected_idx + ndarray + +
    +

    An array of indices of the patches to visualize (if set to None, all the patches will be +visualized).

    +
    +     		+ None +
    colors + ndarray + +
    +

    An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as +they are).

    +
    +     		+ None +
    alpha + float + +
    +

    Set the transparency of the colors to apply to the patches.

    +
    +     		+ 0.6 +
    black_white + bool + +
    +

    Set to True to visualize a binary mask of the patches extracted.

    +
    +     		+ False +
    draw_grid + bool + +
    +

    Set to True to draw a grid on the stitched patches.

    +
    +     		+ False +
    crop_roi + bool + +
    +

    Set to True to crop the visualization to the region of interest (requires a ROI to be set for the +slide beforehand with the set_roi method).

    +
    +     		+ False +
    background_color + tuple[int, int, int] + +
    +

    The color of the background.

    +
    +     		+ (0, 0, 0) +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Image + +
    +

    A PIL image of the stitched patches.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def stitch(
+    self,
+    vis_level: int,
+    selected_idx: np.ndarray = None,
+    colors: np.ndarray = None,
+    alpha: float = 0.6,
+    black_white: bool = False,
+    draw_grid: bool = False,
+    crop_roi: bool = False,
+    background_color: tuple[int, int, int] = (0, 0, 0),
+) -> Image.Image:
+    """Stitch the patches extracted on an image. The patches can be masked and
+    colored depending on the mask and colors provided. Requires the coordinates of
+    the patches to be set for the slide beforehand with the
+    [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method.
+
+    Args:
+        vis_level: The level at which the patches should be visualized.
+        selected_idx: An array of indices of the patches to visualize (if set to None, all the patches will be
+            visualized).
+        colors: An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as
+            they are).
+        alpha: Set the transparency of the colors to apply to the patches.
+        black_white: Set to True to visualize a binary mask of the patches extracted.
+        draw_grid: Set to True to draw a grid on the stitched patches.
+        crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the
+            slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method).
+        background_color: The color of the background.
+
+    Returns:
+        A PIL image of the stitched patches.
+    """
+    assert self.ROI is not None if crop_roi else True, (
+        "no ROI provided, while crop_roi is set to True,"
+        " please set a ROI for the slide or set crop_roi to False."
+    )
+    assert self.coords is not None, (
+        "no coordinates provided for the patches to visualize, please run the "
+        "extract_patches method first or load the coordinates from a file"
+    )
+    if crop_roi:
+        w, h = int(np.ceil(self.ROI_width / self.level_downsamples[vis_level])), int(
+            np.ceil(self.ROI_height / self.level_downsamples[vis_level])
+        )
+    else:
+        w, h = self.level_dimensions[vis_level]
+    patch_size = self.coords_attrs["patch_size"]
+    patch_level = self.coords_attrs["patch_level"]
+    patch_size = int(patch_size * self.level_downsamples[patch_level])
+    canvas = init_image(w, h, mask=black_white, color_bakground=background_color)
+    downsample_vis = self.level_downsamples[vis_level]
+    idxs = np.arange(len(self.coords))
+    if selected_idx is not None:
+        idxs = idxs[selected_idx]
+    patch_size = np.ceil(patch_size / downsample_vis).astype(int)
+    log.info(
+        f"Stitching {len(idxs)} patches at level {vis_level} with patch size {patch_size}, "
+        f"with colors {colors is not None}."
+    )
+    offset = self.ROI[:2] if crop_roi else np.array([0, 0])
+    for idx in idxs:
+        coord = self.coords[idx]
+        coord_downsampled = np.ceil(np.abs(coord - offset) / downsample_vis).astype(
+            int
+        )
+        patch_size_coord = (
+            min(max(w - coord_downsampled[0], 0), patch_size),
+            min(max(h - coord_downsampled[1], 0), patch_size),
+        )
+        if any(val == 0 for val in patch_size_coord):
+            continue
+        if black_white:
+            patch = np.ones(patch_size_coord, dtype="uint8")
+            colors = None
+        else:
+            patch = np.array(
+                self.slide.read_region(
+                    tuple(coord), vis_level, patch_size_coord
+                ).convert("RGB")
+            )
+        if colors is not None:
+            assert len(colors) == len(idxs), (
+                "the number of colors provided must match "
+                "the number of selected coordinates"
+            )
+            color = colors[idx]
+            color_patch = (
+                np.ones((patch_size_coord[1], patch_size_coord[0], 3)) * color
+            ).astype("uint8")
+            canvas[
+                coord_downsampled[1] : coord_downsampled[1] + patch_size_coord[1],
+                coord_downsampled[0] : coord_downsampled[0] + patch_size_coord[0],
+                :,
+            ] = cv2.addWeighted(color_patch, alpha, patch, 1 - alpha, 0, patch)
+        else:
+            canvas[
+                coord_downsampled[1] : coord_downsampled[1] + patch_size_coord[1],
+                coord_downsampled[0] : coord_downsampled[0] + patch_size_coord[0],
+                :,
+            ] = patch
+        if draw_grid:
+            cv2.rectangle(
+                canvas,
+                tuple(np.maximum([0, 0], coord_downsampled - 1)),
+                tuple(coord_downsampled + patch_size_coord),
+                (0, 0, 0, 255),
+                thickness=2,
+            )
+    img = Image.fromarray(canvas)
+    return img
+
    +
    +
    + +
    + + +
    + + + +

    + visualize(vis_level, crop_roi=False, contours_color=(255, 0, 0), line_thickness=500, max_size=None, number_contours=False, black_white=False, view_slide_only=False) + +

    + + +
    + +

    Visualize the slide with or without the contours of the tissue.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    vis_level + int + +
    +

    The level at which the visualization should be performed.

    +
    +     		+ required +
    crop_roi + bool + +
    +

    Set to True to crop the visualization to the region of interest (requires a ROI to be set for the +slide beforehand with the set_roi method).

    +
    +     		+ False +
    contours_color + tuple[int, int, int] + +
    +

    The color to use for the contours.

    +
    +     		+ (255, 0, 0) +
    line_thickness + int + +
    +

    The thickness to use for the contours

    +
    +     		+ 500 +
    max_size + int | None + +
    +

    The maximum size for the visualization for the width or height of the image.

    +
    +     		+ None +
    number_contours + bool + +
    +

    Set to True to number the contours.

    +
    +     		+ False +
    black_white + bool + +
    +

    Set to True to visualize a binary mask of the contoured tissue.

    +
    +     		+ False +
    view_slide_only + bool + +
    +

    Set to True to visualize the slide only (without the contours).

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Image + +
    +

    A PIL image of the visualization.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def visualize(
+    self,
+    vis_level: int,
+    crop_roi: bool = False,
+    contours_color: tuple[int, int, int] = (255, 0, 0),
+    line_thickness: int = 500,
+    max_size: int | None = None,
+    number_contours: bool = False,
+    black_white: bool = False,
+    view_slide_only: bool = False,
+) -> Image.Image:
+    """Visualize the slide with or without the contours of the tissue.
+
+    Args:
+        vis_level: The level at which the visualization should be performed.
+        crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the
+            slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method).
+        contours_color: The color to use for the contours.
+        line_thickness: The thickness to use for the contours
+        max_size: The maximum size for the visualization for the width or height of the image.
+        number_contours: Set to True to number the contours.
+        black_white: Set to True to visualize a binary mask of the contoured tissue.
+        view_slide_only: Set to True to visualize the slide only (without the contours).
+
+    Returns:
+        A PIL image of the visualization.
+    """
+    assert line_thickness > 0, "line_thickness must be greater than 0"
+
+    scale = 1 / self.level_downsamples[vis_level]
+
+    if black_white:
+        img = np.zeros_like(self.create_thumbnail(vis_level, crop_roi), dtype="uint8")
+        line_thickness = -1
+        contours_color = (1, 1, 1)
+    else:
+        img = np.array(self.create_thumbnail(vis_level, crop_roi))
+        line_thickness = int(line_thickness * scale)
+
+    if not view_slide_only:
+        assert (
+            len(self.tissue_contours) > 0
+        ), "No tissue contours found for the slide, please run the detect_tissue method first"
+        offset = self.ROI[:2] if crop_roi else np.array([0, 0])
+        contours = [cont - offset for cont in self.tissue_contours]
+        contours = self.scale_contours(contours, scale)
+        if len(contours) > 0:
+            if not number_contours:
+                cv2.drawContours(
+                    img,
+                    contours,
+                    -1,
+                    contours_color,
+                    line_thickness,
+                    lineType=cv2.LINE_8,
+                )
+            else:  # add numbering to each contour
+                for idx, cont in enumerate(contours):
+                    M = cv2.moments(cont)
+                    cX = int(M["m10"] / (M["m00"] + 1e-9))
+                    cY = int(M["m01"] / (M["m00"] + 1e-9))
+                    # draw the contour and put text next to center
+                    cv2.drawContours(
+                        img,
+                        [cont],
+                        -1,
+                        contours_color,
+                        line_thickness,
+                        lineType=cv2.LINE_8,
+                    )
+                    cv2.putText(
+                        img,
+                        "{}".format(idx),
+                        (cX, cY),
+                        cv2.FONT_HERSHEY_SIMPLEX,
+                        2,
+                        (255, 0, 0),
+                        10,
+                    )
+
+    img = Image.fromarray(img)
+
+    if black_white:
+        img = img.convert("L")
+
+    w, h = img.size
+
+    if max_size is not None and (w > max_size or h > max_size):
+        resizeFactor = max_size / w if w > h else max_size / h
+        img = img.resize((int(w * resizeFactor), int(h * resizeFactor)))
+
+    return img
+
    +
    +
    + +
    + + +
    + + + +

    + worker_init(slide_path, engine) + + + staticmethod + + +

    + + +
    + +

    Initialize the worker process with a wsi object.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_path + str + +
    +

    The path to the slide.

    +
    +     		+ required +
    engine + str + +
    +

    The backend library to use for reading the slide (currently only openslide +and tiffslide are supported)

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    166
+167
+168
+169
+170
+171
+172
+173
+174
+175
+176
    @staticmethod
+def worker_init(slide_path: str, engine: str) -> None:
+    """Initialize the worker process with a wsi object.
+
+    Args:
+        slide_path: The path to the slide.
+        engine: The backend library to use for reading the slide (currently only openslide
+            and tiffslide are supported)
+    """
+    global wsi
+    wsi = WSI.read(slide_path, engine)
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/nucleiseg/index.html b/reference/prismtoolbox/nucleiseg/index.html new file mode 100644 index 0000000..19f694e --- /dev/null +++ b/reference/prismtoolbox/nucleiseg/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    + + + + + + + + + +

    Index

    + +
    + + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/nucleiseg/models/index.html b/reference/prismtoolbox/nucleiseg/models/index.html new file mode 100644 index 0000000..d63ec60 --- /dev/null +++ b/reference/prismtoolbox/nucleiseg/models/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    + + + + + + + + + +

    Index

    + +
    + + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/nucleiseg/models/sop/architectures/index.html b/reference/prismtoolbox/nucleiseg/models/sop/architectures/index.html new file mode 100644 index 0000000..e39e015 --- /dev/null +++ b/reference/prismtoolbox/nucleiseg/models/sop/architectures/index.html @@ -0,0 +1,1976 @@ + + + + + + + + + + + + + + + + Architectures - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    + + + + + + + + + +

    Architectures

    + +
    + + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + ResnetBlock(dim, padding_type, norm_layer, use_dropout, use_bias) + +

    + + +
    +

    + Bases: Module

    + + +

    Define a Resnet block

    + +

    Initialize the Resnet block +A resnet block is a conv block with skip connections +We construct a conv block with build_conv_block function, +and implement skip connections in function. +Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
+    """Initialize the Resnet block
+    A resnet block is a conv block with skip connections
+    We construct a conv block with build_conv_block function,
+    and implement skip connections in <forward> function.
+    Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
+    """
+    super(ResnetBlock, self).__init__()
+    self.conv_block = self.build_conv_block(
+        dim, padding_type, norm_layer, use_dropout, use_bias
+    )
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias) + +

    + + +
    + +

    Construct a convolutional block. +Parameters: + dim (int) -- the number of channels in the conv layer. + padding_type (str) -- the name of padding layer: reflect | replicate | zero + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers. + use_bias (bool) -- if the conv layer uses bias or not +Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
+    """Construct a convolutional block.
+    Parameters:
+        dim (int)           -- the number of channels in the conv layer.
+        padding_type (str)  -- the name of padding layer: reflect | replicate | zero
+        norm_layer          -- normalization layer
+        use_dropout (bool)  -- if use dropout layers.
+        use_bias (bool)     -- if the conv layer uses bias or not
+    Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
+    """
+    conv_block = []
+    p = 0
+    if padding_type == "reflect":
+        conv_block += [nn.ReflectionPad2d(1)]
+    elif padding_type == "replicate":
+        conv_block += [nn.ReplicationPad2d(1)]
+    elif padding_type == "zero":
+        p = 1
+    else:
+        raise NotImplementedError("padding [%s] is not implemented" % padding_type)
+
+    conv_block += [
+        nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
+        norm_layer(dim),
+        nn.ReLU(True),
+    ]
+    if use_dropout:
+        conv_block += [nn.Dropout(0.5)]
+
+    p = 0
+    if padding_type == "reflect":
+        conv_block += [nn.ReflectionPad2d(1)]
+    elif padding_type == "replicate":
+        conv_block += [nn.ReplicationPad2d(1)]
+    elif padding_type == "zero":
+        p = 1
+    else:
+        raise NotImplementedError("padding [%s] is not implemented" % padding_type)
+    conv_block += [
+        nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
+        norm_layer(dim),
+    ]
+
+    return nn.Sequential(*conv_block)
+
    +
    +
    + +
    + + +
    + + + +

    + forward(x) + +

    + + +
    + +

    Forward function (with skip connections)

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def forward(self, x):
+    """Forward function (with skip connections)"""
+    out = x + self.conv_block(x)  # add skip connections
+    return out
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + +
    + + + +

    + ResnetGenerator(input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect') + +

    + + +
    +

    + Bases: Module

    + + +

    Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations. +We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)

    + +

    Construct a Resnet-based generator +Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers + n_blocks (int) -- the number of ResNet blocks + padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def __init__(
+    self,
+    input_nc,
+    output_nc,
+    ngf=64,
+    norm_layer=nn.BatchNorm2d,
+    use_dropout=False,
+    n_blocks=6,
+    padding_type="reflect",
+):
+    """Construct a Resnet-based generator
+    Parameters:
+        input_nc (int)      -- the number of channels in input images
+        output_nc (int)     -- the number of channels in output images
+        ngf (int)           -- the number of filters in the last conv layer
+        norm_layer          -- normalization layer
+        use_dropout (bool)  -- if use dropout layers
+        n_blocks (int)      -- the number of ResNet blocks
+        padding_type (str)  -- the name of padding layer in conv layers: reflect | replicate | zero
+    """
+    assert n_blocks >= 0
+    super(ResnetGenerator, self).__init__()
+    if type(norm_layer) == functools.partial:
+        use_bias = norm_layer.func == nn.InstanceNorm2d
+    else:
+        use_bias = norm_layer == nn.InstanceNorm2d
+
+    model = [
+        nn.ReflectionPad2d(3),
+        nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
+        norm_layer(ngf),
+        nn.ReLU(True),
+    ]
+
+    n_downsampling = 2
+    for i in range(n_downsampling):  # add downsampling layers
+        mult = 2**i
+        model += [
+            nn.Conv2d(
+                ngf * mult,
+                ngf * mult * 2,
+                kernel_size=3,
+                stride=2,
+                padding=1,
+                bias=use_bias,
+            ),
+            norm_layer(ngf * mult * 2),
+            nn.ReLU(True),
+        ]
+
+    mult = 2**n_downsampling
+    for i in range(n_blocks):  # add ResNet blocks
+
+        model += [
+            ResnetBlock(
+                ngf * mult,
+                padding_type=padding_type,
+                norm_layer=norm_layer,
+                use_dropout=use_dropout,
+                use_bias=use_bias,
+            )
+        ]
+
+    for i in range(n_downsampling):  # add upsampling layers
+        mult = 2 ** (n_downsampling - i)
+        model += [
+            nn.ConvTranspose2d(
+                ngf * mult,
+                int(ngf * mult / 2),
+                kernel_size=3,
+                stride=2,
+                padding=1,
+                output_padding=1,
+                bias=use_bias,
+            ),
+            norm_layer(int(ngf * mult / 2)),
+            nn.ReLU(True),
+        ]
+    model += [nn.ReflectionPad2d(3)]
+    model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
+    # Model += [nn.Tanh()]
+
+    self.model = nn.Sequential(*model)
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + forward(input) + +

    + + +
    + +

    Standard forward

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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+270
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    def forward(self, input):
+    """Standard forward"""
+    return self.model(input)
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + +
    + + + +

    + UnetGenerator(input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, dropout_value=0.5, bias_last_conv=True) + +

    + + +
    +

    + Bases: Module

    + + +

    Create a Unet-based generator

    + +

    Construct a Unet generator +Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer +We construct the U-Net from the innermost layer to the outermost layer. +It is a recursive process.

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def __init__(
+    self,
+    input_nc,
+    output_nc,
+    num_downs,
+    ngf=64,
+    norm_layer=nn.BatchNorm2d,
+    use_dropout=False,
+    dropout_value=0.5,
+    bias_last_conv=True,
+):
+    """Construct a Unet generator
+    Parameters:
+        input_nc (int)  -- the number of channels in input images
+        output_nc (int) -- the number of channels in output images
+        num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
+                            image of size 128x128 will become of size 1x1 # at the bottleneck
+        ngf (int)       -- the number of filters in the last conv layer
+        norm_layer      -- normalization layer
+    We construct the U-Net from the innermost layer to the outermost layer.
+    It is a recursive process.
+    """
+    super(UnetGenerator, self).__init__()
+    # construct unet structure
+    unet_block = UnetSkipConnectionBlock(
+        ngf * 8,
+        ngf * 8,
+        input_nc=None,
+        submodule=None,
+        norm_layer=norm_layer,
+        innermost=True,
+    )  # add the innermost layer
+    for i in range(num_downs - 5):  # add intermediate layers with ngf * 8 filters
+        unet_block = UnetSkipConnectionBlock(
+            ngf * 8,
+            ngf * 8,
+            input_nc=None,
+            submodule=unet_block,
+            norm_layer=norm_layer,
+            use_dropout=use_dropout,
+            dropout_value=dropout_value,
+        )
+    # gradually reduce the number of filters from ngf * 8 to ngf
+    unet_block = UnetSkipConnectionBlock(
+        ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer
+    )
+    unet_block = UnetSkipConnectionBlock(
+        ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer
+    )
+    unet_block = UnetSkipConnectionBlock(
+        ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer
+    )
+    self.model = UnetSkipConnectionBlock(
+        output_nc,
+        ngf,
+        input_nc=input_nc,
+        submodule=unet_block,
+        outermost=True,
+        norm_layer=norm_layer,
+        bias_last_conv=bias_last_conv,
+    )  # add the outermost layer
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + forward(input) + +

    + + +
    + +

    Standard forward

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
    405
+406
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    def forward(self, input):
+    """Standard forward"""
+    return self.model(input)
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + +
    + + + +

    + UnetSkipConnectionBlock(outer_nc, inner_nc, input_nc=None, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False, dropout_value=0.5, bias_last_conv=True) + +

    + + +
    +

    + Bases: Module

    + + +

    Defines the Unet submodule with skip connection. +X -------------------identity---------------------- +|-- downsampling -- |submodule| -- upsampling --|

    + +

    Construct a Unet submodule with skip connections. +Parameters: + outer_nc (int) -- the number of filters in the outer conv layer + inner_nc (int) -- the number of filters in the inner conv layer + input_nc (int) -- the number of channels in input images/features + submodule (UnetSkipConnectionBlock) -- previously defined submodules + outermost (bool) -- if this module is the outermost module + innermost (bool) -- if this module is the innermost module + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers.

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def __init__(
+    self,
+    outer_nc,
+    inner_nc,
+    input_nc=None,
+    submodule=None,
+    outermost=False,
+    innermost=False,
+    norm_layer=nn.BatchNorm2d,
+    use_dropout=False,
+    dropout_value=0.5,
+    bias_last_conv=True,
+):
+    """Construct a Unet submodule with skip connections.
+    Parameters:
+        outer_nc (int) -- the number of filters in the outer conv layer
+        inner_nc (int) -- the number of filters in the inner conv layer
+        input_nc (int) -- the number of channels in input images/features
+        submodule (UnetSkipConnectionBlock) -- previously defined submodules
+        outermost (bool)    -- if this module is the outermost module
+        innermost (bool)    -- if this module is the innermost module
+        norm_layer          -- normalization layer
+        use_dropout (bool)  -- if use dropout layers.
+    """
+    super(UnetSkipConnectionBlock, self).__init__()
+    self.outermost = outermost
+    if type(norm_layer) == functools.partial:
+        use_bias = norm_layer.func == nn.InstanceNorm2d
+    else:
+        use_bias = norm_layer == nn.InstanceNorm2d
+    if input_nc is None:
+        input_nc = outer_nc
+    downconv = nn.Conv2d(
+        input_nc, inner_nc, kernel_size=4, stride=2, padding=1, bias=use_bias
+    )
+    downrelu = nn.LeakyReLU(0.2, True)
+    downnorm = norm_layer(inner_nc)
+    uprelu = nn.ReLU(True)
+    upnorm = norm_layer(outer_nc)
+
+    if outermost:
+        upconv = nn.ConvTranspose2d(
+            inner_nc * 2,
+            outer_nc,
+            kernel_size=4,
+            stride=2,
+            padding=1,
+            bias=bias_last_conv,
+        )
+        down = [downconv]
+        up = [uprelu, upconv]
+        model = down + [submodule] + up
+    elif innermost:
+        upconv = nn.ConvTranspose2d(
+            inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias
+        )
+        down = [downrelu, downconv]
+        up = [uprelu, upconv, upnorm]
+        model = down + up
+    else:
+        upconv = nn.ConvTranspose2d(
+            inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias
+        )
+        down = [downrelu, downconv, downnorm]
+        up = [uprelu, upconv, upnorm]
+
+        if use_dropout:
+            model = down + [submodule] + up + [nn.Dropout(dropout_value)]
+        else:
+            model = down + [submodule] + up
+
+    self.model = nn.Sequential(*model)
+
    +
    + + + +
    + + + + + + + + + + + +
    + +
    + + +
    + + + +
    + + + +

    + define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, dropout_value=0.5, init_type='normal', init_gain=0.02, gpu_ids=[], bias_last_conv=True) + +

    + + +
    + +

    Create a generator +Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + ngf (int) -- the number of filters in the last conv layer + netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128 + norm (str) -- the name of normalization layers used in the network: batch | instance | none + use_dropout (bool) -- if use dropout layers. + init_type (str) -- the name of our initialization method. + init_gain (float) -- scaling factor for normal, xavier and orthogonal. + gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 +Returns a generator +Our current implementation provides two types of generators: + U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images) + The original U-Net paper: https://arxiv.org/abs/1505.04597 + Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks) + Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations. + We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style). +The generator has been initialized by . It uses RELU for non-linearity.

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def define_G(
+    input_nc,
+    output_nc,
+    ngf,
+    netG,
+    norm="batch",
+    use_dropout=False,
+    dropout_value=0.5,
+    init_type="normal",
+    init_gain=0.02,
+    gpu_ids=[],
+    bias_last_conv=True,
+):
+    """Create a generator
+    Parameters:
+        input_nc (int) -- the number of channels in input images
+        output_nc (int) -- the number of channels in output images
+        ngf (int) -- the number of filters in the last conv layer
+        netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128
+        norm (str) -- the name of normalization layers used in the network: batch | instance | none
+        use_dropout (bool) -- if use dropout layers.
+        init_type (str)    -- the name of our initialization method.
+        init_gain (float)  -- scaling factor for normal, xavier and orthogonal.
+        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
+    Returns a generator
+    Our current implementation provides two types of generators:
+        U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images)
+        The original U-Net paper: https://arxiv.org/abs/1505.04597
+        Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks)
+        Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations.
+        We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style).
+    The generator has been initialized by <init_net>. It uses RELU for non-linearity.
+    """
+    net = None
+    norm_layer = get_norm_layer(norm_type=norm)
+
+    if netG == "resnet_9blocks":
+        net = ResnetGenerator(
+            input_nc,
+            output_nc,
+            ngf,
+            norm_layer=norm_layer,
+            use_dropout=use_dropout,
+            n_blocks=9,
+        )
+    elif netG == "resnet_6blocks":
+        net = ResnetGenerator(
+            input_nc,
+            output_nc,
+            ngf,
+            norm_layer=norm_layer,
+            use_dropout=use_dropout,
+            n_blocks=6,
+        )
+    elif netG == "unet_128":
+        net = UnetGenerator(
+            input_nc,
+            output_nc,
+            7,
+            ngf,
+            norm_layer=norm_layer,
+            use_dropout=use_dropout,
+            dropout_value=dropout_value,
+            bias_last_conv=bias_last_conv,
+        )
+    elif netG == "unet_256":
+        net = UnetGenerator(
+            input_nc,
+            output_nc,
+            8,
+            ngf,
+            norm_layer=norm_layer,
+            use_dropout=use_dropout,
+            dropout_value=dropout_value,
+            bias_last_conv=bias_last_conv,
+        )
+    else:
+        raise NotImplementedError("Generator model name [%s] is not recognized" % netG)
+    return init_net(net, init_type, init_gain, gpu_ids)
+
    +
    +
    + +
    + + +
    + + + +

    + get_norm_layer(norm_type='instance') + +

    + + +
    + +

    Return a normalization layer +Parameters: + norm_type (str) -- the name of the normalization layer: batch | instance | none +For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). +For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def get_norm_layer(norm_type="instance"):
+    """Return a normalization layer
+    Parameters:
+        norm_type (str) -- the name of the normalization layer: batch | instance | none
+    For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
+    For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
+    """
+    if norm_type == "batch":
+        norm_layer = functools.partial(
+            nn.BatchNorm2d, affine=True, track_running_stats=True
+        )
+    elif norm_type == "instance":
+        norm_layer = functools.partial(
+            nn.InstanceNorm2d, affine=False, track_running_stats=False
+        )
+    elif norm_type == "none":
+
+        def norm_layer(x):
+            return Identity()
+
+    else:
+        raise NotImplementedError("normalization layer [%s] is not found" % norm_type)
+    return norm_layer
+
    +
    +
    + +
    + + +
    + + + +

    + init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]) + +

    + + +
    + +

    Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights +Parameters: + net (network) -- the network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + gain (float) -- scaling factor for normal, xavier and orthogonal. + gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 +Return an initialized network.

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def init_net(net, init_type="normal", init_gain=0.02, gpu_ids=[]):
+    """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
+    Parameters:
+        net (network)      -- the network to be initialized
+        init_type (str)    -- the name of an initialization method: normal | xavier | kaiming | orthogonal
+        gain (float)       -- scaling factor for normal, xavier and orthogonal.
+        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
+    Return an initialized network.
+    """
+    if len(gpu_ids) > 0:
+        assert torch.cuda.is_available()
+        net.to(gpu_ids[0])
+        net = torch.nn.DataParallel(net, gpu_ids)  # multi-GPUs
+    init_weights(net, init_type, init_gain=init_gain)
+    return net
+
    +
    +
    + +
    + + +
    + + + +

    + init_weights(net, init_type='normal', init_gain=0.02) + +

    + + +
    + +

    Initialize network weights. +Parameters: + net (network) -- network to be initialized + init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal + init_gain (float) -- scaling factor for normal, xavier and orthogonal. +We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might +work better for some applications. Feel free to try yourself.

    + +
    + Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py + 
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    def init_weights(net, init_type="normal", init_gain=0.02):
+    """Initialize network weights.
+    Parameters:
+        net (network)   -- network to be initialized
+        init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
+        init_gain (float)    -- scaling factor for normal, xavier and orthogonal.
+    We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
+    work better for some applications. Feel free to try yourself.
+    """
+
+    def init_func(m):  # define the initialization function
+        classname = m.__class__.__name__
+        if hasattr(m, "weight") and (
+            classname.find("Conv") != -1 or classname.find("Linear") != -1
+        ):
+            if init_type == "normal":
+                init.normal_(m.weight.data, 0.0, init_gain)
+            elif init_type == "xavier":
+                init.xavier_normal_(m.weight.data, gain=init_gain)
+            elif init_type == "kaiming":
+                init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
+            elif init_type == "orthogonal":
+                init.orthogonal_(m.weight.data, gain=init_gain)
+            else:
+                raise NotImplementedError(
+                    "initialization method [%s] is not implemented" % init_type
+                )
+            if hasattr(m, "bias") and m.bias is not None:
+                init.constant_(m.bias.data, 0.0)
+        elif (
+            classname.find("BatchNorm2d") != -1
+        ):  # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
+            init.normal_(m.weight.data, 1.0, init_gain)
+            init.constant_(m.bias.data, 0.0)
+
+    print("initialize network with %s" % init_type)
+    net.apply(init_func)  # apply the initialization function <init_func>
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + + + +
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    + + +
    + +
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    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/nucleiseg/models/sop/index.html b/reference/prismtoolbox/nucleiseg/models/sop/index.html new file mode 100644 index 0000000..39236a5 --- /dev/null +++ b/reference/prismtoolbox/nucleiseg/models/sop/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + + +

    Index

    + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/nucleiseg/models/sop/modules/index.html b/reference/prismtoolbox/nucleiseg/models/sop/modules/index.html new file mode 100644 index 0000000..67058da --- /dev/null +++ b/reference/prismtoolbox/nucleiseg/models/sop/modules/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Modules - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Modules

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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/nucleiseg/models/sop/postprocessing/index.html b/reference/prismtoolbox/nucleiseg/models/sop/postprocessing/index.html new file mode 100644 index 0000000..a2fec7a --- /dev/null +++ b/reference/prismtoolbox/nucleiseg/models/sop/postprocessing/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Postprocessing - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + + + + + + + +

    Postprocessing

    + +
    + + + + +
    + + + +
    + + + + + + + + + + + +
    + +
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    + + +
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    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/nucleiseg/seg_utils/index.html b/reference/prismtoolbox/nucleiseg/seg_utils/index.html new file mode 100644 index 0000000..40f34d3 --- /dev/null +++ b/reference/prismtoolbox/nucleiseg/seg_utils/index.html @@ -0,0 +1,788 @@ + + + + + + + + + + + + + + + + Seg utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
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    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    + + + + + + + + + +

    Seg utils

    + +
    + + + + +
    + + + +
    + + + + + + + + + + +
    + + + +

    + solve_conflicts(cells, threshold=0.5, return_indices=False) + +

    + + +
    + +

    Resolve segmentation conflicts (i.e. overlap) after running segmentation on patches

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    cells + list[Polygon] + +
    +

    List of cell polygons

    +
    +     		+ required +
    threshold + float + +
    +

    When two cells are overlapping, we look at the area of intersection over the area of the smallest +cell. If this value is higher than the threshold, the cells are merged

    +
    +     		+ 0.5 +
    return_indices + bool + +
    +

    If True, returns also the cells indices. Merged cells have an index of -1.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray[Polygon] | tuple[ndarray[Polygon], ndarray] + +
    +

    Array of resolved cells polygons. If return_indices, it also returns an array of cell indices.

    +
    +
    + +
    + Source code in src/prismtoolbox/nucleiseg/seg_utils.py + 
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    def solve_conflicts(
+    cells: list[Polygon],
+    threshold: float = 0.5,
+    return_indices: bool = False,
+) -> np.ndarray[Polygon] | tuple[np.ndarray[Polygon], np.ndarray]:
+    """Resolve segmentation conflicts (i.e. overlap) after running segmentation on patches
+
+    Args:
+        cells: List of cell polygons
+        threshold: When two cells are overlapping, we look at the area of intersection over the area of the smallest
+         cell. If this value is higher than the `threshold`, the cells are merged
+        return_indices: If `True`, returns also the cells indices. Merged cells have an index of -1.
+
+    Returns:
+        Array of resolved cells polygons. If `return_indices`, it also returns an array of cell indices.
+    """
+    cells = list(cells)
+    n_cells = len(cells)
+    resolved_indices = np.arange(n_cells)
+
+    assert n_cells > 0, "No cells was segmented, cannot continue"
+
+    tree = shapely.STRtree(cells)
+    conflicts = tree.query(cells, predicate="intersects")
+
+    conflicts = conflicts[:, conflicts[0] != conflicts[1]].T
+    conflicts = np.array([c for c in conflicts if c[0] < c[1]])
+
+    for i1, i2 in tqdm(conflicts, desc="Resolving conflicts"):
+        resolved_i1 = resolved_indices[i1]
+        resolved_i2 = resolved_indices[i2]
+        cell1, cell2 = cells[resolved_i1], cells[resolved_i2]
+
+        intersection = cell1.intersection(cell2).area
+        if intersection >= threshold * min(cell1.area, cell2.area):
+            cell = cell1 | cell2
+
+            resolved_indices[np.isin(resolved_indices, [resolved_i1, resolved_i2])] = len(
+                cells
+            )
+            cells.append(cell)
+
+    unique_indices = np.unique(resolved_indices)
+    unique_cells = MultiPolygon(list(np.array(cells)[unique_indices]))
+
+    if return_indices:
+        return unique_cells, np.where(unique_indices < n_cells, unique_indices, -1)
+
+    return unique_cells
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/nucleiseg/segmenter/index.html b/reference/prismtoolbox/nucleiseg/segmenter/index.html new file mode 100644 index 0000000..517e168 --- /dev/null +++ b/reference/prismtoolbox/nucleiseg/segmenter/index.html @@ -0,0 +1,598 @@ + + + + + + + + + + + + + + + + prismtoolbox.nucleiseg - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + + +

    prismtoolbox.nucleiseg

    + +
    + + + + +
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    + + + + + + + + + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/utils/data_utils/index.html b/reference/prismtoolbox/utils/data_utils/index.html new file mode 100644 index 0000000..5c182ef --- /dev/null +++ b/reference/prismtoolbox/utils/data_utils/index.html @@ -0,0 +1,1220 @@ + + + + + + + + + + + + + + + + Data utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
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    + + + +
    +
    + + + + + + + + + +

    Data utils

    + +
    + + + + +
    + + + +
    + + + + + + + + + + +
    + + + +

    + load_obj_with_json(file_path) + +

    + + +
    + +

    Load an object from a file using json.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    file_path + str + +
    +

    The path to the json file.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Any + +
    +

    A json object from the file.

    +
    +
    + +
    + Source code in src/prismtoolbox/utils/data_utils.py + 
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    def load_obj_with_json(file_path: str) -> Any:
+    """Load an object from a file using json.
+
+    Args:
+        file_path: The path to the json file.
+
+    Returns:
+        A json object from the file.
+    """
+    with open(file_path, "r") as f:
+        return json.load(f)
+
    +
    +
    + +
    + + +
    + + + +

    + load_obj_with_pickle(file_path) + +

    + + +
    + +

    Load an object from a file using pickle.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    file_path + str + +
    +

    The path to the pickle file.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Any + +
    +

    A pickeable object from the file.

    +
    +
    + +
    + Source code in src/prismtoolbox/utils/data_utils.py + 
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    def load_obj_with_pickle(file_path: str) -> Any:
+    """Load an object from a file using pickle.
+
+    Args:
+        file_path: The path to the pickle file.
+
+    Returns:
+        A pickeable object from the file.
+    """
+    with open(file_path, "rb") as f:
+        return pickle.load(f)
+
    +
    +
    + +
    + + +
    + + + +

    + read_h5_file(file_path, key) + +

    + + +
    + +

    Read an object from a h5 file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    file_path + str + +
    +

    The path to the h5 file.

    +
    +     		+ required +
    key + str + +
    +

    The key to select the dataset in the h5 file.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Tuple[ndarray, dict] + +
    +

    A dataset from the h5 file.

    +
    +
    + +
    + Source code in src/prismtoolbox/utils/data_utils.py + 
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    def read_h5_file(file_path: str, key: str) -> Tuple[np.ndarray, dict]:
+    """Read an object from a h5 file.
+
+    Args:
+        file_path: The path to the h5 file.
+        key: The key to select the dataset in the h5 file.
+
+    Returns:
+        A dataset from the h5 file.
+    """
+    with h5py.File(file_path, "r") as f:
+        object = f[key][()]
+        attrs = {key: value for key, value in f[key].attrs.items()}
+    return object, attrs
+
    +
    +
    + +
    + + +
    + + + +

    + read_json_with_geopandas(file_path, offset=(0, 0)) + +

    + + +
    + +

    Read a json file with geopandas.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    file_path + str + +
    +

    The path to a json file.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + GeoDataFrame + +
    +

    A GeoDataFrame object from the json file.

    +
    +
    + +
    + Source code in src/prismtoolbox/utils/data_utils.py + 
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    def read_json_with_geopandas(
+    file_path: str, offset: tuple[int, int] = (0, 0)
+) -> gpd.GeoDataFrame:
+    """Read a json file with geopandas.
+
+    Args:
+        file_path: The path to a json file.
+
+    Returns:
+        A GeoDataFrame object from the json file.
+    """
+    data = load_obj_with_json(file_path)
+    df = gpd.GeoDataFrame.from_features(data)
+    df.translate(xoff=offset[0], yoff=offset[1])
+    if not df.is_valid.any():
+        df.loc[~df.is_valid, :] = df.loc[~df.is_valid, :].buffer(0)
+    if "classification" in df.columns:
+        df["classification"] = df["classification"].apply(
+            lambda x: x["name"] if type(x) == dict else x
+        )
+    return df
+
    +
    +
    + +
    + + +
    + + + +

    + save_obj_with_json(obj, file_path) + +

    + + +
    + +

    Save an object to a file using json.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    obj + object + +
    +

    A json object.

    +
    +     		+ required +
    file_path + str + +
    +

    The path to the file.

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/utils/data_utils.py + 
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    def save_obj_with_json(obj: object, file_path: str) -> None:
+    """Save an object to a file using json.
+
+    Args:
+        obj: A json object.
+        file_path: The path to the file.
+    """
+    with open(file_path, "w") as f:
+        json.dump(obj, f)
+
    +
    +
    + +
    + + +
    + + + +

    + save_obj_with_pickle(obj, file_path) + +

    + + +
    + +

    Save an object to a file using pickle.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    obj + object + +
    +

    A pickeable object.

    +
    +     		+ required +
    file_path + str + +
    +

    The path to the file.

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/utils/data_utils.py + 
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    def save_obj_with_pickle(obj: object, file_path: str) -> None:
+    """Save an object to a file using pickle.
+
+    Args:
+        obj: A pickeable object.
+        file_path: The path to the file.
+    """
+    with open(file_path, "wb") as f:
+        pickle.dump(obj, f)
+
    +
    +
    + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/utils/index.html b/reference/prismtoolbox/utils/index.html new file mode 100644 index 0000000..b376d22 --- /dev/null +++ b/reference/prismtoolbox/utils/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Index

    + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/utils/qupath_utils/index.html b/reference/prismtoolbox/utils/qupath_utils/index.html new file mode 100644 index 0000000..7f4a021 --- /dev/null +++ b/reference/prismtoolbox/utils/qupath_utils/index.html @@ -0,0 +1,1165 @@ + + + + + + + + + + + + + + + + Qupath utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    +
    + + + + + + + + + +

    Qupath utils

    + +
    + + + + +
    + + + +
    + + + + + + + + + + +
    + + + +

    + PolygonsToContours(polygons) + +

    + + +
    + +

    Converts shapely polygons to list of arrays.

    +

    :param polygons: shapely polygons to convert to arrays +:return: list of contours containing the opencv-like contours created from the shapely polygons

    + +
    + Source code in src/prismtoolbox/utils/qupath_utils.py + 
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    def PolygonsToContours(polygons: MultiPolygon):
+    """Converts shapely polygons to list of arrays.
+
+    :param polygons: shapely polygons to convert to arrays
+    :return: list of contours containing the opencv-like contours created from the shapely polygons
+    """
+    return [
+        np.array(poly.exterior.coords)[:-1, None, ...].astype(int)
+        for poly in polygons.geoms
+    ]
+
    +
    +
    + +
    + + +
    + + + +

    + contoursToPolygons(contours, merge=False, make_valid=False) + +

    + + +
    + +

    Converts list of arrays to shapely polygons.

    +

    :param contours: list of contours to convert to shapely polygons +:param merge: optional boolean to merge the polygons +:param make_valid: optional boolean to enforce validity of the polygons +:return: MultiPolygon object containing the polygons created from the arrays

    + +
    + Source code in src/prismtoolbox/utils/qupath_utils.py + 
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    def contoursToPolygons(
+    contours: List[np.ndarray],
+    merge: Optional[bool] = False,
+    make_valid: Optional[bool] = False,
+) -> Union[Polygon, MultiPolygon]:
+    """Converts list of arrays to shapely polygons.
+
+    :param contours: list of contours to convert to shapely polygons
+    :param merge: optional boolean to merge the polygons
+    :param make_valid: optional boolean to enforce validity of the polygons
+    :return: MultiPolygon object containing the polygons created from the arrays
+    """
+    polygons = [Polygon(contour.squeeze()).buffer(0) for contour in contours]
+    result = []
+    for poly in polygons:
+        if poly.is_empty:
+            continue
+        if poly.geom_type == "MultiPolygon":
+            result.append(max(poly.geoms, key=lambda x: x.area))
+        else:
+            result.append(poly)
+    polygons = MultiPolygon(result)
+    if make_valid and not polygons.is_valid:
+        polygons = polygons.buffer(0)
+    if merge:
+        polygons = unary_union(polygons)
+    return polygons
+
    +
    +
    + +
    + + +
    + + + +

    + convert_rgb_to_java_int_signed(rgb) + +

    + + +
    + +

    Converts RGB tuple to Java signed integer.

    +

    :param rgb: RGB tuple +:return: Java signed integer

    + +
    + Source code in src/prismtoolbox/utils/qupath_utils.py + 
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    def convert_rgb_to_java_int_signed(rgb: Tuple[int, int, int]) -> int:
+    """Converts RGB tuple to Java signed integer.
+
+    :param rgb: RGB tuple
+    :return: Java signed integer
+    """
+    r, g, b = rgb
+    java_rgb = (255 << 24) | (r << 16) | (g << 8) | b
+    if java_rgb >= (1 << 31):
+        java_rgb -= 1 << 32
+    return java_rgb
+
    +
    +
    + +
    + + +
    + + + +

    + export_polygons_to_qupath(polygons, path, object_type, offset=(0, 0), label=None, color=None, append_to_existing_file=False, as_feature_collection=False) + +

    + + +
    + +

    Exports polygons to a .json or .geojson file.

    +

    :param polygons: shapely polygons to export +:param path: path to the .geojson file +:param object_type: type of the object (should be either "annotation" or "detection") +:param offset: optional offset to add to each coordinate in the arrays +:param label: optional label of the polygons +:param color: optional color of the polygons +:param append_to_existing_file: optional boolean to append the polygons to an existing file +:param as_feature_collection: optional boolean to save the polygons as a FeatureCollection

    + +
    + Source code in src/prismtoolbox/utils/qupath_utils.py + 
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    def export_polygons_to_qupath(
+    polygons: MultiPolygon,
+    path: str,
+    object_type: str,
+    offset: Optional[Tuple[int, int]] = (0, 0),
+    label: Optional[str] = None,
+    color: Optional[Tuple[int, int, int]] = None,
+    append_to_existing_file: Optional[bool] = False,
+    as_feature_collection: Optional[bool] = False,
+):
+    """Exports polygons to a .json or .geojson file.
+
+    :param polygons: shapely polygons to export
+    :param path: path to the .geojson file
+    :param object_type: type of the object (should be either "annotation" or "detection")
+    :param offset: optional offset to add to each coordinate in the arrays
+    :param label: optional label of the polygons
+    :param color: optional color of the polygons
+    :param append_to_existing_file: optional boolean to append the polygons to an existing file
+    :param as_feature_collection: optional boolean to save the polygons as a FeatureCollection
+    """
+    if isinstance(polygons, Polygon):
+        polygons = MultiPolygon([polygons])
+    features = []
+    properties = {"objectType": object_type}
+    if label is not None:
+        properties["classification"] = {
+            "name": label,
+            "colorRGB": convert_rgb_to_java_int_signed(color),
+        }
+    polygons = translate(polygons, xoff=offset[0], yoff=offset[1])
+    for poly in polygons.geoms:
+        features.append(
+            {
+                "type": "Feature",
+                "id": str(uuid.uuid4()),
+                "geometry": mapping(poly),
+                "properties": properties,
+            }
+        )
+    features = (
+        {"type": "FeatureCollection", "features": features}
+        if as_feature_collection
+        else features
+    )
+    if os.path.exists(path) and append_to_existing_file:
+        previous_features = load_obj_with_json(path)
+        if len(previous_features) == 0:
+            log.warning(
+                "The .geojson file does not contain any features, creating new file."
+            )
+        else:
+            if as_feature_collection:
+                previous_features["features"].extend(features["features"])
+            else:
+                previous_features.extend(features)
+            features = previous_features
+    save_obj_with_json(features, path)
+
    +
    +
    + +
    + + +
    + + + +

    + intersectionPolygons(polygons1, polygons2) + +

    + + +
    + +

    Computes the intersection of two MultiPolygons.

    +

    :param polygons1: first MultiPolygon +:param polygons2: second MultiPolygon +:return: MultiPolygon containing the intersection of the two input MultiPolygons

    + +
    + Source code in src/prismtoolbox/utils/qupath_utils.py + 
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    def intersectionPolygons(
+    polygons1: MultiPolygon, polygons2: MultiPolygon
+) -> MultiPolygon:
+    """Computes the intersection of two MultiPolygons.
+
+    :param polygons1: first MultiPolygon
+    :param polygons2: second MultiPolygon
+    :return: MultiPolygon containing the intersection of the two input MultiPolygons
+    """
+    intersection = polygons1 & polygons2
+    if intersection.geom_type == "MultiPolygon":
+        return intersection
+    elif intersection.geom_type == "GeometryCollection":
+        intersection = MultiPolygon(
+            [poly for poly in intersection.geoms if isinstance(poly, Polygon)]
+        )
+    elif intersection.geom_type == "Polygon":
+        intersection = MultiPolygon([intersection])
+    else:
+        raise ValueError(
+            "Intersection of provided MultiPolygons is not a MultiPolygon or a Polygon"
+        )
+    return intersection
+
    +
    +
    + +
    + + +
    + + + +

    + patchesToPolygons(patches, patch_size, patch_downsample, merge=False) + +

    + + +
    + +

    Converts patches to shapely polygons.

    +

    :param patches: Top left point coordinates of the patches to convert to shapely polygons +:param patch_size: size of the patches +:param merge: optional boolean to merge the polygons +:return: MultiPolygon object containing the polygons created from the patches

    + +
    + Source code in src/prismtoolbox/utils/qupath_utils.py + 
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    def patchesToPolygons(
+    patches: np.ndarray,
+    patch_size: int,
+    patch_downsample: int,
+    merge: Optional[bool] = False,
+) -> Union[Polygon, MultiPolygon]:
+    """Converts patches to shapely polygons.
+
+    :param patches: Top left point coordinates of the patches to convert to shapely polygons
+    :param patch_size: size of the patches
+    :param merge: optional boolean to merge the polygons
+    :return: MultiPolygon object containing the polygons created from the patches
+    """
+    polygons = []
+    ref_patch_size = patch_size * patch_downsample
+    for patch in patches:
+        x, y = patch
+        polygons.append(box(x, y, x + ref_patch_size, y + ref_patch_size, ccw=False))
+    polygons = MultiPolygon(polygons)
+    if merge:
+        polygons = unary_union(polygons)
+    return polygons
+
    +
    +
    + +
    + + +
    + + + +

    + read_qupath_annotations(path, offset=(0, 0), class_name='annotation', column_to_select='objectType') + +

    + + +
    + +

    Reads pathologist annotations from a .geojson file.

    +

    :param path: path to the .geojson file +:param offset: optional offset to add to each coordinate in the arrays +:param class_name: name of the class to select +:param column_to_select: optional column to select +:return: MultiPolygon object containing the polygons of the selected class.

    + +
    + Source code in src/prismtoolbox/utils/qupath_utils.py + 
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    def read_qupath_annotations(
+    path: str,
+    offset: Optional[Tuple[int, int]] = (0, 0),
+    class_name: str = "annotation",
+    column_to_select: str = "objectType",
+):
+    """Reads pathologist annotations from a .geojson file.
+
+    :param path: path to the .geojson file
+    :param offset: optional offset to add to each coordinate in the arrays
+    :param class_name: name of the class to select
+    :param column_to_select: optional column to select
+    :return: MultiPolygon object containing the polygons of the selected class.
+    """
+    df = read_json_with_geopandas(path, offset)
+    polygons = []
+    for poly in df.loc[df[column_to_select] == class_name, "geometry"].values:
+        if poly.geom_type == "Polygon":
+            polygons.append(poly)
+        elif poly.geom_type == "MultiPolygon":
+            polygons.extend(poly.geoms)
+        else:
+            raise ValueError("Geometry type not supported.")
+    polygons = MultiPolygon(polygons)
+    if not polygons.is_valid:
+        polygons = polygons.buffer(0)
+    return polygons
+
    +
    +
    + +
    + + + +
    + +
    + +
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    + + +
    + +
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    +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/utils/stain_utils/index.html b/reference/prismtoolbox/utils/stain_utils/index.html new file mode 100644 index 0000000..de5ebba --- /dev/null +++ b/reference/prismtoolbox/utils/stain_utils/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Stain utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    +
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    + + + +
    +
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    + + + +
    +
    + + + + + + + + + +

    Stain utils

    + +
    + + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
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    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/utils/torch_utils/index.html b/reference/prismtoolbox/utils/torch_utils/index.html new file mode 100644 index 0000000..b289cde --- /dev/null +++ b/reference/prismtoolbox/utils/torch_utils/index.html @@ -0,0 +1,750 @@ + + + + + + + + + + + + + + + + Torch utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
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    + + + +
    +
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    + + + +
    +
    + + + + + + + + + +

    Torch utils

    + +
    + + + + +
    + + + +
    + + + + + + + + + + +
    + + + +

    + create_transforms(transforms_dict) + +

    + + +
    + +

    Create a torchvision.transforms.Compose object from a dictionary of transforms.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    transforms_dict + dict[str, dict[str, any]] + +
    +

    Dictionary of transforms. The keys are the names of the transforms and the values are the +parameters to pass to the transform as a dictionary. Possible transforms are:

    +
      +
    • "totensor": ToTensorv2
      • +
    • "normalize": transformsv2.Normalize
      • +
    • "horizontal_flip": transformsv2.RandomHorizontalFlip
      • +
    • "vertical_flip": transformsv2.RandomVerticalFlip
      • +
    • "rotation": transformsv2.RandomRotation
      • +
    • "resized_crop": transformsv2.RandomResizedCrop
      • +
    • "random_crop": transformsv2.RandomCrop
      • +
    • "center_crop": transformsv2.CenterCrop
      • +
    • "resize": transformsv2.Resize
      • +
    • "clip_custom": ClipCustom
      • +
    +

    Please refer to the torchvision documentation for the +parameters of each torchvision transform.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Compose + +
    +

    A torchvision.transforms.Compose object.

    +
    +
    + +
    + Source code in src/prismtoolbox/utils/torch_utils.py + 
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    def create_transforms(transforms_dict: dict[str, dict[str, any]]) -> transformsv2.Compose:
+    """Create a torchvision.transforms.Compose object from a dictionary of transforms.
+
+    Args:
+        transforms_dict: Dictionary of transforms. The keys are the names of the transforms and the values are the
+            parameters to pass to the transform as a dictionary. Possible transforms are:
+
+            - "totensor": ToTensorv2
+            - "normalize": transformsv2.Normalize
+            - "horizontal_flip": transformsv2.RandomHorizontalFlip
+            - "vertical_flip": transformsv2.RandomVerticalFlip
+            - "rotation": transformsv2.RandomRotation
+            - "resized_crop": transformsv2.RandomResizedCrop
+            - "random_crop": transformsv2.RandomCrop
+            - "center_crop": transformsv2.CenterCrop
+            - "resize": transformsv2.Resize
+            - "clip_custom": ClipCustom
+
+            Please refer to the [torchvision documentation](https://pytorch.org/vision/stable/transforms.html) for the
+            parameters of each torchvision transform.
+
+    Returns:
+        A torchvision.transforms.Compose object.
+    """
+    if any(
+        transform_name not in possible_transforms for transform_name in transforms_dict
+    ):
+        raise ValueError(
+            f"invalid transform name. Possible transforms: {possible_transforms.keys()}"
+        )
+    transforms = transformsv2.Compose(
+        [
+            possible_transforms[transform_name](**transform_params)
+            for transform_name, transform_params in transforms_dict.items()
+        ]
+    )
+    return transforms
+
    +
    +
    + +
    + + + +
    + +
    + +
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    + + +
    + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/utils/vis_utils/index.html b/reference/prismtoolbox/utils/vis_utils/index.html new file mode 100644 index 0000000..787b464 --- /dev/null +++ b/reference/prismtoolbox/utils/vis_utils/index.html @@ -0,0 +1,821 @@ + + + + + + + + + + + + + + + + Vis utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + +
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    +
    +
    + + + +
    +
    +
    + + + +
    +
    + + + + + + + + + +

    Vis utils

    + +
    + + + + +
    + + + +
    + + + + + + + + + + +
    + + + +

    + bbox_from_contours(contours, downsample_factor=1) + +

    + + +
    + +

    Compute the bounding box from a set of contours +:param contours: list of contours +:return: bounding box of the contours

    + +
    + Source code in src/prismtoolbox/utils/vis_utils.py + 
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    def bbox_from_contours(
+    contours: list[np.ndarray], downsample_factor: int = 1
+) -> tuple[int, int, int, int]:
+    """
+    Compute the bounding box from a set of contours
+    :param contours: list of contours
+    :return: bounding box of the contours
+    """
+    flatten_contours = np.concatenate(contours)
+    x_min, y_min = flatten_contours.min(axis=0).squeeze().astype(int) / downsample_factor
+    x_max, y_max = flatten_contours.max(axis=0).squeeze().astype(int) / downsample_factor
+    return x_min, y_min, x_max, y_max
+
    +
    +
    + +
    + + +
    + + + +

    + bbox_from_coords(coords, patch_size=0, downsample_factor=1) + +

    + + +
    + +

    Compute the bounding box from a set of coordinates +:param coords: coordinates of the patches +:param patch_size: size of the patches (at level extraction) +:return: bounding box of the patches

    + +
    + Source code in src/prismtoolbox/utils/vis_utils.py + 
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    def bbox_from_coords(
+    coords: np.ndarray,
+    patch_size: int = 0,
+    downsample_factor: int = 1,
+) -> tuple[int, int, int, int]:
+    """
+    Compute the bounding box from a set of coordinates
+    :param coords: coordinates of the patches
+    :param patch_size: size of the patches (at level extraction)
+    :return: bounding box of the patches
+    """
+    x_min, y_min = coords.min(axis=0).astype(int) / downsample_factor
+    x_max, y_max = coords.max(axis=0).astype(int) / downsample_factor
+    return x_min, y_min, x_max + patch_size, y_max + patch_size
+
    +
    +
    + +
    + + +
    + + + +

    + get_colors_from_cmap(cmap_name, n_colors, scale=255) + +

    + + +
    + +

    Get a list of colors from a matplotlib colormap +:param cmap_name: name of a matplotlib colormap +:param n_colors: number of colors +:return: list of colors

    + +
    + Source code in src/prismtoolbox/utils/vis_utils.py + 
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    def get_colors_from_cmap(cmap_name, n_colors, scale=255):
+    """
+    Get a list of colors from a matplotlib colormap
+    :param cmap_name: name of a matplotlib colormap
+    :param n_colors: number of colors
+    :return: list of colors
+    """
+    cmap_name = plt.get_cmap(cmap_name, n_colors)
+    colors_from_cmap = np.array([to_rgb(cmap_name(i)) for i in range(n_colors)])
+    if scale > 1:
+        return (colors_from_cmap * scale).astype(int)
+    else:
+        return colors_from_cmap
+
    +
    +
    + +
    + + +
    + + + +

    + init_image(w, h, color_bakground=(255, 255, 255), mask=False) + +

    + + +
    + +

    Create an image with the dimension (w,h) and the color of the background set to color_bakground +:param w: width of image +:param h: height of image +:param color_bakground: color of the background +:param mask: set to True to create a binary mask +:return: PIL image with the dimension (w,h), in grayscale when mask if set to True

    + +
    + Source code in src/prismtoolbox/utils/vis_utils.py + 
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    def init_image(
+    w: int,
+    h: int,
+    color_bakground: tuple[int, int, int] = (255, 255, 255),
+    mask: bool = False,
+) -> np.ndarray:
+    """
+    Create an image with the dimension (w,h) and the color of the background set to color_bakground
+    :param w: width of image
+    :param h: height of image
+    :param color_bakground: color of the background
+    :param mask: set to True to create a binary mask
+    :return: PIL image with the dimension (w,h), in grayscale when mask if set to True
+    """
+    if mask:
+        return np.array(ImageOps.grayscale(Image.new(size=(w, h), mode="RGB", color=0)))
+    else:
+        return np.array(Image.new(size=(w, h), mode="RGB", color=color_bakground))
+
    +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsicore/core_utils/index.html b/reference/prismtoolbox/wsicore/core_utils/index.html new file mode 100644 index 0000000..9372683 --- /dev/null +++ b/reference/prismtoolbox/wsicore/core_utils/index.html @@ -0,0 +1,2589 @@ + + + + + + + + + + + + + + + + prismtoolbox.wsicore.core_utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + + + + + + + +

    prismtoolbox.wsicore.core_utils

    + +
    + + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + IsInContour(contour, patch_size, center_shift=0.5, mode='center') + +

    + + +
    + + +

    The IsInContour class checks if a patch is inside a contour.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    contour + ndarray + +
    +

    The contour to check if the patch is inside.

    +
    +     		+ required +
    patch_size + int + +
    +

    The size of the patches that will be checked.

    +
    +     		+ required +
    center_shift + int + +
    +

    The shift of the center of the patch.

    +
    +     		+ 0.5 +
    mode + str + +
    +

    The mode to use for the contour checking. Possible values are:

    +
      +
    • "center" mode checks if the center of the patch is within the contour.
      • +
    • "four_pt" mode checks if one out of the four corners of the patch are within the contour.
      • +
    • "four_pt_hard" mode checks if the four corners of the patch are within the contour.
      • +
    +
    +     		+ 'center' +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def __init__(
+    self,
+    contour: np.ndarray,
+    patch_size: int,
+    center_shift: int = 0.5,
+    mode: str = "center",
+):
+    """The IsInContour class checks if a patch is inside a contour.
+
+    Args:
+        contour: The contour to check if the patch is inside.
+        patch_size: The size of the patches that will be checked.
+        center_shift: The shift of the center of the patch.
+        mode: The mode to use for the contour checking. Possible values are:
+
+            - "center" mode checks if the center of the patch is within the contour.
+            - "four_pt" mode checks if one out of the four corners of the patch are within the contour.
+            - "four_pt_hard" mode checks if the four corners of the patch are within the contour.
+    """
+    self.cont = contour
+    self.patch_size = patch_size
+    self.shift = int(patch_size // 2 * center_shift)
+    self.mode = mode
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + __call__(pt) + +

    + + +
    + +

    Check if a point is inside the contour.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    pt + tuple[int, int] + +
    +

    The top left coordinate of the patch.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + int + +
    +

    1 if the patch is inside the contour, 0 otherwise.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def __call__(self, pt: tuple[int, int]) -> int:
+    """Check if a point is inside the contour.
+
+    Args:
+        pt: The top left coordinate of the patch.
+
+    Returns:
+        1 if the patch is inside the contour, 0 otherwise.
+    """
+    center = (int(pt[0] + self.patch_size // 2), int(pt[1] + self.patch_size // 2))
+    if self.mode == "center":
+        return 1 if cv2.pointPolygonTest(self.cont, center, False) >= 0 else 0
+    else:
+        if self.shift > 0:
+            all_points = [
+                (center[0] - self.shift, center[1] - self.shift),
+                (center[0] + self.shift, center[1] + self.shift),
+                (center[0] + self.shift, center[1] - self.shift),
+                (center[0] - self.shift, center[1] + self.shift),
+            ]
+        else:
+            all_points = [center]
+        # Easy version of 4pt contour checking function - 1 of 4 points need to be in the contour for test to pass
+        if self.mode == "four_pt":
+            for points in all_points:
+                if cv2.pointPolygonTest(self.cont, points, False) >= 0:
+                    return 1
+            return 0
+        # Hard version of 4pt contour checking function - all 4 points need to be in the contour for test to pass
+        elif self.mode == "four_pt_hard":
+            for points in all_points:
+                if cv2.pointPolygonTest(self.cont, points, False) < 0:
+                    return 0
+            return 1
+        else:
+            raise ValueError(f"mode {self.mode} not recognized")
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + + + +
    + + + +

    + apply_bilateral_filter(img) + +

    + + +
    + +

    Apply a bilateral filter on a grayscale image.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    img + ndarray + +
    +

    An input grayscale image.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    The input image after applying a bilateral filter of parameters 3, 3*2, 3/2.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def apply_bilateral_filter(img: np.ndarray) -> np.ndarray:
+    """Apply a bilateral filter on a grayscale image.
+
+    Args:
+        img: An input grayscale image.
+
+    Returns:
+        The input image after applying a bilateral filter of parameters 3, 3*2, 3/2.
+    """
+    assert len(img.shape) == 2, f"Input image should be grayscale, got shape {img.shape}"
+    img_filtered = cv2.bilateralFilter(img, 3, 3 * 2, 3 / 2)
+    return img_filtered
+
    +
    +
    + +
    + + +
    + + + +

    + apply_binary_thresh(img, thresh, inv) + +

    + + +
    + +

    Apply a binary threshold on a grayscale image.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    img + ndarray + +
    +

    An input grayscale image.

    +
    +     		+ required +
    thresh + int + +
    +

    The threshold value.

    +
    +     		+ required +
    inv + bool + +
    +

    Set to True to invert the binary threshold.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    The input image after applying a binary threshold.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def apply_binary_thresh(img: np.ndarray, thresh: int, inv: bool) -> np.ndarray:
+    """Apply a binary threshold on a grayscale image.
+
+    Args:
+        img: An input grayscale image.
+        thresh: The threshold value.
+        inv: Set to True to invert the binary threshold.
+
+    Returns:
+        The input image after applying a binary threshold.
+    """
+    if inv:
+        _, img_thresh = cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY_INV)
+    else:
+        _, img_thresh = cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY)
+    return img_thresh
+
    +
    +
    + +
    + + +
    + + + +

    + compute_law_feats(img, window_size, only_s5=True) + +

    + + +
    + +

    Compute the Law's texture energy for a given grayscale image and window size.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    img + ndarray + +
    +

    An input grayscale image.

    +
    +     		+ required +
    window_size + int + +
    +

    The window size to use for Law's texture energy computation.

    +
    +     		+ required +
    only_s5 + bool + +
    +

    Set to True to only compute the Law's texture energy for the S5S5 filter.

    +
    +     		+ True +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + list[ndarray] + +
    +

    A list of Law's texture energy map for each filter extracted from the input image.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def compute_law_feats(
+    img: np.ndarray, window_size: int, only_s5: bool = True
+) -> list[np.ndarray]:
+    """Compute the Law's texture energy for a given grayscale image and window size.
+
+    Args:
+      img: An input grayscale image.
+      window_size: The window size to use for Law's texture energy computation.
+      only_s5: Set to True to only compute the Law's texture energy for the S5S5 filter.
+
+    Returns:
+        A list of Law's texture energy map for each filter extracted from the input image.
+    """
+    L5 = np.array([1, 4, 6, 4, 1])
+    E5 = np.array([-1, -2, 0, 2, 1])
+    S5 = np.array([-1, 0, 2, 0, -1])
+    R5 = np.array([1, -4, 6, -4, 1])
+
+    vectors = [L5, E5, S5, R5]
+    vectors_name = ["L5", "E5", "S5", "R5"]
+    if only_s5:
+        vectors = [vectors[2]]
+        vectors_name = [vectors_name[2]]
+
+    filters = [
+        np.expand_dims(vectors[i], -1).dot(np.expand_dims(vectors[j], -1).T)
+        for i, j in product(range(len(vectors)), range(len(vectors)))
+    ]
+    filters_name = np.array(
+        [
+            vectors_name[i] + vectors_name[j]
+            for i, j in product(range(len(vectors)), range(len(vectors)))
+        ]
+    )
+
+    imgs_filtered = []
+    for filt in filters:
+        imgs_filtered.append(oaconvolve(img, filt, mode="same"))
+
+    window = np.ones((window_size, window_size))
+    imgs_energy = []
+    for img_filtered in imgs_filtered:
+        imgs_energy.append(oaconvolve(np.abs(img_filtered), window, mode="same"))
+
+    def _get_img_energy(name):
+        return imgs_energy[np.where(filters_name == name)[0].item()]
+
+    if only_s5:
+        imgs_feats = [_get_img_energy("S5S5")]
+    else:
+        imgs_feats = [
+            np.mean(np.array([_get_img_energy("L5E5"), _get_img_energy("E5L5")]), axis=0),
+            np.mean(np.array([_get_img_energy("L5R5"), _get_img_energy("R5L5")]), axis=0),
+            np.mean(np.array([_get_img_energy("E5S5"), _get_img_energy("S5E5")]), axis=0),
+            _get_img_energy("S5S5"),
+            _get_img_energy("R5R5"),
+            np.mean(np.array([_get_img_energy("L5S5"), _get_img_energy("S5L5")]), axis=0),
+            _get_img_energy("E5E5"),
+            np.mean(np.array([_get_img_energy("E5R5"), _get_img_energy("R5E5")]), axis=0),
+            np.mean(np.array([_get_img_energy("S5R5"), _get_img_energy("R5S5")]), axis=0),
+        ]
+
+    return imgs_feats
+
    +
    +
    + +
    + + +
    + + + +

    + contour_mask(mask) + +

    + + +
    + +

    Find the contours in a binary image with opencv findContours.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    mask + ndarray + +
    +

    A binary image.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + list[ndarray] + +
    +

    A list of contours found in the input image with opencv findContours.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def contour_mask(mask: np.ndarray) -> list[np.ndarray]:
+    """Find the contours in a binary image with opencv findContours.
+
+    Args:
+        mask: A binary image.
+
+    Returns:
+        A list of contours found in the input image with opencv findContours.
+    """
+    contours = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    contours = contours[0] if len(contours) == 2 else contours[1]
+    return contours
+
    +
    +
    + +
    + + +
    + + + +

    + floodfill_img(img, start) + +

    + + +
    + +

    Perform the floodfill algorithm on a binary image.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    img + ndarray + +
    +

    A binary image.

    +
    +     		+ required +
    start + tuple[int, int] + +
    +

    coordinates of the starting point for the floodfill algorithm.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    The input image after applying the floodfill algorithm.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def floodfill_img(img: np.ndarray, start: tuple[int, int]) -> np.ndarray:
+    """Perform the floodfill algorithm on a binary image.
+
+    Args:
+        img: A binary image.
+        start: coordinates of the starting point for the floodfill algorithm.
+
+    Returns:
+        The input image after applying the floodfill algorithm.
+    """
+    img_to_floodfill = img.copy()
+    h, w = img.shape[:2]
+    mask = np.zeros((h + 2, w + 2), np.uint8)
+    cv2.floodFill(img_to_floodfill, mask, start, 255)
+    im_floodfill_inv = cv2.bitwise_not(img_to_floodfill)
+    img_out = img | im_floodfill_inv
+    return img_out[1:-1, 1:-1]
+
    +
    +
    + +
    + + +
    + + + +

    + isBlackPatch(patch, rgb_thresh=20, percentage=0.05) + +

    + + +
    + +

    Check if a patch is black.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    patch + Image + +
    +

    An input patch.

    +
    +     		+ required +
    rgb_thresh + int + +
    +

    The threshold value for the RGB channels to be considered black.

    +
    +     		+ 20 +
    percentage + float + +
    +

    The percentage of pixels below the threshold to consider the patch as black.

    +
    +     		+ 0.05 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + bool + +
    +

    True if the patch is black, False otherwise.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def isBlackPatch(
+    patch: Image.Image, rgb_thresh: int = 20, percentage: float = 0.05
+) -> bool:
+    """Check if a patch is black.
+
+    Args:
+        patch: An input patch.
+        rgb_thresh: The threshold value for the RGB channels to be considered black.
+        percentage: The percentage of pixels below the threshold to consider the patch as black.
+
+    Returns:
+        True if the patch is black, False otherwise.
+    """
+    num_pixels = patch.size[0] * patch.size[1]
+    return (
+        True
+        if np.all(np.array(patch) < rgb_thresh, axis=2).sum() > num_pixels * percentage
+        else False
+    )
+
    +
    +
    + +
    + + +
    + + + +

    + isWhitePatch(patch, rgb_thresh=220, percentage=0.2) + +

    + + +
    + +

    Check if a patch is white.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    patch + Image + +
    +

    An input patch.

    +
    +     		+ required +
    rgb_thresh + int + +
    +

    The threshold value for the RGB channels to be considered white.

    +
    +     		+ 220 +
    percentage + float + +
    +

    The percentage of pixels above the threshold to consider the patch as white.

    +
    +     		+ 0.2 +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + bool + +
    +

    True if the patch is white, False otherwise.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def isWhitePatch(
+    patch: Image.Image, rgb_thresh: int = 220, percentage: float = 0.2
+) -> bool:
+    """Check if a patch is white.
+
+    Args:
+        patch: An input patch.
+        rgb_thresh: The threshold value for the RGB channels to be considered white.
+        percentage: The percentage of pixels above the threshold to consider the patch as white.
+
+    Returns:
+        True if the patch is white, False otherwise.
+    """
+    num_pixels = patch.size[0] * patch.size[1]
+    return (
+        True
+        if np.all(np.array(patch) > rgb_thresh, axis=2).sum() > num_pixels * percentage
+        else False
+    )
+
    +
    +
    + +
    + + +
    + + + +

    + local_average(img, window_size) + +

    + + +
    + +

    Perform local averaging on the image for a given window size.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    img + ndarray + +
    +

    An input image.

    +
    +     		+ required +
    window_size + int + +
    +

    The window size to use for local averaging.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    Grayscale image minus the local average on a window of size window_size of the input image.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def local_average(img: np.ndarray, window_size: int) -> np.ndarray:
+    """Perform local averaging on the image for a given window size.
+
+    Args:
+        img: An input image.
+        window_size: The window size to use for local averaging.
+
+    Returns:
+        Grayscale image minus the local average on a window of size window_size of the input image.
+    """
+    window = np.ones((window_size, window_size)) / (window_size**2)
+    img_grayscaled = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    return img_grayscaled - oaconvolve(img_grayscaled, window, mode="same")
+
    +
    +
    + +
    + + +
    + + + +

    + save_patches_with_hdf5(output_path, asset_dict, attr_dict=None) + +

    + + +
    + +

    Save patches to an HDF5 file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    output_path + str + +
    +

    The path to the output HDF5 file.

    +
    +     		+ required +
    asset_dict + dict[str, ndarray] + +
    +

    A dictionary of patches to save (key: name of the dataset, value: array of patches coordinates).

    +
    +     		+ required +
    attr_dict + dict[str, dict[str, str | int | tuple[int, int]]] + +
    +

    A dictionary of attributes to save (key: name of the dataset, value: dictionary of attributes to

    +
    +     		+ None +
    +

    set to the HDF5 dataset).

    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def save_patches_with_hdf5(
+    output_path: str,
+    asset_dict: dict[str, np.ndarray],
+    attr_dict: dict[str, dict[str, str | int | tuple[int, int]]] = None,
+) -> None:
+    """Save patches to an HDF5 file.
+
+    Args:
+      output_path: The path to the output HDF5 file.
+      asset_dict: A dictionary of patches to save (key: name of the dataset, value: array of patches coordinates).
+      attr_dict: A dictionary of attributes to save (key: name of the dataset, value: dictionary of attributes to
+    set to the HDF5 dataset).
+    """
+    file = h5py.File(output_path, "w")
+    for key, val in asset_dict.items():
+        data_shape = val.shape
+        if key not in file:
+            data_type = val.dtype
+            chunk_shape = (1,) + data_shape[1:]
+            maxshape = (None,) + data_shape[1:]
+            dset = file.create_dataset(
+                key,
+                shape=data_shape,
+                maxshape=maxshape,
+                chunks=chunk_shape,
+                dtype=data_type,
+            )
+            dset[:] = val
+            if attr_dict is not None:
+                if key in attr_dict.keys():
+                    for attr_key, attr_val in attr_dict[key].items():
+                        dset.attrs[attr_key] = attr_val
+        else:
+            dset = file[key]
+            dset.resize(len(dset) + data_shape[0], axis=0)
+            dset[-data_shape[0] :] = val
+    file.close()
+
    +
    +
    + +
    + + +
    + + + +

    + select_roi_on_thumbnail(img, scale_factor) + +

    + + +
    + +

    Select a region of interest on the thumbnail of the slide using an interactive +window.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    img + ndarray + +
    +

    A thumbnail of the slide.

    +
    +     		+ required +
    scale_factor + int + +
    +

    The scale factor to apply to convert the coordinates to the original slide dimensions.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    The coordinates of the selected region of interest.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/core_utils.py + 
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    def select_roi_on_thumbnail(img: np.ndarray, scale_factor: int) -> np.ndarray:
+    """Select a region of interest on the thumbnail of the slide using an interactive
+    window.
+
+    Args:
+        img: A thumbnail of the slide.
+        scale_factor: The scale factor to apply to convert the coordinates to the original slide dimensions.
+
+    Returns:
+        The coordinates of the selected region of interest.
+    """
+    drawing = False
+    roi = [0, 0, 0, 0]
+
+    def _draw_rectangle(event, x, y, flags, param):
+        nonlocal roi, drawing
+        if event == cv2.EVENT_LBUTTONDOWN:
+            drawing = True
+            roi[0], roi[1] = x, y
+
+        elif event == cv2.EVENT_MOUSEMOVE:
+            if drawing:
+                roi[2], roi[3] = x, y
+
+        elif event == cv2.EVENT_LBUTTONUP:
+            drawing = False
+            roi[2], roi[3] = x, y
+
+    window_name = "Select ROI (beginning by the top left corner) - press ESC to exit"
+    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+    cv2.resizeWindow(window_name, 800, 800)
+    cv2.setMouseCallback(window_name, _draw_rectangle)
+
+    while True:
+        temp_img = img.copy()
+        if roi[2] and roi[3]:
+            cv2.rectangle(temp_img, (roi[0], roi[1]), (roi[2], roi[3]), (0, 255, 0), 5)
+            width = (roi[2] - roi[0]) * scale_factor
+            height = (roi[3] - roi[1]) * scale_factor
+            cv2.putText(
+                temp_img,
+                f"Width: {width} - Height: {height}",
+                (roi[0], roi[1] - 10),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                1,
+                (0, 255, 0),
+                2,
+            )
+        cv2.imshow(window_name, temp_img)
+
+        k = cv2.waitKey(1) & 0xFF
+        if k == 27:
+            break
+
+    cv2.destroyAllWindows()
+    return np.array(roi)
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsicore/index.html b/reference/prismtoolbox/wsicore/index.html new file mode 100644 index 0000000..339a5bb --- /dev/null +++ b/reference/prismtoolbox/wsicore/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    + + + + + + + + + +

    Index

    + +
    + + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsicore/wsi/index.html b/reference/prismtoolbox/wsicore/wsi/index.html new file mode 100644 index 0000000..8c79247 --- /dev/null +++ b/reference/prismtoolbox/wsicore/wsi/index.html @@ -0,0 +1,5994 @@ + + + + + + + + + + + + + + + + prismtoolbox.wsicore.wsi - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    + + + + + + + + + +

    prismtoolbox.wsicore.wsi

    + +
    + + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + WSI(slide_path, engine='openslide') + +

    + + +
    + + +

    The WSI (Whole Slide Image) class is responsible for handling operations +related to whole slide images.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_path + str + +
    +

    The path to the slide image file.

    +
    +     		+ required +
    engine + str + +
    +

    The engine used to read the slide image.

    +
    +     		+ 'openslide' +
    + + + +

    Attributes:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescription
    slide_path + str + +
    +

    The path to the slide image file.

    +
    +
    engine + str + +
    +

    The engine used to read the slide image.

    +
    +
    slide_name + str + +
    +

    The name of the slide image file. +Retrieved from the slide path using +retrieve_slide_name_ext method.

    +
    +
    slide_ext + str + +
    +

    The extension of the slide image file. +Retrieved from the slide path using +retrieve_slide_name_ext method.

    +
    +
    slide + OpenSlide | TiffSlide + +
    +

    The wsi read from the file using engine.

    +
    +
    dimensions + list[tuple[int, int]] + +
    +

    The dimensions of the slide image. +Set by the set_slide_attributes method.

    +
    +
    level_dimensions + list[tuple[int, int]] + +
    +

    The dimensions of the different levels of the slide image. +Set by the set_slide_attributes method.

    +
    +
    level_downsamples + list[tuple[int, int]] + +
    +

    The downsampling factors of the different levels of +the slide image. Set by the set_slide_attributes +method.

    +
    +
    properties + dict + +
    +

    The properties of the slide image. +Set by the set_slide_attributes method.

    +
    +
    offset + tuple[int, int] + +
    +

    The offset of the slide image. +Set by the set_slide_attributes method.

    +
    +
    ROI + ndarray + +
    +

    The region of interest in the slide image. +Please use the set_roi method to set the ROI.

    +
    +
    ROI_width + int + +
    +

    The width of the region of interest. +Set by the set_roi method.

    +
    +
    ROI_height + int + +
    +

    The height of the region of interest. +Set by the set_roi method.

    +
    +
    tissue_contours + list[ndarray] + +
    +

    The contours of the tissue in the slide image. +Please use the detect_tissue +method to detect the tissue contours.

    +
    +
    coords + ndarray + +
    +

    The coordinates of patches extracted from slide image. +Please use the extract_patches +method to extract patches.

    +
    +
    coords_attrs + dict + +
    +

    The attributes of the coordinates. +Set by the extract_patches method.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def __init__(self, slide_path: str, engine: str = "openslide"):
+    """The WSI (Whole Slide Image) class is responsible for handling operations
+    related to whole slide images.
+
+    Args:
+        slide_path: The path to the slide image file.
+        engine: The engine used to read the slide image.
+
+    Attributes:
+        slide_path (str): The path to the slide image file.
+        engine (str): The engine used to read the slide image.
+        slide_name (str): The name of the slide image file.
+            Retrieved from the slide path using
+            [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method.
+        slide_ext (str): The extension of the slide image file.
+            Retrieved from the slide path using
+            [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method.
+        slide (OpenSlide | TiffSlide): The wsi read from the file using engine.
+        dimensions (list[tuple[int, int]]): The dimensions of the slide image.
+            Set by the set_slide_attributes method.
+        level_dimensions (list[tuple[int, int]]): The dimensions of the different levels of the slide image.
+            Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method.
+        level_downsamples (list[tuple[int, int]]): The downsampling factors of the different levels of
+            the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes]
+            method.
+        properties (dict): The properties of the slide image.
+            Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method.
+        offset (tuple[int, int]): The offset of the slide image.
+            Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method.
+        ROI (ndarray): The region of interest in the slide image.
+            Please use the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method to set the ROI.
+        ROI_width (int): The width of the region of interest.
+            Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method.
+        ROI_height (int): The height of the region of interest.
+            Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method.
+        tissue_contours (list[ndarray]): The contours of the tissue in the slide image.
+            Please use the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue]
+            method to detect the tissue contours.
+        coords (np.ndarray): The coordinates of patches extracted from slide image.
+            Please use the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches]
+            method to extract patches.
+        coords_attrs (dict): The attributes of the coordinates.
+            Set by the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method.
+    """
+    self.slide_path = slide_path
+    self.engine = engine
+    self.slide_name, self.slide_ext = self.retrieve_slide_name_ext(self.slide_path)
+    self.slide = self.read(slide_path, engine)
+    self.dimensions = None
+    self.level_dimensions = None
+    self.level_downsamples = None
+    self.properties = None
+    self.offset = (0, 0)
+    self.set_slide_attributes()
+    self.ROI = None
+    self.ROI_width = None
+    self.ROI_height = None
+    self.tissue_contours = None
+    self.coords = None
+    self.coords_attrs = None
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + apply_pathologist_annotations(path, class_name='annotation', column_to_select='objectType') + +

    + + +
    + +

    Apply pathologist annotations to the tissue contours by intersecting the annotations +with the tissue contours. Requires the tissue contours to be set for the slide beforehand +with the detect_tissue method.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    path + str + +
    +

    The path to the .geojson file containing the annotations extracted from QuPath.

    +
    +     		+ required +
    class_name + str + +
    +

    The class name to use for selecting the annotations to apply.

