From f3d9c3d2744a733cdd2f1da61dde0bc486d5678c Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Fri, 12 Apr 2024 13:05:38 +0000 Subject: [PATCH] build based on 61f153e --- dev/.documenter-siteinfo.json | 1 + dev/_contribute/index.html | 2 +- dev/_intro.qmd | 11 +- dev/assets/documenter.js | 913 +++++++++++-- dev/assets/intro.gif | Bin 0 -> 76276 bytes dev/assets/resources/index.html | 2 +- dev/assets/search.js | 267 ---- dev/assets/themes/documenter-dark.css | 4 +- dev/assets/themes/documenter-light.css | 4 +- dev/assets/themeswap.js | 82 +- dev/assets/warner.js | 87 +- dev/contribute/index.html | 2 +- dev/contribute/performance/index.html | 4 +- dev/explanation/architecture/index.html | 2 +- dev/explanation/categorical/index.html | 4 +- .../generators/clap_roar/index.html | 4 +- dev/explanation/generators/clue/index.html | 4 +- dev/explanation/generators/dice/index.html | 4 +- .../generators/feature_tweak/index.html | 4 +- dev/explanation/generators/generic/index.html | 4 +- .../generators/gravitational/index.html | 4 +- dev/explanation/generators/greedy/index.html | 4 +- .../generators/growing_spheres/index.html | 4 +- .../generators/overview/index.html | 4 +- dev/explanation/generators/probe/index.html | 4 +- dev/explanation/generators/revise/index.html | 6 +- dev/explanation/index.html | 2 +- dev/explanation/optimisers/jsma/index.html | 4 +- .../optimisers/overview/index.html | 2 +- dev/extensions/index.html | 2 + dev/extensions/index.qmd | 9 + dev/extensions/laplace_redux.qmd | 74 ++ dev/extensions/laplace_redux/index.html | 17 + .../figure-commonmark/cell-6-output-1.svg | 1153 +++++++++++++++++ dev/extensions/neurotree.qmd | 81 ++ dev/extensions/neurotree/index.html | 20 + .../figure-commonmark/cell-6-output-1.svg | 1125 ++++++++++++++++ .../custom_generators/index.html | 4 +- dev/how_to_guides/custom_models/index.html | 4 +- dev/how_to_guides/index.html | 2 +- dev/index.html | 6 +- .../figure-commonmark/cell-10-output-1.svg | 26 +- .../figure-commonmark/cell-5-output-1.svg | 168 +-- dev/objects.inv | Bin 0 -> 6402 bytes dev/reference.qmd | 2 +- dev/reference/index.html | 44 +- dev/search/index.html | 2 - dev/search_index.js | 2 +- dev/tutorials/benchmarking/index.html | 4 +- dev/tutorials/data_catalogue/index.html | 4 +- dev/tutorials/data_preprocessing/index.html | 4 +- dev/tutorials/evaluation/index.html | 4 +- dev/tutorials/generators/index.html | 6 +- dev/tutorials/index.html | 2 +- dev/tutorials/model_catalogue/index.html | 4 +- dev/tutorials/models/index.html | 4 +- dev/tutorials/parallelization/index.html | 4 +- dev/tutorials/simple_example/index.html | 4 +- dev/tutorials/whistle_stop/index.html | 4 +- 59 files changed, 3587 insertions(+), 637 deletions(-) create mode 100644 dev/.documenter-siteinfo.json create mode 100644 dev/assets/intro.gif delete mode 100644 dev/assets/search.js create mode 100644 dev/extensions/index.html create mode 100644 dev/extensions/index.qmd create mode 100644 dev/extensions/laplace_redux.qmd create mode 100644 dev/extensions/laplace_redux/index.html create mode 100644 dev/extensions/laplace_redux_files/figure-commonmark/cell-6-output-1.svg create mode 100644 dev/extensions/neurotree.qmd create mode 100644 dev/extensions/neurotree/index.html create mode 100644 dev/extensions/neurotree_files/figure-commonmark/cell-6-output-1.svg create mode 100644 dev/objects.inv delete mode 100644 dev/search/index.html diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json new file mode 100644 index 000000000..28a47bf21 --- /dev/null +++ b/dev/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-12T13:05:18","documenter_version":"1.3.0"}} \ No newline at end of file diff --git a/dev/_contribute/index.html b/dev/_contribute/index.html index fa7c68c82..64e0aed5e 100644 --- a/dev/_contribute/index.html +++ b/dev/_contribute/index.html @@ -1,2 +1,2 @@ -Contributing · CounterfactualExplanations.jl
Edit on GitHub

Contributing

Our goal is to provide a go-to place for Counterfactual Explanations in Julia. To this end, the following is a non-exhaustive list of enhancements we have planned:

  1. Additional counterfactual generators and predictive models.
  2. Additional datasets for testing, evaluation and benchmarking.
  3. Support for regression models.

For a complete list, have a look at outstanding issue.

How to contribute?

Any sort of contribution is welcome, in particular:

  1. Should you spot any errors or something is not working, please just open an issue.
  2. If you want to contribute your code, please proceed as follows:
    • Fork this repo and clone your fork: git clone https://github.com/your_username/CounterfactualExplanations.jl.
    • Implement your modifications and submit a pull request.
  3. For any other questions or comments, you can also start a discussion.
+Contributing · CounterfactualExplanations.jl

Contributing

Our goal is to provide a go-to place for Counterfactual Explanations in Julia. To this end, the following is a non-exhaustive list of enhancements we have planned:

  1. Additional counterfactual generators and predictive models.
  2. Additional datasets for testing, evaluation and benchmarking.
  3. Support for regression models.

For a complete list, have a look at outstanding issue.

How to contribute?

Any sort of contribution is welcome, in particular:

  1. Should you spot any errors or something is not working, please just open an issue.
  2. If you want to contribute your code, please proceed as follows:
    • Fork this repo and clone your fork: git clone https://github.com/your_username/CounterfactualExplanations.jl.
    • Implement your modifications and submit a pull request.
  3. For any other questions or comments, you can also start a discussion.
diff --git a/dev/_intro.qmd b/dev/_intro.qmd index 3bdfd5db5..2cb4d2adb 100644 --- a/dev/_intro.qmd +++ b/dev/_intro.qmd @@ -2,7 +2,7 @@ *Counterfactual Explanations and Algorithmic Recourse in Julia.* -[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/stable) [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/dev) [![Build Status](https://github.com/juliatrustworthyai/CounterfactualExplanations.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/juliatrustworthyai/CounterfactualExplanations.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Coverage](https://codecov.io/gh/juliatrustworthyai/CounterfactualExplanations.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/juliatrustworthyai/CounterfactualExplanations.jl) [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) [![License](https://img.shields.io/github/license/juliatrustworthyai/CounterfactualExplanations.jl)](LICENSE) [![Package Downloads](https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/CounterfactualExplanations/)](https://pkgs.genieframework.com?packages=CounterfactualExplanations) [![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl) +[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/stable) [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliatrustworthyai.github.io/CounterfactualExplanations.jl/dev) [![Build Status](https://github.com/juliatrustworthyai/CounterfactualExplanations.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/juliatrustworthyai/CounterfactualExplanations.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Coverage](https://codecov.io/gh/juliatrustworthyai/CounterfactualExplanations.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/juliatrustworthyai/CounterfactualExplanations.jl) [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle) [![License](https://img.shields.io/github/license/juliatrustworthyai/CounterfactualExplanations.jl)](assets/intro.gif) [![Package Downloads](https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/CounterfactualExplanations/)](https://pkgs.genieframework.com?packages=CounterfactualExplanations) [![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl) ```{julia} #| echo: false @@ -318,17 +318,14 @@ ce = generate_counterfactual( The animation below shows the resulting counterfactual path: ```{julia} +#| output: true #| echo: false anim = animate_path(ce; title="Factual: $(factual) → Target: $(target)") -gif(anim, joinpath(www_path, "intro.gif"), fps=10) -gif(anim, "README_files/intro.gif", fps=10) +anim = gif(anim, fps=10) +display(anim) ``` -![](www/intro.gif) - -![](README_files/intro.gif) - ## ☑️ Implemented Counterfactual Generators Currently, the following counterfactual generators are implemented: diff --git a/dev/assets/documenter.js b/dev/assets/documenter.js index 6adfbbbf4..c6562b558 100644 --- a/dev/assets/documenter.js +++ b/dev/assets/documenter.js @@ -1,15 +1,15 @@ // Generated by Documenter.jl requirejs.config({ paths: { - 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/languages/julia.min', + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', - 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.12.1/jquery-ui.min', - 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.13.24/contrib/auto-render.min', - 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', - 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.13.24/katex.min', - 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/highlight.min', - 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/languages/julia-repl.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', }, shim: { "highlight-julia": { @@ -70,13 +70,91 @@ $(document).ready(function() { hljs.highlightAll(); }) +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on("click", ".docstring header", function () { + let articleToggleTitle = "Expand docstring"; + + debounce(() => { + if ($(this).siblings("section").is(":visible")) { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + $(this) + .find(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + $(this).siblings("section").slideToggle(); + }); +}); + +$(document).on("click", ".docs-article-toggle-button", function (event) { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + let animationSpeed = event.noToggleAnimation ? 0 : 400; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(animationSpeed); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(animationSpeed); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + }) //////////////////////////////////////////////////////////////////////////////// require([], function() { function addCopyButtonCallbacks() { for (const el of document.getElementsByTagName("pre")) { const button = document.createElement("button"); - button.classList.add("copy-button", "fas", "fa-copy"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + el.appendChild(button); const success = function () { @@ -85,7 +163,7 @@ function addCopyButtonCallbacks() { }; const failure = function () { - button.classList.add("error", "fa-times"); + button.classList.add("error", "fa-xmark"); button.classList.remove("fa-copy"); }; @@ -94,7 +172,7 @@ function addCopyButtonCallbacks() { setTimeout(function () { button.classList.add("fa-copy"); - button.classList.remove("success", "fa-check", "fa-times"); + button.classList.remove("success", "fa-check", "fa-xmark"); }, 5000); }); } @@ -138,30 +216,682 @@ require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { // Manages the top navigation bar (hides it when the user starts scrolling down on the // mobile). window.Headroom = Headroom; // work around buggy module loading? -$(document).ready(function() { - $('#documenter .docs-navbar').headroom({ - "tolerance": {"up": 10, "down": 10}, +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, }); +}); + }) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let meta = $("div[data-docstringscollapsed]").data(); + + if (meta?.docstringscollapsed) { + $("#documenter-article-toggle-button").trigger({ + type: "click", + noToggleAnimation: true, + }); + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +/* +To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +PSEUDOCODE: + +Searching happens automatically as the user types or adjusts the selected filters. +To preserve responsiveness, as much as possible of the slow parts of the search are done +in a web worker. Searching and result generation are done in the worker, and filtering and +DOM updates are done in the main thread. The filters are in the main thread as they should +be very quick to apply. This lets filters be changed without re-searching with minisearch +(which is possible even if filtering is on the worker thread) and also lets filters be +changed _while_ the worker is searching and without message passing (neither of which are +possible if filtering is on the worker thread) + +SEARCH WORKER: + +Import minisearch + +Build index + +On message from main thread + run search + find the first 200 unique results from each category, and compute their divs for display + note that this is necessary and sufficient information for the main thread to find the + first 200 unique results from any given filter set + post results to main thread + +MAIN: + +Launch worker + +Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results) + +On text update + if worker is not running, launch_search() + +launch_search + set worker_is_running to true, set last_search_text to the search text + post the search query to worker + +on message from worker + if last_search_text is not the same as the text in the search field, + the latest search result is not reflective of the latest search query, so update again + launch_search() + otherwise + set worker_is_running to false + + regardless, display the new search results to the user + save the unfiltered_results as a global + update_search() + +on filter click + adjust the filter selection + update_search() + +update_search + apply search filters by looping through the unfiltered_results and finding the first 200 + unique results that match the filters + + Update the DOM +*/ + +/////// SEARCH WORKER /////// + +function worker_function(documenterSearchIndex, documenterBaseURL, filters) { + importScripts( + "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js" + ); + + let data = documenterSearchIndex.map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; + }); + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", + ]); + + let index = new MiniSearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + + word = word.toLowerCase(); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not + // find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + prefix: true, + boost: { title: 100 }, + fuzzy: 2, + }, + }); + + index.addAll(data); + + /** + * Used to map characters to HTML entities. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const htmlEscapes = { + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", + }; + + /** + * Used to match HTML entities and HTML characters. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const reUnescapedHtml = /[&<>"']/g; + const reHasUnescapedHtml = RegExp(reUnescapedHtml.source); + + /** + * Escape function from lodash + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + function escape(string) { + return string && reHasUnescapedHtml.test(string) + ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr]) + : string || ""; + } + + /** + * Make the result component given a minisearch result data object and the value + * of the search input as queryString. To view the result object structure, refer: + * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ + function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + + let textindex = new RegExp(`${querystring}`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + text = text.length ? escape(text) : ""; + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`${escape(querystring)}`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${escape(result.title)}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; + } + + self.onmessage = function (e) { + let query = e.data; + let results = index.search(query, { + filter: (result) => { + // Only return relevant results + return result.score >= 1; + }, + }); + + // Pre-filter to deduplicate and limit to 200 per category to the extent + // possible without knowing what the filters are. + let filtered_results = []; + let counts = {}; + for (let filter of filters) { + counts[filter] = 0; + } + let present = {}; + + for (let result of results) { + cat = result.category; + cnt = counts[cat]; + if (cnt < 200) { + id = cat + "---" + result.location; + if (present[id]) { + continue; + } + present[id] = true; + filtered_results.push({ + location: result.location, + category: cat, + div: make_search_result(result, query), + }); + } + } + + postMessage(filtered_results); + }; +} + +// `worker = Threads.@spawn worker_function(documenterSearchIndex)`, but in JavaScript! +const filters = [ + ...new Set(documenterSearchIndex["docs"].map((x) => x.category)), +]; +const worker_str = + "(" + + worker_function.toString() + + ")(" + + JSON.stringify(documenterSearchIndex["docs"]) + + "," + + JSON.stringify(documenterBaseURL) + + "," + + JSON.stringify(filters) + + ")"; +const worker_blob = new Blob([worker_str], { type: "text/javascript" }); +const worker = new Worker(URL.createObjectURL(worker_blob)); + +/////// SEARCH MAIN /////// + +// Whether the worker is currently handling a search. This is a boolean +// as the worker only ever handles 1 or 0 searches at a time. +var worker_is_running = false; + +// The last search text that was sent to the worker. This is used to determine +// if the worker should be launched again when it reports back results. +var last_search_text = ""; + +// The results of the last search. This, in combination with the state of the filters +// in the DOM, is used compute the results to display on calls to update_search. +var unfiltered_results = []; + +// Which filter is currently selected +var selected_filter = ""; + +$(document).on("input", ".documenter-search-input", function (event) { + if (!worker_is_running) { + launch_search(); + } +}); + +function launch_search() { + worker_is_running = true; + last_search_text = $(".documenter-search-input").val(); + worker.postMessage(last_search_text); +} + +worker.onmessage = function (e) { + if (last_search_text !== $(".documenter-search-input").val()) { + launch_search(); + } else { + worker_is_running = false; + } + + unfiltered_results = e.data; + update_search(); +}; + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + selected_filter = ""; + } else { + selected_filter = $(this).text().toLowerCase(); + } + + // This updates search results and toggles classes for UI: + update_search(); +}); + +/** + * Make/Update the search component + */ +function update_search() { + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + if (selected_filter == "") { + results = unfiltered_results; + } else { + results = unfiltered_results.filter((result) => { + return selected_filter == result.category.toLowerCase(); + }); + } + + let search_result_container = ``; + let modal_filters = make_modal_body_filters(); + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + for (var i = 0, n = results.length; i < n && count < 200; ++i) { + let result = results[i]; + if (result.location && !links.includes(result.location)) { + search_results += result.div; + count++; + links.push(result.location); + } + } + + if (count == 1) { + count_str = "1 result"; + } else if (count == 200) { + count_str = "200+ results"; + } else { + count_str = count + " results"; + } + let result_count = `
${count_str}
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(` +
Type something to get started!
+ `); + } +} + +/** + * Make the modal filter html + * + * @returns string + */ +function make_modal_body_filters() { + let str = filters + .map((val) => { + if (selected_filter == val.toLowerCase()) { + return `${val}`; + } else { + return `${val}`; + } + }) + .join(""); + + return ` +
+ Filters: + ${str} +
`; +} }) //////////////////////////////////////////////////////////////////////////////// require(['jquery'], function($) { // Modal settings dialog -$(document).ready(function() { - var settings = $('#documenter-settings'); - $('#documenter-settings-button').click(function(){ - settings.toggleClass('is-active'); +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); }); // Close the dialog if X is clicked - $('#documenter-settings button.delete').click(function(){ - settings.removeClass('is-active'); + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); }); // Close dialog if ESC is pressed - $(document).keyup(function(e) { - if (e.keyCode == 27) settings.removeClass('is-active'); + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+ `; + + let initial_search_body = ` +
Type something to get started!
+ `; + + let search_modal_footer = ` + + `; + + $(document.body).append( + ` + + ` + ); + + document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); + }); + + document + .querySelector(".close-search-modal") + .addEventListener("click", () => { + closeModal(); + }); + + $(document).on("click", ".search-result-link", function () { + closeModal(); + }); + + document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; }); + + // Functions to open and close a modal + function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); + } + + function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); + } + + document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); }); }) @@ -169,150 +899,139 @@ $(document).ready(function() { require(['jquery'], function($) { // Manages the showing and hiding of the sidebar. -$(document).ready(function() { +$(document).ready(function () { var sidebar = $("#documenter > .docs-sidebar"); - var sidebar_button = $("#documenter-sidebar-button") - sidebar_button.click(function(ev) { + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { ev.preventDefault(); - sidebar.toggleClass('visible'); - if (sidebar.hasClass('visible')) { + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { // Makes sure that the current menu item is visible in the sidebar. $("#documenter .docs-menu a.is-active").focus(); } }); - $("#documenter > .docs-main").bind('click', function(ev) { + $("#documenter > .docs-main").bind("click", function (ev) { if ($(ev.target).is(sidebar_button)) { return; } - if (sidebar.hasClass('visible')) { - sidebar.removeClass('visible'); + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); } }); -}) +}); // Resizes the package name / sitename in the sidebar if it is too wide. // Inspired by: https://github.com/davatron5000/FitText.js -$(document).ready(function() { +$(document).ready(function () { e = $("#documenter .docs-autofit"); function resize() { - var L = parseInt(e.css('max-width'), 10); + var L = parseInt(e.css("max-width"), 10); var L0 = e.width(); - if(L0 > L) { - var h0 = parseInt(e.css('font-size'), 10); - e.css('font-size', L * h0 / L0); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); // TODO: make sure it survives resizes? } } // call once and then register events resize(); $(window).resize(resize); - $(window).on('orientationchange', resize); + $(window).on("orientationchange", resize); }); // Scroll the navigation bar to the currently selected menu item -$(document).ready(function() { +$(document).ready(function () { var sidebar = $("#documenter .docs-menu").get(0); var active = $("#documenter .docs-menu .is-active").get(0); - if(typeof active !== 'undefined') { + if (typeof active !== "undefined") { sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; } -}) +}); }) //////////////////////////////////////////////////////////////////////////////// require(['jquery'], function($) { -function set_theme(theme) { - var active = null; - var disabled = []; - for (var i = 0; i < document.styleSheets.length; i++) { - var ss = document.styleSheets[i]; - var themename = ss.ownerNode.getAttribute("data-theme-name"); - if(themename === null) continue; // ignore non-theme stylesheets - // Find the active theme - if(themename === theme) active = ss; - else disabled.push(ss); - } - if(active !== null) { - active.disabled = false; - if(active.ownerNode.getAttribute("data-theme-primary") === null) { - document.getElementsByTagName('html')[0].className = "theme--" + theme; - } else { - document.getElementsByTagName('html')[0].className = ""; - } - disabled.forEach(function(ss){ - ss.disabled = true; - }); - } - - // Store the theme in localStorage - if(typeof(window.localStorage) !== "undefined") { - window.localStorage.setItem("documenter-theme", theme); - } else { - console.error("Browser does not support window.localStorage"); - } -} - // Theme picker setup -$(document).ready(function() { +$(document).ready(function () { // onchange callback - $('#documenter-themepicker').change(function themepick_callback(ev){ - var themename = $('#documenter-themepicker option:selected').attr('value'); - set_theme(themename); + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); }); // Make sure that the themepicker displays the correct theme when the theme is retrieved // from localStorage - if(typeof(window.localStorage) !== "undefined") { - var theme = window.localStorage.getItem("documenter-theme"); - if(theme !== null) { - $('#documenter-themepicker option').each(function(i,e) { - e.selected = (e.value === theme); - }) - } else { - $('#documenter-themepicker option').each(function(i,e) { - e.selected = $("html").hasClass(`theme--${e.value}`); - }) + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); } } -}) +}); }) //////////////////////////////////////////////////////////////////////////////// require(['jquery'], function($) { // update the version selector with info from the siteinfo.js and ../versions.js files -$(document).ready(function() { +$(document).ready(function () { // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the // siteinfo.js file, we just return immediately and not display the version selector. - if (typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === 'boolean' && DOCUMENTER_VERSION_SELECTOR_DISABLED) { + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { return; } var version_selector = $("#documenter .docs-version-selector"); var version_selector_select = $("#documenter .docs-version-selector select"); - version_selector_select.change(function(x) { - target_href = version_selector_select.children("option:selected").get(0).value; + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; window.location.href = target_href; }); // add the current version to the selector based on siteinfo.js, but only if the selector is empty - if (typeof DOCUMENTER_CURRENT_VERSION !== 'undefined' && $('#version-selector > option').length == 0) { - var option = $(""); + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); version_selector_select.append(option); } - if (typeof DOC_VERSIONS !== 'undefined') { + if (typeof DOC_VERSIONS !== "undefined") { var existing_versions = version_selector_select.children("option"); - var existing_versions_texts = existing_versions.map(function(i,x){return x.text}); - DOC_VERSIONS.forEach(function(each) { - var version_url = documenterBaseURL + "/../" + each; + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; var existing_id = $.inArray(each, existing_versions_texts); // if not already in the version selector, add it as a new option, // otherwise update the old option with the URL and enable it if (existing_id == -1) { - var option = $(""); + var option = $( + "" + ); version_selector_select.append(option); } else { var option = existing_versions[existing_id]; @@ -326,6 +1045,6 @@ $(document).ready(function() { if (version_selector_select.children("option").length > 0) { version_selector.toggleClass("visible"); } -}) +}); }) diff --git a/dev/assets/intro.gif b/dev/assets/intro.gif new file mode 100644 index 0000000000000000000000000000000000000000..797f3e56469fed2e00754722798ae31fc1cacbdf GIT binary patch literal 76276 zcmeF2^;Z+_`~L@wF$Rp$4I_lnqenV=NJvXdH%K=M8!<|{!x7TbQX<_TQYs1}(jX{d z0LH$r_uujTJbt=k2*PFAE@B)>=i|APnsf+)cBEYvLAP#8Te zT$n*rju{GJ6A)#S(`IvZVdrAwV4>r_#l>Bi3JGmxrCq&B{%Y8)MFoJKGa zf}4~!nDlv@n%pxrGc*fzH6swrCOyqxx>@{3$ARTg!M*VEdtJ`fB7D}a#kTl(+wyq3 z|8R4%cad-)+;*()ajYdbSRD z_AYsOd3pEW^PbB0(M$6Q6Y~kL^{FWE!+`x&*#df70-x0fPMZgnHw2GA3<(Jd4UY)T zaSXc+4-57W$Ek;R8AKS}ihL*)nGq4WWE^Ek9febg8aIko=Z}erjmZ^{#mU8{>BUw@ z#=h)&m}M9@+!LRY6yJ%(Y71bC?GmuU$-Q3qVXYKB!BlHVx_f^5V?_E>?~IJ}$EFfQ!+I*)lb*u>ViOZ^;~r`xQ4pchP8{vS4o|n-Osn}dSXDm4P1SL{CzWm z{UMAmUJkyjbb0ya)-)bC-NQ3;7&13AH@C{YILW*Ck#p(5W_fveWt(GdZSDOu@5bv- z8~bV>KYrXS27KN@Y|n7-e%#;P-Q63D*na{zT;V@HKDtc-mA5=gTD3Bh{}B!>V<0Al}hA!a`~>4&BAf2WB$(#7W~W3lMS=>1YIT%m<)N>QzuT?a!zFu>EU=>5_`!dt;1^3J2VN|#~naQWHAaBbx3V$qj{{( zN*6@?oaLPwwC!mn+16jpd}QhKAMfOCpIZQA1Oba5Z~vwy$^dc>v(l ziLM1+`v%O+T2!1%Mf?tVIj{r>;6bsAk*tUB0DMM1&M29s5cmvbtSY!jYL2p$ zM&62A-`&gdX|n*jdedAZ7FARHS8Q&DsFf6wd1wdIDzjCMLY3;lZEw^9{fPTya)-V< zTX51u+&#`Xf0s|Up^xA1SV~uTowQFUC9seQ%C0xJk$)URlLSsw=fwjng1yWotq>0N+0QH>83V03C?N0D(22SGA>xh}1E3G631L!Z8Dd)9+|6=5cBU^_=>H+p!ux##6X3 z(GK+CI2{S8z$ov7d0LZMm@=}PW;;*%wLDB2mu^_adSSWYybN+9vc5~hVfcEtN<%GW z-B?Wc(Inbxr&l{3Iy7Uc2;T&pB$}X8-iR=>Ki(pJn>-<>9*eTrTDk zcBXQ~b{0pdGMb8Bw7NN6d09)QajPJBV=!YL^ zkpUt&@cDzxw?3`9h;nJ&Rk5rd{9z1akG;a@Sv<>RbMFdwv;7#3661taL#g?cj)#Q>)I z>Af`16(Lh6A@*#~IR6|(9LC&KWaJK5u#cX?%Ca*vZoXDtmuwv8yQ6ujVNa4sVl*3{ z%xp*suo+Yo2#)x|^UKr4wg@I{1`#xcT7exl3W!sgU0=9&SF&jNo@#t&tKklCOaqj_ zGO6iZc?bw0KunvAdwRWiLJ&*_%B<288e9se5yl+HKaMV&Qyj%7#U7796*pZtMja<( zswSXei?!TKFwnHOO;!L>lU48og>u;BvT*tFBMK)k!$O-C4u2pQ=EsqB{W#Q``3*`u zu?}#-ChiT^WF1tRHhIC2nIouGZ{XIbr#<_&yPrvRXtBub)17zyAAT6%jLm&EZvlKi z{%DP(X%5J#;RP`ox8V(&o%gmeq_0BSvoe}ph4kOg+WhP&de!W%FlSxSEXMwj39Rg? zPGueTv#Z|TSAv7kgWZy##~Isrd+4tZ5l2%gwL~$TBD^rvctiIvjf8FS;)l=VG6FL0g;UyaM@1}?9@98K%P|Q(FWptS0;W+<8r!7wvK6X4lDM3ig z$s5yqk9G2~(rCLAG&*AJ4BU>_HQ1&vy&6XtwWr>j-)4SsHG%YRH~Xu;!@hnsDJ!2W zXWb{UaeUZEnj&EsHIm84aXpPO>c}aa-xXB8o-uyhkymH1Ct`a&Yo#NtIvw~}BKUgl z08XJ;u#_%c8p%?M2`-^DNs=$DBtk^+mo)Nl5=vhJDeZJ8Ibzs@4o@IcN5z9Zk)G_V zHxyc!uByu6LminYh*_3K4X>(~o)9mk&U9C;!mA^r=$jP+^m)Cm;jwAy&1${ib0y~G zOyT=D^ZQJblBo#^8|?Ss)8&-eo%3HDe&4(shIY3lzsd{=xn7(88YDkI?|ZLa@s)c( zFq<#TgDH=&K);Bt;4vmqGb4~~ffT@h>^wDB&`W7yA=SdKzJ|Fdy*Yc_GjL$|Eov9CgI&5KD+beUS`p3_L0fmfo+!8 zR|t;M<0?RhDo&VjMKU!(t3;1`g_v`*3xd`@WUuyQE;SqL@295+yhHLg*d-F zPaNYJ3F^?p)5Bl6-cxrgB><72X_fGFEh8)CGJa8DYB?BU$8Lbl~oc!i8EUZS4Db9+LQ6#i`&kOyJ3t zS5X^>8O#mwTZqS)?o@4)0MaGk5L24zWOSQpj$mXCqBuvSFGoa={M}jFAJ*J^wTW3% zc(;ALH*p`lL;;fa%zEXS#E6XgpzH57m69qCgt3tmSW{UdvW-%*n+4r=JsD4sA%bX= zM6la5Oh+mnc!S7wRCN3O?vaa5BuTG()>>#tQXD!mMG=!Bq5#wfkXL}o?;^8ze&t0K z$Fm)IKN4aQb%CCBKGuI5EcZDF$ySj1-t`|i(r(R{RUymIGOIi}i=azV#p3^TDoNHj zBjqh_4Ux@YogHmm68^66_hj^_deI&CBH7RRX>6qiI=SW^xo-Q3q(|=Vi-9f?Sqq3f z!Mc=v_q^0@AWXO!4UGAOn_CsC8ID9~FG|KKv0I=l4k0pVu%oI%w5yGHra(crERBH0^am zM3->jtK?EZW9h(AZhn2%`<~d+`k+a+l8BiKhW_eH%gX+`5>}-gyBXL%08%|n(_~C( zhN7#h;=dC1{gg#n>j&srXEP0(y`h=@);oHnwg?!uTd zS;+A7qRi;8nqfgmQdIp4n!10u9$Hv6^Y%#(Td@-+c91g96O~u%;a?wKU4btP#x$;` zCR3R}zidIjWIbDNe714(Y#LU#BwXX=0%^TzY=S}I5ETHd{w<~N)gtAo?O*3p+X#vGI4e8J}BdNw=)J<@z zYxHwUq2_7#QdF_K(viE$b*r;?ip(AVQ7_MNJs^fMTfex>^F?22ZMRP}RGp)y@dkFd z*vQV&gqeWp+rkV*JKhnfXU}L7N&`eneZ_3u?QfpW_7>buX^?4Zkeed}MYi28>MWV- zEBn&t!_nW+nBO!Dn|Ew!6``$dGl8j7nFlJ8SN3$KC0Z&s1Bk`XbNikbL=ybpH}5NU zFF`7m(*~*=2k7*n^U5s&RCK@L)VfMN8^)CL$R-GhGU#3Z$LPdQ(Y>NKzG*K4C2fj_ z*!m9j$@2Aqi;(1EjZ*l#pVrzHcEtt-nM>^A{QMhcm0Z=oJ} zIZ;RZ2~O1!Ky!s0)~$VNey?B>(IB}V3RoW=7VQS5_4%ZZcs7mvRTv^$(#Igd30KB3 zyp#{5Ts3`!CFWtI8xdEFJ>vkHPB=s`b?lGvi)o$a{DThC52Y1_9)&4{8kX*W8Ql19 z93{uY)3kAw#__qS@!kT~F2~!wlNPF9vbX>I^=xB>o}@QcIu&< zkR#)?Z;mlZ(Y~JZRXKg7wxXSLcdEy{s;4L`qsqpqNZOx>4s9#JxFg~0dE#6GfaE=T ze9R~n9h9&If+YR-T-6~sp{rT_w(}9q$kyZ!l=u1rt_f64aa6sIkO^dOTHV<5nR2zV z^v81rgVBS0TXAwW5MX?h9 z&Le=smyDNpp86a%A=N(jI!zOQw(_S849FPe)qqhxi}>b9x#(EG=m_ap4-rsqHRcMv zVsE!=oOEO#-)?BwjhWp~Z#$%ZOI^A=1DXS$Q>npW72>cTaNVV0KoT{5&|lcnA~@{j z>rBTUYIQ1y@3guvBaVh4)ksK{@tWbwreyDqG>;{Rw`(8m;(~wny520gD^2nFjaR3< zFBX66ayoUk3kZOj+{;DqP(qB$-iZ+_R~qdh&%|k-0@P?n1R_`8r6<$6{0T`=YHd2B zp`{5WKbfJ3iJY391V_eFz0azv3(`A!!vy}g^bFQA(0$QFw)~x{-dL<^T(Z!`CNTCj zbvsQaax?SHie^OMc((uBok`43>H;KH(<0?NP3Gi%_ES>gK7W67^+jPopZfXp0 zxb`A#BL0o3F$F&70i& zG@tzZ>e=&~kI&cYimhJ}pf-gOM6YhQe}ig+-KS#x3qC{haLUX8$m$t5$MGet32o8J z5omiCz7SD<^61yIUgh_>)SU0VJZJOLW?kp<(u^8C9{aBTWz$-yJmaNqrFl#re^6QR zN`yPm>Y=+0blmRWi`kbA^8S6H&3U?W5G>zpquChqK4SW!KFO>4oPK->B5T{WOZaGHU+~W%&Hn^#~4WLT@)aR$T{-Q76;R z!l@?UpPc&E*7XAa*lc7YcCv_~@5!TgUil9&bDl4$oip8y3~wW6y+0(o^v#I5b&!j? zm_Sv&NcrMfz_&%pUI5KFtk)cxKI(V22gUtsB+a++kgGrTVI5HTO>k+sjGn(#eeKKA zx_@_aPNMzX5V16HhuRrEgZG9o()czLRtH3$JI)+f-HjxaeNyZDBn|QRE_zcRei&Fk z>poO{S@F~R$u~ma7isnHhCiu|=bCb}D7ulYP*vY6BxIhNrURZU*4iqO87TR0ZAxMx z_v3N?UtCzj4$GgbXtwK|`|h`SzT0?_wln`3z5QqW{-4Q5e@3?ek@wx+!F~+%Q=|<4 zO7@1FzM{3b@Ae&ib1S+-iWvE$r*sl2=`kSM=}>Xv+&cKg>Su4}vggomAKIh41<+6= z@%w$^&qu_oC&Zf};+scq0QFXeE-&(51H4uN9}rQ0u3}x@=;cYrK*b%#icxx4#?%gS1T`9-0neF%azVhAl-S=KuDEFEDBj%zxu}Dv!?j4cUMM_@F zRTg#z&=@yow`igtDfxk6Y_Tu2BH^$UTNBWkuh1^FJhkce`#6}$aqp{Lf9UC;#jm^H z90rMvpMjjc)(DYU8Xlc8t8?ecl<4OPoYohvvn)d?C5b-yWfqV+b(g)f4~+@ER(nmp zfgu{sRqXqs?jlxg*3Crr+R1X84TH@&QjW{<;9Pzg^az2qQ3Gc}$&{nkHk^k|t~0Gm zZL)m{XsxY+!Afen8b_I4tCO1-s-Da)3({mF8lCL_Oz>GT3l}gZ;U4tKK(7) z3zv>r?i1&C!~XEi2XT$oRbz)5PCf;a7LXTW2}`cj&T1Uhd0O;2S={zIQcoG&zMm*O zrGJtrtX*cwxS&mN=7c2frFYON9n}ZVD$zxC-7yV%#npicedfsGP@Sn$A|6Ro-Lq-> zBT;ECr_P9y_{3S~+(I*+(sFvsjfZ{D+0#9H@5}Tgog35rG`+Wsukma+Mo(;Db+)@yC`gVBu z(O?PpkE8m#Lw7BP{=U`T#^PsOEukad=jmhSjdYn!&Q`%sJgn&U*kUkYcy4`_(WgZZ z=)b2kDjKlef|_nFjKB3VFbVsS2KZ$bP=@7Lwej`Y2wSQfjFnY}r&w)ogd~GhA6_dk zl}j$y4X}j%&BsxIr#XO2)-t=PF@vA0CpZfcpboR^O^TQR0O5k4 z`%OX+L=BHDO+nW{Fv;7=&{5|iB4-yzUc-r?*iOnEw2*Tz>zj_k`jR;s&=BY}p{$9; zF0NX1T{A7=j~YHXd==tZIendybo2yby(dDlEyDkTK2d8>eIVU#jNbc;Rz+T*hNpou zRTbDlZL&R43t2_yUj&pj$c82wy3qS$#wXkZ@g{jmOs`Q+>I1c-x#UL3g!L3^r@YOuvAMq&}ko!xPl(BQ2BE=*E0N;CXsudyOWybz-6l z8D3?#68@ZyT=-64s(n8R3pJarPyk)JlmH*o3S39z=Wr_CVAqf&As^w|qx7|WI$D9K zzAH%0l6UU)CCN~crx*TwFu0N`*Yc24=uMwdSH!a94^o4F7{$A06*>Ah)8l12?YtYi zD>4MSiR7OFsToYJ^kHxvKBM+U9e@V&bsV145|*6yv??Q|O;hOVD9d9S#NOMc1)=+y z{!y6U(M;>TqMc{1*Tk7Nl4EwhfjdKA=n*UyvX|Vl}&k2Z6vreyyYe;GI&^m|eAGQ|&^~qU6vhZ4Kd`u0C<8qsy zyHl7tGpK&-{V*!Yks1`&YHk#K(BhTr>%U^0s+qSle~?#S^IGIfr@*~U4`Bnjw=Z-< zgiwuA4i^RQ2HqeOU4M7b@)QA}Jiys zx%n3%$6iYVlDTJ?RGGS65s$mcSN7bmUzZO%nq*mrB=f-q*#Zlj5T_XiU+0eXYU zfbF#VQzwP@0nL15*V&mq1QTZX0!A&Q9b&gN%GeLLR_krH^xPexi9_tL_Btll8AVt{ zViGvI+YY1-0k5Mr>$RyjC{xmLJPj83<`eHKk8GExui~8e?w4>fF5=x~-s>FXh$|;^<5t8lkDKW2WvSHk(+kE()HrW%+oBm#gJFs(#7F4uNc>-!5B7Jy z8WfZ=Fe~kS7H5BTjh{V`#_NA`_?lJ{8EqjTs)OE7|6x04l(1@U`X-Q;@fq9 zk;aJgdD5P->7W47Y)wr(fa%J!{;gv&YeGprmI`^vwpOA<9&4sIk`ZgQaLRsE2bfZ- z1F8jl-ryzaLX)p~wbQ)hRQU{Wq`3d}GY*C1hpJ&Z#w)D?EtzoX1z@W_RZU;;zf+D+ ztP-~m<4*!P7l&5xPvZvTsim^wsqMEkXauEFG|@QLLgF_)I(ryHvIXCL_ct@+bg7y} z+t7}y>uUawReDqN%QAo6SCk1dqdE|brj^Ei6>R*j2l3;t@()Vy!lfxdn9-~3ETG)? zAdvZCA{QX;heLuB_GM8VThJ5bkH1{ynxAPfs)pk?nYxmt3`XhQN5N{YPLO4rs?h_#(NcEn0ss%43i>dEJk$ohErAyxuLrU*|`JVGV2N|78Vv?@wTL{bK!5-fXF zoIMkbk=g#Jny~2X18;#NEayyRS-=rnta9PFH2tIBFPU6(wipJn%G5TFq%UD0p2f7- zxaUVNM!>ylCp%QBD}x(^6YrVwa#eiFG@fw{gdU9(Hwq;ZzynCXJHK5*pFX*N!Z z6{i|dZUML*dkD1Yns7!9(5Dq;*2fo~kSYXV;adP9SVEq1VtzD8E{&jA%paAN*w-v> zw?(RhNqCJU&)&{qoJ&&f9ndHOIii3;Rtl=Cwa}QJ2&(bRY=Ivn^o`1O9bK&*xdXef5d?)5$ModxqK*95Fzrt~sl0E;rV;FGqb|mw4+d*2H0QE-$RjAY5)zZ zX<+Y^I9+UL?b2;}@d_w6x3OVZfzkgY%d|ypls8PF50^4o1Y7sA+Im*&tdS}fW%Z`g zP*L&EWsym-k|{2di_p(YUXdzc6ouBX7670`1WsxV>uj9lW6aF_H%l|CNOkS0X??tX zqxc7QoJkhBe^+lMKZP$VuOceJ;}G=HhHcPWm8q9xKTfrFuIv4J7yU^W!-*=noLLWQ z&NSMX2+l8|Jk3;!;<6pbukwKmS?8ZO;6tWMZ{NK5Oqpd+L~h%bXpNc88Y34XkmZ~8 zsbnQe%q3`|Kxi6J*0zZ!J+mURUT5topLn9p_ERaxQHt>lZ~A0SIM6vRK~)+g>ekZu zhmtm3LYqYBIMq;N74qw-r(I=5#?XxSi=ljmvAfAYAhPrg8;unkxtp?V_FR#|75;{* z^flqOgh&hROlczoBoTm9i2!~mNwhPOd1Q)@6-gqY0=e^noxKyx;03zy#8ATo6nzVw zUVBj%IV5IQp_lBRtkMpqjJ>~WEw`+WA-=vdmHQY5e50#$tw99EJfc%PtCVb@ zqQ6Am?o86I7xd{UAxnyZME~9e15O-}pkO?zS}%gDq{eG>qhE*?p@8*TZ_L}sm`0Wh zE4$wL=wi0-Nc}AirmKn$S?iQbbGX%OUcDxNO22cC4_1BpD3Zb?6oyU&p$XzGBIH6~ z99e>5#uACI`D|QAy`wKsY8XTkndF1!sJ(fr5{M5(0fho^sznL5B=6*yRanjmr z+pe42GGD*xenXplm|09LO?x`CmtoxY8YKpF?j^Id zFdU0-e@ks#%BM~$u4VV5VOw>5=TxcNVl&mA>6}5p?W1J6Tcc_R*{MC^LR?TGVo3~4 zA_V_b7E7TVk-*DE_6jhR*Ve~QlQ5AtV1N=nc%#kVl`s!qy>d;lK9Qqf>n|DxEpZwu z2PBI66^lLVVhh$qmZyG@X)cGb8;t5w$vfcv7E?ZM{jp(!TY`y)Zw4uf85AA)Z#93m z2*64#E=UHD{&y7{|FD*-*1yPQrF5bM2I0&wedt3hH7bGnvydGl!2*9bmQ}?b#yxw5 zNgQ4bQBD@@#u;)ay@Wi`i0oR^aA|{CZ+VkK?;LqnfXNWcN2BeWz_(jLK>N390z^mmfYaCiQn+Bar<-X3fH=&w zoMpSMy)wLoGKuOsT~8IQh{;0gk#l~;5>ePBRlK4zIH_ID1~vh9Gxl-S!gcQhoa2{9 z?dqlCK-7B_v8>6?p60g`5&CV3j>to;X}`4Ybg>}8&b}E6ddEDySHVBWtlX5;#rA%r zc(m3VzLGavc$od??KkPQctP~(qh1PzGZH;dKZVmtiS=?S#TiK|j`=Y~Yq-p-MRItU zw$sL`$`$r(9w$D{gmEWV0FX*CY6`8LiL>I^7m-`rrARX`#l?8#g3c~en7CQSy-Eg4 z$7MGLioghvD`9yPJyn*J9lGoFeub?}X^j9anR8(IJ@{<`eERplKSKY$VF-yRN55M6 z00~9-e#4ln4?|3Z%LhJ?-C`oqc`=bva$t`aueZ3GG>gj%-dDxE{i`oYqD6M9MK;G& zNj6XB!A-`a=3@dQ6S^XWACf8sV0R4?765JEaWJQpIp4`7O*lEmD)CPq?qs{f`S z^1lYfRhJ><1yUw;Qee<=yZr@3;%Z$bBHS-dA6Bml$3&ky1$U_P5Kk_dcn zx8l%S-IFRnRg-F;E7lKN*x>t~N~{Jv0sE$`!_-@GOeh=QdVkBJ#F<=<3QdAn#5?wi zBvntR(^Vs-0GuZB<}%T2{Bqv+A*iky?HCbNwDp~ZKB%bCxv3C316?tF*Rb)$dEqFk zH94p)Jd{XW@Nh@rK7|7)9G{OlI&|w&+O@XnUHwdkGk>VcU?)WTy8snY?Vg;xC=4)n z$VIs*4&BzRbA@FGGu()B*VHkf|E3RtEKCDPwI*Ve)^LlrA6ERK?x1@UG7M6Qm?aYg z)>GhpUxDcV3qBb%5`1Qn{{^4q1TV^8PBk+x!_>ypLeQ))(MBgouSi z`}V~x15vXa)&{Rj!G$uV@Qah9OQA)AMl!v)H)CD@qdSGte<>PxSL=<#Wxc$Ah`epK z>G96s7xs~S=scAp?(G}Rj7bf|5Rii@>$(E<^)QqG)ZtA`qObF7v(AC6yy zVT@{hyeG1iS4HHIw#f^0_bsysbqS@M#a`fjkQNiYuuY63FF!dg=j|AvaLyzw_f}!E z-L>nT`6AQD9S^?!Q~h&w5ia=V#$4oKl6l|7w{K6rNY2OYF~^g$M>xh`F?MQmKfJY! zq47^|pTMQ~wqrSP`O=eeo#lzFqSTPdX;$ZubM5;jj60vLzNc$Y!(A|*$(%JLrZ@)r zg$WW0Ii!2Cuip~?oJjAMbE@<;Au$tmfDcX2=Y(d8bA#xZ9 zsFT03FU$_(h+q6j`6*jq6DFL`GQn`vpukfRdE6)T1pTJ)P3*Pa^G}Zw*`w+$oOpW? z)fxSv8R8YUg6;?#$!ctiE6-EuUSPuMo+PI z?P<_h#A|4|SccGrG1t5wDcMZ_Dc#(LEw3libH zt8G)PK?wdY*3+wJ+VIvrb#0(JFh`r&J|vsubqt;Rn$7EGQbX=KfsQH9#Ww6))VjB! zUC-35I=f!CA`<()RqGHza?a4<{{r23A#S?eSMPIPO8$se*o-{*{mp`es+niRDzWpG z+HcuVTICcWhpUkc$$aEkw*G>pTC(evwrihFy1vJ~%j&xwpVFm2$c}_)WFo5D%fL%m zf{7;F1XMo_WwSoUL zxdL*p2D`#-ojNSYr65v1>`S+(M(MXvu7iQlw|)~*QFFI%|J6SC%l|}t#Nho#%^5We zBwJj{^FtBhr~z?`oOS4F!Sx9<`)N(k;o=xNB;LzwiQ1z|hZP;7 z!(6OBDU}tN@x@P{bMox1dhfy0Ljzq>j{3);(p3_VBgXCd<4Xtj&0G6FH$C)^;pfZ< z7^>a9>-gITBFE|2iy7t>9YV&FvK$Vpx43bZgF&){c8EQqL3|brl5pJxO02?7bF**= z|L&Uo|K+R^i`?DAeU5lupw<@+nya98ldbA7mn{w2p*D6R@5LEa3EGA>HUh6xMV_*P zN&Lz#Md_LD%<0^hB(o`%8IB@jGv6)gDLH?hknoDvPPVYPty;djI2#m@wmxw9a}br& zSr%E!Z+48RSC%G+-oH*(1ZFWY{Nc-b>;mV#oXL!F#fB`Ave|OKXR|X8pssF9khX~| z+W$OC6CcklQIE{9?yUqd?`bfznxv`%$5On6lb}c;JQ_%nZ#PY#J#|ce3&i4G&;>SU zypRPnKU2yUmP?C7I*N0LOU-^?s!?m=gw8dZ@`zh=kg@#C&^5+Mi$QmRg0v@bO5mh) zC32JHc5u-1rYMaA)74_0FbQ(XAAhD)0mhk4%y<_2TXWWplH_99+#@iWx|{e<%B1ypL0mv$(4jNdye znezs~c2^VucR#1@#zSpVUCEuME@&Pg7_NPgj!fLQ2)8*O6P1IGH4^*w}_C5@U`zEn= zUr|ein5fz_QnI8aP#q|_Kt9%x^IUE1DQmSSkIVDXRaPgZ}{Bs zSfwf zwv^3`A5337)(ir%G{fIG(VTj^G460c{wCdm>7`pRx;P!7CsocW=fmNew(t0l(dpSP zpRn)G9+5*PiR3+gV+ znP26tQIn~6WDIzwJ*VokE2%^rW^8q|@ekrT1kafilufi-ZpMQX_WCn3h6hn?iClZ} zPEWbX0pv2QS#CUKKX?q#6CF=t>%Z%~_*7Ly^$M`G)ETh6xB>Nicq#&MsMTbwwVFVr zRtm%{zQgMcT#5S8hGV3 zoh5cd&{c9r6olCAun9U;^b!4f_ci~hir;uH%e;<1j^8a(T;MGYVU_SnevnYw{m;n4 zkWazBb;;F)vp(G7i&8@C8Yk z+@E`@c^IfK>sS6*4w(6LQ!9q9AHUR}xAaBZZlK7X5v?Aq$ntN`kyF*0{reNzoBQpQ z1OUAw2{%{`mSUH#;gC<9P%wu}trf=5VboxHe4jOhT;GnSj|nW!v!;*W3`h-m*==^m zP%)ZHh|Ic-twTMAUfJ=r2>WJLix0lK&pwt!%vVINR+JyYdHU&S|2Y!X(OY2Nb2=wF zcD$PLrh=oB^|S`EnG|k-6*tfnis10kHrCKKQd-Tw%+ugAhyT>Gvi`myGCwNPhW#mQ zpcc0&ld(AxICJx|i@!g3C1+prkZ%7|A7+L*1K78DbryI}M$RJ^ z1!Om+9zI4Sa0vTidK3AcBQtN)k%?M4S1*peQ8jx@lR!CI^$VR4@C?pcX(&EP_%ksV zbmSDJa!+!hdhidC52-Unq;olE+mTU9Rr6JMrb*&BF}-~;rrf5HJwDxUopI~mo6o#f z@w;Qn>16zmx4}s}J-(xyCvjZpVJHF4`H%I3uNl|FsqDlRn&s*{U(-}K`C-Q{yv#Lr zFUJrnnU1?82Qva}7Z!r!)<`ST>x>B*_?A|@qYY<`@H|$yhslxJEjrE0|{w6;VuRc4n8+l%Bz=wBVGyf`pO&#B} zwZkFs8d5b?Y0qrC%A9Pc&E-I2^84!w$8VXqrqu05SjUH29R?&o(Jy zZrx;+D=!eKR3;NtDsFiV>e`U<1F4_bs&UwDyT$inJk~~|KaT>B&6EzYscg5fdA9Oj z%WVI=`JwLmJb_z>dz^DXofoOW4%A>z|F01aEGe#Qe!%SK8G0h`HfmhG{Dor51mR!f zyj%8d`vrNK#`@`S2(-PLJx;inxlU>xHBc7o#2jx&X0kB){9NZSqoUDkm!l>=s8*eM zV;CAk+pL&>kGn85RqKRq#A@HkI;bwe=$++vg1&~9TIxYWP9522oOB53;*5s&KEoxj z&XIIe;X4z1ZDK5_PAVl)A&L8jNsF0I!$QJ?-M}5veVk}n zkd54GI4h%^8hTW==9tabP!3AWxgo%nj%?Mv@`CE~9quE=KJz}*6q?sS&SMc-_urlf znH%K_&65BlcA;H1m_Z(oj-7QYCK?^}ocEjMNsV`Rz2H#(C!m}s{~CNoYb&3Pq5^h4 z>6J@cu{$?bjx^j?Yy@QTd0q#&XDtH;@NRXMWiQvB)N?m+S{x=iBLEeR@CK1CC3=Js zdoyzlnBJoZYxwR5D;2h!DrX4`5vDWPDBC1t^ZC^0h!CY3I@Nr-2EVW#y9v1xbE5j) ztFb%I+eJYU8o|hMj-RZp5bH1b0=Q8c?h5F^5g-V=e?gI3lDuUvWK`vU^}+)4Y%l`vuCdVv&xXucl)RCzGmmq6O)N; zw~Eu|^4G1PFAG@r^2ulQUmbXB)OXZcc620mJBHyOP9JSO+jMUcba3qsB66)X5;C71 zg@mC)!&VfseIjGLqiV{xYkd--EGLsKK+k7;4lVneUwJpbm~rbq5==OfN_aZUH+6G( z?J2TuYUeqa(B9pWa6vdx`*tGlbNWHztL1~a^V1*mo+h8YO|^Bmpps#}f(y(NVaMCQ z%S12zg=p0P-zOzbZOE02G;QU{m8QS_Nfp|+Y5mttzU*v&PyyGh5te_x#cArX8fA9f z0xW+^^QBfY$Ml_Ajr#BY2NC}*za!%)QNx>Z+A2i=kPQ08{a5ucB(LBuHom{Z!tzm20#Ccg~6kCl=%C0tjssBigp$Ty1siulFIHkQC*w z){MX31kG~+)%+x%d?|(r>#P#T1-cjgT4X+fm)_dI=yMkR2WCYU5e-acNKGJI9S8@A zB(!nd3#vZ!=Zw~WeE#KV#A%rM>;vrN1N#Z}WeNN?k~h4aR}&4?!il~JBK#%6agf_v^LJ zWkQgYoUWv0oCYhtvqOqp8H}bY1t?T^DoM%`OWNLzUk+hgR&YQaLc z%{rms)+CFhB!R&U0W$K*WFm~iHs#(*G9jBur*F>=8Yi|VefQ@5OCaOS`)-O4iHO%=pw4k`5&o2hoQzE+Pu-p3V0n>`bjyt)%~4o*j<6!xZM4##TtCfw~cfXXmD zXq74zzk>SzA^CD6+j+N+A{-Y1Rbyj|qsyYWCKLOAOkMR`l+o6m85m|@=$N6D?(Rm0 zP8qsk=!OBLQHGN4E*(HhK)M8!mJUIr)Bz+^P*PA)KEC_Qz0dRh0eh|Up7Wfu_u6X@ zx~koTJ!+SwKI`s`>aw}%R*duOR;IgK+7%hf@3O<1xm1-2lPjbM&u02{uTeJVUR91- zF1OnGgXj<1eu)~PVHgYFg87|-(C{o8xgv*zqReormp!YMGIAk5jEyekze;XwUxmEs zE$|03XamX#ddokB$AA0vuOyVtDp&#?Ecj$6%|a$W)tjY(kk7bpQS&b=fuXAPw@n zrn%~5nE3Q9ruiGb4ANKF{2Rci@ZI=#j*WcH{@2TO$#jA*o)k#0Z|lPMBLf6pcG|>0 zcfN^i_3n!pco`k(2WUUWHW=LfBhB)ub0F>IONX*diI<(P|2%)w*Z+;C&1Rah62s#j z_J&+A@sr~p=7_;F$4*G!ked83&o%2w?~2C{GlkY+wxH3-;9d&vmkv>xG^h25>z0O| zH~T^Z(*aWHu+etDyWdVcM=o{G1*g0F%>^U+{IAPHuV)PJ&%Sv%pw&_0C41+7|KYE` z42V)^UsRuiLYDyeEsxR&wm;}kxVc1MPf$cUli!AX`GJrG|@KH?? z3ldW73=CE74^#+P{X4NUP*eD-`Dg#ci>|M*SGyyM<~{@jjdaZTM zw#JDrsXI){p^&mnA(# z0nTqS*|fN|Gi}Gyw$p+Sd-PnibGVIa`dcfi;|s;yjqAcq_KBZLK7x`(eD*lvF$fSb z5HASz|MWTrg80~Xh=1t9*>6VvepGsYZmsx&ZRSw<_F-7^r`ta(Zurwst(1jIBzi1o zvDV`ClYyZJ1rlycn~P@q2HyGwtrczRo@71(+OsvlfT=H1t1cX|UF(&W>@u4*tG+$N ze2ojkutWjB7L|$H%o7EYlOrBfrvf78&ccl~a+_ZB+y#86nvowIZU_(!VZOl$xBd(} zTztbq!w6AMlP+my$Wjj+CCwe|iMe;Pnv={83Z~vt!MWJP4)NwhWYyP-lA6qjP4=Tk z1~VdArKH@QazQdX)TDV5GnL~CL&qs^#+8;*-T?cE6euQ;#6rtrQT&Xj%z-J(Y3*~$ z0(_EmDD-4CuZc}l97qE*TYDS6*YrleEy;|yk%8GevnF|o77^oAvlYu7(zJ3=5%HUN z_!fHasc$^7I}=M0erYxOYce2zm{4kY&Amb@>8^%Q&iur0`H}f&e~rI3SCawsXF&#r z3tv=F3G$V7>p0$gK4PnMi;v*~4}yio-DYJv!6|e;rQA_CSC)2_n~FV_pt)lpG0I6X z#88Y94I0xXdeX4wAvQ;^%~tPgD#5AowkwVMX+LdeG0`DDj=xzSrAO5Fpzvd{=L!u9>Yjg8D1mD zkoPGjzQP-a?E#vwPv!2rm;2qn8f8(>zP#8Fo2u&!QTC~r^EdYA0w7FC#^1eMdf#u3 z`LLcBi1^sfmTCL3WBzTiuk~)MO;C?W*T&g=+_Rs|1NXWNUHI%A+47sLDwVWj%K5mnJ20cc84BDcp zfaVkaei7x@utm27LNfbNY5!oNu1Y(XoWDkatLoP&@1cACagk%YsF;9N^jh#?OpHV^ z8{E52L->2E(vQi~?2@5w(mnUMJERs+vd|ZVor}Cj5jR!LY-_`OD~nM>BRg~#6Iuq9 ziLq+d_7u4t&vLQ^xsd0$f9ex$s|j9uWYncJ91PQ&i>(=<{VoI&y*hT%~bDYIhxDcDSSdMHu^TzADO zx8c29yrL&aO#GgLujVv(y{%-V&rSMdGVA`Zd$`uuG#+&;A_>`g8+VuTZ>Hhug}yoo z)v^0(i{|wFQ&}Y!>YtR(ijCq9vOFEjSUhUrqt`|h#Va;kKdp718xK)e3c2PhHWbOF zZW~ugrE7}_IU_T|&cbYLshEu9F>3ty9JFXg6^Y(uBxa5kxf^BU8)-yRwy zYZ`f8&M~gmz#pqUV2yL6inJVK?;5n&=eELhS zXWN?fu~JP}(4~aDrh?8pMOb|n0kg@u6{X50PgVoS+|%cNNqlDjVh3kc&6lUywNicq zBvtDx!k3+`ojkr30$J7JIf)quX*oromyADN6qV|F7-q@F=VkoVdn5HD9^GTRH@wp8 zn=b6jmzwP$PttBoV<46=AwTa&*6~HxGhm+S2K|W>)%(@XH)x}wcj8rO>IU;$-+_AM zi@R;X1iytWJz33zQ-oAOKSlkaKIj#8*`e>Q$2L1DROPYaoU3=ZkaAG2zmKGN6peUw z8NSX?{GbzmBs+Ls~U$n_pANs3d1|?L{9PP`K*?K*E@|S+% z&|q4bk=tdMvUlPzNHmJIY@_M>N*pUEbFb*kxOq+}-<`SB^44u<;l7n=cccc8WFcO( zR)Zd#q?tTv>$X#U#oj%}s!~kotadkV++6<4Vc?-~!FKUd#9m<5B6S8xU5hk7Yr|^W z5d^v7A4)dw*r!ZzL9ZYp*pGZJewK_H-Fzup8YoDqjVArtPU=MIwx&q{;vn)W!oE8S zzG(Osvez&rV)JN87(bf){`PK@{)JWu`|#-7`S;@a*!pQZbROJoA$cw7v9xma9hS!D zR=Au3F?#`@Tq>v4cwR{oD;8S67$)E<`x7k1aQ1Qhw_TQ_k8{Bb#uSi)v8}mBq0+U} zh8#j8FCW^RhNS;;xnF!f?(nC%26Fd%EK?YWKy+x@;Z8er5ebA3`6UFaJhJSBZ4tN1ltB#;zitPoD`m6UtFDBD$++MsVht4xK;_kI@cE=m;qt&}pb=+gYN#CbDEI(La15fWvIShK*Y)phLA4WIG(|5|x_ z=aOPs3Bll-J)BDTR^-WXT6S-)9))W^?|v&K2A^;*YzV9x_Hy&yhyAZMH@{^#2Y%)r zo>ZDlt>6tKx^J}XhQ|Y1NFnxYh`<6p`R7FX@4ZZ`uRk7#HCsoCxI7TJ3WITP z`fTL9sJs5QvH9ZH{RZ$qECNLdzPZ7HjfGF%4@dM_1jK~_aq#}pbpP1% zHwEOnm;yUtRZbhRgob+17FM88fIOX*$c>dqkm_qCil#P;MuAH4^qA&-986&f_U2$`_;}^q z>$+&NnbUs>qz`o)1mCy~QYT;`yjwKNTO=M-)1R{t)eDGdF<3&Y-}m`*>9`fsVIp1U zlb7Ep3xzvMG60Z_Tc*K5L$JUTerr}DOVJi;UYem+-O&K%{Tk*YZrX>G1Tp?^^~H{^ z(!UsM`!ELJ^-J zwc_}FiY2NG?&?X^7Z=Jfs@IRF_5!fXCnND#vh@YxUnhjc$7J~#8GNcYgSGkcf=XBq}q~*R*+cIVqpPK(btqob3=wnl;CVjvCG+963y>tv9jrB z{PE)AJVE>zJiO=_PM;;?HVE@MBRLeeKNc6!P?7tUBUfs|4mxXlWN7?iRx~-YZVJ-_ zU;GlH54BbKThb=et4b7xr!d{(2+HMm!jiG)lF@evxM8U&vk-wmwM>c5gLdW1Q{^)n z=NsK6?KWK(?UO^^bm*ITD1*zdvhC}XTQ4vbI_?wr3p`(6C5r?tzmp2_DwZsaRlAp6 zVkt;zQCI!Tl5#!uJ;Pq+WXZ&GFfnnUOPZv%w}07@pu;=ymg1)|^^ zfX>*c#E{s1vcUTybZpP_j!zA_hfsQ-jCi!k`&Bil7DZ)nMlB%{V229GcQNo9tKtm@ zN;DVQaQLOl@X)%h<7QkeNr0WOR zv=3{URD=Df93ay=-BOaN<2KRp5RCv6main z_%Ds=>2Ux&dLe5J@*A|J>hBUxXd6kVcR8Q-DhKrVt2~hIXGr?pPhWmLq4urgwpouircJbw%&l5AbY_nkC!9qqfd;=6$cF$;)}k1&6aAy#1%y7}(N^xcsK zRRO$z)T$2ml*m_}`i_>tlqq)-T+Ok}G0GeYOtZ_J30kvQDgYYeQSe`?4p4z9k;F*GY)TPScrIfbHKM!)Nw?aZ) zY>Xv1>I}GNVylaY)2psiSYMmGx42-fw8G!#rW~F`_EO*rBjJ;5YCHU0?;!+b5yi{* z0<2=<50MoAaD1(o6|ZEXMi2y1*gKMzaZKY|jRXpc&BW$S4?oB#-H=<@LUK*+v=L16 z&2gT7;?1$6sKahx!1E>&H#n>}8$(A$qFfAwJ4|5xjLr`Tmvy9~%rXjw(+I7`>Zsh> z)}>U1`MbZ+I0l8zg6{AZC*X9j#m5vb*vBVS zYBz(3h@M2m7L9&kkR1RLhIMWNA;Ym0{|XVStcV!NB*UEKQu-(d3sH8*knR{DvX+uEze$l(v}D9!|4_8{sz)=AZefQ z7ZpW<2>h}bm~jB23de|Dmf;u=1{Knzns?$>N-wO^m`)E)4P2=u(k;aBcs`=-d)a_dH;qN0ZWL+5ojCnN-pQzWEW2* zE7v$Fp2udU1{ugMNcdBovZhPA7jyzHSg_4tLC_XF@QUB-H|nT}B5aE;!jv(b6%v8Z z^gbbJJCXh%A|!SSw`7F4(D4T|;=t>*zRCgVd|3*3@bC}4YX_aR$5R~JNiDC0*F zh0{BJTRQMj5sh*TwJaNhR}2DE&{GzAg9_F0q@by4r~|AKlhj z$%3#nvn?%h{6qc&NKS%|CopdgClGrLO6BWz_{io#ZrUeR~bzTMb(S2?b>B{;NEB*}2SrGFApU?r@S!|Qj0r?wQPEAgV6rGA3W}?gJ z+J{af0T>n%R%Rk1@`7KrlVxII?>R)FQdAU4-_xbkr25BGqFr?uB|?H4oYcOw%}ISA zql+0z-9(K3{}%g&r_%^c9Y?*-j6YPWz)K z{N4+CAW2oD-ub7DR^-+tPCl|Q_p*zEPP|R;lE`@0%9$A$yJ$sM^p?*%8`6O<{}Djd z5lQu3mcegm@OAF%*^*%nX4vn^6x?=7qxlFAFH)VBeCfrFf*so+~yaU1;nOHYQ% zMV0R?hevGc%`gM-f{%tEZ|y#?4A1y(XuRt;?31&r8q97iuz&kNaDC|$A8`7$s)L@f z_8K0p82R}ZD}(G-TcqU{LucmxZ-rJYanKgYXL_I+5*CK;gk8{_4pA9iPVowTL##fJ^q7YP!K$}0y8}1 zpi@B9*)#o9fc)$KDq>GXY6g5--pJF>l#dazQ;Fnh)v6edHUq++D>FpJ(AfXxmpz?g z`D9@BWY-h-9k~5;FoqDusF);Lp&FZCvI(;Q+!?mN=C|%o5~~_dXI6-~hKfI%$mSG# z%MmJEJ(a&!(i?TNHuJ8gSb{je*Q$nugpOZV`>J;V{2c6xUL06_}h$PPYE{vjpRfA>E1XE6JkS)K;N?o52l*iTwT1ZfWT!$vGmvjh*j| zU4Q$h(u@6Df8}fQK~)MB`RBVq<;3vTXe!NXNG+DYjhL?>id^aC`Ilvd8k=M#SG^)( zrNy#dB6kKR#mIkoIOG8VOoNZp;I|{6$tTl#;mMNV$@hQFF@J9EteuS%-8sX@$=;;|L$0DV-T7vr>rF0^?* z!4(hFMOUc%I)iISc$$8-Rb&v6>pZi1$#)KA*A1afwYWcPJ$!p#kC4>BG}*Vy z#D4O|wfp0vhFu->bIuGg=c)5MB{KDsxn%n~$2F(`V^Md=X)>PH)c9Q};ZsW>Bd_JG zIBdryGnNELsn~Loh`5tuh7c)e8;6KAf2ORe#|E#fQ7DdT3NTq;8S4r4wk(baMYMz* z4h26P)|z=LXt_dadCb=4-rNH8a;ST`5C@NL3E;Ob##zS>0u9_>i(2 zZ=W8#a@S7gMTqmbi(8m%_M2S8d(}Mtkw1bd9m>yjHbkEO2G5wijpx6f6k>wgeNLQC zk*XYR5(-Qei8s{LNl5RiPH7W)rK;1q4wy`Rlgg>qJ4Wkeq~~3B-f~Eq`q{WgIwfuV z2F4`5oCIdF>_I<(P4mw@{s!GpI(`e0IyS2qYgzD-mIpNu>Fb$!2|G+8c|5w)Mh zARE(4W9a*j_D96gyU1o}*w-)qw9#*sc)x4RVF?nV2@3ctWP9Gpd8hP0pI{L)p|)rW ztp`QA_Z}}YzAtM8`z&nOZBr8n$yP~C9)ns&19|N?RRif1=*dxUV7j&{+OAZ?bj)LH zgN^f1fVbtyu=jnxRSd!em*FQFkP&)gEn7Pd$Qe3TxTH>iXSa8VXp^w^XB1xPa_YUw zPmTPZlP|;%Xy}Op+}rORro`P81KqYm=`F8RDN=X~M6OzjfG3sWLRq8$8vZ)`|Njm%ksK;f8F4{dSoktj+L_8J6+Dwr21!Xm__u1e;RNUzED{o=|jF$6A zI_}=91Txc6vZmvGn1e@!w)sXs?Mc~doQZ&A; zew&M?Cy5;g7j2EPKk-D^7l73wrcqzHFA8j~D+=8mpHcG?>gZ6%=j7Wua^L8b8AJr) z2nQx)^Sb-R7)BH%lRuvOq3*U<@FWFB%tO9%#?qd18=5iAX%54hQD2U4Z@oCPxXJ3L?ul2XMfF$qtXdk?I z&;%vx3mGXq9M)HGDI`H1s~sBU*{e6^QdO}xu?cQfSvj(Q7#4pU6BDE&%x;`+U(x-} zk^t`8pFnMmO`spxCLuMZ=Rz06F^5$+=Xe{Czb#759(oWcdqzqA@0i34Hv?<_mWt4R zqv3t(DCu$sMuC=Ex|_5J`z?r*kvmRy4N^sxU4Z&`Ydh$q1v3TV^ za)ZEJC#I@@pV04{fnf5j3y)*~NmjaGJ9}M42O~7wZ|l~=bO_QnItJ}91~va%iDfuP zOGYfId0P>zkWOaPq~Hm-{E!lDqi7!E02Z5L0HTQS_GYQ|XOu|Pk-t^?{wq?ojmz*U#%-lYk1l6G&c`~qi$e4}`} zpvDx0E|HqcjC5QxjReHqe{Qde;vu4l)yJ-#ZG9vlaU@ zJe$8zXF9AAj&_*k-PiA3(!3a5RhE-Rvwt}K;MYL%VLMM-45fM`FReB6YddJc0F$s1 zuHielota91t}jzfsCyj~^!@tOD({&1vsWBN4b{SE+Pk>6)gtvf<3~c0?`-!Uxjm8V zdcak^>um5Qf9c7K%Lo0i8jimByi&@Im(O_?aVXOP9mgM0o3L(6=j!D99a8Po*DdU0 zShCz-UX!GvFey9K;4 ze%tpM1moBJ)OwSn7wXmcfu#4mL)NmE7MKb`b8RdAPjzw|Ppe_VYJQGhSB7pM%}ph|yDCTE0K#@*L#DaaUPI1KEXy zJs4CY#dSx#>mZL53@5G#57sHxEw7QXqp&v?_Gx(i-YEK)&NN2kRfVLao5RdQb9?QM z&$8JB3gh!)1+inE`B(wX{IeO)E;4d@irFa-2mcz~mcz91`Xr_s>GJ4q5}SKj+Ji{e zWweUwFdN{#0O7aRA2*ucNw`0tg{wv_YRvC`2i?%FNr-GZTSDK6Y?|%NVKxf*Qq4%UV>5)WoQl37(3>uk#)Lv3Zz}-NVKxKMl00KhdVu<~-VeR6rPg z>$us$N_hbQTw*CZ(4gNu7XRGiIfsH*w}P+t{CAM{^)4Yz=eh%8LOwDMG}>_-W*mwb zv;?IbX&!+tt41a#p(WOK{4)$DDK|C@q2B|wNie0bX<8n$knA`;KZZo9!4>hm3Wa`F zb#UphI8HplC5qy47{&cd3T0MOP%8yskR0Mj4yGhmm!;B5N@g>1sa#P+@1qRNmFl=H z-QHl$e$rZK65bu-c|;xKVC0R3>ZHZq$WOin2?o0Zr3LnR;KDelV2~5*K};tO%GD#9 ziUX^jL0~m(omlqh9*(wlHv~>uVHsn;EH(6rXSIA+nkA@k+8LINq3^s@W~?Z!I9)G1SCEq z?7w&%J!-Px;=6f;_;}^F5=$8;3I(;2k)bIM1_3iC8O=PITH#bW6VwhP@jGBwRG7Wj z+1(c|DM=StRt=f+{Dh9c8!(G`PC`!5(*Q#!Q!AbcqE1n*6_i0ri-t8SynGj?^UiE| z`LSKKQ!hTzGg~imlz3NNa(~%Oa*QZ5)|Pw3RysWQdq|u*r9UB%>ac~v36*h!CD}Np zI2;7v2FXk7lQj4!uM_jEx|4T|BWGJs3nMPBco)No$Y%$U5O-Jnj>wLhx6K;wv)1q# zYT2ho+Pj<_edf0-0gh5JfOS|!BjmVtNIx!+vriBsD78j3*gsgnC3)^MNa_QhK`T!Z za&y`8RLT{G-Qp9oY#!(S&5HveVcWb!UwMhrCrFKXo_;+jz5$d}eDMT8xvO=&gxjP_pNmhKlTPz#${V#s8LQ=#af&E~%D&h$tH>3ErtW}+J5*~7o4&Z-;V8RJLpz?CilALf{~FvHm$s&vV(pHk><7YeEB0SJrdrPoCOi=|bmotH_dW9@94o8>XY5-$cJ zk`>FjsgRL@|JZ>SItvzT!*YgT)(Zr)(mu?SwjHs^r+_jyBSgfK+zJoU5(gH0L#(j_ zism#d?JfrFZ8bFD3Y7vH%~&irP4m$xO7q(_PHEA~z`W?L;v zlAa)k+aM%}!QW9#Mys}e&~NA$iLaPjSv3hpoj8wDQj|vWIaYARoWlaI{k7K7cGZn( ziugenv=78?H_BLs7p)yixQd6rD{)VHNtyDp=_-Km!|x_M|BE8+7Xob9ed_9)pLx`* z7$Z;G3n$Fg=?gQe=B6t;66>h$YMJL4Fh%_HxL*v(L5#2DJfnby{;k$ZE!GkkV8-Jc zkACaetuRif&^J|)$oTyA;6m$m zH_;GkSNpRzY17BmlA=NtDJ;GSQR#YP>J33x7h_p&cvhrJ6fTu%$&GVGtmQXEd>qCt z?vp1f5S{(p9u>&c#yF|M*R4`lHspw2N3wi|yZ_AJ3vV;eZnH#ohX_b*rF0WWMNT=t z=t+FB+xns`qB;Z=Wm1uHHI(u>uE=n?IlG%fH;c_#)3LmPLktft>Xb-$pV*yc@U#Is zhs|zb6^IT%#-3?(@vHo^?hEdrw?J2{dU>vw^z}{mt!?+c-p~=#6DFXyADzBYI|{tZ zH26Y8Oucch9T=V;#ol3$>{y%@Omi}Biez0KH&KZ#RNO~$j}iH7)kLAwo*$EtjFyS+ z!svbA|2V|DdF8kXUIResv}N#Q{_ddZwm+#tf4{vJ|NP8et^Lztd@W&599M#woDhT& zoHET~?K#pzLtu#>e(9?1y$2Ro^!)prME-)9`A>yv6=Y?BTr}9JpaEV-b-!bm<9TW! zxjbTETvUi*%$2IUmkIoPdZgrhq@}jou@Z%TZ}E}`cQiHl@vMGEA&W|oH`qtn`zPP* z?r^4N@1Gq|1VW_~pk=Kx&UZ*=eVi!3jug`9N&cJY?dt^V&aDVtBfre zn4P|Xn|>v(3Lb-`A5%4z!vGO@ftJ413{}=CL3pU(bgHeu3+WttDTnSLR=T9jrg;6Sa* zmQ$Zk72S9Uhw2fhp!Mefu&)o`#Bg>4I*H;o@u}`+Kr9=#*-g#V%p70C8@4+r&JUir z8b#>`nS_THU#%zpS*qVFO*zrcx{g_rOIMHz%)W{K~H zvzP9Je6XRF->T30qwu~+yBh0 zw5G90f4~HOkYQfYBcW;WH_A<1u8kTsl{n669x-kd^LdKZx}aT~@^H_UV0itPCDC^{ zyl0X5;p>8?HrDdBKizA2y7N)q^MV8QBD2y@7)x;_t_#!a@$7=!KKvDD#6kP)LKtA= zk(~DiLQ#cST*~KW41&P09X&9&1QQw4x}n;-_F;fc8Qxet->I;V13YRXQf>N5 zbVFp=jMD_x-yzB#Cm3-?ve~~y)b?&80uS}0a%yj_#f!@l=zcPVT)n`$@n#8DQ_ zDEYDaeuFo8?|Ln}=2w!f;EjuN-`$Zc%F7~{mXq`3Y_R4siFGl+RRt>Iz&F(p>p>!W z)@9QSrhH%gg3xC*`_B(t-x_*-z@^hXD*ZsQaMvBOln~kTN$GjmKbBPA=WyfZr(0HO z;`9-WW-TMyPmHaVumEe6DcOQ%iVIw^1*%PE@<#oJV+AG`km1fs{w3M!!PWG;sE_+= z7yIBbd>NFcKZ^U6V9Jmpj`Yy$!)(8w?-HZ(w}@ixe#$*+`92Rf!dwmcgn%?9R9G|s zp(sI6@sa$D_4`p40o`YuMb=k4Wz$zJtoG3G`fsbV$_VhsxnBGDzVABcSkz0!K??hR zek@kDba3SG1)Z?ozshwQ!5_xo?lW+P%d)bOQWuCYqyLdqhE{;da8NnQ@7u~0uA6|Q zuCB$O=vP=RUXO;qO_i`u>9Yg--+g|(%=qzy_CR&!4*I?SWz?vVY`zJU&g|eIb^5gE z4T14;z;17$kbGBXiQH<(NeceN$Z-Fn!7Q5f#{C8GiPy~PBnELKe;yLmH&;%FynCju zeD;>g>lwp@o54gC?@YC2Pu*(&6|yvD_|RbHvkO{kn=nzf#M)@s8@GbE+WvM z+$%(pyJmMu4a2oIsXMB|?!l9vuiJ@%MF3}FH^uJrIz3V3*Doygtq1vAGX3s}jOuS$ z{T_U9Z#VDWdtz?SlX6WtJZ$@)gVB!@>*LivsCjet6hoBtX!5c;p56(OaU2zHYEStHG_~q6`aW;ic-%taW+@t~bWHqw@MQZtnH=s9OC}(i zjev%|l|`JOjRQYDur>Ai+P31?JO$+q;SI^**gPH?PfRE5vbwOPmPEyM|Jej)Q!b4` z>Q-kuv-AHt)AY^H6Z#B!XjC1#9yxrgGj*}Xp>B1iWi0B4w8)X#c{cTU5e7=0u?#0^GS!-hChVeKW_G#vI%4G zNx*kt>&~O|&-l;!Mj6Z!PcnHNZa#Y1Pd?1%czWv8;uHd|ES5l~V(3xeE~C9quV3%Z zfoL%*AQoMoj`TLZBMuAHhD-Xpsg|KmYL_LI8VG;OEND}H!MFBf6hTH3M~VbV%0-Fp zup->%!D14;10x{hALxOlFjKDouCX6~oUPTjkg7jGTUr7l;$$V*C5@>I0EiR)JoDG( zzW}wU2(~Yuwh>UxBz1r_RLNBHW_n^&VPelFxy$4il4~jBKreV{3VGj>>|&RL=3m6C z43;tsAn|Z}KO6ye85zsY*ok|a)g*w50=EnS>d1{pgWJyl?4 zyhcu1F1_1ZboOm#;6$(H64O@QAEbv$!}@4V+{0VvI#WKaV2d_mqx?{NV4sT_(t;^X zYM9^LxMH|adhj?#O3vquP&V>Ua2-dy(EWOLi^Gs@CFdSM3^Sz%>tlL1kC^Qyrz;Md zoHZ=1OWi9j!Ar#|CNaXBV75uOpAGXoq2o&ByEKlgsx0n-izrgNYxq zuQXU$ZaD=!2>Y=XivRQUl6{!2Nz#d(_$_CLB(ZIsCvDhzsfuJLm8S@arGW93VA#<9 zZHno6&lpb&O8&f>Cs@yuqL+3@`fv4u`#Gx&VDFrQ-QmsiM zIBN0QqI_e+=o_l^Fgmslyw*Z7klYRPs=eAj-dxr=$23gi=D0b*hBNCJDpRs^FX zqwF#U##q7$T$P50svyu3B=(H0wsVr8k7V&e`15vlw$?7={}x?5_{>lG3GDx8^8{;2sH2`PXXD`p%jkG ze{H?y1zJ$u9u48P3lcw969Lu17Qmz<=D%H)6m~SsV*-## zdg)|VHz209&lrR5EjA`PX?8F2Z}(Se_k(WLE{lJ+oXI+;r1^j_Ul+-8d}B4OmgQLZ zW6A8FoStfo3?*6->JH#MP7qyD1z9lEk>dPa5$!mpY=0)>NKTfp(8wtW?AvviUB!!Wyv|y>Kw=*>dvXz`VuP| z{%|LNUXD&jlSu%u>k4aH-Nsf2kNQfxCS&&8-mHa!!Iso+Y~Fy=KRg}Ad;Xxaw1a3r zaNO#^_RZ~_B}#sqZhVYu^|p!G@epDXn}8_!YMNOi`~>^K)eNF%97R2AHf!m5&waP~ zMpv1lMoMgzvD$n|=Du_u)mfujzDlyk2{vER7s%9>57QNlg+yvJxi~o{Dbs`c)^Dc} z4NMbqw{&$jbNQ>@FTt&aTp$~a)-4@Q-^NI1bl$(ObTl7DE;2Vzkr8*oiYBr6Dr$^MzNRXemDpYJz2qE4~ZCi{A=OAgG)@gyJ zU5QBSkLj2W(Zb_!MfIQVwGI4vY5qqC)6`3AZr)o~+9YM_(O-0j`mCU`E1m|%mw54jba_n=HSAxKF{ zZp-}8k#X^*H+M3)TjFJ1YD7C`;-uSh1}fd8YZqqy|COhyRn5QMv}|>e+}Sxl+>nkq ztr^R9zJ&h$BE}RUAYdOsOn-<6D{dAxN{EgF^{H=29lZwoX5DDt;FDPhwM{M|aSZF9T^Wn5RF6!zNA4uVA zdZyqziu}!{_b_G>O32HK$}gP|+x}Wb8=d{0@PY1h#&$mxKEgJVwfjbOD>M3^I@eA6 zwELsnc(ybLXdL5G?V|;SiMymHfhwzJ3k`?cJ|8N7p32LBB z-0n=oC3@T+tGXtBDN_NI8MA8G0M%^luRx?j&T=Ys+A3i6XGDZ|zfC7?%L#tylZb4W zKR8hygxx!&ssz#^?<~_$OM++=k-d6DpL$=5I@JpV@g1DpdxGU&gbg-OA zuaT^#X*w8)(59;Jp{mHWswkFK;7_Aon9M6pB0N@9y8XDMQCf7*=m^kXjnvPSf*)Mm1$TvYS=`@F!X5HZEDyb zYB(H@*)D5L<&rukft?jKUG&E-4+zA1hWY9tAxWB^Wtv`%n%@1IJ~NuWqnZy^HT|zO zG0(=svc>~k5(7!K9_VZN9%=^rXoW^;g{8gmkc5OcYDM;Ig+J4Jpx8B$H86Rsk=Es2!)M9j~vQV5|MuM>{b}J1I>&xlB8yQ9HF?J8ecgeN#K*P&@NlJBw5&n^`AE za58-p;spTf6y3}HT#QK~L}AMdHcKPXL^^;3Op(stn`DS1TBe+N(nE#R8iQT?HOaU3 z7=%mE^}v1*(=`GnaH}L-%OYx{3b}a!&l{&QUG6+1)Na~@aj)jxe$D5Z53`tVjl9|# zsU7JFgRG{r)OCCENP%Ib>yE9xhZLrR)BR~_5lPsHu<2KdIy70YhL%(Bua)Bz^`|W5 z2OMVxDC0b?AVcx`2|l-ziLo;?`Xkqg4?SWfj&*zbA%H9$H&NhpeA?jQ%rZ07%>xuu zH?tBy@lcjPjB>VD5LF((b`r+s3Eiea!FGyn(=yvck&W)mXo5!+s7AdBmN zcnowz&rLed#h?>02XoLQog)`*HvqOXTCcChML);`D{%~@v7GBqBLtuoK9L%(ks8J~ zCVrnWyu6-@NFwrDo$pOcye5V8ks3abHTmH`*ts4sgjtd;TH_TqCt-bcW~n~d`FLM}(uV4fZo zG#OdQ8e4rvDN4HvXnYeAaXDA#nVSn^i%jHSC(-lUE~FoDQ@;+wWliT35EA|ugB(x+ zNC%*~#W3<83>bVG3~EDbD~p1Hl;-+>O$Ah-Q(;g>)*ea@0Lc4H_3^Ta5jr)Ky_^kG zQCmKdbwo+3jz8K}^oN$Mkrr1u!-N#}cNcn1QimDJV)w9g9Whw|?R~4Dy@owS4QPn9 zH>HYpDzjV%CKkNHG{$VcUmwMY=hV)&>7AP>a(32=K_tD3H#26niM0wHcY(?tC>Fu|H1YC zKe)1RSuiq6+ae=TY!+O?8p=mc5YrrT)adD{LRkU7Z%x@l&r1j#59i(Id&znnTab6Q zzDv(A2<9o`qmuQ(O4Q5&5RDgQg=!O|8fq8X{dixGMcAvy{3QXDE2wgzte>X*&s-GB zS}eQp$)^qT$v`(rN=6eZPzT~1GleV^o{kX{W>r}j1Y5^IX&p!Fc3%v>CbK*EQlH>8 z@|Zg^V&|%dHMZjHk@?-QUU!Ar(wMe{FEf|}dbybb4N4A4BmzDV9+!Zv@Q%H0H}-g_ zprZ0$tU#a?kQo5KUBjFIU~R#xupuZVRWQh;A%G~oq9^w-&<9b!UpYS8b#Gco>gy84|V1mv&`qDIXHV?G_OmF|; z>LNx&)thp%;#46p#u%+F)?9LS@xMpS7l1hM?*Ed+ilioG31S6{MOS|xn?)-)a{Gej&AE}tqo+C50apHRx{#~klh&>|{& zNyZ*)QE)`5@Bb07V(LxyjER&5TKRIAh+I33g7SZM%zJ>yEkTU`^A`<*X=nv4(OJ0S zI|{mz%=By!LH8C$7Y`5aVIVmTIdjXRD5x=<*XZlyLIoN`;^9;Ybi!&3k;tuAtFOI4 zLSq%UC-NB7kwajf(8}dNEIM9L&6Sb2Zm@VR4@Bn~aE!#o)EUE6QSrezxIakaSr`d? zb~%=8ipZnTgf$63sfqEKBa${nuowb-=E;#Y5qWSVgI06`8PR-w#grSnw*pyAMwSGp9n8!f0in zrkTZKSn!`ihAbqM1#`>*J|X5b0x+typkis7646mW;D5K}3_u9T31I#&p*R(|AZrV+ z3hO zx(P?kARS@sA#FIIRt%!H$lOCasFFymLioL`NPIc=8Jm+Z6vZ(ySVDq%{C{k{heK1@ z7Bzlv?oERbZfFKf;DR&(ols2>@CJeuDM3I$QG*o03Id7_Y6{Xs42XaV5~T==3fLXT zNdz?%6%`R2OQ?!EBPz})?kcd!jsW`gY zXZ_n$dqkFZHt@S)fqR+wkW`1bS%9e`uJpxL`P(O~?-qV$$yWUl^=n>(C>Llt(mD zs+J)B+KN1x z{C~%*QS!D}G!KBc6f^(XlcE_CZzI&4^0v{Uwbl>+)>L84bJCXVXV;9SDwh^J%1HiQ zv`NrPu#C|rrwsuOL8URAfsD#Ks!$g;4e8mUu|2Ht(J|E;n!;2KelmyrF_LS#R28qSY#&T4r^^E%7+uoIg9Mbnz z(nOhP9$>T1s9DpyLcE<=!=oJ2_a~7Vy-4eYoio?ep!^o4=iq@COIY$9&`@Z}@ukfE6CSXO0d z|Et30ve$7(GE1Bd-iT9vm3Z>cdy^OKOdZT__H+qa=f_r*Z#VVN@LTOCK{|Fbp79@r znaAc2`5ep7h}dQ3JvtB?bdVBe%L5YuNS{(SxZJF&_ddsgEq-FP{_BF;!oM6B$mN(( z%4iAwVBTnHP&t!T`fcj}1lWT%fW`ld4XP5_GX>E@oini^o>2p^&S5%@1&c-2$Ngk^ z*3qnjTbSI04^=&QXxsy$Dpu-`A2X zW>Jni&3;k|s*UQjhm{ggON<0D?t{;%rDW4Af3YSn|0;by?Z)HywRvS`xm$#D{PSqU zMlF|;OB%Yfy@x^1S0Ss-{n*jC^u{!aa$zK)EKU@T@{VkS^-Q9DNAm97*?1-_#{36m z?sMwaJl)NyI$9rIX?yvwoZQ_C%aOCRNk#?c+{6d;SK?3*NE<6J`EfpCN=kkib&viH=AkQP?Kj<)4 zv&pP=0$UB{2a`!@s~j#LIMMEo3XW4#(QY3?OgTM!E+Tsp;)A< zq9mEo;~QSf#nR>k!yqJCGw*3L@10iCkut3nBYS2ut19O1Za z?)s7Fo9)U-cnjXt4P^$&-QnEp>=y;ytri_*I>d#MJC&!EI=b+(UN+;%{AKE^j!AaH z_Q&bn3zwk-^RHsw{pV&0824`|>4a)(lfem3%+PS|j}>cayB3V>dt+9*;4?9Edq2Ho z?s=Mfe71z)k>L-|$rQiX4W3_%xCPc5Vo1mKjHMZQ8S-$TWqZz{)tSJ!2r!P0lt<}4 zYGLuxR#Fc5y_$C`+SWBA*F02zt0jR#FK8F%%?eKSCQqtzt24Ro9U>hpfzbidDiI|M z+)!GYUR08<*0S##>hkdkbYV+Hxzq;7HRUPehfGhuU;QoWT?FR-v$7O?$+2bJ)Rr{^ zsyxK3D{PXeV}g=cJq7CbL&s=F^Rzrt6}16UdRsn=l5nH5g6?hP(CPT%()y9>nMDP+ zL;(StWM&2;RtA>Omr2W-6h0kv)=`oWfGxa8=HF|LrKhEbLHqoSR6A`4-8G97=h+vg0jA%!ZADdW{uU?1HIYX06il?%?{#^Ls z1OGBePdvyQ5{S<;!n2ab6uJ3S*i?VAC#=S6$JJ=_?{kZOTata9eD})etI;bO^?@48 z6HiNw$9ikEH?&0VP&t$zd?S5jK7Ecw(qpYtr}j@{3FHVZxb(Sb-{|mCuRGok-P?#x zb{Buhd(a z?Fy6-5iKs7=R?@;Elg9Jhu_x9%vmb8zfSx{E}a8xK@Y(8+v1T7k5;C(SoGn?KPNEz za}Ph?A^Ey(swVaL?fr~So-&`bPl>?T$=JEV$I?=wZcEifD;=qXoih}jACCneAo3C> zsE#i#ub;X_{M=w*aSFkxT`mY_9VYpLzqG0{2SK_iRba@3o3WEoP+wEng9IbU2g>GN zMll4!NV!~O!D;1s<0o`jsv22=v&TBMwp1iXAj6ig1>uTFY4VucZ}n4SUG9p@#=Zp;;aC zdr-GUhMgjqR4QWsQd>#n%CQ5Xj{J-WF89Mz$xWf{BzpCDA=cG<ltGM{ zgLD9}0MNsq9Gjbf1pcePGRnHoTt3I{i(kZo$cvGs1Tsg69O2V*iXe7E$<3A4qr9*M zd3`10&eAh|LB~ z%01g5Gp(1G>McL&R>I>P69Y0@G*2%YOC^vQ`WxE1I8K-m6tip#D$*!HWYLSUy2FXK zem_C&Px=gx|AGcgHrp)OT4~b9{zUD& zy}xGb;1Q2yd-MI@eLh^Q+i*@G@%}T=JMn3E%?n3p=cgm@-n_WCyIynf9tW!+^mLAk zgYyK~6L<;lR)mBdC+kb_HGZ@zcz)Q;!HiqJIrRq@i91*C}!=qwHjAT8$|iw+<14|U@6|!oYM7Z z+b6|$Ydo}*RFmOrtLY@0Y}>-n)j?@ADMFJt+gx|2>mev&4oCp1hlxg^#Iz(!!XKm~ zf&}p~^Mo4)OhQhN`^NQJ9`gFeo@|#-B#M)2bI=aP;wPgbuGRI9O=P-`@bgP@4eog{ z20k>al;OTE)}LL0aoJ5L+gQqr+4mzs92S?MvRb7;8b$lQC^ciukv>zrMKEHkSI z)1Ny^L#Q5IBaXQ#2`|Iqh%XdO8D%8WlCaD70gz4|c5e%+k+1bod5 z4|rHBDbaquL&BanQvc}|>WvDhEC3Exf zZuaKkTB1Qtr82{NQ0l(_!Ko232p)AI(`-#N0x>O`{DC-qhrgu4OfQ;PvgHN_nRzoY z=Yd8qvc*a*_`y`;i|g=0j?w3fBd%;N)o!xT$yarto=tAN2Gq}IG?5!9-WQW{Z&&|V zY&bAjdq(#Pv0t`Jby7dP9P5E(2Jc_2iqRAx%;Ck^FVP&e z14_GWy=m(nV%}_1O0)bE>a}b+SA)^*`@+cfE$ZWu#+3KG#T+OU|(i`8qGJ0{~`XRl8d`L0v8qjEjo z0!t@2$B=;3@axzmgbDnG)R=_#641@N?&va$qVe7Z)My(y_<-1wxObIB-5!4;(NPjV z#!u-DUZa(PX@zzbL~PmWHClKS?43KlGz%<5?Dgd?2pK|s?(*+2O)$E-JcmT4`t?fX~JGs9TQt$VOjt^AVM#Jdf#2@6#jf^K;>o zXb=7M%$@M|xYf3^O{0Y-)AW)9oc)4iWI_*3Q<$_^KZZU&74tzzk;NF$tPY38N))he zkZAdbK{1gp&fy5>0J#TQV&TStjS7$$xQ7(&j+oicqf-n-SMWDs&Jk}rQc8$Aa;g@^ z^byx}9R(qmns&TYyt@OKYn`KQ;OxN=SmrLFQLW=|pFh6YeD$ZF{`w0b*=oeFQL;wz7kjCg z;F^IFR)GC+N-_pZB!rmJ^9DI@Tct~r%3>Op?=CW)g^|_-E2?(CcqLkc)DyGrb_ZW)k#fLtG?}vC-6x5 z1M~k8DX8^PnzDt?%fWe2mA|9XnT+)&f5otvoBL6p(sVgdhmR8W?TPJtfVNSswM~-) z4vZh8J5oa$JDo?(=aX%aR{lcu z8~3vRxSHS)DVAu+lJI+acdUgEX`(|CmC69*lsyX!)y_jPFI)AB zbV+QEwKSQ<9TMDWCVT4B(zcCVGCcC>%u{;~W%%G(e%rKs2b$-wEPi!E(>wWi9cT?O z=BIx48zK8rSj&4isN>DoX-6QJth$~-6Fm9R#H1B(-8VY_cB0Wn?eQ7)zau!`$^YOh0Qn!q zv+kQ1cxs07p%_&Dou#S3-`FKnemvHiG}*tGFyuEU>+#M}<-U-DoAIHhU-5bs7!}l( zxEN!D0<-*OC8lsHaHD7of0A)fZ!BI4x@K`ypz%?w6 zn8&98x+|CWI;%y_!bZWjMcV+(0X+fEKT-j%VL!02DdYY-6gg8Nk5D6|T0>Y19hOM*-=E8F}9UB;1^m?|4#P@39PDjL@qm}@^#9zIi zIFk_>nFjX;eoDSX;0M`N_Fiyk17LdN&B+K23^BV*$GXh1LVzVuKeOj-3eA2jdXy%Y z*(`0}^nRD3sT(Bg>eFp)S8UU)xn&%C)oS)Iyjp@15)}Dot->2)4%cySKQi9{)tl<$9%lTGc`McxZmI_{&Qv zozg$1!`LBeRe0M3`HNGxJjOe>&|&;CiA;bf2AV_hdycv{w`>WDiL8txL3KN2J+338 zKFFf4FcKG2jIQm4>?iaA&j^Nh81coUoExh~S!4Oe<_(R*fxn(s&&r&C%XXy5r9XLO z-~5K$T0i*wT-Z7x026)smbz3;zJa`no)=J5l6G4l(FLJBH1eb;)C2oATvKeFM!Kn(d^ZqdGX(ZKs{gq1_I9ih&=I}`NL1NzngZTgKbE+gy?F(N#^2B zdq1I*N0ByI9(I5;B(a?z6laLZT7;B<14-3~NIJB}PP}_pl60q7c4@v9GVcOA-~1w( z%E4QQiK4xame!AwGSJGK%1393tvXyGlp}@6RnnC|r-~zTSYFMZkwLj}fhTgSrW>kH2)AV_Ldf;Jv8(PYib4G zu-9L>5S}=9T<{(JLhdTer0#}bFHe8eC#URwDba8ILgkiVqcVP3u%1mAA!3ywzkJEO zTVoZX+__1W%Zjh)?ba_4A3j9l_ufCe!6-w4893A{2`AHlwN>;KhA}z$VCPGIvygOf zd!5cY#FN%SHq0)h=k!qDf|vDqCG-sc^=J5pCmc;90-y0JlT5^3FDex;F!XSskj<{N zh zP&h+tPkyjUg_3gZ68$%c?@9Ef^Q+gj+|7|-##ij&6h(I1cWCj&In?;m`Pr#9@91!NXKEG-j*XME1mVTSbeIoKYrc#fo9 ziP*R#@r>)*u%C=4c$6;>(^asE{^l|^dh^yA*Q2kA795JGcCPdl90auVx?>q+4Iq$E z5P}WBqm7D=nb_;-=B;%3tl@O| z_O?$zi_RDZbn)=-nh2iq$NUlQq$)5*g#HXbE{GVi>SO>8-+5+Gyw4DYx4a9?U&*~b zw;bQ&yF9zHm_Pl>SzhGGXzL~GgkilN&RQM1i)0(8FuBh$-0L8G@gDUqu9V10^vsro z<^G2kj2(>DI&ohdy{XR1yD_ur0QqkNDtSI{`C$%2RF*>9iI^NA@~MpCjDDWHVEfjl zEFkF?)E14d1JQKpmLobD z^d>OqWEJ6A>)b#gy^{e8mOfLv%h#DV=iH(`D>--a459uN05R7H9QZEt@$K1?Z`;jr zJbQxwD|<*c*$>)|-*x_XyLpc~k=ShsV5x$Br4aQgbf~<;H*$gQ+xA-Y4yY?Qx~5u^ zHjP;5m_BY$QJchvz`_ZZ-G{198JUEv)M}&4Wh4rx+V1@zC^J8^^jTrAMCt&YBj|9- zPYtFg=&(2nOtbt_(ufgSiJn<0*Qcd^PF*mcBuV_^`J*nCNTdo1MORLrrh^2ijNX$f z@*Zk3$5K5FhEnkNpYX5I?z<7!NZ2mgUuWyZkZQ!fG>7jiPc4&Z3x3OCqxr7@7M5ZA zb=!@H$2JZ+Wr}mR0mR*ByNoJ*U80X$7zkXGms_Skk1_cA*RLBV;TI1Lf0_lCM&%3x zmqJ(5#m9c*FLOPtZ}449%b%6jaOz6`*&e8n_>qyb-{^~L1(9?~2g5)b65$6le?A_* zi68+>mYE3abnWTfNhW`URkk0So~+#;Qh~X!1%^l;o&S|JbzCq}wQSCgpeA7$nZRo| zSk7ZCC3#x~C4lms3}a=Ha9K({c3Tqh_~4v?0wEl`bS$wlVaC3UOe#nU+CfZRC9r6` zn8ZiKmM^PdV_Srnb@)p#RKl2CT|;^_Ls~z(*&c%?KA7-Xj#?#NH0}abBsI_kbKh@d zAXstOXe#)G+zxo{ckF?_B;8_zu^pdRWfRd0F8wi3=uQWYy-2@kT2^dCZg$1!gNi3_ zFkaG}yRGvGMY9QY4j<^sR~LpS|}&( z3vkhARcUv&C3=&sbjFd(J@vum0~QKLrlq*~2*&s7PDLnS4gWx3$5zo=9E*zWs;A|A zBbgS3sp$HI+RM!78SM2fDlye;9Sd1<@wBoSBKiArA8!4?TnW~M{g1t zUemBrchR&a{lFZ2f9N@;YcNB5X$F>F6HKa}C&qAkl5884Ai=H&Jt|pJTzS7#wBq8Y zL|{q7;GrYM+x_m#QkOmwRpSq4y*!%z+r-PGFaJ!@2e(;Isi;zmI?#8cbztvt!qBVa zn#ZV6S!Z_tBTB}dH&z36s9jujC64mir}?ehs@xM=b)kF6J3XGf##){*ffe?5Ot90l zy4=^Z`5N-06<6s~#RX7e>l)yh!IItubfMeRGXtZqo68=zEWN$@v31$qrf+8{HF!e# z{C7gtoKDs#RpwhLf6E{Xd-Ceujsvh(a(>O`JwZzUp9irQT2PZ&}esiQFshmnytkV%c5KExNmkO&dXEYC< z6Y{dL?u#3RCpPU*I{mr$U^ze>?y8HrjqTIbr}R9sM|^Iw_^Y&fj*D*O0P9D*j%IC? z4oixN4XIs2f)z(BatK#ECUF~!CAQ7q@S9Q$xy?DF_Fj&L9M6TSUiDslt8%Z^N&4g< zw_QO7NoLm?YPTjybM?=!jN>0K*R_HY-Y=Ic#n+OyH6Ner6Slevtv!OryA~ocI{ICD|D=iT~mwM{pf|X+3sFD6|s1^xGsgnu;5@dTHg2F^Yx$!&O zl>p2{`g0y58WAN#bpiFFb+`$UuDSU=2}CwBN16wx2}LkJ1GRTI=@^kt_e8G%?6%e_ zyy(4)<&?!=8Z}&+DDPI}94~)edmcxFu~{U=y-L$RXMMBkPz{)Y0l>n4;s7zpXPJ#b z+uuFJHf)xR$oEbV$@k6R0CCq6G3gG561`8xZs+(J$y`Y-RaYK}JEbuR51s}vYJz1k zv`Lm-i85G<<>;;4(F-Cn{S2pSJ7k6A1Qcpp-{q>rC#f7JxA%fQPXbfmfx?S#ACw`6 zm)Y*vMinjnqt<9D#z*Ko3XW1ZLp}gaa$-2RhiQ3zeWrb0Fri$L?K(0chzL~5VgA1b4j9qHu<$c|{A*}7gVCNvY$ zW-VGJ->^@&Q~zhf%dBJXpAd8@zYydO>K2 z@qlaUg`_d8WG8@=W0zUjR-r{YwlPMNT)&%Ctw=iXS)PC(32p(Q@b8IXYfHayQq7Rf zZZUo-6DfmrUhlY>`X(%7mt5t zfOhGUzd0xZWjm`-P{taxSoc)@{;93uT{=mJ?p!%5bK0kyy{HnZ zi=1(xnjjWR$h57LPSJ8s$$@WlS;qywlj!ulGc5!4QXSBqh)Qgo` z6H9y}N3Et<2T0qov4c@ha_a+5ZR`wNw`x<}3rUrs(zHEz0 znGf(2TZHY&><43aQ~QmTTv9T*Q9)pDv5uEqk0B%ExZcLYc|lj$)$3mSD2}l$y5i|S z>f{{296Jm4Q?;@YfeyqKo;)$aG6*Nz4`=NV?!Rc75pw35mkYi8TZBJ?CyG13{b!<( zhaX0s2(eA0;s{?kIa@}>#C=RgGqe%G_X1?9QwID&ufJxbbCPy6$yK{6b zpjh$b!jgkGe%YbH#BlnL*wAzxA_l-K*}sI;RX{o24rGHVoh@tDRHKWC z*Q>P?q=Ia^llE85_V0oUCYNzs3-wP)Qn|Jksp8(_p{bRe6(4?m zww?_~-&yB9?!dodBB0IM4(yzMod7J=v&$TVJ{li3cyX{7+X4a zhK(Zm0;B4q!0%mn9^gxOFy3I1l_3( zOi`a@ceU@4q@IfTN(pXi)p1aHQ^Y=q#N=o`ow}8lw9m<-0&x27Jlj zQC9r63CQMICoq_nub;WIOG)Y*pXPg!3yDWHslU(8Q-jhVg#!o9hf;ry)?1+8oZ3@5uQMP7?XN%PU0gJBjbj7ZmP4DEkpML1O32U&OxY z|8w1oyFdQ$`#Vkh-c+;D>({<~eDzRPVcOp^0%~~yj5LMyuRz;~J0&dj#-D(e`e3n; zl=jJN#pVgZ4Zi+mn_2c|tPJmHs05znE8z?phe}61h0jUW@WW_exRB+fwa_Z)WA& zdn=6dlO(cqv+!L(6`}+_!7e_e&v00iLvTvBXx1MU?E?rITI~D{H~2)K10>}(&R(!S zAf1I)jwyjg?=SbYIT|1rB8f=I-pIy*rqJ$_D`$s41iWrAf`P^-4JG zlo2zhJ$V40@U3J6IRskm>U*xL0fiBI&b;07=WYB=WPWc^MpxA-=%SXdr0~< zb#{55&GqaM@5G){_Xb6tDl}%t>&!*H(4T+s=8k+9oxX@a^e>_!Moip^{mqKd2>zE# zhM-nz@3Uw)leEH%$h>RrcnF(& zV_9A!EgFucfN2cKZm2!$^V{vERa5B_67%|E?Ep zi*$_=khzcQ;fkpBxNkwGlVUL6S^%ca@+oBT-v11`5Isv{JC`P~Q8M{_RlrA)7uiOpD)!reY&;W^5i4*+Qhy;wz>>nWVC>eXJ{HY^WE<&fkdK^2>N5+qVe z_V(pH)WF1(?KcFr-jcz?BQ7R1Y21x9-OfL~|HF-9=>xng(68iWS-`mi0-p6 zp1ME^pRSYb=UY8rJ0qG$)`T1NBpViiti+Mx?ZMl^QawHx@a3LE{&*j>Zn6yanjcUa zXM~${2Vj2c{jvypT@7ZCFfoQ(cuoD$YX_~zvClLPCz9*X_Vj)BqJ%6CQ!y;{3vbQ2P2!201|tzPo(BHAg8nx|g*M zS%hDA2sz>Lt-DeIu)t`*>!0mTXZVL(uY1q^+g(W;b|vY1<*0js6*F_4kgrJqKG%T+ z_C1t)f z>w*Wwn3T$*AK+ph<-1*<+q+yq-|_VIsG|fOr!lJN7)U@a+pkR-Tb2ChWUnnFwd@(mO*utFAg_) z0sMl0(Jw~r%W0GqvW(OfXr7Wq+09MWTnOB8QHP{bg3w48=XRp4DL^3+MQ3&Y+A16u zQ4rQwi=4Zj{u4jY38@vf$D~TKF>=-6%<{Akz{1KBVeD@KF2zs`%@y1XIij=7!C0qW z6Wa4Agb3ep#`U9M(rhb%PciU(91B?l8Bs>N3#dE`MIgJSF|rWMM&o4*y5Npk5lvC@ zrDiwD$M^aDSTQHWlr^_EIdFV5<^Xpm#VW{d^w&fF8`YIyy$!5LDw55RYj1e~uEafZ_Z!ixJtlI|c zQ&9;KT`pN0Z}5wq)3vVM8WQCZpp3h9Yc)9Td#78>3qVuut_Q6vdp@Wz;R$DB15~8M z!kb0yh3q94aqXM@@Z`!F3Vq4$)6P@+BIVM{;B`l#Fy1@r8hFc*gMYmRURzGluTp3> zUWeyv6H?xmugX#5OE*ICvD04UN-|+02x-r0FY7$4)^mS7B=LQ;D_yxmBp=YfT~b{= z)IM=4O8>*0Q=w<@Jwn2(*_Trxp%rsI;47ftRWTz|BF1Rz_$ zjv$x;mfv%2?vn($6LuPyuDELTZAU!_%mx<#i~d=bjNw0W-x^<;DNC2iv&9g7t^p!) z#V0M&OlyU4J|UHLLM#h+L+>_1Cvwh7?kekn`Yc z`9lr9^Ppv0%|m@v4dL5NDktLzlCv?HZdvYPj1R@_Ic`XCJso&%(?RQfKYV0CSF=98 zer7MU!&xY_JLe=eDWKzYq> zAH2;sYGtQtc9`S;NoPY!BLMlx5XEJENXbH^xsFN^UvoN&MmBQetY&K^n6nYmC_ZeT zj*a(*Xvy!3Fk790!`zr}_y7JKi2uSz-Rld>+WFOMXXHTgq_IplxeegSWw;!GUobEq zM29>N4G(G7?+_W`)W#x1KBfQ& zTM!~qgzU=~)^!n9wd=W)LbZVK^71BnWU(T+W_w0wPk0CkRvF~nDi4zkR zl>T?v&OBw(g*Q<(ZB%y+P+#b2=__LbQb0Zan8SB)seTHaj@}UhaQP~ zvva{!4OpA>aaBA*pxl2-{4!+Nd{f|;bK{fs>Qz^+e06AGPHDvkWcz#SQ}aoGMN=q#*cW0q+1o=G`BU-7dEA-FHQw$i(1qR*IjX3?s1f27 zIkX7h?Vw^7MkMZZUTTyK9|P9G6CV%xOtlOH5Y2@n^n%{&i}UkaHApT&#T~|S${Z1< z?bBBy`!Q)*EWTWTZ}$6Je;Jeqdi9A82rmla0RusSoU|;dX{BDNl%FV%?Sa zPX^fqmY$EA>g(qZ8D>--oW7RPOHJ32w>!VIEht(?4CX|YO<#*w?uzYq1XEcC$%$T> zOu09C)t$bvg=(d=Xkpd21y+j3KH`63Pg3MH)9n+qW*pVlVQd*30)LT9B-|}VI3W#a z830{i84c}htM4G$YHvx_v^Ct>L!uL?lkXcsOGXS{Sx%m7^v&k!kQ}=*j$c1W(#19g zeiBs^VkjY%%P-iga3Ps0#Kfl_pM%JizTcOF{NuQGsJ2g?_w1zb!1MaR;w0e5?;&ls z!>DpVmFApZDITyUNiAp#e%p?rd5s6w_fy2jAinZYfRysfr&c9mIPoYZWr1_NY_7qs zcmz%?M@X7dy4_cH7*#if=-u|ZH7FBIN9jS zU2KHDU5hOGVQ$@F2u6e$op9gBoNs24p05;x#SgylVuqffba_{oZ;Lcjpqs9qh zV95vPr+lsE2n-sa9FpyaHvT?mQS*i*VL5Nu8K~0F>Ks`BXUmEB7N@5Fz!a-g1}*o{ z|HlQM5Xu5E*e0p!bXK+!$$ZMIfT^FC$czazwCliX-~fEi!LX%lq6b8U zBp@tOBRu6y&HqKK-wu?m?Jf^K^{rONvGg@A6Tb`Hd6fl=-bA6-S^7aqJoA2R$lZiW z2oP4TBDj!jxNZtG>G@_B{$b`Z<@=cymZ~`Q?g@M!ma*pL?;t$e#t`W?$30PiH`3A* z?t5O&@^0JwkVmmF$p=SCpB^QuHE$n~azE;n9j*8Q+ojRooBV&Y`l;3TuK)PQ?~^0l zHH#{Msp~{awz||8PJI6uRy@MUPxBscAq6BVRuT~#1SH@DU$ldv5dlBGw~F1Nw#8y~ zw4c}CTwi%C?FL|1a61r!nL>;`QhDND(Sv$*__sz$31AFH0^a|1_*y*`znXaN`Fj-B zik>29aapLidBy_Q?H>{+iHx9T+yXa#Uo9zhi0=x@vh7eol%&@YO++=HPPqCDYk6yN zzDx@rqnd=Rb|jJyYKa0Phw`LC$o6#CBLxBBuVbRoic^C?xHfHI$Hk5Oi<|Db<>N-l zN(uby#cC6c`1#3~OwFH5E>8-7VmdK5&4BGqM?>9fh2V#t=2B|$g=M&{-{A`zBwjLFPvK+?TuBJsG`<11z-u5I+YI?l zH2;3vN4iI$um&2nZq*&{n5;}CIMZ7iD8a~!G+7KCvGMo95`S3OgDE~gLy`AHMhk1@ z-#{x3i10%+L{3fO494yNTWo~HUwD(?7JcQX*}k9q_)3tZbek!=_E7$`!!3e2UcYOISoL)aPEir z$nzpZ0v%uH*%-Q7Ooe3$Izr7v|2de5a1~eq z95^|?JJnlYTZiv84NzR^C^WR|!6DXT zc}652`nfZ^dtai(i>)o=N`tP~sW3yA?ZUF9%Tnv=F%KJ75KA0Qa?Mwd*z%CoG7wED^p>1dTnV}f5F$7C_hDG6+9wtP<-gw82BIKevZ z%f714?>o41yVZ(si(ZZw{lfn(dcinO{>fL>Gm&4Ta@T58@DKP(puMP%k)QzxR?(IV zPM&{-YTrn=kGB|IpseHL#+*s>c4X~>O80cV)EIekuOgGij6hqIVTZncCgRQrZoEK5 zTGq*Q{9E=FR4t8Nxc%Tc=+$bD#GNs-A?;;f&V)cGh#IY16)ws= zDr}&ACjCYOM&y!rFBEMTC8?UdSP45Y$%CIVPnuMvy5nzJRA?Xm^~ij}TZhv&p|7&B zEgByhr;h?$l3>}>T7i%DzB@8MVG3S6R*~Iu{f+GZO1AdBasL-_XT9q?wOK`2iu{QT zHfcuLh4cY4<@MD&uIRY%!T2959=kGG1|1moss5fT%QjiF3NUq%Y~?vbBj8|ypDtv+ zA!jiHx3sb2=?Lzt3>Q5VZGBkst$5oHFmXH7=f9vj(E*Lt>E)rK7p=98Lh-G z+~EyRtYGAiLo*ld)`4qMz3E%##@ktP9%z6{{EkB3whtP7NNk?;#rS-q3K8t<>U)@u z%8wGtZe?>tH^>ZDh!F)}P+8FAM9wFk`e^?6bi!UO|HE!Ji%7{o|L>`H|Jgv^7^MQw z7zz;-0xycsTB*HLr4pb5T&Reh#KwP`FzB=VY%WnkjsvmE$h~&;x<>T*X=yL?C!ncex%b2bK|&n7tP^c<&@#C!yh=L_~oZ z(p-wci9}W0_eP!%l~g|9s9F=s$PM+n*@}i0O!v;IqAq{sjW2&p#wTp}x6+6(VC)qx zo;#NHAJ}V3+!V2C?)}15xrf3F2w`ZFw@KCC*4D)Mk$EcO+C)~6CJeY&J@?7_<%tJv z${&k)Vn9a6MaHUijux3Y7HCu@FC)P8NT!3GFmrA30c91OB6*G_+sb{8bh9}svOZ0C zyLRKWy32LyGZ{YN6?)6)8!u}go*Q2OlN)*++thrBl=-|Me5NJJrc83wQqmnVK|LSO zjb6T78bb=Q>|AQ;2~im5t6~^~IWn{4*FlYM8dNA@tCm$Iy+K8Va_tPWN=01+P&)t7 zp*hOM5;LNA^;La4$yp-VaxBmd1Dg_kUoYu)GnZUk^5VwdW21inqp631u?!$limZBG zW91UFBCU`L9F|cs#s9bOZBOtkkNnZUeS=UU+Le>i+mt zf_enn8#)qya1JOtnM3}tpx)_BG14F= zjQ;D%D$fDQI(!3rYf7oTbo{D7sl|2BTBmeC$2@~fgJO+xlo&d=IN zn3k9we=mt)19<=CUeG7pWs6zb76OWf@!< z!E~$`v*NPcJn)^V&ojE-3@4c@Yj|sJVUdkMsh5WoSMy~=aE5NosCm<^D@z$S)uRX8 zQaD1Q&_a-i(~bWyP2aUUqm>0gdT0WGZCGqwdbO%jf5md1Sf_N_cB@?U(qm#2gRKLU zrOk;~hAO~G=VB1-mq#6JW(xI8lCc3sxyPyl5D0UGWG_Kxz?@@*0_b%kVQeD$TL7L( z1dsno1f!t5=DV)hUuFRKKCZaPA=#CfqWCgy7@K5Ff_E?%#%sBLXWQ@sA}M&+h5+Zd zYLu+o_cVo51EFLdzI7vCT}3C2sTn*b#5MP zjc+Y|#@jkCY-d#aRlLZoWq+HP{~o17+NMF5TZY>63s$8gV)4_COgeCf&;dxgK?mJk z4N6OXw|fo!Zplp)IkiVWYksm)97jI2XikLY@OK*?QU&_`H_dT>W z-sp*IXXcZOq4PRQkF>CF4CD5_3IB(Ekmrk*?HHJl5owJKSY6Qa;U+yPQZe=Nyb4?f zgb=}FKL<*6G)J(jj0O!~hki@E`$?-s1Rq5k_Cv1C*51GX>z1xpCM{J7!lUxLyn*{4 zi+6Bd@6m*OOZfNV2habXgEy($W}7E-zc+L6X~P!?X86kN7S!(&e(-j6e&`Yvm3Oxw zwzKUnIcGGw3!cDfALN`KI2!dq16JevdXz14thW}9F`j3;MvSF7mW=n^KC1+efEf2+ zSEK@j;3^~J@`Y0&Af(NG=I(ZK@KS2j2W<|pG4|Y&cmJtCoc8_a;PvfS^;FtlcTbqD zA9P!>SnR;3kFLZaY?(yg&1SJ1c<0V79Ni*%ql?$cn1b2;w*x!)5V^}@SB27rk1kG} zon#2Em$$8(wK{#$)q#I<-^ZZ?LH~5>+xG_leeiNZM${ttnj4|7TS(W8C<4zlyMBYR zqmOtl%KU4Oj%M~&9unERWZ!*f>ERAeq?=~ivA?Bw zGbg;_e^0pYCcd6Q%AER-q@ujB#V?(Xz@(8GNwsD89F$CR$1kpp`IBQ}yQ=24VSzF@ z`SnwtCP`JbPu#+PDHEa%wN!mi% zPkMfiM4y~Ufc#g^sOcQNXNQ06zJxxxzI8TW8kEK9y^&c%dR54Md!-(vFE|>+OOB!$ z+r5ZHa%A}Z3-T_CcqqNM0rb{)5gks#&CJER2~U?N;S))At@q)@k|6xk!VEF?6 zjE5taZ0iLl0|p+mXD-JisC*}1kTt=- z8=FIsX)ksWA~7V0)Vf%?`qs)Roggnpa!F)`BIwI3E`K8$tps+I~ zk*%ZPg2Jrh1;hUzVPE17<@*2sJhNkrv4+qn*;0*Aw)BjptQnCInrvBG#;(GdF%sIQ zC`)LpDJpxkolz=FB4lfuLY6j_I>YTpU?;n`CuKT%{_xrWI_o3DrEI7Ts zSgYW))$%J#(J3UEM4v!!U)Da1XUr~6ndoth zv-^*CUMeI9dqNxj_4qAr`s2;Q`Px6nT5|DqB^*_gDqwjF`BdAc4M3Q}rin7Zo#)?d zOd%!-;Y^1E=#45e(bZ==K&Z*Al5GT~Gu$dIUdDq;a4x1VC4u~tw(e55(2diR+i$W^ z@WlXS6LR@L1*-Y{a!m%}k$^N_%zYIF^eA(f8?yVO%eSK9-c0zC!JT&_R$_x}FcRt- zrjOQ-vTs=R00SFpR+z5gB#Y$i_n~Mm4j;?MW5TGJvo-DWub|;TWP4^^^-($b#nFzP zIA_air*7@?dE{azwlkONg~n3MH7U%Tk8BuUN|MRrs-WMXs>xrekXzW^M~>?>O^b3E z+`$v#O8tO;3%*5arEXI}Mbq%cvY0b|@ar|)xXMqp<_acsjDVIVuGzUBbEL126p&td zXD@keAUE->7vNaaYBf61n95BSEK~~A4=K&6A2*w(LY6F~$*|C8H3DA61)%Q3WVUE> zmjn=94v5M=2tTsonf%@~vrVo>uHgpG*1 zq?#fqHo2f6Dl)^l6B9H$0mOaWo$&Z^iyq4?cP&TML0%7P$hQs&vu>R#fG8LmhRj>F zwYyAIoI&S>BQUIAnQCBN3q%*N%TX_r$@0Mj@2i#muWA|n4KW|9if3Klxy6gmiK-k1 zgX=2;(MpD~pOnz-Bn7*?YvH;{#Pk7eLY4kUhkNLC7`~w4EQ-w&QPhmn17KVMenr48 zJRnjt?@9q|8JZKvy48dMIc|J5$SWRNv*ewF@acXo4@&rk4ZEG9TVHFP7~n_cM-=)^z)Lb*l7 z0w@;T33OwT^fjHoJ&@IqTo;+cQ^hIE-A%BR!?7HdaXl1sQSp7>E{2;{<1#~BN6EeY zQtQlcdrwCG4ZRf|vs@AMAb8{Y7MD~2y%k&j4ZR697vmX_m&1sw&(>8;p%ePJ`L-P* z5l7Q=#nVq@Vslv~r{3{4(9=9hhBRnPjk6?Ji)AK?(9N=72E2EdGavuRKNKM`UKAdH zyCvzwwJ|UBIt@?!SQh^uyWm>MZ5GFP>hC8*$AK)alsSKvxp3y6%Si5-5p;(d&o` zio86~gR+>$1z%x^rcS%5TAMuPJ$Q)nUs5!RFcK;;*ms~lW<6lvmG1iL%a0IX8n46+ z9muZOxHloc`*IY0{XQ02b)WP-jcy7V{UNsw+y`!!ju<`J#LYfe=L_V(4ad_GZ|cpV`V2s@0R}b? zjJWg3AI_I_$@3V!b`Hti5??J-;JP@ik%mOfjF^0GseK7~tlx4syN^s=tf)lod}&ay zk?*BEe$A%C;N5UxdcgnxW>5i51_Yys7vImfp@-z9yB1PV4v@Y(v8Tnls;K1N`kp0)3NQ$y8r&Y2pb0SXiblyKHfS5&~0 zA|%6bDQ@gkZMjtiYFDa3+h>Xg8;z48gj8SfVq=?Hmrs9w`=kN)YPS9SO5|Z1?N1BEa>>a_Y7QyJAZzC_mL zLn@k!w;SVBL~y!%`Mm(97uB{_%@O2}N*Y5r)fd8z+u;(u{L5fGE^|fGC)EMA8_$Ul#K*@;%Wd z@bW7q8mDoc<4adPl9Vadg@*u87{ilo>)GkS#^UPz%hFy06>?wKLxt$K-q~}gX+Y8lJ0@Y_ zUSBzMm%GF+&%U8*q%Bi58>y?BBGqaGV98-5^LMVi&!cd zBak_bM&zFGpE2Ztn;l{|LPI$*0|t?XJnX93(zA|7KZe%)#FZTcpJCfCJ_F#StD?Vi z(w1?NgraFKu$!`nbwG80qTrNBAQ&Xeed`I1r5(7T>07|DTw0aQUh%__Ss(0x>PMAr(C z@1{v(1f8;eFmU)hn^{ZULONl{+BDLhYI0)}w74;* z#3kJ+bY%rU-ED{VI)I78LY~^H!w^9{j*R=hIBuPtrH-8kst-N*!jFtj00@$zRWkF zr|UTdpJll8L!0EVATbA#40dOBE=}NBV@sAWsBK7?&2c%Zz=c9CLE)5+WT*4ta&-NA zWv)R8(9GOSwencK3rY{++>A3J@A9m}KYZ$NZV&J2b7a;v6_kb4T^>Gx`P7sXg*zAE zjwXn2aVIV6!Lj_WJdU7~a>aQ$$O6|SBJ5tDwBF`QMD!B~M-(8&L&;|)qOE_t50k)p zTmAPS+MhijY_li7wb@WJ0ito#+fv{u0Mi;tK4a-%*UgJV_{Ik0sxX_61pqjb?Rji- z9Nx%Lh!M5QTymR+f<=j5+LMo3(yh@szE>h3u*<89+-z#gkTSSl)J^MXPG7EXx2TKp zXPs^o7LzF6*}L4a!Shy??2hOiPZMmvh^p9sc(>)I&O$YP){pDmmZ=x=^j<^tXyeR8 z;_WZ)f?AX`DmfG3Pri)C&`IE83?4OJCxTPuXI7ix0!g3W5- z`|4I7rTL@8h4Cvl>>UrmSeo4e|DH8(v(4U*b^o*8|C7P(aWsCzstdCz&O$AMRLxNs zZ1VsfTl_go&RAx8}*O$~+~M6g<@5EHOuh(64huF|F!^&#T*HV8c$8 z{(;M_%MXBn_u&bc>S%ymb0KLUb+h|^m?U*w~_W)i0QA6^mw8!LIO zXN2yL)yenM*ufjU(Je)JKN?K}+Ao2j{`G9kx|+|P8p(}CsXKNA@X~rM2z4&CI7_v6xE_^A9|j z@I4K#!ppxc^0gY={9$vLIL5UOT!kG%glTeaH9(k(;)DoOL~wx+VUqt6d4IkXsUP_4 zK)%s<5g<(Ujy*VM`QNjuYWaiG-6NTgqGyDqNRd)$tZEexzjOWjy8By#&Ao7Sc*uuw zZ4cg`w@w0nOu3$mt)|r7D7Z&DbXyS68Q6=h5LP0UXUq>8`gdZ)W`(SZ%ge`rRcUHH zfC(jUXGQmn#Z2A(8NDux_#Cw z4m19)NwTu{ZSwH*IqCLUsOj))__-!Ir@q5plEHHDbsxz~9~75q%5hs?Kb)C(uIFrq zx|E?h8Y$PJD>uvP^WA-4>Om)Ad#-!dGa~oqQACN)GTiaP`Jf1Kc=E92u^+Ex;V#(l zi(N2%@hFhcbqRNYkCiJ7Y&Rd+h;lrT&=pM|ffN%obdP$rgz>;T2<$sXo7CG+8-`C5 zWgXz(KO?OHDJu#*;Cyrlp~hxv6hh;6iUu0H@S!qSkpPnopI$z^1ebU(@7z54uo!L* zSZ)`7evrLNj6l9CSL#p|>F#m+bo36_66TkS!*^MGXy}ChHVEJd9(LcB3t?hWy@xv* z6Vu=-O?iY-Gh$&YrXnvd8;)FTwjjDRT&>#<0Ze~BGLG=IdKdw9nti1Jq zS6(Uk9t}p3^2WCcjZ=q=Wc+9#XFNd(p2kw5D~nnVl*`DQ(hsV%q)~lEb+{uGAOzq3 z)YOo2w&ReA_~$vBDK&0^wX~00R!vYNtcVRTqR?60WhfD%ELEng?>xFZ>dQn#AYA5m z+M`x+_42NZC_F!Mne?v4>WJ|B?T7Tokl$ldP^52U*1uQ2>77!bctysC%P=iCAPYfW z3GPnI3)LNgK`qVdO8S%0VY!7kqn!9y&1Tw@8jV-WTzx<9zB?t)(~|caW1-S7NkUah z?J4Iwm!IwSL3YDy->z8XH>|PgSE3Hknk}qZ$XeF-*D3Kynw&e7k2uab)eRz5u%;(O zne6dqo|LR$&bNpI<%H!Ok4%jYx(9_P0eX%VdMf4(*kstodxvc9z+VSw7Z;+kH8+PVIkEUD^EPs z>J51S2lQ2q7*pR4><$YbT)Pn2<$Vl$<1caTD5hha_?+Uud*W}e&dU&@j`my6yDYwr z2I261PkVNIcgdIaUdsm>U$H5y);oufHSX%qeZO>CnPT_4eJ*w(u^@X2&%vAG`J;kc zuZppv;UzIn(NJ6$_Qh7Q?+(v@y*e{qcf(ejs1nHJyZH-pBQ)fv(stC?Wbk%ipwP~Q zbXh$L&2ldw5S6aUSwej=cO=%yDlj3KD3DQ!nYyM&HZu8nqf5iS(8hnSw_5VMM_0cI zMRk3QaTpmT0=G~o6kS%b5K0taP$1&MvxVnJ!zy=N$gv5}H#;yM45%%xcLK+q{z1uB zZ`)1SGEK@sl>!+R)7R}*BltmGrEbfl3?Y6s4Wys$pvOwmr{6u@r;f13PSL=8_EjBaQWcyC&We4x1 zsQ8oK*DhUd9lB&g#oAYW25Wqpq5T(8mujQCRHjv-q{kMrgvU=5>hFURa7ra+aQYtk zev8yP$vBNLj=}j39R)ug^Np6c-`5B&Cd$$)=1ancevd@+ed39Dc)MWLG$uglntkOp z9_KA)d#7TIkkzv6BX1zvqK)SB9EhsaR$>_`s{NM~FpOmxEFe8y!jTk(Uc_epE5H7xIY7DRS zisl&iqiiMtoJE^o?2XKK3(I_`>)9DuV}`0X^YH%K(a4qlC~cXv1XL2#Mu# z>kjBvf5}EHarssmovn3#&#H7fSFt>VOokfovgWfJKNHK}`?0^yT_T~bhN`Yx#)mDB zpbhbJk#k4*c5K>ui~FJb1elf?B2PE-cFDx;DXDua+)iux;jW4VG(72VTELO+3NXvL zU*mi=?RO(X>hn*-3FBPZ!n5zL%)6~O{k#9|xP;xqn}ti;7XGF=9cjw_Fr&2^VG6LC zS#n~XtKH4ea~sHTx&7z+kZ2AWg@-N2o~)_pd2es_E=T&A@8Xv-Kd6(~V3}?IyUaS% zZ@H(8JLZ@91>YEBj+x;x_$FYPH{9wTGAF* z5%Fvgw(0WRFmaKnjF-m)7F0qPAuT1i@VZLv#fVhyb3XJxryXZohxzsMa%E5*FdZ#7 zjJzl`9sQTT=QJ%4yn*Tue0r$BVcqlVizi`1D}=f?F|;8I#Ha4NGcHzf{e4t?&!S%}x6o{NJ>>Zj zBeJQgH@lIHhC2Jh{U2($ggpOi5(smx-4cu1M2%Wna!ZZ3^^Z57}+SckPwJo*Up`g6Y>@vrbyp z7)(ke#FMk&b8rGZ05mX14^0zzS_D7SkIH}qPx?(%HO-TTrv^amav}4Tw^qn}DN8lc zq^Z^_R&qY2s^P13{Af@gqhcHFhVnNg6=gL$HpGuaAE@YyUAQ8BBU&}aW#M*sve5CU z-1A$uXmI9lA#-G{&I42_{i3va?F5jc%N+6WnA)Dfcv%E`n#6PTtblLYiNB$Rc-oYI zcCSeKZT8NB1<;uYoOa;pNy!L}Q*Uz(xhSKYTO}4CQzH>YO1>3fHsd*jz-TJ{p2^X= z0X5oq=eSfZugiUcS6(;x((pJI{vitb|JoIX_ZuM;T0ZvgUEwVTVU8daIAVN$R~(+j z`RRh{PPi);-(u2fpug1%773ll+$CO4x>FCZc-EXMTjnIvbr#{sq>I*S-v=n=MtMf< zvU@0)f+JG}rG3>3ZE`w~pbL)<_zj(pvsL-!LB&I=BAUQ76z`ehOTd9uq+mT(NCJ1q zsJ$c${hFN>Ii#k2?bRFU+w2dk-ImNaa9z+nQSn^>XQJRY^5Q>MBa$-iAl|t%^9vso z88R;17nfS~g~h1o8hToR}{= zQe5{&D1!fQyzO2Rfx@`&kkg$wH#UQ*{5 z-7*S3Bqr~$y&(oPw$lqS z)`#k!tuf-jz6}5EGPgD1x&O5(#EX_n0;38jT5f!(3acB@OFR zjUq59R=^EXl`2lJ;rCbj;R*CMIe75Q&OW)J~z;%bOsNI9nfRmIJ zTk`AvYqVp{_z0n_Pp=9#NWj2lh93=48u)n2IE{!qqZI2s#n^}~FE>aOqK=}M>~`xZ%lifD_v+c#x`@I1R>fot zStBso-N}8rSA~4%{iuK=bJCAaX!p9~2%+5!I`LRPy<9X_N!WiCg_8oOcz^svJ^luE zyTgA=Khw;&ZpwbsQ}ZyNRl>BAJTUB=L(9?Q$FEvfsafGvMOB&`SMm)3%phf1!lz_I zvudr6gSu#34n9V7xjO{>dQg&fu^Y6#vzRVDVFb=dPZ?pYls*M2>tlO~Fu}Iq_f=!D z1o?`(WrLS?4%O|N1TbIdk~i80r(O5-l)yOskyp{@7zwECm&>n7Gq>F_5wW-!5*gOh zdg^}|tHmJ|hbw@3EOjpPZqJUm0`1-^U2efhNEX4VUAkBpNx?rU9hahK)nJ9IN+i60Akq`%zm{S8})C+{3=K_2aES9QyQP zQIV}r|2F>rz1!76z#wt;7|%Q)^0wS>O39s)I|p-1w#v<=Fz93jGtC$7BcJ+MVc5O> zit+2j{bW{GJ#c2h@=S97nm?b1i(5MV*a63bZx9G={&#_5nQRYOzIW5#p;ozWSpgYi zO+XneKvtaQ%9jhFA|48l>MqU!c664pG;sNFJ4!mv$STy>Ta!v36kV&SpI!<{BmBKJEa_Ro~zOz+7|6=P}pF zorM5%U7q=+?z^gQa1Ee;YwrWhb;hwD>uh+op{9=iwA)cd{%Qno>b0I8{_8Xn>KVuv zD5p2Zuf=@eOfkX)3}kLf)<4Rv2?T0^@Ke@2-z!P6Do56E33w^W!*5ket~;nw(Bngp zBAzCXgbJ26K?iYcc~)HIKm`{I@9ck0J8<%wIT>XYIlYBW)=a7gIn)v0aZs6cJHA&b zcG3e%J+kEZabxYGV`hCy1^8uf@>J1r^^_)|UJKB_SP`25E33S7B~C)&PPXWE5ut`} z{wX>qObyj?^N7{v-b9tb#u^aJSV}(@vubR#kUL@HOaTCH}g zKt28Ja2KjWh;RjNfuR$%>WI6k_U$J*%qzwu0+4mPQGWkF2>45O2pct+;!EneRG-u+ zJtESly!+!51ODz8UKR^&k1oAq3&cl~80SbqVy4-wzm39+{d>P#mX-;t@(IEBs$4hNh-<|-C4&CoFBC1>_~D5gsUV%fjzeTo zWM+VL^$@yKQ%xvmPB4;?nzpIYyop+$HVBkS?5?vqpT@-yN-`K}kRKd5LkED*)>|j$ zQf1EdSIb^M|EDno4_?L%h!Q3@GuS(sAI~qRX6$C&*!!_c*^R+^!e<^wkyz;$44b%1 zZskrqEC(LCkE@&SHRySi%s4W>+TTMqQ+B@{s0Ew9D%E%GcX+t?-c@_X9a^_c zi}=ejy-w!#Pw41OHdAsD=YxAPB)NHdzAERrIw@0R(r?p~&IuvF`UMEkuTsfom@yan z-svM#4onQ3?=fGMW0$DNG^KQZ^NJ&qYeizLJ4GZ(^h^&;?V{T;n3+KGI-0iSF|;c0 z`Ez>Q`$CBmno?P0vq&l=dDtduJ}yk_rK9W7pa;x=lC1P3loRizx#G2XIrBeP;oe6O zj(2WHBuX+^BYF7wz;TXe$p9E__h}$7+aE_CKi!Ps`34{CH+lw9pyLO^_dRZrSobb4 zzgTYg@Q{W>{2lCs_D^JR93%-as!jji87`$OpUn#dbliZ35eDwf(Q?zwBV^ zI&6|Pj^uTPZ{>(m&w*T8-1ghq;tW@%h8n~6w;0hirLM5oK<|-58G?-$_J^ZLIl@Sd zy(0fn{QdBt{AGQK#%}Z$(OgPqL9bfN?Qt_aL9KTJ4l|f{Scl!tM%*O&pBPu8i?AA$ zdvOY9*v~L*>K}7&p((aIQSE;DvBpR?Z70~)6`!k8R^ntS`iG5_;bD|UDK6V_(U*I3 zT}D)HejO59@cx#B*|S(q7eJhn=j%NIhrC|%d7_MK;PV6W^Kql=HCarIn}G+e*`Liu zKr(?h4NU8KjI4~(^mUuuemOTFQi;(Cd$81bXc%JS6uV#}#Z|-Nd_vXVJ*_1M4CbW|Ld=RiE-%mLtMXaVLJ7atiKi?INU7o7Jq98a zs957^W(Dg;reXLdz_p99V~shY#nuP~Nchv_FuWSAE(xn#+oeW4#;J2|=YXvv-&Y6` zZj=S-=1?{ISgM!ZWck*rokOnoqBgGBnumKoD7*C&HT)c_97d3Q0QECGz*NK!I zO#UFZOP7ctau>I=F*wT$!TgV-AU>FW+zbe78%0EiGdkld=f&HWOOMNSEj>8E1d70< zz(+zw;E)n4Y4e}TYWe#_Do=RRL<-!vfQRQfDkDnzFsxzx4aZ8bERV3}qW)^A=bAmU zr25b{iv+m(tzT^U{KR8r*N29)HN%T-H-I@#g6l~3nH;W0a>3_N=CeNt0OOAbB_ z(#rRs8~c%xS5fj1cO}6%FVa&Ef+G=pkLEfL^&d%*|7&kdUm%r-3S}|#d!rAhNzi?c ze6x3GN%_eoAfnfnfH$&@O-zs{s=3r zR{;nsroHchc>9rPjDDnQc3_3j4Y4z7WE4 z22v@({VX2ggO1SNF*y;MNXCjSr$j(-J|dFSd+io6F?EVMnTz^EOD4zf{QN_it}eM2 z>!;Bvf`08np?%r=&F#V8FLZk-Q$+RIbewXsw>gowz0+x#T$4~QWx+ax^RHj1>QBVr zM5St{3sQAAHaU_sWL1UPBPi2Y;vv=3Yx^k3ZW$&On{%bVx1 z6CZO|lsX8vQswIbA}5u|It*nepu3KMfk^_E4vq%WR;x=KvS)TrJl0}Dpp(=fK(;j$ za)OY^G42FM8MALyt_QPp(|MCPfMv2?Z7w%C7^U2{89`TAwH1u)-5kwXYSqGYV_ z9AOVWav3489$-mo%spF~EKF5z*{?X&VgT6QNC2CpD* z8}ab~f%rpjtB`njYMk@##=m0xzO@TJSC_6)fN6Z0rf}{9S8n1Tx7TMSz(|Z>o4ji; zv=`&R>^&e&U%gVQfQL*_*g zp7WjHxoM0>Adyx1tQBzp!ctP;-DfAbi^hTzy3`znx2VrCa$a9lHlcc7F&-bH@ZYF;0J~MF)y%zsVi6+?}4aO^>jfTJ7|(O~N~_-LWk+0rCAB zU`7vm4IPy73l_;68IET6g^}eE3jW4N?2Jv)&#Ia4V+QLNg;w}uEFntgAD)mz-Afr3 zLD5=KQMO=pBkfr2sbe$^401nuhDv6^W(LR^zv*jdpP7sbGL9b5@2!7mXz}k;u=5wo zAJa~QP#%xJ<0j&?@i3HEqHdG7g-xA;1urNQk+$VBfY(8y>)bvxI?4(%W~^z$fGxd$B) zVi!#rs1JQLabW+Ja_=iaVnj{qA1k+3VgFS8%L94;;x1MM`Ky(ZKirb3x5S~Wq1@?G zZtaN>vH`%f5Fn@L%6=LoE>^EMOP!a~=Sto7Vn)9`V}N3eA>^q&xs;2@9V4A`aN?Ow}Yn_%s@5D}W)?9p4fF zs1FPt$TrC&DTv=zrAZHO7_ua>HgJ#X<0|RK;+W8(-)3LOJTUQ?wuJ1aG=nysm=EF@ zok?E09yp(tZkl&K$^7Vi`UdT1piIa6Pj|ziC~hE$HVpnd=?i(81%`fUx zBtZJUzh=&OkzS`=_!Y~s5-@!spztTv+Fo$}HvP9(@RWy6v0+%L$akLc!+eqy%v;~E z6Aa*|DB>vMF9BrjBB-7eB=gA4dluh2z4UiwLqZ}5pjvuK3$m5bFiTu4abtvJtbV~!GdzKYxgyTct2u@3LqpyQV!F@9EwChF?Jn+9 z!?@QV`0W?xk2sU$v858L3&-D;bQ_Eyt(RBWT&g_MgA8C1Qt%WRVWC8K9%xT`Jrdq- zFnOf=MB%Wb=XQrbVFq+7FesA3MNJVRS>rX*}3B=9*YI+MUA6uK|tw;bs|%5{OlNaNsr>Wz!pQs*{C%F81rS47U zMP0x8wOhdClxtaURqCZIm}fzFtA^Wb{R1H9DOVaBY;ely?O3tyc&=6i$KcA>zdRNF z7-8>MiIeFSz;-ybE~uUB*=Wjcy%jD<9hdXn>%lf2rVqRX#qY{+p~9W zx}A^6)Yu%yh5dkG3X77X&6#K9W2sMg?6*eS3M_OdG|xPiouZ}gOdWt}cH6^`=Di~_ zAe>VA=X)iwFACdqr^c4tKQrt3`KLIJ|LYWjaUw7Z#!GYKVlmy9lx$o3 zq8oBYh-7;0GIQAZ?E2w}+SjxSMtk;%aV;OI3NG7db#?R3#}^}>KRsy|{qD)N_&)~4 zch<4IZ@S=@Toerr?YjdQ*6-%BA6m`t)PhJ@z_186usxn-yB{$!I&FzzYuPnGVyOCaYP! zN_gfLbgp|U4ody}L#FUQ*rmYzPc0ClhK%&(u)3FnRx_nx3;n>kP|Z%g3vo+2G18h_ z?Bn0{INddw105>s)2AO1%e@d8cKol}!4o(DgoCbab*7IrLTH#!kqyGYnnKVl;yoZu zG!qWGzLzjr)S96Tt~^6kXkDh^(%y4>D_FO35ZMo9oJot$Leaab_gFPr=47muajpj) z=IS7u=ud39tSzwTEjb-ZRIm+|>h{jxkhL zN>?Mst$n09mB591%TTn6gwnhjFJ(NbCe~hWd?sM*7oz!axM&@Babm)!$L>+)`&ypw zU~EU{{Xq(aDYpg;;CtzmWy3L_kEnPcncl{uN}5@FSN>RC#DE3f{i_A0Z@Usp{MLS1 z`2oEUmC7Tn6V}*D3arg+9gMjCv^8kIGG;~95~o4pLh5Z93Wf=8xeCzxB6{zirZsgvS=UCAVJ|f7Ie%Mh@tUXA>mj1@rd3}> ze6DPL4x78Jo4v|NeY3_%>g$4frC4E;&QqFxa;?2*(c|Aij~)wr)#uw5Gyq-6pR4D4 z@kI3pCPn^<+ysG8PI3WD1=HEcQ|*wK$P}BMEMkcSL)uOJeQ3DRDH$hZYo3h(Bb`#~ zcVUUemB1aKVPhpPwz$31pDu3pS3UhupnMTfmmKE!BoW4k_zSS`c3E$Ig$BAYkp(hg z19wKAJSFoyJQ+4h zgwdJL+5WyiLT%AtqqzKHqgZoQR_&YLD7w${L58^!fmbXPwqkpA5wb&bnx^cFg^X2T zqhMbF38QqtQ8IoVp?OR7jwU5S2vKdcx|9~zM?QgJ)Qr>eF`>q}`^c7DiG}Bamugaw zJ_uU}^6R5meL&cnv@}Hj4N|>BvZ6Ru?(O5epj+{O?#p<|szpzHUY>eNFo=4ByH#Vy zt$e}7PMdvpj|l_PVx_I~X0m^(ka=o#z6S1p1QLKa1+odiLSk>!~a9WD1o> zV@*}fwEth!>H~ZCH-Er1#AP(wj}%av?-ruqry6ogHt`O0u({+ALWyMoIsUy>8N$hut;JC^psK0 zNKK{Gmf4{VEQ=~8YRj!pqwqwR5Mc%|V0`5%tQB7DQNhIWn2Zq*Sp2hSfacN1g6mNd z%T(O@E0?F(2qc)_8w*tsmpHY##!15B!4vHvKW z-&-C2YPHrvi=-z^SzmpO({*<4zER9RGKLS{^|h*&FuUS=I>xK%lp2l#8AIrvuS<5xI$342(L$Ne92Np8 zd@|{`;Kh(C3MXM6^FXypL}u(bBBjmu&Cw1!A$ArX(Ti2 z${?qluwJ7R=~1pSUU{+FCqb<;1z?kee}J^r6Jvl|#X&odNU>o2{#BypMLAK~^pZR| zZGH*XAND^Jf}Q@RO~uW7<;bi5n*P$v1rEcM@TIS?2=88^M6orYi{0( zwO1y`zcmb4WsHDq&Opb@TcpaguqF8xII*9v7 z_re690{pB+0#qs7HKU+CP^>s&bS6ayFTujC@C9iwE%$tI!W~f$5E)m(-hO}IasY>kA*OKA8lMz%Mu}s zjMqN;nHwl2(F8wRgXbXMey8?@)<{Z6!pf_u2AYE6O}V~VNy-(nyF5y^irk62n!fxI z6m>Fcq3wbl1HZ>$lis}QzIeYXa8xR3uOYJq^+fJV#IlCbzSh4jx4*t6^VU+w1q06_ zeCX83&sSlD5TBE~B&Lyw-c!~qv_Vs@dv%)#t^vhz>z1LA-Bi2J59gHpVs#B^tA7va z5iwuMtB=%02?O*M1%plfbCa@V&Kvst2RHt?;n8=w)pr5P--JZL)CmXdsc1?Ve*FgS z!HY+txrUYOW?o8}7x)#l0vEmQ(vyQUGF(K3K%7IG1&r3`lGt;PHCzr{6e->GV<#60 zK1k>Zo?s6Wl)X4mY2uO1ZpdO$m@g3m=aX(fG$S~XY+6hVKr zF{(#9{FiY^%O_urRSYYpo3&s0D4}nbNf2iYS(_PAldG2fhCJ({WxWHs*_iuJhf`OX zOtd}TUUE+nHCr12Fq6z)fSJfA-UEFu!U}|s7Ky$RbmuVR`prOMUv`_^EO5wL7m1}dDArLrzSbwNYw zGJ~#+M|I-E6H8|k?u0zw!d1WEglYp$zsl>>>u03UIehTC+W*Ja2!*k*he*EOrT#6;wqq#c@pfE7 ze9nA=`^7&sq(Q2j(>7>E1k5jutbduuUKymTLKc--pHI`#nWC&3N?8N#R6Ozht+XQf zZ6}jkb|oK22P$nYI4rA6FQiGo(4_Apyzefcxj!CTVgVv7mPBmu0TJM$et<)Cx9l6X z>NDi?lOvf?Bi7#WS^+l5(G;h{0VORx96-pcTm6TSA0Is}GS{+@#&{Csf9Co7=^eox z0{52)F8QN|;`+A=9=Wq)g$MRejsqHg)>N(2Aa+UVE-^am| z*$eit>h~X#Yc}b>^o+mu_7yB~i;_=45ZW{Hi+qV4`}ndm?d4ge?EAk!wSC+2fSc>h zqxzs%4Xq8U%}`3Fs4Tv0^idL)gIx1aT74Z8NYoBA`!blwS!}uK;E}^6jju2Nt`(f@ zJvqDWy5@Ehf-(Kln?0cC$jAW`6Qzk}5}jcUH*QS*`B3ABeqJaQ{C-nY6_^NH-jJ|4 zVHj9BcU>1r7vKF-)-r2XN0AE`MR&n6MX!y%RZ2{wHW3u!Xe-Cb@&sn0BaXiTCv1>d9WSR59UWP`8RN7YwgT1T| zwo`l(f^$Kzm*Jm^V~&H^J$-rKm|%-{c_Kd3z5OU(#SW13mO8R(?R~k@h5K62Xr5H; z?mt&Go_n&zcvRDQEuT%ked~}X2({gpVRA3sU2o>kW;=9WVxHL~yD?S*%Q_LKc7)S~ zr`fY)+21c>XT*iKy)NflTAy@f*H)T)&s~be>@e~i$#*v_ppkf?T;{U)nod#V|J8La z{!pfG7=LHx#T*#pG$Ar1>lEWKLSn|Lmehz26phoEP^(ZV?Tm9$QO+?<<(y=rjauzE z)G|7dQ|!htXr!c4OP^|g&#-+y?XTUx;eFocx$o=xUiYn`8<>gFZsVMUInk=8agz}8 z45Ck!4ULLX6ON00G$4rb8+Bbil)ja-_hQ;SKHH&tG zb0jhKflW9bY@x(PG)(2o*m+fC`07|NXlJTBx3Xa~b!Gsp%okY4eEaSHMEOWo8s4M)yP3`JJv0{gB_-P%0UY41VQ5WM?(xI%vjNZ}fCE?= zC_1I~?*=^!wDQ>u$IPs6po%KopICUrNsg~QMeS(~N!?`s?oZ5=^=0)Mgr8m(v`XrI z?TEzk9hBKJDKzP=N55IS+_PXs;*GOpl=kPY_1o_knC<^d_af)->3wWE<5QvceQ4H$ z$`8gU0xMVAKgkzOxb)Sv+kIHU-+3$2NJSYATVs-gKLLld1SXGZM+auWQrQ6~aVf0s z@bz!fyD2h4YZ7e{-AOQ)V-Q+lH=FWcmxW_(0<-DOh1vFd!ffcL{QZ^p6a+sH=7H%L zo&TJUv5ZAkBq+h9!j|tpJFVd7(>!s{8X$ux_&;H@H8|ukBV}bL55YHZ;QkI*V~Q>{kGNq)8T9$n2qkRJ-4vX8dqz~V$M!&% z4}A(bG!2$3X*1@=rXX6)RaOs0BN^?f2Z&%t6F|jjz=&kD>+81Q zb1HrSXpGsDw8qu_&FZOq6lWvTRhL^ewY1=|D{W1Wo-vB<|y&< zS-@!OLONvJJuvH4-Hyo@!rA&>+a@Z3mT`u#SKxet32HHP=hz={W=;@<1gw7L50?;m z2xVDdE9>=EV5MZy*c)NAbaahxS@pYp2#(jv=mf*unOzsne&uSa_r35f-qLcXd3pzL zxXDyBKKVUkdAr7cOoIYls%d|I68jk<-e4vw^7`e+-Pu05dX><-pai)vBE0QQz-Y%u zbccz!$=m^?VMl6T@Pk@NwOS5d1Ur6YSBwrsJGZ9eZP#dnHAx5+PC*|R5*)vYX#!h| z&X1XonO;GJK&SC4PwHSd>|4C^w-7hA=nKrw>5ZxoW4h;o&16s1i1j}N-T(Z>>K?Ko zSlUv!T-R8HDLxp2cDlhDaQm z6cU#88$PS$0>-wHOFk&_`Dxzuw;I$LY^cT;FsIetHA5-#o$*pY(5036-@a9HH1g%n zTd`8rYjNwY{eDAA+Rm6Dtlj%la&8`gPhXGKMO;%nZns@4NKk-reQ4Z3b3_$8oxHag z)i88@n}?SKtl8IJ7u1#mvSBakv59rRB=o+QfHiX?Rp#s;5_xH(5@l$PQthV3G zYo-3Er4rXeA`HDZp8Zq?c7yZfi|Kc!W+0v6HVJ%6lQQ6TnKl8Q=9A?TMbjWHbO&Bl zN&-R*yegU5UGSCb2n0BprR^$m-k60fYg!n0 z>|g*vS7oH}lH*-^C>?!%v7mb$mUl2Gd&RVfy%`l^#t+_XZn*_P)32IvvVrHkbFn=l zy0fepIpwmL8O@{_)}HV@bl_f-QZg8n_??sbA4~ZCKX@8bw@#HyKOP7r7^NQ`s(|o` z^A$lwF{sMe^i&wGVEbBu{Z_1l;{52Z-`VA(@U0HSBFi9p7DAq$w*FBF!OEc75jI8QChvd3#@S*Y_ z0^`_qEsJtxFmWg4z$Z5=c7HuWI{Lv5;+5f>%p+L+ZmV!aBVu;oojT>k$j{M##ZTtn zyu!F1hFRF~(OU}zaC-+on| zxJ#jws(E-o$UZMtvE&f`kOh>i{${q*TFth2MSVJh0R}va zs&J}g2mY~zr{npPp%*^0KO=i8v;t=3ya5%6-}LlH?C@P4ZH@v6S&AH$O*le>0rFoto?-m6c>E+-5GcyD9AI6hEs&D9DEtIpuznrUI}=5L^1yO( zxU(1LyZFkz0DgwMT^#td?F_I(zq2Cg_|-i2gi-_R&X#1 T*7S`nqebiRO&@|m#j*Z>!RhAn literal 0 HcmV?d00001 diff --git a/dev/assets/resources/index.html b/dev/assets/resources/index.html index a738df788..b54e7f337 100644 --- a/dev/assets/resources/index.html +++ b/dev/assets/resources/index.html @@ -1,2 +1,2 @@ -📚 Additional Resources · CounterfactualExplanations.jl +📚 Additional Resources · CounterfactualExplanations.jl diff --git a/dev/assets/search.js b/dev/assets/search.js deleted file mode 100644 index c133f7410..000000000 --- a/dev/assets/search.js +++ /dev/null @@ -1,267 +0,0 @@ -// Generated by Documenter.jl -requirejs.config({ - paths: { - 'lunr': 'https://cdnjs.cloudflare.com/ajax/libs/lunr.js/2.3.9/lunr.min', - 'lodash': 'https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.21/lodash.min', - 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.min', - } -}); -//////////////////////////////////////////////////////////////////////////////// -require(['jquery', 'lunr', 'lodash'], function($, lunr, _) { - -$(document).ready(function() { - // parseUri 1.2.2 - // (c) Steven Levithan - // MIT License - function parseUri (str) { - var o = parseUri.options, - m = o.parser[o.strictMode ? "strict" : "loose"].exec(str), - uri = {}, - i = 14; - - while (i--) uri[o.key[i]] = m[i] || ""; - - uri[o.q.name] = {}; - uri[o.key[12]].replace(o.q.parser, function ($0, $1, $2) { - if ($1) uri[o.q.name][$1] = $2; - }); - - return uri; - }; - parseUri.options = { - strictMode: false, - key: ["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"], - q: { - name: "queryKey", - parser: /(?:^|&)([^&=]*)=?([^&]*)/g - }, - parser: { - strict: /^(?:([^:\/?#]+):)?(?:\/\/((?:(([^:@]*)(?::([^:@]*))?)?@)?([^:\/?#]*)(?::(\d*))?))?((((?:[^?#\/]*\/)*)([^?#]*))(?:\?([^#]*))?(?:#(.*))?)/, - loose: /^(?:(?![^:@]+:[^:@\/]*@)([^:\/?#.]+):)?(?:\/\/)?((?:(([^:@]*)(?::([^:@]*))?)?@)?([^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/ - } - }; - - $("#search-form").submit(function(e) { - e.preventDefault() - }) - - // list below is the lunr 2.1.3 list minus the intersect with names(Base) - // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) - // ideally we'd just filter the original list but it's not available as a variable - lunr.stopWordFilter = lunr.generateStopWordFilter([ - 'a', - 'able', - 'about', - 'across', - 'after', - 'almost', - 'also', - 'am', - 'among', - 'an', - 'and', - 'are', - 'as', - 'at', - 'be', - 'because', - 'been', - 'but', - 'by', - 'can', - 'cannot', - 'could', - 'dear', - 'did', - 'does', - 'either', - 'ever', - 'every', - 'from', - 'got', - 'had', - 'has', - 'have', - 'he', - 'her', - 'hers', - 'him', - 'his', - 'how', - 'however', - 'i', - 'if', - 'into', - 'it', - 'its', - 'just', - 'least', - 'like', - 'likely', - 'may', - 'me', - 'might', - 'most', - 'must', - 'my', - 'neither', - 'no', - 'nor', - 'not', - 'of', - 'off', - 'often', - 'on', - 'or', - 'other', - 'our', - 'own', - 'rather', - 'said', - 'say', - 'says', - 'she', - 'should', - 'since', - 'so', - 'some', - 'than', - 'that', - 'the', - 'their', - 'them', - 'then', - 'there', - 'these', - 'they', - 'this', - 'tis', - 'to', - 'too', - 'twas', - 'us', - 'wants', - 'was', - 'we', - 'were', - 'what', - 'when', - 'who', - 'whom', - 'why', - 'will', - 'would', - 'yet', - 'you', - 'your' - ]) - - // add . as a separator, because otherwise "title": "Documenter.Anchors.add!" - // would not find anything if searching for "add!", only for the entire qualification - lunr.tokenizer.separator = /[\s\-\.]+/ - - // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names - lunr.trimmer = function (token) { - return token.update(function (s) { - return s.replace(/^[^a-zA-Z0-9@!]+/, '').replace(/[^a-zA-Z0-9@!]+$/, '') - }) - } - - lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'juliaStopWordFilter') - lunr.Pipeline.registerFunction(lunr.trimmer, 'juliaTrimmer') - - var index = lunr(function () { - this.ref('location') - this.field('title',{boost: 100}) - this.field('text') - documenterSearchIndex['docs'].forEach(function(e) { - this.add(e) - }, this) - }) - var store = {} - - documenterSearchIndex['docs'].forEach(function(e) { - store[e.location] = {title: e.title, category: e.category, page: e.page} - }) - - $(function(){ - searchresults = $('#documenter-search-results'); - searchinfo = $('#documenter-search-info'); - searchbox = $('#documenter-search-query'); - searchform = $('.docs-search'); - sidebar = $('.docs-sidebar'); - function update_search(querystring) { - tokens = lunr.tokenizer(querystring) - results = index.query(function (q) { - tokens.forEach(function (t) { - q.term(t.toString(), { - fields: ["title"], - boost: 100, - usePipeline: true, - editDistance: 0, - wildcard: lunr.Query.wildcard.NONE - }) - q.term(t.toString(), { - fields: ["title"], - boost: 10, - usePipeline: true, - editDistance: 2, - wildcard: lunr.Query.wildcard.NONE - }) - q.term(t.toString(), { - fields: ["text"], - boost: 1, - usePipeline: true, - editDistance: 0, - wildcard: lunr.Query.wildcard.NONE - }) - }) - }) - searchinfo.text("Number of results: " + results.length) - searchresults.empty() - results.forEach(function(result) { - data = store[result.ref] - link = $(''+data.title+'') - link.attr('href', documenterBaseURL+'/'+result.ref) - if (data.category != "page"){ - cat = $('('+data.category+', '+data.page+')') - } else { - cat = $('('+data.category+')') - } - li = $('
  • ').append(link).append(" ").append(cat) - searchresults.append(li) - }) - } - - function update_search_box() { - querystring = searchbox.val() - update_search(querystring) - } - - searchbox.keyup(_.debounce(update_search_box, 250)) - searchbox.change(update_search_box) - - // Disable enter-key form submission for the searchbox on the search page - // and just re-run search rather than refresh the whole page. - searchform.keypress( - function(event){ - if (event.which == '13') { - if (sidebar.hasClass('visible')) { - sidebar.removeClass('visible'); - } - update_search_box(); - event.preventDefault(); - } - } - ); - - search_query_uri = parseUri(window.location).queryKey["q"] - if(search_query_uri !== undefined) { - search_query = decodeURIComponent(search_query_uri.replace(/\+/g, '%20')) - searchbox.val(search_query) - } - update_search_box(); - }) -}) - -}) diff --git a/dev/assets/themes/documenter-dark.css b/dev/assets/themes/documenter-dark.css index c94a294dc..53889fb99 100644 --- a/dev/assets/themes/documenter-dark.css +++ b/dev/assets/themes/documenter-dark.css @@ -1,7 +1,7 @@ -@keyframes spinAround{from{transform:rotate(0deg)}to{transform:rotate(359deg)}}html.theme--documenter-dark .tabs,html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .breadcrumb,html.theme--documenter-dark .file,html.theme--documenter-dark .button,.is-unselectable,html.theme--documenter-dark .modal-close,html.theme--documenter-dark .delete{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}html.theme--documenter-dark .navbar-link:not(.is-arrowless)::after,html.theme--documenter-dark .select:not(.is-multiple):not(.is-loading)::after{border:3px solid rgba(0,0,0,0);border-radius:2px;border-right:0;border-top:0;content:" ";display:block;height:0.625em;margin-top:-0.4375em;pointer-events:none;position:absolute;top:50%;transform:rotate(-45deg);transform-origin:center;width:0.625em}html.theme--documenter-dark .admonition:not(:last-child),html.theme--documenter-dark .tabs:not(:last-child),html.theme--documenter-dark .message:not(:last-child),html.theme--documenter-dark .list:not(:last-child),html.theme--documenter-dark .level:not(:last-child),html.theme--documenter-dark .breadcrumb:not(:last-child),html.theme--documenter-dark .highlight:not(:last-child),html.theme--documenter-dark .block:not(:last-child),html.theme--documenter-dark .title:not(:last-child),html.theme--documenter-dark .subtitle:not(:last-child),html.theme--documenter-dark .table-container:not(:last-child),html.theme--documenter-dark .table:not(:last-child),html.theme--documenter-dark .progress:not(:last-child),html.theme--documenter-dark .notification:not(:last-child),html.theme--documenter-dark .content:not(:last-child),html.theme--documenter-dark .box:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .modal-close,html.theme--documenter-dark .delete{-moz-appearance:none;-webkit-appearance:none;background-color:rgba(10,10,10,0.2);border:none;border-radius:290486px;cursor:pointer;pointer-events:auto;display:inline-block;flex-grow:0;flex-shrink:0;font-size:0;height:20px;max-height:20px;max-width:20px;min-height:20px;min-width:20px;outline:none;position:relative;vertical-align:top;width:20px}html.theme--documenter-dark .modal-close::before,html.theme--documenter-dark .delete::before,html.theme--documenter-dark .modal-close::after,html.theme--documenter-dark .delete::after{background-color:#fff;content:"";display:block;left:50%;position:absolute;top:50%;transform:translateX(-50%) translateY(-50%) rotate(45deg);transform-origin:center center}html.theme--documenter-dark .modal-close::before,html.theme--documenter-dark .delete::before{height:2px;width:50%}html.theme--documenter-dark .modal-close::after,html.theme--documenter-dark .delete::after{height:50%;width:2px}html.theme--documenter-dark .modal-close:hover,html.theme--documenter-dark .delete:hover,html.theme--documenter-dark .modal-close:focus,html.theme--documenter-dark .delete:focus{background-color:rgba(10,10,10,0.3)}html.theme--documenter-dark .modal-close:active,html.theme--documenter-dark .delete:active{background-color:rgba(10,10,10,0.4)}html.theme--documenter-dark .is-small.modal-close,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.modal-close,html.theme--documenter-dark .is-small.delete,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.delete{height:16px;max-height:16px;max-width:16px;min-height:16px;min-width:16px;width:16px}html.theme--documenter-dark .is-medium.modal-close,html.theme--documenter-dark .is-medium.delete{height:24px;max-height:24px;max-width:24px;min-height:24px;min-width:24px;width:24px}html.theme--documenter-dark .is-large.modal-close,html.theme--documenter-dark .is-large.delete{height:32px;max-height:32px;max-width:32px;min-height:32px;min-width:32px;width:32px}html.theme--documenter-dark .control.is-loading::after,html.theme--documenter-dark .select.is-loading::after,html.theme--documenter-dark .loader,html.theme--documenter-dark .button.is-loading::after{animation:spinAround 500ms infinite linear;border:2px solid #dbdee0;border-radius:290486px;border-right-color:transparent;border-top-color:transparent;content:"";display:block;height:1em;position:relative;width:1em}html.theme--documenter-dark .hero-video,html.theme--documenter-dark .modal-background,html.theme--documenter-dark .modal,html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--documenter-dark .image.is-16by9 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--documenter-dark .image.is-2by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--documenter-dark .image.is-2by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--documenter-dark .image.is-3by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--documenter-dark .image.is-3by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,html.theme--documenter-dark .image.is-4by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 img,html.theme--documenter-dark .image.is-4by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,html.theme--documenter-dark .image.is-3by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 img,html.theme--documenter-dark .image.is-3by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,html.theme--documenter-dark .image.is-2by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 img,html.theme--documenter-dark .image.is-2by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,html.theme--documenter-dark .image.is-3by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 img,html.theme--documenter-dark .image.is-3by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,html.theme--documenter-dark .image.is-9by16 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 img,html.theme--documenter-dark .image.is-9by16 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,html.theme--documenter-dark .image.is-1by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2 img,html.theme--documenter-dark .image.is-1by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,html.theme--documenter-dark .image.is-1by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3 img,html.theme--documenter-dark .image.is-1by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio,.is-overlay{bottom:0;left:0;position:absolute;right:0;top:0}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .file-cta,html.theme--documenter-dark .file-name,html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid transparent;border-radius:.4em;box-shadow:none;display:inline-flex;font-size:15px;height:2.25em;justify-content:flex-start;line-height:1.5;padding-bottom:calc(0.375em - 1px);padding-left:calc(0.625em - 1px);padding-right:calc(0.625em - 1px);padding-top:calc(0.375em - 1px);position:relative;vertical-align:top}html.theme--documenter-dark .pagination-previous:focus,html.theme--documenter-dark .pagination-next:focus,html.theme--documenter-dark .pagination-link:focus,html.theme--documenter-dark .pagination-ellipsis:focus,html.theme--documenter-dark .file-cta:focus,html.theme--documenter-dark .file-name:focus,html.theme--documenter-dark .select select:focus,html.theme--documenter-dark .textarea:focus,html.theme--documenter-dark .input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:focus,html.theme--documenter-dark .button:focus,html.theme--documenter-dark .is-focused.pagination-previous,html.theme--documenter-dark .is-focused.pagination-next,html.theme--documenter-dark .is-focused.pagination-link,html.theme--documenter-dark .is-focused.pagination-ellipsis,html.theme--documenter-dark .is-focused.file-cta,html.theme--documenter-dark .is-focused.file-name,html.theme--documenter-dark .select select.is-focused,html.theme--documenter-dark .is-focused.textarea,html.theme--documenter-dark .is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-focused.button,html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active,html.theme--documenter-dark .pagination-ellipsis:active,html.theme--documenter-dark .file-cta:active,html.theme--documenter-dark .file-name:active,html.theme--documenter-dark .select select:active,html.theme--documenter-dark .textarea:active,html.theme--documenter-dark .input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:active,html.theme--documenter-dark .button:active,html.theme--documenter-dark .is-active.pagination-previous,html.theme--documenter-dark .is-active.pagination-next,html.theme--documenter-dark .is-active.pagination-link,html.theme--documenter-dark .is-active.pagination-ellipsis,html.theme--documenter-dark .is-active.file-cta,html.theme--documenter-dark .is-active.file-name,html.theme--documenter-dark .select select.is-active,html.theme--documenter-dark .is-active.textarea,html.theme--documenter-dark .is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .is-active.button{outline:none}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark .pagination-next[disabled],html.theme--documenter-dark .pagination-link[disabled],html.theme--documenter-dark .pagination-ellipsis[disabled],html.theme--documenter-dark .file-cta[disabled],html.theme--documenter-dark .file-name[disabled],html.theme--documenter-dark .select select[disabled],html.theme--documenter-dark .textarea[disabled],html.theme--documenter-dark .input[disabled],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled],html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark fieldset[disabled] .pagination-previous,fieldset[disabled] html.theme--documenter-dark .pagination-next,html.theme--documenter-dark fieldset[disabled] .pagination-next,fieldset[disabled] html.theme--documenter-dark .pagination-link,html.theme--documenter-dark fieldset[disabled] .pagination-link,fieldset[disabled] html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark fieldset[disabled] .pagination-ellipsis,fieldset[disabled] html.theme--documenter-dark .file-cta,html.theme--documenter-dark fieldset[disabled] .file-cta,fieldset[disabled] html.theme--documenter-dark .file-name,html.theme--documenter-dark fieldset[disabled] .file-name,fieldset[disabled] html.theme--documenter-dark .select select,fieldset[disabled] html.theme--documenter-dark .textarea,fieldset[disabled] html.theme--documenter-dark .input,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark fieldset[disabled] .select select,html.theme--documenter-dark .select fieldset[disabled] select,html.theme--documenter-dark fieldset[disabled] .textarea,html.theme--documenter-dark fieldset[disabled] .input,html.theme--documenter-dark fieldset[disabled] #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar fieldset[disabled] form.docs-search>input,fieldset[disabled] html.theme--documenter-dark .button,html.theme--documenter-dark fieldset[disabled] .button{cursor:not-allowed}/*! minireset.css v0.0.4 | MIT License | github.com/jgthms/minireset.css */html,body,p,ol,ul,li,dl,dt,dd,blockquote,figure,fieldset,legend,textarea,pre,iframe,hr,h1,h2,h3,h4,h5,h6{margin:0;padding:0}h1,h2,h3,h4,h5,h6{font-size:100%;font-weight:normal}ul{list-style:none}button,input,select,textarea{margin:0}html{box-sizing:border-box}*,*::before,*::after{box-sizing:inherit}img,embed,iframe,object,video{height:auto;max-width:100%}audio{max-width:100%}iframe{border:0}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}td:not([align]),th:not([align]){text-align:left}.is-clearfix::after{clear:both;content:" ";display:table}.is-pulled-left{float:left !important}.is-pulled-right{float:right !important}.is-clipped{overflow:hidden !important}.is-size-1{font-size:3rem !important}.is-size-2{font-size:2.5rem !important}.is-size-3{font-size:2rem !important}.is-size-4{font-size:1.5rem !important}.is-size-5{font-size:1.25rem !important}.is-size-6{font-size:15px !important}.is-size-7,html.theme--documenter-dark .docstring>section>a.docs-sourcelink{font-size:.85em !important}@media screen and (max-width: 768px){.is-size-1-mobile{font-size:3rem !important}.is-size-2-mobile{font-size:2.5rem !important}.is-size-3-mobile{font-size:2rem !important}.is-size-4-mobile{font-size:1.5rem !important}.is-size-5-mobile{font-size:1.25rem !important}.is-size-6-mobile{font-size:15px !important}.is-size-7-mobile{font-size:.85em !important}}@media screen and (min-width: 769px),print{.is-size-1-tablet{font-size:3rem !important}.is-size-2-tablet{font-size:2.5rem !important}.is-size-3-tablet{font-size:2rem !important}.is-size-4-tablet{font-size:1.5rem !important}.is-size-5-tablet{font-size:1.25rem !important}.is-size-6-tablet{font-size:15px !important}.is-size-7-tablet{font-size:.85em !important}}@media screen and (max-width: 1055px){.is-size-1-touch{font-size:3rem !important}.is-size-2-touch{font-size:2.5rem !important}.is-size-3-touch{font-size:2rem !important}.is-size-4-touch{font-size:1.5rem !important}.is-size-5-touch{font-size:1.25rem !important}.is-size-6-touch{font-size:15px !important}.is-size-7-touch{font-size:.85em !important}}@media screen and (min-width: 1056px){.is-size-1-desktop{font-size:3rem !important}.is-size-2-desktop{font-size:2.5rem !important}.is-size-3-desktop{font-size:2rem !important}.is-size-4-desktop{font-size:1.5rem !important}.is-size-5-desktop{font-size:1.25rem !important}.is-size-6-desktop{font-size:15px !important}.is-size-7-desktop{font-size:.85em !important}}@media screen and (min-width: 1216px){.is-size-1-widescreen{font-size:3rem !important}.is-size-2-widescreen{font-size:2.5rem !important}.is-size-3-widescreen{font-size:2rem !important}.is-size-4-widescreen{font-size:1.5rem !important}.is-size-5-widescreen{font-size:1.25rem !important}.is-size-6-widescreen{font-size:15px !important}.is-size-7-widescreen{font-size:.85em !important}}@media screen and (min-width: 1408px){.is-size-1-fullhd{font-size:3rem !important}.is-size-2-fullhd{font-size:2.5rem !important}.is-size-3-fullhd{font-size:2rem !important}.is-size-4-fullhd{font-size:1.5rem !important}.is-size-5-fullhd{font-size:1.25rem !important}.is-size-6-fullhd{font-size:15px !important}.is-size-7-fullhd{font-size:.85em !important}}.has-text-centered{text-align:center !important}.has-text-justified{text-align:justify !important}.has-text-left{text-align:left !important}.has-text-right{text-align:right !important}@media screen and (max-width: 768px){.has-text-centered-mobile{text-align:center !important}}@media screen and (min-width: 769px),print{.has-text-centered-tablet{text-align:center !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-centered-tablet-only{text-align:center !important}}@media screen and (max-width: 1055px){.has-text-centered-touch{text-align:center !important}}@media screen and (min-width: 1056px){.has-text-centered-desktop{text-align:center !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-centered-desktop-only{text-align:center !important}}@media screen and (min-width: 1216px){.has-text-centered-widescreen{text-align:center !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-centered-widescreen-only{text-align:center !important}}@media screen and (min-width: 1408px){.has-text-centered-fullhd{text-align:center !important}}@media screen and (max-width: 768px){.has-text-justified-mobile{text-align:justify !important}}@media screen and (min-width: 769px),print{.has-text-justified-tablet{text-align:justify !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-justified-tablet-only{text-align:justify !important}}@media screen and (max-width: 1055px){.has-text-justified-touch{text-align:justify !important}}@media screen and (min-width: 1056px){.has-text-justified-desktop{text-align:justify !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-justified-desktop-only{text-align:justify !important}}@media screen and (min-width: 1216px){.has-text-justified-widescreen{text-align:justify !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-justified-widescreen-only{text-align:justify !important}}@media screen and (min-width: 1408px){.has-text-justified-fullhd{text-align:justify !important}}@media screen and (max-width: 768px){.has-text-left-mobile{text-align:left !important}}@media screen and (min-width: 769px),print{.has-text-left-tablet{text-align:left !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-left-tablet-only{text-align:left !important}}@media screen and (max-width: 1055px){.has-text-left-touch{text-align:left !important}}@media screen and (min-width: 1056px){.has-text-left-desktop{text-align:left !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-left-desktop-only{text-align:left !important}}@media screen and (min-width: 1216px){.has-text-left-widescreen{text-align:left !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-left-widescreen-only{text-align:left !important}}@media screen and (min-width: 1408px){.has-text-left-fullhd{text-align:left !important}}@media screen and (max-width: 768px){.has-text-right-mobile{text-align:right !important}}@media screen and (min-width: 769px),print{.has-text-right-tablet{text-align:right !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-right-tablet-only{text-align:right !important}}@media screen and (max-width: 1055px){.has-text-right-touch{text-align:right !important}}@media screen and (min-width: 1056px){.has-text-right-desktop{text-align:right !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-right-desktop-only{text-align:right !important}}@media screen and (min-width: 1216px){.has-text-right-widescreen{text-align:right !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-right-widescreen-only{text-align:right !important}}@media screen and (min-width: 1408px){.has-text-right-fullhd{text-align:right !important}}.is-capitalized{text-transform:capitalize !important}.is-lowercase{text-transform:lowercase !important}.is-uppercase{text-transform:uppercase !important}.is-italic{font-style:italic !important}.has-text-white{color:#fff !important}a.has-text-white:hover,a.has-text-white:focus{color:#e6e6e6 !important}.has-background-white{background-color:#fff !important}.has-text-black{color:#0a0a0a !important}a.has-text-black:hover,a.has-text-black:focus{color:#000 !important}.has-background-black{background-color:#0a0a0a !important}.has-text-light{color:#ecf0f1 !important}a.has-text-light:hover,a.has-text-light:focus{color:#cfd9db !important}.has-background-light{background-color:#ecf0f1 !important}.has-text-dark{color:#282f2f !important}a.has-text-dark:hover,a.has-text-dark:focus{color:#111414 !important}.has-background-dark{background-color:#282f2f !important}.has-text-primary{color:#375a7f !important}a.has-text-primary:hover,a.has-text-primary:focus{color:#28415b !important}.has-background-primary{background-color:#375a7f !important}.has-text-link{color:#1abc9c !important}a.has-text-link:hover,a.has-text-link:focus{color:#148f77 !important}.has-background-link{background-color:#1abc9c !important}.has-text-info{color:#024c7d !important}a.has-text-info:hover,a.has-text-info:focus{color:#012d4b !important}.has-background-info{background-color:#024c7d !important}.has-text-success{color:#008438 !important}a.has-text-success:hover,a.has-text-success:focus{color:#005122 !important}.has-background-success{background-color:#008438 !important}.has-text-warning{color:#ad8100 !important}a.has-text-warning:hover,a.has-text-warning:focus{color:#7a5b00 !important}.has-background-warning{background-color:#ad8100 !important}.has-text-danger{color:#9e1b0d !important}a.has-text-danger:hover,a.has-text-danger:focus{color:#6f1309 !important}.has-background-danger{background-color:#9e1b0d !important}.has-text-black-bis{color:#121212 !important}.has-background-black-bis{background-color:#121212 !important}.has-text-black-ter{color:#242424 !important}.has-background-black-ter{background-color:#242424 !important}.has-text-grey-darker{color:#282f2f !important}.has-background-grey-darker{background-color:#282f2f !important}.has-text-grey-dark{color:#343c3d !important}.has-background-grey-dark{background-color:#343c3d !important}.has-text-grey{color:#5e6d6f !important}.has-background-grey{background-color:#5e6d6f !important}.has-text-grey-light{color:#8c9b9d !important}.has-background-grey-light{background-color:#8c9b9d !important}.has-text-grey-lighter{color:#dbdee0 !important}.has-background-grey-lighter{background-color:#dbdee0 !important}.has-text-white-ter{color:#ecf0f1 !important}.has-background-white-ter{background-color:#ecf0f1 !important}.has-text-white-bis{color:#fafafa !important}.has-background-white-bis{background-color:#fafafa !important}.has-text-weight-light{font-weight:300 !important}.has-text-weight-normal{font-weight:400 !important}.has-text-weight-medium{font-weight:500 !important}.has-text-weight-semibold{font-weight:600 !important}.has-text-weight-bold{font-weight:700 !important}.is-family-primary{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-secondary{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-sans-serif{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-monospace{font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace !important}.is-family-code{font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace !important}.is-block{display:block !important}@media screen and (max-width: 768px){.is-block-mobile{display:block !important}}@media screen and (min-width: 769px),print{.is-block-tablet{display:block !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-block-tablet-only{display:block !important}}@media screen and (max-width: 1055px){.is-block-touch{display:block !important}}@media screen and (min-width: 1056px){.is-block-desktop{display:block !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-block-desktop-only{display:block !important}}@media screen and (min-width: 1216px){.is-block-widescreen{display:block !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-block-widescreen-only{display:block !important}}@media screen and (min-width: 1408px){.is-block-fullhd{display:block !important}}.is-flex{display:flex !important}@media screen and (max-width: 768px){.is-flex-mobile{display:flex !important}}@media screen and (min-width: 769px),print{.is-flex-tablet{display:flex !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-flex-tablet-only{display:flex !important}}@media screen and (max-width: 1055px){.is-flex-touch{display:flex !important}}@media screen and (min-width: 1056px){.is-flex-desktop{display:flex !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-flex-desktop-only{display:flex !important}}@media screen and (min-width: 1216px){.is-flex-widescreen{display:flex !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-flex-widescreen-only{display:flex !important}}@media screen and (min-width: 1408px){.is-flex-fullhd{display:flex !important}}.is-inline{display:inline !important}@media screen and (max-width: 768px){.is-inline-mobile{display:inline !important}}@media screen and (min-width: 769px),print{.is-inline-tablet{display:inline !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-tablet-only{display:inline !important}}@media screen and (max-width: 1055px){.is-inline-touch{display:inline !important}}@media screen and (min-width: 1056px){.is-inline-desktop{display:inline !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-desktop-only{display:inline !important}}@media screen and (min-width: 1216px){.is-inline-widescreen{display:inline !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-widescreen-only{display:inline !important}}@media screen and (min-width: 1408px){.is-inline-fullhd{display:inline !important}}.is-inline-block{display:inline-block !important}@media screen and (max-width: 768px){.is-inline-block-mobile{display:inline-block !important}}@media screen and (min-width: 769px),print{.is-inline-block-tablet{display:inline-block !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-block-tablet-only{display:inline-block !important}}@media screen and (max-width: 1055px){.is-inline-block-touch{display:inline-block !important}}@media screen and (min-width: 1056px){.is-inline-block-desktop{display:inline-block !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-block-desktop-only{display:inline-block !important}}@media screen and (min-width: 1216px){.is-inline-block-widescreen{display:inline-block !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-block-widescreen-only{display:inline-block !important}}@media screen and (min-width: 1408px){.is-inline-block-fullhd{display:inline-block !important}}.is-inline-flex{display:inline-flex !important}@media screen and (max-width: 768px){.is-inline-flex-mobile{display:inline-flex !important}}@media screen and (min-width: 769px),print{.is-inline-flex-tablet{display:inline-flex !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-flex-tablet-only{display:inline-flex !important}}@media screen and (max-width: 1055px){.is-inline-flex-touch{display:inline-flex !important}}@media screen and (min-width: 1056px){.is-inline-flex-desktop{display:inline-flex !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-flex-desktop-only{display:inline-flex !important}}@media screen and (min-width: 1216px){.is-inline-flex-widescreen{display:inline-flex !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-flex-widescreen-only{display:inline-flex !important}}@media screen and (min-width: 1408px){.is-inline-flex-fullhd{display:inline-flex !important}}.is-hidden{display:none !important}.is-sr-only{border:none !important;clip:rect(0, 0, 0, 0) !important;height:0.01em !important;overflow:hidden !important;padding:0 !important;position:absolute !important;white-space:nowrap !important;width:0.01em !important}@media screen and (max-width: 768px){.is-hidden-mobile{display:none !important}}@media screen and (min-width: 769px),print{.is-hidden-tablet{display:none !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-hidden-tablet-only{display:none !important}}@media screen and (max-width: 1055px){.is-hidden-touch{display:none !important}}@media screen and (min-width: 1056px){.is-hidden-desktop{display:none !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-hidden-desktop-only{display:none !important}}@media screen and (min-width: 1216px){.is-hidden-widescreen{display:none !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-hidden-widescreen-only{display:none !important}}@media screen and (min-width: 1408px){.is-hidden-fullhd{display:none !important}}.is-invisible{visibility:hidden !important}@media screen and (max-width: 768px){.is-invisible-mobile{visibility:hidden !important}}@media screen and (min-width: 769px),print{.is-invisible-tablet{visibility:hidden !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-invisible-tablet-only{visibility:hidden !important}}@media screen and (max-width: 1055px){.is-invisible-touch{visibility:hidden !important}}@media screen and (min-width: 1056px){.is-invisible-desktop{visibility:hidden !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-invisible-desktop-only{visibility:hidden !important}}@media screen and (min-width: 1216px){.is-invisible-widescreen{visibility:hidden !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-invisible-widescreen-only{visibility:hidden !important}}@media screen and (min-width: 1408px){.is-invisible-fullhd{visibility:hidden !important}}.is-marginless{margin:0 !important}.is-paddingless{padding:0 !important}.is-radiusless{border-radius:0 !important}.is-shadowless{box-shadow:none !important}.is-relative{position:relative !important}html.theme--documenter-dark{/*! +html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .file-cta,html.theme--documenter-dark .file-name,html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid transparent;border-radius:.4em;box-shadow:none;display:inline-flex;font-size:1rem;height:2.5em;justify-content:flex-start;line-height:1.5;padding-bottom:calc(0.5em - 1px);padding-left:calc(0.75em - 1px);padding-right:calc(0.75em - 1px);padding-top:calc(0.5em - 1px);position:relative;vertical-align:top}html.theme--documenter-dark .pagination-previous:focus,html.theme--documenter-dark .pagination-next:focus,html.theme--documenter-dark .pagination-link:focus,html.theme--documenter-dark .pagination-ellipsis:focus,html.theme--documenter-dark .file-cta:focus,html.theme--documenter-dark .file-name:focus,html.theme--documenter-dark .select select:focus,html.theme--documenter-dark .textarea:focus,html.theme--documenter-dark .input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:focus,html.theme--documenter-dark .button:focus,html.theme--documenter-dark .is-focused.pagination-previous,html.theme--documenter-dark .is-focused.pagination-next,html.theme--documenter-dark .is-focused.pagination-link,html.theme--documenter-dark .is-focused.pagination-ellipsis,html.theme--documenter-dark .is-focused.file-cta,html.theme--documenter-dark .is-focused.file-name,html.theme--documenter-dark .select select.is-focused,html.theme--documenter-dark .is-focused.textarea,html.theme--documenter-dark .is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-focused.button,html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active,html.theme--documenter-dark .pagination-ellipsis:active,html.theme--documenter-dark .file-cta:active,html.theme--documenter-dark .file-name:active,html.theme--documenter-dark .select select:active,html.theme--documenter-dark .textarea:active,html.theme--documenter-dark .input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:active,html.theme--documenter-dark .button:active,html.theme--documenter-dark .is-active.pagination-previous,html.theme--documenter-dark .is-active.pagination-next,html.theme--documenter-dark .is-active.pagination-link,html.theme--documenter-dark .is-active.pagination-ellipsis,html.theme--documenter-dark .is-active.file-cta,html.theme--documenter-dark .is-active.file-name,html.theme--documenter-dark .select select.is-active,html.theme--documenter-dark .is-active.textarea,html.theme--documenter-dark .is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .is-active.button{outline:none}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark .pagination-next[disabled],html.theme--documenter-dark .pagination-link[disabled],html.theme--documenter-dark .pagination-ellipsis[disabled],html.theme--documenter-dark .file-cta[disabled],html.theme--documenter-dark .file-name[disabled],html.theme--documenter-dark .select select[disabled],html.theme--documenter-dark .textarea[disabled],html.theme--documenter-dark .input[disabled],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled],html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark fieldset[disabled] .pagination-previous,fieldset[disabled] html.theme--documenter-dark .pagination-next,html.theme--documenter-dark fieldset[disabled] .pagination-next,fieldset[disabled] html.theme--documenter-dark .pagination-link,html.theme--documenter-dark fieldset[disabled] .pagination-link,fieldset[disabled] html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark fieldset[disabled] .pagination-ellipsis,fieldset[disabled] html.theme--documenter-dark .file-cta,html.theme--documenter-dark fieldset[disabled] .file-cta,fieldset[disabled] html.theme--documenter-dark .file-name,html.theme--documenter-dark fieldset[disabled] .file-name,fieldset[disabled] html.theme--documenter-dark .select select,fieldset[disabled] html.theme--documenter-dark .textarea,fieldset[disabled] html.theme--documenter-dark .input,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark fieldset[disabled] .select select,html.theme--documenter-dark .select fieldset[disabled] select,html.theme--documenter-dark fieldset[disabled] .textarea,html.theme--documenter-dark fieldset[disabled] .input,html.theme--documenter-dark fieldset[disabled] #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar fieldset[disabled] form.docs-search>input,fieldset[disabled] html.theme--documenter-dark .button,html.theme--documenter-dark fieldset[disabled] .button{cursor:not-allowed}html.theme--documenter-dark .tabs,html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .breadcrumb,html.theme--documenter-dark .file,html.theme--documenter-dark .button,.is-unselectable{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}html.theme--documenter-dark .navbar-link:not(.is-arrowless)::after,html.theme--documenter-dark .select:not(.is-multiple):not(.is-loading)::after{border:3px solid rgba(0,0,0,0);border-radius:2px;border-right:0;border-top:0;content:" ";display:block;height:0.625em;margin-top:-0.4375em;pointer-events:none;position:absolute;top:50%;transform:rotate(-45deg);transform-origin:center;width:0.625em}html.theme--documenter-dark .admonition:not(:last-child),html.theme--documenter-dark .tabs:not(:last-child),html.theme--documenter-dark .pagination:not(:last-child),html.theme--documenter-dark .message:not(:last-child),html.theme--documenter-dark .level:not(:last-child),html.theme--documenter-dark .breadcrumb:not(:last-child),html.theme--documenter-dark .block:not(:last-child),html.theme--documenter-dark .title:not(:last-child),html.theme--documenter-dark .subtitle:not(:last-child),html.theme--documenter-dark .table-container:not(:last-child),html.theme--documenter-dark .table:not(:last-child),html.theme--documenter-dark .progress:not(:last-child),html.theme--documenter-dark .notification:not(:last-child),html.theme--documenter-dark .content:not(:last-child),html.theme--documenter-dark .box:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .modal-close,html.theme--documenter-dark .delete{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;-moz-appearance:none;-webkit-appearance:none;background-color:rgba(10,10,10,0.2);border:none;border-radius:9999px;cursor:pointer;pointer-events:auto;display:inline-block;flex-grow:0;flex-shrink:0;font-size:0;height:20px;max-height:20px;max-width:20px;min-height:20px;min-width:20px;outline:none;position:relative;vertical-align:top;width:20px}html.theme--documenter-dark .modal-close::before,html.theme--documenter-dark .delete::before,html.theme--documenter-dark .modal-close::after,html.theme--documenter-dark .delete::after{background-color:#fff;content:"";display:block;left:50%;position:absolute;top:50%;transform:translateX(-50%) translateY(-50%) rotate(45deg);transform-origin:center center}html.theme--documenter-dark .modal-close::before,html.theme--documenter-dark .delete::before{height:2px;width:50%}html.theme--documenter-dark .modal-close::after,html.theme--documenter-dark .delete::after{height:50%;width:2px}html.theme--documenter-dark .modal-close:hover,html.theme--documenter-dark .delete:hover,html.theme--documenter-dark .modal-close:focus,html.theme--documenter-dark .delete:focus{background-color:rgba(10,10,10,0.3)}html.theme--documenter-dark .modal-close:active,html.theme--documenter-dark .delete:active{background-color:rgba(10,10,10,0.4)}html.theme--documenter-dark .is-small.modal-close,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.modal-close,html.theme--documenter-dark .is-small.delete,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.delete{height:16px;max-height:16px;max-width:16px;min-height:16px;min-width:16px;width:16px}html.theme--documenter-dark .is-medium.modal-close,html.theme--documenter-dark .is-medium.delete{height:24px;max-height:24px;max-width:24px;min-height:24px;min-width:24px;width:24px}html.theme--documenter-dark .is-large.modal-close,html.theme--documenter-dark .is-large.delete{height:32px;max-height:32px;max-width:32px;min-height:32px;min-width:32px;width:32px}html.theme--documenter-dark .control.is-loading::after,html.theme--documenter-dark .select.is-loading::after,html.theme--documenter-dark .loader,html.theme--documenter-dark .button.is-loading::after{animation:spinAround 500ms infinite linear;border:2px solid #dbdee0;border-radius:9999px;border-right-color:transparent;border-top-color:transparent;content:"";display:block;height:1em;position:relative;width:1em}html.theme--documenter-dark .hero-video,html.theme--documenter-dark .modal-background,html.theme--documenter-dark .modal,html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--documenter-dark .image.is-16by9 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--documenter-dark .image.is-2by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--documenter-dark .image.is-2by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--documenter-dark .image.is-3by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--documenter-dark .image.is-3by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,html.theme--documenter-dark .image.is-4by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 img,html.theme--documenter-dark .image.is-4by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,html.theme--documenter-dark .image.is-3by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 img,html.theme--documenter-dark .image.is-3by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,html.theme--documenter-dark .image.is-2by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 img,html.theme--documenter-dark .image.is-2by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,html.theme--documenter-dark .image.is-3by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 img,html.theme--documenter-dark .image.is-3by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,html.theme--documenter-dark .image.is-9by16 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 img,html.theme--documenter-dark .image.is-9by16 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,html.theme--documenter-dark .image.is-1by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2 img,html.theme--documenter-dark .image.is-1by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,html.theme--documenter-dark .image.is-1by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3 img,html.theme--documenter-dark .image.is-1by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio,.is-overlay{bottom:0;left:0;position:absolute;right:0;top:0}html.theme--documenter-dark .navbar-burger{-moz-appearance:none;-webkit-appearance:none;appearance:none;background:none;border:none;color:currentColor;font-family:inherit;font-size:1em;margin:0;padding:0}/*! minireset.css v0.0.6 | MIT License | github.com/jgthms/minireset.css */html,body,p,ol,ul,li,dl,dt,dd,blockquote,figure,fieldset,legend,textarea,pre,iframe,hr,h1,h2,h3,h4,h5,h6{margin:0;padding:0}h1,h2,h3,h4,h5,h6{font-size:100%;font-weight:normal}ul{list-style:none}button,input,select,textarea{margin:0}html{box-sizing:border-box}*,*::before,*::after{box-sizing:inherit}img,video{height:auto;max-width:100%}iframe{border:0}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}td:not([align]),th:not([align]){text-align:inherit}.has-text-white{color:#fff !important}a.has-text-white:hover,a.has-text-white:focus{color:#e6e6e6 !important}.has-background-white{background-color:#fff !important}.has-text-black{color:#0a0a0a !important}a.has-text-black:hover,a.has-text-black:focus{color:#000 !important}.has-background-black{background-color:#0a0a0a !important}.has-text-light{color:#ecf0f1 !important}a.has-text-light:hover,a.has-text-light:focus{color:#cfd9db !important}.has-background-light{background-color:#ecf0f1 !important}.has-text-dark{color:#282f2f !important}a.has-text-dark:hover,a.has-text-dark:focus{color:#111414 !important}.has-background-dark{background-color:#282f2f !important}.has-text-primary{color:#375a7f !important}a.has-text-primary:hover,a.has-text-primary:focus{color:#28415b !important}.has-background-primary{background-color:#375a7f !important}.has-text-primary-light{color:#f1f5f9 !important}a.has-text-primary-light:hover,a.has-text-primary-light:focus{color:#cddbe9 !important}.has-background-primary-light{background-color:#f1f5f9 !important}.has-text-primary-dark{color:#4d7eb2 !important}a.has-text-primary-dark:hover,a.has-text-primary-dark:focus{color:#7198c1 !important}.has-background-primary-dark{background-color:#4d7eb2 !important}.has-text-link{color:#1abc9c !important}a.has-text-link:hover,a.has-text-link:focus{color:#148f77 !important}.has-background-link{background-color:#1abc9c !important}.has-text-link-light{color:#edfdf9 !important}a.has-text-link-light:hover,a.has-text-link-light:focus{color:#c0f6ec !important}.has-background-link-light{background-color:#edfdf9 !important}.has-text-link-dark{color:#15987e !important}a.has-text-link-dark:hover,a.has-text-link-dark:focus{color:#1bc5a4 !important}.has-background-link-dark{background-color:#15987e !important}.has-text-info{color:#024c7d !important}a.has-text-info:hover,a.has-text-info:focus{color:#012d4b !important}.has-background-info{background-color:#024c7d !important}.has-text-info-light{color:#ebf7ff !important}a.has-text-info-light:hover,a.has-text-info-light:focus{color:#b9e2fe !important}.has-background-info-light{background-color:#ebf7ff !important}.has-text-info-dark{color:#0e9dfb !important}a.has-text-info-dark:hover,a.has-text-info-dark:focus{color:#40b1fc !important}.has-background-info-dark{background-color:#0e9dfb !important}.has-text-success{color:#008438 !important}a.has-text-success:hover,a.has-text-success:focus{color:#005122 !important}.has-background-success{background-color:#008438 !important}.has-text-success-light{color:#ebfff3 !important}a.has-text-success-light:hover,a.has-text-success-light:focus{color:#b8ffd6 !important}.has-background-success-light{background-color:#ebfff3 !important}.has-text-success-dark{color:#00eb64 !important}a.has-text-success-dark:hover,a.has-text-success-dark:focus{color:#1fff7e !important}.has-background-success-dark{background-color:#00eb64 !important}.has-text-warning{color:#ad8100 !important}a.has-text-warning:hover,a.has-text-warning:focus{color:#7a5b00 !important}.has-background-warning{background-color:#ad8100 !important}.has-text-warning-light{color:#fffaeb !important}a.has-text-warning-light:hover,a.has-text-warning-light:focus{color:#ffedb8 !important}.has-background-warning-light{background-color:#fffaeb !important}.has-text-warning-dark{color:#d19c00 !important}a.has-text-warning-dark:hover,a.has-text-warning-dark:focus{color:#ffbf05 !important}.has-background-warning-dark{background-color:#d19c00 !important}.has-text-danger{color:#9e1b0d !important}a.has-text-danger:hover,a.has-text-danger:focus{color:#6f1309 !important}.has-background-danger{background-color:#9e1b0d !important}.has-text-danger-light{color:#fdeeec !important}a.has-text-danger-light:hover,a.has-text-danger-light:focus{color:#fac3bd !important}.has-background-danger-light{background-color:#fdeeec !important}.has-text-danger-dark{color:#ec311d !important}a.has-text-danger-dark:hover,a.has-text-danger-dark:focus{color:#f05c4c !important}.has-background-danger-dark{background-color:#ec311d !important}.has-text-black-bis{color:#121212 !important}.has-background-black-bis{background-color:#121212 !important}.has-text-black-ter{color:#242424 !important}.has-background-black-ter{background-color:#242424 !important}.has-text-grey-darker{color:#282f2f !important}.has-background-grey-darker{background-color:#282f2f !important}.has-text-grey-dark{color:#343c3d !important}.has-background-grey-dark{background-color:#343c3d !important}.has-text-grey{color:#5e6d6f !important}.has-background-grey{background-color:#5e6d6f !important}.has-text-grey-light{color:#8c9b9d !important}.has-background-grey-light{background-color:#8c9b9d !important}.has-text-grey-lighter{color:#dbdee0 !important}.has-background-grey-lighter{background-color:#dbdee0 !important}.has-text-white-ter{color:#ecf0f1 !important}.has-background-white-ter{background-color:#ecf0f1 !important}.has-text-white-bis{color:#fafafa !important}.has-background-white-bis{background-color:#fafafa !important}.is-flex-direction-row{flex-direction:row !important}.is-flex-direction-row-reverse{flex-direction:row-reverse !important}.is-flex-direction-column{flex-direction:column !important}.is-flex-direction-column-reverse{flex-direction:column-reverse !important}.is-flex-wrap-nowrap{flex-wrap:nowrap !important}.is-flex-wrap-wrap{flex-wrap:wrap !important}.is-flex-wrap-wrap-reverse{flex-wrap:wrap-reverse !important}.is-justify-content-flex-start{justify-content:flex-start !important}.is-justify-content-flex-end{justify-content:flex-end !important}.is-justify-content-center{justify-content:center !important}.is-justify-content-space-between{justify-content:space-between !important}.is-justify-content-space-around{justify-content:space-around !important}.is-justify-content-space-evenly{justify-content:space-evenly !important}.is-justify-content-start{justify-content:start !important}.is-justify-content-end{justify-content:end !important}.is-justify-content-left{justify-content:left !important}.is-justify-content-right{justify-content:right !important}.is-align-content-flex-start{align-content:flex-start !important}.is-align-content-flex-end{align-content:flex-end !important}.is-align-content-center{align-content:center !important}.is-align-content-space-between{align-content:space-between !important}.is-align-content-space-around{align-content:space-around !important}.is-align-content-space-evenly{align-content:space-evenly !important}.is-align-content-stretch{align-content:stretch !important}.is-align-content-start{align-content:start !important}.is-align-content-end{align-content:end !important}.is-align-content-baseline{align-content:baseline !important}.is-align-items-stretch{align-items:stretch !important}.is-align-items-flex-start{align-items:flex-start !important}.is-align-items-flex-end{align-items:flex-end !important}.is-align-items-center{align-items:center !important}.is-align-items-baseline{align-items:baseline !important}.is-align-items-start{align-items:start !important}.is-align-items-end{align-items:end !important}.is-align-items-self-start{align-items:self-start !important}.is-align-items-self-end{align-items:self-end !important}.is-align-self-auto{align-self:auto !important}.is-align-self-flex-start{align-self:flex-start !important}.is-align-self-flex-end{align-self:flex-end !important}.is-align-self-center{align-self:center !important}.is-align-self-baseline{align-self:baseline !important}.is-align-self-stretch{align-self:stretch !important}.is-flex-grow-0{flex-grow:0 !important}.is-flex-grow-1{flex-grow:1 !important}.is-flex-grow-2{flex-grow:2 !important}.is-flex-grow-3{flex-grow:3 !important}.is-flex-grow-4{flex-grow:4 !important}.is-flex-grow-5{flex-grow:5 !important}.is-flex-shrink-0{flex-shrink:0 !important}.is-flex-shrink-1{flex-shrink:1 !important}.is-flex-shrink-2{flex-shrink:2 !important}.is-flex-shrink-3{flex-shrink:3 !important}.is-flex-shrink-4{flex-shrink:4 !important}.is-flex-shrink-5{flex-shrink:5 !important}.is-clearfix::after{clear:both;content:" ";display:table}.is-pulled-left{float:left !important}.is-pulled-right{float:right !important}.is-radiusless{border-radius:0 !important}.is-shadowless{box-shadow:none !important}.is-clickable{cursor:pointer !important;pointer-events:all !important}.is-clipped{overflow:hidden !important}.is-relative{position:relative !important}.is-marginless{margin:0 !important}.is-paddingless{padding:0 !important}.m-0{margin:0 !important}.mt-0{margin-top:0 !important}.mr-0{margin-right:0 !important}.mb-0{margin-bottom:0 !important}.ml-0{margin-left:0 !important}.mx-0{margin-left:0 !important;margin-right:0 !important}.my-0{margin-top:0 !important;margin-bottom:0 !important}.m-1{margin:.25rem !important}.mt-1{margin-top:.25rem !important}.mr-1{margin-right:.25rem !important}.mb-1{margin-bottom:.25rem !important}.ml-1{margin-left:.25rem !important}.mx-1{margin-left:.25rem !important;margin-right:.25rem !important}.my-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.m-2{margin:.5rem !important}.mt-2{margin-top:.5rem !important}.mr-2{margin-right:.5rem !important}.mb-2{margin-bottom:.5rem !important}.ml-2{margin-left:.5rem !important}.mx-2{margin-left:.5rem !important;margin-right:.5rem !important}.my-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.m-3{margin:.75rem !important}.mt-3{margin-top:.75rem !important}.mr-3{margin-right:.75rem !important}.mb-3{margin-bottom:.75rem !important}.ml-3{margin-left:.75rem !important}.mx-3{margin-left:.75rem !important;margin-right:.75rem !important}.my-3{margin-top:.75rem !important;margin-bottom:.75rem !important}.m-4{margin:1rem !important}.mt-4{margin-top:1rem !important}.mr-4{margin-right:1rem !important}.mb-4{margin-bottom:1rem !important}.ml-4{margin-left:1rem !important}.mx-4{margin-left:1rem !important;margin-right:1rem !important}.my-4{margin-top:1rem !important;margin-bottom:1rem !important}.m-5{margin:1.5rem !important}.mt-5{margin-top:1.5rem !important}.mr-5{margin-right:1.5rem !important}.mb-5{margin-bottom:1.5rem !important}.ml-5{margin-left:1.5rem !important}.mx-5{margin-left:1.5rem !important;margin-right:1.5rem !important}.my-5{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.m-6{margin:3rem !important}.mt-6{margin-top:3rem !important}.mr-6{margin-right:3rem !important}.mb-6{margin-bottom:3rem !important}.ml-6{margin-left:3rem !important}.mx-6{margin-left:3rem !important;margin-right:3rem !important}.my-6{margin-top:3rem !important;margin-bottom:3rem !important}.m-auto{margin:auto !important}.mt-auto{margin-top:auto !important}.mr-auto{margin-right:auto !important}.mb-auto{margin-bottom:auto !important}.ml-auto{margin-left:auto !important}.mx-auto{margin-left:auto !important;margin-right:auto !important}.my-auto{margin-top:auto !important;margin-bottom:auto !important}.p-0{padding:0 !important}.pt-0{padding-top:0 !important}.pr-0{padding-right:0 !important}.pb-0{padding-bottom:0 !important}.pl-0{padding-left:0 !important}.px-0{padding-left:0 !important;padding-right:0 !important}.py-0{padding-top:0 !important;padding-bottom:0 !important}.p-1{padding:.25rem !important}.pt-1{padding-top:.25rem !important}.pr-1{padding-right:.25rem !important}.pb-1{padding-bottom:.25rem !important}.pl-1{padding-left:.25rem !important}.px-1{padding-left:.25rem !important;padding-right:.25rem !important}.py-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.p-2{padding:.5rem !important}.pt-2{padding-top:.5rem !important}.pr-2{padding-right:.5rem !important}.pb-2{padding-bottom:.5rem !important}.pl-2{padding-left:.5rem !important}.px-2{padding-left:.5rem !important;padding-right:.5rem !important}.py-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.p-3{padding:.75rem !important}.pt-3{padding-top:.75rem !important}.pr-3{padding-right:.75rem !important}.pb-3{padding-bottom:.75rem !important}.pl-3{padding-left:.75rem !important}.px-3{padding-left:.75rem !important;padding-right:.75rem !important}.py-3{padding-top:.75rem !important;padding-bottom:.75rem !important}.p-4{padding:1rem !important}.pt-4{padding-top:1rem !important}.pr-4{padding-right:1rem !important}.pb-4{padding-bottom:1rem !important}.pl-4{padding-left:1rem !important}.px-4{padding-left:1rem !important;padding-right:1rem !important}.py-4{padding-top:1rem !important;padding-bottom:1rem !important}.p-5{padding:1.5rem !important}.pt-5{padding-top:1.5rem !important}.pr-5{padding-right:1.5rem !important}.pb-5{padding-bottom:1.5rem !important}.pl-5{padding-left:1.5rem !important}.px-5{padding-left:1.5rem !important;padding-right:1.5rem !important}.py-5{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.p-6{padding:3rem !important}.pt-6{padding-top:3rem !important}.pr-6{padding-right:3rem !important}.pb-6{padding-bottom:3rem !important}.pl-6{padding-left:3rem !important}.px-6{padding-left:3rem !important;padding-right:3rem !important}.py-6{padding-top:3rem !important;padding-bottom:3rem !important}.p-auto{padding:auto !important}.pt-auto{padding-top:auto !important}.pr-auto{padding-right:auto !important}.pb-auto{padding-bottom:auto !important}.pl-auto{padding-left:auto !important}.px-auto{padding-left:auto !important;padding-right:auto !important}.py-auto{padding-top:auto !important;padding-bottom:auto !important}.is-size-1{font-size:3rem !important}.is-size-2{font-size:2.5rem !important}.is-size-3{font-size:2rem !important}.is-size-4{font-size:1.5rem !important}.is-size-5{font-size:1.25rem !important}.is-size-6{font-size:1rem !important}.is-size-7,html.theme--documenter-dark .docstring>section>a.docs-sourcelink{font-size:.75rem !important}@media screen and (max-width: 768px){.is-size-1-mobile{font-size:3rem !important}.is-size-2-mobile{font-size:2.5rem !important}.is-size-3-mobile{font-size:2rem !important}.is-size-4-mobile{font-size:1.5rem !important}.is-size-5-mobile{font-size:1.25rem !important}.is-size-6-mobile{font-size:1rem !important}.is-size-7-mobile{font-size:.75rem !important}}@media screen and (min-width: 769px),print{.is-size-1-tablet{font-size:3rem !important}.is-size-2-tablet{font-size:2.5rem !important}.is-size-3-tablet{font-size:2rem !important}.is-size-4-tablet{font-size:1.5rem !important}.is-size-5-tablet{font-size:1.25rem !important}.is-size-6-tablet{font-size:1rem !important}.is-size-7-tablet{font-size:.75rem !important}}@media screen and (max-width: 1055px){.is-size-1-touch{font-size:3rem !important}.is-size-2-touch{font-size:2.5rem !important}.is-size-3-touch{font-size:2rem !important}.is-size-4-touch{font-size:1.5rem !important}.is-size-5-touch{font-size:1.25rem !important}.is-size-6-touch{font-size:1rem !important}.is-size-7-touch{font-size:.75rem !important}}@media screen and (min-width: 1056px){.is-size-1-desktop{font-size:3rem !important}.is-size-2-desktop{font-size:2.5rem !important}.is-size-3-desktop{font-size:2rem !important}.is-size-4-desktop{font-size:1.5rem !important}.is-size-5-desktop{font-size:1.25rem !important}.is-size-6-desktop{font-size:1rem !important}.is-size-7-desktop{font-size:.75rem !important}}@media screen and (min-width: 1216px){.is-size-1-widescreen{font-size:3rem !important}.is-size-2-widescreen{font-size:2.5rem !important}.is-size-3-widescreen{font-size:2rem !important}.is-size-4-widescreen{font-size:1.5rem !important}.is-size-5-widescreen{font-size:1.25rem !important}.is-size-6-widescreen{font-size:1rem !important}.is-size-7-widescreen{font-size:.75rem !important}}@media screen and (min-width: 1408px){.is-size-1-fullhd{font-size:3rem !important}.is-size-2-fullhd{font-size:2.5rem !important}.is-size-3-fullhd{font-size:2rem !important}.is-size-4-fullhd{font-size:1.5rem !important}.is-size-5-fullhd{font-size:1.25rem !important}.is-size-6-fullhd{font-size:1rem !important}.is-size-7-fullhd{font-size:.75rem !important}}.has-text-centered{text-align:center !important}.has-text-justified{text-align:justify !important}.has-text-left{text-align:left !important}.has-text-right{text-align:right !important}@media screen and (max-width: 768px){.has-text-centered-mobile{text-align:center !important}}@media screen and (min-width: 769px),print{.has-text-centered-tablet{text-align:center !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-centered-tablet-only{text-align:center !important}}@media screen and (max-width: 1055px){.has-text-centered-touch{text-align:center !important}}@media screen and (min-width: 1056px){.has-text-centered-desktop{text-align:center !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-centered-desktop-only{text-align:center !important}}@media screen and (min-width: 1216px){.has-text-centered-widescreen{text-align:center !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-centered-widescreen-only{text-align:center !important}}@media screen and (min-width: 1408px){.has-text-centered-fullhd{text-align:center !important}}@media screen and (max-width: 768px){.has-text-justified-mobile{text-align:justify !important}}@media screen and (min-width: 769px),print{.has-text-justified-tablet{text-align:justify !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-justified-tablet-only{text-align:justify !important}}@media screen and (max-width: 1055px){.has-text-justified-touch{text-align:justify !important}}@media screen and (min-width: 1056px){.has-text-justified-desktop{text-align:justify !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-justified-desktop-only{text-align:justify !important}}@media screen and (min-width: 1216px){.has-text-justified-widescreen{text-align:justify !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-justified-widescreen-only{text-align:justify !important}}@media screen and (min-width: 1408px){.has-text-justified-fullhd{text-align:justify !important}}@media screen and (max-width: 768px){.has-text-left-mobile{text-align:left !important}}@media screen and (min-width: 769px),print{.has-text-left-tablet{text-align:left !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-left-tablet-only{text-align:left !important}}@media screen and (max-width: 1055px){.has-text-left-touch{text-align:left !important}}@media screen and (min-width: 1056px){.has-text-left-desktop{text-align:left !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-left-desktop-only{text-align:left !important}}@media screen and (min-width: 1216px){.has-text-left-widescreen{text-align:left !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-left-widescreen-only{text-align:left !important}}@media screen and (min-width: 1408px){.has-text-left-fullhd{text-align:left !important}}@media screen and (max-width: 768px){.has-text-right-mobile{text-align:right !important}}@media screen and (min-width: 769px),print{.has-text-right-tablet{text-align:right !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-right-tablet-only{text-align:right !important}}@media screen and (max-width: 1055px){.has-text-right-touch{text-align:right !important}}@media screen and (min-width: 1056px){.has-text-right-desktop{text-align:right !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-right-desktop-only{text-align:right !important}}@media screen and (min-width: 1216px){.has-text-right-widescreen{text-align:right !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-right-widescreen-only{text-align:right !important}}@media screen and (min-width: 1408px){.has-text-right-fullhd{text-align:right !important}}.is-capitalized{text-transform:capitalize !important}.is-lowercase{text-transform:lowercase !important}.is-uppercase{text-transform:uppercase !important}.is-italic{font-style:italic !important}.is-underlined{text-decoration:underline !important}.has-text-weight-light{font-weight:300 !important}.has-text-weight-normal{font-weight:400 !important}.has-text-weight-medium{font-weight:500 !important}.has-text-weight-semibold{font-weight:600 !important}.has-text-weight-bold{font-weight:700 !important}.is-family-primary{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-secondary{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-sans-serif{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-monospace{font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace !important}.is-family-code{font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace !important}.is-block{display:block !important}@media screen and (max-width: 768px){.is-block-mobile{display:block !important}}@media screen and (min-width: 769px),print{.is-block-tablet{display:block !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-block-tablet-only{display:block !important}}@media screen and (max-width: 1055px){.is-block-touch{display:block !important}}@media screen and (min-width: 1056px){.is-block-desktop{display:block !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-block-desktop-only{display:block !important}}@media screen and (min-width: 1216px){.is-block-widescreen{display:block !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-block-widescreen-only{display:block !important}}@media screen and (min-width: 1408px){.is-block-fullhd{display:block !important}}.is-flex{display:flex !important}@media screen and (max-width: 768px){.is-flex-mobile{display:flex !important}}@media screen and (min-width: 769px),print{.is-flex-tablet{display:flex !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-flex-tablet-only{display:flex !important}}@media screen and (max-width: 1055px){.is-flex-touch{display:flex !important}}@media screen and (min-width: 1056px){.is-flex-desktop{display:flex !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-flex-desktop-only{display:flex !important}}@media screen and (min-width: 1216px){.is-flex-widescreen{display:flex !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-flex-widescreen-only{display:flex !important}}@media screen and (min-width: 1408px){.is-flex-fullhd{display:flex !important}}.is-inline{display:inline !important}@media screen and (max-width: 768px){.is-inline-mobile{display:inline !important}}@media screen and (min-width: 769px),print{.is-inline-tablet{display:inline !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-tablet-only{display:inline !important}}@media screen and (max-width: 1055px){.is-inline-touch{display:inline !important}}@media screen and (min-width: 1056px){.is-inline-desktop{display:inline !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-desktop-only{display:inline !important}}@media screen and (min-width: 1216px){.is-inline-widescreen{display:inline !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-widescreen-only{display:inline !important}}@media screen and (min-width: 1408px){.is-inline-fullhd{display:inline !important}}.is-inline-block{display:inline-block !important}@media screen and (max-width: 768px){.is-inline-block-mobile{display:inline-block !important}}@media screen and (min-width: 769px),print{.is-inline-block-tablet{display:inline-block !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-block-tablet-only{display:inline-block !important}}@media screen and (max-width: 1055px){.is-inline-block-touch{display:inline-block !important}}@media screen and (min-width: 1056px){.is-inline-block-desktop{display:inline-block !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-block-desktop-only{display:inline-block !important}}@media screen and (min-width: 1216px){.is-inline-block-widescreen{display:inline-block !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-block-widescreen-only{display:inline-block !important}}@media screen and (min-width: 1408px){.is-inline-block-fullhd{display:inline-block !important}}.is-inline-flex{display:inline-flex !important}@media screen and (max-width: 768px){.is-inline-flex-mobile{display:inline-flex !important}}@media screen and (min-width: 769px),print{.is-inline-flex-tablet{display:inline-flex !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-flex-tablet-only{display:inline-flex !important}}@media screen and (max-width: 1055px){.is-inline-flex-touch{display:inline-flex !important}}@media screen and (min-width: 1056px){.is-inline-flex-desktop{display:inline-flex !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-flex-desktop-only{display:inline-flex !important}}@media screen and (min-width: 1216px){.is-inline-flex-widescreen{display:inline-flex !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-flex-widescreen-only{display:inline-flex !important}}@media screen and (min-width: 1408px){.is-inline-flex-fullhd{display:inline-flex !important}}.is-hidden{display:none !important}.is-sr-only{border:none !important;clip:rect(0, 0, 0, 0) !important;height:0.01em !important;overflow:hidden !important;padding:0 !important;position:absolute !important;white-space:nowrap !important;width:0.01em !important}@media screen and (max-width: 768px){.is-hidden-mobile{display:none !important}}@media screen and (min-width: 769px),print{.is-hidden-tablet{display:none !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-hidden-tablet-only{display:none !important}}@media screen and (max-width: 1055px){.is-hidden-touch{display:none !important}}@media screen and (min-width: 1056px){.is-hidden-desktop{display:none !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-hidden-desktop-only{display:none !important}}@media screen and (min-width: 1216px){.is-hidden-widescreen{display:none !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-hidden-widescreen-only{display:none !important}}@media screen and (min-width: 1408px){.is-hidden-fullhd{display:none !important}}.is-invisible{visibility:hidden !important}@media screen and (max-width: 768px){.is-invisible-mobile{visibility:hidden !important}}@media screen and (min-width: 769px),print{.is-invisible-tablet{visibility:hidden !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-invisible-tablet-only{visibility:hidden !important}}@media screen and (max-width: 1055px){.is-invisible-touch{visibility:hidden !important}}@media screen and (min-width: 1056px){.is-invisible-desktop{visibility:hidden !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-invisible-desktop-only{visibility:hidden !important}}@media screen and (min-width: 1216px){.is-invisible-widescreen{visibility:hidden !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-invisible-widescreen-only{visibility:hidden !important}}@media screen and (min-width: 1408px){.is-invisible-fullhd{visibility:hidden !important}}html.theme--documenter-dark{/*! Theme: a11y-dark Author: @ericwbailey Maintainer: @ericwbailey Based on the Tomorrow Night Eighties theme: https://github.com/isagalaev/highlight.js/blob/master/src/styles/tomorrow-night-eighties.css -*/}html.theme--documenter-dark html{background-color:#1f2424;font-size:16px;-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;min-width:300px;overflow-x:auto;overflow-y:scroll;text-rendering:optimizeLegibility;text-size-adjust:100%}html.theme--documenter-dark article,html.theme--documenter-dark aside,html.theme--documenter-dark figure,html.theme--documenter-dark footer,html.theme--documenter-dark header,html.theme--documenter-dark hgroup,html.theme--documenter-dark section{display:block}html.theme--documenter-dark body,html.theme--documenter-dark button,html.theme--documenter-dark input,html.theme--documenter-dark select,html.theme--documenter-dark textarea{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif}html.theme--documenter-dark code,html.theme--documenter-dark pre{-moz-osx-font-smoothing:auto;-webkit-font-smoothing:auto;font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace}html.theme--documenter-dark body{color:#fff;font-size:1em;font-weight:400;line-height:1.5}html.theme--documenter-dark a{color:#1abc9c;cursor:pointer;text-decoration:none}html.theme--documenter-dark a strong{color:currentColor}html.theme--documenter-dark a:hover{color:#1dd2af}html.theme--documenter-dark code{background-color:rgba(255,255,255,0.05);color:#ececec;font-size:.875em;font-weight:normal;padding:.1em}html.theme--documenter-dark hr{background-color:#282f2f;border:none;display:block;height:2px;margin:1.5rem 0}html.theme--documenter-dark img{height:auto;max-width:100%}html.theme--documenter-dark input[type="checkbox"],html.theme--documenter-dark input[type="radio"]{vertical-align:baseline}html.theme--documenter-dark small{font-size:.875em}html.theme--documenter-dark span{font-style:inherit;font-weight:inherit}html.theme--documenter-dark strong{color:#f2f2f2;font-weight:700}html.theme--documenter-dark fieldset{border:none}html.theme--documenter-dark pre{-webkit-overflow-scrolling:touch;background-color:#282f2f;color:#fff;font-size:.875em;overflow-x:auto;padding:1.25rem 1.5rem;white-space:pre;word-wrap:normal}html.theme--documenter-dark pre code{background-color:transparent;color:currentColor;font-size:1em;padding:0}html.theme--documenter-dark table td,html.theme--documenter-dark table th{vertical-align:top}html.theme--documenter-dark table td:not([align]),html.theme--documenter-dark table th:not([align]){text-align:left}html.theme--documenter-dark table th{color:#f2f2f2}html.theme--documenter-dark .box{background-color:#343c3d;border-radius:8px;box-shadow:none;color:#fff;display:block;padding:1.25rem}html.theme--documenter-dark a.box:hover,html.theme--documenter-dark a.box:focus{box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px #1abc9c}html.theme--documenter-dark a.box:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2),0 0 0 1px #1abc9c}html.theme--documenter-dark .button{background-color:#282f2f;border-color:#4c5759;border-width:1px;color:#375a7f;cursor:pointer;justify-content:center;padding-bottom:calc(0.375em - 1px);padding-left:.75em;padding-right:.75em;padding-top:calc(0.375em - 1px);text-align:center;white-space:nowrap}html.theme--documenter-dark .button strong{color:inherit}html.theme--documenter-dark .button .icon,html.theme--documenter-dark .button .icon.is-small,html.theme--documenter-dark .button #documenter .docs-sidebar form.docs-search>input.icon,html.theme--documenter-dark #documenter .docs-sidebar .button form.docs-search>input.icon,html.theme--documenter-dark .button .icon.is-medium,html.theme--documenter-dark .button .icon.is-large{height:1.5em;width:1.5em}html.theme--documenter-dark .button .icon:first-child:not(:last-child){margin-left:calc(-0.375em - 1px);margin-right:0.1875em}html.theme--documenter-dark .button .icon:last-child:not(:first-child){margin-left:0.1875em;margin-right:calc(-0.375em - 1px)}html.theme--documenter-dark .button .icon:first-child:last-child{margin-left:calc(-0.375em - 1px);margin-right:calc(-0.375em - 1px)}html.theme--documenter-dark .button:hover,html.theme--documenter-dark .button.is-hovered{border-color:#8c9b9d;color:#f2f2f2}html.theme--documenter-dark .button:focus,html.theme--documenter-dark .button.is-focused{border-color:#8c9b9d;color:#17a689}html.theme--documenter-dark .button:focus:not(:active),html.theme--documenter-dark .button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .button:active,html.theme--documenter-dark .button.is-active{border-color:#343c3d;color:#f2f2f2}html.theme--documenter-dark .button.is-text{background-color:transparent;border-color:transparent;color:#fff;text-decoration:underline}html.theme--documenter-dark .button.is-text:hover,html.theme--documenter-dark .button.is-text.is-hovered,html.theme--documenter-dark .button.is-text:focus,html.theme--documenter-dark .button.is-text.is-focused{background-color:#282f2f;color:#f2f2f2}html.theme--documenter-dark .button.is-text:active,html.theme--documenter-dark .button.is-text.is-active{background-color:#1d2122;color:#f2f2f2}html.theme--documenter-dark .button.is-text[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-text{background-color:transparent;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-white{background-color:#fff;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .button.is-white:hover,html.theme--documenter-dark .button.is-white.is-hovered{background-color:#f9f9f9;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .button.is-white:focus,html.theme--documenter-dark .button.is-white.is-focused{border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .button.is-white:focus:not(:active),html.theme--documenter-dark .button.is-white.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--documenter-dark .button.is-white:active,html.theme--documenter-dark .button.is-white.is-active{background-color:#f2f2f2;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .button.is-white[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white{background-color:#fff;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-white.is-inverted{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-white.is-inverted:hover,html.theme--documenter-dark .button.is-white.is-inverted.is-hovered{background-color:#000}html.theme--documenter-dark .button.is-white.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-inverted{background-color:#0a0a0a;border-color:transparent;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-white.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-white.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-white.is-outlined:hover,html.theme--documenter-dark .button.is-white.is-outlined.is-hovered,html.theme--documenter-dark .button.is-white.is-outlined:focus,html.theme--documenter-dark .button.is-white.is-outlined.is-focused{background-color:#fff;border-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-white.is-outlined.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-white.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-white.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-focused{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black{background-color:#0a0a0a;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:hover,html.theme--documenter-dark .button.is-black.is-hovered{background-color:#040404;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:focus,html.theme--documenter-dark .button.is-black.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:focus:not(:active),html.theme--documenter-dark .button.is-black.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}html.theme--documenter-dark .button.is-black:active,html.theme--documenter-dark .button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black{background-color:#0a0a0a;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-black.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;color:#282f2f}html.theme--documenter-dark .button.is-light:hover,html.theme--documenter-dark .button.is-light.is-hovered{background-color:#e5eaec;border-color:transparent;color:#282f2f}html.theme--documenter-dark .button.is-light:focus,html.theme--documenter-dark .button.is-light.is-focused{border-color:transparent;color:#282f2f}html.theme--documenter-dark .button.is-light:focus:not(:active),html.theme--documenter-dark .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light.is-active{background-color:#dde4e6;border-color:transparent;color:#282f2f}html.theme--documenter-dark .button.is-light[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-light.is-inverted{background-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-hovered{background-color:#1d2122}html.theme--documenter-dark .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted{background-color:#282f2f;border-color:transparent;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-outlined.is-focused{background-color:#ecf0f1;border-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-focused{background-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#ecf0f1}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#ecf0f1}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#ecf0f1}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#ecf0f1}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-dark.is-inverted,html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted:hover,html.theme--documenter-dark .content kbd.button.is-inverted:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-hovered{background-color:#dde4e6}html.theme--documenter-dark .button.is-dark.is-inverted[disabled],html.theme--documenter-dark .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#ecf0f1;border-color:transparent;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-loading::after,html.theme--documenter-dark .content kbd.button.is-loading::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#ecf0f1;color:#ecf0f1}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus:not(:active),html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus:not(:active),html.theme--documenter-dark .button.is-primary.is-focused:not(:active),html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(55,90,127,0.25)}html.theme--documenter-dark .button.is-primary:active,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:active,html.theme--documenter-dark .button.is-primary.is-active,html.theme--documenter-dark .docstring>section>a.button.is-active.docs-sourcelink{background-color:#2f4d6d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary[disabled],html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-primary.is-inverted,html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-inverted:hover,html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-inverted.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-hovered.docs-sourcelink{background-color:#f2f2f2}html.theme--documenter-dark .button.is-primary.is-inverted[disabled],html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-inverted,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;border-color:transparent;box-shadow:none;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-loading.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-outlined:hover,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-outlined.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:focus::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined[disabled],html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-outlined,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;box-shadow:none;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined:hover,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-focused.docs-sourcelink{background-color:#fff;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined[disabled],html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-link{background-color:#1abc9c;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-link:hover,html.theme--documenter-dark .button.is-link.is-hovered{background-color:#18b193;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-link:focus,html.theme--documenter-dark .button.is-link.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-link:focus:not(:active),html.theme--documenter-dark .button.is-link.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .button.is-link:active,html.theme--documenter-dark .button.is-link.is-active{background-color:#17a689;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-link[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link{background-color:#1abc9c;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-link.is-inverted{background-color:#fff;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted:hover,html.theme--documenter-dark .button.is-link.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-link.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-link.is-outlined{background-color:transparent;border-color:#1abc9c;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-outlined:hover,html.theme--documenter-dark .button.is-link.is-outlined.is-hovered,html.theme--documenter-dark .button.is-link.is-outlined:focus,html.theme--documenter-dark .button.is-link.is-outlined.is-focused{background-color:#1abc9c;border-color:#1abc9c;color:#fff}html.theme--documenter-dark .button.is-link.is-outlined.is-loading::after{border-color:transparent transparent #1abc9c #1abc9c !important}html.theme--documenter-dark .button.is-link.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-link.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-link.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-link.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-outlined{background-color:transparent;border-color:#1abc9c;box-shadow:none;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-focused{background-color:#fff;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #1abc9c #1abc9c !important}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-info{background-color:#024c7d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-info:hover,html.theme--documenter-dark .button.is-info.is-hovered{background-color:#024470;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-info:focus,html.theme--documenter-dark .button.is-info.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-info:focus:not(:active),html.theme--documenter-dark .button.is-info.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(2,76,125,0.25)}html.theme--documenter-dark .button.is-info:active,html.theme--documenter-dark .button.is-info.is-active{background-color:#023d64;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-info[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-info{background-color:#024c7d;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-info.is-inverted{background-color:#fff;color:#024c7d}html.theme--documenter-dark .button.is-info.is-inverted:hover,html.theme--documenter-dark .button.is-info.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-info.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-info.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#024c7d}html.theme--documenter-dark .button.is-info.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-info.is-outlined{background-color:transparent;border-color:#024c7d;color:#024c7d}html.theme--documenter-dark .button.is-info.is-outlined:hover,html.theme--documenter-dark .button.is-info.is-outlined.is-hovered,html.theme--documenter-dark .button.is-info.is-outlined:focus,html.theme--documenter-dark .button.is-info.is-outlined.is-focused{background-color:#024c7d;border-color:#024c7d;color:#fff}html.theme--documenter-dark .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #024c7d #024c7d !important}html.theme--documenter-dark .button.is-info.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-info.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-info.is-outlined{background-color:transparent;border-color:#024c7d;box-shadow:none;color:#024c7d}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-focused{background-color:#fff;color:#024c7d}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #024c7d #024c7d !important}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-success{background-color:#008438;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-success:hover,html.theme--documenter-dark .button.is-success.is-hovered{background-color:#073;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-success:focus,html.theme--documenter-dark .button.is-success.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-success:focus:not(:active),html.theme--documenter-dark .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(0,132,56,0.25)}html.theme--documenter-dark .button.is-success:active,html.theme--documenter-dark .button.is-success.is-active{background-color:#006b2d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-success[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success{background-color:#008438;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-success.is-inverted{background-color:#fff;color:#008438}html.theme--documenter-dark .button.is-success.is-inverted:hover,html.theme--documenter-dark .button.is-success.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#008438}html.theme--documenter-dark .button.is-success.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#008438;color:#008438}html.theme--documenter-dark .button.is-success.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-outlined.is-focused{background-color:#008438;border-color:#008438;color:#fff}html.theme--documenter-dark .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #008438 #008438 !important}html.theme--documenter-dark .button.is-success.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#008438;box-shadow:none;color:#008438}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#008438}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #008438 #008438 !important}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-warning{background-color:#ad8100;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-warning:hover,html.theme--documenter-dark .button.is-warning.is-hovered{background-color:#a07700;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-warning:focus,html.theme--documenter-dark .button.is-warning.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-warning:focus:not(:active),html.theme--documenter-dark .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(173,129,0,0.25)}html.theme--documenter-dark .button.is-warning:active,html.theme--documenter-dark .button.is-warning.is-active{background-color:#946e00;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-warning[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning{background-color:#ad8100;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-warning.is-inverted{background-color:#fff;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#ad8100;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-outlined.is-focused{background-color:#ad8100;border-color:#ad8100;color:#fff}html.theme--documenter-dark .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #ad8100 #ad8100 !important}html.theme--documenter-dark .button.is-warning.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#ad8100;box-shadow:none;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-focused{background-color:#fff;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ad8100 #ad8100 !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:hover,html.theme--documenter-dark .button.is-danger.is-hovered{background-color:#92190c;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus,html.theme--documenter-dark .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus:not(:active),html.theme--documenter-dark .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(158,27,13,0.25)}html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger.is-active{background-color:#86170b;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-outlined.is-focused{background-color:#9e1b0d;border-color:#9e1b0d;color:#fff}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:3px;font-size:.85em}html.theme--documenter-dark .button.is-normal{font-size:15px}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#dbdee0;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em / 2));top:calc(50% - (1em / 2));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:290486px;padding-left:1em;padding-right:1em}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:0.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){border-radius:3px;font-size:.85em}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}html.theme--documenter-dark .container.is-fluid{margin-left:32px;margin-right:32px;max-width:none}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen{max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd{max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container{max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container{max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:left}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.85em}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--documenter-dark .icon.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--documenter-dark .icon.is-medium{height:2rem;width:2rem}html.theme--documenter-dark .icon.is-large{height:3rem;width:3rem}html.theme--documenter-dark .image,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--documenter-dark .image img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:290486px}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--documenter-dark .image.is-16by9 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--documenter-dark .image.is-2by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--documenter-dark .image.is-2by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--documenter-dark .image.is-3by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--documenter-dark .image.is-3by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,html.theme--documenter-dark .image.is-4by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 img,html.theme--documenter-dark .image.is-4by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,html.theme--documenter-dark .image.is-3by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 img,html.theme--documenter-dark .image.is-3by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,html.theme--documenter-dark .image.is-2by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 img,html.theme--documenter-dark .image.is-2by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,html.theme--documenter-dark .image.is-3by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 img,html.theme--documenter-dark .image.is-3by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,html.theme--documenter-dark .image.is-9by16 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 img,html.theme--documenter-dark .image.is-9by16 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,html.theme--documenter-dark .image.is-1by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2 img,html.theme--documenter-dark .image.is-1by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,html.theme--documenter-dark .image.is-1by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3 img,html.theme--documenter-dark .image.is-1by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio{height:100%;width:100%}html.theme--documenter-dark .image.is-square,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square,html.theme--documenter-dark .image.is-1by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1{padding-top:100%}html.theme--documenter-dark .image.is-5by4,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4{padding-top:80%}html.theme--documenter-dark .image.is-4by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}html.theme--documenter-dark .image.is-3by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}html.theme--documenter-dark .image.is-5by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}html.theme--documenter-dark .image.is-16by9,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}html.theme--documenter-dark .image.is-2by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}html.theme--documenter-dark .image.is-3by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--documenter-dark .image.is-4by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--documenter-dark .image.is-3by4,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--documenter-dark .image.is-2by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--documenter-dark .image.is-3by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--documenter-dark .image.is-9by16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16{padding-top:177.7777%}html.theme--documenter-dark .image.is-1by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2{padding-top:200%}html.theme--documenter-dark .image.is-1by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3{padding-top:300%}html.theme--documenter-dark .image.is-16x16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16x16{height:16px;width:16px}html.theme--documenter-dark .image.is-24x24,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-24x24{height:24px;width:24px}html.theme--documenter-dark .image.is-32x32,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-32x32{height:32px;width:32px}html.theme--documenter-dark .image.is-48x48,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-48x48{height:48px;width:48px}html.theme--documenter-dark .image.is-64x64,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-64x64{height:64px;width:64px}html.theme--documenter-dark .image.is-96x96,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-96x96{height:96px;width:96px}html.theme--documenter-dark .image.is-128x128,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-128x128{height:128px;width:128px}html.theme--documenter-dark .notification{background-color:#282f2f;border-radius:.4em;padding:1.25rem 2.5rem 1.25rem 1.5rem;position:relative}html.theme--documenter-dark .notification a:not(.button):not(.dropdown-item){color:currentColor;text-decoration:underline}html.theme--documenter-dark .notification strong{color:currentColor}html.theme--documenter-dark .notification code,html.theme--documenter-dark .notification pre{background:#fff}html.theme--documenter-dark .notification pre code{background:transparent}html.theme--documenter-dark .notification>.delete{position:absolute;right:0.5rem;top:0.5rem}html.theme--documenter-dark .notification .title,html.theme--documenter-dark .notification .subtitle,html.theme--documenter-dark .notification .content{color:currentColor}html.theme--documenter-dark .notification.is-white{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .notification.is-black{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .notification.is-light{background-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .notification.is-dark,html.theme--documenter-dark .content kbd.notification{background-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .notification.is-primary,html.theme--documenter-dark .docstring>section>a.notification.docs-sourcelink{background-color:#375a7f;color:#fff}html.theme--documenter-dark .notification.is-link{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .notification.is-info{background-color:#024c7d;color:#fff}html.theme--documenter-dark .notification.is-success{background-color:#008438;color:#fff}html.theme--documenter-dark .notification.is-warning{background-color:#ad8100;color:#fff}html.theme--documenter-dark .notification.is-danger{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .progress{-moz-appearance:none;-webkit-appearance:none;border:none;border-radius:290486px;display:block;height:15px;overflow:hidden;padding:0;width:100%}html.theme--documenter-dark .progress::-webkit-progress-bar{background-color:#5e6d6f}html.theme--documenter-dark .progress::-webkit-progress-value{background-color:#dbdee0}html.theme--documenter-dark .progress::-moz-progress-bar{background-color:#dbdee0}html.theme--documenter-dark .progress::-ms-fill{background-color:#dbdee0;border:none}html.theme--documenter-dark .progress.is-white::-webkit-progress-value{background-color:#fff}html.theme--documenter-dark .progress.is-white::-moz-progress-bar{background-color:#fff}html.theme--documenter-dark .progress.is-white::-ms-fill{background-color:#fff}html.theme--documenter-dark .progress.is-white:indeterminate{background-image:linear-gradient(to right, #fff 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-black::-webkit-progress-value{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black::-moz-progress-bar{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black::-ms-fill{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black:indeterminate{background-image:linear-gradient(to right, #0a0a0a 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-light::-webkit-progress-value{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light::-moz-progress-bar{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light::-ms-fill{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light:indeterminate{background-image:linear-gradient(to right, #ecf0f1 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-dark::-webkit-progress-value,html.theme--documenter-dark .content kbd.progress::-webkit-progress-value{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark::-moz-progress-bar,html.theme--documenter-dark .content kbd.progress::-moz-progress-bar{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark::-ms-fill,html.theme--documenter-dark .content kbd.progress::-ms-fill{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark:indeterminate,html.theme--documenter-dark .content kbd.progress:indeterminate{background-image:linear-gradient(to right, #282f2f 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-primary::-webkit-progress-value,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-webkit-progress-value{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary::-moz-progress-bar,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-moz-progress-bar{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary::-ms-fill,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-ms-fill{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary:indeterminate,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink:indeterminate{background-image:linear-gradient(to right, #375a7f 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-link::-webkit-progress-value{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link::-moz-progress-bar{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link::-ms-fill{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link:indeterminate{background-image:linear-gradient(to right, #1abc9c 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-info::-webkit-progress-value{background-color:#024c7d}html.theme--documenter-dark .progress.is-info::-moz-progress-bar{background-color:#024c7d}html.theme--documenter-dark .progress.is-info::-ms-fill{background-color:#024c7d}html.theme--documenter-dark .progress.is-info:indeterminate{background-image:linear-gradient(to right, #024c7d 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-success::-webkit-progress-value{background-color:#008438}html.theme--documenter-dark .progress.is-success::-moz-progress-bar{background-color:#008438}html.theme--documenter-dark .progress.is-success::-ms-fill{background-color:#008438}html.theme--documenter-dark .progress.is-success:indeterminate{background-image:linear-gradient(to right, #008438 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-warning::-webkit-progress-value{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning::-moz-progress-bar{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning::-ms-fill{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning:indeterminate{background-image:linear-gradient(to right, #ad8100 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress.is-danger::-webkit-progress-value{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger::-moz-progress-bar{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger::-ms-fill{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger:indeterminate{background-image:linear-gradient(to right, #9e1b0d 30%, #5e6d6f 30%)}html.theme--documenter-dark .progress:indeterminate{animation-duration:1.5s;animation-iteration-count:infinite;animation-name:moveIndeterminate;animation-timing-function:linear;background-color:#5e6d6f;background-image:linear-gradient(to right, #fff 30%, #5e6d6f 30%);background-position:top left;background-repeat:no-repeat;background-size:150% 150%}html.theme--documenter-dark .progress:indeterminate::-webkit-progress-bar{background-color:transparent}html.theme--documenter-dark .progress:indeterminate::-moz-progress-bar{background-color:transparent}html.theme--documenter-dark .progress.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.progress{height:.85em}html.theme--documenter-dark .progress.is-medium{height:1.25rem}html.theme--documenter-dark .progress.is-large{height:1.5rem}@keyframes moveIndeterminate{from{background-position:200% 0}to{background-position:-200% 0}}html.theme--documenter-dark .table{background-color:#343c3d;color:#fff}html.theme--documenter-dark .table td,html.theme--documenter-dark .table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .table td.is-white,html.theme--documenter-dark .table th.is-white{background-color:#fff;border-color:#fff;color:#0a0a0a}html.theme--documenter-dark .table td.is-black,html.theme--documenter-dark .table th.is-black{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .table td.is-light,html.theme--documenter-dark .table th.is-light{background-color:#ecf0f1;border-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .table td.is-dark,html.theme--documenter-dark .table th.is-dark{background-color:#282f2f;border-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .table td.is-primary,html.theme--documenter-dark .table th.is-primary{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .table td.is-link,html.theme--documenter-dark .table th.is-link{background-color:#1abc9c;border-color:#1abc9c;color:#fff}html.theme--documenter-dark .table td.is-info,html.theme--documenter-dark .table th.is-info{background-color:#024c7d;border-color:#024c7d;color:#fff}html.theme--documenter-dark .table td.is-success,html.theme--documenter-dark .table th.is-success{background-color:#008438;border-color:#008438;color:#fff}html.theme--documenter-dark .table td.is-warning,html.theme--documenter-dark .table th.is-warning{background-color:#ad8100;border-color:#ad8100;color:#fff}html.theme--documenter-dark .table td.is-danger,html.theme--documenter-dark .table th.is-danger{background-color:#9e1b0d;border-color:#9e1b0d;color:#fff}html.theme--documenter-dark .table td.is-narrow,html.theme--documenter-dark .table th.is-narrow{white-space:nowrap;width:1%}html.theme--documenter-dark .table td.is-selected,html.theme--documenter-dark .table th.is-selected{background-color:#375a7f;color:#fff}html.theme--documenter-dark .table td.is-selected a,html.theme--documenter-dark .table td.is-selected strong,html.theme--documenter-dark .table th.is-selected a,html.theme--documenter-dark .table th.is-selected strong{color:currentColor}html.theme--documenter-dark .table th{color:#f2f2f2}html.theme--documenter-dark .table th:not([align]){text-align:left}html.theme--documenter-dark .table tr.is-selected{background-color:#375a7f;color:#fff}html.theme--documenter-dark .table tr.is-selected a,html.theme--documenter-dark .table tr.is-selected strong{color:currentColor}html.theme--documenter-dark .table tr.is-selected td,html.theme--documenter-dark .table tr.is-selected th{border-color:#fff;color:currentColor}html.theme--documenter-dark .table thead{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table thead td,html.theme--documenter-dark .table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .table tfoot{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table tfoot td,html.theme--documenter-dark .table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .table tbody{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table tbody tr:last-child td,html.theme--documenter-dark .table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .table.is-bordered td,html.theme--documenter-dark .table.is-bordered th{border-width:1px}html.theme--documenter-dark .table.is-bordered tr:last-child td,html.theme--documenter-dark .table.is-bordered tr:last-child th{border-bottom-width:1px}html.theme--documenter-dark .table.is-fullwidth{width:100%}html.theme--documenter-dark .table.is-hoverable tbody tr:not(.is-selected):hover{background-color:#282f2f}html.theme--documenter-dark .table.is-hoverable.is-striped tbody tr:not(.is-selected):hover{background-color:#282f2f}html.theme--documenter-dark .table.is-hoverable.is-striped tbody tr:not(.is-selected):hover:nth-child(even){background-color:#2d3435}html.theme--documenter-dark .table.is-narrow td,html.theme--documenter-dark .table.is-narrow th{padding:0.25em 0.5em}html.theme--documenter-dark .table.is-striped tbody tr:not(.is-selected):nth-child(even){background-color:#282f2f}html.theme--documenter-dark .table-container{-webkit-overflow-scrolling:touch;overflow:auto;overflow-y:hidden;max-width:100%}html.theme--documenter-dark .tags{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .tags .tag,html.theme--documenter-dark .tags .content kbd,html.theme--documenter-dark .content .tags kbd,html.theme--documenter-dark .tags .docstring>section>a.docs-sourcelink{margin-bottom:0.5rem}html.theme--documenter-dark .tags .tag:not(:last-child),html.theme--documenter-dark .tags .content kbd:not(:last-child),html.theme--documenter-dark .content .tags kbd:not(:last-child),html.theme--documenter-dark .tags .docstring>section>a.docs-sourcelink:not(:last-child){margin-right:0.5rem}html.theme--documenter-dark .tags:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .tags:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .tags.are-medium .tag:not(.is-normal):not(.is-large),html.theme--documenter-dark .tags.are-medium .content kbd:not(.is-normal):not(.is-large),html.theme--documenter-dark .content .tags.are-medium kbd:not(.is-normal):not(.is-large),html.theme--documenter-dark .tags.are-medium .docstring>section>a.docs-sourcelink:not(.is-normal):not(.is-large){font-size:15px}html.theme--documenter-dark .tags.are-large .tag:not(.is-normal):not(.is-medium),html.theme--documenter-dark .tags.are-large .content kbd:not(.is-normal):not(.is-medium),html.theme--documenter-dark .content .tags.are-large kbd:not(.is-normal):not(.is-medium),html.theme--documenter-dark .tags.are-large .docstring>section>a.docs-sourcelink:not(.is-normal):not(.is-medium){font-size:1.25rem}html.theme--documenter-dark .tags.is-centered{justify-content:center}html.theme--documenter-dark .tags.is-centered .tag,html.theme--documenter-dark .tags.is-centered .content kbd,html.theme--documenter-dark .content .tags.is-centered kbd,html.theme--documenter-dark .tags.is-centered .docstring>section>a.docs-sourcelink{margin-right:0.25rem;margin-left:0.25rem}html.theme--documenter-dark .tags.is-right{justify-content:flex-end}html.theme--documenter-dark .tags.is-right .tag:not(:first-child),html.theme--documenter-dark .tags.is-right .content kbd:not(:first-child),html.theme--documenter-dark .content .tags.is-right kbd:not(:first-child),html.theme--documenter-dark .tags.is-right .docstring>section>a.docs-sourcelink:not(:first-child){margin-left:0.5rem}html.theme--documenter-dark .tags.is-right .tag:not(:last-child),html.theme--documenter-dark .tags.is-right .content kbd:not(:last-child),html.theme--documenter-dark .content .tags.is-right kbd:not(:last-child),html.theme--documenter-dark .tags.is-right .docstring>section>a.docs-sourcelink:not(:last-child){margin-right:0}html.theme--documenter-dark .tags.has-addons .tag,html.theme--documenter-dark .tags.has-addons .content kbd,html.theme--documenter-dark .content .tags.has-addons kbd,html.theme--documenter-dark .tags.has-addons .docstring>section>a.docs-sourcelink{margin-right:0}html.theme--documenter-dark .tags.has-addons .tag:not(:first-child),html.theme--documenter-dark .tags.has-addons .content kbd:not(:first-child),html.theme--documenter-dark .content .tags.has-addons kbd:not(:first-child),html.theme--documenter-dark .tags.has-addons .docstring>section>a.docs-sourcelink:not(:first-child){margin-left:0;border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .tags.has-addons .tag:not(:last-child),html.theme--documenter-dark .tags.has-addons .content kbd:not(:last-child),html.theme--documenter-dark .content .tags.has-addons kbd:not(:last-child),html.theme--documenter-dark .tags.has-addons .docstring>section>a.docs-sourcelink:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0}html.theme--documenter-dark .tag:not(body),html.theme--documenter-dark .content kbd:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body){align-items:center;background-color:#282f2f;border-radius:.4em;color:#fff;display:inline-flex;font-size:.85em;height:2em;justify-content:center;line-height:1.5;padding-left:0.75em;padding-right:0.75em;white-space:nowrap}html.theme--documenter-dark .tag:not(body) .delete,html.theme--documenter-dark .content kbd:not(body) .delete,html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body) .delete{margin-left:0.25rem;margin-right:-0.375rem}html.theme--documenter-dark .tag.is-white:not(body),html.theme--documenter-dark .content kbd.is-white:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-white:not(body){background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .tag.is-black:not(body),html.theme--documenter-dark .content kbd.is-black:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-black:not(body){background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .tag.is-light:not(body),html.theme--documenter-dark .content kbd.is-light:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-light:not(body){background-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .tag.is-dark:not(body),html.theme--documenter-dark .content kbd:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-dark:not(body),html.theme--documenter-dark .content .docstring>section>kbd:not(body){background-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .tag.is-primary:not(body),html.theme--documenter-dark .content kbd.is-primary:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body){background-color:#375a7f;color:#fff}html.theme--documenter-dark .tag.is-link:not(body),html.theme--documenter-dark .content kbd.is-link:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-link:not(body){background-color:#1abc9c;color:#fff}html.theme--documenter-dark .tag.is-info:not(body),html.theme--documenter-dark .content kbd.is-info:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-info:not(body){background-color:#024c7d;color:#fff}html.theme--documenter-dark .tag.is-success:not(body),html.theme--documenter-dark .content kbd.is-success:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-success:not(body){background-color:#008438;color:#fff}html.theme--documenter-dark .tag.is-warning:not(body),html.theme--documenter-dark .content kbd.is-warning:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-warning:not(body){background-color:#ad8100;color:#fff}html.theme--documenter-dark .tag.is-danger:not(body),html.theme--documenter-dark .content kbd.is-danger:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-danger:not(body){background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .tag.is-normal:not(body),html.theme--documenter-dark .content kbd.is-normal:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-normal:not(body){font-size:.85em}html.theme--documenter-dark .tag.is-medium:not(body),html.theme--documenter-dark .content kbd.is-medium:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-medium:not(body){font-size:15px}html.theme--documenter-dark .tag.is-large:not(body),html.theme--documenter-dark .content kbd.is-large:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-large:not(body){font-size:1.25rem}html.theme--documenter-dark .tag:not(body) .icon:first-child:not(:last-child),html.theme--documenter-dark .content kbd:not(body) .icon:first-child:not(:last-child),html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body) .icon:first-child:not(:last-child){margin-left:-0.375em;margin-right:0.1875em}html.theme--documenter-dark .tag:not(body) .icon:last-child:not(:first-child),html.theme--documenter-dark .content kbd:not(body) .icon:last-child:not(:first-child),html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body) .icon:last-child:not(:first-child){margin-left:0.1875em;margin-right:-0.375em}html.theme--documenter-dark .tag:not(body) .icon:first-child:last-child,html.theme--documenter-dark .content kbd:not(body) .icon:first-child:last-child,html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body) .icon:first-child:last-child{margin-left:-0.375em;margin-right:-0.375em}html.theme--documenter-dark .tag.is-delete:not(body),html.theme--documenter-dark .content kbd.is-delete:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body){margin-left:1px;padding:0;position:relative;width:2em}html.theme--documenter-dark .tag.is-delete:not(body)::before,html.theme--documenter-dark .content kbd.is-delete:not(body)::before,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body)::before,html.theme--documenter-dark .tag.is-delete:not(body)::after,html.theme--documenter-dark .content kbd.is-delete:not(body)::after,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body)::after{background-color:currentColor;content:"";display:block;left:50%;position:absolute;top:50%;transform:translateX(-50%) translateY(-50%) rotate(45deg);transform-origin:center center}html.theme--documenter-dark .tag.is-delete:not(body)::before,html.theme--documenter-dark .content kbd.is-delete:not(body)::before,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body)::before{height:1px;width:50%}html.theme--documenter-dark .tag.is-delete:not(body)::after,html.theme--documenter-dark .content kbd.is-delete:not(body)::after,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body)::after{height:50%;width:1px}html.theme--documenter-dark .tag.is-delete:not(body):hover,html.theme--documenter-dark .content kbd.is-delete:not(body):hover,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body):hover,html.theme--documenter-dark .tag.is-delete:not(body):focus,html.theme--documenter-dark .content kbd.is-delete:not(body):focus,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body):focus{background-color:#1d2122}html.theme--documenter-dark .tag.is-delete:not(body):active,html.theme--documenter-dark .content kbd.is-delete:not(body):active,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body):active{background-color:#111414}html.theme--documenter-dark .tag.is-rounded:not(body),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:not(body),html.theme--documenter-dark .content kbd.is-rounded:not(body),html.theme--documenter-dark #documenter .docs-sidebar .content form.docs-search>input:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-rounded:not(body){border-radius:290486px}html.theme--documenter-dark a.tag:hover,html.theme--documenter-dark .docstring>section>a.docs-sourcelink:hover{text-decoration:underline}html.theme--documenter-dark .title,html.theme--documenter-dark .subtitle{word-break:break-word}html.theme--documenter-dark .title em,html.theme--documenter-dark .title span,html.theme--documenter-dark .subtitle em,html.theme--documenter-dark .subtitle span{font-weight:inherit}html.theme--documenter-dark .title sub,html.theme--documenter-dark .subtitle sub{font-size:.75em}html.theme--documenter-dark .title sup,html.theme--documenter-dark .subtitle sup{font-size:.75em}html.theme--documenter-dark .title .tag,html.theme--documenter-dark .title .content kbd,html.theme--documenter-dark .content .title kbd,html.theme--documenter-dark .title .docstring>section>a.docs-sourcelink,html.theme--documenter-dark .subtitle .tag,html.theme--documenter-dark .subtitle .content kbd,html.theme--documenter-dark .content .subtitle kbd,html.theme--documenter-dark .subtitle .docstring>section>a.docs-sourcelink{vertical-align:middle}html.theme--documenter-dark .title{color:#fff;font-size:2rem;font-weight:500;line-height:1.125}html.theme--documenter-dark .title strong{color:inherit;font-weight:inherit}html.theme--documenter-dark .title+.highlight{margin-top:-0.75rem}html.theme--documenter-dark .title:not(.is-spaced)+.subtitle{margin-top:-1.25rem}html.theme--documenter-dark .title.is-1{font-size:3rem}html.theme--documenter-dark .title.is-2{font-size:2.5rem}html.theme--documenter-dark .title.is-3{font-size:2rem}html.theme--documenter-dark .title.is-4{font-size:1.5rem}html.theme--documenter-dark .title.is-5{font-size:1.25rem}html.theme--documenter-dark .title.is-6{font-size:15px}html.theme--documenter-dark .title.is-7{font-size:.85em}html.theme--documenter-dark .subtitle{color:#8c9b9d;font-size:1.25rem;font-weight:400;line-height:1.25}html.theme--documenter-dark .subtitle strong{color:#8c9b9d;font-weight:600}html.theme--documenter-dark .subtitle:not(.is-spaced)+.title{margin-top:-1.25rem}html.theme--documenter-dark .subtitle.is-1{font-size:3rem}html.theme--documenter-dark .subtitle.is-2{font-size:2.5rem}html.theme--documenter-dark .subtitle.is-3{font-size:2rem}html.theme--documenter-dark .subtitle.is-4{font-size:1.5rem}html.theme--documenter-dark .subtitle.is-5{font-size:1.25rem}html.theme--documenter-dark .subtitle.is-6{font-size:15px}html.theme--documenter-dark .subtitle.is-7{font-size:.85em}html.theme--documenter-dark .heading{display:block;font-size:11px;letter-spacing:1px;margin-bottom:5px;text-transform:uppercase}html.theme--documenter-dark .highlight{font-weight:400;max-width:100%;overflow:hidden;padding:0}html.theme--documenter-dark .highlight pre{overflow:auto;max-width:100%}html.theme--documenter-dark .number{align-items:center;background-color:#282f2f;border-radius:290486px;display:inline-flex;font-size:1.25rem;height:2em;justify-content:center;margin-right:1.5rem;min-width:2.5em;padding:0.25rem 0.5rem;text-align:center;vertical-align:top}html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{background-color:#1f2424;border-color:#5e6d6f;border-radius:.4em;color:#dbdee0}html.theme--documenter-dark .select select::-moz-placeholder,html.theme--documenter-dark .textarea::-moz-placeholder,html.theme--documenter-dark .input::-moz-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input::-moz-placeholder{color:rgba(219,222,224,0.3)}html.theme--documenter-dark .select select::-webkit-input-placeholder,html.theme--documenter-dark .textarea::-webkit-input-placeholder,html.theme--documenter-dark .input::-webkit-input-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input::-webkit-input-placeholder{color:rgba(219,222,224,0.3)}html.theme--documenter-dark .select select:-moz-placeholder,html.theme--documenter-dark .textarea:-moz-placeholder,html.theme--documenter-dark .input:-moz-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:-moz-placeholder{color:rgba(219,222,224,0.3)}html.theme--documenter-dark .select select:-ms-input-placeholder,html.theme--documenter-dark .textarea:-ms-input-placeholder,html.theme--documenter-dark .input:-ms-input-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:-ms-input-placeholder{color:rgba(219,222,224,0.3)}html.theme--documenter-dark .select select:hover,html.theme--documenter-dark .textarea:hover,html.theme--documenter-dark .input:hover,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:hover,html.theme--documenter-dark .select select.is-hovered,html.theme--documenter-dark .is-hovered.textarea,html.theme--documenter-dark .is-hovered.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-hovered{border-color:#8c9b9d}html.theme--documenter-dark .select select:focus,html.theme--documenter-dark .textarea:focus,html.theme--documenter-dark .input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:focus,html.theme--documenter-dark .select select.is-focused,html.theme--documenter-dark .is-focused.textarea,html.theme--documenter-dark .is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .select select:active,html.theme--documenter-dark .textarea:active,html.theme--documenter-dark .input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:active,html.theme--documenter-dark .select select.is-active,html.theme--documenter-dark .is-active.textarea,html.theme--documenter-dark .is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{border-color:#1abc9c;box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .select select[disabled],html.theme--documenter-dark .textarea[disabled],html.theme--documenter-dark .input[disabled],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled],fieldset[disabled] html.theme--documenter-dark .select select,fieldset[disabled] html.theme--documenter-dark .textarea,fieldset[disabled] html.theme--documenter-dark .input,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{background-color:#8c9b9d;border-color:#282f2f;box-shadow:none;color:#fff}html.theme--documenter-dark .select select[disabled]::-moz-placeholder,html.theme--documenter-dark .textarea[disabled]::-moz-placeholder,html.theme--documenter-dark .input[disabled]::-moz-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled]::-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .select select::-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .textarea::-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .input::-moz-placeholder,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input::-moz-placeholder{color:rgba(255,255,255,0.3)}html.theme--documenter-dark .select select[disabled]::-webkit-input-placeholder,html.theme--documenter-dark .textarea[disabled]::-webkit-input-placeholder,html.theme--documenter-dark .input[disabled]::-webkit-input-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled]::-webkit-input-placeholder,fieldset[disabled] html.theme--documenter-dark .select select::-webkit-input-placeholder,fieldset[disabled] html.theme--documenter-dark .textarea::-webkit-input-placeholder,fieldset[disabled] html.theme--documenter-dark .input::-webkit-input-placeholder,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input::-webkit-input-placeholder{color:rgba(255,255,255,0.3)}html.theme--documenter-dark .select select[disabled]:-moz-placeholder,html.theme--documenter-dark .textarea[disabled]:-moz-placeholder,html.theme--documenter-dark .input[disabled]:-moz-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled]:-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .select select:-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .textarea:-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .input:-moz-placeholder,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:-moz-placeholder{color:rgba(255,255,255,0.3)}html.theme--documenter-dark .select select[disabled]:-ms-input-placeholder,html.theme--documenter-dark .textarea[disabled]:-ms-input-placeholder,html.theme--documenter-dark .input[disabled]:-ms-input-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled]:-ms-input-placeholder,fieldset[disabled] html.theme--documenter-dark .select select:-ms-input-placeholder,fieldset[disabled] html.theme--documenter-dark .textarea:-ms-input-placeholder,fieldset[disabled] html.theme--documenter-dark .input:-ms-input-placeholder,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:-ms-input-placeholder{color:rgba(255,255,255,0.3)}html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{box-shadow:inset 0 1px 2px rgba(10,10,10,0.1);max-width:100%;width:100%}html.theme--documenter-dark .textarea[readonly],html.theme--documenter-dark .input[readonly],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[readonly]{box-shadow:none}html.theme--documenter-dark .is-white.textarea,html.theme--documenter-dark .is-white.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-white{border-color:#fff}html.theme--documenter-dark .is-white.textarea:focus,html.theme--documenter-dark .is-white.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-white:focus,html.theme--documenter-dark .is-white.is-focused.textarea,html.theme--documenter-dark .is-white.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-white.textarea:active,html.theme--documenter-dark .is-white.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-white:active,html.theme--documenter-dark .is-white.is-active.textarea,html.theme--documenter-dark .is-white.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--documenter-dark .is-black.textarea,html.theme--documenter-dark .is-black.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-black{border-color:#0a0a0a}html.theme--documenter-dark .is-black.textarea:focus,html.theme--documenter-dark .is-black.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-black:focus,html.theme--documenter-dark .is-black.is-focused.textarea,html.theme--documenter-dark .is-black.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-black.textarea:active,html.theme--documenter-dark .is-black.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-black:active,html.theme--documenter-dark .is-black.is-active.textarea,html.theme--documenter-dark .is-black.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}html.theme--documenter-dark .is-light.textarea,html.theme--documenter-dark .is-light.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-light{border-color:#ecf0f1}html.theme--documenter-dark .is-light.textarea:focus,html.theme--documenter-dark .is-light.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-light:focus,html.theme--documenter-dark .is-light.is-focused.textarea,html.theme--documenter-dark .is-light.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-light.textarea:active,html.theme--documenter-dark .is-light.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-light:active,html.theme--documenter-dark .is-light.is-active.textarea,html.theme--documenter-dark .is-light.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .is-dark.textarea,html.theme--documenter-dark .content kbd.textarea,html.theme--documenter-dark .is-dark.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-dark,html.theme--documenter-dark .content kbd.input{border-color:#282f2f}html.theme--documenter-dark .is-dark.textarea:focus,html.theme--documenter-dark .content kbd.textarea:focus,html.theme--documenter-dark .is-dark.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-dark:focus,html.theme--documenter-dark .content kbd.input:focus,html.theme--documenter-dark .is-dark.is-focused.textarea,html.theme--documenter-dark .content kbd.is-focused.textarea,html.theme--documenter-dark .is-dark.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .content kbd.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar .content form.docs-search>input.is-focused,html.theme--documenter-dark .is-dark.textarea:active,html.theme--documenter-dark .content kbd.textarea:active,html.theme--documenter-dark .is-dark.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-dark:active,html.theme--documenter-dark .content kbd.input:active,html.theme--documenter-dark .is-dark.is-active.textarea,html.theme--documenter-dark .content kbd.is-active.textarea,html.theme--documenter-dark .is-dark.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .content kbd.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar .content form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .is-primary.textarea,html.theme--documenter-dark .docstring>section>a.textarea.docs-sourcelink,html.theme--documenter-dark .is-primary.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-primary,html.theme--documenter-dark .docstring>section>a.input.docs-sourcelink{border-color:#375a7f}html.theme--documenter-dark .is-primary.textarea:focus,html.theme--documenter-dark .docstring>section>a.textarea.docs-sourcelink:focus,html.theme--documenter-dark .is-primary.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-primary:focus,html.theme--documenter-dark .docstring>section>a.input.docs-sourcelink:focus,html.theme--documenter-dark .is-primary.is-focused.textarea,html.theme--documenter-dark .docstring>section>a.is-focused.textarea.docs-sourcelink,html.theme--documenter-dark .is-primary.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .docstring>section>a.is-focused.input.docs-sourcelink,html.theme--documenter-dark .is-primary.textarea:active,html.theme--documenter-dark .docstring>section>a.textarea.docs-sourcelink:active,html.theme--documenter-dark .is-primary.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-primary:active,html.theme--documenter-dark .docstring>section>a.input.docs-sourcelink:active,html.theme--documenter-dark .is-primary.is-active.textarea,html.theme--documenter-dark .docstring>section>a.is-active.textarea.docs-sourcelink,html.theme--documenter-dark .is-primary.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .docstring>section>a.is-active.input.docs-sourcelink{box-shadow:0 0 0 0.125em rgba(55,90,127,0.25)}html.theme--documenter-dark .is-link.textarea,html.theme--documenter-dark .is-link.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-link{border-color:#1abc9c}html.theme--documenter-dark .is-link.textarea:focus,html.theme--documenter-dark .is-link.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-link:focus,html.theme--documenter-dark .is-link.is-focused.textarea,html.theme--documenter-dark .is-link.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-link.textarea:active,html.theme--documenter-dark .is-link.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-link:active,html.theme--documenter-dark .is-link.is-active.textarea,html.theme--documenter-dark .is-link.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .is-info.textarea,html.theme--documenter-dark .is-info.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-info{border-color:#024c7d}html.theme--documenter-dark .is-info.textarea:focus,html.theme--documenter-dark .is-info.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-info:focus,html.theme--documenter-dark .is-info.is-focused.textarea,html.theme--documenter-dark .is-info.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-info.textarea:active,html.theme--documenter-dark .is-info.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-info:active,html.theme--documenter-dark .is-info.is-active.textarea,html.theme--documenter-dark .is-info.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(2,76,125,0.25)}html.theme--documenter-dark .is-success.textarea,html.theme--documenter-dark .is-success.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-success{border-color:#008438}html.theme--documenter-dark .is-success.textarea:focus,html.theme--documenter-dark .is-success.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-success:focus,html.theme--documenter-dark .is-success.is-focused.textarea,html.theme--documenter-dark .is-success.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-success.textarea:active,html.theme--documenter-dark .is-success.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-success:active,html.theme--documenter-dark .is-success.is-active.textarea,html.theme--documenter-dark .is-success.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(0,132,56,0.25)}html.theme--documenter-dark .is-warning.textarea,html.theme--documenter-dark .is-warning.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-warning{border-color:#ad8100}html.theme--documenter-dark .is-warning.textarea:focus,html.theme--documenter-dark .is-warning.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-warning:focus,html.theme--documenter-dark .is-warning.is-focused.textarea,html.theme--documenter-dark .is-warning.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-warning.textarea:active,html.theme--documenter-dark .is-warning.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-warning:active,html.theme--documenter-dark .is-warning.is-active.textarea,html.theme--documenter-dark .is-warning.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(173,129,0,0.25)}html.theme--documenter-dark .is-danger.textarea,html.theme--documenter-dark .is-danger.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-danger{border-color:#9e1b0d}html.theme--documenter-dark .is-danger.textarea:focus,html.theme--documenter-dark .is-danger.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-danger:focus,html.theme--documenter-dark .is-danger.is-focused.textarea,html.theme--documenter-dark .is-danger.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-danger.textarea:active,html.theme--documenter-dark .is-danger.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-danger:active,html.theme--documenter-dark .is-danger.is-active.textarea,html.theme--documenter-dark .is-danger.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(158,27,13,0.25)}html.theme--documenter-dark .is-small.textarea,html.theme--documenter-dark .is-small.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{border-radius:3px;font-size:.85em}html.theme--documenter-dark .is-medium.textarea,html.theme--documenter-dark .is-medium.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-medium{font-size:1.25rem}html.theme--documenter-dark .is-large.textarea,html.theme--documenter-dark .is-large.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-large{font-size:1.5rem}html.theme--documenter-dark .is-fullwidth.textarea,html.theme--documenter-dark .is-fullwidth.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-fullwidth{display:block;width:100%}html.theme--documenter-dark .is-inline.textarea,html.theme--documenter-dark .is-inline.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-inline{display:inline;width:auto}html.theme--documenter-dark .input.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{border-radius:290486px;padding-left:1em;padding-right:1em}html.theme--documenter-dark .input.is-static,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-static{background-color:transparent;border-color:transparent;box-shadow:none;padding-left:0;padding-right:0}html.theme--documenter-dark .textarea{display:block;max-width:100%;min-width:100%;padding:0.625em;resize:vertical}html.theme--documenter-dark .textarea:not([rows]){max-height:600px;min-height:120px}html.theme--documenter-dark .textarea[rows]{height:initial}html.theme--documenter-dark .textarea.has-fixed-size{resize:none}html.theme--documenter-dark .radio,html.theme--documenter-dark .checkbox{cursor:pointer;display:inline-block;line-height:1.25;position:relative}html.theme--documenter-dark .radio input,html.theme--documenter-dark .checkbox input{cursor:pointer}html.theme--documenter-dark .radio:hover,html.theme--documenter-dark .checkbox:hover{color:#8c9b9d}html.theme--documenter-dark .radio[disabled],html.theme--documenter-dark .checkbox[disabled],fieldset[disabled] html.theme--documenter-dark .radio,fieldset[disabled] html.theme--documenter-dark .checkbox{color:#fff;cursor:not-allowed}html.theme--documenter-dark .radio+.radio{margin-left:0.5em}html.theme--documenter-dark .select{display:inline-block;max-width:100%;position:relative;vertical-align:top}html.theme--documenter-dark .select:not(.is-multiple){height:2.25em}html.theme--documenter-dark .select:not(.is-multiple):not(.is-loading)::after{border-color:#1abc9c;right:1.125em;z-index:4}html.theme--documenter-dark .select.is-rounded select,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.select select{border-radius:290486px;padding-left:1em}html.theme--documenter-dark .select select{cursor:pointer;display:block;font-size:1em;max-width:100%;outline:none}html.theme--documenter-dark .select select::-ms-expand{display:none}html.theme--documenter-dark .select select[disabled]:hover,fieldset[disabled] html.theme--documenter-dark .select select:hover{border-color:#282f2f}html.theme--documenter-dark .select select:not([multiple]){padding-right:2.5em}html.theme--documenter-dark .select select[multiple]{height:auto;padding:0}html.theme--documenter-dark .select select[multiple] option{padding:0.5em 1em}html.theme--documenter-dark .select:not(.is-multiple):not(.is-loading):hover::after{border-color:#8c9b9d}html.theme--documenter-dark .select.is-white:not(:hover)::after{border-color:#fff}html.theme--documenter-dark .select.is-white select{border-color:#fff}html.theme--documenter-dark .select.is-white select:hover,html.theme--documenter-dark .select.is-white select.is-hovered{border-color:#f2f2f2}html.theme--documenter-dark .select.is-white select:focus,html.theme--documenter-dark .select.is-white select.is-focused,html.theme--documenter-dark .select.is-white select:active,html.theme--documenter-dark .select.is-white select.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--documenter-dark .select.is-black:not(:hover)::after{border-color:#0a0a0a}html.theme--documenter-dark .select.is-black select{border-color:#0a0a0a}html.theme--documenter-dark .select.is-black select:hover,html.theme--documenter-dark .select.is-black select.is-hovered{border-color:#000}html.theme--documenter-dark .select.is-black select:focus,html.theme--documenter-dark .select.is-black select.is-focused,html.theme--documenter-dark .select.is-black select:active,html.theme--documenter-dark .select.is-black select.is-active{box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}html.theme--documenter-dark .select.is-light:not(:hover)::after{border-color:#ecf0f1}html.theme--documenter-dark .select.is-light select{border-color:#ecf0f1}html.theme--documenter-dark .select.is-light select:hover,html.theme--documenter-dark .select.is-light select.is-hovered{border-color:#dde4e6}html.theme--documenter-dark .select.is-light select:focus,html.theme--documenter-dark .select.is-light select.is-focused,html.theme--documenter-dark .select.is-light select:active,html.theme--documenter-dark .select.is-light select.is-active{box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .select.is-dark:not(:hover)::after,html.theme--documenter-dark .content kbd.select:not(:hover)::after{border-color:#282f2f}html.theme--documenter-dark .select.is-dark select,html.theme--documenter-dark .content kbd.select select{border-color:#282f2f}html.theme--documenter-dark .select.is-dark select:hover,html.theme--documenter-dark .content kbd.select select:hover,html.theme--documenter-dark .select.is-dark select.is-hovered,html.theme--documenter-dark .content kbd.select select.is-hovered{border-color:#1d2122}html.theme--documenter-dark .select.is-dark select:focus,html.theme--documenter-dark .content kbd.select select:focus,html.theme--documenter-dark .select.is-dark select.is-focused,html.theme--documenter-dark .content kbd.select select.is-focused,html.theme--documenter-dark .select.is-dark select:active,html.theme--documenter-dark .content kbd.select select:active,html.theme--documenter-dark .select.is-dark select.is-active,html.theme--documenter-dark .content kbd.select select.is-active{box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .select.is-primary:not(:hover)::after,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink:not(:hover)::after{border-color:#375a7f}html.theme--documenter-dark .select.is-primary select,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select{border-color:#375a7f}html.theme--documenter-dark .select.is-primary select:hover,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select:hover,html.theme--documenter-dark .select.is-primary select.is-hovered,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select.is-hovered{border-color:#2f4d6d}html.theme--documenter-dark .select.is-primary select:focus,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select:focus,html.theme--documenter-dark .select.is-primary select.is-focused,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select.is-focused,html.theme--documenter-dark .select.is-primary select:active,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select:active,html.theme--documenter-dark .select.is-primary select.is-active,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select.is-active{box-shadow:0 0 0 0.125em rgba(55,90,127,0.25)}html.theme--documenter-dark .select.is-link:not(:hover)::after{border-color:#1abc9c}html.theme--documenter-dark .select.is-link select{border-color:#1abc9c}html.theme--documenter-dark .select.is-link select:hover,html.theme--documenter-dark .select.is-link select.is-hovered{border-color:#17a689}html.theme--documenter-dark .select.is-link select:focus,html.theme--documenter-dark .select.is-link select.is-focused,html.theme--documenter-dark .select.is-link select:active,html.theme--documenter-dark .select.is-link select.is-active{box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .select.is-info:not(:hover)::after{border-color:#024c7d}html.theme--documenter-dark .select.is-info select{border-color:#024c7d}html.theme--documenter-dark .select.is-info select:hover,html.theme--documenter-dark .select.is-info select.is-hovered{border-color:#023d64}html.theme--documenter-dark .select.is-info select:focus,html.theme--documenter-dark .select.is-info select.is-focused,html.theme--documenter-dark .select.is-info select:active,html.theme--documenter-dark .select.is-info select.is-active{box-shadow:0 0 0 0.125em rgba(2,76,125,0.25)}html.theme--documenter-dark .select.is-success:not(:hover)::after{border-color:#008438}html.theme--documenter-dark .select.is-success select{border-color:#008438}html.theme--documenter-dark .select.is-success select:hover,html.theme--documenter-dark .select.is-success select.is-hovered{border-color:#006b2d}html.theme--documenter-dark .select.is-success select:focus,html.theme--documenter-dark .select.is-success select.is-focused,html.theme--documenter-dark .select.is-success select:active,html.theme--documenter-dark .select.is-success select.is-active{box-shadow:0 0 0 0.125em rgba(0,132,56,0.25)}html.theme--documenter-dark .select.is-warning:not(:hover)::after{border-color:#ad8100}html.theme--documenter-dark .select.is-warning select{border-color:#ad8100}html.theme--documenter-dark .select.is-warning select:hover,html.theme--documenter-dark .select.is-warning select.is-hovered{border-color:#946e00}html.theme--documenter-dark .select.is-warning select:focus,html.theme--documenter-dark .select.is-warning select.is-focused,html.theme--documenter-dark .select.is-warning select:active,html.theme--documenter-dark .select.is-warning select.is-active{box-shadow:0 0 0 0.125em rgba(173,129,0,0.25)}html.theme--documenter-dark .select.is-danger:not(:hover)::after{border-color:#9e1b0d}html.theme--documenter-dark .select.is-danger select{border-color:#9e1b0d}html.theme--documenter-dark .select.is-danger select:hover,html.theme--documenter-dark .select.is-danger select.is-hovered{border-color:#86170b}html.theme--documenter-dark .select.is-danger select:focus,html.theme--documenter-dark .select.is-danger select.is-focused,html.theme--documenter-dark .select.is-danger select:active,html.theme--documenter-dark .select.is-danger select.is-active{box-shadow:0 0 0 0.125em rgba(158,27,13,0.25)}html.theme--documenter-dark .select.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.select{border-radius:3px;font-size:.85em}html.theme--documenter-dark .select.is-medium{font-size:1.25rem}html.theme--documenter-dark .select.is-large{font-size:1.5rem}html.theme--documenter-dark .select.is-disabled::after{border-color:#fff}html.theme--documenter-dark .select.is-fullwidth{width:100%}html.theme--documenter-dark .select.is-fullwidth select{width:100%}html.theme--documenter-dark .select.is-loading::after{margin-top:0;position:absolute;right:0.625em;top:0.625em;transform:none}html.theme--documenter-dark .select.is-loading.is-small:after,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-loading:after{font-size:.85em}html.theme--documenter-dark .select.is-loading.is-medium:after{font-size:1.25rem}html.theme--documenter-dark .select.is-loading.is-large:after{font-size:1.5rem}html.theme--documenter-dark .file{align-items:stretch;display:flex;justify-content:flex-start;position:relative}html.theme--documenter-dark .file.is-white .file-cta{background-color:#fff;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .file.is-white:hover .file-cta,html.theme--documenter-dark .file.is-white.is-hovered .file-cta{background-color:#f9f9f9;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .file.is-white:focus .file-cta,html.theme--documenter-dark .file.is-white.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(255,255,255,0.25);color:#0a0a0a}html.theme--documenter-dark .file.is-white:active .file-cta,html.theme--documenter-dark .file.is-white.is-active .file-cta{background-color:#f2f2f2;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .file.is-black .file-cta{background-color:#0a0a0a;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-black:hover .file-cta,html.theme--documenter-dark .file.is-black.is-hovered .file-cta{background-color:#040404;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-black:focus .file-cta,html.theme--documenter-dark .file.is-black.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(10,10,10,0.25);color:#fff}html.theme--documenter-dark .file.is-black:active .file-cta,html.theme--documenter-dark .file.is-black.is-active .file-cta{background-color:#000;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-light .file-cta{background-color:#ecf0f1;border-color:transparent;color:#282f2f}html.theme--documenter-dark .file.is-light:hover .file-cta,html.theme--documenter-dark .file.is-light.is-hovered .file-cta{background-color:#e5eaec;border-color:transparent;color:#282f2f}html.theme--documenter-dark .file.is-light:focus .file-cta,html.theme--documenter-dark .file.is-light.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(236,240,241,0.25);color:#282f2f}html.theme--documenter-dark .file.is-light:active .file-cta,html.theme--documenter-dark .file.is-light.is-active .file-cta{background-color:#dde4e6;border-color:transparent;color:#282f2f}html.theme--documenter-dark .file.is-dark .file-cta,html.theme--documenter-dark .content kbd.file .file-cta{background-color:#282f2f;border-color:transparent;color:#ecf0f1}html.theme--documenter-dark .file.is-dark:hover .file-cta,html.theme--documenter-dark .content kbd.file:hover .file-cta,html.theme--documenter-dark .file.is-dark.is-hovered .file-cta,html.theme--documenter-dark .content kbd.file.is-hovered .file-cta{background-color:#232829;border-color:transparent;color:#ecf0f1}html.theme--documenter-dark .file.is-dark:focus .file-cta,html.theme--documenter-dark .content kbd.file:focus .file-cta,html.theme--documenter-dark .file.is-dark.is-focused .file-cta,html.theme--documenter-dark .content kbd.file.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(40,47,47,0.25);color:#ecf0f1}html.theme--documenter-dark .file.is-dark:active .file-cta,html.theme--documenter-dark .content kbd.file:active .file-cta,html.theme--documenter-dark .file.is-dark.is-active .file-cta,html.theme--documenter-dark .content kbd.file.is-active .file-cta{background-color:#1d2122;border-color:transparent;color:#ecf0f1}html.theme--documenter-dark .file.is-primary .file-cta,html.theme--documenter-dark .docstring>section>a.file.docs-sourcelink .file-cta{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-primary:hover .file-cta,html.theme--documenter-dark .docstring>section>a.file.docs-sourcelink:hover .file-cta,html.theme--documenter-dark .file.is-primary.is-hovered .file-cta,html.theme--documenter-dark .docstring>section>a.file.is-hovered.docs-sourcelink .file-cta{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-primary:focus .file-cta,html.theme--documenter-dark .docstring>section>a.file.docs-sourcelink:focus .file-cta,html.theme--documenter-dark .file.is-primary.is-focused .file-cta,html.theme--documenter-dark .docstring>section>a.file.is-focused.docs-sourcelink .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(55,90,127,0.25);color:#fff}html.theme--documenter-dark .file.is-primary:active .file-cta,html.theme--documenter-dark .docstring>section>a.file.docs-sourcelink:active .file-cta,html.theme--documenter-dark .file.is-primary.is-active .file-cta,html.theme--documenter-dark .docstring>section>a.file.is-active.docs-sourcelink .file-cta{background-color:#2f4d6d;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-link .file-cta{background-color:#1abc9c;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-link:hover .file-cta,html.theme--documenter-dark .file.is-link.is-hovered .file-cta{background-color:#18b193;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-link:focus .file-cta,html.theme--documenter-dark .file.is-link.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(26,188,156,0.25);color:#fff}html.theme--documenter-dark .file.is-link:active .file-cta,html.theme--documenter-dark .file.is-link.is-active .file-cta{background-color:#17a689;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-info .file-cta{background-color:#024c7d;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-info:hover .file-cta,html.theme--documenter-dark .file.is-info.is-hovered .file-cta{background-color:#024470;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-info:focus .file-cta,html.theme--documenter-dark .file.is-info.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(2,76,125,0.25);color:#fff}html.theme--documenter-dark .file.is-info:active .file-cta,html.theme--documenter-dark .file.is-info.is-active .file-cta{background-color:#023d64;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-success .file-cta{background-color:#008438;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-success:hover .file-cta,html.theme--documenter-dark .file.is-success.is-hovered .file-cta{background-color:#073;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-success:focus .file-cta,html.theme--documenter-dark .file.is-success.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(0,132,56,0.25);color:#fff}html.theme--documenter-dark .file.is-success:active .file-cta,html.theme--documenter-dark .file.is-success.is-active .file-cta{background-color:#006b2d;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-warning .file-cta{background-color:#ad8100;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-warning:hover .file-cta,html.theme--documenter-dark .file.is-warning.is-hovered .file-cta{background-color:#a07700;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-warning:focus .file-cta,html.theme--documenter-dark .file.is-warning.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(173,129,0,0.25);color:#fff}html.theme--documenter-dark .file.is-warning:active .file-cta,html.theme--documenter-dark .file.is-warning.is-active .file-cta{background-color:#946e00;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-danger .file-cta{background-color:#9e1b0d;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-danger:hover .file-cta,html.theme--documenter-dark .file.is-danger.is-hovered .file-cta{background-color:#92190c;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-danger:focus .file-cta,html.theme--documenter-dark .file.is-danger.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(158,27,13,0.25);color:#fff}html.theme--documenter-dark .file.is-danger:active .file-cta,html.theme--documenter-dark .file.is-danger.is-active .file-cta{background-color:#86170b;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.file{font-size:.85em}html.theme--documenter-dark .file.is-medium{font-size:1.25rem}html.theme--documenter-dark .file.is-medium .file-icon .fa{font-size:21px}html.theme--documenter-dark .file.is-large{font-size:1.5rem}html.theme--documenter-dark .file.is-large .file-icon .fa{font-size:28px}html.theme--documenter-dark .file.has-name .file-cta{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--documenter-dark .file.has-name .file-name{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .file.has-name.is-empty .file-cta{border-radius:.4em}html.theme--documenter-dark .file.has-name.is-empty .file-name{display:none}html.theme--documenter-dark .file.is-boxed .file-label{flex-direction:column}html.theme--documenter-dark .file.is-boxed .file-cta{flex-direction:column;height:auto;padding:1em 3em}html.theme--documenter-dark .file.is-boxed .file-name{border-width:0 1px 1px}html.theme--documenter-dark .file.is-boxed .file-icon{height:1.5em;width:1.5em}html.theme--documenter-dark .file.is-boxed .file-icon .fa{font-size:21px}html.theme--documenter-dark .file.is-boxed.is-small .file-icon .fa,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-boxed .file-icon .fa{font-size:14px}html.theme--documenter-dark .file.is-boxed.is-medium .file-icon .fa{font-size:28px}html.theme--documenter-dark .file.is-boxed.is-large .file-icon .fa{font-size:35px}html.theme--documenter-dark .file.is-boxed.has-name .file-cta{border-radius:.4em .4em 0 0}html.theme--documenter-dark .file.is-boxed.has-name .file-name{border-radius:0 0 .4em .4em;border-width:0 1px 1px}html.theme--documenter-dark .file.is-centered{justify-content:center}html.theme--documenter-dark .file.is-fullwidth .file-label{width:100%}html.theme--documenter-dark .file.is-fullwidth .file-name{flex-grow:1;max-width:none}html.theme--documenter-dark .file.is-right{justify-content:flex-end}html.theme--documenter-dark .file.is-right .file-cta{border-radius:0 .4em .4em 0}html.theme--documenter-dark .file.is-right .file-name{border-radius:.4em 0 0 .4em;border-width:1px 0 1px 1px;order:-1}html.theme--documenter-dark .file-label{align-items:stretch;display:flex;cursor:pointer;justify-content:flex-start;overflow:hidden;position:relative}html.theme--documenter-dark .file-label:hover .file-cta{background-color:#e5eaec;color:#282f2f}html.theme--documenter-dark .file-label:hover .file-name{border-color:#596668}html.theme--documenter-dark .file-label:active .file-cta{background-color:#dde4e6;color:#282f2f}html.theme--documenter-dark .file-label:active .file-name{border-color:#535f61}html.theme--documenter-dark .file-input{height:100%;left:0;opacity:0;outline:none;position:absolute;top:0;width:100%}html.theme--documenter-dark .file-cta,html.theme--documenter-dark .file-name{border-color:#5e6d6f;border-radius:.4em;font-size:1em;padding-left:1em;padding-right:1em;white-space:nowrap}html.theme--documenter-dark .file-cta{background-color:#ecf0f1;color:#343c3d}html.theme--documenter-dark .file-name{border-color:#5e6d6f;border-style:solid;border-width:1px 1px 1px 0;display:block;max-width:16em;overflow:hidden;text-align:left;text-overflow:ellipsis}html.theme--documenter-dark .file-icon{align-items:center;display:flex;height:1em;justify-content:center;margin-right:0.5em;width:1em}html.theme--documenter-dark .file-icon .fa{font-size:14px}html.theme--documenter-dark .label{color:#282f2f;display:block;font-size:15px;font-weight:700}html.theme--documenter-dark .label:not(:last-child){margin-bottom:0.5em}html.theme--documenter-dark .label.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.label{font-size:.85em}html.theme--documenter-dark .label.is-medium{font-size:1.25rem}html.theme--documenter-dark .label.is-large{font-size:1.5rem}html.theme--documenter-dark .help{display:block;font-size:.85em;margin-top:0.25rem}html.theme--documenter-dark .help.is-white{color:#fff}html.theme--documenter-dark .help.is-black{color:#0a0a0a}html.theme--documenter-dark .help.is-light{color:#ecf0f1}html.theme--documenter-dark .help.is-dark,html.theme--documenter-dark .content kbd.help{color:#282f2f}html.theme--documenter-dark .help.is-primary,html.theme--documenter-dark .docstring>section>a.help.docs-sourcelink{color:#375a7f}html.theme--documenter-dark .help.is-link{color:#1abc9c}html.theme--documenter-dark .help.is-info{color:#024c7d}html.theme--documenter-dark .help.is-success{color:#008438}html.theme--documenter-dark .help.is-warning{color:#ad8100}html.theme--documenter-dark .help.is-danger{color:#9e1b0d}html.theme--documenter-dark .field:not(:last-child){margin-bottom:0.75rem}html.theme--documenter-dark .field.has-addons{display:flex;justify-content:flex-start}html.theme--documenter-dark .field.has-addons .control:not(:last-child){margin-right:-1px}html.theme--documenter-dark .field.has-addons .control:not(:first-child):not(:last-child) .button,html.theme--documenter-dark .field.has-addons .control:not(:first-child):not(:last-child) .input,html.theme--documenter-dark .field.has-addons .control:not(:first-child):not(:last-child) #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control:not(:first-child):not(:last-child) form.docs-search>input,html.theme--documenter-dark .field.has-addons .control:not(:first-child):not(:last-child) .select select{border-radius:0}html.theme--documenter-dark .field.has-addons .control:first-child:not(:only-child) .button,html.theme--documenter-dark .field.has-addons .control:first-child:not(:only-child) .input,html.theme--documenter-dark .field.has-addons .control:first-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control:first-child:not(:only-child) form.docs-search>input,html.theme--documenter-dark .field.has-addons .control:first-child:not(:only-child) .select select{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--documenter-dark .field.has-addons .control:last-child:not(:only-child) .button,html.theme--documenter-dark .field.has-addons .control:last-child:not(:only-child) .input,html.theme--documenter-dark .field.has-addons .control:last-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control:last-child:not(:only-child) form.docs-search>input,html.theme--documenter-dark .field.has-addons .control:last-child:not(:only-child) .select select{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .button.is-hovered:not([disabled]),html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .input.is-hovered:not([disabled]),html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-hovered:not([disabled]),html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-hovered:not([disabled]),html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .select select.is-hovered:not([disabled]){z-index:2}html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):focus,html.theme--documenter-dark .field.has-addons .control .button.is-focused:not([disabled]),html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):active,html.theme--documenter-dark .field.has-addons .control .button.is-active:not([disabled]),html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):focus,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus,html.theme--documenter-dark .field.has-addons .control .input.is-focused:not([disabled]),html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]),html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]),html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):active,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active,html.theme--documenter-dark .field.has-addons .control .input.is-active:not([disabled]),html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]),html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]),html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):focus,html.theme--documenter-dark .field.has-addons .control .select select.is-focused:not([disabled]),html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):active,html.theme--documenter-dark .field.has-addons .control .select select.is-active:not([disabled]){z-index:3}html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):focus:hover,html.theme--documenter-dark .field.has-addons .control .button.is-focused:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):active:hover,html.theme--documenter-dark .field.has-addons .control .button.is-active:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):focus:hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus:hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus:hover,html.theme--documenter-dark .field.has-addons .control .input.is-focused:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]):hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):active:hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active:hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active:hover,html.theme--documenter-dark .field.has-addons .control .input.is-active:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]):hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):focus:hover,html.theme--documenter-dark .field.has-addons .control .select select.is-focused:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):active:hover,html.theme--documenter-dark .field.has-addons .control .select select.is-active:not([disabled]):hover{z-index:4}html.theme--documenter-dark .field.has-addons .control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .field.has-addons.has-addons-centered{justify-content:center}html.theme--documenter-dark .field.has-addons.has-addons-right{justify-content:flex-end}html.theme--documenter-dark .field.has-addons.has-addons-fullwidth .control{flex-grow:1;flex-shrink:0}html.theme--documenter-dark .field.is-grouped{display:flex;justify-content:flex-start}html.theme--documenter-dark .field.is-grouped>.control{flex-shrink:0}html.theme--documenter-dark .field.is-grouped>.control:not(:last-child){margin-bottom:0;margin-right:0.75rem}html.theme--documenter-dark .field.is-grouped>.control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .field.is-grouped.is-grouped-centered{justify-content:center}html.theme--documenter-dark .field.is-grouped.is-grouped-right{justify-content:flex-end}html.theme--documenter-dark .field.is-grouped.is-grouped-multiline{flex-wrap:wrap}html.theme--documenter-dark .field.is-grouped.is-grouped-multiline>.control:last-child,html.theme--documenter-dark .field.is-grouped.is-grouped-multiline>.control:not(:last-child){margin-bottom:0.75rem}html.theme--documenter-dark .field.is-grouped.is-grouped-multiline:last-child{margin-bottom:-0.75rem}html.theme--documenter-dark .field.is-grouped.is-grouped-multiline:not(:last-child){margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--documenter-dark .field.is-horizontal{display:flex}}html.theme--documenter-dark .field-label .label{font-size:inherit}@media screen and (max-width: 768px){html.theme--documenter-dark .field-label{margin-bottom:0.5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .field-label{flex-basis:0;flex-grow:1;flex-shrink:0;margin-right:1.5rem;text-align:right}html.theme--documenter-dark .field-label.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.field-label{font-size:.85em;padding-top:0.375em}html.theme--documenter-dark .field-label.is-normal{padding-top:0.375em}html.theme--documenter-dark .field-label.is-medium{font-size:1.25rem;padding-top:0.375em}html.theme--documenter-dark .field-label.is-large{font-size:1.5rem;padding-top:0.375em}}html.theme--documenter-dark .field-body .field .field{margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--documenter-dark .field-body{display:flex;flex-basis:0;flex-grow:5;flex-shrink:1}html.theme--documenter-dark .field-body .field{margin-bottom:0}html.theme--documenter-dark .field-body>.field{flex-shrink:1}html.theme--documenter-dark .field-body>.field:not(.is-narrow){flex-grow:1}html.theme--documenter-dark .field-body>.field:not(:last-child){margin-right:0.75rem}}html.theme--documenter-dark .control{box-sizing:border-box;clear:both;font-size:15px;position:relative;text-align:left}html.theme--documenter-dark .control.has-icons-left .input:focus~.icon,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input:focus~.icon,html.theme--documenter-dark .control.has-icons-left .select:focus~.icon,html.theme--documenter-dark .control.has-icons-right .input:focus~.icon,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input:focus~.icon,html.theme--documenter-dark .control.has-icons-right .select:focus~.icon{color:#5e6d6f}html.theme--documenter-dark .control.has-icons-left .input.is-small~.icon,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input~.icon,html.theme--documenter-dark .control.has-icons-left .select.is-small~.icon,html.theme--documenter-dark .control.has-icons-right .input.is-small~.icon,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input~.icon,html.theme--documenter-dark .control.has-icons-right .select.is-small~.icon{font-size:.85em}html.theme--documenter-dark .control.has-icons-left .input.is-medium~.icon,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-medium~.icon,html.theme--documenter-dark .control.has-icons-left .select.is-medium~.icon,html.theme--documenter-dark .control.has-icons-right .input.is-medium~.icon,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-medium~.icon,html.theme--documenter-dark .control.has-icons-right .select.is-medium~.icon{font-size:1.25rem}html.theme--documenter-dark .control.has-icons-left .input.is-large~.icon,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-large~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-large~.icon,html.theme--documenter-dark .control.has-icons-left .select.is-large~.icon,html.theme--documenter-dark .control.has-icons-right .input.is-large~.icon,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-large~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-large~.icon,html.theme--documenter-dark .control.has-icons-right .select.is-large~.icon{font-size:1.5rem}html.theme--documenter-dark .control.has-icons-left .icon,html.theme--documenter-dark .control.has-icons-right .icon{color:#dbdee0;height:2.25em;pointer-events:none;position:absolute;top:0;width:2.25em;z-index:4}html.theme--documenter-dark .control.has-icons-left .input,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input,html.theme--documenter-dark .control.has-icons-left .select select{padding-left:2.25em}html.theme--documenter-dark .control.has-icons-left .icon.is-left{left:0}html.theme--documenter-dark .control.has-icons-right .input,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input,html.theme--documenter-dark .control.has-icons-right .select select{padding-right:2.25em}html.theme--documenter-dark .control.has-icons-right .icon.is-right{right:0}html.theme--documenter-dark .control.is-loading::after{position:absolute !important;right:0.625em;top:0.625em;z-index:4}html.theme--documenter-dark .control.is-loading.is-small:after,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-loading:after{font-size:.85em}html.theme--documenter-dark .control.is-loading.is-medium:after{font-size:1.25rem}html.theme--documenter-dark .control.is-loading.is-large:after{font-size:1.5rem}html.theme--documenter-dark .breadcrumb{font-size:15px;white-space:nowrap}html.theme--documenter-dark .breadcrumb a{align-items:center;color:#1abc9c;display:flex;justify-content:center;padding:0 .75em}html.theme--documenter-dark .breadcrumb a:hover{color:#1dd2af}html.theme--documenter-dark .breadcrumb li{align-items:center;display:flex}html.theme--documenter-dark .breadcrumb li:first-child a{padding-left:0}html.theme--documenter-dark .breadcrumb li.is-active a{color:#f2f2f2;cursor:default;pointer-events:none}html.theme--documenter-dark .breadcrumb li+li::before{color:#8c9b9d;content:"\0002f"}html.theme--documenter-dark .breadcrumb ul,html.theme--documenter-dark .breadcrumb ol{align-items:flex-start;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .breadcrumb .icon:first-child{margin-right:0.5em}html.theme--documenter-dark .breadcrumb .icon:last-child{margin-left:0.5em}html.theme--documenter-dark .breadcrumb.is-centered ol,html.theme--documenter-dark .breadcrumb.is-centered ul{justify-content:center}html.theme--documenter-dark .breadcrumb.is-right ol,html.theme--documenter-dark .breadcrumb.is-right ul{justify-content:flex-end}html.theme--documenter-dark .breadcrumb.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.breadcrumb{font-size:.85em}html.theme--documenter-dark .breadcrumb.is-medium{font-size:1.25rem}html.theme--documenter-dark .breadcrumb.is-large{font-size:1.5rem}html.theme--documenter-dark .breadcrumb.has-arrow-separator li+li::before{content:"\02192"}html.theme--documenter-dark .breadcrumb.has-bullet-separator li+li::before{content:"\02022"}html.theme--documenter-dark .breadcrumb.has-dot-separator li+li::before{content:"\000b7"}html.theme--documenter-dark .breadcrumb.has-succeeds-separator li+li::before{content:"\0227B"}html.theme--documenter-dark .card{background-color:#fff;box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px rgba(10,10,10,0.1);color:#fff;max-width:100%;position:relative}html.theme--documenter-dark .card-header{background-color:rgba(0,0,0,0);align-items:stretch;box-shadow:0 1px 2px rgba(10,10,10,0.1);display:flex}html.theme--documenter-dark .card-header-title{align-items:center;color:#f2f2f2;display:flex;flex-grow:1;font-weight:700;padding:.75rem}html.theme--documenter-dark .card-header-title.is-centered{justify-content:center}html.theme--documenter-dark .card-header-icon{align-items:center;cursor:pointer;display:flex;justify-content:center;padding:.75rem}html.theme--documenter-dark .card-image{display:block;position:relative}html.theme--documenter-dark .card-content{background-color:rgba(0,0,0,0);padding:1.5rem}html.theme--documenter-dark .card-footer{background-color:rgba(0,0,0,0);border-top:1px solid #5e6d6f;align-items:stretch;display:flex}html.theme--documenter-dark .card-footer-item{align-items:center;display:flex;flex-basis:0;flex-grow:1;flex-shrink:0;justify-content:center;padding:.75rem}html.theme--documenter-dark .card-footer-item:not(:last-child){border-right:1px solid #5e6d6f}html.theme--documenter-dark .card .media:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .dropdown{display:inline-flex;position:relative;vertical-align:top}html.theme--documenter-dark .dropdown.is-active .dropdown-menu,html.theme--documenter-dark .dropdown.is-hoverable:hover .dropdown-menu{display:block}html.theme--documenter-dark .dropdown.is-right .dropdown-menu{left:auto;right:0}html.theme--documenter-dark .dropdown.is-up .dropdown-menu{bottom:100%;padding-bottom:4px;padding-top:initial;top:auto}html.theme--documenter-dark .dropdown-menu{display:none;left:0;min-width:12rem;padding-top:4px;position:absolute;top:100%;z-index:20}html.theme--documenter-dark .dropdown-content{background-color:#282f2f;border-radius:.4em;box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px rgba(10,10,10,0.1);padding-bottom:.5rem;padding-top:.5rem}html.theme--documenter-dark .dropdown-item{color:#fff;display:block;font-size:0.875rem;line-height:1.5;padding:0.375rem 1rem;position:relative}html.theme--documenter-dark a.dropdown-item,html.theme--documenter-dark button.dropdown-item{padding-right:3rem;text-align:left;white-space:nowrap;width:100%}html.theme--documenter-dark a.dropdown-item:hover,html.theme--documenter-dark button.dropdown-item:hover{background-color:#282f2f;color:#0a0a0a}html.theme--documenter-dark a.dropdown-item.is-active,html.theme--documenter-dark button.dropdown-item.is-active{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .dropdown-divider{background-color:#5e6d6f;border:none;display:block;height:1px;margin:0.5rem 0}html.theme--documenter-dark .level{align-items:center;justify-content:space-between}html.theme--documenter-dark .level code{border-radius:.4em}html.theme--documenter-dark .level img{display:inline-block;vertical-align:top}html.theme--documenter-dark .level.is-mobile{display:flex}html.theme--documenter-dark .level.is-mobile .level-left,html.theme--documenter-dark .level.is-mobile .level-right{display:flex}html.theme--documenter-dark .level.is-mobile .level-left+.level-right{margin-top:0}html.theme--documenter-dark .level.is-mobile .level-item:not(:last-child){margin-bottom:0;margin-right:.75rem}html.theme--documenter-dark .level.is-mobile .level-item:not(.is-narrow){flex-grow:1}@media screen and (min-width: 769px),print{html.theme--documenter-dark .level{display:flex}html.theme--documenter-dark .level>.level-item:not(.is-narrow){flex-grow:1}}html.theme--documenter-dark .level-item{align-items:center;display:flex;flex-basis:auto;flex-grow:0;flex-shrink:0;justify-content:center}html.theme--documenter-dark .level-item .title,html.theme--documenter-dark .level-item .subtitle{margin-bottom:0}@media screen and (max-width: 768px){html.theme--documenter-dark .level-item:not(:last-child){margin-bottom:.75rem}}html.theme--documenter-dark .level-left,html.theme--documenter-dark .level-right{flex-basis:auto;flex-grow:0;flex-shrink:0}html.theme--documenter-dark .level-left .level-item.is-flexible,html.theme--documenter-dark .level-right .level-item.is-flexible{flex-grow:1}@media screen and (min-width: 769px),print{html.theme--documenter-dark .level-left .level-item:not(:last-child),html.theme--documenter-dark .level-right .level-item:not(:last-child){margin-right:.75rem}}html.theme--documenter-dark .level-left{align-items:center;justify-content:flex-start}@media screen and (max-width: 768px){html.theme--documenter-dark .level-left+.level-right{margin-top:1.5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .level-left{display:flex}}html.theme--documenter-dark .level-right{align-items:center;justify-content:flex-end}@media screen and (min-width: 769px),print{html.theme--documenter-dark .level-right{display:flex}}html.theme--documenter-dark .list{background-color:#fff;border-radius:.4em;box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px rgba(10,10,10,0.1)}html.theme--documenter-dark .list-item{display:block;padding:0.5em 1em}html.theme--documenter-dark .list-item:not(a){color:#fff}html.theme--documenter-dark .list-item:first-child{border-top-left-radius:.4em;border-top-right-radius:.4em}html.theme--documenter-dark .list-item:last-child{border-bottom-left-radius:.4em;border-bottom-right-radius:.4em}html.theme--documenter-dark .list-item:not(:last-child){border-bottom:1px solid #5e6d6f}html.theme--documenter-dark .list-item.is-active{background-color:#1abc9c;color:#fff}html.theme--documenter-dark a.list-item{background-color:#282f2f;cursor:pointer}html.theme--documenter-dark .media{align-items:flex-start;display:flex;text-align:left}html.theme--documenter-dark .media .content:not(:last-child){margin-bottom:0.75rem}html.theme--documenter-dark .media .media{border-top:1px solid rgba(94,109,111,0.5);display:flex;padding-top:0.75rem}html.theme--documenter-dark .media .media .content:not(:last-child),html.theme--documenter-dark .media .media .control:not(:last-child){margin-bottom:0.5rem}html.theme--documenter-dark .media .media .media{padding-top:0.5rem}html.theme--documenter-dark .media .media .media+.media{margin-top:0.5rem}html.theme--documenter-dark .media+.media{border-top:1px solid rgba(94,109,111,0.5);margin-top:1rem;padding-top:1rem}html.theme--documenter-dark .media.is-large+.media{margin-top:1.5rem;padding-top:1.5rem}html.theme--documenter-dark .media-left,html.theme--documenter-dark .media-right{flex-basis:auto;flex-grow:0;flex-shrink:0}html.theme--documenter-dark .media-left{margin-right:1rem}html.theme--documenter-dark .media-right{margin-left:1rem}html.theme--documenter-dark .media-content{flex-basis:auto;flex-grow:1;flex-shrink:1;text-align:left}@media screen and (max-width: 768px){html.theme--documenter-dark .media-content{overflow-x:auto}}html.theme--documenter-dark .menu{font-size:15px}html.theme--documenter-dark .menu.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.menu{font-size:.85em}html.theme--documenter-dark .menu.is-medium{font-size:1.25rem}html.theme--documenter-dark .menu.is-large{font-size:1.5rem}html.theme--documenter-dark .menu-list{line-height:1.25}html.theme--documenter-dark .menu-list a{border-radius:3px;color:#fff;display:block;padding:0.5em 0.75em}html.theme--documenter-dark .menu-list a:hover{background-color:#282f2f;color:#f2f2f2}html.theme--documenter-dark .menu-list a.is-active{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .menu-list li ul{border-left:1px solid #5e6d6f;margin:.75em;padding-left:.75em}html.theme--documenter-dark .menu-label{color:#fff;font-size:.75em;letter-spacing:.1em;text-transform:uppercase}html.theme--documenter-dark .menu-label:not(:first-child){margin-top:1em}html.theme--documenter-dark .menu-label:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .message{background-color:#282f2f;border-radius:.4em;font-size:15px}html.theme--documenter-dark .message strong{color:currentColor}html.theme--documenter-dark .message a:not(.button):not(.tag):not(.dropdown-item){color:currentColor;text-decoration:underline}html.theme--documenter-dark .message.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.message{font-size:.85em}html.theme--documenter-dark .message.is-medium{font-size:1.25rem}html.theme--documenter-dark .message.is-large{font-size:1.5rem}html.theme--documenter-dark .message.is-white{background-color:#fff}html.theme--documenter-dark .message.is-white .message-header{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .message.is-white .message-body{border-color:#fff;color:#4d4d4d}html.theme--documenter-dark .message.is-black{background-color:#fafafa}html.theme--documenter-dark .message.is-black .message-header{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .message.is-black .message-body{border-color:#0a0a0a;color:#090909}html.theme--documenter-dark .message.is-light{background-color:#f9fafb}html.theme--documenter-dark .message.is-light .message-header{background-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .message.is-light .message-body{border-color:#ecf0f1;color:#505050}html.theme--documenter-dark .message.is-dark,html.theme--documenter-dark .content kbd.message{background-color:#f9fafa}html.theme--documenter-dark .message.is-dark .message-header,html.theme--documenter-dark .content kbd.message .message-header{background-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .message.is-dark .message-body,html.theme--documenter-dark .content kbd.message .message-body{border-color:#282f2f;color:#212526}html.theme--documenter-dark .message.is-primary,html.theme--documenter-dark .docstring>section>a.message.docs-sourcelink{background-color:#f8fafc}html.theme--documenter-dark .message.is-primary .message-header,html.theme--documenter-dark .docstring>section>a.message.docs-sourcelink .message-header{background-color:#375a7f;color:#fff}html.theme--documenter-dark .message.is-primary .message-body,html.theme--documenter-dark .docstring>section>a.message.docs-sourcelink .message-body{border-color:#375a7f;color:#2b4159}html.theme--documenter-dark .message.is-link{background-color:#f6fefc}html.theme--documenter-dark .message.is-link .message-header{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .message.is-link .message-body{border-color:#1abc9c;color:#0b2f28}html.theme--documenter-dark .message.is-info{background-color:#f5fbff}html.theme--documenter-dark .message.is-info .message-header{background-color:#024c7d;color:#fff}html.theme--documenter-dark .message.is-info .message-body{border-color:#024c7d;color:#033659}html.theme--documenter-dark .message.is-success{background-color:#f5fff9}html.theme--documenter-dark .message.is-success .message-header{background-color:#008438;color:#fff}html.theme--documenter-dark .message.is-success .message-body{border-color:#008438;color:#023518}html.theme--documenter-dark .message.is-warning{background-color:#fffcf5}html.theme--documenter-dark .message.is-warning .message-header{background-color:#ad8100;color:#fff}html.theme--documenter-dark .message.is-warning .message-body{border-color:#ad8100;color:#3d2e03}html.theme--documenter-dark .message.is-danger{background-color:#fef6f6}html.theme--documenter-dark .message.is-danger .message-header{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .message.is-danger .message-body{border-color:#9e1b0d;color:#7a170c}html.theme--documenter-dark .message-header{align-items:center;background-color:#fff;border-radius:.4em .4em 0 0;color:rgba(0,0,0,0.7);display:flex;font-weight:700;justify-content:space-between;line-height:1.25;padding:0.75em 1em;position:relative}html.theme--documenter-dark .message-header .delete{flex-grow:0;flex-shrink:0;margin-left:0.75em}html.theme--documenter-dark .message-header+.message-body{border-width:0;border-top-left-radius:0;border-top-right-radius:0}html.theme--documenter-dark .message-body{border-color:#5e6d6f;border-radius:.4em;border-style:solid;border-width:0 0 0 4px;color:#fff;padding:1.25em 1.5em}html.theme--documenter-dark .message-body code,html.theme--documenter-dark .message-body pre{background-color:#fff}html.theme--documenter-dark .message-body pre code{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .modal{align-items:center;display:none;flex-direction:column;justify-content:center;overflow:hidden;position:fixed;z-index:40}html.theme--documenter-dark .modal.is-active{display:flex}html.theme--documenter-dark .modal-background{background-color:rgba(10,10,10,0.86)}html.theme--documenter-dark .modal-content,html.theme--documenter-dark .modal-card{margin:0 20px;max-height:calc(100vh - 160px);overflow:auto;position:relative;width:100%}@media screen and (min-width: 769px),print{html.theme--documenter-dark .modal-content,html.theme--documenter-dark .modal-card{margin:0 auto;max-height:calc(100vh - 40px);width:640px}}html.theme--documenter-dark .modal-close{background:none;height:40px;position:fixed;right:20px;top:20px;width:40px}html.theme--documenter-dark .modal-card{display:flex;flex-direction:column;max-height:calc(100vh - 40px);overflow:hidden;-ms-overflow-y:visible}html.theme--documenter-dark .modal-card-head,html.theme--documenter-dark .modal-card-foot{align-items:center;background-color:#282f2f;display:flex;flex-shrink:0;justify-content:flex-start;padding:20px;position:relative}html.theme--documenter-dark .modal-card-head{border-bottom:1px solid #5e6d6f;border-top-left-radius:8px;border-top-right-radius:8px}html.theme--documenter-dark .modal-card-title{color:#f2f2f2;flex-grow:1;flex-shrink:0;font-size:1.5rem;line-height:1}html.theme--documenter-dark .modal-card-foot{border-bottom-left-radius:8px;border-bottom-right-radius:8px;border-top:1px solid #5e6d6f}html.theme--documenter-dark .modal-card-foot .button:not(:last-child){margin-right:0.5em}html.theme--documenter-dark .modal-card-body{-webkit-overflow-scrolling:touch;background-color:#fff;flex-grow:1;flex-shrink:1;overflow:auto;padding:20px}html.theme--documenter-dark .navbar{background-color:#375a7f;min-height:4rem;position:relative;z-index:30}html.theme--documenter-dark .navbar.is-white{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link{color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-white .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-white .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link::after{border-color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-burger{color:#0a0a0a}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-white .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-white .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link{color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-white .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-white .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-white .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-white .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-white .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link::after{border-color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-white .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-white .navbar-item.has-dropdown.is-active .navbar-link{background-color:#f2f2f2;color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-dropdown a.navbar-item.is-active{background-color:#fff;color:#0a0a0a}}html.theme--documenter-dark .navbar.is-black{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-black .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-black .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link.is-active{background-color:#000;color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-black .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-black .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-black .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-black .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-black .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-black .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-black .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link.is-active{background-color:#000;color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-black .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-black .navbar-item.has-dropdown.is-active .navbar-link{background-color:#000;color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-dropdown a.navbar-item.is-active{background-color:#0a0a0a;color:#fff}}html.theme--documenter-dark .navbar.is-light{background-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .navbar.is-light .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link{color:#282f2f}html.theme--documenter-dark .navbar.is-light .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-light .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-light .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link.is-active{background-color:#dde4e6;color:#282f2f}html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link::after{border-color:#282f2f}html.theme--documenter-dark .navbar.is-light .navbar-burger{color:#282f2f}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-light .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-light .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link{color:#282f2f}html.theme--documenter-dark .navbar.is-light .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-light .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-light .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-light .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-light .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-light .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link.is-active{background-color:#dde4e6;color:#282f2f}html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link::after{border-color:#282f2f}html.theme--documenter-dark .navbar.is-light .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-light .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-light .navbar-item.has-dropdown.is-active .navbar-link{background-color:#dde4e6;color:#282f2f}html.theme--documenter-dark .navbar.is-light .navbar-dropdown a.navbar-item.is-active{background-color:#ecf0f1;color:#282f2f}}html.theme--documenter-dark .navbar.is-dark,html.theme--documenter-dark .content kbd.navbar{background-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .navbar.is-dark .navbar-brand>.navbar-item,html.theme--documenter-dark .content kbd.navbar .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link{color:#ecf0f1}html.theme--documenter-dark .navbar.is-dark .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .content kbd.navbar .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-dark .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .content kbd.navbar .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-dark .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link:focus,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link:hover,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link.is-active{background-color:#1d2122;color:#ecf0f1}html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link::after,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link::after{border-color:#ecf0f1}html.theme--documenter-dark .navbar.is-dark .navbar-burger,html.theme--documenter-dark .content kbd.navbar .navbar-burger{color:#ecf0f1}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-dark .navbar-start>.navbar-item,html.theme--documenter-dark .content kbd.navbar .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-dark .navbar-end>.navbar-item,html.theme--documenter-dark .content kbd.navbar .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link{color:#ecf0f1}html.theme--documenter-dark .navbar.is-dark .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .content kbd.navbar .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-dark .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .content kbd.navbar .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-dark .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link:focus,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link:hover,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-dark .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .content kbd.navbar .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-dark .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .content kbd.navbar .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-dark .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link:focus,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link:hover,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link.is-active{background-color:#1d2122;color:#ecf0f1}html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link::after,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link::after,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link::after{border-color:#ecf0f1}html.theme--documenter-dark .navbar.is-dark .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-dark .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-dark .navbar-item.has-dropdown.is-active .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-item.has-dropdown.is-active .navbar-link{background-color:#1d2122;color:#ecf0f1}html.theme--documenter-dark .navbar.is-dark .navbar-dropdown a.navbar-item.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-dropdown a.navbar-item.is-active{background-color:#282f2f;color:#ecf0f1}}html.theme--documenter-dark .navbar.is-primary,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink{background-color:#375a7f;color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-brand>.navbar-item,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-primary .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-primary .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link.is-active{background-color:#2f4d6d;color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link::after,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-burger,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-primary .navbar-start>.navbar-item,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-primary .navbar-end>.navbar-item,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-primary .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-primary .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-primary .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-primary .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-primary .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link.is-active{background-color:#2f4d6d;color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link::after,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link::after,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-primary .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-primary .navbar-item.has-dropdown.is-active .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown.is-active .navbar-link{background-color:#2f4d6d;color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-dropdown a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-dropdown a.navbar-item.is-active{background-color:#375a7f;color:#fff}}html.theme--documenter-dark .navbar.is-link{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-link .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-link .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link.is-active{background-color:#17a689;color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-link .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-link .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-link .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-link .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-link .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-link .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-link .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link.is-active{background-color:#17a689;color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-link .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-link .navbar-item.has-dropdown.is-active .navbar-link{background-color:#17a689;color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-dropdown a.navbar-item.is-active{background-color:#1abc9c;color:#fff}}html.theme--documenter-dark .navbar.is-info{background-color:#024c7d;color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-info .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-info .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link.is-active{background-color:#023d64;color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-info .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-info .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-info .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-info .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-info .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-info .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-info .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link.is-active{background-color:#023d64;color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-info .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-info .navbar-item.has-dropdown.is-active .navbar-link{background-color:#023d64;color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-dropdown a.navbar-item.is-active{background-color:#024c7d;color:#fff}}html.theme--documenter-dark .navbar.is-success{background-color:#008438;color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-success .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-success .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link.is-active{background-color:#006b2d;color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-success .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-success .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-success .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-success .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-success .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-success .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-success .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link.is-active{background-color:#006b2d;color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-success .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-success .navbar-item.has-dropdown.is-active .navbar-link{background-color:#006b2d;color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-dropdown a.navbar-item.is-active{background-color:#008438;color:#fff}}html.theme--documenter-dark .navbar.is-warning{background-color:#ad8100;color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-warning .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-warning .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link.is-active{background-color:#946e00;color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-warning .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-warning .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-warning .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-warning .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-warning .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-warning .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-warning .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link.is-active{background-color:#946e00;color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-warning .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-warning .navbar-item.has-dropdown.is-active .navbar-link{background-color:#946e00;color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-dropdown a.navbar-item.is-active{background-color:#ad8100;color:#fff}}html.theme--documenter-dark .navbar.is-danger{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-danger .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-danger .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link.is-active{background-color:#86170b;color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-danger .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-danger .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-danger .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-danger .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-danger .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-danger .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-danger .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link.is-active{background-color:#86170b;color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-danger .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-danger .navbar-item.has-dropdown.is-active .navbar-link{background-color:#86170b;color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-dropdown a.navbar-item.is-active{background-color:#9e1b0d;color:#fff}}html.theme--documenter-dark .navbar>.container{align-items:stretch;display:flex;min-height:4rem;width:100%}html.theme--documenter-dark .navbar.has-shadow{box-shadow:0 2px 0 0 #282f2f}html.theme--documenter-dark .navbar.is-fixed-bottom,html.theme--documenter-dark .navbar.is-fixed-top{left:0;position:fixed;right:0;z-index:30}html.theme--documenter-dark .navbar.is-fixed-bottom{bottom:0}html.theme--documenter-dark .navbar.is-fixed-bottom.has-shadow{box-shadow:0 -2px 0 0 #282f2f}html.theme--documenter-dark .navbar.is-fixed-top{top:0}html.theme--documenter-dark html.has-navbar-fixed-top,html.theme--documenter-dark body.has-navbar-fixed-top{padding-top:4rem}html.theme--documenter-dark html.has-navbar-fixed-bottom,html.theme--documenter-dark body.has-navbar-fixed-bottom{padding-bottom:4rem}html.theme--documenter-dark .navbar-brand,html.theme--documenter-dark .navbar-tabs{align-items:stretch;display:flex;flex-shrink:0;min-height:4rem}html.theme--documenter-dark .navbar-brand a.navbar-item:focus,html.theme--documenter-dark .navbar-brand a.navbar-item:hover{background-color:transparent}html.theme--documenter-dark .navbar-tabs{-webkit-overflow-scrolling:touch;max-width:100vw;overflow-x:auto;overflow-y:hidden}html.theme--documenter-dark .navbar-burger{color:#fff;cursor:pointer;display:block;height:4rem;position:relative;width:4rem;margin-left:auto}html.theme--documenter-dark .navbar-burger span{background-color:currentColor;display:block;height:1px;left:calc(50% - 8px);position:absolute;transform-origin:center;transition-duration:86ms;transition-property:background-color, opacity, transform;transition-timing-function:ease-out;width:16px}html.theme--documenter-dark .navbar-burger span:nth-child(1){top:calc(50% - 6px)}html.theme--documenter-dark .navbar-burger span:nth-child(2){top:calc(50% - 1px)}html.theme--documenter-dark .navbar-burger span:nth-child(3){top:calc(50% + 4px)}html.theme--documenter-dark .navbar-burger:hover{background-color:rgba(0,0,0,0.05)}html.theme--documenter-dark .navbar-burger.is-active span:nth-child(1){transform:translateY(5px) rotate(45deg)}html.theme--documenter-dark .navbar-burger.is-active span:nth-child(2){opacity:0}html.theme--documenter-dark .navbar-burger.is-active span:nth-child(3){transform:translateY(-5px) rotate(-45deg)}html.theme--documenter-dark .navbar-menu{display:none}html.theme--documenter-dark .navbar-item,html.theme--documenter-dark .navbar-link{color:#fff;display:block;line-height:1.5;padding:0.5rem 0.75rem;position:relative}html.theme--documenter-dark .navbar-item .icon:only-child,html.theme--documenter-dark .navbar-link .icon:only-child{margin-left:-0.25rem;margin-right:-0.25rem}html.theme--documenter-dark a.navbar-item,html.theme--documenter-dark .navbar-link{cursor:pointer}html.theme--documenter-dark a.navbar-item:focus,html.theme--documenter-dark a.navbar-item:focus-within,html.theme--documenter-dark a.navbar-item:hover,html.theme--documenter-dark a.navbar-item.is-active,html.theme--documenter-dark .navbar-link:focus,html.theme--documenter-dark .navbar-link:focus-within,html.theme--documenter-dark .navbar-link:hover,html.theme--documenter-dark .navbar-link.is-active{background-color:rgba(0,0,0,0);color:#1abc9c}html.theme--documenter-dark .navbar-item{display:block;flex-grow:0;flex-shrink:0}html.theme--documenter-dark .navbar-item img{max-height:1.75rem}html.theme--documenter-dark .navbar-item.has-dropdown{padding:0}html.theme--documenter-dark .navbar-item.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .navbar-item.is-tab{border-bottom:1px solid transparent;min-height:4rem;padding-bottom:calc(0.5rem - 1px)}html.theme--documenter-dark .navbar-item.is-tab:focus,html.theme--documenter-dark .navbar-item.is-tab:hover{background-color:rgba(0,0,0,0);border-bottom-color:#1abc9c}html.theme--documenter-dark .navbar-item.is-tab.is-active{background-color:rgba(0,0,0,0);border-bottom-color:#1abc9c;border-bottom-style:solid;border-bottom-width:3px;color:#1abc9c;padding-bottom:calc(0.5rem - 3px)}html.theme--documenter-dark .navbar-content{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .navbar-link:not(.is-arrowless){padding-right:2.5em}html.theme--documenter-dark .navbar-link:not(.is-arrowless)::after{border-color:#fff;margin-top:-0.375em;right:1.125em}html.theme--documenter-dark .navbar-dropdown{font-size:0.875rem;padding-bottom:0.5rem;padding-top:0.5rem}html.theme--documenter-dark .navbar-dropdown .navbar-item{padding-left:1.5rem;padding-right:1.5rem}html.theme--documenter-dark .navbar-divider{background-color:rgba(0,0,0,0.2);border:none;display:none;height:2px;margin:0.5rem 0}@media screen and (max-width: 1055px){html.theme--documenter-dark .navbar>.container{display:block}html.theme--documenter-dark .navbar-brand .navbar-item,html.theme--documenter-dark .navbar-tabs .navbar-item{align-items:center;display:flex}html.theme--documenter-dark .navbar-link::after{display:none}html.theme--documenter-dark .navbar-menu{background-color:#375a7f;box-shadow:0 8px 16px rgba(10,10,10,0.1);padding:0.5rem 0}html.theme--documenter-dark .navbar-menu.is-active{display:block}html.theme--documenter-dark .navbar.is-fixed-bottom-touch,html.theme--documenter-dark .navbar.is-fixed-top-touch{left:0;position:fixed;right:0;z-index:30}html.theme--documenter-dark .navbar.is-fixed-bottom-touch{bottom:0}html.theme--documenter-dark .navbar.is-fixed-bottom-touch.has-shadow{box-shadow:0 -2px 3px rgba(10,10,10,0.1)}html.theme--documenter-dark .navbar.is-fixed-top-touch{top:0}html.theme--documenter-dark .navbar.is-fixed-top .navbar-menu,html.theme--documenter-dark .navbar.is-fixed-top-touch .navbar-menu{-webkit-overflow-scrolling:touch;max-height:calc(100vh - 4rem);overflow:auto}html.theme--documenter-dark html.has-navbar-fixed-top-touch,html.theme--documenter-dark body.has-navbar-fixed-top-touch{padding-top:4rem}html.theme--documenter-dark html.has-navbar-fixed-bottom-touch,html.theme--documenter-dark body.has-navbar-fixed-bottom-touch{padding-bottom:4rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar,html.theme--documenter-dark .navbar-menu,html.theme--documenter-dark .navbar-start,html.theme--documenter-dark .navbar-end{align-items:stretch;display:flex}html.theme--documenter-dark .navbar{min-height:4rem}html.theme--documenter-dark .navbar.is-spaced{padding:1rem 2rem}html.theme--documenter-dark .navbar.is-spaced .navbar-start,html.theme--documenter-dark .navbar.is-spaced .navbar-end{align-items:center}html.theme--documenter-dark .navbar.is-spaced a.navbar-item,html.theme--documenter-dark .navbar.is-spaced .navbar-link{border-radius:.4em}html.theme--documenter-dark .navbar.is-transparent a.navbar-item:focus,html.theme--documenter-dark .navbar.is-transparent a.navbar-item:hover,html.theme--documenter-dark .navbar.is-transparent a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-transparent .navbar-link:focus,html.theme--documenter-dark .navbar.is-transparent .navbar-link:hover,html.theme--documenter-dark .navbar.is-transparent .navbar-link.is-active{background-color:transparent !important}html.theme--documenter-dark .navbar.is-transparent .navbar-item.has-dropdown.is-active .navbar-link,html.theme--documenter-dark .navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:focus .navbar-link,html.theme--documenter-dark .navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:focus-within .navbar-link,html.theme--documenter-dark .navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:hover .navbar-link{background-color:transparent !important}html.theme--documenter-dark .navbar.is-transparent .navbar-dropdown a.navbar-item:focus,html.theme--documenter-dark .navbar.is-transparent .navbar-dropdown a.navbar-item:hover{background-color:rgba(0,0,0,0);color:#dbdee0}html.theme--documenter-dark .navbar.is-transparent .navbar-dropdown a.navbar-item.is-active{background-color:rgba(0,0,0,0);color:#1abc9c}html.theme--documenter-dark .navbar-burger{display:none}html.theme--documenter-dark .navbar-item,html.theme--documenter-dark .navbar-link{align-items:center;display:flex}html.theme--documenter-dark .navbar-item{display:flex}html.theme--documenter-dark .navbar-item.has-dropdown{align-items:stretch}html.theme--documenter-dark .navbar-item.has-dropdown-up .navbar-link::after{transform:rotate(135deg) translate(0.25em, -0.25em)}html.theme--documenter-dark .navbar-item.has-dropdown-up .navbar-dropdown{border-bottom:1px solid rgba(0,0,0,0.2);border-radius:8px 8px 0 0;border-top:none;bottom:100%;box-shadow:0 -8px 8px rgba(10,10,10,0.1);top:auto}html.theme--documenter-dark .navbar-item.is-active .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:focus .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:focus-within .