From 37f860bf9239939d4168cbf1e8c4f172283d7351 Mon Sep 17 00:00:00 2001
From: felipeangelimvieira <felipeangelim@gmail.com>
Date: Tue, 21 Oct 2025 19:39:41 -0300
Subject: [PATCH 1/6] Requirements and random seed

---
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 requirements.txt                              |   11 +
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diff --git a/book/content/pt/part2/panel_data.qmd b/book/content/pt/part2/panel_data.qmd
index ababee0..9846994 100644
--- a/book/content/pt/part2/panel_data.qmd
+++ b/book/content/pt/part2/panel_data.qmd
@@ -165,7 +165,7 @@ from tsbook.forecasting.reduction import ReductionForecaster
 from lightgbm import LGBMRegressor
 
 global_forecaster1 = ReductionForecaster(
-    LGBMRegressor(n_estimators=100, verbose=-1),
+    LGBMRegressor(n_estimators=100, verbose=-1, objective="tweedie", random_state=42),
     window_length=30,
 )
 
diff --git "a/book/latest/Previs\303\243o-de-S\303\251ries-temporais-com-Python--um-pequeno-guia.pdf" "b/book/latest/Previs\303\243o-de-S\303\251ries-temporais-com-Python--um-pequeno-guia.pdf"
deleted file mode 100644
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-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../../../content/pt/extra/sktime_custom.html">Part III: Apêndices</a></li><li class="breadcrumb-item"><a href="../../../content/pt/extra/sktime_custom.html"><span class="chapter-number">11</span>&nbsp; <span class="chapter-title">Criando modelos customizados com sktime</span></a></li></ol></nav>
-<div class="quarto-title">
-<h1 class="title"><span class="chapter-number">11</span>&nbsp; <span class="chapter-title">Criando modelos customizados com sktime</span></h1>
-</div>
-
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-<div class="quarto-title-meta">
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-<p>O sktime oferece um ecossistema robusto, mas em cenarios reais frequentemente precisamos ajustar comportamentos, incorporar dados hierarquicos ou adicionar pre-processamentos especificos. Felizmente, o sktime torna relativamente simples a criacao de modelos customizados, desde que sigamos algumas regras.</p>
-<p>Acredito que essa e uma das grandes vantagens da biblioteca: o foco em ser extensivel e customizavel.</p>
-<section id="o-sistema-de-tags" class="level2" data-number="11.1">
-<h2 data-number="11.1" class="anchored" data-anchor-id="o-sistema-de-tags"><span class="header-section-number">11.1</span> O sistema de tags</h2>
-<p>Entre a chamada dos metodos publicos e privados existe uma camada de validacoes e conversões controlada pelas tags. Elas sinalizam ao <code>BaseForecaster</code> e ao <code>BaseTransformer</code> o que precisa ser garantido antes de executar a implementacao customizada.</p>
-<p>As tags mais importantes para um forecaster podem ser definidas assim:</p>
-<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>_tags <span class="op">=</span> {</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>    <span class="st">"capability:exogenous"</span>: <span class="va">True</span>,</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>    <span class="st">"requires-fh-in-fit"</span>: <span class="va">False</span>,</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>    <span class="st">"X_inner_mtype"</span>: [</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>        <span class="st">"pd.Series"</span>,</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>        <span class="st">"pd.DataFrame"</span>,</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>        <span class="st">"pd-multiindex"</span>,</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>        <span class="st">"pd_multiindex_hier"</span>,</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>    ],</span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>    <span class="st">"y_inner_mtype"</span>: [</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>        <span class="st">"pd.Series"</span>,</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>        <span class="st">"pd.DataFrame"</span>,</span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>        <span class="st">"pd-multiindex"</span>,</span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>        <span class="st">"pd_multiindex_hier"</span>,</span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>    ]</span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<p>Cada uma delas indica o que o modelo é capaz de fazer nos seus métodos privados <code>_fit</code> e <code>_predict</code>:</p>
-<ul>
-<li><code>capability:exogenous</code>: Indica se o modelo suporta variáveis exógenas (X) durante o ajuste e a previsão.</li>
-<li><code>requires-fh-in-fit</code>: Indica se o modelo precisa do horizonte de previsão (fh) durante o ajuste. Alguns modelos precisam devido a sua implementação interna.</li>
-<li><code>y_inner_mtype</code>: Define os tipos de dados aceitos para a variável dependente (y) durante o ajuste e a previsão.</li>
-<li><code>X_inner_mtype</code>: Define os tipos de dados aceitos para as variáveis exógenas (X) durante o ajuste e a previsão.</li>
-</ul>
-<p>Os machine-types (<strong>mtypes</strong>), ou tipos para máquina, são a peça mais crucial nesse sistema.</p>
-<section id="machine-types-disponiveis" class="level3" data-number="11.1.1">
-<h3 data-number="11.1.1" class="anchored" data-anchor-id="machine-types-disponiveis"><span class="header-section-number">11.1.1</span> Machine-types disponiveis</h3>
-<p>Os <code>mtypes</code> definem qual a estrutura de dados que o modelo aceita como entrada e produz como saída. Os principais mtypes para séries temporais são:</p>
-<ul>
-<li><code>np.ndarray</code></li>
-<li><code>pd.Series</code></li>
-<li><code>pd.DataFrame</code></li>
-<li><code>pd-multiindex</code> (ideia de painel)</li>
-<li><code>pd_multiindex_hier</code> (dados hierarquicos)</li>
-</ul>
-<p>Se o modelo suporta um <code>mtype</code> hierárquico e passamos um dado hierárquico, o dado chegará normalmente ao método privado <code>_fit</code> ou <code>_predict</code>. Caso contrário, o sktime tentará converter o dado para um mtype suportado.</p>
-<section id="baixando-exemplos-por-mtype" class="level4" data-number="11.1.1.1">
-<h4 data-number="11.1.1.1" class="anchored" data-anchor-id="baixando-exemplos-por-mtype"><span class="header-section-number">11.1.1.1</span> Baixando exemplos por mtype</h4>
-<p>Para entender melhor cada mtype, podemos baixar exemplos práticos usando a função <code>get_examples</code> do sktime:</p>
-<div id="77134c99" class="cell" data-execution_count="1">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.datatypes <span class="im">import</span> get_examples</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>get_examples(mtype<span class="op">=</span><span class="st">"np.ndarray"</span>, as_scitype<span class="op">=</span><span class="st">"Series"</span>)[<span class="dv">0</span>]</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="1">
-<pre><code>array([[ 1. ],
-       [ 4. ],
-       [ 0.5],
-       [-3. ]])</code></pre>
-</div>
-</div>
-<div id="b477e8ea" class="cell" data-execution_count="2">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>get_examples(mtype<span class="op">=</span><span class="st">"pd.DataFrame"</span>, as_scitype<span class="op">=</span><span class="st">"Series"</span>)[<span class="dv">0</span>].head()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="2">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">a</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">0</th>
-<td>1.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">1</th>
-<td>4.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2</th>
-<td>0.5</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">3</th>
-<td>-3.0</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<div id="720130b7" class="cell" data-execution_count="3">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>get_examples(mtype<span class="op">=</span><span class="st">"pd-multiindex"</span>, as_scitype<span class="op">=</span><span class="st">"Panel"</span>)[<span class="dv">0</span>].head()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="3">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">var_0</th>
-<th data-quarto-table-cell-role="th">var_1</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">instances</th>
-<th data-quarto-table-cell-role="th">timepoints</th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="3" data-quarto-table-cell-role="th" data-valign="top">0</th>
-<th data-quarto-table-cell-role="th">0</th>
-<td>1</td>
-<td>4</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">1</th>
-<td>2</td>
-<td>5</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2</th>
-<td>3</td>
-<td>6</td>
-</tr>
-<tr class="even">
-<th rowspan="2" data-quarto-table-cell-role="th" data-valign="top">1</th>
-<th data-quarto-table-cell-role="th">0</th>
-<td>1</td>
-<td>4</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">1</th>
-<td>2</td>
-<td>55</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<div id="bcc22ae8" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>get_examples(mtype<span class="op">=</span><span class="st">"pd_multiindex_hier"</span>, as_scitype<span class="op">=</span><span class="st">"Hierarchical"</span>)[<span class="dv">0</span>].head()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="4">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">var_0</th>
-<th data-quarto-table-cell-role="th">var_1</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">foo</th>
-<th data-quarto-table-cell-role="th">bar</th>
-<th data-quarto-table-cell-role="th">timepoints</th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">a</th>
-<th rowspan="3" data-quarto-table-cell-role="th" data-valign="top">0</th>
-<th data-quarto-table-cell-role="th">0</th>
-<td>1</td>
-<td>4</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">1</th>
-<td>2</td>
-<td>5</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2</th>
-<td>3</td>
-<td>6</td>
-</tr>
-<tr class="even">
-<th rowspan="2" data-quarto-table-cell-role="th" data-valign="top">1</th>
-<th data-quarto-table-cell-role="th">0</th>
-<td>1</td>
-<td>4</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">1</th>
-<td>2</td>
-<td>55</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<p>Alguns mtypes tem limitações: uma <code>pd.Series</code> simples nao representa problemas hierarquicos, sendo necessario recorrer ao <code>pd_multiindex_hier</code>.</p>
-</section>
-<section id="exemplo-prático" class="level4" data-number="11.1.1.2">
-<h4 data-number="11.1.1.2" class="anchored" data-anchor-id="exemplo-prático"><span class="header-section-number">11.1.1.2</span> Exemplo prático</h4>
-<p>Vamos criar o nosso primeiro esqueleto de forecaster customizado. Para isso, baixamos uma série de exemplo:</p>
-<div id="def5f0d6" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.base <span class="im">import</span> BaseForecaster</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils._testing.series <span class="im">import</span> _make_series</span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> _make_series(<span class="dv">4</span>)</span>
-<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>y</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="6">
-<pre><code>2000-01-01    1.000000
-2000-01-02    1.464179
-2000-01-03    2.706921
-2000-01-04    3.018884
-Freq: D, dtype: float64</code></pre>
-</div>
-</div>
-<p>Nosso protótipo irá apenas printar os dados recebidos no método <code>_fit</code>. O <code>__init__</code> recebe um dicionário de tags para definir as capacidades do modelo.</p>
-<div id="23679353" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="kw">class</span> Logger(BaseForecaster):</span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>    _tags <span class="op">=</span> {</span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>        <span class="st">"requires-fh-in-fit"</span>: <span class="va">False</span>,</span>
-<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>    }</span>
-<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a>    <span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>, tags_to_set):</span>
-<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>        <span class="va">self</span>.tags_to_set <span class="op">=</span> tags_to_set</span>
-<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a>        <span class="bu">super</span>().<span class="fu">__init__</span>()</span>
-<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a>        <span class="va">self</span>.set_tags(<span class="op">**</span>tags_to_set)</span>
-<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a>    <span class="kw">def</span> _fit(<span class="va">self</span>, y, X<span class="op">=</span><span class="va">None</span>, fh<span class="op">=</span><span class="va">None</span>):</span>
-<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a>        <span class="bu">print</span>(<span class="st">"Inside fit:"</span>)</span>
-<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a>        <span class="bu">print</span>(y)</span>
-<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a>        <span class="cf">return</span> <span class="va">self</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="4764d628" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span> : [<span class="st">"pd.Series"</span>] })</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>logger.fit(y)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Inside fit:
-2000-01-01    1.000000
-2000-01-02    1.464179
-2000-01-03    2.706921
-2000-01-04    3.018884
-Freq: D, dtype: float64</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="8">
-<style>#sk-5c6eb362-0746-4770-b114-8469fc2a146f {
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-
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-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
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-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-label label.sk-toggleable__label,
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-5c6eb362-0746-4770-b114-8469fc2a146f a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-5c6eb362-0746-4770-b114-8469fc2a146f" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Logger(tags_to_set={'y_inner_mtype': ['pd.Series']})</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('d40071ad-254c-4c7e-a8e5-e87738f8de7d')" type="checkbox" checked=""><label for="UUID('d40071ad-254c-4c7e-a8e5-e87738f8de7d')" class="sk-toggleable__label sk-toggleable__label-arrow">Logger</label><div class="sk-toggleable__content"><pre>Logger(tags_to_set={'y_inner_mtype': ['pd.Series']})</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="016c728d" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span> : [<span class="st">"np.ndarray"</span>] })</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>logger.fit(y)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Inside fit:
-[[1.        ]
- [1.46417876]
- [2.70692053]
- [3.01888437]]</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="9">
-<style>#sk-76564855-be7f-41be-b81f-c7b8427ed6e8 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 pre {
-    padding: 0;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-label label.sk-toggleable__label,
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-76564855-be7f-41be-b81f-c7b8427ed6e8 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-76564855-be7f-41be-b81f-c7b8427ed6e8" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Logger(tags_to_set={'y_inner_mtype': ['np.ndarray']})</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('25450db5-4f11-4efc-addb-fd8bdc723dc8')" type="checkbox" checked=""><label for="UUID('25450db5-4f11-4efc-addb-fd8bdc723dc8')" class="sk-toggleable__label sk-toggleable__label-arrow">Logger</label><div class="sk-toggleable__content"><pre>Logger(tags_to_set={'y_inner_mtype': ['np.ndarray']})</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="60ff3341" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span> : [<span class="st">"pd.DataFrame"</span>] })</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>logger.fit(y)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Inside fit:
-                   0
-2000-01-01  1.000000
-2000-01-02  1.464179
-2000-01-03  2.706921
-2000-01-04  3.018884</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="10">
-<style>#sk-a40fe2ea-8e04-4238-bf00-50061092592b {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b pre {
-    padding: 0;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-label label.sk-toggleable__label,
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-a40fe2ea-8e04-4238-bf00-50061092592b a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-a40fe2ea-8e04-4238-bf00-50061092592b" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Logger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('5cd38c61-018c-4044-bea5-d2512b816923')" type="checkbox" checked=""><label for="UUID('5cd38c61-018c-4044-bea5-d2512b816923')" class="sk-toggleable__label sk-toggleable__label-arrow">Logger</label><div class="sk-toggleable__content"><pre>Logger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="9eb35fcc" class="cell" data-execution_count="11">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="cf">try</span>:</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>    logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span> : [<span class="st">"pd_multiindex_hier"</span>] })</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>    logger.fit(y)</span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a><span class="cf">except</span> <span class="pp">ValueError</span> <span class="im">as</span> e:</span>
-<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(e)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Error in Logger, no series scitypes supported, likely a bug in estimator: scitypes arg passed to _most_complex_scitype are ['Hierarchical']</code></pre>
-</div>
-</div>
-<div id="b44f379b" class="cell" data-execution_count="12">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="cf">try</span>:</span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>    logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span>: [<span class="st">"pd.DataFrame"</span>, <span class="st">"pd_multiindex_hier"</span>]})</span>
-<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>    logger.fit(y)</span>
-<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a><span class="cf">except</span> <span class="pp">ValueError</span> <span class="im">as</span> e:</span>
-<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(e)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Inside fit:
-                   0
-2000-01-01  1.000000
-2000-01-02  1.464179
-2000-01-03  2.706921
-2000-01-04  3.018884</code></pre>
-</div>
-</div>
-</section>
-<section id="input-hierárquico" class="level4" data-number="11.1.1.3">
-<h4 data-number="11.1.1.3" class="anchored" data-anchor-id="input-hierárquico"><span class="header-section-number">11.1.1.3</span> Input hierárquico</h4>
-<p>Agora veremos como o modelo se comporta com dados hierárquicos. Note que, nos casos onde o modelo não suporta dados hierárquicos, o sktime tentará convertê-los para um mtype suportado.</p>
-<div id="df058e2b" class="cell" data-execution_count="13">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils._testing.hierarchical <span class="im">import</span> _make_hierarchical</span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> _make_hierarchical((<span class="dv">1</span>,<span class="dv">2</span>), max_timepoints<span class="op">=</span><span class="dv">4</span>, min_timepoints<span class="op">=</span><span class="dv">2</span>)</span>
-<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>y</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="13">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">c0</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">h0</th>
-<th data-quarto-table-cell-role="th">h1</th>
-<th data-quarto-table-cell-role="th">time</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="6" data-quarto-table-cell-role="th" data-valign="top">h0_0</th>
-<th rowspan="3" data-quarto-table-cell-role="th" data-valign="top">h1_0</th>
-<th data-quarto-table-cell-role="th">2000-01-02</th>
-<td>1.693028</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2000-01-03</th>
-<td>1.092681</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2000-01-04</th>
-<td>2.352703</td>
-</tr>
-<tr class="even">
-<th rowspan="3" data-quarto-table-cell-role="th" data-valign="top">h1_1</th>
-<th data-quarto-table-cell-role="th">2000-01-02</th>
-<td>2.456702</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2000-01-03</th>
-<td>1.585521</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2000-01-04</th>
-<td>1.000000</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<div id="c81ba750" class="cell" data-execution_count="14">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span> : [<span class="st">"pd.Series"</span>] })</span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>logger.fit(y)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Inside fit:
-time
-2000-01-02    1.693028
-2000-01-03    1.092681
-2000-01-04    2.352703
-Freq: D, dtype: float64
-Inside fit:
-time
-2000-01-02    2.456702
-2000-01-03    1.585521
-2000-01-04    1.000000
-Freq: D, dtype: float64</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="14">
-<style>#sk-c4424be1-b2ec-4553-b398-f92373c77799 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 pre {
-    padding: 0;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-label label.sk-toggleable__label,
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-c4424be1-b2ec-4553-b398-f92373c77799 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-c4424be1-b2ec-4553-b398-f92373c77799" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Logger(tags_to_set={'y_inner_mtype': ['pd.Series']})</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('370f7116-4c02-4043-a1a5-ce65d9c8166c')" type="checkbox" checked=""><label for="UUID('370f7116-4c02-4043-a1a5-ce65d9c8166c')" class="sk-toggleable__label sk-toggleable__label-arrow">Logger</label><div class="sk-toggleable__content"><pre>Logger(tags_to_set={'y_inner_mtype': ['pd.Series']})</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="6bc62e42" class="cell" data-execution_count="15">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span> : [<span class="st">"np.ndarray"</span>] })</span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>logger.fit(y)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Inside fit:
-[[1.69302835]
- [1.092681  ]
- [2.35270294]]
-Inside fit:
-[[2.45670163]
- [1.58552141]
- [1.        ]]</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="15">
-<style>#sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 pre {
-    padding: 0;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-label label.sk-toggleable__label,
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-c0b55e01-d0e8-4c25-a051-19e91516ba51 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-c0b55e01-d0e8-4c25-a051-19e91516ba51" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Logger(tags_to_set={'y_inner_mtype': ['np.ndarray']})</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('02d4268a-b6fc-42ff-8176-8c0645690ee1')" type="checkbox" checked=""><label for="UUID('02d4268a-b6fc-42ff-8176-8c0645690ee1')" class="sk-toggleable__label sk-toggleable__label-arrow">Logger</label><div class="sk-toggleable__content"><pre>Logger(tags_to_set={'y_inner_mtype': ['np.ndarray']})</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="2fe2a235" class="cell" data-execution_count="16">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span> : [<span class="st">"pd.DataFrame"</span>] })</span>
-<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>logger.fit(y)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Inside fit:
-                  c0
-time                
-2000-01-02  1.693028
-2000-01-03  1.092681
-2000-01-04  2.352703
-Inside fit:
-                  c0
-time                
-2000-01-02  2.456702
-2000-01-03  1.585521
-2000-01-04  1.000000</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="16">
-<style>#sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 pre {
-    padding: 0;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-label label.sk-toggleable__label,
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-04b60f19-b56a-458f-893f-2cb8e0e4b8a6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Logger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('998c6414-6897-45f9-937d-667609845d30')" type="checkbox" checked=""><label for="UUID('998c6414-6897-45f9-937d-667609845d30')" class="sk-toggleable__label sk-toggleable__label-arrow">Logger</label><div class="sk-toggleable__content"><pre>Logger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="77e53f3b" class="cell" data-execution_count="17">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="cf">try</span>:</span>
-<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>    logger <span class="op">=</span> Logger(tags_to_set<span class="op">=</span>{<span class="st">"y_inner_mtype"</span>: [<span class="st">"pd_multiindex_hier"</span>]})</span>
-<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>    logger.fit(y)</span>
-<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a><span class="cf">except</span> <span class="pp">ValueError</span> <span class="im">as</span> e:</span>
-<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(e)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>Inside fit:
-                            c0
-h0   h1   time                
-h0_0 h1_0 2000-01-02  1.693028
-          2000-01-03  1.092681
-          2000-01-04  2.352703
-     h1_1 2000-01-02  2.456702
-          2000-01-03  1.585521
-          2000-01-04  1.000000</code></pre>
-</div>
-</div>
-</section>
-</section>
-</section>
-<section id="criando-um-modelo-naive" class="level2" data-number="11.2">
-<h2 data-number="11.2" class="anchored" data-anchor-id="criando-um-modelo-naive"><span class="header-section-number">11.2</span> Criando um modelo naive</h2>
-<p>Agora, vamos implementar um modelo simples de previsão, o <code>CustomNaiveForecaster</code>, que prevê o valor médio dos últimos n pontos da série temporal.</p>
-<p>É um exemplo simples, mas que ilustra bem como criar um forecaster customizado com sktime.</p>
-<div id="eea5081e" class="cell" data-execution_count="18">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"panel"</span>)</span>
-<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>y_train, y_test  <span class="op">=</span> dataset.load(<span class="st">"y_train"</span>, <span class="st">"y_test"</span>)</span>
-<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>y_train</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="18">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">sku_id</th>
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">0</th>
-<th data-quarto-table-cell-role="th">2020-01-01</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-02</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-03</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-04</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-05</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">24</th>
-<th data-quarto-table-cell-role="th">2024-07-01</th>
-<td>218</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-02</th>
-<td>198</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-03</th>
-<td>182</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-04</th>
-<td>210</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-05</th>
-<td>209</td>
-</tr>
-</tbody>
-</table>
-
-<p>41200 rows × 1 columns</p>
-</div>
-</div>
-</div>
-<div id="4fbfbce3" class="cell" data-execution_count="19">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a>    y_train.loc[<span class="dv">0</span>],</span>
-<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a>    y_train.loc[<span class="dv">24</span>],</span>
-<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[</span>
-<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a>        <span class="st">"SKU 0"</span>,</span>
-<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a>        <span class="st">"SKU 24"</span>,</span>
-<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a>    ],</span>
-<span id="cb30-10"><a href="#cb30-10" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="sktime_custom_files/figure-html/cell-20-output-1.png" width="1263" height="338" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Abaixo, implementamos o <code>CustomNaiveForecaster</code> seguindo as regras do sktime (clique para expandir). Em seguida, explicamos passo a passo.</p>
-<div id="da89cf53" class="cell" data-execution_count="20">
-<details class="code-fold">
-<summary>Code</summary>
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.base <span class="im">import</span> BaseForecaster</span>
-<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
-<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a><span class="kw">class</span> CustomNaiveForecaster(BaseForecaster):</span>
-<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>    <span class="co">"""</span></span>
-<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a><span class="co">    A simple naive forecaster</span></span>
-<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a><span class="co">    Parameters</span></span>
-<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a><span class="co">    ----------</span></span>
-<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a><span class="co">    n : int</span></span>
-<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a><span class="co">        Number of past values to use.</span></span>
-<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a><span class="co">    """</span></span>
-<span id="cb31-14"><a href="#cb31-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-15"><a href="#cb31-15" aria-hidden="true" tabindex="-1"></a>    _tags <span class="op">=</span> {</span>
-<span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a>        <span class="st">"requires-fh-in-fit"</span>: <span class="va">False</span>,</span>
-<span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a>        <span class="st">"y_inner_mtype"</span>: [</span>
-<span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a>            <span class="st">"pd.Series"</span>,</span>
-<span id="cb31-19"><a href="#cb31-19" aria-hidden="true" tabindex="-1"></a>        ],</span>
-<span id="cb31-20"><a href="#cb31-20" aria-hidden="true" tabindex="-1"></a>    }</span>
-<span id="cb31-21"><a href="#cb31-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-22"><a href="#cb31-22" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Add hyperparameters in init!</span></span>
-<span id="cb31-23"><a href="#cb31-23" aria-hidden="true" tabindex="-1"></a>    <span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>, n<span class="op">=</span><span class="dv">1</span>):</span>
-<span id="cb31-24"><a href="#cb31-24" aria-hidden="true" tabindex="-1"></a>        <span class="co"># 1. Set hyper-parameters</span></span>
-<span id="cb31-25"><a href="#cb31-25" aria-hidden="true" tabindex="-1"></a>        <span class="va">self</span>.n <span class="op">=</span> n</span>
-<span id="cb31-26"><a href="#cb31-26" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-27"><a href="#cb31-27" aria-hidden="true" tabindex="-1"></a>        <span class="co"># 2. Initialize parent class</span></span>
-<span id="cb31-28"><a href="#cb31-28" aria-hidden="true" tabindex="-1"></a>        <span class="bu">super</span>().<span class="fu">__init__</span>()</span>
-<span id="cb31-29"><a href="#cb31-29" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-30"><a href="#cb31-30" aria-hidden="true" tabindex="-1"></a>        <span class="co"># 3. Check hyper-parameters</span></span>
-<span id="cb31-31"><a href="#cb31-31" aria-hidden="true" tabindex="-1"></a>        <span class="cf">assert</span> <span class="va">self</span>.n <span class="op">&gt;</span> <span class="dv">0</span>, <span class="st">"n must be greater than 0"</span></span>
-<span id="cb31-32"><a href="#cb31-32" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-33"><a href="#cb31-33" aria-hidden="true" tabindex="-1"></a>    <span class="kw">def</span> _fit(<span class="va">self</span>, y, X, fh):</span>
-<span id="cb31-34"><a href="#cb31-34" aria-hidden="true" tabindex="-1"></a>        <span class="co">"""</span></span>
-<span id="cb31-35"><a href="#cb31-35" aria-hidden="true" tabindex="-1"></a><span class="co">        Fit necessary parameters.</span></span>
-<span id="cb31-36"><a href="#cb31-36" aria-hidden="true" tabindex="-1"></a><span class="co">        """</span></span>
-<span id="cb31-37"><a href="#cb31-37" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-38"><a href="#cb31-38" aria-hidden="true" tabindex="-1"></a>        <span class="va">self</span>.value_ <span class="op">=</span> y.iloc[<span class="op">-</span><span class="va">self</span>.n :].mean()</span>
-<span id="cb31-39"><a href="#cb31-39" aria-hidden="true" tabindex="-1"></a>        <span class="cf">return</span> <span class="va">self</span></span>
-<span id="cb31-40"><a href="#cb31-40" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-41"><a href="#cb31-41" aria-hidden="true" tabindex="-1"></a>    <span class="kw">def</span> _predict(<span class="va">self</span>, fh, X):</span>
-<span id="cb31-42"><a href="#cb31-42" aria-hidden="true" tabindex="-1"></a>        <span class="co">"""</span></span>
-<span id="cb31-43"><a href="#cb31-43" aria-hidden="true" tabindex="-1"></a><span class="co">        Use forecasting horizon and optionally X to predict y</span></span>
-<span id="cb31-44"><a href="#cb31-44" aria-hidden="true" tabindex="-1"></a><span class="co">        """</span></span>
-<span id="cb31-45"><a href="#cb31-45" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-46"><a href="#cb31-46" aria-hidden="true" tabindex="-1"></a>        <span class="co"># During fit, BaseForecaster sets</span></span>
-<span id="cb31-47"><a href="#cb31-47" aria-hidden="true" tabindex="-1"></a>        <span class="co"># self.cutoff to the latest cutoff time point</span></span>
-<span id="cb31-48"><a href="#cb31-48" aria-hidden="true" tabindex="-1"></a>        index <span class="op">=</span> fh.to_absolute_index(<span class="va">self</span>.cutoff)</span>
-<span id="cb31-49"><a href="#cb31-49" aria-hidden="true" tabindex="-1"></a>        y_pred <span class="op">=</span> pd.Series(</span>
-<span id="cb31-50"><a href="#cb31-50" aria-hidden="true" tabindex="-1"></a>            index<span class="op">=</span>index,</span>
-<span id="cb31-51"><a href="#cb31-51" aria-hidden="true" tabindex="-1"></a>            data<span class="op">=</span>[<span class="va">self</span>.value_ <span class="cf">for</span> _ <span class="kw">in</span> <span class="bu">range</span>(<span class="bu">len</span>(index))],</span>
-<span id="cb31-52"><a href="#cb31-52" aria-hidden="true" tabindex="-1"></a>        )</span>
-<span id="cb31-53"><a href="#cb31-53" aria-hidden="true" tabindex="-1"></a>        y_pred.name <span class="op">=</span> <span class="va">self</span>._y.name</span>
-<span id="cb31-54"><a href="#cb31-54" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-55"><a href="#cb31-55" aria-hidden="true" tabindex="-1"></a>        <span class="cf">return</span> y_pred</span>
-<span id="cb31-56"><a href="#cb31-56" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-57"><a href="#cb31-57" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Veremos mais tarde como usar esse método</span></span>
-<span id="cb31-58"><a href="#cb31-58" aria-hidden="true" tabindex="-1"></a>    <span class="at">@classmethod</span></span>
-<span id="cb31-59"><a href="#cb31-59" aria-hidden="true" tabindex="-1"></a>    <span class="kw">def</span> get_test_params(cls, parameter_set<span class="op">=</span><span class="st">"default"</span>):</span>
-<span id="cb31-60"><a href="#cb31-60" aria-hidden="true" tabindex="-1"></a>        <span class="cf">return</span> [</span>
-<span id="cb31-61"><a href="#cb31-61" aria-hidden="true" tabindex="-1"></a>            {<span class="st">"n"</span>: <span class="dv">1</span>},</span>
-<span id="cb31-62"><a href="#cb31-62" aria-hidden="true" tabindex="-1"></a>            {<span class="st">"n"</span>: <span class="dv">2</span>},</span>
-<span id="cb31-63"><a href="#cb31-63" aria-hidden="true" tabindex="-1"></a>        ]</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</details>
-</div>
-<section id="definindo-o-método-__init__" class="level3" data-number="11.2.1">
-<h3 data-number="11.2.1" class="anchored" data-anchor-id="definindo-o-método-__init__"><span class="header-section-number">11.2.1</span> Definindo o método <code>__init__</code></h3>
-<p>O método <code>__init__</code> possui 3 etapas:</p>
-<ol type="1">
-<li>A definição dos hiperparametros e seus atributos com mesmo nome.</li>
-<li>A chamada do <code>super().__init__()</code> para inicializar a classe pai.</li>
-<li>A validação dos hiperparâmetros.</li>
-</ol>
-<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Add hyperparameters in init!</span></span>
-<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>, n<span class="op">=</span><span class="dv">1</span>):</span>
-<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>    <span class="co"># 1. Set hyper-parameters</span></span>
-<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>    <span class="va">self</span>.n <span class="op">=</span> n</span>
-<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a>    <span class="co"># 2. Initialize parent class</span></span>
-<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>    <span class="bu">super</span>().<span class="fu">__init__</span>()</span>
-<span id="cb32-8"><a href="#cb32-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb32-9"><a href="#cb32-9" aria-hidden="true" tabindex="-1"></a>    <span class="co"># 3. Check hyper-parameters</span></span>
-<span id="cb32-10"><a href="#cb32-10" aria-hidden="true" tabindex="-1"></a>    <span class="cf">assert</span> <span class="va">self</span>.n <span class="op">&gt;</span> <span class="dv">0</span>, <span class="st">"n must be greater than 0"</span></span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<p>No caso de algum preprocessamento dos hiperparâmetros no <code>__init__</code>, devemos guardar em uma variável com nome diferente do hiperparâmetro. Por exemplo, se tivéssemos interesse em ter um atributo <code>n</code> diferente do passado no <code>__init__</code>, poderíamos fazer:</p>
-<pre><code>self._n = n + 1</code></pre>
-<p>O <code>self.n</code> funciona como uma digital do modelo, e deve ser exatamente o que foi passado no <code>__init__</code>.</p>
-</section>
-<section id="definindo-o-método-_fit" class="level3" data-number="11.2.2">
-<h3 data-number="11.2.2" class="anchored" data-anchor-id="definindo-o-método-_fit"><span class="header-section-number">11.2.2</span> Definindo o método <code>_fit</code></h3>
-<p>No método <code>_fit</code>, devemos implementar a lógica de ajuste do modelo. No nosso caso, calculamos a média dos últimos <code>n</code> valores e armazenamos em <code>self.value_</code>.</p>
-<p>O <code>_</code> após o nome do atributo indica que é um atributo aprendido durante o ajuste, e será retornado quando chamarmos <code>get_fitted_params()</code>.</p>
-<p>Note que podemos supor que <code>y</code> é do tipo definido na tag <code>y_inner_mtype</code>, ou seja, uma <code>pd.Series</code>.</p>
-</section>
-<section id="definindo-o-método-_predict" class="level3" data-number="11.2.3">
-<h3 data-number="11.2.3" class="anchored" data-anchor-id="definindo-o-método-_predict"><span class="header-section-number">11.2.3</span> Definindo o método <code>_predict</code></h3>
-<p>No método <code>_predict</code>, implementamos a lógica de previsão. Usamos o horizonte de previsão <code>fh</code> para determinar os índices futuros e retornamos uma série com o valor previsto para cada ponto no horizonte.</p>
-<p>O <code>fh</code> é um objeto do tipo <code>ForecastingHorizon</code>, que possui o método <code>to_absolute_index(cutoff)</code> para converter o horizonte relativo em índices absolutos, considerando o último ponto conhecido (<code>self.cutoff</code>).</p>
-<p>Retornamos um <code>pd.Series</code> com os índices e os valores previstos.</p>
-</section>
-<section id="usando-o-customnaiveforecaster" class="level3" data-number="11.2.4">
-<h3 data-number="11.2.4" class="anchored" data-anchor-id="usando-o-customnaiveforecaster"><span class="header-section-number">11.2.4</span> Usando o <code>CustomNaiveForecaster</code></h3>
-<p>Agora, já podemos usar o nosso modelo customizado para fazer previsões.</p>
-<div id="66140113" class="cell" data-execution_count="21">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>custom_naive_model <span class="op">=</span> CustomNaiveForecaster()</span>
-<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>custom_naive_model.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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-    display: block;
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-  #sk-bb9bd512-8ffb-4d77-ab1f-f7a81db79245 a.estimator_doc_link {
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-</style><div id="sk-bb9bd512-8ffb-4d77-ab1f-f7a81db79245" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>CustomNaiveForecaster()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('3f04b81c-4cc4-4942-877e-6f7543baee06')" type="checkbox" checked=""><label for="UUID('3f04b81c-4cc4-4942-877e-6f7543baee06')" class="sk-toggleable__label sk-toggleable__label-arrow">CustomNaiveForecaster</label><div class="sk-toggleable__content"><pre>CustomNaiveForecaster()</pre></div></div></div></div></div>
-</div>
-</div>
-<p>Como passamos um dado hierárquico, o sktime converteu automaticamente para <code>pd.Series</code>, que é o mtype suportado pelo nosso modelo. Os modelos internos, para cada série, ficam disponíveis em <code>forecasters_</code>.</p>
-<div id="4919f22a" class="cell" data-execution_count="22">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>custom_naive_model.forecasters_</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="22">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">forecasters</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">0</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">1</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">3</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">4</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">5</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">6</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">7</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">8</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">9</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">10</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">11</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">12</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">13</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">14</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">15</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">16</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">17</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">18</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">19</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">20</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">21</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">22</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">23</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">24</th>
-<td>CustomNaiveForecaster()</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<div id="480fb552" class="cell" data-execution_count="23">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> custom_naive_model.predict(fh<span class="op">=</span>y_test.index.get_level_values(<span class="op">-</span><span class="dv">1</span>).unique())</span>
-<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>fig, _ <span class="op">=</span> plot_series(</span>
-<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a>    y_train.loc[<span class="dv">0</span>],</span>
-<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>    y_pred.loc[<span class="dv">0</span>],</span>
-<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[</span>
-<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>        <span class="st">"SKU 0"</span>,</span>
-<span id="cb36-8"><a href="#cb36-8" aria-hidden="true" tabindex="-1"></a>        <span class="st">"Previsão SKU 0"</span>,</span>
-<span id="cb36-9"><a href="#cb36-9" aria-hidden="true" tabindex="-1"></a>    ],</span>
-<span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb36-11"><a href="#cb36-11" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="sktime_custom_files/figure-html/cell-24-output-1.png" width="1263" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="testes-unitários" class="level2" data-number="11.3">
-<h2 data-number="11.3" class="anchored" data-anchor-id="testes-unitários"><span class="header-section-number">11.3</span> Testes unitários</h2>
-<p>O sktime também fornece uma funcionalidade que traz testes unitários prontos para validar se o modelo customizado está funcionando corretamente.</p>
-<p>Ele usa os hiperparâmetros retornados pelo método <code>get_test_params</code> para criar instâncias do modelo e executar uma série de testes.</p>
-<div id="0039b4df" class="cell" data-execution_count="24">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.estimator_checks <span class="im">import</span> check_estimator</span>
-<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a>check_estimator(CustomNaiveForecaster, tests_to_exclude<span class="op">=</span>[<span class="st">"test_doctest_examples"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stdout">
-<pre><code>All tests PASSED!</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="24">
-<pre><code>{'test_constructor[CustomNaiveForecaster]': 'PASSED',
- 'test_inheritance[CustomNaiveForecaster]': 'PASSED',
- 'test_repr_html[CustomNaiveForecaster-0]': 'PASSED',
- 'test_repr_html[CustomNaiveForecaster-1]': 'PASSED',
- 'test_clone[CustomNaiveForecaster-0]': 'PASSED',
- 'test_clone[CustomNaiveForecaster-1]': 'PASSED',
- 'test_random_tags[CustomNaiveForecaster]': 'PASSED',
- 'test_get_test_params[CustomNaiveForecaster]': 'PASSED',
- 'test_get_params[CustomNaiveForecaster-0]': 'PASSED',
- 'test_get_params[CustomNaiveForecaster-1]': 'PASSED',
- 'test_create_test_instances_and_names[CustomNaiveForecaster]': 'PASSED',
- 'test_valid_estimator_class_tags[CustomNaiveForecaster]': 'PASSED',
- 'test_estimator_tags[CustomNaiveForecaster]': 'PASSED',
- 'test_repr[CustomNaiveForecaster-0]': 'PASSED',
- 'test_repr[CustomNaiveForecaster-1]': 'PASSED',
- 'test_create_test_instance[CustomNaiveForecaster]': 'PASSED',
- 'test_no_cross_test_side_effects_part2[CustomNaiveForecaster-0]': 'PASSED',
- 'test_no_cross_test_side_effects_part2[CustomNaiveForecaster-1]': 'PASSED',
- 'test_get_test_params_coverage[CustomNaiveForecaster]': 'PASSED',
- 'test_has_common_interface[CustomNaiveForecaster]': 'PASSED',
- 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-0]': 'PASSED',
- 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-1]': 'PASSED',
- 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-0]': 'PASSED',
- 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-1]': 'PASSED',
- 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-0]': 'PASSED',
- 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-1]': 'PASSED',
- 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-0]': 'PASSED',
- 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-1]': 'PASSED',
- 'test_set_params_sklearn[CustomNaiveForecaster]': 'PASSED',
- 'test_obj_vs_cls_signature[CustomNaiveForecaster]': 'PASSED',
- 'test_no_cross_test_side_effects_part1[CustomNaiveForecaster-0]': 'PASSED',
- 'test_no_cross_test_side_effects_part1[CustomNaiveForecaster-1]': 'PASSED',
- 'test_valid_estimator_tags[CustomNaiveForecaster-0]': 'PASSED',
- 'test_valid_estimator_tags[CustomNaiveForecaster-1]': 'PASSED',
- 'test_set_params[CustomNaiveForecaster-0]': 'PASSED',
- 'test_set_params[CustomNaiveForecaster-1]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
- 'test_persistence_via_pickle[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_persistence_via_pickle[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_persistence_via_pickle[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_persistence_via_pickle[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_fit_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_fit_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_fit_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_fit_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
- 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
- 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
- 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
- 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
- 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
- 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',
- 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',
- 'test_fit_returns_self[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',
- 'test_fit_returns_self[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',
- 'test_fit_returns_self[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',
- 'test_fit_returns_self[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',
- 'test_dl_constructor_initializes_deeply[CustomNaiveForecaster]': 'PASSED',
- 'test_save_estimators_to_file[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_save_estimators_to_file[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_save_estimators_to_file[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_save_estimators_to_file[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_fit_updates_state[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',
- 'test_fit_updates_state[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',
- 'test_fit_updates_state[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',
- 'test_fit_updates_state[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',
- 'test_multiprocessing_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_multiprocessing_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_multiprocessing_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',
- 'test_multiprocessing_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',
- 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',
- 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',
- 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',
- 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',
- 'test_fh_not_passed_error_handling[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
- 'test_fh_not_passed_error_handling[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
- 'test_fh_not_passed_error_handling[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
- 'test_fh_not_passed_error_handling[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-0]': 'PASSED',
- 'test_raises_not_fitted_error[CustomNaiveForecaster-1]': 'PASSED',
- 'test_predict_series_name_preserved[CustomNaiveForecaster-0]': 'PASSED',
- 'test_predict_series_name_preserved[CustomNaiveForecaster-1]': 'PASSED',
- 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
- 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
- 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
- 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.05]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.1]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.05]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.1]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.05]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.1]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.05]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.1]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.05]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.1]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.05]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.1]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.05]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.1]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.05]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.1]': 'PASSED',
- 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',
- 'test_fit_predict[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
- 'test_fit_predict[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
- 'test_fit_predict[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
- 'test_fit_predict[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=True-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=True-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=True-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=True-1-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=1-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=1-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=1-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=1-1-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=5-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=5-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=5-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:1cols-update_params=False-step=5-1-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=True-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=True-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=True-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=True-1-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=1-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=1-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=1-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=1-1-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=5-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=5-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-0-y:2cols-update_params=False-step=5-1-fh=1]': 'PASSED',
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- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=True-0-fh=1]': 'PASSED',
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- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=1-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=1-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=1-1-fh=1]': 'PASSED',
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- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=5-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=5-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=5-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:1cols-update_params=False-step=5-1-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=True-0-fh=1]': 'PASSED',
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- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=True-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=True-1-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=1-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=1-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=1-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=1-1-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=5-0-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=5-0-fh=[2 5]]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=5-1-fh=1]': 'PASSED',
- 'test_update_predict_predicted_index[CustomNaiveForecaster-1-y:2cols-update_params=False-step=5-1-fh=[2 5]]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-int-int-True]': 'PASSED',
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- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-range-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-range-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-period-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-period-period-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-int-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-int-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-range-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-range-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-period-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-period-period-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-int-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-int-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-range-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-range-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-period-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-period-period-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=1-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-int-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-int-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-range-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-range-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-period-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-period-period-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-int-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-int-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-range-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-range-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-period-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-period-period-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=1-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-int-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-int-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-range-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-range-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-period-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-period-period-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-int-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-int-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-range-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-range-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-period-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-period-period-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-int-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-int-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-range-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-range-int-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-period-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-period-period-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_with_X[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
- 'test_y_multivariate_raises_error[CustomNaiveForecaster-0]': 'PASSED',
- 'test_y_multivariate_raises_error[CustomNaiveForecaster-1]': 'PASSED',
- 'test_score[CustomNaiveForecaster-0-y:1cols-fh=1]': 'PASSED',
- 'test_score[CustomNaiveForecaster-0-y:1cols-fh=[2 5]]': 'PASSED',
- 'test_score[CustomNaiveForecaster-0-y:2cols-fh=1]': 'PASSED',
- 'test_score[CustomNaiveForecaster-0-y:2cols-fh=[2 5]]': 'PASSED',
- 'test_score[CustomNaiveForecaster-1-y:1cols-fh=1]': 'PASSED',
- 'test_score[CustomNaiveForecaster-1-y:1cols-fh=[2 5]]': 'PASSED',
- 'test_score[CustomNaiveForecaster-1-y:2cols-fh=1]': 'PASSED',
- 'test_score[CustomNaiveForecaster-1-y:2cols-fh=[2 5]]': 'PASSED',
- 'test_X_invalid_type_raises_error[CustomNaiveForecaster-0-y:1cols-0]': 'PASSED',
- 'test_X_invalid_type_raises_error[CustomNaiveForecaster-0-y:1cols-1]': 'PASSED',
- 'test_X_invalid_type_raises_error[CustomNaiveForecaster-0-y:2cols-0]': 'PASSED',
- 'test_X_invalid_type_raises_error[CustomNaiveForecaster-0-y:2cols-1]': 'PASSED',
- 'test_X_invalid_type_raises_error[CustomNaiveForecaster-1-y:1cols-0]': 'PASSED',
- 'test_X_invalid_type_raises_error[CustomNaiveForecaster-1-y:1cols-1]': 'PASSED',
- 'test_X_invalid_type_raises_error[CustomNaiveForecaster-1-y:2cols-0]': 'PASSED',
- 'test_X_invalid_type_raises_error[CustomNaiveForecaster-1-y:2cols-1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-0-y:1cols-update_params=True-fh=1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-0-y:1cols-update_params=True-fh=[2 5]]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-0-y:1cols-update_params=False-fh=1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-0-y:1cols-update_params=False-fh=[2 5]]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-0-y:2cols-update_params=True-fh=1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-0-y:2cols-update_params=True-fh=[2 5]]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-0-y:2cols-update_params=False-fh=1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-0-y:2cols-update_params=False-fh=[2 5]]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-1-y:1cols-update_params=True-fh=1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-1-y:1cols-update_params=True-fh=[2 5]]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-1-y:1cols-update_params=False-fh=1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-1-y:1cols-update_params=False-fh=[2 5]]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-1-y:2cols-update_params=True-fh=1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-1-y:2cols-update_params=True-fh=[2 5]]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-1-y:2cols-update_params=False-fh=1]': 'PASSED',
- 'test_update_predict_single[CustomNaiveForecaster-1-y:2cols-update_params=False-fh=[2 5]]': 'PASSED',
- 'test_get_fitted_params[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',
- 'test_get_fitted_params[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',
- 'test_get_fitted_params[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',
- 'test_get_fitted_params[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',
- 'test_hierarchical_with_exogeneous[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
- 'test_hierarchical_with_exogeneous[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
- 'test_hierarchical_with_exogeneous[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
- 'test_hierarchical_with_exogeneous[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
- 'test_pred_int_tag[CustomNaiveForecaster-0]': 'PASSED',
- 'test_pred_int_tag[CustomNaiveForecaster-1]': 'PASSED',
- 'test__y_when_refitting[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
- 'test__y_when_refitting[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
- 'test__y_when_refitting[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
- 'test__y_when_refitting[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.05-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.05-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.1-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.1-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=[0.25, 0.75]-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=[0.25, 0.75]-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.05-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.05-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.1-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.1-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.05-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.05-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.1-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.1-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=[0.25, 0.75]-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=[0.25, 0.75]-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.05-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.05-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.1-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.1-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.05-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.05-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.1-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.1-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=[0.25, 0.75]-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=[0.25, 0.75]-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.05-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.05-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.1-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.1-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.05-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.05-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.1-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.1-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=[0.25, 0.75]-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=[0.25, 0.75]-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.05-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.05-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.1-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.1-1]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]-0]': 'PASSED',
- 'test_predict_interval[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]-1]': 'PASSED',
- 'test_update_with_exogenous_variables[CustomNaiveForecaster-0-y:1cols-update_params=True]': 'PASSED',
- 'test_update_with_exogenous_variables[CustomNaiveForecaster-0-y:1cols-update_params=False]': 'PASSED',
- 'test_update_with_exogenous_variables[CustomNaiveForecaster-0-y:2cols-update_params=True]': 'PASSED',
- 'test_update_with_exogenous_variables[CustomNaiveForecaster-0-y:2cols-update_params=False]': 'PASSED',
- 'test_update_with_exogenous_variables[CustomNaiveForecaster-1-y:1cols-update_params=True]': 'PASSED',
- 'test_update_with_exogenous_variables[CustomNaiveForecaster-1-y:1cols-update_params=False]': 'PASSED',
- 'test_update_with_exogenous_variables[CustomNaiveForecaster-1-y:2cols-update_params=True]': 'PASSED',
- 'test_update_with_exogenous_variables[CustomNaiveForecaster-1-y:2cols-update_params=False]': 'PASSED',
- 'test_fh_attribute[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
- 'test_fh_attribute[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
- 'test_fh_attribute[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
- 'test_fh_attribute[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
- 'test_categorical_y_raises_error[CustomNaiveForecaster-0]': 'PASSED',
- 'test_categorical_y_raises_error[CustomNaiveForecaster-1]': 'PASSED',
- 'test_y_invalid_type_raises_error[CustomNaiveForecaster-0-0]': 'PASSED',
- 'test_y_invalid_type_raises_error[CustomNaiveForecaster-0-1]': 'PASSED',
- 'test_y_invalid_type_raises_error[CustomNaiveForecaster-1-0]': 'PASSED',
- 'test_y_invalid_type_raises_error[CustomNaiveForecaster-1-1]': 'PASSED',
- 'test_predict_proba[CustomNaiveForecaster-0-y:1cols-fh=1]': 'PASSED',
- 'test_predict_proba[CustomNaiveForecaster-0-y:1cols-fh=[2 5]]': 'PASSED',
- 'test_predict_proba[CustomNaiveForecaster-0-y:2cols-fh=1]': 'PASSED',
- 'test_predict_proba[CustomNaiveForecaster-0-y:2cols-fh=[2 5]]': 'PASSED',
- 'test_predict_proba[CustomNaiveForecaster-1-y:1cols-fh=1]': 'PASSED',
- 'test_predict_proba[CustomNaiveForecaster-1-y:1cols-fh=[2 5]]': 'PASSED',
- 'test_predict_proba[CustomNaiveForecaster-1-y:2cols-fh=1]': 'PASSED',
- 'test_predict_proba[CustomNaiveForecaster-1-y:2cols-fh=[2 5]]': 'PASSED',
- 'test__y_and_cutoff[CustomNaiveForecaster-0-y:1cols]': 'PASSED',
- 'test__y_and_cutoff[CustomNaiveForecaster-0-y:2cols]': 'PASSED',
- 'test__y_and_cutoff[CustomNaiveForecaster-1-y:1cols]': 'PASSED',
- 'test__y_and_cutoff[CustomNaiveForecaster-1-y:2cols]': 'PASSED',
- 'test_categorical_X_raises_error[CustomNaiveForecaster-0]': 'PASSED',
- 'test_categorical_X_raises_error[CustomNaiveForecaster-1]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=1-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=-3-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-2 -5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=0-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:1cols-fh=[-3  2]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=1-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=-3-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-2 -5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=0-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-0-y:2cols-fh=[-3  2]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=1-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=-3-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-2 -5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=0-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:1cols-fh=[-3  2]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=1-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=-3-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-2 -5]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=0-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-int-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-int-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-range-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-range-int-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-period-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-period-period-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-datetime-int-True]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index[CustomNaiveForecaster-1-y:2cols-fh=[-3  2]-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-int-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-int-int-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-range-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-range-int-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-period-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-period-period-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:1cols-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-int-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-int-int-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-range-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-range-int-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-period-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-period-period-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-int-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-int-int-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-range-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-range-int-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-period-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-period-period-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-timedelta-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-int-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-int-int-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-range-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-range-int-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-period-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-period-period-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-int-True]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-datetime-False]': 'PASSED',
- 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-timedelta-True]': 'PASSED'}</code></pre>
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-<div id="quarto-sidebar-glass" class="quarto-sidebar-collapse-item" data-bs-toggle="collapse" data-bs-target=".quarto-sidebar-collapse-item"></div>
-<!-- margin-sidebar -->
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-        <nav id="TOC" role="doc-toc" class="toc-active">
-    <h2 id="toc-title">Table of contents</h2>
-   
-  <ul>
-  <li><a href="#importando-dados" id="toc-importando-dados" class="nav-link active" data-scroll-target="#importando-dados"><span class="header-section-number">3.1</span> Importando dados</a></li>
-  <li><a href="#auto-correlação" id="toc-auto-correlação" class="nav-link" data-scroll-target="#auto-correlação"><span class="header-section-number">3.2</span> Auto-correlação</a></li>
-  <li><a href="#componentes-de-séries-temporais" id="toc-componentes-de-séries-temporais" class="nav-link" data-scroll-target="#componentes-de-séries-temporais"><span class="header-section-number">3.3</span> Componentes de séries temporais</a>
-  <ul class="collapse">
-  <li><a href="#decompondo-a-série-temporal" id="toc-decompondo-a-série-temporal" class="nav-link" data-scroll-target="#decompondo-a-série-temporal"><span class="header-section-number">3.3.1</span> Decompondo a série temporal</a></li>
-  </ul></li>
-  <li><a href="#séries-estacionárias" id="toc-séries-estacionárias" class="nav-link" data-scroll-target="#séries-estacionárias"><span class="header-section-number">3.4</span> Séries estacionárias</a>
-  <ul class="collapse">
-  <li><a href="#diferenciação" id="toc-diferenciação" class="nav-link" data-scroll-target="#diferenciação"><span class="header-section-number">3.4.1</span> Diferenciação</a></li>
-  </ul></li>
-  <li><a href="#criando-um-pipeline-com-diferenciação-e-naive" id="toc-criando-um-pipeline-com-diferenciação-e-naive" class="nav-link" data-scroll-target="#criando-um-pipeline-com-diferenciação-e-naive"><span class="header-section-number">3.5</span> Criando um pipeline com diferenciação e Naive</a></li>
-  </ul>
-</nav>
-    </div>
-<!-- main -->
-<main class="content" id="quarto-document-content">
-
-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../../../content/pt/part1/index.html">Part I: Básico</a></li><li class="breadcrumb-item"><a href="../../../content/pt/part1/components_and_diff.html"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Decomposição de séries temporais e modelos clássicos</span></a></li></ol></nav>
-<div class="quarto-title">
-<h1 class="title"><span class="chapter-number">3</span>&nbsp; <span class="chapter-title">Decomposição de séries temporais e modelos clássicos</span></h1>
-</div>
-
-
-
-<div class="quarto-title-meta">
-
-    
-  
-    
-  </div>
-  
-
-
-</header>
-
-
-<p>Nesse capítulo, vamos começar a usar um dataset mais realista, com dados simulando vendas diárias de uma empresa de varejo.</p>
-<p>Vamos aprender os seguintes pontos:</p>
-<ul>
-<li>Definição de séries integradas, e uso de diferenciação</li>
-<li>Modelos estatísticos clássicos: modelos de suavização exponencial e modelos autoregressivos (AR)</li>
-<li>Como usar esses modelos com a biblioteca <code>sktime</code></li>
-</ul>
-<section id="importando-dados" class="level2" data-number="3.1">
-<h2 data-number="3.1" class="anchored" data-anchor-id="importando-dados"><span class="header-section-number">3.1</span> Importando dados</h2>
-<p>Para acessar os dados, vamos usar a classe <code>SyntheticRetail</code> da biblioteca <code>tsbook</code>, que contém dados simulados de vendas diárias de uma empresa de varejo.</p>
-<div id="8b1085c3" class="cell" data-execution_count="1">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"univariate"</span>)</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>y_train, y_test <span class="op">=</span> dataset.load(<span class="st">"y_train"</span>, <span class="st">"y_test"</span>)</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-2-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Os dados são diários, e vemos que sempre positivos. Também notamos que existe alguma sazonalidade, aparentemente algo mensal e anual, que aumenta de magnitude ao longo do tempo.</p>
-<p>Algo que deve chamar a atenção nesse gráfico é que a magnitude da série temporal está aumentando ao longo do tempo. Isso não deve passar desapercebido, pois é um ponto importante para entendermos o que vem a seguir.</p>
-</section>
-<section id="auto-correlação" class="level2" data-number="3.2">
-<h2 data-number="3.2" class="anchored" data-anchor-id="auto-correlação"><span class="header-section-number">3.2</span> Auto-correlação</h2>
-<p>É interessante entender o quanto de informação o passado de uma série temporal carrega sobre o seu futuro. Uma maneira de capturar essa relação (considerando variações lineares) é através da auto-correlação. A autocorrelação para um determinado lag <span class="math inline">\(k\)</span> é definida como:</p>
-<p><span class="math display">\[
-\text{Corr}(Y_t,Y_{t-k})=\frac{\text{Cov}(Y_t,Y_{t-k})}{\sqrt{\text{Var}(Y_t)\text{Var}(Y_{t-k})}} = \frac{E[(Y_t - \mu)(Y_{t-k} - \mu)]}{\sqrt{\text{Var}(Y_t)\text{Var}(Y_{t-k})}}
-\]</span></p>
-<p>Em outras palavras, quando o valor em <span class="math inline">\(k\)</span> observações atrás está acima (ou abaixo) da média, o valor atual também tende a estar acima (ou abaixo) da média?</p>
-<p>Com <code>plot_correlations</code>, podemos visualizar algumas informações úteis:</p>
-<ol type="1">
-<li>No plot superior, vemos a série temporal original.</li>
-<li>No canto inferior esquerdo, temos o gráfico de autocorrelação (ACF), que mostra a correlação entre a série temporal e suas versões defasadas (lags). Valores próximos de 1 ou -1 indicam uma forte correlação positiva ou negativa, respectivamente.</li>
-<li>No canto inferior direito, temos o gráfico de autocorrelação parcial (PACF), que mostra a correlação entre a série temporal e suas versões defasadas, controlando para as correlações intermediárias. É útil para entender o efeito isolado de um lag.</li>
-</ol>
-<div id="9b61a5e1" class="cell" data-execution_count="2">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_correlations</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plot_correlations(y_train, lags<span class="op">=</span><span class="dv">60</span>)</span>
-<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/_2/9y4tsvdd2n3gqjgd2zmlr1km0000gn/T/ipykernel_24222/2914460266.py:5: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
-  fig.show()</code></pre>
-</div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-3-output-2.png" width="1163" height="779" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>No gráfico de autocorrelação, vemos algumas características interessantes:</p>
-<ol type="1">
-<li>Valores são extremamente altos, e decaem lentamente ao longo do tempo.</li>
-<li>Existem oscilações claras, indicando padrões sazonais na série temporal.</li>
-</ol>
-<div class="callout callout-style-default callout-tip callout-titled">
-<div class="callout-header d-flex align-content-center">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Tip
-</div>
-</div>
-<div class="callout-body-container callout-body">
-<p>É um erro comum usar a correlação de lags de uma série para seleção de variáveis (lags). Sempre que possível, devemos eliminar a tendência antes de analisar a autocorrelação.</p>
-</div>
-</div>
-<p>Veremos que esses padrões são indicativos de que a série temporal possui componentes importantes, e que valores passados dizem muito sobre valores futuros.</p>
-</section>
-<section id="componentes-de-séries-temporais" class="level2" data-number="3.3">
-<h2 data-number="3.3" class="anchored" data-anchor-id="componentes-de-séries-temporais"><span class="header-section-number">3.3</span> Componentes de séries temporais</h2>
-<p>Séries temporais podem ser decompostas em 3 componentes principais:</p>
-<ul>
-<li>Tendência: padrão de longo prazo na série temporal</li>
-<li>Sazonalidade: padrões que se repetem em intervalos regulares, como diariamente, semanalmente ou anualmente</li>
-<li>Ruído: variação aleatória que não pode ser explicada pelos outros componentes</li>
-</ul>
-<p>Uma série aditiva pode ser representada como:</p>
-<p><span class="math display">\[
-Y(t) = T(t) + S(t) + R(t)
-\]</span></p>
-<p>onde <span class="math inline">\(T(t)\)</span> é a tendência, <span class="math inline">\(S(t)\)</span> é a sazonalidade, e <span class="math inline">\(R(t)\)</span> é o ruído.</p>
-<p>Em séries aditivas, o impacto da sazonalidade se dá em termos absolutos, dizemos: “em janeiro, as vendas aumentam em 100 unidades com relação a média do ano”.</p>
-<p>Mas também existem séries multiplicativas, onde os componentes interagem de forma diferente:</p>
-<p><span class="math display">\[
-Y(t) = T(t) \cdot S(t) \cdot R(t)
-\]</span></p>
-<p>Nessas séries, o impacto da sazonalidade se dá em termos relativos, dizemos: “em janeiro, as vendas aumentam em 20% com relação a média do ano”. Esse é o caso mais comum para séries não-negativas, como vendas. Isso vem por definição: se temos vendas muito baixas, por exemplo, 10 unidades, não faz sentido dizer que em janeiro as vendas diminuem em 100 unidades, pois isso levaria a vendas negativas. Já dizer que as vendas diminuem em 20% é perfeitamente razoável.</p>
-<div class="callout callout-style-default callout-tip callout-titled">
-<div class="callout-header d-flex align-content-center">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Tip
-</div>
-</div>
-<div class="callout-body-container callout-body">
-<p>Em alguns casos, as séries multiplicativas são definidas como:</p>
-<p><span class="math display">\[
-Y(t) = T(t) + T(t) \cdot S(t) + T(t) \cdot R(t)
-\]</span></p>
-</div>
-</div>
-<p>Quando a série é multiplicativa, podemos fazer recurso ao logaritmo para transformá-la em aditiva:</p>
-<p><span class="math display">\[
-log(Y(t)) = log(T(t)) + log(S(t)) + log(R(t))
-\]</span></p>
-<p>Para fazer isso no sktime, usamos o transformador <code>LogTransformer</code>. Transformadores são usados para pré-processar ou pós-processar os dados antes de aplicar um modelo de previsão, e sua interface é similar a dos modelos de previsão:</p>
-<ul>
-<li><code>__init__</code>: define os hiperparâmetros do transformador</li>
-<li><code>fit</code>: aprende os parâmetros do transformador a partir dos dados</li>
-<li><code>transform</code>: aplica a transformação nos dados</li>
-<li><code>inverse_transform</code> (opcional): aplica a transformação inversa nos dados</li>
-</ul>
-<div id="bca6ac8d" class="cell" data-execution_count="3">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.boxcox <span class="im">import</span> LogTransformer</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>log_transformer <span class="op">=</span> LogTransformer()</span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>log_transformer.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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-  }
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-label label.sk-toggleable__label,
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-a544e557-4cac-406e-846c-1b90a4821d7f a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-a544e557-4cac-406e-846c-1b90a4821d7f" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogTransformer()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('a55b80fe-7a52-48e3-bde7-dd0858dd5fe3')" type="checkbox" checked=""><label for="UUID('a55b80fe-7a52-48e3-bde7-dd0858dd5fe3')" class="sk-toggleable__label sk-toggleable__label-arrow">LogTransformer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.boxcox.LogTransformer.html">?<span>Documentation for LogTransformer</span></a></label><div class="sk-toggleable__content"><pre>LogTransformer()</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="d9298bac" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>y_train_log <span class="op">=</span> log_transformer.transform(y_train)</span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>plot_series(y_train_log, labels<span class="op">=</span>[<span class="st">"Logaritmo"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-5-output-1.png" width="1246" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Ainda que não esteja perfeito, essa transformação estabiliza as variações da série temporal, o que é importante para alguns modelos de previsão.</p>
-<section id="decompondo-a-série-temporal" class="level3" data-number="3.3.1">
-<h3 data-number="3.3.1" class="anchored" data-anchor-id="decompondo-a-série-temporal"><span class="header-section-number">3.3.1</span> Decompondo a série temporal</h3>
-<p>Sktime fornece algumas opções para decompor séries temporais. Aqui, vamos usar o <code>Detrender</code> para remover a tendência, e o <code>Deseasonalizer</code> para remover a sazonalidade.</p>
-<section id="tendência" class="level4" data-number="3.3.1.1">
-<h4 data-number="3.3.1.1" class="anchored" data-anchor-id="tendência"><span class="header-section-number">3.3.1.1</span> Tendência</h4>
-<div id="2ad98872" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.detrend <span class="im">import</span> Detrender, Deseasonalizer</span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>detrender <span class="op">=</span> LogTransformer() <span class="op">*</span> Detrender(model<span class="op">=</span><span class="st">"additive"</span>)</span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>detrender.fit(y_train)</span>
-<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>y_train_detrended <span class="op">=</span> detrender.transform(y_train)</span>
-<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>plot_series(y_train_detrended, labels<span class="op">=</span>[<span class="st">"Detrended"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-6-output-1.png" width="1278" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Vemos uma mudança importante no gráfico de autocorrelação:</p>
-<div id="53d0fa2d" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>fig, _ <span class="op">=</span> plot_correlations(y_train_detrended, lags<span class="op">=</span><span class="dv">60</span>)</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/_2/9y4tsvdd2n3gqjgd2zmlr1km0000gn/T/ipykernel_24222/935228866.py:2: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
-  fig.show()</code></pre>
-</div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-7-output-2.png" width="1163" height="779" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>O que indica que, ao eliminar a tendencia, a informação que o passado carrega sobre o futuro diminuiu bastante. Na verdade, a existência da tendência - um efeito de longo prazo - faz com que valores passados sejam altamente correlacionados com valores futuros, e pode dar a falsa impressão de que a série é “fácil” de modelar.</p>
-</section>
-<section id="sazonalidade" class="level4" data-number="3.3.1.2">
-<h4 data-number="3.3.1.2" class="anchored" data-anchor-id="sazonalidade"><span class="header-section-number">3.3.1.2</span> Sazonalidade</h4>
-<p>Agora, usamos o <code>Deseasonalizer</code> para remover a sazonalidade:</p>
-<div id="d2b4a8ac" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>deseasonalizer <span class="op">=</span> LogTransformer() <span class="op">*</span> Deseasonalizer(model<span class="op">=</span><span class="st">"additive"</span>, sp<span class="op">=</span><span class="dv">365</span>)</span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>deseasonalizer.fit(y_train)</span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>y_train_deseasonalized <span class="op">=</span> deseasonalizer.transform(y_train)</span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>plot_series(y_train_log, y_train_deseasonalized, labels<span class="op">=</span>[<span class="st">"Log with seasonality"</span>, <span class="st">"Deseasonalized"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-8-output-1.png" width="1246" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Podemos usar o <code>Detrender</code> e o <code>Deseasonalizer</code> juntos para remover ambos os componentes:</p>
-<div id="ade24011" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>remove_components <span class="op">=</span> LogTransformer() <span class="op">*</span> Detrender(model<span class="op">=</span><span class="st">"additive"</span>) <span class="op">*</span> Deseasonalizer(model<span class="op">=</span><span class="st">"additive"</span>, sp<span class="op">=</span><span class="dv">365</span>) </span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>remove_components.fit(y_train)</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>y_train_removed <span class="op">=</span> remove_components.transform(y_train)</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>plot_series(y_train_log, y_train_removed, labels<span class="op">=</span>[<span class="st">"Log with seasonality"</span>, <span class="st">"Deseasonalized and detrended"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-9-output-1.png" width="1246" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<div id="6959c389" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>fig, _ <span class="op">=</span> plot_correlations(y_train_removed, lags<span class="op">=</span><span class="dv">60</span>)</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/_2/9y4tsvdd2n3gqjgd2zmlr1km0000gn/T/ipykernel_24222/10449850.py:2: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