    +
    +     		+ 'annotation' +
    column_to_select + str + +
    +

    The column to select in the GeoDataFrame.

    +
    +     		+ 'objectType' +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def apply_pathologist_annotations(
+    self,
+    path: str,
+    class_name: str = "annotation",
+    column_to_select: str = "objectType",
+) -> None:
+    """Apply pathologist annotations to the tissue contours by intersecting the annotations
+    with the tissue contours. Requires the tissue contours to be set for the slide beforehand
+    with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method.
+
+    Args:
+        path: The path to the .geojson file containing the annotations extracted from QuPath.
+        class_name: The class name to use for selecting the annotations to apply.
+        column_to_select: The column to select in the GeoDataFrame.
+    """
+    assert (
+        self.tissue_contours is not None
+    ), "No tissue contours found for the slide, please run the detect_tissue method first"
+    offset = (-self.offset[0], -self.offset[1])
+    pathologist_annotations = read_qupath_annotations(
+        path, offset=offset, class_name=class_name, column_to_select=column_to_select
+    )
+    polygons = contoursToPolygons(self.tissue_contours, make_valid=True)
+    intersection = intersectionPolygons(polygons, pathologist_annotations)
+    self.tissue_contours = PolygonsToContours(intersection)
+
    +
    +
    + +
    + + +
    + + + +

    + convert_micrometer_to_pixel(value, level, axis='x') + +

    + + +
    + +

    Convert a value from micrometer to pixel.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    value + float + +
    +

    The value to convert (in micrometer).

    +
    +     		+ required +
    level + int + +
    +

    The level at which the conversion should be performed.

    +
    +     		+ required +
    axis + str + +
    +

    The axis to use for getting the conversion factor (x or y).

    +
    +     		+ 'x' +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + int + +
    +

    The input value in pixel.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def convert_micrometer_to_pixel(
+    self,
+    value: float,
+    level: int,
+    axis: str = "x",
+) -> int:
+    """Convert a value from micrometer to pixel.
+
+    Args:
+        value: The value to convert (in micrometer).
+        level: The level at which the conversion should be performed.
+        axis: The axis to use for getting the conversion factor (x or y).
+
+    Returns:
+        The input value in pixel.
+    """
+    return int(value / float(self.properties[f"{self.engine}.mpp-{axis}"])) // int(
+        self.level_downsamples[level]
+    )
+
    +
    +
    + +
    + + +
    + + + +

    + convert_pixel_to_micrometer(value, level, axis='x') + +

    + + +
    + +

    Convert a value from pixel to micrometer.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    value + float + +
    +

    The value to convert (in pixel).

    +
    +     		+ required +
    level + int + +
    +

    The level at which the conversion should be performed.

    +
    +     		+ required +
    axis + str + +
    +

    The axis to use for getting the conversion factor (x or y).

    +
    +     		+ 'x' +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + float + +
    +

    The input value in micrometer.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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+274
    def convert_pixel_to_micrometer(
+    self,
+    value: float,
+    level: int,
+    axis: str = "x",
+) -> float:
+    """Convert a value from pixel to micrometer.
+
+    Args:
+        value: The value to convert (in pixel).
+        level: The level at which the conversion should be performed.
+        axis: The axis to use for getting the conversion factor (x or y).
+
+    Returns:
+        The input value in micrometer.
+    """
+    return (
+        value
+        * float(self.properties[f"{self.engine}.mpp-{axis}"])
+        * self.level_downsamples[level]
+    )
+
    +
    +
    + +
    + + +
    + + + +

    + convert_units(value, level, from_unit, to_unit='px', axis='x') + +

    + + +
    + +

    Convert a value from one unit to another.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    value + float + +
    +

    The value to convert.

    +
    +     		+ required +
    level + int + +
    +

    The level at which the conversion should be performed.

    +
    +     		+ required +
    from_unit + str + +
    +

    The unit to convert from (px or micro).

    +
    +     		+ required +
    to_unit + str + +
    +

    The unit to convert to (px or micro).

    +
    +     		+ 'px' +
    axis + str + +
    +

    The axis to use for getting the conversion factor (x or y).

    +
    +     		+ 'x' +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + int + +
    +

    The input value converted in the desired unit.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def convert_units(
+    self,
+    value: float,
+    level: int,
+    from_unit: str,
+    to_unit: str = "px",
+    axis: str = "x",
+) -> int:
+    """Convert a value from one unit to another.
+
+    Args:
+        value: The value to convert.
+        level: The level at which the conversion should be performed.
+        from_unit: The unit to convert from (px or micro).
+        to_unit: The unit to convert to (px or micro).
+        axis: The axis to use for getting the conversion factor (x or y).
+
+    Returns:
+        The input value converted in the desired unit.
+    """
+    if from_unit == "micro" and to_unit == "px":
+        value = self.convert_micrometer_to_pixel(value, level, axis)
+    elif from_unit == "px" and to_unit == "micro":
+        value = self.convert_pixel_to_micrometer(value, level, axis)
+    elif from_unit == to_unit:
+        pass
+    else:
+        raise ValueError(f"Conversion from {from_unit} to {to_unit} not supported")
+    return value
+
    +
    +
    + +
    + + +
    + + + +

    + create_thumbnail(level, crop_roi=False) + +

    + + +
    + +

    Create a thumbnail of the slide at a given level.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    level + int + +
    +

    The level at which the thumbnail should be created.

    +
    +     		+ required +
    crop_roi + bool + +
    +

    A boolean to crop the thumbnail to the region of interest defined for the slide +(requires a ROI to be set for the slide beforehand with the +set_roi method)

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Image + +
    +

    A thumbnail of the slide as a PIL image.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    499
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+526
    def create_thumbnail(
+    self,
+    level: int,
+    crop_roi: bool = False,
+) -> Image.Image:
+    """Create a thumbnail of the slide at a given level.
+
+    Args:
+        level: The level at which the thumbnail should be created.
+        crop_roi: A boolean to crop the thumbnail to the region of interest defined for the slide
+            (requires a ROI to be set for the slide beforehand with the
+            [set_roi][prismtoolbox.wsicore.WSI.set_roi] method)
+
+    Returns:
+        A thumbnail of the slide as a PIL image.
+    """
+    assert self.ROI is not None if crop_roi else True, (
+        "No ROI provided, while crop_roi is set to True. Please set a ROI for "
+        "the slide or set crop_roi to False."
+    )
+    thumb = self.read_region((0, 0), level, self.level_dimensions[level]).convert(
+        "RGB"
+    )
+    if crop_roi:
+        log.info(f"Creating thumbnail with ROI {self.ROI}.")
+        coords_roi = (self.ROI / self.level_downsamples[level]).astype(int)
+        thumb = thumb.crop(coords_roi)
+    return thumb
+
    +
    +
    + +
    + + +
    + + + +

    + detect_tissue(seg_level, window_avg, window_eng, thresh, inside_roi=False, inv_thresh=False, area_min=200000.0, start=(0, 0)) + +

    + + +
    + +

    Segment the tissue on the slide based on a threshold on the Law's texture +energy spot map and floodfill algorithm to fill the holes in the mask. The +tissue contours are stored in the tissue_contours attribute.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    seg_level + int + +
    +

    The level at which the segmentation should be performed.

    +
    +     		+ required +
    window_avg + int + +
    +

    The size of the window for local averaging.

    +
    +     		+ required +
    window_eng + int + +
    +

    The size of the window for Law's texture energy computation.

    +
    +     		+ required +
    thresh + int + +
    +

    The threshold for binarization on the Law's texture energy spot map.

    +
    +     		+ required +
    inside_roi + bool + +
    +

    Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide +beforehand with the set_roi method).

    +
    +     		+ False +
    inv_thresh + bool + +
    +

    Set to True to invert the thresholding.

    +
    +     		+ False +
    area_min + float + +
    +

    The minimum area for a contour to be kept.

    +
    +     		+ 200000.0 +
    start + tuple[int, int] + +
    +

    The starting point for the floodfill algorithm (should be left at (0, 0) in most cases).

    +
    +     		+ (0, 0) +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
    567
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    def detect_tissue(
+    self,
+    seg_level: int,
+    window_avg: int,
+    window_eng: int,
+    thresh: int,
+    inside_roi: bool = False,
+    inv_thresh: bool = False,
+    area_min: float = 2e5,
+    start: tuple[int, int] = (0, 0),
+) -> None:
+    """Segment the tissue on the slide based on a threshold on the Law's texture
+    energy spot map and floodfill algorithm to fill the holes in the mask. The
+    tissue contours are stored in the tissue_contours attribute.
+
+    Args:
+        seg_level: The level at which the segmentation should be performed.
+        window_avg: The size of the window for local averaging.
+        window_eng: The size of the window for Law's texture energy computation.
+        thresh: The threshold for binarization on the Law's texture energy spot map.
+        inside_roi: Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide
+            beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method).
+        inv_thresh: Set to True to invert the thresholding.
+        area_min: The minimum area for a contour to be kept.
+        start: The starting point for the floodfill algorithm (should be left at (0, 0) in most cases).
+    """
+    final_contours = []
+    img = np.array(self.create_thumbnail(seg_level, inside_roi))
+    img_avg = local_average(np.asarray(img), window_avg)
+    law_feats = compute_law_feats(img_avg, window_eng)[0]
+    filterred_img = apply_bilateral_filter(np.clip(law_feats, 0, 255).astype("uint8"))
+    threshed_img = apply_binary_thresh(filterred_img, thresh, inv_thresh)
+    flooded_img = floodfill_img(np.pad(threshed_img, 1), start)
+    contours = contour_mask(flooded_img)
+    for contour in contours:
+        c = contour.copy()
+        area = cv2.contourArea(c)
+        if area > area_min:
+            final_contours.append(contour)
+    if len(final_contours) == 0:
+        self.tissue_contours = []
+        print(f"No contours found for slide {self.slide_name}.")
+        return
+    else:
+        scale = self.level_downsamples[seg_level]
+        offset = np.array(self.ROI[:2]) if self.ROI is not None else np.array([0, 0])
+        final_contours = self.scale_contours(final_contours, scale)
+        final_contours = [cont + offset for cont in final_contours]
+        # Sanity check to ensure that the contours are all within a ROI is provided
+        if self.ROI is not None:
+            assert all(
+                [
+                    np.all(cont >= self.ROI[:2]) and np.all(cont <= self.ROI[2:])
+                    for cont in final_contours
+                ]
+            )
+        self.tissue_contours = final_contours
+        print(
+            f"Identified {len(final_contours)} contours for slide {self.slide_name}."
+        )
+        return
+
    +
    +
    + +
    + + +
    + + + +

    + extract_patches(patch_size, patch_level, mode, step_size=None, overlap=None, units=('px', 'px'), use_padding=True, contours_mode=None, rgb_threshs=(2, 220), percentages=(0.6, 0.9)) + +

    + + +
    + +

    Extract valid patches from the slide with different extraction modes. A patch +is considered valid if it is not black or white and is within the region of +interest or the tissue contours if relevant. The extracted patches are stored as +coordinates in the coords attribute, and the attributes of the coordinates are +stored in the coords_attrs attribute.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    patch_size + float + +
    +

    The size of the patches to extract (assumed to be square).

    +
    +     		+ required +
    patch_level + int + +
    +

    The level at which the patches should be extracted.

    +
    +     		+ required +
    mode + str + +
    +

    The mode to use for the extraction:

    +
      +
    • "contours" mode extracts patches within the tissue contours (requires the tissue contours +to be set for the slide beforehand with the detect_tissue +method).
      • +
    • "roi" mode extracts patches within the region of interest (requires the ROI to be set +for the slide beforehand with the set_roi method).
      • +
    • "all" mode extracts patches from the entire slide
      • +
    +
    +     		+ required +
    step_size + float | None + +
    +

    The step size for the sliding window (if set to None, the step size will be computed based on the +overlap).

    +
    +     		+ None +
    overlap + float | None + +
    +

    The overlap between patches as an absolute value (must be provided if step_size is set to None).

    +
    +     		+ None +
    units + tuple[str, str] + +
    +

    The units for the patch size and step size/overlap values (pixels: px, micrometers: micro).

    +
    +     		+ ('px', 'px') +
    use_padding + bool + +
    +

    Set to True to use padding for the extraction.

    +
    +     		+ True +
    contours_mode + str | None + +
    +

    The mode to use for the contour checking function (must be provided if mode is +set to contours). See IsInContour for +more details.

    +
    +     		+ None +
    rgb_threshs + tuple[int, int] + +
    +

    The tuple of thresholds for the RGB channels (black threshold, white threshold).

    +
    +     		+ (2, 220) +
    percentages + tuple[float, float] + +
    +

    The tuple of percentages of pixels below/above the thresholds to consider the patch as +black/white.

    +
    +     		+ (0.6, 0.9) +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def extract_patches(
+    self,
+    patch_size: float,
+    patch_level: int,
+    mode: str,
+    step_size: float | None = None,
+    overlap: float | None = None,
+    units: tuple[str, str] = ("px", "px"),
+    use_padding: bool = True,
+    contours_mode: str | None = None,
+    rgb_threshs: tuple[int, int] = (2, 220),
+    percentages: tuple[float, float] = (0.6, 0.9),
+) -> None:
+    """Extract valid patches from the slide with different extraction modes. A patch
+    is considered valid if it is not black or white and is within the region of
+    interest or the tissue contours if relevant. The extracted patches are stored as
+    coordinates in the coords attribute, and the attributes of the coordinates are
+    stored in the coords_attrs attribute.
+
+    Args:
+        patch_size: The size of the patches to extract (assumed to be square).
+        patch_level: The level at which the patches should be extracted.
+        mode: The mode to use for the extraction:
+
+            - "contours" mode extracts patches within the tissue contours (requires the tissue contours
+            to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue]
+            method).
+            - "roi" mode extracts patches within the region of interest (requires the ROI to be set
+            for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method).
+            - "all" mode extracts patches from the entire slide
+
+        step_size: The step size for the sliding window (if set to None, the step size will be computed based on the
+            overlap).
+        overlap: The overlap between patches as an absolute value (must be provided if step_size is set to None).
+        units: The units for the patch size and step size/overlap values (pixels: px, micrometers: micro).
+        use_padding: Set to True to use padding for the extraction.
+        contours_mode: The mode to use for the contour checking function (must be provided if mode is
+            set to contours). See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for
+            more details.
+        rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold).
+        percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as
+            black/white.
+    """
+    assert (
+        step_size is not None or overlap is not None
+    ), "Either step_size or overlap must be provided"
+    assert all(
+        [unit in ["micro", "px"] for unit in units]
+    ), "Units must be either 'micro' or 'px'"
+
+    patch_size = self.convert_units(patch_size, patch_level, units[0])
+    if step_size is None:
+        step_size = patch_size - self.convert_units(overlap, patch_level, units[1])
+    else:
+        step_size = self.convert_units(step_size, patch_level, units[1])
+
+    log.info(
+        f"Extracting patches of size {patch_size} at level {patch_level} with step size {step_size}."
+    )
+
+    if mode == "contours":
+        log.info("Extracting patches with 'contours' mode.")
+        assert self.tissue_contours is not None
+        assert len(self.tissue_contours) > 0
+        assert contours_mode is not None
+        valid_coords = []
+        for cont in self.tissue_contours:
+            roi_dim = cv2.boundingRect(cont)
+            log.info(f"Processing ROI of dimensions: {roi_dim}")
+            valid_coords.extend(
+                self.extract_patches_roi(
+                    patch_level,
+                    patch_size,
+                    step_size,
+                    roi_dim,
+                    use_padding,
+                    cont,
+                    contours_mode,
+                    rgb_threshs=rgb_threshs,
+                    percentages=percentages,
+                )
+            )
+        valid_coords = np.array(valid_coords)
+    elif mode == "roi":
+        log.info("Extracting patches with 'roi' mode.")
+        assert self.ROI is not None
+        roi_dim = self.ROI[0], self.ROI[1], self.ROI_width, self.ROI_height
+        log.info("Processing ROI of dimensions:", roi_dim)
+        valid_coords = self.extract_patches_roi(
+            patch_level,
+            patch_size,
+            step_size,
+            roi_dim,
+            use_padding,
+            rgb_threshs=rgb_threshs,
+            percentages=percentages,
+        )
+    elif mode == "all":
+        roi_dim = 0, 0, self.level_dimensions[0][0], self.level_dimensions[0][1]
+        log.info("Processing ROI of dimensions:", roi_dim)
+        valid_coords = self.extract_patches_roi(
+            patch_level,
+            patch_size,
+            step_size,
+            roi_dim,
+            use_padding,
+            rgb_threshs=rgb_threshs,
+            percentages=percentages,
+        )
+    else:
+        raise ValueError(f"Mode {mode} not supported")
+
+    attr = {
+        "patch_size": patch_size,
+        "patch_level": patch_level,
+        "downsample": self.level_downsamples[patch_level],
+        "downsampled_level_dim": tuple(np.array(self.level_dimensions[patch_level])),
+        "level_dim": self.level_dimensions[patch_level],
+        "name": self.slide_name,
+    }
+
+    if len(valid_coords) == 0:
+        log.warning(f"No valid coordinates found for slide {self.slide_name}.")
+    else:
+        print(
+            f"Identified a total of {len(valid_coords)}  valid coordinates in the slide {self.slide_name}."
+        )
+        self.coords = valid_coords
+        self.coords_attrs = attr
+
    +
    +
    + +
    + + +
    + + + +

    + extract_patches_roi(patch_level, patch_size, step_size=None, roi_dim=None, use_padding=True, contour=None, contours_mode=None, coord_candidates=None, rgb_threshs=(2, 220), percentages=(0.6, 0.9), return_indices=False) + +

    + + +
    + +

    Extract valid patches from a region of interest, i.e if the patch is not +black or white and is within the region of interest/contours if relevant).

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    patch_level + int + +
    +

    The level at which the patches should be extracted.

    +
    +     		+ required +
    patch_size + int + +
    +

    The size of the patches to extract (assumed to be square).

    +
    +     		+ required +
    step_size + int | None + +
    +

    The step size to use for the sliding window.

    +
    +     		+ None +
    roi_dim + tuple[int, int, int, int] | None + +
    +

    The top-left corner coordinates and dimensions of the region of interest. Must be provided if +coord_candidates is set to None.

    +
    +     		+ None +
    use_padding + bool + +
    +

    Set to True to use padding for the extraction.

    +
    +     		+ True +
    contour + ndarray | None + +
    +

    The tissue contour to use for the extraction. If set to None, will not check if patches are within +a contour.

    +
    +     		+ None +
    contours_mode + str | None + +
    +

    The mode for the contour checking function. +See IsInContour for more details. Must be provided +if mode is set to contours. Otherwise, will not check if patches are within the contours.

    +
    +     		+ None +
    coord_candidates + ndarray | None + +
    +

    Precomputed candidate coordinates for the patches. If set to None, will compute the +candidate coordinates based on the ROI dimensions and the step size.

    +
    +     		+ None +
    rgb_threshs + tuple[int, int] + +
    +

    The tuple of thresholds for the RGB channels (black threshold, white threshold).

    +
    +     		+ (2, 220) +
    percentages + tuple[float, float] + +
    +

    The tuple of percentages of pixels below/above the thresholds to consider the patch as +black/white.

    +
    +     		+ (0.6, 0.9) +
    return_indices + bool + +
    +

    Set to True to return the indices of the valid coordinates.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches).

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def extract_patches_roi(
+    self,
+    patch_level: int,
+    patch_size: int,
+    step_size: int | None = None,
+    roi_dim: tuple[int, int, int, int] | None = None,
+    use_padding: bool = True,
+    contour: np.ndarray | None = None,
+    contours_mode: str | None = None,
+    coord_candidates: np.ndarray | None = None,
+    rgb_threshs: tuple[int, int] = (2, 220),
+    percentages: tuple[float, float] = (0.6, 0.9),
+    return_indices: bool = False,
+) -> np.ndarray:
+    """Extract valid patches from a region of interest, i.e if the patch is not
+    black or white and is within the region of interest/contours if relevant).
+
+    Args:
+        patch_level: The level at which the patches should be extracted.
+        patch_size: The size of the patches to extract (assumed to be square).
+        step_size: The step size to use for the sliding window.
+        roi_dim: The top-left corner coordinates and dimensions of the region of interest. Must be provided if
+            coord_candidates is set to None.
+        use_padding: Set to True to use padding for the extraction.
+        contour: The tissue contour to use for the extraction. If set to None, will not check if patches are within
+            a contour.
+        contours_mode: The mode for the contour checking function.
+            See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. Must be provided
+            if mode is set to contours. Otherwise, will not check if patches are within the contours.
+        coord_candidates: Precomputed candidate coordinates for the patches. If set to None, will compute the
+            candidate coordinates based on the ROI dimensions and the step size.
+        rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold).
+        percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as
+            black/white.
+        return_indices: Set to True to return the indices of the valid coordinates.
+
+    Returns:
+        An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches).
+    """
+    assert contours_mode is not None if contour is not None else True, (
+        "A contour mode must be provided if patch "
+        "extraction mode is set to contours"
+    )
+    assert (
+        roi_dim is not None and step_size is not None
+    ) or coord_candidates is not None, (
+        "Either (roi_dim and step_size) or coord_candidates must be provided"
+    )
+    patch_downsample = int(self.level_downsamples[patch_level])
+    ref_patch_size = patch_size * patch_downsample
+
+    if coord_candidates is None:
+        start_x, start_y, w, h = roi_dim
+
+        img_w, img_h = self.level_dimensions[0]
+
+        if use_padding:
+            stop_y = start_y + h
+            stop_x = start_x + w
+        else:
+            stop_y = min(start_y + h, img_h - ref_patch_size + 1)
+            stop_x = min(start_x + w, img_w - ref_patch_size + 1)
+
+        step_size = step_size * patch_downsample
+
+        x_range = np.arange(start_x, stop_x, step=step_size)
+        y_range = np.arange(start_y, stop_y, step=step_size)
+        x_coords, y_coords = np.meshgrid(x_range, y_range, indexing="ij")
+        coord_candidates = np.array(
+            [x_coords.flatten(), y_coords.flatten()]
+        ).transpose()
+
+    if contour is not None:
+        cont_check_fn = IsInContour(
+            contour, patch_size=ref_patch_size, center_shift=0.5, mode=contours_mode
+        )
+        log.info(
+            f"Extracting patches with contour checking function mode {contours_mode}."
+        )
+    else:
+        cont_check_fn = None
+
+    num_workers = mp.cpu_count()
+    pool = mp.Pool(
+        num_workers,
+        initializer=WSI.worker_init,
+        initargs=(
+            self.slide_path,
+            self.engine,
+        ),
+    )
+    iterable = [
+        (coord, cont_check_fn, patch_level, patch_size, rgb_threshs, percentages)
+        for coord in coord_candidates
+    ]
+    valid_coords = pool.starmap(WSI.process_coord_candidate, iterable)
+    pool.close()
+
+    valid_indices = [i for i, coord in enumerate(valid_coords) if coord is not None]
+    valid_coords = np.array([coord_candidates[i] for i in valid_indices])
+
+    log.info(
+        f"Identified {len(valid_coords)}  valid coordinates in the ROI {roi_dim}."
+    )
+
+    if return_indices:
+        return valid_coords, valid_indices
+    else:
+        return valid_coords
+
    +
    +
    + +
    + + +
    + + + +

    + is_black_white(patch, rgb_threshs=(2, 220), percentages=(0.6, 0.9)) + + + staticmethod + + +

    + + +
    + +

    Check if a patch is black or white.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    patch + Image + +
    +

    The input patch.

    +
    +     		+ required +
    rgb_threshs + tuple[int, int] + +
    +

    The tuple of thresholds for the RGB channels (black threshold, white threshold).

    +
    +     		+ (2, 220) +
    percentages + tuple[float, float] + +
    +

    The tuple of percentages of pixels below/above the thresholds to consider the patch +as black/white.

    +
    +     		+ (0.6, 0.9) +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + bool + +
    +

    True if the patch is black or white, False otherwise.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def is_black_white(
+    patch: Image.Image,
+    rgb_threshs: tuple[int, int] = (2, 220),
+    percentages: tuple[float, float] = (0.6, 0.9),
+) -> bool:
+    """Check if a patch is black or white.
+
+    Args:
+        patch: The input patch.
+        rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold).
+        percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch
+            as black/white.
+
+    Returns:
+        True if the patch is black or white, False otherwise.
+    """
+    return isBlackPatch(
+        patch, rgb_thresh=rgb_threshs[0], percentage=percentages[0]
+    ) or isWhitePatch(patch, rgb_thresh=rgb_threshs[1], percentage=percentages[1])
+
    +
    +
    + +
    + + +
    + + + +

    + load_patches(file_path) + +

    + + +
    + +

    Load the patches from a hdf5 file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    file_path + str + +
    +

    The path to the hdf5 file containing the patches.

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def load_patches(self, file_path: str) -> None:
+    """Load the patches from a hdf5 file.
+
+    Args:
+        file_path: The path to the hdf5 file containing the patches.
+    """
+    log.info(f"Loading patches for slide {self.slide_name} from {file_path}.")
+    self.coords, self.coords_attrs = read_h5_file(file_path, "coords")
+
    +
    +
    + +
    + + +
    + + + +

    + load_tissue_contours(file_path) + +

    + + +
    + +

    Load the tissue contours from a pickle file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    file_path + str + +
    +

    The path to the pickle file containing the tissue contours.

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def load_tissue_contours(self, file_path: str) -> None:
+    """Load the tissue contours from a pickle file.
+
+    Args:
+        file_path: The path to the pickle file containing the tissue contours.
+    """
+    log.info(f"Loading tissue contours for slide {self.slide_name} from {file_path}.")
+    self.tissue_contours = load_obj_with_pickle(file_path)
+
    +
    +
    + +
    + + +
    + + + +

    + process_coord_candidate(coord, cont_check_fn, patch_level, patch_size, rgb_threshs=(2, 220), percentages=(0.6, 0.9)) + + + staticmethod + + +

    + + +
    + +

    Determine if a candidate coordinate is valid based on a contour checking +function and/or black/white thresholding.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    coord + tuple[int, int] + +
    +

    The candidate coordinate.

    +
    +     		+ required +
    cont_check_fn + IsInContour | None + +
    +

    The contour checking function.

    +
    +     		+ required +
    patch_level + int + +
    +

    The level at which the patch should be extracted.

    +
    +     		+ required +
    patch_size + int + +
    +

    The size of the patch (assumed to be square).

    +
    +     		+ required +
    rgb_threshs + tuple[int, int] + +
    +

    The tuple of thresholds for the RGB channels (black threshold, white threshold).

    +
    +     		+ (2, 220) +
    percentages + tuple[float, float] + +
    +

    The tuple of percentages of pixels below/above the thresholds to consider the patch as +black/white.

    +
    +     		+ (0.6, 0.9) +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + tuple[int, int] | None + +
    +

    The coordinate if it is valid, None otherwise.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def process_coord_candidate(
+    coord: tuple[int, int],
+    cont_check_fn: IsInContour | None,
+    patch_level: int,
+    patch_size: int,
+    rgb_threshs: tuple[int, int] = (2, 220),
+    percentages: tuple[float, float] = (0.6, 0.9),
+) -> tuple[int, int] | None:
+    """Determine if a candidate coordinate is valid based on a contour checking
+    function and/or black/white thresholding.
+
+    Args:
+        coord: The candidate coordinate.
+        cont_check_fn: The contour checking function.
+        patch_level: The level at which the patch should be extracted.
+        patch_size: The size of the patch (assumed to be square).
+        rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold).
+        percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as
+            black/white.
+
+    Returns:
+        The coordinate if it is valid, None otherwise.
+    """
+    if cont_check_fn is None or cont_check_fn(coord):
+        patch = wsi.read_region(
+            tuple(coord), patch_level, (patch_size, patch_size)
+        ).convert("RGB")
+        if not WSI.is_black_white(patch, rgb_threshs, percentages):
+            return coord
+        else:
+            return None
+    else:
+        return None
+
    +
    +
    + +
    + + +
    + + + +

    + read(slide_path, engine) + + + staticmethod + + +

    + + +
    + +

    Read a slide with a given engine.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_path + str + +
    +

    The path to the slide.

    +
    +     		+ required +
    engine + str + +
    +

    The backend library to use for reading the slide +(currently only openslide and tiffslide are supported).

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + +
    +

    A slide object.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def read(slide_path: str, engine: str):
+    """Read a slide with a given engine.
+
+    Args:
+        slide_path: The path to the slide.
+        engine: The backend library to use for reading the slide
+            (currently only openslide and tiffslide are supported).
+
+    Returns:
+        A slide object.
+    """
+    if engine == "openslide":
+        import openslide
+
+        slide = openslide.OpenSlide(slide_path)
+    elif engine == "tiffslide":
+        import tiffslide
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=UserWarning)
+            slide = tiffslide.TiffSlide(slide_path)
+    else:
+        raise NotImplementedError(f"engine {engine} not supported")
+    return slide
+
    +
    +
    + +
    + + +
    + + + +

    + read_region(location, level, size) + +

    + + +
    + +

    Read a region from the slide for a given level and size.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    location + tuple[int, int] + +
    +

    The coordinates of the top left corner of the region (in pixels).

    +
    +     		+ required +
    level + int + +
    +

    The level at which the region should be read.

    +
    +     		+ required +
    size + tuple[int, int] + +
    +

    The size of the region (in pixels).

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Image + +
    +

    The desired region of the slide as a PIL image.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def read_region(
+    self,
+    location: tuple[int, int],
+    level: int,
+    size: tuple[int, int],
+) -> Image.Image:
+    """Read a region from the slide for a given level and size.
+
+    Args:
+        location: The coordinates of the top left corner of the region (in pixels).
+        level: The level at which the region should be read.
+        size: The size of the region (in pixels).
+
+    Returns:
+        The desired region of the slide as a PIL image.
+    """
+    return self.slide.read_region(location, level, size).convert("RGB")
+
    +
    +
    + +
    + + +
    + + + +

    + retrieve_slide_name_ext(slide_path) + + + staticmethod + + +

    + + +
    + +

    Retrieves slide name and slide extension from slide path.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_path + str + +
    +

    The path to the slide.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + tuple[str, str] + +
    +

    A tuple (slide name, slide ext).

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def retrieve_slide_name_ext(slide_path: str) -> tuple[str, str]:
+    """Retrieves slide name and slide extension from slide path.
+
+    Args:
+        slide_path: The path to the slide.
+
+    Returns:
+        A tuple (slide name, slide ext).
+    """
+    slide_ext = re.search(r"(?<=\.)[\w\.]+$", slide_path).group(0)
+    slide_name = re.search(r"([^/]+?)(?=\.[\w\.]+$)", slide_path).group(0)
+    return slide_name, slide_ext
+
    +
    +
    + +
    + + +
    + + + +

    + save_patches(save_dir, file_format='h5', selected_idx=None, merge=False, label=None, color=(255, 0, 0), append_to_existing_file=False) + +

    + + +
    + +

    Save the patches in a hdf5 or geojson file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    save_dir + str + +
    +

    The path to the directory where the patches will be saved.

    +
    +     		+ required +
    file_format + str + +
    +

    The format for the saving (h5 for python processing, geojson for QuPath processing).

    +
    +     		+ 'h5' +
    selected_idx + ndarray | None + +
    +

    An array of indices of the patches to save (if set to None, all the patches will be saved).

    +
    +     		+ None +
    merge + bool + +
    +

    Set to True to merge the patches into a single polygon (for geojson format only).

    +
    +     		+ False +
    label + str | None + +
    +

    An optional label to assign to the patches (for geojson format only).

    +
    +     		+ None +
    color + tuple[int, int, int] + +
    +

    An optional color to assign to the patches (for geojson format only).

    +
    +     		+ (255, 0, 0) +
    append_to_existing_file + bool + +
    +

    Set to True to append the patches to an existing geojson file.

    +
    +     		+ False +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def save_patches(
+    self,
+    save_dir: str,
+    file_format: str = "h5",
+    selected_idx: np.ndarray | None = None,
+    merge: bool = False,
+    label: str | None = None,
+    color: tuple[int, int, int] = (255, 0, 0),
+    append_to_existing_file: bool = False,
+) -> None:
+    """Save the patches in a hdf5 or geojson file.
+
+    Args:
+        save_dir: The path to the directory where the patches will be saved.
+        file_format: The format for the saving (h5 for python processing, geojson for QuPath processing).
+        selected_idx: An array of indices of the patches to save (if set to None, all the patches will be saved).
+        merge: Set to True to merge the patches into a single polygon (for geojson format only).
+        label: An optional label to assign to the patches (for geojson format only).
+        color: An optional color to assign to the patches (for geojson format only).
+        append_to_existing_file: Set to True to append the patches to an existing geojson file.
+    """
+    if selected_idx is not None:
+        coords = self.coords[selected_idx]
+    else:
+        coords = self.coords
+    if not os.path.isdir(save_dir):
+        log.warning(f"Folder {save_dir} does not exist, creating new folder...")
+        pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
+    if file_format == "h5":
+        asset_dict = {"coords": self.coords}
+        attr_dict = {"coords": self.coords_attrs}
+        file_path = os.path.join(save_dir, f"{self.slide_name}.h5")
+        log.info(
+            f"Saving patches for slide {self.slide_name} at {file_path} with hdf5."
+        )
+        save_patches_with_hdf5(file_path, asset_dict, attr_dict)
+    elif file_format == "geojson":
+        file_path = os.path.join(save_dir, f"{self.slide_name}.geojson")
+        patch_downsample = int(
+            self.level_downsamples[self.coords_attrs["patch_level"]]
+        )
+        polygons = patchesToPolygons(
+            coords, self.coords_attrs["patch_size"], patch_downsample, merge
+        )
+        log.info(
+            f"Saving {len(coords)} patches for slide {self.slide_name} at {file_path} with geojson."
+        )
+        export_polygons_to_qupath(
+            polygons,
+            file_path,
+            "annotation",
+            offset=self.offset,
+            label=label,
+            color=color,
+            append_to_existing_file=append_to_existing_file,
+        )
+    elif file_format == "jpg" or file_format == "png":
+        save_dir = os.path.join(save_dir, self.slide_name)
+        if not os.path.isdir(save_dir):
+            log.warning(f"Folder {save_dir} does not exist, creating new folder...")
+            pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
+        log.info(
+            f"Saving {len(coords)} patches for slide {self.slide_name} at {save_dir} with {file_format}."
+        )
+        for coord in coords:
+            patch = self.read_region(
+                coord,
+                self.coords_attrs["patch_level"],
+                (self.coords_attrs["patch_size"], self.coords_attrs["patch_size"]),
+            ).convert("RGB")
+            patch.save(os.path.join(save_dir, f"{coord[0]}_{coord[1]}.{file_format}"))
+    else:
+        raise ValueError(f"format {file_format} not supported")
+
    +
    +
    + +
    + + +
    + + + +

    + save_tissue_contours(save_dir, selected_idx=None, file_format='pickle', merge=False, label=None, color=(255, 0, 0), append_to_existing_file=False, make_valid=False) + +

    + + +
    + +

    Save the tissue contours in a pickle or geojson file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    save_dir + str + +
    +

    The path to the directory where the contours will be saved.

    +
    +     		+ required +
    selected_idx + ndarray | None + +
    +

    An array of indices of the contours to save +(if set to None, all the contours will be saved).

    +
    +     		+ None +
    file_format + str + +
    +

    The file format for saving the contours +(pickle for python processing, geojson for QuPath processing).

    +
    +     		+ 'pickle' +
    merge + bool + +
    +

    Set to True to merge the contours into a single polygon (for geojson format only).

    +
    +     		+ False +
    label + str | None + +
    +

    An optional label to assign to the tissue contours (for geojson format only).

    +
    +     		+ None +
    color + tuple[int, int, int] + +
    +

    An optional color to assign to the tissue contours (for geojson format only).

    +
    +     		+ (255, 0, 0) +
    append_to_existing_file + bool + +
    +

    Set to True to append the contours to an existing geojson file.

    +
    +     		+ False +
    make_valid + bool + +
    +

    Set to True to make the polygons valid (for geojson format only).

    +
    +     		+ False +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def save_tissue_contours(
+    self,
+    save_dir: str,
+    selected_idx: np.ndarray | None = None,
+    file_format: str = "pickle",
+    merge: bool = False,
+    label: str | None = None,
+    color: tuple[int, int, int] = (255, 0, 0),
+    append_to_existing_file: bool = False,
+    make_valid: bool = False,
+) -> None:
+    """Save the tissue contours in a pickle or geojson file.
+
+    Args:
+        save_dir: The path to the directory where the contours will be saved.
+        selected_idx: An array of indices of the contours to save
+            (if set to None, all the contours will be saved).
+        file_format: The file format for saving the contours
+            (pickle for python processing, geojson for QuPath processing).
+        merge: Set to True to merge the contours into a single polygon (for geojson format only).
+        label: An optional label to assign to the tissue contours (for geojson format only).
+        color: An optional color to assign to the tissue contours (for geojson format only).
+        append_to_existing_file: Set to True to append the contours to an existing geojson file.
+        make_valid: Set to True to make the polygons valid (for geojson format only).
+    """
+    assert self.tissue_contours is not None, (
+        "No tissue contours found for the slide, "
+        "please run the detect_tissue method first"
+    )
+    if selected_idx is not None:
+        tissue_contours = [self.tissue_contours[idx] for idx in selected_idx]
+    else:
+        tissue_contours = self.tissue_contours
+    if not os.path.isdir(save_dir):
+        log.warning(f"Folder {save_dir} does not exist, creating new folder...")
+        pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
+    if file_format == "pickle":
+        file_path = os.path.join(save_dir, f"{self.slide_name}.pkl")
+        log.info(
+            f"Saving tissue contours for slide {self.slide_name} at {file_path} with pickle."
+        )
+        save_obj_with_pickle(tissue_contours, file_path)
+    elif file_format == "geojson":
+        file_path = os.path.join(save_dir, f"{self.slide_name}.geojson")
+        log.info(
+            f"Saving {selected_idx} tissue contours for slide {self.slide_name} at {file_path} with geojson."
+        )
+        polygons = contoursToPolygons(tissue_contours, merge, make_valid)
+        export_polygons_to_qupath(
+            polygons,
+            file_path,
+            "annotation",
+            offset=self.offset,
+            label=label,
+            color=color,
+            append_to_existing_file=append_to_existing_file,
+        )
+    else:
+        raise ValueError(f"format {file_format} not supported")
+
    +
    +
    + +
    + + +
    + + + +

    + scale_contours(contours, scale) + + + staticmethod + + +

    + + +
    + +

    Scale the contours by a given factor.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    contours + list[ndarray] + +
    +

    The contours to scale.

    +
    +     		+ required +
    scale + int + +
    +

    The scale factor to apply.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + list[ndarray] + +
    +

    The input list with scaled contours.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def scale_contours(
+    contours: list[np.ndarray],
+    scale: int,
+) -> list[np.ndarray]:
+    """Scale the contours by a given factor.
+
+    Args:
+        contours: The contours to scale.
+        scale: The scale factor to apply.
+
+    Returns:
+         The input list with scaled contours.
+    """
+    scaled_contours = [np.array(cont * scale, dtype="int") for cont in contours]
+    return scaled_contours
+
    +
    +
    + +
    + + +
    + + + +

    + set_roi(roi=None, rois_df_path=None) + +

    + + +
    + +

    Set the region of interest for the slide. Can be set manually or by selecting +a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    roi + tuple[int, int, int, int] | None + +
    +

    Set the region of interest manually as a tuple (x1, y1, x2, y2).

    +
    +     		+ None +
    rois_df_path + str | None + +
    +

    The path to dataframe containing the ROIs with a slide_id column identifying the slide.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + None + +
    +

    The region of interest set for the slide as a tuple (x1, y1, x2, y2).

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def set_roi(
+    self,
+    roi: tuple[int, int, int, int] | None = None,
+    rois_df_path: str | None = None,
+) -> None:
+    """Set the region of interest for the slide. Can be set manually or by selecting
+    a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute.
+
+    Args:
+        roi: Set the region of interest manually as a tuple (x1, y1, x2, y2).
+        rois_df_path: The path to dataframe containing the ROIs with a slide_id column identifying the slide.
+
+    Returns:
+        The region of interest set for the slide as a tuple (x1, y1, x2, y2).
+    """
+    if roi is not None:
+        ROI = np.array(roi).astype(int)
+    elif rois_df_path is not None:
+        rois_df = pd.read_csv(rois_df_path)
+        ROI = rois_df[rois_df.slide_id == self.slide_name].values[0].astype(int)
+    else:
+        log.info("No ROI provided, prompting user to select one.")
+        level = input(
+            f"No ROI was provided, please select a level at which the ROI should be created (max level: {len(self.level_dimensions)-1}): "
+        )
+        if not level:
+            log.warning("No level provided, setting the ROI at the highest level.")
+            level = len(self.level_downsamples) - 1
+        else:
+            level = int(level)
+        img = np.array(self.create_thumbnail(level))
+        ROI = select_roi_on_thumbnail(img, int(self.level_downsamples[level]))
+        ROI = (ROI * self.level_downsamples[level]).astype(int)
+    self.ROI = ROI
+    self.ROI_width = ROI[2] - ROI[0]
+    self.ROI_height = ROI[3] - ROI[1]
+    print(f"ROI for slide {self.slide_name} has been set to {self.ROI}.")
+    return ROI
+
    +
    +
    + +
    + + +
    + + + +

    + set_slide_attributes() + +

    + + +
    + +

    Set the slide attributes.

    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def set_slide_attributes(self):
+    """Set the slide attributes."""
+    if self.engine == "openslide":
+        self.dimensions = self.slide.dimensions
+        self.level_dimensions = self.slide.level_dimensions
+        self.level_downsamples = self.slide.level_downsamples
+        self.properties = self.slide.properties
+    elif self.engine == "tiffslide":
+        self.dimensions = self.slide.dimensions
+        self.level_dimensions = self.slide.level_dimensions
+        self.level_downsamples = self.slide.level_downsamples
+        self.properties = self.slide.properties
+    else:
+        raise NotImplementedError(f"engine {self.engine} not supported")
+    if (
+        f"{self.engine}.bounds-x" in self.properties.keys()
+        and self.properties[f"{self.engine}.bounds-x"] is not None
+    ):
+        self.offset = (
+            -int(self.properties[f"{self.engine}.bounds-x"]),
+            -int(self.properties[f"{self.engine}.bounds-y"]),
+        )
+
    +
    +
    + +
    + + +
    + + + +

    + stitch(vis_level, selected_idx=None, colors=None, alpha=0.6, black_white=False, draw_grid=False, crop_roi=False, background_color=(0, 0, 0)) + +

    + + +
    + +

    Stitch the patches extracted on an image. The patches can be masked and +colored depending on the mask and colors provided. Requires the coordinates of +the patches to be set for the slide beforehand with the +extract_patches method.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    vis_level + int + +
    +

    The level at which the patches should be visualized.

    +
    +     		+ required +
    selected_idx + ndarray + +
    +

    An array of indices of the patches to visualize (if set to None, all the patches will be +visualized).

    +
    +     		+ None +
    colors + ndarray + +
    +

    An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as +they are).

    +
    +     		+ None +
    alpha + float + +
    +

    Set the transparency of the colors to apply to the patches.

    +
    +     		+ 0.6 +
    black_white + bool + +
    +

    Set to True to visualize a binary mask of the patches extracted.

    +
    +     		+ False +
    draw_grid + bool + +
    +

    Set to True to draw a grid on the stitched patches.

    +
    +     		+ False +
    crop_roi + bool + +
    +

    Set to True to crop the visualization to the region of interest (requires a ROI to be set for the +slide beforehand with the set_roi method).

    +
    +     		+ False +
    background_color + tuple[int, int, int] + +
    +

    The color of the background.

    +
    +     		+ (0, 0, 0) +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Image + +
    +

    A PIL image of the stitched patches.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def stitch(
+    self,
+    vis_level: int,
+    selected_idx: np.ndarray = None,
+    colors: np.ndarray = None,
+    alpha: float = 0.6,
+    black_white: bool = False,
+    draw_grid: bool = False,
+    crop_roi: bool = False,
+    background_color: tuple[int, int, int] = (0, 0, 0),
+) -> Image.Image:
+    """Stitch the patches extracted on an image. The patches can be masked and
+    colored depending on the mask and colors provided. Requires the coordinates of
+    the patches to be set for the slide beforehand with the
+    [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method.
+
+    Args:
+        vis_level: The level at which the patches should be visualized.
+        selected_idx: An array of indices of the patches to visualize (if set to None, all the patches will be
+            visualized).
+        colors: An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as
+            they are).
+        alpha: Set the transparency of the colors to apply to the patches.
+        black_white: Set to True to visualize a binary mask of the patches extracted.
+        draw_grid: Set to True to draw a grid on the stitched patches.
+        crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the
+            slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method).
+        background_color: The color of the background.
+
+    Returns:
+        A PIL image of the stitched patches.
+    """
+    assert self.ROI is not None if crop_roi else True, (
+        "no ROI provided, while crop_roi is set to True,"
+        " please set a ROI for the slide or set crop_roi to False."
+    )
+    assert self.coords is not None, (
+        "no coordinates provided for the patches to visualize, please run the "
+        "extract_patches method first or load the coordinates from a file"
+    )
+    if crop_roi:
+        w, h = int(np.ceil(self.ROI_width / self.level_downsamples[vis_level])), int(
+            np.ceil(self.ROI_height / self.level_downsamples[vis_level])
+        )
+    else:
+        w, h = self.level_dimensions[vis_level]
+    patch_size = self.coords_attrs["patch_size"]
+    patch_level = self.coords_attrs["patch_level"]
+    patch_size = int(patch_size * self.level_downsamples[patch_level])
+    canvas = init_image(w, h, mask=black_white, color_bakground=background_color)
+    downsample_vis = self.level_downsamples[vis_level]
+    idxs = np.arange(len(self.coords))
+    if selected_idx is not None:
+        idxs = idxs[selected_idx]
+    patch_size = np.ceil(patch_size / downsample_vis).astype(int)
+    log.info(
+        f"Stitching {len(idxs)} patches at level {vis_level} with patch size {patch_size}, "
+        f"with colors {colors is not None}."
+    )
+    offset = self.ROI[:2] if crop_roi else np.array([0, 0])
+    for idx in idxs:
+        coord = self.coords[idx]
+        coord_downsampled = np.ceil(np.abs(coord - offset) / downsample_vis).astype(
+            int
+        )
+        patch_size_coord = (
+            min(max(w - coord_downsampled[0], 0), patch_size),
+            min(max(h - coord_downsampled[1], 0), patch_size),
+        )
+        if any(val == 0 for val in patch_size_coord):
+            continue
+        if black_white:
+            patch = np.ones(patch_size_coord, dtype="uint8")
+            colors = None
+        else:
+            patch = np.array(
+                self.slide.read_region(
+                    tuple(coord), vis_level, patch_size_coord
+                ).convert("RGB")
+            )
+        if colors is not None:
+            assert len(colors) == len(idxs), (
+                "the number of colors provided must match "
+                "the number of selected coordinates"
+            )
+            color = colors[idx]
+            color_patch = (
+                np.ones((patch_size_coord[1], patch_size_coord[0], 3)) * color
+            ).astype("uint8")
+            canvas[
+                coord_downsampled[1] : coord_downsampled[1] + patch_size_coord[1],
+                coord_downsampled[0] : coord_downsampled[0] + patch_size_coord[0],
+                :,
+            ] = cv2.addWeighted(color_patch, alpha, patch, 1 - alpha, 0, patch)
+        else:
+            canvas[
+                coord_downsampled[1] : coord_downsampled[1] + patch_size_coord[1],
+                coord_downsampled[0] : coord_downsampled[0] + patch_size_coord[0],
+                :,
+            ] = patch
+        if draw_grid:
+            cv2.rectangle(
+                canvas,
+                tuple(np.maximum([0, 0], coord_downsampled - 1)),
+                tuple(coord_downsampled + patch_size_coord),
+                (0, 0, 0, 255),
+                thickness=2,
+            )
+    img = Image.fromarray(canvas)
+    return img
+
    +
    +
    + +
    + + +
    + + + +

    + visualize(vis_level, crop_roi=False, contours_color=(255, 0, 0), line_thickness=500, max_size=None, number_contours=False, black_white=False, view_slide_only=False) + +

    + + +
    + +

    Visualize the slide with or without the contours of the tissue.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    vis_level + int + +
    +

    The level at which the visualization should be performed.

    +
    +     		+ required +
    crop_roi + bool + +
    +

    Set to True to crop the visualization to the region of interest (requires a ROI to be set for the +slide beforehand with the set_roi method).

    +
    +     		+ False +
    contours_color + tuple[int, int, int] + +
    +

    The color to use for the contours.

    +
    +     		+ (255, 0, 0) +
    line_thickness + int + +
    +

    The thickness to use for the contours

    +
    +     		+ 500 +
    max_size + int | None + +
    +

    The maximum size for the visualization for the width or height of the image.

    +
    +     		+ None +
    number_contours + bool + +
    +

    Set to True to number the contours.

    +
    +     		+ False +
    black_white + bool + +
    +

    Set to True to visualize a binary mask of the contoured tissue.

    +
    +     		+ False +
    view_slide_only + bool + +
    +

    Set to True to visualize the slide only (without the contours).

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Image + +
    +

    A PIL image of the visualization.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    def visualize(
+    self,
+    vis_level: int,
+    crop_roi: bool = False,
+    contours_color: tuple[int, int, int] = (255, 0, 0),
+    line_thickness: int = 500,
+    max_size: int | None = None,
+    number_contours: bool = False,
+    black_white: bool = False,
+    view_slide_only: bool = False,
+) -> Image.Image:
+    """Visualize the slide with or without the contours of the tissue.
+
+    Args:
+        vis_level: The level at which the visualization should be performed.
+        crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the
+            slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method).
+        contours_color: The color to use for the contours.
+        line_thickness: The thickness to use for the contours
+        max_size: The maximum size for the visualization for the width or height of the image.
+        number_contours: Set to True to number the contours.
+        black_white: Set to True to visualize a binary mask of the contoured tissue.
+        view_slide_only: Set to True to visualize the slide only (without the contours).
+
+    Returns:
+        A PIL image of the visualization.
+    """
+    assert line_thickness > 0, "line_thickness must be greater than 0"
+
+    scale = 1 / self.level_downsamples[vis_level]
+
+    if black_white:
+        img = np.zeros_like(self.create_thumbnail(vis_level, crop_roi), dtype="uint8")
+        line_thickness = -1
+        contours_color = (1, 1, 1)
+    else:
+        img = np.array(self.create_thumbnail(vis_level, crop_roi))
+        line_thickness = int(line_thickness * scale)
+
+    if not view_slide_only:
+        assert (
+            len(self.tissue_contours) > 0
+        ), "No tissue contours found for the slide, please run the detect_tissue method first"
+        offset = self.ROI[:2] if crop_roi else np.array([0, 0])
+        contours = [cont - offset for cont in self.tissue_contours]
+        contours = self.scale_contours(contours, scale)
+        if len(contours) > 0:
+            if not number_contours:
+                cv2.drawContours(
+                    img,
+                    contours,
+                    -1,
+                    contours_color,
+                    line_thickness,
+                    lineType=cv2.LINE_8,
+                )
+            else:  # add numbering to each contour
+                for idx, cont in enumerate(contours):
+                    M = cv2.moments(cont)
+                    cX = int(M["m10"] / (M["m00"] + 1e-9))
+                    cY = int(M["m01"] / (M["m00"] + 1e-9))
+                    # draw the contour and put text next to center
+                    cv2.drawContours(
+                        img,
+                        [cont],
+                        -1,
+                        contours_color,
+                        line_thickness,
+                        lineType=cv2.LINE_8,
+                    )
+                    cv2.putText(
+                        img,
+                        "{}".format(idx),
+                        (cX, cY),
+                        cv2.FONT_HERSHEY_SIMPLEX,
+                        2,
+                        (255, 0, 0),
+                        10,
+                    )
+
+    img = Image.fromarray(img)
+
+    if black_white:
+        img = img.convert("L")
+
+    w, h = img.size
+
+    if max_size is not None and (w > max_size or h > max_size):
+        resizeFactor = max_size / w if w > h else max_size / h
+        img = img.resize((int(w * resizeFactor), int(h * resizeFactor)))
+
+    return img
+
    +
    +
    + +
    + + +
    + + + +

    + worker_init(slide_path, engine) + + + staticmethod + + +

    + + +
    + +

    Initialize the worker process with a wsi object.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_path + str + +
    +

    The path to the slide.

    +
    +     		+ required +
    engine + str + +
    +

    The backend library to use for reading the slide (currently only openslide +and tiffslide are supported)

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/wsicore/wsi.py + 
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    @staticmethod
+def worker_init(slide_path: str, engine: str) -> None:
+    """Initialize the worker process with a wsi object.
+
+    Args:
+        slide_path: The path to the slide.
+        engine: The backend library to use for reading the slide (currently only openslide
+            and tiffslide are supported)
+    """
+    global wsi
+    wsi = WSI.read(slide_path, engine)
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/emb_utils/index.html b/reference/prismtoolbox/wsiemb/emb_utils/index.html new file mode 100644 index 0000000..a6e9fdc --- /dev/null +++ b/reference/prismtoolbox/wsiemb/emb_utils/index.html @@ -0,0 +1,1812 @@ + + + + + + + + + + + + + + + + prismtoolbox.wsiemb.emb_utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + + +

    prismtoolbox.wsiemb.emb_utils

    + +
    + + + + +
    + + + +
    + + + + + + + + + + +
    + + + +

    + compute_cell_features(cells_df_with_classes) + +

    + + +
    + +

    Compute the features of the cells from an input geodataframe for each class.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    cells_df_with_classes + GeoDataFrame + +
    +

    A geodataframe containing the cells and their classes.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    The cells based features for each class.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/emb_utils.py + 
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    def compute_cell_features(cells_df_with_classes: gpd.GeoDataFrame) -> np.ndarray:
+    """Compute the features of the cells from an input geodataframe for each class.
+
+    Args:
+        cells_df_with_classes: A geodataframe containing the cells and their classes.
+
+    Returns:
+        The cells based features for each class.
+    """
+    assert (
+        "classification" in cells_df_with_classes.columns
+    ), "The input geodataframe must contain a 'classification' column"
+    cells_feats = np.ravel(
+        cells_df_with_classes.groupby("classification", sort=False)
+        .apply(get_cell_properties, include_groups=False)
+        .to_list()
+    )
+    return cells_feats
+
    +
    +
    + +
    + + +
    + + + +

    + compute_optimal_number_clusters(embeddings_matrix, model_name, metric_name='davies_bouldin', min_clusters=2, max_clusters=10, **kwargs) + +

    + + +
    + +

    Compute the optimal number of clusters to retrieve from the embedding matrix +using the specified clustering model and metric.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    embeddings_matrix + ndarray + +
    +

    An array of shape (n_samples, n_features) containing the embeddings.

    +
    +     		+ required +
    model_name + str + +
    +

    The name of the clustering model. At the moment, possible models are:

    + +
    +     		+ required +
    metric_name + str + +
    +

    The name of the metric used to evaluate the clustering quality. +At the moment, possible metrics are:

    + +
    +     		+ 'davies_bouldin' +
    min_clusters + int + +
    +

    The minimum number of clusters to consider.

    +
    +     		+ 2 +
    max_clusters + int + +
    +

    The maximum number of clusters to consider.

    +
    +     		+ 10 +
    **kwargs + +
    +

    Some additional arguments for the clustering model (see the documentation of the clustering model).

    +
    +     		+ {} +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + tuple[int, list[float]] + +
    +

    A tuple containing the optimal number of clusters and the quality scores for each number of clusters.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/emb_utils.py + 
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    def compute_optimal_number_clusters(
+    embeddings_matrix: np.ndarray,
+    model_name: str,
+    metric_name: str = "davies_bouldin",
+    min_clusters: int = 2,
+    max_clusters: int = 10,
+    **kwargs,
+) -> tuple[int, list[float]]:
+    """Compute the optimal number of clusters to retrieve from the embedding matrix
+    using the specified clustering model and metric.
+
+    Args:
+        embeddings_matrix: An array of shape (n_samples, n_features) containing the embeddings.
+        model_name: The name of the clustering model. At the moment, possible models are:
+
+            - "[kmeans](https://scikit-learn.org/stable/modules/generated/
+            sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)"
+            - "[kmeans_mini_batch](https://scikit-learn.org/stable/modules/generated/
+            sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans)"
+
+        metric_name: The name of the metric used to evaluate the clustering quality.
+            At the moment, possible metrics are:
+
+            - "[silhouette](https://scikit-learn.org/stable/modules/generated/
+            sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score)"
+            - "[calinski_harabasz](https://scikit-learn.org/stable/modules/generated/
+            sklearn.metrics.calinski_harabasz_score.html#sklearn.metrics.calinski_harabasz_score)"
+            - "[davies_bouldin](https://scikit-learn.org/stable/modules/generated/
+            sklearn.metrics.davies_bouldin_score.html#sklearn.metrics.davies_bouldin_score)"
+
+        min_clusters: The minimum number of clusters to consider.
+        max_clusters: The maximum number of clusters to consider.
+
+        **kwargs: Some additional arguments for the clustering model (see the documentation of the clustering model).
+
+    Returns:
+        A tuple containing the optimal number of clusters and the quality scores for each number of clusters.
+    """
+    if metric_name not in clustering_metrics.keys():
+        raise ValueError(f"Metric {metric_name} not implemented")
+    metric = clustering_metrics[metric_name]
+    if model_name == "kmeans":
+        cluster_model = skl_cluster.KMeans(**kwargs)
+    elif model_name == "kmeans_mini_batch":
+        cluster_model = skl_cluster.MiniBatchKMeans(**kwargs)
+    else:
+        raise ValueError(f"Model {model_name} not implemented")
+
+    scores = []
+    for n_clusters in tqdm(
+        range(min_clusters, max_clusters + 1),
+        desc="Computing clustering quality " "scores for each number of clusters",
+    ):
+        cluster_model.set_params(n_clusters=n_clusters)
+        cluster_assignments = cluster_model.fit_predict(embeddings_matrix)
+        scores.append(metric(embeddings_matrix, cluster_assignments))
+    if metric_name == "davies_bouldin":
+        optimal_number = np.argmin(scores) + min_clusters
+    else:
+        optimal_number = np.argmax(scores) + min_clusters
+    return optimal_number, scores
+
    +
    +
    + +
    + + +
    + + + +

    + create_model(arch_name, pretrained_weights=None) + +

    + + +
    + +

    Create a model from the arch_name and load the pretrained weights if specified.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    arch_name + str + +
    +

    The architecture name. At the moment, possible models are:

    + +
    +     		+ required +
    pretrained_weights + str | None + +
    +

    The path to the pretrained weights or the name of the pretrained weights.

    +
      +
    • For torchvision models and CLAM, possible weights are: "IMAGENET1K_V1", "IMAGENET1K_V2", and +"ciga" for ResNet18.
      • +
    • For Pathoduet, possible weights are: "pathoduet_HE", "pathoduet_IHC".
      • +
    • For Phikon, Uni, and Conch, the weights are automatically downloaded from the Hugging Face model hub.
      • +
    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + tuple[Module, Compose | None] + +
    +

    A tuple containing a torch model and the transforms used to preprocess the images (set to None if no pretrained +weights were used).

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/emb_utils.py + 
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    def create_model(
+    arch_name: str, pretrained_weights: str | None = None
+) -> tuple[nn.Module, transformsv2.Compose | None]:
+    """Create a model from the arch_name and load the pretrained weights if specified.
+
+    Args:
+        arch_name: The architecture name. At the moment, possible models are:
+
+            - [torchvision based models](https://pytorch.org/vision/stable/models.html): "resnet18", "resnet50",
+             "resnet101".
+            - [CLAM](https://github.com/mahmoodlab/CLAM), i.e ResNet50 truncated after the third convolutional
+            block: "clam".
+            - [Pathoduet model](https://github.com/openmedlab/PathoDuet): "pathoduet".
+            - [Phikon model](https://huggingface.co/owkin/phikon): "phikon".
+            - [Conch model](https://huggingface.co/MahmoodLab/CONCH): "conch".
+            - [Uni model](https://huggingface.co/MahmoodLab/UNI): "uni".
+
+        pretrained_weights: The path to the pretrained weights or the name of the pretrained weights.
+
+            - For torchvision models and CLAM, possible weights are: "IMAGENET1K_V1", "IMAGENET1K_V2", and
+            "[ciga](https://github.com/ozanciga/self-supervised-histopathology)" for ResNet18.
+            - For Pathoduet, possible weights are: "pathoduet_HE", "pathoduet_IHC".
+            - For Phikon, Uni, and Conch, the weights are automatically downloaded from the Hugging Face model hub.
+
+    Returns:
+        A tuple containing a torch model and the transforms used to preprocess the images (set to None if no pretrained
+            weights were used).
+    """
+    if arch_name not in arch_dict:
+        raise ValueError(f"invalid model name. Possible models: {arch_dict.keys()}")
+    model, pretrained_transforms = arch_dict[arch_name](weights=pretrained_weights)
+    return model, pretrained_transforms
+
    +
    +
    + +
    + + +
    + + + +

    + extract_stain_features(patches, conv_matrix_name='HED') + +

    + + +
    + +

    Extract the stain features from the images.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    imgs + +
    +

    A tensor of shape (n_samples, n_channels, height, width) containing the images.

    +
    +     		+ required +
    conv_matrix_name + str + +
    +

    The name of the conversion matrix. At the moment, possible values are:

    +
      +
    • "HED": Hematoxylin, Eosin and DAB.
      • +
    • "HD": Hematoxylin and DAB.
      • +
    • "HD_custom": Custom Hematoxylin and DAB matrix.
      • +
    +
    +     		+ 'HED' +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + Tensor + +
    +

    A tensor of shape (n_samples, n_channels, height, width) containing the stain features.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/emb_utils.py + 
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    def extract_stain_features(
+    patches: torch.Tensor, conv_matrix_name: str = "HED"
+) -> torch.Tensor:
+    """Extract the stain features from the images.
+
+    Args:
+        imgs: A tensor of shape (n_samples, n_channels, height, width) containing the images.
+        conv_matrix_name: The name of the conversion matrix. At the moment, possible values are:
+
+            - "HED": Hematoxylin, Eosin and DAB.
+            - "HD": Hematoxylin and DAB.
+            - "HD_custom": Custom Hematoxylin and DAB matrix.
+
+    Returns:
+        A tensor of shape (n_samples, n_channels, height, width) containing the stain features.
+    """
+    conv_matrix = torch.tensor(
+        retrieve_conv_matrix(conv_matrix_name), dtype=torch.float32
+    )
+    eps = torch.tensor([1e-6], dtype=torch.float32)
+    patches = torch.maximum(patches, eps)
+    log_adjust = torch.log(eps)
+    stains = torch.einsum("bcij,cd->bdij", torch.log(patches) / log_adjust, conv_matrix)
+    stains = torch.maximum(stains, torch.tensor([0.0], dtype=torch.float32))
+    if conv_matrix_name.startswith("HD"):
+        stains = stains[:, :2, ...]
+    stains_flattened = stains.view(stains.size(0), stains.size(1), -1)
+    feats = torch.concatenate(
+        [
+            stains.mean(dim=(2, 3)),
+            stains.std(dim=(2, 3)),
+            stains.amin(dim=(2, 3)),
+            stains.amax(dim=(2, 3)),
+            stains_flattened.median(dim=-1).values,
+            stains_flattened.quantile(0.25, dim=-1),
+            stains_flattened.quantile(0.75, dim=-1),
+        ],
+        dim=1,
+    )
+    return feats
+
    +
    +
    + +
    + + +
    + + + +

    + get_cell_properties(cells_df) + +

    + + +
    + +

    Get the properties of the cells in the input dataframe.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    cells_df + GeoDataFrame + +
    +

    A geodataframe containing the cells.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + tuple[list[float], list[str]] + +
    +

    A list of 7 features describing the number of cells and their average morphological properties.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/emb_utils.py + 
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    def get_cell_properties(cells_df: gpd.GeoDataFrame) -> tuple[list[float], list[str]]:
+    """Get the properties of the cells in the input dataframe.
+
+    Args:
+        cells_df: A geodataframe containing the cells.
+
+    Returns:
+        A list of 7 features describing the number of cells and their average morphological properties.
+    """
+    if cells_df.geometry.isnull().all():
+        return [0.0] * 7
+    else:
+        N_cells = len(cells_df)
+        avg_cells_area = cells_df.area.mean()
+        avg_cells_perimeter = cells_df.length.mean()
+        avg_cells_compactness = (
+            (4 * np.pi * cells_df.area) / (cells_df.length**2)
+        ).mean()
+        avg_cells_roundness = (
+            (4 * cells_df.area) / (cells_df.convex_hull.length**2)
+        ).mean()
+        avg_cells_solidity = (cells_df.area / cells_df.convex_hull.area).mean()
+        avg_cells_elongation = (
+            (cells_df.bounds["maxx"] - cells_df.bounds["minx"])
+            / (cells_df.bounds["maxy"] - cells_df.bounds["miny"])
+        ).mean()
+        return [
+            N_cells,
+            avg_cells_area,
+            avg_cells_perimeter,
+            avg_cells_compactness,
+            avg_cells_roundness,
+            avg_cells_solidity,
+            avg_cells_elongation,
+        ]
+
    +
    +
    + +
    + + +
    + + + +

    + get_cells_in_patch(cells_df, coord, patch_size, cell_classes) + +

    + + +
    + +

    Get the cells whose centroids are within the patch defined by the input coordinates and size.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    cells_df + GeoDataFrame + +
    +

    A geodataframe containing the cells.

    +
    +     		+ required +
    coord + Tensor + +
    +

    A tensor of shape (2,) containing the coordinates of the patch.

    +
    +     		+ required +
    patch_size + int + +
    +

    The size of the patch.

    +
    +     		+ required +
    cell_classes + list[str] + +
    +

    A list of cell classes.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + GeoDataFrame + +
    +

    A geodataframe containing the cells in the patch ordered by cell classes.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/emb_utils.py + 
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    def get_cells_in_patch(
+    cells_df: gpd.GeoDataFrame,
+    coord: torch.Tensor,
+    patch_size: int,
+    cell_classes: list[str],
+) -> gpd.GeoDataFrame:
+    """Get the cells whose centroids are within the patch defined by the input coordinates and size.
+
+    Args:
+        cells_df: A geodataframe containing the cells.
+        coord: A tensor of shape (2,) containing the coordinates of the patch.
+        patch_size: The size of the patch.
+        cell_classes: A list of cell classes.
+
+    Returns:
+        A geodataframe containing the cells in the patch ordered by cell classes.
+    """
+    x, y = coord.numpy()
+    patch_polygon = box(x, y, x + patch_size, y + patch_size, ccw=False)
+    cells_in_patch = cells_df.loc[
+        cells_df.centroid.within(patch_polygon),
+    ]
+    cells_in_patch_grouped = []
+    for cell_class in cell_classes:
+        if cell_class in cells_in_patch["classification"].values:
+            cell_class_in_patch_df = cells_in_patch[
+                cells_in_patch["classification"] == cell_class
+            ]
+        else:
+            cell_class_in_patch_df = gpd.GeoDataFrame(
+                columns=cells_df.columns,
+                data=[pd.Series({col: None for col in cells_df.columns})],
+            )
+            cell_class_in_patch_df["classification"] = cell_class
+        cells_in_patch_grouped.append(cell_class_in_patch_df)
+    result = pd.concat(cells_in_patch_grouped, ignore_index=True)
+    return result
+
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/embedder/index.html b/reference/prismtoolbox/wsiemb/embedder/index.html new file mode 100644 index 0000000..73d4471 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/embedder/index.html @@ -0,0 +1,3214 @@ + + + + + + + + + + + + + + + + prismtoolbox.wsiemb.embedder - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + + +

    prismtoolbox.wsiemb.embedder

    + +
    + + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + PatchEmbedder(arch_name, batch_size, num_workers, pretrained_weights=None, transforms_dict=None, device='cuda', need_login=False) + +

    + + +
    +

    + Bases: BasePatchHandler

    + + +

    The PatchEmbedder class is used to extract embeddings from patches extracted as images in a folder.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    arch_name + str + +
    +

    The name of the architecture to use. +See create_model for available architectures.

    +
    +     		+ required +
    batch_size + int + +
    +

    The batch size to use for the dataloader.

    +
    +     		+ required +
    num_workers + int + +
    +

    The number of workers to use for the dataloader.

    +
    +     		+ required +
    pretrained_weights + str | None + +
    +

    The path to the pretrained weights or the name of the pretrained weights. See +create_model for available weights for each architecture.

    +
    +     		+ None +
    transforms_dict + dict[str, dict[str, any]] | None + +
    +

    The dictionary of transforms to use. +See create_transforms for more information. +If None, the pretrained transforms will be used.

    +
    +     		+ None +
    device + str + +
    +

    The device to use for the model.

    +
    +     		+ 'cuda' +
    need_login + bool + +
    +

    Whether to login to the HuggingFace Hub (for Uni and Conch models).

    +
    +     		+ False +
    + + + +

    Attributes:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescription
    batch_size + +
    +

    The batch size to use for the dataloader.

    +
    +
    num_workers + +
    +

    The number of workers to use for the dataloader.

    +
    +
    transforms_dict + +
    +

    The dictionary of transforms to use.

    +
    +
    device + +
    +

    The device to use for the model.

    +
    +
    model + +
    +

    The model to use for creating the embeddings.

    +
    +
    pretrained_transforms + +
    +

    The transforms used when pretraining the model.

    +
    +
    embeddings + +
    +

    A dictionary containing the extracted embeddings for each image.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def __init__(
+    self,
+    arch_name: str,
+    batch_size: int,
+    num_workers: int,
+    pretrained_weights: str | None = None,
+    transforms_dict: dict[str, dict[str, any]] | None = None,
+    device: str = "cuda",
+    need_login: bool = False,
+):
+    """The PatchEmbedder class is used to extract embeddings from patches extracted as images in a folder.
+
+    Args:
+        arch_name: The name of the architecture to use.
+            See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available architectures.
+        batch_size: The batch size to use for the dataloader.
+        num_workers: The number of workers to use for the dataloader.
+        pretrained_weights: The path to the pretrained weights or the name of the pretrained weights. See
+            [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available weights for each architecture.
+        transforms_dict: The dictionary of transforms to use.
+            See [create_transforms][prismtoolbox.utils.torch_utils.create_transforms] for more information.
+            If None, the pretrained transforms will be used.
+        device: The device to use for the model.
+        need_login: Whether to login to the HuggingFace Hub (for Uni and Conch models).
+
+    Attributes:
+        batch_size: The batch size to use for the dataloader.
+        num_workers: The number of workers to use for the dataloader.
+        transforms_dict: The dictionary of transforms to use.
+        device: The device to use for the model.
+        model: The model to use for creating the embeddings.
+        pretrained_transforms: The transforms used when pretraining the model.
+        embeddings: A dictionary containing the extracted embeddings for each image.
+
+    """
+    super().__init__(batch_size, num_workers, transforms_dict)
+    self.device = device
+
+    if need_login:
+        from huggingface_hub import login
+
+        login()
+
+    self.model, self.pretrained_transforms = create_model(
+        arch_name, pretrained_weights
+    )
+    log.info(
+        f"Model {arch_name} loaded with pretrained weights {pretrained_weights}."
+    )
+    self.model.eval()
+    self.model.to(self.device)
+
+    self.embeddings = {}
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + extract_embeddings(img_folder, show_progress=True) + +

    + + +
    + +

    Extract embeddings from the images in the img_folder.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    img_folder + +
    +

    A folder containing a series of subfolders, each containing images. +For example, img_folder could be a folder where the subfolders correspond to different slides.

    +
    +     		+ required +
    show_progress + bool + +
    +

    Whether to show the progress bar.

    +
    +     		+ True +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def extract_embeddings(self, img_folder, show_progress: bool = True):
+    """Extract embeddings from the images in the img_folder.
+
+    Args:
+        img_folder: A folder containing a series of subfolders, each containing images.
+            For example, img_folder could be a folder where the subfolders correspond to different slides.
+        show_progress: Whether to show the progress bar.
+    """
+    log.info(f"Extracting embeddings from images in {img_folder}.")
+    dataset = self.create_dataset(img_folder=img_folder)
+    dataloader = self.create_dataloader(dataset)
+    start_time = time.time()
+    embeddings = [[] for _ in range(len(dataset.classes))]
+    img_ids = []
+    for i in range(len(dataset.classes)):
+        img_ids.append(np.array(dataset.imgs)[np.array(dataset.targets) == i][:, 0])
+    for imgs, folder_id in tqdm(
+        dataloader,
+        desc=f"Extracting embeddings from images in {img_folder}",
+        disable=not show_progress,
+    ):
+        imgs = imgs.to(self.device)
+        with torch.no_grad():
+            output = self.model(imgs)
+            for i in range(len(dataset.classes)):
+                embeddings[i].append(output[folder_id == i].cpu())
+    log.info(f"Embedding time: {time.time() - start_time}.")
+    log.info(f"Extracted {len(embeddings)} from images in {img_folder}.")
+    for img_id, embedding in zip(img_ids, embeddings):
+        self.embeddings[img_id] = torch.cat(embedding, dim=0)
+
    +
    +
    + +
    + + +
    + + + +

    + get_transforms() + +

    + + +
    + +

    Get the transforms to use for creating the embeddings.

    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + +
    +

    The transforms to use when loading the patches.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def get_transforms(self):
+    """Get the transforms to use for creating the embeddings.
+
+    Returns:
+        The transforms to use when loading the patches.
+    """
+    if self.transforms_dict is not None:
+        super().get_transforms()
+    elif self.pretrained_transforms is not None:
+        log.info("No transforms dict found, using pretrained transforms.")
+        transforms = self.pretrained_transforms
+    else:
+        log.info("No transforms dict or pretrained transforms found.")
+        transforms = None
+    return transforms
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + +
    + + + +

    + SlideEmbedder(slide_dir, batch_size, num_workers, arch_name=None, pretrained_weights=None, transforms_dict=None, device='cuda', engine='openslide', coords_dir=None, patch_size=None, patch_level=None, patch_downsample=None, need_login=False) + +

    + + +
    +

    + Bases: BaseSlideHandler

    + + +

    The SlideEmbedder class is used to extract embeddings from patches extracted direclty from the slides.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_dir + str + +
    +

    The directory containing the slides.

    +
    +     		+ required +
    batch_size + int + +
    +

    The batch size to use for the dataloader.

    +
    +     		+ required +
    num_workers + int + +
    +

    The number of workers to use for the dataloader.

    +
    +     		+ required +
    arch_name + str | None + +
    +

    The name of the architecture to use. +See create_model for available architectures.

    +
    +     		+ None +
    pretrained_weights + str | None + +
    +

    The path to the pretrained weights or the name of the pretrained weights. See +create_model for available weights for each architecture.

    +
    +     		+ None +
    transforms_dict + dict[str, dict[str, any]] | None + +
    +

    The dictionary of transforms to use. +See create_transforms +for more information. If None, the pretrained transforms will be used.

    +
    +     		+ None +
    device + str + +
    +

    The device to use for the model.

    +
    +     		+ 'cuda' +
    engine + str + +
    +

    The engine to use for reading the slides.

    +
    +     		+ 'openslide' +
    coords_dir + str | None + +
    +

    The directory containing the coordinates of the patches as hdf5 files. If None, +the patch_size and patch_level must be provided.

    +
    +     		+ None +
    patch_size + int | None + +
    +

    The size of the patches. If None, it will be extracted from the hdf5 files.

    +
    +     		+ None +
    patch_level + int | None + +
    +

    The level of the patches. If None, it will be extracted from the hdf5 files.

    +
    +     		+ None +
    patch_downsample + int | None + +
    +

    The downsample of the patches. If None, it will be extracted from the hdf5 files.

    +
    +     		+ None +
    need_login + bool + +
    +

    Whether to login to the HuggingFace Hub (for Uni and Conch models).

    +
    +     		+ False +
    + + + +

    Attributes:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescription
    slide_dir + +
    +

    The directory containing the slides.

    +
    +
    batch_size + +
    +

    The batch size to use for the dataloader.

    +
    +
    num_workers + +
    +

    The number of workers to use for the dataloader.

    +
    +
    transforms_dict + +
    +

    The dictionary of transforms to use.

    +
    +
    device + +
    +

    The device to use for the model.

    +
    +
    engine + +
    +

    The engine to use for reading the slides.

    +
    +
    coords_dir + +
    +

    The directory containing the coordinates of the patches as hdf5 files.

    +
    +
    patch_size + +
    +

    The size of the patches.

    +
    +
    patch_level + +
    +

    The level of the patches.

    +
    +
    patch_downsample + +
    +

    The downsample of the patches.

    +
    +
    arch_name + +
    +

    The name of the architecture to use.

    +
    +
    model + +
    +

    The model to use for creating the embeddings.

    +
    +
    pretrained_transforms + +
    +

    The transforms used for the pretrained model.

    +
    +
    model_based_embeddings + +
    +

    A dictionary containing the extracted embeddings for each slide +with the pretrained model.

    +
    +
    stain_based_embeddings + +
    +

    A dictionary containing the extracted embeddings for each slide +with the stain based features.

    +
    +
    cell_based_embeddings + +
    +

    A dictionary containing the extracted embeddings for each slide +with the cell based features.

    +
    +
    model_based_embedding_names + +
    +

    The names of the features extracted with the pretrained model.

    +
    +
    stain_based_embedding_names + +
    +

    The names of the features extracted with the stain based features.

    +
    +
    cell_based_embedding_names + +
    +

    The names of the features extracted with the cell based features.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def __init__(
+    self,
+    slide_dir: str,
+    batch_size: int,
+    num_workers: int,
+    arch_name: str | None = None,
+    pretrained_weights: str | None = None,
+    transforms_dict: dict[str, dict[str, any]] | None = None,
+    device: str = "cuda",
+    engine: str = "openslide",
+    coords_dir: str | None = None,
+    patch_size: int | None = None,
+    patch_level: int | None = None,
+    patch_downsample: int | None = None,
+    need_login: bool = False,
+):
+    """The SlideEmbedder class is used to extract embeddings from patches extracted direclty from the slides.
+
+    Args:
+        slide_dir: The directory containing the slides.
+        batch_size: The batch size to use for the dataloader.
+        num_workers: The number of workers to use for the dataloader.
+        arch_name: The name of the architecture to use.
+            See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available architectures.
+        pretrained_weights: The path to the pretrained weights or the name of the pretrained weights. See
+            [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available weights for each architecture.
+        transforms_dict: The dictionary of transforms to use.
+            See [create_transforms][prismtoolbox.utils.torch_utils.create_transforms]
+            for more information. If None, the pretrained transforms will be used.
+        device: The device to use for the model.
+        engine: The engine to use for reading the slides.
+        coords_dir: The directory containing the coordinates of the patches as hdf5 files. If None,
+            the patch_size and patch_level must be provided.
+        patch_size: The size of the patches. If None, it will be extracted from the hdf5 files.
+        patch_level: The level of the patches. If None, it will be extracted from the hdf5 files.
+        patch_downsample: The downsample of the patches. If None, it will be extracted from the hdf5 files.
+        need_login: Whether to login to the HuggingFace Hub (for Uni and Conch models).
+
+    Attributes:
+        slide_dir: The directory containing the slides.
+        batch_size: The batch size to use for the dataloader.
+        num_workers: The number of workers to use for the dataloader.
+        transforms_dict: The dictionary of transforms to use.
+        device: The device to use for the model.
+        engine: The engine to use for reading the slides.
+        coords_dir: The directory containing the coordinates of the patches as hdf5 files.
+        patch_size: The size of the patches.
+        patch_level: The level of the patches.
+        patch_downsample: The downsample of the patches.
+        arch_name: The name of the architecture to use.
+        model: The model to use for creating the embeddings.
+        pretrained_transforms: The transforms used for the pretrained model.
+        model_based_embeddings: A dictionary containing the extracted embeddings for each slide
+            with the pretrained model.
+        stain_based_embeddings: A dictionary containing the extracted embeddings for each slide
+            with the stain based features.
+        cell_based_embeddings: A dictionary containing the extracted embeddings for each slide
+            with the cell based features.
+        model_based_embedding_names: The names of the features extracted with the pretrained model.
+        stain_based_embedding_names: The names of the features extracted with the stain based features.
+        cell_based_embedding_names: The names of the features extracted with the cell based features.
+
+    """
+    super().__init__(
+        slide_dir,
+        batch_size,
+        num_workers,
+        transforms_dict,
+        engine,
+        coords_dir,
+        patch_size,
+        patch_level,
+        patch_downsample,
+    )
+
+    if need_login:
+        from huggingface_hub import login
+
+        login()
+
+    self.device = device
+    self.arch_name = arch_name
+    if self.arch_name is not None:
+        self.model, self.pretrained_transforms = create_model(
+            arch_name, pretrained_weights
+        )
+        log.info(
+            f"Model {self.arch_name} loaded with pretrained weights {pretrained_weights}."
+        )
+        self.model.eval()
+        self.model.to(self.device)
+    else:
+        self.model = None
+        self.pretrained_transforms = None
+
+    self.model_based_embeddings = {}
+    self.stain_based_embeddings = {}
+    self.cell_based_embeddings = {}
+    self.model_based_embedding_names = []
+    self.stain_based_embedding_names = []
+    self.cell_based_embedding_names = []
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + extract_cell_based_embeddings(slide_name, slide_ext, coords=None, cells_path=None, cell_classes=None, with_offset=True, show_progress=True) + +

    + + +
    + +

    Extract embeddings from the patches of a slide using the cell based features.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_name + str + +
    +

    The name of the slide to extract the embeddings from (without the extension).