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:hover .navbar-dropdown{display:block}.navbar.is-spaced html.theme--documenter-dark .navbar-item.is-active .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-active .navbar-dropdown.is-boxed,.navbar.is-spaced html.theme--documenter-dark .navbar-item.is-hoverable:focus .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:focus .navbar-dropdown.is-boxed,.navbar.is-spaced html.theme--documenter-dark .navbar-item.is-hoverable:focus-within .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:focus-within .navbar-dropdown.is-boxed,.navbar.is-spaced html.theme--documenter-dark .navbar-item.is-hoverable:hover .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:hover .navbar-dropdown.is-boxed{opacity:1;pointer-events:auto;transform:translateY(0)}html.theme--documenter-dark .navbar-menu{flex-grow:1;flex-shrink:0}html.theme--documenter-dark .navbar-start{justify-content:flex-start;margin-right:auto}html.theme--documenter-dark .navbar-end{justify-content:flex-end;margin-left:auto}html.theme--documenter-dark .navbar-dropdown{background-color:#375a7f;border-bottom-left-radius:8px;border-bottom-right-radius:8px;border-top:1px solid rgba(0,0,0,0.2);box-shadow:0 8px 8px rgba(10,10,10,0.1);display:none;font-size:0.875rem;left:0;min-width:100%;position:absolute;top:100%;z-index:20}html.theme--documenter-dark .navbar-dropdown .navbar-item{padding:0.375rem 1rem;white-space:nowrap}html.theme--documenter-dark .navbar-dropdown a.navbar-item{padding-right:3rem}html.theme--documenter-dark .navbar-dropdown a.navbar-item:focus,html.theme--documenter-dark .navbar-dropdown a.navbar-item:hover{background-color:rgba(0,0,0,0);color:#dbdee0}html.theme--documenter-dark .navbar-dropdown a.navbar-item.is-active{background-color:rgba(0,0,0,0);color:#1abc9c}.navbar.is-spaced html.theme--documenter-dark .navbar-dropdown,html.theme--documenter-dark .navbar-dropdown.is-boxed{border-radius:8px;border-top:none;box-shadow:0 8px 8px rgba(10,10,10,0.1), 0 0 0 1px rgba(10,10,10,0.1);display:block;opacity:0;pointer-events:none;top:calc(100% + (-4px));transform:translateY(-5px);transition-duration:86ms;transition-property:opacity, transform}html.theme--documenter-dark .navbar-dropdown.is-right{left:auto;right:0}html.theme--documenter-dark .navbar-divider{display:block}html.theme--documenter-dark .navbar>.container .navbar-brand,html.theme--documenter-dark .container>.navbar .navbar-brand{margin-left:-.75rem}html.theme--documenter-dark .navbar>.container .navbar-menu,html.theme--documenter-dark .container>.navbar .navbar-menu{margin-right:-.75rem}html.theme--documenter-dark .navbar.is-fixed-bottom-desktop,html.theme--documenter-dark .navbar.is-fixed-top-desktop{left:0;position:fixed;right:0;z-index:30}html.theme--documenter-dark .navbar.is-fixed-bottom-desktop{bottom:0}html.theme--documenter-dark .navbar.is-fixed-bottom-desktop.has-shadow{box-shadow:0 -2px 3px rgba(10,10,10,0.1)}html.theme--documenter-dark .navbar.is-fixed-top-desktop{top:0}html.theme--documenter-dark html.has-navbar-fixed-top-desktop,html.theme--documenter-dark body.has-navbar-fixed-top-desktop{padding-top:4rem}html.theme--documenter-dark html.has-navbar-fixed-bottom-desktop,html.theme--documenter-dark body.has-navbar-fixed-bottom-desktop{padding-bottom:4rem}html.theme--documenter-dark html.has-spaced-navbar-fixed-top,html.theme--documenter-dark body.has-spaced-navbar-fixed-top{padding-top:6rem}html.theme--documenter-dark html.has-spaced-navbar-fixed-bottom,html.theme--documenter-dark body.has-spaced-navbar-fixed-bottom{padding-bottom:6rem}html.theme--documenter-dark a.navbar-item.is-active,html.theme--documenter-dark .navbar-link.is-active{color:#1abc9c}html.theme--documenter-dark a.navbar-item.is-active:not(:focus):not(:hover),html.theme--documenter-dark .navbar-link.is-active:not(:focus):not(:hover){background-color:rgba(0,0,0,0)}html.theme--documenter-dark .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar-item.has-dropdown.is-active .navbar-link{background-color:rgba(0,0,0,0)}}html.theme--documenter-dark .hero.is-fullheight-with-navbar{min-height:calc(100vh - 4rem)}html.theme--documenter-dark .pagination{font-size:15px;margin:-.25rem}html.theme--documenter-dark .pagination.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination{font-size:.85em}html.theme--documenter-dark .pagination.is-medium{font-size:1.25rem}html.theme--documenter-dark .pagination.is-large{font-size:1.5rem}html.theme--documenter-dark .pagination.is-rounded .pagination-previous,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-previous,html.theme--documenter-dark .pagination.is-rounded .pagination-next,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-next{padding-left:1em;padding-right:1em;border-radius:290486px}html.theme--documenter-dark .pagination.is-rounded .pagination-link,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-link{border-radius:290486px}html.theme--documenter-dark .pagination,html.theme--documenter-dark .pagination-list{align-items:center;display:flex;justify-content:center;text-align:center}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis{font-size:1em;justify-content:center;margin:.25rem;padding-left:.5em;padding-right:.5em;text-align:center}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link{border-color:#5e6d6f;color:#1abc9c;min-width:2.25em}html.theme--documenter-dark .pagination-previous:hover,html.theme--documenter-dark .pagination-next:hover,html.theme--documenter-dark .pagination-link:hover{border-color:#8c9b9d;color:#1dd2af}html.theme--documenter-dark .pagination-previous:focus,html.theme--documenter-dark .pagination-next:focus,html.theme--documenter-dark .pagination-link:focus{border-color:#8c9b9d}html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2)}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark .pagination-next[disabled],html.theme--documenter-dark .pagination-link[disabled]{background-color:#dbdee0;border-color:#dbdee0;box-shadow:none;color:#5e6d6f;opacity:0.5}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next{padding-left:0.75em;padding-right:0.75em;white-space:nowrap}html.theme--documenter-dark .pagination-link.is-current{background-color:#1abc9c;border-color:#1abc9c;color:#fff}html.theme--documenter-dark .pagination-ellipsis{color:#8c9b9d;pointer-events:none}html.theme--documenter-dark .pagination-list{flex-wrap:wrap}@media screen and (max-width: 768px){html.theme--documenter-dark .pagination{flex-wrap:wrap}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .pagination-list li{flex-grow:1;flex-shrink:1}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .pagination-list{flex-grow:1;flex-shrink:1;justify-content:flex-start;order:1}html.theme--documenter-dark .pagination-previous{order:2}html.theme--documenter-dark .pagination-next{order:3}html.theme--documenter-dark .pagination{justify-content:space-between}html.theme--documenter-dark .pagination.is-centered .pagination-previous{order:1}html.theme--documenter-dark .pagination.is-centered .pagination-list{justify-content:center;order:2}html.theme--documenter-dark .pagination.is-centered .pagination-next{order:3}html.theme--documenter-dark .pagination.is-right .pagination-previous{order:1}html.theme--documenter-dark .pagination.is-right .pagination-next{order:2}html.theme--documenter-dark .pagination.is-right .pagination-list{justify-content:flex-end;order:3}}html.theme--documenter-dark .panel{font-size:15px}html.theme--documenter-dark .panel:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .panel-heading,html.theme--documenter-dark .panel-tabs,html.theme--documenter-dark .panel-block{border-bottom:1px solid #5e6d6f;border-left:1px solid #5e6d6f;border-right:1px solid #5e6d6f}html.theme--documenter-dark .panel-heading:first-child,html.theme--documenter-dark .panel-tabs:first-child,html.theme--documenter-dark .panel-block:first-child{border-top:1px solid #5e6d6f}html.theme--documenter-dark .panel-heading{background-color:#282f2f;border-radius:.4em .4em 0 0;color:#f2f2f2;font-size:1.25em;font-weight:300;line-height:1.25;padding:0.5em 0.75em}html.theme--documenter-dark .panel-tabs{align-items:flex-end;display:flex;font-size:.875em;justify-content:center}html.theme--documenter-dark .panel-tabs a{border-bottom:1px solid #5e6d6f;margin-bottom:-1px;padding:0.5em}html.theme--documenter-dark .panel-tabs a.is-active{border-bottom-color:#343c3d;color:#17a689}html.theme--documenter-dark .panel-list a{color:#fff}html.theme--documenter-dark .panel-list a:hover{color:#1abc9c}html.theme--documenter-dark .panel-block{align-items:center;color:#f2f2f2;display:flex;justify-content:flex-start;padding:0.5em 0.75em}html.theme--documenter-dark .panel-block input[type="checkbox"]{margin-right:0.75em}html.theme--documenter-dark .panel-block>.control{flex-grow:1;flex-shrink:1;width:100%}html.theme--documenter-dark .panel-block.is-wrapped{flex-wrap:wrap}html.theme--documenter-dark .panel-block.is-active{border-left-color:#1abc9c;color:#17a689}html.theme--documenter-dark .panel-block.is-active .panel-icon{color:#1abc9c}html.theme--documenter-dark a.panel-block,html.theme--documenter-dark label.panel-block{cursor:pointer}html.theme--documenter-dark a.panel-block:hover,html.theme--documenter-dark label.panel-block:hover{background-color:#282f2f}html.theme--documenter-dark .panel-icon{display:inline-block;font-size:14px;height:1em;line-height:1em;text-align:center;vertical-align:top;width:1em;color:#fff;margin-right:0.75em}html.theme--documenter-dark .panel-icon .fa{font-size:inherit;line-height:inherit}html.theme--documenter-dark .tabs{-webkit-overflow-scrolling:touch;align-items:stretch;display:flex;font-size:15px;justify-content:space-between;overflow:hidden;overflow-x:auto;white-space:nowrap}html.theme--documenter-dark .tabs a{align-items:center;border-bottom-color:#5e6d6f;border-bottom-style:solid;border-bottom-width:1px;color:#fff;display:flex;justify-content:center;margin-bottom:-1px;padding:0.5em 1em;vertical-align:top}html.theme--documenter-dark .tabs a:hover{border-bottom-color:#f2f2f2;color:#f2f2f2}html.theme--documenter-dark .tabs li{display:block}html.theme--documenter-dark .tabs li.is-active a{border-bottom-color:#1abc9c;color:#1abc9c}html.theme--documenter-dark .tabs ul{align-items:center;border-bottom-color:#5e6d6f;border-bottom-style:solid;border-bottom-width:1px;display:flex;flex-grow:1;flex-shrink:0;justify-content:flex-start}html.theme--documenter-dark .tabs ul.is-left{padding-right:0.75em}html.theme--documenter-dark .tabs ul.is-center{flex:none;justify-content:center;padding-left:0.75em;padding-right:0.75em}html.theme--documenter-dark .tabs ul.is-right{justify-content:flex-end;padding-left:0.75em}html.theme--documenter-dark .tabs .icon:first-child{margin-right:0.5em}html.theme--documenter-dark .tabs .icon:last-child{margin-left:0.5em}html.theme--documenter-dark .tabs.is-centered ul{justify-content:center}html.theme--documenter-dark .tabs.is-right ul{justify-content:flex-end}html.theme--documenter-dark .tabs.is-boxed a{border:1px solid transparent;border-radius:.4em .4em 0 0}html.theme--documenter-dark .tabs.is-boxed a:hover{background-color:#282f2f;border-bottom-color:#5e6d6f}html.theme--documenter-dark .tabs.is-boxed li.is-active a{background-color:#fff;border-color:#5e6d6f;border-bottom-color:rgba(0,0,0,0) !important}html.theme--documenter-dark .tabs.is-fullwidth li{flex-grow:1;flex-shrink:0}html.theme--documenter-dark .tabs.is-toggle a{border-color:#5e6d6f;border-style:solid;border-width:1px;margin-bottom:0;position:relative}html.theme--documenter-dark .tabs.is-toggle a:hover{background-color:#282f2f;border-color:#8c9b9d;z-index:2}html.theme--documenter-dark .tabs.is-toggle li+li{margin-left:-1px}html.theme--documenter-dark .tabs.is-toggle li:first-child a{border-radius:.4em 0 0 .4em}html.theme--documenter-dark .tabs.is-toggle li:last-child a{border-radius:0 .4em .4em 0}html.theme--documenter-dark .tabs.is-toggle li.is-active a{background-color:#1abc9c;border-color:#1abc9c;color:#fff;z-index:1}html.theme--documenter-dark .tabs.is-toggle ul{border-bottom:none}html.theme--documenter-dark .tabs.is-toggle.is-toggle-rounded li:first-child a{border-bottom-left-radius:290486px;border-top-left-radius:290486px;padding-left:1.25em}html.theme--documenter-dark .tabs.is-toggle.is-toggle-rounded li:last-child a{border-bottom-right-radius:290486px;border-top-right-radius:290486px;padding-right:1.25em}html.theme--documenter-dark .tabs.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.tabs{font-size:.85em}html.theme--documenter-dark .tabs.is-medium{font-size:1.25rem}html.theme--documenter-dark .tabs.is-large{font-size:1.5rem}html.theme--documenter-dark .column{display:block;flex-basis:0;flex-grow:1;flex-shrink:1;padding:.75rem}.columns.is-mobile>html.theme--documenter-dark .column.is-narrow{flex:none}.columns.is-mobile>html.theme--documenter-dark .column.is-full{flex:none;width:100%}.columns.is-mobile>html.theme--documenter-dark .column.is-three-quarters{flex:none;width:75%}.columns.is-mobile>html.theme--documenter-dark .column.is-two-thirds{flex:none;width:66.6666%}.columns.is-mobile>html.theme--documenter-dark .column.is-half{flex:none;width:50%}.columns.is-mobile>html.theme--documenter-dark .column.is-one-third{flex:none;width:33.3333%}.columns.is-mobile>html.theme--documenter-dark .column.is-one-quarter{flex:none;width:25%}.columns.is-mobile>html.theme--documenter-dark .column.is-one-fifth{flex:none;width:20%}.columns.is-mobile>html.theme--documenter-dark .column.is-two-fifths{flex:none;width:40%}.columns.is-mobile>html.theme--documenter-dark .column.is-three-fifths{flex:none;width:60%}.columns.is-mobile>html.theme--documenter-dark .column.is-four-fifths{flex:none;width:80%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-three-quarters{margin-left:75%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-two-thirds{margin-left:66.6666%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-half{margin-left:50%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-one-third{margin-left:33.3333%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-one-quarter{margin-left:25%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-one-fifth{margin-left:20%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-two-fifths{margin-left:40%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-three-fifths{margin-left:60%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-four-fifths{margin-left:80%}.columns.is-mobile>html.theme--documenter-dark .column.is-0{flex:none;width:0%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-0{margin-left:0%}.columns.is-mobile>html.theme--documenter-dark .column.is-1{flex:none;width:8.3333333333%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-1{margin-left:8.3333333333%}.columns.is-mobile>html.theme--documenter-dark .column.is-2{flex:none;width:16.6666666667%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-2{margin-left:16.6666666667%}.columns.is-mobile>html.theme--documenter-dark .column.is-3{flex:none;width:25%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-3{margin-left:25%}.columns.is-mobile>html.theme--documenter-dark .column.is-4{flex:none;width:33.3333333333%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-4{margin-left:33.3333333333%}.columns.is-mobile>html.theme--documenter-dark .column.is-5{flex:none;width:41.6666666667%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-5{margin-left:41.6666666667%}.columns.is-mobile>html.theme--documenter-dark .column.is-6{flex:none;width:50%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-6{margin-left:50%}.columns.is-mobile>html.theme--documenter-dark .column.is-7{flex:none;width:58.3333333333%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-7{margin-left:58.3333333333%}.columns.is-mobile>html.theme--documenter-dark .column.is-8{flex:none;width:66.6666666667%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-8{margin-left:66.6666666667%}.columns.is-mobile>html.theme--documenter-dark .column.is-9{flex:none;width:75%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-9{margin-left:75%}.columns.is-mobile>html.theme--documenter-dark .column.is-10{flex:none;width:83.3333333333%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-10{margin-left:83.3333333333%}.columns.is-mobile>html.theme--documenter-dark .column.is-11{flex:none;width:91.6666666667%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-11{margin-left:91.6666666667%}.columns.is-mobile>html.theme--documenter-dark .column.is-12{flex:none;width:100%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-12{margin-left:100%}@media screen and (max-width: 768px){html.theme--documenter-dark .column.is-narrow-mobile{flex:none}html.theme--documenter-dark .column.is-full-mobile{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-mobile{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-mobile{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-mobile{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-mobile{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-mobile{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-mobile{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-mobile{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-mobile{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-mobile{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-mobile{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-mobile{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-mobile{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-mobile{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-mobile{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-mobile{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-mobile{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-mobile{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-mobile{margin-left:80%}html.theme--documenter-dark .column.is-0-mobile{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-mobile{margin-left:0%}html.theme--documenter-dark .column.is-1-mobile{flex:none;width:8.3333333333%}html.theme--documenter-dark .column.is-offset-1-mobile{margin-left:8.3333333333%}html.theme--documenter-dark .column.is-2-mobile{flex:none;width:16.6666666667%}html.theme--documenter-dark .column.is-offset-2-mobile{margin-left:16.6666666667%}html.theme--documenter-dark .column.is-3-mobile{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-mobile{margin-left:25%}html.theme--documenter-dark .column.is-4-mobile{flex:none;width:33.3333333333%}html.theme--documenter-dark .column.is-offset-4-mobile{margin-left:33.3333333333%}html.theme--documenter-dark .column.is-5-mobile{flex:none;width:41.6666666667%}html.theme--documenter-dark .column.is-offset-5-mobile{margin-left:41.6666666667%}html.theme--documenter-dark .column.is-6-mobile{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-mobile{margin-left:50%}html.theme--documenter-dark .column.is-7-mobile{flex:none;width:58.3333333333%}html.theme--documenter-dark .column.is-offset-7-mobile{margin-left:58.3333333333%}html.theme--documenter-dark .column.is-8-mobile{flex:none;width:66.6666666667%}html.theme--documenter-dark .column.is-offset-8-mobile{margin-left:66.6666666667%}html.theme--documenter-dark .column.is-9-mobile{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-mobile{margin-left:75%}html.theme--documenter-dark .column.is-10-mobile{flex:none;width:83.3333333333%}html.theme--documenter-dark .column.is-offset-10-mobile{margin-left:83.3333333333%}html.theme--documenter-dark .column.is-11-mobile{flex:none;width:91.6666666667%}html.theme--documenter-dark .column.is-offset-11-mobile{margin-left:91.6666666667%}html.theme--documenter-dark .column.is-12-mobile{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-mobile{margin-left:100%}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .column.is-narrow,html.theme--documenter-dark .column.is-narrow-tablet{flex:none}html.theme--documenter-dark .column.is-full,html.theme--documenter-dark .column.is-full-tablet{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters,html.theme--documenter-dark .column.is-three-quarters-tablet{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds,html.theme--documenter-dark .column.is-two-thirds-tablet{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half,html.theme--documenter-dark .column.is-half-tablet{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third,html.theme--documenter-dark .column.is-one-third-tablet{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter,html.theme--documenter-dark .column.is-one-quarter-tablet{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth,html.theme--documenter-dark .column.is-one-fifth-tablet{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths,html.theme--documenter-dark .column.is-two-fifths-tablet{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths,html.theme--documenter-dark .column.is-three-fifths-tablet{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths,html.theme--documenter-dark .column.is-four-fifths-tablet{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters,html.theme--documenter-dark .column.is-offset-three-quarters-tablet{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds,html.theme--documenter-dark .column.is-offset-two-thirds-tablet{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half,html.theme--documenter-dark .column.is-offset-half-tablet{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third,html.theme--documenter-dark .column.is-offset-one-third-tablet{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter,html.theme--documenter-dark .column.is-offset-one-quarter-tablet{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth,html.theme--documenter-dark .column.is-offset-one-fifth-tablet{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths,html.theme--documenter-dark .column.is-offset-two-fifths-tablet{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths,html.theme--documenter-dark .column.is-offset-three-fifths-tablet{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths,html.theme--documenter-dark .column.is-offset-four-fifths-tablet{margin-left:80%}html.theme--documenter-dark .column.is-0,html.theme--documenter-dark .column.is-0-tablet{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0,html.theme--documenter-dark .column.is-offset-0-tablet{margin-left:0%}html.theme--documenter-dark .column.is-1,html.theme--documenter-dark .column.is-1-tablet{flex:none;width:8.3333333333%}html.theme--documenter-dark .column.is-offset-1,html.theme--documenter-dark .column.is-offset-1-tablet{margin-left:8.3333333333%}html.theme--documenter-dark .column.is-2,html.theme--documenter-dark .column.is-2-tablet{flex:none;width:16.6666666667%}html.theme--documenter-dark .column.is-offset-2,html.theme--documenter-dark .column.is-offset-2-tablet{margin-left:16.6666666667%}html.theme--documenter-dark .column.is-3,html.theme--documenter-dark .column.is-3-tablet{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3,html.theme--documenter-dark .column.is-offset-3-tablet{margin-left:25%}html.theme--documenter-dark .column.is-4,html.theme--documenter-dark .column.is-4-tablet{flex:none;width:33.3333333333%}html.theme--documenter-dark .column.is-offset-4,html.theme--documenter-dark .column.is-offset-4-tablet{margin-left:33.3333333333%}html.theme--documenter-dark .column.is-5,html.theme--documenter-dark .column.is-5-tablet{flex:none;width:41.6666666667%}html.theme--documenter-dark .column.is-offset-5,html.theme--documenter-dark .column.is-offset-5-tablet{margin-left:41.6666666667%}html.theme--documenter-dark .column.is-6,html.theme--documenter-dark .column.is-6-tablet{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6,html.theme--documenter-dark .column.is-offset-6-tablet{margin-left:50%}html.theme--documenter-dark .column.is-7,html.theme--documenter-dark .column.is-7-tablet{flex:none;width:58.3333333333%}html.theme--documenter-dark .column.is-offset-7,html.theme--documenter-dark .column.is-offset-7-tablet{margin-left:58.3333333333%}html.theme--documenter-dark .column.is-8,html.theme--documenter-dark .column.is-8-tablet{flex:none;width:66.6666666667%}html.theme--documenter-dark .column.is-offset-8,html.theme--documenter-dark .column.is-offset-8-tablet{margin-left:66.6666666667%}html.theme--documenter-dark .column.is-9,html.theme--documenter-dark .column.is-9-tablet{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9,html.theme--documenter-dark .column.is-offset-9-tablet{margin-left:75%}html.theme--documenter-dark .column.is-10,html.theme--documenter-dark .column.is-10-tablet{flex:none;width:83.3333333333%}html.theme--documenter-dark .column.is-offset-10,html.theme--documenter-dark .column.is-offset-10-tablet{margin-left:83.3333333333%}html.theme--documenter-dark .column.is-11,html.theme--documenter-dark .column.is-11-tablet{flex:none;width:91.6666666667%}html.theme--documenter-dark .column.is-offset-11,html.theme--documenter-dark .column.is-offset-11-tablet{margin-left:91.6666666667%}html.theme--documenter-dark .column.is-12,html.theme--documenter-dark .column.is-12-tablet{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12,html.theme--documenter-dark .column.is-offset-12-tablet{margin-left:100%}}@media screen and (max-width: 1055px){html.theme--documenter-dark .column.is-narrow-touch{flex:none}html.theme--documenter-dark .column.is-full-touch{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-touch{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-touch{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-touch{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-touch{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-touch{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-touch{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-touch{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-touch{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-touch{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-touch{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-touch{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-touch{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-touch{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-touch{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-touch{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-touch{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-touch{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-touch{margin-left:80%}html.theme--documenter-dark .column.is-0-touch{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-touch{margin-left:0%}html.theme--documenter-dark .column.is-1-touch{flex:none;width:8.3333333333%}html.theme--documenter-dark .column.is-offset-1-touch{margin-left:8.3333333333%}html.theme--documenter-dark .column.is-2-touch{flex:none;width:16.6666666667%}html.theme--documenter-dark .column.is-offset-2-touch{margin-left:16.6666666667%}html.theme--documenter-dark .column.is-3-touch{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-touch{margin-left:25%}html.theme--documenter-dark .column.is-4-touch{flex:none;width:33.3333333333%}html.theme--documenter-dark .column.is-offset-4-touch{margin-left:33.3333333333%}html.theme--documenter-dark .column.is-5-touch{flex:none;width:41.6666666667%}html.theme--documenter-dark .column.is-offset-5-touch{margin-left:41.6666666667%}html.theme--documenter-dark .column.is-6-touch{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-touch{margin-left:50%}html.theme--documenter-dark .column.is-7-touch{flex:none;width:58.3333333333%}html.theme--documenter-dark .column.is-offset-7-touch{margin-left:58.3333333333%}html.theme--documenter-dark .column.is-8-touch{flex:none;width:66.6666666667%}html.theme--documenter-dark .column.is-offset-8-touch{margin-left:66.6666666667%}html.theme--documenter-dark .column.is-9-touch{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-touch{margin-left:75%}html.theme--documenter-dark .column.is-10-touch{flex:none;width:83.3333333333%}html.theme--documenter-dark .column.is-offset-10-touch{margin-left:83.3333333333%}html.theme--documenter-dark .column.is-11-touch{flex:none;width:91.6666666667%}html.theme--documenter-dark .column.is-offset-11-touch{margin-left:91.6666666667%}html.theme--documenter-dark .column.is-12-touch{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-touch{margin-left:100%}}@media screen and (min-width: 1056px){html.theme--documenter-dark .column.is-narrow-desktop{flex:none}html.theme--documenter-dark .column.is-full-desktop{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-desktop{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-desktop{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-desktop{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-desktop{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-desktop{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-desktop{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-desktop{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-desktop{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-desktop{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-desktop{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-desktop{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-desktop{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-desktop{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-desktop{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-desktop{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-desktop{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-desktop{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-desktop{margin-left:80%}html.theme--documenter-dark .column.is-0-desktop{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-desktop{margin-left:0%}html.theme--documenter-dark .column.is-1-desktop{flex:none;width:8.3333333333%}html.theme--documenter-dark .column.is-offset-1-desktop{margin-left:8.3333333333%}html.theme--documenter-dark .column.is-2-desktop{flex:none;width:16.6666666667%}html.theme--documenter-dark .column.is-offset-2-desktop{margin-left:16.6666666667%}html.theme--documenter-dark .column.is-3-desktop{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-desktop{margin-left:25%}html.theme--documenter-dark .column.is-4-desktop{flex:none;width:33.3333333333%}html.theme--documenter-dark .column.is-offset-4-desktop{margin-left:33.3333333333%}html.theme--documenter-dark .column.is-5-desktop{flex:none;width:41.6666666667%}html.theme--documenter-dark .column.is-offset-5-desktop{margin-left:41.6666666667%}html.theme--documenter-dark .column.is-6-desktop{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-desktop{margin-left:50%}html.theme--documenter-dark .column.is-7-desktop{flex:none;width:58.3333333333%}html.theme--documenter-dark .column.is-offset-7-desktop{margin-left:58.3333333333%}html.theme--documenter-dark .column.is-8-desktop{flex:none;width:66.6666666667%}html.theme--documenter-dark .column.is-offset-8-desktop{margin-left:66.6666666667%}html.theme--documenter-dark .column.is-9-desktop{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-desktop{margin-left:75%}html.theme--documenter-dark .column.is-10-desktop{flex:none;width:83.3333333333%}html.theme--documenter-dark .column.is-offset-10-desktop{margin-left:83.3333333333%}html.theme--documenter-dark .column.is-11-desktop{flex:none;width:91.6666666667%}html.theme--documenter-dark .column.is-offset-11-desktop{margin-left:91.6666666667%}html.theme--documenter-dark .column.is-12-desktop{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-desktop{margin-left:100%}}@media screen and (min-width: 1216px){html.theme--documenter-dark .column.is-narrow-widescreen{flex:none}html.theme--documenter-dark .column.is-full-widescreen{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-widescreen{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-widescreen{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-widescreen{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-widescreen{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-widescreen{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-widescreen{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-widescreen{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-widescreen{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-widescreen{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-widescreen{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-widescreen{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-widescreen{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-widescreen{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-widescreen{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-widescreen{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-widescreen{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-widescreen{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-widescreen{margin-left:80%}html.theme--documenter-dark .column.is-0-widescreen{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-widescreen{margin-left:0%}html.theme--documenter-dark .column.is-1-widescreen{flex:none;width:8.3333333333%}html.theme--documenter-dark .column.is-offset-1-widescreen{margin-left:8.3333333333%}html.theme--documenter-dark .column.is-2-widescreen{flex:none;width:16.6666666667%}html.theme--documenter-dark .column.is-offset-2-widescreen{margin-left:16.6666666667%}html.theme--documenter-dark .column.is-3-widescreen{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-widescreen{margin-left:25%}html.theme--documenter-dark .column.is-4-widescreen{flex:none;width:33.3333333333%}html.theme--documenter-dark .column.is-offset-4-widescreen{margin-left:33.3333333333%}html.theme--documenter-dark .column.is-5-widescreen{flex:none;width:41.6666666667%}html.theme--documenter-dark .column.is-offset-5-widescreen{margin-left:41.6666666667%}html.theme--documenter-dark .column.is-6-widescreen{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-widescreen{margin-left:50%}html.theme--documenter-dark .column.is-7-widescreen{flex:none;width:58.3333333333%}html.theme--documenter-dark .column.is-offset-7-widescreen{margin-left:58.3333333333%}html.theme--documenter-dark .column.is-8-widescreen{flex:none;width:66.6666666667%}html.theme--documenter-dark .column.is-offset-8-widescreen{margin-left:66.6666666667%}html.theme--documenter-dark .column.is-9-widescreen{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-widescreen{margin-left:75%}html.theme--documenter-dark .column.is-10-widescreen{flex:none;width:83.3333333333%}html.theme--documenter-dark .column.is-offset-10-widescreen{margin-left:83.3333333333%}html.theme--documenter-dark .column.is-11-widescreen{flex:none;width:91.6666666667%}html.theme--documenter-dark .column.is-offset-11-widescreen{margin-left:91.6666666667%}html.theme--documenter-dark .column.is-12-widescreen{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-widescreen{margin-left:100%}}@media screen and (min-width: 1408px){html.theme--documenter-dark .column.is-narrow-fullhd{flex:none}html.theme--documenter-dark .column.is-full-fullhd{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-fullhd{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-fullhd{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-fullhd{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-fullhd{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-fullhd{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-fullhd{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-fullhd{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-fullhd{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-fullhd{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-fullhd{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-fullhd{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-fullhd{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-fullhd{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-fullhd{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-fullhd{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-fullhd{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-fullhd{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-fullhd{margin-left:80%}html.theme--documenter-dark .column.is-0-fullhd{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-fullhd{margin-left:0%}html.theme--documenter-dark .column.is-1-fullhd{flex:none;width:8.3333333333%}html.theme--documenter-dark .column.is-offset-1-fullhd{margin-left:8.3333333333%}html.theme--documenter-dark .column.is-2-fullhd{flex:none;width:16.6666666667%}html.theme--documenter-dark .column.is-offset-2-fullhd{margin-left:16.6666666667%}html.theme--documenter-dark .column.is-3-fullhd{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-fullhd{margin-left:25%}html.theme--documenter-dark .column.is-4-fullhd{flex:none;width:33.3333333333%}html.theme--documenter-dark .column.is-offset-4-fullhd{margin-left:33.3333333333%}html.theme--documenter-dark .column.is-5-fullhd{flex:none;width:41.6666666667%}html.theme--documenter-dark .column.is-offset-5-fullhd{margin-left:41.6666666667%}html.theme--documenter-dark .column.is-6-fullhd{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-fullhd{margin-left:50%}html.theme--documenter-dark .column.is-7-fullhd{flex:none;width:58.3333333333%}html.theme--documenter-dark .column.is-offset-7-fullhd{margin-left:58.3333333333%}html.theme--documenter-dark .column.is-8-fullhd{flex:none;width:66.6666666667%}html.theme--documenter-dark .column.is-offset-8-fullhd{margin-left:66.6666666667%}html.theme--documenter-dark .column.is-9-fullhd{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-fullhd{margin-left:75%}html.theme--documenter-dark .column.is-10-fullhd{flex:none;width:83.3333333333%}html.theme--documenter-dark .column.is-offset-10-fullhd{margin-left:83.3333333333%}html.theme--documenter-dark .column.is-11-fullhd{flex:none;width:91.6666666667%}html.theme--documenter-dark .column.is-offset-11-fullhd{margin-left:91.6666666667%}html.theme--documenter-dark .column.is-12-fullhd{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-fullhd{margin-left:100%}}html.theme--documenter-dark .columns{margin-left:-.75rem;margin-right:-.75rem;margin-top:-.75rem}html.theme--documenter-dark .columns:last-child{margin-bottom:-.75rem}html.theme--documenter-dark .columns:not(:last-child){margin-bottom:calc(1.5rem - .75rem)}html.theme--documenter-dark .columns.is-centered{justify-content:center}html.theme--documenter-dark .columns.is-gapless{margin-left:0;margin-right:0;margin-top:0}html.theme--documenter-dark .columns.is-gapless>.column{margin:0;padding:0 !important}html.theme--documenter-dark .columns.is-gapless:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .columns.is-gapless:last-child{margin-bottom:0}html.theme--documenter-dark .columns.is-mobile{display:flex}html.theme--documenter-dark .columns.is-multiline{flex-wrap:wrap}html.theme--documenter-dark .columns.is-vcentered{align-items:center}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns:not(.is-desktop){display:flex}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-desktop{display:flex}}html.theme--documenter-dark .columns.is-variable{--columnGap: 0.75rem;margin-left:calc(-1 * var(--columnGap));margin-right:calc(-1 * var(--columnGap))}html.theme--documenter-dark .columns.is-variable .column{padding-left:var(--columnGap);padding-right:var(--columnGap)}html.theme--documenter-dark .columns.is-variable.is-0{--columnGap: 0rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-0-mobile{--columnGap: 0rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-0-tablet{--columnGap: 0rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-0-tablet-only{--columnGap: 0rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-0-touch{--columnGap: 0rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-0-desktop{--columnGap: 0rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-0-desktop-only{--columnGap: 0rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-0-widescreen{--columnGap: 0rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-0-widescreen-only{--columnGap: 0rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-0-fullhd{--columnGap: 0rem}}html.theme--documenter-dark .columns.is-variable.is-1{--columnGap: .25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-1-mobile{--columnGap: .25rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-1-tablet{--columnGap: .25rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-1-tablet-only{--columnGap: .25rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-1-touch{--columnGap: .25rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-1-desktop{--columnGap: .25rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-1-desktop-only{--columnGap: .25rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-1-widescreen{--columnGap: .25rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-1-widescreen-only{--columnGap: .25rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-1-fullhd{--columnGap: .25rem}}html.theme--documenter-dark .columns.is-variable.is-2{--columnGap: .5rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-2-mobile{--columnGap: .5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-2-tablet{--columnGap: .5rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-2-tablet-only{--columnGap: .5rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-2-touch{--columnGap: .5rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-2-desktop{--columnGap: .5rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-2-desktop-only{--columnGap: .5rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-2-widescreen{--columnGap: .5rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-2-widescreen-only{--columnGap: .5rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-2-fullhd{--columnGap: .5rem}}html.theme--documenter-dark .columns.is-variable.is-3{--columnGap: .75rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-3-mobile{--columnGap: .75rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-3-tablet{--columnGap: .75rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-3-tablet-only{--columnGap: .75rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-3-touch{--columnGap: .75rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-3-desktop{--columnGap: .75rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-3-desktop-only{--columnGap: .75rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-3-widescreen{--columnGap: .75rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-3-widescreen-only{--columnGap: .75rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-3-fullhd{--columnGap: .75rem}}html.theme--documenter-dark .columns.is-variable.is-4{--columnGap: 1rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-4-mobile{--columnGap: 1rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-4-tablet{--columnGap: 1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-4-tablet-only{--columnGap: 1rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-4-touch{--columnGap: 1rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-4-desktop{--columnGap: 1rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-4-desktop-only{--columnGap: 1rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-4-widescreen{--columnGap: 1rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-4-widescreen-only{--columnGap: 1rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-4-fullhd{--columnGap: 1rem}}html.theme--documenter-dark .columns.is-variable.is-5{--columnGap: 1.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-5-mobile{--columnGap: 1.25rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-5-tablet{--columnGap: 1.25rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-5-tablet-only{--columnGap: 1.25rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-5-touch{--columnGap: 1.25rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-5-desktop{--columnGap: 1.25rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-5-desktop-only{--columnGap: 1.25rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-5-widescreen{--columnGap: 1.25rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-5-widescreen-only{--columnGap: 1.25rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-5-fullhd{--columnGap: 1.25rem}}html.theme--documenter-dark .columns.is-variable.is-6{--columnGap: 1.5rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-6-mobile{--columnGap: 1.5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-6-tablet{--columnGap: 1.5rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-6-tablet-only{--columnGap: 1.5rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-6-touch{--columnGap: 1.5rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-6-desktop{--columnGap: 1.5rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-6-desktop-only{--columnGap: 1.5rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-6-widescreen{--columnGap: 1.5rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-6-widescreen-only{--columnGap: 1.5rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-6-fullhd{--columnGap: 1.5rem}}html.theme--documenter-dark .columns.is-variable.is-7{--columnGap: 1.75rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-7-mobile{--columnGap: 1.75rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-7-tablet{--columnGap: 1.75rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-7-tablet-only{--columnGap: 1.75rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-7-touch{--columnGap: 1.75rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-7-desktop{--columnGap: 1.75rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-7-desktop-only{--columnGap: 1.75rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-7-widescreen{--columnGap: 1.75rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-7-widescreen-only{--columnGap: 1.75rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-7-fullhd{--columnGap: 1.75rem}}html.theme--documenter-dark .columns.is-variable.is-8{--columnGap: 2rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-8-mobile{--columnGap: 2rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-8-tablet{--columnGap: 2rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-8-tablet-only{--columnGap: 2rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-8-touch{--columnGap: 2rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-8-desktop{--columnGap: 2rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-8-desktop-only{--columnGap: 2rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-8-widescreen{--columnGap: 2rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-8-widescreen-only{--columnGap: 2rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-8-fullhd{--columnGap: 2rem}}html.theme--documenter-dark .tile{align-items:stretch;display:block;flex-basis:0;flex-grow:1;flex-shrink:1;min-height:min-content}html.theme--documenter-dark .tile.is-ancestor{margin-left:-.75rem;margin-right:-.75rem;margin-top:-.75rem}html.theme--documenter-dark .tile.is-ancestor:last-child{margin-bottom:-.75rem}html.theme--documenter-dark .tile.is-ancestor:not(:last-child){margin-bottom:.75rem}html.theme--documenter-dark .tile.is-child{margin:0 !important}html.theme--documenter-dark .tile.is-parent{padding:.75rem}html.theme--documenter-dark .tile.is-vertical{flex-direction:column}html.theme--documenter-dark .tile.is-vertical>.tile.is-child:not(:last-child){margin-bottom:1.5rem !important}@media screen and (min-width: 769px),print{html.theme--documenter-dark .tile:not(.is-child){display:flex}html.theme--documenter-dark .tile.is-1{flex:none;width:8.3333333333%}html.theme--documenter-dark .tile.is-2{flex:none;width:16.6666666667%}html.theme--documenter-dark .tile.is-3{flex:none;width:25%}html.theme--documenter-dark .tile.is-4{flex:none;width:33.3333333333%}html.theme--documenter-dark .tile.is-5{flex:none;width:41.6666666667%}html.theme--documenter-dark .tile.is-6{flex:none;width:50%}html.theme--documenter-dark .tile.is-7{flex:none;width:58.3333333333%}html.theme--documenter-dark .tile.is-8{flex:none;width:66.6666666667%}html.theme--documenter-dark .tile.is-9{flex:none;width:75%}html.theme--documenter-dark .tile.is-10{flex:none;width:83.3333333333%}html.theme--documenter-dark .tile.is-11{flex:none;width:91.6666666667%}html.theme--documenter-dark .tile.is-12{flex:none;width:100%}}html.theme--documenter-dark .hero{align-items:stretch;display:flex;flex-direction:column;justify-content:space-between}html.theme--documenter-dark .hero .navbar{background:none}html.theme--documenter-dark .hero .tabs ul{border-bottom:none}html.theme--documenter-dark .hero.is-white{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .hero.is-white a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-white strong{color:inherit}html.theme--documenter-dark .hero.is-white .title{color:#0a0a0a}html.theme--documenter-dark .hero.is-white .subtitle{color:rgba(10,10,10,0.9)}html.theme--documenter-dark .hero.is-white .subtitle a:not(.button),html.theme--documenter-dark .hero.is-white .subtitle strong{color:#0a0a0a}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-white .navbar-menu{background-color:#fff}}html.theme--documenter-dark .hero.is-white .navbar-item,html.theme--documenter-dark .hero.is-white .navbar-link{color:rgba(10,10,10,0.7)}html.theme--documenter-dark .hero.is-white a.navbar-item:hover,html.theme--documenter-dark .hero.is-white a.navbar-item.is-active,html.theme--documenter-dark .hero.is-white .navbar-link:hover,html.theme--documenter-dark .hero.is-white .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}html.theme--documenter-dark .hero.is-white .tabs a{color:#0a0a0a;opacity:0.9}html.theme--documenter-dark .hero.is-white .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-white .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-white .tabs.is-boxed a,html.theme--documenter-dark .hero.is-white .tabs.is-toggle a{color:#0a0a0a}html.theme--documenter-dark .hero.is-white .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-white .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-white .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-white .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-white .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-white .tabs.is-toggle li.is-active a:hover{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .hero.is-white.is-bold{background-image:linear-gradient(141deg, #e8e3e4 0%, #fff 71%, #fff 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-white.is-bold .navbar-menu{background-image:linear-gradient(141deg, #e8e3e4 0%, #fff 71%, #fff 100%)}}html.theme--documenter-dark .hero.is-black{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .hero.is-black a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-black strong{color:inherit}html.theme--documenter-dark .hero.is-black .title{color:#fff}html.theme--documenter-dark .hero.is-black .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-black .subtitle a:not(.button),html.theme--documenter-dark .hero.is-black .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-black .navbar-menu{background-color:#0a0a0a}}html.theme--documenter-dark .hero.is-black .navbar-item,html.theme--documenter-dark .hero.is-black .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-black a.navbar-item:hover,html.theme--documenter-dark .hero.is-black a.navbar-item.is-active,html.theme--documenter-dark .hero.is-black .navbar-link:hover,html.theme--documenter-dark .hero.is-black .navbar-link.is-active{background-color:#000;color:#fff}html.theme--documenter-dark .hero.is-black .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-black .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-black .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-black .tabs.is-boxed a,html.theme--documenter-dark .hero.is-black .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-black .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-black .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-black .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-black .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-black .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-black .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#0a0a0a}html.theme--documenter-dark .hero.is-black.is-bold{background-image:linear-gradient(141deg, #000 0%, #0a0a0a 71%, #181616 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-black.is-bold .navbar-menu{background-image:linear-gradient(141deg, #000 0%, #0a0a0a 71%, #181616 100%)}}html.theme--documenter-dark .hero.is-light{background-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .hero.is-light a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-light strong{color:inherit}html.theme--documenter-dark .hero.is-light .title{color:#282f2f}html.theme--documenter-dark .hero.is-light .subtitle{color:rgba(40,47,47,0.9)}html.theme--documenter-dark .hero.is-light .subtitle a:not(.button),html.theme--documenter-dark .hero.is-light .subtitle strong{color:#282f2f}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-light .navbar-menu{background-color:#ecf0f1}}html.theme--documenter-dark .hero.is-light .navbar-item,html.theme--documenter-dark .hero.is-light .navbar-link{color:rgba(40,47,47,0.7)}html.theme--documenter-dark .hero.is-light a.navbar-item:hover,html.theme--documenter-dark .hero.is-light a.navbar-item.is-active,html.theme--documenter-dark .hero.is-light .navbar-link:hover,html.theme--documenter-dark .hero.is-light .navbar-link.is-active{background-color:#dde4e6;color:#282f2f}html.theme--documenter-dark .hero.is-light .tabs a{color:#282f2f;opacity:0.9}html.theme--documenter-dark .hero.is-light .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-light .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-light .tabs.is-boxed a,html.theme--documenter-dark .hero.is-light .tabs.is-toggle a{color:#282f2f}html.theme--documenter-dark .hero.is-light .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-light .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-light .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-light .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-light .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-light .tabs.is-toggle li.is-active a:hover{background-color:#282f2f;border-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .hero.is-light.is-bold{background-image:linear-gradient(141deg, #cadfe0 0%, #ecf0f1 71%, #fafbfc 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-light.is-bold .navbar-menu{background-image:linear-gradient(141deg, #cadfe0 0%, #ecf0f1 71%, #fafbfc 100%)}}html.theme--documenter-dark .hero.is-dark,html.theme--documenter-dark .content kbd.hero{background-color:#282f2f;color:#ecf0f1}html.theme--documenter-dark .hero.is-dark a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .content kbd.hero a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-dark strong,html.theme--documenter-dark .content kbd.hero strong{color:inherit}html.theme--documenter-dark .hero.is-dark .title,html.theme--documenter-dark .content kbd.hero .title{color:#ecf0f1}html.theme--documenter-dark .hero.is-dark .subtitle,html.theme--documenter-dark .content kbd.hero .subtitle{color:rgba(236,240,241,0.9)}html.theme--documenter-dark .hero.is-dark .subtitle a:not(.button),html.theme--documenter-dark .content kbd.hero .subtitle a:not(.button),html.theme--documenter-dark .hero.is-dark .subtitle strong,html.theme--documenter-dark .content kbd.hero .subtitle strong{color:#ecf0f1}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-dark .navbar-menu,html.theme--documenter-dark .content kbd.hero .navbar-menu{background-color:#282f2f}}html.theme--documenter-dark .hero.is-dark .navbar-item,html.theme--documenter-dark .content kbd.hero .navbar-item,html.theme--documenter-dark .hero.is-dark .navbar-link,html.theme--documenter-dark .content kbd.hero .navbar-link{color:rgba(236,240,241,0.7)}html.theme--documenter-dark .hero.is-dark a.navbar-item:hover,html.theme--documenter-dark .content kbd.hero a.navbar-item:hover,html.theme--documenter-dark .hero.is-dark a.navbar-item.is-active,html.theme--documenter-dark .content kbd.hero a.navbar-item.is-active,html.theme--documenter-dark .hero.is-dark .navbar-link:hover,html.theme--documenter-dark .content kbd.hero .navbar-link:hover,html.theme--documenter-dark .hero.is-dark .navbar-link.is-active,html.theme--documenter-dark .content kbd.hero .navbar-link.is-active{background-color:#1d2122;color:#ecf0f1}html.theme--documenter-dark .hero.is-dark .tabs a,html.theme--documenter-dark .content kbd.hero .tabs a{color:#ecf0f1;opacity:0.9}html.theme--documenter-dark .hero.is-dark .tabs a:hover,html.theme--documenter-dark .content kbd.hero .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-dark .tabs li.is-active a,html.theme--documenter-dark .content kbd.hero .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-dark .tabs.is-boxed a,html.theme--documenter-dark .content kbd.hero .tabs.is-boxed a,html.theme--documenter-dark .hero.is-dark .tabs.is-toggle a,html.theme--documenter-dark .content kbd.hero .tabs.is-toggle a{color:#ecf0f1}html.theme--documenter-dark .hero.is-dark .tabs.is-boxed a:hover,html.theme--documenter-dark .content kbd.hero .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-dark .tabs.is-toggle a:hover,html.theme--documenter-dark .content kbd.hero .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-dark .tabs.is-boxed li.is-active a,html.theme--documenter-dark .content kbd.hero .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-dark .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-dark .tabs.is-toggle li.is-active a,html.theme--documenter-dark .content kbd.hero .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-dark .tabs.is-toggle li.is-active a:hover{background-color:#ecf0f1;border-color:#ecf0f1;color:#282f2f}html.theme--documenter-dark .hero.is-dark.is-bold,html.theme--documenter-dark .content kbd.hero.is-bold{background-image:linear-gradient(141deg, #0f1615 0%, #282f2f 71%, #313c40 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-dark.is-bold .navbar-menu,html.theme--documenter-dark .content kbd.hero.is-bold .navbar-menu{background-image:linear-gradient(141deg, #0f1615 0%, #282f2f 71%, #313c40 100%)}}html.theme--documenter-dark .hero.is-primary,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink{background-color:#375a7f;color:#fff}html.theme--documenter-dark .hero.is-primary a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-primary strong,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink strong{color:inherit}html.theme--documenter-dark .hero.is-primary .title,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .title{color:#fff}html.theme--documenter-dark .hero.is-primary .subtitle,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-primary .subtitle a:not(.button),html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .subtitle a:not(.button),html.theme--documenter-dark .hero.is-primary .subtitle strong,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-primary .navbar-menu,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-menu{background-color:#375a7f}}html.theme--documenter-dark .hero.is-primary .navbar-item,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-item,html.theme--documenter-dark .hero.is-primary .navbar-link,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-primary a.navbar-item:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink a.navbar-item:hover,html.theme--documenter-dark .hero.is-primary a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink a.navbar-item.is-active,html.theme--documenter-dark .hero.is-primary .navbar-link:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-link:hover,html.theme--documenter-dark .hero.is-primary .navbar-link.is-active,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-link.is-active{background-color:#2f4d6d;color:#fff}html.theme--documenter-dark .hero.is-primary .tabs a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-primary .tabs a:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-primary .tabs li.is-active a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-primary .tabs.is-boxed a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-boxed a,html.theme--documenter-dark .hero.is-primary .tabs.is-toggle a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-primary .tabs.is-boxed a:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-primary .tabs.is-toggle a:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-primary .tabs.is-boxed li.is-active a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-primary .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-primary .tabs.is-toggle li.is-active a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-primary .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#375a7f}html.theme--documenter-dark .hero.is-primary.is-bold,html.theme--documenter-dark .docstring>section>a.hero.is-bold.docs-sourcelink{background-image:linear-gradient(141deg, #214b62 0%, #375a7f 71%, #3a5796 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-primary.is-bold .navbar-menu,html.theme--documenter-dark .docstring>section>a.hero.is-bold.docs-sourcelink .navbar-menu{background-image:linear-gradient(141deg, #214b62 0%, #375a7f 71%, #3a5796 100%)}}html.theme--documenter-dark .hero.is-link{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .hero.is-link a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-link strong{color:inherit}html.theme--documenter-dark .hero.is-link .title{color:#fff}html.theme--documenter-dark .hero.is-link .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-link .subtitle a:not(.button),html.theme--documenter-dark .hero.is-link .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-link .navbar-menu{background-color:#1abc9c}}html.theme--documenter-dark .hero.is-link .navbar-item,html.theme--documenter-dark .hero.is-link .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-link a.navbar-item:hover,html.theme--documenter-dark .hero.is-link a.navbar-item.is-active,html.theme--documenter-dark .hero.is-link .navbar-link:hover,html.theme--documenter-dark .hero.is-link .navbar-link.is-active{background-color:#17a689;color:#fff}html.theme--documenter-dark .hero.is-link .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-link .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-link .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-link .tabs.is-boxed a,html.theme--documenter-dark .hero.is-link .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-link .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-link .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-link .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-link .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-link .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-link .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#1abc9c}html.theme--documenter-dark .hero.is-link.is-bold{background-image:linear-gradient(141deg, #0c9764 0%, #1abc9c 71%, #17d8d2 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-link.is-bold .navbar-menu{background-image:linear-gradient(141deg, #0c9764 0%, #1abc9c 71%, #17d8d2 100%)}}html.theme--documenter-dark .hero.is-info{background-color:#024c7d;color:#fff}html.theme--documenter-dark .hero.is-info a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-info strong{color:inherit}html.theme--documenter-dark .hero.is-info .title{color:#fff}html.theme--documenter-dark .hero.is-info .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-info .subtitle a:not(.button),html.theme--documenter-dark .hero.is-info .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-info .navbar-menu{background-color:#024c7d}}html.theme--documenter-dark .hero.is-info .navbar-item,html.theme--documenter-dark .hero.is-info .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-info a.navbar-item:hover,html.theme--documenter-dark .hero.is-info a.navbar-item.is-active,html.theme--documenter-dark .hero.is-info .navbar-link:hover,html.theme--documenter-dark .hero.is-info .navbar-link.is-active{background-color:#023d64;color:#fff}html.theme--documenter-dark .hero.is-info .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-info .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-info .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-info .tabs.is-boxed a,html.theme--documenter-dark .hero.is-info .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-info .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-info .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-info .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-info .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-info .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-info .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#024c7d}html.theme--documenter-dark .hero.is-info.is-bold{background-image:linear-gradient(141deg, #003a4c 0%, #024c7d 71%, #004299 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-info.is-bold .navbar-menu{background-image:linear-gradient(141deg, #003a4c 0%, #024c7d 71%, #004299 100%)}}html.theme--documenter-dark .hero.is-success{background-color:#008438;color:#fff}html.theme--documenter-dark .hero.is-success a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-success strong{color:inherit}html.theme--documenter-dark .hero.is-success .title{color:#fff}html.theme--documenter-dark .hero.is-success .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-success .subtitle a:not(.button),html.theme--documenter-dark .hero.is-success .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-success .navbar-menu{background-color:#008438}}html.theme--documenter-dark .hero.is-success .navbar-item,html.theme--documenter-dark .hero.is-success .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-success a.navbar-item:hover,html.theme--documenter-dark .hero.is-success a.navbar-item.is-active,html.theme--documenter-dark .hero.is-success .navbar-link:hover,html.theme--documenter-dark .hero.is-success .navbar-link.is-active{background-color:#006b2d;color:#fff}html.theme--documenter-dark .hero.is-success .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-success .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-success .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-success .tabs.is-boxed a,html.theme--documenter-dark .hero.is-success .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-success .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-success .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-success .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-success .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-success .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-success .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#008438}html.theme--documenter-dark .hero.is-success.is-bold{background-image:linear-gradient(141deg, #005115 0%, #008438 71%, #009e5d 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-success.is-bold .navbar-menu{background-image:linear-gradient(141deg, #005115 0%, #008438 71%, #009e5d 100%)}}html.theme--documenter-dark .hero.is-warning{background-color:#ad8100;color:#fff}html.theme--documenter-dark .hero.is-warning a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-warning strong{color:inherit}html.theme--documenter-dark .hero.is-warning .title{color:#fff}html.theme--documenter-dark .hero.is-warning .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-warning .subtitle a:not(.button),html.theme--documenter-dark .hero.is-warning .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-warning .navbar-menu{background-color:#ad8100}}html.theme--documenter-dark .hero.is-warning .navbar-item,html.theme--documenter-dark .hero.is-warning .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-warning a.navbar-item:hover,html.theme--documenter-dark .hero.is-warning a.navbar-item.is-active,html.theme--documenter-dark .hero.is-warning .navbar-link:hover,html.theme--documenter-dark .hero.is-warning .navbar-link.is-active{background-color:#946e00;color:#fff}html.theme--documenter-dark .hero.is-warning .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-warning .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-warning .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-warning .tabs.is-boxed a,html.theme--documenter-dark .hero.is-warning .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-warning .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-warning .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-warning .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-warning .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-warning .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-warning .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#ad8100}html.theme--documenter-dark .hero.is-warning.is-bold{background-image:linear-gradient(141deg, #7a4700 0%, #ad8100 71%, #c7b500 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-warning.is-bold .navbar-menu{background-image:linear-gradient(141deg, #7a4700 0%, #ad8100 71%, #c7b500 100%)}}html.theme--documenter-dark .hero.is-danger{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .hero.is-danger a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-danger strong{color:inherit}html.theme--documenter-dark .hero.is-danger .title{color:#fff}html.theme--documenter-dark .hero.is-danger .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-danger .subtitle a:not(.button),html.theme--documenter-dark .hero.is-danger .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-danger .navbar-menu{background-color:#9e1b0d}}html.theme--documenter-dark .hero.is-danger .navbar-item,html.theme--documenter-dark .hero.is-danger .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-danger a.navbar-item:hover,html.theme--documenter-dark .hero.is-danger a.navbar-item.is-active,html.theme--documenter-dark .hero.is-danger .navbar-link:hover,html.theme--documenter-dark .hero.is-danger .navbar-link.is-active{background-color:#86170b;color:#fff}html.theme--documenter-dark .hero.is-danger .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-danger .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-danger .tabs li.is-active a{opacity:1}html.theme--documenter-dark .hero.is-danger .tabs.is-boxed a,html.theme--documenter-dark .hero.is-danger .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-danger .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-danger .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-danger .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-danger .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-danger .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-danger .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#9e1b0d}html.theme--documenter-dark .hero.is-danger.is-bold{background-image:linear-gradient(141deg, #75030b 0%, #9e1b0d 71%, #ba380a 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-danger.is-bold .navbar-menu{background-image:linear-gradient(141deg, #75030b 0%, #9e1b0d 71%, #ba380a 100%)}}html.theme--documenter-dark .hero.is-small .hero-body,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.hero .hero-body{padding-bottom:1.5rem;padding-top:1.5rem}@media screen and (min-width: 769px),print{html.theme--documenter-dark .hero.is-medium .hero-body{padding-bottom:9rem;padding-top:9rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .hero.is-large .hero-body{padding-bottom:18rem;padding-top:18rem}}html.theme--documenter-dark .hero.is-halfheight .hero-body,html.theme--documenter-dark .hero.is-fullheight .hero-body,html.theme--documenter-dark .hero.is-fullheight-with-navbar .hero-body{align-items:center;display:flex}html.theme--documenter-dark .hero.is-halfheight .hero-body>.container,html.theme--documenter-dark .hero.is-fullheight .hero-body>.container,html.theme--documenter-dark .hero.is-fullheight-with-navbar .hero-body>.container{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .hero.is-halfheight{min-height:50vh}html.theme--documenter-dark .hero.is-fullheight{min-height:100vh}html.theme--documenter-dark .hero-video{overflow:hidden}html.theme--documenter-dark .hero-video video{left:50%;min-height:100%;min-width:100%;position:absolute;top:50%;transform:translate3d(-50%, -50%, 0)}html.theme--documenter-dark .hero-video.is-transparent{opacity:0.3}@media screen and (max-width: 768px){html.theme--documenter-dark .hero-video{display:none}}html.theme--documenter-dark .hero-buttons{margin-top:1.5rem}@media screen and (max-width: 768px){html.theme--documenter-dark .hero-buttons .button{display:flex}html.theme--documenter-dark .hero-buttons .button:not(:last-child){margin-bottom:0.75rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .hero-buttons{display:flex;justify-content:center}html.theme--documenter-dark .hero-buttons .button:not(:last-child){margin-right:1.5rem}}html.theme--documenter-dark .hero-head,html.theme--documenter-dark .hero-foot{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .hero-body{flex-grow:1;flex-shrink:0;padding:3rem 1.5rem}html.theme--documenter-dark .section{padding:3rem 1.5rem}@media screen and (min-width: 1056px){html.theme--documenter-dark .section.is-medium{padding:9rem 1.5rem}html.theme--documenter-dark .section.is-large{padding:18rem 1.5rem}}html.theme--documenter-dark .footer{background-color:#282f2f;padding:3rem 1.5rem 6rem}html.theme--documenter-dark hr{height:1px}html.theme--documenter-dark h6{text-transform:uppercase;letter-spacing:0.5px}html.theme--documenter-dark .hero{background-color:#343c3d}html.theme--documenter-dark a{transition:all 200ms ease}html.theme--documenter-dark .button{transition:all 200ms ease;border-width:1px;color:#fff}html.theme--documenter-dark .button.is-active,html.theme--documenter-dark .button.is-focused,html.theme--documenter-dark .button:active,html.theme--documenter-dark .button:focus{box-shadow:0 0 0 2px rgba(140,155,157,0.5)}html.theme--documenter-dark .button.is-white.is-hovered,html.theme--documenter-dark .button.is-white:hover{background-color:#fff}html.theme--documenter-dark .button.is-white.is-active,html.theme--documenter-dark .button.is-white.is-focused,html.theme--documenter-dark .button.is-white:active,html.theme--documenter-dark .button.is-white:focus{border-color:#fff;box-shadow:0 0 0 2px rgba(255,255,255,0.5)}html.theme--documenter-dark .button.is-black.is-hovered,html.theme--documenter-dark .button.is-black:hover{background-color:#1d1d1d}html.theme--documenter-dark .button.is-black.is-active,html.theme--documenter-dark .button.is-black.is-focused,html.theme--documenter-dark .button.is-black:active,html.theme--documenter-dark .button.is-black:focus{border-color:#0a0a0a;box-shadow:0 0 0 2px rgba(10,10,10,0.5)}html.theme--documenter-dark .button.is-light.is-hovered,html.theme--documenter-dark .button.is-light:hover{background-color:#fff}html.theme--documenter-dark .button.is-light.is-active,html.theme--documenter-dark .button.is-light.is-focused,html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light:focus{border-color:#ecf0f1;box-shadow:0 0 0 2px rgba(236,240,241,0.5)}html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered,html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover{background-color:#3a4344}html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused,html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus{border-color:#282f2f;box-shadow:0 0 0 2px rgba(40,47,47,0.5)}html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover{background-color:#436d9a}html.theme--documenter-dark .button.is-primary.is-active,html.theme--documenter-dark .docstring>section>a.button.is-active.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink,html.theme--documenter-dark .button.is-primary:active,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:active,html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus{border-color:#375a7f;box-shadow:0 0 0 2px rgba(55,90,127,0.5)}html.theme--documenter-dark .button.is-link.is-hovered,html.theme--documenter-dark .button.is-link:hover{background-color:#1fdeb8}html.theme--documenter-dark .button.is-link.is-active,html.theme--documenter-dark .button.is-link.is-focused,html.theme--documenter-dark .button.is-link:active,html.theme--documenter-dark .button.is-link:focus{border-color:#1abc9c;box-shadow:0 0 0 2px rgba(26,188,156,0.5)}html.theme--documenter-dark .button.is-info.is-hovered,html.theme--documenter-dark .button.is-info:hover{background-color:#0363a3}html.theme--documenter-dark .button.is-info.is-active,html.theme--documenter-dark .button.is-info.is-focused,html.theme--documenter-dark .button.is-info:active,html.theme--documenter-dark .button.is-info:focus{border-color:#024c7d;box-shadow:0 0 0 2px rgba(2,76,125,0.5)}html.theme--documenter-dark .button.is-success.is-hovered,html.theme--documenter-dark .button.is-success:hover{background-color:#00aa48}html.theme--documenter-dark .button.is-success.is-active,html.theme--documenter-dark .button.is-success.is-focused,html.theme--documenter-dark .button.is-success:active,html.theme--documenter-dark .button.is-success:focus{border-color:#008438;box-shadow:0 0 0 2px rgba(0,132,56,0.5)}html.theme--documenter-dark .button.is-warning.is-hovered,html.theme--documenter-dark .button.is-warning:hover{background-color:#d39e00}html.theme--documenter-dark .button.is-warning.is-active,html.theme--documenter-dark .button.is-warning.is-focused,html.theme--documenter-dark .button.is-warning:active,html.theme--documenter-dark .button.is-warning:focus{border-color:#ad8100;box-shadow:0 0 0 2px rgba(173,129,0,0.5)}html.theme--documenter-dark .button.is-danger.is-hovered,html.theme--documenter-dark .button.is-danger:hover{background-color:#c12110}html.theme--documenter-dark .button.is-danger.is-active,html.theme--documenter-dark .button.is-danger.is-focused,html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger:focus{border-color:#9e1b0d;box-shadow:0 0 0 2px rgba(158,27,13,0.5)}html.theme--documenter-dark .label{color:#dbdee0}html.theme--documenter-dark .button,html.theme--documenter-dark .control.has-icons-left .icon,html.theme--documenter-dark .control.has-icons-right .icon,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .select,html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea{height:2.5em}html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .textarea{transition:all 200ms ease;box-shadow:none;border-width:1px;padding-left:1em;padding-right:1em}html.theme--documenter-dark .select:after,html.theme--documenter-dark .select select{border-width:1px}html.theme--documenter-dark .control.has-addons .button,html.theme--documenter-dark .control.has-addons .input,html.theme--documenter-dark .control.has-addons #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .control.has-addons form.docs-search>input,html.theme--documenter-dark .control.has-addons .select{margin-right:-1px}html.theme--documenter-dark .notification{background-color:#343c3d}html.theme--documenter-dark .card{box-shadow:none;border:1px solid #343c3d;background-color:#282f2f;border-radius:.4em}html.theme--documenter-dark .card .card-image img{border-radius:.4em .4em 0 0}html.theme--documenter-dark .card .card-header{box-shadow:none;background-color:rgba(18,18,18,0.2);border-radius:.4em .4em 0 0}html.theme--documenter-dark .card .card-footer{background-color:rgba(18,18,18,0.2)}html.theme--documenter-dark .card .card-footer,html.theme--documenter-dark .card .card-footer-item{border-width:1px;border-color:#343c3d}html.theme--documenter-dark .notification.is-white a:not(.button){color:#0a0a0a;text-decoration:underline}html.theme--documenter-dark .notification.is-black a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-light a:not(.button){color:#282f2f;text-decoration:underline}html.theme--documenter-dark .notification.is-dark a:not(.button),html.theme--documenter-dark .content kbd.notification a:not(.button){color:#ecf0f1;text-decoration:underline}html.theme--documenter-dark .notification.is-primary a:not(.button),html.theme--documenter-dark .docstring>section>a.notification.docs-sourcelink a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-link a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-info a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-success a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-warning a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-danger a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .tag,html.theme--documenter-dark .content kbd,html.theme--documenter-dark .docstring>section>a.docs-sourcelink{border-radius:.4em}html.theme--documenter-dark .menu-list a{transition:all 300ms ease}html.theme--documenter-dark .modal-card-body{background-color:#282f2f}html.theme--documenter-dark .modal-card-foot,html.theme--documenter-dark .modal-card-head{border-color:#343c3d}html.theme--documenter-dark .message-header{font-weight:700;background-color:#343c3d;color:#fff}html.theme--documenter-dark .message-body{border-width:1px;border-color:#343c3d}html.theme--documenter-dark .navbar{border-radius:.4em}html.theme--documenter-dark .navbar.is-transparent{background:none}html.theme--documenter-dark .navbar.is-primary .navbar-dropdown a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-dropdown a.navbar-item.is-active{background-color:#1abc9c}@media screen and (max-width: 1055px){html.theme--documenter-dark .navbar .navbar-menu{background-color:#375a7f;border-radius:0 0 .4em .4em}}html.theme--documenter-dark .hero .navbar,html.theme--documenter-dark body>.navbar{border-radius:0}html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-previous{border-width:1px}html.theme--documenter-dark .panel-block,html.theme--documenter-dark .panel-heading,html.theme--documenter-dark .panel-tabs{border-width:1px}html.theme--documenter-dark .panel-block:first-child,html.theme--documenter-dark .panel-heading:first-child,html.theme--documenter-dark .panel-tabs:first-child{border-top-width:1px}html.theme--documenter-dark .panel-heading{font-weight:700}html.theme--documenter-dark .panel-tabs a{border-width:1px;margin-bottom:-1px}html.theme--documenter-dark .panel-tabs a.is-active{border-bottom-color:#17a689}html.theme--documenter-dark .panel-block:hover{color:#1dd2af}html.theme--documenter-dark .panel-block:hover .panel-icon{color:#1dd2af}html.theme--documenter-dark .panel-block.is-active .panel-icon{color:#17a689}html.theme--documenter-dark .tabs a{border-bottom-width:1px;margin-bottom:-1px}html.theme--documenter-dark .tabs ul{border-bottom-width:1px}html.theme--documenter-dark .tabs.is-boxed a{border-width:1px}html.theme--documenter-dark .tabs.is-boxed li.is-active a{background-color:#1f2424}html.theme--documenter-dark .tabs.is-toggle li a{border-width:1px;margin-bottom:0}html.theme--documenter-dark .tabs.is-toggle li+li{margin-left:-1px}html.theme--documenter-dark .hero.is-white .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-black .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-light .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-dark .navbar .navbar-dropdown .navbar-item:hover,html.theme--documenter-dark .content kbd.hero .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-primary .navbar .navbar-dropdown .navbar-item:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-link .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-info .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-success .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-warning .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-danger .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark h1 .docs-heading-anchor,html.theme--documenter-dark h1 .docs-heading-anchor:hover,html.theme--documenter-dark h1 .docs-heading-anchor:visited,html.theme--documenter-dark h2 .docs-heading-anchor,html.theme--documenter-dark h2 .docs-heading-anchor:hover,html.theme--documenter-dark h2 .docs-heading-anchor:visited,html.theme--documenter-dark h3 .docs-heading-anchor,html.theme--documenter-dark h3 .docs-heading-anchor:hover,html.theme--documenter-dark h3 .docs-heading-anchor:visited,html.theme--documenter-dark h4 .docs-heading-anchor,html.theme--documenter-dark h4 .docs-heading-anchor:hover,html.theme--documenter-dark h4 .docs-heading-anchor:visited,html.theme--documenter-dark h5 .docs-heading-anchor,html.theme--documenter-dark h5 .docs-heading-anchor:hover,html.theme--documenter-dark h5 .docs-heading-anchor:visited,html.theme--documenter-dark h6 .docs-heading-anchor,html.theme--documenter-dark h6 .docs-heading-anchor:hover,html.theme--documenter-dark h6 .docs-heading-anchor:visited{color:#f2f2f2}html.theme--documenter-dark h1 .docs-heading-anchor-permalink,html.theme--documenter-dark h2 .docs-heading-anchor-permalink,html.theme--documenter-dark h3 .docs-heading-anchor-permalink,html.theme--documenter-dark h4 .docs-heading-anchor-permalink,html.theme--documenter-dark h5 .docs-heading-anchor-permalink,html.theme--documenter-dark h6 .docs-heading-anchor-permalink{visibility:hidden;vertical-align:middle;margin-left:0.5em;font-size:0.7rem}html.theme--documenter-dark h1 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h2 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h3 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h4 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h5 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h6 .docs-heading-anchor-permalink::before{font-family:"Font Awesome 5 Free";font-weight:900;content:"\f0c1"}html.theme--documenter-dark h1:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h2:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h3:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h4:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h5:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h6:hover .docs-heading-anchor-permalink{visibility:visible}html.theme--documenter-dark .docs-light-only{display:none !important}html.theme--documenter-dark pre{position:relative;overflow:hidden}html.theme--documenter-dark pre code,html.theme--documenter-dark pre code.hljs{padding:0 .75rem !important;overflow:auto;display:block}html.theme--documenter-dark pre code:first-of-type,html.theme--documenter-dark pre code.hljs:first-of-type{padding-top:0.5rem !important}html.theme--documenter-dark pre code:last-of-type,html.theme--documenter-dark pre code.hljs:last-of-type{padding-bottom:0.5rem !important}html.theme--documenter-dark pre .copy-button{opacity:0.2;transition:opacity 0.2s;position:absolute;right:0em;top:0em;padding:0.5em;width:2.5em;height:2.5em;background:transparent;border:none;font-family:"Font Awesome 5 Free";color:#fff;cursor:pointer;text-align:center}html.theme--documenter-dark pre .copy-button:focus,html.theme--documenter-dark pre .copy-button:hover{opacity:1;background:rgba(255,255,255,0.1);color:#1abc9c}html.theme--documenter-dark pre .copy-button.success{color:#259a12;opacity:1}html.theme--documenter-dark pre .copy-button.error{color:#cb3c33;opacity:1}html.theme--documenter-dark pre:hover .copy-button{opacity:1}html.theme--documenter-dark .admonition{background-color:#282f2f;border-style:solid;border-width:1px;border-color:#5e6d6f;border-radius:.4em;font-size:15px}html.theme--documenter-dark .admonition strong{color:currentColor}html.theme--documenter-dark .admonition.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.admonition{font-size:.85em}html.theme--documenter-dark .admonition.is-medium{font-size:1.25rem}html.theme--documenter-dark .admonition.is-large{font-size:1.5rem}html.theme--documenter-dark .admonition.is-default{background-color:#282f2f;border-color:#5e6d6f}html.theme--documenter-dark .admonition.is-default>.admonition-header{background-color:#5e6d6f;color:#fff}html.theme--documenter-dark .admonition.is-default>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-info{background-color:#282f2f;border-color:#024c7d}html.theme--documenter-dark .admonition.is-info>.admonition-header{background-color:#024c7d;color:#fff}html.theme--documenter-dark .admonition.is-info>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-success{background-color:#282f2f;border-color:#008438}html.theme--documenter-dark .admonition.is-success>.admonition-header{background-color:#008438;color:#fff}html.theme--documenter-dark .admonition.is-success>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-warning{background-color:#282f2f;border-color:#ad8100}html.theme--documenter-dark .admonition.is-warning>.admonition-header{background-color:#ad8100;color:#fff}html.theme--documenter-dark .admonition.is-warning>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-danger{background-color:#282f2f;border-color:#9e1b0d}html.theme--documenter-dark .admonition.is-danger>.admonition-header{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .admonition.is-danger>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-compat{background-color:#282f2f;border-color:#137886}html.theme--documenter-dark .admonition.is-compat>.admonition-header{background-color:#137886;color:#fff}html.theme--documenter-dark .admonition.is-compat>.admonition-body{color:#fff}html.theme--documenter-dark .admonition-header{color:#fff;background-color:#5e6d6f;align-items:center;font-weight:700;justify-content:space-between;line-height:1.25;padding:0.5rem .75rem;position:relative}html.theme--documenter-dark .admonition-header:before{font-family:"Font Awesome 5 Free";font-weight:900;margin-right:.75rem;content:"\f06a"}html.theme--documenter-dark .admonition-body{color:#fff;padding:0.5rem .75rem}html.theme--documenter-dark .admonition-body pre{background-color:#282f2f}html.theme--documenter-dark .admonition-body code{background-color:rgba(255,255,255,0.05)}html.theme--documenter-dark .docstring{margin-bottom:1em;background-color:rgba(0,0,0,0);border:1px solid #5e6d6f;box-shadow:none;max-width:100%}html.theme--documenter-dark .docstring>header{display:flex;flex-grow:1;align-items:stretch;padding:0.5rem .75rem;background-color:#282f2f;box-shadow:0 1px 2px rgba(10,10,10,0.1);box-shadow:none;border-bottom:1px solid #5e6d6f}html.theme--documenter-dark .docstring>header code{background-color:transparent}html.theme--documenter-dark .docstring>header .docstring-binding{margin-right:0.3em}html.theme--documenter-dark .docstring>header .docstring-category{margin-left:0.3em}html.theme--documenter-dark .docstring>section{position:relative;padding:.75rem .75rem;border-bottom:1px solid #5e6d6f}html.theme--documenter-dark .docstring>section:last-child{border-bottom:none}html.theme--documenter-dark .docstring>section>a.docs-sourcelink{transition:opacity 0.3s;opacity:0;position:absolute;right:.375rem;bottom:.375rem}html.theme--documenter-dark .docstring>section>a.docs-sourcelink:focus{opacity:1 !important}html.theme--documenter-dark .docstring:hover>section>a.docs-sourcelink{opacity:0.2}html.theme--documenter-dark .docstring:focus-within>section>a.docs-sourcelink{opacity:0.2}html.theme--documenter-dark .docstring>section:hover a.docs-sourcelink{opacity:1}html.theme--documenter-dark .documenter-example-output{background-color:#1f2424}html.theme--documenter-dark .outdated-warning-overlay{position:fixed;top:0;left:0;right:0;box-shadow:0 0 10px rgba(0,0,0,0.3);z-index:999;background-color:#282f2f;color:#fff;border-bottom:3px solid #9e1b0d;padding:10px 35px;text-align:center;font-size:15px}html.theme--documenter-dark .outdated-warning-overlay .outdated-warning-closer{position:absolute;top:calc(50% - 10px);right:18px;cursor:pointer;width:12px}html.theme--documenter-dark .outdated-warning-overlay a{color:#1abc9c}html.theme--documenter-dark .outdated-warning-overlay a:hover{color:#1dd2af}html.theme--documenter-dark .content pre{border:1px solid #5e6d6f}html.theme--documenter-dark .content code{font-weight:inherit}html.theme--documenter-dark .content a code{color:#1abc9c}html.theme--documenter-dark .content h1 code,html.theme--documenter-dark .content h2 code,html.theme--documenter-dark .content h3 code,html.theme--documenter-dark .content h4 code,html.theme--documenter-dark .content h5 code,html.theme--documenter-dark .content h6 code{color:#f2f2f2}html.theme--documenter-dark .content table{display:block;width:initial;max-width:100%;overflow-x:auto}html.theme--documenter-dark .content blockquote>ul:first-child,html.theme--documenter-dark .content blockquote>ol:first-child,html.theme--documenter-dark .content .admonition-body>ul:first-child,html.theme--documenter-dark .content .admonition-body>ol:first-child{margin-top:0}html.theme--documenter-dark pre,html.theme--documenter-dark code{font-variant-ligatures:no-contextual}html.theme--documenter-dark .breadcrumb a.is-disabled{cursor:default;pointer-events:none}html.theme--documenter-dark .breadcrumb a.is-disabled,html.theme--documenter-dark .breadcrumb a.is-disabled:hover{color:#f2f2f2}html.theme--documenter-dark .hljs{background:initial !important}html.theme--documenter-dark .katex .katex-mathml{top:0;right:0}html.theme--documenter-dark .katex-display,html.theme--documenter-dark mjx-container,html.theme--documenter-dark .MathJax_Display{margin:0.5em 0 !important}html.theme--documenter-dark html{-moz-osx-font-smoothing:auto;-webkit-font-smoothing:auto}html.theme--documenter-dark li.no-marker{list-style:none}html.theme--documenter-dark #documenter .docs-main>article{overflow-wrap:break-word}html.theme--documenter-dark #documenter .docs-main>article .math-container{overflow-x:auto;overflow-y:hidden}@media screen and (min-width: 1056px){html.theme--documenter-dark #documenter .docs-main{max-width:52rem;margin-left:20rem;padding-right:1rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-main{width:100%}html.theme--documenter-dark #documenter .docs-main>article{max-width:52rem;margin-left:auto;margin-right:auto;margin-bottom:1rem;padding:0 1rem}html.theme--documenter-dark #documenter .docs-main>header,html.theme--documenter-dark #documenter .docs-main>nav{max-width:100%;width:100%;margin:0}}html.theme--documenter-dark #documenter .docs-main header.docs-navbar{background-color:#1f2424;border-bottom:1px solid #5e6d6f;z-index:2;min-height:4rem;margin-bottom:1rem;display:flex}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .breadcrumb{flex-grow:1}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right{display:flex;white-space:nowrap}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-icon,html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-label,html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-sidebar-button{display:inline-block}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-label{padding:0;margin-left:0.3em}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-settings-button{margin:auto 0 auto 1rem}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-sidebar-button{font-size:1.5rem;margin:auto 0 auto 1rem}html.theme--documenter-dark #documenter .docs-main header.docs-navbar>*{margin:auto 0}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-main header.docs-navbar{position:sticky;top:0;padding:0 1rem;transition-property:top, box-shadow;-webkit-transition-property:top, box-shadow;transition-duration:0.3s;-webkit-transition-duration:0.3s}html.theme--documenter-dark #documenter .docs-main header.docs-navbar.headroom--not-top{box-shadow:.2rem 0rem .4rem #171717;transition-duration:0.7s;-webkit-transition-duration:0.7s}html.theme--documenter-dark #documenter .docs-main header.docs-navbar.headroom--unpinned.headroom--not-top.headroom--not-bottom{top:-4.5rem;transition-duration:0.7s;-webkit-transition-duration:0.7s}}html.theme--documenter-dark #documenter .docs-main section.footnotes{border-top:1px solid #5e6d6f}html.theme--documenter-dark #documenter .docs-main section.footnotes li .tag:first-child,html.theme--documenter-dark #documenter .docs-main section.footnotes li .docstring>section>a.docs-sourcelink:first-child,html.theme--documenter-dark #documenter .docs-main section.footnotes li .content kbd:first-child,html.theme--documenter-dark .content #documenter .docs-main section.footnotes li kbd:first-child{margin-right:1em;margin-bottom:0.4em}html.theme--documenter-dark #documenter .docs-main .docs-footer{display:flex;flex-wrap:wrap;margin-left:0;margin-right:0;border-top:1px solid #5e6d6f;padding-top:1rem;padding-bottom:1rem}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-main .docs-footer{padding-left:1rem;padding-right:1rem}}html.theme--documenter-dark #documenter .docs-main .docs-footer .docs-footer-nextpage,html.theme--documenter-dark #documenter .docs-main .docs-footer .docs-footer-prevpage{flex-grow:1}html.theme--documenter-dark #documenter .docs-main .docs-footer .docs-footer-nextpage{text-align:right}html.theme--documenter-dark #documenter .docs-main .docs-footer .flexbox-break{flex-basis:100%;height:0}html.theme--documenter-dark #documenter .docs-main .docs-footer .footer-message{font-size:0.8em;margin:0.5em auto 0 auto;text-align:center}html.theme--documenter-dark #documenter .docs-sidebar{display:flex;flex-direction:column;color:#fff;background-color:#282f2f;border-right:1px solid #5e6d6f;padding:0;flex:0 0 18rem;z-index:5;font-size:15px;position:fixed;left:-18rem;width:18rem;height:100%;transition:left 0.3s}html.theme--documenter-dark #documenter .docs-sidebar.visible{left:0;box-shadow:.4rem 0rem .8rem #171717}@media screen and (min-width: 1056px){html.theme--documenter-dark #documenter .docs-sidebar.visible{box-shadow:none}}@media screen and (min-width: 1056px){html.theme--documenter-dark #documenter .docs-sidebar{left:0;top:0}}html.theme--documenter-dark #documenter .docs-sidebar .docs-logo{margin-top:1rem;padding:0 1rem}html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{max-height:6rem;margin:auto}html.theme--documenter-dark #documenter .docs-sidebar .docs-package-name{flex-shrink:0;font-size:1.5rem;font-weight:700;text-align:center;white-space:nowrap;overflow:hidden;padding:0.5rem 0}html.theme--documenter-dark #documenter .docs-sidebar .docs-package-name .docs-autofit{max-width:16.2rem}html.theme--documenter-dark #documenter .docs-sidebar .docs-package-name a,html.theme--documenter-dark #documenter .docs-sidebar .docs-package-name a:hover{color:#fff}html.theme--documenter-dark #documenter .docs-sidebar .docs-version-selector{border-top:1px solid #5e6d6f;display:none;padding:0.5rem}html.theme--documenter-dark #documenter .docs-sidebar .docs-version-selector.visible{display:flex}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu{flex-grow:1;user-select:none;border-top:1px solid #5e6d6f;padding-bottom:1.5rem}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu>li>.tocitem{font-weight:bold}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu>li li{font-size:14.25px;margin-left:1em;border-left:1px solid #5e6d6f}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu input.collapse-toggle{display:none}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.collapsed{display:none}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu input:checked~ul.collapsed{display:block}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem{display:flex}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem .docs-label{flex-grow:2}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem .docs-chevron{display:inline-block;font-style:normal;font-variant:normal;text-rendering:auto;line-height:1;font-size:11.25px;margin-left:1rem;margin-top:auto;margin-bottom:auto}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem .docs-chevron::before{font-family:"Font Awesome 5 Free";font-weight:900;content:"\f054"}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu input:checked~label.tocitem .docs-chevron::before{content:"\f078"}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu .tocitem{display:block;padding:0.5rem 0.5rem}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu .tocitem,html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu .tocitem:hover{color:#fff;background:#282f2f}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu a.tocitem:hover,html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem:hover{color:#fff;background-color:#32393a}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu li.is-active{border-top:1px solid #5e6d6f;border-bottom:1px solid #5e6d6f;background-color:#1f2424}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu li.is-active .tocitem,html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu li.is-active .tocitem:hover{background-color:#1f2424;color:#fff}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu li.is-active ul.internal .tocitem:hover{background-color:#32393a;color:#fff}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu>li.is-active:first-child{border-top:none}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.internal{margin:0 0.5rem 0.5rem;border-top:1px solid #5e6d6f}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.internal li{font-size:12.75px;border-left:none;margin-left:0;margin-top:0.5rem}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.internal .tocitem{width:100%;padding:0}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.internal .tocitem::before{content:"⚬";margin-right:0.4em}html.theme--documenter-dark #documenter .docs-sidebar form.docs-search{margin:auto;margin-top:0.5rem;margin-bottom:0.5rem}html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{width:14.4rem}@media screen and (min-width: 1056px){html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu{overflow-y:auto;-webkit-overflow-scroll:touch}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar{width:.3rem;background:none}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar-thumb{border-radius:5px 0px 0px 5px;background:#3b4445}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar-thumb:hover{background:#4e5a5c}}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-sidebar{overflow-y:auto;-webkit-overflow-scroll:touch}html.theme--documenter-dark #documenter .docs-sidebar::-webkit-scrollbar{width:.3rem;background:none}html.theme--documenter-dark #documenter .docs-sidebar::-webkit-scrollbar-thumb{border-radius:5px 0px 0px 5px;background:#3b4445}html.theme--documenter-dark #documenter .docs-sidebar::-webkit-scrollbar-thumb:hover{background:#4e5a5c}}html.theme--documenter-dark #documenter .docs-main #documenter-search-info{margin-bottom:1rem}html.theme--documenter-dark #documenter .docs-main #documenter-search-results{list-style-type:circle;list-style-position:outside}html.theme--documenter-dark #documenter .docs-main #documenter-search-results li{margin-left:2rem}html.theme--documenter-dark #documenter .docs-main #documenter-search-results .docs-highlight{background-color:yellow}html.theme--documenter-dark{background-color:#1f2424;font-size:16px;min-width:300px;overflow-x:auto;overflow-y:scroll;text-rendering:optimizeLegibility;text-size-adjust:100%}html.theme--documenter-dark .ansi span.sgr1{font-weight:bolder}html.theme--documenter-dark .ansi span.sgr2{font-weight:lighter}html.theme--documenter-dark .ansi span.sgr3{font-style:italic}html.theme--documenter-dark .ansi span.sgr4{text-decoration:underline}html.theme--documenter-dark .ansi span.sgr7{color:#1f2424;background-color:#fff}html.theme--documenter-dark .ansi span.sgr8{color:transparent}html.theme--documenter-dark .ansi span.sgr8 span{color:transparent}html.theme--documenter-dark .ansi span.sgr9{text-decoration:line-through}html.theme--documenter-dark .ansi span.sgr30{color:#242424}html.theme--documenter-dark .ansi span.sgr31{color:#f6705f}html.theme--documenter-dark .ansi span.sgr32{color:#4fb43a}html.theme--documenter-dark .ansi span.sgr33{color:#f4c72f}html.theme--documenter-dark .ansi span.sgr34{color:#7587f0}html.theme--documenter-dark .ansi span.sgr35{color:#bc89d3}html.theme--documenter-dark .ansi span.sgr36{color:#49b6ca}html.theme--documenter-dark .ansi span.sgr37{color:#b3bdbe}html.theme--documenter-dark .ansi span.sgr40{background-color:#242424}html.theme--documenter-dark .ansi span.sgr41{background-color:#f6705f}html.theme--documenter-dark .ansi span.sgr42{background-color:#4fb43a}html.theme--documenter-dark .ansi span.sgr43{background-color:#f4c72f}html.theme--documenter-dark .ansi span.sgr44{background-color:#7587f0}html.theme--documenter-dark .ansi span.sgr45{background-color:#bc89d3}html.theme--documenter-dark .ansi span.sgr46{background-color:#49b6ca}html.theme--documenter-dark .ansi span.sgr47{background-color:#b3bdbe}html.theme--documenter-dark .ansi span.sgr90{color:#92a0a2}html.theme--documenter-dark .ansi span.sgr91{color:#ff8674}html.theme--documenter-dark .ansi span.sgr92{color:#79d462}html.theme--documenter-dark .ansi span.sgr93{color:#ffe76b}html.theme--documenter-dark .ansi span.sgr94{color:#8a98ff}html.theme--documenter-dark .ansi span.sgr95{color:#d2a4e6}html.theme--documenter-dark .ansi span.sgr96{color:#6bc8db}html.theme--documenter-dark .ansi span.sgr97{color:#ecf0f1}html.theme--documenter-dark .ansi span.sgr100{background-color:#92a0a2}html.theme--documenter-dark .ansi span.sgr101{background-color:#ff8674}html.theme--documenter-dark .ansi span.sgr102{background-color:#79d462}html.theme--documenter-dark .ansi span.sgr103{background-color:#ffe76b}html.theme--documenter-dark .ansi span.sgr104{background-color:#8a98ff}html.theme--documenter-dark .ansi span.sgr105{background-color:#d2a4e6}html.theme--documenter-dark .ansi span.sgr106{background-color:#6bc8db}html.theme--documenter-dark .ansi span.sgr107{background-color:#ecf0f1}html.theme--documenter-dark code.language-julia-repl>span.hljs-meta{color:#4fb43a;font-weight:bolder}html.theme--documenter-dark .hljs{background:#2b2b2b;color:#f8f8f2}html.theme--documenter-dark .hljs-comment,html.theme--documenter-dark .hljs-quote{color:#d4d0ab}html.theme--documenter-dark .hljs-variable,html.theme--documenter-dark .hljs-template-variable,html.theme--documenter-dark .hljs-tag,html.theme--documenter-dark .hljs-name,html.theme--documenter-dark .hljs-selector-id,html.theme--documenter-dark .hljs-selector-class,html.theme--documenter-dark .hljs-regexp,html.theme--documenter-dark .hljs-deletion{color:#ffa07a}html.theme--documenter-dark .hljs-number,html.theme--documenter-dark .hljs-built_in,html.theme--documenter-dark .hljs-literal,html.theme--documenter-dark .hljs-type,html.theme--documenter-dark .hljs-params,html.theme--documenter-dark .hljs-meta,html.theme--documenter-dark .hljs-link{color:#f5ab35}html.theme--documenter-dark .hljs-attribute{color:#ffd700}html.theme--documenter-dark .hljs-string,html.theme--documenter-dark .hljs-symbol,html.theme--documenter-dark .hljs-bullet,html.theme--documenter-dark .hljs-addition{color:#abe338}html.theme--documenter-dark .hljs-title,html.theme--documenter-dark .hljs-section{color:#00e0e0}html.theme--documenter-dark .hljs-keyword,html.theme--documenter-dark .hljs-selector-tag{color:#dcc6e0}html.theme--documenter-dark .hljs-emphasis{font-style:italic}html.theme--documenter-dark .hljs-strong{font-weight:bold}@media screen and (-ms-high-contrast: active){html.theme--documenter-dark .hljs-addition,html.theme--documenter-dark .hljs-attribute,html.theme--documenter-dark .hljs-built_in,html.theme--documenter-dark .hljs-bullet,html.theme--documenter-dark .hljs-comment,html.theme--documenter-dark .hljs-link,html.theme--documenter-dark .hljs-literal,html.theme--documenter-dark .hljs-meta,html.theme--documenter-dark .hljs-number,html.theme--documenter-dark .hljs-params,html.theme--documenter-dark .hljs-string,html.theme--documenter-dark .hljs-symbol,html.theme--documenter-dark .hljs-type,html.theme--documenter-dark .hljs-quote{color:highlight}html.theme--documenter-dark .hljs-keyword,html.theme--documenter-dark .hljs-selector-tag{font-weight:bold}}html.theme--documenter-dark .hljs-subst{color:#f8f8f2} +*/}html.theme--documenter-dark html{background-color:#1f2424;font-size:16px;-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;min-width:300px;overflow-x:auto;overflow-y:scroll;text-rendering:optimizeLegibility;text-size-adjust:100%}html.theme--documenter-dark article,html.theme--documenter-dark aside,html.theme--documenter-dark figure,html.theme--documenter-dark footer,html.theme--documenter-dark header,html.theme--documenter-dark hgroup,html.theme--documenter-dark section{display:block}html.theme--documenter-dark body,html.theme--documenter-dark button,html.theme--documenter-dark input,html.theme--documenter-dark optgroup,html.theme--documenter-dark select,html.theme--documenter-dark textarea{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif}html.theme--documenter-dark code,html.theme--documenter-dark pre{-moz-osx-font-smoothing:auto;-webkit-font-smoothing:auto;font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace}html.theme--documenter-dark body{color:#fff;font-size:1em;font-weight:400;line-height:1.5}html.theme--documenter-dark a{color:#1abc9c;cursor:pointer;text-decoration:none}html.theme--documenter-dark a strong{color:currentColor}html.theme--documenter-dark a:hover{color:#1dd2af}html.theme--documenter-dark code{background-color:rgba(255,255,255,0.05);color:#ececec;font-size:.875em;font-weight:normal;padding:.1em}html.theme--documenter-dark hr{background-color:#282f2f;border:none;display:block;height:2px;margin:1.5rem 0}html.theme--documenter-dark img{height:auto;max-width:100%}html.theme--documenter-dark input[type="checkbox"],html.theme--documenter-dark input[type="radio"]{vertical-align:baseline}html.theme--documenter-dark small{font-size:.875em}html.theme--documenter-dark span{font-style:inherit;font-weight:inherit}html.theme--documenter-dark strong{color:#f2f2f2;font-weight:700}html.theme--documenter-dark fieldset{border:none}html.theme--documenter-dark pre{-webkit-overflow-scrolling:touch;background-color:#282f2f;color:#fff;font-size:.875em;overflow-x:auto;padding:1.25rem 1.5rem;white-space:pre;word-wrap:normal}html.theme--documenter-dark pre code{background-color:transparent;color:currentColor;font-size:1em;padding:0}html.theme--documenter-dark table td,html.theme--documenter-dark table th{vertical-align:top}html.theme--documenter-dark table td:not([align]),html.theme--documenter-dark table th:not([align]){text-align:inherit}html.theme--documenter-dark table th{color:#f2f2f2}html.theme--documenter-dark .box{background-color:#343c3d;border-radius:8px;box-shadow:none;color:#fff;display:block;padding:1.25rem}html.theme--documenter-dark a.box:hover,html.theme--documenter-dark a.box:focus{box-shadow:0 0.5em 1em -0.125em rgba(10,10,10,0.1),0 0 0 1px #1abc9c}html.theme--documenter-dark a.box:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2),0 0 0 1px #1abc9c}html.theme--documenter-dark .button{background-color:#282f2f;border-color:#4c5759;border-width:1px;color:#375a7f;cursor:pointer;justify-content:center;padding-bottom:calc(0.5em - 1px);padding-left:1em;padding-right:1em;padding-top:calc(0.5em - 1px);text-align:center;white-space:nowrap}html.theme--documenter-dark .button strong{color:inherit}html.theme--documenter-dark .button .icon,html.theme--documenter-dark .button .icon.is-small,html.theme--documenter-dark .button #documenter .docs-sidebar form.docs-search>input.icon,html.theme--documenter-dark #documenter .docs-sidebar .button form.docs-search>input.icon,html.theme--documenter-dark .button .icon.is-medium,html.theme--documenter-dark .button .icon.is-large{height:1.5em;width:1.5em}html.theme--documenter-dark .button .icon:first-child:not(:last-child){margin-left:calc(-0.5em - 1px);margin-right:.25em}html.theme--documenter-dark .button .icon:last-child:not(:first-child){margin-left:.25em;margin-right:calc(-0.5em - 1px)}html.theme--documenter-dark .button .icon:first-child:last-child{margin-left:calc(-0.5em - 1px);margin-right:calc(-0.5em - 1px)}html.theme--documenter-dark .button:hover,html.theme--documenter-dark .button.is-hovered{border-color:#8c9b9d;color:#f2f2f2}html.theme--documenter-dark .button:focus,html.theme--documenter-dark .button.is-focused{border-color:#8c9b9d;color:#17a689}html.theme--documenter-dark .button:focus:not(:active),html.theme--documenter-dark .button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .button:active,html.theme--documenter-dark .button.is-active{border-color:#343c3d;color:#f2f2f2}html.theme--documenter-dark .button.is-text{background-color:transparent;border-color:transparent;color:#fff;text-decoration:underline}html.theme--documenter-dark .button.is-text:hover,html.theme--documenter-dark .button.is-text.is-hovered,html.theme--documenter-dark .button.is-text:focus,html.theme--documenter-dark .button.is-text.is-focused{background-color:#282f2f;color:#f2f2f2}html.theme--documenter-dark .button.is-text:active,html.theme--documenter-dark .button.is-text.is-active{background-color:#1d2122;color:#f2f2f2}html.theme--documenter-dark .button.is-text[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-text{background-color:transparent;border-color:transparent;box-shadow:none}html.theme--documenter-dark .button.is-ghost{background:none;border-color:rgba(0,0,0,0);color:#1abc9c;text-decoration:none}html.theme--documenter-dark .button.is-ghost:hover,html.theme--documenter-dark .button.is-ghost.is-hovered{color:#1abc9c;text-decoration:underline}html.theme--documenter-dark .button.is-white{background-color:#fff;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .button.is-white:hover,html.theme--documenter-dark .button.is-white.is-hovered{background-color:#f9f9f9;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .button.is-white:focus,html.theme--documenter-dark .button.is-white.is-focused{border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .button.is-white:focus:not(:active),html.theme--documenter-dark .button.is-white.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--documenter-dark .button.is-white:active,html.theme--documenter-dark .button.is-white.is-active{background-color:#f2f2f2;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .button.is-white[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white{background-color:#fff;border-color:#fff;box-shadow:none}html.theme--documenter-dark .button.is-white.is-inverted{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-white.is-inverted:hover,html.theme--documenter-dark .button.is-white.is-inverted.is-hovered{background-color:#000}html.theme--documenter-dark .button.is-white.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-inverted{background-color:#0a0a0a;border-color:transparent;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-white.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-white.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-white.is-outlined:hover,html.theme--documenter-dark .button.is-white.is-outlined.is-hovered,html.theme--documenter-dark .button.is-white.is-outlined:focus,html.theme--documenter-dark .button.is-white.is-outlined.is-focused{background-color:#fff;border-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-white.is-outlined.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-white.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-white.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-focused{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black{background-color:#0a0a0a;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:hover,html.theme--documenter-dark .button.is-black.is-hovered{background-color:#040404;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:focus,html.theme--documenter-dark .button.is-black.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:focus:not(:active),html.theme--documenter-dark .button.is-black.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}html.theme--documenter-dark .button.is-black:active,html.theme--documenter-dark .button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black{background-color:#0a0a0a;border-color:#0a0a0a;box-shadow:none}html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-black.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:hover,html.theme--documenter-dark .button.is-light.is-hovered{background-color:#e5eaec;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus,html.theme--documenter-dark .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus:not(:active),html.theme--documenter-dark .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light.is-active{background-color:#dde4e6;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:#ecf0f1;box-shadow:none}html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-outlined.is-focused{background-color:#ecf0f1;border-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:#282f2f;box-shadow:none}html.theme--documenter-dark .button.is-dark.is-inverted,html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted:hover,html.theme--documenter-dark .content kbd.button.is-inverted:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-dark.is-inverted[disabled],html.theme--documenter-dark .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-loading::after,html.theme--documenter-dark .content kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus:not(:active),html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus:not(:active),html.theme--documenter-dark .button.is-primary.is-focused:not(:active),html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(55,90,127,0.25)}html.theme--documenter-dark .button.is-primary:active,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:active,html.theme--documenter-dark .button.is-primary.is-active,html.theme--documenter-dark .docstring>section>a.button.is-active.docs-sourcelink{background-color:#2f4d6d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary[disabled],html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:#375a7f;box-shadow:none}html.theme--documenter-dark .button.is-primary.is-inverted,html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-inverted:hover,html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-inverted.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-hovered.docs-sourcelink{background-color:#f2f2f2}html.theme--documenter-dark .button.is-primary.is-inverted[disabled],html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-inverted,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;border-color:transparent;box-shadow:none;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-loading.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-outlined:hover,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-outlined.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading:focus::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--documenter-dark .button.is-primary.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .docstring>section>a.button.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-primary.is-outlined[disabled],html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-outlined,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#375a7f;box-shadow:none;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined:hover,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined:focus,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-focused.docs-sourcelink{background-color:#fff;color:#375a7f}html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:hover::after,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-loading.is-hovered.docs-sourcelink::after,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:focus::after,html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #375a7f #375a7f !important}html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined[disabled],html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary.is-light,html.theme--documenter-dark .docstring>section>a.button.is-light.docs-sourcelink{background-color:#f1f5f9;color:#4d7eb2}html.theme--documenter-dark .button.is-primary.is-light:hover,html.theme--documenter-dark .docstring>section>a.button.is-light.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-light.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-light.is-hovered.docs-sourcelink{background-color:#e8eef5;border-color:transparent;color:#4d7eb2}html.theme--documenter-dark .button.is-primary.is-light:active,html.theme--documenter-dark .docstring>section>a.button.is-light.docs-sourcelink:active,html.theme--documenter-dark .button.is-primary.is-light.is-active,html.theme--documenter-dark .docstring>section>a.button.is-light.is-active.docs-sourcelink{background-color:#dfe8f1;border-color:transparent;color:#4d7eb2}html.theme--documenter-dark .button.is-link{background-color:#1abc9c;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-link:hover,html.theme--documenter-dark .button.is-link.is-hovered{background-color:#18b193;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-link:focus,html.theme--documenter-dark .button.is-link.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-link:focus:not(:active),html.theme--documenter-dark .button.is-link.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .button.is-link:active,html.theme--documenter-dark .button.is-link.is-active{background-color:#17a689;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-link[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link{background-color:#1abc9c;border-color:#1abc9c;box-shadow:none}html.theme--documenter-dark .button.is-link.is-inverted{background-color:#fff;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted:hover,html.theme--documenter-dark .button.is-link.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-link.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-link.is-outlined{background-color:transparent;border-color:#1abc9c;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-outlined:hover,html.theme--documenter-dark .button.is-link.is-outlined.is-hovered,html.theme--documenter-dark .button.is-link.is-outlined:focus,html.theme--documenter-dark .button.is-link.is-outlined.is-focused{background-color:#1abc9c;border-color:#1abc9c;color:#fff}html.theme--documenter-dark .button.is-link.is-outlined.is-loading::after{border-color:transparent transparent #1abc9c #1abc9c !important}html.theme--documenter-dark .button.is-link.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-link.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-link.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-link.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-outlined{background-color:transparent;border-color:#1abc9c;box-shadow:none;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-focused{background-color:#fff;color:#1abc9c}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-link.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #1abc9c #1abc9c !important}html.theme--documenter-dark .button.is-link.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-link.is-light{background-color:#edfdf9;color:#15987e}html.theme--documenter-dark .button.is-link.is-light:hover,html.theme--documenter-dark .button.is-link.is-light.is-hovered{background-color:#e2fbf6;border-color:transparent;color:#15987e}html.theme--documenter-dark .button.is-link.is-light:active,html.theme--documenter-dark .button.is-link.is-light.is-active{background-color:#d7f9f3;border-color:transparent;color:#15987e}html.theme--documenter-dark .button.is-info{background-color:#024c7d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-info:hover,html.theme--documenter-dark .button.is-info.is-hovered{background-color:#024470;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-info:focus,html.theme--documenter-dark .button.is-info.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-info:focus:not(:active),html.theme--documenter-dark .button.is-info.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(2,76,125,0.25)}html.theme--documenter-dark .button.is-info:active,html.theme--documenter-dark .button.is-info.is-active{background-color:#023d64;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-info[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-info{background-color:#024c7d;border-color:#024c7d;box-shadow:none}html.theme--documenter-dark .button.is-info.is-inverted{background-color:#fff;color:#024c7d}html.theme--documenter-dark .button.is-info.is-inverted:hover,html.theme--documenter-dark .button.is-info.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-info.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-info.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#024c7d}html.theme--documenter-dark .button.is-info.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-info.is-outlined{background-color:transparent;border-color:#024c7d;color:#024c7d}html.theme--documenter-dark .button.is-info.is-outlined:hover,html.theme--documenter-dark .button.is-info.is-outlined.is-hovered,html.theme--documenter-dark .button.is-info.is-outlined:focus,html.theme--documenter-dark .button.is-info.is-outlined.is-focused{background-color:#024c7d;border-color:#024c7d;color:#fff}html.theme--documenter-dark .button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #024c7d #024c7d !important}html.theme--documenter-dark .button.is-info.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-info.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-info.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-info.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-info.is-outlined{background-color:transparent;border-color:#024c7d;box-shadow:none;color:#024c7d}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-focused{background-color:#fff;color:#024c7d}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #024c7d #024c7d !important}html.theme--documenter-dark .button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-info.is-light{background-color:#ebf7ff;color:#0e9dfb}html.theme--documenter-dark .button.is-info.is-light:hover,html.theme--documenter-dark .button.is-info.is-light.is-hovered{background-color:#def2fe;border-color:transparent;color:#0e9dfb}html.theme--documenter-dark .button.is-info.is-light:active,html.theme--documenter-dark .button.is-info.is-light.is-active{background-color:#d2edfe;border-color:transparent;color:#0e9dfb}html.theme--documenter-dark .button.is-success{background-color:#008438;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-success:hover,html.theme--documenter-dark .button.is-success.is-hovered{background-color:#073;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-success:focus,html.theme--documenter-dark .button.is-success.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-success:focus:not(:active),html.theme--documenter-dark .button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(0,132,56,0.25)}html.theme--documenter-dark .button.is-success:active,html.theme--documenter-dark .button.is-success.is-active{background-color:#006b2d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-success[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success{background-color:#008438;border-color:#008438;box-shadow:none}html.theme--documenter-dark .button.is-success.is-inverted{background-color:#fff;color:#008438}html.theme--documenter-dark .button.is-success.is-inverted:hover,html.theme--documenter-dark .button.is-success.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-success.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#008438}html.theme--documenter-dark .button.is-success.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#008438;color:#008438}html.theme--documenter-dark .button.is-success.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-outlined.is-focused{background-color:#008438;border-color:#008438;color:#fff}html.theme--documenter-dark .button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #008438 #008438 !important}html.theme--documenter-dark .button.is-success.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#008438;box-shadow:none;color:#008438}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#008438}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #008438 #008438 !important}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-success.is-light{background-color:#ebfff3;color:#00eb64}html.theme--documenter-dark .button.is-success.is-light:hover,html.theme--documenter-dark .button.is-success.is-light.is-hovered{background-color:#deffec;border-color:transparent;color:#00eb64}html.theme--documenter-dark .button.is-success.is-light:active,html.theme--documenter-dark .button.is-success.is-light.is-active{background-color:#d1ffe5;border-color:transparent;color:#00eb64}html.theme--documenter-dark .button.is-warning{background-color:#ad8100;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-warning:hover,html.theme--documenter-dark .button.is-warning.is-hovered{background-color:#a07700;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-warning:focus,html.theme--documenter-dark .button.is-warning.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-warning:focus:not(:active),html.theme--documenter-dark .button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(173,129,0,0.25)}html.theme--documenter-dark .button.is-warning:active,html.theme--documenter-dark .button.is-warning.is-active{background-color:#946e00;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-warning[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning{background-color:#ad8100;border-color:#ad8100;box-shadow:none}html.theme--documenter-dark .button.is-warning.is-inverted{background-color:#fff;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-warning.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#ad8100;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-outlined.is-focused{background-color:#ad8100;border-color:#ad8100;color:#fff}html.theme--documenter-dark .button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #ad8100 #ad8100 !important}html.theme--documenter-dark .button.is-warning.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#ad8100;box-shadow:none;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-focused{background-color:#fff;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ad8100 #ad8100 !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-warning.is-light{background-color:#fffaeb;color:#d19c00}html.theme--documenter-dark .button.is-warning.is-light:hover,html.theme--documenter-dark .button.is-warning.is-light.is-hovered{background-color:#fff7de;border-color:transparent;color:#d19c00}html.theme--documenter-dark .button.is-warning.is-light:active,html.theme--documenter-dark .button.is-warning.is-light.is-active{background-color:#fff3d1;border-color:transparent;color:#d19c00}html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:hover,html.theme--documenter-dark .button.is-danger.is-hovered{background-color:#92190c;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus,html.theme--documenter-dark .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus:not(:active),html.theme--documenter-dark .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(158,27,13,0.25)}html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger.is-active{background-color:#86170b;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:#9e1b0d;box-shadow:none}html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-outlined.is-focused{background-color:#9e1b0d;border-color:#9e1b0d;color:#fff}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#fce3e0;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#fcd8d5;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--documenter-dark .icon.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--documenter-dark .icon.is-medium{height:2rem;width:2rem}html.theme--documenter-dark .icon.is-large{height:3rem;width:3rem}html.theme--documenter-dark .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--documenter-dark .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--documenter-dark .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--documenter-dark div.icon-text{display:flex}html.theme--documenter-dark .image,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--documenter-dark .image img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--documenter-dark .image.is-16by9 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--documenter-dark .image.is-2by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--documenter-dark .image.is-2by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--documenter-dark .image.is-3by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--documenter-dark .image.is-3by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,html.theme--documenter-dark .image.is-4by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 img,html.theme--documenter-dark .image.is-4by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,html.theme--documenter-dark .image.is-3by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 img,html.theme--documenter-dark .image.is-3by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,html.theme--documenter-dark .image.is-2by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 img,html.theme--documenter-dark .image.is-2by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,html.theme--documenter-dark .image.is-3by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 img,html.theme--documenter-dark .image.is-3by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,html.theme--documenter-dark .image.is-9by16 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 img,html.theme--documenter-dark .image.is-9by16 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,html.theme--documenter-dark .image.is-1by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2 img,html.theme--documenter-dark .image.is-1by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,html.theme--documenter-dark .image.is-1by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3 img,html.theme--documenter-dark .image.is-1by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio{height:100%;width:100%}html.theme--documenter-dark .image.is-square,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square,html.theme--documenter-dark .image.is-1by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1{padding-top:100%}html.theme--documenter-dark .image.is-5by4,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4{padding-top:80%}html.theme--documenter-dark .image.is-4by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}html.theme--documenter-dark .image.is-3by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}html.theme--documenter-dark .image.is-5by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}html.theme--documenter-dark .image.is-16by9,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}html.theme--documenter-dark .image.is-2by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}html.theme--documenter-dark .image.is-3by1,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}html.theme--documenter-dark .image.is-4by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}html.theme--documenter-dark .image.is-3by4,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}html.theme--documenter-dark .image.is-2by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}html.theme--documenter-dark .image.is-3by5,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}html.theme--documenter-dark .image.is-9by16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-9by16{padding-top:177.7777%}html.theme--documenter-dark .image.is-1by2,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by2{padding-top:200%}html.theme--documenter-dark .image.is-1by3,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by3{padding-top:300%}html.theme--documenter-dark .image.is-16x16,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16x16{height:16px;width:16px}html.theme--documenter-dark .image.is-24x24,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-24x24{height:24px;width:24px}html.theme--documenter-dark .image.is-32x32,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-32x32{height:32px;width:32px}html.theme--documenter-dark .image.is-48x48,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-48x48{height:48px;width:48px}html.theme--documenter-dark .image.is-64x64,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-64x64{height:64px;width:64px}html.theme--documenter-dark .image.is-96x96,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-96x96{height:96px;width:96px}html.theme--documenter-dark .image.is-128x128,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-128x128{height:128px;width:128px}html.theme--documenter-dark .notification{background-color:#282f2f;border-radius:.4em;position:relative;padding:1.25rem 2.5rem 1.25rem 1.5rem}html.theme--documenter-dark .notification a:not(.button):not(.dropdown-item){color:currentColor;text-decoration:underline}html.theme--documenter-dark .notification strong{color:currentColor}html.theme--documenter-dark .notification code,html.theme--documenter-dark .notification pre{background:#fff}html.theme--documenter-dark .notification pre code{background:transparent}html.theme--documenter-dark .notification>.delete{right:.5rem;position:absolute;top:0.5rem}html.theme--documenter-dark .notification .title,html.theme--documenter-dark .notification .subtitle,html.theme--documenter-dark .notification .content{color:currentColor}html.theme--documenter-dark .notification.is-white{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .notification.is-black{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .notification.is-light{background-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .notification.is-dark,html.theme--documenter-dark .content kbd.notification{background-color:#282f2f;color:#fff}html.theme--documenter-dark .notification.is-primary,html.theme--documenter-dark .docstring>section>a.notification.docs-sourcelink{background-color:#375a7f;color:#fff}html.theme--documenter-dark .notification.is-primary.is-light,html.theme--documenter-dark .docstring>section>a.notification.is-light.docs-sourcelink{background-color:#f1f5f9;color:#4d7eb2}html.theme--documenter-dark .notification.is-link{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .notification.is-link.is-light{background-color:#edfdf9;color:#15987e}html.theme--documenter-dark .notification.is-info{background-color:#024c7d;color:#fff}html.theme--documenter-dark .notification.is-info.is-light{background-color:#ebf7ff;color:#0e9dfb}html.theme--documenter-dark .notification.is-success{background-color:#008438;color:#fff}html.theme--documenter-dark .notification.is-success.is-light{background-color:#ebfff3;color:#00eb64}html.theme--documenter-dark .notification.is-warning{background-color:#ad8100;color:#fff}html.theme--documenter-dark .notification.is-warning.is-light{background-color:#fffaeb;color:#d19c00}html.theme--documenter-dark .notification.is-danger{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .notification.is-danger.is-light{background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .progress{-moz-appearance:none;-webkit-appearance:none;border:none;border-radius:9999px;display:block;height:1rem;overflow:hidden;padding:0;width:100%}html.theme--documenter-dark .progress::-webkit-progress-bar{background-color:#343c3d}html.theme--documenter-dark .progress::-webkit-progress-value{background-color:#dbdee0}html.theme--documenter-dark .progress::-moz-progress-bar{background-color:#dbdee0}html.theme--documenter-dark .progress::-ms-fill{background-color:#dbdee0;border:none}html.theme--documenter-dark .progress.is-white::-webkit-progress-value{background-color:#fff}html.theme--documenter-dark .progress.is-white::-moz-progress-bar{background-color:#fff}html.theme--documenter-dark .progress.is-white::-ms-fill{background-color:#fff}html.theme--documenter-dark .progress.is-white:indeterminate{background-image:linear-gradient(to right, #fff 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-black::-webkit-progress-value{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black::-moz-progress-bar{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black::-ms-fill{background-color:#0a0a0a}html.theme--documenter-dark .progress.is-black:indeterminate{background-image:linear-gradient(to right, #0a0a0a 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-light::-webkit-progress-value{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light::-moz-progress-bar{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light::-ms-fill{background-color:#ecf0f1}html.theme--documenter-dark .progress.is-light:indeterminate{background-image:linear-gradient(to right, #ecf0f1 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-dark::-webkit-progress-value,html.theme--documenter-dark .content kbd.progress::-webkit-progress-value{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark::-moz-progress-bar,html.theme--documenter-dark .content kbd.progress::-moz-progress-bar{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark::-ms-fill,html.theme--documenter-dark .content kbd.progress::-ms-fill{background-color:#282f2f}html.theme--documenter-dark .progress.is-dark:indeterminate,html.theme--documenter-dark .content kbd.progress:indeterminate{background-image:linear-gradient(to right, #282f2f 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-primary::-webkit-progress-value,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-webkit-progress-value{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary::-moz-progress-bar,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-moz-progress-bar{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary::-ms-fill,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink::-ms-fill{background-color:#375a7f}html.theme--documenter-dark .progress.is-primary:indeterminate,html.theme--documenter-dark .docstring>section>a.progress.docs-sourcelink:indeterminate{background-image:linear-gradient(to right, #375a7f 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-link::-webkit-progress-value{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link::-moz-progress-bar{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link::-ms-fill{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link:indeterminate{background-image:linear-gradient(to right, #1abc9c 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-info::-webkit-progress-value{background-color:#024c7d}html.theme--documenter-dark .progress.is-info::-moz-progress-bar{background-color:#024c7d}html.theme--documenter-dark .progress.is-info::-ms-fill{background-color:#024c7d}html.theme--documenter-dark .progress.is-info:indeterminate{background-image:linear-gradient(to right, #024c7d 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-success::-webkit-progress-value{background-color:#008438}html.theme--documenter-dark .progress.is-success::-moz-progress-bar{background-color:#008438}html.theme--documenter-dark .progress.is-success::-ms-fill{background-color:#008438}html.theme--documenter-dark .progress.is-success:indeterminate{background-image:linear-gradient(to right, #008438 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-warning::-webkit-progress-value{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning::-moz-progress-bar{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning::-ms-fill{background-color:#ad8100}html.theme--documenter-dark .progress.is-warning:indeterminate{background-image:linear-gradient(to right, #ad8100 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-danger::-webkit-progress-value{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger::-moz-progress-bar{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger::-ms-fill{background-color:#9e1b0d}html.theme--documenter-dark .progress.is-danger:indeterminate{background-image:linear-gradient(to right, #9e1b0d 30%, #343c3d 30%)}html.theme--documenter-dark .progress:indeterminate{animation-duration:1.5s;animation-iteration-count:infinite;animation-name:moveIndeterminate;animation-timing-function:linear;background-color:#343c3d;background-image:linear-gradient(to right, #fff 30%, #343c3d 30%);background-position:top left;background-repeat:no-repeat;background-size:150% 150%}html.theme--documenter-dark .progress:indeterminate::-webkit-progress-bar{background-color:transparent}html.theme--documenter-dark .progress:indeterminate::-moz-progress-bar{background-color:transparent}html.theme--documenter-dark .progress:indeterminate::-ms-fill{animation-name:none}html.theme--documenter-dark .progress.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.progress{height:.75rem}html.theme--documenter-dark .progress.is-medium{height:1.25rem}html.theme--documenter-dark .progress.is-large{height:1.5rem}@keyframes moveIndeterminate{from{background-position:200% 0}to{background-position:-200% 0}}html.theme--documenter-dark .table{background-color:#343c3d;color:#fff}html.theme--documenter-dark .table td,html.theme--documenter-dark .table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .table td.is-white,html.theme--documenter-dark .table th.is-white{background-color:#fff;border-color:#fff;color:#0a0a0a}html.theme--documenter-dark .table td.is-black,html.theme--documenter-dark .table th.is-black{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .table td.is-light,html.theme--documenter-dark .table th.is-light{background-color:#ecf0f1;border-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .table td.is-dark,html.theme--documenter-dark .table th.is-dark{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .table td.is-primary,html.theme--documenter-dark .table th.is-primary{background-color:#375a7f;border-color:#375a7f;color:#fff}html.theme--documenter-dark .table td.is-link,html.theme--documenter-dark .table th.is-link{background-color:#1abc9c;border-color:#1abc9c;color:#fff}html.theme--documenter-dark .table td.is-info,html.theme--documenter-dark .table th.is-info{background-color:#024c7d;border-color:#024c7d;color:#fff}html.theme--documenter-dark .table td.is-success,html.theme--documenter-dark .table th.is-success{background-color:#008438;border-color:#008438;color:#fff}html.theme--documenter-dark .table td.is-warning,html.theme--documenter-dark .table th.is-warning{background-color:#ad8100;border-color:#ad8100;color:#fff}html.theme--documenter-dark .table td.is-danger,html.theme--documenter-dark .table th.is-danger{background-color:#9e1b0d;border-color:#9e1b0d;color:#fff}html.theme--documenter-dark .table td.is-narrow,html.theme--documenter-dark .table th.is-narrow{white-space:nowrap;width:1%}html.theme--documenter-dark .table td.is-selected,html.theme--documenter-dark .table th.is-selected{background-color:#375a7f;color:#fff}html.theme--documenter-dark .table td.is-selected a,html.theme--documenter-dark .table td.is-selected strong,html.theme--documenter-dark .table th.is-selected a,html.theme--documenter-dark .table th.is-selected strong{color:currentColor}html.theme--documenter-dark .table td.is-vcentered,html.theme--documenter-dark .table th.is-vcentered{vertical-align:middle}html.theme--documenter-dark .table th{color:#f2f2f2}html.theme--documenter-dark .table th:not([align]){text-align:left}html.theme--documenter-dark .table tr.is-selected{background-color:#375a7f;color:#fff}html.theme--documenter-dark .table tr.is-selected a,html.theme--documenter-dark .table tr.is-selected strong{color:currentColor}html.theme--documenter-dark .table tr.is-selected td,html.theme--documenter-dark .table tr.is-selected th{border-color:#fff;color:currentColor}html.theme--documenter-dark .table thead{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table thead td,html.theme--documenter-dark .table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .table tfoot{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table tfoot td,html.theme--documenter-dark .table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .table tbody{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .table tbody tr:last-child td,html.theme--documenter-dark .table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .table.is-bordered td,html.theme--documenter-dark .table.is-bordered th{border-width:1px}html.theme--documenter-dark .table.is-bordered tr:last-child td,html.theme--documenter-dark .table.is-bordered tr:last-child th{border-bottom-width:1px}html.theme--documenter-dark .table.is-fullwidth{width:100%}html.theme--documenter-dark .table.is-hoverable tbody tr:not(.is-selected):hover{background-color:#282f2f}html.theme--documenter-dark .table.is-hoverable.is-striped tbody tr:not(.is-selected):hover{background-color:#282f2f}html.theme--documenter-dark .table.is-hoverable.is-striped tbody tr:not(.is-selected):hover:nth-child(even){background-color:#2d3435}html.theme--documenter-dark .table.is-narrow td,html.theme--documenter-dark .table.is-narrow th{padding:0.25em 0.5em}html.theme--documenter-dark .table.is-striped tbody tr:not(.is-selected):nth-child(even){background-color:#282f2f}html.theme--documenter-dark .table-container{-webkit-overflow-scrolling:touch;overflow:auto;overflow-y:hidden;max-width:100%}html.theme--documenter-dark .tags{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .tags .tag,html.theme--documenter-dark .tags .content kbd,html.theme--documenter-dark .content .tags kbd,html.theme--documenter-dark .tags .docstring>section>a.docs-sourcelink{margin-bottom:0.5rem}html.theme--documenter-dark .tags .tag:not(:last-child),html.theme--documenter-dark .tags .content kbd:not(:last-child),html.theme--documenter-dark .content .tags kbd:not(:last-child),html.theme--documenter-dark .tags .docstring>section>a.docs-sourcelink:not(:last-child){margin-right:.5rem}html.theme--documenter-dark .tags:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .tags:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .tags.are-medium .tag:not(.is-normal):not(.is-large),html.theme--documenter-dark .tags.are-medium .content kbd:not(.is-normal):not(.is-large),html.theme--documenter-dark .content .tags.are-medium kbd:not(.is-normal):not(.is-large),html.theme--documenter-dark .tags.are-medium .docstring>section>a.docs-sourcelink:not(.is-normal):not(.is-large){font-size:1rem}html.theme--documenter-dark .tags.are-large .tag:not(.is-normal):not(.is-medium),html.theme--documenter-dark .tags.are-large .content kbd:not(.is-normal):not(.is-medium),html.theme--documenter-dark .content .tags.are-large kbd:not(.is-normal):not(.is-medium),html.theme--documenter-dark .tags.are-large .docstring>section>a.docs-sourcelink:not(.is-normal):not(.is-medium){font-size:1.25rem}html.theme--documenter-dark .tags.is-centered{justify-content:center}html.theme--documenter-dark .tags.is-centered .tag,html.theme--documenter-dark .tags.is-centered .content kbd,html.theme--documenter-dark .content .tags.is-centered kbd,html.theme--documenter-dark .tags.is-centered .docstring>section>a.docs-sourcelink{margin-right:0.25rem;margin-left:0.25rem}html.theme--documenter-dark .tags.is-right{justify-content:flex-end}html.theme--documenter-dark .tags.is-right .tag:not(:first-child),html.theme--documenter-dark .tags.is-right .content kbd:not(:first-child),html.theme--documenter-dark .content .tags.is-right kbd:not(:first-child),html.theme--documenter-dark .tags.is-right .docstring>section>a.docs-sourcelink:not(:first-child){margin-left:0.5rem}html.theme--documenter-dark .tags.is-right .tag:not(:last-child),html.theme--documenter-dark .tags.is-right .content kbd:not(:last-child),html.theme--documenter-dark .content .tags.is-right kbd:not(:last-child),html.theme--documenter-dark .tags.is-right .docstring>section>a.docs-sourcelink:not(:last-child){margin-right:0}html.theme--documenter-dark .tags.has-addons .tag,html.theme--documenter-dark .tags.has-addons .content kbd,html.theme--documenter-dark .content .tags.has-addons kbd,html.theme--documenter-dark .tags.has-addons .docstring>section>a.docs-sourcelink{margin-right:0}html.theme--documenter-dark .tags.has-addons .tag:not(:first-child),html.theme--documenter-dark .tags.has-addons .content kbd:not(:first-child),html.theme--documenter-dark .content .tags.has-addons kbd:not(:first-child),html.theme--documenter-dark .tags.has-addons .docstring>section>a.docs-sourcelink:not(:first-child){margin-left:0;border-top-left-radius:0;border-bottom-left-radius:0}html.theme--documenter-dark .tags.has-addons .tag:not(:last-child),html.theme--documenter-dark .tags.has-addons .content kbd:not(:last-child),html.theme--documenter-dark .content .tags.has-addons kbd:not(:last-child),html.theme--documenter-dark .tags.has-addons .docstring>section>a.docs-sourcelink:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}html.theme--documenter-dark .tag:not(body),html.theme--documenter-dark .content kbd:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body){align-items:center;background-color:#282f2f;border-radius:.4em;color:#fff;display:inline-flex;font-size:.75rem;height:2em;justify-content:center;line-height:1.5;padding-left:0.75em;padding-right:0.75em;white-space:nowrap}html.theme--documenter-dark .tag:not(body) .delete,html.theme--documenter-dark .content kbd:not(body) .delete,html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body) .delete{margin-left:.25rem;margin-right:-.375rem}html.theme--documenter-dark .tag.is-white:not(body),html.theme--documenter-dark .content kbd.is-white:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-white:not(body){background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .tag.is-black:not(body),html.theme--documenter-dark .content kbd.is-black:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-black:not(body){background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .tag.is-light:not(body),html.theme--documenter-dark .content kbd.is-light:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-light:not(body){background-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .tag.is-dark:not(body),html.theme--documenter-dark .content kbd:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-dark:not(body),html.theme--documenter-dark .content .docstring>section>kbd:not(body){background-color:#282f2f;color:#fff}html.theme--documenter-dark .tag.is-primary:not(body),html.theme--documenter-dark .content kbd.is-primary:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body){background-color:#375a7f;color:#fff}html.theme--documenter-dark .tag.is-primary.is-light:not(body),html.theme--documenter-dark .content kbd.is-primary.is-light:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-light:not(body){background-color:#f1f5f9;color:#4d7eb2}html.theme--documenter-dark .tag.is-link:not(body),html.theme--documenter-dark .content kbd.is-link:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-link:not(body){background-color:#1abc9c;color:#fff}html.theme--documenter-dark .tag.is-link.is-light:not(body),html.theme--documenter-dark .content kbd.is-link.is-light:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-link.is-light:not(body){background-color:#edfdf9;color:#15987e}html.theme--documenter-dark .tag.is-info:not(body),html.theme--documenter-dark .content kbd.is-info:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-info:not(body){background-color:#024c7d;color:#fff}html.theme--documenter-dark .tag.is-info.is-light:not(body),html.theme--documenter-dark .content kbd.is-info.is-light:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-info.is-light:not(body){background-color:#ebf7ff;color:#0e9dfb}html.theme--documenter-dark .tag.is-success:not(body),html.theme--documenter-dark .content kbd.is-success:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-success:not(body){background-color:#008438;color:#fff}html.theme--documenter-dark .tag.is-success.is-light:not(body),html.theme--documenter-dark .content kbd.is-success.is-light:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-success.is-light:not(body){background-color:#ebfff3;color:#00eb64}html.theme--documenter-dark .tag.is-warning:not(body),html.theme--documenter-dark .content kbd.is-warning:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-warning:not(body){background-color:#ad8100;color:#fff}html.theme--documenter-dark .tag.is-warning.is-light:not(body),html.theme--documenter-dark .content kbd.is-warning.is-light:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-warning.is-light:not(body){background-color:#fffaeb;color:#d19c00}html.theme--documenter-dark .tag.is-danger:not(body),html.theme--documenter-dark .content kbd.is-danger:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-danger:not(body){background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .tag.is-danger.is-light:not(body),html.theme--documenter-dark .content kbd.is-danger.is-light:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-danger.is-light:not(body){background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .tag.is-normal:not(body),html.theme--documenter-dark .content kbd.is-normal:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-normal:not(body){font-size:.75rem}html.theme--documenter-dark .tag.is-medium:not(body),html.theme--documenter-dark .content kbd.is-medium:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-medium:not(body){font-size:1rem}html.theme--documenter-dark .tag.is-large:not(body),html.theme--documenter-dark .content kbd.is-large:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-large:not(body){font-size:1.25rem}html.theme--documenter-dark .tag:not(body) .icon:first-child:not(:last-child),html.theme--documenter-dark .content kbd:not(body) .icon:first-child:not(:last-child),html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body) .icon:first-child:not(:last-child){margin-left:-.375em;margin-right:.1875em}html.theme--documenter-dark .tag:not(body) .icon:last-child:not(:first-child),html.theme--documenter-dark .content kbd:not(body) .icon:last-child:not(:first-child),html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body) .icon:last-child:not(:first-child){margin-left:.1875em;margin-right:-.375em}html.theme--documenter-dark .tag:not(body) .icon:first-child:last-child,html.theme--documenter-dark .content kbd:not(body) .icon:first-child:last-child,html.theme--documenter-dark .docstring>section>a.docs-sourcelink:not(body) .icon:first-child:last-child{margin-left:-.375em;margin-right:-.375em}html.theme--documenter-dark .tag.is-delete:not(body),html.theme--documenter-dark .content kbd.is-delete:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body){margin-left:1px;padding:0;position:relative;width:2em}html.theme--documenter-dark .tag.is-delete:not(body)::before,html.theme--documenter-dark .content kbd.is-delete:not(body)::before,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body)::before,html.theme--documenter-dark .tag.is-delete:not(body)::after,html.theme--documenter-dark .content kbd.is-delete:not(body)::after,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body)::after{background-color:currentColor;content:"";display:block;left:50%;position:absolute;top:50%;transform:translateX(-50%) translateY(-50%) rotate(45deg);transform-origin:center center}html.theme--documenter-dark .tag.is-delete:not(body)::before,html.theme--documenter-dark .content kbd.is-delete:not(body)::before,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body)::before{height:1px;width:50%}html.theme--documenter-dark .tag.is-delete:not(body)::after,html.theme--documenter-dark .content kbd.is-delete:not(body)::after,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body)::after{height:50%;width:1px}html.theme--documenter-dark .tag.is-delete:not(body):hover,html.theme--documenter-dark .content kbd.is-delete:not(body):hover,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body):hover,html.theme--documenter-dark .tag.is-delete:not(body):focus,html.theme--documenter-dark .content kbd.is-delete:not(body):focus,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body):focus{background-color:#1d2122}html.theme--documenter-dark .tag.is-delete:not(body):active,html.theme--documenter-dark .content kbd.is-delete:not(body):active,html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-delete:not(body):active{background-color:#111414}html.theme--documenter-dark .tag.is-rounded:not(body),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:not(body),html.theme--documenter-dark .content kbd.is-rounded:not(body),html.theme--documenter-dark #documenter .docs-sidebar .content form.docs-search>input:not(body),html.theme--documenter-dark .docstring>section>a.docs-sourcelink.is-rounded:not(body){border-radius:9999px}html.theme--documenter-dark a.tag:hover,html.theme--documenter-dark .docstring>section>a.docs-sourcelink:hover{text-decoration:underline}html.theme--documenter-dark .title,html.theme--documenter-dark .subtitle{word-break:break-word}html.theme--documenter-dark .title em,html.theme--documenter-dark .title span,html.theme--documenter-dark .subtitle em,html.theme--documenter-dark .subtitle span{font-weight:inherit}html.theme--documenter-dark .title sub,html.theme--documenter-dark .subtitle sub{font-size:.75em}html.theme--documenter-dark .title sup,html.theme--documenter-dark .subtitle sup{font-size:.75em}html.theme--documenter-dark .title .tag,html.theme--documenter-dark .title .content kbd,html.theme--documenter-dark .content .title kbd,html.theme--documenter-dark .title .docstring>section>a.docs-sourcelink,html.theme--documenter-dark .subtitle .tag,html.theme--documenter-dark .subtitle .content kbd,html.theme--documenter-dark .content .subtitle kbd,html.theme--documenter-dark .subtitle .docstring>section>a.docs-sourcelink{vertical-align:middle}html.theme--documenter-dark .title{color:#fff;font-size:2rem;font-weight:500;line-height:1.125}html.theme--documenter-dark .title strong{color:inherit;font-weight:inherit}html.theme--documenter-dark .title:not(.is-spaced)+.subtitle{margin-top:-1.25rem}html.theme--documenter-dark .title.is-1{font-size:3rem}html.theme--documenter-dark .title.is-2{font-size:2.5rem}html.theme--documenter-dark .title.is-3{font-size:2rem}html.theme--documenter-dark .title.is-4{font-size:1.5rem}html.theme--documenter-dark .title.is-5{font-size:1.25rem}html.theme--documenter-dark .title.is-6{font-size:1rem}html.theme--documenter-dark .title.is-7{font-size:.75rem}html.theme--documenter-dark .subtitle{color:#8c9b9d;font-size:1.25rem;font-weight:400;line-height:1.25}html.theme--documenter-dark .subtitle strong{color:#8c9b9d;font-weight:600}html.theme--documenter-dark .subtitle:not(.is-spaced)+.title{margin-top:-1.25rem}html.theme--documenter-dark .subtitle.is-1{font-size:3rem}html.theme--documenter-dark .subtitle.is-2{font-size:2.5rem}html.theme--documenter-dark .subtitle.is-3{font-size:2rem}html.theme--documenter-dark .subtitle.is-4{font-size:1.5rem}html.theme--documenter-dark .subtitle.is-5{font-size:1.25rem}html.theme--documenter-dark .subtitle.is-6{font-size:1rem}html.theme--documenter-dark .subtitle.is-7{font-size:.75rem}html.theme--documenter-dark .heading{display:block;font-size:11px;letter-spacing:1px;margin-bottom:5px;text-transform:uppercase}html.theme--documenter-dark .number{align-items:center;background-color:#282f2f;border-radius:9999px;display:inline-flex;font-size:1.25rem;height:2em;justify-content:center;margin-right:1.5rem;min-width:2.5em;padding:0.25rem 0.5rem;text-align:center;vertical-align:top}html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{background-color:#1f2424;border-color:#5e6d6f;border-radius:.4em;color:#dbdee0}html.theme--documenter-dark .select select::-moz-placeholder,html.theme--documenter-dark .textarea::-moz-placeholder,html.theme--documenter-dark .input::-moz-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input::-moz-placeholder{color:#868c98}html.theme--documenter-dark .select select::-webkit-input-placeholder,html.theme--documenter-dark .textarea::-webkit-input-placeholder,html.theme--documenter-dark .input::-webkit-input-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input::-webkit-input-placeholder{color:#868c98}html.theme--documenter-dark .select select:-moz-placeholder,html.theme--documenter-dark .textarea:-moz-placeholder,html.theme--documenter-dark .input:-moz-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:-moz-placeholder{color:#868c98}html.theme--documenter-dark .select select:-ms-input-placeholder,html.theme--documenter-dark .textarea:-ms-input-placeholder,html.theme--documenter-dark .input:-ms-input-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:-ms-input-placeholder{color:#868c98}html.theme--documenter-dark .select select:hover,html.theme--documenter-dark .textarea:hover,html.theme--documenter-dark .input:hover,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:hover,html.theme--documenter-dark .select select.is-hovered,html.theme--documenter-dark .is-hovered.textarea,html.theme--documenter-dark .is-hovered.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-hovered{border-color:#8c9b9d}html.theme--documenter-dark .select select:focus,html.theme--documenter-dark .textarea:focus,html.theme--documenter-dark .input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:focus,html.theme--documenter-dark .select select.is-focused,html.theme--documenter-dark .is-focused.textarea,html.theme--documenter-dark .is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .select select:active,html.theme--documenter-dark .textarea:active,html.theme--documenter-dark .input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:active,html.theme--documenter-dark .select select.is-active,html.theme--documenter-dark .is-active.textarea,html.theme--documenter-dark .is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{border-color:#1abc9c;box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .select select[disabled],html.theme--documenter-dark .textarea[disabled],html.theme--documenter-dark .input[disabled],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled],fieldset[disabled] html.theme--documenter-dark .select select,fieldset[disabled] html.theme--documenter-dark .textarea,fieldset[disabled] html.theme--documenter-dark .input,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{background-color:#8c9b9d;border-color:#282f2f;box-shadow:none;color:#fff}html.theme--documenter-dark .select select[disabled]::-moz-placeholder,html.theme--documenter-dark .textarea[disabled]::-moz-placeholder,html.theme--documenter-dark .input[disabled]::-moz-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled]::-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .select select::-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .textarea::-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .input::-moz-placeholder,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input::-moz-placeholder{color:rgba(255,255,255,0.3)}html.theme--documenter-dark .select select[disabled]::-webkit-input-placeholder,html.theme--documenter-dark .textarea[disabled]::-webkit-input-placeholder,html.theme--documenter-dark .input[disabled]::-webkit-input-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled]::-webkit-input-placeholder,fieldset[disabled] html.theme--documenter-dark .select select::-webkit-input-placeholder,fieldset[disabled] html.theme--documenter-dark .textarea::-webkit-input-placeholder,fieldset[disabled] html.theme--documenter-dark .input::-webkit-input-placeholder,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input::-webkit-input-placeholder{color:rgba(255,255,255,0.3)}html.theme--documenter-dark .select select[disabled]:-moz-placeholder,html.theme--documenter-dark .textarea[disabled]:-moz-placeholder,html.theme--documenter-dark .input[disabled]:-moz-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled]:-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .select select:-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .textarea:-moz-placeholder,fieldset[disabled] html.theme--documenter-dark .input:-moz-placeholder,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:-moz-placeholder{color:rgba(255,255,255,0.3)}html.theme--documenter-dark .select select[disabled]:-ms-input-placeholder,html.theme--documenter-dark .textarea[disabled]:-ms-input-placeholder,html.theme--documenter-dark .input[disabled]:-ms-input-placeholder,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled]:-ms-input-placeholder,fieldset[disabled] html.theme--documenter-dark .select select:-ms-input-placeholder,fieldset[disabled] html.theme--documenter-dark .textarea:-ms-input-placeholder,fieldset[disabled] html.theme--documenter-dark .input:-ms-input-placeholder,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:-ms-input-placeholder{color:rgba(255,255,255,0.3)}html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{box-shadow:inset 0 0.0625em 0.125em rgba(10,10,10,0.05);max-width:100%;width:100%}html.theme--documenter-dark .textarea[readonly],html.theme--documenter-dark .input[readonly],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[readonly]{box-shadow:none}html.theme--documenter-dark .is-white.textarea,html.theme--documenter-dark .is-white.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-white{border-color:#fff}html.theme--documenter-dark .is-white.textarea:focus,html.theme--documenter-dark .is-white.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-white:focus,html.theme--documenter-dark .is-white.is-focused.textarea,html.theme--documenter-dark .is-white.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-white.textarea:active,html.theme--documenter-dark .is-white.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-white:active,html.theme--documenter-dark .is-white.is-active.textarea,html.theme--documenter-dark .is-white.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--documenter-dark .is-black.textarea,html.theme--documenter-dark .is-black.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-black{border-color:#0a0a0a}html.theme--documenter-dark .is-black.textarea:focus,html.theme--documenter-dark .is-black.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-black:focus,html.theme--documenter-dark .is-black.is-focused.textarea,html.theme--documenter-dark .is-black.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-black.textarea:active,html.theme--documenter-dark .is-black.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-black:active,html.theme--documenter-dark .is-black.is-active.textarea,html.theme--documenter-dark .is-black.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}html.theme--documenter-dark .is-light.textarea,html.theme--documenter-dark .is-light.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-light{border-color:#ecf0f1}html.theme--documenter-dark .is-light.textarea:focus,html.theme--documenter-dark .is-light.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-light:focus,html.theme--documenter-dark .is-light.is-focused.textarea,html.theme--documenter-dark .is-light.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-light.textarea:active,html.theme--documenter-dark .is-light.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-light:active,html.theme--documenter-dark .is-light.is-active.textarea,html.theme--documenter-dark .is-light.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .is-dark.textarea,html.theme--documenter-dark .content kbd.textarea,html.theme--documenter-dark .is-dark.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-dark,html.theme--documenter-dark .content kbd.input{border-color:#282f2f}html.theme--documenter-dark .is-dark.textarea:focus,html.theme--documenter-dark .content kbd.textarea:focus,html.theme--documenter-dark .is-dark.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-dark:focus,html.theme--documenter-dark .content kbd.input:focus,html.theme--documenter-dark .is-dark.is-focused.textarea,html.theme--documenter-dark .content kbd.is-focused.textarea,html.theme--documenter-dark .is-dark.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .content kbd.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar .content form.docs-search>input.is-focused,html.theme--documenter-dark .is-dark.textarea:active,html.theme--documenter-dark .content kbd.textarea:active,html.theme--documenter-dark .is-dark.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-dark:active,html.theme--documenter-dark .content kbd.input:active,html.theme--documenter-dark .is-dark.is-active.textarea,html.theme--documenter-dark .content kbd.is-active.textarea,html.theme--documenter-dark .is-dark.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .content kbd.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar .content form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .is-primary.textarea,html.theme--documenter-dark .docstring>section>a.textarea.docs-sourcelink,html.theme--documenter-dark .is-primary.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-primary,html.theme--documenter-dark .docstring>section>a.input.docs-sourcelink{border-color:#375a7f}html.theme--documenter-dark .is-primary.textarea:focus,html.theme--documenter-dark .docstring>section>a.textarea.docs-sourcelink:focus,html.theme--documenter-dark .is-primary.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-primary:focus,html.theme--documenter-dark .docstring>section>a.input.docs-sourcelink:focus,html.theme--documenter-dark .is-primary.is-focused.textarea,html.theme--documenter-dark .docstring>section>a.is-focused.textarea.docs-sourcelink,html.theme--documenter-dark .is-primary.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .docstring>section>a.is-focused.input.docs-sourcelink,html.theme--documenter-dark .is-primary.textarea:active,html.theme--documenter-dark .docstring>section>a.textarea.docs-sourcelink:active,html.theme--documenter-dark .is-primary.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-primary:active,html.theme--documenter-dark .docstring>section>a.input.docs-sourcelink:active,html.theme--documenter-dark .is-primary.is-active.textarea,html.theme--documenter-dark .docstring>section>a.is-active.textarea.docs-sourcelink,html.theme--documenter-dark .is-primary.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .docstring>section>a.is-active.input.docs-sourcelink{box-shadow:0 0 0 0.125em rgba(55,90,127,0.25)}html.theme--documenter-dark .is-link.textarea,html.theme--documenter-dark .is-link.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-link{border-color:#1abc9c}html.theme--documenter-dark .is-link.textarea:focus,html.theme--documenter-dark .is-link.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-link:focus,html.theme--documenter-dark .is-link.is-focused.textarea,html.theme--documenter-dark .is-link.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-link.textarea:active,html.theme--documenter-dark .is-link.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-link:active,html.theme--documenter-dark .is-link.is-active.textarea,html.theme--documenter-dark .is-link.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .is-info.textarea,html.theme--documenter-dark .is-info.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-info{border-color:#024c7d}html.theme--documenter-dark .is-info.textarea:focus,html.theme--documenter-dark .is-info.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-info:focus,html.theme--documenter-dark .is-info.is-focused.textarea,html.theme--documenter-dark .is-info.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-info.textarea:active,html.theme--documenter-dark .is-info.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-info:active,html.theme--documenter-dark .is-info.is-active.textarea,html.theme--documenter-dark .is-info.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(2,76,125,0.25)}html.theme--documenter-dark .is-success.textarea,html.theme--documenter-dark .is-success.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-success{border-color:#008438}html.theme--documenter-dark .is-success.textarea:focus,html.theme--documenter-dark .is-success.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-success:focus,html.theme--documenter-dark .is-success.is-focused.textarea,html.theme--documenter-dark .is-success.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-success.textarea:active,html.theme--documenter-dark .is-success.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-success:active,html.theme--documenter-dark .is-success.is-active.textarea,html.theme--documenter-dark .is-success.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(0,132,56,0.25)}html.theme--documenter-dark .is-warning.textarea,html.theme--documenter-dark .is-warning.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-warning{border-color:#ad8100}html.theme--documenter-dark .is-warning.textarea:focus,html.theme--documenter-dark .is-warning.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-warning:focus,html.theme--documenter-dark .is-warning.is-focused.textarea,html.theme--documenter-dark .is-warning.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-warning.textarea:active,html.theme--documenter-dark .is-warning.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-warning:active,html.theme--documenter-dark .is-warning.is-active.textarea,html.theme--documenter-dark .is-warning.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(173,129,0,0.25)}html.theme--documenter-dark .is-danger.textarea,html.theme--documenter-dark .is-danger.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-danger{border-color:#9e1b0d}html.theme--documenter-dark .is-danger.textarea:focus,html.theme--documenter-dark .is-danger.input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-danger:focus,html.theme--documenter-dark .is-danger.is-focused.textarea,html.theme--documenter-dark .is-danger.is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-danger.textarea:active,html.theme--documenter-dark .is-danger.input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-danger:active,html.theme--documenter-dark .is-danger.is-active.textarea,html.theme--documenter-dark .is-danger.is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(158,27,13,0.25)}html.theme--documenter-dark .is-small.textarea,html.theme--documenter-dark .is-small.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{border-radius:3px;font-size:.75rem}html.theme--documenter-dark .is-medium.textarea,html.theme--documenter-dark .is-medium.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-medium{font-size:1.25rem}html.theme--documenter-dark .is-large.textarea,html.theme--documenter-dark .is-large.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-large{font-size:1.5rem}html.theme--documenter-dark .is-fullwidth.textarea,html.theme--documenter-dark .is-fullwidth.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-fullwidth{display:block;width:100%}html.theme--documenter-dark .is-inline.textarea,html.theme--documenter-dark .is-inline.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-inline{display:inline;width:auto}html.theme--documenter-dark .input.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{border-radius:9999px;padding-left:calc(calc(0.75em - 1px) + 0.375em);padding-right:calc(calc(0.75em - 1px) + 0.375em)}html.theme--documenter-dark .input.is-static,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-static{background-color:transparent;border-color:transparent;box-shadow:none;padding-left:0;padding-right:0}html.theme--documenter-dark .textarea{display:block;max-width:100%;min-width:100%;padding:calc(0.75em - 1px);resize:vertical}html.theme--documenter-dark .textarea:not([rows]){max-height:40em;min-height:8em}html.theme--documenter-dark .textarea[rows]{height:initial}html.theme--documenter-dark .textarea.has-fixed-size{resize:none}html.theme--documenter-dark .radio,html.theme--documenter-dark .checkbox{cursor:pointer;display:inline-block;line-height:1.25;position:relative}html.theme--documenter-dark .radio input,html.theme--documenter-dark .checkbox input{cursor:pointer}html.theme--documenter-dark .radio:hover,html.theme--documenter-dark .checkbox:hover{color:#8c9b9d}html.theme--documenter-dark .radio[disabled],html.theme--documenter-dark .checkbox[disabled],fieldset[disabled] html.theme--documenter-dark .radio,fieldset[disabled] html.theme--documenter-dark .checkbox,html.theme--documenter-dark .radio input[disabled],html.theme--documenter-dark .checkbox input[disabled]{color:#fff;cursor:not-allowed}html.theme--documenter-dark .radio+.radio{margin-left:.5em}html.theme--documenter-dark .select{display:inline-block;max-width:100%;position:relative;vertical-align:top}html.theme--documenter-dark .select:not(.is-multiple){height:2.5em}html.theme--documenter-dark .select:not(.is-multiple):not(.is-loading)::after{border-color:#1abc9c;right:1.125em;z-index:4}html.theme--documenter-dark .select.is-rounded select,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.select select{border-radius:9999px;padding-left:1em}html.theme--documenter-dark .select select{cursor:pointer;display:block;font-size:1em;max-width:100%;outline:none}html.theme--documenter-dark .select select::-ms-expand{display:none}html.theme--documenter-dark .select select[disabled]:hover,fieldset[disabled] html.theme--documenter-dark .select select:hover{border-color:#282f2f}html.theme--documenter-dark .select select:not([multiple]){padding-right:2.5em}html.theme--documenter-dark .select select[multiple]{height:auto;padding:0}html.theme--documenter-dark .select select[multiple] option{padding:0.5em 1em}html.theme--documenter-dark .select:not(.is-multiple):not(.is-loading):hover::after{border-color:#8c9b9d}html.theme--documenter-dark .select.is-white:not(:hover)::after{border-color:#fff}html.theme--documenter-dark .select.is-white select{border-color:#fff}html.theme--documenter-dark .select.is-white select:hover,html.theme--documenter-dark .select.is-white select.is-hovered{border-color:#f2f2f2}html.theme--documenter-dark .select.is-white select:focus,html.theme--documenter-dark .select.is-white select.is-focused,html.theme--documenter-dark .select.is-white select:active,html.theme--documenter-dark .select.is-white select.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}html.theme--documenter-dark .select.is-black:not(:hover)::after{border-color:#0a0a0a}html.theme--documenter-dark .select.is-black select{border-color:#0a0a0a}html.theme--documenter-dark .select.is-black select:hover,html.theme--documenter-dark .select.is-black select.is-hovered{border-color:#000}html.theme--documenter-dark .select.is-black select:focus,html.theme--documenter-dark .select.is-black select.is-focused,html.theme--documenter-dark .select.is-black select:active,html.theme--documenter-dark .select.is-black select.is-active{box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}html.theme--documenter-dark .select.is-light:not(:hover)::after{border-color:#ecf0f1}html.theme--documenter-dark .select.is-light select{border-color:#ecf0f1}html.theme--documenter-dark .select.is-light select:hover,html.theme--documenter-dark .select.is-light select.is-hovered{border-color:#dde4e6}html.theme--documenter-dark .select.is-light select:focus,html.theme--documenter-dark .select.is-light select.is-focused,html.theme--documenter-dark .select.is-light select:active,html.theme--documenter-dark .select.is-light select.is-active{box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .select.is-dark:not(:hover)::after,html.theme--documenter-dark .content kbd.select:not(:hover)::after{border-color:#282f2f}html.theme--documenter-dark .select.is-dark select,html.theme--documenter-dark .content kbd.select select{border-color:#282f2f}html.theme--documenter-dark .select.is-dark select:hover,html.theme--documenter-dark .content kbd.select select:hover,html.theme--documenter-dark .select.is-dark select.is-hovered,html.theme--documenter-dark .content kbd.select select.is-hovered{border-color:#1d2122}html.theme--documenter-dark .select.is-dark select:focus,html.theme--documenter-dark .content kbd.select select:focus,html.theme--documenter-dark .select.is-dark select.is-focused,html.theme--documenter-dark .content kbd.select select.is-focused,html.theme--documenter-dark .select.is-dark select:active,html.theme--documenter-dark .content kbd.select select:active,html.theme--documenter-dark .select.is-dark select.is-active,html.theme--documenter-dark .content kbd.select select.is-active{box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .select.is-primary:not(:hover)::after,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink:not(:hover)::after{border-color:#375a7f}html.theme--documenter-dark .select.is-primary select,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select{border-color:#375a7f}html.theme--documenter-dark .select.is-primary select:hover,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select:hover,html.theme--documenter-dark .select.is-primary select.is-hovered,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select.is-hovered{border-color:#2f4d6d}html.theme--documenter-dark .select.is-primary select:focus,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select:focus,html.theme--documenter-dark .select.is-primary select.is-focused,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select.is-focused,html.theme--documenter-dark .select.is-primary select:active,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select:active,html.theme--documenter-dark .select.is-primary select.is-active,html.theme--documenter-dark .docstring>section>a.select.docs-sourcelink select.is-active{box-shadow:0 0 0 0.125em rgba(55,90,127,0.25)}html.theme--documenter-dark .select.is-link:not(:hover)::after{border-color:#1abc9c}html.theme--documenter-dark .select.is-link select{border-color:#1abc9c}html.theme--documenter-dark .select.is-link select:hover,html.theme--documenter-dark .select.is-link select.is-hovered{border-color:#17a689}html.theme--documenter-dark .select.is-link select:focus,html.theme--documenter-dark .select.is-link select.is-focused,html.theme--documenter-dark .select.is-link select:active,html.theme--documenter-dark .select.is-link select.is-active{box-shadow:0 0 0 0.125em rgba(26,188,156,0.25)}html.theme--documenter-dark .select.is-info:not(:hover)::after{border-color:#024c7d}html.theme--documenter-dark .select.is-info select{border-color:#024c7d}html.theme--documenter-dark .select.is-info select:hover,html.theme--documenter-dark .select.is-info select.is-hovered{border-color:#023d64}html.theme--documenter-dark .select.is-info select:focus,html.theme--documenter-dark .select.is-info select.is-focused,html.theme--documenter-dark .select.is-info select:active,html.theme--documenter-dark .select.is-info select.is-active{box-shadow:0 0 0 0.125em rgba(2,76,125,0.25)}html.theme--documenter-dark .select.is-success:not(:hover)::after{border-color:#008438}html.theme--documenter-dark .select.is-success select{border-color:#008438}html.theme--documenter-dark .select.is-success select:hover,html.theme--documenter-dark .select.is-success select.is-hovered{border-color:#006b2d}html.theme--documenter-dark .select.is-success select:focus,html.theme--documenter-dark .select.is-success select.is-focused,html.theme--documenter-dark .select.is-success select:active,html.theme--documenter-dark .select.is-success select.is-active{box-shadow:0 0 0 0.125em rgba(0,132,56,0.25)}html.theme--documenter-dark .select.is-warning:not(:hover)::after{border-color:#ad8100}html.theme--documenter-dark .select.is-warning select{border-color:#ad8100}html.theme--documenter-dark .select.is-warning select:hover,html.theme--documenter-dark .select.is-warning select.is-hovered{border-color:#946e00}html.theme--documenter-dark .select.is-warning select:focus,html.theme--documenter-dark .select.is-warning select.is-focused,html.theme--documenter-dark .select.is-warning select:active,html.theme--documenter-dark .select.is-warning select.is-active{box-shadow:0 0 0 0.125em rgba(173,129,0,0.25)}html.theme--documenter-dark .select.is-danger:not(:hover)::after{border-color:#9e1b0d}html.theme--documenter-dark .select.is-danger select{border-color:#9e1b0d}html.theme--documenter-dark .select.is-danger select:hover,html.theme--documenter-dark .select.is-danger select.is-hovered{border-color:#86170b}html.theme--documenter-dark .select.is-danger select:focus,html.theme--documenter-dark .select.is-danger select.is-focused,html.theme--documenter-dark .select.is-danger select:active,html.theme--documenter-dark .select.is-danger select.is-active{box-shadow:0 0 0 0.125em rgba(158,27,13,0.25)}html.theme--documenter-dark .select.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.select{border-radius:3px;font-size:.75rem}html.theme--documenter-dark .select.is-medium{font-size:1.25rem}html.theme--documenter-dark .select.is-large{font-size:1.5rem}html.theme--documenter-dark .select.is-disabled::after{border-color:#fff !important;opacity:0.5}html.theme--documenter-dark .select.is-fullwidth{width:100%}html.theme--documenter-dark .select.is-fullwidth select{width:100%}html.theme--documenter-dark .select.is-loading::after{margin-top:0;position:absolute;right:.625em;top:0.625em;transform:none}html.theme--documenter-dark .select.is-loading.is-small:after,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-loading:after{font-size:.75rem}html.theme--documenter-dark .select.is-loading.is-medium:after{font-size:1.25rem}html.theme--documenter-dark .select.is-loading.is-large:after{font-size:1.5rem}html.theme--documenter-dark .file{align-items:stretch;display:flex;justify-content:flex-start;position:relative}html.theme--documenter-dark .file.is-white .file-cta{background-color:#fff;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .file.is-white:hover .file-cta,html.theme--documenter-dark .file.is-white.is-hovered .file-cta{background-color:#f9f9f9;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .file.is-white:focus .file-cta,html.theme--documenter-dark .file.is-white.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(255,255,255,0.25);color:#0a0a0a}html.theme--documenter-dark .file.is-white:active .file-cta,html.theme--documenter-dark .file.is-white.is-active .file-cta{background-color:#f2f2f2;border-color:transparent;color:#0a0a0a}html.theme--documenter-dark .file.is-black .file-cta{background-color:#0a0a0a;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-black:hover .file-cta,html.theme--documenter-dark .file.is-black.is-hovered .file-cta{background-color:#040404;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-black:focus .file-cta,html.theme--documenter-dark .file.is-black.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(10,10,10,0.25);color:#fff}html.theme--documenter-dark .file.is-black:active .file-cta,html.theme--documenter-dark .file.is-black.is-active .file-cta{background-color:#000;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-light .file-cta{background-color:#ecf0f1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .file.is-light:hover .file-cta,html.theme--documenter-dark .file.is-light.is-hovered .file-cta{background-color:#e5eaec;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .file.is-light:focus .file-cta,html.theme--documenter-dark .file.is-light.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(236,240,241,0.25);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .file.is-light:active .file-cta,html.theme--documenter-dark .file.is-light.is-active .file-cta{background-color:#dde4e6;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .file.is-dark .file-cta,html.theme--documenter-dark .content kbd.file .file-cta{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-dark:hover .file-cta,html.theme--documenter-dark .content kbd.file:hover .file-cta,html.theme--documenter-dark .file.is-dark.is-hovered .file-cta,html.theme--documenter-dark .content kbd.file.is-hovered .file-cta{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-dark:focus .file-cta,html.theme--documenter-dark .content kbd.file:focus .file-cta,html.theme--documenter-dark .file.is-dark.is-focused .file-cta,html.theme--documenter-dark .content kbd.file.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(40,47,47,0.25);color:#fff}html.theme--documenter-dark .file.is-dark:active .file-cta,html.theme--documenter-dark .content kbd.file:active .file-cta,html.theme--documenter-dark .file.is-dark.is-active .file-cta,html.theme--documenter-dark .content kbd.file.is-active .file-cta{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-primary .file-cta,html.theme--documenter-dark .docstring>section>a.file.docs-sourcelink .file-cta{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-primary:hover .file-cta,html.theme--documenter-dark .docstring>section>a.file.docs-sourcelink:hover .file-cta,html.theme--documenter-dark .file.is-primary.is-hovered .file-cta,html.theme--documenter-dark .docstring>section>a.file.is-hovered.docs-sourcelink .file-cta{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-primary:focus .file-cta,html.theme--documenter-dark .docstring>section>a.file.docs-sourcelink:focus .file-cta,html.theme--documenter-dark .file.is-primary.is-focused .file-cta,html.theme--documenter-dark .docstring>section>a.file.is-focused.docs-sourcelink .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(55,90,127,0.25);color:#fff}html.theme--documenter-dark .file.is-primary:active .file-cta,html.theme--documenter-dark .docstring>section>a.file.docs-sourcelink:active .file-cta,html.theme--documenter-dark .file.is-primary.is-active .file-cta,html.theme--documenter-dark .docstring>section>a.file.is-active.docs-sourcelink .file-cta{background-color:#2f4d6d;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-link .file-cta{background-color:#1abc9c;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-link:hover .file-cta,html.theme--documenter-dark .file.is-link.is-hovered .file-cta{background-color:#18b193;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-link:focus .file-cta,html.theme--documenter-dark .file.is-link.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(26,188,156,0.25);color:#fff}html.theme--documenter-dark .file.is-link:active .file-cta,html.theme--documenter-dark .file.is-link.is-active .file-cta{background-color:#17a689;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-info .file-cta{background-color:#024c7d;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-info:hover .file-cta,html.theme--documenter-dark .file.is-info.is-hovered .file-cta{background-color:#024470;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-info:focus .file-cta,html.theme--documenter-dark .file.is-info.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(2,76,125,0.25);color:#fff}html.theme--documenter-dark .file.is-info:active .file-cta,html.theme--documenter-dark .file.is-info.is-active .file-cta{background-color:#023d64;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-success .file-cta{background-color:#008438;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-success:hover .file-cta,html.theme--documenter-dark .file.is-success.is-hovered .file-cta{background-color:#073;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-success:focus .file-cta,html.theme--documenter-dark .file.is-success.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(0,132,56,0.25);color:#fff}html.theme--documenter-dark .file.is-success:active .file-cta,html.theme--documenter-dark .file.is-success.is-active .file-cta{background-color:#006b2d;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-warning .file-cta{background-color:#ad8100;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-warning:hover .file-cta,html.theme--documenter-dark .file.is-warning.is-hovered .file-cta{background-color:#a07700;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-warning:focus .file-cta,html.theme--documenter-dark .file.is-warning.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(173,129,0,0.25);color:#fff}html.theme--documenter-dark .file.is-warning:active .file-cta,html.theme--documenter-dark .file.is-warning.is-active .file-cta{background-color:#946e00;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-danger .file-cta{background-color:#9e1b0d;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-danger:hover .file-cta,html.theme--documenter-dark .file.is-danger.is-hovered .file-cta{background-color:#92190c;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-danger:focus .file-cta,html.theme--documenter-dark .file.is-danger.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(158,27,13,0.25);color:#fff}html.theme--documenter-dark .file.is-danger:active .file-cta,html.theme--documenter-dark .file.is-danger.is-active .file-cta{background-color:#86170b;border-color:transparent;color:#fff}html.theme--documenter-dark .file.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.file{font-size:.75rem}html.theme--documenter-dark .file.is-normal{font-size:1rem}html.theme--documenter-dark .file.is-medium{font-size:1.25rem}html.theme--documenter-dark .file.is-medium .file-icon .fa{font-size:21px}html.theme--documenter-dark .file.is-large{font-size:1.5rem}html.theme--documenter-dark .file.is-large .file-icon .fa{font-size:28px}html.theme--documenter-dark .file.has-name .file-cta{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--documenter-dark .file.has-name .file-name{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .file.has-name.is-empty .file-cta{border-radius:.4em}html.theme--documenter-dark .file.has-name.is-empty .file-name{display:none}html.theme--documenter-dark .file.is-boxed .file-label{flex-direction:column}html.theme--documenter-dark .file.is-boxed .file-cta{flex-direction:column;height:auto;padding:1em 3em}html.theme--documenter-dark .file.is-boxed .file-name{border-width:0 1px 1px}html.theme--documenter-dark .file.is-boxed .file-icon{height:1.5em;width:1.5em}html.theme--documenter-dark .file.is-boxed .file-icon .fa{font-size:21px}html.theme--documenter-dark .file.is-boxed.is-small .file-icon .fa,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-boxed .file-icon .fa{font-size:14px}html.theme--documenter-dark .file.is-boxed.is-medium .file-icon .fa{font-size:28px}html.theme--documenter-dark .file.is-boxed.is-large .file-icon .fa{font-size:35px}html.theme--documenter-dark .file.is-boxed.has-name .file-cta{border-radius:.4em .4em 0 0}html.theme--documenter-dark .file.is-boxed.has-name .file-name{border-radius:0 0 .4em .4em;border-width:0 1px 1px}html.theme--documenter-dark .file.is-centered{justify-content:center}html.theme--documenter-dark .file.is-fullwidth .file-label{width:100%}html.theme--documenter-dark .file.is-fullwidth .file-name{flex-grow:1;max-width:none}html.theme--documenter-dark .file.is-right{justify-content:flex-end}html.theme--documenter-dark .file.is-right .file-cta{border-radius:0 .4em .4em 0}html.theme--documenter-dark .file.is-right .file-name{border-radius:.4em 0 0 .4em;border-width:1px 0 1px 1px;order:-1}html.theme--documenter-dark .file-label{align-items:stretch;display:flex;cursor:pointer;justify-content:flex-start;overflow:hidden;position:relative}html.theme--documenter-dark .file-label:hover .file-cta{background-color:#232829;color:#f2f2f2}html.theme--documenter-dark .file-label:hover .file-name{border-color:#596668}html.theme--documenter-dark .file-label:active .file-cta{background-color:#1d2122;color:#f2f2f2}html.theme--documenter-dark .file-label:active .file-name{border-color:#535f61}html.theme--documenter-dark .file-input{height:100%;left:0;opacity:0;outline:none;position:absolute;top:0;width:100%}html.theme--documenter-dark .file-cta,html.theme--documenter-dark .file-name{border-color:#5e6d6f;border-radius:.4em;font-size:1em;padding-left:1em;padding-right:1em;white-space:nowrap}html.theme--documenter-dark .file-cta{background-color:#282f2f;color:#fff}html.theme--documenter-dark .file-name{border-color:#5e6d6f;border-style:solid;border-width:1px 1px 1px 0;display:block;max-width:16em;overflow:hidden;text-align:inherit;text-overflow:ellipsis}html.theme--documenter-dark .file-icon{align-items:center;display:flex;height:1em;justify-content:center;margin-right:.5em;width:1em}html.theme--documenter-dark .file-icon .fa{font-size:14px}html.theme--documenter-dark .label{color:#f2f2f2;display:block;font-size:1rem;font-weight:700}html.theme--documenter-dark .label:not(:last-child){margin-bottom:0.5em}html.theme--documenter-dark .label.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.label{font-size:.75rem}html.theme--documenter-dark .label.is-medium{font-size:1.25rem}html.theme--documenter-dark .label.is-large{font-size:1.5rem}html.theme--documenter-dark .help{display:block;font-size:.75rem;margin-top:0.25rem}html.theme--documenter-dark .help.is-white{color:#fff}html.theme--documenter-dark .help.is-black{color:#0a0a0a}html.theme--documenter-dark .help.is-light{color:#ecf0f1}html.theme--documenter-dark .help.is-dark,html.theme--documenter-dark .content kbd.help{color:#282f2f}html.theme--documenter-dark .help.is-primary,html.theme--documenter-dark .docstring>section>a.help.docs-sourcelink{color:#375a7f}html.theme--documenter-dark .help.is-link{color:#1abc9c}html.theme--documenter-dark .help.is-info{color:#024c7d}html.theme--documenter-dark .help.is-success{color:#008438}html.theme--documenter-dark .help.is-warning{color:#ad8100}html.theme--documenter-dark .help.is-danger{color:#9e1b0d}html.theme--documenter-dark .field:not(:last-child){margin-bottom:0.75rem}html.theme--documenter-dark .field.has-addons{display:flex;justify-content:flex-start}html.theme--documenter-dark .field.has-addons .control:not(:last-child){margin-right:-1px}html.theme--documenter-dark .field.has-addons .control:not(:first-child):not(:last-child) .button,html.theme--documenter-dark .field.has-addons .control:not(:first-child):not(:last-child) .input,html.theme--documenter-dark .field.has-addons .control:not(:first-child):not(:last-child) #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control:not(:first-child):not(:last-child) form.docs-search>input,html.theme--documenter-dark .field.has-addons .control:not(:first-child):not(:last-child) .select select{border-radius:0}html.theme--documenter-dark .field.has-addons .control:first-child:not(:only-child) .button,html.theme--documenter-dark .field.has-addons .control:first-child:not(:only-child) .input,html.theme--documenter-dark .field.has-addons .control:first-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control:first-child:not(:only-child) form.docs-search>input,html.theme--documenter-dark .field.has-addons .control:first-child:not(:only-child) .select select{border-bottom-right-radius:0;border-top-right-radius:0}html.theme--documenter-dark .field.has-addons .control:last-child:not(:only-child) .button,html.theme--documenter-dark .field.has-addons .control:last-child:not(:only-child) .input,html.theme--documenter-dark .field.has-addons .control:last-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control:last-child:not(:only-child) form.docs-search>input,html.theme--documenter-dark .field.has-addons .control:last-child:not(:only-child) .select select{border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .button.is-hovered:not([disabled]),html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .input.is-hovered:not([disabled]),html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-hovered:not([disabled]),html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-hovered:not([disabled]),html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .select select.is-hovered:not([disabled]){z-index:2}html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):focus,html.theme--documenter-dark .field.has-addons .control .button.is-focused:not([disabled]),html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):active,html.theme--documenter-dark .field.has-addons .control .button.is-active:not([disabled]),html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):focus,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus,html.theme--documenter-dark .field.has-addons .control .input.is-focused:not([disabled]),html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]),html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]),html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):active,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active,html.theme--documenter-dark .field.has-addons .control .input.is-active:not([disabled]),html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]),html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]),html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):focus,html.theme--documenter-dark .field.has-addons .control .select select.is-focused:not([disabled]),html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):active,html.theme--documenter-dark .field.has-addons .control .select select.is-active:not([disabled]){z-index:3}html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):focus:hover,html.theme--documenter-dark .field.has-addons .control .button.is-focused:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .button:not([disabled]):active:hover,html.theme--documenter-dark .field.has-addons .control .button.is-active:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):focus:hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus:hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus:hover,html.theme--documenter-dark .field.has-addons .control .input.is-focused:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]):hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .input:not([disabled]):active:hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active:hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active:hover,html.theme--documenter-dark .field.has-addons .control .input.is-active:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]):hover,html.theme--documenter-dark #documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):focus:hover,html.theme--documenter-dark .field.has-addons .control .select select.is-focused:not([disabled]):hover,html.theme--documenter-dark .field.has-addons .control .select select:not([disabled]):active:hover,html.theme--documenter-dark .field.has-addons .control .select select.is-active:not([disabled]):hover{z-index:4}html.theme--documenter-dark .field.has-addons .control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .field.has-addons.has-addons-centered{justify-content:center}html.theme--documenter-dark .field.has-addons.has-addons-right{justify-content:flex-end}html.theme--documenter-dark .field.has-addons.has-addons-fullwidth .control{flex-grow:1;flex-shrink:0}html.theme--documenter-dark .field.is-grouped{display:flex;justify-content:flex-start}html.theme--documenter-dark .field.is-grouped>.control{flex-shrink:0}html.theme--documenter-dark .field.is-grouped>.control:not(:last-child){margin-bottom:0;margin-right:.75rem}html.theme--documenter-dark .field.is-grouped>.control.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .field.is-grouped.is-grouped-centered{justify-content:center}html.theme--documenter-dark .field.is-grouped.is-grouped-right{justify-content:flex-end}html.theme--documenter-dark .field.is-grouped.is-grouped-multiline{flex-wrap:wrap}html.theme--documenter-dark .field.is-grouped.is-grouped-multiline>.control:last-child,html.theme--documenter-dark .field.is-grouped.is-grouped-multiline>.control:not(:last-child){margin-bottom:0.75rem}html.theme--documenter-dark .field.is-grouped.is-grouped-multiline:last-child{margin-bottom:-0.75rem}html.theme--documenter-dark .field.is-grouped.is-grouped-multiline:not(:last-child){margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--documenter-dark .field.is-horizontal{display:flex}}html.theme--documenter-dark .field-label .label{font-size:inherit}@media screen and (max-width: 768px){html.theme--documenter-dark .field-label{margin-bottom:0.5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .field-label{flex-basis:0;flex-grow:1;flex-shrink:0;margin-right:1.5rem;text-align:right}html.theme--documenter-dark .field-label.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.field-label{font-size:.75rem;padding-top:0.375em}html.theme--documenter-dark .field-label.is-normal{padding-top:0.375em}html.theme--documenter-dark .field-label.is-medium{font-size:1.25rem;padding-top:0.375em}html.theme--documenter-dark .field-label.is-large{font-size:1.5rem;padding-top:0.375em}}html.theme--documenter-dark .field-body .field .field{margin-bottom:0}@media screen and (min-width: 769px),print{html.theme--documenter-dark .field-body{display:flex;flex-basis:0;flex-grow:5;flex-shrink:1}html.theme--documenter-dark .field-body .field{margin-bottom:0}html.theme--documenter-dark .field-body>.field{flex-shrink:1}html.theme--documenter-dark .field-body>.field:not(.is-narrow){flex-grow:1}html.theme--documenter-dark .field-body>.field:not(:last-child){margin-right:.75rem}}html.theme--documenter-dark .control{box-sizing:border-box;clear:both;font-size:1rem;position:relative;text-align:inherit}html.theme--documenter-dark .control.has-icons-left .input:focus~.icon,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input:focus~.icon,html.theme--documenter-dark .control.has-icons-left .select:focus~.icon,html.theme--documenter-dark .control.has-icons-right .input:focus~.icon,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input:focus~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input:focus~.icon,html.theme--documenter-dark .control.has-icons-right .select:focus~.icon{color:#282f2f}html.theme--documenter-dark .control.has-icons-left .input.is-small~.icon,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input~.icon,html.theme--documenter-dark .control.has-icons-left .select.is-small~.icon,html.theme--documenter-dark .control.has-icons-right .input.is-small~.icon,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input~.icon,html.theme--documenter-dark .control.has-icons-right .select.is-small~.icon{font-size:.75rem}html.theme--documenter-dark .control.has-icons-left .input.is-medium~.icon,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-medium~.icon,html.theme--documenter-dark .control.has-icons-left .select.is-medium~.icon,html.theme--documenter-dark .control.has-icons-right .input.is-medium~.icon,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-medium~.icon,html.theme--documenter-dark .control.has-icons-right .select.is-medium~.icon{font-size:1.25rem}html.theme--documenter-dark .control.has-icons-left .input.is-large~.icon,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-large~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-large~.icon,html.theme--documenter-dark .control.has-icons-left .select.is-large~.icon,html.theme--documenter-dark .control.has-icons-right .input.is-large~.icon,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-large~.icon,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-large~.icon,html.theme--documenter-dark .control.has-icons-right .select.is-large~.icon{font-size:1.5rem}html.theme--documenter-dark .control.has-icons-left .icon,html.theme--documenter-dark .control.has-icons-right .icon{color:#5e6d6f;height:2.5em;pointer-events:none;position:absolute;top:0;width:2.5em;z-index:4}html.theme--documenter-dark .control.has-icons-left .input,html.theme--documenter-dark .control.has-icons-left #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-left form.docs-search>input,html.theme--documenter-dark .control.has-icons-left .select select{padding-left:2.5em}html.theme--documenter-dark .control.has-icons-left .icon.is-left{left:0}html.theme--documenter-dark .control.has-icons-right .input,html.theme--documenter-dark .control.has-icons-right #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .control.has-icons-right form.docs-search>input,html.theme--documenter-dark .control.has-icons-right .select select{padding-right:2.5em}html.theme--documenter-dark .control.has-icons-right .icon.is-right{right:0}html.theme--documenter-dark .control.is-loading::after{position:absolute !important;right:.625em;top:0.625em;z-index:4}html.theme--documenter-dark .control.is-loading.is-small:after,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-loading:after{font-size:.75rem}html.theme--documenter-dark .control.is-loading.is-medium:after{font-size:1.25rem}html.theme--documenter-dark .control.is-loading.is-large:after{font-size:1.5rem}html.theme--documenter-dark .breadcrumb{font-size:1rem;white-space:nowrap}html.theme--documenter-dark .breadcrumb a{align-items:center;color:#1abc9c;display:flex;justify-content:center;padding:0 .75em}html.theme--documenter-dark .breadcrumb a:hover{color:#1dd2af}html.theme--documenter-dark .breadcrumb li{align-items:center;display:flex}html.theme--documenter-dark .breadcrumb li:first-child a{padding-left:0}html.theme--documenter-dark .breadcrumb li.is-active a{color:#f2f2f2;cursor:default;pointer-events:none}html.theme--documenter-dark .breadcrumb li+li::before{color:#8c9b9d;content:"\0002f"}html.theme--documenter-dark .breadcrumb ul,html.theme--documenter-dark .breadcrumb ol{align-items:flex-start;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .breadcrumb .icon:first-child{margin-right:.5em}html.theme--documenter-dark .breadcrumb .icon:last-child{margin-left:.5em}html.theme--documenter-dark .breadcrumb.is-centered ol,html.theme--documenter-dark .breadcrumb.is-centered ul{justify-content:center}html.theme--documenter-dark .breadcrumb.is-right ol,html.theme--documenter-dark .breadcrumb.is-right ul{justify-content:flex-end}html.theme--documenter-dark .breadcrumb.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.breadcrumb{font-size:.75rem}html.theme--documenter-dark .breadcrumb.is-medium{font-size:1.25rem}html.theme--documenter-dark .breadcrumb.is-large{font-size:1.5rem}html.theme--documenter-dark .breadcrumb.has-arrow-separator li+li::before{content:"\02192"}html.theme--documenter-dark .breadcrumb.has-bullet-separator li+li::before{content:"\02022"}html.theme--documenter-dark .breadcrumb.has-dot-separator li+li::before{content:"\000b7"}html.theme--documenter-dark .breadcrumb.has-succeeds-separator li+li::before{content:"\0227B"}html.theme--documenter-dark .card{background-color:#fff;border-radius:.25rem;box-shadow:#171717;color:#fff;max-width:100%;position:relative}html.theme--documenter-dark .card-footer:first-child,html.theme--documenter-dark .card-content:first-child,html.theme--documenter-dark .card-header:first-child{border-top-left-radius:.25rem;border-top-right-radius:.25rem}html.theme--documenter-dark .card-footer:last-child,html.theme--documenter-dark .card-content:last-child,html.theme--documenter-dark .card-header:last-child{border-bottom-left-radius:.25rem;border-bottom-right-radius:.25rem}html.theme--documenter-dark .card-header{background-color:rgba(0,0,0,0);align-items:stretch;box-shadow:0 0.125em 0.25em rgba(10,10,10,0.1);display:flex}html.theme--documenter-dark .card-header-title{align-items:center;color:#f2f2f2;display:flex;flex-grow:1;font-weight:700;padding:0.75rem 1rem}html.theme--documenter-dark .card-header-title.is-centered{justify-content:center}html.theme--documenter-dark .card-header-icon{-moz-appearance:none;-webkit-appearance:none;appearance:none;background:none;border:none;color:currentColor;font-family:inherit;font-size:1em;margin:0;padding:0;align-items:center;cursor:pointer;display:flex;justify-content:center;padding:0.75rem 1rem}html.theme--documenter-dark .card-image{display:block;position:relative}html.theme--documenter-dark .card-image:first-child img{border-top-left-radius:.25rem;border-top-right-radius:.25rem}html.theme--documenter-dark .card-image:last-child img{border-bottom-left-radius:.25rem;border-bottom-right-radius:.25rem}html.theme--documenter-dark .card-content{background-color:rgba(0,0,0,0);padding:1.5rem}html.theme--documenter-dark .card-footer{background-color:rgba(0,0,0,0);border-top:1px solid #ededed;align-items:stretch;display:flex}html.theme--documenter-dark .card-footer-item{align-items:center;display:flex;flex-basis:0;flex-grow:1;flex-shrink:0;justify-content:center;padding:.75rem}html.theme--documenter-dark .card-footer-item:not(:last-child){border-right:1px solid #ededed}html.theme--documenter-dark .card .media:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .dropdown{display:inline-flex;position:relative;vertical-align:top}html.theme--documenter-dark .dropdown.is-active .dropdown-menu,html.theme--documenter-dark .dropdown.is-hoverable:hover .dropdown-menu{display:block}html.theme--documenter-dark .dropdown.is-right .dropdown-menu{left:auto;right:0}html.theme--documenter-dark .dropdown.is-up .dropdown-menu{bottom:100%;padding-bottom:4px;padding-top:initial;top:auto}html.theme--documenter-dark .dropdown-menu{display:none;left:0;min-width:12rem;padding-top:4px;position:absolute;top:100%;z-index:20}html.theme--documenter-dark .dropdown-content{background-color:#282f2f;border-radius:.4em;box-shadow:#171717;padding-bottom:.5rem;padding-top:.5rem}html.theme--documenter-dark .dropdown-item{color:#fff;display:block;font-size:0.875rem;line-height:1.5;padding:0.375rem 1rem;position:relative}html.theme--documenter-dark a.dropdown-item,html.theme--documenter-dark button.dropdown-item{padding-right:3rem;text-align:inherit;white-space:nowrap;width:100%}html.theme--documenter-dark a.dropdown-item:hover,html.theme--documenter-dark button.dropdown-item:hover{background-color:#282f2f;color:#0a0a0a}html.theme--documenter-dark a.dropdown-item.is-active,html.theme--documenter-dark button.dropdown-item.is-active{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .dropdown-divider{background-color:#ededed;border:none;display:block;height:1px;margin:0.5rem 0}html.theme--documenter-dark .level{align-items:center;justify-content:space-between}html.theme--documenter-dark .level code{border-radius:.4em}html.theme--documenter-dark .level img{display:inline-block;vertical-align:top}html.theme--documenter-dark .level.is-mobile{display:flex}html.theme--documenter-dark .level.is-mobile .level-left,html.theme--documenter-dark .level.is-mobile .level-right{display:flex}html.theme--documenter-dark .level.is-mobile .level-left+.level-right{margin-top:0}html.theme--documenter-dark .level.is-mobile .level-item:not(:last-child){margin-bottom:0;margin-right:.75rem}html.theme--documenter-dark .level.is-mobile .level-item:not(.is-narrow){flex-grow:1}@media screen and (min-width: 769px),print{html.theme--documenter-dark .level{display:flex}html.theme--documenter-dark .level>.level-item:not(.is-narrow){flex-grow:1}}html.theme--documenter-dark .level-item{align-items:center;display:flex;flex-basis:auto;flex-grow:0;flex-shrink:0;justify-content:center}html.theme--documenter-dark .level-item .title,html.theme--documenter-dark .level-item .subtitle{margin-bottom:0}@media screen and (max-width: 768px){html.theme--documenter-dark .level-item:not(:last-child){margin-bottom:.75rem}}html.theme--documenter-dark .level-left,html.theme--documenter-dark .level-right{flex-basis:auto;flex-grow:0;flex-shrink:0}html.theme--documenter-dark .level-left .level-item.is-flexible,html.theme--documenter-dark .level-right .level-item.is-flexible{flex-grow:1}@media screen and (min-width: 769px),print{html.theme--documenter-dark .level-left .level-item:not(:last-child),html.theme--documenter-dark .level-right .level-item:not(:last-child){margin-right:.75rem}}html.theme--documenter-dark .level-left{align-items:center;justify-content:flex-start}@media screen and (max-width: 768px){html.theme--documenter-dark .level-left+.level-right{margin-top:1.5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .level-left{display:flex}}html.theme--documenter-dark .level-right{align-items:center;justify-content:flex-end}@media screen and (min-width: 769px),print{html.theme--documenter-dark .level-right{display:flex}}html.theme--documenter-dark .media{align-items:flex-start;display:flex;text-align:inherit}html.theme--documenter-dark .media .content:not(:last-child){margin-bottom:.75rem}html.theme--documenter-dark .media .media{border-top:1px solid rgba(94,109,111,0.5);display:flex;padding-top:.75rem}html.theme--documenter-dark .media .media .content:not(:last-child),html.theme--documenter-dark .media .media .control:not(:last-child){margin-bottom:.5rem}html.theme--documenter-dark .media .media .media{padding-top:.5rem}html.theme--documenter-dark .media .media .media+.media{margin-top:.5rem}html.theme--documenter-dark .media+.media{border-top:1px solid rgba(94,109,111,0.5);margin-top:1rem;padding-top:1rem}html.theme--documenter-dark .media.is-large+.media{margin-top:1.5rem;padding-top:1.5rem}html.theme--documenter-dark .media-left,html.theme--documenter-dark .media-right{flex-basis:auto;flex-grow:0;flex-shrink:0}html.theme--documenter-dark .media-left{margin-right:1rem}html.theme--documenter-dark .media-right{margin-left:1rem}html.theme--documenter-dark .media-content{flex-basis:auto;flex-grow:1;flex-shrink:1;text-align:inherit}@media screen and (max-width: 768px){html.theme--documenter-dark .media-content{overflow-x:auto}}html.theme--documenter-dark .menu{font-size:1rem}html.theme--documenter-dark .menu.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.menu{font-size:.75rem}html.theme--documenter-dark .menu.is-medium{font-size:1.25rem}html.theme--documenter-dark .menu.is-large{font-size:1.5rem}html.theme--documenter-dark .menu-list{line-height:1.25}html.theme--documenter-dark .menu-list a{border-radius:3px;color:#fff;display:block;padding:0.5em 0.75em}html.theme--documenter-dark .menu-list a:hover{background-color:#282f2f;color:#f2f2f2}html.theme--documenter-dark .menu-list a.is-active{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .menu-list li ul{border-left:1px solid #5e6d6f;margin:.75em;padding-left:.75em}html.theme--documenter-dark .menu-label{color:#fff;font-size:.75em;letter-spacing:.1em;text-transform:uppercase}html.theme--documenter-dark .menu-label:not(:first-child){margin-top:1em}html.theme--documenter-dark .menu-label:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .message{background-color:#282f2f;border-radius:.4em;font-size:1rem}html.theme--documenter-dark .message strong{color:currentColor}html.theme--documenter-dark .message a:not(.button):not(.tag):not(.dropdown-item){color:currentColor;text-decoration:underline}html.theme--documenter-dark .message.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.message{font-size:.75rem}html.theme--documenter-dark .message.is-medium{font-size:1.25rem}html.theme--documenter-dark .message.is-large{font-size:1.5rem}html.theme--documenter-dark .message.is-white{background-color:#fff}html.theme--documenter-dark .message.is-white .message-header{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .message.is-white .message-body{border-color:#fff}html.theme--documenter-dark .message.is-black{background-color:#fafafa}html.theme--documenter-dark .message.is-black .message-header{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .message.is-black .message-body{border-color:#0a0a0a}html.theme--documenter-dark .message.is-light{background-color:#f9fafb}html.theme--documenter-dark .message.is-light .message-header{background-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .message.is-light .message-body{border-color:#ecf0f1}html.theme--documenter-dark .message.is-dark,html.theme--documenter-dark .content kbd.message{background-color:#f9fafa}html.theme--documenter-dark .message.is-dark .message-header,html.theme--documenter-dark .content kbd.message .message-header{background-color:#282f2f;color:#fff}html.theme--documenter-dark .message.is-dark .message-body,html.theme--documenter-dark .content kbd.message .message-body{border-color:#282f2f}html.theme--documenter-dark .message.is-primary,html.theme--documenter-dark .docstring>section>a.message.docs-sourcelink{background-color:#f1f5f9}html.theme--documenter-dark .message.is-primary .message-header,html.theme--documenter-dark .docstring>section>a.message.docs-sourcelink .message-header{background-color:#375a7f;color:#fff}html.theme--documenter-dark .message.is-primary .message-body,html.theme--documenter-dark .docstring>section>a.message.docs-sourcelink .message-body{border-color:#375a7f;color:#4d7eb2}html.theme--documenter-dark .message.is-link{background-color:#edfdf9}html.theme--documenter-dark .message.is-link .message-header{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .message.is-link .message-body{border-color:#1abc9c;color:#15987e}html.theme--documenter-dark .message.is-info{background-color:#ebf7ff}html.theme--documenter-dark .message.is-info .message-header{background-color:#024c7d;color:#fff}html.theme--documenter-dark .message.is-info .message-body{border-color:#024c7d;color:#0e9dfb}html.theme--documenter-dark .message.is-success{background-color:#ebfff3}html.theme--documenter-dark .message.is-success .message-header{background-color:#008438;color:#fff}html.theme--documenter-dark .message.is-success .message-body{border-color:#008438;color:#00eb64}html.theme--documenter-dark .message.is-warning{background-color:#fffaeb}html.theme--documenter-dark .message.is-warning .message-header{background-color:#ad8100;color:#fff}html.theme--documenter-dark .message.is-warning .message-body{border-color:#ad8100;color:#d19c00}html.theme--documenter-dark .message.is-danger{background-color:#fdeeec}html.theme--documenter-dark .message.is-danger .message-header{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .message.is-danger .message-body{border-color:#9e1b0d;color:#ec311d}html.theme--documenter-dark .message-header{align-items:center;background-color:#fff;border-radius:.4em .4em 0 0;color:rgba(0,0,0,0.7);display:flex;font-weight:700;justify-content:space-between;line-height:1.25;padding:0.75em 1em;position:relative}html.theme--documenter-dark .message-header .delete{flex-grow:0;flex-shrink:0;margin-left:.75em}html.theme--documenter-dark .message-header+.message-body{border-width:0;border-top-left-radius:0;border-top-right-radius:0}html.theme--documenter-dark .message-body{border-color:#5e6d6f;border-radius:.4em;border-style:solid;border-width:0 0 0 4px;color:#fff;padding:1.25em 1.5em}html.theme--documenter-dark .message-body code,html.theme--documenter-dark .message-body pre{background-color:#fff}html.theme--documenter-dark .message-body pre code{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .modal{align-items:center;display:none;flex-direction:column;justify-content:center;overflow:hidden;position:fixed;z-index:40}html.theme--documenter-dark .modal.is-active{display:flex}html.theme--documenter-dark .modal-background{background-color:rgba(10,10,10,0.86)}html.theme--documenter-dark .modal-content,html.theme--documenter-dark .modal-card{margin:0 20px;max-height:calc(100vh - 160px);overflow:auto;position:relative;width:100%}@media screen and (min-width: 769px){html.theme--documenter-dark .modal-content,html.theme--documenter-dark .modal-card{margin:0 auto;max-height:calc(100vh - 40px);width:640px}}html.theme--documenter-dark .modal-close{background:none;height:40px;position:fixed;right:20px;top:20px;width:40px}html.theme--documenter-dark .modal-card{display:flex;flex-direction:column;max-height:calc(100vh - 40px);overflow:hidden;-ms-overflow-y:visible}html.theme--documenter-dark .modal-card-head,html.theme--documenter-dark .modal-card-foot{align-items:center;background-color:#282f2f;display:flex;flex-shrink:0;justify-content:flex-start;padding:20px;position:relative}html.theme--documenter-dark .modal-card-head{border-bottom:1px solid #5e6d6f;border-top-left-radius:8px;border-top-right-radius:8px}html.theme--documenter-dark .modal-card-title{color:#f2f2f2;flex-grow:1;flex-shrink:0;font-size:1.5rem;line-height:1}html.theme--documenter-dark .modal-card-foot{border-bottom-left-radius:8px;border-bottom-right-radius:8px;border-top:1px solid #5e6d6f}html.theme--documenter-dark .modal-card-foot .button:not(:last-child){margin-right:.5em}html.theme--documenter-dark .modal-card-body{-webkit-overflow-scrolling:touch;background-color:#fff;flex-grow:1;flex-shrink:1;overflow:auto;padding:20px}html.theme--documenter-dark .navbar{background-color:#375a7f;min-height:4rem;position:relative;z-index:30}html.theme--documenter-dark .navbar.is-white{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link{color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-white .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-white .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-brand .navbar-link::after{border-color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-burger{color:#0a0a0a}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-white .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-white .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link{color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-white .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-white .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-white .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-white .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-white .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-white .navbar-end .navbar-link::after{border-color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-white .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-white .navbar-item.has-dropdown.is-active .navbar-link{background-color:#f2f2f2;color:#0a0a0a}html.theme--documenter-dark .navbar.is-white .navbar-dropdown a.navbar-item.is-active{background-color:#fff;color:#0a0a0a}}html.theme--documenter-dark .navbar.is-black{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-black .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-black .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link.is-active{background-color:#000;color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-black .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-black .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-black .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-black .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-black .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-black .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-black .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link.is-active{background-color:#000;color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-black .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-black .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-black .navbar-item.has-dropdown.is-active .navbar-link{background-color:#000;color:#fff}html.theme--documenter-dark .navbar.is-black .navbar-dropdown a.navbar-item.is-active{background-color:#0a0a0a;color:#fff}}html.theme--documenter-dark .navbar.is-light{background-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .navbar.is-light .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link{color:rgba(0,0,0,0.7)}html.theme--documenter-dark .navbar.is-light .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-light .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-light .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link.is-active{background-color:#dde4e6;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .navbar.is-light .navbar-brand .navbar-link::after{border-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .navbar.is-light .navbar-burger{color:rgba(0,0,0,0.7)}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-light .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-light .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link{color:rgba(0,0,0,0.7)}html.theme--documenter-dark .navbar.is-light .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-light .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-light .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-light .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-light .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-light .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link.is-active{background-color:#dde4e6;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .navbar.is-light .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-light .navbar-end .navbar-link::after{border-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .navbar.is-light .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-light .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-light .navbar-item.has-dropdown.is-active .navbar-link{background-color:#dde4e6;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .navbar.is-light .navbar-dropdown a.navbar-item.is-active{background-color:#ecf0f1;color:rgba(0,0,0,0.7)}}html.theme--documenter-dark .navbar.is-dark,html.theme--documenter-dark .content kbd.navbar{background-color:#282f2f;color:#fff}html.theme--documenter-dark .navbar.is-dark .navbar-brand>.navbar-item,html.theme--documenter-dark .content kbd.navbar .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-dark .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .content kbd.navbar .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-dark .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .content kbd.navbar .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-dark .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link:focus,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link:hover,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link.is-active{background-color:#1d2122;color:#fff}html.theme--documenter-dark .navbar.is-dark .navbar-brand .navbar-link::after,html.theme--documenter-dark .content kbd.navbar .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-dark .navbar-burger,html.theme--documenter-dark .content kbd.navbar .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-dark .navbar-start>.navbar-item,html.theme--documenter-dark .content kbd.navbar .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-dark .navbar-end>.navbar-item,html.theme--documenter-dark .content kbd.navbar .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-dark .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .content kbd.navbar .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-dark .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .content kbd.navbar .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-dark .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link:focus,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link:hover,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-dark .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .content kbd.navbar .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-dark .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .content kbd.navbar .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-dark .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link:focus,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link:hover,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link.is-active{background-color:#1d2122;color:#fff}html.theme--documenter-dark .navbar.is-dark .navbar-start .navbar-link::after,html.theme--documenter-dark .content kbd.navbar .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-dark .navbar-end .navbar-link::after,html.theme--documenter-dark .content kbd.navbar .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-dark .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-dark .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-dark .navbar-item.has-dropdown.is-active .navbar-link,html.theme--documenter-dark .content kbd.navbar .navbar-item.has-dropdown.is-active .navbar-link{background-color:#1d2122;color:#fff}html.theme--documenter-dark .navbar.is-dark .navbar-dropdown a.navbar-item.is-active,html.theme--documenter-dark .content kbd.navbar .navbar-dropdown a.navbar-item.is-active{background-color:#282f2f;color:#fff}}html.theme--documenter-dark .navbar.is-primary,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink{background-color:#375a7f;color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-brand>.navbar-item,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-primary .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-primary .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link.is-active{background-color:#2f4d6d;color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-brand .navbar-link::after,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-burger,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-primary .navbar-start>.navbar-item,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-primary .navbar-end>.navbar-item,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-primary .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-primary .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-primary .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-primary .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-primary .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link:focus,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link:hover,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link.is-active{background-color:#2f4d6d;color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-start .navbar-link::after,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-primary .navbar-end .navbar-link::after,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-primary .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-primary .navbar-item.has-dropdown.is-active .navbar-link,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown.is-active .navbar-link{background-color:#2f4d6d;color:#fff}html.theme--documenter-dark .navbar.is-primary .navbar-dropdown a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-dropdown a.navbar-item.is-active{background-color:#375a7f;color:#fff}}html.theme--documenter-dark .navbar.is-link{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-link .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-link .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link.is-active{background-color:#17a689;color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-link .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-link .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-link .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-link .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-link .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-link .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-link .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link.is-active{background-color:#17a689;color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-link .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-link .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-link .navbar-item.has-dropdown.is-active .navbar-link{background-color:#17a689;color:#fff}html.theme--documenter-dark .navbar.is-link .navbar-dropdown a.navbar-item.is-active{background-color:#1abc9c;color:#fff}}html.theme--documenter-dark .navbar.is-info{background-color:#024c7d;color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-info .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-info .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link.is-active{background-color:#023d64;color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-info .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-info .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-info .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-info .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-info .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-info .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-info .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link.is-active{background-color:#023d64;color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-info .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-info .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-info .navbar-item.has-dropdown.is-active .navbar-link{background-color:#023d64;color:#fff}html.theme--documenter-dark .navbar.is-info .navbar-dropdown a.navbar-item.is-active{background-color:#024c7d;color:#fff}}html.theme--documenter-dark .navbar.is-success{background-color:#008438;color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-success .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-success .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link.is-active{background-color:#006b2d;color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-success .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-success .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-success .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-success .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-success .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-success .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-success .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link.is-active{background-color:#006b2d;color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-success .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-success .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-success .navbar-item.has-dropdown.is-active .navbar-link{background-color:#006b2d;color:#fff}html.theme--documenter-dark .navbar.is-success .navbar-dropdown a.navbar-item.is-active{background-color:#008438;color:#fff}}html.theme--documenter-dark .navbar.is-warning{background-color:#ad8100;color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-warning .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-warning .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link.is-active{background-color:#946e00;color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-warning .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-warning .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-warning .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-warning .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-warning .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-warning .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-warning .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link.is-active{background-color:#946e00;color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-warning .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-warning .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-warning .navbar-item.has-dropdown.is-active .navbar-link{background-color:#946e00;color:#fff}html.theme--documenter-dark .navbar.is-warning .navbar-dropdown a.navbar-item.is-active{background-color:#ad8100;color:#fff}}html.theme--documenter-dark .navbar.is-danger{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-brand>.navbar-item,html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-brand>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-danger .navbar-brand>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-danger .navbar-brand>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link:focus,html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link:hover,html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link.is-active{background-color:#86170b;color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-brand .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-burger{color:#fff}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar.is-danger .navbar-start>.navbar-item,html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link,html.theme--documenter-dark .navbar.is-danger .navbar-end>.navbar-item,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link{color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-start>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-danger .navbar-start>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-danger .navbar-start>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link:focus,html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link:hover,html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link.is-active,html.theme--documenter-dark .navbar.is-danger .navbar-end>a.navbar-item:focus,html.theme--documenter-dark .navbar.is-danger .navbar-end>a.navbar-item:hover,html.theme--documenter-dark .navbar.is-danger .navbar-end>a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link:focus,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link:hover,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link.is-active{background-color:#86170b;color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-start .navbar-link::after,html.theme--documenter-dark .navbar.is-danger .navbar-end .navbar-link::after{border-color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar.is-danger .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar.is-danger .navbar-item.has-dropdown.is-active .navbar-link{background-color:#86170b;color:#fff}html.theme--documenter-dark .navbar.is-danger .navbar-dropdown a.navbar-item.is-active{background-color:#9e1b0d;color:#fff}}html.theme--documenter-dark .navbar>.container{align-items:stretch;display:flex;min-height:4rem;width:100%}html.theme--documenter-dark .navbar.has-shadow{box-shadow:0 2px 0 0 #282f2f}html.theme--documenter-dark .navbar.is-fixed-bottom,html.theme--documenter-dark .navbar.is-fixed-top{left:0;position:fixed;right:0;z-index:30}html.theme--documenter-dark .navbar.is-fixed-bottom{bottom:0}html.theme--documenter-dark .navbar.is-fixed-bottom.has-shadow{box-shadow:0 -2px 0 0 #282f2f}html.theme--documenter-dark .navbar.is-fixed-top{top:0}html.theme--documenter-dark html.has-navbar-fixed-top,html.theme--documenter-dark body.has-navbar-fixed-top{padding-top:4rem}html.theme--documenter-dark html.has-navbar-fixed-bottom,html.theme--documenter-dark body.has-navbar-fixed-bottom{padding-bottom:4rem}html.theme--documenter-dark .navbar-brand,html.theme--documenter-dark .navbar-tabs{align-items:stretch;display:flex;flex-shrink:0;min-height:4rem}html.theme--documenter-dark .navbar-brand a.navbar-item:focus,html.theme--documenter-dark .navbar-brand a.navbar-item:hover{background-color:transparent}html.theme--documenter-dark .navbar-tabs{-webkit-overflow-scrolling:touch;max-width:100vw;overflow-x:auto;overflow-y:hidden}html.theme--documenter-dark .navbar-burger{color:#fff;-moz-appearance:none;-webkit-appearance:none;appearance:none;background:none;border:none;cursor:pointer;display:block;height:4rem;position:relative;width:4rem;margin-left:auto}html.theme--documenter-dark .navbar-burger span{background-color:currentColor;display:block;height:1px;left:calc(50% - 8px);position:absolute;transform-origin:center;transition-duration:86ms;transition-property:background-color, opacity, transform;transition-timing-function:ease-out;width:16px}html.theme--documenter-dark .navbar-burger span:nth-child(1){top:calc(50% - 6px)}html.theme--documenter-dark .navbar-burger span:nth-child(2){top:calc(50% - 1px)}html.theme--documenter-dark .navbar-burger span:nth-child(3){top:calc(50% + 4px)}html.theme--documenter-dark .navbar-burger:hover{background-color:rgba(0,0,0,0.05)}html.theme--documenter-dark .navbar-burger.is-active span:nth-child(1){transform:translateY(5px) rotate(45deg)}html.theme--documenter-dark .navbar-burger.is-active span:nth-child(2){opacity:0}html.theme--documenter-dark .navbar-burger.is-active span:nth-child(3){transform:translateY(-5px) rotate(-45deg)}html.theme--documenter-dark .navbar-menu{display:none}html.theme--documenter-dark .navbar-item,html.theme--documenter-dark .navbar-link{color:#fff;display:block;line-height:1.5;padding:0.5rem 0.75rem;position:relative}html.theme--documenter-dark .navbar-item .icon:only-child,html.theme--documenter-dark .navbar-link .icon:only-child{margin-left:-0.25rem;margin-right:-0.25rem}html.theme--documenter-dark a.navbar-item,html.theme--documenter-dark .navbar-link{cursor:pointer}html.theme--documenter-dark a.navbar-item:focus,html.theme--documenter-dark a.navbar-item:focus-within,html.theme--documenter-dark a.navbar-item:hover,html.theme--documenter-dark a.navbar-item.is-active,html.theme--documenter-dark .navbar-link:focus,html.theme--documenter-dark .navbar-link:focus-within,html.theme--documenter-dark .navbar-link:hover,html.theme--documenter-dark .navbar-link.is-active{background-color:rgba(0,0,0,0);color:#1abc9c}html.theme--documenter-dark .navbar-item{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .navbar-item img{max-height:1.75rem}html.theme--documenter-dark .navbar-item.has-dropdown{padding:0}html.theme--documenter-dark .navbar-item.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .navbar-item.is-tab{border-bottom:1px solid transparent;min-height:4rem;padding-bottom:calc(0.5rem - 1px)}html.theme--documenter-dark .navbar-item.is-tab:focus,html.theme--documenter-dark .navbar-item.is-tab:hover{background-color:rgba(0,0,0,0);border-bottom-color:#1abc9c}html.theme--documenter-dark .navbar-item.is-tab.is-active{background-color:rgba(0,0,0,0);border-bottom-color:#1abc9c;border-bottom-style:solid;border-bottom-width:3px;color:#1abc9c;padding-bottom:calc(0.5rem - 3px)}html.theme--documenter-dark .navbar-content{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .navbar-link:not(.is-arrowless){padding-right:2.5em}html.theme--documenter-dark .navbar-link:not(.is-arrowless)::after{border-color:#fff;margin-top:-0.375em;right:1.125em}html.theme--documenter-dark .navbar-dropdown{font-size:0.875rem;padding-bottom:0.5rem;padding-top:0.5rem}html.theme--documenter-dark .navbar-dropdown .navbar-item{padding-left:1.5rem;padding-right:1.5rem}html.theme--documenter-dark .navbar-divider{background-color:rgba(0,0,0,0.2);border:none;display:none;height:2px;margin:0.5rem 0}@media screen and (max-width: 1055px){html.theme--documenter-dark .navbar>.container{display:block}html.theme--documenter-dark .navbar-brand .navbar-item,html.theme--documenter-dark .navbar-tabs .navbar-item{align-items:center;display:flex}html.theme--documenter-dark .navbar-link::after{display:none}html.theme--documenter-dark .navbar-menu{background-color:#375a7f;box-shadow:0 8px 16px rgba(10,10,10,0.1);padding:0.5rem 0}html.theme--documenter-dark .navbar-menu.is-active{display:block}html.theme--documenter-dark .navbar.is-fixed-bottom-touch,html.theme--documenter-dark .navbar.is-fixed-top-touch{left:0;position:fixed;right:0;z-index:30}html.theme--documenter-dark .navbar.is-fixed-bottom-touch{bottom:0}html.theme--documenter-dark .navbar.is-fixed-bottom-touch.has-shadow{box-shadow:0 -2px 3px rgba(10,10,10,0.1)}html.theme--documenter-dark .navbar.is-fixed-top-touch{top:0}html.theme--documenter-dark .navbar.is-fixed-top .navbar-menu,html.theme--documenter-dark .navbar.is-fixed-top-touch .navbar-menu{-webkit-overflow-scrolling:touch;max-height:calc(100vh - 4rem);overflow:auto}html.theme--documenter-dark html.has-navbar-fixed-top-touch,html.theme--documenter-dark body.has-navbar-fixed-top-touch{padding-top:4rem}html.theme--documenter-dark html.has-navbar-fixed-bottom-touch,html.theme--documenter-dark body.has-navbar-fixed-bottom-touch{padding-bottom:4rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .navbar,html.theme--documenter-dark .navbar-menu,html.theme--documenter-dark .navbar-start,html.theme--documenter-dark .navbar-end{align-items:stretch;display:flex}html.theme--documenter-dark .navbar{min-height:4rem}html.theme--documenter-dark .navbar.is-spaced{padding:1rem 2rem}html.theme--documenter-dark .navbar.is-spaced .navbar-start,html.theme--documenter-dark .navbar.is-spaced .navbar-end{align-items:center}html.theme--documenter-dark .navbar.is-spaced a.navbar-item,html.theme--documenter-dark .navbar.is-spaced .navbar-link{border-radius:.4em}html.theme--documenter-dark .navbar.is-transparent a.navbar-item:focus,html.theme--documenter-dark .navbar.is-transparent a.navbar-item:hover,html.theme--documenter-dark .navbar.is-transparent a.navbar-item.is-active,html.theme--documenter-dark .navbar.is-transparent .navbar-link:focus,html.theme--documenter-dark .navbar.is-transparent .navbar-link:hover,html.theme--documenter-dark .navbar.is-transparent .navbar-link.is-active{background-color:transparent !important}html.theme--documenter-dark .navbar.is-transparent .navbar-item.has-dropdown.is-active .navbar-link,html.theme--documenter-dark .navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:focus .navbar-link,html.theme--documenter-dark .navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:focus-within .navbar-link,html.theme--documenter-dark .navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:hover .navbar-link{background-color:transparent !important}html.theme--documenter-dark .navbar.is-transparent .navbar-dropdown a.navbar-item:focus,html.theme--documenter-dark .navbar.is-transparent .navbar-dropdown a.navbar-item:hover{background-color:rgba(0,0,0,0);color:#dbdee0}html.theme--documenter-dark .navbar.is-transparent .navbar-dropdown a.navbar-item.is-active{background-color:rgba(0,0,0,0);color:#1abc9c}html.theme--documenter-dark .navbar-burger{display:none}html.theme--documenter-dark .navbar-item,html.theme--documenter-dark .navbar-link{align-items:center;display:flex}html.theme--documenter-dark .navbar-item.has-dropdown{align-items:stretch}html.theme--documenter-dark .navbar-item.has-dropdown-up .navbar-link::after{transform:rotate(135deg) translate(0.25em, -0.25em)}html.theme--documenter-dark .navbar-item.has-dropdown-up .navbar-dropdown{border-bottom:1px solid rgba(0,0,0,0.2);border-radius:8px 8px 0 0;border-top:none;bottom:100%;box-shadow:0 -8px 8px rgba(10,10,10,0.1);top:auto}html.theme--documenter-dark .navbar-item.is-active .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:focus .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:focus-within .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:hover .navbar-dropdown{display:block}.navbar.is-spaced html.theme--documenter-dark .navbar-item.is-active .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-active .navbar-dropdown.is-boxed,.navbar.is-spaced html.theme--documenter-dark .navbar-item.is-hoverable:focus .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:focus .navbar-dropdown.is-boxed,.navbar.is-spaced html.theme--documenter-dark .navbar-item.is-hoverable:focus-within .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:focus-within .navbar-dropdown.is-boxed,.navbar.is-spaced html.theme--documenter-dark .navbar-item.is-hoverable:hover .navbar-dropdown,html.theme--documenter-dark .navbar-item.is-hoverable:hover .navbar-dropdown.is-boxed{opacity:1;pointer-events:auto;transform:translateY(0)}html.theme--documenter-dark .navbar-menu{flex-grow:1;flex-shrink:0}html.theme--documenter-dark .navbar-start{justify-content:flex-start;margin-right:auto}html.theme--documenter-dark .navbar-end{justify-content:flex-end;margin-left:auto}html.theme--documenter-dark .navbar-dropdown{background-color:#375a7f;border-bottom-left-radius:8px;border-bottom-right-radius:8px;border-top:1px solid rgba(0,0,0,0.2);box-shadow:0 8px 8px rgba(10,10,10,0.1);display:none;font-size:0.875rem;left:0;min-width:100%;position:absolute;top:100%;z-index:20}html.theme--documenter-dark .navbar-dropdown .navbar-item{padding:0.375rem 1rem;white-space:nowrap}html.theme--documenter-dark .navbar-dropdown a.navbar-item{padding-right:3rem}html.theme--documenter-dark .navbar-dropdown a.navbar-item:focus,html.theme--documenter-dark .navbar-dropdown a.navbar-item:hover{background-color:rgba(0,0,0,0);color:#dbdee0}html.theme--documenter-dark .navbar-dropdown a.navbar-item.is-active{background-color:rgba(0,0,0,0);color:#1abc9c}.navbar.is-spaced html.theme--documenter-dark .navbar-dropdown,html.theme--documenter-dark .navbar-dropdown.is-boxed{border-radius:8px;border-top:none;box-shadow:0 8px 8px rgba(10,10,10,0.1), 0 0 0 1px rgba(10,10,10,0.1);display:block;opacity:0;pointer-events:none;top:calc(100% + (-4px));transform:translateY(-5px);transition-duration:86ms;transition-property:opacity, transform}html.theme--documenter-dark .navbar-dropdown.is-right{left:auto;right:0}html.theme--documenter-dark .navbar-divider{display:block}html.theme--documenter-dark .navbar>.container .navbar-brand,html.theme--documenter-dark .container>.navbar .navbar-brand{margin-left:-.75rem}html.theme--documenter-dark .navbar>.container .navbar-menu,html.theme--documenter-dark .container>.navbar .navbar-menu{margin-right:-.75rem}html.theme--documenter-dark .navbar.is-fixed-bottom-desktop,html.theme--documenter-dark .navbar.is-fixed-top-desktop{left:0;position:fixed;right:0;z-index:30}html.theme--documenter-dark .navbar.is-fixed-bottom-desktop{bottom:0}html.theme--documenter-dark .navbar.is-fixed-bottom-desktop.has-shadow{box-shadow:0 -2px 3px rgba(10,10,10,0.1)}html.theme--documenter-dark .navbar.is-fixed-top-desktop{top:0}html.theme--documenter-dark html.has-navbar-fixed-top-desktop,html.theme--documenter-dark body.has-navbar-fixed-top-desktop{padding-top:4rem}html.theme--documenter-dark html.has-navbar-fixed-bottom-desktop,html.theme--documenter-dark body.has-navbar-fixed-bottom-desktop{padding-bottom:4rem}html.theme--documenter-dark html.has-spaced-navbar-fixed-top,html.theme--documenter-dark body.has-spaced-navbar-fixed-top{padding-top:6rem}html.theme--documenter-dark html.has-spaced-navbar-fixed-bottom,html.theme--documenter-dark body.has-spaced-navbar-fixed-bottom{padding-bottom:6rem}html.theme--documenter-dark a.navbar-item.is-active,html.theme--documenter-dark .navbar-link.is-active{color:#1abc9c}html.theme--documenter-dark a.navbar-item.is-active:not(:focus):not(:hover),html.theme--documenter-dark .navbar-link.is-active:not(:focus):not(:hover){background-color:rgba(0,0,0,0)}html.theme--documenter-dark .navbar-item.has-dropdown:focus .navbar-link,html.theme--documenter-dark .navbar-item.has-dropdown:hover .navbar-link,html.theme--documenter-dark .navbar-item.has-dropdown.is-active .navbar-link{background-color:rgba(0,0,0,0)}}html.theme--documenter-dark .hero.is-fullheight-with-navbar{min-height:calc(100vh - 4rem)}html.theme--documenter-dark .pagination{font-size:1rem;margin:-.25rem}html.theme--documenter-dark .pagination.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination{font-size:.75rem}html.theme--documenter-dark .pagination.is-medium{font-size:1.25rem}html.theme--documenter-dark .pagination.is-large{font-size:1.5rem}html.theme--documenter-dark .pagination.is-rounded .pagination-previous,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-previous,html.theme--documenter-dark .pagination.is-rounded .pagination-next,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-next{padding-left:1em;padding-right:1em;border-radius:9999px}html.theme--documenter-dark .pagination.is-rounded .pagination-link,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-link{border-radius:9999px}html.theme--documenter-dark .pagination,html.theme--documenter-dark .pagination-list{align-items:center;display:flex;justify-content:center;text-align:center}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis{font-size:1em;justify-content:center;margin:.25rem;padding-left:.5em;padding-right:.5em;text-align:center}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link{border-color:#5e6d6f;color:#1abc9c;min-width:2.5em}html.theme--documenter-dark .pagination-previous:hover,html.theme--documenter-dark .pagination-next:hover,html.theme--documenter-dark .pagination-link:hover{border-color:#8c9b9d;color:#1dd2af}html.theme--documenter-dark .pagination-previous:focus,html.theme--documenter-dark .pagination-next:focus,html.theme--documenter-dark .pagination-link:focus{border-color:#8c9b9d}html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2)}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark .pagination-previous.is-disabled,html.theme--documenter-dark .pagination-next[disabled],html.theme--documenter-dark .pagination-next.is-disabled,html.theme--documenter-dark .pagination-link[disabled],html.theme--documenter-dark .pagination-link.is-disabled{background-color:#5e6d6f;border-color:#5e6d6f;box-shadow:none;color:#fff;opacity:0.5}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next{padding-left:.75em;padding-right:.75em;white-space:nowrap}html.theme--documenter-dark .pagination-link.is-current{background-color:#1abc9c;border-color:#1abc9c;color:#fff}html.theme--documenter-dark .pagination-ellipsis{color:#8c9b9d;pointer-events:none}html.theme--documenter-dark .pagination-list{flex-wrap:wrap}html.theme--documenter-dark .pagination-list li{list-style:none}@media screen and (max-width: 768px){html.theme--documenter-dark .pagination{flex-wrap:wrap}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .pagination-list li{flex-grow:1;flex-shrink:1}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .pagination-list{flex-grow:1;flex-shrink:1;justify-content:flex-start;order:1}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis{margin-bottom:0;margin-top:0}html.theme--documenter-dark .pagination-previous{order:2}html.theme--documenter-dark .pagination-next{order:3}html.theme--documenter-dark .pagination{justify-content:space-between;margin-bottom:0;margin-top:0}html.theme--documenter-dark .pagination.is-centered .pagination-previous{order:1}html.theme--documenter-dark .pagination.is-centered .pagination-list{justify-content:center;order:2}html.theme--documenter-dark .pagination.is-centered .pagination-next{order:3}html.theme--documenter-dark .pagination.is-right .pagination-previous{order:1}html.theme--documenter-dark .pagination.is-right .pagination-next{order:2}html.theme--documenter-dark .pagination.is-right .pagination-list{justify-content:flex-end;order:3}}html.theme--documenter-dark .panel{border-radius:8px;box-shadow:#171717;font-size:1rem}html.theme--documenter-dark .panel:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .panel.is-white .panel-heading{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .panel.is-white .panel-tabs a.is-active{border-bottom-color:#fff}html.theme--documenter-dark .panel.is-white .panel-block.is-active .panel-icon{color:#fff}html.theme--documenter-dark .panel.is-black .panel-heading{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .panel.is-black .panel-tabs a.is-active{border-bottom-color:#0a0a0a}html.theme--documenter-dark .panel.is-black .panel-block.is-active .panel-icon{color:#0a0a0a}html.theme--documenter-dark .panel.is-light .panel-heading{background-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .panel.is-light .panel-tabs a.is-active{border-bottom-color:#ecf0f1}html.theme--documenter-dark .panel.is-light .panel-block.is-active .panel-icon{color:#ecf0f1}html.theme--documenter-dark .panel.is-dark .panel-heading,html.theme--documenter-dark .content kbd.panel .panel-heading{background-color:#282f2f;color:#fff}html.theme--documenter-dark .panel.is-dark .panel-tabs a.is-active,html.theme--documenter-dark .content kbd.panel .panel-tabs a.is-active{border-bottom-color:#282f2f}html.theme--documenter-dark .panel.is-dark .panel-block.is-active .panel-icon,html.theme--documenter-dark .content kbd.panel .panel-block.is-active .panel-icon{color:#282f2f}html.theme--documenter-dark .panel.is-primary .panel-heading,html.theme--documenter-dark .docstring>section>a.panel.docs-sourcelink .panel-heading{background-color:#375a7f;color:#fff}html.theme--documenter-dark .panel.is-primary .panel-tabs a.is-active,html.theme--documenter-dark .docstring>section>a.panel.docs-sourcelink .panel-tabs a.is-active{border-bottom-color:#375a7f}html.theme--documenter-dark .panel.is-primary .panel-block.is-active .panel-icon,html.theme--documenter-dark .docstring>section>a.panel.docs-sourcelink .panel-block.is-active .panel-icon{color:#375a7f}html.theme--documenter-dark .panel.is-link .panel-heading{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .panel.is-link .panel-tabs a.is-active{border-bottom-color:#1abc9c}html.theme--documenter-dark .panel.is-link .panel-block.is-active .panel-icon{color:#1abc9c}html.theme--documenter-dark .panel.is-info .panel-heading{background-color:#024c7d;color:#fff}html.theme--documenter-dark .panel.is-info .panel-tabs a.is-active{border-bottom-color:#024c7d}html.theme--documenter-dark .panel.is-info .panel-block.is-active .panel-icon{color:#024c7d}html.theme--documenter-dark .panel.is-success .panel-heading{background-color:#008438;color:#fff}html.theme--documenter-dark .panel.is-success .panel-tabs a.is-active{border-bottom-color:#008438}html.theme--documenter-dark .panel.is-success .panel-block.is-active .panel-icon{color:#008438}html.theme--documenter-dark .panel.is-warning .panel-heading{background-color:#ad8100;color:#fff}html.theme--documenter-dark .panel.is-warning .panel-tabs a.is-active{border-bottom-color:#ad8100}html.theme--documenter-dark .panel.is-warning .panel-block.is-active .panel-icon{color:#ad8100}html.theme--documenter-dark .panel.is-danger .panel-heading{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .panel.is-danger .panel-tabs a.is-active{border-bottom-color:#9e1b0d}html.theme--documenter-dark .panel.is-danger .panel-block.is-active .panel-icon{color:#9e1b0d}html.theme--documenter-dark .panel-tabs:not(:last-child),html.theme--documenter-dark .panel-block:not(:last-child){border-bottom:1px solid #ededed}html.theme--documenter-dark .panel-heading{background-color:#343c3d;border-radius:8px 8px 0 0;color:#f2f2f2;font-size:1.25em;font-weight:700;line-height:1.25;padding:0.75em 1em}html.theme--documenter-dark .panel-tabs{align-items:flex-end;display:flex;font-size:.875em;justify-content:center}html.theme--documenter-dark .panel-tabs a{border-bottom:1px solid #5e6d6f;margin-bottom:-1px;padding:0.5em}html.theme--documenter-dark .panel-tabs a.is-active{border-bottom-color:#343c3d;color:#17a689}html.theme--documenter-dark .panel-list a{color:#fff}html.theme--documenter-dark .panel-list a:hover{color:#1abc9c}html.theme--documenter-dark .panel-block{align-items:center;color:#f2f2f2;display:flex;justify-content:flex-start;padding:0.5em 0.75em}html.theme--documenter-dark .panel-block input[type="checkbox"]{margin-right:.75em}html.theme--documenter-dark .panel-block>.control{flex-grow:1;flex-shrink:1;width:100%}html.theme--documenter-dark .panel-block.is-wrapped{flex-wrap:wrap}html.theme--documenter-dark .panel-block.is-active{border-left-color:#1abc9c;color:#17a689}html.theme--documenter-dark .panel-block.is-active .panel-icon{color:#1abc9c}html.theme--documenter-dark .panel-block:last-child{border-bottom-left-radius:8px;border-bottom-right-radius:8px}html.theme--documenter-dark a.panel-block,html.theme--documenter-dark label.panel-block{cursor:pointer}html.theme--documenter-dark a.panel-block:hover,html.theme--documenter-dark label.panel-block:hover{background-color:#282f2f}html.theme--documenter-dark .panel-icon{display:inline-block;font-size:14px;height:1em;line-height:1em;text-align:center;vertical-align:top;width:1em;color:#fff;margin-right:.75em}html.theme--documenter-dark .panel-icon .fa{font-size:inherit;line-height:inherit}html.theme--documenter-dark .tabs{-webkit-overflow-scrolling:touch;align-items:stretch;display:flex;font-size:1rem;justify-content:space-between;overflow:hidden;overflow-x:auto;white-space:nowrap}html.theme--documenter-dark .tabs a{align-items:center;border-bottom-color:#5e6d6f;border-bottom-style:solid;border-bottom-width:1px;color:#fff;display:flex;justify-content:center;margin-bottom:-1px;padding:0.5em 1em;vertical-align:top}html.theme--documenter-dark .tabs a:hover{border-bottom-color:#f2f2f2;color:#f2f2f2}html.theme--documenter-dark .tabs li{display:block}html.theme--documenter-dark .tabs li.is-active a{border-bottom-color:#1abc9c;color:#1abc9c}html.theme--documenter-dark .tabs ul{align-items:center;border-bottom-color:#5e6d6f;border-bottom-style:solid;border-bottom-width:1px;display:flex;flex-grow:1;flex-shrink:0;justify-content:flex-start}html.theme--documenter-dark .tabs ul.is-left{padding-right:0.75em}html.theme--documenter-dark .tabs ul.is-center{flex:none;justify-content:center;padding-left:0.75em;padding-right:0.75em}html.theme--documenter-dark .tabs ul.is-right{justify-content:flex-end;padding-left:0.75em}html.theme--documenter-dark .tabs .icon:first-child{margin-right:.5em}html.theme--documenter-dark .tabs .icon:last-child{margin-left:.5em}html.theme--documenter-dark .tabs.is-centered ul{justify-content:center}html.theme--documenter-dark .tabs.is-right ul{justify-content:flex-end}html.theme--documenter-dark .tabs.is-boxed a{border:1px solid transparent;border-radius:.4em .4em 0 0}html.theme--documenter-dark .tabs.is-boxed a:hover{background-color:#282f2f;border-bottom-color:#5e6d6f}html.theme--documenter-dark .tabs.is-boxed li.is-active a{background-color:#fff;border-color:#5e6d6f;border-bottom-color:rgba(0,0,0,0) !important}html.theme--documenter-dark .tabs.is-fullwidth li{flex-grow:1;flex-shrink:0}html.theme--documenter-dark .tabs.is-toggle a{border-color:#5e6d6f;border-style:solid;border-width:1px;margin-bottom:0;position:relative}html.theme--documenter-dark .tabs.is-toggle a:hover{background-color:#282f2f;border-color:#8c9b9d;z-index:2}html.theme--documenter-dark .tabs.is-toggle li+li{margin-left:-1px}html.theme--documenter-dark .tabs.is-toggle li:first-child a{border-top-left-radius:.4em;border-bottom-left-radius:.4em}html.theme--documenter-dark .tabs.is-toggle li:last-child a{border-top-right-radius:.4em;border-bottom-right-radius:.4em}html.theme--documenter-dark .tabs.is-toggle li.is-active a{background-color:#1abc9c;border-color:#1abc9c;color:#fff;z-index:1}html.theme--documenter-dark .tabs.is-toggle ul{border-bottom:none}html.theme--documenter-dark .tabs.is-toggle.is-toggle-rounded li:first-child a{border-bottom-left-radius:9999px;border-top-left-radius:9999px;padding-left:1.25em}html.theme--documenter-dark .tabs.is-toggle.is-toggle-rounded li:last-child a{border-bottom-right-radius:9999px;border-top-right-radius:9999px;padding-right:1.25em}html.theme--documenter-dark .tabs.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.tabs{font-size:.75rem}html.theme--documenter-dark .tabs.is-medium{font-size:1.25rem}html.theme--documenter-dark .tabs.is-large{font-size:1.5rem}html.theme--documenter-dark .column{display:block;flex-basis:0;flex-grow:1;flex-shrink:1;padding:.75rem}.columns.is-mobile>html.theme--documenter-dark .column.is-narrow{flex:none;width:unset}.columns.is-mobile>html.theme--documenter-dark .column.is-full{flex:none;width:100%}.columns.is-mobile>html.theme--documenter-dark .column.is-three-quarters{flex:none;width:75%}.columns.is-mobile>html.theme--documenter-dark .column.is-two-thirds{flex:none;width:66.6666%}.columns.is-mobile>html.theme--documenter-dark .column.is-half{flex:none;width:50%}.columns.is-mobile>html.theme--documenter-dark .column.is-one-third{flex:none;width:33.3333%}.columns.is-mobile>html.theme--documenter-dark .column.is-one-quarter{flex:none;width:25%}.columns.is-mobile>html.theme--documenter-dark .column.is-one-fifth{flex:none;width:20%}.columns.is-mobile>html.theme--documenter-dark .column.is-two-fifths{flex:none;width:40%}.columns.is-mobile>html.theme--documenter-dark .column.is-three-fifths{flex:none;width:60%}.columns.is-mobile>html.theme--documenter-dark .column.is-four-fifths{flex:none;width:80%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-three-quarters{margin-left:75%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-two-thirds{margin-left:66.6666%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-half{margin-left:50%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-one-third{margin-left:33.3333%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-one-quarter{margin-left:25%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-one-fifth{margin-left:20%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-two-fifths{margin-left:40%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-three-fifths{margin-left:60%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-four-fifths{margin-left:80%}.columns.is-mobile>html.theme--documenter-dark .column.is-0{flex:none;width:0%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-0{margin-left:0%}.columns.is-mobile>html.theme--documenter-dark .column.is-1{flex:none;width:8.33333337%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-1{margin-left:8.33333337%}.columns.is-mobile>html.theme--documenter-dark .column.is-2{flex:none;width:16.66666674%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-2{margin-left:16.66666674%}.columns.is-mobile>html.theme--documenter-dark .column.is-3{flex:none;width:25%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-3{margin-left:25%}.columns.is-mobile>html.theme--documenter-dark .column.is-4{flex:none;width:33.33333337%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-4{margin-left:33.33333337%}.columns.is-mobile>html.theme--documenter-dark .column.is-5{flex:none;width:41.66666674%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-5{margin-left:41.66666674%}.columns.is-mobile>html.theme--documenter-dark .column.is-6{flex:none;width:50%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-6{margin-left:50%}.columns.is-mobile>html.theme--documenter-dark .column.is-7{flex:none;width:58.33333337%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-7{margin-left:58.33333337%}.columns.is-mobile>html.theme--documenter-dark .column.is-8{flex:none;width:66.66666674%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-8{margin-left:66.66666674%}.columns.is-mobile>html.theme--documenter-dark .column.is-9{flex:none;width:75%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-9{margin-left:75%}.columns.is-mobile>html.theme--documenter-dark .column.is-10{flex:none;width:83.33333337%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-10{margin-left:83.33333337%}.columns.is-mobile>html.theme--documenter-dark .column.is-11{flex:none;width:91.66666674%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-11{margin-left:91.66666674%}.columns.is-mobile>html.theme--documenter-dark .column.is-12{flex:none;width:100%}.columns.is-mobile>html.theme--documenter-dark .column.is-offset-12{margin-left:100%}@media screen and (max-width: 768px){html.theme--documenter-dark .column.is-narrow-mobile{flex:none;width:unset}html.theme--documenter-dark .column.is-full-mobile{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-mobile{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-mobile{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-mobile{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-mobile{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-mobile{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-mobile{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-mobile{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-mobile{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-mobile{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-mobile{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-mobile{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-mobile{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-mobile{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-mobile{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-mobile{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-mobile{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-mobile{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-mobile{margin-left:80%}html.theme--documenter-dark .column.is-0-mobile{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-mobile{margin-left:0%}html.theme--documenter-dark .column.is-1-mobile{flex:none;width:8.33333337%}html.theme--documenter-dark .column.is-offset-1-mobile{margin-left:8.33333337%}html.theme--documenter-dark .column.is-2-mobile{flex:none;width:16.66666674%}html.theme--documenter-dark .column.is-offset-2-mobile{margin-left:16.66666674%}html.theme--documenter-dark .column.is-3-mobile{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-mobile{margin-left:25%}html.theme--documenter-dark .column.is-4-mobile{flex:none;width:33.33333337%}html.theme--documenter-dark .column.is-offset-4-mobile{margin-left:33.33333337%}html.theme--documenter-dark .column.is-5-mobile{flex:none;width:41.66666674%}html.theme--documenter-dark .column.is-offset-5-mobile{margin-left:41.66666674%}html.theme--documenter-dark .column.is-6-mobile{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-mobile{margin-left:50%}html.theme--documenter-dark .column.is-7-mobile{flex:none;width:58.33333337%}html.theme--documenter-dark .column.is-offset-7-mobile{margin-left:58.33333337%}html.theme--documenter-dark .column.is-8-mobile{flex:none;width:66.66666674%}html.theme--documenter-dark .column.is-offset-8-mobile{margin-left:66.66666674%}html.theme--documenter-dark .column.is-9-mobile{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-mobile{margin-left:75%}html.theme--documenter-dark .column.is-10-mobile{flex:none;width:83.33333337%}html.theme--documenter-dark .column.is-offset-10-mobile{margin-left:83.33333337%}html.theme--documenter-dark .column.is-11-mobile{flex:none;width:91.66666674%}html.theme--documenter-dark .column.is-offset-11-mobile{margin-left:91.66666674%}html.theme--documenter-dark .column.is-12-mobile{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-mobile{margin-left:100%}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .column.is-narrow,html.theme--documenter-dark .column.is-narrow-tablet{flex:none;width:unset}html.theme--documenter-dark .column.is-full,html.theme--documenter-dark .column.is-full-tablet{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters,html.theme--documenter-dark .column.is-three-quarters-tablet{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds,html.theme--documenter-dark .column.is-two-thirds-tablet{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half,html.theme--documenter-dark .column.is-half-tablet{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third,html.theme--documenter-dark .column.is-one-third-tablet{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter,html.theme--documenter-dark .column.is-one-quarter-tablet{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth,html.theme--documenter-dark .column.is-one-fifth-tablet{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths,html.theme--documenter-dark .column.is-two-fifths-tablet{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths,html.theme--documenter-dark .column.is-three-fifths-tablet{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths,html.theme--documenter-dark .column.is-four-fifths-tablet{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters,html.theme--documenter-dark .column.is-offset-three-quarters-tablet{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds,html.theme--documenter-dark .column.is-offset-two-thirds-tablet{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half,html.theme--documenter-dark .column.is-offset-half-tablet{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third,html.theme--documenter-dark .column.is-offset-one-third-tablet{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter,html.theme--documenter-dark .column.is-offset-one-quarter-tablet{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth,html.theme--documenter-dark .column.is-offset-one-fifth-tablet{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths,html.theme--documenter-dark .column.is-offset-two-fifths-tablet{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths,html.theme--documenter-dark .column.is-offset-three-fifths-tablet{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths,html.theme--documenter-dark .column.is-offset-four-fifths-tablet{margin-left:80%}html.theme--documenter-dark .column.is-0,html.theme--documenter-dark .column.is-0-tablet{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0,html.theme--documenter-dark .column.is-offset-0-tablet{margin-left:0%}html.theme--documenter-dark .column.is-1,html.theme--documenter-dark .column.is-1-tablet{flex:none;width:8.33333337%}html.theme--documenter-dark .column.is-offset-1,html.theme--documenter-dark .column.is-offset-1-tablet{margin-left:8.33333337%}html.theme--documenter-dark .column.is-2,html.theme--documenter-dark .column.is-2-tablet{flex:none;width:16.66666674%}html.theme--documenter-dark .column.is-offset-2,html.theme--documenter-dark .column.is-offset-2-tablet{margin-left:16.66666674%}html.theme--documenter-dark .column.is-3,html.theme--documenter-dark .column.is-3-tablet{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3,html.theme--documenter-dark .column.is-offset-3-tablet{margin-left:25%}html.theme--documenter-dark .column.is-4,html.theme--documenter-dark .column.is-4-tablet{flex:none;width:33.33333337%}html.theme--documenter-dark .column.is-offset-4,html.theme--documenter-dark .column.is-offset-4-tablet{margin-left:33.33333337%}html.theme--documenter-dark .column.is-5,html.theme--documenter-dark .column.is-5-tablet{flex:none;width:41.66666674%}html.theme--documenter-dark .column.is-offset-5,html.theme--documenter-dark .column.is-offset-5-tablet{margin-left:41.66666674%}html.theme--documenter-dark .column.is-6,html.theme--documenter-dark .column.is-6-tablet{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6,html.theme--documenter-dark .column.is-offset-6-tablet{margin-left:50%}html.theme--documenter-dark .column.is-7,html.theme--documenter-dark .column.is-7-tablet{flex:none;width:58.33333337%}html.theme--documenter-dark .column.is-offset-7,html.theme--documenter-dark .column.is-offset-7-tablet{margin-left:58.33333337%}html.theme--documenter-dark .column.is-8,html.theme--documenter-dark .column.is-8-tablet{flex:none;width:66.66666674%}html.theme--documenter-dark .column.is-offset-8,html.theme--documenter-dark .column.is-offset-8-tablet{margin-left:66.66666674%}html.theme--documenter-dark .column.is-9,html.theme--documenter-dark .column.is-9-tablet{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9,html.theme--documenter-dark .column.is-offset-9-tablet{margin-left:75%}html.theme--documenter-dark .column.is-10,html.theme--documenter-dark .column.is-10-tablet{flex:none;width:83.33333337%}html.theme--documenter-dark .column.is-offset-10,html.theme--documenter-dark .column.is-offset-10-tablet{margin-left:83.33333337%}html.theme--documenter-dark .column.is-11,html.theme--documenter-dark .column.is-11-tablet{flex:none;width:91.66666674%}html.theme--documenter-dark .column.is-offset-11,html.theme--documenter-dark .column.is-offset-11-tablet{margin-left:91.66666674%}html.theme--documenter-dark .column.is-12,html.theme--documenter-dark .column.is-12-tablet{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12,html.theme--documenter-dark .column.is-offset-12-tablet{margin-left:100%}}@media screen and (max-width: 1055px){html.theme--documenter-dark .column.is-narrow-touch{flex:none;width:unset}html.theme--documenter-dark .column.is-full-touch{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-touch{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-touch{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-touch{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-touch{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-touch{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-touch{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-touch{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-touch{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-touch{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-touch{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-touch{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-touch{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-touch{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-touch{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-touch{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-touch{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-touch{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-touch{margin-left:80%}html.theme--documenter-dark .column.is-0-touch{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-touch{margin-left:0%}html.theme--documenter-dark .column.is-1-touch{flex:none;width:8.33333337%}html.theme--documenter-dark .column.is-offset-1-touch{margin-left:8.33333337%}html.theme--documenter-dark .column.is-2-touch{flex:none;width:16.66666674%}html.theme--documenter-dark .column.is-offset-2-touch{margin-left:16.66666674%}html.theme--documenter-dark .column.is-3-touch{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-touch{margin-left:25%}html.theme--documenter-dark .column.is-4-touch{flex:none;width:33.33333337%}html.theme--documenter-dark .column.is-offset-4-touch{margin-left:33.33333337%}html.theme--documenter-dark .column.is-5-touch{flex:none;width:41.66666674%}html.theme--documenter-dark .column.is-offset-5-touch{margin-left:41.66666674%}html.theme--documenter-dark .column.is-6-touch{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-touch{margin-left:50%}html.theme--documenter-dark .column.is-7-touch{flex:none;width:58.33333337%}html.theme--documenter-dark .column.is-offset-7-touch{margin-left:58.33333337%}html.theme--documenter-dark .column.is-8-touch{flex:none;width:66.66666674%}html.theme--documenter-dark .column.is-offset-8-touch{margin-left:66.66666674%}html.theme--documenter-dark .column.is-9-touch{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-touch{margin-left:75%}html.theme--documenter-dark .column.is-10-touch{flex:none;width:83.33333337%}html.theme--documenter-dark .column.is-offset-10-touch{margin-left:83.33333337%}html.theme--documenter-dark .column.is-11-touch{flex:none;width:91.66666674%}html.theme--documenter-dark .column.is-offset-11-touch{margin-left:91.66666674%}html.theme--documenter-dark .column.is-12-touch{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-touch{margin-left:100%}}@media screen and (min-width: 1056px){html.theme--documenter-dark .column.is-narrow-desktop{flex:none;width:unset}html.theme--documenter-dark .column.is-full-desktop{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-desktop{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-desktop{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-desktop{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-desktop{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-desktop{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-desktop{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-desktop{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-desktop{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-desktop{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-desktop{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-desktop{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-desktop{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-desktop{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-desktop{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-desktop{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-desktop{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-desktop{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-desktop{margin-left:80%}html.theme--documenter-dark .column.is-0-desktop{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-desktop{margin-left:0%}html.theme--documenter-dark .column.is-1-desktop{flex:none;width:8.33333337%}html.theme--documenter-dark .column.is-offset-1-desktop{margin-left:8.33333337%}html.theme--documenter-dark .column.is-2-desktop{flex:none;width:16.66666674%}html.theme--documenter-dark .column.is-offset-2-desktop{margin-left:16.66666674%}html.theme--documenter-dark .column.is-3-desktop{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-desktop{margin-left:25%}html.theme--documenter-dark .column.is-4-desktop{flex:none;width:33.33333337%}html.theme--documenter-dark .column.is-offset-4-desktop{margin-left:33.33333337%}html.theme--documenter-dark .column.is-5-desktop{flex:none;width:41.66666674%}html.theme--documenter-dark .column.is-offset-5-desktop{margin-left:41.66666674%}html.theme--documenter-dark .column.is-6-desktop{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-desktop{margin-left:50%}html.theme--documenter-dark .column.is-7-desktop{flex:none;width:58.33333337%}html.theme--documenter-dark .column.is-offset-7-desktop{margin-left:58.33333337%}html.theme--documenter-dark .column.is-8-desktop{flex:none;width:66.66666674%}html.theme--documenter-dark .column.is-offset-8-desktop{margin-left:66.66666674%}html.theme--documenter-dark .column.is-9-desktop{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-desktop{margin-left:75%}html.theme--documenter-dark .column.is-10-desktop{flex:none;width:83.33333337%}html.theme--documenter-dark .column.is-offset-10-desktop{margin-left:83.33333337%}html.theme--documenter-dark .column.is-11-desktop{flex:none;width:91.66666674%}html.theme--documenter-dark .column.is-offset-11-desktop{margin-left:91.66666674%}html.theme--documenter-dark .column.is-12-desktop{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-desktop{margin-left:100%}}@media screen and (min-width: 1216px){html.theme--documenter-dark .column.is-narrow-widescreen{flex:none;width:unset}html.theme--documenter-dark .column.is-full-widescreen{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-widescreen{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-widescreen{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-widescreen{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-widescreen{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-widescreen{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-widescreen{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-widescreen{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-widescreen{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-widescreen{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-widescreen{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-widescreen{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-widescreen{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-widescreen{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-widescreen{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-widescreen{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-widescreen{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-widescreen{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-widescreen{margin-left:80%}html.theme--documenter-dark .column.is-0-widescreen{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-widescreen{margin-left:0%}html.theme--documenter-dark .column.is-1-widescreen{flex:none;width:8.33333337%}html.theme--documenter-dark .column.is-offset-1-widescreen{margin-left:8.33333337%}html.theme--documenter-dark .column.is-2-widescreen{flex:none;width:16.66666674%}html.theme--documenter-dark .column.is-offset-2-widescreen{margin-left:16.66666674%}html.theme--documenter-dark .column.is-3-widescreen{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-widescreen{margin-left:25%}html.theme--documenter-dark .column.is-4-widescreen{flex:none;width:33.33333337%}html.theme--documenter-dark .column.is-offset-4-widescreen{margin-left:33.33333337%}html.theme--documenter-dark .column.is-5-widescreen{flex:none;width:41.66666674%}html.theme--documenter-dark .column.is-offset-5-widescreen{margin-left:41.66666674%}html.theme--documenter-dark .column.is-6-widescreen{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-widescreen{margin-left:50%}html.theme--documenter-dark .column.is-7-widescreen{flex:none;width:58.33333337%}html.theme--documenter-dark .column.is-offset-7-widescreen{margin-left:58.33333337%}html.theme--documenter-dark .column.is-8-widescreen{flex:none;width:66.66666674%}html.theme--documenter-dark .column.is-offset-8-widescreen{margin-left:66.66666674%}html.theme--documenter-dark .column.is-9-widescreen{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-widescreen{margin-left:75%}html.theme--documenter-dark .column.is-10-widescreen{flex:none;width:83.33333337%}html.theme--documenter-dark .column.is-offset-10-widescreen{margin-left:83.33333337%}html.theme--documenter-dark .column.is-11-widescreen{flex:none;width:91.66666674%}html.theme--documenter-dark .column.is-offset-11-widescreen{margin-left:91.66666674%}html.theme--documenter-dark .column.is-12-widescreen{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-widescreen{margin-left:100%}}@media screen and (min-width: 1408px){html.theme--documenter-dark .column.is-narrow-fullhd{flex:none;width:unset}html.theme--documenter-dark .column.is-full-fullhd{flex:none;width:100%}html.theme--documenter-dark .column.is-three-quarters-fullhd{flex:none;width:75%}html.theme--documenter-dark .column.is-two-thirds-fullhd{flex:none;width:66.6666%}html.theme--documenter-dark .column.is-half-fullhd{flex:none;width:50%}html.theme--documenter-dark .column.is-one-third-fullhd{flex:none;width:33.3333%}html.theme--documenter-dark .column.is-one-quarter-fullhd{flex:none;width:25%}html.theme--documenter-dark .column.is-one-fifth-fullhd{flex:none;width:20%}html.theme--documenter-dark .column.is-two-fifths-fullhd{flex:none;width:40%}html.theme--documenter-dark .column.is-three-fifths-fullhd{flex:none;width:60%}html.theme--documenter-dark .column.is-four-fifths-fullhd{flex:none;width:80%}html.theme--documenter-dark .column.is-offset-three-quarters-fullhd{margin-left:75%}html.theme--documenter-dark .column.is-offset-two-thirds-fullhd{margin-left:66.6666%}html.theme--documenter-dark .column.is-offset-half-fullhd{margin-left:50%}html.theme--documenter-dark .column.is-offset-one-third-fullhd{margin-left:33.3333%}html.theme--documenter-dark .column.is-offset-one-quarter-fullhd{margin-left:25%}html.theme--documenter-dark .column.is-offset-one-fifth-fullhd{margin-left:20%}html.theme--documenter-dark .column.is-offset-two-fifths-fullhd{margin-left:40%}html.theme--documenter-dark .column.is-offset-three-fifths-fullhd{margin-left:60%}html.theme--documenter-dark .column.is-offset-four-fifths-fullhd{margin-left:80%}html.theme--documenter-dark .column.is-0-fullhd{flex:none;width:0%}html.theme--documenter-dark .column.is-offset-0-fullhd{margin-left:0%}html.theme--documenter-dark .column.is-1-fullhd{flex:none;width:8.33333337%}html.theme--documenter-dark .column.is-offset-1-fullhd{margin-left:8.33333337%}html.theme--documenter-dark .column.is-2-fullhd{flex:none;width:16.66666674%}html.theme--documenter-dark .column.is-offset-2-fullhd{margin-left:16.66666674%}html.theme--documenter-dark .column.is-3-fullhd{flex:none;width:25%}html.theme--documenter-dark .column.is-offset-3-fullhd{margin-left:25%}html.theme--documenter-dark .column.is-4-fullhd{flex:none;width:33.33333337%}html.theme--documenter-dark .column.is-offset-4-fullhd{margin-left:33.33333337%}html.theme--documenter-dark .column.is-5-fullhd{flex:none;width:41.66666674%}html.theme--documenter-dark .column.is-offset-5-fullhd{margin-left:41.66666674%}html.theme--documenter-dark .column.is-6-fullhd{flex:none;width:50%}html.theme--documenter-dark .column.is-offset-6-fullhd{margin-left:50%}html.theme--documenter-dark .column.is-7-fullhd{flex:none;width:58.33333337%}html.theme--documenter-dark .column.is-offset-7-fullhd{margin-left:58.33333337%}html.theme--documenter-dark .column.is-8-fullhd{flex:none;width:66.66666674%}html.theme--documenter-dark .column.is-offset-8-fullhd{margin-left:66.66666674%}html.theme--documenter-dark .column.is-9-fullhd{flex:none;width:75%}html.theme--documenter-dark .column.is-offset-9-fullhd{margin-left:75%}html.theme--documenter-dark .column.is-10-fullhd{flex:none;width:83.33333337%}html.theme--documenter-dark .column.is-offset-10-fullhd{margin-left:83.33333337%}html.theme--documenter-dark .column.is-11-fullhd{flex:none;width:91.66666674%}html.theme--documenter-dark .column.is-offset-11-fullhd{margin-left:91.66666674%}html.theme--documenter-dark .column.is-12-fullhd{flex:none;width:100%}html.theme--documenter-dark .column.is-offset-12-fullhd{margin-left:100%}}html.theme--documenter-dark .columns{margin-left:-.75rem;margin-right:-.75rem;margin-top:-.75rem}html.theme--documenter-dark .columns:last-child{margin-bottom:-.75rem}html.theme--documenter-dark .columns:not(:last-child){margin-bottom:calc(1.5rem - .75rem)}html.theme--documenter-dark .columns.is-centered{justify-content:center}html.theme--documenter-dark .columns.is-gapless{margin-left:0;margin-right:0;margin-top:0}html.theme--documenter-dark .columns.is-gapless>.column{margin:0;padding:0 !important}html.theme--documenter-dark .columns.is-gapless:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .columns.is-gapless:last-child{margin-bottom:0}html.theme--documenter-dark .columns.is-mobile{display:flex}html.theme--documenter-dark .columns.is-multiline{flex-wrap:wrap}html.theme--documenter-dark .columns.is-vcentered{align-items:center}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns:not(.is-desktop){display:flex}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-desktop{display:flex}}html.theme--documenter-dark .columns.is-variable{--columnGap: 0.75rem;margin-left:calc(-1 * var(--columnGap));margin-right:calc(-1 * var(--columnGap))}html.theme--documenter-dark .columns.is-variable>.column{padding-left:var(--columnGap);padding-right:var(--columnGap)}html.theme--documenter-dark .columns.is-variable.is-0{--columnGap: 0rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-0-mobile{--columnGap: 0rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-0-tablet{--columnGap: 0rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-0-tablet-only{--columnGap: 0rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-0-touch{--columnGap: 0rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-0-desktop{--columnGap: 0rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-0-desktop-only{--columnGap: 0rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-0-widescreen{--columnGap: 0rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-0-widescreen-only{--columnGap: 0rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-0-fullhd{--columnGap: 0rem}}html.theme--documenter-dark .columns.is-variable.is-1{--columnGap: .25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-1-mobile{--columnGap: .25rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-1-tablet{--columnGap: .25rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-1-tablet-only{--columnGap: .25rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-1-touch{--columnGap: .25rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-1-desktop{--columnGap: .25rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-1-desktop-only{--columnGap: .25rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-1-widescreen{--columnGap: .25rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-1-widescreen-only{--columnGap: .25rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-1-fullhd{--columnGap: .25rem}}html.theme--documenter-dark .columns.is-variable.is-2{--columnGap: .5rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-2-mobile{--columnGap: .5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-2-tablet{--columnGap: .5rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-2-tablet-only{--columnGap: .5rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-2-touch{--columnGap: .5rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-2-desktop{--columnGap: .5rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-2-desktop-only{--columnGap: .5rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-2-widescreen{--columnGap: .5rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-2-widescreen-only{--columnGap: .5rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-2-fullhd{--columnGap: .5rem}}html.theme--documenter-dark .columns.is-variable.is-3{--columnGap: .75rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-3-mobile{--columnGap: .75rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-3-tablet{--columnGap: .75rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-3-tablet-only{--columnGap: .75rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-3-touch{--columnGap: .75rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-3-desktop{--columnGap: .75rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-3-desktop-only{--columnGap: .75rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-3-widescreen{--columnGap: .75rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-3-widescreen-only{--columnGap: .75rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-3-fullhd{--columnGap: .75rem}}html.theme--documenter-dark .columns.is-variable.is-4{--columnGap: 1rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-4-mobile{--columnGap: 1rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-4-tablet{--columnGap: 1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-4-tablet-only{--columnGap: 1rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-4-touch{--columnGap: 1rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-4-desktop{--columnGap: 1rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-4-desktop-only{--columnGap: 1rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-4-widescreen{--columnGap: 1rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-4-widescreen-only{--columnGap: 1rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-4-fullhd{--columnGap: 1rem}}html.theme--documenter-dark .columns.is-variable.is-5{--columnGap: 1.