-  fig.show()</code></pre>
-</div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-10-output-2.png" width="1163" height="779" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<div class="callout callout-style-default callout-tip callout-titled">
-<div class="callout-header d-flex align-content-center">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Tip
-</div>
-</div>
-<div class="callout-body-container callout-body">
-<p>Tente adicionar mais um deseasonalizer para remover a sazonalidade semanal.</p>
-</div>
-</div>
-</section>
-</section>
-</section>
-<section id="séries-estacionárias" class="level2" data-number="3.4">
-<h2 data-number="3.4" class="anchored" data-anchor-id="séries-estacionárias"><span class="header-section-number">3.4</span> Séries estacionárias</h2>
-<p>O conceito de estacionariedade é fundamental em séries temporais. Uma série temporal é dita estacionária se suas propriedades estatísticas, como média, variância e autocovariância, são constantes ao longo do tempo, não importando a janela e quando ela é observada.</p>
-<div class=".{.callout-tip}">
-<p>Mais precisamente, se <span class="math inline">\(Y(t)\)</span>, onde <span class="math inline">\(t\)</span> é o indice temporal, então dizemos que ela é estacionária se:</p>
-<p><span class="math display">\[
-P(Y(t_{start}:t_{end})) = P(Y(t_{start}+k:t_{end}+k)), \quad \forall k, t_{start}, t_{end} \in \mathbb{Z}
-\]</span></p>
-</div>
-<p>Claramente, a série temporal que estamos analisando não é estacionária. Basta percebermos que para valores maiores de <span class="math inline">\(t\)</span>, a média e a variância são maiores.</p>
-<p>O aumento da média da série ao longo do tempo é chamado de <strong>tendência</strong>. A tendência é um padrão de longo prazo na série temporal, e um grande desafio para previsões de longo prazo.</p>
-<p>Existem definições mais “suaves” de estacionariedade, como a estacionariedade fraca, que requer apenas que propriedades como média e autovariância sejam constantes ao longo do tempo.</p>
-<p>No fundo, o que nos interessa mais é ter uma série temporal que seja “fácil”de modelar. Para alguns algoritmos, como Naive, é importante que ela seja o mais próxima possível de estacionária. Veja abaixo o problema geraodo quando aplicamos o modelo Naive diretamente na série temporal original.</p>
-<div id="cfb044b0" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>naive <span class="op">=</span> NaiveForecaster(strategy<span class="op">=</span><span class="st">"mean"</span>, window_length<span class="op">=</span><span class="dv">24</span>)</span>
-<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>naive.fit(y_train)</span>
-<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> naive.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão Naive"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-11-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<section id="diferenciação" class="level3" data-number="3.4.1">
-<h3 data-number="3.4.1" class="anchored" data-anchor-id="diferenciação"><span class="header-section-number">3.4.1</span> Diferenciação</h3>
-<p>Uma técnica simples e eficaz para lidar com séries não estacionárias é a diferenciação. Calculamos:</p>
-<p><span class="math display">\[
-Y'(t) = Y(t) - Y(t-1)
-\]</span></p>
-<p>e fazemos previsões em <span class="math inline">\(Y'(t)\)</span> ao invés de <span class="math inline">\(Y(t)\)</span>. Para obter a previsão de <span class="math inline">\(Y(t)\)</span>, precisamos fazer o processo inverso: somar a previsão de <span class="math inline">\(Y'(t)\)</span> com o valor anterior de <span class="math inline">\(Y(t-1)\)</span>.</p>
-<p><span class="math display">\[
-\hat{Y(t)} = \hat{Y'}(t) + \hat{Y}(t-1), \quad \hat{Y}(0) \text{ conhecido}
-\]</span></p>
-<p>Com sktime, isso é extremamente fácil. Aqui, vamos usar um <strong>transformador</strong> chamado <code>Differencer</code>.</p>
-<div id="d2f3bba5" class="cell" data-execution_count="11">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.difference <span class="im">import</span> Differencer</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>diff <span class="op">=</span> Differencer()</span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>diff.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="11">
-<style>#sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
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-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e {
-    color: var(--sklearn-color-text);
-  }
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-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
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-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-parallel-item {
-    display: flex;
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-
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-    align-self: flex-end;
-    width: 50%;
-  }
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-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
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-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-label label.sk-toggleable__label,
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-d2af06d7-6b6a-4560-ae0f-655b007f4f3e" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Differencer()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('be2ae25f-aa85-4dc1-ac00-1eab28191d30')" type="checkbox" checked=""><label for="UUID('be2ae25f-aa85-4dc1-ac00-1eab28191d30')" class="sk-toggleable__label sk-toggleable__label-arrow">Differencer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.difference.Differencer.html">?<span>Documentation for Differencer</span></a></label><div class="sk-toggleable__content"><pre>Differencer()</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="5155cd17" class="cell" data-execution_count="12">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>y_train_diff <span class="op">=</span> diff.transform(y_train)</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_train_diff, labels<span class="op">=</span>[<span class="st">"Original"</span>, <span class="st">"Diferenciado"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-13-output-1.png" width="1274" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="criando-um-pipeline-com-diferenciação-e-naive" class="level2" data-number="3.5">
-<h2 data-number="3.5" class="anchored" data-anchor-id="criando-um-pipeline-com-diferenciação-e-naive"><span class="header-section-number">3.5</span> Criando um pipeline com diferenciação e Naive</h2>
-<p>Agora, podemos criar um modelo de forecasting mais complexo, composto por dois passos:</p>
-<ul>
-<li>Diferenciação dos dados</li>
-<li>Modelo Naive aplicado nos dados diferenciados</li>
-</ul>
-<p>Para isso, usamos a classe <code>TransformedTargetForecaster</code>, que cria um pipeline de transformadores e um modelo de previsão.</p>
-<div id="515d6dd1" class="cell" data-execution_count="13">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.compose <span class="im">import</span> TransformedTargetForecaster</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> TransformedTargetForecaster(steps<span class="op">=</span>[</span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>    (<span class="st">"differencer"</span>, Differencer()),</span>
-<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>    (<span class="st">"naive"</span>, NaiveForecaster(strategy<span class="op">=</span><span class="st">"mean"</span>, window_length<span class="op">=</span><span class="dv">24</span>))</span>
-<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>])</span>
-<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="13">
-<style>#sk-734ea791-ec25-40c5-8e42-24c28cb359f7 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 pre {
-    padding: 0;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-label label.sk-toggleable__label,
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-734ea791-ec25-40c5-8e42-24c28cb359f7 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-734ea791-ec25-40c5-8e42-24c28cb359f7" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TransformedTargetForecaster(steps=[('differencer', Differencer()),
-                                   ('naive',
-                                    NaiveForecaster(strategy='mean',
-                                                    window_length=24))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('ae603978-32a0-40d9-a3ff-65b9273f3710')" type="checkbox"><label for="UUID('ae603978-32a0-40d9-a3ff-65b9273f3710')" class="sk-toggleable__label sk-toggleable__label-arrow">TransformedTargetForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.TransformedTargetForecaster.html">?<span>Documentation for TransformedTargetForecaster</span></a></label><div class="sk-toggleable__content"><pre>TransformedTargetForecaster(steps=[('differencer', Differencer()),
-                                   ('naive',
-                                    NaiveForecaster(strategy='mean',
-                                                    window_length=24))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('be19f33a-25f9-4db6-8843-d7a854e27d20')" type="checkbox"><label for="UUID('be19f33a-25f9-4db6-8843-d7a854e27d20')" class="sk-toggleable__label sk-toggleable__label-arrow">Differencer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.difference.Differencer.html">?<span>Documentation for Differencer</span></a></label><div class="sk-toggleable__content"><pre>Differencer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('fc2e4b53-eb19-44b6-aa0d-3ffdd304f3e7')" type="checkbox"><label for="UUID('fc2e4b53-eb19-44b6-aa0d-3ffdd304f3e7')" class="sk-toggleable__label sk-toggleable__label-arrow">NaiveForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.naive.NaiveForecaster.html">?<span>Documentation for NaiveForecaster</span></a></label><div class="sk-toggleable__content"><pre>NaiveForecaster(strategy='mean', window_length=24)</pre></div></div></div></div></div></div></div>
-</div>
-</div>
-<p>Ou apenas:</p>
-<div id="b24097f1" class="cell" data-execution_count="14">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> Differencer() <span class="op">*</span> NaiveForecaster(strategy<span class="op">=</span><span class="st">"mean"</span>, window_length<span class="op">=</span><span class="dv">24</span>)</span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="14">
-<style>#sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 pre {
-    padding: 0;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-label label.sk-toggleable__label,
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-f0daf872-6083-464c-8d4f-d288bf26f6b7 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-f0daf872-6083-464c-8d4f-d288bf26f6b7" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TransformedTargetForecaster(steps=[Differencer(),
-                                   NaiveForecaster(strategy='mean',
-                                                   window_length=24)])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('39099b24-fee7-4650-b146-ba2fab9159f7')" type="checkbox"><label for="UUID('39099b24-fee7-4650-b146-ba2fab9159f7')" class="sk-toggleable__label sk-toggleable__label-arrow">TransformedTargetForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.TransformedTargetForecaster.html">?<span>Documentation for TransformedTargetForecaster</span></a></label><div class="sk-toggleable__content"><pre>TransformedTargetForecaster(steps=[Differencer(),
-                                   NaiveForecaster(strategy='mean',
-                                                   window_length=24)])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('3b73a26c-b9a0-456f-ad26-97e2f27664e5')" type="checkbox"><label for="UUID('3b73a26c-b9a0-456f-ad26-97e2f27664e5')" class="sk-toggleable__label sk-toggleable__label-arrow">Differencer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.difference.Differencer.html">?<span>Documentation for Differencer</span></a></label><div class="sk-toggleable__content"><pre>Differencer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('a8b218d3-eed9-43c0-a4c7-f6c73208e8be')" type="checkbox"><label for="UUID('a8b218d3-eed9-43c0-a4c7-f6c73208e8be')" class="sk-toggleable__label sk-toggleable__label-arrow">NaiveForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.naive.NaiveForecaster.html">?<span>Documentation for NaiveForecaster</span></a></label><div class="sk-toggleable__content"><pre>NaiveForecaster(strategy='mean', window_length=24)</pre></div></div></div></div></div></div></div>
-</div>
-</div>
-<p>E agora podemos prever:</p>
-<div id="1dcd226c" class="cell" data-execution_count="15">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão Naive com diferenciação"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-16-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Existem ainda alguns problemas com a diferenciação. Note que a variância da série temporal diferenciada não é constante ao longo do tempo. Aqui, podemos combinar nossa transformação logarítmica com a diferenciação.</p>
-<p>Primeiro, vamos criar um transformador que combina as duas transformações:</p>
-<div id="9f7d6df9" class="cell" data-execution_count="16">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.compose <span class="im">import</span> TransformerPipeline</span>
-<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>log_diff <span class="op">=</span> TransformerPipeline(steps<span class="op">=</span>[</span>
-<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>    (<span class="st">"log"</span>, LogTransformer()),</span>
-<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a>    (<span class="st">"diff"</span>, Differencer())</span>
-<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>])</span>
-<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a>log_diff.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="16">
-<style>#sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e pre {
-    padding: 0;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-label label.sk-toggleable__label,
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-81cbfce9-ef27-4a3a-8ece-1001f9703e8e" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TransformerPipeline(steps=[('log', LogTransformer()), ('diff', Differencer())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('f3405748-54c7-4c1d-ab55-b9181f691880')" type="checkbox"><label for="UUID('f3405748-54c7-4c1d-ab55-b9181f691880')" class="sk-toggleable__label sk-toggleable__label-arrow">TransformerPipeline<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.compose.TransformerPipeline.html">?<span>Documentation for TransformerPipeline</span></a></label><div class="sk-toggleable__content"><pre>TransformerPipeline(steps=[('log', LogTransformer()), ('diff', Differencer())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('617391fd-5bb4-4a8a-99ca-1c4be780492b')" type="checkbox"><label for="UUID('617391fd-5bb4-4a8a-99ca-1c4be780492b')" class="sk-toggleable__label sk-toggleable__label-arrow">LogTransformer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.boxcox.LogTransformer.html">?<span>Documentation for LogTransformer</span></a></label><div class="sk-toggleable__content"><pre>LogTransformer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('20ff0f2d-9d99-4f83-a8e0-32de3d589714')" type="checkbox"><label for="UUID('20ff0f2d-9d99-4f83-a8e0-32de3d589714')" class="sk-toggleable__label sk-toggleable__label-arrow">Differencer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.difference.Differencer.html">?<span>Documentation for Differencer</span></a></label><div class="sk-toggleable__content"><pre>Differencer()</pre></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="08f587fb" class="cell" data-execution_count="17">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>y_train_log_diff <span class="op">=</span> log_diff.transform(y_train)</span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>plot_series(y_train_log_diff, labels<span class="op">=</span>[<span class="st">"Log + Diferenciado"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-18-output-1.png" width="1270" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Para fazer forecast, criamos um pipeline com o transformador combinado e o modelo Naive:</p>
-<div id="5998428c" class="cell" data-execution_count="18">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> log_diff <span class="op">*</span> NaiveForecaster(strategy<span class="op">=</span><span class="st">"mean"</span>, window_length<span class="op">=</span><span class="dv">24</span>)</span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span>
-<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão Naive com log + diferenciação"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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-<div>
-<figure class="figure">
-<p><img src="components_and_diff_files/figure-html/cell-19-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
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-<section id="exponential-smoothing" class="level2" data-number="4.1">
-<h2 data-number="4.1" class="anchored" data-anchor-id="exponential-smoothing"><span class="header-section-number">4.1</span> Exponential Smoothing</h2>
-<p>O Modelo Naive, que calculada a média dos últimos valores, é um modelo muito simples, mas que pode ser melhorado. Analizando o que ele faz:</p>
-<p><span class="math display">\[
-\hat{Y}(t) = \frac{Y(t-1) + Y(t-2) + \dots + Y(t-n)}{n}
-\]</span></p>
-<p>Ele basicamente atribui o mesmo peso para todas as observações passadas. Mas, intuitivamente, faz mais sentido dar mais peso para as observações mais recentes, e menos peso para as observações mais antigas.</p>
-<p>O exponential smoothing faz exatamente isso. Ele atribui pesos decrescentes para observações mais antigas. O peso da observação decresce exponencialmente segundo um fator <span class="math inline">\(0 &lt; \alpha &lt; 1\)</span> <span class="citation" data-cites="hyndman2018forecasting">(<a href="#ref-hyndman2018forecasting" role="doc-biblioref">Hyndman and Athanasopoulos 2018</a>)</span>:</p>
-<p><span class="math display">\[
-\hat{Y}(t) = \alpha Y(t-1) + \alpha(1 - \alpha)Y(t-1) + \alpha(1 - \alpha)^2 Y(t-2) + \dots
-\]</span></p>
-<div id="0de03b64" class="cell" data-execution_count="1">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(<span class="dv">1</span>, <span class="dv">1</span>, figsize<span class="op">=</span>(<span class="dv">6</span>, <span class="dv">4</span>))</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>alpha <span class="op">=</span> <span class="fl">0.3</span></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>weights <span class="op">=</span> [alpha <span class="op">*</span> (<span class="dv">1</span> <span class="op">-</span> alpha) <span class="op">**</span> i <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="dv">20</span>)]</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>ax.bar(<span class="bu">range</span>(<span class="bu">len</span>(weights)), weights)</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>ax.set_title(<span class="st">"Pesos do Exponential Smoothing"</span>)</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>ax.set_xlabel(<span class="st">"Observações passadas"</span>)</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>ax.set_ylabel(<span class="st">"Peso"</span>)</span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ets_and_ar_files/figure-html/cell-2-output-1.png" width="530" height="376" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<section id="usando-exponential-smoothing-com-sktime" class="level3" data-number="4.1.1">
-<h3 data-number="4.1.1" class="anchored" data-anchor-id="usando-exponential-smoothing-com-sktime"><span class="header-section-number">4.1.1</span> Usando Exponential Smoothing com sktime</h3>
-<div id="98fe3d96" class="cell" data-execution_count="2">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"univariate"</span>)</span>
-<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>y_train, y_test <span class="op">=</span> dataset.load(<span class="st">"y_train"</span>, <span class="st">"y_test"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="3e620051" class="cell" data-execution_count="3">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.exp_smoothing <span class="im">import</span> ExponentialSmoothing</span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ExponentialSmoothing()</span>
-<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="3">
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-  /* Toggleable content - dropdown */
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-label label.sk-toggleable__label,
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-89c952dd-8f3d-49be-9a8f-1f6de782bdf1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ExponentialSmoothing()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('d5341bf6-a14c-4c4d-9838-6ba98c205517')" type="checkbox" checked=""><label for="UUID('d5341bf6-a14c-4c4d-9838-6ba98c205517')" class="sk-toggleable__label sk-toggleable__label-arrow">ExponentialSmoothing<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.exp_smoothing.ExponentialSmoothing.html">?<span>Documentation for ExponentialSmoothing</span></a></label><div class="sk-toggleable__content"><pre>ExponentialSmoothing()</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="ff318bc6" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão Exponential Smoothing"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ets_and_ar_files/figure-html/cell-5-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Existem versões alternativas que consideram sazonalidade e tendências. Veja a <a href="https://www.sktime.org/en/stable/api_reference/auto_generated/sktime.forecasting.exp_smoothing.ExponentialSmoothing.html">documentação</a> para mais detalhes.</p>
-<div id="787b9ace" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ExponentialSmoothing(trend<span class="op">=</span><span class="st">"add"</span>, seasonal<span class="op">=</span><span class="st">"add"</span>, sp<span class="op">=</span><span class="dv">7</span>)</span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>    y_train,</span>
-<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>    y_test,</span>
-<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>    y_pred,</span>
-<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão Exponential Smoothing"</span>],</span>
-<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ets_and_ar_files/figure-html/cell-6-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="modelos-autoregressivos-ar" class="level2" data-number="4.2">
-<h2 data-number="4.2" class="anchored" data-anchor-id="modelos-autoregressivos-ar"><span class="header-section-number">4.2</span> Modelos autoregressivos (AR)</h2>
-<p>Modelos autoregressivos (AR) são modelos que prevem o valor atual de uma série temporal como uma combinação dos valores passados. O modelo AR(p) usa os últimos p valores para fazer a previsão:</p>
-<p><span class="math display">\[
-\hat{Y}(t) = \phi_1 Y(t-1) + \phi_2 Y(t-2) + \dots + \phi_p Y(t-p)
-\]</span></p>
-<p>onde <span class="math inline">\(\phi_1, \phi_2, \dots, \phi_p\)</span> são os parâmetros do modelo que precisam ser estimados a partir dos dados.</p>
-<div id="612b15ff" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.auto_reg <span class="im">import</span> AutoREG</span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> AutoREG(lags<span class="op">=</span><span class="dv">31</span>)</span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span>
-<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>    y_train,</span>
-<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>    y_test,</span>
-<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>    y_pred,</span>
-<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão AR"</span>],</span>
-<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ets_and_ar_files/figure-html/cell-7-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Um modelo auto-regressivo mais complexo é o ARIMA, que combina autoregressão (AR), média móvel (MA) e diferenciação integrada (I) para lidar com séries temporais não estacionárias. Não vamos estudar o ARIMA aqui pois envolve conceitos mais avançados, mas temos ele disponível no sktime, <code>sktime.forecasting.arima.ARIMA</code>.</p>
-</section>
-<section id="stl-dividir-e-conquistar" class="level2" data-number="4.3">
-<h2 data-number="4.3" class="anchored" data-anchor-id="stl-dividir-e-conquistar"><span class="header-section-number">4.3</span> STL: dividir e conquistar</h2>
-<p>Sabemos que séries temporais podem ser decompostas em componentes de tendência, sazonalidade e resíduos. O modelo STL (Seasonal and Trend decomposition using Loess) é uma técnica que permite fazer essa decomposição de forma robusta.</p>
-<p>Temos no sktime o <code>STLTransformer</code>, que permite fazer a decomposição STL:</p>
-<div id="4954b20d" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.detrend <span class="im">import</span> STLTransformer</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>stl <span class="op">=</span> STLTransformer(sp<span class="op">=</span><span class="dv">365</span>)</span>
-<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>stl.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="7">
-<style>#sk-861ed953-145b-401c-846c-05d041681bb3 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 pre {
-    padding: 0;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-861ed953-145b-401c-846c-05d041681bb3 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-label label.sk-toggleable__label,
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-861ed953-145b-401c-846c-05d041681bb3 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-861ed953-145b-401c-846c-05d041681bb3 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-861ed953-145b-401c-846c-05d041681bb3 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-861ed953-145b-401c-846c-05d041681bb3" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>STLTransformer(sp=365)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('c3fb95cc-c19d-42df-ac81-ba1f743d04b3')" type="checkbox" checked=""><label for="UUID('c3fb95cc-c19d-42df-ac81-ba1f743d04b3')" class="sk-toggleable__label sk-toggleable__label-arrow">STLTransformer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.detrend.STLTransformer.html">?<span>Documentation for STLTransformer</span></a></label><div class="sk-toggleable__content"><pre>STLTransformer(sp=365)</pre></div></div></div></div></div>
-</div>
-</div>
-<p>E agora podemos inspecionar os componentes:</p>
-<div id="c43f4ee7" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(<span class="dv">3</span>, <span class="dv">1</span>, figsize<span class="op">=</span>(<span class="dv">10</span>, <span class="dv">8</span>), sharex<span class="op">=</span><span class="va">True</span>)</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>stl.trend_.plot.line(ax<span class="op">=</span>ax[<span class="dv">0</span>])</span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>ax[<span class="dv">0</span>].set_title(<span class="st">"Tendência"</span>)</span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>stl.seasonal_.plot.line(ax<span class="op">=</span>ax[<span class="dv">1</span>])</span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>ax[<span class="dv">1</span>].set_title(<span class="st">"Sazonalidade"</span>)</span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>stl.resid_.plot.line(ax<span class="op">=</span>ax[<span class="dv">2</span>])</span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>ax[<span class="dv">2</span>].set_title(<span class="st">"Resíduos"</span>)</span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/var/folders/_2/9y4tsvdd2n3gqjgd2zmlr1km0000gn/T/ipykernel_24269/1542615419.py:8: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
-  fig.show()</code></pre>
-</div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ets_and_ar_files/figure-html/cell-9-output-2.png" width="809" height="653" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Uma possibilidade, uma vez que temos os diferentes componentes, é modelar cada componente separadamente e depois combinar as previsões. O sktime tem o <code>STLForecaster</code>, que faz exatamente isso:</p>
-<div id="54f0eac9" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.trend <span class="im">import</span> STLForecaster</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> STLForecaster(</span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>    forecaster_trend<span class="op">=</span>AutoREG(lags<span class="op">=</span><span class="dv">31</span>),</span>
-<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>    forecaster_seasonal<span class="op">=</span>NaiveForecaster(sp<span class="op">=</span><span class="dv">7</span>),</span>
-<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a>    forecaster_resid<span class="op">=</span>AutoREG(lags<span class="op">=</span><span class="dv">31</span>),</span>
-<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a>    sp<span class="op">=</span><span class="dv">7</span>,</span>
-<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span>
-<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb10-15"><a href="#cb10-15" aria-hidden="true" tabindex="-1"></a>    y_train,</span>
-<span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a>    y_test,</span>
-<span id="cb10-17"><a href="#cb10-17" aria-hidden="true" tabindex="-1"></a>    y_pred,</span>
-<span id="cb10-18"><a href="#cb10-18" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão STL + AR"</span>],</span>
-<span id="cb10-19"><a href="#cb10-19" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ets_and_ar_files/figure-html/cell-10-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Para fins de demonstração, podemos complicar um pouco mais o modelo, modelando os resíduos com outro <code>STLForecaster</code>:</p>
-<div id="5adf52ac" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> STLForecaster(</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>    forecaster_trend<span class="op">=</span>AutoREG(lags<span class="op">=</span><span class="dv">31</span>),</span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>    forecaster_seasonal<span class="op">=</span>NaiveForecaster(sp<span class="op">=</span><span class="dv">7</span>),</span>
-<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>    forecaster_resid<span class="op">=</span>STLForecaster(</span>
-<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>        forecaster_trend<span class="op">=</span>AutoREG(lags<span class="op">=</span><span class="dv">31</span>),</span>
-<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>        forecaster_seasonal<span class="op">=</span>NaiveForecaster(sp<span class="op">=</span><span class="dv">365</span>),</span>
-<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>        forecaster_resid<span class="op">=</span>AutoREG(lags<span class="op">=</span><span class="dv">31</span>),</span>
-<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>        sp<span class="op">=</span><span class="dv">365</span>,</span>
-<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a>    ),</span>
-<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>    sp<span class="op">=</span><span class="dv">7</span>,</span>
-<span id="cb11-11"><a href="#cb11-11" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb11-12"><a href="#cb11-12" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb11-13"><a href="#cb11-13" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="10">
-<style>#sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f {
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-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
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-    position: relative;
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-    display: none;
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-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
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-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
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-
-  /* Parallel-specific style estimator block */
-
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-    content: "";
-    width: 100%;
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-    width: 50%;
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-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-parallel-item:last-child::after {
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-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
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-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
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-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
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-    max-width: 0;
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-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-toggleable__content pre {
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-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f input.sk-toggleable__control:checked~div.sk-toggleable__content {
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-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-label label.sk-toggleable__label,
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-4eb95fd7-1016-4edb-9643-b80d4f5c069f" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>STLForecaster(forecaster_resid=STLForecaster(forecaster_resid=AutoREG(lags=31),
-                                             forecaster_seasonal=NaiveForecaster(sp=365),
-                                             forecaster_trend=AutoREG(lags=31),
-                                             sp=365),
-              forecaster_seasonal=NaiveForecaster(sp=7),
-              forecaster_trend=AutoREG(lags=31), sp=7)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('7354f07a-6975-4c31-a36f-5459ad2a14f1')" type="checkbox"><label for="UUID('7354f07a-6975-4c31-a36f-5459ad2a14f1')" class="sk-toggleable__label sk-toggleable__label-arrow">STLForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.trend.STLForecaster.html">?<span>Documentation for STLForecaster</span></a></label><div class="sk-toggleable__content"><pre>STLForecaster(forecaster_resid=STLForecaster(forecaster_resid=AutoREG(lags=31),
-                                             forecaster_seasonal=NaiveForecaster(sp=365),
-                                             forecaster_trend=AutoREG(lags=31),
-                                             sp=365),
-              forecaster_seasonal=NaiveForecaster(sp=7),
-              forecaster_trend=AutoREG(lags=31), sp=7)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('49dde800-1452-46d5-b2b9-308e2afe8776')" type="checkbox"><label for="UUID('49dde800-1452-46d5-b2b9-308e2afe8776')" class="sk-toggleable__label sk-toggleable__label-arrow">forecaster_resid: STLForecaster</label><div class="sk-toggleable__content"><pre>STLForecaster(forecaster_resid=AutoREG(lags=31),
-              forecaster_seasonal=NaiveForecaster(sp=365),
-              forecaster_trend=AutoREG(lags=31), sp=365)</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('a94e9d84-e0e3-4299-831c-f0d150acb33f')" type="checkbox"><label for="UUID('a94e9d84-e0e3-4299-831c-f0d150acb33f')" class="sk-toggleable__label sk-toggleable__label-arrow">forecaster_resid: AutoREG</label><div class="sk-toggleable__content"><pre>AutoREG(lags=31)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('f0db60be-0d10-4362-862b-c87bf5a0bfb8')" type="checkbox"><label for="UUID('f0db60be-0d10-4362-862b-c87bf5a0bfb8')" class="sk-toggleable__label sk-toggleable__label-arrow">AutoREG<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.auto_reg.AutoREG.html">?<span>Documentation for AutoREG</span></a></label><div class="sk-toggleable__content"><pre>AutoREG(lags=31)</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('92b52453-0161-4109-9dd2-0b3d5e6524af')" type="checkbox"><label for="UUID('92b52453-0161-4109-9dd2-0b3d5e6524af')" class="sk-toggleable__label sk-toggleable__label-arrow">forecaster_seasonal: NaiveForecaster</label><div class="sk-toggleable__content"><pre>NaiveForecaster(sp=365)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('e62353e2-2a6b-48c6-9c80-6504f9349121')" type="checkbox"><label for="UUID('e62353e2-2a6b-48c6-9c80-6504f9349121')" class="sk-toggleable__label sk-toggleable__label-arrow">NaiveForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.naive.NaiveForecaster.html">?<span>Documentation for NaiveForecaster</span></a></label><div class="sk-toggleable__content"><pre>NaiveForecaster(sp=365)</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('bf1bcecb-edf8-456f-8813-7b38050c17c1')" type="checkbox"><label for="UUID('bf1bcecb-edf8-456f-8813-7b38050c17c1')" class="sk-toggleable__label sk-toggleable__label-arrow">forecaster_trend: AutoREG</label><div class="sk-toggleable__content"><pre>AutoREG(lags=31)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('a33ac400-4784-4632-9e5f-3319098adc4e')" type="checkbox"><label for="UUID('a33ac400-4784-4632-9e5f-3319098adc4e')" class="sk-toggleable__label sk-toggleable__label-arrow">AutoREG<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.auto_reg.AutoREG.html">?<span>Documentation for AutoREG</span></a></label><div class="sk-toggleable__content"><pre>AutoREG(lags=31)</pre></div></div></div></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('2bf6d5ca-dcda-49bf-8ac2-9ba3439698ea')" type="checkbox"><label for="UUID('2bf6d5ca-dcda-49bf-8ac2-9ba3439698ea')" class="sk-toggleable__label sk-toggleable__label-arrow">forecaster_seasonal: NaiveForecaster</label><div class="sk-toggleable__content"><pre>NaiveForecaster(sp=7)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('9fdee7c2-2606-4545-92dc-eaaf8399846a')" type="checkbox"><label for="UUID('9fdee7c2-2606-4545-92dc-eaaf8399846a')" class="sk-toggleable__label sk-toggleable__label-arrow">NaiveForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.naive.NaiveForecaster.html">?<span>Documentation for NaiveForecaster</span></a></label><div class="sk-toggleable__content"><pre>NaiveForecaster(sp=7)</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('af36a547-5bdf-4d1c-b1af-6ca48ba244e8')" type="checkbox"><label for="UUID('af36a547-5bdf-4d1c-b1af-6ca48ba244e8')" class="sk-toggleable__label sk-toggleable__label-arrow">forecaster_trend: AutoREG</label><div class="sk-toggleable__content"><pre>AutoREG(lags=31)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('8055c5bd-3d80-4bf6-b2dd-96fb869b2337')" type="checkbox"><label for="UUID('8055c5bd-3d80-4bf6-b2dd-96fb869b2337')" class="sk-toggleable__label sk-toggleable__label-arrow">AutoREG<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.auto_reg.AutoREG.html">?<span>Documentation for AutoREG</span></a></label><div class="sk-toggleable__content"><pre>AutoREG(lags=31)</pre></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="87447479" class="cell" data-execution_count="11">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>    y_train,</span>
-<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>    y_test,</span>
-<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>    y_pred,</span>
-<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão STL + AR"</span>],</span>
-<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ets_and_ar_files/figure-html/cell-12-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-
-
-<div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
-<div id="ref-hyndman2018forecasting" class="csl-entry" role="listitem">
-Hyndman, Rob J, and George Athanasopoulos. 2018. <em>Forecasting: Principles and Practice</em>. OTexts.
-</div>
-</div>
-</section>
-
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-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../../../content/pt/part1/index.html">Part I: Básico</a></li><li class="breadcrumb-item"><a href="../../../content/pt/part1/index.html"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Definição do problema</span></a></li></ol></nav>
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-<p>Em modelos de regressão e classificação básicos, supomos que os dados são i.i.d: variaveis aleatórias independentes e identicamente distribuidas. Ou seja, a ordem que observamos os dados não tem nenhum impacto em nossa habilidade de prever quais serão as próximas observações.</p>
-<p>Vamos considerar um experimento simples: temos uma caixa com bolas pretas e vermelhas dentro. Digamos que as bolas pretas valem 1 e as bolas vermelhas valem 0. Queremos descobrir qual a quantidade de pontos vamos ter na próxima rodada.</p>
-<div class="quarto-figure quarto-figure-center">
-<figure class="figure">
-<p><img src="./balls1.png" class="img-fluid figure-img" width="300"></p>
-<figcaption>Bolas pretas e vermelhas</figcaption>
-</figure>
-</div>
-<p>Se as bolas estão distribuidas aleatóriamente dentro da caixa, podemos esperar ver algo como a seguinte sequência de pontos:</p>
-<p><span class="math display">\[
-\{0, 1, 1, 0, 1, 0, 0, \dots \}
-\]</span></p>
-<p>Nesse caso, a ordem dos pontos não importa. Não importa o que vimos até então; a próxima bola que vamos tirar da caixa é independente das bolas que já tiramos.</p>
-<p>Vamos supor um caso ligeiramente diferente. Agora, as bolas estão organizadas em blocos: primeiro todas as bolas pretas, depois todas as bolas vermelhas, depois todas as bolas pretas de novo, e assim por diante. Nesse caso, podemos esperar ver algo como:</p>
-<p><span class="math display">\[
-\{1, 1, 1, 0, 0, 0, 1, 1, 1, \dots \}
-\]</span></p>
-<p>Nesse caso, a ordem dos pontos importa. Se vimos muitas bolas pretas recentemente, é mais provável que vejamos uma bola preta na próxima rodada. A próxima bola que vamos tirar da caixa depende das bolas que já tiramos. Esse é um caso básico de aplicação de <strong>modelos de previsão de séries temporais</strong>.</p>
-<p>Considerando que cada observação é indexada por uma variável temporal, podemos definir uma série temporal como uma sequência de observações ordenadas no tempo. Exemplos comuns de séries temporais incluem:</p>
-<ul>
-<li>Varejo: vendas diárias, semanais ou mensais de um produto</li>
-<li>Finanças: preços diários de ações, taxas de câmbio</li>
-<li>Saúde: número diário de novos casos de uma doença</li>
-<li>Clima: temperatura diária, precipitação mensal</li>
-</ul>
-<p>Esse livro é uma introdução prática a modelos de séries temporais, com objetivo de dar contexto das ferramentas mais relevantes em 3h de leitura.</p>
-<p>O conteúdo está organizado nas seguintes seções:</p>
-<ol type="1">
-<li><strong>Introdução a séries temporais</strong>:
-<ol type="1">
-<li>Modelo Naive, Modelo Naive sazonal</li>
-<li>Séries integradas, diferenciação e estacionariedade</li>
-<li>Modelo de Suavização Exponencial (Exponential Smoothing)</li>
-<li>Modelos autoregressivos (AR)</li>
-<li>Métricas de avaliação de modelos de séries temporais</li>
-<li>Engenharia de features para séries temporais</li>
-</ol></li>
-<li><strong>Modelos avançados e caso de uso</strong>
-<ol type="1">
-<li>Forecast com modelos de Machine Learning</li>
-<li>Forecast com modelos fundacionais</li>
-<li>Previsão de vendas totais agregadas</li>
-<li>Previsão de vendas por região e modelos globais</li>
-<li>Previsão hierárquica e reconciliação</li>
-</ol></li>
-<li><strong>Customização de modelos com sktime</strong>
-<ol type="1">
-<li>Como customizar e criar modelos em sktime</li>
-<li>Criando um wrapper de biblioteca externa</li>
-</ol></li>
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-<p>Até então, vimos como criar modelos de séries temporais e avaliamos qualitativamente seu desempenho, em apenas uma simples divisão treino-teste. Agora, vamos explorar como avaliar modelos de séries temporais de forma mais robusta, utilizando métricas específicas e técnicas de validação cruzada adaptadas para dados temporais.</p>
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-<p>Primeiro vamos baixar os dados e criar um modelo simples.</p>
-<div id="92e17c72" class="cell" data-execution_count="1">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"univariate"</span>)</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>y_train, y_test <span class="op">=</span> dataset.load(<span class="st">"y_train"</span>, <span class="st">"y_test"</span>)</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Predict</span></span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> NaiveForecaster(strategy<span class="op">=</span><span class="st">"last"</span>)</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<p>Para avaliar o desempenho de modelos de séries temporais, existem diversas métricas que podem ser utilizadas.</p>
-<p>Duas métricas básicas são:</p>
-<ul>
-<li><strong>Mean Absolute Error (MAE)</strong>: Média dos erros absolutos entre as previsões e os valores reais. <span class="math display">\[
-MAE = \frac{1}{h} \sum_{t=T+1}^{T+h} |y_t - \hat{y}_t|
-\]</span></li>
-</ul>
-<div id="0cdc55bd" class="cell" data-execution_count="2">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanAbsoluteError</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanAbsoluteError()</span>
-<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="2">
-<pre><code>np.float64(615.0555555555555)</code></pre>
-</div>
-</div>
-<ul>
-<li><strong>Mean Squared Error (MSE)</strong>: Média dos erros quadráticos entre as previsões e os valores reais.</li>
-</ul>
-<p><span class="math display">\[
-MSE = \frac{1}{h} \sum_{t=T+1}^{T+h} (y_t - \hat{y}_t)^2
-\]</span></p>
-<div id="611c5759" class="cell" data-execution_count="3">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanSquaredError</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanSquaredError()</span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="3">
-<pre><code>np.float64(633773.8777777777)</code></pre>
-</div>
-</div>
-<p>No entanto, essas métricas não levam em consideração a escala dos dados, o que pode ser um problema ao comparar desempenho entre diferentes séries temporais, e também na comunicação dos resultados para stakeholders.</p>
-<p>Em muitos meios, é comum usar métricas que eliminam o fator escala, como:</p>
-<ul>
-<li><strong>Mean Absolute Percentage Error (MAPE)</strong>: Média dos erros percentuais absolutos entre as previsões e os valores reais.</li>
-</ul>
-<p><span class="math display">\[
-MAPE = \frac{1}{h} \sum_{t=T+1}^{T+h} \left| \frac{y_t - \hat{y}_t}{y_t} \right|
-\]</span></p>
-<div id="c8ecb93c" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanAbsolutePercentageError</span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanAbsolutePercentageError()</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="4">
-<pre><code>np.float64(0.21292470853237802)</code></pre>
-</div>
-</div>
-<ul>
-<li><strong>Symmetric Mean Absolute Percentage Error (sMAPE)</strong>: Média dos erros percentuais absolutos simétricos entre as previsões e os valores reais.</li>
-</ul>
-<div id="f90df67c" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanAbsolutePercentageError(symmetric<span class="op">=</span><span class="va">True</span>)</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="5">
-<pre><code>np.float64(0.22359840490570626)</code></pre>
-</div>
-</div>
-<p><span class="math display">\[
-sMAPE = \frac{1}{h} \sum_{t=T+1}^{T+h} \frac{|y_t - \hat{y}_t|}{(|y_t| + |\hat{y}_t|)/2}
-\]</span></p>
-<p>Essas métricas, no entanto, apresentam seus próprios problemas. Note que os valores podem ser exageradamente alto quando o denominador é próximo de zero. Ou seja, o MAPE e sMAPE podem ser problemáticos quando a série temporal contém valores próximos de zero.</p>
-<p>Outro tema é que essas métricas não consideram a dificuldade da série. Por exemplo, 5% de erro pode ser muito em uma série, e pouco em outra. Para isso, foram propostas métricas “escalas”:</p>
-<ul>
-<li><strong>Mean Absolute Scaled Error (MASE)</strong>: dividimos o erro absoluto pelo erro absoluto médio de um naive nos dados de treino. Se:</li>
-</ul>
-<p><span class="math display">\[
-e_{naive} = \frac{1}{T-1} \sum_{t=2}^{T} |y_t - y_{t-1}|
-\]</span></p>
-<p>são os erros nos dados de treino, então:</p>
-<p><span class="math display">\[
-MASE = \frac{1}{h} \sum_{t=T+1}^{T+h} \frac{|y_t - \hat{y}_t|}{e_{naive}}
-\]</span></p>
-<div id="52c674a1" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanAbsoluteScaledError</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanAbsoluteScaledError()</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred, y_train<span class="op">=</span>y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="6">
-<pre><code>np.float64(10.127736897882464)</code></pre>
-</div>
-</div>
-<ul>
-<li><strong>Mean Squared Scaled Error (MSSE)</strong>: dividimos o erro quadrático pelo quadrado de <span class="math inline">\(e_{naive}\)</span>.</li>
-</ul>
-<p><span class="math display">\[
-MSSE = \frac{1}{h} \sum_{t=T+1}^{T+h} \frac{(y_t - \hat{y}_t)^2}{e_{naive}^2}
-\]</span></p>
-<div id="66d143c4" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanSquaredScaledError</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanSquaredScaledError()</span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred, y_train<span class="op">=</span>y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="7">
-<pre><code>np.float64(57.09087480132447)</code></pre>
-</div>
-</div>
-</section>
-<section id="cross-validation-para-séries-temporais" class="level2" data-number="5.2">
-<h2 data-number="5.2" class="anchored" data-anchor-id="cross-validation-para-séries-temporais"><span class="header-section-number">5.2</span> Cross-validation para séries temporais</h2>
-<p>Em um cross-validation, nossa intenção é estimar corretamente o erro de generalização do modelo, ou seja, o erro que o modelo terá em dados futuros.</p>
-<p><span class="math display">\[
-\mathbb{E}[L(Y, \hat{Y})]
-\]</span></p>
-<p>Onde a média é sobre a distribuição que mais representa o que será visto em produção.</p>
-<p>Tipicamente, fazemos back-testing com dois tipos de janelas:</p>
-<ul>
-<li><strong>Sliding window</strong>: a janela de treino tem tamanho fixo, e “desliza” ao longo do tempo. A cada passo, o modelo é re-treinado com os dados mais recentes.</li>
-</ul>
-<div id="145b9eff" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_windows</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.split <span class="im">import</span> SlidingWindowSplitter</span>
-<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>sliding_window_cv <span class="op">=</span> SlidingWindowSplitter(</span>
-<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>    window_length<span class="op">=</span><span class="dv">365</span> <span class="op">*</span> <span class="dv">3</span>, step_length<span class="op">=</span><span class="dv">100</span>, fh<span class="op">=</span><span class="bu">list</span>(<span class="bu">range</span>(<span class="dv">1</span>, <span class="dv">90</span> <span class="op">+</span> <span class="dv">1</span>))</span>
-<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a>plot_windows(cv<span class="op">=</span>sliding_window_cv, y<span class="op">=</span>y_train)</span>
-<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="metricas_files/figure-html/cell-9-output-1.png" width="1246" height="415" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<ul>
-<li><strong>Expanding window</strong>: a janela de treino começa com um tamanho mínimo, e vai aumentando ao longo do tempo, incluindo todos os dados anteriores. A cada passo, o modelo é re-treinado com todos os dados disponíveis até aquele ponto.</li>
-</ul>
-<div id="bf7cddb7" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.split <span class="im">import</span> ExpandingWindowSplitter</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>expanding_window_cv <span class="op">=</span> ExpandingWindowSplitter(</span>
-<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>    initial_window<span class="op">=</span><span class="dv">365</span> <span class="op">*</span> <span class="dv">3</span>, step_length<span class="op">=</span><span class="dv">100</span>, fh<span class="op">=</span><span class="bu">list</span>(<span class="bu">range</span>(<span class="dv">1</span>, <span class="dv">90</span> <span class="op">+</span> <span class="dv">1</span>))</span>
-<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>plot_windows(cv<span class="op">=</span>expanding_window_cv, y<span class="op">=</span>y_train)</span>
-<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="metricas_files/figure-html/cell-10-output-1.png" width="1246" height="415" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<div class=".{callout-tip}">
-<p>Qual escolher? Bom, escolha o que mais representa o que você espera ver em produção.</p>
-</div>
-</section>
-<section id="executando-o-cross-validation" class="level2" data-number="5.3">
-<h2 data-number="5.3" class="anchored" data-anchor-id="executando-o-cross-validation"><span class="header-section-number">5.3</span> Executando o cross-validation</h2>
-<p>Para executar cross-validation, usamos a função <code>evaluate</code>, que recebe o modelo, o esquema de cross-validation, os dados, e a métrica a ser usada.</p>
-<div id="710c3cbd" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.model_evaluation <span class="im">import</span> evaluate</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanAbsoluteScaledError</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> NaiveForecaster(strategy<span class="op">=</span><span class="st">"last"</span>)</span>
-<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>evaluate(</span>
-<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>    forecaster<span class="op">=</span>model,</span>
-<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>    cv<span class="op">=</span>expanding_window_cv,</span>
-<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>    y<span class="op">=</span>y_train,</span>
-<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a>    X<span class="op">=</span><span class="va">None</span>, <span class="co"># Veremos na próxima seçao!</span></span>
-<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a>    scoring<span class="op">=</span>MeanAbsoluteScaledError(),</span>
-<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a>    error_score<span class="op">=</span><span class="st">"raise"</span>,</span>
-<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a>    return_data<span class="op">=</span><span class="va">True</span>,</span>
-<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="10">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">test_MeanAbsoluteScaledError</th>
-<th data-quarto-table-cell-role="th">fit_time</th>
-<th data-quarto-table-cell-role="th">pred_time</th>
-<th data-quarto-table-cell-role="th">len_train_window</th>
-<th data-quarto-table-cell-role="th">cutoff</th>
-<th data-quarto-table-cell-role="th">y_train</th>
-<th data-quarto-table-cell-role="th">y_test</th>
-<th data-quarto-table-cell-role="th">y_pred</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">0</th>
-<td>12.839683</td>
-<td>0.002296</td>
-<td>0.005386</td>
-<td>1095</td>
-<td>2022-12-30</td>
-<td>sales date 2020-01-01...</td>
-<td>sales date 2022-12-31...</td>
-<td>sales 2022-12-31 458.0 2023-01-01...</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">1</th>
-<td>3.813045</td>
-<td>0.001674</td>
-<td>0.003905</td>
-<td>1195</td>
-<td>2023-04-09</td>
-<td>sales date 2020-01-01...</td>
-<td>sales date 2023-04-10...</td>
-<td>sales 2023-04-10 866.0 2023-04-11...</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2</th>
-<td>8.068216</td>
-<td>0.001615</td>
-<td>0.003741</td>
-<td>1295</td>
-<td>2023-07-18</td>
-<td>sales date 2020-01-01...</td>
-<td>sales date 2023-07-19...</td>
-<td>sales 2023-07-19 748.0 2023-07-20...</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">3</th>
-<td>7.854970</td>
-<td>0.001626</td>
-<td>0.003664</td>
-<td>1395</td>
-<td>2023-10-26</td>
-<td>sales date 2020-01-01...</td>
-<td>sales date 2023-10-27...</td>
-<td>sales 2023-10-27 1229.0 2023-10-...</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">4</th>
-<td>5.955709</td>
-<td>0.001576</td>
-<td>0.003626</td>
-<td>1495</td>
-<td>2024-02-03</td>
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-  <li><a href="#exemplo-prático-com-sktime" id="toc-exemplo-prático-com-sktime" class="nav-link active" data-scroll-target="#exemplo-prático-com-sktime"><span class="header-section-number">2.1</span> Exemplo prático com sktime</a>
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-  <li><a href="#criando-o-modelo" id="toc-criando-o-modelo" class="nav-link" data-scroll-target="#criando-o-modelo"><span class="header-section-number">2.1.1</span> Criando o modelo</a></li>
-  <li><a href="#ajustando-hiperparâmetros" id="toc-ajustando-hiperparâmetros" class="nav-link" data-scroll-target="#ajustando-hiperparâmetros"><span class="header-section-number">2.1.2</span> Ajustando hiperparâmetros</a></li>
-  </ul></li>
-  </ul>
-</nav>
-    </div>
-<!-- main -->
-<main class="content" id="quarto-document-content">
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-<header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../../../content/pt/part1/index.html">Part I: Básico</a></li><li class="breadcrumb-item"><a href="../../../content/pt/part1/naive.html"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Primeiro passos com sktime e modelo Naive</span></a></li></ol></nav>
-<div class="quarto-title">
-<h1 class="title"><span class="chapter-number">2</span>&nbsp; <span class="chapter-title">Primeiro passos com sktime e modelo Naive</span></h1>
-</div>
-
-
-
-<div class="quarto-title-meta">
-
-    
-  
-    
-  </div>
-  
-
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-</header>
-
-
-<p>Considerando o nosso exemplo da caixa com bolas pretas e vermelhas, um modelo simples para prever a próxima bola que vamos tirar da caixa é o <strong>Modelo Naive</strong>.</p>
-<p>Existem algumas versões de modelo naive:</p>
-<ul>
-<li><strong>Naive (last)</strong>: a previsão para o próximo ponto é igual ao último ponto observado.</li>
-<li><strong>Naive (mean)</strong>: a previsão para o próximo ponto é igual à média dos pontos observados numa janela passada.</li>
-<li><strong>Naive Sazonal</strong>: a previsão para o próximo ponto é igual ao ponto observado na mesma posição em um ciclo anterior. Por exemplo, se estamos prevendo vendas diárias e hoje é segunda-feira, a previsão para hoje é igual às vendas da última segunda-feira.</li>
-</ul>
-<p>Esse modelo é simples, mas é extremamente eficaz e dificil de ser vencido em muitos casos. No cenário onde os dados não vêm organizados em nenhuma ordem específica, a melhor aposta que podemos fazer é o valor médio das bolas no histórico recente.</p>
-<p>Vamos ver como fazer um forecast simples com sktime, usando o modelo Naive.</p>
-<section id="exemplo-prático-com-sktime" class="level2" data-number="2.1">
-<h2 data-number="2.1" class="anchored" data-anchor-id="exemplo-prático-com-sktime"><span class="header-section-number">2.1</span> Exemplo prático com sktime</h2>
-<p>Aqui, baixamos o dataset simples que vem na biblioteca desse repositório.</p>
-<div id="85e23e26" class="cell" data-execution_count="1">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.simple <span class="im">import</span> SimpleDataset</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SimpleDataset(<span class="va">True</span>)</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>y <span class="op">=</span> dataset.load(<span class="st">"y"</span>)</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>y.head()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="1">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">points</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2021-01-01</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2021-01-02</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2021-01-03</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2021-01-04</th>
-<td>1</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2021-01-05</th>
-<td>1</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<p>Esse é um dataset simples com uma série temporal mensal. Vamos dividir os dados em treino e teste, usando os últimos 36 meses como teste. Para isso, devemos respeitar a ordem temporal dos dados.</p>
-<p>A função <code>temporal_train_test_split</code> faz isso para nós.</p>
-<div id="fc187c4b" class="cell" data-execution_count="2">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.model_selection <span class="im">import</span> temporal_train_test_split</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>y_train, y_test <span class="op">=</span> temporal_train_test_split(y, test_size<span class="op">=</span><span class="dv">36</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<p>Também temos uma função de plotagem simples para visualizar séries temporais.</p>
-<div id="c166cf05" class="cell" data-execution_count="3">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>plot_series(y, labels<span class="op">=</span>[<span class="st">"Observações"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="naive_files/figure-html/cell-4-output-1.png" width="1258" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<section id="criando-o-modelo" class="level3" data-number="2.1.1">
-<h3 data-number="2.1.1" class="anchored" data-anchor-id="criando-o-modelo"><span class="header-section-number">2.1.1</span> Criando o modelo</h3>
-<p>No sktime, os modelos são usados em 3 passos:</p>
-<ul>
-<li>Inicialização (<code>__init__</code>): aqui, definimos os hiperparâmetros do modelo. Pense nessa parte como a configuração do modelo.</li>
-<li>Treinamento (<code>fit</code>): aqui, o modelo aprende com os dados de treino.</li>
-<li>Previsão (<code>predict</code>): com esse método, o modelo faz previsões para os dados futuros.</li>
-</ul>
-<p>Quando inicializamos o modelo em um notebook, o sktime mostra uma ilustração do modelo, o que é útil para entender o que está acontecendo “por baixo dos panos”. Em casos mais complexos com composição de modelos, isso pode ser útil para ilustrar o que estamos fazendo para outros cientistas.</p>
-<div id="45182c3c" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> NaiveForecaster(strategy<span class="op">=</span><span class="st">"last"</span>)</span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>model</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="4">
-<style>#sk-ea763fda-5545-4634-b5ea-6072c214db30 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
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-
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-    color: var(--sklearn-color-text);
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-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 input.sk-hidden--visually {
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-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
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-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
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-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
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-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-text-repr-fallback {
-    display: none;
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-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
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-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-parallel-item {
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-    flex-direction: column;
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-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
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-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
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-    /* Default theme specific background. It is overwritten whether we have a
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-
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-    cursor: pointer;
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-    /* Arrow on the left of the label */
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-    float: left;
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-    color: var(--sklearn-color-icon);
-  }
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-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 label.sk-toggleable__label-arrow:hover:before {
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-
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-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
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-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-toggleable__content pre {
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-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
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-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-label label.sk-toggleable__label,
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
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-
-  /* On hover */
-  #sk-ea763fda-5545-4634-b5ea-6072c214db30 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-ea763fda-5545-4634-b5ea-6072c214db30" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>NaiveForecaster()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('3808289d-4a17-4fb2-b37b-4444d623baaf')" type="checkbox" checked=""><label for="UUID('3808289d-4a17-4fb2-b37b-4444d623baaf')" class="sk-toggleable__label sk-toggleable__label-arrow">NaiveForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.naive.NaiveForecaster.html">?<span>Documentation for NaiveForecaster</span></a></label><div class="sk-toggleable__content"><pre>NaiveForecaster()</pre></div></div></div></div></div>
-</div>
-</div>
-<p>Ao treinar o modelo, passamos dados de treinamento.</p>
-<div id="bcfb0ed7" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="5">
-<style>#sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 {
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-
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-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
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-
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-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 input.sk-hidden--visually {
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-    border: 1px dashed var(--sklearn-color-line);
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-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
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-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-container {
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-       but bootstrap.min.css set `[hidden] { display: none !important; }`
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-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
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-
-  /* Parallel-specific style estimator block */
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-    content: "";
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-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
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-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-parallel {
-    display: flex;
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-    background-color: var(--sklearn-color-background);
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-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-label label.sk-toggleable__label,
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-070f5c92-d3e8-4321-9cdd-d4216052ecf4" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>NaiveForecaster()</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('376eea0d-fd74-4e0d-9367-4e52967280a4')" type="checkbox" checked=""><label for="UUID('376eea0d-fd74-4e0d-9367-4e52967280a4')" class="sk-toggleable__label sk-toggleable__label-arrow">NaiveForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.naive.NaiveForecaster.html">?<span>Documentation for NaiveForecaster</span></a></label><div class="sk-toggleable__content"><pre>NaiveForecaster()</pre></div></div></div></div></div>
-</div>
-</div>
-<p>Para a previsão, temos que passar um argumento obrigatório: <code>fh</code>, abreviatura de “forecasting horizon” (horizonte de previsão). Tipicamente passamos um <code>fh</code> relativo, ou seja, no formato de uma lista</p>
-<div id="a22238cf" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>model.predict(fh<span class="op">=</span>[<span class="dv">1</span>,<span class="dv">2</span>,<span class="dv">3</span>,<span class="dv">4</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="6">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">points</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2021-03-06</th>
-<td>1.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2021-03-07</th>
-<td>1.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2021-03-08</th>
-<td>1.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2021-03-09</th>
-<td>1.0</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<p>Onde cada número representa o número de períodos à frente que queremos prever. Também podemos passar o <code>fh</code> como um índice de tempo absoluto, que é o que faremos aqui. Vamos passar o índice de tempo do conjunto de teste.</p>
-<div id="6ac25e76" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="naive_files/figure-html/cell-8-output-1.png" width="1258" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-</section>
-<section id="ajustando-hiperparâmetros" class="level3" data-number="2.1.2">
-<h3 data-number="2.1.2" class="anchored" data-anchor-id="ajustando-hiperparâmetros"><span class="header-section-number">2.1.2</span> Ajustando hiperparâmetros</h3>
-<p>Para alterar hiperparametros de um modelo já existente, podemos usar o método <code>set_params</code>, que modifica <em>in-place</em> os hiperparâmetros do modelo.</p>
-<div id="d5b8f3b2" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>model.set_params(</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>    strategy<span class="op">=</span><span class="st">"mean"</span>,</span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>    window_length<span class="op">=</span><span class="dv">12</span>,</span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="naive_files/figure-html/cell-9-output-1.png" width="1258" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Podemos também testar o Naive sazonal, que repete a última observação de <code>6</code> períodos atrás.</p>
-<div id="f08ae681" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>model.set_params(</span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>    sp<span class="op">=</span><span class="dv">6</span>,</span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>    strategy<span class="op">=</span><span class="st">"last"</span></span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>model.fit(y_train)</span>
-<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index)</span>
-<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
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-<p>Além de modelos de ML simples, também podemos usar modelos de deep learning para forecasting. Podemos com certeza usar uma rede neural simples como um regressor, assim como fizemos com os modelos de ML tradicionais. No entanto, existem alguns modelos com arquiteturas específicas para forecasting de séries temporais. Por exemplo, o N-BEATS é um modelo de deep learning que pode ser usado para forecasting.</p>
-<div class="callout callout-style-default callout-tip callout-titled">
-<div class="callout-header d-flex align-content-center">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
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-<div class="callout-title-container flex-fill">
-Tip
-</div>
-</div>
-<div class="callout-body-container callout-body">
-<p>Importante notar que esse livro/tutorial tem o objetivo de ser curto e prático, então não entraremos em muitos detalhes sobre deep learning ou sobre todos métodos existentes.</p>
-</div>
-</div>
-<div id="b2ead3c2" class="cell" data-execution_count="2">
-<details class="code-fold">
-<summary>Code</summary>
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</details>
-</div>
-<div id="7bd6aa13" class="cell" data-execution_count="3">
-<details class="code-fold">
-<summary>Code</summary>
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"panel"</span>)</span>
-<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>y_train, X_train, y_test, X_test <span class="op">=</span> dataset.load(</span>
-<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>    <span class="st">"y_train"</span>, <span class="st">"X_train"</span>, <span class="st">"y_test"</span>, <span class="st">"X_test"</span></span>
-<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>fh <span class="op">=</span> y_test.index.get_level_values(<span class="op">-</span><span class="dv">1</span>).unique()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</details>
-</div>
-<section id="n-beats" class="level2" data-number="10.1">
-<h2 data-number="10.1" class="anchored" data-anchor-id="n-beats"><span class="header-section-number">10.1</span> N-Beats</h2>
-<p>N-BEATS é um modelo de séries temporais totalmente baseado em camadas densas (MLP)—nada de RNN/LSTM nem convolução. Ele pega uma janela do passado e entrega diretamente a previsão do futuro, aprendendo padrões como tendência e sazonalidade de forma pura, só com perceptrons.</p>
-<p>O N-BEATS usa bases para construir sinais interpretáveis:</p>
-<ul>
-<li>Base de tendência: combina funções polinomiais (captura subidas/descidas suaves).</li>
-<li>Base sazonal: combina senos/cossenos (captura repetições periódicas).</li>
-<li>Base genérica: aprende formas livres (sem pressupor forma analítica).</li>
-</ul>
-<p>Assim, internamente, ele determina os coeficientes das funções base para modelar a série temporal, baseado no histórico observado, fazendo uma espécie de meta-aprendizado interno.</p>
-<p>Os blocos são empilhados e executados de forma sucessiva. Pense numa fila de especialistas olhando a mesma janela do passado:</p>
-<ul>
-<li>Cada um explica a sua parte do que viu (remove do passado o que já foi entendido = backcast), e propõe um pedaço da previsão (seu forecast).</li>
-<li>O que não foi explicado segue para o próximo especialista. No final, a previsão é a soma do que cada um sugeriu.</li>
-</ul>
-<p><img src="img/nbeats_simplified.png" class="img-fluid"></p>
-<section id="pytorch-forecasting" class="level3" data-number="10.1.1">
-<h3 data-number="10.1.1" class="anchored" data-anchor-id="pytorch-forecasting"><span class="header-section-number">10.1.1</span> Pytorch Forecasting</h3>
-<p>O Sktime nao é uma biblioteca especializada em deep learning, mas sim uma API uniforme que provê acesso aos mais diversos algoritmos.</p>
-<p>Logo, também provemos acesso a bibliotecas especializadas em deep learning, como o Pytorch Forecasting, que implementa o N-BEATS.</p>
-<p>Aqui, temos que definir os hiperparâmetros do modelo, como o número de blocos, o tamanho do contexto (janela de entrada), e o número de coeficientes para as funcões de base.</p>
-<div id="4ce7945e" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.pytorchforecasting <span class="im">import</span> PytorchForecastingNBeats</span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pytorch_forecasting.data.encoders <span class="im">import</span> EncoderNormalizer</span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>CONTEXT_LENGTH <span class="op">=</span> <span class="dv">120</span></span>
-<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>nbeats <span class="op">=</span> PytorchForecastingNBeats(</span>
-<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>    train_to_dataloader_params<span class="op">=</span>{<span class="st">"batch_size"</span>: <span class="dv">256</span>},</span>
-<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>    trainer_params<span class="op">=</span>{<span class="st">"max_epochs"</span>: <span class="dv">1</span>},</span>
-<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>    model_params<span class="op">=</span>{</span>
-<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>        <span class="st">"stack_types"</span>: [<span class="st">"trend"</span>, <span class="st">"seasonality"</span>], <span class="co"># One of the following values: “generic”, “seasonality” or “trend”.</span></span>
-<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>        <span class="st">"num_blocks"</span> : [<span class="dv">2</span>,<span class="dv">2</span>], <span class="co"># The number of blocks per stack. </span></span>
-<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>        <span class="st">"context_length"</span>: CONTEXT_LENGTH, <span class="co"># lookback period</span></span>
-<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>        <span class="st">"expansion_coefficient_lengths"</span> : [<span class="dv">2</span>, <span class="dv">5</span>],</span>
-<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a>        <span class="st">"learning_rate"</span>: <span class="fl">1e-3</span>,</span>
-<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a>    },</span>
-<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a>    dataset_params<span class="op">=</span>{</span>
-<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a>        <span class="st">"max_encoder_length"</span>: CONTEXT_LENGTH,</span>
-<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a>        <span class="st">"target_normalizer"</span>: EncoderNormalizer()</span>
-<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a>    },</span>
-<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a>nbeats.fit(y_train.astype(<span class="bu">float</span>), fh<span class="op">=</span>fh)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
-GPU available: True (mps), used: True
-TPU available: False, using: 0 TPU cores
-HPU available: False, using: 0 HPUs
-
-  | Name            | Type       | Params | Mode 
--------------------------------------------------------
-0 | loss            | MASE       | 0      | train
-1 | logging_metrics | ModuleList | 0      | train
-2 | net_blocks      | ModuleList | 1.4 M  | train
--------------------------------------------------------
-1.4 M     Trainable params
-0         Non-trainable params
-1.4 M     Total params
-5.484     Total estimated model params size (MB)
-60        Modules in train mode
-0         Modules in eval mode</code></pre>
-</div>
-<div class="cell-output cell-output-display">
-<script type="application/vnd.jupyter.widget-view+json">
-{"model_id":"62d3db570d84401b9fe5979878033087","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
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-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>`Trainer.fit` stopped: `max_epochs=1` reached.</code></pre>
-</div>
-<div class="cell-output cell-output-display" data-execution_count="4">
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-
-  div.sk-parallel-item,
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-
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-
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-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
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-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
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-    /* Expand drop-down */
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-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
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-  }
-
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e div.sk-label label.sk-toggleable__label,
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
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-
-  /* On hover, darken the color of the background */
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-
-  /* Estimator label */
-
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-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e div.sk-label-container {
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-
-  /* Estimator-specific */
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-
-  /* on hover */
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
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-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
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-    display: block;
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-
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e a.estimator_doc_link {
-    float: right;
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-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
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-
-  /* On hover */
-  #sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-3dc749a5-42c8-4c43-9cb5-1314f804a67e" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>PytorchForecastingNBeats(dataset_params={'max_encoder_length': 120,
-                                         'target_normalizer': EncoderNormalizer(
-    method='standard',
-    center=True,
-    max_length=None,
-    transformation=None,
-    method_kwargs={}
-)},
-                         model_params={'context_length': 120,
-                                       'expansion_coefficient_lengths': [2, 5],
-                                       'learning_rate': 0.001,
-                                       'num_blocks': [2, 2],
-                                       'stack_types': ['trend', 'seasonality']},
-                         train_to_dataloader_params={'batch_size': 256},
-                         trainer_params={'max_epochs': 1})</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('5e840a87-3275-4576-a07f-a19819711fdb')" type="checkbox" checked=""><label for="UUID('5e840a87-3275-4576-a07f-a19819711fdb')" class="sk-toggleable__label sk-toggleable__label-arrow">PytorchForecastingNBeats<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.pytorchforecasting.PytorchForecastingNBeats.html">?<span>Documentation for PytorchForecastingNBeats</span></a></label><div class="sk-toggleable__content"><pre>PytorchForecastingNBeats(dataset_params={'max_encoder_length': 120,
-                                         'target_normalizer': EncoderNormalizer(
-    method='standard',
-    center=True,
-    max_length=None,
-    transformation=None,
-    method_kwargs={}
-)},
-                         model_params={'context_length': 120,
-                                       'expansion_coefficient_lengths': [2, 5],
-                                       'learning_rate': 0.001,
-                                       'num_blocks': [2, 2],
-                                       'stack_types': ['trend', 'seasonality']},
-                         train_to_dataloader_params={'batch_size': 256},
-                         trainer_params={'max_epochs': 1})</pre></div></div></div></div></div>
-</div>
-</div>
-<div id="bb2965d8" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>y_pred_nbeats <span class="op">=</span> nbeats.predict(fh<span class="op">=</span>fh, X<span class="op">=</span>X_test)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/felipeangelim/Workspace/python_brasil_2025/.venv/lib/python3.11/site-packages/sktime/forecasting/base/adapters/_pytorchforecasting.py:655: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
-  _y.fillna(0, inplace=True)
-💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
-GPU available: True (mps), used: True
-TPU available: False, using: 0 TPU cores
-HPU available: False, using: 0 HPUs</code></pre>
-</div>
-</div>
-<div id="ec05571a" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanSquaredScaledError</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanSquaredScaledError(multilevel<span class="op">=</span><span class="st">"uniform_average_time"</span>)</span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_nbeats, y_train<span class="op">=</span>y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="6">
-<pre><code>np.float64(16.453837132521112)</code></pre>
-</div>
-</div>
-<p>Agora, podemos visualizar o forecast para uma das séries. Vemos que, mesmo com poucas épocas de treinamento ou tuning, o N-BEATS já consegue capturar a tendência.</p>
-<div id="9fd942bf" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">10</span>, <span class="dv">4</span>))</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>y_train.loc[<span class="dv">10</span>].plot(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"Train"</span>)</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>y_test.loc[<span class="dv">10</span>].plot(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"Test"</span>)</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>y_pred_nbeats.loc[<span class="dv">10</span>].plot(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"N-BEATS"</span>)</span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="deep_learning_files/figure-html/cell-8-output-1.png" width="815" height="356" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="zero-shot-forecasting-com-n-beats" class="level2" data-number="10.2">
-<h2 data-number="10.2" class="anchored" data-anchor-id="zero-shot-forecasting-com-n-beats"><span class="header-section-number">10.2</span> Zero-shot forecasting com N-BEATS</h2>
-<p>Zero-shot forecasting se refere ao fato de fazer previsão para uma série jamais vista pelo modeo, sem utilizar a série para treinar ou ajustar parâmetros dele.</p>
-<p>Aqui, para simular esse cenário, vamos criar uma nova série temporal combinando duas séries do conjunto de treino.</p>
-<div id="3c106bc9" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>new_y_train <span class="op">=</span> (y_train.loc[<span class="dv">0</span>]<span class="op">**</span><span class="dv">2</span> <span class="op">+</span> y_train.loc[<span class="dv">20</span>]).astype(<span class="bu">float</span>)</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>new_y_test <span class="op">=</span> (y_test.loc[<span class="dv">0</span>]<span class="op">**</span><span class="dv">2</span> <span class="op">+</span> y_test.loc[<span class="dv">20</span>]).astype(<span class="bu">float</span>)</span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Plotting the new series</span></span>
-<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">10</span>, <span class="dv">4</span>))</span>
-<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>new_y_train[<span class="st">"sales"</span>].plot.line(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"New Train"</span>)</span>
-<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>new_y_test[<span class="st">"sales"</span>].plot.line(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"New Test"</span>)</span>
-<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="deep_learning_files/figure-html/cell-9-output-1.png" width="840" height="359" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Na interface atual do sktime, usamos o argumento <code>y</code> do método <code>predict</code> para passar a nova série temporal para o modelo:</p>
-<div id="62cd23f5" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>y_pred_zeroshot <span class="op">=</span> nbeats.predict(fh<span class="op">=</span>fh, y<span class="op">=</span>new_y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>/Users/felipeangelim/Workspace/python_brasil_2025/.venv/lib/python3.11/site-packages/sktime/forecasting/base/adapters/_pytorchforecasting.py:655: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
-  _y.fillna(0, inplace=True)
-💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.