    +
    +     		+ required +
    slide_ext + str + +
    +

    The extension of the slide.

    +
    +     		+ required +
    coords + ndarray | None + +
    +

    The coordinates of the patches to extract the embeddings from. +If None, the coordinates will be loaded from the hdf5 file located in coords_dir.

    +
    +     		+ None +
    cells_path + str | None + +
    +

    The path to the cells geojson file.

    +
    +     		+ None +
    cell_classes + list[str] | None + +
    +

    The classes of the cells to extract the embeddings from. If None, all the classes will be used.

    +
    +     		+ None +
    with_offset + bool + +
    +

    Whether to offset the coordinates of the cells by the slide offset.

    +
    +     		+ True +
    show_progress + bool + +
    +

    Whether to show the progress bar.

    +
    +     		+ True +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def extract_cell_based_embeddings(
+    self,
+    slide_name: str,
+    slide_ext: str,
+    coords: np.ndarray | None = None,
+    cells_path: str | None = None,
+    cell_classes: list[str] | None = None,
+    with_offset: bool = True,
+    show_progress: bool = True,
+):
+    """Extract embeddings from the patches of a slide using the cell based features.
+
+    Args:
+        slide_name: The name of the slide to extract the embeddings from (without the extension).
+        slide_ext: The extension of the slide.
+        coords: The coordinates of the patches to extract the embeddings from.
+            If None, the coordinates will be loaded from the hdf5 file located in coords_dir.
+        cells_path: The path to the cells geojson file.
+        cell_classes: The classes of the cells to extract the embeddings from. If None, all the classes will be used.
+        with_offset: Whether to offset the coordinates of the cells by the slide offset.
+        show_progress: Whether to show the progress bar.
+    """
+    dataset = self.create_dataset(
+        slide_name, slide_ext=slide_ext, coords=coords, coords_only=True
+    )
+    dataloader = self.create_dataloader(dataset, num_workers=1)
+    patch_size = dataset.patch_size
+    patch_downsample = dataset.downsample
+    ref_patch_size = patch_size * patch_downsample
+    offset = dataset.slide_offset if with_offset else (0, 0)
+    offset = (-offset[0], -offset[1])
+    cells_df = read_json_with_geopandas(cells_path, offset=offset)
+    if "classification" not in cells_df.columns:
+        raise ValueError(
+            "The 'classification' column is missing in the cells dataframe."
+        )
+    cell_classes = (
+        cells_df.classification.unique() if cell_classes is None else cell_classes
+    )
+    self.cell_based_embedding_names = [
+        f"{cell_class}_{feature}"
+        for cell_class in cell_classes
+        for feature in CELL_FEATURE_NAMES
+    ]
+    start_time = time.time()
+    embeddings = []
+    if self.num_workers > 1:
+        pool = mp.Pool(self.num_workers)
+    for coords in tqdm(
+        dataloader,
+        desc=f"Extracting cell based features from the patches of {slide_name}",
+        disable=not show_progress,
+    ):
+        if self.num_workers > 1:
+            cells_df_in_patches = pool.starmap(
+                get_cells_in_patch,
+                [(cells_df, coord, ref_patch_size, cell_classes) for coord in coords],
+            )
+            cells_features = np.array(
+                pool.map(compute_cell_features, cells_df_in_patches)
+            )
+        else:
+            cells_df_in_patches = [
+                get_cells_in_patch(cells_df, coord, ref_patch_size, cell_classes)
+                for coord in coords
+            ]
+            cells_features = np.array(
+                [
+                    compute_cell_features(cells_df_in_patch)
+                    for cells_df_in_patch in cells_df_in_patches
+                ]
+            )
+        embeddings.append(torch.tensor(cells_features))
+    if self.num_workers > 1:
+        pool.close()
+    self.cell_based_embeddings[slide_name] = torch.cat(embeddings, dim=0)
+    log.info(f"Embedding time: {time.time() - start_time}.")
+    log.info(f"Extracted embeddings from {len(embeddings)} patches of {slide_name}.")
+
    +
    +
    + +
    + + +
    + + + +

    + extract_model_based_embeddings(slide_name, slide_ext, coords=None, show_progress=True) + +

    + + +
    + +

    Extract embeddings from the patches of a slide using the pretrained model.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_name + str + +
    +

    The name of the slide to extract the embeddings from (without the extension).

    +
    +     		+ required +
    slide_ext + str + +
    +

    The extension of the slide.

    +
    +     		+ required +
    coords + ndarray | None + +
    +

    The coordinates of the patches to extract the embeddings from. +If None, the coordinates will be loaded from the hdf5 file located in coords_dir.

    +
    +     		+ None +
    show_progress + bool + +
    +

    Whether to show the progress bar.

    +
    +     		+ True +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def extract_model_based_embeddings(
+    self,
+    slide_name: str,
+    slide_ext: str,
+    coords: np.ndarray | None = None,
+    show_progress: bool = True,
+):
+    """Extract embeddings from the patches of a slide using the pretrained model.
+
+    Args:
+        slide_name: The name of the slide to extract the embeddings from (without the extension).
+        slide_ext: The extension of the slide.
+        coords: The coordinates of the patches to extract the embeddings from.
+            If None, the coordinates will be loaded from the hdf5 file located in coords_dir.
+        show_progress: Whether to show the progress bar.
+    """
+    assert (
+        self.model is not None
+    ), "Model not found. Please provide an architecture name when initializing the SlideEmbedder."
+    log.info(f"Extracting embeddings from the patches of {slide_name}.")
+    dataset = self.create_dataset(slide_name, slide_ext=slide_ext, coords=coords)
+    dataloader = self.create_dataloader(dataset)
+    start_time = time.time()
+    embeddings = []
+    for patches, _ in tqdm(
+        dataloader,
+        desc=f"Extracting embeddings from the patches of {slide_name}",
+        disable=not show_progress,
+    ):
+        patches = patches.to(self.device)
+        with torch.no_grad():
+            output = self.model(patches)
+            embeddings.append(output.cpu())
+    log.info(f"Embedding time: {time.time() - start_time}.")
+    self.model_based_embeddings[slide_name] = torch.cat(embeddings, dim=0)
+    embeddings_dim = self.model_based_embeddings[slide_name].shape[1]
+    self.model_based_embedding_names = [
+        f"{self.arch_name}_{i}" for i in range(embeddings_dim)
+    ]
+    log.info(f"Extracted embeddings from {len(embeddings)} patches of {slide_name}.")
+
    +
    +
    + +
    + + +
    + + + +

    + extract_stain_based_embeddings(slide_name, slide_ext, coords=None, conv_matrix_name='HED', show_progress=True) + +

    + + +
    + +

    Extract embeddings from the patches of a slide using the stain based features.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_name + str + +
    +

    The name of the slide to extract the embeddings from (without the extension).

    +
    +     		+ required +
    slide_ext + str + +
    +

    The extension of the slide.

    +
    +     		+ required +
    coords + ndarray | None + +
    +

    The coordinates of the patches to extract the embeddings from. +If None, the coordinates will be loaded from the hdf5 file located in coords_dir.

    +
    +     		+ None +
    conv_matrix_name + str + +
    +

    The name of the convolutional matrix to use for the stain based features. See +extract_stain_features for available matrices.

    +
    +     		+ 'HED' +
    show_progress + bool + +
    +

    Whether to show the progress bar.

    +
    +     		+ True +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def extract_stain_based_embeddings(
+    self,
+    slide_name: str,
+    slide_ext: str,
+    coords: np.ndarray | None = None,
+    conv_matrix_name: str = "HED",
+    show_progress: bool = True,
+):
+    """Extract embeddings from the patches of a slide using the stain based features.
+
+    Args:
+        slide_name: The name of the slide to extract the embeddings from (without the extension).
+        slide_ext: The extension of the slide.
+        coords: The coordinates of the patches to extract the embeddings from.
+            If None, the coordinates will be loaded from the hdf5 file located in coords_dir.
+        conv_matrix_name: The name of the convolutional matrix to use for the stain based features. See
+            [extract_stain_features][prismtoolbox.wsiemb.emb_utils.extract_stain_features] for available matrices.
+        show_progress: Whether to show the progress bar.
+    """
+    log.info(f"Extracting embeddings from the patches of {slide_name}.")
+    dataset = self.create_dataset(
+        slide_name, slide_ext=slide_ext, coords=coords, no_transforms=True
+    )
+    dataloader = self.create_dataloader(dataset)
+    if conv_matrix_name == "HED":
+        stain_names = ["Hematoxylin", "Eosin", "DAB"]
+    else:
+        stain_names = ["Hematoxylin", "DAB"]
+    self.stain_based_embedding_names = [
+        f"{stain_name}_{feature}"
+        for feature in STAIN_FEATURE_NAMES
+        for stain_name in stain_names
+    ]
+    start_time = time.time()
+    embeddings = []
+    for patches, _ in tqdm(
+        dataloader,
+        desc=f"Extracting stain based features from the patches of {slide_name}",
+        disable=not show_progress,
+    ):
+        embeddings.append(extract_stain_features(patches, conv_matrix_name))
+    log.info(f"Embedding time: {time.time() - start_time}.")
+    self.stain_based_embeddings[slide_name] = torch.cat(embeddings, dim=0)
+    log.info(f"Extracted embeddings from {len(embeddings)} patches of {slide_name}.")
+
    +
    +
    + +
    + + +
    + + + +

    + get_transforms() + +

    + + +
    + +

    Get the transforms to use for creating the dataset.

    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + +
    +

    The transforms to use when loading the patches.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def get_transforms(self):
+    """Get the transforms to use for creating the dataset.
+
+    Returns:
+        The transforms to use when loading the patches.
+    """
+    if self.transforms_dict is not None:
+        transforms = super().get_transforms()
+    elif self.pretrained_transforms is not None:
+        log.info("No transforms dict found, using pretrained transforms.")
+        transforms = self.pretrained_transforms
+    else:
+        log.info("No transforms dict or pretrained transforms found.")
+        transforms = None
+    return transforms
+
    +
    +
    + +
    + + +
    + + + +

    + save_embeddings(save_dir, flush_memory=False, format='pt', merge=False) + +

    + + +
    + +

    Save the extracted embeddings to the chosen format under slide_name.format.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    save_dir + str + +
    +

    The path to the directory where to save the embeddings.

    +
    +     		+ required +
    flush_memory + bool + +
    +

    Whether to remove the embeddings from self.embeddings after saving.

    +
    +     		+ False +
    format + str + +
    +

    The format to save the embeddings in. Possible formats: ['pt', 'npy']

    +
    +     		+ 'pt' +
    merge + bool + +
    +

    Whether to merge the different types of embeddings before saving.

    +
    +     		+ False +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def save_embeddings(
+    self,
+    save_dir: str,
+    flush_memory: bool = False,
+    format: str = "pt",
+    merge: bool = False,
+):
+    """Save the extracted embeddings to the chosen format under slide_name.format.
+
+    Args:
+        save_dir: The path to the directory where to save the embeddings.
+        flush_memory: Whether to remove the embeddings from self.embeddings after saving.
+        format: The format to save the embeddings in. Possible formats: ['pt', 'npy']
+        merge: Whether to merge the different types of embeddings before saving.
+
+    """
+    if not os.path.isdir(save_dir):
+        log.warning(f"Folder {save_dir} does not exist, creating new folder...")
+        pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
+    if format not in ["pt", "npy"]:
+        raise ValueError("invalid format, possible formats: ['pt', 'npy']")
+    slides_processed = set(self.model_based_embeddings.keys()).union(
+        self.stain_based_embeddings.keys(), self.cell_based_embeddings.keys()
+    )
+    for slide_name in slides_processed:
+        if merge:
+            embeddings_list = [
+                (
+                    "",
+                    torch.cat(
+                        [
+                            emb_dict[slide_name]
+                            for emb_dict in [
+                                self.model_based_embeddings,
+                                self.stain_based_embeddings,
+                                self.cell_based_embeddings,
+                            ]
+                            if slide_name in emb_dict.keys()
+                        ],
+                        dim=1,
+                    ),
+                )
+            ]
+        else:
+            embeddings_list = [
+                (emb_type, emb_dict[slide_name])
+                for emb_type, emb_dict in zip(
+                    ["_model_based", "_stain_based", "_cell_based"],
+                    [
+                        self.model_based_embeddings,
+                        self.stain_based_embeddings,
+                        self.cell_based_embeddings,
+                    ],
+                )
+                if slide_name in emb_dict.keys()
+            ]
+        for emb_type, emb in embeddings_list:
+            output_path = os.path.join(save_dir, f"{slide_name+emb_type}.{format}")
+            if format == "pt":
+                torch.save(emb, output_path)
+            elif format == "npy":
+                emb = emb.numpy()
+                np.save(output_path, emb)
+            else:
+                raise NotImplementedError
+        log.info(f"Embeddings for slide {slide_name} saved at {output_path}.")
+
+    if flush_memory:
+        self.model_based_embeddings = {}
+        self.stain_based_embeddings = {}
+        self.cell_based_embeddings = {}
+        log.info("Memory flushed.")
+
    +
    +
    + +
    + + +
    + + + +

    + save_embeddings_names(save_dir, format='csv', flush_memory=False, merge=False) + +

    + + +
    + +

    Save the extracted embeddings names to the chosen format under slide_name.format.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    save_dir + str + +
    +

    The path to the directory where to save the embeddings.

    +
    +     		+ required +
    format + str + +
    +

    The format to save the embeddings in. Possible formats: ['csv']

    +
    +     		+ 'csv' +
    flush_memory + bool + +
    +

    Whether to remove the reset the embeddings names after saving.

    +
    +     		+ False +
    merge + bool + +
    +

    Whether the embeddings were merged before saving.

    +
    +     		+ False +
    + +
    + Source code in src/prismtoolbox/wsiemb/embedder.py + 
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    def save_embeddings_names(
+    self,
+    save_dir: str,
+    format: str = "csv",
+    flush_memory: bool = False,
+    merge: bool = False,
+):
+    """Save the extracted embeddings names to the chosen format under slide_name.format.
+
+    Args:
+        save_dir: The path to the directory where to save the embeddings.
+        format: The format to save the embeddings in. Possible formats: ['csv']
+        flush_memory: Whether to remove the reset the embeddings names after saving.
+        merge: Whether the embeddings were merged before saving.
+    """
+    if not os.path.isdir(save_dir):
+        log.warning(f"Folder {save_dir} does not exist, creating new folder...")
+        pathlib.Path(save_dir).mkdir(parents=True, exist_ok=True)
+    if format not in ["csv"]:
+        raise ValueError("invalid format, possible formats: ['csv']")
+
+    if merge:
+        output_path = os.path.join(save_dir, f"embeddings_names.{format}")
+        if os.path.exists(output_path):
+            log.warning(
+                f"File embeddings_names.{format} already exists in {save_dir}. Overwriting..."
+            )
+        embeddings_names = (
+            self.model_based_embedding_names
+            + self.stain_based_embedding_names
+            + self.cell_based_embedding_names
+        )
+        df = pd.DataFrame(embeddings_names, columns=["feature_names"])
+        df.to_csv(output_path, index=False)
+    else:
+        for emb_type, emb_names in zip(
+            ["model_based", "stain_based", "cell_based"],
+            [
+                self.model_based_embedding_names,
+                self.stain_based_embedding_names,
+                self.cell_based_embedding_names,
+            ],
+        ):
+            if len(emb_names) == 0:
+                continue
+            output_path = os.path.join(
+                save_dir, f"embeddings_names_{emb_type}.{format}"
+            )
+            if os.path.exists(output_path):
+                log.warning(
+                    f"File embeddings_names_{emb_type}.{format} already exists in {save_dir}. Overwriting..."
+                )
+            df = pd.DataFrame(emb_names, columns=["feature_names"])
+            df.to_csv(output_path, index=False)
+    log.info(f"Embeddings names saved at {output_path}.")
+    if flush_memory:
+        self.model_based_embedding_names = []
+        self.stain_based_embedding_names = []
+        self.cell_based_embedding_names = []
+        log.info("Memory flushed.")
+
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    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/index.html b/reference/prismtoolbox/wsiemb/index.html new file mode 100644 index 0000000..325f484 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Index

    + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/clam/index.html b/reference/prismtoolbox/wsiemb/models/clam/index.html new file mode 100644 index 0000000..2e5cdfc --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/clam/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Clam - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Clam

    + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/conch_model/coca_model/index.html b/reference/prismtoolbox/wsiemb/models/conch_model/coca_model/index.html new file mode 100644 index 0000000..8747cb7 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/conch_model/coca_model/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Coca model - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Coca model

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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/conch_model/conch/index.html b/reference/prismtoolbox/wsiemb/models/conch_model/conch/index.html new file mode 100644 index 0000000..865fe14 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/conch_model/conch/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Conch - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Conch

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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/conch_model/index.html b/reference/prismtoolbox/wsiemb/models/conch_model/index.html new file mode 100644 index 0000000..fdfe2d3 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/conch_model/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Index

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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/conch_model/transformer/index.html b/reference/prismtoolbox/wsiemb/models/conch_model/transformer/index.html new file mode 100644 index 0000000..6562060 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/conch_model/transformer/index.html @@ -0,0 +1,731 @@ + + + + + + + + + + + + + + + + Transformer - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Transformer

    + +
    + + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + LayerNorm + + +

    + + +
    +

    + Bases: LayerNorm

    + + +

    Subclass torch's LayerNorm (with cast back to input dtype).

    + + + + +
    + + + + + + + + + + + +
    + +
    + + +
    + +
    + + + +

    + LayerNormFp32 + + +

    + + +
    +

    + Bases: LayerNorm

    + + +

    Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).

    + + + + +
    + + + + + + + + + + + +
    + +
    + + +
    + +
    + + + +

    + PatchDropout(prob, exclude_first_token=True) + +

    + + +
    +

    + Bases: Module

    + + +

    https://arxiv.org/abs/2212.00794

    + +
    + Source code in src/prismtoolbox/wsiemb/models/conch_model/transformer.py + 
    53
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    def __init__(self, prob, exclude_first_token=True):
+    super().__init__()
+    assert 0 <= prob < 1.0
+    self.prob = prob
+    self.exclude_first_token = exclude_first_token  # exclude CLS token
+
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    + + + +
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    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/conch_model/utils/index.html b/reference/prismtoolbox/wsiemb/models/conch_model/utils/index.html new file mode 100644 index 0000000..ebca6e2 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/conch_model/utils/index.html @@ -0,0 +1,769 @@ + + + + + + + + + + + + + + + + Utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + + +

    Utils

    + +
    + + + + +
    + + + +
    + + + + + + + + + + +
    + + + +

    + freeze_batch_norm_2d(module, module_match={}, name='') + +

    + + +
    + +

    Converts all BatchNorm2d and SyncBatchNorm layers of provided module into FrozenBatchNorm2d. If module is +itself an instance of either BatchNorm2d or SyncBatchNorm, it is converted into FrozenBatchNorm2d and +returned. Otherwise, the module is walked recursively and submodules are converted in place.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    module + Module + +
    +

    Any PyTorch module.

    +
    +     		+ required +
    module_match + dict + +
    +

    Dictionary of full module names to freeze (all if empty)

    +
    +     		+ {} +
    name + str + +
    +

    Full module name (prefix)

    +
    +     		+ '' +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + +
    +

    torch.nn.Module: Resulting module

    +
    +
    +

    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762

    + +
    + Source code in src/prismtoolbox/wsiemb/models/conch_model/utils.py + 
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    def freeze_batch_norm_2d(module, module_match={}, name=""):
+    """
+    Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is
+    itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and
+    returned. Otherwise, the module is walked recursively and submodules are converted in place.
+
+    Args:
+        module (torch.nn.Module): Any PyTorch module.
+        module_match (dict): Dictionary of full module names to freeze (all if empty)
+        name (str): Full module name (prefix)
+
+    Returns:
+        torch.nn.Module: Resulting module
+
+    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
+    """
+    res = module
+    is_match = True
+    if module_match:
+        is_match = name in module_match
+    if is_match and isinstance(
+        module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)
+    ):
+        res = FrozenBatchNorm2d(module.num_features)
+        res.num_features = module.num_features
+        res.affine = module.affine
+        if module.affine:
+            res.weight.data = module.weight.data.clone().detach()
+            res.bias.data = module.bias.data.clone().detach()
+        res.running_mean.data = module.running_mean.data
+        res.running_var.data = module.running_var.data
+        res.eps = module.eps
+    else:
+        for child_name, child in module.named_children():
+            full_child_name = ".".join([name, child_name]) if name else child_name
+            new_child = freeze_batch_norm_2d(child, module_match, full_child_name)
+            if new_child is not child:
+                res.add_module(child_name, new_child)
+    return res
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/conch_model/vision_tower/index.html b/reference/prismtoolbox/wsiemb/models/conch_model/vision_tower/index.html new file mode 100644 index 0000000..f84cddc --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/conch_model/vision_tower/index.html @@ -0,0 +1,856 @@ + + + + + + + + + + + + + + + + Vision tower - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + +
    +
    + + + + + + + + + +

    Vision tower

    + +
    + + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + VisualModel(embed_dim_contrast, embed_dim_caption, trunk, image_size=224, proj='', proj_bias=False, drop=0.0, global_average_pool=False, use_attentional_pool_contrast=False, use_attentional_pool_caption=False, n_queries_contrast=1, n_queries_caption=256, attn_pooler_heads=8, norm_layer=nn.LayerNorm, output_tokens=False, trunk_kwargs={}) + +

    + + +
    +

    + Bases: Module

    + + +
    + Source code in src/prismtoolbox/wsiemb/models/conch_model/vision_tower.py + 
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    def __init__(
+    self,
+    embed_dim_contrast,
+    embed_dim_caption,
+    trunk,
+    image_size=224,
+    proj="",
+    proj_bias=False,
+    drop=0.0,
+    global_average_pool=False,
+    use_attentional_pool_contrast=False,
+    use_attentional_pool_caption=False,
+    n_queries_contrast=1,
+    n_queries_caption=256,
+    attn_pooler_heads=8,
+    norm_layer=nn.LayerNorm,
+    output_tokens=False,
+    trunk_kwargs={},
+):
+    super().__init__()
+
+    self.trunk = trunk
+    self.trunk_kwargs = trunk_kwargs
+    self.image_size = to_2tuple(image_size)
+    prev_chs = self.trunk.num_features
+    head_layers = OrderedDict()
+
+    # whether to use attentional pooling
+    self.use_attentional_pool_contrast = use_attentional_pool_contrast
+    self.use_attentional_pool_caption = use_attentional_pool_caption
+    self.global_average_pool = global_average_pool
+    self.output_tokens = output_tokens
+    if use_attentional_pool_contrast:
+        scale = prev_chs**-0.5
+        self.attn_pool_contrast = AttentionalPooler(
+            d_model=embed_dim_contrast,
+            context_dim=prev_chs,
+            n_head=attn_pooler_heads,
+            n_queries=n_queries_contrast,
+        )
+        self.ln_contrast = norm_layer(embed_dim_contrast)
+        self.proj_contrast = nn.Parameter(
+            scale * torch.randn(embed_dim_contrast, embed_dim_contrast)
+        )
+    else:
+        assert proj, "projection layer needed if not using attentional pooling."
+        # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used
+        if proj == "linear":
+            head_layers["drop"] = nn.Dropout(drop)
+            head_layers["proj"] = nn.Linear(
+                prev_chs, embed_dim_contrast, bias=proj_bias
+            )
+        elif proj == "mlp":
+            head_layers["mlp"] = Mlp(
+                prev_chs,
+                2 * embed_dim_contrast,
+                embed_dim_contrast,
+                drop=(drop, 0),
+                bias=(True, proj_bias),
+            )
+
+    self.head = nn.Sequential(head_layers)
+
+    if use_attentional_pool_caption:
+        self.attn_pool_caption = AttentionalPooler(
+            d_model=embed_dim_caption,
+            context_dim=prev_chs,
+            n_head=attn_pooler_heads,
+            n_queries=n_queries_caption,
+        )
+        self.ln_caption = norm_layer(embed_dim_caption)
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + lock(unlocked_groups=0, freeze_bn_stats=False) + +

    + + +
    + +

    lock modules +Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0)

    + +
    + Source code in src/prismtoolbox/wsiemb/models/conch_model/vision_tower.py + 
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    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
+    """lock modules
+    Args:
+        unlocked_groups (int): leave last n layer groups unlocked (default: 0)
+    """
+    if not unlocked_groups:
+        # lock full model
+        for param in self.trunk.parameters():
+            param.requires_grad = False
+        if freeze_bn_stats:
+            freeze_batch_norm_2d(self.trunk)
+    else:
+        from timm.models.helpers import group_modules, group_parameters
+
+        matcher = self.trunk.group_matcher()
+        gparams = group_parameters(self.trunk, matcher)
+        max_layer_id = max(gparams.keys())
+        max_layer_id = max_layer_id - unlocked_groups
+        for group_idx in range(max_layer_id + 1):
+            group = gparams[group_idx]
+            for param in group:
+                self.trunk.get_parameter(param).requires_grad = False
+        if freeze_bn_stats:
+            gmodules = group_modules(self.trunk, matcher, reverse=True)
+            gmodules = {k for k, v in gmodules.items() if v <= max_layer_id}
+            freeze_batch_norm_2d(self.trunk, gmodules)
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + + + + +
    + +
    + +
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    + +
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    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/generic/index.html b/reference/prismtoolbox/wsiemb/models/generic/index.html new file mode 100644 index 0000000..e601ce0 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/generic/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Generic - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Generic

    + +
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    + +
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    + + +
    + +
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    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/index.html b/reference/prismtoolbox/wsiemb/models/index.html new file mode 100644 index 0000000..50f1df4 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Index - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Index

    + +
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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/pathoduet/index.html b/reference/prismtoolbox/wsiemb/models/pathoduet/index.html new file mode 100644 index 0000000..abd6a61 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/pathoduet/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Pathoduet - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Pathoduet

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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/phikon/index.html b/reference/prismtoolbox/wsiemb/models/phikon/index.html new file mode 100644 index 0000000..78dae8d --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/phikon/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Phikon - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Phikon

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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/models/utils/index.html b/reference/prismtoolbox/wsiemb/models/utils/index.html new file mode 100644 index 0000000..36ed0b7 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/models/utils/index.html @@ -0,0 +1,565 @@ + + + + + + + + + + + + + + + + Utils - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Utils

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    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reference/prismtoolbox/wsiemb/processing/index.html b/reference/prismtoolbox/wsiemb/processing/index.html new file mode 100644 index 0000000..c54a903 --- /dev/null +++ b/reference/prismtoolbox/wsiemb/processing/index.html @@ -0,0 +1,3343 @@ + + + + + + + + + + + + + + + + prismtoolbox.wsiemb.processing - PrismToolBox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    prismtoolbox.wsiemb.processing

    + +
    + + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + EmbeddingProcessor(embeddings, embeddings_names=None, slide_ids=None, cmap='Set1', seed=None) + +

    + + +
    + + +

    summary

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    embeddings + list[ndarray | Tensor | str] + +
    +

    The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings.

    +
    +     		+ required +
    embeddings_names + list[str] | str | None + +
    +

    The names of the embeddings. Should be a list of strings with the same length as the embeddings,

    +
    +     		+ None +
    slide_ids + list[str] | None + +
    +

    The ids of the slides.

    +
    +     		+ None +
    cmap + str + +
    +

    The colormap to use for visualizations.

    +
    +     		+ 'Set1' +
    seed + int + +
    +

    The seed to use for reproducibility.

    +
    +     		+ None +
    + + + +

    Attributes:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescription
    embeddings + +
    +

    The embeddings to process as a list of numpy arrays.

    +
    +
    embeddings_matrix + +
    +

    The embeddings to process concatenated into a single numpy array.

    +
    +
    embeddings_stats + +
    +

    The statistics of the embeddings (mean, std, min, max).

    +
    +
    slide_ids + +
    +

    The ids of the slides. If not provided, the slide ids are generated.

    +
    +
    slide_ids_matrix + +
    +

    The slide ids of each embedding concatenated into a single numpy array.

    +
    +
    cmap + +
    +

    The colormap to use for visualizations.

    +
    +
    seed + +
    +

    The seed to use for reproducibility.

    +
    +
    cluster_model + +
    +

    The clustering model used to cluster the embeddings. It is set using the

    +
    +
    n_clusters + +
    +

    The number of clusters in the clustering model. It is set using the

    +
    +
    cluster_colors + +
    +

    The colors of the clusters in the clustering model. It is set using the

    +
    +
    eps + +
    +

    A small value to avoid division by zero.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def __init__(
+    self,
+    embeddings: list[np.ndarray | torch.Tensor | str],
+    embeddings_names: list[str] | str | None = None,
+    slide_ids: list[str] | None = None,
+    cmap: str = "Set1",
+    seed: int = None,
+):
+    """_summary_
+
+    Args:
+        embeddings: The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings.
+        embeddings_names: The names of the embeddings. Should be a list of strings with the same length as the embeddings,
+        or the path to a file containing the names. If not provided, the names are generated.
+        slide_ids: The ids of the slides.
+        cmap: The colormap to use for visualizations.
+        seed: The seed to use for reproducibility.
+
+    Attributes:
+        embeddings: The embeddings to process as a list of numpy arrays.
+        embeddings_matrix: The embeddings to process concatenated into a single numpy array.
+        embeddings_stats: The statistics of the embeddings (mean, std, min, max).
+        slide_ids: The ids of the slides. If not provided, the slide ids are generated.
+        slide_ids_matrix: The slide ids of each embedding concatenated into a single numpy array.
+        cmap: The colormap to use for visualizations.
+        seed: The seed to use for reproducibility.
+        cluster_model: The clustering model used to cluster the embeddings. It is set using the
+        [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method.
+        n_clusters: The number of clusters in the clustering model. It is set using the
+        [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method.
+        cluster_colors: The colors of the clusters in the clustering model. It is set using the
+        [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method.
+        eps: A small value to avoid division by zero.
+    """
+    self.embeddings = self.load_embeddings(embeddings)
+    self.embeddings_names = self.load_embeddings_names(embeddings_names)
+    self.embeddings_matrix = np.concatenate(self.embeddings, axis=0)
+    self.embeddings_stats = self.compute_embeddings_stats(self.embeddings_matrix)
+    self.slide_ids = (
+        np.array(slide_ids) if slide_ids is not None else np.arange(len(embeddings))
+    )
+    self.slide_ids_matrix = np.concatenate(
+        [
+            np.repeat(slide_id, len(emb))
+            for slide_id, emb in zip(self.slide_ids, self.embeddings)
+        ],
+        axis=0,
+    )
+    self.cmap = cmap
+    self.seed = seed
+    self.cluster_model = None
+    self.n_clusters = None
+    self.cluster_colors = None
+    self.eps = 1e-6
+
    +
    + + + +
    + + + + + + + + + + +
    + + + +

    + compute_embeddings_stats(embeddings_matrix) + + + staticmethod + + +

    + + +
    + +

    Compute the statistics of an input embeddings matrix.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    embeddings_matrix + ndarray + +
    +

    The embeddings matrix to compute the statistics of.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + dict[str, ndarray] + +
    +

    The statistics of the embeddings matrix (mean, std, min, max) as a dictionary.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    @staticmethod
+def compute_embeddings_stats(embeddings_matrix: np.ndarray) -> dict[str, np.ndarray]:
+    """Compute the statistics of an input embeddings matrix.
+
+    Args:
+        embeddings_matrix: The embeddings matrix to compute the statistics of.
+
+    Returns:
+        The statistics of the embeddings matrix (mean, std, min, max) as a dictionary.
+    """
+    return {
+        "mean": np.mean(embeddings_matrix, axis=0),
+        "std": np.std(embeddings_matrix, axis=0),
+        "min": np.min(embeddings_matrix, axis=0),
+        "max": np.max(embeddings_matrix, axis=0),
+    }
+
    +
    +
    + +
    + + +
    + + + +

    + create_cluster_model(model_name, normalize=True, n_samples=None, selected_features=None, **kwargs) + +

    + + +
    + +

    Create a clustering model trained on the embeddings matrix. The resulting model is stored in + self.cluster_model.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    model_name + str + +
    +

    The clustering model to use. Possible models are:

    + +
    +     		+ required +
    normalize + bool + +
    +

    Whether to normalize the embeddings or not according to the mean and std of +self.embeddings_stats.

    +
    +     		+ True +
    n_samples + int | float | None + +
    +

    The number of samples to subsample. If None, the whole embeddings matrix is used.

    +
    +     		+ None +
    selected_features + list[str] | None + +
    +

    The names of the embeddings to subsample. If None, all embeddings are used.

    +
    +     		+ None +
    **kwargs + +
    +

    Additional arguments for the clustering model (see the documentation of the clustering model).

    +
    +     		+ {} +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def create_cluster_model(
+    self,
+    model_name: str,
+    normalize: bool = True,
+    n_samples: int | float | None = None,
+    selected_features: list[str] | None = None,
+    **kwargs,
+):
+    """Create a clustering model trained on the embeddings matrix. The resulting model is stored in
+        self.cluster_model.
+
+    Args:
+        model_name: The clustering model to use. Possible models are:
+
+            - "[kmeans](https://scikit-learn.org/stable/modules/generated/
+            sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)"
+            - "[kmeans_mini_batch](https://scikit-learn.org/stable/modules/generated/
+            sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans)"
+
+        normalize: Whether to normalize the embeddings or not according to the mean and std of
+            self.embeddings_stats.
+        n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used.
+        selected_features: The names of the embeddings to subsample. If None, all embeddings are used.
+        **kwargs: Additional arguments for the clustering model (see the documentation of the clustering model).
+
+    """
+    if model_name == "kmeans":
+        self.cluster_model = skl_cluster.KMeans(**kwargs)
+    elif model_name == "kmeans_mini_batch":
+        self.cluster_model = skl_cluster.MiniBatchKMeans(**kwargs)
+    else:
+        raise ValueError(f"model {model_name} not implemented")
+    emb_mean, emb_std = (
+        (self.embeddings_stats["mean"], self.embeddings_stats["std"])
+        if normalize
+        else (0, 1)
+    )
+    if n_samples is not None:
+        embeddings_matrix = self.return_subsampled_embeddings(n_samples)
+    else:
+        embeddings_matrix = self.embeddings_matrix
+    embeddings_matrix = (embeddings_matrix - emb_mean) / (emb_std + self.eps)
+    if selected_features is not None:
+        selected_feats = np.array(
+            [
+                i
+                for i, name in enumerate(self.embeddings_names)
+                if name in selected_features
+            ]
+        )
+        embeddings_matrix = embeddings_matrix[:, selected_feats]
+    self.cluster_model.fit(embeddings_matrix)
+    self.n_clusters = self.cluster_model.n_clusters
+    self.cluster_colors = get_colors_from_cmap(self.cmap, self.n_clusters)
+
    +
    +
    + +
    + + +
    + + + +

    + export_clusters_to_qupath(WSI_object, save_dir, normalize=True, selected_features=None) + +

    + + +
    + +

    Export the clusters as polygons to a geojson file for a slide.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    WSI_object + WSI + +
    +

    An instance of the WSI class created from the slide.

    +
    +     		+ required +
    save_dir + str + +
    +

    The directory to save the geojson file to.

    +
    +     		+ required +
    normalize + bool + +
    +

    Whether the embeddings were normalized or not when creating the cluster model.

    +
    +     		+ True +
    selected_features + list[str] | None + +
    +

    The names of the embeddings to subsample. If None, all embeddings are used.

    +
    +     		+ None +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def export_clusters_to_qupath(
+    self,
+    WSI_object: WSI,
+    save_dir: str,
+    normalize: bool = True,
+    selected_features: list[str] | None = None,
+):
+    """Export the clusters as polygons to a geojson file for a slide.
+
+    Args:
+        WSI_object: An instance of the WSI class created from the slide.
+        save_dir: The directory to save the geojson file to.
+        normalize: Whether the embeddings were normalized or not when creating the cluster model.
+        selected_features: The names of the embeddings to subsample. If None, all embeddings are used.
+    """
+    assert (
+        self.n_clusters is not None
+    ), "no cluster model created, please create a cluster model first"
+    cluster_assignments = self.get_cluster_assignments_for_slide(
+        WSI_object.slide_name,
+        normalize,
+        selected_features,
+    )
+    idx = np.arange(len(WSI_object.coords))
+    assert len(cluster_assignments) == len(
+        idx
+    ), "Number of cluster assignments and number of patches do not match"
+    for cluster in range(self.n_clusters):
+        WSI_object.save_patches(
+            save_dir,
+            file_format="geojson",
+            selected_idx=idx[cluster_assignments == cluster],
+            merge=True,
+            label=f"cluster_{cluster}",
+            color=self.cluster_colors[cluster].tolist(),
+            append_to_existing_file=True,
+        )
+
    +
    +
    + +
    + + +
    + + + +

    + get_cluster_assignments_for_slide(slide_id, normalize=True, selected_features=None) + +

    + + +
    + +

    Get the cluster assignments for a specific slide. Requires a cluster model to be created first + with the create_cluster_model + method.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_id + str + +
    +

    The id of the slide to get the cluster assignments for.

    +
    +     		+ required +
    normalize + bool + +
    +

    Whether the embeddings were normalized or not when creating the cluster model.

    +
    +     		+ True +
    selected_features + list[str] | None + +
    +

    The names of the embeddings to subsample. If None, all embeddings are used.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    The cluster assignments for the slide.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def get_cluster_assignments_for_slide(
+    self,
+    slide_id: str,
+    normalize: bool = True,
+    selected_features: list[str] | None = None,
+) -> np.ndarray:
+    """Get the cluster assignments for a specific slide. Requires a cluster model to be created first
+        with the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model]
+        method.
+
+    Args:
+        slide_id: The id of the slide to get the cluster assignments for.
+        normalize: Whether the embeddings were normalized or not when creating the cluster model.
+        selected_features: The names of the embeddings to subsample. If None, all embeddings are used.
+
+    Returns:
+        The cluster assignments for the slide.
+    """
+    assert (
+        self.cluster_model is not None
+    ), "no cluster model created, please create a cluster model first"
+    if slide_id not in self.slide_ids:
+        raise ValueError(f"slide {slide_id} not found in slide ids")
+    idx = np.where(self.slide_ids == slide_id)[0].item()
+    embeddings = self.embeddings[idx]
+    emb_mean, emb_std = (
+        (self.embeddings_stats["mean"], self.embeddings_stats["std"])
+        if normalize
+        else (0, 1)
+    )
+    embeddings = (embeddings - emb_mean) / (emb_std + self.eps)
+    if selected_features is not None:
+        selected_feats = np.array(
+            [
+                i
+                for i, name in enumerate(self.embeddings_names)
+                if name in selected_features
+            ]
+        )
+        embeddings = embeddings[:, selected_feats]
+    return self.cluster_model.predict(embeddings)
+
    +
    +
    + +
    + + +
    + + + +

    + get_cluster_percentages_for_slide(slide_id, normalize=True, selected_features=None) + +

    + + +
    + +

    Get the cluster percentages for a specific slide. Requires a cluster model to be created first + with the create_cluster_model + method.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_id + str + +
    +

    The id of the slide to get the cluster percentages for.

    +
    +     		+ required +
    normalize + bool + +
    +

    Whether the embeddings were normalized or not when creating the cluster model.

    +
    +     		+ True +
    selected_features + list[str] | None + +
    +

    The names of the embeddings to subsample. If None, all embeddings are used.

    +
    +     		+ None +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + dict[str, float] + +
    +

    The cluster percentages for the slide.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def get_cluster_percentages_for_slide(
+    self,
+    slide_id: str,
+    normalize: bool = True,
+    selected_features: list[str] | None = None,
+) -> dict[str, float]:
+    """Get the cluster percentages for a specific slide. Requires a cluster model to be created first
+        with the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model]
+        method.
+
+    Args:
+        slide_id: The id of the slide to get the cluster percentages for.
+        normalize: Whether the embeddings were normalized or not when creating the cluster model.
+        selected_features: The names of the embeddings to subsample. If None, all embeddings are used.
+
+    Returns:
+        The cluster percentages for the slide.
+    """
+    cluster_assignments = self.get_cluster_assignments_for_slide(
+        slide_id, normalize, selected_features
+    )
+    cluster_percentage = {}
+    for cluster in range(self.n_clusters):
+        cluster_percentage[f"cluster_{cluster}"] = (
+            cluster_assignments == cluster
+        ).sum() / len(cluster_assignments)
+    return cluster_percentage
+
    +
    +
    + +
    + + +
    + + + +

    + get_embedding_for_slide(slide_id, normalize=True) + +

    + + +
    + +

    Get the embeddings for a specific slide.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    slide_id + str + +
    +

    The id of the slide to get the embeddings for.

    +
    +     		+ required +
    normalize + bool + +
    +

    Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats.

    +
    +     		+ True +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    The embeddings for the slide.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def get_embedding_for_slide(
+    self, slide_id: str, normalize: bool = True
+) -> np.ndarray:
+    """Get the embeddings for a specific slide.
+
+    Args:
+        slide_id: The id of the slide to get the embeddings for.
+        normalize: Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats.
+
+    Returns:
+        The embeddings for the slide.
+    """
+    if slide_id not in self.slide_ids:
+        raise ValueError(f"slide {slide_id} not found in slide ids")
+    idx = np.where(self.slide_ids == slide_id)[0].item()
+    emb_mean, emb_std = (
+        (self.embeddings_stats["mean"], self.embeddings_stats["std"])
+        if normalize
+        else (0, 1)
+    )
+    return (self.embeddings[idx] - emb_mean) / (emb_std + self.eps)
+
    +
    +
    + +
    + + +
    + + + +

    + get_optimal_number_clusters(model_name, normalize=True, metric_name='davies_bouldin', min_clusters=2, max_clusters=10, with_scores=True, n_samples=None, selected_features=None, **kwargs) + +

    + + +
    + +

    Compute the optimal number of clusters for the embeddings.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    model_name + str + +
    +

    The clustering model to use. +See compute_optimal_number_clusters +for the available models.

    +
    +     		+ required +
    normalize + bool + +
    +

    Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats.

    +
    +     		+ True +
    metric_name + str + +
    +

    The metric to use to compute the optimal number of clusters. +See compute_optimal_number_clusters +for the available metrics.

    +
    +     		+ 'davies_bouldin' +
    min_clusters + int + +
    +

    The minimum number of clusters to consider.

    +
    +     		+ 2 +
    max_clusters + int + +
    +

    The maximum number of clusters to consider.

    +
    +     		+ 10 +
    with_scores + bool + +
    +

    Whether to return the scores or not.

    +
    +     		+ True +
    n_samples + int | float | None + +
    +

    The number of samples to subsample. If None, the whole embeddings matrix is used.

    +
    +     		+ None +
    selected_features + list[str] | None + +
    +

    The names of the embeddings to subsample. If None, all embeddings are used.

    +
    +     		+ None +
    **kwargs + +
    +

    Additional arguments for the clustering model (see the documentation of the clustering model).

    +
    +     		+ {} +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + int | tuple[int, list[float]] + +
    +

    The optimal number of clusters according to the metric. If with_scores, it also returns the scores.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def get_optimal_number_clusters(
+    self,
+    model_name: str,
+    normalize: bool = True,
+    metric_name: str = "davies_bouldin",
+    min_clusters: int = 2,
+    max_clusters: int = 10,
+    with_scores: bool = True,
+    n_samples: int | float | None = None,
+    selected_features: list[str] | None = None,
+    **kwargs,
+) -> int | tuple[int, list[float]]:
+    """Compute the optimal number of clusters for the embeddings.
+
+    Args:
+        model_name: The clustering model to use.
+            See [compute_optimal_number_clusters][prismtoolbox.wsiemb.emb_utils.compute_optimal_number_clusters]
+            for the available models.
+        normalize: Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats.
+        metric_name: The metric to use to compute the optimal number of clusters.
+            See [compute_optimal_number_clusters][prismtoolbox.wsiemb.emb_utils.compute_optimal_number_clusters]
+            for the available metrics.
+        min_clusters: The minimum number of clusters to consider.
+        max_clusters: The maximum number of clusters to consider.
+        with_scores: Whether to return the scores or not.
+        n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used.
+        selected_features: The names of the embeddings to subsample. If None, all embeddings are used.
+        **kwargs: Additional arguments for the clustering model (see the documentation of the clustering model).
+
+    Returns:
+        The optimal number of clusters according to the metric. If with_scores, it also returns the scores.
+    """
+    emb_mean, emb_std = (
+        (self.embeddings_stats["mean"], self.embeddings_stats["std"])
+        if normalize
+        else (0, 1)
+    )
+    if n_samples is not None:
+        embeddings_matrix = self.return_subsampled_embeddings(n_samples)
+    else:
+        embeddings_matrix = self.embeddings_matrix
+    embeddings_matrix = (embeddings_matrix - emb_mean) / (emb_std + self.eps)
+    if selected_features is not None:
+        selected_feats = np.array(
+            [
+                i
+                for i, name in enumerate(self.embeddings_names)
+                if name in selected_features
+            ]
+        )
+        embeddings_matrix = embeddings_matrix[:, selected_feats]
+    optimal_number, scores = compute_optimal_number_clusters(
+        embeddings_matrix,
+        model_name,
+        metric_name=metric_name,
+        min_clusters=min_clusters,
+        max_clusters=max_clusters,
+        **kwargs,
+    )
+    if with_scores:
+        return optimal_number, scores
+    else:
+        return optimal_number
+
    +
    +
    + +
    + + +
    + + + +

    + import_cluster_model(input_path) + +

    + + +
    + +

    Import the clustering model from a pickle file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    input_path + str + +
    +

    The path to import the clustering model from.

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def import_cluster_model(self, input_path: str):
+    """Import the clustering model from a pickle file.
+
+    Args:
+        input_path: The path to import the clustering model from.
+    """
+    self.cluster_model = load_obj_with_pickle(input_path)
+    self.n_clusters = self.cluster_model.n_clusters
+    self.cluster_colors = get_colors_from_cmap(self.cmap, self.n_clusters)
+
    +
    +
    + +
    + + +
    + + + +

    + load_embeddings(embeddings) + + + staticmethod + + +

    + + +
    + +

    Process the embeddings to load them as numpy arrays.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    embeddings + list[ndarray | Tensor | str] + +
    +

    The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + list[ndarray] + +
    +

    The embeddings loaded as numpy arrays.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    @staticmethod
+def load_embeddings(
+    embeddings: list[np.ndarray | torch.Tensor | str],
+) -> list[np.ndarray]:
+    """Process the embeddings to load them as numpy arrays.
+
+    Args:
+        embeddings: The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings.
+
+    Returns:
+        The embeddings loaded as numpy arrays.
+    """
+    embeddings_loaded = []
+    for emb in embeddings:
+        if isinstance(emb, torch.Tensor):
+            emb = emb.numpy()
+        elif isinstance(emb, np.ndarray):
+            emb = emb
+        elif isinstance(emb, str):
+            emb = torch.load(emb).numpy()
+        else:
+            raise ValueError("embedding type not supported")
+        embeddings_loaded.append(emb)
+    return embeddings_loaded
+
    +
    +
    + +
    + + +
    + + + +

    + return_subsampled_embeddings(n_samples, by_slide=True, labels=False) + +

    + + +
    + +

    Creates a subsample version of the embeddings matrix.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    n_samples + int | float + +
    +

    The number of samples to subsample. If float, it is the percentage of samples to subsample. +If None, the whole embeddings matrix is used.

    +
    +     		+ required +
    by_slide + bool + +
    +

    Whether to subsample by slide or not.

    +
    +     		+ True +
    labels + ndarray | None + +
    +

    Labels to subsample along with the embeddings.

    +
    +     		+ False +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray | tuple[ndarray, ndarray] + +
    +

    The subsampled embeddings matrix. If labels, it also returns the subsampled labels.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def return_subsampled_embeddings(
+    self,
+    n_samples: int | float,
+    by_slide: bool = True,
+    labels: np.ndarray | None = False,
+) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
+    """Creates a subsample version of the embeddings matrix.
+
+    Args:
+        n_samples: The number of samples to subsample. If float, it is the percentage of samples to subsample.
+            If None, the whole embeddings matrix is used.
+        by_slide: Whether to subsample by slide or not.
+        labels: Labels to subsample along with the embeddings.
+
+    Returns:
+        The subsampled embeddings matrix. If labels, it also returns the subsampled labels.
+    """
+
+    rng = np.random.default_rng(self.seed)
+    if by_slide:
+        subsampled_idx = []
+        current_idx = 0
+        for emb in self.embeddings:
+            if n_samples < 1:
+                n_samples_per_slide = int(n_samples * len(emb))
+            else:
+                n_samples_per_slide = n_samples // len(self.embeddings)
+            subsampled_idx.extend(
+                rng.choice(len(emb), n_samples_per_slide, replace=False) + current_idx
+            )
+            current_idx += len(emb)
+        log.info(f"Subsampled {n_samples} embeddings from each slide.")
+    else:
+        if n_samples < 1:
+            n_samples = int(n_samples * len(self.embeddings_matrix))
+        subsampled_idx = rng.choice(
+            len(self.embeddings_matrix), n_samples, replace=False
+        )
+        log.info(f"Subsampled {n_samples} embeddings.")
+    if labels is not None:
+        assert len(labels) == len(
+            self.embeddings_matrix
+        ), "labels and embeddings have different lengths"
+        return (
+            self.embeddings_matrix[subsampled_idx],
+            labels[subsampled_idx],
+        )
+    else:
+        return self.embeddings_matrix[subsampled_idx]
+
    +
    +
    + +
    + + +
    + + + +

    + save_cluster_model(output_path) + +

    + + +
    + +

    Save the clustering model to a pickle file.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    output_path + str + +
    +

    The path to save the clustering model to.

    +
    +     		+ required +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def save_cluster_model(self, output_path: str):
+    """Save the clustering model to a pickle file.
+
+    Args:
+        output_path: The path to save the clustering model to.
+    """
+    save_obj_with_pickle(self.cluster_model, output_path)
+
    +
    +
    + +
    + + +
    + + + +

    + scale_to_01_range(x) + + + staticmethod + + +

    + + +
    + +

    Scale an array to the [0; 1] range.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    x + ndarray + +
    +

    The array to scale.

    +
    +     		+ required +
    + + + +

    Returns:

    + + + + + + + + + + + + + +
    TypeDescription
    + ndarray + +
    +

    The array scaled to the [0; 1] range.

    +
    +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    @staticmethod
+def scale_to_01_range(x: np.ndarray) -> np.ndarray:
+    """Scale an array to the [0; 1] range.
+
+    Args:
+        x: The array to scale.
+
+    Returns:
+        The array scaled to the [0; 1] range.
+    """
+    # compute the distribution range
+    value_range = np.max(x) - np.min(x)
+
+    # move the distribution so that it starts from zero
+    # by extracting the minimal value from all its values
+    starts_from_zero = x - np.min(x)
+
+    # make the distribution fit [0; 1] by dividing by its range
+    return starts_from_zero / value_range
+
    +
    +
    + +
    + + +
    + + + +

    + visualize(model_name, labels=None, n_samples=None, selected_features=None, **kwargs) + +

    + + +
    + +

    Visualize the embeddings using a dimensionality reduction model.

    + + + +

    Parameters:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    NameTypeDescriptionDefault
    model_name + str + +
    +

    The name of the dimensionality reduction model to use. Possible models are:

    + +
    +     		+ required +
    labels + ndarray | None + +
    +

    The labels to use for the visualization. If None, no labels are used.

    +
    +     		+ None +
    n_samples + int | float | None + +
    +

    The number of samples to subsample. If None, the whole embeddings matrix is used.

    +
    +     		+ None +
    selected_features + list[str] | None + +
    +

    The names of the embeddings to subsample. If None, all embeddings are used.

    +
    +     		+ None +
    **kwargs + +
    +

    Additional arguments for the dimensionality reduction model +(see the documentation of the dimensionality reduction model).