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-5-mobile{--columnGap: 1.25rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-5-tablet{--columnGap: 1.25rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-5-tablet-only{--columnGap: 1.25rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-5-touch{--columnGap: 1.25rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-5-desktop{--columnGap: 1.25rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-5-desktop-only{--columnGap: 1.25rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-5-widescreen{--columnGap: 1.25rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-5-widescreen-only{--columnGap: 1.25rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-5-fullhd{--columnGap: 1.25rem}}html.theme--documenter-dark .columns.is-variable.is-6{--columnGap: 1.5rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-6-mobile{--columnGap: 1.5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-6-tablet{--columnGap: 1.5rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-6-tablet-only{--columnGap: 1.5rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-6-touch{--columnGap: 1.5rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-6-desktop{--columnGap: 1.5rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-6-desktop-only{--columnGap: 1.5rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-6-widescreen{--columnGap: 1.5rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-6-widescreen-only{--columnGap: 1.5rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-6-fullhd{--columnGap: 1.5rem}}html.theme--documenter-dark .columns.is-variable.is-7{--columnGap: 1.75rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-7-mobile{--columnGap: 1.75rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-7-tablet{--columnGap: 1.75rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-7-tablet-only{--columnGap: 1.75rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-7-touch{--columnGap: 1.75rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-7-desktop{--columnGap: 1.75rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-7-desktop-only{--columnGap: 1.75rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-7-widescreen{--columnGap: 1.75rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-7-widescreen-only{--columnGap: 1.75rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-7-fullhd{--columnGap: 1.75rem}}html.theme--documenter-dark .columns.is-variable.is-8{--columnGap: 2rem}@media screen and (max-width: 768px){html.theme--documenter-dark .columns.is-variable.is-8-mobile{--columnGap: 2rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .columns.is-variable.is-8-tablet{--columnGap: 2rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-8-tablet-only{--columnGap: 2rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark .columns.is-variable.is-8-touch{--columnGap: 2rem}}@media screen and (min-width: 1056px){html.theme--documenter-dark .columns.is-variable.is-8-desktop{--columnGap: 2rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){html.theme--documenter-dark .columns.is-variable.is-8-desktop-only{--columnGap: 2rem}}@media screen and (min-width: 1216px){html.theme--documenter-dark .columns.is-variable.is-8-widescreen{--columnGap: 2rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){html.theme--documenter-dark .columns.is-variable.is-8-widescreen-only{--columnGap: 2rem}}@media screen and (min-width: 1408px){html.theme--documenter-dark .columns.is-variable.is-8-fullhd{--columnGap: 2rem}}html.theme--documenter-dark .tile{align-items:stretch;display:block;flex-basis:0;flex-grow:1;flex-shrink:1;min-height:min-content}html.theme--documenter-dark .tile.is-ancestor{margin-left:-.75rem;margin-right:-.75rem;margin-top:-.75rem}html.theme--documenter-dark .tile.is-ancestor:last-child{margin-bottom:-.75rem}html.theme--documenter-dark .tile.is-ancestor:not(:last-child){margin-bottom:.75rem}html.theme--documenter-dark .tile.is-child{margin:0 !important}html.theme--documenter-dark .tile.is-parent{padding:.75rem}html.theme--documenter-dark .tile.is-vertical{flex-direction:column}html.theme--documenter-dark .tile.is-vertical>.tile.is-child:not(:last-child){margin-bottom:1.5rem !important}@media screen and (min-width: 769px),print{html.theme--documenter-dark .tile:not(.is-child){display:flex}html.theme--documenter-dark .tile.is-1{flex:none;width:8.33333337%}html.theme--documenter-dark .tile.is-2{flex:none;width:16.66666674%}html.theme--documenter-dark .tile.is-3{flex:none;width:25%}html.theme--documenter-dark .tile.is-4{flex:none;width:33.33333337%}html.theme--documenter-dark .tile.is-5{flex:none;width:41.66666674%}html.theme--documenter-dark .tile.is-6{flex:none;width:50%}html.theme--documenter-dark .tile.is-7{flex:none;width:58.33333337%}html.theme--documenter-dark .tile.is-8{flex:none;width:66.66666674%}html.theme--documenter-dark .tile.is-9{flex:none;width:75%}html.theme--documenter-dark .tile.is-10{flex:none;width:83.33333337%}html.theme--documenter-dark .tile.is-11{flex:none;width:91.66666674%}html.theme--documenter-dark .tile.is-12{flex:none;width:100%}}html.theme--documenter-dark .hero{align-items:stretch;display:flex;flex-direction:column;justify-content:space-between}html.theme--documenter-dark .hero .navbar{background:none}html.theme--documenter-dark .hero .tabs ul{border-bottom:none}html.theme--documenter-dark .hero.is-white{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .hero.is-white a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-white strong{color:inherit}html.theme--documenter-dark .hero.is-white .title{color:#0a0a0a}html.theme--documenter-dark .hero.is-white .subtitle{color:rgba(10,10,10,0.9)}html.theme--documenter-dark .hero.is-white .subtitle a:not(.button),html.theme--documenter-dark .hero.is-white .subtitle strong{color:#0a0a0a}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-white .navbar-menu{background-color:#fff}}html.theme--documenter-dark .hero.is-white .navbar-item,html.theme--documenter-dark .hero.is-white .navbar-link{color:rgba(10,10,10,0.7)}html.theme--documenter-dark .hero.is-white a.navbar-item:hover,html.theme--documenter-dark .hero.is-white a.navbar-item.is-active,html.theme--documenter-dark .hero.is-white .navbar-link:hover,html.theme--documenter-dark .hero.is-white .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}html.theme--documenter-dark .hero.is-white .tabs a{color:#0a0a0a;opacity:0.9}html.theme--documenter-dark .hero.is-white .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-white .tabs li.is-active a{color:#fff !important;opacity:1}html.theme--documenter-dark .hero.is-white .tabs.is-boxed a,html.theme--documenter-dark .hero.is-white .tabs.is-toggle a{color:#0a0a0a}html.theme--documenter-dark .hero.is-white .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-white .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-white .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-white .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-white .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-white .tabs.is-toggle li.is-active a:hover{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .hero.is-white.is-bold{background-image:linear-gradient(141deg, #e8e3e4 0%, #fff 71%, #fff 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-white.is-bold .navbar-menu{background-image:linear-gradient(141deg, #e8e3e4 0%, #fff 71%, #fff 100%)}}html.theme--documenter-dark .hero.is-black{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .hero.is-black a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-black strong{color:inherit}html.theme--documenter-dark .hero.is-black .title{color:#fff}html.theme--documenter-dark .hero.is-black .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-black .subtitle a:not(.button),html.theme--documenter-dark .hero.is-black .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-black .navbar-menu{background-color:#0a0a0a}}html.theme--documenter-dark .hero.is-black .navbar-item,html.theme--documenter-dark .hero.is-black .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-black a.navbar-item:hover,html.theme--documenter-dark .hero.is-black a.navbar-item.is-active,html.theme--documenter-dark .hero.is-black .navbar-link:hover,html.theme--documenter-dark .hero.is-black .navbar-link.is-active{background-color:#000;color:#fff}html.theme--documenter-dark .hero.is-black .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-black .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-black .tabs li.is-active a{color:#0a0a0a !important;opacity:1}html.theme--documenter-dark .hero.is-black .tabs.is-boxed a,html.theme--documenter-dark .hero.is-black .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-black .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-black .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-black .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-black .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-black .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-black .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#0a0a0a}html.theme--documenter-dark .hero.is-black.is-bold{background-image:linear-gradient(141deg, #000 0%, #0a0a0a 71%, #181616 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-black.is-bold .navbar-menu{background-image:linear-gradient(141deg, #000 0%, #0a0a0a 71%, #181616 100%)}}html.theme--documenter-dark .hero.is-light{background-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .hero.is-light a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-light strong{color:inherit}html.theme--documenter-dark .hero.is-light .title{color:rgba(0,0,0,0.7)}html.theme--documenter-dark .hero.is-light .subtitle{color:rgba(0,0,0,0.9)}html.theme--documenter-dark .hero.is-light .subtitle a:not(.button),html.theme--documenter-dark .hero.is-light .subtitle strong{color:rgba(0,0,0,0.7)}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-light .navbar-menu{background-color:#ecf0f1}}html.theme--documenter-dark .hero.is-light .navbar-item,html.theme--documenter-dark .hero.is-light .navbar-link{color:rgba(0,0,0,0.7)}html.theme--documenter-dark .hero.is-light a.navbar-item:hover,html.theme--documenter-dark .hero.is-light a.navbar-item.is-active,html.theme--documenter-dark .hero.is-light .navbar-link:hover,html.theme--documenter-dark .hero.is-light .navbar-link.is-active{background-color:#dde4e6;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .hero.is-light .tabs a{color:rgba(0,0,0,0.7);opacity:0.9}html.theme--documenter-dark .hero.is-light .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-light .tabs li.is-active a{color:#ecf0f1 !important;opacity:1}html.theme--documenter-dark .hero.is-light .tabs.is-boxed a,html.theme--documenter-dark .hero.is-light .tabs.is-toggle a{color:rgba(0,0,0,0.7)}html.theme--documenter-dark .hero.is-light .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-light .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-light .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-light .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-light .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-light .tabs.is-toggle li.is-active a:hover{background-color:rgba(0,0,0,0.7);border-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .hero.is-light.is-bold{background-image:linear-gradient(141deg, #cadfe0 0%, #ecf0f1 71%, #fafbfc 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-light.is-bold .navbar-menu{background-image:linear-gradient(141deg, #cadfe0 0%, #ecf0f1 71%, #fafbfc 100%)}}html.theme--documenter-dark .hero.is-dark,html.theme--documenter-dark .content kbd.hero{background-color:#282f2f;color:#fff}html.theme--documenter-dark .hero.is-dark a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .content kbd.hero a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-dark strong,html.theme--documenter-dark .content kbd.hero strong{color:inherit}html.theme--documenter-dark .hero.is-dark .title,html.theme--documenter-dark .content kbd.hero .title{color:#fff}html.theme--documenter-dark .hero.is-dark .subtitle,html.theme--documenter-dark .content kbd.hero .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-dark .subtitle a:not(.button),html.theme--documenter-dark .content kbd.hero .subtitle a:not(.button),html.theme--documenter-dark .hero.is-dark .subtitle strong,html.theme--documenter-dark .content kbd.hero .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-dark .navbar-menu,html.theme--documenter-dark .content kbd.hero .navbar-menu{background-color:#282f2f}}html.theme--documenter-dark .hero.is-dark .navbar-item,html.theme--documenter-dark .content kbd.hero .navbar-item,html.theme--documenter-dark .hero.is-dark .navbar-link,html.theme--documenter-dark .content kbd.hero .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-dark a.navbar-item:hover,html.theme--documenter-dark .content kbd.hero a.navbar-item:hover,html.theme--documenter-dark .hero.is-dark a.navbar-item.is-active,html.theme--documenter-dark .content kbd.hero a.navbar-item.is-active,html.theme--documenter-dark .hero.is-dark .navbar-link:hover,html.theme--documenter-dark .content kbd.hero .navbar-link:hover,html.theme--documenter-dark .hero.is-dark .navbar-link.is-active,html.theme--documenter-dark .content kbd.hero .navbar-link.is-active{background-color:#1d2122;color:#fff}html.theme--documenter-dark .hero.is-dark .tabs a,html.theme--documenter-dark .content kbd.hero .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-dark .tabs a:hover,html.theme--documenter-dark .content kbd.hero .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-dark .tabs li.is-active a,html.theme--documenter-dark .content kbd.hero .tabs li.is-active a{color:#282f2f !important;opacity:1}html.theme--documenter-dark .hero.is-dark .tabs.is-boxed a,html.theme--documenter-dark .content kbd.hero .tabs.is-boxed a,html.theme--documenter-dark .hero.is-dark .tabs.is-toggle a,html.theme--documenter-dark .content kbd.hero .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-dark .tabs.is-boxed a:hover,html.theme--documenter-dark .content kbd.hero .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-dark .tabs.is-toggle a:hover,html.theme--documenter-dark .content kbd.hero .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-dark .tabs.is-boxed li.is-active a,html.theme--documenter-dark .content kbd.hero .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-dark .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-dark .tabs.is-toggle li.is-active a,html.theme--documenter-dark .content kbd.hero .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-dark .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#282f2f}html.theme--documenter-dark .hero.is-dark.is-bold,html.theme--documenter-dark .content kbd.hero.is-bold{background-image:linear-gradient(141deg, #0f1615 0%, #282f2f 71%, #313c40 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-dark.is-bold .navbar-menu,html.theme--documenter-dark .content kbd.hero.is-bold .navbar-menu{background-image:linear-gradient(141deg, #0f1615 0%, #282f2f 71%, #313c40 100%)}}html.theme--documenter-dark .hero.is-primary,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink{background-color:#375a7f;color:#fff}html.theme--documenter-dark .hero.is-primary a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-primary strong,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink strong{color:inherit}html.theme--documenter-dark .hero.is-primary .title,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .title{color:#fff}html.theme--documenter-dark .hero.is-primary .subtitle,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-primary .subtitle a:not(.button),html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .subtitle a:not(.button),html.theme--documenter-dark .hero.is-primary .subtitle strong,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-primary .navbar-menu,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-menu{background-color:#375a7f}}html.theme--documenter-dark .hero.is-primary .navbar-item,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-item,html.theme--documenter-dark .hero.is-primary .navbar-link,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-primary a.navbar-item:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink a.navbar-item:hover,html.theme--documenter-dark .hero.is-primary a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink a.navbar-item.is-active,html.theme--documenter-dark .hero.is-primary .navbar-link:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-link:hover,html.theme--documenter-dark .hero.is-primary .navbar-link.is-active,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar-link.is-active{background-color:#2f4d6d;color:#fff}html.theme--documenter-dark .hero.is-primary .tabs a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-primary .tabs a:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-primary .tabs li.is-active a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs li.is-active a{color:#375a7f !important;opacity:1}html.theme--documenter-dark .hero.is-primary .tabs.is-boxed a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-boxed a,html.theme--documenter-dark .hero.is-primary .tabs.is-toggle a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-primary .tabs.is-boxed a:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-primary .tabs.is-toggle a:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-primary .tabs.is-boxed li.is-active a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-primary .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-primary .tabs.is-toggle li.is-active a,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-primary .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#375a7f}html.theme--documenter-dark .hero.is-primary.is-bold,html.theme--documenter-dark .docstring>section>a.hero.is-bold.docs-sourcelink{background-image:linear-gradient(141deg, #214b62 0%, #375a7f 71%, #3a5796 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-primary.is-bold .navbar-menu,html.theme--documenter-dark .docstring>section>a.hero.is-bold.docs-sourcelink .navbar-menu{background-image:linear-gradient(141deg, #214b62 0%, #375a7f 71%, #3a5796 100%)}}html.theme--documenter-dark .hero.is-link{background-color:#1abc9c;color:#fff}html.theme--documenter-dark .hero.is-link a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-link strong{color:inherit}html.theme--documenter-dark .hero.is-link .title{color:#fff}html.theme--documenter-dark .hero.is-link .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-link .subtitle a:not(.button),html.theme--documenter-dark .hero.is-link .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-link .navbar-menu{background-color:#1abc9c}}html.theme--documenter-dark .hero.is-link .navbar-item,html.theme--documenter-dark .hero.is-link .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-link a.navbar-item:hover,html.theme--documenter-dark .hero.is-link a.navbar-item.is-active,html.theme--documenter-dark .hero.is-link .navbar-link:hover,html.theme--documenter-dark .hero.is-link .navbar-link.is-active{background-color:#17a689;color:#fff}html.theme--documenter-dark .hero.is-link .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-link .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-link .tabs li.is-active a{color:#1abc9c !important;opacity:1}html.theme--documenter-dark .hero.is-link .tabs.is-boxed a,html.theme--documenter-dark .hero.is-link .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-link .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-link .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-link .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-link .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-link .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-link .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#1abc9c}html.theme--documenter-dark .hero.is-link.is-bold{background-image:linear-gradient(141deg, #0c9764 0%, #1abc9c 71%, #17d8d2 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-link.is-bold .navbar-menu{background-image:linear-gradient(141deg, #0c9764 0%, #1abc9c 71%, #17d8d2 100%)}}html.theme--documenter-dark .hero.is-info{background-color:#024c7d;color:#fff}html.theme--documenter-dark .hero.is-info a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-info strong{color:inherit}html.theme--documenter-dark .hero.is-info .title{color:#fff}html.theme--documenter-dark .hero.is-info .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-info .subtitle a:not(.button),html.theme--documenter-dark .hero.is-info .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-info .navbar-menu{background-color:#024c7d}}html.theme--documenter-dark .hero.is-info .navbar-item,html.theme--documenter-dark .hero.is-info .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-info a.navbar-item:hover,html.theme--documenter-dark .hero.is-info a.navbar-item.is-active,html.theme--documenter-dark .hero.is-info .navbar-link:hover,html.theme--documenter-dark .hero.is-info .navbar-link.is-active{background-color:#023d64;color:#fff}html.theme--documenter-dark .hero.is-info .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-info .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-info .tabs li.is-active a{color:#024c7d !important;opacity:1}html.theme--documenter-dark .hero.is-info .tabs.is-boxed a,html.theme--documenter-dark .hero.is-info .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-info .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-info .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-info .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-info .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-info .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-info .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#024c7d}html.theme--documenter-dark .hero.is-info.is-bold{background-image:linear-gradient(141deg, #003a4c 0%, #024c7d 71%, #004299 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-info.is-bold .navbar-menu{background-image:linear-gradient(141deg, #003a4c 0%, #024c7d 71%, #004299 100%)}}html.theme--documenter-dark .hero.is-success{background-color:#008438;color:#fff}html.theme--documenter-dark .hero.is-success a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-success strong{color:inherit}html.theme--documenter-dark .hero.is-success .title{color:#fff}html.theme--documenter-dark .hero.is-success .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-success .subtitle a:not(.button),html.theme--documenter-dark .hero.is-success .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-success .navbar-menu{background-color:#008438}}html.theme--documenter-dark .hero.is-success .navbar-item,html.theme--documenter-dark .hero.is-success .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-success a.navbar-item:hover,html.theme--documenter-dark .hero.is-success a.navbar-item.is-active,html.theme--documenter-dark .hero.is-success .navbar-link:hover,html.theme--documenter-dark .hero.is-success .navbar-link.is-active{background-color:#006b2d;color:#fff}html.theme--documenter-dark .hero.is-success .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-success .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-success .tabs li.is-active a{color:#008438 !important;opacity:1}html.theme--documenter-dark .hero.is-success .tabs.is-boxed a,html.theme--documenter-dark .hero.is-success .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-success .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-success .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-success .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-success .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-success .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-success .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#008438}html.theme--documenter-dark .hero.is-success.is-bold{background-image:linear-gradient(141deg, #005115 0%, #008438 71%, #009e5d 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-success.is-bold .navbar-menu{background-image:linear-gradient(141deg, #005115 0%, #008438 71%, #009e5d 100%)}}html.theme--documenter-dark .hero.is-warning{background-color:#ad8100;color:#fff}html.theme--documenter-dark .hero.is-warning a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-warning strong{color:inherit}html.theme--documenter-dark .hero.is-warning .title{color:#fff}html.theme--documenter-dark .hero.is-warning .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-warning .subtitle a:not(.button),html.theme--documenter-dark .hero.is-warning .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-warning .navbar-menu{background-color:#ad8100}}html.theme--documenter-dark .hero.is-warning .navbar-item,html.theme--documenter-dark .hero.is-warning .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-warning a.navbar-item:hover,html.theme--documenter-dark .hero.is-warning a.navbar-item.is-active,html.theme--documenter-dark .hero.is-warning .navbar-link:hover,html.theme--documenter-dark .hero.is-warning .navbar-link.is-active{background-color:#946e00;color:#fff}html.theme--documenter-dark .hero.is-warning .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-warning .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-warning .tabs li.is-active a{color:#ad8100 !important;opacity:1}html.theme--documenter-dark .hero.is-warning .tabs.is-boxed a,html.theme--documenter-dark .hero.is-warning .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-warning .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-warning .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-warning .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-warning .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-warning .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-warning .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#ad8100}html.theme--documenter-dark .hero.is-warning.is-bold{background-image:linear-gradient(141deg, #7a4700 0%, #ad8100 71%, #c7b500 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-warning.is-bold .navbar-menu{background-image:linear-gradient(141deg, #7a4700 0%, #ad8100 71%, #c7b500 100%)}}html.theme--documenter-dark .hero.is-danger{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .hero.is-danger a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),html.theme--documenter-dark .hero.is-danger strong{color:inherit}html.theme--documenter-dark .hero.is-danger .title{color:#fff}html.theme--documenter-dark .hero.is-danger .subtitle{color:rgba(255,255,255,0.9)}html.theme--documenter-dark .hero.is-danger .subtitle a:not(.button),html.theme--documenter-dark .hero.is-danger .subtitle strong{color:#fff}@media screen and (max-width: 1055px){html.theme--documenter-dark .hero.is-danger .navbar-menu{background-color:#9e1b0d}}html.theme--documenter-dark .hero.is-danger .navbar-item,html.theme--documenter-dark .hero.is-danger .navbar-link{color:rgba(255,255,255,0.7)}html.theme--documenter-dark .hero.is-danger a.navbar-item:hover,html.theme--documenter-dark .hero.is-danger a.navbar-item.is-active,html.theme--documenter-dark .hero.is-danger .navbar-link:hover,html.theme--documenter-dark .hero.is-danger .navbar-link.is-active{background-color:#86170b;color:#fff}html.theme--documenter-dark .hero.is-danger .tabs a{color:#fff;opacity:0.9}html.theme--documenter-dark .hero.is-danger .tabs a:hover{opacity:1}html.theme--documenter-dark .hero.is-danger .tabs li.is-active a{color:#9e1b0d !important;opacity:1}html.theme--documenter-dark .hero.is-danger .tabs.is-boxed a,html.theme--documenter-dark .hero.is-danger .tabs.is-toggle a{color:#fff}html.theme--documenter-dark .hero.is-danger .tabs.is-boxed a:hover,html.theme--documenter-dark .hero.is-danger .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}html.theme--documenter-dark .hero.is-danger .tabs.is-boxed li.is-active a,html.theme--documenter-dark .hero.is-danger .tabs.is-boxed li.is-active a:hover,html.theme--documenter-dark .hero.is-danger .tabs.is-toggle li.is-active a,html.theme--documenter-dark .hero.is-danger .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#9e1b0d}html.theme--documenter-dark .hero.is-danger.is-bold{background-image:linear-gradient(141deg, #75030b 0%, #9e1b0d 71%, #ba380a 100%)}@media screen and (max-width: 768px){html.theme--documenter-dark .hero.is-danger.is-bold .navbar-menu{background-image:linear-gradient(141deg, #75030b 0%, #9e1b0d 71%, #ba380a 100%)}}html.theme--documenter-dark .hero.is-small .hero-body,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.hero .hero-body{padding:1.5rem}@media screen and (min-width: 769px),print{html.theme--documenter-dark .hero.is-medium .hero-body{padding:9rem 4.5rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .hero.is-large .hero-body{padding:18rem 6rem}}html.theme--documenter-dark .hero.is-halfheight .hero-body,html.theme--documenter-dark .hero.is-fullheight .hero-body,html.theme--documenter-dark .hero.is-fullheight-with-navbar .hero-body{align-items:center;display:flex}html.theme--documenter-dark .hero.is-halfheight .hero-body>.container,html.theme--documenter-dark .hero.is-fullheight .hero-body>.container,html.theme--documenter-dark .hero.is-fullheight-with-navbar .hero-body>.container{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .hero.is-halfheight{min-height:50vh}html.theme--documenter-dark .hero.is-fullheight{min-height:100vh}html.theme--documenter-dark .hero-video{overflow:hidden}html.theme--documenter-dark .hero-video video{left:50%;min-height:100%;min-width:100%;position:absolute;top:50%;transform:translate3d(-50%, -50%, 0)}html.theme--documenter-dark .hero-video.is-transparent{opacity:0.3}@media screen and (max-width: 768px){html.theme--documenter-dark .hero-video{display:none}}html.theme--documenter-dark .hero-buttons{margin-top:1.5rem}@media screen and (max-width: 768px){html.theme--documenter-dark .hero-buttons .button{display:flex}html.theme--documenter-dark .hero-buttons .button:not(:last-child){margin-bottom:0.75rem}}@media screen and (min-width: 769px),print{html.theme--documenter-dark .hero-buttons{display:flex;justify-content:center}html.theme--documenter-dark .hero-buttons .button:not(:last-child){margin-right:1.5rem}}html.theme--documenter-dark .hero-head,html.theme--documenter-dark .hero-foot{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .hero-body{flex-grow:1;flex-shrink:0;padding:3rem 1.5rem}@media screen and (min-width: 769px),print{html.theme--documenter-dark .hero-body{padding:3rem 3rem}}html.theme--documenter-dark .section{padding:3rem 1.5rem}@media screen and (min-width: 1056px){html.theme--documenter-dark .section{padding:3rem 3rem}html.theme--documenter-dark .section.is-medium{padding:9rem 4.5rem}html.theme--documenter-dark .section.is-large{padding:18rem 6rem}}html.theme--documenter-dark .footer{background-color:#282f2f;padding:3rem 1.5rem 6rem}html.theme--documenter-dark hr{height:1px}html.theme--documenter-dark h6{text-transform:uppercase;letter-spacing:0.5px}html.theme--documenter-dark .hero{background-color:#343c3d}html.theme--documenter-dark a{transition:all 200ms ease}html.theme--documenter-dark .button{transition:all 200ms ease;border-width:1px;color:#fff}html.theme--documenter-dark .button.is-active,html.theme--documenter-dark .button.is-focused,html.theme--documenter-dark .button:active,html.theme--documenter-dark .button:focus{box-shadow:0 0 0 2px rgba(140,155,157,0.5)}html.theme--documenter-dark .button.is-white.is-hovered,html.theme--documenter-dark .button.is-white:hover{background-color:#fff}html.theme--documenter-dark .button.is-white.is-active,html.theme--documenter-dark .button.is-white.is-focused,html.theme--documenter-dark .button.is-white:active,html.theme--documenter-dark .button.is-white:focus{border-color:#fff;box-shadow:0 0 0 2px rgba(255,255,255,0.5)}html.theme--documenter-dark .button.is-black.is-hovered,html.theme--documenter-dark .button.is-black:hover{background-color:#1d1d1d}html.theme--documenter-dark .button.is-black.is-active,html.theme--documenter-dark .button.is-black.is-focused,html.theme--documenter-dark .button.is-black:active,html.theme--documenter-dark .button.is-black:focus{border-color:#0a0a0a;box-shadow:0 0 0 2px rgba(10,10,10,0.5)}html.theme--documenter-dark .button.is-light.is-hovered,html.theme--documenter-dark .button.is-light:hover{background-color:#fff}html.theme--documenter-dark .button.is-light.is-active,html.theme--documenter-dark .button.is-light.is-focused,html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light:focus{border-color:#ecf0f1;box-shadow:0 0 0 2px rgba(236,240,241,0.5)}html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered,html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover{background-color:#3a4344}html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused,html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus{border-color:#282f2f;box-shadow:0 0 0 2px rgba(40,47,47,0.5)}html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink,html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover{background-color:#436d9a}html.theme--documenter-dark .button.is-primary.is-active,html.theme--documenter-dark .docstring>section>a.button.is-active.docs-sourcelink,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink,html.theme--documenter-dark .button.is-primary:active,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:active,html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus{border-color:#375a7f;box-shadow:0 0 0 2px rgba(55,90,127,0.5)}html.theme--documenter-dark .button.is-link.is-hovered,html.theme--documenter-dark .button.is-link:hover{background-color:#1fdeb8}html.theme--documenter-dark .button.is-link.is-active,html.theme--documenter-dark .button.is-link.is-focused,html.theme--documenter-dark .button.is-link:active,html.theme--documenter-dark .button.is-link:focus{border-color:#1abc9c;box-shadow:0 0 0 2px rgba(26,188,156,0.5)}html.theme--documenter-dark .button.is-info.is-hovered,html.theme--documenter-dark .button.is-info:hover{background-color:#0363a3}html.theme--documenter-dark .button.is-info.is-active,html.theme--documenter-dark .button.is-info.is-focused,html.theme--documenter-dark .button.is-info:active,html.theme--documenter-dark .button.is-info:focus{border-color:#024c7d;box-shadow:0 0 0 2px rgba(2,76,125,0.5)}html.theme--documenter-dark .button.is-success.is-hovered,html.theme--documenter-dark .button.is-success:hover{background-color:#00aa48}html.theme--documenter-dark .button.is-success.is-active,html.theme--documenter-dark .button.is-success.is-focused,html.theme--documenter-dark .button.is-success:active,html.theme--documenter-dark .button.is-success:focus{border-color:#008438;box-shadow:0 0 0 2px rgba(0,132,56,0.5)}html.theme--documenter-dark .button.is-warning.is-hovered,html.theme--documenter-dark .button.is-warning:hover{background-color:#d39e00}html.theme--documenter-dark .button.is-warning.is-active,html.theme--documenter-dark .button.is-warning.is-focused,html.theme--documenter-dark .button.is-warning:active,html.theme--documenter-dark .button.is-warning:focus{border-color:#ad8100;box-shadow:0 0 0 2px rgba(173,129,0,0.5)}html.theme--documenter-dark .button.is-danger.is-hovered,html.theme--documenter-dark .button.is-danger:hover{background-color:#c12110}html.theme--documenter-dark .button.is-danger.is-active,html.theme--documenter-dark .button.is-danger.is-focused,html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger:focus{border-color:#9e1b0d;box-shadow:0 0 0 2px rgba(158,27,13,0.5)}html.theme--documenter-dark .label{color:#dbdee0}html.theme--documenter-dark .button,html.theme--documenter-dark .control.has-icons-left .icon,html.theme--documenter-dark .control.has-icons-right .icon,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .select,html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea{height:2.5em}html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .textarea{transition:all 200ms ease;box-shadow:none;border-width:1px;padding-left:1em;padding-right:1em}html.theme--documenter-dark .select:after,html.theme--documenter-dark .select select{border-width:1px}html.theme--documenter-dark .control.has-addons .button,html.theme--documenter-dark .control.has-addons .input,html.theme--documenter-dark .control.has-addons #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark #documenter .docs-sidebar .control.has-addons form.docs-search>input,html.theme--documenter-dark .control.has-addons .select{margin-right:-1px}html.theme--documenter-dark .notification{background-color:#343c3d}html.theme--documenter-dark .card{box-shadow:none;border:1px solid #343c3d;background-color:#282f2f;border-radius:.4em}html.theme--documenter-dark .card .card-image img{border-radius:.4em .4em 0 0}html.theme--documenter-dark .card .card-header{box-shadow:none;background-color:rgba(18,18,18,0.2);border-radius:.4em .4em 0 0}html.theme--documenter-dark .card .card-footer{background-color:rgba(18,18,18,0.2)}html.theme--documenter-dark .card .card-footer,html.theme--documenter-dark .card .card-footer-item{border-width:1px;border-color:#343c3d}html.theme--documenter-dark .notification.is-white a:not(.button){color:#0a0a0a;text-decoration:underline}html.theme--documenter-dark .notification.is-black a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-light a:not(.button){color:rgba(0,0,0,0.7);text-decoration:underline}html.theme--documenter-dark .notification.is-dark a:not(.button),html.theme--documenter-dark .content kbd.notification a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-primary a:not(.button),html.theme--documenter-dark .docstring>section>a.notification.docs-sourcelink a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-link a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-info a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-success a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-warning a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .notification.is-danger a:not(.button){color:#fff;text-decoration:underline}html.theme--documenter-dark .tag,html.theme--documenter-dark .content kbd,html.theme--documenter-dark .docstring>section>a.docs-sourcelink{border-radius:.4em}html.theme--documenter-dark .menu-list a{transition:all 300ms ease}html.theme--documenter-dark .modal-card-body{background-color:#282f2f}html.theme--documenter-dark .modal-card-foot,html.theme--documenter-dark .modal-card-head{border-color:#343c3d}html.theme--documenter-dark .message-header{font-weight:700;background-color:#343c3d;color:#fff}html.theme--documenter-dark .message-body{border-width:1px;border-color:#343c3d}html.theme--documenter-dark .navbar{border-radius:.4em}html.theme--documenter-dark .navbar.is-transparent{background:none}html.theme--documenter-dark .navbar.is-primary .navbar-dropdown a.navbar-item.is-active,html.theme--documenter-dark .docstring>section>a.navbar.docs-sourcelink .navbar-dropdown a.navbar-item.is-active{background-color:#1abc9c}@media screen and (max-width: 1055px){html.theme--documenter-dark .navbar .navbar-menu{background-color:#375a7f;border-radius:0 0 .4em .4em}}html.theme--documenter-dark .hero .navbar,html.theme--documenter-dark body>.navbar{border-radius:0}html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-previous{border-width:1px}html.theme--documenter-dark .panel-block,html.theme--documenter-dark .panel-heading,html.theme--documenter-dark .panel-tabs{border-width:1px}html.theme--documenter-dark .panel-block:first-child,html.theme--documenter-dark .panel-heading:first-child,html.theme--documenter-dark .panel-tabs:first-child{border-top-width:1px}html.theme--documenter-dark .panel-heading{font-weight:700}html.theme--documenter-dark .panel-tabs a{border-width:1px;margin-bottom:-1px}html.theme--documenter-dark .panel-tabs a.is-active{border-bottom-color:#17a689}html.theme--documenter-dark .panel-block:hover{color:#1dd2af}html.theme--documenter-dark .panel-block:hover .panel-icon{color:#1dd2af}html.theme--documenter-dark .panel-block.is-active .panel-icon{color:#17a689}html.theme--documenter-dark .tabs a{border-bottom-width:1px;margin-bottom:-1px}html.theme--documenter-dark .tabs ul{border-bottom-width:1px}html.theme--documenter-dark .tabs.is-boxed a{border-width:1px}html.theme--documenter-dark .tabs.is-boxed li.is-active a{background-color:#1f2424}html.theme--documenter-dark .tabs.is-toggle li a{border-width:1px;margin-bottom:0}html.theme--documenter-dark .tabs.is-toggle li+li{margin-left:-1px}html.theme--documenter-dark .hero.is-white .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-black .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-light .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-dark .navbar .navbar-dropdown .navbar-item:hover,html.theme--documenter-dark .content kbd.hero .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-primary .navbar .navbar-dropdown .navbar-item:hover,html.theme--documenter-dark .docstring>section>a.hero.docs-sourcelink .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-link .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-info .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-success .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-warning .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark .hero.is-danger .navbar .navbar-dropdown .navbar-item:hover{background-color:rgba(0,0,0,0)}html.theme--documenter-dark h1 .docs-heading-anchor,html.theme--documenter-dark h1 .docs-heading-anchor:hover,html.theme--documenter-dark h1 .docs-heading-anchor:visited,html.theme--documenter-dark h2 .docs-heading-anchor,html.theme--documenter-dark h2 .docs-heading-anchor:hover,html.theme--documenter-dark h2 .docs-heading-anchor:visited,html.theme--documenter-dark h3 .docs-heading-anchor,html.theme--documenter-dark h3 .docs-heading-anchor:hover,html.theme--documenter-dark h3 .docs-heading-anchor:visited,html.theme--documenter-dark h4 .docs-heading-anchor,html.theme--documenter-dark h4 .docs-heading-anchor:hover,html.theme--documenter-dark h4 .docs-heading-anchor:visited,html.theme--documenter-dark h5 .docs-heading-anchor,html.theme--documenter-dark h5 .docs-heading-anchor:hover,html.theme--documenter-dark h5 .docs-heading-anchor:visited,html.theme--documenter-dark h6 .docs-heading-anchor,html.theme--documenter-dark h6 .docs-heading-anchor:hover,html.theme--documenter-dark h6 .docs-heading-anchor:visited{color:#f2f2f2}html.theme--documenter-dark h1 .docs-heading-anchor-permalink,html.theme--documenter-dark h2 .docs-heading-anchor-permalink,html.theme--documenter-dark h3 .docs-heading-anchor-permalink,html.theme--documenter-dark h4 .docs-heading-anchor-permalink,html.theme--documenter-dark h5 .docs-heading-anchor-permalink,html.theme--documenter-dark h6 .docs-heading-anchor-permalink{visibility:hidden;vertical-align:middle;margin-left:0.5em;font-size:0.7rem}html.theme--documenter-dark h1 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h2 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h3 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h4 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h5 .docs-heading-anchor-permalink::before,html.theme--documenter-dark h6 .docs-heading-anchor-permalink::before{font-family:"Font Awesome 6 Free";font-weight:900;content:"\f0c1"}html.theme--documenter-dark h1:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h2:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h3:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h4:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h5:hover .docs-heading-anchor-permalink,html.theme--documenter-dark h6:hover .docs-heading-anchor-permalink{visibility:visible}html.theme--documenter-dark .docs-light-only{display:none !important}html.theme--documenter-dark pre{position:relative;overflow:hidden}html.theme--documenter-dark pre code,html.theme--documenter-dark pre code.hljs{padding:0 .75rem !important;overflow:auto;display:block}html.theme--documenter-dark pre code:first-of-type,html.theme--documenter-dark pre code.hljs:first-of-type{padding-top:0.5rem !important}html.theme--documenter-dark pre code:last-of-type,html.theme--documenter-dark pre code.hljs:last-of-type{padding-bottom:0.5rem !important}html.theme--documenter-dark pre .copy-button{opacity:0.2;transition:opacity 0.2s;position:absolute;right:0em;top:0em;padding:0.5em;width:2.5em;height:2.5em;background:transparent;border:none;font-family:"Font Awesome 6 Free";color:#fff;cursor:pointer;text-align:center}html.theme--documenter-dark pre .copy-button:focus,html.theme--documenter-dark pre .copy-button:hover{opacity:1;background:rgba(255,255,255,0.1);color:#1abc9c}html.theme--documenter-dark pre .copy-button.success{color:#259a12;opacity:1}html.theme--documenter-dark pre .copy-button.error{color:#cb3c33;opacity:1}html.theme--documenter-dark pre:hover .copy-button{opacity:1}html.theme--documenter-dark .admonition{background-color:#282f2f;border-style:solid;border-width:1px;border-color:#5e6d6f;border-radius:.4em;font-size:1rem}html.theme--documenter-dark .admonition strong{color:currentColor}html.theme--documenter-dark .admonition.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.admonition{font-size:.75rem}html.theme--documenter-dark .admonition.is-medium{font-size:1.25rem}html.theme--documenter-dark .admonition.is-large{font-size:1.5rem}html.theme--documenter-dark .admonition.is-default{background-color:#282f2f;border-color:#5e6d6f}html.theme--documenter-dark .admonition.is-default>.admonition-header{background-color:#5e6d6f;color:#fff}html.theme--documenter-dark .admonition.is-default>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-info{background-color:#282f2f;border-color:#024c7d}html.theme--documenter-dark .admonition.is-info>.admonition-header{background-color:#024c7d;color:#fff}html.theme--documenter-dark .admonition.is-info>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-success{background-color:#282f2f;border-color:#008438}html.theme--documenter-dark .admonition.is-success>.admonition-header{background-color:#008438;color:#fff}html.theme--documenter-dark .admonition.is-success>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-warning{background-color:#282f2f;border-color:#ad8100}html.theme--documenter-dark .admonition.is-warning>.admonition-header{background-color:#ad8100;color:#fff}html.theme--documenter-dark .admonition.is-warning>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-danger{background-color:#282f2f;border-color:#9e1b0d}html.theme--documenter-dark .admonition.is-danger>.admonition-header{background-color:#9e1b0d;color:#fff}html.theme--documenter-dark .admonition.is-danger>.admonition-body{color:#fff}html.theme--documenter-dark .admonition.is-compat{background-color:#282f2f;border-color:#137886}html.theme--documenter-dark .admonition.is-compat>.admonition-header{background-color:#137886;color:#fff}html.theme--documenter-dark .admonition.is-compat>.admonition-body{color:#fff}html.theme--documenter-dark .admonition-header{color:#fff;background-color:#5e6d6f;align-items:center;font-weight:700;justify-content:space-between;line-height:1.25;padding:0.5rem .75rem;position:relative}html.theme--documenter-dark .admonition-header:before{font-family:"Font Awesome 6 Free";font-weight:900;margin-right:.75rem;content:"\f06a"}html.theme--documenter-dark details.admonition.is-details>.admonition-header{list-style:none}html.theme--documenter-dark details.admonition.is-details>.admonition-header:before{font-family:"Font Awesome 6 Free";font-weight:900;content:"\f055"}html.theme--documenter-dark details.admonition.is-details[open]>.admonition-header:before{font-family:"Font Awesome 6 Free";font-weight:900;content:"\f056"}html.theme--documenter-dark .admonition-body{color:#fff;padding:0.5rem .75rem}html.theme--documenter-dark .admonition-body pre{background-color:#282f2f}html.theme--documenter-dark .admonition-body code{background-color:rgba(255,255,255,0.05)}html.theme--documenter-dark .docstring{margin-bottom:1em;background-color:rgba(0,0,0,0);border:1px solid #5e6d6f;box-shadow:none;max-width:100%}html.theme--documenter-dark .docstring>header{cursor:pointer;display:flex;flex-grow:1;align-items:stretch;padding:0.5rem .75rem;background-color:#282f2f;box-shadow:0 0.125em 0.25em rgba(10,10,10,0.1);box-shadow:none;border-bottom:1px solid #5e6d6f;overflow:auto}html.theme--documenter-dark .docstring>header code{background-color:transparent}html.theme--documenter-dark .docstring>header .docstring-article-toggle-button{min-width:1.1rem;padding:0.2rem 0.2rem 0.2rem 0}html.theme--documenter-dark .docstring>header .docstring-binding{margin-right:0.3em}html.theme--documenter-dark .docstring>header .docstring-category{margin-left:0.3em}html.theme--documenter-dark .docstring>section{position:relative;padding:.75rem .75rem;border-bottom:1px solid #5e6d6f}html.theme--documenter-dark .docstring>section:last-child{border-bottom:none}html.theme--documenter-dark .docstring>section>a.docs-sourcelink{transition:opacity 0.3s;opacity:0;position:absolute;right:.375rem;bottom:.375rem}html.theme--documenter-dark .docstring>section>a.docs-sourcelink:focus{opacity:1 !important}html.theme--documenter-dark .docstring:hover>section>a.docs-sourcelink{opacity:0.2}html.theme--documenter-dark .docstring:focus-within>section>a.docs-sourcelink{opacity:0.2}html.theme--documenter-dark .docstring>section:hover a.docs-sourcelink{opacity:1}html.theme--documenter-dark .documenter-example-output{background-color:#1f2424}html.theme--documenter-dark .outdated-warning-overlay{position:fixed;top:0;left:0;right:0;box-shadow:0 0 10px rgba(0,0,0,0.3);z-index:999;background-color:#282f2f;color:#fff;border-bottom:3px solid #9e1b0d;padding:10px 35px;text-align:center;font-size:15px}html.theme--documenter-dark .outdated-warning-overlay .outdated-warning-closer{position:absolute;top:calc(50% - 10px);right:18px;cursor:pointer;width:12px}html.theme--documenter-dark .outdated-warning-overlay a{color:#1abc9c}html.theme--documenter-dark .outdated-warning-overlay a:hover{color:#1dd2af}html.theme--documenter-dark .content pre{border:1px solid #5e6d6f}html.theme--documenter-dark .content code{font-weight:inherit}html.theme--documenter-dark .content a code{color:#1abc9c}html.theme--documenter-dark .content h1 code,html.theme--documenter-dark .content h2 code,html.theme--documenter-dark .content h3 code,html.theme--documenter-dark .content h4 code,html.theme--documenter-dark .content h5 code,html.theme--documenter-dark .content h6 code{color:#f2f2f2}html.theme--documenter-dark .content table{display:block;width:initial;max-width:100%;overflow-x:auto}html.theme--documenter-dark .content blockquote>ul:first-child,html.theme--documenter-dark .content blockquote>ol:first-child,html.theme--documenter-dark .content .admonition-body>ul:first-child,html.theme--documenter-dark .content .admonition-body>ol:first-child{margin-top:0}html.theme--documenter-dark pre,html.theme--documenter-dark code{font-variant-ligatures:no-contextual}html.theme--documenter-dark .breadcrumb a.is-disabled{cursor:default;pointer-events:none}html.theme--documenter-dark .breadcrumb a.is-disabled,html.theme--documenter-dark .breadcrumb a.is-disabled:hover{color:#f2f2f2}html.theme--documenter-dark .hljs{background:initial !important}html.theme--documenter-dark .katex .katex-mathml{top:0;right:0}html.theme--documenter-dark .katex-display,html.theme--documenter-dark mjx-container,html.theme--documenter-dark .MathJax_Display{margin:0.5em 0 !important}html.theme--documenter-dark html{-moz-osx-font-smoothing:auto;-webkit-font-smoothing:auto}html.theme--documenter-dark li.no-marker{list-style:none}html.theme--documenter-dark #documenter .docs-main>article{overflow-wrap:break-word}html.theme--documenter-dark #documenter .docs-main>article .math-container{overflow-x:auto;overflow-y:hidden}@media screen and (min-width: 1056px){html.theme--documenter-dark #documenter .docs-main{max-width:52rem;margin-left:20rem;padding-right:1rem}}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-main{width:100%}html.theme--documenter-dark #documenter .docs-main>article{max-width:52rem;margin-left:auto;margin-right:auto;margin-bottom:1rem;padding:0 1rem}html.theme--documenter-dark #documenter .docs-main>header,html.theme--documenter-dark #documenter .docs-main>nav{max-width:100%;width:100%;margin:0}}html.theme--documenter-dark #documenter .docs-main header.docs-navbar{background-color:#1f2424;border-bottom:1px solid #5e6d6f;z-index:2;min-height:4rem;margin-bottom:1rem;display:flex}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .breadcrumb{flex-grow:1;overflow-x:hidden}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-sidebar-button{display:block;font-size:1.5rem;padding-bottom:0.1rem;margin-right:1rem}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right{display:flex;white-space:nowrap;gap:1rem;align-items:center}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-icon,html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-label{display:inline-block}html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-label{padding:0;margin-left:0.3em}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-main header.docs-navbar .docs-right .docs-navbar-link{margin-left:0.4rem;margin-right:0.4rem}}html.theme--documenter-dark #documenter .docs-main header.docs-navbar>*{margin:auto 0}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-main header.docs-navbar{position:sticky;top:0;padding:0 1rem;transition-property:top, box-shadow;-webkit-transition-property:top, box-shadow;transition-duration:0.3s;-webkit-transition-duration:0.3s}html.theme--documenter-dark #documenter .docs-main header.docs-navbar.headroom--not-top{box-shadow:.2rem 0rem .4rem #171717;transition-duration:0.7s;-webkit-transition-duration:0.7s}html.theme--documenter-dark #documenter .docs-main header.docs-navbar.headroom--unpinned.headroom--not-top.headroom--not-bottom{top:-4.5rem;transition-duration:0.7s;-webkit-transition-duration:0.7s}}html.theme--documenter-dark #documenter .docs-main section.footnotes{border-top:1px solid #5e6d6f}html.theme--documenter-dark #documenter .docs-main section.footnotes li .tag:first-child,html.theme--documenter-dark #documenter .docs-main section.footnotes li .docstring>section>a.docs-sourcelink:first-child,html.theme--documenter-dark #documenter .docs-main section.footnotes li .content kbd:first-child,html.theme--documenter-dark .content #documenter .docs-main section.footnotes li kbd:first-child{margin-right:1em;margin-bottom:0.4em}html.theme--documenter-dark #documenter .docs-main .docs-footer{display:flex;flex-wrap:wrap;margin-left:0;margin-right:0;border-top:1px solid #5e6d6f;padding-top:1rem;padding-bottom:1rem}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-main .docs-footer{padding-left:1rem;padding-right:1rem}}html.theme--documenter-dark #documenter .docs-main .docs-footer .docs-footer-nextpage,html.theme--documenter-dark #documenter .docs-main .docs-footer .docs-footer-prevpage{flex-grow:1}html.theme--documenter-dark #documenter .docs-main .docs-footer .docs-footer-nextpage{text-align:right}html.theme--documenter-dark #documenter .docs-main .docs-footer .flexbox-break{flex-basis:100%;height:0}html.theme--documenter-dark #documenter .docs-main .docs-footer .footer-message{font-size:0.8em;margin:0.5em auto 0 auto;text-align:center}html.theme--documenter-dark #documenter .docs-sidebar{display:flex;flex-direction:column;color:#fff;background-color:#282f2f;border-right:1px solid #5e6d6f;padding:0;flex:0 0 18rem;z-index:5;font-size:1rem;position:fixed;left:-18rem;width:18rem;height:100%;transition:left 0.3s}html.theme--documenter-dark #documenter .docs-sidebar.visible{left:0;box-shadow:.4rem 0rem .8rem #171717}@media screen and (min-width: 1056px){html.theme--documenter-dark #documenter .docs-sidebar.visible{box-shadow:none}}@media screen and (min-width: 1056px){html.theme--documenter-dark #documenter .docs-sidebar{left:0;top:0}}html.theme--documenter-dark #documenter .docs-sidebar .docs-logo{margin-top:1rem;padding:0 1rem}html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{max-height:6rem;margin:auto}html.theme--documenter-dark #documenter .docs-sidebar .docs-package-name{flex-shrink:0;font-size:1.5rem;font-weight:700;text-align:center;white-space:nowrap;overflow:hidden;padding:0.5rem 0}html.theme--documenter-dark #documenter .docs-sidebar .docs-package-name .docs-autofit{max-width:16.2rem}html.theme--documenter-dark #documenter .docs-sidebar .docs-package-name a,html.theme--documenter-dark #documenter .docs-sidebar .docs-package-name a:hover{color:#fff}html.theme--documenter-dark #documenter .docs-sidebar .docs-version-selector{border-top:1px solid #5e6d6f;display:none;padding:0.5rem}html.theme--documenter-dark #documenter .docs-sidebar .docs-version-selector.visible{display:flex}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu{flex-grow:1;user-select:none;border-top:1px solid #5e6d6f;padding-bottom:1.5rem}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu>li>.tocitem{font-weight:bold}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu>li li{font-size:.95rem;margin-left:1em;border-left:1px solid #5e6d6f}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu input.collapse-toggle{display:none}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.collapsed{display:none}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu input:checked~ul.collapsed{display:block}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem{display:flex}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem .docs-label{flex-grow:2}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem .docs-chevron{display:inline-block;font-style:normal;font-variant:normal;text-rendering:auto;line-height:1;font-size:.75rem;margin-left:1rem;margin-top:auto;margin-bottom:auto}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem .docs-chevron::before{font-family:"Font Awesome 6 Free";font-weight:900;content:"\f054"}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu input:checked~label.tocitem .docs-chevron::before{content:"\f078"}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu .tocitem{display:block;padding:0.5rem 0.5rem}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu .tocitem,html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu .tocitem:hover{color:#fff;background:#282f2f}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu a.tocitem:hover,html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu label.tocitem:hover{color:#fff;background-color:#32393a}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu li.is-active{border-top:1px solid #5e6d6f;border-bottom:1px solid #5e6d6f;background-color:#1f2424}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu li.is-active .tocitem,html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu li.is-active .tocitem:hover{background-color:#1f2424;color:#fff}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu li.is-active ul.internal .tocitem:hover{background-color:#32393a;color:#fff}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu>li.is-active:first-child{border-top:none}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.internal{margin:0 0.5rem 0.5rem;border-top:1px solid #5e6d6f}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.internal li{font-size:.85rem;border-left:none;margin-left:0;margin-top:0.5rem}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.internal .tocitem{width:100%;padding:0}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu ul.internal .tocitem::before{content:"⚬";margin-right:0.4em}html.theme--documenter-dark #documenter .docs-sidebar form.docs-search{margin:auto;margin-top:0.5rem;margin-bottom:0.5rem}html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input{width:14.4rem}html.theme--documenter-dark #documenter .docs-sidebar #documenter-search-query{color:#868c98;width:14.4rem;box-shadow:inset 0 1px 2px rgba(10,10,10,0.1)}@media screen and (min-width: 1056px){html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu{overflow-y:auto;-webkit-overflow-scroll:touch}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar{width:.3rem;background:none}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar-thumb{border-radius:5px 0px 0px 5px;background:#3b4445}html.theme--documenter-dark #documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar-thumb:hover{background:#4e5a5c}}@media screen and (max-width: 1055px){html.theme--documenter-dark #documenter .docs-sidebar{overflow-y:auto;-webkit-overflow-scroll:touch}html.theme--documenter-dark #documenter .docs-sidebar::-webkit-scrollbar{width:.3rem;background:none}html.theme--documenter-dark #documenter .docs-sidebar::-webkit-scrollbar-thumb{border-radius:5px 0px 0px 5px;background:#3b4445}html.theme--documenter-dark #documenter .docs-sidebar::-webkit-scrollbar-thumb:hover{background:#4e5a5c}}html.theme--documenter-dark kbd.search-modal-key-hints{border-radius:0.25rem;border:1px solid rgba(245,245,245,0.6);box-shadow:0 2px 0 1px rgba(245,245,245,0.6);cursor:default;font-size:0.9rem;line-height:1.5;min-width:0.75rem;text-align:center;padding:0.1rem 0.3rem;position:relative;top:-1px}html.theme--documenter-dark .search-min-width-50{min-width:50%}html.theme--documenter-dark .search-min-height-100{min-height:100%}html.theme--documenter-dark .search-modal-card-body{max-height:calc(100vh - 15rem)}html.theme--documenter-dark .search-result-link{border-radius:0.7em;transition:all 300ms}html.theme--documenter-dark .search-result-link:hover,html.theme--documenter-dark .search-result-link:focus{background-color:rgba(0,128,128,0.1)}html.theme--documenter-dark .search-result-link .property-search-result-badge,html.theme--documenter-dark .search-result-link .search-filter{transition:all 300ms}html.theme--documenter-dark .property-search-result-badge,html.theme--documenter-dark .search-filter{padding:0.15em 0.5em;font-size:0.8em;font-style:italic;text-transform:none !important;line-height:1.5;color:#f5f5f5;background-color:rgba(51,65,85,0.501961);border-radius:0.6rem}html.theme--documenter-dark .search-result-link:hover .property-search-result-badge,html.theme--documenter-dark .search-result-link:hover .search-filter,html.theme--documenter-dark .search-result-link:focus .property-search-result-badge,html.theme--documenter-dark .search-result-link:focus .search-filter{color:#333;background-color:#f1f5f9}html.theme--documenter-dark .search-filter{color:#333;background-color:#f5f5f5;transition:all 300ms}html.theme--documenter-dark .search-filter:hover,html.theme--documenter-dark .search-filter:focus{color:#333}html.theme--documenter-dark .search-filter-selected{color:#f5f5f5;background-color:rgba(139,0,139,0.5)}html.theme--documenter-dark .search-filter-selected:hover,html.theme--documenter-dark .search-filter-selected:focus{color:#f5f5f5}html.theme--documenter-dark .search-result-highlight{background-color:#ffdd57;color:black}html.theme--documenter-dark .search-divider{border-bottom:1px solid #5e6d6f}html.theme--documenter-dark .search-result-title{width:85%;color:#f5f5f5}html.theme--documenter-dark .search-result-code-title{font-size:0.875rem;font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace}html.theme--documenter-dark #search-modal .modal-card-body::-webkit-scrollbar,html.theme--documenter-dark #search-modal .filter-tabs::-webkit-scrollbar{height:10px;width:10px;background-color:transparent}html.theme--documenter-dark #search-modal .modal-card-body::-webkit-scrollbar-thumb,html.theme--documenter-dark #search-modal .filter-tabs::-webkit-scrollbar-thumb{background-color:gray;border-radius:1rem}html.theme--documenter-dark #search-modal .modal-card-body::-webkit-scrollbar-track,html.theme--documenter-dark #search-modal .filter-tabs::-webkit-scrollbar-track{-webkit-box-shadow:inset 0 0 6px rgba(0,0,0,0.6);background-color:transparent}html.theme--documenter-dark .w-100{width:100%}html.theme--documenter-dark .gap-2{gap:0.5rem}html.theme--documenter-dark .gap-4{gap:1rem}html.theme--documenter-dark .gap-8{gap:2rem}html.theme--documenter-dark{background-color:#1f2424;font-size:16px;min-width:300px;overflow-x:auto;overflow-y:scroll;text-rendering:optimizeLegibility;text-size-adjust:100%}html.theme--documenter-dark .ansi span.sgr1{font-weight:bolder}html.theme--documenter-dark .ansi span.sgr2{font-weight:lighter}html.theme--documenter-dark .ansi span.sgr3{font-style:italic}html.theme--documenter-dark .ansi span.sgr4{text-decoration:underline}html.theme--documenter-dark .ansi span.sgr7{color:#1f2424;background-color:#fff}html.theme--documenter-dark .ansi span.sgr8{color:transparent}html.theme--documenter-dark .ansi span.sgr8 span{color:transparent}html.theme--documenter-dark .ansi span.sgr9{text-decoration:line-through}html.theme--documenter-dark .ansi span.sgr30{color:#242424}html.theme--documenter-dark .ansi span.sgr31{color:#f6705f}html.theme--documenter-dark .ansi span.sgr32{color:#4fb43a}html.theme--documenter-dark .ansi span.sgr33{color:#f4c72f}html.theme--documenter-dark .ansi span.sgr34{color:#7587f0}html.theme--documenter-dark .ansi span.sgr35{color:#bc89d3}html.theme--documenter-dark .ansi span.sgr36{color:#49b6ca}html.theme--documenter-dark .ansi span.sgr37{color:#b3bdbe}html.theme--documenter-dark .ansi span.sgr40{background-color:#242424}html.theme--documenter-dark .ansi span.sgr41{background-color:#f6705f}html.theme--documenter-dark .ansi span.sgr42{background-color:#4fb43a}html.theme--documenter-dark .ansi span.sgr43{background-color:#f4c72f}html.theme--documenter-dark .ansi span.sgr44{background-color:#7587f0}html.theme--documenter-dark .ansi span.sgr45{background-color:#bc89d3}html.theme--documenter-dark .ansi span.sgr46{background-color:#49b6ca}html.theme--documenter-dark .ansi span.sgr47{background-color:#b3bdbe}html.theme--documenter-dark .ansi span.sgr90{color:#92a0a2}html.theme--documenter-dark .ansi span.sgr91{color:#ff8674}html.theme--documenter-dark .ansi span.sgr92{color:#79d462}html.theme--documenter-dark .ansi span.sgr93{color:#ffe76b}html.theme--documenter-dark .ansi span.sgr94{color:#8a98ff}html.theme--documenter-dark .ansi span.sgr95{color:#d2a4e6}html.theme--documenter-dark .ansi span.sgr96{color:#6bc8db}html.theme--documenter-dark .ansi span.sgr97{color:#ecf0f1}html.theme--documenter-dark .ansi span.sgr100{background-color:#92a0a2}html.theme--documenter-dark .ansi span.sgr101{background-color:#ff8674}html.theme--documenter-dark .ansi span.sgr102{background-color:#79d462}html.theme--documenter-dark .ansi span.sgr103{background-color:#ffe76b}html.theme--documenter-dark .ansi span.sgr104{background-color:#8a98ff}html.theme--documenter-dark .ansi span.sgr105{background-color:#d2a4e6}html.theme--documenter-dark .ansi span.sgr106{background-color:#6bc8db}html.theme--documenter-dark .ansi span.sgr107{background-color:#ecf0f1}html.theme--documenter-dark code.language-julia-repl>span.hljs-meta{color:#4fb43a;font-weight:bolder}html.theme--documenter-dark .hljs{background:#2b2b2b;color:#f8f8f2}html.theme--documenter-dark .hljs-comment,html.theme--documenter-dark .hljs-quote{color:#d4d0ab}html.theme--documenter-dark .hljs-variable,html.theme--documenter-dark .hljs-template-variable,html.theme--documenter-dark .hljs-tag,html.theme--documenter-dark .hljs-name,html.theme--documenter-dark .hljs-selector-id,html.theme--documenter-dark .hljs-selector-class,html.theme--documenter-dark .hljs-regexp,html.theme--documenter-dark .hljs-deletion{color:#ffa07a}html.theme--documenter-dark .hljs-number,html.theme--documenter-dark .hljs-built_in,html.theme--documenter-dark .hljs-literal,html.theme--documenter-dark .hljs-type,html.theme--documenter-dark .hljs-params,html.theme--documenter-dark .hljs-meta,html.theme--documenter-dark .hljs-link{color:#f5ab35}html.theme--documenter-dark .hljs-attribute{color:#ffd700}html.theme--documenter-dark .hljs-string,html.theme--documenter-dark .hljs-symbol,html.theme--documenter-dark .hljs-bullet,html.theme--documenter-dark .hljs-addition{color:#abe338}html.theme--documenter-dark .hljs-title,html.theme--documenter-dark .hljs-section{color:#00e0e0}html.theme--documenter-dark .hljs-keyword,html.theme--documenter-dark .hljs-selector-tag{color:#dcc6e0}html.theme--documenter-dark .hljs-emphasis{font-style:italic}html.theme--documenter-dark .hljs-strong{font-weight:bold}@media screen and (-ms-high-contrast: active){html.theme--documenter-dark .hljs-addition,html.theme--documenter-dark .hljs-attribute,html.theme--documenter-dark .hljs-built_in,html.theme--documenter-dark .hljs-bullet,html.theme--documenter-dark .hljs-comment,html.theme--documenter-dark .hljs-link,html.theme--documenter-dark .hljs-literal,html.theme--documenter-dark .hljs-meta,html.theme--documenter-dark .hljs-number,html.theme--documenter-dark .hljs-params,html.theme--documenter-dark .hljs-string,html.theme--documenter-dark .hljs-symbol,html.theme--documenter-dark .hljs-type,html.theme--documenter-dark .hljs-quote{color:highlight}html.theme--documenter-dark .hljs-keyword,html.theme--documenter-dark .hljs-selector-tag{font-weight:bold}}html.theme--documenter-dark .hljs-subst{color:#f8f8f2}html.theme--documenter-dark .search-result-link{border-radius:0.7em;transition:all 300ms}html.theme--documenter-dark .search-result-link:hover,html.theme--documenter-dark .search-result-link:focus{background-color:rgba(0,128,128,0.1)}html.theme--documenter-dark .search-result-link .property-search-result-badge,html.theme--documenter-dark .search-result-link .search-filter{transition:all 300ms}html.theme--documenter-dark .search-result-link:hover .property-search-result-badge,html.theme--documenter-dark .search-result-link:hover .search-filter,html.theme--documenter-dark .search-result-link:focus .property-search-result-badge,html.theme--documenter-dark .search-result-link:focus .search-filter{color:#333 !important;background-color:#f1f5f9 !important}html.theme--documenter-dark .search-result-title{color:whitesmoke}html.theme--documenter-dark .search-result-highlight{background-color:greenyellow;color:black}html.theme--documenter-dark .search-divider{border-bottom:1px solid #5e6d6f50}html.theme--documenter-dark .w-100{width:100%}html.theme--documenter-dark .gap-2{gap:0.5rem}html.theme--documenter-dark .gap-4{gap:1rem} diff --git a/dev/assets/themes/documenter-light.css b/dev/assets/themes/documenter-light.css index 9b9a14b04..2f168c77b 100644 --- a/dev/assets/themes/documenter-light.css +++ b/dev/assets/themes/documenter-light.css @@ -1,4 +1,4 @@ -@keyframes spinAround{from{transform:rotate(0deg)}to{transform:rotate(359deg)}}.tabs,.pagination-previous,.pagination-next,.pagination-link,.pagination-ellipsis,.breadcrumb,.file,.button,.is-unselectable,.modal-close,.delete{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.navbar-link:not(.is-arrowless)::after,.select:not(.is-multiple):not(.is-loading)::after{border:3px solid rgba(0,0,0,0);border-radius:2px;border-right:0;border-top:0;content:" ";display:block;height:0.625em;margin-top:-0.4375em;pointer-events:none;position:absolute;top:50%;transform:rotate(-45deg);transform-origin:center;width:0.625em}.admonition:not(:last-child),.tabs:not(:last-child),.message:not(:last-child),.list:not(:last-child),.level:not(:last-child),.breadcrumb:not(:last-child),.highlight:not(:last-child),.block:not(:last-child),.title:not(:last-child),.subtitle:not(:last-child),.table-container:not(:last-child),.table:not(:last-child),.progress:not(:last-child),.notification:not(:last-child),.content:not(:last-child),.box:not(:last-child){margin-bottom:1.5rem}.modal-close,.delete{-moz-appearance:none;-webkit-appearance:none;background-color:rgba(10,10,10,0.2);border:none;border-radius:290486px;cursor:pointer;pointer-events:auto;display:inline-block;flex-grow:0;flex-shrink:0;font-size:0;height:20px;max-height:20px;max-width:20px;min-height:20px;min-width:20px;outline:none;position:relative;vertical-align:top;width:20px}.modal-close::before,.delete::before,.modal-close::after,.delete::after{background-color:#fff;content:"";display:block;left:50%;position:absolute;top:50%;transform:translateX(-50%) translateY(-50%) rotate(45deg);transform-origin:center center}.modal-close::before,.delete::before{height:2px;width:50%}.modal-close::after,.delete::after{height:50%;width:2px}.modal-close:hover,.delete:hover,.modal-close:focus,.delete:focus{background-color:rgba(10,10,10,0.3)}.modal-close:active,.delete:active{background-color:rgba(10,10,10,0.4)}.is-small.modal-close,#documenter .docs-sidebar form.docs-search>input.modal-close,.is-small.delete,#documenter .docs-sidebar form.docs-search>input.delete{height:16px;max-height:16px;max-width:16px;min-height:16px;min-width:16px;width:16px}.is-medium.modal-close,.is-medium.delete{height:24px;max-height:24px;max-width:24px;min-height:24px;min-width:24px;width:24px}.is-large.modal-close,.is-large.delete{height:32px;max-height:32px;max-width:32px;min-height:32px;min-width:32px;width:32px}.control.is-loading::after,.select.is-loading::after,.loader,.button.is-loading::after{animation:spinAround 500ms infinite linear;border:2px solid #dbdbdb;border-radius:290486px;border-right-color:transparent;border-top-color:transparent;content:"";display:block;height:1em;position:relative;width:1em}.hero-video,.modal-background,.modal,.image.is-square img,#documenter .docs-sidebar .docs-logo>img.is-square img,.image.is-square .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,.image.is-1by1 img,#documenter .docs-sidebar .docs-logo>img.is-1by1 img,.image.is-1by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,.image.is-5by4 img,#documenter .docs-sidebar .docs-logo>img.is-5by4 img,.image.is-5by4 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,.image.is-4by3 img,#documenter .docs-sidebar .docs-logo>img.is-4by3 img,.image.is-4by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,.image.is-3by2 img,#documenter .docs-sidebar .docs-logo>img.is-3by2 img,.image.is-3by2 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,.image.is-5by3 img,#documenter .docs-sidebar .docs-logo>img.is-5by3 img,.image.is-5by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,.image.is-16by9 img,#documenter .docs-sidebar .docs-logo>img.is-16by9 img,.image.is-16by9 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,.image.is-2by1 img,#documenter .docs-sidebar .docs-logo>img.is-2by1 img,.image.is-2by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,.image.is-3by1 img,#documenter .docs-sidebar .docs-logo>img.is-3by1 img,.image.is-3by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,.image.is-4by5 img,#documenter .docs-sidebar .docs-logo>img.is-4by5 img,.image.is-4by5 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,.image.is-3by4 img,#documenter .docs-sidebar .docs-logo>img.is-3by4 img,.image.is-3by4 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,.image.is-2by3 img,#documenter .docs-sidebar .docs-logo>img.is-2by3 img,.image.is-2by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,.image.is-3by5 img,#documenter .docs-sidebar .docs-logo>img.is-3by5 img,.image.is-3by5 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,.image.is-9by16 img,#documenter .docs-sidebar .docs-logo>img.is-9by16 img,.image.is-9by16 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,.image.is-1by2 img,#documenter .docs-sidebar .docs-logo>img.is-1by2 img,.image.is-1by2 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,.image.is-1by3 img,#documenter .docs-sidebar .docs-logo>img.is-1by3 img,.image.is-1by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio,.is-overlay{bottom:0;left:0;position:absolute;right:0;top:0}.pagination-previous,.pagination-next,.pagination-link,.pagination-ellipsis,.file-cta,.file-name,.select select,.textarea,.input,#documenter .docs-sidebar form.docs-search>input,.button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid transparent;border-radius:4px;box-shadow:none;display:inline-flex;font-size:1rem;height:2.25em;justify-content:flex-start;line-height:1.5;padding-bottom:calc(0.375em - 1px);padding-left:calc(0.625em - 1px);padding-right:calc(0.625em - 1px);padding-top:calc(0.375em - 1px);position:relative;vertical-align:top}.pagination-previous:focus,.pagination-next:focus,.pagination-link:focus,.pagination-ellipsis:focus,.file-cta:focus,.file-name:focus,.select select:focus,.textarea:focus,.input:focus,#documenter .docs-sidebar form.docs-search>input:focus,.button:focus,.is-focused.pagination-previous,.is-focused.pagination-next,.is-focused.pagination-link,.is-focused.pagination-ellipsis,.is-focused.file-cta,.is-focused.file-name,.select select.is-focused,.is-focused.textarea,.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-focused.button,.pagination-previous:active,.pagination-next:active,.pagination-link:active,.pagination-ellipsis:active,.file-cta:active,.file-name:active,.select select:active,.textarea:active,.input:active,#documenter .docs-sidebar form.docs-search>input:active,.button:active,.is-active.pagination-previous,.is-active.pagination-next,.is-active.pagination-link,.is-active.pagination-ellipsis,.is-active.file-cta,.is-active.file-name,.select select.is-active,.is-active.textarea,.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active,.is-active.button{outline:none}.pagination-previous[disabled],.pagination-next[disabled],.pagination-link[disabled],.pagination-ellipsis[disabled],.file-cta[disabled],.file-name[disabled],.select select[disabled],.textarea[disabled],.input[disabled],#documenter .docs-sidebar form.docs-search>input[disabled],.button[disabled],fieldset[disabled] .pagination-previous,fieldset[disabled] .pagination-next,fieldset[disabled] .pagination-link,fieldset[disabled] .pagination-ellipsis,fieldset[disabled] .file-cta,fieldset[disabled] .file-name,fieldset[disabled] .select select,.select fieldset[disabled] select,fieldset[disabled] .textarea,fieldset[disabled] .input,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input,fieldset[disabled] .button{cursor:not-allowed}/*! minireset.css v0.0.4 | MIT License | github.com/jgthms/minireset.css */html,body,p,ol,ul,li,dl,dt,dd,blockquote,figure,fieldset,legend,textarea,pre,iframe,hr,h1,h2,h3,h4,h5,h6{margin:0;padding:0}h1,h2,h3,h4,h5,h6{font-size:100%;font-weight:normal}ul{list-style:none}button,input,select,textarea{margin:0}html{box-sizing:border-box}*,*::before,*::after{box-sizing:inherit}img,embed,iframe,object,video{height:auto;max-width:100%}audio{max-width:100%}iframe{border:0}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}td:not([align]),th:not([align]){text-align:left}html{background-color:#fff;font-size:16px;-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;min-width:300px;overflow-x:auto;overflow-y:scroll;text-rendering:optimizeLegibility;text-size-adjust:100%}article,aside,figure,footer,header,hgroup,section{display:block}body,button,input,select,textarea{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif}code,pre{-moz-osx-font-smoothing:auto;-webkit-font-smoothing:auto;font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace}body{color:#222;font-size:1em;font-weight:400;line-height:1.5}a{color:#2e63b8;cursor:pointer;text-decoration:none}a strong{color:currentColor}a:hover{color:#363636}code{background-color:rgba(0,0,0,0.05);color:#000;font-size:.875em;font-weight:normal;padding:.1em}hr{background-color:#f5f5f5;border:none;display:block;height:2px;margin:1.5rem 0}img{height:auto;max-width:100%}input[type="checkbox"],input[type="radio"]{vertical-align:baseline}small{font-size:.875em}span{font-style:inherit;font-weight:inherit}strong{color:#222;font-weight:700}fieldset{border:none}pre{-webkit-overflow-scrolling:touch;background-color:#f5f5f5;color:#222;font-size:.875em;overflow-x:auto;padding:1.25rem 1.5rem;white-space:pre;word-wrap:normal}pre code{background-color:transparent;color:currentColor;font-size:1em;padding:0}table td,table th{vertical-align:top}table td:not([align]),table th:not([align]){text-align:left}table th{color:#222}.is-clearfix::after{clear:both;content:" ";display:table}.is-pulled-left{float:left !important}.is-pulled-right{float:right !important}.is-clipped{overflow:hidden !important}.is-size-1{font-size:3rem !important}.is-size-2{font-size:2.5rem !important}.is-size-3{font-size:2rem !important}.is-size-4{font-size:1.5rem !important}.is-size-5{font-size:1.25rem !important}.is-size-6{font-size:1rem !important}.is-size-7,.docstring>section>a.docs-sourcelink{font-size:.75rem !important}@media screen and (max-width: 768px){.is-size-1-mobile{font-size:3rem !important}.is-size-2-mobile{font-size:2.5rem !important}.is-size-3-mobile{font-size:2rem !important}.is-size-4-mobile{font-size:1.5rem !important}.is-size-5-mobile{font-size:1.25rem !important}.is-size-6-mobile{font-size:1rem !important}.is-size-7-mobile{font-size:.75rem !important}}@media screen and (min-width: 769px),print{.is-size-1-tablet{font-size:3rem !important}.is-size-2-tablet{font-size:2.5rem !important}.is-size-3-tablet{font-size:2rem !important}.is-size-4-tablet{font-size:1.5rem !important}.is-size-5-tablet{font-size:1.25rem !important}.is-size-6-tablet{font-size:1rem !important}.is-size-7-tablet{font-size:.75rem !important}}@media screen and (max-width: 1055px){.is-size-1-touch{font-size:3rem !important}.is-size-2-touch{font-size:2.5rem !important}.is-size-3-touch{font-size:2rem !important}.is-size-4-touch{font-size:1.5rem !important}.is-size-5-touch{font-size:1.25rem !important}.is-size-6-touch{font-size:1rem !important}.is-size-7-touch{font-size:.75rem !important}}@media screen and (min-width: 1056px){.is-size-1-desktop{font-size:3rem !important}.is-size-2-desktop{font-size:2.5rem !important}.is-size-3-desktop{font-size:2rem !important}.is-size-4-desktop{font-size:1.5rem !important}.is-size-5-desktop{font-size:1.25rem !important}.is-size-6-desktop{font-size:1rem !important}.is-size-7-desktop{font-size:.75rem !important}}@media screen and (min-width: 1216px){.is-size-1-widescreen{font-size:3rem !important}.is-size-2-widescreen{font-size:2.5rem !important}.is-size-3-widescreen{font-size:2rem !important}.is-size-4-widescreen{font-size:1.5rem !important}.is-size-5-widescreen{font-size:1.25rem !important}.is-size-6-widescreen{font-size:1rem !important}.is-size-7-widescreen{font-size:.75rem !important}}@media screen and (min-width: 1408px){.is-size-1-fullhd{font-size:3rem !important}.is-size-2-fullhd{font-size:2.5rem !important}.is-size-3-fullhd{font-size:2rem !important}.is-size-4-fullhd{font-size:1.5rem !important}.is-size-5-fullhd{font-size:1.25rem !important}.is-size-6-fullhd{font-size:1rem !important}.is-size-7-fullhd{font-size:.75rem !important}}.has-text-centered{text-align:center !important}.has-text-justified{text-align:justify !important}.has-text-left{text-align:left !important}.has-text-right{text-align:right !important}@media screen and (max-width: 768px){.has-text-centered-mobile{text-align:center !important}}@media screen and (min-width: 769px),print{.has-text-centered-tablet{text-align:center !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-centered-tablet-only{text-align:center !important}}@media screen and (max-width: 1055px){.has-text-centered-touch{text-align:center !important}}@media screen and (min-width: 1056px){.has-text-centered-desktop{text-align:center !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-centered-desktop-only{text-align:center !important}}@media screen and (min-width: 1216px){.has-text-centered-widescreen{text-align:center !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-centered-widescreen-only{text-align:center !important}}@media screen and (min-width: 1408px){.has-text-centered-fullhd{text-align:center !important}}@media screen and (max-width: 768px){.has-text-justified-mobile{text-align:justify !important}}@media screen and (min-width: 769px),print{.has-text-justified-tablet{text-align:justify !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-justified-tablet-only{text-align:justify !important}}@media screen and (max-width: 1055px){.has-text-justified-touch{text-align:justify !important}}@media screen and (min-width: 1056px){.has-text-justified-desktop{text-align:justify !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-justified-desktop-only{text-align:justify !important}}@media screen and (min-width: 1216px){.has-text-justified-widescreen{text-align:justify !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-justified-widescreen-only{text-align:justify !important}}@media screen and (min-width: 1408px){.has-text-justified-fullhd{text-align:justify !important}}@media screen and (max-width: 768px){.has-text-left-mobile{text-align:left !important}}@media screen and (min-width: 769px),print{.has-text-left-tablet{text-align:left !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-left-tablet-only{text-align:left !important}}@media screen and (max-width: 1055px){.has-text-left-touch{text-align:left !important}}@media screen and (min-width: 1056px){.has-text-left-desktop{text-align:left !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-left-desktop-only{text-align:left !important}}@media screen and (min-width: 1216px){.has-text-left-widescreen{text-align:left !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-left-widescreen-only{text-align:left !important}}@media screen and (min-width: 1408px){.has-text-left-fullhd{text-align:left !important}}@media screen and (max-width: 768px){.has-text-right-mobile{text-align:right !important}}@media screen and (min-width: 769px),print{.has-text-right-tablet{text-align:right !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-right-tablet-only{text-align:right !important}}@media screen and (max-width: 1055px){.has-text-right-touch{text-align:right !important}}@media screen and (min-width: 1056px){.has-text-right-desktop{text-align:right !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-right-desktop-only{text-align:right !important}}@media screen and (min-width: 1216px){.has-text-right-widescreen{text-align:right !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-right-widescreen-only{text-align:right !important}}@media screen and (min-width: 1408px){.has-text-right-fullhd{text-align:right !important}}.is-capitalized{text-transform:capitalize !important}.is-lowercase{text-transform:lowercase !important}.is-uppercase{text-transform:uppercase !important}.is-italic{font-style:italic !important}.has-text-white{color:#fff !important}a.has-text-white:hover,a.has-text-white:focus{color:#e6e6e6 !important}.has-background-white{background-color:#fff !important}.has-text-black{color:#0a0a0a !important}a.has-text-black:hover,a.has-text-black:focus{color:#000 !important}.has-background-black{background-color:#0a0a0a !important}.has-text-light{color:#f5f5f5 !important}a.has-text-light:hover,a.has-text-light:focus{color:#dbdbdb !important}.has-background-light{background-color:#f5f5f5 !important}.has-text-dark{color:#363636 !important}a.has-text-dark:hover,a.has-text-dark:focus{color:#1c1c1c !important}.has-background-dark{background-color:#363636 !important}.has-text-primary{color:#4eb5de !important}a.has-text-primary:hover,a.has-text-primary:focus{color:#27a1d2 !important}.has-background-primary{background-color:#4eb5de !important}.has-text-link{color:#2e63b8 !important}a.has-text-link:hover,a.has-text-link:focus{color:#244d8f !important}.has-background-link{background-color:#2e63b8 !important}.has-text-info{color:#209cee !important}a.has-text-info:hover,a.has-text-info:focus{color:#1081cb !important}.has-background-info{background-color:#209cee !important}.has-text-success{color:#22c35b !important}a.has-text-success:hover,a.has-text-success:focus{color:#1a9847 !important}.has-background-success{background-color:#22c35b !important}.has-text-warning{color:#ffdd57 !important}a.has-text-warning:hover,a.has-text-warning:focus{color:#ffd324 !important}.has-background-warning{background-color:#ffdd57 !important}.has-text-danger{color:#da0b00 !important}a.has-text-danger:hover,a.has-text-danger:focus{color:#a70800 !important}.has-background-danger{background-color:#da0b00 !important}.has-text-black-bis{color:#121212 !important}.has-background-black-bis{background-color:#121212 !important}.has-text-black-ter{color:#242424 !important}.has-background-black-ter{background-color:#242424 !important}.has-text-grey-darker{color:#363636 !important}.has-background-grey-darker{background-color:#363636 !important}.has-text-grey-dark{color:#4a4a4a !important}.has-background-grey-dark{background-color:#4a4a4a !important}.has-text-grey{color:#6b6b6b !important}.has-background-grey{background-color:#6b6b6b !important}.has-text-grey-light{color:#b5b5b5 !important}.has-background-grey-light{background-color:#b5b5b5 !important}.has-text-grey-lighter{color:#dbdbdb !important}.has-background-grey-lighter{background-color:#dbdbdb !important}.has-text-white-ter{color:#f5f5f5 !important}.has-background-white-ter{background-color:#f5f5f5 !important}.has-text-white-bis{color:#fafafa !important}.has-background-white-bis{background-color:#fafafa !important}.has-text-weight-light{font-weight:300 !important}.has-text-weight-normal{font-weight:400 !important}.has-text-weight-medium{font-weight:500 !important}.has-text-weight-semibold{font-weight:600 !important}.has-text-weight-bold{font-weight:700 !important}.is-family-primary{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-secondary{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-sans-serif{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-monospace{font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace !important}.is-family-code{font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace !important}.is-block{display:block !important}@media screen and (max-width: 768px){.is-block-mobile{display:block !important}}@media screen and (min-width: 769px),print{.is-block-tablet{display:block !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-block-tablet-only{display:block !important}}@media screen and (max-width: 1055px){.is-block-touch{display:block !important}}@media screen and (min-width: 1056px){.is-block-desktop{display:block !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-block-desktop-only{display:block !important}}@media screen and (min-width: 1216px){.is-block-widescreen{display:block !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-block-widescreen-only{display:block !important}}@media screen and (min-width: 1408px){.is-block-fullhd{display:block !important}}.is-flex{display:flex !important}@media screen and (max-width: 768px){.is-flex-mobile{display:flex !important}}@media screen and (min-width: 769px),print{.is-flex-tablet{display:flex !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-flex-tablet-only{display:flex !important}}@media screen and (max-width: 1055px){.is-flex-touch{display:flex !important}}@media screen and (min-width: 1056px){.is-flex-desktop{display:flex !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-flex-desktop-only{display:flex !important}}@media screen and (min-width: 1216px){.is-flex-widescreen{display:flex !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-flex-widescreen-only{display:flex !important}}@media screen and (min-width: 1408px){.is-flex-fullhd{display:flex !important}}.is-inline{display:inline !important}@media screen and (max-width: 768px){.is-inline-mobile{display:inline !important}}@media screen and (min-width: 769px),print{.is-inline-tablet{display:inline !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-tablet-only{display:inline !important}}@media screen and (max-width: 1055px){.is-inline-touch{display:inline !important}}@media screen and (min-width: 1056px){.is-inline-desktop{display:inline !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-desktop-only{display:inline !important}}@media screen and (min-width: 1216px){.is-inline-widescreen{display:inline !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-widescreen-only{display:inline !important}}@media screen and (min-width: 1408px){.is-inline-fullhd{display:inline !important}}.is-inline-block{display:inline-block !important}@media screen and (max-width: 768px){.is-inline-block-mobile{display:inline-block !important}}@media screen and (min-width: 769px),print{.is-inline-block-tablet{display:inline-block !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-block-tablet-only{display:inline-block !important}}@media screen and (max-width: 1055px){.is-inline-block-touch{display:inline-block !important}}@media screen and (min-width: 1056px){.is-inline-block-desktop{display:inline-block !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-block-desktop-only{display:inline-block !important}}@media screen and (min-width: 1216px){.is-inline-block-widescreen{display:inline-block !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-block-widescreen-only{display:inline-block !important}}@media screen and (min-width: 1408px){.is-inline-block-fullhd{display:inline-block !important}}.is-inline-flex{display:inline-flex !important}@media screen and (max-width: 768px){.is-inline-flex-mobile{display:inline-flex !important}}@media screen and (min-width: 769px),print{.is-inline-flex-tablet{display:inline-flex !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-flex-tablet-only{display:inline-flex !important}}@media screen and (max-width: 1055px){.is-inline-flex-touch{display:inline-flex !important}}@media screen and (min-width: 1056px){.is-inline-flex-desktop{display:inline-flex !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-flex-desktop-only{display:inline-flex !important}}@media screen and (min-width: 1216px){.is-inline-flex-widescreen{display:inline-flex !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-flex-widescreen-only{display:inline-flex !important}}@media screen and (min-width: 1408px){.is-inline-flex-fullhd{display:inline-flex !important}}.is-hidden{display:none !important}.is-sr-only{border:none !important;clip:rect(0, 0, 0, 0) !important;height:0.01em !important;overflow:hidden !important;padding:0 !important;position:absolute !important;white-space:nowrap !important;width:0.01em !important}@media screen and (max-width: 768px){.is-hidden-mobile{display:none !important}}@media screen and (min-width: 769px),print{.is-hidden-tablet{display:none !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-hidden-tablet-only{display:none !important}}@media screen and (max-width: 1055px){.is-hidden-touch{display:none !important}}@media screen and (min-width: 1056px){.is-hidden-desktop{display:none !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-hidden-desktop-only{display:none !important}}@media screen and (min-width: 1216px){.is-hidden-widescreen{display:none !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-hidden-widescreen-only{display:none !important}}@media screen and (min-width: 1408px){.is-hidden-fullhd{display:none !important}}.is-invisible{visibility:hidden !important}@media screen and (max-width: 768px){.is-invisible-mobile{visibility:hidden !important}}@media screen and (min-width: 769px),print{.is-invisible-tablet{visibility:hidden !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-invisible-tablet-only{visibility:hidden !important}}@media screen and (max-width: 1055px){.is-invisible-touch{visibility:hidden !important}}@media screen and (min-width: 1056px){.is-invisible-desktop{visibility:hidden !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-invisible-desktop-only{visibility:hidden !important}}@media screen and (min-width: 1216px){.is-invisible-widescreen{visibility:hidden !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-invisible-widescreen-only{visibility:hidden !important}}@media screen and (min-width: 1408px){.is-invisible-fullhd{visibility:hidden !important}}.is-marginless{margin:0 !important}.is-paddingless{padding:0 !important}.is-radiusless{border-radius:0 !important}.is-shadowless{box-shadow:none !important}.is-relative{position:relative !important}.box{background-color:#fff;border-radius:6px;box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px rgba(10,10,10,0.1);color:#222;display:block;padding:1.25rem}a.box:hover,a.box:focus{box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px #2e63b8}a.box:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2),0 0 0 1px #2e63b8}.button{background-color:#fff;border-color:#dbdbdb;border-width:1px;color:#363636;cursor:pointer;justify-content:center;padding-bottom:calc(0.375em - 1px);padding-left:.75em;padding-right:.75em;padding-top:calc(0.375em - 1px);text-align:center;white-space:nowrap}.button strong{color:inherit}.button .icon,.button .icon.is-small,.button #documenter .docs-sidebar form.docs-search>input.icon,#documenter .docs-sidebar .button form.docs-search>input.icon,.button .icon.is-medium,.button .icon.is-large{height:1.5em;width:1.5em}.button .icon:first-child:not(:last-child){margin-left:calc(-0.375em - 1px);margin-right:0.1875em}.button .icon:last-child:not(:first-child){margin-left:0.1875em;margin-right:calc(-0.375em - 1px)}.button .icon:first-child:last-child{margin-left:calc(-0.375em - 1px);margin-right:calc(-0.375em - 1px)}.button:hover,.button.is-hovered{border-color:#b5b5b5;color:#363636}.button:focus,.button.is-focused{border-color:#3c5dcd;color:#363636}.button:focus:not(:active),.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.button:active,.button.is-active{border-color:#4a4a4a;color:#363636}.button.is-text{background-color:transparent;border-color:transparent;color:#222;text-decoration:underline}.button.is-text:hover,.button.is-text.is-hovered,.button.is-text:focus,.button.is-text.is-focused{background-color:#f5f5f5;color:#222}.button.is-text:active,.button.is-text.is-active{background-color:#e8e8e8;color:#222}.button.is-text[disabled],fieldset[disabled] .button.is-text{background-color:transparent;border-color:transparent;box-shadow:none}.button.is-white{background-color:#fff;border-color:transparent;color:#0a0a0a}.button.is-white:hover,.button.is-white.is-hovered{background-color:#f9f9f9;border-color:transparent;color:#0a0a0a}.button.is-white:focus,.button.is-white.is-focused{border-color:transparent;color:#0a0a0a}.button.is-white:focus:not(:active),.button.is-white.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}.button.is-white:active,.button.is-white.is-active{background-color:#f2f2f2;border-color:transparent;color:#0a0a0a}.button.is-white[disabled],fieldset[disabled] .button.is-white{background-color:#fff;border-color:transparent;box-shadow:none}.button.is-white.is-inverted{background-color:#0a0a0a;color:#fff}.button.is-white.is-inverted:hover,.button.is-white.is-inverted.is-hovered{background-color:#000}.button.is-white.is-inverted[disabled],fieldset[disabled] .button.is-white.is-inverted{background-color:#0a0a0a;border-color:transparent;box-shadow:none;color:#fff}.button.is-white.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}.button.is-white.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-white.is-outlined:hover,.button.is-white.is-outlined.is-hovered,.button.is-white.is-outlined:focus,.button.is-white.is-outlined.is-focused{background-color:#fff;border-color:#fff;color:#0a0a0a}.button.is-white.is-outlined.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-white.is-outlined.is-loading:hover::after,.button.is-white.is-outlined.is-loading.is-hovered::after,.button.is-white.is-outlined.is-loading:focus::after,.button.is-white.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}.button.is-white.is-outlined[disabled],fieldset[disabled] .button.is-white.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}.button.is-white.is-inverted.is-outlined:hover,.button.is-white.is-inverted.is-outlined.is-hovered,.button.is-white.is-inverted.is-outlined:focus,.button.is-white.is-inverted.is-outlined.is-focused{background-color:#0a0a0a;color:#fff}.button.is-white.is-inverted.is-outlined.is-loading:hover::after,.button.is-white.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-white.is-inverted.is-outlined.is-loading:focus::after,.button.is-white.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-white.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}.button.is-black{background-color:#0a0a0a;border-color:transparent;color:#fff}.button.is-black:hover,.button.is-black.is-hovered{background-color:#040404;border-color:transparent;color:#fff}.button.is-black:focus,.button.is-black.is-focused{border-color:transparent;color:#fff}.button.is-black:focus:not(:active),.button.is-black.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}.button.is-black:active,.button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}.button.is-black[disabled],fieldset[disabled] .button.is-black{background-color:#0a0a0a;border-color:transparent;box-shadow:none}.button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}.button.is-black.is-inverted:hover,.button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-black.is-inverted[disabled],fieldset[disabled] .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}.button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}.button.is-black.is-outlined:hover,.button.is-black.is-outlined.is-hovered,.button.is-black.is-outlined:focus,.button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}.button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}.button.is-black.is-outlined.is-loading:hover::after,.button.is-black.is-outlined.is-loading.is-hovered::after,.button.is-black.is-outlined.is-loading:focus::after,.button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-black.is-outlined[disabled],fieldset[disabled] .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}.button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-black.is-inverted.is-outlined:hover,.button.is-black.is-inverted.is-outlined.is-hovered,.button.is-black.is-inverted.is-outlined:focus,.button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}.button.is-black.is-inverted.is-outlined.is-loading:hover::after,.button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-black.is-inverted.is-outlined.is-loading:focus::after,.button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}.button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-light{background-color:#f5f5f5;border-color:transparent;color:#363636}.button.is-light:hover,.button.is-light.is-hovered{background-color:#eee;border-color:transparent;color:#363636}.button.is-light:focus,.button.is-light.is-focused{border-color:transparent;color:#363636}.button.is-light:focus:not(:active),.button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}.button.is-light:active,.button.is-light.is-active{background-color:#e8e8e8;border-color:transparent;color:#363636}.button.is-light[disabled],fieldset[disabled] .button.is-light{background-color:#f5f5f5;border-color:transparent;box-shadow:none}.button.is-light.is-inverted{background-color:#363636;color:#f5f5f5}.button.is-light.is-inverted:hover,.button.is-light.is-inverted.is-hovered{background-color:#292929}.button.is-light.is-inverted[disabled],fieldset[disabled] .button.is-light.is-inverted{background-color:#363636;border-color:transparent;box-shadow:none;color:#f5f5f5}.button.is-light.is-loading::after{border-color:transparent transparent #363636 #363636 !important}.button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;color:#f5f5f5}.button.is-light.is-outlined:hover,.button.is-light.is-outlined.is-hovered,.button.is-light.is-outlined:focus,.button.is-light.is-outlined.is-focused{background-color:#f5f5f5;border-color:#f5f5f5;color:#363636}.button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}.button.is-light.is-outlined.is-loading:hover::after,.button.is-light.is-outlined.is-loading.is-hovered::after,.button.is-light.is-outlined.is-loading:focus::after,.button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #363636 #363636 !important}.button.is-light.is-outlined[disabled],fieldset[disabled] .button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;box-shadow:none;color:#f5f5f5}.button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:#363636;color:#363636}.button.is-light.is-inverted.is-outlined:hover,.button.is-light.is-inverted.is-outlined.is-hovered,.button.is-light.is-inverted.is-outlined:focus,.button.is-light.is-inverted.is-outlined.is-focused{background-color:#363636;color:#f5f5f5}.button.is-light.is-inverted.is-outlined.is-loading:hover::after,.button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-light.is-inverted.is-outlined.is-loading:focus::after,.button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}.button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:#363636;box-shadow:none;color:#363636}.button.is-dark,.content kbd.button{background-color:#363636;border-color:transparent;color:#f5f5f5}.button.is-dark:hover,.content kbd.button:hover,.button.is-dark.is-hovered,.content kbd.button.is-hovered{background-color:#2f2f2f;border-color:transparent;color:#f5f5f5}.button.is-dark:focus,.content kbd.button:focus,.button.is-dark.is-focused,.content kbd.button.is-focused{border-color:transparent;color:#f5f5f5}.button.is-dark:focus:not(:active),.content kbd.button:focus:not(:active),.button.is-dark.is-focused:not(:active),.content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(54,54,54,0.25)}.button.is-dark:active,.content kbd.button:active,.button.is-dark.is-active,.content kbd.button.is-active{background-color:#292929;border-color:transparent;color:#f5f5f5}.button.is-dark[disabled],.content kbd.button[disabled],fieldset[disabled] .button.is-dark,fieldset[disabled] .content kbd.button,.content fieldset[disabled] kbd.button{background-color:#363636;border-color:transparent;box-shadow:none}.button.is-dark.is-inverted,.content kbd.button.is-inverted{background-color:#f5f5f5;color:#363636}.button.is-dark.is-inverted:hover,.content kbd.button.is-inverted:hover,.button.is-dark.is-inverted.is-hovered,.content kbd.button.is-inverted.is-hovered{background-color:#e8e8e8}.button.is-dark.is-inverted[disabled],.content kbd.button.is-inverted[disabled],fieldset[disabled] .button.is-dark.is-inverted,fieldset[disabled] .content kbd.button.is-inverted,.content fieldset[disabled] kbd.button.is-inverted{background-color:#f5f5f5;border-color:transparent;box-shadow:none;color:#363636}.button.is-dark.is-loading::after,.content kbd.button.is-loading::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}.button.is-dark.is-outlined,.content kbd.button.is-outlined{background-color:transparent;border-color:#363636;color:#363636}.button.is-dark.is-outlined:hover,.content kbd.button.is-outlined:hover,.button.is-dark.is-outlined.is-hovered,.content kbd.button.is-outlined.is-hovered,.button.is-dark.is-outlined:focus,.content kbd.button.is-outlined:focus,.button.is-dark.is-outlined.is-focused,.content kbd.button.is-outlined.is-focused{background-color:#363636;border-color:#363636;color:#f5f5f5}.button.is-dark.is-outlined.is-loading::after,.content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #363636 #363636 !important}.button.is-dark.is-outlined.is-loading:hover::after,.content kbd.button.is-outlined.is-loading:hover::after,.button.is-dark.is-outlined.is-loading.is-hovered::after,.content kbd.button.is-outlined.is-loading.is-hovered::after,.button.is-dark.is-outlined.is-loading:focus::after,.content kbd.button.is-outlined.is-loading:focus::after,.button.is-dark.is-outlined.is-loading.is-focused::after,.content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}.button.is-dark.is-outlined[disabled],.content kbd.button.is-outlined[disabled],fieldset[disabled] .button.is-dark.is-outlined,fieldset[disabled] .content kbd.button.is-outlined,.content fieldset[disabled] kbd.button.is-outlined{background-color:transparent;border-color:#363636;box-shadow:none;color:#363636}.button.is-dark.is-inverted.is-outlined,.content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#f5f5f5;color:#f5f5f5}.button.is-dark.is-inverted.is-outlined:hover,.content kbd.button.is-inverted.is-outlined:hover,.button.is-dark.is-inverted.is-outlined.is-hovered,.content kbd.button.is-inverted.is-outlined.is-hovered,.button.is-dark.is-inverted.is-outlined:focus,.content kbd.button.is-inverted.is-outlined:focus,.button.is-dark.is-inverted.is-outlined.is-focused,.content kbd.button.is-inverted.is-outlined.is-focused{background-color:#f5f5f5;color:#363636}.button.is-dark.is-inverted.is-outlined.is-loading:hover::after,.content kbd.button.is-inverted.is-outlined.is-loading:hover::after,.button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,.content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-dark.is-inverted.is-outlined.is-loading:focus::after,.content kbd.button.is-inverted.is-outlined.is-loading:focus::after,.button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,.content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #363636 #363636 !important}.button.is-dark.is-inverted.is-outlined[disabled],.content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-dark.is-inverted.is-outlined,fieldset[disabled] .content kbd.button.is-inverted.is-outlined,.content fieldset[disabled] kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#f5f5f5;box-shadow:none;color:#f5f5f5}.button.is-primary,.docstring>section>a.button.docs-sourcelink{background-color:#4eb5de;border-color:transparent;color:#fff}.button.is-primary:hover,.docstring>section>a.button.docs-sourcelink:hover,.button.is-primary.is-hovered,.docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#43b1dc;border-color:transparent;color:#fff}.button.is-primary:focus,.docstring>section>a.button.docs-sourcelink:focus,.button.is-primary.is-focused,.docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}.button.is-primary:focus:not(:active),.docstring>section>a.button.docs-sourcelink:focus:not(:active),.button.is-primary.is-focused:not(:active),.docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(78,181,222,0.25)}.button.is-primary:active,.docstring>section>a.button.docs-sourcelink:active,.button.is-primary.is-active,.docstring>section>a.button.is-active.docs-sourcelink{background-color:#39acda;border-color:transparent;color:#fff}.button.is-primary[disabled],.docstring>section>a.button.docs-sourcelink[disabled],fieldset[disabled] .button.is-primary,fieldset[disabled] .docstring>section>a.button.docs-sourcelink{background-color:#4eb5de;border-color:transparent;box-shadow:none}.button.is-primary.is-inverted,.docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;color:#4eb5de}.button.is-primary.is-inverted:hover,.docstring>section>a.button.is-inverted.docs-sourcelink:hover,.button.is-primary.is-inverted.is-hovered,.docstring>section>a.button.is-inverted.is-hovered.docs-sourcelink{background-color:#f2f2f2}.button.is-primary.is-inverted[disabled],.docstring>section>a.button.is-inverted.docs-sourcelink[disabled],fieldset[disabled] .button.is-primary.is-inverted,fieldset[disabled] .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;border-color:transparent;box-shadow:none;color:#4eb5de}.button.is-primary.is-loading::after,.docstring>section>a.button.is-loading.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}.button.is-primary.is-outlined,.docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#4eb5de;color:#4eb5de}.button.is-primary.is-outlined:hover,.docstring>section>a.button.is-outlined.docs-sourcelink:hover,.button.is-primary.is-outlined.is-hovered,.docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,.button.is-primary.is-outlined:focus,.docstring>section>a.button.is-outlined.docs-sourcelink:focus,.button.is-primary.is-outlined.is-focused,.docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#4eb5de;border-color:#4eb5de;color:#fff}.button.is-primary.is-outlined.is-loading::after,.docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #4eb5de #4eb5de !important}.button.is-primary.is-outlined.is-loading:hover::after,.docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,.button.is-primary.is-outlined.is-loading.is-hovered::after,.docstring>section>a.button.is-outlined.is-loading.is-hovered.docs-sourcelink::after,.button.is-primary.is-outlined.is-loading:focus::after,.docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:focus::after,.button.is-primary.is-outlined.is-loading.is-focused::after,.docstring>section>a.button.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}.button.is-primary.is-outlined[disabled],.docstring>section>a.button.is-outlined.docs-sourcelink[disabled],fieldset[disabled] .button.is-primary.is-outlined,fieldset[disabled] .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#4eb5de;box-shadow:none;color:#4eb5de}.button.is-primary.is-inverted.is-outlined,.docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;color:#fff}.button.is-primary.is-inverted.is-outlined:hover,.docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink:hover,.button.is-primary.is-inverted.is-outlined.is-hovered,.docstring>section>a.button.is-inverted.is-outlined.is-hovered.docs-sourcelink,.button.is-primary.is-inverted.is-outlined:focus,.docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink:focus,.button.is-primary.is-inverted.is-outlined.is-focused,.docstring>section>a.button.is-inverted.is-outlined.is-focused.docs-sourcelink{background-color:#fff;color:#4eb5de}.button.is-primary.is-inverted.is-outlined.is-loading:hover::after,.docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:hover::after,.button.is-primary.is-inverted.is-outlined.is-loading.is-hovered::after,.docstring>section>a.button.is-inverted.is-outlined.is-loading.is-hovered.docs-sourcelink::after,.button.is-primary.is-inverted.is-outlined.is-loading:focus::after,.docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:focus::after,.button.is-primary.is-inverted.is-outlined.is-loading.is-focused::after,.docstring>section>a.button.is-inverted.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #4eb5de #4eb5de !important}.button.is-primary.is-inverted.is-outlined[disabled],.docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink[disabled],fieldset[disabled] .button.is-primary.is-inverted.is-outlined,fieldset[disabled] .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-link{background-color:#2e63b8;border-color:transparent;color:#fff}.button.is-link:hover,.button.is-link.is-hovered{background-color:#2b5eae;border-color:transparent;color:#fff}.button.is-link:focus,.button.is-link.is-focused{border-color:transparent;color:#fff}.button.is-link:focus:not(:active),.button.is-link.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.button.is-link:active,.button.is-link.is-active{background-color:#2958a4;border-color:transparent;color:#fff}.button.is-link[disabled],fieldset[disabled] .button.is-link{background-color:#2e63b8;border-color:transparent;box-shadow:none}.button.is-link.is-inverted{background-color:#fff;color:#2e63b8}.button.is-link.is-inverted:hover,.button.is-link.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-link.is-inverted[disabled],fieldset[disabled] .button.is-link.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#2e63b8}.button.is-link.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-link.is-outlined{background-color:transparent;border-color:#2e63b8;color:#2e63b8}.button.is-link.is-outlined:hover,.button.is-link.is-outlined.is-hovered,.button.is-link.is-outlined:focus,.button.is-link.is-outlined.is-focused{background-color:#2e63b8;border-color:#2e63b8;color:#fff}.button.is-link.is-outlined.is-loading::after{border-color:transparent transparent #2e63b8 #2e63b8 !important}.button.is-link.is-outlined.is-loading:hover::after,.button.is-link.is-outlined.is-loading.is-hovered::after,.button.is-link.is-outlined.is-loading:focus::after,.button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-link.is-outlined[disabled],fieldset[disabled] .button.is-link.is-outlined{background-color:transparent;border-color:#2e63b8;box-shadow:none;color:#2e63b8}.button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-link.is-inverted.is-outlined:hover,.button.is-link.is-inverted.is-outlined.is-hovered,.button.is-link.is-inverted.is-outlined:focus,.button.is-link.is-inverted.is-outlined.is-focused{background-color:#fff;color:#2e63b8}.button.is-link.is-inverted.is-outlined.is-loading:hover::after,.button.is-link.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-link.is-inverted.is-outlined.is-loading:focus::after,.button.is-link.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #2e63b8 #2e63b8 !important}.button.is-link.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-info{background-color:#209cee;border-color:transparent;color:#fff}.button.is-info:hover,.button.is-info.is-hovered{background-color:#1497ed;border-color:transparent;color:#fff}.button.is-info:focus,.button.is-info.is-focused{border-color:transparent;color:#fff}.button.is-info:focus:not(:active),.button.is-info.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(32,156,238,0.25)}.button.is-info:active,.button.is-info.is-active{background-color:#1190e3;border-color:transparent;color:#fff}.button.is-info[disabled],fieldset[disabled] .button.is-info{background-color:#209cee;border-color:transparent;box-shadow:none}.button.is-info.is-inverted{background-color:#fff;color:#209cee}.button.is-info.is-inverted:hover,.button.is-info.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-info.is-inverted[disabled],fieldset[disabled] .button.is-info.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#209cee}.button.is-info.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-info.is-outlined{background-color:transparent;border-color:#209cee;color:#209cee}.button.is-info.is-outlined:hover,.button.is-info.is-outlined.is-hovered,.button.is-info.is-outlined:focus,.button.is-info.is-outlined.is-focused{background-color:#209cee;border-color:#209cee;color:#fff}.button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #209cee #209cee !important}.button.is-info.is-outlined.is-loading:hover::after,.button.is-info.is-outlined.is-loading.is-hovered::after,.button.is-info.is-outlined.is-loading:focus::after,.button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-info.is-outlined[disabled],fieldset[disabled] .button.is-info.is-outlined{background-color:transparent;border-color:#209cee;box-shadow:none;color:#209cee}.button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-info.is-inverted.is-outlined:hover,.button.is-info.is-inverted.is-outlined.is-hovered,.button.is-info.is-inverted.is-outlined:focus,.button.is-info.is-inverted.is-outlined.is-focused{background-color:#fff;color:#209cee}.button.is-info.is-inverted.is-outlined.is-loading:hover::after,.button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-info.is-inverted.is-outlined.is-loading:focus::after,.button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #209cee #209cee !important}.button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-success{background-color:#22c35b;border-color:transparent;color:#fff}.button.is-success:hover,.button.is-success.is-hovered{background-color:#20b856;border-color:transparent;color:#fff}.button.is-success:focus,.button.is-success.is-focused{border-color:transparent;color:#fff}.button.is-success:focus:not(:active),.button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(34,195,91,0.25)}.button.is-success:active,.button.is-success.is-active{background-color:#1ead51;border-color:transparent;color:#fff}.button.is-success[disabled],fieldset[disabled] .button.is-success{background-color:#22c35b;border-color:transparent;box-shadow:none}.button.is-success.is-inverted{background-color:#fff;color:#22c35b}.button.is-success.is-inverted:hover,.button.is-success.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-success.is-inverted[disabled],fieldset[disabled] .button.is-success.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#22c35b}.button.is-success.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-success.is-outlined{background-color:transparent;border-color:#22c35b;color:#22c35b}.button.is-success.is-outlined:hover,.button.is-success.is-outlined.is-hovered,.button.is-success.is-outlined:focus,.button.is-success.is-outlined.is-focused{background-color:#22c35b;border-color:#22c35b;color:#fff}.button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #22c35b #22c35b !important}.button.is-success.is-outlined.is-loading:hover::after,.button.is-success.is-outlined.is-loading.is-hovered::after,.button.is-success.is-outlined.is-loading:focus::after,.button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-success.is-outlined[disabled],fieldset[disabled] .button.is-success.is-outlined{background-color:transparent;border-color:#22c35b;box-shadow:none;color:#22c35b}.button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-success.is-inverted.is-outlined:hover,.button.is-success.is-inverted.is-outlined.is-hovered,.button.is-success.is-inverted.is-outlined:focus,.button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#22c35b}.button.is-success.is-inverted.is-outlined.is-loading:hover::after,.button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-success.is-inverted.is-outlined.is-loading:focus::after,.button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #22c35b #22c35b !important}.button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-warning{background-color:#ffdd57;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-warning:hover,.button.is-warning.is-hovered{background-color:#ffda4a;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-warning:focus,.button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-warning:focus:not(:active),.button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(255,221,87,0.25)}.button.is-warning:active,.button.is-warning.is-active{background-color:#ffd83e;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-warning[disabled],fieldset[disabled] .button.is-warning{background-color:#ffdd57;border-color:transparent;box-shadow:none}.button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#ffdd57}.button.is-warning.is-inverted:hover,.button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}.button.is-warning.is-inverted[disabled],fieldset[disabled] .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ffdd57}.button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}.button.is-warning.is-outlined{background-color:transparent;border-color:#ffdd57;color:#ffdd57}.button.is-warning.is-outlined:hover,.button.is-warning.is-outlined.is-hovered,.button.is-warning.is-outlined:focus,.button.is-warning.is-outlined.is-focused{background-color:#ffdd57;border-color:#ffdd57;color:rgba(0,0,0,0.7)}.button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #ffdd57 #ffdd57 !important}.button.is-warning.is-outlined.is-loading:hover::after,.button.is-warning.is-outlined.is-loading.is-hovered::after,.button.is-warning.is-outlined.is-loading:focus::after,.button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}.button.is-warning.is-outlined[disabled],fieldset[disabled] .button.is-warning.is-outlined{background-color:transparent;border-color:#ffdd57;box-shadow:none;color:#ffdd57}.button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}.button.is-warning.is-inverted.is-outlined:hover,.button.is-warning.is-inverted.is-outlined.is-hovered,.button.is-warning.is-inverted.is-outlined:focus,.button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ffdd57}.button.is-warning.is-inverted.is-outlined.is-loading:hover::after,.button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-warning.is-inverted.is-outlined.is-loading:focus::after,.button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ffdd57 #ffdd57 !important}.button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}.button.is-danger{background-color:#da0b00;border-color:transparent;color:#fff}.button.is-danger:hover,.button.is-danger.is-hovered{background-color:#cd0a00;border-color:transparent;color:#fff}.button.is-danger:focus,.button.is-danger.is-focused{border-color:transparent;color:#fff}.button.is-danger:focus:not(:active),.button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(218,11,0,0.25)}.button.is-danger:active,.button.is-danger.is-active{background-color:#c10a00;border-color:transparent;color:#fff}.button.is-danger[disabled],fieldset[disabled] .button.is-danger{background-color:#da0b00;border-color:transparent;box-shadow:none}.button.is-danger.is-inverted{background-color:#fff;color:#da0b00}.button.is-danger.is-inverted:hover,.button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-danger.is-inverted[disabled],fieldset[disabled] .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#da0b00}.button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-danger.is-outlined{background-color:transparent;border-color:#da0b00;color:#da0b00}.button.is-danger.is-outlined:hover,.button.is-danger.is-outlined.is-hovered,.button.is-danger.is-outlined:focus,.button.is-danger.is-outlined.is-focused{background-color:#da0b00;border-color:#da0b00;color:#fff}.button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #da0b00 #da0b00 !important}.button.is-danger.is-outlined.is-loading:hover::after,.button.is-danger.is-outlined.is-loading.is-hovered::after,.button.is-danger.is-outlined.is-loading:focus::after,.button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-danger.is-outlined[disabled],fieldset[disabled] .button.is-danger.is-outlined{background-color:transparent;border-color:#da0b00;box-shadow:none;color:#da0b00}.button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-danger.is-inverted.is-outlined:hover,.button.is-danger.is-inverted.is-outlined.is-hovered,.button.is-danger.is-inverted.is-outlined:focus,.button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#da0b00}.button.is-danger.is-inverted.is-outlined.is-loading:hover::after,.button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-danger.is-inverted.is-outlined.is-loading:focus::after,.button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #da0b00 #da0b00 !important}.button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-small,#documenter .docs-sidebar form.docs-search>input.button{border-radius:2px;font-size:.75rem}.button.is-normal{font-size:1rem}.button.is-medium{font-size:1.25rem}.button.is-large{font-size:1.5rem}.button[disabled],fieldset[disabled] .button{background-color:#fff;border-color:#dbdbdb;box-shadow:none;opacity:.5}.button.is-fullwidth{display:flex;width:100%}.button.is-loading{color:transparent !important;pointer-events:none}.button.is-loading::after{position:absolute;left:calc(50% - (1em / 2));top:calc(50% - (1em / 2));position:absolute !important}.button.is-static{background-color:#f5f5f5;border-color:#dbdbdb;color:#6b6b6b;box-shadow:none;pointer-events:none}.button.is-rounded,#documenter .docs-sidebar form.docs-search>input.button{border-radius:290486px;padding-left:1em;padding-right:1em}.buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}.buttons .button{margin-bottom:0.5rem}.buttons .button:not(:last-child):not(.is-fullwidth){margin-right:0.5rem}.buttons:last-child{margin-bottom:-0.5rem}.buttons:not(:last-child){margin-bottom:1rem}.buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){border-radius:2px;font-size:.75rem}.buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}.buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}.buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}.buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}.buttons.has-addons .button:last-child{margin-right:0}.buttons.has-addons .button:hover,.buttons.has-addons .button.is-hovered{z-index:2}.buttons.has-addons .button:focus,.buttons.has-addons .button.is-focused,.buttons.has-addons .button:active,.buttons.has-addons .button.is-active,.buttons.has-addons .button.is-selected{z-index:3}.buttons.has-addons .button:focus:hover,.buttons.has-addons .button.is-focused:hover,.buttons.has-addons .button:active:hover,.buttons.has-addons .button.is-active:hover,.buttons.has-addons .button.is-selected:hover{z-index:4}.buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}.buttons.is-centered{justify-content:center}.buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}.buttons.is-right{justify-content:flex-end}.buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}.container{flex-grow:1;margin:0 auto;position:relative;width:auto}@media screen and (min-width: 1056px){.container{max-width:992px}.container.is-fluid{margin-left:32px;margin-right:32px;max-width:none}}@media screen and (max-width: 1215px){.container.is-widescreen{max-width:1152px}}@media screen and (max-width: 1407px){.container.is-fullhd{max-width:1344px}}@media screen and (min-width: 1216px){.container{max-width:1152px}}@media screen and (min-width: 1408px){.container{max-width:1344px}}.content li+li{margin-top:0.25em}.content p:not(:last-child),.content dl:not(:last-child),.content ol:not(:last-child),.content ul:not(:last-child),.content blockquote:not(:last-child),.content pre:not(:last-child),.content table:not(:last-child){margin-bottom:1em}.content h1,.content h2,.content h3,.content h4,.content h5,.content h6{color:#222;font-weight:600;line-height:1.125}.content h1{font-size:2em;margin-bottom:0.5em}.content h1:not(:first-child){margin-top:1em}.content h2{font-size:1.75em;margin-bottom:0.5714em}.content h2:not(:first-child){margin-top:1.1428em}.content h3{font-size:1.5em;margin-bottom:0.6666em}.content h3:not(:first-child){margin-top:1.3333em}.content h4{font-size:1.25em;margin-bottom:0.8em}.content h5{font-size:1.125em;margin-bottom:0.8888em}.content h6{font-size:1em;margin-bottom:1em}.content blockquote{background-color:#f5f5f5;border-left:5px solid #dbdbdb;padding:1.25em 1.5em}.content ol{list-style-position:outside;margin-left:2em;margin-top:1em}.content ol:not([type]){list-style-type:decimal}.content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}.content ol.is-lower-roman:not([type]){list-style-type:lower-roman}.content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}.content ol.is-upper-roman:not([type]){list-style-type:upper-roman}.content ul{list-style:disc outside;margin-left:2em;margin-top:1em}.content ul ul{list-style-type:circle;margin-top:0.5em}.content ul ul ul{list-style-type:square}.content dd{margin-left:2em}.content figure{margin-left:2em;margin-right:2em;text-align:center}.content figure:not(:first-child){margin-top:2em}.content figure:not(:last-child){margin-bottom:2em}.content figure img{display:inline-block}.content figure figcaption{font-style:italic}.content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}.content sup,.content sub{font-size:75%}.content table{width:100%}.content table td,.content table th{border:1px solid #dbdbdb;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}.content table th{color:#222}.content table th:not([align]){text-align:left}.content table thead td,.content table thead th{border-width:0 0 2px;color:#222}.content table tfoot td,.content table tfoot th{border-width:2px 0 0;color:#222}.content table tbody tr:last-child td,.content table tbody tr:last-child th{border-bottom-width:0}.content .tabs li+li{margin-top:0}.content.is-small,#documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}.content.is-medium{font-size:1.25rem}.content.is-large{font-size:1.5rem}.icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}.icon.is-small,#documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}.icon.is-medium{height:2rem;width:2rem}.icon.is-large{height:3rem;width:3rem}.image,#documenter .docs-sidebar .docs-logo>img{display:block;position:relative}.image img,#documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}.image img.is-rounded,#documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:290486px}.image.is-square img,#documenter .docs-sidebar .docs-logo>img.is-square img,.image.is-square .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,.image.is-1by1 img,#documenter .docs-sidebar .docs-logo>img.is-1by1 img,.image.is-1by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,.image.is-5by4 img,#documenter .docs-sidebar .docs-logo>img.is-5by4 img,.image.is-5by4 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,.image.is-4by3 img,#documenter .docs-sidebar .docs-logo>img.is-4by3 img,.image.is-4by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,.image.is-3by2 img,#documenter .docs-sidebar .docs-logo>img.is-3by2 img,.image.is-3by2 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,.image.is-5by3 img,#documenter .docs-sidebar .docs-logo>img.is-5by3 img,.image.is-5by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,.image.is-16by9 img,#documenter .docs-sidebar .docs-logo>img.is-16by9 img,.image.is-16by9 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,.image.is-2by1 img,#documenter .docs-sidebar .docs-logo>img.is-2by1 img,.image.is-2by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,.image.is-3by1 img,#documenter .docs-sidebar .docs-logo>img.is-3by1 img,.image.is-3by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,.image.is-4by5 img,#documenter .docs-sidebar .docs-logo>img.is-4by5 img,.image.is-4by5 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,.image.is-3by4 img,#documenter .docs-sidebar .docs-logo>img.is-3by4 img,.image.is-3by4 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,.image.is-2by3 img,#documenter .docs-sidebar .docs-logo>img.is-2by3 img,.image.is-2by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,.image.is-3by5 img,#documenter .docs-sidebar .docs-logo>img.is-3by5 img,.image.is-3by5 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,.image.is-9by16 img,#documenter .docs-sidebar .docs-logo>img.is-9by16 img,.image.is-9by16 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,.image.is-1by2 img,#documenter .docs-sidebar .docs-logo>img.is-1by2 img,.image.is-1by2 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,.image.is-1by3 img,#documenter .docs-sidebar .docs-logo>img.is-1by3 img,.image.is-1by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio{height:100%;width:100%}.image.is-square,#documenter .docs-sidebar .docs-logo>img.is-square,.image.is-1by1,#documenter .docs-sidebar .docs-logo>img.is-1by1{padding-top:100%}.image.is-5by4,#documenter .docs-sidebar .docs-logo>img.is-5by4{padding-top:80%}.image.is-4by3,#documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}.image.is-3by2,#documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}.image.is-5by3,#documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}.image.is-16by9,#documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}.image.is-2by1,#documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}.image.is-3by1,#documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}.image.is-4by5,#documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}.image.is-3by4,#documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}.image.is-2by3,#documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}.image.is-3by5,#documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}.image.is-9by16,#documenter .docs-sidebar .docs-logo>img.is-9by16{padding-top:177.7777%}.image.is-1by2,#documenter .docs-sidebar .docs-logo>img.is-1by2{padding-top:200%}.image.is-1by3,#documenter .docs-sidebar .docs-logo>img.is-1by3{padding-top:300%}.image.is-16x16,#documenter .docs-sidebar .docs-logo>img.is-16x16{height:16px;width:16px}.image.is-24x24,#documenter .docs-sidebar .docs-logo>img.is-24x24{height:24px;width:24px}.image.is-32x32,#documenter .docs-sidebar .docs-logo>img.is-32x32{height:32px;width:32px}.image.is-48x48,#documenter .docs-sidebar .docs-logo>img.is-48x48{height:48px;width:48px}.image.is-64x64,#documenter .docs-sidebar .docs-logo>img.is-64x64{height:64px;width:64px}.image.is-96x96,#documenter .docs-sidebar .docs-logo>img.is-96x96{height:96px;width:96px}.image.is-128x128,#documenter .docs-sidebar .docs-logo>img.is-128x128{height:128px;width:128px}.notification{background-color:#f5f5f5;border-radius:4px;padding:1.25rem 2.5rem 1.25rem 1.5rem;position:relative}.notification a:not(.button):not(.dropdown-item){color:currentColor;text-decoration:underline}.notification strong{color:currentColor}.notification code,.notification pre{background:#fff}.notification pre code{background:transparent}.notification>.delete{position:absolute;right:0.5rem;top:0.5rem}.notification .title,.notification .subtitle,.notification .content{color:currentColor}.notification.is-white{background-color:#fff;color:#0a0a0a}.notification.is-black{background-color:#0a0a0a;color:#fff}.notification.is-light{background-color:#f5f5f5;color:#363636}.notification.is-dark,.content kbd.notification{background-color:#363636;color:#f5f5f5}.notification.is-primary,.docstring>section>a.notification.docs-sourcelink{background-color:#4eb5de;color:#fff}.notification.is-link{background-color:#2e63b8;color:#fff}.notification.is-info{background-color:#209cee;color:#fff}.notification.is-success{background-color:#22c35b;color:#fff}.notification.is-warning{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.notification.is-danger{background-color:#da0b00;color:#fff}.progress{-moz-appearance:none;-webkit-appearance:none;border:none;border-radius:290486px;display:block;height:1rem;overflow:hidden;padding:0;width:100%}.progress::-webkit-progress-bar{background-color:#dbdbdb}.progress::-webkit-progress-value{background-color:#222}.progress::-moz-progress-bar{background-color:#222}.progress::-ms-fill{background-color:#222;border:none}.progress.is-white::-webkit-progress-value{background-color:#fff}.progress.is-white::-moz-progress-bar{background-color:#fff}.progress.is-white::-ms-fill{background-color:#fff}.progress.is-white:indeterminate{background-image:linear-gradient(to right, #fff 30%, #dbdbdb 30%)}.progress.is-black::-webkit-progress-value{background-color:#0a0a0a}.progress.is-black::-moz-progress-bar{background-color:#0a0a0a}.progress.is-black::-ms-fill{background-color:#0a0a0a}.progress.is-black:indeterminate{background-image:linear-gradient(to right, #0a0a0a 30%, #dbdbdb 30%)}.progress.is-light::-webkit-progress-value{background-color:#f5f5f5}.progress.is-light::-moz-progress-bar{background-color:#f5f5f5}.progress.is-light::-ms-fill{background-color:#f5f5f5}.progress.is-light:indeterminate{background-image:linear-gradient(to right, #f5f5f5 30%, #dbdbdb 30%)}.progress.is-dark::-webkit-progress-value,.content kbd.progress::-webkit-progress-value{background-color:#363636}.progress.is-dark::-moz-progress-bar,.content kbd.progress::-moz-progress-bar{background-color:#363636}.progress.is-dark::-ms-fill,.content kbd.progress::-ms-fill{background-color:#363636}.progress.is-dark:indeterminate,.content kbd.progress:indeterminate{background-image:linear-gradient(to right, #363636 30%, #dbdbdb 30%)}.progress.is-primary::-webkit-progress-value,.docstring>section>a.progress.docs-sourcelink::-webkit-progress-value{background-color:#4eb5de}.progress.is-primary::-moz-progress-bar,.docstring>section>a.progress.docs-sourcelink::-moz-progress-bar{background-color:#4eb5de}.progress.is-primary::-ms-fill,.docstring>section>a.progress.docs-sourcelink::-ms-fill{background-color:#4eb5de}.progress.is-primary:indeterminate,.docstring>section>a.progress.docs-sourcelink:indeterminate{background-image:linear-gradient(to right, #4eb5de 30%, #dbdbdb 30%)}.progress.is-link::-webkit-progress-value{background-color:#2e63b8}.progress.is-link::-moz-progress-bar{background-color:#2e63b8}.progress.is-link::-ms-fill{background-color:#2e63b8}.progress.is-link:indeterminate{background-image:linear-gradient(to right, #2e63b8 30%, #dbdbdb 30%)}.progress.is-info::-webkit-progress-value{background-color:#209cee}.progress.is-info::-moz-progress-bar{background-color:#209cee}.progress.is-info::-ms-fill{background-color:#209cee}.progress.is-info:indeterminate{background-image:linear-gradient(to right, #209cee 30%, #dbdbdb 30%)}.progress.is-success::-webkit-progress-value{background-color:#22c35b}.progress.is-success::-moz-progress-bar{background-color:#22c35b}.progress.is-success::-ms-fill{background-color:#22c35b}.progress.is-success:indeterminate{background-image:linear-gradient(to right, #22c35b 30%, #dbdbdb 30%)}.progress.is-warning::-webkit-progress-value{background-color:#ffdd57}.progress.is-warning::-moz-progress-bar{background-color:#ffdd57}.progress.is-warning::-ms-fill{background-color:#ffdd57}.progress.is-warning:indeterminate{background-image:linear-gradient(to right, #ffdd57 30%, #dbdbdb 30%)}.progress.is-danger::-webkit-progress-value{background-color:#da0b00}.progress.is-danger::-moz-progress-bar{background-color:#da0b00}.progress.is-danger::-ms-fill{background-color:#da0b00}.progress.is-danger:indeterminate{background-image:linear-gradient(to right, #da0b00 30%, #dbdbdb 30%)}.progress:indeterminate{animation-duration:1.5s;animation-iteration-count:infinite;animation-name:moveIndeterminate;animation-timing-function:linear;background-color:#dbdbdb;background-image:linear-gradient(to right, #222 30%, #dbdbdb 30%);background-position:top left;background-repeat:no-repeat;background-size:150% 150%}.progress:indeterminate::-webkit-progress-bar{background-color:transparent}.progress:indeterminate::-moz-progress-bar{background-color:transparent}.progress.is-small,#documenter .docs-sidebar form.docs-search>input.progress{height:.75rem}.progress.is-medium{height:1.25rem}.progress.is-large{height:1.5rem}@keyframes moveIndeterminate{from{background-position:200% 0}to{background-position:-200% 0}}.table{background-color:#fff;color:#363636}.table td,.table th{border:1px solid #dbdbdb;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}.table td.is-white,.table th.is-white{background-color:#fff;border-color:#fff;color:#0a0a0a}.table td.is-black,.table th.is-black{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}.table td.is-light,.table th.is-light{background-color:#f5f5f5;border-color:#f5f5f5;color:#363636}.table td.is-dark,.table th.is-dark{background-color:#363636;border-color:#363636;color:#f5f5f5}.table td.is-primary,.table th.is-primary{background-color:#4eb5de;border-color:#4eb5de;color:#fff}.table td.is-link,.table th.is-link{background-color:#2e63b8;border-color:#2e63b8;color:#fff}.table td.is-info,.table th.is-info{background-color:#209cee;border-color:#209cee;color:#fff}.table td.is-success,.table th.is-success{background-color:#22c35b;border-color:#22c35b;color:#fff}.table td.is-warning,.table th.is-warning{background-color:#ffdd57;border-color:#ffdd57;color:rgba(0,0,0,0.7)}.table td.is-danger,.table th.is-danger{background-color:#da0b00;border-color:#da0b00;color:#fff}.table td.is-narrow,.table th.is-narrow{white-space:nowrap;width:1%}.table td.is-selected,.table th.is-selected{background-color:#4eb5de;color:#fff}.table td.is-selected a,.table td.is-selected strong,.table th.is-selected a,.table th.is-selected strong{color:currentColor}.table th{color:#222}.table th:not([align]){text-align:left}.table tr.is-selected{background-color:#4eb5de;color:#fff}.table tr.is-selected a,.table tr.is-selected strong{color:currentColor}.table tr.is-selected td,.table tr.is-selected th{border-color:#fff;color:currentColor}.table thead{background-color:rgba(0,0,0,0)}.table thead td,.table thead th{border-width:0 0 2px;color:#222}.table tfoot{background-color:rgba(0,0,0,0)}.table tfoot td,.table tfoot th{border-width:2px 0 0;color:#222}.table tbody{background-color:rgba(0,0,0,0)}.table tbody tr:last-child td,.table tbody tr:last-child th{border-bottom-width:0}.table.is-bordered td,.table.is-bordered th{border-width:1px}.table.is-bordered tr:last-child td,.table.is-bordered tr:last-child th{border-bottom-width:1px}.table.is-fullwidth{width:100%}.table.is-hoverable tbody tr:not(.is-selected):hover{background-color:#fafafa}.table.is-hoverable.is-striped tbody tr:not(.is-selected):hover{background-color:#fafafa}.table.is-hoverable.is-striped tbody tr:not(.is-selected):hover:nth-child(even){background-color:#f5f5f5}.table.is-narrow td,.table.is-narrow th{padding:0.25em 0.5em}.table.is-striped tbody tr:not(.is-selected):nth-child(even){background-color:#fafafa}.table-container{-webkit-overflow-scrolling:touch;overflow:auto;overflow-y:hidden;max-width:100%}.tags{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}.tags .tag,.tags .content kbd,.content .tags kbd,.tags .docstring>section>a.docs-sourcelink{margin-bottom:0.5rem}.tags .tag:not(:last-child),.tags .content kbd:not(:last-child),.content .tags kbd:not(:last-child),.tags .docstring>section>a.docs-sourcelink:not(:last-child){margin-right:0.5rem}.tags:last-child{margin-bottom:-0.5rem}.tags:not(:last-child){margin-bottom:1rem}.tags.are-medium .tag:not(.is-normal):not(.is-large),.tags.are-medium .content kbd:not(.is-normal):not(.is-large),.content .tags.are-medium kbd:not(.is-normal):not(.is-large),.tags.are-medium .docstring>section>a.docs-sourcelink:not(.is-normal):not(.is-large){font-size:1rem}.tags.are-large .tag:not(.is-normal):not(.is-medium),.tags.are-large .content kbd:not(.is-normal):not(.is-medium),.content .tags.are-large kbd:not(.is-normal):not(.is-medium),.tags.are-large .docstring>section>a.docs-sourcelink:not(.is-normal):not(.is-medium){font-size:1.25rem}.tags.is-centered{justify-content:center}.tags.is-centered .tag,.tags.is-centered .content kbd,.content .tags.is-centered kbd,.tags.is-centered .docstring>section>a.docs-sourcelink{margin-right:0.25rem;margin-left:0.25rem}.tags.is-right{justify-content:flex-end}.tags.is-right .tag:not(:first-child),.tags.is-right .content kbd:not(:first-child),.content .tags.is-right kbd:not(:first-child),.tags.is-right .docstring>section>a.docs-sourcelink:not(:first-child){margin-left:0.5rem}.tags.is-right .tag:not(:last-child),.tags.is-right .content kbd:not(:last-child),.content .tags.is-right kbd:not(:last-child),.tags.is-right .docstring>section>a.docs-sourcelink:not(:last-child){margin-right:0}.tags.has-addons .tag,.tags.has-addons .content kbd,.content .tags.has-addons kbd,.tags.has-addons .docstring>section>a.docs-sourcelink{margin-right:0}.tags.has-addons .tag:not(:first-child),.tags.has-addons .content kbd:not(:first-child),.content .tags.has-addons kbd:not(:first-child),.tags.has-addons .docstring>section>a.docs-sourcelink:not(:first-child){margin-left:0;border-bottom-left-radius:0;border-top-left-radius:0}.tags.has-addons .tag:not(:last-child),.tags.has-addons .content kbd:not(:last-child),.content .tags.has-addons kbd:not(:last-child),.tags.has-addons .docstring>section>a.docs-sourcelink:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0}.tag:not(body),.content kbd:not(body),.docstring>section>a.docs-sourcelink:not(body){align-items:center;background-color:#f5f5f5;border-radius:4px;color:#222;display:inline-flex;font-size:.75rem;height:2em;justify-content:center;line-height:1.5;padding-left:0.75em;padding-right:0.75em;white-space:nowrap}.tag:not(body) .delete,.content kbd:not(body) .delete,.docstring>section>a.docs-sourcelink:not(body) .delete{margin-left:0.25rem;margin-right:-0.375rem}.tag.is-white:not(body),.content kbd.is-white:not(body),.docstring>section>a.docs-sourcelink.is-white:not(body){background-color:#fff;color:#0a0a0a}.tag.is-black:not(body),.content kbd.is-black:not(body),.docstring>section>a.docs-sourcelink.is-black:not(body){background-color:#0a0a0a;color:#fff}.tag.is-light:not(body),.content kbd.is-light:not(body),.docstring>section>a.docs-sourcelink.is-light:not(body){background-color:#f5f5f5;color:#363636}.tag.is-dark:not(body),.content kbd:not(body),.docstring>section>a.docs-sourcelink.is-dark:not(body),.content .docstring>section>kbd:not(body){background-color:#363636;color:#f5f5f5}.tag.is-primary:not(body),.content kbd.is-primary:not(body),.docstring>section>a.docs-sourcelink:not(body){background-color:#4eb5de;color:#fff}.tag.is-link:not(body),.content kbd.is-link:not(body),.docstring>section>a.docs-sourcelink.is-link:not(body){background-color:#2e63b8;color:#fff}.tag.is-info:not(body),.content kbd.is-info:not(body),.docstring>section>a.docs-sourcelink.is-info:not(body){background-color:#209cee;color:#fff}.tag.is-success:not(body),.content kbd.is-success:not(body),.docstring>section>a.docs-sourcelink.is-success:not(body){background-color:#22c35b;color:#fff}.tag.is-warning:not(body),.content kbd.is-warning:not(body),.docstring>section>a.docs-sourcelink.is-warning:not(body){background-color:#ffdd57;color:rgba(0,0,0,0.7)}.tag.is-danger:not(body),.content kbd.is-danger:not(body),.docstring>section>a.docs-sourcelink.is-danger:not(body){background-color:#da0b00;color:#fff}.tag.is-normal:not(body),.content kbd.is-normal:not(body),.docstring>section>a.docs-sourcelink.is-normal:not(body){font-size:.75rem}.tag.is-medium:not(body),.content kbd.is-medium:not(body),.docstring>section>a.docs-sourcelink.is-medium:not(body){font-size:1rem}.tag.is-large:not(body),.content kbd.is-large:not(body),.docstring>section>a.docs-sourcelink.is-large:not(body){font-size:1.25rem}.tag:not(body) .icon:first-child:not(:last-child),.content kbd:not(body) .icon:first-child:not(:last-child),.docstring>section>a.docs-sourcelink:not(body) .icon:first-child:not(:last-child){margin-left:-0.375em;margin-right:0.1875em}.tag:not(body) .icon:last-child:not(:first-child),.content kbd:not(body) .icon:last-child:not(:first-child),.docstring>section>a.docs-sourcelink:not(body) .icon:last-child:not(:first-child){margin-left:0.1875em;margin-right:-0.375em}.tag:not(body) .icon:first-child:last-child,.content kbd:not(body) .icon:first-child:last-child,.docstring>section>a.docs-sourcelink:not(body) .icon:first-child:last-child{margin-left:-0.375em;margin-right:-0.375em}.tag.is-delete:not(body),.content kbd.is-delete:not(body),.docstring>section>a.docs-sourcelink.is-delete:not(body){margin-left:1px;padding:0;position:relative;width:2em}.tag.is-delete:not(body)::before,.content kbd.is-delete:not(body)::before,.docstring>section>a.docs-sourcelink.is-delete:not(body)::before,.tag.is-delete:not(body)::after,.content kbd.is-delete:not(body)::after,.docstring>section>a.docs-sourcelink.is-delete:not(body)::after{background-color:currentColor;content:"";display:block;left:50%;position:absolute;top:50%;transform:translateX(-50%) translateY(-50%) rotate(45deg);transform-origin:center center}.tag.is-delete:not(body)::before,.content kbd.is-delete:not(body)::before,.docstring>section>a.docs-sourcelink.is-delete:not(body)::before{height:1px;width:50%}.tag.is-delete:not(body)::after,.content kbd.is-delete:not(body)::after,.docstring>section>a.docs-sourcelink.is-delete:not(body)::after{height:50%;width:1px}.tag.is-delete:not(body):hover,.content kbd.is-delete:not(body):hover,.docstring>section>a.docs-sourcelink.is-delete:not(body):hover,.tag.is-delete:not(body):focus,.content kbd.is-delete:not(body):focus,.docstring>section>a.docs-sourcelink.is-delete:not(body):focus{background-color:#e8e8e8}.tag.is-delete:not(body):active,.content kbd.is-delete:not(body):active,.docstring>section>a.docs-sourcelink.is-delete:not(body):active{background-color:#dbdbdb}.tag.is-rounded:not(body),#documenter .docs-sidebar form.docs-search>input:not(body),.content kbd.is-rounded:not(body),#documenter .docs-sidebar .content form.docs-search>input:not(body),.docstring>section>a.docs-sourcelink.is-rounded:not(body){border-radius:290486px}a.tag:hover,.docstring>section>a.docs-sourcelink:hover{text-decoration:underline}.title,.subtitle{word-break:break-word}.title em,.title span,.subtitle em,.subtitle span{font-weight:inherit}.title sub,.subtitle sub{font-size:.75em}.title sup,.subtitle sup{font-size:.75em}.title .tag,.title .content kbd,.content .title kbd,.title .docstring>section>a.docs-sourcelink,.subtitle .tag,.subtitle .content kbd,.content .subtitle kbd,.subtitle .docstring>section>a.docs-sourcelink{vertical-align:middle}.title{color:#363636;font-size:2rem;font-weight:600;line-height:1.125}.title strong{color:inherit;font-weight:inherit}.title+.highlight{margin-top:-0.75rem}.title:not(.is-spaced)+.subtitle{margin-top:-1.25rem}.title.is-1{font-size:3rem}.title.is-2{font-size:2.5rem}.title.is-3{font-size:2rem}.title.is-4{font-size:1.5rem}.title.is-5{font-size:1.25rem}.title.is-6{font-size:1rem}.title.is-7{font-size:.75rem}.subtitle{color:#4a4a4a;font-size:1.25rem;font-weight:400;line-height:1.25}.subtitle strong{color:#363636;font-weight:600}.subtitle:not(.is-spaced)+.title{margin-top:-1.25rem}.subtitle.is-1{font-size:3rem}.subtitle.is-2{font-size:2.5rem}.subtitle.is-3{font-size:2rem}.subtitle.is-4{font-size:1.5rem}.subtitle.is-5{font-size:1.25rem}.subtitle.is-6{font-size:1rem}.subtitle.is-7{font-size:.75rem}.heading{display:block;font-size:11px;letter-spacing:1px;margin-bottom:5px;text-transform:uppercase}.highlight{font-weight:400;max-width:100%;overflow:hidden;padding:0}.highlight pre{overflow:auto;max-width:100%}.number{align-items:center;background-color:#f5f5f5;border-radius:290486px;display:inline-flex;font-size:1.25rem;height:2em;justify-content:center;margin-right:1.5rem;min-width:2.5em;padding:0.25rem 0.5rem;text-align:center;vertical-align:top}.select select,.textarea,.input,#documenter .docs-sidebar form.docs-search>input{background-color:#fff;border-color:#dbdbdb;border-radius:4px;color:#363636}.select select::-moz-placeholder,.textarea::-moz-placeholder,.input::-moz-placeholder,#documenter .docs-sidebar form.docs-search>input::-moz-placeholder{color:rgba(54,54,54,0.3)}.select select::-webkit-input-placeholder,.textarea::-webkit-input-placeholder,.input::-webkit-input-placeholder,#documenter .docs-sidebar form.docs-search>input::-webkit-input-placeholder{color:rgba(54,54,54,0.3)}.select select:-moz-placeholder,.textarea:-moz-placeholder,.input:-moz-placeholder,#documenter .docs-sidebar form.docs-search>input:-moz-placeholder{color:rgba(54,54,54,0.3)}.select select:-ms-input-placeholder,.textarea:-ms-input-placeholder,.input:-ms-input-placeholder,#documenter .docs-sidebar form.docs-search>input:-ms-input-placeholder{color:rgba(54,54,54,0.3)}.select select:hover,.textarea:hover,.input:hover,#documenter .docs-sidebar form.docs-search>input:hover,.select select.is-hovered,.is-hovered.textarea,.is-hovered.input,#documenter .docs-sidebar form.docs-search>input.is-hovered{border-color:#b5b5b5}.select select:focus,.textarea:focus,.input:focus,#documenter .docs-sidebar form.docs-search>input:focus,.select select.is-focused,.is-focused.textarea,.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.select select:active,.textarea:active,.input:active,#documenter .docs-sidebar form.docs-search>input:active,.select select.is-active,.is-active.textarea,.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{border-color:#2e63b8;box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.select select[disabled],.textarea[disabled],.input[disabled],#documenter .docs-sidebar form.docs-search>input[disabled],fieldset[disabled] .select select,.select fieldset[disabled] select,fieldset[disabled] .textarea,fieldset[disabled] .input,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input{background-color:#f5f5f5;border-color:#f5f5f5;box-shadow:none;color:#6b6b6b}.select select[disabled]::-moz-placeholder,.textarea[disabled]::-moz-placeholder,.input[disabled]::-moz-placeholder,#documenter .docs-sidebar form.docs-search>input[disabled]::-moz-placeholder,fieldset[disabled] .select select::-moz-placeholder,.select fieldset[disabled] select::-moz-placeholder,fieldset[disabled] .textarea::-moz-placeholder,fieldset[disabled] .input::-moz-placeholder,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input::-moz-placeholder,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input::-moz-placeholder{color:rgba(107,107,107,0.3)}.select select[disabled]::-webkit-input-placeholder,.textarea[disabled]::-webkit-input-placeholder,.input[disabled]::-webkit-input-placeholder,#documenter .docs-sidebar form.docs-search>input[disabled]::-webkit-input-placeholder,fieldset[disabled] .select select::-webkit-input-placeholder,.select fieldset[disabled] select::-webkit-input-placeholder,fieldset[disabled] .textarea::-webkit-input-placeholder,fieldset[disabled] .input::-webkit-input-placeholder,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input::-webkit-input-placeholder,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input::-webkit-input-placeholder{color:rgba(107,107,107,0.3)}.select select[disabled]:-moz-placeholder,.textarea[disabled]:-moz-placeholder,.input[disabled]:-moz-placeholder,#documenter .docs-sidebar form.docs-search>input[disabled]:-moz-placeholder,fieldset[disabled] .select select:-moz-placeholder,.select fieldset[disabled] select:-moz-placeholder,fieldset[disabled] .textarea:-moz-placeholder,fieldset[disabled] .input:-moz-placeholder,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input:-moz-placeholder,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input:-moz-placeholder{color:rgba(107,107,107,0.3)}.select select[disabled]:-ms-input-placeholder,.textarea[disabled]:-ms-input-placeholder,.input[disabled]:-ms-input-placeholder,#documenter .docs-sidebar form.docs-search>input[disabled]:-ms-input-placeholder,fieldset[disabled] .select select:-ms-input-placeholder,.select fieldset[disabled] select:-ms-input-placeholder,fieldset[disabled] .textarea:-ms-input-placeholder,fieldset[disabled] .input:-ms-input-placeholder,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input:-ms-input-placeholder,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input:-ms-input-placeholder{color:rgba(107,107,107,0.3)}.textarea,.input,#documenter .docs-sidebar form.docs-search>input{box-shadow:inset 0 1px 2px rgba(10,10,10,0.1);max-width:100%;width:100%}.textarea[readonly],.input[readonly],#documenter .docs-sidebar form.docs-search>input[readonly]{box-shadow:none}.is-white.textarea,.is-white.input,#documenter .docs-sidebar form.docs-search>input.is-white{border-color:#fff}.is-white.textarea:focus,.is-white.input:focus,#documenter .docs-sidebar form.docs-search>input.is-white:focus,.is-white.is-focused.textarea,.is-white.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-white.textarea:active,.is-white.input:active,#documenter .docs-sidebar form.docs-search>input.is-white:active,.is-white.is-active.textarea,.is-white.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}.is-black.textarea,.is-black.input,#documenter .docs-sidebar form.docs-search>input.is-black{border-color:#0a0a0a}.is-black.textarea:focus,.is-black.input:focus,#documenter .docs-sidebar form.docs-search>input.is-black:focus,.is-black.is-focused.textarea,.is-black.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-black.textarea:active,.is-black.input:active,#documenter .docs-sidebar form.docs-search>input.is-black:active,.is-black.is-active.textarea,.is-black.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}.is-light.textarea,.is-light.input,#documenter .docs-sidebar form.docs-search>input.is-light{border-color:#f5f5f5}.is-light.textarea:focus,.is-light.input:focus,#documenter .docs-sidebar form.docs-search>input.is-light:focus,.is-light.is-focused.textarea,.is-light.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-light.textarea:active,.is-light.input:active,#documenter .docs-sidebar form.docs-search>input.is-light:active,.is-light.is-active.textarea,.is-light.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}.is-dark.textarea,.content kbd.textarea,.is-dark.input,#documenter .docs-sidebar form.docs-search>input.is-dark,.content kbd.input{border-color:#363636}.is-dark.textarea:focus,.content kbd.textarea:focus,.is-dark.input:focus,#documenter .docs-sidebar form.docs-search>input.is-dark:focus,.content kbd.input:focus,.is-dark.is-focused.textarea,.content kbd.is-focused.textarea,.is-dark.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.content kbd.is-focused.input,#documenter .docs-sidebar .content form.docs-search>input.is-focused,.is-dark.textarea:active,.content kbd.textarea:active,.is-dark.input:active,#documenter .docs-sidebar form.docs-search>input.is-dark:active,.content kbd.input:active,.is-dark.is-active.textarea,.content kbd.is-active.textarea,.is-dark.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active,.content kbd.is-active.input,#documenter .docs-sidebar .content form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(54,54,54,0.25)}.is-primary.textarea,.docstring>section>a.textarea.docs-sourcelink,.is-primary.input,#documenter .docs-sidebar form.docs-search>input.is-primary,.docstring>section>a.input.docs-sourcelink{border-color:#4eb5de}.is-primary.textarea:focus,.docstring>section>a.textarea.docs-sourcelink:focus,.is-primary.input:focus,#documenter .docs-sidebar form.docs-search>input.is-primary:focus,.docstring>section>a.input.docs-sourcelink:focus,.is-primary.is-focused.textarea,.docstring>section>a.is-focused.textarea.docs-sourcelink,.is-primary.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.docstring>section>a.is-focused.input.docs-sourcelink,.is-primary.textarea:active,.docstring>section>a.textarea.docs-sourcelink:active,.is-primary.input:active,#documenter .docs-sidebar form.docs-search>input.is-primary:active,.docstring>section>a.input.docs-sourcelink:active,.is-primary.is-active.textarea,.docstring>section>a.is-active.textarea.docs-sourcelink,.is-primary.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active,.docstring>section>a.is-active.input.docs-sourcelink{box-shadow:0 0 0 0.125em rgba(78,181,222,0.25)}.is-link.textarea,.is-link.input,#documenter .docs-sidebar form.docs-search>input.is-link{border-color:#2e63b8}.is-link.textarea:focus,.is-link.input:focus,#documenter .docs-sidebar form.docs-search>input.is-link:focus,.is-link.is-focused.textarea,.is-link.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-link.textarea:active,.is-link.input:active,#documenter .docs-sidebar form.docs-search>input.is-link:active,.is-link.is-active.textarea,.is-link.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.is-info.textarea,.is-info.input,#documenter .docs-sidebar form.docs-search>input.is-info{border-color:#209cee}.is-info.textarea:focus,.is-info.input:focus,#documenter .docs-sidebar form.docs-search>input.is-info:focus,.is-info.is-focused.textarea,.is-info.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-info.textarea:active,.is-info.input:active,#documenter .docs-sidebar form.docs-search>input.is-info:active,.is-info.is-active.textarea,.is-info.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(32,156,238,0.25)}.is-success.textarea,.is-success.input,#documenter .docs-sidebar form.docs-search>input.is-success{border-color:#22c35b}.is-success.textarea:focus,.is-success.input:focus,#documenter .docs-sidebar form.docs-search>input.is-success:focus,.is-success.is-focused.textarea,.is-success.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-success.textarea:active,.is-success.input:active,#documenter .docs-sidebar form.docs-search>input.is-success:active,.is-success.is-active.textarea,.is-success.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(34,195,91,0.25)}.is-warning.textarea,.is-warning.input,#documenter .docs-sidebar form.docs-search>input.is-warning{border-color:#ffdd57}.is-warning.textarea:focus,.is-warning.input:focus,#documenter .docs-sidebar form.docs-search>input.is-warning:focus,.is-warning.is-focused.textarea,.is-warning.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-warning.textarea:active,.is-warning.input:active,#documenter .docs-sidebar form.docs-search>input.is-warning:active,.is-warning.is-active.textarea,.is-warning.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,221,87,0.25)}.is-danger.textarea,.is-danger.input,#documenter .docs-sidebar form.docs-search>input.is-danger{border-color:#da0b00}.is-danger.textarea:focus,.is-danger.input:focus,#documenter .docs-sidebar form.docs-search>input.is-danger:focus,.is-danger.is-focused.textarea,.is-danger.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-danger.textarea:active,.is-danger.input:active,#documenter .docs-sidebar form.docs-search>input.is-danger:active,.is-danger.is-active.textarea,.is-danger.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(218,11,0,0.25)}.is-small.textarea,.is-small.input,#documenter .docs-sidebar form.docs-search>input{border-radius:2px;font-size:.75rem}.is-medium.textarea,.is-medium.input,#documenter .docs-sidebar form.docs-search>input.is-medium{font-size:1.25rem}.is-large.textarea,.is-large.input,#documenter .docs-sidebar form.docs-search>input.is-large{font-size:1.5rem}.is-fullwidth.textarea,.is-fullwidth.input,#documenter .docs-sidebar form.docs-search>input.is-fullwidth{display:block;width:100%}.is-inline.textarea,.is-inline.input,#documenter .docs-sidebar form.docs-search>input.is-inline{display:inline;width:auto}.input.is-rounded,#documenter .docs-sidebar form.docs-search>input{border-radius:290486px;padding-left:1em;padding-right:1em}.input.is-static,#documenter .docs-sidebar form.docs-search>input.is-static{background-color:transparent;border-color:transparent;box-shadow:none;padding-left:0;padding-right:0}.textarea{display:block;max-width:100%;min-width:100%;padding:0.625em;resize:vertical}.textarea:not([rows]){max-height:600px;min-height:120px}.textarea[rows]{height:initial}.textarea.has-fixed-size{resize:none}.radio,.checkbox{cursor:pointer;display:inline-block;line-height:1.25;position:relative}.radio input,.checkbox input{cursor:pointer}.radio:hover,.checkbox:hover{color:#363636}.radio[disabled],.checkbox[disabled],fieldset[disabled] .radio,fieldset[disabled] .checkbox{color:#6b6b6b;cursor:not-allowed}.radio+.radio{margin-left:0.5em}.select{display:inline-block;max-width:100%;position:relative;vertical-align:top}.select:not(.is-multiple){height:2.25em}.select:not(.is-multiple):not(.is-loading)::after{border-color:#2e63b8;right:1.125em;z-index:4}.select.is-rounded select,#documenter .docs-sidebar form.docs-search>input.select select{border-radius:290486px;padding-left:1em}.select select{cursor:pointer;display:block;font-size:1em;max-width:100%;outline:none}.select select::-ms-expand{display:none}.select select[disabled]:hover,fieldset[disabled] .select select:hover{border-color:#f5f5f5}.select select:not([multiple]){padding-right:2.5em}.select select[multiple]{height:auto;padding:0}.select select[multiple] option{padding:0.5em 1em}.select:not(.is-multiple):not(.is-loading):hover::after{border-color:#363636}.select.is-white:not(:hover)::after{border-color:#fff}.select.is-white select{border-color:#fff}.select.is-white select:hover,.select.is-white select.is-hovered{border-color:#f2f2f2}.select.is-white select:focus,.select.is-white select.is-focused,.select.is-white select:active,.select.is-white select.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}.select.is-black:not(:hover)::after{border-color:#0a0a0a}.select.is-black select{border-color:#0a0a0a}.select.is-black select:hover,.select.is-black select.is-hovered{border-color:#000}.select.is-black select:focus,.select.is-black select.is-focused,.select.is-black select:active,.select.is-black select.is-active{box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}.select.is-light:not(:hover)::after{border-color:#f5f5f5}.select.is-light select{border-color:#f5f5f5}.select.is-light select:hover,.select.is-light select.is-hovered{border-color:#e8e8e8}.select.is-light select:focus,.select.is-light select.is-focused,.select.is-light select:active,.select.is-light select.is-active{box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}.select.is-dark:not(:hover)::after,.content kbd.select:not(:hover)::after{border-color:#363636}.select.is-dark select,.content kbd.select select{border-color:#363636}.select.is-dark select:hover,.content kbd.select select:hover,.select.is-dark select.is-hovered,.content kbd.select select.is-hovered{border-color:#292929}.select.is-dark select:focus,.content kbd.select select:focus,.select.is-dark select.is-focused,.content kbd.select select.is-focused,.select.is-dark select:active,.content kbd.select select:active,.select.is-dark select.is-active,.content kbd.select select.is-active{box-shadow:0 0 0 0.125em rgba(54,54,54,0.25)}.select.is-primary:not(:hover)::after,.docstring>section>a.select.docs-sourcelink:not(:hover)::after{border-color:#4eb5de}.select.is-primary select,.docstring>section>a.select.docs-sourcelink select{border-color:#4eb5de}.select.is-primary select:hover,.docstring>section>a.select.docs-sourcelink select:hover,.select.is-primary select.is-hovered,.docstring>section>a.select.docs-sourcelink select.is-hovered{border-color:#39acda}.select.is-primary select:focus,.docstring>section>a.select.docs-sourcelink select:focus,.select.is-primary select.is-focused,.docstring>section>a.select.docs-sourcelink select.is-focused,.select.is-primary select:active,.docstring>section>a.select.docs-sourcelink select:active,.select.is-primary select.is-active,.docstring>section>a.select.docs-sourcelink select.is-active{box-shadow:0 0 0 0.125em rgba(78,181,222,0.25)}.select.is-link:not(:hover)::after{border-color:#2e63b8}.select.is-link select{border-color:#2e63b8}.select.is-link select:hover,.select.is-link select.is-hovered{border-color:#2958a4}.select.is-link select:focus,.select.is-link select.is-focused,.select.is-link select:active,.select.is-link select.is-active{box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.select.is-info:not(:hover)::after{border-color:#209cee}.select.is-info select{border-color:#209cee}.select.is-info select:hover,.select.is-info select.is-hovered{border-color:#1190e3}.select.is-info select:focus,.select.is-info select.is-focused,.select.is-info select:active,.select.is-info select.is-active{box-shadow:0 0 0 0.125em rgba(32,156,238,0.25)}.select.is-success:not(:hover)::after{border-color:#22c35b}.select.is-success select{border-color:#22c35b}.select.is-success select:hover,.select.is-success select.is-hovered{border-color:#1ead51}.select.is-success select:focus,.select.is-success select.is-focused,.select.is-success select:active,.select.is-success select.is-active{box-shadow:0 0 0 0.125em rgba(34,195,91,0.25)}.select.is-warning:not(:hover)::after{border-color:#ffdd57}.select.is-warning select{border-color:#ffdd57}.select.is-warning select:hover,.select.is-warning select.is-hovered{border-color:#ffd83e}.select.is-warning select:focus,.select.is-warning select.is-focused,.select.is-warning select:active,.select.is-warning select.is-active{box-shadow:0 0 0 0.125em rgba(255,221,87,0.25)}.select.is-danger:not(:hover)::after{border-color:#da0b00}.select.is-danger select{border-color:#da0b00}.select.is-danger select:hover,.select.is-danger select.is-hovered{border-color:#c10a00}.select.is-danger select:focus,.select.is-danger select.is-focused,.select.is-danger select:active,.select.is-danger select.is-active{box-shadow:0 0 0 0.125em rgba(218,11,0,0.25)}.select.is-small,#documenter .docs-sidebar form.docs-search>input.select{border-radius:2px;font-size:.75rem}.select.is-medium{font-size:1.25rem}.select.is-large{font-size:1.5rem}.select.is-disabled::after{border-color:#6b6b6b}.select.is-fullwidth{width:100%}.select.is-fullwidth select{width:100%}.select.is-loading::after{margin-top:0;position:absolute;right:0.625em;top:0.625em;transform:none}.select.is-loading.is-small:after,#documenter .docs-sidebar form.docs-search>input.is-loading:after{font-size:.75rem}.select.is-loading.is-medium:after{font-size:1.25rem}.select.is-loading.is-large:after{font-size:1.5rem}.file{align-items:stretch;display:flex;justify-content:flex-start;position:relative}.file.is-white .file-cta{background-color:#fff;border-color:transparent;color:#0a0a0a}.file.is-white:hover .file-cta,.file.is-white.is-hovered .file-cta{background-color:#f9f9f9;border-color:transparent;color:#0a0a0a}.file.is-white:focus .file-cta,.file.is-white.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(255,255,255,0.25);color:#0a0a0a}.file.is-white:active .file-cta,.file.is-white.is-active .file-cta{background-color:#f2f2f2;border-color:transparent;color:#0a0a0a}.file.is-black .file-cta{background-color:#0a0a0a;border-color:transparent;color:#fff}.file.is-black:hover .file-cta,.file.is-black.is-hovered .file-cta{background-color:#040404;border-color:transparent;color:#fff}.file.is-black:focus .file-cta,.file.is-black.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(10,10,10,0.25);color:#fff}.file.is-black:active .file-cta,.file.is-black.is-active .file-cta{background-color:#000;border-color:transparent;color:#fff}.file.is-light .file-cta{background-color:#f5f5f5;border-color:transparent;color:#363636}.file.is-light:hover .file-cta,.file.is-light.is-hovered .file-cta{background-color:#eee;border-color:transparent;color:#363636}.file.is-light:focus .file-cta,.file.is-light.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(245,245,245,0.25);color:#363636}.file.is-light:active .file-cta,.file.is-light.is-active .file-cta{background-color:#e8e8e8;border-color:transparent;color:#363636}.file.is-dark .file-cta,.content kbd.file .file-cta{background-color:#363636;border-color:transparent;color:#f5f5f5}.file.is-dark:hover .file-cta,.content kbd.file:hover .file-cta,.file.is-dark.is-hovered .file-cta,.content kbd.file.is-hovered .file-cta{background-color:#2f2f2f;border-color:transparent;color:#f5f5f5}.file.is-dark:focus .file-cta,.content kbd.file:focus .file-cta,.file.is-dark.is-focused .file-cta,.content kbd.file.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(54,54,54,0.25);color:#f5f5f5}.file.is-dark:active .file-cta,.content kbd.file:active .file-cta,.file.is-dark.is-active .file-cta,.content kbd.file.is-active .file-cta{background-color:#292929;border-color:transparent;color:#f5f5f5}.file.is-primary .file-cta,.docstring>section>a.file.docs-sourcelink .file-cta{background-color:#4eb5de;border-color:transparent;color:#fff}.file.is-primary:hover .file-cta,.docstring>section>a.file.docs-sourcelink:hover .file-cta,.file.is-primary.is-hovered .file-cta,.docstring>section>a.file.is-hovered.docs-sourcelink .file-cta{background-color:#43b1dc;border-color:transparent;color:#fff}.file.is-primary:focus .file-cta,.docstring>section>a.file.docs-sourcelink:focus .file-cta,.file.is-primary.is-focused .file-cta,.docstring>section>a.file.is-focused.docs-sourcelink .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(78,181,222,0.25);color:#fff}.file.is-primary:active .file-cta,.docstring>section>a.file.docs-sourcelink:active .file-cta,.file.is-primary.is-active .file-cta,.docstring>section>a.file.is-active.docs-sourcelink .file-cta{background-color:#39acda;border-color:transparent;color:#fff}.file.is-link .file-cta{background-color:#2e63b8;border-color:transparent;color:#fff}.file.is-link:hover .file-cta,.file.is-link.is-hovered .file-cta{background-color:#2b5eae;border-color:transparent;color:#fff}.file.is-link:focus .file-cta,.file.is-link.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(46,99,184,0.25);color:#fff}.file.is-link:active .file-cta,.file.is-link.is-active .file-cta{background-color:#2958a4;border-color:transparent;color:#fff}.file.is-info .file-cta{background-color:#209cee;border-color:transparent;color:#fff}.file.is-info:hover .file-cta,.file.is-info.is-hovered .file-cta{background-color:#1497ed;border-color:transparent;color:#fff}.file.is-info:focus .file-cta,.file.is-info.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(32,156,238,0.25);color:#fff}.file.is-info:active .file-cta,.file.is-info.is-active .file-cta{background-color:#1190e3;border-color:transparent;color:#fff}.file.is-success .file-cta{background-color:#22c35b;border-color:transparent;color:#fff}.file.is-success:hover .file-cta,.file.is-success.is-hovered .file-cta{background-color:#20b856;border-color:transparent;color:#fff}.file.is-success:focus .file-cta,.file.is-success.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(34,195,91,0.25);color:#fff}.file.is-success:active .file-cta,.file.is-success.is-active .file-cta{background-color:#1ead51;border-color:transparent;color:#fff}.file.is-warning .file-cta{background-color:#ffdd57;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-warning:hover .file-cta,.file.is-warning.is-hovered .file-cta{background-color:#ffda4a;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-warning:focus .file-cta,.file.is-warning.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(255,221,87,0.25);color:rgba(0,0,0,0.7)}.file.is-warning:active .file-cta,.file.is-warning.is-active .file-cta{background-color:#ffd83e;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-danger .file-cta{background-color:#da0b00;border-color:transparent;color:#fff}.file.is-danger:hover .file-cta,.file.is-danger.is-hovered .file-cta{background-color:#cd0a00;border-color:transparent;color:#fff}.file.is-danger:focus .file-cta,.file.is-danger.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(218,11,0,0.25);color:#fff}.file.is-danger:active .file-cta,.file.is-danger.is-active .file-cta{background-color:#c10a00;border-color:transparent;color:#fff}.file.is-small,#documenter .docs-sidebar form.docs-search>input.file{font-size:.75rem}.file.is-medium{font-size:1.25rem}.file.is-medium .file-icon .fa{font-size:21px}.file.is-large{font-size:1.5rem}.file.is-large .file-icon .fa{font-size:28px}.file.has-name .file-cta{border-bottom-right-radius:0;border-top-right-radius:0}.file.has-name .file-name{border-bottom-left-radius:0;border-top-left-radius:0}.file.has-name.is-empty .file-cta{border-radius:4px}.file.has-name.is-empty .file-name{display:none}.file.is-boxed .file-label{flex-direction:column}.file.is-boxed .file-cta{flex-direction:column;height:auto;padding:1em 3em}.file.is-boxed .file-name{border-width:0 1px 1px}.file.is-boxed .file-icon{height:1.5em;width:1.5em}.file.is-boxed .file-icon .fa{font-size:21px}.file.is-boxed.is-small .file-icon .fa,#documenter .docs-sidebar form.docs-search>input.is-boxed .file-icon .fa{font-size:14px}.file.is-boxed.is-medium .file-icon .fa{font-size:28px}.file.is-boxed.is-large .file-icon .fa{font-size:35px}.file.is-boxed.has-name .file-cta{border-radius:4px 4px 0 0}.file.is-boxed.has-name .file-name{border-radius:0 0 4px 4px;border-width:0 1px 1px}.file.is-centered{justify-content:center}.file.is-fullwidth .file-label{width:100%}.file.is-fullwidth .file-name{flex-grow:1;max-width:none}.file.is-right{justify-content:flex-end}.file.is-right .file-cta{border-radius:0 4px 4px 0}.file.is-right .file-name{border-radius:4px 0 0 4px;border-width:1px 0 1px 1px;order:-1}.file-label{align-items:stretch;display:flex;cursor:pointer;justify-content:flex-start;overflow:hidden;position:relative}.file-label:hover .file-cta{background-color:#eee;color:#363636}.file-label:hover .file-name{border-color:#d5d5d5}.file-label:active .file-cta{background-color:#e8e8e8;color:#363636}.file-label:active .file-name{border-color:#cfcfcf}.file-input{height:100%;left:0;opacity:0;outline:none;position:absolute;top:0;width:100%}.file-cta,.file-name{border-color:#dbdbdb;border-radius:4px;font-size:1em;padding-left:1em;padding-right:1em;white-space:nowrap}.file-cta{background-color:#f5f5f5;color:#4a4a4a}.file-name{border-color:#dbdbdb;border-style:solid;border-width:1px 1px 1px 0;display:block;max-width:16em;overflow:hidden;text-align:left;text-overflow:ellipsis}.file-icon{align-items:center;display:flex;height:1em;justify-content:center;margin-right:0.5em;width:1em}.file-icon .fa{font-size:14px}.label{color:#363636;display:block;font-size:1rem;font-weight:700}.label:not(:last-child){margin-bottom:0.5em}.label.is-small,#documenter .docs-sidebar form.docs-search>input.label{font-size:.75rem}.label.is-medium{font-size:1.25rem}.label.is-large{font-size:1.5rem}.help{display:block;font-size:.75rem;margin-top:0.25rem}.help.is-white{color:#fff}.help.is-black{color:#0a0a0a}.help.is-light{color:#f5f5f5}.help.is-dark,.content kbd.help{color:#363636}.help.is-primary,.docstring>section>a.help.docs-sourcelink{color:#4eb5de}.help.is-link{color:#2e63b8}.help.is-info{color:#209cee}.help.is-success{color:#22c35b}.help.is-warning{color:#ffdd57}.help.is-danger{color:#da0b00}.field:not(:last-child){margin-bottom:0.75rem}.field.has-addons{display:flex;justify-content:flex-start}.field.has-addons .control:not(:last-child){margin-right:-1px}.field.has-addons .control:not(:first-child):not(:last-child) .button,.field.has-addons .control:not(:first-child):not(:last-child) .input,.field.has-addons .control:not(:first-child):not(:last-child) #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .field.has-addons .control:not(:first-child):not(:last-child) form.docs-search>input,.field.has-addons .control:not(:first-child):not(:last-child) .select select{border-radius:0}.field.has-addons .control:first-child:not(:only-child) .button,.field.has-addons .control:first-child:not(:only-child) .input,.field.has-addons .control:first-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .field.has-addons .control:first-child:not(:only-child) form.docs-search>input,.field.has-addons .control:first-child:not(:only-child) .select select{border-bottom-right-radius:0;border-top-right-radius:0}.field.has-addons .control:last-child:not(:only-child) .button,.field.has-addons .control:last-child:not(:only-child) .input,.field.has-addons .control:last-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .field.has-addons .control:last-child:not(:only-child) form.docs-search>input,.field.has-addons .control:last-child:not(:only-child) .select select{border-bottom-left-radius:0;border-top-left-radius:0}.field.has-addons .control .button:not([disabled]):hover,.field.has-addons .control .button.is-hovered:not([disabled]),.field.has-addons .control .input:not([disabled]):hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):hover,.field.has-addons .control .input.is-hovered:not([disabled]),.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-hovered:not([disabled]),#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-hovered:not([disabled]),.field.has-addons .control .select select:not([disabled]):hover,.field.has-addons .control .select select.is-hovered:not([disabled]){z-index:2}.field.has-addons .control .button:not([disabled]):focus,.field.has-addons .control .button.is-focused:not([disabled]),.field.has-addons .control .button:not([disabled]):active,.field.has-addons .control .button.is-active:not([disabled]),.field.has-addons .control .input:not([disabled]):focus,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus,.field.has-addons .control .input.is-focused:not([disabled]),.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]),#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]),.field.has-addons .control .input:not([disabled]):active,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active,.field.has-addons .control .input.is-active:not([disabled]),.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]),#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]),.field.has-addons .control .select select:not([disabled]):focus,.field.has-addons .control .select select.is-focused:not([disabled]),.field.has-addons .control .select select:not([disabled]):active,.field.has-addons .control .select select.is-active:not([disabled]){z-index:3}.field.has-addons .control .button:not([disabled]):focus:hover,.field.has-addons .control .button.is-focused:not([disabled]):hover,.field.has-addons .control .button:not([disabled]):active:hover,.field.has-addons .control .button.is-active:not([disabled]):hover,.field.has-addons .control .input:not([disabled]):focus:hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus:hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus:hover,.field.has-addons .control .input.is-focused:not([disabled]):hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]):hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]):hover,.field.has-addons .control .input:not([disabled]):active:hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active:hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active:hover,.field.has-addons .control .input.is-active:not([disabled]):hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]):hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]):hover,.field.has-addons .control .select select:not([disabled]):focus:hover,.field.has-addons .control .select select.is-focused:not([disabled]):hover,.field.has-addons .control .select select:not([disabled]):active:hover,.field.has-addons .control .select select.is-active:not([disabled]):hover{z-index:4}.field.has-addons .control.is-expanded{flex-grow:1;flex-shrink:1}.field.has-addons.has-addons-centered{justify-content:center}.field.has-addons.has-addons-right{justify-content:flex-end}.field.has-addons.has-addons-fullwidth .control{flex-grow:1;flex-shrink:0}.field.is-grouped{display:flex;justify-content:flex-start}.field.is-grouped>.control{flex-shrink:0}.field.is-grouped>.control:not(:last-child){margin-bottom:0;margin-right:0.75rem}.field.is-grouped>.control.is-expanded{flex-grow:1;flex-shrink:1}.field.is-grouped.is-grouped-centered{justify-content:center}.field.is-grouped.is-grouped-right{justify-content:flex-end}.field.is-grouped.is-grouped-multiline{flex-wrap:wrap}.field.is-grouped.is-grouped-multiline>.control:last-child,.field.is-grouped.is-grouped-multiline>.control:not(:last-child){margin-bottom:0.75rem}.field.is-grouped.is-grouped-multiline:last-child{margin-bottom:-0.75rem}.field.is-grouped.is-grouped-multiline:not(:last-child){margin-bottom:0}@media screen and (min-width: 769px),print{.field.is-horizontal{display:flex}}.field-label .label{font-size:inherit}@media screen and (max-width: 768px){.field-label{margin-bottom:0.5rem}}@media screen and (min-width: 769px),print{.field-label{flex-basis:0;flex-grow:1;flex-shrink:0;margin-right:1.5rem;text-align:right}.field-label.is-small,#documenter .docs-sidebar form.docs-search>input.field-label{font-size:.75rem;padding-top:0.375em}.field-label.is-normal{padding-top:0.375em}.field-label.is-medium{font-size:1.25rem;padding-top:0.375em}.field-label.is-large{font-size:1.5rem;padding-top:0.375em}}.field-body .field .field{margin-bottom:0}@media screen and (min-width: 769px),print{.field-body{display:flex;flex-basis:0;flex-grow:5;flex-shrink:1}.field-body .field{margin-bottom:0}.field-body>.field{flex-shrink:1}.field-body>.field:not(.is-narrow){flex-grow:1}.field-body>.field:not(:last-child){margin-right:0.75rem}}.control{box-sizing:border-box;clear:both;font-size:1rem;position:relative;text-align:left}.control.has-icons-left .input:focus~.icon,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input:focus~.icon,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input:focus~.icon,.control.has-icons-left .select:focus~.icon,.control.has-icons-right .input:focus~.icon,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input:focus~.icon,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input:focus~.icon,.control.has-icons-right .select:focus~.icon{color:#6b6b6b}.control.has-icons-left .input.is-small~.icon,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input~.icon,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input~.icon,.control.has-icons-left .select.is-small~.icon,.control.has-icons-right .input.is-small~.icon,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input~.icon,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input~.icon,.control.has-icons-right .select.is-small~.icon{font-size:.75rem}.control.has-icons-left .input.is-medium~.icon,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-medium~.icon,.control.has-icons-left .select.is-medium~.icon,.control.has-icons-right .input.is-medium~.icon,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-medium~.icon,.control.has-icons-right .select.is-medium~.icon{font-size:1.25rem}.control.has-icons-left .input.is-large~.icon,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-large~.icon,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-large~.icon,.control.has-icons-left .select.is-large~.icon,.control.has-icons-right .input.is-large~.icon,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-large~.icon,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-large~.icon,.control.has-icons-right .select.is-large~.icon{font-size:1.5rem}.control.has-icons-left .icon,.control.has-icons-right .icon{color:#dbdbdb;height:2.25em;pointer-events:none;position:absolute;top:0;width:2.25em;z-index:4}.control.has-icons-left .input,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input,.control.has-icons-left .select select{padding-left:2.25em}.control.has-icons-left .icon.is-left{left:0}.control.has-icons-right .input,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input,.control.has-icons-right .select select{padding-right:2.25em}.control.has-icons-right .icon.is-right{right:0}.control.is-loading::after{position:absolute !important;right:0.625em;top:0.625em;z-index:4}.control.is-loading.is-small:after,#documenter .docs-sidebar form.docs-search>input.is-loading:after{font-size:.75rem}.control.is-loading.is-medium:after{font-size:1.25rem}.control.is-loading.is-large:after{font-size:1.5rem}.breadcrumb{font-size:1rem;white-space:nowrap}.breadcrumb a{align-items:center;color:#2e63b8;display:flex;justify-content:center;padding:0 .75em}.breadcrumb a:hover{color:#363636}.breadcrumb li{align-items:center;display:flex}.breadcrumb li:first-child a{padding-left:0}.breadcrumb li.is-active a{color:#222;cursor:default;pointer-events:none}.breadcrumb li+li::before{color:#b5b5b5;content:"\0002f"}.breadcrumb ul,.breadcrumb ol{align-items:flex-start;display:flex;flex-wrap:wrap;justify-content:flex-start}.breadcrumb .icon:first-child{margin-right:0.5em}.breadcrumb .icon:last-child{margin-left:0.5em}.breadcrumb.is-centered ol,.breadcrumb.is-centered ul{justify-content:center}.breadcrumb.is-right ol,.breadcrumb.is-right ul{justify-content:flex-end}.breadcrumb.is-small,#documenter .docs-sidebar form.docs-search>input.breadcrumb{font-size:.75rem}.breadcrumb.is-medium{font-size:1.25rem}.breadcrumb.is-large{font-size:1.5rem}.breadcrumb.has-arrow-separator li+li::before{content:"\02192"}.breadcrumb.has-bullet-separator li+li::before{content:"\02022"}.breadcrumb.has-dot-separator li+li::before{content:"\000b7"}.breadcrumb.has-succeeds-separator li+li::before{content:"\0227B"}.card{background-color:#fff;box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px rgba(10,10,10,0.1);color:#222;max-width:100%;position:relative}.card-header{background-color:rgba(0,0,0,0);align-items:stretch;box-shadow:0 1px 2px rgba(10,10,10,0.1);display:flex}.card-header-title{align-items:center;color:#222;display:flex;flex-grow:1;font-weight:700;padding:.75rem}.card-header-title.is-centered{justify-content:center}.card-header-icon{align-items:center;cursor:pointer;display:flex;justify-content:center;padding:.75rem}.card-image{display:block;position:relative}.card-content{background-color:rgba(0,0,0,0);padding:1.5rem}.card-footer{background-color:rgba(0,0,0,0);border-top:1px solid #dbdbdb;align-items:stretch;display:flex}.card-footer-item{align-items:center;display:flex;flex-basis:0;flex-grow:1;flex-shrink:0;justify-content:center;padding:.75rem}.card-footer-item:not(:last-child){border-right:1px solid #dbdbdb}.card .media:not(:last-child){margin-bottom:1.5rem}.dropdown{display:inline-flex;position:relative;vertical-align:top}.dropdown.is-active .dropdown-menu,.dropdown.is-hoverable:hover .dropdown-menu{display:block}.dropdown.is-right .dropdown-menu{left:auto;right:0}.dropdown.is-up .dropdown-menu{bottom:100%;padding-bottom:4px;padding-top:initial;top:auto}.dropdown-menu{display:none;left:0;min-width:12rem;padding-top:4px;position:absolute;top:100%;z-index:20}.dropdown-content{background-color:#fff;border-radius:4px;box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px rgba(10,10,10,0.1);padding-bottom:.5rem;padding-top:.5rem}.dropdown-item{color:#4a4a4a;display:block;font-size:0.875rem;line-height:1.5;padding:0.375rem 1rem;position:relative}a.dropdown-item,button.dropdown-item{padding-right:3rem;text-align:left;white-space:nowrap;width:100%}a.dropdown-item:hover,button.dropdown-item:hover{background-color:#f5f5f5;color:#0a0a0a}a.dropdown-item.is-active,button.dropdown-item.is-active{background-color:#2e63b8;color:#fff}.dropdown-divider{background-color:#dbdbdb;border:none;display:block;height:1px;margin:0.5rem 0}.level{align-items:center;justify-content:space-between}.level code{border-radius:4px}.level img{display:inline-block;vertical-align:top}.level.is-mobile{display:flex}.level.is-mobile .level-left,.level.is-mobile .level-right{display:flex}.level.is-mobile .level-left+.level-right{margin-top:0}.level.is-mobile .level-item:not(:last-child){margin-bottom:0;margin-right:.75rem}.level.is-mobile .level-item:not(.is-narrow){flex-grow:1}@media screen and (min-width: 769px),print{.level{display:flex}.level>.level-item:not(.is-narrow){flex-grow:1}}.level-item{align-items:center;display:flex;flex-basis:auto;flex-grow:0;flex-shrink:0;justify-content:center}.level-item .title,.level-item .subtitle{margin-bottom:0}@media screen and (max-width: 768px){.level-item:not(:last-child){margin-bottom:.75rem}}.level-left,.level-right{flex-basis:auto;flex-grow:0;flex-shrink:0}.level-left .level-item.is-flexible,.level-right .level-item.is-flexible{flex-grow:1}@media screen and (min-width: 769px),print{.level-left .level-item:not(:last-child),.level-right .level-item:not(:last-child){margin-right:.75rem}}.level-left{align-items:center;justify-content:flex-start}@media screen and (max-width: 768px){.level-left+.level-right{margin-top:1.5rem}}@media screen and (min-width: 769px),print{.level-left{display:flex}}.level-right{align-items:center;justify-content:flex-end}@media screen and (min-width: 769px),print{.level-right{display:flex}}.list{background-color:#fff;border-radius:4px;box-shadow:0 2px 3px rgba(10,10,10,0.1),0 0 0 1px rgba(10,10,10,0.1)}.list-item{display:block;padding:0.5em 1em}.list-item:not(a){color:#222}.list-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-item:last-child{border-bottom-left-radius:4px;border-bottom-right-radius:4px}.list-item:not(:last-child){border-bottom:1px solid #dbdbdb}.list-item.is-active{background-color:#2e63b8;color:#fff}a.list-item{background-color:#f5f5f5;cursor:pointer}.media{align-items:flex-start;display:flex;text-align:left}.media .content:not(:last-child){margin-bottom:0.75rem}.media .media{border-top:1px solid rgba(219,219,219,0.5);display:flex;padding-top:0.75rem}.media .media .content:not(:last-child),.media .media .control:not(:last-child){margin-bottom:0.5rem}.media .media .media{padding-top:0.5rem}.media .media .media+.media{margin-top:0.5rem}.media+.media{border-top:1px solid rgba(219,219,219,0.5);margin-top:1rem;padding-top:1rem}.media.is-large+.media{margin-top:1.5rem;padding-top:1.5rem}.media-left,.media-right{flex-basis:auto;flex-grow:0;flex-shrink:0}.media-left{margin-right:1rem}.media-right{margin-left:1rem}.media-content{flex-basis:auto;flex-grow:1;flex-shrink:1;text-align:left}@media screen and (max-width: 768px){.media-content{overflow-x:auto}}.menu{font-size:1rem}.menu.is-small,#documenter .docs-sidebar form.docs-search>input.menu{font-size:.75rem}.menu.is-medium{font-size:1.25rem}.menu.is-large{font-size:1.5rem}.menu-list{line-height:1.25}.menu-list a{border-radius:2px;color:#222;display:block;padding:0.5em 0.75em}.menu-list a:hover{background-color:#f5f5f5;color:#222}.menu-list a.is-active{background-color:#2e63b8;color:#fff}.menu-list li ul{border-left:1px solid #dbdbdb;margin:.75em;padding-left:.75em}.menu-label{color:#6b6b6b;font-size:.75em;letter-spacing:.1em;text-transform:uppercase}.menu-label:not(:first-child){margin-top:1em}.menu-label:not(:last-child){margin-bottom:1em}.message{background-color:#f5f5f5;border-radius:4px;font-size:1rem}.message strong{color:currentColor}.message a:not(.button):not(.tag):not(.dropdown-item){color:currentColor;text-decoration:underline}.message.is-small,#documenter .docs-sidebar form.docs-search>input.message{font-size:.75rem}.message.is-medium{font-size:1.25rem}.message.is-large{font-size:1.5rem}.message.is-white{background-color:#fff}.message.is-white .message-header{background-color:#fff;color:#0a0a0a}.message.is-white .message-body{border-color:#fff;color:#4d4d4d}.message.is-black{background-color:#fafafa}.message.is-black .message-header{background-color:#0a0a0a;color:#fff}.message.is-black .message-body{border-color:#0a0a0a;color:#090909}.message.is-light{background-color:#fafafa}.message.is-light .message-header{background-color:#f5f5f5;color:#363636}.message.is-light .message-body{border-color:#f5f5f5;color:#505050}.message.is-dark,.content kbd.message{background-color:#fafafa}.message.is-dark .message-header,.content kbd.message .message-header{background-color:#363636;color:#f5f5f5}.message.is-dark .message-body,.content kbd.message .message-body{border-color:#363636;color:#2a2a2a}.message.is-primary,.docstring>section>a.message.docs-sourcelink{background-color:#f6fbfd}.message.is-primary .message-header,.docstring>section>a.message.docs-sourcelink .message-header{background-color:#4eb5de;color:#fff}.message.is-primary .message-body,.docstring>section>a.message.docs-sourcelink .message-body{border-color:#4eb5de;color:#1f556a}.message.is-link{background-color:#f7f9fd}.message.is-link .message-header{background-color:#2e63b8;color:#fff}.message.is-link .message-body{border-color:#2e63b8;color:#264981}.message.is-info{background-color:#f6fbfe}.message.is-info .message-header{background-color:#209cee;color:#fff}.message.is-info .message-body{border-color:#209cee;color:#12537d}.message.is-success{background-color:#f6fdf9}.message.is-success .message-header{background-color:#22c35b;color:#fff}.message.is-success .message-body{border-color:#22c35b;color:#0f361d}.message.is-warning{background-color:#fffdf5}.message.is-warning .message-header{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.message.is-warning .message-body{border-color:#ffdd57;color:#3c3108}.message.is-danger{background-color:#fff5f5}.message.is-danger .message-header{background-color:#da0b00;color:#fff}.message.is-danger .message-body{border-color:#da0b00;color:#9b0c04}.message-header{align-items:center;background-color:#222;border-radius:4px 4px 0 0;color:#fff;display:flex;font-weight:700;justify-content:space-between;line-height:1.25;padding:0.75em 1em;position:relative}.message-header .delete{flex-grow:0;flex-shrink:0;margin-left:0.75em}.message-header+.message-body{border-width:0;border-top-left-radius:0;border-top-right-radius:0}.message-body{border-color:#dbdbdb;border-radius:4px;border-style:solid;border-width:0 0 0 4px;color:#222;padding:1.25em 1.5em}.message-body code,.message-body pre{background-color:#fff}.message-body pre code{background-color:rgba(0,0,0,0)}.modal{align-items:center;display:none;flex-direction:column;justify-content:center;overflow:hidden;position:fixed;z-index:40}.modal.is-active{display:flex}.modal-background{background-color:rgba(10,10,10,0.86)}.modal-content,.modal-card{margin:0 20px;max-height:calc(100vh - 160px);overflow:auto;position:relative;width:100%}@media screen and (min-width: 769px),print{.modal-content,.modal-card{margin:0 auto;max-height:calc(100vh - 40px);width:640px}}.modal-close{background:none;height:40px;position:fixed;right:20px;top:20px;width:40px}.modal-card{display:flex;flex-direction:column;max-height:calc(100vh - 40px);overflow:hidden;-ms-overflow-y:visible}.modal-card-head,.modal-card-foot{align-items:center;background-color:#f5f5f5;display:flex;flex-shrink:0;justify-content:flex-start;padding:20px;position:relative}.modal-card-head{border-bottom:1px solid #dbdbdb;border-top-left-radius:6px;border-top-right-radius:6px}.modal-card-title{color:#222;flex-grow:1;flex-shrink:0;font-size:1.5rem;line-height:1}.modal-card-foot{border-bottom-left-radius:6px;border-bottom-right-radius:6px;border-top:1px solid #dbdbdb}.modal-card-foot .button:not(:last-child){margin-right:0.5em}.modal-card-body{-webkit-overflow-scrolling:touch;background-color:#fff;flex-grow:1;flex-shrink:1;overflow:auto;padding:20px}.navbar{background-color:#fff;min-height:3.25rem;position:relative;z-index:30}.navbar.is-white{background-color:#fff;color:#0a0a0a}.navbar.is-white .navbar-brand>.navbar-item,.navbar.is-white .navbar-brand .navbar-link{color:#0a0a0a}.navbar.is-white .navbar-brand>a.navbar-item:focus,.navbar.is-white .navbar-brand>a.navbar-item:hover,.navbar.is-white .navbar-brand>a.navbar-item.is-active,.navbar.is-white .navbar-brand .navbar-link:focus,.navbar.is-white .navbar-brand .navbar-link:hover,.navbar.is-white .navbar-brand .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}.navbar.is-white .navbar-brand .navbar-link::after{border-color:#0a0a0a}.navbar.is-white .navbar-burger{color:#0a0a0a}@media screen and (min-width: 1056px){.navbar.is-white .navbar-start>.navbar-item,.navbar.is-white .navbar-start .navbar-link,.navbar.is-white .navbar-end>.navbar-item,.navbar.is-white .navbar-end .navbar-link{color:#0a0a0a}.navbar.is-white .navbar-start>a.navbar-item:focus,.navbar.is-white .navbar-start>a.navbar-item:hover,.navbar.is-white .navbar-start>a.navbar-item.is-active,.navbar.is-white .navbar-start .navbar-link:focus,.navbar.is-white .navbar-start .navbar-link:hover,.navbar.is-white .navbar-start .navbar-link.is-active,.navbar.is-white .navbar-end>a.navbar-item:focus,.navbar.is-white .navbar-end>a.navbar-item:hover,.navbar.is-white .navbar-end>a.navbar-item.is-active,.navbar.is-white .navbar-end .navbar-link:focus,.navbar.is-white .navbar-end .navbar-link:hover,.navbar.is-white .navbar-end .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}.navbar.is-white .navbar-start .navbar-link::after,.navbar.is-white .navbar-end .navbar-link::after{border-color:#0a0a0a}.navbar.is-white .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-white .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-white .navbar-item.has-dropdown.is-active .navbar-link{background-color:#f2f2f2;color:#0a0a0a}.navbar.is-white .navbar-dropdown a.navbar-item.is-active{background-color:#fff;color:#0a0a0a}}.navbar.is-black{background-color:#0a0a0a;color:#fff}.navbar.is-black .navbar-brand>.navbar-item,.navbar.is-black .navbar-brand .navbar-link{color:#fff}.navbar.is-black .navbar-brand>a.navbar-item:focus,.navbar.is-black .navbar-brand>a.navbar-item:hover,.navbar.is-black .navbar-brand>a.navbar-item.is-active,.navbar.is-black .navbar-brand .navbar-link:focus,.navbar.is-black .navbar-brand .navbar-link:hover,.navbar.is-black .navbar-brand .navbar-link.is-active{background-color:#000;color:#fff}.navbar.is-black .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-black .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-black .navbar-start>.navbar-item,.navbar.is-black .navbar-start .navbar-link,.navbar.is-black .navbar-end>.navbar-item,.navbar.is-black .navbar-end .navbar-link{color:#fff}.navbar.is-black .navbar-start>a.navbar-item:focus,.navbar.is-black .navbar-start>a.navbar-item:hover,.navbar.is-black .navbar-start>a.navbar-item.is-active,.navbar.is-black .navbar-start .navbar-link:focus,.navbar.is-black .navbar-start .navbar-link:hover,.navbar.is-black .navbar-start .navbar-link.is-active,.navbar.is-black .navbar-end>a.navbar-item:focus,.navbar.is-black .navbar-end>a.navbar-item:hover,.navbar.is-black .navbar-end>a.navbar-item.is-active,.navbar.is-black .navbar-end .navbar-link:focus,.navbar.is-black .navbar-end .navbar-link:hover,.navbar.is-black .navbar-end .navbar-link.is-active{background-color:#000;color:#fff}.navbar.is-black .navbar-start .navbar-link::after,.navbar.is-black .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-black .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-black .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-black .navbar-item.has-dropdown.is-active .navbar-link{background-color:#000;color:#fff}.navbar.is-black .navbar-dropdown a.navbar-item.is-active{background-color:#0a0a0a;color:#fff}}.navbar.is-light{background-color:#f5f5f5;color:#363636}.navbar.is-light .navbar-brand>.navbar-item,.navbar.is-light .navbar-brand .navbar-link{color:#363636}.navbar.is-light .navbar-brand>a.navbar-item:focus,.navbar.is-light .navbar-brand>a.navbar-item:hover,.navbar.is-light .navbar-brand>a.navbar-item.is-active,.navbar.is-light .navbar-brand .navbar-link:focus,.navbar.is-light .navbar-brand .navbar-link:hover,.navbar.is-light .navbar-brand .navbar-link.is-active{background-color:#e8e8e8;color:#363636}.navbar.is-light .navbar-brand .navbar-link::after{border-color:#363636}.navbar.is-light .navbar-burger{color:#363636}@media screen and (min-width: 1056px){.navbar.is-light .navbar-start>.navbar-item,.navbar.is-light .navbar-start .navbar-link,.navbar.is-light .navbar-end>.navbar-item,.navbar.is-light .navbar-end .navbar-link{color:#363636}.navbar.is-light .navbar-start>a.navbar-item:focus,.navbar.is-light .navbar-start>a.navbar-item:hover,.navbar.is-light .navbar-start>a.navbar-item.is-active,.navbar.is-light .navbar-start .navbar-link:focus,.navbar.is-light .navbar-start .navbar-link:hover,.navbar.is-light .navbar-start .navbar-link.is-active,.navbar.is-light .navbar-end>a.navbar-item:focus,.navbar.is-light .navbar-end>a.navbar-item:hover,.navbar.is-light .navbar-end>a.navbar-item.is-active,.navbar.is-light .navbar-end .navbar-link:focus,.navbar.is-light .navbar-end .navbar-link:hover,.navbar.is-light .navbar-end .navbar-link.is-active{background-color:#e8e8e8;color:#363636}.navbar.is-light .navbar-start .navbar-link::after,.navbar.is-light .navbar-end .navbar-link::after{border-color:#363636}.navbar.is-light .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-light .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-light .navbar-item.has-dropdown.is-active .navbar-link{background-color:#e8e8e8;color:#363636}.navbar.is-light .navbar-dropdown a.navbar-item.is-active{background-color:#f5f5f5;color:#363636}}.navbar.is-dark,.content kbd.navbar{background-color:#363636;color:#f5f5f5}.navbar.is-dark .navbar-brand>.navbar-item,.content kbd.navbar .navbar-brand>.navbar-item,.navbar.is-dark .navbar-brand .navbar-link,.content kbd.navbar .navbar-brand .navbar-link{color:#f5f5f5}.navbar.is-dark .navbar-brand>a.navbar-item:focus,.content kbd.navbar .navbar-brand>a.navbar-item:focus,.navbar.is-dark .navbar-brand>a.navbar-item:hover,.content kbd.navbar .navbar-brand>a.navbar-item:hover,.navbar.is-dark .navbar-brand>a.navbar-item.is-active,.content kbd.navbar .navbar-brand>a.navbar-item.is-active,.navbar.is-dark .navbar-brand .navbar-link:focus,.content kbd.navbar .navbar-brand .navbar-link:focus,.navbar.is-dark .navbar-brand .navbar-link:hover,.content kbd.navbar .navbar-brand .navbar-link:hover,.navbar.is-dark .navbar-brand .navbar-link.is-active,.content kbd.navbar .navbar-brand .navbar-link.is-active{background-color:#292929;color:#f5f5f5}.navbar.is-dark .navbar-brand .navbar-link::after,.content kbd.navbar .navbar-brand .navbar-link::after{border-color:#f5f5f5}.navbar.is-dark .navbar-burger,.content kbd.navbar .navbar-burger{color:#f5f5f5}@media screen and (min-width: 1056px){.navbar.is-dark .navbar-start>.navbar-item,.content kbd.navbar .navbar-start>.navbar-item,.navbar.is-dark .navbar-start .navbar-link,.content kbd.navbar .navbar-start .navbar-link,.navbar.is-dark .navbar-end>.navbar-item,.content kbd.navbar .navbar-end>.navbar-item,.navbar.is-dark .navbar-end .navbar-link,.content kbd.navbar .navbar-end .navbar-link{color:#f5f5f5}.navbar.is-dark .navbar-start>a.navbar-item:focus,.content kbd.navbar .navbar-start>a.navbar-item:focus,.navbar.is-dark .navbar-start>a.navbar-item:hover,.content kbd.navbar .navbar-start>a.navbar-item:hover,.navbar.is-dark .navbar-start>a.navbar-item.is-active,.content kbd.navbar .navbar-start>a.navbar-item.is-active,.navbar.is-dark .navbar-start .navbar-link:focus,.content kbd.navbar .navbar-start .navbar-link:focus,.navbar.is-dark .navbar-start .navbar-link:hover,.content kbd.navbar .navbar-start .navbar-link:hover,.navbar.is-dark .navbar-start .navbar-link.is-active,.content kbd.navbar .navbar-start .navbar-link.is-active,.navbar.is-dark .navbar-end>a.navbar-item:focus,.content kbd.navbar .navbar-end>a.navbar-item:focus,.navbar.is-dark .navbar-end>a.navbar-item:hover,.content kbd.navbar .navbar-end>a.navbar-item:hover,.navbar.is-dark .navbar-end>a.navbar-item.is-active,.content kbd.navbar .navbar-end>a.navbar-item.is-active,.navbar.is-dark .navbar-end .navbar-link:focus,.content kbd.navbar .navbar-end .navbar-link:focus,.navbar.is-dark .navbar-end .navbar-link:hover,.content kbd.navbar .navbar-end .navbar-link:hover,.navbar.is-dark .navbar-end .navbar-link.is-active,.content kbd.navbar .navbar-end .navbar-link.is-active{background-color:#292929;color:#f5f5f5}.navbar.is-dark .navbar-start .navbar-link::after,.content kbd.navbar .navbar-start .navbar-link::after,.navbar.is-dark .navbar-end .navbar-link::after,.content kbd.navbar .navbar-end .navbar-link::after{border-color:#f5f5f5}.navbar.is-dark .navbar-item.has-dropdown:focus .navbar-link,.content kbd.navbar .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-dark .navbar-item.has-dropdown:hover .navbar-link,.content kbd.navbar .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-dark .navbar-item.has-dropdown.is-active .navbar-link,.content kbd.navbar .navbar-item.has-dropdown.is-active .navbar-link{background-color:#292929;color:#f5f5f5}.navbar.is-dark .navbar-dropdown a.navbar-item.is-active,.content kbd.navbar .navbar-dropdown a.navbar-item.is-active{background-color:#363636;color:#f5f5f5}}.navbar.is-primary,.docstring>section>a.navbar.docs-sourcelink{background-color:#4eb5de;color:#fff}.navbar.is-primary .navbar-brand>.navbar-item,.docstring>section>a.navbar.docs-sourcelink .navbar-brand>.navbar-item,.navbar.is-primary .navbar-brand .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link{color:#fff}.navbar.is-primary .navbar-brand>a.navbar-item:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item:focus,.navbar.is-primary .navbar-brand>a.navbar-item:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item:hover,.navbar.is-primary .navbar-brand>a.navbar-item.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item.is-active,.navbar.is-primary .navbar-brand .navbar-link:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link:focus,.navbar.is-primary .navbar-brand .navbar-link:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link:hover,.navbar.is-primary .navbar-brand .navbar-link.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link.is-active{background-color:#39acda;color:#fff}.navbar.is-primary .navbar-brand .navbar-link::after,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-primary .navbar-burger,.docstring>section>a.navbar.docs-sourcelink .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-primary .navbar-start>.navbar-item,.docstring>section>a.navbar.docs-sourcelink .navbar-start>.navbar-item,.navbar.is-primary .navbar-start .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link,.navbar.is-primary .navbar-end>.navbar-item,.docstring>section>a.navbar.docs-sourcelink .navbar-end>.navbar-item,.navbar.is-primary .navbar-end .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link{color:#fff}.navbar.is-primary .navbar-start>a.navbar-item:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item:focus,.navbar.is-primary .navbar-start>a.navbar-item:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item:hover,.navbar.is-primary .navbar-start>a.navbar-item.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item.is-active,.navbar.is-primary .navbar-start .navbar-link:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link:focus,.navbar.is-primary .navbar-start .navbar-link:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link:hover,.navbar.is-primary .navbar-start .navbar-link.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link.is-active,.navbar.is-primary .navbar-end>a.navbar-item:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item:focus,.navbar.is-primary .navbar-end>a.navbar-item:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item:hover,.navbar.is-primary .navbar-end>a.navbar-item.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item.is-active,.navbar.is-primary .navbar-end .navbar-link:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link:focus,.navbar.is-primary .navbar-end .navbar-link:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link:hover,.navbar.is-primary .navbar-end .navbar-link.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link.is-active{background-color:#39acda;color:#fff}.navbar.is-primary .navbar-start .navbar-link::after,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link::after,.navbar.is-primary .navbar-end .navbar-link::after,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-primary .navbar-item.has-dropdown:focus .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-primary .navbar-item.has-dropdown:hover .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-primary .navbar-item.has-dropdown.is-active .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown.is-active .navbar-link{background-color:#39acda;color:#fff}.navbar.is-primary .navbar-dropdown a.navbar-item.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-dropdown a.navbar-item.is-active{background-color:#4eb5de;color:#fff}}.navbar.is-link{background-color:#2e63b8;color:#fff}.navbar.is-link .navbar-brand>.navbar-item,.navbar.is-link .navbar-brand .navbar-link{color:#fff}.navbar.is-link .navbar-brand>a.navbar-item:focus,.navbar.is-link .navbar-brand>a.navbar-item:hover,.navbar.is-link .navbar-brand>a.navbar-item.is-active,.navbar.is-link .navbar-brand .navbar-link:focus,.navbar.is-link .navbar-brand .navbar-link:hover,.navbar.is-link .navbar-brand .navbar-link.is-active{background-color:#2958a4;color:#fff}.navbar.is-link .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-link .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-link .navbar-start>.navbar-item,.navbar.is-link .navbar-start .navbar-link,.navbar.is-link .navbar-end>.navbar-item,.navbar.is-link .navbar-end .navbar-link{color:#fff}.navbar.is-link .navbar-start>a.navbar-item:focus,.navbar.is-link .navbar-start>a.navbar-item:hover,.navbar.is-link .navbar-start>a.navbar-item.is-active,.navbar.is-link .navbar-start .navbar-link:focus,.navbar.is-link .navbar-start .navbar-link:hover,.navbar.is-link .navbar-start .navbar-link.is-active,.navbar.is-link .navbar-end>a.navbar-item:focus,.navbar.is-link .navbar-end>a.navbar-item:hover,.navbar.is-link .navbar-end>a.navbar-item.is-active,.navbar.is-link .navbar-end .navbar-link:focus,.navbar.is-link .navbar-end .navbar-link:hover,.navbar.is-link .navbar-end .navbar-link.is-active{background-color:#2958a4;color:#fff}.navbar.is-link .navbar-start .navbar-link::after,.navbar.is-link .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-link .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-link .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-link .navbar-item.has-dropdown.is-active .navbar-link{background-color:#2958a4;color:#fff}.navbar.is-link .navbar-dropdown a.navbar-item.is-active{background-color:#2e63b8;color:#fff}}.navbar.is-info{background-color:#209cee;color:#fff}.navbar.is-info .navbar-brand>.navbar-item,.navbar.is-info .navbar-brand .navbar-link{color:#fff}.navbar.is-info .navbar-brand>a.navbar-item:focus,.navbar.is-info .navbar-brand>a.navbar-item:hover,.navbar.is-info .navbar-brand>a.navbar-item.is-active,.navbar.is-info .navbar-brand .navbar-link:focus,.navbar.is-info .navbar-brand .navbar-link:hover,.navbar.is-info .navbar-brand .navbar-link.is-active{background-color:#1190e3;color:#fff}.navbar.is-info .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-info .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-info .navbar-start>.navbar-item,.navbar.is-info .navbar-start .navbar-link,.navbar.is-info .navbar-end>.navbar-item,.navbar.is-info .navbar-end .navbar-link{color:#fff}.navbar.is-info .navbar-start>a.navbar-item:focus,.navbar.is-info .navbar-start>a.navbar-item:hover,.navbar.is-info .navbar-start>a.navbar-item.is-active,.navbar.is-info .navbar-start .navbar-link:focus,.navbar.is-info .navbar-start .navbar-link:hover,.navbar.is-info .navbar-start .navbar-link.is-active,.navbar.is-info .navbar-end>a.navbar-item:focus,.navbar.is-info .navbar-end>a.navbar-item:hover,.navbar.is-info .navbar-end>a.navbar-item.is-active,.navbar.is-info .navbar-end .navbar-link:focus,.navbar.is-info .navbar-end .navbar-link:hover,.navbar.is-info .navbar-end .navbar-link.is-active{background-color:#1190e3;color:#fff}.navbar.is-info .navbar-start .navbar-link::after,.navbar.is-info .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-info .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-info .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-info .navbar-item.has-dropdown.is-active .navbar-link{background-color:#1190e3;color:#fff}.navbar.is-info .navbar-dropdown a.navbar-item.is-active{background-color:#209cee;color:#fff}}.navbar.is-success{background-color:#22c35b;color:#fff}.navbar.is-success .navbar-brand>.navbar-item,.navbar.is-success .navbar-brand .navbar-link{color:#fff}.navbar.is-success .navbar-brand>a.navbar-item:focus,.navbar.is-success .navbar-brand>a.navbar-item:hover,.navbar.is-success .navbar-brand>a.navbar-item.is-active,.navbar.is-success .navbar-brand .navbar-link:focus,.navbar.is-success .navbar-brand .navbar-link:hover,.navbar.is-success .navbar-brand .navbar-link.is-active{background-color:#1ead51;color:#fff}.navbar.is-success .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-success .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-success .navbar-start>.navbar-item,.navbar.is-success .navbar-start .navbar-link,.navbar.is-success .navbar-end>.navbar-item,.navbar.is-success .navbar-end .navbar-link{color:#fff}.navbar.is-success .navbar-start>a.navbar-item:focus,.navbar.is-success .navbar-start>a.navbar-item:hover,.navbar.is-success .navbar-start>a.navbar-item.is-active,.navbar.is-success .navbar-start .navbar-link:focus,.navbar.is-success .navbar-start .navbar-link:hover,.navbar.is-success .navbar-start .navbar-link.is-active,.navbar.is-success .navbar-end>a.navbar-item:focus,.navbar.is-success .navbar-end>a.navbar-item:hover,.navbar.is-success .navbar-end>a.navbar-item.is-active,.navbar.is-success .navbar-end .navbar-link:focus,.navbar.is-success .navbar-end .navbar-link:hover,.navbar.is-success .navbar-end .navbar-link.is-active{background-color:#1ead51;color:#fff}.navbar.is-success .navbar-start .navbar-link::after,.navbar.is-success .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-success .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-success .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-success .navbar-item.has-dropdown.is-active .navbar-link{background-color:#1ead51;color:#fff}.navbar.is-success .navbar-dropdown a.navbar-item.is-active{background-color:#22c35b;color:#fff}}.navbar.is-warning{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-brand>.navbar-item,.navbar.is-warning .navbar-brand .navbar-link{color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-brand>a.navbar-item:focus,.navbar.is-warning .navbar-brand>a.navbar-item:hover,.navbar.is-warning .navbar-brand>a.navbar-item.is-active,.navbar.is-warning .navbar-brand .navbar-link:focus,.navbar.is-warning .navbar-brand .navbar-link:hover,.navbar.is-warning .navbar-brand .navbar-link.is-active{background-color:#ffd83e;color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-brand .navbar-link::after{border-color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-burger{color:rgba(0,0,0,0.7)}@media screen and (min-width: 1056px){.navbar.is-warning .navbar-start>.navbar-item,.navbar.is-warning .navbar-start .navbar-link,.navbar.is-warning .navbar-end>.navbar-item,.navbar.is-warning .navbar-end .navbar-link{color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-start>a.navbar-item:focus,.navbar.is-warning .navbar-start>a.navbar-item:hover,.navbar.is-warning .navbar-start>a.navbar-item.is-active,.navbar.is-warning .navbar-start .navbar-link:focus,.navbar.is-warning .navbar-start .navbar-link:hover,.navbar.is-warning .navbar-start .navbar-link.is-active,.navbar.is-warning .navbar-end>a.navbar-item:focus,.navbar.is-warning .navbar-end>a.navbar-item:hover,.navbar.is-warning .navbar-end>a.navbar-item.is-active,.navbar.is-warning .navbar-end .navbar-link:focus,.navbar.is-warning .navbar-end .navbar-link:hover,.navbar.is-warning .navbar-end .navbar-link.is-active{background-color:#ffd83e;color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-start .navbar-link::after,.navbar.is-warning .navbar-end .navbar-link::after{border-color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-warning .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-warning .navbar-item.has-dropdown.is-active .navbar-link{background-color:#ffd83e;color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-dropdown a.navbar-item.is-active{background-color:#ffdd57;color:rgba(0,0,0,0.7)}}.navbar.is-danger{background-color:#da0b00;color:#fff}.navbar.is-danger .navbar-brand>.navbar-item,.navbar.is-danger .navbar-brand .navbar-link{color:#fff}.navbar.is-danger .navbar-brand>a.navbar-item:focus,.navbar.is-danger .navbar-brand>a.navbar-item:hover,.navbar.is-danger .navbar-brand>a.navbar-item.is-active,.navbar.is-danger .navbar-brand .navbar-link:focus,.navbar.is-danger .navbar-brand .navbar-link:hover,.navbar.is-danger .navbar-brand .navbar-link.is-active{background-color:#c10a00;color:#fff}.navbar.is-danger .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-danger .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-danger .navbar-start>.navbar-item,.navbar.is-danger .navbar-start .navbar-link,.navbar.is-danger .navbar-end>.navbar-item,.navbar.is-danger .navbar-end .navbar-link{color:#fff}.navbar.is-danger .navbar-start>a.navbar-item:focus,.navbar.is-danger .navbar-start>a.navbar-item:hover,.navbar.is-danger .navbar-start>a.navbar-item.is-active,.navbar.is-danger .navbar-start .navbar-link:focus,.navbar.is-danger .navbar-start .navbar-link:hover,.navbar.is-danger .navbar-start .navbar-link.is-active,.navbar.is-danger .navbar-end>a.navbar-item:focus,.navbar.is-danger .navbar-end>a.navbar-item:hover,.navbar.is-danger .navbar-end>a.navbar-item.is-active,.navbar.is-danger .navbar-end .navbar-link:focus,.navbar.is-danger .navbar-end .navbar-link:hover,.navbar.is-danger .navbar-end .navbar-link.is-active{background-color:#c10a00;color:#fff}.navbar.is-danger .navbar-start .navbar-link::after,.navbar.is-danger .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-danger .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-danger .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-danger .navbar-item.has-dropdown.is-active .navbar-link{background-color:#c10a00;color:#fff}.navbar.is-danger .navbar-dropdown a.navbar-item.is-active{background-color:#da0b00;color:#fff}}.navbar>.container{align-items:stretch;display:flex;min-height:3.25rem;width:100%}.navbar.has-shadow{box-shadow:0 2px 0 0 #f5f5f5}.navbar.is-fixed-bottom,.navbar.is-fixed-top{left:0;position:fixed;right:0;z-index:30}.navbar.is-fixed-bottom{bottom:0}.navbar.is-fixed-bottom.has-shadow{box-shadow:0 -2px 0 0 #f5f5f5}.navbar.is-fixed-top{top:0}html.has-navbar-fixed-top,body.has-navbar-fixed-top{padding-top:3.25rem}html.has-navbar-fixed-bottom,body.has-navbar-fixed-bottom{padding-bottom:3.25rem}.navbar-brand,.navbar-tabs{align-items:stretch;display:flex;flex-shrink:0;min-height:3.25rem}.navbar-brand a.navbar-item:focus,.navbar-brand a.navbar-item:hover{background-color:transparent}.navbar-tabs{-webkit-overflow-scrolling:touch;max-width:100vw;overflow-x:auto;overflow-y:hidden}.navbar-burger{color:#4a4a4a;cursor:pointer;display:block;height:3.25rem;position:relative;width:3.25rem;margin-left:auto}.navbar-burger span{background-color:currentColor;display:block;height:1px;left:calc(50% - 8px);position:absolute;transform-origin:center;transition-duration:86ms;transition-property:background-color, opacity, transform;transition-timing-function:ease-out;width:16px}.navbar-burger span:nth-child(1){top:calc(50% - 6px)}.navbar-burger span:nth-child(2){top:calc(50% - 1px)}.navbar-burger span:nth-child(3){top:calc(50% + 4px)}.navbar-burger:hover{background-color:rgba(0,0,0,0.05)}.navbar-burger.is-active span:nth-child(1){transform:translateY(5px) rotate(45deg)}.navbar-burger.is-active span:nth-child(2){opacity:0}.navbar-burger.is-active span:nth-child(3){transform:translateY(-5px) rotate(-45deg)}.navbar-menu{display:none}.navbar-item,.navbar-link{color:#4a4a4a;display:block;line-height:1.5;padding:0.5rem 0.75rem;position:relative}.navbar-item .icon:only-child,.navbar-link .icon:only-child{margin-left:-0.25rem;margin-right:-0.25rem}a.navbar-item,.navbar-link{cursor:pointer}a.navbar-item:focus,a.navbar-item:focus-within,a.navbar-item:hover,a.navbar-item.is-active,.navbar-link:focus,.navbar-link:focus-within,.navbar-link:hover,.navbar-link.is-active{background-color:#fafafa;color:#2e63b8}.navbar-item{display:block;flex-grow:0;flex-shrink:0}.navbar-item img{max-height:1.75rem}.navbar-item.has-dropdown{padding:0}.navbar-item.is-expanded{flex-grow:1;flex-shrink:1}.navbar-item.is-tab{border-bottom:1px solid transparent;min-height:3.25rem;padding-bottom:calc(0.5rem - 1px)}.navbar-item.is-tab:focus,.navbar-item.is-tab:hover{background-color:rgba(0,0,0,0);border-bottom-color:#2e63b8}.navbar-item.is-tab.is-active{background-color:rgba(0,0,0,0);border-bottom-color:#2e63b8;border-bottom-style:solid;border-bottom-width:3px;color:#2e63b8;padding-bottom:calc(0.5rem - 3px)}.navbar-content{flex-grow:1;flex-shrink:1}.navbar-link:not(.is-arrowless){padding-right:2.5em}.navbar-link:not(.is-arrowless)::after{border-color:#2e63b8;margin-top:-0.375em;right:1.125em}.navbar-dropdown{font-size:0.875rem;padding-bottom:0.5rem;padding-top:0.5rem}.navbar-dropdown .navbar-item{padding-left:1.5rem;padding-right:1.5rem}.navbar-divider{background-color:#f5f5f5;border:none;display:none;height:2px;margin:0.5rem 0}@media screen and (max-width: 1055px){.navbar>.container{display:block}.navbar-brand .navbar-item,.navbar-tabs .navbar-item{align-items:center;display:flex}.navbar-link::after{display:none}.navbar-menu{background-color:#fff;box-shadow:0 8px 16px rgba(10,10,10,0.1);padding:0.5rem 0}.navbar-menu.is-active{display:block}.navbar.is-fixed-bottom-touch,.navbar.is-fixed-top-touch{left:0;position:fixed;right:0;z-index:30}.navbar.is-fixed-bottom-touch{bottom:0}.navbar.is-fixed-bottom-touch.has-shadow{box-shadow:0 -2px 3px rgba(10,10,10,0.1)}.navbar.is-fixed-top-touch{top:0}.navbar.is-fixed-top .navbar-menu,.navbar.is-fixed-top-touch .navbar-menu{-webkit-overflow-scrolling:touch;max-height:calc(100vh - 3.25rem);overflow:auto}html.has-navbar-fixed-top-touch,body.has-navbar-fixed-top-touch{padding-top:3.25rem}html.has-navbar-fixed-bottom-touch,body.has-navbar-fixed-bottom-touch{padding-bottom:3.25rem}}@media screen and (min-width: 1056px){.navbar,.navbar-menu,.navbar-start,.navbar-end{align-items:stretch;display:flex}.navbar{min-height:3.25rem}.navbar.is-spaced{padding:1rem 2rem}.navbar.is-spaced .navbar-start,.navbar.is-spaced .navbar-end{align-items:center}.navbar.is-spaced a.navbar-item,.navbar.is-spaced .navbar-link{border-radius:4px}.navbar.is-transparent a.navbar-item:focus,.navbar.is-transparent a.navbar-item:hover,.navbar.is-transparent a.navbar-item.is-active,.navbar.is-transparent .navbar-link:focus,.navbar.is-transparent .navbar-link:hover,.navbar.is-transparent .navbar-link.is-active{background-color:transparent !important}.navbar.is-transparent .navbar-item.has-dropdown.is-active .navbar-link,.navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:focus .navbar-link,.navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:focus-within .navbar-link,.navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:hover .navbar-link{background-color:transparent !important}.navbar.is-transparent .navbar-dropdown a.navbar-item:focus,.navbar.is-transparent .navbar-dropdown a.navbar-item:hover{background-color:#f5f5f5;color:#0a0a0a}.navbar.is-transparent .navbar-dropdown a.navbar-item.is-active{background-color:#f5f5f5;color:#2e63b8}.navbar-burger{display:none}.navbar-item,.navbar-link{align-items:center;display:flex}.navbar-item{display:flex}.navbar-item.has-dropdown{align-items:stretch}.navbar-item.has-dropdown-up .navbar-link::after{transform:rotate(135deg) translate(0.25em, -0.25em)}.navbar-item.has-dropdown-up .navbar-dropdown{border-bottom:2px solid #dbdbdb;border-radius:6px 6px 0 0;border-top:none;bottom:100%;box-shadow:0 -8px 8px rgba(10,10,10,0.1);top:auto}.navbar-item.is-active .navbar-dropdown,.navbar-item.is-hoverable:focus .navbar-dropdown,.navbar-item.is-hoverable:focus-within .navbar-dropdown,.navbar-item.is-hoverable:hover .navbar-dropdown{display:block}.navbar.is-spaced .navbar-item.is-active .navbar-dropdown,.navbar-item.is-active .navbar-dropdown.is-boxed,.navbar.is-spaced .navbar-item.is-hoverable:focus .navbar-dropdown,.navbar-item.is-hoverable:focus .navbar-dropdown.is-boxed,.navbar.is-spaced .navbar-item.is-hoverable:focus-within .navbar-dropdown,.navbar-item.is-hoverable:focus-within .navbar-dropdown.is-boxed,.navbar.is-spaced .navbar-item.is-hoverable:hover .navbar-dropdown,.navbar-item.is-hoverable:hover .navbar-dropdown.is-boxed{opacity:1;pointer-events:auto;transform:translateY(0)}.navbar-menu{flex-grow:1;flex-shrink:0}.navbar-start{justify-content:flex-start;margin-right:auto}.navbar-end{justify-content:flex-end;margin-left:auto}.navbar-dropdown{background-color:#fff;border-bottom-left-radius:6px;border-bottom-right-radius:6px;border-top:2px solid #dbdbdb;box-shadow:0 8px 8px rgba(10,10,10,0.1);display:none;font-size:0.875rem;left:0;min-width:100%;position:absolute;top:100%;z-index:20}.navbar-dropdown .navbar-item{padding:0.375rem 1rem;white-space:nowrap}.navbar-dropdown a.navbar-item{padding-right:3rem}.navbar-dropdown a.navbar-item:focus,.navbar-dropdown a.navbar-item:hover{background-color:#f5f5f5;color:#0a0a0a}.navbar-dropdown a.navbar-item.is-active{background-color:#f5f5f5;color:#2e63b8}.navbar.is-spaced .navbar-dropdown,.navbar-dropdown.is-boxed{border-radius:6px;border-top:none;box-shadow:0 8px 8px rgba(10,10,10,0.1), 0 0 0 1px rgba(10,10,10,0.1);display:block;opacity:0;pointer-events:none;top:calc(100% + (-4px));transform:translateY(-5px);transition-duration:86ms;transition-property:opacity, transform}.navbar-dropdown.is-right{left:auto;right:0}.navbar-divider{display:block}.navbar>.container .navbar-brand,.container>.navbar .navbar-brand{margin-left:-.75rem}.navbar>.container .navbar-menu,.container>.navbar .navbar-menu{margin-right:-.75rem}.navbar.is-fixed-bottom-desktop,.navbar.is-fixed-top-desktop{left:0;position:fixed;right:0;z-index:30}.navbar.is-fixed-bottom-desktop{bottom:0}.navbar.is-fixed-bottom-desktop.has-shadow{box-shadow:0 -2px 3px rgba(10,10,10,0.1)}.navbar.is-fixed-top-desktop{top:0}html.has-navbar-fixed-top-desktop,body.has-navbar-fixed-top-desktop{padding-top:3.25rem}html.has-navbar-fixed-bottom-desktop,body.has-navbar-fixed-bottom-desktop{padding-bottom:3.25rem}html.has-spaced-navbar-fixed-top,body.has-spaced-navbar-fixed-top{padding-top:5.25rem}html.has-spaced-navbar-fixed-bottom,body.has-spaced-navbar-fixed-bottom{padding-bottom:5.25rem}a.navbar-item.is-active,.navbar-link.is-active{color:#0a0a0a}a.navbar-item.is-active:not(:focus):not(:hover),.navbar-link.is-active:not(:focus):not(:hover){background-color:rgba(0,0,0,0)}.navbar-item.has-dropdown:focus .navbar-link,.navbar-item.has-dropdown:hover .navbar-link,.navbar-item.has-dropdown.is-active .navbar-link{background-color:#fafafa}}.hero.is-fullheight-with-navbar{min-height:calc(100vh - 3.25rem)}.pagination{font-size:1rem;margin:-.25rem}.pagination.is-small,#documenter .docs-sidebar form.docs-search>input.pagination{font-size:.75rem}.pagination.is-medium{font-size:1.25rem}.pagination.is-large{font-size:1.5rem}.pagination.is-rounded .pagination-previous,#documenter .docs-sidebar form.docs-search>input.pagination .pagination-previous,.pagination.is-rounded .pagination-next,#documenter .docs-sidebar form.docs-search>input.pagination .pagination-next{padding-left:1em;padding-right:1em;border-radius:290486px}.pagination.is-rounded .pagination-link,#documenter .docs-sidebar form.docs-search>input.pagination .pagination-link{border-radius:290486px}.pagination,.pagination-list{align-items:center;display:flex;justify-content:center;text-align:center}.pagination-previous,.pagination-next,.pagination-link,.pagination-ellipsis{font-size:1em;justify-content:center;margin:.25rem;padding-left:.5em;padding-right:.5em;text-align:center}.pagination-previous,.pagination-next,.pagination-link{border-color:#dbdbdb;color:#363636;min-width:2.25em}.pagination-previous:hover,.pagination-next:hover,.pagination-link:hover{border-color:#b5b5b5;color:#363636}.pagination-previous:focus,.pagination-next:focus,.pagination-link:focus{border-color:#3c5dcd}.pagination-previous:active,.pagination-next:active,.pagination-link:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2)}.pagination-previous[disabled],.pagination-next[disabled],.pagination-link[disabled]{background-color:#dbdbdb;border-color:#dbdbdb;box-shadow:none;color:#6b6b6b;opacity:0.5}.pagination-previous,.pagination-next{padding-left:0.75em;padding-right:0.75em;white-space:nowrap}.pagination-link.is-current{background-color:#2e63b8;border-color:#2e63b8;color:#fff}.pagination-ellipsis{color:#b5b5b5;pointer-events:none}.pagination-list{flex-wrap:wrap}@media screen and (max-width: 768px){.pagination{flex-wrap:wrap}.pagination-previous,.pagination-next{flex-grow:1;flex-shrink:1}.pagination-list li{flex-grow:1;flex-shrink:1}}@media screen and (min-width: 769px),print{.pagination-list{flex-grow:1;flex-shrink:1;justify-content:flex-start;order:1}.pagination-previous{order:2}.pagination-next{order:3}.pagination{justify-content:space-between}.pagination.is-centered .pagination-previous{order:1}.pagination.is-centered .pagination-list{justify-content:center;order:2}.pagination.is-centered .pagination-next{order:3}.pagination.is-right .pagination-previous{order:1}.pagination.is-right .pagination-next{order:2}.pagination.is-right .pagination-list{justify-content:flex-end;order:3}}.panel{font-size:1rem}.panel:not(:last-child){margin-bottom:1.5rem}.panel-heading,.panel-tabs,.panel-block{border-bottom:1px solid #dbdbdb;border-left:1px solid #dbdbdb;border-right:1px solid #dbdbdb}.panel-heading:first-child,.panel-tabs:first-child,.panel-block:first-child{border-top:1px solid #dbdbdb}.panel-heading{background-color:#f5f5f5;border-radius:4px 4px 0 0;color:#222;font-size:1.25em;font-weight:300;line-height:1.25;padding:0.5em 0.75em}.panel-tabs{align-items:flex-end;display:flex;font-size:.875em;justify-content:center}.panel-tabs a{border-bottom:1px solid #dbdbdb;margin-bottom:-1px;padding:0.5em}.panel-tabs a.is-active{border-bottom-color:#4a4a4a;color:#363636}.panel-list a{color:#222}.panel-list a:hover{color:#2e63b8}.panel-block{align-items:center;color:#222;display:flex;justify-content:flex-start;padding:0.5em 0.75em}.panel-block input[type="checkbox"]{margin-right:0.75em}.panel-block>.control{flex-grow:1;flex-shrink:1;width:100%}.panel-block.is-wrapped{flex-wrap:wrap}.panel-block.is-active{border-left-color:#2e63b8;color:#363636}.panel-block.is-active .panel-icon{color:#2e63b8}a.panel-block,label.panel-block{cursor:pointer}a.panel-block:hover,label.panel-block:hover{background-color:#f5f5f5}.panel-icon{display:inline-block;font-size:14px;height:1em;line-height:1em;text-align:center;vertical-align:top;width:1em;color:#6b6b6b;margin-right:0.75em}.panel-icon .fa{font-size:inherit;line-height:inherit}.tabs{-webkit-overflow-scrolling:touch;align-items:stretch;display:flex;font-size:1rem;justify-content:space-between;overflow:hidden;overflow-x:auto;white-space:nowrap}.tabs a{align-items:center;border-bottom-color:#dbdbdb;border-bottom-style:solid;border-bottom-width:1px;color:#222;display:flex;justify-content:center;margin-bottom:-1px;padding:0.5em 1em;vertical-align:top}.tabs a:hover{border-bottom-color:#222;color:#222}.tabs li{display:block}.tabs li.is-active a{border-bottom-color:#2e63b8;color:#2e63b8}.tabs ul{align-items:center;border-bottom-color:#dbdbdb;border-bottom-style:solid;border-bottom-width:1px;display:flex;flex-grow:1;flex-shrink:0;justify-content:flex-start}.tabs ul.is-left{padding-right:0.75em}.tabs ul.is-center{flex:none;justify-content:center;padding-left:0.75em;padding-right:0.75em}.tabs ul.is-right{justify-content:flex-end;padding-left:0.75em}.tabs .icon:first-child{margin-right:0.5em}.tabs .icon:last-child{margin-left:0.5em}.tabs.is-centered ul{justify-content:center}.tabs.is-right ul{justify-content:flex-end}.tabs.is-boxed a{border:1px solid transparent;border-radius:4px 4px 0 0}.tabs.is-boxed a:hover{background-color:#f5f5f5;border-bottom-color:#dbdbdb}.tabs.is-boxed li.is-active a{background-color:#fff;border-color:#dbdbdb;border-bottom-color:rgba(0,0,0,0) !important}.tabs.is-fullwidth li{flex-grow:1;flex-shrink:0}.tabs.is-toggle a{border-color:#dbdbdb;border-style:solid;border-width:1px;margin-bottom:0;position:relative}.tabs.is-toggle a:hover{background-color:#f5f5f5;border-color:#b5b5b5;z-index:2}.tabs.is-toggle li+li{margin-left:-1px}.tabs.is-toggle li:first-child a{border-radius:4px 0 0 4px}.tabs.is-toggle li:last-child a{border-radius:0 4px 4px 0}.tabs.is-toggle li.is-active a{background-color:#2e63b8;border-color:#2e63b8;color:#fff;z-index:1}.tabs.is-toggle ul{border-bottom:none}.tabs.is-toggle.is-toggle-rounded li:first-child a{border-bottom-left-radius:290486px;border-top-left-radius:290486px;padding-left:1.25em}.tabs.is-toggle.is-toggle-rounded li:last-child a{border-bottom-right-radius:290486px;border-top-right-radius:290486px;padding-right:1.25em}.tabs.is-small,#documenter .docs-sidebar form.docs-search>input.tabs{font-size:.75rem}.tabs.is-medium{font-size:1.25rem}.tabs.is-large{font-size:1.5rem}.column{display:block;flex-basis:0;flex-grow:1;flex-shrink:1;padding:.75rem}.columns.is-mobile>.column.is-narrow{flex:none}.columns.is-mobile>.column.is-full{flex:none;width:100%}.columns.is-mobile>.column.is-three-quarters{flex:none;width:75%}.columns.is-mobile>.column.is-two-thirds{flex:none;width:66.6666%}.columns.is-mobile>.column.is-half{flex:none;width:50%}.columns.is-mobile>.column.is-one-third{flex:none;width:33.3333%}.columns.is-mobile>.column.is-one-quarter{flex:none;width:25%}.columns.is-mobile>.column.is-one-fifth{flex:none;width:20%}.columns.is-mobile>.column.is-two-fifths{flex:none;width:40%}.columns.is-mobile>.column.is-three-fifths{flex:none;width:60%}.columns.is-mobile>.column.is-four-fifths{flex:none;width:80%}.columns.is-mobile>.column.is-offset-three-quarters{margin-left:75%}.columns.is-mobile>.column.is-offset-two-thirds{margin-left:66.6666%}.columns.is-mobile>.column.is-offset-half{margin-left:50%}.columns.is-mobile>.column.is-offset-one-third{margin-left:33.3333%}.columns.is-mobile>.column.is-offset-one-quarter{margin-left:25%}.columns.is-mobile>.column.is-offset-one-fifth{margin-left:20%}.columns.is-mobile>.column.is-offset-two-fifths{margin-left:40%}.columns.is-mobile>.column.is-offset-three-fifths{margin-left:60%}.columns.is-mobile>.column.is-offset-four-fifths{margin-left:80%}.columns.is-mobile>.column.is-0{flex:none;width:0%}.columns.is-mobile>.column.is-offset-0{margin-left:0%}.columns.is-mobile>.column.is-1{flex:none;width:8.3333333333%}.columns.is-mobile>.column.is-offset-1{margin-left:8.3333333333%}.columns.is-mobile>.column.is-2{flex:none;width:16.6666666667%}.columns.is-mobile>.column.is-offset-2{margin-left:16.6666666667%}.columns.is-mobile>.column.is-3{flex:none;width:25%}.columns.is-mobile>.column.is-offset-3{margin-left:25%}.columns.is-mobile>.column.is-4{flex:none;width:33.3333333333%}.columns.is-mobile>.column.is-offset-4{margin-left:33.3333333333%}.columns.is-mobile>.column.is-5{flex:none;width:41.6666666667%}.columns.is-mobile>.column.is-offset-5{margin-left:41.6666666667%}.columns.is-mobile>.column.is-6{flex:none;width:50%}.columns.is-mobile>.column.is-offset-6{margin-left:50%}.columns.is-mobile>.column.is-7{flex:none;width:58.3333333333%}.columns.is-mobile>.column.is-offset-7{margin-left:58.3333333333%}.columns.is-mobile>.column.is-8{flex:none;width:66.6666666667%}.columns.is-mobile>.column.is-offset-8{margin-left:66.6666666667%}.columns.is-mobile>.column.is-9{flex:none;width:75%}.columns.is-mobile>.column.is-offset-9{margin-left:75%}.columns.is-mobile>.column.is-10{flex:none;width:83.3333333333%}.columns.is-mobile>.column.is-offset-10{margin-left:83.3333333333%}.columns.is-mobile>.column.is-11{flex:none;width:91.6666666667%}.columns.is-mobile>.column.is-offset-11{margin-left:91.6666666667%}.columns.is-mobile>.column.is-12{flex:none;width:100%}.columns.is-mobile>.column.is-offset-12{margin-left:100%}@media screen and (max-width: 768px){.column.is-narrow-mobile{flex:none}.column.is-full-mobile{flex:none;width:100%}.column.is-three-quarters-mobile{flex:none;width:75%}.column.is-two-thirds-mobile{flex:none;width:66.6666%}.column.is-half-mobile{flex:none;width:50%}.column.is-one-third-mobile{flex:none;width:33.3333%}.column.is-one-quarter-mobile{flex:none;width:25%}.column.is-one-fifth-mobile{flex:none;width:20%}.column.is-two-fifths-mobile{flex:none;width:40%}.column.is-three-fifths-mobile{flex:none;width:60%}.column.is-four-fifths-mobile{flex:none;width:80%}.column.is-offset-three-quarters-mobile{margin-left:75%}.column.is-offset-two-thirds-mobile{margin-left:66.6666%}.column.is-offset-half-mobile{margin-left:50%}.column.is-offset-one-third-mobile{margin-left:33.3333%}.column.is-offset-one-quarter-mobile{margin-left:25%}.column.is-offset-one-fifth-mobile{margin-left:20%}.column.is-offset-two-fifths-mobile{margin-left:40%}.column.is-offset-three-fifths-mobile{margin-left:60%}.column.is-offset-four-fifths-mobile{margin-left:80%}.column.is-0-mobile{flex:none;width:0%}.column.is-offset-0-mobile{margin-left:0%}.column.is-1-mobile{flex:none;width:8.3333333333%}.column.is-offset-1-mobile{margin-left:8.3333333333%}.column.is-2-mobile{flex:none;width:16.6666666667%}.column.is-offset-2-mobile{margin-left:16.6666666667%}.column.is-3-mobile{flex:none;width:25%}.column.is-offset-3-mobile{margin-left:25%}.column.is-4-mobile{flex:none;width:33.3333333333%}.column.is-offset-4-mobile{margin-left:33.3333333333%}.column.is-5-mobile{flex:none;width:41.6666666667%}.column.is-offset-5-mobile{margin-left:41.6666666667%}.column.is-6-mobile{flex:none;width:50%}.column.is-offset-6-mobile{margin-left:50%}.column.is-7-mobile{flex:none;width:58.3333333333%}.column.is-offset-7-mobile{margin-left:58.3333333333%}.column.is-8-mobile{flex:none;width:66.6666666667%}.column.is-offset-8-mobile{margin-left:66.6666666667%}.column.is-9-mobile{flex:none;width:75%}.column.is-offset-9-mobile{margin-left:75%}.column.is-10-mobile{flex:none;width:83.3333333333%}.column.is-offset-10-mobile{margin-left:83.3333333333%}.column.is-11-mobile{flex:none;width:91.6666666667%}.column.is-offset-11-mobile{margin-left:91.6666666667%}.column.is-12-mobile{flex:none;width:100%}.column.is-offset-12-mobile{margin-left:100%}}@media screen and (min-width: 769px),print{.column.is-narrow,.column.is-narrow-tablet{flex:none}.column.is-full,.column.is-full-tablet{flex:none;width:100%}.column.is-three-quarters,.column.is-three-quarters-tablet{flex:none;width:75%}.column.is-two-thirds,.column.is-two-thirds-tablet{flex:none;width:66.6666%}.column.is-half,.column.is-half-tablet{flex:none;width:50%}.column.is-one-third,.column.is-one-third-tablet{flex:none;width:33.3333%}.column.is-one-quarter,.column.is-one-quarter-tablet{flex:none;width:25%}.column.is-one-fifth,.column.is-one-fifth-tablet{flex:none;width:20%}.column.is-two-fifths,.column.is-two-fifths-tablet{flex:none;width:40%}.column.is-three-fifths,.column.is-three-fifths-tablet{flex:none;width:60%}.column.is-four-fifths,.column.is-four-fifths-tablet{flex:none;width:80%}.column.is-offset-three-quarters,.column.is-offset-three-quarters-tablet{margin-left:75%}.column.is-offset-two-thirds,.column.is-offset-two-thirds-tablet{margin-left:66.6666%}.column.is-offset-half,.column.is-offset-half-tablet{margin-left:50%}.column.is-offset-one-third,.column.is-offset-one-third-tablet{margin-left:33.3333%}.column.is-offset-one-quarter,.column.is-offset-one-quarter-tablet{margin-left:25%}.column.is-offset-one-fifth,.column.is-offset-one-fifth-tablet{margin-left:20%}.column.is-offset-two-fifths,.column.is-offset-two-fifths-tablet{margin-left:40%}.column.is-offset-three-fifths,.column.is-offset-three-fifths-tablet{margin-left:60%}.column.is-offset-four-fifths,.column.is-offset-four-fifths-tablet{margin-left:80%}.column.is-0,.column.is-0-tablet{flex:none;width:0%}.column.is-offset-0,.column.is-offset-0-tablet{margin-left:0%}.column.is-1,.column.is-1-tablet{flex:none;width:8.3333333333%}.column.is-offset-1,.column.is-offset-1-tablet{margin-left:8.3333333333%}.column.is-2,.column.is-2-tablet{flex:none;width:16.6666666667%}.column.is-offset-2,.column.is-offset-2-tablet{margin-left:16.6666666667%}.column.is-3,.column.is-3-tablet{flex:none;width:25%}.column.is-offset-3,.column.is-offset-3-tablet{margin-left:25%}.column.is-4,.column.is-4-tablet{flex:none;width:33.3333333333%}.column.is-offset-4,.column.is-offset-4-tablet{margin-left:33.3333333333%}.column.is-5,.column.is-5-tablet{flex:none;width:41.6666666667%}.column.is-offset-5,.column.is-offset-5-tablet{margin-left:41.6666666667%}.column.is-6,.column.is-6-tablet{flex:none;width:50%}.column.is-offset-6,.column.is-offset-6-tablet{margin-left:50%}.column.is-7,.column.is-7-tablet{flex:none;width:58.3333333333%}.column.is-offset-7,.column.is-offset-7-tablet{margin-left:58.3333333333%}.column.is-8,.column.is-8-tablet{flex:none;width:66.6666666667%}.column.is-offset-8,.column.is-offset-8-tablet{margin-left:66.6666666667%}.column.is-9,.column.is-9-tablet{flex:none;width:75%}.column.is-offset-9,.column.is-offset-9-tablet{margin-left:75%}.column.is-10,.column.is-10-tablet{flex:none;width:83.3333333333%}.column.is-offset-10,.column.is-offset-10-tablet{margin-left:83.3333333333%}.column.is-11,.column.is-11-tablet{flex:none;width:91.6666666667%}.column.is-offset-11,.column.is-offset-11-tablet{margin-left:91.6666666667%}.column.is-12,.column.is-12-tablet{flex:none;width:100%}.column.is-offset-12,.column.is-offset-12-tablet{margin-left:100%}}@media screen and (max-width: 1055px){.column.is-narrow-touch{flex:none}.column.is-full-touch{flex:none;width:100%}.column.is-three-quarters-touch{flex:none;width:75%}.column.is-two-thirds-touch{flex:none;width:66.6666%}.column.is-half-touch{flex:none;width:50%}.column.is-one-third-touch{flex:none;width:33.3333%}.column.is-one-quarter-touch{flex:none;width:25%}.column.is-one-fifth-touch{flex:none;width:20%}.column.is-two-fifths-touch{flex:none;width:40%}.column.is-three-fifths-touch{flex:none;width:60%}.column.is-four-fifths-touch{flex:none;width:80%}.column.is-offset-three-quarters-touch{margin-left:75%}.column.is-offset-two-thirds-touch{margin-left:66.6666%}.column.is-offset-half-touch{margin-left:50%}.column.is-offset-one-third-touch{margin-left:33.3333%}.column.is-offset-one-quarter-touch{margin-left:25%}.column.is-offset-one-fifth-touch{margin-left:20%}.column.is-offset-two-fifths-touch{margin-left:40%}.column.is-offset-three-fifths-touch{margin-left:60%}.column.is-offset-four-fifths-touch{margin-left:80%}.column.is-0-touch{flex:none;width:0%}.column.is-offset-0-touch{margin-left:0%}.column.is-1-touch{flex:none;width:8.3333333333%}.column.is-offset-1-touch{margin-left:8.3333333333%}.column.is-2-touch{flex:none;width:16.6666666667%}.column.is-offset-2-touch{margin-left:16.6666666667%}.column.is-3-touch{flex:none;width:25%}.column.is-offset-3-touch{margin-left:25%}.column.is-4-touch{flex:none;width:33.3333333333%}.column.is-offset-4-touch{margin-left:33.3333333333%}.column.is-5-touch{flex:none;width:41.6666666667%}.column.is-offset-5-touch{margin-left:41.6666666667%}.column.is-6-touch{flex:none;width:50%}.column.is-offset-6-touch{margin-left:50%}.column.is-7-touch{flex:none;width:58.3333333333%}.column.is-offset-7-touch{margin-left:58.3333333333%}.column.is-8-touch{flex:none;width:66.6666666667%}.column.is-offset-8-touch{margin-left:66.6666666667%}.column.is-9-touch{flex:none;width:75%}.column.is-offset-9-touch{margin-left:75%}.column.is-10-touch{flex:none;width:83.3333333333%}.column.is-offset-10-touch{margin-left:83.3333333333%}.column.is-11-touch{flex:none;width:91.6666666667%}.column.is-offset-11-touch{margin-left:91.6666666667%}.column.is-12-touch{flex:none;width:100%}.column.is-offset-12-touch{margin-left:100%}}@media screen and (min-width: 1056px){.column.is-narrow-desktop{flex:none}.column.is-full-desktop{flex:none;width:100%}.column.is-three-quarters-desktop{flex:none;width:75%}.column.is-two-thirds-desktop{flex:none;width:66.6666%}.column.is-half-desktop{flex:none;width:50%}.column.is-one-third-desktop{flex:none;width:33.3333%}.column.is-one-quarter-desktop{flex:none;width:25%}.column.is-one-fifth-desktop{flex:none;width:20%}.column.is-two-fifths-desktop{flex:none;width:40%}.column.is-three-fifths-desktop{flex:none;width:60%}.column.is-four-fifths-desktop{flex:none;width:80%}.column.is-offset-three-quarters-desktop{margin-left:75%}.column.is-offset-two-thirds-desktop{margin-left:66.6666%}.column.is-offset-half-desktop{margin-left:50%}.column.is-offset-one-third-desktop{margin-left:33.3333%}.column.is-offset-one-quarter-desktop{margin-left:25%}.column.is-offset-one-fifth-desktop{margin-left:20%}.column.is-offset-two-fifths-desktop{margin-left:40%}.column.is-offset-three-fifths-desktop{margin-left:60%}.column.is-offset-four-fifths-desktop{margin-left:80%}.column.is-0-desktop{flex:none;width:0%}.column.is-offset-0-desktop{margin-left:0%}.column.is-1-desktop{flex:none;width:8.3333333333%}.column.is-offset-1-desktop{margin-left:8.3333333333%}.column.is-2-desktop{flex:none;width:16.6666666667%}.column.is-offset-2-desktop{margin-left:16.6666666667%}.column.is-3-desktop{flex:none;width:25%}.column.is-offset-3-desktop{margin-left:25%}.column.is-4-desktop{flex:none;width:33.3333333333%}.column.is-offset-4-desktop{margin-left:33.3333333333%}.column.is-5-desktop{flex:none;width:41.6666666667%}.column.is-offset-5-desktop{margin-left:41.6666666667%}.column.is-6-desktop{flex:none;width:50%}.column.is-offset-6-desktop{margin-left:50%}.column.is-7-desktop{flex:none;width:58.3333333333%}.column.is-offset-7-desktop{margin-left:58.3333333333%}.column.is-8-desktop{flex:none;width:66.6666666667%}.column.is-offset-8-desktop{margin-left:66.6666666667%}.column.is-9-desktop{flex:none;width:75%}.column.is-offset-9-desktop{margin-left:75%}.column.is-10-desktop{flex:none;width:83.3333333333%}.column.is-offset-10-desktop{margin-left:83.3333333333%}.column.is-11-desktop{flex:none;width:91.6666666667%}.column.is-offset-11-desktop{margin-left:91.6666666667%}.column.is-12-desktop{flex:none;width:100%}.column.is-offset-12-desktop{margin-left:100%}}@media screen and (min-width: 1216px){.column.is-narrow-widescreen{flex:none}.column.is-full-widescreen{flex:none;width:100%}.column.is-three-quarters-widescreen{flex:none;width:75%}.column.is-two-thirds-widescreen{flex:none;width:66.6666%}.column.is-half-widescreen{flex:none;width:50%}.column.is-one-third-widescreen{flex:none;width:33.3333%}.column.is-one-quarter-widescreen{flex:none;width:25%}.column.is-one-fifth-widescreen{flex:none;width:20%}.column.is-two-fifths-widescreen{flex:none;width:40%}.column.is-three-fifths-widescreen{flex:none;width:60%}.column.is-four-fifths-widescreen{flex:none;width:80%}.column.is-offset-three-quarters-widescreen{margin-left:75%}.column.is-offset-two-thirds-widescreen{margin-left:66.6666%}.column.is-offset-half-widescreen{margin-left:50%}.column.is-offset-one-third-widescreen{margin-left:33.3333%}.column.is-offset-one-quarter-widescreen{margin-left:25%}.column.is-offset-one-fifth-widescreen{margin-left:20%}.column.is-offset-two-fifths-widescreen{margin-left:40%}.column.is-offset-three-fifths-widescreen{margin-left:60%}.column.is-offset-four-fifths-widescreen{margin-left:80%}.column.is-0-widescreen{flex:none;width:0%}.column.is-offset-0-widescreen{margin-left:0%}.column.is-1-widescreen{flex:none;width:8.3333333333%}.column.is-offset-1-widescreen{margin-left:8.3333333333%}.column.is-2-widescreen{flex:none;width:16.6666666667%}.column.is-offset-2-widescreen{margin-left:16.6666666667%}.column.is-3-widescreen{flex:none;width:25%}.column.is-offset-3-widescreen{margin-left:25%}.column.is-4-widescreen{flex:none;width:33.3333333333%}.column.is-offset-4-widescreen{margin-left:33.3333333333%}.column.is-5-widescreen{flex:none;width:41.6666666667%}.column.is-offset-5-widescreen{margin-left:41.6666666667%}.column.is-6-widescreen{flex:none;width:50%}.column.is-offset-6-widescreen{margin-left:50%}.column.is-7-widescreen{flex:none;width:58.3333333333%}.column.is-offset-7-widescreen{margin-left:58.3333333333%}.column.is-8-widescreen{flex:none;width:66.6666666667%}.column.is-offset-8-widescreen{margin-left:66.6666666667%}.column.is-9-widescreen{flex:none;width:75%}.column.is-offset-9-widescreen{margin-left:75%}.column.is-10-widescreen{flex:none;width:83.3333333333%}.column.is-offset-10-widescreen{margin-left:83.3333333333%}.column.is-11-widescreen{flex:none;width:91.6666666667%}.column.is-offset-11-widescreen{margin-left:91.6666666667%}.column.is-12-widescreen{flex:none;width:100%}.column.is-offset-12-widescreen{margin-left:100%}}@media screen and (min-width: 1408px){.column.is-narrow-fullhd{flex:none}.column.is-full-fullhd{flex:none;width:100%}.column.is-three-quarters-fullhd{flex:none;width:75%}.column.is-two-thirds-fullhd{flex:none;width:66.6666%}.column.is-half-fullhd{flex:none;width:50%}.column.is-one-third-fullhd{flex:none;width:33.3333%}.column.is-one-quarter-fullhd{flex:none;width:25%}.column.is-one-fifth-fullhd{flex:none;width:20%}.column.is-two-fifths-fullhd{flex:none;width:40%}.column.is-three-fifths-fullhd{flex:none;width:60%}.column.is-four-fifths-fullhd{flex:none;width:80%}.column.is-offset-three-quarters-fullhd{margin-left:75%}.column.is-offset-two-thirds-fullhd{margin-left:66.6666%}.column.is-offset-half-fullhd{margin-left:50%}.column.is-offset-one-third-fullhd{margin-left:33.3333%}.column.is-offset-one-quarter-fullhd{margin-left:25%}.column.is-offset-one-fifth-fullhd{margin-left:20%}.column.is-offset-two-fifths-fullhd{margin-left:40%}.column.is-offset-three-fifths-fullhd{margin-left:60%}.column.is-offset-four-fifths-fullhd{margin-left:80%}.column.is-0-fullhd{flex:none;width:0%}.column.is-offset-0-fullhd{margin-left:0%}.column.is-1-fullhd{flex:none;width:8.3333333333%}.column.is-offset-1-fullhd{margin-left:8.3333333333%}.column.is-2-fullhd{flex:none;width:16.6666666667%}.column.is-offset-2-fullhd{margin-left:16.6666666667%}.column.is-3-fullhd{flex:none;width:25%}.column.is-offset-3-fullhd{margin-left:25%}.column.is-4-fullhd{flex:none;width:33.3333333333%}.column.is-offset-4-fullhd{margin-left:33.3333333333%}.column.is-5-fullhd{flex:none;width:41.6666666667%}.column.is-offset-5-fullhd{margin-left:41.6666666667%}.column.is-6-fullhd{flex:none;width:50%}.column.is-offset-6-fullhd{margin-left:50%}.column.is-7-fullhd{flex:none;width:58.3333333333%}.column.is-offset-7-fullhd{margin-left:58.3333333333%}.column.is-8-fullhd{flex:none;width:66.6666666667%}.column.is-offset-8-fullhd{margin-left:66.6666666667%}.column.is-9-fullhd{flex:none;width:75%}.column.is-offset-9-fullhd{margin-left:75%}.column.is-10-fullhd{flex:none;width:83.3333333333%}.column.is-offset-10-fullhd{margin-left:83.3333333333%}.column.is-11-fullhd{flex:none;width:91.6666666667%}.column.is-offset-11-fullhd{margin-left:91.6666666667%}.column.is-12-fullhd{flex:none;width:100%}.column.is-offset-12-fullhd{margin-left:100%}}.columns{margin-left:-.75rem;margin-right:-.75rem;margin-top:-.75rem}.columns:last-child{margin-bottom:-.75rem}.columns:not(:last-child){margin-bottom:calc(1.5rem - .75rem)}.columns.is-centered{justify-content:center}.columns.is-gapless{margin-left:0;margin-right:0;margin-top:0}.columns.is-gapless>.column{margin:0;padding:0 !important}.columns.is-gapless:not(:last-child){margin-bottom:1.5rem}.columns.is-gapless:last-child{margin-bottom:0}.columns.is-mobile{display:flex}.columns.is-multiline{flex-wrap:wrap}.columns.is-vcentered{align-items:center}@media screen and (min-width: 769px),print{.columns:not(.is-desktop){display:flex}}@media screen and (min-width: 1056px){.columns.is-desktop{display:flex}}.columns.is-variable{--columnGap: 0.75rem;margin-left:calc(-1 * var(--columnGap));margin-right:calc(-1 * var(--columnGap))}.columns.is-variable .column{padding-left:var(--columnGap);padding-right:var(--columnGap)}.columns.is-variable.is-0{--columnGap: 0rem}@media screen and (max-width: 768px){.columns.is-variable.is-0-mobile{--columnGap: 0rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-0-tablet{--columnGap: 0rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-0-tablet-only{--columnGap: 0rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-0-touch{--columnGap: 0rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-0-desktop{--columnGap: 0rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-0-desktop-only{--columnGap: 0rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-0-widescreen{--columnGap: 0rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-0-widescreen-only{--columnGap: 0rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-0-fullhd{--columnGap: 0rem}}.columns.is-variable.is-1{--columnGap: .25rem}@media screen and (max-width: 768px){.columns.is-variable.is-1-mobile{--columnGap: .25rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-1-tablet{--columnGap: .25rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-1-tablet-only{--columnGap: .25rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-1-touch{--columnGap: .25rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-1-desktop{--columnGap: .25rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-1-desktop-only{--columnGap: .25rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-1-widescreen{--columnGap: .25rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-1-widescreen-only{--columnGap: .25rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-1-fullhd{--columnGap: .25rem}}.columns.is-variable.is-2{--columnGap: .5rem}@media screen and (max-width: 768px){.columns.is-variable.is-2-mobile{--columnGap: .5rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-2-tablet{--columnGap: .5rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-2-tablet-only{--columnGap: .5rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-2-touch{--columnGap: .5rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-2-desktop{--columnGap: .5rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-2-desktop-only{--columnGap: .5rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-2-widescreen{--columnGap: .5rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-2-widescreen-only{--columnGap: .5rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-2-fullhd{--columnGap: .5rem}}.columns.is-variable.is-3{--columnGap: .75rem}@media screen and (max-width: 768px){.columns.is-variable.is-3-mobile{--columnGap: .75rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-3-tablet{--columnGap: .75rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-3-tablet-only{--columnGap: .75rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-3-touch{--columnGap: .75rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-3-desktop{--columnGap: .75rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-3-desktop-only{--columnGap: .75rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-3-widescreen{--columnGap: .75rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-3-widescreen-only{--columnGap: .75rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-3-fullhd{--columnGap: .75rem}}.columns.is-variable.is-4{--columnGap: 1rem}@media screen and (max-width: 768px){.columns.is-variable.is-4-mobile{--columnGap: 1rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-4-tablet{--columnGap: 1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-4-tablet-only{--columnGap: 1rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-4-touch{--columnGap: 1rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-4-desktop{--columnGap: 1rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-4-desktop-only{--columnGap: 1rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-4-widescreen{--columnGap: 1rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-4-widescreen-only{--columnGap: 1rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-4-fullhd{--columnGap: 1rem}}.columns.is-variable.is-5{--columnGap: 1.25rem}@media screen and (max-width: 768px){.columns.is-variable.is-5-mobile{--columnGap: 1.25rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-5-tablet{--columnGap: 1.25rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-5-tablet-only{--columnGap: 1.25rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-5-touch{--columnGap: 1.25rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-5-desktop{--columnGap: 1.25rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-5-desktop-only{--columnGap: 1.25rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-5-widescreen{--columnGap: 1.25rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-5-widescreen-only{--columnGap: 1.25rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-5-fullhd{--columnGap: 1.25rem}}.columns.is-variable.is-6{--columnGap: 1.5rem}@media screen and (max-width: 768px){.columns.is-variable.is-6-mobile{--columnGap: 1.5rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-6-tablet{--columnGap: 1.5rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-6-tablet-only{--columnGap: 1.5rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-6-touch{--columnGap: 1.5rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-6-desktop{--columnGap: 1.5rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-6-desktop-only{--columnGap: 1.5rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-6-widescreen{--columnGap: 1.5rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-6-widescreen-only{--columnGap: 1.5rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-6-fullhd{--columnGap: 1.5rem}}.columns.is-variable.is-7{--columnGap: 1.75rem}@media screen and (max-width: 768px){.columns.is-variable.is-7-mobile{--columnGap: 1.75rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-7-tablet{--columnGap: 1.75rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-7-tablet-only{--columnGap: 1.75rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-7-touch{--columnGap: 1.75rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-7-desktop{--columnGap: 1.75rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-7-desktop-only{--columnGap: 1.75rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-7-widescreen{--columnGap: 1.75rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-7-widescreen-only{--columnGap: 1.75rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-7-fullhd{--columnGap: 1.75rem}}.columns.is-variable.is-8{--columnGap: 2rem}@media screen and (max-width: 768px){.columns.is-variable.is-8-mobile{--columnGap: 2rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-8-tablet{--columnGap: 2rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-8-tablet-only{--columnGap: 2rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-8-touch{--columnGap: 2rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-8-desktop{--columnGap: 2rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-8-desktop-only{--columnGap: 2rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-8-widescreen{--columnGap: 2rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-8-widescreen-only{--columnGap: 2rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-8-fullhd{--columnGap: 2rem}}.tile{align-items:stretch;display:block;flex-basis:0;flex-grow:1;flex-shrink:1;min-height:min-content}.tile.is-ancestor{margin-left:-.75rem;margin-right:-.75rem;margin-top:-.75rem}.tile.is-ancestor:last-child{margin-bottom:-.75rem}.tile.is-ancestor:not(:last-child){margin-bottom:.75rem}.tile.is-child{margin:0 !important}.tile.is-parent{padding:.75rem}.tile.is-vertical{flex-direction:column}.tile.is-vertical>.tile.is-child:not(:last-child){margin-bottom:1.5rem !important}@media screen and (min-width: 769px),print{.tile:not(.is-child){display:flex}.tile.is-1{flex:none;width:8.3333333333%}.tile.is-2{flex:none;width:16.6666666667%}.tile.is-3{flex:none;width:25%}.tile.is-4{flex:none;width:33.3333333333%}.tile.is-5{flex:none;width:41.6666666667%}.tile.is-6{flex:none;width:50%}.tile.is-7{flex:none;width:58.3333333333%}.tile.is-8{flex:none;width:66.6666666667%}.tile.is-9{flex:none;width:75%}.tile.is-10{flex:none;width:83.3333333333%}.tile.is-11{flex:none;width:91.6666666667%}.tile.is-12{flex:none;width:100%}}.hero{align-items:stretch;display:flex;flex-direction:column;justify-content:space-between}.hero .navbar{background:none}.hero .tabs ul{border-bottom:none}.hero.is-white{background-color:#fff;color:#0a0a0a}.hero.is-white a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-white strong{color:inherit}.hero.is-white .title{color:#0a0a0a}.hero.is-white .subtitle{color:rgba(10,10,10,0.9)}.hero.is-white .subtitle a:not(.button),.hero.is-white .subtitle strong{color:#0a0a0a}@media screen and (max-width: 1055px){.hero.is-white .navbar-menu{background-color:#fff}}.hero.is-white .navbar-item,.hero.is-white .navbar-link{color:rgba(10,10,10,0.7)}.hero.is-white a.navbar-item:hover,.hero.is-white a.navbar-item.is-active,.hero.is-white .navbar-link:hover,.hero.is-white .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}.hero.is-white .tabs a{color:#0a0a0a;opacity:0.9}.hero.is-white .tabs a:hover{opacity:1}.hero.is-white .tabs li.is-active a{opacity:1}.hero.is-white .tabs.is-boxed a,.hero.is-white .tabs.is-toggle a{color:#0a0a0a}.hero.is-white .tabs.is-boxed a:hover,.hero.is-white .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-white .tabs.is-boxed li.is-active a,.hero.is-white .tabs.is-boxed li.is-active a:hover,.hero.is-white .tabs.is-toggle li.is-active a,.hero.is-white .tabs.is-toggle li.is-active a:hover{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}.hero.is-white.is-bold{background-image:linear-gradient(141deg, #e8e3e4 0%, #fff 71%, #fff 100%)}@media screen and (max-width: 768px){.hero.is-white.is-bold .navbar-menu{background-image:linear-gradient(141deg, #e8e3e4 0%, #fff 71%, #fff 100%)}}.hero.is-black{background-color:#0a0a0a;color:#fff}.hero.is-black a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-black strong{color:inherit}.hero.is-black .title{color:#fff}.hero.is-black .subtitle{color:rgba(255,255,255,0.9)}.hero.is-black .subtitle a:not(.button),.hero.is-black .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-black .navbar-menu{background-color:#0a0a0a}}.hero.is-black .navbar-item,.hero.is-black .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-black a.navbar-item:hover,.hero.is-black a.navbar-item.is-active,.hero.is-black .navbar-link:hover,.hero.is-black .navbar-link.is-active{background-color:#000;color:#fff}.hero.is-black .tabs a{color:#fff;opacity:0.9}.hero.is-black .tabs a:hover{opacity:1}.hero.is-black .tabs li.is-active a{opacity:1}.hero.is-black .tabs.is-boxed a,.hero.is-black .tabs.is-toggle a{color:#fff}.hero.is-black .tabs.is-boxed a:hover,.hero.is-black .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-black .tabs.is-boxed li.is-active a,.hero.is-black .tabs.is-boxed li.is-active a:hover,.hero.is-black .tabs.is-toggle li.is-active a,.hero.is-black .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#0a0a0a}.hero.is-black.is-bold{background-image:linear-gradient(141deg, #000 0%, #0a0a0a 71%, #181616 100%)}@media screen and (max-width: 768px){.hero.is-black.is-bold .navbar-menu{background-image:linear-gradient(141deg, #000 0%, #0a0a0a 71%, #181616 100%)}}.hero.is-light{background-color:#f5f5f5;color:#363636}.hero.is-light a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-light strong{color:inherit}.hero.is-light .title{color:#363636}.hero.is-light .subtitle{color:rgba(54,54,54,0.9)}.hero.is-light .subtitle a:not(.button),.hero.is-light .subtitle strong{color:#363636}@media screen and (max-width: 1055px){.hero.is-light .navbar-menu{background-color:#f5f5f5}}.hero.is-light .navbar-item,.hero.is-light .navbar-link{color:rgba(54,54,54,0.7)}.hero.is-light a.navbar-item:hover,.hero.is-light a.navbar-item.is-active,.hero.is-light .navbar-link:hover,.hero.is-light .navbar-link.is-active{background-color:#e8e8e8;color:#363636}.hero.is-light .tabs a{color:#363636;opacity:0.9}.hero.is-light .tabs a:hover{opacity:1}.hero.is-light .tabs li.is-active a{opacity:1}.hero.is-light .tabs.is-boxed a,.hero.is-light .tabs.is-toggle a{color:#363636}.hero.is-light .tabs.is-boxed a:hover,.hero.is-light .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-light .tabs.is-boxed li.is-active a,.hero.is-light .tabs.is-boxed li.is-active a:hover,.hero.is-light .tabs.is-toggle li.is-active a,.hero.is-light .tabs.is-toggle li.is-active a:hover{background-color:#363636;border-color:#363636;color:#f5f5f5}.hero.is-light.is-bold{background-image:linear-gradient(141deg, #dfd8d9 0%, #f5f5f5 71%, #fff 100%)}@media screen and (max-width: 768px){.hero.is-light.is-bold .navbar-menu{background-image:linear-gradient(141deg, #dfd8d9 0%, #f5f5f5 71%, #fff 100%)}}.hero.is-dark,.content kbd.hero{background-color:#363636;color:#f5f5f5}.hero.is-dark a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.content kbd.hero a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-dark strong,.content kbd.hero strong{color:inherit}.hero.is-dark .title,.content kbd.hero .title{color:#f5f5f5}.hero.is-dark .subtitle,.content kbd.hero .subtitle{color:rgba(245,245,245,0.9)}.hero.is-dark .subtitle a:not(.button),.content kbd.hero .subtitle a:not(.button),.hero.is-dark .subtitle strong,.content kbd.hero .subtitle strong{color:#f5f5f5}@media screen and (max-width: 1055px){.hero.is-dark .navbar-menu,.content kbd.hero .navbar-menu{background-color:#363636}}.hero.is-dark .navbar-item,.content kbd.hero .navbar-item,.hero.is-dark .navbar-link,.content kbd.hero .navbar-link{color:rgba(245,245,245,0.7)}.hero.is-dark a.navbar-item:hover,.content kbd.hero a.navbar-item:hover,.hero.is-dark a.navbar-item.is-active,.content kbd.hero a.navbar-item.is-active,.hero.is-dark .navbar-link:hover,.content kbd.hero .navbar-link:hover,.hero.is-dark .navbar-link.is-active,.content kbd.hero .navbar-link.is-active{background-color:#292929;color:#f5f5f5}.hero.is-dark .tabs a,.content kbd.hero .tabs a{color:#f5f5f5;opacity:0.9}.hero.is-dark .tabs a:hover,.content kbd.hero .tabs a:hover{opacity:1}.hero.is-dark .tabs li.is-active a,.content kbd.hero .tabs li.is-active a{opacity:1}.hero.is-dark .tabs.is-boxed a,.content kbd.hero .tabs.is-boxed a,.hero.is-dark .tabs.is-toggle a,.content kbd.hero .tabs.is-toggle a{color:#f5f5f5}.hero.is-dark .tabs.is-boxed a:hover,.content kbd.hero .tabs.is-boxed a:hover,.hero.is-dark .tabs.is-toggle a:hover,.content kbd.hero .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-dark .tabs.is-boxed li.is-active a,.content kbd.hero .tabs.is-boxed li.is-active a,.hero.is-dark .tabs.is-boxed li.is-active a:hover,.hero.is-dark .tabs.is-toggle li.is-active a,.content kbd.hero .tabs.is-toggle li.is-active a,.hero.is-dark .tabs.is-toggle li.is-active a:hover{background-color:#f5f5f5;border-color:#f5f5f5;color:#363636}.hero.is-dark.is-bold,.content kbd.hero.is-bold{background-image:linear-gradient(141deg, #1f191a 0%, #363636 71%, #46403f 100%)}@media screen and (max-width: 768px){.hero.is-dark.is-bold .navbar-menu,.content kbd.hero.is-bold .navbar-menu{background-image:linear-gradient(141deg, #1f191a 0%, #363636 71%, #46403f 100%)}}.hero.is-primary,.docstring>section>a.hero.docs-sourcelink{background-color:#4eb5de;color:#fff}.hero.is-primary a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.docstring>section>a.hero.docs-sourcelink a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-primary strong,.docstring>section>a.hero.docs-sourcelink strong{color:inherit}.hero.is-primary .title,.docstring>section>a.hero.docs-sourcelink .title{color:#fff}.hero.is-primary .subtitle,.docstring>section>a.hero.docs-sourcelink .subtitle{color:rgba(255,255,255,0.9)}.hero.is-primary .subtitle a:not(.button),.docstring>section>a.hero.docs-sourcelink .subtitle a:not(.button),.hero.is-primary .subtitle strong,.docstring>section>a.hero.docs-sourcelink .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-primary .navbar-menu,.docstring>section>a.hero.docs-sourcelink .navbar-menu{background-color:#4eb5de}}.hero.is-primary .navbar-item,.docstring>section>a.hero.docs-sourcelink .navbar-item,.hero.is-primary .navbar-link,.docstring>section>a.hero.docs-sourcelink .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-primary a.navbar-item:hover,.docstring>section>a.hero.docs-sourcelink a.navbar-item:hover,.hero.is-primary a.navbar-item.is-active,.docstring>section>a.hero.docs-sourcelink a.navbar-item.is-active,.hero.is-primary .navbar-link:hover,.docstring>section>a.hero.docs-sourcelink .navbar-link:hover,.hero.is-primary .navbar-link.is-active,.docstring>section>a.hero.docs-sourcelink .navbar-link.is-active{background-color:#39acda;color:#fff}.hero.is-primary .tabs a,.docstring>section>a.hero.docs-sourcelink .tabs a{color:#fff;opacity:0.9}.hero.is-primary .tabs a:hover,.docstring>section>a.hero.docs-sourcelink .tabs a:hover{opacity:1}.hero.is-primary .tabs li.is-active a,.docstring>section>a.hero.docs-sourcelink .tabs li.is-active a{opacity:1}.hero.is-primary .tabs.is-boxed a,.docstring>section>a.hero.docs-sourcelink .tabs.is-boxed a,.hero.is-primary .tabs.is-toggle a,.docstring>section>a.hero.docs-sourcelink .tabs.is-toggle a{color:#fff}.hero.is-primary .tabs.is-boxed a:hover,.docstring>section>a.hero.docs-sourcelink .tabs.is-boxed a:hover,.hero.is-primary .tabs.is-toggle a:hover,.docstring>section>a.hero.docs-sourcelink .