-GPU available: True (mps), used: True
-TPU available: False, using: 0 TPU cores
-HPU available: False, using: 0 HPUs</code></pre>
-</div>
-</div>
-<div id="6502b5a7" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">10</span>, <span class="dv">4</span>))</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>new_y_train[<span class="st">"sales"</span>].plot.line(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"New Train"</span>)</span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>new_y_test[<span class="st">"sales"</span>].plot.line(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"New Test"</span>)</span>
-<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>y_pred_zeroshot[<span class="st">"sales"</span>].plot.line(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"N-BEATS Zero-shot"</span>)</span>
-<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>plt.legend()</span>
-<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="deep_learning_files/figure-html/cell-11-output-1.png" width="840" height="359" class="figure-img"></p>
-</figure>
-</div>
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-  <li><a href="#solução-3-usar-valores-defasados-lags-da-variável-exógena" id="toc-solução-3-usar-valores-defasados-lags-da-variável-exógena" class="nav-link" data-scroll-target="#solução-3-usar-valores-defasados-lags-da-variável-exógena"><span class="header-section-number">6.3.3</span> Solução 3: Usar valores defasados (lags) da variável exógena</a></li>
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-<div class="quarto-title">
-<h1 class="title"><span class="chapter-number">6</span>&nbsp; <span class="chapter-title">Variáveis Exógenas</span></h1>
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-<p>Como séries temporais possuem uma ordem temporal, a sua própria história tem poder preditivo muito forte. No entanto, em alguns casos, apenas a história não é suficiente para fazer previsões precisas.</p>
-<p>Por exemplo, em vendas de varejo, fatores como promoções, feriados e eventos sazonais podem impactar significativamente as vendas.</p>
-<p>Agora, vamos ver como usar variáveis exógenas em modelos de séries temporais com sktime, as famosas “features”.</p>
-<p>A interface é sempre a mesma, vamos ver que a diferença é o uso do parâmetro <code>X</code> nos métodos <code>fit</code> e <code>predict</code>.</p>
-<p>Usaremos os mesmos dados de varejo sintético dos exemplos anteriores. Agora, teremos também <code>X_train</code> e <code>X_test</code>, que são as variáveis exógenas.</p>
-<div id="a2211a9d" class="cell" data-execution_count="1">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"univariate"</span>, macro_trend<span class="op">=</span><span class="va">True</span>)</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>y_train, X_train, y_test, X_test <span class="op">=</span> dataset.load(</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>    <span class="st">"y_train"</span>, <span class="st">"X_train"</span>, <span class="st">"y_test"</span>, <span class="st">"X_test"</span></span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>X_train.head()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="1">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">promo</th>
-<th data-quarto-table-cell-role="th">macro_trend</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-01</th>
-<td>0.0</td>
-<td>1.304000</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-02</th>
-<td>0.0</td>
-<td>1.327826</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-03</th>
-<td>0.0</td>
-<td>1.350638</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-04</th>
-<td>0.0</td>
-<td>1.361667</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-05</th>
-<td>0.0</td>
-<td>1.361633</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<div id="9a4d2a36" class="cell" data-execution_count="2">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>X_train.plot.line()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="exog_variables_files/figure-html/cell-3-output-1.png" width="566" height="429" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<div id="51c1ee52" class="cell" data-execution_count="3">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots( figsize<span class="op">=</span>(<span class="dv">10</span>, <span class="dv">6</span>))</span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>y_train.plot(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"y"</span>)</span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>X_train[<span class="st">"macro_trend"</span>].plot(ax<span class="op">=</span>ax.twinx(), label<span class="op">=</span><span class="st">"macro_trend"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="exog_variables_files/figure-html/cell-4-output-1.png" width="833" height="503" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Nem todos modelos suportam variáveis exógenas. Para ver uma lista de possibilidades, podemos usar a função <code>all_estimators</code> do sktime.</p>
-<div id="76b7c3ea" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.registry <span class="im">import</span> all_estimators</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>all_estimators(</span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>    <span class="st">"forecaster"</span>, filter_tags<span class="op">=</span>{<span class="st">"capability:exogenous"</span>: <span class="va">True</span>}, as_dataframe<span class="op">=</span><span class="va">True</span></span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="4">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">name</th>
-<th data-quarto-table-cell-role="th">object</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">0</th>
-<td>ARDL</td>
-<td>&lt;class 'sktime.forecasting.ardl.ARDL'&gt;</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">1</th>
-<td>ARIMA</td>
-<td>&lt;class 'sktime.forecasting.arima._pmdarima.ARI...</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2</th>
-<td>AutoARIMA</td>
-<td>&lt;class 'sktime.forecasting.arima._pmdarima.Aut...</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">3</th>
-<td>AutoEnsembleForecaster</td>
-<td>&lt;class 'sktime.forecasting.compose._ensemble.A...</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">4</th>
-<td>AutoREG</td>
-<td>&lt;class 'sktime.forecasting.auto_reg.AutoREG'&gt;</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">77</th>
-<td>UpdateEvery</td>
-<td>&lt;class 'sktime.forecasting.stream._update.Upda...</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">78</th>
-<td>UpdateRefitsEvery</td>
-<td>&lt;class 'sktime.forecasting.stream._update.Upda...</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">79</th>
-<td>VARMAX</td>
-<td>&lt;class 'sktime.forecasting.varmax.VARMAX'&gt;</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">80</th>
-<td>VECM</td>
-<td>&lt;class 'sktime.forecasting.vecm.VECM'&gt;</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">81</th>
-<td>YfromX</td>
-<td>&lt;class 'sktime.forecasting.compose._reduce.Yfr...</td>
-</tr>
-</tbody>
-</table>
-
-<p>82 rows × 2 columns</p>
-</div>
-</div>
-</div>
-<section id="tipos-de-variáveis-exógenas" class="level2" data-number="6.1">
-<h2 data-number="6.1" class="anchored" data-anchor-id="tipos-de-variáveis-exógenas"><span class="header-section-number">6.1</span> Tipos de Variáveis Exógenas</h2>
-<p>Antes de prosseguirmos, vamos separar as variáveis exógenas em dois tipos:</p>
-<ul>
-<li><p><strong>Variáveis exógenas com valores futuros conhecidos</strong>: São variáveis cujos valores futuros já são conhecidos no momento da previsão. Variáveis indicadoras (dummies) para feriados ou eventos especiais, bem como recursos de sazonalidade, são exemplos comuns desse tipo de variável.</p></li>
-<li><p><strong>Variáveis exógenas com valores futuros desconhecidos</strong>: São variáveis cujos valores futuros não são conhecidos para o horizonte de previsão. Por exemplo, se quisermos incluir indicadores econômicos que ainda não foram divulgados, devemos tratá-los como variáveis exógenas de valor futuro desconhecido.</p></li>
-</ul>
-<p>Nesse último cenário, para realizar a previsão, existem três opções: 1. Prever os valores futuros das variáveis exógenas usando um modelo separado (ou o mesmo modelo, se ele permitir) e usar essas previsões como entrada para o modelo principal de previsão. 2. Usar um valor de preenchimento (por exemplo, o último valor conhecido) para as variáveis exógenas de valor futuro desconhecido durante a previsão. 3. Usar valores defasados (lags) das variáveis exógenas como características (features), o que pode ser útil se o modelo conseguir aprender com os valores passados dessas variáveis.</p>
-</section>
-<section id="usando-variáveis-exógenas-com-sktime" class="level2" data-number="6.2">
-<h2 data-number="6.2" class="anchored" data-anchor-id="usando-variáveis-exógenas-com-sktime"><span class="header-section-number">6.2</span> Usando variáveis exógenas com sktime</h2>
-<p>Vamos usar <code>AutoREG</code> como modelo base para nosso exemplo de vairáveis exógenas. Primeiramente, vamos supor que conhecemos a variável <code>macro_trend</code> no futuro</p>
-<div id="14fb2bce" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.auto_reg <span class="im">import</span> AutoREG</span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> AutoREG(lags<span class="op">=</span><span class="dv">30</span>)</span>
-<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train)</span>
-<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index, X<span class="op">=</span>X_test)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="353d3726" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com Exógenas"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="exog_variables_files/figure-html/cell-7-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Fácil, no caso que conhecemos a variável exógena no futuro, basta passar <code>X</code> em <code>fit</code> e <code>predict</code>.</p>
-<p>Antes de avançar, é importante revisar que os transformadores com <code>fit</code> e <code>transform</code> também podem ser aplicados a variávei exógenas! Inclusive, podemos fazer pipelines compostos de várias etapas de preprocessamento de exógenas. Isso será importante para os próximos exemplos.</p>
-</section>
-<section id="variável-observada-mas-desconhecida-no-futuro" class="level2" data-number="6.3">
-<h2 data-number="6.3" class="anchored" data-anchor-id="variável-observada-mas-desconhecida-no-futuro"><span class="header-section-number">6.3</span> Variável observada, mas desconhecida no futuro</h2>
-<p>Vamos eliminar a variável <code>macro_trend</code> do conjunto de teste, para simular o cenário onde não conhecemos o valor futuro dessa variável.</p>
-<div id="00f8464d" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>X_test_missing <span class="op">=</span> X_test.copy()</span>
-<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>X_test_missing[<span class="st">"macro_trend"</span>] <span class="op">=</span> np.nan</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<section id="solução-1-prever-a-variável-exógena" class="level3" data-number="6.3.1">
-<h3 data-number="6.3.1" class="anchored" data-anchor-id="solução-1-prever-a-variável-exógena"><span class="header-section-number">6.3.1</span> Solução 1: Prever a variável exógena</h3>
-<p>Agora, vamos supor que não sabemos o valor futuro de <code>macro_trend</code>. Nesse caso, podemos criar um modelo separado para prever <code>macro_trend</code> e usar essa previsão como entrada para o modelo principal.</p>
-<p>Sktime possui uma funcionalidade pronta para isso: um forecaster chamado <code>ForecastX</code>. Ele é composto de dois modelos, um para a variável exógena, e outro para o alvo principal.</p>
-<p>Aqui, o forecaster necessita que o horizonte de previsão (<code>fh</code>) seja passado já na etapa de <code>fit</code>.</p>
-<div id="afc0e771" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.compose <span class="im">import</span> ForecastX</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ForecastX(</span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>    forecaster_y<span class="op">=</span>AutoREG(lags<span class="op">=</span><span class="dv">30</span>),</span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>    forecaster_X<span class="op">=</span>AutoREG(lags<span class="op">=</span><span class="dv">30</span>),</span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>fh <span class="op">=</span> [i <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="dv">1</span>, <span class="bu">len</span>(y_test) <span class="op">+</span> <span class="dv">1</span>)]</span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train, fh<span class="op">=</span>fh)</span>
-<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>y_pred_case1 <span class="op">=</span> model.predict(X<span class="op">=</span>X_test_missing)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="6868b5c0" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred_case1, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com Exógenas Previstas"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="exog_variables_files/figure-html/cell-10-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-</section>
-<section id="solução-2-usar-valor-de-preenchimento-imputação" class="level3" data-number="6.3.2">
-<h3 data-number="6.3.2" class="anchored" data-anchor-id="solução-2-usar-valor-de-preenchimento-imputação"><span class="header-section-number">6.3.2</span> Solução 2: Usar valor de preenchimento (imputação)</h3>
-<p>Para essa solução, vamos usar transformadores para preencher os valores faltantes na variável exógena. Aqui, usaremos o <code>Imputer</code> do sktime, que suporta variáveis exógenas.</p>
-<div id="24eea33a" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.impute <span class="im">import</span> Imputer</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>imputer <span class="op">=</span> Imputer(method<span class="op">=</span><span class="st">"mean"</span>)</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>imputer.fit(X_train)</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Agora imputamos</span></span>
-<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>X_test_imputed <span class="op">=</span> imputer.transform(X_test_missing)</span>
-<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a>X_test_imputed.tail()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="10">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">promo</th>
-<th data-quarto-table-cell-role="th">macro_trend</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-12-28</th>
-<td>0.0</td>
-<td>22.805784</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-12-29</th>
-<td>0.0</td>
-<td>22.805784</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-12-30</th>
-<td>0.0</td>
-<td>22.805784</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-12-31</th>
-<td>1.0</td>
-<td>22.805784</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2025-01-01</th>
-<td>0.0</td>
-<td>22.805784</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<p>Para usar preprocessamento de exógenas + forecasting, podemos usar a composição <code>ForecastingPipeline</code>.</p>
-<div id="dbe1ecbb" class="cell" data-execution_count="11">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.compose <span class="im">import</span> ForecastingPipeline</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.difference <span class="im">import</span> Differencer</span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ForecastingPipeline(</span>
-<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>    steps<span class="op">=</span>[(<span class="st">"imputer"</span>, Imputer(method<span class="op">=</span><span class="st">"mean"</span>)), (<span class="st">"forecaster"</span>, AutoREG(lags<span class="op">=</span><span class="dv">30</span>))]</span>
-<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="11">
-<style>#sk-28749f64-6212-47e6-be13-6164e659a7fc {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
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-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
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-    color: var(--sklearn-color-text);
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-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
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-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-container {
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-       but bootstrap.min.css set `[hidden] { display: none !important; }`
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-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
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-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-label label.sk-toggleable__label,
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-28749f64-6212-47e6-be13-6164e659a7fc a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-28749f64-6212-47e6-be13-6164e659a7fc" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ForecastingPipeline(steps=[('imputer', Imputer(method='mean')),
-                           ('forecaster', AutoREG(lags=30))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('b8e2865a-a844-4f20-b43f-322daf64405f')" type="checkbox"><label for="UUID('b8e2865a-a844-4f20-b43f-322daf64405f')" class="sk-toggleable__label sk-toggleable__label-arrow">ForecastingPipeline<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.ForecastingPipeline.html">?<span>Documentation for ForecastingPipeline</span></a></label><div class="sk-toggleable__content"><pre>ForecastingPipeline(steps=[('imputer', Imputer(method='mean')),
-                           ('forecaster', AutoREG(lags=30))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('ef09f756-f5fe-414d-a675-77810f05d9fc')" type="checkbox"><label for="UUID('ef09f756-f5fe-414d-a675-77810f05d9fc')" class="sk-toggleable__label sk-toggleable__label-arrow">Imputer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.impute.Imputer.html">?<span>Documentation for Imputer</span></a></label><div class="sk-toggleable__content"><pre>Imputer(method='mean')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('3af483cf-0ddc-489f-ad16-11a877f906e7')" type="checkbox"><label for="UUID('3af483cf-0ddc-489f-ad16-11a877f906e7')" class="sk-toggleable__label sk-toggleable__label-arrow">AutoREG<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.auto_reg.AutoREG.html">?<span>Documentation for AutoREG</span></a></label><div class="sk-toggleable__content"><pre>AutoREG(lags=30)</pre></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="926122ea" class="cell" data-execution_count="12">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>y_pred_case2 <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index, X<span class="op">=</span>X_test_missing)</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred_case2, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com Exógenas Imputadas"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="exog_variables_files/figure-html/cell-13-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Nossa previsão não ficou boa. Claro! A variável exógena possui uma tendência - que naturalmente faz com que a imputação do ultimo valor ou a média não funcione bem. A solução ótima varia de caso para caso.</p>
-<div class="callout callout-style-default callout-tip callout-titled">
-<div class="callout-header d-flex align-content-center">
-<div class="callout-icon-container">
-<i class="callout-icon"></i>
-</div>
-<div class="callout-title-container flex-fill">
-Tip
-</div>
-</div>
-<div class="callout-body-container callout-body">
-<p><strong>Dica</strong>: Podemos também usar o operador <code>**</code> como atalho para criar pipelines de variáveis exógenas.</p>
-<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> Imputer(method<span class="op">=</span><span class="st">"mean"</span>) <span class="op">**</span> AutoREG(lags<span class="op">=</span><span class="dv">30</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<p>Note a diferença do que aprendemos para criar pipelines com transformações na variável target, que usam <code>*</code> como operador. Claramente, podemos fazer composições mais complexas, como:</p>
-<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> Imputer(method<span class="op">=</span><span class="st">"mean"</span>) <span class="op">**</span> (Differencer() <span class="op">*</span> AutoREG())</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-</div>
-</section>
-<section id="solução-3-usar-valores-defasados-lags-da-variável-exógena" class="level3" data-number="6.3.3">
-<h3 data-number="6.3.3" class="anchored" data-anchor-id="solução-3-usar-valores-defasados-lags-da-variável-exógena"><span class="header-section-number">6.3.3</span> Solução 3: Usar valores defasados (lags) da variável exógena</h3>
-<p>Outra opção é criar versões defasadas das variáveis exógenas e usá-las como features.</p>
-<p>Para isso, podemos usar o transformador Lag do sktime. Ao utilizar defasagens (lags), surgem dois desafios principais:</p>
-<ol type="1">
-<li><p>O aparecimento de valores NaN, que muitos modelos de previsão não conseguem tratar.</p></li>
-<li><p>O número de variáveis exógenas pode aumentar significativamente, o que pode levar a overfitting ou, no caso do nosso conjunto de dados, a um número de features maior que o número de amostras — o que pode gerar erros no processo de ajuste (fitting).</p></li>
-</ol>
-<p>Para lidar com isso, no exemplo abaixo utilizamos um TransformerPipeline que realiza as seguintes etapas:</p>
-<ul>
-<li>Seleção de variáveis: executa uma seleção das variáveis exógenas, mantendo apenas as mais relevantes.</li>
-<li>Defasagem: aplica o transformador Lag para criar versões defasadas das variáveis exógenas.</li>
-<li>Imputação: usa o transformador Imputer para preencher os valores NaN criados pelo processo de defasagem. Neste caso, é usado o método backfill (preenchimento a partir de valores posteriores).</li>
-</ul>
-<div id="e8dcc1e9" class="cell" data-execution_count="13">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.compose <span class="im">import</span> TransformerPipeline</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.feature_selection <span class="im">import</span> FeatureSelection</span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.impute <span class="im">import</span> Imputer</span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.lag <span class="im">import</span> Lag</span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.subset <span class="im">import</span> IndexSubset</span>
-<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>transformer_pipeline <span class="op">=</span> TransformerPipeline(</span>
-<span id="cb15-9"><a href="#cb15-9" aria-hidden="true" tabindex="-1"></a>    steps<span class="op">=</span>[</span>
-<span id="cb15-10"><a href="#cb15-10" aria-hidden="true" tabindex="-1"></a>        (<span class="st">"lag"</span>, Lag(lags<span class="op">=</span><span class="bu">list</span>(<span class="bu">range</span>(<span class="dv">1</span>, <span class="dv">180</span> <span class="op">+</span> <span class="dv">1</span>)))),  <span class="co"># Cria lags 3 e 4</span></span>
-<span id="cb15-11"><a href="#cb15-11" aria-hidden="true" tabindex="-1"></a>        (<span class="st">"subset"</span>, IndexSubset()),  <span class="co"># Seleciona apenas macro_trend</span></span>
-<span id="cb15-12"><a href="#cb15-12" aria-hidden="true" tabindex="-1"></a>        (<span class="st">"impute"</span>, Imputer(method<span class="op">=</span><span class="st">"backfill"</span>, value<span class="op">=</span><span class="dv">0</span>)),  <span class="co"># Imputa valores NaN</span></span>
-<span id="cb15-13"><a href="#cb15-13" aria-hidden="true" tabindex="-1"></a>        (<span class="st">"feature_selection"</span>, FeatureSelection()),  <span class="co"># Seleciona features</span></span>
-<span id="cb15-14"><a href="#cb15-14" aria-hidden="true" tabindex="-1"></a>    ]</span>
-<span id="cb15-15"><a href="#cb15-15" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="39865a06" class="cell" data-execution_count="14">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>transformer_pipeline.fit(X<span class="op">=</span>X_train, y<span class="op">=</span>y_train)</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>X_test_transformed <span class="op">=</span> transformer_pipeline.transform(X<span class="op">=</span>X_test_missing)</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>X_test_transformed.head()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="14">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
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-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">lag_98__macro_trend</th>
-<th data-quarto-table-cell-role="th">lag_34__macro_trend</th>
-<th data-quarto-table-cell-role="th">lag_29__macro_trend</th>
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-<td>81.838222</td>
-<td>82.233778</td>
-<td>0.0</td>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-08</th>
-<td>82.319556</td>
-<td>82.084889</td>
-<td>82.460889</td>
-<td>81.724444</td>
-<td>82.063111</td>
-<td>81.823556</td>
-<td>81.306222</td>
-<td>82.756444</td>
-<td>83.440444</td>
-<td>82.657333</td>
-<td>...</td>
-<td>0.0</td>
-<td>77.896000</td>
-<td>82.393778</td>
-<td>0.0</td>
-<td>76.268889</td>
-<td>0.0</td>
-<td>82.044889</td>
-<td>82.288444</td>
-<td>0.0</td>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-09</th>
-<td>82.393778</td>
-<td>82.555556</td>
-<td>82.498667</td>
-<td>81.875556</td>
-<td>82.175556</td>
-<td>82.084889</td>
-<td>81.565333</td>
-<td>82.488444</td>
-<td>83.594667</td>
-<td>82.607111</td>
-<td>...</td>
-<td>0.0</td>
-<td>78.460444</td>
-<td>82.383556</td>
-<td>0.0</td>
-<td>76.441333</td>
-<td>0.0</td>
-<td>82.003111</td>
-<td>82.319556</td>
-<td>0.0</td>
-<td>1.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-10</th>
-<td>82.383556</td>
-<td>82.657333</td>
-<td>82.444444</td>
-<td>82.086222</td>
-<td>82.288000</td>
-<td>82.555556</td>
-<td>81.838222</td>
-<td>82.042667</td>
-<td>83.721778</td>
-<td>82.515556</td>
-<td>...</td>
-<td>0.0</td>
-<td>79.001778</td>
-<td>82.550222</td>
-<td>0.0</td>
-<td>76.692889</td>
-<td>0.0</td>
-<td>81.840889</td>
-<td>82.393778</td>
-<td>0.0</td>
-<td>0.0</td>
-</tr>
-</tbody>
-</table>
-
-<p>5 rows × 180 columns</p>
-</div>
-</div>
-</div>
-<div id="cdc4bf98" class="cell" data-execution_count="15">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ForecastingPipeline(</span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>    steps<span class="op">=</span>[</span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>        (<span class="st">"preprocessing"</span>, transformer_pipeline),</span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>        (<span class="st">"forecaster"</span>, AutoREG(lags<span class="op">=</span><span class="dv">30</span>)),</span>
-<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>    ]</span>
-<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>).fit(X<span class="op">=</span>X_train, y<span class="op">=</span>y_train)</span>
-<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>model</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="15">
-<style>#sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 pre {
-    padding: 0;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-label label.sk-toggleable__label,
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-ca71daeb-9ed7-4f7d-b315-91a3f3d4bdb2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ForecastingPipeline(steps=[('preprocessing',
-                            TransformerPipeline(steps=[('lag',
-                                                        Lag(lags=[1, 2, 3, 4, 5,
-                                                                  6, 7, 8, 9,
-                                                                  10, 11, 12,
-                                                                  13, 14, 15,
-                                                                  16, 17, 18,
-                                                                  19, 20, 21,
-                                                                  22, 23, 24,
-                                                                  25, 26, 27,
-                                                                  28, 29, 30, ...])),
-                                                       ('subset',
-                                                        IndexSubset()),
-                                                       ('impute',
-                                                        Imputer(method='backfill',
-                                                                value=0)),
-                                                       ('feature_selection',
-                                                        FeatureSelection())])),
-                           ('forecaster', AutoREG(lags=30))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('65000fa5-ea91-46e2-bbc8-6a01465b4914')" type="checkbox"><label for="UUID('65000fa5-ea91-46e2-bbc8-6a01465b4914')" class="sk-toggleable__label sk-toggleable__label-arrow">ForecastingPipeline<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.ForecastingPipeline.html">?<span>Documentation for ForecastingPipeline</span></a></label><div class="sk-toggleable__content"><pre>ForecastingPipeline(steps=[('preprocessing',
-                            TransformerPipeline(steps=[('lag',
-                                                        Lag(lags=[1, 2, 3, 4, 5,
-                                                                  6, 7, 8, 9,
-                                                                  10, 11, 12,
-                                                                  13, 14, 15,
-                                                                  16, 17, 18,
-                                                                  19, 20, 21,
-                                                                  22, 23, 24,
-                                                                  25, 26, 27,
-                                                                  28, 29, 30, ...])),
-                                                       ('subset',
-                                                        IndexSubset()),
-                                                       ('impute',
-                                                        Imputer(method='backfill',
-                                                                value=0)),
-                                                       ('feature_selection',
-                                                        FeatureSelection())])),
-                           ('forecaster', AutoREG(lags=30))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('af6058fa-1f57-47e6-a320-56cb67da6310')" type="checkbox"><label for="UUID('af6058fa-1f57-47e6-a320-56cb67da6310')" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessing<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.compose.TransformerPipeline.html">?<span>Documentation for preprocessing</span></a></label><div class="sk-toggleable__content"><pre>TransformerPipeline(steps=[('lag',
-                            Lag(lags=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
-                                      14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
-                                      24, 25, 26, 27, 28, 29, 30, ...])),
-                           ('subset', IndexSubset()),
-                           ('impute', Imputer(method='backfill', value=0)),
-                           ('feature_selection', FeatureSelection())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('74a684ad-c4b3-4020-ab42-e617d24dda89')" type="checkbox"><label for="UUID('74a684ad-c4b3-4020-ab42-e617d24dda89')" class="sk-toggleable__label sk-toggleable__label-arrow">Lag<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.lag.Lag.html">?<span>Documentation for Lag</span></a></label><div class="sk-toggleable__content"><pre>Lag(lags=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
-          21, 22, 23, 24, 25, 26, 27, 28, 29, 30, ...])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('94fd33d2-ce45-4bd2-922c-5a880c87d1c2')" type="checkbox"><label for="UUID('94fd33d2-ce45-4bd2-922c-5a880c87d1c2')" class="sk-toggleable__label sk-toggleable__label-arrow">IndexSubset<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.subset.IndexSubset.html">?<span>Documentation for IndexSubset</span></a></label><div class="sk-toggleable__content"><pre>IndexSubset()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('e7817d78-3bac-41b8-b3ec-edcf996fecde')" type="checkbox"><label for="UUID('e7817d78-3bac-41b8-b3ec-edcf996fecde')" class="sk-toggleable__label sk-toggleable__label-arrow">Imputer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.impute.Imputer.html">?<span>Documentation for Imputer</span></a></label><div class="sk-toggleable__content"><pre>Imputer(method='backfill', value=0)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('17fdffc6-2b3e-4ce6-9525-5299bd7081c3')" type="checkbox"><label for="UUID('17fdffc6-2b3e-4ce6-9525-5299bd7081c3')" class="sk-toggleable__label sk-toggleable__label-arrow">FeatureSelection<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.feature_selection.FeatureSelection.html">?<span>Documentation for FeatureSelection</span></a></label><div class="sk-toggleable__content"><pre>FeatureSelection()</pre></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('83425904-82e5-4626-a809-a5cdd01d0ea5')" type="checkbox"><label for="UUID('83425904-82e5-4626-a809-a5cdd01d0ea5')" class="sk-toggleable__label sk-toggleable__label-arrow">AutoREG<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.auto_reg.AutoREG.html">?<span>Documentation for AutoREG</span></a></label><div class="sk-toggleable__content"><pre>AutoREG(lags=30)</pre></div></div></div></div></div></div></div>
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-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred_case3, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com Exógenas Lag"</span>])</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
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-  <li><a href="#previsão-sem-reconciliação" id="toc-previsão-sem-reconciliação" class="nav-link" data-scroll-target="#previsão-sem-reconciliação"><span class="header-section-number">9.3</span> Previsão sem reconciliação</a></li>
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-  <li><a href="#comparando-resultados" id="toc-comparando-resultados" class="nav-link" data-scroll-target="#comparando-resultados"><span class="header-section-number">9.8</span> Comparando resultados</a></li>
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-<div class="quarto-title">
-<h1 class="title"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Forecasting Hierárquico</span></h1>
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-<p>Muitas vezes, não apenas temos múltiplas séries temporais, mas essas séries também estão organizadas em uma hierarquia. Por exemplo, vendas de produtos podem ser organizadas por SKU, categoria, departamento e total da loja.</p>
-<p>Vamos usar o mesmo dataset sintético, mas agora com uma hierarquia de produtos.</p>
-<div class="cell" data-layout-align="default">
-<div class="cell-output-display">
-<div>
-<p></p><figure class="figure"><p></p>
-<div>
-<pre class="mermaid mermaid-js">graph TD
-  root["__total"]
-
-  %% group -1
-  root --&gt; g_minus1["-1"]
-  g_minus1 --&gt; sku20["20"]
-  g_minus1 --&gt; sku21["21"]
-  g_minus1 --&gt; sku22["22"]
-  g_minus1 --&gt; sku23["23"]
-  g_minus1 --&gt; sku24["24"]
-
-  %% group 0
-  root --&gt; g0["0"]
-  g0 --&gt; sku0["0"]
-  g0 --&gt; sku1["1"]
-  g0 --&gt; sku2["2"]
-  g0 --&gt; sku3["3"]
-  g0 --&gt; sku4["4"]
-
-  %% group 1
-  root --&gt; g1["..."]
-
-  
-  %% group 3
-  root --&gt; g3["3"]
-  g3 --&gt; sku15["15"]
-  g3 --&gt; sku16["16"]
-  g3 --&gt; sku17["17"]
-  g3 --&gt; sku18["18"]
-  g3 --&gt; sku19["19"]
-</pre>
-</div>
-<p></p></figure><p></p>
-</div>
-</div>
-</div>
-<p>Ao mesmo tempo que dados hierarárquicos são interessantes pois nos trazem mais informação, eles também trazem desafios adicionais. Imagine que queremos prever as vendas futuras de cada produto. Se fizermos previsões independetes para cada produto, não há garantia que a soma das previsões dos produtos será igual à previsão do total da loja. Isso é chamado de incoerência nas previsões hierárquicas. O processo de ajustar as previsões para garantir coerência é chamado de <strong>reconciliação</strong>.</p>
-<section id="carregando-dados" class="level2" data-number="9.1">
-<h2 data-number="9.1" class="anchored" data-anchor-id="carregando-dados"><span class="header-section-number">9.1</span> Carregando dados</h2>
-<p>Vamos usar os dados sintéticos, agora com sua versao hierárquica.</p>
-<div id="c78343cd" class="cell" data-execution_count="2">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"hierarchical"</span>)</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>y_train, X_train, y_test, X_test <span class="op">=</span> dataset.load(<span class="st">"y_train"</span>, <span class="st">"X_train"</span>, <span class="st">"y_test"</span>, <span class="st">"X_test"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-</section>
-<section id="uso-de-pandas-e-dados-hierárquicos" class="level2" data-number="9.2">
-<h2 data-number="9.2" class="anchored" data-anchor-id="uso-de-pandas-e-dados-hierárquicos"><span class="header-section-number">9.2</span> Uso de pandas e dados hierárquicos</h2>
-<p>Agora, os dataframes possuem mais de 2 ou mais índices, representando a hierarquia.</p>
-<div id="0bd65c54" class="cell" data-execution_count="3">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>y_train</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="3">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">group_id</th>
-<th data-quarto-table-cell-role="th">sku_id</th>
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">-1</th>
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">20</th>
-<th data-quarto-table-cell-role="th">2020-01-01</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-02</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-03</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-04</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-05</th>
-<td>2</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">__total</th>
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">__total</th>
-<th data-quarto-table-cell-role="th">2024-07-01</th>
-<td>2000</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-02</th>
-<td>1616</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-03</th>
-<td>1917</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-04</th>
-<td>2384</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-05</th>
-<td>2462</td>
-</tr>
-</tbody>
-</table>
-
-<p>51088 rows × 1 columns</p>
-</div>
-</div>
-</div>
-<p>Para obter o número de pontos de série únicos (séries temporais individuais), podemos fazer o seguinte:</p>
-<div id="aacf53f3" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>y_train.index.droplevel(<span class="op">-</span><span class="dv">1</span>).nunique()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="4">
-<pre><code>31</code></pre>
-</div>
-</div>
-<p>Note que existem algumas séries com um identificador <code>__total</code>. Esse identificador representa o total para aquele nível da hierarquia. Por exemplo, se o id completo é <code>(-1, "__total")</code>, isso representa o total do grupo -1.</p>
-<div id="75470011" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>y_train.loc[(<span class="op">-</span><span class="dv">1</span>, <span class="st">"__total"</span>)].head()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="5">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-01</th>
-<td>4</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-02</th>
-<td>2</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-03</th>
-<td>3</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-04</th>
-<td>14</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-05</th>
-<td>16</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<p>O total de todas as séries é representado por <code>("__total", "__total")</code>.</p>
-<div id="22e1a0b8" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>y_train.loc[(<span class="st">"__total"</span>, <span class="st">"__total"</span>)]</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="6">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-01</th>
-<td>24</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-02</th>
-<td>19</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-03</th>
-<td>14</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-04</th>
-<td>23</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-05</th>
-<td>23</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-01</th>
-<td>2000</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-02</th>
-<td>1616</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-03</th>
-<td>1917</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-04</th>
-<td>2384</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-05</th>
-<td>2462</td>
-</tr>
-</tbody>
-</table>
-
-<p>1648 rows × 1 columns</p>
-</div>
-</div>
-</div>
-<p>Para contabilizar o número de séries temporais individuais, podemos fazer o seguinte:</p>
-<div id="5bb40c95" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>y_train.index.droplevel(<span class="op">-</span><span class="dv">1</span>).nunique()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="7">
-<pre><code>31</code></pre>
-</div>
-</div>
-</section>
-<section id="previsão-sem-reconciliação" class="level2" data-number="9.3">
-<h2 data-number="9.3" class="anchored" data-anchor-id="previsão-sem-reconciliação"><span class="header-section-number">9.3</span> Previsão sem reconciliação</h2>
-<p>Vamos fazer uma previsão e entender o problema da incoerência.</p>
-<div id="6a5469e6" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>fh <span class="op">=</span> y_test.index.get_level_values(<span class="op">-</span><span class="dv">1</span>).unique()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="f2f0e01e" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.forecasting.reduction <span class="im">import</span> ReductionForecaster</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> lightgbm <span class="im">import</span> LGBMRegressor</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>forecaster <span class="op">=</span> ReductionForecaster(</span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>    LGBMRegressor(n_estimators<span class="op">=</span><span class="dv">50</span>, verbose<span class="op">=-</span><span class="dv">1</span>, objective<span class="op">=</span><span class="st">"tweedie"</span>),</span>
-<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>    window_length<span class="op">=</span><span class="dv">30</span>,</span>
-<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a>    normalization_strategy<span class="op">=</span><span class="st">"divide_mean"</span>,</span>
-<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a>forecaster.fit(y_train, X<span class="op">=</span>X_train)</span>
-<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> forecaster.predict(fh, X<span class="op">=</span>X_test)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<p>Para somar as previsões de baixo para cima, podemos usar o transformador <code>Aggregator</code>. Vamos ver que, quando somarmos as previsões das séries filhas, o resultado não é igual à previsão da série total.</p>
-<div id="03267def" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.hierarchical.aggregate <span class="im">import</span> Aggregator</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>Aggregator().fit_transform(y_pred) <span class="op">-</span> y_pred</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="10">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">group_id</th>
-<th data-quarto-table-cell-role="th">sku_id</th>
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">-1</th>
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">20</th>
-<th data-quarto-table-cell-role="th">2024-07-06</th>
-<td>0.000000</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-07</th>
-<td>0.000000</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-08</th>
-<td>0.000000</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-09</th>
-<td>0.000000</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-10</th>
-<td>0.000000</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">__total</th>
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">__total</th>
-<th data-quarto-table-cell-role="th">2024-12-28</th>
-<td>-195.866868</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-12-29</th>
-<td>-113.260571</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-12-30</th>
-<td>-294.224862</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-12-31</th>
-<td>-311.490070</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2025-01-01</th>
-<td>-256.487505</td>
-</tr>
-</tbody>
-</table>
-
-<p>5580 rows × 1 columns</p>
-</div>
-</div>
-</div>
-<p>Existe uma diferença… ou seja, os valores não batem. Imagine o impacto de levar previsões incoerentes para a tomada de decisão em uma empresa? A raiz do problema é que temos mais modelos que graus de liberdade. Para ilustrar, suponha que temos 3 séries: <span class="math inline">\(A\)</span>, <span class="math inline">\(B\)</span> e <span class="math inline">\(C\)</span>, onde:</p>
-<p><span class="math display">\[
-C(t) = A(t) + B(t)
-\]</span></p>
-<p>Aqui, temos 3 séries, mas apenas 2 graus de liberdade, pois <span class="math inline">\(C\)</span> é completamente determinado por <span class="math inline">\(A\)</span> e <span class="math inline">\(B\)</span>. Se fizermos previsões independentes para <span class="math inline">\(A\)</span>, <span class="math inline">\(B\)</span> e <span class="math inline">\(C\)</span>, não há garantia de que a relação acima será mantida nas previsões.</p>
-</section>
-<section id="reconciliação-de-previsões-hierárquicas" class="level2" data-number="9.4">
-<h2 data-number="9.4" class="anchored" data-anchor-id="reconciliação-de-previsões-hierárquicas"><span class="header-section-number">9.4</span> Reconciliação de previsões hierárquicas</h2>
-<p><img src="img/hierarchical_reconciled_vs_not.png" class="img-fluid"></p>
-<p>Existem diferentes métodos para reconciliar previsões em séries temporais hierárquicas. Não existe uma solução única, e o melhor método depende dos dados e do contexto.</p>
-</section>
-<section id="bottom-up" class="level2" data-number="9.5">
-<h2 data-number="9.5" class="anchored" data-anchor-id="bottom-up"><span class="header-section-number">9.5</span> Bottom-up</h2>
-<p>A maneira mais simples de reconcialiar previsões hierárquicas é a abordagem <strong>bottom-up</strong>. Nessa abordagem, fazemos previsões apenas para as séries mais baixas na hierarquia (as séries filhas) e depois somamos essas previsões para obter as previsões das séries superiores (as séries pais).</p>
-<p><img src="img/hierarchical_bottomup.png" alt="Hierarchical Bottom-up" width="450"></p>
-<p>Lados positivos:</p>
-<ul>
-<li>Simplicidade: fácil de entender e implementar.</li>
-<li>Coerência garantida: a soma das previsões das séries filhas sempre será igual à previsão da série pai.</li>
-<li>Sérias filhas podem capturar detalhes específicos que podem ser perdidos em níveis superiores.</li>
-</ul>
-<p>No entanto, essa abordagem também tem desvantagens: é sucetível ao ruído nas séries filhas, e se as séries filhas tiverem pouca informação, as previsões podem ser ruins. Por exemplo, muitos zeros nas séries de níveis baixos pode levar a previsões ruins a niveis agregados.</p>
-<div id="443a165e" class="cell" data-execution_count="11">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.hierarchical.reconcile <span class="im">import</span> BottomUpReconciler</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>bottom_up <span class="op">=</span> BottomUpReconciler() <span class="op">*</span> forecaster</span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>bottom_up.fit(y_train)</span>
-<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>y_pred_bottomup <span class="op">=</span> bottom_up.predict(fh<span class="op">=</span>fh)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<p>Agora vemos que as previsões são coerentes:</p>
-<div id="deb36773" class="cell" data-execution_count="12">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>Aggregator().fit_transform(y_pred_bottomup) <span class="op">-</span> y_pred_bottomup</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="12">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">group_id</th>
-<th data-quarto-table-cell-role="th">sku_id</th>
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">-1</th>
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">20</th>
-<th data-quarto-table-cell-role="th">2024-07-06</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-07</th>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-08</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-09</th>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-10</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">__total</th>
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">__total</th>
-<th data-quarto-table-cell-role="th">2024-12-28</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-12-29</th>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-12-30</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-12-31</th>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2025-01-01</th>
-<td>0.0</td>
-</tr>
-</tbody>
-</table>
-
-<p>5580 rows × 1 columns</p>
-</div>
-</div>
-</div>
-</section>
-<section id="top-down-forecast-proportions" class="level2" data-number="9.6">
-<h2 data-number="9.6" class="anchored" data-anchor-id="top-down-forecast-proportions"><span class="header-section-number">9.6</span> Top-down (forecast proportions)</h2>
-<p>Outra abordagem é a <strong>top-down</strong>. Nessa abordagem, fazemos previsões apenas para as séries superiores na hierarquia (as séries pais) e depois distribuímos essas previsões para as séries filhas com base em proporções previstas.</p>
-<p>Suponha que temos a seguinte hierarquia <span class="math inline">\(C(t) = A(t) + B(t)\)</span>. Considere <span class="math inline">\(\hat{C}(t)\)</span>, <span class="math inline">\(\hat{A}(t)\)</span> e <span class="math inline">\(\hat{B}(t)\)</span> como as previsões para <span class="math inline">\(C\)</span>, <span class="math inline">\(A\)</span> e <span class="math inline">\(B\)</span>, respectivamente. Na abordagem top-down, faríamos o seguinte:</p>
-<ol type="1">
-<li>Prever <span class="math inline">\(\hat{C}(t)\)</span>, <span class="math inline">\(\hat{A}(t)\)</span> e <span class="math inline">\(\hat{B}(t)\)</span> independentemente.</li>
-<li>Calcular as proporções previstas para os níveis mais baixos: <span class="math display">\[
-p_A(t) = \frac{\hat{A}(t)}{\hat{A}(t) + \hat{B}(t)}
-\]</span></li>
-</ol>
-<p><span class="math display">\[
-p_B(t) = \frac{\hat{B}(t)}{\hat{A}(t) + \hat{B}(t)}
-\]</span></p>
-<ol start="3" type="1">
-<li>Distribuir a previsão de <span class="math inline">\(C\)</span> para <span class="math inline">\(A\)</span> e <span class="math inline">\(B\)</span> usando essas proporções: <span class="math display">\[
-\tilde{A}(t) = p_A(t) \cdot \hat{C}(t)
-\]</span></li>
-</ol>
-<p><span class="math display">\[
-\tilde{B}(t) = p_B(t) \cdot \hat{C}(t)
-\]</span></p>
-<p>Essa abordagem é capaz de usufruir da qualidade do forecast total, e ainda consegue distribuir para as séries filhas baseadas no histórico.</p>
-<p><img src="img/hierarchical_td_fcst.png" alt="Topdown Forecast" width="900"></p>
-<p>O que chamam de “Proporções históricas” é equivalente a esse método, mas com um modelo Naive para prever as proporções.</p>
-<p>Esse método pode ser bom quando o forecast total é de boa qualidade. No entanto, dependemos profundamente da qualidade do forecast total e das proporções.</p>
-<div id="94fa98bb" class="cell" data-execution_count="13">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.hierarchical.reconcile <span class="im">import</span> TopdownReconciler</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>top_down_fcst <span class="op">=</span> TopdownReconciler() <span class="op">*</span> forecaster</span>
-<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>top_down_fcst.fit(y_train)</span>
-<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>y_pred_topdown <span class="op">=</span> top_down_fcst.predict(fh<span class="op">=</span>fh)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-</section>
-<section id="reconciliação-ótima" class="level2" data-number="9.7">
-<h2 data-number="9.7" class="anchored" data-anchor-id="reconciliação-ótima"><span class="header-section-number">9.7</span> Reconciliação ótima</h2>
-<p>Existe uma abordagem mais sofisticada, com uma intuição geométrica interessante. A ideia é ajustar as previsões iniciais para que elas satisfaçam as restrições de soma da hierarquia. Por exemplo, para a hierarquia <span class="math inline">\(C(t) = A(t) + B(t)\)</span>, queremos garantir que:</p>
-<p><span class="math display">\[
-\hat{C}(t) = \hat{A}(t) + \hat{B}(t)
-\]</span></p>
-<p>Se consideramos nosso espaço 3D de observações <span class="math inline">\((\hat{A}, \hat{B}, \hat{C})\)</span>, a condição acima é satisfeita para um plano 2D nesse universo.</p>
-<p><img src="img/coherent_plane.png" class="img-fluid"></p>
-<p>Podemos então projetar nossas previsões iniciais nesse plano para obter previsões coerentes. Essa projeção pode ser feita de várias maneiras, levando a diferentes métodos de reconciliação ótima. Os métodos levam o nome “OLS” pois a projeção é feita minimizando o erro quadrático (Ordinary Least Squares).</p>
-<ul>
-<li><strong>OLS</strong> : projetar ortogonalmente todas as previsões base na espaço de reconciliação, tratando todas as séries igualmente.</li>
-<li><strong>Weighted OLS</strong>: projetar obliquamente, ou seja, considerando pesos diferentes para cada série, permitindo dar mais importância a certas séries na reconciliação. A projeção não faz mais uma perpendicular, mas sim uma oblíqua.</li>
-<li><strong>Minimum trace (MinT)</strong>: use a matriz de covariância do erro para encontrar as previsões reconciliadas ótimas. Chamado de “ótimo”.</li>
-</ul>
-<p>Para a reconciliação ótima com OLS, podemos usar o <code>OptimalReconciler</code> do sktime:</p>
-<div id="6f5d03d3" class="cell" data-execution_count="14">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.hierarchical.reconcile <span class="im">import</span> OptimalReconciler</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>optimal <span class="op">=</span> OptimalReconciler(<span class="st">"ols"</span>) <span class="op">*</span> forecaster</span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>optimal.fit(y_train)</span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>y_pred_optimal <span class="op">=</span> optimal.predict(fh<span class="op">=</span>fh)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="48b5aab7" class="cell" data-execution_count="15">
-<details class="code-fold">
-<summary>Code</summary>
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>idx <span class="op">=</span> y_train.index.droplevel(<span class="op">-</span><span class="dv">1</span>).unique()[<span class="dv">10</span>]</span>
-<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>    y_train.loc[idx,],</span>
-<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>    y_test.loc[idx,],</span>
-<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a>    y_pred.loc[idx,],</span>
-<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a>    y_pred_optimal.loc[idx,],</span>
-<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[<span class="st">"Train"</span>, <span class="st">"Test"</span>, <span class="st">"Predicted (sem reconciliação)"</span>, <span class="st">"Predicted (ótimo)"</span>],</span>
-<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a>plt.xlim(pd.to_datetime(<span class="st">"2024-05-01"</span>), <span class="va">None</span>)</span>
-<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</details>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="hierarchical_forecasting_files/figure-html/cell-16-output-1.png" width="1286" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Para reconciliações ótimas (que usam a covariância do erro), podemos usar o <code>ReconcilerForecaster</code> do sktime, que internamente já faz o cálculo da covariância do erro:</p>
-<div id="5c5759af" class="cell" data-execution_count="16">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.reconcile <span class="im">import</span> ReconcilerForecaster</span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>mint_forecaster <span class="op">=</span> ReconcilerForecaster(</span>
-<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>    forecaster<span class="op">=</span>forecaster,</span>
-<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>    method<span class="op">=</span><span class="st">"mint_shrink"</span>)</span>
-<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a>mint_forecaster.fit(y_train)</span>
-<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>y_pred_mint <span class="op">=</span> mint_forecaster.predict(fh<span class="op">=</span>fh)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-</section>
-<section id="comparando-resultados" class="level2" data-number="9.8">
-<h2 data-number="9.8" class="anchored" data-anchor-id="comparando-resultados"><span class="header-section-number">9.8</span> Comparando resultados</h2>
-<div id="a0401069" class="cell" data-execution_count="17">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanSquaredScaledError</span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanSquaredScaledError(multilevel<span class="op">=</span><span class="st">"uniform_average_time"</span>)</span>
-<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>pd.DataFrame(</span>
-<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>    {</span>
-<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a>        <span class="st">"Baseline"</span>: metric(y_test, y_pred, y_train<span class="op">=</span>y_train),</span>
-<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a>        <span class="st">"BottomUpReconciler"</span>: metric(y_test, y_pred_bottomup, y_train<span class="op">=</span>y_train),</span>
-<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a>        <span class="st">"TopDownReconciler"</span>: metric(y_test, y_pred_topdown, y_train<span class="op">=</span>y_train),</span>
-<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a>        <span class="st">"OptimalReconciler (ols)"</span>: metric(y_test, y_pred_optimal, y_train<span class="op">=</span>y_train),</span>
-<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a>        <span class="st">"Mint Reconciler"</span>: metric(y_test, y_pred_mint, y_train<span class="op">=</span>y_train),</span>
-<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a>    },</span>
-<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a>    index<span class="op">=</span>[<span class="st">"Mean Absolute Scaled Error"</span>],</span>
-<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="17">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">Baseline</th>
-<th data-quarto-table-cell-role="th">BottomUpReconciler</th>
-<th data-quarto-table-cell-role="th">TopDownReconciler</th>
-<th data-quarto-table-cell-role="th">OptimalReconciler (ols)</th>
-<th data-quarto-table-cell-role="th">Mint Reconciler</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">Mean Absolute Scaled Error</th>
-<td>40.172892</td>
-<td>82.744957</td>
-<td>38.150928</td>
-<td>39.847359</td>
-<td>33.014674</td>
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diff --git a/book/latest/content/pt/part2/img/reduction.png b/book/latest/content/pt/part2/img/reduction.png
deleted file mode 100644
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-  <li><a href="#o-problema-da-tendência" id="toc-o-problema-da-tendência" class="nav-link active" data-scroll-target="#o-problema-da-tendência"><span class="header-section-number">7.1</span> O problema da tendência</a></li>
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-  <li><a href="#modelo-direto-e-recursivo" id="toc-modelo-direto-e-recursivo" class="nav-link" data-scroll-target="#modelo-direto-e-recursivo"><span class="header-section-number">7.2.3</span> Modelo direto e recursivo</a></li>
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-<h1 class="title"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">Modelos de Machine Learning</span></h1>
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-<p>Nesse capítulo vamos ver as maneiras de usar modelos de Machine Learning para forecasting. Aqui é onde mais acontecem erros de novos praticantes, pois muitas vezes tentam aplicar modelos de ML diretamente na série temporal.</p>
-<p>Para usar um modelo de ML, precisamos transformar a série temporal em um problema de regressão tradicional. Isso é feito criando janelas deslizantes (sliding windows) da série temporal, onde cada janela é usada como uma amostra de treinamento para o modelo de ML.</p>
-<p>Ou seja, se temos uma série temporal <span class="math inline">\((y_t)\)</span>, podemos criar janelas de tamanho <span class="math inline">\(n\)</span> e usar os valores <span class="math inline">\(y_{t-n}, y_{t-n+1}, \ldots, y_{t-1}\)</span> como características (features) para prever o valor <span class="math inline">\(y_t\)</span>.</p>
-<div class="quarto-figure quarto-figure-center">
-<figure class="figure">
-<p><img src="img/reduction.png" class="img-fluid figure-img"></p>
-<figcaption>img/reduction.png</figcaption>
-</figure>
-</div>
-<p>Para prever mais de um passo à frente, existem duas abordagens:</p>
-<ol type="1">
-<li><strong>Previsão recursive</strong>: se queremos prever <span class="math inline">\(h\)</span> passos à frente, podemos usar o modelo para prever <span class="math inline">\(y_{t+1}\)</span>, depois usar essa previsão para prever <span class="math inline">\(y_{t+2}\)</span>, e assim por diante, até <span class="math inline">\(y_{t+h}\)</span>. Isso pode levar a erros acumulados, pois cada previsão depende das previsões anteriores.</li>
-<li><strong>Previsão direta</strong>: em vez de prever um passo de cada vez, podemos treinar o modelo para prever todos os <span class="math inline">\(h\)</span> passos à frente de uma vez. Isso pode ser feito usando um modelo para cada <span class="math inline">\(h\)</span> ou usando um modelo que prevê um vetor de <span class="math inline">\(h\)</span> valores.</li>
-</ol>
-<p>A verdade é que as duas abordagens podem ser vistas como uma: a previsão recursiva pode ser vista como uma previsão direta para <span class="math inline">\(h=1\)</span>.</p>
-<div id="7a0ed700" class="cell" data-execution_count="2">
-<details class="code-fold">
-<summary>Code</summary>
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"univariate"</span>)</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>y_train, X_train, y_test, X_test <span class="op">=</span> dataset.load(</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>    <span class="st">"y_train"</span>, <span class="st">"X_train"</span>, <span class="st">"y_test"</span>, <span class="st">"X_test"</span></span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</details>
-</div>
-<section id="o-problema-da-tendência" class="level2" data-number="7.1">
-<h2 data-number="7.1" class="anchored" data-anchor-id="o-problema-da-tendência"><span class="header-section-number">7.1</span> O problema da tendência</h2>
-<p>A tendência em séries temporais é como um constante problema de data drift:</p>