    +
    +     		+ {} +
    + +
    + Source code in src/prismtoolbox/wsiemb/processing.py + 
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    def visualize(
+    self,
+    model_name: str,
+    labels: np.ndarray | None = None,
+    n_samples: int | float | None = None,
+    selected_features: list[str] | None = None,
+    **kwargs,
+):
+    """Visualize the embeddings using a dimensionality reduction model.
+
+    Args:
+        model_name: The name of the dimensionality reduction model to use. Possible models are:
+
+            - "[PCA](https://scikit-learn.org/stable/modules/generated/
+            sklearn.decomposition.PCA.html#sklearn.decomposition.PCA)"
+            - "[TSNE](https://scikit-learn.org/stable/modules/generated/
+            sklearn.manifold.TSNE.html#sklearn.manifold.TSNE)"
+            - "[UMAP](https://umap-learn.readthedocs.io/en/latest/)"
+        labels: The labels to use for the visualization. If None, no labels are used.
+        n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used.
+        selected_features: The names of the embeddings to subsample. If None, all embeddings are used.
+        **kwargs: Additional arguments for the dimensionality reduction model
+            (see the documentation of the dimensionality reduction model).
+
+    """
+    if model_name == "PCA":
+        dimensionality_reduction_model = skl_decomposition.PCA(
+            n_components=2, **kwargs
+        )
+    elif model_name == "TSNE":
+        dimensionality_reduction_model = skl_manifold.TSNE(n_components=2, **kwargs)
+    elif model_name == "UMAP":
+        dimensionality_reduction_model = umap.UMAP(n_components=2, **kwargs)
+    else:
+        raise ValueError(f"model {model_name} not implemented")
+    if n_samples is not None:
+        subsampled_arrays = self.return_subsampled_embeddings(
+            n_samples, labels=labels
+        )
+        embeddings_matrix = (
+            subsampled_arrays if labels is None else subsampled_arrays[0]
+        )
+        labels = subsampled_arrays[1] if labels is not None else None
+    else:
+        embeddings_matrix = self.embeddings_matrix
+    if selected_features is not None:
+        selected_feats = np.array(
+            [
+                i
+                for i, name in enumerate(self.embeddings_names)
+                if name in selected_features
+            ]
+        )
+        embeddings_matrix = embeddings_matrix[:, selected_feats]
+    embeddings_reduced = dimensionality_reduction_model.fit_transform(
+        embeddings_matrix
+    )
+    plot_scatter(
+        self.scale_to_01_range(embeddings_reduced[:, 0]),
+        self.scale_to_01_range(embeddings_reduced[:, 1]),
+        self.cmap,
+        labels,
+    )
+
    +
    +
    + +
    + + + +
    + +
    + + +
    + + + + +
    + +
    + +
    + + + + + + +
    +
    + + +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 0000000..610fdbc --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"PrismToolBox Usage Guide","title":"Home"},{"location":"#prismtoolbox-usage-guide","text":"","title":"PrismToolBox Usage Guide"},{"location":"reference/SUMMARY/","text":"prismtoolbox nucleiseg models sop architectures modules postprocessing seg_utils segmenter utils data_utils qupath_utils stain_utils torch_utils vis_utils wsicore core_utils wsi wsiemb emb_utils embedder models clam conch_model coca_model conch transformer utils vision_tower generic pathoduet phikon utils processing","title":"SUMMARY"},{"location":"reference/prismtoolbox/","text":"WSI ( slide_path , engine = 'openslide' ) The WSI (Whole Slide Image) class is responsible for handling operations related to whole slide images. Parameters: Name Type Description Default slide_path str The path to the slide image file. required engine str The engine used to read the slide image. 'openslide' Attributes: Name Type Description slide_path str The path to the slide image file. engine str The engine used to read the slide image. slide_name str The name of the slide image file. Retrieved from the slide path using retrieve_slide_name_ext method. slide_ext str The extension of the slide image file. Retrieved from the slide path using retrieve_slide_name_ext method. slide OpenSlide | TiffSlide The wsi read from the file using engine. dimensions list [ tuple [ int , int ]] The dimensions of the slide image. Set by the set_slide_attributes method. level_dimensions list [ tuple [ int , int ]] The dimensions of the different levels of the slide image. Set by the set_slide_attributes method. level_downsamples list [ tuple [ int , int ]] The downsampling factors of the different levels of the slide image. Set by the set_slide_attributes method. properties dict The properties of the slide image. Set by the set_slide_attributes method. offset tuple [ int , int ] The offset of the slide image. Set by the set_slide_attributes method. ROI ndarray The region of interest in the slide image. Please use the set_roi method to set the ROI. ROI_width int The width of the region of interest. Set by the set_roi method. ROI_height int The height of the region of interest. Set by the set_roi method. tissue_contours list [ ndarray ] The contours of the tissue in the slide image. Please use the detect_tissue method to detect the tissue contours. coords ndarray The coordinates of patches extracted from slide image. Please use the extract_patches method to extract patches. coords_attrs dict The attributes of the coordinates. Set by the extract_patches method. Source code in src/prismtoolbox/wsicore/wsi.py 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 def __init__ ( self , slide_path : str , engine : str = \"openslide\" ): \"\"\"The WSI (Whole Slide Image) class is responsible for handling operations related to whole slide images. Args: slide_path: The path to the slide image file. engine: The engine used to read the slide image. Attributes: slide_path (str): The path to the slide image file. engine (str): The engine used to read the slide image. slide_name (str): The name of the slide image file. Retrieved from the slide path using [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method. slide_ext (str): The extension of the slide image file. Retrieved from the slide path using [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method. slide (OpenSlide | TiffSlide): The wsi read from the file using engine. dimensions (list[tuple[int, int]]): The dimensions of the slide image. Set by the set_slide_attributes method. level_dimensions (list[tuple[int, int]]): The dimensions of the different levels of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. level_downsamples (list[tuple[int, int]]): The downsampling factors of the different levels of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. properties (dict): The properties of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. offset (tuple[int, int]): The offset of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. ROI (ndarray): The region of interest in the slide image. Please use the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method to set the ROI. ROI_width (int): The width of the region of interest. Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method. ROI_height (int): The height of the region of interest. Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method. tissue_contours (list[ndarray]): The contours of the tissue in the slide image. Please use the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method to detect the tissue contours. coords (np.ndarray): The coordinates of patches extracted from slide image. Please use the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method to extract patches. coords_attrs (dict): The attributes of the coordinates. Set by the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method. \"\"\" self . slide_path = slide_path self . engine = engine self . slide_name , self . slide_ext = self . retrieve_slide_name_ext ( self . slide_path ) self . slide = self . read ( slide_path , engine ) self . dimensions = None self . level_dimensions = None self . level_downsamples = None self . properties = None self . offset = ( 0 , 0 ) self . set_slide_attributes () self . ROI = None self . ROI_width = None self . ROI_height = None self . tissue_contours = None self . coords = None self . coords_attrs = None apply_pathologist_annotations ( path , class_name = 'annotation' , column_to_select = 'objectType' ) Apply pathologist annotations to the tissue contours by intersecting the annotations with the tissue contours. Requires the tissue contours to be set for the slide beforehand with the detect_tissue method. Parameters: Name Type Description Default path str The path to the .geojson file containing the annotations extracted from QuPath. required class_name str The class name to use for selecting the annotations to apply. 'annotation' column_to_select str The column to select in the GeoDataFrame. 'objectType' Source code in src/prismtoolbox/wsicore/wsi.py 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 def apply_pathologist_annotations ( self , path : str , class_name : str = \"annotation\" , column_to_select : str = \"objectType\" , ) -> None : \"\"\"Apply pathologist annotations to the tissue contours by intersecting the annotations with the tissue contours. Requires the tissue contours to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method. Args: path: The path to the .geojson file containing the annotations extracted from QuPath. class_name: The class name to use for selecting the annotations to apply. column_to_select: The column to select in the GeoDataFrame. \"\"\" assert ( self . tissue_contours is not None ), \"No tissue contours found for the slide, please run the detect_tissue method first\" offset = ( - self . offset [ 0 ], - self . offset [ 1 ]) pathologist_annotations = read_qupath_annotations ( path , offset = offset , class_name = class_name , column_to_select = column_to_select ) polygons = contoursToPolygons ( self . tissue_contours , make_valid = True ) intersection = intersectionPolygons ( polygons , pathologist_annotations ) self . tissue_contours = PolygonsToContours ( intersection ) convert_micrometer_to_pixel ( value , level , axis = 'x' ) Convert a value from micrometer to pixel. Parameters: Name Type Description Default value float The value to convert (in micrometer). required level int The level at which the conversion should be performed. required axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description int The input value in pixel. Source code in src/prismtoolbox/wsicore/wsi.py 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 def convert_micrometer_to_pixel ( self , value : float , level : int , axis : str = \"x\" , ) -> int : \"\"\"Convert a value from micrometer to pixel. Args: value: The value to convert (in micrometer). level: The level at which the conversion should be performed. axis: The axis to use for getting the conversion factor (x or y). Returns: The input value in pixel. \"\"\" return int ( value / float ( self . properties [ f \" { self . engine } .mpp- { axis } \" ])) // int ( self . level_downsamples [ level ] ) convert_pixel_to_micrometer ( value , level , axis = 'x' ) Convert a value from pixel to micrometer. Parameters: Name Type Description Default value float The value to convert (in pixel). required level int The level at which the conversion should be performed. required axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description float The input value in micrometer. Source code in src/prismtoolbox/wsicore/wsi.py 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 def convert_pixel_to_micrometer ( self , value : float , level : int , axis : str = \"x\" , ) -> float : \"\"\"Convert a value from pixel to micrometer. Args: value: The value to convert (in pixel). level: The level at which the conversion should be performed. axis: The axis to use for getting the conversion factor (x or y). Returns: The input value in micrometer. \"\"\" return ( value * float ( self . properties [ f \" { self . engine } .mpp- { axis } \" ]) * self . level_downsamples [ level ] ) convert_units ( value , level , from_unit , to_unit = 'px' , axis = 'x' ) Convert a value from one unit to another. Parameters: Name Type Description Default value float The value to convert. required level int The level at which the conversion should be performed. required from_unit str The unit to convert from (px or micro). required to_unit str The unit to convert to (px or micro). 'px' axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description int The input value converted in the desired unit. Source code in src/prismtoolbox/wsicore/wsi.py 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 def convert_units ( self , value : float , level : int , from_unit : str , to_unit : str = \"px\" , axis : str = \"x\" , ) -> int : \"\"\"Convert a value from one unit to another. Args: value: The value to convert. level: The level at which the conversion should be performed. from_unit: The unit to convert from (px or micro). to_unit: The unit to convert to (px or micro). axis: The axis to use for getting the conversion factor (x or y). Returns: The input value converted in the desired unit. \"\"\" if from_unit == \"micro\" and to_unit == \"px\" : value = self . convert_micrometer_to_pixel ( value , level , axis ) elif from_unit == \"px\" and to_unit == \"micro\" : value = self . convert_pixel_to_micrometer ( value , level , axis ) elif from_unit == to_unit : pass else : raise ValueError ( f \"Conversion from { from_unit } to { to_unit } not supported\" ) return value create_thumbnail ( level , crop_roi = False ) Create a thumbnail of the slide at a given level. Parameters: Name Type Description Default level int The level at which the thumbnail should be created. required crop_roi bool A boolean to crop the thumbnail to the region of interest defined for the slide (requires a ROI to be set for the slide beforehand with the set_roi method) False Returns: Type Description Image A thumbnail of the slide as a PIL image. Source code in src/prismtoolbox/wsicore/wsi.py 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 def create_thumbnail ( self , level : int , crop_roi : bool = False , ) -> Image . Image : \"\"\"Create a thumbnail of the slide at a given level. Args: level: The level at which the thumbnail should be created. crop_roi: A boolean to crop the thumbnail to the region of interest defined for the slide (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method) Returns: A thumbnail of the slide as a PIL image. \"\"\" assert self . ROI is not None if crop_roi else True , ( \"No ROI provided, while crop_roi is set to True. Please set a ROI for \" \"the slide or set crop_roi to False.\" ) thumb = self . read_region (( 0 , 0 ), level , self . level_dimensions [ level ]) . convert ( \"RGB\" ) if crop_roi : log . info ( f \"Creating thumbnail with ROI { self . ROI } .\" ) coords_roi = ( self . ROI / self . level_downsamples [ level ]) . astype ( int ) thumb = thumb . crop ( coords_roi ) return thumb detect_tissue ( seg_level , window_avg , window_eng , thresh , inside_roi = False , inv_thresh = False , area_min = 200000.0 , start = ( 0 , 0 )) Segment the tissue on the slide based on a threshold on the Law's texture energy spot map and floodfill algorithm to fill the holes in the mask. The tissue contours are stored in the tissue_contours attribute. Parameters: Name Type Description Default seg_level int The level at which the segmentation should be performed. required window_avg int The size of the window for local averaging. required window_eng int The size of the window for Law's texture energy computation. required thresh int The threshold for binarization on the Law's texture energy spot map. required inside_roi bool Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide beforehand with the set_roi method). False inv_thresh bool Set to True to invert the thresholding. False area_min float The minimum area for a contour to be kept. 200000.0 start tuple [ int , int ] The starting point for the floodfill algorithm (should be left at (0, 0) in most cases). (0, 0) Source code in src/prismtoolbox/wsicore/wsi.py 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 def detect_tissue ( self , seg_level : int , window_avg : int , window_eng : int , thresh : int , inside_roi : bool = False , inv_thresh : bool = False , area_min : float = 2e5 , start : tuple [ int , int ] = ( 0 , 0 ), ) -> None : \"\"\"Segment the tissue on the slide based on a threshold on the Law's texture energy spot map and floodfill algorithm to fill the holes in the mask. The tissue contours are stored in the tissue_contours attribute. Args: seg_level: The level at which the segmentation should be performed. window_avg: The size of the window for local averaging. window_eng: The size of the window for Law's texture energy computation. thresh: The threshold for binarization on the Law's texture energy spot map. inside_roi: Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). inv_thresh: Set to True to invert the thresholding. area_min: The minimum area for a contour to be kept. start: The starting point for the floodfill algorithm (should be left at (0, 0) in most cases). \"\"\" final_contours = [] img = np . array ( self . create_thumbnail ( seg_level , inside_roi )) img_avg = local_average ( np . asarray ( img ), window_avg ) law_feats = compute_law_feats ( img_avg , window_eng )[ 0 ] filterred_img = apply_bilateral_filter ( np . clip ( law_feats , 0 , 255 ) . astype ( \"uint8\" )) threshed_img = apply_binary_thresh ( filterred_img , thresh , inv_thresh ) flooded_img = floodfill_img ( np . pad ( threshed_img , 1 ), start ) contours = contour_mask ( flooded_img ) for contour in contours : c = contour . copy () area = cv2 . contourArea ( c ) if area > area_min : final_contours . append ( contour ) if len ( final_contours ) == 0 : self . tissue_contours = [] print ( f \"No contours found for slide { self . slide_name } .\" ) return else : scale = self . level_downsamples [ seg_level ] offset = np . array ( self . ROI [: 2 ]) if self . ROI is not None else np . array ([ 0 , 0 ]) final_contours = self . scale_contours ( final_contours , scale ) final_contours = [ cont + offset for cont in final_contours ] # Sanity check to ensure that the contours are all within a ROI is provided if self . ROI is not None : assert all ( [ np . all ( cont >= self . ROI [: 2 ]) and np . all ( cont <= self . ROI [ 2 :]) for cont in final_contours ] ) self . tissue_contours = final_contours print ( f \"Identified { len ( final_contours ) } contours for slide { self . slide_name } .\" ) return extract_patches ( patch_size , patch_level , mode , step_size = None , overlap = None , units = ( 'px' , 'px' ), use_padding = True , contours_mode = None , rgb_threshs = ( 2 , 220 ), percentages = ( 0.6 , 0.9 )) Extract valid patches from the slide with different extraction modes. A patch is considered valid if it is not black or white and is within the region of interest or the tissue contours if relevant. The extracted patches are stored as coordinates in the coords attribute, and the attributes of the coordinates are stored in the coords_attrs attribute. Parameters: Name Type Description Default patch_size float The size of the patches to extract (assumed to be square). required patch_level int The level at which the patches should be extracted. required mode str The mode to use for the extraction: \"contours\" mode extracts patches within the tissue contours (requires the tissue contours to be set for the slide beforehand with the detect_tissue method). \"roi\" mode extracts patches within the region of interest (requires the ROI to be set for the slide beforehand with the set_roi method). \"all\" mode extracts patches from the entire slide required step_size float | None The step size for the sliding window (if set to None, the step size will be computed based on the overlap). None overlap float | None The overlap between patches as an absolute value (must be provided if step_size is set to None). None units tuple [ str , str ] The units for the patch size and step size/overlap values (pixels: px, micrometers: micro). ('px', 'px') use_padding bool Set to True to use padding for the extraction. True contours_mode str | None The mode to use for the contour checking function (must be provided if mode is set to contours). See IsInContour for more details. None rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Source code in src/prismtoolbox/wsicore/wsi.py 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 def extract_patches ( self , patch_size : float , patch_level : int , mode : str , step_size : float | None = None , overlap : float | None = None , units : tuple [ str , str ] = ( \"px\" , \"px\" ), use_padding : bool = True , contours_mode : str | None = None , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> None : \"\"\"Extract valid patches from the slide with different extraction modes. A patch is considered valid if it is not black or white and is within the region of interest or the tissue contours if relevant. The extracted patches are stored as coordinates in the coords attribute, and the attributes of the coordinates are stored in the coords_attrs attribute. Args: patch_size: The size of the patches to extract (assumed to be square). patch_level: The level at which the patches should be extracted. mode: The mode to use for the extraction: - \"contours\" mode extracts patches within the tissue contours (requires the tissue contours to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method). - \"roi\" mode extracts patches within the region of interest (requires the ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). - \"all\" mode extracts patches from the entire slide step_size: The step size for the sliding window (if set to None, the step size will be computed based on the overlap). overlap: The overlap between patches as an absolute value (must be provided if step_size is set to None). units: The units for the patch size and step size/overlap values (pixels: px, micrometers: micro). use_padding: Set to True to use padding for the extraction. contours_mode: The mode to use for the contour checking function (must be provided if mode is set to contours). See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. \"\"\" assert ( step_size is not None or overlap is not None ), \"Either step_size or overlap must be provided\" assert all ( [ unit in [ \"micro\" , \"px\" ] for unit in units ] ), \"Units must be either 'micro' or 'px'\" patch_size = self . convert_units ( patch_size , patch_level , units [ 0 ]) if step_size is None : step_size = patch_size - self . convert_units ( overlap , patch_level , units [ 1 ]) else : step_size = self . convert_units ( step_size , patch_level , units [ 1 ]) log . info ( f \"Extracting patches of size { patch_size } at level { patch_level } with step size { step_size } .\" ) if mode == \"contours\" : log . info ( \"Extracting patches with 'contours' mode.\" ) assert self . tissue_contours is not None assert len ( self . tissue_contours ) > 0 assert contours_mode is not None valid_coords = [] for cont in self . tissue_contours : roi_dim = cv2 . boundingRect ( cont ) log . info ( f \"Processing ROI of dimensions: { roi_dim } \" ) valid_coords . extend ( self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , cont , contours_mode , rgb_threshs = rgb_threshs , percentages = percentages , ) ) valid_coords = np . array ( valid_coords ) elif mode == \"roi\" : log . info ( \"Extracting patches with 'roi' mode.\" ) assert self . ROI is not None roi_dim = self . ROI [ 0 ], self . ROI [ 1 ], self . ROI_width , self . ROI_height log . info ( \"Processing ROI of dimensions:\" , roi_dim ) valid_coords = self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , rgb_threshs = rgb_threshs , percentages = percentages , ) elif mode == \"all\" : roi_dim = 0 , 0 , self . level_dimensions [ 0 ][ 0 ], self . level_dimensions [ 0 ][ 1 ] log . info ( \"Processing ROI of dimensions:\" , roi_dim ) valid_coords = self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , rgb_threshs = rgb_threshs , percentages = percentages , ) else : raise ValueError ( f \"Mode { mode } not supported\" ) attr = { \"patch_size\" : patch_size , \"patch_level\" : patch_level , \"downsample\" : self . level_downsamples [ patch_level ], \"downsampled_level_dim\" : tuple ( np . array ( self . level_dimensions [ patch_level ])), \"level_dim\" : self . level_dimensions [ patch_level ], \"name\" : self . slide_name , } if len ( valid_coords ) == 0 : log . warning ( f \"No valid coordinates found for slide { self . slide_name } .\" ) else : print ( f \"Identified a total of { len ( valid_coords ) } valid coordinates in the slide { self . slide_name } .\" ) self . coords = valid_coords self . coords_attrs = attr extract_patches_roi ( patch_level , patch_size , step_size = None , roi_dim = None , use_padding = True , contour = None , contours_mode = None , coord_candidates = None , rgb_threshs = ( 2 , 220 ), percentages = ( 0.6 , 0.9 ), return_indices = False ) Extract valid patches from a region of interest, i.e if the patch is not black or white and is within the region of interest/contours if relevant). Parameters: Name Type Description Default patch_level int The level at which the patches should be extracted. required patch_size int The size of the patches to extract (assumed to be square). required step_size int | None The step size to use for the sliding window. None roi_dim tuple [ int , int , int , int ] | None The top-left corner coordinates and dimensions of the region of interest. Must be provided if coord_candidates is set to None. None use_padding bool Set to True to use padding for the extraction. True contour ndarray | None The tissue contour to use for the extraction. If set to None, will not check if patches are within a contour. None contours_mode str | None The mode for the contour checking function. See IsInContour for more details. Must be provided if mode is set to contours. Otherwise, will not check if patches are within the contours. None coord_candidates ndarray | None Precomputed candidate coordinates for the patches. If set to None, will compute the candidate coordinates based on the ROI dimensions and the step size. None rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) return_indices bool Set to True to return the indices of the valid coordinates. False Returns: Type Description ndarray An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches). Source code in src/prismtoolbox/wsicore/wsi.py 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 def extract_patches_roi ( self , patch_level : int , patch_size : int , step_size : int | None = None , roi_dim : tuple [ int , int , int , int ] | None = None , use_padding : bool = True , contour : np . ndarray | None = None , contours_mode : str | None = None , coord_candidates : np . ndarray | None = None , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), return_indices : bool = False , ) -> np . ndarray : \"\"\"Extract valid patches from a region of interest, i.e if the patch is not black or white and is within the region of interest/contours if relevant). Args: patch_level: The level at which the patches should be extracted. patch_size: The size of the patches to extract (assumed to be square). step_size: The step size to use for the sliding window. roi_dim: The top-left corner coordinates and dimensions of the region of interest. Must be provided if coord_candidates is set to None. use_padding: Set to True to use padding for the extraction. contour: The tissue contour to use for the extraction. If set to None, will not check if patches are within a contour. contours_mode: The mode for the contour checking function. See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. Must be provided if mode is set to contours. Otherwise, will not check if patches are within the contours. coord_candidates: Precomputed candidate coordinates for the patches. If set to None, will compute the candidate coordinates based on the ROI dimensions and the step size. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. return_indices: Set to True to return the indices of the valid coordinates. Returns: An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches). \"\"\" assert contours_mode is not None if contour is not None else True , ( \"A contour mode must be provided if patch \" \"extraction mode is set to contours\" ) assert ( roi_dim is not None and step_size is not None ) or coord_candidates is not None , ( \"Either (roi_dim and step_size) or coord_candidates must be provided\" ) patch_downsample = int ( self . level_downsamples [ patch_level ]) ref_patch_size = patch_size * patch_downsample if coord_candidates is None : start_x , start_y , w , h = roi_dim img_w , img_h = self . level_dimensions [ 0 ] if use_padding : stop_y = start_y + h stop_x = start_x + w else : stop_y = min ( start_y + h , img_h - ref_patch_size + 1 ) stop_x = min ( start_x + w , img_w - ref_patch_size + 1 ) step_size = step_size * patch_downsample x_range = np . arange ( start_x , stop_x , step = step_size ) y_range = np . arange ( start_y , stop_y , step = step_size ) x_coords , y_coords = np . meshgrid ( x_range , y_range , indexing = \"ij\" ) coord_candidates = np . array ( [ x_coords . flatten (), y_coords . flatten ()] ) . transpose () if contour is not None : cont_check_fn = IsInContour ( contour , patch_size = ref_patch_size , center_shift = 0.5 , mode = contours_mode ) log . info ( f \"Extracting patches with contour checking function mode { contours_mode } .\" ) else : cont_check_fn = None num_workers = mp . cpu_count () pool = mp . Pool ( num_workers , initializer = WSI . worker_init , initargs = ( self . slide_path , self . engine , ), ) iterable = [ ( coord , cont_check_fn , patch_level , patch_size , rgb_threshs , percentages ) for coord in coord_candidates ] valid_coords = pool . starmap ( WSI . process_coord_candidate , iterable ) pool . close () valid_indices = [ i for i , coord in enumerate ( valid_coords ) if coord is not None ] valid_coords = np . array ([ coord_candidates [ i ] for i in valid_indices ]) log . info ( f \"Identified { len ( valid_coords ) } valid coordinates in the ROI { roi_dim } .\" ) if return_indices : return valid_coords , valid_indices else : return valid_coords is_black_white ( patch , rgb_threshs = ( 2 , 220 ), percentages = ( 0.6 , 0.9 )) staticmethod Check if a patch is black or white. Parameters: Name Type Description Default patch Image The input patch. required rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Returns: Type Description bool True if the patch is black or white, False otherwise. Source code in src/prismtoolbox/wsicore/wsi.py 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 @staticmethod def is_black_white ( patch : Image . Image , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> bool : \"\"\"Check if a patch is black or white. Args: patch: The input patch. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. Returns: True if the patch is black or white, False otherwise. \"\"\" return isBlackPatch ( patch , rgb_thresh = rgb_threshs [ 0 ], percentage = percentages [ 0 ] ) or isWhitePatch ( patch , rgb_thresh = rgb_threshs [ 1 ], percentage = percentages [ 1 ]) load_patches ( file_path ) Load the patches from a hdf5 file. Parameters: Name Type Description Default file_path str The path to the hdf5 file containing the patches. required Source code in src/prismtoolbox/wsicore/wsi.py 449 450 451 452 453 454 455 456 def load_patches ( self , file_path : str ) -> None : \"\"\"Load the patches from a hdf5 file. Args: file_path: The path to the hdf5 file containing the patches. \"\"\" log . info ( f \"Loading patches for slide { self . slide_name } from { file_path } .\" ) self . coords , self . coords_attrs = read_h5_file ( file_path , \"coords\" ) load_tissue_contours ( file_path ) Load the tissue contours from a pickle file. Parameters: Name Type Description Default file_path str The path to the pickle file containing the tissue contours. required Source code in src/prismtoolbox/wsicore/wsi.py 366 367 368 369 370 371 372 373 def load_tissue_contours ( self , file_path : str ) -> None : \"\"\"Load the tissue contours from a pickle file. Args: file_path: The path to the pickle file containing the tissue contours. \"\"\" log . info ( f \"Loading tissue contours for slide { self . slide_name } from { file_path } .\" ) self . tissue_contours = load_obj_with_pickle ( file_path ) process_coord_candidate ( coord , cont_check_fn , patch_level , patch_size , rgb_threshs = ( 2 , 220 ), percentages = ( 0.6 , 0.9 )) staticmethod Determine if a candidate coordinate is valid based on a contour checking function and/or black/white thresholding. Parameters: Name Type Description Default coord tuple [ int , int ] The candidate coordinate. required cont_check_fn IsInContour | None The contour checking function. required patch_level int The level at which the patch should be extracted. required patch_size int The size of the patch (assumed to be square). required rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Returns: Type Description tuple [ int , int ] | None The coordinate if it is valid, None otherwise. Source code in src/prismtoolbox/wsicore/wsi.py 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 @staticmethod def process_coord_candidate ( coord : tuple [ int , int ], cont_check_fn : IsInContour | None , patch_level : int , patch_size : int , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> tuple [ int , int ] | None : \"\"\"Determine if a candidate coordinate is valid based on a contour checking function and/or black/white thresholding. Args: coord: The candidate coordinate. cont_check_fn: The contour checking function. patch_level: The level at which the patch should be extracted. patch_size: The size of the patch (assumed to be square). rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. Returns: The coordinate if it is valid, None otherwise. \"\"\" if cont_check_fn is None or cont_check_fn ( coord ): patch = wsi . read_region ( tuple ( coord ), patch_level , ( patch_size , patch_size ) ) . convert ( \"RGB\" ) if not WSI . is_black_white ( patch , rgb_threshs , percentages ): return coord else : return None else : return None read ( slide_path , engine ) staticmethod Read a slide with a given engine. Parameters: Name Type Description Default slide_path str The path to the slide. required engine str The backend library to use for reading the slide (currently only openslide and tiffslide are supported). required Returns: Type Description A slide object. Source code in src/prismtoolbox/wsicore/wsi.py 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 @staticmethod def read ( slide_path : str , engine : str ): \"\"\"Read a slide with a given engine. Args: slide_path: The path to the slide. engine: The backend library to use for reading the slide (currently only openslide and tiffslide are supported). Returns: A slide object. \"\"\" if engine == \"openslide\" : import openslide slide = openslide . OpenSlide ( slide_path ) elif engine == \"tiffslide\" : import tiffslide with warnings . catch_warnings (): warnings . simplefilter ( \"ignore\" , category = UserWarning ) slide = tiffslide . TiffSlide ( slide_path ) else : raise NotImplementedError ( f \"engine { engine } not supported\" ) return slide read_region ( location , level , size ) Read a region from the slide for a given level and size. Parameters: Name Type Description Default location tuple [ int , int ] The coordinates of the top left corner of the region (in pixels). required level int The level at which the region should be read. required size tuple [ int , int ] The size of the region (in pixels). required Returns: Type Description Image The desired region of the slide as a PIL image. Source code in src/prismtoolbox/wsicore/wsi.py 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 def read_region ( self , location : tuple [ int , int ], level : int , size : tuple [ int , int ], ) -> Image . Image : \"\"\"Read a region from the slide for a given level and size. Args: location: The coordinates of the top left corner of the region (in pixels). level: The level at which the region should be read. size: The size of the region (in pixels). Returns: The desired region of the slide as a PIL image. \"\"\" return self . slide . read_region ( location , level , size ) . convert ( \"RGB\" ) retrieve_slide_name_ext ( slide_path ) staticmethod Retrieves slide name and slide extension from slide path. Parameters: Name Type Description Default slide_path str The path to the slide. required Returns: Type Description tuple [ str , str ] A tuple (slide name, slide ext). Source code in src/prismtoolbox/wsicore/wsi.py 109 110 111 112 113 114 115 116 117 118 119 120 121 @staticmethod def retrieve_slide_name_ext ( slide_path : str ) -> tuple [ str , str ]: \"\"\"Retrieves slide name and slide extension from slide path. Args: slide_path: The path to the slide. Returns: A tuple (slide name, slide ext). \"\"\" slide_ext = re . search ( r \"(?<=\\.)[\\w\\.]+$\" , slide_path ) . group ( 0 ) slide_name = re . search ( r \"([^/]+?)(?=\\.[\\w\\.]+$)\" , slide_path ) . group ( 0 ) return slide_name , slide_ext save_patches ( save_dir , file_format = 'h5' , selected_idx = None , merge = False , label = None , color = ( 255 , 0 , 0 ), append_to_existing_file = False ) Save the patches in a hdf5 or geojson file. Parameters: Name Type Description Default save_dir str The path to the directory where the patches will be saved. required file_format str The format for the saving (h5 for python processing, geojson for QuPath processing). 'h5' selected_idx ndarray | None An array of indices of the patches to save (if set to None, all the patches will be saved). None merge bool Set to True to merge the patches into a single polygon (for geojson format only). False label str | None An optional label to assign to the patches (for geojson format only). None color tuple [ int , int , int ] An optional color to assign to the patches (for geojson format only). (255, 0, 0) append_to_existing_file bool Set to True to append the patches to an existing geojson file. False Source code in src/prismtoolbox/wsicore/wsi.py 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 def save_patches ( self , save_dir : str , file_format : str = \"h5\" , selected_idx : np . ndarray | None = None , merge : bool = False , label : str | None = None , color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), append_to_existing_file : bool = False , ) -> None : \"\"\"Save the patches in a hdf5 or geojson file. Args: save_dir: The path to the directory where the patches will be saved. file_format: The format for the saving (h5 for python processing, geojson for QuPath processing). selected_idx: An array of indices of the patches to save (if set to None, all the patches will be saved). merge: Set to True to merge the patches into a single polygon (for geojson format only). label: An optional label to assign to the patches (for geojson format only). color: An optional color to assign to the patches (for geojson format only). append_to_existing_file: Set to True to append the patches to an existing geojson file. \"\"\" if selected_idx is not None : coords = self . coords [ selected_idx ] else : coords = self . coords if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if file_format == \"h5\" : asset_dict = { \"coords\" : self . coords } attr_dict = { \"coords\" : self . coords_attrs } file_path = os . path . join ( save_dir , f \" { self . slide_name } .h5\" ) log . info ( f \"Saving patches for slide { self . slide_name } at { file_path } with hdf5.\" ) save_patches_with_hdf5 ( file_path , asset_dict , attr_dict ) elif file_format == \"geojson\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .geojson\" ) patch_downsample = int ( self . level_downsamples [ self . coords_attrs [ \"patch_level\" ]] ) polygons = patchesToPolygons ( coords , self . coords_attrs [ \"patch_size\" ], patch_downsample , merge ) log . info ( f \"Saving { len ( coords ) } patches for slide { self . slide_name } at { file_path } with geojson.\" ) export_polygons_to_qupath ( polygons , file_path , \"annotation\" , offset = self . offset , label = label , color = color , append_to_existing_file = append_to_existing_file , ) elif file_format == \"jpg\" or file_format == \"png\" : save_dir = os . path . join ( save_dir , self . slide_name ) if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) log . info ( f \"Saving { len ( coords ) } patches for slide { self . slide_name } at { save_dir } with { file_format } .\" ) for coord in coords : patch = self . read_region ( coord , self . coords_attrs [ \"patch_level\" ], ( self . coords_attrs [ \"patch_size\" ], self . coords_attrs [ \"patch_size\" ]), ) . convert ( \"RGB\" ) patch . save ( os . path . join ( save_dir , f \" { coord [ 0 ] } _ { coord [ 1 ] } . { file_format } \" )) else : raise ValueError ( f \"format { file_format } not supported\" ) save_tissue_contours ( save_dir , selected_idx = None , file_format = 'pickle' , merge = False , label = None , color = ( 255 , 0 , 0 ), append_to_existing_file = False , make_valid = False ) Save the tissue contours in a pickle or geojson file. Parameters: Name Type Description Default save_dir str The path to the directory where the contours will be saved. required selected_idx ndarray | None An array of indices of the contours to save (if set to None, all the contours will be saved). None file_format str The file format for saving the contours (pickle for python processing, geojson for QuPath processing). 'pickle' merge bool Set to True to merge the contours into a single polygon (for geojson format only). False label str | None An optional label to assign to the tissue contours (for geojson format only). None color tuple [ int , int , int ] An optional color to assign to the tissue contours (for geojson format only). (255, 0, 0) append_to_existing_file bool Set to True to append the contours to an existing geojson file. False make_valid bool Set to True to make the polygons valid (for geojson format only). False Source code in src/prismtoolbox/wsicore/wsi.py 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 def save_tissue_contours ( self , save_dir : str , selected_idx : np . ndarray | None = None , file_format : str = \"pickle\" , merge : bool = False , label : str | None = None , color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), append_to_existing_file : bool = False , make_valid : bool = False , ) -> None : \"\"\"Save the tissue contours in a pickle or geojson file. Args: save_dir: The path to the directory where the contours will be saved. selected_idx: An array of indices of the contours to save (if set to None, all the contours will be saved). file_format: The file format for saving the contours (pickle for python processing, geojson for QuPath processing). merge: Set to True to merge the contours into a single polygon (for geojson format only). label: An optional label to assign to the tissue contours (for geojson format only). color: An optional color to assign to the tissue contours (for geojson format only). append_to_existing_file: Set to True to append the contours to an existing geojson file. make_valid: Set to True to make the polygons valid (for geojson format only). \"\"\" assert self . tissue_contours is not None , ( \"No tissue contours found for the slide, \" \"please run the detect_tissue method first\" ) if selected_idx is not None : tissue_contours = [ self . tissue_contours [ idx ] for idx in selected_idx ] else : tissue_contours = self . tissue_contours if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if file_format == \"pickle\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .pkl\" ) log . info ( f \"Saving tissue contours for slide { self . slide_name } at { file_path } with pickle.\" ) save_obj_with_pickle ( tissue_contours , file_path ) elif file_format == \"geojson\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .geojson\" ) log . info ( f \"Saving { selected_idx } tissue contours for slide { self . slide_name } at { file_path } with geojson.\" ) polygons = contoursToPolygons ( tissue_contours , merge , make_valid ) export_polygons_to_qupath ( polygons , file_path , \"annotation\" , offset = self . offset , label = label , color = color , append_to_existing_file = append_to_existing_file , ) else : raise ValueError ( f \"format { file_format } not supported\" ) scale_contours ( contours , scale ) staticmethod Scale the contours by a given factor. Parameters: Name Type Description Default contours list [ ndarray ] The contours to scale. required scale int The scale factor to apply. required Returns: Type Description list [ ndarray ] The input list with scaled contours. Source code in src/prismtoolbox/wsicore/wsi.py 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 @staticmethod def scale_contours ( contours : list [ np . ndarray ], scale : int , ) -> list [ np . ndarray ]: \"\"\"Scale the contours by a given factor. Args: contours: The contours to scale. scale: The scale factor to apply. Returns: The input list with scaled contours. \"\"\" scaled_contours = [ np . array ( cont * scale , dtype = \"int\" ) for cont in contours ] return scaled_contours set_roi ( roi = None , rois_df_path = None ) Set the region of interest for the slide. Can be set manually or by selecting a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute. Parameters: Name Type Description Default roi tuple [ int , int , int , int ] | None Set the region of interest manually as a tuple (x1, y1, x2, y2). None rois_df_path str | None The path to dataframe containing the ROIs with a slide_id column identifying the slide. None Returns: Type Description None The region of interest set for the slide as a tuple (x1, y1, x2, y2). Source code in src/prismtoolbox/wsicore/wsi.py 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 def set_roi ( self , roi : tuple [ int , int , int , int ] | None = None , rois_df_path : str | None = None , ) -> None : \"\"\"Set the region of interest for the slide. Can be set manually or by selecting a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute. Args: roi: Set the region of interest manually as a tuple (x1, y1, x2, y2). rois_df_path: The path to dataframe containing the ROIs with a slide_id column identifying the slide. Returns: The region of interest set for the slide as a tuple (x1, y1, x2, y2). \"\"\" if roi is not None : ROI = np . array ( roi ) . astype ( int ) elif rois_df_path is not None : rois_df = pd . read_csv ( rois_df_path ) ROI = rois_df [ rois_df . slide_id == self . slide_name ] . values [ 0 ] . astype ( int ) else : log . info ( \"No ROI provided, prompting user to select one.\" ) level = input ( f \"No ROI was provided, please select a level at which the ROI should be created (max level: { len ( self . level_dimensions ) - 1 } ): \" ) if not level : log . warning ( \"No level provided, setting the ROI at the highest level.\" ) level = len ( self . level_downsamples ) - 1 else : level = int ( level ) img = np . array ( self . create_thumbnail ( level )) ROI = select_roi_on_thumbnail ( img , int ( self . level_downsamples [ level ])) ROI = ( ROI * self . level_downsamples [ level ]) . astype ( int ) self . ROI = ROI self . ROI_width = ROI [ 2 ] - ROI [ 0 ] self . ROI_height = ROI [ 3 ] - ROI [ 1 ] print ( f \"ROI for slide { self . slide_name } has been set to { self . ROI } .\" ) return ROI set_slide_attributes () Set the slide attributes. Source code in src/prismtoolbox/wsicore/wsi.py 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 def set_slide_attributes ( self ): \"\"\"Set the slide attributes.\"\"\" if self . engine == \"openslide\" : self . dimensions = self . slide . dimensions self . level_dimensions = self . slide . level_dimensions self . level_downsamples = self . slide . level_downsamples self . properties = self . slide . properties elif self . engine == \"tiffslide\" : self . dimensions = self . slide . dimensions self . level_dimensions = self . slide . level_dimensions self . level_downsamples = self . slide . level_downsamples self . properties = self . slide . properties else : raise NotImplementedError ( f \"engine { self . engine } not supported\" ) if ( f \" { self . engine } .bounds-x\" in self . properties . keys () and self . properties [ f \" { self . engine } .bounds-x\" ] is not None ): self . offset = ( - int ( self . properties [ f \" { self . engine } .bounds-x\" ]), - int ( self . properties [ f \" { self . engine } .bounds-y\" ]), ) stitch ( vis_level , selected_idx = None , colors = None , alpha = 0.6 , black_white = False , draw_grid = False , crop_roi = False , background_color = ( 0 , 0 , 0 )) Stitch the patches extracted on an image. The patches can be masked and colored depending on the mask and colors provided. Requires the coordinates of the patches to be set for the slide beforehand with the extract_patches method. Parameters: Name Type Description Default vis_level int The level at which the patches should be visualized. required selected_idx ndarray An array of indices of the patches to visualize (if set to None, all the patches will be visualized). None colors ndarray An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as they are). None alpha float Set the transparency of the colors to apply to the patches. 0.6 black_white bool Set to True to visualize a binary mask of the patches extracted. False draw_grid bool Set to True to draw a grid on the stitched patches. False crop_roi bool Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the set_roi method). False background_color tuple [ int , int , int ] The color of the background. (0, 0, 0) Returns: Type Description Image A PIL image of the stitched patches. Source code in src/prismtoolbox/wsicore/wsi.py 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 def stitch ( self , vis_level : int , selected_idx : np . ndarray = None , colors : np . ndarray = None , alpha : float = 0.6 , black_white : bool = False , draw_grid : bool = False , crop_roi : bool = False , background_color : tuple [ int , int , int ] = ( 0 , 0 , 0 ), ) -> Image . Image : \"\"\"Stitch the patches extracted on an image. The patches can be masked and colored depending on the mask and colors provided. Requires the coordinates of the patches to be set for the slide beforehand with the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method. Args: vis_level: The level at which the patches should be visualized. selected_idx: An array of indices of the patches to visualize (if set to None, all the patches will be visualized). colors: An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as they are). alpha: Set the transparency of the colors to apply to the patches. black_white: Set to True to visualize a binary mask of the patches extracted. draw_grid: Set to True to draw a grid on the stitched patches. crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). background_color: The color of the background. Returns: A PIL image of the stitched patches. \"\"\" assert self . ROI is not None if crop_roi else True , ( \"no ROI provided, while crop_roi is set to True,\" \" please set a ROI for the slide or set crop_roi to False.\" ) assert self . coords is not None , ( \"no coordinates provided for the patches to visualize, please run the \" \"extract_patches method first or load the coordinates from a file\" ) if crop_roi : w , h = int ( np . ceil ( self . ROI_width / self . level_downsamples [ vis_level ])), int ( np . ceil ( self . ROI_height / self . level_downsamples [ vis_level ]) ) else : w , h = self . level_dimensions [ vis_level ] patch_size = self . coords_attrs [ \"patch_size\" ] patch_level = self . coords_attrs [ \"patch_level\" ] patch_size = int ( patch_size * self . level_downsamples [ patch_level ]) canvas = init_image ( w , h , mask = black_white , color_bakground = background_color ) downsample_vis = self . level_downsamples [ vis_level ] idxs = np . arange ( len ( self . coords )) if selected_idx is not None : idxs = idxs [ selected_idx ] patch_size = np . ceil ( patch_size / downsample_vis ) . astype ( int ) log . info ( f \"Stitching { len ( idxs ) } patches at level { vis_level } with patch size { patch_size } , \" f \"with colors { colors is not None } .\" ) offset = self . ROI [: 2 ] if crop_roi else np . array ([ 0 , 0 ]) for idx in idxs : coord = self . coords [ idx ] coord_downsampled = np . ceil ( np . abs ( coord - offset ) / downsample_vis ) . astype ( int ) patch_size_coord = ( min ( max ( w - coord_downsampled [ 0 ], 0 ), patch_size ), min ( max ( h - coord_downsampled [ 1 ], 0 ), patch_size ), ) if any ( val == 0 for val in patch_size_coord ): continue if black_white : patch = np . ones ( patch_size_coord , dtype = \"uint8\" ) colors = None else : patch = np . array ( self . slide . read_region ( tuple ( coord ), vis_level , patch_size_coord ) . convert ( \"RGB\" ) ) if colors is not None : assert len ( colors ) == len ( idxs ), ( \"the number of colors provided must match \" \"the number of selected coordinates\" ) color = colors [ idx ] color_patch = ( np . ones (( patch_size_coord [ 1 ], patch_size_coord [ 0 ], 3 )) * color ) . astype ( \"uint8\" ) canvas [ coord_downsampled [ 1 ] : coord_downsampled [ 1 ] + patch_size_coord [ 1 ], coord_downsampled [ 0 ] : coord_downsampled [ 0 ] + patch_size_coord [ 0 ], :, ] = cv2 . addWeighted ( color_patch , alpha , patch , 1 - alpha , 0 , patch ) else : canvas [ coord_downsampled [ 1 ] : coord_downsampled [ 1 ] + patch_size_coord [ 1 ], coord_downsampled [ 0 ] : coord_downsampled [ 0 ] + patch_size_coord [ 0 ], :, ] = patch if draw_grid : cv2 . rectangle ( canvas , tuple ( np . maximum ([ 0 , 0 ], coord_downsampled - 1 )), tuple ( coord_downsampled + patch_size_coord ), ( 0 , 0 , 0 , 255 ), thickness = 2 , ) img = Image . fromarray ( canvas ) return img visualize ( vis_level , crop_roi = False , contours_color = ( 255 , 0 , 0 ), line_thickness = 500 , max_size = None , number_contours = False , black_white = False , view_slide_only = False ) Visualize the slide with or without the contours of the tissue. Parameters: Name Type Description Default vis_level int The level at which the visualization should be performed. required crop_roi bool Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the set_roi method). False contours_color tuple [ int , int , int ] The color to use for the contours. (255, 0, 0) line_thickness int The thickness to use for the contours 500 max_size int | None The maximum size for the visualization for the width or height of the image. None number_contours bool Set to True to number the contours. False black_white bool Set to True to visualize a binary mask of the contoured tissue. False view_slide_only bool Set to True to visualize the slide only (without the contours). False Returns: Type Description Image A PIL image of the visualization. Source code in src/prismtoolbox/wsicore/wsi.py 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 def visualize ( self , vis_level : int , crop_roi : bool = False , contours_color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), line_thickness : int = 500 , max_size : int | None = None , number_contours : bool = False , black_white : bool = False , view_slide_only : bool = False , ) -> Image . Image : \"\"\"Visualize the slide with or without the contours of the tissue. Args: vis_level: The level at which the visualization should be performed. crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). contours_color: The color to use for the contours. line_thickness: The thickness to use for the contours max_size: The maximum size for the visualization for the width or height of the image. number_contours: Set to True to number the contours. black_white: Set to True to visualize a binary mask of the contoured tissue. view_slide_only: Set to True to visualize the slide only (without the contours). Returns: A PIL image of the visualization. \"\"\" assert line_thickness > 0 , \"line_thickness must be greater than 0\" scale = 1 / self . level_downsamples [ vis_level ] if black_white : img = np . zeros_like ( self . create_thumbnail ( vis_level , crop_roi ), dtype = \"uint8\" ) line_thickness = - 1 contours_color = ( 1 , 1 , 1 ) else : img = np . array ( self . create_thumbnail ( vis_level , crop_roi )) line_thickness = int ( line_thickness * scale ) if not view_slide_only : assert ( len ( self . tissue_contours ) > 0 ), \"No tissue contours found for the slide, please run the detect_tissue method first\" offset = self . ROI [: 2 ] if crop_roi else np . array ([ 0 , 0 ]) contours = [ cont - offset for cont in self . tissue_contours ] contours = self . scale_contours ( contours , scale ) if len ( contours ) > 0 : if not number_contours : cv2 . drawContours ( img , contours , - 1 , contours_color , line_thickness , lineType = cv2 . LINE_8 , ) else : # add numbering to each contour for idx , cont in enumerate ( contours ): M = cv2 . moments ( cont ) cX = int ( M [ \"m10\" ] / ( M [ \"m00\" ] + 1e-9 )) cY = int ( M [ \"m01\" ] / ( M [ \"m00\" ] + 1e-9 )) # draw the contour and put text next to center cv2 . drawContours ( img , [ cont ], - 1 , contours_color , line_thickness , lineType = cv2 . LINE_8 , ) cv2 . putText ( img , \" {} \" . format ( idx ), ( cX , cY ), cv2 . FONT_HERSHEY_SIMPLEX , 2 , ( 255 , 0 , 0 ), 10 , ) img = Image . fromarray ( img ) if black_white : img = img . convert ( \"L\" ) w , h = img . size if max_size is not None and ( w > max_size or h > max_size ): resizeFactor = max_size / w if w > h else max_size / h img = img . resize (( int ( w * resizeFactor ), int ( h * resizeFactor ))) return img worker_init ( slide_path , engine ) staticmethod Initialize the worker process with a wsi object. Parameters: Name Type Description Default slide_path str The path to the slide. required engine str The backend library to use for reading the slide (currently only openslide and tiffslide are supported) required Source code in src/prismtoolbox/wsicore/wsi.py 166 167 168 169 170 171 172 173 174 175 176 @staticmethod def worker_init ( slide_path : str , engine : str ) -> None : \"\"\"Initialize the worker process with a wsi object. Args: slide_path: The path to the slide. engine: The backend library to use for reading the slide (currently only openslide and tiffslide are supported) \"\"\" global wsi wsi = WSI . read ( slide_path , engine )","title":"Index"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI","text":"The WSI (Whole Slide Image) class is responsible for handling operations related to whole slide images. Parameters: Name Type Description Default slide_path str The path to the slide image file. required engine str The engine used to read the slide image. 'openslide' Attributes: Name Type Description slide_path str The path to the slide image file. engine str The engine used to read the slide image. slide_name str The name of the slide image file. Retrieved from the slide path using retrieve_slide_name_ext method. slide_ext str The extension of the slide image file. Retrieved from the slide path using retrieve_slide_name_ext method. slide OpenSlide | TiffSlide The wsi read from the file using engine. dimensions list [ tuple [ int , int ]] The dimensions of the slide image. Set by the set_slide_attributes method. level_dimensions list [ tuple [ int , int ]] The dimensions of the different levels of the slide image. Set by the set_slide_attributes method. level_downsamples list [ tuple [ int , int ]] The downsampling factors of the different levels of the slide image. Set by the set_slide_attributes method. properties dict The properties of the slide image. Set by the set_slide_attributes method. offset tuple [ int , int ] The offset of the slide image. Set by the set_slide_attributes method. ROI ndarray The region of interest in the slide image. Please use the set_roi method to set the ROI. ROI_width int The width of the region of interest. Set by the set_roi method. ROI_height int The height of the region of interest. Set by the set_roi method. tissue_contours list [ ndarray ] The contours of the tissue in the slide image. Please use the detect_tissue method to detect the tissue contours. coords ndarray The coordinates of patches extracted from slide image. Please use the extract_patches method to extract patches. coords_attrs dict The attributes of the coordinates. Set by the extract_patches method. Source code in src/prismtoolbox/wsicore/wsi.py 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 def __init__ ( self , slide_path : str , engine : str = \"openslide\" ): \"\"\"The WSI (Whole Slide Image) class is responsible for handling operations related to whole slide images. Args: slide_path: The path to the slide image file. engine: The engine used to read the slide image. Attributes: slide_path (str): The path to the slide image file. engine (str): The engine used to read the slide image. slide_name (str): The name of the slide image file. Retrieved from the slide path using [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method. slide_ext (str): The extension of the slide image file. Retrieved from the slide path using [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method. slide (OpenSlide | TiffSlide): The wsi read from the file using engine. dimensions (list[tuple[int, int]]): The dimensions of the slide image. Set by the set_slide_attributes method. level_dimensions (list[tuple[int, int]]): The dimensions of the different levels of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. level_downsamples (list[tuple[int, int]]): The downsampling factors of the different levels of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. properties (dict): The properties of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. offset (tuple[int, int]): The offset of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. ROI (ndarray): The region of interest in the slide image. Please use the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method to set the ROI. ROI_width (int): The width of the region of interest. Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method. ROI_height (int): The height of the region of interest. Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method. tissue_contours (list[ndarray]): The contours of the tissue in the slide image. Please use the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method to detect the tissue contours. coords (np.ndarray): The coordinates of patches extracted from slide image. Please use the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method to extract patches. coords_attrs (dict): The attributes of the coordinates. Set by the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method. \"\"\" self . slide_path = slide_path self . engine = engine self . slide_name , self . slide_ext = self . retrieve_slide_name_ext ( self . slide_path ) self . slide = self . read ( slide_path , engine ) self . dimensions = None self . level_dimensions = None self . level_downsamples = None self . properties = None self . offset = ( 0 , 0 ) self . set_slide_attributes () self . ROI = None self . ROI_width = None self . ROI_height = None self . tissue_contours = None self . coords = None self . coords_attrs = None","title":" WSI"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.apply_pathologist_annotations","text":"Apply pathologist annotations to the tissue contours by intersecting the annotations with the tissue contours. Requires the tissue contours to be set for the slide beforehand with the detect_tissue method. Parameters: Name Type Description Default path str The path to the .geojson file containing the annotations extracted from QuPath. required class_name str The class name to use for selecting the annotations to apply. 'annotation' column_to_select str The column to select in the GeoDataFrame. 'objectType' Source code in src/prismtoolbox/wsicore/wsi.py 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 def apply_pathologist_annotations ( self , path : str , class_name : str = \"annotation\" , column_to_select : str = \"objectType\" , ) -> None : \"\"\"Apply pathologist annotations to the tissue contours by intersecting the annotations with the tissue contours. Requires the tissue contours to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method. Args: path: The path to the .geojson file containing the annotations extracted from QuPath. class_name: The class name to use for selecting the annotations to apply. column_to_select: The column to select in the GeoDataFrame. \"\"\" assert ( self . tissue_contours is not None ), \"No tissue contours found for the slide, please run the detect_tissue method first\" offset = ( - self . offset [ 0 ], - self . offset [ 1 ]) pathologist_annotations = read_qupath_annotations ( path , offset = offset , class_name = class_name , column_to_select = column_to_select ) polygons = contoursToPolygons ( self . tissue_contours , make_valid = True ) intersection = intersectionPolygons ( polygons , pathologist_annotations ) self . tissue_contours = PolygonsToContours ( intersection )","title":" apply_pathologist_annotations"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.convert_micrometer_to_pixel","text":"Convert a value from micrometer to pixel. Parameters: Name Type Description Default value float The value to convert (in micrometer). required level int The level at which the conversion should be performed. required axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description int The input value in pixel. Source code in src/prismtoolbox/wsicore/wsi.py 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 def convert_micrometer_to_pixel ( self , value : float , level : int , axis : str = \"x\" , ) -> int : \"\"\"Convert a value from micrometer to pixel. Args: value: The value to convert (in micrometer). level: The level at which the conversion should be performed. axis: The axis to use for getting the conversion factor (x or y). Returns: The input value in pixel. \"\"\" return int ( value / float ( self . properties [ f \" { self . engine } .mpp- { axis } \" ])) // int ( self . level_downsamples [ level ] )","title":" convert_micrometer_to_pixel"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.convert_pixel_to_micrometer","text":"Convert a value from pixel to micrometer. Parameters: Name Type Description Default value float The value to convert (in pixel). required level int The level at which the conversion should be performed. required axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description float The input value in micrometer. Source code in src/prismtoolbox/wsicore/wsi.py 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 def convert_pixel_to_micrometer ( self , value : float , level : int , axis : str = \"x\" , ) -> float : \"\"\"Convert a value from pixel to micrometer. Args: value: The value to convert (in pixel). level: The level at which the conversion should be performed. axis: The axis to use for getting the conversion factor (x or y). Returns: The input value in micrometer. \"\"\" return ( value * float ( self . properties [ f \" { self . engine } .mpp- { axis } \" ]) * self . level_downsamples [ level ] )","title":" convert_pixel_to_micrometer"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.convert_units","text":"Convert a value from one unit to another. Parameters: Name Type Description Default value float The value to convert. required level int The level at which the conversion should be performed. required from_unit str The unit to convert from (px or micro). required to_unit str The unit to convert to (px or micro). 'px' axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description int The input value converted in the desired unit. Source code in src/prismtoolbox/wsicore/wsi.py 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 def convert_units ( self , value : float , level : int , from_unit : str , to_unit : str = \"px\" , axis : str = \"x\" , ) -> int : \"\"\"Convert a value from one unit to another. Args: value: The value to convert. level: The level at which the conversion should be performed. from_unit: The unit to convert from (px or micro). to_unit: The unit to convert to (px or micro). axis: The axis to use for getting the conversion factor (x or y). Returns: The input value converted in the desired unit. \"\"\" if from_unit == \"micro\" and to_unit == \"px\" : value = self . convert_micrometer_to_pixel ( value , level , axis ) elif from_unit == \"px\" and to_unit == \"micro\" : value = self . convert_pixel_to_micrometer ( value , level , axis ) elif from_unit == to_unit : pass else : raise ValueError ( f \"Conversion from { from_unit } to { to_unit } not supported\" ) return value","title":" convert_units"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.create_thumbnail","text":"Create a thumbnail of the slide at a given level. Parameters: Name Type Description Default level int The level at which the thumbnail should be created. required crop_roi bool A boolean to crop the thumbnail to the region of interest defined for the slide (requires a ROI to be set for the slide beforehand with the set_roi method) False Returns: Type Description Image A thumbnail of the slide as a PIL image. Source code in src/prismtoolbox/wsicore/wsi.py 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 def create_thumbnail ( self , level : int , crop_roi : bool = False , ) -> Image . Image : \"\"\"Create a thumbnail of the slide at a given level. Args: level: The level at which the thumbnail should be created. crop_roi: A boolean to crop the thumbnail to the region of interest defined for the slide (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method) Returns: A thumbnail of the slide as a PIL image. \"\"\" assert self . ROI is not None if crop_roi else True , ( \"No ROI provided, while crop_roi is set to True. Please set a ROI for \" \"the slide or set crop_roi to False.\" ) thumb = self . read_region (( 0 , 0 ), level , self . level_dimensions [ level ]) . convert ( \"RGB\" ) if crop_roi : log . info ( f \"Creating thumbnail with ROI { self . ROI } .\" ) coords_roi = ( self . ROI / self . level_downsamples [ level ]) . astype ( int ) thumb = thumb . crop ( coords_roi ) return thumb","title":" create_thumbnail"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.detect_tissue","text":"Segment the tissue on the slide based on a threshold on the Law's texture energy spot map and floodfill algorithm to fill the holes in the mask. The tissue contours are stored in the tissue_contours attribute. Parameters: Name Type Description Default seg_level int The level at which the segmentation should be performed. required window_avg int The size of the window for local averaging. required window_eng int The size of the window for Law's texture energy computation. required thresh int The threshold for binarization on the Law's texture energy spot map. required inside_roi bool Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide beforehand with the set_roi method). False inv_thresh bool Set to True to invert the thresholding. False area_min float The minimum area for a contour to be kept. 200000.0 start tuple [ int , int ] The starting point for the floodfill algorithm (should be left at (0, 0) in most cases). (0, 0) Source code in src/prismtoolbox/wsicore/wsi.py 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 def detect_tissue ( self , seg_level : int , window_avg : int , window_eng : int , thresh : int , inside_roi : bool = False , inv_thresh : bool = False , area_min : float = 2e5 , start : tuple [ int , int ] = ( 0 , 0 ), ) -> None : \"\"\"Segment the tissue on the slide based on a threshold on the Law's texture energy spot map and floodfill algorithm to fill the holes in the mask. The tissue contours are stored in the tissue_contours attribute. Args: seg_level: The level at which the segmentation should be performed. window_avg: The size of the window for local averaging. window_eng: The size of the window for Law's texture energy computation. thresh: The threshold for binarization on the Law's texture energy spot map. inside_roi: Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). inv_thresh: Set to True to invert the thresholding. area_min: The minimum area for a contour to be kept. start: The starting point for the floodfill algorithm (should be left at (0, 0) in most cases). \"\"\" final_contours = [] img = np . array ( self . create_thumbnail ( seg_level , inside_roi )) img_avg = local_average ( np . asarray ( img ), window_avg ) law_feats = compute_law_feats ( img_avg , window_eng )[ 0 ] filterred_img = apply_bilateral_filter ( np . clip ( law_feats , 0 , 255 ) . astype ( \"uint8\" )) threshed_img = apply_binary_thresh ( filterred_img , thresh , inv_thresh ) flooded_img = floodfill_img ( np . pad ( threshed_img , 1 ), start ) contours = contour_mask ( flooded_img ) for contour in contours : c = contour . copy () area = cv2 . contourArea ( c ) if area > area_min : final_contours . append ( contour ) if len ( final_contours ) == 0 : self . tissue_contours = [] print ( f \"No contours found for slide { self . slide_name } .\" ) return else : scale = self . level_downsamples [ seg_level ] offset = np . array ( self . ROI [: 2 ]) if self . ROI is not None else np . array ([ 0 , 0 ]) final_contours = self . scale_contours ( final_contours , scale ) final_contours = [ cont + offset for cont in final_contours ] # Sanity check to ensure that the contours are all within a ROI is provided if self . ROI is not None : assert all ( [ np . all ( cont >= self . ROI [: 2 ]) and np . all ( cont <= self . ROI [ 2 :]) for cont in final_contours ] ) self . tissue_contours = final_contours print ( f \"Identified { len ( final_contours ) } contours for slide { self . slide_name } .\" ) return","title":" detect_tissue"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.extract_patches","text":"Extract valid patches from the slide with different extraction modes. A patch is considered valid if it is not black or white and is within the region of interest or the tissue contours if relevant. The extracted patches are stored as coordinates in the coords attribute, and the attributes of the coordinates are stored in the coords_attrs attribute. Parameters: Name Type Description Default patch_size float The size of the patches to extract (assumed to be square). required patch_level int The level at which the patches should be extracted. required mode str The mode to use for the extraction: \"contours\" mode extracts patches within the tissue contours (requires the tissue contours to be set for the slide beforehand with the detect_tissue method). \"roi\" mode extracts patches within the region of interest (requires the ROI to be set for the slide beforehand with the set_roi method). \"all\" mode extracts patches from the entire slide required step_size float | None The step size for the sliding window (if set to None, the step size will be computed based on the overlap). None overlap float | None The overlap between patches as an absolute value (must be provided if step_size is set to None). None units tuple [ str , str ] The units for the patch size and step size/overlap values (pixels: px, micrometers: micro). ('px', 'px') use_padding bool Set to True to use padding for the extraction. True contours_mode str | None The mode to use for the contour checking function (must be provided if mode is set to contours). See IsInContour for more details. None rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Source code in src/prismtoolbox/wsicore/wsi.py 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 def extract_patches ( self , patch_size : float , patch_level : int , mode : str , step_size : float | None = None , overlap : float | None = None , units : tuple [ str , str ] = ( \"px\" , \"px\" ), use_padding : bool = True , contours_mode : str | None = None , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> None : \"\"\"Extract valid patches from the slide with different extraction modes. A patch is considered valid if it is not black or white and is within the region of interest or the tissue contours if relevant. The extracted patches are stored as coordinates in the coords attribute, and the attributes of the coordinates are stored in the coords_attrs attribute. Args: patch_size: The size of the patches to extract (assumed to be square). patch_level: The level at which the patches should be extracted. mode: The mode to use for the extraction: - \"contours\" mode extracts patches within the tissue contours (requires the tissue contours to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method). - \"roi\" mode extracts patches within the region of interest (requires the ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). - \"all\" mode extracts patches from the entire slide step_size: The step size for the sliding window (if set to None, the step size will be computed based on the overlap). overlap: The overlap between patches as an absolute value (must be provided if step_size is set to None). units: The units for the patch size and step size/overlap values (pixels: px, micrometers: micro). use_padding: Set to True to use padding for the extraction. contours_mode: The mode to use for the contour checking function (must be provided if mode is set to contours). See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. \"\"\" assert ( step_size is not None or overlap is not None ), \"Either step_size or overlap must be provided\" assert all ( [ unit in [ \"micro\" , \"px\" ] for unit in units ] ), \"Units must be either 'micro' or 'px'\" patch_size = self . convert_units ( patch_size , patch_level , units [ 0 ]) if step_size is None : step_size = patch_size - self . convert_units ( overlap , patch_level , units [ 1 ]) else : step_size = self . convert_units ( step_size , patch_level , units [ 1 ]) log . info ( f \"Extracting patches of size { patch_size } at level { patch_level } with step size { step_size } .\" ) if mode == \"contours\" : log . info ( \"Extracting patches with 'contours' mode.\" ) assert self . tissue_contours is not None assert len ( self . tissue_contours ) > 0 assert contours_mode is not None valid_coords = [] for cont in self . tissue_contours : roi_dim = cv2 . boundingRect ( cont ) log . info ( f \"Processing ROI of dimensions: { roi_dim } \" ) valid_coords . extend ( self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , cont , contours_mode , rgb_threshs = rgb_threshs , percentages = percentages , ) ) valid_coords = np . array ( valid_coords ) elif mode == \"roi\" : log . info ( \"Extracting patches with 'roi' mode.\" ) assert self . ROI is not None roi_dim = self . ROI [ 0 ], self . ROI [ 1 ], self . ROI_width , self . ROI_height log . info ( \"Processing ROI of dimensions:\" , roi_dim ) valid_coords = self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , rgb_threshs = rgb_threshs , percentages = percentages , ) elif mode == \"all\" : roi_dim = 0 , 0 , self . level_dimensions [ 0 ][ 0 ], self . level_dimensions [ 0 ][ 1 ] log . info ( \"Processing ROI of dimensions:\" , roi_dim ) valid_coords = self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , rgb_threshs = rgb_threshs , percentages = percentages , ) else : raise ValueError ( f \"Mode { mode } not supported\" ) attr = { \"patch_size\" : patch_size , \"patch_level\" : patch_level , \"downsample\" : self . level_downsamples [ patch_level ], \"downsampled_level_dim\" : tuple ( np . array ( self . level_dimensions [ patch_level ])), \"level_dim\" : self . level_dimensions [ patch_level ], \"name\" : self . slide_name , } if len ( valid_coords ) == 0 : log . warning ( f \"No valid coordinates found for slide { self . slide_name } .\" ) else : print ( f \"Identified a total of { len ( valid_coords ) } valid coordinates in the slide { self . slide_name } .\" ) self . coords = valid_coords self . coords_attrs = attr","title":" extract_patches"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.extract_patches_roi","text":"Extract valid patches from a region of interest, i.e if the patch is not black or white and is within the region of interest/contours if relevant). Parameters: Name Type Description Default patch_level int The level at which the patches should be extracted. required patch_size int The size of the patches to extract (assumed to be square). required step_size int | None The step size to use for the sliding window. None roi_dim tuple [ int , int , int , int ] | None The top-left corner coordinates and dimensions of the region of interest. Must be provided if coord_candidates is set to None. None use_padding bool Set to True to use padding for the extraction. True contour ndarray | None The tissue contour to use for the extraction. If set to None, will not check if patches are within a contour. None contours_mode str | None The mode for the contour checking function. See IsInContour for more details. Must be provided if mode is set to contours. Otherwise, will not check if patches are within the contours. None coord_candidates ndarray | None Precomputed candidate coordinates for the patches. If set to None, will compute the candidate coordinates based on the ROI dimensions and the step size. None rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) return_indices bool Set to True to return the indices of the valid coordinates. False Returns: Type Description ndarray An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches). Source code in src/prismtoolbox/wsicore/wsi.py 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 def extract_patches_roi ( self , patch_level : int , patch_size : int , step_size : int | None = None , roi_dim : tuple [ int , int , int , int ] | None = None , use_padding : bool = True , contour : np . ndarray | None = None , contours_mode : str | None = None , coord_candidates : np . ndarray | None = None , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), return_indices : bool = False , ) -> np . ndarray : \"\"\"Extract valid patches from a region of interest, i.e if the patch is not black or white and is within the region of interest/contours if relevant). Args: patch_level: The level at which the patches should be extracted. patch_size: The size of the patches to extract (assumed to be square). step_size: The step size to use for the sliding window. roi_dim: The top-left corner coordinates and dimensions of the region of interest. Must be provided if coord_candidates is set to None. use_padding: Set to True to use padding for the extraction. contour: The tissue contour to use for the extraction. If set to None, will not check if patches are within a contour. contours_mode: The mode for the contour checking function. See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. Must be provided if mode is set to contours. Otherwise, will not check if patches are within the contours. coord_candidates: Precomputed candidate coordinates for the patches. If set to None, will compute the candidate coordinates based on the ROI dimensions and the step size. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. return_indices: Set to True to return the indices of the valid coordinates. Returns: An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches). \"\"\" assert contours_mode is not None if contour is not None else True , ( \"A contour mode must be provided if patch \" \"extraction mode is set to contours\" ) assert ( roi_dim is not None and step_size is not None ) or coord_candidates is not None , ( \"Either (roi_dim and step_size) or coord_candidates must be provided\" ) patch_downsample = int ( self . level_downsamples [ patch_level ]) ref_patch_size = patch_size * patch_downsample if coord_candidates is None : start_x , start_y , w , h = roi_dim img_w , img_h = self . level_dimensions [ 0 ] if use_padding : stop_y = start_y + h stop_x = start_x + w else : stop_y = min ( start_y + h , img_h - ref_patch_size + 1 ) stop_x = min ( start_x + w , img_w - ref_patch_size + 1 ) step_size = step_size * patch_downsample x_range = np . arange ( start_x , stop_x , step = step_size ) y_range = np . arange ( start_y , stop_y , step = step_size ) x_coords , y_coords = np . meshgrid ( x_range , y_range , indexing = \"ij\" ) coord_candidates = np . array ( [ x_coords . flatten (), y_coords . flatten ()] ) . transpose () if contour is not None : cont_check_fn = IsInContour ( contour , patch_size = ref_patch_size , center_shift = 0.5 , mode = contours_mode ) log . info ( f \"Extracting patches with contour checking function mode { contours_mode } .\" ) else : cont_check_fn = None num_workers = mp . cpu_count () pool = mp . Pool ( num_workers , initializer = WSI . worker_init , initargs = ( self . slide_path , self . engine , ), ) iterable = [ ( coord , cont_check_fn , patch_level , patch_size , rgb_threshs , percentages ) for coord in coord_candidates ] valid_coords = pool . starmap ( WSI . process_coord_candidate , iterable ) pool . close () valid_indices = [ i for i , coord in enumerate ( valid_coords ) if coord is not None ] valid_coords = np . array ([ coord_candidates [ i ] for i in valid_indices ]) log . info ( f \"Identified { len ( valid_coords ) } valid coordinates in the ROI { roi_dim } .\" ) if return_indices : return valid_coords , valid_indices else : return valid_coords","title":" extract_patches_roi"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.is_black_white","text":"Check if a patch is black or white. Parameters: Name Type Description Default patch Image The input patch. required rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Returns: Type Description bool True if the patch is black or white, False otherwise. Source code in src/prismtoolbox/wsicore/wsi.py 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 @staticmethod def is_black_white ( patch : Image . Image , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> bool : \"\"\"Check if a patch is black or white. Args: patch: The input patch. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. Returns: True if the patch is black or white, False otherwise. \"\"\" return isBlackPatch ( patch , rgb_thresh = rgb_threshs [ 0 ], percentage = percentages [ 0 ] ) or isWhitePatch ( patch , rgb_thresh = rgb_threshs [ 1 ], percentage = percentages [ 1 ])","title":" is_black_white"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.load_patches","text":"Load the patches from a hdf5 file. Parameters: Name Type Description Default file_path str The path to the hdf5 file containing the patches. required Source code in src/prismtoolbox/wsicore/wsi.py 449 450 451 452 453 454 455 456 def load_patches ( self , file_path : str ) -> None : \"\"\"Load the patches from a hdf5 file. Args: file_path: The path to the hdf5 file containing the patches. \"\"\" log . info ( f \"Loading patches for slide { self . slide_name } from { file_path } .\" ) self . coords , self . coords_attrs = read_h5_file ( file_path , \"coords\" )","title":" load_patches"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.load_tissue_contours","text":"Load the tissue contours from a pickle file. Parameters: Name Type Description Default file_path str The path to the pickle file containing the tissue contours. required Source code in src/prismtoolbox/wsicore/wsi.py 366 367 368 369 370 371 372 373 def load_tissue_contours ( self , file_path : str ) -> None : \"\"\"Load the tissue contours from a pickle file. Args: file_path: The path to the pickle file containing the tissue contours. \"\"\" log . info ( f \"Loading tissue contours for slide { self . slide_name } from { file_path } .\" ) self . tissue_contours = load_obj_with_pickle ( file_path )","title":" load_tissue_contours"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.process_coord_candidate","text":"Determine if a candidate coordinate is valid based on a contour checking function and/or black/white thresholding. Parameters: Name Type Description Default coord tuple [ int , int ] The candidate coordinate. required cont_check_fn IsInContour | None The contour checking function. required patch_level int The level at which the patch should be extracted. required patch_size int The size of the patch (assumed to be square). required rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Returns: Type Description tuple [ int , int ] | None The coordinate if it is valid, None otherwise. Source code in src/prismtoolbox/wsicore/wsi.py 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 @staticmethod def process_coord_candidate ( coord : tuple [ int , int ], cont_check_fn : IsInContour | None , patch_level : int , patch_size : int , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> tuple [ int , int ] | None : \"\"\"Determine if a candidate coordinate is valid based on a contour checking function and/or black/white thresholding. Args: coord: The candidate coordinate. cont_check_fn: The contour checking function. patch_level: The level at which the patch should be extracted. patch_size: The size of the patch (assumed to be square). rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. Returns: The coordinate if it is valid, None otherwise. \"\"\" if cont_check_fn is None or cont_check_fn ( coord ): patch = wsi . read_region ( tuple ( coord ), patch_level , ( patch_size , patch_size ) ) . convert ( \"RGB\" ) if not WSI . is_black_white ( patch , rgb_threshs , percentages ): return coord else : return None else : return None","title":" process_coord_candidate"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.read","text":"Read a slide with a given engine. Parameters: Name Type Description Default slide_path str The path to the slide. required engine str The backend library to use for reading the slide (currently only openslide and tiffslide are supported). required Returns: Type Description A slide object. Source code in src/prismtoolbox/wsicore/wsi.py 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 @staticmethod def read ( slide_path : str , engine : str ): \"\"\"Read a slide with a given engine. Args: slide_path: The path to the slide. engine: The backend library to use for reading the slide (currently only openslide and tiffslide are supported). Returns: A slide object. \"\"\" if engine == \"openslide\" : import openslide slide = openslide . OpenSlide ( slide_path ) elif engine == \"tiffslide\" : import tiffslide with warnings . catch_warnings (): warnings . simplefilter ( \"ignore\" , category = UserWarning ) slide = tiffslide . TiffSlide ( slide_path ) else : raise NotImplementedError ( f \"engine { engine } not supported\" ) return slide","title":" read"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.read_region","text":"Read a region from the slide for a given level and size. Parameters: Name Type Description Default location tuple [ int , int ] The coordinates of the top left corner of the region (in pixels). required level int The level at which the region should be read. required size tuple [ int , int ] The size of the region (in pixels). required Returns: Type Description Image The desired region of the slide as a PIL image. Source code in src/prismtoolbox/wsicore/wsi.py 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 def read_region ( self , location : tuple [ int , int ], level : int , size : tuple [ int , int ], ) -> Image . Image : \"\"\"Read a region from the slide for a given level and size. Args: location: The coordinates of the top left corner of the region (in pixels). level: The level at which the region should be read. size: The size of the region (in pixels). Returns: The desired region of the slide as a PIL image. \"\"\" return self . slide . read_region ( location , level , size ) . convert ( \"RGB\" )","title":" read_region"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.retrieve_slide_name_ext","text":"Retrieves slide name and slide extension from slide path. Parameters: Name Type Description Default slide_path str The path to the slide. required Returns: Type Description tuple [ str , str ] A tuple (slide name, slide ext). Source code in src/prismtoolbox/wsicore/wsi.py 109 110 111 112 113 114 115 116 117 118 119 120 121 @staticmethod def retrieve_slide_name_ext ( slide_path : str ) -> tuple [ str , str ]: \"\"\"Retrieves slide name and slide extension from slide path. Args: slide_path: The path to the slide. Returns: A tuple (slide name, slide ext). \"\"\" slide_ext = re . search ( r \"(?<=\\.)[\\w\\.]+$\" , slide_path ) . group ( 0 ) slide_name = re . search ( r \"([^/]+?)(?=\\.[\\w\\.]+$)\" , slide_path ) . group ( 0 ) return slide_name , slide_ext","title":" retrieve_slide_name_ext"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.save_patches","text":"Save the patches in a hdf5 or geojson file. Parameters: Name Type Description Default save_dir str The path to the directory where the patches will be saved. required file_format str The format for the saving (h5 for python processing, geojson for QuPath processing). 'h5' selected_idx ndarray | None An array of indices of the patches to save (if set to None, all the patches will be saved). None merge bool Set to True to merge the patches into a single polygon (for geojson format only). False label str | None An optional label to assign to the patches (for geojson format only). None color tuple [ int , int , int ] An optional color to assign to the patches (for geojson format only). (255, 0, 0) append_to_existing_file bool Set to True to append the patches to an existing geojson file. False Source code in src/prismtoolbox/wsicore/wsi.py 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 def save_patches ( self , save_dir : str , file_format : str = \"h5\" , selected_idx : np . ndarray | None = None , merge : bool = False , label : str | None = None , color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), append_to_existing_file : bool = False , ) -> None : \"\"\"Save the patches in a hdf5 or geojson file. Args: save_dir: The path to the directory where the patches will be saved. file_format: The format for the saving (h5 for python processing, geojson for QuPath processing). selected_idx: An array of indices of the patches to save (if set to None, all the patches will be saved). merge: Set to True to merge the patches into a single polygon (for geojson format only). label: An optional label to assign to the patches (for geojson format only). color: An optional color to assign to the patches (for geojson format only). append_to_existing_file: Set to True to append the patches to an existing geojson file. \"\"\" if selected_idx is not None : coords = self . coords [ selected_idx ] else : coords = self . coords if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if file_format == \"h5\" : asset_dict = { \"coords\" : self . coords } attr_dict = { \"coords\" : self . coords_attrs } file_path = os . path . join ( save_dir , f \" { self . slide_name } .h5\" ) log . info ( f \"Saving patches for slide { self . slide_name } at { file_path } with hdf5.\" ) save_patches_with_hdf5 ( file_path , asset_dict , attr_dict ) elif file_format == \"geojson\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .geojson\" ) patch_downsample = int ( self . level_downsamples [ self . coords_attrs [ \"patch_level\" ]] ) polygons = patchesToPolygons ( coords , self . coords_attrs [ \"patch_size\" ], patch_downsample , merge ) log . info ( f \"Saving { len ( coords ) } patches for slide { self . slide_name } at { file_path } with geojson.\" ) export_polygons_to_qupath ( polygons , file_path , \"annotation\" , offset = self . offset , label = label , color = color , append_to_existing_file = append_to_existing_file , ) elif file_format == \"jpg\" or file_format == \"png\" : save_dir = os . path . join ( save_dir , self . slide_name ) if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) log . info ( f \"Saving { len ( coords ) } patches for slide { self . slide_name } at { save_dir } with { file_format } .\" ) for coord in coords : patch = self . read_region ( coord , self . coords_attrs [ \"patch_level\" ], ( self . coords_attrs [ \"patch_size\" ], self . coords_attrs [ \"patch_size\" ]), ) . convert ( \"RGB\" ) patch . save ( os . path . join ( save_dir , f \" { coord [ 0 ] } _ { coord [ 1 ] } . { file_format } \" )) else : raise ValueError ( f \"format { file_format } not supported\" )","title":" save_patches"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.save_tissue_contours","text":"Save the tissue contours in a pickle or geojson file. Parameters: Name Type Description Default save_dir str The path to the directory where the contours will be saved. required selected_idx ndarray | None An array of indices of the contours to save (if set to None, all the contours will be saved). None file_format str The file format for saving the contours (pickle for python processing, geojson for QuPath processing). 'pickle' merge bool Set to True to merge the contours into a single polygon (for geojson format only). False label str | None An optional label to assign to the tissue contours (for geojson format only). None color tuple [ int , int , int ] An optional color to assign to the tissue contours (for geojson format only). (255, 0, 0) append_to_existing_file bool Set to True to append the contours to an existing geojson file. False make_valid bool Set to True to make the polygons valid (for geojson format only). False Source code in src/prismtoolbox/wsicore/wsi.py 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 def save_tissue_contours ( self , save_dir : str , selected_idx : np . ndarray | None = None , file_format : str = \"pickle\" , merge : bool = False , label : str | None = None , color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), append_to_existing_file : bool = False , make_valid : bool = False , ) -> None : \"\"\"Save the tissue contours in a pickle or geojson file. Args: save_dir: The path to the directory where the contours will be saved. selected_idx: An array of indices of the contours to save (if set to None, all the contours will be saved). file_format: The file format for saving the contours (pickle for python processing, geojson for QuPath processing). merge: Set to True to merge the contours into a single polygon (for geojson format only). label: An optional label to assign to the tissue contours (for geojson format only). color: An optional color to assign to the tissue contours (for geojson format only). append_to_existing_file: Set to True to append the contours to an existing geojson file. make_valid: Set to True to make the polygons valid (for geojson format only). \"\"\" assert self . tissue_contours is not None , ( \"No tissue contours found for the slide, \" \"please run the detect_tissue method first\" ) if selected_idx is not None : tissue_contours = [ self . tissue_contours [ idx ] for idx in selected_idx ] else : tissue_contours = self . tissue_contours if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if file_format == \"pickle\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .pkl\" ) log . info ( f \"Saving tissue contours for slide { self . slide_name } at { file_path } with pickle.