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-primary .tabs.is-boxed li.is-active a,.docstring>section>a.hero.docs-sourcelink .tabs.is-boxed li.is-active a,.hero.is-primary .tabs.is-boxed li.is-active a:hover,.hero.is-primary .tabs.is-toggle li.is-active a,.docstring>section>a.hero.docs-sourcelink .tabs.is-toggle li.is-active a,.hero.is-primary .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#4eb5de}.hero.is-primary.is-bold,.docstring>section>a.hero.is-bold.docs-sourcelink{background-image:linear-gradient(141deg, #1bc7de 0%, #4eb5de 71%, #5fa9e7 100%)}@media screen and (max-width: 768px){.hero.is-primary.is-bold .navbar-menu,.docstring>section>a.hero.is-bold.docs-sourcelink .navbar-menu{background-image:linear-gradient(141deg, #1bc7de 0%, #4eb5de 71%, #5fa9e7 100%)}}.hero.is-link{background-color:#2e63b8;color:#fff}.hero.is-link a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-link strong{color:inherit}.hero.is-link .title{color:#fff}.hero.is-link .subtitle{color:rgba(255,255,255,0.9)}.hero.is-link .subtitle a:not(.button),.hero.is-link .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-link .navbar-menu{background-color:#2e63b8}}.hero.is-link .navbar-item,.hero.is-link .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-link a.navbar-item:hover,.hero.is-link a.navbar-item.is-active,.hero.is-link .navbar-link:hover,.hero.is-link .navbar-link.is-active{background-color:#2958a4;color:#fff}.hero.is-link .tabs a{color:#fff;opacity:0.9}.hero.is-link .tabs a:hover{opacity:1}.hero.is-link .tabs li.is-active a{opacity:1}.hero.is-link .tabs.is-boxed a,.hero.is-link .tabs.is-toggle a{color:#fff}.hero.is-link .tabs.is-boxed a:hover,.hero.is-link .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-link .tabs.is-boxed li.is-active a,.hero.is-link .tabs.is-boxed li.is-active a:hover,.hero.is-link .tabs.is-toggle li.is-active a,.hero.is-link .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#2e63b8}.hero.is-link.is-bold{background-image:linear-gradient(141deg, #1b6098 0%, #2e63b8 71%, #2d51d2 100%)}@media screen and (max-width: 768px){.hero.is-link.is-bold .navbar-menu{background-image:linear-gradient(141deg, #1b6098 0%, #2e63b8 71%, #2d51d2 100%)}}.hero.is-info{background-color:#209cee;color:#fff}.hero.is-info a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-info strong{color:inherit}.hero.is-info .title{color:#fff}.hero.is-info .subtitle{color:rgba(255,255,255,0.9)}.hero.is-info .subtitle a:not(.button),.hero.is-info .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-info .navbar-menu{background-color:#209cee}}.hero.is-info .navbar-item,.hero.is-info .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-info a.navbar-item:hover,.hero.is-info a.navbar-item.is-active,.hero.is-info .navbar-link:hover,.hero.is-info .navbar-link.is-active{background-color:#1190e3;color:#fff}.hero.is-info .tabs a{color:#fff;opacity:0.9}.hero.is-info .tabs a:hover{opacity:1}.hero.is-info .tabs li.is-active a{opacity:1}.hero.is-info .tabs.is-boxed a,.hero.is-info .tabs.is-toggle a{color:#fff}.hero.is-info .tabs.is-boxed a:hover,.hero.is-info .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-info .tabs.is-boxed li.is-active a,.hero.is-info .tabs.is-boxed li.is-active a:hover,.hero.is-info .tabs.is-toggle li.is-active a,.hero.is-info .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#209cee}.hero.is-info.is-bold{background-image:linear-gradient(141deg, #05a6d6 0%, #209cee 71%, #3287f5 100%)}@media screen and (max-width: 768px){.hero.is-info.is-bold .navbar-menu{background-image:linear-gradient(141deg, #05a6d6 0%, #209cee 71%, #3287f5 100%)}}.hero.is-success{background-color:#22c35b;color:#fff}.hero.is-success a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-success strong{color:inherit}.hero.is-success .title{color:#fff}.hero.is-success .subtitle{color:rgba(255,255,255,0.9)}.hero.is-success .subtitle a:not(.button),.hero.is-success .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-success .navbar-menu{background-color:#22c35b}}.hero.is-success .navbar-item,.hero.is-success .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-success a.navbar-item:hover,.hero.is-success a.navbar-item.is-active,.hero.is-success .navbar-link:hover,.hero.is-success .navbar-link.is-active{background-color:#1ead51;color:#fff}.hero.is-success .tabs a{color:#fff;opacity:0.9}.hero.is-success .tabs a:hover{opacity:1}.hero.is-success .tabs li.is-active a{opacity:1}.hero.is-success .tabs.is-boxed a,.hero.is-success .tabs.is-toggle a{color:#fff}.hero.is-success .tabs.is-boxed a:hover,.hero.is-success .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-success .tabs.is-boxed li.is-active a,.hero.is-success .tabs.is-boxed li.is-active a:hover,.hero.is-success .tabs.is-toggle li.is-active a,.hero.is-success .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#22c35b}.hero.is-success.is-bold{background-image:linear-gradient(141deg, #12a02c 0%, #22c35b 71%, #1fdf83 100%)}@media screen and (max-width: 768px){.hero.is-success.is-bold .navbar-menu{background-image:linear-gradient(141deg, #12a02c 0%, #22c35b 71%, #1fdf83 100%)}}.hero.is-warning{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.hero.is-warning a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-warning strong{color:inherit}.hero.is-warning .title{color:rgba(0,0,0,0.7)}.hero.is-warning .subtitle{color:rgba(0,0,0,0.9)}.hero.is-warning .subtitle a:not(.button),.hero.is-warning .subtitle strong{color:rgba(0,0,0,0.7)}@media screen and (max-width: 1055px){.hero.is-warning .navbar-menu{background-color:#ffdd57}}.hero.is-warning .navbar-item,.hero.is-warning .navbar-link{color:rgba(0,0,0,0.7)}.hero.is-warning a.navbar-item:hover,.hero.is-warning a.navbar-item.is-active,.hero.is-warning .navbar-link:hover,.hero.is-warning .navbar-link.is-active{background-color:#ffd83e;color:rgba(0,0,0,0.7)}.hero.is-warning .tabs a{color:rgba(0,0,0,0.7);opacity:0.9}.hero.is-warning .tabs a:hover{opacity:1}.hero.is-warning .tabs li.is-active a{opacity:1}.hero.is-warning .tabs.is-boxed a,.hero.is-warning .tabs.is-toggle a{color:rgba(0,0,0,0.7)}.hero.is-warning .tabs.is-boxed a:hover,.hero.is-warning .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-warning .tabs.is-boxed li.is-active a,.hero.is-warning .tabs.is-boxed li.is-active a:hover,.hero.is-warning .tabs.is-toggle li.is-active a,.hero.is-warning .tabs.is-toggle li.is-active a:hover{background-color:rgba(0,0,0,0.7);border-color:rgba(0,0,0,0.7);color:#ffdd57}.hero.is-warning.is-bold{background-image:linear-gradient(141deg, #ffae24 0%, #ffdd57 71%, #fffa71 100%)}@media screen and (max-width: 768px){.hero.is-warning.is-bold .navbar-menu{background-image:linear-gradient(141deg, #ffae24 0%, #ffdd57 71%, #fffa71 100%)}}.hero.is-danger{background-color:#da0b00;color:#fff}.hero.is-danger a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-danger strong{color:inherit}.hero.is-danger .title{color:#fff}.hero.is-danger .subtitle{color:rgba(255,255,255,0.9)}.hero.is-danger .subtitle a:not(.button),.hero.is-danger .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-danger .navbar-menu{background-color:#da0b00}}.hero.is-danger .navbar-item,.hero.is-danger .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-danger a.navbar-item:hover,.hero.is-danger a.navbar-item.is-active,.hero.is-danger .navbar-link:hover,.hero.is-danger .navbar-link.is-active{background-color:#c10a00;color:#fff}.hero.is-danger .tabs a{color:#fff;opacity:0.9}.hero.is-danger .tabs a:hover{opacity:1}.hero.is-danger .tabs li.is-active a{opacity:1}.hero.is-danger .tabs.is-boxed a,.hero.is-danger .tabs.is-toggle a{color:#fff}.hero.is-danger .tabs.is-boxed a:hover,.hero.is-danger .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-danger .tabs.is-boxed li.is-active a,.hero.is-danger .tabs.is-boxed li.is-active a:hover,.hero.is-danger .tabs.is-toggle li.is-active a,.hero.is-danger .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#da0b00}.hero.is-danger.is-bold{background-image:linear-gradient(141deg, #a70013 0%, #da0b00 71%, #f43500 100%)}@media screen and (max-width: 768px){.hero.is-danger.is-bold .navbar-menu{background-image:linear-gradient(141deg, #a70013 0%, #da0b00 71%, #f43500 100%)}}.hero.is-small .hero-body,#documenter .docs-sidebar form.docs-search>input.hero .hero-body{padding-bottom:1.5rem;padding-top:1.5rem}@media screen and (min-width: 769px),print{.hero.is-medium .hero-body{padding-bottom:9rem;padding-top:9rem}}@media screen and (min-width: 769px),print{.hero.is-large .hero-body{padding-bottom:18rem;padding-top:18rem}}.hero.is-halfheight .hero-body,.hero.is-fullheight .hero-body,.hero.is-fullheight-with-navbar .hero-body{align-items:center;display:flex}.hero.is-halfheight .hero-body>.container,.hero.is-fullheight .hero-body>.container,.hero.is-fullheight-with-navbar .hero-body>.container{flex-grow:1;flex-shrink:1}.hero.is-halfheight{min-height:50vh}.hero.is-fullheight{min-height:100vh}.hero-video{overflow:hidden}.hero-video video{left:50%;min-height:100%;min-width:100%;position:absolute;top:50%;transform:translate3d(-50%, -50%, 0)}.hero-video.is-transparent{opacity:0.3}@media screen and (max-width: 768px){.hero-video{display:none}}.hero-buttons{margin-top:1.5rem}@media screen and (max-width: 768px){.hero-buttons .button{display:flex}.hero-buttons .button:not(:last-child){margin-bottom:0.75rem}}@media screen and (min-width: 769px),print{.hero-buttons{display:flex;justify-content:center}.hero-buttons .button:not(:last-child){margin-right:1.5rem}}.hero-head,.hero-foot{flex-grow:0;flex-shrink:0}.hero-body{flex-grow:1;flex-shrink:0;padding:3rem 1.5rem}.section{padding:3rem 1.5rem}@media screen and (min-width: 1056px){.section.is-medium{padding:9rem 1.5rem}.section.is-large{padding:18rem 1.5rem}}.footer{background-color:#fafafa;padding:3rem 1.5rem 6rem}h1 .docs-heading-anchor,h1 .docs-heading-anchor:hover,h1 .docs-heading-anchor:visited,h2 .docs-heading-anchor,h2 .docs-heading-anchor:hover,h2 .docs-heading-anchor:visited,h3 .docs-heading-anchor,h3 .docs-heading-anchor:hover,h3 .docs-heading-anchor:visited,h4 .docs-heading-anchor,h4 .docs-heading-anchor:hover,h4 .docs-heading-anchor:visited,h5 .docs-heading-anchor,h5 .docs-heading-anchor:hover,h5 .docs-heading-anchor:visited,h6 .docs-heading-anchor,h6 .docs-heading-anchor:hover,h6 .docs-heading-anchor:visited{color:#222}h1 .docs-heading-anchor-permalink,h2 .docs-heading-anchor-permalink,h3 .docs-heading-anchor-permalink,h4 .docs-heading-anchor-permalink,h5 .docs-heading-anchor-permalink,h6 .docs-heading-anchor-permalink{visibility:hidden;vertical-align:middle;margin-left:0.5em;font-size:0.7rem}h1 .docs-heading-anchor-permalink::before,h2 .docs-heading-anchor-permalink::before,h3 .docs-heading-anchor-permalink::before,h4 .docs-heading-anchor-permalink::before,h5 .docs-heading-anchor-permalink::before,h6 .docs-heading-anchor-permalink::before{font-family:"Font Awesome 5 Free";font-weight:900;content:"\f0c1"}h1:hover .docs-heading-anchor-permalink,h2:hover .docs-heading-anchor-permalink,h3:hover .docs-heading-anchor-permalink,h4:hover .docs-heading-anchor-permalink,h5:hover .docs-heading-anchor-permalink,h6:hover .docs-heading-anchor-permalink{visibility:visible}.docs-dark-only{display:none !important}pre{position:relative;overflow:hidden}pre code,pre code.hljs{padding:0 .75rem !important;overflow:auto;display:block}pre code:first-of-type,pre code.hljs:first-of-type{padding-top:0.5rem !important}pre code:last-of-type,pre code.hljs:last-of-type{padding-bottom:0.5rem !important}pre .copy-button{opacity:0.2;transition:opacity 0.2s;position:absolute;right:0em;top:0em;padding:0.5em;width:2.5em;height:2.5em;background:transparent;border:none;font-family:"Font Awesome 5 Free";color:#222;cursor:pointer;text-align:center}pre .copy-button:focus,pre .copy-button:hover{opacity:1;background:rgba(34,34,34,0.1);color:#2e63b8}pre .copy-button.success{color:#259a12;opacity:1}pre .copy-button.error{color:#cb3c33;opacity:1}pre:hover .copy-button{opacity:1}.admonition{background-color:#b5b5b5;border-style:solid;border-width:1px;border-color:#363636;border-radius:4px;font-size:1rem}.admonition strong{color:currentColor}.admonition.is-small,#documenter .docs-sidebar form.docs-search>input.admonition{font-size:.75rem}.admonition.is-medium{font-size:1.25rem}.admonition.is-large{font-size:1.5rem}.admonition.is-default{background-color:#b5b5b5;border-color:#363636}.admonition.is-default>.admonition-header{background-color:#363636;color:#fff}.admonition.is-default>.admonition-body{color:#fff}.admonition.is-info{background-color:#def0fc;border-color:#209cee}.admonition.is-info>.admonition-header{background-color:#209cee;color:#fff}.admonition.is-info>.admonition-body{color:rgba(0,0,0,0.7)}.admonition.is-success{background-color:#bdf4d1;border-color:#22c35b}.admonition.is-success>.admonition-header{background-color:#22c35b;color:#fff}.admonition.is-success>.admonition-body{color:rgba(0,0,0,0.7)}.admonition.is-warning{background-color:#fff3c5;border-color:#ffdd57}.admonition.is-warning>.admonition-header{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.admonition.is-warning>.admonition-body{color:rgba(0,0,0,0.7)}.admonition.is-danger{background-color:#ffaba7;border-color:#da0b00}.admonition.is-danger>.admonition-header{background-color:#da0b00;color:#fff}.admonition.is-danger>.admonition-body{color:rgba(0,0,0,0.7)}.admonition.is-compat{background-color:#bdeff5;border-color:#1db5c9}.admonition.is-compat>.admonition-header{background-color:#1db5c9;color:#fff}.admonition.is-compat>.admonition-body{color:rgba(0,0,0,0.7)}.admonition-header{color:#fff;background-color:#363636;align-items:center;font-weight:700;justify-content:space-between;line-height:1.25;padding:0.5rem .75rem;position:relative}.admonition-header:before{font-family:"Font Awesome 5 Free";font-weight:900;margin-right:.75rem;content:"\f06a"}.admonition-body{color:#222;padding:0.5rem .75rem}.admonition-body pre{background-color:#f5f5f5}.admonition-body code{background-color:rgba(0,0,0,0.05)}.docstring{margin-bottom:1em;background-color:rgba(0,0,0,0);border:1px solid #dbdbdb;box-shadow:2px 2px 3px rgba(10,10,10,0.1);max-width:100%}.docstring>header{display:flex;flex-grow:1;align-items:stretch;padding:0.5rem .75rem;background-color:#f5f5f5;box-shadow:0 1px 2px rgba(10,10,10,0.1);box-shadow:none;border-bottom:1px solid #dbdbdb}.docstring>header code{background-color:transparent}.docstring>header .docstring-binding{margin-right:0.3em}.docstring>header .docstring-category{margin-left:0.3em}.docstring>section{position:relative;padding:.75rem .75rem;border-bottom:1px solid #dbdbdb}.docstring>section:last-child{border-bottom:none}.docstring>section>a.docs-sourcelink{transition:opacity 0.3s;opacity:0;position:absolute;right:.375rem;bottom:.375rem}.docstring>section>a.docs-sourcelink:focus{opacity:1 !important}.docstring:hover>section>a.docs-sourcelink{opacity:0.2}.docstring:focus-within>section>a.docs-sourcelink{opacity:0.2}.docstring>section:hover a.docs-sourcelink{opacity:1}.documenter-example-output{background-color:#fff}.outdated-warning-overlay{position:fixed;top:0;left:0;right:0;box-shadow:0 0 10px rgba(0,0,0,0.3);z-index:999;background-color:#ffaba7;color:rgba(0,0,0,0.7);border-bottom:3px solid #da0b00;padding:10px 35px;text-align:center;font-size:15px}.outdated-warning-overlay .outdated-warning-closer{position:absolute;top:calc(50% - 10px);right:18px;cursor:pointer;width:12px}.outdated-warning-overlay a{color:#2e63b8}.outdated-warning-overlay a:hover{color:#363636}.content pre{border:1px solid #dbdbdb}.content code{font-weight:inherit}.content a code{color:#2e63b8}.content h1 code,.content h2 code,.content h3 code,.content h4 code,.content h5 code,.content h6 code{color:#222}.content table{display:block;width:initial;max-width:100%;overflow-x:auto}.content blockquote>ul:first-child,.content blockquote>ol:first-child,.content .admonition-body>ul:first-child,.content .admonition-body>ol:first-child{margin-top:0}pre,code{font-variant-ligatures:no-contextual}.breadcrumb a.is-disabled{cursor:default;pointer-events:none}.breadcrumb a.is-disabled,.breadcrumb a.is-disabled:hover{color:#222}.hljs{background:initial !important}.katex .katex-mathml{top:0;right:0}.katex-display,mjx-container,.MathJax_Display{margin:0.5em 0 !important}html{-moz-osx-font-smoothing:auto;-webkit-font-smoothing:auto}li.no-marker{list-style:none}#documenter .docs-main>article{overflow-wrap:break-word}#documenter .docs-main>article .math-container{overflow-x:auto;overflow-y:hidden}@media screen and (min-width: 1056px){#documenter .docs-main{max-width:52rem;margin-left:20rem;padding-right:1rem}}@media screen and (max-width: 1055px){#documenter .docs-main{width:100%}#documenter .docs-main>article{max-width:52rem;margin-left:auto;margin-right:auto;margin-bottom:1rem;padding:0 1rem}#documenter .docs-main>header,#documenter .docs-main>nav{max-width:100%;width:100%;margin:0}}#documenter .docs-main header.docs-navbar{background-color:#fff;border-bottom:1px solid #dbdbdb;z-index:2;min-height:4rem;margin-bottom:1rem;display:flex}#documenter .docs-main header.docs-navbar .breadcrumb{flex-grow:1}#documenter .docs-main header.docs-navbar .docs-right{display:flex;white-space:nowrap}#documenter .docs-main header.docs-navbar .docs-right .docs-icon,#documenter .docs-main header.docs-navbar .docs-right .docs-label,#documenter .docs-main header.docs-navbar .docs-right .docs-sidebar-button{display:inline-block}#documenter .docs-main header.docs-navbar .docs-right .docs-label{padding:0;margin-left:0.3em}#documenter .docs-main header.docs-navbar .docs-right .docs-settings-button{margin:auto 0 auto 1rem}#documenter .docs-main header.docs-navbar .docs-right .docs-sidebar-button{font-size:1.5rem;margin:auto 0 auto 1rem}#documenter .docs-main header.docs-navbar>*{margin:auto 0}@media screen and (max-width: 1055px){#documenter .docs-main header.docs-navbar{position:sticky;top:0;padding:0 1rem;transition-property:top, box-shadow;-webkit-transition-property:top, box-shadow;transition-duration:0.3s;-webkit-transition-duration:0.3s}#documenter .docs-main header.docs-navbar.headroom--not-top{box-shadow:.2rem 0rem .4rem #bbb;transition-duration:0.7s;-webkit-transition-duration:0.7s}#documenter .docs-main header.docs-navbar.headroom--unpinned.headroom--not-top.headroom--not-bottom{top:-4.5rem;transition-duration:0.7s;-webkit-transition-duration:0.7s}}#documenter .docs-main section.footnotes{border-top:1px solid #dbdbdb}#documenter .docs-main section.footnotes li .tag:first-child,#documenter .docs-main section.footnotes li .docstring>section>a.docs-sourcelink:first-child,#documenter .docs-main section.footnotes li .content kbd:first-child,.content #documenter .docs-main section.footnotes li kbd:first-child{margin-right:1em;margin-bottom:0.4em}#documenter .docs-main .docs-footer{display:flex;flex-wrap:wrap;margin-left:0;margin-right:0;border-top:1px solid #dbdbdb;padding-top:1rem;padding-bottom:1rem}@media screen and (max-width: 1055px){#documenter .docs-main .docs-footer{padding-left:1rem;padding-right:1rem}}#documenter .docs-main .docs-footer .docs-footer-nextpage,#documenter .docs-main .docs-footer .docs-footer-prevpage{flex-grow:1}#documenter .docs-main .docs-footer .docs-footer-nextpage{text-align:right}#documenter .docs-main .docs-footer .flexbox-break{flex-basis:100%;height:0}#documenter .docs-main .docs-footer .footer-message{font-size:0.8em;margin:0.5em auto 0 auto;text-align:center}#documenter .docs-sidebar{display:flex;flex-direction:column;color:#0a0a0a;background-color:#f5f5f5;border-right:1px solid #dbdbdb;padding:0;flex:0 0 18rem;z-index:5;font-size:1rem;position:fixed;left:-18rem;width:18rem;height:100%;transition:left 0.3s}#documenter .docs-sidebar.visible{left:0;box-shadow:.4rem 0rem .8rem #bbb}@media screen and (min-width: 1056px){#documenter .docs-sidebar.visible{box-shadow:none}}@media screen and (min-width: 1056px){#documenter .docs-sidebar{left:0;top:0}}#documenter .docs-sidebar .docs-logo{margin-top:1rem;padding:0 1rem}#documenter .docs-sidebar .docs-logo>img{max-height:6rem;margin:auto}#documenter .docs-sidebar .docs-package-name{flex-shrink:0;font-size:1.5rem;font-weight:700;text-align:center;white-space:nowrap;overflow:hidden;padding:0.5rem 0}#documenter .docs-sidebar .docs-package-name .docs-autofit{max-width:16.2rem}#documenter .docs-sidebar .docs-package-name a,#documenter .docs-sidebar .docs-package-name a:hover{color:#0a0a0a}#documenter .docs-sidebar .docs-version-selector{border-top:1px solid #dbdbdb;display:none;padding:0.5rem}#documenter .docs-sidebar .docs-version-selector.visible{display:flex}#documenter .docs-sidebar ul.docs-menu{flex-grow:1;user-select:none;border-top:1px solid #dbdbdb;padding-bottom:1.5rem}#documenter .docs-sidebar ul.docs-menu>li>.tocitem{font-weight:bold}#documenter .docs-sidebar ul.docs-menu>li li{font-size:.95rem;margin-left:1em;border-left:1px solid #dbdbdb}#documenter .docs-sidebar ul.docs-menu input.collapse-toggle{display:none}#documenter .docs-sidebar ul.docs-menu ul.collapsed{display:none}#documenter .docs-sidebar ul.docs-menu input:checked~ul.collapsed{display:block}#documenter .docs-sidebar ul.docs-menu label.tocitem{display:flex}#documenter .docs-sidebar ul.docs-menu label.tocitem .docs-label{flex-grow:2}#documenter .docs-sidebar ul.docs-menu label.tocitem .docs-chevron{display:inline-block;font-style:normal;font-variant:normal;text-rendering:auto;line-height:1;font-size:.75rem;margin-left:1rem;margin-top:auto;margin-bottom:auto}#documenter .docs-sidebar ul.docs-menu label.tocitem .docs-chevron::before{font-family:"Font Awesome 5 Free";font-weight:900;content:"\f054"}#documenter .docs-sidebar ul.docs-menu input:checked~label.tocitem .docs-chevron::before{content:"\f078"}#documenter .docs-sidebar ul.docs-menu .tocitem{display:block;padding:0.5rem 0.5rem}#documenter .docs-sidebar ul.docs-menu .tocitem,#documenter .docs-sidebar ul.docs-menu .tocitem:hover{color:#0a0a0a;background:#f5f5f5}#documenter .docs-sidebar ul.docs-menu a.tocitem:hover,#documenter .docs-sidebar ul.docs-menu label.tocitem:hover{color:#0a0a0a;background-color:#ebebeb}#documenter .docs-sidebar ul.docs-menu li.is-active{border-top:1px solid #dbdbdb;border-bottom:1px solid #dbdbdb;background-color:#fff}#documenter .docs-sidebar ul.docs-menu li.is-active .tocitem,#documenter .docs-sidebar ul.docs-menu li.is-active .tocitem:hover{background-color:#fff;color:#0a0a0a}#documenter .docs-sidebar ul.docs-menu li.is-active ul.internal .tocitem:hover{background-color:#ebebeb;color:#0a0a0a}#documenter .docs-sidebar ul.docs-menu>li.is-active:first-child{border-top:none}#documenter .docs-sidebar ul.docs-menu ul.internal{margin:0 0.5rem 0.5rem;border-top:1px solid #dbdbdb}#documenter .docs-sidebar ul.docs-menu ul.internal li{font-size:.85rem;border-left:none;margin-left:0;margin-top:0.5rem}#documenter .docs-sidebar ul.docs-menu ul.internal .tocitem{width:100%;padding:0}#documenter .docs-sidebar ul.docs-menu ul.internal .tocitem::before{content:"⚬";margin-right:0.4em}#documenter .docs-sidebar form.docs-search{margin:auto;margin-top:0.5rem;margin-bottom:0.5rem}#documenter .docs-sidebar form.docs-search>input{width:14.4rem}@media screen and (min-width: 1056px){#documenter .docs-sidebar ul.docs-menu{overflow-y:auto;-webkit-overflow-scroll:touch}#documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar{width:.3rem;background:none}#documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar-thumb{border-radius:5px 0px 0px 5px;background:#e0e0e0}#documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar-thumb:hover{background:#ccc}}@media screen and (max-width: 1055px){#documenter .docs-sidebar{overflow-y:auto;-webkit-overflow-scroll:touch}#documenter .docs-sidebar::-webkit-scrollbar{width:.3rem;background:none}#documenter .docs-sidebar::-webkit-scrollbar-thumb{border-radius:5px 0px 0px 5px;background:#e0e0e0}#documenter .docs-sidebar::-webkit-scrollbar-thumb:hover{background:#ccc}}#documenter .docs-main #documenter-search-info{margin-bottom:1rem}#documenter .docs-main #documenter-search-results{list-style-type:circle;list-style-position:outside}#documenter .docs-main #documenter-search-results li{margin-left:2rem}#documenter .docs-main #documenter-search-results .docs-highlight{background-color:yellow}.ansi span.sgr1{font-weight:bolder}.ansi span.sgr2{font-weight:lighter}.ansi span.sgr3{font-style:italic}.ansi span.sgr4{text-decoration:underline}.ansi span.sgr7{color:#fff;background-color:#222}.ansi span.sgr8{color:transparent}.ansi span.sgr8 span{color:transparent}.ansi span.sgr9{text-decoration:line-through}.ansi span.sgr30{color:#242424}.ansi span.sgr31{color:#a7201f}.ansi span.sgr32{color:#066f00}.ansi span.sgr33{color:#856b00}.ansi span.sgr34{color:#2149b0}.ansi span.sgr35{color:#7d4498}.ansi span.sgr36{color:#007989}.ansi span.sgr37{color:gray}.ansi span.sgr40{background-color:#242424}.ansi span.sgr41{background-color:#a7201f}.ansi span.sgr42{background-color:#066f00}.ansi span.sgr43{background-color:#856b00}.ansi span.sgr44{background-color:#2149b0}.ansi span.sgr45{background-color:#7d4498}.ansi span.sgr46{background-color:#007989}.ansi span.sgr47{background-color:gray}.ansi span.sgr90{color:#616161}.ansi span.sgr91{color:#cb3c33}.ansi span.sgr92{color:#0e8300}.ansi span.sgr93{color:#a98800}.ansi span.sgr94{color:#3c5dcd}.ansi span.sgr95{color:#9256af}.ansi span.sgr96{color:#008fa3}.ansi span.sgr97{color:#f5f5f5}.ansi span.sgr100{background-color:#616161}.ansi span.sgr101{background-color:#cb3c33}.ansi span.sgr102{background-color:#0e8300}.ansi span.sgr103{background-color:#a98800}.ansi span.sgr104{background-color:#3c5dcd}.ansi span.sgr105{background-color:#9256af}.ansi span.sgr106{background-color:#008fa3}.ansi span.sgr107{background-color:#f5f5f5}code.language-julia-repl>span.hljs-meta{color:#066f00;font-weight:bolder}/*! +.pagination-previous,.pagination-next,.pagination-link,.pagination-ellipsis,.file-cta,.file-name,.select select,.textarea,.input,#documenter .docs-sidebar form.docs-search>input,.button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid transparent;border-radius:4px;box-shadow:none;display:inline-flex;font-size:1rem;height:2.5em;justify-content:flex-start;line-height:1.5;padding-bottom:calc(0.5em - 1px);padding-left:calc(0.75em - 1px);padding-right:calc(0.75em - 1px);padding-top:calc(0.5em - 1px);position:relative;vertical-align:top}.pagination-previous:focus,.pagination-next:focus,.pagination-link:focus,.pagination-ellipsis:focus,.file-cta:focus,.file-name:focus,.select select:focus,.textarea:focus,.input:focus,#documenter .docs-sidebar form.docs-search>input:focus,.button:focus,.is-focused.pagination-previous,.is-focused.pagination-next,.is-focused.pagination-link,.is-focused.pagination-ellipsis,.is-focused.file-cta,.is-focused.file-name,.select select.is-focused,.is-focused.textarea,.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-focused.button,.pagination-previous:active,.pagination-next:active,.pagination-link:active,.pagination-ellipsis:active,.file-cta:active,.file-name:active,.select select:active,.textarea:active,.input:active,#documenter .docs-sidebar form.docs-search>input:active,.button:active,.is-active.pagination-previous,.is-active.pagination-next,.is-active.pagination-link,.is-active.pagination-ellipsis,.is-active.file-cta,.is-active.file-name,.select select.is-active,.is-active.textarea,.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active,.is-active.button{outline:none}.pagination-previous[disabled],.pagination-next[disabled],.pagination-link[disabled],.pagination-ellipsis[disabled],.file-cta[disabled],.file-name[disabled],.select select[disabled],.textarea[disabled],.input[disabled],#documenter .docs-sidebar form.docs-search>input[disabled],.button[disabled],fieldset[disabled] .pagination-previous,fieldset[disabled] .pagination-next,fieldset[disabled] .pagination-link,fieldset[disabled] .pagination-ellipsis,fieldset[disabled] .file-cta,fieldset[disabled] .file-name,fieldset[disabled] .select select,.select fieldset[disabled] select,fieldset[disabled] .textarea,fieldset[disabled] .input,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input,fieldset[disabled] .button{cursor:not-allowed}.tabs,.pagination-previous,.pagination-next,.pagination-link,.pagination-ellipsis,.breadcrumb,.file,.button,.is-unselectable{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.navbar-link:not(.is-arrowless)::after,.select:not(.is-multiple):not(.is-loading)::after{border:3px solid rgba(0,0,0,0);border-radius:2px;border-right:0;border-top:0;content:" ";display:block;height:0.625em;margin-top:-0.4375em;pointer-events:none;position:absolute;top:50%;transform:rotate(-45deg);transform-origin:center;width:0.625em}.admonition:not(:last-child),.tabs:not(:last-child),.pagination:not(:last-child),.message:not(:last-child),.level:not(:last-child),.breadcrumb:not(:last-child),.block:not(:last-child),.title:not(:last-child),.subtitle:not(:last-child),.table-container:not(:last-child),.table:not(:last-child),.progress:not(:last-child),.notification:not(:last-child),.content:not(:last-child),.box:not(:last-child){margin-bottom:1.5rem}.modal-close,.delete{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;-moz-appearance:none;-webkit-appearance:none;background-color:rgba(10,10,10,0.2);border:none;border-radius:9999px;cursor:pointer;pointer-events:auto;display:inline-block;flex-grow:0;flex-shrink:0;font-size:0;height:20px;max-height:20px;max-width:20px;min-height:20px;min-width:20px;outline:none;position:relative;vertical-align:top;width:20px}.modal-close::before,.delete::before,.modal-close::after,.delete::after{background-color:#fff;content:"";display:block;left:50%;position:absolute;top:50%;transform:translateX(-50%) translateY(-50%) rotate(45deg);transform-origin:center center}.modal-close::before,.delete::before{height:2px;width:50%}.modal-close::after,.delete::after{height:50%;width:2px}.modal-close:hover,.delete:hover,.modal-close:focus,.delete:focus{background-color:rgba(10,10,10,0.3)}.modal-close:active,.delete:active{background-color:rgba(10,10,10,0.4)}.is-small.modal-close,#documenter .docs-sidebar form.docs-search>input.modal-close,.is-small.delete,#documenter .docs-sidebar form.docs-search>input.delete{height:16px;max-height:16px;max-width:16px;min-height:16px;min-width:16px;width:16px}.is-medium.modal-close,.is-medium.delete{height:24px;max-height:24px;max-width:24px;min-height:24px;min-width:24px;width:24px}.is-large.modal-close,.is-large.delete{height:32px;max-height:32px;max-width:32px;min-height:32px;min-width:32px;width:32px}.control.is-loading::after,.select.is-loading::after,.loader,.button.is-loading::after{animation:spinAround 500ms infinite linear;border:2px solid #dbdbdb;border-radius:9999px;border-right-color:transparent;border-top-color:transparent;content:"";display:block;height:1em;position:relative;width:1em}.hero-video,.modal-background,.modal,.image.is-square img,#documenter .docs-sidebar .docs-logo>img.is-square img,.image.is-square .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,.image.is-1by1 img,#documenter .docs-sidebar .docs-logo>img.is-1by1 img,.image.is-1by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,.image.is-5by4 img,#documenter .docs-sidebar .docs-logo>img.is-5by4 img,.image.is-5by4 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,.image.is-4by3 img,#documenter .docs-sidebar .docs-logo>img.is-4by3 img,.image.is-4by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,.image.is-3by2 img,#documenter .docs-sidebar .docs-logo>img.is-3by2 img,.image.is-3by2 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,.image.is-5by3 img,#documenter .docs-sidebar .docs-logo>img.is-5by3 img,.image.is-5by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,.image.is-16by9 img,#documenter .docs-sidebar .docs-logo>img.is-16by9 img,.image.is-16by9 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,.image.is-2by1 img,#documenter .docs-sidebar .docs-logo>img.is-2by1 img,.image.is-2by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,.image.is-3by1 img,#documenter .docs-sidebar .docs-logo>img.is-3by1 img,.image.is-3by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,.image.is-4by5 img,#documenter .docs-sidebar .docs-logo>img.is-4by5 img,.image.is-4by5 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,.image.is-3by4 img,#documenter .docs-sidebar .docs-logo>img.is-3by4 img,.image.is-3by4 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,.image.is-2by3 img,#documenter .docs-sidebar .docs-logo>img.is-2by3 img,.image.is-2by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,.image.is-3by5 img,#documenter .docs-sidebar .docs-logo>img.is-3by5 img,.image.is-3by5 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,.image.is-9by16 img,#documenter .docs-sidebar .docs-logo>img.is-9by16 img,.image.is-9by16 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,.image.is-1by2 img,#documenter .docs-sidebar .docs-logo>img.is-1by2 img,.image.is-1by2 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,.image.is-1by3 img,#documenter .docs-sidebar .docs-logo>img.is-1by3 img,.image.is-1by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio,.is-overlay{bottom:0;left:0;position:absolute;right:0;top:0}.navbar-burger{-moz-appearance:none;-webkit-appearance:none;appearance:none;background:none;border:none;color:currentColor;font-family:inherit;font-size:1em;margin:0;padding:0}.has-text-white{color:#fff !important}a.has-text-white:hover,a.has-text-white:focus{color:#e6e6e6 !important}.has-background-white{background-color:#fff !important}.has-text-black{color:#0a0a0a !important}a.has-text-black:hover,a.has-text-black:focus{color:#000 !important}.has-background-black{background-color:#0a0a0a !important}.has-text-light{color:#f5f5f5 !important}a.has-text-light:hover,a.has-text-light:focus{color:#dbdbdb !important}.has-background-light{background-color:#f5f5f5 !important}.has-text-dark{color:#363636 !important}a.has-text-dark:hover,a.has-text-dark:focus{color:#1c1c1c !important}.has-background-dark{background-color:#363636 !important}.has-text-primary{color:#4eb5de !important}a.has-text-primary:hover,a.has-text-primary:focus{color:#27a1d2 !important}.has-background-primary{background-color:#4eb5de !important}.has-text-primary-light{color:#eef8fc !important}a.has-text-primary-light:hover,a.has-text-primary-light:focus{color:#c3e6f4 !important}.has-background-primary-light{background-color:#eef8fc !important}.has-text-primary-dark{color:#1a6d8e !important}a.has-text-primary-dark:hover,a.has-text-primary-dark:focus{color:#228eb9 !important}.has-background-primary-dark{background-color:#1a6d8e !important}.has-text-link{color:#2e63b8 !important}a.has-text-link:hover,a.has-text-link:focus{color:#244d8f !important}.has-background-link{background-color:#2e63b8 !important}.has-text-link-light{color:#eff3fb !important}a.has-text-link-light:hover,a.has-text-link-light:focus{color:#c6d6f1 !important}.has-background-link-light{background-color:#eff3fb !important}.has-text-link-dark{color:#3169c4 !important}a.has-text-link-dark:hover,a.has-text-link-dark:focus{color:#5485d4 !important}.has-background-link-dark{background-color:#3169c4 !important}.has-text-info{color:#209cee !important}a.has-text-info:hover,a.has-text-info:focus{color:#1081cb !important}.has-background-info{background-color:#209cee !important}.has-text-info-light{color:#ecf7fe !important}a.has-text-info-light:hover,a.has-text-info-light:focus{color:#bde2fa !important}.has-background-info-light{background-color:#ecf7fe !important}.has-text-info-dark{color:#0e72b4 !important}a.has-text-info-dark:hover,a.has-text-info-dark:focus{color:#1190e3 !important}.has-background-info-dark{background-color:#0e72b4 !important}.has-text-success{color:#22c35b !important}a.has-text-success:hover,a.has-text-success:focus{color:#1a9847 !important}.has-background-success{background-color:#22c35b !important}.has-text-success-light{color:#eefcf3 !important}a.has-text-success-light:hover,a.has-text-success-light:focus{color:#c2f4d4 !important}.has-background-success-light{background-color:#eefcf3 !important}.has-text-success-dark{color:#198f43 !important}a.has-text-success-dark:hover,a.has-text-success-dark:focus{color:#21bb57 !important}.has-background-success-dark{background-color:#198f43 !important}.has-text-warning{color:#ffdd57 !important}a.has-text-warning:hover,a.has-text-warning:focus{color:#ffd324 !important}.has-background-warning{background-color:#ffdd57 !important}.has-text-warning-light{color:#fffbeb !important}a.has-text-warning-light:hover,a.has-text-warning-light:focus{color:#fff1b8 !important}.has-background-warning-light{background-color:#fffbeb !important}.has-text-warning-dark{color:#947600 !important}a.has-text-warning-dark:hover,a.has-text-warning-dark:focus{color:#c79f00 !important}.has-background-warning-dark{background-color:#947600 !important}.has-text-danger{color:#da0b00 !important}a.has-text-danger:hover,a.has-text-danger:focus{color:#a70800 !important}.has-background-danger{background-color:#da0b00 !important}.has-text-danger-light{color:#ffeceb !important}a.has-text-danger-light:hover,a.has-text-danger-light:focus{color:#ffbbb8 !important}.has-background-danger-light{background-color:#ffeceb !important}.has-text-danger-dark{color:#f50c00 !important}a.has-text-danger-dark:hover,a.has-text-danger-dark:focus{color:#ff3429 !important}.has-background-danger-dark{background-color:#f50c00 !important}.has-text-black-bis{color:#121212 !important}.has-background-black-bis{background-color:#121212 !important}.has-text-black-ter{color:#242424 !important}.has-background-black-ter{background-color:#242424 !important}.has-text-grey-darker{color:#363636 !important}.has-background-grey-darker{background-color:#363636 !important}.has-text-grey-dark{color:#4a4a4a !important}.has-background-grey-dark{background-color:#4a4a4a !important}.has-text-grey{color:#6b6b6b !important}.has-background-grey{background-color:#6b6b6b !important}.has-text-grey-light{color:#b5b5b5 !important}.has-background-grey-light{background-color:#b5b5b5 !important}.has-text-grey-lighter{color:#dbdbdb !important}.has-background-grey-lighter{background-color:#dbdbdb !important}.has-text-white-ter{color:#f5f5f5 !important}.has-background-white-ter{background-color:#f5f5f5 !important}.has-text-white-bis{color:#fafafa !important}.has-background-white-bis{background-color:#fafafa !important}.is-flex-direction-row{flex-direction:row !important}.is-flex-direction-row-reverse{flex-direction:row-reverse !important}.is-flex-direction-column{flex-direction:column !important}.is-flex-direction-column-reverse{flex-direction:column-reverse !important}.is-flex-wrap-nowrap{flex-wrap:nowrap !important}.is-flex-wrap-wrap{flex-wrap:wrap !important}.is-flex-wrap-wrap-reverse{flex-wrap:wrap-reverse !important}.is-justify-content-flex-start{justify-content:flex-start !important}.is-justify-content-flex-end{justify-content:flex-end !important}.is-justify-content-center{justify-content:center !important}.is-justify-content-space-between{justify-content:space-between !important}.is-justify-content-space-around{justify-content:space-around !important}.is-justify-content-space-evenly{justify-content:space-evenly !important}.is-justify-content-start{justify-content:start !important}.is-justify-content-end{justify-content:end !important}.is-justify-content-left{justify-content:left !important}.is-justify-content-right{justify-content:right !important}.is-align-content-flex-start{align-content:flex-start !important}.is-align-content-flex-end{align-content:flex-end !important}.is-align-content-center{align-content:center !important}.is-align-content-space-between{align-content:space-between !important}.is-align-content-space-around{align-content:space-around !important}.is-align-content-space-evenly{align-content:space-evenly !important}.is-align-content-stretch{align-content:stretch !important}.is-align-content-start{align-content:start !important}.is-align-content-end{align-content:end !important}.is-align-content-baseline{align-content:baseline !important}.is-align-items-stretch{align-items:stretch !important}.is-align-items-flex-start{align-items:flex-start !important}.is-align-items-flex-end{align-items:flex-end !important}.is-align-items-center{align-items:center !important}.is-align-items-baseline{align-items:baseline !important}.is-align-items-start{align-items:start !important}.is-align-items-end{align-items:end !important}.is-align-items-self-start{align-items:self-start !important}.is-align-items-self-end{align-items:self-end !important}.is-align-self-auto{align-self:auto !important}.is-align-self-flex-start{align-self:flex-start !important}.is-align-self-flex-end{align-self:flex-end !important}.is-align-self-center{align-self:center !important}.is-align-self-baseline{align-self:baseline !important}.is-align-self-stretch{align-self:stretch !important}.is-flex-grow-0{flex-grow:0 !important}.is-flex-grow-1{flex-grow:1 !important}.is-flex-grow-2{flex-grow:2 !important}.is-flex-grow-3{flex-grow:3 !important}.is-flex-grow-4{flex-grow:4 !important}.is-flex-grow-5{flex-grow:5 !important}.is-flex-shrink-0{flex-shrink:0 !important}.is-flex-shrink-1{flex-shrink:1 !important}.is-flex-shrink-2{flex-shrink:2 !important}.is-flex-shrink-3{flex-shrink:3 !important}.is-flex-shrink-4{flex-shrink:4 !important}.is-flex-shrink-5{flex-shrink:5 !important}.is-clearfix::after{clear:both;content:" ";display:table}.is-pulled-left{float:left !important}.is-pulled-right{float:right !important}.is-radiusless{border-radius:0 !important}.is-shadowless{box-shadow:none !important}.is-clickable{cursor:pointer !important;pointer-events:all !important}.is-clipped{overflow:hidden !important}.is-relative{position:relative !important}.is-marginless{margin:0 !important}.is-paddingless{padding:0 !important}.m-0{margin:0 !important}.mt-0{margin-top:0 !important}.mr-0{margin-right:0 !important}.mb-0{margin-bottom:0 !important}.ml-0{margin-left:0 !important}.mx-0{margin-left:0 !important;margin-right:0 !important}.my-0{margin-top:0 !important;margin-bottom:0 !important}.m-1{margin:.25rem !important}.mt-1{margin-top:.25rem !important}.mr-1{margin-right:.25rem !important}.mb-1{margin-bottom:.25rem !important}.ml-1{margin-left:.25rem !important}.mx-1{margin-left:.25rem !important;margin-right:.25rem !important}.my-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.m-2{margin:.5rem !important}.mt-2{margin-top:.5rem !important}.mr-2{margin-right:.5rem !important}.mb-2{margin-bottom:.5rem !important}.ml-2{margin-left:.5rem !important}.mx-2{margin-left:.5rem !important;margin-right:.5rem !important}.my-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.m-3{margin:.75rem !important}.mt-3{margin-top:.75rem !important}.mr-3{margin-right:.75rem !important}.mb-3{margin-bottom:.75rem !important}.ml-3{margin-left:.75rem !important}.mx-3{margin-left:.75rem !important;margin-right:.75rem !important}.my-3{margin-top:.75rem !important;margin-bottom:.75rem !important}.m-4{margin:1rem !important}.mt-4{margin-top:1rem !important}.mr-4{margin-right:1rem !important}.mb-4{margin-bottom:1rem !important}.ml-4{margin-left:1rem !important}.mx-4{margin-left:1rem !important;margin-right:1rem !important}.my-4{margin-top:1rem !important;margin-bottom:1rem !important}.m-5{margin:1.5rem !important}.mt-5{margin-top:1.5rem !important}.mr-5{margin-right:1.5rem !important}.mb-5{margin-bottom:1.5rem !important}.ml-5{margin-left:1.5rem !important}.mx-5{margin-left:1.5rem !important;margin-right:1.5rem !important}.my-5{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.m-6{margin:3rem !important}.mt-6{margin-top:3rem !important}.mr-6{margin-right:3rem !important}.mb-6{margin-bottom:3rem !important}.ml-6{margin-left:3rem !important}.mx-6{margin-left:3rem !important;margin-right:3rem !important}.my-6{margin-top:3rem !important;margin-bottom:3rem !important}.m-auto{margin:auto !important}.mt-auto{margin-top:auto !important}.mr-auto{margin-right:auto !important}.mb-auto{margin-bottom:auto !important}.ml-auto{margin-left:auto !important}.mx-auto{margin-left:auto !important;margin-right:auto !important}.my-auto{margin-top:auto !important;margin-bottom:auto !important}.p-0{padding:0 !important}.pt-0{padding-top:0 !important}.pr-0{padding-right:0 !important}.pb-0{padding-bottom:0 !important}.pl-0{padding-left:0 !important}.px-0{padding-left:0 !important;padding-right:0 !important}.py-0{padding-top:0 !important;padding-bottom:0 !important}.p-1{padding:.25rem !important}.pt-1{padding-top:.25rem !important}.pr-1{padding-right:.25rem !important}.pb-1{padding-bottom:.25rem !important}.pl-1{padding-left:.25rem !important}.px-1{padding-left:.25rem !important;padding-right:.25rem !important}.py-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.p-2{padding:.5rem !important}.pt-2{padding-top:.5rem !important}.pr-2{padding-right:.5rem !important}.pb-2{padding-bottom:.5rem !important}.pl-2{padding-left:.5rem !important}.px-2{padding-left:.5rem !important;padding-right:.5rem !important}.py-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.p-3{padding:.75rem !important}.pt-3{padding-top:.75rem !important}.pr-3{padding-right:.75rem !important}.pb-3{padding-bottom:.75rem !important}.pl-3{padding-left:.75rem !important}.px-3{padding-left:.75rem !important;padding-right:.75rem !important}.py-3{padding-top:.75rem !important;padding-bottom:.75rem !important}.p-4{padding:1rem !important}.pt-4{padding-top:1rem !important}.pr-4{padding-right:1rem !important}.pb-4{padding-bottom:1rem !important}.pl-4{padding-left:1rem !important}.px-4{padding-left:1rem !important;padding-right:1rem !important}.py-4{padding-top:1rem !important;padding-bottom:1rem !important}.p-5{padding:1.5rem !important}.pt-5{padding-top:1.5rem !important}.pr-5{padding-right:1.5rem !important}.pb-5{padding-bottom:1.5rem !important}.pl-5{padding-left:1.5rem !important}.px-5{padding-left:1.5rem !important;padding-right:1.5rem !important}.py-5{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.p-6{padding:3rem !important}.pt-6{padding-top:3rem !important}.pr-6{padding-right:3rem !important}.pb-6{padding-bottom:3rem !important}.pl-6{padding-left:3rem !important}.px-6{padding-left:3rem !important;padding-right:3rem !important}.py-6{padding-top:3rem !important;padding-bottom:3rem !important}.p-auto{padding:auto !important}.pt-auto{padding-top:auto !important}.pr-auto{padding-right:auto !important}.pb-auto{padding-bottom:auto !important}.pl-auto{padding-left:auto !important}.px-auto{padding-left:auto !important;padding-right:auto !important}.py-auto{padding-top:auto !important;padding-bottom:auto !important}.is-size-1{font-size:3rem !important}.is-size-2{font-size:2.5rem !important}.is-size-3{font-size:2rem !important}.is-size-4{font-size:1.5rem !important}.is-size-5{font-size:1.25rem !important}.is-size-6{font-size:1rem !important}.is-size-7,.docstring>section>a.docs-sourcelink{font-size:.75rem !important}@media screen and (max-width: 768px){.is-size-1-mobile{font-size:3rem !important}.is-size-2-mobile{font-size:2.5rem !important}.is-size-3-mobile{font-size:2rem !important}.is-size-4-mobile{font-size:1.5rem !important}.is-size-5-mobile{font-size:1.25rem !important}.is-size-6-mobile{font-size:1rem !important}.is-size-7-mobile{font-size:.75rem !important}}@media screen and (min-width: 769px),print{.is-size-1-tablet{font-size:3rem !important}.is-size-2-tablet{font-size:2.5rem !important}.is-size-3-tablet{font-size:2rem !important}.is-size-4-tablet{font-size:1.5rem !important}.is-size-5-tablet{font-size:1.25rem !important}.is-size-6-tablet{font-size:1rem !important}.is-size-7-tablet{font-size:.75rem !important}}@media screen and (max-width: 1055px){.is-size-1-touch{font-size:3rem !important}.is-size-2-touch{font-size:2.5rem !important}.is-size-3-touch{font-size:2rem !important}.is-size-4-touch{font-size:1.5rem !important}.is-size-5-touch{font-size:1.25rem !important}.is-size-6-touch{font-size:1rem !important}.is-size-7-touch{font-size:.75rem !important}}@media screen and (min-width: 1056px){.is-size-1-desktop{font-size:3rem !important}.is-size-2-desktop{font-size:2.5rem !important}.is-size-3-desktop{font-size:2rem !important}.is-size-4-desktop{font-size:1.5rem !important}.is-size-5-desktop{font-size:1.25rem !important}.is-size-6-desktop{font-size:1rem !important}.is-size-7-desktop{font-size:.75rem !important}}@media screen and (min-width: 1216px){.is-size-1-widescreen{font-size:3rem !important}.is-size-2-widescreen{font-size:2.5rem !important}.is-size-3-widescreen{font-size:2rem !important}.is-size-4-widescreen{font-size:1.5rem !important}.is-size-5-widescreen{font-size:1.25rem !important}.is-size-6-widescreen{font-size:1rem !important}.is-size-7-widescreen{font-size:.75rem !important}}@media screen and (min-width: 1408px){.is-size-1-fullhd{font-size:3rem !important}.is-size-2-fullhd{font-size:2.5rem !important}.is-size-3-fullhd{font-size:2rem !important}.is-size-4-fullhd{font-size:1.5rem !important}.is-size-5-fullhd{font-size:1.25rem !important}.is-size-6-fullhd{font-size:1rem !important}.is-size-7-fullhd{font-size:.75rem !important}}.has-text-centered{text-align:center !important}.has-text-justified{text-align:justify !important}.has-text-left{text-align:left !important}.has-text-right{text-align:right !important}@media screen and (max-width: 768px){.has-text-centered-mobile{text-align:center !important}}@media screen and (min-width: 769px),print{.has-text-centered-tablet{text-align:center !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-centered-tablet-only{text-align:center !important}}@media screen and (max-width: 1055px){.has-text-centered-touch{text-align:center !important}}@media screen and (min-width: 1056px){.has-text-centered-desktop{text-align:center !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-centered-desktop-only{text-align:center !important}}@media screen and (min-width: 1216px){.has-text-centered-widescreen{text-align:center !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-centered-widescreen-only{text-align:center !important}}@media screen and (min-width: 1408px){.has-text-centered-fullhd{text-align:center !important}}@media screen and (max-width: 768px){.has-text-justified-mobile{text-align:justify !important}}@media screen and (min-width: 769px),print{.has-text-justified-tablet{text-align:justify !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-justified-tablet-only{text-align:justify !important}}@media screen and (max-width: 1055px){.has-text-justified-touch{text-align:justify !important}}@media screen and (min-width: 1056px){.has-text-justified-desktop{text-align:justify !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-justified-desktop-only{text-align:justify !important}}@media screen and (min-width: 1216px){.has-text-justified-widescreen{text-align:justify !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-justified-widescreen-only{text-align:justify !important}}@media screen and (min-width: 1408px){.has-text-justified-fullhd{text-align:justify !important}}@media screen and (max-width: 768px){.has-text-left-mobile{text-align:left !important}}@media screen and (min-width: 769px),print{.has-text-left-tablet{text-align:left !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-left-tablet-only{text-align:left !important}}@media screen and (max-width: 1055px){.has-text-left-touch{text-align:left !important}}@media screen and (min-width: 1056px){.has-text-left-desktop{text-align:left !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-left-desktop-only{text-align:left !important}}@media screen and (min-width: 1216px){.has-text-left-widescreen{text-align:left !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-left-widescreen-only{text-align:left !important}}@media screen and (min-width: 1408px){.has-text-left-fullhd{text-align:left !important}}@media screen and (max-width: 768px){.has-text-right-mobile{text-align:right !important}}@media screen and (min-width: 769px),print{.has-text-right-tablet{text-align:right !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.has-text-right-tablet-only{text-align:right !important}}@media screen and (max-width: 1055px){.has-text-right-touch{text-align:right !important}}@media screen and (min-width: 1056px){.has-text-right-desktop{text-align:right !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.has-text-right-desktop-only{text-align:right !important}}@media screen and (min-width: 1216px){.has-text-right-widescreen{text-align:right !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.has-text-right-widescreen-only{text-align:right !important}}@media screen and (min-width: 1408px){.has-text-right-fullhd{text-align:right !important}}.is-capitalized{text-transform:capitalize !important}.is-lowercase{text-transform:lowercase !important}.is-uppercase{text-transform:uppercase !important}.is-italic{font-style:italic !important}.is-underlined{text-decoration:underline !important}.has-text-weight-light{font-weight:300 !important}.has-text-weight-normal{font-weight:400 !important}.has-text-weight-medium{font-weight:500 !important}.has-text-weight-semibold{font-weight:600 !important}.has-text-weight-bold{font-weight:700 !important}.is-family-primary{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-secondary{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-sans-serif{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif !important}.is-family-monospace{font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace !important}.is-family-code{font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace !important}.is-block{display:block !important}@media screen and (max-width: 768px){.is-block-mobile{display:block !important}}@media screen and (min-width: 769px),print{.is-block-tablet{display:block !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-block-tablet-only{display:block !important}}@media screen and (max-width: 1055px){.is-block-touch{display:block !important}}@media screen and (min-width: 1056px){.is-block-desktop{display:block !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-block-desktop-only{display:block !important}}@media screen and (min-width: 1216px){.is-block-widescreen{display:block !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-block-widescreen-only{display:block !important}}@media screen and (min-width: 1408px){.is-block-fullhd{display:block !important}}.is-flex{display:flex !important}@media screen and (max-width: 768px){.is-flex-mobile{display:flex !important}}@media screen and (min-width: 769px),print{.is-flex-tablet{display:flex !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-flex-tablet-only{display:flex !important}}@media screen and (max-width: 1055px){.is-flex-touch{display:flex !important}}@media screen and (min-width: 1056px){.is-flex-desktop{display:flex !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-flex-desktop-only{display:flex !important}}@media screen and (min-width: 1216px){.is-flex-widescreen{display:flex !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-flex-widescreen-only{display:flex !important}}@media screen and (min-width: 1408px){.is-flex-fullhd{display:flex !important}}.is-inline{display:inline !important}@media screen and (max-width: 768px){.is-inline-mobile{display:inline !important}}@media screen and (min-width: 769px),print{.is-inline-tablet{display:inline !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-tablet-only{display:inline !important}}@media screen and (max-width: 1055px){.is-inline-touch{display:inline !important}}@media screen and (min-width: 1056px){.is-inline-desktop{display:inline !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-desktop-only{display:inline !important}}@media screen and (min-width: 1216px){.is-inline-widescreen{display:inline !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-widescreen-only{display:inline !important}}@media screen and (min-width: 1408px){.is-inline-fullhd{display:inline !important}}.is-inline-block{display:inline-block !important}@media screen and (max-width: 768px){.is-inline-block-mobile{display:inline-block !important}}@media screen and (min-width: 769px),print{.is-inline-block-tablet{display:inline-block !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-block-tablet-only{display:inline-block !important}}@media screen and (max-width: 1055px){.is-inline-block-touch{display:inline-block !important}}@media screen and (min-width: 1056px){.is-inline-block-desktop{display:inline-block !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-block-desktop-only{display:inline-block !important}}@media screen and (min-width: 1216px){.is-inline-block-widescreen{display:inline-block !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-block-widescreen-only{display:inline-block !important}}@media screen and (min-width: 1408px){.is-inline-block-fullhd{display:inline-block !important}}.is-inline-flex{display:inline-flex !important}@media screen and (max-width: 768px){.is-inline-flex-mobile{display:inline-flex !important}}@media screen and (min-width: 769px),print{.is-inline-flex-tablet{display:inline-flex !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-inline-flex-tablet-only{display:inline-flex !important}}@media screen and (max-width: 1055px){.is-inline-flex-touch{display:inline-flex !important}}@media screen and (min-width: 1056px){.is-inline-flex-desktop{display:inline-flex !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-inline-flex-desktop-only{display:inline-flex !important}}@media screen and (min-width: 1216px){.is-inline-flex-widescreen{display:inline-flex !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-inline-flex-widescreen-only{display:inline-flex !important}}@media screen and (min-width: 1408px){.is-inline-flex-fullhd{display:inline-flex !important}}.is-hidden{display:none !important}.is-sr-only{border:none !important;clip:rect(0, 0, 0, 0) !important;height:0.01em !important;overflow:hidden !important;padding:0 !important;position:absolute !important;white-space:nowrap !important;width:0.01em !important}@media screen and (max-width: 768px){.is-hidden-mobile{display:none !important}}@media screen and (min-width: 769px),print{.is-hidden-tablet{display:none !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-hidden-tablet-only{display:none !important}}@media screen and (max-width: 1055px){.is-hidden-touch{display:none !important}}@media screen and (min-width: 1056px){.is-hidden-desktop{display:none !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-hidden-desktop-only{display:none !important}}@media screen and (min-width: 1216px){.is-hidden-widescreen{display:none !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-hidden-widescreen-only{display:none !important}}@media screen and (min-width: 1408px){.is-hidden-fullhd{display:none !important}}.is-invisible{visibility:hidden !important}@media screen and (max-width: 768px){.is-invisible-mobile{visibility:hidden !important}}@media screen and (min-width: 769px),print{.is-invisible-tablet{visibility:hidden !important}}@media screen and (min-width: 769px) and (max-width: 1055px){.is-invisible-tablet-only{visibility:hidden !important}}@media screen and (max-width: 1055px){.is-invisible-touch{visibility:hidden !important}}@media screen and (min-width: 1056px){.is-invisible-desktop{visibility:hidden !important}}@media screen and (min-width: 1056px) and (max-width: 1215px){.is-invisible-desktop-only{visibility:hidden !important}}@media screen and (min-width: 1216px){.is-invisible-widescreen{visibility:hidden !important}}@media screen and (min-width: 1216px) and (max-width: 1407px){.is-invisible-widescreen-only{visibility:hidden !important}}@media screen and (min-width: 1408px){.is-invisible-fullhd{visibility:hidden !important}}/*! minireset.css v0.0.6 | MIT License | github.com/jgthms/minireset.css */html,body,p,ol,ul,li,dl,dt,dd,blockquote,figure,fieldset,legend,textarea,pre,iframe,hr,h1,h2,h3,h4,h5,h6{margin:0;padding:0}h1,h2,h3,h4,h5,h6{font-size:100%;font-weight:normal}ul{list-style:none}button,input,select,textarea{margin:0}html{box-sizing:border-box}*,*::before,*::after{box-sizing:inherit}img,video{height:auto;max-width:100%}iframe{border:0}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}td:not([align]),th:not([align]){text-align:inherit}html{background-color:#fff;font-size:16px;-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;min-width:300px;overflow-x:auto;overflow-y:scroll;text-rendering:optimizeLegibility;text-size-adjust:100%}article,aside,figure,footer,header,hgroup,section{display:block}body,button,input,optgroup,select,textarea{font-family:"Lato Medium",-apple-system,BlinkMacSystemFont,"Segoe UI","Helvetica Neue","Helvetica","Arial",sans-serif}code,pre{-moz-osx-font-smoothing:auto;-webkit-font-smoothing:auto;font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace}body{color:#222;font-size:1em;font-weight:400;line-height:1.5}a{color:#2e63b8;cursor:pointer;text-decoration:none}a strong{color:currentColor}a:hover{color:#363636}code{background-color:rgba(0,0,0,0.05);color:#000;font-size:.875em;font-weight:normal;padding:.1em}hr{background-color:#f5f5f5;border:none;display:block;height:2px;margin:1.5rem 0}img{height:auto;max-width:100%}input[type="checkbox"],input[type="radio"]{vertical-align:baseline}small{font-size:.875em}span{font-style:inherit;font-weight:inherit}strong{color:#222;font-weight:700}fieldset{border:none}pre{-webkit-overflow-scrolling:touch;background-color:#f5f5f5;color:#222;font-size:.875em;overflow-x:auto;padding:1.25rem 1.5rem;white-space:pre;word-wrap:normal}pre code{background-color:transparent;color:currentColor;font-size:1em;padding:0}table td,table th{vertical-align:top}table td:not([align]),table th:not([align]){text-align:inherit}table th{color:#222}@keyframes spinAround{from{transform:rotate(0deg)}to{transform:rotate(359deg)}}.box{background-color:#fff;border-radius:6px;box-shadow:#bbb;color:#222;display:block;padding:1.25rem}a.box:hover,a.box:focus{box-shadow:0 0.5em 1em -0.125em rgba(10,10,10,0.1),0 0 0 1px #2e63b8}a.box:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2),0 0 0 1px #2e63b8}.button{background-color:#fff;border-color:#dbdbdb;border-width:1px;color:#222;cursor:pointer;justify-content:center;padding-bottom:calc(0.5em - 1px);padding-left:1em;padding-right:1em;padding-top:calc(0.5em - 1px);text-align:center;white-space:nowrap}.button strong{color:inherit}.button .icon,.button .icon.is-small,.button #documenter .docs-sidebar form.docs-search>input.icon,#documenter .docs-sidebar .button form.docs-search>input.icon,.button .icon.is-medium,.button .icon.is-large{height:1.5em;width:1.5em}.button .icon:first-child:not(:last-child){margin-left:calc(-0.5em - 1px);margin-right:.25em}.button .icon:last-child:not(:first-child){margin-left:.25em;margin-right:calc(-0.5em - 1px)}.button .icon:first-child:last-child{margin-left:calc(-0.5em - 1px);margin-right:calc(-0.5em - 1px)}.button:hover,.button.is-hovered{border-color:#b5b5b5;color:#363636}.button:focus,.button.is-focused{border-color:#3c5dcd;color:#363636}.button:focus:not(:active),.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.button:active,.button.is-active{border-color:#4a4a4a;color:#363636}.button.is-text{background-color:transparent;border-color:transparent;color:#222;text-decoration:underline}.button.is-text:hover,.button.is-text.is-hovered,.button.is-text:focus,.button.is-text.is-focused{background-color:#f5f5f5;color:#222}.button.is-text:active,.button.is-text.is-active{background-color:#e8e8e8;color:#222}.button.is-text[disabled],fieldset[disabled] .button.is-text{background-color:transparent;border-color:transparent;box-shadow:none}.button.is-ghost{background:none;border-color:rgba(0,0,0,0);color:#2e63b8;text-decoration:none}.button.is-ghost:hover,.button.is-ghost.is-hovered{color:#2e63b8;text-decoration:underline}.button.is-white{background-color:#fff;border-color:transparent;color:#0a0a0a}.button.is-white:hover,.button.is-white.is-hovered{background-color:#f9f9f9;border-color:transparent;color:#0a0a0a}.button.is-white:focus,.button.is-white.is-focused{border-color:transparent;color:#0a0a0a}.button.is-white:focus:not(:active),.button.is-white.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}.button.is-white:active,.button.is-white.is-active{background-color:#f2f2f2;border-color:transparent;color:#0a0a0a}.button.is-white[disabled],fieldset[disabled] .button.is-white{background-color:#fff;border-color:#fff;box-shadow:none}.button.is-white.is-inverted{background-color:#0a0a0a;color:#fff}.button.is-white.is-inverted:hover,.button.is-white.is-inverted.is-hovered{background-color:#000}.button.is-white.is-inverted[disabled],fieldset[disabled] .button.is-white.is-inverted{background-color:#0a0a0a;border-color:transparent;box-shadow:none;color:#fff}.button.is-white.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}.button.is-white.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-white.is-outlined:hover,.button.is-white.is-outlined.is-hovered,.button.is-white.is-outlined:focus,.button.is-white.is-outlined.is-focused{background-color:#fff;border-color:#fff;color:#0a0a0a}.button.is-white.is-outlined.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-white.is-outlined.is-loading:hover::after,.button.is-white.is-outlined.is-loading.is-hovered::after,.button.is-white.is-outlined.is-loading:focus::after,.button.is-white.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}.button.is-white.is-outlined[disabled],fieldset[disabled] .button.is-white.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}.button.is-white.is-inverted.is-outlined:hover,.button.is-white.is-inverted.is-outlined.is-hovered,.button.is-white.is-inverted.is-outlined:focus,.button.is-white.is-inverted.is-outlined.is-focused{background-color:#0a0a0a;color:#fff}.button.is-white.is-inverted.is-outlined.is-loading:hover::after,.button.is-white.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-white.is-inverted.is-outlined.is-loading:focus::after,.button.is-white.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-white.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}.button.is-black{background-color:#0a0a0a;border-color:transparent;color:#fff}.button.is-black:hover,.button.is-black.is-hovered{background-color:#040404;border-color:transparent;color:#fff}.button.is-black:focus,.button.is-black.is-focused{border-color:transparent;color:#fff}.button.is-black:focus:not(:active),.button.is-black.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}.button.is-black:active,.button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}.button.is-black[disabled],fieldset[disabled] .button.is-black{background-color:#0a0a0a;border-color:#0a0a0a;box-shadow:none}.button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}.button.is-black.is-inverted:hover,.button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-black.is-inverted[disabled],fieldset[disabled] .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}.button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}.button.is-black.is-outlined:hover,.button.is-black.is-outlined.is-hovered,.button.is-black.is-outlined:focus,.button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}.button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}.button.is-black.is-outlined.is-loading:hover::after,.button.is-black.is-outlined.is-loading.is-hovered::after,.button.is-black.is-outlined.is-loading:focus::after,.button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-black.is-outlined[disabled],fieldset[disabled] .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}.button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-black.is-inverted.is-outlined:hover,.button.is-black.is-inverted.is-outlined.is-hovered,.button.is-black.is-inverted.is-outlined:focus,.button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}.button.is-black.is-inverted.is-outlined.is-loading:hover::after,.button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-black.is-inverted.is-outlined.is-loading:focus::after,.button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}.button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-light{background-color:#f5f5f5;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-light:hover,.button.is-light.is-hovered{background-color:#eee;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-light:focus,.button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-light:focus:not(:active),.button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}.button.is-light:active,.button.is-light.is-active{background-color:#e8e8e8;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-light[disabled],fieldset[disabled] .button.is-light{background-color:#f5f5f5;border-color:#f5f5f5;box-shadow:none}.button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#f5f5f5}.button.is-light.is-inverted:hover,.button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}.button.is-light.is-inverted[disabled],fieldset[disabled] .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#f5f5f5}.button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}.button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;color:#f5f5f5}.button.is-light.is-outlined:hover,.button.is-light.is-outlined.is-hovered,.button.is-light.is-outlined:focus,.button.is-light.is-outlined.is-focused{background-color:#f5f5f5;border-color:#f5f5f5;color:rgba(0,0,0,0.7)}.button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}.button.is-light.is-outlined.is-loading:hover::after,.button.is-light.is-outlined.is-loading.is-hovered::after,.button.is-light.is-outlined.is-loading:focus::after,.button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}.button.is-light.is-outlined[disabled],fieldset[disabled] .button.is-light.is-outlined{background-color:transparent;border-color:#f5f5f5;box-shadow:none;color:#f5f5f5}.button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}.button.is-light.is-inverted.is-outlined:hover,.button.is-light.is-inverted.is-outlined.is-hovered,.button.is-light.is-inverted.is-outlined:focus,.button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f5f5f5}.button.is-light.is-inverted.is-outlined.is-loading:hover::after,.button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-light.is-inverted.is-outlined.is-loading:focus::after,.button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}.button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}.button.is-dark,.content kbd.button{background-color:#363636;border-color:transparent;color:#fff}.button.is-dark:hover,.content kbd.button:hover,.button.is-dark.is-hovered,.content kbd.button.is-hovered{background-color:#2f2f2f;border-color:transparent;color:#fff}.button.is-dark:focus,.content kbd.button:focus,.button.is-dark.is-focused,.content kbd.button.is-focused{border-color:transparent;color:#fff}.button.is-dark:focus:not(:active),.content kbd.button:focus:not(:active),.button.is-dark.is-focused:not(:active),.content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(54,54,54,0.25)}.button.is-dark:active,.content kbd.button:active,.button.is-dark.is-active,.content kbd.button.is-active{background-color:#292929;border-color:transparent;color:#fff}.button.is-dark[disabled],.content kbd.button[disabled],fieldset[disabled] .button.is-dark,fieldset[disabled] .content kbd.button,.content fieldset[disabled] kbd.button{background-color:#363636;border-color:#363636;box-shadow:none}.button.is-dark.is-inverted,.content kbd.button.is-inverted{background-color:#fff;color:#363636}.button.is-dark.is-inverted:hover,.content kbd.button.is-inverted:hover,.button.is-dark.is-inverted.is-hovered,.content kbd.button.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-dark.is-inverted[disabled],.content kbd.button.is-inverted[disabled],fieldset[disabled] .button.is-dark.is-inverted,fieldset[disabled] .content kbd.button.is-inverted,.content fieldset[disabled] kbd.button.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#363636}.button.is-dark.is-loading::after,.content kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-dark.is-outlined,.content kbd.button.is-outlined{background-color:transparent;border-color:#363636;color:#363636}.button.is-dark.is-outlined:hover,.content kbd.button.is-outlined:hover,.button.is-dark.is-outlined.is-hovered,.content kbd.button.is-outlined.is-hovered,.button.is-dark.is-outlined:focus,.content kbd.button.is-outlined:focus,.button.is-dark.is-outlined.is-focused,.content kbd.button.is-outlined.is-focused{background-color:#363636;border-color:#363636;color:#fff}.button.is-dark.is-outlined.is-loading::after,.content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #363636 #363636 !important}.button.is-dark.is-outlined.is-loading:hover::after,.content kbd.button.is-outlined.is-loading:hover::after,.button.is-dark.is-outlined.is-loading.is-hovered::after,.content kbd.button.is-outlined.is-loading.is-hovered::after,.button.is-dark.is-outlined.is-loading:focus::after,.content kbd.button.is-outlined.is-loading:focus::after,.button.is-dark.is-outlined.is-loading.is-focused::after,.content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-dark.is-outlined[disabled],.content kbd.button.is-outlined[disabled],fieldset[disabled] .button.is-dark.is-outlined,fieldset[disabled] .content kbd.button.is-outlined,.content fieldset[disabled] kbd.button.is-outlined{background-color:transparent;border-color:#363636;box-shadow:none;color:#363636}.button.is-dark.is-inverted.is-outlined,.content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-dark.is-inverted.is-outlined:hover,.content kbd.button.is-inverted.is-outlined:hover,.button.is-dark.is-inverted.is-outlined.is-hovered,.content kbd.button.is-inverted.is-outlined.is-hovered,.button.is-dark.is-inverted.is-outlined:focus,.content kbd.button.is-inverted.is-outlined:focus,.button.is-dark.is-inverted.is-outlined.is-focused,.content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#363636}.button.is-dark.is-inverted.is-outlined.is-loading:hover::after,.content kbd.button.is-inverted.is-outlined.is-loading:hover::after,.button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,.content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-dark.is-inverted.is-outlined.is-loading:focus::after,.content kbd.button.is-inverted.is-outlined.is-loading:focus::after,.button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,.content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #363636 #363636 !important}.button.is-dark.is-inverted.is-outlined[disabled],.content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-dark.is-inverted.is-outlined,fieldset[disabled] .content kbd.button.is-inverted.is-outlined,.content fieldset[disabled] kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-primary,.docstring>section>a.button.docs-sourcelink{background-color:#4eb5de;border-color:transparent;color:#fff}.button.is-primary:hover,.docstring>section>a.button.docs-sourcelink:hover,.button.is-primary.is-hovered,.docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#43b1dc;border-color:transparent;color:#fff}.button.is-primary:focus,.docstring>section>a.button.docs-sourcelink:focus,.button.is-primary.is-focused,.docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}.button.is-primary:focus:not(:active),.docstring>section>a.button.docs-sourcelink:focus:not(:active),.button.is-primary.is-focused:not(:active),.docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(78,181,222,0.25)}.button.is-primary:active,.docstring>section>a.button.docs-sourcelink:active,.button.is-primary.is-active,.docstring>section>a.button.is-active.docs-sourcelink{background-color:#39acda;border-color:transparent;color:#fff}.button.is-primary[disabled],.docstring>section>a.button.docs-sourcelink[disabled],fieldset[disabled] .button.is-primary,fieldset[disabled] .docstring>section>a.button.docs-sourcelink{background-color:#4eb5de;border-color:#4eb5de;box-shadow:none}.button.is-primary.is-inverted,.docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;color:#4eb5de}.button.is-primary.is-inverted:hover,.docstring>section>a.button.is-inverted.docs-sourcelink:hover,.button.is-primary.is-inverted.is-hovered,.docstring>section>a.button.is-inverted.is-hovered.docs-sourcelink{background-color:#f2f2f2}.button.is-primary.is-inverted[disabled],.docstring>section>a.button.is-inverted.docs-sourcelink[disabled],fieldset[disabled] .button.is-primary.is-inverted,fieldset[disabled] .docstring>section>a.button.is-inverted.docs-sourcelink{background-color:#fff;border-color:transparent;box-shadow:none;color:#4eb5de}.button.is-primary.is-loading::after,.docstring>section>a.button.is-loading.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}.button.is-primary.is-outlined,.docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#4eb5de;color:#4eb5de}.button.is-primary.is-outlined:hover,.docstring>section>a.button.is-outlined.docs-sourcelink:hover,.button.is-primary.is-outlined.is-hovered,.docstring>section>a.button.is-outlined.is-hovered.docs-sourcelink,.button.is-primary.is-outlined:focus,.docstring>section>a.button.is-outlined.docs-sourcelink:focus,.button.is-primary.is-outlined.is-focused,.docstring>section>a.button.is-outlined.is-focused.docs-sourcelink{background-color:#4eb5de;border-color:#4eb5de;color:#fff}.button.is-primary.is-outlined.is-loading::after,.docstring>section>a.button.is-outlined.is-loading.docs-sourcelink::after{border-color:transparent transparent #4eb5de #4eb5de !important}.button.is-primary.is-outlined.is-loading:hover::after,.docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:hover::after,.button.is-primary.is-outlined.is-loading.is-hovered::after,.docstring>section>a.button.is-outlined.is-loading.is-hovered.docs-sourcelink::after,.button.is-primary.is-outlined.is-loading:focus::after,.docstring>section>a.button.is-outlined.is-loading.docs-sourcelink:focus::after,.button.is-primary.is-outlined.is-loading.is-focused::after,.docstring>section>a.button.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #fff #fff !important}.button.is-primary.is-outlined[disabled],.docstring>section>a.button.is-outlined.docs-sourcelink[disabled],fieldset[disabled] .button.is-primary.is-outlined,fieldset[disabled] .docstring>section>a.button.is-outlined.docs-sourcelink{background-color:transparent;border-color:#4eb5de;box-shadow:none;color:#4eb5de}.button.is-primary.is-inverted.is-outlined,.docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;color:#fff}.button.is-primary.is-inverted.is-outlined:hover,.docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink:hover,.button.is-primary.is-inverted.is-outlined.is-hovered,.docstring>section>a.button.is-inverted.is-outlined.is-hovered.docs-sourcelink,.button.is-primary.is-inverted.is-outlined:focus,.docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink:focus,.button.is-primary.is-inverted.is-outlined.is-focused,.docstring>section>a.button.is-inverted.is-outlined.is-focused.docs-sourcelink{background-color:#fff;color:#4eb5de}.button.is-primary.is-inverted.is-outlined.is-loading:hover::after,.docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:hover::after,.button.is-primary.is-inverted.is-outlined.is-loading.is-hovered::after,.docstring>section>a.button.is-inverted.is-outlined.is-loading.is-hovered.docs-sourcelink::after,.button.is-primary.is-inverted.is-outlined.is-loading:focus::after,.docstring>section>a.button.is-inverted.is-outlined.is-loading.docs-sourcelink:focus::after,.button.is-primary.is-inverted.is-outlined.is-loading.is-focused::after,.docstring>section>a.button.is-inverted.is-outlined.is-loading.is-focused.docs-sourcelink::after{border-color:transparent transparent #4eb5de #4eb5de !important}.button.is-primary.is-inverted.is-outlined[disabled],.docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink[disabled],fieldset[disabled] .button.is-primary.is-inverted.is-outlined,fieldset[disabled] .docstring>section>a.button.is-inverted.is-outlined.docs-sourcelink{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-primary.is-light,.docstring>section>a.button.is-light.docs-sourcelink{background-color:#eef8fc;color:#1a6d8e}.button.is-primary.is-light:hover,.docstring>section>a.button.is-light.docs-sourcelink:hover,.button.is-primary.is-light.is-hovered,.docstring>section>a.button.is-light.is-hovered.docs-sourcelink{background-color:#e3f3fa;border-color:transparent;color:#1a6d8e}.button.is-primary.is-light:active,.docstring>section>a.button.is-light.docs-sourcelink:active,.button.is-primary.is-light.is-active,.docstring>section>a.button.is-light.is-active.docs-sourcelink{background-color:#d8eff8;border-color:transparent;color:#1a6d8e}.button.is-link{background-color:#2e63b8;border-color:transparent;color:#fff}.button.is-link:hover,.button.is-link.is-hovered{background-color:#2b5eae;border-color:transparent;color:#fff}.button.is-link:focus,.button.is-link.is-focused{border-color:transparent;color:#fff}.button.is-link:focus:not(:active),.button.is-link.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.button.is-link:active,.button.is-link.is-active{background-color:#2958a4;border-color:transparent;color:#fff}.button.is-link[disabled],fieldset[disabled] .button.is-link{background-color:#2e63b8;border-color:#2e63b8;box-shadow:none}.button.is-link.is-inverted{background-color:#fff;color:#2e63b8}.button.is-link.is-inverted:hover,.button.is-link.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-link.is-inverted[disabled],fieldset[disabled] .button.is-link.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#2e63b8}.button.is-link.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-link.is-outlined{background-color:transparent;border-color:#2e63b8;color:#2e63b8}.button.is-link.is-outlined:hover,.button.is-link.is-outlined.is-hovered,.button.is-link.is-outlined:focus,.button.is-link.is-outlined.is-focused{background-color:#2e63b8;border-color:#2e63b8;color:#fff}.button.is-link.is-outlined.is-loading::after{border-color:transparent transparent #2e63b8 #2e63b8 !important}.button.is-link.is-outlined.is-loading:hover::after,.button.is-link.is-outlined.is-loading.is-hovered::after,.button.is-link.is-outlined.is-loading:focus::after,.button.is-link.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-link.is-outlined[disabled],fieldset[disabled] .button.is-link.is-outlined{background-color:transparent;border-color:#2e63b8;box-shadow:none;color:#2e63b8}.button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-link.is-inverted.is-outlined:hover,.button.is-link.is-inverted.is-outlined.is-hovered,.button.is-link.is-inverted.is-outlined:focus,.button.is-link.is-inverted.is-outlined.is-focused{background-color:#fff;color:#2e63b8}.button.is-link.is-inverted.is-outlined.is-loading:hover::after,.button.is-link.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-link.is-inverted.is-outlined.is-loading:focus::after,.button.is-link.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #2e63b8 #2e63b8 !important}.button.is-link.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-link.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-link.is-light{background-color:#eff3fb;color:#3169c4}.button.is-link.is-light:hover,.button.is-link.is-light.is-hovered{background-color:#e4ecf8;border-color:transparent;color:#3169c4}.button.is-link.is-light:active,.button.is-link.is-light.is-active{background-color:#dae5f6;border-color:transparent;color:#3169c4}.button.is-info{background-color:#209cee;border-color:transparent;color:#fff}.button.is-info:hover,.button.is-info.is-hovered{background-color:#1497ed;border-color:transparent;color:#fff}.button.is-info:focus,.button.is-info.is-focused{border-color:transparent;color:#fff}.button.is-info:focus:not(:active),.button.is-info.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(32,156,238,0.25)}.button.is-info:active,.button.is-info.is-active{background-color:#1190e3;border-color:transparent;color:#fff}.button.is-info[disabled],fieldset[disabled] .button.is-info{background-color:#209cee;border-color:#209cee;box-shadow:none}.button.is-info.is-inverted{background-color:#fff;color:#209cee}.button.is-info.is-inverted:hover,.button.is-info.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-info.is-inverted[disabled],fieldset[disabled] .button.is-info.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#209cee}.button.is-info.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-info.is-outlined{background-color:transparent;border-color:#209cee;color:#209cee}.button.is-info.is-outlined:hover,.button.is-info.is-outlined.is-hovered,.button.is-info.is-outlined:focus,.button.is-info.is-outlined.is-focused{background-color:#209cee;border-color:#209cee;color:#fff}.button.is-info.is-outlined.is-loading::after{border-color:transparent transparent #209cee #209cee !important}.button.is-info.is-outlined.is-loading:hover::after,.button.is-info.is-outlined.is-loading.is-hovered::after,.button.is-info.is-outlined.is-loading:focus::after,.button.is-info.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-info.is-outlined[disabled],fieldset[disabled] .button.is-info.is-outlined{background-color:transparent;border-color:#209cee;box-shadow:none;color:#209cee}.button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-info.is-inverted.is-outlined:hover,.button.is-info.is-inverted.is-outlined.is-hovered,.button.is-info.is-inverted.is-outlined:focus,.button.is-info.is-inverted.is-outlined.is-focused{background-color:#fff;color:#209cee}.button.is-info.is-inverted.is-outlined.is-loading:hover::after,.button.is-info.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-info.is-inverted.is-outlined.is-loading:focus::after,.button.is-info.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #209cee #209cee !important}.button.is-info.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-info.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-info.is-light{background-color:#ecf7fe;color:#0e72b4}.button.is-info.is-light:hover,.button.is-info.is-light.is-hovered{background-color:#e0f1fd;border-color:transparent;color:#0e72b4}.button.is-info.is-light:active,.button.is-info.is-light.is-active{background-color:#d4ecfc;border-color:transparent;color:#0e72b4}.button.is-success{background-color:#22c35b;border-color:transparent;color:#fff}.button.is-success:hover,.button.is-success.is-hovered{background-color:#20b856;border-color:transparent;color:#fff}.button.is-success:focus,.button.is-success.is-focused{border-color:transparent;color:#fff}.button.is-success:focus:not(:active),.button.is-success.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(34,195,91,0.25)}.button.is-success:active,.button.is-success.is-active{background-color:#1ead51;border-color:transparent;color:#fff}.button.is-success[disabled],fieldset[disabled] .button.is-success{background-color:#22c35b;border-color:#22c35b;box-shadow:none}.button.is-success.is-inverted{background-color:#fff;color:#22c35b}.button.is-success.is-inverted:hover,.button.is-success.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-success.is-inverted[disabled],fieldset[disabled] .button.is-success.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#22c35b}.button.is-success.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-success.is-outlined{background-color:transparent;border-color:#22c35b;color:#22c35b}.button.is-success.is-outlined:hover,.button.is-success.is-outlined.is-hovered,.button.is-success.is-outlined:focus,.button.is-success.is-outlined.is-focused{background-color:#22c35b;border-color:#22c35b;color:#fff}.button.is-success.is-outlined.is-loading::after{border-color:transparent transparent #22c35b #22c35b !important}.button.is-success.is-outlined.is-loading:hover::after,.button.is-success.is-outlined.is-loading.is-hovered::after,.button.is-success.is-outlined.is-loading:focus::after,.button.is-success.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-success.is-outlined[disabled],fieldset[disabled] .button.is-success.is-outlined{background-color:transparent;border-color:#22c35b;box-shadow:none;color:#22c35b}.button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-success.is-inverted.is-outlined:hover,.button.is-success.is-inverted.is-outlined.is-hovered,.button.is-success.is-inverted.is-outlined:focus,.button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#22c35b}.button.is-success.is-inverted.is-outlined.is-loading:hover::after,.button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-success.is-inverted.is-outlined.is-loading:focus::after,.button.is-success.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #22c35b #22c35b !important}.button.is-success.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-success.is-light{background-color:#eefcf3;color:#198f43}.button.is-success.is-light:hover,.button.is-success.is-light.is-hovered{background-color:#e3faeb;border-color:transparent;color:#198f43}.button.is-success.is-light:active,.button.is-success.is-light.is-active{background-color:#d8f8e3;border-color:transparent;color:#198f43}.button.is-warning{background-color:#ffdd57;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-warning:hover,.button.is-warning.is-hovered{background-color:#ffda4a;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-warning:focus,.button.is-warning.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-warning:focus:not(:active),.button.is-warning.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(255,221,87,0.25)}.button.is-warning:active,.button.is-warning.is-active{background-color:#ffd83e;border-color:transparent;color:rgba(0,0,0,0.7)}.button.is-warning[disabled],fieldset[disabled] .button.is-warning{background-color:#ffdd57;border-color:#ffdd57;box-shadow:none}.button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);color:#ffdd57}.button.is-warning.is-inverted:hover,.button.is-warning.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}.button.is-warning.is-inverted[disabled],fieldset[disabled] .button.is-warning.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ffdd57}.button.is-warning.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}.button.is-warning.is-outlined{background-color:transparent;border-color:#ffdd57;color:#ffdd57}.button.is-warning.is-outlined:hover,.button.is-warning.is-outlined.is-hovered,.button.is-warning.is-outlined:focus,.button.is-warning.is-outlined.is-focused{background-color:#ffdd57;border-color:#ffdd57;color:rgba(0,0,0,0.7)}.button.is-warning.is-outlined.is-loading::after{border-color:transparent transparent #ffdd57 #ffdd57 !important}.button.is-warning.is-outlined.is-loading:hover::after,.button.is-warning.is-outlined.is-loading.is-hovered::after,.button.is-warning.is-outlined.is-loading:focus::after,.button.is-warning.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}.button.is-warning.is-outlined[disabled],fieldset[disabled] .button.is-warning.is-outlined{background-color:transparent;border-color:#ffdd57;box-shadow:none;color:#ffdd57}.button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}.button.is-warning.is-inverted.is-outlined:hover,.button.is-warning.is-inverted.is-outlined.is-hovered,.button.is-warning.is-inverted.is-outlined:focus,.button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ffdd57}.button.is-warning.is-inverted.is-outlined.is-loading:hover::after,.button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-warning.is-inverted.is-outlined.is-loading:focus::after,.button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ffdd57 #ffdd57 !important}.button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}.button.is-warning.is-light{background-color:#fffbeb;color:#947600}.button.is-warning.is-light:hover,.button.is-warning.is-light.is-hovered{background-color:#fff8de;border-color:transparent;color:#947600}.button.is-warning.is-light:active,.button.is-warning.is-light.is-active{background-color:#fff6d1;border-color:transparent;color:#947600}.button.is-danger{background-color:#da0b00;border-color:transparent;color:#fff}.button.is-danger:hover,.button.is-danger.is-hovered{background-color:#cd0a00;border-color:transparent;color:#fff}.button.is-danger:focus,.button.is-danger.is-focused{border-color:transparent;color:#fff}.button.is-danger:focus:not(:active),.button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(218,11,0,0.25)}.button.is-danger:active,.button.is-danger.is-active{background-color:#c10a00;border-color:transparent;color:#fff}.button.is-danger[disabled],fieldset[disabled] .button.is-danger{background-color:#da0b00;border-color:#da0b00;box-shadow:none}.button.is-danger.is-inverted{background-color:#fff;color:#da0b00}.button.is-danger.is-inverted:hover,.button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}.button.is-danger.is-inverted[disabled],fieldset[disabled] .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#da0b00}.button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}.button.is-danger.is-outlined{background-color:transparent;border-color:#da0b00;color:#da0b00}.button.is-danger.is-outlined:hover,.button.is-danger.is-outlined.is-hovered,.button.is-danger.is-outlined:focus,.button.is-danger.is-outlined.is-focused{background-color:#da0b00;border-color:#da0b00;color:#fff}.button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #da0b00 #da0b00 !important}.button.is-danger.is-outlined.is-loading:hover::after,.button.is-danger.is-outlined.is-loading.is-hovered::after,.button.is-danger.is-outlined.is-loading:focus::after,.button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}.button.is-danger.is-outlined[disabled],fieldset[disabled] .button.is-danger.is-outlined{background-color:transparent;border-color:#da0b00;box-shadow:none;color:#da0b00}.button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}.button.is-danger.is-inverted.is-outlined:hover,.button.is-danger.is-inverted.is-outlined.is-hovered,.button.is-danger.is-inverted.is-outlined:focus,.button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#da0b00}.button.is-danger.is-inverted.is-outlined.is-loading:hover::after,.button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,.button.is-danger.is-inverted.is-outlined.is-loading:focus::after,.button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #da0b00 #da0b00 !important}.button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}.button.is-danger.is-light{background-color:#ffeceb;color:#f50c00}.button.is-danger.is-light:hover,.button.is-danger.is-light.is-hovered{background-color:#ffe0de;border-color:transparent;color:#f50c00}.button.is-danger.is-light:active,.button.is-danger.is-light.is-active{background-color:#ffd3d1;border-color:transparent;color:#f50c00}.button.is-small,#documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}.button.is-small:not(.is-rounded),#documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:2px}.button.is-normal{font-size:1rem}.button.is-medium{font-size:1.25rem}.button.is-large{font-size:1.5rem}.button[disabled],fieldset[disabled] .button{background-color:#fff;border-color:#dbdbdb;box-shadow:none;opacity:.5}.button.is-fullwidth{display:flex;width:100%}.button.is-loading{color:transparent !important;pointer-events:none}.button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}.button.is-static{background-color:#f5f5f5;border-color:#dbdbdb;color:#6b6b6b;box-shadow:none;pointer-events:none}.button.is-rounded,#documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}.buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}.buttons .button{margin-bottom:0.5rem}.buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}.buttons:last-child{margin-bottom:-0.5rem}.buttons:not(:last-child){margin-bottom:1rem}.buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}.buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:2px}.buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}.buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}.buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}.buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}.buttons.has-addons .button:last-child{margin-right:0}.buttons.has-addons .button:hover,.buttons.has-addons .button.is-hovered{z-index:2}.buttons.has-addons .button:focus,.buttons.has-addons .button.is-focused,.buttons.has-addons .button:active,.buttons.has-addons .button.is-active,.buttons.has-addons .button.is-selected{z-index:3}.buttons.has-addons .button:focus:hover,.buttons.has-addons .button.is-focused:hover,.buttons.has-addons .button:active:hover,.buttons.has-addons .button.is-active:hover,.buttons.has-addons .button.is-selected:hover{z-index:4}.buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}.buttons.is-centered{justify-content:center}.buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}.buttons.is-right{justify-content:flex-end}.buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){.button.is-responsive.is-small,#documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}.button.is-responsive,.button.is-responsive.is-normal{font-size:.65625rem}.button.is-responsive.is-medium{font-size:.75rem}.button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.button.is-responsive.is-small,#documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}.button.is-responsive,.button.is-responsive.is-normal{font-size:.75rem}.button.is-responsive.is-medium{font-size:1rem}.button.is-responsive.is-large{font-size:1.25rem}}.container{flex-grow:1;margin:0 auto;position:relative;width:auto}.container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){.container{max-width:992px}}@media screen and (max-width: 1215px){.container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){.container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){.container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){.container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}.content li+li{margin-top:0.25em}.content p:not(:last-child),.content dl:not(:last-child),.content ol:not(:last-child),.content ul:not(:last-child),.content blockquote:not(:last-child),.content pre:not(:last-child),.content table:not(:last-child){margin-bottom:1em}.content h1,.content h2,.content h3,.content h4,.content h5,.content h6{color:#222;font-weight:600;line-height:1.125}.content h1{font-size:2em;margin-bottom:0.5em}.content h1:not(:first-child){margin-top:1em}.content h2{font-size:1.75em;margin-bottom:0.5714em}.content h2:not(:first-child){margin-top:1.1428em}.content h3{font-size:1.5em;margin-bottom:0.6666em}.content h3:not(:first-child){margin-top:1.3333em}.content h4{font-size:1.25em;margin-bottom:0.8em}.content h5{font-size:1.125em;margin-bottom:0.8888em}.content h6{font-size:1em;margin-bottom:1em}.content blockquote{background-color:#f5f5f5;border-left:5px solid #dbdbdb;padding:1.25em 1.5em}.content ol{list-style-position:outside;margin-left:2em;margin-top:1em}.content ol:not([type]){list-style-type:decimal}.content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}.content ol.is-lower-roman:not([type]){list-style-type:lower-roman}.content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}.content ol.is-upper-roman:not([type]){list-style-type:upper-roman}.content ul{list-style:disc outside;margin-left:2em;margin-top:1em}.content ul ul{list-style-type:circle;margin-top:0.5em}.content ul ul ul{list-style-type:square}.content dd{margin-left:2em}.content figure{margin-left:2em;margin-right:2em;text-align:center}.content figure:not(:first-child){margin-top:2em}.content figure:not(:last-child){margin-bottom:2em}.content figure img{display:inline-block}.content figure figcaption{font-style:italic}.content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}.content sup,.content sub{font-size:75%}.content table{width:100%}.content table td,.content table th{border:1px solid #dbdbdb;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}.content table th{color:#222}.content table th:not([align]){text-align:inherit}.content table thead td,.content table thead th{border-width:0 0 2px;color:#222}.content table tfoot td,.content table tfoot th{border-width:2px 0 0;color:#222}.content table tbody tr:last-child td,.content table tbody tr:last-child th{border-bottom-width:0}.content .tabs li+li{margin-top:0}.content.is-small,#documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}.content.is-normal{font-size:1rem}.content.is-medium{font-size:1.25rem}.content.is-large{font-size:1.5rem}.icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}.icon.is-small,#documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}.icon.is-medium{height:2rem;width:2rem}.icon.is-large{height:3rem;width:3rem}.icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}.icon-text .icon{flex-grow:0;flex-shrink:0}.icon-text .icon:not(:last-child){margin-right:.25em}.icon-text .icon:not(:first-child){margin-left:.25em}div.icon-text{display:flex}.image,#documenter .docs-sidebar .docs-logo>img{display:block;position:relative}.image img,#documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}.image img.is-rounded,#documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}.image.is-fullwidth,#documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}.image.is-square img,#documenter .docs-sidebar .docs-logo>img.is-square img,.image.is-square .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,.image.is-1by1 img,#documenter .docs-sidebar .docs-logo>img.is-1by1 img,.image.is-1by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,.image.is-5by4 img,#documenter .docs-sidebar .docs-logo>img.is-5by4 img,.image.is-5by4 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,.image.is-4by3 img,#documenter .docs-sidebar .docs-logo>img.is-4by3 img,.image.is-4by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,.image.is-3by2 img,#documenter .docs-sidebar .docs-logo>img.is-3by2 img,.image.is-3by2 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,.image.is-5by3 img,#documenter .docs-sidebar .docs-logo>img.is-5by3 img,.image.is-5by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,.image.is-16by9 img,#documenter .docs-sidebar .docs-logo>img.is-16by9 img,.image.is-16by9 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,.image.is-2by1 img,#documenter .docs-sidebar .docs-logo>img.is-2by1 img,.image.is-2by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,.image.is-3by1 img,#documenter .docs-sidebar .docs-logo>img.is-3by1 img,.image.is-3by1 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,.image.is-4by5 img,#documenter .docs-sidebar .docs-logo>img.is-4by5 img,.image.is-4by5 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,.image.is-3by4 img,#documenter .docs-sidebar .docs-logo>img.is-3by4 img,.image.is-3by4 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by4 .has-ratio,.image.is-2by3 img,#documenter .docs-sidebar .docs-logo>img.is-2by3 img,.image.is-2by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-2by3 .has-ratio,.image.is-3by5 img,#documenter .docs-sidebar .docs-logo>img.is-3by5 img,.image.is-3by5 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-3by5 .has-ratio,.image.is-9by16 img,#documenter .docs-sidebar .docs-logo>img.is-9by16 img,.image.is-9by16 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-9by16 .has-ratio,.image.is-1by2 img,#documenter .docs-sidebar .docs-logo>img.is-1by2 img,.image.is-1by2 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by2 .has-ratio,.image.is-1by3 img,#documenter .docs-sidebar .docs-logo>img.is-1by3 img,.image.is-1by3 .has-ratio,#documenter .docs-sidebar .docs-logo>img.is-1by3 .has-ratio{height:100%;width:100%}.image.is-square,#documenter .docs-sidebar .docs-logo>img.is-square,.image.is-1by1,#documenter .docs-sidebar .docs-logo>img.is-1by1{padding-top:100%}.image.is-5by4,#documenter .docs-sidebar .docs-logo>img.is-5by4{padding-top:80%}.image.is-4by3,#documenter .docs-sidebar .docs-logo>img.is-4by3{padding-top:75%}.image.is-3by2,#documenter .docs-sidebar .docs-logo>img.is-3by2{padding-top:66.6666%}.image.is-5by3,#documenter .docs-sidebar .docs-logo>img.is-5by3{padding-top:60%}.image.is-16by9,#documenter .docs-sidebar .docs-logo>img.is-16by9{padding-top:56.25%}.image.is-2by1,#documenter .docs-sidebar .docs-logo>img.is-2by1{padding-top:50%}.image.is-3by1,#documenter .docs-sidebar .docs-logo>img.is-3by1{padding-top:33.3333%}.image.is-4by5,#documenter .docs-sidebar .docs-logo>img.is-4by5{padding-top:125%}.image.is-3by4,#documenter .docs-sidebar .docs-logo>img.is-3by4{padding-top:133.3333%}.image.is-2by3,#documenter .docs-sidebar .docs-logo>img.is-2by3{padding-top:150%}.image.is-3by5,#documenter .docs-sidebar .docs-logo>img.is-3by5{padding-top:166.6666%}.image.is-9by16,#documenter .docs-sidebar .docs-logo>img.is-9by16{padding-top:177.7777%}.image.is-1by2,#documenter .docs-sidebar .docs-logo>img.is-1by2{padding-top:200%}.image.is-1by3,#documenter .docs-sidebar .docs-logo>img.is-1by3{padding-top:300%}.image.is-16x16,#documenter .docs-sidebar .docs-logo>img.is-16x16{height:16px;width:16px}.image.is-24x24,#documenter .docs-sidebar .docs-logo>img.is-24x24{height:24px;width:24px}.image.is-32x32,#documenter .docs-sidebar .docs-logo>img.is-32x32{height:32px;width:32px}.image.is-48x48,#documenter .docs-sidebar .docs-logo>img.is-48x48{height:48px;width:48px}.image.is-64x64,#documenter .docs-sidebar .docs-logo>img.is-64x64{height:64px;width:64px}.image.is-96x96,#documenter .docs-sidebar .docs-logo>img.is-96x96{height:96px;width:96px}.image.is-128x128,#documenter .docs-sidebar .docs-logo>img.is-128x128{height:128px;width:128px}.notification{background-color:#f5f5f5;border-radius:4px;position:relative;padding:1.25rem 2.5rem 1.25rem 1.5rem}.notification a:not(.button):not(.dropdown-item){color:currentColor;text-decoration:underline}.notification strong{color:currentColor}.notification code,.notification pre{background:#fff}.notification pre code{background:transparent}.notification>.delete{right:.5rem;position:absolute;top:0.5rem}.notification .title,.notification .subtitle,.notification .content{color:currentColor}.notification.is-white{background-color:#fff;color:#0a0a0a}.notification.is-black{background-color:#0a0a0a;color:#fff}.notification.is-light{background-color:#f5f5f5;color:rgba(0,0,0,0.7)}.notification.is-dark,.content kbd.notification{background-color:#363636;color:#fff}.notification.is-primary,.docstring>section>a.notification.docs-sourcelink{background-color:#4eb5de;color:#fff}.notification.is-primary.is-light,.docstring>section>a.notification.is-light.docs-sourcelink{background-color:#eef8fc;color:#1a6d8e}.notification.is-link{background-color:#2e63b8;color:#fff}.notification.is-link.is-light{background-color:#eff3fb;color:#3169c4}.notification.is-info{background-color:#209cee;color:#fff}.notification.is-info.is-light{background-color:#ecf7fe;color:#0e72b4}.notification.is-success{background-color:#22c35b;color:#fff}.notification.is-success.is-light{background-color:#eefcf3;color:#198f43}.notification.is-warning{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.notification.is-warning.is-light{background-color:#fffbeb;color:#947600}.notification.is-danger{background-color:#da0b00;color:#fff}.notification.is-danger.is-light{background-color:#ffeceb;color:#f50c00}.progress{-moz-appearance:none;-webkit-appearance:none;border:none;border-radius:9999px;display:block;height:1rem;overflow:hidden;padding:0;width:100%}.progress::-webkit-progress-bar{background-color:#ededed}.progress::-webkit-progress-value{background-color:#222}.progress::-moz-progress-bar{background-color:#222}.progress::-ms-fill{background-color:#222;border:none}.progress.is-white::-webkit-progress-value{background-color:#fff}.progress.is-white::-moz-progress-bar{background-color:#fff}.progress.is-white::-ms-fill{background-color:#fff}.progress.is-white:indeterminate{background-image:linear-gradient(to right, #fff 30%, #ededed 30%)}.progress.is-black::-webkit-progress-value{background-color:#0a0a0a}.progress.is-black::-moz-progress-bar{background-color:#0a0a0a}.progress.is-black::-ms-fill{background-color:#0a0a0a}.progress.is-black:indeterminate{background-image:linear-gradient(to right, #0a0a0a 30%, #ededed 30%)}.progress.is-light::-webkit-progress-value{background-color:#f5f5f5}.progress.is-light::-moz-progress-bar{background-color:#f5f5f5}.progress.is-light::-ms-fill{background-color:#f5f5f5}.progress.is-light:indeterminate{background-image:linear-gradient(to right, #f5f5f5 30%, #ededed 30%)}.progress.is-dark::-webkit-progress-value,.content kbd.progress::-webkit-progress-value{background-color:#363636}.progress.is-dark::-moz-progress-bar,.content kbd.progress::-moz-progress-bar{background-color:#363636}.progress.is-dark::-ms-fill,.content kbd.progress::-ms-fill{background-color:#363636}.progress.is-dark:indeterminate,.content kbd.progress:indeterminate{background-image:linear-gradient(to right, #363636 30%, #ededed 30%)}.progress.is-primary::-webkit-progress-value,.docstring>section>a.progress.docs-sourcelink::-webkit-progress-value{background-color:#4eb5de}.progress.is-primary::-moz-progress-bar,.docstring>section>a.progress.docs-sourcelink::-moz-progress-bar{background-color:#4eb5de}.progress.is-primary::-ms-fill,.docstring>section>a.progress.docs-sourcelink::-ms-fill{background-color:#4eb5de}.progress.is-primary:indeterminate,.docstring>section>a.progress.docs-sourcelink:indeterminate{background-image:linear-gradient(to right, #4eb5de 30%, #ededed 30%)}.progress.is-link::-webkit-progress-value{background-color:#2e63b8}.progress.is-link::-moz-progress-bar{background-color:#2e63b8}.progress.is-link::-ms-fill{background-color:#2e63b8}.progress.is-link:indeterminate{background-image:linear-gradient(to right, #2e63b8 30%, #ededed 30%)}.progress.is-info::-webkit-progress-value{background-color:#209cee}.progress.is-info::-moz-progress-bar{background-color:#209cee}.progress.is-info::-ms-fill{background-color:#209cee}.progress.is-info:indeterminate{background-image:linear-gradient(to right, #209cee 30%, #ededed 30%)}.progress.is-success::-webkit-progress-value{background-color:#22c35b}.progress.is-success::-moz-progress-bar{background-color:#22c35b}.progress.is-success::-ms-fill{background-color:#22c35b}.progress.is-success:indeterminate{background-image:linear-gradient(to right, #22c35b 30%, #ededed 30%)}.progress.is-warning::-webkit-progress-value{background-color:#ffdd57}.progress.is-warning::-moz-progress-bar{background-color:#ffdd57}.progress.is-warning::-ms-fill{background-color:#ffdd57}.progress.is-warning:indeterminate{background-image:linear-gradient(to right, #ffdd57 30%, #ededed 30%)}.progress.is-danger::-webkit-progress-value{background-color:#da0b00}.progress.is-danger::-moz-progress-bar{background-color:#da0b00}.progress.is-danger::-ms-fill{background-color:#da0b00}.progress.is-danger:indeterminate{background-image:linear-gradient(to right, #da0b00 30%, #ededed 30%)}.progress:indeterminate{animation-duration:1.5s;animation-iteration-count:infinite;animation-name:moveIndeterminate;animation-timing-function:linear;background-color:#ededed;background-image:linear-gradient(to right, #222 30%, #ededed 30%);background-position:top left;background-repeat:no-repeat;background-size:150% 150%}.progress:indeterminate::-webkit-progress-bar{background-color:transparent}.progress:indeterminate::-moz-progress-bar{background-color:transparent}.progress:indeterminate::-ms-fill{animation-name:none}.progress.is-small,#documenter .docs-sidebar form.docs-search>input.progress{height:.75rem}.progress.is-medium{height:1.25rem}.progress.is-large{height:1.5rem}@keyframes moveIndeterminate{from{background-position:200% 0}to{background-position:-200% 0}}.table{background-color:#fff;color:#222}.table td,.table th{border:1px solid #dbdbdb;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}.table td.is-white,.table th.is-white{background-color:#fff;border-color:#fff;color:#0a0a0a}.table td.is-black,.table th.is-black{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}.table td.is-light,.table th.is-light{background-color:#f5f5f5;border-color:#f5f5f5;color:rgba(0,0,0,0.7)}.table td.is-dark,.table th.is-dark{background-color:#363636;border-color:#363636;color:#fff}.table td.is-primary,.table th.is-primary{background-color:#4eb5de;border-color:#4eb5de;color:#fff}.table td.is-link,.table th.is-link{background-color:#2e63b8;border-color:#2e63b8;color:#fff}.table td.is-info,.table th.is-info{background-color:#209cee;border-color:#209cee;color:#fff}.table td.is-success,.table th.is-success{background-color:#22c35b;border-color:#22c35b;color:#fff}.table td.is-warning,.table th.is-warning{background-color:#ffdd57;border-color:#ffdd57;color:rgba(0,0,0,0.7)}.table td.is-danger,.table th.is-danger{background-color:#da0b00;border-color:#da0b00;color:#fff}.table td.is-narrow,.table th.is-narrow{white-space:nowrap;width:1%}.table td.is-selected,.table th.is-selected{background-color:#4eb5de;color:#fff}.table td.is-selected a,.table td.is-selected strong,.table th.is-selected a,.table th.is-selected strong{color:currentColor}.table td.is-vcentered,.table th.is-vcentered{vertical-align:middle}.table th{color:#222}.table th:not([align]){text-align:left}.table tr.is-selected{background-color:#4eb5de;color:#fff}.table tr.is-selected a,.table tr.is-selected strong{color:currentColor}.table tr.is-selected td,.table tr.is-selected th{border-color:#fff;color:currentColor}.table thead{background-color:rgba(0,0,0,0)}.table thead td,.table thead th{border-width:0 0 2px;color:#222}.table tfoot{background-color:rgba(0,0,0,0)}.table tfoot td,.table tfoot th{border-width:2px 0 0;color:#222}.table tbody{background-color:rgba(0,0,0,0)}.table tbody tr:last-child td,.table tbody tr:last-child th{border-bottom-width:0}.table.is-bordered td,.table.is-bordered th{border-width:1px}.table.is-bordered tr:last-child td,.table.is-bordered tr:last-child th{border-bottom-width:1px}.table.is-fullwidth{width:100%}.table.is-hoverable tbody tr:not(.is-selected):hover{background-color:#fafafa}.table.is-hoverable.is-striped tbody tr:not(.is-selected):hover{background-color:#fafafa}.table.is-hoverable.is-striped tbody tr:not(.is-selected):hover:nth-child(even){background-color:#f5f5f5}.table.is-narrow td,.table.is-narrow th{padding:0.25em 0.5em}.table.is-striped tbody tr:not(.is-selected):nth-child(even){background-color:#fafafa}.table-container{-webkit-overflow-scrolling:touch;overflow:auto;overflow-y:hidden;max-width:100%}.tags{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}.tags .tag,.tags .content kbd,.content .tags kbd,.tags .docstring>section>a.docs-sourcelink{margin-bottom:0.5rem}.tags .tag:not(:last-child),.tags .content kbd:not(:last-child),.content .tags kbd:not(:last-child),.tags .docstring>section>a.docs-sourcelink:not(:last-child){margin-right:.5rem}.tags:last-child{margin-bottom:-0.5rem}.tags:not(:last-child){margin-bottom:1rem}.tags.are-medium .tag:not(.is-normal):not(.is-large),.tags.are-medium .content kbd:not(.is-normal):not(.is-large),.content .tags.are-medium kbd:not(.is-normal):not(.is-large),.tags.are-medium .docstring>section>a.docs-sourcelink:not(.is-normal):not(.is-large){font-size:1rem}.tags.are-large .tag:not(.is-normal):not(.is-medium),.tags.are-large .content kbd:not(.is-normal):not(.is-medium),.content .tags.are-large kbd:not(.is-normal):not(.is-medium),.tags.are-large .docstring>section>a.docs-sourcelink:not(.is-normal):not(.is-medium){font-size:1.25rem}.tags.is-centered{justify-content:center}.tags.is-centered .tag,.tags.is-centered .content kbd,.content .tags.is-centered kbd,.tags.is-centered .docstring>section>a.docs-sourcelink{margin-right:0.25rem;margin-left:0.25rem}.tags.is-right{justify-content:flex-end}.tags.is-right .tag:not(:first-child),.tags.is-right .content kbd:not(:first-child),.content .tags.is-right kbd:not(:first-child),.tags.is-right .docstring>section>a.docs-sourcelink:not(:first-child){margin-left:0.5rem}.tags.is-right .tag:not(:last-child),.tags.is-right .content kbd:not(:last-child),.content .tags.is-right kbd:not(:last-child),.tags.is-right .docstring>section>a.docs-sourcelink:not(:last-child){margin-right:0}.tags.has-addons .tag,.tags.has-addons .content kbd,.content .tags.has-addons kbd,.tags.has-addons .docstring>section>a.docs-sourcelink{margin-right:0}.tags.has-addons .tag:not(:first-child),.tags.has-addons .content kbd:not(:first-child),.content .tags.has-addons kbd:not(:first-child),.tags.has-addons .docstring>section>a.docs-sourcelink:not(:first-child){margin-left:0;border-top-left-radius:0;border-bottom-left-radius:0}.tags.has-addons .tag:not(:last-child),.tags.has-addons .content kbd:not(:last-child),.content .tags.has-addons kbd:not(:last-child),.tags.has-addons .docstring>section>a.docs-sourcelink:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.tag:not(body),.content kbd:not(body),.docstring>section>a.docs-sourcelink:not(body){align-items:center;background-color:#f5f5f5;border-radius:4px;color:#222;display:inline-flex;font-size:.75rem;height:2em;justify-content:center;line-height:1.5;padding-left:0.75em;padding-right:0.75em;white-space:nowrap}.tag:not(body) .delete,.content kbd:not(body) .delete,.docstring>section>a.docs-sourcelink:not(body) .delete{margin-left:.25rem;margin-right:-.375rem}.tag.is-white:not(body),.content kbd.is-white:not(body),.docstring>section>a.docs-sourcelink.is-white:not(body){background-color:#fff;color:#0a0a0a}.tag.is-black:not(body),.content kbd.is-black:not(body),.docstring>section>a.docs-sourcelink.is-black:not(body){background-color:#0a0a0a;color:#fff}.tag.is-light:not(body),.content kbd.is-light:not(body),.docstring>section>a.docs-sourcelink.is-light:not(body){background-color:#f5f5f5;color:rgba(0,0,0,0.7)}.tag.is-dark:not(body),.content kbd:not(body),.docstring>section>a.docs-sourcelink.is-dark:not(body),.content .docstring>section>kbd:not(body){background-color:#363636;color:#fff}.tag.is-primary:not(body),.content kbd.is-primary:not(body),.docstring>section>a.docs-sourcelink:not(body){background-color:#4eb5de;color:#fff}.tag.is-primary.is-light:not(body),.content kbd.is-primary.is-light:not(body),.docstring>section>a.docs-sourcelink.is-light:not(body){background-color:#eef8fc;color:#1a6d8e}.tag.is-link:not(body),.content kbd.is-link:not(body),.docstring>section>a.docs-sourcelink.is-link:not(body){background-color:#2e63b8;color:#fff}.tag.is-link.is-light:not(body),.content kbd.is-link.is-light:not(body),.docstring>section>a.docs-sourcelink.is-link.is-light:not(body){background-color:#eff3fb;color:#3169c4}.tag.is-info:not(body),.content kbd.is-info:not(body),.docstring>section>a.docs-sourcelink.is-info:not(body){background-color:#209cee;color:#fff}.tag.is-info.is-light:not(body),.content kbd.is-info.is-light:not(body),.docstring>section>a.docs-sourcelink.is-info.is-light:not(body){background-color:#ecf7fe;color:#0e72b4}.tag.is-success:not(body),.content kbd.is-success:not(body),.docstring>section>a.docs-sourcelink.is-success:not(body){background-color:#22c35b;color:#fff}.tag.is-success.is-light:not(body),.content kbd.is-success.is-light:not(body),.docstring>section>a.docs-sourcelink.is-success.is-light:not(body){background-color:#eefcf3;color:#198f43}.tag.is-warning:not(body),.content kbd.is-warning:not(body),.docstring>section>a.docs-sourcelink.is-warning:not(body){background-color:#ffdd57;color:rgba(0,0,0,0.7)}.tag.is-warning.is-light:not(body),.content kbd.is-warning.is-light:not(body),.docstring>section>a.docs-sourcelink.is-warning.is-light:not(body){background-color:#fffbeb;color:#947600}.tag.is-danger:not(body),.content kbd.is-danger:not(body),.docstring>section>a.docs-sourcelink.is-danger:not(body){background-color:#da0b00;color:#fff}.tag.is-danger.is-light:not(body),.content kbd.is-danger.is-light:not(body),.docstring>section>a.docs-sourcelink.is-danger.is-light:not(body){background-color:#ffeceb;color:#f50c00}.tag.is-normal:not(body),.content kbd.is-normal:not(body),.docstring>section>a.docs-sourcelink.is-normal:not(body){font-size:.75rem}.tag.is-medium:not(body),.content kbd.is-medium:not(body),.docstring>section>a.docs-sourcelink.is-medium:not(body){font-size:1rem}.tag.is-large:not(body),.content kbd.is-large:not(body),.docstring>section>a.docs-sourcelink.is-large:not(body){font-size:1.25rem}.tag:not(body) .icon:first-child:not(:last-child),.content kbd:not(body) .icon:first-child:not(:last-child),.docstring>section>a.docs-sourcelink:not(body) .icon:first-child:not(:last-child){margin-left:-.375em;margin-right:.1875em}.tag:not(body) .icon:last-child:not(:first-child),.content kbd:not(body) .icon:last-child:not(:first-child),.docstring>section>a.docs-sourcelink:not(body) .icon:last-child:not(:first-child){margin-left:.1875em;margin-right:-.375em}.tag:not(body) .icon:first-child:last-child,.content kbd:not(body) .icon:first-child:last-child,.docstring>section>a.docs-sourcelink:not(body) .icon:first-child:last-child{margin-left:-.375em;margin-right:-.375em}.tag.is-delete:not(body),.content kbd.is-delete:not(body),.docstring>section>a.docs-sourcelink.is-delete:not(body){margin-left:1px;padding:0;position:relative;width:2em}.tag.is-delete:not(body)::before,.content kbd.is-delete:not(body)::before,.docstring>section>a.docs-sourcelink.is-delete:not(body)::before,.tag.is-delete:not(body)::after,.content kbd.is-delete:not(body)::after,.docstring>section>a.docs-sourcelink.is-delete:not(body)::after{background-color:currentColor;content:"";display:block;left:50%;position:absolute;top:50%;transform:translateX(-50%) translateY(-50%) rotate(45deg);transform-origin:center center}.tag.is-delete:not(body)::before,.content kbd.is-delete:not(body)::before,.docstring>section>a.docs-sourcelink.is-delete:not(body)::before{height:1px;width:50%}.tag.is-delete:not(body)::after,.content kbd.is-delete:not(body)::after,.docstring>section>a.docs-sourcelink.is-delete:not(body)::after{height:50%;width:1px}.tag.is-delete:not(body):hover,.content kbd.is-delete:not(body):hover,.docstring>section>a.docs-sourcelink.is-delete:not(body):hover,.tag.is-delete:not(body):focus,.content kbd.is-delete:not(body):focus,.docstring>section>a.docs-sourcelink.is-delete:not(body):focus{background-color:#e8e8e8}.tag.is-delete:not(body):active,.content kbd.is-delete:not(body):active,.docstring>section>a.docs-sourcelink.is-delete:not(body):active{background-color:#dbdbdb}.tag.is-rounded:not(body),#documenter .docs-sidebar form.docs-search>input:not(body),.content kbd.is-rounded:not(body),#documenter .docs-sidebar .content form.docs-search>input:not(body),.docstring>section>a.docs-sourcelink.is-rounded:not(body){border-radius:9999px}a.tag:hover,.docstring>section>a.docs-sourcelink:hover{text-decoration:underline}.title,.subtitle{word-break:break-word}.title em,.title span,.subtitle em,.subtitle span{font-weight:inherit}.title sub,.subtitle sub{font-size:.75em}.title sup,.subtitle sup{font-size:.75em}.title .tag,.title .content kbd,.content .title kbd,.title .docstring>section>a.docs-sourcelink,.subtitle .tag,.subtitle .content kbd,.content .subtitle kbd,.subtitle .docstring>section>a.docs-sourcelink{vertical-align:middle}.title{color:#222;font-size:2rem;font-weight:600;line-height:1.125}.title strong{color:inherit;font-weight:inherit}.title:not(.is-spaced)+.subtitle{margin-top:-1.25rem}.title.is-1{font-size:3rem}.title.is-2{font-size:2.5rem}.title.is-3{font-size:2rem}.title.is-4{font-size:1.5rem}.title.is-5{font-size:1.25rem}.title.is-6{font-size:1rem}.title.is-7{font-size:.75rem}.subtitle{color:#222;font-size:1.25rem;font-weight:400;line-height:1.25}.subtitle strong{color:#222;font-weight:600}.subtitle:not(.is-spaced)+.title{margin-top:-1.25rem}.subtitle.is-1{font-size:3rem}.subtitle.is-2{font-size:2.5rem}.subtitle.is-3{font-size:2rem}.subtitle.is-4{font-size:1.5rem}.subtitle.is-5{font-size:1.25rem}.subtitle.is-6{font-size:1rem}.subtitle.is-7{font-size:.75rem}.heading{display:block;font-size:11px;letter-spacing:1px;margin-bottom:5px;text-transform:uppercase}.number{align-items:center;background-color:#f5f5f5;border-radius:9999px;display:inline-flex;font-size:1.25rem;height:2em;justify-content:center;margin-right:1.5rem;min-width:2.5em;padding:0.25rem 0.5rem;text-align:center;vertical-align:top}.select select,.textarea,.input,#documenter .docs-sidebar form.docs-search>input{background-color:#fff;border-color:#dbdbdb;border-radius:4px;color:#222}.select select::-moz-placeholder,.textarea::-moz-placeholder,.input::-moz-placeholder,#documenter .docs-sidebar form.docs-search>input::-moz-placeholder{color:#707070}.select select::-webkit-input-placeholder,.textarea::-webkit-input-placeholder,.input::-webkit-input-placeholder,#documenter .docs-sidebar form.docs-search>input::-webkit-input-placeholder{color:#707070}.select select:-moz-placeholder,.textarea:-moz-placeholder,.input:-moz-placeholder,#documenter .docs-sidebar form.docs-search>input:-moz-placeholder{color:#707070}.select select:-ms-input-placeholder,.textarea:-ms-input-placeholder,.input:-ms-input-placeholder,#documenter .docs-sidebar form.docs-search>input:-ms-input-placeholder{color:#707070}.select select:hover,.textarea:hover,.input:hover,#documenter .docs-sidebar form.docs-search>input:hover,.select select.is-hovered,.is-hovered.textarea,.is-hovered.input,#documenter .docs-sidebar form.docs-search>input.is-hovered{border-color:#b5b5b5}.select select:focus,.textarea:focus,.input:focus,#documenter .docs-sidebar form.docs-search>input:focus,.select select.is-focused,.is-focused.textarea,.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.select select:active,.textarea:active,.input:active,#documenter .docs-sidebar form.docs-search>input:active,.select select.is-active,.is-active.textarea,.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{border-color:#2e63b8;box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.select select[disabled],.textarea[disabled],.input[disabled],#documenter .docs-sidebar form.docs-search>input[disabled],fieldset[disabled] .select select,.select fieldset[disabled] select,fieldset[disabled] .textarea,fieldset[disabled] .input,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input{background-color:#f5f5f5;border-color:#f5f5f5;box-shadow:none;color:#6b6b6b}.select select[disabled]::-moz-placeholder,.textarea[disabled]::-moz-placeholder,.input[disabled]::-moz-placeholder,#documenter .docs-sidebar form.docs-search>input[disabled]::-moz-placeholder,fieldset[disabled] .select select::-moz-placeholder,.select fieldset[disabled] select::-moz-placeholder,fieldset[disabled] .textarea::-moz-placeholder,fieldset[disabled] .input::-moz-placeholder,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input::-moz-placeholder,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input::-moz-placeholder{color:rgba(107,107,107,0.3)}.select select[disabled]::-webkit-input-placeholder,.textarea[disabled]::-webkit-input-placeholder,.input[disabled]::-webkit-input-placeholder,#documenter .docs-sidebar form.docs-search>input[disabled]::-webkit-input-placeholder,fieldset[disabled] .select select::-webkit-input-placeholder,.select fieldset[disabled] select::-webkit-input-placeholder,fieldset[disabled] .textarea::-webkit-input-placeholder,fieldset[disabled] .input::-webkit-input-placeholder,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input::-webkit-input-placeholder,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input::-webkit-input-placeholder{color:rgba(107,107,107,0.3)}.select select[disabled]:-moz-placeholder,.textarea[disabled]:-moz-placeholder,.input[disabled]:-moz-placeholder,#documenter .docs-sidebar form.docs-search>input[disabled]:-moz-placeholder,fieldset[disabled] .select select:-moz-placeholder,.select fieldset[disabled] select:-moz-placeholder,fieldset[disabled] .textarea:-moz-placeholder,fieldset[disabled] .input:-moz-placeholder,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input:-moz-placeholder,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input:-moz-placeholder{color:rgba(107,107,107,0.3)}.select select[disabled]:-ms-input-placeholder,.textarea[disabled]:-ms-input-placeholder,.input[disabled]:-ms-input-placeholder,#documenter .docs-sidebar form.docs-search>input[disabled]:-ms-input-placeholder,fieldset[disabled] .select select:-ms-input-placeholder,.select fieldset[disabled] select:-ms-input-placeholder,fieldset[disabled] .textarea:-ms-input-placeholder,fieldset[disabled] .input:-ms-input-placeholder,fieldset[disabled] #documenter .docs-sidebar form.docs-search>input:-ms-input-placeholder,#documenter .docs-sidebar fieldset[disabled] form.docs-search>input:-ms-input-placeholder{color:rgba(107,107,107,0.3)}.textarea,.input,#documenter .docs-sidebar form.docs-search>input{box-shadow:inset 0 0.0625em 0.125em rgba(10,10,10,0.05);max-width:100%;width:100%}.textarea[readonly],.input[readonly],#documenter .docs-sidebar form.docs-search>input[readonly]{box-shadow:none}.is-white.textarea,.is-white.input,#documenter .docs-sidebar form.docs-search>input.is-white{border-color:#fff}.is-white.textarea:focus,.is-white.input:focus,#documenter .docs-sidebar form.docs-search>input.is-white:focus,.is-white.is-focused.textarea,.is-white.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-white.textarea:active,.is-white.input:active,#documenter .docs-sidebar form.docs-search>input.is-white:active,.is-white.is-active.textarea,.is-white.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}.is-black.textarea,.is-black.input,#documenter .docs-sidebar form.docs-search>input.is-black{border-color:#0a0a0a}.is-black.textarea:focus,.is-black.input:focus,#documenter .docs-sidebar form.docs-search>input.is-black:focus,.is-black.is-focused.textarea,.is-black.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-black.textarea:active,.is-black.input:active,#documenter .docs-sidebar form.docs-search>input.is-black:active,.is-black.is-active.textarea,.is-black.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}.is-light.textarea,.is-light.input,#documenter .docs-sidebar form.docs-search>input.is-light{border-color:#f5f5f5}.is-light.textarea:focus,.is-light.input:focus,#documenter .docs-sidebar form.docs-search>input.is-light:focus,.is-light.is-focused.textarea,.is-light.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-light.textarea:active,.is-light.input:active,#documenter .docs-sidebar form.docs-search>input.is-light:active,.is-light.is-active.textarea,.is-light.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}.is-dark.textarea,.content kbd.textarea,.is-dark.input,#documenter .docs-sidebar form.docs-search>input.is-dark,.content kbd.input{border-color:#363636}.is-dark.textarea:focus,.content kbd.textarea:focus,.is-dark.input:focus,#documenter .docs-sidebar form.docs-search>input.is-dark:focus,.content kbd.input:focus,.is-dark.is-focused.textarea,.content kbd.is-focused.textarea,.is-dark.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.content kbd.is-focused.input,#documenter .docs-sidebar .content form.docs-search>input.is-focused,.is-dark.textarea:active,.content kbd.textarea:active,.is-dark.input:active,#documenter .docs-sidebar form.docs-search>input.is-dark:active,.content kbd.input:active,.is-dark.is-active.textarea,.content kbd.is-active.textarea,.is-dark.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active,.content kbd.is-active.input,#documenter .docs-sidebar .content form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(54,54,54,0.25)}.is-primary.textarea,.docstring>section>a.textarea.docs-sourcelink,.is-primary.input,#documenter .docs-sidebar form.docs-search>input.is-primary,.docstring>section>a.input.docs-sourcelink{border-color:#4eb5de}.is-primary.textarea:focus,.docstring>section>a.textarea.docs-sourcelink:focus,.is-primary.input:focus,#documenter .docs-sidebar form.docs-search>input.is-primary:focus,.docstring>section>a.input.docs-sourcelink:focus,.is-primary.is-focused.textarea,.docstring>section>a.is-focused.textarea.docs-sourcelink,.is-primary.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.docstring>section>a.is-focused.input.docs-sourcelink,.is-primary.textarea:active,.docstring>section>a.textarea.docs-sourcelink:active,.is-primary.input:active,#documenter .docs-sidebar form.docs-search>input.is-primary:active,.docstring>section>a.input.docs-sourcelink:active,.is-primary.is-active.textarea,.docstring>section>a.is-active.textarea.docs-sourcelink,.is-primary.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active,.docstring>section>a.is-active.input.docs-sourcelink{box-shadow:0 0 0 0.125em rgba(78,181,222,0.25)}.is-link.textarea,.is-link.input,#documenter .docs-sidebar form.docs-search>input.is-link{border-color:#2e63b8}.is-link.textarea:focus,.is-link.input:focus,#documenter .docs-sidebar form.docs-search>input.is-link:focus,.is-link.is-focused.textarea,.is-link.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-link.textarea:active,.is-link.input:active,#documenter .docs-sidebar form.docs-search>input.is-link:active,.is-link.is-active.textarea,.is-link.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.is-info.textarea,.is-info.input,#documenter .docs-sidebar form.docs-search>input.is-info{border-color:#209cee}.is-info.textarea:focus,.is-info.input:focus,#documenter .docs-sidebar form.docs-search>input.is-info:focus,.is-info.is-focused.textarea,.is-info.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-info.textarea:active,.is-info.input:active,#documenter .docs-sidebar form.docs-search>input.is-info:active,.is-info.is-active.textarea,.is-info.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(32,156,238,0.25)}.is-success.textarea,.is-success.input,#documenter .docs-sidebar form.docs-search>input.is-success{border-color:#22c35b}.is-success.textarea:focus,.is-success.input:focus,#documenter .docs-sidebar form.docs-search>input.is-success:focus,.is-success.is-focused.textarea,.is-success.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-success.textarea:active,.is-success.input:active,#documenter .docs-sidebar form.docs-search>input.is-success:active,.is-success.is-active.textarea,.is-success.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(34,195,91,0.25)}.is-warning.textarea,.is-warning.input,#documenter .docs-sidebar form.docs-search>input.is-warning{border-color:#ffdd57}.is-warning.textarea:focus,.is-warning.input:focus,#documenter .docs-sidebar form.docs-search>input.is-warning:focus,.is-warning.is-focused.textarea,.is-warning.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-warning.textarea:active,.is-warning.input:active,#documenter .docs-sidebar form.docs-search>input.is-warning:active,.is-warning.is-active.textarea,.is-warning.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(255,221,87,0.25)}.is-danger.textarea,.is-danger.input,#documenter .docs-sidebar form.docs-search>input.is-danger{border-color:#da0b00}.is-danger.textarea:focus,.is-danger.input:focus,#documenter .docs-sidebar form.docs-search>input.is-danger:focus,.is-danger.is-focused.textarea,.is-danger.is-focused.input,#documenter .docs-sidebar form.docs-search>input.is-focused,.is-danger.textarea:active,.is-danger.input:active,#documenter .docs-sidebar form.docs-search>input.is-danger:active,.is-danger.is-active.textarea,.is-danger.is-active.input,#documenter .docs-sidebar form.docs-search>input.is-active{box-shadow:0 0 0 0.125em rgba(218,11,0,0.25)}.is-small.textarea,.is-small.input,#documenter .docs-sidebar form.docs-search>input{border-radius:2px;font-size:.75rem}.is-medium.textarea,.is-medium.input,#documenter .docs-sidebar form.docs-search>input.is-medium{font-size:1.25rem}.is-large.textarea,.is-large.input,#documenter .docs-sidebar form.docs-search>input.is-large{font-size:1.5rem}.is-fullwidth.textarea,.is-fullwidth.input,#documenter .docs-sidebar form.docs-search>input.is-fullwidth{display:block;width:100%}.is-inline.textarea,.is-inline.input,#documenter .docs-sidebar form.docs-search>input.is-inline{display:inline;width:auto}.input.is-rounded,#documenter .docs-sidebar form.docs-search>input{border-radius:9999px;padding-left:calc(calc(0.75em - 1px) + 0.375em);padding-right:calc(calc(0.75em - 1px) + 0.375em)}.input.is-static,#documenter .docs-sidebar form.docs-search>input.is-static{background-color:transparent;border-color:transparent;box-shadow:none;padding-left:0;padding-right:0}.textarea{display:block;max-width:100%;min-width:100%;padding:calc(0.75em - 1px);resize:vertical}.textarea:not([rows]){max-height:40em;min-height:8em}.textarea[rows]{height:initial}.textarea.has-fixed-size{resize:none}.radio,.checkbox{cursor:pointer;display:inline-block;line-height:1.25;position:relative}.radio input,.checkbox input{cursor:pointer}.radio:hover,.checkbox:hover{color:#222}.radio[disabled],.checkbox[disabled],fieldset[disabled] .radio,fieldset[disabled] .checkbox,.radio input[disabled],.checkbox input[disabled]{color:#6b6b6b;cursor:not-allowed}.radio+.radio{margin-left:.5em}.select{display:inline-block;max-width:100%;position:relative;vertical-align:top}.select:not(.is-multiple){height:2.5em}.select:not(.is-multiple):not(.is-loading)::after{border-color:#2e63b8;right:1.125em;z-index:4}.select.is-rounded select,#documenter .docs-sidebar form.docs-search>input.select select{border-radius:9999px;padding-left:1em}.select select{cursor:pointer;display:block;font-size:1em;max-width:100%;outline:none}.select select::-ms-expand{display:none}.select select[disabled]:hover,fieldset[disabled] .select select:hover{border-color:#f5f5f5}.select select:not([multiple]){padding-right:2.5em}.select select[multiple]{height:auto;padding:0}.select select[multiple] option{padding:0.5em 1em}.select:not(.is-multiple):not(.is-loading):hover::after{border-color:#222}.select.is-white:not(:hover)::after{border-color:#fff}.select.is-white select{border-color:#fff}.select.is-white select:hover,.select.is-white select.is-hovered{border-color:#f2f2f2}.select.is-white select:focus,.select.is-white select.is-focused,.select.is-white select:active,.select.is-white select.is-active{box-shadow:0 0 0 0.125em rgba(255,255,255,0.25)}.select.is-black:not(:hover)::after{border-color:#0a0a0a}.select.is-black select{border-color:#0a0a0a}.select.is-black select:hover,.select.is-black select.is-hovered{border-color:#000}.select.is-black select:focus,.select.is-black select.is-focused,.select.is-black select:active,.select.is-black select.is-active{box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}.select.is-light:not(:hover)::after{border-color:#f5f5f5}.select.is-light select{border-color:#f5f5f5}.select.is-light select:hover,.select.is-light select.is-hovered{border-color:#e8e8e8}.select.is-light select:focus,.select.is-light select.is-focused,.select.is-light select:active,.select.is-light select.is-active{box-shadow:0 0 0 0.125em rgba(245,245,245,0.25)}.select.is-dark:not(:hover)::after,.content kbd.select:not(:hover)::after{border-color:#363636}.select.is-dark select,.content kbd.select select{border-color:#363636}.select.is-dark select:hover,.content kbd.select select:hover,.select.is-dark select.is-hovered,.content kbd.select select.is-hovered{border-color:#292929}.select.is-dark select:focus,.content kbd.select select:focus,.select.is-dark select.is-focused,.content kbd.select select.is-focused,.select.is-dark select:active,.content kbd.select select:active,.select.is-dark select.is-active,.content kbd.select select.is-active{box-shadow:0 0 0 0.125em rgba(54,54,54,0.25)}.select.is-primary:not(:hover)::after,.docstring>section>a.select.docs-sourcelink:not(:hover)::after{border-color:#4eb5de}.select.is-primary select,.docstring>section>a.select.docs-sourcelink select{border-color:#4eb5de}.select.is-primary select:hover,.docstring>section>a.select.docs-sourcelink select:hover,.select.is-primary select.is-hovered,.docstring>section>a.select.docs-sourcelink select.is-hovered{border-color:#39acda}.select.is-primary select:focus,.docstring>section>a.select.docs-sourcelink select:focus,.select.is-primary select.is-focused,.docstring>section>a.select.docs-sourcelink select.is-focused,.select.is-primary select:active,.docstring>section>a.select.docs-sourcelink select:active,.select.is-primary select.is-active,.docstring>section>a.select.docs-sourcelink select.is-active{box-shadow:0 0 0 0.125em rgba(78,181,222,0.25)}.select.is-link:not(:hover)::after{border-color:#2e63b8}.select.is-link select{border-color:#2e63b8}.select.is-link select:hover,.select.is-link select.is-hovered{border-color:#2958a4}.select.is-link select:focus,.select.is-link select.is-focused,.select.is-link select:active,.select.is-link select.is-active{box-shadow:0 0 0 0.125em rgba(46,99,184,0.25)}.select.is-info:not(:hover)::after{border-color:#209cee}.select.is-info select{border-color:#209cee}.select.is-info select:hover,.select.is-info select.is-hovered{border-color:#1190e3}.select.is-info select:focus,.select.is-info select.is-focused,.select.is-info select:active,.select.is-info select.is-active{box-shadow:0 0 0 0.125em rgba(32,156,238,0.25)}.select.is-success:not(:hover)::after{border-color:#22c35b}.select.is-success select{border-color:#22c35b}.select.is-success select:hover,.select.is-success select.is-hovered{border-color:#1ead51}.select.is-success select:focus,.select.is-success select.is-focused,.select.is-success select:active,.select.is-success select.is-active{box-shadow:0 0 0 0.125em rgba(34,195,91,0.25)}.select.is-warning:not(:hover)::after{border-color:#ffdd57}.select.is-warning select{border-color:#ffdd57}.select.is-warning select:hover,.select.is-warning select.is-hovered{border-color:#ffd83e}.select.is-warning select:focus,.select.is-warning select.is-focused,.select.is-warning select:active,.select.is-warning select.is-active{box-shadow:0 0 0 0.125em rgba(255,221,87,0.25)}.select.is-danger:not(:hover)::after{border-color:#da0b00}.select.is-danger select{border-color:#da0b00}.select.is-danger select:hover,.select.is-danger select.is-hovered{border-color:#c10a00}.select.is-danger select:focus,.select.is-danger select.is-focused,.select.is-danger select:active,.select.is-danger select.is-active{box-shadow:0 0 0 0.125em rgba(218,11,0,0.25)}.select.is-small,#documenter .docs-sidebar form.docs-search>input.select{border-radius:2px;font-size:.75rem}.select.is-medium{font-size:1.25rem}.select.is-large{font-size:1.5rem}.select.is-disabled::after{border-color:#6b6b6b !important;opacity:0.5}.select.is-fullwidth{width:100%}.select.is-fullwidth select{width:100%}.select.is-loading::after{margin-top:0;position:absolute;right:.625em;top:0.625em;transform:none}.select.is-loading.is-small:after,#documenter .docs-sidebar form.docs-search>input.is-loading:after{font-size:.75rem}.select.is-loading.is-medium:after{font-size:1.25rem}.select.is-loading.is-large:after{font-size:1.5rem}.file{align-items:stretch;display:flex;justify-content:flex-start;position:relative}.file.is-white .file-cta{background-color:#fff;border-color:transparent;color:#0a0a0a}.file.is-white:hover .file-cta,.file.is-white.is-hovered .file-cta{background-color:#f9f9f9;border-color:transparent;color:#0a0a0a}.file.is-white:focus .file-cta,.file.is-white.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(255,255,255,0.25);color:#0a0a0a}.file.is-white:active .file-cta,.file.is-white.is-active .file-cta{background-color:#f2f2f2;border-color:transparent;color:#0a0a0a}.file.is-black .file-cta{background-color:#0a0a0a;border-color:transparent;color:#fff}.file.is-black:hover .file-cta,.file.is-black.is-hovered .file-cta{background-color:#040404;border-color:transparent;color:#fff}.file.is-black:focus .file-cta,.file.is-black.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(10,10,10,0.25);color:#fff}.file.is-black:active .file-cta,.file.is-black.is-active .file-cta{background-color:#000;border-color:transparent;color:#fff}.file.is-light .file-cta{background-color:#f5f5f5;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-light:hover .file-cta,.file.is-light.is-hovered .file-cta{background-color:#eee;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-light:focus .file-cta,.file.is-light.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(245,245,245,0.25);color:rgba(0,0,0,0.7)}.file.is-light:active .file-cta,.file.is-light.is-active .file-cta{background-color:#e8e8e8;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-dark .file-cta,.content kbd.file .file-cta{background-color:#363636;border-color:transparent;color:#fff}.file.is-dark:hover .file-cta,.content kbd.file:hover .file-cta,.file.is-dark.is-hovered .file-cta,.content kbd.file.is-hovered .file-cta{background-color:#2f2f2f;border-color:transparent;color:#fff}.file.is-dark:focus .file-cta,.content kbd.file:focus .file-cta,.file.is-dark.is-focused .file-cta,.content kbd.file.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(54,54,54,0.25);color:#fff}.file.is-dark:active .file-cta,.content kbd.file:active .file-cta,.file.is-dark.is-active .file-cta,.content kbd.file.is-active .file-cta{background-color:#292929;border-color:transparent;color:#fff}.file.is-primary .file-cta,.docstring>section>a.file.docs-sourcelink .file-cta{background-color:#4eb5de;border-color:transparent;color:#fff}.file.is-primary:hover .file-cta,.docstring>section>a.file.docs-sourcelink:hover .file-cta,.file.is-primary.is-hovered .file-cta,.docstring>section>a.file.is-hovered.docs-sourcelink .file-cta{background-color:#43b1dc;border-color:transparent;color:#fff}.file.is-primary:focus .file-cta,.docstring>section>a.file.docs-sourcelink:focus .file-cta,.file.is-primary.is-focused .file-cta,.docstring>section>a.file.is-focused.docs-sourcelink .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(78,181,222,0.25);color:#fff}.file.is-primary:active .file-cta,.docstring>section>a.file.docs-sourcelink:active .file-cta,.file.is-primary.is-active .file-cta,.docstring>section>a.file.is-active.docs-sourcelink .file-cta{background-color:#39acda;border-color:transparent;color:#fff}.file.is-link .file-cta{background-color:#2e63b8;border-color:transparent;color:#fff}.file.is-link:hover .file-cta,.file.is-link.is-hovered .file-cta{background-color:#2b5eae;border-color:transparent;color:#fff}.file.is-link:focus .file-cta,.file.is-link.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(46,99,184,0.25);color:#fff}.file.is-link:active .file-cta,.file.is-link.is-active .file-cta{background-color:#2958a4;border-color:transparent;color:#fff}.file.is-info .file-cta{background-color:#209cee;border-color:transparent;color:#fff}.file.is-info:hover .file-cta,.file.is-info.is-hovered .file-cta{background-color:#1497ed;border-color:transparent;color:#fff}.file.is-info:focus .file-cta,.file.is-info.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(32,156,238,0.25);color:#fff}.file.is-info:active .file-cta,.file.is-info.is-active .file-cta{background-color:#1190e3;border-color:transparent;color:#fff}.file.is-success .file-cta{background-color:#22c35b;border-color:transparent;color:#fff}.file.is-success:hover .file-cta,.file.is-success.is-hovered .file-cta{background-color:#20b856;border-color:transparent;color:#fff}.file.is-success:focus .file-cta,.file.is-success.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(34,195,91,0.25);color:#fff}.file.is-success:active .file-cta,.file.is-success.is-active .file-cta{background-color:#1ead51;border-color:transparent;color:#fff}.file.is-warning .file-cta{background-color:#ffdd57;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-warning:hover .file-cta,.file.is-warning.is-hovered .file-cta{background-color:#ffda4a;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-warning:focus .file-cta,.file.is-warning.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(255,221,87,0.25);color:rgba(0,0,0,0.7)}.file.is-warning:active .file-cta,.file.is-warning.is-active .file-cta{background-color:#ffd83e;border-color:transparent;color:rgba(0,0,0,0.7)}.file.is-danger .file-cta{background-color:#da0b00;border-color:transparent;color:#fff}.file.is-danger:hover .file-cta,.file.is-danger.is-hovered .file-cta{background-color:#cd0a00;border-color:transparent;color:#fff}.file.is-danger:focus .file-cta,.file.is-danger.is-focused .file-cta{border-color:transparent;box-shadow:0 0 0.5em rgba(218,11,0,0.25);color:#fff}.file.is-danger:active .file-cta,.file.is-danger.is-active .file-cta{background-color:#c10a00;border-color:transparent;color:#fff}.file.is-small,#documenter .docs-sidebar form.docs-search>input.file{font-size:.75rem}.file.is-normal{font-size:1rem}.file.is-medium{font-size:1.25rem}.file.is-medium .file-icon .fa{font-size:21px}.file.is-large{font-size:1.5rem}.file.is-large .file-icon .fa{font-size:28px}.file.has-name .file-cta{border-bottom-right-radius:0;border-top-right-radius:0}.file.has-name .file-name{border-bottom-left-radius:0;border-top-left-radius:0}.file.has-name.is-empty .file-cta{border-radius:4px}.file.has-name.is-empty .file-name{display:none}.file.is-boxed .file-label{flex-direction:column}.file.is-boxed .file-cta{flex-direction:column;height:auto;padding:1em 3em}.file.is-boxed .file-name{border-width:0 1px 1px}.file.is-boxed .file-icon{height:1.5em;width:1.5em}.file.is-boxed .file-icon .fa{font-size:21px}.file.is-boxed.is-small .file-icon .fa,#documenter .docs-sidebar form.docs-search>input.is-boxed .file-icon .fa{font-size:14px}.file.is-boxed.is-medium .file-icon .fa{font-size:28px}.file.is-boxed.is-large .file-icon .fa{font-size:35px}.file.is-boxed.has-name .file-cta{border-radius:4px 4px 0 0}.file.is-boxed.has-name .file-name{border-radius:0 0 4px 4px;border-width:0 1px 1px}.file.is-centered{justify-content:center}.file.is-fullwidth .file-label{width:100%}.file.is-fullwidth .file-name{flex-grow:1;max-width:none}.file.is-right{justify-content:flex-end}.file.is-right .file-cta{border-radius:0 4px 4px 0}.file.is-right .file-name{border-radius:4px 0 0 4px;border-width:1px 0 1px 1px;order:-1}.file-label{align-items:stretch;display:flex;cursor:pointer;justify-content:flex-start;overflow:hidden;position:relative}.file-label:hover .file-cta{background-color:#eee;color:#222}.file-label:hover .file-name{border-color:#d5d5d5}.file-label:active .file-cta{background-color:#e8e8e8;color:#222}.file-label:active .file-name{border-color:#cfcfcf}.file-input{height:100%;left:0;opacity:0;outline:none;position:absolute;top:0;width:100%}.file-cta,.file-name{border-color:#dbdbdb;border-radius:4px;font-size:1em;padding-left:1em;padding-right:1em;white-space:nowrap}.file-cta{background-color:#f5f5f5;color:#222}.file-name{border-color:#dbdbdb;border-style:solid;border-width:1px 1px 1px 0;display:block;max-width:16em;overflow:hidden;text-align:inherit;text-overflow:ellipsis}.file-icon{align-items:center;display:flex;height:1em;justify-content:center;margin-right:.5em;width:1em}.file-icon .fa{font-size:14px}.label{color:#222;display:block;font-size:1rem;font-weight:700}.label:not(:last-child){margin-bottom:0.5em}.label.is-small,#documenter .docs-sidebar form.docs-search>input.label{font-size:.75rem}.label.is-medium{font-size:1.25rem}.label.is-large{font-size:1.5rem}.help{display:block;font-size:.75rem;margin-top:0.25rem}.help.is-white{color:#fff}.help.is-black{color:#0a0a0a}.help.is-light{color:#f5f5f5}.help.is-dark,.content kbd.help{color:#363636}.help.is-primary,.docstring>section>a.help.docs-sourcelink{color:#4eb5de}.help.is-link{color:#2e63b8}.help.is-info{color:#209cee}.help.is-success{color:#22c35b}.help.is-warning{color:#ffdd57}.help.is-danger{color:#da0b00}.field:not(:last-child){margin-bottom:0.75rem}.field.has-addons{display:flex;justify-content:flex-start}.field.has-addons .control:not(:last-child){margin-right:-1px}.field.has-addons .control:not(:first-child):not(:last-child) .button,.field.has-addons .control:not(:first-child):not(:last-child) .input,.field.has-addons .control:not(:first-child):not(:last-child) #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .field.has-addons .control:not(:first-child):not(:last-child) form.docs-search>input,.field.has-addons .control:not(:first-child):not(:last-child) .select select{border-radius:0}.field.has-addons .control:first-child:not(:only-child) .button,.field.has-addons .control:first-child:not(:only-child) .input,.field.has-addons .control:first-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .field.has-addons .control:first-child:not(:only-child) form.docs-search>input,.field.has-addons .control:first-child:not(:only-child) .select select{border-bottom-right-radius:0;border-top-right-radius:0}.field.has-addons .control:last-child:not(:only-child) .button,.field.has-addons .control:last-child:not(:only-child) .input,.field.has-addons .control:last-child:not(:only-child) #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .field.has-addons .control:last-child:not(:only-child) form.docs-search>input,.field.has-addons .control:last-child:not(:only-child) .select select{border-bottom-left-radius:0;border-top-left-radius:0}.field.has-addons .control .button:not([disabled]):hover,.field.has-addons .control .button.is-hovered:not([disabled]),.field.has-addons .control .input:not([disabled]):hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):hover,.field.has-addons .control .input.is-hovered:not([disabled]),.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-hovered:not([disabled]),#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-hovered:not([disabled]),.field.has-addons .control .select select:not([disabled]):hover,.field.has-addons .control .select select.is-hovered:not([disabled]){z-index:2}.field.has-addons .control .button:not([disabled]):focus,.field.has-addons .control .button.is-focused:not([disabled]),.field.has-addons .control .button:not([disabled]):active,.field.has-addons .control .button.is-active:not([disabled]),.field.has-addons .control .input:not([disabled]):focus,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus,.field.has-addons .control .input.is-focused:not([disabled]),.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]),#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]),.field.has-addons .control .input:not([disabled]):active,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active,.field.has-addons .control .input.is-active:not([disabled]),.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]),#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]),.field.has-addons .control .select select:not([disabled]):focus,.field.has-addons .control .select select.is-focused:not([disabled]),.field.has-addons .control .select select:not([disabled]):active,.field.has-addons .control .select select.is-active:not([disabled]){z-index:3}.field.has-addons .control .button:not([disabled]):focus:hover,.field.has-addons .control .button.is-focused:not([disabled]):hover,.field.has-addons .control .button:not([disabled]):active:hover,.field.has-addons .control .button.is-active:not([disabled]):hover,.field.has-addons .control .input:not([disabled]):focus:hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):focus:hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):focus:hover,.field.has-addons .control .input.is-focused:not([disabled]):hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-focused:not([disabled]):hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-focused:not([disabled]):hover,.field.has-addons .control .input:not([disabled]):active:hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input:not([disabled]):active:hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input:not([disabled]):active:hover,.field.has-addons .control .input.is-active:not([disabled]):hover,.field.has-addons .control #documenter .docs-sidebar form.docs-search>input.is-active:not([disabled]):hover,#documenter .docs-sidebar .field.has-addons .control form.docs-search>input.is-active:not([disabled]):hover,.field.has-addons .control .select select:not([disabled]):focus:hover,.field.has-addons .control .select select.is-focused:not([disabled]):hover,.field.has-addons .control .select select:not([disabled]):active:hover,.field.has-addons .control .select select.is-active:not([disabled]):hover{z-index:4}.field.has-addons .control.is-expanded{flex-grow:1;flex-shrink:1}.field.has-addons.has-addons-centered{justify-content:center}.field.has-addons.has-addons-right{justify-content:flex-end}.field.has-addons.has-addons-fullwidth .control{flex-grow:1;flex-shrink:0}.field.is-grouped{display:flex;justify-content:flex-start}.field.is-grouped>.control{flex-shrink:0}.field.is-grouped>.control:not(:last-child){margin-bottom:0;margin-right:.75rem}.field.is-grouped>.control.is-expanded{flex-grow:1;flex-shrink:1}.field.is-grouped.is-grouped-centered{justify-content:center}.field.is-grouped.is-grouped-right{justify-content:flex-end}.field.is-grouped.is-grouped-multiline{flex-wrap:wrap}.field.is-grouped.is-grouped-multiline>.control:last-child,.field.is-grouped.is-grouped-multiline>.control:not(:last-child){margin-bottom:0.75rem}.field.is-grouped.is-grouped-multiline:last-child{margin-bottom:-0.75rem}.field.is-grouped.is-grouped-multiline:not(:last-child){margin-bottom:0}@media screen and (min-width: 769px),print{.field.is-horizontal{display:flex}}.field-label .label{font-size:inherit}@media screen and (max-width: 768px){.field-label{margin-bottom:0.5rem}}@media screen and (min-width: 769px),print{.field-label{flex-basis:0;flex-grow:1;flex-shrink:0;margin-right:1.5rem;text-align:right}.field-label.is-small,#documenter .docs-sidebar form.docs-search>input.field-label{font-size:.75rem;padding-top:0.375em}.field-label.is-normal{padding-top:0.375em}.field-label.is-medium{font-size:1.25rem;padding-top:0.375em}.field-label.is-large{font-size:1.5rem;padding-top:0.375em}}.field-body .field .field{margin-bottom:0}@media screen and (min-width: 769px),print{.field-body{display:flex;flex-basis:0;flex-grow:5;flex-shrink:1}.field-body .field{margin-bottom:0}.field-body>.field{flex-shrink:1}.field-body>.field:not(.is-narrow){flex-grow:1}.field-body>.field:not(:last-child){margin-right:.75rem}}.control{box-sizing:border-box;clear:both;font-size:1rem;position:relative;text-align:inherit}.control.has-icons-left .input:focus~.icon,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input:focus~.icon,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input:focus~.icon,.control.has-icons-left .select:focus~.icon,.control.has-icons-right .input:focus~.icon,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input:focus~.icon,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input:focus~.icon,.control.has-icons-right .select:focus~.icon{color:#222}.control.has-icons-left .input.is-small~.icon,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input~.icon,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input~.icon,.control.has-icons-left .select.is-small~.icon,.control.has-icons-right .input.is-small~.icon,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input~.icon,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input~.icon,.control.has-icons-right .select.is-small~.icon{font-size:.75rem}.control.has-icons-left .input.is-medium~.icon,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-medium~.icon,.control.has-icons-left .select.is-medium~.icon,.control.has-icons-right .input.is-medium~.icon,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-medium~.icon,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-medium~.icon,.control.has-icons-right .select.is-medium~.icon{font-size:1.25rem}.control.has-icons-left .input.is-large~.icon,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input.is-large~.icon,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input.is-large~.icon,.control.has-icons-left .select.is-large~.icon,.control.has-icons-right .input.is-large~.icon,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input.is-large~.icon,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input.is-large~.icon,.control.has-icons-right .select.is-large~.icon{font-size:1.5rem}.control.has-icons-left .icon,.control.has-icons-right .icon{color:#dbdbdb;height:2.5em;pointer-events:none;position:absolute;top:0;width:2.5em;z-index:4}.control.has-icons-left .input,.control.has-icons-left #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .control.has-icons-left form.docs-search>input,.control.has-icons-left .select select{padding-left:2.5em}.control.has-icons-left .icon.is-left{left:0}.control.has-icons-right .input,.control.has-icons-right #documenter .docs-sidebar form.docs-search>input,#documenter .docs-sidebar .control.has-icons-right form.docs-search>input,.control.has-icons-right .select select{padding-right:2.5em}.control.has-icons-right .icon.is-right{right:0}.control.is-loading::after{position:absolute !important;right:.625em;top:0.625em;z-index:4}.control.is-loading.is-small:after,#documenter .docs-sidebar form.docs-search>input.is-loading:after{font-size:.75rem}.control.is-loading.is-medium:after{font-size:1.25rem}.control.is-loading.is-large:after{font-size:1.5rem}.breadcrumb{font-size:1rem;white-space:nowrap}.breadcrumb a{align-items:center;color:#2e63b8;display:flex;justify-content:center;padding:0 .75em}.breadcrumb a:hover{color:#363636}.breadcrumb li{align-items:center;display:flex}.breadcrumb li:first-child a{padding-left:0}.breadcrumb li.is-active a{color:#222;cursor:default;pointer-events:none}.breadcrumb li+li::before{color:#b5b5b5;content:"\0002f"}.breadcrumb ul,.breadcrumb ol{align-items:flex-start;display:flex;flex-wrap:wrap;justify-content:flex-start}.breadcrumb .icon:first-child{margin-right:.5em}.breadcrumb .icon:last-child{margin-left:.5em}.breadcrumb.is-centered ol,.breadcrumb.is-centered ul{justify-content:center}.breadcrumb.is-right ol,.breadcrumb.is-right ul{justify-content:flex-end}.breadcrumb.is-small,#documenter .docs-sidebar form.docs-search>input.breadcrumb{font-size:.75rem}.breadcrumb.is-medium{font-size:1.25rem}.breadcrumb.is-large{font-size:1.5rem}.breadcrumb.has-arrow-separator li+li::before{content:"\02192"}.breadcrumb.has-bullet-separator li+li::before{content:"\02022"}.breadcrumb.has-dot-separator li+li::before{content:"\000b7"}.breadcrumb.has-succeeds-separator li+li::before{content:"\0227B"}.card{background-color:#fff;border-radius:.25rem;box-shadow:#bbb;color:#222;max-width:100%;position:relative}.card-footer:first-child,.card-content:first-child,.card-header:first-child{border-top-left-radius:.25rem;border-top-right-radius:.25rem}.card-footer:last-child,.card-content:last-child,.card-header:last-child{border-bottom-left-radius:.25rem;border-bottom-right-radius:.25rem}.card-header{background-color:rgba(0,0,0,0);align-items:stretch;box-shadow:0 0.125em 0.25em rgba(10,10,10,0.1);display:flex}.card-header-title{align-items:center;color:#222;display:flex;flex-grow:1;font-weight:700;padding:0.75rem 1rem}.card-header-title.is-centered{justify-content:center}.card-header-icon{-moz-appearance:none;-webkit-appearance:none;appearance:none;background:none;border:none;color:currentColor;font-family:inherit;font-size:1em;margin:0;padding:0;align-items:center;cursor:pointer;display:flex;justify-content:center;padding:0.75rem 1rem}.card-image{display:block;position:relative}.card-image:first-child img{border-top-left-radius:.25rem;border-top-right-radius:.25rem}.card-image:last-child img{border-bottom-left-radius:.25rem;border-bottom-right-radius:.25rem}.card-content{background-color:rgba(0,0,0,0);padding:1.5rem}.card-footer{background-color:rgba(0,0,0,0);border-top:1px solid #ededed;align-items:stretch;display:flex}.card-footer-item{align-items:center;display:flex;flex-basis:0;flex-grow:1;flex-shrink:0;justify-content:center;padding:.75rem}.card-footer-item:not(:last-child){border-right:1px solid #ededed}.card .media:not(:last-child){margin-bottom:1.5rem}.dropdown{display:inline-flex;position:relative;vertical-align:top}.dropdown.is-active .dropdown-menu,.dropdown.is-hoverable:hover .dropdown-menu{display:block}.dropdown.is-right .dropdown-menu{left:auto;right:0}.dropdown.is-up .dropdown-menu{bottom:100%;padding-bottom:4px;padding-top:initial;top:auto}.dropdown-menu{display:none;left:0;min-width:12rem;padding-top:4px;position:absolute;top:100%;z-index:20}.dropdown-content{background-color:#fff;border-radius:4px;box-shadow:#bbb;padding-bottom:.5rem;padding-top:.5rem}.dropdown-item{color:#222;display:block;font-size:0.875rem;line-height:1.5;padding:0.375rem 1rem;position:relative}a.dropdown-item,button.dropdown-item{padding-right:3rem;text-align:inherit;white-space:nowrap;width:100%}a.dropdown-item:hover,button.dropdown-item:hover{background-color:#f5f5f5;color:#0a0a0a}a.dropdown-item.is-active,button.dropdown-item.is-active{background-color:#2e63b8;color:#fff}.dropdown-divider{background-color:#ededed;border:none;display:block;height:1px;margin:0.5rem 0}.level{align-items:center;justify-content:space-between}.level code{border-radius:4px}.level img{display:inline-block;vertical-align:top}.level.is-mobile{display:flex}.level.is-mobile .level-left,.level.is-mobile .level-right{display:flex}.level.is-mobile .level-left+.level-right{margin-top:0}.level.is-mobile .level-item:not(:last-child){margin-bottom:0;margin-right:.75rem}.level.is-mobile .level-item:not(.is-narrow){flex-grow:1}@media screen and (min-width: 769px),print{.level{display:flex}.level>.level-item:not(.is-narrow){flex-grow:1}}.level-item{align-items:center;display:flex;flex-basis:auto;flex-grow:0;flex-shrink:0;justify-content:center}.level-item .title,.level-item .subtitle{margin-bottom:0}@media screen and (max-width: 768px){.level-item:not(:last-child){margin-bottom:.75rem}}.level-left,.level-right{flex-basis:auto;flex-grow:0;flex-shrink:0}.level-left .level-item.is-flexible,.level-right .level-item.is-flexible{flex-grow:1}@media screen and (min-width: 769px),print{.level-left .level-item:not(:last-child),.level-right .level-item:not(:last-child){margin-right:.75rem}}.level-left{align-items:center;justify-content:flex-start}@media screen and (max-width: 768px){.level-left+.level-right{margin-top:1.5rem}}@media screen and (min-width: 769px),print{.level-left{display:flex}}.level-right{align-items:center;justify-content:flex-end}@media screen and (min-width: 769px),print{.level-right{display:flex}}.media{align-items:flex-start;display:flex;text-align:inherit}.media .content:not(:last-child){margin-bottom:.75rem}.media .media{border-top:1px solid rgba(219,219,219,0.5);display:flex;padding-top:.75rem}.media .media .content:not(:last-child),.media .media .control:not(:last-child){margin-bottom:.5rem}.media .media .media{padding-top:.5rem}.media .media .media+.media{margin-top:.5rem}.media+.media{border-top:1px solid rgba(219,219,219,0.5);margin-top:1rem;padding-top:1rem}.media.is-large+.media{margin-top:1.5rem;padding-top:1.5rem}.media-left,.media-right{flex-basis:auto;flex-grow:0;flex-shrink:0}.media-left{margin-right:1rem}.media-right{margin-left:1rem}.media-content{flex-basis:auto;flex-grow:1;flex-shrink:1;text-align:inherit}@media screen and (max-width: 768px){.media-content{overflow-x:auto}}.menu{font-size:1rem}.menu.is-small,#documenter .docs-sidebar form.docs-search>input.menu{font-size:.75rem}.menu.is-medium{font-size:1.25rem}.menu.is-large{font-size:1.5rem}.menu-list{line-height:1.25}.menu-list a{border-radius:2px;color:#222;display:block;padding:0.5em 0.75em}.menu-list a:hover{background-color:#f5f5f5;color:#222}.menu-list a.is-active{background-color:#2e63b8;color:#fff}.menu-list li ul{border-left:1px solid #dbdbdb;margin:.75em;padding-left:.75em}.menu-label{color:#6b6b6b;font-size:.75em;letter-spacing:.1em;text-transform:uppercase}.menu-label:not(:first-child){margin-top:1em}.menu-label:not(:last-child){margin-bottom:1em}.message{background-color:#f5f5f5;border-radius:4px;font-size:1rem}.message strong{color:currentColor}.message a:not(.button):not(.tag):not(.dropdown-item){color:currentColor;text-decoration:underline}.message.is-small,#documenter .docs-sidebar form.docs-search>input.message{font-size:.75rem}.message.is-medium{font-size:1.25rem}.message.is-large{font-size:1.5rem}.message.is-white{background-color:#fff}.message.is-white .message-header{background-color:#fff;color:#0a0a0a}.message.is-white .message-body{border-color:#fff}.message.is-black{background-color:#fafafa}.message.is-black .message-header{background-color:#0a0a0a;color:#fff}.message.is-black .message-body{border-color:#0a0a0a}.message.is-light{background-color:#fafafa}.message.is-light .message-header{background-color:#f5f5f5;color:rgba(0,0,0,0.7)}.message.is-light .message-body{border-color:#f5f5f5}.message.is-dark,.content kbd.message{background-color:#fafafa}.message.is-dark .message-header,.content kbd.message .message-header{background-color:#363636;color:#fff}.message.is-dark .message-body,.content kbd.message .message-body{border-color:#363636}.message.is-primary,.docstring>section>a.message.docs-sourcelink{background-color:#eef8fc}.message.is-primary .message-header,.docstring>section>a.message.docs-sourcelink .message-header{background-color:#4eb5de;color:#fff}.message.is-primary .message-body,.docstring>section>a.message.docs-sourcelink .message-body{border-color:#4eb5de;color:#1a6d8e}.message.is-link{background-color:#eff3fb}.message.is-link .message-header{background-color:#2e63b8;color:#fff}.message.is-link .message-body{border-color:#2e63b8;color:#3169c4}.message.is-info{background-color:#ecf7fe}.message.is-info .message-header{background-color:#209cee;color:#fff}.message.is-info .message-body{border-color:#209cee;color:#0e72b4}.message.is-success{background-color:#eefcf3}.message.is-success .message-header{background-color:#22c35b;color:#fff}.message.is-success .message-body{border-color:#22c35b;color:#198f43}.message.is-warning{background-color:#fffbeb}.message.is-warning .message-header{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.message.is-warning .message-body{border-color:#ffdd57;color:#947600}.message.is-danger{background-color:#ffeceb}.message.is-danger .message-header{background-color:#da0b00;color:#fff}.message.is-danger .message-body{border-color:#da0b00;color:#f50c00}.message-header{align-items:center;background-color:#222;border-radius:4px 4px 0 0;color:#fff;display:flex;font-weight:700;justify-content:space-between;line-height:1.25;padding:0.75em 1em;position:relative}.message-header .delete{flex-grow:0;flex-shrink:0;margin-left:.75em}.message-header+.message-body{border-width:0;border-top-left-radius:0;border-top-right-radius:0}.message-body{border-color:#dbdbdb;border-radius:4px;border-style:solid;border-width:0 0 0 4px;color:#222;padding:1.25em 1.5em}.message-body code,.message-body pre{background-color:#fff}.message-body pre code{background-color:rgba(0,0,0,0)}.modal{align-items:center;display:none;flex-direction:column;justify-content:center;overflow:hidden;position:fixed;z-index:40}.modal.is-active{display:flex}.modal-background{background-color:rgba(10,10,10,0.86)}.modal-content,.modal-card{margin:0 20px;max-height:calc(100vh - 160px);overflow:auto;position:relative;width:100%}@media screen and (min-width: 769px){.modal-content,.modal-card{margin:0 auto;max-height:calc(100vh - 40px);width:640px}}.modal-close{background:none;height:40px;position:fixed;right:20px;top:20px;width:40px}.modal-card{display:flex;flex-direction:column;max-height:calc(100vh - 40px);overflow:hidden;-ms-overflow-y:visible}.modal-card-head,.modal-card-foot{align-items:center;background-color:#f5f5f5;display:flex;flex-shrink:0;justify-content:flex-start;padding:20px;position:relative}.modal-card-head{border-bottom:1px solid #dbdbdb;border-top-left-radius:6px;border-top-right-radius:6px}.modal-card-title{color:#222;flex-grow:1;flex-shrink:0;font-size:1.5rem;line-height:1}.modal-card-foot{border-bottom-left-radius:6px;border-bottom-right-radius:6px;border-top:1px solid #dbdbdb}.modal-card-foot .button:not(:last-child){margin-right:.5em}.modal-card-body{-webkit-overflow-scrolling:touch;background-color:#fff;flex-grow:1;flex-shrink:1;overflow:auto;padding:20px}.navbar{background-color:#fff;min-height:3.25rem;position:relative;z-index:30}.navbar.is-white{background-color:#fff;color:#0a0a0a}.navbar.is-white .navbar-brand>.navbar-item,.navbar.is-white .navbar-brand .navbar-link{color:#0a0a0a}.navbar.is-white .navbar-brand>a.navbar-item:focus,.navbar.is-white .navbar-brand>a.navbar-item:hover,.navbar.is-white .navbar-brand>a.navbar-item.is-active,.navbar.is-white .navbar-brand .navbar-link:focus,.navbar.is-white .navbar-brand .navbar-link:hover,.navbar.is-white .navbar-brand .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}.navbar.is-white .navbar-brand .navbar-link::after{border-color:#0a0a0a}.navbar.is-white .navbar-burger{color:#0a0a0a}@media screen and (min-width: 1056px){.navbar.is-white .navbar-start>.navbar-item,.navbar.is-white .navbar-start .navbar-link,.navbar.is-white .navbar-end>.navbar-item,.navbar.is-white .navbar-end .navbar-link{color:#0a0a0a}.navbar.is-white .navbar-start>a.navbar-item:focus,.navbar.is-white .navbar-start>a.navbar-item:hover,.navbar.is-white .navbar-start>a.navbar-item.is-active,.navbar.is-white .navbar-start .navbar-link:focus,.navbar.is-white .navbar-start .navbar-link:hover,.navbar.is-white .navbar-start .navbar-link.is-active,.navbar.is-white .navbar-end>a.navbar-item:focus,.navbar.is-white .navbar-end>a.navbar-item:hover,.navbar.is-white .navbar-end>a.navbar-item.is-active,.navbar.is-white .navbar-end .navbar-link:focus,.navbar.is-white .navbar-end .navbar-link:hover,.navbar.is-white .navbar-end .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}.navbar.is-white .navbar-start .navbar-link::after,.navbar.is-white .navbar-end .navbar-link::after{border-color:#0a0a0a}.navbar.is-white .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-white .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-white .navbar-item.has-dropdown.is-active .navbar-link{background-color:#f2f2f2;color:#0a0a0a}.navbar.is-white .navbar-dropdown a.navbar-item.is-active{background-color:#fff;color:#0a0a0a}}.navbar.is-black{background-color:#0a0a0a;color:#fff}.navbar.is-black .navbar-brand>.navbar-item,.navbar.is-black .navbar-brand .navbar-link{color:#fff}.navbar.is-black .navbar-brand>a.navbar-item:focus,.navbar.is-black .navbar-brand>a.navbar-item:hover,.navbar.is-black .navbar-brand>a.navbar-item.is-active,.navbar.is-black .navbar-brand .navbar-link:focus,.navbar.is-black .navbar-brand .navbar-link:hover,.navbar.is-black .navbar-brand .navbar-link.is-active{background-color:#000;color:#fff}.navbar.is-black .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-black .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-black .navbar-start>.navbar-item,.navbar.is-black .navbar-start .navbar-link,.navbar.is-black .navbar-end>.navbar-item,.navbar.is-black .navbar-end .navbar-link{color:#fff}.navbar.is-black .navbar-start>a.navbar-item:focus,.navbar.is-black .navbar-start>a.navbar-item:hover,.navbar.is-black .navbar-start>a.navbar-item.is-active,.navbar.is-black .navbar-start .navbar-link:focus,.navbar.is-black .navbar-start .navbar-link:hover,.navbar.is-black .navbar-start .navbar-link.is-active,.navbar.is-black .navbar-end>a.navbar-item:focus,.navbar.is-black .navbar-end>a.navbar-item:hover,.navbar.is-black .navbar-end>a.navbar-item.is-active,.navbar.is-black .navbar-end .navbar-link:focus,.navbar.is-black .navbar-end .navbar-link:hover,.navbar.is-black .navbar-end .navbar-link.is-active{background-color:#000;color:#fff}.navbar.is-black .navbar-start .navbar-link::after,.navbar.is-black .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-black .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-black .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-black .navbar-item.has-dropdown.is-active .navbar-link{background-color:#000;color:#fff}.navbar.is-black .navbar-dropdown a.navbar-item.is-active{background-color:#0a0a0a;color:#fff}}.navbar.is-light{background-color:#f5f5f5;color:rgba(0,0,0,0.7)}.navbar.is-light .navbar-brand>.navbar-item,.navbar.is-light .navbar-brand .navbar-link{color:rgba(0,0,0,0.7)}.navbar.is-light .navbar-brand>a.navbar-item:focus,.navbar.is-light .navbar-brand>a.navbar-item:hover,.navbar.is-light .navbar-brand>a.navbar-item.is-active,.navbar.is-light .navbar-brand .navbar-link:focus,.navbar.is-light .navbar-brand .navbar-link:hover,.navbar.is-light .navbar-brand .navbar-link.is-active{background-color:#e8e8e8;color:rgba(0,0,0,0.7)}.navbar.is-light .navbar-brand .navbar-link::after{border-color:rgba(0,0,0,0.7)}.navbar.is-light .navbar-burger{color:rgba(0,0,0,0.7)}@media screen and (min-width: 1056px){.navbar.is-light .navbar-start>.navbar-item,.navbar.is-light .navbar-start .navbar-link,.navbar.is-light .navbar-end>.navbar-item,.navbar.is-light .navbar-end .navbar-link{color:rgba(0,0,0,0.7)}.navbar.is-light .navbar-start>a.navbar-item:focus,.navbar.is-light .navbar-start>a.navbar-item:hover,.navbar.is-light .navbar-start>a.navbar-item.is-active,.navbar.is-light .navbar-start .navbar-link:focus,.navbar.is-light .navbar-start .navbar-link:hover,.navbar.is-light .navbar-start .navbar-link.is-active,.navbar.is-light .navbar-end>a.navbar-item:focus,.navbar.is-light .navbar-end>a.navbar-item:hover,.navbar.is-light .navbar-end>a.navbar-item.is-active,.navbar.is-light .navbar-end .navbar-link:focus,.navbar.is-light .navbar-end .navbar-link:hover,.navbar.is-light .navbar-end .navbar-link.is-active{background-color:#e8e8e8;color:rgba(0,0,0,0.7)}.navbar.is-light .navbar-start .navbar-link::after,.navbar.is-light .navbar-end .navbar-link::after{border-color:rgba(0,0,0,0.7)}.navbar.is-light .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-light .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-light .navbar-item.has-dropdown.is-active .navbar-link{background-color:#e8e8e8;color:rgba(0,0,0,0.7)}.navbar.is-light .navbar-dropdown a.navbar-item.is-active{background-color:#f5f5f5;color:rgba(0,0,0,0.7)}}.navbar.is-dark,.content kbd.navbar{background-color:#363636;color:#fff}.navbar.is-dark .navbar-brand>.navbar-item,.content kbd.navbar .navbar-brand>.navbar-item,.navbar.is-dark .navbar-brand .navbar-link,.content kbd.navbar .navbar-brand .navbar-link{color:#fff}.navbar.is-dark .navbar-brand>a.navbar-item:focus,.content kbd.navbar .navbar-brand>a.navbar-item:focus,.navbar.is-dark .navbar-brand>a.navbar-item:hover,.content kbd.navbar .navbar-brand>a.navbar-item:hover,.navbar.is-dark .navbar-brand>a.navbar-item.is-active,.content kbd.navbar .navbar-brand>a.navbar-item.is-active,.navbar.is-dark .navbar-brand .navbar-link:focus,.content kbd.navbar .navbar-brand .navbar-link:focus,.navbar.is-dark .navbar-brand .navbar-link:hover,.content kbd.navbar .navbar-brand .navbar-link:hover,.navbar.is-dark .navbar-brand .navbar-link.is-active,.content kbd.navbar .navbar-brand .navbar-link.is-active{background-color:#292929;color:#fff}.navbar.is-dark .navbar-brand .navbar-link::after,.content kbd.navbar .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-dark .navbar-burger,.content kbd.navbar .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-dark .navbar-start>.navbar-item,.content kbd.navbar .navbar-start>.navbar-item,.navbar.is-dark .navbar-start .navbar-link,.content kbd.navbar .navbar-start .navbar-link,.navbar.is-dark .navbar-end>.navbar-item,.content kbd.navbar .navbar-end>.navbar-item,.navbar.is-dark .navbar-end .navbar-link,.content kbd.navbar .navbar-end .navbar-link{color:#fff}.navbar.is-dark .navbar-start>a.navbar-item:focus,.content kbd.navbar .navbar-start>a.navbar-item:focus,.navbar.is-dark .navbar-start>a.navbar-item:hover,.content kbd.navbar .navbar-start>a.navbar-item:hover,.navbar.is-dark .navbar-start>a.navbar-item.is-active,.content kbd.navbar .navbar-start>a.navbar-item.is-active,.navbar.is-dark .navbar-start .navbar-link:focus,.content kbd.navbar .navbar-start .navbar-link:focus,.navbar.is-dark .navbar-start .navbar-link:hover,.content kbd.navbar .navbar-start .navbar-link:hover,.navbar.is-dark .navbar-start .navbar-link.is-active,.content kbd.navbar .navbar-start .navbar-link.is-active,.navbar.is-dark .navbar-end>a.navbar-item:focus,.content kbd.navbar .navbar-end>a.navbar-item:focus,.navbar.is-dark .navbar-end>a.navbar-item:hover,.content kbd.navbar .navbar-end>a.navbar-item:hover,.navbar.is-dark .navbar-end>a.navbar-item.is-active,.content kbd.navbar .navbar-end>a.navbar-item.is-active,.navbar.is-dark .navbar-end .navbar-link:focus,.content kbd.navbar .navbar-end .navbar-link:focus,.navbar.is-dark .navbar-end .navbar-link:hover,.content kbd.navbar .navbar-end .navbar-link:hover,.navbar.is-dark .navbar-end .navbar-link.is-active,.content kbd.navbar .navbar-end .navbar-link.is-active{background-color:#292929;color:#fff}.navbar.is-dark .navbar-start .navbar-link::after,.content kbd.navbar .navbar-start .navbar-link::after,.navbar.is-dark .navbar-end .navbar-link::after,.content kbd.navbar .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-dark .navbar-item.has-dropdown:focus .navbar-link,.content kbd.navbar .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-dark .navbar-item.has-dropdown:hover .navbar-link,.content kbd.navbar .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-dark .navbar-item.has-dropdown.is-active .navbar-link,.content kbd.navbar .navbar-item.has-dropdown.is-active .navbar-link{background-color:#292929;color:#fff}.navbar.is-dark .navbar-dropdown a.navbar-item.is-active,.content kbd.navbar .navbar-dropdown a.navbar-item.is-active{background-color:#363636;color:#fff}}.navbar.is-primary,.docstring>section>a.navbar.docs-sourcelink{background-color:#4eb5de;color:#fff}.navbar.is-primary .navbar-brand>.navbar-item,.docstring>section>a.navbar.docs-sourcelink .navbar-brand>.navbar-item,.navbar.is-primary .navbar-brand .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link{color:#fff}.navbar.is-primary .navbar-brand>a.navbar-item:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item:focus,.navbar.is-primary .navbar-brand>a.navbar-item:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item:hover,.navbar.is-primary .navbar-brand>a.navbar-item.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-brand>a.navbar-item.is-active,.navbar.is-primary .navbar-brand .navbar-link:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link:focus,.navbar.is-primary .navbar-brand .navbar-link:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link:hover,.navbar.is-primary .navbar-brand .navbar-link.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link.is-active{background-color:#39acda;color:#fff}.navbar.is-primary .navbar-brand .navbar-link::after,.docstring>section>a.navbar.docs-sourcelink .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-primary .navbar-burger,.docstring>section>a.navbar.docs-sourcelink .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-primary .navbar-start>.navbar-item,.docstring>section>a.navbar.docs-sourcelink .navbar-start>.navbar-item,.navbar.is-primary .navbar-start .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link,.navbar.is-primary .navbar-end>.navbar-item,.docstring>section>a.navbar.docs-sourcelink .navbar-end>.navbar-item,.navbar.is-primary .navbar-end .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link{color:#fff}.navbar.is-primary .navbar-start>a.navbar-item:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item:focus,.navbar.is-primary .navbar-start>a.navbar-item:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item:hover,.navbar.is-primary .navbar-start>a.navbar-item.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-start>a.navbar-item.is-active,.navbar.is-primary .navbar-start .navbar-link:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link:focus,.navbar.is-primary .navbar-start .navbar-link:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link:hover,.navbar.is-primary .navbar-start .navbar-link.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link.is-active,.navbar.is-primary .navbar-end>a.navbar-item:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item:focus,.navbar.is-primary .navbar-end>a.navbar-item:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item:hover,.navbar.is-primary .navbar-end>a.navbar-item.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-end>a.navbar-item.is-active,.navbar.is-primary .navbar-end .navbar-link:focus,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link:focus,.navbar.is-primary .navbar-end .navbar-link:hover,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link:hover,.navbar.is-primary .navbar-end .navbar-link.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link.is-active{background-color:#39acda;color:#fff}.navbar.is-primary .navbar-start .navbar-link::after,.docstring>section>a.navbar.docs-sourcelink .navbar-start .navbar-link::after,.navbar.is-primary .navbar-end .navbar-link::after,.docstring>section>a.navbar.docs-sourcelink .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-primary .navbar-item.has-dropdown:focus .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-primary .navbar-item.has-dropdown:hover .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-primary .navbar-item.has-dropdown.is-active .navbar-link,.docstring>section>a.navbar.docs-sourcelink .navbar-item.has-dropdown.is-active .navbar-link{background-color:#39acda;color:#fff}.navbar.is-primary .navbar-dropdown a.navbar-item.is-active,.docstring>section>a.navbar.docs-sourcelink .navbar-dropdown a.navbar-item.is-active{background-color:#4eb5de;color:#fff}}.navbar.is-link{background-color:#2e63b8;color:#fff}.navbar.is-link .navbar-brand>.navbar-item,.navbar.is-link .navbar-brand .navbar-link{color:#fff}.navbar.is-link .navbar-brand>a.navbar-item:focus,.navbar.is-link .navbar-brand>a.navbar-item:hover,.navbar.is-link .navbar-brand>a.navbar-item.is-active,.navbar.is-link .navbar-brand .navbar-link:focus,.navbar.is-link .navbar-brand .navbar-link:hover,.navbar.is-link .navbar-brand .navbar-link.is-active{background-color:#2958a4;color:#fff}.navbar.is-link .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-link .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-link .navbar-start>.navbar-item,.navbar.is-link .navbar-start .navbar-link,.navbar.is-link .navbar-end>.navbar-item,.navbar.is-link .navbar-end .navbar-link{color:#fff}.navbar.is-link .navbar-start>a.navbar-item:focus,.navbar.is-link .navbar-start>a.navbar-item:hover,.navbar.is-link .navbar-start>a.navbar-item.is-active,.navbar.is-link .navbar-start .navbar-link:focus,.navbar.is-link .navbar-start .navbar-link:hover,.navbar.is-link .navbar-start .navbar-link.is-active,.navbar.is-link .navbar-end>a.navbar-item:focus,.navbar.is-link .navbar-end>a.navbar-item:hover,.navbar.is-link .navbar-end>a.navbar-item.is-active,.navbar.is-link .navbar-end .navbar-link:focus,.navbar.is-link .navbar-end .navbar-link:hover,.navbar.is-link .navbar-end .navbar-link.is-active{background-color:#2958a4;color:#fff}.navbar.is-link .navbar-start .navbar-link::after,.navbar.is-link .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-link .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-link .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-link .navbar-item.has-dropdown.is-active .navbar-link{background-color:#2958a4;color:#fff}.navbar.is-link .navbar-dropdown a.navbar-item.is-active{background-color:#2e63b8;color:#fff}}.navbar.is-info{background-color:#209cee;color:#fff}.navbar.is-info .navbar-brand>.navbar-item,.navbar.is-info .navbar-brand .navbar-link{color:#fff}.navbar.is-info .navbar-brand>a.navbar-item:focus,.navbar.is-info .navbar-brand>a.navbar-item:hover,.navbar.is-info .navbar-brand>a.navbar-item.is-active,.navbar.is-info .navbar-brand .navbar-link:focus,.navbar.is-info .navbar-brand .navbar-link:hover,.navbar.is-info .navbar-brand .navbar-link.is-active{background-color:#1190e3;color:#fff}.navbar.is-info .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-info .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-info .navbar-start>.navbar-item,.navbar.is-info .navbar-start .navbar-link,.navbar.is-info .navbar-end>.navbar-item,.navbar.is-info .navbar-end .navbar-link{color:#fff}.navbar.is-info .navbar-start>a.navbar-item:focus,.navbar.is-info .navbar-start>a.navbar-item:hover,.navbar.is-info .navbar-start>a.navbar-item.is-active,.navbar.is-info .navbar-start .navbar-link:focus,.navbar.is-info .navbar-start .navbar-link:hover,.navbar.is-info .navbar-start .navbar-link.is-active,.navbar.is-info .navbar-end>a.navbar-item:focus,.navbar.is-info .navbar-end>a.navbar-item:hover,.navbar.is-info .navbar-end>a.navbar-item.is-active,.navbar.is-info .navbar-end .navbar-link:focus,.navbar.is-info .navbar-end .navbar-link:hover,.navbar.is-info .navbar-end .navbar-link.is-active{background-color:#1190e3;color:#fff}.navbar.is-info .navbar-start .navbar-link::after,.navbar.is-info .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-info .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-info .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-info .navbar-item.has-dropdown.is-active .navbar-link{background-color:#1190e3;color:#fff}.navbar.is-info .navbar-dropdown a.navbar-item.is-active{background-color:#209cee;color:#fff}}.navbar.is-success{background-color:#22c35b;color:#fff}.navbar.is-success .navbar-brand>.navbar-item,.navbar.is-success .navbar-brand .navbar-link{color:#fff}.navbar.is-success .navbar-brand>a.navbar-item:focus,.navbar.is-success .navbar-brand>a.navbar-item:hover,.navbar.is-success .navbar-brand>a.navbar-item.is-active,.navbar.is-success .navbar-brand .navbar-link:focus,.navbar.is-success .navbar-brand .navbar-link:hover,.navbar.is-success .navbar-brand .navbar-link.is-active{background-color:#1ead51;color:#fff}.navbar.is-success .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-success .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-success .navbar-start>.navbar-item,.navbar.is-success .navbar-start .navbar-link,.navbar.is-success .navbar-end>.navbar-item,.navbar.is-success .navbar-end .navbar-link{color:#fff}.navbar.is-success .navbar-start>a.navbar-item:focus,.navbar.is-success .navbar-start>a.navbar-item:hover,.navbar.is-success .navbar-start>a.navbar-item.is-active,.navbar.is-success .navbar-start .navbar-link:focus,.navbar.is-success .navbar-start .navbar-link:hover,.navbar.is-success .navbar-start .navbar-link.is-active,.navbar.is-success .navbar-end>a.navbar-item:focus,.navbar.is-success .navbar-end>a.navbar-item:hover,.navbar.is-success .navbar-end>a.navbar-item.is-active,.navbar.is-success .navbar-end .navbar-link:focus,.navbar.is-success .navbar-end .navbar-link:hover,.navbar.is-success .navbar-end .navbar-link.is-active{background-color:#1ead51;color:#fff}.navbar.is-success .navbar-start .navbar-link::after,.navbar.is-success .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-success .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-success .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-success .navbar-item.has-dropdown.is-active .navbar-link{background-color:#1ead51;color:#fff}.navbar.is-success .navbar-dropdown a.navbar-item.is-active{background-color:#22c35b;color:#fff}}.navbar.is-warning{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-brand>.navbar-item,.navbar.is-warning .navbar-brand .navbar-link{color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-brand>a.navbar-item:focus,.navbar.is-warning .navbar-brand>a.navbar-item:hover,.navbar.is-warning .navbar-brand>a.navbar-item.is-active,.navbar.is-warning .navbar-brand .navbar-link:focus,.navbar.is-warning .navbar-brand .navbar-link:hover,.navbar.is-warning .navbar-brand .navbar-link.is-active{background-color:#ffd83e;color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-brand .navbar-link::after{border-color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-burger{color:rgba(0,0,0,0.7)}@media screen and (min-width: 1056px){.navbar.is-warning .navbar-start>.navbar-item,.navbar.is-warning .navbar-start .navbar-link,.navbar.is-warning .navbar-end>.navbar-item,.navbar.is-warning .navbar-end .navbar-link{color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-start>a.navbar-item:focus,.navbar.is-warning .navbar-start>a.navbar-item:hover,.navbar.is-warning .navbar-start>a.navbar-item.is-active,.navbar.is-warning .navbar-start .navbar-link:focus,.navbar.is-warning .navbar-start .navbar-link:hover,.navbar.is-warning .navbar-start .navbar-link.is-active,.navbar.is-warning .navbar-end>a.navbar-item:focus,.navbar.is-warning .navbar-end>a.navbar-item:hover,.navbar.is-warning .navbar-end>a.navbar-item.is-active,.navbar.is-warning .navbar-end .navbar-link:focus,.navbar.is-warning .navbar-end .navbar-link:hover,.navbar.is-warning .navbar-end .navbar-link.is-active{background-color:#ffd83e;color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-start .navbar-link::after,.navbar.is-warning .navbar-end .navbar-link::after{border-color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-warning .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-warning .navbar-item.has-dropdown.is-active .navbar-link{background-color:#ffd83e;color:rgba(0,0,0,0.7)}.navbar.is-warning .navbar-dropdown a.navbar-item.is-active{background-color:#ffdd57;color:rgba(0,0,0,0.7)}}.navbar.is-danger{background-color:#da0b00;color:#fff}.navbar.is-danger .navbar-brand>.navbar-item,.navbar.is-danger .navbar-brand .navbar-link{color:#fff}.navbar.is-danger .navbar-brand>a.navbar-item:focus,.navbar.is-danger .navbar-brand>a.navbar-item:hover,.navbar.is-danger .navbar-brand>a.navbar-item.is-active,.navbar.is-danger .navbar-brand .navbar-link:focus,.navbar.is-danger .navbar-brand .navbar-link:hover,.navbar.is-danger .navbar-brand .navbar-link.is-active{background-color:#c10a00;color:#fff}.navbar.is-danger .navbar-brand .navbar-link::after{border-color:#fff}.navbar.is-danger .navbar-burger{color:#fff}@media screen and (min-width: 1056px){.navbar.is-danger .navbar-start>.navbar-item,.navbar.is-danger .navbar-start .navbar-link,.navbar.is-danger .navbar-end>.navbar-item,.navbar.is-danger .navbar-end .navbar-link{color:#fff}.navbar.is-danger .navbar-start>a.navbar-item:focus,.navbar.is-danger .navbar-start>a.navbar-item:hover,.navbar.is-danger .navbar-start>a.navbar-item.is-active,.navbar.is-danger .navbar-start .navbar-link:focus,.navbar.is-danger .navbar-start .navbar-link:hover,.navbar.is-danger .navbar-start .navbar-link.is-active,.navbar.is-danger .navbar-end>a.navbar-item:focus,.navbar.is-danger .navbar-end>a.navbar-item:hover,.navbar.is-danger .navbar-end>a.navbar-item.is-active,.navbar.is-danger .navbar-end .navbar-link:focus,.navbar.is-danger .navbar-end .navbar-link:hover,.navbar.is-danger .navbar-end .navbar-link.is-active{background-color:#c10a00;color:#fff}.navbar.is-danger .navbar-start .navbar-link::after,.navbar.is-danger .navbar-end .navbar-link::after{border-color:#fff}.navbar.is-danger .navbar-item.has-dropdown:focus .navbar-link,.navbar.is-danger .navbar-item.has-dropdown:hover .navbar-link,.navbar.is-danger .navbar-item.has-dropdown.is-active .navbar-link{background-color:#c10a00;color:#fff}.navbar.is-danger .navbar-dropdown a.navbar-item.is-active{background-color:#da0b00;color:#fff}}.navbar>.container{align-items:stretch;display:flex;min-height:3.25rem;width:100%}.navbar.has-shadow{box-shadow:0 2px 0 0 #f5f5f5}.navbar.is-fixed-bottom,.navbar.is-fixed-top{left:0;position:fixed;right:0;z-index:30}.navbar.is-fixed-bottom{bottom:0}.navbar.is-fixed-bottom.has-shadow{box-shadow:0 -2px 0 0 #f5f5f5}.navbar.is-fixed-top{top:0}html.has-navbar-fixed-top,body.has-navbar-fixed-top{padding-top:3.25rem}html.has-navbar-fixed-bottom,body.has-navbar-fixed-bottom{padding-bottom:3.25rem}.navbar-brand,.navbar-tabs{align-items:stretch;display:flex;flex-shrink:0;min-height:3.25rem}.navbar-brand a.navbar-item:focus,.navbar-brand a.navbar-item:hover{background-color:transparent}.navbar-tabs{-webkit-overflow-scrolling:touch;max-width:100vw;overflow-x:auto;overflow-y:hidden}.navbar-burger{color:#222;-moz-appearance:none;-webkit-appearance:none;appearance:none;background:none;border:none;cursor:pointer;display:block;height:3.25rem;position:relative;width:3.25rem;margin-left:auto}.navbar-burger span{background-color:currentColor;display:block;height:1px;left:calc(50% - 8px);position:absolute;transform-origin:center;transition-duration:86ms;transition-property:background-color, opacity, transform;transition-timing-function:ease-out;width:16px}.navbar-burger span:nth-child(1){top:calc(50% - 6px)}.navbar-burger span:nth-child(2){top:calc(50% - 1px)}.navbar-burger span:nth-child(3){top:calc(50% + 4px)}.navbar-burger:hover{background-color:rgba(0,0,0,0.05)}.navbar-burger.is-active span:nth-child(1){transform:translateY(5px) rotate(45deg)}.navbar-burger.is-active span:nth-child(2){opacity:0}.navbar-burger.is-active span:nth-child(3){transform:translateY(-5px) rotate(-45deg)}.navbar-menu{display:none}.navbar-item,.navbar-link{color:#222;display:block;line-height:1.5;padding:0.5rem 0.75rem;position:relative}.navbar-item .icon:only-child,.navbar-link .icon:only-child{margin-left:-0.25rem;margin-right:-0.25rem}a.navbar-item,.navbar-link{cursor:pointer}a.navbar-item:focus,a.navbar-item:focus-within,a.navbar-item:hover,a.navbar-item.is-active,.navbar-link:focus,.navbar-link:focus-within,.navbar-link:hover,.navbar-link.is-active{background-color:#fafafa;color:#2e63b8}.navbar-item{flex-grow:0;flex-shrink:0}.navbar-item img{max-height:1.75rem}.navbar-item.has-dropdown{padding:0}.navbar-item.is-expanded{flex-grow:1;flex-shrink:1}.navbar-item.is-tab{border-bottom:1px solid transparent;min-height:3.25rem;padding-bottom:calc(0.5rem - 1px)}.navbar-item.is-tab:focus,.navbar-item.is-tab:hover{background-color:rgba(0,0,0,0);border-bottom-color:#2e63b8}.navbar-item.is-tab.is-active{background-color:rgba(0,0,0,0);border-bottom-color:#2e63b8;border-bottom-style:solid;border-bottom-width:3px;color:#2e63b8;padding-bottom:calc(0.5rem - 3px)}.navbar-content{flex-grow:1;flex-shrink:1}.navbar-link:not(.is-arrowless){padding-right:2.5em}.navbar-link:not(.is-arrowless)::after{border-color:#2e63b8;margin-top:-0.375em;right:1.125em}.navbar-dropdown{font-size:0.875rem;padding-bottom:0.5rem;padding-top:0.5rem}.navbar-dropdown .navbar-item{padding-left:1.5rem;padding-right:1.5rem}.navbar-divider{background-color:#f5f5f5;border:none;display:none;height:2px;margin:0.5rem 0}@media screen and (max-width: 1055px){.navbar>.container{display:block}.navbar-brand .navbar-item,.navbar-tabs .navbar-item{align-items:center;display:flex}.navbar-link::after{display:none}.navbar-menu{background-color:#fff;box-shadow:0 8px 16px rgba(10,10,10,0.1);padding:0.5rem 0}.navbar-menu.is-active{display:block}.navbar.is-fixed-bottom-touch,.navbar.is-fixed-top-touch{left:0;position:fixed;right:0;z-index:30}.navbar.is-fixed-bottom-touch{bottom:0}.navbar.is-fixed-bottom-touch.has-shadow{box-shadow:0 -2px 3px rgba(10,10,10,0.1)}.navbar.is-fixed-top-touch{top:0}.navbar.is-fixed-top .navbar-menu,.navbar.is-fixed-top-touch .navbar-menu{-webkit-overflow-scrolling:touch;max-height:calc(100vh - 3.25rem);overflow:auto}html.has-navbar-fixed-top-touch,body.has-navbar-fixed-top-touch{padding-top:3.25rem}html.has-navbar-fixed-bottom-touch,body.has-navbar-fixed-bottom-touch{padding-bottom:3.25rem}}@media screen and (min-width: 1056px){.navbar,.navbar-menu,.navbar-start,.navbar-end{align-items:stretch;display:flex}.navbar{min-height:3.25rem}.navbar.is-spaced{padding:1rem 2rem}.navbar.is-spaced .navbar-start,.navbar.is-spaced .navbar-end{align-items:center}.navbar.is-spaced a.navbar-item,.navbar.is-spaced .navbar-link{border-radius:4px}.navbar.is-transparent a.navbar-item:focus,.navbar.is-transparent a.navbar-item:hover,.navbar.is-transparent a.navbar-item.is-active,.navbar.is-transparent .navbar-link:focus,.navbar.is-transparent .navbar-link:hover,.navbar.is-transparent .navbar-link.is-active{background-color:transparent !important}.navbar.is-transparent .navbar-item.has-dropdown.is-active .navbar-link,.navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:focus .navbar-link,.navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:focus-within .navbar-link,.navbar.is-transparent .navbar-item.has-dropdown.is-hoverable:hover .navbar-link{background-color:transparent !important}.navbar.is-transparent .navbar-dropdown a.navbar-item:focus,.navbar.is-transparent .navbar-dropdown a.navbar-item:hover{background-color:#f5f5f5;color:#0a0a0a}.navbar.is-transparent .navbar-dropdown a.navbar-item.is-active{background-color:#f5f5f5;color:#2e63b8}.navbar-burger{display:none}.navbar-item,.navbar-link{align-items:center;display:flex}.navbar-item.has-dropdown{align-items:stretch}.navbar-item.has-dropdown-up .navbar-link::after{transform:rotate(135deg) translate(0.25em, -0.25em)}.navbar-item.has-dropdown-up .navbar-dropdown{border-bottom:2px solid #dbdbdb;border-radius:6px 6px 0 0;border-top:none;bottom:100%;box-shadow:0 -8px 8px rgba(10,10,10,0.1);top:auto}.navbar-item.is-active .navbar-dropdown,.navbar-item.is-hoverable:focus .navbar-dropdown,.navbar-item.is-hoverable:focus-within .navbar-dropdown,.navbar-item.is-hoverable:hover .navbar-dropdown{display:block}.navbar.is-spaced .navbar-item.is-active .navbar-dropdown,.navbar-item.is-active .navbar-dropdown.is-boxed,.navbar.is-spaced .navbar-item.is-hoverable:focus .navbar-dropdown,.navbar-item.is-hoverable:focus .navbar-dropdown.is-boxed,.navbar.is-spaced .navbar-item.is-hoverable:focus-within .navbar-dropdown,.navbar-item.is-hoverable:focus-within .navbar-dropdown.is-boxed,.navbar.is-spaced .navbar-item.is-hoverable:hover .navbar-dropdown,.navbar-item.is-hoverable:hover .navbar-dropdown.is-boxed{opacity:1;pointer-events:auto;transform:translateY(0)}.navbar-menu{flex-grow:1;flex-shrink:0}.navbar-start{justify-content:flex-start;margin-right:auto}.navbar-end{justify-content:flex-end;margin-left:auto}.navbar-dropdown{background-color:#fff;border-bottom-left-radius:6px;border-bottom-right-radius:6px;border-top:2px solid #dbdbdb;box-shadow:0 8px 8px rgba(10,10,10,0.1);display:none;font-size:0.875rem;left:0;min-width:100%;position:absolute;top:100%;z-index:20}.navbar-dropdown .navbar-item{padding:0.375rem 1rem;white-space:nowrap}.navbar-dropdown a.navbar-item{padding-right:3rem}.navbar-dropdown a.navbar-item:focus,.navbar-dropdown a.navbar-item:hover{background-color:#f5f5f5;color:#0a0a0a}.navbar-dropdown a.navbar-item.is-active{background-color:#f5f5f5;color:#2e63b8}.navbar.is-spaced .navbar-dropdown,.navbar-dropdown.is-boxed{border-radius:6px;border-top:none;box-shadow:0 8px 8px rgba(10,10,10,0.1), 0 0 0 1px rgba(10,10,10,0.1);display:block;opacity:0;pointer-events:none;top:calc(100% + (-4px));transform:translateY(-5px);transition-duration:86ms;transition-property:opacity, transform}.navbar-dropdown.is-right{left:auto;right:0}.navbar-divider{display:block}.navbar>.container .navbar-brand,.container>.navbar .navbar-brand{margin-left:-.75rem}.navbar>.container .navbar-menu,.container>.navbar .navbar-menu{margin-right:-.75rem}.navbar.is-fixed-bottom-desktop,.navbar.is-fixed-top-desktop{left:0;position:fixed;right:0;z-index:30}.navbar.is-fixed-bottom-desktop{bottom:0}.navbar.is-fixed-bottom-desktop.has-shadow{box-shadow:0 -2px 3px rgba(10,10,10,0.1)}.navbar.is-fixed-top-desktop{top:0}html.has-navbar-fixed-top-desktop,body.has-navbar-fixed-top-desktop{padding-top:3.25rem}html.has-navbar-fixed-bottom-desktop,body.has-navbar-fixed-bottom-desktop{padding-bottom:3.25rem}html.has-spaced-navbar-fixed-top,body.has-spaced-navbar-fixed-top{padding-top:5.25rem}html.has-spaced-navbar-fixed-bottom,body.has-spaced-navbar-fixed-bottom{padding-bottom:5.25rem}a.navbar-item.is-active,.navbar-link.is-active{color:#0a0a0a}a.navbar-item.is-active:not(:focus):not(:hover),.navbar-link.is-active:not(:focus):not(:hover){background-color:rgba(0,0,0,0)}.navbar-item.has-dropdown:focus .navbar-link,.navbar-item.has-dropdown:hover .navbar-link,.navbar-item.has-dropdown.is-active .navbar-link{background-color:#fafafa}}.hero.is-fullheight-with-navbar{min-height:calc(100vh - 3.25rem)}.pagination{font-size:1rem;margin:-.25rem}.pagination.is-small,#documenter .docs-sidebar form.docs-search>input.pagination{font-size:.75rem}.pagination.is-medium{font-size:1.25rem}.pagination.is-large{font-size:1.5rem}.pagination.is-rounded .pagination-previous,#documenter .docs-sidebar form.docs-search>input.pagination .pagination-previous,.pagination.is-rounded .pagination-next,#documenter .docs-sidebar form.docs-search>input.pagination .pagination-next{padding-left:1em;padding-right:1em;border-radius:9999px}.pagination.is-rounded .pagination-link,#documenter .docs-sidebar form.docs-search>input.pagination .pagination-link{border-radius:9999px}.pagination,.pagination-list{align-items:center;display:flex;justify-content:center;text-align:center}.pagination-previous,.pagination-next,.pagination-link,.pagination-ellipsis{font-size:1em;justify-content:center;margin:.25rem;padding-left:.5em;padding-right:.5em;text-align:center}.pagination-previous,.pagination-next,.pagination-link{border-color:#dbdbdb;color:#222;min-width:2.5em}.pagination-previous:hover,.pagination-next:hover,.pagination-link:hover{border-color:#b5b5b5;color:#363636}.pagination-previous:focus,.pagination-next:focus,.pagination-link:focus{border-color:#3c5dcd}.pagination-previous:active,.pagination-next:active,.pagination-link:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2)}.pagination-previous[disabled],.pagination-previous.is-disabled,.pagination-next[disabled],.pagination-next.is-disabled,.pagination-link[disabled],.pagination-link.is-disabled{background-color:#dbdbdb;border-color:#dbdbdb;box-shadow:none;color:#6b6b6b;opacity:0.5}.pagination-previous,.pagination-next{padding-left:.75em;padding-right:.75em;white-space:nowrap}.pagination-link.is-current{background-color:#2e63b8;border-color:#2e63b8;color:#fff}.pagination-ellipsis{color:#b5b5b5;pointer-events:none}.pagination-list{flex-wrap:wrap}.pagination-list li{list-style:none}@media screen and (max-width: 768px){.pagination{flex-wrap:wrap}.pagination-previous,.pagination-next{flex-grow:1;flex-shrink:1}.pagination-list li{flex-grow:1;flex-shrink:1}}@media screen and (min-width: 769px),print{.pagination-list{flex-grow:1;flex-shrink:1;justify-content:flex-start;order:1}.pagination-previous,.pagination-next,.pagination-link,.pagination-ellipsis{margin-bottom:0;margin-top:0}.pagination-previous{order:2}.pagination-next{order:3}.pagination{justify-content:space-between;margin-bottom:0;margin-top:0}.pagination.is-centered .pagination-previous{order:1}.pagination.is-centered .pagination-list{justify-content:center;order:2}.pagination.is-centered .pagination-next{order:3}.pagination.is-right .pagination-previous{order:1}.pagination.is-right .pagination-next{order:2}.pagination.is-right .pagination-list{justify-content:flex-end;order:3}}.panel{border-radius:6px;box-shadow:#bbb;font-size:1rem}.panel:not(:last-child){margin-bottom:1.5rem}.panel.is-white .panel-heading{background-color:#fff;color:#0a0a0a}.panel.is-white .panel-tabs a.is-active{border-bottom-color:#fff}.panel.is-white .panel-block.is-active .panel-icon{color:#fff}.panel.is-black .panel-heading{background-color:#0a0a0a;color:#fff}.panel.is-black .panel-tabs a.is-active{border-bottom-color:#0a0a0a}.panel.is-black .panel-block.is-active .panel-icon{color:#0a0a0a}.panel.is-light .panel-heading{background-color:#f5f5f5;color:rgba(0,0,0,0.7)}.panel.is-light .panel-tabs a.is-active{border-bottom-color:#f5f5f5}.panel.is-light .panel-block.is-active .panel-icon{color:#f5f5f5}.panel.is-dark .panel-heading,.content kbd.panel .panel-heading{background-color:#363636;color:#fff}.panel.is-dark .panel-tabs a.is-active,.content kbd.panel .panel-tabs a.is-active{border-bottom-color:#363636}.panel.is-dark .panel-block.is-active .panel-icon,.content kbd.panel .panel-block.is-active .panel-icon{color:#363636}.panel.is-primary .panel-heading,.docstring>section>a.panel.docs-sourcelink .panel-heading{background-color:#4eb5de;color:#fff}.panel.is-primary .panel-tabs a.is-active,.docstring>section>a.panel.docs-sourcelink .panel-tabs a.is-active{border-bottom-color:#4eb5de}.panel.is-primary .panel-block.is-active .panel-icon,.docstring>section>a.panel.docs-sourcelink .panel-block.is-active .panel-icon{color:#4eb5de}.panel.is-link .panel-heading{background-color:#2e63b8;color:#fff}.panel.is-link .panel-tabs a.is-active{border-bottom-color:#2e63b8}.panel.is-link .panel-block.is-active .panel-icon{color:#2e63b8}.panel.is-info .panel-heading{background-color:#209cee;color:#fff}.panel.is-info .panel-tabs a.is-active{border-bottom-color:#209cee}.panel.is-info .panel-block.is-active .panel-icon{color:#209cee}.panel.is-success .panel-heading{background-color:#22c35b;color:#fff}.panel.is-success .panel-tabs a.is-active{border-bottom-color:#22c35b}.panel.is-success .panel-block.is-active .panel-icon{color:#22c35b}.panel.is-warning .panel-heading{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.panel.is-warning .panel-tabs a.is-active{border-bottom-color:#ffdd57}.panel.is-warning .panel-block.is-active .panel-icon{color:#ffdd57}.panel.is-danger .panel-heading{background-color:#da0b00;color:#fff}.panel.is-danger .panel-tabs a.is-active{border-bottom-color:#da0b00}.panel.is-danger .panel-block.is-active .panel-icon{color:#da0b00}.panel-tabs:not(:last-child),.panel-block:not(:last-child){border-bottom:1px solid #ededed}.panel-heading{background-color:#ededed;border-radius:6px 6px 0 0;color:#222;font-size:1.25em;font-weight:700;line-height:1.25;padding:0.75em 1em}.panel-tabs{align-items:flex-end;display:flex;font-size:.875em;justify-content:center}.panel-tabs a{border-bottom:1px solid #dbdbdb;margin-bottom:-1px;padding:0.5em}.panel-tabs a.is-active{border-bottom-color:#4a4a4a;color:#363636}.panel-list a{color:#222}.panel-list a:hover{color:#2e63b8}.panel-block{align-items:center;color:#222;display:flex;justify-content:flex-start;padding:0.5em 0.75em}.panel-block input[type="checkbox"]{margin-right:.75em}.panel-block>.control{flex-grow:1;flex-shrink:1;width:100%}.panel-block.is-wrapped{flex-wrap:wrap}.panel-block.is-active{border-left-color:#2e63b8;color:#363636}.panel-block.is-active .panel-icon{color:#2e63b8}.panel-block:last-child{border-bottom-left-radius:6px;border-bottom-right-radius:6px}a.panel-block,label.panel-block{cursor:pointer}a.panel-block:hover,label.panel-block:hover{background-color:#f5f5f5}.panel-icon{display:inline-block;font-size:14px;height:1em;line-height:1em;text-align:center;vertical-align:top;width:1em;color:#6b6b6b;margin-right:.75em}.panel-icon .fa{font-size:inherit;line-height:inherit}.tabs{-webkit-overflow-scrolling:touch;align-items:stretch;display:flex;font-size:1rem;justify-content:space-between;overflow:hidden;overflow-x:auto;white-space:nowrap}.tabs a{align-items:center;border-bottom-color:#dbdbdb;border-bottom-style:solid;border-bottom-width:1px;color:#222;display:flex;justify-content:center;margin-bottom:-1px;padding:0.5em 1em;vertical-align:top}.tabs a:hover{border-bottom-color:#222;color:#222}.tabs li{display:block}.tabs li.is-active a{border-bottom-color:#2e63b8;color:#2e63b8}.tabs ul{align-items:center;border-bottom-color:#dbdbdb;border-bottom-style:solid;border-bottom-width:1px;display:flex;flex-grow:1;flex-shrink:0;justify-content:flex-start}.tabs ul.is-left{padding-right:0.75em}.tabs ul.is-center{flex:none;justify-content:center;padding-left:0.75em;padding-right:0.75em}.tabs ul.is-right{justify-content:flex-end;padding-left:0.75em}.tabs .icon:first-child{margin-right:.5em}.tabs .icon:last-child{margin-left:.5em}.tabs.is-centered ul{justify-content:center}.tabs.is-right ul{justify-content:flex-end}.tabs.is-boxed a{border:1px solid transparent;border-radius:4px 4px 0 0}.tabs.is-boxed a:hover{background-color:#f5f5f5;border-bottom-color:#dbdbdb}.tabs.is-boxed li.is-active a{background-color:#fff;border-color:#dbdbdb;border-bottom-color:rgba(0,0,0,0) !important}.tabs.is-fullwidth li{flex-grow:1;flex-shrink:0}.tabs.is-toggle a{border-color:#dbdbdb;border-style:solid;border-width:1px;margin-bottom:0;position:relative}.tabs.is-toggle a:hover{background-color:#f5f5f5;border-color:#b5b5b5;z-index:2}.tabs.is-toggle li+li{margin-left:-1px}.tabs.is-toggle li:first-child a{border-top-left-radius:4px;border-bottom-left-radius:4px}.tabs.is-toggle li:last-child a{border-top-right-radius:4px;border-bottom-right-radius:4px}.tabs.is-toggle li.is-active a{background-color:#2e63b8;border-color:#2e63b8;color:#fff;z-index:1}.tabs.is-toggle ul{border-bottom:none}.tabs.is-toggle.is-toggle-rounded li:first-child a{border-bottom-left-radius:9999px;border-top-left-radius:9999px;padding-left:1.25em}.tabs.is-toggle.is-toggle-rounded li:last-child a{border-bottom-right-radius:9999px;border-top-right-radius:9999px;padding-right:1.25em}.tabs.is-small,#documenter .docs-sidebar form.docs-search>input.tabs{font-size:.75rem}.tabs.is-medium{font-size:1.25rem}.tabs.is-large{font-size:1.5rem}.column{display:block;flex-basis:0;flex-grow:1;flex-shrink:1;padding:.75rem}.columns.is-mobile>.column.is-narrow{flex:none;width:unset}.columns.is-mobile>.column.is-full{flex:none;width:100%}.columns.is-mobile>.column.is-three-quarters{flex:none;width:75%}.columns.is-mobile>.column.is-two-thirds{flex:none;width:66.6666%}.columns.is-mobile>.column.is-half{flex:none;width:50%}.columns.is-mobile>.column.is-one-third{flex:none;width:33.3333%}.columns.is-mobile>.column.is-one-quarter{flex:none;width:25%}.columns.is-mobile>.column.is-one-fifth{flex:none;width:20%}.columns.is-mobile>.column.is-two-fifths{flex:none;width:40%}.columns.is-mobile>.column.is-three-fifths{flex:none;width:60%}.columns.is-mobile>.column.is-four-fifths{flex:none;width:80%}.columns.is-mobile>.column.is-offset-three-quarters{margin-left:75%}.columns.is-mobile>.column.is-offset-two-thirds{margin-left:66.6666%}.columns.is-mobile>.column.is-offset-half{margin-left:50%}.columns.is-mobile>.column.is-offset-one-third{margin-left:33.3333%}.columns.is-mobile>.column.is-offset-one-quarter{margin-left:25%}.columns.is-mobile>.column.is-offset-one-fifth{margin-left:20%}.columns.is-mobile>.column.is-offset-two-fifths{margin-left:40%}.columns.is-mobile>.column.is-offset-three-fifths{margin-left:60%}.columns.is-mobile>.column.is-offset-four-fifths{margin-left:80%}.columns.is-mobile>.column.is-0{flex:none;width:0%}.columns.is-mobile>.column.is-offset-0{margin-left:0%}.columns.is-mobile>.column.is-1{flex:none;width:8.33333337%}.columns.is-mobile>.column.is-offset-1{margin-left:8.33333337%}.columns.is-mobile>.column.is-2{flex:none;width:16.66666674%}.columns.is-mobile>.column.is-offset-2{margin-left:16.66666674%}.columns.is-mobile>.column.is-3{flex:none;width:25%}.columns.is-mobile>.column.is-offset-3{margin-left:25%}.columns.is-mobile>.column.is-4{flex:none;width:33.33333337%}.columns.is-mobile>.column.is-offset-4{margin-left:33.33333337%}.columns.is-mobile>.column.is-5{flex:none;width:41.66666674%}.columns.is-mobile>.column.is-offset-5{margin-left:41.66666674%}.columns.is-mobile>.column.is-6{flex:none;width:50%}.columns.is-mobile>.column.is-offset-6{margin-left:50%}.columns.is-mobile>.column.is-7{flex:none;width:58.33333337%}.columns.is-mobile>.column.is-offset-7{margin-left:58.33333337%}.columns.is-mobile>.column.is-8{flex:none;width:66.66666674%}.columns.is-mobile>.column.is-offset-8{margin-left:66.66666674%}.columns.is-mobile>.column.is-9{flex:none;width:75%}.columns.is-mobile>.column.is-offset-9{margin-left:75%}.columns.is-mobile>.column.is-10{flex:none;width:83.33333337%}.columns.is-mobile>.column.is-offset-10{margin-left:83.33333337%}.columns.is-mobile>.column.is-11{flex:none;width:91.66666674%}.columns.is-mobile>.column.is-offset-11{margin-left:91.66666674%}.columns.is-mobile>.column.is-12{flex:none;width:100%}.columns.is-mobile>.column.is-offset-12{margin-left:100%}@media screen and (max-width: 768px){.column.is-narrow-mobile{flex:none;width:unset}.column.is-full-mobile{flex:none;width:100%}.column.is-three-quarters-mobile{flex:none;width:75%}.column.is-two-thirds-mobile{flex:none;width:66.6666%}.column.is-half-mobile{flex:none;width:50%}.column.is-one-third-mobile{flex:none;width:33.3333%}.column.is-one-quarter-mobile{flex:none;width:25%}.column.is-one-fifth-mobile{flex:none;width:20%}.column.is-two-fifths-mobile{flex:none;width:40%}.column.is-three-fifths-mobile{flex:none;width:60%}.column.is-four-fifths-mobile{flex:none;width:80%}.column.is-offset-three-quarters-mobile{margin-left:75%}.column.is-offset-two-thirds-mobile{margin-left:66.6666%}.column.is-offset-half-mobile{margin-left:50%}.column.is-offset-one-third-mobile{margin-left:33.3333%}.column.is-offset-one-quarter-mobile{margin-left:25%}.column.is-offset-one-fifth-mobile{margin-left:20%}.column.is-offset-two-fifths-mobile{margin-left:40%}.column.is-offset-three-fifths-mobile{margin-left:60%}.column.is-offset-four-fifths-mobile{margin-left:80%}.column.is-0-mobile{flex:none;width:0%}.column.is-offset-0-mobile{margin-left:0%}.column.is-1-mobile{flex:none;width:8.33333337%}.column.is-offset-1-mobile{margin-left:8.33333337%}.column.is-2-mobile{flex:none;width:16.66666674%}.column.is-offset-2-mobile{margin-left:16.66666674%}.column.is-3-mobile{flex:none;width:25%}.column.is-offset-3-mobile{margin-left:25%}.column.is-4-mobile{flex:none;width:33.33333337%}.column.is-offset-4-mobile{margin-left:33.33333337%}.column.is-5-mobile{flex:none;width:41.66666674%}.column.is-offset-5-mobile{margin-left:41.66666674%}.column.is-6-mobile{flex:none;width:50%}.column.is-offset-6-mobile{margin-left:50%}.column.is-7-mobile{flex:none;width:58.33333337%}.column.is-offset-7-mobile{margin-left:58.33333337%}.column.is-8-mobile{flex:none;width:66.66666674%}.column.is-offset-8-mobile{margin-left:66.66666674%}.column.is-9-mobile{flex:none;width:75%}.column.is-offset-9-mobile{margin-left:75%}.column.is-10-mobile{flex:none;width:83.33333337%}.column.is-offset-10-mobile{margin-left:83.33333337%}.column.is-11-mobile{flex:none;width:91.66666674%}.column.is-offset-11-mobile{margin-left:91.66666674%}.column.is-12-mobile{flex:none;width:100%}.column.is-offset-12-mobile{margin-left:100%}}@media screen and (min-width: 769px),print{.column.is-narrow,.column.is-narrow-tablet{flex:none;width:unset}.column.is-full,.column.is-full-tablet{flex:none;width:100%}.column.is-three-quarters,.column.is-three-quarters-tablet{flex:none;width:75%}.column.is-two-thirds,.column.is-two-thirds-tablet{flex:none;width:66.6666%}.column.is-half,.column.is-half-tablet{flex:none;width:50%}.column.is-one-third,.column.is-one-third-tablet{flex:none;width:33.3333%}.column.is-one-quarter,.column.is-one-quarter-tablet{flex:none;width:25%}.column.is-one-fifth,.column.is-one-fifth-tablet{flex:none;width:20%}.column.is-two-fifths,.column.is-two-fifths-tablet{flex:none;width:40%}.column.is-three-fifths,.column.is-three-fifths-tablet{flex:none;width:60%}.column.is-four-fifths,.column.is-four-fifths-tablet{flex:none;width:80%}.column.is-offset-three-quarters,.column.is-offset-three-quarters-tablet{margin-left:75%}.column.is-offset-two-thirds,.column.is-offset-two-thirds-tablet{margin-left:66.6666%}.column.is-offset-half,.column.is-offset-half-tablet{margin-left:50%}.column.is-offset-one-third,.column.is-offset-one-third-tablet{margin-left:33.3333%}.column.is-offset-one-quarter,.column.is-offset-one-quarter-tablet{margin-left:25%}.column.is-offset-one-fifth,.column.is-offset-one-fifth-tablet{margin-left:20%}.column.is-offset-two-fifths,.column.is-offset-two-fifths-tablet{margin-left:40%}.column.is-offset-three-fifths,.column.is-offset-three-fifths-tablet{margin-left:60%}.column.is-offset-four-fifths,.column.is-offset-four-fifths-tablet{margin-left:80%}.column.is-0,.column.is-0-tablet{flex:none;width:0%}.column.is-offset-0,.column.is-offset-0-tablet{margin-left:0%}.column.is-1,.column.is-1-tablet{flex:none;width:8.33333337%}.column.is-offset-1,.column.is-offset-1-tablet{margin-left:8.33333337%}.column.is-2,.column.is-2-tablet{flex:none;width:16.66666674%}.column.is-offset-2,.column.is-offset-2-tablet{margin-left:16.66666674%}.column.is-3,.column.is-3-tablet{flex:none;width:25%}.column.is-offset-3,.column.is-offset-3-tablet{margin-left:25%}.column.is-4,.column.is-4-tablet{flex:none;width:33.33333337%}.column.is-offset-4,.column.is-offset-4-tablet{margin-left:33.33333337%}.column.is-5,.column.is-5-tablet{flex:none;width:41.66666674%}.column.is-offset-5,.column.is-offset-5-tablet{margin-left:41.66666674%}.column.is-6,.column.is-6-tablet{flex:none;width:50%}.column.is-offset-6,.column.is-offset-6-tablet{margin-left:50%}.column.is-7,.column.is-7-tablet{flex:none;width:58.33333337%}.column.is-offset-7,.column.is-offset-7-tablet{margin-left:58.33333337%}.column.is-8,.column.is-8-tablet{flex:none;width:66.66666674%}.column.is-offset-8,.column.is-offset-8-tablet{margin-left:66.66666674%}.column.is-9,.column.is-9-tablet{flex:none;width:75%}.column.is-offset-9,.column.is-offset-9-tablet{margin-left:75%}.column.is-10,.column.is-10-tablet{flex:none;width:83.33333337%}.column.is-offset-10,.column.is-offset-10-tablet{margin-left:83.33333337%}.column.is-11,.column.is-11-tablet{flex:none;width:91.66666674%}.column.is-offset-11,.column.is-offset-11-tablet{margin-left:91.66666674%}.column.is-12,.column.is-12-tablet{flex:none;width:100%}.column.is-offset-12,.column.is-offset-12-tablet{margin-left:100%}}@media screen and (max-width: 1055px){.column.is-narrow-touch{flex:none;width:unset}.column.is-full-touch{flex:none;width:100%}.column.is-three-quarters-touch{flex:none;width:75%}.column.is-two-thirds-touch{flex:none;width:66.6666%}.column.is-half-touch{flex:none;width:50%}.column.is-one-third-touch{flex:none;width:33.3333%}.column.is-one-quarter-touch{flex:none;width:25%}.column.is-one-fifth-touch{flex:none;width:20%}.column.is-two-fifths-touch{flex:none;width:40%}.column.is-three-fifths-touch{flex:none;width:60%}.column.is-four-fifths-touch{flex:none;width:80%}.column.is-offset-three-quarters-touch{margin-left:75%}.column.is-offset-two-thirds-touch{margin-left:66.6666%}.column.is-offset-half-touch{margin-left:50%}.column.is-offset-one-third-touch{margin-left:33.3333%}.column.is-offset-one-quarter-touch{margin-left:25%}.column.is-offset-one-fifth-touch{margin-left:20%}.column.is-offset-two-fifths-touch{margin-left:40%}.column.is-offset-three-fifths-touch{margin-left:60%}.column.is-offset-four-fifths-touch{margin-left:80%}.column.is-0-touch{flex:none;width:0%}.column.is-offset-0-touch{margin-left:0%}.column.is-1-touch{flex:none;width:8.33333337%}.column.is-offset-1-touch{margin-left:8.33333337%}.column.is-2-touch{flex:none;width:16.66666674%}.column.is-offset-2-touch{margin-left:16.66666674%}.column.is-3-touch{flex:none;width:25%}.column.is-offset-3-touch{margin-left:25%}.column.is-4-touch{flex:none;width:33.33333337%}.column.is-offset-4-touch{margin-left:33.33333337%}.column.is-5-touch{flex:none;width:41.66666674%}.column.is-offset-5-touch{margin-left:41.66666674%}.column.is-6-touch{flex:none;width:50%}.column.is-offset-6-touch{margin-left:50%}.column.is-7-touch{flex:none;width:58.33333337%}.column.is-offset-7-touch{margin-left:58.33333337%}.column.is-8-touch{flex:none;width:66.66666674%}.column.is-offset-8-touch{margin-left:66.66666674%}.column.is-9-touch{flex:none;width:75%}.column.is-offset-9-touch{margin-left:75%}.column.is-10-touch{flex:none;width:83.33333337%}.column.is-offset-10-touch{margin-left:83.33333337%}.column.is-11-touch{flex:none;width:91.66666674%}.column.is-offset-11-touch{margin-left:91.66666674%}.column.is-12-touch{flex:none;width:100%}.column.is-offset-12-touch{margin-left:100%}}@media screen and (min-width: 1056px){.column.is-narrow-desktop{flex:none;width:unset}.column.is-full-desktop{flex:none;width:100%}.column.is-three-quarters-desktop{flex:none;width:75%}.column.is-two-thirds-desktop{flex:none;width:66.6666%}.column.is-half-desktop{flex:none;width:50%}.column.is-one-third-desktop{flex:none;width:33.3333%}.column.is-one-quarter-desktop{flex:none;width:25%}.column.is-one-fifth-desktop{flex:none;width:20%}.column.is-two-fifths-desktop{flex:none;width:40%}.column.is-three-fifths-desktop{flex:none;width:60%}.column.is-four-fifths-desktop{flex:none;width:80%}.column.is-offset-three-quarters-desktop{margin-left:75%}.column.is-offset-two-thirds-desktop{margin-left:66.6666%}.column.is-offset-half-desktop{margin-left:50%}.column.is-offset-one-third-desktop{margin-left:33.3333%}.column.is-offset-one-quarter-desktop{margin-left:25%}.column.is-offset-one-fifth-desktop{margin-left:20%}.column.is-offset-two-fifths-desktop{margin-left:40%}.column.is-offset-three-fifths-desktop{margin-left:60%}.column.is-offset-four-fifths-desktop{margin-left:80%}.column.is-0-desktop{flex:none;width:0%}.column.is-offset-0-desktop{margin-left:0%}.column.is-1-desktop{flex:none;width:8.33333337%}.column.is-offset-1-desktop{margin-left:8.33333337%}.column.is-2-desktop{flex:none;width:16.66666674%}.column.is-offset-2-desktop{margin-left:16.66666674%}.column.is-3-desktop{flex:none;width:25%}.column.is-offset-3-desktop{margin-left:25%}.column.is-4-desktop{flex:none;width:33.33333337%}.column.is-offset-4-desktop{margin-left:33.33333337%}.column.is-5-desktop{flex:none;width:41.66666674%}.column.is-offset-5-desktop{margin-left:41.66666674%}.column.is-6-desktop{flex:none;width:50%}.column.is-offset-6-desktop{margin-left:50%}.column.is-7-desktop{flex:none;width:58.33333337%}.column.is-offset-7-desktop{margin-left:58.33333337%}.column.is-8-desktop{flex:none;width:66.66666674%}.column.is-offset-8-desktop{margin-left:66.66666674%}.column.is-9-desktop{flex:none;width:75%}.column.is-offset-9-desktop{margin-left:75%}.column.is-10-desktop{flex:none;width:83.33333337%}.column.is-offset-10-desktop{margin-left:83.33333337%}.column.is-11-desktop{flex:none;width:91.66666674%}.column.is-offset-11-desktop{margin-left:91.66666674%}.column.is-12-desktop{flex:none;width:100%}.column.is-offset-12-desktop{margin-left:100%}}@media screen and (min-width: 1216px){.column.is-narrow-widescreen{flex:none;width:unset}.column.is-full-widescreen{flex:none;width:100%}.column.is-three-quarters-widescreen{flex:none;width:75%}.column.is-two-thirds-widescreen{flex:none;width:66.6666%}.column.is-half-widescreen{flex:none;width:50%}.column.is-one-third-widescreen{flex:none;width:33.3333%}.column.is-one-quarter-widescreen{flex:none;width:25%}.column.is-one-fifth-widescreen{flex:none;width:20%}.column.is-two-fifths-widescreen{flex:none;width:40%}.column.is-three-fifths-widescreen{flex:none;width:60%}.column.is-four-fifths-widescreen{flex:none;width:80%}.column.is-offset-three-quarters-widescreen{margin-left:75%}.column.is-offset-two-thirds-widescreen{margin-left:66.6666%}.column.is-offset-half-widescreen{margin-left:50%}.column.is-offset-one-third-widescreen{margin-left:33.3333%}.column.is-offset-one-quarter-widescreen{margin-left:25%}.column.is-offset-one-fifth-widescreen{margin-left:20%}.column.is-offset-two-fifths-widescreen{margin-left:40%}.column.is-offset-three-fifths-widescreen{margin-left:60%}.column.is-offset-four-fifths-widescreen{margin-left:80%}.column.is-0-widescreen{flex:none;width:0%}.column.is-offset-0-widescreen{margin-left:0%}.column.is-1-widescreen{flex:none;width:8.33333337%}.column.is-offset-1-widescreen{margin-left:8.33333337%}.column.is-2-widescreen{flex:none;width:16.66666674%}.column.is-offset-2-widescreen{margin-left:16.66666674%}.column.is-3-widescreen{flex:none;width:25%}.column.is-offset-3-widescreen{margin-left:25%}.column.is-4-widescreen{flex:none;width:33.33333337%}.column.is-offset-4-widescreen{margin-left:33.33333337%}.column.is-5-widescreen{flex:none;width:41.66666674%}.column.is-offset-5-widescreen{margin-left:41.66666674%}.column.is-6-widescreen{flex:none;width:50%}.column.is-offset-6-widescreen{margin-left:50%}.column.is-7-widescreen{flex:none;width:58.33333337%}.column.is-offset-7-widescreen{margin-left:58.33333337%}.column.is-8-widescreen{flex:none;width:66.66666674%}.column.is-offset-8-widescreen{margin-left:66.66666674%}.column.is-9-widescreen{flex:none;width:75%}.column.is-offset-9-widescreen{margin-left:75%}.column.is-10-widescreen{flex:none;width:83.33333337%}.column.is-offset-10-widescreen{margin-left:83.33333337%}.column.is-11-widescreen{flex:none;width:91.66666674%}.column.is-offset-11-widescreen{margin-left:91.66666674%}.column.is-12-widescreen{flex:none;width:100%}.column.is-offset-12-widescreen{margin-left:100%}}@media screen and (min-width: 1408px){.column.is-narrow-fullhd{flex:none;width:unset}.column.is-full-fullhd{flex:none;width:100%}.column.is-three-quarters-fullhd{flex:none;width:75%}.column.is-two-thirds-fullhd{flex:none;width:66.6666%}.column.is-half-fullhd{flex:none;width:50%}.column.is-one-third-fullhd{flex:none;width:33.3333%}.column.is-one-quarter-fullhd{flex:none;width:25%}.column.is-one-fifth-fullhd{flex:none;width:20%}.column.is-two-fifths-fullhd{flex:none;width:40%}.column.is-three-fifths-fullhd{flex:none;width:60%}.column.is-four-fifths-fullhd{flex:none;width:80%}.column.is-offset-three-quarters-fullhd{margin-left:75%}.column.is-offset-two-thirds-fullhd{margin-left:66.6666%}.column.is-offset-half-fullhd{margin-left:50%}.column.is-offset-one-third-fullhd{margin-left:33.3333%}.column.is-offset-one-quarter-fullhd{margin-left:25%}.column.is-offset-one-fifth-fullhd{margin-left:20%}.column.is-offset-two-fifths-fullhd{margin-left:40%}.column.is-offset-three-fifths-fullhd{margin-left:60%}.column.is-offset-four-fifths-fullhd{margin-left:80%}.column.is-0-fullhd{flex:none;width:0%}.column.is-offset-0-fullhd{margin-left:0%}.column.is-1-fullhd{flex:none;width:8.33333337%}.column.is-offset-1-fullhd{margin-left:8.33333337%}.column.is-2-fullhd{flex:none;width:16.66666674%}.column.is-offset-2-fullhd{margin-left:16.66666674%}.column.is-3-fullhd{flex:none;width:25%}.column.is-offset-3-fullhd{margin-left:25%}.column.is-4-fullhd{flex:none;width:33.33333337%}.column.is-offset-4-fullhd{margin-left:33.33333337%}.column.is-5-fullhd{flex:none;width:41.66666674%}.column.is-offset-5-fullhd{margin-left:41.66666674%}.column.is-6-fullhd{flex:none;width:50%}.column.is-offset-6-fullhd{margin-left:50%}.column.is-7-fullhd{flex:none;width:58.33333337%}.column.is-offset-7-fullhd{margin-left:58.33333337%}.column.is-8-fullhd{flex:none;width:66.66666674%}.column.is-offset-8-fullhd{margin-left:66.66666674%}.column.is-9-fullhd{flex:none;width:75%}.column.is-offset-9-fullhd{margin-left:75%}.column.is-10-fullhd{flex:none;width:83.33333337%}.column.is-offset-10-fullhd{margin-left:83.33333337%}.column.is-11-fullhd{flex:none;width:91.66666674%}.column.is-offset-11-fullhd{margin-left:91.66666674%}.column.is-12-fullhd{flex:none;width:100%}.column.is-offset-12-fullhd{margin-left:100%}}.columns{margin-left:-.75rem;margin-right:-.75rem;margin-top:-.75rem}.columns:last-child{margin-bottom:-.75rem}.columns:not(:last-child){margin-bottom:calc(1.5rem - .75rem)}.columns.is-centered{justify-content:center}.columns.is-gapless{margin-left:0;margin-right:0;margin-top:0}.columns.is-gapless>.column{margin:0;padding:0 !important}.columns.is-gapless:not(:last-child){margin-bottom:1.5rem}.columns.is-gapless:last-child{margin-bottom:0}.columns.is-mobile{display:flex}.columns.is-multiline{flex-wrap:wrap}.columns.is-vcentered{align-items:center}@media screen and (min-width: 769px),print{.columns:not(.is-desktop){display:flex}}@media screen and (min-width: 1056px){.columns.is-desktop{display:flex}}.columns.is-variable{--columnGap: 0.75rem;margin-left:calc(-1 * var(--columnGap));margin-right:calc(-1 * var(--columnGap))}.columns.is-variable>.column{padding-left:var(--columnGap);padding-right:var(--columnGap)}.columns.is-variable.is-0{--columnGap: 0rem}@media screen and (max-width: 768px){.columns.is-variable.is-0-mobile{--columnGap: 0rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-0-tablet{--columnGap: 0rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-0-tablet-only{--columnGap: 0rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-0-touch{--columnGap: 0rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-0-desktop{--columnGap: 0rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-0-desktop-only{--columnGap: 0rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-0-widescreen{--columnGap: 0rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-0-widescreen-only{--columnGap: 0rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-0-fullhd{--columnGap: 0rem}}.columns.is-variable.is-1{--columnGap: .25rem}@media screen and (max-width: 768px){.columns.is-variable.is-1-mobile{--columnGap: .25rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-1-tablet{--columnGap: .25rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-1-tablet-only{--columnGap: .25rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-1-touch{--columnGap: .25rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-1-desktop{--columnGap: .25rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-1-desktop-only{--columnGap: .25rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-1-widescreen{--columnGap: .25rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-1-widescreen-only{--columnGap: .25rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-1-fullhd{--columnGap: .25rem}}.columns.is-variable.is-2{--columnGap: .5rem}@media screen and (max-width: 768px){.columns.is-variable.is-2-mobile{--columnGap: .5rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-2-tablet{--columnGap: .5rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-2-tablet-only{--columnGap: .5rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-2-touch{--columnGap: .5rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-2-desktop{--columnGap: .5rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-2-desktop-only{--columnGap: .5rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-2-widescreen{--columnGap: .5rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-2-widescreen-only{--columnGap: .5rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-2-fullhd{--columnGap: .5rem}}.columns.is-variable.is-3{--columnGap: .75rem}@media screen and (max-width: 768px){.columns.is-variable.is-3-mobile{--columnGap: .75rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-3-tablet{--columnGap: .75rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-3-tablet-only{--columnGap: .75rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-3-touch{--columnGap: .75rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-3-desktop{--columnGap: .75rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-3-desktop-only{--columnGap: .75rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-3-widescreen{--columnGap: .75rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-3-widescreen-only{--columnGap: .75rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-3-fullhd{--columnGap: .75rem}}.columns.is-variable.is-4{--columnGap: 1rem}@media screen and (max-width: 768px){.columns.is-variable.is-4-mobile{--columnGap: 1rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-4-tablet{--columnGap: 1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-4-tablet-only{--columnGap: 1rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-4-touch{--columnGap: 1rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-4-desktop{--columnGap: 1rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-4-desktop-only{--columnGap: 1rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-4-widescreen{--columnGap: 1rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-4-widescreen-only{--columnGap: 1rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-4-fullhd{--columnGap: 1rem}}.columns.is-variable.is-5{--columnGap: 1.25rem}@media screen and (max-width: 768px){.columns.is-variable.is-5-mobile{--columnGap: 1.25rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-5-tablet{--columnGap: 1.25rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-5-tablet-only{--columnGap: 1.25rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-5-touch{--columnGap: 1.25rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-5-desktop{--columnGap: 1.25rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-5-desktop-only{--columnGap: 1.25rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-5-widescreen{--columnGap: 1.25rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-5-widescreen-only{--columnGap: 1.25rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-5-fullhd{--columnGap: 1.25rem}}.columns.is-variable.is-6{--columnGap: 1.5rem}@media screen and (max-width: 768px){.columns.is-variable.is-6-mobile{--columnGap: 1.5rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-6-tablet{--columnGap: 1.5rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-6-tablet-only{--columnGap: 1.5rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-6-touch{--columnGap: 1.5rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-6-desktop{--columnGap: 1.5rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-6-desktop-only{--columnGap: 1.5rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-6-widescreen{--columnGap: 1.5rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-6-widescreen-only{--columnGap: 1.5rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-6-fullhd{--columnGap: 1.5rem}}.columns.is-variable.is-7{--columnGap: 1.75rem}@media screen and (max-width: 768px){.columns.is-variable.is-7-mobile{--columnGap: 1.75rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-7-tablet{--columnGap: 1.75rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-7-tablet-only{--columnGap: 1.75rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-7-touch{--columnGap: 1.75rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-7-desktop{--columnGap: 1.75rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-7-desktop-only{--columnGap: 1.75rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-7-widescreen{--columnGap: 1.75rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-7-widescreen-only{--columnGap: 1.75rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-7-fullhd{--columnGap: 1.75rem}}.columns.is-variable.is-8{--columnGap: 2rem}@media screen and (max-width: 768px){.columns.is-variable.is-8-mobile{--columnGap: 2rem}}@media screen and (min-width: 769px),print{.columns.is-variable.is-8-tablet{--columnGap: 2rem}}@media screen and (min-width: 769px) and (max-width: 1055px){.columns.is-variable.is-8-tablet-only{--columnGap: 2rem}}@media screen and (max-width: 1055px){.columns.is-variable.is-8-touch{--columnGap: 2rem}}@media screen and (min-width: 1056px){.columns.is-variable.is-8-desktop{--columnGap: 2rem}}@media screen and (min-width: 1056px) and (max-width: 1215px){.columns.is-variable.is-8-desktop-only{--columnGap: 2rem}}@media screen and (min-width: 1216px){.columns.is-variable.is-8-widescreen{--columnGap: 2rem}}@media screen and (min-width: 1216px) and (max-width: 1407px){.columns.is-variable.is-8-widescreen-only{--columnGap: 2rem}}@media screen and (min-width: 1408px){.columns.is-variable.is-8-fullhd{--columnGap: 2rem}}.tile{align-items:stretch;display:block;flex-basis:0;flex-grow:1;flex-shrink:1;min-height:min-content}.tile.is-ancestor{margin-left:-.75rem;margin-right:-.75rem;margin-top:-.75rem}.tile.is-ancestor:last-child{margin-bottom:-.75rem}.tile.is-ancestor:not(:last-child){margin-bottom:.75rem}.tile.is-child{margin:0 !important}.tile.is-parent{padding:.75rem}.tile.is-vertical{flex-direction:column}.tile.is-vertical>.tile.is-child:not(:last-child){margin-bottom:1.5rem !important}@media screen and (min-width: 769px),print{.tile:not(.is-child){display:flex}.tile.is-1{flex:none;width:8.33333337%}.tile.is-2{flex:none;width:16.66666674%}.tile.is-3{flex:none;width:25%}.tile.is-4{flex:none;width:33.33333337%}.tile.is-5{flex:none;width:41.66666674%}.tile.is-6{flex:none;width:50%}.tile.is-7{flex:none;width:58.33333337%}.tile.is-8{flex:none;width:66.66666674%}.tile.is-9{flex:none;width:75%}.tile.is-10{flex:none;width:83.33333337%}.tile.is-11{flex:none;width:91.66666674%}.tile.is-12{flex:none;width:100%}}.hero{align-items:stretch;display:flex;flex-direction:column;justify-content:space-between}.hero .navbar{background:none}.hero .tabs ul{border-bottom:none}.hero.is-white{background-color:#fff;color:#0a0a0a}.hero.is-white a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-white strong{color:inherit}.hero.is-white .title{color:#0a0a0a}.hero.is-white .subtitle{color:rgba(10,10,10,0.9)}.hero.is-white .subtitle a:not(.button),.hero.is-white .subtitle strong{color:#0a0a0a}@media screen and (max-width: 1055px){.hero.is-white .navbar-menu{background-color:#fff}}.hero.is-white .navbar-item,.hero.is-white .navbar-link{color:rgba(10,10,10,0.7)}.hero.is-white a.navbar-item:hover,.hero.is-white a.navbar-item.is-active,.hero.is-white .navbar-link:hover,.hero.is-white .navbar-link.is-active{background-color:#f2f2f2;color:#0a0a0a}.hero.is-white .tabs a{color:#0a0a0a;opacity:0.9}.hero.is-white .tabs a:hover{opacity:1}.hero.is-white .tabs li.is-active a{color:#fff !important;opacity:1}.hero.is-white .tabs.is-boxed a,.hero.is-white .tabs.is-toggle a{color:#0a0a0a}.hero.is-white .tabs.is-boxed a:hover,.hero.is-white .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-white .tabs.is-boxed li.is-active a,.hero.is-white .tabs.is-boxed li.is-active a:hover,.hero.is-white .tabs.is-toggle li.is-active a,.hero.is-white .tabs.is-toggle li.is-active a:hover{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}.hero.is-white.is-bold{background-image:linear-gradient(141deg, #e8e3e4 0%, #fff 71%, #fff 100%)}@media screen and (max-width: 768px){.hero.is-white.is-bold .navbar-menu{background-image:linear-gradient(141deg, #e8e3e4 0%, #fff 71%, #fff 100%)}}.hero.is-black{background-color:#0a0a0a;color:#fff}.hero.is-black a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-black strong{color:inherit}.hero.is-black .title{color:#fff}.hero.is-black .subtitle{color:rgba(255,255,255,0.9)}.hero.is-black .subtitle a:not(.button),.hero.is-black .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-black .navbar-menu{background-color:#0a0a0a}}.hero.is-black .navbar-item,.hero.is-black .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-black a.navbar-item:hover,.hero.is-black a.navbar-item.is-active,.hero.is-black .navbar-link:hover,.hero.is-black .navbar-link.is-active{background-color:#000;color:#fff}.hero.is-black .tabs a{color:#fff;opacity:0.9}.hero.is-black .tabs a:hover{opacity:1}.hero.is-black .tabs li.is-active a{color:#0a0a0a !important;opacity:1}.hero.is-black .tabs.is-boxed a,.hero.is-black .tabs.is-toggle a{color:#fff}.hero.is-black .tabs.is-boxed a:hover,.hero.is-black .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-black .tabs.is-boxed li.is-active a,.hero.is-black .tabs.is-boxed li.is-active a:hover,.hero.is-black .tabs.is-toggle li.is-active a,.hero.is-black .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#0a0a0a}.hero.is-black.is-bold{background-image:linear-gradient(141deg, #000 0%, #0a0a0a 71%, #181616 100%)}@media screen and (max-width: 768px){.hero.is-black.is-bold .navbar-menu{background-image:linear-gradient(141deg, #000 0%, #0a0a0a 71%, #181616 100%)}}.hero.is-light{background-color:#f5f5f5;color:rgba(0,0,0,0.7)}.hero.is-light a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-light strong{color:inherit}.hero.is-light .title{color:rgba(0,0,0,0.7)}.hero.is-light .subtitle{color:rgba(0,0,0,0.9)}.hero.is-light .subtitle a:not(.button),.hero.is-light .subtitle strong{color:rgba(0,0,0,0.7)}@media screen and (max-width: 1055px){.hero.is-light .navbar-menu{background-color:#f5f5f5}}.hero.is-light .navbar-item,.hero.is-light .navbar-link{color:rgba(0,0,0,0.7)}.hero.is-light a.navbar-item:hover,.hero.is-light a.navbar-item.is-active,.hero.is-light .navbar-link:hover,.hero.is-light .navbar-link.is-active{background-color:#e8e8e8;color:rgba(0,0,0,0.7)}.hero.is-light .tabs a{color:rgba(0,0,0,0.7);opacity:0.9}.hero.is-light .tabs a:hover{opacity:1}.hero.is-light .tabs li.is-active a{color:#f5f5f5 !important;opacity:1}.hero.is-light .tabs.is-boxed a,.hero.is-light .tabs.is-toggle a{color:rgba(0,0,0,0.7)}.hero.is-light .tabs.is-boxed a:hover,.hero.is-light .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-light .tabs.is-boxed li.is-active a,.hero.is-light .tabs.is-boxed li.is-active a:hover,.hero.is-light .tabs.is-toggle li.is-active a,.hero.is-light .tabs.is-toggle li.is-active a:hover{background-color:rgba(0,0,0,0.7);border-color:rgba(0,0,0,0.7);color:#f5f5f5}.hero.is-light.is-bold{background-image:linear-gradient(141deg, #dfd8d9 0%, #f5f5f5 71%, #fff 100%)}@media screen and (max-width: 768px){.hero.is-light.is-bold .navbar-menu{background-image:linear-gradient(141deg, #dfd8d9 0%, #f5f5f5 71%, #fff 100%)}}.hero.is-dark,.content kbd.hero{background-color:#363636;color:#fff}.hero.is-dark a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.content kbd.hero a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-dark strong,.content kbd.hero strong{color:inherit}.hero.is-dark .title,.content kbd.hero .title{color:#fff}.hero.is-dark .subtitle,.content kbd.hero .subtitle{color:rgba(255,255,255,0.9)}.hero.is-dark .subtitle a:not(.button),.content kbd.hero .subtitle a:not(.button),.hero.is-dark .subtitle strong,.content kbd.hero .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-dark .navbar-menu,.content kbd.hero .navbar-menu{background-color:#363636}}.hero.is-dark .navbar-item,.content kbd.hero .navbar-item,.hero.is-dark .navbar-link,.content kbd.hero .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-dark a.navbar-item:hover,.content kbd.hero a.navbar-item:hover,.hero.is-dark a.navbar-item.is-active,.content kbd.hero a.navbar-item.is-active,.hero.is-dark .navbar-link:hover,.content kbd.hero .navbar-link:hover,.hero.is-dark .navbar-link.is-active,.content kbd.hero .navbar-link.is-active{background-color:#292929;color:#fff}.hero.is-dark .tabs a,.content kbd.hero .tabs a{color:#fff;opacity:0.9}.hero.is-dark .tabs a:hover,.content kbd.hero .tabs a:hover{opacity:1}.hero.is-dark .tabs li.is-active a,.content kbd.hero .tabs li.is-active a{color:#363636 !important;opacity:1}.hero.is-dark .tabs.is-boxed a,.content kbd.hero .tabs.is-boxed a,.hero.is-dark .tabs.is-toggle a,.content kbd.hero .tabs.is-toggle a{color:#fff}.hero.is-dark .tabs.is-boxed a:hover,.content kbd.hero .tabs.is-boxed a:hover,.hero.is-dark .tabs.is-toggle a:hover,.content kbd.hero .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-dark .tabs.is-boxed li.is-active a,.content kbd.hero .tabs.is-boxed li.is-active a,.hero.is-dark .tabs.is-boxed li.is-active a:hover,.hero.is-dark .tabs.is-toggle li.is-active a,.content kbd.hero .tabs.is-toggle li.is-active a,.hero.is-dark .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#363636}.hero.is-dark.is-bold,.content kbd.hero.is-bold{background-image:linear-gradient(141deg, #1f191a 0%, #363636 71%, #46403f 100%)}@media screen and (max-width: 768px){.hero.is-dark.is-bold .navbar-menu,.content kbd.hero.is-bold .navbar-menu{background-image:linear-gradient(141deg, #1f191a 0%, #363636 71%, #46403f 100%)}}.hero.is-primary,.docstring>section>a.hero.docs-sourcelink{background-color:#4eb5de;color:#fff}.hero.is-primary a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.docstring>section>a.hero.docs-sourcelink a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-primary strong,.docstring>section>a.hero.docs-sourcelink strong{color:inherit}.hero.is-primary .title,.docstring>section>a.hero.docs-sourcelink .title{color:#fff}.hero.is-primary .subtitle,.docstring>section>a.hero.docs-sourcelink .subtitle{color:rgba(255,255,255,0.9)}.hero.is-primary .subtitle a:not(.button),.docstring>section>a.hero.docs-sourcelink .subtitle a:not(.button),.hero.is-primary .subtitle strong,.docstring>section>a.hero.docs-sourcelink .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-primary .navbar-menu,.docstring>section>a.hero.docs-sourcelink .navbar-menu{background-color:#4eb5de}}.hero.is-primary .navbar-item,.docstring>section>a.hero.docs-sourcelink .navbar-item,.hero.is-primary .navbar-link,.docstring>section>a.hero.docs-sourcelink .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-primary a.navbar-item:hover,.docstring>section>a.hero.docs-sourcelink a.navbar-item:hover,.hero.is-primary a.navbar-item.is-active,.docstring>section>a.hero.docs-sourcelink a.navbar-item.is-active,.hero.is-primary .navbar-link:hover,.docstring>section>a.hero.docs-sourcelink .navbar-link:hover,.hero.is-primary .navbar-link.is-active,.docstring>section>a.hero.docs-sourcelink .navbar-link.is-active{background-color:#39acda;color:#fff}.hero.is-primary .tabs a,.docstring>section>a.hero.docs-sourcelink .tabs a{color:#fff;opacity:0.9}.hero.is-primary .tabs a:hover,.docstring>section>a.hero.docs-sourcelink .tabs a:hover{opacity:1}.hero.is-primary .tabs li.is-active a,.docstring>section>a.hero.docs-sourcelink .tabs li.is-active a{color:#4eb5de !important;opacity:1}.hero.is-primary .tabs.is-boxed a,.docstring>section>a.hero.docs-sourcelink .tabs.is-boxed a,.hero.is-primary .tabs.is-toggle a,.docstring>section>a.hero.docs-sourcelink .tabs.is-toggle a{color:#fff}.hero.is-primary .tabs.is-boxed a:hover,.docstring>section>a.hero.docs-sourcelink .tabs.is-boxed a:hover,.hero.is-primary .tabs.is-toggle a:hover,.docstring>section>a.hero.docs-sourcelink .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-primary .tabs.is-boxed li.is-active a,.docstring>section>a.hero.docs-sourcelink .tabs.is-boxed li.is-active a,.hero.is-primary .tabs.is-boxed li.is-active a:hover,.hero.is-primary .tabs.is-toggle li.is-active a,.docstring>section>a.hero.docs-sourcelink .tabs.is-toggle li.is-active a,.hero.is-primary .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#4eb5de}.hero.is-primary.is-bold,.docstring>section>a.hero.is-bold.docs-sourcelink{background-image:linear-gradient(141deg, #1bc7de 0%, #4eb5de 71%, #5fa9e7 100%)}@media screen and (max-width: 768px){.hero.is-primary.is-bold .navbar-menu,.docstring>section>a.hero.is-bold.docs-sourcelink .navbar-menu{background-image:linear-gradient(141deg, #1bc7de 0%, #4eb5de 71%, #5fa9e7 100%)}}.hero.is-link{background-color:#2e63b8;color:#fff}.hero.is-link a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-link strong{color:inherit}.hero.is-link .title{color:#fff}.hero.is-link .subtitle{color:rgba(255,255,255,0.9)}.hero.is-link .subtitle a:not(.button),.hero.is-link .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-link .navbar-menu{background-color:#2e63b8}}.hero.is-link .navbar-item,.hero.is-link .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-link a.navbar-item:hover,.hero.is-link a.navbar-item.is-active,.hero.is-link .navbar-link:hover,.hero.is-link .navbar-link.is-active{background-color:#2958a4;color:#fff}.hero.is-link .tabs a{color:#fff;opacity:0.9}.hero.is-link .tabs a:hover{opacity:1}.hero.is-link .tabs li.is-active a{color:#2e63b8 !important;opacity:1}.hero.is-link .tabs.is-boxed a,.hero.is-link .tabs.is-toggle a{color:#fff}.hero.is-link .tabs.is-boxed a:hover,.hero.is-link .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-link .tabs.is-boxed li.is-active a,.hero.is-link .tabs.is-boxed li.is-active a:hover,.hero.is-link .tabs.is-toggle li.is-active a,.hero.is-link .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#2e63b8}.hero.is-link.is-bold{background-image:linear-gradient(141deg, #1b6098 0%, #2e63b8 71%, #2d51d2 100%)}@media screen and (max-width: 768px){.hero.is-link.is-bold .navbar-menu{background-image:linear-gradient(141deg, #1b6098 0%, #2e63b8 71%, #2d51d2 100%)}}.hero.is-info{background-color:#209cee;color:#fff}.hero.is-info a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-info strong{color:inherit}.hero.is-info .title{color:#fff}.hero.is-info .subtitle{color:rgba(255,255,255,0.9)}.hero.is-info .subtitle a:not(.button),.hero.is-info .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-info .navbar-menu{background-color:#209cee}}.hero.is-info .navbar-item,.hero.is-info .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-info a.navbar-item:hover,.hero.is-info a.navbar-item.is-active,.hero.is-info .navbar-link:hover,.hero.is-info .navbar-link.is-active{background-color:#1190e3;color:#fff}.hero.is-info .tabs a{color:#fff;opacity:0.9}.hero.is-info .tabs a:hover{opacity:1}.hero.is-info .tabs li.is-active a{color:#209cee !important;opacity:1}.hero.is-info .tabs.is-boxed a,.hero.is-info .tabs.is-toggle a{color:#fff}.hero.is-info .tabs.is-boxed a:hover,.hero.is-info .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-info .tabs.is-boxed li.is-active a,.hero.is-info .tabs.is-boxed li.is-active a:hover,.hero.is-info .tabs.is-toggle li.is-active a,.hero.is-info .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#209cee}.hero.is-info.is-bold{background-image:linear-gradient(141deg, #05a6d6 0%, #209cee 71%, #3287f5 100%)}@media screen and (max-width: 768px){.hero.is-info.is-bold .navbar-menu{background-image:linear-gradient(141deg, #05a6d6 0%, #209cee 71%, #3287f5 100%)}}.hero.is-success{background-color:#22c35b;color:#fff}.hero.is-success a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-success strong{color:inherit}.hero.is-success .title{color:#fff}.hero.is-success .subtitle{color:rgba(255,255,255,0.9)}.hero.is-success .subtitle a:not(.button),.hero.is-success .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-success .navbar-menu{background-color:#22c35b}}.hero.is-success .navbar-item,.hero.is-success .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-success a.navbar-item:hover,.hero.is-success a.navbar-item.is-active,.hero.is-success .navbar-link:hover,.hero.is-success .navbar-link.is-active{background-color:#1ead51;color:#fff}.hero.is-success .tabs a{color:#fff;opacity:0.9}.hero.is-success .tabs a:hover{opacity:1}.hero.is-success .tabs li.is-active a{color:#22c35b !important;opacity:1}.hero.is-success .tabs.is-boxed a,.hero.is-success .tabs.is-toggle a{color:#fff}.hero.is-success .tabs.is-boxed a:hover,.hero.is-success .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-success .tabs.is-boxed li.is-active a,.hero.is-success .tabs.is-boxed li.is-active a:hover,.hero.is-success .tabs.is-toggle li.is-active a,.hero.is-success .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#22c35b}.hero.is-success.is-bold{background-image:linear-gradient(141deg, #12a02c 0%, #22c35b 71%, #1fdf83 100%)}@media screen and (max-width: 768px){.hero.is-success.is-bold .navbar-menu{background-image:linear-gradient(141deg, #12a02c 0%, #22c35b 71%, #1fdf83 100%)}}.hero.is-warning{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.hero.is-warning a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-warning strong{color:inherit}.hero.is-warning .title{color:rgba(0,0,0,0.7)}.hero.is-warning .subtitle{color:rgba(0,0,0,0.9)}.hero.is-warning .subtitle a:not(.button),.hero.is-warning .subtitle strong{color:rgba(0,0,0,0.7)}@media screen and (max-width: 1055px){.hero.is-warning .navbar-menu{background-color:#ffdd57}}.hero.is-warning .navbar-item,.hero.is-warning .navbar-link{color:rgba(0,0,0,0.7)}.hero.is-warning a.navbar-item:hover,.hero.is-warning a.navbar-item.is-active,.hero.is-warning .navbar-link:hover,.hero.is-warning .navbar-link.is-active{background-color:#ffd83e;color:rgba(0,0,0,0.7)}.hero.is-warning .tabs a{color:rgba(0,0,0,0.7);opacity:0.9}.hero.is-warning .tabs a:hover{opacity:1}.hero.is-warning .tabs li.is-active a{color:#ffdd57 !important;opacity:1}.hero.is-warning .tabs.is-boxed a,.hero.is-warning .tabs.is-toggle a{color:rgba(0,0,0,0.7)}.hero.is-warning .tabs.is-boxed a:hover,.hero.is-warning .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-warning .tabs.is-boxed li.is-active a,.hero.is-warning .tabs.is-boxed li.is-active a:hover,.hero.is-warning .tabs.is-toggle li.is-active a,.hero.is-warning .tabs.is-toggle li.is-active a:hover{background-color:rgba(0,0,0,0.7);border-color:rgba(0,0,0,0.7);color:#ffdd57}.hero.is-warning.is-bold{background-image:linear-gradient(141deg, #ffae24 0%, #ffdd57 71%, #fffa71 100%)}@media screen and (max-width: 768px){.hero.is-warning.is-bold .navbar-menu{background-image:linear-gradient(141deg, #ffae24 0%, #ffdd57 71%, #fffa71 100%)}}.hero.is-danger{background-color:#da0b00;color:#fff}.hero.is-danger a:not(.button):not(.dropdown-item):not(.tag):not(.pagination-link.is-current),.hero.is-danger strong{color:inherit}.hero.is-danger .title{color:#fff}.hero.is-danger .subtitle{color:rgba(255,255,255,0.9)}.hero.is-danger .subtitle a:not(.button),.hero.is-danger .subtitle strong{color:#fff}@media screen and (max-width: 1055px){.hero.is-danger .navbar-menu{background-color:#da0b00}}.hero.is-danger .navbar-item,.hero.is-danger .navbar-link{color:rgba(255,255,255,0.7)}.hero.is-danger a.navbar-item:hover,.hero.is-danger a.navbar-item.is-active,.hero.is-danger .navbar-link:hover,.hero.is-danger .navbar-link.is-active{background-color:#c10a00;color:#fff}.hero.is-danger .tabs a{color:#fff;opacity:0.9}.hero.is-danger .tabs a:hover{opacity:1}.hero.is-danger .tabs li.is-active a{color:#da0b00 !important;opacity:1}.hero.is-danger .tabs.is-boxed a,.hero.is-danger .tabs.is-toggle a{color:#fff}.hero.is-danger .tabs.is-boxed a:hover,.hero.is-danger .tabs.is-toggle a:hover{background-color:rgba(10,10,10,0.1)}.hero.is-danger .tabs.is-boxed li.is-active a,.hero.is-danger .tabs.is-boxed li.is-active a:hover,.hero.is-danger .tabs.is-toggle li.is-active a,.hero.is-danger .tabs.is-toggle li.is-active a:hover{background-color:#fff;border-color:#fff;color:#da0b00}.hero.is-danger.is-bold{background-image:linear-gradient(141deg, #a70013 0%, #da0b00 71%, #f43500 100%)}@media screen and (max-width: 768px){.hero.is-danger.is-bold .navbar-menu{background-image:linear-gradient(141deg, #a70013 0%, #da0b00 71%, #f43500 100%)}}.hero.is-small .hero-body,#documenter .docs-sidebar form.docs-search>input.hero .hero-body{padding:1.5rem}@media screen and (min-width: 769px),print{.hero.is-medium .hero-body{padding:9rem 4.5rem}}@media screen and (min-width: 769px),print{.hero.is-large .hero-body{padding:18rem 6rem}}.hero.is-halfheight .hero-body,.hero.is-fullheight .hero-body,.hero.is-fullheight-with-navbar .hero-body{align-items:center;display:flex}.hero.is-halfheight .hero-body>.container,.hero.is-fullheight .hero-body>.container,.hero.is-fullheight-with-navbar .hero-body>.container{flex-grow:1;flex-shrink:1}.hero.is-halfheight{min-height:50vh}.hero.is-fullheight{min-height:100vh}.hero-video{overflow:hidden}.hero-video video{left:50%;min-height:100%;min-width:100%;position:absolute;top:50%;transform:translate3d(-50%, -50%, 0)}.hero-video.is-transparent{opacity:0.3}@media screen and (max-width: 768px){.hero-video{display:none}}.hero-buttons{margin-top:1.5rem}@media screen and (max-width: 768px){.hero-buttons .button{display:flex}.hero-buttons .button:not(:last-child){margin-bottom:0.75rem}}@media screen and (min-width: 769px),print{.hero-buttons{display:flex;justify-content:center}.hero-buttons .button:not(:last-child){margin-right:1.5rem}}.hero-head,.hero-foot{flex-grow:0;flex-shrink:0}.hero-body{flex-grow:1;flex-shrink:0;padding:3rem 1.5rem}@media screen and (min-width: 769px),print{.hero-body{padding:3rem 3rem}}.section{padding:3rem 1.5rem}@media screen and (min-width: 1056px){.section{padding:3rem 3rem}.section.is-medium{padding:9rem 4.5rem}.section.is-large{padding:18rem 6rem}}.footer{background-color:#fafafa;padding:3rem 1.5rem 6rem}h1 .docs-heading-anchor,h1 .docs-heading-anchor:hover,h1 .docs-heading-anchor:visited,h2 .docs-heading-anchor,h2 .docs-heading-anchor:hover,h2 .docs-heading-anchor:visited,h3 .docs-heading-anchor,h3 .docs-heading-anchor:hover,h3 .docs-heading-anchor:visited,h4 .docs-heading-anchor,h4 .docs-heading-anchor:hover,h4 .docs-heading-anchor:visited,h5 .docs-heading-anchor,h5 .docs-heading-anchor:hover,h5 .docs-heading-anchor:visited,h6 .docs-heading-anchor,h6 .docs-heading-anchor:hover,h6 .docs-heading-anchor:visited{color:#222}h1 .docs-heading-anchor-permalink,h2 .docs-heading-anchor-permalink,h3 .docs-heading-anchor-permalink,h4 .docs-heading-anchor-permalink,h5 .docs-heading-anchor-permalink,h6 .docs-heading-anchor-permalink{visibility:hidden;vertical-align:middle;margin-left:0.5em;font-size:0.7rem}h1 .docs-heading-anchor-permalink::before,h2 .docs-heading-anchor-permalink::before,h3 .docs-heading-anchor-permalink::before,h4 .docs-heading-anchor-permalink::before,h5 .docs-heading-anchor-permalink::before,h6 .docs-heading-anchor-permalink::before{font-family:"Font Awesome 6 Free";font-weight:900;content:"\f0c1"}h1:hover .docs-heading-anchor-permalink,h2:hover .docs-heading-anchor-permalink,h3:hover .docs-heading-anchor-permalink,h4:hover .docs-heading-anchor-permalink,h5:hover .docs-heading-anchor-permalink,h6:hover .docs-heading-anchor-permalink{visibility:visible}.docs-dark-only{display:none !important}pre{position:relative;overflow:hidden}pre code,pre code.hljs{padding:0 .75rem !important;overflow:auto;display:block}pre code:first-of-type,pre code.hljs:first-of-type{padding-top:0.5rem !important}pre code:last-of-type,pre code.hljs:last-of-type{padding-bottom:0.5rem !important}pre .copy-button{opacity:0.2;transition:opacity 0.2s;position:absolute;right:0em;top:0em;padding:0.5em;width:2.5em;height:2.5em;background:transparent;border:none;font-family:"Font Awesome 6 Free";color:#222;cursor:pointer;text-align:center}pre .copy-button:focus,pre .copy-button:hover{opacity:1;background:rgba(34,34,34,0.1);color:#2e63b8}pre .copy-button.success{color:#259a12;opacity:1}pre .copy-button.error{color:#cb3c33;opacity:1}pre:hover .copy-button{opacity:1}.admonition{background-color:#b5b5b5;border-style:solid;border-width:1px;border-color:#363636;border-radius:4px;font-size:1rem}.admonition strong{color:currentColor}.admonition.is-small,#documenter .docs-sidebar form.docs-search>input.admonition{font-size:.75rem}.admonition.is-medium{font-size:1.25rem}.admonition.is-large{font-size:1.5rem}.admonition.is-default{background-color:#b5b5b5;border-color:#363636}.admonition.is-default>.admonition-header{background-color:#363636;color:#fff}.admonition.is-default>.admonition-body{color:#fff}.admonition.is-info{background-color:#def0fc;border-color:#209cee}.admonition.is-info>.admonition-header{background-color:#209cee;color:#fff}.admonition.is-info>.admonition-body{color:rgba(0,0,0,0.7)}.admonition.is-success{background-color:#bdf4d1;border-color:#22c35b}.admonition.is-success>.admonition-header{background-color:#22c35b;color:#fff}.admonition.is-success>.admonition-body{color:rgba(0,0,0,0.7)}.admonition.is-warning{background-color:#fff3c5;border-color:#ffdd57}.admonition.is-warning>.admonition-header{background-color:#ffdd57;color:rgba(0,0,0,0.7)}.admonition.is-warning>.admonition-body{color:rgba(0,0,0,0.7)}.admonition.is-danger{background-color:#ffaba7;border-color:#da0b00}.admonition.is-danger>.admonition-header{background-color:#da0b00;color:#fff}.admonition.is-danger>.admonition-body{color:rgba(0,0,0,0.7)}.admonition.is-compat{background-color:#bdeff5;border-color:#1db5c9}.admonition.is-compat>.admonition-header{background-color:#1db5c9;color:#fff}.admonition.is-compat>.admonition-body{color:rgba(0,0,0,0.7)}.admonition-header{color:#fff;background-color:#363636;align-items:center;font-weight:700;justify-content:space-between;line-height:1.25;padding:0.5rem .75rem;position:relative}.admonition-header:before{font-family:"Font Awesome 6 Free";font-weight:900;margin-right:.75rem;content:"\f06a"}details.admonition.is-details>.admonition-header{list-style:none}details.admonition.is-details>.admonition-header:before{font-family:"Font Awesome 6 Free";font-weight:900;content:"\f055"}details.admonition.is-details[open]>.admonition-header:before{font-family:"Font Awesome 6 Free";font-weight:900;content:"\f056"}.admonition-body{color:#222;padding:0.5rem .75rem}.admonition-body pre{background-color:#f5f5f5}.admonition-body code{background-color:rgba(0,0,0,0.05)}.docstring{margin-bottom:1em;background-color:rgba(0,0,0,0);border:1px solid #dbdbdb;box-shadow:2px 2px 3px rgba(10,10,10,0.1);max-width:100%}.docstring>header{cursor:pointer;display:flex;flex-grow:1;align-items:stretch;padding:0.5rem .75rem;background-color:#f5f5f5;box-shadow:0 0.125em 0.25em rgba(10,10,10,0.1);box-shadow:none;border-bottom:1px solid #dbdbdb;overflow:auto}.docstring>header code{background-color:transparent}.docstring>header .docstring-article-toggle-button{min-width:1.1rem;padding:0.2rem 0.2rem 0.2rem 0}.docstring>header .docstring-binding{margin-right:0.3em}.docstring>header .docstring-category{margin-left:0.3em}.docstring>section{position:relative;padding:.75rem .75rem;border-bottom:1px solid #dbdbdb}.docstring>section:last-child{border-bottom:none}.docstring>section>a.docs-sourcelink{transition:opacity 0.3s;opacity:0;position:absolute;right:.375rem;bottom:.375rem}.docstring>section>a.docs-sourcelink:focus{opacity:1 !important}.docstring:hover>section>a.docs-sourcelink{opacity:0.2}.docstring:focus-within>section>a.docs-sourcelink{opacity:0.2}.docstring>section:hover a.docs-sourcelink{opacity:1}.documenter-example-output{background-color:#fff}.outdated-warning-overlay{position:fixed;top:0;left:0;right:0;box-shadow:0 0 10px rgba(0,0,0,0.3);z-index:999;background-color:#ffaba7;color:rgba(0,0,0,0.7);border-bottom:3px solid #da0b00;padding:10px 35px;text-align:center;font-size:15px}.outdated-warning-overlay .outdated-warning-closer{position:absolute;top:calc(50% - 10px);right:18px;cursor:pointer;width:12px}.outdated-warning-overlay a{color:#2e63b8}.outdated-warning-overlay a:hover{color:#363636}.content pre{border:1px solid #dbdbdb}.content code{font-weight:inherit}.content a code{color:#2e63b8}.content h1 code,.content h2 code,.content h3 code,.content h4 code,.content h5 code,.content h6 code{color:#222}.content table{display:block;width:initial;max-width:100%;overflow-x:auto}.content blockquote>ul:first-child,.content blockquote>ol:first-child,.content .admonition-body>ul:first-child,.content .admonition-body>ol:first-child{margin-top:0}pre,code{font-variant-ligatures:no-contextual}.breadcrumb a.is-disabled{cursor:default;pointer-events:none}.breadcrumb a.is-disabled,.breadcrumb a.is-disabled:hover{color:#222}.hljs{background:initial !important}.katex .katex-mathml{top:0;right:0}.katex-display,mjx-container,.MathJax_Display{margin:0.5em 0 !important}html{-moz-osx-font-smoothing:auto;-webkit-font-smoothing:auto}li.no-marker{list-style:none}#documenter .docs-main>article{overflow-wrap:break-word}#documenter .docs-main>article .math-container{overflow-x:auto;overflow-y:hidden}@media screen and (min-width: 1056px){#documenter .docs-main{max-width:52rem;margin-left:20rem;padding-right:1rem}}@media screen and (max-width: 1055px){#documenter .docs-main{width:100%}#documenter .docs-main>article{max-width:52rem;margin-left:auto;margin-right:auto;margin-bottom:1rem;padding:0 1rem}#documenter .docs-main>header,#documenter .docs-main>nav{max-width:100%;width:100%;margin:0}}#documenter .docs-main header.docs-navbar{background-color:#fff;border-bottom:1px solid #dbdbdb;z-index:2;min-height:4rem;margin-bottom:1rem;display:flex}#documenter .docs-main header.docs-navbar .breadcrumb{flex-grow:1;overflow-x:hidden}#documenter .docs-main header.docs-navbar .docs-sidebar-button{display:block;font-size:1.5rem;padding-bottom:0.1rem;margin-right:1rem}#documenter .docs-main header.docs-navbar .docs-right{display:flex;white-space:nowrap;gap:1rem;align-items:center}#documenter .docs-main header.docs-navbar .docs-right .docs-icon,#documenter .docs-main header.docs-navbar .docs-right .docs-label{display:inline-block}#documenter .docs-main header.docs-navbar .docs-right .docs-label{padding:0;margin-left:0.3em}@media screen and (max-width: 1055px){#documenter .docs-main header.docs-navbar .docs-right .docs-navbar-link{margin-left:0.4rem;margin-right:0.4rem}}#documenter .docs-main header.docs-navbar>*{margin:auto 0}@media screen and (max-width: 1055px){#documenter .docs-main header.docs-navbar{position:sticky;top:0;padding:0 1rem;transition-property:top, box-shadow;-webkit-transition-property:top, box-shadow;transition-duration:0.3s;-webkit-transition-duration:0.3s}#documenter .docs-main header.docs-navbar.headroom--not-top{box-shadow:.2rem 0rem .4rem #bbb;transition-duration:0.7s;-webkit-transition-duration:0.7s}#documenter .docs-main header.docs-navbar.headroom--unpinned.headroom--not-top.headroom--not-bottom{top:-4.5rem;transition-duration:0.7s;-webkit-transition-duration:0.7s}}#documenter .docs-main section.footnotes{border-top:1px solid #dbdbdb}#documenter .docs-main section.footnotes li .tag:first-child,#documenter .docs-main section.footnotes li .docstring>section>a.docs-sourcelink:first-child,#documenter .docs-main section.footnotes li .content kbd:first-child,.content #documenter .docs-main section.footnotes li kbd:first-child{margin-right:1em;margin-bottom:0.4em}#documenter .docs-main .docs-footer{display:flex;flex-wrap:wrap;margin-left:0;margin-right:0;border-top:1px solid #dbdbdb;padding-top:1rem;padding-bottom:1rem}@media screen and (max-width: 1055px){#documenter .docs-main .docs-footer{padding-left:1rem;padding-right:1rem}}#documenter .docs-main .docs-footer .docs-footer-nextpage,#documenter .docs-main .docs-footer .docs-footer-prevpage{flex-grow:1}#documenter .docs-main .docs-footer .docs-footer-nextpage{text-align:right}#documenter .docs-main .docs-footer .flexbox-break{flex-basis:100%;height:0}#documenter .docs-main .docs-footer .footer-message{font-size:0.8em;margin:0.5em auto 0 auto;text-align:center}#documenter .docs-sidebar{display:flex;flex-direction:column;color:#0a0a0a;background-color:#f5f5f5;border-right:1px solid #dbdbdb;padding:0;flex:0 0 18rem;z-index:5;font-size:1rem;position:fixed;left:-18rem;width:18rem;height:100%;transition:left 0.3s}#documenter .docs-sidebar.visible{left:0;box-shadow:.4rem 0rem .8rem #bbb}@media screen and (min-width: 1056px){#documenter .docs-sidebar.visible{box-shadow:none}}@media screen and (min-width: 1056px){#documenter .docs-sidebar{left:0;top:0}}#documenter .docs-sidebar .docs-logo{margin-top:1rem;padding:0 1rem}#documenter .docs-sidebar .docs-logo>img{max-height:6rem;margin:auto}#documenter .docs-sidebar .docs-package-name{flex-shrink:0;font-size:1.5rem;font-weight:700;text-align:center;white-space:nowrap;overflow:hidden;padding:0.5rem 0}#documenter .docs-sidebar .docs-package-name .docs-autofit{max-width:16.2rem}#documenter .docs-sidebar .docs-package-name a,#documenter .docs-sidebar .docs-package-name a:hover{color:#0a0a0a}#documenter .docs-sidebar .docs-version-selector{border-top:1px solid #dbdbdb;display:none;padding:0.5rem}#documenter .docs-sidebar .docs-version-selector.visible{display:flex}#documenter .docs-sidebar ul.docs-menu{flex-grow:1;user-select:none;border-top:1px solid #dbdbdb;padding-bottom:1.5rem}#documenter .docs-sidebar ul.docs-menu>li>.tocitem{font-weight:bold}#documenter .docs-sidebar ul.docs-menu>li li{font-size:.95rem;margin-left:1em;border-left:1px solid #dbdbdb}#documenter .docs-sidebar ul.docs-menu input.collapse-toggle{display:none}#documenter .docs-sidebar ul.docs-menu ul.collapsed{display:none}#documenter .docs-sidebar ul.docs-menu input:checked~ul.collapsed{display:block}#documenter .docs-sidebar ul.docs-menu label.tocitem{display:flex}#documenter .docs-sidebar ul.docs-menu label.tocitem .docs-label{flex-grow:2}#documenter .docs-sidebar ul.docs-menu label.tocitem .docs-chevron{display:inline-block;font-style:normal;font-variant:normal;text-rendering:auto;line-height:1;font-size:.75rem;margin-left:1rem;margin-top:auto;margin-bottom:auto}#documenter .docs-sidebar ul.docs-menu label.tocitem .docs-chevron::before{font-family:"Font Awesome 6 Free";font-weight:900;content:"\f054"}#documenter .docs-sidebar ul.docs-menu input:checked~label.tocitem .docs-chevron::before{content:"\f078"}#documenter .docs-sidebar ul.docs-menu .tocitem{display:block;padding:0.5rem 0.5rem}#documenter .docs-sidebar ul.docs-menu .tocitem,#documenter .docs-sidebar ul.docs-menu .tocitem:hover{color:#0a0a0a;background:#f5f5f5}#documenter .docs-sidebar ul.docs-menu a.tocitem:hover,#documenter .docs-sidebar ul.docs-menu label.tocitem:hover{color:#0a0a0a;background-color:#ebebeb}#documenter .docs-sidebar ul.docs-menu li.is-active{border-top:1px solid #dbdbdb;border-bottom:1px solid #dbdbdb;background-color:#fff}#documenter .docs-sidebar ul.docs-menu li.is-active .tocitem,#documenter .docs-sidebar ul.docs-menu li.is-active .tocitem:hover{background-color:#fff;color:#0a0a0a}#documenter .docs-sidebar ul.docs-menu li.is-active ul.internal .tocitem:hover{background-color:#ebebeb;color:#0a0a0a}#documenter .docs-sidebar ul.docs-menu>li.is-active:first-child{border-top:none}#documenter .docs-sidebar ul.docs-menu ul.internal{margin:0 0.5rem 0.5rem;border-top:1px solid #dbdbdb}#documenter .docs-sidebar ul.docs-menu ul.internal li{font-size:.85rem;border-left:none;margin-left:0;margin-top:0.5rem}#documenter .docs-sidebar ul.docs-menu ul.internal .tocitem{width:100%;padding:0}#documenter .docs-sidebar ul.docs-menu ul.internal .tocitem::before{content:"⚬";margin-right:0.4em}#documenter .docs-sidebar form.docs-search{margin:auto;margin-top:0.5rem;margin-bottom:0.5rem}#documenter .docs-sidebar form.docs-search>input{width:14.4rem}#documenter .docs-sidebar #documenter-search-query{color:#707070;width:14.4rem;box-shadow:inset 0 1px 2px rgba(10,10,10,0.1)}@media screen and (min-width: 1056px){#documenter .docs-sidebar ul.docs-menu{overflow-y:auto;-webkit-overflow-scroll:touch}#documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar{width:.3rem;background:none}#documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar-thumb{border-radius:5px 0px 0px 5px;background:#e0e0e0}#documenter .docs-sidebar ul.docs-menu::-webkit-scrollbar-thumb:hover{background:#ccc}}@media screen and (max-width: 1055px){#documenter .docs-sidebar{overflow-y:auto;-webkit-overflow-scroll:touch}#documenter .docs-sidebar::-webkit-scrollbar{width:.3rem;background:none}#documenter .docs-sidebar::-webkit-scrollbar-thumb{border-radius:5px 0px 0px 5px;background:#e0e0e0}#documenter .docs-sidebar::-webkit-scrollbar-thumb:hover{background:#ccc}}kbd.search-modal-key-hints{border-radius:0.25rem;border:1px solid rgba(0,0,0,0.6);box-shadow:0 2px 0 1px rgba(0,0,0,0.6);cursor:default;font-size:0.9rem;line-height:1.5;min-width:0.75rem;text-align:center;padding:0.1rem 0.3rem;position:relative;top:-1px}.search-min-width-50{min-width:50%}.search-min-height-100{min-height:100%}.search-modal-card-body{max-height:calc(100vh - 15rem)}.search-result-link{border-radius:0.7em;transition:all 300ms}.search-result-link:hover,.search-result-link:focus{background-color:rgba(0,128,128,0.1)}.search-result-link .property-search-result-badge,.search-result-link .search-filter{transition:all 300ms}.property-search-result-badge,.search-filter{padding:0.15em 0.5em;font-size:0.8em;font-style:italic;text-transform:none !important;line-height:1.5;color:#f5f5f5;background-color:rgba(51,65,85,0.501961);border-radius:0.6rem}.search-result-link:hover .property-search-result-badge,.search-result-link:hover .search-filter,.search-result-link:focus .property-search-result-badge,.search-result-link:focus .search-filter{color:#f1f5f9;background-color:#333}.search-filter{color:#333;background-color:#f5f5f5;transition:all 300ms}.search-filter:hover,.search-filter:focus{color:#333}.search-filter-selected{color:#f5f5f5;background-color:rgba(139,0,139,0.5)}.search-filter-selected:hover,.search-filter-selected:focus{color:#f5f5f5}.search-result-highlight{background-color:#ffdd57;color:black}.search-divider{border-bottom:1px solid #dbdbdb}.search-result-title{width:85%;color:#333}.search-result-code-title{font-size:0.875rem;font-family:"JuliaMono","SFMono-Regular","Menlo","Consolas","Liberation Mono","DejaVu Sans Mono",monospace}#search-modal .modal-card-body::-webkit-scrollbar,#search-modal .filter-tabs::-webkit-scrollbar{height:10px;width:10px;background-color:transparent}#search-modal .modal-card-body::-webkit-scrollbar-thumb,#search-modal .filter-tabs::-webkit-scrollbar-thumb{background-color:gray;border-radius:1rem}#search-modal .modal-card-body::-webkit-scrollbar-track,#search-modal .filter-tabs::-webkit-scrollbar-track{-webkit-box-shadow:inset 0 0 6px rgba(0,0,0,0.6);background-color:transparent}.w-100{width:100%}.gap-2{gap:0.5rem}.gap-4{gap:1rem}.gap-8{gap:2rem}.ansi span.sgr1{font-weight:bolder}.ansi span.sgr2{font-weight:lighter}.ansi span.sgr3{font-style:italic}.ansi span.sgr4{text-decoration:underline}.ansi span.sgr7{color:#fff;background-color:#222}.ansi span.sgr8{color:transparent}.ansi span.sgr8 span{color:transparent}.ansi span.sgr9{text-decoration:line-through}.ansi span.sgr30{color:#242424}.ansi span.sgr31{color:#a7201f}.ansi span.sgr32{color:#066f00}.ansi span.sgr33{color:#856b00}.ansi span.sgr34{color:#2149b0}.ansi span.sgr35{color:#7d4498}.ansi span.sgr36{color:#007989}.ansi span.sgr37{color:gray}.ansi span.sgr40{background-color:#242424}.ansi span.sgr41{background-color:#a7201f}.ansi span.sgr42{background-color:#066f00}.ansi span.sgr43{background-color:#856b00}.ansi span.sgr44{background-color:#2149b0}.ansi span.sgr45{background-color:#7d4498}.ansi span.sgr46{background-color:#007989}.ansi span.sgr47{background-color:gray}.ansi span.sgr90{color:#616161}.ansi span.sgr91{color:#cb3c33}.ansi span.sgr92{color:#0e8300}.ansi span.sgr93{color:#a98800}.ansi span.sgr94{color:#3c5dcd}.ansi span.sgr95{color:#9256af}.ansi span.sgr96{color:#008fa3}.ansi span.sgr97{color:#f5f5f5}.ansi span.sgr100{background-color:#616161}.ansi span.sgr101{background-color:#cb3c33}.ansi span.sgr102{background-color:#0e8300}.ansi span.sgr103{background-color:#a98800}.ansi span.sgr104{background-color:#3c5dcd}.ansi span.sgr105{background-color:#9256af}.ansi span.sgr106{background-color:#008fa3}.ansi span.sgr107{background-color:#f5f5f5}code.language-julia-repl>span.hljs-meta{color:#066f00;font-weight:bolder}/*! Theme: Default Description: Original highlight.js style Author: (c) Ivan Sagalaev @@ -6,4 +6,4 @@ Website: https://highlightjs.org/ License: see project LICENSE Touched: 2021 -*/pre code.hljs{display:block;overflow-x:auto}code.hljs{padding:3px 5px}.hljs{background:#F0F0F0;color:#444}.hljs-comment{color:#888888}.hljs-tag,.hljs-punctuation{color:#444a}.hljs-tag .hljs-name,.hljs-tag .hljs-attr{color:#444}.hljs-keyword,.hljs-attribute,.hljs-selector-tag,.hljs-meta .hljs-keyword,.hljs-doctag,.hljs-name{font-weight:bold}.hljs-type,.hljs-string,.hljs-number,.hljs-selector-id,.hljs-selector-class,.hljs-quote,.hljs-template-tag,.hljs-deletion{color:#880000}.hljs-title,.hljs-section{color:#880000;font-weight:bold}.hljs-regexp,.hljs-symbol,.hljs-variable,.hljs-template-variable,.hljs-link,.hljs-selector-attr,.hljs-operator,.hljs-selector-pseudo{color:#BC6060}.hljs-literal{color:#78A960}.hljs-built_in,.hljs-bullet,.hljs-code,.hljs-addition{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta .hljs-string{color:#4d99bf}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold} +*/pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}.hljs{background:#F3F3F3;color:#444}.hljs-comment{color:#697070}.hljs-tag,.hljs-punctuation{color:#444a}.hljs-tag .hljs-name,.hljs-tag .hljs-attr{color:#444}.hljs-keyword,.hljs-attribute,.hljs-selector-tag,.hljs-meta .hljs-keyword,.hljs-doctag,.hljs-name{font-weight:bold}.hljs-type,.hljs-string,.hljs-number,.hljs-selector-id,.hljs-selector-class,.hljs-quote,.hljs-template-tag,.hljs-deletion{color:#880000}.hljs-title,.hljs-section{color:#880000;font-weight:bold}.hljs-regexp,.hljs-symbol,.hljs-variable,.hljs-template-variable,.hljs-link,.hljs-selector-attr,.hljs-operator,.hljs-selector-pseudo{color:#ab5656}.hljs-literal{color:#695}.hljs-built_in,.hljs-bullet,.hljs-code,.hljs-addition{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta .hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem} diff --git a/dev/assets/themeswap.js b/dev/assets/themeswap.js index c58e993e3..9f5eebe6a 100644 --- a/dev/assets/themeswap.js +++ b/dev/assets/themeswap.js @@ -1,20 +1,17 @@ // Small function to quickly swap out themes. Gets put into the tag.. function set_theme_from_local_storage() { - // Intialize the theme to null, which means default + // Initialize the theme to null, which means default var theme = null; // If the browser supports the localstorage and is not disabled then try to get the // documenter theme - if(window.localStorage != null) { + if (window.localStorage != null) { // Get the user-picked theme from localStorage. May be `null`, which means the default // theme. - theme = window.localStorage.getItem("documenter-theme"); + theme = window.localStorage.getItem("documenter-theme"); } - // Check if the browser supports user color preference - var darkPreference = false; // Check if the users preference is for dark color scheme - if(window.matchMedia('(prefers-color-scheme: dark)').matches === true) { - darkPreference = true; - } + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; // Initialize a few variables for the loop: // // - active: will contain the index of the theme that should be active. Note that there @@ -24,43 +21,64 @@ function set_theme_from_local_storage() { // // - disabled: style sheets that should be disabled (i.e. all the theme style sheets // that are not the currently active theme) - var active = null; var disabled = []; var darkTheme = null; + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; for (var i = 0; i < document.styleSheets.length; i++) { var ss = document.styleSheets[i]; // The tag of each style sheet is expected to have a data-theme-name attribute // which must contain the name of the theme. The names in localStorage much match this. var themename = ss.ownerNode.getAttribute("data-theme-name"); // attribute not set => non-theme stylesheet => ignore - if(themename === null) continue; + if (themename === null) continue; // To distinguish the default (primary) theme, it needs to have the data-theme-primary // attribute set. - var isprimary = (ss.ownerNode.getAttribute("data-theme-primary") !== null); - // Check if the theme is primary dark theme - var isDarkTheme = (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null); - // If ss is for dark theme then set the value of darkTheme to the name of the theme - if(isDarkTheme) darkTheme = themename; + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } // If we find a matching theme (and it's not the default), we'll set active to non-null - if(themename === theme) active = i; + if (themename === theme) active = i; // Store the style sheets of inactive themes so that we could disable them - if(themename !== theme) disabled.push(ss); + if (themename !== theme) disabled.push(ss); } - if(active !== null) { + var activeTheme = null; + if (active !== null) { // If we did find an active theme, we'll (1) add the theme--$(theme) class to - document.getElementsByTagName('html')[0].className = "theme--" + theme; - // and (2) disable all the other theme stylesheets - disabled.forEach(function(ss){ - ss.disabled = true; - }); + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. That's only for non-primary or the primary dark theme. + if (darkPreference) { + document.getElementsByTagName("html")[0].className = + "theme--" + activeTheme; + } else { + document.getElementsByTagName("html")[0].className = ""; + } } - else if(darkTheme !== null && darkPreference === true) { - // If we did find an active theme, we'll (1) add the theme--$(theme) class to - document.getElementsByTagName('html')[0].className = "theme--" + darkTheme; - // and (2) disable all the other theme stylesheets - disabled.forEach(function(ss){ - if (ss.ownerNode.getAttribute("data-theme-name") !== darkTheme) { - ss.disabled = true; - } - }); + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); } } set_theme_from_local_storage(); diff --git a/dev/assets/warner.js b/dev/assets/warner.js index 5531c8851..3f6f5d008 100644 --- a/dev/assets/warner.js +++ b/dev/assets/warner.js @@ -1,49 +1,52 @@ -function maybeAddWarning () { - // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE - // in siteinfo.js. - // If either of these are undefined something went horribly wrong, so we abort. - if ( - window.DOCUMENTER_NEWEST === undefined || - window.DOCUMENTER_CURRENT_VERSION === undefined || - window.DOCUMENTER_STABLE === undefined - ) { - return - }; +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } - // Current version is not a version number, so we can't tell if it's the newest version. Abort. - if (!/v(\d+\.)*\d+/.test(window.DOCUMENTER_CURRENT_VERSION)) { - return - }; + // Current version is not a version number, so we can't tell if it's the newest version. Abort. + if (!/v(\d+\.)*\d+/.test(window.DOCUMENTER_CURRENT_VERSION)) { + return; + } - // Current version is newest version, so no need to add a warning. - if (window.DOCUMENTER_NEWEST === window.DOCUMENTER_CURRENT_VERSION) { - return - }; + // Current version is newest version, so no need to add a warning. + if (window.DOCUMENTER_NEWEST === window.DOCUMENTER_CURRENT_VERSION) { + return; + } - // Add a noindex meta tag (unless one exists) so that search engines don't index this version of the docs. - if (document.body.querySelector('meta[name="robots"]') === null) { - const meta = document.createElement('meta'); - meta.name = 'robots'; - meta.content = 'noindex'; + // Add a noindex meta tag (unless one exists) so that search engines don't index this version of the docs. + if (document.body.querySelector('meta[name="robots"]') === null) { + const meta = document.createElement("meta"); + meta.name = "robots"; + meta.content = "noindex"; - document.getElementsByTagName('head')[0].appendChild(meta); - }; + document.getElementsByTagName("head")[0].appendChild(meta); + } - const div = document.createElement('div'); - div.classList.add('outdated-warning-overlay'); - const closer = document.createElement('button'); - closer.classList.add('outdated-warning-closer', 'delete'); - closer.addEventListener('click', function () { - document.body.removeChild(div); - }); - const href = window.documenterBaseURL + '/../' + window.DOCUMENTER_STABLE; - div.innerHTML = 'This documentation is not for the latest stable release, but for either the development version or an older release.
    Click here to go to the documentation for the latest stable release.'; - div.appendChild(closer); - document.body.appendChild(div); -}; + const div = document.createElement("div"); + div.classList.add("outdated-warning-overlay"); + const closer = document.createElement("button"); + closer.classList.add("outdated-warning-closer", "delete"); + closer.addEventListener("click", function () { + document.body.removeChild(div); + }); + const href = window.documenterBaseURL + "/../" + window.DOCUMENTER_STABLE; + div.innerHTML = + 'This documentation is not for the latest stable release, but for either the development version or an older release.
    Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} -if (document.readyState === 'loading') { - document.addEventListener('DOMContentLoaded', maybeAddWarning); +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); } else { - maybeAddWarning(); -}; + maybeAddWarning(); +} diff --git a/dev/contribute/index.html b/dev/contribute/index.html index cca3510a8..f6352eac5 100644 --- a/dev/contribute/index.html +++ b/dev/contribute/index.html @@ -1,2 +1,2 @@ -🛠 Contribute · CounterfactualExplanations.jl
    Edit on GitHub