-<div id="5a68b9e2" class="cell" data-execution_count="3">
-<details class="code-fold">
-<summary>Code</summary>
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>_X <span class="op">=</span> [y_train.iloc[i : i <span class="op">+</span> <span class="dv">7</span>] <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="dv">0</span>, <span class="dv">700</span>)]</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>_X_test <span class="op">=</span> [y_train.iloc[i : i <span class="op">+</span> <span class="dv">7</span>] <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="dv">700</span>, <span class="dv">800</span>)]</span>
-<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> set_index(x):</span>
-<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a>    x.index <span class="op">=</span> <span class="bu">range</span>(<span class="bu">len</span>(x))</span>
-<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> x</span>
-<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a>_X <span class="op">=</span> [set_index(x) <span class="cf">for</span> x <span class="kw">in</span> _X]</span>
-<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a>_X_test <span class="op">=</span> [set_index(x) <span class="cf">for</span> x <span class="kw">in</span> _X_test]</span>
-<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots()</span>
-<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> x <span class="kw">in</span> _X:</span>
-<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a>    ax.plot(x, color<span class="op">=</span><span class="st">"gray"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>)</span>
-<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> x <span class="kw">in</span> _X_test:</span>
-<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a>    ax.plot(x, color<span class="op">=</span><span class="st">"red"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>)</span>
-<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a><span class="co"># Add legend, with 1 red line for test and 1 gray for train</span></span>
-<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> matplotlib.lines <span class="im">import</span> Line2D</span>
-<span id="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a>legend_handles <span class="op">=</span> [</span>
-<span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a>    Line2D([<span class="dv">0</span>], [<span class="dv">0</span>], color<span class="op">=</span><span class="st">"gray"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>, lw<span class="op">=</span><span class="dv">2</span>, label<span class="op">=</span><span class="st">"Treino"</span>),</span>
-<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a>    Line2D([<span class="dv">0</span>], [<span class="dv">0</span>], color<span class="op">=</span><span class="st">"red"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>, lw<span class="op">=</span><span class="dv">2</span>, label<span class="op">=</span><span class="st">"Teste"</span>),</span>
-<span id="cb2-28"><a href="#cb2-28" aria-hidden="true" tabindex="-1"></a>]</span>
-<span id="cb2-29"><a href="#cb2-29" aria-hidden="true" tabindex="-1"></a>ax.legend(handles<span class="op">=</span>legend_handles, loc<span class="op">=</span><span class="st">"best"</span>)</span>
-<span id="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a>ax.set_title(<span class="st">"Série original - Magnitudes diferentes para cada janela"</span>)</span>
-<span id="cb2-31"><a href="#cb2-31" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</details>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ml_models_files/figure-html/cell-4-output-1.png" width="575" height="432" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Quando criamos nossas janelas e olhamos treine e teste, esse problema fica claro. A informação de uma série em treino não é util para prever a série de teste, pois elas estão em magnitudes diferentes.</p>
-<p>Uma possível solução para isso é normalizar cada janela, dividindo pelo valor médio da janela. Assim, todas as janelas ficam na mesma escala:</p>
-<div id="ace59291" class="cell" data-execution_count="4">
-<details class="code-fold">
-<summary>Code</summary>
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>_X <span class="op">=</span> [x <span class="op">/</span> x.mean() <span class="cf">for</span> x <span class="kw">in</span> _X]</span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>_X_test <span class="op">=</span> [x <span class="op">/</span> x.mean() <span class="cf">for</span> x <span class="kw">in</span> _X_test]</span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots()</span>
-<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> x <span class="kw">in</span> _X:</span>
-<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>    ax.plot(x, color<span class="op">=</span><span class="st">"gray"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>)</span>
-<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> x <span class="kw">in</span> _X_test:</span>
-<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>    ax.plot(x, color<span class="op">=</span><span class="st">"red"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>)</span>
-<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>ax.legend(handles<span class="op">=</span>legend_handles, loc<span class="op">=</span><span class="st">"best"</span>)</span>
-<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>ax.set_title(<span class="st">"Série normalizada"</span>)</span>
-<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</details>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ml_models_files/figure-html/cell-5-output-1.png" width="571" height="431" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>e podemos prever sem problemas.</p>
-<p>Outra possibilidade é a diferenciação, como já vimos em capítulos anteriores. A diferenciação remove a tendência da série, tornando-a estacionária.</p>
-</section>
-<section id="usando-modelos-de-ml-com-sktime" class="level2" data-number="7.2">
-<h2 data-number="7.2" class="anchored" data-anchor-id="usando-modelos-de-ml-com-sktime"><span class="header-section-number">7.2</span> Usando modelos de ML com sktime</h2>
-<p>Primeiro, vamos import <code>ReductionForecaster</code>, que é a classe que implementa a abordagem de janelas deslizantes para usar modelos de ML em séries temporais. Vamos testar um primeiro caso sem nenhum tipo de preprocessamento, apenas criando as janelas:</p>
-<div id="64046b80" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.forecasting.reduction <span class="im">import</span> ReductionForecaster</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.ensemble <span class="im">import</span> RandomForestRegressor</span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ReductionForecaster(</span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>    RandomForestRegressor(n_estimators<span class="op">=</span><span class="dv">100</span>, random_state<span class="op">=</span><span class="dv">42</span>),</span>
-<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>    window_length<span class="op">=</span><span class="dv">30</span>,</span>
-<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="5">
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-
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-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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-  }
-
-  /* Toggleable label */
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-    margin-right: 0.25em;
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-    background-color: var(--sklearn-color-level-2);
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-
-  /* Specification for estimator info */
-
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-  a:link.sk-estimator-doc-link,
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-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
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-
-  /* On hover */
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-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ReductionForecaster(estimator=RandomForestRegressor(random_state=42),
-                    window_length=30)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('90a45fbe-c3cd-4173-b11e-e327ff0c462c')" type="checkbox"><label for="UUID('90a45fbe-c3cd-4173-b11e-e327ff0c462c')" class="sk-toggleable__label sk-toggleable__label-arrow">ReductionForecaster</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=RandomForestRegressor(random_state=42),
-                    window_length=30)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('15ba3e0c-7b66-4b74-ae6f-1c8c37f97177')" type="checkbox"><label for="UUID('15ba3e0c-7b66-4b74-ae6f-1c8c37f97177')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: RandomForestRegressor</label><div class="sk-toggleable__content"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('80405df2-f98a-4388-8663-0e63b94f37e5')" type="checkbox"><label for="UUID('80405df2-f98a-4388-8663-0e63b94f37e5')" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestRegressor<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></label><div class="sk-toggleable__content"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="10c64b0b" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index, X<span class="op">=</span>X_test)</span>
-<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com ML"</span>])</span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ml_models_files/figure-html/cell-7-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Claramente, tivemos o problema que mencionamos anteriormente.</p>
-<section id="solução-1-diferenciação" class="level3" data-number="7.2.1">
-<h3 data-number="7.2.1" class="anchored" data-anchor-id="solução-1-diferenciação"><span class="header-section-number">7.2.1</span> Solução 1: Diferenciação</h3>
-<p>Uma solução é usar a diferenciação para remover a tendência da série.</p>
-<div id="54b3feda" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.difference <span class="im">import</span> Differencer</span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>regressor <span class="op">=</span> RandomForestRegressor(n_estimators<span class="op">=</span><span class="dv">100</span>, random_state<span class="op">=</span><span class="dv">42</span>)</span>
-<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> Differencer() <span class="op">*</span> ReductionForecaster(</span>
-<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>    regressor,</span>
-<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>    window_length<span class="op">=</span><span class="dv">30</span>,</span>
-<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>    steps_ahead<span class="op">=</span><span class="dv">1</span>,</span>
-<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="7">
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-
-
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-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
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-  /* Estimator label */
-
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-
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-
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-
-  /* Specification for estimator info */
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-  }
-
-  /* On hover */
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-  .sk-estimator-doc-link:hover,
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-    color: var(--sklearn-color-background);
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-
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-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-7ce17bde-d7c2-4d10-971d-23b955150077 a.estimator_doc_link {
-    float: right;
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-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-7ce17bde-d7c2-4d10-971d-23b955150077 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-7ce17bde-d7c2-4d10-971d-23b955150077" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TransformedTargetForecaster(steps=[Differencer(),
-                                   ReductionForecaster(estimator=RandomForestRegressor(random_state=42),
-                                                       window_length=30)])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('6f61633f-415a-4c99-9c2f-20a752187da5')" type="checkbox"><label for="UUID('6f61633f-415a-4c99-9c2f-20a752187da5')" class="sk-toggleable__label sk-toggleable__label-arrow">TransformedTargetForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.TransformedTargetForecaster.html">?<span>Documentation for TransformedTargetForecaster</span></a></label><div class="sk-toggleable__content"><pre>TransformedTargetForecaster(steps=[Differencer(),
-                                   ReductionForecaster(estimator=RandomForestRegressor(random_state=42),
-                                                       window_length=30)])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('14fcfdad-207e-4a47-b98c-a66bb4b7323d')" type="checkbox"><label for="UUID('14fcfdad-207e-4a47-b98c-a66bb4b7323d')" class="sk-toggleable__label sk-toggleable__label-arrow">Differencer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.difference.Differencer.html">?<span>Documentation for Differencer</span></a></label><div class="sk-toggleable__content"><pre>Differencer()</pre></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('d163b341-7476-40f5-b56a-fac17156efe4')" type="checkbox"><label for="UUID('d163b341-7476-40f5-b56a-fac17156efe4')" class="sk-toggleable__label sk-toggleable__label-arrow">ReductionForecaster</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=RandomForestRegressor(random_state=42),
-                    window_length=30)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('24a0a953-2de7-4918-b15b-6a72e5b40109')" type="checkbox"><label for="UUID('24a0a953-2de7-4918-b15b-6a72e5b40109')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: RandomForestRegressor</label><div class="sk-toggleable__content"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('493c3594-6ebf-4ec1-8812-023baf5c962b')" type="checkbox"><label for="UUID('493c3594-6ebf-4ec1-8812-023baf5c962b')" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestRegressor<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></label><div class="sk-toggleable__content"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="5c626c7c" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>y_pred_diff <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index, X<span class="op">=</span>X_test)</span>
-<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>    y_train, y_test, y_pred_diff, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com ML + Diferença"</span>]</span>
-<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ml_models_files/figure-html/cell-9-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Aqui, já vemos uma melhora significativa. Mas tem algo que podemos melhorar para realizar a diferenciação? Sim. Lembre que essa série tem um padrão multiplicativo. Então, antes de aplicar a diferenciação, podemos aplicar uma transformação logarítmica para estabilizar a variância:</p>
-<div id="f5e40c86" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.boxcox <span class="im">import</span> LogTransformer</span>
-<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>model_log <span class="op">=</span> LogTransformer() <span class="op">*</span> model</span>
-<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>model_log.fit(y_train, X<span class="op">=</span>X_train)</span>
-<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>y_pred_log_diff <span class="op">=</span> model_log.predict(fh<span class="op">=</span>y_test.index, X<span class="op">=</span>X_test)</span>
-<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>    y_train,</span>
-<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>    y_test,</span>
-<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>    y_pred_log_diff,</span>
-<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com ML + Log + Diferença"</span>],</span>
-<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ml_models_files/figure-html/cell-10-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-</section>
-<section id="solução-2-normalização-por-janela" class="level3" data-number="7.2.2">
-<h3 data-number="7.2.2" class="anchored" data-anchor-id="solução-2-normalização-por-janela"><span class="header-section-number">7.2.2</span> Solução 2: Normalização por janela</h3>
-<p>A diferenciação aumenta o ruído da série, o que pode dificultar o trabalho do modelo de ML. Outra opção é normalizar em cada janela. A classe <code>ReductionForecaster</code> tem um parâmetro chamado <code>normalization_strategy</code>, que pode ser usado para determinar a estratégia de normalização. Vamos usar a estratégia <code>divide_mean</code>, que divide cada janela pelo seu valor médio.</p>
-<div id="128b9d5a" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ReductionForecaster(</span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>    regressor,</span>
-<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>    window_length<span class="op">=</span><span class="dv">30</span>,</span>
-<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>    steps_ahead<span class="op">=</span><span class="dv">1</span>,</span>
-<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>    normalization_strategy<span class="op">=</span><span class="st">"divide_mean"</span>,</span>
-<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train)</span>
-<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a>y_pred_norm <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index, X<span class="op">=</span>X_test)</span>
-<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a>    y_train, y_test, y_pred_norm, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com ML + Normalização"</span>]</span>
-<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ml_models_files/figure-html/cell-11-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-</section>
-<section id="modelo-direto-e-recursivo" class="level3" data-number="7.2.3">
-<h3 data-number="7.2.3" class="anchored" data-anchor-id="modelo-direto-e-recursivo"><span class="header-section-number">7.2.3</span> Modelo direto e recursivo</h3>
-<p>Podemos fazer um conjunto de modelos, um para cada passo à frente. Abaixo, definimos <code>steps_ahead=12</code>, o que significa que o modelo vai prever 12 passos à frente diretamente.</p>
-<div id="4e5b7dc3" class="cell" data-execution_count="11">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ReductionForecaster(</span>
-<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>    regressor,</span>
-<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>    window_length<span class="op">=</span><span class="dv">30</span>,</span>
-<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>    steps_ahead<span class="op">=</span><span class="dv">12</span>,</span>
-<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>    normalization_strategy<span class="op">=</span><span class="st">"divide_mean"</span>,</span>
-<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train)</span>
-<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a>y_pred_norm_direct <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index, X<span class="op">=</span>X_test)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="df87f47f" class="cell" data-execution_count="12">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>plot_series(</span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>    y_train,</span>
-<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>    y_test,</span>
-<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a>    y_pred_norm_direct,</span>
-<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>    labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com ML + Normalização"</span>],</span>
-<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="ml_models_files/figure-html/cell-13-output-1.png" width="1271" height="337" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Podemos comparar o MAPE de todos os modelos:</p>
-<div id="52151c2a" class="cell" data-execution_count="13">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanAbsolutePercentageError</span>
-<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>mape <span class="op">=</span> MeanAbsolutePercentageError()</span>
-<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>results <span class="op">=</span> {}</span>
-<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> _y_pred, label <span class="kw">in</span> <span class="bu">zip</span>(</span>
-<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a>    [</span>
-<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>        y_pred,</span>
-<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a>        y_pred_diff,</span>
-<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a>        y_pred_log_diff,</span>
-<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a>        y_pred_norm,</span>
-<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>        y_pred_norm_direct,</span>
-<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>    ],</span>
-<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>    [</span>
-<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a>        <span class="st">"ML"</span>,</span>
-<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a>        <span class="st">"ML + Diferença"</span>,</span>
-<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a>        <span class="st">"ML + Log + Diferença"</span>,</span>
-<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a>        <span class="st">"ML + Normalização"</span>,</span>
-<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a>        <span class="st">"ML + Normalização + Direto"</span>,</span>
-<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>    ],</span>
-<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a>):</span>
-<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a>    results[label] <span class="op">=</span> mape(y_test, _y_pred)</span>
-<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
-<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a>results <span class="op">=</span> pd.DataFrame.from_dict(results, orient<span class="op">=</span><span class="st">"index"</span>, columns<span class="op">=</span>[<span class="st">"MAPE"</span>])</span>
-<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a>results.sort_values(<span class="st">"MAPE"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="13">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">MAPE</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">ML + Log + Diferença</th>
-<td>0.125494</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">ML + Normalização</th>
-<td>0.128436</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">ML + Normalização + Direto</th>
-<td>0.138206</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">ML + Diferença</th>
-<td>0.178693</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">ML</th>
-<td>0.243593</td>
-</tr>
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-  <li><a href="#acessando-os-dados" id="toc-acessando-os-dados" class="nav-link active" data-scroll-target="#acessando-os-dados"><span class="header-section-number">8.1</span> Acessando os dados</a>
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-  </ul></li>
-  <li><a href="#upcasting-automático" id="toc-upcasting-automático" class="nav-link" data-scroll-target="#upcasting-automático"><span class="header-section-number">8.2</span> Upcasting automático</a></li>
-  <li><a href="#métricas" id="toc-métricas" class="nav-link" data-scroll-target="#métricas"><span class="header-section-number">8.3</span> Métricas</a></li>
-  <li><a href="#modelos-globais-de-machine-learning" id="toc-modelos-globais-de-machine-learning" class="nav-link" data-scroll-target="#modelos-globais-de-machine-learning"><span class="header-section-number">8.4</span> Modelos globais de Machine Learning</a></li>
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-  <li><a href="#normalização-por-janela" id="toc-normalização-por-janela" class="nav-link" data-scroll-target="#normalização-por-janela"><span class="header-section-number">8.5.1</span> Normalização por janela</a></li>
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-  </ul></li>
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-  <li><a href="#resumo" id="toc-resumo" class="nav-link" data-scroll-target="#resumo"><span class="header-section-number">8.7</span> Resumo</a></li>
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-<div class="quarto-title">
-<h1 class="title"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">Dados em painel (multi-series)</span></h1>
-</div>
-
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-
-<p>Em muitas aplicações, não temos acesso a uma única série temporal, mas sim a um conjunto de séries temporais relacionadas. Isso é comum em cenários como vendas de produtos em diferentes lojas, consumo de energia em diferentes regiões, etc. Esses dados são chamados de dados em painel.</p>
-<p>Uma ideia poderosa é aproveitar a similaridade entre as séries para melhorar as previsões. Chamamos de <strong>modelos globais</strong> os modelos capazes de aprender padrões comuns entre as séries, ao contrário dos <strong>modelos locais</strong> que aprendem apenas com uma única série.</p>
-<p>A maioria dos modelos clássicos de séries temporais são locais. Modelos globais são, em geral, baseados em modelos tabulares de ML ou deep learning. Segundo competições de séries temporais, como a M5, em forecasts de painel os modelos globais são os que apresentam melhor desempenho <span class="citation" data-cites="makridakis2022m5">(<a href="#ref-makridakis2022m5" role="doc-biblioref">Makridakis, Spiliotis, and Assimakopoulos 2022</a>)</span>.</p>
-<section id="acessando-os-dados" class="level2" data-number="8.1">
-<h2 data-number="8.1" class="anchored" data-anchor-id="acessando-os-dados"><span class="header-section-number">8.1</span> Acessando os dados</h2>
-<p>Aqui, vamos usar o dataset sintético que vimos antes, mas agora teremos acesso às várias séries temporais que compõe o total.</p>
-<p>Esse dataset é feito para simular um caso de varejo, onde temos vendas diárias de vários produtos:</p>
-<div id="505f9534" class="cell" data-execution_count="2">
-<details class="code-fold">
-<summary>Code</summary>
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</details>
-</div>
-<div id="881cc1f2" class="cell" data-execution_count="3">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"panel"</span>)</span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>y_train, X_train, y_test, X_test <span class="op">=</span> dataset.load(</span>
-<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>    <span class="st">"y_train"</span>, <span class="st">"X_train"</span>, <span class="st">"y_test"</span>, <span class="st">"X_test"</span></span>
-<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<p>Note que, para dados em painel, os dataframes possuem mais um nível de índice, que identifica a série temporal a que cada observação pertence:</p>
-<div id="ce0e4918" class="cell" data-execution_count="4">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>display(X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">promo</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">sku_id</th>
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">0</th>
-<th data-quarto-table-cell-role="th">2020-01-01</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-02</th>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-03</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-04</th>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-05</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">24</th>
-<th data-quarto-table-cell-role="th">2024-07-01</th>
-<td>1.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-02</th>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-03</th>
-<td>0.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-04</th>
-<td>0.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-05</th>
-<td>0.0</td>
-</tr>
-</tbody>
-</table>
-
-<p>41200 rows × 1 columns</p>
-</div>
-</div>
-</div>
-<p>Podemos visualizar algumas séries. Vemos que há mais zeros nesse dataset, em comparação ao que usamos antes.</p>
-<div id="12dd6991" class="cell" data-execution_count="5">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">10</span>, <span class="dv">4</span>))</span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>y_train.unstack(level<span class="op">=</span><span class="dv">0</span>).droplevel(<span class="dv">0</span>, axis<span class="op">=</span><span class="dv">1</span>).iloc[:, [<span class="dv">0</span>,<span class="dv">10</span>]].plot(ax<span class="op">=</span>ax, alpha<span class="op">=</span><span class="fl">0.7</span>)</span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="panel_data_files/figure-html/cell-6-output-1.png" width="798" height="356" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<section id="pandas-e-multi-índices" class="level3" data-number="8.1.1">
-<h3 data-number="8.1.1" class="anchored" data-anchor-id="pandas-e-multi-índices"><span class="header-section-number">8.1.1</span> Pandas e multi-índices</h3>
-<p>Para trabalhar com essas estruturas de dados, é importante revisar algumas operações do pandas.</p>
-<div id="f32f8844" class="cell" data-execution_count="6">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>y_train.index.get_level_values(<span class="op">-</span><span class="dv">1</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="6">
-<pre><code>PeriodIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',
-             '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',
-             '2020-01-09', '2020-01-10',
-             ...
-             '2024-06-26', '2024-06-27', '2024-06-28', '2024-06-29',
-             '2024-06-30', '2024-07-01', '2024-07-02', '2024-07-03',
-             '2024-07-04', '2024-07-05'],
-            dtype='period[D]', name='date', length=41200)</code></pre>
-</div>
-</div>
-<p>As seguintes operações são bem úteis para trabalhar com multi-índices:</p>
-<div id="1eedeff8" class="cell" data-execution_count="7">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>y_train.index</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="7">
-<pre><code>MultiIndex([( 0, '2020-01-01'),
-            ( 0, '2020-01-02'),
-            ( 0, '2020-01-03'),
-            ( 0, '2020-01-04'),
-            ( 0, '2020-01-05'),
-            ( 0, '2020-01-06'),
-            ( 0, '2020-01-07'),
-            ( 0, '2020-01-08'),
-            ( 0, '2020-01-09'),
-            ( 0, '2020-01-10'),
-            ...
-            (24, '2024-06-26'),
-            (24, '2024-06-27'),
-            (24, '2024-06-28'),
-            (24, '2024-06-29'),
-            (24, '2024-06-30'),
-            (24, '2024-07-01'),
-            (24, '2024-07-02'),
-            (24, '2024-07-03'),
-            (24, '2024-07-04'),
-            (24, '2024-07-05')],
-           names=['sku_id', 'date'], length=41200)</code></pre>
-</div>
-</div>
-<p>Acessar valores únicos no primeiro nivel (nível 0, mais à esquerda):</p>
-<div id="8ad72c1b" class="cell" data-execution_count="8">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>y_train.index.get_level_values(<span class="dv">0</span>).unique()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="8">
-<pre><code>Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
-       18, 19, 20, 21, 22, 23, 24],
-      dtype='int64', name='sku_id')</code></pre>
-</div>
-</div>
-<p>Selecionar uma série específica (nível 0 igual a 0):</p>
-<div id="6577fc97" class="cell" data-execution_count="9">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>y_train.loc[<span class="dv">0</span>]</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="9">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-01</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-02</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-03</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-04</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-05</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-01</th>
-<td>220</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-02</th>
-<td>128</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-03</th>
-<td>47</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-04</th>
-<td>73</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-05</th>
-<td>165</td>
-</tr>
-</tbody>
-</table>
-
-<p>1648 rows × 1 columns</p>
-</div>
-</div>
-</div>
-<p>Aqui, podemos usar <code>pd.IndexSlice</code> para selecionar várias séries ao mesmo tempo. Note que pd.IndexSlice é passado diretamente para <code>.loc</code>:</p>
-<div id="ebb41b96" class="cell" data-execution_count="10">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>y_train.loc[pd.IndexSlice[[<span class="dv">0</span>,<span class="dv">2</span>], :]]</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="10">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">sku_id</th>
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">0</th>
-<th data-quarto-table-cell-role="th">2020-01-01</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-02</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-03</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2020-01-04</th>
-<td>0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2020-01-05</th>
-<td>0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">2</th>
-<th data-quarto-table-cell-role="th">2024-07-01</th>
-<td>125</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-02</th>
-<td>185</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-03</th>
-<td>179</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-04</th>
-<td>210</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-05</th>
-<td>236</td>
-</tr>
-</tbody>
-</table>
-
-<p>3296 rows × 1 columns</p>
-</div>
-</div>
-</div>
-<p>Agora, para selecionar o horizonte de forecasting, temos que chamar <code>unique</code>:</p>
-<div id="636b22ca" class="cell" data-execution_count="11">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>fh <span class="op">=</span> y_test.index.get_level_values(<span class="dv">1</span>).unique()</span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>fh</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="11">
-<pre><code>PeriodIndex(['2024-07-06', '2024-07-07', '2024-07-08', '2024-07-09',
-             '2024-07-10', '2024-07-11', '2024-07-12', '2024-07-13',
-             '2024-07-14', '2024-07-15',
-             ...
-             '2024-12-23', '2024-12-24', '2024-12-25', '2024-12-26',
-             '2024-12-27', '2024-12-28', '2024-12-29', '2024-12-30',
-             '2024-12-31', '2025-01-01'],
-            dtype='period[D]', name='date', length=180)</code></pre>
-</div>
-</div>
-</section>
-</section>
-<section id="upcasting-automático" class="level2" data-number="8.2">
-<h2 data-number="8.2" class="anchored" data-anchor-id="upcasting-automático"><span class="header-section-number">8.2</span> Upcasting automático</h2>
-<p>Nem todos modelos suportam nativamente dados em painel. Por exemplo, exponential smoothing. Aqui, temos uma boa notícia: sem linhas extras necessárias. O sktime faz <em>upcasting</em> automático para dados em painel ao usar estimadores do <code>sktime</code>.</p>
-<div id="9d92bc4b" class="cell" data-execution_count="12">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>naive_forecaster <span class="op">=</span> NaiveForecaster(strategy<span class="op">=</span><span class="st">"last"</span>, window_length<span class="op">=</span><span class="dv">1</span>)</span>
-<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a>naive_forecaster.fit(y_train)</span>
-<span id="cb15-6"><a href="#cb15-6" aria-hidden="true" tabindex="-1"></a>y_pred_naive <span class="op">=</span> naive_forecaster.predict(fh<span class="op">=</span>fh)</span>
-<span id="cb15-7"><a href="#cb15-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb15-8"><a href="#cb15-8" aria-hidden="true" tabindex="-1"></a>y_pred_naive</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="12">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">sales</th>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">sku_id</th>
-<th data-quarto-table-cell-role="th">date</th>
-<th data-quarto-table-cell-role="th"></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">0</th>
-<th data-quarto-table-cell-role="th">2024-07-06</th>
-<td>165.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-07</th>
-<td>165.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-08</th>
-<td>165.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-07-09</th>
-<td>165.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-07-10</th>
-<td>165.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">...</th>
-<th data-quarto-table-cell-role="th">...</th>
-<td>...</td>
-</tr>
-<tr class="odd">
-<th rowspan="5" data-quarto-table-cell-role="th" data-valign="top">24</th>
-<th data-quarto-table-cell-role="th">2024-12-28</th>
-<td>209.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-12-29</th>
-<td>209.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2024-12-30</th>
-<td>209.0</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">2024-12-31</th>
-<td>209.0</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2025-01-01</th>
-<td>209.0</td>
-</tr>
-</tbody>
-</table>
-
-<p>4500 rows × 1 columns</p>
-</div>
-</div>
-</div>
-<p>Internamente, o <code>sktime</code> cria um clone do estimador para cada série nos dados em painel. Em seguida, cada clone é treinado com a série correspondente. Isso é feito de forma transparente para usuário, mas sem exigir esforço.</p>
-<p>O atributo <code>forecasters_</code> armazena um DataFrame com os estimatores de cada série.</p>
-<div id="c00d21f2" class="cell" data-execution_count="13">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>naive_forecaster.forecasters_.head()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="13">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">forecasters</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">0</th>
-<td>NaiveForecaster(window_length=1)</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">1</th>
-<td>NaiveForecaster(window_length=1)</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">2</th>
-<td>NaiveForecaster(window_length=1)</td>
-</tr>
-<tr class="even">
-<th data-quarto-table-cell-role="th">3</th>
-<td>NaiveForecaster(window_length=1)</td>
-</tr>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">4</th>
-<td>NaiveForecaster(window_length=1)</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<p>É dificil explicar o quanto isso é extremamente útil para código limpo e prototipagem rápida. Foi um dos motivos que me levaram a usar o <code>sktime</code>.</p>
-</section>
-<section id="métricas" class="level2" data-number="8.3">
-<h2 data-number="8.3" class="anchored" data-anchor-id="métricas"><span class="header-section-number">8.3</span> Métricas</h2>
-<p>Agora que temos várias séries, precisamos explicar como calcular métricas de avaliação. O sktime oferece duas opções para isso, como argumentos na criação da métrica:</p>
-<ul>
-<li><code>multilevel="uniform_average_time"</code> para calcular a média das séries temporais no painel.</li>
-<li><code>multilevel="raw_values"</code> para obter o erro por série.</li>
-</ul>
-<div id="9410dffe" class="cell" data-execution_count="14">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.performance_metrics.forecasting <span class="im">import</span> MeanSquaredScaledError</span>
-<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>metric <span class="op">=</span> MeanSquaredScaledError(multilevel<span class="op">=</span><span class="st">"uniform_average_time"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="b60dea3d" class="cell" data-execution_count="15">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_naive, y_train<span class="op">=</span>y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="15">
-<pre><code>np.float64(19.04893558607091)</code></pre>
-</div>
-</div>
-<p>Na prática, as métricas que a sua aplicação exige podem ser diferentes. Por exemplo, as séries temporais podem ter diferentes importâncias, e você pode querer ponderar as métricas de acordo.</p>
-<p>Para isso, é possível criar uma métrica customizada no sktime, mas não entraremos nesse mérito aqui.</p>
-</section>
-<section id="modelos-globais-de-machine-learning" class="level2" data-number="8.4">
-<h2 data-number="8.4" class="anchored" data-anchor-id="modelos-globais-de-machine-learning"><span class="header-section-number">8.4</span> Modelos globais de Machine Learning</h2>
-<p>Quando vimos como usar modelos de Machine Learning para forecasting, já mencionamos como é necessário traduzir o problema de séries temporais para um problema de regressão tradicional.</p>
-<p>No caso de dados em painel, também podemos usar essa abordagem, mas agora aproveitando todas as séries temporais para treinar um único modelo global.</p>
-<p><img src="img/global_reduction.png" class="img-fluid"></p>
-<p>Abaixo, vamos comparar um LightGBM global com um local. Veremos o seguinte: o modelo local é <strong>melhor</strong> que o modelo global, <strong>se não processarmos os dados</strong> corretamente para o modelo global aproveitar as <strong>similaridades</strong> entre as séries!</p>
-<div id="f187058b" class="cell" data-execution_count="16">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.forecasting.reduction <span class="im">import</span> ReductionForecaster</span>
-<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> lightgbm <span class="im">import</span> LGBMRegressor</span>
-<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a>global_forecaster1 <span class="op">=</span> ReductionForecaster(</span>
-<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>    LGBMRegressor(n_estimators<span class="op">=</span><span class="dv">100</span>, verbose<span class="op">=-</span><span class="dv">1</span>),</span>
-<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a>    window_length<span class="op">=</span><span class="dv">30</span>,</span>
-<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb20-8"><a href="#cb20-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb20-9"><a href="#cb20-9" aria-hidden="true" tabindex="-1"></a>global_forecaster1.fit(y_train, X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="16">
-<style>#sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 pre {
-    padding: 0;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-label label.sk-toggleable__label,
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-cf94f6c6-79a3-4b1a-b989-433b94415f98 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-cf94f6c6-79a3-4b1a-b989-433b94415f98" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1), window_length=30)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('be9b78ca-f43e-4775-a01e-64287ff4001c')" type="checkbox"><label for="UUID('be9b78ca-f43e-4775-a01e-64287ff4001c')" class="sk-toggleable__label sk-toggleable__label-arrow">ReductionForecaster</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1), window_length=30)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('ee6d9fc9-013b-4aac-a3a2-8e47ecb83e4e')" type="checkbox"><label for="UUID('ee6d9fc9-013b-4aac-a3a2-8e47ecb83e4e')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('dbc6f469-d9e4-498e-83dc-2134dac16436')" type="checkbox"><label for="UUID('dbc6f469-d9e4-498e-83dc-2134dac16436')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="7538663d" class="cell" data-execution_count="17">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>y_pred_global1 <span class="op">=</span> global_forecaster1.predict(fh<span class="op">=</span>fh, X<span class="op">=</span>X_test)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<div id="22c4b1b4" class="cell" data-execution_count="18">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots(figsize<span class="op">=</span>(<span class="dv">10</span>, <span class="dv">4</span>))</span>
-<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>y_train.loc[<span class="dv">10</span>, <span class="st">"sales"</span>].plot(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"Treino"</span>)</span>
-<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>y_test.loc[<span class="dv">10</span>, <span class="st">"sales"</span>].plot(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"Teste"</span>)</span>
-<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>y_pred_global1.loc[<span class="dv">10</span>, <span class="st">"sales"</span>].plot(ax<span class="op">=</span>ax, label<span class="op">=</span><span class="st">"Global 1"</span>)</span>
-<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a>plt.legend()</span>
-<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="panel_data_files/figure-html/cell-19-output-1.png" width="815" height="356" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<p>Para forçar que um modelo global funcione como um modelo local, podemos usar <code>ForecastByLevel</code>, que cria um modelo separado para cada série temporal, mesmo quando o estimador suporta dados em painel.</p>
-<div id="2e4704cb" class="cell" data-execution_count="19">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.compose <span class="im">import</span> ForecastByLevel</span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>local_forecaster1 <span class="op">=</span> ForecastByLevel(global_forecaster1, groupby<span class="op">=</span><span class="st">"local"</span>)</span>
-<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>local_forecaster1.fit(y_train, X<span class="op">=</span>X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="19">
-<style>#sk-61402fd1-17bd-4598-a37e-952ca0b7e879 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 pre {
-    padding: 0;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-label label.sk-toggleable__label,
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-61402fd1-17bd-4598-a37e-952ca0b7e879 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-61402fd1-17bd-4598-a37e-952ca0b7e879" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ForecastByLevel(forecaster=ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                               window_length=30))</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('420a852a-1abd-40a8-bc98-dd04ba14baaf')" type="checkbox"><label for="UUID('420a852a-1abd-40a8-bc98-dd04ba14baaf')" class="sk-toggleable__label sk-toggleable__label-arrow">ForecastByLevel<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.ForecastByLevel.html">?<span>Documentation for ForecastByLevel</span></a></label><div class="sk-toggleable__content"><pre>ForecastByLevel(forecaster=ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                               window_length=30))</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('933bca9d-18c1-4452-827b-5cb80e91420f')" type="checkbox"><label for="UUID('933bca9d-18c1-4452-827b-5cb80e91420f')" class="sk-toggleable__label sk-toggleable__label-arrow">forecaster: ReductionForecaster</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1), window_length=30)</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('7d2a6bd8-ef53-42eb-8ccb-9a0985ca675f')" type="checkbox"><label for="UUID('7d2a6bd8-ef53-42eb-8ccb-9a0985ca675f')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('56c8c3a1-9332-47a1-875d-96c1bdf8c6d4')" type="checkbox"><label for="UUID('56c8c3a1-9332-47a1-875d-96c1bdf8c6d4')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="649b1ed5" class="cell" data-execution_count="20">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>y_pred_local1 <span class="op">=</span> local_forecaster1.predict(fh<span class="op">=</span>fh, X<span class="op">=</span>X_test)</span>
-<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>err_global1 <span class="op">=</span> metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_global1, y_train<span class="op">=</span>y_train)</span>
-<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>err_local1 <span class="op">=</span> metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_local1, y_train<span class="op">=</span>y_train)</span>
-<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a>errors <span class="op">=</span> pd.DataFrame(</span>
-<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a>    {</span>
-<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a>        <span class="st">"Global (1)"</span>: [err_global1],</span>
-<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a>        <span class="st">"Local (1)"</span>: [err_local1],</span>
-<span id="cb24-10"><a href="#cb24-10" aria-hidden="true" tabindex="-1"></a>    },</span>
-<span id="cb24-11"><a href="#cb24-11" aria-hidden="true" tabindex="-1"></a>    index<span class="op">=</span>[<span class="st">"MSE"</span>],</span>
-<span id="cb24-12"><a href="#cb24-12" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-</section>
-<section id="preprocessamento-e-engenharia-de-features" class="level2" data-number="8.5">
-<h2 data-number="8.5" class="anchored" data-anchor-id="preprocessamento-e-engenharia-de-features"><span class="header-section-number">8.5</span> Preprocessamento e engenharia de features</h2>
-<p>Sabemos como preprocessar séries temporais univariadas para melhorar o desempenho dos modelos de ML. Aplicamos da mesma maneira que fizemos anteriormente o <code>Differencer</code>, com objetivo de remover tendências.</p>
-<div id="c69e8d9e" class="cell" data-execution_count="21">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.difference <span class="im">import</span> Differencer</span>
-<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>global_forecaster2 <span class="op">=</span> Differencer() <span class="op">*</span> global_forecaster1</span>
-<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>global_forecaster2.fit(y_train, X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="21">
-<style>#sk-3f7a1a81-192b-4cf0-8379-9caa8612707c {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c pre {
-    padding: 0;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-label label.sk-toggleable__label,
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-3f7a1a81-192b-4cf0-8379-9caa8612707c a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-3f7a1a81-192b-4cf0-8379-9caa8612707c" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>TransformedTargetForecaster(steps=[Differencer(),
-                                   ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                       window_length=30)])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('cc0879eb-3a43-48fd-a4cc-eee7c5ac5f23')" type="checkbox"><label for="UUID('cc0879eb-3a43-48fd-a4cc-eee7c5ac5f23')" class="sk-toggleable__label sk-toggleable__label-arrow">TransformedTargetForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.TransformedTargetForecaster.html">?<span>Documentation for TransformedTargetForecaster</span></a></label><div class="sk-toggleable__content"><pre>TransformedTargetForecaster(steps=[Differencer(),
-                                   ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                       window_length=30)])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('cc1ebf7e-0604-4910-8fd3-65197868f899')" type="checkbox"><label for="UUID('cc1ebf7e-0604-4910-8fd3-65197868f899')" class="sk-toggleable__label sk-toggleable__label-arrow">Differencer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.difference.Differencer.html">?<span>Documentation for Differencer</span></a></label><div class="sk-toggleable__content"><pre>Differencer()</pre></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('8397a39e-cb86-41ba-b33d-7651c751396f')" type="checkbox"><label for="UUID('8397a39e-cb86-41ba-b33d-7651c751396f')" class="sk-toggleable__label sk-toggleable__label-arrow">ReductionForecaster</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1), window_length=30)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('29209e0f-0f60-4991-b6c6-6df9a6ad1a5f')" type="checkbox"><label for="UUID('29209e0f-0f60-4991-b6c6-6df9a6ad1a5f')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('ff4d3dfb-949d-47e0-8adc-76f8bf730fcc')" type="checkbox"><label for="UUID('ff4d3dfb-949d-47e0-8adc-76f8bf730fcc')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="a5a7a8ca" class="cell" data-execution_count="22">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>y_pred_global2 <span class="op">=</span> global_forecaster2.predict(fh<span class="op">=</span>fh, X<span class="op">=</span>X_test)</span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>metric_global2 <span class="op">=</span> metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_global2, y_train<span class="op">=</span>y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<p>E agora sua versão local:</p>
-<div id="7a1a8b09" class="cell" data-execution_count="23">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>local_forecaster2 <span class="op">=</span> ForecastByLevel(global_forecaster2, groupby<span class="op">=</span><span class="st">"local"</span>)</span>
-<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>local_forecaster2.fit(y_train, X<span class="op">=</span>X_train)</span>
-<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>y_pred_local2 <span class="op">=</span> local_forecaster2.predict(fh<span class="op">=</span>fh, X<span class="op">=</span>X_test)</span>
-<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a>metric_local2 <span class="op">=</span> metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_local2, y_train<span class="op">=</span>y_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-</div>
-<p>Agora, podemos comparar:</p>
-<div id="2a681407" class="cell" data-execution_count="24">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>errors[<span class="st">"Global (2)"</span>] <span class="op">=</span> metric_global2</span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>errors[<span class="st">"Local (2)"</span>] <span class="op">=</span> metric_local2</span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>errors</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="24">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">Global (1)</th>
-<th data-quarto-table-cell-role="th">Local (1)</th>
-<th data-quarto-table-cell-role="th">Global (2)</th>
-<th data-quarto-table-cell-role="th">Local (2)</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">MSE</th>
-<td>41.057873</td>
-<td>23.720592</td>
-<td>22.238809</td>
-<td>67.626298</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<p>Note como já superamos o modelo global incial, e o modelo local. Isso é para destacar que é <strong>essencial</strong> realizar um bom preprocessamento e engenharia de features para que modelos de Machine Learning tenham bom desempenho em dados em painel.</p>
-<section id="normalização-por-janela" class="level3" data-number="8.5.1">
-<h3 data-number="8.5.1" class="anchored" data-anchor-id="normalização-por-janela"><span class="header-section-number">8.5.1</span> Normalização por janela</h3>
-<p>Agora, vamos usar a normalização por janela, que é especialmente útil em dados em painel, onde as séries podem ter diferentes escalas.</p>
-<div id="1b7cb0ff" class="cell" data-execution_count="25">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>global_forecaster3 <span class="op">=</span> global_forecaster1.clone().set_params(</span>
-<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>    normalization_strategy<span class="op">=</span><span class="st">"divide_mean"</span></span>
-<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>global_forecaster3.fit(y_train, X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="25">
-<style>#sk-73113c0e-dc48-4802-9eae-6006bf3dd48f {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f pre {
-    padding: 0;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-label label.sk-toggleable__label,
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-73113c0e-dc48-4802-9eae-6006bf3dd48f a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-73113c0e-dc48-4802-9eae-6006bf3dd48f" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                    normalization_strategy='divide_mean', window_length=30)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('dd732dde-adf2-4835-90a0-b8c2c95e7dc8')" type="checkbox"><label for="UUID('dd732dde-adf2-4835-90a0-b8c2c95e7dc8')" class="sk-toggleable__label sk-toggleable__label-arrow">ReductionForecaster</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                    normalization_strategy='divide_mean', window_length=30)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('19e97d8d-964a-4fa3-abc4-93e502ea8825')" type="checkbox"><label for="UUID('19e97d8d-964a-4fa3-abc4-93e502ea8825')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('415be7e7-a958-4e1e-a907-71b6a4eb952e')" type="checkbox"><label for="UUID('415be7e7-a958-4e1e-a907-71b6a4eb952e')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="88f8aa7f" class="cell" data-execution_count="26">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Predict</span></span>
-<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>y_pred_global3 <span class="op">=</span> global_forecaster3.predict(fh<span class="op">=</span>fh, X<span class="op">=</span>X_test)</span>
-<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Métrica</span></span>
-<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a>metric_global3 <span class="op">=</span> metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_global3, y_train<span class="op">=</span>y_train)</span>
-<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a>errors[<span class="st">"Global 3 (window norm)"</span>] <span class="op">=</span> metric_global3</span>
-<span id="cb30-8"><a href="#cb30-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb30-9"><a href="#cb30-9" aria-hidden="true" tabindex="-1"></a>display(errors)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">Global (1)</th>
-<th data-quarto-table-cell-role="th">Local (1)</th>
-<th data-quarto-table-cell-role="th">Global (2)</th>
-<th data-quarto-table-cell-role="th">Local (2)</th>
-<th data-quarto-table-cell-role="th">Global 3 (window norm)</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">MSE</th>
-<td>41.057873</td>
-<td>23.720592</td>
-<td>22.238809</td>
-<td>67.626298</td>
-<td>15.317953</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-<p>Vemos que resultados são ainda melhores!</p>
-</section>
-<section id="pipelines-de-features-exógenas" class="level3" data-number="8.5.2">
-<h3 data-number="8.5.2" class="anchored" data-anchor-id="pipelines-de-features-exógenas"><span class="header-section-number">8.5.2</span> Pipelines de features exógenas</h3>
-<p>Podemos ajudar o modelo a capturar sazonalidades adicionando features de Fourier como features exógenas.</p>
-<p>Usamos <code>**</code> para criar um pipeline aplicado sobre as features exógenas:</p>
-<div id="025fecbf" class="cell" data-execution_count="27">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.fourier <span class="im">import</span> FourierFeatures</span>
-<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>fourier_features <span class="op">=</span> FourierFeatures(</span>
-<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a>    sp_list<span class="op">=</span>[<span class="fl">365.25</span>, <span class="fl">365.25</span> <span class="op">/</span> <span class="dv">12</span>], fourier_terms_list<span class="op">=</span>[<span class="dv">1</span>, <span class="dv">1</span>], freq<span class="op">=</span><span class="st">"D"</span></span>
-<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a>)</span>
-<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a>global_forecaster4 <span class="op">=</span> fourier_features<span class="op">**</span>global_forecaster3</span>
-<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a>global_forecaster4.fit(y_train, X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="27">
-<style>#sk-d4980bde-7371-498e-a983-2ca415b8a766 {
-    /* Definition of color scheme common for light and dark mode */
-    --sklearn-color-text: black;
-    --sklearn-color-line: gray;
-    /* Definition of color scheme for objects */
-    --sklearn-color-level-0: #fff5e6;
-    --sklearn-color-level-1: #f6e4d2;
-    --sklearn-color-level-2: #ffe0b3;
-    --sklearn-color-level-3: chocolate;
-
-    /* Specific color for light theme */
-    --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
-    --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
-    --sklearn-color-icon: #696969;
-
-    @media (prefers-color-scheme: dark) {
-      /* Redefinition of color scheme for dark theme */
-      --sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
-      --sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
-      --sklearn-color-icon: #878787;
-    }
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 {
-    color: var(--sklearn-color-text);
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 pre {
-    padding: 0;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 input.sk-hidden--visually {
-    border: 0;
-    clip: rect(1px 1px 1px 1px);
-    clip: rect(1px, 1px, 1px, 1px);
-    height: 1px;
-    margin: -1px;
-    overflow: hidden;
-    padding: 0;
-    position: absolute;
-    width: 1px;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-dashed-wrapped {
-    border: 1px dashed var(--sklearn-color-line);
-    margin: 0 0.4em 0.5em 0.4em;
-    box-sizing: border-box;
-    padding-bottom: 0.4em;
-    background-color: var(--sklearn-color-background);
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-container {
-    /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-       but bootstrap.min.css set `[hidden] { display: none !important; }`
-       so we also need the `!important` here to be able to override the
-       default hidden behavior on the sphinx rendered scikit-learn.org.
-       See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-    display: inline-block !important;
-    position: relative;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-text-repr-fallback {
-    display: none;
-  }
-
-  div.sk-parallel-item,
-  div.sk-serial,
-  div.sk-item {
-    /* draw centered vertical line to link estimators */
-    background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-    background-size: 2px 100%;
-    background-repeat: no-repeat;
-    background-position: center center;
-  }
-
-  /* Parallel-specific style estimator block */
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-parallel-item::after {
-    content: "";
-    width: 100%;
-    border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-    flex-grow: 1;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-parallel {
-    display: flex;
-    align-items: stretch;
-    justify-content: center;
-    background-color: var(--sklearn-color-background);
-    position: relative;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-parallel-item {
-    display: flex;
-    flex-direction: column;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-parallel-item:first-child::after {
-    align-self: flex-end;
-    width: 50%;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-parallel-item:last-child::after {
-    align-self: flex-start;
-    width: 50%;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-parallel-item:only-child::after {
-    width: 0;
-  }
-
-  /* Serial-specific style estimator block */
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-serial {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    background-color: var(--sklearn-color-background);
-    padding-right: 1em;
-    padding-left: 1em;
-  }
-
-
-  /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-  clickable and can be expanded/collapsed.