\" ) save_obj_with_pickle ( tissue_contours , file_path ) elif file_format == \"geojson\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .geojson\" ) log . info ( f \"Saving { selected_idx } tissue contours for slide { self . slide_name } at { file_path } with geojson.\" ) polygons = contoursToPolygons ( tissue_contours , merge , make_valid ) export_polygons_to_qupath ( polygons , file_path , \"annotation\" , offset = self . offset , label = label , color = color , append_to_existing_file = append_to_existing_file , ) else : raise ValueError ( f \"format { file_format } not supported\" )","title":" save_tissue_contours"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.scale_contours","text":"Scale the contours by a given factor. Parameters: Name Type Description Default contours list [ ndarray ] The contours to scale. required scale int The scale factor to apply. required Returns: Type Description list [ ndarray ] The input list with scaled contours. Source code in src/prismtoolbox/wsicore/wsi.py 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 @staticmethod def scale_contours ( contours : list [ np . ndarray ], scale : int , ) -> list [ np . ndarray ]: \"\"\"Scale the contours by a given factor. Args: contours: The contours to scale. scale: The scale factor to apply. Returns: The input list with scaled contours. \"\"\" scaled_contours = [ np . array ( cont * scale , dtype = \"int\" ) for cont in contours ] return scaled_contours","title":" scale_contours"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.set_roi","text":"Set the region of interest for the slide. Can be set manually or by selecting a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute. Parameters: Name Type Description Default roi tuple [ int , int , int , int ] | None Set the region of interest manually as a tuple (x1, y1, x2, y2). None rois_df_path str | None The path to dataframe containing the ROIs with a slide_id column identifying the slide. None Returns: Type Description None The region of interest set for the slide as a tuple (x1, y1, x2, y2). Source code in src/prismtoolbox/wsicore/wsi.py 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 def set_roi ( self , roi : tuple [ int , int , int , int ] | None = None , rois_df_path : str | None = None , ) -> None : \"\"\"Set the region of interest for the slide. Can be set manually or by selecting a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute. Args: roi: Set the region of interest manually as a tuple (x1, y1, x2, y2). rois_df_path: The path to dataframe containing the ROIs with a slide_id column identifying the slide. Returns: The region of interest set for the slide as a tuple (x1, y1, x2, y2). \"\"\" if roi is not None : ROI = np . array ( roi ) . astype ( int ) elif rois_df_path is not None : rois_df = pd . read_csv ( rois_df_path ) ROI = rois_df [ rois_df . slide_id == self . slide_name ] . values [ 0 ] . astype ( int ) else : log . info ( \"No ROI provided, prompting user to select one.\" ) level = input ( f \"No ROI was provided, please select a level at which the ROI should be created (max level: { len ( self . level_dimensions ) - 1 } ): \" ) if not level : log . warning ( \"No level provided, setting the ROI at the highest level.\" ) level = len ( self . level_downsamples ) - 1 else : level = int ( level ) img = np . array ( self . create_thumbnail ( level )) ROI = select_roi_on_thumbnail ( img , int ( self . level_downsamples [ level ])) ROI = ( ROI * self . level_downsamples [ level ]) . astype ( int ) self . ROI = ROI self . ROI_width = ROI [ 2 ] - ROI [ 0 ] self . ROI_height = ROI [ 3 ] - ROI [ 1 ] print ( f \"ROI for slide { self . slide_name } has been set to { self . ROI } .\" ) return ROI","title":" set_roi"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.set_slide_attributes","text":"Set the slide attributes. Source code in src/prismtoolbox/wsicore/wsi.py 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 def set_slide_attributes ( self ): \"\"\"Set the slide attributes.\"\"\" if self . engine == \"openslide\" : self . dimensions = self . slide . dimensions self . level_dimensions = self . slide . level_dimensions self . level_downsamples = self . slide . level_downsamples self . properties = self . slide . properties elif self . engine == \"tiffslide\" : self . dimensions = self . slide . dimensions self . level_dimensions = self . slide . level_dimensions self . level_downsamples = self . slide . level_downsamples self . properties = self . slide . properties else : raise NotImplementedError ( f \"engine { self . engine } not supported\" ) if ( f \" { self . engine } .bounds-x\" in self . properties . keys () and self . properties [ f \" { self . engine } .bounds-x\" ] is not None ): self . offset = ( - int ( self . properties [ f \" { self . engine } .bounds-x\" ]), - int ( self . properties [ f \" { self . engine } .bounds-y\" ]), )","title":" set_slide_attributes"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.stitch","text":"Stitch the patches extracted on an image. The patches can be masked and colored depending on the mask and colors provided. Requires the coordinates of the patches to be set for the slide beforehand with the extract_patches method. Parameters: Name Type Description Default vis_level int The level at which the patches should be visualized. required selected_idx ndarray An array of indices of the patches to visualize (if set to None, all the patches will be visualized). None colors ndarray An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as they are). None alpha float Set the transparency of the colors to apply to the patches. 0.6 black_white bool Set to True to visualize a binary mask of the patches extracted. False draw_grid bool Set to True to draw a grid on the stitched patches. False crop_roi bool Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the set_roi method). False background_color tuple [ int , int , int ] The color of the background. (0, 0, 0) Returns: Type Description Image A PIL image of the stitched patches. Source code in src/prismtoolbox/wsicore/wsi.py 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 def stitch ( self , vis_level : int , selected_idx : np . ndarray = None , colors : np . ndarray = None , alpha : float = 0.6 , black_white : bool = False , draw_grid : bool = False , crop_roi : bool = False , background_color : tuple [ int , int , int ] = ( 0 , 0 , 0 ), ) -> Image . Image : \"\"\"Stitch the patches extracted on an image. The patches can be masked and colored depending on the mask and colors provided. Requires the coordinates of the patches to be set for the slide beforehand with the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method. Args: vis_level: The level at which the patches should be visualized. selected_idx: An array of indices of the patches to visualize (if set to None, all the patches will be visualized). colors: An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as they are). alpha: Set the transparency of the colors to apply to the patches. black_white: Set to True to visualize a binary mask of the patches extracted. draw_grid: Set to True to draw a grid on the stitched patches. crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). background_color: The color of the background. Returns: A PIL image of the stitched patches. \"\"\" assert self . ROI is not None if crop_roi else True , ( \"no ROI provided, while crop_roi is set to True,\" \" please set a ROI for the slide or set crop_roi to False.\" ) assert self . coords is not None , ( \"no coordinates provided for the patches to visualize, please run the \" \"extract_patches method first or load the coordinates from a file\" ) if crop_roi : w , h = int ( np . ceil ( self . ROI_width / self . level_downsamples [ vis_level ])), int ( np . ceil ( self . ROI_height / self . level_downsamples [ vis_level ]) ) else : w , h = self . level_dimensions [ vis_level ] patch_size = self . coords_attrs [ \"patch_size\" ] patch_level = self . coords_attrs [ \"patch_level\" ] patch_size = int ( patch_size * self . level_downsamples [ patch_level ]) canvas = init_image ( w , h , mask = black_white , color_bakground = background_color ) downsample_vis = self . level_downsamples [ vis_level ] idxs = np . arange ( len ( self . coords )) if selected_idx is not None : idxs = idxs [ selected_idx ] patch_size = np . ceil ( patch_size / downsample_vis ) . astype ( int ) log . info ( f \"Stitching { len ( idxs ) } patches at level { vis_level } with patch size { patch_size } , \" f \"with colors { colors is not None } .\" ) offset = self . ROI [: 2 ] if crop_roi else np . array ([ 0 , 0 ]) for idx in idxs : coord = self . coords [ idx ] coord_downsampled = np . ceil ( np . abs ( coord - offset ) / downsample_vis ) . astype ( int ) patch_size_coord = ( min ( max ( w - coord_downsampled [ 0 ], 0 ), patch_size ), min ( max ( h - coord_downsampled [ 1 ], 0 ), patch_size ), ) if any ( val == 0 for val in patch_size_coord ): continue if black_white : patch = np . ones ( patch_size_coord , dtype = \"uint8\" ) colors = None else : patch = np . array ( self . slide . read_region ( tuple ( coord ), vis_level , patch_size_coord ) . convert ( \"RGB\" ) ) if colors is not None : assert len ( colors ) == len ( idxs ), ( \"the number of colors provided must match \" \"the number of selected coordinates\" ) color = colors [ idx ] color_patch = ( np . ones (( patch_size_coord [ 1 ], patch_size_coord [ 0 ], 3 )) * color ) . astype ( \"uint8\" ) canvas [ coord_downsampled [ 1 ] : coord_downsampled [ 1 ] + patch_size_coord [ 1 ], coord_downsampled [ 0 ] : coord_downsampled [ 0 ] + patch_size_coord [ 0 ], :, ] = cv2 . addWeighted ( color_patch , alpha , patch , 1 - alpha , 0 , patch ) else : canvas [ coord_downsampled [ 1 ] : coord_downsampled [ 1 ] + patch_size_coord [ 1 ], coord_downsampled [ 0 ] : coord_downsampled [ 0 ] + patch_size_coord [ 0 ], :, ] = patch if draw_grid : cv2 . rectangle ( canvas , tuple ( np . maximum ([ 0 , 0 ], coord_downsampled - 1 )), tuple ( coord_downsampled + patch_size_coord ), ( 0 , 0 , 0 , 255 ), thickness = 2 , ) img = Image . fromarray ( canvas ) return img","title":" stitch"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.visualize","text":"Visualize the slide with or without the contours of the tissue. Parameters: Name Type Description Default vis_level int The level at which the visualization should be performed. required crop_roi bool Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the set_roi method). False contours_color tuple [ int , int , int ] The color to use for the contours. (255, 0, 0) line_thickness int The thickness to use for the contours 500 max_size int | None The maximum size for the visualization for the width or height of the image. None number_contours bool Set to True to number the contours. False black_white bool Set to True to visualize a binary mask of the contoured tissue. False view_slide_only bool Set to True to visualize the slide only (without the contours). False Returns: Type Description Image A PIL image of the visualization. Source code in src/prismtoolbox/wsicore/wsi.py 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 def visualize ( self , vis_level : int , crop_roi : bool = False , contours_color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), line_thickness : int = 500 , max_size : int | None = None , number_contours : bool = False , black_white : bool = False , view_slide_only : bool = False , ) -> Image . Image : \"\"\"Visualize the slide with or without the contours of the tissue. Args: vis_level: The level at which the visualization should be performed. crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). contours_color: The color to use for the contours. line_thickness: The thickness to use for the contours max_size: The maximum size for the visualization for the width or height of the image. number_contours: Set to True to number the contours. black_white: Set to True to visualize a binary mask of the contoured tissue. view_slide_only: Set to True to visualize the slide only (without the contours). Returns: A PIL image of the visualization. \"\"\" assert line_thickness > 0 , \"line_thickness must be greater than 0\" scale = 1 / self . level_downsamples [ vis_level ] if black_white : img = np . zeros_like ( self . create_thumbnail ( vis_level , crop_roi ), dtype = \"uint8\" ) line_thickness = - 1 contours_color = ( 1 , 1 , 1 ) else : img = np . array ( self . create_thumbnail ( vis_level , crop_roi )) line_thickness = int ( line_thickness * scale ) if not view_slide_only : assert ( len ( self . tissue_contours ) > 0 ), \"No tissue contours found for the slide, please run the detect_tissue method first\" offset = self . ROI [: 2 ] if crop_roi else np . array ([ 0 , 0 ]) contours = [ cont - offset for cont in self . tissue_contours ] contours = self . scale_contours ( contours , scale ) if len ( contours ) > 0 : if not number_contours : cv2 . drawContours ( img , contours , - 1 , contours_color , line_thickness , lineType = cv2 . LINE_8 , ) else : # add numbering to each contour for idx , cont in enumerate ( contours ): M = cv2 . moments ( cont ) cX = int ( M [ \"m10\" ] / ( M [ \"m00\" ] + 1e-9 )) cY = int ( M [ \"m01\" ] / ( M [ \"m00\" ] + 1e-9 )) # draw the contour and put text next to center cv2 . drawContours ( img , [ cont ], - 1 , contours_color , line_thickness , lineType = cv2 . LINE_8 , ) cv2 . putText ( img , \" {} \" . format ( idx ), ( cX , cY ), cv2 . FONT_HERSHEY_SIMPLEX , 2 , ( 255 , 0 , 0 ), 10 , ) img = Image . fromarray ( img ) if black_white : img = img . convert ( \"L\" ) w , h = img . size if max_size is not None and ( w > max_size or h > max_size ): resizeFactor = max_size / w if w > h else max_size / h img = img . resize (( int ( w * resizeFactor ), int ( h * resizeFactor ))) return img","title":" visualize"},{"location":"reference/prismtoolbox/#prismtoolbox.WSI.worker_init","text":"Initialize the worker process with a wsi object. Parameters: Name Type Description Default slide_path str The path to the slide. required engine str The backend library to use for reading the slide (currently only openslide and tiffslide are supported) required Source code in src/prismtoolbox/wsicore/wsi.py 166 167 168 169 170 171 172 173 174 175 176 @staticmethod def worker_init ( slide_path : str , engine : str ) -> None : \"\"\"Initialize the worker process with a wsi object. Args: slide_path: The path to the slide. engine: The backend library to use for reading the slide (currently only openslide and tiffslide are supported) \"\"\" global wsi wsi = WSI . read ( slide_path , engine )","title":" worker_init"},{"location":"reference/prismtoolbox/nucleiseg/","text":"","title":"Index"},{"location":"reference/prismtoolbox/nucleiseg/seg_utils/","text":"solve_conflicts ( cells , threshold = 0.5 , return_indices = False ) Resolve segmentation conflicts (i.e. overlap) after running segmentation on patches Parameters: Name Type Description Default cells list [ Polygon ] List of cell polygons required threshold float When two cells are overlapping, we look at the area of intersection over the area of the smallest cell. If this value is higher than the threshold , the cells are merged 0.5 return_indices bool If True , returns also the cells indices. Merged cells have an index of -1. False Returns: Type Description ndarray [ Polygon ] | tuple [ ndarray [ Polygon ], ndarray ] Array of resolved cells polygons. If return_indices , it also returns an array of cell indices. Source code in src/prismtoolbox/nucleiseg/seg_utils.py 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def solve_conflicts ( cells : list [ Polygon ], threshold : float = 0.5 , return_indices : bool = False , ) -> np . ndarray [ Polygon ] | tuple [ np . ndarray [ Polygon ], np . ndarray ]: \"\"\"Resolve segmentation conflicts (i.e. overlap) after running segmentation on patches Args: cells: List of cell polygons threshold: When two cells are overlapping, we look at the area of intersection over the area of the smallest cell. If this value is higher than the `threshold`, the cells are merged return_indices: If `True`, returns also the cells indices. Merged cells have an index of -1. Returns: Array of resolved cells polygons. If `return_indices`, it also returns an array of cell indices. \"\"\" cells = list ( cells ) n_cells = len ( cells ) resolved_indices = np . arange ( n_cells ) assert n_cells > 0 , \"No cells was segmented, cannot continue\" tree = shapely . STRtree ( cells ) conflicts = tree . query ( cells , predicate = \"intersects\" ) conflicts = conflicts [:, conflicts [ 0 ] != conflicts [ 1 ]] . T conflicts = np . array ([ c for c in conflicts if c [ 0 ] < c [ 1 ]]) for i1 , i2 in tqdm ( conflicts , desc = \"Resolving conflicts\" ): resolved_i1 = resolved_indices [ i1 ] resolved_i2 = resolved_indices [ i2 ] cell1 , cell2 = cells [ resolved_i1 ], cells [ resolved_i2 ] intersection = cell1 . intersection ( cell2 ) . area if intersection >= threshold * min ( cell1 . area , cell2 . area ): cell = cell1 | cell2 resolved_indices [ np . isin ( resolved_indices , [ resolved_i1 , resolved_i2 ])] = len ( cells ) cells . append ( cell ) unique_indices = np . unique ( resolved_indices ) unique_cells = MultiPolygon ( list ( np . array ( cells )[ unique_indices ])) if return_indices : return unique_cells , np . where ( unique_indices < n_cells , unique_indices , - 1 ) return unique_cells","title":"Seg utils"},{"location":"reference/prismtoolbox/nucleiseg/seg_utils/#prismtoolbox.nucleiseg.seg_utils.solve_conflicts","text":"Resolve segmentation conflicts (i.e. overlap) after running segmentation on patches Parameters: Name Type Description Default cells list [ Polygon ] List of cell polygons required threshold float When two cells are overlapping, we look at the area of intersection over the area of the smallest cell. If this value is higher than the threshold , the cells are merged 0.5 return_indices bool If True , returns also the cells indices. Merged cells have an index of -1. False Returns: Type Description ndarray [ Polygon ] | tuple [ ndarray [ Polygon ], ndarray ] Array of resolved cells polygons. If return_indices , it also returns an array of cell indices. Source code in src/prismtoolbox/nucleiseg/seg_utils.py 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 def solve_conflicts ( cells : list [ Polygon ], threshold : float = 0.5 , return_indices : bool = False , ) -> np . ndarray [ Polygon ] | tuple [ np . ndarray [ Polygon ], np . ndarray ]: \"\"\"Resolve segmentation conflicts (i.e. overlap) after running segmentation on patches Args: cells: List of cell polygons threshold: When two cells are overlapping, we look at the area of intersection over the area of the smallest cell. If this value is higher than the `threshold`, the cells are merged return_indices: If `True`, returns also the cells indices. Merged cells have an index of -1. Returns: Array of resolved cells polygons. If `return_indices`, it also returns an array of cell indices. \"\"\" cells = list ( cells ) n_cells = len ( cells ) resolved_indices = np . arange ( n_cells ) assert n_cells > 0 , \"No cells was segmented, cannot continue\" tree = shapely . STRtree ( cells ) conflicts = tree . query ( cells , predicate = \"intersects\" ) conflicts = conflicts [:, conflicts [ 0 ] != conflicts [ 1 ]] . T conflicts = np . array ([ c for c in conflicts if c [ 0 ] < c [ 1 ]]) for i1 , i2 in tqdm ( conflicts , desc = \"Resolving conflicts\" ): resolved_i1 = resolved_indices [ i1 ] resolved_i2 = resolved_indices [ i2 ] cell1 , cell2 = cells [ resolved_i1 ], cells [ resolved_i2 ] intersection = cell1 . intersection ( cell2 ) . area if intersection >= threshold * min ( cell1 . area , cell2 . area ): cell = cell1 | cell2 resolved_indices [ np . isin ( resolved_indices , [ resolved_i1 , resolved_i2 ])] = len ( cells ) cells . append ( cell ) unique_indices = np . unique ( resolved_indices ) unique_cells = MultiPolygon ( list ( np . array ( cells )[ unique_indices ])) if return_indices : return unique_cells , np . where ( unique_indices < n_cells , unique_indices , - 1 ) return unique_cells","title":" solve_conflicts"},{"location":"reference/prismtoolbox/nucleiseg/segmenter/","text":"","title":"prismtoolbox.nucleiseg"},{"location":"reference/prismtoolbox/nucleiseg/models/","text":"","title":"Index"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/","text":"","title":"Index"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/","text":"ResnetBlock ( dim , padding_type , norm_layer , use_dropout , use_bias ) Bases: Module Define a Resnet block Initialize the Resnet block A resnet block is a conv block with skip connections We construct a conv block with build_conv_block function, and implement skip connections in function. Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 277 278 279 280 281 282 283 284 285 286 287 def __init__ ( self , dim , padding_type , norm_layer , use_dropout , use_bias ): \"\"\"Initialize the Resnet block A resnet block is a conv block with skip connections We construct a conv block with build_conv_block function, and implement skip connections in function. Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf \"\"\" super ( ResnetBlock , self ) . __init__ () self . conv_block = self . build_conv_block ( dim , padding_type , norm_layer , use_dropout , use_bias ) build_conv_block ( dim , padding_type , norm_layer , use_dropout , use_bias ) Construct a convolutional block. Parameters: dim (int) -- the number of channels in the conv layer. padding_type (str) -- the name of padding layer: reflect | replicate | zero norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. use_bias (bool) -- if the conv layer uses bias or not Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU)) Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 def build_conv_block ( self , dim , padding_type , norm_layer , use_dropout , use_bias ): \"\"\"Construct a convolutional block. Parameters: dim (int) -- the number of channels in the conv layer. padding_type (str) -- the name of padding layer: reflect | replicate | zero norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. use_bias (bool) -- if the conv layer uses bias or not Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU)) \"\"\" conv_block = [] p = 0 if padding_type == \"reflect\" : conv_block += [ nn . ReflectionPad2d ( 1 )] elif padding_type == \"replicate\" : conv_block += [ nn . ReplicationPad2d ( 1 )] elif padding_type == \"zero\" : p = 1 else : raise NotImplementedError ( \"padding [ %s ] is not implemented\" % padding_type ) conv_block += [ nn . Conv2d ( dim , dim , kernel_size = 3 , padding = p , bias = use_bias ), norm_layer ( dim ), nn . ReLU ( True ), ] if use_dropout : conv_block += [ nn . Dropout ( 0.5 )] p = 0 if padding_type == \"reflect\" : conv_block += [ nn . ReflectionPad2d ( 1 )] elif padding_type == \"replicate\" : conv_block += [ nn . ReplicationPad2d ( 1 )] elif padding_type == \"zero\" : p = 1 else : raise NotImplementedError ( \"padding [ %s ] is not implemented\" % padding_type ) conv_block += [ nn . Conv2d ( dim , dim , kernel_size = 3 , padding = p , bias = use_bias ), norm_layer ( dim ), ] return nn . Sequential ( * conv_block ) forward ( x ) Forward function (with skip connections) Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 334 335 336 337 def forward ( self , x ): \"\"\"Forward function (with skip connections)\"\"\" out = x + self . conv_block ( x ) # add skip connections return out ResnetGenerator ( input_nc , output_nc , ngf = 64 , norm_layer = nn . BatchNorm2d , use_dropout = False , n_blocks = 6 , padding_type = 'reflect' ) Bases: Module Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations. We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style) Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers n_blocks (int) -- the number of ResNet blocks padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 def __init__ ( self , input_nc , output_nc , ngf = 64 , norm_layer = nn . BatchNorm2d , use_dropout = False , n_blocks = 6 , padding_type = \"reflect\" , ): \"\"\"Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers n_blocks (int) -- the number of ResNet blocks padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero \"\"\" assert n_blocks >= 0 super ( ResnetGenerator , self ) . __init__ () if type ( norm_layer ) == functools . partial : use_bias = norm_layer . func == nn . InstanceNorm2d else : use_bias = norm_layer == nn . InstanceNorm2d model = [ nn . ReflectionPad2d ( 3 ), nn . Conv2d ( input_nc , ngf , kernel_size = 7 , padding = 0 , bias = use_bias ), norm_layer ( ngf ), nn . ReLU ( True ), ] n_downsampling = 2 for i in range ( n_downsampling ): # add downsampling layers mult = 2 ** i model += [ nn . Conv2d ( ngf * mult , ngf * mult * 2 , kernel_size = 3 , stride = 2 , padding = 1 , bias = use_bias , ), norm_layer ( ngf * mult * 2 ), nn . ReLU ( True ), ] mult = 2 ** n_downsampling for i in range ( n_blocks ): # add ResNet blocks model += [ ResnetBlock ( ngf * mult , padding_type = padding_type , norm_layer = norm_layer , use_dropout = use_dropout , use_bias = use_bias , ) ] for i in range ( n_downsampling ): # add upsampling layers mult = 2 ** ( n_downsampling - i ) model += [ nn . ConvTranspose2d ( ngf * mult , int ( ngf * mult / 2 ), kernel_size = 3 , stride = 2 , padding = 1 , output_padding = 1 , bias = use_bias , ), norm_layer ( int ( ngf * mult / 2 )), nn . ReLU ( True ), ] model += [ nn . ReflectionPad2d ( 3 )] model += [ nn . Conv2d ( ngf , output_nc , kernel_size = 7 , padding = 0 )] # Model += [nn.Tanh()] self . model = nn . Sequential ( * model ) forward ( input ) Standard forward Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 269 270 271 def forward ( self , input ): \"\"\"Standard forward\"\"\" return self . model ( input ) UnetGenerator ( input_nc , output_nc , num_downs , ngf = 64 , norm_layer = nn . BatchNorm2d , use_dropout = False , dropout_value = 0.5 , bias_last_conv = True ) Bases: Module Create a Unet-based generator Construct a Unet generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer We construct the U-Net from the innermost layer to the outermost layer. It is a recursive process. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 def __init__ ( self , input_nc , output_nc , num_downs , ngf = 64 , norm_layer = nn . BatchNorm2d , use_dropout = False , dropout_value = 0.5 , bias_last_conv = True , ): \"\"\"Construct a Unet generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer We construct the U-Net from the innermost layer to the outermost layer. It is a recursive process. \"\"\" super ( UnetGenerator , self ) . __init__ () # construct unet structure unet_block = UnetSkipConnectionBlock ( ngf * 8 , ngf * 8 , input_nc = None , submodule = None , norm_layer = norm_layer , innermost = True , ) # add the innermost layer for i in range ( num_downs - 5 ): # add intermediate layers with ngf * 8 filters unet_block = UnetSkipConnectionBlock ( ngf * 8 , ngf * 8 , input_nc = None , submodule = unet_block , norm_layer = norm_layer , use_dropout = use_dropout , dropout_value = dropout_value , ) # gradually reduce the number of filters from ngf * 8 to ngf unet_block = UnetSkipConnectionBlock ( ngf * 4 , ngf * 8 , input_nc = None , submodule = unet_block , norm_layer = norm_layer ) unet_block = UnetSkipConnectionBlock ( ngf * 2 , ngf * 4 , input_nc = None , submodule = unet_block , norm_layer = norm_layer ) unet_block = UnetSkipConnectionBlock ( ngf , ngf * 2 , input_nc = None , submodule = unet_block , norm_layer = norm_layer ) self . model = UnetSkipConnectionBlock ( output_nc , ngf , input_nc = input_nc , submodule = unet_block , outermost = True , norm_layer = norm_layer , bias_last_conv = bias_last_conv , ) # add the outermost layer forward ( input ) Standard forward Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 405 406 407 def forward ( self , input ): \"\"\"Standard forward\"\"\" return self . model ( input ) UnetSkipConnectionBlock ( outer_nc , inner_nc , input_nc = None , submodule = None , outermost = False , innermost = False , norm_layer = nn . BatchNorm2d , use_dropout = False , dropout_value = 0.5 , bias_last_conv = True ) Bases: Module Defines the Unet submodule with skip connection. X -------------------identity---------------------- |-- downsampling -- |submodule| -- upsampling --| Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer inner_nc (int) -- the number of filters in the inner conv layer input_nc (int) -- the number of channels in input images/features submodule (UnetSkipConnectionBlock) -- previously defined submodules outermost (bool) -- if this module is the outermost module innermost (bool) -- if this module is the innermost module norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 def __init__ ( self , outer_nc , inner_nc , input_nc = None , submodule = None , outermost = False , innermost = False , norm_layer = nn . BatchNorm2d , use_dropout = False , dropout_value = 0.5 , bias_last_conv = True , ): \"\"\"Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer inner_nc (int) -- the number of filters in the inner conv layer input_nc (int) -- the number of channels in input images/features submodule (UnetSkipConnectionBlock) -- previously defined submodules outermost (bool) -- if this module is the outermost module innermost (bool) -- if this module is the innermost module norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. \"\"\" super ( UnetSkipConnectionBlock , self ) . __init__ () self . outermost = outermost if type ( norm_layer ) == functools . partial : use_bias = norm_layer . func == nn . InstanceNorm2d else : use_bias = norm_layer == nn . InstanceNorm2d if input_nc is None : input_nc = outer_nc downconv = nn . Conv2d ( input_nc , inner_nc , kernel_size = 4 , stride = 2 , padding = 1 , bias = use_bias ) downrelu = nn . LeakyReLU ( 0.2 , True ) downnorm = norm_layer ( inner_nc ) uprelu = nn . ReLU ( True ) upnorm = norm_layer ( outer_nc ) if outermost : upconv = nn . ConvTranspose2d ( inner_nc * 2 , outer_nc , kernel_size = 4 , stride = 2 , padding = 1 , bias = bias_last_conv , ) down = [ downconv ] up = [ uprelu , upconv ] model = down + [ submodule ] + up elif innermost : upconv = nn . ConvTranspose2d ( inner_nc , outer_nc , kernel_size = 4 , stride = 2 , padding = 1 , bias = use_bias ) down = [ downrelu , downconv ] up = [ uprelu , upconv , upnorm ] model = down + up else : upconv = nn . ConvTranspose2d ( inner_nc * 2 , outer_nc , kernel_size = 4 , stride = 2 , padding = 1 , bias = use_bias ) down = [ downrelu , downconv , downnorm ] up = [ uprelu , upconv , upnorm ] if use_dropout : model = down + [ submodule ] + up + [ nn . Dropout ( dropout_value )] else : model = down + [ submodule ] + up self . model = nn . Sequential ( * model ) define_G ( input_nc , output_nc , ngf , netG , norm = 'batch' , use_dropout = False , dropout_value = 0.5 , init_type = 'normal' , init_gain = 0.02 , gpu_ids = [], bias_last_conv = True ) Create a generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128 norm (str) -- the name of normalization layers used in the network: batch | instance | none use_dropout (bool) -- if use dropout layers. init_type (str) -- the name of our initialization method. init_gain (float) -- scaling factor for normal, xavier and orthogonal. gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 Returns a generator Our current implementation provides two types of generators: U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images) The original U-Net paper: https://arxiv.org/abs/1505.04597 Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks) Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations. We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style). The generator has been initialized by . It uses RELU for non-linearity. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 def define_G ( input_nc , output_nc , ngf , netG , norm = \"batch\" , use_dropout = False , dropout_value = 0.5 , init_type = \"normal\" , init_gain = 0.02 , gpu_ids = [], bias_last_conv = True , ): \"\"\"Create a generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128 norm (str) -- the name of normalization layers used in the network: batch | instance | none use_dropout (bool) -- if use dropout layers. init_type (str) -- the name of our initialization method. init_gain (float) -- scaling factor for normal, xavier and orthogonal. gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 Returns a generator Our current implementation provides two types of generators: U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images) The original U-Net paper: https://arxiv.org/abs/1505.04597 Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks) Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations. We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style). The generator has been initialized by . It uses RELU for non-linearity. \"\"\" net = None norm_layer = get_norm_layer ( norm_type = norm ) if netG == \"resnet_9blocks\" : net = ResnetGenerator ( input_nc , output_nc , ngf , norm_layer = norm_layer , use_dropout = use_dropout , n_blocks = 9 , ) elif netG == \"resnet_6blocks\" : net = ResnetGenerator ( input_nc , output_nc , ngf , norm_layer = norm_layer , use_dropout = use_dropout , n_blocks = 6 , ) elif netG == \"unet_128\" : net = UnetGenerator ( input_nc , output_nc , 7 , ngf , norm_layer = norm_layer , use_dropout = use_dropout , dropout_value = dropout_value , bias_last_conv = bias_last_conv , ) elif netG == \"unet_256\" : net = UnetGenerator ( input_nc , output_nc , 8 , ngf , norm_layer = norm_layer , use_dropout = use_dropout , dropout_value = dropout_value , bias_last_conv = bias_last_conv , ) else : raise NotImplementedError ( \"Generator model name [ %s ] is not recognized\" % netG ) return init_net ( net , init_type , init_gain , gpu_ids ) get_norm_layer ( norm_type = 'instance' ) Return a normalization layer Parameters: norm_type (str) -- the name of the normalization layer: batch | instance | none For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def get_norm_layer ( norm_type = \"instance\" ): \"\"\"Return a normalization layer Parameters: norm_type (str) -- the name of the normalization layer: batch | instance | none For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics. \"\"\" if norm_type == \"batch\" : norm_layer = functools . partial ( nn . BatchNorm2d , affine = True , track_running_stats = True ) elif norm_type == \"instance\" : norm_layer = functools . partial ( nn . InstanceNorm2d , affine = False , track_running_stats = False ) elif norm_type == \"none\" : def norm_layer ( x ): return Identity () else : raise NotImplementedError ( \"normalization layer [ %s ] is not found\" % norm_type ) return norm_layer init_net ( net , init_type = 'normal' , init_gain = 0.02 , gpu_ids = []) Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights Parameters: net (network) -- the network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal gain (float) -- scaling factor for normal, xavier and orthogonal. gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 Return an initialized network. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 def init_net ( net , init_type = \"normal\" , init_gain = 0.02 , gpu_ids = []): \"\"\"Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights Parameters: net (network) -- the network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal gain (float) -- scaling factor for normal, xavier and orthogonal. gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 Return an initialized network. \"\"\" if len ( gpu_ids ) > 0 : assert torch . cuda . is_available () net . to ( gpu_ids [ 0 ]) net = torch . nn . DataParallel ( net , gpu_ids ) # multi-GPUs init_weights ( net , init_type , init_gain = init_gain ) return net init_weights ( net , init_type = 'normal' , init_gain = 0.02 ) Initialize network weights. Parameters: net (network) -- network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal init_gain (float) -- scaling factor for normal, xavier and orthogonal. We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might work better for some applications. Feel free to try yourself. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def init_weights ( net , init_type = \"normal\" , init_gain = 0.02 ): \"\"\"Initialize network weights. Parameters: net (network) -- network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal init_gain (float) -- scaling factor for normal, xavier and orthogonal. We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might work better for some applications. Feel free to try yourself. \"\"\" def init_func ( m ): # define the initialization function classname = m . __class__ . __name__ if hasattr ( m , \"weight\" ) and ( classname . find ( \"Conv\" ) != - 1 or classname . find ( \"Linear\" ) != - 1 ): if init_type == \"normal\" : init . normal_ ( m . weight . data , 0.0 , init_gain ) elif init_type == \"xavier\" : init . xavier_normal_ ( m . weight . data , gain = init_gain ) elif init_type == \"kaiming\" : init . kaiming_normal_ ( m . weight . data , a = 0 , mode = \"fan_in\" ) elif init_type == \"orthogonal\" : init . orthogonal_ ( m . weight . data , gain = init_gain ) else : raise NotImplementedError ( \"initialization method [ %s ] is not implemented\" % init_type ) if hasattr ( m , \"bias\" ) and m . bias is not None : init . constant_ ( m . bias . data , 0.0 ) elif ( classname . find ( \"BatchNorm2d\" ) != - 1 ): # BatchNorm Layer's weight is not a matrix; only normal distribution applies. init . normal_ ( m . weight . data , 1.0 , init_gain ) init . constant_ ( m . bias . data , 0.0 ) print ( \"initialize network with %s \" % init_type ) net . apply ( init_func ) # apply the initialization function ","title":"Architectures"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.ResnetBlock","text":"Bases: Module Define a Resnet block Initialize the Resnet block A resnet block is a conv block with skip connections We construct a conv block with build_conv_block function, and implement skip connections in function. Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 277 278 279 280 281 282 283 284 285 286 287 def __init__ ( self , dim , padding_type , norm_layer , use_dropout , use_bias ): \"\"\"Initialize the Resnet block A resnet block is a conv block with skip connections We construct a conv block with build_conv_block function, and implement skip connections in function. Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf \"\"\" super ( ResnetBlock , self ) . __init__ () self . conv_block = self . build_conv_block ( dim , padding_type , norm_layer , use_dropout , use_bias )","title":" ResnetBlock"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.ResnetBlock.build_conv_block","text":"Construct a convolutional block. Parameters: dim (int) -- the number of channels in the conv layer. padding_type (str) -- the name of padding layer: reflect | replicate | zero norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. use_bias (bool) -- if the conv layer uses bias or not Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU)) Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 def build_conv_block ( self , dim , padding_type , norm_layer , use_dropout , use_bias ): \"\"\"Construct a convolutional block. Parameters: dim (int) -- the number of channels in the conv layer. padding_type (str) -- the name of padding layer: reflect | replicate | zero norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. use_bias (bool) -- if the conv layer uses bias or not Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU)) \"\"\" conv_block = [] p = 0 if padding_type == \"reflect\" : conv_block += [ nn . ReflectionPad2d ( 1 )] elif padding_type == \"replicate\" : conv_block += [ nn . ReplicationPad2d ( 1 )] elif padding_type == \"zero\" : p = 1 else : raise NotImplementedError ( \"padding [ %s ] is not implemented\" % padding_type ) conv_block += [ nn . Conv2d ( dim , dim , kernel_size = 3 , padding = p , bias = use_bias ), norm_layer ( dim ), nn . ReLU ( True ), ] if use_dropout : conv_block += [ nn . Dropout ( 0.5 )] p = 0 if padding_type == \"reflect\" : conv_block += [ nn . ReflectionPad2d ( 1 )] elif padding_type == \"replicate\" : conv_block += [ nn . ReplicationPad2d ( 1 )] elif padding_type == \"zero\" : p = 1 else : raise NotImplementedError ( \"padding [ %s ] is not implemented\" % padding_type ) conv_block += [ nn . Conv2d ( dim , dim , kernel_size = 3 , padding = p , bias = use_bias ), norm_layer ( dim ), ] return nn . Sequential ( * conv_block )","title":" build_conv_block"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.ResnetBlock.forward","text":"Forward function (with skip connections) Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 334 335 336 337 def forward ( self , x ): \"\"\"Forward function (with skip connections)\"\"\" out = x + self . conv_block ( x ) # add skip connections return out","title":" forward"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.ResnetGenerator","text":"Bases: Module Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations. We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style) Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers n_blocks (int) -- the number of ResNet blocks padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 def __init__ ( self , input_nc , output_nc , ngf = 64 , norm_layer = nn . BatchNorm2d , use_dropout = False , n_blocks = 6 , padding_type = \"reflect\" , ): \"\"\"Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers n_blocks (int) -- the number of ResNet blocks padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero \"\"\" assert n_blocks >= 0 super ( ResnetGenerator , self ) . __init__ () if type ( norm_layer ) == functools . partial : use_bias = norm_layer . func == nn . InstanceNorm2d else : use_bias = norm_layer == nn . InstanceNorm2d model = [ nn . ReflectionPad2d ( 3 ), nn . Conv2d ( input_nc , ngf , kernel_size = 7 , padding = 0 , bias = use_bias ), norm_layer ( ngf ), nn . ReLU ( True ), ] n_downsampling = 2 for i in range ( n_downsampling ): # add downsampling layers mult = 2 ** i model += [ nn . Conv2d ( ngf * mult , ngf * mult * 2 , kernel_size = 3 , stride = 2 , padding = 1 , bias = use_bias , ), norm_layer ( ngf * mult * 2 ), nn . ReLU ( True ), ] mult = 2 ** n_downsampling for i in range ( n_blocks ): # add ResNet blocks model += [ ResnetBlock ( ngf * mult , padding_type = padding_type , norm_layer = norm_layer , use_dropout = use_dropout , use_bias = use_bias , ) ] for i in range ( n_downsampling ): # add upsampling layers mult = 2 ** ( n_downsampling - i ) model += [ nn . ConvTranspose2d ( ngf * mult , int ( ngf * mult / 2 ), kernel_size = 3 , stride = 2 , padding = 1 , output_padding = 1 , bias = use_bias , ), norm_layer ( int ( ngf * mult / 2 )), nn . ReLU ( True ), ] model += [ nn . ReflectionPad2d ( 3 )] model += [ nn . Conv2d ( ngf , output_nc , kernel_size = 7 , padding = 0 )] # Model += [nn.Tanh()] self . model = nn . Sequential ( * model )","title":" ResnetGenerator"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.ResnetGenerator.forward","text":"Standard forward Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 269 270 271 def forward ( self , input ): \"\"\"Standard forward\"\"\" return self . model ( input )","title":" forward"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.UnetGenerator","text":"Bases: Module Create a Unet-based generator Construct a Unet generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer We construct the U-Net from the innermost layer to the outermost layer. It is a recursive process. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 def __init__ ( self , input_nc , output_nc , num_downs , ngf = 64 , norm_layer = nn . BatchNorm2d , use_dropout = False , dropout_value = 0.5 , bias_last_conv = True , ): \"\"\"Construct a Unet generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, image of size 128x128 will become of size 1x1 # at the bottleneck ngf (int) -- the number of filters in the last conv layer norm_layer -- normalization layer We construct the U-Net from the innermost layer to the outermost layer. It is a recursive process. \"\"\" super ( UnetGenerator , self ) . __init__ () # construct unet structure unet_block = UnetSkipConnectionBlock ( ngf * 8 , ngf * 8 , input_nc = None , submodule = None , norm_layer = norm_layer , innermost = True , ) # add the innermost layer for i in range ( num_downs - 5 ): # add intermediate layers with ngf * 8 filters unet_block = UnetSkipConnectionBlock ( ngf * 8 , ngf * 8 , input_nc = None , submodule = unet_block , norm_layer = norm_layer , use_dropout = use_dropout , dropout_value = dropout_value , ) # gradually reduce the number of filters from ngf * 8 to ngf unet_block = UnetSkipConnectionBlock ( ngf * 4 , ngf * 8 , input_nc = None , submodule = unet_block , norm_layer = norm_layer ) unet_block = UnetSkipConnectionBlock ( ngf * 2 , ngf * 4 , input_nc = None , submodule = unet_block , norm_layer = norm_layer ) unet_block = UnetSkipConnectionBlock ( ngf , ngf * 2 , input_nc = None , submodule = unet_block , norm_layer = norm_layer ) self . model = UnetSkipConnectionBlock ( output_nc , ngf , input_nc = input_nc , submodule = unet_block , outermost = True , norm_layer = norm_layer , bias_last_conv = bias_last_conv , ) # add the outermost layer","title":" UnetGenerator"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.UnetGenerator.forward","text":"Standard forward Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 405 406 407 def forward ( self , input ): \"\"\"Standard forward\"\"\" return self . model ( input )","title":" forward"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.UnetSkipConnectionBlock","text":"Bases: Module Defines the Unet submodule with skip connection. X -------------------identity---------------------- |-- downsampling -- |submodule| -- upsampling --| Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer inner_nc (int) -- the number of filters in the inner conv layer input_nc (int) -- the number of channels in input images/features submodule (UnetSkipConnectionBlock) -- previously defined submodules outermost (bool) -- if this module is the outermost module innermost (bool) -- if this module is the innermost module norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 def __init__ ( self , outer_nc , inner_nc , input_nc = None , submodule = None , outermost = False , innermost = False , norm_layer = nn . BatchNorm2d , use_dropout = False , dropout_value = 0.5 , bias_last_conv = True , ): \"\"\"Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer inner_nc (int) -- the number of filters in the inner conv layer input_nc (int) -- the number of channels in input images/features submodule (UnetSkipConnectionBlock) -- previously defined submodules outermost (bool) -- if this module is the outermost module innermost (bool) -- if this module is the innermost module norm_layer -- normalization layer use_dropout (bool) -- if use dropout layers. \"\"\" super ( UnetSkipConnectionBlock , self ) . __init__ () self . outermost = outermost if type ( norm_layer ) == functools . partial : use_bias = norm_layer . func == nn . InstanceNorm2d else : use_bias = norm_layer == nn . InstanceNorm2d if input_nc is None : input_nc = outer_nc downconv = nn . Conv2d ( input_nc , inner_nc , kernel_size = 4 , stride = 2 , padding = 1 , bias = use_bias ) downrelu = nn . LeakyReLU ( 0.2 , True ) downnorm = norm_layer ( inner_nc ) uprelu = nn . ReLU ( True ) upnorm = norm_layer ( outer_nc ) if outermost : upconv = nn . ConvTranspose2d ( inner_nc * 2 , outer_nc , kernel_size = 4 , stride = 2 , padding = 1 , bias = bias_last_conv , ) down = [ downconv ] up = [ uprelu , upconv ] model = down + [ submodule ] + up elif innermost : upconv = nn . ConvTranspose2d ( inner_nc , outer_nc , kernel_size = 4 , stride = 2 , padding = 1 , bias = use_bias ) down = [ downrelu , downconv ] up = [ uprelu , upconv , upnorm ] model = down + up else : upconv = nn . ConvTranspose2d ( inner_nc * 2 , outer_nc , kernel_size = 4 , stride = 2 , padding = 1 , bias = use_bias ) down = [ downrelu , downconv , downnorm ] up = [ uprelu , upconv , upnorm ] if use_dropout : model = down + [ submodule ] + up + [ nn . Dropout ( dropout_value )] else : model = down + [ submodule ] + up self . model = nn . Sequential ( * model )","title":" UnetSkipConnectionBlock"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.define_G","text":"Create a generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128 norm (str) -- the name of normalization layers used in the network: batch | instance | none use_dropout (bool) -- if use dropout layers. init_type (str) -- the name of our initialization method. init_gain (float) -- scaling factor for normal, xavier and orthogonal. gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 Returns a generator Our current implementation provides two types of generators: U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images) The original U-Net paper: https://arxiv.org/abs/1505.04597 Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks) Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations. We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style). The generator has been initialized by . It uses RELU for non-linearity. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 def define_G ( input_nc , output_nc , ngf , netG , norm = \"batch\" , use_dropout = False , dropout_value = 0.5 , init_type = \"normal\" , init_gain = 0.02 , gpu_ids = [], bias_last_conv = True , ): \"\"\"Create a generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels in output images ngf (int) -- the number of filters in the last conv layer netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128 norm (str) -- the name of normalization layers used in the network: batch | instance | none use_dropout (bool) -- if use dropout layers. init_type (str) -- the name of our initialization method. init_gain (float) -- scaling factor for normal, xavier and orthogonal. gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 Returns a generator Our current implementation provides two types of generators: U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images) The original U-Net paper: https://arxiv.org/abs/1505.04597 Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks) Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations. We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style). The generator has been initialized by . It uses RELU for non-linearity. \"\"\" net = None norm_layer = get_norm_layer ( norm_type = norm ) if netG == \"resnet_9blocks\" : net = ResnetGenerator ( input_nc , output_nc , ngf , norm_layer = norm_layer , use_dropout = use_dropout , n_blocks = 9 , ) elif netG == \"resnet_6blocks\" : net = ResnetGenerator ( input_nc , output_nc , ngf , norm_layer = norm_layer , use_dropout = use_dropout , n_blocks = 6 , ) elif netG == \"unet_128\" : net = UnetGenerator ( input_nc , output_nc , 7 , ngf , norm_layer = norm_layer , use_dropout = use_dropout , dropout_value = dropout_value , bias_last_conv = bias_last_conv , ) elif netG == \"unet_256\" : net = UnetGenerator ( input_nc , output_nc , 8 , ngf , norm_layer = norm_layer , use_dropout = use_dropout , dropout_value = dropout_value , bias_last_conv = bias_last_conv , ) else : raise NotImplementedError ( \"Generator model name [ %s ] is not recognized\" % netG ) return init_net ( net , init_type , init_gain , gpu_ids )","title":" define_G"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.get_norm_layer","text":"Return a normalization layer Parameters: norm_type (str) -- the name of the normalization layer: batch | instance | none For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def get_norm_layer ( norm_type = \"instance\" ): \"\"\"Return a normalization layer Parameters: norm_type (str) -- the name of the normalization layer: batch | instance | none For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics. \"\"\" if norm_type == \"batch\" : norm_layer = functools . partial ( nn . BatchNorm2d , affine = True , track_running_stats = True ) elif norm_type == \"instance\" : norm_layer = functools . partial ( nn . InstanceNorm2d , affine = False , track_running_stats = False ) elif norm_type == \"none\" : def norm_layer ( x ): return Identity () else : raise NotImplementedError ( \"normalization layer [ %s ] is not found\" % norm_type ) return norm_layer","title":" get_norm_layer"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.init_net","text":"Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights Parameters: net (network) -- the network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal gain (float) -- scaling factor for normal, xavier and orthogonal. gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 Return an initialized network. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 def init_net ( net , init_type = \"normal\" , init_gain = 0.02 , gpu_ids = []): \"\"\"Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights Parameters: net (network) -- the network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal gain (float) -- scaling factor for normal, xavier and orthogonal. gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2 Return an initialized network. \"\"\" if len ( gpu_ids ) > 0 : assert torch . cuda . is_available () net . to ( gpu_ids [ 0 ]) net = torch . nn . DataParallel ( net , gpu_ids ) # multi-GPUs init_weights ( net , init_type , init_gain = init_gain ) return net","title":" init_net"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/architectures/#prismtoolbox.nucleiseg.models.sop.architectures.init_weights","text":"Initialize network weights. Parameters: net (network) -- network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal init_gain (float) -- scaling factor for normal, xavier and orthogonal. We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might work better for some applications. Feel free to try yourself. Source code in src/prismtoolbox/nucleiseg/models/sop/architectures.py 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def init_weights ( net , init_type = \"normal\" , init_gain = 0.02 ): \"\"\"Initialize network weights. Parameters: net (network) -- network to be initialized init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal init_gain (float) -- scaling factor for normal, xavier and orthogonal. We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might work better for some applications. Feel free to try yourself. \"\"\" def init_func ( m ): # define the initialization function classname = m . __class__ . __name__ if hasattr ( m , \"weight\" ) and ( classname . find ( \"Conv\" ) != - 1 or classname . find ( \"Linear\" ) != - 1 ): if init_type == \"normal\" : init . normal_ ( m . weight . data , 0.0 , init_gain ) elif init_type == \"xavier\" : init . xavier_normal_ ( m . weight . data , gain = init_gain ) elif init_type == \"kaiming\" : init . kaiming_normal_ ( m . weight . data , a = 0 , mode = \"fan_in\" ) elif init_type == \"orthogonal\" : init . orthogonal_ ( m . weight . data , gain = init_gain ) else : raise NotImplementedError ( \"initialization method [ %s ] is not implemented\" % init_type ) if hasattr ( m , \"bias\" ) and m . bias is not None : init . constant_ ( m . bias . data , 0.0 ) elif ( classname . find ( \"BatchNorm2d\" ) != - 1 ): # BatchNorm Layer's weight is not a matrix; only normal distribution applies. init . normal_ ( m . weight . data , 1.0 , init_gain ) init . constant_ ( m . bias . data , 0.0 ) print ( \"initialize network with %s \" % init_type ) net . apply ( init_func ) # apply the initialization function ","title":" init_weights"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/modules/","text":"","title":"Modules"},{"location":"reference/prismtoolbox/nucleiseg/models/sop/postprocessing/","text":"","title":"Postprocessing"},{"location":"reference/prismtoolbox/utils/","text":"","title":"Index"},{"location":"reference/prismtoolbox/utils/data_utils/","text":"load_obj_with_json ( file_path ) Load an object from a file using json. Parameters: Name Type Description Default file_path str The path to the json file. required Returns: Type Description Any A json object from the file. Source code in src/prismtoolbox/utils/data_utils.py 47 48 49 50 51 52 53 54 55 56 57 def load_obj_with_json ( file_path : str ) -> Any : \"\"\"Load an object from a file using json. Args: file_path: The path to the json file. Returns: A json object from the file. \"\"\" with open ( file_path , \"r\" ) as f : return json . load ( f ) load_obj_with_pickle ( file_path ) Load an object from a file using pickle. Parameters: Name Type Description Default file_path str The path to the pickle file. required Returns: Type Description Any A pickeable object from the file. Source code in src/prismtoolbox/utils/data_utils.py 34 35 36 37 38 39 40 41 42 43 44 def load_obj_with_pickle ( file_path : str ) -> Any : \"\"\"Load an object from a file using pickle. Args: file_path: The path to the pickle file. Returns: A pickeable object from the file. \"\"\" with open ( file_path , \"rb\" ) as f : return pickle . load ( f ) read_h5_file ( file_path , key ) Read an object from a h5 file. Parameters: Name Type Description Default file_path str The path to the h5 file. required key str The key to select the dataset in the h5 file. required Returns: Type Description Tuple [ ndarray , dict ] A dataset from the h5 file. Source code in src/prismtoolbox/utils/data_utils.py 60 61 62 63 64 65 66 67 68 69 70 71 72 73 def read_h5_file ( file_path : str , key : str ) -> Tuple [ np . ndarray , dict ]: \"\"\"Read an object from a h5 file. Args: file_path: The path to the h5 file. key: The key to select the dataset in the h5 file. Returns: A dataset from the h5 file. \"\"\" with h5py . File ( file_path , \"r\" ) as f : object = f [ key ][()] attrs = { key : value for key , value in f [ key ] . attrs . items ()} return object , attrs read_json_with_geopandas ( file_path , offset = ( 0 , 0 )) Read a json file with geopandas. Parameters: Name Type Description Default file_path str The path to a json file. required Returns: Type Description GeoDataFrame A GeoDataFrame object from the json file. Source code in src/prismtoolbox/utils/data_utils.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 def read_json_with_geopandas ( file_path : str , offset : tuple [ int , int ] = ( 0 , 0 ) ) -> gpd . GeoDataFrame : \"\"\"Read a json file with geopandas. Args: file_path: The path to a json file. Returns: A GeoDataFrame object from the json file. \"\"\" data = load_obj_with_json ( file_path ) df = gpd . GeoDataFrame . from_features ( data ) df . translate ( xoff = offset [ 0 ], yoff = offset [ 1 ]) if not df . is_valid . any (): df . loc [ ~ df . is_valid , :] = df . loc [ ~ df . is_valid , :] . buffer ( 0 ) if \"classification\" in df . columns : df [ \"classification\" ] = df [ \"classification\" ] . apply ( lambda x : x [ \"name\" ] if type ( x ) == dict else x ) return df save_obj_with_json ( obj , file_path ) Save an object to a file using json. Parameters: Name Type Description Default obj object A json object. required file_path str The path to the file. required Source code in src/prismtoolbox/utils/data_utils.py 23 24 25 26 27 28 29 30 31 def save_obj_with_json ( obj : object , file_path : str ) -> None : \"\"\"Save an object to a file using json. Args: obj: A json object. file_path: The path to the file. \"\"\" with open ( file_path , \"w\" ) as f : json . dump ( obj , f ) save_obj_with_pickle ( obj , file_path ) Save an object to a file using pickle. Parameters: Name Type Description Default obj object A pickeable object. required file_path str The path to the file. required Source code in src/prismtoolbox/utils/data_utils.py 12 13 14 15 16 17 18 19 20 def save_obj_with_pickle ( obj : object , file_path : str ) -> None : \"\"\"Save an object to a file using pickle. Args: obj: A pickeable object. file_path: The path to the file. \"\"\" with open ( file_path , \"wb\" ) as f : pickle . dump ( obj , f )","title":"Data utils"},{"location":"reference/prismtoolbox/utils/data_utils/#prismtoolbox.utils.data_utils.load_obj_with_json","text":"Load an object from a file using json. Parameters: Name Type Description Default file_path str The path to the json file. required Returns: Type Description Any A json object from the file. Source code in src/prismtoolbox/utils/data_utils.py 47 48 49 50 51 52 53 54 55 56 57 def load_obj_with_json ( file_path : str ) -> Any : \"\"\"Load an object from a file using json. Args: file_path: The path to the json file. Returns: A json object from the file. \"\"\" with open ( file_path , \"r\" ) as f : return json . load ( f )","title":" load_obj_with_json"},{"location":"reference/prismtoolbox/utils/data_utils/#prismtoolbox.utils.data_utils.load_obj_with_pickle","text":"Load an object from a file using pickle. Parameters: Name Type Description Default file_path str The path to the pickle file. required Returns: Type Description Any A pickeable object from the file. Source code in src/prismtoolbox/utils/data_utils.py 34 35 36 37 38 39 40 41 42 43 44 def load_obj_with_pickle ( file_path : str ) -> Any : \"\"\"Load an object from a file using pickle. Args: file_path: The path to the pickle file. Returns: A pickeable object from the file. \"\"\" with open ( file_path , \"rb\" ) as f : return pickle . load ( f )","title":" load_obj_with_pickle"},{"location":"reference/prismtoolbox/utils/data_utils/#prismtoolbox.utils.data_utils.read_h5_file","text":"Read an object from a h5 file. Parameters: Name Type Description Default file_path str The path to the h5 file. required key str The key to select the dataset in the h5 file. required Returns: Type Description Tuple [ ndarray , dict ] A dataset from the h5 file. Source code in src/prismtoolbox/utils/data_utils.py 60 61 62 63 64 65 66 67 68 69 70 71 72 73 def read_h5_file ( file_path : str , key : str ) -> Tuple [ np . ndarray , dict ]: \"\"\"Read an object from a h5 file. Args: file_path: The path to the h5 file. key: The key to select the dataset in the h5 file. Returns: A dataset from the h5 file. \"\"\" with h5py . File ( file_path , \"r\" ) as f : object = f [ key ][()] attrs = { key : value for key , value in f [ key ] . attrs . items ()} return object , attrs","title":" read_h5_file"},{"location":"reference/prismtoolbox/utils/data_utils/#prismtoolbox.utils.data_utils.read_json_with_geopandas","text":"Read a json file with geopandas. Parameters: Name Type Description Default file_path str The path to a json file. required Returns: Type Description GeoDataFrame A GeoDataFrame object from the json file. Source code in src/prismtoolbox/utils/data_utils.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 def read_json_with_geopandas ( file_path : str , offset : tuple [ int , int ] = ( 0 , 0 ) ) -> gpd . GeoDataFrame : \"\"\"Read a json file with geopandas. Args: file_path: The path to a json file. Returns: A GeoDataFrame object from the json file. \"\"\" data = load_obj_with_json ( file_path ) df = gpd . GeoDataFrame . from_features ( data ) df . translate ( xoff = offset [ 0 ], yoff = offset [ 1 ]) if not df . is_valid . any (): df . loc [ ~ df . is_valid , :] = df . loc [ ~ df . is_valid , :] . buffer ( 0 ) if \"classification\" in df . columns : df [ \"classification\" ] = df [ \"classification\" ] . apply ( lambda x : x [ \"name\" ] if type ( x ) == dict else x ) return df","title":" read_json_with_geopandas"},{"location":"reference/prismtoolbox/utils/data_utils/#prismtoolbox.utils.data_utils.save_obj_with_json","text":"Save an object to a file using json. Parameters: Name Type Description Default obj object A json object. required file_path str The path to the file. required Source code in src/prismtoolbox/utils/data_utils.py 23 24 25 26 27 28 29 30 31 def save_obj_with_json ( obj : object , file_path : str ) -> None : \"\"\"Save an object to a file using json. Args: obj: A json object. file_path: The path to the file. \"\"\" with open ( file_path , \"w\" ) as f : json . dump ( obj , f )","title":" save_obj_with_json"},{"location":"reference/prismtoolbox/utils/data_utils/#prismtoolbox.utils.data_utils.save_obj_with_pickle","text":"Save an object to a file using pickle. Parameters: Name Type Description Default obj object A pickeable object. required file_path str The path to the file. required Source code in src/prismtoolbox/utils/data_utils.py 12 13 14 15 16 17 18 19 20 def save_obj_with_pickle ( obj : object , file_path : str ) -> None : \"\"\"Save an object to a file using pickle. Args: obj: A pickeable object. file_path: The path to the file. \"\"\" with open ( file_path , \"wb\" ) as f : pickle . dump ( obj , f )","title":" save_obj_with_pickle"},{"location":"reference/prismtoolbox/utils/qupath_utils/","text":"PolygonsToContours ( polygons ) Converts shapely polygons to list of arrays. :param polygons: shapely polygons to convert to arrays :return: list of contours containing the opencv-like contours created from the shapely polygons Source code in src/prismtoolbox/utils/qupath_utils.py 46 47 48 49 50 51 52 53 54 55 def PolygonsToContours ( polygons : MultiPolygon ): \"\"\"Converts shapely polygons to list of arrays. :param polygons: shapely polygons to convert to arrays :return: list of contours containing the opencv-like contours created from the shapely polygons \"\"\" return [ np . array ( poly . exterior . coords )[: - 1 , None , ... ] . astype ( int ) for poly in polygons . geoms ] contoursToPolygons ( contours , merge = False , make_valid = False ) Converts list of arrays to shapely polygons. :param contours: list of contours to convert to shapely polygons :param merge: optional boolean to merge the polygons :param make_valid: optional boolean to enforce validity of the polygons :return: MultiPolygon object containing the polygons created from the arrays Source code in src/prismtoolbox/utils/qupath_utils.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 def contoursToPolygons ( contours : List [ np . ndarray ], merge : Optional [ bool ] = False , make_valid : Optional [ bool ] = False , ) -> Union [ Polygon , MultiPolygon ]: \"\"\"Converts list of arrays to shapely polygons. :param contours: list of contours to convert to shapely polygons :param merge: optional boolean to merge the polygons :param make_valid: optional boolean to enforce validity of the polygons :return: MultiPolygon object containing the polygons created from the arrays \"\"\" polygons = [ Polygon ( contour . squeeze ()) . buffer ( 0 ) for contour in contours ] result = [] for poly in polygons : if poly . is_empty : continue if poly . geom_type == \"MultiPolygon\" : result . append ( max ( poly . geoms , key = lambda x : x . area )) else : result . append ( poly ) polygons = MultiPolygon ( result ) if make_valid and not polygons . is_valid : polygons = polygons . buffer ( 0 ) if merge : polygons = unary_union ( polygons ) return polygons convert_rgb_to_java_int_signed ( rgb ) Converts RGB tuple to Java signed integer. :param rgb: RGB tuple :return: Java signed integer Source code in src/prismtoolbox/utils/qupath_utils.py 87 88 89 90 91 92 93 94 95 96 97 def convert_rgb_to_java_int_signed ( rgb : Tuple [ int , int , int ]) -> int : \"\"\"Converts RGB tuple to Java signed integer. :param rgb: RGB tuple :return: Java signed integer \"\"\" r , g , b = rgb java_rgb = ( 255 << 24 ) | ( r << 16 ) | ( g << 8 ) | b if java_rgb >= ( 1 << 31 ): java_rgb -= 1 << 32 return java_rgb export_polygons_to_qupath ( polygons , path , object_type , offset = ( 0 , 0 ), label = None , color = None , append_to_existing_file = False , as_feature_collection = False ) Exports polygons to a .json or .geojson file. :param polygons: shapely polygons to export :param path: path to the .geojson file :param object_type: type of the object (should be either \"annotation\" or \"detection\") :param offset: optional offset to add to each coordinate in the arrays :param label: optional label of the polygons :param color: optional color of the polygons :param append_to_existing_file: optional boolean to append the polygons to an existing file :param as_feature_collection: optional boolean to save the polygons as a FeatureCollection Source code in src/prismtoolbox/utils/qupath_utils.py 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def export_polygons_to_qupath ( polygons : MultiPolygon , path : str , object_type : str , offset : Optional [ Tuple [ int , int ]] = ( 0 , 0 ), label : Optional [ str ] = None , color : Optional [ Tuple [ int , int , int ]] = None , append_to_existing_file : Optional [ bool ] = False , as_feature_collection : Optional [ bool ] = False , ): \"\"\"Exports polygons to a .json or .geojson file. :param polygons: shapely polygons to export :param path: path to the .geojson file :param object_type: type of the object (should be either \"annotation\" or \"detection\") :param offset: optional offset to add to each coordinate in the arrays :param label: optional label of the polygons :param color: optional color of the polygons :param append_to_existing_file: optional boolean to append the polygons to an existing file :param as_feature_collection: optional boolean to save the polygons as a FeatureCollection \"\"\" if isinstance ( polygons , Polygon ): polygons = MultiPolygon ([ polygons ]) features = [] properties = { \"objectType\" : object_type } if label is not None : properties [ \"classification\" ] = { \"name\" : label , \"colorRGB\" : convert_rgb_to_java_int_signed ( color ), } polygons = translate ( polygons , xoff = offset [ 0 ], yoff = offset [ 1 ]) for poly in polygons . geoms : features . append ( { \"type\" : \"Feature\" , \"id\" : str ( uuid . uuid4 ()), \"geometry\" : mapping ( poly ), \"properties\" : properties , } ) features = ( { \"type\" : \"FeatureCollection\" , \"features\" : features } if as_feature_collection else features ) if os . path . exists ( path ) and append_to_existing_file : previous_features = load_obj_with_json ( path ) if len ( previous_features ) == 0 : log . warning ( \"The .geojson file does not contain any features, creating new file.\" ) else : if as_feature_collection : previous_features [ \"features\" ] . extend ( features [ \"features\" ]) else : previous_features . extend ( features ) features = previous_features save_obj_with_json ( features , path ) intersectionPolygons ( polygons1 , polygons2 ) Computes the intersection of two MultiPolygons. :param polygons1: first MultiPolygon :param polygons2: second MultiPolygon :return: MultiPolygon containing the intersection of the two input MultiPolygons Source code in src/prismtoolbox/utils/qupath_utils.py 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 def intersectionPolygons ( polygons1 : MultiPolygon , polygons2 : MultiPolygon ) -> MultiPolygon : \"\"\"Computes the intersection of two MultiPolygons. :param polygons1: first MultiPolygon :param polygons2: second MultiPolygon :return: MultiPolygon containing the intersection of the two input MultiPolygons \"\"\" intersection = polygons1 & polygons2 if intersection . geom_type == \"MultiPolygon\" : return intersection elif intersection . geom_type == \"GeometryCollection\" : intersection = MultiPolygon ( [ poly for poly in intersection . geoms if isinstance ( poly , Polygon )] ) elif intersection . geom_type == \"Polygon\" : intersection = MultiPolygon ([ intersection ]) else : raise ValueError ( \"Intersection of provided MultiPolygons is not a MultiPolygon or a Polygon\" ) return intersection patchesToPolygons ( patches , patch_size , patch_downsample , merge = False ) Converts patches to shapely polygons. :param patches: Top left point coordinates of the patches to convert to shapely polygons :param patch_size: size of the patches :param merge: optional boolean to merge the polygons :return: MultiPolygon object containing the polygons created from the patches Source code in src/prismtoolbox/utils/qupath_utils.py 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 def patchesToPolygons ( patches : np . ndarray , patch_size : int , patch_downsample : int , merge : Optional [ bool ] = False , ) -> Union [ Polygon , MultiPolygon ]: \"\"\"Converts patches to shapely polygons. :param patches: Top left point coordinates of the patches to convert to shapely polygons :param patch_size: size of the patches :param merge: optional boolean to merge the polygons :return: MultiPolygon object containing the polygons created from the patches \"\"\" polygons = [] ref_patch_size = patch_size * patch_downsample for patch in patches : x , y = patch polygons . append ( box ( x , y , x + ref_patch_size , y + ref_patch_size , ccw = False )) polygons = MultiPolygon ( polygons ) if merge : polygons = unary_union ( polygons ) return polygons read_qupath_annotations ( path , offset = ( 0 , 0 ), class_name = 'annotation' , column_to_select = 'objectType' ) Reads pathologist annotations from a .geojson file. :param path: path to the .geojson file :param offset: optional offset to add to each coordinate in the arrays :param class_name: name of the class to select :param column_to_select: optional column to select :return: MultiPolygon object containing the polygons of the selected class. Source code in src/prismtoolbox/utils/qupath_utils.py 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 def read_qupath_annotations ( path : str , offset : Optional [ Tuple [ int , int ]] = ( 0 , 0 ), class_name : str = \"annotation\" , column_to_select : str = \"objectType\" , ): \"\"\"Reads pathologist annotations from a .geojson file. :param path: path to the .geojson file :param offset: optional offset to add to each coordinate in the arrays :param class_name: name of the class to select :param column_to_select: optional column to select :return: MultiPolygon object containing the polygons of the selected class. \"\"\" df = read_json_with_geopandas ( path , offset ) polygons = [] for poly in df . loc [ df [ column_to_select ] == class_name , \"geometry\" ] . values : if poly . geom_type == \"Polygon\" : polygons . append ( poly ) elif poly . geom_type == \"MultiPolygon\" : polygons . extend ( poly . geoms ) else : raise ValueError ( \"Geometry type not supported.\" ) polygons = MultiPolygon ( polygons ) if not polygons . is_valid : polygons = polygons . buffer ( 0 ) return polygons","title":"Qupath utils"},{"location":"reference/prismtoolbox/utils/qupath_utils/#prismtoolbox.utils.qupath_utils.PolygonsToContours","text":"Converts shapely polygons to list of arrays. :param polygons: shapely polygons to convert to arrays :return: list of contours containing the opencv-like contours created from the shapely polygons Source code in src/prismtoolbox/utils/qupath_utils.py 46 47 48 49 50 51 52 53 54 55 def PolygonsToContours ( polygons : MultiPolygon ): \"\"\"Converts shapely polygons to list of arrays. :param polygons: shapely polygons to convert to arrays :return: list of contours containing the opencv-like contours created from the shapely polygons \"\"\" return [ np . array ( poly . exterior . coords )[: - 1 , None , ... ] . astype ( int ) for poly in polygons . geoms ]","title":" PolygonsToContours"},{"location":"reference/prismtoolbox/utils/qupath_utils/#prismtoolbox.utils.qupath_utils.contoursToPolygons","text":"Converts list of arrays to shapely polygons. :param contours: list of contours to convert to shapely polygons :param merge: optional boolean to merge the polygons :param make_valid: optional boolean to enforce validity of the polygons :return: MultiPolygon object containing the polygons created from the arrays Source code in src/prismtoolbox/utils/qupath_utils.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 def contoursToPolygons ( contours : List [ np . ndarray ], merge : Optional [ bool ] = False , make_valid : Optional [ bool ] = False , ) -> Union [ Polygon , MultiPolygon ]: \"\"\"Converts list of arrays to shapely polygons. :param contours: list of contours to convert to shapely polygons :param merge: optional boolean to merge the polygons :param make_valid: optional boolean to enforce validity of the polygons :return: MultiPolygon object containing the polygons created from the arrays \"\"\" polygons = [ Polygon ( contour . squeeze ()) . buffer ( 0 ) for contour in contours ] result = [] for poly in polygons : if poly . is_empty : continue if poly . geom_type == \"MultiPolygon\" : result . append ( max ( poly . geoms , key = lambda x : x . area )) else : result . append ( poly ) polygons = MultiPolygon ( result ) if make_valid and not polygons . is_valid : polygons = polygons . buffer ( 0 ) if merge : polygons = unary_union ( polygons ) return polygons","title":" contoursToPolygons"},{"location":"reference/prismtoolbox/utils/qupath_utils/#prismtoolbox.utils.qupath_utils.convert_rgb_to_java_int_signed","text":"Converts RGB tuple to Java signed integer. :param rgb: RGB tuple :return: Java signed integer Source code in src/prismtoolbox/utils/qupath_utils.py 87 88 89 90 91 92 93 94 95 96 97 def convert_rgb_to_java_int_signed ( rgb : Tuple [ int , int , int ]) -> int : \"\"\"Converts RGB tuple to Java signed integer. :param rgb: RGB tuple :return: Java signed integer \"\"\" r , g , b = rgb java_rgb = ( 255 << 24 ) | ( r << 16 ) | ( g << 8 ) | b if java_rgb >= ( 1 << 31 ): java_rgb -= 1 << 32 return java_rgb","title":" convert_rgb_to_java_int_signed"},{"location":"reference/prismtoolbox/utils/qupath_utils/#prismtoolbox.utils.qupath_utils.export_polygons_to_qupath","text":"Exports polygons to a .json or .geojson file. :param polygons: shapely polygons to export :param path: path to the .geojson file :param object_type: type of the object (should be either \"annotation\" or \"detection\") :param offset: optional offset to add to each coordinate in the arrays :param label: optional label of the polygons :param color: optional color of the polygons :param append_to_existing_file: optional boolean to append the polygons to an existing file :param as_feature_collection: optional boolean to save the polygons as a FeatureCollection Source code in src/prismtoolbox/utils/qupath_utils.py 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 def export_polygons_to_qupath ( polygons : MultiPolygon , path : str , object_type : str , offset : Optional [ Tuple [ int , int ]] = ( 0 , 0 ), label : Optional [ str ] = None , color : Optional [ Tuple [ int , int , int ]] = None , append_to_existing_file : Optional [ bool ] = False , as_feature_collection : Optional [ bool ] = False , ): \"\"\"Exports polygons to a .json or .geojson file. :param polygons: shapely polygons to export :param path: path to the .geojson file :param object_type: type of the object (should be either \"annotation\" or \"detection\") :param offset: optional offset to add to each coordinate in the arrays :param label: optional label of the polygons :param color: optional color of the polygons :param append_to_existing_file: optional boolean to append the polygons to an existing file :param as_feature_collection: optional boolean to save the polygons as a FeatureCollection \"\"\" if isinstance ( polygons , Polygon ): polygons = MultiPolygon ([ polygons ]) features = [] properties = { \"objectType\" : object_type } if label is not None : properties [ \"classification\" ] = { \"name\" : label , \"colorRGB\" : convert_rgb_to_java_int_signed ( color ), } polygons = translate ( polygons , xoff = offset [ 0 ], yoff = offset [ 1 ]) for poly in polygons . geoms : features . append ( { \"type\" : \"Feature\" , \"id\" : str ( uuid . uuid4 ()), \"geometry\" : mapping ( poly ), \"properties\" : properties , } ) features = ( { \"type\" : \"FeatureCollection\" , \"features\" : features } if as_feature_collection else features ) if os . path . exists ( path ) and append_to_existing_file : previous_features = load_obj_with_json ( path ) if len ( previous_features ) == 0 : log . warning ( \"The .geojson file does not contain any features, creating new file.\" ) else : if as_feature_collection : previous_features [ \"features\" ] . extend ( features [ \"features\" ]) else : previous_features . extend ( features ) features = previous_features save_obj_with_json ( features , path )","title":" export_polygons_to_qupath"},{"location":"reference/prismtoolbox/utils/qupath_utils/#prismtoolbox.utils.qupath_utils.intersectionPolygons","text":"Computes the intersection of two MultiPolygons. :param polygons1: first MultiPolygon :param polygons2: second MultiPolygon :return: MultiPolygon containing the intersection of the two input MultiPolygons Source code in src/prismtoolbox/utils/qupath_utils.py 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 def intersectionPolygons ( polygons1 : MultiPolygon , polygons2 : MultiPolygon ) -> MultiPolygon : \"\"\"Computes the intersection of two MultiPolygons. :param polygons1: first MultiPolygon :param polygons2: second MultiPolygon :return: MultiPolygon containing the intersection of the two input MultiPolygons \"\"\" intersection = polygons1 & polygons2 if intersection . geom_type == \"MultiPolygon\" : return intersection elif intersection . geom_type == \"GeometryCollection\" : intersection = MultiPolygon ( [ poly for poly in intersection . geoms if isinstance ( poly , Polygon )] ) elif intersection . geom_type == \"Polygon\" : intersection = MultiPolygon ([ intersection ]) else : raise ValueError ( \"Intersection of provided MultiPolygons is not a MultiPolygon or a Polygon\" ) return intersection","title":" intersectionPolygons"},{"location":"reference/prismtoolbox/utils/qupath_utils/#prismtoolbox.utils.qupath_utils.patchesToPolygons","text":"Converts patches to shapely polygons. :param patches: Top left point coordinates of the patches to convert to shapely polygons :param patch_size: size of the patches :param merge: optional boolean to merge the polygons :return: MultiPolygon object containing the polygons created from the patches Source code in src/prismtoolbox/utils/qupath_utils.py 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 def patchesToPolygons ( patches : np . ndarray , patch_size : int , patch_downsample : int , merge : Optional [ bool ] = False , ) -> Union [ Polygon , MultiPolygon ]: \"\"\"Converts patches to shapely polygons. :param patches: Top left point coordinates of the patches to convert to shapely polygons :param patch_size: size of the patches :param merge: optional boolean to merge the polygons :return: MultiPolygon object containing the polygons created from the patches \"\"\" polygons = [] ref_patch_size = patch_size * patch_downsample for patch in patches : x , y = patch polygons . append ( box ( x , y , x + ref_patch_size , y + ref_patch_size , ccw = False )) polygons = MultiPolygon ( polygons ) if merge : polygons = unary_union ( polygons ) return polygons","title":" patchesToPolygons"},{"location":"reference/prismtoolbox/utils/qupath_utils/#prismtoolbox.utils.qupath_utils.read_qupath_annotations","text":"Reads pathologist annotations from a .geojson file. :param path: path to the .geojson file :param offset: optional offset to add to each coordinate in the arrays :param class_name: name of the class to select :param column_to_select: optional column to select :return: MultiPolygon object containing the polygons of the selected class. Source code in src/prismtoolbox/utils/qupath_utils.py 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 def read_qupath_annotations ( path : str , offset : Optional [ Tuple [ int , int ]] = ( 0 , 0 ), class_name : str = \"annotation\" , column_to_select : str = \"objectType\" , ): \"\"\"Reads pathologist annotations from a .geojson file. :param path: path to the .geojson file :param offset: optional offset to add to each coordinate in the arrays :param class_name: name of the class to select :param column_to_select: optional column to select :return: MultiPolygon object containing the polygons of the selected class. \"\"\" df = read_json_with_geopandas ( path , offset ) polygons = [] for poly in df . loc [ df [ column_to_select ] == class_name , \"geometry\" ] . values : if poly . geom_type == \"Polygon\" : polygons . append ( poly ) elif poly . geom_type == \"MultiPolygon\" : polygons . extend ( poly . geoms ) else : raise ValueError ( \"Geometry type not supported.\" ) polygons = MultiPolygon ( polygons ) if not polygons . is_valid : polygons = polygons . buffer ( 0 ) return polygons","title":" read_qupath_annotations"},{"location":"reference/prismtoolbox/utils/stain_utils/","text":"","title":"Stain utils"},{"location":"reference/prismtoolbox/utils/torch_utils/","text":"create_transforms ( transforms_dict ) Create a torchvision.transforms.Compose object from a dictionary of transforms. Parameters: Name Type Description Default transforms_dict dict [ str , dict [ str , any ]] Dictionary of transforms. The keys are the names of the transforms and the values are the parameters to pass to the transform as a dictionary. Possible transforms are: \"totensor\": ToTensorv2 \"normalize\": transformsv2.Normalize \"horizontal_flip\": transformsv2.RandomHorizontalFlip \"vertical_flip\": transformsv2.RandomVerticalFlip \"rotation\": transformsv2.RandomRotation \"resized_crop\": transformsv2.RandomResizedCrop \"random_crop\": transformsv2.RandomCrop \"center_crop\": transformsv2.CenterCrop \"resize\": transformsv2.Resize \"clip_custom\": ClipCustom Please refer to the torchvision documentation for the parameters of each torchvision transform. required Returns: Type Description Compose A torchvision.transforms.Compose object. Source code in src/prismtoolbox/utils/torch_utils.py 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 def create_transforms ( transforms_dict : dict [ str , dict [ str , any ]]) -> transformsv2 . Compose : \"\"\"Create a torchvision.transforms.Compose object from a dictionary of transforms. Args: transforms_dict: Dictionary of transforms. The keys are the names of the transforms and the values are the parameters to pass to the transform as a dictionary. Possible transforms are: - \"totensor\": ToTensorv2 - \"normalize\": transformsv2.Normalize - \"horizontal_flip\": transformsv2.RandomHorizontalFlip - \"vertical_flip\": transformsv2.RandomVerticalFlip - \"rotation\": transformsv2.RandomRotation - \"resized_crop\": transformsv2.RandomResizedCrop - \"random_crop\": transformsv2.RandomCrop - \"center_crop\": transformsv2.CenterCrop - \"resize\": transformsv2.Resize - \"clip_custom\": ClipCustom Please refer to the [torchvision documentation](https://pytorch.org/vision/stable/transforms.html) for the parameters of each torchvision transform. Returns: A torchvision.transforms.Compose object. \"\"\" if any ( transform_name not in possible_transforms for transform_name in transforms_dict ): raise ValueError ( f \"invalid transform name. Possible transforms: { possible_transforms . keys () } \" ) transforms = transformsv2 . Compose ( [ possible_transforms [ transform_name ]( ** transform_params ) for transform_name , transform_params in transforms_dict . items () ] ) return transforms","title":"Torch utils"},{"location":"reference/prismtoolbox/utils/torch_utils/#prismtoolbox.utils.torch_utils.create_transforms","text":"Create a torchvision.transforms.Compose object from a dictionary of transforms. Parameters: Name Type Description Default transforms_dict dict [ str , dict [ str , any ]] Dictionary of transforms. The keys are the names of the transforms and the values are the parameters to pass to the transform as a dictionary. Possible transforms are: \"totensor\": ToTensorv2 \"normalize\": transformsv2.Normalize \"horizontal_flip\": transformsv2.RandomHorizontalFlip \"vertical_flip\": transformsv2.RandomVerticalFlip \"rotation\": transformsv2.RandomRotation \"resized_crop\": transformsv2.RandomResizedCrop \"random_crop\": transformsv2.RandomCrop \"center_crop\": transformsv2.CenterCrop \"resize\": transformsv2.Resize \"clip_custom\": ClipCustom Please refer to the torchvision documentation for the parameters of each torchvision transform. required Returns: Type Description Compose A torchvision.transforms.Compose object. Source code in src/prismtoolbox/utils/torch_utils.py 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 def create_transforms ( transforms_dict : dict [ str , dict [ str , any ]]) -> transformsv2 . Compose : \"\"\"Create a torchvision.transforms.Compose object from a dictionary of transforms. Args: transforms_dict: Dictionary of transforms. The keys are the names of the transforms and the values are the parameters to pass to the transform as a dictionary. Possible transforms are: - \"totensor\": ToTensorv2 - \"normalize\": transformsv2.Normalize - \"horizontal_flip\": transformsv2.RandomHorizontalFlip - \"vertical_flip\": transformsv2.RandomVerticalFlip - \"rotation\": transformsv2.RandomRotation - \"resized_crop\": transformsv2.RandomResizedCrop - \"random_crop\": transformsv2.RandomCrop - \"center_crop\": transformsv2.CenterCrop - \"resize\": transformsv2.Resize - \"clip_custom\": ClipCustom Please refer to the [torchvision documentation](https://pytorch.org/vision/stable/transforms.html) for the parameters of each torchvision transform. Returns: A torchvision.transforms.Compose object. \"\"\" if any ( transform_name not in possible_transforms for transform_name in transforms_dict ): raise ValueError ( f \"invalid transform name. Possible transforms: { possible_transforms . keys () } \" ) transforms = transformsv2 . Compose ( [ possible_transforms [ transform_name ]( ** transform_params ) for transform_name , transform_params in transforms_dict . items () ] ) return transforms","title":" create_transforms"},{"location":"reference/prismtoolbox/utils/vis_utils/","text":"bbox_from_contours ( contours , downsample_factor = 1 ) Compute the bounding box from a set of contours :param contours: list of contours :return: bounding box of the contours Source code in src/prismtoolbox/utils/vis_utils.py 29 30 31 32 33 34 35 36 37 38 39 40 def bbox_from_contours ( contours : list [ np . ndarray ], downsample_factor : int = 1 ) -> tuple [ int , int , int , int ]: \"\"\" Compute the bounding box from a set of contours :param contours: list of contours :return: bounding box of the contours \"\"\" flatten_contours = np . concatenate ( contours ) x_min , y_min = flatten_contours . min ( axis = 0 ) . squeeze () . astype ( int ) / downsample_factor x_max , y_max = flatten_contours . max ( axis = 0 ) . squeeze () . astype ( int ) / downsample_factor return x_min , y_min , x_max , y_max bbox_from_coords ( coords , patch_size = 0 , downsample_factor = 1 ) Compute the bounding box from a set of coordinates :param coords: coordinates of the patches :param patch_size: size of the patches (at level extraction) :return: bounding box of the patches Source code in src/prismtoolbox/utils/vis_utils.py 43 44 45 46 47 48 49 50 51 52 53 54 55 56 def bbox_from_coords ( coords : np . ndarray , patch_size : int = 0 , downsample_factor : int = 1 , ) -> tuple [ int , int , int , int ]: \"\"\" Compute the bounding box from a set of coordinates :param coords: coordinates of the patches :param patch_size: size of the patches (at level extraction) :return: bounding box of the patches \"\"\" x_min , y_min = coords . min ( axis = 0 ) . astype ( int ) / downsample_factor x_max , y_max = coords . max ( axis = 0 ) . astype ( int ) / downsample_factor return x_min , y_min , x_max + patch_size , y_max + patch_size get_colors_from_cmap ( cmap_name , n_colors , scale = 255 ) Get a list of colors from a matplotlib colormap :param cmap_name: name of a matplotlib colormap :param n_colors: number of colors :return: list of colors Source code in src/prismtoolbox/utils/vis_utils.py 59 60 61 62 63 64 65 66 67 68 69 70 71 def get_colors_from_cmap ( cmap_name , n_colors , scale = 255 ): \"\"\" Get a list of colors from a matplotlib colormap :param cmap_name: name of a matplotlib colormap :param n_colors: number of colors :return: list of colors \"\"\" cmap_name = plt . get_cmap ( cmap_name , n_colors ) colors_from_cmap = np . array ([ to_rgb ( cmap_name ( i )) for i in range ( n_colors )]) if scale > 1 : return ( colors_from_cmap * scale ) . astype ( int ) else : return colors_from_cmap init_image ( w , h , color_bakground = ( 255 , 255 , 255 ), mask = False ) Create an image with the dimension (w,h) and the color of the background set to color_bakground :param w: width of image :param h: height of image :param color_bakground: color of the background :param mask: set to True to create a binary mask :return: PIL image with the dimension (w,h), in grayscale when mask if set to True Source code in src/prismtoolbox/utils/vis_utils.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 def init_image ( w : int , h : int , color_bakground : tuple [ int , int , int ] = ( 255 , 255 , 255 ), mask : bool = False , ) -> np . ndarray : \"\"\" Create an image with the dimension (w,h) and the color of the background set to color_bakground :param w: width of image :param h: height of image :param color_bakground: color of the background :param mask: set to True to create a binary mask :return: PIL image with the dimension (w,h), in grayscale when mask if set to True \"\"\" if mask : return np . array ( ImageOps . grayscale ( Image . new ( size = ( w , h ), mode = \"RGB\" , color = 0 ))) else : return np . array ( Image . new ( size = ( w , h ), mode = \"RGB\" , color = color_bakground ))","title":"Vis utils"},{"location":"reference/prismtoolbox/utils/vis_utils/#prismtoolbox.utils.vis_utils.bbox_from_contours","text":"Compute the bounding box from a set of contours :param contours: list of contours :return: bounding box of the contours Source code in src/prismtoolbox/utils/vis_utils.py 29 30 31 32 33 34 35 36 37 38 39 40 def bbox_from_contours ( contours : list [ np . ndarray ], downsample_factor : int = 1 ) -> tuple [ int , int , int , int ]: \"\"\" Compute the bounding box from a set of contours :param contours: list of contours :return: bounding box of the contours \"\"\" flatten_contours = np . concatenate ( contours ) x_min , y_min = flatten_contours . min ( axis = 0 ) . squeeze () . astype ( int ) / downsample_factor x_max , y_max = flatten_contours . max ( axis = 0 ) . squeeze () . astype ( int ) / downsample_factor return x_min , y_min , x_max , y_max","title":" bbox_from_contours"},{"location":"reference/prismtoolbox/utils/vis_utils/#prismtoolbox.utils.vis_utils.bbox_from_coords","text":"Compute the bounding box from a set of coordinates :param coords: coordinates of the patches :param patch_size: size of the patches (at level extraction) :return: bounding box of the patches Source code in src/prismtoolbox/utils/vis_utils.py 43 44 45 46 47 48 49 50 51 52 53 54 55 56 def bbox_from_coords ( coords : np . ndarray , patch_size : int = 0 , downsample_factor : int = 1 , ) -> tuple [ int , int , int , int ]: \"\"\" Compute the bounding box from a set of coordinates :param coords: coordinates of the patches :param patch_size: size of the patches (at level extraction) :return: bounding box of the patches \"\"\" x_min , y_min = coords . min ( axis = 0 ) . astype ( int ) / downsample_factor x_max , y_max = coords . max ( axis = 0 ) . astype ( int ) / downsample_factor return x_min , y_min , x_max + patch_size , y_max + patch_size","title":" bbox_from_coords"},{"location":"reference/prismtoolbox/utils/vis_utils/#prismtoolbox.utils.vis_utils.get_colors_from_cmap","text":"Get a list of colors from a matplotlib colormap :param cmap_name: name of a matplotlib colormap :param n_colors: number of colors :return: list of colors Source code in src/prismtoolbox/utils/vis_utils.py 59 60 61 62 63 64 65 66 67 68 69 70 71 def get_colors_from_cmap ( cmap_name , n_colors , scale = 255 ): \"\"\" Get a list of colors from a matplotlib colormap :param cmap_name: name of a matplotlib colormap :param n_colors: number of colors :return: list of colors \"\"\" cmap_name = plt . get_cmap ( cmap_name , n_colors ) colors_from_cmap = np . array ([ to_rgb ( cmap_name ( i )) for i in range ( n_colors )]) if scale > 1 : return ( colors_from_cmap * scale ) . astype ( int ) else : return colors_from_cmap","title":" get_colors_from_cmap"},{"location":"reference/prismtoolbox/utils/vis_utils/#prismtoolbox.utils.vis_utils.init_image","text":"Create an image with the dimension (w,h) and the color of the background set to color_bakground :param w: width of image :param h: height of image :param color_bakground: color of the background :param mask: set to True to create a binary mask :return: PIL image with the dimension (w,h), in grayscale when mask if set to True Source code in src/prismtoolbox/utils/vis_utils.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 def init_image ( w : int , h : int , color_bakground : tuple [ int , int , int ] = ( 255 , 255 , 255 ), mask : bool = False , ) -> np . ndarray : \"\"\" Create an image with the dimension (w,h) and the color of the background set to color_bakground :param w: width of image :param h: height of image :param color_bakground: color of the background :param mask: set to True to create a binary mask :return: PIL image with the dimension (w,h), in grayscale when mask if set to True \"\"\" if mask : return np . array ( ImageOps . grayscale ( Image . new ( size = ( w , h ), mode = \"RGB\" , color = 0 ))) else : return np . array ( Image . new ( size = ( w , h ), mode = \"RGB\" , color = color_bakground ))","title":" init_image"},{"location":"reference/prismtoolbox/wsicore/","text":"","title":"Index"},{"location":"reference/prismtoolbox/wsicore/core_utils/","text":"IsInContour ( contour , patch_size , center_shift = 0.5 , mode = 'center' ) The IsInContour class checks if a patch is inside a contour. Parameters: Name Type Description Default contour ndarray The contour to check if the patch is inside. required patch_size int The size of the patches that will be checked. required center_shift int The shift of the center of the patch. 0.5 mode str The mode to use for the contour checking. Possible values are: \"center\" mode checks if the center of the patch is within the contour. \"four_pt\" mode checks if one out of the four corners of the patch are within the contour. \"four_pt_hard\" mode checks if the four corners of the patch are within the contour. 