    🛠 Contribute

    Contributions of any kind are very much welcome! Take a look at the issue to see what things we are currently working on.

    If any of the below applies to you, this might be the right open-source project for you:

    • You’re an expert in Counterfactual Explanations or Explainable AI more broadly and you are curious about Julia.
    • You’re experienced with Julia and are happy to help someone less experienced to up their game. Ideally, you are also curious about Trustworthy AI.
    • You’re new to Julia and open-source development and would like to start your learning journey by contributing to a recent and active development. Ideally, you are familiar with machine learning.

    @pat-alt here: I am still very much at the beginning of my Julia journey, so if you spot any issues or have any suggestions for design improvement, please just open issue or start a discussion.

    For more details on how to contribute see here. Please follow the SciML ColPrac guide.

    +🛠 Contribute · CounterfactualExplanations.jl

    🛠 Contribute

    Contributions of any kind are very much welcome! Take a look at the issue to see what things we are currently working on.

    If any of the below applies to you, this might be the right open-source project for you:

    • You’re an expert in Counterfactual Explanations or Explainable AI more broadly and you are curious about Julia.
    • You’re experienced with Julia and are happy to help someone less experienced to up their game. Ideally, you are also curious about Trustworthy AI.
    • You’re new to Julia and open-source development and would like to start your learning journey by contributing to a recent and active development. Ideally, you are familiar with machine learning.

    @pat-alt here: I am still very much at the beginning of my Julia journey, so if you spot any issues or have any suggestions for design improvement, please just open issue or start a discussion.

    For more details on how to contribute see here. Please follow the SciML ColPrac guide.

    diff --git a/dev/contribute/performance/index.html b/dev/contribute/performance/index.html index 579fd8f23..4d9ed2d0a 100644 --- a/dev/contribute/performance/index.html +++ b/dev/contribute/performance/index.html @@ -1,5 +1,5 @@ -- · CounterfactualExplanations.jl
    Edit on GitHub
    Random.seed!(42)
+- · CounterfactualExplanations.jl
    Random.seed!(42)
 # Counteractual data and model:
 data = TaijaData.load_linearly_separable()
 counterfactual_data = DataPreprocessing.CounterfactualData(data...)
@@ -12,4 +12,4 @@
 # Search:
 generator = GenericGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
    data_large = TaijaData.load_linearly_separable(100000)
-counterfactual_data_large = DataPreprocessing.CounterfactualData(data_large...)
    @time generate_counterfactual(x, target, counterfactual_data, M, generator)
    @time generate_counterfactual(x, target, counterfactual_data_large, M, generator)
    
+counterfactual_data_large = DataPreprocessing.CounterfactualData(data_large...)
    @time generate_counterfactual(x, target, counterfactual_data, M, generator)
    @time generate_counterfactual(x, target, counterfactual_data_large, M, generator)
    diff --git a/dev/explanation/architecture/index.html b/dev/explanation/architecture/index.html index a3f7fb036..b572039aa 100644 --- a/dev/explanation/architecture/index.html +++ b/dev/explanation/architecture/index.html @@ -1,2 +1,2 @@ -Package Architecture · CounterfactualExplanations.jl
    Edit on GitHub

    Package Architecture

    Modular, composable, scalable!

    The diagram below provides an overview of the package architecture. It is built around two core modules that are designed to be as extensible as possible through dispatch: 1) Models is concerned with making any arbitrary model compatible with the package; 2) Generators is used to implement arbitrary counterfactual search algorithms.[1] The core function of the package generate_counterfactual uses an instance of type <: AbstractFittedModel produced by the Models module and an instance of type <: AbstractGenerator produced by the Generators module.

    [1] We have made an effort to keep the code base a flexible and extensible as possible, but cannot guarantee at this point that any counterfactual generator can be implemented without further adaptation.

    +Package Architecture · CounterfactualExplanations.jl

    Package Architecture

    Modular, composable, scalable!

    The diagram below provides an overview of the package architecture. It is built around two core modules that are designed to be as extensible as possible through dispatch: 1) Models is concerned with making any arbitrary model compatible with the package; 2) Generators is used to implement arbitrary counterfactual search algorithms.[1] The core function of the package generate_counterfactual uses an instance of type <: AbstractFittedModel produced by the Models module and an instance of type <: AbstractGenerator produced by the Generators module.

    [1] We have made an effort to keep the code base a flexible and extensible as possible, but cannot guarantee at this point that any counterfactual generator can be implemented without further adaptation.

    diff --git a/dev/explanation/categorical/index.html b/dev/explanation/categorical/index.html index 405edaa5b..a5dc76168 100644 --- a/dev/explanation/categorical/index.html +++ b/dev/explanation/categorical/index.html @@ -1,5 +1,5 @@ -Categorical Features · CounterfactualExplanations.jl
    Edit on GitHub

    Categorical Features

    To illustrate how data is preprocessed under the hood, we consider a simple toy dataset with three categorical features (name, grade and sex) and one continuous feature (age):

    X = (
+Categorical Features · CounterfactualExplanations.jl
    Categorical Features
    To illustrate how data is preprocessed under the hood, we consider a simple toy dataset with three categorical features (name, grade and sex) and one continuous feature (age):
    X = (
     name=categorical(["Danesh", "Lee", "Mary", "John"]),
     grade=categorical(["A", "B", "A", "C"], ordered=true),
     sex=categorical(["male","female","male","male"]),
@@ -117,4 +117,4 @@
  0.0
  0.0
  1.0
- 1.85
    
+ 1.85
    diff --git a/dev/explanation/generators/clap_roar/index.html b/dev/explanation/generators/clap_roar/index.html index 070985196..b8a06e423 100644 --- a/dev/explanation/generators/clap_roar/index.html +++ b/dev/explanation/generators/clap_roar/index.html @@ -1,6 +1,6 @@ -ClaPROAR · CounterfactualExplanations.jl
    Edit on GitHub

    ClaPROARGenerator

    The ClaPROARGenerator was introduced in Altmeyer et al. (2023).

    Description

    The acronym Clap stands for classifier-preserving. The approach is loosely inspired by ROAR (Upadhyay, Joshi, and Lakkaraju 2021). Altmeyer et al. (2023) propose to explicitly penalize the loss incurred by the classifer when evaluated on the counterfactual $x^\prime$ at given parameter values. Formally, we have

    \[\begin{aligned} +ClaPROAR · CounterfactualExplanations.jl

    ClaPROARGenerator

    The ClaPROARGenerator was introduced in Altmeyer et al. (2023).

    Description

    The acronym Clap stands for classifier-preserving. The approach is loosely inspired by ROAR (Upadhyay, Joshi, and Lakkaraju 2021). Altmeyer et al. (2023) propose to explicitly penalize the loss incurred by the classifer when evaluated on the counterfactual $x^\prime$ at given parameter values. Formally, we have

    \[\begin{aligned} \text{extcost}(f(\mathbf{s}^\prime)) = l(M(f(\mathbf{s}^\prime)),y^\prime) \end{aligned}\]

    for each counterfactual $k$ where $l$ denotes the loss function used to train $M$. This approach is based on the intuition that (endogenous) model shifts will be triggered by counterfactuals that increase classifier loss (Altmeyer et al. 2023).

    Usage

    The approach can be used in our package as follows:

    generator = ClaPROARGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-plot(ce)

    Comparison to GenericGenerator

    The figure below compares the outcome for the GenericGenerator and the ClaPROARGenerator.

    References

    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.

    Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. 2021. “Towards Robust and Reliable Algorithmic Recourse.” https://arxiv.org/abs/2102.13620.

    +plot(ce)

    Comparison to GenericGenerator

    The figure below compares the outcome for the GenericGenerator and the ClaPROARGenerator.

    References

    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.

    Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. 2021. “Towards Robust and Reliable Algorithmic Recourse.” https://arxiv.org/abs/2102.13620.

    diff --git a/dev/explanation/generators/clue/index.html b/dev/explanation/generators/clue/index.html index 2e77b4903..7893b89ae 100644 --- a/dev/explanation/generators/clue/index.html +++ b/dev/explanation/generators/clue/index.html @@ -1,5 +1,5 @@ -CLUE · CounterfactualExplanations.jl
    Edit on GitHub

    CLUEGenerator

    In this tutorial, we introduce the CLUEGenerator, a counterfactual generator based on the Counterfactual Latent Uncertainty Explanations (CLUE) method proposed by Antorán et al. (2020).

    Description

    The CLUEGenerator leverages differentiable probabilistic models, such as Bayesian Neural Networks (BNNs), to estimate uncertainty in predictions. It aims to provide interpretable counterfactual explanations by identifying input patterns that lead to predictive uncertainty. The generator utilizes a latent variable framework and employs a decoder from a variational autoencoder (VAE) to generate counterfactual samples in latent space.

    The CLUE algorithm minimizes a loss function that combines uncertainty estimates and the distance between the generated counterfactual and the original input. By optimizing this loss function iteratively, the CLUEGenerator generates counterfactuals that are similar to the original observation but assigned low uncertainty.

    The formula for predictive entropy is as follow:

    \[\begin{aligned} +CLUE · CounterfactualExplanations.jl

    CLUEGenerator

    In this tutorial, we introduce the CLUEGenerator, a counterfactual generator based on the Counterfactual Latent Uncertainty Explanations (CLUE) method proposed by Antorán et al. (2020).

    Description

    The CLUEGenerator leverages differentiable probabilistic models, such as Bayesian Neural Networks (BNNs), to estimate uncertainty in predictions. It aims to provide interpretable counterfactual explanations by identifying input patterns that lead to predictive uncertainty. The generator utilizes a latent variable framework and employs a decoder from a variational autoencoder (VAE) to generate counterfactual samples in latent space.

    The CLUE algorithm minimizes a loss function that combines uncertainty estimates and the distance between the generated counterfactual and the original input. By optimizing this loss function iteratively, the CLUEGenerator generates counterfactuals that are similar to the original observation but assigned low uncertainty.

    The formula for predictive entropy is as follow:

    \[\begin{aligned} H(y^*|x^*, D) &= - \sum_{k=1}^{K} p(y^*=c_k|x^*, D) \log p(y^*=c_k|x^*, D) \end{aligned}\]

    Usage

    While using one must keep in mind that the CLUE algorithim is meant to find a more robust datapoint of the same class, using CLUE generator without any additional penalties/losses will mean that it is not a counterfactual generator. The generated result will be of the same class as the original input, but a more robust datapoint.

    CLUE works best for BNN’s. The CLUEGenerator can be used with any differentiable probabilistic model, but the results may not be as good as with BNNs.

    The CLUEGenerator can be used in the following manner:

    generator = CLUEGenerator()
 M = fit_model(counterfactual_data, :DeepEnsemble)
@@ -7,4 +7,4 @@
 ce = generate_counterfactual(
     x, target, counterfactual_data, M, generator;
     convergence=conv)
-plot(ce)

    Extra: The CLUE generator can also be used upon already having achieved a counterfactual with a different generator. In this case, you can use CLUE and make the counterfactual more robust.

    Note: The above documentation is based on the information provided in the CLUE paper. Please refer to the original paper for more detailed explanations and implementation specifics.

    References

    Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. “Getting a Clue: A Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.

    +plot(ce)

    Extra: The CLUE generator can also be used upon already having achieved a counterfactual with a different generator. In this case, you can use CLUE and make the counterfactual more robust.

    Note: The above documentation is based on the information provided in the CLUE paper. Please refer to the original paper for more detailed explanations and implementation specifics.

    References

    Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. “Getting a Clue: A Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.

    diff --git a/dev/explanation/generators/dice/index.html b/dev/explanation/generators/dice/index.html index 4ab9840e3..96d69ba0d 100644 --- a/dev/explanation/generators/dice/index.html +++ b/dev/explanation/generators/dice/index.html @@ -1,5 +1,5 @@ -DiCE · CounterfactualExplanations.jl
    Edit on GitHub

    DiCEGenerator

    The DiCEGenerator can be used to generate multiple diverse counterfactuals for a single factual.

    Description

    Counterfactual Explanations are not unique and there are therefore many different ways through which valid counterfactuals can be generated. In the context of Algorithmic Recourse this can be leveraged to offer individuals not one, but possibly many different ways to change a negative outcome into a positive one. One might argue that it makes sense for those different options to be as diverse as possible. This idea is at the core of DiCE, a counterfactual generator introduce by Mothilal, Sharma, and Tan (2020) that generate a diverse set of counterfactual explanations.

    Defining Diversity

    To ensure that the generated counterfactuals are diverse, Mothilal, Sharma, and Tan (2020) add a diversity constraint to the counterfactual search objective. In particular, diversity is explicitly proxied via Determinantal Point Processes (DDP).

    We can implement DDP in Julia as follows:[1]

    using LinearAlgebra
+DiCE · CounterfactualExplanations.jl
    DiCEGenerator
    The DiCEGenerator can be used to generate multiple diverse counterfactuals for a single factual.
    Description
    Counterfactual Explanations are not unique and there are therefore many different ways through which valid counterfactuals can be generated. In the context of Algorithmic Recourse this can be leveraged to offer individuals not one, but possibly many different ways to change a negative outcome into a positive one. One might argue that it makes sense for those different options to be as diverse as possible. This idea is at the core of DiCE, a counterfactual generator introduce by Mothilal, Sharma, and Tan (2020) that generate a diverse set of counterfactual explanations.
    Defining Diversity
    To ensure that the generated counterfactuals are diverse, Mothilal, Sharma, and Tan (2020) add a diversity constraint to the counterfactual search objective. In particular, diversity is explicitly proxied via Determinantal Point Processes (DDP).
    We can implement DDP in Julia as follows:[1]
    using LinearAlgebra
 function ddp_diversity(X::AbstractArray{<:Real, 3})
     xs = eachslice(X, dims = ndims(X))
     K = [1/(1 + norm(x .- y)) for x in xs, y in xs]
@@ -40,4 +40,4 @@
             num_counterfactuals=n_cf, convergence=conv
       )
     )
-end
    The figure below shows the resulting counterfactual paths. As expected, the resulting counterfactuals are more dispersed across the feature domain for higher choices of $\lambda_2$
    References
    Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.
    [1] With thanks to the respondents on Discourse
    
+end

    The figure below shows the resulting counterfactual paths. As expected, the resulting counterfactuals are more dispersed across the feature domain for higher choices of $\lambda_2$

    References

    Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.

    [1] With thanks to the respondents on Discourse

    diff --git a/dev/explanation/generators/feature_tweak/index.html b/dev/explanation/generators/feature_tweak/index.html index ffff8c69e..0a8cd6d4d 100644 --- a/dev/explanation/generators/feature_tweak/index.html +++ b/dev/explanation/generators/feature_tweak/index.html @@ -1,5 +1,5 @@ -FeatureTweak · CounterfactualExplanations.jl
    Edit on GitHub

    FeatureTweakGenerator

    Feature Tweak refers to the generator introduced by Tolomei et al. (2017). Our implementation takes inspiration from the featureTweakPy library.

    Description

    Feature Tweak is a powerful recourse algorithm for ensembles of tree-based classifiers such as random forests. Though the problem of understanding how an input to an ensemble model could be transformed in such a way that the model changes its original prediction has been proven to be NP-hard (Tolomei et al. 2017), Feature Tweak provides an algorithm that manages to tractably solve this problem in multiple real-world applications. An example of a problem Feature Tweak is able to efficiently solve, explored in depth in Tolomei et al. (2017) is the problem of transforming an advertisement that has been classified by the ensemble model as a low-quality advertisement to a high-quality one through small changes to its features. With the help of Feature Tweak, advertisers can both learn about the reasons a particular ad was marked to have a low quality, as well as receive actionable suggestions about how to convert a low-quality ad into a high-quality one.

    Though Feature Tweak is a powerful way of avoiding brute-force search in an exponential search space, it does not come without disadvantages. The primary limitations of the approach are that it’s currently only applicable to tree-based classifiers and works only in the setting of binary classification. Another problem is that though the algorithm avoids exponential-time search, it is often still computationally expensive. The algorithm may be improved in the future to tackle all of these shortcomings.

    The following equation displays how a true negative instance x can be transformed into a positively predicted instance x’. To be more precise, x’ is the best possible transformation among all transformations **x***, computed with a cost function δ.

    \[\begin{aligned} +FeatureTweak · CounterfactualExplanations.jl

    FeatureTweakGenerator

    Feature Tweak refers to the generator introduced by Tolomei et al. (2017). Our implementation takes inspiration from the featureTweakPy library.

    Description

    Feature Tweak is a powerful recourse algorithm for ensembles of tree-based classifiers such as random forests. Though the problem of understanding how an input to an ensemble model could be transformed in such a way that the model changes its original prediction has been proven to be NP-hard (Tolomei et al. 2017), Feature Tweak provides an algorithm that manages to tractably solve this problem in multiple real-world applications. An example of a problem Feature Tweak is able to efficiently solve, explored in depth in Tolomei et al. (2017) is the problem of transforming an advertisement that has been classified by the ensemble model as a low-quality advertisement to a high-quality one through small changes to its features. With the help of Feature Tweak, advertisers can both learn about the reasons a particular ad was marked to have a low quality, as well as receive actionable suggestions about how to convert a low-quality ad into a high-quality one.

    Though Feature Tweak is a powerful way of avoiding brute-force search in an exponential search space, it does not come without disadvantages. The primary limitations of the approach are that it’s currently only applicable to tree-based classifiers and works only in the setting of binary classification. Another problem is that though the algorithm avoids exponential-time search, it is often still computationally expensive. The algorithm may be improved in the future to tackle all of these shortcomings.

    The following equation displays how a true negative instance x can be transformed into a positively predicted instance x’. To be more precise, x’ is the best possible transformation among all transformations **x***, computed with a cost function δ.

    \[\begin{aligned} \mathbf{x}^\prime = \arg_{\mathbf{x^*}} \min \{ {\delta(\mathbf{x}, \mathbf{x^*}) | \hat{f}(\mathbf{x}) = -1 \wedge \hat{f}(\mathbf{x^*}) = +1} \} \end{aligned}\]

    Example

    In this example we apply the Feature Tweak algorithm to a decision tree and a random forest trained on the moons dataset. We first load the data and fit the models:

    n = 500
 counterfactual_data = CounterfactualData(TaijaData.load_moons(n)...)
@@ -31,4 +31,4 @@
     colorbar=false,
 )
 
-display(plot(p1, p2; size=(800, 400)))

    References

    Tolomei, Gabriele, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017. “Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 465–74. https://doi.org/10.1145/3097983.3098039.

    +display(plot(p1, p2; size=(800, 400)))

    References

    Tolomei, Gabriele, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017. “Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 465–74. https://doi.org/10.1145/3097983.3098039.

    diff --git a/dev/explanation/generators/generic/index.html b/dev/explanation/generators/generic/index.html index 5a3cbcac0..e4ab23410 100644 --- a/dev/explanation/generators/generic/index.html +++ b/dev/explanation/generators/generic/index.html @@ -1,4 +1,4 @@ -Generic · CounterfactualExplanations.jl
    Edit on GitHub

    GenericGenerator

    We use the term generic to relate to the basic counterfactual generator proposed by Wachter, Mittelstadt, and Russell (2017) with $L1$-norm regularization. There is also a variant of this generator that uses the distance metric proposed in Wachter, Mittelstadt, and Russell (2017), which we call WachterGenerator.

    Description

    As the term indicates, this approach is simple: it forms the baseline approach for gradient-based counterfactual generators. Wachter, Mittelstadt, and Russell (2017) were among the first to realise that

    […] explanations can, in principle, be offered without opening the “black box.”

    — Wachter, Mittelstadt, and Russell (2017)

    Gradient descent is performed directly in the feature space. Concerning the cost heuristic, the authors choose to penalize the distance of counterfactuals from the factual value. This is based on the intuitive notion that larger feature perturbations require greater effort.

    Usage

    The approach can be used in our package as follows:

    generator = GenericGenerator()
+Generic · CounterfactualExplanations.jl
    GenericGenerator
    We use the term generic to relate to the basic counterfactual generator proposed by Wachter, Mittelstadt, and Russell (2017) with $L1$-norm regularization. There is also a variant of this generator that uses the distance metric proposed in Wachter, Mittelstadt, and Russell (2017), which we call WachterGenerator.
    Description
    As the term indicates, this approach is simple: it forms the baseline approach for gradient-based counterfactual generators. Wachter, Mittelstadt, and Russell (2017) were among the first to realise that
    […] explanations can, in principle, be offered without opening the “black box.”
    — Wachter, Mittelstadt, and Russell (2017)
    Gradient descent is performed directly in the feature space. Concerning the cost heuristic, the authors choose to penalize the distance of counterfactuals from the factual value. This is based on the intuitive notion that larger feature perturbations require greater effort.
    Usage
    The approach can be used in our package as follows:
    generator = GenericGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-plot(ce)
    References
    Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harv. JL & Tech. 31: 841. https://doi.org/10.2139/ssrn.3063289.
    
+plot(ce)

    References

    Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harv. JL & Tech. 31: 841. https://doi.org/10.2139/ssrn.3063289.

    diff --git a/dev/explanation/generators/gravitational/index.html b/dev/explanation/generators/gravitational/index.html index 90849b7fc..1d8dba282 100644 --- a/dev/explanation/generators/gravitational/index.html +++ b/dev/explanation/generators/gravitational/index.html @@ -1,8 +1,8 @@ -Gravitational · CounterfactualExplanations.jl
    Edit on GitHub

    GravitationalGenerator

    The GravitationalGenerator was introduced in Altmeyer et al. (2023). It is named so because it generates counterfactuals that gravitate towards some sensible point in the target domain.

    Description

    Altmeyer et al. (2023) extend the general framework as follows,

    \[\begin{aligned} +Gravitational · CounterfactualExplanations.jl

    GravitationalGenerator

    The GravitationalGenerator was introduced in Altmeyer et al. (2023). It is named so because it generates counterfactuals that gravitate towards some sensible point in the target domain.

    Description

    Altmeyer et al. (2023) extend the general framework as follows,

    \[\begin{aligned} \mathbf{s}^\prime &= \arg \min_{\mathbf{s}^\prime \in \mathcal{S}} \{ {\text{yloss}(M(f(\mathbf{s}^\prime)),y^*)} \\ &+ \lambda_1 {\text{cost}(f(\mathbf{s}^\prime))} + \lambda_2 {\text{extcost}(f(\mathbf{s}^\prime))} \} \end{aligned} \]

    where $\text{cost}(f(\mathbf{s}^\prime))$ denotes the proxy for costs faced by the individual. “The newly introduced term $\text{extcost}(f(\mathbf{s}^\prime))$ is meant to capture and address external costs incurred by the collective of individuals in response to changes in $\mathbf{s}^\prime$.” (Altmeyer et al. 2023)

    For the GravitationalGenerator we have,

    \[\begin{aligned} \text{extcost}(f(\mathbf{s}^\prime)) = \text{dist}(f(\mathbf{s}^\prime),\bar{x}^*) \end{aligned}\]

    where $\bar{x}$ is some sensible point in the target domain, for example, the subsample average $\bar{x}^*=\text{mean}(x)$, $x \in \mathcal{D}_1$.

    There is a tradeoff then, between the distance of counterfactuals from their factual value and the chosen point in the target domain. The chart below illustrates how the counterfactual outcome changes as the penalty $\lambda_2$ on the distance to the point in the target domain is increased from left to right (holding the other penalty term constant).

    Usage

    The approach can be used in our package as follows:

    generator = GravitationalGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-display(plot(ce))

    Comparison to GenericGenerator

    The figure below compares the outcome for the GenericGenerator and the GravitationalGenerator.

    References

    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.

    +display(plot(ce))

    Comparison to GenericGenerator

    The figure below compares the outcome for the GenericGenerator and the GravitationalGenerator.

    References

    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.

    diff --git a/dev/explanation/generators/greedy/index.html b/dev/explanation/generators/greedy/index.html index 213b4ee55..ae1f4ec23 100644 --- a/dev/explanation/generators/greedy/index.html +++ b/dev/explanation/generators/greedy/index.html @@ -1,5 +1,5 @@ -Greedy · CounterfactualExplanations.jl
    Edit on GitHub

    GreedyGenerator

    We use the term greedy to describe the counterfactual generator introduced by Schut et al. (2021).

    Description

    The Greedy generator works under the premise of generating realistic counterfactuals by minimizing predictive uncertainty. Schut et al. (2021) show that for models that incorporates predictive uncertainty in their predictions, maximizing the predictive probability corresponds to minimizing the predictive uncertainty: by construction, the generated counterfactual will therefore be realistic (low epistemic uncertainty) and unambiguous (low aleatoric uncertainty).

    For the counterfactual search Schut et al. (2021) propose using a Jacobian-based Saliency Map Attack(JSMA). It is greedy in the sense that it is an “iterative algorithm that updates the most salient feature, i.e. the feature that has the largest influence on the classification, by $\delta$ at each step” (Schut et al. 2021).

    Usage

    The approach can be used in our package as follows:

    M = fit_model(counterfactual_data, :DeepEnsemble)
+Greedy · CounterfactualExplanations.jl
    GreedyGenerator
    We use the term greedy to describe the counterfactual generator introduced by Schut et al. (2021).
    Description
    The Greedy generator works under the premise of generating realistic counterfactuals by minimizing predictive uncertainty. Schut et al. (2021) show that for models that incorporates predictive uncertainty in their predictions, maximizing the predictive probability corresponds to minimizing the predictive uncertainty: by construction, the generated counterfactual will therefore be realistic (low epistemic uncertainty) and unambiguous (low aleatoric uncertainty).
    For the counterfactual search Schut et al. (2021) propose using a Jacobian-based Saliency Map Attack(JSMA). It is greedy in the sense that it is an “iterative algorithm that updates the most salient feature, i.e. the feature that has the largest influence on the classification, by $\delta$ at each step” (Schut et al. 2021).
    Usage
    The approach can be used in our package as follows:
    M = fit_model(counterfactual_data, :DeepEnsemble)
 generator = GreedyGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-plot(ce)
    References
    Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.
    
+plot(ce)

    References

    Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.

    diff --git a/dev/explanation/generators/growing_spheres/index.html b/dev/explanation/generators/growing_spheres/index.html index f927bed83..df927d8e8 100644 --- a/dev/explanation/generators/growing_spheres/index.html +++ b/dev/explanation/generators/growing_spheres/index.html @@ -1,6 +1,6 @@ -GrowingSpheres · CounterfactualExplanations.jl
    Edit on GitHub

    GrowingSpheres

    Growing Spheres refers to the generator introduced by Laugel et al. (2017). Our implementation takes inspiration from the CARLA library.

    Principle of the Proposed Approach

    In order to interpret a prediction through comparison, the Growing Spheres algorithm focuses on finding an observation belonging to the other class and answers the question: “Considering an observation and a classifier, what is the minimal change we need to apply in order to change the prediction of this observation?”. This problem is similar to inverse classification but applied to interpretability.

    Explaining how to change a prediction can help the user understand what the model considers as locally important. The Growing Spheres approach provides insights into the classifier’s behavior without claiming any causal knowledge. It differs from other interpretability approaches and is not concerned with the global behavior of the model. Instead, it aims to provide local insights into the classifier’s decision-making process.

    The algorithm finds the closest “ennemy” observation, which is an observation classified into the other class than the input observation. The final explanation is the difference vector between the input observation and the ennemy.

    Finding the Closest Ennemy

    The algorithm solves the following minimization problem to find the closest ennemy:

    \[e^* = \arg \min_{e \in X} \{ c(x, e) \,|\, f(e) \neq f(x) \}\]

    The cost function c(x, e) is defined as:

    \[c(x, e) = ||x - e||_2 + \gamma ||x - e||_0\]

    where ||.||_2 is the Euclidean norm and ||.||_0 is the sparsity measure. The weight gamma balances the importance of sparsity in the cost function.

    To approximate the solution, the Growing Spheres algorithm uses a two-step heuristic approach. The first step is the Generation phase, where observations are generated in spherical layers around the input observation. The second step is the Feature Selection phase, where the generated observation with the smallest change in each feature is selected.

    Example

    generator = GrowingSpheresGenerator()
+GrowingSpheres · CounterfactualExplanations.jl
    GrowingSpheres
    Growing Spheres refers to the generator introduced by Laugel et al. (2017). Our implementation takes inspiration from the CARLA library.
    Principle of the Proposed Approach
    In order to interpret a prediction through comparison, the Growing Spheres algorithm focuses on finding an observation belonging to the other class and answers the question: “Considering an observation and a classifier, what is the minimal change we need to apply in order to change the prediction of this observation?”. This problem is similar to inverse classification but applied to interpretability.
    Explaining how to change a prediction can help the user understand what the model considers as locally important. The Growing Spheres approach provides insights into the classifier’s behavior without claiming any causal knowledge. It differs from other interpretability approaches and is not concerned with the global behavior of the model. Instead, it aims to provide local insights into the classifier’s decision-making process.
    The algorithm finds the closest “ennemy” observation, which is an observation classified into the other class than the input observation. The final explanation is the difference vector between the input observation and the ennemy.
    Finding the Closest Ennemy
    The algorithm solves the following minimization problem to find the closest ennemy:
    \[e^* = \arg \min_{e \in X} \{ c(x, e) \,|\, f(e) \neq f(x) \}\]
    The cost function c(x, e) is defined as:
    \[c(x, e) = ||x - e||_2 + \gamma ||x - e||_0\]
    where ||.||_2 is the Euclidean norm and ||.||_0 is the sparsity measure. The weight gamma balances the importance of sparsity in the cost function.
    To approximate the solution, the Growing Spheres algorithm uses a two-step heuristic approach. The first step is the Generation phase, where observations are generated in spherical layers around the input observation. The second step is the Feature Selection phase, where the generated observation with the smallest change in each feature is selected.
    Example
    generator = GrowingSpheresGenerator()
 M = fit_model(counterfactual_data, :DeepEnsemble)
 ce = generate_counterfactual(
     x, target, counterfactual_data, M, generator)
-plot(ce)
    References
    Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” arXiv. https://doi.org/10.48550/arXiv.1712.08443.
    
+plot(ce)

    References

    Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” arXiv. https://doi.org/10.48550/arXiv.1712.08443.

    diff --git a/dev/explanation/generators/overview/index.html b/dev/explanation/generators/overview/index.html index 75ccf17a2..2f79d4e45 100644 --- a/dev/explanation/generators/overview/index.html +++ b/dev/explanation/generators/overview/index.html @@ -1,5 +1,5 @@ -Overview · CounterfactualExplanations.jl
    Edit on GitHub

    Counterfactual Generators

    Counterfactual generators form the very core of this package. The generator_catalogue can be used to inspect the available generators:

    generator_catalogue
    Dict{Symbol, Any} with 11 entries:
+Overview · CounterfactualExplanations.jl
    Counterfactual Generators
    Counterfactual generators form the very core of this package. The generator_catalogue can be used to inspect the available generators:
    generator_catalogue
    Dict{Symbol, Any} with 11 entries:
   :gravitational   => GravitationalGenerator
   :growing_spheres => GrowingSpheresGenerator
   :revise          => REVISEGenerator
@@ -12,4 +12,4 @@
   :generic         => GenericGenerator
   :greedy          => GreedyGenerator
    The following sections provide brief descriptions of all of them.
    Gradient-based Counterfactual Generators
    At the time of writing, all generators are gradient-based: that is, counterfactuals are searched through gradient descent. In Altmeyer et al. (2023) we lay out a general methodological framework that can be applied to all of these generators:
    \[\begin{aligned}
 \mathbf{s}^\prime &= \arg \min_{\mathbf{s}^\prime \in \mathcal{S}} \left\{  {\text{yloss}(M(f(\mathbf{s}^\prime)),y^*)}+ \lambda {\text{cost}(f(\mathbf{s}^\prime)) }  \right\} 
-\end{aligned} \]
    “Here $\mathbf{s}^\prime=\left\{s_k^\prime\right\}_K$ is a $K$-dimensional array of counterfactual states and $f: \mathcal{S} \mapsto \mathcal{X}$ maps from the counterfactual state space to the feature space.” (Altmeyer et al. 2023)
    For most generators, the state space is the feature space ($f$ is the identity function) and the number of counterfactuals $K$ is one. Latent Space generators instead search counterfactuals in some latent space $\mathcal{S}$. In this case, $f$ corresponds to the decoder part of the generative model, that is the function that maps back from the latent space to inputs.
    References
    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.
    
+\end{aligned} \]
    “Here $\mathbf{s}^\prime=\left\{s_k^\prime\right\}_K$ is a $K$-dimensional array of counterfactual states and $f: \mathcal{S} \mapsto \mathcal{X}$ maps from the counterfactual state space to the feature space.” (Altmeyer et al. 2023)
    For most generators, the state space is the feature space ($f$ is the identity function) and the number of counterfactuals $K$ is one. Latent Space generators instead search counterfactuals in some latent space $\mathcal{S}$. In this case, $f$ corresponds to the decoder part of the generative model, that is the function that maps back from the latent space to inputs.
    References
    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.
    diff --git a/dev/explanation/generators/probe/index.html b/dev/explanation/generators/probe/index.html index de5bdc31b..393e0c5b4 100644 --- a/dev/explanation/generators/probe/index.html +++ b/dev/explanation/generators/probe/index.html @@ -1,5 +1,5 @@ -PROBE · CounterfactualExplanations.jl
    Edit on GitHub

    ProbeGenerator

    The ProbeGenerator is designed to navigate the trade-offs between costs and robustness in Algorithmic Recourse (Pawelczyk et al. 2022).

    Description

    The goal of ProbeGenerator is to find a recourse x’ whose prediction at any point y within some set around x’ belongs to the positive class with probability 1 - r, where r is the recourse invalidation rate. It minimizes the gap between the achieved and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction.

    Explanation

    The loss function this generator is defined below. R is a hinge loss parameter which helps control for robustness. The loss and penalty functions can still be chosen freely.

    \[\begin{aligned} +PROBE · CounterfactualExplanations.jl

    ProbeGenerator

    The ProbeGenerator is designed to navigate the trade-offs between costs and robustness in Algorithmic Recourse (Pawelczyk et al. 2022).

    Description

    The goal of ProbeGenerator is to find a recourse x’ whose prediction at any point y within some set around x’ belongs to the positive class with probability 1 - r, where r is the recourse invalidation rate. It minimizes the gap between the achieved and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction.

    Explanation

    The loss function this generator is defined below. R is a hinge loss parameter which helps control for robustness. The loss and penalty functions can still be chosen freely.

    \[\begin{aligned} R(x'; \sigma^2 I) + l(f(x'), s) + \lambda d_c(x', x) \end{aligned}\]

    R uses the following formula to control for noise. It generates small perturbations and checks how often the counterfactual explanation flips back to a factual one, when small amounts of noise are added to it.

    \[\begin{aligned} \Delta(x^{\hat{E}}) &= E_{\varepsilon}[h(x^{\hat{E}}) - h(x^{\hat{E}} + \varepsilon)] @@ -10,4 +10,4 @@ generator = CounterfactualExplanations.Generators.ProbeGenerator(opt=opt) conv = CounterfactualExplanations.Convergence.InvalidationRateConvergence(;invalidation_rate=0.5) ce = generate_counterfactual(x, target, counterfactual_data, M, generator, convergence=conv) -plot(ce)

    Choosing different invalidation rates makes the counterfactual more or less robust. The following plot shows the counterfactuals generated for different invalidation rates.

    References

    Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” arXiv Preprint arXiv:2203.06768.

    +plot(ce)

    Choosing different invalidation rates makes the counterfactual more or less robust. The following plot shows the counterfactuals generated for different invalidation rates.

    References

    Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” arXiv Preprint arXiv:2203.06768.

    diff --git a/dev/explanation/generators/revise/index.html b/dev/explanation/generators/revise/index.html index fd2261d79..0257e42f7 100644 --- a/dev/explanation/generators/revise/index.html +++ b/dev/explanation/generators/revise/index.html @@ -1,5 +1,5 @@ -REVISE · CounterfactualExplanations.jl
    Edit on GitHub

    REVISEGenerator

    REVISE is a Latent Space generator introduced by Joshi et al. (2019).

    Description

    The current consensus in the literature is that Counterfactual Explanations should be realistic: the generated counterfactuals should look like they were generated by the data-generating process (DGP) that governs the problem at hand. With respect to Algorithmic Recourse, it is certainly true that counterfactuals should be realistic in order to be actionable for individuals.[1] To address this need, researchers have come up with various approaches in recent years. Among the most popular approaches is Latent Space Search, which was first proposed in Joshi et al. (2019): instead of traversing the feature space directly, this approach relies on a separate generative model that learns a latent space representation of the DGP. Assuming the generative model is well-specified, access to the learned latent embeddings of the data comes with two advantages:

    1. Since the learned DGP is encoded in the latent space, the generated counterfactuals will respect the learned representation of the data. In practice, this means that counterfactuals will be realistic.
    2. The latent space is typically a compressed (i.e. lower dimensional) version of the feature space. This makes the counterfactual search less costly.

    There are also certain disadvantages though:

    1. Learning generative models is (typically) an expensive task, which may well outweigh the benefits associated with utlimately traversing a lower dimensional space.
    2. If the generative model is poorly specified, this will affect the quality of the counterfactuals.[2]

    Anyway, traversing latent embeddings is a powerful idea that may be very useful depending on the specific context. This tutorial introduces the concept and how it is implemented in this package.

    Usage

    The approach can be used in our package as follows:

    generator = REVISEGenerator()
+REVISE · CounterfactualExplanations.jl
    REVISEGenerator
    REVISE is a Latent Space generator introduced by Joshi et al. (2019).
    Description
    The current consensus in the literature is that Counterfactual Explanations should be realistic: the generated counterfactuals should look like they were generated by the data-generating process (DGP) that governs the problem at hand. With respect to Algorithmic Recourse, it is certainly true that counterfactuals should be realistic in order to be actionable for individuals.[1] To address this need, researchers have come up with various approaches in recent years. Among the most popular approaches is Latent Space Search, which was first proposed in Joshi et al. (2019): instead of traversing the feature space directly, this approach relies on a separate generative model that learns a latent space representation of the DGP. Assuming the generative model is well-specified, access to the learned latent embeddings of the data comes with two advantages:
    1. Since the learned DGP is encoded in the latent space, the generated counterfactuals will respect the learned representation of the data. In practice, this means that counterfactuals will be realistic.
    2. The latent space is typically a compressed (i.e. lower dimensional) version of the feature space. This makes the counterfactual search less costly.
    There are also certain disadvantages though:
    1. Learning generative models is (typically) an expensive task, which may well outweigh the benefits associated with utlimately traversing a lower dimensional space.
    2. If the generative model is poorly specified, this will affect the quality of the counterfactuals.[2]
    Anyway, traversing latent embeddings is a powerful idea that may be very useful depending on the specific context. This tutorial introduces the concept and how it is implemented in this package.
    Usage
    The approach can be used in our package as follows:
    generator = REVISEGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
 plot(ce)
    Worked 2D Examples
    Below we load 2D data and train a VAE on it and plot the original samples against their reconstructions.
    # output: true
 
@@ -24,7 +24,7 @@
 x = select_factual(counterfactual_data, chosen)
    Finally, we generate and visualize the generated counterfactual:
    # Search:
 generator = REVISEGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-plot(ce)
    3D Example
    To illustrate the notion of Latent Space search, let’s look at an example involving 3-dimensional input data, which we can still visualize. The code chunk below loads the data and implements the counterfactual search.
    # Data and Classifier:
+plot(ce)
    3D Example
    To illustrate the notion of Latent Space search, let’s look at an example involving 3-dimensional input data, which we can still visualize. The code chunk below loads the data and implements the counterfactual search.
    # Data and Classifier:
 counterfactual_data = CounterfactualData(load_blobs(k=3)...)
 X = counterfactual_data.X
 ys = counterfactual_data.output_encoder.labels.refs
@@ -52,4 +52,4 @@
 # Define generator:
 generator = REVISEGenerator(opt=Flux.Adam(0.1))
 # Generate recourse:
-ce = generate_counterfactual(x, target, counterfactual_data, M, generator; convergence=conv)
    The chart below shows the results:
    References
    Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.
    [1] In general, we believe that there may be a trade-off between creating counterfactuals that respect the DGP vs. counterfactuals reflect the behaviour of the black-model in question - both accurately and complete.
    [2] We believe that there is another potentially crucial disadvantage of relying on a separate generative model: it reallocates the task of learning realistic explanations for the data from the black-box model to the generative model.
    
+ce = generate_counterfactual(x, target, counterfactual_data, M, generator; convergence=conv)

    The chart below shows the results:

    References

    Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.

    [1] In general, we believe that there may be a trade-off between creating counterfactuals that respect the DGP vs. counterfactuals reflect the behaviour of the black-model in question - both accurately and complete.

    [2] We believe that there is another potentially crucial disadvantage of relying on a separate generative model: it reallocates the task of learning realistic explanations for the data from the black-box model to the generative model.

    diff --git a/dev/explanation/index.html b/dev/explanation/index.html index 480d8244f..2d80fe185 100644 --- a/dev/explanation/index.html +++ b/dev/explanation/index.html @@ -1,2 +1,2 @@ -Overview · CounterfactualExplanations.jl
    Edit on GitHub

    Explanation

    In this section you will find detailed explanations about the methodology and code.

    Explanation clarifies, deepens and broadens the reader’s understanding of a subject.

    — Diátaxis

    In other words, you come here because you are interested in understanding how all of this actually works 🤓.

    +Overview · CounterfactualExplanations.jl

    Explanation

    In this section you will find detailed explanations about the methodology and code.

    Explanation clarifies, deepens and broadens the reader’s understanding of a subject.

    — Diátaxis

    In other words, you come here because you are interested in understanding how all of this actually works 🤓.

    diff --git a/dev/explanation/optimisers/jsma/index.html b/dev/explanation/optimisers/jsma/index.html index 301049319..f96a1ac5c 100644 --- a/dev/explanation/optimisers/jsma/index.html +++ b/dev/explanation/optimisers/jsma/index.html @@ -1,5 +1,5 @@ -JSMA · CounterfactualExplanations.jl
    Edit on GitHub

    Jacobian-based Saliency Map Attack

    To search counterfactuals, Schut et al. (2021) propose to use a Jacobian-Based Saliency Map Attack (JSMA) inspired by the literature on adversarial attacks. It works by moving in the direction of the most salient feature at a fixed step size in each iteration. Schut et al. (2021) use this optimisation rule in the context of Bayesian classifiers and demonstrate good results in terms of plausibility — how realistic counterfactuals are — and redundancy — how sparse the proposed feature changes are.

    JSMADescent

    To implement this approach in a reusable manner, we have added JSMA as a Flux optimiser. In particular, we have added a class JSMADescent<:Flux.Optimise.AbstractOptimiser, for which we have overloaded the Flux.Optimise.apply! method. This makes it possible to reuse JSMADescent as an optimiser in composable generators.

    The optimiser can be used with with any generator as follows:

    using CounterfactualExplanations.Generators: JSMADescent
+JSMA · CounterfactualExplanations.jl
    Jacobian-based Saliency Map Attack
    To search counterfactuals, Schut et al. (2021) propose to use a Jacobian-Based Saliency Map Attack (JSMA) inspired by the literature on adversarial attacks. It works by moving in the direction of the most salient feature at a fixed step size in each iteration. Schut et al. (2021) use this optimisation rule in the context of Bayesian classifiers and demonstrate good results in terms of plausibility — how realistic counterfactuals are — and redundancy — how sparse the proposed feature changes are.
    JSMADescent
    To implement this approach in a reusable manner, we have added JSMA as a Flux optimiser. In particular, we have added a class JSMADescent<:Flux.Optimise.AbstractOptimiser, for which we have overloaded the Flux.Optimise.apply! method. This makes it possible to reuse JSMADescent as an optimiser in composable generators.
    The optimiser can be used with with any generator as follows:
    using CounterfactualExplanations.Generators: JSMADescent
 generator = GenericGenerator() |>
     gen -> @with_optimiser(gen,JSMADescent(;η=0.1))
-ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
    The figure below compares the resulting counterfactual search outcome to the corresponding outcome with generic Descent.
    plot(p1,p2,size=(1000,400))
    Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.
    
+ce = generate_counterfactual(x, target, counterfactual_data, M, generator)

    The figure below compares the resulting counterfactual search outcome to the corresponding outcome with generic Descent.

    plot(p1,p2,size=(1000,400))

    Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.

    diff --git a/dev/explanation/optimisers/overview/index.html b/dev/explanation/optimisers/overview/index.html index e80351917..697c9e623 100644 --- a/dev/explanation/optimisers/overview/index.html +++ b/dev/explanation/optimisers/overview/index.html @@ -1,2 +1,2 @@ -Overview · CounterfactualExplanations.jl
    Edit on GitHub

    Optimisation Rules

    Counterfactual search is an optimization problem. Consequently, the choice of the optimisation rule affects the generated counterfactuals. In the short term, we aim to enable users to choose any of the available Flux optimisers. This has not been sufficiently tested yet, and you may run into issues.

    Custom Optimisation Rules

    Flux optimisers are specifically designed for deep learning, and in particular, for learning model parameters. In counterfactual search, the features are the free parameters that we are optimising over. To this end, some custom optimisation rules are necessary to incorporate ideas presented in the literature. In the following, we introduce those rules.

    +Overview · CounterfactualExplanations.jl

    Optimisation Rules

    Counterfactual search is an optimization problem. Consequently, the choice of the optimisation rule affects the generated counterfactuals. In the short term, we aim to enable users to choose any of the available Flux optimisers. This has not been sufficiently tested yet, and you may run into issues.

    Custom Optimisation Rules

    Flux optimisers are specifically designed for deep learning, and in particular, for learning model parameters. In counterfactual search, the features are the free parameters that we are optimising over. To this end, some custom optimisation rules are necessary to incorporate ideas presented in the literature. In the following, we introduce those rules.

    diff --git a/dev/extensions/index.html b/dev/extensions/index.html new file mode 100644 index 000000000..0de18575a --- /dev/null +++ b/dev/extensions/index.html @@ -0,0 +1,2 @@ + +Overview · CounterfactualExplanations.jl

    ⛓️ Extensions

    In this section, you will find information about package extensions of the CounterfactualExplanations package. Extensions are a relatively new feature of Julia that allows users to conditionally load code based on the presence of other packages. This is useful for creating packages that extend the functionality of other packages, without requiring the user to install the package being extended.

    diff --git a/dev/extensions/index.qmd b/dev/extensions/index.qmd new file mode 100644 index 000000000..393c7b56c --- /dev/null +++ b/dev/extensions/index.qmd @@ -0,0 +1,9 @@ + +```@meta +CurrentModule = CounterfactualExplanations +``` + +# ⛓️ Extensions + +In this section, you will find information about package extensions of the `CounterfactualExplanations` package. [Extensions](https://pkgdocs.julialang.org/v1/creating-packages/#Conditional-loading-of-code-in-packages-(Extensions)) are a relatively new feature of Julia that allows users to conditionally load code based on the presence of other packages. This is useful for creating packages that extend the functionality of other packages, without requiring the user to install the package being extended. + diff --git a/dev/extensions/laplace_redux.qmd b/dev/extensions/laplace_redux.qmd new file mode 100644 index 000000000..cac652fc4 --- /dev/null +++ b/dev/extensions/laplace_redux.qmd @@ -0,0 +1,74 @@ +``` @meta +CurrentModule = CounterfactualExplanations +``` + +```{julia} +#| echo: false +include("$(pwd())/docs/setup_docs.jl") +eval(setup_docs) +``` + +# [LaplaceRedux.jl](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl) + +[LaplaceRedux.jl](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl) is one of Taija's own packages that provides a framework for Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks. The methodology was first proposed by @immer2020improving and implemented in Python by @daxberger2021laplace. This is relevant to the work on counterfactual explanations (CE), because research has shown that counterfactual explanations for Bayesian models are typically more plausible, because Bayesian models are able to capture the uncertainty in the data [@schut2021generating]. + +```{=commonmark} +!!! tip "Read More" + To learn more about Laplace Redux, head over to the [official documentation](https://juliatrustworthyai.github.io/LaplaceRedux.jl/stable/). +``` + +## Example + +```{julia} +#| echo: false + +using CounterfactualExplanations +using CounterfactualExplanations.Models +using Plots +using TaijaData +using TaijaPlotting +``` + +The extension will be loaded automatically when loading the `LaplaceRedux` package (assuming the `CounterfactualExplanations` package is also loaded). + +```{julia} +#| output: true +#| echo: true + +using LaplaceRedux +``` + +Next, we will fit a neural network with Laplace Approximation to the moons dataset using our standard package API for doing so. By default, the Bayesian prior is optimized through empirical Bayes using the `LaplaceRedux` package. + +```{julia} +#| output: true +#| echo: true + +# Fit model to data: +data = CounterfactualData(load_moons()...) +M = fit_model(data, :LaplaceRedux; n_hidden=16) +``` + +Finally, we select a factual instance and generate a counterfactual explanation for it using the generic gradient-based CE method. + +```{julia} +#| output: true +#| echo: true + +# Select a factual instance: +target = 1 +factual = 0 +chosen = rand(findall(predict_label(M, data) .== factual)) +x = select_factual(data, chosen) + +# Generate counterfactual explanation: +η = 0.01 +generator = GenericGenerator(; opt=Descent(η), λ=0.01) +conv = CounterfactualExplanations.Convergence.DecisionThresholdConvergence(; + decision_threshold=0.9, max_iter=100 +) +ce = generate_counterfactual(x, target, data, M, generator; convergence=conv) +plot(ce, alpha=0.1) +``` + +# References \ No newline at end of file diff --git a/dev/extensions/laplace_redux/index.html b/dev/extensions/laplace_redux/index.html new file mode 100644 index 000000000..d0e28fe0f --- /dev/null +++ b/dev/extensions/laplace_redux/index.html @@ -0,0 +1,17 @@ + +LaplaceRedux · CounterfactualExplanations.jl

    LaplaceRedux.jl

    LaplaceRedux.jl is one of Taija’s own packages that provides a framework for Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks. The methodology was first proposed by Immer, Korzepa, and Bauer (2020) and implemented in Python by Daxberger et al. (2021). This is relevant to the work on counterfactual explanations (CE), because research has shown that counterfactual explanations for Bayesian models are typically more plausible, because Bayesian models are able to capture the uncertainty in the data (Schut et al. 2021).

    Read More

    To learn more about Laplace Redux, head over to the official documentation.

    Example

    The extension will be loaded automatically when loading the LaplaceRedux package (assuming the CounterfactualExplanations package is also loaded).

    using LaplaceRedux

    Next, we will fit a neural network with Laplace Approximation to the moons dataset using our standard package API for doing so. By default, the Bayesian prior is optimized through empirical Bayes using the LaplaceRedux package.

    # Fit model to data:
+data = CounterfactualData(load_moons()...)
+M = fit_model(data, :LaplaceRedux; n_hidden=16)
    LaplaceReduxExt.LaplaceReduxModel(Laplace(Chain(Dense(2 => 16, relu), Dense(16 => 2)), :classification, :all, nothing, :full, LaplaceRedux.Curvature.GGN(Chain(Dense(2 => 16, relu), Dense(16 => 2)), :classification, Flux.Losses.logitcrossentropy, Array{Float32}[[-1.3098596 0.59241515; 0.91760206 0.02950162; … ; -0.018356863 0.12850936; -0.5381665 -0.7872097], [-0.2581085, -0.90997887, -0.5418944, -0.23735572, 0.81020063, -0.3033359, -0.47902864, -0.6432098, -0.038013518, 0.028280666, 0.009903266, -0.8796683, 0.41090682, 0.011093224, -0.1580453, 0.7911349], [3.092321 -2.4660816 … -0.3446268 -1.465249; -2.9468734 3.167357 … 0.31758657 1.7140366], [-0.3107697, 0.31076983]], 1.0, :all, nothing), 1.0, 0.0, Float32[-1.3098596, 0.91760206, 0.5239727, -1.1579771, -0.851813, -1.9411169, 0.47409698, 0.6679365, 0.8944433, 0.663116  …  -0.3172857, 0.15530388, 1.3264753, -0.3506721, -0.3446268, 0.31758657, -1.465249, 1.7140366, -0.3107697, 0.31076983], [0.10530027048093525 0.0 … 0.0 0.0; 0.0 0.10530027048093525 … 0.0 0.0; … ; 0.0 0.0 … 0.10530027048093525 0.0; 0.0 0.0 … 0.0 0.10530027048093525], [0.10066431429751965 0.0 … -0.030656783425475176 0.030656334963944154; 0.0 20.93513766443357 … -2.3185940232360736 2.3185965484008193; … ; -0.030656783425475176 -2.3185940232360736 … 1.0101450999063672 -1.0101448118057204; 0.030656334963944154 2.3185965484008193 … -1.0101448118057204 1.0101451389641771], [1.1006643142975197 0.0 … -0.030656783425475176 0.030656334963944154; 0.0 21.93513766443357 … -2.3185940232360736 2.3185965484008193; … ; -0.030656783425475176 -2.3185940232360736 … 2.0101450999063672 -1.0101448118057204; 0.030656334963944154 2.3185965484008193 … -1.0101448118057204 2.010145138964177], [0.9412600568016627 0.003106911671721699 … 0.003743740333409532 -0.003743452315572739; 0.003106912946573237 0.6539263732691709 … 0.0030385955287734246 -0.0030390041204196414; … ; 0.0037437406323562283 0.003038591829991259 … 0.9624905710233649 0.03750911813897676; -0.0037434526145225856 -0.0030390004216833593 … 0.03750911813898124 0.9624905774453485], 82, 250, 2, 997.8087484836578), :classification_multi)

    Finally, we select a factual instance and generate a counterfactual explanation for it using the generic gradient-based CE method.

    # Select a factual instance:
+target = 1
+factual = 0
+chosen = rand(findall(predict_label(M, data) .== factual))
+x = select_factual(data, chosen)
+
+# Generate counterfactual explanation:
+η = 0.01
+generator = GenericGenerator(; opt=Descent(η), λ=0.01)
+conv = CounterfactualExplanations.Convergence.DecisionThresholdConvergence(;
+    decision_threshold=0.9, max_iter=100
+)
+ce = generate_counterfactual(x, target, data, M, generator; convergence=conv)
+plot(ce, alpha=0.1)

    References

    Daxberger, Erik, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, and Philipp Hennig. 2021. “Laplace Redux-Effortless Bayesian Deep Learning.” Advances in Neural Information Processing Systems 34.

    Immer, Alexander, Maciej Korzepa, and Matthias Bauer. 2020. “Improving Predictions of Bayesian Neural Networks via Local Linearization.” https://arxiv.org/abs/2008.08400.

    Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.

    diff --git a/dev/extensions/laplace_redux_files/figure-commonmark/cell-6-output-1.svg b/dev/extensions/laplace_redux_files/figure-commonmark/cell-6-output-1.svg new file mode 100644 index 000000000..2806e8836 --- /dev/null +++ b/dev/extensions/laplace_redux_files/figure-commonmark/cell-6-output-1.svg @@ -0,0 +1,1153 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/extensions/neurotree.qmd b/dev/extensions/neurotree.qmd new file mode 100644 index 000000000..b394eef5a --- /dev/null +++ b/dev/extensions/neurotree.qmd @@ -0,0 +1,81 @@ +``` @meta +CurrentModule = CounterfactualExplanations +``` + +```{julia} +#| echo: false +include("$(pwd())/docs/setup_docs.jl") +eval(setup_docs) +``` + +# [NeuroTreeModels.jl](https://evovest.github.io/NeuroTreeModels.jl/dev/) + +[NeuroTreeModels.jl](https://evovest.github.io/NeuroTreeModels.jl/dev/) is a package that provides a framework for training differentiable tree-based models. This is relevant to the work on counterfactual explanations (CE), which often assumes that the underlying black-box model is differentiable with respect to its input. The literature on CE therefore regularly focuses exclusively on explaining deep learning models. This is at odds with the fact that the literature also typically focuses on tabular data, which is often best modeled by tree-based models [@grinsztajn2022why]. The extension for NeuroTreeModels.jl provides a way to bridge this gap by allowing users to apply existing gradient-based CE methods to differentiable tree-based models. + +```{=commonmark} +!!! warning "Experimental Feature" + Please note that this extension is still experimental. Neither the behaviour of differentiable tree-based models nor their interplay with counterfactual explanations is well understood at this point. If you encounter any issues, please report them to the package maintainers. Your feedback is highly appreciated. + + Please also note that this extension is only tested on Julia 1.9 and higher, due to compatibility [issues](https://github.com/Evovest/NeuroTreeModels.jl/pull/7). +``` + +## Example + +```{julia} +#| echo: false + +using CounterfactualExplanations +using CounterfactualExplanations.Models +using Flux +using MLJBase +using Plots +using TaijaData +using TaijaPlotting +``` + +The extension will be loaded automatically when loading the `NeuroTreeModels` package (assuming the `CounterfactualExplanations` package is also loaded). + +```{julia} +#| output: true +#| echo: true + +using NeuroTreeModels +``` + +Next, we will fit a NeuroTree model to the moons dataset using our standard package API for doing so. + +```{julia} +#| output: true +#| echo: true + +# Fit model to data: +data = CounterfactualData(load_moons()...) +M = fit_model( + data, :NeuroTree; + depth=2, lr=5e-2, nrounds=50, batchsize=10 +) +``` + +Finally, we select a factual instance and generate a counterfactual explanation for it using the generic gradient-based CE method. + +```{julia} +#| output: true +#| echo: true + +# Select a factual instance: +target = 1 +factual = 0 +chosen = rand(findall(predict_label(M, data) .== factual)) +x = select_factual(data, chosen) + +# Generate counterfactual explanation: +η = 0.01 +generator = GenericGenerator(; opt=Descent(η), λ=0.01) +conv = CounterfactualExplanations.Convergence.DecisionThresholdConvergence(; + decision_threshold=0.9, max_iter=100 +) +ce = generate_counterfactual(x, target, data, M, generator; convergence=conv) +plot(ce, alpha=0.1) +``` + +# References \ No newline at end of file diff --git a/dev/extensions/neurotree/index.html b/dev/extensions/neurotree/index.html new file mode 100644 index 000000000..ebf81e7f5 --- /dev/null +++ b/dev/extensions/neurotree/index.html @@ -0,0 +1,20 @@ + +NeuroTrees · CounterfactualExplanations.jl

    NeuroTreeModels.jl

    NeuroTreeModels.jl is a package that provides a framework for training differentiable tree-based models. This is relevant to the work on counterfactual explanations (CE), which often assumes that the underlying black-box model is differentiable with respect to its input. The literature on CE therefore regularly focuses exclusively on explaining deep learning models. This is at odds with the fact that the literature also typically focuses on tabular data, which is often best modeled by tree-based models (Grinsztajn, Oyallon, and Varoquaux 2022). The extension for NeuroTreeModels.jl provides a way to bridge this gap by allowing users to apply existing gradient-based CE methods to differentiable tree-based models.

    Experimental Feature

    Please note that this extension is still experimental. Neither the behaviour of differentiable tree-based models nor their interplay with counterfactual explanations is well understood at this point. If you encounter any issues, please report them to the package maintainers. Your feedback is highly appreciated.

    Please also note that this extension is only tested on Julia 1.9 and higher, due to compatibility issues.

    Example

    The extension will be loaded automatically when loading the NeuroTreeModels package (assuming the CounterfactualExplanations package is also loaded).

    using NeuroTreeModels

    Next, we will fit a NeuroTree model to the moons dataset using our standard package API for doing so.

    # Fit model to data:
+data = CounterfactualData(load_moons()...)
+M = fit_model(
+    data, :NeuroTree; 
+    depth=2, lr=5e-2, nrounds=50, batchsize=10
+)
    NeuroTreeExt.NeuroTreeModel(NeuroTreeRegressor(loss = mlogloss, …), :classification_multi, NeuroTreeModels.NeuroTreeModel{NeuroTreeModels.MLogLoss, Chain{Tuple{BatchNorm{typeof(identity), Vector{Float32}, Float32, Vector{Float32}}, NeuroTreeModels.StackTree}}}(NeuroTreeModels.MLogLoss, Chain(BatchNorm(2, active=false), NeuroTreeModels.StackTree(NeuroTree[NeuroTree{Matrix{Float32}, Vector{Float32}, Array{Float32, 3}}(Float32[1.8824593 -0.28222033; -2.680499 0.67347014; … ; -1.0722864 1.3651229; -2.0926774 1.63557], Float32[-3.4070241, 4.545113, 1.0882677, -0.3497498, -2.766766, 1.9072449, -0.9736261, 3.9750721, 1.726214, 3.7279263  …  -0.0664266, -0.4214582, -2.3816268, -3.1371245, 0.76548636, 2.636373, 2.4558601, 0.893434, -1.9484522, 4.793434], Float32[3.44271 -6.334693 -0.6308845 3.385659; -3.4316056 6.297003 0.7254221 -3.3283486;;; -3.7011054 -0.17596768 0.15429471 2.270125; 3.4926674 0.026218029 -0.19753197 -2.2337704;;; 1.1795454 -4.315231 0.28486454 1.9995956; -0.9651108 4.0999455 -0.05312265 -1.8039354;;; … ;;; 2.5076811 -0.46358463 -3.5438805 0.0686823; -2.592356 0.47884527 3.781507 -0.022692114;;; -0.59115165 -3.234046 0.09896194 2.375202; 0.5592871 3.3082843 -0.014032216 -2.1876256;;; 2.039389 -0.10134532 2.6637273 -4.999703; -2.0289893 0.3368772 -2.5739825 5.069934], tanh)])), Dict{Symbol, Any}(:feature_names => [:x1, :x2], :nrounds => 50, :device => :cpu)))

    Finally, we select a factual instance and generate a counterfactual explanation for it using the generic gradient-based CE method.

    # Select a factual instance:
+target = 1
+factual = 0
+chosen = rand(findall(predict_label(M, data) .== factual))
+x = select_factual(data, chosen)
+
+# Generate counterfactual explanation:
+η = 0.01
+generator = GenericGenerator(; opt=Descent(η), λ=0.01)
+conv = CounterfactualExplanations.Convergence.DecisionThresholdConvergence(;
+    decision_threshold=0.9, max_iter=100
+)
+ce = generate_counterfactual(x, target, data, M, generator; convergence=conv)
+plot(ce, alpha=0.1)

    References

    Grinsztajn, Léo, Edouard Oyallon, and Gaël Varoquaux. 2022. “Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?” https://arxiv.org/abs/2207.08815.

    diff --git a/dev/extensions/neurotree_files/figure-commonmark/cell-6-output-1.svg b/dev/extensions/neurotree_files/figure-commonmark/cell-6-output-1.svg new file mode 100644 index 000000000..6306c1308 --- /dev/null +++ b/dev/extensions/neurotree_files/figure-commonmark/cell-6-output-1.svg @@ -0,0 +1,1125 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/how_to_guides/custom_generators/index.html b/dev/how_to_guides/custom_generators/index.html index 74795ac0f..7fd6d71a4 100644 --- a/dev/how_to_guides/custom_generators/index.html +++ b/dev/how_to_guides/custom_generators/index.html @@ -1,5 +1,5 @@ -... add custom generators · CounterfactualExplanations.jl
    Edit on GitHub

    How to add Custom Generators

    As we will see in this short tutorial, building custom counterfactual generators is straightforward. We hope that this will facilitate contributions through the community.

    Generic generator with dropout

    To illustrate how custom generators can be implemented we will consider a simple example of a generator that extends the functionality of our GenericGenerator. We have noted elsewhere that the effectiveness of counterfactual explanations depends to some degree on the quality of the fitted model. Another, perhaps trivial, thing to note is that counterfactual explanations are not unique: there are potentially many valid counterfactual paths. One interesting (or silly) idea following these two observations might be to introduce some form of regularization in the counterfactual search. For example, we could use dropout to randomly switch features on and off in each iteration. Without dwelling further on the usefulness of this idea, let us see how it can be implemented.

    The first code chunk below implements two important steps: 1) create an abstract subtype of the AbstractGradientBasedGenerator and 2) create a constructor similar to the GenericConstructor, but with one additional field for the probability of dropout.

    # Abstract suptype:
+... add custom generators · CounterfactualExplanations.jl
    How to add Custom Generators
    As we will see in this short tutorial, building custom counterfactual generators is straightforward. We hope that this will facilitate contributions through the community.
    Generic generator with dropout
    To illustrate how custom generators can be implemented we will consider a simple example of a generator that extends the functionality of our GenericGenerator. We have noted elsewhere that the effectiveness of counterfactual explanations depends to some degree on the quality of the fitted model. Another, perhaps trivial, thing to note is that counterfactual explanations are not unique: there are potentially many valid counterfactual paths. One interesting (or silly) idea following these two observations might be to introduce some form of regularization in the counterfactual search. For example, we could use dropout to randomly switch features on and off in each iteration. Without dwelling further on the usefulness of this idea, let us see how it can be implemented.
    The first code chunk below implements two important steps: 1) create an abstract subtype of the AbstractGradientBasedGenerator and 2) create a constructor similar to the GenericConstructor, but with one additional field for the probability of dropout.
    # Abstract suptype:
 abstract type AbstractDropoutGenerator <: AbstractGradientBasedGenerator end
 
 # Constructor:
@@ -55,4 +55,4 @@
     x, target, counterfactual_data, M, generator;
     num_counterfactuals=5)
 
-plot(ce)
    
+plot(ce)

    diff --git a/dev/how_to_guides/custom_models/index.html b/dev/how_to_guides/custom_models/index.html index 86c1873cb..c29f30661 100644 --- a/dev/how_to_guides/custom_models/index.html +++ b/dev/how_to_guides/custom_models/index.html @@ -1,5 +1,5 @@ -... add custom models · CounterfactualExplanations.jl
    Edit on GitHub

    How to add Custom Models

    Adding custom models is possible and relatively straightforward, as we will demonstrate in this guide.

    Custom Models

    Apart from the default models you can use any arbitrary (differentiable) model and generate recourse in the same way as before. Only two steps are necessary to make your own Julia model compatible with this package:

    1. The model needs to be declared as a subtype of <:CounterfactualExplanations.Models.AbstractFittedModel.
    2. You need to extend the functions CounterfactualExplanations.Models.logits and CounterfactualExplanations.Models.probs for your custom model.

    How FluxModel was added

    To demonstrate how this can be done in practice, we will reiterate here how native support for Flux.jl models was enabled (Innes 2018). Once again we use synthetic data for an illustrative example. The code below loads the data and builds a simple model architecture that can be used for a multi-class prediction task. Note how outputs from the final layer are not passed through a softmax activation function, since the counterfactual loss is evaluated with respect to logits. The model is trained with dropout.

    # Data:
+... add custom models · CounterfactualExplanations.jl
    How to add Custom Models
    Adding custom models is possible and relatively straightforward, as we will demonstrate in this guide.
    Custom Models
    Apart from the default models you can use any arbitrary (differentiable) model and generate recourse in the same way as before. Only two steps are necessary to make your own Julia model compatible with this package:
    1. The model needs to be declared as a subtype of <:CounterfactualExplanations.Models.AbstractFittedModel.
    2. You need to extend the functions CounterfactualExplanations.Models.logits and CounterfactualExplanations.Models.probs for your custom model.
    How FluxModel was added
    To demonstrate how this can be done in practice, we will reiterate here how native support for Flux.jl models was enabled (Innes 2018). Once again we use synthetic data for an illustrative example. The code below loads the data and builds a simple model architecture that can be used for a multi-class prediction task. Note how outputs from the final layer are not passed through a softmax activation function, since the counterfactual loss is evaluated with respect to logits. The model is trained with dropout.
    # Data:
 N = 200
 data = TaijaData.load_blobs(N; centers=4, cluster_std=0.5)
 counterfactual_data = DataPreprocessing.CounterfactualData(data...)
@@ -51,4 +51,4 @@
 # Counterfactual search:
 generator = GenericGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-plot(ce)
    References
    Innes, Mike. 2018. “Flux: Elegant Machine Learning with Julia.” Journal of Open Source Software 3 (25): 602. https://doi.org/10.21105/joss.00602.
    
+plot(ce)

    References

    Innes, Mike. 2018. “Flux: Elegant Machine Learning with Julia.” Journal of Open Source Software 3 (25): 602. https://doi.org/10.21105/joss.00602.

    diff --git a/dev/how_to_guides/index.html b/dev/how_to_guides/index.html index 287364a26..d14d4d5b0 100644 --- a/dev/how_to_guides/index.html +++ b/dev/how_to_guides/index.html @@ -1,2 +1,2 @@ -Overview · CounterfactualExplanations.jl
    Edit on GitHub

    How-To Guides

    In this section, you will find a series of how-to-guides that showcase specific use cases of counterfactual explanations (CE).

    How-to guides are directions that take the reader through the steps required to solve a real-world problem. How-to guides are goal-oriented.

    — Diátaxis

    In other words, you come here because you may have some particular problem in mind, would like to see how it can be solved using CE and then most likely head off again 🫡.

    +Overview · CounterfactualExplanations.jl

    How-To Guides

    In this section, you will find a series of how-to-guides that showcase specific use cases of counterfactual explanations (CE).

    How-to guides are directions that take the reader through the steps required to solve a real-world problem. How-to guides are goal-oriented.

    — Diátaxis

    In other words, you come here because you may have some particular problem in mind, would like to see how it can be solved using CE and then most likely head off again 🫡.

    diff --git a/dev/index.html b/dev/index.html index 6e89524ba..971c4ddf6 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,5 +1,5 @@ -🏠 Home · CounterfactualExplanations.jl
    Edit on GitHub

    Documentation for CounterfactualExplanations.jl.

    CounterfactualExplanations

    Counterfactual Explanations and Algorithmic Recourse in Julia.

    Stable Dev Build Status Coverage Code Style: Blue License Package Downloads Aqua QA

    CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs.

    There is also a corresponding paper, Explaining Black-Box Models through Counterfactuals, which has been published in JuliaCon Proceedings. Please consider citing the paper, if you use this package in your work:

    DOI DOI

    @article{Altmeyer2023,
+🏠 Home · CounterfactualExplanations.jl
    Documentation for CounterfactualExplanations.jl.
    CounterfactualExplanations
    Counterfactual Explanations and Algorithmic Recourse in Julia.
    Stable Dev Build Status Coverage Code Style: Blue License Package Downloads Aqua QA
    CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs.
    There is also a corresponding paper, Explaining Black-Box Models through Counterfactuals, which has been published in JuliaCon Proceedings. Please consider citing the paper, if you use this package in your work:
    DOI DOI
    @article{Altmeyer2023,
   doi = {10.21105/jcon.00130},
   url = {https://doi.org/10.21105/jcon.00130},
   year = {2023},
@@ -32,7 +32,7 @@
 ce = generate_counterfactual(
   x, target, counterfactual_data, M, generator; 
   num_counterfactuals=3, convergence=conv,
-)
    The animation below shows the resulting counterfactual path:
    ☑️ Implemented Counterfactual Generators
    Currently, the following counterfactual generators are implemented:
    • ClaPROAR (Altmeyer et al. 2023)
    • CLUE (Antorán et al. 2020)
    • DiCE (Mothilal, Sharma, and Tan 2020)
    • FeatureTweak (Tolomei et al. 2017)
    • Generic
    • GravitationalGenerator (Altmeyer et al. 2023)
    • Greedy (Schut et al. 2021)
    • GrowingSpheres (Laugel et al. 2017)
    • PROBE (Pawelczyk et al. 2022)
    • REVISE (Joshi et al. 2019)
    • Wachter (Wachter, Mittelstadt, and Russell 2017)
    🎯 Goals and limitations
    The goal of this library is to contribute to efforts towards trustworthy machine learning in Julia. The Julia language has an edge when it comes to trustworthiness: it is very transparent. Packages like this one are generally written in pure Julia, which makes it easy for users and developers to understand and contribute to open-source code. Eventually, this project aims to offer a one-stop-shop of counterfactual explanations.
    Our ambition is to enhance the package through the following features:
    1. Support for all supervised machine learning models trained in MLJ.jl.
    2. Support for regression models.
    🛠 Contribute
    Contributions of any kind are very much welcome! Take a look at the issue to see what things we are currently working on.
    If any of the below applies to you, this might be the right open-source project for you:
    • You’re an expert in Counterfactual Explanations or Explainable AI more broadly and you are curious about Julia.
    • You’re experienced with Julia and are happy to help someone less experienced to up their game. Ideally, you are also curious about Trustworthy AI.
    • You’re new to Julia and open-source development and would like to start your learning journey by contributing to a recent and active development. Ideally, you are familiar with machine learning.
    @pat-alt here: I am still very much at the beginning of my Julia journey, so if you spot any issues or have any suggestions for design improvement, please just open issue or start a discussion.
    For more details on how to contribute see here. Please follow the SciML ColPrac guide.
    There are also some general pointers for people looking to contribute to any of our Taija packages here.
    🎓 Citation
    If you want to use this codebase, please consider citing the corresponding paper:
    @article{Altmeyer2023,
+)
    The animation below shows the resulting counterfactual path:
    <img src="data:image/gif;base64,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" />
    ☑️ Implemented Counterfactual Generators
    Currently, the following counterfactual generators are implemented:
    • ClaPROAR (Altmeyer et al. 2023)
    • CLUE (Antorán et al. 2020)
    • DiCE (Mothilal, Sharma, and Tan 2020)
    • FeatureTweak (Tolomei et al. 2017)
    • Generic
    • GravitationalGenerator (Altmeyer et al. 2023)
    • Greedy (Schut et al. 2021)
    • GrowingSpheres (Laugel et al. 2017)
    • PROBE (Pawelczyk et al. 2022)
    • REVISE (Joshi et al. 2019)
    • Wachter (Wachter, Mittelstadt, and Russell 2017)
    🎯 Goals and limitations
    The goal of this library is to contribute to efforts towards trustworthy machine learning in Julia. The Julia language has an edge when it comes to trustworthiness: it is very transparent. Packages like this one are generally written in pure Julia, which makes it easy for users and developers to understand and contribute to open-source code. Eventually, this project aims to offer a one-stop-shop of counterfactual explanations.
    Our ambition is to enhance the package through the following features:
    1. Support for all supervised machine learning models trained in MLJ.jl.
    2. Support for regression models.
    🛠 Contribute
    Contributions of any kind are very much welcome! Take a look at the issue to see what things we are currently working on.
    If any of the below applies to you, this might be the right open-source project for you:
    • You’re an expert in Counterfactual Explanations or Explainable AI more broadly and you are curious about Julia.
    • You’re experienced with Julia and are happy to help someone less experienced to up their game. Ideally, you are also curious about Trustworthy AI.
    • You’re new to Julia and open-source development and would like to start your learning journey by contributing to a recent and active development. Ideally, you are familiar with machine learning.
    @pat-alt here: I am still very much at the beginning of my Julia journey, so if you spot any issues or have any suggestions for design improvement, please just open issue or start a discussion.
    For more details on how to contribute see here. Please follow the SciML ColPrac guide.
    There are also some general pointers for people looking to contribute to any of our Taija packages here.
    🎓 Citation
    If you want to use this codebase, please consider citing the corresponding paper:
    @article{Altmeyer2023,
   doi = {10.21105/jcon.00130},
   url = {https://doi.org/10.21105/jcon.00130},
   year = {2023},
@@ -43,4 +43,4 @@
   author = {Patrick Altmeyer and Arie van Deursen and Cynthia C. s. Liem},
   title = {Explaining Black-Box Models through Counterfactuals},
   journal = {Proceedings of the JuliaCon Conferences}
-}
    📚 References
    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.
    Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. “Getting a Clue: A Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.
    Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.
    Kaggle. 2011. “Give Me Some Credit, Improve on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the Next Two Years.” Kaggle. https://www.kaggle.com/c/GiveMeSomeCredit.
    Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” https://arxiv.org/abs/1712.08443.
    Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.
    Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” arXiv Preprint arXiv:2203.06768.
    Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.
    Tolomei, Gabriele, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017. “Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 465–74. https://doi.org/10.1145/3097983.3098039.
    Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harv. JL & Tech. 31: 841. https://doi.org/10.2139/ssrn.3063289.
    
+}

    📚 References

    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.

    Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. “Getting a Clue: A Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.

    Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.

    Kaggle. 2011. “Give Me Some Credit, Improve on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the Next Two Years.” Kaggle. https://www.kaggle.com/c/GiveMeSomeCredit.

    Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” https://arxiv.org/abs/1712.08443.

    Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.

    Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” arXiv Preprint arXiv:2203.06768.

    Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.

    Tolomei, Gabriele, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017. “Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 465–74. https://doi.org/10.1145/3097983.3098039.

    Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harv. JL & Tech. 31: 841. https://doi.org/10.2139/ssrn.3063289.

    diff --git a/dev/index_files/figure-commonmark/cell-10-output-1.svg b/dev/index_files/figure-commonmark/cell-10-output-1.svg index 50fb91d15..a0739b82f 100644 --- a/dev/index_files/figure-commonmark/cell-10-output-1.svg +++ b/dev/index_files/figure-commonmark/cell-10-output-1.svg @@ -1,25 +1,25 @@ - + - + - + - + - + - - - + + + - + - + - - - + + + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/objects.inv b/dev/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..bec6ae3c4a8d1566e053da9bfdacee0487811b20 GIT binary patch literal 6402 zcmV+d8U5xXAX9K?X>NERX>N99Zgg*Qc_4OWa&u{KZXhxWBOp+6Z)#;@bUGkIZ*^{T zWpZX=V{~<4Y(;o*Y+-I;bZKvHb1rIZ3L_v^WpZMd?av*PJAarPHb0B7EY-J#6b0A}HZE$jBb8}^6Aa!$TZf78RY-wUH3V58=Jza0z zMt1M_SIi<6&;?9gNA@;}u|QXrWXFMJ3z8B8yC{eeIail9FL}Wa$=bj#ivr!3c3UJ5 z#qLA)p+M1>_O-t!f1vpZJ@Y|wNY0Q$a(LwcL99#8oHHNi`+*4r!@2y|5ZmVj+ypM%lmgubhU_mP2ZJv=|R$L=L#fi5LGU8=O)4j1vQxW!oh>3h~+b z>%((B*j&4&jb;Gs<<}=VKz`tv0HV!+(}N=|pcTP%yWH%S;CBWPYwefl8UJ}ne`$9o z!8bw19^=qtkL32SQ}Rze4nq7pBjB_lg-ZX`gNtv0Pu+C8CJ9N6;MRIj1nj{D|E)s= z!u`r*BUwA(wDZ?bwOg5xn;I#qM<;_(Wk;)L&NO47_Zi6XMOtHU%DNq|K_L;w*9h=@a6Jda^e z4vfMHfeX33ZHyfN{Pe@W{10MH5i~_o(}l1CoWrL5pd4q-f&jou8{(8?n3}E5Do&iE zIFB-ttT4R8p(C46x+ShzV1T56hX5X`!>gNQFR1yyA@JUB;?->|fdBE||Ly<&_fJUb z+(aU6?(bv*-+vqa!{4s6Y@0q=F0X@ZonP&H@n-o|9tQX_0Y<)!lWcu=_+r`U_RHoH zYxwDtyaJobw<)`=-l`s&%Kn%u$%?TLaIiqeBq1=!^T%=8rpQq2E^ z^34--uK$E}07CBU^3xCB|2N{GI|abu4GsWwNSsw3d7VP0>5svr%HF8#O+^YY5#7+U zyUQ4)oU<89CM5_YKoq+DWsFNjaSnS@LLUHJz+kH39Ay>w#hV0gw^P9vKosyV^JqNl z@Xj^PfsKS^0?Dn}y2@^#WGxy?J9SdZP_1mG!u~JcQDI*-UYr3f#mNQ(Wap$FYf#KZ ziZ)R7SM^0z-^FPjW&vmu=ak@-TCWeB3z#lEXb`Ufk`}DJgCh;JqA4oTBHpmUGkknN z7YX6SW6mPZLIl~KIL|>s!JwRrcmpE=broby*5*wqbE_H`kPv+dV@C8P;x8BH1Z2RQ zI0=1+MZ7daY(&RkgjGi#Z97v0XH4f>YMq0J2L}Tb4Q*Z#0UZWyY;5in+gNmDBKtCS zo-<+GC8xP$hPH^eT@(^t5QG_37ZP1`TAamT!G0Ng&eLY_%wRUOGYrbBMbb*Evt-WU zVzn?mOAv)v1s#fE2?tJE^r~iEDN|R{u|~?bbgb!OYE4P;Cl#Vztx@XSf;~4$Zdb5T z12&?t72>?1elG|$^>r8<0r>VbdH2O-;NLl~^K6@EX1hQ;Al^Yzy|q;sr+RCX1(HsP zlO;HaK;LvLuNFO5JuA&6MB?eI7Z;a$A;@2sVvqe?yk4!?PG77^xEkz#)_TjtMP-L` zR14=DiO)uQq4c#|9PdhgIXC|cAG%jw*RvzC7tJL0izo&3p@Q9*C;R9BLUN16Q=A5# z^Gw(QIBc2Reb^ZQR0FU$^hXQ4L%%KVMA&+mZ&d`n+3G&a!IdM4b6yK|bgCoKMHS{S zu-EG04MFKgF)&oh4D6wlT6L|FX>c^q<18E-M>6&Yo1c}XFD53OqKc;fzJuCZjrSfs zD?B0z!h$il?xRn*9ll4K8u8&##wNqqmTdi9#zd*G_-2IxWG44J!TFr53$qTm!{EHQi(pt8Xv<-HR`wGf6-{alV<(;mV{G(w%I*HDyraH9-~TgWO^|4^ zsAuHHhR~gcf!h(0qK#|W%c0U&&Vw>&r)MvQ`JLE{uL2wiQn>(|1o-aGImKJ&Fw1~^ z2g&j}I8=Z`01k0*3kw2BA$eYGHhIQ0bdkmzW|@`!t2E#VV7$xLluL2u#6@n&K#o;C z5zY|px!~fXbOeHkMYHzbcJn)f~C8%V~#^m~=a8 z_;W^nRVy^aBwqv6qNsxyM(|Z31=o{F5P59aG51YCUV!BX>wCC`?Zr*6(wv7&$TXZy z1#AHs6_6K?N-JZak@2XsE*9sttfg*qLdh zplB@R3V4dLH$0-?i)O{6K5J!4%ldp)r2s}G(KYhFD|@E5koKwJcP6XneZ1XBvb!xoEj7^r^!3$S0`v%MzV8D`P}k56gJ>mIuo$5h z!k888#tG}yi?w&H+MBe-DYfl{tyFM241&iDv=n29M2xVjDP{On6T*ow_cAIsTo`Z6 zyyTs7MfWJIaYa3|;8G_KUj;ON>TWRzf7P5P(ytN9)&z%;uTY*<5y}|WmgGgFszxlz zn7Q`>g=r7nGNw0nuul>m8=JGGIbxt~qv4mmO4G;!&*=x!HNW;yCMu(prP&zi(@7Kt z&LCO^=Za*%=wHQ>BWOl_la5(6hHCHNQWk(Szfto17s#1nXeFGM9oOI~JF=N)_gVAz~J(_WdUighr z3`H6%-C~avawZZQ4|_>GN>)66s0U9aM>O4`vO^smpiOAxD2{GOa!oOULBs#{`xT|! z?U;B04FX-Rf$`RH=u0nWlIk6!?$)@bL634=RH5`O;;Xb_8`jJhdrnM= zNV5A{M#N|*r52jEBiI!nO$55++n+=!*?=X_y^ATfP33VkOy=Un6c402Ph*pY)xgJ5 zt7O=~Ux6QFcXI?3v}o+a6Dn346-VIa{kOk<@+#k4kz|yGsN}}cp#0V+Iw`! z$2HK3{PB-}ST~(hQET#)RuV{_>Q4OA&-Xq*ShpQi<&b93tm>K&TYiilwuxlxcB6$y z^Xn&^Dn88RP`6?RbGXu_v&1R8AoSz=a<1Efl3ur3O2YVC*DsFx?JDup#%U$-Bat($ zvLS95r%eN9eoMV$6t!8-;Ld1XO%0rsIdJKE9stQwJUGh6f|V?O z?F50V1~ov?7THyn)**?Twh8PiWbJiz#!8 zX;rpXfrvsXEiMZwkv3A8RS(*U>5=X@<|rdhJd?{K=ipT>nn^sWO1085<>|qX=?pV> z<=_zh89{Z*>hFV{*P9>A>Q}mHew7lvAP4h;hr?Njc$9(BgS5knErZuQK5bxL2zuFs zZ^a3FyHUU&|M&+*31Ux{M-k=QVG5|DR?#38q!|042rxV5`4Y@~YU`(DGV>_mnX?8{ zOHY#+`yNiS{j1_HcIs9u+q9$U2d9xzLy`mfg4Z%p#aevWL5iTS-nEf!60MJIB@N*5 zCV(IPg->H9+JtT+SI)yZLwUMzTNXIP`ywm$qp}w)En`HO|wSw1X0b4HZEPBI%9gH-S&xLodWYFROqgBWkQB z$m+gf3@oo2d;sa2!;`7GZ;)L4w))hi7ptT&dVip;K9}XBAKHD?X8k5E98KE=&kexJ zT4&jY2hS|X03H0W6aqGYZoPg-Z=&D3MQ-=t0TmK*l!h22+m`=&_sBDiyv- zUj`-zeeH(9I}!%#I4%STPl(p*MO*V5m3~p;g9#P6%tno>dQPUwro*ovZBK*xpJ+|1 z>#Xb;iY18$5EzZ1DfHXMrDTEs-Dy&v?uHQN-RiMhfEcI70_drRV(~t!5{KJ6C|PrOPy+4 ze-5yP8W+)(N4?@`SCx7C5!kKKlZ!w4i$o`9!rJKiD?*3myRtX~$B+5BvxHZh;BBy? zI1OSe6)ZvB^4k(&z+l^aBwnOqO!whF@-kljWi=Ym65pgyxsnr0h1EEQo}g76_2&lb zTqph8&<%BB5CvcnhQaUabZ3>G)HrNJvUo9Hb(P-E%4^h!Emzod+<{?dGM$%Rj2JL2 zox%xonrSd^PW7uqWkt&>cFl_hn_fVP;w&3+WV$_(zm`M78)W@}`1&%;ir zU`C7Fo+-}s!#2*C3mv)<39i>yah}Xd<~>NQ5WFM>=tnIhAJem zScC%3fYT5xgDJ3=XFM#c9SH}Fti&=}!^pADAYupi*&8=$o1j)n!2q!p+7|0ZOR#*L z__LH~twA#+!ufN(I8J<4(^1F;dcE;ANg57WEB;jbIWYstN_5aA*l{-2%W=(?yIZC2#p zi%0VVa*u z6m8T2l$WJ4y4+#W)<}fO*u7<=*Gb;g*5yHl{a#3Z-Z^aAZf^xuTsAa)+&iv# z;->0Oi=2dYJf^Ox?k#asV^<%ZXd<&=+_2ido!s~-W3qSU=Uhu~X_YA%3Rx9IK6!5; zyQc;@G?_Obklzf~e;h%4Rgm6p0AF!j`c)iROp_~(=1+HSrrXrZQZ&3ouqnR&*cp~& z@unLOtIyEtkd)53jRf+8|i9EntUiyI>DU;NmcxB|q5uYO|eLYrvXJxZx~G_rr{4 zTCs*NgNWc+u3eQOj|=sJ0JcHYE3htHmWZb{zuVHpm~!P!vv#1kZ*unXfdbKJX(6I; zyS)XfjdY${pn8@5RH@i;j&Eo7Q15|0%XW~2+651merd*yV3Taba68s>ci-ykT~OQK z=rc{NDnGY%?-^C)s6L`^Slw^fdi2Mvb{w@Hxwz$qACo}V>s!pJW1M99T zo%dtJJEuTs3(hU(L3_Uj$%=q8sswlQqWUrgS+;E=kT z@a@bRrW5NpZ8P~p1^4xhf-yP%QPz>=-2Z5sXz)GPgkKYdsaC#ogNUaj&0NREk%rN0 zU^pBzuTgQH!E8~)!mfgh$AfJ|CfJ{WXFKw(ggP)``4}vNO0Im##L4p9iiklbg{im-G(XfxTRP_mX*3!{Vlvau^R2bOpth`KU z*OP0ZwtZ1tXUhGf$+X)$v~5sjG~h<=ep`Rgeca}DwOq41TgjvbNxy70Siu2?pV1Od z3ygR;^m2Mdh6_ToC86gceVWS-AfanN?Y5hO~Y4C?+{7jyF9 z!@QS&>TuYW?9ch7Qy2WsscF@fuSlL)aO4%a;QOeVQ)Y*EQZpB`@&fJ6-V>Rq3Efao zgRAV1-cp6pvSS-*r`%(!9;pNPfXhJn_h0^L;KA5?{rJmgqYlbW{pL%*FE1C^3<~Sm ziC`(~Zw7~UMi@boo6pk0^m7X-J)_=RJ3g@jQi*Mn-fu?%dG0o&$%Ib}zqEPAVzd@9 QLe~h{E40P`132${VI)UQ)c^nh literal 0 HcmV?d00001 diff --git a/dev/reference.qmd b/dev/reference.qmd index 9bfb8d703..b002d017a 100644 --- a/dev/reference.qmd +++ b/dev/reference.qmd @@ -15,7 +15,7 @@ In other words, you come here because you want to take a very close look at the ```@contents Pages = ["reference.md"] -Depth = 3 +Depth = 2 ``` ## Exported functions diff --git a/dev/reference/index.html b/dev/reference/index.html index 9fd2b8b06..14c028863 100644 --- a/dev/reference/index.html +++ b/dev/reference/index.html @@ -1,5 +1,5 @@ -🧐 Reference · CounterfactualExplanations.jl
    Edit on GitHub

    Reference

    In this reference, you will find a detailed overview of the package API.

    Reference guides are technical descriptions of the machinery and how to operate it. Reference material is information-oriented.

    — Diátaxis

    In other words, you come here because you want to take a very close look at the code 🧐.

    Content

    Exported functions

    CounterfactualExplanations.CounterfactualExplanation — Method
    function CounterfactualExplanation(;
+🧐 Reference · CounterfactualExplanations.jl
    Reference
    In this reference, you will find a detailed overview of the package API.
    Reference guides are technical descriptions of the machinery and how to operate it. Reference material is information-oriented.
    — Diátaxis
    In other words, you come here because you want to take a very close look at the code 🧐.
    Content
    Exported functions
    CounterfactualExplanations.CounterfactualExplanation — Method
    function CounterfactualExplanation(;
 	x::AbstractArray,
 	target::RawTargetType,
 	data::CounterfactualData,
@@ -8,14 +8,14 @@
 	num_counterfactuals::Int = 1,
 	initialization::Symbol = :add_perturbation,
     convergence::Union{AbstractConvergence,Symbol}=:decision_threshold,
-)
    Outer method to construct a CounterfactualExplanation structure.
    source
    CounterfactualExplanations.generate_counterfactual — Method
    generate_counterfactual(
     x::Base.Iterators.Zip,
     target::RawTargetType,
     data::CounterfactualData,
     M::Models.AbstractFittedModel,
     generator::AbstractGenerator;
     kwargs...,
-)
    Overloads the generate_counterfactual method to accept a zip of factuals x and return a vector of counterfactuals.
    source
    CounterfactualExplanations.generate_counterfactual — Method
    generate_counterfactual(
     x::Matrix,
     target::RawTargetType,
     data::CounterfactualData,
@@ -49,7 +49,7 @@
 
 # Counterfactual search:
 generator = GenericGenerator()
-ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
    source
    CounterfactualExplanations.generate_counterfactual — Method
    generate_counterfactual(
     x::Matrix,
     target::RawTargetType,
     data::DataPreprocessing.CounterfactualData,
@@ -60,17 +60,17 @@
         decision_threshold=(1 / length(data.y_levels)), max_iter=1000
     ),
     kwrgs...,
-)
    Overloads the generate_counterfactual for the GrowingSpheresGenerator generator.
    source
    CounterfactualExplanations.generate_counterfactual — Method
    generate_counterfactual(
     x::Vector{<:Matrix},
     target::RawTargetType,
     data::CounterfactualData,
     M::Models.AbstractFittedModel,
     generator::AbstractGenerator;
     kwargs...,
-)
    Overloads the generate_counterfactual method to accept a vector of factuals x and return a vector of counterfactuals.
    source
    CounterfactualExplanations.target_probs — Function
    target_probs(
     ce::CounterfactualExplanation,
     x::Union{AbstractArray,Nothing}=nothing,
-)
    Returns the predicted probability of the target class for x. If x is nothing, the predicted probability corresponding to the counterfactual value is returned.
    source
    CounterfactualExplanations.update! — Method
    update!(ce::CounterfactualExplanation)
    An important subroutine that updates the counterfactual explanation. It takes a snapshot of the current counterfactual search state and passes it to the generator. Based on the current state the generator generates perturbations. Various constraints are then applied to the proposed vector of feature perturbations. Finally, the counterfactual search state is updated.
    source
    CounterfactualExplanations.Convergence.converged — Method
    converged(convergence::MaxIterConvergence, ce::CounterfactualExplanation)
    Checks if the counterfactual search has converged when the convergence criterion is maximum iterations. This means the counterfactual search will not terminate until the maximum number of iterations has been reached independently of the other convergence criteria.
    source
    CounterfactualExplanations.Convergence.hinge_loss — Method
    hinge_loss(convergence::InvalidationRateConvergence, ce::AbstractCounterfactualExplanation)
    Calculates the hinge loss of a counterfactual explanation.
    Arguments
    • convergence::InvalidationRateConvergence: The convergence criterion to use.
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation to calculate the hinge loss for.
    Returns
    The hinge loss of the counterfactual explanation.
    source
    CounterfactualExplanations.Convergence.invalidation_rate — Method
    invalidation_rate(ce::AbstractCounterfactualExplanation)
    Calculates the invalidation rate of a counterfactual explanation.
    Arguments
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation to calculate the invalidation rate for.
    • kwargs: Additional keyword arguments to pass to the function.
    Returns
    The invalidation rate of the counterfactual explanation.
    source
    CounterfactualExplanations.update! — Method
    update!(ce::CounterfactualExplanation)
    An important subroutine that updates the counterfactual explanation. It takes a snapshot of the current counterfactual search state and passes it to the generator. Based on the current state the generator generates perturbations. Various constraints are then applied to the proposed vector of feature perturbations. Finally, the counterfactual search state is updated.
    source
    CounterfactualExplanations.Convergence.converged — Method
    converged(convergence::MaxIterConvergence, ce::CounterfactualExplanation)
    Checks if the counterfactual search has converged when the convergence criterion is maximum iterations. This means the counterfactual search will not terminate until the maximum number of iterations has been reached independently of the other convergence criteria.
    source
    CounterfactualExplanations.Convergence.hinge_loss — Method
    hinge_loss(convergence::InvalidationRateConvergence, ce::AbstractCounterfactualExplanation)
    Calculates the hinge loss of a counterfactual explanation.
    Arguments
    • convergence::InvalidationRateConvergence: The convergence criterion to use.
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation to calculate the hinge loss for.
    Returns
    The hinge loss of the counterfactual explanation.
    source
    CounterfactualExplanations.Convergence.invalidation_rate — Method
    invalidation_rate(ce::AbstractCounterfactualExplanation)
    Calculates the invalidation rate of a counterfactual explanation.
    Arguments
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation to calculate the invalidation rate for.
    • kwargs: Additional keyword arguments to pass to the function.
    Returns
    The invalidation rate of the counterfactual explanation.
    source
    CounterfactualExplanations.Evaluation.benchmark — Method
    benchmark(
     data::CounterfactualData;
     models::Dict{<:Any,<:Any}=standard_models_catalogue,
     generators::Union{Nothing,Dict{<:Any,<:AbstractGenerator}}=nothing,
@@ -82,7 +82,7 @@
     store_ce::Bool=false,
     parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     kwrgs...,
-)
    Runs the benchmarking exercise as follows:
    1. Randomly choose a factual and target label unless specified. 
    2. If no pretrained models are provided, it is assumed that a dictionary of callable model objects is provided (by default using the standard_models_catalogue). 
    3. Each of these models is then trained on the data. 
    4. For each model separately choose n_individuals randomly from the non-target (factual) class. For each generator create a benchmark as in benchmark(xs::Union{AbstractArray,Base.Iterators.Zip}).
    5. Finally, concatenate the results.
    If vertical_splits is specified to an integer, the computations are split vertically into vertical_splits chunks. In this case, the results are stored in a temporary directory and concatenated afterwards. 
    source
    CounterfactualExplanations.Evaluation.benchmark — Method
    benchmark(
+)
    Runs the benchmarking exercise as follows:
    1. Randomly choose a factual and target label unless specified. 
    2. If no pretrained models are provided, it is assumed that a dictionary of callable model objects is provided (by default using the standard_models_catalogue). 
    3. Each of these models is then trained on the data. 
    4. For each model separately choose n_individuals randomly from the non-target (factual) class. For each generator create a benchmark as in benchmark(xs::Union{AbstractArray,Base.Iterators.Zip}).
    5. Finally, concatenate the results.
    If vertical_splits is specified to an integer, the computations are split vertically into vertical_splits chunks. In this case, the results are stored in a temporary directory and concatenated afterwards. 
    source
    CounterfactualExplanations.Evaluation.benchmark — Method
    benchmark(
     x::Union{AbstractArray,Base.Iterators.Zip},
     target::RawTargetType,
     data::CounterfactualData;
@@ -95,19 +95,19 @@
     store_ce::Bool=false,
     parallelizer::Union{Nothing,AbstractParallelizer}=nothing,
     kwrgs...,
-)
    First generates counterfactual explanations for factual x, the target and data using each of the provided models and generators. Then generates a Benchmark for the vector of counterfactual explanations as in benchmark(counterfactual_explanations::Vector{CounterfactualExplanation}).
    source
    CounterfactualExplanations.Evaluation.benchmark — Method
    benchmark(
     counterfactual_explanations::Vector{CounterfactualExplanation};
     meta_data::Union{Nothing,<:Vector{<:Dict}}=nothing,
     measure::Union{Function,Vector{Function}}=default_measures,
     store_ce::Bool=false,
-)
    Generates a Benchmark for a vector of counterfactual explanations. Optionally meta_data describing each individual counterfactual explanation can be supplied. This should be a vector of dictionaries of the same length as the vector of counterfactuals. If no meta_data is supplied, it will be automatically inferred. All measure functions are applied to each counterfactual explanation. If store_ce=true, the counterfactual explanations are stored in the benchmark.
    source
    CounterfactualExplanations.Evaluation.evaluate — Function
    evaluate(
+)
    Generates a Benchmark for a vector of counterfactual explanations. Optionally meta_data describing each individual counterfactual explanation can be supplied. This should be a vector of dictionaries of the same length as the vector of counterfactuals. If no meta_data is supplied, it will be automatically inferred. All measure functions are applied to each counterfactual explanation. If store_ce=true, the counterfactual explanations are stored in the benchmark.
    source
    CounterfactualExplanations.Evaluation.evaluate — Function
    evaluate(
     ce::CounterfactualExplanation;
     measure::Union{Function,Vector{Function}}=default_measures,
     agg::Function=mean,
     report_each::Bool=false,
     output_format::Symbol=:Vector,
     pivot_longer::Bool=true
-)
    Just computes evaluation measures for the counterfactual explanation. By default, no meta data is reported. For report_meta=true, meta data is automatically inferred, unless this overwritten by meta_data. The optional meta_data argument should be a vector of dictionaries of the same length as the vector of counterfactual explanations. 
    source
    CounterfactualExplanations.Evaluation.validity — Method
    validity(ce::CounterfactualExplanation; γ=0.5)
    Checks of the counterfactual search has been successful with respect to the probability threshold γ. In case multiple counterfactuals were generated, the function returns the proportion of successful counterfactuals.
    source
    CounterfactualExplanations.DataPreprocessing.CounterfactualData — Method
    CounterfactualData(
+)
    Just computes evaluation measures for the counterfactual explanation. By default, no meta data is reported. For report_meta=true, meta data is automatically inferred, unless this overwritten by meta_data. The optional meta_data argument should be a vector of dictionaries of the same length as the vector of counterfactual explanations. 
    source
    CounterfactualExplanations.Evaluation.validity — Method
    validity(ce::CounterfactualExplanation; γ=0.5)
    Checks of the counterfactual search has been successful with respect to the probability threshold γ. In case multiple counterfactuals were generated, the function returns the proportion of successful counterfactuals.
    source
    CounterfactualExplanations.DataPreprocessing.CounterfactualData — Method
    CounterfactualData(
     X::AbstractMatrix, y::AbstractMatrix;
     mutability::Union{Vector{Symbol},Nothing}=nothing,
     domain::Union{Any,Nothing}=nothing,
@@ -117,43 +117,43 @@
 )
    This outer constructor method prepares features X and labels y to be used with the package. Mutability and domain constraints can be added for the features. The function also accepts arguments that specify which features are categorical and which are continues. These arguments are currently not used. 
    Examples
    using CounterfactualExplanations.Data
 x, y = toy_data_linear()
 X = hcat(x...)
-counterfactual_data = CounterfactualData(X,y')
    source
    CounterfactualExplanations.Models.DecisionTreeModel — Method
    DecisionTreeModel(data::CounterfactualData; kwargs...)
    Constructs a new TreeModel object wrapped around a decision tree from the data in a CounterfactualData object. Not called by the user directly.
    Arguments
    • data::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.
    Returns
    • model::TreeModel: A TreeModel object.
    source
    CounterfactualExplanations.Models.Linear — Method
    Linear(data::CounterfactualData; kwargs...)
    Constructs a model with one linear layer. If the output is binary, this corresponds to logistic regression, since model outputs are passed through the sigmoid function. If the output is multi-class, this corresponds to multinomial logistic regression, since model outputs are passed through the softmax function.
    source
    CounterfactualExplanations.Models.RandomForestModel — Method
    RandomForestModel(data::CounterfactualData; kwargs...)
    Constructs a new TreeModel object wrapped around a random forest from the data in a CounterfactualData object. Not called by the user directly.
    Arguments
    • data::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.
    Returns
    • model::TreeModel: A TreeModel object.
    source
    CounterfactualExplanations.Models.fit_model — Function
    fit_model(
+)
    Outer constructor method that accepts a Tables.MatrixTable. By default, the indices of categorical and continuous features are automatically inferred the features' scitype.
    source
    CounterfactualExplanations.Models.DecisionTreeModel — Method
    DecisionTreeModel(data::CounterfactualData; kwargs...)
    Constructs a new TreeModel object wrapped around a decision tree from the data in a CounterfactualData object. Not called by the user directly.
    Arguments
    • data::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.
    Returns
    • model::TreeModel: A TreeModel object.
    source
    CounterfactualExplanations.Models.Linear — Method
    Linear(data::CounterfactualData; kwargs...)
    Constructs a model with one linear layer. If the output is binary, this corresponds to logistic regression, since model outputs are passed through the sigmoid function. If the output is multi-class, this corresponds to multinomial logistic regression, since model outputs are passed through the softmax function.
    source
    CounterfactualExplanations.Models.RandomForestModel — Method
    RandomForestModel(data::CounterfactualData; kwargs...)
    Constructs a new TreeModel object wrapped around a random forest from the data in a CounterfactualData object. Not called by the user directly.
    Arguments
    • data::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.
    Returns
    • model::TreeModel: A TreeModel object.
    source
    CounterfactualExplanations.Models.fit_model — Function
    fit_model(
     counterfactual_data::CounterfactualData, model::Symbol=:MLP;
     kwrgs...
-)
    Fits one of the available default models to the counterfactual_data. The model argument can be used to specify the desired model. The available values correspond to the keys of the all_models_catalogue dictionary.
    source
    CounterfactualExplanations.Models.logits — Method
    logits(M::AbstractFittedModel, X::AbstractArray)
    Generic method that is compulsory for all models. It returns the raw model predictions. In classification this is sometimes referred to as logits: the non-normalized predictions that are fed into a link function to produce predicted probabilities. In regression (not currently implemented) raw outputs typically correspond to final outputs. In other words, there is typically no normalization involved.
    source
    CounterfactualExplanations.Models.logits — Method
    logits(M::TreeModel, X::AbstractArray)
    Calculates the logit scores output by the model M for the input data X.
    Arguments
    • M::TreeModel: The model selected by the user.
    • X::AbstractArray: The feature vector for which the logit scores are calculated.
    Returns
    • logits::Matrix: A matrix of logits for each output class for each data point in X.
    Example
    logits = Models.logits(M, x) # calculates the logit scores for each output class for the data point x
    source
    CounterfactualExplanations.Models.model_evaluation — Method
    model_evaluation(M::AbstractFittedModel, test_data::CounterfactualData)
    Helper function to compute F-Score for AbstractFittedModel on a (test) data set. By default, it computes the accuracy. Any other measure, e.g. from the StatisticalMeasures package, can be passed as an argument. Currently, only measures applicable to classification tasks are supported.
    source
    CounterfactualExplanations.Models.predict_label — Method
    predict_label(M::TreeModel, X::AbstractArray)
    Returns the predicted label for X.
    Arguments
    • M::TreeModel: The model selected by the user.
    • X::AbstractArray: The input array for which the label is predicted.
    Returns
    • labels::AbstractArray: The predicted label for each data point in X.
    Example
    label = Models.predict_label(M, x) # returns the predicted label for each data point in x
    source
    CounterfactualExplanations.Models.predict_proba — Method
    predict_proba(M::AbstractFittedModel, counterfactual_data::CounterfactualData, X::Union{Nothing,AbstractArray})
    Returns the predicted output probabilities for a given model M, data set counterfactual_data and input data X.
    source
    CounterfactualExplanations.Models.probs — Method
    probs(M::AbstractFittedModel, X::AbstractArray)
    Generic method that is compulsory for all models. It returns the normalized model predictions, so the predicted probabilities in the case of classification. In regression (not currently implemented) this method is redundant. 
    source
    CounterfactualExplanations.Models.probs — Method
    probs(M::TreeModel, X::AbstractArray{<:Number, 2})
    Calculates the probability scores for each output class for the two-dimensional input data matrix X.
    Arguments
    • M::TreeModel: The TreeModel.
    • X::AbstractArray: The feature vector for which the predictions are made.
    Returns
    • p::Matrix: A matrix of probability scores for each output class for each data point in X.
    Example
    probabilities = Models.probs(M, X) # calculates the probability scores for each output class for each data point in X.
    source
    CounterfactualExplanations.Models.probs — Method
    probs(M::TreeModel, X::AbstractArray{<:Number, 1})
    Works the same way as the probs(M::TreeModel, X::AbstractArray{<:Number, 2}) method above, but handles 1-dimensional rather than 2-dimensional input data.
    source
    CounterfactualExplanations.Generators.FeatureTweakGenerator — Method
    FeatureTweakGenerator(; penalty::Union{Nothing,Function,Vector{Function}}=Objectives.distance_l2, ϵ::AbstractFloat=0.1)
    Constructs a new Feature Tweak Generator object.
    Uses the L2-norm as the penalty to measure the distance between the counterfactual and the factual. According to the paper by Tolomei et al., another recommended choice for the penalty in addition to the L2-norm is the L0-norm. The L0-norm simply minimizes the number of features that are changed through the tweak.
    Arguments
    • penalty::Union{Nothing,Function,Vector{Function}}: The penalty function to use for the generator. Defaults to distance_l2.
    • ϵ::AbstractFloat: The tolerance value for the feature tweaks. Described at length in Tolomei et al. (https://arxiv.org/pdf/1706.06691.pdf). Defaults to 0.1.
    Returns
    • generator::FeatureTweakGenerator: A non-gradient-based generator that can be used to generate counterfactuals using the feature tweak method.
    source
    CounterfactualExplanations.Models.logits — Method
    logits(M::AbstractFittedModel, X::AbstractArray)
    Generic method that is compulsory for all models. It returns the raw model predictions. In classification this is sometimes referred to as logits: the non-normalized predictions that are fed into a link function to produce predicted probabilities. In regression (not currently implemented) raw outputs typically correspond to final outputs. In other words, there is typically no normalization involved.
    source
    CounterfactualExplanations.Models.logits — Method
    logits(M::TreeModel, X::AbstractArray)
    Calculates the logit scores output by the model M for the input data X.
    Arguments
    • M::TreeModel: The model selected by the user.
    • X::AbstractArray: The feature vector for which the logit scores are calculated.
    Returns
    • logits::Matrix: A matrix of logits for each output class for each data point in X.
    Example
    logits = Models.logits(M, x) # calculates the logit scores for each output class for the data point x
    source
    CounterfactualExplanations.Models.model_evaluation — Method
    model_evaluation(M::AbstractFittedModel, test_data::CounterfactualData)
    Helper function to compute F-Score for AbstractFittedModel on a (test) data set. By default, it computes the accuracy. Any other measure, e.g. from the StatisticalMeasures package, can be passed as an argument. Currently, only measures applicable to classification tasks are supported.
    source
    CounterfactualExplanations.Models.predict_label — Method
    predict_label(M::TreeModel, X::AbstractArray)
    Returns the predicted label for X.
    Arguments
    • M::TreeModel: The model selected by the user.
    • X::AbstractArray: The input array for which the label is predicted.
    Returns
    • labels::AbstractArray: The predicted label for each data point in X.
    Example
    label = Models.predict_label(M, x) # returns the predicted label for each data point in x
    source
    CounterfactualExplanations.Models.predict_proba — Method
    predict_proba(M::AbstractFittedModel, counterfactual_data::CounterfactualData, X::Union{Nothing,AbstractArray})
    Returns the predicted output probabilities for a given model M, data set counterfactual_data and input data X.
    source
    CounterfactualExplanations.Models.probs — Method
    probs(M::AbstractFittedModel, X::AbstractArray)
    Generic method that is compulsory for all models. It returns the normalized model predictions, so the predicted probabilities in the case of classification. In regression (not currently implemented) this method is redundant. 
    source
    CounterfactualExplanations.Models.probs — Method
    probs(M::TreeModel, X::AbstractArray{<:Number, 2})
    Calculates the probability scores for each output class for the two-dimensional input data matrix X.
    Arguments
    • M::TreeModel: The TreeModel.
    • X::AbstractArray: The feature vector for which the predictions are made.
    Returns
    • p::Matrix: A matrix of probability scores for each output class for each data point in X.
    Example
    probabilities = Models.probs(M, X) # calculates the probability scores for each output class for each data point in X.
    source
    CounterfactualExplanations.Models.probs — Method
    probs(M::TreeModel, X::AbstractArray{<:Number, 1})
    Works the same way as the probs(M::TreeModel, X::AbstractArray{<:Number, 2}) method above, but handles 1-dimensional rather than 2-dimensional input data.
    source
    CounterfactualExplanations.Generators.FeatureTweakGenerator — Method
    FeatureTweakGenerator(; penalty::Union{Nothing,Function,Vector{Function}}=Objectives.distance_l2, ϵ::AbstractFloat=0.1)
    Constructs a new Feature Tweak Generator object.
    Uses the L2-norm as the penalty to measure the distance between the counterfactual and the factual. According to the paper by Tolomei et al., another recommended choice for the penalty in addition to the L2-norm is the L0-norm. The L0-norm simply minimizes the number of features that are changed through the tweak.
    Arguments
    • penalty::Union{Nothing,Function,Vector{Function}}: The penalty function to use for the generator. Defaults to distance_l2.
    • ϵ::AbstractFloat: The tolerance value for the feature tweaks. Described at length in Tolomei et al. (https://arxiv.org/pdf/1706.06691.pdf). Defaults to 0.1.
    Returns
    • generator::FeatureTweakGenerator: A non-gradient-based generator that can be used to generate counterfactuals using the feature tweak method.
    source
    CounterfactualExplanations.Generators.GradientBasedGenerator — Method
    GradientBasedGenerator(;
 	loss::Union{Nothing,Function}=nothing,
 	penalty::Penalty=nothing,
 	λ::Union{Nothing,AbstractFloat,Vector{AbstractFloat}}=nothing,
 	latent_space::Bool::false,
 	opt::Flux.Optimise.AbstractOptimiser=Flux.Descent(),
     generative_model_params::NamedTuple=(;),
-)
    Default outer constructor for GradientBasedGenerator.
    Arguments
    • loss::Union{Nothing,Function}=nothing: The loss function used by the model.
    • penalty::Penalty=nothing: A penalty function for the generator to penalize counterfactuals too far from the original point.
    • λ::Union{Nothing,AbstractFloat,Vector{AbstractFloat}}=nothing: The weight of the penalty function.
    • latent_space::Bool=false: Whether to use the latent space of a generative model to generate counterfactuals.
    • opt::Flux.Optimise.AbstractOptimiser=Flux.Descent(): The optimizer to use for the generator.
    • generative_model_params::NamedTuple: The parameters of the generative model associated with the generator.
    Returns
    • generator::GradientBasedGenerator: A gradient-based counterfactual generator.
    source
    CounterfactualExplanations.Generators.conditions_satisfied — Method
    conditions_satisfied(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)
    The default method to check if the all conditions for convergence of the counterfactual search have been satisified for gradient-based generators. By default, gradient-based search is considered to have converged as soon as the proposed feature changes for all features are smaller than one percent of its standard deviation.
    source
    CounterfactualExplanations.Generators.feature_tweaking! — Method
    feature_tweaking!(ce::AbstractCounterfactualExplanation)
    Returns a counterfactual instance of ce.x based on the ensemble of classifiers provided.
    Arguments
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation object.
    Returns
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation object.
    Example
    ce = feature_tweaking!(ce) # returns a counterfactual inside the ce.s′ field based on the ensemble of classifiers provided
    source
    CounterfactualExplanations.Generators.hinge_loss — Method
    hinge_loss(convergence::AbstractConvergence, ce::AbstractCounterfactualExplanation)
    The default hinge loss for any convergence criterion. Can be overridden inside the Convergence module as part of the definition of specific convergence criteria.
    source
    CounterfactualExplanations.Objectives.ddp_diversity — Method
    ddp_diversity(
+)
    Default outer constructor for GradientBasedGenerator.
    Arguments
    • loss::Union{Nothing,Function}=nothing: The loss function used by the model.
    • penalty::Penalty=nothing: A penalty function for the generator to penalize counterfactuals too far from the original point.
    • λ::Union{Nothing,AbstractFloat,Vector{AbstractFloat}}=nothing: The weight of the penalty function.
    • latent_space::Bool=false: Whether to use the latent space of a generative model to generate counterfactuals.
    • opt::Flux.Optimise.AbstractOptimiser=Flux.Descent(): The optimizer to use for the generator.
    • generative_model_params::NamedTuple: The parameters of the generative model associated with the generator.
    Returns
    • generator::GradientBasedGenerator: A gradient-based counterfactual generator.
    source
    CounterfactualExplanations.Generators.conditions_satisfied — Method
    conditions_satisfied(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)
    The default method to check if the all conditions for convergence of the counterfactual search have been satisified for gradient-based generators. By default, gradient-based search is considered to have converged as soon as the proposed feature changes for all features are smaller than one percent of its standard deviation.
    source
    CounterfactualExplanations.Generators.feature_tweaking! — Method
    feature_tweaking!(ce::AbstractCounterfactualExplanation)
    Returns a counterfactual instance of ce.x based on the ensemble of classifiers provided.
    Arguments
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation object.
    Returns
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation object.
    Example
    ce = feature_tweaking!(ce) # returns a counterfactual inside the ce.s′ field based on the ensemble of classifiers provided
    source
    CounterfactualExplanations.Generators.hinge_loss — Method
    hinge_loss(convergence::AbstractConvergence, ce::AbstractCounterfactualExplanation)
    The default hinge loss for any convergence criterion. Can be overridden inside the Convergence module as part of the definition of specific convergence criteria.
    source
    Flux.Losses.logitbinarycrossentropy — Method
    Flux.Losses.logitbinarycrossentropy(ce::AbstractCounterfactualExplanation)
    Simply extends the logitbinarycrossentropy method to work with objects of type AbstractCounterfactualExplanation.
    source
    Flux.Losses.logitcrossentropy — Method
    Flux.Losses.logitcrossentropy(ce::AbstractCounterfactualExplanation)
    Simply extends the logitcrossentropy method to work with objects of type AbstractCounterfactualExplanation.
    source
    Flux.Losses.mse — Method
    Flux.Losses.mse(ce::AbstractCounterfactualExplanation)
    Simply extends the mse method to work with objects of type AbstractCounterfactualExplanation.
    source
    Internal functions
    Flux.Losses.logitbinarycrossentropy — Method
    Flux.Losses.logitbinarycrossentropy(ce::AbstractCounterfactualExplanation)
    Simply extends the logitbinarycrossentropy method to work with objects of type AbstractCounterfactualExplanation.
    source
    Flux.Losses.logitcrossentropy — Method
    Flux.Losses.logitcrossentropy(ce::AbstractCounterfactualExplanation)
    Simply extends the logitcrossentropy method to work with objects of type AbstractCounterfactualExplanation.
    source
    Flux.Losses.mse — Method
    Flux.Losses.mse(ce::AbstractCounterfactualExplanation)
    Simply extends the mse method to work with objects of type AbstractCounterfactualExplanation.
    source
    Internal functions
    CounterfactualExplanations.decode_array — Method
    decode_array(dt::MultivariateStats.AbstractDimensionalityReduction, x::AbstractArray)
    Helper function to decode an array x using a data transform dt::MultivariateStats.AbstractDimensionalityReduction.
    source
    CounterfactualExplanations.decode_state — Function
    function decode_state(     ce::CounterfactualExplanation,     x::Union{AbstractArray,Nothing}=nothing, )
    Applies all the applicable decoding functions:
    1. If applicable, map the state variable back from the latent space to the feature space.
    2. If and where applicable, inverse-transform features.
    3. Reconstruct all categorical encodings.
    Finally, the decoded counterfactual is returned.
    source
    CounterfactualExplanations.encode_array — Method
    encode_array(dt::MultivariateStats.AbstractDimensionalityReduction, x::AbstractArray)
    Helper function to encode an array x using a data transform dt::MultivariateStats.AbstractDimensionalityReduction.
    source
    CounterfactualExplanations.encode_state — Function
    function encode_state(     ce::CounterfactualExplanation,      x::Union{AbstractArray,Nothing} = nothing, )
    Applies all required encodings to x:
    1. If applicable, it maps x to the latent space learned by the generative model.
    2. If and where applicable, it rescales features. 
    Finally, it returns the encoded state variable.
    source
    CounterfactualExplanations.guess_likelihood — Method
    guess_likelihood(y::RawOutputArrayType)
    Guess the likelihood based on the scientific type of the output array. Returns a symbol indicating the guessed likelihood and the scientific type of the output array.
    source
    CounterfactualExplanations.initialize_state — Method
    initialize_state(ce::CounterfactualExplanation)
    Initializes the starting point for the factual(s):
    1. If ce.initialization is set to :identity or counterfactuals are searched in a latent space, then nothing is done.
    2. If ce.initialization is set to :add_perturbation, then a random perturbation is added to the factual following following Slack (2021): https://arxiv.org/abs/2106.02666. The authors show that this improves adversarial robustness.
    source
    CounterfactualExplanations.decode_array — Method
    decode_array(dt::MultivariateStats.AbstractDimensionalityReduction, x::AbstractArray)
    Helper function to decode an array x using a data transform dt::MultivariateStats.AbstractDimensionalityReduction.
    source
    CounterfactualExplanations.decode_state — Function
    function decode_state(     ce::CounterfactualExplanation,     x::Union{AbstractArray,Nothing}=nothing, )
    Applies all the applicable decoding functions:
    1. If applicable, map the state variable back from the latent space to the feature space.
    2. If and where applicable, inverse-transform features.
    3. Reconstruct all categorical encodings.
    Finally, the decoded counterfactual is returned.
    source
    CounterfactualExplanations.encode_array — Method
    encode_array(dt::MultivariateStats.AbstractDimensionalityReduction, x::AbstractArray)
    Helper function to encode an array x using a data transform dt::MultivariateStats.AbstractDimensionalityReduction.
    source
    CounterfactualExplanations.encode_state — Function
    function encode_state(     ce::CounterfactualExplanation,      x::Union{AbstractArray,Nothing} = nothing, )
    Applies all required encodings to x:
    1. If applicable, it maps x to the latent space learned by the generative model.
    2. If and where applicable, it rescales features. 
    Finally, it returns the encoded state variable.
    source
    CounterfactualExplanations.guess_likelihood — Method
    guess_likelihood(y::RawOutputArrayType)
    Guess the likelihood based on the scientific type of the output array. Returns a symbol indicating the guessed likelihood and the scientific type of the output array.
    source
    CounterfactualExplanations.initialize_state — Method
    initialize_state(ce::CounterfactualExplanation)
    Initializes the starting point for the factual(s):
    1. If ce.initialization is set to :identity or counterfactuals are searched in a latent space, then nothing is done.
    2. If ce.initialization is set to :add_perturbation, then a random perturbation is added to the factual following following Slack (2021): https://arxiv.org/abs/2106.02666. The authors show that this improves adversarial robustness.
    source
    CounterfactualExplanations.map_to_latent — Function
    function maptolatent(         ce::CounterfactualExplanation,         x::Union{AbstractArray,Nothing}=nothing,     ) 
    Maps x from the feature space $\mathcal{X}$ to the latent space learned by the generative model.
    source
    Base.vcat — Method
    Base.vcat(bmk1::Benchmark, bmk2::Benchmark)
    Vertically concatenates two Benchmark objects.
    source
    CounterfactualExplanations.Evaluation.compute_measure — Method
    compute_measure(ce::CounterfactualExplanation, measure::Function, agg::Function)
    Computes a single measure for a counterfactual explanation. The measure is applied to the counterfactual explanation ce and aggregated using the aggregation function agg.
    source
    CounterfactualExplanations.map_to_latent — Function
    function maptolatent(         ce::CounterfactualExplanation,         x::Union{AbstractArray,Nothing}=nothing,     ) 
    Maps x from the feature space $\mathcal{X}$ to the latent space learned by the generative model.
    source
    Base.vcat — Method
    Base.vcat(bmk1::Benchmark, bmk2::Benchmark)
    Vertically concatenates two Benchmark objects.
    source
    CounterfactualExplanations.Evaluation.compute_measure — Method
    compute_measure(ce::CounterfactualExplanation, measure::Function, agg::Function)
    Computes a single measure for a counterfactual explanation. The measure is applied to the counterfactual explanation ce and aggregated using the aggregation function agg.
    source
    CounterfactualExplanations.Evaluation.to_dataframe — Method
    evaluate_dataframe(
     ce::CounterfactualExplanation,
     measure::Vector{Function},
     agg::Function,
     report_each::Bool,
     pivot_longer::Bool,
     store_ce::Bool,
-)
    Evaluates a counterfactual explanation and returns a dataframe of evaluation measures.
    source
    CounterfactualExplanations.DataPreprocessing.convert_to_1d — Method
    convert_to_1d(y::Matrix, y_levels::AbstractArray)
    Helper function to convert a one-hot encoded matrix to a vector of labels. This is necessary because MLJ models require the labels to be represented as a vector, but the synthetic datasets in this package hold the labels in one-hot encoded form.
    Arguments
    • y::Matrix: The one-hot encoded matrix.
    • y_levels::AbstractArray: The levels of the categorical variable.
    Returns
    • labels: A vector of labels.
    source
    CounterfactualExplanations.DataPreprocessing.get_generative_model — Method
    get_generative_model(counterfactual_data::CounterfactualData)
    Returns the underlying generative model. If there is no existing model available, the default generative model (VAE) is used. Otherwise it is expected that existing generative model has been pre-trained or else a warning is triggered.
    source
    CounterfactualExplanations.DataPreprocessing.preprocess_data_for_mlj — Method
    preprocess_data_for_mlj(data::CounterfactualData)
    Helper function to preprocess data::CounterfactualData for MLJ models.
    Arguments
    • data::CounterfactualData: The data to be preprocessed.
    Returns
    • (df_x, y): A tuple containing the preprocessed data, with df_x being a DataFrame object and y being a categorical vector.
    Example
    X, y = preprocessdatafor_mlj(data)
    source
    CounterfactualExplanations.DataPreprocessing.train_test_split — Method
    train_test_split(data::CounterfactualData;test_size=0.2,keep_class_ratio=false)
    Splits data into train and test split.
    Arguments
    • data::CounterfactualData: The data to be preprocessed.
    • test_size=0.2: Proportion of the data to be used for testing. 
    • keep_class_ratio=false: Decides whether to sample equally from each class, or keep their relative size.
    Returns
    • (train_data::CounterfactualData, test_data::CounterfactualData): A tuple containing the train and test splits.
    Example
    train, test = traintestsplit(data, testsize=0.1, keepclass_ratio=true)
    source
    CounterfactualExplanations.Models.TreeModel — Type
    TreeModel <: AbstractNonDifferentiableJuliaModel
    Constructor for tree-based models from the MLJ library. 
    Arguments
    • model::Any: The model selected by the user. Must be a model from the MLJ library.
    • likelihood::Symbol: The likelihood of the model. Must be one of [:classification_binary, :classification_multi].
    Returns
    • TreeModel: A tree-based model from the MLJ library wrapped inside the TreeModel class.
    source
    CounterfactualExplanations.Models.get_individual_classifiers — Method
    get_individual_classifiers(M::TreeModel)
    Returns the individual classifiers in the forest. If the input is a decision tree, the method returns the decision tree itself inside an array.
    Arguments
    • M::TreeModel: The model selected by the user.
    Returns
    • classifiers::AbstractArray: An array of individual classifiers in the forest.
    Example
    classifiers = Models.getindividualclassifiers(M) # returns the individual classifiers in the forest
    source
    CounterfactualExplanations.Models.train — Method
    train(M::TreeModel, data::CounterfactualData; kwargs...)
    Fits the model M to the data in the CounterfactualData object. This method is not called by the user directly.
    Arguments
    • M::TreeModel: The wrapper for a TreeModel.
    • data::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.
    Returns
    • M::TreeModel: The fitted TreeModel.
    source
    Base.rand — Function
    Random.rand(encoder::Encoder, x, device=cpu)
    Draws random samples from the latent distribution.
    source
    CounterfactualExplanations.Generators.calculate_delta — Method
    calculate_delta(ce::AbstractCounterfactualExplanation, penalty::Vector{Function})
    Calculates the penalty for the proposed feature tweak.
    Arguments
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation object.
    Returns
    • delta::Float64: The calculated penalty for the proposed feature tweak.
    source
    CounterfactualExplanations.Generators.esatisfactory_instance — Method
    esatisfactory_instance(generator::FeatureTweakGenerator, x::AbstractArray, paths::Dict{String, Dict{String, Any}})
    Returns an epsilon-satisfactory counterfactual for x based on the paths provided.
    Arguments
    • generator::FeatureTweakGenerator: The feature tweak generator.
    • x::AbstractArray: The factual instance.
    • paths::Dict{String, Dict{String, Any}}: A list of paths to the leaves of the tree to be used for tweaking the feature.
    Returns
    • esatisfactory::AbstractArray: The epsilon-satisfactory instance.
    Example
    esatisfactory = esatisfactory_instance(generator, x, paths) # returns an epsilon-satisfactory counterfactual for x based on the paths provided
    source
    CounterfactualExplanations.Generators.feature_selection! — Method
    feature_selection!(ce::AbstractCounterfactualExplanation)
    Perform feature selection to find the dimension with the closest (but not equal) values between the ce.x (factual) and ce.s′ (counterfactual) arrays.
    Arguments
    • ce::AbstractCounterfactualExplanation: An instance of the AbstractCounterfactualExplanation type representing the counterfactual explanation.
    Returns
    • nothing
    The function iteratively modifies the ce.s′ counterfactual array by updating its elements to match the corresponding elements in the ce.x factual array, one dimension at a time, until the predicted label of the modified ce.s′ matches the predicted label of the ce.x array.
    source
    CounterfactualExplanations.Generators.find_closest_dimension — Method
    find_closest_dimension(factual, counterfactual)
    Find the dimension with the closest (but not equal) values between the factual and counterfactual arrays.
    Arguments
    • factual: The factual array.
    • counterfactual: The counterfactual array.
    Returns
    • closest_dimension: The index of the dimension with the closest values.
    The function iterates over the indices of the factual array and calculates the absolute difference between the corresponding elements in the factual and counterfactual arrays. It returns the index of the dimension with the smallest difference, excluding dimensions where the values in factual and counterfactual are equal.
    source
    CounterfactualExplanations.Generators.find_counterfactual — Method
    find_counterfactual(model, factual_class, counterfactual_data, counterfactual_candidates)
    Find the first counterfactual index by predicting labels.
    Arguments
    • model: The fitted model used for prediction.
    • target_class: Expected target class.
    • counterfactual_data: Data required for counterfactual generation.
    • counterfactual_candidates: The array of counterfactual candidates.
    Returns
    • counterfactual: The index of the first counterfactual found.
    source
    CounterfactualExplanations.Generators.growing_spheres_generation! — Method
    growing_spheres_generation(ce::AbstractCounterfactualExplanation)
    Generate counterfactual candidates using the growing spheres generation algorithm.
    Arguments
    • ce::AbstractCounterfactualExplanation: An instance of the AbstractCounterfactualExplanation type representing the counterfactual explanation.
    Returns
    • nothing
    This function applies the growing spheres generation algorithm to generate counterfactual candidates. It starts by generating random points uniformly on a sphere, gradually reducing the search space until no counterfactuals are found. Then it expands the search space until at least one counterfactual is found or the maximum number of iterations is reached.
    The algorithm iteratively generates counterfactual candidates and predicts their labels using the model stored in ce.M. It checks if any of the predicted labels are different from the factual class. The process of reducing the search space involves halving the search radius, while the process of expanding the search space involves increasing the search radius.
    source
    CounterfactualExplanations.Generators.h — Method
    h(generator::AbstractGenerator, penalty::Function, ce::AbstractCounterfactualExplanation)
    Overloads the h function for the case where a single penalty function is provided.
    source
    CounterfactualExplanations.Generators.h — Method
    h(generator::AbstractGenerator, penalty::Tuple, ce::AbstractCounterfactualExplanation)
    Overloads the h function for the case where a single penalty function is provided with additional keyword arguments.
    source
    CounterfactualExplanations.Generators.h — Method
    h(generator::AbstractGenerator, penalty::Tuple, ce::AbstractCounterfactualExplanation)
    Overloads the h function for the case where a single penalty function is provided with additional keyword arguments.
    source
    CounterfactualExplanations.Generators.hyper_sphere_coordinates — Method
    hyper_sphere_coordinates(n_search_samples::Int, instance::Vector{Float64}, low::Int, high::Int; p_norm::Int=2)
    Generates candidate counterfactuals using the growing spheres method based on hyper-sphere coordinates.
    The implementation follows the Random Point Picking over a sphere algorithm described in the paper: "Learning Counterfactual Explanations for Tabular Data" by Pawelczyk, Broelemann & Kascneci (2020), presented at The Web Conference 2020 (WWW). It ensures that points are sampled uniformly at random using insights from: http://mathworld.wolfram.com/HyperspherePointPicking.html
    The growing spheres method is originally proposed in the paper: "Comparison-based Inverse Classification for Interpretability in Machine Learning" by Thibaut Laugel et al (2018), presented at the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2018).
    Arguments
    • n_search_samples::Int: The number of search samples (int > 0).
    • instance::AbstractArray: The input point array.
    • low::AbstractFloat: The lower bound (float >= 0, l < h).
    • high::AbstractFloat: The upper bound (float >= 0, h > l).
    • p_norm::Integer: The norm parameter (int >= 1).
    Returns
    • candidate_counterfactuals::Array: An array of candidate counterfactuals.
    source
    CounterfactualExplanations.Generators.search_path — Function
    search_path(tree::Union{DecisionTree.Leaf, DecisionTree.Node}, target::RawTargetType, path::AbstractArray)
    Return a path index list with the inequality symbols, thresholds and feature indices.
    Arguments
    • tree::Union{DecisionTree.Leaf, DecisionTree.Node}: The root node of a decision tree.
    • target::RawTargetType: The target class.
    • path::AbstractArray: A list containing the paths found thus far.
    Returns
    • paths::AbstractArray: A list of paths to the leaves of the tree to be used for tweaking the feature.
    Example
    paths = search_path(tree, target) # returns a list of paths to the leaves of the tree to be used for tweaking the feature
    source
    CounterfactualExplanations.Generators.∂h — Method
    ∂h(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)
    The default method to compute the gradient of the complexity penalty at the current counterfactual state for gradient-based generators. It assumes that Zygote.jl has gradient access. 
    If the penalty is not provided, it returns 0.0. By default, Zygote never works out the gradient for constants and instead returns 'nothing', so we need to add a manual step to override this behaviour. See here: https://discourse.julialang.org/t/zygote-gradient/26715.
    source
    CounterfactualExplanations.Generators.∂ℓ — Method
    ∂ℓ(generator::AbstractGradientBasedGenerator, M::Union{Models.LogisticModel, Models.BayesianLogisticModel}, ce::AbstractCounterfactualExplanation)
    The default method to compute the gradient of the loss function at the current counterfactual state for gradient-based generators. It assumes that Zygote.jl has gradient access.
    source
    CounterfactualExplanations.Generators.∇ — Method
    ∇(generator::AbstractGradientBasedGenerator, M::Models.AbstractDifferentiableModel, ce::AbstractCounterfactualExplanation)
    The default method to compute the gradient of the counterfactual search objective for gradient-based generators. It simply computes the weighted sum over partial derivates. It assumes that Zygote.jl has gradient access. If the counterfactual is being generated using Probe, the hinge loss is added to the gradient.
    source
    CounterfactualExplanations.DataPreprocessing.convert_to_1d — Method
    convert_to_1d(y::Matrix, y_levels::AbstractArray)
    Helper function to convert a one-hot encoded matrix to a vector of labels. This is necessary because MLJ models require the labels to be represented as a vector, but the synthetic datasets in this package hold the labels in one-hot encoded form.
    Arguments
    • y::Matrix: The one-hot encoded matrix.
    • y_levels::AbstractArray: The levels of the categorical variable.
    Returns
    • labels: A vector of labels.
    source
    CounterfactualExplanations.DataPreprocessing.get_generative_model — Method
    get_generative_model(counterfactual_data::CounterfactualData)
    Returns the underlying generative model. If there is no existing model available, the default generative model (VAE) is used. Otherwise it is expected that existing generative model has been pre-trained or else a warning is triggered.
    source
    CounterfactualExplanations.DataPreprocessing.preprocess_data_for_mlj — Method
    preprocess_data_for_mlj(data::CounterfactualData)
    Helper function to preprocess data::CounterfactualData for MLJ models.
    Arguments
    • data::CounterfactualData: The data to be preprocessed.
    Returns
    • (df_x, y): A tuple containing the preprocessed data, with df_x being a DataFrame object and y being a categorical vector.
    Example
    X, y = preprocessdatafor_mlj(data)
    source
    CounterfactualExplanations.DataPreprocessing.train_test_split — Method
    train_test_split(data::CounterfactualData;test_size=0.2,keep_class_ratio=false)
    Splits data into train and test split.
    Arguments
    • data::CounterfactualData: The data to be preprocessed.
    • test_size=0.2: Proportion of the data to be used for testing. 
    • keep_class_ratio=false: Decides whether to sample equally from each class, or keep their relative size.
    Returns
    • (train_data::CounterfactualData, test_data::CounterfactualData): A tuple containing the train and test splits.
    Example
    train, test = traintestsplit(data, testsize=0.1, keepclass_ratio=true)
    source
    CounterfactualExplanations.Models.TreeModel — Type
    TreeModel <: AbstractNonDifferentiableJuliaModel
    Constructor for tree-based models from the MLJ library. 
    Arguments
    • model::Any: The model selected by the user. Must be a model from the MLJ library.
    • likelihood::Symbol: The likelihood of the model. Must be one of [:classification_binary, :classification_multi].
    Returns
    • TreeModel: A tree-based model from the MLJ library wrapped inside the TreeModel class.
    source
    CounterfactualExplanations.Models.get_individual_classifiers — Method
    get_individual_classifiers(M::TreeModel)
    Returns the individual classifiers in the forest. If the input is a decision tree, the method returns the decision tree itself inside an array.
    Arguments
    • M::TreeModel: The model selected by the user.
    Returns
    • classifiers::AbstractArray: An array of individual classifiers in the forest.
    Example
    classifiers = Models.getindividualclassifiers(M) # returns the individual classifiers in the forest
    source
    CounterfactualExplanations.Models.train — Method
    train(M::TreeModel, data::CounterfactualData; kwargs...)
    Fits the model M to the data in the CounterfactualData object. This method is not called by the user directly.
    Arguments
    • M::TreeModel: The wrapper for a TreeModel.
    • data::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.
    Returns
    • M::TreeModel: The fitted TreeModel.
    source
    Base.rand — Function
    Random.rand(encoder::Encoder, x, device=cpu)
    Draws random samples from the latent distribution.
    source
    CounterfactualExplanations.Generators.calculate_delta — Method
    calculate_delta(ce::AbstractCounterfactualExplanation, penalty::Vector{Function})
    Calculates the penalty for the proposed feature tweak.
    Arguments
    • ce::AbstractCounterfactualExplanation: The counterfactual explanation object.
    Returns
    • delta::Float64: The calculated penalty for the proposed feature tweak.
    source
    CounterfactualExplanations.Generators.esatisfactory_instance — Method
    esatisfactory_instance(generator::FeatureTweakGenerator, x::AbstractArray, paths::Dict{String, Dict{String, Any}})
    Returns an epsilon-satisfactory counterfactual for x based on the paths provided.
    Arguments
    • generator::FeatureTweakGenerator: The feature tweak generator.
    • x::AbstractArray: The factual instance.
    • paths::Dict{String, Dict{String, Any}}: A list of paths to the leaves of the tree to be used for tweaking the feature.
    Returns
    • esatisfactory::AbstractArray: The epsilon-satisfactory instance.
    Example
    esatisfactory = esatisfactory_instance(generator, x, paths) # returns an epsilon-satisfactory counterfactual for x based on the paths provided
    source
    CounterfactualExplanations.Generators.feature_selection! — Method
    feature_selection!(ce::AbstractCounterfactualExplanation)
    Perform feature selection to find the dimension with the closest (but not equal) values between the ce.x (factual) and ce.s′ (counterfactual) arrays.
    Arguments
    • ce::AbstractCounterfactualExplanation: An instance of the AbstractCounterfactualExplanation type representing the counterfactual explanation.
    Returns
    • nothing
    The function iteratively modifies the ce.s′ counterfactual array by updating its elements to match the corresponding elements in the ce.x factual array, one dimension at a time, until the predicted label of the modified ce.s′ matches the predicted label of the ce.x array.
    source
    CounterfactualExplanations.Generators.find_closest_dimension — Method
    find_closest_dimension(factual, counterfactual)
    Find the dimension with the closest (but not equal) values between the factual and counterfactual arrays.
    Arguments
    • factual: The factual array.
    • counterfactual: The counterfactual array.
    Returns
    • closest_dimension: The index of the dimension with the closest values.
    The function iterates over the indices of the factual array and calculates the absolute difference between the corresponding elements in the factual and counterfactual arrays. It returns the index of the dimension with the smallest difference, excluding dimensions where the values in factual and counterfactual are equal.
    source
    CounterfactualExplanations.Generators.find_counterfactual — Method
    find_counterfactual(model, factual_class, counterfactual_data, counterfactual_candidates)
    Find the first counterfactual index by predicting labels.
    Arguments
    • model: The fitted model used for prediction.
    • target_class: Expected target class.
    • counterfactual_data: Data required for counterfactual generation.
    • counterfactual_candidates: The array of counterfactual candidates.
    Returns
    • counterfactual: The index of the first counterfactual found.
    source
    CounterfactualExplanations.Generators.growing_spheres_generation! — Method
    growing_spheres_generation(ce::AbstractCounterfactualExplanation)
    Generate counterfactual candidates using the growing spheres generation algorithm.
    Arguments
    • ce::AbstractCounterfactualExplanation: An instance of the AbstractCounterfactualExplanation type representing the counterfactual explanation.
    Returns
    • nothing
    This function applies the growing spheres generation algorithm to generate counterfactual candidates. It starts by generating random points uniformly on a sphere, gradually reducing the search space until no counterfactuals are found. Then it expands the search space until at least one counterfactual is found or the maximum number of iterations is reached.
    The algorithm iteratively generates counterfactual candidates and predicts their labels using the model stored in ce.M. It checks if any of the predicted labels are different from the factual class. The process of reducing the search space involves halving the search radius, while the process of expanding the search space involves increasing the search radius.
    source
    CounterfactualExplanations.Generators.h — Method
    h(generator::AbstractGenerator, penalty::Function, ce::AbstractCounterfactualExplanation)
    Overloads the h function for the case where a single penalty function is provided.
    source
    CounterfactualExplanations.Generators.h — Method
    h(generator::AbstractGenerator, penalty::Tuple, ce::AbstractCounterfactualExplanation)
    Overloads the h function for the case where a single penalty function is provided with additional keyword arguments.
    source
    CounterfactualExplanations.Generators.h — Method
    h(generator::AbstractGenerator, penalty::Tuple, ce::AbstractCounterfactualExplanation)
    Overloads the h function for the case where a single penalty function is provided with additional keyword arguments.
    source
    CounterfactualExplanations.Generators.hyper_sphere_coordinates — Method
    hyper_sphere_coordinates(n_search_samples::Int, instance::Vector{Float64}, low::Int, high::Int; p_norm::Int=2)
    Generates candidate counterfactuals using the growing spheres method based on hyper-sphere coordinates.
    The implementation follows the Random Point Picking over a sphere algorithm described in the paper: "Learning Counterfactual Explanations for Tabular Data" by Pawelczyk, Broelemann & Kascneci (2020), presented at The Web Conference 2020 (WWW). It ensures that points are sampled uniformly at random using insights from: http://mathworld.wolfram.com/HyperspherePointPicking.html
    The growing spheres method is originally proposed in the paper: "Comparison-based Inverse Classification for Interpretability in Machine Learning" by Thibaut Laugel et al (2018), presented at the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2018).
    Arguments
    • n_search_samples::Int: The number of search samples (int > 0).
    • instance::AbstractArray: The input point array.
    • low::AbstractFloat: The lower bound (float >= 0, l < h).
    • high::AbstractFloat: The upper bound (float >= 0, h > l).
    • p_norm::Integer: The norm parameter (int >= 1).
    Returns
    • candidate_counterfactuals::Array: An array of candidate counterfactuals.
    source
    CounterfactualExplanations.Generators.search_path — Function
    search_path(tree::Union{DecisionTree.Leaf, DecisionTree.Node}, target::RawTargetType, path::AbstractArray)
    Return a path index list with the inequality symbols, thresholds and feature indices.
    Arguments
    • tree::Union{DecisionTree.Leaf, DecisionTree.Node}: The root node of a decision tree.
    • target::RawTargetType: The target class.
    • path::AbstractArray: A list containing the paths found thus far.
    Returns
    • paths::AbstractArray: A list of paths to the leaves of the tree to be used for tweaking the feature.
    Example
    paths = search_path(tree, target) # returns a list of paths to the leaves of the tree to be used for tweaking the feature
    source
    CounterfactualExplanations.Generators.∂h — Method
    ∂h(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)
    The default method to compute the gradient of the complexity penalty at the current counterfactual state for gradient-based generators. It assumes that Zygote.jl has gradient access. 
    If the penalty is not provided, it returns 0.0. By default, Zygote never works out the gradient for constants and instead returns 'nothing', so we need to add a manual step to override this behaviour. See here: https://discourse.julialang.org/t/zygote-gradient/26715.
    source
    CounterfactualExplanations.Generators.∂ℓ — Method
    ∂ℓ(generator::AbstractGradientBasedGenerator, M::Union{Models.LogisticModel, Models.BayesianLogisticModel}, ce::AbstractCounterfactualExplanation)
    The default method to compute the gradient of the loss function at the current counterfactual state for gradient-based generators. It assumes that Zygote.jl has gradient access.
    source
    CounterfactualExplanations.Generators.∇ — Method
    ∇(generator::AbstractGradientBasedGenerator, M::Models.AbstractDifferentiableModel, ce::AbstractCounterfactualExplanation)
    The default method to compute the gradient of the counterfactual search objective for gradient-based generators. It simply computes the weighted sum over partial derivates. It assumes that Zygote.jl has gradient access. If the counterfactual is being generated using Probe, the hinge loss is added to the gradient.
    source
    