-  - Pipeline and ColumnTransformer use this feature and define the default style
-  - Estimators will overwrite some part of the style using the `sk-estimator` class
-  */
-
-  /* Pipeline and ColumnTransformer style (default) */
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-toggleable {
-    /* Default theme specific background. It is overwritten whether we have a
-    specific estimator or a Pipeline/ColumnTransformer */
-    background-color: var(--sklearn-color-background);
-  }
-
-  /* Toggleable label */
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 label.sk-toggleable__label {
-    cursor: pointer;
-    display: block;
-    width: 100%;
-    margin-bottom: 0;
-    padding: 0.5em;
-    box-sizing: border-box;
-    text-align: center;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 label.sk-toggleable__label-arrow:before {
-    /* Arrow on the left of the label */
-    content: "▸";
-    float: left;
-    margin-right: 0.25em;
-    color: var(--sklearn-color-icon);
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 label.sk-toggleable__label-arrow:hover:before {
-    color: var(--sklearn-color-text);
-  }
-
-  /* Toggleable content - dropdown */
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-toggleable__content {
-    max-height: 0;
-    max-width: 0;
-    overflow: hidden;
-    text-align: left;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-toggleable__content pre {
-    margin: 0.2em;
-    border-radius: 0.25em;
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-    /* Expand drop-down */
-    max-height: 200px;
-    max-width: 100%;
-    overflow: auto;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-    content: "▾";
-  }
-
-  /* Pipeline/ColumnTransformer-specific style */
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator-specific style */
-
-  /* Colorize estimator box */
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-    /* unfitted */
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-label label.sk-toggleable__label,
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-label label {
-    /* The background is the default theme color */
-    color: var(--sklearn-color-text-on-default-background);
-  }
-
-  /* On hover, darken the color of the background */
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-label:hover label.sk-toggleable__label {
-    color: var(--sklearn-color-text);
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Estimator label */
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-label label {
-    font-family: monospace;
-    font-weight: bold;
-    display: inline-block;
-    line-height: 1.2em;
-  }
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-label-container {
-    text-align: center;
-  }
-
-  /* Estimator-specific */
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-estimator {
-    font-family: monospace;
-    border: 1px dotted var(--sklearn-color-border-box);
-    border-radius: 0.25em;
-    box-sizing: border-box;
-    margin-bottom: 0.5em;
-    background-color: var(--sklearn-color-level-0);
-  }
-
-  /* on hover */
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 div.sk-estimator:hover {
-    background-color: var(--sklearn-color-level-2);
-  }
-
-  /* Specification for estimator info */
-
-  .sk-estimator-doc-link,
-  a:link.sk-estimator-doc-link,
-  a:visited.sk-estimator-doc-link {
-    float: right;
-    font-size: smaller;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1em;
-    height: 1em;
-    width: 1em;
-    text-decoration: none !important;
-    margin-left: 1ex;
-    border: var(--sklearn-color-level-1) 1pt solid;
-    color: var(--sklearn-color-level-1);
-  }
-
-  /* On hover */
-  div.sk-estimator:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover,
-  div.sk-label-container:hover .sk-estimator-doc-link:hover,
-  .sk-estimator-doc-link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-
-  /* Span, style for the box shown on hovering the info icon */
-  .sk-estimator-doc-link span {
-    display: none;
-    z-index: 9999;
-    position: relative;
-    font-weight: normal;
-    right: .2ex;
-    padding: .5ex;
-    margin: .5ex;
-    width: min-content;
-    min-width: 20ex;
-    max-width: 50ex;
-    color: var(--sklearn-color-text);
-    box-shadow: 2pt 2pt 4pt #999;
-    background: var(--sklearn-color-level-0);
-    border: .5pt solid var(--sklearn-color-level-3);
-  }
-
-  .sk-estimator-doc-link:hover span {
-    display: block;
-  }
-
-  /* "?"-specific style due to the `<a>` HTML tag */
-
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 a.estimator_doc_link {
-    float: right;
-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
-    height: 1rem;
-    width: 1rem;
-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-d4980bde-7371-498e-a983-2ca415b8a766 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-d4980bde-7371-498e-a983-2ca415b8a766" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ForecastingPipeline(steps=[FourierFeatures(fourier_terms_list=[1, 1], freq='D',
-                                           sp_list=[365.25, 30.4375]),
-                           ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                               normalization_strategy='divide_mean',
-                                               window_length=30)])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('0cb415fd-255b-45a6-b285-12e7b69e1404')" type="checkbox"><label for="UUID('0cb415fd-255b-45a6-b285-12e7b69e1404')" class="sk-toggleable__label sk-toggleable__label-arrow">ForecastingPipeline<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.ForecastingPipeline.html">?<span>Documentation for ForecastingPipeline</span></a></label><div class="sk-toggleable__content"><pre>ForecastingPipeline(steps=[FourierFeatures(fourier_terms_list=[1, 1], freq='D',
-                                           sp_list=[365.25, 30.4375]),
-                           ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                               normalization_strategy='divide_mean',
-                                               window_length=30)])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('bf1bfc44-b957-47df-aa95-5f18ee01c461')" type="checkbox"><label for="UUID('bf1bfc44-b957-47df-aa95-5f18ee01c461')" class="sk-toggleable__label sk-toggleable__label-arrow">FourierFeatures<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.fourier.FourierFeatures.html">?<span>Documentation for FourierFeatures</span></a></label><div class="sk-toggleable__content"><pre>FourierFeatures(fourier_terms_list=[1, 1], freq='D', sp_list=[365.25, 30.4375])</pre></div></div></div><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('a2632da2-d79b-4a31-b387-f46b56f367ce')" type="checkbox"><label for="UUID('a2632da2-d79b-4a31-b387-f46b56f367ce')" class="sk-toggleable__label sk-toggleable__label-arrow">ReductionForecaster</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                    normalization_strategy='divide_mean', window_length=30)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('8e29d4af-4033-4a0e-b3c5-b660526b1c5e')" type="checkbox"><label for="UUID('8e29d4af-4033-4a0e-b3c5-b660526b1c5e')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('f7a948ea-a13e-41df-8b91-91faa6a1fcff')" type="checkbox"><label for="UUID('f7a948ea-a13e-41df-8b91-91faa6a1fcff')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="1892c109" class="cell" data-execution_count="28">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>y_pred_global4 <span class="op">=</span> global_forecaster4.predict(fh<span class="op">=</span>fh, X<span class="op">=</span>X_test)</span>
-<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>metric_global4 <span class="op">=</span> metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_global4, y_train<span class="op">=</span>y_train)</span>
-<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>errors[<span class="st">"Global 4 (fourier)"</span>] <span class="op">=</span> metric_global4</span>
-<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a>errors</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="28">
-<div>
-
-
-<table class="dataframe caption-top table table-sm table-striped small" data-border="1">
-<thead>
-<tr class="header">
-<th data-quarto-table-cell-role="th"></th>
-<th data-quarto-table-cell-role="th">Global (1)</th>
-<th data-quarto-table-cell-role="th">Local (1)</th>
-<th data-quarto-table-cell-role="th">Global (2)</th>
-<th data-quarto-table-cell-role="th">Local (2)</th>
-<th data-quarto-table-cell-role="th">Global 3 (window norm)</th>
-<th data-quarto-table-cell-role="th">Global 4 (fourier)</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<th data-quarto-table-cell-role="th">MSE</th>
-<td>41.057873</td>
-<td>23.720592</td>
-<td>22.238809</td>
-<td>67.626298</td>
-<td>15.317953</td>
-<td>15.143648</td>
-</tr>
-</tbody>
-</table>
-
-</div>
-</div>
-</div>
-</section>
-</section>
-<section id="agrupamento-modelos-globais" class="level2" data-number="8.6">
-<h2 data-number="8.6" class="anchored" data-anchor-id="agrupamento-modelos-globais"><span class="header-section-number">8.6</span> Agrupamento + Modelos globais</h2>
-<p>Uma técnica muito adotada é fazer modelos globais por clusters de séries temporais similares.</p>
-<p>Uma maneira de categorizar é usando ADI (Average Demand Interval) e CV² (squared Coefficient of Variation). O componente ADI é calculado como o percentual de períodos com demanda (y&gt;0), e <span class="math inline">\(CV^2\)</span> é o quadrado do coeficiente de variação das demandas positivas.</p>
-<table class="caption-top table">
-<colgroup>
-<col style="width: 22%">
-<col style="width: 13%">
-<col style="width: 13%">
-<col style="width: 28%">
-<col style="width: 21%">
-</colgroup>
-<thead>
-<tr class="header">
-<th style="text-align: left;"><strong>Categoria</strong></th>
-<th style="text-align: left;"><strong>ADI</strong></th>
-<th style="text-align: left;"><strong>CV²</strong></th>
-<th style="text-align: left;"><strong>Padrão típico</strong></th>
-<th style="text-align: left;"><strong>Exemplos</strong></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td style="text-align: left;"><strong>Suave (Smooth)</strong></td>
-<td style="text-align: left;">≤ 1,32</td>
-<td style="text-align: left;">≤ 0,49</td>
-<td style="text-align: left;">Demanda contínua e estável</td>
-<td style="text-align: left;">Itens de consumo diário, alimentos</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;"><strong>Errática (Erratic)</strong></td>
-<td style="text-align: left;">≤ 1,32</td>
-<td style="text-align: left;">&gt; 0,49</td>
-<td style="text-align: left;">Demanda contínua, porém muito variável</td>
-<td style="text-align: left;">Moda, eletrônicos</td>
-</tr>
-<tr class="odd">
-<td style="text-align: left;"><strong>Intermitente (Intermittent)</strong></td>
-<td style="text-align: left;">&gt; 1,32</td>
-<td style="text-align: left;">≤ 0,49</td>
-<td style="text-align: left;">Muitos períodos sem venda, mas valores estáveis quando ocorre</td>
-<td style="text-align: left;">Peças de reposição, ferramentas</td>
-</tr>
-<tr class="even">
-<td style="text-align: left;"><strong>Irregular (Lumpy)</strong></td>
-<td style="text-align: left;">&gt; 1,32</td>
-<td style="text-align: left;">&gt; 0,49</td>
-<td style="text-align: left;">Muitos períodos com zero e valores muito variáveis quando há demanda</td>
-<td style="text-align: left;">Equipamentos caros, sobressalentes grandes</td>
-</tr>
-</tbody>
-</table>
-<div id="9fff793e" class="cell" data-execution_count="29">
-<div class="cell-output cell-output-display">
-<div>
-<figure class="figure">
-<p><img src="panel_data_files/figure-html/cell-30-output-1.png" width="950" height="566" class="figure-img"></p>
-</figure>
-</div>
-</div>
-</div>
-<div id="fb7217f5" class="cell" data-execution_count="30">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.compose <span class="im">import</span> GroupbyCategoryForecaster</span>
-<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.adi_cv <span class="im">import</span> ADICVTransformer</span>
-<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a><span class="co"># </span><span class="al">TODO</span><span class="co">: customize yours!</span></span>
-<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a>group_forecaster <span class="op">=</span> GroupbyCategoryForecaster(</span>
-<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>    forecasters <span class="op">=</span></span>
-<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a>        {<span class="st">"smooth"</span>: global_forecaster3.clone(),</span>
-<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a>        <span class="st">"erratic"</span>: global_forecaster3.clone(),</span>
-<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a>        <span class="st">"intermittent"</span>: global_forecaster3.clone(),</span>
-<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a>        <span class="st">"lumpy"</span>: global_forecaster3.clone(),</span>
-<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a>        },</span>
-<span id="cb33-12"><a href="#cb33-12" aria-hidden="true" tabindex="-1"></a>    transformer<span class="op">=</span>ADICVTransformer(features<span class="op">=</span>[<span class="st">"class"</span>],))</span>
-<span id="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-14"><a href="#cb33-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a>group_forecaster.fit(y_train, X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="30">
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-    font-size: 1rem;
-    line-height: 1em;
-    font-family: monospace;
-    background-color: var(--sklearn-color-background);
-    border-radius: 1rem;
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-    text-decoration: none;
-    color: var(--sklearn-color-level-1);
-    border: var(--sklearn-color-level-1) 1pt solid;
-  }
-
-  /* On hover */
-  #sk-cfb4db2e-976b-4014-b72c-c46ce633c722 a.estimator_doc_link:hover {
-    background-color: var(--sklearn-color-level-3);
-    color: var(--sklearn-color-background);
-    text-decoration: none;
-  }
-</style><div id="sk-cfb4db2e-976b-4014-b72c-c46ce633c722" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GroupbyCategoryForecaster(forecasters={'erratic': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                                      normalization_strategy='divide_mean',
-                                                                      window_length=30),
-                                       'intermittent': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                                           normalization_strategy='divide_mean',
-                                                                           window_length=30),
-                                       'lumpy': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                                    normalization_strategy='divide_mean',
-                                                                    window_length=30),
-                                       'smooth': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                                     normalization_strategy='divide_mean',
-                                                                     window_length=30)},
-                          transformer=ADICVTransformer(features=['class']))</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('d9e8f1b7-eb65-4a83-908f-b10ac9a56872')" type="checkbox"><label for="UUID('d9e8f1b7-eb65-4a83-908f-b10ac9a56872')" class="sk-toggleable__label sk-toggleable__label-arrow">GroupbyCategoryForecaster<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.forecasting.compose.GroupbyCategoryForecaster.html">?<span>Documentation for GroupbyCategoryForecaster</span></a></label><div class="sk-toggleable__content"><pre>GroupbyCategoryForecaster(forecasters={'erratic': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                                      normalization_strategy='divide_mean',
-                                                                      window_length=30),
-                                       'intermittent': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                                           normalization_strategy='divide_mean',
-                                                                           window_length=30),
-                                       'lumpy': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                                    normalization_strategy='divide_mean',
-                                                                    window_length=30),
-                                       'smooth': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                                                                     normalization_strategy='divide_mean',
-                                                                     window_length=30)},
-                          transformer=ADICVTransformer(features=['class']))</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('dc5a74d9-216d-4d59-89e1-b3d4f08a3efd')" type="checkbox"><label for="UUID('dc5a74d9-216d-4d59-89e1-b3d4f08a3efd')" class="sk-toggleable__label sk-toggleable__label-arrow">ADICVTransformer</label><div class="sk-toggleable__content"><pre>ADICVTransformer(features=['class'])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('1fea5c2a-4394-4411-8907-61b74e8708fe')" type="checkbox"><label for="UUID('1fea5c2a-4394-4411-8907-61b74e8708fe')" class="sk-toggleable__label sk-toggleable__label-arrow">ADICVTransformer<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://www.sktime.net/en/v0.39.0/api_reference/auto_generated/sktime.transformations.series.adi_cv.ADICVTransformer.html">?<span>Documentation for ADICVTransformer</span></a></label><div class="sk-toggleable__content"><pre>ADICVTransformer(features=['class'])</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('6ecaa260-b06c-47dd-8323-13236d6a3674')" type="checkbox"><label for="UUID('6ecaa260-b06c-47dd-8323-13236d6a3674')" class="sk-toggleable__label sk-toggleable__label-arrow">smooth</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                    normalization_strategy='divide_mean', window_length=30)</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('5eedc69f-6c18-4bc7-a5c5-6b149b3bd54c')" type="checkbox"><label for="UUID('5eedc69f-6c18-4bc7-a5c5-6b149b3bd54c')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('c26e779b-e8d2-4d68-ae61-f307c47d7675')" type="checkbox"><label for="UUID('c26e779b-e8d2-4d68-ae61-f307c47d7675')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('1e28f2d6-4d87-4c0e-8508-c5ff2b540fd9')" type="checkbox"><label for="UUID('1e28f2d6-4d87-4c0e-8508-c5ff2b540fd9')" class="sk-toggleable__label sk-toggleable__label-arrow">erratic</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                    normalization_strategy='divide_mean', window_length=30)</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('45bf4f08-a38e-4f82-9359-64fc9e69bb50')" type="checkbox"><label for="UUID('45bf4f08-a38e-4f82-9359-64fc9e69bb50')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('f773f238-7798-4d84-90f0-2e918fa36b62')" type="checkbox"><label for="UUID('f773f238-7798-4d84-90f0-2e918fa36b62')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('4039f344-0a35-4806-8b85-e6e471712903')" type="checkbox"><label for="UUID('4039f344-0a35-4806-8b85-e6e471712903')" class="sk-toggleable__label sk-toggleable__label-arrow">intermittent</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                    normalization_strategy='divide_mean', window_length=30)</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('d7216e34-ed1e-4b6f-be91-d2498bf64c61')" type="checkbox"><label for="UUID('d7216e34-ed1e-4b6f-be91-d2498bf64c61')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('7a6b15e1-720e-41e9-86d1-e5b7f72ea779')" type="checkbox"><label for="UUID('7a6b15e1-720e-41e9-86d1-e5b7f72ea779')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('b52c7e35-f9c1-4efc-8af0-5410489e6291')" type="checkbox"><label for="UUID('b52c7e35-f9c1-4efc-8af0-5410489e6291')" class="sk-toggleable__label sk-toggleable__label-arrow">lumpy</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=LGBMRegressor(verbose=-1),
-                    normalization_strategy='divide_mean', window_length=30)</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('a1038f99-1e5c-4889-9ebd-08a22e27e3ce')" type="checkbox"><label for="UUID('a1038f99-1e5c-4889-9ebd-08a22e27e3ce')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('fe8e92f5-31cd-4905-afff-4a9d16fc1dd8')" type="checkbox"><label for="UUID('fe8e92f5-31cd-4905-afff-4a9d16fc1dd8')" class="sk-toggleable__label sk-toggleable__label-arrow">LGBMRegressor</label><div class="sk-toggleable__content"><pre>LGBMRegressor(verbose=-1)</pre></div></div></div></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('88ff3179-965d-4a0a-8e4f-4afa34197191')" type="checkbox"><label for="UUID('88ff3179-965d-4a0a-8e4f-4afa34197191')" class="sk-toggleable__label sk-toggleable__label-arrow">fallback_forecaster</label><div class="sk-toggleable__content"><pre>None</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('48930e51-f857-4966-bce0-84449c117a38')" type="checkbox"><label for="UUID('48930e51-f857-4966-bce0-84449c117a38')" class="sk-toggleable__label sk-toggleable__label-arrow">None</label><div class="sk-toggleable__content"><pre>None</pre></div></div></div></div></div></div></div></div></div></div>
-</div>
-</div>
-<div id="9155f56a" class="cell" data-execution_count="31">
-<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>y_pred_group <span class="op">=</span> group_forecaster.predict(fh<span class="op">=</span>fh, X<span class="op">=</span>X_test)</span>
-<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a>metric_group <span class="op">=</span> metric(y_true<span class="op">=</span>y_test, y_pred<span class="op">=</span>y_pred_group, y_train<span class="op">=</span>y_train)</span>
-<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb34-4"><a href="#cb34-4" aria-hidden="true" tabindex="-1"></a>metric_group</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
-<div class="cell-output cell-output-display" data-execution_count="31">
-<pre><code>np.float64(53.56051503265084)</code></pre>
-</div>
-</div>
-<p>Although it did not perform better than the best global model in this synthetic example, in real-world scenarios this approach can be very effective, particularly when there are large samples for each category, allowing the model to learn specific patterns for each group of series.</p>
-<p>In addition, this can be useful when there are computation constraints, as training multiple smaller models for each cluster can be more efficient than training a single large global model.</p>
-</section>
-<section id="resumo" class="level2" data-number="8.7">
-<h2 data-number="8.7" class="anchored" data-anchor-id="resumo"><span class="header-section-number">8.7</span> Resumo</h2>
-<p>Aqui, vimos como trabalhar com dados em painel (multi-series) usando o sktime. Vimos, especialmente, como criar modelos globais de Machine Learning que aproveitam as similaridades entre as séries para melhorar o desempenho das previsões. Também destacamos a importância do preprocessamento e da engenharia de features para obter bons resultados com esses modelos.</p>
-<p>Para uma análise mais aprofundada, recomendo o artigo de <span class="citation" data-cites="montero2021principles">(<a href="#ref-montero2021principles" role="doc-biblioref">Montero-Manso and Hyndman 2021</a>)</span>, que discute princípios para forecasting em dados em painel usando modelos globais de Machine Learning.</p>
-
-
-<div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
-<div id="ref-makridakis2022m5" class="csl-entry" role="listitem">
-Makridakis, Spyros, Evangelos Spiliotis, and Vassilios Assimakopoulos. 2022. <span>“M5 Accuracy Competition: Results, Findings, and Conclusions.”</span> <em>International Journal of Forecasting</em> 38 (4): 1346–64.
-</div>
-<div id="ref-montero2021principles" class="csl-entry" role="listitem">
-Montero-Manso, Pablo, and Rob J Hyndman. 2021. <span>“Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality.”</span> <em>International Journal of Forecasting</em> 37 (4): 1632–53.
-</div>
-</div>
-</section>
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\ No newline at end of file
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-             <p>Felipe Angelim </p>
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-<h1 class="unnumbered">Prefácio</h1><p><img src="logo.jpeg" title="Previsão de Séries temporais com Python: um pequeno guia" class="quarto-cover-image nolightbox img-fluid" alt="Uma ilustração de séries temporais"></p>
-<p>Esse é um pequeno livro feito para o workshop de sktime na Python Brasil 2025. O objetivo é apresentar os conceitos básicos de séries temporais e como usar o sktime para modelagem e previsão.</p>
-<p>Esse livro é raso em diversos aspectos, mas pode ser útil como ponto de partida para quem quer aprender mais sobre séries temporais em Python.</p>
-<p>Caso queira contribuir para sktime, visite o <a href="https://github.com/sktime/sktime">repositório oficial no GitHub</a> e participe do nosso <a href="https://discord.com/invite/54ACzaFsn7">Discord</a>.</p>
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-    "text": "Prefácio\nEsse é um pequeno livro feito para o workshop de sktime na Python Brasil 2025. O objetivo é apresentar os conceitos básicos de séries temporais e como usar o sktime para modelagem e previsão.\nEsse livro é raso em diversos aspectos, mas pode ser útil como ponto de partida para quem quer aprender mais sobre séries temporais em Python.\nCaso queira contribuir para sktime, visite o repositório oficial no GitHub e participe do nosso Discord.",
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-    "text": "Em modelos de regressão e classificação básicos, supomos que os dados são i.i.d: variaveis aleatórias independentes e identicamente distribuidas. Ou seja, a ordem que observamos os dados não tem nenhum impacto em nossa habilidade de prever quais serão as próximas observações.\nVamos considerar um experimento simples: temos uma caixa com bolas pretas e vermelhas dentro. Digamos que as bolas pretas valem 1 e as bolas vermelhas valem 0. Queremos descobrir qual a quantidade de pontos vamos ter na próxima rodada.\n\n\n\nBolas pretas e vermelhas\n\n\nSe as bolas estão distribuidas aleatóriamente dentro da caixa, podemos esperar ver algo como a seguinte sequência de pontos:\n\\[\n\\{0, 1, 1, 0, 1, 0, 0, \\dots \\}\n\\]\nNesse caso, a ordem dos pontos não importa. Não importa o que vimos até então; a próxima bola que vamos tirar da caixa é independente das bolas que já tiramos.\nVamos supor um caso ligeiramente diferente. Agora, as bolas estão organizadas em blocos: primeiro todas as bolas pretas, depois todas as bolas vermelhas, depois todas as bolas pretas de novo, e assim por diante. Nesse caso, podemos esperar ver algo como:\n\\[\n\\{1, 1, 1, 0, 0, 0, 1, 1, 1, \\dots \\}\n\\]\nNesse caso, a ordem dos pontos importa. Se vimos muitas bolas pretas recentemente, é mais provável que vejamos uma bola preta na próxima rodada. A próxima bola que vamos tirar da caixa depende das bolas que já tiramos. Esse é um caso básico de aplicação de modelos de previsão de séries temporais.\nConsiderando que cada observação é indexada por uma variável temporal, podemos definir uma série temporal como uma sequência de observações ordenadas no tempo. Exemplos comuns de séries temporais incluem:\n\nVarejo: vendas diárias, semanais ou mensais de um produto\nFinanças: preços diários de ações, taxas de câmbio\nSaúde: número diário de novos casos de uma doença\nClima: temperatura diária, precipitação mensal\n\nEsse livro é uma introdução prática a modelos de séries temporais, com objetivo de dar contexto das ferramentas mais relevantes em 3h de leitura.\nO conteúdo está organizado nas seguintes seções:\n\nIntrodução a séries temporais:\n\nModelo Naive, Modelo Naive sazonal\nSéries integradas, diferenciação e estacionariedade\nModelo de Suavização Exponencial (Exponential Smoothing)\nModelos autoregressivos (AR)\nMétricas de avaliação de modelos de séries temporais\nEngenharia de features para séries temporais\n\nModelos avançados e caso de uso\n\nForecast com modelos de Machine Learning\nForecast com modelos fundacionais\nPrevisão de vendas totais agregadas\nPrevisão de vendas por região e modelos globais\nPrevisão hierárquica e reconciliação\n\nCustomização de modelos com sktime\n\nComo customizar e criar modelos em sktime\nCriando um wrapper de biblioteca externa",
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-      "Part I: Básico",
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-    "text": "2.1 Exemplo prático com sktime\nConsiderando o nosso exemplo da caixa com bolas pretas e vermelhas, um modelo simples para prever a próxima bola que vamos tirar da caixa é o Modelo Naive.\nExistem algumas versões de modelo naive:\nEsse modelo é simples, mas é extremamente eficaz e dificil de ser vencido em muitos casos. No cenário onde os dados não vêm organizados em nenhuma ordem específica, a melhor aposta que podemos fazer é o valor médio das bolas no histórico recente.\nVamos ver como fazer um forecast simples com sktime, usando o modelo Naive.\nAqui, baixamos o dataset simples que vem na biblioteca desse repositório.\nfrom tsbook.datasets.simple import SimpleDataset\n\n\ndataset = SimpleDataset(True)\ny = dataset.load(\"y\")\n\ny.head()\n\n\n\n\n\n\n\n\npoints\n\n\n\n\n2021-01-01\n0\n\n\n2021-01-02\n0\n\n\n2021-01-03\n0\n\n\n2021-01-04\n1\n\n\n2021-01-05\n1\nEsse é um dataset simples com uma série temporal mensal. Vamos dividir os dados em treino e teste, usando os últimos 36 meses como teste. Para isso, devemos respeitar a ordem temporal dos dados.\nA função temporal_train_test_split faz isso para nós.\nfrom sktime.forecasting.model_selection import temporal_train_test_split\ny_train, y_test = temporal_train_test_split(y, test_size=36)\nTambém temos uma função de plotagem simples para visualizar séries temporais.\nfrom sktime.utils.plotting import plot_series\n\nplot_series(y, labels=[\"Observações\"])",
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-    "text": "2.1.1 Criando o modelo\nNo sktime, os modelos são usados em 3 passos:\n\nInicialização (__init__): aqui, definimos os hiperparâmetros do modelo. Pense nessa parte como a configuração do modelo.\nTreinamento (fit): aqui, o modelo aprende com os dados de treino.\nPrevisão (predict): com esse método, o modelo faz previsões para os dados futuros.\n\nQuando inicializamos o modelo em um notebook, o sktime mostra uma ilustração do modelo, o que é útil para entender o que está acontecendo “por baixo dos panos”. Em casos mais complexos com composição de modelos, isso pode ser útil para ilustrar o que estamos fazendo para outros cientistas.\n\nfrom sktime.forecasting.naive import NaiveForecaster\n\nmodel = NaiveForecaster(strategy=\"last\")\nmodel\n\nNaiveForecaster()Please rerun this cell to show the HTML repr or trust the notebook.NaiveForecaster?Documentation for NaiveForecasterNaiveForecaster()\n\n\nAo treinar o modelo, passamos dados de treinamento.\n\nmodel.fit(y_train)\n\nNaiveForecaster()Please rerun this cell to show the HTML repr or trust the notebook.NaiveForecaster?Documentation for NaiveForecasterNaiveForecaster()\n\n\nPara a previsão, temos que passar um argumento obrigatório: fh, abreviatura de “forecasting horizon” (horizonte de previsão). Tipicamente passamos um fh relativo, ou seja, no formato de uma lista\n\nmodel.predict(fh=[1,2,3,4])\n\n\n\n\n\n\n\n\npoints\n\n\n\n\n2021-03-06\n1.0\n\n\n2021-03-07\n1.0\n\n\n2021-03-08\n1.0\n\n\n2021-03-09\n1.0\n\n\n\n\n\n\n\nOnde cada número representa o número de períodos à frente que queremos prever. Também podemos passar o fh como um índice de tempo absoluto, que é o que faremos aqui. Vamos passar o índice de tempo do conjunto de teste.\n\ny_pred = model.predict(fh=y_test.index)\n\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão\"])\n\n\n\n\n\n\n\n\n\n\n2.1.2 Ajustando hiperparâmetros\nPara alterar hiperparametros de um modelo já existente, podemos usar o método set_params, que modifica in-place os hiperparâmetros do modelo.\n\nmodel.set_params(\n    strategy=\"mean\",\n    window_length=12,\n)\n\nmodel.fit(y_train)\ny_pred = model.predict(fh=y_test.index)\n\n\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão\"])\n\n\n\n\n\n\n\n\nPodemos também testar o Naive sazonal, que repete a última observação de 6 períodos atrás.\n\nmodel.set_params(\n    sp=6,\n    strategy=\"last\"\n\n)\nmodel.fit(y_train)\ny_pred = model.predict(fh=y_test.index)\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão\"])\n\n\n\n\n\n\n\n\nClaro, como esse exemplo é muito simples, o modelo naive sazonal captura perfeitamente a sazonalidade dos dados. No entanto, vamos ver no próximo capítulo um caso de uso mais realista, com dados de varejo, e estudaremos modelos mais avançados.",
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-    "text": "3.1 Importando dados\nNesse capítulo, vamos começar a usar um dataset mais realista, com dados simulando vendas diárias de uma empresa de varejo.\nVamos aprender os seguintes pontos:\nPara acessar os dados, vamos usar a classe SyntheticRetail da biblioteca tsbook, que contém dados simulados de vendas diárias de uma empresa de varejo.\nfrom tsbook.datasets.retail import SyntheticRetail\nfrom sktime.utils.plotting import plot_series\n\ndataset = SyntheticRetail(\"univariate\")\ny_train, y_test = dataset.load(\"y_train\", \"y_test\")\n\nplot_series(y_train, y_test, labels=[\"Treino\", \"Teste\"])\nOs dados são diários, e vemos que sempre positivos. Também notamos que existe alguma sazonalidade, aparentemente algo mensal e anual, que aumenta de magnitude ao longo do tempo.\nAlgo que deve chamar a atenção nesse gráfico é que a magnitude da série temporal está aumentando ao longo do tempo. Isso não deve passar desapercebido, pois é um ponto importante para entendermos o que vem a seguir.",
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-    "text": "3.2 Auto-correlação\nÉ interessante entender o quanto de informação o passado de uma série temporal carrega sobre o seu futuro. Uma maneira de capturar essa relação (considerando variações lineares) é através da auto-correlação. A autocorrelação para um determinado lag \\(k\\) é definida como:\n\\[\n\\text{Corr}(Y_t,Y_{t-k})=\\frac{\\text{Cov}(Y_t,Y_{t-k})}{\\sqrt{\\text{Var}(Y_t)\\text{Var}(Y_{t-k})}} = \\frac{E[(Y_t - \\mu)(Y_{t-k} - \\mu)]}{\\sqrt{\\text{Var}(Y_t)\\text{Var}(Y_{t-k})}}\n\\]\nEm outras palavras, quando o valor em \\(k\\) observações atrás está acima (ou abaixo) da média, o valor atual também tende a estar acima (ou abaixo) da média?\nCom plot_correlations, podemos visualizar algumas informações úteis:\n\nNo plot superior, vemos a série temporal original.\nNo canto inferior esquerdo, temos o gráfico de autocorrelação (ACF), que mostra a correlação entre a série temporal e suas versões defasadas (lags). Valores próximos de 1 ou -1 indicam uma forte correlação positiva ou negativa, respectivamente.\nNo canto inferior direito, temos o gráfico de autocorrelação parcial (PACF), que mostra a correlação entre a série temporal e suas versões defasadas, controlando para as correlações intermediárias. É útil para entender o efeito isolado de um lag.\n\n\nfrom sktime.utils.plotting import plot_correlations\n\n\nfig, ax = plot_correlations(y_train, lags=60)\nfig.show()\n\n/var/folders/_2/9y4tsvdd2n3gqjgd2zmlr1km0000gn/T/ipykernel_24222/2914460266.py:5: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n  fig.show()\n\n\n\n\n\n\n\n\n\nNo gráfico de autocorrelação, vemos algumas características interessantes:\n\nValores são extremamente altos, e decaem lentamente ao longo do tempo.\nExistem oscilações claras, indicando padrões sazonais na série temporal.\n\n\n\n\n\n\n\nTip\n\n\n\nÉ um erro comum usar a correlação de lags de uma série para seleção de variáveis (lags). Sempre que possível, devemos eliminar a tendência antes de analisar a autocorrelação.\n\n\nVeremos que esses padrões são indicativos de que a série temporal possui componentes importantes, e que valores passados dizem muito sobre valores futuros.",
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-    "text": "3.3 Componentes de séries temporais\nSéries temporais podem ser decompostas em 3 componentes principais:\n\nTendência: padrão de longo prazo na série temporal\nSazonalidade: padrões que se repetem em intervalos regulares, como diariamente, semanalmente ou anualmente\nRuído: variação aleatória que não pode ser explicada pelos outros componentes\n\nUma série aditiva pode ser representada como:\n\\[\nY(t) = T(t) + S(t) + R(t)\n\\]\nonde \\(T(t)\\) é a tendência, \\(S(t)\\) é a sazonalidade, e \\(R(t)\\) é o ruído.\nEm séries aditivas, o impacto da sazonalidade se dá em termos absolutos, dizemos: “em janeiro, as vendas aumentam em 100 unidades com relação a média do ano”.\nMas também existem séries multiplicativas, onde os componentes interagem de forma diferente:\n\\[\nY(t) = T(t) \\cdot S(t) \\cdot R(t)\n\\]\nNessas séries, o impacto da sazonalidade se dá em termos relativos, dizemos: “em janeiro, as vendas aumentam em 20% com relação a média do ano”. Esse é o caso mais comum para séries não-negativas, como vendas. Isso vem por definição: se temos vendas muito baixas, por exemplo, 10 unidades, não faz sentido dizer que em janeiro as vendas diminuem em 100 unidades, pois isso levaria a vendas negativas. Já dizer que as vendas diminuem em 20% é perfeitamente razoável.\n\n\n\n\n\n\nTip\n\n\n\nEm alguns casos, as séries multiplicativas são definidas como:\n\\[\nY(t) = T(t) + T(t) \\cdot S(t) + T(t) \\cdot R(t)\n\\]\n\n\nQuando a série é multiplicativa, podemos fazer recurso ao logaritmo para transformá-la em aditiva:\n\\[\nlog(Y(t)) = log(T(t)) + log(S(t)) + log(R(t))\n\\]\nPara fazer isso no sktime, usamos o transformador LogTransformer. Transformadores são usados para pré-processar ou pós-processar os dados antes de aplicar um modelo de previsão, e sua interface é similar a dos modelos de previsão:\n\n__init__: define os hiperparâmetros do transformador\nfit: aprende os parâmetros do transformador a partir dos dados\ntransform: aplica a transformação nos dados\ninverse_transform (opcional): aplica a transformação inversa nos dados\n\n\nfrom sktime.transformations.series.boxcox import LogTransformer\nlog_transformer = LogTransformer()\nlog_transformer.fit(y_train)\n\nLogTransformer()Please rerun this cell to show the HTML repr or trust the notebook.LogTransformer?Documentation for LogTransformerLogTransformer()\n\n\n\ny_train_log = log_transformer.transform(y_train)\nplot_series(y_train_log, labels=[\"Logaritmo\"])\n\n\n\n\n\n\n\n\nAinda que não esteja perfeito, essa transformação estabiliza as variações da série temporal, o que é importante para alguns modelos de previsão.\n\n3.3.1 Decompondo a série temporal\nSktime fornece algumas opções para decompor séries temporais. Aqui, vamos usar o Detrender para remover a tendência, e o Deseasonalizer para remover a sazonalidade.\n\n3.3.1.1 Tendência\n\nfrom sktime.transformations.series.detrend import Detrender, Deseasonalizer\n\n\ndetrender = LogTransformer() * Detrender(model=\"additive\")\ndetrender.fit(y_train)\ny_train_detrended = detrender.transform(y_train)\nplot_series(y_train_detrended, labels=[\"Detrended\"])\n\n\n\n\n\n\n\n\nVemos uma mudança importante no gráfico de autocorrelação:\n\nfig, _ = plot_correlations(y_train_detrended, lags=60)\nfig.show()\n\n/var/folders/_2/9y4tsvdd2n3gqjgd2zmlr1km0000gn/T/ipykernel_24222/935228866.py:2: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n  fig.show()\n\n\n\n\n\n\n\n\n\nO que indica que, ao eliminar a tendencia, a informação que o passado carrega sobre o futuro diminuiu bastante. Na verdade, a existência da tendência - um efeito de longo prazo - faz com que valores passados sejam altamente correlacionados com valores futuros, e pode dar a falsa impressão de que a série é “fácil” de modelar.\n\n\n3.3.1.2 Sazonalidade\nAgora, usamos o Deseasonalizer para remover a sazonalidade:\n\ndeseasonalizer = LogTransformer() * Deseasonalizer(model=\"additive\", sp=365)\ndeseasonalizer.fit(y_train)\ny_train_deseasonalized = deseasonalizer.transform(y_train)\nplot_series(y_train_log, y_train_deseasonalized, labels=[\"Log with seasonality\", \"Deseasonalized\"])\n\n\n\n\n\n\n\n\nPodemos usar o Detrender e o Deseasonalizer juntos para remover ambos os componentes:\n\nremove_components = LogTransformer() * Detrender(model=\"additive\") * Deseasonalizer(model=\"additive\", sp=365) \nremove_components.fit(y_train)\ny_train_removed = remove_components.transform(y_train)\nplot_series(y_train_log, y_train_removed, labels=[\"Log with seasonality\", \"Deseasonalized and detrended\"])\n\n\n\n\n\n\n\n\n\nfig, _ = plot_correlations(y_train_removed, lags=60)\nfig.show()\n\n/var/folders/_2/9y4tsvdd2n3gqjgd2zmlr1km0000gn/T/ipykernel_24222/10449850.py:2: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n  fig.show()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTip\n\n\n\nTente adicionar mais um deseasonalizer para remover a sazonalidade semanal.",
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-    "text": "3.4 Séries estacionárias\nO conceito de estacionariedade é fundamental em séries temporais. Uma série temporal é dita estacionária se suas propriedades estatísticas, como média, variância e autocovariância, são constantes ao longo do tempo, não importando a janela e quando ela é observada.\n\nMais precisamente, se \\(Y(t)\\), onde \\(t\\) é o indice temporal, então dizemos que ela é estacionária se:\n\\[\nP(Y(t_{start}:t_{end})) = P(Y(t_{start}+k:t_{end}+k)), \\quad \\forall k, t_{start}, t_{end} \\in \\mathbb{Z}\n\\]\n\nClaramente, a série temporal que estamos analisando não é estacionária. Basta percebermos que para valores maiores de \\(t\\), a média e a variância são maiores.\nO aumento da média da série ao longo do tempo é chamado de tendência. A tendência é um padrão de longo prazo na série temporal, e um grande desafio para previsões de longo prazo.\nExistem definições mais “suaves” de estacionariedade, como a estacionariedade fraca, que requer apenas que propriedades como média e autovariância sejam constantes ao longo do tempo.\nNo fundo, o que nos interessa mais é ter uma série temporal que seja “fácil”de modelar. Para alguns algoritmos, como Naive, é importante que ela seja o mais próxima possível de estacionária. Veja abaixo o problema geraodo quando aplicamos o modelo Naive diretamente na série temporal original.\n\nfrom sktime.forecasting.naive import NaiveForecaster\n\nnaive = NaiveForecaster(strategy=\"mean\", window_length=24)\nnaive.fit(y_train)\ny_pred = naive.predict(fh=y_test.index)\n\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão Naive\"])\n\n\n\n\n\n\n\n\n\n3.4.1 Diferenciação\nUma técnica simples e eficaz para lidar com séries não estacionárias é a diferenciação. Calculamos:\n\\[\nY'(t) = Y(t) - Y(t-1)\n\\]\ne fazemos previsões em \\(Y'(t)\\) ao invés de \\(Y(t)\\). Para obter a previsão de \\(Y(t)\\), precisamos fazer o processo inverso: somar a previsão de \\(Y'(t)\\) com o valor anterior de \\(Y(t-1)\\).\n\\[\n\\hat{Y(t)} = \\hat{Y'}(t) + \\hat{Y}(t-1), \\quad \\hat{Y}(0) \\text{ conhecido}\n\\]\nCom sktime, isso é extremamente fácil. Aqui, vamos usar um transformador chamado Differencer.\n\nfrom sktime.transformations.series.difference import Differencer\ndiff = Differencer()\ndiff.fit(y_train)\n\nDifferencer()Please rerun this cell to show the HTML repr or trust the notebook.Differencer?Documentation for DifferencerDifferencer()\n\n\n\ny_train_diff = diff.transform(y_train)\n\nplot_series(y_train, y_train_diff, labels=[\"Original\", \"Diferenciado\"])",
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-    "text": "3.5 Criando um pipeline com diferenciação e Naive\nAgora, podemos criar um modelo de forecasting mais complexo, composto por dois passos:\n\nDiferenciação dos dados\nModelo Naive aplicado nos dados diferenciados\n\nPara isso, usamos a classe TransformedTargetForecaster, que cria um pipeline de transformadores e um modelo de previsão.\n\nfrom sktime.forecasting.compose import TransformedTargetForecaster\n\nmodel = TransformedTargetForecaster(steps=[\n    (\"differencer\", Differencer()),\n    (\"naive\", NaiveForecaster(strategy=\"mean\", window_length=24))\n])\nmodel.fit(y_train)\n\nTransformedTargetForecaster(steps=[('differencer', Differencer()),\n                                   ('naive',\n                                    NaiveForecaster(strategy='mean',\n                                                    window_length=24))])Please rerun this cell to show the HTML repr or trust the notebook.TransformedTargetForecaster?Documentation for TransformedTargetForecasterTransformedTargetForecaster(steps=[('differencer', Differencer()),\n                                   ('naive',\n                                    NaiveForecaster(strategy='mean',\n                                                    window_length=24))])Differencer?Documentation for DifferencerDifferencer()NaiveForecaster?Documentation for NaiveForecasterNaiveForecaster(strategy='mean', window_length=24)\n\n\nOu apenas:\n\nmodel = Differencer() * NaiveForecaster(strategy=\"mean\", window_length=24)\nmodel.fit(y_train)\n\nTransformedTargetForecaster(steps=[Differencer(),\n                                   NaiveForecaster(strategy='mean',\n                                                   window_length=24)])Please rerun this cell to show the HTML repr or trust the notebook.TransformedTargetForecaster?Documentation for TransformedTargetForecasterTransformedTargetForecaster(steps=[Differencer(),\n                                   NaiveForecaster(strategy='mean',\n                                                   window_length=24)])Differencer?Documentation for DifferencerDifferencer()NaiveForecaster?Documentation for NaiveForecasterNaiveForecaster(strategy='mean', window_length=24)\n\n\nE agora podemos prever:\n\ny_pred = model.predict(fh=y_test.index)\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão Naive com diferenciação\"])\n\n\n\n\n\n\n\n\nExistem ainda alguns problemas com a diferenciação. Note que a variância da série temporal diferenciada não é constante ao longo do tempo. Aqui, podemos combinar nossa transformação logarítmica com a diferenciação.\nPrimeiro, vamos criar um transformador que combina as duas transformações:\n\nfrom sktime.transformations.compose import TransformerPipeline\n\nlog_diff = TransformerPipeline(steps=[\n    (\"log\", LogTransformer()),\n    (\"diff\", Differencer())\n])\nlog_diff.fit(y_train)\n\nTransformerPipeline(steps=[('log', LogTransformer()), ('diff', Differencer())])Please rerun this cell to show the HTML repr or trust the notebook.TransformerPipeline?Documentation for TransformerPipelineTransformerPipeline(steps=[('log', LogTransformer()), ('diff', Differencer())])LogTransformer?Documentation for LogTransformerLogTransformer()Differencer?Documentation for DifferencerDifferencer()\n\n\n\ny_train_log_diff = log_diff.transform(y_train)\nplot_series(y_train_log_diff, labels=[\"Log + Diferenciado\"])\n\n\n\n\n\n\n\n\nPara fazer forecast, criamos um pipeline com o transformador combinado e o modelo Naive:\n\nmodel = log_diff * NaiveForecaster(strategy=\"mean\", window_length=24)\nmodel.fit(y_train)\ny_pred = model.predict(fh=y_test.index)\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão Naive com log + diferenciação\"])",
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-    "text": "4.1 Exponential Smoothing\nO Modelo Naive, que calculada a média dos últimos valores, é um modelo muito simples, mas que pode ser melhorado. Analizando o que ele faz:\n\\[\n\\hat{Y}(t) = \\frac{Y(t-1) + Y(t-2) + \\dots + Y(t-n)}{n}\n\\]\nEle basicamente atribui o mesmo peso para todas as observações passadas. Mas, intuitivamente, faz mais sentido dar mais peso para as observações mais recentes, e menos peso para as observações mais antigas.\nO exponential smoothing faz exatamente isso. Ele atribui pesos decrescentes para observações mais antigas. O peso da observação decresce exponencialmente segundo um fator \\(0 &lt; \\alpha &lt; 1\\) (Hyndman and Athanasopoulos 2018):\n\\[\n\\hat{Y}(t) = \\alpha Y(t-1) + \\alpha(1 - \\alpha)Y(t-1) + \\alpha(1 - \\alpha)^2 Y(t-2) + \\dots\n\\]\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(1, 1, figsize=(6, 4))\nalpha = 0.3\nweights = [alpha * (1 - alpha) ** i for i in range(20)]\nax.bar(range(len(weights)), weights)\nax.set_title(\"Pesos do Exponential Smoothing\")\nax.set_xlabel(\"Observações passadas\")\nax.set_ylabel(\"Peso\")\nplt.show()",
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-    "text": "4.1.1 Usando Exponential Smoothing com sktime\n\nfrom tsbook.datasets.retail import SyntheticRetail\nfrom sktime.utils.plotting import plot_series\n\ndataset = SyntheticRetail(\"univariate\")\ny_train, y_test = dataset.load(\"y_train\", \"y_test\")\n\n\nfrom sktime.utils.plotting import plot_series\nfrom sktime.forecasting.exp_smoothing import ExponentialSmoothing\n\nmodel = ExponentialSmoothing()\nmodel.fit(y_train)\n\nExponentialSmoothing()Please rerun this cell to show the HTML repr or trust the notebook.ExponentialSmoothing?Documentation for ExponentialSmoothingExponentialSmoothing()\n\n\n\ny_pred = model.predict(fh=y_test.index)\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão Exponential Smoothing\"])\n\n\n\n\n\n\n\n\nExistem versões alternativas que consideram sazonalidade e tendências. Veja a documentação para mais detalhes.\n\nmodel = ExponentialSmoothing(trend=\"add\", seasonal=\"add\", sp=7)\nmodel.fit(y_train)\ny_pred = model.predict(fh=y_test.index)\nplot_series(\n    y_train,\n    y_test,\n    y_pred,\n    labels=[\"Treino\", \"Teste\", \"Previsão Exponential Smoothing\"],\n)",
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-    "text": "4.2 Modelos autoregressivos (AR)\nModelos autoregressivos (AR) são modelos que prevem o valor atual de uma série temporal como uma combinação dos valores passados. O modelo AR(p) usa os últimos p valores para fazer a previsão:\n\\[\n\\hat{Y}(t) = \\phi_1 Y(t-1) + \\phi_2 Y(t-2) + \\dots + \\phi_p Y(t-p)\n\\]\nonde \\(\\phi_1, \\phi_2, \\dots, \\phi_p\\) são os parâmetros do modelo que precisam ser estimados a partir dos dados.\n\nfrom sktime.forecasting.auto_reg import AutoREG\nfrom sktime.utils.plotting import plot_series\n\nmodel = AutoREG(lags=31)\nmodel.fit(y_train)\n\ny_pred = model.predict(fh=y_test.index)\nplot_series(\n    y_train,\n    y_test,\n    y_pred,\n    labels=[\"Treino\", \"Teste\", \"Previsão AR\"],\n)\n\n\n\n\n\n\n\n\nUm modelo auto-regressivo mais complexo é o ARIMA, que combina autoregressão (AR), média móvel (MA) e diferenciação integrada (I) para lidar com séries temporais não estacionárias. Não vamos estudar o ARIMA aqui pois envolve conceitos mais avançados, mas temos ele disponível no sktime, sktime.forecasting.arima.ARIMA.",
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-    "text": "4.3 STL: dividir e conquistar\nSabemos que séries temporais podem ser decompostas em componentes de tendência, sazonalidade e resíduos. O modelo STL (Seasonal and Trend decomposition using Loess) é uma técnica que permite fazer essa decomposição de forma robusta.\nTemos no sktime o STLTransformer, que permite fazer a decomposição STL:\n\nfrom sktime.transformations.series.detrend import STLTransformer\n\nstl = STLTransformer(sp=365)\nstl.fit(y_train)\n\nSTLTransformer(sp=365)Please rerun this cell to show the HTML repr or trust the notebook.STLTransformer?Documentation for STLTransformerSTLTransformer(sp=365)\n\n\nE agora podemos inspecionar os componentes:\n\nfig, ax = plt.subplots(3, 1, figsize=(10, 8), sharex=True)\nstl.trend_.plot.line(ax=ax[0])\nax[0].set_title(\"Tendência\")\nstl.seasonal_.plot.line(ax=ax[1])\nax[1].set_title(\"Sazonalidade\")\nstl.resid_.plot.line(ax=ax[2])\nax[2].set_title(\"Resíduos\")\nfig.show()\n\n/var/folders/_2/9y4tsvdd2n3gqjgd2zmlr1km0000gn/T/ipykernel_24269/1542615419.py:8: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n  fig.show()\n\n\n\n\n\n\n\n\n\nUma possibilidade, uma vez que temos os diferentes componentes, é modelar cada componente separadamente e depois combinar as previsões. O sktime tem o STLForecaster, que faz exatamente isso:\n\nfrom sktime.forecasting.trend import STLForecaster\nfrom sktime.forecasting.naive import NaiveForecaster\n\nmodel = STLForecaster(\n    forecaster_trend=AutoREG(lags=31),\n    forecaster_seasonal=NaiveForecaster(sp=7),\n    forecaster_resid=AutoREG(lags=31),\n    sp=7,\n)\n\nmodel.fit(y_train)\ny_pred = model.predict(fh=y_test.index)\n\nplot_series(\n    y_train,\n    y_test,\n    y_pred,\n    labels=[\"Treino\", \"Teste\", \"Previsão STL + AR\"],\n)\n\n\n\n\n\n\n\n\nPara fins de demonstração, podemos complicar um pouco mais o modelo, modelando os resíduos com outro STLForecaster:\n\nmodel = STLForecaster(\n    forecaster_trend=AutoREG(lags=31),\n    forecaster_seasonal=NaiveForecaster(sp=7),\n    forecaster_resid=STLForecaster(\n        forecaster_trend=AutoREG(lags=31),\n        forecaster_seasonal=NaiveForecaster(sp=365),\n        forecaster_resid=AutoREG(lags=31),\n        sp=365,\n    ),\n    sp=7,\n)\n\nmodel.fit(y_train)\n\nSTLForecaster(forecaster_resid=STLForecaster(forecaster_resid=AutoREG(lags=31),\n                                             forecaster_seasonal=NaiveForecaster(sp=365),\n                                             forecaster_trend=AutoREG(lags=31),\n                                             sp=365),\n              forecaster_seasonal=NaiveForecaster(sp=7),\n              forecaster_trend=AutoREG(lags=31), sp=7)Please rerun this cell to show the HTML repr or trust the notebook.STLForecaster?Documentation for STLForecasterSTLForecaster(forecaster_resid=STLForecaster(forecaster_resid=AutoREG(lags=31),\n                                             forecaster_seasonal=NaiveForecaster(sp=365),\n                                             forecaster_trend=AutoREG(lags=31),\n                                             sp=365),\n              forecaster_seasonal=NaiveForecaster(sp=7),\n              forecaster_trend=AutoREG(lags=31), sp=7)forecaster_resid: STLForecasterSTLForecaster(forecaster_resid=AutoREG(lags=31),\n              forecaster_seasonal=NaiveForecaster(sp=365),\n              forecaster_trend=AutoREG(lags=31), sp=365)forecaster_resid: AutoREGAutoREG(lags=31)AutoREG?Documentation for AutoREGAutoREG(lags=31)forecaster_seasonal: NaiveForecasterNaiveForecaster(sp=365)NaiveForecaster?Documentation for NaiveForecasterNaiveForecaster(sp=365)forecaster_trend: AutoREGAutoREG(lags=31)AutoREG?Documentation for AutoREGAutoREG(lags=31)forecaster_seasonal: NaiveForecasterNaiveForecaster(sp=7)NaiveForecaster?Documentation for NaiveForecasterNaiveForecaster(sp=7)forecaster_trend: AutoREGAutoREG(lags=31)AutoREG?Documentation for AutoREGAutoREG(lags=31)\n\n\n\ny_pred = model.predict(fh=y_test.index)\n\nplot_series(\n    y_train,\n    y_test,\n    y_pred,\n    labels=[\"Treino\", \"Teste\", \"Previsão STL + AR\"],\n)\n\n\n\n\n\n\n\n\n\n\n\n\nHyndman, Rob J, and George Athanasopoulos. 2018. Forecasting: Principles and Practice. OTexts.",
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-    "text": "5.1 Métricas\nAté então, vimos como criar modelos de séries temporais e avaliamos qualitativamente seu desempenho, em apenas uma simples divisão treino-teste. Agora, vamos explorar como avaliar modelos de séries temporais de forma mais robusta, utilizando métricas específicas e técnicas de validação cruzada adaptadas para dados temporais.\nPrimeiro vamos baixar os dados e criar um modelo simples.\nimport matplotlib.pyplot as plt\nfrom tsbook.datasets.retail import SyntheticRetail\nfrom sktime.utils.plotting import plot_series\nfrom sktime.forecasting.naive import NaiveForecaster\n\ndataset = SyntheticRetail(\"univariate\")\ny_train, y_test = dataset.load(\"y_train\", \"y_test\")\n\n# Predict\nmodel = NaiveForecaster(strategy=\"last\")\nmodel.fit(y_train)\ny_pred = model.predict(fh=y_test.index)\nPara avaliar o desempenho de modelos de séries temporais, existem diversas métricas que podem ser utilizadas.\nDuas métricas básicas são:\nfrom sktime.performance_metrics.forecasting import MeanAbsoluteError\n\nmetric = MeanAbsoluteError()\nmetric(y_true=y_test, y_pred=y_pred)\n\nnp.float64(615.0555555555555)\n\\[\nMSE = \\frac{1}{h} \\sum_{t=T+1}^{T+h} (y_t - \\hat{y}_t)^2\n\\]\nfrom sktime.performance_metrics.forecasting import MeanSquaredError\n\nmetric = MeanSquaredError()\nmetric(y_true=y_test, y_pred=y_pred)\n\nnp.float64(633773.8777777777)\nNo entanto, essas métricas não levam em consideração a escala dos dados, o que pode ser um problema ao comparar desempenho entre diferentes séries temporais, e também na comunicação dos resultados para stakeholders.\nEm muitos meios, é comum usar métricas que eliminam o fator escala, como:\n\\[\nMAPE = \\frac{1}{h} \\sum_{t=T+1}^{T+h} \\left| \\frac{y_t - \\hat{y}_t}{y_t} \\right|\n\\]\nfrom sktime.performance_metrics.forecasting import MeanAbsolutePercentageError\n\nmetric = MeanAbsolutePercentageError()\nmetric(y_true=y_test, y_pred=y_pred)\n\nnp.float64(0.21292470853237802)\nmetric = MeanAbsolutePercentageError(symmetric=True)\nmetric(y_true=y_test, y_pred=y_pred)\n\nnp.float64(0.22359840490570626)\n\\[\nsMAPE = \\frac{1}{h} \\sum_{t=T+1}^{T+h} \\frac{|y_t - \\hat{y}_t|}{(|y_t| + |\\hat{y}_t|)/2}\n\\]\nEssas métricas, no entanto, apresentam seus próprios problemas. Note que os valores podem ser exageradamente alto quando o denominador é próximo de zero. Ou seja, o MAPE e sMAPE podem ser problemáticos quando a série temporal contém valores próximos de zero.\nOutro tema é que essas métricas não consideram a dificuldade da série. Por exemplo, 5% de erro pode ser muito em uma série, e pouco em outra. Para isso, foram propostas métricas “escalas”:\n\\[\ne_{naive} = \\frac{1}{T-1} \\sum_{t=2}^{T} |y_t - y_{t-1}|\n\\]\nsão os erros nos dados de treino, então:\n\\[\nMASE = \\frac{1}{h} \\sum_{t=T+1}^{T+h} \\frac{|y_t - \\hat{y}_t|}{e_{naive}}\n\\]\nfrom sktime.performance_metrics.forecasting import MeanAbsoluteScaledError\n\nmetric = MeanAbsoluteScaledError()\nmetric(y_true=y_test, y_pred=y_pred, y_train=y_train)\n\nnp.float64(10.127736897882464)\n\\[\nMSSE = \\frac{1}{h} \\sum_{t=T+1}^{T+h} \\frac{(y_t - \\hat{y}_t)^2}{e_{naive}^2}\n\\]\nfrom sktime.performance_metrics.forecasting import MeanSquaredScaledError\n\nmetric = MeanSquaredScaledError()\nmetric(y_true=y_test, y_pred=y_pred, y_train=y_train)\n\nnp.float64(57.09087480132447)",