'center' Source code in src/prismtoolbox/wsicore/core_utils.py 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 def __init__ ( self , contour : np . ndarray , patch_size : int , center_shift : int = 0.5 , mode : str = \"center\" , ): \"\"\"The IsInContour class checks if a patch is inside a contour. Args: contour: The contour to check if the patch is inside. patch_size: The size of the patches that will be checked. center_shift: The shift of the center of the patch. mode: The mode to use for the contour checking. Possible values are: - \"center\" mode checks if the center of the patch is within the contour. - \"four_pt\" mode checks if one out of the four corners of the patch are within the contour. - \"four_pt_hard\" mode checks if the four corners of the patch are within the contour. \"\"\" self . cont = contour self . patch_size = patch_size self . shift = int ( patch_size // 2 * center_shift ) self . mode = mode __call__ ( pt ) Check if a point is inside the contour. Parameters: Name Type Description Default pt tuple [ int , int ] The top left coordinate of the patch. required Returns: Type Description int 1 if the patch is inside the contour, 0 otherwise. Source code in src/prismtoolbox/wsicore/core_utils.py 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 def __call__ ( self , pt : tuple [ int , int ]) -> int : \"\"\"Check if a point is inside the contour. Args: pt: The top left coordinate of the patch. Returns: 1 if the patch is inside the contour, 0 otherwise. \"\"\" center = ( int ( pt [ 0 ] + self . patch_size // 2 ), int ( pt [ 1 ] + self . patch_size // 2 )) if self . mode == \"center\" : return 1 if cv2 . pointPolygonTest ( self . cont , center , False ) >= 0 else 0 else : if self . shift > 0 : all_points = [ ( center [ 0 ] - self . shift , center [ 1 ] - self . shift ), ( center [ 0 ] + self . shift , center [ 1 ] + self . shift ), ( center [ 0 ] + self . shift , center [ 1 ] - self . shift ), ( center [ 0 ] - self . shift , center [ 1 ] + self . shift ), ] else : all_points = [ center ] # Easy version of 4pt contour checking function - 1 of 4 points need to be in the contour for test to pass if self . mode == \"four_pt\" : for points in all_points : if cv2 . pointPolygonTest ( self . cont , points , False ) >= 0 : return 1 return 0 # Hard version of 4pt contour checking function - all 4 points need to be in the contour for test to pass elif self . mode == \"four_pt_hard\" : for points in all_points : if cv2 . pointPolygonTest ( self . cont , points , False ) < 0 : return 0 return 1 else : raise ValueError ( f \"mode { self . mode } not recognized\" ) apply_bilateral_filter ( img ) Apply a bilateral filter on a grayscale image. Parameters: Name Type Description Default img ndarray An input grayscale image. required Returns: Type Description ndarray The input image after applying a bilateral filter of parameters 3, 3*2, 3/2. Source code in src/prismtoolbox/wsicore/core_utils.py 150 151 152 153 154 155 156 157 158 159 160 161 def apply_bilateral_filter ( img : np . ndarray ) -> np . ndarray : \"\"\"Apply a bilateral filter on a grayscale image. Args: img: An input grayscale image. Returns: The input image after applying a bilateral filter of parameters 3, 3*2, 3/2. \"\"\" assert len ( img . shape ) == 2 , f \"Input image should be grayscale, got shape { img . shape } \" img_filtered = cv2 . bilateralFilter ( img , 3 , 3 * 2 , 3 / 2 ) return img_filtered apply_binary_thresh ( img , thresh , inv ) Apply a binary threshold on a grayscale image. Parameters: Name Type Description Default img ndarray An input grayscale image. required thresh int The threshold value. required inv bool Set to True to invert the binary threshold. required Returns: Type Description ndarray The input image after applying a binary threshold. Source code in src/prismtoolbox/wsicore/core_utils.py 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 def apply_binary_thresh ( img : np . ndarray , thresh : int , inv : bool ) -> np . ndarray : \"\"\"Apply a binary threshold on a grayscale image. Args: img: An input grayscale image. thresh: The threshold value. inv: Set to True to invert the binary threshold. Returns: The input image after applying a binary threshold. \"\"\" if inv : _ , img_thresh = cv2 . threshold ( img , thresh , 255 , cv2 . THRESH_BINARY_INV ) else : _ , img_thresh = cv2 . threshold ( img , thresh , 255 , cv2 . THRESH_BINARY ) return img_thresh compute_law_feats ( img , window_size , only_s5 = True ) Compute the Law's texture energy for a given grayscale image and window size. Parameters: Name Type Description Default img ndarray An input grayscale image. required window_size int The window size to use for Law's texture energy computation. required only_s5 bool Set to True to only compute the Law's texture energy for the S5S5 filter. True Returns: Type Description list [ ndarray ] A list of Law's texture energy map for each filter extracted from the input image. Source code in src/prismtoolbox/wsicore/core_utils.py 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 def compute_law_feats ( img : np . ndarray , window_size : int , only_s5 : bool = True ) -> list [ np . ndarray ]: \"\"\"Compute the Law's texture energy for a given grayscale image and window size. Args: img: An input grayscale image. window_size: The window size to use for Law's texture energy computation. only_s5: Set to True to only compute the Law's texture energy for the S5S5 filter. Returns: A list of Law's texture energy map for each filter extracted from the input image. \"\"\" L5 = np . array ([ 1 , 4 , 6 , 4 , 1 ]) E5 = np . array ([ - 1 , - 2 , 0 , 2 , 1 ]) S5 = np . array ([ - 1 , 0 , 2 , 0 , - 1 ]) R5 = np . array ([ 1 , - 4 , 6 , - 4 , 1 ]) vectors = [ L5 , E5 , S5 , R5 ] vectors_name = [ \"L5\" , \"E5\" , \"S5\" , \"R5\" ] if only_s5 : vectors = [ vectors [ 2 ]] vectors_name = [ vectors_name [ 2 ]] filters = [ np . expand_dims ( vectors [ i ], - 1 ) . dot ( np . expand_dims ( vectors [ j ], - 1 ) . T ) for i , j in product ( range ( len ( vectors )), range ( len ( vectors ))) ] filters_name = np . array ( [ vectors_name [ i ] + vectors_name [ j ] for i , j in product ( range ( len ( vectors )), range ( len ( vectors ))) ] ) imgs_filtered = [] for filt in filters : imgs_filtered . append ( oaconvolve ( img , filt , mode = \"same\" )) window = np . ones (( window_size , window_size )) imgs_energy = [] for img_filtered in imgs_filtered : imgs_energy . append ( oaconvolve ( np . abs ( img_filtered ), window , mode = \"same\" )) def _get_img_energy ( name ): return imgs_energy [ np . where ( filters_name == name )[ 0 ] . item ()] if only_s5 : imgs_feats = [ _get_img_energy ( \"S5S5\" )] else : imgs_feats = [ np . mean ( np . array ([ _get_img_energy ( \"L5E5\" ), _get_img_energy ( \"E5L5\" )]), axis = 0 ), np . mean ( np . array ([ _get_img_energy ( \"L5R5\" ), _get_img_energy ( \"R5L5\" )]), axis = 0 ), np . mean ( np . array ([ _get_img_energy ( \"E5S5\" ), _get_img_energy ( \"S5E5\" )]), axis = 0 ), _get_img_energy ( \"S5S5\" ), _get_img_energy ( \"R5R5\" ), np . mean ( np . array ([ _get_img_energy ( \"L5S5\" ), _get_img_energy ( \"S5L5\" )]), axis = 0 ), _get_img_energy ( \"E5E5\" ), np . mean ( np . array ([ _get_img_energy ( \"E5R5\" ), _get_img_energy ( \"R5E5\" )]), axis = 0 ), np . mean ( np . array ([ _get_img_energy ( \"S5R5\" ), _get_img_energy ( \"R5S5\" )]), axis = 0 ), ] return imgs_feats contour_mask ( mask ) Find the contours in a binary image with opencv findContours. Parameters: Name Type Description Default mask ndarray A binary image. required Returns: Type Description list [ ndarray ] A list of contours found in the input image with opencv findContours. Source code in src/prismtoolbox/wsicore/core_utils.py 201 202 203 204 205 206 207 208 209 210 211 212 def contour_mask ( mask : np . ndarray ) -> list [ np . ndarray ]: \"\"\"Find the contours in a binary image with opencv findContours. Args: mask: A binary image. Returns: A list of contours found in the input image with opencv findContours. \"\"\" contours = cv2 . findContours ( mask , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE ) contours = contours [ 0 ] if len ( contours ) == 2 else contours [ 1 ] return contours floodfill_img ( img , start ) Perform the floodfill algorithm on a binary image. Parameters: Name Type Description Default img ndarray A binary image. required start tuple [ int , int ] coordinates of the starting point for the floodfill algorithm. required Returns: Type Description ndarray The input image after applying the floodfill algorithm. Source code in src/prismtoolbox/wsicore/core_utils.py 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def floodfill_img ( img : np . ndarray , start : tuple [ int , int ]) -> np . ndarray : \"\"\"Perform the floodfill algorithm on a binary image. Args: img: A binary image. start: coordinates of the starting point for the floodfill algorithm. Returns: The input image after applying the floodfill algorithm. \"\"\" img_to_floodfill = img . copy () h , w = img . shape [: 2 ] mask = np . zeros (( h + 2 , w + 2 ), np . uint8 ) cv2 . floodFill ( img_to_floodfill , mask , start , 255 ) im_floodfill_inv = cv2 . bitwise_not ( img_to_floodfill ) img_out = img | im_floodfill_inv return img_out [ 1 : - 1 , 1 : - 1 ] isBlackPatch ( patch , rgb_thresh = 20 , percentage = 0.05 ) Check if a patch is black. Parameters: Name Type Description Default patch Image An input patch. required rgb_thresh int The threshold value for the RGB channels to be considered black. 20 percentage float The percentage of pixels below the threshold to consider the patch as black. 0.05 Returns: Type Description bool True if the patch is black, False otherwise. Source code in src/prismtoolbox/wsicore/core_utils.py 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 def isBlackPatch ( patch : Image . Image , rgb_thresh : int = 20 , percentage : float = 0.05 ) -> bool : \"\"\"Check if a patch is black. Args: patch: An input patch. rgb_thresh: The threshold value for the RGB channels to be considered black. percentage: The percentage of pixels below the threshold to consider the patch as black. Returns: True if the patch is black, False otherwise. \"\"\" num_pixels = patch . size [ 0 ] * patch . size [ 1 ] return ( True if np . all ( np . array ( patch ) < rgb_thresh , axis = 2 ) . sum () > num_pixels * percentage else False ) isWhitePatch ( patch , rgb_thresh = 220 , percentage = 0.2 ) Check if a patch is white. Parameters: Name Type Description Default patch Image An input patch. required rgb_thresh int The threshold value for the RGB channels to be considered white. 220 percentage float The percentage of pixels above the threshold to consider the patch as white. 0.2 Returns: Type Description bool True if the patch is white, False otherwise. Source code in src/prismtoolbox/wsicore/core_utils.py 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 def isWhitePatch ( patch : Image . Image , rgb_thresh : int = 220 , percentage : float = 0.2 ) -> bool : \"\"\"Check if a patch is white. Args: patch: An input patch. rgb_thresh: The threshold value for the RGB channels to be considered white. percentage: The percentage of pixels above the threshold to consider the patch as white. Returns: True if the patch is white, False otherwise. \"\"\" num_pixels = patch . size [ 0 ] * patch . size [ 1 ] return ( True if np . all ( np . array ( patch ) > rgb_thresh , axis = 2 ) . sum () > num_pixels * percentage else False ) local_average ( img , window_size ) Perform local averaging on the image for a given window size. Parameters: Name Type Description Default img ndarray An input image. required window_size int The window size to use for local averaging. required Returns: Type Description ndarray Grayscale image minus the local average on a window of size window_size of the input image. Source code in src/prismtoolbox/wsicore/core_utils.py 70 71 72 73 74 75 76 77 78 79 80 81 82 def local_average ( img : np . ndarray , window_size : int ) -> np . ndarray : \"\"\"Perform local averaging on the image for a given window size. Args: img: An input image. window_size: The window size to use for local averaging. Returns: Grayscale image minus the local average on a window of size window_size of the input image. \"\"\" window = np . ones (( window_size , window_size )) / ( window_size ** 2 ) img_grayscaled = cv2 . cvtColor ( img , cv2 . COLOR_RGB2GRAY ) return img_grayscaled - oaconvolve ( img_grayscaled , window , mode = \"same\" ) save_patches_with_hdf5 ( output_path , asset_dict , attr_dict = None ) Save patches to an HDF5 file. Parameters: Name Type Description Default output_path str The path to the output HDF5 file. required asset_dict dict [ str , ndarray ] A dictionary of patches to save (key: name of the dataset, value: array of patches coordinates). required attr_dict dict [ str , dict [ str , str | int | tuple [ int , int ]]] A dictionary of attributes to save (key: name of the dataset, value: dictionary of attributes to None set to the HDF5 dataset). Source code in src/prismtoolbox/wsicore/core_utils.py 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 def save_patches_with_hdf5 ( output_path : str , asset_dict : dict [ str , np . ndarray ], attr_dict : dict [ str , dict [ str , str | int | tuple [ int , int ]]] = None , ) -> None : \"\"\"Save patches to an HDF5 file. Args: output_path: The path to the output HDF5 file. asset_dict: A dictionary of patches to save (key: name of the dataset, value: array of patches coordinates). attr_dict: A dictionary of attributes to save (key: name of the dataset, value: dictionary of attributes to set to the HDF5 dataset). \"\"\" file = h5py . File ( output_path , \"w\" ) for key , val in asset_dict . items (): data_shape = val . shape if key not in file : data_type = val . dtype chunk_shape = ( 1 ,) + data_shape [ 1 :] maxshape = ( None ,) + data_shape [ 1 :] dset = file . create_dataset ( key , shape = data_shape , maxshape = maxshape , chunks = chunk_shape , dtype = data_type , ) dset [:] = val if attr_dict is not None : if key in attr_dict . keys (): for attr_key , attr_val in attr_dict [ key ] . items (): dset . attrs [ attr_key ] = attr_val else : dset = file [ key ] dset . resize ( len ( dset ) + data_shape [ 0 ], axis = 0 ) dset [ - data_shape [ 0 ] :] = val file . close () select_roi_on_thumbnail ( img , scale_factor ) Select a region of interest on the thumbnail of the slide using an interactive window. Parameters: Name Type Description Default img ndarray A thumbnail of the slide. required scale_factor int The scale factor to apply to convert the coordinates to the original slide dimensions. required Returns: Type Description ndarray The coordinates of the selected region of interest. Source code in src/prismtoolbox/wsicore/core_utils.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 def select_roi_on_thumbnail ( img : np . ndarray , scale_factor : int ) -> np . ndarray : \"\"\"Select a region of interest on the thumbnail of the slide using an interactive window. Args: img: A thumbnail of the slide. scale_factor: The scale factor to apply to convert the coordinates to the original slide dimensions. Returns: The coordinates of the selected region of interest. \"\"\" drawing = False roi = [ 0 , 0 , 0 , 0 ] def _draw_rectangle ( event , x , y , flags , param ): nonlocal roi , drawing if event == cv2 . EVENT_LBUTTONDOWN : drawing = True roi [ 0 ], roi [ 1 ] = x , y elif event == cv2 . EVENT_MOUSEMOVE : if drawing : roi [ 2 ], roi [ 3 ] = x , y elif event == cv2 . EVENT_LBUTTONUP : drawing = False roi [ 2 ], roi [ 3 ] = x , y window_name = \"Select ROI (beginning by the top left corner) - press ESC to exit\" cv2 . namedWindow ( window_name , cv2 . WINDOW_NORMAL ) cv2 . resizeWindow ( window_name , 800 , 800 ) cv2 . setMouseCallback ( window_name , _draw_rectangle ) while True : temp_img = img . copy () if roi [ 2 ] and roi [ 3 ]: cv2 . rectangle ( temp_img , ( roi [ 0 ], roi [ 1 ]), ( roi [ 2 ], roi [ 3 ]), ( 0 , 255 , 0 ), 5 ) width = ( roi [ 2 ] - roi [ 0 ]) * scale_factor height = ( roi [ 3 ] - roi [ 1 ]) * scale_factor cv2 . putText ( temp_img , f \"Width: { width } - Height: { height } \" , ( roi [ 0 ], roi [ 1 ] - 10 ), cv2 . FONT_HERSHEY_SIMPLEX , 1 , ( 0 , 255 , 0 ), 2 , ) cv2 . imshow ( window_name , temp_img ) k = cv2 . waitKey ( 1 ) & 0xFF if k == 27 : break cv2 . destroyAllWindows () return np . array ( roi )","title":"prismtoolbox.wsicore.core_utils"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.IsInContour","text":"The IsInContour class checks if a patch is inside a contour. Parameters: Name Type Description Default contour ndarray The contour to check if the patch is inside. required patch_size int The size of the patches that will be checked. required center_shift int The shift of the center of the patch. 0.5 mode str The mode to use for the contour checking. Possible values are: \"center\" mode checks if the center of the patch is within the contour. \"four_pt\" mode checks if one out of the four corners of the patch are within the contour. \"four_pt_hard\" mode checks if the four corners of the patch are within the contour. 'center' Source code in src/prismtoolbox/wsicore/core_utils.py 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 def __init__ ( self , contour : np . ndarray , patch_size : int , center_shift : int = 0.5 , mode : str = \"center\" , ): \"\"\"The IsInContour class checks if a patch is inside a contour. Args: contour: The contour to check if the patch is inside. patch_size: The size of the patches that will be checked. center_shift: The shift of the center of the patch. mode: The mode to use for the contour checking. Possible values are: - \"center\" mode checks if the center of the patch is within the contour. - \"four_pt\" mode checks if one out of the four corners of the patch are within the contour. - \"four_pt_hard\" mode checks if the four corners of the patch are within the contour. \"\"\" self . cont = contour self . patch_size = patch_size self . shift = int ( patch_size // 2 * center_shift ) self . mode = mode","title":" IsInContour"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.IsInContour.__call__","text":"Check if a point is inside the contour. Parameters: Name Type Description Default pt tuple [ int , int ] The top left coordinate of the patch. required Returns: Type Description int 1 if the patch is inside the contour, 0 otherwise. Source code in src/prismtoolbox/wsicore/core_utils.py 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 def __call__ ( self , pt : tuple [ int , int ]) -> int : \"\"\"Check if a point is inside the contour. Args: pt: The top left coordinate of the patch. Returns: 1 if the patch is inside the contour, 0 otherwise. \"\"\" center = ( int ( pt [ 0 ] + self . patch_size // 2 ), int ( pt [ 1 ] + self . patch_size // 2 )) if self . mode == \"center\" : return 1 if cv2 . pointPolygonTest ( self . cont , center , False ) >= 0 else 0 else : if self . shift > 0 : all_points = [ ( center [ 0 ] - self . shift , center [ 1 ] - self . shift ), ( center [ 0 ] + self . shift , center [ 1 ] + self . shift ), ( center [ 0 ] + self . shift , center [ 1 ] - self . shift ), ( center [ 0 ] - self . shift , center [ 1 ] + self . shift ), ] else : all_points = [ center ] # Easy version of 4pt contour checking function - 1 of 4 points need to be in the contour for test to pass if self . mode == \"four_pt\" : for points in all_points : if cv2 . pointPolygonTest ( self . cont , points , False ) >= 0 : return 1 return 0 # Hard version of 4pt contour checking function - all 4 points need to be in the contour for test to pass elif self . mode == \"four_pt_hard\" : for points in all_points : if cv2 . pointPolygonTest ( self . cont , points , False ) < 0 : return 0 return 1 else : raise ValueError ( f \"mode { self . mode } not recognized\" )","title":" __call__"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.apply_bilateral_filter","text":"Apply a bilateral filter on a grayscale image. Parameters: Name Type Description Default img ndarray An input grayscale image. required Returns: Type Description ndarray The input image after applying a bilateral filter of parameters 3, 3*2, 3/2. Source code in src/prismtoolbox/wsicore/core_utils.py 150 151 152 153 154 155 156 157 158 159 160 161 def apply_bilateral_filter ( img : np . ndarray ) -> np . ndarray : \"\"\"Apply a bilateral filter on a grayscale image. Args: img: An input grayscale image. Returns: The input image after applying a bilateral filter of parameters 3, 3*2, 3/2. \"\"\" assert len ( img . shape ) == 2 , f \"Input image should be grayscale, got shape { img . shape } \" img_filtered = cv2 . bilateralFilter ( img , 3 , 3 * 2 , 3 / 2 ) return img_filtered","title":" apply_bilateral_filter"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.apply_binary_thresh","text":"Apply a binary threshold on a grayscale image. Parameters: Name Type Description Default img ndarray An input grayscale image. required thresh int The threshold value. required inv bool Set to True to invert the binary threshold. required Returns: Type Description ndarray The input image after applying a binary threshold. Source code in src/prismtoolbox/wsicore/core_utils.py 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 def apply_binary_thresh ( img : np . ndarray , thresh : int , inv : bool ) -> np . ndarray : \"\"\"Apply a binary threshold on a grayscale image. Args: img: An input grayscale image. thresh: The threshold value. inv: Set to True to invert the binary threshold. Returns: The input image after applying a binary threshold. \"\"\" if inv : _ , img_thresh = cv2 . threshold ( img , thresh , 255 , cv2 . THRESH_BINARY_INV ) else : _ , img_thresh = cv2 . threshold ( img , thresh , 255 , cv2 . THRESH_BINARY ) return img_thresh","title":" apply_binary_thresh"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.compute_law_feats","text":"Compute the Law's texture energy for a given grayscale image and window size. Parameters: Name Type Description Default img ndarray An input grayscale image. required window_size int The window size to use for Law's texture energy computation. required only_s5 bool Set to True to only compute the Law's texture energy for the S5S5 filter. True Returns: Type Description list [ ndarray ] A list of Law's texture energy map for each filter extracted from the input image. Source code in src/prismtoolbox/wsicore/core_utils.py 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 def compute_law_feats ( img : np . ndarray , window_size : int , only_s5 : bool = True ) -> list [ np . ndarray ]: \"\"\"Compute the Law's texture energy for a given grayscale image and window size. Args: img: An input grayscale image. window_size: The window size to use for Law's texture energy computation. only_s5: Set to True to only compute the Law's texture energy for the S5S5 filter. Returns: A list of Law's texture energy map for each filter extracted from the input image. \"\"\" L5 = np . array ([ 1 , 4 , 6 , 4 , 1 ]) E5 = np . array ([ - 1 , - 2 , 0 , 2 , 1 ]) S5 = np . array ([ - 1 , 0 , 2 , 0 , - 1 ]) R5 = np . array ([ 1 , - 4 , 6 , - 4 , 1 ]) vectors = [ L5 , E5 , S5 , R5 ] vectors_name = [ \"L5\" , \"E5\" , \"S5\" , \"R5\" ] if only_s5 : vectors = [ vectors [ 2 ]] vectors_name = [ vectors_name [ 2 ]] filters = [ np . expand_dims ( vectors [ i ], - 1 ) . dot ( np . expand_dims ( vectors [ j ], - 1 ) . T ) for i , j in product ( range ( len ( vectors )), range ( len ( vectors ))) ] filters_name = np . array ( [ vectors_name [ i ] + vectors_name [ j ] for i , j in product ( range ( len ( vectors )), range ( len ( vectors ))) ] ) imgs_filtered = [] for filt in filters : imgs_filtered . append ( oaconvolve ( img , filt , mode = \"same\" )) window = np . ones (( window_size , window_size )) imgs_energy = [] for img_filtered in imgs_filtered : imgs_energy . append ( oaconvolve ( np . abs ( img_filtered ), window , mode = \"same\" )) def _get_img_energy ( name ): return imgs_energy [ np . where ( filters_name == name )[ 0 ] . item ()] if only_s5 : imgs_feats = [ _get_img_energy ( \"S5S5\" )] else : imgs_feats = [ np . mean ( np . array ([ _get_img_energy ( \"L5E5\" ), _get_img_energy ( \"E5L5\" )]), axis = 0 ), np . mean ( np . array ([ _get_img_energy ( \"L5R5\" ), _get_img_energy ( \"R5L5\" )]), axis = 0 ), np . mean ( np . array ([ _get_img_energy ( \"E5S5\" ), _get_img_energy ( \"S5E5\" )]), axis = 0 ), _get_img_energy ( \"S5S5\" ), _get_img_energy ( \"R5R5\" ), np . mean ( np . array ([ _get_img_energy ( \"L5S5\" ), _get_img_energy ( \"S5L5\" )]), axis = 0 ), _get_img_energy ( \"E5E5\" ), np . mean ( np . array ([ _get_img_energy ( \"E5R5\" ), _get_img_energy ( \"R5E5\" )]), axis = 0 ), np . mean ( np . array ([ _get_img_energy ( \"S5R5\" ), _get_img_energy ( \"R5S5\" )]), axis = 0 ), ] return imgs_feats","title":" compute_law_feats"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.contour_mask","text":"Find the contours in a binary image with opencv findContours. Parameters: Name Type Description Default mask ndarray A binary image. required Returns: Type Description list [ ndarray ] A list of contours found in the input image with opencv findContours. Source code in src/prismtoolbox/wsicore/core_utils.py 201 202 203 204 205 206 207 208 209 210 211 212 def contour_mask ( mask : np . ndarray ) -> list [ np . ndarray ]: \"\"\"Find the contours in a binary image with opencv findContours. Args: mask: A binary image. Returns: A list of contours found in the input image with opencv findContours. \"\"\" contours = cv2 . findContours ( mask , cv2 . RETR_TREE , cv2 . CHAIN_APPROX_SIMPLE ) contours = contours [ 0 ] if len ( contours ) == 2 else contours [ 1 ] return contours","title":" contour_mask"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.floodfill_img","text":"Perform the floodfill algorithm on a binary image. Parameters: Name Type Description Default img ndarray A binary image. required start tuple [ int , int ] coordinates of the starting point for the floodfill algorithm. required Returns: Type Description ndarray The input image after applying the floodfill algorithm. Source code in src/prismtoolbox/wsicore/core_utils.py 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def floodfill_img ( img : np . ndarray , start : tuple [ int , int ]) -> np . ndarray : \"\"\"Perform the floodfill algorithm on a binary image. Args: img: A binary image. start: coordinates of the starting point for the floodfill algorithm. Returns: The input image after applying the floodfill algorithm. \"\"\" img_to_floodfill = img . copy () h , w = img . shape [: 2 ] mask = np . zeros (( h + 2 , w + 2 ), np . uint8 ) cv2 . floodFill ( img_to_floodfill , mask , start , 255 ) im_floodfill_inv = cv2 . bitwise_not ( img_to_floodfill ) img_out = img | im_floodfill_inv return img_out [ 1 : - 1 , 1 : - 1 ]","title":" floodfill_img"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.isBlackPatch","text":"Check if a patch is black. Parameters: Name Type Description Default patch Image An input patch. required rgb_thresh int The threshold value for the RGB channels to be considered black. 20 percentage float The percentage of pixels below the threshold to consider the patch as black. 0.05 Returns: Type Description bool True if the patch is black, False otherwise. Source code in src/prismtoolbox/wsicore/core_utils.py 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 def isBlackPatch ( patch : Image . Image , rgb_thresh : int = 20 , percentage : float = 0.05 ) -> bool : \"\"\"Check if a patch is black. Args: patch: An input patch. rgb_thresh: The threshold value for the RGB channels to be considered black. percentage: The percentage of pixels below the threshold to consider the patch as black. Returns: True if the patch is black, False otherwise. \"\"\" num_pixels = patch . size [ 0 ] * patch . size [ 1 ] return ( True if np . all ( np . array ( patch ) < rgb_thresh , axis = 2 ) . sum () > num_pixels * percentage else False )","title":" isBlackPatch"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.isWhitePatch","text":"Check if a patch is white. Parameters: Name Type Description Default patch Image An input patch. required rgb_thresh int The threshold value for the RGB channels to be considered white. 220 percentage float The percentage of pixels above the threshold to consider the patch as white. 0.2 Returns: Type Description bool True if the patch is white, False otherwise. Source code in src/prismtoolbox/wsicore/core_utils.py 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 def isWhitePatch ( patch : Image . Image , rgb_thresh : int = 220 , percentage : float = 0.2 ) -> bool : \"\"\"Check if a patch is white. Args: patch: An input patch. rgb_thresh: The threshold value for the RGB channels to be considered white. percentage: The percentage of pixels above the threshold to consider the patch as white. Returns: True if the patch is white, False otherwise. \"\"\" num_pixels = patch . size [ 0 ] * patch . size [ 1 ] return ( True if np . all ( np . array ( patch ) > rgb_thresh , axis = 2 ) . sum () > num_pixels * percentage else False )","title":" isWhitePatch"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.local_average","text":"Perform local averaging on the image for a given window size. Parameters: Name Type Description Default img ndarray An input image. required window_size int The window size to use for local averaging. required Returns: Type Description ndarray Grayscale image minus the local average on a window of size window_size of the input image. Source code in src/prismtoolbox/wsicore/core_utils.py 70 71 72 73 74 75 76 77 78 79 80 81 82 def local_average ( img : np . ndarray , window_size : int ) -> np . ndarray : \"\"\"Perform local averaging on the image for a given window size. Args: img: An input image. window_size: The window size to use for local averaging. Returns: Grayscale image minus the local average on a window of size window_size of the input image. \"\"\" window = np . ones (( window_size , window_size )) / ( window_size ** 2 ) img_grayscaled = cv2 . cvtColor ( img , cv2 . COLOR_RGB2GRAY ) return img_grayscaled - oaconvolve ( img_grayscaled , window , mode = \"same\" )","title":" local_average"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.save_patches_with_hdf5","text":"Save patches to an HDF5 file. Parameters: Name Type Description Default output_path str The path to the output HDF5 file. required asset_dict dict [ str , ndarray ] A dictionary of patches to save (key: name of the dataset, value: array of patches coordinates). required attr_dict dict [ str , dict [ str , str | int | tuple [ int , int ]]] A dictionary of attributes to save (key: name of the dataset, value: dictionary of attributes to None set to the HDF5 dataset). Source code in src/prismtoolbox/wsicore/core_utils.py 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 def save_patches_with_hdf5 ( output_path : str , asset_dict : dict [ str , np . ndarray ], attr_dict : dict [ str , dict [ str , str | int | tuple [ int , int ]]] = None , ) -> None : \"\"\"Save patches to an HDF5 file. Args: output_path: The path to the output HDF5 file. asset_dict: A dictionary of patches to save (key: name of the dataset, value: array of patches coordinates). attr_dict: A dictionary of attributes to save (key: name of the dataset, value: dictionary of attributes to set to the HDF5 dataset). \"\"\" file = h5py . File ( output_path , \"w\" ) for key , val in asset_dict . items (): data_shape = val . shape if key not in file : data_type = val . dtype chunk_shape = ( 1 ,) + data_shape [ 1 :] maxshape = ( None ,) + data_shape [ 1 :] dset = file . create_dataset ( key , shape = data_shape , maxshape = maxshape , chunks = chunk_shape , dtype = data_type , ) dset [:] = val if attr_dict is not None : if key in attr_dict . keys (): for attr_key , attr_val in attr_dict [ key ] . items (): dset . attrs [ attr_key ] = attr_val else : dset = file [ key ] dset . resize ( len ( dset ) + data_shape [ 0 ], axis = 0 ) dset [ - data_shape [ 0 ] :] = val file . close ()","title":" save_patches_with_hdf5"},{"location":"reference/prismtoolbox/wsicore/core_utils/#prismtoolbox.wsicore.core_utils.select_roi_on_thumbnail","text":"Select a region of interest on the thumbnail of the slide using an interactive window. Parameters: Name Type Description Default img ndarray A thumbnail of the slide. required scale_factor int The scale factor to apply to convert the coordinates to the original slide dimensions. required Returns: Type Description ndarray The coordinates of the selected region of interest. Source code in src/prismtoolbox/wsicore/core_utils.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 def select_roi_on_thumbnail ( img : np . ndarray , scale_factor : int ) -> np . ndarray : \"\"\"Select a region of interest on the thumbnail of the slide using an interactive window. Args: img: A thumbnail of the slide. scale_factor: The scale factor to apply to convert the coordinates to the original slide dimensions. Returns: The coordinates of the selected region of interest. \"\"\" drawing = False roi = [ 0 , 0 , 0 , 0 ] def _draw_rectangle ( event , x , y , flags , param ): nonlocal roi , drawing if event == cv2 . EVENT_LBUTTONDOWN : drawing = True roi [ 0 ], roi [ 1 ] = x , y elif event == cv2 . EVENT_MOUSEMOVE : if drawing : roi [ 2 ], roi [ 3 ] = x , y elif event == cv2 . EVENT_LBUTTONUP : drawing = False roi [ 2 ], roi [ 3 ] = x , y window_name = \"Select ROI (beginning by the top left corner) - press ESC to exit\" cv2 . namedWindow ( window_name , cv2 . WINDOW_NORMAL ) cv2 . resizeWindow ( window_name , 800 , 800 ) cv2 . setMouseCallback ( window_name , _draw_rectangle ) while True : temp_img = img . copy () if roi [ 2 ] and roi [ 3 ]: cv2 . rectangle ( temp_img , ( roi [ 0 ], roi [ 1 ]), ( roi [ 2 ], roi [ 3 ]), ( 0 , 255 , 0 ), 5 ) width = ( roi [ 2 ] - roi [ 0 ]) * scale_factor height = ( roi [ 3 ] - roi [ 1 ]) * scale_factor cv2 . putText ( temp_img , f \"Width: { width } - Height: { height } \" , ( roi [ 0 ], roi [ 1 ] - 10 ), cv2 . FONT_HERSHEY_SIMPLEX , 1 , ( 0 , 255 , 0 ), 2 , ) cv2 . imshow ( window_name , temp_img ) k = cv2 . waitKey ( 1 ) & 0xFF if k == 27 : break cv2 . destroyAllWindows () return np . array ( roi )","title":" select_roi_on_thumbnail"},{"location":"reference/prismtoolbox/wsicore/wsi/","text":"WSI ( slide_path , engine = 'openslide' ) The WSI (Whole Slide Image) class is responsible for handling operations related to whole slide images. Parameters: Name Type Description Default slide_path str The path to the slide image file. required engine str The engine used to read the slide image. 'openslide' Attributes: Name Type Description slide_path str The path to the slide image file. engine str The engine used to read the slide image. slide_name str The name of the slide image file. Retrieved from the slide path using retrieve_slide_name_ext method. slide_ext str The extension of the slide image file. Retrieved from the slide path using retrieve_slide_name_ext method. slide OpenSlide | TiffSlide The wsi read from the file using engine. dimensions list [ tuple [ int , int ]] The dimensions of the slide image. Set by the set_slide_attributes method. level_dimensions list [ tuple [ int , int ]] The dimensions of the different levels of the slide image. Set by the set_slide_attributes method. level_downsamples list [ tuple [ int , int ]] The downsampling factors of the different levels of the slide image. Set by the set_slide_attributes method. properties dict The properties of the slide image. Set by the set_slide_attributes method. offset tuple [ int , int ] The offset of the slide image. Set by the set_slide_attributes method. ROI ndarray The region of interest in the slide image. Please use the set_roi method to set the ROI. ROI_width int The width of the region of interest. Set by the set_roi method. ROI_height int The height of the region of interest. Set by the set_roi method. tissue_contours list [ ndarray ] The contours of the tissue in the slide image. Please use the detect_tissue method to detect the tissue contours. coords ndarray The coordinates of patches extracted from slide image. Please use the extract_patches method to extract patches. coords_attrs dict The attributes of the coordinates. Set by the extract_patches method. Source code in src/prismtoolbox/wsicore/wsi.py 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 def __init__ ( self , slide_path : str , engine : str = \"openslide\" ): \"\"\"The WSI (Whole Slide Image) class is responsible for handling operations related to whole slide images. Args: slide_path: The path to the slide image file. engine: The engine used to read the slide image. Attributes: slide_path (str): The path to the slide image file. engine (str): The engine used to read the slide image. slide_name (str): The name of the slide image file. Retrieved from the slide path using [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method. slide_ext (str): The extension of the slide image file. Retrieved from the slide path using [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method. slide (OpenSlide | TiffSlide): The wsi read from the file using engine. dimensions (list[tuple[int, int]]): The dimensions of the slide image. Set by the set_slide_attributes method. level_dimensions (list[tuple[int, int]]): The dimensions of the different levels of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. level_downsamples (list[tuple[int, int]]): The downsampling factors of the different levels of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. properties (dict): The properties of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. offset (tuple[int, int]): The offset of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. ROI (ndarray): The region of interest in the slide image. Please use the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method to set the ROI. ROI_width (int): The width of the region of interest. Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method. ROI_height (int): The height of the region of interest. Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method. tissue_contours (list[ndarray]): The contours of the tissue in the slide image. Please use the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method to detect the tissue contours. coords (np.ndarray): The coordinates of patches extracted from slide image. Please use the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method to extract patches. coords_attrs (dict): The attributes of the coordinates. Set by the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method. \"\"\" self . slide_path = slide_path self . engine = engine self . slide_name , self . slide_ext = self . retrieve_slide_name_ext ( self . slide_path ) self . slide = self . read ( slide_path , engine ) self . dimensions = None self . level_dimensions = None self . level_downsamples = None self . properties = None self . offset = ( 0 , 0 ) self . set_slide_attributes () self . ROI = None self . ROI_width = None self . ROI_height = None self . tissue_contours = None self . coords = None self . coords_attrs = None apply_pathologist_annotations ( path , class_name = 'annotation' , column_to_select = 'objectType' ) Apply pathologist annotations to the tissue contours by intersecting the annotations with the tissue contours. Requires the tissue contours to be set for the slide beforehand with the detect_tissue method. Parameters: Name Type Description Default path str The path to the .geojson file containing the annotations extracted from QuPath. required class_name str The class name to use for selecting the annotations to apply. 'annotation' column_to_select str The column to select in the GeoDataFrame. 'objectType' Source code in src/prismtoolbox/wsicore/wsi.py 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 def apply_pathologist_annotations ( self , path : str , class_name : str = \"annotation\" , column_to_select : str = \"objectType\" , ) -> None : \"\"\"Apply pathologist annotations to the tissue contours by intersecting the annotations with the tissue contours. Requires the tissue contours to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method. Args: path: The path to the .geojson file containing the annotations extracted from QuPath. class_name: The class name to use for selecting the annotations to apply. column_to_select: The column to select in the GeoDataFrame. \"\"\" assert ( self . tissue_contours is not None ), \"No tissue contours found for the slide, please run the detect_tissue method first\" offset = ( - self . offset [ 0 ], - self . offset [ 1 ]) pathologist_annotations = read_qupath_annotations ( path , offset = offset , class_name = class_name , column_to_select = column_to_select ) polygons = contoursToPolygons ( self . tissue_contours , make_valid = True ) intersection = intersectionPolygons ( polygons , pathologist_annotations ) self . tissue_contours = PolygonsToContours ( intersection ) convert_micrometer_to_pixel ( value , level , axis = 'x' ) Convert a value from micrometer to pixel. Parameters: Name Type Description Default value float The value to convert (in micrometer). required level int The level at which the conversion should be performed. required axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description int The input value in pixel. Source code in src/prismtoolbox/wsicore/wsi.py 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 def convert_micrometer_to_pixel ( self , value : float , level : int , axis : str = \"x\" , ) -> int : \"\"\"Convert a value from micrometer to pixel. Args: value: The value to convert (in micrometer). level: The level at which the conversion should be performed. axis: The axis to use for getting the conversion factor (x or y). Returns: The input value in pixel. \"\"\" return int ( value / float ( self . properties [ f \" { self . engine } .mpp- { axis } \" ])) // int ( self . level_downsamples [ level ] ) convert_pixel_to_micrometer ( value , level , axis = 'x' ) Convert a value from pixel to micrometer. Parameters: Name Type Description Default value float The value to convert (in pixel). required level int The level at which the conversion should be performed. required axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description float The input value in micrometer. Source code in src/prismtoolbox/wsicore/wsi.py 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 def convert_pixel_to_micrometer ( self , value : float , level : int , axis : str = \"x\" , ) -> float : \"\"\"Convert a value from pixel to micrometer. Args: value: The value to convert (in pixel). level: The level at which the conversion should be performed. axis: The axis to use for getting the conversion factor (x or y). Returns: The input value in micrometer. \"\"\" return ( value * float ( self . properties [ f \" { self . engine } .mpp- { axis } \" ]) * self . level_downsamples [ level ] ) convert_units ( value , level , from_unit , to_unit = 'px' , axis = 'x' ) Convert a value from one unit to another. Parameters: Name Type Description Default value float The value to convert. required level int The level at which the conversion should be performed. required from_unit str The unit to convert from (px or micro). required to_unit str The unit to convert to (px or micro). 'px' axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description int The input value converted in the desired unit. Source code in src/prismtoolbox/wsicore/wsi.py 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 def convert_units ( self , value : float , level : int , from_unit : str , to_unit : str = \"px\" , axis : str = \"x\" , ) -> int : \"\"\"Convert a value from one unit to another. Args: value: The value to convert. level: The level at which the conversion should be performed. from_unit: The unit to convert from (px or micro). to_unit: The unit to convert to (px or micro). axis: The axis to use for getting the conversion factor (x or y). Returns: The input value converted in the desired unit. \"\"\" if from_unit == \"micro\" and to_unit == \"px\" : value = self . convert_micrometer_to_pixel ( value , level , axis ) elif from_unit == \"px\" and to_unit == \"micro\" : value = self . convert_pixel_to_micrometer ( value , level , axis ) elif from_unit == to_unit : pass else : raise ValueError ( f \"Conversion from { from_unit } to { to_unit } not supported\" ) return value create_thumbnail ( level , crop_roi = False ) Create a thumbnail of the slide at a given level. Parameters: Name Type Description Default level int The level at which the thumbnail should be created. required crop_roi bool A boolean to crop the thumbnail to the region of interest defined for the slide (requires a ROI to be set for the slide beforehand with the set_roi method) False Returns: Type Description Image A thumbnail of the slide as a PIL image. Source code in src/prismtoolbox/wsicore/wsi.py 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 def create_thumbnail ( self , level : int , crop_roi : bool = False , ) -> Image . Image : \"\"\"Create a thumbnail of the slide at a given level. Args: level: The level at which the thumbnail should be created. crop_roi: A boolean to crop the thumbnail to the region of interest defined for the slide (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method) Returns: A thumbnail of the slide as a PIL image. \"\"\" assert self . ROI is not None if crop_roi else True , ( \"No ROI provided, while crop_roi is set to True. Please set a ROI for \" \"the slide or set crop_roi to False.\" ) thumb = self . read_region (( 0 , 0 ), level , self . level_dimensions [ level ]) . convert ( \"RGB\" ) if crop_roi : log . info ( f \"Creating thumbnail with ROI { self . ROI } .\" ) coords_roi = ( self . ROI / self . level_downsamples [ level ]) . astype ( int ) thumb = thumb . crop ( coords_roi ) return thumb detect_tissue ( seg_level , window_avg , window_eng , thresh , inside_roi = False , inv_thresh = False , area_min = 200000.0 , start = ( 0 , 0 )) Segment the tissue on the slide based on a threshold on the Law's texture energy spot map and floodfill algorithm to fill the holes in the mask. The tissue contours are stored in the tissue_contours attribute. Parameters: Name Type Description Default seg_level int The level at which the segmentation should be performed. required window_avg int The size of the window for local averaging. required window_eng int The size of the window for Law's texture energy computation. required thresh int The threshold for binarization on the Law's texture energy spot map. required inside_roi bool Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide beforehand with the set_roi method). False inv_thresh bool Set to True to invert the thresholding. False area_min float The minimum area for a contour to be kept. 200000.0 start tuple [ int , int ] The starting point for the floodfill algorithm (should be left at (0, 0) in most cases). (0, 0) Source code in src/prismtoolbox/wsicore/wsi.py 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 def detect_tissue ( self , seg_level : int , window_avg : int , window_eng : int , thresh : int , inside_roi : bool = False , inv_thresh : bool = False , area_min : float = 2e5 , start : tuple [ int , int ] = ( 0 , 0 ), ) -> None : \"\"\"Segment the tissue on the slide based on a threshold on the Law's texture energy spot map and floodfill algorithm to fill the holes in the mask. The tissue contours are stored in the tissue_contours attribute. Args: seg_level: The level at which the segmentation should be performed. window_avg: The size of the window for local averaging. window_eng: The size of the window for Law's texture energy computation. thresh: The threshold for binarization on the Law's texture energy spot map. inside_roi: Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). inv_thresh: Set to True to invert the thresholding. area_min: The minimum area for a contour to be kept. start: The starting point for the floodfill algorithm (should be left at (0, 0) in most cases). \"\"\" final_contours = [] img = np . array ( self . create_thumbnail ( seg_level , inside_roi )) img_avg = local_average ( np . asarray ( img ), window_avg ) law_feats = compute_law_feats ( img_avg , window_eng )[ 0 ] filterred_img = apply_bilateral_filter ( np . clip ( law_feats , 0 , 255 ) . astype ( \"uint8\" )) threshed_img = apply_binary_thresh ( filterred_img , thresh , inv_thresh ) flooded_img = floodfill_img ( np . pad ( threshed_img , 1 ), start ) contours = contour_mask ( flooded_img ) for contour in contours : c = contour . copy () area = cv2 . contourArea ( c ) if area > area_min : final_contours . append ( contour ) if len ( final_contours ) == 0 : self . tissue_contours = [] print ( f \"No contours found for slide { self . slide_name } .\" ) return else : scale = self . level_downsamples [ seg_level ] offset = np . array ( self . ROI [: 2 ]) if self . ROI is not None else np . array ([ 0 , 0 ]) final_contours = self . scale_contours ( final_contours , scale ) final_contours = [ cont + offset for cont in final_contours ] # Sanity check to ensure that the contours are all within a ROI is provided if self . ROI is not None : assert all ( [ np . all ( cont >= self . ROI [: 2 ]) and np . all ( cont <= self . ROI [ 2 :]) for cont in final_contours ] ) self . tissue_contours = final_contours print ( f \"Identified { len ( final_contours ) } contours for slide { self . slide_name } .\" ) return extract_patches ( patch_size , patch_level , mode , step_size = None , overlap = None , units = ( 'px' , 'px' ), use_padding = True , contours_mode = None , rgb_threshs = ( 2 , 220 ), percentages = ( 0.6 , 0.9 )) Extract valid patches from the slide with different extraction modes. A patch is considered valid if it is not black or white and is within the region of interest or the tissue contours if relevant. The extracted patches are stored as coordinates in the coords attribute, and the attributes of the coordinates are stored in the coords_attrs attribute. Parameters: Name Type Description Default patch_size float The size of the patches to extract (assumed to be square). required patch_level int The level at which the patches should be extracted. required mode str The mode to use for the extraction: \"contours\" mode extracts patches within the tissue contours (requires the tissue contours to be set for the slide beforehand with the detect_tissue method). \"roi\" mode extracts patches within the region of interest (requires the ROI to be set for the slide beforehand with the set_roi method). \"all\" mode extracts patches from the entire slide required step_size float | None The step size for the sliding window (if set to None, the step size will be computed based on the overlap). None overlap float | None The overlap between patches as an absolute value (must be provided if step_size is set to None). None units tuple [ str , str ] The units for the patch size and step size/overlap values (pixels: px, micrometers: micro). ('px', 'px') use_padding bool Set to True to use padding for the extraction. True contours_mode str | None The mode to use for the contour checking function (must be provided if mode is set to contours). See IsInContour for more details. None rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Source code in src/prismtoolbox/wsicore/wsi.py 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 def extract_patches ( self , patch_size : float , patch_level : int , mode : str , step_size : float | None = None , overlap : float | None = None , units : tuple [ str , str ] = ( \"px\" , \"px\" ), use_padding : bool = True , contours_mode : str | None = None , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> None : \"\"\"Extract valid patches from the slide with different extraction modes. A patch is considered valid if it is not black or white and is within the region of interest or the tissue contours if relevant. The extracted patches are stored as coordinates in the coords attribute, and the attributes of the coordinates are stored in the coords_attrs attribute. Args: patch_size: The size of the patches to extract (assumed to be square). patch_level: The level at which the patches should be extracted. mode: The mode to use for the extraction: - \"contours\" mode extracts patches within the tissue contours (requires the tissue contours to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method). - \"roi\" mode extracts patches within the region of interest (requires the ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). - \"all\" mode extracts patches from the entire slide step_size: The step size for the sliding window (if set to None, the step size will be computed based on the overlap). overlap: The overlap between patches as an absolute value (must be provided if step_size is set to None). units: The units for the patch size and step size/overlap values (pixels: px, micrometers: micro). use_padding: Set to True to use padding for the extraction. contours_mode: The mode to use for the contour checking function (must be provided if mode is set to contours). See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. \"\"\" assert ( step_size is not None or overlap is not None ), \"Either step_size or overlap must be provided\" assert all ( [ unit in [ \"micro\" , \"px\" ] for unit in units ] ), \"Units must be either 'micro' or 'px'\" patch_size = self . convert_units ( patch_size , patch_level , units [ 0 ]) if step_size is None : step_size = patch_size - self . convert_units ( overlap , patch_level , units [ 1 ]) else : step_size = self . convert_units ( step_size , patch_level , units [ 1 ]) log . info ( f \"Extracting patches of size { patch_size } at level { patch_level } with step size { step_size } .\" ) if mode == \"contours\" : log . info ( \"Extracting patches with 'contours' mode.\" ) assert self . tissue_contours is not None assert len ( self . tissue_contours ) > 0 assert contours_mode is not None valid_coords = [] for cont in self . tissue_contours : roi_dim = cv2 . boundingRect ( cont ) log . info ( f \"Processing ROI of dimensions: { roi_dim } \" ) valid_coords . extend ( self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , cont , contours_mode , rgb_threshs = rgb_threshs , percentages = percentages , ) ) valid_coords = np . array ( valid_coords ) elif mode == \"roi\" : log . info ( \"Extracting patches with 'roi' mode.\" ) assert self . ROI is not None roi_dim = self . ROI [ 0 ], self . ROI [ 1 ], self . ROI_width , self . ROI_height log . info ( \"Processing ROI of dimensions:\" , roi_dim ) valid_coords = self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , rgb_threshs = rgb_threshs , percentages = percentages , ) elif mode == \"all\" : roi_dim = 0 , 0 , self . level_dimensions [ 0 ][ 0 ], self . level_dimensions [ 0 ][ 1 ] log . info ( \"Processing ROI of dimensions:\" , roi_dim ) valid_coords = self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , rgb_threshs = rgb_threshs , percentages = percentages , ) else : raise ValueError ( f \"Mode { mode } not supported\" ) attr = { \"patch_size\" : patch_size , \"patch_level\" : patch_level , \"downsample\" : self . level_downsamples [ patch_level ], \"downsampled_level_dim\" : tuple ( np . array ( self . level_dimensions [ patch_level ])), \"level_dim\" : self . level_dimensions [ patch_level ], \"name\" : self . slide_name , } if len ( valid_coords ) == 0 : log . warning ( f \"No valid coordinates found for slide { self . slide_name } .\" ) else : print ( f \"Identified a total of { len ( valid_coords ) } valid coordinates in the slide { self . slide_name } .\" ) self . coords = valid_coords self . coords_attrs = attr extract_patches_roi ( patch_level , patch_size , step_size = None , roi_dim = None , use_padding = True , contour = None , contours_mode = None , coord_candidates = None , rgb_threshs = ( 2 , 220 ), percentages = ( 0.6 , 0.9 ), return_indices = False ) Extract valid patches from a region of interest, i.e if the patch is not black or white and is within the region of interest/contours if relevant). Parameters: Name Type Description Default patch_level int The level at which the patches should be extracted. required patch_size int The size of the patches to extract (assumed to be square). required step_size int | None The step size to use for the sliding window. None roi_dim tuple [ int , int , int , int ] | None The top-left corner coordinates and dimensions of the region of interest. Must be provided if coord_candidates is set to None. None use_padding bool Set to True to use padding for the extraction. True contour ndarray | None The tissue contour to use for the extraction. If set to None, will not check if patches are within a contour. None contours_mode str | None The mode for the contour checking function. See IsInContour for more details. Must be provided if mode is set to contours. Otherwise, will not check if patches are within the contours. None coord_candidates ndarray | None Precomputed candidate coordinates for the patches. If set to None, will compute the candidate coordinates based on the ROI dimensions and the step size. None rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) return_indices bool Set to True to return the indices of the valid coordinates. False Returns: Type Description ndarray An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches). Source code in src/prismtoolbox/wsicore/wsi.py 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 def extract_patches_roi ( self , patch_level : int , patch_size : int , step_size : int | None = None , roi_dim : tuple [ int , int , int , int ] | None = None , use_padding : bool = True , contour : np . ndarray | None = None , contours_mode : str | None = None , coord_candidates : np . ndarray | None = None , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), return_indices : bool = False , ) -> np . ndarray : \"\"\"Extract valid patches from a region of interest, i.e if the patch is not black or white and is within the region of interest/contours if relevant). Args: patch_level: The level at which the patches should be extracted. patch_size: The size of the patches to extract (assumed to be square). step_size: The step size to use for the sliding window. roi_dim: The top-left corner coordinates and dimensions of the region of interest. Must be provided if coord_candidates is set to None. use_padding: Set to True to use padding for the extraction. contour: The tissue contour to use for the extraction. If set to None, will not check if patches are within a contour. contours_mode: The mode for the contour checking function. See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. Must be provided if mode is set to contours. Otherwise, will not check if patches are within the contours. coord_candidates: Precomputed candidate coordinates for the patches. If set to None, will compute the candidate coordinates based on the ROI dimensions and the step size. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. return_indices: Set to True to return the indices of the valid coordinates. Returns: An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches). \"\"\" assert contours_mode is not None if contour is not None else True , ( \"A contour mode must be provided if patch \" \"extraction mode is set to contours\" ) assert ( roi_dim is not None and step_size is not None ) or coord_candidates is not None , ( \"Either (roi_dim and step_size) or coord_candidates must be provided\" ) patch_downsample = int ( self . level_downsamples [ patch_level ]) ref_patch_size = patch_size * patch_downsample if coord_candidates is None : start_x , start_y , w , h = roi_dim img_w , img_h = self . level_dimensions [ 0 ] if use_padding : stop_y = start_y + h stop_x = start_x + w else : stop_y = min ( start_y + h , img_h - ref_patch_size + 1 ) stop_x = min ( start_x + w , img_w - ref_patch_size + 1 ) step_size = step_size * patch_downsample x_range = np . arange ( start_x , stop_x , step = step_size ) y_range = np . arange ( start_y , stop_y , step = step_size ) x_coords , y_coords = np . meshgrid ( x_range , y_range , indexing = \"ij\" ) coord_candidates = np . array ( [ x_coords . flatten (), y_coords . flatten ()] ) . transpose () if contour is not None : cont_check_fn = IsInContour ( contour , patch_size = ref_patch_size , center_shift = 0.5 , mode = contours_mode ) log . info ( f \"Extracting patches with contour checking function mode { contours_mode } .\" ) else : cont_check_fn = None num_workers = mp . cpu_count () pool = mp . Pool ( num_workers , initializer = WSI . worker_init , initargs = ( self . slide_path , self . engine , ), ) iterable = [ ( coord , cont_check_fn , patch_level , patch_size , rgb_threshs , percentages ) for coord in coord_candidates ] valid_coords = pool . starmap ( WSI . process_coord_candidate , iterable ) pool . close () valid_indices = [ i for i , coord in enumerate ( valid_coords ) if coord is not None ] valid_coords = np . array ([ coord_candidates [ i ] for i in valid_indices ]) log . info ( f \"Identified { len ( valid_coords ) } valid coordinates in the ROI { roi_dim } .\" ) if return_indices : return valid_coords , valid_indices else : return valid_coords is_black_white ( patch , rgb_threshs = ( 2 , 220 ), percentages = ( 0.6 , 0.9 )) staticmethod Check if a patch is black or white. Parameters: Name Type Description Default patch Image The input patch. required rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Returns: Type Description bool True if the patch is black or white, False otherwise. Source code in src/prismtoolbox/wsicore/wsi.py 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 @staticmethod def is_black_white ( patch : Image . Image , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> bool : \"\"\"Check if a patch is black or white. Args: patch: The input patch. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. Returns: True if the patch is black or white, False otherwise. \"\"\" return isBlackPatch ( patch , rgb_thresh = rgb_threshs [ 0 ], percentage = percentages [ 0 ] ) or isWhitePatch ( patch , rgb_thresh = rgb_threshs [ 1 ], percentage = percentages [ 1 ]) load_patches ( file_path ) Load the patches from a hdf5 file. Parameters: Name Type Description Default file_path str The path to the hdf5 file containing the patches. required Source code in src/prismtoolbox/wsicore/wsi.py 449 450 451 452 453 454 455 456 def load_patches ( self , file_path : str ) -> None : \"\"\"Load the patches from a hdf5 file. Args: file_path: The path to the hdf5 file containing the patches. \"\"\" log . info ( f \"Loading patches for slide { self . slide_name } from { file_path } .\" ) self . coords , self . coords_attrs = read_h5_file ( file_path , \"coords\" ) load_tissue_contours ( file_path ) Load the tissue contours from a pickle file. Parameters: Name Type Description Default file_path str The path to the pickle file containing the tissue contours. required Source code in src/prismtoolbox/wsicore/wsi.py 366 367 368 369 370 371 372 373 def load_tissue_contours ( self , file_path : str ) -> None : \"\"\"Load the tissue contours from a pickle file. Args: file_path: The path to the pickle file containing the tissue contours. \"\"\" log . info ( f \"Loading tissue contours for slide { self . slide_name } from { file_path } .\" ) self . tissue_contours = load_obj_with_pickle ( file_path ) process_coord_candidate ( coord , cont_check_fn , patch_level , patch_size , rgb_threshs = ( 2 , 220 ), percentages = ( 0.6 , 0.9 )) staticmethod Determine if a candidate coordinate is valid based on a contour checking function and/or black/white thresholding. Parameters: Name Type Description Default coord tuple [ int , int ] The candidate coordinate. required cont_check_fn IsInContour | None The contour checking function. required patch_level int The level at which the patch should be extracted. required patch_size int The size of the patch (assumed to be square). required rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Returns: Type Description tuple [ int , int ] | None The coordinate if it is valid, None otherwise. Source code in src/prismtoolbox/wsicore/wsi.py 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 @staticmethod def process_coord_candidate ( coord : tuple [ int , int ], cont_check_fn : IsInContour | None , patch_level : int , patch_size : int , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> tuple [ int , int ] | None : \"\"\"Determine if a candidate coordinate is valid based on a contour checking function and/or black/white thresholding. Args: coord: The candidate coordinate. cont_check_fn: The contour checking function. patch_level: The level at which the patch should be extracted. patch_size: The size of the patch (assumed to be square). rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. Returns: The coordinate if it is valid, None otherwise. \"\"\" if cont_check_fn is None or cont_check_fn ( coord ): patch = wsi . read_region ( tuple ( coord ), patch_level , ( patch_size , patch_size ) ) . convert ( \"RGB\" ) if not WSI . is_black_white ( patch , rgb_threshs , percentages ): return coord else : return None else : return None read ( slide_path , engine ) staticmethod Read a slide with a given engine. Parameters: Name Type Description Default slide_path str The path to the slide. required engine str The backend library to use for reading the slide (currently only openslide and tiffslide are supported). required Returns: Type Description A slide object. Source code in src/prismtoolbox/wsicore/wsi.py 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 @staticmethod def read ( slide_path : str , engine : str ): \"\"\"Read a slide with a given engine. Args: slide_path: The path to the slide. engine: The backend library to use for reading the slide (currently only openslide and tiffslide are supported). Returns: A slide object. \"\"\" if engine == \"openslide\" : import openslide slide = openslide . OpenSlide ( slide_path ) elif engine == \"tiffslide\" : import tiffslide with warnings . catch_warnings (): warnings . simplefilter ( \"ignore\" , category = UserWarning ) slide = tiffslide . TiffSlide ( slide_path ) else : raise NotImplementedError ( f \"engine { engine } not supported\" ) return slide read_region ( location , level , size ) Read a region from the slide for a given level and size. Parameters: Name Type Description Default location tuple [ int , int ] The coordinates of the top left corner of the region (in pixels). required level int The level at which the region should be read. required size tuple [ int , int ] The size of the region (in pixels). required Returns: Type Description Image The desired region of the slide as a PIL image. Source code in src/prismtoolbox/wsicore/wsi.py 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 def read_region ( self , location : tuple [ int , int ], level : int , size : tuple [ int , int ], ) -> Image . Image : \"\"\"Read a region from the slide for a given level and size. Args: location: The coordinates of the top left corner of the region (in pixels). level: The level at which the region should be read. size: The size of the region (in pixels). Returns: The desired region of the slide as a PIL image. \"\"\" return self . slide . read_region ( location , level , size ) . convert ( \"RGB\" ) retrieve_slide_name_ext ( slide_path ) staticmethod Retrieves slide name and slide extension from slide path. Parameters: Name Type Description Default slide_path str The path to the slide. required Returns: Type Description tuple [ str , str ] A tuple (slide name, slide ext). Source code in src/prismtoolbox/wsicore/wsi.py 109 110 111 112 113 114 115 116 117 118 119 120 121 @staticmethod def retrieve_slide_name_ext ( slide_path : str ) -> tuple [ str , str ]: \"\"\"Retrieves slide name and slide extension from slide path. Args: slide_path: The path to the slide. Returns: A tuple (slide name, slide ext). \"\"\" slide_ext = re . search ( r \"(?<=\\.)[\\w\\.]+$\" , slide_path ) . group ( 0 ) slide_name = re . search ( r \"([^/]+?)(?=\\.[\\w\\.]+$)\" , slide_path ) . group ( 0 ) return slide_name , slide_ext save_patches ( save_dir , file_format = 'h5' , selected_idx = None , merge = False , label = None , color = ( 255 , 0 , 0 ), append_to_existing_file = False ) Save the patches in a hdf5 or geojson file. Parameters: Name Type Description Default save_dir str The path to the directory where the patches will be saved. required file_format str The format for the saving (h5 for python processing, geojson for QuPath processing). 'h5' selected_idx ndarray | None An array of indices of the patches to save (if set to None, all the patches will be saved). None merge bool Set to True to merge the patches into a single polygon (for geojson format only). False label str | None An optional label to assign to the patches (for geojson format only). None color tuple [ int , int , int ] An optional color to assign to the patches (for geojson format only). (255, 0, 0) append_to_existing_file bool Set to True to append the patches to an existing geojson file. False Source code in src/prismtoolbox/wsicore/wsi.py 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 def save_patches ( self , save_dir : str , file_format : str = \"h5\" , selected_idx : np . ndarray | None = None , merge : bool = False , label : str | None = None , color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), append_to_existing_file : bool = False , ) -> None : \"\"\"Save the patches in a hdf5 or geojson file. Args: save_dir: The path to the directory where the patches will be saved. file_format: The format for the saving (h5 for python processing, geojson for QuPath processing). selected_idx: An array of indices of the patches to save (if set to None, all the patches will be saved). merge: Set to True to merge the patches into a single polygon (for geojson format only). label: An optional label to assign to the patches (for geojson format only). color: An optional color to assign to the patches (for geojson format only). append_to_existing_file: Set to True to append the patches to an existing geojson file. \"\"\" if selected_idx is not None : coords = self . coords [ selected_idx ] else : coords = self . coords if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if file_format == \"h5\" : asset_dict = { \"coords\" : self . coords } attr_dict = { \"coords\" : self . coords_attrs } file_path = os . path . join ( save_dir , f \" { self . slide_name } .h5\" ) log . info ( f \"Saving patches for slide { self . slide_name } at { file_path } with hdf5.\" ) save_patches_with_hdf5 ( file_path , asset_dict , attr_dict ) elif file_format == \"geojson\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .geojson\" ) patch_downsample = int ( self . level_downsamples [ self . coords_attrs [ \"patch_level\" ]] ) polygons = patchesToPolygons ( coords , self . coords_attrs [ \"patch_size\" ], patch_downsample , merge ) log . info ( f \"Saving { len ( coords ) } patches for slide { self . slide_name } at { file_path } with geojson.\" ) export_polygons_to_qupath ( polygons , file_path , \"annotation\" , offset = self . offset , label = label , color = color , append_to_existing_file = append_to_existing_file , ) elif file_format == \"jpg\" or file_format == \"png\" : save_dir = os . path . join ( save_dir , self . slide_name ) if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) log . info ( f \"Saving { len ( coords ) } patches for slide { self . slide_name } at { save_dir } with { file_format } .\" ) for coord in coords : patch = self . read_region ( coord , self . coords_attrs [ \"patch_level\" ], ( self . coords_attrs [ \"patch_size\" ], self . coords_attrs [ \"patch_size\" ]), ) . convert ( \"RGB\" ) patch . save ( os . path . join ( save_dir , f \" { coord [ 0 ] } _ { coord [ 1 ] } . { file_format } \" )) else : raise ValueError ( f \"format { file_format } not supported\" ) save_tissue_contours ( save_dir , selected_idx = None , file_format = 'pickle' , merge = False , label = None , color = ( 255 , 0 , 0 ), append_to_existing_file = False , make_valid = False ) Save the tissue contours in a pickle or geojson file. Parameters: Name Type Description Default save_dir str The path to the directory where the contours will be saved. required selected_idx ndarray | None An array of indices of the contours to save (if set to None, all the contours will be saved). None file_format str The file format for saving the contours (pickle for python processing, geojson for QuPath processing). 'pickle' merge bool Set to True to merge the contours into a single polygon (for geojson format only). False label str | None An optional label to assign to the tissue contours (for geojson format only). None color tuple [ int , int , int ] An optional color to assign to the tissue contours (for geojson format only). (255, 0, 0) append_to_existing_file bool Set to True to append the contours to an existing geojson file. False make_valid bool Set to True to make the polygons valid (for geojson format only). False Source code in src/prismtoolbox/wsicore/wsi.py 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 def save_tissue_contours ( self , save_dir : str , selected_idx : np . ndarray | None = None , file_format : str = \"pickle\" , merge : bool = False , label : str | None = None , color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), append_to_existing_file : bool = False , make_valid : bool = False , ) -> None : \"\"\"Save the tissue contours in a pickle or geojson file. Args: save_dir: The path to the directory where the contours will be saved. selected_idx: An array of indices of the contours to save (if set to None, all the contours will be saved). file_format: The file format for saving the contours (pickle for python processing, geojson for QuPath processing). merge: Set to True to merge the contours into a single polygon (for geojson format only). label: An optional label to assign to the tissue contours (for geojson format only). color: An optional color to assign to the tissue contours (for geojson format only). append_to_existing_file: Set to True to append the contours to an existing geojson file. make_valid: Set to True to make the polygons valid (for geojson format only). \"\"\" assert self . tissue_contours is not None , ( \"No tissue contours found for the slide, \" \"please run the detect_tissue method first\" ) if selected_idx is not None : tissue_contours = [ self . tissue_contours [ idx ] for idx in selected_idx ] else : tissue_contours = self . tissue_contours if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if file_format == \"pickle\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .pkl\" ) log . info ( f \"Saving tissue contours for slide { self . slide_name } at { file_path } with pickle.\" ) save_obj_with_pickle ( tissue_contours , file_path ) elif file_format == \"geojson\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .geojson\" ) log . info ( f \"Saving { selected_idx } tissue contours for slide { self . slide_name } at { file_path } with geojson.\" ) polygons = contoursToPolygons ( tissue_contours , merge , make_valid ) export_polygons_to_qupath ( polygons , file_path , \"annotation\" , offset = self . offset , label = label , color = color , append_to_existing_file = append_to_existing_file , ) else : raise ValueError ( f \"format { file_format } not supported\" ) scale_contours ( contours , scale ) staticmethod Scale the contours by a given factor. Parameters: Name Type Description Default contours list [ ndarray ] The contours to scale. required scale int The scale factor to apply. required Returns: Type Description list [ ndarray ] The input list with scaled contours. Source code in src/prismtoolbox/wsicore/wsi.py 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 @staticmethod def scale_contours ( contours : list [ np . ndarray ], scale : int , ) -> list [ np . ndarray ]: \"\"\"Scale the contours by a given factor. Args: contours: The contours to scale. scale: The scale factor to apply. Returns: The input list with scaled contours. \"\"\" scaled_contours = [ np . array ( cont * scale , dtype = \"int\" ) for cont in contours ] return scaled_contours set_roi ( roi = None , rois_df_path = None ) Set the region of interest for the slide. Can be set manually or by selecting a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute. Parameters: Name Type Description Default roi tuple [ int , int , int , int ] | None Set the region of interest manually as a tuple (x1, y1, x2, y2). None rois_df_path str | None The path to dataframe containing the ROIs with a slide_id column identifying the slide. None Returns: Type Description None The region of interest set for the slide as a tuple (x1, y1, x2, y2). Source code in src/prismtoolbox/wsicore/wsi.py 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 def set_roi ( self , roi : tuple [ int , int , int , int ] | None = None , rois_df_path : str | None = None , ) -> None : \"\"\"Set the region of interest for the slide. Can be set manually or by selecting a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute. Args: roi: Set the region of interest manually as a tuple (x1, y1, x2, y2). rois_df_path: The path to dataframe containing the ROIs with a slide_id column identifying the slide. Returns: The region of interest set for the slide as a tuple (x1, y1, x2, y2). \"\"\" if roi is not None : ROI = np . array ( roi ) . astype ( int ) elif rois_df_path is not None : rois_df = pd . read_csv ( rois_df_path ) ROI = rois_df [ rois_df . slide_id == self . slide_name ] . values [ 0 ] . astype ( int ) else : log . info ( \"No ROI provided, prompting user to select one.\" ) level = input ( f \"No ROI was provided, please select a level at which the ROI should be created (max level: { len ( self . level_dimensions ) - 1 } ): \" ) if not level : log . warning ( \"No level provided, setting the ROI at the highest level.\" ) level = len ( self . level_downsamples ) - 1 else : level = int ( level ) img = np . array ( self . create_thumbnail ( level )) ROI = select_roi_on_thumbnail ( img , int ( self . level_downsamples [ level ])) ROI = ( ROI * self . level_downsamples [ level ]) . astype ( int ) self . ROI = ROI self . ROI_width = ROI [ 2 ] - ROI [ 0 ] self . ROI_height = ROI [ 3 ] - ROI [ 1 ] print ( f \"ROI for slide { self . slide_name } has been set to { self . ROI } .\" ) return ROI set_slide_attributes () Set the slide attributes. Source code in src/prismtoolbox/wsicore/wsi.py 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 def set_slide_attributes ( self ): \"\"\"Set the slide attributes.\"\"\" if self . engine == \"openslide\" : self . dimensions = self . slide . dimensions self . level_dimensions = self . slide . level_dimensions self . level_downsamples = self . slide . level_downsamples self . properties = self . slide . properties elif self . engine == \"tiffslide\" : self . dimensions = self . slide . dimensions self . level_dimensions = self . slide . level_dimensions self . level_downsamples = self . slide . level_downsamples self . properties = self . slide . properties else : raise NotImplementedError ( f \"engine { self . engine } not supported\" ) if ( f \" { self . engine } .bounds-x\" in self . properties . keys () and self . properties [ f \" { self . engine } .bounds-x\" ] is not None ): self . offset = ( - int ( self . properties [ f \" { self . engine } .bounds-x\" ]), - int ( self . properties [ f \" { self . engine } .bounds-y\" ]), ) stitch ( vis_level , selected_idx = None , colors = None , alpha = 0.6 , black_white = False , draw_grid = False , crop_roi = False , background_color = ( 0 , 0 , 0 )) Stitch the patches extracted on an image. The patches can be masked and colored depending on the mask and colors provided. Requires the coordinates of the patches to be set for the slide beforehand with the extract_patches method. Parameters: Name Type Description Default vis_level int The level at which the patches should be visualized. required selected_idx ndarray An array of indices of the patches to visualize (if set to None, all the patches will be visualized). None colors ndarray An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as they are). None alpha float Set the transparency of the colors to apply to the patches. 0.6 black_white bool Set to True to visualize a binary mask of the patches extracted. False draw_grid bool Set to True to draw a grid on the stitched patches. False crop_roi bool Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the set_roi method). False background_color tuple [ int , int , int ] The color of the background. (0, 0, 0) Returns: Type Description Image A PIL image of the stitched patches. Source code in src/prismtoolbox/wsicore/wsi.py 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 def stitch ( self , vis_level : int , selected_idx : np . ndarray = None , colors : np . ndarray = None , alpha : float = 0.6 , black_white : bool = False , draw_grid : bool = False , crop_roi : bool = False , background_color : tuple [ int , int , int ] = ( 0 , 0 , 0 ), ) -> Image . Image : \"\"\"Stitch the patches extracted on an image. The patches can be masked and colored depending on the mask and colors provided. Requires the coordinates of the patches to be set for the slide beforehand with the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method. Args: vis_level: The level at which the patches should be visualized. selected_idx: An array of indices of the patches to visualize (if set to None, all the patches will be visualized). colors: An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as they are). alpha: Set the transparency of the colors to apply to the patches. black_white: Set to True to visualize a binary mask of the patches extracted. draw_grid: Set to True to draw a grid on the stitched patches. crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). background_color: The color of the background. Returns: A PIL image of the stitched patches. \"\"\" assert self . ROI is not None if crop_roi else True , ( \"no ROI provided, while crop_roi is set to True,\" \" please set a ROI for the slide or set crop_roi to False.\" ) assert self . coords is not None , ( \"no coordinates provided for the patches to visualize, please run the \" \"extract_patches method first or load the coordinates from a file\" ) if crop_roi : w , h = int ( np . ceil ( self . ROI_width / self . level_downsamples [ vis_level ])), int ( np . ceil ( self . ROI_height / self . level_downsamples [ vis_level ]) ) else : w , h = self . level_dimensions [ vis_level ] patch_size = self . coords_attrs [ \"patch_size\" ] patch_level = self . coords_attrs [ \"patch_level\" ] patch_size = int ( patch_size * self . level_downsamples [ patch_level ]) canvas = init_image ( w , h , mask = black_white , color_bakground = background_color ) downsample_vis = self . level_downsamples [ vis_level ] idxs = np . arange ( len ( self . coords )) if selected_idx is not None : idxs = idxs [ selected_idx ] patch_size = np . ceil ( patch_size / downsample_vis ) . astype ( int ) log . info ( f \"Stitching { len ( idxs ) } patches at level { vis_level } with patch size { patch_size } , \" f \"with colors { colors is not None } .\" ) offset = self . ROI [: 2 ] if crop_roi else np . array ([ 0 , 0 ]) for idx in idxs : coord = self . coords [ idx ] coord_downsampled = np . ceil ( np . abs ( coord - offset ) / downsample_vis ) . astype ( int ) patch_size_coord = ( min ( max ( w - coord_downsampled [ 0 ], 0 ), patch_size ), min ( max ( h - coord_downsampled [ 1 ], 0 ), patch_size ), ) if any ( val == 0 for val in patch_size_coord ): continue if black_white : patch = np . ones ( patch_size_coord , dtype = \"uint8\" ) colors = None else : patch = np . array ( self . slide . read_region ( tuple ( coord ), vis_level , patch_size_coord ) . convert ( \"RGB\" ) ) if colors is not None : assert len ( colors ) == len ( idxs ), ( \"the number of colors provided must match \" \"the number of selected coordinates\" ) color = colors [ idx ] color_patch = ( np . ones (( patch_size_coord [ 1 ], patch_size_coord [ 0 ], 3 )) * color ) . astype ( \"uint8\" ) canvas [ coord_downsampled [ 1 ] : coord_downsampled [ 1 ] + patch_size_coord [ 1 ], coord_downsampled [ 0 ] : coord_downsampled [ 0 ] + patch_size_coord [ 0 ], :, ] = cv2 . addWeighted ( color_patch , alpha , patch , 1 - alpha , 0 , patch ) else : canvas [ coord_downsampled [ 1 ] : coord_downsampled [ 1 ] + patch_size_coord [ 1 ], coord_downsampled [ 0 ] : coord_downsampled [ 0 ] + patch_size_coord [ 0 ], :, ] = patch if draw_grid : cv2 . rectangle ( canvas , tuple ( np . maximum ([ 0 , 0 ], coord_downsampled - 1 )), tuple ( coord_downsampled + patch_size_coord ), ( 0 , 0 , 0 , 255 ), thickness = 2 , ) img = Image . fromarray ( canvas ) return img visualize ( vis_level , crop_roi = False , contours_color = ( 255 , 0 , 0 ), line_thickness = 500 , max_size = None , number_contours = False , black_white = False , view_slide_only = False ) Visualize the slide with or without the contours of the tissue. Parameters: Name Type Description Default vis_level int The level at which the visualization should be performed. required crop_roi bool Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the set_roi method). False contours_color tuple [ int , int , int ] The color to use for the contours. (255, 0, 0) line_thickness int The thickness to use for the contours 500 max_size int | None The maximum size for the visualization for the width or height of the image. None number_contours bool Set to True to number the contours. False black_white bool Set to True to visualize a binary mask of the contoured tissue. False view_slide_only bool Set to True to visualize the slide only (without the contours). False Returns: Type Description Image A PIL image of the visualization. Source code in src/prismtoolbox/wsicore/wsi.py 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 def visualize ( self , vis_level : int , crop_roi : bool = False , contours_color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), line_thickness : int = 500 , max_size : int | None = None , number_contours : bool = False , black_white : bool = False , view_slide_only : bool = False , ) -> Image . Image : \"\"\"Visualize the slide with or without the contours of the tissue. Args: vis_level: The level at which the visualization should be performed. crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). contours_color: The color to use for the contours. line_thickness: The thickness to use for the contours max_size: The maximum size for the visualization for the width or height of the image. number_contours: Set to True to number the contours. black_white: Set to True to visualize a binary mask of the contoured tissue. view_slide_only: Set to True to visualize the slide only (without the contours). Returns: A PIL image of the visualization. \"\"\" assert line_thickness > 0 , \"line_thickness must be greater than 0\" scale = 1 / self . level_downsamples [ vis_level ] if black_white : img = np . zeros_like ( self . create_thumbnail ( vis_level , crop_roi ), dtype = \"uint8\" ) line_thickness = - 1 contours_color = ( 1 , 1 , 1 ) else : img = np . array ( self . create_thumbnail ( vis_level , crop_roi )) line_thickness = int ( line_thickness * scale ) if not view_slide_only : assert ( len ( self . tissue_contours ) > 0 ), \"No tissue contours found for the slide, please run the detect_tissue method first\" offset = self . ROI [: 2 ] if crop_roi else np . array ([ 0 , 0 ]) contours = [ cont - offset for cont in self . tissue_contours ] contours = self . scale_contours ( contours , scale ) if len ( contours ) > 0 : if not number_contours : cv2 . drawContours ( img , contours , - 1 , contours_color , line_thickness , lineType = cv2 . LINE_8 , ) else : # add numbering to each contour for idx , cont in enumerate ( contours ): M = cv2 . moments ( cont ) cX = int ( M [ \"m10\" ] / ( M [ \"m00\" ] + 1e-9 )) cY = int ( M [ \"m01\" ] / ( M [ \"m00\" ] + 1e-9 )) # draw the contour and put text next to center cv2 . drawContours ( img , [ cont ], - 1 , contours_color , line_thickness , lineType = cv2 . LINE_8 , ) cv2 . putText ( img , \" {} \" . format ( idx ), ( cX , cY ), cv2 . FONT_HERSHEY_SIMPLEX , 2 , ( 255 , 0 , 0 ), 10 , ) img = Image . fromarray ( img ) if black_white : img = img . convert ( \"L\" ) w , h = img . size if max_size is not None and ( w > max_size or h > max_size ): resizeFactor = max_size / w if w > h else max_size / h img = img . resize (( int ( w * resizeFactor ), int ( h * resizeFactor ))) return img worker_init ( slide_path , engine ) staticmethod Initialize the worker process with a wsi object. Parameters: Name Type Description Default slide_path str The path to the slide. required engine str The backend library to use for reading the slide (currently only openslide and tiffslide are supported) required Source code in src/prismtoolbox/wsicore/wsi.py 166 167 168 169 170 171 172 173 174 175 176 @staticmethod def worker_init ( slide_path : str , engine : str ) -> None : \"\"\"Initialize the worker process with a wsi object. Args: slide_path: The path to the slide. engine: The backend library to use for reading the slide (currently only openslide and tiffslide are supported) \"\"\" global wsi wsi = WSI . read ( slide_path , engine )","title":"prismtoolbox.wsicore.wsi"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI","text":"The WSI (Whole Slide Image) class is responsible for handling operations related to whole slide images. Parameters: Name Type Description Default slide_path str The path to the slide image file. required engine str The engine used to read the slide image. 'openslide' Attributes: Name Type Description slide_path str The path to the slide image file. engine str The engine used to read the slide image. slide_name str The name of the slide image file. Retrieved from the slide path using retrieve_slide_name_ext method. slide_ext str The extension of the slide image file. Retrieved from the slide path using retrieve_slide_name_ext method. slide OpenSlide | TiffSlide The wsi read from the file using engine. dimensions list [ tuple [ int , int ]] The dimensions of the slide image. Set by the set_slide_attributes method. level_dimensions list [ tuple [ int , int ]] The dimensions of the different levels of the slide image. Set by the set_slide_attributes method. level_downsamples list [ tuple [ int , int ]] The downsampling factors of the different levels of the slide image. Set by the set_slide_attributes method. properties dict The properties of the slide image. Set by the set_slide_attributes method. offset tuple [ int , int ] The offset of the slide image. Set by the set_slide_attributes method. ROI ndarray The region of interest in the slide image. Please use the set_roi method to set the ROI. ROI_width int The width of the region of interest. Set by the set_roi method. ROI_height int The height of the region of interest. Set by the set_roi method. tissue_contours list [ ndarray ] The contours of the tissue in the slide image. Please use the detect_tissue method to detect the tissue contours. coords ndarray The coordinates of patches extracted from slide image. Please use the extract_patches method to extract patches. coords_attrs dict The attributes of the coordinates. Set by the extract_patches method. Source code in src/prismtoolbox/wsicore/wsi.py 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 def __init__ ( self , slide_path : str , engine : str = \"openslide\" ): \"\"\"The WSI (Whole Slide Image) class is responsible for handling operations related to whole slide images. Args: slide_path: The path to the slide image file. engine: The engine used to read the slide image. Attributes: slide_path (str): The path to the slide image file. engine (str): The engine used to read the slide image. slide_name (str): The name of the slide image file. Retrieved from the slide path using [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method. slide_ext (str): The extension of the slide image file. Retrieved from the slide path using [retrieve_slide_name_ext][prismtoolbox.wsicore.WSI.retrieve_slide_name_ext] method. slide (OpenSlide | TiffSlide): The wsi read from the file using engine. dimensions (list[tuple[int, int]]): The dimensions of the slide image. Set by the set_slide_attributes method. level_dimensions (list[tuple[int, int]]): The dimensions of the different levels of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. level_downsamples (list[tuple[int, int]]): The downsampling factors of the different levels of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. properties (dict): The properties of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. offset (tuple[int, int]): The offset of the slide image. Set by the [set_slide_attributes][prismtoolbox.wsicore.WSI.set_slide_attributes] method. ROI (ndarray): The region of interest in the slide image. Please use the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method to set the ROI. ROI_width (int): The width of the region of interest. Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method. ROI_height (int): The height of the region of interest. Set by the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method. tissue_contours (list[ndarray]): The contours of the tissue in the slide image. Please use the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method to detect the tissue contours. coords (np.ndarray): The coordinates of patches extracted from slide image. Please use the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method to extract patches. coords_attrs (dict): The attributes of the coordinates. Set by the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method. \"\"\" self . slide_path = slide_path self . engine = engine self . slide_name , self . slide_ext = self . retrieve_slide_name_ext ( self . slide_path ) self . slide = self . read ( slide_path , engine ) self . dimensions = None self . level_dimensions = None self . level_downsamples = None self . properties = None self . offset = ( 0 , 0 ) self . set_slide_attributes () self . ROI = None self . ROI_width = None self . ROI_height = None self . tissue_contours = None self . coords = None self . coords_attrs = None","title":" WSI"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.apply_pathologist_annotations","text":"Apply pathologist annotations to the tissue contours by intersecting the annotations with the tissue contours. Requires the tissue contours to be set for the slide beforehand with the detect_tissue method. Parameters: Name Type Description Default path str The path to the .geojson file containing the annotations extracted from QuPath. required class_name str The class name to use for selecting the annotations to apply. 'annotation' column_to_select str The column to select in the GeoDataFrame. 'objectType' Source code in src/prismtoolbox/wsicore/wsi.py 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 def apply_pathologist_annotations ( self , path : str , class_name : str = \"annotation\" , column_to_select : str = \"objectType\" , ) -> None : \"\"\"Apply pathologist annotations to the tissue contours by intersecting the annotations with the tissue contours. Requires the tissue contours to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method. Args: path: The path to the .geojson file containing the annotations extracted from QuPath. class_name: The class name to use for selecting the annotations to apply. column_to_select: The column to select in the GeoDataFrame. \"\"\" assert ( self . tissue_contours is not None ), \"No tissue contours found for the slide, please run the detect_tissue method first\" offset = ( - self . offset [ 0 ], - self . offset [ 1 ]) pathologist_annotations = read_qupath_annotations ( path , offset = offset , class_name = class_name , column_to_select = column_to_select ) polygons = contoursToPolygons ( self . tissue_contours , make_valid = True ) intersection = intersectionPolygons ( polygons , pathologist_annotations ) self . tissue_contours = PolygonsToContours ( intersection )","title":" apply_pathologist_annotations"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.convert_micrometer_to_pixel","text":"Convert a value from micrometer to pixel. Parameters: Name Type Description Default value float The value to convert (in micrometer). required level int The level at which the conversion should be performed. required axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description int The input value in pixel. Source code in src/prismtoolbox/wsicore/wsi.py 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 def convert_micrometer_to_pixel ( self , value : float , level : int , axis : str = \"x\" , ) -> int : \"\"\"Convert a value from micrometer to pixel. Args: value: The value to convert (in micrometer). level: The level at which the conversion should be performed. axis: The axis to use for getting the conversion factor (x or y). Returns: The input value in pixel. \"\"\" return int ( value / float ( self . properties [ f \" { self . engine } .mpp- { axis } \" ])) // int ( self . level_downsamples [ level ] )","title":" convert_micrometer_to_pixel"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.convert_pixel_to_micrometer","text":"Convert a value from pixel to micrometer. Parameters: Name Type Description Default value float The value to convert (in pixel). required level int The level at which the conversion should be performed. required axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description float The input value in micrometer. Source code in src/prismtoolbox/wsicore/wsi.py 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 def convert_pixel_to_micrometer ( self , value : float , level : int , axis : str = \"x\" , ) -> float : \"\"\"Convert a value from pixel to micrometer. Args: value: The value to convert (in pixel). level: The level at which the conversion should be performed. axis: The axis to use for getting the conversion factor (x or y). Returns: The input value in micrometer. \"\"\" return ( value * float ( self . properties [ f \" { self . engine } .mpp- { axis } \" ]) * self . level_downsamples [ level ] )","title":" convert_pixel_to_micrometer"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.convert_units","text":"Convert a value from one unit to another. Parameters: Name Type Description Default value float The value to convert. required level int The level at which the conversion should be performed. required from_unit str The unit to convert from (px or micro). required to_unit str The unit to convert to (px or micro). 'px' axis str The axis to use for getting the conversion factor (x or y). 