+)

    Additional penalty for ClaPROARGenerator.

    source
    diff --git a/dev/search/index.html b/dev/search/index.html deleted file mode 100644 index 2d5c2169f..000000000 --- a/dev/search/index.html +++ /dev/null @@ -1,2 +0,0 @@ - -Search · CounterfactualExplanations.jl diff --git a/dev/search_index.js b/dev/search_index.js index 3f749d559..2861ab9c8 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/dice/#DiCEGenerator","page":"DiCE","title":"DiCEGenerator","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"The DiCEGenerator can be used to generate multiple diverse counterfactuals for a single factual.","category":"page"},{"location":"explanation/generators/dice/#Description","page":"DiCE","title":"Description","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"Counterfactual Explanations are not unique and there are therefore many different ways through which valid counterfactuals can be generated. In the context of Algorithmic Recourse this can be leveraged to offer individuals not one, but possibly many different ways to change a negative outcome into a positive one. One might argue that it makes sense for those different options to be as diverse as possible. This idea is at the core of DiCE, a counterfactual generator introduce by Mothilal, Sharma, and Tan (2020) that generate a diverse set of counterfactual explanations.","category":"page"},{"location":"explanation/generators/dice/#Defining-Diversity","page":"DiCE","title":"Defining Diversity","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"To ensure that the generated counterfactuals are diverse, Mothilal, Sharma, and Tan (2020) add a diversity constraint to the counterfactual search objective. In particular, diversity is explicitly proxied via Determinantal Point Processes (DDP).","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"We can implement DDP in Julia as follows:[1]","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"using LinearAlgebra\nfunction ddp_diversity(X::AbstractArray{<:Real, 3})\n xs = eachslice(X, dims = ndims(X))\n K = [1/(1 + norm(x .- y)) for x in xs, y in xs]\n return det(K)\nend","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"Below we generate some random points in mathbbR^2 and apply gradient ascent on this function evaluated at the whole array of points. As we can see in the animation below, the points are sent away from each other. In other words, diversity across the array of points increases as we ascend the ddp_diversity function.","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"lims = 5\nN = 5\nX = rand(2,1,N)\nT = 50\nη = 0.1\nanim = @animate for t in 1:T\n X .+= gradient(ddp_diversity, X)[1]\n Z = reshape(X,2,N)\n scatter(\n Z[1,:],Z[2,:],ms=25, \n xlims=(-lims,lims),ylims=(-lims,lims),\n label=\"\",colour=1:N,\n size=(500,500),\n title=\"Diverse Counterfactuals\"\n )\nend\ngif(anim, joinpath(www_path, \"dice_intro.gif\"))","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"(Image: )","category":"page"},{"location":"explanation/generators/dice/#Usage","page":"DiCE","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"generator = DiCEGenerator()\nconv = CounterfactualExplanations.Convergence.GeneratorConditionsConvergence()\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator; \n num_counterfactuals=5, convergence=conv\n)\nplot(ce)","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"(Image: )","category":"page"},{"location":"explanation/generators/dice/#Effect-of-Penalty","page":"DiCE","title":"Effect of Penalty","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"Λ₂ = [0.1, 1.0, 5.0]\nces = []\nn_cf = 5\nusing Flux\nfor λ₂ ∈ Λ₂ \n λ = [0.00, λ₂]\n generator = DiCEGenerator(λ=λ)\n ces = vcat(\n ces...,\n generate_counterfactual(\n x, target, counterfactual_data, M, generator; \n num_counterfactuals=n_cf, convergence=conv\n )\n )\nend","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"The figure below shows the resulting counterfactual paths. As expected, the resulting counterfactuals are more dispersed across the feature domain for higher choices of lambda_2","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"(Image: )","category":"page"},{"location":"explanation/generators/dice/#References","page":"DiCE","title":"References","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"[1] With thanks to the respondents on Discourse","category":"page"},{"location":"how_to_guides/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"how_to_guides/#How-To-Guides","page":"Overview","title":"How-To Guides","text":"","category":"section"},{"location":"how_to_guides/","page":"Overview","title":"Overview","text":"In this section, you will find a series of how-to-guides that showcase specific use cases of counterfactual explanations (CE).","category":"page"},{"location":"how_to_guides/","page":"Overview","title":"Overview","text":"How-to guides are directions that take the reader through the steps required to solve a real-world problem. How-to guides are goal-oriented.— Diátaxis","category":"page"},{"location":"how_to_guides/","page":"Overview","title":"Overview","text":"In other words, you come here because you may have some particular problem in mind, would like to see how it can be solved using CE and then most likely head off again 🫡.","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/generators/#Handling-Generators","page":"Handling Generators","title":"Handling Generators","text":"","category":"section"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Generating Counterfactual Explanations can be seen as a generative modelling task because it involves generating samples in the input space: x sim mathcalX. In this tutorial, we will introduce how Counterfactual GradientBasedGenerators are used. They are discussed in more detail in the explanatory section of the documentation.","category":"page"},{"location":"tutorials/generators/#Composable-Generators","page":"Handling Generators","title":"Composable Generators","text":"","category":"section"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"warning: Breaking Changes Expected\nWork on this feature is still in its very early stages and breaking changes should be expected. ","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"One of the key objectives for this package is Composability. It turns out that many of the various counterfactual generators that have been proposed in the literature, essentially do the same thing: they optimize an objective function. Formally we have,","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"\nbeginaligned\nmathbfs^prime = arg min_mathbfs^prime in mathcalS left textyloss(M(f(mathbfs^prime))y^*)+ lambda textcost(f(mathbfs^prime)) right \nendaligned \n qquad(1)","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"where textyloss denotes the main loss function and textcost is a penalty term (Altmeyer et al. 2023).","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Without going into further detail here, the important thing to mention is that Equation 1 very closely describes how counterfactual search is actually implemented in the package. In other words, all off-the-shelf generators currently implemented work with that same objective. They just vary in the way that penalties are defined, for example. This gives rise to an interesting idea:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Why not compose generators that combine ideas from different off-the-shelf generators?","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"The GradientBasedGenerator class provides a straightforward way to do this, without requiring users to build custom GradientBasedGenerators from scratch. It can be instantiated as follows:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"generator = GradientBasedGenerator()","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"By default, this creates a generator that simply performs gradient descent without any penalties. To modify the behaviour of the generator, you can define the counterfactual search objective function using the @objective macro:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"@objective(generator, logitbinarycrossentropy + 0.1distance_l2 + 1.0ddp_diversity)","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Here we have essentially created a version of the DiCEGenerator:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"ce = generate_counterfactual(x, target, counterfactual_data, M, generator; num_counterfactuals=5)\nplot(ce)","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"(Image: )","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Multiple macros can be chained using Chains.jl making it easy to create entirely new flavours of counterfactual generators. The following generator, for example, combines ideas from DiCE (Mothilal, Sharma, and Tan 2020) and REVISE (Joshi et al. 2019):","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"@chain generator begin\n @objective logitcrossentropy + 1.0ddp_diversity # DiCE (Mothilal et al. 2020)\n @with_optimiser Flux.Adam(0.1) \n @search_latent_space # REVISE (Joshi et al. 2019)\nend","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Let’s take this generator to our MNIST dataset and generate a counterfactual explanation for turning a 0 into a 8.","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"(Image: )","category":"page"},{"location":"tutorials/generators/#Off-the-Shelf-Generators","page":"Handling Generators","title":"Off-the-Shelf Generators","text":"","category":"section"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Off-the-shelf generators are just default recipes for counterfactual generators. Currently, the following off-the-shelf counterfactual generators are implemented in the package:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"generator_catalogue","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Dict{Symbol, Any} with 11 entries:\n :gravitational => GravitationalGenerator\n :growing_spheres => GrowingSpheresGenerator\n :revise => REVISEGenerator\n :clue => CLUEGenerator\n :probe => ProbeGenerator\n :dice => DiCEGenerator\n :feature_tweak => FeatureTweakGenerator\n :claproar => ClaPROARGenerator\n :wachter => WachterGenerator\n :generic => GenericGenerator\n :greedy => GreedyGenerator","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"To specify the type of generator you want to use, you can simply instantiate it:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"# Search:\ngenerator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"(Image: )","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"We generally make an effort to follow the literature as closely as possible when implementing off-the-shelf generators.","category":"page"},{"location":"tutorials/generators/#References","page":"Handling Generators","title":"References","text":"","category":"section"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/simple_example/#Simple-Example","page":"Simple Example","title":"Simple Example","text":"","category":"section"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"In this tutorial, we will go through a simple example involving synthetic data and a generic counterfactual generator.","category":"page"},{"location":"tutorials/simple_example/#Data-and-Classifier","page":"Simple Example","title":"Data and Classifier","text":"","category":"section"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"Below we generate some linearly separable data and fit a simple MLP classifier with batch normalization to it. For more information on generating data and models, refer to the Handling Data and Handling Models tutorials respectively.","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"# Counteractual data and model:\nflux_training_params.batchsize = 10\ndata = TaijaData.load_linearly_separable()\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\ncounterfactual_data.standardize = true\nM = fit_model(counterfactual_data, :MLP, batch_norm=true)","category":"page"},{"location":"tutorials/simple_example/#Counterfactual-Search","page":"Simple Example","title":"Counterfactual Search","text":"","category":"section"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"Next, determine a target and factual class for our counterfactual search and select a random factual instance to explain.","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"target = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"Finally, we generate and visualize the generated counterfactual:","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"# Search:\ngenerator = WachterGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"(Image: )","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/optimisers/jsma/#Jacobian-based-Saliency-Map-Attack","page":"JSMA","title":"Jacobian-based Saliency Map Attack","text":"","category":"section"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"To search counterfactuals, Schut et al. (2021) propose to use a Jacobian-Based Saliency Map Attack (JSMA) inspired by the literature on adversarial attacks. It works by moving in the direction of the most salient feature at a fixed step size in each iteration. Schut et al. (2021) use this optimisation rule in the context of Bayesian classifiers and demonstrate good results in terms of plausibility — how realistic counterfactuals are — and redundancy — how sparse the proposed feature changes are.","category":"page"},{"location":"explanation/optimisers/jsma/#JSMADescent","page":"JSMA","title":"JSMADescent","text":"","category":"section"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"To implement this approach in a reusable manner, we have added JSMA as a Flux optimiser. In particular, we have added a class JSMADescent<:Flux.Optimise.AbstractOptimiser, for which we have overloaded the Flux.Optimise.apply! method. This makes it possible to reuse JSMADescent as an optimiser in composable generators.","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"The optimiser can be used with with any generator as follows:","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"using CounterfactualExplanations.Generators: JSMADescent\ngenerator = GenericGenerator() |>\n gen -> @with_optimiser(gen,JSMADescent(;η=0.1))\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"The figure below compares the resulting counterfactual search outcome to the corresponding outcome with generic Descent.","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"plot(p1,p2,size=(1000,400))","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"(Image: )","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/data_catalogue/#Data-Catalogue","page":"Data Catalogue","title":"Data Catalogue","text":"","category":"section"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"To allow researchers and practitioners to test and compare counterfactual generators, the TAIJA environment includes the package TaijaData.jl which comes with pre-processed synthetic and real-world benchmark datasets from different domains. This page explains how to use TaijaData.jl in tandem with CounterfactualExplanations.jl.","category":"page"},{"location":"tutorials/data_catalogue/#Synthetic-Data","page":"Data Catalogue","title":"Synthetic Data","text":"","category":"section"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"The following dictionary can be used to inspect the available methods to generate synthetic datasets where the key indicates the name of the data and the value is the corresponding method:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"TaijaData.data_catalogue[:synthetic]","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"Dict{Symbol, Function} with 6 entries:\n :overlapping => load_overlapping\n :linearly_separable => load_linearly_separable\n :blobs => load_blobs\n :moons => load_moons\n :circles => load_circles\n :multi_class => load_multi_class","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"The chart below shows the generated data using default parameters:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"plts = []\n_height = 200\n_n = length(keys(data_catalogue[:synthetic]))\nfor (key, fun) in data_catalogue[:synthetic]\n data = fun()\n counterfactual_data = DataPreprocessing.CounterfactualData(data...)\n plt = plot()\n scatter!(counterfactual_data, title=key)\n plts = [plts..., plt]\nend\nplot(plts..., size=(_n * _height, _height), layout=(1, _n))","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"(Image: )","category":"page"},{"location":"tutorials/data_catalogue/#Real-World-Data","page":"Data Catalogue","title":"Real-World Data","text":"","category":"section"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"As for real-world data, the same dictionary can be used to inspect the available data from different domains.","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"TaijaData.data_catalogue[:tabular]","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"Dict{Symbol, Function} with 5 entries:\n :german_credit => load_german_credit\n :california_housing => load_california_housing\n :credit_default => load_credit_default\n :adult => load_uci_adult\n :gmsc => load_gmsc","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"TaijaData.data_catalogue[:vision]","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"Dict{Symbol, Function} with 3 entries:\n :fashion_mnist => load_fashion_mnist\n :mnist => load_mnist\n :cifar_10 => load_cifar_10","category":"page"},{"location":"tutorials/data_catalogue/#Loading-Data","page":"Data Catalogue","title":"Loading Data","text":"","category":"section"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"To load or generate any of the datasets listed above, you can just use the corresponding method, for example:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"data = TaijaData.load_linearly_separable()\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"Optionally, you can specify how many samples you want to generate like so:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"n = 100\ndata = TaijaData.load_overlapping(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"This also applies to real-world datasets, which by default are loaded in their entirety. If n is supplied, the dataset will be randomly undersampled:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"data = TaijaData.load_mnist(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"The undersampled dataset is automatically balanced:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"sum(counterfactual_data.y; dims=2)","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"10×1 Matrix{Int64}:\n 10\n 10\n 10\n 10\n 10\n 10\n 10\n 10\n 10\n 10","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"We can also use a helper function to split the data into train and test sets:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"train_data, test_data = \n CounterfactualExplanations.DataPreprocessing.train_test_split(counterfactual_data)","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"how_to_guides/custom_generators/#How-to-add-Custom-Generators","page":"... add custom generators","title":"How to add Custom Generators","text":"","category":"section"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"As we will see in this short tutorial, building custom counterfactual generators is straightforward. We hope that this will facilitate contributions through the community.","category":"page"},{"location":"how_to_guides/custom_generators/#Generic-generator-with-dropout","page":"... add custom generators","title":"Generic generator with dropout","text":"","category":"section"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"To illustrate how custom generators can be implemented we will consider a simple example of a generator that extends the functionality of our GenericGenerator. We have noted elsewhere that the effectiveness of counterfactual explanations depends to some degree on the quality of the fitted model. Another, perhaps trivial, thing to note is that counterfactual explanations are not unique: there are potentially many valid counterfactual paths. One interesting (or silly) idea following these two observations might be to introduce some form of regularization in the counterfactual search. For example, we could use dropout to randomly switch features on and off in each iteration. Without dwelling further on the usefulness of this idea, let us see how it can be implemented.","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"The first code chunk below implements two important steps: 1) create an abstract subtype of the AbstractGradientBasedGenerator and 2) create a constructor similar to the GenericConstructor, but with one additional field for the probability of dropout.","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"# Abstract suptype:\nabstract type AbstractDropoutGenerator <: AbstractGradientBasedGenerator end\n\n# Constructor:\nstruct DropoutGenerator <: AbstractDropoutGenerator\n loss::Function # loss function\n penalty::Function\n λ::AbstractFloat # strength of penalty\n latent_space::Bool\n opt::Any # optimizer\n generative_model_params::NamedTuple\n p_dropout::AbstractFloat # dropout rate\nend\n\n# Instantiate:\ngenerator = DropoutGenerator(\n Flux.logitbinarycrossentropy,\n CounterfactualExplanations.Objectives.distance_l1,\n 0.1,\n false,\n Flux.Optimise.Descent(0.1),\n (;),\n 0.5,\n)","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"Next, we define how feature perturbations are generated for our dropout generator: in particular, we extend the relevant function through a method that implemented the dropout logic.","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"using CounterfactualExplanations.Generators\nusing StatsBase\nfunction Generators.generate_perturbations(\n generator::AbstractDropoutGenerator, \n ce::CounterfactualExplanation\n)\n s′ = deepcopy(ce.s′)\n new_s′ = Generators.propose_state(generator, ce)\n Δs′ = new_s′ - s′ # gradient step\n\n # Dropout:\n set_to_zero = sample(\n 1:length(Δs′),\n Int(round(generator.p_dropout*length(Δs′))),\n replace=false\n )\n Δs′[set_to_zero] .= 0\n return Δs′\nend","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"Finally, we proceed to generate counterfactuals in the same way we always do:","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"# Data and Classifier:\nM = fit_model(counterfactual_data, :DeepEnsemble)\n\n# Factual and Target:\nyhat = predict_label(M, counterfactual_data)\ntarget = 2 # target label\ncandidates = findall(vec(yhat) .!= target)\nchosen = rand(candidates)\nx = select_factual(counterfactual_data, chosen)\n\n# Counterfactual search:\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator;\n num_counterfactuals=5)\n\nplot(ce)","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"(Image: )","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/parallelization/#Parallelization","page":"Parallelization","title":"Parallelization","text":"","category":"section"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Version 0.1.15 adds support for parallelization through multi-processing. Currently, the only available backend for parallelization is MPI.jl.","category":"page"},{"location":"tutorials/parallelization/#Available-functions","page":"Parallelization","title":"Available functions","text":"","category":"section"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Parallelization is only available for certain functions. To check if a function is parallelizable, you can use parallelizable function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"using CounterfactualExplanations.Evaluation: evaluate, benchmark\nprintln(parallelizable(generate_counterfactual))\nprintln(parallelizable(evaluate))\nprintln(parallelizable(predict_label))","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"true\ntrue\nfalse","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"In the following, we will generate multiple counterfactuals and evaluate them in parallel:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"chosen = rand(findall(predict_label(M, counterfactual_data) .== factual), 1000)\nxs = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"tutorials/parallelization/#Multi-threading","page":"Parallelization","title":"Multi-threading","text":"","category":"section"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"We first instantiate an ThreadParallelizer object:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"parallelizer = ThreadsParallelizer()","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"ThreadsParallelizer()","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"To generate counterfactuals in parallel, we use the parallelize function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"ces = @with_parallelizer parallelizer begin\n generate_counterfactual(\n xs,\n target,\n counterfactual_data,\n M,\n generator\n )\nend","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Generating counterfactuals ... 0%| | ETA: 0:01:29 (89.14 ms/it)Generating counterfactuals ... 100%|███████| Time: 0:00:01 ( 1.59 ms/it)\n\n1000-element Vector{AbstractCounterfactualExplanation}:\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n ⋮\n CounterfactualExplanation\nConvergence: ✅ after 9 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"To evaluate counterfactuals in parallel, we again use the parallelize function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"@with_parallelizer parallelizer evaluate(ces)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Evaluating counterfactuals ... 0%| | ETA: 0:07:03 ( 0.42 s/it)Evaluating counterfactuals ... 100%|███████| Time: 0:00:00 ( 0.86 ms/it)\n\n1000-element Vector{Any}:\n Vector[[1.0], Float32[3.2939816], [0.0]]\n Vector[[1.0], Float32[3.019046], [0.0]]\n Vector[[1.0], Float32[3.701171], [0.0]]\n Vector[[1.0], Float32[2.5611918], [0.0]]\n Vector[[1.0], Float32[2.9027307], [0.0]]\n Vector[[1.0], Float32[3.7893882], [0.0]]\n Vector[[1.0], Float32[3.5026522], [0.0]]\n Vector[[1.0], Float32[3.6317568], [0.0]]\n Vector[[1.0], Float32[3.084984], [0.0]]\n Vector[[1.0], Float32[3.2268934], [0.0]]\n Vector[[1.0], Float32[2.834947], [0.0]]\n Vector[[1.0], Float32[3.656587], [0.0]]\n Vector[[1.0], Float32[2.5985842], [0.0]]\n ⋮\n Vector[[1.0], Float32[4.067538], [0.0]]\n Vector[[1.0], Float32[3.02231], [0.0]]\n Vector[[1.0], Float32[2.748292], [0.0]]\n Vector[[1.0], Float32[2.9483426], [0.0]]\n Vector[[1.0], Float32[3.066149], [0.0]]\n Vector[[1.0], Float32[3.6018147], [0.0]]\n Vector[[1.0], Float32[3.0138078], [0.0]]\n Vector[[1.0], Float32[3.5724509], [0.0]]\n Vector[[1.0], Float32[3.117551], [0.0]]\n Vector[[1.0], Float32[2.9670508], [0.0]]\n Vector[[1.0], Float32[3.4107168], [0.0]]\n Vector[[1.0], Float32[3.0252533], [0.0]]","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Benchmarks can also be run with parallelization by specifying parallelizer argument:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"# Models:\nbmk = benchmark(counterfactual_data; parallelizer = parallelizer)","category":"page"},{"location":"tutorials/parallelization/#MPI","page":"Parallelization","title":"MPI","text":"","category":"section"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"note: Note\nTo use MPI, you need to have MPI installed on your machine. Running the following code straight from a running Julia session will work if you have MPI installed on your machine, but it will be run on a single process. To execute the code on multiple processes, you need to run it from the command line with mpirun or mpiexec. For example, to run a script on 4 processes, you can run the following command from the command line:\n\nmpiexecjl --project -n 4 julia -e 'include(\"docs/src/srcipts/mpi.jl\")'For more information, see MPI.jl. ","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"We first instantiate an MPIParallelizer object:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"import MPI\nMPI.Init()\nparallelizer = MPIParallelizer(MPI.COMM_WORLD; threaded=true)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Precompiling MPIExt\n ✓ TaijaParallel → MPIExt\n 1 dependency successfully precompiled in 3 seconds. 255 already precompiled.\n[ Info: Precompiling MPIExt [48137b38-b316-530b-be8a-261f41e68c23]\n┌ Warning: Module TaijaParallel with build ID ffffffff-ffff-ffff-0001-2d458926c256 is missing from the cache.\n│ This may mean TaijaParallel [bf1c2c22-5e42-4e78-8b6b-92e6c673eeb0] does not support precompilation but is imported by a module that does.\n└ @ Base loading.jl:1948\n[ Info: Skipping precompilation since __precompile__(false). Importing MPIExt [48137b38-b316-530b-be8a-261f41e68c23].\n[ Info: Using `MPI.jl` for multi-processing.\n\nRunning on 1 processes.\n\nMPIExt.MPIParallelizer(MPI.Comm(1140850688), 0, 1, nothing, true)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"To generate counterfactuals in parallel, we use the parallelize function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"ces = @with_parallelizer parallelizer begin\n generate_counterfactual(\n xs,\n target,\n counterfactual_data,\n M,\n generator\n )\nend","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Generating counterfactuals ... 9%|▋ | ETA: 0:00:01 ( 1.15 ms/it)Generating counterfactuals ... 19%|█▍ | ETA: 0:00:01 ( 1.07 ms/it)Generating counterfactuals ... 29%|██ | ETA: 0:00:01 ( 1.10 ms/it)Generating counterfactuals ... 39%|██▊ | ETA: 0:00:01 ( 1.08 ms/it)Generating counterfactuals ... 49%|███▍ | ETA: 0:00:01 ( 1.08 ms/it)Generating counterfactuals ... 59%|████▏ | ETA: 0:00:00 ( 1.08 ms/it)Generating counterfactuals ... 69%|████▊ | ETA: 0:00:00 ( 1.08 ms/it)Generating counterfactuals ... 79%|█████▌ | ETA: 0:00:00 ( 1.07 ms/it)Generating counterfactuals ... 89%|██████▎| ETA: 0:00:00 ( 1.07 ms/it)Generating counterfactuals ... 99%|██████▉| ETA: 0:00:00 ( 1.06 ms/it)Generating counterfactuals ... 100%|███████| Time: 0:00:01 ( 1.06 ms/it)\n\n1000-element Vector{AbstractCounterfactualExplanation}:\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n ⋮\n CounterfactualExplanation\nConvergence: ✅ after 9 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"To evaluate counterfactuals in parallel, we again use the parallelize function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"@with_parallelizer parallelizer evaluate(ces)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"1000-element Vector{Any}:\n Vector[[1.0], Float32[3.0941274], [0.0]]\n Vector[[1.0], Float32[3.0894346], [0.0]]\n Vector[[1.0], Float32[3.5737448], [0.0]]\n Vector[[1.0], Float32[2.6201036], [0.0]]\n Vector[[1.0], Float32[2.8519764], [0.0]]\n Vector[[1.0], Float32[3.7762523], [0.0]]\n Vector[[1.0], Float32[3.4162796], [0.0]]\n Vector[[1.0], Float32[3.6095932], [0.0]]\n Vector[[1.0], Float32[3.1347957], [0.0]]\n Vector[[1.0], Float32[3.0313473], [0.0]]\n Vector[[1.0], Float32[2.7612567], [0.0]]\n Vector[[1.0], Float32[3.6191392], [0.0]]\n Vector[[1.0], Float32[2.610616], [0.0]]\n ⋮\n Vector[[1.0], Float32[4.0844703], [0.0]]\n Vector[[1.0], Float32[3.0119], [0.0]]\n Vector[[1.0], Float32[2.4461186], [0.0]]\n Vector[[1.0], Float32[3.071967], [0.0]]\n Vector[[1.0], Float32[3.132917], [0.0]]\n Vector[[1.0], Float32[3.5403214], [0.0]]\n Vector[[1.0], Float32[3.0588162], [0.0]]\n Vector[[1.0], Float32[3.5600657], [0.0]]\n Vector[[1.0], Float32[3.2205954], [0.0]]\n Vector[[1.0], Float32[2.896302], [0.0]]\n Vector[[1.0], Float32[3.2603998], [0.0]]\n Vector[[1.0], Float32[3.1369917], [0.0]]","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"tip: Tip\nNote that parallelizable processes can be supplied as input to the macro either as a block or directly as an expression.","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Benchmarks can also be run with parallelization by specifying parallelizer argument:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"# Models:\nbmk = benchmark(counterfactual_data; parallelizer = parallelizer)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"The following code snippet shows a complete example script that uses MPI for running a benchmark in parallel:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"using CounterfactualExplanations\nusing CounterfactualExplanations.Evaluation: benchmark\nusing CounterfactualExplanations.Models\nimport MPI\n\nMPI.Init()\n\ndata = TaijaData.load_linearly_separable()\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\nM = fit_model(counterfactual_data, :Linear)\nfactual = 1\ntarget = 2\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual), 100)\nxs = select_factual(counterfactual_data, chosen)\ngenerator = GenericGenerator()\n\nparallelizer = MPIParallelizer(MPI.COMM_WORLD)\n\nbmk = benchmark(counterfactual_data; parallelizer=parallelizer)\n\nMPI.Finalize()","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"The file can be executed from the command line as follows:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"mpiexecjl --project -n 4 julia -e 'include(\"docs/src/srcipts/mpi.jl\")'","category":"page"},{"location":"tutorials/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanation","category":"page"},{"location":"tutorials/#Tutorials","page":"Overview","title":"Tutorials","text":"","category":"section"},{"location":"tutorials/","page":"Overview","title":"Overview","text":"In this section, you will find a series of tutorials that should help you gain a basic understanding of Conformal Prediction and how to apply it in Julia using this package.","category":"page"},{"location":"tutorials/","page":"Overview","title":"Overview","text":"Tutorials are lessons that take the reader by the hand through a series of steps to complete a project of some kind. Tutorials are learning-oriented.— Diátaxis","category":"page"},{"location":"tutorials/","page":"Overview","title":"Overview","text":"In other words, you come here because you are new to this topic and are looking for a first peek at the methodology and code 🫣.","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"explanation/generators/growing_spheres/#GrowingSpheres","page":"GrowingSpheres","title":"GrowingSpheres","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"Growing Spheres refers to the generator introduced by Laugel et al. (2017). Our implementation takes inspiration from the CARLA library.","category":"page"},{"location":"explanation/generators/growing_spheres/#Principle-of-the-Proposed-Approach","page":"GrowingSpheres","title":"Principle of the Proposed Approach","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"In order to interpret a prediction through comparison, the Growing Spheres algorithm focuses on finding an observation belonging to the other class and answers the question: “Considering an observation and a classifier, what is the minimal change we need to apply in order to change the prediction of this observation?”. This problem is similar to inverse classification but applied to interpretability.","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"Explaining how to change a prediction can help the user understand what the model considers as locally important. The Growing Spheres approach provides insights into the classifier’s behavior without claiming any causal knowledge. It differs from other interpretability approaches and is not concerned with the global behavior of the model. Instead, it aims to provide local insights into the classifier’s decision-making process.","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"The algorithm finds the closest “ennemy” observation, which is an observation classified into the other class than the input observation. The final explanation is the difference vector between the input observation and the ennemy.","category":"page"},{"location":"explanation/generators/growing_spheres/#Finding-the-Closest-Ennemy","page":"GrowingSpheres","title":"Finding the Closest Ennemy","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"The algorithm solves the following minimization problem to find the closest ennemy:","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"e^* = arg min_e in X c(x e) f(e) neq f(x) ","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"The cost function c(x, e) is defined as:","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"c(x e) = x - e_2 + gamma x - e_0","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"where ||.||_2 is the Euclidean norm and ||.||_0 is the sparsity measure. The weight gamma balances the importance of sparsity in the cost function.","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"To approximate the solution, the Growing Spheres algorithm uses a two-step heuristic approach. The first step is the Generation phase, where observations are generated in spherical layers around the input observation. The second step is the Feature Selection phase, where the generated observation with the smallest change in each feature is selected.","category":"page"},{"location":"explanation/generators/growing_spheres/#Example","page":"GrowingSpheres","title":"Example","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"generator = GrowingSpheresGenerator()\nM = fit_model(counterfactual_data, :DeepEnsemble)\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"(Image: )","category":"page"},{"location":"explanation/generators/growing_spheres/#References","page":"GrowingSpheres","title":"References","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” arXiv. https://doi.org/10.48550/arXiv.1712.08443.","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"contribute/#Contribute","page":"🛠 Contribute","title":"🛠 Contribute","text":"","category":"section"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"Contributions of any kind are very much welcome! Take a look at the issue to see what things we are currently working on.","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"If any of the below applies to you, this might be the right open-source project for you:","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"You’re an expert in Counterfactual Explanations or Explainable AI more broadly and you are curious about Julia.\nYou’re experienced with Julia and are happy to help someone less experienced to up their game. Ideally, you are also curious about Trustworthy AI.\nYou’re new to Julia and open-source development and would like to start your learning journey by contributing to a recent and active development. Ideally, you are familiar with machine learning.","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"@pat-alt here: I am still very much at the beginning of my Julia journey, so if you spot any issues or have any suggestions for design improvement, please just open issue or start a discussion.","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"For more details on how to contribute see here. Please follow the SciML ColPrac guide.","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/clap_roar/#ClaPROARGenerator","page":"ClaPROAR","title":"ClaPROARGenerator","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"The ClaPROARGenerator was introduced in Altmeyer et al. (2023).","category":"page"},{"location":"explanation/generators/clap_roar/#Description","page":"ClaPROAR","title":"Description","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"The acronym Clap stands for classifier-preserving. The approach is loosely inspired by ROAR (Upadhyay, Joshi, and Lakkaraju 2021). Altmeyer et al. (2023) propose to explicitly penalize the loss incurred by the classifer when evaluated on the counterfactual x^prime at given parameter values. Formally, we have","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"beginaligned\ntextextcost(f(mathbfs^prime)) = l(M(f(mathbfs^prime))y^prime)\nendaligned","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"for each counterfactual k where l denotes the loss function used to train M. This approach is based on the intuition that (endogenous) model shifts will be triggered by counterfactuals that increase classifier loss (Altmeyer et al. 2023).","category":"page"},{"location":"explanation/generators/clap_roar/#Usage","page":"ClaPROAR","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"generator = ClaPROARGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"(Image: )","category":"page"},{"location":"explanation/generators/clap_roar/#Comparison-to-GenericGenerator","page":"ClaPROAR","title":"Comparison to GenericGenerator","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"The figure below compares the outcome for the GenericGenerator and the ClaPROARGenerator.","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"(Image: )","category":"page"},{"location":"explanation/generators/clap_roar/#References","page":"ClaPROAR","title":"References","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. 2021. “Towards Robust and Reliable Algorithmic Recourse.” https://arxiv.org/abs/2102.13620.","category":"page"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/architecture/#Package-Architecture","page":"Package Architecture","title":"Package Architecture","text":"","category":"section"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"Modular, composable, scalable!","category":"page"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"The diagram below provides an overview of the package architecture. It is built around two core modules that are designed to be as extensible as possible through dispatch: 1) Models is concerned with making any arbitrary model compatible with the package; 2) Generators is used to implement arbitrary counterfactual search algorithms.[1] The core function of the package generate_counterfactual uses an instance of type <: AbstractFittedModel produced by the Models module and an instance of type <: AbstractGenerator produced by the Generators module.","category":"page"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"(Image: )","category":"page"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"[1] We have made an effort to keep the code base a flexible and extensible as possible, but cannot guarantee at this point that any counterfactual generator can be implemented without further adaptation.","category":"page"},{"location":"explanation/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"explanation/#Explanation","page":"Overview","title":"Explanation","text":"","category":"section"},{"location":"explanation/","page":"Overview","title":"Overview","text":"In this section you will find detailed explanations about the methodology and code.","category":"page"},{"location":"explanation/","page":"Overview","title":"Overview","text":"Explanation clarifies, deepens and broadens the reader’s understanding of a subject.— Diátaxis","category":"page"},{"location":"explanation/","page":"Overview","title":"Overview","text":"In other words, you come here because you are interested in understanding how all of this actually works 🤓.","category":"page"},{"location":"contribute/performance/","page":"-","title":"-","text":"Random.seed!(42)\n# Counteractual data and model:\ndata = TaijaData.load_linearly_separable()\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\nM = fit_model(counterfactual_data, :Linear)\ntarget = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)\n\n# Search:\ngenerator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"contribute/performance/","page":"-","title":"-","text":"data_large = TaijaData.load_linearly_separable(100000)\ncounterfactual_data_large = DataPreprocessing.CounterfactualData(data_large...)","category":"page"},{"location":"contribute/performance/","page":"-","title":"-","text":"@time generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"contribute/performance/","page":"-","title":"-","text":"@time generate_counterfactual(x, target, counterfactual_data_large, M, generator)","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/feature_tweak/#FeatureTweakGenerator","page":"FeatureTweak","title":"FeatureTweakGenerator","text":"","category":"section"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Feature Tweak refers to the generator introduced by Tolomei et al. (2017). Our implementation takes inspiration from the featureTweakPy library.","category":"page"},{"location":"explanation/generators/feature_tweak/#Description","page":"FeatureTweak","title":"Description","text":"","category":"section"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Feature Tweak is a powerful recourse algorithm for ensembles of tree-based classifiers such as random forests. Though the problem of understanding how an input to an ensemble model could be transformed in such a way that the model changes its original prediction has been proven to be NP-hard (Tolomei et al. 2017), Feature Tweak provides an algorithm that manages to tractably solve this problem in multiple real-world applications. An example of a problem Feature Tweak is able to efficiently solve, explored in depth in Tolomei et al. (2017) is the problem of transforming an advertisement that has been classified by the ensemble model as a low-quality advertisement to a high-quality one through small changes to its features. With the help of Feature Tweak, advertisers can both learn about the reasons a particular ad was marked to have a low quality, as well as receive actionable suggestions about how to convert a low-quality ad into a high-quality one.","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Though Feature Tweak is a powerful way of avoiding brute-force search in an exponential search space, it does not come without disadvantages. The primary limitations of the approach are that it’s currently only applicable to tree-based classifiers and works only in the setting of binary classification. Another problem is that though the algorithm avoids exponential-time search, it is often still computationally expensive. The algorithm may be improved in the future to tackle all of these shortcomings.","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"The following equation displays how a true negative instance x can be transformed into a positively predicted instance x’. To be more precise, x’ is the best possible transformation among all transformations **x***, computed with a cost function δ.","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"beginaligned\nmathbfx^prime = arg_mathbfx^* min delta(mathbfx mathbfx^*) hatf(mathbfx) = -1 wedge hatf(mathbfx^*) = +1 \nendaligned","category":"page"},{"location":"explanation/generators/feature_tweak/#Example","page":"FeatureTweak","title":"Example","text":"","category":"section"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"In this example we apply the Feature Tweak algorithm to a decision tree and a random forest trained on the moons dataset. We first load the data and fit the models:","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"n = 500\ncounterfactual_data = CounterfactualData(TaijaData.load_moons(n)...)\n\n# Classifiers\ndecision_tree = CounterfactualExplanations.Models.fit_model(\n counterfactual_data, :DecisionTree; max_depth=5, min_samples_leaf=3\n)\nforest = Models.fit_model(counterfactual_data, :RandomForest)","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Next, we select a point to explain and a target class to transform the point to. We then search for counterfactuals using the FeatureTweakGenerator:","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"# Select a point to explain:\nx = float32.([1, -0.5])[:,:]\nfactual = Models.predict_label(forest, x)\ntarget = counterfactual_data.y_levels[findall(counterfactual_data.y_levels != factual)][1]\n\n# Search for counterfactuals:\ngenerator = FeatureTweakGenerator(ϵ=0.1)\ntree_counterfactual = generate_counterfactual(\n x, target, counterfactual_data, decision_tree, generator\n)\nforest_counterfactual = generate_counterfactual(\n x, target, counterfactual_data, forest, generator\n)","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"The resulting counterfactuals are shown below:","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"p1 = plot(\n tree_counterfactual;\n colorbar=false,\n title=\"Decision Tree\",\n)\n\np2 = plot(\n forest_counterfactual; title=\"Random Forest\",\n colorbar=false,\n)\n\ndisplay(plot(p1, p2; size=(800, 400)))","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"(Image: )","category":"page"},{"location":"explanation/generators/feature_tweak/#References","page":"FeatureTweak","title":"References","text":"","category":"section"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Tolomei, Gabriele, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017. “Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 465–74. https://doi.org/10.1145/3097983.3098039.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/clue/#CLUEGenerator","page":"CLUE","title":"CLUEGenerator","text":"","category":"section"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"In this tutorial, we introduce the CLUEGenerator, a counterfactual generator based on the Counterfactual Latent Uncertainty Explanations (CLUE) method proposed by Antorán et al. (2020).","category":"page"},{"location":"explanation/generators/clue/#Description","page":"CLUE","title":"Description","text":"","category":"section"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"The CLUEGenerator leverages differentiable probabilistic models, such as Bayesian Neural Networks (BNNs), to estimate uncertainty in predictions. It aims to provide interpretable counterfactual explanations by identifying input patterns that lead to predictive uncertainty. The generator utilizes a latent variable framework and employs a decoder from a variational autoencoder (VAE) to generate counterfactual samples in latent space.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"The CLUE algorithm minimizes a loss function that combines uncertainty estimates and the distance between the generated counterfactual and the original input. By optimizing this loss function iteratively, the CLUEGenerator generates counterfactuals that are similar to the original observation but assigned low uncertainty.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"The formula for predictive entropy is as follow:","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"beginaligned\nH(y^*x^* D) = - sum_k=1^K p(y^*=c_kx^* D) log p(y^*=c_kx^* D)\nendaligned","category":"page"},{"location":"explanation/generators/clue/#Usage","page":"CLUE","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"While using one must keep in mind that the CLUE algorithim is meant to find a more robust datapoint of the same class, using CLUE generator without any additional penalties/losses will mean that it is not a counterfactual generator. The generated result will be of the same class as the original input, but a more robust datapoint.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"CLUE works best for BNN’s. The CLUEGenerator can be used with any differentiable probabilistic model, but the results may not be as good as with BNNs.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"The CLUEGenerator can be used in the following manner:","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"generator = CLUEGenerator()\nM = fit_model(counterfactual_data, :DeepEnsemble)\nconv = CounterfactualExplanations.Convergence.MaxIterConvergence(max_iter=1000)\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator;\n convergence=conv)\nplot(ce)","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"(Image: )","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"Extra: The CLUE generator can also be used upon already having achieved a counterfactual with a different generator. In this case, you can use CLUE and make the counterfactual more robust.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"Note: The above documentation is based on the information provided in the CLUE paper. Please refer to the original paper for more detailed explanations and implementation specifics.","category":"page"},{"location":"explanation/generators/clue/#References","page":"CLUE","title":"References","text":"","category":"section"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. “Getting a Clue: A Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/revise/#REVISEGenerator","page":"REVISE","title":"REVISEGenerator","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"REVISE is a Latent Space generator introduced by Joshi et al. (2019).","category":"page"},{"location":"explanation/generators/revise/#Description","page":"REVISE","title":"Description","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The current consensus in the literature is that Counterfactual Explanations should be realistic: the generated counterfactuals should look like they were generated by the data-generating process (DGP) that governs the problem at hand. With respect to Algorithmic Recourse, it is certainly true that counterfactuals should be realistic in order to be actionable for individuals.[1] To address this need, researchers have come up with various approaches in recent years. Among the most popular approaches is Latent Space Search, which was first proposed in Joshi et al. (2019): instead of traversing the feature space directly, this approach relies on a separate generative model that learns a latent space representation of the DGP. Assuming the generative model is well-specified, access to the learned latent embeddings of the data comes with two advantages:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Since the learned DGP is encoded in the latent space, the generated counterfactuals will respect the learned representation of the data. In practice, this means that counterfactuals will be realistic.\nThe latent space is typically a compressed (i.e. lower dimensional) version of the feature space. This makes the counterfactual search less costly.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"There are also certain disadvantages though:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Learning generative models is (typically) an expensive task, which may well outweigh the benefits associated with utlimately traversing a lower dimensional space.\nIf the generative model is poorly specified, this will affect the quality of the counterfactuals.[2]","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Anyway, traversing latent embeddings is a powerful idea that may be very useful depending on the specific context. This tutorial introduces the concept and how it is implemented in this package.","category":"page"},{"location":"explanation/generators/revise/#Usage","page":"REVISE","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"generator = REVISEGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"(Image: )","category":"page"},{"location":"explanation/generators/revise/#Worked-2D-Examples","page":"REVISE","title":"Worked 2D Examples","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Below we load 2D data and train a VAE on it and plot the original samples against their reconstructions.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"# output: true\n\ncounterfactual_data = CounterfactualData(load_overlapping()...)\nX = counterfactual_data.X\ny = counterfactual_data.y\ninput_dim = size(X, 1)\nusing CounterfactualExplanations.GenerativeModels: VAE, train!, reconstruct\nvae = VAE(input_dim; nll=Flux.Losses.mse, epochs=100, λ=0.01, latent_dim=2, hidden_dim=32)\nflux_training_params.verbose = true\ntrain!(vae, X, y)\nX̂ = reconstruct(vae, X)[1]\np0 = scatter(X[1, :], X[2, :], color=:blue, label=\"Original\", xlab=\"x₁\", ylab=\"x₂\")\nscatter!(X̂[1, :], X̂[2, :], color=:orange, label=\"Reconstructed\", xlab=\"x₁\", ylab=\"x₂\")\np1 = scatter(X[1, :], X̂[1, :], color=:purple, label=\"\", xlab=\"x₁\", ylab=\"x̂₁\")\np2 = scatter(X[2, :], X̂[2, :], color=:purple, label=\"\", xlab=\"x₂\", ylab=\"x̂₂\")\nplt2 = plot(p1,p2, layout=(1,2), size=(800, 400))\nplot(p0, plt2, layout=(2,1), size=(800, 600))","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Next, we train a simple MLP for the classification task. Then we determine a target and factual class for our counterfactual search and select a random factual instance to explain.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"M = fit_model(counterfactual_data, :MLP)\ntarget = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Finally, we generate and visualize the generated counterfactual:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"# Search:\ngenerator = REVISEGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"(Image: )","category":"page"},{"location":"explanation/generators/revise/#D-Example","page":"REVISE","title":"3D Example","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"To illustrate the notion of Latent Space search, let’s look at an example involving 3-dimensional input data, which we can still visualize. The code chunk below loads the data and implements the counterfactual search.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"# Data and Classifier:\ncounterfactual_data = CounterfactualData(load_blobs(k=3)...)\nX = counterfactual_data.X\nys = counterfactual_data.output_encoder.labels.refs\nM = fit_model(counterfactual_data, :MLP)\n\n# Randomly selected factual:\nx = select_factual(counterfactual_data,rand(1:size(counterfactual_data.X,2)))\ny = predict_label(M, counterfactual_data, x)[1]\ntarget = counterfactual_data.y_levels[counterfactual_data.y_levels .!= y][1]\n\n# Generate recourse:\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The figure below demonstrates the idea of searching counterfactuals in a lower-dimensional latent space: on the left, we can see the counterfactual search in the 3-dimensional feature space, while on the right we can see the corresponding search in the latent space.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"(Image: )","category":"page"},{"location":"explanation/generators/revise/#MNIST-data","page":"REVISE","title":"MNIST data","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Let’s carry the ideas introduced above over to a more complex example. The code below loads MNIST data as well as a pre-trained classifier and generative model for the data.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"using CounterfactualExplanations.Models: load_mnist_mlp, load_mnist_ensemble, load_mnist_vae\ncounterfactual_data = CounterfactualData(load_mnist()...)\nX, y = CounterfactualExplanations.DataPreprocessing.unpack_data(counterfactual_data)\ninput_dim, n_obs = size(counterfactual_data.X)\nM = load_mnist_mlp()\nvae = load_mnist_vae()","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The F1-score of our pre-trained image classifier on test data is: 0.94","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Before continuing, we supply the pre-trained generative model to our data container:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"counterfactual_data.generative_model = vae # assign generative model","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Now let’s define a factual and target label:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"# Randomly selected factual:\nRandom.seed!(2023)\nfactual_label = 8\nx = reshape(X[:,rand(findall(predict_label(M, counterfactual_data).==factual_label))],input_dim,1)\ntarget = 3\nfactual = predict_label(M, counterfactual_data, x)[1]","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Using REVISE, we are going to turn a randomly drawn 8 into a 3.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The API call is the same as always:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"γ = 0.95\nconv = \n CounterfactualExplanations.Convergence.DecisionThresholdConvergence(decision_threshold=γ)\n# Define generator:\ngenerator = REVISEGenerator(opt=Flux.Adam(0.1))\n# Generate recourse:\nce = generate_counterfactual(x, target, counterfactual_data, M, generator; convergence=conv)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The chart below shows the results:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"(Image: )","category":"page"},{"location":"explanation/generators/revise/#References","page":"REVISE","title":"References","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"[1] In general, we believe that there may be a trade-off between creating counterfactuals that respect the DGP vs. counterfactuals reflect the behaviour of the black-model in question - both accurately and complete.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"[2] We believe that there is another potentially crucial disadvantage of relying on a separate generative model: it reallocates the task of learning realistic explanations for the data from the black-box model to the generative model.","category":"page"},{"location":"explanation/optimisers/overview/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/optimisers/overview/#Optimisation-Rules","page":"Overview","title":"Optimisation Rules","text":"","category":"section"},{"location":"explanation/optimisers/overview/","page":"Overview","title":"Overview","text":"Counterfactual search is an optimization problem. Consequently, the choice of the optimisation rule affects the generated counterfactuals. In the short term, we aim to enable users to choose any of the available Flux optimisers. This has not been sufficiently tested yet, and you may run into issues.","category":"page"},{"location":"explanation/optimisers/overview/#Custom-Optimisation-Rules","page":"Overview","title":"Custom Optimisation Rules","text":"","category":"section"},{"location":"explanation/optimisers/overview/","page":"Overview","title":"Overview","text":"Flux optimisers are specifically designed for deep learning, and in particular, for learning model parameters. In counterfactual search, the features are the free parameters that we are optimising over. To this end, some custom optimisation rules are necessary to incorporate ideas presented in the literature. In the following, we introduce those rules.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/data_preprocessing/#Handling-Data","page":"Handling Data","title":"Handling Data","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The package works with custom data containers that contain the input and output data as well as information about the type and mutability of features. In this tutorial, we will see how data can be prepared for use with the package.","category":"page"},{"location":"tutorials/data_preprocessing/#Basic-Functionality","page":"Handling Data","title":"Basic Functionality","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"To demonstrate the basic way to prepare data, let’s look at a standard benchmark dataset: Fisher’s classic iris dataset. We can use MLDatasets to load this data.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"dataset = Iris()","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Our data constructor CounterfactualData needs at least two inputs: features X and targets y.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"X = dataset.features\ny = dataset.targets","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Next, we convert the input data to a Tables.MatrixTable (following MLJ.jl) convention. Concerning the target variable, we just assign grab the first column of the data frame.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"X = table(Tables.matrix(X))\ny = y[:,1]","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Now we can feed these two ingredients to our constructor:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data = CounterfactualData(X, y)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Under the hood, the constructor performs basic preprocessing steps. For example, the output variable y is automatically one-hot encoded:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data.y","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"3×150 Matrix{Bool}:\n 1 1 1 1 1 1 1 1 1 1 1 1 1 … 0 0 0 0 0 0 0 0 0 0 0 0\n 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\n 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Similarly, a transformer used to scale continuous input features is automatically fitted:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data.dt","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"ZScoreTransform{Float64, Vector{Float64}}(4, 2, [5.843333333333335, 3.0540000000000007, 3.7586666666666693, 1.1986666666666672], [0.8280661279778629, 0.4335943113621737, 1.7644204199522617, 0.7631607417008414])","category":"page"},{"location":"tutorials/data_preprocessing/#Categorical-Features","page":"Handling Data","title":"Categorical Features","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"For the counterfactual search, it is important to distinguish between continuous and categorical features. This is because categorical features cannot be perturbed arbitrarily: they can take specific discrete values, but not just any value on the real line.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Consider the following example:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"y = rand([1,0],4)\nX = (\n name=categorical([\"Danesh\", \"Lee\", \"Mary\", \"John\"]),\n grade=categorical([\"A\", \"B\", \"A\", \"C\"], ordered=true),\n sex=categorical([\"male\",\"female\",\"male\",\"male\"]),\n height=[1.85, 1.67, 1.5, 1.67],\n)\nschema(X)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"┌────────┬──────────────────┬──────────────────────────────────┐\n│ names │ scitypes │ types │\n├────────┼──────────────────┼──────────────────────────────────┤\n│ name │ Multiclass{4} │ CategoricalValue{String, UInt32} │\n│ grade │ OrderedFactor{3} │ CategoricalValue{String, UInt32} │\n│ sex │ Multiclass{2} │ CategoricalValue{String, UInt32} │\n│ height │ Continuous │ Float64 │\n└────────┴──────────────────┴──────────────────────────────────┘","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Typically, in the context of Unserpervised Learning, categorical features are one-hot or dummy encoded. To this end, we could use MLJ, for example:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"hot = OneHotEncoder()\nmach = MLJBase.fit!(machine(hot, X))\nW = MLJBase.transform(mach, X)\nX = permutedims(MLJBase.matrix(W))","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"In all likelihood, this pre-processing step already happens at the stage, when the supervised model is trained. Since our counterfactual generators need to work in the same feature domain as the model they are intended to explain, we assume that categorical features are already encoded.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The CounterfactualData constructor takes two optional arguments that can be used to specify the indices of categorical and continuous features. By default, all features are assumed to be continuous. For categorical features, the constructor expects an array of arrays of integers (Vector{Vector{Int}}) where each subarray includes the indices of all one-hot encoded rows related to a single categorical feature. In the example above, the name feature is one-hot encoded across rows 1, 2, 3 and 4 of X, the grade feature is encoded across the following three rows, etc.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"schema(W)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"┌──────────────┬────────────┬─────────┐\n│ names │ scitypes │ types │\n├──────────────┼────────────┼─────────┤\n│ name__Danesh │ Continuous │ Float64 │\n│ name__John │ Continuous │ Float64 │\n│ name__Lee │ Continuous │ Float64 │\n│ name__Mary │ Continuous │ Float64 │\n│ grade__A │ Continuous │ Float64 │\n│ grade__B │ Continuous │ Float64 │\n│ grade__C │ Continuous │ Float64 │\n│ sex__female │ Continuous │ Float64 │\n│ sex__male │ Continuous │ Float64 │\n│ height │ Continuous │ Float64 │\n└──────────────┴────────────┴─────────┘","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The code chunk below assigns the categorical and continuous feature indices:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"features_categorical = [\n [1,2,3,4], # name\n [5,6,7], # grade\n [8,9] # sex\n]\nfeatures_continuous = [10]","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"When instantiating the data container, these indices just need to be supplied as keyword arguments:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data = CounterfactualData(\n X,y;\n features_categorical = features_categorical,\n features_continuous = features_continuous\n)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"This will ensure that the discrete domain of categorical features is respected in the counterfactual search. We achieve this through a form of Projected Gradient Descent and it works for any of our counterfactual generators.","category":"page"},{"location":"tutorials/data_preprocessing/#Example","page":"Handling Data","title":"Example","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"To see this in action, let’s load some synthetic data using MLJ:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"N = 1000\nX, ys = MLJBase.make_blobs(N, 2; centers=2, as_table=false, center_box=(-5 => 5), cluster_std=0.5)\nys .= ys.==2","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Next, we generate a synthetic categorical feature based on the output variable. First, we define the discrete levels:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"cat_values = [\"X\",\"Y\",\"Z\"]","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Next, we impose that the categorical feature is most likely to take the first discrete level, namely X, whenever y is equal to 1.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"xcat = map(ys) do y\n if y==1\n x = sample(cat_values, Weights([0.8,0.1,0.1]))\n else\n x = sample(cat_values, Weights([0.1,0.1,0.8]))\n end\nend\nxcat = categorical(xcat)\nX = (\n x1 = X[:,1],\n x2 = X[:,2],\n x3 = xcat\n)\nschema(X)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"As above, we use a OneHotEncoder to transform the data:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"hot = OneHotEncoder()\nmach = MLJBase.fit!(machine(hot, X))\nW = MLJBase.transform(mach, X)\nschema(W)\nX = permutedims(MLJBase.matrix(W))","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Finally, we assign the categorical indices and instantiate our data container:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"features_categorical = [collect(3:size(X,1))]\ncounterfactual_data = CounterfactualData(\n X,ys';\n features_categorical = features_categorical,\n)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"With the data pre-processed we can use the fit_model function to train a simple classifier:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"M = fit_model(counterfactual_data, :Linear)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Now it is finally time to generate counterfactuals. We first define 1 as our target and then choose a random sample from the non-target class:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"target = 1\nfactual = 0\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen) ","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"5×1 Matrix{Float32}:\n -2.9433472\n 0.5782963\n 0.0\n 0.0\n 1.0","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The factual x belongs to group Z.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"We generate a counterfactual for x using the standard API call:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"generator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"CounterfactualExplanation\nConvergence: ✅ after 7 steps.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The search yields the following counterfactual:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"x′ = counterfactual(ce)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"5-element Vector{Float32}:\n 1.1222683\n 0.7145791\n 0.0\n 0.0\n 1.0","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"It belongs to group Z.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"This is intuitive because by construction the categorical variable is most likely to take that value when y is equal to the target outcome.","category":"page"},{"location":"tutorials/data_preprocessing/#Immutable-Features","page":"Handling Data","title":"Immutable Features","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"In practice, features usually cannot be perturbed arbitrarily. Suppose, for example, that one of the features used by a bank to predict the creditworthiness of its clients is gender. If a counterfactual explanation for the prediction model indicates that female clients should change their gender to improve their creditworthiness, then this is an interesting insight (it reveals gender bias), but it is not usually an actionable transformation in practice. In such cases, we may want to constrain the mutability of features to ensure actionable and realistic recourse.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"To illustrate how this can be implemented in CounterfactualExplanations.jl we will continue to work with the synthetic data from the previous section. Mutability of features can be defined in terms of four different options: 1) the feature is mutable in both directions, 2) the feature can only increase (e.g. age), 3) the feature can only decrease (e.g. time left until your next deadline) and 4) the feature is not mutable (e.g. skin colour, ethnicity, …). To specify which category a feature belongs to, you can pass a vector of symbols containing the mutability constraints at the pre-processing stage. For each feature you can choose from these four options: :both (mutable in both directions), :increase (only up), :decrease (only down) and :none (immutable). By default, nothing is passed to that keyword argument and it is assumed that all features are mutable in both directions.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Below we impose that the second feature is immutable.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data = CounterfactualData(load_linearly_separable()...)\nM = fit_model(counterfactual_data, :Linear)\ncounterfactual_data.mutability = [:both, :none]","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"target = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen) \nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The resulting counterfactual path is shown in the chart below. Since only the first feature can be perturbed, the sample can only move along the horizontal axis.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"plot(ce)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"(Image: ) ","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/model_catalogue/#Model-Catalogue","page":"Model Catalogue","title":"Model Catalogue","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"While in general it is assumed that users will use this package to explain their pre-trained models, we provide out-of-the-box functionality to train various simple default models. In this tutorial, we will see how these models can be fitted to CounterfactualData.","category":"page"},{"location":"tutorials/model_catalogue/#Available-Models","page":"Model Catalogue","title":"Available Models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The standard_models_catalogue can be used to inspect the available default models:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"standard_models_catalogue","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Dict{Symbol, Any} with 4 entries:\n :Linear => Linear\n :LaplaceRedux => LaplaceReduxModel\n :DeepEnsemble => FluxEnsemble\n :MLP => FluxModel","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The dictionary keys correspond to the model names. In this case, the dictionary values are constructors that can be used called on instances of type CounterfactualData to fit the corresponding model. In most cases, users will find it most convenient to use the fit_model API call instead.","category":"page"},{"location":"tutorials/model_catalogue/#Fitting-Models","page":"Model Catalogue","title":"Fitting Models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Models from the standard model catalogue are a core part of the package and thus compatible with all offered counterfactual generators and functionalities.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The all_models_catalogue can be used to inspect all models offered by the package:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"all_models_catalogue","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"However, when using models not included in the standard_models_catalogue, additional caution is advised: they might not be supported by all counterfactual generators or they might not be models native to Julia. Thus, a more thorough reading of their documentation may be necessary to make sure that they are used correctly.","category":"page"},{"location":"tutorials/model_catalogue/#Fitting-Flux-Models","page":"Model Catalogue","title":"Fitting Flux Models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"First, let’s load one of the synthetic datasets. For this, we’ll first need to import the TaijaData.jl package:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"n = 500\ndata = TaijaData.load_multi_class(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"We could use a Deep Ensemble (Lakshminarayanan, Pritzel, and Blundell 2016) as follows:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"M = fit_model(counterfactual_data, :DeepEnsemble)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The returned object is an instance of type FluxEnsemble <: AbstractFittedModel and can be used in downstream tasks without further ado. For example, the resulting fit can be visualised using the generic plot() method as:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"plts = []\nfor target in counterfactual_data.y_levels\n plt = plot(M, counterfactual_data; target=target, title=\"p(y=$(target)|x,θ)\")\n plts = [plts..., plt]\nend\nplot(plts...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"(Image: )","category":"page"},{"location":"tutorials/model_catalogue/#Importing-PyTorch-models","page":"Model Catalogue","title":"Importing PyTorch models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The package supports generating counterfactuals for any neural network that has been previously defined and trained using PyTorch, regardless of the specific architectural details of the model. To generate counterfactuals for a PyTorch model, save the model inside a .pt file and call the following function:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_loaded = TaijaInteroperability.pytorch_model_loader(\n \"$(pwd())/docs/src/tutorials/miscellaneous\",\n \"neural_network_class\",\n \"NeuralNetwork\",\n \"$(pwd())/docs/src/tutorials/miscellaneous/pretrained_model.pt\"\n)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The method pytorch_model_loader requires four arguments:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The path to the folder with a .py file where the PyTorch model is defined\nThe name of the file where the PyTorch model is defined\nThe name of the class of the PyTorch model\nThe path to the Pickle file that holds the model weights","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"In the above case:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The file defining the model is inside $(pwd())/docs/src/tutorials/miscellaneous\nThe name of the .py file holding the model definition is neural_network_class\nThe name of the model class is NeuralNetwork\nThe Pickle file is located at $(pwd())/docs/src/tutorials/miscellaneous/pretrained_model.pt","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Though the model file and Pickle file are inside the same directory in this tutorial, this does not necessarily have to be the case.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The reason why the model file and Pickle file have to be provided separately is that the package expects an already trained PyTorch model as input. It is also possible to define new PyTorch models within the package, but since this is not the expected use of our package, special support is not offered for that. A guide for defining Python and PyTorch classes in Julia through PythonCall.jl can be found here.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Once the PyTorch model has been loaded into the package, wrap it inside the PyTorchModel class:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_pytorch = TaijaInteroperability.PyTorchModel(model_loaded, counterfactual_data.likelihood)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"This model can now be passed into the generators like any other.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Please note that the functionality for generating counterfactuals for Python models is only available if your Julia version is 1.8 or above. For Julia 1.7 users, we recommend upgrading the version to 1.8 or 1.9 before loading a PyTorch model into the package.","category":"page"},{"location":"tutorials/model_catalogue/#Importing-R-torch-models","page":"Model Catalogue","title":"Importing R torch models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"warning: Not fully tested\nPlease note that due to the incompatibility between RCall and PythonCall, it is not feasible to test both PyTorch and RTorch implementations within the same pipeline. While the RTorch implementation has been manually tested, we cannot ensure its consistent functionality as it is inherently susceptible to bugs.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The CounterfactualExplanations package supports generating counterfactuals for neural networks that have been defined and trained using R torch. Regardless of the specific architectural details of the model, you can easily generate counterfactual explanations by following these steps.","category":"page"},{"location":"tutorials/model_catalogue/#Saving-the-R-torch-model","page":"Model Catalogue","title":"Saving the R torch model","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"First, save your trained R torch model as a .pt file using the torch_save() function provided by the R torch library. This function allows you to serialize the model and save it to a file. For example:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"torch_save(model, file = \"$(pwd())/docs/src/tutorials/miscellaneous/r_model.pt\")","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Make sure to specify the correct file path where you want to save the model.","category":"page"},{"location":"tutorials/model_catalogue/#Loading-the-R-torch-model","page":"Model Catalogue","title":"Loading the R torch model","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"To import the R torch model into the CounterfactualExplanations package, use the rtorch_model_loader() function. This function loads the model from the previously saved .pt file. Here is an example of how to load the R torch model:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_loaded = TaijaInteroperability.rtorch_model_loader(\"$(pwd())/docs/src/tutorials/miscellaneous/r_model.pt\")","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The rtorch_model_loader() function requires only one argument:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_path: The path to the .pt file that contains the trained R torch model.","category":"page"},{"location":"tutorials/model_catalogue/#Wrapping-the-R-torch-model","page":"Model Catalogue","title":"Wrapping the R torch model","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Once the R torch model has been loaded into the package, wrap it inside the RTorchModel class. This step prepares the model to be used by the counterfactual generators. Here is an example:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_R = TaijaInteroperability.RTorchModel(model_loaded, counterfactual_data.likelihood)","category":"page"},{"location":"tutorials/model_catalogue/#Generating-counterfactuals-with-the-R-torch-model","page":"Model Catalogue","title":"Generating counterfactuals with the R torch model","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Now that the R torch model has been wrapped inside the RTorchModel class, you can pass it into the counterfactual generators as you would with any other model.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Please note that RCall is not fully compatible with PythonCall. Therefore, it is advisable not to import both R torch and PyTorch models within the same Julia session. Additionally, it’s worth mentioning that the R torch integration is still untested in the CounterfactualExplanations package.","category":"page"},{"location":"tutorials/model_catalogue/#Tuning-Flux-Models","page":"Model Catalogue","title":"Tuning Flux Models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"By default, model architectures are very simple. Through optional arguments, users have some control over the neural network architecture and can choose to impose regularization through dropout. Let’s tackle a more challenging dataset: MNIST (LeCun 1998).","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"data = TaijaData.load_mnist(10000)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\ntrain_data, test_data = \n CounterfactualExplanations.DataPreprocessing.train_test_split(counterfactual_data)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"(Image: )","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"In this case, we will use a Multi-Layer Perceptron (MLP) but we will adjust the model and training hyperparameters. Parameters related to training of Flux.jl models are currently stored in a mutable container:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"flux_training_params","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"CounterfactualExplanations.FluxModelParams\n loss: Symbol logitbinarycrossentropy\n opt: Symbol Adam\n n_epochs: Int64 100\n batchsize: Int64 1\n verbose: Bool false","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"In cases like this one, where model training can be expected to take a few moments, it can be useful to activate verbosity, so let’s set the corresponding field value to true. We’ll also impose mini-batch training:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"flux_training_params.verbose = true\nflux_training_params.batchsize = round(size(train_data.X,2)/10)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"To account for the fact that this is a slightly more challenging task, we will use an appropriate number of hidden neurons per layer. We will also activate dropout regularization. To scale networks up further, it is also possible to adjust the number of hidden layers, which we will not do here.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_params = (\n n_hidden = 32,\n dropout = true\n)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The model_params can be supplied to the familiar API call:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"M = fit_model(train_data, :MLP; model_params...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"FluxModel(Chain(Dense(784 => 32, relu), Dropout(0.25, active=false), Dense(32 => 10)), :classification_multi)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The model performance on our test set can be evaluated as follows:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_evaluation(M, test_data)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"1-element Vector{Float64}:\n 0.9185","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Finally, let’s restore the default training parameters:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"CounterfactualExplanations.reset!(flux_training_params)","category":"page"},{"location":"tutorials/model_catalogue/#Fitting-and-tuning-MLJ-models","page":"Model Catalogue","title":"Fitting and tuning MLJ models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Among models from the MLJ library, two models are integrated as part of the core functionality of the package:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"mlj_models_catalogue","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"These models are compatible with the Feature Tweak generator. Support for other generators has not been implemented, as both decision trees and random forests are non-differentiable tree-based models and thus, gradient-based generators don’t apply for them.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Tuning MLJ models is very simple. As the first step, let’s reload the dataset:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"n = 500\ndata = TaijaData.load_moons(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Using the usual procedure for fitting models, we can call the following method:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"tree = CounterfactualExplanations.Models.fit_model(counterfactual_data, :DecisionTree)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"However, it’s also possible to tune the DecisionTreeClassifier’s parameters. This can be done using the keyword arguments when calling fit_model() as follows:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"tree = CounterfactualExplanations.Models.fit_model(counterfactual_data, :DecisionTree; max_depth=2, min_samples_leaf=3)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"For all supported MLJ models, every tunable parameter they have is supported as a keyword argument. The tunable parameters for the DecisionTreeModel and the RandomForestModel can be found from the documentation of the DecisionTree.jl package under the Decision Tree Classifier and Random Forest Classifier sections.","category":"page"},{"location":"tutorials/model_catalogue/#Package-extension-models","page":"Model Catalogue","title":"Package extension models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The package also includes two models which don’t form a part of the core functionality of the package, but which can be accessed as package extensions. These are the EvoTreeModel from the MLJ library and the LaplaceReduxModel from LaplaceRedux.jl.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"To trigger the package extensions, the weak dependency first has to be loaded with the using keyword:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"using EvoTrees","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Once this is done, the extension models can be used like any other model:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"M = fit_model(counterfactual_data, :EvoTree; model_params...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"EvoTreesExt.EvoTreeModel(machine(EvoTreeClassifier{EvoTrees.MLogLoss}\n - nrounds: 100\n - L2: 0.0\n - lambda: 0.0\n - gamma: 0.0\n - eta: 0.1\n - max_depth: 6\n - min_weight: 1.0\n - rowsample: 1.0\n - colsample: 1.0\n - nbins: 64\n - alpha: 0.5\n - tree_type: binary\n - rng: MersenneTwister(123, (0, 9018, 8016, 884))\n, …), :classification_multi)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The tunable parameters for the EvoTreeModel can be found from the documentation of the EvoTrees.jl package under the EvoTreeClassifier section.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Please note that support for counterfactual generation with both LaplaceReduxModel and EvoTreeModel is not yet fully implemented.","category":"page"},{"location":"tutorials/model_catalogue/#References","page":"Model Catalogue","title":"References","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” https://arxiv.org/abs/1612.01474.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"LeCun, Yann. 1998. “The MNIST Database of Handwritten Digits.”","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/overview/#generators_explanation","page":"Overview","title":"Counterfactual Generators","text":"","category":"section"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"Counterfactual generators form the very core of this package. The generator_catalogue can be used to inspect the available generators:","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"generator_catalogue","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"Dict{Symbol, Any} with 11 entries:\n :gravitational => GravitationalGenerator\n :growing_spheres => GrowingSpheresGenerator\n :revise => REVISEGenerator\n :clue => CLUEGenerator\n :probe => ProbeGenerator\n :dice => DiCEGenerator\n :feature_tweak => FeatureTweakGenerator\n :claproar => ClaPROARGenerator\n :wachter => WachterGenerator\n :generic => GenericGenerator\n :greedy => GreedyGenerator","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"The following sections provide brief descriptions of all of them.","category":"page"},{"location":"explanation/generators/overview/#Gradient-based-Counterfactual-Generators","page":"Overview","title":"Gradient-based Counterfactual Generators","text":"","category":"section"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"At the time of writing, all generators are gradient-based: that is, counterfactuals are searched through gradient descent. In Altmeyer et al. (2023) we lay out a general methodological framework that can be applied to all of these generators:","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"beginaligned\nmathbfs^prime = arg min_mathbfs^prime in mathcalS left textyloss(M(f(mathbfs^prime))y^*)+ lambda textcost(f(mathbfs^prime)) right \nendaligned ","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"“Here mathbfs^prime=lefts_k^primeright_K is a K-dimensional array of counterfactual states and f mathcalS mapsto mathcalX maps from the counterfactual state space to the feature space.” (Altmeyer et al. 2023)","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"For most generators, the state space is the feature space (f is the identity function) and the number of counterfactuals K is one. Latent Space generators instead search counterfactuals in some latent space mathcalS. In this case, f corresponds to the decoder part of the generative model, that is the function that maps back from the latent space to inputs.","category":"page"},{"location":"explanation/generators/overview/#References","page":"Overview","title":"References","text":"","category":"section"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"_contribute/#Contributing","page":"Contributing","title":"Contributing","text":"","category":"section"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"Our goal is to provide a go-to place for Counterfactual Explanations in Julia. To this end, the following is a non-exhaustive list of enhancements we have planned:","category":"page"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"Additional counterfactual generators and predictive models.\nAdditional datasets for testing, evaluation and benchmarking.\nSupport for regression models.","category":"page"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"For a complete list, have a look at outstanding issue.","category":"page"},{"location":"_contribute/#How-to-contribute?","page":"Contributing","title":"How to contribute?","text":"","category":"section"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"Any sort of contribution is welcome, in particular:","category":"page"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"Should you spot any errors or something is not working, please just open an issue.\nIf you want to contribute your code, please proceed as follows:\nFork this repo and clone your fork: git clone https://github.com/your_username/CounterfactualExplanations.jl.\nImplement your modifications and submit a pull request.\nFor any other questions or comments, you can also start a discussion.","category":"page"},{"location":"assets/resources/#Further-Resources","page":"📚 Additional Resources","title":"Further Resources","text":"","category":"section"},{"location":"assets/resources/#JuliaCon-2022","page":"📚 Additional Resources","title":"JuliaCon 2022","text":"","category":"section"},{"location":"assets/resources/","page":"📚 Additional Resources","title":"📚 Additional Resources","text":"Slides: link","category":"page"},{"location":"assets/resources/#JuliaCon-Proceedings-Paper","page":"📚 Additional Resources","title":"JuliaCon Proceedings Paper","text":"","category":"section"},{"location":"assets/resources/","page":"📚 Additional Resources","title":"📚 Additional Resources","text":"TBD","category":"page"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"reference/#Reference","page":"🧐 Reference","title":"Reference","text":"","category":"section"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"In this reference, you will find a detailed overview of the package API.","category":"page"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"Reference guides are technical descriptions of the machinery and how to operate it. Reference material is information-oriented.— Diátaxis","category":"page"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"In other words, you come here because you want to take a very close look at the code 🧐.","category":"page"},{"location":"reference/#Content","page":"🧐 Reference","title":"Content","text":"","category":"section"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"Pages = [\"reference.md\"]\nDepth = 3","category":"page"},{"location":"reference/#Exported-functions","page":"🧐 Reference","title":"Exported functions","text":"","category":"section"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"Modules = [\n CounterfactualExplanations, \n CounterfactualExplanations.Convergence,\n CounterfactualExplanations.Evaluation,\n CounterfactualExplanations.DataPreprocessing,\n CounterfactualExplanations.Models,\n CounterfactualExplanations.GenerativeModels, \n CounterfactualExplanations.Generators, \n CounterfactualExplanations.Objectives\n]\nPrivate = false","category":"page"},{"location":"reference/#CounterfactualExplanations.RawOutputArrayType","page":"🧐 Reference","title":"CounterfactualExplanations.RawOutputArrayType","text":"RawOutputArrayType\n\nA type union for the allowed type for the output array y.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.RawTargetType","page":"🧐 Reference","title":"CounterfactualExplanations.RawTargetType","text":"RawTargetType\n\nA type union for the allowed types for the target variable.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.flux_training_params","page":"🧐 Reference","title":"CounterfactualExplanations.flux_training_params","text":"flux_training_params\n\nThe default training parameter for FluxModels etc.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.AbstractConvergence","page":"🧐 Reference","title":"CounterfactualExplanations.AbstractConvergence","text":"An abstract type that serves as the base type for convergence objects.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.AbstractCounterfactualExplanation","page":"🧐 Reference","title":"CounterfactualExplanations.AbstractCounterfactualExplanation","text":"Base type for counterfactual explanations.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.AbstractFittedModel","page":"🧐 Reference","title":"CounterfactualExplanations.AbstractFittedModel","text":"Base type for fitted models.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.AbstractGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.AbstractGenerator","text":"An abstract type that serves as the base type for counterfactual generators.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.CounterfactualExplanation","page":"🧐 Reference","title":"CounterfactualExplanations.CounterfactualExplanation","text":"A struct that collects all information relevant to a specific counterfactual explanation for a single individual.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.CounterfactualExplanation-Tuple{AbstractArray, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, AbstractGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.CounterfactualExplanation","text":"function CounterfactualExplanation(;\n\tx::AbstractArray,\n\ttarget::RawTargetType,\n\tdata::CounterfactualData,\n\tM::Models.AbstractFittedModel,\n\tgenerator::Generators.AbstractGenerator,\n\tnum_counterfactuals::Int = 1,\n\tinitialization::Symbol = :add_perturbation,\n convergence::Union{AbstractConvergence,Symbol}=:decision_threshold,\n)\n\nOuter method to construct a CounterfactualExplanation structure.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.EncodedOutputArrayType","page":"🧐 Reference","title":"CounterfactualExplanations.EncodedOutputArrayType","text":"EncodedOutputArrayType\n\nType of encoded output array.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.EncodedTargetType","page":"🧐 Reference","title":"CounterfactualExplanations.EncodedTargetType","text":"EncodedTargetType\n\nType of encoded target variable.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.OutputEncoder","page":"🧐 Reference","title":"CounterfactualExplanations.OutputEncoder","text":"OutputEncoder\n\nThe OutputEncoder takes a raw output array (y) and encodes it.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.OutputEncoder-Tuple{Union{Int64, AbstractFloat, String, Symbol}}","page":"🧐 Reference","title":"CounterfactualExplanations.OutputEncoder","text":"(encoder::OutputEncoder)(ynew::RawTargetType)\n\nWhen called on a new value ynew, the OutputEncoder encodes it based on the initial encoding.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.OutputEncoder-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.OutputEncoder","text":"(encoder::OutputEncoder)()\n\nOn call, the OutputEncoder returns the encoded output array.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.EvoTreeModel","page":"🧐 Reference","title":"CounterfactualExplanations.EvoTreeModel","text":"EvoTreeModel\n\nExposes the EvoTreeModel from the EvoTreesExt extension.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.LaplaceReduxModel","page":"🧐 Reference","title":"CounterfactualExplanations.LaplaceReduxModel","text":"LaplaceReduxModel\n\nExposes the LaplaceReduxModel from the LaplaceReduxExt extension.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Base.Iterators.Zip, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, AbstractGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"generate_counterfactual(\n x::Base.Iterators.Zip,\n target::RawTargetType,\n data::CounterfactualData,\n M::Models.AbstractFittedModel,\n generator::AbstractGenerator;\n kwargs...,\n)\n\nOverloads the generate_counterfactual method to accept a zip of factuals x and return a vector of counterfactuals.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Matrix, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, AbstractGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"generate_counterfactual(\n x::Matrix,\n target::RawTargetType,\n data::CounterfactualData,\n M::Models.AbstractFittedModel,\n generator::AbstractGenerator;\n num_counterfactuals::Int=1,\n initialization::Symbol=:add_perturbation,\n convergence::Union{AbstractConvergence,Symbol}=:decision_threshold,\n timeout::Union{Nothing,Real}=nothing,\n)\n\nThe core function that is used to run counterfactual search for a given factual x, target, counterfactual data, model and generator. Keywords can be used to specify the desired threshold for the predicted target class probability and the maximum number of iterations.\n\nArguments\n\nx::Matrix: Factual data point.\ntarget::RawTargetType: Target class.\ndata::CounterfactualData: Counterfactual data.\nM::Models.AbstractFittedModel: Fitted model.\ngenerator::AbstractGenerator: Generator.\nnum_counterfactuals::Int=1: Number of counterfactuals to generate for factual.\ninitialization::Symbol=:add_perturbation: Initialization method. By default, the initialization is done by adding a small random perturbation to the factual to achieve more robustness.\nconvergence::Union{AbstractConvergence,Symbol}=:decision_threshold: Convergence criterion. By default, the convergence is based on the decision threshold.\ntimeout::Union{Nothing,Int}=nothing: Timeout in seconds.\n\nExamples\n\nGeneric generator\n\nusing CounterfactualExplanations\n\n# Data:\nusing CounterfactualExplanations.Data\nusing Random\nRandom.seed!(1234)\nxs, ys = Data.toy_data_linear()\nX = hcat(xs...)\ncounterfactual_data = CounterfactualData(X,ys')\n\n# Model\nusing CounterfactualExplanations.Models: LogisticModel, probs \n# Logit model:\nw = [1.0 1.0] # true coefficients\nb = 0\nM = LogisticModel(w, [b])\n\n# Randomly selected factual:\nx = select_factual(counterfactual_data,rand(1:size(X)[2]))\ny = round(probs(M, x)[1])\ntarget = round(probs(M, x)[1])==0 ? 1 : 0 \n\n# Counterfactual search:\ngenerator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Matrix, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, GrowingSpheresGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"generate_counterfactual(\n x::Matrix,\n target::RawTargetType,\n data::DataPreprocessing.CounterfactualData,\n M::Models.AbstractFittedModel,\n generator::Generators.GrowingSpheresGenerator;\n num_counterfactuals::Int=1,\n convergence::Union{AbstractConvergence,Symbol}=Convergence.DecisionThresholdConvergence(;\n decision_threshold=(1 / length(data.y_levels)), max_iter=1000\n ),\n kwrgs...,\n)\n\nOverloads the generate_counterfactual for the GrowingSpheresGenerator generator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Tuple{AbstractArray}, Vararg{Any}}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"Overloads the generate_counterfactual method to accept a tuple containing and array. This allows for broadcasting over Zip iterators.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Vector{<:Matrix}, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, AbstractGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"generate_counterfactual(\n x::Vector{<:Matrix},\n target::RawTargetType,\n data::CounterfactualData,\n M::Models.AbstractFittedModel,\n generator::AbstractGenerator;\n kwargs...,\n)\n\nOverloads the generate_counterfactual method to accept a vector of factuals x and return a vector of counterfactuals.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.get_target_index-Tuple{Any, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.get_target_index","text":"get_target_index(y_levels, target)\n\nUtility that returns the index of target in y_levels.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.path-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.path","text":"path(ce::CounterfactualExplanation)\n\nA convenience method that returns the entire counterfactual path.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.target_probs","page":"🧐 Reference","title":"CounterfactualExplanations.target_probs","text":"target_probs(\n ce::CounterfactualExplanation,\n x::Union{AbstractArray,Nothing}=nothing,\n)\n\nReturns the predicted probability of the target class for x. If x is nothing, the predicted probability corresponding to the counterfactual value is returned.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.terminated-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.terminated","text":"terminated(ce::CounterfactualExplanation)\n\nA convenience method that checks if the counterfactual search has terminated.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.total_steps-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.total_steps","text":"total_steps(ce::CounterfactualExplanation)\n\nA convenience method that returns the total number of steps of the counterfactual search.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.update!-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.update!","text":"update!(ce::CounterfactualExplanation)\n\nAn important subroutine that updates the counterfactual explanation. It takes a snapshot of the current counterfactual search state and passes it to the generator. Based on the current state the generator generates perturbations. Various constraints are then applied to the proposed vector of feature perturbations. Finally, the counterfactual search state is updated.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.convergence_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.convergence_catalogue","text":"convergence_catalogue\n\nA dictionary containing all convergence criteria.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Convergence.converged-Tuple{CounterfactualExplanations.Convergence.DecisionThresholdConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.converged","text":"converged(convergence::DecisionThresholdConvergence, ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has converged when the convergence criterion is the decision threshold.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.converged-Tuple{CounterfactualExplanations.Convergence.GeneratorConditionsConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.converged","text":"converged(convergence::GeneratorConditionsConvergence, ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has converged when the convergence criterion is generator_conditions.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.converged-Tuple{CounterfactualExplanations.Convergence.InvalidationRateConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.converged","text":"converged(convergence::InvalidationRateConvergence, ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has converged when the convergence criterion is invalidation rate.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.converged-Tuple{CounterfactualExplanations.Convergence.MaxIterConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.converged","text":"converged(convergence::MaxIterConvergence, ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has converged when the convergence criterion is maximum iterations. This means the counterfactual search will not terminate until the maximum number of iterations has been reached independently of the other convergence criteria.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.get_convergence_type-Tuple{AbstractConvergence, AbstractVector}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.get_convergence_type","text":"get_convergence_type(convergence::AbstractConvergence)\n\nReturns the convergence object.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.get_convergence_type-Tuple{Symbol, AbstractVector}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.get_convergence_type","text":"get_convergence_type(convergence::Symbol)\n\nReturns the convergence object from the dictionary of default convergence types.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.hinge_loss-Tuple{CounterfactualExplanations.Convergence.InvalidationRateConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.hinge_loss","text":"hinge_loss(convergence::InvalidationRateConvergence, ce::AbstractCounterfactualExplanation)\n\nCalculates the hinge loss of a counterfactual explanation.\n\nArguments\n\nconvergence::InvalidationRateConvergence: The convergence criterion to use.\nce::AbstractCounterfactualExplanation: The counterfactual explanation to calculate the hinge loss for.\n\nReturns\n\nThe hinge loss of the counterfactual explanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.invalidation_rate-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.invalidation_rate","text":"invalidation_rate(ce::AbstractCounterfactualExplanation)\n\nCalculates the invalidation rate of a counterfactual explanation.\n\nArguments\n\nce::AbstractCounterfactualExplanation: The counterfactual explanation to calculate the invalidation rate for.\nkwargs: Additional keyword arguments to pass to the function.\n\nReturns\n\nThe invalidation rate of the counterfactual explanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.threshold_reached-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.threshold_reached","text":"threshold_reached(ce::CounterfactualExplanation)\n\nDetermines if the predefined threshold for the target class probability has been reached.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.default_measures","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.default_measures","text":"The default evaluation measures.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Evaluation.Benchmark","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.Benchmark","text":"A container for benchmarks of counterfactual explanations. Instead of subtyping DataFrame, it contains a DataFrame of evaluation measures (see this discussion for why we don't subtype DataFrame directly).\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Evaluation.Benchmark-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.Benchmark","text":"(bmk::Benchmark)(; agg=mean)\n\nReturns a DataFrame containing evaluation measures aggregated by num_counterfactual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.benchmark-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.benchmark","text":"benchmark(\n data::CounterfactualData;\n models::Dict{<:Any,<:Any}=standard_models_catalogue,\n generators::Union{Nothing,Dict{<:Any,<:AbstractGenerator}}=nothing,\n measure::Union{Function,Vector{Function}}=default_measures,\n n_individuals::Int=5,\n suppress_training::Bool=false,\n factual::Union{Nothing,RawTargetType}=nothing,\n target::Union{Nothing,RawTargetType}=nothing,\n store_ce::Bool=false,\n parallelizer::Union{Nothing,AbstractParallelizer}=nothing,\n kwrgs...,\n)\n\nRuns the benchmarking exercise as follows:\n\nRandomly choose a factual and target label unless specified. \nIf no pretrained models are provided, it is assumed that a dictionary of callable model objects is provided (by default using the standard_models_catalogue). \nEach of these models is then trained on the data. \nFor each model separately choose n_individuals randomly from the non-target (factual) class. For each generator create a benchmark as in benchmark(xs::Union{AbstractArray,Base.Iterators.Zip}).\nFinally, concatenate the results.\n\nIf vertical_splits is specified to an integer, the computations are split vertically into vertical_splits chunks. In this case, the results are stored in a temporary directory and concatenated afterwards. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.benchmark-Tuple{Union{AbstractArray, Base.Iterators.Zip}, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.benchmark","text":"benchmark(\n x::Union{AbstractArray,Base.Iterators.Zip},\n target::RawTargetType,\n data::CounterfactualData;\n models::Dict{<:Any,<:AbstractFittedModel},\n generators::Dict{<:Any,<:AbstractGenerator},\n measure::Union{Function,Vector{Function}}=default_measures,\n xids::Union{Nothing,AbstractArray}=nothing,\n dataname::Union{Nothing,Symbol,String}=nothing,\n verbose::Bool=true,\n store_ce::Bool=false,\n parallelizer::Union{Nothing,AbstractParallelizer}=nothing,\n kwrgs...,\n)\n\nFirst generates counterfactual explanations for factual x, the target and data using each of the provided models and generators. Then generates a Benchmark for the vector of counterfactual explanations as in benchmark(counterfactual_explanations::Vector{CounterfactualExplanation}).\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.benchmark-Tuple{Vector{CounterfactualExplanation}}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.benchmark","text":"benchmark(\n counterfactual_explanations::Vector{CounterfactualExplanation};\n meta_data::Union{Nothing,<:Vector{<:Dict}}=nothing,\n measure::Union{Function,Vector{Function}}=default_measures,\n store_ce::Bool=false,\n)\n\nGenerates a Benchmark for a vector of counterfactual explanations. Optionally meta_data describing each individual counterfactual explanation can be supplied. This should be a vector of dictionaries of the same length as the vector of counterfactuals. If no meta_data is supplied, it will be automatically inferred. All measure functions are applied to each counterfactual explanation. If store_ce=true, the counterfactual explanations are stored in the benchmark.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.evaluate","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.evaluate","text":"evaluate(\n ce::CounterfactualExplanation;\n measure::Union{Function,Vector{Function}}=default_measures,\n agg::Function=mean,\n report_each::Bool=false,\n output_format::Symbol=:Vector,\n pivot_longer::Bool=true\n)\n\nJust computes evaluation measures for the counterfactual explanation. By default, no meta data is reported. For report_meta=true, meta data is automatically inferred, unless this overwritten by meta_data. The optional meta_data argument should be a vector of dictionaries of the same length as the vector of counterfactual explanations. \n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Evaluation.redundancy-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.redundancy","text":"redundancy(ce::CounterfactualExplanation)\n\nComputes the feature redundancy: that is, the number of features that remain unchanged from their original, factual values.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.validity-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.validity","text":"validity(ce::CounterfactualExplanation; γ=0.5)\n\nChecks of the counterfactual search has been successful with respect to the probability threshold γ. In case multiple counterfactuals were generated, the function returns the proportion of successful counterfactuals.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.CounterfactualData-Tuple{AbstractMatrix, Union{AbstractMatrix, AbstractVector}}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.CounterfactualData","text":"CounterfactualData(\n X::AbstractMatrix, y::AbstractMatrix;\n mutability::Union{Vector{Symbol},Nothing}=nothing,\n domain::Union{Any,Nothing}=nothing,\n features_categorical::Union{Vector{Int},Nothing}=nothing,\n features_continuous::Union{Vector{Int},Nothing}=nothing,\n standardize::Bool=false\n)\n\nThis outer constructor method prepares features X and labels y to be used with the package. Mutability and domain constraints can be added for the features. The function also accepts arguments that specify which features are categorical and which are continues. These arguments are currently not used. \n\nExamples\n\nusing CounterfactualExplanations.Data\nx, y = toy_data_linear()\nX = hcat(x...)\ncounterfactual_data = CounterfactualData(X,y')\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.CounterfactualData-Tuple{Tables.MatrixTable, Union{AbstractMatrix, AbstractVector}}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.CounterfactualData","text":"function CounterfactualData(\n X::Tables.MatrixTable,\n y::RawOutputArrayType;\n kwrgs...\n)\n\nOuter constructor method that accepts a Tables.MatrixTable. By default, the indices of categorical and continuous features are automatically inferred the features' scitype.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.apply_domain_constraints-Tuple{CounterfactualData, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.apply_domain_constraints","text":"apply_domain_constraints(counterfactual_data::CounterfactualData, x::AbstractArray)\n\nA subroutine that is used to apply the predetermined domain constraints.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.select_factual-Tuple{CounterfactualData, Int64}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.select_factual","text":"select_factual(counterfactual_data::CounterfactualData, index::Int)\n\nA convenience method that can be used to access the feature matrix.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.select_factual-Tuple{CounterfactualData, Union{UnitRange{Int64}, Vector{Int64}}}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.select_factual","text":"select_factual(counterfactual_data::CounterfactualData, index::Union{Vector{Int},UnitRange{Int}})\n\nA convenience method that can be used to access the feature matrix.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.transformable_features-Tuple{CounterfactualData, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.transformable_features","text":"transformable_features(counterfactual_data::CounterfactualData, dt::Any)\n\nBy default, all continuous features are transformable. This function returns the indices of all continuous features.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.transformable_features-Tuple{CounterfactualData, StatsBase.ZScoreTransform}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.transformable_features","text":"transformable_features(counterfactual_data::CounterfactualData, dt::ZScoreTransform)\n\nReturns the indices of all continuous features that can be transformed. For constant features ZScoreTransform returns NaN.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.transformable_features-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.transformable_features","text":"transformable_features(counterfactual_data::CounterfactualData)\n\nDispatches the transformable_features function to the appropriate method based on the type of the dt field.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.all_models_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Models.all_models_catalogue","text":"all_models_catalogue\n\nA dictionary containing both differentiable and non-differentiable machine learning models.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Models.mlj_models_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Models.mlj_models_catalogue","text":"mlj_models_catalogue\n\nA dictionary containing all machine learning models from the MLJ model registry that the package supports.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Models.standard_models_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Models.standard_models_catalogue","text":"standard_models_catalogue\n\nA dictionary containing all differentiable machine learning models.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Models.AbstractDifferentiableModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractDifferentiableModel","text":"Base type for differentiable models.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.FluxEnsemble","page":"🧐 Reference","title":"CounterfactualExplanations.Models.FluxEnsemble","text":"FluxEnsemble <: AbstractFluxModel\n\nConstructor for deep ensembles trained in Flux.jl. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.FluxModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.FluxModel","text":"FluxModel <: AbstractFluxModel\n\nConstructor for models trained in Flux.jl. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.FluxModel-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.FluxModel","text":"FluxModel(data::CounterfactualData; kwargs...)\n\nConstructs a multi-layer perceptron (MLP).\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.DecisionTreeModel-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.DecisionTreeModel","text":"DecisionTreeModel(data::CounterfactualData; kwargs...)\n\nConstructs a new TreeModel object wrapped around a decision tree from the data in a CounterfactualData object. Not called by the user directly.\n\nArguments\n\ndata::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.\n\nReturns\n\nmodel::TreeModel: A TreeModel object.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.Linear-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.Linear","text":"Linear(data::CounterfactualData; kwargs...)\n\nConstructs a model with one linear layer. If the output is binary, this corresponds to logistic regression, since model outputs are passed through the sigmoid function. If the output is multi-class, this corresponds to multinomial logistic regression, since model outputs are passed through the softmax function.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.RandomForestModel-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.RandomForestModel","text":"RandomForestModel(data::CounterfactualData; kwargs...)\n\nConstructs a new TreeModel object wrapped around a random forest from the data in a CounterfactualData object. Not called by the user directly.\n\nArguments\n\ndata::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.\n\nReturns\n\nmodel::TreeModel: A TreeModel object.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.fit_model","page":"🧐 Reference","title":"CounterfactualExplanations.Models.fit_model","text":"fit_model(\n counterfactual_data::CounterfactualData, model::Symbol=:MLP;\n kwrgs...\n)\n\nFits one of the available default models to the counterfactual_data. The model argument can be used to specify the desired model. The available values correspond to the keys of the all_models_catalogue dictionary.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Models.logits-Tuple{AbstractFittedModel, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.logits","text":"logits(M::AbstractFittedModel, X::AbstractArray)\n\nGeneric method that is compulsory for all models. It returns the raw model predictions. In classification this is sometimes referred to as logits: the non-normalized predictions that are fed into a link function to produce predicted probabilities. In regression (not currently implemented) raw outputs typically correspond to final outputs. In other words, there is typically no normalization involved.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.logits-Tuple{CounterfactualExplanations.Models.TreeModel, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.logits","text":"logits(M::TreeModel, X::AbstractArray)\n\nCalculates the logit scores output by the model M for the input data X.\n\nArguments\n\nM::TreeModel: The model selected by the user.\nX::AbstractArray: The feature vector for which the logit scores are calculated.\n\nReturns\n\nlogits::Matrix: A matrix of logits for each output class for each data point in X.\n\nExample\n\nlogits = Models.logits(M, x) # calculates the logit scores for each output class for the data point x\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.model_evaluation-Tuple{AbstractFittedModel, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.model_evaluation","text":"model_evaluation(M::AbstractFittedModel, test_data::CounterfactualData)\n\nHelper function to compute F-Score for AbstractFittedModel on a (test) data set. By default, it computes the accuracy. Any other measure, e.g. from the StatisticalMeasures package, can be passed as an argument. Currently, only measures applicable to classification tasks are supported.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.predict_label-Tuple{AbstractFittedModel, CounterfactualData, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.predict_label","text":"predict_label(M::AbstractFittedModel, counterfactual_data::CounterfactualData, X::AbstractArray)\n\nReturns the predicted output label for a given model M, data set counterfactual_data and input data X.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.predict_label-Tuple{AbstractFittedModel, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.predict_label","text":"predict_label(M::AbstractFittedModel, counterfactual_data::CounterfactualData)\n\nReturns the predicted output labels for all data points of data set counterfactual_data for a given model M.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.predict_label-Tuple{CounterfactualExplanations.Models.TreeModel, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.predict_label","text":"predict_label(M::TreeModel, X::AbstractArray)\n\nReturns the predicted label for X.\n\nArguments\n\nM::TreeModel: The model selected by the user.\nX::AbstractArray: The input array for which the label is predicted.\n\nReturns\n\nlabels::AbstractArray: The predicted label for each data point in X.\n\nExample\n\nlabel = Models.predict_label(M, x) # returns the predicted label for each data point in x\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.predict_proba-Tuple{AbstractFittedModel, Union{Nothing, CounterfactualData}, Union{Nothing, AbstractArray}}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.predict_proba","text":"predict_proba(M::AbstractFittedModel, counterfactual_data::CounterfactualData, X::Union{Nothing,AbstractArray})\n\nReturns the predicted output probabilities for a given model M, data set counterfactual_data and input data X.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.probs-Tuple{AbstractFittedModel, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.probs","text":"probs(M::AbstractFittedModel, X::AbstractArray)\n\nGeneric method that is compulsory for all models. It returns the normalized model predictions, so the predicted probabilities in the case of classification. In regression (not currently implemented) this method is redundant. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.probs-Tuple{CounterfactualExplanations.Models.TreeModel, AbstractMatrix{<:Number}}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.probs","text":"probs(M::TreeModel, X::AbstractArray{<:Number, 2})\n\nCalculates the probability scores for each output class for the two-dimensional input data matrix X.\n\nArguments\n\nM::TreeModel: The TreeModel.\nX::AbstractArray: The feature vector for which the predictions are made.\n\nReturns\n\np::Matrix: A matrix of probability scores for each output class for each data point in X.\n\nExample\n\nprobabilities = Models.probs(M, X) # calculates the probability scores for each output class for each data point in X.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.probs-Tuple{CounterfactualExplanations.Models.TreeModel, AbstractVector{<:Number}}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.probs","text":"probs(M::TreeModel, X::AbstractArray{<:Number, 1})\n\nWorks the same way as the probs(M::TreeModel, X::AbstractArray{<:Number, 2}) method above, but handles 1-dimensional rather than 2-dimensional input data.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.generator_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.generator_catalogue","text":"A dictionary containing the constructors of all available counterfactual generators.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Generators.AbstractGradientBasedGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.AbstractGradientBasedGenerator","text":"AbstractGradientBasedGenerator\n\nAn abstract type that serves as the base type for gradient-based counterfactual generators. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.AbstractNonGradientBasedGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.AbstractNonGradientBasedGenerator","text":"AbstractNonGradientBasedGenerator\n\nAn abstract type that serves as the base type for non gradient-based counterfactual generators. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.FeatureTweakGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.FeatureTweakGenerator","text":"Feature Tweak counterfactual generator class.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.FeatureTweakGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.FeatureTweakGenerator","text":"FeatureTweakGenerator(; penalty::Union{Nothing,Function,Vector{Function}}=Objectives.distance_l2, ϵ::AbstractFloat=0.1)\n\nConstructs a new Feature Tweak Generator object.\n\nUses the L2-norm as the penalty to measure the distance between the counterfactual and the factual. According to the paper by Tolomei et al., another recommended choice for the penalty in addition to the L2-norm is the L0-norm. The L0-norm simply minimizes the number of features that are changed through the tweak.\n\nArguments\n\npenalty::Union{Nothing,Function,Vector{Function}}: The penalty function to use for the generator. Defaults to distance_l2.\nϵ::AbstractFloat: The tolerance value for the feature tweaks. Described at length in Tolomei et al. (https://arxiv.org/pdf/1706.06691.pdf). Defaults to 0.1.\n\nReturns\n\ngenerator::FeatureTweakGenerator: A non-gradient-based generator that can be used to generate counterfactuals using the feature tweak method.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GradientBasedGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GradientBasedGenerator","text":"Base class for gradient-based counterfactual generators.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.GradientBasedGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GradientBasedGenerator","text":"GradientBasedGenerator(;\n\tloss::Union{Nothing,Function}=nothing,\n\tpenalty::Penalty=nothing,\n\tλ::Union{Nothing,AbstractFloat,Vector{AbstractFloat}}=nothing,\n\tlatent_space::Bool::false,\n\topt::Flux.Optimise.AbstractOptimiser=Flux.Descent(),\n generative_model_params::NamedTuple=(;),\n)\n\nDefault outer constructor for GradientBasedGenerator.\n\nArguments\n\nloss::Union{Nothing,Function}=nothing: The loss function used by the model.\npenalty::Penalty=nothing: A penalty function for the generator to penalize counterfactuals too far from the original point.\nλ::Union{Nothing,AbstractFloat,Vector{AbstractFloat}}=nothing: The weight of the penalty function.\nlatent_space::Bool=false: Whether to use the latent space of a generative model to generate counterfactuals.\nopt::Flux.Optimise.AbstractOptimiser=Flux.Descent(): The optimizer to use for the generator.\ngenerative_model_params::NamedTuple: The parameters of the generative model associated with the generator.\n\nReturns\n\ngenerator::GradientBasedGenerator: A gradient-based counterfactual generator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GrowingSpheresGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GrowingSpheresGenerator","text":"Growing Spheres counterfactual generator class.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.GrowingSpheresGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GrowingSpheresGenerator","text":"GrowingSpheresGenerator(; n::Int=100, η::Float64=0.1, kwargs...)\n\nConstructs a new Growing Spheres Generator object.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.JSMADescent","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.JSMADescent","text":"An optimisation rule that can be used to implement a Jacobian-based Saliency Map Attack.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.JSMADescent-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.JSMADescent","text":"Outer constructor for the JSMADescent rule.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.CLUEGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.CLUEGenerator","text":"Constructor for CLUEGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.ClaPROARGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ClaPROARGenerator","text":"Constructor for ClaPGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.DiCEGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.DiCEGenerator","text":"Constructor for DiCEGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GenericGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GenericGenerator","text":"Constructor for GenericGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GravitationalGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GravitationalGenerator","text":"Constructor for GravitationalGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GreedyGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GreedyGenerator","text":"Constructor for GreedyGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.ProbeGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ProbeGenerator","text":"Constructor for ProbeGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.REVISEGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.REVISEGenerator","text":"Constructor for REVISEGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.WachterGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.WachterGenerator","text":"Constructor for WachterGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.conditions_satisfied-Tuple{AbstractGradientBasedGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.conditions_satisfied","text":"conditions_satisfied(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nThe default method to check if the all conditions for convergence of the counterfactual search have been satisified for gradient-based generators. By default, gradient-based search is considered to have converged as soon as the proposed feature changes for all features are smaller than one percent of its standard deviation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.feature_tweaking!-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.feature_tweaking!","text":"feature_tweaking!(ce::AbstractCounterfactualExplanation)\n\nReturns a counterfactual instance of ce.x based on the ensemble of classifiers provided.\n\nArguments\n\nce::AbstractCounterfactualExplanation: The counterfactual explanation object.\n\nReturns\n\nce::AbstractCounterfactualExplanation: The counterfactual explanation object.\n\nExample\n\nce = feature_tweaking!(ce) # returns a counterfactual inside the ce.s′ field based on the ensemble of classifiers provided\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.generate_perturbations-Tuple{AbstractGradientBasedGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.generate_perturbations","text":"generate_perturbations(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nThe default method to generate feature perturbations for gradient-based generators through simple gradient descent.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.generate_perturbations-Tuple{FeatureTweakGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.generate_perturbations","text":"generate_perturbations(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nThe default method to generate feature perturbations for gradient-based generators through simple gradient descent.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.hinge_loss-Tuple{AbstractConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.hinge_loss","text":"hinge_loss(convergence::AbstractConvergence, ce::AbstractCounterfactualExplanation)\n\nThe default hinge loss for any convergence criterion. Can be overridden inside the Convergence module as part of the definition of specific convergence criteria.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.@objective-Tuple{Any, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.@objective","text":"objective(generator, ex)\n\nA macro that can be used to define the counterfactual search objective.\n\n\n\n\n\n","category":"macro"},{"location":"reference/#CounterfactualExplanations.Generators.@search_feature_space-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.@search_feature_space","text":"search_feature_space(generator)\n\nA simple macro that can be used to specify feature space search.\n\n\n\n\n\n","category":"macro"},{"location":"reference/#CounterfactualExplanations.Generators.@search_latent_space-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.@search_latent_space","text":"search_latent_space(generator)\n\nA simple macro that can be used to specify latent space search.\n\n\n\n\n\n","category":"macro"},{"location":"reference/#CounterfactualExplanations.Generators.@with_optimiser-Tuple{Any, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.@with_optimiser","text":"with_optimiser(generator, optimiser)\n\nA simple macro that can be used to specify the optimiser to be used.\n\n\n\n\n\n","category":"macro"},{"location":"reference/#CounterfactualExplanations.Objectives.ddp_diversity-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.ddp_diversity","text":"ddp_diversity(\n ce::AbstractCounterfactualExplanation;\n perturbation_size=1e-5\n)\n\nEvaluates how diverse the counterfactuals are using a Determinantal Point Process (DDP).\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance","text":"distance(ce::AbstractCounterfactualExplanation, p::Real=2)\n\nComputes the distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_l0-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_l0","text":"distance_l0(ce::AbstractCounterfactualExplanation)\n\nComputes the L0 distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_l1-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_l1","text":"distance_l1(ce::AbstractCounterfactualExplanation)\n\nComputes the L1 distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_l2-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_l2","text":"distance_l2(ce::AbstractCounterfactualExplanation)\n\nComputes the L2 (Euclidean) distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_linf-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_linf","text":"distance_linf(ce::AbstractCounterfactualExplanation)\n\nComputes the L-inf distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_mad-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_mad","text":"distance_mad(ce::AbstractCounterfactualExplanation; agg=mean)\n\nThis is the distance measure proposed by Wachter et al. (2017).\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.predictive_entropy-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.predictive_entropy","text":"predictive_entropy(ce::AbstractCounterfactualExplanation; agg=Statistics.mean)\n\nComputes the predictive entropy of the counterfactuals. Explained in https://arxiv.org/abs/1406.2541.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Flux.Losses.logitbinarycrossentropy-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"Flux.Losses.logitbinarycrossentropy","text":"Flux.Losses.logitbinarycrossentropy(ce::AbstractCounterfactualExplanation)\n\nSimply extends the logitbinarycrossentropy method to work with objects of type AbstractCounterfactualExplanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Flux.Losses.logitcrossentropy-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"Flux.Losses.logitcrossentropy","text":"Flux.Losses.logitcrossentropy(ce::AbstractCounterfactualExplanation)\n\nSimply extends the logitcrossentropy method to work with objects of type AbstractCounterfactualExplanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Flux.Losses.mse-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"Flux.Losses.mse","text":"Flux.Losses.mse(ce::AbstractCounterfactualExplanation)\n\nSimply extends the mse method to work with objects of type AbstractCounterfactualExplanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Internal-functions","page":"🧐 Reference","title":"Internal functions","text":"","category":"section"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"Modules = [\n CounterfactualExplanations, \n CounterfactualExplanations.Convergence,\n CounterfactualExplanations.Evaluation,\n CounterfactualExplanations.DataPreprocessing,\n CounterfactualExplanations.Models, \n CounterfactualExplanations.GenerativeModels,\n CounterfactualExplanations.Generators, \n CounterfactualExplanations.Objectives\n]\nPublic = false","category":"page"},{"location":"reference/#CounterfactualExplanations.FluxModelParams","page":"🧐 Reference","title":"CounterfactualExplanations.FluxModelParams","text":"FluxModelParams\n\nDefault MLP training parameters.\n\n\n\n\n\n","category":"type"},{"location":"reference/#Base.Broadcast.broadcastable-Tuple{AbstractFittedModel}","page":"🧐 Reference","title":"Base.Broadcast.broadcastable","text":"Treat AbstractFittedModel as scalar when broadcasting.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Base.Broadcast.broadcastable-Tuple{AbstractGenerator}","page":"🧐 Reference","title":"Base.Broadcast.broadcastable","text":"Treat AbstractGenerator as scalar when broadcasting.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.adjust_shape!-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.adjust_shape!","text":"adjust_shape!(ce::CounterfactualExplanation)\n\nA convenience method that adjusts the dimensions of the counterfactual state and related fields.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.adjust_shape-Tuple{CounterfactualExplanation, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.adjust_shape","text":"adjust_shape(\n ce::CounterfactualExplanation, \n x::AbstractArray\n)\n\nA convenience method that adjusts the dimensions of x.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.apply_domain_constraints!-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.apply_domain_constraints!","text":"apply_domain_constraints!(ce::CounterfactualExplanation)\n\nWrapper function that applies underlying domain constraints.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.apply_mutability-Tuple{CounterfactualExplanation, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.apply_mutability","text":"apply_mutability(\n ce::CounterfactualExplanation,\n Δs′::AbstractArray,\n)\n\nA subroutine that applies mutability constraints to the proposed vector of feature perturbations.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.counterfactual-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual","text":"counterfactual(ce::CounterfactualExplanation)\n\nA convenience method that returns the counterfactual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.counterfactual_label-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual_label","text":"counterfactual_label(ce::CounterfactualExplanation)\n\nA convenience method that returns the predicted label of the counterfactual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.counterfactual_label_path-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual_label_path","text":"counterfactual_label_path(ce::CounterfactualExplanation)\n\nReturns the counterfactual labels for each step of the search.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.counterfactual_probability","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual_probability","text":"counterfactual_probability(ce::CounterfactualExplanation)\n\nA convenience method that computes the class probabilities of the counterfactual.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.counterfactual_probability_path-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual_probability_path","text":"counterfactual_probability_path(ce::CounterfactualExplanation)\n\nReturns the counterfactual probabilities for each step of the search.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.decode_array-Tuple{MultivariateStats.AbstractDimensionalityReduction, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.decode_array","text":"decode_array(dt::MultivariateStats.AbstractDimensionalityReduction, x::AbstractArray)\n\nHelper function to decode an array x using a data transform dt::MultivariateStats.AbstractDimensionalityReduction.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.decode_array-Tuple{StatsBase.AbstractDataTransform, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.decode_array","text":"decode_array(dt::StatsBase.AbstractDataTransform, x::AbstractArray)\n\nHelper function to decode an array x using a data transform dt::StatsBase.AbstractDataTransform.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.decode_state","page":"🧐 Reference","title":"CounterfactualExplanations.decode_state","text":"function decode_state( ce::CounterfactualExplanation, x::Union{AbstractArray,Nothing}=nothing, )\n\nApplies all the applicable decoding functions:\n\nIf applicable, map the state variable back from the latent space to the feature space.\nIf and where applicable, inverse-transform features.\nReconstruct all categorical encodings.\n\nFinally, the decoded counterfactual is returned.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.encode_array-Tuple{MultivariateStats.AbstractDimensionalityReduction, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.encode_array","text":"encode_array(dt::MultivariateStats.AbstractDimensionalityReduction, x::AbstractArray)\n\nHelper function to encode an array x using a data transform dt::MultivariateStats.AbstractDimensionalityReduction.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.encode_array-Tuple{StatsBase.AbstractDataTransform, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.encode_array","text":"encode_array(dt::StatsBase.AbstractDataTransform, x::AbstractArray)\n\nHelper function to encode an array x using a data transform dt::StatsBase.AbstractDataTransform.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.encode_state","page":"🧐 Reference","title":"CounterfactualExplanations.encode_state","text":"function encode_state( ce::CounterfactualExplanation, x::Union{AbstractArray,Nothing} = nothing, )\n\nApplies all required encodings to x:\n\nIf applicable, it maps x to the latent space learned by the generative model.\nIf and where applicable, it rescales features. \n\nFinally, it returns the encoded state variable.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.factual-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.factual","text":"factual(ce::CounterfactualExplanation)\n\nA convenience method to retrieve the factual x.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.factual_label-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.factual_label","text":"factual_label(ce::CounterfactualExplanation)\n\nA convenience method to get the predicted label associated with the factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.factual_probability-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.factual_probability","text":"factual_probability(ce::CounterfactualExplanation)\n\nA convenience method to compute the class probabilities of the factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.find_potential_neighbours-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.find_potential_neighbours","text":"find_potential_neighbors(ce::AbstractCounterfactualExplanation)\n\nFinds potential neighbors for the selected factual data point.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.get_meta-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.get_meta","text":"get_meta(ce::CounterfactualExplanation)\n\nReturns meta data for a counterfactual explanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.guess_likelihood-Tuple{Union{AbstractMatrix, AbstractVector}}","page":"🧐 Reference","title":"CounterfactualExplanations.guess_likelihood","text":"guess_likelihood(y::RawOutputArrayType)\n\nGuess the likelihood based on the scientific type of the output array. Returns a symbol indicating the guessed likelihood and the scientific type of the output array.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.guess_loss-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.guess_loss","text":"guess_loss(ce::CounterfactualExplanation)\n\nGuesses the loss function to be used for the counterfactual search in case likelihood field is specified for the AbstractFittedModel instance and no loss function was explicitly declared for AbstractGenerator instance.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.in_target_class-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.in_target_class","text":"in_target_class(ce::CounterfactualExplanation)\n\nCheck if the counterfactual is in the target class.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.initialize_state-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.initialize_state","text":"initialize_state(ce::CounterfactualExplanation)\n\nInitializes the starting point for the factual(s):\n\nIf ce.initialization is set to :identity or counterfactuals are searched in a latent space, then nothing is done.\nIf ce.initialization is set to :add_perturbation, then a random perturbation is added to the factual following following Slack (2021): https://arxiv.org/abs/2106.02666. The authors show that this improves adversarial robustness.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.map_from_latent","page":"🧐 Reference","title":"CounterfactualExplanations.map_from_latent","text":"map_from_latent(\n ce::CounterfactualExplanation,\n x::Union{AbstractArray,Nothing}=nothing,\n)\n\nMaps the state variable back from the latent space to the feature space.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.map_to_latent","page":"🧐 Reference","title":"CounterfactualExplanations.map_to_latent","text":"function maptolatent( ce::CounterfactualExplanation, x::Union{AbstractArray,Nothing}=nothing, ) \n\nMaps x from the feature space mathcalX to the latent space learned by the generative model.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.output_dim-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.output_dim","text":"output_dim(ce::CounterfactualExplanation)\n\nA convenience method that returns the output dimension of the predictive model.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.reset!-Tuple{CounterfactualExplanations.FluxModelParams}","page":"🧐 Reference","title":"CounterfactualExplanations.reset!","text":"reset!(flux_training_params::FluxModelParams)\n\nRestores the default parameter values.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.steps_exhausted-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.steps_exhausted","text":"steps_exhausted(ce::CounterfactualExplanation)\n\nA convenience method that checks if the number of maximum iterations has been exhausted.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.target_probs_path-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.target_probs_path","text":"target_probs_path(ce::CounterfactualExplanation)\n\nReturns the target probabilities for each step of the search.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.distance_measures","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.distance_measures","text":"All distance measures.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#Base.vcat-Tuple{CounterfactualExplanations.Evaluation.Benchmark, CounterfactualExplanations.Evaluation.Benchmark}","page":"🧐 Reference","title":"Base.vcat","text":"Base.vcat(bmk1::Benchmark, bmk2::Benchmark)\n\nVertically concatenates two Benchmark objects.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.compute_measure-Tuple{CounterfactualExplanation, Function, Function}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.compute_measure","text":"compute_measure(ce::CounterfactualExplanation, measure::Function, agg::Function)\n\nComputes a single measure for a counterfactual explanation. The measure is applied to the counterfactual explanation ce and aggregated using the aggregation function agg.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.to_dataframe-Tuple{Vector, Any, Bool, Bool, Bool, CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.to_dataframe","text":"evaluate_dataframe(\n ce::CounterfactualExplanation,\n measure::Vector{Function},\n agg::Function,\n report_each::Bool,\n pivot_longer::Bool,\n store_ce::Bool,\n)\n\nEvaluates a counterfactual explanation and returns a dataframe of evaluation measures.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.validity_strict-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.validity_strict","text":"validity_strict(ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has been strictly valid in the sense that it has converged with respect to the pre-specified target probability γ.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Base.Broadcast.broadcastable-Tuple{CounterfactualData}","page":"🧐 Reference","title":"Base.Broadcast.broadcastable","text":"Treat CounterfactualData as scalar when broadcasting.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing._subset-Tuple{CounterfactualData, Vector{Int64}}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing._subset","text":"_subset(data::CounterfactualData, idx::Vector{Int})\n\nCreates a subset of the data.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.convert_to_1d-Tuple{Matrix, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.convert_to_1d","text":"convert_to_1d(y::Matrix, y_levels::AbstractArray)\n\nHelper function to convert a one-hot encoded matrix to a vector of labels. This is necessary because MLJ models require the labels to be represented as a vector, but the synthetic datasets in this package hold the labels in one-hot encoded form.\n\nArguments\n\ny::Matrix: The one-hot encoded matrix.\ny_levels::AbstractArray: The levels of the categorical variable.\n\nReturns\n\nlabels: A vector of labels.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.get_generative_model-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.get_generative_model","text":"get_generative_model(counterfactual_data::CounterfactualData)\n\nReturns the underlying generative model. If there is no existing model available, the default generative model (VAE) is used. Otherwise it is expected that existing generative model has been pre-trained or else a warning is triggered.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.has_pretrained_generative_model-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.has_pretrained_generative_model","text":"has_pretrained_generative_model(counterfactual_data::CounterfactualData)\n\nChecks if generative model is present and trained.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.input_dim-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.input_dim","text":"input_dim(counterfactual_data::CounterfactualData)\n\nHelper function that returns the input dimension (number of features) of the data. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.mutability_constraints-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.mutability_constraints","text":"mutability_constraints(counterfactual_data::CounterfactualData)\n\nA convenience function that returns the mutability constraints. If none were specified, it is assumed that all features are mutable in :both directions.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.preprocess_data_for_mlj-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.preprocess_data_for_mlj","text":"preprocess_data_for_mlj(data::CounterfactualData)\n\nHelper function to preprocess data::CounterfactualData for MLJ models.\n\nArguments\n\ndata::CounterfactualData: The data to be preprocessed.\n\nReturns\n\n(df_x, y): A tuple containing the preprocessed data, with df_x being a DataFrame object and y being a categorical vector.\n\nExample\n\nX, y = preprocessdatafor_mlj(data)\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.reconstruct_cat_encoding-Tuple{CounterfactualData, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.reconstruct_cat_encoding","text":"reconstruct_cat_encoding(counterfactual_data::CounterfactualData, x::Vector)\n\nReconstruct the categorical encoding for a single instance. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.subsample-Tuple{CounterfactualData, Int64}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.subsample","text":"subsample(data::CounterfactualData, n::Int)\n\nHelper function to randomly subsample data::CounterfactualData.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.train_test_split-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.train_test_split","text":"train_test_split(data::CounterfactualData;test_size=0.2,keep_class_ratio=false)\n\nSplits data into train and test split.\n\nArguments\n\ndata::CounterfactualData: The data to be preprocessed.\ntest_size=0.2: Proportion of the data to be used for testing. \nkeep_class_ratio=false: Decides whether to sample equally from each class, or keep their relative size.\n\nReturns\n\n(train_data::CounterfactualData, test_data::CounterfactualData): A tuple containing the train and test splits.\n\nExample\n\ntrain, test = traintestsplit(data, testsize=0.1, keepclass_ratio=true)\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.unpack_data-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.unpack_data","text":"unpack_data(data::CounterfactualData)\n\nHelper function that unpacks data.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.AbstractCustomDifferentiableModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractCustomDifferentiableModel","text":"Base type for custom differentiable models.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.AbstractFluxModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractFluxModel","text":"Base type for differentiable models written in Flux.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.AbstractMLJModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractMLJModel","text":"Base type for differentiable models from the MLJ library.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.AbstractNonDifferentiableJuliaModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractNonDifferentiableJuliaModel","text":"Base type for non-differentiable models written in pure Julia.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.AbstractNonDifferentiableModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractNonDifferentiableModel","text":"Base type for non-differentiable models.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.FluxEnsembleParams","page":"🧐 Reference","title":"CounterfactualExplanations.Models.FluxEnsembleParams","text":"FluxModelParams\n\nDefault Deep Ensemble training parameters.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.TreeModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.TreeModel","text":"TreeModel <: AbstractNonDifferentiableJuliaModel\n\nConstructor for tree-based models from the MLJ library. \n\nArguments\n\nmodel::Any: The model selected by the user. Must be a model from the MLJ library.\nlikelihood::Symbol: The likelihood of the model. Must be one of [:classification_binary, :classification_multi].\n\nReturns\n\nTreeModel: A tree-based model from the MLJ library wrapped inside the TreeModel class.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.TreeModel-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.TreeModel","text":"Outer constructor method for TreeModel.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.binary_to_onehot-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.binary_to_onehot","text":"binary_to_onehot(p)\n\nHelper function to turn dummy-encoded variable into onehot-encoded variable.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.build_ensemble-Tuple{Int64}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.build_ensemble","text":"build_ensemble(K::Int;kw=(input_dim=2,n_hidden=32,output_dim=1))\n\nHelper function that builds an ensemble of K models.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.build_mlp-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.build_mlp","text":"build_mlp()\n\nHelper function to build simple MLP.\n\nExamples\n\nnn = build_mlp()\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.data_loader-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.data_loader","text":"data_loader(data::CounterfactualData)\n\nPrepares counterfactual data for training in Flux.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.get_individual_classifiers-Tuple{CounterfactualExplanations.Models.TreeModel}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.get_individual_classifiers","text":"get_individual_classifiers(M::TreeModel)\n\nReturns the individual classifiers in the forest. If the input is a decision tree, the method returns the decision tree itself inside an array.\n\nArguments\n\nM::TreeModel: The model selected by the user.\n\nReturns\n\nclassifiers::AbstractArray: An array of individual classifiers in the forest.\n\nExample\n\nclassifiers = Models.getindividualclassifiers(M) # returns the individual classifiers in the forest\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.train-Tuple{CounterfactualExplanations.Models.TreeModel, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.train","text":"train(M::TreeModel, data::CounterfactualData; kwargs...)\n\nFits the model M to the data in the CounterfactualData object. This method is not called by the user directly.\n\nArguments\n\nM::TreeModel: The wrapper for a TreeModel.\ndata::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.\n\nReturns\n\nM::TreeModel: The fitted TreeModel.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.train-Tuple{FluxEnsemble, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.train","text":"train(M::FluxEnsemble, data::CounterfactualData; kwargs...)\n\nWrapper function to retrain.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.train-Tuple{FluxModel, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.train","text":"train(M::FluxModel, data::CounterfactualData; kwargs...)\n\nWrapper function to retrain FluxModel.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.AbstractGMParams","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.AbstractGMParams","text":"Base type of generative model hyperparameter container.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.AbstractGenerativeModel","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.AbstractGenerativeModel","text":"Base type for generative model.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.Encoder","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.Encoder","text":"Encoder\n\nConstructs encoder part of VAE: a simple Flux neural network with one hidden layer and two linear output layers for the first two moments of the latent distribution.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.VAE","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.VAE","text":"VAE <: AbstractGenerativeModel\n\nConstructs the Variational Autoencoder. The VAE is a subtype of AbstractGenerativeModel. Any (sub-)type of AbstractGenerativeModel is accepted by latent space generators. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.VAE-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.VAE","text":"VAE(input_dim;kws...)\n\nOuter method for instantiating a VAE.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.VAEParams","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.VAEParams","text":"VAEParams <: AbstractGMParams\n\nThe default VAE parameters describing both the encoder/decoder architecture and the training process.\n\n\n\n\n\n","category":"type"},{"location":"reference/#Base.rand","page":"🧐 Reference","title":"Base.rand","text":"Random.rand(encoder::Encoder, x, device=cpu)\n\nDraws random samples from the latent distribution.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.Decoder-Tuple{Int64, Int64, Int64}","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.Decoder","text":"Decoder(input_dim::Int, latent_dim::Int, hidden_dim::Int; activation=relu)\n\nThe default decoder architecture is just a Flux Chain with one hidden layer and a linear output layer. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.get_data-Tuple{AbstractArray, AbstractArray, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.get_data","text":"get_data(X::AbstractArray, y::AbstractArray, batch_size)\n\nPreparing data for mini-batch training .\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.reconstruct","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.reconstruct","text":"reconstruct(generative_model::VAE, x, device=cpu)\n\nImplements a full pass of some input x through the VAE: x ↦ x̂.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.reparameterization_trick","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.reparameterization_trick","text":"reparameterization_trick(μ,logσ,device=cpu)\n\nHelper function that implements the reparameterization trick: z ∼ 𝒩(μ,σ²) ⇔ z=μ + σ ⊙ ε, ε ∼ 𝒩(0,I).\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Generators.Penalty","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.Penalty","text":"Type union for acceptable argument types for the penalty field of GradientBasedGenerator.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators._replace_nans","page":"🧐 Reference","title":"CounterfactualExplanations.Generators._replace_nans","text":"_replace_nans(Δs′::AbstractArray, old_new::Pair=(NaN => 0))\n\nHelper function to deal with exploding gradients. This is only a temporary fix and will be improved.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Generators.calculate_delta-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.calculate_delta","text":"calculate_delta(ce::AbstractCounterfactualExplanation, penalty::Vector{Function})\n\nCalculates the penalty for the proposed feature tweak.\n\nArguments\n\nce::AbstractCounterfactualExplanation: The counterfactual explanation object.\n\nReturns\n\ndelta::Float64: The calculated penalty for the proposed feature tweak.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.esatisfactory_instance-Tuple{FeatureTweakGenerator, AbstractArray, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.esatisfactory_instance","text":"esatisfactory_instance(generator::FeatureTweakGenerator, x::AbstractArray, paths::Dict{String, Dict{String, Any}})\n\nReturns an epsilon-satisfactory counterfactual for x based on the paths provided.\n\nArguments\n\ngenerator::FeatureTweakGenerator: The feature tweak generator.\nx::AbstractArray: The factual instance.\npaths::Dict{String, Dict{String, Any}}: A list of paths to the leaves of the tree to be used for tweaking the feature.\n\nReturns\n\nesatisfactory::AbstractArray: The epsilon-satisfactory instance.\n\nExample\n\nesatisfactory = esatisfactory_instance(generator, x, paths) # returns an epsilon-satisfactory counterfactual for x based on the paths provided\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.feature_selection!-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.feature_selection!","text":"feature_selection!(ce::AbstractCounterfactualExplanation)\n\nPerform feature selection to find the dimension with the closest (but not equal) values between the ce.x (factual) and ce.s′ (counterfactual) arrays.\n\nArguments\n\nce::AbstractCounterfactualExplanation: An instance of the AbstractCounterfactualExplanation type representing the counterfactual explanation.\n\nReturns\n\nnothing\n\nThe function iteratively modifies the ce.s′ counterfactual array by updating its elements to match the corresponding elements in the ce.x factual array, one dimension at a time, until the predicted label of the modified ce.s′ matches the predicted label of the ce.x array.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.find_closest_dimension-Tuple{Any, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.find_closest_dimension","text":"find_closest_dimension(factual, counterfactual)\n\nFind the dimension with the closest (but not equal) values between the factual and counterfactual arrays.\n\nArguments\n\nfactual: The factual array.\ncounterfactual: The counterfactual array.\n\nReturns\n\nclosest_dimension: The index of the dimension with the closest values.\n\nThe function iterates over the indices of the factual array and calculates the absolute difference between the corresponding elements in the factual and counterfactual arrays. It returns the index of the dimension with the smallest difference, excluding dimensions where the values in factual and counterfactual are equal.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.find_counterfactual-NTuple{4, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.find_counterfactual","text":"find_counterfactual(model, factual_class, counterfactual_data, counterfactual_candidates)\n\nFind the first counterfactual index by predicting labels.\n\nArguments\n\nmodel: The fitted model used for prediction.\ntarget_class: Expected target class.\ncounterfactual_data: Data required for counterfactual generation.\ncounterfactual_candidates: The array of counterfactual candidates.\n\nReturns\n\ncounterfactual: The index of the first counterfactual found.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.growing_spheres_generation!-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.growing_spheres_generation!","text":"growing_spheres_generation(ce::AbstractCounterfactualExplanation)\n\nGenerate counterfactual candidates using the growing spheres generation algorithm.\n\nArguments\n\nce::AbstractCounterfactualExplanation: An instance of the AbstractCounterfactualExplanation type representing the counterfactual explanation.\n\nReturns\n\nnothing\n\nThis function applies the growing spheres generation algorithm to generate counterfactual candidates. It starts by generating random points uniformly on a sphere, gradually reducing the search space until no counterfactuals are found. Then it expands the search space until at least one counterfactual is found or the maximum number of iterations is reached.\n\nThe algorithm iteratively generates counterfactual candidates and predicts their labels using the model stored in ce.M. It checks if any of the predicted labels are different from the factual class. The process of reducing the search space involves halving the search radius, while the process of expanding the search space involves increasing the search radius.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, ce::AbstractCounterfactualExplanation)\n\nDispatches to the appropriate complexity function for any generator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, Function, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, penalty::Function, ce::AbstractCounterfactualExplanation)\n\nOverloads the h function for the case where a single penalty function is provided.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, Nothing, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, penalty::Nothing, ce::AbstractCounterfactualExplanation)\n\nOverloads the h function for the case where no penalty is provided.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, Vector{<:Tuple}, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, penalty::Tuple, ce::AbstractCounterfactualExplanation)\n\nOverloads the h function for the case where a single penalty function is provided with additional keyword arguments.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, Vector{Function}, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, penalty::Tuple, ce::AbstractCounterfactualExplanation)\n\nOverloads the h function for the case where a single penalty function is provided with additional keyword arguments.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.hyper_sphere_coordinates-Tuple{Integer, AbstractArray, AbstractFloat, AbstractFloat}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.hyper_sphere_coordinates","text":"hyper_sphere_coordinates(n_search_samples::Int, instance::Vector{Float64}, low::Int, high::Int; p_norm::Int=2)\n\nGenerates candidate counterfactuals using the growing spheres method based on hyper-sphere coordinates.\n\nThe implementation follows the Random Point Picking over a sphere algorithm described in the paper: \"Learning Counterfactual Explanations for Tabular Data\" by Pawelczyk, Broelemann & Kascneci (2020), presented at The Web Conference 2020 (WWW). It ensures that points are sampled uniformly at random using insights from: http://mathworld.wolfram.com/HyperspherePointPicking.html\n\nThe growing spheres method is originally proposed in the paper: \"Comparison-based Inverse Classification for Interpretability in Machine Learning\" by Thibaut Laugel et al (2018), presented at the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2018).\n\nArguments\n\nn_search_samples::Int: The number of search samples (int > 0).\ninstance::AbstractArray: The input point array.\nlow::AbstractFloat: The lower bound (float >= 0, l < h).\nhigh::AbstractFloat: The upper bound (float >= 0, h > l).\np_norm::Integer: The norm parameter (int >= 1).\n\nReturns\n\ncandidate_counterfactuals::Array: An array of candidate counterfactuals.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.propose_state-Tuple{AbstractGradientBasedGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.propose_state","text":"propose_state(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nProposes new state based on backpropagation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.search_path","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.search_path","text":"search_path(tree::Union{DecisionTree.Leaf, DecisionTree.Node}, target::RawTargetType, path::AbstractArray)\n\nReturn a path index list with the inequality symbols, thresholds and feature indices.\n\nArguments\n\ntree::Union{DecisionTree.Leaf, DecisionTree.Node}: The root node of a decision tree.\ntarget::RawTargetType: The target class.\npath::AbstractArray: A list containing the paths found thus far.\n\nReturns\n\npaths::AbstractArray: A list of paths to the leaves of the tree to be used for tweaking the feature.\n\nExample\n\npaths = search_path(tree, target) # returns a list of paths to the leaves of the tree to be used for tweaking the feature\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Generators.ℓ-Tuple{AbstractGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ℓ","text":"ℓ(generator::AbstractGenerator, ce::AbstractCounterfactualExplanation)\n\nDispatches to the appropriate loss function for any generator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.ℓ-Tuple{AbstractGenerator, Function, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ℓ","text":"ℓ(generator::AbstractGenerator, loss::Function, ce::AbstractCounterfactualExplanation)\n\nOverloads the ℓ function for the case where a single loss function is provided.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.ℓ-Tuple{AbstractGenerator, Nothing, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ℓ","text":"ℓ(generator::AbstractGenerator, loss::Nothing, ce::AbstractCounterfactualExplanation)\n\nOverloads the ℓ function for the case where no loss function is provided.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.∂h-Tuple{AbstractGradientBasedGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.∂h","text":"∂h(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nThe default method to compute the gradient of the complexity penalty at the current counterfactual state for gradient-based generators. It assumes that Zygote.jl has gradient access. \n\nIf the penalty is not provided, it returns 0.0. By default, Zygote never works out the gradient for constants and instead returns 'nothing', so we need to add a manual step to override this behaviour. See here: https://discourse.julialang.org/t/zygote-gradient/26715.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.∂ℓ-Tuple{AbstractGradientBasedGenerator, AbstractDifferentiableModel, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.∂ℓ","text":"∂ℓ(generator::AbstractGradientBasedGenerator, M::Union{Models.LogisticModel, Models.BayesianLogisticModel}, ce::AbstractCounterfactualExplanation)\n\nThe default method to compute the gradient of the loss function at the current counterfactual state for gradient-based generators. It assumes that Zygote.jl has gradient access.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.∇-Tuple{AbstractGradientBasedGenerator, AbstractDifferentiableModel, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.∇","text":"∇(generator::AbstractGradientBasedGenerator, M::Models.AbstractDifferentiableModel, ce::AbstractCounterfactualExplanation)\n\nThe default method to compute the gradient of the counterfactual search objective for gradient-based generators. It simply computes the weighted sum over partial derivates. It assumes that Zygote.jl has gradient access. If the counterfactual is being generated using Probe, the hinge loss is added to the gradient.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_from_target-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_from_target","text":"distance_from_target(\n ce::AbstractCounterfactualExplanation;\n K::Int=50\n)\n\nComputes the distance of the counterfactual from a point in the target main.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.model_loss_penalty-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.model_loss_penalty","text":"function model_loss_penalty(\n ce::AbstractCounterfactualExplanation;\n agg=mean\n)\n\nAdditional penalty for ClaPROARGenerator.\n\n\n\n\n\n","category":"method"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/evaluation/#Performance-Evaluation","page":"Evaluating Explanations","title":"Performance Evaluation","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Now that we know how to generate counterfactual explanations in Julia, you may have a few follow-up questions: How do I know if the counterfactual search has been successful? How good is my counterfactual explanation? What does ‘good’ even mean in this context? In this tutorial, we will see how counterfactual explanations can be evaluated with respect to their performance.","category":"page"},{"location":"tutorials/evaluation/#Default-Measures","page":"Evaluating Explanations","title":"Default Measures","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Numerous evaluation measures for counterfactual explanations have been proposed. In what follows, we will cover some of the most important measures.","category":"page"},{"location":"tutorials/evaluation/#Single-Measure,-Single-Counterfactual","page":"Evaluating Explanations","title":"Single Measure, Single Counterfactual","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"One of the most important measures is validity, which simply determines whether or not a counterfactual explanation x^prime is valid in the sense that it yields the target prediction: M(x^prime)=t. We can evaluate the validity of a single counterfactual explanation ce using the Evaluation.evaluate function as follows:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"using CounterfactualExplanations.Evaluation: evaluate, validity\nevaluate(ce; measure=validity)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"1-element Vector{Vector{Float64}}:\n [1.0]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"For a single counterfactual explanation, this evaluation measure can only take two values: it is either equal to 1, if the explanation is valid or 0 otherwise. Another important measure is distance, which relates to the distance between the factual x and the counterfactual x^prime. In the context of Algorithmic Recourse, higher distances are typically associated with higher costs to individuals seeking recourse.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"using CounterfactualExplanations.Objectives: distance\nevaluate(ce; measure=distance)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"1-element Vector{Vector{Float32}}:\n [3.2273161]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"By default, distance computes the L2 (Euclidean) distance.","category":"page"},{"location":"tutorials/evaluation/#Multiple-Measures,-Single-Counterfactual","page":"Evaluating Explanations","title":"Multiple Measures, Single Counterfactual","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"You might be interested in computing not just the L2 distance, but various LP norms. This can be done by supplying a vector of functions to the measure key argument. For convenience, all default distance measures have already been collected in a vector:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"using CounterfactualExplanations.Evaluation: distance_measures\ndistance_measures","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"4-element Vector{Function}:\n distance_l0 (generic function with 1 method)\n distance_l1 (generic function with 1 method)\n distance_l2 (generic function with 1 method)\n distance_linf (generic function with 1 method)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"We can use this vector of evaluation measures as follows:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ce; measure=distance_measures)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"4-element Vector{Vector{Float32}}:\n [2.0]\n [3.2273161]\n [2.7737978]\n [2.7285953]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"If no measure is specified, the evaluate method will return all default measures,","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ce)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"3-element Vector{Vector}:\n [1.0]\n Float32[3.2273161]\n [0.0]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"which include:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"CounterfactualExplanations.Evaluation.default_measures","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"3-element Vector{Function}:\n validity (generic function with 1 method)\n distance (generic function with 1 method)\n redundancy (generic function with 1 method)","category":"page"},{"location":"tutorials/evaluation/#Multiple-Measures-and-Counterfactuals","page":"Evaluating Explanations","title":"Multiple Measures and Counterfactuals","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"We can also evaluate multiple counterfactual explanations at once:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"generator = DiCEGenerator()\nces = generate_counterfactual(x, target, counterfactual_data, M, generator; num_counterfactuals=5)\nevaluate(ces)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"3-element Vector{Vector}:\n [1.0]\n Float32[3.1955845]\n [[0.0, 0.0, 0.0, 0.0, 0.0]]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"By default, each evaluation measure is aggregated across all counterfactual explanations. To return individual measures for each counterfactual explanation you can specify report_each=true","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ces; report_each=true)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"3-element Vector{Vector}:\n BitVector[[1, 1, 1, 1, 1]]\n Vector{Float32}[[3.3671722, 3.1028512, 3.2829392, 3.0728922, 3.1520686]]\n [[0.0, 0.0, 0.0, 0.0, 0.0]]","category":"page"},{"location":"tutorials/evaluation/#Custom-Measures","page":"Evaluating Explanations","title":"Custom Measures","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"A measure is just a method that takes a CounterfactualExplanation as its only positional argument and agg::Function as a key argument specifying how measures should be aggregated across counterfactuals. Defining custom measures is therefore straightforward. For example, we could define a measure to compute the inverse target probability as follows:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"my_measure(ce::CounterfactualExplanation; agg=mean) = agg(1 .- CounterfactualExplanations.target_probs(ce))\nevaluate(ce; measure=my_measure)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"1-element Vector{Vector{Float32}}:\n [0.41711217]","category":"page"},{"location":"tutorials/evaluation/#Tidy-Output","page":"Evaluating Explanations","title":"Tidy Output","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"By default, evaluate returns vectors of evaluation measures. The optional key argument output_format::Symbol can be used to post-process the output in two ways: firstly, to return the output as a dictionary, specify output_format=:Dict:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ces; output_format=:Dict, report_each=true)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Dict{Symbol, Vector} with 3 entries:\n :validity => BitVector[[1, 1, 1, 1, 1]]\n :redundancy => [[0.0, 0.0, 0.0, 0.0, 0.0]]\n :distance => Vector{Float32}[[3.36717, 3.10285, 3.28294, 3.07289, 3.15207]]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Secondly, to return the output as a data frame, specify output_format=:DataFrame.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ces; output_format=:DataFrame, report_each=true)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"By default, data frames are pivoted to long format using individual counterfactuals as the id column. This behaviour can be suppressed by specifying pivot_longer=false.","category":"page"},{"location":"tutorials/evaluation/#Multiple-Counterfactual-Explanations","page":"Evaluating Explanations","title":"Multiple Counterfactual Explanations","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"It may be necessary to generate counterfactual explanations for multiple individuals.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Below, for example, we first select multiple samples (5) from the non-target class and then generate counterfactual explanations for all of them.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"This can be done using broadcasting:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"# Factual and target:\nids = rand(findall(predict_label(M, counterfactual_data) .== factual), n_individuals)\nxs = select_factual(counterfactual_data, ids)\nces = generate_counterfactual(xs, target, counterfactual_data, M, generator; num_counterfactuals=5)\nevaluation = evaluate.(ces)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"5-element Vector{Vector{Vector}}:\n [[1.0], Float32[3.351181], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n [[1.0], Float32[2.6405892], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n [[1.0], Float32[2.935012], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n [[1.0], Float32[3.5348382], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n [[1.0], Float32[3.9373996], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n\nVector{Vector}[[[1.0], Float32[3.351181], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[2.6405892], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[2.935012], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[3.5348382], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[3.9373996], [[0.0, 0.0, 0.0, 0.0, 0.0]]]]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"This leads us to our next topic: Performance Benchmarks.","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/generic/#GenericGenerator","page":"Generic","title":"GenericGenerator","text":"","category":"section"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"We use the term generic to relate to the basic counterfactual generator proposed by Wachter, Mittelstadt, and Russell (2017) with L1-norm regularization. There is also a variant of this generator that uses the distance metric proposed in Wachter, Mittelstadt, and Russell (2017), which we call WachterGenerator.","category":"page"},{"location":"explanation/generators/generic/#Description","page":"Generic","title":"Description","text":"","category":"section"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"As the term indicates, this approach is simple: it forms the baseline approach for gradient-based counterfactual generators. Wachter, Mittelstadt, and Russell (2017) were among the first to realise that","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"[…] explanations can, in principle, be offered without opening the “black box.”— Wachter, Mittelstadt, and Russell (2017)","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"Gradient descent is performed directly in the feature space. Concerning the cost heuristic, the authors choose to penalize the distance of counterfactuals from the factual value. This is based on the intuitive notion that larger feature perturbations require greater effort.","category":"page"},{"location":"explanation/generators/generic/#Usage","page":"Generic","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"generator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"(Image: )","category":"page"},{"location":"explanation/generators/generic/#References","page":"Generic","title":"References","text":"","category":"section"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harv. JL & Tech. 31: 841. https://doi.org/10.2139/ssrn.3063289.","category":"page"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/greedy/#GreedyGenerator","page":"Greedy","title":"GreedyGenerator","text":"","category":"section"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"We use the term greedy to describe the counterfactual generator introduced by Schut et al. (2021).","category":"page"},{"location":"explanation/generators/greedy/#Description","page":"Greedy","title":"Description","text":"","category":"section"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"The Greedy generator works under the premise of generating realistic counterfactuals by minimizing predictive uncertainty. Schut et al. (2021) show that for models that incorporates predictive uncertainty in their predictions, maximizing the predictive probability corresponds to minimizing the predictive uncertainty: by construction, the generated counterfactual will therefore be realistic (low epistemic uncertainty) and unambiguous (low aleatoric uncertainty).","category":"page"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"For the counterfactual search Schut et al. (2021) propose using a Jacobian-based Saliency Map Attack(JSMA). It is greedy in the sense that it is an “iterative algorithm that updates the most salient feature, i.e. the feature that has the largest influence on the classification, by delta at each step” (Schut et al. 2021).","category":"page"},{"location":"explanation/generators/greedy/#Usage","page":"Greedy","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"M = fit_model(counterfactual_data, :DeepEnsemble)\ngenerator = GreedyGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"(Image: )","category":"page"},{"location":"explanation/generators/greedy/#References","page":"Greedy","title":"References","text":"","category":"section"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/probe/#ProbeGenerator","page":"PROBE","title":"ProbeGenerator","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"The ProbeGenerator is designed to navigate the trade-offs between costs and robustness in Algorithmic Recourse (Pawelczyk et al. 2022).","category":"page"},{"location":"explanation/generators/probe/#Description","page":"PROBE","title":"Description","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"The goal of ProbeGenerator is to find a recourse x’ whose prediction at any point y within some set around x’ belongs to the positive class with probability 1 - r, where r is the recourse invalidation rate. It minimizes the gap between the achieved and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction.","category":"page"},{"location":"explanation/generators/probe/#Explanation","page":"PROBE","title":"Explanation","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"The loss function this generator is defined below. R is a hinge loss parameter which helps control for robustness. The loss and penalty functions can still be chosen freely.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"beginaligned\nR(x sigma^2 I) + l(f(x) s) + lambda d_c(x x)\nendaligned","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"R uses the following formula to control for noise. It generates small perturbations and checks how often the counterfactual explanation flips back to a factual one, when small amounts of noise are added to it.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"beginaligned\nDelta(x^hatE) = E_varepsilonh(x^hatE) - h(x^hatE + varepsilon)\nendaligned","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"The above formula is not differentiable. For this reason the generator uses the closed form version of the formula below.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"beginequation\nDelta tilde(x^hatE sigma^2 I) = 1 - Phi left(fracsqrtf(x^hatE)sqrtnabla f(x^hatE)^T sigma^2 I nabla f(x^hatE)right) \nendequation","category":"page"},{"location":"explanation/generators/probe/#Usage","page":"PROBE","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"Generating a counterfactual with the data loaded and generator chosen works as follows:","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"Note: It is important to set the convergence to “:invalidation_rate” here.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"M = fit_model(counterfactual_data, :DeepEnsemble)\nopt = Descent(0.01)\ngenerator = CounterfactualExplanations.Generators.ProbeGenerator(opt=opt)\nconv = CounterfactualExplanations.Convergence.InvalidationRateConvergence(;invalidation_rate=0.5)\nce = generate_counterfactual(x, target, counterfactual_data, M, generator, convergence=conv)\nplot(ce)","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"Choosing different invalidation rates makes the counterfactual more or less robust. The following plot shows the counterfactuals generated for different invalidation rates.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"(Image: )","category":"page"},{"location":"explanation/generators/probe/#References","page":"PROBE","title":"References","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” arXiv Preprint arXiv:2203.06768.","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/gravitational/#GravitationalGenerator","page":"Gravitational","title":"GravitationalGenerator","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"The GravitationalGenerator was introduced in Altmeyer et al. (2023). It is named so because it generates counterfactuals that gravitate towards some sensible point in the target domain.","category":"page"},{"location":"explanation/generators/gravitational/#Description","page":"Gravitational","title":"Description","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"Altmeyer et al. (2023) extend the general framework as follows,","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"beginaligned\nmathbfs^prime = arg min_mathbfs^prime in mathcalS textyloss(M(f(mathbfs^prime))y^*) + lambda_1 textcost(f(mathbfs^prime)) + lambda_2 textextcost(f(mathbfs^prime)) \nendaligned ","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"where textcost(f(mathbfs^prime)) denotes the proxy for costs faced by the individual. “The newly introduced term textextcost(f(mathbfs^prime)) is meant to capture and address external costs incurred by the collective of individuals in response to changes in mathbfs^prime.” (Altmeyer et al. 2023)","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"For the GravitationalGenerator we have,","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"beginaligned\ntextextcost(f(mathbfs^prime)) = textdist(f(mathbfs^prime)barx^*) \nendaligned","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"where barx is some sensible point in the target domain, for example, the subsample average barx^*=textmean(x), x in mathcalD_1.","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"There is a tradeoff then, between the distance of counterfactuals from their factual value and the chosen point in the target domain. The chart below illustrates how the counterfactual outcome changes as the penalty lambda_2 on the distance to the point in the target domain is increased from left to right (holding the other penalty term constant).","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"(Image: )","category":"page"},{"location":"explanation/generators/gravitational/#Usage","page":"Gravitational","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"generator = GravitationalGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\ndisplay(plot(ce))","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"(Image: )","category":"page"},{"location":"explanation/generators/gravitational/#Comparison-to-GenericGenerator","page":"Gravitational","title":"Comparison to GenericGenerator","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"The figure below compares the outcome for the GenericGenerator and the GravitationalGenerator.","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"(Image: )","category":"page"},{"location":"explanation/generators/gravitational/#References","page":"Gravitational","title":"References","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/benchmarking/#Performance-Benchmarks","page":"Benchmarking Explanations","title":"Performance Benchmarks","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"In the previous tutorial, we have seen how counterfactual explanations can be evaluated. An important follow-up task is to compare the performance of different counterfactual generators is an important task. Researchers can use benchmarks to test new ideas they want to implement. Practitioners can find the right counterfactual generator for their specific use case through benchmarks. In this tutorial, we will see how to run benchmarks for counterfactual generators.","category":"page"},{"location":"tutorials/benchmarking/#Post-Hoc-Benchmarking","page":"Benchmarking Explanations","title":"Post Hoc Benchmarking","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"We begin by continuing the discussion from the previous tutorial: suppose you have generated multiple counterfactual explanations for multiple individuals, like below:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"# Factual and target:\nn_individuals = 5\nids = rand(findall(predict_label(M, counterfactual_data) .== factual), n_individuals)\nxs = select_factual(counterfactual_data, ids)\nces = generate_counterfactual(xs, target, counterfactual_data, M, generator; num_counterfactuals=5)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"You may be interested in comparing the outcomes across individuals. To benchmark the various counterfactual explanations using default evaluation measures, you can simply proceed as follows:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk = benchmark(ces)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Under the hood, the benchmark(counterfactual_explanations::Vector{CounterfactualExplanation}) uses CounterfactualExplanations.Evaluation.evaluate(ce::CounterfactualExplanation) to generate a Benchmark object, which contains the evaluation in its most granular form as a DataFrame.","category":"page"},{"location":"tutorials/benchmarking/#Working-with-Benchmarks","page":"Benchmarking Explanations","title":"Working with Benchmarks","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"For convenience, the DataFrame containing the evaluation can be returned by simply calling the Benchmark object. By default, the aggregated evaluation measures across id (in line with the default behaviour of evaluate).","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk()","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"15×7 DataFrame\n Row │ sample variable value generator ⋯\n │ Base.UUID String Float64 Symbol ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 239104d0-f59f-11ee-3d0c-d1db071927ff distance 3.17243 GradientBase ⋯\n 2 │ 239104d0-f59f-11ee-3d0c-d1db071927ff redundancy 0.0 GradientBase\n 3 │ 239104d0-f59f-11ee-3d0c-d1db071927ff validity 1.0 GradientBase\n 4 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b distance 3.07148 GradientBase\n 5 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b redundancy 0.0 GradientBase ⋯\n 6 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b validity 1.0 GradientBase\n 7 │ 2398b916-f59f-11ee-3f13-bd00858a39af distance 3.62159 GradientBase\n 8 │ 2398b916-f59f-11ee-3f13-bd00858a39af redundancy 0.0 GradientBase\n 9 │ 2398b916-f59f-11ee-3f13-bd00858a39af validity 1.0 GradientBase ⋯\n 10 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b distance 2.62783 GradientBase\n 11 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b redundancy 0.0 GradientBase\n 12 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b validity 1.0 GradientBase\n 13 │ 2398c08a-f59f-11ee-175b-81c155750752 distance 2.91985 GradientBase ⋯\n 14 │ 2398c08a-f59f-11ee-175b-81c155750752 redundancy 0.0 GradientBase\n 15 │ 2398c08a-f59f-11ee-175b-81c155750752 validity 1.0 GradientBase\n 4 columns omitted","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"To retrieve the granular dataset, simply do:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk(agg=nothing)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"75×8 DataFrame\n Row │ sample num_counterfactual variable v ⋯\n │ Base.UUID Int64 String F ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 1 distance 3 ⋯\n 2 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 2 distance 3\n 3 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 3 distance 3\n 4 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 4 distance 3\n 5 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 5 distance 3 ⋯\n 6 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 1 redundancy 0\n 7 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 2 redundancy 0\n 8 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 3 redundancy 0\n 9 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 4 redundancy 0 ⋯\n 10 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 5 redundancy 0\n 11 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 1 validity 1\n ⋮ │ ⋮ ⋮ ⋮ ⋱\n 66 │ 2398c08a-f59f-11ee-175b-81c155750752 1 redundancy 0\n 67 │ 2398c08a-f59f-11ee-175b-81c155750752 2 redundancy 0 ⋯\n 68 │ 2398c08a-f59f-11ee-175b-81c155750752 3 redundancy 0\n 69 │ 2398c08a-f59f-11ee-175b-81c155750752 4 redundancy 0\n 70 │ 2398c08a-f59f-11ee-175b-81c155750752 5 redundancy 0\n 71 │ 2398c08a-f59f-11ee-175b-81c155750752 1 validity 1 ⋯\n 72 │ 2398c08a-f59f-11ee-175b-81c155750752 2 validity 1\n 73 │ 2398c08a-f59f-11ee-175b-81c155750752 3 validity 1\n 74 │ 2398c08a-f59f-11ee-175b-81c155750752 4 validity 1\n 75 │ 2398c08a-f59f-11ee-175b-81c155750752 5 validity 1 ⋯\n 5 columns and 54 rows omitted","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Since benchmarks return a DataFrame object on call, post-processing is straightforward. For example, we could use Tidier.jl:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"using Tidier\n@chain bmk() begin\n @filter(variable == \"distance\")\n @select(sample, variable, value)\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"5×3 DataFrame\n Row │ sample variable value \n │ Base.UUID String Float64 \n─────┼─────────────────────────────────────────────────────────\n 1 │ 239104d0-f59f-11ee-3d0c-d1db071927ff distance 3.17243\n 2 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b distance 3.07148\n 3 │ 2398b916-f59f-11ee-3f13-bd00858a39af distance 3.62159\n 4 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b distance 2.62783\n 5 │ 2398c08a-f59f-11ee-175b-81c155750752 distance 2.91985","category":"page"},{"location":"tutorials/benchmarking/#Metadata-for-Counterfactual-Explanations","page":"Benchmarking Explanations","title":"Metadata for Counterfactual Explanations","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Benchmarks always report metadata for each counterfactual explanation, which is automatically inferred by default. The default metadata concerns the explained model and the employed generator. In the current example, we used the same model and generator for each individual:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"@chain bmk() begin\n @group_by(sample)\n @select(sample, model, generator)\n @summarize(model=first(model),generator=first(generator))\n @ungroup\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"5×3 DataFrame\n Row │ sample model ⋯\n │ Base.UUID Symbol ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 239104d0-f59f-11ee-3d0c-d1db071927ff FluxModel(Chain(Dense(2 => 2)), … ⋯\n 2 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b FluxModel(Chain(Dense(2 => 2)), …\n 3 │ 2398b916-f59f-11ee-3f13-bd00858a39af FluxModel(Chain(Dense(2 => 2)), …\n 4 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b FluxModel(Chain(Dense(2 => 2)), …\n 5 │ 2398c08a-f59f-11ee-175b-81c155750752 FluxModel(Chain(Dense(2 => 2)), … ⋯\n 1 column omitted","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Metadata can also be provided as an optional key argument.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"meta_data = Dict(\n :generator => \"Generic\",\n :model => \"MLP\",\n)\nmeta_data = [meta_data for i in 1:length(ces)]\nbmk = benchmark(ces; meta_data=meta_data)\n@chain bmk() begin\n @group_by(sample)\n @select(sample, model, generator)\n @summarize(model=first(model),generator=first(generator))\n @ungroup\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"5×3 DataFrame\n Row │ sample model generator \n │ Base.UUID String String \n─────┼─────────────────────────────────────────────────────────\n 1 │ 27fae496-f59f-11ee-2c30-f35d1025a6d4 MLP Generic\n 2 │ 27fdcc6a-f59f-11ee-030b-152c9794c5f1 MLP Generic\n 3 │ 27fdd04a-f59f-11ee-2010-e1732ff5d8d2 MLP Generic\n 4 │ 27fdd340-f59f-11ee-1d20-050a69dcacef MLP Generic\n 5 │ 27fdd5fc-f59f-11ee-02e8-d198e436abb3 MLP Generic","category":"page"},{"location":"tutorials/benchmarking/#Ad-Hoc-Benchmarking","page":"Benchmarking Explanations","title":"Ad Hoc Benchmarking","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"So far we have assumed the following workflow:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Fit some machine learning model.\nGenerate counterfactual explanations for some individual(s) (generate_counterfactual).\nEvaluate and benchmark them (benchmark(ces::Vector{CounterfactualExplanation})).","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"In many cases, it may be preferable to combine these steps. To this end, we have added support for two scenarios of Ad Hoc Benchmarking.","category":"page"},{"location":"tutorials/benchmarking/#Pre-trained-Models","page":"Benchmarking Explanations","title":"Pre-trained Models","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"In the first scenario, it is assumed that the machine learning models have been pre-trained and so the workflow can be summarized as follows:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Fit some machine learning model(s).\nGenerate counterfactual explanations and benchmark them.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"We suspect that this is the most common workflow for practitioners who are interested in benchmarking counterfactual explanations for the pre-trained machine learning models. Let’s go through this workflow using a simple example. We first train some models and store them in a dictionary:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"models = Dict(\n :MLP => fit_model(counterfactual_data, :MLP),\n :Linear => fit_model(counterfactual_data, :Linear),\n)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Next, we store the counterfactual generators of interest in a dictionary as well:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"generators = Dict(\n :Generic => GenericGenerator(),\n :Gravitational => GravitationalGenerator(),\n :Wachter => WachterGenerator(),\n :ClaPROAR => ClaPROARGenerator(),\n)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Then we can run a benchmark for individual(s) x, a pre-specified target and counterfactual_data as follows:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk = benchmark(x, target, counterfactual_data; models=models, generators=generators)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"In this case, metadata is automatically inferred from the dictionaries:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"@chain bmk() begin\n @filter(variable == \"distance\")\n @select(sample, variable, value, model, generator)\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"8×5 DataFrame\n Row │ sample variable value model ⋯\n │ Base.UUID String Float64 Tuple… ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 2cba5eee-f59f-11ee-1844-cbc7a8372a38 distance 4.38877 (:Linear, Flux ⋯\n 2 │ 2cd740fe-f59f-11ee-35c3-1157eb1b7583 distance 4.17021 (:Linear, Flux\n 3 │ 2cd741e2-f59f-11ee-2b09-0d55ef9892b9 distance 4.31145 (:Linear, Flux\n 4 │ 2cd7420c-f59f-11ee-1996-6fa75e23bb57 distance 4.17035 (:Linear, Flux\n 5 │ 2cd74234-f59f-11ee-0ad0-9f21949f5932 distance 5.73182 (:MLP, FluxMod ⋯\n 6 │ 2cd7425c-f59f-11ee-3eb4-af34f85ffd3d distance 5.50606 (:MLP, FluxMod\n 7 │ 2cd7427a-f59f-11ee-10d3-a1df6c8dc125 distance 5.2114 (:MLP, FluxMod\n 8 │ 2cd74298-f59f-11ee-32d1-f501c104fea8 distance 5.3623 (:MLP, FluxMod\n 2 columns omitted","category":"page"},{"location":"tutorials/benchmarking/#Everything-at-once","page":"Benchmarking Explanations","title":"Everything at once","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Researchers, in particular, may be interested in combining all steps into one. This is the second scenario of Ad Hoc Benchmarking:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Fit some machine learning model(s), generate counterfactual explanations and benchmark them.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"It involves calling benchmark directly on counterfactual data (the only positional argument):","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk = benchmark(counterfactual_data)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"This will use the default models from standard_models_catalogue and train them on the data. All available generators from generator_catalogue will also be used:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"@chain bmk() begin\n @filter(variable == \"validity\")\n @select(sample, variable, value, model, generator)\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"200×5 DataFrame\n Row │ sample variable value model genera ⋯\n │ Base.UUID String Float64 Symbol Symbol ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear gravit ⋯\n 2 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear growin\n 3 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear revise\n 4 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear clue\n 5 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear probe ⋯\n 6 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear dice\n 7 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear clapro\n 8 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear wachte\n 9 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear generi ⋯\n 10 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear greedy\n 11 │ 32d255e8-f59f-11ee-3e8d-a9e9f6e23ea8 validity 1.0 Linear gravit\n ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n 191 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP gravit\n 192 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP growin ⋯\n 193 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP revise\n 194 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP clue\n 195 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP probe\n 196 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP dice ⋯\n 197 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP clapro\n 198 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP wachte\n 199 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP generi\n 200 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP greedy ⋯\n 1 column and 179 rows omitted","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Optionally, you can instead provide a dictionary of models and generators as before. Each value in the models dictionary should be one of two things:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Either be an object M of type AbstractFittedModel that implements the Models.train method.\nOr a DataType that can be called on CounterfactualData to create an object M as in (a).","category":"page"},{"location":"tutorials/benchmarking/#Multiple-Datasets","page":"Benchmarking Explanations","title":"Multiple Datasets","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Benchmarks are run on single instances of type CounterfactualData. This is our design choice for two reasons:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"We want to avoid the loops inside the benchmark method(s) from getting too nested and convoluted.\nWhile it is straightforward to infer metadata for models and generators, this is not the case for datasets.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Fortunately, it is very easy to run benchmarks for multiple datasets anyway, since Benchmark instances can be concatenated. To see how, let’s consider an example involving multiple datasets, models and generators:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"# Data:\ndatasets = Dict(\n :moons => CounterfactualData(load_moons()...),\n :circles => CounterfactualData(load_circles()...),\n)\n\n# Models:\nmodels = Dict(\n :MLP => FluxModel,\n :Linear => Linear,\n)\n\n# Generators:\ngenerators = Dict(\n :Generic => GenericGenerator(),\n :Greedy => GreedyGenerator(),\n)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Then we can simply loop over the datasets and eventually concatenate the results like so:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"using CounterfactualExplanations.Evaluation: distance_measures\nbmks = []\nfor (dataname, dataset) in datasets\n bmk = benchmark(dataset; models=models, generators=generators, measure=distance_measures)\n push!(bmks, bmk)\nend\nbmk = vcat(bmks[1], bmks[2]; ids=collect(keys(datasets)))","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"When ids are supplied, then a new id column is added to the evaluation data frame that contains unique identifiers for the different benchmarks. The optional idcol_name argument can be used to specify the name for that indicator column (defaults to \"dataset\"):","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"@chain bmk() begin\n @group_by(dataset, generator)\n @filter(model == :MLP)\n @filter(variable == \"distance_l1\")\n @summarize(L1_norm=mean(value))\n @ungroup\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"4×3 DataFrame\n Row │ dataset generator L1_norm \n │ Symbol Symbol Float32 \n─────┼──────────────────────────────\n 1 │ moons Generic 1.56555\n 2 │ moons Greedy 0.819269\n 3 │ circles Generic 1.83524\n 4 │ circles Greedy 0.498953","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/whistle_stop/#Whistle-Stop-Tour","page":"Whiste-Stop Tour","title":"Whistle-Stop Tour","text":"","category":"section"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"In this tutorial, we will go through a slightly more complex example involving synthetic data. We will generate Counterfactual Explanations using different generators and visualize the results.","category":"page"},{"location":"tutorials/whistle_stop/#Data-and-Classifier","page":"Whiste-Stop Tour","title":"Data and Classifier","text":"","category":"section"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"# Choose some values for data and a model:\nn_dim = 2\nn_classes = 4\nn_samples = 400\nmodel_name = :MLP","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"The code chunk below generates synthetic data and uses it to fit a classifier. The outcome variable counterfactual_data.y consists of 4 classes. The input data counterfactual_data.X consists of 2 features. We generate a total of 400 samples. On the model side, we have specified model_name = :MLP. The fit_model can be used to fit a number of default models.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"data = TaijaData.load_multi_class(n_samples)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\nM = fit_model(counterfactual_data, model_name)","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"The chart below visualizes our data along with the model predictions. In particular, the contour indicates the predicted probabilities generated by our classifier. By default, these are the predicted probabilities for y=1, the first label. For multi-dimensional input data is compressed into two dimensions and the decision boundary is approximated using Nearest Neighbors (this is still somewhat experimental).","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"plot(M, counterfactual_data)","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"(Image: )","category":"page"},{"location":"tutorials/whistle_stop/#Counterfactual-Explanation","page":"Whiste-Stop Tour","title":"Counterfactual Explanation","text":"","category":"section"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"Next, we begin by specifying our target and factual label. We then draw a random sample from the non-target (factual) class.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"# Factual and target:\ntarget = 2\nfactual = 4\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"This sets the baseline for our counterfactual search: we plan to perturb the factual x to change the predicted label from y=4 to our target label target=2.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"Counterfactual generators accept several default parameters that can be used to adjust the counterfactual search at a high level: for example, a Flux.jl optimizer can be supplied to define how exactly gradient steps are performed. Importantly, one can also define the threshold probability at which the counterfactual search will converge. This relates to the probability predicted by the underlying black-box model, that the counterfactual belongs to the target class. A higher decision threshold typically prolongs the counterfactual search.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"# Search params:\ndecision_threshold = 0.75\nnum_counterfactuals = 3","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"The code below runs the counterfactual search for each generator available in the generator_catalogue. In each case, we also call the generic plot() method on the generated instance of type CounterfactualExplanation. This generates a simple plot that visualizes the entire counterfactual path. The chart below shows the results for all counterfactual generators: Factual: 4 → Target: 2.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"ces = Dict()\nplts = []\nplottable_generators = filter(((k,v),) -> k ∉ [:growing_spheres, :feature_tweak], generator_catalogue)\n# Search:\nfor (key, Generator) in plottable_generators\n generator = Generator()\n ce = generate_counterfactual(\n x, target, counterfactual_data, M, generator;\n num_counterfactuals = num_counterfactuals,\n convergence=GeneratorConditionsConvergence(\n decision_threshold=decision_threshold\n )\n )\n ces[key] = ce\n plts = [plts..., plot(ce; title=key, colorbar=false)]\nend","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"(Image: )","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"how_to_guides/custom_models/#How-to-add-Custom-Models","page":"... add custom models","title":"How to add Custom Models","text":"","category":"section"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"Adding custom models is possible and relatively straightforward, as we will demonstrate in this guide.","category":"page"},{"location":"how_to_guides/custom_models/#Custom-Models","page":"... add custom models","title":"Custom Models","text":"","category":"section"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"Apart from the default models you can use any arbitrary (differentiable) model and generate recourse in the same way as before. Only two steps are necessary to make your own Julia model compatible with this package:","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"The model needs to be declared as a subtype of <:CounterfactualExplanations.Models.AbstractFittedModel.\nYou need to extend the functions CounterfactualExplanations.Models.logits and CounterfactualExplanations.Models.probs for your custom model.","category":"page"},{"location":"how_to_guides/custom_models/#How-FluxModel-was-added","page":"... add custom models","title":"How FluxModel was added","text":"","category":"section"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"To demonstrate how this can be done in practice, we will reiterate here how native support for Flux.jl models was enabled (Innes 2018). Once again we use synthetic data for an illustrative example. The code below loads the data and builds a simple model architecture that can be used for a multi-class prediction task. Note how outputs from the final layer are not passed through a softmax activation function, since the counterfactual loss is evaluated with respect to logits. The model is trained with dropout.","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"# Data:\nN = 200\ndata = TaijaData.load_blobs(N; centers=4, cluster_std=0.5)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\ny = counterfactual_data.y\nX = counterfactual_data.X\n\n# Flux model setup: \nusing Flux\ndata = Flux.DataLoader((X,y), batchsize=1)\nn_hidden = 32\noutput_dim = size(y,1)\ninput_dim = 2\nactivation = σ\nmodel = Chain(\n Dense(input_dim, n_hidden, activation),\n Dropout(0.1),\n Dense(n_hidden, output_dim)\n) \nloss(x, y) = Flux.Losses.logitcrossentropy(model(x), y)\n\n# Flux model training:\nusing Flux.Optimise: update!, Adam\nopt = Adam()\nepochs = 50\nfor epoch = 1:epochs\n for d in data\n gs = gradient(Flux.params(model)) do\n l = loss(d...)\n end\n update!(opt, Flux.params(model), gs)\n end\nend","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"The code below implements the two steps that were necessary to make Flux models compatible with the package. We first declare our new struct as a subtype of <:AbstractDifferentiableModel, which itself is an abstract subtype of <:AbstractFittedModel. Computing logits amounts to just calling the model on inputs. Predicted probabilities for labels can in this case be computed by passing predicted logits through the softmax function. Finally, we just instantiate our model in the same way as always.","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"# Step 1)\nstruct MyFluxModel <: AbstractDifferentiableModel\n model::Any\n likelihood::Symbol\nend\n\n# Step 2)\n# import functions in order to extend\nimport CounterfactualExplanations.Models: logits\nimport CounterfactualExplanations.Models: probs \nlogits(M::MyFluxModel, X::AbstractArray) = M.model(X)\nprobs(M::MyFluxModel, X::AbstractArray) = softmax(logits(M, X))\nM = MyFluxModel(model, :classification_multi)","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"The code below implements the counterfactual search and plots the results:","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"factual_label = 4\ntarget = 2\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual_label))\nx = select_factual(counterfactual_data, chosen) \n\n# Counterfactual search:\ngenerator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"(Image: )","category":"page"},{"location":"how_to_guides/custom_models/#References","page":"... add custom models","title":"References","text":"","category":"section"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"Innes, Mike. 2018. “Flux: Elegant Machine Learning with Julia.” Journal of Open Source Software 3 (25): 602. https://doi.org/10.21105/joss.00602.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: )","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Documentation for CounterfactualExplanations.jl.","category":"page"},{"location":"#CounterfactualExplanations","page":"🏠 Home","title":"CounterfactualExplanations","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Counterfactual Explanations and Algorithmic Recourse in Julia.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: Stable) (Image: Dev) (Image: Build Status) (Image: Coverage) (Image: Code Style: Blue) (Image: License) (Image: Package Downloads) (Image: Aqua QA)","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"There is also a corresponding paper, Explaining Black-Box Models through Counterfactuals, which has been published in JuliaCon Proceedings. Please consider citing the paper, if you use this package in your work:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: DOI) (Image: DOI)","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"@article{Altmeyer2023,\n doi = {10.21105/jcon.00130},\n url = {https://doi.org/10.21105/jcon.00130},\n year = {2023},\n publisher = {The Open Journal},\n volume = {1},\n number = {1},\n pages = {130},\n author = {Patrick Altmeyer and Arie van Deursen and Cynthia C. s. Liem},\n title = {Explaining Black-Box Models through Counterfactuals},\n journal = {Proceedings of the JuliaCon Conferences}\n}","category":"page"},{"location":"#Installation","page":"🏠 Home","title":"🚩 Installation","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"You can install the stable release from Julia’s General Registry as follows:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"using Pkg\nPkg.add(\"CounterfactualExplanations\")","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"CounterfactualExplanations.jl is under active development. To install the development version of the package you can run the following command:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"using Pkg\nPkg.add(url=\"https://github.com/juliatrustworthyai/CounterfactualExplanations.jl\")","category":"page"},{"location":"#Background-and-Motivation","page":"🏠 Home","title":"🤔 Background and Motivation","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Machine learning models like Deep Neural Networks have become so complex, opaque and underspecified in the data that they are generally considered Black Boxes. Nonetheless, such models often play a key role in data-driven decision-making systems. This creates the following problem: human operators in charge of such systems have to rely on them blindly, while those individuals subject to them generally have no way of challenging an undesirable outcome:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"“You cannot appeal to (algorithms). They do not listen. Nor do they bend.”— Cathy O’Neil in Weapons of Math Destruction, 2016","category":"page"},{"location":"#Enter:-Counterfactual-Explanations","page":"🏠 Home","title":"🔮 Enter: Counterfactual Explanations","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Counterfactual Explanations can help human stakeholders make sense of the systems they develop, use or endure: they explain how inputs into a system need to change for it to produce different decisions. Explainability benefits internal as well as external quality assurance.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Counterfactual Explanations have a few properties that are desirable in the context of Explainable Artificial Intelligence (XAI). These include:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Full fidelity to the black-box model, since no proxy is involved.\nNo need for (reasonably) interpretable features as opposed to LIME and SHAP.\nClear link to Algorithmic Recourse and Causal Inference.\nLess susceptible to adversarial attacks than LIME and SHAP.","category":"page"},{"location":"#Example:-Give-Me-Some-Credit","page":"🏠 Home","title":"Example: Give Me Some Credit","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Consider the following real-world scenario: a retail bank is using a black-box model trained on their clients’ credit history to decide whether they will provide credit to new applicants. To simulate this scenario, we have pre-trained a binary classifier on the publicly available Give Me Some Credit dataset that ships with this package (Kaggle 2011).","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"The figure below shows counterfactuals for 10 randomly chosen individuals that would have been denied credit initially.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: )","category":"page"},{"location":"#Example:-MNIST","page":"🏠 Home","title":"Example: MNIST","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"The figure below shows a counterfactual generated for an image classifier trained on MNIST: in particular, it demonstrates which pixels need to change in order for the classifier to predict 3 instead of 8.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Since v0.1.9 counterfactual generators are fully composable. Here we have composed a generator that combines ideas from Wachter, Mittelstadt, and Russell (2017) and Altmeyer et al. (2023):","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"# Compose generator:\nusing CounterfactualExplanations.Objectives: distance_from_target\ngenerator = GradientBasedGenerator()\n@chain generator begin\n @objective logitcrossentropy + 0.1distance_mad + 0.1distance_from_target\n @with_optimiser Adam(0.1) \nend\ncounterfactual_data.generative_model = vae # assign generative model","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: )","category":"page"},{"location":"#Usage-example","page":"🏠 Home","title":"🔍 Usage example","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Generating counterfactuals will typically look like follows. Below we first fit a simple model to a synthetic dataset with linearly separable features and then draw a random sample:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"# Data and Classifier:\ncounterfactual_data = CounterfactualData(load_linearly_separable()...)\nM = fit_model(counterfactual_data, :Linear)\n\n# Select random sample:\ntarget = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"To this end, we specify a counterfactual generator of our choice:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"# Counterfactual search:\ngenerator = DiCEGenerator(λ=[0.1,0.3])","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Here, we have chosen to use the GradientBasedGenerator to move the individual from its factual label 1 to the target label 2.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"With all of our ingredients specified, we finally generate counterfactuals using a simple API call:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"conv = conv = CounterfactualExplanations.Convergence.GeneratorConditionsConvergence()\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator; \n num_counterfactuals=3, convergence=conv,\n)","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"The animation below shows the resulting counterfactual path:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: )","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: )","category":"page"},{"location":"#Implemented-Counterfactual-Generators","page":"🏠 Home","title":"☑️ Implemented Counterfactual Generators","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Currently, the following counterfactual generators are implemented:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"ClaPROAR (Altmeyer et al. 2023)\nCLUE (Antorán et al. 2020)\nDiCE (Mothilal, Sharma, and Tan 2020)\nFeatureTweak (Tolomei et al. 2017)\nGeneric\nGravitationalGenerator (Altmeyer et al. 2023)\nGreedy (Schut et al. 2021)\nGrowingSpheres (Laugel et al. 2017)\nPROBE (Pawelczyk et al. 2022)\nREVISE (Joshi et al. 2019)\nWachter (Wachter, Mittelstadt, and Russell 2017)","category":"page"},{"location":"#Goals-and-limitations","page":"🏠 Home","title":"🎯 Goals and limitations","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"The goal of this library is to contribute to efforts towards trustworthy machine learning in Julia. The Julia language has an edge when it comes to trustworthiness: it is very transparent. Packages like this one are generally written in pure Julia, which makes it easy for users and developers to understand and contribute to open-source code. Eventually, this project aims to offer a one-stop-shop of counterfactual explanations.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Our ambition is to enhance the package through the following features:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Support for all supervised machine learning models trained in MLJ.jl.\nSupport for regression models.","category":"page"},{"location":"#Contribute","page":"🏠 Home","title":"🛠 Contribute","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Contributions of any kind are very much welcome! Take a look at the issue to see what things we are currently working on.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"If any of the below applies to you, this might be the right open-source project for you:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"You’re an expert in Counterfactual Explanations or Explainable AI more broadly and you are curious about Julia.\nYou’re experienced with Julia and are happy to help someone less experienced to up their game. Ideally, you are also curious about Trustworthy AI.\nYou’re new to Julia and open-source development and would like to start your learning journey by contributing to a recent and active development. Ideally, you are familiar with machine learning.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"@pat-alt here: I am still very much at the beginning of my Julia journey, so if you spot any issues or have any suggestions for design improvement, please just open issue or start a discussion.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"For more details on how to contribute see here. Please follow the SciML ColPrac guide.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"There are also some general pointers for people looking to contribute to any of our Taija packages here.","category":"page"},{"location":"#Citation","page":"🏠 Home","title":"🎓 Citation","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"If you want to use this codebase, please consider citing the corresponding paper:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"@article{Altmeyer2023,\n doi = {10.21105/jcon.00130},\n url = {https://doi.org/10.21105/jcon.00130},\n year = {2023},\n publisher = {The Open Journal},\n volume = {1},\n number = {1},\n pages = {130},\n author = {Patrick Altmeyer and Arie van Deursen and Cynthia C. s. Liem},\n title = {Explaining Black-Box Models through Counterfactuals},\n journal = {Proceedings of the JuliaCon Conferences}\n}","category":"page"},{"location":"#References","page":"🏠 Home","title":"📚 References","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. “Getting a Clue: A Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Kaggle. 2011. “Give Me Some Credit, Improve on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the Next Two Years.” Kaggle. https://www.kaggle.com/c/GiveMeSomeCredit.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” https://arxiv.org/abs/1712.08443.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” arXiv Preprint arXiv:2203.06768.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Tolomei, Gabriele, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017. “Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 465–74. https://doi.org/10.1145/3097983.3098039.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harv. JL & Tech. 31: 841. https://doi.org/10.2139/ssrn.3063289.","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/categorical/#Categorical-Features","page":"Categorical Features","title":"Categorical Features","text":"","category":"section"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"To illustrate how data is preprocessed under the hood, we consider a simple toy dataset with three categorical features (name, grade and sex) and one continuous feature (age):","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"X = (\n name=categorical([\"Danesh\", \"Lee\", \"Mary\", \"John\"]),\n grade=categorical([\"A\", \"B\", \"A\", \"C\"], ordered=true),\n sex=categorical([\"male\",\"female\",\"male\",\"male\"]),\n height=[1.85, 1.67, 1.5, 1.67],\n)\nschema(X)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"Categorical features are expected to be one-hot or dummy encoded. To this end, we could use MLJ, for example:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"hot = OneHotEncoder()\nmach = fit!(machine(hot, X))\nW = transform(mach, X)\nschema(W)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"┌──────────────┬────────────┬─────────┐\n│ names │ scitypes │ types │\n├──────────────┼────────────┼─────────┤\n│ name__Danesh │ Continuous │ Float64 │\n│ name__John │ Continuous │ Float64 │\n│ name__Lee │ Continuous │ Float64 │\n│ name__Mary │ Continuous │ Float64 │\n│ grade__A │ Continuous │ Float64 │\n│ grade__B │ Continuous │ Float64 │\n│ grade__C │ Continuous │ Float64 │\n│ sex__female │ Continuous │ Float64 │\n│ sex__male │ Continuous │ Float64 │\n│ height │ Continuous │ Float64 │\n└──────────────┴────────────┴─────────┘","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The matrix that will be perturbed during the counterfactual search looks as follows:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"X = permutedims(MLJBase.matrix(W))","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10×4 Matrix{Float64}:\n 1.0 0.0 0.0 0.0\n 0.0 0.0 0.0 1.0\n 0.0 1.0 0.0 0.0\n 0.0 0.0 1.0 0.0\n 1.0 0.0 1.0 0.0\n 0.0 1.0 0.0 0.0\n 0.0 0.0 0.0 1.0\n 0.0 1.0 0.0 0.0\n 1.0 0.0 1.0 1.0\n 1.85 1.67 1.5 1.67","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The CounterfactualData constructor takes two optional arguments that can be used to specify the indices of categorical and continuous features. If nothing is supplied, all features are assumed to be continuous. For categorical features, the constructor expects and array of arrays of integers (Vector{Vector{Int}}) where each subarray includes the indices of a all one-hot encoded rows related to a single categorical feature. In the example above, the name feature is one-hot encoded across rows 1, 2 and 3 of X.","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"features_categorical = [\n [1,2,3,4], # name\n [5,6,7], # grade\n [8,9] # sex\n]\nfeatures_continuous = [10]","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"We propose the following simple logic for reconstructing categorical encodings after perturbations:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"For one-hot encoded features with multiple classes, choose the maximum.\nFor binary features, clip the perturbed value to fall into 01 and round to the nearest of the two integers.","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"function reconstruct_cat_encoding(x)\n map(features_categorical) do cat_group_index\n if length(cat_group_index) > 1\n x[cat_group_index] = Int.(x[cat_group_index] .== maximum(x[cat_group_index]))\n if sum(x[cat_group_index]) > 1\n ties = findall(x[cat_group_index] .== 1)\n _x = zeros(length(x[cat_group_index]))\n winner = rand(ties,1)[1]\n _x[winner] = 1\n x[cat_group_index] = _x\n end\n else\n x[cat_group_index] = [round(clamp(x[cat_group_index][1],0,1))]\n end\n end\n return x\nend","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"Let’s look at a few simple examples to see how this function works. Firstly, consider the case of perturbing a single element:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"x = X[:,1]\nx[1] = 1.1\nx","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 1.1\n 0.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The reconstructed one-hot-encoded vector will look like this:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"reconstruct_cat_encoding(x)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"Next, consider the case of perturbing multiple elements:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"x[2] = 1.1\nx[3] = -1.2\nx","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 1.0\n 1.1\n -1.2\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The reconstructed one-hot-encoded vector will look like this:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"reconstruct_cat_encoding(x)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 0.0\n 1.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"Finally, let’s introduce a tie:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"x[1] = 1.0\nx","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 1.0\n 1.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The reconstructed one-hot-encoded vector will look like this:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"reconstruct_cat_encoding(x)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 0.0\n 1.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/models/#Handling-Models","page":"Handling Models","title":"Handling Models","text":"","category":"section"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"The typical use-case for Counterfactual Explanations and Algorithmic Recourse is as follows: users have trained some supervised model that is not inherently interpretable and are looking for a way to explain it. In this tutorial, we will see how pre-trained models can be used with this package.","category":"page"},{"location":"tutorials/models/#Models-trained-in-Flux.jl","page":"Handling Models","title":"Models trained in Flux.jl","text":"","category":"section"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"We will train a simple binary classifier in Flux.jl on the popular Moons dataset:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"n = 500\ndata = TaijaData.load_moons(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\nX = counterfactual_data.X\ny = counterfactual_data.y\nplt = plot()\nscatter!(counterfactual_data)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"(Image: )","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"The following code chunk sets up a Deep Neural Network for the task at hand:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"data = Flux.DataLoader((X,y),batchsize=1)\ninput_dim = size(X,1)\nn_hidden = 32\nactivation = relu\noutput_dim = 2\nnn = Chain(\n Dense(input_dim, n_hidden, activation),\n Dropout(0.1),\n Dense(n_hidden, output_dim)\n)\nloss(yhat, y) = Flux.Losses.logitcrossentropy(nn(yhat), y)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"Next, we fit the network to the data:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"using Flux.Optimise: update!, Adam\nopt = Adam()\nepochs = 100\navg_loss(data) = mean(map(d -> loss(d[1],d[2]), data))\nshow_every = epochs/5\n# Training:\nfor epoch = 1:epochs\n for d in data\n gs = gradient(Flux.params(nn)) do\n l = loss(d...)\n end\n update!(opt, Flux.params(nn), gs)\n end\n if epoch % show_every == 0\n println(\"Epoch \" * string(epoch))\n @show avg_loss(data)\n end\nend","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"Epoch 20\navg_loss(data) = 0.1407434f0\nEpoch 40\navg_loss(data) = 0.11345118f0\nEpoch 60\navg_loss(data) = 0.046319224f0\nEpoch 80\navg_loss(data) = 0.011847609f0\nEpoch 100\navg_loss(data) = 0.007242911f0","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"To prepare the fitted model for use with our package, we need to wrap it inside a container. For plain-vanilla models trained in Flux.jl, the corresponding constructor is called FluxModel. There is also a separate constructor called FluxEnsemble, which applies to Deep Ensembles. Deep Ensembles are a popular approach to approximate Bayesian Deep Learning and have been shown to generate good predictive uncertainty estimates (Lakshminarayanan, Pritzel, and Blundell 2016).","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"The appropriate API call to wrap our simple network in a container follows below:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"M = FluxModel(nn)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"FluxModel(Chain(Dense(2 => 32, relu), Dropout(0.1, active=false), Dense(32 => 2)), :classification_binary)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"The likelihood function of the output variable is automatically inferred from the data. The generic plot() method can be called on the model and data to visualise the results:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"plot(M, counterfactual_data)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"(Image: )","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"Our model M is now ready for use with the package.","category":"page"},{"location":"tutorials/models/#References","page":"Handling Models","title":"References","text":"","category":"section"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” https://arxiv.org/abs/1612.01474.","category":"page"}] +[{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/models/#Handling-Models","page":"Handling Models","title":"Handling Models","text":"","category":"section"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"The typical use-case for Counterfactual Explanations and Algorithmic Recourse is as follows: users have trained some supervised model that is not inherently interpretable and are looking for a way to explain it. In this tutorial, we will see how pre-trained models can be used with this package.","category":"page"},{"location":"tutorials/models/#Models-trained-in-Flux.jl","page":"Handling Models","title":"Models trained in Flux.jl","text":"","category":"section"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"We will train a simple binary classifier in Flux.jl on the popular Moons dataset:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"n = 500\ndata = TaijaData.load_moons(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\nX = counterfactual_data.X\ny = counterfactual_data.y\nplt = plot()\nscatter!(counterfactual_data)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"(Image: )","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"The following code chunk sets up a Deep Neural Network for the task at hand:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"data = Flux.DataLoader((X,y),batchsize=1)\ninput_dim = size(X,1)\nn_hidden = 32\nactivation = relu\noutput_dim = 2\nnn = Chain(\n Dense(input_dim, n_hidden, activation),\n Dropout(0.1),\n Dense(n_hidden, output_dim)\n)\nloss(yhat, y) = Flux.Losses.logitcrossentropy(nn(yhat), y)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"Next, we fit the network to the data:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"using Flux.Optimise: update!, Adam\nopt = Adam()\nepochs = 100\navg_loss(data) = mean(map(d -> loss(d[1],d[2]), data))\nshow_every = epochs/5\n# Training:\nfor epoch = 1:epochs\n for d in data\n gs = gradient(Flux.params(nn)) do\n l = loss(d...)\n end\n update!(opt, Flux.params(nn), gs)\n end\n if epoch % show_every == 0\n println(\"Epoch \" * string(epoch))\n @show avg_loss(data)\n end\nend","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"Epoch 20\navg_loss(data) = 0.1407434f0\nEpoch 40\navg_loss(data) = 0.11345118f0\nEpoch 60\navg_loss(data) = 0.046319224f0\nEpoch 80\navg_loss(data) = 0.011847609f0\nEpoch 100\navg_loss(data) = 0.007242911f0","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"To prepare the fitted model for use with our package, we need to wrap it inside a container. For plain-vanilla models trained in Flux.jl, the corresponding constructor is called FluxModel. There is also a separate constructor called FluxEnsemble, which applies to Deep Ensembles. Deep Ensembles are a popular approach to approximate Bayesian Deep Learning and have been shown to generate good predictive uncertainty estimates (Lakshminarayanan, Pritzel, and Blundell 2016).","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"The appropriate API call to wrap our simple network in a container follows below:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"M = FluxModel(nn)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"FluxModel(Chain(Dense(2 => 32, relu), Dropout(0.1, active=false), Dense(32 => 2)), :classification_binary)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"The likelihood function of the output variable is automatically inferred from the data. The generic plot() method can be called on the model and data to visualise the results:","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"plot(M, counterfactual_data)","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"(Image: )","category":"page"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"Our model M is now ready for use with the package.","category":"page"},{"location":"tutorials/models/#References","page":"Handling Models","title":"References","text":"","category":"section"},{"location":"tutorials/models/","page":"Handling Models","title":"Handling Models","text":"Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” https://arxiv.org/abs/1612.01474.","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/dice/#DiCEGenerator","page":"DiCE","title":"DiCEGenerator","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"The DiCEGenerator can be used to generate multiple diverse counterfactuals for a single factual.","category":"page"},{"location":"explanation/generators/dice/#Description","page":"DiCE","title":"Description","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"Counterfactual Explanations are not unique and there are therefore many different ways through which valid counterfactuals can be generated. In the context of Algorithmic Recourse this can be leveraged to offer individuals not one, but possibly many different ways to change a negative outcome into a positive one. One might argue that it makes sense for those different options to be as diverse as possible. This idea is at the core of DiCE, a counterfactual generator introduce by Mothilal, Sharma, and Tan (2020) that generate a diverse set of counterfactual explanations.","category":"page"},{"location":"explanation/generators/dice/#Defining-Diversity","page":"DiCE","title":"Defining Diversity","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"To ensure that the generated counterfactuals are diverse, Mothilal, Sharma, and Tan (2020) add a diversity constraint to the counterfactual search objective. In particular, diversity is explicitly proxied via Determinantal Point Processes (DDP).","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"We can implement DDP in Julia as follows:[1]","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"using LinearAlgebra\nfunction ddp_diversity(X::AbstractArray{<:Real, 3})\n xs = eachslice(X, dims = ndims(X))\n K = [1/(1 + norm(x .- y)) for x in xs, y in xs]\n return det(K)\nend","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"Below we generate some random points in mathbbR^2 and apply gradient ascent on this function evaluated at the whole array of points. As we can see in the animation below, the points are sent away from each other. In other words, diversity across the array of points increases as we ascend the ddp_diversity function.","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"lims = 5\nN = 5\nX = rand(2,1,N)\nT = 50\nη = 0.1\nanim = @animate for t in 1:T\n X .+= gradient(ddp_diversity, X)[1]\n Z = reshape(X,2,N)\n scatter(\n Z[1,:],Z[2,:],ms=25, \n xlims=(-lims,lims),ylims=(-lims,lims),\n label=\"\",colour=1:N,\n size=(500,500),\n title=\"Diverse Counterfactuals\"\n )\nend\ngif(anim, joinpath(www_path, \"dice_intro.gif\"))","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"(Image: )","category":"page"},{"location":"explanation/generators/dice/#Usage","page":"DiCE","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"generator = DiCEGenerator()\nconv = CounterfactualExplanations.Convergence.GeneratorConditionsConvergence()\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator; \n num_counterfactuals=5, convergence=conv\n)\nplot(ce)","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"(Image: )","category":"page"},{"location":"explanation/generators/dice/#Effect-of-Penalty","page":"DiCE","title":"Effect of Penalty","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"Λ₂ = [0.1, 1.0, 5.0]\nces = []\nn_cf = 5\nusing Flux\nfor λ₂ ∈ Λ₂ \n λ = [0.00, λ₂]\n generator = DiCEGenerator(λ=λ)\n ces = vcat(\n ces...,\n generate_counterfactual(\n x, target, counterfactual_data, M, generator; \n num_counterfactuals=n_cf, convergence=conv\n )\n )\nend","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"The figure below shows the resulting counterfactual paths. As expected, the resulting counterfactuals are more dispersed across the feature domain for higher choices of lambda_2","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"(Image: )","category":"page"},{"location":"explanation/generators/dice/#References","page":"DiCE","title":"References","text":"","category":"section"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.","category":"page"},{"location":"explanation/generators/dice/","page":"DiCE","title":"DiCE","text":"[1] With thanks to the respondents on Discourse","category":"page"},{"location":"how_to_guides/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"how_to_guides/#How-To-Guides","page":"Overview","title":"How-To Guides","text":"","category":"section"},{"location":"how_to_guides/","page":"Overview","title":"Overview","text":"In this section, you will find a series of how-to-guides that showcase specific use cases of counterfactual explanations (CE).","category":"page"},{"location":"how_to_guides/","page":"Overview","title":"Overview","text":"How-to guides are directions that take the reader through the steps required to solve a real-world problem. How-to guides are goal-oriented.— Diátaxis","category":"page"},{"location":"how_to_guides/","page":"Overview","title":"Overview","text":"In other words, you come here because you may have some particular problem in mind, would like to see how it can be solved using CE and then most likely head off again 🫡.","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/generators/#Handling-Generators","page":"Handling Generators","title":"Handling Generators","text":"","category":"section"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Generating Counterfactual Explanations can be seen as a generative modelling task because it involves generating samples in the input space: x sim mathcalX. In this tutorial, we will introduce how Counterfactual GradientBasedGenerators are used. They are discussed in more detail in the explanatory section of the documentation.","category":"page"},{"location":"tutorials/generators/#Composable-Generators","page":"Handling Generators","title":"Composable Generators","text":"","category":"section"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"warning: Breaking Changes Expected\nWork on this feature is still in its very early stages and breaking changes should be expected. ","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"One of the key objectives for this package is Composability. It turns out that many of the various counterfactual generators that have been proposed in the literature, essentially do the same thing: they optimize an objective function. Formally we have,","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"\nbeginaligned\nmathbfs^prime = arg min_mathbfs^prime in mathcalS left textyloss(M(f(mathbfs^prime))y^*)+ lambda textcost(f(mathbfs^prime)) right \nendaligned \n qquad(1)","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"where textyloss denotes the main loss function and textcost is a penalty term (Altmeyer et al. 2023).","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Without going into further detail here, the important thing to mention is that Equation 1 very closely describes how counterfactual search is actually implemented in the package. In other words, all off-the-shelf generators currently implemented work with that same objective. They just vary in the way that penalties are defined, for example. This gives rise to an interesting idea:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Why not compose generators that combine ideas from different off-the-shelf generators?","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"The GradientBasedGenerator class provides a straightforward way to do this, without requiring users to build custom GradientBasedGenerators from scratch. It can be instantiated as follows:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"generator = GradientBasedGenerator()","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"By default, this creates a generator that simply performs gradient descent without any penalties. To modify the behaviour of the generator, you can define the counterfactual search objective function using the @objective macro:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"@objective(generator, logitbinarycrossentropy + 0.1distance_l2 + 1.0ddp_diversity)","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Here we have essentially created a version of the DiCEGenerator:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"ce = generate_counterfactual(x, target, counterfactual_data, M, generator; num_counterfactuals=5)\nplot(ce)","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"(Image: )","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Multiple macros can be chained using Chains.jl making it easy to create entirely new flavours of counterfactual generators. The following generator, for example, combines ideas from DiCE (Mothilal, Sharma, and Tan 2020) and REVISE (Joshi et al. 2019):","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"@chain generator begin\n @objective logitcrossentropy + 1.0ddp_diversity # DiCE (Mothilal et al. 2020)\n @with_optimiser Flux.Adam(0.1) \n @search_latent_space # REVISE (Joshi et al. 2019)\nend","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Let’s take this generator to our MNIST dataset and generate a counterfactual explanation for turning a 0 into a 8.","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"(Image: )","category":"page"},{"location":"tutorials/generators/#Off-the-Shelf-Generators","page":"Handling Generators","title":"Off-the-Shelf Generators","text":"","category":"section"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Off-the-shelf generators are just default recipes for counterfactual generators. Currently, the following off-the-shelf counterfactual generators are implemented in the package:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"generator_catalogue","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Dict{Symbol, Any} with 11 entries:\n :gravitational => GravitationalGenerator\n :growing_spheres => GrowingSpheresGenerator\n :revise => REVISEGenerator\n :clue => CLUEGenerator\n :probe => ProbeGenerator\n :dice => DiCEGenerator\n :feature_tweak => FeatureTweakGenerator\n :claproar => ClaPROARGenerator\n :wachter => WachterGenerator\n :generic => GenericGenerator\n :greedy => GreedyGenerator","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"To specify the type of generator you want to use, you can simply instantiate it:","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"# Search:\ngenerator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"(Image: )","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"We generally make an effort to follow the literature as closely as possible when implementing off-the-shelf generators.","category":"page"},{"location":"tutorials/generators/#References","page":"Handling Generators","title":"References","text":"","category":"section"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.","category":"page"},{"location":"tutorials/generators/","page":"Handling Generators","title":"Handling Generators","text":"Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/simple_example/#Simple-Example","page":"Simple Example","title":"Simple Example","text":"","category":"section"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"In this tutorial, we will go through a simple example involving synthetic data and a generic counterfactual generator.","category":"page"},{"location":"tutorials/simple_example/#Data-and-Classifier","page":"Simple Example","title":"Data and Classifier","text":"","category":"section"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"Below we generate some linearly separable data and fit a simple MLP classifier with batch normalization to it. For more information on generating data and models, refer to the Handling Data and Handling Models tutorials respectively.","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"# Counteractual data and model:\nflux_training_params.batchsize = 10\ndata = TaijaData.load_linearly_separable()\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\ncounterfactual_data.standardize = true\nM = fit_model(counterfactual_data, :MLP, batch_norm=true)","category":"page"},{"location":"tutorials/simple_example/#Counterfactual-Search","page":"Simple Example","title":"Counterfactual Search","text":"","category":"section"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"Next, determine a target and factual class for our counterfactual search and select a random factual instance to explain.","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"target = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"Finally, we generate and visualize the generated counterfactual:","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"# Search:\ngenerator = WachterGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"tutorials/simple_example/","page":"Simple Example","title":"Simple Example","text":"(Image: )","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/optimisers/jsma/#Jacobian-based-Saliency-Map-Attack","page":"JSMA","title":"Jacobian-based Saliency Map Attack","text":"","category":"section"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"To search counterfactuals, Schut et al. (2021) propose to use a Jacobian-Based Saliency Map Attack (JSMA) inspired by the literature on adversarial attacks. It works by moving in the direction of the most salient feature at a fixed step size in each iteration. Schut et al. (2021) use this optimisation rule in the context of Bayesian classifiers and demonstrate good results in terms of plausibility — how realistic counterfactuals are — and redundancy — how sparse the proposed feature changes are.","category":"page"},{"location":"explanation/optimisers/jsma/#JSMADescent","page":"JSMA","title":"JSMADescent","text":"","category":"section"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"To implement this approach in a reusable manner, we have added JSMA as a Flux optimiser. In particular, we have added a class JSMADescent<:Flux.Optimise.AbstractOptimiser, for which we have overloaded the Flux.Optimise.apply! method. This makes it possible to reuse JSMADescent as an optimiser in composable generators.","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"The optimiser can be used with with any generator as follows:","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"using CounterfactualExplanations.Generators: JSMADescent\ngenerator = GenericGenerator() |>\n gen -> @with_optimiser(gen,JSMADescent(;η=0.1))\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"The figure below compares the resulting counterfactual search outcome to the corresponding outcome with generic Descent.","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"plot(p1,p2,size=(1000,400))","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"(Image: )","category":"page"},{"location":"explanation/optimisers/jsma/","page":"JSMA","title":"JSMA","text":"Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/data_catalogue/#Data-Catalogue","page":"Data Catalogue","title":"Data Catalogue","text":"","category":"section"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"To allow researchers and practitioners to test and compare counterfactual generators, the TAIJA environment includes the package TaijaData.jl which comes with pre-processed synthetic and real-world benchmark datasets from different domains. This page explains how to use TaijaData.jl in tandem with CounterfactualExplanations.jl.","category":"page"},{"location":"tutorials/data_catalogue/#Synthetic-Data","page":"Data Catalogue","title":"Synthetic Data","text":"","category":"section"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"The following dictionary can be used to inspect the available methods to generate synthetic datasets where the key indicates the name of the data and the value is the corresponding method:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"TaijaData.data_catalogue[:synthetic]","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"Dict{Symbol, Function} with 6 entries:\n :overlapping => load_overlapping\n :linearly_separable => load_linearly_separable\n :blobs => load_blobs\n :moons => load_moons\n :circles => load_circles\n :multi_class => load_multi_class","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"The chart below shows the generated data using default parameters:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"plts = []\n_height = 200\n_n = length(keys(data_catalogue[:synthetic]))\nfor (key, fun) in data_catalogue[:synthetic]\n data = fun()\n counterfactual_data = DataPreprocessing.CounterfactualData(data...)\n plt = plot()\n scatter!(counterfactual_data, title=key)\n plts = [plts..., plt]\nend\nplot(plts..., size=(_n * _height, _height), layout=(1, _n))","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"(Image: )","category":"page"},{"location":"tutorials/data_catalogue/#Real-World-Data","page":"Data Catalogue","title":"Real-World Data","text":"","category":"section"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"As for real-world data, the same dictionary can be used to inspect the available data from different domains.","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"TaijaData.data_catalogue[:tabular]","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"Dict{Symbol, Function} with 5 entries:\n :german_credit => load_german_credit\n :california_housing => load_california_housing\n :credit_default => load_credit_default\n :adult => load_uci_adult\n :gmsc => load_gmsc","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"TaijaData.data_catalogue[:vision]","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"Dict{Symbol, Function} with 3 entries:\n :fashion_mnist => load_fashion_mnist\n :mnist => load_mnist\n :cifar_10 => load_cifar_10","category":"page"},{"location":"tutorials/data_catalogue/#Loading-Data","page":"Data Catalogue","title":"Loading Data","text":"","category":"section"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"To load or generate any of the datasets listed above, you can just use the corresponding method, for example:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"data = TaijaData.load_linearly_separable()\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"Optionally, you can specify how many samples you want to generate like so:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"n = 100\ndata = TaijaData.load_overlapping(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"This also applies to real-world datasets, which by default are loaded in their entirety. If n is supplied, the dataset will be randomly undersampled:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"data = TaijaData.load_mnist(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"The undersampled dataset is automatically balanced:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"sum(counterfactual_data.y; dims=2)","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"10×1 Matrix{Int64}:\n 10\n 10\n 10\n 10\n 10\n 10\n 10\n 10\n 10\n 10","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"We can also use a helper function to split the data into train and test sets:","category":"page"},{"location":"tutorials/data_catalogue/","page":"Data Catalogue","title":"Data Catalogue","text":"train_data, test_data = \n CounterfactualExplanations.DataPreprocessing.train_test_split(counterfactual_data)","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"how_to_guides/custom_generators/#How-to-add-Custom-Generators","page":"... add custom generators","title":"How to add Custom Generators","text":"","category":"section"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"As we will see in this short tutorial, building custom counterfactual generators is straightforward. We hope that this will facilitate contributions through the community.","category":"page"},{"location":"how_to_guides/custom_generators/#Generic-generator-with-dropout","page":"... add custom generators","title":"Generic generator with dropout","text":"","category":"section"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"To illustrate how custom generators can be implemented we will consider a simple example of a generator that extends the functionality of our GenericGenerator. We have noted elsewhere that the effectiveness of counterfactual explanations depends to some degree on the quality of the fitted model. Another, perhaps trivial, thing to note is that counterfactual explanations are not unique: there are potentially many valid counterfactual paths. One interesting (or silly) idea following these two observations might be to introduce some form of regularization in the counterfactual search. For example, we could use dropout to randomly switch features on and off in each iteration. Without dwelling further on the usefulness of this idea, let us see how it can be implemented.","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"The first code chunk below implements two important steps: 1) create an abstract subtype of the AbstractGradientBasedGenerator and 2) create a constructor similar to the GenericConstructor, but with one additional field for the probability of dropout.","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"# Abstract suptype:\nabstract type AbstractDropoutGenerator <: AbstractGradientBasedGenerator end\n\n# Constructor:\nstruct DropoutGenerator <: AbstractDropoutGenerator\n loss::Function # loss function\n penalty::Function\n λ::AbstractFloat # strength of penalty\n latent_space::Bool\n opt::Any # optimizer\n generative_model_params::NamedTuple\n p_dropout::AbstractFloat # dropout rate\nend\n\n# Instantiate:\ngenerator = DropoutGenerator(\n Flux.logitbinarycrossentropy,\n CounterfactualExplanations.Objectives.distance_l1,\n 0.1,\n false,\n Flux.Optimise.Descent(0.1),\n (;),\n 0.5,\n)","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"Next, we define how feature perturbations are generated for our dropout generator: in particular, we extend the relevant function through a method that implemented the dropout logic.","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"using CounterfactualExplanations.Generators\nusing StatsBase\nfunction Generators.generate_perturbations(\n generator::AbstractDropoutGenerator, \n ce::CounterfactualExplanation\n)\n s′ = deepcopy(ce.s′)\n new_s′ = Generators.propose_state(generator, ce)\n Δs′ = new_s′ - s′ # gradient step\n\n # Dropout:\n set_to_zero = sample(\n 1:length(Δs′),\n Int(round(generator.p_dropout*length(Δs′))),\n replace=false\n )\n Δs′[set_to_zero] .= 0\n return Δs′\nend","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"Finally, we proceed to generate counterfactuals in the same way we always do:","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"# Data and Classifier:\nM = fit_model(counterfactual_data, :DeepEnsemble)\n\n# Factual and Target:\nyhat = predict_label(M, counterfactual_data)\ntarget = 2 # target label\ncandidates = findall(vec(yhat) .!= target)\nchosen = rand(candidates)\nx = select_factual(counterfactual_data, chosen)\n\n# Counterfactual search:\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator;\n num_counterfactuals=5)\n\nplot(ce)","category":"page"},{"location":"how_to_guides/custom_generators/","page":"... add custom generators","title":"... add custom generators","text":"(Image: )","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/parallelization/#Parallelization","page":"Parallelization","title":"Parallelization","text":"","category":"section"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Version 0.1.15 adds support for parallelization through multi-processing. Currently, the only available backend for parallelization is MPI.jl.","category":"page"},{"location":"tutorials/parallelization/#Available-functions","page":"Parallelization","title":"Available functions","text":"","category":"section"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Parallelization is only available for certain functions. To check if a function is parallelizable, you can use parallelizable function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"using CounterfactualExplanations.Evaluation: evaluate, benchmark\nprintln(parallelizable(generate_counterfactual))\nprintln(parallelizable(evaluate))\nprintln(parallelizable(predict_label))","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"true\ntrue\nfalse","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"In the following, we will generate multiple counterfactuals and evaluate them in parallel:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"chosen = rand(findall(predict_label(M, counterfactual_data) .== factual), 1000)\nxs = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"tutorials/parallelization/#Multi-threading","page":"Parallelization","title":"Multi-threading","text":"","category":"section"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"We first instantiate an ThreadParallelizer object:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"parallelizer = ThreadsParallelizer()","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"ThreadsParallelizer()","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"To generate counterfactuals in parallel, we use the parallelize function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"ces = @with_parallelizer parallelizer begin\n generate_counterfactual(\n xs,\n target,\n counterfactual_data,\n M,\n generator\n )\nend","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Generating counterfactuals ... 0%| | ETA: 0:01:29 (89.14 ms/it)Generating counterfactuals ... 100%|███████| Time: 0:00:01 ( 1.59 ms/it)\n\n1000-element Vector{AbstractCounterfactualExplanation}:\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n ⋮\n CounterfactualExplanation\nConvergence: ✅ after 9 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"To evaluate counterfactuals in parallel, we again use the parallelize function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"@with_parallelizer parallelizer evaluate(ces)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Evaluating counterfactuals ... 0%| | ETA: 0:07:03 ( 0.42 s/it)Evaluating counterfactuals ... 100%|███████| Time: 0:00:00 ( 0.86 ms/it)\n\n1000-element Vector{Any}:\n Vector[[1.0], Float32[3.2939816], [0.0]]\n Vector[[1.0], Float32[3.019046], [0.0]]\n Vector[[1.0], Float32[3.701171], [0.0]]\n Vector[[1.0], Float32[2.5611918], [0.0]]\n Vector[[1.0], Float32[2.9027307], [0.0]]\n Vector[[1.0], Float32[3.7893882], [0.0]]\n Vector[[1.0], Float32[3.5026522], [0.0]]\n Vector[[1.0], Float32[3.6317568], [0.0]]\n Vector[[1.0], Float32[3.084984], [0.0]]\n Vector[[1.0], Float32[3.2268934], [0.0]]\n Vector[[1.0], Float32[2.834947], [0.0]]\n Vector[[1.0], Float32[3.656587], [0.0]]\n Vector[[1.0], Float32[2.5985842], [0.0]]\n ⋮\n Vector[[1.0], Float32[4.067538], [0.0]]\n Vector[[1.0], Float32[3.02231], [0.0]]\n Vector[[1.0], Float32[2.748292], [0.0]]\n Vector[[1.0], Float32[2.9483426], [0.0]]\n Vector[[1.0], Float32[3.066149], [0.0]]\n Vector[[1.0], Float32[3.6018147], [0.0]]\n Vector[[1.0], Float32[3.0138078], [0.0]]\n Vector[[1.0], Float32[3.5724509], [0.0]]\n Vector[[1.0], Float32[3.117551], [0.0]]\n Vector[[1.0], Float32[2.9670508], [0.0]]\n Vector[[1.0], Float32[3.4107168], [0.0]]\n Vector[[1.0], Float32[3.0252533], [0.0]]","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Benchmarks can also be run with parallelization by specifying parallelizer argument:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"# Models:\nbmk = benchmark(counterfactual_data; parallelizer = parallelizer)","category":"page"},{"location":"tutorials/parallelization/#MPI","page":"Parallelization","title":"MPI","text":"","category":"section"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"note: Note\nTo use MPI, you need to have MPI installed on your machine. Running the following code straight from a running Julia session will work if you have MPI installed on your machine, but it will be run on a single process. To execute the code on multiple processes, you need to run it from the command line with mpirun or mpiexec. For example, to run a script on 4 processes, you can run the following command from the command line:\n\nmpiexecjl --project -n 4 julia -e 'include(\"docs/src/srcipts/mpi.jl\")'For more information, see MPI.jl. ","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"We first instantiate an MPIParallelizer object:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"import MPI\nMPI.Init()\nparallelizer = MPIParallelizer(MPI.COMM_WORLD; threaded=true)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Precompiling MPIExt\n ✓ TaijaParallel → MPIExt\n 1 dependency successfully precompiled in 3 seconds. 255 already precompiled.\n[ Info: Precompiling MPIExt [48137b38-b316-530b-be8a-261f41e68c23]\n┌ Warning: Module TaijaParallel with build ID ffffffff-ffff-ffff-0001-2d458926c256 is missing from the cache.\n│ This may mean TaijaParallel [bf1c2c22-5e42-4e78-8b6b-92e6c673eeb0] does not support precompilation but is imported by a module that does.\n└ @ Base loading.jl:1948\n[ Info: Skipping precompilation since __precompile__(false). Importing MPIExt [48137b38-b316-530b-be8a-261f41e68c23].\n[ Info: Using `MPI.jl` for multi-processing.\n\nRunning on 1 processes.\n\nMPIExt.MPIParallelizer(MPI.Comm(1140850688), 0, 1, nothing, true)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"To generate counterfactuals in parallel, we use the parallelize function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"ces = @with_parallelizer parallelizer begin\n generate_counterfactual(\n xs,\n target,\n counterfactual_data,\n M,\n generator\n )\nend","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Generating counterfactuals ... 9%|▋ | ETA: 0:00:01 ( 1.15 ms/it)Generating counterfactuals ... 19%|█▍ | ETA: 0:00:01 ( 1.07 ms/it)Generating counterfactuals ... 29%|██ | ETA: 0:00:01 ( 1.10 ms/it)Generating counterfactuals ... 39%|██▊ | ETA: 0:00:01 ( 1.08 ms/it)Generating counterfactuals ... 49%|███▍ | ETA: 0:00:01 ( 1.08 ms/it)Generating counterfactuals ... 59%|████▏ | ETA: 0:00:00 ( 1.08 ms/it)Generating counterfactuals ... 69%|████▊ | ETA: 0:00:00 ( 1.08 ms/it)Generating counterfactuals ... 79%|█████▌ | ETA: 0:00:00 ( 1.07 ms/it)Generating counterfactuals ... 89%|██████▎| ETA: 0:00:00 ( 1.07 ms/it)Generating counterfactuals ... 99%|██████▉| ETA: 0:00:00 ( 1.06 ms/it)Generating counterfactuals ... 100%|███████| Time: 0:00:01 ( 1.06 ms/it)\n\n1000-element Vector{AbstractCounterfactualExplanation}:\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n ⋮\n CounterfactualExplanation\nConvergence: ✅ after 9 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 6 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 8 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.\n CounterfactualExplanation\nConvergence: ✅ after 7 steps.","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"To evaluate counterfactuals in parallel, we again use the parallelize function:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"@with_parallelizer parallelizer evaluate(ces)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"1000-element Vector{Any}:\n Vector[[1.0], Float32[3.0941274], [0.0]]\n Vector[[1.0], Float32[3.0894346], [0.0]]\n Vector[[1.0], Float32[3.5737448], [0.0]]\n Vector[[1.0], Float32[2.6201036], [0.0]]\n Vector[[1.0], Float32[2.8519764], [0.0]]\n Vector[[1.0], Float32[3.7762523], [0.0]]\n Vector[[1.0], Float32[3.4162796], [0.0]]\n Vector[[1.0], Float32[3.6095932], [0.0]]\n Vector[[1.0], Float32[3.1347957], [0.0]]\n Vector[[1.0], Float32[3.0313473], [0.0]]\n Vector[[1.0], Float32[2.7612567], [0.0]]\n Vector[[1.0], Float32[3.6191392], [0.0]]\n Vector[[1.0], Float32[2.610616], [0.0]]\n ⋮\n Vector[[1.0], Float32[4.0844703], [0.0]]\n Vector[[1.0], Float32[3.0119], [0.0]]\n Vector[[1.0], Float32[2.4461186], [0.0]]\n Vector[[1.0], Float32[3.071967], [0.0]]\n Vector[[1.0], Float32[3.132917], [0.0]]\n Vector[[1.0], Float32[3.5403214], [0.0]]\n Vector[[1.0], Float32[3.0588162], [0.0]]\n Vector[[1.0], Float32[3.5600657], [0.0]]\n Vector[[1.0], Float32[3.2205954], [0.0]]\n Vector[[1.0], Float32[2.896302], [0.0]]\n Vector[[1.0], Float32[3.2603998], [0.0]]\n Vector[[1.0], Float32[3.1369917], [0.0]]","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"tip: Tip\nNote that parallelizable processes can be supplied as input to the macro either as a block or directly as an expression.","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"Benchmarks can also be run with parallelization by specifying parallelizer argument:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"# Models:\nbmk = benchmark(counterfactual_data; parallelizer = parallelizer)","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"The following code snippet shows a complete example script that uses MPI for running a benchmark in parallel:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"using CounterfactualExplanations\nusing CounterfactualExplanations.Evaluation: benchmark\nusing CounterfactualExplanations.Models\nimport MPI\n\nMPI.Init()\n\ndata = TaijaData.load_linearly_separable()\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\nM = fit_model(counterfactual_data, :Linear)\nfactual = 1\ntarget = 2\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual), 100)\nxs = select_factual(counterfactual_data, chosen)\ngenerator = GenericGenerator()\n\nparallelizer = MPIParallelizer(MPI.COMM_WORLD)\n\nbmk = benchmark(counterfactual_data; parallelizer=parallelizer)\n\nMPI.Finalize()","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"The file can be executed from the command line as follows:","category":"page"},{"location":"tutorials/parallelization/","page":"Parallelization","title":"Parallelization","text":"mpiexecjl --project -n 4 julia -e 'include(\"docs/src/srcipts/mpi.jl\")'","category":"page"},{"location":"tutorials/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanation","category":"page"},{"location":"tutorials/#Tutorials","page":"Overview","title":"Tutorials","text":"","category":"section"},{"location":"tutorials/","page":"Overview","title":"Overview","text":"In this section, you will find a series of tutorials that should help you gain a basic understanding of Conformal Prediction and how to apply it in Julia using this package.","category":"page"},{"location":"tutorials/","page":"Overview","title":"Overview","text":"Tutorials are lessons that take the reader by the hand through a series of steps to complete a project of some kind. Tutorials are learning-oriented.— Diátaxis","category":"page"},{"location":"tutorials/","page":"Overview","title":"Overview","text":"In other words, you come here because you are new to this topic and are looking for a first peek at the methodology and code 🫣.","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"explanation/generators/growing_spheres/#GrowingSpheres","page":"GrowingSpheres","title":"GrowingSpheres","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"Growing Spheres refers to the generator introduced by Laugel et al. (2017). Our implementation takes inspiration from the CARLA library.","category":"page"},{"location":"explanation/generators/growing_spheres/#Principle-of-the-Proposed-Approach","page":"GrowingSpheres","title":"Principle of the Proposed Approach","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"In order to interpret a prediction through comparison, the Growing Spheres algorithm focuses on finding an observation belonging to the other class and answers the question: “Considering an observation and a classifier, what is the minimal change we need to apply in order to change the prediction of this observation?”. This problem is similar to inverse classification but applied to interpretability.","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"Explaining how to change a prediction can help the user understand what the model considers as locally important. The Growing Spheres approach provides insights into the classifier’s behavior without claiming any causal knowledge. It differs from other interpretability approaches and is not concerned with the global behavior of the model. Instead, it aims to provide local insights into the classifier’s decision-making process.","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"The algorithm finds the closest “ennemy” observation, which is an observation classified into the other class than the input observation. The final explanation is the difference vector between the input observation and the ennemy.","category":"page"},{"location":"explanation/generators/growing_spheres/#Finding-the-Closest-Ennemy","page":"GrowingSpheres","title":"Finding the Closest Ennemy","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"The algorithm solves the following minimization problem to find the closest ennemy:","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"e^* = arg min_e in X c(x e) f(e) neq f(x) ","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"The cost function c(x, e) is defined as:","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"c(x e) = x - e_2 + gamma x - e_0","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"where ||.||_2 is the Euclidean norm and ||.||_0 is the sparsity measure. The weight gamma balances the importance of sparsity in the cost function.","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"To approximate the solution, the Growing Spheres algorithm uses a two-step heuristic approach. The first step is the Generation phase, where observations are generated in spherical layers around the input observation. The second step is the Feature Selection phase, where the generated observation with the smallest change in each feature is selected.","category":"page"},{"location":"explanation/generators/growing_spheres/#Example","page":"GrowingSpheres","title":"Example","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"generator = GrowingSpheresGenerator()\nM = fit_model(counterfactual_data, :DeepEnsemble)\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"(Image: )","category":"page"},{"location":"explanation/generators/growing_spheres/#References","page":"GrowingSpheres","title":"References","text":"","category":"section"},{"location":"explanation/generators/growing_spheres/","page":"GrowingSpheres","title":"GrowingSpheres","text":"Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” arXiv. https://doi.org/10.48550/arXiv.1712.08443.","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"contribute/#Contribute","page":"🛠 Contribute","title":"🛠 Contribute","text":"","category":"section"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"Contributions of any kind are very much welcome! Take a look at the issue to see what things we are currently working on.","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"If any of the below applies to you, this might be the right open-source project for you:","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"You’re an expert in Counterfactual Explanations or Explainable AI more broadly and you are curious about Julia.\nYou’re experienced with Julia and are happy to help someone less experienced to up their game. Ideally, you are also curious about Trustworthy AI.\nYou’re new to Julia and open-source development and would like to start your learning journey by contributing to a recent and active development. Ideally, you are familiar with machine learning.","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"@pat-alt here: I am still very much at the beginning of my Julia journey, so if you spot any issues or have any suggestions for design improvement, please just open issue or start a discussion.","category":"page"},{"location":"contribute/","page":"🛠 Contribute","title":"🛠 Contribute","text":"For more details on how to contribute see here. Please follow the SciML ColPrac guide.","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/clap_roar/#ClaPROARGenerator","page":"ClaPROAR","title":"ClaPROARGenerator","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"The ClaPROARGenerator was introduced in Altmeyer et al. (2023).","category":"page"},{"location":"explanation/generators/clap_roar/#Description","page":"ClaPROAR","title":"Description","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"The acronym Clap stands for classifier-preserving. The approach is loosely inspired by ROAR (Upadhyay, Joshi, and Lakkaraju 2021). Altmeyer et al. (2023) propose to explicitly penalize the loss incurred by the classifer when evaluated on the counterfactual x^prime at given parameter values. Formally, we have","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"beginaligned\ntextextcost(f(mathbfs^prime)) = l(M(f(mathbfs^prime))y^prime)\nendaligned","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"for each counterfactual k where l denotes the loss function used to train M. This approach is based on the intuition that (endogenous) model shifts will be triggered by counterfactuals that increase classifier loss (Altmeyer et al. 2023).","category":"page"},{"location":"explanation/generators/clap_roar/#Usage","page":"ClaPROAR","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"generator = ClaPROARGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"(Image: )","category":"page"},{"location":"explanation/generators/clap_roar/#Comparison-to-GenericGenerator","page":"ClaPROAR","title":"Comparison to GenericGenerator","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"The figure below compares the outcome for the GenericGenerator and the ClaPROARGenerator.","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"(Image: )","category":"page"},{"location":"explanation/generators/clap_roar/#References","page":"ClaPROAR","title":"References","text":"","category":"section"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"explanation/generators/clap_roar/","page":"ClaPROAR","title":"ClaPROAR","text":"Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. 2021. “Towards Robust and Reliable Algorithmic Recourse.” https://arxiv.org/abs/2102.13620.","category":"page"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/architecture/#Package-Architecture","page":"Package Architecture","title":"Package Architecture","text":"","category":"section"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"Modular, composable, scalable!","category":"page"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"The diagram below provides an overview of the package architecture. It is built around two core modules that are designed to be as extensible as possible through dispatch: 1) Models is concerned with making any arbitrary model compatible with the package; 2) Generators is used to implement arbitrary counterfactual search algorithms.[1] The core function of the package generate_counterfactual uses an instance of type <: AbstractFittedModel produced by the Models module and an instance of type <: AbstractGenerator produced by the Generators module.","category":"page"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"(Image: )","category":"page"},{"location":"explanation/architecture/","page":"Package Architecture","title":"Package Architecture","text":"[1] We have made an effort to keep the code base a flexible and extensible as possible, but cannot guarantee at this point that any counterfactual generator can be implemented without further adaptation.","category":"page"},{"location":"explanation/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"explanation/#Explanation","page":"Overview","title":"Explanation","text":"","category":"section"},{"location":"explanation/","page":"Overview","title":"Overview","text":"In this section you will find detailed explanations about the methodology and code.","category":"page"},{"location":"explanation/","page":"Overview","title":"Overview","text":"Explanation clarifies, deepens and broadens the reader’s understanding of a subject.— Diátaxis","category":"page"},{"location":"explanation/","page":"Overview","title":"Overview","text":"In other words, you come here because you are interested in understanding how all of this actually works 🤓.","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"extensions/laplace_redux/#[LaplaceRedux.jl](https://github.com/JuliaTrustworthyAI/LaplaceRedux.jl)","page":"LaplaceRedux","title":"LaplaceRedux.jl","text":"","category":"section"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"LaplaceRedux.jl is one of Taija’s own packages that provides a framework for Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks. The methodology was first proposed by Immer, Korzepa, and Bauer (2020) and implemented in Python by Daxberger et al. (2021). This is relevant to the work on counterfactual explanations (CE), because research has shown that counterfactual explanations for Bayesian models are typically more plausible, because Bayesian models are able to capture the uncertainty in the data (Schut et al. 2021).","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"tip: Read More\nTo learn more about Laplace Redux, head over to the official documentation.","category":"page"},{"location":"extensions/laplace_redux/#Example","page":"LaplaceRedux","title":"Example","text":"","category":"section"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"The extension will be loaded automatically when loading the LaplaceRedux package (assuming the CounterfactualExplanations package is also loaded).","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"using LaplaceRedux","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"Next, we will fit a neural network with Laplace Approximation to the moons dataset using our standard package API for doing so. By default, the Bayesian prior is optimized through empirical Bayes using the LaplaceRedux package.","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"# Fit model to data:\ndata = CounterfactualData(load_moons()...)\nM = fit_model(data, :LaplaceRedux; n_hidden=16)","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"LaplaceReduxExt.LaplaceReduxModel(Laplace(Chain(Dense(2 => 16, relu), Dense(16 => 2)), :classification, :all, nothing, :full, LaplaceRedux.Curvature.GGN(Chain(Dense(2 => 16, relu), Dense(16 => 2)), :classification, Flux.Losses.logitcrossentropy, Array{Float32}[[-1.3098596 0.59241515; 0.91760206 0.02950162; … ; -0.018356863 0.12850936; -0.5381665 -0.7872097], [-0.2581085, -0.90997887, -0.5418944, -0.23735572, 0.81020063, -0.3033359, -0.47902864, -0.6432098, -0.038013518, 0.028280666, 0.009903266, -0.8796683, 0.41090682, 0.011093224, -0.1580453, 0.7911349], [3.092321 -2.4660816 … -0.3446268 -1.465249; -2.9468734 3.167357 … 0.31758657 1.7140366], [-0.3107697, 0.31076983]], 1.0, :all, nothing), 1.0, 0.0, Float32[-1.3098596, 0.91760206, 0.5239727, -1.1579771, -0.851813, -1.9411169, 0.47409698, 0.6679365, 0.8944433, 0.663116 … -0.3172857, 0.15530388, 1.3264753, -0.3506721, -0.3446268, 0.31758657, -1.465249, 1.7140366, -0.3107697, 0.31076983], [0.10530027048093525 0.0 … 0.0 0.0; 0.0 0.10530027048093525 … 0.0 0.0; … ; 0.0 0.0 … 0.10530027048093525 0.0; 0.0 0.0 … 0.0 0.10530027048093525], [0.10066431429751965 0.0 … -0.030656783425475176 0.030656334963944154; 0.0 20.93513766443357 … -2.3185940232360736 2.3185965484008193; … ; -0.030656783425475176 -2.3185940232360736 … 1.0101450999063672 -1.0101448118057204; 0.030656334963944154 2.3185965484008193 … -1.0101448118057204 1.0101451389641771], [1.1006643142975197 0.0 … -0.030656783425475176 0.030656334963944154; 0.0 21.93513766443357 … -2.3185940232360736 2.3185965484008193; … ; -0.030656783425475176 -2.3185940232360736 … 2.0101450999063672 -1.0101448118057204; 0.030656334963944154 2.3185965484008193 … -1.0101448118057204 2.010145138964177], [0.9412600568016627 0.003106911671721699 … 0.003743740333409532 -0.003743452315572739; 0.003106912946573237 0.6539263732691709 … 0.0030385955287734246 -0.0030390041204196414; … ; 0.0037437406323562283 0.003038591829991259 … 0.9624905710233649 0.03750911813897676; -0.0037434526145225856 -0.0030390004216833593 … 0.03750911813898124 0.9624905774453485], 82, 250, 2, 997.8087484836578), :classification_multi)","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"Finally, we select a factual instance and generate a counterfactual explanation for it using the generic gradient-based CE method.","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"# Select a factual instance:\ntarget = 1\nfactual = 0\nchosen = rand(findall(predict_label(M, data) .== factual))\nx = select_factual(data, chosen)\n\n# Generate counterfactual explanation:\nη = 0.01\ngenerator = GenericGenerator(; opt=Descent(η), λ=0.01)\nconv = CounterfactualExplanations.Convergence.DecisionThresholdConvergence(;\n decision_threshold=0.9, max_iter=100\n)\nce = generate_counterfactual(x, target, data, M, generator; convergence=conv)\nplot(ce, alpha=0.1)","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"(Image: )","category":"page"},{"location":"extensions/laplace_redux/#References","page":"LaplaceRedux","title":"References","text":"","category":"section"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"Daxberger, Erik, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, and Philipp Hennig. 2021. “Laplace Redux-Effortless Bayesian Deep Learning.” Advances in Neural Information Processing Systems 34.","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"Immer, Alexander, Maciej Korzepa, and Matthias Bauer. 2020. “Improving Predictions of Bayesian Neural Networks via Local Linearization.” https://arxiv.org/abs/2008.08400.","category":"page"},{"location":"extensions/laplace_redux/","page":"LaplaceRedux","title":"LaplaceRedux","text":"Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.","category":"page"},{"location":"contribute/performance/","page":"-","title":"-","text":"Random.seed!(42)\n# Counteractual data and model:\ndata = TaijaData.load_linearly_separable()\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\nM = fit_model(counterfactual_data, :Linear)\ntarget = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)\n\n# Search:\ngenerator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"contribute/performance/","page":"-","title":"-","text":"data_large = TaijaData.load_linearly_separable(100000)\ncounterfactual_data_large = DataPreprocessing.CounterfactualData(data_large...)","category":"page"},{"location":"contribute/performance/","page":"-","title":"-","text":"@time generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"contribute/performance/","page":"-","title":"-","text":"@time generate_counterfactual(x, target, counterfactual_data_large, M, generator)","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/feature_tweak/#FeatureTweakGenerator","page":"FeatureTweak","title":"FeatureTweakGenerator","text":"","category":"section"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Feature Tweak refers to the generator introduced by Tolomei et al. (2017). Our implementation takes inspiration from the featureTweakPy library.","category":"page"},{"location":"explanation/generators/feature_tweak/#Description","page":"FeatureTweak","title":"Description","text":"","category":"section"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Feature Tweak is a powerful recourse algorithm for ensembles of tree-based classifiers such as random forests. Though the problem of understanding how an input to an ensemble model could be transformed in such a way that the model changes its original prediction has been proven to be NP-hard (Tolomei et al. 2017), Feature Tweak provides an algorithm that manages to tractably solve this problem in multiple real-world applications. An example of a problem Feature Tweak is able to efficiently solve, explored in depth in Tolomei et al. (2017) is the problem of transforming an advertisement that has been classified by the ensemble model as a low-quality advertisement to a high-quality one through small changes to its features. With the help of Feature Tweak, advertisers can both learn about the reasons a particular ad was marked to have a low quality, as well as receive actionable suggestions about how to convert a low-quality ad into a high-quality one.","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Though Feature Tweak is a powerful way of avoiding brute-force search in an exponential search space, it does not come without disadvantages. The primary limitations of the approach are that it’s currently only applicable to tree-based classifiers and works only in the setting of binary classification. Another problem is that though the algorithm avoids exponential-time search, it is often still computationally expensive. The algorithm may be improved in the future to tackle all of these shortcomings.","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"The following equation displays how a true negative instance x can be transformed into a positively predicted instance x’. To be more precise, x’ is the best possible transformation among all transformations **x***, computed with a cost function δ.","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"beginaligned\nmathbfx^prime = arg_mathbfx^* min delta(mathbfx mathbfx^*) hatf(mathbfx) = -1 wedge hatf(mathbfx^*) = +1 \nendaligned","category":"page"},{"location":"explanation/generators/feature_tweak/#Example","page":"FeatureTweak","title":"Example","text":"","category":"section"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"In this example we apply the Feature Tweak algorithm to a decision tree and a random forest trained on the moons dataset. We first load the data and fit the models:","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"n = 500\ncounterfactual_data = CounterfactualData(TaijaData.load_moons(n)...)\n\n# Classifiers\ndecision_tree = CounterfactualExplanations.Models.fit_model(\n counterfactual_data, :DecisionTree; max_depth=5, min_samples_leaf=3\n)\nforest = Models.fit_model(counterfactual_data, :RandomForest)","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Next, we select a point to explain and a target class to transform the point to. We then search for counterfactuals using the FeatureTweakGenerator:","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"# Select a point to explain:\nx = float32.([1, -0.5])[:,:]\nfactual = Models.predict_label(forest, x)\ntarget = counterfactual_data.y_levels[findall(counterfactual_data.y_levels != factual)][1]\n\n# Search for counterfactuals:\ngenerator = FeatureTweakGenerator(ϵ=0.1)\ntree_counterfactual = generate_counterfactual(\n x, target, counterfactual_data, decision_tree, generator\n)\nforest_counterfactual = generate_counterfactual(\n x, target, counterfactual_data, forest, generator\n)","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"The resulting counterfactuals are shown below:","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"p1 = plot(\n tree_counterfactual;\n colorbar=false,\n title=\"Decision Tree\",\n)\n\np2 = plot(\n forest_counterfactual; title=\"Random Forest\",\n colorbar=false,\n)\n\ndisplay(plot(p1, p2; size=(800, 400)))","category":"page"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"(Image: )","category":"page"},{"location":"explanation/generators/feature_tweak/#References","page":"FeatureTweak","title":"References","text":"","category":"section"},{"location":"explanation/generators/feature_tweak/","page":"FeatureTweak","title":"FeatureTweak","text":"Tolomei, Gabriele, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017. “Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 465–74. https://doi.org/10.1145/3097983.3098039.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/clue/#CLUEGenerator","page":"CLUE","title":"CLUEGenerator","text":"","category":"section"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"In this tutorial, we introduce the CLUEGenerator, a counterfactual generator based on the Counterfactual Latent Uncertainty Explanations (CLUE) method proposed by Antorán et al. (2020).","category":"page"},{"location":"explanation/generators/clue/#Description","page":"CLUE","title":"Description","text":"","category":"section"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"The CLUEGenerator leverages differentiable probabilistic models, such as Bayesian Neural Networks (BNNs), to estimate uncertainty in predictions. It aims to provide interpretable counterfactual explanations by identifying input patterns that lead to predictive uncertainty. The generator utilizes a latent variable framework and employs a decoder from a variational autoencoder (VAE) to generate counterfactual samples in latent space.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"The CLUE algorithm minimizes a loss function that combines uncertainty estimates and the distance between the generated counterfactual and the original input. By optimizing this loss function iteratively, the CLUEGenerator generates counterfactuals that are similar to the original observation but assigned low uncertainty.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"The formula for predictive entropy is as follow:","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"beginaligned\nH(y^*x^* D) = - sum_k=1^K p(y^*=c_kx^* D) log p(y^*=c_kx^* D)\nendaligned","category":"page"},{"location":"explanation/generators/clue/#Usage","page":"CLUE","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"While using one must keep in mind that the CLUE algorithim is meant to find a more robust datapoint of the same class, using CLUE generator without any additional penalties/losses will mean that it is not a counterfactual generator. The generated result will be of the same class as the original input, but a more robust datapoint.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"CLUE works best for BNN’s. The CLUEGenerator can be used with any differentiable probabilistic model, but the results may not be as good as with BNNs.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"The CLUEGenerator can be used in the following manner:","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"generator = CLUEGenerator()\nM = fit_model(counterfactual_data, :DeepEnsemble)\nconv = CounterfactualExplanations.Convergence.MaxIterConvergence(max_iter=1000)\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator;\n convergence=conv)\nplot(ce)","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"(Image: )","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"Extra: The CLUE generator can also be used upon already having achieved a counterfactual with a different generator. In this case, you can use CLUE and make the counterfactual more robust.","category":"page"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"Note: The above documentation is based on the information provided in the CLUE paper. Please refer to the original paper for more detailed explanations and implementation specifics.","category":"page"},{"location":"explanation/generators/clue/#References","page":"CLUE","title":"References","text":"","category":"section"},{"location":"explanation/generators/clue/","page":"CLUE","title":"CLUE","text":"Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. “Getting a Clue: A Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/revise/#REVISEGenerator","page":"REVISE","title":"REVISEGenerator","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"REVISE is a Latent Space generator introduced by Joshi et al. (2019).","category":"page"},{"location":"explanation/generators/revise/#Description","page":"REVISE","title":"Description","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The current consensus in the literature is that Counterfactual Explanations should be realistic: the generated counterfactuals should look like they were generated by the data-generating process (DGP) that governs the problem at hand. With respect to Algorithmic Recourse, it is certainly true that counterfactuals should be realistic in order to be actionable for individuals.[1] To address this need, researchers have come up with various approaches in recent years. Among the most popular approaches is Latent Space Search, which was first proposed in Joshi et al. (2019): instead of traversing the feature space directly, this approach relies on a separate generative model that learns a latent space representation of the DGP. Assuming the generative model is well-specified, access to the learned latent embeddings of the data comes with two advantages:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Since the learned DGP is encoded in the latent space, the generated counterfactuals will respect the learned representation of the data. In practice, this means that counterfactuals will be realistic.\nThe latent space is typically a compressed (i.e. lower dimensional) version of the feature space. This makes the counterfactual search less costly.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"There are also certain disadvantages though:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Learning generative models is (typically) an expensive task, which may well outweigh the benefits associated with utlimately traversing a lower dimensional space.\nIf the generative model is poorly specified, this will affect the quality of the counterfactuals.[2]","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Anyway, traversing latent embeddings is a powerful idea that may be very useful depending on the specific context. This tutorial introduces the concept and how it is implemented in this package.","category":"page"},{"location":"explanation/generators/revise/#Usage","page":"REVISE","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"generator = REVISEGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"(Image: )","category":"page"},{"location":"explanation/generators/revise/#Worked-2D-Examples","page":"REVISE","title":"Worked 2D Examples","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Below we load 2D data and train a VAE on it and plot the original samples against their reconstructions.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"# output: true\n\ncounterfactual_data = CounterfactualData(load_overlapping()...)\nX = counterfactual_data.X\ny = counterfactual_data.y\ninput_dim = size(X, 1)\nusing CounterfactualExplanations.GenerativeModels: VAE, train!, reconstruct\nvae = VAE(input_dim; nll=Flux.Losses.mse, epochs=100, λ=0.01, latent_dim=2, hidden_dim=32)\nflux_training_params.verbose = true\ntrain!(vae, X, y)\nX̂ = reconstruct(vae, X)[1]\np0 = scatter(X[1, :], X[2, :], color=:blue, label=\"Original\", xlab=\"x₁\", ylab=\"x₂\")\nscatter!(X̂[1, :], X̂[2, :], color=:orange, label=\"Reconstructed\", xlab=\"x₁\", ylab=\"x₂\")\np1 = scatter(X[1, :], X̂[1, :], color=:purple, label=\"\", xlab=\"x₁\", ylab=\"x̂₁\")\np2 = scatter(X[2, :], X̂[2, :], color=:purple, label=\"\", xlab=\"x₂\", ylab=\"x̂₂\")\nplt2 = plot(p1,p2, layout=(1,2), size=(800, 400))\nplot(p0, plt2, layout=(2,1), size=(800, 600))","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Next, we train a simple MLP for the classification task. Then we determine a target and factual class for our counterfactual search and select a random factual instance to explain.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"M = fit_model(counterfactual_data, :MLP)\ntarget = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Finally, we generate and visualize the generated counterfactual:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"# Search:\ngenerator = REVISEGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"(Image: )","category":"page"},{"location":"explanation/generators/revise/#3D-Example","page":"REVISE","title":"3D Example","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"To illustrate the notion of Latent Space search, let’s look at an example involving 3-dimensional input data, which we can still visualize. The code chunk below loads the data and implements the counterfactual search.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"# Data and Classifier:\ncounterfactual_data = CounterfactualData(load_blobs(k=3)...)\nX = counterfactual_data.X\nys = counterfactual_data.output_encoder.labels.refs\nM = fit_model(counterfactual_data, :MLP)\n\n# Randomly selected factual:\nx = select_factual(counterfactual_data,rand(1:size(counterfactual_data.X,2)))\ny = predict_label(M, counterfactual_data, x)[1]\ntarget = counterfactual_data.y_levels[counterfactual_data.y_levels .!= y][1]\n\n# Generate recourse:\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The figure below demonstrates the idea of searching counterfactuals in a lower-dimensional latent space: on the left, we can see the counterfactual search in the 3-dimensional feature space, while on the right we can see the corresponding search in the latent space.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"(Image: )","category":"page"},{"location":"explanation/generators/revise/#MNIST-data","page":"REVISE","title":"MNIST data","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Let’s carry the ideas introduced above over to a more complex example. The code below loads MNIST data as well as a pre-trained classifier and generative model for the data.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"using CounterfactualExplanations.Models: load_mnist_mlp, load_mnist_ensemble, load_mnist_vae\ncounterfactual_data = CounterfactualData(load_mnist()...)\nX, y = CounterfactualExplanations.DataPreprocessing.unpack_data(counterfactual_data)\ninput_dim, n_obs = size(counterfactual_data.X)\nM = load_mnist_mlp()\nvae = load_mnist_vae()","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The F1-score of our pre-trained image classifier on test data is: 0.94","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Before continuing, we supply the pre-trained generative model to our data container:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"counterfactual_data.generative_model = vae # assign generative model","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Now let’s define a factual and target label:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"# Randomly selected factual:\nRandom.seed!(2023)\nfactual_label = 8\nx = reshape(X[:,rand(findall(predict_label(M, counterfactual_data).==factual_label))],input_dim,1)\ntarget = 3\nfactual = predict_label(M, counterfactual_data, x)[1]","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Using REVISE, we are going to turn a randomly drawn 8 into a 3.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The API call is the same as always:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"γ = 0.95\nconv = \n CounterfactualExplanations.Convergence.DecisionThresholdConvergence(decision_threshold=γ)\n# Define generator:\ngenerator = REVISEGenerator(opt=Flux.Adam(0.1))\n# Generate recourse:\nce = generate_counterfactual(x, target, counterfactual_data, M, generator; convergence=conv)","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"The chart below shows the results:","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"(Image: )","category":"page"},{"location":"explanation/generators/revise/#References","page":"REVISE","title":"References","text":"","category":"section"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"[1] In general, we believe that there may be a trade-off between creating counterfactuals that respect the DGP vs. counterfactuals reflect the behaviour of the black-model in question - both accurately and complete.","category":"page"},{"location":"explanation/generators/revise/","page":"REVISE","title":"REVISE","text":"[2] We believe that there is another potentially crucial disadvantage of relying on a separate generative model: it reallocates the task of learning realistic explanations for the data from the black-box model to the generative model.","category":"page"},{"location":"explanation/optimisers/overview/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/optimisers/overview/#Optimisation-Rules","page":"Overview","title":"Optimisation Rules","text":"","category":"section"},{"location":"explanation/optimisers/overview/","page":"Overview","title":"Overview","text":"Counterfactual search is an optimization problem. Consequently, the choice of the optimisation rule affects the generated counterfactuals. In the short term, we aim to enable users to choose any of the available Flux optimisers. This has not been sufficiently tested yet, and you may run into issues.","category":"page"},{"location":"explanation/optimisers/overview/#Custom-Optimisation-Rules","page":"Overview","title":"Custom Optimisation Rules","text":"","category":"section"},{"location":"explanation/optimisers/overview/","page":"Overview","title":"Overview","text":"Flux optimisers are specifically designed for deep learning, and in particular, for learning model parameters. In counterfactual search, the features are the free parameters that we are optimising over. To this end, some custom optimisation rules are necessary to incorporate ideas presented in the literature. In the following, we introduce those rules.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/data_preprocessing/#Handling-Data","page":"Handling Data","title":"Handling Data","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The package works with custom data containers that contain the input and output data as well as information about the type and mutability of features. In this tutorial, we will see how data can be prepared for use with the package.","category":"page"},{"location":"tutorials/data_preprocessing/#Basic-Functionality","page":"Handling Data","title":"Basic Functionality","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"To demonstrate the basic way to prepare data, let’s look at a standard benchmark dataset: Fisher’s classic iris dataset. We can use MLDatasets to load this data.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"dataset = Iris()","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Our data constructor CounterfactualData needs at least two inputs: features X and targets y.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"X = dataset.features\ny = dataset.targets","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Next, we convert the input data to a Tables.MatrixTable (following MLJ.jl) convention. Concerning the target variable, we just assign grab the first column of the data frame.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"X = table(Tables.matrix(X))\ny = y[:,1]","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Now we can feed these two ingredients to our constructor:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data = CounterfactualData(X, y)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Under the hood, the constructor performs basic preprocessing steps. For example, the output variable y is automatically one-hot encoded:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data.y","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"3×150 Matrix{Bool}:\n 1 1 1 1 1 1 1 1 1 1 1 1 1 … 0 0 0 0 0 0 0 0 0 0 0 0\n 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\n 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Similarly, a transformer used to scale continuous input features is automatically fitted:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data.dt","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"ZScoreTransform{Float64, Vector{Float64}}(4, 2, [5.843333333333335, 3.0540000000000007, 3.7586666666666693, 1.1986666666666672], [0.8280661279778629, 0.4335943113621737, 1.7644204199522617, 0.7631607417008414])","category":"page"},{"location":"tutorials/data_preprocessing/#Categorical-Features","page":"Handling Data","title":"Categorical Features","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"For the counterfactual search, it is important to distinguish between continuous and categorical features. This is because categorical features cannot be perturbed arbitrarily: they can take specific discrete values, but not just any value on the real line.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Consider the following example:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"y = rand([1,0],4)\nX = (\n name=categorical([\"Danesh\", \"Lee\", \"Mary\", \"John\"]),\n grade=categorical([\"A\", \"B\", \"A\", \"C\"], ordered=true),\n sex=categorical([\"male\",\"female\",\"male\",\"male\"]),\n height=[1.85, 1.67, 1.5, 1.67],\n)\nschema(X)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"┌────────┬──────────────────┬──────────────────────────────────┐\n│ names │ scitypes │ types │\n├────────┼──────────────────┼──────────────────────────────────┤\n│ name │ Multiclass{4} │ CategoricalValue{String, UInt32} │\n│ grade │ OrderedFactor{3} │ CategoricalValue{String, UInt32} │\n│ sex │ Multiclass{2} │ CategoricalValue{String, UInt32} │\n│ height │ Continuous │ Float64 │\n└────────┴──────────────────┴──────────────────────────────────┘","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Typically, in the context of Unserpervised Learning, categorical features are one-hot or dummy encoded. To this end, we could use MLJ, for example:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"hot = OneHotEncoder()\nmach = MLJBase.fit!(machine(hot, X))\nW = MLJBase.transform(mach, X)\nX = permutedims(MLJBase.matrix(W))","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"In all likelihood, this pre-processing step already happens at the stage, when the supervised model is trained. Since our counterfactual generators need to work in the same feature domain as the model they are intended to explain, we assume that categorical features are already encoded.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The CounterfactualData constructor takes two optional arguments that can be used to specify the indices of categorical and continuous features. By default, all features are assumed to be continuous. For categorical features, the constructor expects an array of arrays of integers (Vector{Vector{Int}}) where each subarray includes the indices of all one-hot encoded rows related to a single categorical feature. In the example above, the name feature is one-hot encoded across rows 1, 2, 3 and 4 of X, the grade feature is encoded across the following three rows, etc.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"schema(W)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"┌──────────────┬────────────┬─────────┐\n│ names │ scitypes │ types │\n├──────────────┼────────────┼─────────┤\n│ name__Danesh │ Continuous │ Float64 │\n│ name__John │ Continuous │ Float64 │\n│ name__Lee │ Continuous │ Float64 │\n│ name__Mary │ Continuous │ Float64 │\n│ grade__A │ Continuous │ Float64 │\n│ grade__B │ Continuous │ Float64 │\n│ grade__C │ Continuous │ Float64 │\n│ sex__female │ Continuous │ Float64 │\n│ sex__male │ Continuous │ Float64 │\n│ height │ Continuous │ Float64 │\n└──────────────┴────────────┴─────────┘","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The code chunk below assigns the categorical and continuous feature indices:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"features_categorical = [\n [1,2,3,4], # name\n [5,6,7], # grade\n [8,9] # sex\n]\nfeatures_continuous = [10]","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"When instantiating the data container, these indices just need to be supplied as keyword arguments:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data = CounterfactualData(\n X,y;\n features_categorical = features_categorical,\n features_continuous = features_continuous\n)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"This will ensure that the discrete domain of categorical features is respected in the counterfactual search. We achieve this through a form of Projected Gradient Descent and it works for any of our counterfactual generators.","category":"page"},{"location":"tutorials/data_preprocessing/#Example","page":"Handling Data","title":"Example","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"To see this in action, let’s load some synthetic data using MLJ:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"N = 1000\nX, ys = MLJBase.make_blobs(N, 2; centers=2, as_table=false, center_box=(-5 => 5), cluster_std=0.5)\nys .= ys.==2","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Next, we generate a synthetic categorical feature based on the output variable. First, we define the discrete levels:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"cat_values = [\"X\",\"Y\",\"Z\"]","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Next, we impose that the categorical feature is most likely to take the first discrete level, namely X, whenever y is equal to 1.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"xcat = map(ys) do y\n if y==1\n x = sample(cat_values, Weights([0.8,0.1,0.1]))\n else\n x = sample(cat_values, Weights([0.1,0.1,0.8]))\n end\nend\nxcat = categorical(xcat)\nX = (\n x1 = X[:,1],\n x2 = X[:,2],\n x3 = xcat\n)\nschema(X)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"As above, we use a OneHotEncoder to transform the data:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"hot = OneHotEncoder()\nmach = MLJBase.fit!(machine(hot, X))\nW = MLJBase.transform(mach, X)\nschema(W)\nX = permutedims(MLJBase.matrix(W))","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Finally, we assign the categorical indices and instantiate our data container:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"features_categorical = [collect(3:size(X,1))]\ncounterfactual_data = CounterfactualData(\n X,ys';\n features_categorical = features_categorical,\n)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"With the data pre-processed we can use the fit_model function to train a simple classifier:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"M = fit_model(counterfactual_data, :Linear)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Now it is finally time to generate counterfactuals. We first define 1 as our target and then choose a random sample from the non-target class:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"target = 1\nfactual = 0\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen) ","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"5×1 Matrix{Float32}:\n -2.9433472\n 0.5782963\n 0.0\n 0.0\n 1.0","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The factual x belongs to group Z.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"We generate a counterfactual for x using the standard API call:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"generator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"CounterfactualExplanation\nConvergence: ✅ after 7 steps.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The search yields the following counterfactual:","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"x′ = counterfactual(ce)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"5-element Vector{Float32}:\n 1.1222683\n 0.7145791\n 0.0\n 0.0\n 1.0","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"It belongs to group Z.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"This is intuitive because by construction the categorical variable is most likely to take that value when y is equal to the target outcome.","category":"page"},{"location":"tutorials/data_preprocessing/#Immutable-Features","page":"Handling Data","title":"Immutable Features","text":"","category":"section"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"In practice, features usually cannot be perturbed arbitrarily. Suppose, for example, that one of the features used by a bank to predict the creditworthiness of its clients is gender. If a counterfactual explanation for the prediction model indicates that female clients should change their gender to improve their creditworthiness, then this is an interesting insight (it reveals gender bias), but it is not usually an actionable transformation in practice. In such cases, we may want to constrain the mutability of features to ensure actionable and realistic recourse.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"To illustrate how this can be implemented in CounterfactualExplanations.jl we will continue to work with the synthetic data from the previous section. Mutability of features can be defined in terms of four different options: 1) the feature is mutable in both directions, 2) the feature can only increase (e.g. age), 3) the feature can only decrease (e.g. time left until your next deadline) and 4) the feature is not mutable (e.g. skin colour, ethnicity, …). To specify which category a feature belongs to, you can pass a vector of symbols containing the mutability constraints at the pre-processing stage. For each feature you can choose from these four options: :both (mutable in both directions), :increase (only up), :decrease (only down) and :none (immutable). By default, nothing is passed to that keyword argument and it is assumed that all features are mutable in both directions.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"Below we impose that the second feature is immutable.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"counterfactual_data = CounterfactualData(load_linearly_separable()...)\nM = fit_model(counterfactual_data, :Linear)\ncounterfactual_data.mutability = [:both, :none]","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"target = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen) \nce = generate_counterfactual(x, target, counterfactual_data, M, generator)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"The resulting counterfactual path is shown in the chart below. Since only the first feature can be perturbed, the sample can only move along the horizontal axis.","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"plot(ce)","category":"page"},{"location":"tutorials/data_preprocessing/","page":"Handling Data","title":"Handling Data","text":"(Image: ) ","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/model_catalogue/#Model-Catalogue","page":"Model Catalogue","title":"Model Catalogue","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"While in general it is assumed that users will use this package to explain their pre-trained models, we provide out-of-the-box functionality to train various simple default models. In this tutorial, we will see how these models can be fitted to CounterfactualData.","category":"page"},{"location":"tutorials/model_catalogue/#Available-Models","page":"Model Catalogue","title":"Available Models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The standard_models_catalogue can be used to inspect the available default models:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"standard_models_catalogue","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Dict{Symbol, Any} with 4 entries:\n :Linear => Linear\n :LaplaceRedux => LaplaceReduxModel\n :DeepEnsemble => FluxEnsemble\n :MLP => FluxModel","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The dictionary keys correspond to the model names. In this case, the dictionary values are constructors that can be used called on instances of type CounterfactualData to fit the corresponding model. In most cases, users will find it most convenient to use the fit_model API call instead.","category":"page"},{"location":"tutorials/model_catalogue/#Fitting-Models","page":"Model Catalogue","title":"Fitting Models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Models from the standard model catalogue are a core part of the package and thus compatible with all offered counterfactual generators and functionalities.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The all_models_catalogue can be used to inspect all models offered by the package:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"all_models_catalogue","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"However, when using models not included in the standard_models_catalogue, additional caution is advised: they might not be supported by all counterfactual generators or they might not be models native to Julia. Thus, a more thorough reading of their documentation may be necessary to make sure that they are used correctly.","category":"page"},{"location":"tutorials/model_catalogue/#Fitting-Flux-Models","page":"Model Catalogue","title":"Fitting Flux Models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"First, let’s load one of the synthetic datasets. For this, we’ll first need to import the TaijaData.jl package:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"n = 500\ndata = TaijaData.load_multi_class(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"We could use a Deep Ensemble (Lakshminarayanan, Pritzel, and Blundell 2016) as follows:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"M = fit_model(counterfactual_data, :DeepEnsemble)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The returned object is an instance of type FluxEnsemble <: AbstractFittedModel and can be used in downstream tasks without further ado. For example, the resulting fit can be visualised using the generic plot() method as:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"plts = []\nfor target in counterfactual_data.y_levels\n plt = plot(M, counterfactual_data; target=target, title=\"p(y=$(target)|x,θ)\")\n plts = [plts..., plt]\nend\nplot(plts...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"(Image: )","category":"page"},{"location":"tutorials/model_catalogue/#Importing-PyTorch-models","page":"Model Catalogue","title":"Importing PyTorch models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The package supports generating counterfactuals for any neural network that has been previously defined and trained using PyTorch, regardless of the specific architectural details of the model. To generate counterfactuals for a PyTorch model, save the model inside a .pt file and call the following function:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_loaded = TaijaInteroperability.pytorch_model_loader(\n \"$(pwd())/docs/src/tutorials/miscellaneous\",\n \"neural_network_class\",\n \"NeuralNetwork\",\n \"$(pwd())/docs/src/tutorials/miscellaneous/pretrained_model.pt\"\n)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The method pytorch_model_loader requires four arguments:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The path to the folder with a .py file where the PyTorch model is defined\nThe name of the file where the PyTorch model is defined\nThe name of the class of the PyTorch model\nThe path to the Pickle file that holds the model weights","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"In the above case:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The file defining the model is inside $(pwd())/docs/src/tutorials/miscellaneous\nThe name of the .py file holding the model definition is neural_network_class\nThe name of the model class is NeuralNetwork\nThe Pickle file is located at $(pwd())/docs/src/tutorials/miscellaneous/pretrained_model.pt","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Though the model file and Pickle file are inside the same directory in this tutorial, this does not necessarily have to be the case.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The reason why the model file and Pickle file have to be provided separately is that the package expects an already trained PyTorch model as input. It is also possible to define new PyTorch models within the package, but since this is not the expected use of our package, special support is not offered for that. A guide for defining Python and PyTorch classes in Julia through PythonCall.jl can be found here.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Once the PyTorch model has been loaded into the package, wrap it inside the PyTorchModel class:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_pytorch = TaijaInteroperability.PyTorchModel(model_loaded, counterfactual_data.likelihood)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"This model can now be passed into the generators like any other.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Please note that the functionality for generating counterfactuals for Python models is only available if your Julia version is 1.8 or above. For Julia 1.7 users, we recommend upgrading the version to 1.8 or 1.9 before loading a PyTorch model into the package.","category":"page"},{"location":"tutorials/model_catalogue/#Importing-R-torch-models","page":"Model Catalogue","title":"Importing R torch models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"warning: Not fully tested\nPlease note that due to the incompatibility between RCall and PythonCall, it is not feasible to test both PyTorch and RTorch implementations within the same pipeline. While the RTorch implementation has been manually tested, we cannot ensure its consistent functionality as it is inherently susceptible to bugs.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The CounterfactualExplanations package supports generating counterfactuals for neural networks that have been defined and trained using R torch. Regardless of the specific architectural details of the model, you can easily generate counterfactual explanations by following these steps.","category":"page"},{"location":"tutorials/model_catalogue/#Saving-the-R-torch-model","page":"Model Catalogue","title":"Saving the R torch model","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"First, save your trained R torch model as a .pt file using the torch_save() function provided by the R torch library. This function allows you to serialize the model and save it to a file. For example:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"torch_save(model, file = \"$(pwd())/docs/src/tutorials/miscellaneous/r_model.pt\")","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Make sure to specify the correct file path where you want to save the model.","category":"page"},{"location":"tutorials/model_catalogue/#Loading-the-R-torch-model","page":"Model Catalogue","title":"Loading the R torch model","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"To import the R torch model into the CounterfactualExplanations package, use the rtorch_model_loader() function. This function loads the model from the previously saved .pt file. Here is an example of how to load the R torch model:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_loaded = TaijaInteroperability.rtorch_model_loader(\"$(pwd())/docs/src/tutorials/miscellaneous/r_model.pt\")","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The rtorch_model_loader() function requires only one argument:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_path: The path to the .pt file that contains the trained R torch model.","category":"page"},{"location":"tutorials/model_catalogue/#Wrapping-the-R-torch-model","page":"Model Catalogue","title":"Wrapping the R torch model","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Once the R torch model has been loaded into the package, wrap it inside the RTorchModel class. This step prepares the model to be used by the counterfactual generators. Here is an example:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_R = TaijaInteroperability.RTorchModel(model_loaded, counterfactual_data.likelihood)","category":"page"},{"location":"tutorials/model_catalogue/#Generating-counterfactuals-with-the-R-torch-model","page":"Model Catalogue","title":"Generating counterfactuals with the R torch model","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Now that the R torch model has been wrapped inside the RTorchModel class, you can pass it into the counterfactual generators as you would with any other model.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Please note that RCall is not fully compatible with PythonCall. Therefore, it is advisable not to import both R torch and PyTorch models within the same Julia session. Additionally, it’s worth mentioning that the R torch integration is still untested in the CounterfactualExplanations package.","category":"page"},{"location":"tutorials/model_catalogue/#Tuning-Flux-Models","page":"Model Catalogue","title":"Tuning Flux Models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"By default, model architectures are very simple. Through optional arguments, users have some control over the neural network architecture and can choose to impose regularization through dropout. Let’s tackle a more challenging dataset: MNIST (LeCun 1998).","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"data = TaijaData.load_mnist(10000)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\ntrain_data, test_data = \n CounterfactualExplanations.DataPreprocessing.train_test_split(counterfactual_data)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"(Image: )","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"In this case, we will use a Multi-Layer Perceptron (MLP) but we will adjust the model and training hyperparameters. Parameters related to training of Flux.jl models are currently stored in a mutable container:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"flux_training_params","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"CounterfactualExplanations.FluxModelParams\n loss: Symbol logitbinarycrossentropy\n opt: Symbol Adam\n n_epochs: Int64 100\n batchsize: Int64 1\n verbose: Bool false","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"In cases like this one, where model training can be expected to take a few moments, it can be useful to activate verbosity, so let’s set the corresponding field value to true. We’ll also impose mini-batch training:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"flux_training_params.verbose = true\nflux_training_params.batchsize = round(size(train_data.X,2)/10)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"To account for the fact that this is a slightly more challenging task, we will use an appropriate number of hidden neurons per layer. We will also activate dropout regularization. To scale networks up further, it is also possible to adjust the number of hidden layers, which we will not do here.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_params = (\n n_hidden = 32,\n dropout = true\n)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The model_params can be supplied to the familiar API call:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"M = fit_model(train_data, :MLP; model_params...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"FluxModel(Chain(Dense(784 => 32, relu), Dropout(0.25, active=false), Dense(32 => 10)), :classification_multi)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The model performance on our test set can be evaluated as follows:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"model_evaluation(M, test_data)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"1-element Vector{Float64}:\n 0.9185","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Finally, let’s restore the default training parameters:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"CounterfactualExplanations.reset!(flux_training_params)","category":"page"},{"location":"tutorials/model_catalogue/#Fitting-and-tuning-MLJ-models","page":"Model Catalogue","title":"Fitting and tuning MLJ models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Among models from the MLJ library, two models are integrated as part of the core functionality of the package:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"mlj_models_catalogue","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"These models are compatible with the Feature Tweak generator. Support for other generators has not been implemented, as both decision trees and random forests are non-differentiable tree-based models and thus, gradient-based generators don’t apply for them.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Tuning MLJ models is very simple. As the first step, let’s reload the dataset:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"n = 500\ndata = TaijaData.load_moons(n)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Using the usual procedure for fitting models, we can call the following method:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"tree = CounterfactualExplanations.Models.fit_model(counterfactual_data, :DecisionTree)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"However, it’s also possible to tune the DecisionTreeClassifier’s parameters. This can be done using the keyword arguments when calling fit_model() as follows:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"tree = CounterfactualExplanations.Models.fit_model(counterfactual_data, :DecisionTree; max_depth=2, min_samples_leaf=3)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"For all supported MLJ models, every tunable parameter they have is supported as a keyword argument. The tunable parameters for the DecisionTreeModel and the RandomForestModel can be found from the documentation of the DecisionTree.jl package under the Decision Tree Classifier and Random Forest Classifier sections.","category":"page"},{"location":"tutorials/model_catalogue/#Package-extension-models","page":"Model Catalogue","title":"Package extension models","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The package also includes two models which don’t form a part of the core functionality of the package, but which can be accessed as package extensions. These are the EvoTreeModel from the MLJ library and the LaplaceReduxModel from LaplaceRedux.jl.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"To trigger the package extensions, the weak dependency first has to be loaded with the using keyword:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"using EvoTrees","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Once this is done, the extension models can be used like any other model:","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"M = fit_model(counterfactual_data, :EvoTree; model_params...)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"EvoTreesExt.EvoTreeModel(machine(EvoTreeClassifier{EvoTrees.MLogLoss}\n - nrounds: 100\n - L2: 0.0\n - lambda: 0.0\n - gamma: 0.0\n - eta: 0.1\n - max_depth: 6\n - min_weight: 1.0\n - rowsample: 1.0\n - colsample: 1.0\n - nbins: 64\n - alpha: 0.5\n - tree_type: binary\n - rng: MersenneTwister(123, (0, 9018, 8016, 884))\n, …), :classification_multi)","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"The tunable parameters for the EvoTreeModel can be found from the documentation of the EvoTrees.jl package under the EvoTreeClassifier section.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Please note that support for counterfactual generation with both LaplaceReduxModel and EvoTreeModel is not yet fully implemented.","category":"page"},{"location":"tutorials/model_catalogue/#References","page":"Model Catalogue","title":"References","text":"","category":"section"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” https://arxiv.org/abs/1612.01474.","category":"page"},{"location":"tutorials/model_catalogue/","page":"Model Catalogue","title":"Model Catalogue","text":"LeCun, Yann. 1998. “The MNIST Database of Handwritten Digits.”","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/overview/#generators_explanation","page":"Overview","title":"Counterfactual Generators","text":"","category":"section"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"Counterfactual generators form the very core of this package. The generator_catalogue can be used to inspect the available generators:","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"generator_catalogue","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"Dict{Symbol, Any} with 11 entries:\n :gravitational => GravitationalGenerator\n :growing_spheres => GrowingSpheresGenerator\n :revise => REVISEGenerator\n :clue => CLUEGenerator\n :probe => ProbeGenerator\n :dice => DiCEGenerator\n :feature_tweak => FeatureTweakGenerator\n :claproar => ClaPROARGenerator\n :wachter => WachterGenerator\n :generic => GenericGenerator\n :greedy => GreedyGenerator","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"The following sections provide brief descriptions of all of them.","category":"page"},{"location":"explanation/generators/overview/#Gradient-based-Counterfactual-Generators","page":"Overview","title":"Gradient-based Counterfactual Generators","text":"","category":"section"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"At the time of writing, all generators are gradient-based: that is, counterfactuals are searched through gradient descent. In Altmeyer et al. (2023) we lay out a general methodological framework that can be applied to all of these generators:","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"beginaligned\nmathbfs^prime = arg min_mathbfs^prime in mathcalS left textyloss(M(f(mathbfs^prime))y^*)+ lambda textcost(f(mathbfs^prime)) right \nendaligned ","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"“Here mathbfs^prime=lefts_k^primeright_K is a K-dimensional array of counterfactual states and f mathcalS mapsto mathcalX maps from the counterfactual state space to the feature space.” (Altmeyer et al. 2023)","category":"page"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"For most generators, the state space is the feature space (f is the identity function) and the number of counterfactuals K is one. Latent Space generators instead search counterfactuals in some latent space mathcalS. In this case, f corresponds to the decoder part of the generative model, that is the function that maps back from the latent space to inputs.","category":"page"},{"location":"explanation/generators/overview/#References","page":"Overview","title":"References","text":"","category":"section"},{"location":"explanation/generators/overview/","page":"Overview","title":"Overview","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"_contribute/#Contributing","page":"Contributing","title":"Contributing","text":"","category":"section"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"Our goal is to provide a go-to place for Counterfactual Explanations in Julia. To this end, the following is a non-exhaustive list of enhancements we have planned:","category":"page"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"Additional counterfactual generators and predictive models.\nAdditional datasets for testing, evaluation and benchmarking.\nSupport for regression models.","category":"page"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"For a complete list, have a look at outstanding issue.","category":"page"},{"location":"_contribute/#How-to-contribute?","page":"Contributing","title":"How to contribute?","text":"","category":"section"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"Any sort of contribution is welcome, in particular:","category":"page"},{"location":"_contribute/","page":"Contributing","title":"Contributing","text":"Should you spot any errors or something is not working, please just open an issue.\nIf you want to contribute your code, please proceed as follows:\nFork this repo and clone your fork: git clone https://github.com/your_username/CounterfactualExplanations.jl.\nImplement your modifications and submit a pull request.\nFor any other questions or comments, you can also start a discussion.","category":"page"},{"location":"assets/resources/#Further-Resources","page":"📚 Additional Resources","title":"Further Resources","text":"","category":"section"},{"location":"assets/resources/#JuliaCon-2022","page":"📚 Additional Resources","title":"JuliaCon 2022","text":"","category":"section"},{"location":"assets/resources/","page":"📚 Additional Resources","title":"📚 Additional Resources","text":"Slides: link","category":"page"},{"location":"assets/resources/#JuliaCon-Proceedings-Paper","page":"📚 Additional Resources","title":"JuliaCon Proceedings Paper","text":"","category":"section"},{"location":"assets/resources/","page":"📚 Additional Resources","title":"📚 Additional Resources","text":"TBD","category":"page"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"reference/#Reference","page":"🧐 Reference","title":"Reference","text":"","category":"section"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"In this reference, you will find a detailed overview of the package API.","category":"page"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"Reference guides are technical descriptions of the machinery and how to operate it. Reference material is information-oriented.— Diátaxis","category":"page"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"In other words, you come here because you want to take a very close look at the code 🧐.","category":"page"},{"location":"reference/#Content","page":"🧐 Reference","title":"Content","text":"","category":"section"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"Pages = [\"reference.md\"]\nDepth = 2","category":"page"},{"location":"reference/#Exported-functions","page":"🧐 Reference","title":"Exported functions","text":"","category":"section"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"Modules = [\n CounterfactualExplanations, \n CounterfactualExplanations.Convergence,\n CounterfactualExplanations.Evaluation,\n CounterfactualExplanations.DataPreprocessing,\n CounterfactualExplanations.Models,\n CounterfactualExplanations.GenerativeModels, \n CounterfactualExplanations.Generators, \n CounterfactualExplanations.Objectives\n]\nPrivate = false","category":"page"},{"location":"reference/#CounterfactualExplanations.RawOutputArrayType","page":"🧐 Reference","title":"CounterfactualExplanations.RawOutputArrayType","text":"RawOutputArrayType\n\nA type union for the allowed type for the output array y.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.RawTargetType","page":"🧐 Reference","title":"CounterfactualExplanations.RawTargetType","text":"RawTargetType\n\nA type union for the allowed types for the target variable.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.flux_training_params","page":"🧐 Reference","title":"CounterfactualExplanations.flux_training_params","text":"flux_training_params\n\nThe default training parameter for FluxModels etc.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.AbstractConvergence","page":"🧐 Reference","title":"CounterfactualExplanations.AbstractConvergence","text":"An abstract type that serves as the base type for convergence objects.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.AbstractCounterfactualExplanation","page":"🧐 Reference","title":"CounterfactualExplanations.AbstractCounterfactualExplanation","text":"Base type for counterfactual explanations.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.AbstractFittedModel","page":"🧐 Reference","title":"CounterfactualExplanations.AbstractFittedModel","text":"Base type for fitted models.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.AbstractGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.AbstractGenerator","text":"An abstract type that serves as the base type for counterfactual generators.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.CounterfactualExplanation","page":"🧐 Reference","title":"CounterfactualExplanations.CounterfactualExplanation","text":"A struct that collects all information relevant to a specific counterfactual explanation for a single individual.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.CounterfactualExplanation-Tuple{AbstractArray, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, AbstractGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.CounterfactualExplanation","text":"function CounterfactualExplanation(;\n\tx::AbstractArray,\n\ttarget::RawTargetType,\n\tdata::CounterfactualData,\n\tM::Models.AbstractFittedModel,\n\tgenerator::Generators.AbstractGenerator,\n\tnum_counterfactuals::Int = 1,\n\tinitialization::Symbol = :add_perturbation,\n convergence::Union{AbstractConvergence,Symbol}=:decision_threshold,\n)\n\nOuter method to construct a CounterfactualExplanation structure.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.EncodedOutputArrayType","page":"🧐 Reference","title":"CounterfactualExplanations.EncodedOutputArrayType","text":"EncodedOutputArrayType\n\nType of encoded output array.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.EncodedTargetType","page":"🧐 Reference","title":"CounterfactualExplanations.EncodedTargetType","text":"EncodedTargetType\n\nType of encoded target variable.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.OutputEncoder","page":"🧐 Reference","title":"CounterfactualExplanations.OutputEncoder","text":"OutputEncoder\n\nThe OutputEncoder takes a raw output array (y) and encodes it.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.OutputEncoder-Tuple{Union{Int64, AbstractFloat, String, Symbol}}","page":"🧐 Reference","title":"CounterfactualExplanations.OutputEncoder","text":"(encoder::OutputEncoder)(ynew::RawTargetType)\n\nWhen called on a new value ynew, the OutputEncoder encodes it based on the initial encoding.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.OutputEncoder-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.OutputEncoder","text":"(encoder::OutputEncoder)()\n\nOn call, the OutputEncoder returns the encoded output array.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.EvoTreeModel","page":"🧐 Reference","title":"CounterfactualExplanations.EvoTreeModel","text":"EvoTreeModel\n\nExposes the EvoTreeModel from the EvoTreesExt extension.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.LaplaceReduxModel","page":"🧐 Reference","title":"CounterfactualExplanations.LaplaceReduxModel","text":"LaplaceReduxModel\n\nExposes the LaplaceReduxModel from the LaplaceReduxExt extension.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.NeuroTreeModel","page":"🧐 Reference","title":"CounterfactualExplanations.NeuroTreeModel","text":"NeuroTreeModel\n\nExposes the NeuroTreeModel from the NeuroTreeExt extension.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Base.Iterators.Zip, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, AbstractGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"generate_counterfactual(\n x::Base.Iterators.Zip,\n target::RawTargetType,\n data::CounterfactualData,\n M::Models.AbstractFittedModel,\n generator::AbstractGenerator;\n kwargs...,\n)\n\nOverloads the generate_counterfactual method to accept a zip of factuals x and return a vector of counterfactuals.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Matrix, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, AbstractGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"generate_counterfactual(\n x::Matrix,\n target::RawTargetType,\n data::CounterfactualData,\n M::Models.AbstractFittedModel,\n generator::AbstractGenerator;\n num_counterfactuals::Int=1,\n initialization::Symbol=:add_perturbation,\n convergence::Union{AbstractConvergence,Symbol}=:decision_threshold,\n timeout::Union{Nothing,Real}=nothing,\n)\n\nThe core function that is used to run counterfactual search for a given factual x, target, counterfactual data, model and generator. Keywords can be used to specify the desired threshold for the predicted target class probability and the maximum number of iterations.\n\nArguments\n\nx::Matrix: Factual data point.\ntarget::RawTargetType: Target class.\ndata::CounterfactualData: Counterfactual data.\nM::Models.AbstractFittedModel: Fitted model.\ngenerator::AbstractGenerator: Generator.\nnum_counterfactuals::Int=1: Number of counterfactuals to generate for factual.\ninitialization::Symbol=:add_perturbation: Initialization method. By default, the initialization is done by adding a small random perturbation to the factual to achieve more robustness.\nconvergence::Union{AbstractConvergence,Symbol}=:decision_threshold: Convergence criterion. By default, the convergence is based on the decision threshold.\ntimeout::Union{Nothing,Int}=nothing: Timeout in seconds.\n\nExamples\n\nGeneric generator\n\nusing CounterfactualExplanations\n\n# Data:\nusing CounterfactualExplanations.Data\nusing Random\nRandom.seed!(1234)\nxs, ys = Data.toy_data_linear()\nX = hcat(xs...)\ncounterfactual_data = CounterfactualData(X,ys')\n\n# Model\nusing CounterfactualExplanations.Models: LogisticModel, probs \n# Logit model:\nw = [1.0 1.0] # true coefficients\nb = 0\nM = LogisticModel(w, [b])\n\n# Randomly selected factual:\nx = select_factual(counterfactual_data,rand(1:size(X)[2]))\ny = round(probs(M, x)[1])\ntarget = round(probs(M, x)[1])==0 ? 1 : 0 \n\n# Counterfactual search:\ngenerator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Matrix, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, GrowingSpheresGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"generate_counterfactual(\n x::Matrix,\n target::RawTargetType,\n data::DataPreprocessing.CounterfactualData,\n M::Models.AbstractFittedModel,\n generator::Generators.GrowingSpheresGenerator;\n num_counterfactuals::Int=1,\n convergence::Union{AbstractConvergence,Symbol}=Convergence.DecisionThresholdConvergence(;\n decision_threshold=(1 / length(data.y_levels)), max_iter=1000\n ),\n kwrgs...,\n)\n\nOverloads the generate_counterfactual for the GrowingSpheresGenerator generator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Tuple{AbstractArray}, Vararg{Any}}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"Overloads the generate_counterfactual method to accept a tuple containing and array. This allows for broadcasting over Zip iterators.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.generate_counterfactual-Tuple{Vector{<:Matrix}, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData, AbstractFittedModel, AbstractGenerator}","page":"🧐 Reference","title":"CounterfactualExplanations.generate_counterfactual","text":"generate_counterfactual(\n x::Vector{<:Matrix},\n target::RawTargetType,\n data::CounterfactualData,\n M::Models.AbstractFittedModel,\n generator::AbstractGenerator;\n kwargs...,\n)\n\nOverloads the generate_counterfactual method to accept a vector of factuals x and return a vector of counterfactuals.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.get_target_index-Tuple{Any, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.get_target_index","text":"get_target_index(y_levels, target)\n\nUtility that returns the index of target in y_levels.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.path-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.path","text":"path(ce::CounterfactualExplanation)\n\nA convenience method that returns the entire counterfactual path.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.target_probs","page":"🧐 Reference","title":"CounterfactualExplanations.target_probs","text":"target_probs(\n ce::CounterfactualExplanation,\n x::Union{AbstractArray,Nothing}=nothing,\n)\n\nReturns the predicted probability of the target class for x. If x is nothing, the predicted probability corresponding to the counterfactual value is returned.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.terminated-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.terminated","text":"terminated(ce::CounterfactualExplanation)\n\nA convenience method that checks if the counterfactual search has terminated.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.total_steps-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.total_steps","text":"total_steps(ce::CounterfactualExplanation)\n\nA convenience method that returns the total number of steps of the counterfactual search.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.update!-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.update!","text":"update!(ce::CounterfactualExplanation)\n\nAn important subroutine that updates the counterfactual explanation. It takes a snapshot of the current counterfactual search state and passes it to the generator. Based on the current state the generator generates perturbations. Various constraints are then applied to the proposed vector of feature perturbations. Finally, the counterfactual search state is updated.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.convergence_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.convergence_catalogue","text":"convergence_catalogue\n\nA dictionary containing all convergence criteria.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Convergence.converged-Tuple{CounterfactualExplanations.Convergence.DecisionThresholdConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.converged","text":"converged(convergence::DecisionThresholdConvergence, ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has converged when the convergence criterion is the decision threshold.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.converged-Tuple{CounterfactualExplanations.Convergence.GeneratorConditionsConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.converged","text":"converged(convergence::GeneratorConditionsConvergence, ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has converged when the convergence criterion is generator_conditions.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.converged-Tuple{CounterfactualExplanations.Convergence.InvalidationRateConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.converged","text":"converged(convergence::InvalidationRateConvergence, ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has converged when the convergence criterion is invalidation rate.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.converged-Tuple{CounterfactualExplanations.Convergence.MaxIterConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.converged","text":"converged(convergence::MaxIterConvergence, ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has converged when the convergence criterion is maximum iterations. This means the counterfactual search will not terminate until the maximum number of iterations has been reached independently of the other convergence criteria.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.get_convergence_type-Tuple{AbstractConvergence, AbstractVector}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.get_convergence_type","text":"get_convergence_type(convergence::AbstractConvergence)\n\nReturns the convergence object.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.get_convergence_type-Tuple{Symbol, AbstractVector}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.get_convergence_type","text":"get_convergence_type(convergence::Symbol)\n\nReturns the convergence object from the dictionary of default convergence types.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.hinge_loss-Tuple{CounterfactualExplanations.Convergence.InvalidationRateConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.hinge_loss","text":"hinge_loss(convergence::InvalidationRateConvergence, ce::AbstractCounterfactualExplanation)\n\nCalculates the hinge loss of a counterfactual explanation.\n\nArguments\n\nconvergence::InvalidationRateConvergence: The convergence criterion to use.\nce::AbstractCounterfactualExplanation: The counterfactual explanation to calculate the hinge loss for.\n\nReturns\n\nThe hinge loss of the counterfactual explanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.invalidation_rate-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.invalidation_rate","text":"invalidation_rate(ce::AbstractCounterfactualExplanation)\n\nCalculates the invalidation rate of a counterfactual explanation.\n\nArguments\n\nce::AbstractCounterfactualExplanation: The counterfactual explanation to calculate the invalidation rate for.\nkwargs: Additional keyword arguments to pass to the function.\n\nReturns\n\nThe invalidation rate of the counterfactual explanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Convergence.threshold_reached-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Convergence.threshold_reached","text":"threshold_reached(ce::CounterfactualExplanation)\n\nDetermines if the predefined threshold for the target class probability has been reached.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.default_measures","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.default_measures","text":"The default evaluation measures.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Evaluation.Benchmark","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.Benchmark","text":"A container for benchmarks of counterfactual explanations. Instead of subtyping DataFrame, it contains a DataFrame of evaluation measures (see this discussion for why we don't subtype DataFrame directly).\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Evaluation.Benchmark-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.Benchmark","text":"(bmk::Benchmark)(; agg=mean)\n\nReturns a DataFrame containing evaluation measures aggregated by num_counterfactual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.benchmark-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.benchmark","text":"benchmark(\n data::CounterfactualData;\n models::Dict{<:Any,<:Any}=standard_models_catalogue,\n generators::Union{Nothing,Dict{<:Any,<:AbstractGenerator}}=nothing,\n measure::Union{Function,Vector{Function}}=default_measures,\n n_individuals::Int=5,\n suppress_training::Bool=false,\n factual::Union{Nothing,RawTargetType}=nothing,\n target::Union{Nothing,RawTargetType}=nothing,\n store_ce::Bool=false,\n parallelizer::Union{Nothing,AbstractParallelizer}=nothing,\n kwrgs...,\n)\n\nRuns the benchmarking exercise as follows:\n\nRandomly choose a factual and target label unless specified. \nIf no pretrained models are provided, it is assumed that a dictionary of callable model objects is provided (by default using the standard_models_catalogue). \nEach of these models is then trained on the data. \nFor each model separately choose n_individuals randomly from the non-target (factual) class. For each generator create a benchmark as in benchmark(xs::Union{AbstractArray,Base.Iterators.Zip}).\nFinally, concatenate the results.\n\nIf vertical_splits is specified to an integer, the computations are split vertically into vertical_splits chunks. In this case, the results are stored in a temporary directory and concatenated afterwards. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.benchmark-Tuple{Union{AbstractArray, Base.Iterators.Zip}, Union{Int64, AbstractFloat, String, Symbol}, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.benchmark","text":"benchmark(\n x::Union{AbstractArray,Base.Iterators.Zip},\n target::RawTargetType,\n data::CounterfactualData;\n models::Dict{<:Any,<:AbstractFittedModel},\n generators::Dict{<:Any,<:AbstractGenerator},\n measure::Union{Function,Vector{Function}}=default_measures,\n xids::Union{Nothing,AbstractArray}=nothing,\n dataname::Union{Nothing,Symbol,String}=nothing,\n verbose::Bool=true,\n store_ce::Bool=false,\n parallelizer::Union{Nothing,AbstractParallelizer}=nothing,\n kwrgs...,\n)\n\nFirst generates counterfactual explanations for factual x, the target and data using each of the provided models and generators. Then generates a Benchmark for the vector of counterfactual explanations as in benchmark(counterfactual_explanations::Vector{CounterfactualExplanation}).\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.benchmark-Tuple{Vector{CounterfactualExplanation}}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.benchmark","text":"benchmark(\n counterfactual_explanations::Vector{CounterfactualExplanation};\n meta_data::Union{Nothing,<:Vector{<:Dict}}=nothing,\n measure::Union{Function,Vector{Function}}=default_measures,\n store_ce::Bool=false,\n)\n\nGenerates a Benchmark for a vector of counterfactual explanations. Optionally meta_data describing each individual counterfactual explanation can be supplied. This should be a vector of dictionaries of the same length as the vector of counterfactuals. If no meta_data is supplied, it will be automatically inferred. All measure functions are applied to each counterfactual explanation. If store_ce=true, the counterfactual explanations are stored in the benchmark.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.evaluate","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.evaluate","text":"evaluate(\n ce::CounterfactualExplanation;\n measure::Union{Function,Vector{Function}}=default_measures,\n agg::Function=mean,\n report_each::Bool=false,\n output_format::Symbol=:Vector,\n pivot_longer::Bool=true\n)\n\nJust computes evaluation measures for the counterfactual explanation. By default, no meta data is reported. For report_meta=true, meta data is automatically inferred, unless this overwritten by meta_data. The optional meta_data argument should be a vector of dictionaries of the same length as the vector of counterfactual explanations. \n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Evaluation.redundancy-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.redundancy","text":"redundancy(ce::CounterfactualExplanation)\n\nComputes the feature redundancy: that is, the number of features that remain unchanged from their original, factual values.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.validity-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.validity","text":"validity(ce::CounterfactualExplanation; γ=0.5)\n\nChecks of the counterfactual search has been successful with respect to the probability threshold γ. In case multiple counterfactuals were generated, the function returns the proportion of successful counterfactuals.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.CounterfactualData-Tuple{AbstractMatrix, Union{AbstractMatrix, AbstractVector}}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.CounterfactualData","text":"CounterfactualData(\n X::AbstractMatrix, y::AbstractMatrix;\n mutability::Union{Vector{Symbol},Nothing}=nothing,\n domain::Union{Any,Nothing}=nothing,\n features_categorical::Union{Vector{Int},Nothing}=nothing,\n features_continuous::Union{Vector{Int},Nothing}=nothing,\n standardize::Bool=false\n)\n\nThis outer constructor method prepares features X and labels y to be used with the package. Mutability and domain constraints can be added for the features. The function also accepts arguments that specify which features are categorical and which are continues. These arguments are currently not used. \n\nExamples\n\nusing CounterfactualExplanations.Data\nx, y = toy_data_linear()\nX = hcat(x...)\ncounterfactual_data = CounterfactualData(X,y')\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.CounterfactualData-Tuple{Tables.MatrixTable, Union{AbstractMatrix, AbstractVector}}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.CounterfactualData","text":"function CounterfactualData(\n X::Tables.MatrixTable,\n y::RawOutputArrayType;\n kwrgs...\n)\n\nOuter constructor method that accepts a Tables.MatrixTable. By default, the indices of categorical and continuous features are automatically inferred the features' scitype.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.apply_domain_constraints-Tuple{CounterfactualData, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.apply_domain_constraints","text":"apply_domain_constraints(counterfactual_data::CounterfactualData, x::AbstractArray)\n\nA subroutine that is used to apply the predetermined domain constraints.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.select_factual-Tuple{CounterfactualData, Int64}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.select_factual","text":"select_factual(counterfactual_data::CounterfactualData, index::Int)\n\nA convenience method that can be used to access the feature matrix.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.select_factual-Tuple{CounterfactualData, Union{UnitRange{Int64}, Vector{Int64}}}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.select_factual","text":"select_factual(counterfactual_data::CounterfactualData, index::Union{Vector{Int},UnitRange{Int}})\n\nA convenience method that can be used to access the feature matrix.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.transformable_features-Tuple{CounterfactualData, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.transformable_features","text":"transformable_features(counterfactual_data::CounterfactualData, dt::Any)\n\nBy default, all continuous features are transformable. This function returns the indices of all continuous features.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.transformable_features-Tuple{CounterfactualData, StatsBase.ZScoreTransform}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.transformable_features","text":"transformable_features(counterfactual_data::CounterfactualData, dt::ZScoreTransform)\n\nReturns the indices of all continuous features that can be transformed. For constant features ZScoreTransform returns NaN.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.transformable_features-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.transformable_features","text":"transformable_features(counterfactual_data::CounterfactualData)\n\nDispatches the transformable_features function to the appropriate method based on the type of the dt field.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.all_models_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Models.all_models_catalogue","text":"all_models_catalogue\n\nA dictionary containing both differentiable and non-differentiable machine learning models.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Models.mlj_models_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Models.mlj_models_catalogue","text":"mlj_models_catalogue\n\nA dictionary containing all machine learning models from the MLJ model registry that the package supports.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Models.standard_models_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Models.standard_models_catalogue","text":"standard_models_catalogue\n\nA dictionary containing all differentiable machine learning models.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Models.AbstractDifferentiableModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractDifferentiableModel","text":"Base type for differentiable models.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.FluxEnsemble","page":"🧐 Reference","title":"CounterfactualExplanations.Models.FluxEnsemble","text":"FluxEnsemble <: AbstractFluxModel\n\nConstructor for deep ensembles trained in Flux.jl. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.FluxModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.FluxModel","text":"FluxModel <: AbstractFluxModel\n\nConstructor for models trained in Flux.jl. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.FluxModel-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.FluxModel","text":"FluxModel(data::CounterfactualData; kwargs...)\n\nConstructs a multi-layer perceptron (MLP).\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.DecisionTreeModel-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.DecisionTreeModel","text":"DecisionTreeModel(data::CounterfactualData; kwargs...)\n\nConstructs a new TreeModel object wrapped around a decision tree from the data in a CounterfactualData object. Not called by the user directly.\n\nArguments\n\ndata::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.\n\nReturns\n\nmodel::TreeModel: A TreeModel object.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.Linear-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.Linear","text":"Linear(data::CounterfactualData; kwargs...)\n\nConstructs a model with one linear layer. If the output is binary, this corresponds to logistic regression, since model outputs are passed through the sigmoid function. If the output is multi-class, this corresponds to multinomial logistic regression, since model outputs are passed through the softmax function.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.RandomForestModel-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.RandomForestModel","text":"RandomForestModel(data::CounterfactualData; kwargs...)\n\nConstructs a new TreeModel object wrapped around a random forest from the data in a CounterfactualData object. Not called by the user directly.\n\nArguments\n\ndata::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.\n\nReturns\n\nmodel::TreeModel: A TreeModel object.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.fit_model","page":"🧐 Reference","title":"CounterfactualExplanations.Models.fit_model","text":"fit_model(\n counterfactual_data::CounterfactualData, model::Symbol=:MLP;\n kwrgs...\n)\n\nFits one of the available default models to the counterfactual_data. The model argument can be used to specify the desired model. The available values correspond to the keys of the all_models_catalogue dictionary.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Models.logits-Tuple{AbstractFittedModel, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.logits","text":"logits(M::AbstractFittedModel, X::AbstractArray)\n\nGeneric method that is compulsory for all models. It returns the raw model predictions. In classification this is sometimes referred to as logits: the non-normalized predictions that are fed into a link function to produce predicted probabilities. In regression (not currently implemented) raw outputs typically correspond to final outputs. In other words, there is typically no normalization involved.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.logits-Tuple{CounterfactualExplanations.Models.TreeModel, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.logits","text":"logits(M::TreeModel, X::AbstractArray)\n\nCalculates the logit scores output by the model M for the input data X.\n\nArguments\n\nM::TreeModel: The model selected by the user.\nX::AbstractArray: The feature vector for which the logit scores are calculated.\n\nReturns\n\nlogits::Matrix: A matrix of logits for each output class for each data point in X.\n\nExample\n\nlogits = Models.logits(M, x) # calculates the logit scores for each output class for the data point x\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.model_evaluation-Tuple{AbstractFittedModel, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.model_evaluation","text":"model_evaluation(M::AbstractFittedModel, test_data::CounterfactualData)\n\nHelper function to compute F-Score for AbstractFittedModel on a (test) data set. By default, it computes the accuracy. Any other measure, e.g. from the StatisticalMeasures package, can be passed as an argument. Currently, only measures applicable to classification tasks are supported.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.predict_label-Tuple{AbstractFittedModel, CounterfactualData, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.predict_label","text":"predict_label(M::AbstractFittedModel, counterfactual_data::CounterfactualData, X::AbstractArray)\n\nReturns the predicted output label for a given model M, data set counterfactual_data and input data X.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.predict_label-Tuple{AbstractFittedModel, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.predict_label","text":"predict_label(M::AbstractFittedModel, counterfactual_data::CounterfactualData)\n\nReturns the predicted output labels for all data points of data set counterfactual_data for a given model M.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.predict_label-Tuple{CounterfactualExplanations.Models.TreeModel, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.predict_label","text":"predict_label(M::TreeModel, X::AbstractArray)\n\nReturns the predicted label for X.\n\nArguments\n\nM::TreeModel: The model selected by the user.\nX::AbstractArray: The input array for which the label is predicted.\n\nReturns\n\nlabels::AbstractArray: The predicted label for each data point in X.\n\nExample\n\nlabel = Models.predict_label(M, x) # returns the predicted label for each data point in x\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.predict_proba-Tuple{AbstractFittedModel, Union{Nothing, CounterfactualData}, Union{Nothing, AbstractArray}}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.predict_proba","text":"predict_proba(M::AbstractFittedModel, counterfactual_data::CounterfactualData, X::Union{Nothing,AbstractArray})\n\nReturns the predicted output probabilities for a given model M, data set counterfactual_data and input data X.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.probs-Tuple{AbstractFittedModel, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.probs","text":"probs(M::AbstractFittedModel, X::AbstractArray)\n\nGeneric method that is compulsory for all models. It returns the normalized model predictions, so the predicted probabilities in the case of classification. In regression (not currently implemented) this method is redundant. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.probs-Tuple{CounterfactualExplanations.Models.TreeModel, AbstractMatrix{<:Number}}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.probs","text":"probs(M::TreeModel, X::AbstractArray{<:Number, 2})\n\nCalculates the probability scores for each output class for the two-dimensional input data matrix X.\n\nArguments\n\nM::TreeModel: The TreeModel.\nX::AbstractArray: The feature vector for which the predictions are made.\n\nReturns\n\np::Matrix: A matrix of probability scores for each output class for each data point in X.\n\nExample\n\nprobabilities = Models.probs(M, X) # calculates the probability scores for each output class for each data point in X.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.probs-Tuple{CounterfactualExplanations.Models.TreeModel, AbstractVector{<:Number}}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.probs","text":"probs(M::TreeModel, X::AbstractArray{<:Number, 1})\n\nWorks the same way as the probs(M::TreeModel, X::AbstractArray{<:Number, 2}) method above, but handles 1-dimensional rather than 2-dimensional input data.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.generator_catalogue","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.generator_catalogue","text":"A dictionary containing the constructors of all available counterfactual generators.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#CounterfactualExplanations.Generators.AbstractGradientBasedGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.AbstractGradientBasedGenerator","text":"AbstractGradientBasedGenerator\n\nAn abstract type that serves as the base type for gradient-based counterfactual generators. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.AbstractNonGradientBasedGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.AbstractNonGradientBasedGenerator","text":"AbstractNonGradientBasedGenerator\n\nAn abstract type that serves as the base type for non gradient-based counterfactual generators. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.FeatureTweakGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.FeatureTweakGenerator","text":"Feature Tweak counterfactual generator class.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.FeatureTweakGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.FeatureTweakGenerator","text":"FeatureTweakGenerator(; penalty::Union{Nothing,Function,Vector{Function}}=Objectives.distance_l2, ϵ::AbstractFloat=0.1)\n\nConstructs a new Feature Tweak Generator object.\n\nUses the L2-norm as the penalty to measure the distance between the counterfactual and the factual. According to the paper by Tolomei et al., another recommended choice for the penalty in addition to the L2-norm is the L0-norm. The L0-norm simply minimizes the number of features that are changed through the tweak.\n\nArguments\n\npenalty::Union{Nothing,Function,Vector{Function}}: The penalty function to use for the generator. Defaults to distance_l2.\nϵ::AbstractFloat: The tolerance value for the feature tweaks. Described at length in Tolomei et al. (https://arxiv.org/pdf/1706.06691.pdf). Defaults to 0.1.\n\nReturns\n\ngenerator::FeatureTweakGenerator: A non-gradient-based generator that can be used to generate counterfactuals using the feature tweak method.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GradientBasedGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GradientBasedGenerator","text":"Base class for gradient-based counterfactual generators.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.GradientBasedGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GradientBasedGenerator","text":"GradientBasedGenerator(;\n\tloss::Union{Nothing,Function}=nothing,\n\tpenalty::Penalty=nothing,\n\tλ::Union{Nothing,AbstractFloat,Vector{AbstractFloat}}=nothing,\n\tlatent_space::Bool::false,\n\topt::Flux.Optimise.AbstractOptimiser=Flux.Descent(),\n generative_model_params::NamedTuple=(;),\n)\n\nDefault outer constructor for GradientBasedGenerator.\n\nArguments\n\nloss::Union{Nothing,Function}=nothing: The loss function used by the model.\npenalty::Penalty=nothing: A penalty function for the generator to penalize counterfactuals too far from the original point.\nλ::Union{Nothing,AbstractFloat,Vector{AbstractFloat}}=nothing: The weight of the penalty function.\nlatent_space::Bool=false: Whether to use the latent space of a generative model to generate counterfactuals.\nopt::Flux.Optimise.AbstractOptimiser=Flux.Descent(): The optimizer to use for the generator.\ngenerative_model_params::NamedTuple: The parameters of the generative model associated with the generator.\n\nReturns\n\ngenerator::GradientBasedGenerator: A gradient-based counterfactual generator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GrowingSpheresGenerator","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GrowingSpheresGenerator","text":"Growing Spheres counterfactual generator class.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.GrowingSpheresGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GrowingSpheresGenerator","text":"GrowingSpheresGenerator(; n::Int=100, η::Float64=0.1, kwargs...)\n\nConstructs a new Growing Spheres Generator object.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.JSMADescent","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.JSMADescent","text":"An optimisation rule that can be used to implement a Jacobian-based Saliency Map Attack.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators.JSMADescent-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.JSMADescent","text":"Outer constructor for the JSMADescent rule.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.CLUEGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.CLUEGenerator","text":"Constructor for CLUEGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.ClaPROARGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ClaPROARGenerator","text":"Constructor for ClaPGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.DiCEGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.DiCEGenerator","text":"Constructor for DiCEGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GenericGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GenericGenerator","text":"Constructor for GenericGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GravitationalGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GravitationalGenerator","text":"Constructor for GravitationalGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.GreedyGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.GreedyGenerator","text":"Constructor for GreedyGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.ProbeGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ProbeGenerator","text":"Constructor for ProbeGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.REVISEGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.REVISEGenerator","text":"Constructor for REVISEGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.WachterGenerator-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.WachterGenerator","text":"Constructor for WachterGenerator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.conditions_satisfied-Tuple{AbstractGradientBasedGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.conditions_satisfied","text":"conditions_satisfied(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nThe default method to check if the all conditions for convergence of the counterfactual search have been satisified for gradient-based generators. By default, gradient-based search is considered to have converged as soon as the proposed feature changes for all features are smaller than one percent of its standard deviation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.feature_tweaking!-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.feature_tweaking!","text":"feature_tweaking!(ce::AbstractCounterfactualExplanation)\n\nReturns a counterfactual instance of ce.x based on the ensemble of classifiers provided.\n\nArguments\n\nce::AbstractCounterfactualExplanation: The counterfactual explanation object.\n\nReturns\n\nce::AbstractCounterfactualExplanation: The counterfactual explanation object.\n\nExample\n\nce = feature_tweaking!(ce) # returns a counterfactual inside the ce.s′ field based on the ensemble of classifiers provided\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.generate_perturbations-Tuple{AbstractGradientBasedGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.generate_perturbations","text":"generate_perturbations(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nThe default method to generate feature perturbations for gradient-based generators through simple gradient descent.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.generate_perturbations-Tuple{FeatureTweakGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.generate_perturbations","text":"generate_perturbations(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nThe default method to generate feature perturbations for gradient-based generators through simple gradient descent.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.hinge_loss-Tuple{AbstractConvergence, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.hinge_loss","text":"hinge_loss(convergence::AbstractConvergence, ce::AbstractCounterfactualExplanation)\n\nThe default hinge loss for any convergence criterion. Can be overridden inside the Convergence module as part of the definition of specific convergence criteria.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.@objective-Tuple{Any, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.@objective","text":"objective(generator, ex)\n\nA macro that can be used to define the counterfactual search objective.\n\n\n\n\n\n","category":"macro"},{"location":"reference/#CounterfactualExplanations.Generators.@search_feature_space-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.@search_feature_space","text":"search_feature_space(generator)\n\nA simple macro that can be used to specify feature space search.\n\n\n\n\n\n","category":"macro"},{"location":"reference/#CounterfactualExplanations.Generators.@search_latent_space-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.@search_latent_space","text":"search_latent_space(generator)\n\nA simple macro that can be used to specify latent space search.\n\n\n\n\n\n","category":"macro"},{"location":"reference/#CounterfactualExplanations.Generators.@with_optimiser-Tuple{Any, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.@with_optimiser","text":"with_optimiser(generator, optimiser)\n\nA simple macro that can be used to specify the optimiser to be used.\n\n\n\n\n\n","category":"macro"},{"location":"reference/#CounterfactualExplanations.Objectives.ddp_diversity-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.ddp_diversity","text":"ddp_diversity(\n ce::AbstractCounterfactualExplanation;\n perturbation_size=1e-5\n)\n\nEvaluates how diverse the counterfactuals are using a Determinantal Point Process (DDP).\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance","text":"distance(ce::AbstractCounterfactualExplanation, p::Real=2)\n\nComputes the distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_l0-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_l0","text":"distance_l0(ce::AbstractCounterfactualExplanation)\n\nComputes the L0 distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_l1-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_l1","text":"distance_l1(ce::AbstractCounterfactualExplanation)\n\nComputes the L1 distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_l2-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_l2","text":"distance_l2(ce::AbstractCounterfactualExplanation)\n\nComputes the L2 (Euclidean) distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_linf-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_linf","text":"distance_linf(ce::AbstractCounterfactualExplanation)\n\nComputes the L-inf distance of the counterfactual to the original factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_mad-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_mad","text":"distance_mad(ce::AbstractCounterfactualExplanation; agg=mean)\n\nThis is the distance measure proposed by Wachter et al. (2017).\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.predictive_entropy-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.predictive_entropy","text":"predictive_entropy(ce::AbstractCounterfactualExplanation; agg=Statistics.mean)\n\nComputes the predictive entropy of the counterfactuals. Explained in https://arxiv.org/abs/1406.2541.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Flux.Losses.logitbinarycrossentropy-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"Flux.Losses.logitbinarycrossentropy","text":"Flux.Losses.logitbinarycrossentropy(ce::AbstractCounterfactualExplanation)\n\nSimply extends the logitbinarycrossentropy method to work with objects of type AbstractCounterfactualExplanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Flux.Losses.logitcrossentropy-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"Flux.Losses.logitcrossentropy","text":"Flux.Losses.logitcrossentropy(ce::AbstractCounterfactualExplanation)\n\nSimply extends the logitcrossentropy method to work with objects of type AbstractCounterfactualExplanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Flux.Losses.mse-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"Flux.Losses.mse","text":"Flux.Losses.mse(ce::AbstractCounterfactualExplanation)\n\nSimply extends the mse method to work with objects of type AbstractCounterfactualExplanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Internal-functions","page":"🧐 Reference","title":"Internal functions","text":"","category":"section"},{"location":"reference/","page":"🧐 Reference","title":"🧐 Reference","text":"Modules = [\n CounterfactualExplanations, \n CounterfactualExplanations.Convergence,\n CounterfactualExplanations.Evaluation,\n CounterfactualExplanations.DataPreprocessing,\n CounterfactualExplanations.Models, \n CounterfactualExplanations.GenerativeModels,\n CounterfactualExplanations.Generators, \n CounterfactualExplanations.Objectives\n]\nPublic = false","category":"page"},{"location":"reference/#CounterfactualExplanations.FluxModelParams","page":"🧐 Reference","title":"CounterfactualExplanations.FluxModelParams","text":"FluxModelParams\n\nDefault MLP training parameters.\n\n\n\n\n\n","category":"type"},{"location":"reference/#Base.Broadcast.broadcastable-Tuple{AbstractFittedModel}","page":"🧐 Reference","title":"Base.Broadcast.broadcastable","text":"Treat AbstractFittedModel as scalar when broadcasting.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Base.Broadcast.broadcastable-Tuple{AbstractGenerator}","page":"🧐 Reference","title":"Base.Broadcast.broadcastable","text":"Treat AbstractGenerator as scalar when broadcasting.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.adjust_shape!-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.adjust_shape!","text":"adjust_shape!(ce::CounterfactualExplanation)\n\nA convenience method that adjusts the dimensions of the counterfactual state and related fields.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.adjust_shape-Tuple{CounterfactualExplanation, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.adjust_shape","text":"adjust_shape(\n ce::CounterfactualExplanation, \n x::AbstractArray\n)\n\nA convenience method that adjusts the dimensions of x.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.apply_domain_constraints!-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.apply_domain_constraints!","text":"apply_domain_constraints!(ce::CounterfactualExplanation)\n\nWrapper function that applies underlying domain constraints.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.apply_mutability-Tuple{CounterfactualExplanation, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.apply_mutability","text":"apply_mutability(\n ce::CounterfactualExplanation,\n Δs′::AbstractArray,\n)\n\nA subroutine that applies mutability constraints to the proposed vector of feature perturbations.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.counterfactual-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual","text":"counterfactual(ce::CounterfactualExplanation)\n\nA convenience method that returns the counterfactual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.counterfactual_label-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual_label","text":"counterfactual_label(ce::CounterfactualExplanation)\n\nA convenience method that returns the predicted label of the counterfactual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.counterfactual_label_path-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual_label_path","text":"counterfactual_label_path(ce::CounterfactualExplanation)\n\nReturns the counterfactual labels for each step of the search.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.counterfactual_probability","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual_probability","text":"counterfactual_probability(ce::CounterfactualExplanation)\n\nA convenience method that computes the class probabilities of the counterfactual.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.counterfactual_probability_path-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.counterfactual_probability_path","text":"counterfactual_probability_path(ce::CounterfactualExplanation)\n\nReturns the counterfactual probabilities for each step of the search.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.decode_array-Tuple{MultivariateStats.AbstractDimensionalityReduction, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.decode_array","text":"decode_array(dt::MultivariateStats.AbstractDimensionalityReduction, x::AbstractArray)\n\nHelper function to decode an array x using a data transform dt::MultivariateStats.AbstractDimensionalityReduction.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.decode_array-Tuple{StatsBase.AbstractDataTransform, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.decode_array","text":"decode_array(dt::StatsBase.AbstractDataTransform, x::AbstractArray)\n\nHelper function to decode an array x using a data transform dt::StatsBase.AbstractDataTransform.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.decode_state","page":"🧐 Reference","title":"CounterfactualExplanations.decode_state","text":"function decode_state( ce::CounterfactualExplanation, x::Union{AbstractArray,Nothing}=nothing, )\n\nApplies all the applicable decoding functions:\n\nIf applicable, map the state variable back from the latent space to the feature space.\nIf and where applicable, inverse-transform features.\nReconstruct all categorical encodings.\n\nFinally, the decoded counterfactual is returned.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.encode_array-Tuple{MultivariateStats.AbstractDimensionalityReduction, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.encode_array","text":"encode_array(dt::MultivariateStats.AbstractDimensionalityReduction, x::AbstractArray)\n\nHelper function to encode an array x using a data transform dt::MultivariateStats.AbstractDimensionalityReduction.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.encode_array-Tuple{StatsBase.AbstractDataTransform, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.encode_array","text":"encode_array(dt::StatsBase.AbstractDataTransform, x::AbstractArray)\n\nHelper function to encode an array x using a data transform dt::StatsBase.AbstractDataTransform.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.encode_state","page":"🧐 Reference","title":"CounterfactualExplanations.encode_state","text":"function encode_state( ce::CounterfactualExplanation, x::Union{AbstractArray,Nothing} = nothing, )\n\nApplies all required encodings to x:\n\nIf applicable, it maps x to the latent space learned by the generative model.\nIf and where applicable, it rescales features. \n\nFinally, it returns the encoded state variable.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.factual-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.factual","text":"factual(ce::CounterfactualExplanation)\n\nA convenience method to retrieve the factual x.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.factual_label-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.factual_label","text":"factual_label(ce::CounterfactualExplanation)\n\nA convenience method to get the predicted label associated with the factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.factual_probability-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.factual_probability","text":"factual_probability(ce::CounterfactualExplanation)\n\nA convenience method to compute the class probabilities of the factual.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.find_potential_neighbours-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.find_potential_neighbours","text":"find_potential_neighbors(ce::AbstractCounterfactualExplanation)\n\nFinds potential neighbors for the selected factual data point.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.get_meta-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.get_meta","text":"get_meta(ce::CounterfactualExplanation)\n\nReturns meta data for a counterfactual explanation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.guess_likelihood-Tuple{Union{AbstractMatrix, AbstractVector}}","page":"🧐 Reference","title":"CounterfactualExplanations.guess_likelihood","text":"guess_likelihood(y::RawOutputArrayType)\n\nGuess the likelihood based on the scientific type of the output array. Returns a symbol indicating the guessed likelihood and the scientific type of the output array.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.guess_loss-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.guess_loss","text":"guess_loss(ce::CounterfactualExplanation)\n\nGuesses the loss function to be used for the counterfactual search in case likelihood field is specified for the AbstractFittedModel instance and no loss function was explicitly declared for AbstractGenerator instance.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.in_target_class-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.in_target_class","text":"in_target_class(ce::CounterfactualExplanation)\n\nCheck if the counterfactual is in the target class.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.initialize_state-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.initialize_state","text":"initialize_state(ce::CounterfactualExplanation)\n\nInitializes the starting point for the factual(s):\n\nIf ce.initialization is set to :identity or counterfactuals are searched in a latent space, then nothing is done.\nIf ce.initialization is set to :add_perturbation, then a random perturbation is added to the factual following following Slack (2021): https://arxiv.org/abs/2106.02666. The authors show that this improves adversarial robustness.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.map_from_latent","page":"🧐 Reference","title":"CounterfactualExplanations.map_from_latent","text":"map_from_latent(\n ce::CounterfactualExplanation,\n x::Union{AbstractArray,Nothing}=nothing,\n)\n\nMaps the state variable back from the latent space to the feature space.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.map_to_latent","page":"🧐 Reference","title":"CounterfactualExplanations.map_to_latent","text":"function maptolatent( ce::CounterfactualExplanation, x::Union{AbstractArray,Nothing}=nothing, ) \n\nMaps x from the feature space mathcalX to the latent space learned by the generative model.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.output_dim-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.output_dim","text":"output_dim(ce::CounterfactualExplanation)\n\nA convenience method that returns the output dimension of the predictive model.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.reset!-Tuple{CounterfactualExplanations.FluxModelParams}","page":"🧐 Reference","title":"CounterfactualExplanations.reset!","text":"reset!(flux_training_params::FluxModelParams)\n\nRestores the default parameter values.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.steps_exhausted-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.steps_exhausted","text":"steps_exhausted(ce::CounterfactualExplanation)\n\nA convenience method that checks if the number of maximum iterations has been exhausted.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.target_probs_path-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.target_probs_path","text":"target_probs_path(ce::CounterfactualExplanation)\n\nReturns the target probabilities for each step of the search.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.distance_measures","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.distance_measures","text":"All distance measures.\n\n\n\n\n\n","category":"constant"},{"location":"reference/#Base.vcat-Tuple{CounterfactualExplanations.Evaluation.Benchmark, CounterfactualExplanations.Evaluation.Benchmark}","page":"🧐 Reference","title":"Base.vcat","text":"Base.vcat(bmk1::Benchmark, bmk2::Benchmark)\n\nVertically concatenates two Benchmark objects.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.compute_measure-Tuple{CounterfactualExplanation, Function, Function}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.compute_measure","text":"compute_measure(ce::CounterfactualExplanation, measure::Function, agg::Function)\n\nComputes a single measure for a counterfactual explanation. The measure is applied to the counterfactual explanation ce and aggregated using the aggregation function agg.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.to_dataframe-Tuple{Vector, Any, Bool, Bool, Bool, CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.to_dataframe","text":"evaluate_dataframe(\n ce::CounterfactualExplanation,\n measure::Vector{Function},\n agg::Function,\n report_each::Bool,\n pivot_longer::Bool,\n store_ce::Bool,\n)\n\nEvaluates a counterfactual explanation and returns a dataframe of evaluation measures.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Evaluation.validity_strict-Tuple{CounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Evaluation.validity_strict","text":"validity_strict(ce::CounterfactualExplanation)\n\nChecks if the counterfactual search has been strictly valid in the sense that it has converged with respect to the pre-specified target probability γ.\n\n\n\n\n\n","category":"method"},{"location":"reference/#Base.Broadcast.broadcastable-Tuple{CounterfactualData}","page":"🧐 Reference","title":"Base.Broadcast.broadcastable","text":"Treat CounterfactualData as scalar when broadcasting.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing._subset-Tuple{CounterfactualData, Vector{Int64}}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing._subset","text":"_subset(data::CounterfactualData, idx::Vector{Int})\n\nCreates a subset of the data.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.convert_to_1d-Tuple{Matrix, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.convert_to_1d","text":"convert_to_1d(y::Matrix, y_levels::AbstractArray)\n\nHelper function to convert a one-hot encoded matrix to a vector of labels. This is necessary because MLJ models require the labels to be represented as a vector, but the synthetic datasets in this package hold the labels in one-hot encoded form.\n\nArguments\n\ny::Matrix: The one-hot encoded matrix.\ny_levels::AbstractArray: The levels of the categorical variable.\n\nReturns\n\nlabels: A vector of labels.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.get_generative_model-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.get_generative_model","text":"get_generative_model(counterfactual_data::CounterfactualData)\n\nReturns the underlying generative model. If there is no existing model available, the default generative model (VAE) is used. Otherwise it is expected that existing generative model has been pre-trained or else a warning is triggered.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.has_pretrained_generative_model-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.has_pretrained_generative_model","text":"has_pretrained_generative_model(counterfactual_data::CounterfactualData)\n\nChecks if generative model is present and trained.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.input_dim-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.input_dim","text":"input_dim(counterfactual_data::CounterfactualData)\n\nHelper function that returns the input dimension (number of features) of the data. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.mutability_constraints-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.mutability_constraints","text":"mutability_constraints(counterfactual_data::CounterfactualData)\n\nA convenience function that returns the mutability constraints. If none were specified, it is assumed that all features are mutable in :both directions.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.preprocess_data_for_mlj-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.preprocess_data_for_mlj","text":"preprocess_data_for_mlj(data::CounterfactualData)\n\nHelper function to preprocess data::CounterfactualData for MLJ models.\n\nArguments\n\ndata::CounterfactualData: The data to be preprocessed.\n\nReturns\n\n(df_x, y): A tuple containing the preprocessed data, with df_x being a DataFrame object and y being a categorical vector.\n\nExample\n\nX, y = preprocessdatafor_mlj(data)\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.reconstruct_cat_encoding-Tuple{CounterfactualData, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.reconstruct_cat_encoding","text":"reconstruct_cat_encoding(counterfactual_data::CounterfactualData, x::Vector)\n\nReconstruct the categorical encoding for a single instance. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.subsample-Tuple{CounterfactualData, Int64}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.subsample","text":"subsample(data::CounterfactualData, n::Int)\n\nHelper function to randomly subsample data::CounterfactualData.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.train_test_split-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.train_test_split","text":"train_test_split(data::CounterfactualData;test_size=0.2,keep_class_ratio=false)\n\nSplits data into train and test split.\n\nArguments\n\ndata::CounterfactualData: The data to be preprocessed.\ntest_size=0.2: Proportion of the data to be used for testing. \nkeep_class_ratio=false: Decides whether to sample equally from each class, or keep their relative size.\n\nReturns\n\n(train_data::CounterfactualData, test_data::CounterfactualData): A tuple containing the train and test splits.\n\nExample\n\ntrain, test = traintestsplit(data, testsize=0.1, keepclass_ratio=true)\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.DataPreprocessing.unpack_data-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.DataPreprocessing.unpack_data","text":"unpack_data(data::CounterfactualData)\n\nHelper function that unpacks data.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.AbstractCustomDifferentiableModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractCustomDifferentiableModel","text":"Base type for custom differentiable models.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.AbstractFluxModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractFluxModel","text":"Base type for differentiable models written in Flux.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.AbstractMLJModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractMLJModel","text":"Base type for differentiable models from the MLJ library.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.AbstractNonDifferentiableJuliaModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractNonDifferentiableJuliaModel","text":"Base type for non-differentiable models written in pure Julia.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.AbstractNonDifferentiableModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.AbstractNonDifferentiableModel","text":"Base type for non-differentiable models.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.FluxEnsembleParams","page":"🧐 Reference","title":"CounterfactualExplanations.Models.FluxEnsembleParams","text":"FluxModelParams\n\nDefault Deep Ensemble training parameters.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.TreeModel","page":"🧐 Reference","title":"CounterfactualExplanations.Models.TreeModel","text":"TreeModel <: AbstractNonDifferentiableJuliaModel\n\nConstructor for tree-based models from the MLJ library. \n\nArguments\n\nmodel::Any: The model selected by the user. Must be a model from the MLJ library.\nlikelihood::Symbol: The likelihood of the model. Must be one of [:classification_binary, :classification_multi].\n\nReturns\n\nTreeModel: A tree-based model from the MLJ library wrapped inside the TreeModel class.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Models.TreeModel-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.TreeModel","text":"Outer constructor method for TreeModel.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.binary_to_onehot-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.binary_to_onehot","text":"binary_to_onehot(p)\n\nHelper function to turn dummy-encoded variable into onehot-encoded variable.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.build_ensemble-Tuple{Int64}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.build_ensemble","text":"build_ensemble(K::Int;kw=(input_dim=2,n_hidden=32,output_dim=1))\n\nHelper function that builds an ensemble of K models.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.build_mlp-Tuple{}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.build_mlp","text":"build_mlp()\n\nHelper function to build simple MLP.\n\nExamples\n\nnn = build_mlp()\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.data_loader-Tuple{CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.data_loader","text":"data_loader(data::CounterfactualData)\n\nPrepares counterfactual data for training in Flux.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.get_individual_classifiers-Tuple{CounterfactualExplanations.Models.TreeModel}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.get_individual_classifiers","text":"get_individual_classifiers(M::TreeModel)\n\nReturns the individual classifiers in the forest. If the input is a decision tree, the method returns the decision tree itself inside an array.\n\nArguments\n\nM::TreeModel: The model selected by the user.\n\nReturns\n\nclassifiers::AbstractArray: An array of individual classifiers in the forest.\n\nExample\n\nclassifiers = Models.getindividualclassifiers(M) # returns the individual classifiers in the forest\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.train-Tuple{CounterfactualExplanations.Models.TreeModel, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.train","text":"train(M::TreeModel, data::CounterfactualData; kwargs...)\n\nFits the model M to the data in the CounterfactualData object. This method is not called by the user directly.\n\nArguments\n\nM::TreeModel: The wrapper for a TreeModel.\ndata::CounterfactualData: The CounterfactualData object containing the data to be used for training the model.\n\nReturns\n\nM::TreeModel: The fitted TreeModel.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.train-Tuple{FluxEnsemble, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.train","text":"train(M::FluxEnsemble, data::CounterfactualData; kwargs...)\n\nWrapper function to retrain.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Models.train-Tuple{FluxModel, CounterfactualData}","page":"🧐 Reference","title":"CounterfactualExplanations.Models.train","text":"train(M::FluxModel, data::CounterfactualData; kwargs...)\n\nWrapper function to retrain FluxModel.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.AbstractGMParams","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.AbstractGMParams","text":"Base type of generative model hyperparameter container.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.AbstractGenerativeModel","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.AbstractGenerativeModel","text":"Base type for generative model.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.Encoder","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.Encoder","text":"Encoder\n\nConstructs encoder part of VAE: a simple Flux neural network with one hidden layer and two linear output layers for the first two moments of the latent distribution.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.VAE","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.VAE","text":"VAE <: AbstractGenerativeModel\n\nConstructs the Variational Autoencoder. The VAE is a subtype of AbstractGenerativeModel. Any (sub-)type of AbstractGenerativeModel is accepted by latent space generators. \n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.VAE-Tuple{Any}","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.VAE","text":"VAE(input_dim;kws...)\n\nOuter method for instantiating a VAE.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.VAEParams","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.VAEParams","text":"VAEParams <: AbstractGMParams\n\nThe default VAE parameters describing both the encoder/decoder architecture and the training process.\n\n\n\n\n\n","category":"type"},{"location":"reference/#Base.rand","page":"🧐 Reference","title":"Base.rand","text":"Random.rand(encoder::Encoder, x, device=cpu)\n\nDraws random samples from the latent distribution.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.Decoder-Tuple{Int64, Int64, Int64}","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.Decoder","text":"Decoder(input_dim::Int, latent_dim::Int, hidden_dim::Int; activation=relu)\n\nThe default decoder architecture is just a Flux Chain with one hidden layer and a linear output layer. \n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.get_data-Tuple{AbstractArray, AbstractArray, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.get_data","text":"get_data(X::AbstractArray, y::AbstractArray, batch_size)\n\nPreparing data for mini-batch training .\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.reconstruct","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.reconstruct","text":"reconstruct(generative_model::VAE, x, device=cpu)\n\nImplements a full pass of some input x through the VAE: x ↦ x̂.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.GenerativeModels.reparameterization_trick","page":"🧐 Reference","title":"CounterfactualExplanations.GenerativeModels.reparameterization_trick","text":"reparameterization_trick(μ,logσ,device=cpu)\n\nHelper function that implements the reparameterization trick: z ∼ 𝒩(μ,σ²) ⇔ z=μ + σ ⊙ ε, ε ∼ 𝒩(0,I).\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Generators.Penalty","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.Penalty","text":"Type union for acceptable argument types for the penalty field of GradientBasedGenerator.\n\n\n\n\n\n","category":"type"},{"location":"reference/#CounterfactualExplanations.Generators._replace_nans","page":"🧐 Reference","title":"CounterfactualExplanations.Generators._replace_nans","text":"_replace_nans(Δs′::AbstractArray, old_new::Pair=(NaN => 0))\n\nHelper function to deal with exploding gradients. This is only a temporary fix and will be improved.\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Generators.calculate_delta-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.calculate_delta","text":"calculate_delta(ce::AbstractCounterfactualExplanation, penalty::Vector{Function})\n\nCalculates the penalty for the proposed feature tweak.\n\nArguments\n\nce::AbstractCounterfactualExplanation: The counterfactual explanation object.\n\nReturns\n\ndelta::Float64: The calculated penalty for the proposed feature tweak.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.esatisfactory_instance-Tuple{FeatureTweakGenerator, AbstractArray, AbstractArray}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.esatisfactory_instance","text":"esatisfactory_instance(generator::FeatureTweakGenerator, x::AbstractArray, paths::Dict{String, Dict{String, Any}})\n\nReturns an epsilon-satisfactory counterfactual for x based on the paths provided.\n\nArguments\n\ngenerator::FeatureTweakGenerator: The feature tweak generator.\nx::AbstractArray: The factual instance.\npaths::Dict{String, Dict{String, Any}}: A list of paths to the leaves of the tree to be used for tweaking the feature.\n\nReturns\n\nesatisfactory::AbstractArray: The epsilon-satisfactory instance.\n\nExample\n\nesatisfactory = esatisfactory_instance(generator, x, paths) # returns an epsilon-satisfactory counterfactual for x based on the paths provided\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.feature_selection!-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.feature_selection!","text":"feature_selection!(ce::AbstractCounterfactualExplanation)\n\nPerform feature selection to find the dimension with the closest (but not equal) values between the ce.x (factual) and ce.s′ (counterfactual) arrays.\n\nArguments\n\nce::AbstractCounterfactualExplanation: An instance of the AbstractCounterfactualExplanation type representing the counterfactual explanation.\n\nReturns\n\nnothing\n\nThe function iteratively modifies the ce.s′ counterfactual array by updating its elements to match the corresponding elements in the ce.x factual array, one dimension at a time, until the predicted label of the modified ce.s′ matches the predicted label of the ce.x array.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.find_closest_dimension-Tuple{Any, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.find_closest_dimension","text":"find_closest_dimension(factual, counterfactual)\n\nFind the dimension with the closest (but not equal) values between the factual and counterfactual arrays.\n\nArguments\n\nfactual: The factual array.\ncounterfactual: The counterfactual array.\n\nReturns\n\nclosest_dimension: The index of the dimension with the closest values.\n\nThe function iterates over the indices of the factual array and calculates the absolute difference between the corresponding elements in the factual and counterfactual arrays. It returns the index of the dimension with the smallest difference, excluding dimensions where the values in factual and counterfactual are equal.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.find_counterfactual-NTuple{4, Any}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.find_counterfactual","text":"find_counterfactual(model, factual_class, counterfactual_data, counterfactual_candidates)\n\nFind the first counterfactual index by predicting labels.\n\nArguments\n\nmodel: The fitted model used for prediction.\ntarget_class: Expected target class.\ncounterfactual_data: Data required for counterfactual generation.\ncounterfactual_candidates: The array of counterfactual candidates.\n\nReturns\n\ncounterfactual: The index of the first counterfactual found.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.growing_spheres_generation!-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.growing_spheres_generation!","text":"growing_spheres_generation(ce::AbstractCounterfactualExplanation)\n\nGenerate counterfactual candidates using the growing spheres generation algorithm.\n\nArguments\n\nce::AbstractCounterfactualExplanation: An instance of the AbstractCounterfactualExplanation type representing the counterfactual explanation.\n\nReturns\n\nnothing\n\nThis function applies the growing spheres generation algorithm to generate counterfactual candidates. It starts by generating random points uniformly on a sphere, gradually reducing the search space until no counterfactuals are found. Then it expands the search space until at least one counterfactual is found or the maximum number of iterations is reached.\n\nThe algorithm iteratively generates counterfactual candidates and predicts their labels using the model stored in ce.M. It checks if any of the predicted labels are different from the factual class. The process of reducing the search space involves halving the search radius, while the process of expanding the search space involves increasing the search radius.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, ce::AbstractCounterfactualExplanation)\n\nDispatches to the appropriate complexity function for any generator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, Function, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, penalty::Function, ce::AbstractCounterfactualExplanation)\n\nOverloads the h function for the case where a single penalty function is provided.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, Nothing, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, penalty::Nothing, ce::AbstractCounterfactualExplanation)\n\nOverloads the h function for the case where no penalty is provided.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, Vector{<:Tuple}, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, penalty::Tuple, ce::AbstractCounterfactualExplanation)\n\nOverloads the h function for the case where a single penalty function is provided with additional keyword arguments.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.h-Tuple{AbstractGenerator, Vector{Function}, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.h","text":"h(generator::AbstractGenerator, penalty::Tuple, ce::AbstractCounterfactualExplanation)\n\nOverloads the h function for the case where a single penalty function is provided with additional keyword arguments.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.hyper_sphere_coordinates-Tuple{Integer, AbstractArray, AbstractFloat, AbstractFloat}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.hyper_sphere_coordinates","text":"hyper_sphere_coordinates(n_search_samples::Int, instance::Vector{Float64}, low::Int, high::Int; p_norm::Int=2)\n\nGenerates candidate counterfactuals using the growing spheres method based on hyper-sphere coordinates.\n\nThe implementation follows the Random Point Picking over a sphere algorithm described in the paper: \"Learning Counterfactual Explanations for Tabular Data\" by Pawelczyk, Broelemann & Kascneci (2020), presented at The Web Conference 2020 (WWW). It ensures that points are sampled uniformly at random using insights from: http://mathworld.wolfram.com/HyperspherePointPicking.html\n\nThe growing spheres method is originally proposed in the paper: \"Comparison-based Inverse Classification for Interpretability in Machine Learning\" by Thibaut Laugel et al (2018), presented at the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2018).\n\nArguments\n\nn_search_samples::Int: The number of search samples (int > 0).\ninstance::AbstractArray: The input point array.\nlow::AbstractFloat: The lower bound (float >= 0, l < h).\nhigh::AbstractFloat: The upper bound (float >= 0, h > l).\np_norm::Integer: The norm parameter (int >= 1).\n\nReturns\n\ncandidate_counterfactuals::Array: An array of candidate counterfactuals.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.propose_state-Tuple{AbstractGradientBasedGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.propose_state","text":"propose_state(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nProposes new state based on backpropagation.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.search_path","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.search_path","text":"search_path(tree::Union{DecisionTree.Leaf, DecisionTree.Node}, target::RawTargetType, path::AbstractArray)\n\nReturn a path index list with the inequality symbols, thresholds and feature indices.\n\nArguments\n\ntree::Union{DecisionTree.Leaf, DecisionTree.Node}: The root node of a decision tree.\ntarget::RawTargetType: The target class.\npath::AbstractArray: A list containing the paths found thus far.\n\nReturns\n\npaths::AbstractArray: A list of paths to the leaves of the tree to be used for tweaking the feature.\n\nExample\n\npaths = search_path(tree, target) # returns a list of paths to the leaves of the tree to be used for tweaking the feature\n\n\n\n\n\n","category":"function"},{"location":"reference/#CounterfactualExplanations.Generators.ℓ-Tuple{AbstractGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ℓ","text":"ℓ(generator::AbstractGenerator, ce::AbstractCounterfactualExplanation)\n\nDispatches to the appropriate loss function for any generator.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.ℓ-Tuple{AbstractGenerator, Function, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ℓ","text":"ℓ(generator::AbstractGenerator, loss::Function, ce::AbstractCounterfactualExplanation)\n\nOverloads the ℓ function for the case where a single loss function is provided.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.ℓ-Tuple{AbstractGenerator, Nothing, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.ℓ","text":"ℓ(generator::AbstractGenerator, loss::Nothing, ce::AbstractCounterfactualExplanation)\n\nOverloads the ℓ function for the case where no loss function is provided.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.∂h-Tuple{AbstractGradientBasedGenerator, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.∂h","text":"∂h(generator::AbstractGradientBasedGenerator, ce::AbstractCounterfactualExplanation)\n\nThe default method to compute the gradient of the complexity penalty at the current counterfactual state for gradient-based generators. It assumes that Zygote.jl has gradient access. \n\nIf the penalty is not provided, it returns 0.0. By default, Zygote never works out the gradient for constants and instead returns 'nothing', so we need to add a manual step to override this behaviour. See here: https://discourse.julialang.org/t/zygote-gradient/26715.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.∂ℓ-Tuple{AbstractGradientBasedGenerator, AbstractDifferentiableModel, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.∂ℓ","text":"∂ℓ(generator::AbstractGradientBasedGenerator, M::Union{Models.LogisticModel, Models.BayesianLogisticModel}, ce::AbstractCounterfactualExplanation)\n\nThe default method to compute the gradient of the loss function at the current counterfactual state for gradient-based generators. It assumes that Zygote.jl has gradient access.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Generators.∇-Tuple{AbstractGradientBasedGenerator, AbstractDifferentiableModel, AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Generators.∇","text":"∇(generator::AbstractGradientBasedGenerator, M::Models.AbstractDifferentiableModel, ce::AbstractCounterfactualExplanation)\n\nThe default method to compute the gradient of the counterfactual search objective for gradient-based generators. It simply computes the weighted sum over partial derivates. It assumes that Zygote.jl has gradient access. If the counterfactual is being generated using Probe, the hinge loss is added to the gradient.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.distance_from_target-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.distance_from_target","text":"distance_from_target(\n ce::AbstractCounterfactualExplanation;\n K::Int=50\n)\n\nComputes the distance of the counterfactual from a point in the target main.\n\n\n\n\n\n","category":"method"},{"location":"reference/#CounterfactualExplanations.Objectives.model_loss_penalty-Tuple{AbstractCounterfactualExplanation}","page":"🧐 Reference","title":"CounterfactualExplanations.Objectives.model_loss_penalty","text":"function model_loss_penalty(\n ce::AbstractCounterfactualExplanation;\n agg=mean\n)\n\nAdditional penalty for ClaPROARGenerator.\n\n\n\n\n\n","category":"method"},{"location":"extensions/","page":"Overview","title":"Overview","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"extensions/#Extensions","page":"Overview","title":"⛓️ Extensions","text":"","category":"section"},{"location":"extensions/","page":"Overview","title":"Overview","text":"In this section, you will find information about package extensions of the CounterfactualExplanations package. Extensions are a relatively new feature of Julia that allows users to conditionally load code based on the presence of other packages. This is useful for creating packages that extend the functionality of other packages, without requiring the user to install the package being extended.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/evaluation/#Performance-Evaluation","page":"Evaluating Explanations","title":"Performance Evaluation","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Now that we know how to generate counterfactual explanations in Julia, you may have a few follow-up questions: How do I know if the counterfactual search has been successful? How good is my counterfactual explanation? What does ‘good’ even mean in this context? In this tutorial, we will see how counterfactual explanations can be evaluated with respect to their performance.","category":"page"},{"location":"tutorials/evaluation/#Default-Measures","page":"Evaluating Explanations","title":"Default Measures","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Numerous evaluation measures for counterfactual explanations have been proposed. In what follows, we will cover some of the most important measures.","category":"page"},{"location":"tutorials/evaluation/#Single-Measure,-Single-Counterfactual","page":"Evaluating Explanations","title":"Single Measure, Single Counterfactual","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"One of the most important measures is validity, which simply determines whether or not a counterfactual explanation x^prime is valid in the sense that it yields the target prediction: M(x^prime)=t. We can evaluate the validity of a single counterfactual explanation ce using the Evaluation.evaluate function as follows:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"using CounterfactualExplanations.Evaluation: evaluate, validity\nevaluate(ce; measure=validity)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"1-element Vector{Vector{Float64}}:\n [1.0]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"For a single counterfactual explanation, this evaluation measure can only take two values: it is either equal to 1, if the explanation is valid or 0 otherwise. Another important measure is distance, which relates to the distance between the factual x and the counterfactual x^prime. In the context of Algorithmic Recourse, higher distances are typically associated with higher costs to individuals seeking recourse.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"using CounterfactualExplanations.Objectives: distance\nevaluate(ce; measure=distance)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"1-element Vector{Vector{Float32}}:\n [3.2273161]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"By default, distance computes the L2 (Euclidean) distance.","category":"page"},{"location":"tutorials/evaluation/#Multiple-Measures,-Single-Counterfactual","page":"Evaluating Explanations","title":"Multiple Measures, Single Counterfactual","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"You might be interested in computing not just the L2 distance, but various LP norms. This can be done by supplying a vector of functions to the measure key argument. For convenience, all default distance measures have already been collected in a vector:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"using CounterfactualExplanations.Evaluation: distance_measures\ndistance_measures","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"4-element Vector{Function}:\n distance_l0 (generic function with 1 method)\n distance_l1 (generic function with 1 method)\n distance_l2 (generic function with 1 method)\n distance_linf (generic function with 1 method)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"We can use this vector of evaluation measures as follows:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ce; measure=distance_measures)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"4-element Vector{Vector{Float32}}:\n [2.0]\n [3.2273161]\n [2.7737978]\n [2.7285953]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"If no measure is specified, the evaluate method will return all default measures,","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ce)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"3-element Vector{Vector}:\n [1.0]\n Float32[3.2273161]\n [0.0]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"which include:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"CounterfactualExplanations.Evaluation.default_measures","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"3-element Vector{Function}:\n validity (generic function with 1 method)\n distance (generic function with 1 method)\n redundancy (generic function with 1 method)","category":"page"},{"location":"tutorials/evaluation/#Multiple-Measures-and-Counterfactuals","page":"Evaluating Explanations","title":"Multiple Measures and Counterfactuals","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"We can also evaluate multiple counterfactual explanations at once:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"generator = DiCEGenerator()\nces = generate_counterfactual(x, target, counterfactual_data, M, generator; num_counterfactuals=5)\nevaluate(ces)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"3-element Vector{Vector}:\n [1.0]\n Float32[3.1955845]\n [[0.0, 0.0, 0.0, 0.0, 0.0]]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"By default, each evaluation measure is aggregated across all counterfactual explanations. To return individual measures for each counterfactual explanation you can specify report_each=true","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ces; report_each=true)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"3-element Vector{Vector}:\n BitVector[[1, 1, 1, 1, 1]]\n Vector{Float32}[[3.3671722, 3.1028512, 3.2829392, 3.0728922, 3.1520686]]\n [[0.0, 0.0, 0.0, 0.0, 0.0]]","category":"page"},{"location":"tutorials/evaluation/#Custom-Measures","page":"Evaluating Explanations","title":"Custom Measures","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"A measure is just a method that takes a CounterfactualExplanation as its only positional argument and agg::Function as a key argument specifying how measures should be aggregated across counterfactuals. Defining custom measures is therefore straightforward. For example, we could define a measure to compute the inverse target probability as follows:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"my_measure(ce::CounterfactualExplanation; agg=mean) = agg(1 .- CounterfactualExplanations.target_probs(ce))\nevaluate(ce; measure=my_measure)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"1-element Vector{Vector{Float32}}:\n [0.41711217]","category":"page"},{"location":"tutorials/evaluation/#Tidy-Output","page":"Evaluating Explanations","title":"Tidy Output","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"By default, evaluate returns vectors of evaluation measures. The optional key argument output_format::Symbol can be used to post-process the output in two ways: firstly, to return the output as a dictionary, specify output_format=:Dict:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ces; output_format=:Dict, report_each=true)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Dict{Symbol, Vector} with 3 entries:\n :validity => BitVector[[1, 1, 1, 1, 1]]\n :redundancy => [[0.0, 0.0, 0.0, 0.0, 0.0]]\n :distance => Vector{Float32}[[3.36717, 3.10285, 3.28294, 3.07289, 3.15207]]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Secondly, to return the output as a data frame, specify output_format=:DataFrame.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"evaluate(ces; output_format=:DataFrame, report_each=true)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"By default, data frames are pivoted to long format using individual counterfactuals as the id column. This behaviour can be suppressed by specifying pivot_longer=false.","category":"page"},{"location":"tutorials/evaluation/#Multiple-Counterfactual-Explanations","page":"Evaluating Explanations","title":"Multiple Counterfactual Explanations","text":"","category":"section"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"It may be necessary to generate counterfactual explanations for multiple individuals.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"Below, for example, we first select multiple samples (5) from the non-target class and then generate counterfactual explanations for all of them.","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"This can be done using broadcasting:","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"# Factual and target:\nids = rand(findall(predict_label(M, counterfactual_data) .== factual), n_individuals)\nxs = select_factual(counterfactual_data, ids)\nces = generate_counterfactual(xs, target, counterfactual_data, M, generator; num_counterfactuals=5)\nevaluation = evaluate.(ces)","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"5-element Vector{Vector{Vector}}:\n [[1.0], Float32[3.351181], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n [[1.0], Float32[2.6405892], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n [[1.0], Float32[2.935012], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n [[1.0], Float32[3.5348382], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n [[1.0], Float32[3.9373996], [[0.0, 0.0, 0.0, 0.0, 0.0]]]\n\nVector{Vector}[[[1.0], Float32[3.351181], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[2.6405892], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[2.935012], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[3.5348382], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[3.9373996], [[0.0, 0.0, 0.0, 0.0, 0.0]]]]","category":"page"},{"location":"tutorials/evaluation/","page":"Evaluating Explanations","title":"Evaluating Explanations","text":"This leads us to our next topic: Performance Benchmarks.","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/generic/#GenericGenerator","page":"Generic","title":"GenericGenerator","text":"","category":"section"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"We use the term generic to relate to the basic counterfactual generator proposed by Wachter, Mittelstadt, and Russell (2017) with L1-norm regularization. There is also a variant of this generator that uses the distance metric proposed in Wachter, Mittelstadt, and Russell (2017), which we call WachterGenerator.","category":"page"},{"location":"explanation/generators/generic/#Description","page":"Generic","title":"Description","text":"","category":"section"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"As the term indicates, this approach is simple: it forms the baseline approach for gradient-based counterfactual generators. Wachter, Mittelstadt, and Russell (2017) were among the first to realise that","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"[…] explanations can, in principle, be offered without opening the “black box.”— Wachter, Mittelstadt, and Russell (2017)","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"Gradient descent is performed directly in the feature space. Concerning the cost heuristic, the authors choose to penalize the distance of counterfactuals from the factual value. This is based on the intuitive notion that larger feature perturbations require greater effort.","category":"page"},{"location":"explanation/generators/generic/#Usage","page":"Generic","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"generator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"(Image: )","category":"page"},{"location":"explanation/generators/generic/#References","page":"Generic","title":"References","text":"","category":"section"},{"location":"explanation/generators/generic/","page":"Generic","title":"Generic","text":"Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harv. JL & Tech. 31: 841. https://doi.org/10.2139/ssrn.3063289.","category":"page"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/greedy/#GreedyGenerator","page":"Greedy","title":"GreedyGenerator","text":"","category":"section"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"We use the term greedy to describe the counterfactual generator introduced by Schut et al. (2021).","category":"page"},{"location":"explanation/generators/greedy/#Description","page":"Greedy","title":"Description","text":"","category":"section"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"The Greedy generator works under the premise of generating realistic counterfactuals by minimizing predictive uncertainty. Schut et al. (2021) show that for models that incorporates predictive uncertainty in their predictions, maximizing the predictive probability corresponds to minimizing the predictive uncertainty: by construction, the generated counterfactual will therefore be realistic (low epistemic uncertainty) and unambiguous (low aleatoric uncertainty).","category":"page"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"For the counterfactual search Schut et al. (2021) propose using a Jacobian-based Saliency Map Attack(JSMA). It is greedy in the sense that it is an “iterative algorithm that updates the most salient feature, i.e. the feature that has the largest influence on the classification, by delta at each step” (Schut et al. 2021).","category":"page"},{"location":"explanation/generators/greedy/#Usage","page":"Greedy","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"M = fit_model(counterfactual_data, :DeepEnsemble)\ngenerator = GreedyGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"(Image: )","category":"page"},{"location":"explanation/generators/greedy/#References","page":"Greedy","title":"References","text":"","category":"section"},{"location":"explanation/generators/greedy/","page":"Greedy","title":"Greedy","text":"Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/probe/#ProbeGenerator","page":"PROBE","title":"ProbeGenerator","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"The ProbeGenerator is designed to navigate the trade-offs between costs and robustness in Algorithmic Recourse (Pawelczyk et al. 2022).","category":"page"},{"location":"explanation/generators/probe/#Description","page":"PROBE","title":"Description","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"The goal of ProbeGenerator is to find a recourse x’ whose prediction at any point y within some set around x’ belongs to the positive class with probability 1 - r, where r is the recourse invalidation rate. It minimizes the gap between the achieved and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction.","category":"page"},{"location":"explanation/generators/probe/#Explanation","page":"PROBE","title":"Explanation","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"The loss function this generator is defined below. R is a hinge loss parameter which helps control for robustness. The loss and penalty functions can still be chosen freely.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"beginaligned\nR(x sigma^2 I) + l(f(x) s) + lambda d_c(x x)\nendaligned","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"R uses the following formula to control for noise. It generates small perturbations and checks how often the counterfactual explanation flips back to a factual one, when small amounts of noise are added to it.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"beginaligned\nDelta(x^hatE) = E_varepsilonh(x^hatE) - h(x^hatE + varepsilon)\nendaligned","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"The above formula is not differentiable. For this reason the generator uses the closed form version of the formula below.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"beginequation\nDelta tilde(x^hatE sigma^2 I) = 1 - Phi left(fracsqrtf(x^hatE)sqrtnabla f(x^hatE)^T sigma^2 I nabla f(x^hatE)right) \nendequation","category":"page"},{"location":"explanation/generators/probe/#Usage","page":"PROBE","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"Generating a counterfactual with the data loaded and generator chosen works as follows:","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"Note: It is important to set the convergence to “:invalidation_rate” here.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"M = fit_model(counterfactual_data, :DeepEnsemble)\nopt = Descent(0.01)\ngenerator = CounterfactualExplanations.Generators.ProbeGenerator(opt=opt)\nconv = CounterfactualExplanations.Convergence.InvalidationRateConvergence(;invalidation_rate=0.5)\nce = generate_counterfactual(x, target, counterfactual_data, M, generator, convergence=conv)\nplot(ce)","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"Choosing different invalidation rates makes the counterfactual more or less robust. The following plot shows the counterfactuals generated for different invalidation rates.","category":"page"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"(Image: )","category":"page"},{"location":"explanation/generators/probe/#References","page":"PROBE","title":"References","text":"","category":"section"},{"location":"explanation/generators/probe/","page":"PROBE","title":"PROBE","text":"Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” arXiv Preprint arXiv:2203.06768.","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/generators/gravitational/#GravitationalGenerator","page":"Gravitational","title":"GravitationalGenerator","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"The GravitationalGenerator was introduced in Altmeyer et al. (2023). It is named so because it generates counterfactuals that gravitate towards some sensible point in the target domain.","category":"page"},{"location":"explanation/generators/gravitational/#Description","page":"Gravitational","title":"Description","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"Altmeyer et al. (2023) extend the general framework as follows,","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"beginaligned\nmathbfs^prime = arg min_mathbfs^prime in mathcalS textyloss(M(f(mathbfs^prime))y^*) + lambda_1 textcost(f(mathbfs^prime)) + lambda_2 textextcost(f(mathbfs^prime)) \nendaligned ","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"where textcost(f(mathbfs^prime)) denotes the proxy for costs faced by the individual. “The newly introduced term textextcost(f(mathbfs^prime)) is meant to capture and address external costs incurred by the collective of individuals in response to changes in mathbfs^prime.” (Altmeyer et al. 2023)","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"For the GravitationalGenerator we have,","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"beginaligned\ntextextcost(f(mathbfs^prime)) = textdist(f(mathbfs^prime)barx^*) \nendaligned","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"where barx is some sensible point in the target domain, for example, the subsample average barx^*=textmean(x), x in mathcalD_1.","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"There is a tradeoff then, between the distance of counterfactuals from their factual value and the chosen point in the target domain. The chart below illustrates how the counterfactual outcome changes as the penalty lambda_2 on the distance to the point in the target domain is increased from left to right (holding the other penalty term constant).","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"(Image: )","category":"page"},{"location":"explanation/generators/gravitational/#Usage","page":"Gravitational","title":"Usage","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"The approach can be used in our package as follows:","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"generator = GravitationalGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\ndisplay(plot(ce))","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"(Image: )","category":"page"},{"location":"explanation/generators/gravitational/#Comparison-to-GenericGenerator","page":"Gravitational","title":"Comparison to GenericGenerator","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"The figure below compares the outcome for the GenericGenerator and the GravitationalGenerator.","category":"page"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"(Image: )","category":"page"},{"location":"explanation/generators/gravitational/#References","page":"Gravitational","title":"References","text":"","category":"section"},{"location":"explanation/generators/gravitational/","page":"Gravitational","title":"Gravitational","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/benchmarking/#Performance-Benchmarks","page":"Benchmarking Explanations","title":"Performance Benchmarks","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"In the previous tutorial, we have seen how counterfactual explanations can be evaluated. An important follow-up task is to compare the performance of different counterfactual generators is an important task. Researchers can use benchmarks to test new ideas they want to implement. Practitioners can find the right counterfactual generator for their specific use case through benchmarks. In this tutorial, we will see how to run benchmarks for counterfactual generators.","category":"page"},{"location":"tutorials/benchmarking/#Post-Hoc-Benchmarking","page":"Benchmarking Explanations","title":"Post Hoc Benchmarking","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"We begin by continuing the discussion from the previous tutorial: suppose you have generated multiple counterfactual explanations for multiple individuals, like below:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"# Factual and target:\nn_individuals = 5\nids = rand(findall(predict_label(M, counterfactual_data) .== factual), n_individuals)\nxs = select_factual(counterfactual_data, ids)\nces = generate_counterfactual(xs, target, counterfactual_data, M, generator; num_counterfactuals=5)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"You may be interested in comparing the outcomes across individuals. To benchmark the various counterfactual explanations using default evaluation measures, you can simply proceed as follows:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk = benchmark(ces)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Under the hood, the benchmark(counterfactual_explanations::Vector{CounterfactualExplanation}) uses CounterfactualExplanations.Evaluation.evaluate(ce::CounterfactualExplanation) to generate a Benchmark object, which contains the evaluation in its most granular form as a DataFrame.","category":"page"},{"location":"tutorials/benchmarking/#Working-with-Benchmarks","page":"Benchmarking Explanations","title":"Working with Benchmarks","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"For convenience, the DataFrame containing the evaluation can be returned by simply calling the Benchmark object. By default, the aggregated evaluation measures across id (in line with the default behaviour of evaluate).","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk()","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"15×7 DataFrame\n Row │ sample variable value generator ⋯\n │ Base.UUID String Float64 Symbol ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 239104d0-f59f-11ee-3d0c-d1db071927ff distance 3.17243 GradientBase ⋯\n 2 │ 239104d0-f59f-11ee-3d0c-d1db071927ff redundancy 0.0 GradientBase\n 3 │ 239104d0-f59f-11ee-3d0c-d1db071927ff validity 1.0 GradientBase\n 4 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b distance 3.07148 GradientBase\n 5 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b redundancy 0.0 GradientBase ⋯\n 6 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b validity 1.0 GradientBase\n 7 │ 2398b916-f59f-11ee-3f13-bd00858a39af distance 3.62159 GradientBase\n 8 │ 2398b916-f59f-11ee-3f13-bd00858a39af redundancy 0.0 GradientBase\n 9 │ 2398b916-f59f-11ee-3f13-bd00858a39af validity 1.0 GradientBase ⋯\n 10 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b distance 2.62783 GradientBase\n 11 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b redundancy 0.0 GradientBase\n 12 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b validity 1.0 GradientBase\n 13 │ 2398c08a-f59f-11ee-175b-81c155750752 distance 2.91985 GradientBase ⋯\n 14 │ 2398c08a-f59f-11ee-175b-81c155750752 redundancy 0.0 GradientBase\n 15 │ 2398c08a-f59f-11ee-175b-81c155750752 validity 1.0 GradientBase\n 4 columns omitted","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"To retrieve the granular dataset, simply do:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk(agg=nothing)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"75×8 DataFrame\n Row │ sample num_counterfactual variable v ⋯\n │ Base.UUID Int64 String F ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 1 distance 3 ⋯\n 2 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 2 distance 3\n 3 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 3 distance 3\n 4 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 4 distance 3\n 5 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 5 distance 3 ⋯\n 6 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 1 redundancy 0\n 7 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 2 redundancy 0\n 8 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 3 redundancy 0\n 9 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 4 redundancy 0 ⋯\n 10 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 5 redundancy 0\n 11 │ 239104d0-f59f-11ee-3d0c-d1db071927ff 1 validity 1\n ⋮ │ ⋮ ⋮ ⋮ ⋱\n 66 │ 2398c08a-f59f-11ee-175b-81c155750752 1 redundancy 0\n 67 │ 2398c08a-f59f-11ee-175b-81c155750752 2 redundancy 0 ⋯\n 68 │ 2398c08a-f59f-11ee-175b-81c155750752 3 redundancy 0\n 69 │ 2398c08a-f59f-11ee-175b-81c155750752 4 redundancy 0\n 70 │ 2398c08a-f59f-11ee-175b-81c155750752 5 redundancy 0\n 71 │ 2398c08a-f59f-11ee-175b-81c155750752 1 validity 1 ⋯\n 72 │ 2398c08a-f59f-11ee-175b-81c155750752 2 validity 1\n 73 │ 2398c08a-f59f-11ee-175b-81c155750752 3 validity 1\n 74 │ 2398c08a-f59f-11ee-175b-81c155750752 4 validity 1\n 75 │ 2398c08a-f59f-11ee-175b-81c155750752 5 validity 1 ⋯\n 5 columns and 54 rows omitted","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Since benchmarks return a DataFrame object on call, post-processing is straightforward. For example, we could use Tidier.jl:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"using Tidier\n@chain bmk() begin\n @filter(variable == \"distance\")\n @select(sample, variable, value)\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"5×3 DataFrame\n Row │ sample variable value \n │ Base.UUID String Float64 \n─────┼─────────────────────────────────────────────────────────\n 1 │ 239104d0-f59f-11ee-3d0c-d1db071927ff distance 3.17243\n 2 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b distance 3.07148\n 3 │ 2398b916-f59f-11ee-3f13-bd00858a39af distance 3.62159\n 4 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b distance 2.62783\n 5 │ 2398c08a-f59f-11ee-175b-81c155750752 distance 2.91985","category":"page"},{"location":"tutorials/benchmarking/#Metadata-for-Counterfactual-Explanations","page":"Benchmarking Explanations","title":"Metadata for Counterfactual Explanations","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Benchmarks always report metadata for each counterfactual explanation, which is automatically inferred by default. The default metadata concerns the explained model and the employed generator. In the current example, we used the same model and generator for each individual:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"@chain bmk() begin\n @group_by(sample)\n @select(sample, model, generator)\n @summarize(model=first(model),generator=first(generator))\n @ungroup\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"5×3 DataFrame\n Row │ sample model ⋯\n │ Base.UUID Symbol ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 239104d0-f59f-11ee-3d0c-d1db071927ff FluxModel(Chain(Dense(2 => 2)), … ⋯\n 2 │ 2398b3e2-f59f-11ee-3323-13d53fb7e75b FluxModel(Chain(Dense(2 => 2)), …\n 3 │ 2398b916-f59f-11ee-3f13-bd00858a39af FluxModel(Chain(Dense(2 => 2)), …\n 4 │ 2398bce8-f59f-11ee-37c1-ef7c6de27b6b FluxModel(Chain(Dense(2 => 2)), …\n 5 │ 2398c08a-f59f-11ee-175b-81c155750752 FluxModel(Chain(Dense(2 => 2)), … ⋯\n 1 column omitted","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Metadata can also be provided as an optional key argument.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"meta_data = Dict(\n :generator => \"Generic\",\n :model => \"MLP\",\n)\nmeta_data = [meta_data for i in 1:length(ces)]\nbmk = benchmark(ces; meta_data=meta_data)\n@chain bmk() begin\n @group_by(sample)\n @select(sample, model, generator)\n @summarize(model=first(model),generator=first(generator))\n @ungroup\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"5×3 DataFrame\n Row │ sample model generator \n │ Base.UUID String String \n─────┼─────────────────────────────────────────────────────────\n 1 │ 27fae496-f59f-11ee-2c30-f35d1025a6d4 MLP Generic\n 2 │ 27fdcc6a-f59f-11ee-030b-152c9794c5f1 MLP Generic\n 3 │ 27fdd04a-f59f-11ee-2010-e1732ff5d8d2 MLP Generic\n 4 │ 27fdd340-f59f-11ee-1d20-050a69dcacef MLP Generic\n 5 │ 27fdd5fc-f59f-11ee-02e8-d198e436abb3 MLP Generic","category":"page"},{"location":"tutorials/benchmarking/#Ad-Hoc-Benchmarking","page":"Benchmarking Explanations","title":"Ad Hoc Benchmarking","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"So far we have assumed the following workflow:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Fit some machine learning model.\nGenerate counterfactual explanations for some individual(s) (generate_counterfactual).\nEvaluate and benchmark them (benchmark(ces::Vector{CounterfactualExplanation})).","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"In many cases, it may be preferable to combine these steps. To this end, we have added support for two scenarios of Ad Hoc Benchmarking.","category":"page"},{"location":"tutorials/benchmarking/#Pre-trained-Models","page":"Benchmarking Explanations","title":"Pre-trained Models","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"In the first scenario, it is assumed that the machine learning models have been pre-trained and so the workflow can be summarized as follows:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Fit some machine learning model(s).\nGenerate counterfactual explanations and benchmark them.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"We suspect that this is the most common workflow for practitioners who are interested in benchmarking counterfactual explanations for the pre-trained machine learning models. Let’s go through this workflow using a simple example. We first train some models and store them in a dictionary:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"models = Dict(\n :MLP => fit_model(counterfactual_data, :MLP),\n :Linear => fit_model(counterfactual_data, :Linear),\n)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Next, we store the counterfactual generators of interest in a dictionary as well:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"generators = Dict(\n :Generic => GenericGenerator(),\n :Gravitational => GravitationalGenerator(),\n :Wachter => WachterGenerator(),\n :ClaPROAR => ClaPROARGenerator(),\n)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Then we can run a benchmark for individual(s) x, a pre-specified target and counterfactual_data as follows:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk = benchmark(x, target, counterfactual_data; models=models, generators=generators)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"In this case, metadata is automatically inferred from the dictionaries:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"@chain bmk() begin\n @filter(variable == \"distance\")\n @select(sample, variable, value, model, generator)\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"8×5 DataFrame\n Row │ sample variable value model ⋯\n │ Base.UUID String Float64 Tuple… ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 2cba5eee-f59f-11ee-1844-cbc7a8372a38 distance 4.38877 (:Linear, Flux ⋯\n 2 │ 2cd740fe-f59f-11ee-35c3-1157eb1b7583 distance 4.17021 (:Linear, Flux\n 3 │ 2cd741e2-f59f-11ee-2b09-0d55ef9892b9 distance 4.31145 (:Linear, Flux\n 4 │ 2cd7420c-f59f-11ee-1996-6fa75e23bb57 distance 4.17035 (:Linear, Flux\n 5 │ 2cd74234-f59f-11ee-0ad0-9f21949f5932 distance 5.73182 (:MLP, FluxMod ⋯\n 6 │ 2cd7425c-f59f-11ee-3eb4-af34f85ffd3d distance 5.50606 (:MLP, FluxMod\n 7 │ 2cd7427a-f59f-11ee-10d3-a1df6c8dc125 distance 5.2114 (:MLP, FluxMod\n 8 │ 2cd74298-f59f-11ee-32d1-f501c104fea8 distance 5.3623 (:MLP, FluxMod\n 2 columns omitted","category":"page"},{"location":"tutorials/benchmarking/#Everything-at-once","page":"Benchmarking Explanations","title":"Everything at once","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Researchers, in particular, may be interested in combining all steps into one. This is the second scenario of Ad Hoc Benchmarking:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Fit some machine learning model(s), generate counterfactual explanations and benchmark them.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"It involves calling benchmark directly on counterfactual data (the only positional argument):","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"bmk = benchmark(counterfactual_data)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"This will use the default models from standard_models_catalogue and train them on the data. All available generators from generator_catalogue will also be used:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"@chain bmk() begin\n @filter(variable == \"validity\")\n @select(sample, variable, value, model, generator)\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"200×5 DataFrame\n Row │ sample variable value model genera ⋯\n │ Base.UUID String Float64 Symbol Symbol ⋯\n─────┼──────────────────────────────────────────────────────────────────────────\n 1 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear gravit ⋯\n 2 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear growin\n 3 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear revise\n 4 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear clue\n 5 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear probe ⋯\n 6 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear dice\n 7 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear clapro\n 8 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear wachte\n 9 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear generi ⋯\n 10 │ 32d1817e-f59f-11ee-152f-a30b18c2e6f7 validity 1.0 Linear greedy\n 11 │ 32d255e8-f59f-11ee-3e8d-a9e9f6e23ea8 validity 1.0 Linear gravit\n ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋱\n 191 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP gravit\n 192 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP growin ⋯\n 193 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP revise\n 194 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP clue\n 195 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP probe\n 196 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP dice ⋯\n 197 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP clapro\n 198 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP wachte\n 199 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP generi\n 200 │ 3382d08a-f59f-11ee-10b3-f7d18cf7d3b5 validity 1.0 MLP greedy ⋯\n 1 column and 179 rows omitted","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Optionally, you can instead provide a dictionary of models and generators as before. Each value in the models dictionary should be one of two things:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Either be an object M of type AbstractFittedModel that implements the Models.train method.\nOr a DataType that can be called on CounterfactualData to create an object M as in (a).","category":"page"},{"location":"tutorials/benchmarking/#Multiple-Datasets","page":"Benchmarking Explanations","title":"Multiple Datasets","text":"","category":"section"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Benchmarks are run on single instances of type CounterfactualData. This is our design choice for two reasons:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"We want to avoid the loops inside the benchmark method(s) from getting too nested and convoluted.\nWhile it is straightforward to infer metadata for models and generators, this is not the case for datasets.","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Fortunately, it is very easy to run benchmarks for multiple datasets anyway, since Benchmark instances can be concatenated. To see how, let’s consider an example involving multiple datasets, models and generators:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"# Data:\ndatasets = Dict(\n :moons => CounterfactualData(load_moons()...),\n :circles => CounterfactualData(load_circles()...),\n)\n\n# Models:\nmodels = Dict(\n :MLP => FluxModel,\n :Linear => Linear,\n)\n\n# Generators:\ngenerators = Dict(\n :Generic => GenericGenerator(),\n :Greedy => GreedyGenerator(),\n)","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"Then we can simply loop over the datasets and eventually concatenate the results like so:","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"using CounterfactualExplanations.Evaluation: distance_measures\nbmks = []\nfor (dataname, dataset) in datasets\n bmk = benchmark(dataset; models=models, generators=generators, measure=distance_measures)\n push!(bmks, bmk)\nend\nbmk = vcat(bmks[1], bmks[2]; ids=collect(keys(datasets)))","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"When ids are supplied, then a new id column is added to the evaluation data frame that contains unique identifiers for the different benchmarks. The optional idcol_name argument can be used to specify the name for that indicator column (defaults to \"dataset\"):","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"@chain bmk() begin\n @group_by(dataset, generator)\n @filter(model == :MLP)\n @filter(variable == \"distance_l1\")\n @summarize(L1_norm=mean(value))\n @ungroup\nend","category":"page"},{"location":"tutorials/benchmarking/","page":"Benchmarking Explanations","title":"Benchmarking Explanations","text":"4×3 DataFrame\n Row │ dataset generator L1_norm \n │ Symbol Symbol Float32 \n─────┼──────────────────────────────\n 1 │ moons Generic 1.56555\n 2 │ moons Greedy 0.819269\n 3 │ circles Generic 1.83524\n 4 │ circles Greedy 0.498953","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"tutorials/whistle_stop/#Whistle-Stop-Tour","page":"Whiste-Stop Tour","title":"Whistle-Stop Tour","text":"","category":"section"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"In this tutorial, we will go through a slightly more complex example involving synthetic data. We will generate Counterfactual Explanations using different generators and visualize the results.","category":"page"},{"location":"tutorials/whistle_stop/#Data-and-Classifier","page":"Whiste-Stop Tour","title":"Data and Classifier","text":"","category":"section"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"# Choose some values for data and a model:\nn_dim = 2\nn_classes = 4\nn_samples = 400\nmodel_name = :MLP","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"The code chunk below generates synthetic data and uses it to fit a classifier. The outcome variable counterfactual_data.y consists of 4 classes. The input data counterfactual_data.X consists of 2 features. We generate a total of 400 samples. On the model side, we have specified model_name = :MLP. The fit_model can be used to fit a number of default models.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"data = TaijaData.load_multi_class(n_samples)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\nM = fit_model(counterfactual_data, model_name)","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"The chart below visualizes our data along with the model predictions. In particular, the contour indicates the predicted probabilities generated by our classifier. By default, these are the predicted probabilities for y=1, the first label. For multi-dimensional input data is compressed into two dimensions and the decision boundary is approximated using Nearest Neighbors (this is still somewhat experimental).","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"plot(M, counterfactual_data)","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"(Image: )","category":"page"},{"location":"tutorials/whistle_stop/#Counterfactual-Explanation","page":"Whiste-Stop Tour","title":"Counterfactual Explanation","text":"","category":"section"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"Next, we begin by specifying our target and factual label. We then draw a random sample from the non-target (factual) class.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"# Factual and target:\ntarget = 2\nfactual = 4\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"This sets the baseline for our counterfactual search: we plan to perturb the factual x to change the predicted label from y=4 to our target label target=2.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"Counterfactual generators accept several default parameters that can be used to adjust the counterfactual search at a high level: for example, a Flux.jl optimizer can be supplied to define how exactly gradient steps are performed. Importantly, one can also define the threshold probability at which the counterfactual search will converge. This relates to the probability predicted by the underlying black-box model, that the counterfactual belongs to the target class. A higher decision threshold typically prolongs the counterfactual search.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"# Search params:\ndecision_threshold = 0.75\nnum_counterfactuals = 3","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"The code below runs the counterfactual search for each generator available in the generator_catalogue. In each case, we also call the generic plot() method on the generated instance of type CounterfactualExplanation. This generates a simple plot that visualizes the entire counterfactual path. The chart below shows the results for all counterfactual generators: Factual: 4 → Target: 2.","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"ces = Dict()\nplts = []\nplottable_generators = filter(((k,v),) -> k ∉ [:growing_spheres, :feature_tweak], generator_catalogue)\n# Search:\nfor (key, Generator) in plottable_generators\n generator = Generator()\n ce = generate_counterfactual(\n x, target, counterfactual_data, M, generator;\n num_counterfactuals = num_counterfactuals,\n convergence=GeneratorConditionsConvergence(\n decision_threshold=decision_threshold\n )\n )\n ces[key] = ce\n plts = [plts..., plot(ce; title=key, colorbar=false)]\nend","category":"page"},{"location":"tutorials/whistle_stop/","page":"Whiste-Stop Tour","title":"Whiste-Stop Tour","text":"(Image: )","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"how_to_guides/custom_models/#How-to-add-Custom-Models","page":"... add custom models","title":"How to add Custom Models","text":"","category":"section"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"Adding custom models is possible and relatively straightforward, as we will demonstrate in this guide.","category":"page"},{"location":"how_to_guides/custom_models/#Custom-Models","page":"... add custom models","title":"Custom Models","text":"","category":"section"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"Apart from the default models you can use any arbitrary (differentiable) model and generate recourse in the same way as before. Only two steps are necessary to make your own Julia model compatible with this package:","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"The model needs to be declared as a subtype of <:CounterfactualExplanations.Models.AbstractFittedModel.\nYou need to extend the functions CounterfactualExplanations.Models.logits and CounterfactualExplanations.Models.probs for your custom model.","category":"page"},{"location":"how_to_guides/custom_models/#How-FluxModel-was-added","page":"... add custom models","title":"How FluxModel was added","text":"","category":"section"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"To demonstrate how this can be done in practice, we will reiterate here how native support for Flux.jl models was enabled (Innes 2018). Once again we use synthetic data for an illustrative example. The code below loads the data and builds a simple model architecture that can be used for a multi-class prediction task. Note how outputs from the final layer are not passed through a softmax activation function, since the counterfactual loss is evaluated with respect to logits. The model is trained with dropout.","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"# Data:\nN = 200\ndata = TaijaData.load_blobs(N; centers=4, cluster_std=0.5)\ncounterfactual_data = DataPreprocessing.CounterfactualData(data...)\ny = counterfactual_data.y\nX = counterfactual_data.X\n\n# Flux model setup: \nusing Flux\ndata = Flux.DataLoader((X,y), batchsize=1)\nn_hidden = 32\noutput_dim = size(y,1)\ninput_dim = 2\nactivation = σ\nmodel = Chain(\n Dense(input_dim, n_hidden, activation),\n Dropout(0.1),\n Dense(n_hidden, output_dim)\n) \nloss(x, y) = Flux.Losses.logitcrossentropy(model(x), y)\n\n# Flux model training:\nusing Flux.Optimise: update!, Adam\nopt = Adam()\nepochs = 50\nfor epoch = 1:epochs\n for d in data\n gs = gradient(Flux.params(model)) do\n l = loss(d...)\n end\n update!(opt, Flux.params(model), gs)\n end\nend","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"The code below implements the two steps that were necessary to make Flux models compatible with the package. We first declare our new struct as a subtype of <:AbstractDifferentiableModel, which itself is an abstract subtype of <:AbstractFittedModel. Computing logits amounts to just calling the model on inputs. Predicted probabilities for labels can in this case be computed by passing predicted logits through the softmax function. Finally, we just instantiate our model in the same way as always.","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"# Step 1)\nstruct MyFluxModel <: AbstractDifferentiableModel\n model::Any\n likelihood::Symbol\nend\n\n# Step 2)\n# import functions in order to extend\nimport CounterfactualExplanations.Models: logits\nimport CounterfactualExplanations.Models: probs \nlogits(M::MyFluxModel, X::AbstractArray) = M.model(X)\nprobs(M::MyFluxModel, X::AbstractArray) = softmax(logits(M, X))\nM = MyFluxModel(model, :classification_multi)","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"The code below implements the counterfactual search and plots the results:","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"factual_label = 4\ntarget = 2\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual_label))\nx = select_factual(counterfactual_data, chosen) \n\n# Counterfactual search:\ngenerator = GenericGenerator()\nce = generate_counterfactual(x, target, counterfactual_data, M, generator)\nplot(ce)","category":"page"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"(Image: )","category":"page"},{"location":"how_to_guides/custom_models/#References","page":"... add custom models","title":"References","text":"","category":"section"},{"location":"how_to_guides/custom_models/","page":"... add custom models","title":"... add custom models","text":"Innes, Mike. 2018. “Flux: Elegant Machine Learning with Julia.” Journal of Open Source Software 3 (25): 602. https://doi.org/10.21105/joss.00602.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"CurrentModule = CounterfactualExplanations","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: )","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Documentation for CounterfactualExplanations.jl.","category":"page"},{"location":"#CounterfactualExplanations","page":"🏠 Home","title":"CounterfactualExplanations","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Counterfactual Explanations and Algorithmic Recourse in Julia.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: Stable) (Image: Dev) (Image: Build Status) (Image: Coverage) (Image: Code Style: Blue) (Image: License) (Image: Package Downloads) (Image: Aqua QA)","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"There is also a corresponding paper, Explaining Black-Box Models through Counterfactuals, which has been published in JuliaCon Proceedings. Please consider citing the paper, if you use this package in your work:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: DOI) (Image: DOI)","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"@article{Altmeyer2023,\n doi = {10.21105/jcon.00130},\n url = {https://doi.org/10.21105/jcon.00130},\n year = {2023},\n publisher = {The Open Journal},\n volume = {1},\n number = {1},\n pages = {130},\n author = {Patrick Altmeyer and Arie van Deursen and Cynthia C. s. Liem},\n title = {Explaining Black-Box Models through Counterfactuals},\n journal = {Proceedings of the JuliaCon Conferences}\n}","category":"page"},{"location":"#Installation","page":"🏠 Home","title":"🚩 Installation","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"You can install the stable release from Julia’s General Registry as follows:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"using Pkg\nPkg.add(\"CounterfactualExplanations\")","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"CounterfactualExplanations.jl is under active development. To install the development version of the package you can run the following command:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"using Pkg\nPkg.add(url=\"https://github.com/juliatrustworthyai/CounterfactualExplanations.jl\")","category":"page"},{"location":"#Background-and-Motivation","page":"🏠 Home","title":"🤔 Background and Motivation","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Machine learning models like Deep Neural Networks have become so complex, opaque and underspecified in the data that they are generally considered Black Boxes. Nonetheless, such models often play a key role in data-driven decision-making systems. This creates the following problem: human operators in charge of such systems have to rely on them blindly, while those individuals subject to them generally have no way of challenging an undesirable outcome:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"“You cannot appeal to (algorithms). They do not listen. Nor do they bend.”— Cathy O’Neil in Weapons of Math Destruction, 2016","category":"page"},{"location":"#Enter:-Counterfactual-Explanations","page":"🏠 Home","title":"🔮 Enter: Counterfactual Explanations","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Counterfactual Explanations can help human stakeholders make sense of the systems they develop, use or endure: they explain how inputs into a system need to change for it to produce different decisions. Explainability benefits internal as well as external quality assurance.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Counterfactual Explanations have a few properties that are desirable in the context of Explainable Artificial Intelligence (XAI). These include:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Full fidelity to the black-box model, since no proxy is involved.\nNo need for (reasonably) interpretable features as opposed to LIME and SHAP.\nClear link to Algorithmic Recourse and Causal Inference.\nLess susceptible to adversarial attacks than LIME and SHAP.","category":"page"},{"location":"#Example:-Give-Me-Some-Credit","page":"🏠 Home","title":"Example: Give Me Some Credit","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Consider the following real-world scenario: a retail bank is using a black-box model trained on their clients’ credit history to decide whether they will provide credit to new applicants. To simulate this scenario, we have pre-trained a binary classifier on the publicly available Give Me Some Credit dataset that ships with this package (Kaggle 2011).","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"The figure below shows counterfactuals for 10 randomly chosen individuals that would have been denied credit initially.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: )","category":"page"},{"location":"#Example:-MNIST","page":"🏠 Home","title":"Example: MNIST","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"The figure below shows a counterfactual generated for an image classifier trained on MNIST: in particular, it demonstrates which pixels need to change in order for the classifier to predict 3 instead of 8.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Since v0.1.9 counterfactual generators are fully composable. Here we have composed a generator that combines ideas from Wachter, Mittelstadt, and Russell (2017) and Altmeyer et al. (2023):","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"# Compose generator:\nusing CounterfactualExplanations.Objectives: distance_from_target\ngenerator = GradientBasedGenerator()\n@chain generator begin\n @objective logitcrossentropy + 0.1distance_mad + 0.1distance_from_target\n @with_optimiser Adam(0.1) \nend\ncounterfactual_data.generative_model = vae # assign generative model","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"(Image: )","category":"page"},{"location":"#Usage-example","page":"🏠 Home","title":"🔍 Usage example","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Generating counterfactuals will typically look like follows. Below we first fit a simple model to a synthetic dataset with linearly separable features and then draw a random sample:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"# Data and Classifier:\ncounterfactual_data = CounterfactualData(load_linearly_separable()...)\nM = fit_model(counterfactual_data, :Linear)\n\n# Select random sample:\ntarget = 2\nfactual = 1\nchosen = rand(findall(predict_label(M, counterfactual_data) .== factual))\nx = select_factual(counterfactual_data, chosen)","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"To this end, we specify a counterfactual generator of our choice:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"# Counterfactual search:\ngenerator = DiCEGenerator(λ=[0.1,0.3])","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Here, we have chosen to use the GradientBasedGenerator to move the individual from its factual label 1 to the target label 2.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"With all of our ingredients specified, we finally generate counterfactuals using a simple API call:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"conv = conv = CounterfactualExplanations.Convergence.GeneratorConditionsConvergence()\nce = generate_counterfactual(\n x, target, counterfactual_data, M, generator; \n num_counterfactuals=3, convergence=conv,\n)","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"The animation below shows the resulting counterfactual path:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"","category":"page"},{"location":"#Implemented-Counterfactual-Generators","page":"🏠 Home","title":"☑️ Implemented Counterfactual Generators","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Currently, the following counterfactual generators are implemented:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"ClaPROAR (Altmeyer et al. 2023)\nCLUE (Antorán et al. 2020)\nDiCE (Mothilal, Sharma, and Tan 2020)\nFeatureTweak (Tolomei et al. 2017)\nGeneric\nGravitationalGenerator (Altmeyer et al. 2023)\nGreedy (Schut et al. 2021)\nGrowingSpheres (Laugel et al. 2017)\nPROBE (Pawelczyk et al. 2022)\nREVISE (Joshi et al. 2019)\nWachter (Wachter, Mittelstadt, and Russell 2017)","category":"page"},{"location":"#Goals-and-limitations","page":"🏠 Home","title":"🎯 Goals and limitations","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"The goal of this library is to contribute to efforts towards trustworthy machine learning in Julia. The Julia language has an edge when it comes to trustworthiness: it is very transparent. Packages like this one are generally written in pure Julia, which makes it easy for users and developers to understand and contribute to open-source code. Eventually, this project aims to offer a one-stop-shop of counterfactual explanations.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Our ambition is to enhance the package through the following features:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Support for all supervised machine learning models trained in MLJ.jl.\nSupport for regression models.","category":"page"},{"location":"#Contribute","page":"🏠 Home","title":"🛠 Contribute","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Contributions of any kind are very much welcome! Take a look at the issue to see what things we are currently working on.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"If any of the below applies to you, this might be the right open-source project for you:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"You’re an expert in Counterfactual Explanations or Explainable AI more broadly and you are curious about Julia.\nYou’re experienced with Julia and are happy to help someone less experienced to up their game. Ideally, you are also curious about Trustworthy AI.\nYou’re new to Julia and open-source development and would like to start your learning journey by contributing to a recent and active development. Ideally, you are familiar with machine learning.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"@pat-alt here: I am still very much at the beginning of my Julia journey, so if you spot any issues or have any suggestions for design improvement, please just open issue or start a discussion.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"For more details on how to contribute see here. Please follow the SciML ColPrac guide.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"There are also some general pointers for people looking to contribute to any of our Taija packages here.","category":"page"},{"location":"#Citation","page":"🏠 Home","title":"🎓 Citation","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"If you want to use this codebase, please consider citing the corresponding paper:","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"@article{Altmeyer2023,\n doi = {10.21105/jcon.00130},\n url = {https://doi.org/10.21105/jcon.00130},\n year = {2023},\n publisher = {The Open Journal},\n volume = {1},\n number = {1},\n pages = {130},\n author = {Patrick Altmeyer and Arie van Deursen and Cynthia C. s. Liem},\n title = {Explaining Black-Box Models through Counterfactuals},\n journal = {Proceedings of the JuliaCon Conferences}\n}","category":"page"},{"location":"#References","page":"🏠 Home","title":"📚 References","text":"","category":"section"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. “Getting a Clue: A Method for Explaining Uncertainty Estimates.” https://arxiv.org/abs/2006.06848.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Kaggle. 2011. “Give Me Some Credit, Improve on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the Next Two Years.” Kaggle. https://www.kaggle.com/c/GiveMeSomeCredit.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. 2017. “Inverse Classification for Comparison-Based Interpretability in Machine Learning.” https://arxiv.org/abs/1712.08443.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. “Probabilistically Robust Recourse: Navigating the Trade-Offs Between Costs and Robustness in Algorithmic Recourse.” arXiv Preprint arXiv:2203.06768.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. “Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties.” In International Conference on Artificial Intelligence and Statistics, 1756–64. PMLR.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Tolomei, Gabriele, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas. 2017. “Interpretable Predictions of Tree-Based Ensembles via Actionable Feature Tweaking.” In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 465–74. https://doi.org/10.1145/3097983.3098039.","category":"page"},{"location":"","page":"🏠 Home","title":"🏠 Home","text":"Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harv. JL & Tech. 31: 841. https://doi.org/10.2139/ssrn.3063289.","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"explanation/categorical/#Categorical-Features","page":"Categorical Features","title":"Categorical Features","text":"","category":"section"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"To illustrate how data is preprocessed under the hood, we consider a simple toy dataset with three categorical features (name, grade and sex) and one continuous feature (age):","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"X = (\n name=categorical([\"Danesh\", \"Lee\", \"Mary\", \"John\"]),\n grade=categorical([\"A\", \"B\", \"A\", \"C\"], ordered=true),\n sex=categorical([\"male\",\"female\",\"male\",\"male\"]),\n height=[1.85, 1.67, 1.5, 1.67],\n)\nschema(X)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"Categorical features are expected to be one-hot or dummy encoded. To this end, we could use MLJ, for example:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"hot = OneHotEncoder()\nmach = fit!(machine(hot, X))\nW = transform(mach, X)\nschema(W)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"┌──────────────┬────────────┬─────────┐\n│ names │ scitypes │ types │\n├──────────────┼────────────┼─────────┤\n│ name__Danesh │ Continuous │ Float64 │\n│ name__John │ Continuous │ Float64 │\n│ name__Lee │ Continuous │ Float64 │\n│ name__Mary │ Continuous │ Float64 │\n│ grade__A │ Continuous │ Float64 │\n│ grade__B │ Continuous │ Float64 │\n│ grade__C │ Continuous │ Float64 │\n│ sex__female │ Continuous │ Float64 │\n│ sex__male │ Continuous │ Float64 │\n│ height │ Continuous │ Float64 │\n└──────────────┴────────────┴─────────┘","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The matrix that will be perturbed during the counterfactual search looks as follows:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"X = permutedims(MLJBase.matrix(W))","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10×4 Matrix{Float64}:\n 1.0 0.0 0.0 0.0\n 0.0 0.0 0.0 1.0\n 0.0 1.0 0.0 0.0\n 0.0 0.0 1.0 0.0\n 1.0 0.0 1.0 0.0\n 0.0 1.0 0.0 0.0\n 0.0 0.0 0.0 1.0\n 0.0 1.0 0.0 0.0\n 1.0 0.0 1.0 1.0\n 1.85 1.67 1.5 1.67","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The CounterfactualData constructor takes two optional arguments that can be used to specify the indices of categorical and continuous features. If nothing is supplied, all features are assumed to be continuous. For categorical features, the constructor expects and array of arrays of integers (Vector{Vector{Int}}) where each subarray includes the indices of a all one-hot encoded rows related to a single categorical feature. In the example above, the name feature is one-hot encoded across rows 1, 2 and 3 of X.","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"features_categorical = [\n [1,2,3,4], # name\n [5,6,7], # grade\n [8,9] # sex\n]\nfeatures_continuous = [10]","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"We propose the following simple logic for reconstructing categorical encodings after perturbations:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"For one-hot encoded features with multiple classes, choose the maximum.\nFor binary features, clip the perturbed value to fall into 01 and round to the nearest of the two integers.","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"function reconstruct_cat_encoding(x)\n map(features_categorical) do cat_group_index\n if length(cat_group_index) > 1\n x[cat_group_index] = Int.(x[cat_group_index] .== maximum(x[cat_group_index]))\n if sum(x[cat_group_index]) > 1\n ties = findall(x[cat_group_index] .== 1)\n _x = zeros(length(x[cat_group_index]))\n winner = rand(ties,1)[1]\n _x[winner] = 1\n x[cat_group_index] = _x\n end\n else\n x[cat_group_index] = [round(clamp(x[cat_group_index][1],0,1))]\n end\n end\n return x\nend","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"Let’s look at a few simple examples to see how this function works. Firstly, consider the case of perturbing a single element:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"x = X[:,1]\nx[1] = 1.1\nx","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 1.1\n 0.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The reconstructed one-hot-encoded vector will look like this:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"reconstruct_cat_encoding(x)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"Next, consider the case of perturbing multiple elements:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"x[2] = 1.1\nx[3] = -1.2\nx","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 1.0\n 1.1\n -1.2\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The reconstructed one-hot-encoded vector will look like this:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"reconstruct_cat_encoding(x)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 0.0\n 1.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"Finally, let’s introduce a tie:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"x[1] = 1.0\nx","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 1.0\n 1.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"The reconstructed one-hot-encoded vector will look like this:","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"reconstruct_cat_encoding(x)","category":"page"},{"location":"explanation/categorical/","page":"Categorical Features","title":"Categorical Features","text":"10-element Vector{Float64}:\n 0.0\n 1.0\n 0.0\n 0.0\n 1.0\n 0.0\n 0.0\n 0.0\n 1.0\n 1.85","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"CurrentModule = CounterfactualExplanations ","category":"page"},{"location":"extensions/neurotree/#[NeuroTreeModels.jl](https://evovest.github.io/NeuroTreeModels.jl/dev/)","page":"NeuroTrees","title":"NeuroTreeModels.jl","text":"","category":"section"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"NeuroTreeModels.jl is a package that provides a framework for training differentiable tree-based models. This is relevant to the work on counterfactual explanations (CE), which often assumes that the underlying black-box model is differentiable with respect to its input. The literature on CE therefore regularly focuses exclusively on explaining deep learning models. This is at odds with the fact that the literature also typically focuses on tabular data, which is often best modeled by tree-based models (Grinsztajn, Oyallon, and Varoquaux 2022). The extension for NeuroTreeModels.jl provides a way to bridge this gap by allowing users to apply existing gradient-based CE methods to differentiable tree-based models.","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"warning: Experimental Feature\nPlease note that this extension is still experimental. Neither the behaviour of differentiable tree-based models nor their interplay with counterfactual explanations is well understood at this point. If you encounter any issues, please report them to the package maintainers. Your feedback is highly appreciated.Please also note that this extension is only tested on Julia 1.9 and higher, due to compatibility issues.","category":"page"},{"location":"extensions/neurotree/#Example","page":"NeuroTrees","title":"Example","text":"","category":"section"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"The extension will be loaded automatically when loading the NeuroTreeModels package (assuming the CounterfactualExplanations package is also loaded).","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"using NeuroTreeModels","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"Next, we will fit a NeuroTree model to the moons dataset using our standard package API for doing so.","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"# Fit model to data:\ndata = CounterfactualData(load_moons()...)\nM = fit_model(\n data, :NeuroTree; \n depth=2, lr=5e-2, nrounds=50, batchsize=10\n)","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"NeuroTreeExt.NeuroTreeModel(NeuroTreeRegressor(loss = mlogloss, …), :classification_multi, NeuroTreeModels.NeuroTreeModel{NeuroTreeModels.MLogLoss, Chain{Tuple{BatchNorm{typeof(identity), Vector{Float32}, Float32, Vector{Float32}}, NeuroTreeModels.StackTree}}}(NeuroTreeModels.MLogLoss, Chain(BatchNorm(2, active=false), NeuroTreeModels.StackTree(NeuroTree[NeuroTree{Matrix{Float32}, Vector{Float32}, Array{Float32, 3}}(Float32[1.8824593 -0.28222033; -2.680499 0.67347014; … ; -1.0722864 1.3651229; -2.0926774 1.63557], Float32[-3.4070241, 4.545113, 1.0882677, -0.3497498, -2.766766, 1.9072449, -0.9736261, 3.9750721, 1.726214, 3.7279263 … -0.0664266, -0.4214582, -2.3816268, -3.1371245, 0.76548636, 2.636373, 2.4558601, 0.893434, -1.9484522, 4.793434], Float32[3.44271 -6.334693 -0.6308845 3.385659; -3.4316056 6.297003 0.7254221 -3.3283486;;; -3.7011054 -0.17596768 0.15429471 2.270125; 3.4926674 0.026218029 -0.19753197 -2.2337704;;; 1.1795454 -4.315231 0.28486454 1.9995956; -0.9651108 4.0999455 -0.05312265 -1.8039354;;; … ;;; 2.5076811 -0.46358463 -3.5438805 0.0686823; -2.592356 0.47884527 3.781507 -0.022692114;;; -0.59115165 -3.234046 0.09896194 2.375202; 0.5592871 3.3082843 -0.014032216 -2.1876256;;; 2.039389 -0.10134532 2.6637273 -4.999703; -2.0289893 0.3368772 -2.5739825 5.069934], tanh)])), Dict{Symbol, Any}(:feature_names => [:x1, :x2], :nrounds => 50, :device => :cpu)))","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"Finally, we select a factual instance and generate a counterfactual explanation for it using the generic gradient-based CE method.","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"# Select a factual instance:\ntarget = 1\nfactual = 0\nchosen = rand(findall(predict_label(M, data) .== factual))\nx = select_factual(data, chosen)\n\n# Generate counterfactual explanation:\nη = 0.01\ngenerator = GenericGenerator(; opt=Descent(η), λ=0.01)\nconv = CounterfactualExplanations.Convergence.DecisionThresholdConvergence(;\n decision_threshold=0.9, max_iter=100\n)\nce = generate_counterfactual(x, target, data, M, generator; convergence=conv)\nplot(ce, alpha=0.1)","category":"page"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"(Image: )","category":"page"},{"location":"extensions/neurotree/#References","page":"NeuroTrees","title":"References","text":"","category":"section"},{"location":"extensions/neurotree/","page":"NeuroTrees","title":"NeuroTrees","text":"Grinsztajn, Léo, Edouard Oyallon, and Gaël Varoquaux. 2022. “Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?” https://arxiv.org/abs/2207.08815.","category":"page"}] } diff --git a/dev/tutorials/benchmarking/index.html b/dev/tutorials/benchmarking/index.html index d4d06569c..f362c2ae8 100644 --- a/dev/tutorials/benchmarking/index.html +++ b/dev/tutorials/benchmarking/index.html @@ -1,5 +1,5 @@ -Benchmarking Explanations · CounterfactualExplanations.jl
    Edit on GitHub