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-    "text": "Mean Absolute Error (MAE): Média dos erros absolutos entre as previsões e os valores reais. \\[\nMAE = \\frac{1}{h} \\sum_{t=T+1}^{T+h} |y_t - \\hat{y}_t|\n\\]\n\n\n\nMean Squared Error (MSE): Média dos erros quadráticos entre as previsões e os valores reais.\n\n\n\n\n\n\nMean Absolute Percentage Error (MAPE): Média dos erros percentuais absolutos entre as previsões e os valores reais.\n\n\n\n\nSymmetric Mean Absolute Percentage Error (sMAPE): Média dos erros percentuais absolutos simétricos entre as previsões e os valores reais.\n\n\n\n\n\n\nMean Absolute Scaled Error (MASE): dividimos o erro absoluto pelo erro absoluto médio de um naive nos dados de treino. Se:\n\n\n\n\n\n\nMean Squared Scaled Error (MSSE): dividimos o erro quadrático pelo quadrado de \\(e_{naive}\\).",
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-    "text": "5.2 Cross-validation para séries temporais\nEm um cross-validation, nossa intenção é estimar corretamente o erro de generalização do modelo, ou seja, o erro que o modelo terá em dados futuros.\n\\[\n\\mathbb{E}[L(Y, \\hat{Y})]\n\\]\nOnde a média é sobre a distribuição que mais representa o que será visto em produção.\nTipicamente, fazemos back-testing com dois tipos de janelas:\n\nSliding window: a janela de treino tem tamanho fixo, e “desliza” ao longo do tempo. A cada passo, o modelo é re-treinado com os dados mais recentes.\n\n\nfrom sktime.utils.plotting import plot_windows\n\nfrom sktime.split import SlidingWindowSplitter\n\nsliding_window_cv = SlidingWindowSplitter(\n    window_length=365 * 3, step_length=100, fh=list(range(1, 90 + 1))\n)\nplot_windows(cv=sliding_window_cv, y=y_train)\nplt.show()\n\n\n\n\n\n\n\n\n\nExpanding window: a janela de treino começa com um tamanho mínimo, e vai aumentando ao longo do tempo, incluindo todos os dados anteriores. A cada passo, o modelo é re-treinado com todos os dados disponíveis até aquele ponto.\n\n\nfrom sktime.split import ExpandingWindowSplitter\n\n\n\nexpanding_window_cv = ExpandingWindowSplitter(\n    initial_window=365 * 3, step_length=100, fh=list(range(1, 90 + 1))\n)\nplot_windows(cv=expanding_window_cv, y=y_train)\nplt.show()\n\n\n\n\n\n\n\n\n\nQual escolher? Bom, escolha o que mais representa o que você espera ver em produção.",
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-    "text": "5.3 Executando o cross-validation\nPara executar cross-validation, usamos a função evaluate, que recebe o modelo, o esquema de cross-validation, os dados, e a métrica a ser usada.\n\nfrom sktime.forecasting.model_evaluation import evaluate\nfrom sktime.performance_metrics.forecasting import MeanAbsoluteScaledError\nfrom sktime.forecasting.naive import NaiveForecaster\n\nmodel = NaiveForecaster(strategy=\"last\")\n\nevaluate(\n    forecaster=model,\n    cv=expanding_window_cv,\n    y=y_train,\n    X=None, # Veremos na próxima seçao!\n    scoring=MeanAbsoluteScaledError(),\n    error_score=\"raise\",\n    return_data=True,\n)\n\n\n\n\n\n\n\n\ntest_MeanAbsoluteScaledError\nfit_time\npred_time\nlen_train_window\ncutoff\ny_train\ny_test\ny_pred\n\n\n\n\n0\n12.839683\n0.002296\n0.005386\n1095\n2022-12-30\nsales date 2020-01-01...\nsales date 2022-12-31...\nsales 2022-12-31 458.0 2023-01-01...\n\n\n1\n3.813045\n0.001674\n0.003905\n1195\n2023-04-09\nsales date 2020-01-01...\nsales date 2023-04-10...\nsales 2023-04-10 866.0 2023-04-11...\n\n\n2\n8.068216\n0.001615\n0.003741\n1295\n2023-07-18\nsales date 2020-01-01...\nsales date 2023-07-19...\nsales 2023-07-19 748.0 2023-07-20...\n\n\n3\n7.854970\n0.001626\n0.003664\n1395\n2023-10-26\nsales date 2020-01-01...\nsales date 2023-10-27...\nsales 2023-10-27 1229.0 2023-10-...\n\n\n4\n5.955709\n0.001576\n0.003626\n1495\n2024-02-03\nsales date 2020-01-01...\nsales date 2024-02-04...\nsales 2024-02-04 1916.0 2024-02-...",
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-    "text": "6.1 Tipos de Variáveis Exógenas\nComo séries temporais possuem uma ordem temporal, a sua própria história tem poder preditivo muito forte. No entanto, em alguns casos, apenas a história não é suficiente para fazer previsões precisas.\nPor exemplo, em vendas de varejo, fatores como promoções, feriados e eventos sazonais podem impactar significativamente as vendas.\nAgora, vamos ver como usar variáveis exógenas em modelos de séries temporais com sktime, as famosas “features”.\nA interface é sempre a mesma, vamos ver que a diferença é o uso do parâmetro X nos métodos fit e predict.\nUsaremos os mesmos dados de varejo sintético dos exemplos anteriores. Agora, teremos também X_train e X_test, que são as variáveis exógenas.\nNem todos modelos suportam variáveis exógenas. Para ver uma lista de possibilidades, podemos usar a função all_estimators do sktime.\nAntes de prosseguirmos, vamos separar as variáveis exógenas em dois tipos:\nNesse último cenário, para realizar a previsão, existem três opções: 1. Prever os valores futuros das variáveis exógenas usando um modelo separado (ou o mesmo modelo, se ele permitir) e usar essas previsões como entrada para o modelo principal de previsão. 2. Usar um valor de preenchimento (por exemplo, o último valor conhecido) para as variáveis exógenas de valor futuro desconhecido durante a previsão. 3. Usar valores defasados (lags) das variáveis exógenas como características (features), o que pode ser útil se o modelo conseguir aprender com os valores passados dessas variáveis.",
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-    "text": "Variáveis exógenas com valores futuros conhecidos: São variáveis cujos valores futuros já são conhecidos no momento da previsão. Variáveis indicadoras (dummies) para feriados ou eventos especiais, bem como recursos de sazonalidade, são exemplos comuns desse tipo de variável.\nVariáveis exógenas com valores futuros desconhecidos: São variáveis cujos valores futuros não são conhecidos para o horizonte de previsão. Por exemplo, se quisermos incluir indicadores econômicos que ainda não foram divulgados, devemos tratá-los como variáveis exógenas de valor futuro desconhecido.",
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-    "text": "6.2 Usando variáveis exógenas com sktime\nVamos usar AutoREG como modelo base para nosso exemplo de vairáveis exógenas. Primeiramente, vamos supor que conhecemos a variável macro_trend no futuro\n\nfrom sktime.forecasting.auto_reg import AutoREG\n\nmodel = AutoREG(lags=30)\nmodel.fit(y_train, X=X_train)\ny_pred = model.predict(fh=y_test.index, X=X_test)\n\n\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão com Exógenas\"])\n\n\n\n\n\n\n\n\nFácil, no caso que conhecemos a variável exógena no futuro, basta passar X em fit e predict.\nAntes de avançar, é importante revisar que os transformadores com fit e transform também podem ser aplicados a variávei exógenas! Inclusive, podemos fazer pipelines compostos de várias etapas de preprocessamento de exógenas. Isso será importante para os próximos exemplos.",
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-    "text": "6.3 Variável observada, mas desconhecida no futuro\nVamos eliminar a variável macro_trend do conjunto de teste, para simular o cenário onde não conhecemos o valor futuro dessa variável.\n\nimport numpy as np\n\nX_test_missing = X_test.copy()\nX_test_missing[\"macro_trend\"] = np.nan\n\n\n6.3.1 Solução 1: Prever a variável exógena\nAgora, vamos supor que não sabemos o valor futuro de macro_trend. Nesse caso, podemos criar um modelo separado para prever macro_trend e usar essa previsão como entrada para o modelo principal.\nSktime possui uma funcionalidade pronta para isso: um forecaster chamado ForecastX. Ele é composto de dois modelos, um para a variável exógena, e outro para o alvo principal.\nAqui, o forecaster necessita que o horizonte de previsão (fh) seja passado já na etapa de fit.\n\nfrom sktime.forecasting.compose import ForecastX\n\nmodel = ForecastX(\n    forecaster_y=AutoREG(lags=30),\n    forecaster_X=AutoREG(lags=30),\n)\n\nfh = [i for i in range(1, len(y_test) + 1)]\nmodel.fit(y_train, X=X_train, fh=fh)\n\ny_pred_case1 = model.predict(X=X_test_missing)\n\n\nplot_series(y_train, y_test, y_pred_case1, labels=[\"Treino\", \"Teste\", \"Previsão com Exógenas Previstas\"])\n\n\n\n\n\n\n\n\n\n\n6.3.2 Solução 2: Usar valor de preenchimento (imputação)\nPara essa solução, vamos usar transformadores para preencher os valores faltantes na variável exógena. Aqui, usaremos o Imputer do sktime, que suporta variáveis exógenas.\n\nfrom sktime.transformations.series.impute import Imputer\nimputer = Imputer(method=\"mean\")\nimputer.fit(X_train)\n\n# Agora imputamos\nX_test_imputed = imputer.transform(X_test_missing)\nX_test_imputed.tail()\n\n\n\n\n\n\n\n\npromo\nmacro_trend\n\n\ndate\n\n\n\n\n\n\n2024-12-28\n0.0\n22.805784\n\n\n2024-12-29\n0.0\n22.805784\n\n\n2024-12-30\n0.0\n22.805784\n\n\n2024-12-31\n1.0\n22.805784\n\n\n2025-01-01\n0.0\n22.805784\n\n\n\n\n\n\n\nPara usar preprocessamento de exógenas + forecasting, podemos usar a composição ForecastingPipeline.\n\nfrom sktime.forecasting.compose import ForecastingPipeline\nfrom sktime.transformations.series.difference import Differencer\n\n\nmodel = ForecastingPipeline(\n    steps=[(\"imputer\", Imputer(method=\"mean\")), (\"forecaster\", AutoREG(lags=30))]\n)\nmodel.fit(y_train, X=X_train)\n\nForecastingPipeline(steps=[('imputer', Imputer(method='mean')),\n                           ('forecaster', AutoREG(lags=30))])Please rerun this cell to show the HTML repr or trust the notebook.ForecastingPipeline?Documentation for ForecastingPipelineForecastingPipeline(steps=[('imputer', Imputer(method='mean')),\n                           ('forecaster', AutoREG(lags=30))])Imputer?Documentation for ImputerImputer(method='mean')AutoREG?Documentation for AutoREGAutoREG(lags=30)\n\n\n\ny_pred_case2 = model.predict(fh=y_test.index, X=X_test_missing)\nplot_series(y_train, y_test, y_pred_case2, labels=[\"Treino\", \"Teste\", \"Previsão com Exógenas Imputadas\"])\n\n\n\n\n\n\n\n\nNossa previsão não ficou boa. Claro! A variável exógena possui uma tendência - que naturalmente faz com que a imputação do ultimo valor ou a média não funcione bem. A solução ótima varia de caso para caso.\n\n\n\n\n\n\nTip\n\n\n\nDica: Podemos também usar o operador ** como atalho para criar pipelines de variáveis exógenas.\nmodel = Imputer(method=\"mean\") ** AutoREG(lags=30)\nNote a diferença do que aprendemos para criar pipelines com transformações na variável target, que usam * como operador. Claramente, podemos fazer composições mais complexas, como:\nmodel = Imputer(method=\"mean\") ** (Differencer() * AutoREG())\n\n\n\n\n6.3.3 Solução 3: Usar valores defasados (lags) da variável exógena\nOutra opção é criar versões defasadas das variáveis exógenas e usá-las como features.\nPara isso, podemos usar o transformador Lag do sktime. Ao utilizar defasagens (lags), surgem dois desafios principais:\n\nO aparecimento de valores NaN, que muitos modelos de previsão não conseguem tratar.\nO número de variáveis exógenas pode aumentar significativamente, o que pode levar a overfitting ou, no caso do nosso conjunto de dados, a um número de features maior que o número de amostras — o que pode gerar erros no processo de ajuste (fitting).\n\nPara lidar com isso, no exemplo abaixo utilizamos um TransformerPipeline que realiza as seguintes etapas:\n\nSeleção de variáveis: executa uma seleção das variáveis exógenas, mantendo apenas as mais relevantes.\nDefasagem: aplica o transformador Lag para criar versões defasadas das variáveis exógenas.\nImputação: usa o transformador Imputer para preencher os valores NaN criados pelo processo de defasagem. Neste caso, é usado o método backfill (preenchimento a partir de valores posteriores).\n\n\nfrom sktime.transformations.compose import TransformerPipeline\nfrom sktime.transformations.series.feature_selection import FeatureSelection\nfrom sktime.transformations.series.impute import Imputer\nfrom sktime.transformations.series.lag import Lag\nfrom sktime.transformations.series.subset import IndexSubset\n\n\ntransformer_pipeline = TransformerPipeline(\n    steps=[\n        (\"lag\", Lag(lags=list(range(1, 180 + 1)))),  # Cria lags 3 e 4\n        (\"subset\", IndexSubset()),  # Seleciona apenas macro_trend\n        (\"impute\", Imputer(method=\"backfill\", value=0)),  # Imputa valores NaN\n        (\"feature_selection\", FeatureSelection()),  # Seleciona features\n    ]\n)\n\n\ntransformer_pipeline.fit(X=X_train, y=y_train)\nX_test_transformed = transformer_pipeline.transform(X=X_test_missing)\n\nX_test_transformed.head()\n\n\n\n\n\n\n\n\nlag_98__macro_trend\nlag_34__macro_trend\nlag_29__macro_trend\nlag_104__macro_trend\nlag_67__macro_trend\nlag_35__macro_trend\nlag_75__macro_trend\nlag_39__macro_trend\nlag_6__macro_trend\nlag_32__macro_trend\n...\nlag_66__promo\nlag_129__macro_trend\nlag_97__macro_trend\nlag_174__promo\nlag_136__macro_trend\nlag_119__promo\nlag_72__macro_trend\nlag_99__macro_trend\nlag_33__promo\nlag_29__promo\n\n\ndate\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n2024-07-06\n82.233778\n81.711556\n82.607111\n81.633778\n81.764000\n82.042667\n81.536000\n82.793778\n82.909333\n82.084889\n...\n0.0\n77.054667\n82.288444\n0.0\n76.021333\n0.0\n81.565333\n82.200000\n0.0\n0.0\n\n\n2024-07-07\n82.288444\n81.823556\n82.515556\n81.660444\n81.880444\n81.711556\n81.282667\n82.855556\n83.153778\n82.555556\n...\n0.0\n77.324889\n82.319556\n0.0\n76.131111\n0.0\n81.838222\n82.233778\n0.0\n0.0\n\n\n2024-07-08\n82.319556\n82.084889\n82.460889\n81.724444\n82.063111\n81.823556\n81.306222\n82.756444\n83.440444\n82.657333\n...\n0.0\n77.896000\n82.393778\n0.0\n76.268889\n0.0\n82.044889\n82.288444\n0.0\n0.0\n\n\n2024-07-09\n82.393778\n82.555556\n82.498667\n81.875556\n82.175556\n82.084889\n81.565333\n82.488444\n83.594667\n82.607111\n...\n0.0\n78.460444\n82.383556\n0.0\n76.441333\n0.0\n82.003111\n82.319556\n0.0\n1.0\n\n\n2024-07-10\n82.383556\n82.657333\n82.444444\n82.086222\n82.288000\n82.555556\n81.838222\n82.042667\n83.721778\n82.515556\n...\n0.0\n79.001778\n82.550222\n0.0\n76.692889\n0.0\n81.840889\n82.393778\n0.0\n0.0\n\n\n\n\n5 rows × 180 columns\n\n\n\n\nmodel = ForecastingPipeline(\n    steps=[\n        (\"preprocessing\", transformer_pipeline),\n        (\"forecaster\", AutoREG(lags=30)),\n    ]\n).fit(X=X_train, y=y_train)\n\n\nmodel\n\nForecastingPipeline(steps=[('preprocessing',\n                            TransformerPipeline(steps=[('lag',\n                                                        Lag(lags=[1, 2, 3, 4, 5,\n                                                                  6, 7, 8, 9,\n                                                                  10, 11, 12,\n                                                                  13, 14, 15,\n                                                                  16, 17, 18,\n                                                                  19, 20, 21,\n                                                                  22, 23, 24,\n                                                                  25, 26, 27,\n                                                                  28, 29, 30, ...])),\n                                                       ('subset',\n                                                        IndexSubset()),\n                                                       ('impute',\n                                                        Imputer(method='backfill',\n                                                                value=0)),\n                                                       ('feature_selection',\n                                                        FeatureSelection())])),\n                           ('forecaster', AutoREG(lags=30))])Please rerun this cell to show the HTML repr or trust the notebook.ForecastingPipeline?Documentation for ForecastingPipelineForecastingPipeline(steps=[('preprocessing',\n                            TransformerPipeline(steps=[('lag',\n                                                        Lag(lags=[1, 2, 3, 4, 5,\n                                                                  6, 7, 8, 9,\n                                                                  10, 11, 12,\n                                                                  13, 14, 15,\n                                                                  16, 17, 18,\n                                                                  19, 20, 21,\n                                                                  22, 23, 24,\n                                                                  25, 26, 27,\n                                                                  28, 29, 30, ...])),\n                                                       ('subset',\n                                                        IndexSubset()),\n                                                       ('impute',\n                                                        Imputer(method='backfill',\n                                                                value=0)),\n                                                       ('feature_selection',\n                                                        FeatureSelection())])),\n                           ('forecaster', AutoREG(lags=30))])preprocessing?Documentation for preprocessingTransformerPipeline(steps=[('lag',\n                            Lag(lags=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n                                      14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n                                      24, 25, 26, 27, 28, 29, 30, ...])),\n                           ('subset', IndexSubset()),\n                           ('impute', Imputer(method='backfill', value=0)),\n                           ('feature_selection', FeatureSelection())])Lag?Documentation for LagLag(lags=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n          21, 22, 23, 24, 25, 26, 27, 28, 29, 30, ...])IndexSubset?Documentation for IndexSubsetIndexSubset()Imputer?Documentation for ImputerImputer(method='backfill', value=0)FeatureSelection?Documentation for FeatureSelectionFeatureSelection()AutoREG?Documentation for AutoREGAutoREG(lags=30)\n\n\n\ny_pred_case3 = model.predict(fh=y_test.index, X=X_test_missing)\nplot_series(y_train, y_test, y_pred_case3, labels=[\"Treino\", \"Teste\", \"Previsão com Exógenas Lag\"])",
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-    "text": "7.1 O problema da tendência\nNesse capítulo vamos ver as maneiras de usar modelos de Machine Learning para forecasting. Aqui é onde mais acontecem erros de novos praticantes, pois muitas vezes tentam aplicar modelos de ML diretamente na série temporal.\nPara usar um modelo de ML, precisamos transformar a série temporal em um problema de regressão tradicional. Isso é feito criando janelas deslizantes (sliding windows) da série temporal, onde cada janela é usada como uma amostra de treinamento para o modelo de ML.\nOu seja, se temos uma série temporal \\((y_t)\\), podemos criar janelas de tamanho \\(n\\) e usar os valores \\(y_{t-n}, y_{t-n+1}, \\ldots, y_{t-1}\\) como características (features) para prever o valor \\(y_t\\).\nPara prever mais de um passo à frente, existem duas abordagens:\nA verdade é que as duas abordagens podem ser vistas como uma: a previsão recursiva pode ser vista como uma previsão direta para \\(h=1\\).\nA tendência em séries temporais é como um constante problema de data drift:\nCode\n_X = [y_train.iloc[i : i + 7] for i in range(0, 700)]\n\n_X_test = [y_train.iloc[i : i + 7] for i in range(700, 800)]\n\n\ndef set_index(x):\n    x.index = range(len(x))\n    return x\n\n\n_X = [set_index(x) for x in _X]\n_X_test = [set_index(x) for x in _X_test]\n\nimport matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\nfor x in _X:\n    ax.plot(x, color=\"gray\", alpha=0.3)\nfor x in _X_test:\n    ax.plot(x, color=\"red\", alpha=0.3)\n\n# Add legend, with 1 red line for test and 1 gray for train\nfrom matplotlib.lines import Line2D\n\nlegend_handles = [\n    Line2D([0], [0], color=\"gray\", alpha=0.3, lw=2, label=\"Treino\"),\n    Line2D([0], [0], color=\"red\", alpha=0.3, lw=2, label=\"Teste\"),\n]\nax.legend(handles=legend_handles, loc=\"best\")\nax.set_title(\"Série original - Magnitudes diferentes para cada janela\")\nfig.show()\nQuando criamos nossas janelas e olhamos treine e teste, esse problema fica claro. A informação de uma série em treino não é util para prever a série de teste, pois elas estão em magnitudes diferentes.\nUma possível solução para isso é normalizar cada janela, dividindo pelo valor médio da janela. Assim, todas as janelas ficam na mesma escala:\nCode\n_X = [x / x.mean() for x in _X]\n_X_test = [x / x.mean() for x in _X_test]\n\nfig, ax = plt.subplots()\nfor x in _X:\n    ax.plot(x, color=\"gray\", alpha=0.3)\nfor x in _X_test:\n    ax.plot(x, color=\"red\", alpha=0.3)\n\nax.legend(handles=legend_handles, loc=\"best\")\nax.set_title(\"Série normalizada\")\nfig.show()\ne podemos prever sem problemas.\nOutra possibilidade é a diferenciação, como já vimos em capítulos anteriores. A diferenciação remove a tendência da série, tornando-a estacionária.",
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-    "text": "7.2 Usando modelos de ML com sktime\nPrimeiro, vamos import ReductionForecaster, que é a classe que implementa a abordagem de janelas deslizantes para usar modelos de ML em séries temporais. Vamos testar um primeiro caso sem nenhum tipo de preprocessamento, apenas criando as janelas:\n\nfrom tsbook.forecasting.reduction import ReductionForecaster\nfrom sklearn.ensemble import RandomForestRegressor\n\nmodel = ReductionForecaster(\n    RandomForestRegressor(n_estimators=100, random_state=42),\n    window_length=30,\n)\n\nmodel.fit(y_train, X=X_train)\n\nReductionForecaster(estimator=RandomForestRegressor(random_state=42),\n                    window_length=30)Please rerun this cell to show the HTML repr or trust the notebook.ReductionForecasterReductionForecaster(estimator=RandomForestRegressor(random_state=42),\n                    window_length=30)estimator: RandomForestRegressorRandomForestRegressor(random_state=42)RandomForestRegressor?Documentation for RandomForestRegressorRandomForestRegressor(random_state=42)\n\n\n\ny_pred = model.predict(fh=y_test.index, X=X_test)\nplot_series(y_train, y_test, y_pred, labels=[\"Treino\", \"Teste\", \"Previsão com ML\"])\nplt.show()\n\n\n\n\n\n\n\n\nClaramente, tivemos o problema que mencionamos anteriormente.\n\n7.2.1 Solução 1: Diferenciação\nUma solução é usar a diferenciação para remover a tendência da série.\n\nfrom sktime.transformations.series.difference import Differencer\n\nregressor = RandomForestRegressor(n_estimators=100, random_state=42)\n\nmodel = Differencer() * ReductionForecaster(\n    regressor,\n    window_length=30,\n    steps_ahead=1,\n)\n\nmodel.fit(y_train, X=X_train)\n\nTransformedTargetForecaster(steps=[Differencer(),\n                                   ReductionForecaster(estimator=RandomForestRegressor(random_state=42),\n                                                       window_length=30)])Please rerun this cell to show the HTML repr or trust the notebook.TransformedTargetForecaster?Documentation for TransformedTargetForecasterTransformedTargetForecaster(steps=[Differencer(),\n                                   ReductionForecaster(estimator=RandomForestRegressor(random_state=42),\n                                                       window_length=30)])Differencer?Documentation for DifferencerDifferencer()ReductionForecasterReductionForecaster(estimator=RandomForestRegressor(random_state=42),\n                    window_length=30)estimator: RandomForestRegressorRandomForestRegressor(random_state=42)RandomForestRegressor?Documentation for RandomForestRegressorRandomForestRegressor(random_state=42)\n\n\n\ny_pred_diff = model.predict(fh=y_test.index, X=X_test)\n\nplot_series(\n    y_train, y_test, y_pred_diff, labels=[\"Treino\", \"Teste\", \"Previsão com ML + Diferença\"]\n)\nplt.show()\n\n\n\n\n\n\n\n\nAqui, já vemos uma melhora significativa. Mas tem algo que podemos melhorar para realizar a diferenciação? Sim. Lembre que essa série tem um padrão multiplicativo. Então, antes de aplicar a diferenciação, podemos aplicar uma transformação logarítmica para estabilizar a variância:\n\nfrom sktime.transformations.series.boxcox import LogTransformer\n\nmodel_log = LogTransformer() * model\n\nmodel_log.fit(y_train, X=X_train)\ny_pred_log_diff = model_log.predict(fh=y_test.index, X=X_test)\nplot_series(\n    y_train,\n    y_test,\n    y_pred_log_diff,\n    labels=[\"Treino\", \"Teste\", \"Previsão com ML + Log + Diferença\"],\n)\n\n\n\n\n\n\n\n\n\n\n7.2.2 Solução 2: Normalização por janela\nA diferenciação aumenta o ruído da série, o que pode dificultar o trabalho do modelo de ML. Outra opção é normalizar em cada janela. A classe ReductionForecaster tem um parâmetro chamado normalization_strategy, que pode ser usado para determinar a estratégia de normalização. Vamos usar a estratégia divide_mean, que divide cada janela pelo seu valor médio.\n\nmodel = ReductionForecaster(\n    regressor,\n    window_length=30,\n    steps_ahead=1,\n    normalization_strategy=\"divide_mean\",\n)\n\nmodel.fit(y_train, X=X_train)\ny_pred_norm = model.predict(fh=y_test.index, X=X_test)\n\nplot_series(\n    y_train, y_test, y_pred_norm, labels=[\"Treino\", \"Teste\", \"Previsão com ML + Normalização\"]\n)\n\n\n\n\n\n\n\n\n\n\n7.2.3 Modelo direto e recursivo\nPodemos fazer um conjunto de modelos, um para cada passo à frente. Abaixo, definimos steps_ahead=12, o que significa que o modelo vai prever 12 passos à frente diretamente.\n\nmodel = ReductionForecaster(\n    regressor,\n    window_length=30,\n    steps_ahead=12,\n    normalization_strategy=\"divide_mean\",\n)\n\nmodel.fit(y_train, X=X_train)\ny_pred_norm_direct = model.predict(fh=y_test.index, X=X_test)\n\n\nplot_series(\n    y_train,\n    y_test,\n    y_pred_norm_direct,\n    labels=[\"Treino\", \"Teste\", \"Previsão com ML + Normalização\"],\n)\n\n\n\n\n\n\n\n\nPodemos comparar o MAPE de todos os modelos:\n\nfrom sktime.performance_metrics.forecasting import MeanAbsolutePercentageError\n\nmape = MeanAbsolutePercentageError()\n\nresults = {}\nfor _y_pred, label in zip(\n    [\n        y_pred,\n        y_pred_diff,\n        y_pred_log_diff,\n        y_pred_norm,\n        y_pred_norm_direct,\n    ],\n    [\n        \"ML\",\n        \"ML + Diferença\",\n        \"ML + Log + Diferença\",\n        \"ML + Normalização\",\n        \"ML + Normalização + Direto\",\n    ],\n):\n    results[label] = mape(y_test, _y_pred)\n\nimport pandas as pd\n\nresults = pd.DataFrame.from_dict(results, orient=\"index\", columns=[\"MAPE\"])\nresults.sort_values(\"MAPE\")\n\n\n\n\n\n\n\n\nMAPE\n\n\n\n\nML + Log + Diferença\n0.125494\n\n\nML + Normalização\n0.128436\n\n\nML + Normalização + Direto\n0.138206\n\n\nML + Diferença\n0.178693\n\n\nML\n0.243593",
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-    "text": "8.1 Acessando os dados\nEm muitas aplicações, não temos acesso a uma única série temporal, mas sim a um conjunto de séries temporais relacionadas. Isso é comum em cenários como vendas de produtos em diferentes lojas, consumo de energia em diferentes regiões, etc. Esses dados são chamados de dados em painel.\nUma ideia poderosa é aproveitar a similaridade entre as séries para melhorar as previsões. Chamamos de modelos globais os modelos capazes de aprender padrões comuns entre as séries, ao contrário dos modelos locais que aprendem apenas com uma única série.\nA maioria dos modelos clássicos de séries temporais são locais. Modelos globais são, em geral, baseados em modelos tabulares de ML ou deep learning. Segundo competições de séries temporais, como a M5, em forecasts de painel os modelos globais são os que apresentam melhor desempenho (Makridakis, Spiliotis, and Assimakopoulos 2022).\nAqui, vamos usar o dataset sintético que vimos antes, mas agora teremos acesso às várias séries temporais que compõe o total.\nEsse dataset é feito para simular um caso de varejo, onde temos vendas diárias de vários produtos:\nCode\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sktime.utils.plotting import plot_series\nfrom tsbook.datasets.retail import SyntheticRetail\ndataset = SyntheticRetail(\"panel\")\ny_train, X_train, y_test, X_test = dataset.load(\n    \"y_train\", \"X_train\", \"y_test\", \"X_test\"\n)\nNote que, para dados em painel, os dataframes possuem mais um nível de índice, que identifica a série temporal a que cada observação pertence:\ndisplay(X_train)\n\n\n\n\n\n\n\n\n\npromo\n\n\nsku_id\ndate\n\n\n\n\n\n0\n2020-01-01\n0.0\n\n\n2020-01-02\n0.0\n\n\n2020-01-03\n0.0\n\n\n2020-01-04\n0.0\n\n\n2020-01-05\n0.0\n\n\n...\n...\n...\n\n\n24\n2024-07-01\n1.0\n\n\n2024-07-02\n0.0\n\n\n2024-07-03\n0.0\n\n\n2024-07-04\n0.0\n\n\n2024-07-05\n0.0\n\n\n\n\n41200 rows × 1 columns\nPodemos visualizar algumas séries. Vemos que há mais zeros nesse dataset, em comparação ao que usamos antes.\nfrom sktime.utils.plotting import plot_series\n\nfig, ax = plt.subplots(figsize=(10, 4))\ny_train.unstack(level=0).droplevel(0, axis=1).iloc[:, [0,10]].plot(ax=ax, alpha=0.7)\nplt.show()",
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-    "text": "8.1.1 Pandas e multi-índices\nPara trabalhar com essas estruturas de dados, é importante revisar algumas operações do pandas.\n\ny_train.index.get_level_values(-1)\n\nPeriodIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',\n             '2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',\n             '2020-01-09', '2020-01-10',\n             ...\n             '2024-06-26', '2024-06-27', '2024-06-28', '2024-06-29',\n             '2024-06-30', '2024-07-01', '2024-07-02', '2024-07-03',\n             '2024-07-04', '2024-07-05'],\n            dtype='period[D]', name='date', length=41200)\n\n\nAs seguintes operações são bem úteis para trabalhar com multi-índices:\n\ny_train.index\n\nMultiIndex([( 0, '2020-01-01'),\n            ( 0, '2020-01-02'),\n            ( 0, '2020-01-03'),\n            ( 0, '2020-01-04'),\n            ( 0, '2020-01-05'),\n            ( 0, '2020-01-06'),\n            ( 0, '2020-01-07'),\n            ( 0, '2020-01-08'),\n            ( 0, '2020-01-09'),\n            ( 0, '2020-01-10'),\n            ...\n            (24, '2024-06-26'),\n            (24, '2024-06-27'),\n            (24, '2024-06-28'),\n            (24, '2024-06-29'),\n            (24, '2024-06-30'),\n            (24, '2024-07-01'),\n            (24, '2024-07-02'),\n            (24, '2024-07-03'),\n            (24, '2024-07-04'),\n            (24, '2024-07-05')],\n           names=['sku_id', 'date'], length=41200)\n\n\nAcessar valores únicos no primeiro nivel (nível 0, mais à esquerda):\n\ny_train.index.get_level_values(0).unique()\n\nIndex([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n       18, 19, 20, 21, 22, 23, 24],\n      dtype='int64', name='sku_id')\n\n\nSelecionar uma série específica (nível 0 igual a 0):\n\ny_train.loc[0]\n\n\n\n\n\n\n\n\nsales\n\n\ndate\n\n\n\n\n\n2020-01-01\n0\n\n\n2020-01-02\n0\n\n\n2020-01-03\n0\n\n\n2020-01-04\n0\n\n\n2020-01-05\n0\n\n\n...\n...\n\n\n2024-07-01\n220\n\n\n2024-07-02\n128\n\n\n2024-07-03\n47\n\n\n2024-07-04\n73\n\n\n2024-07-05\n165\n\n\n\n\n1648 rows × 1 columns\n\n\n\nAqui, podemos usar pd.IndexSlice para selecionar várias séries ao mesmo tempo. Note que pd.IndexSlice é passado diretamente para .loc:\n\ny_train.loc[pd.IndexSlice[[0,2], :]]\n\n\n\n\n\n\n\n\n\nsales\n\n\nsku_id\ndate\n\n\n\n\n\n0\n2020-01-01\n0\n\n\n2020-01-02\n0\n\n\n2020-01-03\n0\n\n\n2020-01-04\n0\n\n\n2020-01-05\n0\n\n\n...\n...\n...\n\n\n2\n2024-07-01\n125\n\n\n2024-07-02\n185\n\n\n2024-07-03\n179\n\n\n2024-07-04\n210\n\n\n2024-07-05\n236\n\n\n\n\n3296 rows × 1 columns\n\n\n\nAgora, para selecionar o horizonte de forecasting, temos que chamar unique:\n\nfh = y_test.index.get_level_values(1).unique()\n\nfh\n\nPeriodIndex(['2024-07-06', '2024-07-07', '2024-07-08', '2024-07-09',\n             '2024-07-10', '2024-07-11', '2024-07-12', '2024-07-13',\n             '2024-07-14', '2024-07-15',\n             ...\n             '2024-12-23', '2024-12-24', '2024-12-25', '2024-12-26',\n             '2024-12-27', '2024-12-28', '2024-12-29', '2024-12-30',\n             '2024-12-31', '2025-01-01'],\n            dtype='period[D]', name='date', length=180)",
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-    "text": "8.2 Upcasting automático\nNem todos modelos suportam nativamente dados em painel. Por exemplo, exponential smoothing. Aqui, temos uma boa notícia: sem linhas extras necessárias. O sktime faz upcasting automático para dados em painel ao usar estimadores do sktime.\n\nfrom sktime.forecasting.naive import NaiveForecaster\n\n\nnaive_forecaster = NaiveForecaster(strategy=\"last\", window_length=1)\nnaive_forecaster.fit(y_train)\ny_pred_naive = naive_forecaster.predict(fh=fh)\n\ny_pred_naive\n\n\n\n\n\n\n\n\n\nsales\n\n\nsku_id\ndate\n\n\n\n\n\n0\n2024-07-06\n165.0\n\n\n2024-07-07\n165.0\n\n\n2024-07-08\n165.0\n\n\n2024-07-09\n165.0\n\n\n2024-07-10\n165.0\n\n\n...\n...\n...\n\n\n24\n2024-12-28\n209.0\n\n\n2024-12-29\n209.0\n\n\n2024-12-30\n209.0\n\n\n2024-12-31\n209.0\n\n\n2025-01-01\n209.0\n\n\n\n\n4500 rows × 1 columns\n\n\n\nInternamente, o sktime cria um clone do estimador para cada série nos dados em painel. Em seguida, cada clone é treinado com a série correspondente. Isso é feito de forma transparente para usuário, mas sem exigir esforço.\nO atributo forecasters_ armazena um DataFrame com os estimatores de cada série.\n\nnaive_forecaster.forecasters_.head()\n\n\n\n\n\n\n\n\nforecasters\n\n\n\n\n0\nNaiveForecaster(window_length=1)\n\n\n1\nNaiveForecaster(window_length=1)\n\n\n2\nNaiveForecaster(window_length=1)\n\n\n3\nNaiveForecaster(window_length=1)\n\n\n4\nNaiveForecaster(window_length=1)\n\n\n\n\n\n\n\nÉ dificil explicar o quanto isso é extremamente útil para código limpo e prototipagem rápida. Foi um dos motivos que me levaram a usar o sktime.",
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-    "text": "8.3 Métricas\nAgora que temos várias séries, precisamos explicar como calcular métricas de avaliação. O sktime oferece duas opções para isso, como argumentos na criação da métrica:\n\nmultilevel=\"uniform_average_time\" para calcular a média das séries temporais no painel.\nmultilevel=\"raw_values\" para obter o erro por série.\n\n\nfrom sktime.performance_metrics.forecasting import MeanSquaredScaledError\n\nmetric = MeanSquaredScaledError(multilevel=\"uniform_average_time\")\n\n\nmetric(y_true=y_test, y_pred=y_pred_naive, y_train=y_train)\n\nnp.float64(19.04893558607091)\n\n\nNa prática, as métricas que a sua aplicação exige podem ser diferentes. Por exemplo, as séries temporais podem ter diferentes importâncias, e você pode querer ponderar as métricas de acordo.\nPara isso, é possível criar uma métrica customizada no sktime, mas não entraremos nesse mérito aqui.",
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-    "text": "8.4 Modelos globais de Machine Learning\nQuando vimos como usar modelos de Machine Learning para forecasting, já mencionamos como é necessário traduzir o problema de séries temporais para um problema de regressão tradicional.\nNo caso de dados em painel, também podemos usar essa abordagem, mas agora aproveitando todas as séries temporais para treinar um único modelo global.\n\nAbaixo, vamos comparar um LightGBM global com um local. Veremos o seguinte: o modelo local é melhor que o modelo global, se não processarmos os dados corretamente para o modelo global aproveitar as similaridades entre as séries!\n\nfrom tsbook.forecasting.reduction import ReductionForecaster\nfrom lightgbm import LGBMRegressor\n\nglobal_forecaster1 = ReductionForecaster(\n    LGBMRegressor(n_estimators=100, verbose=-1),\n    window_length=30,\n)\n\nglobal_forecaster1.fit(y_train, X_train)\n\nReductionForecaster(estimator=LGBMRegressor(verbose=-1), window_length=30)Please rerun this cell to show the HTML repr or trust the notebook.ReductionForecasterReductionForecaster(estimator=LGBMRegressor(verbose=-1), window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)\n\n\n\ny_pred_global1 = global_forecaster1.predict(fh=fh, X=X_test)\n\n\nfig, ax = plt.subplots(figsize=(10, 4))\ny_train.loc[10, \"sales\"].plot(ax=ax, label=\"Treino\")\ny_test.loc[10, \"sales\"].plot(ax=ax, label=\"Teste\")\ny_pred_global1.loc[10, \"sales\"].plot(ax=ax, label=\"Global 1\")\nplt.legend()\nplt.show()\n\n\n\n\n\n\n\n\nPara forçar que um modelo global funcione como um modelo local, podemos usar ForecastByLevel, que cria um modelo separado para cada série temporal, mesmo quando o estimador suporta dados em painel.\n\nfrom sktime.forecasting.compose import ForecastByLevel\n\nlocal_forecaster1 = ForecastByLevel(global_forecaster1, groupby=\"local\")\n\nlocal_forecaster1.fit(y_train, X=X_train)\n\nForecastByLevel(forecaster=ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                               window_length=30))Please rerun this cell to show the HTML repr or trust the notebook.ForecastByLevel?Documentation for ForecastByLevelForecastByLevel(forecaster=ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                               window_length=30))forecaster: ReductionForecasterReductionForecaster(estimator=LGBMRegressor(verbose=-1), window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)\n\n\n\ny_pred_local1 = local_forecaster1.predict(fh=fh, X=X_test)\n\nerr_global1 = metric(y_true=y_test, y_pred=y_pred_global1, y_train=y_train)\nerr_local1 = metric(y_true=y_test, y_pred=y_pred_local1, y_train=y_train)\n\nerrors = pd.DataFrame(\n    {\n        \"Global (1)\": [err_global1],\n        \"Local (1)\": [err_local1],\n    },\n    index=[\"MSE\"],\n)",
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-    "text": "8.5 Preprocessamento e engenharia de features\nSabemos como preprocessar séries temporais univariadas para melhorar o desempenho dos modelos de ML. Aplicamos da mesma maneira que fizemos anteriormente o Differencer, com objetivo de remover tendências.\n\nfrom sktime.transformations.series.difference import Differencer\n\n\nglobal_forecaster2 = Differencer() * global_forecaster1\nglobal_forecaster2.fit(y_train, X_train)\n\nTransformedTargetForecaster(steps=[Differencer(),\n                                   ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                       window_length=30)])Please rerun this cell to show the HTML repr or trust the notebook.TransformedTargetForecaster?Documentation for TransformedTargetForecasterTransformedTargetForecaster(steps=[Differencer(),\n                                   ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                       window_length=30)])Differencer?Documentation for DifferencerDifferencer()ReductionForecasterReductionForecaster(estimator=LGBMRegressor(verbose=-1), window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)\n\n\n\ny_pred_global2 = global_forecaster2.predict(fh=fh, X=X_test)\nmetric_global2 = metric(y_true=y_test, y_pred=y_pred_global2, y_train=y_train)\n\nE agora sua versão local:\n\nlocal_forecaster2 = ForecastByLevel(global_forecaster2, groupby=\"local\")\nlocal_forecaster2.fit(y_train, X=X_train)\n\ny_pred_local2 = local_forecaster2.predict(fh=fh, X=X_test)\nmetric_local2 = metric(y_true=y_test, y_pred=y_pred_local2, y_train=y_train)\n\nAgora, podemos comparar:\n\nerrors[\"Global (2)\"] = metric_global2\nerrors[\"Local (2)\"] = metric_local2\n\nerrors\n\n\n\n\n\n\n\n\nGlobal (1)\nLocal (1)\nGlobal (2)\nLocal (2)\n\n\n\n\nMSE\n41.057873\n23.720592\n22.238809\n67.626298\n\n\n\n\n\n\n\nNote como já superamos o modelo global incial, e o modelo local. Isso é para destacar que é essencial realizar um bom preprocessamento e engenharia de features para que modelos de Machine Learning tenham bom desempenho em dados em painel.\n\n8.5.1 Normalização por janela\nAgora, vamos usar a normalização por janela, que é especialmente útil em dados em painel, onde as séries podem ter diferentes escalas.\n\nglobal_forecaster3 = global_forecaster1.clone().set_params(\n    normalization_strategy=\"divide_mean\"\n)\n\nglobal_forecaster3.fit(y_train, X_train)\n\nReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                    normalization_strategy='divide_mean', window_length=30)Please rerun this cell to show the HTML repr or trust the notebook.ReductionForecasterReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                    normalization_strategy='divide_mean', window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)\n\n\n\n# Predict\ny_pred_global3 = global_forecaster3.predict(fh=fh, X=X_test)\n\n# Métrica\nmetric_global3 = metric(y_true=y_test, y_pred=y_pred_global3, y_train=y_train)\n\nerrors[\"Global 3 (window norm)\"] = metric_global3\n\ndisplay(errors)\n\n\n\n\n\n\n\n\nGlobal (1)\nLocal (1)\nGlobal (2)\nLocal (2)\nGlobal 3 (window norm)\n\n\n\n\nMSE\n41.057873\n23.720592\n22.238809\n67.626298\n15.317953\n\n\n\n\n\n\n\nVemos que resultados são ainda melhores!\n\n\n8.5.2 Pipelines de features exógenas\nPodemos ajudar o modelo a capturar sazonalidades adicionando features de Fourier como features exógenas.\nUsamos ** para criar um pipeline aplicado sobre as features exógenas:\n\nfrom sktime.transformations.series.fourier import FourierFeatures\n\nfourier_features = FourierFeatures(\n    sp_list=[365.25, 365.25 / 12], fourier_terms_list=[1, 1], freq=\"D\"\n)\n\nglobal_forecaster4 = fourier_features**global_forecaster3\nglobal_forecaster4.fit(y_train, X_train)\n\nForecastingPipeline(steps=[FourierFeatures(fourier_terms_list=[1, 1], freq='D',\n                                           sp_list=[365.25, 30.4375]),\n                           ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                               normalization_strategy='divide_mean',\n                                               window_length=30)])Please rerun this cell to show the HTML repr or trust the notebook.ForecastingPipeline?Documentation for ForecastingPipelineForecastingPipeline(steps=[FourierFeatures(fourier_terms_list=[1, 1], freq='D',\n                                           sp_list=[365.25, 30.4375]),\n                           ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                               normalization_strategy='divide_mean',\n                                               window_length=30)])FourierFeatures?Documentation for FourierFeaturesFourierFeatures(fourier_terms_list=[1, 1], freq='D', sp_list=[365.25, 30.4375])ReductionForecasterReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                    normalization_strategy='divide_mean', window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)\n\n\n\ny_pred_global4 = global_forecaster4.predict(fh=fh, X=X_test)\nmetric_global4 = metric(y_true=y_test, y_pred=y_pred_global4, y_train=y_train)\n\nerrors[\"Global 4 (fourier)\"] = metric_global4\nerrors\n\n\n\n\n\n\n\n\nGlobal (1)\nLocal (1)\nGlobal (2)\nLocal (2)\nGlobal 3 (window norm)\nGlobal 4 (fourier)\n\n\n\n\nMSE\n41.057873\n23.720592\n22.238809\n67.626298\n15.317953\n15.143648",
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-    "text": "8.6 Agrupamento + Modelos globais\nUma técnica muito adotada é fazer modelos globais por clusters de séries temporais similares.\nUma maneira de categorizar é usando ADI (Average Demand Interval) e CV² (squared Coefficient of Variation). O componente ADI é calculado como o percentual de períodos com demanda (y&gt;0), e \\(CV^2\\) é o quadrado do coeficiente de variação das demandas positivas.\n\n\n\n\n\n\n\n\n\n\nCategoria\nADI\nCV²\nPadrão típico\nExemplos\n\n\n\n\nSuave (Smooth)\n≤ 1,32\n≤ 0,49\nDemanda contínua e estável\nItens de consumo diário, alimentos\n\n\nErrática (Erratic)\n≤ 1,32\n&gt; 0,49\nDemanda contínua, porém muito variável\nModa, eletrônicos\n\n\nIntermitente (Intermittent)\n&gt; 1,32\n≤ 0,49\nMuitos períodos sem venda, mas valores estáveis quando ocorre\nPeças de reposição, ferramentas\n\n\nIrregular (Lumpy)\n&gt; 1,32\n&gt; 0,49\nMuitos períodos com zero e valores muito variáveis quando há demanda\nEquipamentos caros, sobressalentes grandes\n\n\n\n\n\n\n\n\n\n\n\n\n\nfrom sktime.forecasting.compose import GroupbyCategoryForecaster\nfrom sktime.transformations.series.adi_cv import ADICVTransformer\n\n# TODO: customize yours!\ngroup_forecaster = GroupbyCategoryForecaster(\n    forecasters =\n        {\"smooth\": global_forecaster3.clone(),\n        \"erratic\": global_forecaster3.clone(),\n        \"intermittent\": global_forecaster3.clone(),\n        \"lumpy\": global_forecaster3.clone(),\n        },\n    transformer=ADICVTransformer(features=[\"class\"],))\n\n\ngroup_forecaster.fit(y_train, X_train)\n\nGroupbyCategoryForecaster(forecasters={'erratic': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                                      normalization_strategy='divide_mean',\n                                                                      window_length=30),\n                                       'intermittent': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                                           normalization_strategy='divide_mean',\n                                                                           window_length=30),\n                                       'lumpy': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                                    normalization_strategy='divide_mean',\n                                                                    window_length=30),\n                                       'smooth': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                                     normalization_strategy='divide_mean',\n                                                                     window_length=30)},\n                          transformer=ADICVTransformer(features=['class']))Please rerun this cell to show the HTML repr or trust the notebook.GroupbyCategoryForecaster?Documentation for GroupbyCategoryForecasterGroupbyCategoryForecaster(forecasters={'erratic': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                                      normalization_strategy='divide_mean',\n                                                                      window_length=30),\n                                       'intermittent': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                                           normalization_strategy='divide_mean',\n                                                                           window_length=30),\n                                       'lumpy': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                                    normalization_strategy='divide_mean',\n                                                                    window_length=30),\n                                       'smooth': ReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                                                                     normalization_strategy='divide_mean',\n                                                                     window_length=30)},\n                          transformer=ADICVTransformer(features=['class']))ADICVTransformerADICVTransformer(features=['class'])ADICVTransformer?Documentation for ADICVTransformerADICVTransformer(features=['class'])smoothReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                    normalization_strategy='divide_mean', window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)erraticReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                    normalization_strategy='divide_mean', window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)intermittentReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                    normalization_strategy='divide_mean', window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)lumpyReductionForecaster(estimator=LGBMRegressor(verbose=-1),\n                    normalization_strategy='divide_mean', window_length=30)estimator: LGBMRegressorLGBMRegressor(verbose=-1)LGBMRegressorLGBMRegressor(verbose=-1)fallback_forecasterNoneNoneNone\n\n\n\ny_pred_group = group_forecaster.predict(fh=fh, X=X_test)\nmetric_group = metric(y_true=y_test, y_pred=y_pred_group, y_train=y_train)\n\nmetric_group\n\nnp.float64(53.56051503265084)\n\n\nAlthough it did not perform better than the best global model in this synthetic example, in real-world scenarios this approach can be very effective, particularly when there are large samples for each category, allowing the model to learn specific patterns for each group of series.\nIn addition, this can be useful when there are computation constraints, as training multiple smaller models for each cluster can be more efficient than training a single large global model.",
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-    "text": "8.7 Resumo\nAqui, vimos como trabalhar com dados em painel (multi-series) usando o sktime. Vimos, especialmente, como criar modelos globais de Machine Learning que aproveitam as similaridades entre as séries para melhorar o desempenho das previsões. Também destacamos a importância do preprocessamento e da engenharia de features para obter bons resultados com esses modelos.\nPara uma análise mais aprofundada, recomendo o artigo de (Montero-Manso and Hyndman 2021), que discute princípios para forecasting em dados em painel usando modelos globais de Machine Learning.\n\n\n\n\nMakridakis, Spyros, Evangelos Spiliotis, and Vassilios Assimakopoulos. 2022. “M5 Accuracy Competition: Results, Findings, and Conclusions.” International Journal of Forecasting 38 (4): 1346–64.\n\n\nMontero-Manso, Pablo, and Rob J Hyndman. 2021. “Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality.” International Journal of Forecasting 37 (4): 1632–53.",
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-    "text": "9.1 Carregando dados\nMuitas vezes, não apenas temos múltiplas séries temporais, mas essas séries também estão organizadas em uma hierarquia. Por exemplo, vendas de produtos podem ser organizadas por SKU, categoria, departamento e total da loja.\nVamos usar o mesmo dataset sintético, mas agora com uma hierarquia de produtos.\nAo mesmo tempo que dados hierarárquicos são interessantes pois nos trazem mais informação, eles também trazem desafios adicionais. Imagine que queremos prever as vendas futuras de cada produto. Se fizermos previsões independetes para cada produto, não há garantia que a soma das previsões dos produtos será igual à previsão do total da loja. Isso é chamado de incoerência nas previsões hierárquicas. O processo de ajustar as previsões para garantir coerência é chamado de reconciliação.\nVamos usar os dados sintéticos, agora com sua versao hierárquica.\nfrom tsbook.datasets.retail import SyntheticRetail\n\ndataset = SyntheticRetail(\"hierarchical\")\ny_train, X_train, y_test, X_test = dataset.load(\"y_train\", \"X_train\", \"y_test\", \"X_test\")",
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-    "text": "9.2 Uso de pandas e dados hierárquicos\nAgora, os dataframes possuem mais de 2 ou mais índices, representando a hierarquia.\n\ny_train\n\n\n\n\n\n\n\n\n\n\nsales\n\n\ngroup_id\nsku_id\ndate\n\n\n\n\n\n-1\n20\n2020-01-01\n0\n\n\n2020-01-02\n0\n\n\n2020-01-03\n0\n\n\n2020-01-04\n0\n\n\n2020-01-05\n2\n\n\n...\n...\n...\n...\n\n\n__total\n__total\n2024-07-01\n2000\n\n\n2024-07-02\n1616\n\n\n2024-07-03\n1917\n\n\n2024-07-04\n2384\n\n\n2024-07-05\n2462\n\n\n\n\n51088 rows × 1 columns\n\n\n\nPara obter o número de pontos de série únicos (séries temporais individuais), podemos fazer o seguinte:\n\ny_train.index.droplevel(-1).nunique()\n\n31\n\n\nNote que existem algumas séries com um identificador __total. Esse identificador representa o total para aquele nível da hierarquia. Por exemplo, se o id completo é (-1, \"__total\"), isso representa o total do grupo -1.\n\ny_train.loc[(-1, \"__total\")].head()\n\n\n\n\n\n\n\n\nsales\n\n\ndate\n\n\n\n\n\n2020-01-01\n4\n\n\n2020-01-02\n2\n\n\n2020-01-03\n3\n\n\n2020-01-04\n14\n\n\n2020-01-05\n16\n\n\n\n\n\n\n\nO total de todas as séries é representado por (\"__total\", \"__total\").\n\ny_train.loc[(\"__total\", \"__total\")]\n\n\n\n\n\n\n\n\nsales\n\n\ndate\n\n\n\n\n\n2020-01-01\n24\n\n\n2020-01-02\n19\n\n\n2020-01-03\n14\n\n\n2020-01-04\n23\n\n\n2020-01-05\n23\n\n\n...\n...\n\n\n2024-07-01\n2000\n\n\n2024-07-02\n1616\n\n\n2024-07-03\n1917\n\n\n2024-07-04\n2384\n\n\n2024-07-05\n2462\n\n\n\n\n1648 rows × 1 columns\n\n\n\nPara contabilizar o número de séries temporais individuais, podemos fazer o seguinte:\n\ny_train.index.droplevel(-1).nunique()\n\n31",
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-    "text": "9.3 Previsão sem reconciliação\nVamos fazer uma previsão e entender o problema da incoerência.\n\nfh = y_test.index.get_level_values(-1).unique()\n\n\nfrom tsbook.forecasting.reduction import ReductionForecaster\nfrom lightgbm import LGBMRegressor\n\nforecaster = ReductionForecaster(\n    LGBMRegressor(n_estimators=50, verbose=-1, objective=\"tweedie\"),\n    window_length=30,\n    normalization_strategy=\"divide_mean\",\n)\nforecaster.fit(y_train, X=X_train)\ny_pred = forecaster.predict(fh, X=X_test)\n\nPara somar as previsões de baixo para cima, podemos usar o transformador Aggregator. Vamos ver que, quando somarmos as previsões das séries filhas, o resultado não é igual à previsão da série total.\n\nfrom sktime.transformations.hierarchical.aggregate import Aggregator\n\nAggregator().fit_transform(y_pred) - y_pred\n\n\n\n\n\n\n\n\n\n\nsales\n\n\ngroup_id\nsku_id\ndate\n\n\n\n\n\n-1\n20\n2024-07-06\n0.000000\n\n\n2024-07-07\n0.000000\n\n\n2024-07-08\n0.000000\n\n\n2024-07-09\n0.000000\n\n\n2024-07-10\n0.000000\n\n\n...\n...\n...\n...\n\n\n__total\n__total\n2024-12-28\n-195.866868\n\n\n2024-12-29\n-113.260571\n\n\n2024-12-30\n-294.224862\n\n\n2024-12-31\n-311.490070\n\n\n2025-01-01\n-256.487505\n\n\n\n\n5580 rows × 1 columns\n\n\n\nExiste uma diferença… ou seja, os valores não batem. Imagine o impacto de levar previsões incoerentes para a tomada de decisão em uma empresa? A raiz do problema é que temos mais modelos que graus de liberdade. Para ilustrar, suponha que temos 3 séries: \\(A\\), \\(B\\) e \\(C\\), onde:\n\\[\nC(t) = A(t) + B(t)\n\\]\nAqui, temos 3 séries, mas apenas 2 graus de liberdade, pois \\(C\\) é completamente determinado por \\(A\\) e \\(B\\). Se fizermos previsões independentes para \\(A\\), \\(B\\) e \\(C\\), não há garantia de que a relação acima será mantida nas previsões.",
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-    "text": "9.4 Reconciliação de previsões hierárquicas\n\nExistem diferentes métodos para reconciliar previsões em séries temporais hierárquicas. Não existe uma solução única, e o melhor método depende dos dados e do contexto.",
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-    "text": "9.5 Bottom-up\nA maneira mais simples de reconcialiar previsões hierárquicas é a abordagem bottom-up. Nessa abordagem, fazemos previsões apenas para as séries mais baixas na hierarquia (as séries filhas) e depois somamos essas previsões para obter as previsões das séries superiores (as séries pais).\n\nLados positivos:\n\nSimplicidade: fácil de entender e implementar.\nCoerência garantida: a soma das previsões das séries filhas sempre será igual à previsão da série pai.\nSérias filhas podem capturar detalhes específicos que podem ser perdidos em níveis superiores.\n\nNo entanto, essa abordagem também tem desvantagens: é sucetível ao ruído nas séries filhas, e se as séries filhas tiverem pouca informação, as previsões podem ser ruins. Por exemplo, muitos zeros nas séries de níveis baixos pode levar a previsões ruins a niveis agregados.\n\nfrom sktime.transformations.hierarchical.reconcile import BottomUpReconciler\n\nbottom_up = BottomUpReconciler() * forecaster\nbottom_up.fit(y_train)\n\ny_pred_bottomup = bottom_up.predict(fh=fh)\n\nAgora vemos que as previsões são coerentes:\n\nAggregator().fit_transform(y_pred_bottomup) - y_pred_bottomup\n\n\n\n\n\n\n\n\n\n\nsales\n\n\ngroup_id\nsku_id\ndate\n\n\n\n\n\n-1\n20\n2024-07-06\n0.0\n\n\n2024-07-07\n0.0\n\n\n2024-07-08\n0.0\n\n\n2024-07-09\n0.0\n\n\n2024-07-10\n0.0\n\n\n...\n...\n...\n...\n\n\n__total\n__total\n2024-12-28\n0.0\n\n\n2024-12-29\n0.0\n\n\n2024-12-30\n0.0\n\n\n2024-12-31\n0.0\n\n\n2025-01-01\n0.0\n\n\n\n\n5580 rows × 1 columns",