'x' Returns: Type Description int The input value converted in the desired unit. Source code in src/prismtoolbox/wsicore/wsi.py 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 def convert_units ( self , value : float , level : int , from_unit : str , to_unit : str = \"px\" , axis : str = \"x\" , ) -> int : \"\"\"Convert a value from one unit to another. Args: value: The value to convert. level: The level at which the conversion should be performed. from_unit: The unit to convert from (px or micro). to_unit: The unit to convert to (px or micro). axis: The axis to use for getting the conversion factor (x or y). Returns: The input value converted in the desired unit. \"\"\" if from_unit == \"micro\" and to_unit == \"px\" : value = self . convert_micrometer_to_pixel ( value , level , axis ) elif from_unit == \"px\" and to_unit == \"micro\" : value = self . convert_pixel_to_micrometer ( value , level , axis ) elif from_unit == to_unit : pass else : raise ValueError ( f \"Conversion from { from_unit } to { to_unit } not supported\" ) return value","title":" convert_units"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.create_thumbnail","text":"Create a thumbnail of the slide at a given level. Parameters: Name Type Description Default level int The level at which the thumbnail should be created. required crop_roi bool A boolean to crop the thumbnail to the region of interest defined for the slide (requires a ROI to be set for the slide beforehand with the set_roi method) False Returns: Type Description Image A thumbnail of the slide as a PIL image. Source code in src/prismtoolbox/wsicore/wsi.py 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 def create_thumbnail ( self , level : int , crop_roi : bool = False , ) -> Image . Image : \"\"\"Create a thumbnail of the slide at a given level. Args: level: The level at which the thumbnail should be created. crop_roi: A boolean to crop the thumbnail to the region of interest defined for the slide (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method) Returns: A thumbnail of the slide as a PIL image. \"\"\" assert self . ROI is not None if crop_roi else True , ( \"No ROI provided, while crop_roi is set to True. Please set a ROI for \" \"the slide or set crop_roi to False.\" ) thumb = self . read_region (( 0 , 0 ), level , self . level_dimensions [ level ]) . convert ( \"RGB\" ) if crop_roi : log . info ( f \"Creating thumbnail with ROI { self . ROI } .\" ) coords_roi = ( self . ROI / self . level_downsamples [ level ]) . astype ( int ) thumb = thumb . crop ( coords_roi ) return thumb","title":" create_thumbnail"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.detect_tissue","text":"Segment the tissue on the slide based on a threshold on the Law's texture energy spot map and floodfill algorithm to fill the holes in the mask. The tissue contours are stored in the tissue_contours attribute. Parameters: Name Type Description Default seg_level int The level at which the segmentation should be performed. required window_avg int The size of the window for local averaging. required window_eng int The size of the window for Law's texture energy computation. required thresh int The threshold for binarization on the Law's texture energy spot map. required inside_roi bool Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide beforehand with the set_roi method). False inv_thresh bool Set to True to invert the thresholding. False area_min float The minimum area for a contour to be kept. 200000.0 start tuple [ int , int ] The starting point for the floodfill algorithm (should be left at (0, 0) in most cases). (0, 0) Source code in src/prismtoolbox/wsicore/wsi.py 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 def detect_tissue ( self , seg_level : int , window_avg : int , window_eng : int , thresh : int , inside_roi : bool = False , inv_thresh : bool = False , area_min : float = 2e5 , start : tuple [ int , int ] = ( 0 , 0 ), ) -> None : \"\"\"Segment the tissue on the slide based on a threshold on the Law's texture energy spot map and floodfill algorithm to fill the holes in the mask. The tissue contours are stored in the tissue_contours attribute. Args: seg_level: The level at which the segmentation should be performed. window_avg: The size of the window for local averaging. window_eng: The size of the window for Law's texture energy computation. thresh: The threshold for binarization on the Law's texture energy spot map. inside_roi: Set to True to identify the tissue only within a ROI (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). inv_thresh: Set to True to invert the thresholding. area_min: The minimum area for a contour to be kept. start: The starting point for the floodfill algorithm (should be left at (0, 0) in most cases). \"\"\" final_contours = [] img = np . array ( self . create_thumbnail ( seg_level , inside_roi )) img_avg = local_average ( np . asarray ( img ), window_avg ) law_feats = compute_law_feats ( img_avg , window_eng )[ 0 ] filterred_img = apply_bilateral_filter ( np . clip ( law_feats , 0 , 255 ) . astype ( \"uint8\" )) threshed_img = apply_binary_thresh ( filterred_img , thresh , inv_thresh ) flooded_img = floodfill_img ( np . pad ( threshed_img , 1 ), start ) contours = contour_mask ( flooded_img ) for contour in contours : c = contour . copy () area = cv2 . contourArea ( c ) if area > area_min : final_contours . append ( contour ) if len ( final_contours ) == 0 : self . tissue_contours = [] print ( f \"No contours found for slide { self . slide_name } .\" ) return else : scale = self . level_downsamples [ seg_level ] offset = np . array ( self . ROI [: 2 ]) if self . ROI is not None else np . array ([ 0 , 0 ]) final_contours = self . scale_contours ( final_contours , scale ) final_contours = [ cont + offset for cont in final_contours ] # Sanity check to ensure that the contours are all within a ROI is provided if self . ROI is not None : assert all ( [ np . all ( cont >= self . ROI [: 2 ]) and np . all ( cont <= self . ROI [ 2 :]) for cont in final_contours ] ) self . tissue_contours = final_contours print ( f \"Identified { len ( final_contours ) } contours for slide { self . slide_name } .\" ) return","title":" detect_tissue"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.extract_patches","text":"Extract valid patches from the slide with different extraction modes. A patch is considered valid if it is not black or white and is within the region of interest or the tissue contours if relevant. The extracted patches are stored as coordinates in the coords attribute, and the attributes of the coordinates are stored in the coords_attrs attribute. Parameters: Name Type Description Default patch_size float The size of the patches to extract (assumed to be square). required patch_level int The level at which the patches should be extracted. required mode str The mode to use for the extraction: \"contours\" mode extracts patches within the tissue contours (requires the tissue contours to be set for the slide beforehand with the detect_tissue method). \"roi\" mode extracts patches within the region of interest (requires the ROI to be set for the slide beforehand with the set_roi method). \"all\" mode extracts patches from the entire slide required step_size float | None The step size for the sliding window (if set to None, the step size will be computed based on the overlap). None overlap float | None The overlap between patches as an absolute value (must be provided if step_size is set to None). None units tuple [ str , str ] The units for the patch size and step size/overlap values (pixels: px, micrometers: micro). ('px', 'px') use_padding bool Set to True to use padding for the extraction. True contours_mode str | None The mode to use for the contour checking function (must be provided if mode is set to contours). See IsInContour for more details. None rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Source code in src/prismtoolbox/wsicore/wsi.py 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 def extract_patches ( self , patch_size : float , patch_level : int , mode : str , step_size : float | None = None , overlap : float | None = None , units : tuple [ str , str ] = ( \"px\" , \"px\" ), use_padding : bool = True , contours_mode : str | None = None , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> None : \"\"\"Extract valid patches from the slide with different extraction modes. A patch is considered valid if it is not black or white and is within the region of interest or the tissue contours if relevant. The extracted patches are stored as coordinates in the coords attribute, and the attributes of the coordinates are stored in the coords_attrs attribute. Args: patch_size: The size of the patches to extract (assumed to be square). patch_level: The level at which the patches should be extracted. mode: The mode to use for the extraction: - \"contours\" mode extracts patches within the tissue contours (requires the tissue contours to be set for the slide beforehand with the [detect_tissue][prismtoolbox.wsicore.WSI.detect_tissue] method). - \"roi\" mode extracts patches within the region of interest (requires the ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). - \"all\" mode extracts patches from the entire slide step_size: The step size for the sliding window (if set to None, the step size will be computed based on the overlap). overlap: The overlap between patches as an absolute value (must be provided if step_size is set to None). units: The units for the patch size and step size/overlap values (pixels: px, micrometers: micro). use_padding: Set to True to use padding for the extraction. contours_mode: The mode to use for the contour checking function (must be provided if mode is set to contours). See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. \"\"\" assert ( step_size is not None or overlap is not None ), \"Either step_size or overlap must be provided\" assert all ( [ unit in [ \"micro\" , \"px\" ] for unit in units ] ), \"Units must be either 'micro' or 'px'\" patch_size = self . convert_units ( patch_size , patch_level , units [ 0 ]) if step_size is None : step_size = patch_size - self . convert_units ( overlap , patch_level , units [ 1 ]) else : step_size = self . convert_units ( step_size , patch_level , units [ 1 ]) log . info ( f \"Extracting patches of size { patch_size } at level { patch_level } with step size { step_size } .\" ) if mode == \"contours\" : log . info ( \"Extracting patches with 'contours' mode.\" ) assert self . tissue_contours is not None assert len ( self . tissue_contours ) > 0 assert contours_mode is not None valid_coords = [] for cont in self . tissue_contours : roi_dim = cv2 . boundingRect ( cont ) log . info ( f \"Processing ROI of dimensions: { roi_dim } \" ) valid_coords . extend ( self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , cont , contours_mode , rgb_threshs = rgb_threshs , percentages = percentages , ) ) valid_coords = np . array ( valid_coords ) elif mode == \"roi\" : log . info ( \"Extracting patches with 'roi' mode.\" ) assert self . ROI is not None roi_dim = self . ROI [ 0 ], self . ROI [ 1 ], self . ROI_width , self . ROI_height log . info ( \"Processing ROI of dimensions:\" , roi_dim ) valid_coords = self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , rgb_threshs = rgb_threshs , percentages = percentages , ) elif mode == \"all\" : roi_dim = 0 , 0 , self . level_dimensions [ 0 ][ 0 ], self . level_dimensions [ 0 ][ 1 ] log . info ( \"Processing ROI of dimensions:\" , roi_dim ) valid_coords = self . extract_patches_roi ( patch_level , patch_size , step_size , roi_dim , use_padding , rgb_threshs = rgb_threshs , percentages = percentages , ) else : raise ValueError ( f \"Mode { mode } not supported\" ) attr = { \"patch_size\" : patch_size , \"patch_level\" : patch_level , \"downsample\" : self . level_downsamples [ patch_level ], \"downsampled_level_dim\" : tuple ( np . array ( self . level_dimensions [ patch_level ])), \"level_dim\" : self . level_dimensions [ patch_level ], \"name\" : self . slide_name , } if len ( valid_coords ) == 0 : log . warning ( f \"No valid coordinates found for slide { self . slide_name } .\" ) else : print ( f \"Identified a total of { len ( valid_coords ) } valid coordinates in the slide { self . slide_name } .\" ) self . coords = valid_coords self . coords_attrs = attr","title":" extract_patches"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.extract_patches_roi","text":"Extract valid patches from a region of interest, i.e if the patch is not black or white and is within the region of interest/contours if relevant). Parameters: Name Type Description Default patch_level int The level at which the patches should be extracted. required patch_size int The size of the patches to extract (assumed to be square). required step_size int | None The step size to use for the sliding window. None roi_dim tuple [ int , int , int , int ] | None The top-left corner coordinates and dimensions of the region of interest. Must be provided if coord_candidates is set to None. None use_padding bool Set to True to use padding for the extraction. True contour ndarray | None The tissue contour to use for the extraction. If set to None, will not check if patches are within a contour. None contours_mode str | None The mode for the contour checking function. See IsInContour for more details. Must be provided if mode is set to contours. Otherwise, will not check if patches are within the contours. None coord_candidates ndarray | None Precomputed candidate coordinates for the patches. If set to None, will compute the candidate coordinates based on the ROI dimensions and the step size. None rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) return_indices bool Set to True to return the indices of the valid coordinates. False Returns: Type Description ndarray An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches). Source code in src/prismtoolbox/wsicore/wsi.py 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 def extract_patches_roi ( self , patch_level : int , patch_size : int , step_size : int | None = None , roi_dim : tuple [ int , int , int , int ] | None = None , use_padding : bool = True , contour : np . ndarray | None = None , contours_mode : str | None = None , coord_candidates : np . ndarray | None = None , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), return_indices : bool = False , ) -> np . ndarray : \"\"\"Extract valid patches from a region of interest, i.e if the patch is not black or white and is within the region of interest/contours if relevant). Args: patch_level: The level at which the patches should be extracted. patch_size: The size of the patches to extract (assumed to be square). step_size: The step size to use for the sliding window. roi_dim: The top-left corner coordinates and dimensions of the region of interest. Must be provided if coord_candidates is set to None. use_padding: Set to True to use padding for the extraction. contour: The tissue contour to use for the extraction. If set to None, will not check if patches are within a contour. contours_mode: The mode for the contour checking function. See [IsInContour][prismtoolbox.wsicore.core_utils.IsInContour] for more details. Must be provided if mode is set to contours. Otherwise, will not check if patches are within the contours. coord_candidates: Precomputed candidate coordinates for the patches. If set to None, will compute the candidate coordinates based on the ROI dimensions and the step size. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. return_indices: Set to True to return the indices of the valid coordinates. Returns: An array of valid coordinates for the patches (i.e. coordinates of the top-left corner of the patches). \"\"\" assert contours_mode is not None if contour is not None else True , ( \"A contour mode must be provided if patch \" \"extraction mode is set to contours\" ) assert ( roi_dim is not None and step_size is not None ) or coord_candidates is not None , ( \"Either (roi_dim and step_size) or coord_candidates must be provided\" ) patch_downsample = int ( self . level_downsamples [ patch_level ]) ref_patch_size = patch_size * patch_downsample if coord_candidates is None : start_x , start_y , w , h = roi_dim img_w , img_h = self . level_dimensions [ 0 ] if use_padding : stop_y = start_y + h stop_x = start_x + w else : stop_y = min ( start_y + h , img_h - ref_patch_size + 1 ) stop_x = min ( start_x + w , img_w - ref_patch_size + 1 ) step_size = step_size * patch_downsample x_range = np . arange ( start_x , stop_x , step = step_size ) y_range = np . arange ( start_y , stop_y , step = step_size ) x_coords , y_coords = np . meshgrid ( x_range , y_range , indexing = \"ij\" ) coord_candidates = np . array ( [ x_coords . flatten (), y_coords . flatten ()] ) . transpose () if contour is not None : cont_check_fn = IsInContour ( contour , patch_size = ref_patch_size , center_shift = 0.5 , mode = contours_mode ) log . info ( f \"Extracting patches with contour checking function mode { contours_mode } .\" ) else : cont_check_fn = None num_workers = mp . cpu_count () pool = mp . Pool ( num_workers , initializer = WSI . worker_init , initargs = ( self . slide_path , self . engine , ), ) iterable = [ ( coord , cont_check_fn , patch_level , patch_size , rgb_threshs , percentages ) for coord in coord_candidates ] valid_coords = pool . starmap ( WSI . process_coord_candidate , iterable ) pool . close () valid_indices = [ i for i , coord in enumerate ( valid_coords ) if coord is not None ] valid_coords = np . array ([ coord_candidates [ i ] for i in valid_indices ]) log . info ( f \"Identified { len ( valid_coords ) } valid coordinates in the ROI { roi_dim } .\" ) if return_indices : return valid_coords , valid_indices else : return valid_coords","title":" extract_patches_roi"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.is_black_white","text":"Check if a patch is black or white. Parameters: Name Type Description Default patch Image The input patch. required rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Returns: Type Description bool True if the patch is black or white, False otherwise. Source code in src/prismtoolbox/wsicore/wsi.py 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 @staticmethod def is_black_white ( patch : Image . Image , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> bool : \"\"\"Check if a patch is black or white. Args: patch: The input patch. rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. Returns: True if the patch is black or white, False otherwise. \"\"\" return isBlackPatch ( patch , rgb_thresh = rgb_threshs [ 0 ], percentage = percentages [ 0 ] ) or isWhitePatch ( patch , rgb_thresh = rgb_threshs [ 1 ], percentage = percentages [ 1 ])","title":" is_black_white"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.load_patches","text":"Load the patches from a hdf5 file. Parameters: Name Type Description Default file_path str The path to the hdf5 file containing the patches. required Source code in src/prismtoolbox/wsicore/wsi.py 449 450 451 452 453 454 455 456 def load_patches ( self , file_path : str ) -> None : \"\"\"Load the patches from a hdf5 file. Args: file_path: The path to the hdf5 file containing the patches. \"\"\" log . info ( f \"Loading patches for slide { self . slide_name } from { file_path } .\" ) self . coords , self . coords_attrs = read_h5_file ( file_path , \"coords\" )","title":" load_patches"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.load_tissue_contours","text":"Load the tissue contours from a pickle file. Parameters: Name Type Description Default file_path str The path to the pickle file containing the tissue contours. required Source code in src/prismtoolbox/wsicore/wsi.py 366 367 368 369 370 371 372 373 def load_tissue_contours ( self , file_path : str ) -> None : \"\"\"Load the tissue contours from a pickle file. Args: file_path: The path to the pickle file containing the tissue contours. \"\"\" log . info ( f \"Loading tissue contours for slide { self . slide_name } from { file_path } .\" ) self . tissue_contours = load_obj_with_pickle ( file_path )","title":" load_tissue_contours"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.process_coord_candidate","text":"Determine if a candidate coordinate is valid based on a contour checking function and/or black/white thresholding. Parameters: Name Type Description Default coord tuple [ int , int ] The candidate coordinate. required cont_check_fn IsInContour | None The contour checking function. required patch_level int The level at which the patch should be extracted. required patch_size int The size of the patch (assumed to be square). required rgb_threshs tuple [ int , int ] The tuple of thresholds for the RGB channels (black threshold, white threshold). (2, 220) percentages tuple [ float , float ] The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. (0.6, 0.9) Returns: Type Description tuple [ int , int ] | None The coordinate if it is valid, None otherwise. Source code in src/prismtoolbox/wsicore/wsi.py 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 @staticmethod def process_coord_candidate ( coord : tuple [ int , int ], cont_check_fn : IsInContour | None , patch_level : int , patch_size : int , rgb_threshs : tuple [ int , int ] = ( 2 , 220 ), percentages : tuple [ float , float ] = ( 0.6 , 0.9 ), ) -> tuple [ int , int ] | None : \"\"\"Determine if a candidate coordinate is valid based on a contour checking function and/or black/white thresholding. Args: coord: The candidate coordinate. cont_check_fn: The contour checking function. patch_level: The level at which the patch should be extracted. patch_size: The size of the patch (assumed to be square). rgb_threshs: The tuple of thresholds for the RGB channels (black threshold, white threshold). percentages: The tuple of percentages of pixels below/above the thresholds to consider the patch as black/white. Returns: The coordinate if it is valid, None otherwise. \"\"\" if cont_check_fn is None or cont_check_fn ( coord ): patch = wsi . read_region ( tuple ( coord ), patch_level , ( patch_size , patch_size ) ) . convert ( \"RGB\" ) if not WSI . is_black_white ( patch , rgb_threshs , percentages ): return coord else : return None else : return None","title":" process_coord_candidate"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.read","text":"Read a slide with a given engine. Parameters: Name Type Description Default slide_path str The path to the slide. required engine str The backend library to use for reading the slide (currently only openslide and tiffslide are supported). required Returns: Type Description A slide object. Source code in src/prismtoolbox/wsicore/wsi.py 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 @staticmethod def read ( slide_path : str , engine : str ): \"\"\"Read a slide with a given engine. Args: slide_path: The path to the slide. engine: The backend library to use for reading the slide (currently only openslide and tiffslide are supported). Returns: A slide object. \"\"\" if engine == \"openslide\" : import openslide slide = openslide . OpenSlide ( slide_path ) elif engine == \"tiffslide\" : import tiffslide with warnings . catch_warnings (): warnings . simplefilter ( \"ignore\" , category = UserWarning ) slide = tiffslide . TiffSlide ( slide_path ) else : raise NotImplementedError ( f \"engine { engine } not supported\" ) return slide","title":" read"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.read_region","text":"Read a region from the slide for a given level and size. Parameters: Name Type Description Default location tuple [ int , int ] The coordinates of the top left corner of the region (in pixels). required level int The level at which the region should be read. required size tuple [ int , int ] The size of the region (in pixels). required Returns: Type Description Image The desired region of the slide as a PIL image. Source code in src/prismtoolbox/wsicore/wsi.py 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 def read_region ( self , location : tuple [ int , int ], level : int , size : tuple [ int , int ], ) -> Image . Image : \"\"\"Read a region from the slide for a given level and size. Args: location: The coordinates of the top left corner of the region (in pixels). level: The level at which the region should be read. size: The size of the region (in pixels). Returns: The desired region of the slide as a PIL image. \"\"\" return self . slide . read_region ( location , level , size ) . convert ( \"RGB\" )","title":" read_region"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.retrieve_slide_name_ext","text":"Retrieves slide name and slide extension from slide path. Parameters: Name Type Description Default slide_path str The path to the slide. required Returns: Type Description tuple [ str , str ] A tuple (slide name, slide ext). Source code in src/prismtoolbox/wsicore/wsi.py 109 110 111 112 113 114 115 116 117 118 119 120 121 @staticmethod def retrieve_slide_name_ext ( slide_path : str ) -> tuple [ str , str ]: \"\"\"Retrieves slide name and slide extension from slide path. Args: slide_path: The path to the slide. Returns: A tuple (slide name, slide ext). \"\"\" slide_ext = re . search ( r \"(?<=\\.)[\\w\\.]+$\" , slide_path ) . group ( 0 ) slide_name = re . search ( r \"([^/]+?)(?=\\.[\\w\\.]+$)\" , slide_path ) . group ( 0 ) return slide_name , slide_ext","title":" retrieve_slide_name_ext"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.save_patches","text":"Save the patches in a hdf5 or geojson file. Parameters: Name Type Description Default save_dir str The path to the directory where the patches will be saved. required file_format str The format for the saving (h5 for python processing, geojson for QuPath processing). 'h5' selected_idx ndarray | None An array of indices of the patches to save (if set to None, all the patches will be saved). None merge bool Set to True to merge the patches into a single polygon (for geojson format only). False label str | None An optional label to assign to the patches (for geojson format only). None color tuple [ int , int , int ] An optional color to assign to the patches (for geojson format only). (255, 0, 0) append_to_existing_file bool Set to True to append the patches to an existing geojson file. False Source code in src/prismtoolbox/wsicore/wsi.py 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 def save_patches ( self , save_dir : str , file_format : str = \"h5\" , selected_idx : np . ndarray | None = None , merge : bool = False , label : str | None = None , color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), append_to_existing_file : bool = False , ) -> None : \"\"\"Save the patches in a hdf5 or geojson file. Args: save_dir: The path to the directory where the patches will be saved. file_format: The format for the saving (h5 for python processing, geojson for QuPath processing). selected_idx: An array of indices of the patches to save (if set to None, all the patches will be saved). merge: Set to True to merge the patches into a single polygon (for geojson format only). label: An optional label to assign to the patches (for geojson format only). color: An optional color to assign to the patches (for geojson format only). append_to_existing_file: Set to True to append the patches to an existing geojson file. \"\"\" if selected_idx is not None : coords = self . coords [ selected_idx ] else : coords = self . coords if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if file_format == \"h5\" : asset_dict = { \"coords\" : self . coords } attr_dict = { \"coords\" : self . coords_attrs } file_path = os . path . join ( save_dir , f \" { self . slide_name } .h5\" ) log . info ( f \"Saving patches for slide { self . slide_name } at { file_path } with hdf5.\" ) save_patches_with_hdf5 ( file_path , asset_dict , attr_dict ) elif file_format == \"geojson\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .geojson\" ) patch_downsample = int ( self . level_downsamples [ self . coords_attrs [ \"patch_level\" ]] ) polygons = patchesToPolygons ( coords , self . coords_attrs [ \"patch_size\" ], patch_downsample , merge ) log . info ( f \"Saving { len ( coords ) } patches for slide { self . slide_name } at { file_path } with geojson.\" ) export_polygons_to_qupath ( polygons , file_path , \"annotation\" , offset = self . offset , label = label , color = color , append_to_existing_file = append_to_existing_file , ) elif file_format == \"jpg\" or file_format == \"png\" : save_dir = os . path . join ( save_dir , self . slide_name ) if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) log . info ( f \"Saving { len ( coords ) } patches for slide { self . slide_name } at { save_dir } with { file_format } .\" ) for coord in coords : patch = self . read_region ( coord , self . coords_attrs [ \"patch_level\" ], ( self . coords_attrs [ \"patch_size\" ], self . coords_attrs [ \"patch_size\" ]), ) . convert ( \"RGB\" ) patch . save ( os . path . join ( save_dir , f \" { coord [ 0 ] } _ { coord [ 1 ] } . { file_format } \" )) else : raise ValueError ( f \"format { file_format } not supported\" )","title":" save_patches"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.save_tissue_contours","text":"Save the tissue contours in a pickle or geojson file. Parameters: Name Type Description Default save_dir str The path to the directory where the contours will be saved. required selected_idx ndarray | None An array of indices of the contours to save (if set to None, all the contours will be saved). None file_format str The file format for saving the contours (pickle for python processing, geojson for QuPath processing). 'pickle' merge bool Set to True to merge the contours into a single polygon (for geojson format only). False label str | None An optional label to assign to the tissue contours (for geojson format only). None color tuple [ int , int , int ] An optional color to assign to the tissue contours (for geojson format only). (255, 0, 0) append_to_existing_file bool Set to True to append the contours to an existing geojson file. False make_valid bool Set to True to make the polygons valid (for geojson format only). False Source code in src/prismtoolbox/wsicore/wsi.py 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 def save_tissue_contours ( self , save_dir : str , selected_idx : np . ndarray | None = None , file_format : str = \"pickle\" , merge : bool = False , label : str | None = None , color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), append_to_existing_file : bool = False , make_valid : bool = False , ) -> None : \"\"\"Save the tissue contours in a pickle or geojson file. Args: save_dir: The path to the directory where the contours will be saved. selected_idx: An array of indices of the contours to save (if set to None, all the contours will be saved). file_format: The file format for saving the contours (pickle for python processing, geojson for QuPath processing). merge: Set to True to merge the contours into a single polygon (for geojson format only). label: An optional label to assign to the tissue contours (for geojson format only). color: An optional color to assign to the tissue contours (for geojson format only). append_to_existing_file: Set to True to append the contours to an existing geojson file. make_valid: Set to True to make the polygons valid (for geojson format only). \"\"\" assert self . tissue_contours is not None , ( \"No tissue contours found for the slide, \" \"please run the detect_tissue method first\" ) if selected_idx is not None : tissue_contours = [ self . tissue_contours [ idx ] for idx in selected_idx ] else : tissue_contours = self . tissue_contours if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if file_format == \"pickle\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .pkl\" ) log . info ( f \"Saving tissue contours for slide { self . slide_name } at { file_path } with pickle.\" ) save_obj_with_pickle ( tissue_contours , file_path ) elif file_format == \"geojson\" : file_path = os . path . join ( save_dir , f \" { self . slide_name } .geojson\" ) log . info ( f \"Saving { selected_idx } tissue contours for slide { self . slide_name } at { file_path } with geojson.\" ) polygons = contoursToPolygons ( tissue_contours , merge , make_valid ) export_polygons_to_qupath ( polygons , file_path , \"annotation\" , offset = self . offset , label = label , color = color , append_to_existing_file = append_to_existing_file , ) else : raise ValueError ( f \"format { file_format } not supported\" )","title":" save_tissue_contours"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.scale_contours","text":"Scale the contours by a given factor. Parameters: Name Type Description Default contours list [ ndarray ] The contours to scale. required scale int The scale factor to apply. required Returns: Type Description list [ ndarray ] The input list with scaled contours. Source code in src/prismtoolbox/wsicore/wsi.py 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 @staticmethod def scale_contours ( contours : list [ np . ndarray ], scale : int , ) -> list [ np . ndarray ]: \"\"\"Scale the contours by a given factor. Args: contours: The contours to scale. scale: The scale factor to apply. Returns: The input list with scaled contours. \"\"\" scaled_contours = [ np . array ( cont * scale , dtype = \"int\" ) for cont in contours ] return scaled_contours","title":" scale_contours"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.set_roi","text":"Set the region of interest for the slide. Can be set manually or by selecting a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute. Parameters: Name Type Description Default roi tuple [ int , int , int , int ] | None Set the region of interest manually as a tuple (x1, y1, x2, y2). None rois_df_path str | None The path to dataframe containing the ROIs with a slide_id column identifying the slide. None Returns: Type Description None The region of interest set for the slide as a tuple (x1, y1, x2, y2). Source code in src/prismtoolbox/wsicore/wsi.py 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 def set_roi ( self , roi : tuple [ int , int , int , int ] | None = None , rois_df_path : str | None = None , ) -> None : \"\"\"Set the region of interest for the slide. Can be set manually or by selecting a region on a thumbnail. The ROI is stored as a tuple in the ROI attribute. Args: roi: Set the region of interest manually as a tuple (x1, y1, x2, y2). rois_df_path: The path to dataframe containing the ROIs with a slide_id column identifying the slide. Returns: The region of interest set for the slide as a tuple (x1, y1, x2, y2). \"\"\" if roi is not None : ROI = np . array ( roi ) . astype ( int ) elif rois_df_path is not None : rois_df = pd . read_csv ( rois_df_path ) ROI = rois_df [ rois_df . slide_id == self . slide_name ] . values [ 0 ] . astype ( int ) else : log . info ( \"No ROI provided, prompting user to select one.\" ) level = input ( f \"No ROI was provided, please select a level at which the ROI should be created (max level: { len ( self . level_dimensions ) - 1 } ): \" ) if not level : log . warning ( \"No level provided, setting the ROI at the highest level.\" ) level = len ( self . level_downsamples ) - 1 else : level = int ( level ) img = np . array ( self . create_thumbnail ( level )) ROI = select_roi_on_thumbnail ( img , int ( self . level_downsamples [ level ])) ROI = ( ROI * self . level_downsamples [ level ]) . astype ( int ) self . ROI = ROI self . ROI_width = ROI [ 2 ] - ROI [ 0 ] self . ROI_height = ROI [ 3 ] - ROI [ 1 ] print ( f \"ROI for slide { self . slide_name } has been set to { self . ROI } .\" ) return ROI","title":" set_roi"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.set_slide_attributes","text":"Set the slide attributes. Source code in src/prismtoolbox/wsicore/wsi.py 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 def set_slide_attributes ( self ): \"\"\"Set the slide attributes.\"\"\" if self . engine == \"openslide\" : self . dimensions = self . slide . dimensions self . level_dimensions = self . slide . level_dimensions self . level_downsamples = self . slide . level_downsamples self . properties = self . slide . properties elif self . engine == \"tiffslide\" : self . dimensions = self . slide . dimensions self . level_dimensions = self . slide . level_dimensions self . level_downsamples = self . slide . level_downsamples self . properties = self . slide . properties else : raise NotImplementedError ( f \"engine { self . engine } not supported\" ) if ( f \" { self . engine } .bounds-x\" in self . properties . keys () and self . properties [ f \" { self . engine } .bounds-x\" ] is not None ): self . offset = ( - int ( self . properties [ f \" { self . engine } .bounds-x\" ]), - int ( self . properties [ f \" { self . engine } .bounds-y\" ]), )","title":" set_slide_attributes"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.stitch","text":"Stitch the patches extracted on an image. The patches can be masked and colored depending on the mask and colors provided. Requires the coordinates of the patches to be set for the slide beforehand with the extract_patches method. Parameters: Name Type Description Default vis_level int The level at which the patches should be visualized. required selected_idx ndarray An array of indices of the patches to visualize (if set to None, all the patches will be visualized). None colors ndarray An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as they are). None alpha float Set the transparency of the colors to apply to the patches. 0.6 black_white bool Set to True to visualize a binary mask of the patches extracted. False draw_grid bool Set to True to draw a grid on the stitched patches. False crop_roi bool Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the set_roi method). False background_color tuple [ int , int , int ] The color of the background. (0, 0, 0) Returns: Type Description Image A PIL image of the stitched patches. Source code in src/prismtoolbox/wsicore/wsi.py 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 def stitch ( self , vis_level : int , selected_idx : np . ndarray = None , colors : np . ndarray = None , alpha : float = 0.6 , black_white : bool = False , draw_grid : bool = False , crop_roi : bool = False , background_color : tuple [ int , int , int ] = ( 0 , 0 , 0 ), ) -> Image . Image : \"\"\"Stitch the patches extracted on an image. The patches can be masked and colored depending on the mask and colors provided. Requires the coordinates of the patches to be set for the slide beforehand with the [extract_patches][prismtoolbox.wsicore.WSI.extract_patches] method. Args: vis_level: The level at which the patches should be visualized. selected_idx: An array of indices of the patches to visualize (if set to None, all the patches will be visualized). colors: An array of RGB colors to apply to the patches (if set to None, the patches will be visualized as they are). alpha: Set the transparency of the colors to apply to the patches. black_white: Set to True to visualize a binary mask of the patches extracted. draw_grid: Set to True to draw a grid on the stitched patches. crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). background_color: The color of the background. Returns: A PIL image of the stitched patches. \"\"\" assert self . ROI is not None if crop_roi else True , ( \"no ROI provided, while crop_roi is set to True,\" \" please set a ROI for the slide or set crop_roi to False.\" ) assert self . coords is not None , ( \"no coordinates provided for the patches to visualize, please run the \" \"extract_patches method first or load the coordinates from a file\" ) if crop_roi : w , h = int ( np . ceil ( self . ROI_width / self . level_downsamples [ vis_level ])), int ( np . ceil ( self . ROI_height / self . level_downsamples [ vis_level ]) ) else : w , h = self . level_dimensions [ vis_level ] patch_size = self . coords_attrs [ \"patch_size\" ] patch_level = self . coords_attrs [ \"patch_level\" ] patch_size = int ( patch_size * self . level_downsamples [ patch_level ]) canvas = init_image ( w , h , mask = black_white , color_bakground = background_color ) downsample_vis = self . level_downsamples [ vis_level ] idxs = np . arange ( len ( self . coords )) if selected_idx is not None : idxs = idxs [ selected_idx ] patch_size = np . ceil ( patch_size / downsample_vis ) . astype ( int ) log . info ( f \"Stitching { len ( idxs ) } patches at level { vis_level } with patch size { patch_size } , \" f \"with colors { colors is not None } .\" ) offset = self . ROI [: 2 ] if crop_roi else np . array ([ 0 , 0 ]) for idx in idxs : coord = self . coords [ idx ] coord_downsampled = np . ceil ( np . abs ( coord - offset ) / downsample_vis ) . astype ( int ) patch_size_coord = ( min ( max ( w - coord_downsampled [ 0 ], 0 ), patch_size ), min ( max ( h - coord_downsampled [ 1 ], 0 ), patch_size ), ) if any ( val == 0 for val in patch_size_coord ): continue if black_white : patch = np . ones ( patch_size_coord , dtype = \"uint8\" ) colors = None else : patch = np . array ( self . slide . read_region ( tuple ( coord ), vis_level , patch_size_coord ) . convert ( \"RGB\" ) ) if colors is not None : assert len ( colors ) == len ( idxs ), ( \"the number of colors provided must match \" \"the number of selected coordinates\" ) color = colors [ idx ] color_patch = ( np . ones (( patch_size_coord [ 1 ], patch_size_coord [ 0 ], 3 )) * color ) . astype ( \"uint8\" ) canvas [ coord_downsampled [ 1 ] : coord_downsampled [ 1 ] + patch_size_coord [ 1 ], coord_downsampled [ 0 ] : coord_downsampled [ 0 ] + patch_size_coord [ 0 ], :, ] = cv2 . addWeighted ( color_patch , alpha , patch , 1 - alpha , 0 , patch ) else : canvas [ coord_downsampled [ 1 ] : coord_downsampled [ 1 ] + patch_size_coord [ 1 ], coord_downsampled [ 0 ] : coord_downsampled [ 0 ] + patch_size_coord [ 0 ], :, ] = patch if draw_grid : cv2 . rectangle ( canvas , tuple ( np . maximum ([ 0 , 0 ], coord_downsampled - 1 )), tuple ( coord_downsampled + patch_size_coord ), ( 0 , 0 , 0 , 255 ), thickness = 2 , ) img = Image . fromarray ( canvas ) return img","title":" stitch"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.visualize","text":"Visualize the slide with or without the contours of the tissue. Parameters: Name Type Description Default vis_level int The level at which the visualization should be performed. required crop_roi bool Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the set_roi method). False contours_color tuple [ int , int , int ] The color to use for the contours. (255, 0, 0) line_thickness int The thickness to use for the contours 500 max_size int | None The maximum size for the visualization for the width or height of the image. None number_contours bool Set to True to number the contours. False black_white bool Set to True to visualize a binary mask of the contoured tissue. False view_slide_only bool Set to True to visualize the slide only (without the contours). False Returns: Type Description Image A PIL image of the visualization. Source code in src/prismtoolbox/wsicore/wsi.py 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 def visualize ( self , vis_level : int , crop_roi : bool = False , contours_color : tuple [ int , int , int ] = ( 255 , 0 , 0 ), line_thickness : int = 500 , max_size : int | None = None , number_contours : bool = False , black_white : bool = False , view_slide_only : bool = False , ) -> Image . Image : \"\"\"Visualize the slide with or without the contours of the tissue. Args: vis_level: The level at which the visualization should be performed. crop_roi: Set to True to crop the visualization to the region of interest (requires a ROI to be set for the slide beforehand with the [set_roi][prismtoolbox.wsicore.WSI.set_roi] method). contours_color: The color to use for the contours. line_thickness: The thickness to use for the contours max_size: The maximum size for the visualization for the width or height of the image. number_contours: Set to True to number the contours. black_white: Set to True to visualize a binary mask of the contoured tissue. view_slide_only: Set to True to visualize the slide only (without the contours). Returns: A PIL image of the visualization. \"\"\" assert line_thickness > 0 , \"line_thickness must be greater than 0\" scale = 1 / self . level_downsamples [ vis_level ] if black_white : img = np . zeros_like ( self . create_thumbnail ( vis_level , crop_roi ), dtype = \"uint8\" ) line_thickness = - 1 contours_color = ( 1 , 1 , 1 ) else : img = np . array ( self . create_thumbnail ( vis_level , crop_roi )) line_thickness = int ( line_thickness * scale ) if not view_slide_only : assert ( len ( self . tissue_contours ) > 0 ), \"No tissue contours found for the slide, please run the detect_tissue method first\" offset = self . ROI [: 2 ] if crop_roi else np . array ([ 0 , 0 ]) contours = [ cont - offset for cont in self . tissue_contours ] contours = self . scale_contours ( contours , scale ) if len ( contours ) > 0 : if not number_contours : cv2 . drawContours ( img , contours , - 1 , contours_color , line_thickness , lineType = cv2 . LINE_8 , ) else : # add numbering to each contour for idx , cont in enumerate ( contours ): M = cv2 . moments ( cont ) cX = int ( M [ \"m10\" ] / ( M [ \"m00\" ] + 1e-9 )) cY = int ( M [ \"m01\" ] / ( M [ \"m00\" ] + 1e-9 )) # draw the contour and put text next to center cv2 . drawContours ( img , [ cont ], - 1 , contours_color , line_thickness , lineType = cv2 . LINE_8 , ) cv2 . putText ( img , \" {} \" . format ( idx ), ( cX , cY ), cv2 . FONT_HERSHEY_SIMPLEX , 2 , ( 255 , 0 , 0 ), 10 , ) img = Image . fromarray ( img ) if black_white : img = img . convert ( \"L\" ) w , h = img . size if max_size is not None and ( w > max_size or h > max_size ): resizeFactor = max_size / w if w > h else max_size / h img = img . resize (( int ( w * resizeFactor ), int ( h * resizeFactor ))) return img","title":" visualize"},{"location":"reference/prismtoolbox/wsicore/wsi/#prismtoolbox.wsicore.wsi.WSI.worker_init","text":"Initialize the worker process with a wsi object. Parameters: Name Type Description Default slide_path str The path to the slide. required engine str The backend library to use for reading the slide (currently only openslide and tiffslide are supported) required Source code in src/prismtoolbox/wsicore/wsi.py 166 167 168 169 170 171 172 173 174 175 176 @staticmethod def worker_init ( slide_path : str , engine : str ) -> None : \"\"\"Initialize the worker process with a wsi object. Args: slide_path: The path to the slide. engine: The backend library to use for reading the slide (currently only openslide and tiffslide are supported) \"\"\" global wsi wsi = WSI . read ( slide_path , engine )","title":" worker_init"},{"location":"reference/prismtoolbox/wsiemb/","text":"","title":"Index"},{"location":"reference/prismtoolbox/wsiemb/emb_utils/","text":"compute_cell_features ( cells_df_with_classes ) Compute the features of the cells from an input geodataframe for each class. Parameters: Name Type Description Default cells_df_with_classes GeoDataFrame A geodataframe containing the cells and their classes. required Returns: Type Description ndarray The cells based features for each class. Source code in src/prismtoolbox/wsiemb/emb_utils.py 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 def compute_cell_features ( cells_df_with_classes : gpd . GeoDataFrame ) -> np . ndarray : \"\"\"Compute the features of the cells from an input geodataframe for each class. Args: cells_df_with_classes: A geodataframe containing the cells and their classes. Returns: The cells based features for each class. \"\"\" assert ( \"classification\" in cells_df_with_classes . columns ), \"The input geodataframe must contain a 'classification' column\" cells_feats = np . ravel ( cells_df_with_classes . groupby ( \"classification\" , sort = False ) . apply ( get_cell_properties , include_groups = False ) . to_list () ) return cells_feats compute_optimal_number_clusters ( embeddings_matrix , model_name , metric_name = 'davies_bouldin' , min_clusters = 2 , max_clusters = 10 , ** kwargs ) Compute the optimal number of clusters to retrieve from the embedding matrix using the specified clustering model and metric. Parameters: Name Type Description Default embeddings_matrix ndarray An array of shape (n_samples, n_features) containing the embeddings. required model_name str The name of the clustering model. At the moment, possible models are: \" kmeans \" \" kmeans_mini_batch \" required metric_name str The name of the metric used to evaluate the clustering quality. At the moment, possible metrics are: \" silhouette \" \" calinski_harabasz \" \" davies_bouldin \" 'davies_bouldin' min_clusters int The minimum number of clusters to consider. 2 max_clusters int The maximum number of clusters to consider. 10 **kwargs Some additional arguments for the clustering model (see the documentation of the clustering model). {} Returns: Type Description tuple [ int , list [ float ]] A tuple containing the optimal number of clusters and the quality scores for each number of clusters. Source code in src/prismtoolbox/wsiemb/emb_utils.py 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 def compute_optimal_number_clusters ( embeddings_matrix : np . ndarray , model_name : str , metric_name : str = \"davies_bouldin\" , min_clusters : int = 2 , max_clusters : int = 10 , ** kwargs , ) -> tuple [ int , list [ float ]]: \"\"\"Compute the optimal number of clusters to retrieve from the embedding matrix using the specified clustering model and metric. Args: embeddings_matrix: An array of shape (n_samples, n_features) containing the embeddings. model_name: The name of the clustering model. At the moment, possible models are: - \"[kmeans](https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\" - \"[kmeans_mini_batch](https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans)\" metric_name: The name of the metric used to evaluate the clustering quality. At the moment, possible metrics are: - \"[silhouette](https://scikit-learn.org/stable/modules/generated/ sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score)\" - \"[calinski_harabasz](https://scikit-learn.org/stable/modules/generated/ sklearn.metrics.calinski_harabasz_score.html#sklearn.metrics.calinski_harabasz_score)\" - \"[davies_bouldin](https://scikit-learn.org/stable/modules/generated/ sklearn.metrics.davies_bouldin_score.html#sklearn.metrics.davies_bouldin_score)\" min_clusters: The minimum number of clusters to consider. max_clusters: The maximum number of clusters to consider. **kwargs: Some additional arguments for the clustering model (see the documentation of the clustering model). Returns: A tuple containing the optimal number of clusters and the quality scores for each number of clusters. \"\"\" if metric_name not in clustering_metrics . keys (): raise ValueError ( f \"Metric { metric_name } not implemented\" ) metric = clustering_metrics [ metric_name ] if model_name == \"kmeans\" : cluster_model = skl_cluster . KMeans ( ** kwargs ) elif model_name == \"kmeans_mini_batch\" : cluster_model = skl_cluster . MiniBatchKMeans ( ** kwargs ) else : raise ValueError ( f \"Model { model_name } not implemented\" ) scores = [] for n_clusters in tqdm ( range ( min_clusters , max_clusters + 1 ), desc = \"Computing clustering quality \" \"scores for each number of clusters\" , ): cluster_model . set_params ( n_clusters = n_clusters ) cluster_assignments = cluster_model . fit_predict ( embeddings_matrix ) scores . append ( metric ( embeddings_matrix , cluster_assignments )) if metric_name == \"davies_bouldin\" : optimal_number = np . argmin ( scores ) + min_clusters else : optimal_number = np . argmax ( scores ) + min_clusters return optimal_number , scores create_model ( arch_name , pretrained_weights = None ) Create a model from the arch_name and load the pretrained weights if specified. Parameters: Name Type Description Default arch_name str The architecture name. At the moment, possible models are: torchvision based models : \"resnet18\", \"resnet50\", \"resnet101\". CLAM , i.e ResNet50 truncated after the third convolutional block: \"clam\". Pathoduet model : \"pathoduet\". Phikon model : \"phikon\". Conch model : \"conch\". Uni model : \"uni\". required pretrained_weights str | None The path to the pretrained weights or the name of the pretrained weights. For torchvision models and CLAM, possible weights are: \"IMAGENET1K_V1\", \"IMAGENET1K_V2\", and \" ciga \" for ResNet18. For Pathoduet, possible weights are: \"pathoduet_HE\", \"pathoduet_IHC\". For Phikon, Uni, and Conch, the weights are automatically downloaded from the Hugging Face model hub. None Returns: Type Description tuple [ Module , Compose | None] A tuple containing a torch model and the transforms used to preprocess the images (set to None if no pretrained weights were used). Source code in src/prismtoolbox/wsiemb/emb_utils.py 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 def create_model ( arch_name : str , pretrained_weights : str | None = None ) -> tuple [ nn . Module , transformsv2 . Compose | None ]: \"\"\"Create a model from the arch_name and load the pretrained weights if specified. Args: arch_name: The architecture name. At the moment, possible models are: - [torchvision based models](https://pytorch.org/vision/stable/models.html): \"resnet18\", \"resnet50\", \"resnet101\". - [CLAM](https://github.com/mahmoodlab/CLAM), i.e ResNet50 truncated after the third convolutional block: \"clam\". - [Pathoduet model](https://github.com/openmedlab/PathoDuet): \"pathoduet\". - [Phikon model](https://huggingface.co/owkin/phikon): \"phikon\". - [Conch model](https://huggingface.co/MahmoodLab/CONCH): \"conch\". - [Uni model](https://huggingface.co/MahmoodLab/UNI): \"uni\". pretrained_weights: The path to the pretrained weights or the name of the pretrained weights. - For torchvision models and CLAM, possible weights are: \"IMAGENET1K_V1\", \"IMAGENET1K_V2\", and \"[ciga](https://github.com/ozanciga/self-supervised-histopathology)\" for ResNet18. - For Pathoduet, possible weights are: \"pathoduet_HE\", \"pathoduet_IHC\". - For Phikon, Uni, and Conch, the weights are automatically downloaded from the Hugging Face model hub. Returns: A tuple containing a torch model and the transforms used to preprocess the images (set to None if no pretrained weights were used). \"\"\" if arch_name not in arch_dict : raise ValueError ( f \"invalid model name. Possible models: { arch_dict . keys () } \" ) model , pretrained_transforms = arch_dict [ arch_name ]( weights = pretrained_weights ) return model , pretrained_transforms extract_stain_features ( patches , conv_matrix_name = 'HED' ) Extract the stain features from the images. Parameters: Name Type Description Default imgs A tensor of shape (n_samples, n_channels, height, width) containing the images. required conv_matrix_name str The name of the conversion matrix. At the moment, possible values are: \"HED\": Hematoxylin, Eosin and DAB. \"HD\": Hematoxylin and DAB. \"HD_custom\": Custom Hematoxylin and DAB matrix. 'HED' Returns: Type Description Tensor A tensor of shape (n_samples, n_channels, height, width) containing the stain features. Source code in src/prismtoolbox/wsiemb/emb_utils.py 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 def extract_stain_features ( patches : torch . Tensor , conv_matrix_name : str = \"HED\" ) -> torch . Tensor : \"\"\"Extract the stain features from the images. Args: imgs: A tensor of shape (n_samples, n_channels, height, width) containing the images. conv_matrix_name: The name of the conversion matrix. At the moment, possible values are: - \"HED\": Hematoxylin, Eosin and DAB. - \"HD\": Hematoxylin and DAB. - \"HD_custom\": Custom Hematoxylin and DAB matrix. Returns: A tensor of shape (n_samples, n_channels, height, width) containing the stain features. \"\"\" conv_matrix = torch . tensor ( retrieve_conv_matrix ( conv_matrix_name ), dtype = torch . float32 ) eps = torch . tensor ([ 1e-6 ], dtype = torch . float32 ) patches = torch . maximum ( patches , eps ) log_adjust = torch . log ( eps ) stains = torch . einsum ( \"bcij,cd->bdij\" , torch . log ( patches ) / log_adjust , conv_matrix ) stains = torch . maximum ( stains , torch . tensor ([ 0.0 ], dtype = torch . float32 )) if conv_matrix_name . startswith ( \"HD\" ): stains = stains [:, : 2 , ... ] stains_flattened = stains . view ( stains . size ( 0 ), stains . size ( 1 ), - 1 ) feats = torch . concatenate ( [ stains . mean ( dim = ( 2 , 3 )), stains . std ( dim = ( 2 , 3 )), stains . amin ( dim = ( 2 , 3 )), stains . amax ( dim = ( 2 , 3 )), stains_flattened . median ( dim =- 1 ) . values , stains_flattened . quantile ( 0.25 , dim =- 1 ), stains_flattened . quantile ( 0.75 , dim =- 1 ), ], dim = 1 , ) return feats get_cell_properties ( cells_df ) Get the properties of the cells in the input dataframe. Parameters: Name Type Description Default cells_df GeoDataFrame A geodataframe containing the cells. required Returns: Type Description tuple [ list [ float ], list [ str ]] A list of 7 features describing the number of cells and their average morphological properties. Source code in src/prismtoolbox/wsiemb/emb_utils.py 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 def get_cell_properties ( cells_df : gpd . GeoDataFrame ) -> tuple [ list [ float ], list [ str ]]: \"\"\"Get the properties of the cells in the input dataframe. Args: cells_df: A geodataframe containing the cells. Returns: A list of 7 features describing the number of cells and their average morphological properties. \"\"\" if cells_df . geometry . isnull () . all (): return [ 0.0 ] * 7 else : N_cells = len ( cells_df ) avg_cells_area = cells_df . area . mean () avg_cells_perimeter = cells_df . length . mean () avg_cells_compactness = ( ( 4 * np . pi * cells_df . area ) / ( cells_df . length ** 2 ) ) . mean () avg_cells_roundness = ( ( 4 * cells_df . area ) / ( cells_df . convex_hull . length ** 2 ) ) . mean () avg_cells_solidity = ( cells_df . area / cells_df . convex_hull . area ) . mean () avg_cells_elongation = ( ( cells_df . bounds [ \"maxx\" ] - cells_df . bounds [ \"minx\" ]) / ( cells_df . bounds [ \"maxy\" ] - cells_df . bounds [ \"miny\" ]) ) . mean () return [ N_cells , avg_cells_area , avg_cells_perimeter , avg_cells_compactness , avg_cells_roundness , avg_cells_solidity , avg_cells_elongation , ] get_cells_in_patch ( cells_df , coord , patch_size , cell_classes ) Get the cells whose centroids are within the patch defined by the input coordinates and size. Parameters: Name Type Description Default cells_df GeoDataFrame A geodataframe containing the cells. required coord Tensor A tensor of shape (2,) containing the coordinates of the patch. required patch_size int The size of the patch. required cell_classes list [ str ] A list of cell classes. required Returns: Type Description GeoDataFrame A geodataframe containing the cells in the patch ordered by cell classes. Source code in src/prismtoolbox/wsiemb/emb_utils.py 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 def get_cells_in_patch ( cells_df : gpd . GeoDataFrame , coord : torch . Tensor , patch_size : int , cell_classes : list [ str ], ) -> gpd . GeoDataFrame : \"\"\"Get the cells whose centroids are within the patch defined by the input coordinates and size. Args: cells_df: A geodataframe containing the cells. coord: A tensor of shape (2,) containing the coordinates of the patch. patch_size: The size of the patch. cell_classes: A list of cell classes. Returns: A geodataframe containing the cells in the patch ordered by cell classes. \"\"\" x , y = coord . numpy () patch_polygon = box ( x , y , x + patch_size , y + patch_size , ccw = False ) cells_in_patch = cells_df . loc [ cells_df . centroid . within ( patch_polygon ), ] cells_in_patch_grouped = [] for cell_class in cell_classes : if cell_class in cells_in_patch [ \"classification\" ] . values : cell_class_in_patch_df = cells_in_patch [ cells_in_patch [ \"classification\" ] == cell_class ] else : cell_class_in_patch_df = gpd . GeoDataFrame ( columns = cells_df . columns , data = [ pd . Series ({ col : None for col in cells_df . columns })], ) cell_class_in_patch_df [ \"classification\" ] = cell_class cells_in_patch_grouped . append ( cell_class_in_patch_df ) result = pd . concat ( cells_in_patch_grouped , ignore_index = True ) return result","title":"prismtoolbox.wsiemb.emb_utils"},{"location":"reference/prismtoolbox/wsiemb/emb_utils/#prismtoolbox.wsiemb.emb_utils.compute_cell_features","text":"Compute the features of the cells from an input geodataframe for each class. Parameters: Name Type Description Default cells_df_with_classes GeoDataFrame A geodataframe containing the cells and their classes. required Returns: Type Description ndarray The cells based features for each class. Source code in src/prismtoolbox/wsiemb/emb_utils.py 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 def compute_cell_features ( cells_df_with_classes : gpd . GeoDataFrame ) -> np . ndarray : \"\"\"Compute the features of the cells from an input geodataframe for each class. Args: cells_df_with_classes: A geodataframe containing the cells and their classes. Returns: The cells based features for each class. \"\"\" assert ( \"classification\" in cells_df_with_classes . columns ), \"The input geodataframe must contain a 'classification' column\" cells_feats = np . ravel ( cells_df_with_classes . groupby ( \"classification\" , sort = False ) . apply ( get_cell_properties , include_groups = False ) . to_list () ) return cells_feats","title":" compute_cell_features"},{"location":"reference/prismtoolbox/wsiemb/emb_utils/#prismtoolbox.wsiemb.emb_utils.compute_optimal_number_clusters","text":"Compute the optimal number of clusters to retrieve from the embedding matrix using the specified clustering model and metric. Parameters: Name Type Description Default embeddings_matrix ndarray An array of shape (n_samples, n_features) containing the embeddings. required model_name str The name of the clustering model. At the moment, possible models are: \" kmeans \" \" kmeans_mini_batch \" required metric_name str The name of the metric used to evaluate the clustering quality. At the moment, possible metrics are: \" silhouette \" \" calinski_harabasz \" \" davies_bouldin \" 'davies_bouldin' min_clusters int The minimum number of clusters to consider. 2 max_clusters int The maximum number of clusters to consider. 10 **kwargs Some additional arguments for the clustering model (see the documentation of the clustering model). {} Returns: Type Description tuple [ int , list [ float ]] A tuple containing the optimal number of clusters and the quality scores for each number of clusters. Source code in src/prismtoolbox/wsiemb/emb_utils.py 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 def compute_optimal_number_clusters ( embeddings_matrix : np . ndarray , model_name : str , metric_name : str = \"davies_bouldin\" , min_clusters : int = 2 , max_clusters : int = 10 , ** kwargs , ) -> tuple [ int , list [ float ]]: \"\"\"Compute the optimal number of clusters to retrieve from the embedding matrix using the specified clustering model and metric. Args: embeddings_matrix: An array of shape (n_samples, n_features) containing the embeddings. model_name: The name of the clustering model. At the moment, possible models are: - \"[kmeans](https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\" - \"[kmeans_mini_batch](https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans)\" metric_name: The name of the metric used to evaluate the clustering quality. At the moment, possible metrics are: - \"[silhouette](https://scikit-learn.org/stable/modules/generated/ sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score)\" - \"[calinski_harabasz](https://scikit-learn.org/stable/modules/generated/ sklearn.metrics.calinski_harabasz_score.html#sklearn.metrics.calinski_harabasz_score)\" - \"[davies_bouldin](https://scikit-learn.org/stable/modules/generated/ sklearn.metrics.davies_bouldin_score.html#sklearn.metrics.davies_bouldin_score)\" min_clusters: The minimum number of clusters to consider. max_clusters: The maximum number of clusters to consider. **kwargs: Some additional arguments for the clustering model (see the documentation of the clustering model). Returns: A tuple containing the optimal number of clusters and the quality scores for each number of clusters. \"\"\" if metric_name not in clustering_metrics . keys (): raise ValueError ( f \"Metric { metric_name } not implemented\" ) metric = clustering_metrics [ metric_name ] if model_name == \"kmeans\" : cluster_model = skl_cluster . KMeans ( ** kwargs ) elif model_name == \"kmeans_mini_batch\" : cluster_model = skl_cluster . MiniBatchKMeans ( ** kwargs ) else : raise ValueError ( f \"Model { model_name } not implemented\" ) scores = [] for n_clusters in tqdm ( range ( min_clusters , max_clusters + 1 ), desc = \"Computing clustering quality \" \"scores for each number of clusters\" , ): cluster_model . set_params ( n_clusters = n_clusters ) cluster_assignments = cluster_model . fit_predict ( embeddings_matrix ) scores . append ( metric ( embeddings_matrix , cluster_assignments )) if metric_name == \"davies_bouldin\" : optimal_number = np . argmin ( scores ) + min_clusters else : optimal_number = np . argmax ( scores ) + min_clusters return optimal_number , scores","title":" compute_optimal_number_clusters"},{"location":"reference/prismtoolbox/wsiemb/emb_utils/#prismtoolbox.wsiemb.emb_utils.create_model","text":"Create a model from the arch_name and load the pretrained weights if specified. Parameters: Name Type Description Default arch_name str The architecture name. At the moment, possible models are: torchvision based models : \"resnet18\", \"resnet50\", \"resnet101\". CLAM , i.e ResNet50 truncated after the third convolutional block: \"clam\". Pathoduet model : \"pathoduet\". Phikon model : \"phikon\". Conch model : \"conch\". Uni model : \"uni\". required pretrained_weights str | None The path to the pretrained weights or the name of the pretrained weights. For torchvision models and CLAM, possible weights are: \"IMAGENET1K_V1\", \"IMAGENET1K_V2\", and \" ciga \" for ResNet18. For Pathoduet, possible weights are: \"pathoduet_HE\", \"pathoduet_IHC\". For Phikon, Uni, and Conch, the weights are automatically downloaded from the Hugging Face model hub. None Returns: Type Description tuple [ Module , Compose | None] A tuple containing a torch model and the transforms used to preprocess the images (set to None if no pretrained weights were used). Source code in src/prismtoolbox/wsiemb/emb_utils.py 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 def create_model ( arch_name : str , pretrained_weights : str | None = None ) -> tuple [ nn . Module , transformsv2 . Compose | None ]: \"\"\"Create a model from the arch_name and load the pretrained weights if specified. Args: arch_name: The architecture name. At the moment, possible models are: - [torchvision based models](https://pytorch.org/vision/stable/models.html): \"resnet18\", \"resnet50\", \"resnet101\". - [CLAM](https://github.com/mahmoodlab/CLAM), i.e ResNet50 truncated after the third convolutional block: \"clam\". - [Pathoduet model](https://github.com/openmedlab/PathoDuet): \"pathoduet\". - [Phikon model](https://huggingface.co/owkin/phikon): \"phikon\". - [Conch model](https://huggingface.co/MahmoodLab/CONCH): \"conch\". - [Uni model](https://huggingface.co/MahmoodLab/UNI): \"uni\". pretrained_weights: The path to the pretrained weights or the name of the pretrained weights. - For torchvision models and CLAM, possible weights are: \"IMAGENET1K_V1\", \"IMAGENET1K_V2\", and \"[ciga](https://github.com/ozanciga/self-supervised-histopathology)\" for ResNet18. - For Pathoduet, possible weights are: \"pathoduet_HE\", \"pathoduet_IHC\". - For Phikon, Uni, and Conch, the weights are automatically downloaded from the Hugging Face model hub. Returns: A tuple containing a torch model and the transforms used to preprocess the images (set to None if no pretrained weights were used). \"\"\" if arch_name not in arch_dict : raise ValueError ( f \"invalid model name. Possible models: { arch_dict . keys () } \" ) model , pretrained_transforms = arch_dict [ arch_name ]( weights = pretrained_weights ) return model , pretrained_transforms","title":" create_model"},{"location":"reference/prismtoolbox/wsiemb/emb_utils/#prismtoolbox.wsiemb.emb_utils.extract_stain_features","text":"Extract the stain features from the images. Parameters: Name Type Description Default imgs A tensor of shape (n_samples, n_channels, height, width) containing the images. required conv_matrix_name str The name of the conversion matrix. At the moment, possible values are: \"HED\": Hematoxylin, Eosin and DAB. \"HD\": Hematoxylin and DAB. \"HD_custom\": Custom Hematoxylin and DAB matrix. 'HED' Returns: Type Description Tensor A tensor of shape (n_samples, n_channels, height, width) containing the stain features. Source code in src/prismtoolbox/wsiemb/emb_utils.py 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 def extract_stain_features ( patches : torch . Tensor , conv_matrix_name : str = \"HED\" ) -> torch . Tensor : \"\"\"Extract the stain features from the images. Args: imgs: A tensor of shape (n_samples, n_channels, height, width) containing the images. conv_matrix_name: The name of the conversion matrix. At the moment, possible values are: - \"HED\": Hematoxylin, Eosin and DAB. - \"HD\": Hematoxylin and DAB. - \"HD_custom\": Custom Hematoxylin and DAB matrix. Returns: A tensor of shape (n_samples, n_channels, height, width) containing the stain features. \"\"\" conv_matrix = torch . tensor ( retrieve_conv_matrix ( conv_matrix_name ), dtype = torch . float32 ) eps = torch . tensor ([ 1e-6 ], dtype = torch . float32 ) patches = torch . maximum ( patches , eps ) log_adjust = torch . log ( eps ) stains = torch . einsum ( \"bcij,cd->bdij\" , torch . log ( patches ) / log_adjust , conv_matrix ) stains = torch . maximum ( stains , torch . tensor ([ 0.0 ], dtype = torch . float32 )) if conv_matrix_name . startswith ( \"HD\" ): stains = stains [:, : 2 , ... ] stains_flattened = stains . view ( stains . size ( 0 ), stains . size ( 1 ), - 1 ) feats = torch . concatenate ( [ stains . mean ( dim = ( 2 , 3 )), stains . std ( dim = ( 2 , 3 )), stains . amin ( dim = ( 2 , 3 )), stains . amax ( dim = ( 2 , 3 )), stains_flattened . median ( dim =- 1 ) . values , stains_flattened . quantile ( 0.25 , dim =- 1 ), stains_flattened . quantile ( 0.75 , dim =- 1 ), ], dim = 1 , ) return feats","title":" extract_stain_features"},{"location":"reference/prismtoolbox/wsiemb/emb_utils/#prismtoolbox.wsiemb.emb_utils.get_cell_properties","text":"Get the properties of the cells in the input dataframe. Parameters: Name Type Description Default cells_df GeoDataFrame A geodataframe containing the cells. required Returns: Type Description tuple [ list [ float ], list [ str ]] A list of 7 features describing the number of cells and their average morphological properties. Source code in src/prismtoolbox/wsiemb/emb_utils.py 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 def get_cell_properties ( cells_df : gpd . GeoDataFrame ) -> tuple [ list [ float ], list [ str ]]: \"\"\"Get the properties of the cells in the input dataframe. Args: cells_df: A geodataframe containing the cells. Returns: A list of 7 features describing the number of cells and their average morphological properties. \"\"\" if cells_df . geometry . isnull () . all (): return [ 0.0 ] * 7 else : N_cells = len ( cells_df ) avg_cells_area = cells_df . area . mean () avg_cells_perimeter = cells_df . length . mean () avg_cells_compactness = ( ( 4 * np . pi * cells_df . area ) / ( cells_df . length ** 2 ) ) . mean () avg_cells_roundness = ( ( 4 * cells_df . area ) / ( cells_df . convex_hull . length ** 2 ) ) . mean () avg_cells_solidity = ( cells_df . area / cells_df . convex_hull . area ) . mean () avg_cells_elongation = ( ( cells_df . bounds [ \"maxx\" ] - cells_df . bounds [ \"minx\" ]) / ( cells_df . bounds [ \"maxy\" ] - cells_df . bounds [ \"miny\" ]) ) . mean () return [ N_cells , avg_cells_area , avg_cells_perimeter , avg_cells_compactness , avg_cells_roundness , avg_cells_solidity , avg_cells_elongation , ]","title":" get_cell_properties"},{"location":"reference/prismtoolbox/wsiemb/emb_utils/#prismtoolbox.wsiemb.emb_utils.get_cells_in_patch","text":"Get the cells whose centroids are within the patch defined by the input coordinates and size. Parameters: Name Type Description Default cells_df GeoDataFrame A geodataframe containing the cells. required coord Tensor A tensor of shape (2,) containing the coordinates of the patch. required patch_size int The size of the patch. required cell_classes list [ str ] A list of cell classes. required Returns: Type Description GeoDataFrame A geodataframe containing the cells in the patch ordered by cell classes. Source code in src/prismtoolbox/wsiemb/emb_utils.py 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 def get_cells_in_patch ( cells_df : gpd . GeoDataFrame , coord : torch . Tensor , patch_size : int , cell_classes : list [ str ], ) -> gpd . GeoDataFrame : \"\"\"Get the cells whose centroids are within the patch defined by the input coordinates and size. Args: cells_df: A geodataframe containing the cells. coord: A tensor of shape (2,) containing the coordinates of the patch. patch_size: The size of the patch. cell_classes: A list of cell classes. Returns: A geodataframe containing the cells in the patch ordered by cell classes. \"\"\" x , y = coord . numpy () patch_polygon = box ( x , y , x + patch_size , y + patch_size , ccw = False ) cells_in_patch = cells_df . loc [ cells_df . centroid . within ( patch_polygon ), ] cells_in_patch_grouped = [] for cell_class in cell_classes : if cell_class in cells_in_patch [ \"classification\" ] . values : cell_class_in_patch_df = cells_in_patch [ cells_in_patch [ \"classification\" ] == cell_class ] else : cell_class_in_patch_df = gpd . GeoDataFrame ( columns = cells_df . columns , data = [ pd . Series ({ col : None for col in cells_df . columns })], ) cell_class_in_patch_df [ \"classification\" ] = cell_class cells_in_patch_grouped . append ( cell_class_in_patch_df ) result = pd . concat ( cells_in_patch_grouped , ignore_index = True ) return result","title":" get_cells_in_patch"},{"location":"reference/prismtoolbox/wsiemb/embedder/","text":"PatchEmbedder ( arch_name , batch_size , num_workers , pretrained_weights = None , transforms_dict = None , device = 'cuda' , need_login = False ) Bases: BasePatchHandler The PatchEmbedder class is used to extract embeddings from patches extracted as images in a folder. Parameters: Name Type Description Default arch_name str The name of the architecture to use. See create_model for available architectures. required batch_size int The batch size to use for the dataloader. required num_workers int The number of workers to use for the dataloader. required pretrained_weights str | None The path to the pretrained weights or the name of the pretrained weights. See create_model for available weights for each architecture. None transforms_dict dict [ str , dict [ str , any ]] | None The dictionary of transforms to use. See create_transforms for more information. If None, the pretrained transforms will be used. None device str The device to use for the model. 'cuda' need_login bool Whether to login to the HuggingFace Hub (for Uni and Conch models). False Attributes: Name Type Description batch_size The batch size to use for the dataloader. num_workers The number of workers to use for the dataloader. transforms_dict The dictionary of transforms to use. device The device to use for the model. model The model to use for creating the embeddings. pretrained_transforms The transforms used when pretraining the model. embeddings A dictionary containing the extracted embeddings for each image. Source code in src/prismtoolbox/wsiemb/embedder.py 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 def __init__ ( self , arch_name : str , batch_size : int , num_workers : int , pretrained_weights : str | None = None , transforms_dict : dict [ str , dict [ str , any ]] | None = None , device : str = \"cuda\" , need_login : bool = False , ): \"\"\"The PatchEmbedder class is used to extract embeddings from patches extracted as images in a folder. Args: arch_name: The name of the architecture to use. See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available architectures. batch_size: The batch size to use for the dataloader. num_workers: The number of workers to use for the dataloader. pretrained_weights: The path to the pretrained weights or the name of the pretrained weights. See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available weights for each architecture. transforms_dict: The dictionary of transforms to use. See [create_transforms][prismtoolbox.utils.torch_utils.create_transforms] for more information. If None, the pretrained transforms will be used. device: The device to use for the model. need_login: Whether to login to the HuggingFace Hub (for Uni and Conch models). Attributes: batch_size: The batch size to use for the dataloader. num_workers: The number of workers to use for the dataloader. transforms_dict: The dictionary of transforms to use. device: The device to use for the model. model: The model to use for creating the embeddings. pretrained_transforms: The transforms used when pretraining the model. embeddings: A dictionary containing the extracted embeddings for each image. \"\"\" super () . __init__ ( batch_size , num_workers , transforms_dict ) self . device = device if need_login : from huggingface_hub import login login () self . model , self . pretrained_transforms = create_model ( arch_name , pretrained_weights ) log . info ( f \"Model { arch_name } loaded with pretrained weights { pretrained_weights } .\" ) self . model . eval () self . model . to ( self . device ) self . embeddings = {} extract_embeddings ( img_folder , show_progress = True ) Extract embeddings from the images in the img_folder. Parameters: Name Type Description Default img_folder A folder containing a series of subfolders, each containing images. For example, img_folder could be a folder where the subfolders correspond to different slides. required show_progress bool Whether to show the progress bar. True Source code in src/prismtoolbox/wsiemb/embedder.py 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 def extract_embeddings ( self , img_folder , show_progress : bool = True ): \"\"\"Extract embeddings from the images in the img_folder. Args: img_folder: A folder containing a series of subfolders, each containing images. For example, img_folder could be a folder where the subfolders correspond to different slides. show_progress: Whether to show the progress bar. \"\"\" log . info ( f \"Extracting embeddings from images in { img_folder } .\" ) dataset = self . create_dataset ( img_folder = img_folder ) dataloader = self . create_dataloader ( dataset ) start_time = time . time () embeddings = [[] for _ in range ( len ( dataset . classes ))] img_ids = [] for i in range ( len ( dataset . classes )): img_ids . append ( np . array ( dataset . imgs )[ np . array ( dataset . targets ) == i ][:, 0 ]) for imgs , folder_id in tqdm ( dataloader , desc = f \"Extracting embeddings from images in { img_folder } \" , disable = not show_progress , ): imgs = imgs . to ( self . device ) with torch . no_grad (): output = self . model ( imgs ) for i in range ( len ( dataset . classes )): embeddings [ i ] . append ( output [ folder_id == i ] . cpu ()) log . info ( f \"Embedding time: { time . time () - start_time } .\" ) log . info ( f \"Extracted { len ( embeddings ) } from images in { img_folder } .\" ) for img_id , embedding in zip ( img_ids , embeddings ): self . embeddings [ img_id ] = torch . cat ( embedding , dim = 0 ) get_transforms () Get the transforms to use for creating the embeddings. Returns: Type Description The transforms to use when loading the patches. Source code in src/prismtoolbox/wsiemb/embedder.py 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 def get_transforms ( self ): \"\"\"Get the transforms to use for creating the embeddings. Returns: The transforms to use when loading the patches. \"\"\" if self . transforms_dict is not None : super () . get_transforms () elif self . pretrained_transforms is not None : log . info ( \"No transforms dict found, using pretrained transforms.\" ) transforms = self . pretrained_transforms else : log . info ( \"No transforms dict or pretrained transforms found.\" ) transforms = None return transforms SlideEmbedder ( slide_dir , batch_size , num_workers , arch_name = None , pretrained_weights = None , transforms_dict = None , device = 'cuda' , engine = 'openslide' , coords_dir = None , patch_size = None , patch_level = None , patch_downsample = None , need_login = False ) Bases: BaseSlideHandler The SlideEmbedder class is used to extract embeddings from patches extracted direclty from the slides. Parameters: Name Type Description Default slide_dir str The directory containing the slides. required batch_size int The batch size to use for the dataloader. required num_workers int The number of workers to use for the dataloader. required arch_name str | None The name of the architecture to use. See create_model for available architectures. None pretrained_weights str | None The path to the pretrained weights or the name of the pretrained weights. See create_model for available weights for each architecture. None transforms_dict dict [ str , dict [ str , any ]] | None The dictionary of transforms to use. See create_transforms for more information. If None, the pretrained transforms will be used. None device str The device to use for the model. 'cuda' engine str The engine to use for reading the slides. 'openslide' coords_dir str | None The directory containing the coordinates of the patches as hdf5 files. If None, the patch_size and patch_level must be provided. None patch_size int | None The size of the patches. If None, it will be extracted from the hdf5 files. None patch_level int | None The level of the patches. If None, it will be extracted from the hdf5 files. None patch_downsample int | None The downsample of the patches. If None, it will be extracted from the hdf5 files. None need_login bool Whether to login to the HuggingFace Hub (for Uni and Conch models). False Attributes: Name Type Description slide_dir The directory containing the slides. batch_size The batch size to use for the dataloader. num_workers The number of workers to use for the dataloader. transforms_dict The dictionary of transforms to use. device The device to use for the model. engine The engine to use for reading the slides. coords_dir The directory containing the coordinates of the patches as hdf5 files. patch_size The size of the patches. patch_level The level of the patches. patch_downsample The downsample of the patches. arch_name The name of the architecture to use. model The model to use for creating the embeddings. pretrained_transforms The transforms used for the pretrained model. model_based_embeddings A dictionary containing the extracted embeddings for each slide with the pretrained model. stain_based_embeddings A dictionary containing the extracted embeddings for each slide with the stain based features. cell_based_embeddings A dictionary containing the extracted embeddings for each slide with the cell based features. model_based_embedding_names The names of the features extracted with the pretrained model. stain_based_embedding_names The names of the features extracted with the stain based features. cell_based_embedding_names The names of the features extracted with the cell based features. Source code in src/prismtoolbox/wsiemb/embedder.py 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 def __init__ ( self , slide_dir : str , batch_size : int , num_workers : int , arch_name : str | None = None , pretrained_weights : str | None = None , transforms_dict : dict [ str , dict [ str , any ]] | None = None , device : str = \"cuda\" , engine : str = \"openslide\" , coords_dir : str | None = None , patch_size : int | None = None , patch_level : int | None = None , patch_downsample : int | None = None , need_login : bool = False , ): \"\"\"The SlideEmbedder class is used to extract embeddings from patches extracted direclty from the slides. Args: slide_dir: The directory containing the slides. batch_size: The batch size to use for the dataloader. num_workers: The number of workers to use for the dataloader. arch_name: The name of the architecture to use. See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available architectures. pretrained_weights: The path to the pretrained weights or the name of the pretrained weights. See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available weights for each architecture. transforms_dict: The dictionary of transforms to use. See [create_transforms][prismtoolbox.utils.torch_utils.create_transforms] for more information. If None, the pretrained transforms will be used. device: The device to use for the model. engine: The engine to use for reading the slides. coords_dir: The directory containing the coordinates of the patches as hdf5 files. If None, the patch_size and patch_level must be provided. patch_size: The size of the patches. If None, it will be extracted from the hdf5 files. patch_level: The level of the patches. If None, it will be extracted from the hdf5 files. patch_downsample: The downsample of the patches. If None, it will be extracted from the hdf5 files. need_login: Whether to login to the HuggingFace Hub (for Uni and Conch models). Attributes: slide_dir: The directory containing the slides. batch_size: The batch size to use for the dataloader. num_workers: The number of workers to use for the dataloader. transforms_dict: The dictionary of transforms to use. device: The device to use for the model. engine: The engine to use for reading the slides. coords_dir: The directory containing the coordinates of the patches as hdf5 files. patch_size: The size of the patches. patch_level: The level of the patches. patch_downsample: The downsample of the patches. arch_name: The name of the architecture to use. model: The model to use for creating the embeddings. pretrained_transforms: The transforms used for the pretrained model. model_based_embeddings: A dictionary containing the extracted embeddings for each slide with the pretrained model. stain_based_embeddings: A dictionary containing the extracted embeddings for each slide with the stain based features. cell_based_embeddings: A dictionary containing the extracted embeddings for each slide with the cell based features. model_based_embedding_names: The names of the features extracted with the pretrained model. stain_based_embedding_names: The names of the features extracted with the stain based features. cell_based_embedding_names: The names of the features extracted with the cell based features. \"\"\" super () . __init__ ( slide_dir , batch_size , num_workers , transforms_dict , engine , coords_dir , patch_size , patch_level , patch_downsample , ) if need_login : from huggingface_hub import login login () self . device = device self . arch_name = arch_name if self . arch_name is not None : self . model , self . pretrained_transforms = create_model ( arch_name , pretrained_weights ) log . info ( f \"Model { self . arch_name } loaded with pretrained weights { pretrained_weights } .\" ) self . model . eval () self . model . to ( self . device ) else : self . model = None self . pretrained_transforms = None self . model_based_embeddings = {} self . stain_based_embeddings = {} self . cell_based_embeddings = {} self . model_based_embedding_names = [] self . stain_based_embedding_names = [] self . cell_based_embedding_names = [] extract_cell_based_embeddings ( slide_name , slide_ext , coords = None , cells_path = None , cell_classes = None , with_offset = True , show_progress = True ) Extract embeddings from the patches of a slide using the cell based features. Parameters: Name Type Description Default slide_name str The name of the slide to extract the embeddings from (without the extension). required slide_ext str The extension of the slide. required coords ndarray | None The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. None cells_path str | None The path to the cells geojson file. None cell_classes list [ str ] | None The classes of the cells to extract the embeddings from. If None, all the classes will be used. None with_offset bool Whether to offset the coordinates of the cells by the slide offset. True show_progress bool Whether to show the progress bar. True Source code in src/prismtoolbox/wsiemb/embedder.py 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 def extract_cell_based_embeddings ( self , slide_name : str , slide_ext : str , coords : np . ndarray | None = None , cells_path : str | None = None , cell_classes : list [ str ] | None = None , with_offset : bool = True , show_progress : bool = True , ): \"\"\"Extract embeddings from the patches of a slide using the cell based features. Args: slide_name: The name of the slide to extract the embeddings from (without the extension). slide_ext: The extension of the slide. coords: The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. cells_path: The path to the cells geojson file. cell_classes: The classes of the cells to extract the embeddings from. If None, all the classes will be used. with_offset: Whether to offset the coordinates of the cells by the slide offset. show_progress: Whether to show the progress bar. \"\"\" dataset = self . create_dataset ( slide_name , slide_ext = slide_ext , coords = coords , coords_only = True ) dataloader = self . create_dataloader ( dataset , num_workers = 1 ) patch_size = dataset . patch_size patch_downsample = dataset . downsample ref_patch_size = patch_size * patch_downsample offset = dataset . slide_offset if with_offset else ( 0 , 0 ) offset = ( - offset [ 0 ], - offset [ 1 ]) cells_df = read_json_with_geopandas ( cells_path , offset = offset ) if \"classification\" not in cells_df . columns : raise ValueError ( \"The 'classification' column is missing in the cells dataframe.\" ) cell_classes = ( cells_df . classification . unique () if cell_classes is None else cell_classes ) self . cell_based_embedding_names = [ f \" { cell_class } _ { feature } \" for cell_class in cell_classes for feature in CELL_FEATURE_NAMES ] start_time = time . time () embeddings = [] if self . num_workers > 1 : pool = mp . Pool ( self . num_workers ) for coords in tqdm ( dataloader , desc = f \"Extracting cell based features from the patches of { slide_name } \" , disable = not show_progress , ): if self . num_workers > 1 : cells_df_in_patches = pool . starmap ( get_cells_in_patch , [( cells_df , coord , ref_patch_size , cell_classes ) for coord in coords ], ) cells_features = np . array ( pool . map ( compute_cell_features , cells_df_in_patches ) ) else : cells_df_in_patches = [ get_cells_in_patch ( cells_df , coord , ref_patch_size , cell_classes ) for coord in coords ] cells_features = np . array ( [ compute_cell_features ( cells_df_in_patch ) for cells_df_in_patch in cells_df_in_patches ] ) embeddings . append ( torch . tensor ( cells_features )) if self . num_workers > 1 : pool . close () self . cell_based_embeddings [ slide_name ] = torch . cat ( embeddings , dim = 0 ) log . info ( f \"Embedding time: { time . time () - start_time } .\" ) log . info ( f \"Extracted embeddings from { len ( embeddings ) } patches of { slide_name } .\" ) extract_model_based_embeddings ( slide_name , slide_ext , coords = None , show_progress = True ) Extract embeddings from the patches of a slide using the pretrained model. Parameters: Name Type Description Default slide_name str The name of the slide to extract the embeddings from (without the extension). required slide_ext str The extension of the slide. required coords ndarray | None The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. None show_progress bool Whether to show the progress bar. True Source code in src/prismtoolbox/wsiemb/embedder.py 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 def extract_model_based_embeddings ( self , slide_name : str , slide_ext : str , coords : np . ndarray | None = None , show_progress : bool = True , ): \"\"\"Extract embeddings from the patches of a slide using the pretrained model. Args: slide_name: The name of the slide to extract the embeddings from (without the extension). slide_ext: The extension of the slide. coords: The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. show_progress: Whether to show the progress bar. \"\"\" assert ( self . model is not None ), \"Model not found. Please provide an architecture name when initializing the SlideEmbedder.\" log . info ( f \"Extracting embeddings from the patches of { slide_name } .\" ) dataset = self . create_dataset ( slide_name , slide_ext = slide_ext , coords = coords ) dataloader = self . create_dataloader ( dataset ) start_time = time . time () embeddings = [] for patches , _ in tqdm ( dataloader , desc = f \"Extracting embeddings from the patches of { slide_name } \" , disable = not show_progress , ): patches = patches . to ( self . device ) with torch . no_grad (): output = self . model ( patches ) embeddings . append ( output . cpu ()) log . info ( f \"Embedding time: { time . time () - start_time } .\" ) self . model_based_embeddings [ slide_name ] = torch . cat ( embeddings , dim = 0 ) embeddings_dim = self . model_based_embeddings [ slide_name ] . shape [ 1 ] self . model_based_embedding_names = [ f \" { self . arch_name } _ { i } \" for i in range ( embeddings_dim ) ] log . info ( f \"Extracted embeddings from { len ( embeddings ) } patches of { slide_name } .\" ) extract_stain_based_embeddings ( slide_name , slide_ext , coords = None , conv_matrix_name = 'HED' , show_progress = True ) Extract embeddings from the patches of a slide using the stain based features. Parameters: Name Type Description Default slide_name str The name of the slide to extract the embeddings from (without the extension). required slide_ext str The extension of the slide. required coords ndarray | None The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. None conv_matrix_name str The name of the convolutional matrix to use for the stain based features. See extract_stain_features for available matrices. 'HED' show_progress bool Whether to show the progress bar. True Source code in src/prismtoolbox/wsiemb/embedder.py 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 def extract_stain_based_embeddings ( self , slide_name : str , slide_ext : str , coords : np . ndarray | None = None , conv_matrix_name : str = \"HED\" , show_progress : bool = True , ): \"\"\"Extract embeddings from the patches of a slide using the stain based features. Args: slide_name: The name of the slide to extract the embeddings from (without the extension). slide_ext: The extension of the slide. coords: The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. conv_matrix_name: The name of the convolutional matrix to use for the stain based features. See [extract_stain_features][prismtoolbox.wsiemb.emb_utils.extract_stain_features] for available matrices. show_progress: Whether to show the progress bar. \"\"\" log . info ( f \"Extracting embeddings from the patches of { slide_name } .\" ) dataset = self . create_dataset ( slide_name , slide_ext = slide_ext , coords = coords , no_transforms = True ) dataloader = self . create_dataloader ( dataset ) if conv_matrix_name == \"HED\" : stain_names = [ \"Hematoxylin\" , \"Eosin\" , \"DAB\" ] else : stain_names = [ \"Hematoxylin\" , \"DAB\" ] self . stain_based_embedding_names = [ f \" { stain_name } _ { feature } \" for feature in STAIN_FEATURE_NAMES for stain_name in stain_names ] start_time = time . time () embeddings = [] for patches , _ in tqdm ( dataloader , desc = f \"Extracting stain based features from the patches of { slide_name } \" , disable = not show_progress , ): embeddings . append ( extract_stain_features ( patches , conv_matrix_name )) log . info ( f \"Embedding time: { time . time () - start_time } .\" ) self . stain_based_embeddings [ slide_name ] = torch . cat ( embeddings , dim = 0 ) log . info ( f \"Extracted embeddings from { len ( embeddings ) } patches of { slide_name } .\" ) get_transforms () Get the transforms to use for creating the dataset. Returns: Type Description The transforms to use when loading the patches. Source code in src/prismtoolbox/wsiemb/embedder.py 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 def get_transforms ( self ): \"\"\"Get the transforms to use for creating the dataset. Returns: The transforms to use when loading the patches. \"\"\" if self . transforms_dict is not None : transforms = super () . get_transforms () elif self . pretrained_transforms is not None : log . info ( \"No transforms dict found, using pretrained transforms.\" ) transforms = self . pretrained_transforms else : log . info ( \"No transforms dict or pretrained transforms found.\" ) transforms = None return transforms save_embeddings ( save_dir , flush_memory = False , format = 'pt' , merge = False ) Save the extracted embeddings to the chosen format under slide_name.format. Parameters: Name Type Description Default save_dir str The path to the directory where to save the embeddings. required flush_memory bool Whether to remove the embeddings from self.embeddings after saving. False format str The format to save the embeddings in. Possible formats: ['pt', 'npy'] 'pt' merge bool Whether to merge the different types of embeddings before saving. False Source code in src/prismtoolbox/wsiemb/embedder.py 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 def save_embeddings ( self , save_dir : str , flush_memory : bool = False , format : str = \"pt\" , merge : bool = False , ): \"\"\"Save the extracted embeddings to the chosen format under slide_name.format. Args: save_dir: The path to the directory where to save the embeddings. flush_memory: Whether to remove the embeddings from self.embeddings after saving. format: The format to save the embeddings in. Possible formats: ['pt', 'npy'] merge: Whether to merge the different types of embeddings before saving. \"\"\" if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if format not in [ \"pt\" , \"npy\" ]: raise ValueError ( \"invalid format, possible formats: ['pt', 'npy']\" ) slides_processed = set ( self . model_based_embeddings . keys ()) . union ( self . stain_based_embeddings . keys (), self . cell_based_embeddings . keys () ) for slide_name in slides_processed : if merge : embeddings_list = [ ( \"\" , torch . cat ( [ emb_dict [ slide_name ] for emb_dict in [ self . model_based_embeddings , self . stain_based_embeddings , self . cell_based_embeddings , ] if slide_name in emb_dict . keys () ], dim = 1 , ), ) ] else : embeddings_list = [ ( emb_type , emb_dict [ slide_name ]) for emb_type , emb_dict in zip ( [ \"_model_based\" , \"_stain_based\" , \"_cell_based\" ], [ self . model_based_embeddings , self . stain_based_embeddings , self . cell_based_embeddings , ], ) if slide_name in emb_dict . keys () ] for emb_type , emb in embeddings_list : output_path = os . path . join ( save_dir , f \" { slide_name + emb_type } . { format } \" ) if format == \"pt\" : torch . save ( emb , output_path ) elif format == \"npy\" : emb = emb . numpy () np . save ( output_path , emb ) else : raise NotImplementedError log . info ( f \"Embeddings for slide { slide_name } saved at { output_path } .