    Performance Benchmarks

    In the previous tutorial, we have seen how counterfactual explanations can be evaluated. An important follow-up task is to compare the performance of different counterfactual generators is an important task. Researchers can use benchmarks to test new ideas they want to implement. Practitioners can find the right counterfactual generator for their specific use case through benchmarks. In this tutorial, we will see how to run benchmarks for counterfactual generators.

    Post Hoc Benchmarking

    We begin by continuing the discussion from the previous tutorial: suppose you have generated multiple counterfactual explanations for multiple individuals, like below:

    # Factual and target:
+Benchmarking Explanations · CounterfactualExplanations.jl
    Performance Benchmarks
    In the previous tutorial, we have seen how counterfactual explanations can be evaluated. An important follow-up task is to compare the performance of different counterfactual generators is an important task. Researchers can use benchmarks to test new ideas they want to implement. Practitioners can find the right counterfactual generator for their specific use case through benchmarks. In this tutorial, we will see how to run benchmarks for counterfactual generators.
    Post Hoc Benchmarking
    We begin by continuing the discussion from the previous tutorial: suppose you have generated multiple counterfactual explanations for multiple individuals, like below:
    # Factual and target:
 n_individuals = 5
 ids = rand(findall(predict_label(M, counterfactual_data) .== factual), n_individuals)
 xs = select_factual(counterfactual_data, ids)
@@ -180,4 +180,4 @@
    1 │ moons    Generic    1.56555
    2 │ moons    Greedy     0.819269
    3 │ circles  Generic    1.83524
-   4 │ circles  Greedy     0.498953
    
+   4 │ circles  Greedy     0.498953
    diff --git a/dev/tutorials/data_catalogue/index.html b/dev/tutorials/data_catalogue/index.html index aa3fae5c8..4b39a4ff2 100644 --- a/dev/tutorials/data_catalogue/index.html +++ b/dev/tutorials/data_catalogue/index.html @@ -1,5 +1,5 @@ -Data Catalogue · CounterfactualExplanations.jl
    Edit on GitHub

    Data Catalogue

    To allow researchers and practitioners to test and compare counterfactual generators, the TAIJA environment includes the package TaijaData.jl which comes with pre-processed synthetic and real-world benchmark datasets from different domains. This page explains how to use TaijaData.jl in tandem with CounterfactualExplanations.jl.

    Synthetic Data

    The following dictionary can be used to inspect the available methods to generate synthetic datasets where the key indicates the name of the data and the value is the corresponding method:

    TaijaData.data_catalogue[:synthetic]
    Dict{Symbol, Function} with 6 entries:
+Data Catalogue · CounterfactualExplanations.jl
    Data Catalogue
    To allow researchers and practitioners to test and compare counterfactual generators, the TAIJA environment includes the package TaijaData.jl which comes with pre-processed synthetic and real-world benchmark datasets from different domains. This page explains how to use TaijaData.jl in tandem with CounterfactualExplanations.jl.
    Synthetic Data
    The following dictionary can be used to inspect the available methods to generate synthetic datasets where the key indicates the name of the data and the value is the corresponding method:
    TaijaData.data_catalogue[:synthetic]
    Dict{Symbol, Function} with 6 entries:
   :overlapping        => load_overlapping
   :linearly_separable => load_linearly_separable
   :blobs              => load_blobs
@@ -38,4 +38,4 @@
  10
  10
  10
    We can also use a helper function to split the data into train and test sets:
    train_data, test_data = 
-    CounterfactualExplanations.DataPreprocessing.train_test_split(counterfactual_data)
    
+    CounterfactualExplanations.DataPreprocessing.train_test_split(counterfactual_data)
    diff --git a/dev/tutorials/data_preprocessing/index.html b/dev/tutorials/data_preprocessing/index.html index c6457df9c..01ca3ea32 100644 --- a/dev/tutorials/data_preprocessing/index.html +++ b/dev/tutorials/data_preprocessing/index.html @@ -1,5 +1,5 @@ -Handling Data · CounterfactualExplanations.jl
    Edit on GitHub

    Handling Data

    The package works with custom data containers that contain the input and output data as well as information about the type and mutability of features. In this tutorial, we will see how data can be prepared for use with the package.

    Basic Functionality

    To demonstrate the basic way to prepare data, let’s look at a standard benchmark dataset: Fisher’s classic iris dataset. We can use MLDatasets to load this data.

    dataset = Iris()

    Our data constructor CounterfactualData needs at least two inputs: features X and targets y.

    X = dataset.features
+Handling Data · CounterfactualExplanations.jl
    Handling Data
    The package works with custom data containers that contain the input and output data as well as information about the type and mutability of features. In this tutorial, we will see how data can be prepared for use with the package.
    Basic Functionality
    To demonstrate the basic way to prepare data, let’s look at a standard benchmark dataset: Fisher’s classic iris dataset. We can use MLDatasets to load this data.
    dataset = Iris()
    Our data constructor CounterfactualData needs at least two inputs: features X and targets y.
    X = dataset.features
 y = dataset.targets
    Next, we convert the input data to a Tables.MatrixTable (following MLJ.jl) convention. Concerning the target variable, we just assign grab the first column of the data frame.
    X = table(Tables.matrix(X))
 y = y[:,1]
    Now we can feed these two ingredients to our constructor:
    counterfactual_data = CounterfactualData(X, y)
    Under the hood, the constructor performs basic preprocessing steps. For example, the output variable y is automatically one-hot encoded:
    counterfactual_data.y
    3×150 Matrix{Bool}:
  1  1  1  1  1  1  1  1  1  1  1  1  1  …  0  0  0  0  0  0  0  0  0  0  0  0
@@ -90,4 +90,4 @@
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
    The resulting counterfactual path is shown in the chart below. Since only the first feature can be perturbed, the sample can only move along the horizontal axis.
    plot(ce)
     <!– ## Domain constraints &#10;In some cases, we may also want to constrain the domain of some feature. For example, age as a feature is constrained to a range from 0 to some upper bound corresponding perhaps to the average life expectancy of humans. Below, for example, we impose an upper bound of $0.5$ for our two features. &#10;```{.julia} counterfactualdata.mutability = [:both, :both] counterfactualdata.domain = [(0,0) for var in counterfactualdata.featurescontinuous]
    &#10;This results in the counterfactual path shown below: since features are not allowed to be perturbed beyond the upper bound, the resulting counterfactual falls just short of the threshold probability $\gamma$.
 &#10;```{.julia}
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-plot(ce)
    –>
    
+plot(ce)

    –>

    diff --git a/dev/tutorials/evaluation/index.html b/dev/tutorials/evaluation/index.html index db581aaff..5de35acaf 100644 --- a/dev/tutorials/evaluation/index.html +++ b/dev/tutorials/evaluation/index.html @@ -1,5 +1,5 @@ -Evaluating Explanations · CounterfactualExplanations.jl
    Edit on GitHub

    Performance Evaluation

    Now that we know how to generate counterfactual explanations in Julia, you may have a few follow-up questions: How do I know if the counterfactual search has been successful? How good is my counterfactual explanation? What does ‘good’ even mean in this context? In this tutorial, we will see how counterfactual explanations can be evaluated with respect to their performance.

    Default Measures

    Numerous evaluation measures for counterfactual explanations have been proposed. In what follows, we will cover some of the most important measures.

    Single Measure, Single Counterfactual

    One of the most important measures is validity, which simply determines whether or not a counterfactual explanation $x^{\prime}$ is valid in the sense that it yields the target prediction: $M(x^{\prime})=t$. We can evaluate the validity of a single counterfactual explanation ce using the Evaluation.evaluate function as follows:

    using CounterfactualExplanations.Evaluation: evaluate, validity
+Evaluating Explanations · CounterfactualExplanations.jl
    Performance Evaluation
    Now that we know how to generate counterfactual explanations in Julia, you may have a few follow-up questions: How do I know if the counterfactual search has been successful? How good is my counterfactual explanation? What does ‘good’ even mean in this context? In this tutorial, we will see how counterfactual explanations can be evaluated with respect to their performance.
    Default Measures
    Numerous evaluation measures for counterfactual explanations have been proposed. In what follows, we will cover some of the most important measures.
    Single Measure, Single Counterfactual
    One of the most important measures is validity, which simply determines whether or not a counterfactual explanation $x^{\prime}$ is valid in the sense that it yields the target prediction: $M(x^{\prime})=t$. We can evaluate the validity of a single counterfactual explanation ce using the Evaluation.evaluate function as follows:
    using CounterfactualExplanations.Evaluation: evaluate, validity
 evaluate(ce; measure=validity)
    1-element Vector{Vector{Float64}}:
  [1.0]
    For a single counterfactual explanation, this evaluation measure can only take two values: it is either equal to 1, if the explanation is valid or 0 otherwise. Another important measure is distance, which relates to the distance between the factual $x$ and the counterfactual $x^{\prime}$. In the context of Algorithmic Recourse, higher distances are typically associated with higher costs to individuals seeking recourse.
    using CounterfactualExplanations.Objectives: distance
 evaluate(ce; measure=distance)
    1-element Vector{Vector{Float32}}:
@@ -42,4 +42,4 @@
  [[1.0], Float32[3.5348382], [[0.0, 0.0, 0.0, 0.0, 0.0]]]
  [[1.0], Float32[3.9373996], [[0.0, 0.0, 0.0, 0.0, 0.0]]]
 
-Vector{Vector}[[[1.0], Float32[3.351181], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[2.6405892], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[2.935012], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[3.5348382], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[3.9373996], [[0.0, 0.0, 0.0, 0.0, 0.0]]]]
    This leads us to our next topic: Performance Benchmarks.
    
+Vector{Vector}[[[1.0], Float32[3.351181], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[2.6405892], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[2.935012], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[3.5348382], [[0.0, 0.0, 0.0, 0.0, 0.0]]], [[1.0], Float32[3.9373996], [[0.0, 0.0, 0.0, 0.0, 0.0]]]]

    This leads us to our next topic: Performance Benchmarks.

    diff --git a/dev/tutorials/generators/index.html b/dev/tutorials/generators/index.html index 67ce8d8f5..e1923ef2d 100644 --- a/dev/tutorials/generators/index.html +++ b/dev/tutorials/generators/index.html @@ -1,9 +1,9 @@ -Handling Generators · CounterfactualExplanations.jl
    Edit on GitHub

    Handling Generators

    Generating Counterfactual Explanations can be seen as a generative modelling task because it involves generating samples in the input space: $x \sim \mathcal{X}$. In this tutorial, we will introduce how Counterfactual GradientBasedGenerators are used. They are discussed in more detail in the explanatory section of the documentation.

    Composable Generators

    Breaking Changes Expected

    Work on this feature is still in its very early stages and breaking changes should be expected.

    One of the key objectives for this package is Composability. It turns out that many of the various counterfactual generators that have been proposed in the literature, essentially do the same thing: they optimize an objective function. Formally we have,

    \[ +Handling Generators · CounterfactualExplanations.jl

    Handling Generators

    Generating Counterfactual Explanations can be seen as a generative modelling task because it involves generating samples in the input space: $x \sim \mathcal{X}$. In this tutorial, we will introduce how Counterfactual GradientBasedGenerators are used. They are discussed in more detail in the explanatory section of the documentation.

    Composable Generators

    Breaking Changes Expected

    Work on this feature is still in its very early stages and breaking changes should be expected.

    One of the key objectives for this package is Composability. It turns out that many of the various counterfactual generators that have been proposed in the literature, essentially do the same thing: they optimize an objective function. Formally we have,

    \[ \begin{aligned} \mathbf{s}^\prime &= \arg \min_{\mathbf{s}^\prime \in \mathcal{S}} \left\{ {\text{yloss}(M(f(\mathbf{s}^\prime)),y^*)}+ \lambda {\text{cost}(f(\mathbf{s}^\prime)) } \right\} \end{aligned} - \qquad(1)\]

    where $\text{yloss}$ denotes the main loss function and $\text{cost}$ is a penalty term (Altmeyer et al. 2023).

    Without going into further detail here, the important thing to mention is that Equation 1 very closely describes how counterfactual search is actually implemented in the package. In other words, all off-the-shelf generators currently implemented work with that same objective. They just vary in the way that penalties are defined, for example. This gives rise to an interesting idea:

    Why not compose generators that combine ideas from different off-the-shelf generators?

    The GradientBasedGenerator class provides a straightforward way to do this, without requiring users to build custom GradientBasedGenerators from scratch. It can be instantiated as follows:

    generator = GradientBasedGenerator()

    By default, this creates a generator that simply performs gradient descent without any penalties. To modify the behaviour of the generator, you can define the counterfactual search objective function using the @objective macro:

    @objective(generator, logitbinarycrossentropy + 0.1distance_l2 + 1.0ddp_diversity)

    Here we have essentially created a version of the DiCEGenerator:

    ce = generate_counterfactual(x, target, counterfactual_data, M, generator; num_counterfactuals=5)
+ \qquad(1)\]
    where $\text{yloss}$ denotes the main loss function and $\text{cost}$ is a penalty term (Altmeyer et al. 2023).
    Without going into further detail here, the important thing to mention is that Equation 1 very closely describes how counterfactual search is actually implemented in the package. In other words, all off-the-shelf generators currently implemented work with that same objective. They just vary in the way that penalties are defined, for example. This gives rise to an interesting idea:
    Why not compose generators that combine ideas from different off-the-shelf generators?
    The GradientBasedGenerator class provides a straightforward way to do this, without requiring users to build custom GradientBasedGenerators from scratch. It can be instantiated as follows:
    generator = GradientBasedGenerator()
    By default, this creates a generator that simply performs gradient descent without any penalties. To modify the behaviour of the generator, you can define the counterfactual search objective function using the @objective macro:
    @objective(generator, logitbinarycrossentropy + 0.1distance_l2 + 1.0ddp_diversity)
    Here we have essentially created a version of the DiCEGenerator:
    ce = generate_counterfactual(x, target, counterfactual_data, M, generator; num_counterfactuals=5)
 plot(ce)
    Multiple macros can be chained using Chains.jl making it easy to create entirely new flavours of counterfactual generators. The following generator, for example, combines ideas from DiCE (Mothilal, Sharma, and Tan 2020) and REVISE (Joshi et al. 2019):
    @chain generator begin
     @objective logitcrossentropy + 1.0ddp_diversity     # DiCE (Mothilal et al. 2020)
     @with_optimiser Flux.Adam(0.1)                      
@@ -22,4 +22,4 @@
   :greedy          => GreedyGenerator
    To specify the type of generator you want to use, you can simply instantiate it:
    # Search:
 generator = GenericGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-plot(ce)
    We generally make an effort to follow the literature as closely as possible when implementing off-the-shelf generators.
    References
    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.
    Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.
    Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.
    +plot(ce)

    We generally make an effort to follow the literature as closely as possible when implementing off-the-shelf generators.

    References

    Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In First IEEE Conference on Secure and Trustworthy Machine Learning. https://doi.org/10.1109/satml54575.2023.00036.

    Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. “Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.” https://arxiv.org/abs/1907.09615.

    Mothilal, Ramaravind K, Amit Sharma, and Chenhao Tan. 2020. “Explaining Machine Learning Classifiers Through Diverse Counterfactual Explanations.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–17. https://doi.org/10.1145/3351095.3372850.

    diff --git a/dev/tutorials/index.html b/dev/tutorials/index.html index f12c5be49..defb31e84 100644 --- a/dev/tutorials/index.html +++ b/dev/tutorials/index.html @@ -1,2 +1,2 @@ -Overview · CounterfactualExplanations.jl
    Edit on GitHub

    Tutorials

    In this section, you will find a series of tutorials that should help you gain a basic understanding of Conformal Prediction and how to apply it in Julia using this package.

    Tutorials are lessons that take the reader by the hand through a series of steps to complete a project of some kind. Tutorials are learning-oriented.

    — Diátaxis

    In other words, you come here because you are new to this topic and are looking for a first peek at the methodology and code 🫣.

    +Overview · CounterfactualExplanations.jl

    Tutorials

    In this section, you will find a series of tutorials that should help you gain a basic understanding of Conformal Prediction and how to apply it in Julia using this package.

    Tutorials are lessons that take the reader by the hand through a series of steps to complete a project of some kind. Tutorials are learning-oriented.

    — Diátaxis

    In other words, you come here because you are new to this topic and are looking for a first peek at the methodology and code 🫣.

    diff --git a/dev/tutorials/model_catalogue/index.html b/dev/tutorials/model_catalogue/index.html index 8c502f9a7..a1fd05522 100644 --- a/dev/tutorials/model_catalogue/index.html +++ b/dev/tutorials/model_catalogue/index.html @@ -1,5 +1,5 @@ -Model Catalogue · CounterfactualExplanations.jl
    Edit on GitHub

    Model Catalogue

    While in general it is assumed that users will use this package to explain their pre-trained models, we provide out-of-the-box functionality to train various simple default models. In this tutorial, we will see how these models can be fitted to CounterfactualData.

    Available Models

    The standard_models_catalogue can be used to inspect the available default models:

    standard_models_catalogue
    Dict{Symbol, Any} with 4 entries:
+Model Catalogue · CounterfactualExplanations.jl
    Model Catalogue
    While in general it is assumed that users will use this package to explain their pre-trained models, we provide out-of-the-box functionality to train various simple default models. In this tutorial, we will see how these models can be fitted to CounterfactualData.
    Available Models
    The standard_models_catalogue can be used to inspect the available default models:
    standard_models_catalogue
    Dict{Symbol, Any} with 4 entries:
   :Linear       => Linear
   :LaplaceRedux => LaplaceReduxModel
   :DeepEnsemble => FluxEnsemble
@@ -44,4 +44,4 @@
  - alpha: 0.5
  - tree_type: binary
  - rng: MersenneTwister(123, (0, 9018, 8016, 884))
-, …), :classification_multi)
    The tunable parameters for the EvoTreeModel can be found from the documentation of the EvoTrees.jl package under the EvoTreeClassifier section.
    Please note that support for counterfactual generation with both LaplaceReduxModel and EvoTreeModel is not yet fully implemented.
    References
    Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” https://arxiv.org/abs/1612.01474.
    LeCun, Yann. 1998. “The MNIST Database of Handwritten Digits.”
    
+, …), :classification_multi)

    The tunable parameters for the EvoTreeModel can be found from the documentation of the EvoTrees.jl package under the EvoTreeClassifier section.

    Please note that support for counterfactual generation with both LaplaceReduxModel and EvoTreeModel is not yet fully implemented.

    References

    Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” https://arxiv.org/abs/1612.01474.

    LeCun, Yann. 1998. “The MNIST Database of Handwritten Digits.”

    diff --git a/dev/tutorials/models/index.html b/dev/tutorials/models/index.html index 85ff86c10..833a12ef4 100644 --- a/dev/tutorials/models/index.html +++ b/dev/tutorials/models/index.html @@ -1,5 +1,5 @@ -Handling Models · CounterfactualExplanations.jl
    Edit on GitHub

    Handling Models

    The typical use-case for Counterfactual Explanations and Algorithmic Recourse is as follows: users have trained some supervised model that is not inherently interpretable and are looking for a way to explain it. In this tutorial, we will see how pre-trained models can be used with this package.

    Models trained in Flux.jl

    We will train a simple binary classifier in Flux.jl on the popular Moons dataset:

    n = 500
+Handling Models · CounterfactualExplanations.jl
    Handling Models
    The typical use-case for Counterfactual Explanations and Algorithmic Recourse is as follows: users have trained some supervised model that is not inherently interpretable and are looking for a way to explain it. In this tutorial, we will see how pre-trained models can be used with this package.
    Models trained in Flux.jl
    We will train a simple binary classifier in Flux.jl on the popular Moons dataset:
    n = 500
 data = TaijaData.load_moons(n)
 counterfactual_data = DataPreprocessing.CounterfactualData(data...)
 X = counterfactual_data.X
@@ -41,4 +41,4 @@
 Epoch 80
 avg_loss(data) = 0.011847609f0
 Epoch 100
-avg_loss(data) = 0.007242911f0
    To prepare the fitted model for use with our package, we need to wrap it inside a container. For plain-vanilla models trained in Flux.jl, the corresponding constructor is called FluxModel. There is also a separate constructor called FluxEnsemble, which applies to Deep Ensembles. Deep Ensembles are a popular approach to approximate Bayesian Deep Learning and have been shown to generate good predictive uncertainty estimates (Lakshminarayanan, Pritzel, and Blundell 2016).
    The appropriate API call to wrap our simple network in a container follows below:
    M = FluxModel(nn)
    FluxModel(Chain(Dense(2 => 32, relu), Dropout(0.1, active=false), Dense(32 => 2)), :classification_binary)
    The likelihood function of the output variable is automatically inferred from the data. The generic plot() method can be called on the model and data to visualise the results:
    plot(M, counterfactual_data)
    Our model M is now ready for use with the package.
    References
    Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” https://arxiv.org/abs/1612.01474.
    
+avg_loss(data) = 0.007242911f0

    To prepare the fitted model for use with our package, we need to wrap it inside a container. For plain-vanilla models trained in Flux.jl, the corresponding constructor is called FluxModel. There is also a separate constructor called FluxEnsemble, which applies to Deep Ensembles. Deep Ensembles are a popular approach to approximate Bayesian Deep Learning and have been shown to generate good predictive uncertainty estimates (Lakshminarayanan, Pritzel, and Blundell 2016).

    The appropriate API call to wrap our simple network in a container follows below:

    M = FluxModel(nn)
    FluxModel(Chain(Dense(2 => 32, relu), Dropout(0.1, active=false), Dense(32 => 2)), :classification_binary)

    The likelihood function of the output variable is automatically inferred from the data. The generic plot() method can be called on the model and data to visualise the results:

    plot(M, counterfactual_data)

    Our model M is now ready for use with the package.

    References

    Lakshminarayanan, Balaji, Alexander Pritzel, and Charles Blundell. 2016. “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles.” https://arxiv.org/abs/1612.01474.

    diff --git a/dev/tutorials/parallelization/index.html b/dev/tutorials/parallelization/index.html index f6baf24d4..4688a7f18 100644 --- a/dev/tutorials/parallelization/index.html +++ b/dev/tutorials/parallelization/index.html @@ -1,5 +1,5 @@ -Parallelization · CounterfactualExplanations.jl
    Edit on GitHub

    Parallelization

    Version 0.1.15 adds support for parallelization through multi-processing. Currently, the only available backend for parallelization is MPI.jl.

    Available functions

    Parallelization is only available for certain functions. To check if a function is parallelizable, you can use parallelizable function:

    using CounterfactualExplanations.Evaluation: evaluate, benchmark
+Parallelization · CounterfactualExplanations.jl
    Parallelization
    Version 0.1.15 adds support for parallelization through multi-processing. Currently, the only available backend for parallelization is MPI.jl.
    Available functions
    Parallelization is only available for certain functions. To check if a function is parallelizable, you can use parallelizable function:
    using CounterfactualExplanations.Evaluation: evaluate, benchmark
 println(parallelizable(generate_counterfactual))
 println(parallelizable(evaluate))
 println(parallelizable(predict_label))
    true
@@ -219,4 +219,4 @@
 
 bmk = benchmark(counterfactual_data; parallelizer=parallelizer)
 
-MPI.Finalize()
    The file can be executed from the command line as follows:
    mpiexecjl --project -n 4 julia -e 'include("docs/src/srcipts/mpi.jl")'
    
+MPI.Finalize()

    The file can be executed from the command line as follows:

    mpiexecjl --project -n 4 julia -e 'include("docs/src/srcipts/mpi.jl")'
    diff --git a/dev/tutorials/simple_example/index.html b/dev/tutorials/simple_example/index.html index 34a6ed514..037c18cf8 100644 --- a/dev/tutorials/simple_example/index.html +++ b/dev/tutorials/simple_example/index.html @@ -1,5 +1,5 @@ -Simple Example · CounterfactualExplanations.jl
    Edit on GitHub

    Simple Example

    In this tutorial, we will go through a simple example involving synthetic data and a generic counterfactual generator.

    Data and Classifier

    Below we generate some linearly separable data and fit a simple MLP classifier with batch normalization to it. For more information on generating data and models, refer to the Handling Data and Handling Models tutorials respectively.

    # Counteractual data and model:
+Simple Example · CounterfactualExplanations.jl
    Simple Example
    In this tutorial, we will go through a simple example involving synthetic data and a generic counterfactual generator.
    Data and Classifier
    Below we generate some linearly separable data and fit a simple MLP classifier with batch normalization to it. For more information on generating data and models, refer to the Handling Data and Handling Models tutorials respectively.
    # Counteractual data and model:
 flux_training_params.batchsize = 10
 data = TaijaData.load_linearly_separable()
 counterfactual_data = DataPreprocessing.CounterfactualData(data...)
@@ -10,4 +10,4 @@
 x = select_factual(counterfactual_data, chosen)
    Finally, we generate and visualize the generated counterfactual:
    # Search:
 generator = WachterGenerator()
 ce = generate_counterfactual(x, target, counterfactual_data, M, generator)
-plot(ce)
    
+plot(ce)

    diff --git a/dev/tutorials/whistle_stop/index.html b/dev/tutorials/whistle_stop/index.html index e24f8ef5b..37e55d06c 100644 --- a/dev/tutorials/whistle_stop/index.html +++ b/dev/tutorials/whistle_stop/index.html @@ -1,5 +1,5 @@ -Whiste-Stop Tour · CounterfactualExplanations.jl
    Edit on GitHub

    Whistle-Stop Tour

    In this tutorial, we will go through a slightly more complex example involving synthetic data. We will generate Counterfactual Explanations using different generators and visualize the results.

    Data and Classifier

    # Choose some values for data and a model:
+Whiste-Stop Tour · CounterfactualExplanations.jl
    Whistle-Stop Tour
    In this tutorial, we will go through a slightly more complex example involving synthetic data. We will generate Counterfactual Explanations using different generators and visualize the results.
    Data and Classifier
    # Choose some values for data and a model:
 n_dim = 2
 n_classes = 4
 n_samples = 400
@@ -26,4 +26,4 @@
     )
     ces[key] = ce
     plts = [plts..., plot(ce; title=key, colorbar=false)]
-end
    
+end