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-    "text": "9.6 Top-down (forecast proportions)\nOutra abordagem é a top-down. Nessa abordagem, fazemos previsões apenas para as séries superiores na hierarquia (as séries pais) e depois distribuímos essas previsões para as séries filhas com base em proporções previstas.\nSuponha que temos a seguinte hierarquia \\(C(t) = A(t) + B(t)\\). Considere \\(\\hat{C}(t)\\), \\(\\hat{A}(t)\\) e \\(\\hat{B}(t)\\) como as previsões para \\(C\\), \\(A\\) e \\(B\\), respectivamente. Na abordagem top-down, faríamos o seguinte:\n\nPrever \\(\\hat{C}(t)\\), \\(\\hat{A}(t)\\) e \\(\\hat{B}(t)\\) independentemente.\nCalcular as proporções previstas para os níveis mais baixos: \\[\np_A(t) = \\frac{\\hat{A}(t)}{\\hat{A}(t) + \\hat{B}(t)}\n\\]\n\n\\[\np_B(t) = \\frac{\\hat{B}(t)}{\\hat{A}(t) + \\hat{B}(t)}\n\\]\n\nDistribuir a previsão de \\(C\\) para \\(A\\) e \\(B\\) usando essas proporções: \\[\n\\tilde{A}(t) = p_A(t) \\cdot \\hat{C}(t)\n\\]\n\n\\[\n\\tilde{B}(t) = p_B(t) \\cdot \\hat{C}(t)\n\\]\nEssa abordagem é capaz de usufruir da qualidade do forecast total, e ainda consegue distribuir para as séries filhas baseadas no histórico.\n\nO que chamam de “Proporções históricas” é equivalente a esse método, mas com um modelo Naive para prever as proporções.\nEsse método pode ser bom quando o forecast total é de boa qualidade. No entanto, dependemos profundamente da qualidade do forecast total e das proporções.\n\nfrom sktime.transformations.hierarchical.reconcile import TopdownReconciler\n\ntop_down_fcst = TopdownReconciler() * forecaster\ntop_down_fcst.fit(y_train)\n\ny_pred_topdown = top_down_fcst.predict(fh=fh)",
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-    "text": "9.7 Reconciliação ótima\nExiste uma abordagem mais sofisticada, com uma intuição geométrica interessante. A ideia é ajustar as previsões iniciais para que elas satisfaçam as restrições de soma da hierarquia. Por exemplo, para a hierarquia \\(C(t) = A(t) + B(t)\\), queremos garantir que:\n\\[\n\\hat{C}(t) = \\hat{A}(t) + \\hat{B}(t)\n\\]\nSe consideramos nosso espaço 3D de observações \\((\\hat{A}, \\hat{B}, \\hat{C})\\), a condição acima é satisfeita para um plano 2D nesse universo.\n\nPodemos então projetar nossas previsões iniciais nesse plano para obter previsões coerentes. Essa projeção pode ser feita de várias maneiras, levando a diferentes métodos de reconciliação ótima. Os métodos levam o nome “OLS” pois a projeção é feita minimizando o erro quadrático (Ordinary Least Squares).\n\nOLS : projetar ortogonalmente todas as previsões base na espaço de reconciliação, tratando todas as séries igualmente.\nWeighted OLS: projetar obliquamente, ou seja, considerando pesos diferentes para cada série, permitindo dar mais importância a certas séries na reconciliação. A projeção não faz mais uma perpendicular, mas sim uma oblíqua.\nMinimum trace (MinT): use a matriz de covariância do erro para encontrar as previsões reconciliadas ótimas. Chamado de “ótimo”.\n\nPara a reconciliação ótima com OLS, podemos usar o OptimalReconciler do sktime:\n\nfrom sktime.transformations.hierarchical.reconcile import OptimalReconciler\n\noptimal = OptimalReconciler(\"ols\") * forecaster\noptimal.fit(y_train)\ny_pred_optimal = optimal.predict(fh=fh)\n\n\n\nCode\nfrom sktime.utils.plotting import plot_series\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n\nidx = y_train.index.droplevel(-1).unique()[10]\n\nplot_series(\n    y_train.loc[idx,],\n    y_test.loc[idx,],\n    y_pred.loc[idx,],\n    y_pred_optimal.loc[idx,],\n    labels=[\"Train\", \"Test\", \"Predicted (sem reconciliação)\", \"Predicted (ótimo)\"],\n)\nplt.xlim(pd.to_datetime(\"2024-05-01\"), None)\nplt.show()\n\n\n\n\n\n\n\n\n\nPara reconciliações ótimas (que usam a covariância do erro), podemos usar o ReconcilerForecaster do sktime, que internamente já faz o cálculo da covariância do erro:\n\nfrom sktime.forecasting.reconcile import ReconcilerForecaster\n\n\nmint_forecaster = ReconcilerForecaster(\n    forecaster=forecaster,\n    method=\"mint_shrink\")\n\nmint_forecaster.fit(y_train)\ny_pred_mint = mint_forecaster.predict(fh=fh)",
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-    "text": "9.8 Comparando resultados\n\nfrom sktime.performance_metrics.forecasting import MeanSquaredScaledError\n\nmetric = MeanSquaredScaledError(multilevel=\"uniform_average_time\")\n\npd.DataFrame(\n    {\n        \"Baseline\": metric(y_test, y_pred, y_train=y_train),\n        \"BottomUpReconciler\": metric(y_test, y_pred_bottomup, y_train=y_train),\n        \"TopDownReconciler\": metric(y_test, y_pred_topdown, y_train=y_train),\n        \"OptimalReconciler (ols)\": metric(y_test, y_pred_optimal, y_train=y_train),\n        \"Mint Reconciler\": metric(y_test, y_pred_mint, y_train=y_train),\n    },\n    index=[\"Mean Absolute Scaled Error\"],\n)\n\n\n\n\n\n\n\n\nBaseline\nBottomUpReconciler\nTopDownReconciler\nOptimalReconciler (ols)\nMint Reconciler\n\n\n\n\nMean Absolute Scaled Error\n40.172892\n82.744957\n38.150928\n39.847359\n33.014674",
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-    "text": "10.1 N-Beats\nAlém de modelos de ML simples, também podemos usar modelos de deep learning para forecasting. Podemos com certeza usar uma rede neural simples como um regressor, assim como fizemos com os modelos de ML tradicionais. No entanto, existem alguns modelos com arquiteturas específicas para forecasting de séries temporais. Por exemplo, o N-BEATS é um modelo de deep learning que pode ser usado para forecasting.\nN-BEATS é um modelo de séries temporais totalmente baseado em camadas densas (MLP)—nada de RNN/LSTM nem convolução. Ele pega uma janela do passado e entrega diretamente a previsão do futuro, aprendendo padrões como tendência e sazonalidade de forma pura, só com perceptrons.\nO N-BEATS usa bases para construir sinais interpretáveis:\nAssim, internamente, ele determina os coeficientes das funções base para modelar a série temporal, baseado no histórico observado, fazendo uma espécie de meta-aprendizado interno.\nOs blocos são empilhados e executados de forma sucessiva. Pense numa fila de especialistas olhando a mesma janela do passado:",
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-    "text": "Base de tendência: combina funções polinomiais (captura subidas/descidas suaves).\nBase sazonal: combina senos/cossenos (captura repetições periódicas).\nBase genérica: aprende formas livres (sem pressupor forma analítica).\n\n\n\n\nCada um explica a sua parte do que viu (remove do passado o que já foi entendido = backcast), e propõe um pedaço da previsão (seu forecast).\nO que não foi explicado segue para o próximo especialista. No final, a previsão é a soma do que cada um sugeriu.\n\n\n\n10.1.1 Pytorch Forecasting\nO Sktime nao é uma biblioteca especializada em deep learning, mas sim uma API uniforme que provê acesso aos mais diversos algoritmos.\nLogo, também provemos acesso a bibliotecas especializadas em deep learning, como o Pytorch Forecasting, que implementa o N-BEATS.\nAqui, temos que definir os hiperparâmetros do modelo, como o número de blocos, o tamanho do contexto (janela de entrada), e o número de coeficientes para as funcões de base.\n\nfrom sktime.forecasting.pytorchforecasting import PytorchForecastingNBeats\nfrom pytorch_forecasting.data.encoders import EncoderNormalizer\n\nCONTEXT_LENGTH = 120\nnbeats = PytorchForecastingNBeats(\n    train_to_dataloader_params={\"batch_size\": 256},\n    trainer_params={\"max_epochs\": 1},\n    model_params={\n        \"stack_types\": [\"trend\", \"seasonality\"], # One of the following values: “generic”, “seasonality” or “trend”.\n        \"num_blocks\" : [2,2], # The number of blocks per stack. \n        \"context_length\": CONTEXT_LENGTH, # lookback period\n        \"expansion_coefficient_lengths\" : [2, 5],\n        \"learning_rate\": 1e-3,\n    },\n    dataset_params={\n\n        \"max_encoder_length\": CONTEXT_LENGTH,\n        \"target_normalizer\": EncoderNormalizer()\n    },\n)\n\nnbeats.fit(y_train.astype(float), fh=fh)\n\n💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\nGPU available: True (mps), used: True\nTPU available: False, using: 0 TPU cores\nHPU available: False, using: 0 HPUs\n\n  | Name            | Type       | Params | Mode \n-------------------------------------------------------\n0 | loss            | MASE       | 0      | train\n1 | logging_metrics | ModuleList | 0      | train\n2 | net_blocks      | ModuleList | 1.4 M  | train\n-------------------------------------------------------\n1.4 M     Trainable params\n0         Non-trainable params\n1.4 M     Total params\n5.484     Total estimated model params size (MB)\n60        Modules in train mode\n0         Modules in eval mode\n\n\n\n\n\n\n\n\n\n\n\n`Trainer.fit` stopped: `max_epochs=1` reached.\n\n\nPytorchForecastingNBeats(dataset_params={'max_encoder_length': 120,\n                                         'target_normalizer': EncoderNormalizer(\n    method='standard',\n    center=True,\n    max_length=None,\n    transformation=None,\n    method_kwargs={}\n)},\n                         model_params={'context_length': 120,\n                                       'expansion_coefficient_lengths': [2, 5],\n                                       'learning_rate': 0.001,\n                                       'num_blocks': [2, 2],\n                                       'stack_types': ['trend', 'seasonality']},\n                         train_to_dataloader_params={'batch_size': 256},\n                         trainer_params={'max_epochs': 1})Please rerun this cell to show the HTML repr or trust the notebook.PytorchForecastingNBeats?Documentation for PytorchForecastingNBeatsPytorchForecastingNBeats(dataset_params={'max_encoder_length': 120,\n                                         'target_normalizer': EncoderNormalizer(\n    method='standard',\n    center=True,\n    max_length=None,\n    transformation=None,\n    method_kwargs={}\n)},\n                         model_params={'context_length': 120,\n                                       'expansion_coefficient_lengths': [2, 5],\n                                       'learning_rate': 0.001,\n                                       'num_blocks': [2, 2],\n                                       'stack_types': ['trend', 'seasonality']},\n                         train_to_dataloader_params={'batch_size': 256},\n                         trainer_params={'max_epochs': 1})\n\n\n\ny_pred_nbeats = nbeats.predict(fh=fh, X=X_test)\n\n/Users/felipeangelim/Workspace/python_brasil_2025/.venv/lib/python3.11/site-packages/sktime/forecasting/base/adapters/_pytorchforecasting.py:655: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n  _y.fillna(0, inplace=True)\n💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\nGPU available: True (mps), used: True\nTPU available: False, using: 0 TPU cores\nHPU available: False, using: 0 HPUs\n\n\n\nfrom sktime.performance_metrics.forecasting import MeanSquaredScaledError\n\nmetric = MeanSquaredScaledError(multilevel=\"uniform_average_time\")\nmetric(y_true=y_test, y_pred=y_pred_nbeats, y_train=y_train)\n\nnp.float64(16.453837132521112)\n\n\nAgora, podemos visualizar o forecast para uma das séries. Vemos que, mesmo com poucas épocas de treinamento ou tuning, o N-BEATS já consegue capturar a tendência.\n\nfig, ax = plt.subplots(figsize=(10, 4))\ny_train.loc[10].plot(ax=ax, label=\"Train\")\ny_test.loc[10].plot(ax=ax, label=\"Test\")\ny_pred_nbeats.loc[10].plot(ax=ax, label=\"N-BEATS\")\nfig.show()",
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-    "text": "10.2 Zero-shot forecasting com N-BEATS\nZero-shot forecasting se refere ao fato de fazer previsão para uma série jamais vista pelo modeo, sem utilizar a série para treinar ou ajustar parâmetros dele.\nAqui, para simular esse cenário, vamos criar uma nova série temporal combinando duas séries do conjunto de treino.\n\nnew_y_train = (y_train.loc[0]**2 + y_train.loc[20]).astype(float)\nnew_y_test = (y_test.loc[0]**2 + y_test.loc[20]).astype(float)\n\n# Plotting the new series\nfig, ax = plt.subplots(figsize=(10, 4))\nnew_y_train[\"sales\"].plot.line(ax=ax, label=\"New Train\")\nnew_y_test[\"sales\"].plot.line(ax=ax, label=\"New Test\")\nfig.show()\n\n\n\n\n\n\n\n\nNa interface atual do sktime, usamos o argumento y do método predict para passar a nova série temporal para o modelo:\n\ny_pred_zeroshot = nbeats.predict(fh=fh, y=new_y_train)\n\n/Users/felipeangelim/Workspace/python_brasil_2025/.venv/lib/python3.11/site-packages/sktime/forecasting/base/adapters/_pytorchforecasting.py:655: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n  _y.fillna(0, inplace=True)\n💡 Tip: For seamless cloud uploads and versioning, try installing [litmodels](https://pypi.org/project/litmodels/) to enable LitModelCheckpoint, which syncs automatically with the Lightning model registry.\nGPU available: True (mps), used: True\nTPU available: False, using: 0 TPU cores\nHPU available: False, using: 0 HPUs\n\n\n\nfig, ax = plt.subplots(figsize=(10, 4))\nnew_y_train[\"sales\"].plot.line(ax=ax, label=\"New Train\")\nnew_y_test[\"sales\"].plot.line(ax=ax, label=\"New Test\")\ny_pred_zeroshot[\"sales\"].plot.line(ax=ax, label=\"N-BEATS Zero-shot\")\nplt.legend()\nplt.show()",
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-    "text": "11.1 O sistema de tags\nO sktime oferece um ecossistema robusto, mas em cenarios reais frequentemente precisamos ajustar comportamentos, incorporar dados hierarquicos ou adicionar pre-processamentos especificos. Felizmente, o sktime torna relativamente simples a criacao de modelos customizados, desde que sigamos algumas regras.\nAcredito que essa e uma das grandes vantagens da biblioteca: o foco em ser extensivel e customizavel.\nEntre a chamada dos metodos publicos e privados existe uma camada de validacoes e conversões controlada pelas tags. Elas sinalizam ao BaseForecaster e ao BaseTransformer o que precisa ser garantido antes de executar a implementacao customizada.\nAs tags mais importantes para um forecaster podem ser definidas assim:\nCada uma delas indica o que o modelo é capaz de fazer nos seus métodos privados _fit e _predict:\nOs machine-types (mtypes), ou tipos para máquina, são a peça mais crucial nesse sistema.",
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-    "text": "_tags = {\n    \"capability:exogenous\": True,\n    \"requires-fh-in-fit\": False,\n    \"X_inner_mtype\": [\n        \"pd.Series\",\n        \"pd.DataFrame\",\n        \"pd-multiindex\",\n        \"pd_multiindex_hier\",\n    ],\n    \"y_inner_mtype\": [\n        \"pd.Series\",\n        \"pd.DataFrame\",\n        \"pd-multiindex\",\n        \"pd_multiindex_hier\",\n    ]\n}\n\n\ncapability:exogenous: Indica se o modelo suporta variáveis exógenas (X) durante o ajuste e a previsão.\nrequires-fh-in-fit: Indica se o modelo precisa do horizonte de previsão (fh) durante o ajuste. Alguns modelos precisam devido a sua implementação interna.\ny_inner_mtype: Define os tipos de dados aceitos para a variável dependente (y) durante o ajuste e a previsão.\nX_inner_mtype: Define os tipos de dados aceitos para as variáveis exógenas (X) durante o ajuste e a previsão.\n\n\n\n11.1.1 Machine-types disponiveis\nOs mtypes definem qual a estrutura de dados que o modelo aceita como entrada e produz como saída. Os principais mtypes para séries temporais são:\n\nnp.ndarray\npd.Series\npd.DataFrame\npd-multiindex (ideia de painel)\npd_multiindex_hier (dados hierarquicos)\n\nSe o modelo suporta um mtype hierárquico e passamos um dado hierárquico, o dado chegará normalmente ao método privado _fit ou _predict. Caso contrário, o sktime tentará converter o dado para um mtype suportado.\n\n11.1.1.1 Baixando exemplos por mtype\nPara entender melhor cada mtype, podemos baixar exemplos práticos usando a função get_examples do sktime:\n\nfrom sktime.datatypes import get_examples\n\nget_examples(mtype=\"np.ndarray\", as_scitype=\"Series\")[0]\n\narray([[ 1. ],\n       [ 4. ],\n       [ 0.5],\n       [-3. ]])\n\n\n\nget_examples(mtype=\"pd.DataFrame\", as_scitype=\"Series\")[0].head()\n\n\n\n\n\n\n\n\na\n\n\n\n\n0\n1.0\n\n\n1\n4.0\n\n\n2\n0.5\n\n\n3\n-3.0\n\n\n\n\n\n\n\n\nget_examples(mtype=\"pd-multiindex\", as_scitype=\"Panel\")[0].head()\n\n\n\n\n\n\n\n\n\nvar_0\nvar_1\n\n\ninstances\ntimepoints\n\n\n\n\n\n\n0\n0\n1\n4\n\n\n1\n2\n5\n\n\n2\n3\n6\n\n\n1\n0\n1\n4\n\n\n1\n2\n55\n\n\n\n\n\n\n\n\nget_examples(mtype=\"pd_multiindex_hier\", as_scitype=\"Hierarchical\")[0].head()\n\n\n\n\n\n\n\n\n\n\nvar_0\nvar_1\n\n\nfoo\nbar\ntimepoints\n\n\n\n\n\n\na\n0\n0\n1\n4\n\n\n1\n2\n5\n\n\n2\n3\n6\n\n\n1\n0\n1\n4\n\n\n1\n2\n55\n\n\n\n\n\n\n\nAlguns mtypes tem limitações: uma pd.Series simples nao representa problemas hierarquicos, sendo necessario recorrer ao pd_multiindex_hier.\n\n\n11.1.1.2 Exemplo prático\nVamos criar o nosso primeiro esqueleto de forecaster customizado. Para isso, baixamos uma série de exemplo:\n\nfrom sktime.forecasting.base import BaseForecaster\nfrom sktime.utils._testing.series import _make_series\n\ny = _make_series(4)\ny\n\n2000-01-01    1.000000\n2000-01-02    1.464179\n2000-01-03    2.706921\n2000-01-04    3.018884\nFreq: D, dtype: float64\n\n\nNosso protótipo irá apenas printar os dados recebidos no método _fit. O __init__ recebe um dicionário de tags para definir as capacidades do modelo.\n\nclass Logger(BaseForecaster):\n\n    _tags = {\n        \"requires-fh-in-fit\": False,\n    }\n\n    def __init__(self, tags_to_set):\n        self.tags_to_set = tags_to_set\n        super().__init__()\n\n        self.set_tags(**tags_to_set)\n    \n    def _fit(self, y, X=None, fh=None):\n        print(\"Inside fit:\")\n        print(y)\n        return self\n\n\nlogger = Logger(tags_to_set={\"y_inner_mtype\" : [\"pd.Series\"] })\nlogger.fit(y)\n\nInside fit:\n2000-01-01    1.000000\n2000-01-02    1.464179\n2000-01-03    2.706921\n2000-01-04    3.018884\nFreq: D, dtype: float64\n\n\nLogger(tags_to_set={'y_inner_mtype': ['pd.Series']})Please rerun this cell to show the HTML repr or trust the notebook.LoggerLogger(tags_to_set={'y_inner_mtype': ['pd.Series']})\n\n\n\nlogger = Logger(tags_to_set={\"y_inner_mtype\" : [\"np.ndarray\"] })\nlogger.fit(y)\n\nInside fit:\n[[1.        ]\n [1.46417876]\n [2.70692053]\n [3.01888437]]\n\n\nLogger(tags_to_set={'y_inner_mtype': ['np.ndarray']})Please rerun this cell to show the HTML repr or trust the notebook.LoggerLogger(tags_to_set={'y_inner_mtype': ['np.ndarray']})\n\n\n\nlogger = Logger(tags_to_set={\"y_inner_mtype\" : [\"pd.DataFrame\"] })\nlogger.fit(y)\n\nInside fit:\n                   0\n2000-01-01  1.000000\n2000-01-02  1.464179\n2000-01-03  2.706921\n2000-01-04  3.018884\n\n\nLogger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})Please rerun this cell to show the HTML repr or trust the notebook.LoggerLogger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})\n\n\n\ntry:\n    logger = Logger(tags_to_set={\"y_inner_mtype\" : [\"pd_multiindex_hier\"] })\n    logger.fit(y)\nexcept ValueError as e:\n    print(e)\n\nError in Logger, no series scitypes supported, likely a bug in estimator: scitypes arg passed to _most_complex_scitype are ['Hierarchical']\n\n\n\ntry:\n    logger = Logger(tags_to_set={\"y_inner_mtype\": [\"pd.DataFrame\", \"pd_multiindex_hier\"]})\n    logger.fit(y)\nexcept ValueError as e:\n    print(e)\n\nInside fit:\n                   0\n2000-01-01  1.000000\n2000-01-02  1.464179\n2000-01-03  2.706921\n2000-01-04  3.018884\n\n\n\n\n11.1.1.3 Input hierárquico\nAgora veremos como o modelo se comporta com dados hierárquicos. Note que, nos casos onde o modelo não suporta dados hierárquicos, o sktime tentará convertê-los para um mtype suportado.\n\nfrom sktime.utils._testing.hierarchical import _make_hierarchical\n\ny = _make_hierarchical((1,2), max_timepoints=4, min_timepoints=2)\ny\n\n\n\n\n\n\n\n\n\n\nc0\n\n\nh0\nh1\ntime\n\n\n\n\n\nh0_0\nh1_0\n2000-01-02\n1.693028\n\n\n2000-01-03\n1.092681\n\n\n2000-01-04\n2.352703\n\n\nh1_1\n2000-01-02\n2.456702\n\n\n2000-01-03\n1.585521\n\n\n2000-01-04\n1.000000\n\n\n\n\n\n\n\n\nlogger = Logger(tags_to_set={\"y_inner_mtype\" : [\"pd.Series\"] })\nlogger.fit(y)\n\nInside fit:\ntime\n2000-01-02    1.693028\n2000-01-03    1.092681\n2000-01-04    2.352703\nFreq: D, dtype: float64\nInside fit:\ntime\n2000-01-02    2.456702\n2000-01-03    1.585521\n2000-01-04    1.000000\nFreq: D, dtype: float64\n\n\nLogger(tags_to_set={'y_inner_mtype': ['pd.Series']})Please rerun this cell to show the HTML repr or trust the notebook.LoggerLogger(tags_to_set={'y_inner_mtype': ['pd.Series']})\n\n\n\nlogger = Logger(tags_to_set={\"y_inner_mtype\" : [\"np.ndarray\"] })\nlogger.fit(y)\n\nInside fit:\n[[1.69302835]\n [1.092681  ]\n [2.35270294]]\nInside fit:\n[[2.45670163]\n [1.58552141]\n [1.        ]]\n\n\nLogger(tags_to_set={'y_inner_mtype': ['np.ndarray']})Please rerun this cell to show the HTML repr or trust the notebook.LoggerLogger(tags_to_set={'y_inner_mtype': ['np.ndarray']})\n\n\n\nlogger = Logger(tags_to_set={\"y_inner_mtype\" : [\"pd.DataFrame\"] })\nlogger.fit(y)\n\nInside fit:\n                  c0\ntime                \n2000-01-02  1.693028\n2000-01-03  1.092681\n2000-01-04  2.352703\nInside fit:\n                  c0\ntime                \n2000-01-02  2.456702\n2000-01-03  1.585521\n2000-01-04  1.000000\n\n\nLogger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})Please rerun this cell to show the HTML repr or trust the notebook.LoggerLogger(tags_to_set={'y_inner_mtype': ['pd.DataFrame']})\n\n\n\ntry:\n    logger = Logger(tags_to_set={\"y_inner_mtype\": [\"pd_multiindex_hier\"]})\n    logger.fit(y)\nexcept ValueError as e:\n    print(e)\n\nInside fit:\n                            c0\nh0   h1   time                \nh0_0 h1_0 2000-01-02  1.693028\n          2000-01-03  1.092681\n          2000-01-04  2.352703\n     h1_1 2000-01-02  2.456702\n          2000-01-03  1.585521\n          2000-01-04  1.000000",
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-    "text": "11.2 Criando um modelo naive\nAgora, vamos implementar um modelo simples de previsão, o CustomNaiveForecaster, que prevê o valor médio dos últimos n pontos da série temporal.\nÉ um exemplo simples, mas que ilustra bem como criar um forecaster customizado com sktime.\n\nfrom tsbook.datasets.retail import SyntheticRetail\n\ndataset = SyntheticRetail(\"panel\")\ny_train, y_test  = dataset.load(\"y_train\", \"y_test\")\ny_train\n\n\n\n\n\n\n\n\n\nsales\n\n\nsku_id\ndate\n\n\n\n\n\n0\n2020-01-01\n0\n\n\n2020-01-02\n0\n\n\n2020-01-03\n0\n\n\n2020-01-04\n0\n\n\n2020-01-05\n0\n\n\n...\n...\n...\n\n\n24\n2024-07-01\n218\n\n\n2024-07-02\n198\n\n\n2024-07-03\n182\n\n\n2024-07-04\n210\n\n\n2024-07-05\n209\n\n\n\n\n41200 rows × 1 columns\n\n\n\n\nfrom sktime.utils.plotting import plot_series\n\nplot_series(\n    y_train.loc[0],\n    y_train.loc[24],\n    labels=[\n        \"SKU 0\",\n        \"SKU 24\",\n    ],\n)\n\n\n\n\n\n\n\n\nAbaixo, implementamos o CustomNaiveForecaster seguindo as regras do sktime (clique para expandir). Em seguida, explicamos passo a passo.\n\n\nCode\nfrom sktime.forecasting.base import BaseForecaster\nimport pandas as pd\n\n\nclass CustomNaiveForecaster(BaseForecaster):\n    \"\"\"\n    A simple naive forecaster\n\n    Parameters\n    ----------\n    n : int\n        Number of past values to use.\n    \"\"\"\n\n    _tags = {\n        \"requires-fh-in-fit\": False,\n        \"y_inner_mtype\": [\n            \"pd.Series\",\n        ],\n    }\n\n    # Add hyperparameters in init!\n    def __init__(self, n=1):\n        # 1. Set hyper-parameters\n        self.n = n\n\n        # 2. Initialize parent class\n        super().__init__()\n\n        # 3. Check hyper-parameters\n        assert self.n &gt; 0, \"n must be greater than 0\"\n\n    def _fit(self, y, X, fh):\n        \"\"\"\n        Fit necessary parameters.\n        \"\"\"\n\n        self.value_ = y.iloc[-self.n :].mean()\n        return self\n\n    def _predict(self, fh, X):\n        \"\"\"\n        Use forecasting horizon and optionally X to predict y\n        \"\"\"\n\n        # During fit, BaseForecaster sets\n        # self.cutoff to the latest cutoff time point\n        index = fh.to_absolute_index(self.cutoff)\n        y_pred = pd.Series(\n            index=index,\n            data=[self.value_ for _ in range(len(index))],\n        )\n        y_pred.name = self._y.name\n\n        return y_pred\n\n    # Veremos mais tarde como usar esse método\n    @classmethod\n    def get_test_params(cls, parameter_set=\"default\"):\n        return [\n            {\"n\": 1},\n            {\"n\": 2},\n        ]\n\n\n\n11.2.1 Definindo o método __init__\nO método __init__ possui 3 etapas:\n\nA definição dos hiperparametros e seus atributos com mesmo nome.\nA chamada do super().__init__() para inicializar a classe pai.\nA validação dos hiperparâmetros.\n\n# Add hyperparameters in init!\ndef __init__(self, n=1):\n    # 1. Set hyper-parameters\n    self.n = n\n\n    # 2. Initialize parent class\n    super().__init__()\n\n    # 3. Check hyper-parameters\n    assert self.n &gt; 0, \"n must be greater than 0\"\nNo caso de algum preprocessamento dos hiperparâmetros no __init__, devemos guardar em uma variável com nome diferente do hiperparâmetro. Por exemplo, se tivéssemos interesse em ter um atributo n diferente do passado no __init__, poderíamos fazer:\nself._n = n + 1\nO self.n funciona como uma digital do modelo, e deve ser exatamente o que foi passado no __init__.\n\n\n11.2.2 Definindo o método _fit\nNo método _fit, devemos implementar a lógica de ajuste do modelo. No nosso caso, calculamos a média dos últimos n valores e armazenamos em self.value_.\nO _ após o nome do atributo indica que é um atributo aprendido durante o ajuste, e será retornado quando chamarmos get_fitted_params().\nNote que podemos supor que y é do tipo definido na tag y_inner_mtype, ou seja, uma pd.Series.\n\n\n11.2.3 Definindo o método _predict\nNo método _predict, implementamos a lógica de previsão. Usamos o horizonte de previsão fh para determinar os índices futuros e retornamos uma série com o valor previsto para cada ponto no horizonte.\nO fh é um objeto do tipo ForecastingHorizon, que possui o método to_absolute_index(cutoff) para converter o horizonte relativo em índices absolutos, considerando o último ponto conhecido (self.cutoff).\nRetornamos um pd.Series com os índices e os valores previstos.\n\n\n11.2.4 Usando o CustomNaiveForecaster\nAgora, já podemos usar o nosso modelo customizado para fazer previsões.\n\ncustom_naive_model = CustomNaiveForecaster()\ncustom_naive_model.fit(y_train)\n\nCustomNaiveForecaster()Please rerun this cell to show the HTML repr or trust the notebook.CustomNaiveForecasterCustomNaiveForecaster()\n\n\nComo passamos um dado hierárquico, o sktime converteu automaticamente para pd.Series, que é o mtype suportado pelo nosso modelo. Os modelos internos, para cada série, ficam disponíveis em forecasters_.\n\ncustom_naive_model.forecasters_\n\n\n\n\n\n\n\n\nforecasters\n\n\n\n\n0\nCustomNaiveForecaster()\n\n\n1\nCustomNaiveForecaster()\n\n\n2\nCustomNaiveForecaster()\n\n\n3\nCustomNaiveForecaster()\n\n\n4\nCustomNaiveForecaster()\n\n\n5\nCustomNaiveForecaster()\n\n\n6\nCustomNaiveForecaster()\n\n\n7\nCustomNaiveForecaster()\n\n\n8\nCustomNaiveForecaster()\n\n\n9\nCustomNaiveForecaster()\n\n\n10\nCustomNaiveForecaster()\n\n\n11\nCustomNaiveForecaster()\n\n\n12\nCustomNaiveForecaster()\n\n\n13\nCustomNaiveForecaster()\n\n\n14\nCustomNaiveForecaster()\n\n\n15\nCustomNaiveForecaster()\n\n\n16\nCustomNaiveForecaster()\n\n\n17\nCustomNaiveForecaster()\n\n\n18\nCustomNaiveForecaster()\n\n\n19\nCustomNaiveForecaster()\n\n\n20\nCustomNaiveForecaster()\n\n\n21\nCustomNaiveForecaster()\n\n\n22\nCustomNaiveForecaster()\n\n\n23\nCustomNaiveForecaster()\n\n\n24\nCustomNaiveForecaster()\n\n\n\n\n\n\n\n\ny_pred = custom_naive_model.predict(fh=y_test.index.get_level_values(-1).unique())\n\nfig, _ = plot_series(\n    y_train.loc[0],\n    y_pred.loc[0],\n    labels=[\n        \"SKU 0\",\n        \"Previsão SKU 0\",\n    ],\n)\nfig.show()",
-    "crumbs": [
-      "Part III: Apêndices",
-      "<span class='chapter-number'>11</span>  <span class='chapter-title'>Criando modelos customizados com sktime</span>"
-    ]
-  },
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-    "objectID": "content/pt/extra/sktime_custom.html#testes-unitários",
-    "href": "content/pt/extra/sktime_custom.html#testes-unitários",
-    "title": "11  Criando modelos customizados com sktime",
-    "section": "11.3 Testes unitários",
-    "text": "11.3 Testes unitários\nO sktime também fornece uma funcionalidade que traz testes unitários prontos para validar se o modelo customizado está funcionando corretamente.\nEle usa os hiperparâmetros retornados pelo método get_test_params para criar instâncias do modelo e executar uma série de testes.\n\nfrom sktime.utils.estimator_checks import check_estimator\n\n\ncheck_estimator(CustomNaiveForecaster, tests_to_exclude=[\"test_doctest_examples\"])\n\nAll tests PASSED!\n\n\n{'test_constructor[CustomNaiveForecaster]': 'PASSED',\n 'test_inheritance[CustomNaiveForecaster]': 'PASSED',\n 'test_repr_html[CustomNaiveForecaster-0]': 'PASSED',\n 'test_repr_html[CustomNaiveForecaster-1]': 'PASSED',\n 'test_clone[CustomNaiveForecaster-0]': 'PASSED',\n 'test_clone[CustomNaiveForecaster-1]': 'PASSED',\n 'test_random_tags[CustomNaiveForecaster]': 'PASSED',\n 'test_get_test_params[CustomNaiveForecaster]': 'PASSED',\n 'test_get_params[CustomNaiveForecaster-0]': 'PASSED',\n 'test_get_params[CustomNaiveForecaster-1]': 'PASSED',\n 'test_create_test_instances_and_names[CustomNaiveForecaster]': 'PASSED',\n 'test_valid_estimator_class_tags[CustomNaiveForecaster]': 'PASSED',\n 'test_estimator_tags[CustomNaiveForecaster]': 'PASSED',\n 'test_repr[CustomNaiveForecaster-0]': 'PASSED',\n 'test_repr[CustomNaiveForecaster-1]': 'PASSED',\n 'test_create_test_instance[CustomNaiveForecaster]': 'PASSED',\n 'test_no_cross_test_side_effects_part2[CustomNaiveForecaster-0]': 'PASSED',\n 'test_no_cross_test_side_effects_part2[CustomNaiveForecaster-1]': 'PASSED',\n 'test_get_test_params_coverage[CustomNaiveForecaster]': 'PASSED',\n 'test_has_common_interface[CustomNaiveForecaster]': 'PASSED',\n 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-0]': 'PASSED',\n 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-1]': 'PASSED',\n 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-0]': 'PASSED',\n 'test_no_between_test_case_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-1]': 'PASSED',\n 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-0]': 'PASSED',\n 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-1]': 'PASSED',\n 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-0]': 'PASSED',\n 'test_no_between_test_case_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-1]': 'PASSED',\n 'test_set_params_sklearn[CustomNaiveForecaster]': 'PASSED',\n 'test_obj_vs_cls_signature[CustomNaiveForecaster]': 'PASSED',\n 'test_no_cross_test_side_effects_part1[CustomNaiveForecaster-0]': 'PASSED',\n 'test_no_cross_test_side_effects_part1[CustomNaiveForecaster-1]': 'PASSED',\n 'test_valid_estimator_tags[CustomNaiveForecaster-0]': 'PASSED',\n 'test_valid_estimator_tags[CustomNaiveForecaster-1]': 'PASSED',\n 'test_set_params[CustomNaiveForecaster-0]': 'PASSED',\n 'test_set_params[CustomNaiveForecaster-1]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',\n 'test_persistence_via_pickle[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_persistence_via_pickle[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_persistence_via_pickle[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_persistence_via_pickle[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_fit_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_fit_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_fit_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_fit_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',\n 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_non_state_changing_method_contract[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',\n 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',\n 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_non_state_changing_method_contract[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',\n 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',\n 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_methods_have_no_side_effects[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',\n 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-get_fitted_params]': 'PASSED',\n 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_methods_have_no_side_effects[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-get_fitted_params]': 'PASSED',\n 'test_fit_returns_self[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',\n 'test_fit_returns_self[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',\n 'test_fit_returns_self[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',\n 'test_fit_returns_self[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',\n 'test_dl_constructor_initializes_deeply[CustomNaiveForecaster]': 'PASSED',\n 'test_save_estimators_to_file[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_save_estimators_to_file[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_save_estimators_to_file[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_save_estimators_to_file[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_fit_updates_state[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',\n 'test_fit_updates_state[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',\n 'test_fit_updates_state[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',\n 'test_fit_updates_state[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',\n 'test_multiprocessing_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_multiprocessing_idempotent[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_multiprocessing_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX-predict]': 'PASSED',\n 'test_multiprocessing_idempotent[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX-predict]': 'PASSED',\n 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-0-ForecasterFitPredictUnivariateWithX]': 'PASSED',\n 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-0-ForecasterFitPredictMultivariateNoX]': 'PASSED',\n 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-1-ForecasterFitPredictUnivariateWithX]': 'PASSED',\n 'test_fit_does_not_overwrite_hyper_params[CustomNaiveForecaster-1-ForecasterFitPredictMultivariateNoX]': 'PASSED',\n 'test_fh_not_passed_error_handling[CustomNaiveForecaster-0-y:1cols]': 'PASSED',\n 'test_fh_not_passed_error_handling[CustomNaiveForecaster-0-y:2cols]': 'PASSED',\n 'test_fh_not_passed_error_handling[CustomNaiveForecaster-1-y:1cols]': 'PASSED',\n 'test_fh_not_passed_error_handling[CustomNaiveForecaster-1-y:2cols]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-0]': 'PASSED',\n 'test_raises_not_fitted_error[CustomNaiveForecaster-1]': 'PASSED',\n 'test_predict_series_name_preserved[CustomNaiveForecaster-0]': 'PASSED',\n 'test_predict_series_name_preserved[CustomNaiveForecaster-1]': 'PASSED',\n 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-0-y:1cols]': 'PASSED',\n 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-0-y:2cols]': 'PASSED',\n 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-1-y:1cols]': 'PASSED',\n 'test_different_fh_in_fit_and_predict_error_handling[CustomNaiveForecaster-1-y:2cols]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.05]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=0.1]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.05]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=0.1]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.05]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=0.1]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.05]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=0.1]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-0-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.05]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=0.1]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.05]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=0.1]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:1cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.05]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=0.1]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=1-alpha=[0.25, 0.75]]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.05]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=0.1]': 'PASSED',\n 'test_predict_quantiles[CustomNaiveForecaster-1-y:2cols-fh=[2 5]-alpha=[0.25, 0.75]]': 'PASSED',\n 'test_fit_predict[CustomNaiveForecaster-0-y:1cols]': 'PASSED',\n 'test_fit_predict[CustomNaiveForecaster-0-y:2cols]': 'PASSED',\n 'test_fit_predict[CustomNaiveForecaster-1-y:1cols]': 'PASSED',\n 'test_fit_predict[CustomNaiveForecaster-1-y:2cols]': 'PASSED',\n 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'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-range-int-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-period-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-period-period-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-datetime-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-0-y:2cols-datetime-timedelta-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-int-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-int-int-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-range-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-range-int-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-period-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-period-period-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-datetime-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:1cols-datetime-timedelta-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-int-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-int-int-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-range-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-range-int-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-period-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-period-period-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-int-True]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-datetime-False]': 'PASSED',\n 'test_predict_time_index_in_sample_full[CustomNaiveForecaster-1-y:2cols-datetime-timedelta-True]': 'PASSED'}",
-    "crumbs": [
-      "Part III: Apêndices",
-      "<span class='chapter-number'>11</span>  <span class='chapter-title'>Criando modelos customizados com sktime</span>"
-    ]
-  }
-]
\ No newline at end of file
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deleted file mode 100644
index 2e4ee04..0000000
--- a/book/latest/site_libs/bootstrap/bootstrap-27c261d06b905028a18691de25d09dde.min.css
+++ /dev/null
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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: rgb(84.32, 84.66, 85);--bs-navbar-hover-color: rgba(31.2, 77.6, 181.6, 0.8);--bs-navbar-disabled-color: rgba(84.32, 84.66, 85, 0.75);--bs-navbar-active-color: rgb(31.2, 77.6, 181.6);--bs-navbar-brand-color: rgb(84.32, 84.66, 85);--bs-navbar-brand-hover-color: rgb(31.2, 77.6, 181.6);--bs-navbar-toggler-border-color: rgba(84.32, 84.66, 85, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%2884.32, 84.66, 85%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%2884.32, 84.66, 85%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: 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viewBox='0 0 16 16' fill='rgb%2815.6, 51.2, 90.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(147, 191.5, 241);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(15.6, 51.2, 90.8);--bs-accordion-active-bg: rgb(211.8, 229.6, 249.4)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: rgb(31.2, 77.6, 181.6);--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: rgb(31.2, 77.6, 181.6);--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 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var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:400;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box 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300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid 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;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: rgb(190.2, 216.9, 246.6);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-file-button-color: #343a40;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #2761e3;--quarto-scss-export-nav-link-hover-color: rgb(31.2, 77.6, 181.6);--quarto-scss-export-nav-link-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #343a40;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #343a40;--quarto-scss-export-dropdown-link-hover-color: #343a40;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #2761e3;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: rgb(31.2, 77.6, 181.6);--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: rgb(31.2, 77.6, 181.6);--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #343a40;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #343a40;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: rgb(211.8, 229.6, 249.4);--quarto-scss-export-accordion-button-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-accordion-button-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-accordion-icon-color: #343a40;--quarto-scss-export-accordion-icon-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #343a40;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #343a40;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #343a40;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #343a40;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #343a40;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #f8f9fa;--quarto-scss-export-bslib-page-sidebar-title-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: rgb(89.25, 89.25, 89.25);--quarto-scss-export-sidebar-hover-color: rgba(32.76, 81.48, 190.68, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(89.25, 89.25, 89.25, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-mermaid-edge-color: #343a40;--quarto-scss-export-mermaid-node-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color--lighter: rgb(74.8620689655, 83.5, 92.1379310345);--quarto-scss-export-mermaid-fg-color--lightest: rgb(97.724137931, 109, 120.275862069);--quarto-scss-export-mermaid-label-bg-color: #fff;--quarto-scss-export-mermaid-label-fg-color: #2780e3;--quarto-scss-export-mermaid-node-bg-color: rgba(39, 128, 227, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518}
\ No newline at end of file
diff --git a/book/latest/site_libs/bootstrap/bootstrap-icons.css b/book/latest/site_libs/bootstrap/bootstrap-icons.css
deleted file mode 100644
index 82b40f5..0000000
--- a/book/latest/site_libs/bootstrap/bootstrap-icons.css
+++ /dev/null
@@ -1,2106 +0,0 @@
-/*!
- * Bootstrap Icons v1.13.1 (https://icons.getbootstrap.com/)
- * Copyright 2019-2024 The Bootstrap Authors
- * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE)
- */
-
-@font-face {
-  font-display: block;
-  font-family: "bootstrap-icons";
-  src: 
-url("./bootstrap-icons.woff?e34853135f9e39acf64315236852cd5a") format("woff");
-}
-
-.bi::before,
-[class^="bi-"]::before,
-[class*=" bi-"]::before {
-  display: inline-block;
-  font-family: bootstrap-icons !important;
-  font-style: normal;
-  font-weight: normal !important;
-  font-variant: normal;
-  text-transform: none;
-  line-height: 1;
-  vertical-align: -.125em;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-
-.bi-123::before { content: "\f67f"; }
-.bi-alarm-fill::before { content: "\f101"; }
-.bi-alarm::before { content: "\f102"; }
-.bi-align-bottom::before { content: "\f103"; }
-.bi-align-center::before { content: "\f104"; }
-.bi-align-end::before { content: "\f105"; }
-.bi-align-middle::before { content: "\f106"; }
-.bi-align-start::before { content: "\f107"; }
-.bi-align-top::before { content: "\f108"; }
-.bi-alt::before { content: "\f109"; }
-.bi-app-indicator::before { content: "\f10a"; }
-.bi-app::before { content: "\f10b"; }
-.bi-archive-fill::before { content: "\f10c"; }
-.bi-archive::before { content: "\f10d"; }
-.bi-arrow-90deg-down::before { content: "\f10e"; }
-.bi-arrow-90deg-left::before { content: "\f10f"; }
-.bi-arrow-90deg-right::before { content: "\f110"; }
-.bi-arrow-90deg-up::before { content: "\f111"; }
-.bi-arrow-bar-down::before { content: "\f112"; }
-.bi-arrow-bar-left::before { content: "\f113"; }
-.bi-arrow-bar-right::before { content: "\f114"; }
-.bi-arrow-bar-up::before { content: "\f115"; }
-.bi-arrow-clockwise::before { content: "\f116"; }
-.bi-arrow-counterclockwise::before { content: "\f117"; }
-.bi-arrow-down-circle-fill::before { content: "\f118"; }
-.bi-arrow-down-circle::before { content: "\f119"; }
-.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
-.bi-arrow-down-left-circle::before { content: "\f11b"; }
-.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
-.bi-arrow-down-left-square::before { content: "\f11d"; }
-.bi-arrow-down-left::before { content: "\f11e"; }
-.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
-.bi-arrow-down-right-circle::before { content: "\f120"; }
-.bi-arrow-down-right-square-fill::before { content: "\f121"; }
-.bi-arrow-down-right-square::before { content: "\f122"; }
-.bi-arrow-down-right::before { content: "\f123"; }
-.bi-arrow-down-short::before { content: "\f124"; }
-.bi-arrow-down-square-fill::before { content: "\f125"; }
-.bi-arrow-down-square::before { content: "\f126"; }
-.bi-arrow-down-up::before { content: "\f127"; }
-.bi-arrow-down::before { content: "\f128"; }
-.bi-arrow-left-circle-fill::before { content: "\f129"; }
-.bi-arrow-left-circle::before { content: "\f12a"; }
-.bi-arrow-left-right::before { content: "\f12b"; }
-.bi-arrow-left-short::before { content: "\f12c"; }
-.bi-arrow-left-square-fill::before { content: "\f12d"; }
-.bi-arrow-left-square::before { content: "\f12e"; }
-.bi-arrow-left::before { content: "\f12f"; }
-.bi-arrow-repeat::before { content: "\f130"; }
-.bi-arrow-return-left::before { content: "\f131"; }
-.bi-arrow-return-right::before { content: "\f132"; }
-.bi-arrow-right-circle-fill::before { content: "\f133"; }
-.bi-arrow-right-circle::before { content: "\f134"; }
-.bi-arrow-right-short::before { content: "\f135"; }
-.bi-arrow-right-square-fill::before { content: "\f136"; }
-.bi-arrow-right-square::before { content: "\f137"; }
-.bi-arrow-right::before { content: "\f138"; }
-.bi-arrow-up-circle-fill::before { content: "\f139"; }
-.bi-arrow-up-circle::before { content: "\f13a"; }
-.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
-.bi-arrow-up-left-circle::before { content: "\f13c"; }
-.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
-.bi-arrow-up-left-square::before { content: "\f13e"; }
-.bi-arrow-up-left::before { content: "\f13f"; }
-.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
-.bi-arrow-up-right-circle::before { content: "\f141"; }
-.bi-arrow-up-right-square-fill::before { content: "\f142"; }
-.bi-arrow-up-right-square::before { content: "\f143"; }
-.bi-arrow-up-right::before { content: "\f144"; }
-.bi-arrow-up-short::before { content: "\f145"; }
-.bi-arrow-up-square-fill::before { content: "\f146"; }
-.bi-arrow-up-square::before { content: "\f147"; }
-.bi-arrow-up::before { content: "\f148"; }
-.bi-arrows-angle-contract::before { content: "\f149"; }
-.bi-arrows-angle-expand::before { content: "\f14a"; }
-.bi-arrows-collapse::before { content: "\f14b"; }
-.bi-arrows-expand::before { content: "\f14c"; }
-.bi-arrows-fullscreen::before { content: "\f14d"; }
-.bi-arrows-move::before { content: "\f14e"; }
-.bi-aspect-ratio-fill::before { content: "\f14f"; }
-.bi-aspect-ratio::before { content: "\f150"; }
-.bi-asterisk::before { content: "\f151"; }
-.bi-at::before { content: "\f152"; }
-.bi-award-fill::before { content: "\f153"; }
-.bi-award::before { content: "\f154"; }
-.bi-back::before { content: "\f155"; }
-.bi-backspace-fill::before { content: "\f156"; }
-.bi-backspace-reverse-fill::before { content: "\f157"; }
-.bi-backspace-reverse::before { content: "\f158"; }
-.bi-backspace::before { content: "\f159"; }
-.bi-badge-3d-fill::before { content: "\f15a"; }
-.bi-badge-3d::before { content: "\f15b"; }
-.bi-badge-4k-fill::before { content: "\f15c"; }
-.bi-badge-4k::before { content: "\f15d"; }
-.bi-badge-8k-fill::before { content: "\f15e"; }
-.bi-badge-8k::before { content: "\f15f"; }
-.bi-badge-ad-fill::before { content: "\f160"; }
-.bi-badge-ad::before { content: "\f161"; }
-.bi-badge-ar-fill::before { content: "\f162"; }
-.bi-badge-ar::before { content: "\f163"; }
-.bi-badge-cc-fill::before { content: "\f164"; }
-.bi-badge-cc::before { content: "\f165"; }
-.bi-badge-hd-fill::before { content: "\f166"; }
-.bi-badge-hd::before { content: "\f167"; }
-.bi-badge-tm-fill::before { content: "\f168"; }
-.bi-badge-tm::before { content: "\f169"; }
-.bi-badge-vo-fill::before { content: "\f16a"; }
-.bi-badge-vo::before { content: "\f16b"; }
-.bi-badge-vr-fill::before { content: "\f16c"; }
-.bi-badge-vr::before { content: "\f16d"; }
-.bi-badge-wc-fill::before { content: "\f16e"; }
-.bi-badge-wc::before { content: "\f16f"; }
-.bi-bag-check-fill::before { content: "\f170"; }
-.bi-bag-check::before { content: "\f171"; }
-.bi-bag-dash-fill::before { content: "\f172"; }
-.bi-bag-dash::before { content: "\f173"; }
-.bi-bag-fill::before { content: "\f174"; }
-.bi-bag-plus-fill::before { content: "\f175"; }
-.bi-bag-plus::before { content: "\f176"; }
-.bi-bag-x-fill::before { content: "\f177"; }
-.bi-bag-x::before { content: "\f178"; }
-.bi-bag::before { content: "\f179"; }
-.bi-bar-chart-fill::before { content: "\f17a"; }
-.bi-bar-chart-line-fill::before { content: "\f17b"; }
-.bi-bar-chart-line::before { content: "\f17c"; }
-.bi-bar-chart-steps::before { content: "\f17d"; }
-.bi-bar-chart::before { content: "\f17e"; }
-.bi-basket-fill::before { content: "\f17f"; }
-.bi-basket::before { content: "\f180"; }
-.bi-basket2-fill::before { content: "\f181"; }
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diff --git a/book/latest/site_libs/bootstrap/bootstrap.min.js b/book/latest/site_libs/bootstrap/bootstrap.min.js
deleted file mode 100644
index e8f21f7..0000000
--- a/book/latest/site_libs/bootstrap/bootstrap.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:pe(t)?Je(t):t.contextElement?Je(t.contextElement):[],popper:Je(e)};var r,c,u=function(t){var e=ui(t);return de.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),"fixed"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,je,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px 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t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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-//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/book/latest/site_libs/clipboard/clipboard.min.js b/book/latest/site_libs/clipboard/clipboard.min.js
deleted file mode 100644
index 1103f81..0000000
--- a/book/latest/site_libs/clipboard/clipboard.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
- * clipboard.js v2.0.11
- * https://clipboardjs.com/
- *
- * Licensed MIT © Zeno Rocha
- */
-!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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diff --git a/book/latest/site_libs/quarto-diagram/mermaid-init.js b/book/latest/site_libs/quarto-diagram/mermaid-init.js
deleted file mode 100644
index 48b7f5c..0000000
--- a/book/latest/site_libs/quarto-diagram/mermaid-init.js
+++ /dev/null
@@ -1,275 +0,0 @@
-// mermaid-init.js
-// Initializes the quarto-mermaid JS runtime
-//
-// Copyright (C) 2022 Posit Software, PBC
-
-/**
- * String.prototype.replaceAll() polyfill
- * https://gomakethings.com/how-to-replace-a-section-of-a-string-with-another-one-with-vanilla-js/
- * @author Chris Ferdinandi
- * @license MIT
- */
-if (!String.prototype.replaceAll) {
-  String.prototype.replaceAll = function (str, newStr) {
-    // If a regex pattern
-    if (
-      Object.prototype.toString.call(str).toLowerCase() === "[object regexp]"
-    ) {
-      return this.replace(str, newStr);
-    }
-
-    // If a string
-    return this.replace(new RegExp(str, "g"), newStr);
-  };
-}
-
-const mermaidOpts = {
-  startOnLoad: false,
-};
-// this CSS is adapted from
-// mkdocs-material
-// Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>
-
-const defaultCSS =
-  ".label text{fill:var(--mermaid-fg-color)}.node circle,.node ellipse,.node path,.node polygon,.node rect{fill:var(--mermaid-node-bg-color);stroke:var(--mermaid-node-fg-color)}marker{fill:var(--mermaid-edge-color)!important}.edgeLabel .label rect{fill:#0000}.label{color:var(--mermaid-label-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}.label foreignObject{line-height:normal;overflow:visible}.label div .edgeLabel{color:var(--mermaid-label-fg-color)}.edgeLabel,.edgeLabel rect,.label div .edgeLabel{background-color:var(--mermaid-label-bg-color)}.edgeLabel,.edgeLabel rect{fill:var(--mermaid-label-bg-color);color:var(--mermaid-edge-color)}.edgePath .path,.flowchart-link{stroke:var(--mermaid-edge-color)}.edgePath .arrowheadPath{fill:var(--mermaid-edge-color);stroke:none}.cluster rect{fill:var(--mermaid-fg-color--lightest);stroke:var(--mermaid-fg-color--lighter)}.cluster span{color:var(--mermaid-label-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}defs #flowchart-circleEnd,defs #flowchart-circleStart,defs #flowchart-crossEnd,defs #flowchart-crossStart,defs #flowchart-pointEnd,defs #flowchart-pointStart{stroke:none}g.classGroup line,g.classGroup rect{fill:var(--mermaid-node-bg-color);stroke:var(--mermaid-node-fg-color)}g.classGroup text{fill:var(--mermaid-label-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}.classLabel .box{fill:var(--mermaid-label-bg-color);background-color:var(--mermaid-label-bg-color);opacity:1}.classLabel .label{fill:var(--mermaid-label-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}.node .divider{stroke:var(--mermaid-node-fg-color)}.relation{stroke:var(--mermaid-edge-color)}.cardinality{fill:var(--mermaid-label-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}.cardinality text{fill:inherit!important}defs #classDiagram-compositionEnd,defs #classDiagram-compositionStart,defs #classDiagram-dependencyEnd,defs #classDiagram-dependencyStart,defs #classDiagram-extensionEnd,defs #classDiagram-extensionStart{fill:var(--mermaid-edge-color)!important;stroke:var(--mermaid-edge-color)!important}defs #classDiagram-aggregationEnd,defs #classDiagram-aggregationStart{fill:var(--mermaid-label-bg-color)!important;stroke:var(--mermaid-edge-color)!important}g.stateGroup rect{fill:var(--mermaid-node-bg-color);stroke:var(--mermaid-node-fg-color)}g.stateGroup .state-title{fill:var(--mermaid-label-fg-color)!important;font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}g.stateGroup .composit{fill:var(--mermaid-label-bg-color)}.nodeLabel{color:var(--mermaid-label-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--mermaid-label-bg-color)}.transition{stroke:var(--mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--mermaid-bg-color)}.statediagram-cluster rect{fill:var(--mermaid-node-bg-color);stroke:var(--mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--mermaid-fg-color--lightest);stroke:var(--mermaid-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--mermaid-edge-color)}.entityBox{fill:var(--mermaid-label-bg-color);stroke:var(--mermaid-node-fg-color)}.entityLabel{fill:var(--mermaid-label-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}.relationshipLabelBox{fill:var(--mermaid-label-bg-color);fill-opacity:1;background-color:var(--mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--mermaid-label-fg-color)}.relationshipLine{stroke:var(--mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--mermaid-edge-color)!important}.actor,defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--mermaid-label-bg-color)}.actor{stroke:var(--mermaid-node-fg-color)}text.actor>tspan{fill:var(--mermaid-label-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}line{stroke:var(--mermaid-fg-color--lighter)}.messageLine0,.messageLine1{stroke:var(--mermaid-edge-color)}.loopText>tspan,.messageText,.noteText>tspan{fill:var(--mermaid-edge-color);stroke:none;font-family:var(--mermaid-font-family)!important;font-weight:var(--mermaid-font-weight)!important}.noteText>tspan{fill:#000}#arrowhead path{fill:var(--mermaid-edge-color);stroke:none}.loopLine{stroke:var(--mermaid-node-fg-color)}.labelBox,.loopLine{fill:var(--mermaid-node-bg-color)}.labelBox{stroke:none}.labelText,.labelText>span{fill:var(--mermaid-node-fg-color);font-family:var(--mermaid-font-family);font-weight:var(--mermaid-font-weight)}";
-
-const mermaidThemeEl = document.querySelector('meta[name="mermaid-theme"]');
-if (mermaidThemeEl) {
-  mermaidOpts.theme = mermaidThemeEl.content;
-} else {
-  mermaidOpts.themeCSS = defaultCSS;
-}
-
-mermaid.initialize(mermaidOpts);
-
-const _quartoMermaid = {
-  // NB: there's effectively a copy of this function
-  // in `core/svg.ts`.
-  // if you change something here, you must keep it consistent there as well.
-  setSvgSize(svg) {
-    const { widthInPoints, heightInPoints, explicitHeight, explicitWidth } =
-      this.resolveSize(svg);
-
-    if (explicitWidth && explicitHeight) {
-      svg.setAttribute("width", widthInPoints);
-      svg.setAttribute("height", heightInPoints);
-      svg.style.maxWidth = null; // remove mermaid's default max-width
-    } else {
-      if (explicitWidth) {
-        svg.style.maxWidth = `${widthInPoints}px`;
-      }
-      if (explicitHeight) {
-        svg.style.maxHeight = `${heightInPoints}px`;
-      }
-    }
-  },
-
-  // NB: there's effectively a copy of this function
-  // in `core/svg.ts`.
-  // if you change something here, you must keep it consistent there as well.
-  makeResponsive(svg) {
-    const width = svg.getAttribute("width");
-    if (width === null) {
-      throw new Error("Couldn't find SVG width");
-    }
-    const numWidth = Number(width.slice(0, -2));
-
-    if (numWidth > 650) {
-      changed = true;
-      svg.setAttribute("width", "100%");
-      svg.removeAttribute("height");
-    }
-  },
-
-  // NB: there's effectively a copy of this function
-  // in `core/svg.ts`.
-  // if you change something here, you must keep it consistent there as well.