\" ) if flush_memory : self . model_based_embeddings = {} self . stain_based_embeddings = {} self . cell_based_embeddings = {} log . info ( \"Memory flushed.\" ) save_embeddings_names ( save_dir , format = 'csv' , flush_memory = False , merge = False ) Save the extracted embeddings names to the chosen format under slide_name.format. Parameters: Name Type Description Default save_dir str The path to the directory where to save the embeddings. required format str The format to save the embeddings in. Possible formats: ['csv'] 'csv' flush_memory bool Whether to remove the reset the embeddings names after saving. False merge bool Whether the embeddings were merged before saving. False Source code in src/prismtoolbox/wsiemb/embedder.py 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 def save_embeddings_names ( self , save_dir : str , format : str = \"csv\" , flush_memory : bool = False , merge : bool = False , ): \"\"\"Save the extracted embeddings names to the chosen format under slide_name.format. Args: save_dir: The path to the directory where to save the embeddings. format: The format to save the embeddings in. Possible formats: ['csv'] flush_memory: Whether to remove the reset the embeddings names after saving. merge: Whether the embeddings were merged before saving. \"\"\" if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if format not in [ \"csv\" ]: raise ValueError ( \"invalid format, possible formats: ['csv']\" ) if merge : output_path = os . path . join ( save_dir , f \"embeddings_names. { format } \" ) if os . path . exists ( output_path ): log . warning ( f \"File embeddings_names. { format } already exists in { save_dir } . Overwriting...\" ) embeddings_names = ( self . model_based_embedding_names + self . stain_based_embedding_names + self . cell_based_embedding_names ) df = pd . DataFrame ( embeddings_names , columns = [ \"feature_names\" ]) df . to_csv ( output_path , index = False ) else : for emb_type , emb_names in zip ( [ \"model_based\" , \"stain_based\" , \"cell_based\" ], [ self . model_based_embedding_names , self . stain_based_embedding_names , self . cell_based_embedding_names , ], ): if len ( emb_names ) == 0 : continue output_path = os . path . join ( save_dir , f \"embeddings_names_ { emb_type } . { format } \" ) if os . path . exists ( output_path ): log . warning ( f \"File embeddings_names_ { emb_type } . { format } already exists in { save_dir } . Overwriting...\" ) df = pd . DataFrame ( emb_names , columns = [ \"feature_names\" ]) df . to_csv ( output_path , index = False ) log . info ( f \"Embeddings names saved at { output_path } .\" ) if flush_memory : self . model_based_embedding_names = [] self . stain_based_embedding_names = [] self . cell_based_embedding_names = [] log . info ( \"Memory flushed.\" )","title":"prismtoolbox.wsiemb.embedder"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.PatchEmbedder","text":"Bases: BasePatchHandler The PatchEmbedder class is used to extract embeddings from patches extracted as images in a folder. Parameters: Name Type Description Default arch_name str The name of the architecture to use. See create_model for available architectures. required batch_size int The batch size to use for the dataloader. required num_workers int The number of workers to use for the dataloader. required pretrained_weights str | None The path to the pretrained weights or the name of the pretrained weights. See create_model for available weights for each architecture. None transforms_dict dict [ str , dict [ str , any ]] | None The dictionary of transforms to use. See create_transforms for more information. If None, the pretrained transforms will be used. None device str The device to use for the model. 'cuda' need_login bool Whether to login to the HuggingFace Hub (for Uni and Conch models). False Attributes: Name Type Description batch_size The batch size to use for the dataloader. num_workers The number of workers to use for the dataloader. transforms_dict The dictionary of transforms to use. device The device to use for the model. model The model to use for creating the embeddings. pretrained_transforms The transforms used when pretraining the model. embeddings A dictionary containing the extracted embeddings for each image. Source code in src/prismtoolbox/wsiemb/embedder.py 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 def __init__ ( self , arch_name : str , batch_size : int , num_workers : int , pretrained_weights : str | None = None , transforms_dict : dict [ str , dict [ str , any ]] | None = None , device : str = \"cuda\" , need_login : bool = False , ): \"\"\"The PatchEmbedder class is used to extract embeddings from patches extracted as images in a folder. Args: arch_name: The name of the architecture to use. See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available architectures. batch_size: The batch size to use for the dataloader. num_workers: The number of workers to use for the dataloader. pretrained_weights: The path to the pretrained weights or the name of the pretrained weights. See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available weights for each architecture. transforms_dict: The dictionary of transforms to use. See [create_transforms][prismtoolbox.utils.torch_utils.create_transforms] for more information. If None, the pretrained transforms will be used. device: The device to use for the model. need_login: Whether to login to the HuggingFace Hub (for Uni and Conch models). Attributes: batch_size: The batch size to use for the dataloader. num_workers: The number of workers to use for the dataloader. transforms_dict: The dictionary of transforms to use. device: The device to use for the model. model: The model to use for creating the embeddings. pretrained_transforms: The transforms used when pretraining the model. embeddings: A dictionary containing the extracted embeddings for each image. \"\"\" super () . __init__ ( batch_size , num_workers , transforms_dict ) self . device = device if need_login : from huggingface_hub import login login () self . model , self . pretrained_transforms = create_model ( arch_name , pretrained_weights ) log . info ( f \"Model { arch_name } loaded with pretrained weights { pretrained_weights } .\" ) self . model . eval () self . model . to ( self . device ) self . embeddings = {}","title":" PatchEmbedder"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.PatchEmbedder.extract_embeddings","text":"Extract embeddings from the images in the img_folder. Parameters: Name Type Description Default img_folder A folder containing a series of subfolders, each containing images. For example, img_folder could be a folder where the subfolders correspond to different slides. required show_progress bool Whether to show the progress bar. True Source code in src/prismtoolbox/wsiemb/embedder.py 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 def extract_embeddings ( self , img_folder , show_progress : bool = True ): \"\"\"Extract embeddings from the images in the img_folder. Args: img_folder: A folder containing a series of subfolders, each containing images. For example, img_folder could be a folder where the subfolders correspond to different slides. show_progress: Whether to show the progress bar. \"\"\" log . info ( f \"Extracting embeddings from images in { img_folder } .\" ) dataset = self . create_dataset ( img_folder = img_folder ) dataloader = self . create_dataloader ( dataset ) start_time = time . time () embeddings = [[] for _ in range ( len ( dataset . classes ))] img_ids = [] for i in range ( len ( dataset . classes )): img_ids . append ( np . array ( dataset . imgs )[ np . array ( dataset . targets ) == i ][:, 0 ]) for imgs , folder_id in tqdm ( dataloader , desc = f \"Extracting embeddings from images in { img_folder } \" , disable = not show_progress , ): imgs = imgs . to ( self . device ) with torch . no_grad (): output = self . model ( imgs ) for i in range ( len ( dataset . classes )): embeddings [ i ] . append ( output [ folder_id == i ] . cpu ()) log . info ( f \"Embedding time: { time . time () - start_time } .\" ) log . info ( f \"Extracted { len ( embeddings ) } from images in { img_folder } .\" ) for img_id , embedding in zip ( img_ids , embeddings ): self . embeddings [ img_id ] = torch . cat ( embedding , dim = 0 )","title":" extract_embeddings"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.PatchEmbedder.get_transforms","text":"Get the transforms to use for creating the embeddings. Returns: Type Description The transforms to use when loading the patches. Source code in src/prismtoolbox/wsiemb/embedder.py 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 def get_transforms ( self ): \"\"\"Get the transforms to use for creating the embeddings. Returns: The transforms to use when loading the patches. \"\"\" if self . transforms_dict is not None : super () . get_transforms () elif self . pretrained_transforms is not None : log . info ( \"No transforms dict found, using pretrained transforms.\" ) transforms = self . pretrained_transforms else : log . info ( \"No transforms dict or pretrained transforms found.\" ) transforms = None return transforms","title":" get_transforms"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.SlideEmbedder","text":"Bases: BaseSlideHandler The SlideEmbedder class is used to extract embeddings from patches extracted direclty from the slides. Parameters: Name Type Description Default slide_dir str The directory containing the slides. required batch_size int The batch size to use for the dataloader. required num_workers int The number of workers to use for the dataloader. required arch_name str | None The name of the architecture to use. See create_model for available architectures. None pretrained_weights str | None The path to the pretrained weights or the name of the pretrained weights. See create_model for available weights for each architecture. None transforms_dict dict [ str , dict [ str , any ]] | None The dictionary of transforms to use. See create_transforms for more information. If None, the pretrained transforms will be used. None device str The device to use for the model. 'cuda' engine str The engine to use for reading the slides. 'openslide' coords_dir str | None The directory containing the coordinates of the patches as hdf5 files. If None, the patch_size and patch_level must be provided. None patch_size int | None The size of the patches. If None, it will be extracted from the hdf5 files. None patch_level int | None The level of the patches. If None, it will be extracted from the hdf5 files. None patch_downsample int | None The downsample of the patches. If None, it will be extracted from the hdf5 files. None need_login bool Whether to login to the HuggingFace Hub (for Uni and Conch models). False Attributes: Name Type Description slide_dir The directory containing the slides. batch_size The batch size to use for the dataloader. num_workers The number of workers to use for the dataloader. transforms_dict The dictionary of transforms to use. device The device to use for the model. engine The engine to use for reading the slides. coords_dir The directory containing the coordinates of the patches as hdf5 files. patch_size The size of the patches. patch_level The level of the patches. patch_downsample The downsample of the patches. arch_name The name of the architecture to use. model The model to use for creating the embeddings. pretrained_transforms The transforms used for the pretrained model. model_based_embeddings A dictionary containing the extracted embeddings for each slide with the pretrained model. stain_based_embeddings A dictionary containing the extracted embeddings for each slide with the stain based features. cell_based_embeddings A dictionary containing the extracted embeddings for each slide with the cell based features. model_based_embedding_names The names of the features extracted with the pretrained model. stain_based_embedding_names The names of the features extracted with the stain based features. cell_based_embedding_names The names of the features extracted with the cell based features. Source code in src/prismtoolbox/wsiemb/embedder.py 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 def __init__ ( self , slide_dir : str , batch_size : int , num_workers : int , arch_name : str | None = None , pretrained_weights : str | None = None , transforms_dict : dict [ str , dict [ str , any ]] | None = None , device : str = \"cuda\" , engine : str = \"openslide\" , coords_dir : str | None = None , patch_size : int | None = None , patch_level : int | None = None , patch_downsample : int | None = None , need_login : bool = False , ): \"\"\"The SlideEmbedder class is used to extract embeddings from patches extracted direclty from the slides. Args: slide_dir: The directory containing the slides. batch_size: The batch size to use for the dataloader. num_workers: The number of workers to use for the dataloader. arch_name: The name of the architecture to use. See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available architectures. pretrained_weights: The path to the pretrained weights or the name of the pretrained weights. See [create_model][prismtoolbox.wsiemb.emb_utils.create_model] for available weights for each architecture. transforms_dict: The dictionary of transforms to use. See [create_transforms][prismtoolbox.utils.torch_utils.create_transforms] for more information. If None, the pretrained transforms will be used. device: The device to use for the model. engine: The engine to use for reading the slides. coords_dir: The directory containing the coordinates of the patches as hdf5 files. If None, the patch_size and patch_level must be provided. patch_size: The size of the patches. If None, it will be extracted from the hdf5 files. patch_level: The level of the patches. If None, it will be extracted from the hdf5 files. patch_downsample: The downsample of the patches. If None, it will be extracted from the hdf5 files. need_login: Whether to login to the HuggingFace Hub (for Uni and Conch models). Attributes: slide_dir: The directory containing the slides. batch_size: The batch size to use for the dataloader. num_workers: The number of workers to use for the dataloader. transforms_dict: The dictionary of transforms to use. device: The device to use for the model. engine: The engine to use for reading the slides. coords_dir: The directory containing the coordinates of the patches as hdf5 files. patch_size: The size of the patches. patch_level: The level of the patches. patch_downsample: The downsample of the patches. arch_name: The name of the architecture to use. model: The model to use for creating the embeddings. pretrained_transforms: The transforms used for the pretrained model. model_based_embeddings: A dictionary containing the extracted embeddings for each slide with the pretrained model. stain_based_embeddings: A dictionary containing the extracted embeddings for each slide with the stain based features. cell_based_embeddings: A dictionary containing the extracted embeddings for each slide with the cell based features. model_based_embedding_names: The names of the features extracted with the pretrained model. stain_based_embedding_names: The names of the features extracted with the stain based features. cell_based_embedding_names: The names of the features extracted with the cell based features. \"\"\" super () . __init__ ( slide_dir , batch_size , num_workers , transforms_dict , engine , coords_dir , patch_size , patch_level , patch_downsample , ) if need_login : from huggingface_hub import login login () self . device = device self . arch_name = arch_name if self . arch_name is not None : self . model , self . pretrained_transforms = create_model ( arch_name , pretrained_weights ) log . info ( f \"Model { self . arch_name } loaded with pretrained weights { pretrained_weights } .\" ) self . model . eval () self . model . to ( self . device ) else : self . model = None self . pretrained_transforms = None self . model_based_embeddings = {} self . stain_based_embeddings = {} self . cell_based_embeddings = {} self . model_based_embedding_names = [] self . stain_based_embedding_names = [] self . cell_based_embedding_names = []","title":" SlideEmbedder"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.SlideEmbedder.extract_cell_based_embeddings","text":"Extract embeddings from the patches of a slide using the cell based features. Parameters: Name Type Description Default slide_name str The name of the slide to extract the embeddings from (without the extension). required slide_ext str The extension of the slide. required coords ndarray | None The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. None cells_path str | None The path to the cells geojson file. None cell_classes list [ str ] | None The classes of the cells to extract the embeddings from. If None, all the classes will be used. None with_offset bool Whether to offset the coordinates of the cells by the slide offset. True show_progress bool Whether to show the progress bar. True Source code in src/prismtoolbox/wsiemb/embedder.py 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 def extract_cell_based_embeddings ( self , slide_name : str , slide_ext : str , coords : np . ndarray | None = None , cells_path : str | None = None , cell_classes : list [ str ] | None = None , with_offset : bool = True , show_progress : bool = True , ): \"\"\"Extract embeddings from the patches of a slide using the cell based features. Args: slide_name: The name of the slide to extract the embeddings from (without the extension). slide_ext: The extension of the slide. coords: The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. cells_path: The path to the cells geojson file. cell_classes: The classes of the cells to extract the embeddings from. If None, all the classes will be used. with_offset: Whether to offset the coordinates of the cells by the slide offset. show_progress: Whether to show the progress bar. \"\"\" dataset = self . create_dataset ( slide_name , slide_ext = slide_ext , coords = coords , coords_only = True ) dataloader = self . create_dataloader ( dataset , num_workers = 1 ) patch_size = dataset . patch_size patch_downsample = dataset . downsample ref_patch_size = patch_size * patch_downsample offset = dataset . slide_offset if with_offset else ( 0 , 0 ) offset = ( - offset [ 0 ], - offset [ 1 ]) cells_df = read_json_with_geopandas ( cells_path , offset = offset ) if \"classification\" not in cells_df . columns : raise ValueError ( \"The 'classification' column is missing in the cells dataframe.\" ) cell_classes = ( cells_df . classification . unique () if cell_classes is None else cell_classes ) self . cell_based_embedding_names = [ f \" { cell_class } _ { feature } \" for cell_class in cell_classes for feature in CELL_FEATURE_NAMES ] start_time = time . time () embeddings = [] if self . num_workers > 1 : pool = mp . Pool ( self . num_workers ) for coords in tqdm ( dataloader , desc = f \"Extracting cell based features from the patches of { slide_name } \" , disable = not show_progress , ): if self . num_workers > 1 : cells_df_in_patches = pool . starmap ( get_cells_in_patch , [( cells_df , coord , ref_patch_size , cell_classes ) for coord in coords ], ) cells_features = np . array ( pool . map ( compute_cell_features , cells_df_in_patches ) ) else : cells_df_in_patches = [ get_cells_in_patch ( cells_df , coord , ref_patch_size , cell_classes ) for coord in coords ] cells_features = np . array ( [ compute_cell_features ( cells_df_in_patch ) for cells_df_in_patch in cells_df_in_patches ] ) embeddings . append ( torch . tensor ( cells_features )) if self . num_workers > 1 : pool . close () self . cell_based_embeddings [ slide_name ] = torch . cat ( embeddings , dim = 0 ) log . info ( f \"Embedding time: { time . time () - start_time } .\" ) log . info ( f \"Extracted embeddings from { len ( embeddings ) } patches of { slide_name } .\" )","title":" extract_cell_based_embeddings"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.SlideEmbedder.extract_model_based_embeddings","text":"Extract embeddings from the patches of a slide using the pretrained model. Parameters: Name Type Description Default slide_name str The name of the slide to extract the embeddings from (without the extension). required slide_ext str The extension of the slide. required coords ndarray | None The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. None show_progress bool Whether to show the progress bar. True Source code in src/prismtoolbox/wsiemb/embedder.py 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 def extract_model_based_embeddings ( self , slide_name : str , slide_ext : str , coords : np . ndarray | None = None , show_progress : bool = True , ): \"\"\"Extract embeddings from the patches of a slide using the pretrained model. Args: slide_name: The name of the slide to extract the embeddings from (without the extension). slide_ext: The extension of the slide. coords: The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. show_progress: Whether to show the progress bar. \"\"\" assert ( self . model is not None ), \"Model not found. Please provide an architecture name when initializing the SlideEmbedder.\" log . info ( f \"Extracting embeddings from the patches of { slide_name } .\" ) dataset = self . create_dataset ( slide_name , slide_ext = slide_ext , coords = coords ) dataloader = self . create_dataloader ( dataset ) start_time = time . time () embeddings = [] for patches , _ in tqdm ( dataloader , desc = f \"Extracting embeddings from the patches of { slide_name } \" , disable = not show_progress , ): patches = patches . to ( self . device ) with torch . no_grad (): output = self . model ( patches ) embeddings . append ( output . cpu ()) log . info ( f \"Embedding time: { time . time () - start_time } .\" ) self . model_based_embeddings [ slide_name ] = torch . cat ( embeddings , dim = 0 ) embeddings_dim = self . model_based_embeddings [ slide_name ] . shape [ 1 ] self . model_based_embedding_names = [ f \" { self . arch_name } _ { i } \" for i in range ( embeddings_dim ) ] log . info ( f \"Extracted embeddings from { len ( embeddings ) } patches of { slide_name } .\" )","title":" extract_model_based_embeddings"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.SlideEmbedder.extract_stain_based_embeddings","text":"Extract embeddings from the patches of a slide using the stain based features. Parameters: Name Type Description Default slide_name str The name of the slide to extract the embeddings from (without the extension). required slide_ext str The extension of the slide. required coords ndarray | None The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. None conv_matrix_name str The name of the convolutional matrix to use for the stain based features. See extract_stain_features for available matrices. 'HED' show_progress bool Whether to show the progress bar. True Source code in src/prismtoolbox/wsiemb/embedder.py 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 def extract_stain_based_embeddings ( self , slide_name : str , slide_ext : str , coords : np . ndarray | None = None , conv_matrix_name : str = \"HED\" , show_progress : bool = True , ): \"\"\"Extract embeddings from the patches of a slide using the stain based features. Args: slide_name: The name of the slide to extract the embeddings from (without the extension). slide_ext: The extension of the slide. coords: The coordinates of the patches to extract the embeddings from. If None, the coordinates will be loaded from the hdf5 file located in coords_dir. conv_matrix_name: The name of the convolutional matrix to use for the stain based features. See [extract_stain_features][prismtoolbox.wsiemb.emb_utils.extract_stain_features] for available matrices. show_progress: Whether to show the progress bar. \"\"\" log . info ( f \"Extracting embeddings from the patches of { slide_name } .\" ) dataset = self . create_dataset ( slide_name , slide_ext = slide_ext , coords = coords , no_transforms = True ) dataloader = self . create_dataloader ( dataset ) if conv_matrix_name == \"HED\" : stain_names = [ \"Hematoxylin\" , \"Eosin\" , \"DAB\" ] else : stain_names = [ \"Hematoxylin\" , \"DAB\" ] self . stain_based_embedding_names = [ f \" { stain_name } _ { feature } \" for feature in STAIN_FEATURE_NAMES for stain_name in stain_names ] start_time = time . time () embeddings = [] for patches , _ in tqdm ( dataloader , desc = f \"Extracting stain based features from the patches of { slide_name } \" , disable = not show_progress , ): embeddings . append ( extract_stain_features ( patches , conv_matrix_name )) log . info ( f \"Embedding time: { time . time () - start_time } .\" ) self . stain_based_embeddings [ slide_name ] = torch . cat ( embeddings , dim = 0 ) log . info ( f \"Extracted embeddings from { len ( embeddings ) } patches of { slide_name } .\" )","title":" extract_stain_based_embeddings"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.SlideEmbedder.get_transforms","text":"Get the transforms to use for creating the dataset. Returns: Type Description The transforms to use when loading the patches. Source code in src/prismtoolbox/wsiemb/embedder.py 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 def get_transforms ( self ): \"\"\"Get the transforms to use for creating the dataset. Returns: The transforms to use when loading the patches. \"\"\" if self . transforms_dict is not None : transforms = super () . get_transforms () elif self . pretrained_transforms is not None : log . info ( \"No transforms dict found, using pretrained transforms.\" ) transforms = self . pretrained_transforms else : log . info ( \"No transforms dict or pretrained transforms found.\" ) transforms = None return transforms","title":" get_transforms"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.SlideEmbedder.save_embeddings","text":"Save the extracted embeddings to the chosen format under slide_name.format. Parameters: Name Type Description Default save_dir str The path to the directory where to save the embeddings. required flush_memory bool Whether to remove the embeddings from self.embeddings after saving. False format str The format to save the embeddings in. Possible formats: ['pt', 'npy'] 'pt' merge bool Whether to merge the different types of embeddings before saving. False Source code in src/prismtoolbox/wsiemb/embedder.py 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 def save_embeddings ( self , save_dir : str , flush_memory : bool = False , format : str = \"pt\" , merge : bool = False , ): \"\"\"Save the extracted embeddings to the chosen format under slide_name.format. Args: save_dir: The path to the directory where to save the embeddings. flush_memory: Whether to remove the embeddings from self.embeddings after saving. format: The format to save the embeddings in. Possible formats: ['pt', 'npy'] merge: Whether to merge the different types of embeddings before saving. \"\"\" if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if format not in [ \"pt\" , \"npy\" ]: raise ValueError ( \"invalid format, possible formats: ['pt', 'npy']\" ) slides_processed = set ( self . model_based_embeddings . keys ()) . union ( self . stain_based_embeddings . keys (), self . cell_based_embeddings . keys () ) for slide_name in slides_processed : if merge : embeddings_list = [ ( \"\" , torch . cat ( [ emb_dict [ slide_name ] for emb_dict in [ self . model_based_embeddings , self . stain_based_embeddings , self . cell_based_embeddings , ] if slide_name in emb_dict . keys () ], dim = 1 , ), ) ] else : embeddings_list = [ ( emb_type , emb_dict [ slide_name ]) for emb_type , emb_dict in zip ( [ \"_model_based\" , \"_stain_based\" , \"_cell_based\" ], [ self . model_based_embeddings , self . stain_based_embeddings , self . cell_based_embeddings , ], ) if slide_name in emb_dict . keys () ] for emb_type , emb in embeddings_list : output_path = os . path . join ( save_dir , f \" { slide_name + emb_type } . { format } \" ) if format == \"pt\" : torch . save ( emb , output_path ) elif format == \"npy\" : emb = emb . numpy () np . save ( output_path , emb ) else : raise NotImplementedError log . info ( f \"Embeddings for slide { slide_name } saved at { output_path } .\" ) if flush_memory : self . model_based_embeddings = {} self . stain_based_embeddings = {} self . cell_based_embeddings = {} log . info ( \"Memory flushed.\" )","title":" save_embeddings"},{"location":"reference/prismtoolbox/wsiemb/embedder/#prismtoolbox.wsiemb.embedder.SlideEmbedder.save_embeddings_names","text":"Save the extracted embeddings names to the chosen format under slide_name.format. Parameters: Name Type Description Default save_dir str The path to the directory where to save the embeddings. required format str The format to save the embeddings in. Possible formats: ['csv'] 'csv' flush_memory bool Whether to remove the reset the embeddings names after saving. False merge bool Whether the embeddings were merged before saving. False Source code in src/prismtoolbox/wsiemb/embedder.py 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 def save_embeddings_names ( self , save_dir : str , format : str = \"csv\" , flush_memory : bool = False , merge : bool = False , ): \"\"\"Save the extracted embeddings names to the chosen format under slide_name.format. Args: save_dir: The path to the directory where to save the embeddings. format: The format to save the embeddings in. Possible formats: ['csv'] flush_memory: Whether to remove the reset the embeddings names after saving. merge: Whether the embeddings were merged before saving. \"\"\" if not os . path . isdir ( save_dir ): log . warning ( f \"Folder { save_dir } does not exist, creating new folder...\" ) pathlib . Path ( save_dir ) . mkdir ( parents = True , exist_ok = True ) if format not in [ \"csv\" ]: raise ValueError ( \"invalid format, possible formats: ['csv']\" ) if merge : output_path = os . path . join ( save_dir , f \"embeddings_names. { format } \" ) if os . path . exists ( output_path ): log . warning ( f \"File embeddings_names. { format } already exists in { save_dir } . Overwriting...\" ) embeddings_names = ( self . model_based_embedding_names + self . stain_based_embedding_names + self . cell_based_embedding_names ) df = pd . DataFrame ( embeddings_names , columns = [ \"feature_names\" ]) df . to_csv ( output_path , index = False ) else : for emb_type , emb_names in zip ( [ \"model_based\" , \"stain_based\" , \"cell_based\" ], [ self . model_based_embedding_names , self . stain_based_embedding_names , self . cell_based_embedding_names , ], ): if len ( emb_names ) == 0 : continue output_path = os . path . join ( save_dir , f \"embeddings_names_ { emb_type } . { format } \" ) if os . path . exists ( output_path ): log . warning ( f \"File embeddings_names_ { emb_type } . { format } already exists in { save_dir } . Overwriting...\" ) df = pd . DataFrame ( emb_names , columns = [ \"feature_names\" ]) df . to_csv ( output_path , index = False ) log . info ( f \"Embeddings names saved at { output_path } .\" ) if flush_memory : self . model_based_embedding_names = [] self . stain_based_embedding_names = [] self . cell_based_embedding_names = [] log . info ( \"Memory flushed.\" )","title":" save_embeddings_names"},{"location":"reference/prismtoolbox/wsiemb/processing/","text":"EmbeddingProcessor ( embeddings , embeddings_names = None , slide_ids = None , cmap = 'Set1' , seed = None ) summary Parameters: Name Type Description Default embeddings list [ ndarray | Tensor | str ] The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings. required embeddings_names list [ str ] | str | None The names of the embeddings. Should be a list of strings with the same length as the embeddings, None slide_ids list [ str ] | None The ids of the slides. None cmap str The colormap to use for visualizations. 'Set1' seed int The seed to use for reproducibility. None Attributes: Name Type Description embeddings The embeddings to process as a list of numpy arrays. embeddings_matrix The embeddings to process concatenated into a single numpy array. embeddings_stats The statistics of the embeddings (mean, std, min, max). slide_ids The ids of the slides. If not provided, the slide ids are generated. slide_ids_matrix The slide ids of each embedding concatenated into a single numpy array. cmap The colormap to use for visualizations. seed The seed to use for reproducibility. cluster_model The clustering model used to cluster the embeddings. It is set using the n_clusters The number of clusters in the clustering model. It is set using the cluster_colors The colors of the clusters in the clustering model. It is set using the eps A small value to avoid division by zero. Source code in src/prismtoolbox/wsiemb/processing.py 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 def __init__ ( self , embeddings : list [ np . ndarray | torch . Tensor | str ], embeddings_names : list [ str ] | str | None = None , slide_ids : list [ str ] | None = None , cmap : str = \"Set1\" , seed : int = None , ): \"\"\"_summary_ Args: embeddings: The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings. embeddings_names: The names of the embeddings. Should be a list of strings with the same length as the embeddings, or the path to a file containing the names. If not provided, the names are generated. slide_ids: The ids of the slides. cmap: The colormap to use for visualizations. seed: The seed to use for reproducibility. Attributes: embeddings: The embeddings to process as a list of numpy arrays. embeddings_matrix: The embeddings to process concatenated into a single numpy array. embeddings_stats: The statistics of the embeddings (mean, std, min, max). slide_ids: The ids of the slides. If not provided, the slide ids are generated. slide_ids_matrix: The slide ids of each embedding concatenated into a single numpy array. cmap: The colormap to use for visualizations. seed: The seed to use for reproducibility. cluster_model: The clustering model used to cluster the embeddings. It is set using the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. n_clusters: The number of clusters in the clustering model. It is set using the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. cluster_colors: The colors of the clusters in the clustering model. It is set using the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. eps: A small value to avoid division by zero. \"\"\" self . embeddings = self . load_embeddings ( embeddings ) self . embeddings_names = self . load_embeddings_names ( embeddings_names ) self . embeddings_matrix = np . concatenate ( self . embeddings , axis = 0 ) self . embeddings_stats = self . compute_embeddings_stats ( self . embeddings_matrix ) self . slide_ids = ( np . array ( slide_ids ) if slide_ids is not None else np . arange ( len ( embeddings )) ) self . slide_ids_matrix = np . concatenate ( [ np . repeat ( slide_id , len ( emb )) for slide_id , emb in zip ( self . slide_ids , self . embeddings ) ], axis = 0 , ) self . cmap = cmap self . seed = seed self . cluster_model = None self . n_clusters = None self . cluster_colors = None self . eps = 1e-6 compute_embeddings_stats ( embeddings_matrix ) staticmethod Compute the statistics of an input embeddings matrix. Parameters: Name Type Description Default embeddings_matrix ndarray The embeddings matrix to compute the statistics of. required Returns: Type Description dict [ str , ndarray ] The statistics of the embeddings matrix (mean, std, min, max) as a dictionary. Source code in src/prismtoolbox/wsiemb/processing.py 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 @staticmethod def compute_embeddings_stats ( embeddings_matrix : np . ndarray ) -> dict [ str , np . ndarray ]: \"\"\"Compute the statistics of an input embeddings matrix. Args: embeddings_matrix: The embeddings matrix to compute the statistics of. Returns: The statistics of the embeddings matrix (mean, std, min, max) as a dictionary. \"\"\" return { \"mean\" : np . mean ( embeddings_matrix , axis = 0 ), \"std\" : np . std ( embeddings_matrix , axis = 0 ), \"min\" : np . min ( embeddings_matrix , axis = 0 ), \"max\" : np . max ( embeddings_matrix , axis = 0 ), } create_cluster_model ( model_name , normalize = True , n_samples = None , selected_features = None , ** kwargs ) Create a clustering model trained on the embeddings matrix. The resulting model is stored in self.cluster_model. Parameters: Name Type Description Default model_name str The clustering model to use. Possible models are: \" kmeans \" \" kmeans_mini_batch \" required normalize bool Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. True n_samples int | float | None The number of samples to subsample. If None, the whole embeddings matrix is used. None selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None **kwargs Additional arguments for the clustering model (see the documentation of the clustering model). {} Source code in src/prismtoolbox/wsiemb/processing.py 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 def create_cluster_model ( self , model_name : str , normalize : bool = True , n_samples : int | float | None = None , selected_features : list [ str ] | None = None , ** kwargs , ): \"\"\"Create a clustering model trained on the embeddings matrix. The resulting model is stored in self.cluster_model. Args: model_name: The clustering model to use. Possible models are: - \"[kmeans](https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\" - \"[kmeans_mini_batch](https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans)\" normalize: Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. **kwargs: Additional arguments for the clustering model (see the documentation of the clustering model). \"\"\" if model_name == \"kmeans\" : self . cluster_model = skl_cluster . KMeans ( ** kwargs ) elif model_name == \"kmeans_mini_batch\" : self . cluster_model = skl_cluster . MiniBatchKMeans ( ** kwargs ) else : raise ValueError ( f \"model { model_name } not implemented\" ) emb_mean , emb_std = ( ( self . embeddings_stats [ \"mean\" ], self . embeddings_stats [ \"std\" ]) if normalize else ( 0 , 1 ) ) if n_samples is not None : embeddings_matrix = self . return_subsampled_embeddings ( n_samples ) else : embeddings_matrix = self . embeddings_matrix embeddings_matrix = ( embeddings_matrix - emb_mean ) / ( emb_std + self . eps ) if selected_features is not None : selected_feats = np . array ( [ i for i , name in enumerate ( self . embeddings_names ) if name in selected_features ] ) embeddings_matrix = embeddings_matrix [:, selected_feats ] self . cluster_model . fit ( embeddings_matrix ) self . n_clusters = self . cluster_model . n_clusters self . cluster_colors = get_colors_from_cmap ( self . cmap , self . n_clusters ) export_clusters_to_qupath ( WSI_object , save_dir , normalize = True , selected_features = None ) Export the clusters as polygons to a geojson file for a slide. Parameters: Name Type Description Default WSI_object WSI An instance of the WSI class created from the slide. required save_dir str The directory to save the geojson file to. required normalize bool Whether the embeddings were normalized or not when creating the cluster model. True selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None Source code in src/prismtoolbox/wsiemb/processing.py 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 def export_clusters_to_qupath ( self , WSI_object : WSI , save_dir : str , normalize : bool = True , selected_features : list [ str ] | None = None , ): \"\"\"Export the clusters as polygons to a geojson file for a slide. Args: WSI_object: An instance of the WSI class created from the slide. save_dir: The directory to save the geojson file to. normalize: Whether the embeddings were normalized or not when creating the cluster model. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. \"\"\" assert ( self . n_clusters is not None ), \"no cluster model created, please create a cluster model first\" cluster_assignments = self . get_cluster_assignments_for_slide ( WSI_object . slide_name , normalize , selected_features , ) idx = np . arange ( len ( WSI_object . coords )) assert len ( cluster_assignments ) == len ( idx ), \"Number of cluster assignments and number of patches do not match\" for cluster in range ( self . n_clusters ): WSI_object . save_patches ( save_dir , file_format = \"geojson\" , selected_idx = idx [ cluster_assignments == cluster ], merge = True , label = f \"cluster_ { cluster } \" , color = self . cluster_colors [ cluster ] . tolist (), append_to_existing_file = True , ) get_cluster_assignments_for_slide ( slide_id , normalize = True , selected_features = None ) Get the cluster assignments for a specific slide. Requires a cluster model to be created first with the create_cluster_model method. Parameters: Name Type Description Default slide_id str The id of the slide to get the cluster assignments for. required normalize bool Whether the embeddings were normalized or not when creating the cluster model. True selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None Returns: Type Description ndarray The cluster assignments for the slide. Source code in src/prismtoolbox/wsiemb/processing.py 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 def get_cluster_assignments_for_slide ( self , slide_id : str , normalize : bool = True , selected_features : list [ str ] | None = None , ) -> np . ndarray : \"\"\"Get the cluster assignments for a specific slide. Requires a cluster model to be created first with the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. Args: slide_id: The id of the slide to get the cluster assignments for. normalize: Whether the embeddings were normalized or not when creating the cluster model. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. Returns: The cluster assignments for the slide. \"\"\" assert ( self . cluster_model is not None ), \"no cluster model created, please create a cluster model first\" if slide_id not in self . slide_ids : raise ValueError ( f \"slide { slide_id } not found in slide ids\" ) idx = np . where ( self . slide_ids == slide_id )[ 0 ] . item () embeddings = self . embeddings [ idx ] emb_mean , emb_std = ( ( self . embeddings_stats [ \"mean\" ], self . embeddings_stats [ \"std\" ]) if normalize else ( 0 , 1 ) ) embeddings = ( embeddings - emb_mean ) / ( emb_std + self . eps ) if selected_features is not None : selected_feats = np . array ( [ i for i , name in enumerate ( self . embeddings_names ) if name in selected_features ] ) embeddings = embeddings [:, selected_feats ] return self . cluster_model . predict ( embeddings ) get_cluster_percentages_for_slide ( slide_id , normalize = True , selected_features = None ) Get the cluster percentages for a specific slide. Requires a cluster model to be created first with the create_cluster_model method. Parameters: Name Type Description Default slide_id str The id of the slide to get the cluster percentages for. required normalize bool Whether the embeddings were normalized or not when creating the cluster model. True selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None Returns: Type Description dict [ str , float ] The cluster percentages for the slide. Source code in src/prismtoolbox/wsiemb/processing.py 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 def get_cluster_percentages_for_slide ( self , slide_id : str , normalize : bool = True , selected_features : list [ str ] | None = None , ) -> dict [ str , float ]: \"\"\"Get the cluster percentages for a specific slide. Requires a cluster model to be created first with the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. Args: slide_id: The id of the slide to get the cluster percentages for. normalize: Whether the embeddings were normalized or not when creating the cluster model. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. Returns: The cluster percentages for the slide. \"\"\" cluster_assignments = self . get_cluster_assignments_for_slide ( slide_id , normalize , selected_features ) cluster_percentage = {} for cluster in range ( self . n_clusters ): cluster_percentage [ f \"cluster_ { cluster } \" ] = ( cluster_assignments == cluster ) . sum () / len ( cluster_assignments ) return cluster_percentage get_embedding_for_slide ( slide_id , normalize = True ) Get the embeddings for a specific slide. Parameters: Name Type Description Default slide_id str The id of the slide to get the embeddings for. required normalize bool Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. True Returns: Type Description ndarray The embeddings for the slide. Source code in src/prismtoolbox/wsiemb/processing.py 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 def get_embedding_for_slide ( self , slide_id : str , normalize : bool = True ) -> np . ndarray : \"\"\"Get the embeddings for a specific slide. Args: slide_id: The id of the slide to get the embeddings for. normalize: Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. Returns: The embeddings for the slide. \"\"\" if slide_id not in self . slide_ids : raise ValueError ( f \"slide { slide_id } not found in slide ids\" ) idx = np . where ( self . slide_ids == slide_id )[ 0 ] . item () emb_mean , emb_std = ( ( self . embeddings_stats [ \"mean\" ], self . embeddings_stats [ \"std\" ]) if normalize else ( 0 , 1 ) ) return ( self . embeddings [ idx ] - emb_mean ) / ( emb_std + self . eps ) get_optimal_number_clusters ( model_name , normalize = True , metric_name = 'davies_bouldin' , min_clusters = 2 , max_clusters = 10 , with_scores = True , n_samples = None , selected_features = None , ** kwargs ) Compute the optimal number of clusters for the embeddings. Parameters: Name Type Description Default model_name str The clustering model to use. See compute_optimal_number_clusters for the available models. required normalize bool Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. True metric_name str The metric to use to compute the optimal number of clusters. See compute_optimal_number_clusters for the available metrics. 'davies_bouldin' min_clusters int The minimum number of clusters to consider. 2 max_clusters int The maximum number of clusters to consider. 10 with_scores bool Whether to return the scores or not. True n_samples int | float | None The number of samples to subsample. If None, the whole embeddings matrix is used. None selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None **kwargs Additional arguments for the clustering model (see the documentation of the clustering model). {} Returns: Type Description int | tuple [ int , list [ float ]] The optimal number of clusters according to the metric. If with_scores, it also returns the scores. Source code in src/prismtoolbox/wsiemb/processing.py 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 def get_optimal_number_clusters ( self , model_name : str , normalize : bool = True , metric_name : str = \"davies_bouldin\" , min_clusters : int = 2 , max_clusters : int = 10 , with_scores : bool = True , n_samples : int | float | None = None , selected_features : list [ str ] | None = None , ** kwargs , ) -> int | tuple [ int , list [ float ]]: \"\"\"Compute the optimal number of clusters for the embeddings. Args: model_name: The clustering model to use. See [compute_optimal_number_clusters][prismtoolbox.wsiemb.emb_utils.compute_optimal_number_clusters] for the available models. normalize: Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. metric_name: The metric to use to compute the optimal number of clusters. See [compute_optimal_number_clusters][prismtoolbox.wsiemb.emb_utils.compute_optimal_number_clusters] for the available metrics. min_clusters: The minimum number of clusters to consider. max_clusters: The maximum number of clusters to consider. with_scores: Whether to return the scores or not. n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. **kwargs: Additional arguments for the clustering model (see the documentation of the clustering model). Returns: The optimal number of clusters according to the metric. If with_scores, it also returns the scores. \"\"\" emb_mean , emb_std = ( ( self . embeddings_stats [ \"mean\" ], self . embeddings_stats [ \"std\" ]) if normalize else ( 0 , 1 ) ) if n_samples is not None : embeddings_matrix = self . return_subsampled_embeddings ( n_samples ) else : embeddings_matrix = self . embeddings_matrix embeddings_matrix = ( embeddings_matrix - emb_mean ) / ( emb_std + self . eps ) if selected_features is not None : selected_feats = np . array ( [ i for i , name in enumerate ( self . embeddings_names ) if name in selected_features ] ) embeddings_matrix = embeddings_matrix [:, selected_feats ] optimal_number , scores = compute_optimal_number_clusters ( embeddings_matrix , model_name , metric_name = metric_name , min_clusters = min_clusters , max_clusters = max_clusters , ** kwargs , ) if with_scores : return optimal_number , scores else : return optimal_number import_cluster_model ( input_path ) Import the clustering model from a pickle file. Parameters: Name Type Description Default input_path str The path to import the clustering model from. required Source code in src/prismtoolbox/wsiemb/processing.py 527 528 529 530 531 532 533 534 535 def import_cluster_model ( self , input_path : str ): \"\"\"Import the clustering model from a pickle file. Args: input_path: The path to import the clustering model from. \"\"\" self . cluster_model = load_obj_with_pickle ( input_path ) self . n_clusters = self . cluster_model . n_clusters self . cluster_colors = get_colors_from_cmap ( self . cmap , self . n_clusters ) load_embeddings ( embeddings ) staticmethod Process the embeddings to load them as numpy arrays. Parameters: Name Type Description Default embeddings list [ ndarray | Tensor | str ] The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings. required Returns: Type Description list [ ndarray ] The embeddings loaded as numpy arrays. Source code in src/prismtoolbox/wsiemb/processing.py 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 @staticmethod def load_embeddings ( embeddings : list [ np . ndarray | torch . Tensor | str ], ) -> list [ np . ndarray ]: \"\"\"Process the embeddings to load them as numpy arrays. Args: embeddings: The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings. Returns: The embeddings loaded as numpy arrays. \"\"\" embeddings_loaded = [] for emb in embeddings : if isinstance ( emb , torch . Tensor ): emb = emb . numpy () elif isinstance ( emb , np . ndarray ): emb = emb elif isinstance ( emb , str ): emb = torch . load ( emb ) . numpy () else : raise ValueError ( \"embedding type not supported\" ) embeddings_loaded . append ( emb ) return embeddings_loaded return_subsampled_embeddings ( n_samples , by_slide = True , labels = False ) Creates a subsample version of the embeddings matrix. Parameters: Name Type Description Default n_samples int | float The number of samples to subsample. If float, it is the percentage of samples to subsample. If None, the whole embeddings matrix is used. required by_slide bool Whether to subsample by slide or not. True labels ndarray | None Labels to subsample along with the embeddings. False Returns: Type Description ndarray | tuple [ ndarray , ndarray ] The subsampled embeddings matrix. If labels, it also returns the subsampled labels. Source code in src/prismtoolbox/wsiemb/processing.py 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 def return_subsampled_embeddings ( self , n_samples : int | float , by_slide : bool = True , labels : np . ndarray | None = False , ) -> np . ndarray | tuple [ np . ndarray , np . ndarray ]: \"\"\"Creates a subsample version of the embeddings matrix. Args: n_samples: The number of samples to subsample. If float, it is the percentage of samples to subsample. If None, the whole embeddings matrix is used. by_slide: Whether to subsample by slide or not. labels: Labels to subsample along with the embeddings. Returns: The subsampled embeddings matrix. If labels, it also returns the subsampled labels. \"\"\" rng = np . random . default_rng ( self . seed ) if by_slide : subsampled_idx = [] current_idx = 0 for emb in self . embeddings : if n_samples < 1 : n_samples_per_slide = int ( n_samples * len ( emb )) else : n_samples_per_slide = n_samples // len ( self . embeddings ) subsampled_idx . extend ( rng . choice ( len ( emb ), n_samples_per_slide , replace = False ) + current_idx ) current_idx += len ( emb ) log . info ( f \"Subsampled { n_samples } embeddings from each slide.\" ) else : if n_samples < 1 : n_samples = int ( n_samples * len ( self . embeddings_matrix )) subsampled_idx = rng . choice ( len ( self . embeddings_matrix ), n_samples , replace = False ) log . info ( f \"Subsampled { n_samples } embeddings.\" ) if labels is not None : assert len ( labels ) == len ( self . embeddings_matrix ), \"labels and embeddings have different lengths\" return ( self . embeddings_matrix [ subsampled_idx ], labels [ subsampled_idx ], ) else : return self . embeddings_matrix [ subsampled_idx ] save_cluster_model ( output_path ) Save the clustering model to a pickle file. Parameters: Name Type Description Default output_path str The path to save the clustering model to. required Source code in src/prismtoolbox/wsiemb/processing.py 519 520 521 522 523 524 525 def save_cluster_model ( self , output_path : str ): \"\"\"Save the clustering model to a pickle file. Args: output_path: The path to save the clustering model to. \"\"\" save_obj_with_pickle ( self . cluster_model , output_path ) scale_to_01_range ( x ) staticmethod Scale an array to the [0; 1] range. Parameters: Name Type Description Default x ndarray The array to scale. required Returns: Type Description ndarray The array scaled to the [0; 1] range. Source code in src/prismtoolbox/wsiemb/processing.py 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 @staticmethod def scale_to_01_range ( x : np . ndarray ) -> np . ndarray : \"\"\"Scale an array to the [0; 1] range. Args: x: The array to scale. Returns: The array scaled to the [0; 1] range. \"\"\" # compute the distribution range value_range = np . max ( x ) - np . min ( x ) # move the distribution so that it starts from zero # by extracting the minimal value from all its values starts_from_zero = x - np . min ( x ) # make the distribution fit [0; 1] by dividing by its range return starts_from_zero / value_range visualize ( model_name , labels = None , n_samples = None , selected_features = None , ** kwargs ) Visualize the embeddings using a dimensionality reduction model. Parameters: Name Type Description Default model_name str The name of the dimensionality reduction model to use. Possible models are: \" PCA \" \" TSNE \" \" UMAP \" required labels ndarray | None The labels to use for the visualization. If None, no labels are used. None n_samples int | float | None The number of samples to subsample. If None, the whole embeddings matrix is used. None selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None **kwargs Additional arguments for the dimensionality reduction model (see the documentation of the dimensionality reduction model). {} Source code in src/prismtoolbox/wsiemb/processing.py 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 def visualize ( self , model_name : str , labels : np . ndarray | None = None , n_samples : int | float | None = None , selected_features : list [ str ] | None = None , ** kwargs , ): \"\"\"Visualize the embeddings using a dimensionality reduction model. Args: model_name: The name of the dimensionality reduction model to use. Possible models are: - \"[PCA](https://scikit-learn.org/stable/modules/generated/ sklearn.decomposition.PCA.html#sklearn.decomposition.PCA)\" - \"[TSNE](https://scikit-learn.org/stable/modules/generated/ sklearn.manifold.TSNE.html#sklearn.manifold.TSNE)\" - \"[UMAP](https://umap-learn.readthedocs.io/en/latest/)\" labels: The labels to use for the visualization. If None, no labels are used. n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. **kwargs: Additional arguments for the dimensionality reduction model (see the documentation of the dimensionality reduction model). \"\"\" if model_name == \"PCA\" : dimensionality_reduction_model = skl_decomposition . PCA ( n_components = 2 , ** kwargs ) elif model_name == \"TSNE\" : dimensionality_reduction_model = skl_manifold . TSNE ( n_components = 2 , ** kwargs ) elif model_name == \"UMAP\" : dimensionality_reduction_model = umap . UMAP ( n_components = 2 , ** kwargs ) else : raise ValueError ( f \"model { model_name } not implemented\" ) if n_samples is not None : subsampled_arrays = self . return_subsampled_embeddings ( n_samples , labels = labels ) embeddings_matrix = ( subsampled_arrays if labels is None else subsampled_arrays [ 0 ] ) labels = subsampled_arrays [ 1 ] if labels is not None else None else : embeddings_matrix = self . embeddings_matrix if selected_features is not None : selected_feats = np . array ( [ i for i , name in enumerate ( self . embeddings_names ) if name in selected_features ] ) embeddings_matrix = embeddings_matrix [:, selected_feats ] embeddings_reduced = dimensionality_reduction_model . fit_transform ( embeddings_matrix ) plot_scatter ( self . scale_to_01_range ( embeddings_reduced [:, 0 ]), self . scale_to_01_range ( embeddings_reduced [:, 1 ]), self . cmap , labels , )","title":"prismtoolbox.wsiemb.processing"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor","text":"summary Parameters: Name Type Description Default embeddings list [ ndarray | Tensor | str ] The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings. required embeddings_names list [ str ] | str | None The names of the embeddings. Should be a list of strings with the same length as the embeddings, None slide_ids list [ str ] | None The ids of the slides. None cmap str The colormap to use for visualizations. 'Set1' seed int The seed to use for reproducibility. None Attributes: Name Type Description embeddings The embeddings to process as a list of numpy arrays. embeddings_matrix The embeddings to process concatenated into a single numpy array. embeddings_stats The statistics of the embeddings (mean, std, min, max). slide_ids The ids of the slides. If not provided, the slide ids are generated. slide_ids_matrix The slide ids of each embedding concatenated into a single numpy array. cmap The colormap to use for visualizations. seed The seed to use for reproducibility. cluster_model The clustering model used to cluster the embeddings. It is set using the n_clusters The number of clusters in the clustering model. It is set using the cluster_colors The colors of the clusters in the clustering model. It is set using the eps A small value to avoid division by zero. Source code in src/prismtoolbox/wsiemb/processing.py 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 def __init__ ( self , embeddings : list [ np . ndarray | torch . Tensor | str ], embeddings_names : list [ str ] | str | None = None , slide_ids : list [ str ] | None = None , cmap : str = \"Set1\" , seed : int = None , ): \"\"\"_summary_ Args: embeddings: The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings. embeddings_names: The names of the embeddings. Should be a list of strings with the same length as the embeddings, or the path to a file containing the names. If not provided, the names are generated. slide_ids: The ids of the slides. cmap: The colormap to use for visualizations. seed: The seed to use for reproducibility. Attributes: embeddings: The embeddings to process as a list of numpy arrays. embeddings_matrix: The embeddings to process concatenated into a single numpy array. embeddings_stats: The statistics of the embeddings (mean, std, min, max). slide_ids: The ids of the slides. If not provided, the slide ids are generated. slide_ids_matrix: The slide ids of each embedding concatenated into a single numpy array. cmap: The colormap to use for visualizations. seed: The seed to use for reproducibility. cluster_model: The clustering model used to cluster the embeddings. It is set using the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. n_clusters: The number of clusters in the clustering model. It is set using the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. cluster_colors: The colors of the clusters in the clustering model. It is set using the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. eps: A small value to avoid division by zero. \"\"\" self . embeddings = self . load_embeddings ( embeddings ) self . embeddings_names = self . load_embeddings_names ( embeddings_names ) self . embeddings_matrix = np . concatenate ( self . embeddings , axis = 0 ) self . embeddings_stats = self . compute_embeddings_stats ( self . embeddings_matrix ) self . slide_ids = ( np . array ( slide_ids ) if slide_ids is not None else np . arange ( len ( embeddings )) ) self . slide_ids_matrix = np . concatenate ( [ np . repeat ( slide_id , len ( emb )) for slide_id , emb in zip ( self . slide_ids , self . embeddings ) ], axis = 0 , ) self . cmap = cmap self . seed = seed self . cluster_model = None self . n_clusters = None self . cluster_colors = None self . eps = 1e-6","title":" EmbeddingProcessor"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.compute_embeddings_stats","text":"Compute the statistics of an input embeddings matrix. Parameters: Name Type Description Default embeddings_matrix ndarray The embeddings matrix to compute the statistics of. required Returns: Type Description dict [ str , ndarray ] The statistics of the embeddings matrix (mean, std, min, max) as a dictionary. Source code in src/prismtoolbox/wsiemb/processing.py 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 @staticmethod def compute_embeddings_stats ( embeddings_matrix : np . ndarray ) -> dict [ str , np . ndarray ]: \"\"\"Compute the statistics of an input embeddings matrix. Args: embeddings_matrix: The embeddings matrix to compute the statistics of. Returns: The statistics of the embeddings matrix (mean, std, min, max) as a dictionary. \"\"\" return { \"mean\" : np . mean ( embeddings_matrix , axis = 0 ), \"std\" : np . std ( embeddings_matrix , axis = 0 ), \"min\" : np . min ( embeddings_matrix , axis = 0 ), \"max\" : np . max ( embeddings_matrix , axis = 0 ), }","title":" compute_embeddings_stats"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model","text":"Create a clustering model trained on the embeddings matrix. The resulting model is stored in self.cluster_model. Parameters: Name Type Description Default model_name str The clustering model to use. Possible models are: \" kmeans \" \" kmeans_mini_batch \" required normalize bool Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. True n_samples int | float | None The number of samples to subsample. If None, the whole embeddings matrix is used. None selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None **kwargs Additional arguments for the clustering model (see the documentation of the clustering model). {} Source code in src/prismtoolbox/wsiemb/processing.py 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 def create_cluster_model ( self , model_name : str , normalize : bool = True , n_samples : int | float | None = None , selected_features : list [ str ] | None = None , ** kwargs , ): \"\"\"Create a clustering model trained on the embeddings matrix. The resulting model is stored in self.cluster_model. Args: model_name: The clustering model to use. Possible models are: - \"[kmeans](https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\" - \"[kmeans_mini_batch](https://scikit-learn.org/stable/modules/generated/ sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans)\" normalize: Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. **kwargs: Additional arguments for the clustering model (see the documentation of the clustering model). \"\"\" if model_name == \"kmeans\" : self . cluster_model = skl_cluster . KMeans ( ** kwargs ) elif model_name == \"kmeans_mini_batch\" : self . cluster_model = skl_cluster . MiniBatchKMeans ( ** kwargs ) else : raise ValueError ( f \"model { model_name } not implemented\" ) emb_mean , emb_std = ( ( self . embeddings_stats [ \"mean\" ], self . embeddings_stats [ \"std\" ]) if normalize else ( 0 , 1 ) ) if n_samples is not None : embeddings_matrix = self . return_subsampled_embeddings ( n_samples ) else : embeddings_matrix = self . embeddings_matrix embeddings_matrix = ( embeddings_matrix - emb_mean ) / ( emb_std + self . eps ) if selected_features is not None : selected_feats = np . array ( [ i for i , name in enumerate ( self . embeddings_names ) if name in selected_features ] ) embeddings_matrix = embeddings_matrix [:, selected_feats ] self . cluster_model . fit ( embeddings_matrix ) self . n_clusters = self . cluster_model . n_clusters self . cluster_colors = get_colors_from_cmap ( self . cmap , self . n_clusters )","title":" create_cluster_model"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.export_clusters_to_qupath","text":"Export the clusters as polygons to a geojson file for a slide. Parameters: Name Type Description Default WSI_object WSI An instance of the WSI class created from the slide. required save_dir str The directory to save the geojson file to. required normalize bool Whether the embeddings were normalized or not when creating the cluster model. True selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None Source code in src/prismtoolbox/wsiemb/processing.py 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 def export_clusters_to_qupath ( self , WSI_object : WSI , save_dir : str , normalize : bool = True , selected_features : list [ str ] | None = None , ): \"\"\"Export the clusters as polygons to a geojson file for a slide. Args: WSI_object: An instance of the WSI class created from the slide. save_dir: The directory to save the geojson file to. normalize: Whether the embeddings were normalized or not when creating the cluster model. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. \"\"\" assert ( self . n_clusters is not None ), \"no cluster model created, please create a cluster model first\" cluster_assignments = self . get_cluster_assignments_for_slide ( WSI_object . slide_name , normalize , selected_features , ) idx = np . arange ( len ( WSI_object . coords )) assert len ( cluster_assignments ) == len ( idx ), \"Number of cluster assignments and number of patches do not match\" for cluster in range ( self . n_clusters ): WSI_object . save_patches ( save_dir , file_format = \"geojson\" , selected_idx = idx [ cluster_assignments == cluster ], merge = True , label = f \"cluster_ { cluster } \" , color = self . cluster_colors [ cluster ] . tolist (), append_to_existing_file = True , )","title":" export_clusters_to_qupath"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.get_cluster_assignments_for_slide","text":"Get the cluster assignments for a specific slide. Requires a cluster model to be created first with the create_cluster_model method. Parameters: Name Type Description Default slide_id str The id of the slide to get the cluster assignments for. required normalize bool Whether the embeddings were normalized or not when creating the cluster model. True selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None Returns: Type Description ndarray The cluster assignments for the slide. Source code in src/prismtoolbox/wsiemb/processing.py 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 def get_cluster_assignments_for_slide ( self , slide_id : str , normalize : bool = True , selected_features : list [ str ] | None = None , ) -> np . ndarray : \"\"\"Get the cluster assignments for a specific slide. Requires a cluster model to be created first with the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. Args: slide_id: The id of the slide to get the cluster assignments for. normalize: Whether the embeddings were normalized or not when creating the cluster model. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. Returns: The cluster assignments for the slide. \"\"\" assert ( self . cluster_model is not None ), \"no cluster model created, please create a cluster model first\" if slide_id not in self . slide_ids : raise ValueError ( f \"slide { slide_id } not found in slide ids\" ) idx = np . where ( self . slide_ids == slide_id )[ 0 ] . item () embeddings = self . embeddings [ idx ] emb_mean , emb_std = ( ( self . embeddings_stats [ \"mean\" ], self . embeddings_stats [ \"std\" ]) if normalize else ( 0 , 1 ) ) embeddings = ( embeddings - emb_mean ) / ( emb_std + self . eps ) if selected_features is not None : selected_feats = np . array ( [ i for i , name in enumerate ( self . embeddings_names ) if name in selected_features ] ) embeddings = embeddings [:, selected_feats ] return self . cluster_model . predict ( embeddings )","title":" get_cluster_assignments_for_slide"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.get_cluster_percentages_for_slide","text":"Get the cluster percentages for a specific slide. Requires a cluster model to be created first with the create_cluster_model method. Parameters: Name Type Description Default slide_id str The id of the slide to get the cluster percentages for. required normalize bool Whether the embeddings were normalized or not when creating the cluster model. True selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None Returns: Type Description dict [ str , float ] The cluster percentages for the slide. Source code in src/prismtoolbox/wsiemb/processing.py 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 def get_cluster_percentages_for_slide ( self , slide_id : str , normalize : bool = True , selected_features : list [ str ] | None = None , ) -> dict [ str , float ]: \"\"\"Get the cluster percentages for a specific slide. Requires a cluster model to be created first with the [create_cluster_model][prismtoolbox.wsiemb.processing.EmbeddingProcessor.create_cluster_model] method. Args: slide_id: The id of the slide to get the cluster percentages for. normalize: Whether the embeddings were normalized or not when creating the cluster model. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. Returns: The cluster percentages for the slide. \"\"\" cluster_assignments = self . get_cluster_assignments_for_slide ( slide_id , normalize , selected_features ) cluster_percentage = {} for cluster in range ( self . n_clusters ): cluster_percentage [ f \"cluster_ { cluster } \" ] = ( cluster_assignments == cluster ) . sum () / len ( cluster_assignments ) return cluster_percentage","title":" get_cluster_percentages_for_slide"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.get_embedding_for_slide","text":"Get the embeddings for a specific slide. Parameters: Name Type Description Default slide_id str The id of the slide to get the embeddings for. required normalize bool Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. True Returns: Type Description ndarray The embeddings for the slide. Source code in src/prismtoolbox/wsiemb/processing.py 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 def get_embedding_for_slide ( self , slide_id : str , normalize : bool = True ) -> np . ndarray : \"\"\"Get the embeddings for a specific slide. Args: slide_id: The id of the slide to get the embeddings for. normalize: Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. Returns: The embeddings for the slide. \"\"\" if slide_id not in self . slide_ids : raise ValueError ( f \"slide { slide_id } not found in slide ids\" ) idx = np . where ( self . slide_ids == slide_id )[ 0 ] . item () emb_mean , emb_std = ( ( self . embeddings_stats [ \"mean\" ], self . embeddings_stats [ \"std\" ]) if normalize else ( 0 , 1 ) ) return ( self . embeddings [ idx ] - emb_mean ) / ( emb_std + self . eps )","title":" get_embedding_for_slide"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.get_optimal_number_clusters","text":"Compute the optimal number of clusters for the embeddings. Parameters: Name Type Description Default model_name str The clustering model to use. See compute_optimal_number_clusters for the available models. required normalize bool Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. True metric_name str The metric to use to compute the optimal number of clusters. See compute_optimal_number_clusters for the available metrics. 'davies_bouldin' min_clusters int The minimum number of clusters to consider. 2 max_clusters int The maximum number of clusters to consider. 10 with_scores bool Whether to return the scores or not. True n_samples int | float | None The number of samples to subsample. If None, the whole embeddings matrix is used. None selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None **kwargs Additional arguments for the clustering model (see the documentation of the clustering model). {} Returns: Type Description int | tuple [ int , list [ float ]] The optimal number of clusters according to the metric. If with_scores, it also returns the scores. Source code in src/prismtoolbox/wsiemb/processing.py 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 def get_optimal_number_clusters ( self , model_name : str , normalize : bool = True , metric_name : str = \"davies_bouldin\" , min_clusters : int = 2 , max_clusters : int = 10 , with_scores : bool = True , n_samples : int | float | None = None , selected_features : list [ str ] | None = None , ** kwargs , ) -> int | tuple [ int , list [ float ]]: \"\"\"Compute the optimal number of clusters for the embeddings. Args: model_name: The clustering model to use. See [compute_optimal_number_clusters][prismtoolbox.wsiemb.emb_utils.compute_optimal_number_clusters] for the available models. normalize: Whether to normalize the embeddings or not according to the mean and std of self.embeddings_stats. metric_name: The metric to use to compute the optimal number of clusters. See [compute_optimal_number_clusters][prismtoolbox.wsiemb.emb_utils.compute_optimal_number_clusters] for the available metrics. min_clusters: The minimum number of clusters to consider. max_clusters: The maximum number of clusters to consider. with_scores: Whether to return the scores or not. n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. **kwargs: Additional arguments for the clustering model (see the documentation of the clustering model). Returns: The optimal number of clusters according to the metric. If with_scores, it also returns the scores. \"\"\" emb_mean , emb_std = ( ( self . embeddings_stats [ \"mean\" ], self . embeddings_stats [ \"std\" ]) if normalize else ( 0 , 1 ) ) if n_samples is not None : embeddings_matrix = self . return_subsampled_embeddings ( n_samples ) else : embeddings_matrix = self . embeddings_matrix embeddings_matrix = ( embeddings_matrix - emb_mean ) / ( emb_std + self . eps ) if selected_features is not None : selected_feats = np . array ( [ i for i , name in enumerate ( self . embeddings_names ) if name in selected_features ] ) embeddings_matrix = embeddings_matrix [:, selected_feats ] optimal_number , scores = compute_optimal_number_clusters ( embeddings_matrix , model_name , metric_name = metric_name , min_clusters = min_clusters , max_clusters = max_clusters , ** kwargs , ) if with_scores : return optimal_number , scores else : return optimal_number","title":" get_optimal_number_clusters"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.import_cluster_model","text":"Import the clustering model from a pickle file. Parameters: Name Type Description Default input_path str The path to import the clustering model from. required Source code in src/prismtoolbox/wsiemb/processing.py 527 528 529 530 531 532 533 534 535 def import_cluster_model ( self , input_path : str ): \"\"\"Import the clustering model from a pickle file. Args: input_path: The path to import the clustering model from. \"\"\" self . cluster_model = load_obj_with_pickle ( input_path ) self . n_clusters = self . cluster_model . n_clusters self . cluster_colors = get_colors_from_cmap ( self . cmap , self . n_clusters )","title":" import_cluster_model"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.load_embeddings","text":"Process the embeddings to load them as numpy arrays. Parameters: Name Type Description Default embeddings list [ ndarray | Tensor | str ] The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings. required Returns: Type Description list [ ndarray ] The embeddings loaded as numpy arrays. Source code in src/prismtoolbox/wsiemb/processing.py 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 @staticmethod def load_embeddings ( embeddings : list [ np . ndarray | torch . Tensor | str ], ) -> list [ np . ndarray ]: \"\"\"Process the embeddings to load them as numpy arrays. Args: embeddings: The embeddings to process. Can be a list of numpy arrays, torch tensors or paths to embeddings. Returns: The embeddings loaded as numpy arrays. \"\"\" embeddings_loaded = [] for emb in embeddings : if isinstance ( emb , torch . Tensor ): emb = emb . numpy () elif isinstance ( emb , np . ndarray ): emb = emb elif isinstance ( emb , str ): emb = torch . load ( emb ) . numpy () else : raise ValueError ( \"embedding type not supported\" ) embeddings_loaded . append ( emb ) return embeddings_loaded","title":" load_embeddings"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.return_subsampled_embeddings","text":"Creates a subsample version of the embeddings matrix. Parameters: Name Type Description Default n_samples int | float The number of samples to subsample. If float, it is the percentage of samples to subsample. If None, the whole embeddings matrix is used. required by_slide bool Whether to subsample by slide or not. True labels ndarray | None Labels to subsample along with the embeddings. False Returns: Type Description ndarray | tuple [ ndarray , ndarray ] The subsampled embeddings matrix. If labels, it also returns the subsampled labels. Source code in src/prismtoolbox/wsiemb/processing.py 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 def return_subsampled_embeddings ( self , n_samples : int | float , by_slide : bool = True , labels : np . ndarray | None = False , ) -> np . ndarray | tuple [ np . ndarray , np . ndarray ]: \"\"\"Creates a subsample version of the embeddings matrix. Args: n_samples: The number of samples to subsample. If float, it is the percentage of samples to subsample. If None, the whole embeddings matrix is used. by_slide: Whether to subsample by slide or not. labels: Labels to subsample along with the embeddings. Returns: The subsampled embeddings matrix. If labels, it also returns the subsampled labels. \"\"\" rng = np . random . default_rng ( self . seed ) if by_slide : subsampled_idx = [] current_idx = 0 for emb in self . embeddings : if n_samples < 1 : n_samples_per_slide = int ( n_samples * len ( emb )) else : n_samples_per_slide = n_samples // len ( self . embeddings ) subsampled_idx . extend ( rng . choice ( len ( emb ), n_samples_per_slide , replace = False ) + current_idx ) current_idx += len ( emb ) log . info ( f \"Subsampled { n_samples } embeddings from each slide.\" ) else : if n_samples < 1 : n_samples = int ( n_samples * len ( self . embeddings_matrix )) subsampled_idx = rng . choice ( len ( self . embeddings_matrix ), n_samples , replace = False ) log . info ( f \"Subsampled { n_samples } embeddings.\" ) if labels is not None : assert len ( labels ) == len ( self . embeddings_matrix ), \"labels and embeddings have different lengths\" return ( self . embeddings_matrix [ subsampled_idx ], labels [ subsampled_idx ], ) else : return self . embeddings_matrix [ subsampled_idx ]","title":" return_subsampled_embeddings"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.save_cluster_model","text":"Save the clustering model to a pickle file. Parameters: Name Type Description Default output_path str The path to save the clustering model to. required Source code in src/prismtoolbox/wsiemb/processing.py 519 520 521 522 523 524 525 def save_cluster_model ( self , output_path : str ): \"\"\"Save the clustering model to a pickle file. Args: output_path: The path to save the clustering model to. \"\"\" save_obj_with_pickle ( self . cluster_model , output_path )","title":" save_cluster_model"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.scale_to_01_range","text":"Scale an array to the [0; 1] range. Parameters: Name Type Description Default x ndarray The array to scale. required Returns: Type Description ndarray The array scaled to the [0; 1] range. Source code in src/prismtoolbox/wsiemb/processing.py 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 @staticmethod def scale_to_01_range ( x : np . ndarray ) -> np . ndarray : \"\"\"Scale an array to the [0; 1] range. Args: x: The array to scale. Returns: The array scaled to the [0; 1] range. \"\"\" # compute the distribution range value_range = np . max ( x ) - np . min ( x ) # move the distribution so that it starts from zero # by extracting the minimal value from all its values starts_from_zero = x - np . min ( x ) # make the distribution fit [0; 1] by dividing by its range return starts_from_zero / value_range","title":" scale_to_01_range"},{"location":"reference/prismtoolbox/wsiemb/processing/#prismtoolbox.wsiemb.processing.EmbeddingProcessor.visualize","text":"Visualize the embeddings using a dimensionality reduction model. Parameters: Name Type Description Default model_name str The name of the dimensionality reduction model to use. Possible models are: \" PCA \" \" TSNE \" \" UMAP \" required labels ndarray | None The labels to use for the visualization. If None, no labels are used. None n_samples int | float | None The number of samples to subsample. If None, the whole embeddings matrix is used. None selected_features list [ str ] | None The names of the embeddings to subsample. If None, all embeddings are used. None **kwargs Additional arguments for the dimensionality reduction model (see the documentation of the dimensionality reduction model). {} Source code in src/prismtoolbox/wsiemb/processing.py 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 def visualize ( self , model_name : str , labels : np . ndarray | None = None , n_samples : int | float | None = None , selected_features : list [ str ] | None = None , ** kwargs , ): \"\"\"Visualize the embeddings using a dimensionality reduction model. Args: model_name: The name of the dimensionality reduction model to use. Possible models are: - \"[PCA](https://scikit-learn.org/stable/modules/generated/ sklearn.decomposition.PCA.html#sklearn.decomposition.PCA)\" - \"[TSNE](https://scikit-learn.org/stable/modules/generated/ sklearn.manifold.TSNE.html#sklearn.manifold.TSNE)\" - \"[UMAP](https://umap-learn.readthedocs.io/en/latest/)\" labels: The labels to use for the visualization. If None, no labels are used. n_samples: The number of samples to subsample. If None, the whole embeddings matrix is used. selected_features: The names of the embeddings to subsample. If None, all embeddings are used. **kwargs: Additional arguments for the dimensionality reduction model (see the documentation of the dimensionality reduction model). \"\"\" if model_name == \"PCA\" : dimensionality_reduction_model = skl_decomposition . PCA ( n_components = 2 , ** kwargs ) elif model_name == \"TSNE\" : dimensionality_reduction_model = skl_manifold . TSNE ( n_components = 2 , ** kwargs ) elif model_name == \"UMAP\" : dimensionality_reduction_model = umap . UMAP ( n_components = 2 , ** kwargs ) else : raise ValueError ( f \"model { model_name } not implemented\" ) if n_samples is not None : subsampled_arrays = self . return_subsampled_embeddings ( n_samples , labels = labels ) embeddings_matrix = ( subsampled_arrays if labels is None else subsampled_arrays [ 0 ] ) labels = subsampled_arrays [ 1 ] if labels is not None else None else : embeddings_matrix = self . embeddings_matrix if selected_features is not None : selected_feats = np . array ( [ i for i , name in enumerate ( self . embeddings_names ) if name in selected_features ] ) embeddings_matrix = embeddings_matrix [:, selected_feats ] embeddings_reduced = dimensionality_reduction_model . fit_transform ( embeddings_matrix ) plot_scatter ( self . scale_to_01_range ( embeddings_reduced [:, 0 ]), self . scale_to_01_range ( embeddings_reduced [:, 1 ]), self . cmap , labels , )","title":" visualize"},{"location":"reference/prismtoolbox/wsiemb/models/","text":"","title":"Index"},{"location":"reference/prismtoolbox/wsiemb/models/clam/","text":"","title":"Clam"},{"location":"reference/prismtoolbox/wsiemb/models/generic/","text":"","title":"Generic"},{"location":"reference/prismtoolbox/wsiemb/models/pathoduet/","text":"","title":"Pathoduet"},{"location":"reference/prismtoolbox/wsiemb/models/phikon/","text":"","title":"Phikon"},{"location":"reference/prismtoolbox/wsiemb/models/utils/","text":"","title":"Utils"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/","text":"","title":"Index"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/coca_model/","text":"","title":"Coca model"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/conch/","text":"","title":"Conch"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/transformer/","text":"LayerNorm Bases: LayerNorm Subclass torch's LayerNorm (with cast back to input dtype). LayerNormFp32 Bases: LayerNorm Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back). PatchDropout ( prob , exclude_first_token = True ) Bases: Module https://arxiv.org/abs/2212.00794 Source code in src/prismtoolbox/wsiemb/models/conch_model/transformer.py 53 54 55 56 57 def __init__ ( self , prob , exclude_first_token = True ): super () . __init__ () assert 0 <= prob < 1.0 self . prob = prob self . exclude_first_token = exclude_first_token # exclude CLS token","title":"Transformer"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/transformer/#prismtoolbox.wsiemb.models.conch_model.transformer.LayerNorm","text":"Bases: LayerNorm Subclass torch's LayerNorm (with cast back to input dtype).","title":" LayerNorm"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/transformer/#prismtoolbox.wsiemb.models.conch_model.transformer.LayerNormFp32","text":"Bases: LayerNorm Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).","title":" LayerNormFp32"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/transformer/#prismtoolbox.wsiemb.models.conch_model.transformer.PatchDropout","text":"Bases: Module https://arxiv.org/abs/2212.00794 Source code in src/prismtoolbox/wsiemb/models/conch_model/transformer.py 53 54 55 56 57 def __init__ ( self , prob , exclude_first_token = True ): super () . __init__ () assert 0 <= prob < 1.0 self . prob = prob self . exclude_first_token = exclude_first_token # exclude CLS token","title":" PatchDropout"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/utils/","text":"freeze_batch_norm_2d ( module , module_match = {}, name = '' ) Converts all BatchNorm2d and SyncBatchNorm layers of provided module into FrozenBatchNorm2d . If module is itself an instance of either BatchNorm2d or SyncBatchNorm , it is converted into FrozenBatchNorm2d and returned. Otherwise, the module is walked recursively and submodules are converted in place. Parameters: Name Type Description Default module Module Any PyTorch module. required module_match dict Dictionary of full module names to freeze (all if empty) {} name str Full module name (prefix) '' Returns: Type Description torch.nn.Module: Resulting module Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 Source code in src/prismtoolbox/wsiemb/models/conch_model/utils.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def freeze_batch_norm_2d ( module , module_match = {}, name = \"\" ): \"\"\" Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and returned. Otherwise, the module is walked recursively and submodules are converted in place. Args: module (torch.nn.Module): Any PyTorch module. module_match (dict): Dictionary of full module names to freeze (all if empty) name (str): Full module name (prefix) Returns: torch.nn.Module: Resulting module Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 \"\"\" res = module is_match = True if module_match : is_match = name in module_match if is_match and isinstance ( module , ( nn . modules . batchnorm . BatchNorm2d , nn . modules . batchnorm . SyncBatchNorm ) ): res = FrozenBatchNorm2d ( module . num_features ) res . num_features = module . num_features res . affine = module . affine if module . affine : res . weight . data = module . weight . data . clone () . detach () res . bias . data = module . bias . data . clone () . detach () res . running_mean . data = module . running_mean . data res . running_var . data = module . running_var . data res . eps = module . eps else : for child_name , child in module . named_children (): full_child_name = \".\" . join ([ name , child_name ]) if name else child_name new_child = freeze_batch_norm_2d ( child , module_match , full_child_name ) if new_child is not child : res . add_module ( child_name , new_child ) return res","title":"Utils"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/utils/#prismtoolbox.wsiemb.models.conch_model.utils.freeze_batch_norm_2d","text":"Converts all BatchNorm2d and SyncBatchNorm layers of provided module into FrozenBatchNorm2d . If module is itself an instance of either BatchNorm2d or SyncBatchNorm , it is converted into FrozenBatchNorm2d and returned. Otherwise, the module is walked recursively and submodules are converted in place. Parameters: Name Type Description Default module Module Any PyTorch module. required module_match dict Dictionary of full module names to freeze (all if empty) {} name str Full module name (prefix) '' Returns: Type Description torch.nn.Module: Resulting module Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 Source code in src/prismtoolbox/wsiemb/models/conch_model/utils.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def freeze_batch_norm_2d ( module , module_match = {}, name = \"\" ): \"\"\" Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and returned. Otherwise, the module is walked recursively and submodules are converted in place. Args: module (torch.nn.Module): Any PyTorch module. module_match (dict): Dictionary of full module names to freeze (all if empty) name (str): Full module name (prefix) Returns: torch.nn.Module: Resulting module Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 \"\"\" res = module is_match = True if module_match : is_match = name in module_match if is_match and isinstance ( module , ( nn . modules . batchnorm . BatchNorm2d , nn . modules . batchnorm . SyncBatchNorm ) ): res = FrozenBatchNorm2d ( module . num_features ) res . num_features = module . num_features res . affine = module . affine if module . affine : res . weight . data = module . weight . data . clone () . detach () res . bias . data = module . bias . data . clone () . detach () res . running_mean . data = module . running_mean . data res . running_var . data = module . running_var . data res . eps = module . eps else : for child_name , child in module . named_children (): full_child_name = \".\" . join ([ name , child_name ]) if name else child_name new_child = freeze_batch_norm_2d ( child , module_match , full_child_name ) if new_child is not child : res . add_module ( child_name , new_child ) return res","title":" freeze_batch_norm_2d"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/vision_tower/","text":"VisualModel ( embed_dim_contrast , embed_dim_caption , trunk , image_size = 224 , proj = '' , proj_bias = False , drop = 0.0 , global_average_pool = False , use_attentional_pool_contrast = False , use_attentional_pool_caption = False , n_queries_contrast = 1 , n_queries_caption = 256 , attn_pooler_heads = 8 , norm_layer = nn . LayerNorm , output_tokens = False , trunk_kwargs = {}) Bases: Module Source code in src/prismtoolbox/wsiemb/models/conch_model/vision_tower.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 def __init__ ( self , embed_dim_contrast , embed_dim_caption , trunk , image_size = 224 , proj = \"\" , proj_bias = False , drop = 0.0 , global_average_pool = False , use_attentional_pool_contrast = False , use_attentional_pool_caption = False , n_queries_contrast = 1 , n_queries_caption = 256 , attn_pooler_heads = 8 , norm_layer = nn . LayerNorm , output_tokens = False , trunk_kwargs = {}, ): super () . __init__ () self . trunk = trunk self . trunk_kwargs = trunk_kwargs self . image_size = to_2tuple ( image_size ) prev_chs = self . trunk . num_features head_layers = OrderedDict () # whether to use attentional pooling self . use_attentional_pool_contrast = use_attentional_pool_contrast self . use_attentional_pool_caption = use_attentional_pool_caption self . global_average_pool = global_average_pool self . output_tokens = output_tokens if use_attentional_pool_contrast : scale = prev_chs **- 0.5 self . attn_pool_contrast = AttentionalPooler ( d_model = embed_dim_contrast , context_dim = prev_chs , n_head = attn_pooler_heads , n_queries = n_queries_contrast , ) self . ln_contrast = norm_layer ( embed_dim_contrast ) self . proj_contrast = nn . Parameter ( scale * torch . randn ( embed_dim_contrast , embed_dim_contrast ) ) else : assert proj , \"projection layer needed if not using attentional pooling.\" # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used if proj == \"linear\" : head_layers [ \"drop\" ] = nn . Dropout ( drop ) head_layers [ \"proj\" ] = nn . Linear ( prev_chs , embed_dim_contrast , bias = proj_bias ) elif proj == \"mlp\" : head_layers [ \"mlp\" ] = Mlp ( prev_chs , 2 * embed_dim_contrast , embed_dim_contrast , drop = ( drop , 0 ), bias = ( True , proj_bias ), ) self . head = nn . Sequential ( head_layers ) if use_attentional_pool_caption : self . attn_pool_caption = AttentionalPooler ( d_model = embed_dim_caption , context_dim = prev_chs , n_head = attn_pooler_heads , n_queries = n_queries_caption , ) self . ln_caption = norm_layer ( embed_dim_caption ) lock ( unlocked_groups = 0 , freeze_bn_stats = False ) lock modules Args: unlocked_groups (int): leave last n layer groups unlocked (default: 0) Source code in src/prismtoolbox/wsiemb/models/conch_model/vision_tower.py 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 def lock ( self , unlocked_groups = 0 , freeze_bn_stats = False ): \"\"\"lock modules Args: unlocked_groups (int): leave last n layer groups unlocked (default: 0) \"\"\" if not unlocked_groups : # lock full model for param in self . trunk . parameters (): param . requires_grad = False if freeze_bn_stats : freeze_batch_norm_2d ( self . trunk ) else : from timm.models.helpers import group_modules , group_parameters matcher = self . trunk . group_matcher () gparams = group_parameters ( self . trunk , matcher ) max_layer_id = max ( gparams . keys ()) max_layer_id = max_layer_id - unlocked_groups for group_idx in range ( max_layer_id + 1 ): group = gparams [ group_idx ] for param in group : self . trunk . get_parameter ( param ) . requires_grad = False if freeze_bn_stats : gmodules = group_modules ( self . trunk , matcher , reverse = True ) gmodules = { k for k , v in gmodules . items () if v <= max_layer_id } freeze_batch_norm_2d ( self . trunk , gmodules )","title":"Vision tower"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/vision_tower/#prismtoolbox.wsiemb.models.conch_model.vision_tower.VisualModel","text":"Bases: Module Source code in src/prismtoolbox/wsiemb/models/conch_model/vision_tower.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 def __init__ ( self , embed_dim_contrast , embed_dim_caption , trunk , image_size = 224 , proj = \"\" , proj_bias = False , drop = 0.0 , global_average_pool = False , use_attentional_pool_contrast = False , use_attentional_pool_caption = False , n_queries_contrast = 1 , n_queries_caption = 256 , attn_pooler_heads = 8 , norm_layer = nn . LayerNorm , output_tokens = False , trunk_kwargs = {}, ): super () . __init__ () self . trunk = trunk self . trunk_kwargs = trunk_kwargs self . image_size = to_2tuple ( image_size ) prev_chs = self . trunk . num_features head_layers = OrderedDict () # whether to use attentional pooling self . use_attentional_pool_contrast = use_attentional_pool_contrast self . use_attentional_pool_caption = use_attentional_pool_caption self . global_average_pool = global_average_pool self . output_tokens = output_tokens if use_attentional_pool_contrast : scale = prev_chs **- 0.5 self . attn_pool_contrast = AttentionalPooler ( d_model = embed_dim_contrast , context_dim = prev_chs , n_head = attn_pooler_heads , n_queries = n_queries_contrast , ) self . ln_contrast = norm_layer ( embed_dim_contrast ) self . proj_contrast = nn . Parameter ( scale * torch . randn ( embed_dim_contrast , embed_dim_contrast ) ) else : assert proj , \"projection layer needed if not using attentional pooling.\" # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used if proj == \"linear\" : head_layers [ \"drop\" ] = nn . Dropout ( drop ) head_layers [ \"proj\" ] = nn . Linear ( prev_chs , embed_dim_contrast , bias = proj_bias ) elif proj == \"mlp\" : head_layers [ \"mlp\" ] = Mlp ( prev_chs , 2 * embed_dim_contrast , embed_dim_contrast , drop = ( drop , 0 ), bias = ( True , proj_bias ), ) self . head = nn . Sequential ( head_layers ) if use_attentional_pool_caption : self . attn_pool_caption = AttentionalPooler ( d_model = embed_dim_caption , context_dim = prev_chs , n_head = attn_pooler_heads , n_queries = n_queries_caption , ) self . ln_caption = norm_layer ( embed_dim_caption )","title":" VisualModel"},{"location":"reference/prismtoolbox/wsiemb/models/conch_model/vision_tower/#prismtoolbox.wsiemb.models.conch_model.vision_tower.VisualModel.lock","text":"lock modules Args: unlocked_groups (int): leave last n layer groups unlocked (default: 0) Source code in src/prismtoolbox/wsiemb/models/conch_model/vision_tower.py 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 def lock ( self , unlocked_groups = 0 , freeze_bn_stats = False ): \"\"\"lock modules Args: unlocked_groups (int): leave last n layer groups unlocked (default: 0) \"\"\" if not unlocked_groups : # lock full model for param in self . trunk . parameters (): param . requires_grad = False if freeze_bn_stats : freeze_batch_norm_2d ( self . trunk ) else : from timm.models.helpers import group_modules , group_parameters matcher = self . trunk . group_matcher () gparams = group_parameters ( self . trunk , matcher ) max_layer_id = max ( gparams . keys ()) max_layer_id = max_layer_id - unlocked_groups for group_idx in range ( max_layer_id + 1 ): group = gparams [ group_idx ] for param in group : self . trunk . get_parameter ( param ) . requires_grad = False if freeze_bn_stats : gmodules = group_modules ( self . trunk , matcher , reverse = True ) gmodules = { k for k , v in gmodules . items () if v <= max_layer_id } freeze_batch_norm_2d ( self . trunk , gmodules )","title":" lock"}]} \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..0f8724e --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz new file mode 100644 index 0000000..82a04c4 Binary files /dev/null and b/sitemap.xml.gz differ