-  fixupAlignment(svg, align) {
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-      heightInInches = svgHeightInInches;
-      widthInInches = svgWidthInInches;
-    }
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-    return {
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-      heightInInches,
-      widthInPoints: Math.round(widthInInches * 96),
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-      explicitWidth: options?.[kFigWidth] !== undefined,
-      explicitHeight: options?.[kFigHeight] !== undefined,
-    };
-  },
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-  postProcess(svg) {
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-      options["figWidth"] === undefined &&
-      options["figHeight"] === undefined
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-      this.makeResponsive(svg);
-    } else {
-      this.setSvgSize(svg);
-    }
-    if (options["reveal"]) {
-      this.fixupAlignment(svg, options["figAlign"] || "center");
-    }
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-    // forward align attributes to the correct parent dif
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-  },
-};
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-        const style = svg.querySelector("style");
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-        );
-        svg.id = el.dataset.label + "-mermaid";
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-    }
-  },
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diff --git a/book/latest/site_libs/quarto-diagram/mermaid.css b/book/latest/site_libs/quarto-diagram/mermaid.css
deleted file mode 100644
index 9f8bb7c..0000000
--- a/book/latest/site_libs/quarto-diagram/mermaid.css
+++ /dev/null
@@ -1,13 +0,0 @@
-.mermaidTooltip {
-  position: absolute;
-  text-align: center;
-  max-width: 200px;
-  padding: 2px;
-  font-family: "trebuchet ms", verdana, arial;
-  font-size: 12px;
-  background: #ffffde;
-  border: 1px solid #aaaa33;
-  border-radius: 2px;
-  pointer-events: none;
-  z-index: 1000;
-}
diff --git a/book/latest/site_libs/quarto-diagram/mermaid.min.js b/book/latest/site_libs/quarto-diagram/mermaid.min.js
deleted file mode 100644
index 6e12566..0000000
--- a/book/latest/site_libs/quarto-diagram/mermaid.min.js
+++ /dev/null
@@ -1,2607 +0,0 @@
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ale3||Me(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Me(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Me(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Me(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Me(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Me(this.primaryColor,{h:210,l:150}),this.cScale9=this.cScale9||Me(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Me(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Me(this.primaryColor,{h:330}),this.darkMode)for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScale"+r]=Ot(this["cScale"+r],75);else for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScale"+r]=Ot(this["cScale"+r],25);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleInv"+r]=this["cScaleInv"+r]||wt(this["cScale"+r]);for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this.darkMode?this["cScalePeer"+r]=this["cScalePeer"+r]||Dt(this["cScale"+r],10):this["cScalePeer"+r]=this["cScalePeer"+r]||Ot(this["cScale"+r],10);this.scaleLabelColor=this.scaleLabelColor||this.labelTextColor;for(let r=0;r<this.THEME_COLOR_LIMIT;r++)this["cScaleLabel"+r]=this["cScaleLabel"+r]||this.scaleLabelColor;let e=this.darkMode?-4:-1;for(let r=0;r<5;r++)this["surface"+r]=this["surface"+r]||Me(this.mainBkg,{h:180,s:-15,l:e*(5+r*3)}),this["surfacePeer"+r]=this["surfacePeer"+r]||Me(this.mainBkg,{h:180,s:-15,l:e*(8+r*3)});this.classText=this.classText||this.textColor,this.fillType0=this.fillType0||this.primaryColor,this.fillType1=this.fillType1||this.secondaryColor,this.fillType2=this.fillType2||Me(this.primaryColor,{h:64}),this.fillType3=this.fillType3||Me(this.secondaryColor,{h:64}),this.fillType4=this.fillType4||Me(this.primaryColor,{h:-64}),this.fillType5=this.fillType5||Me(this.secondaryColor,{h:-64}),this.fillType6=this.fillType6||Me(this.primaryColor,{h:128}),this.fillType7=this.fillType7||Me(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||Me(this.primaryColor,{l:-10}),this.pie5=this.pie5||Me(this.secondaryColor,{l:-10}),this.pie6=this.pie6||Me(this.tertiaryColor,{l:-10}),this.pie7=this.pie7||Me(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||Me(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||Me(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||Me(this.primaryColor,{h:60,l:-20}),this.pie11=this.pie11||Me(this.primaryColor,{h:-60,l:-20}),this.pie12=this.pie12||Me(this.primaryColor,{h:120,l:-10}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.archEdgeColor=this.archEdgeColor||"#777",this.archEdgeArrowColor=this.archEdgeArrowColor||"#777",this.archEdgeWidth=this.archEdgeWidth||"3",this.archGroupBorderColor=this.archGroupBorderColor||"#000",this.archGroupBorderWidth=this.archGroupBorderWidth||"2px",this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||Me(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||Me(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||Me(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||Me(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||Me(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||Me(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ca(this.quadrant1Fill)?Dt(this.quadrant1Fill):Ot(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#FFF4DD,#FFD8B1,#FFA07A,#ECEFF1,#D6DBDF,#C3E0A8,#FFB6A4,#FFD74D,#738FA7,#FFFFF0"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Ot(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||Me(this.primaryColor,{h:-30}),this.git4=this.git4||Me(this.primaryColor,{h:-60}),this.git5=this.git5||Me(this.primaryColor,{h:-90}),this.git6=this.git6||Me(this.primaryColor,{h:60}),this.git7=this.git7||Me(this.primaryColor,{h:120}),this.darkMode?(this.git0=Dt(this.git0,25),this.git1=Dt(this.git1,25),this.git2=Dt(this.git2,25),this.git3=Dt(this.git3,25),this.git4=Dt(this.git4,25),this.git5=Dt(this.git5,25),this.git6=Dt(this.git6,25),this.git7=Dt(this.git7,25)):(this.git0=Ot(this.git0,25),this.git1=Ot(this.git1,25),this.git2=Ot(this.git2,25),this.git3=Ot(this.git3,25),this.git4=Ot(this.git4,25),this.git5=Ot(this.git5,25),this.git6=Ot(this.git6,25),this.git7=Ot(this.git7,25)),this.gitInv0=this.gitInv0||wt(this.git0),this.gitInv1=this.gitInv1||wt(this.git1),this.gitInv2=this.gitInv2||wt(this.git2),this.gitInv3=this.gitInv3||wt(this.git3),this.gitInv4=this.gitInv4||wt(this.git4),this.gitInv5=this.gitInv5||wt(this.git5),this.gitInv6=this.gitInv6||wt(this.git6),this.gitInv7=this.gitInv7||wt(this.git7),this.branchLabelColor=this.branchLabelColor||(this.darkMode?"black":this.labelTextColor),this.gitBranchLabel0=this.gitBranchLabel0||this.branchLabelColor,this.gitBranchLabel1=this.gitBranchLabel1||this.branchLabelColor,this.gitBranchLabel2=this.gitBranchLabel2||this.branchLabelColor,this.gitBranchLabel3=this.gitBranchLabel3||this.branchLabelColor,this.gitBranchLabel4=this.gitBranchLabel4||this.branchLabelColor,this.gitBranchLabel5=this.gitBranchLabel5||this.branchLabelColor,this.gitBranchLabel6=this.gitBranchLabel6||this.branchLabelColor,this.gitBranchLabel7=this.gitBranchLabel7||this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||ah,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||sh}calculate(e){if(typeof e!="object"){this.updateColors();return}let r=Object.keys(e);r.forEach(n=>{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},_$=o(t=>{let e=new jC;return e.calculate(t),e},"getThemeVariables")});var KC,L$,R$=N(()=>{"use strict";Ys();o0();KC=class{static{o(this,"Theme")}constructor(){this.background="#333",this.primaryColor="#1f2020",this.secondaryColor=Dt(this.primaryColor,16),this.tertiaryColor=Me(this.primaryColor,{h:-160}),this.primaryBorderColor=wt(this.background),this.secondaryBorderColor=Ti(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Ti(this.tertiaryColor,this.darkMode),this.primaryTextColor=wt(this.primaryColor),this.secondaryTextColor=wt(this.secondaryColor),this.tertiaryTextColor=wt(this.tertiaryColor),this.lineColor=wt(this.background),this.textColor=wt(this.background),this.mainBkg="#1f2020",this.secondBkg="calculated",this.mainContrastColor="lightgrey",this.darkTextColor=Dt(wt("#323D47"),10),this.lineColor="calculated",this.border1="#ccc",this.border2=qa(255,255,255,.25),this.arrowheadColor="calculated",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="#181818",this.textColor="#ccc",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#F9FFFE",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="calculated",this.activationBkgColor="calculated",this.sequenceNumberColor="black",this.sectionBkgColor=Ot("#EAE8D9",30),this.altSectionBkgColor="calculated",this.sectionBkgColor2="#EAE8D9",this.excludeBkgColor=Ot(this.sectionBkgColor,10),this.taskBorderColor=qa(255,255,255,70),this.taskBkgColor="calculated",this.taskTextColor="calculated",this.taskTextLightColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor=qa(255,255,255,50),this.activeTaskBkgColor="#81B1DB",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="grey",this.critBorderColor="#E83737",this.critBkgColor="#E83737",this.taskTextDarkColor="calculated",this.todayLineColor="#DB5757",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd=this.rowOdd||Dt(this.mainBkg,5)||"#ffffff",this.rowEven=this.rowEven||Ot(this.mainBkg,10),this.labelColor="calculated",this.errorBkgColor="#a44141",this.errorTextColor="#ddd"}updateColors(){this.secondBkg=Dt(this.mainBkg,16),this.lineColor=this.mainContrastColor,this.arrowheadColor=this.mainContrastColor,this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.edgeLabelBackground=Dt(this.labelBackground,25),this.actorBorder=this.border1,this.actorBkg=this.mainBkg,this.actorTextColor=this.mainContrastColor,this.actorLineColor=this.actorBorder,this.signalColor=this.mainContrastColor,this.signalTextColor=this.mainContrastColor,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.mainContrastColor,this.loopTextColor=this.mainContrastColor,this.noteBorderColor=this.secondaryBorderColor,this.noteBkgColor=this.secondBkg,this.noteTextColor=this.secondaryTextColor,this.activationBorderColor=this.border1,this.activationBkgColor=this.secondBkg,this.altSectionBkgColor=this.background,this.taskBkgColor=Dt(this.mainBkg,23),this.taskTextColor=this.darkTextColor,this.taskTextLightColor=this.mainContrastColor,this.taskTextOutsideColor=this.taskTextLightColor,this.gridColor=this.mainContrastColor,this.doneTaskBkgColor=this.mainContrastColor,this.taskTextDarkColor=this.darkTextColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#555",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#f4f4f4",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Me(this.primaryColor,{h:64}),this.fillType3=Me(this.secondaryColor,{h:64}),this.fillType4=Me(this.primaryColor,{h:-64}),this.fillType5=Me(this.secondaryColor,{h:-64}),this.fillType6=Me(this.primaryColor,{h:128}),this.fillType7=Me(this.secondaryColor,{h:128}),this.cScale1=this.cScale1||"#0b0000",this.cScale2=this.cScale2||"#4d1037",this.cScale3=this.cScale3||"#3f5258",this.cScale4=this.cScale4||"#4f2f1b",this.cScale5=this.cScale5||"#6e0a0a",this.cScale6=this.cScale6||"#3b0048",this.cScale7=this.cScale7||"#995a01",this.cScale8=this.cScale8||"#154706",this.cScale9=this.cScale9||"#161722",this.cScale10=this.cScale10||"#00296f",this.cScale11=this.cScale11||"#01629c",this.cScale12=this.cScale12||"#010029",this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Me(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Me(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Me(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Me(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Me(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Me(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Me(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Me(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Me(this.primaryColor,{h:330});for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScaleInv"+e]=this["cScaleInv"+e]||wt(this["cScale"+e]);for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScalePeer"+e]=this["cScalePeer"+e]||Dt(this["cScale"+e],10);for(let e=0;e<5;e++)this["surface"+e]=this["surface"+e]||Me(this.mainBkg,{h:30,s:-30,l:-(-10+e*4)}),this["surfacePeer"+e]=this["surfacePeer"+e]||Me(this.mainBkg,{h:30,s:-30,l:-(-7+e*4)});this.scaleLabelColor=this.scaleLabelColor||(this.darkMode?"black":this.labelTextColor);for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScaleLabel"+e]=this["cScaleLabel"+e]||this.scaleLabelColor;for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["pie"+e]=this["cScale"+e];this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||Me(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||Me(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||Me(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||Me(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||Me(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||Me(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ca(this.quadrant1Fill)?Dt(this.quadrant1Fill):Ot(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#3498db,#2ecc71,#e74c3c,#f1c40f,#bdc3c7,#ffffff,#34495e,#9b59b6,#1abc9c,#e67e22"},this.packet={startByteColor:this.primaryTextColor,endByteColor:this.primaryTextColor,labelColor:this.primaryTextColor,titleColor:this.primaryTextColor,blockStrokeColor:this.primaryTextColor,blockFillColor:this.background},this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.classText=this.primaryTextColor,this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||(this.darkMode?Ot(this.secondaryColor,30):this.secondaryColor),this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=Dt(this.secondaryColor,20),this.git1=Dt(this.pie2||this.secondaryColor,20),this.git2=Dt(this.pie3||this.tertiaryColor,20),this.git3=Dt(this.pie4||Me(this.primaryColor,{h:-30}),20),this.git4=Dt(this.pie5||Me(this.primaryColor,{h:-60}),20),this.git5=Dt(this.pie6||Me(this.primaryColor,{h:-90}),10),this.git6=Dt(this.pie7||Me(this.primaryColor,{h:60}),10),this.git7=Dt(this.pie8||Me(this.primaryColor,{h:120}),20),this.gitInv0=this.gitInv0||wt(this.git0),this.gitInv1=this.gitInv1||wt(this.git1),this.gitInv2=this.gitInv2||wt(this.git2),this.gitInv3=this.gitInv3||wt(this.git3),this.gitInv4=this.gitInv4||wt(this.git4),this.gitInv5=this.gitInv5||wt(this.git5),this.gitInv6=this.gitInv6||wt(this.git6),this.gitInv7=this.gitInv7||wt(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||wt(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||wt(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||Dt(this.background,12),this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||Dt(this.background,2),this.nodeBorder=this.nodeBorder||"#999"}calculate(e){if(typeof e!="object"){this.updateColors();return}let r=Object.keys(e);r.forEach(n=>{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},L$=o(t=>{let e=new KC;return e.calculate(t),e},"getThemeVariables")});var QC,oh,_y=N(()=>{"use strict";Ys();o0();Ay();QC=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#ECECFF",this.secondaryColor=Me(this.primaryColor,{h:120}),this.secondaryColor="#ffffde",this.tertiaryColor=Me(this.primaryColor,{h:-160}),this.primaryBorderColor=Ti(this.primaryColor,this.darkMode),this.secondaryBorderColor=Ti(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Ti(this.tertiaryColor,this.darkMode),this.primaryTextColor=wt(this.primaryColor),this.secondaryTextColor=wt(this.secondaryColor),this.tertiaryTextColor=wt(this.tertiaryColor),this.lineColor=wt(this.background),this.textColor=wt(this.background),this.background="white",this.mainBkg="#ECECFF",this.secondBkg="#ffffde",this.lineColor="#333333",this.border1="#9370DB",this.border2="#aaaa33",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.labelBackground="rgba(232,232,232, 0.8)",this.textColor="#333",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="calculated",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="calculated",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="calculated",this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor="calculated",this.taskTextOutsideColor=this.taskTextDarkColor,this.taskTextClickableColor="calculated",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBorderColor="calculated",this.critBkgColor="calculated",this.todayLineColor="calculated",this.sectionBkgColor=qa(102,102,255,.49),this.altSectionBkgColor="white",this.sectionBkgColor2="#fff400",this.taskBorderColor="#534fbc",this.taskBkgColor="#8a90dd",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="#534fbc",this.activeTaskBkgColor="#bfc7ff",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd="calculated",this.rowEven="calculated",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222",this.updateColors()}updateColors(){this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Me(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Me(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Me(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Me(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Me(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Me(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Me(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Me(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Me(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Ot(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Ot(this.tertiaryColor,40);for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScale"+e]=Ot(this["cScale"+e],10),this["cScalePeer"+e]=this["cScalePeer"+e]||Ot(this["cScale"+e],25);for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScaleInv"+e]=this["cScaleInv"+e]||Me(this["cScale"+e],{h:180});for(let e=0;e<5;e++)this["surface"+e]=this["surface"+e]||Me(this.mainBkg,{h:30,l:-(5+e*5)}),this["surfacePeer"+e]=this["surfacePeer"+e]||Me(this.mainBkg,{h:30,l:-(7+e*5)});if(this.scaleLabelColor=this.scaleLabelColor!=="calculated"&&this.scaleLabelColor?this.scaleLabelColor:this.labelTextColor,this.labelTextColor!=="calculated"){this.cScaleLabel0=this.cScaleLabel0||wt(this.labelTextColor),this.cScaleLabel3=this.cScaleLabel3||wt(this.labelTextColor);for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScaleLabel"+e]=this["cScaleLabel"+e]||this.labelTextColor}this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.textColor,this.edgeLabelBackground=this.labelBackground,this.actorBorder=Dt(this.border1,23),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.signalColor=this.textColor,this.signalTextColor=this.textColor,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.rowOdd=this.rowOdd||Dt(this.primaryColor,75)||"#ffffff",this.rowEven=this.rowEven||Dt(this.primaryColor,1),this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.nodeBorder,this.specialStateColor=this.lineColor,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Me(this.primaryColor,{h:64}),this.fillType3=Me(this.secondaryColor,{h:64}),this.fillType4=Me(this.primaryColor,{h:-64}),this.fillType5=Me(this.secondaryColor,{h:-64}),this.fillType6=Me(this.primaryColor,{h:128}),this.fillType7=Me(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||Me(this.tertiaryColor,{l:-40}),this.pie4=this.pie4||Me(this.primaryColor,{l:-10}),this.pie5=this.pie5||Me(this.secondaryColor,{l:-30}),this.pie6=this.pie6||Me(this.tertiaryColor,{l:-20}),this.pie7=this.pie7||Me(this.primaryColor,{h:60,l:-20}),this.pie8=this.pie8||Me(this.primaryColor,{h:-60,l:-40}),this.pie9=this.pie9||Me(this.primaryColor,{h:120,l:-40}),this.pie10=this.pie10||Me(this.primaryColor,{h:60,l:-40}),this.pie11=this.pie11||Me(this.primaryColor,{h:-90,l:-40}),this.pie12=this.pie12||Me(this.primaryColor,{h:120,l:-30}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||Me(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||Me(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||Me(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||Me(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||Me(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||Me(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ca(this.quadrant1Fill)?Dt(this.quadrant1Fill):Ot(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#ECECFF,#8493A6,#FFC3A0,#DCDDE1,#B8E994,#D1A36F,#C3CDE6,#FFB6C1,#496078,#F8F3E3"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.labelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||Me(this.primaryColor,{h:-30}),this.git4=this.git4||Me(this.primaryColor,{h:-60}),this.git5=this.git5||Me(this.primaryColor,{h:-90}),this.git6=this.git6||Me(this.primaryColor,{h:60}),this.git7=this.git7||Me(this.primaryColor,{h:120}),this.darkMode?(this.git0=Dt(this.git0,25),this.git1=Dt(this.git1,25),this.git2=Dt(this.git2,25),this.git3=Dt(this.git3,25),this.git4=Dt(this.git4,25),this.git5=Dt(this.git5,25),this.git6=Dt(this.git6,25),this.git7=Dt(this.git7,25)):(this.git0=Ot(this.git0,25),this.git1=Ot(this.git1,25),this.git2=Ot(this.git2,25),this.git3=Ot(this.git3,25),this.git4=Ot(this.git4,25),this.git5=Ot(this.git5,25),this.git6=Ot(this.git6,25),this.git7=Ot(this.git7,25)),this.gitInv0=this.gitInv0||Ot(wt(this.git0),25),this.gitInv1=this.gitInv1||wt(this.git1),this.gitInv2=this.gitInv2||wt(this.git2),this.gitInv3=this.gitInv3||wt(this.git3),this.gitInv4=this.gitInv4||wt(this.git4),this.gitInv5=this.gitInv5||wt(this.git5),this.gitInv6=this.gitInv6||wt(this.git6),this.gitInv7=this.gitInv7||wt(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||wt(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||wt(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||ah,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||sh}calculate(e){if(Object.keys(this).forEach(n=>{this[n]==="calculated"&&(this[n]=void 0)}),typeof e!="object"){this.updateColors();return}let r=Object.keys(e);r.forEach(n=>{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},oh=o(t=>{let e=new QC;return e.calculate(t),e},"getThemeVariables")});var ZC,N$,M$=N(()=>{"use strict";Ys();Ay();o0();ZC=class{static{o(this,"Theme")}constructor(){this.background="#f4f4f4",this.primaryColor="#cde498",this.secondaryColor="#cdffb2",this.background="white",this.mainBkg="#cde498",this.secondBkg="#cdffb2",this.lineColor="green",this.border1="#13540c",this.border2="#6eaa49",this.arrowheadColor="green",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.tertiaryColor=Dt("#cde498",10),this.primaryBorderColor=Ti(this.primaryColor,this.darkMode),this.secondaryBorderColor=Ti(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Ti(this.tertiaryColor,this.darkMode),this.primaryTextColor=wt(this.primaryColor),this.secondaryTextColor=wt(this.secondaryColor),this.tertiaryTextColor=wt(this.primaryColor),this.lineColor=wt(this.background),this.textColor=wt(this.background),this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="#333",this.edgeLabelBackground="#e8e8e8",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="black",this.actorLineColor="calculated",this.signalColor="#333",this.signalTextColor="#333",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="#326932",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="#fff5ad",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="#6eaa49",this.altSectionBkgColor="white",this.sectionBkgColor2="#6eaa49",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="#487e3a",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="black",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="lightgrey",this.doneTaskBkgColor="lightgrey",this.doneTaskBorderColor="grey",this.critBorderColor="#ff8888",this.critBkgColor="red",this.todayLineColor="red",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.actorBorder=Ot(this.mainBkg,20),this.actorBkg=this.mainBkg,this.labelBoxBkgColor=this.actorBkg,this.labelTextColor=this.actorTextColor,this.loopTextColor=this.actorTextColor,this.noteBorderColor=this.border2,this.noteTextColor=this.actorTextColor,this.actorLineColor=this.actorBorder,this.cScale0=this.cScale0||this.primaryColor,this.cScale1=this.cScale1||this.secondaryColor,this.cScale2=this.cScale2||this.tertiaryColor,this.cScale3=this.cScale3||Me(this.primaryColor,{h:30}),this.cScale4=this.cScale4||Me(this.primaryColor,{h:60}),this.cScale5=this.cScale5||Me(this.primaryColor,{h:90}),this.cScale6=this.cScale6||Me(this.primaryColor,{h:120}),this.cScale7=this.cScale7||Me(this.primaryColor,{h:150}),this.cScale8=this.cScale8||Me(this.primaryColor,{h:210}),this.cScale9=this.cScale9||Me(this.primaryColor,{h:270}),this.cScale10=this.cScale10||Me(this.primaryColor,{h:300}),this.cScale11=this.cScale11||Me(this.primaryColor,{h:330}),this.cScalePeer1=this.cScalePeer1||Ot(this.secondaryColor,45),this.cScalePeer2=this.cScalePeer2||Ot(this.tertiaryColor,40);for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScale"+e]=Ot(this["cScale"+e],10),this["cScalePeer"+e]=this["cScalePeer"+e]||Ot(this["cScale"+e],25);for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScaleInv"+e]=this["cScaleInv"+e]||Me(this["cScale"+e],{h:180});this.scaleLabelColor=this.scaleLabelColor!=="calculated"&&this.scaleLabelColor?this.scaleLabelColor:this.labelTextColor;for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScaleLabel"+e]=this["cScaleLabel"+e]||this.scaleLabelColor;for(let e=0;e<5;e++)this["surface"+e]=this["surface"+e]||Me(this.mainBkg,{h:30,s:-30,l:-(5+e*5)}),this["surfacePeer"+e]=this["surfacePeer"+e]||Me(this.mainBkg,{h:30,s:-30,l:-(8+e*5)});this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.taskBorderColor=this.border1,this.taskTextColor=this.taskTextLightColor,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.rowOdd=this.rowOdd||Dt(this.mainBkg,75)||"#ffffff",this.rowEven=this.rowEven||Dt(this.mainBkg,20),this.transitionColor=this.transitionColor||this.lineColor,this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f0f0f0",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.compositeBorder=this.compositeBorder||this.nodeBorder,this.innerEndBackground=this.primaryBorderColor,this.specialStateColor=this.lineColor,this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.transitionColor=this.transitionColor||this.lineColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Me(this.primaryColor,{h:64}),this.fillType3=Me(this.secondaryColor,{h:64}),this.fillType4=Me(this.primaryColor,{h:-64}),this.fillType5=Me(this.secondaryColor,{h:-64}),this.fillType6=Me(this.primaryColor,{h:128}),this.fillType7=Me(this.secondaryColor,{h:128}),this.pie1=this.pie1||this.primaryColor,this.pie2=this.pie2||this.secondaryColor,this.pie3=this.pie3||this.tertiaryColor,this.pie4=this.pie4||Me(this.primaryColor,{l:-30}),this.pie5=this.pie5||Me(this.secondaryColor,{l:-30}),this.pie6=this.pie6||Me(this.tertiaryColor,{h:40,l:-40}),this.pie7=this.pie7||Me(this.primaryColor,{h:60,l:-10}),this.pie8=this.pie8||Me(this.primaryColor,{h:-60,l:-10}),this.pie9=this.pie9||Me(this.primaryColor,{h:120,l:0}),this.pie10=this.pie10||Me(this.primaryColor,{h:60,l:-50}),this.pie11=this.pie11||Me(this.primaryColor,{h:-60,l:-50}),this.pie12=this.pie12||Me(this.primaryColor,{h:120,l:-50}),this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||Me(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||Me(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||Me(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||Me(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||Me(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||Me(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ca(this.quadrant1Fill)?Dt(this.quadrant1Fill):Ot(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.packet={startByteColor:this.primaryTextColor,endByteColor:this.primaryTextColor,labelColor:this.primaryTextColor,titleColor:this.primaryTextColor,blockStrokeColor:this.primaryTextColor,blockFillColor:this.mainBkg},this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#CDE498,#FF6B6B,#A0D2DB,#D7BDE2,#F0F0F0,#FFC3A0,#7FD8BE,#FF9A8B,#FAF3E0,#FFF176"},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.edgeLabelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=this.git0||this.primaryColor,this.git1=this.git1||this.secondaryColor,this.git2=this.git2||this.tertiaryColor,this.git3=this.git3||Me(this.primaryColor,{h:-30}),this.git4=this.git4||Me(this.primaryColor,{h:-60}),this.git5=this.git5||Me(this.primaryColor,{h:-90}),this.git6=this.git6||Me(this.primaryColor,{h:60}),this.git7=this.git7||Me(this.primaryColor,{h:120}),this.darkMode?(this.git0=Dt(this.git0,25),this.git1=Dt(this.git1,25),this.git2=Dt(this.git2,25),this.git3=Dt(this.git3,25),this.git4=Dt(this.git4,25),this.git5=Dt(this.git5,25),this.git6=Dt(this.git6,25),this.git7=Dt(this.git7,25)):(this.git0=Ot(this.git0,25),this.git1=Ot(this.git1,25),this.git2=Ot(this.git2,25),this.git3=Ot(this.git3,25),this.git4=Ot(this.git4,25),this.git5=Ot(this.git5,25),this.git6=Ot(this.git6,25),this.git7=Ot(this.git7,25)),this.gitInv0=this.gitInv0||wt(this.git0),this.gitInv1=this.gitInv1||wt(this.git1),this.gitInv2=this.gitInv2||wt(this.git2),this.gitInv3=this.gitInv3||wt(this.git3),this.gitInv4=this.gitInv4||wt(this.git4),this.gitInv5=this.gitInv5||wt(this.git5),this.gitInv6=this.gitInv6||wt(this.git6),this.gitInv7=this.gitInv7||wt(this.git7),this.gitBranchLabel0=this.gitBranchLabel0||wt(this.labelTextColor),this.gitBranchLabel1=this.gitBranchLabel1||this.labelTextColor,this.gitBranchLabel2=this.gitBranchLabel2||this.labelTextColor,this.gitBranchLabel3=this.gitBranchLabel3||wt(this.labelTextColor),this.gitBranchLabel4=this.gitBranchLabel4||this.labelTextColor,this.gitBranchLabel5=this.gitBranchLabel5||this.labelTextColor,this.gitBranchLabel6=this.gitBranchLabel6||this.labelTextColor,this.gitBranchLabel7=this.gitBranchLabel7||this.labelTextColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=this.tagBorder||this.primaryBorderColor,this.tagLabelFontSize=this.tagLabelFontSize||"10px",this.commitLabelColor=this.commitLabelColor||this.secondaryTextColor,this.commitLabelBackground=this.commitLabelBackground||this.secondaryColor,this.commitLabelFontSize=this.commitLabelFontSize||"10px",this.attributeBackgroundColorOdd=this.attributeBackgroundColorOdd||ah,this.attributeBackgroundColorEven=this.attributeBackgroundColorEven||sh}calculate(e){if(typeof e!="object"){this.updateColors();return}let r=Object.keys(e);r.forEach(n=>{this[n]=e[n]}),this.updateColors(),r.forEach(n=>{this[n]=e[n]})}},N$=o(t=>{let e=new ZC;return e.calculate(t),e},"getThemeVariables")});var JC,I$,O$=N(()=>{"use strict";Ys();o0();Ay();JC=class{static{o(this,"Theme")}constructor(){this.primaryColor="#eee",this.contrast="#707070",this.secondaryColor=Dt(this.contrast,55),this.background="#ffffff",this.tertiaryColor=Me(this.primaryColor,{h:-160}),this.primaryBorderColor=Ti(this.primaryColor,this.darkMode),this.secondaryBorderColor=Ti(this.secondaryColor,this.darkMode),this.tertiaryBorderColor=Ti(this.tertiaryColor,this.darkMode),this.primaryTextColor=wt(this.primaryColor),this.secondaryTextColor=wt(this.secondaryColor),this.tertiaryTextColor=wt(this.tertiaryColor),this.lineColor=wt(this.background),this.textColor=wt(this.background),this.mainBkg="#eee",this.secondBkg="calculated",this.lineColor="#666",this.border1="#999",this.border2="calculated",this.note="#ffa",this.text="#333",this.critical="#d42",this.done="#bbb",this.arrowheadColor="#333333",this.fontFamily='"trebuchet ms", verdana, arial, sans-serif',this.fontSize="16px",this.THEME_COLOR_LIMIT=12,this.nodeBkg="calculated",this.nodeBorder="calculated",this.clusterBkg="calculated",this.clusterBorder="calculated",this.defaultLinkColor="calculated",this.titleColor="calculated",this.edgeLabelBackground="white",this.actorBorder="calculated",this.actorBkg="calculated",this.actorTextColor="calculated",this.actorLineColor=this.actorBorder,this.signalColor="calculated",this.signalTextColor="calculated",this.labelBoxBkgColor="calculated",this.labelBoxBorderColor="calculated",this.labelTextColor="calculated",this.loopTextColor="calculated",this.noteBorderColor="calculated",this.noteBkgColor="calculated",this.noteTextColor="calculated",this.activationBorderColor="#666",this.activationBkgColor="#f4f4f4",this.sequenceNumberColor="white",this.sectionBkgColor="calculated",this.altSectionBkgColor="white",this.sectionBkgColor2="calculated",this.excludeBkgColor="#eeeeee",this.taskBorderColor="calculated",this.taskBkgColor="calculated",this.taskTextLightColor="white",this.taskTextColor="calculated",this.taskTextDarkColor="calculated",this.taskTextOutsideColor="calculated",this.taskTextClickableColor="#003163",this.activeTaskBorderColor="calculated",this.activeTaskBkgColor="calculated",this.gridColor="calculated",this.doneTaskBkgColor="calculated",this.doneTaskBorderColor="calculated",this.critBkgColor="calculated",this.critBorderColor="calculated",this.todayLineColor="calculated",this.personBorder=this.primaryBorderColor,this.personBkg=this.mainBkg,this.archEdgeColor="calculated",this.archEdgeArrowColor="calculated",this.archEdgeWidth="3",this.archGroupBorderColor=this.primaryBorderColor,this.archGroupBorderWidth="2px",this.rowOdd=this.rowOdd||Dt(this.mainBkg,75)||"#ffffff",this.rowEven=this.rowEven||"#f4f4f4",this.labelColor="black",this.errorBkgColor="#552222",this.errorTextColor="#552222"}updateColors(){this.secondBkg=Dt(this.contrast,55),this.border2=this.contrast,this.actorBorder=Dt(this.border1,23),this.actorBkg=this.mainBkg,this.actorTextColor=this.text,this.actorLineColor=this.actorBorder,this.signalColor=this.text,this.signalTextColor=this.text,this.labelBoxBkgColor=this.actorBkg,this.labelBoxBorderColor=this.actorBorder,this.labelTextColor=this.text,this.loopTextColor=this.text,this.noteBorderColor="#999",this.noteBkgColor="#666",this.noteTextColor="#fff",this.cScale0=this.cScale0||"#555",this.cScale1=this.cScale1||"#F4F4F4",this.cScale2=this.cScale2||"#555",this.cScale3=this.cScale3||"#BBB",this.cScale4=this.cScale4||"#777",this.cScale5=this.cScale5||"#999",this.cScale6=this.cScale6||"#DDD",this.cScale7=this.cScale7||"#FFF",this.cScale8=this.cScale8||"#DDD",this.cScale9=this.cScale9||"#BBB",this.cScale10=this.cScale10||"#999",this.cScale11=this.cScale11||"#777";for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScaleInv"+e]=this["cScaleInv"+e]||wt(this["cScale"+e]);for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this.darkMode?this["cScalePeer"+e]=this["cScalePeer"+e]||Dt(this["cScale"+e],10):this["cScalePeer"+e]=this["cScalePeer"+e]||Ot(this["cScale"+e],10);this.scaleLabelColor=this.scaleLabelColor||(this.darkMode?"black":this.labelTextColor),this.cScaleLabel0=this.cScaleLabel0||this.cScale1,this.cScaleLabel2=this.cScaleLabel2||this.cScale1;for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["cScaleLabel"+e]=this["cScaleLabel"+e]||this.scaleLabelColor;for(let e=0;e<5;e++)this["surface"+e]=this["surface"+e]||Me(this.mainBkg,{l:-(5+e*5)}),this["surfacePeer"+e]=this["surfacePeer"+e]||Me(this.mainBkg,{l:-(8+e*5)});this.nodeBkg=this.mainBkg,this.nodeBorder=this.border1,this.clusterBkg=this.secondBkg,this.clusterBorder=this.border2,this.defaultLinkColor=this.lineColor,this.titleColor=this.text,this.sectionBkgColor=Dt(this.contrast,30),this.sectionBkgColor2=Dt(this.contrast,30),this.taskBorderColor=Ot(this.contrast,10),this.taskBkgColor=this.contrast,this.taskTextColor=this.taskTextLightColor,this.taskTextDarkColor=this.text,this.taskTextOutsideColor=this.taskTextDarkColor,this.activeTaskBorderColor=this.taskBorderColor,this.activeTaskBkgColor=this.mainBkg,this.gridColor=Dt(this.border1,30),this.doneTaskBkgColor=this.done,this.doneTaskBorderColor=this.lineColor,this.critBkgColor=this.critical,this.critBorderColor=Ot(this.critBkgColor,10),this.todayLineColor=this.critBkgColor,this.archEdgeColor=this.lineColor,this.archEdgeArrowColor=this.lineColor,this.transitionColor=this.transitionColor||"#000",this.transitionLabelColor=this.transitionLabelColor||this.textColor,this.stateLabelColor=this.stateLabelColor||this.stateBkg||this.primaryTextColor,this.stateBkg=this.stateBkg||this.mainBkg,this.labelBackgroundColor=this.labelBackgroundColor||this.stateBkg,this.compositeBackground=this.compositeBackground||this.background||this.tertiaryColor,this.altBackground=this.altBackground||"#f4f4f4",this.compositeTitleBackground=this.compositeTitleBackground||this.mainBkg,this.stateBorder=this.stateBorder||"#000",this.innerEndBackground=this.primaryBorderColor,this.specialStateColor="#222",this.errorBkgColor=this.errorBkgColor||this.tertiaryColor,this.errorTextColor=this.errorTextColor||this.tertiaryTextColor,this.classText=this.primaryTextColor,this.fillType0=this.primaryColor,this.fillType1=this.secondaryColor,this.fillType2=Me(this.primaryColor,{h:64}),this.fillType3=Me(this.secondaryColor,{h:64}),this.fillType4=Me(this.primaryColor,{h:-64}),this.fillType5=Me(this.secondaryColor,{h:-64}),this.fillType6=Me(this.primaryColor,{h:128}),this.fillType7=Me(this.secondaryColor,{h:128});for(let e=0;e<this.THEME_COLOR_LIMIT;e++)this["pie"+e]=this["cScale"+e];this.pie12=this.pie0,this.pieTitleTextSize=this.pieTitleTextSize||"25px",this.pieTitleTextColor=this.pieTitleTextColor||this.taskTextDarkColor,this.pieSectionTextSize=this.pieSectionTextSize||"17px",this.pieSectionTextColor=this.pieSectionTextColor||this.textColor,this.pieLegendTextSize=this.pieLegendTextSize||"17px",this.pieLegendTextColor=this.pieLegendTextColor||this.taskTextDarkColor,this.pieStrokeColor=this.pieStrokeColor||"black",this.pieStrokeWidth=this.pieStrokeWidth||"2px",this.pieOuterStrokeWidth=this.pieOuterStrokeWidth||"2px",this.pieOuterStrokeColor=this.pieOuterStrokeColor||"black",this.pieOpacity=this.pieOpacity||"0.7",this.quadrant1Fill=this.quadrant1Fill||this.primaryColor,this.quadrant2Fill=this.quadrant2Fill||Me(this.primaryColor,{r:5,g:5,b:5}),this.quadrant3Fill=this.quadrant3Fill||Me(this.primaryColor,{r:10,g:10,b:10}),this.quadrant4Fill=this.quadrant4Fill||Me(this.primaryColor,{r:15,g:15,b:15}),this.quadrant1TextFill=this.quadrant1TextFill||this.primaryTextColor,this.quadrant2TextFill=this.quadrant2TextFill||Me(this.primaryTextColor,{r:-5,g:-5,b:-5}),this.quadrant3TextFill=this.quadrant3TextFill||Me(this.primaryTextColor,{r:-10,g:-10,b:-10}),this.quadrant4TextFill=this.quadrant4TextFill||Me(this.primaryTextColor,{r:-15,g:-15,b:-15}),this.quadrantPointFill=this.quadrantPointFill||ca(this.quadrant1Fill)?Dt(this.quadrant1Fill):Ot(this.quadrant1Fill),this.quadrantPointTextFill=this.quadrantPointTextFill||this.primaryTextColor,this.quadrantXAxisTextFill=this.quadrantXAxisTextFill||this.primaryTextColor,this.quadrantYAxisTextFill=this.quadrantYAxisTextFill||this.primaryTextColor,this.quadrantInternalBorderStrokeFill=this.quadrantInternalBorderStrokeFill||this.primaryBorderColor,this.quadrantExternalBorderStrokeFill=this.quadrantExternalBorderStrokeFill||this.primaryBorderColor,this.quadrantTitleFill=this.quadrantTitleFill||this.primaryTextColor,this.xyChart={backgroundColor:this.xyChart?.backgroundColor||this.background,titleColor:this.xyChart?.titleColor||this.primaryTextColor,xAxisTitleColor:this.xyChart?.xAxisTitleColor||this.primaryTextColor,xAxisLabelColor:this.xyChart?.xAxisLabelColor||this.primaryTextColor,xAxisTickColor:this.xyChart?.xAxisTickColor||this.primaryTextColor,xAxisLineColor:this.xyChart?.xAxisLineColor||this.primaryTextColor,yAxisTitleColor:this.xyChart?.yAxisTitleColor||this.primaryTextColor,yAxisLabelColor:this.xyChart?.yAxisLabelColor||this.primaryTextColor,yAxisTickColor:this.xyChart?.yAxisTickColor||this.primaryTextColor,yAxisLineColor:this.xyChart?.yAxisLineColor||this.primaryTextColor,plotColorPalette:this.xyChart?.plotColorPalette||"#EEE,#6BB8E4,#8ACB88,#C7ACD6,#E8DCC2,#FFB2A8,#FFF380,#7E8D91,#FFD8B1,#FAF3E0"},this.radar={axisColor:this.radar?.axisColor||this.lineColor,axisStrokeWidth:this.radar?.axisStrokeWidth||2,axisLabelFontSize:this.radar?.axisLabelFontSize||12,curveOpacity:this.radar?.curveOpacity||.5,curveStrokeWidth:this.radar?.curveStrokeWidth||2,graticuleColor:this.radar?.graticuleColor||"#DEDEDE",graticuleStrokeWidth:this.radar?.graticuleStrokeWidth||1,graticuleOpacity:this.radar?.graticuleOpacity||.3,legendBoxSize:this.radar?.legendBoxSize||12,legendFontSize:this.radar?.legendFontSize||12},this.requirementBackground=this.requirementBackground||this.primaryColor,this.requirementBorderColor=this.requirementBorderColor||this.primaryBorderColor,this.requirementBorderSize=this.requirementBorderSize||"1",this.requirementTextColor=this.requirementTextColor||this.primaryTextColor,this.relationColor=this.relationColor||this.lineColor,this.relationLabelBackground=this.relationLabelBackground||this.edgeLabelBackground,this.relationLabelColor=this.relationLabelColor||this.actorTextColor,this.git0=Ot(this.pie1,25)||this.primaryColor,this.git1=this.pie2||this.secondaryColor,this.git2=this.pie3||this.tertiaryColor,this.git3=this.pie4||Me(this.primaryColor,{h:-30}),this.git4=this.pie5||Me(this.primaryColor,{h:-60}),this.git5=this.pie6||Me(this.primaryColor,{h:-90}),this.git6=this.pie7||Me(this.primaryColor,{h:60}),this.git7=this.pie8||Me(this.primaryColor,{h:120}),this.gitInv0=this.gitInv0||wt(this.git0),this.gitInv1=this.gitInv1||wt(this.git1),this.gitInv2=this.gitInv2||wt(this.git2),this.gitInv3=this.gitInv3||wt(this.git3),this.gitInv4=this.gitInv4||wt(this.git4),this.gitInv5=this.gitInv5||wt(this.git5),this.gitInv6=this.gitInv6||wt(this.git6),this.gitInv7=this.gitInv7||wt(this.git7),this.branchLabelColor=this.branchLabelColor||this.labelTextColor,this.gitBranchLabel0=this.branchLabelColor,this.gitBranchLabel1="white",this.gitBranchLabel2=this.branchLabelColor,this.gitBranchLabel3="white",this.gitBranchLabel4=this.branchLabelColor,this.gitBranchLabel5=this.branchLabelColor,this.gitBranchLabel6=this.branchLabelColor,this.gitBranchLabel7=this.branchLabelColor,this.tagLabelColor=this.tagLabelColor||this.primaryTextColor,this.tagLabelBackground=this.tagLabelBackground||this.primaryColor,this.tagLabelBorder=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t{static{o(this,"Parser")}constructor(e,r){this.mode=void 0,this.gullet=void 0,this.settings=void 0,this.leftrightDepth=void 0,this.nextToken=void 0,this.mode="math",this.gullet=new I7(e,r,this.mode),this.settings=r,this.leftrightDepth=0}expect(e,r){if(r===void 0&&(r=!0),this.fetch().text!==e)throw new gt("Expected '"+e+"', got '"+this.fetch().text+"'",this.fetch());r&&this.consume()}consume(){this.nextToken=null}fetch(){return this.nextToken==null&&(this.nextToken=this.gullet.expandNextToken()),this.nextToken}switchMode(e){this.mode=e,this.gullet.switchMode(e)}parse(){this.settings.globalGroup||this.gullet.beginGroup(),this.settings.colorIsTextColor&&this.gullet.macros.set("\\color","\\textcolor");try{var e=this.parseExpression(!1);return this.expect("EOF"),this.settings.globalGroup||this.gullet.endGroup(),e}finally{this.gullet.endGroups()}}subparse(e){var r=this.nextToken;this.consume(),this.gullet.pushToken(new So("}")),this.gullet.pushTokens(e);var n=this.parseExpression(!1);return this.expect("}"),this.nextToken=r,n}parseExpression(e,r){for(var n=[];;){this.mode==="math"&&this.consumeSpaces();var i=this.fetch();if(t.endOfExpression.indexOf(i.text)!==-1||r&&i.text===r||e&&hh[i.text]&&hh[i.text].infix)break;var a=this.parseAtom(r);if(a){if(a.type==="internal")continue}else break;n.push(a)}return this.mode==="text"&&this.formLigatures(n),this.handleInfixNodes(n)}handleInfixNodes(e){for(var r=-1,n,i=0;i<e.length;i++)if(e[i].type==="infix"){if(r!==-1)throw new gt("only one infix operator per group",e[i].token);r=i,n=e[i].replaceWith}if(r!==-1&&n){var a,s,l=e.slice(0,r),u=e.slice(r+1);l.length===1&&l[0].type==="ordgroup"?a=l[0]:a={type:"ordgroup",mode:this.mode,body:l},u.length===1&&u[0].type==="ordgroup"?s=u[0]:s={type:"ordgroup",mode:this.mode,body:u};var h;return n==="\\\\abovefrac"?h=this.callFunction(n,[a,e[r],s],[]):h=this.callFunction(n,[a,s],[]),[h]}else return e}handleSupSubscript(e){var r=this.fetch(),n=r.text;this.consume(),this.consumeSpaces();var i=this.parseGroup(e);if(!i)throw new gt("Expected group after '"+n+"'",r);return i}formatUnsupportedCmd(e){for(var r=[],n=0;n<e.length;n++)r.push({type:"textord",mode:"text",text:e[n]});var i={type:"text",mode:this.mode,body:r},a={type:"color",mode:this.mode,color:this.settings.errorColor,body:[i]};return a}parseAtom(e){var r=this.parseGroup("atom",e);if(this.mode==="text")return r;for(var n,i;;){this.consumeSpaces();var a=this.fetch();if(a.text==="\\limits"||a.text==="\\nolimits"){if(r&&r.type==="op"){var s=a.text==="\\limits";r.limits=s,r.alwaysHandleSupSub=!0}else if(r&&r.type==="operatorname")r.alwaysHandleSupSub&&(r.limits=a.text==="\\limits");else throw new gt("Limit controls must follow a math operator",a);this.consume()}else if(a.text==="^"){if(n)throw new gt("Double superscript",a);n=this.handleSupSubscript("superscript")}else if(a.text==="_"){if(i)throw new gt("Double subscript",a);i=this.handleSupSubscript("subscript")}else if(a.text==="'"){if(n)throw new gt("Double 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-`,e=e.substring(A.length+1),p=S.slice(y)}}a.loose||(l?a.loose=!0:this.rules.other.doubleBlankLine.test(f)&&(l=!0));let v=null,x;this.options.gfm&&(v=this.rules.other.listIsTask.exec(d),v&&(x=v[0]!=="[ ] ",d=d.replace(this.rules.other.listReplaceTask,""))),a.items.push({type:"list_item",raw:f,task:!!v,checked:x,loose:!1,text:d,tokens:[]}),a.raw+=f}let u=a.items.at(-1);if(u)u.raw=u.raw.trimEnd(),u.text=u.text.trimEnd();else return;a.raw=a.raw.trimEnd();for(let h=0;h<a.items.length;h++)if(this.lexer.state.top=!1,a.items[h].tokens=this.lexer.blockTokens(a.items[h].text,[]),!a.loose){let f=a.items[h].tokens.filter(p=>p.type==="space"),d=f.length>0&&f.some(p=>this.rules.other.anyLine.test(p.raw));a.loose=d}if(a.loose)for(let h=0;h<a.items.length;h++)a.items[h].loose=!0;return a}}html(e){let r=this.rules.block.html.exec(e);if(r)return{type:"html",block:!0,raw:r[0],pre:r[1]==="pre"||r[1]==="script"||r[1]==="style",text:r[0]}}def(e){let r=this.rules.block.def.exec(e);if(r){let n=r[1].toLowerCase().replace(this.rules.other.multipleSpaceGlobal," "),i=r[2]?r[2].replace(this.rules.other.hrefBrackets,"$1").replace(this.rules.inline.anyPunctuation,"$1"):"",a=r[3]?r[3].substring(1,r[3].length-1).replace(this.rules.inline.anyPunctuation,"$1"):r[3];return{type:"def",tag:n,raw:r[0],href:i,title:a}}}table(e){let r=this.rules.block.table.exec(e);if(!r||!this.rules.other.tableDelimiter.test(r[2]))return;let n=$j(r[1]),i=r[2].replace(this.rules.other.tableAlignChars,"").split("|"),a=r[3]?.trim()?r[3].replace(this.rules.other.tableRowBlankLine,"").split(`
-`):[],s={type:"table",raw:r[0],header:[],align:[],rows:[]};if(n.length===i.length){for(let l of i)this.rules.other.tableAlignRight.test(l)?s.align.push("right"):this.rules.other.tableAlignCenter.test(l)?s.align.push("center"):this.rules.other.tableAlignLeft.test(l)?s.align.push("left"):s.align.push(null);for(let l=0;l<n.length;l++)s.header.push({text:n[l],tokens:this.lexer.inline(n[l]),header:!0,align:s.align[l]});for(let l of a)s.rows.push($j(l,s.header.length).map((u,h)=>({text:u,tokens:this.lexer.inline(u),header:!1,align:s.align[h]})));return s}}lheading(e){let r=this.rules.block.lheading.exec(e);if(r)return{type:"heading",raw:r[0],depth:r[2].charAt(0)==="="?1:2,text:r[1],tokens:this.lexer.inline(r[1])}}paragraph(e){let r=this.rules.block.paragraph.exec(e);if(r){let n=r[1].charAt(r[1].length-1)===`
-`?r[1].slice(0,-1):r[1];return{type:"paragraph",raw:r[0],text:n,tokens:this.lexer.inline(n)}}}text(e){let r=this.rules.block.text.exec(e);if(r)return{type:"text",raw:r[0],text:r[0],tokens:this.lexer.inline(r[0])}}escape(e){let r=this.rules.inline.escape.exec(e);if(r)return{type:"escape",raw:r[0],text:r[1]}}tag(e){let r=this.rules.inline.tag.exec(e);if(r)return!this.lexer.state.inLink&&this.rules.other.startATag.test(r[0])?this.lexer.state.inLink=!0:this.lexer.state.inLink&&this.rules.other.endATag.test(r[0])&&(this.lexer.state.inLink=!1),!this.lexer.state.inRawBlock&&this.rules.other.startPreScriptTag.test(r[0])?this.lexer.state.inRawBlock=!0:this.lexer.state.inRawBlock&&this.rules.other.endPreScriptTag.test(r[0])&&(this.lexer.state.inRawBlock=!1),{type:"html",raw:r[0],inLink:this.lexer.state.inLink,inRawBlock:this.lexer.state.inRawBlock,block:!1,text:r[0]}}link(e){let r=this.rules.inline.link.exec(e);if(r){let n=r[2].trim();if(!this.options.pedantic&&this.rules.other.startAngleBracket.test(n)){if(!this.rules.other.endAngleBracket.test(n))return;let s=f2(n.slice(0,-1),"\\");if((n.length-s.length)%2===0)return}else{let s=$8e(r[2],"()");if(s>-1){let u=(r[0].indexOf("!")===0?5:4)+r[1].length+s;r[2]=r[2].substring(0,s),r[0]=r[0].substring(0,u).trim(),r[3]=""}}let i=r[2],a="";if(this.options.pedantic){let s=this.rules.other.pedanticHrefTitle.exec(i);s&&(i=s[1],a=s[3])}else a=r[3]?r[3].slice(1,-1):"";return i=i.trim(),this.rules.other.startAngleBracket.test(i)&&(this.options.pedantic&&!this.rules.other.endAngleBracket.test(n)?i=i.slice(1):i=i.slice(1,-1)),zj(r,{href:i&&i.replace(this.rules.inline.anyPunctuation,"$1"),title:a&&a.replace(this.rules.inline.anyPunctuation,"$1")},r[0],this.lexer,this.rules)}}reflink(e,r){let n;if((n=this.rules.inline.reflink.exec(e))||(n=this.rules.inline.nolink.exec(e))){let i=(n[2]||n[1]).replace(this.rules.other.multipleSpaceGlobal," "),a=r[i.toLowerCase()];if(!a){let s=n[0].charAt(0);return{type:"text",raw:s,text:s}}return zj(n,a,n[0],this.lexer,this.rules)}}emStrong(e,r,n=""){let i=this.rules.inline.emStrongLDelim.exec(e);if(!i||i[3]&&n.match(this.rules.other.unicodeAlphaNumeric))return;if(!(i[1]||i[2]||"")||!n||this.rules.inline.punctuation.exec(n)){let s=[...i[0]].length-1,l,u,h=s,f=0,d=i[0][0]==="*"?this.rules.inline.emStrongRDelimAst:this.rules.inline.emStrongRDelimUnd;for(d.lastIndex=0,r=r.slice(-1*e.length+s);(i=d.exec(r))!=null;){if(l=i[1]||i[2]||i[3]||i[4]||i[5]||i[6],!l)continue;if(u=[...l].length,i[3]||i[4]){h+=u;continue}else if((i[5]||i[6])&&s%3&&!((s+u)%3)){f+=u;continue}if(h-=u,h>0)continue;u=Math.min(u,u+h+f);let p=[...i[0]][0].length,m=e.slice(0,s+i.index+p+u);if(Math.min(s,u)%2){let y=m.slice(1,-1);return{type:"em",raw:m,text:y,tokens:this.lexer.inlineTokens(y)}}let g=m.slice(2,-2);return{type:"strong",raw:m,text:g,tokens:this.lexer.inlineTokens(g)}}}}codespan(e){let r=this.rules.inline.code.exec(e);if(r){let n=r[2].replace(this.rules.other.newLineCharGlobal," "),i=this.rules.other.nonSpaceChar.test(n),a=this.rules.other.startingSpaceChar.test(n)&&this.rules.other.endingSpaceChar.test(n);return i&&a&&(n=n.substring(1,n.length-1)),{type:"codespan",raw:r[0],text:n}}}br(e){let r=this.rules.inline.br.exec(e);if(r)return{type:"br",raw:r[0]}}del(e){let r=this.rules.inline.del.exec(e);if(r)return{type:"del",raw:r[0],text:r[2],tokens:this.lexer.inlineTokens(r[2])}}autolink(e){let r=this.rules.inline.autolink.exec(e);if(r){let n,i;return r[2]==="@"?(n=r[1],i="mailto:"+n):(n=r[1],i=n),{type:"link",raw:r[0],text:n,href:i,tokens:[{type:"text",raw:n,text:n}]}}}url(e){let r;if(r=this.rules.inline.url.exec(e)){let n,i;if(r[2]==="@")n=r[0],i="mailto:"+n;else{let a;do a=r[0],r[0]=this.rules.inline._backpedal.exec(r[0])?.[0]??"";while(a!==r[0]);n=r[0],r[1]==="www."?i="http://"+r[0]:i=r[0]}return{type:"link",raw:r[0],text:n,href:i,tokens:[{type:"text",raw:n,text:n}]}}}inlineText(e){let r=this.rules.inline.text.exec(e);if(r){let n=this.lexer.state.inRawBlock;return{type:"text",raw:r[0],text:r[0],escaped:n}}}},Al=class t{static{o(this,"_Lexer")}tokens;options;state;tokenizer;inlineQueue;constructor(e){this.tokens=[],this.tokens.links=Object.create(null),this.options=e||Id,this.options.tokenizer=this.options.tokenizer||new hm,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};let r={other:ts,block:Sw.normal,inline:h2.normal};this.options.pedantic?(r.block=Sw.pedantic,r.inline=h2.pedantic):this.options.gfm&&(r.block=Sw.gfm,this.options.breaks?r.inline=h2.breaks:r.inline=h2.gfm),this.tokenizer.rules=r}static get rules(){return{block:Sw,inline:h2}}static lex(e,r){return new t(r).lex(e)}static lexInline(e,r){return new t(r).inlineTokens(e)}lex(e){e=e.replace(ts.carriageReturn,`
-`),this.blockTokens(e,this.tokens);for(let r=0;r<this.inlineQueue.length;r++){let n=this.inlineQueue[r];this.inlineTokens(n.src,n.tokens)}return this.inlineQueue=[],this.tokens}blockTokens(e,r=[],n=!1){for(this.options.pedantic&&(e=e.replace(ts.tabCharGlobal,"    ").replace(ts.spaceLine,""));e;){let i;if(this.options.extensions?.block?.some(s=>(i=s.call({lexer:this},e,r))?(e=e.substring(i.raw.length),r.push(i),!0):!1))continue;if(i=this.tokenizer.space(e)){e=e.substring(i.raw.length);let s=r.at(-1);i.raw.length===1&&s!==void 0?s.raw+=`
-`:r.push(i);continue}if(i=this.tokenizer.code(e)){e=e.substring(i.raw.length);let s=r.at(-1);s?.type==="paragraph"||s?.type==="text"?(s.raw+=`
-`+i.raw,s.text+=`
-`+i.text,this.inlineQueue.at(-1).src=s.text):r.push(i);continue}if(i=this.tokenizer.fences(e)){e=e.substring(i.raw.length),r.push(i);continue}if(i=this.tokenizer.heading(e)){e=e.substring(i.raw.length),r.push(i);continue}if(i=this.tokenizer.hr(e)){e=e.substring(i.raw.length),r.push(i);continue}if(i=this.tokenizer.blockquote(e)){e=e.substring(i.raw.length),r.push(i);continue}if(i=this.tokenizer.list(e)){e=e.substring(i.raw.length),r.push(i);continue}if(i=this.tokenizer.html(e)){e=e.substring(i.raw.length),r.push(i);continue}if(i=this.tokenizer.def(e)){e=e.substring(i.raw.length);let s=r.at(-1);s?.type==="paragraph"||s?.type==="text"?(s.raw+=`
-`+i.raw,s.text+=`
-`+i.raw,this.inlineQueue.at(-1).src=s.text):this.tokens.links[i.tag]||(this.tokens.links[i.tag]={href:i.href,title:i.title});continue}if(i=this.tokenizer.table(e)){e=e.substring(i.raw.length),r.push(i);continue}if(i=this.tokenizer.lheading(e)){e=e.substring(i.raw.length),r.push(i);continue}let a=e;if(this.options.extensions?.startBlock){let s=1/0,l=e.slice(1),u;this.options.extensions.startBlock.forEach(h=>{u=h.call({lexer:this},l),typeof u=="number"&&u>=0&&(s=Math.min(s,u))}),s<1/0&&s>=0&&(a=e.substring(0,s+1))}if(this.state.top&&(i=this.tokenizer.paragraph(a))){let s=r.at(-1);n&&s?.type==="paragraph"?(s.raw+=`
-`+i.raw,s.text+=`
-`+i.text,this.inlineQueue.pop(),this.inlineQueue.at(-1).src=s.text):r.push(i),n=a.length!==e.length,e=e.substring(i.raw.length);continue}if(i=this.tokenizer.text(e)){e=e.substring(i.raw.length);let s=r.at(-1);s?.type==="text"?(s.raw+=`
-`+i.raw,s.text+=`
-`+i.text,this.inlineQueue.pop(),this.inlineQueue.at(-1).src=s.text):r.push(i);continue}if(e){let s="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(s);break}else throw new Error(s)}}return this.state.top=!0,r}inline(e,r=[]){return this.inlineQueue.push({src:e,tokens:r}),r}inlineTokens(e,r=[]){let n=e,i=null;if(this.tokens.links){let l=Object.keys(this.tokens.links);if(l.length>0)for(;(i=this.tokenizer.rules.inline.reflinkSearch.exec(n))!=null;)l.includes(i[0].slice(i[0].lastIndexOf("[")+1,-1))&&(n=n.slice(0,i.index)+"["+"a".repeat(i[0].length-2)+"]"+n.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(i=this.tokenizer.rules.inline.blockSkip.exec(n))!=null;)n=n.slice(0,i.index)+"["+"a".repeat(i[0].length-2)+"]"+n.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;(i=this.tokenizer.rules.inline.anyPunctuation.exec(n))!=null;)n=n.slice(0,i.index)+"++"+n.slice(this.tokenizer.rules.inline.anyPunctuation.lastIndex);let a=!1,s="";for(;e;){a||(s=""),a=!1;let l;if(this.options.extensions?.inline?.some(h=>(l=h.call({lexer:this},e,r))?(e=e.substring(l.raw.length),r.push(l),!0):!1))continue;if(l=this.tokenizer.escape(e)){e=e.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.tag(e)){e=e.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.link(e)){e=e.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.reflink(e,this.tokens.links)){e=e.substring(l.raw.length);let h=r.at(-1);l.type==="text"&&h?.type==="text"?(h.raw+=l.raw,h.text+=l.text):r.push(l);continue}if(l=this.tokenizer.emStrong(e,n,s)){e=e.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.codespan(e)){e=e.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.br(e)){e=e.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.del(e)){e=e.substring(l.raw.length),r.push(l);continue}if(l=this.tokenizer.autolink(e)){e=e.substring(l.raw.length),r.push(l);continue}if(!this.state.inLink&&(l=this.tokenizer.url(e))){e=e.substring(l.raw.length),r.push(l);continue}let u=e;if(this.options.extensions?.startInline){let h=1/0,f=e.slice(1),d;this.options.extensions.startInline.forEach(p=>{d=p.call({lexer:this},f),typeof d=="number"&&d>=0&&(h=Math.min(h,d))}),h<1/0&&h>=0&&(u=e.substring(0,h+1))}if(l=this.tokenizer.inlineText(u)){e=e.substring(l.raw.length),l.raw.slice(-1)!=="_"&&(s=l.raw.slice(-1)),a=!0;let h=r.at(-1);h?.type==="text"?(h.raw+=l.raw,h.text+=l.text):r.push(l);continue}if(e){let h="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(h);break}else throw new Error(h)}}return r}},fm=class{static{o(this,"_Renderer")}options;parser;constructor(e){this.options=e||Id}space(e){return""}code({text:e,lang:r,escaped:n}){let i=(r||"").match(ts.notSpaceStart)?.[0],a=e.replace(ts.endingNewline,"")+`
-`;return i?'<pre><code class="language-'+pc(i)+'">'+(n?a:pc(a,!0))+`</code></pre>
-`:"<pre><code>"+(n?a:pc(a,!0))+`</code></pre>
-`}blockquote({tokens:e}){return`<blockquote>
-${this.parser.parse(e)}</blockquote>
-`}html({text:e}){return e}heading({tokens:e,depth:r}){return`<h${r}>${this.parser.parseInline(e)}</h${r}>
-`}hr(e){return`<hr>
-`}list(e){let r=e.ordered,n=e.start,i="";for(let l=0;l<e.items.length;l++){let u=e.items[l];i+=this.listitem(u)}let a=r?"ol":"ul",s=r&&n!==1?' start="'+n+'"':"";return"<"+a+s+`>
-`+i+"</"+a+`>
-`}listitem(e){let r="";if(e.task){let n=this.checkbox({checked:!!e.checked});e.loose?e.tokens[0]?.type==="paragraph"?(e.tokens[0].text=n+" "+e.tokens[0].text,e.tokens[0].tokens&&e.tokens[0].tokens.length>0&&e.tokens[0].tokens[0].type==="text"&&(e.tokens[0].tokens[0].text=n+" "+pc(e.tokens[0].tokens[0].text),e.tokens[0].tokens[0].escaped=!0)):e.tokens.unshift({type:"text",raw:n+" ",text:n+" ",escaped:!0}):r+=n+" "}return r+=this.parser.parse(e.tokens,!!e.loose),`<li>${r}</li>
-`}checkbox({checked:e}){return"<input "+(e?'checked="" ':"")+'disabled="" type="checkbox">'}paragraph({tokens:e}){return`<p>${this.parser.parseInline(e)}</p>
-`}table(e){let r="",n="";for(let a=0;a<e.header.length;a++)n+=this.tablecell(e.header[a]);r+=this.tablerow({text:n});let i="";for(let a=0;a<e.rows.length;a++){let s=e.rows[a];n="";for(let l=0;l<s.length;l++)n+=this.tablecell(s[l]);i+=this.tablerow({text:n})}return i&&(i=`<tbody>${i}</tbody>`),`<table>
-<thead>
-`+r+`</thead>
-`+i+`</table>
-`}tablerow({text:e}){return`<tr>
-${e}</tr>
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Wa,wC,Hf,xo,Tnt,TC,Jp={},_4,Jc,SF,D4;;){if(Hf=Kt[Kt.length-1],this.defaultActions[Hf]?xo=this.defaultActions[Hf]:((Wa===null||typeof Wa>"u")&&(Wa=P2e()),xo=Vf[Hf]&&Vf[Hf][Wa]),typeof xo>"u"||!xo.length||!xo[0]){var kC="";D4=[];for(_4 in Vf[Hf])this.terminals_[_4]&&_4>M2e&&D4.push("'"+this.terminals_[_4]+"'");Xi.showPosition?kC="Parse error on line "+(wi+1)+`:
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-Expecting `+D4.join(", ")+", got '"+(this.terminals_[Wa]||Wa)+"'":kC="Parse error on line "+(wi+1)+": Unexpected "+(Wa==EF?"end of input":"'"+(this.terminals_[Wa]||Wa)+"'"),this.parseError(kC,{text:Xi.match,token:this.terminals_[Wa]||Wa,line:Xi.yylineno,loc:bC,expected:D4})}if(xo[0]instanceof Array&&xo.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Hf+", token: "+Wa);switch(xo[0]){case 1:Kt.push(Wa),Cn.push(Xi.yytext),ye.push(Xi.yylloc),Kt.push(xo[1]),Wa=null,wC?(Wa=wC,wC=null):(TF=Xi.yyleng,Te=Xi.yytext,wi=Xi.yylineno,bC=Xi.yylloc,kF>0&&kF--);break;case 2:if(Jc=this.productions_[xo[1]][1],Jp.$=Cn[Cn.length-Jc],Jp._$={first_line:ye[ye.length-(Jc||1)].first_line,last_line:ye[ye.length-1].last_line,first_column:ye[ye.length-(Jc||1)].first_column,last_column:ye[ye.length-1].last_column},O2e&&(Jp._$.range=[ye[ye.length-(Jc||1)].range[0],ye[ye.length-1].range[1]]),TC=this.performAction.apply(Jp,[Te,TF,wi,Uf.yy,xo[1],Cn,ye].concat(I2e)),typeof TC<"u")return TC;Jc&&(Kt=Kt.slice(0,-1*Jc*2),Cn=Cn.slice(0,-1*Jc),ye=ye.slice(0,-1*Jc)),Kt.push(this.productions_[xo[1]][0]),Cn.push(Jp.$),ye.push(Jp._$),SF=Vf[Kt[Kt.length-2]][Kt[Kt.length-1]],Kt.push(SF);break;case 3:return!0}}return!0},"parse")},Zn=function(){var Hr={EOF:1,parseError:o(function(mt,Kt){if(this.yy.parser)this.yy.parser.parseError(mt,Kt);else throw new Error(mt)},"parseError"),setInput:o(function(et,mt){return this.yy=mt||this.yy||{},this._input=et,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var et=this._input[0];this.yytext+=et,this.yyleng++,this.offset++,this.match+=et,this.matched+=et;var mt=et.match(/(?:\r\n?|\n).*/g);return mt?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),et},"input"),unput:o(function(et){var mt=et.length,Kt=et.split(/(?:\r\n?|\n)/g);this._input=et+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-mt),this.offset-=mt;var lt=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),Kt.length-1&&(this.yylineno-=Kt.length-1);var Cn=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:Kt?(Kt.length===lt.length?this.yylloc.first_column:0)+lt[lt.length-Kt.length].length-Kt[0].length:this.yylloc.first_column-mt},this.options.ranges&&(this.yylloc.range=[Cn[0],Cn[0]+this.yyleng-mt]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
-`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(et){this.unput(this.match.slice(et))},"less"),pastInput:o(function(){var et=this.matched.substr(0,this.matched.length-this.match.length);return(et.length>20?"...":"")+et.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var et=this.match;return et.length<20&&(et+=this._input.substr(0,20-et.length)),(et.substr(0,20)+(et.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var et=this.pastInput(),mt=new Array(et.length+1).join("-");return et+this.upcomingInput()+`
-`+mt+"^"},"showPosition"),test_match:o(function(et,mt){var Kt,lt,Cn;if(this.options.backtrack_lexer&&(Cn={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(Cn.yylloc.range=this.yylloc.range.slice(0))),lt=et[0].match(/(?:\r\n?|\n).*/g),lt&&(this.yylineno+=lt.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:lt?lt[lt.length-1].length-lt[lt.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+et[0].length},this.yytext+=et[0],this.match+=et[0],this.matches=et,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(et[0].length),this.matched+=et[0],Kt=this.performAction.call(this,this.yy,this,mt,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),Kt)return Kt;if(this._backtrack){for(var ye in Cn)this[ye]=Cn[ye];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var et,mt,Kt,lt;this._more||(this.yytext="",this.match="");for(var Cn=this._currentRules(),ye=0;ye<Cn.length;ye++)if(Kt=this._input.match(this.rules[Cn[ye]]),Kt&&(!mt||Kt[0].length>mt[0].length)){if(mt=Kt,lt=ye,this.options.backtrack_lexer){if(et=this.test_match(Kt,Cn[ye]),et!==!1)return et;if(this._backtrack){mt=!1;continue}else return!1}else if(!this.options.flex)break}return mt?(et=this.test_match(mt,Cn[lt]),et!==!1?et:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
-`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var mt=this.next();return mt||this.lex()},"lex"),begin:o(function(mt){this.conditionStack.push(mt)},"begin"),popState:o(function(){var mt=this.conditionStack.length-1;return mt>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(mt){return mt=this.conditionStack.length-1-Math.abs(mt||0),mt>=0?this.conditionStack[mt]:"INITIAL"},"topState"),pushState:o(function(mt){this.begin(mt)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(mt,Kt,lt,Cn){var ye=Cn;switch(lt){case 0:return this.begin("acc_title"),34;break;case 1:return this.popState(),"acc_title_value";break;case 2:return this.begin("acc_descr"),36;break;case 3:return this.popState(),"acc_descr_value";break;case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:return this.pushState("shapeData"),Kt.yytext="",40;break;case 8:return this.pushState("shapeDataStr"),40;break;case 9:return this.popState(),40;break;case 10:let Vf=/\n\s*/g;return Kt.yytext=Kt.yytext.replace(Vf,"<br/>"),40;break;case 11:return 40;case 12:this.popState();break;case 13:this.begin("callbackname");break;case 14:this.popState();break;case 15:this.popState(),this.begin("callbackargs");break;case 16:return 95;case 17:this.popState();break;case 18:return 96;case 19:return"MD_STR";case 20:this.popState();break;case 21:this.begin("md_string");break;case 22:return"STR";case 23:this.popState();break;case 24:this.pushState("string");break;case 25:return 84;case 26:return 102;case 27:return 85;case 28:return 104;case 29:return 86;case 30:return 87;case 31:return 97;case 32:this.begin("click");break;case 33:this.popState();break;case 34:return 88;case 35:return mt.lex.firstGraph()&&this.begin("dir"),12;break;case 36:return mt.lex.firstGraph()&&this.begin("dir"),12;break;case 37:return mt.lex.firstGraph()&&this.begin("dir"),12;break;case 38:return 27;case 39:return 32;case 40:return 98;case 41:return 98;case 42:return 98;case 43:return 98;case 44:return this.popState(),13;break;case 45:return this.popState(),14;break;case 46:return this.popState(),14;break;case 47:return this.popState(),14;break;case 48:return this.popState(),14;break;case 49:return this.popState(),14;break;case 50:return this.popState(),14;break;case 51:return this.popState(),14;break;case 52:return this.popState(),14;break;case 53:return this.popState(),14;break;case 54:return this.popState(),14;break;case 55:return 121;case 56:return 122;case 57:return 123;case 58:return 124;case 59:return 78;case 60:return 105;case 61:return 111;case 62:return 46;case 63:return 60;case 64:return 44;case 65:return 8;case 66:return 106;case 67:return 115;case 68:return this.popState(),77;break;case 69:return this.pushState("edgeText"),75;break;case 70:return 119;case 71:return this.popState(),77;break;case 72:return this.pushState("thickEdgeText"),75;break;case 73:return 119;case 74:return this.popState(),77;break;case 75:return this.pushState("dottedEdgeText"),75;break;case 76:return 119;case 77:return 77;case 78:return this.popState(),53;break;case 79:return"TEXT";case 80:return this.pushState("ellipseText"),52;break;case 81:return this.popState(),55;break;case 82:return this.pushState("text"),54;break;case 83:return this.popState(),57;break;case 84:return this.pushState("text"),56;break;case 85:return 58;case 86:return this.pushState("text"),67;break;case 87:return this.popState(),64;break;case 88:return this.pushState("text"),63;break;case 89:return this.popState(),49;break;case 90:return this.pushState("text"),48;break;case 91:return this.popState(),69;break;case 92:return this.popState(),71;break;case 93:return 117;case 94:return this.pushState("trapText"),68;break;case 95:return this.pushState("trapText"),70;break;case 96:return 118;case 97:return 67;case 98:return 90;case 99:return"SEP";case 100:return 89;case 101:return 115;case 102:return 111;case 103:return 44;case 104:return 109;case 105:return 114;case 106:return 116;case 107:return this.popState(),62;break;case 108:return this.pushState("text"),62;break;case 109:return this.popState(),51;break;case 110:return this.pushState("text"),50;break;case 111:return this.popState(),31;break;case 112:return this.pushState("text"),29;break;case 113:return this.popState(),66;break;case 114:return this.pushState("text"),65;break;case 115:return"TEXT";case 116:return"QUOTE";case 117:return 9;case 118:return 10;case 119:return 11}},"anonymous"),rules:[/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:@\{)/,/^(?:["])/,/^(?:["])/,/^(?:[^\"]+)/,/^(?:[^}^"]+)/,/^(?:\})/,/^(?:call[\s]+)/,/^(?:\([\s]*\))/,/^(?:\()/,/^(?:[^(]*)/,/^(?:\))/,/^(?:[^)]*)/,/^(?:[^`"]+)/,/^(?:[`]["])/,/^(?:["][`])/,/^(?:[^"]+)/,/^(?:["])/,/^(?:["])/,/^(?:style\b)/,/^(?:default\b)/,/^(?:linkStyle\b)/,/^(?:interpolate\b)/,/^(?:classDef\b)/,/^(?:class\b)/,/^(?:href[\s])/,/^(?:click[\s]+)/,/^(?:[\s\n])/,/^(?:[^\s\n]*)/,/^(?:flowchart-elk\b)/,/^(?:graph\b)/,/^(?:flowchart\b)/,/^(?:subgraph\b)/,/^(?:end\b\s*)/,/^(?:_self\b)/,/^(?:_blank\b)/,/^(?:_parent\b)/,/^(?:_top\b)/,/^(?:(\r?\n)*\s*\n)/,/^(?:\s*LR\b)/,/^(?:\s*RL\b)/,/^(?:\s*TB\b)/,/^(?:\s*BT\b)/,/^(?:\s*TD\b)/,/^(?:\s*BR\b)/,/^(?:\s*<)/,/^(?:\s*>)/,/^(?:\s*\^)/,/^(?:\s*v\b)/,/^(?:.*direction\s+TB[^\n]*)/,/^(?:.*direction\s+BT[^\n]*)/,/^(?:.*direction\s+RL[^\n]*)/,/^(?:.*direction\s+LR[^\n]*)/,/^(?:[^\s\"]+@(?=[^\{\"]))/,/^(?:[0-9]+)/,/^(?:#)/,/^(?::::)/,/^(?::)/,/^(?:&)/,/^(?:;)/,/^(?:,)/,/^(?:\*)/,/^(?:\s*[xo<]?--+[-xo>]\s*)/,/^(?:\s*[xo<]?--\s*)/,/^(?:[^-]|-(?!-)+)/,/^(?:\s*[xo<]?==+[=xo>]\s*)/,/^(?:\s*[xo<]?==\s*)/,/^(?:[^=]|=(?!))/,/^(?:\s*[xo<]?-?\.+-[xo>]?\s*)/,/^(?:\s*[xo<]?-\.\s*)/,/^(?:[^\.]|\.(?!))/,/^(?:\s*~~[\~]+\s*)/,/^(?:[-/\)][\)])/,/^(?:[^\(\)\[\]\{\}]|!\)+)/,/^(?:\(-)/,/^(?:\]\))/,/^(?:\(\[)/,/^(?:\]\])/,/^(?:\[\[)/,/^(?:\[\|)/,/^(?:>)/,/^(?:\)\])/,/^(?:\[\()/,/^(?:\)\)\))/,/^(?:\(\(\()/,/^(?:[\\(?=\])][\]])/,/^(?:\/(?=\])\])/,/^(?:\/(?!\])|\\(?!\])|[^\\\[\]\(\)\{\}\/]+)/,/^(?:\[\/)/,/^(?:\[\\)/,/^(?:<)/,/^(?:>)/,/^(?:\^)/,/^(?:\\\|)/,/^(?:v\b)/,/^(?:\*)/,/^(?:#)/,/^(?:&)/,/^(?:([A-Za-z0-9!"\#$%&'*+\.`?\\_\/]|-(?=[^\>\-\.])|(?!))+)/,/^(?:-)/,/^(?:[\u00AA\u00B5\u00BA\u00C0-\u00D6\u00D8-\u00F6]|[\u00F8-\u02C1\u02C6-\u02D1\u02E0-\u02E4\u02EC\u02EE\u0370-\u0374\u0376\u0377]|[\u037A-\u037D\u0386\u0388-\u038A\u038C\u038E-\u03A1\u03A3-\u03F5]|[\u03F7-\u0481\u048A-\u0527\u0531-\u0556\u0559\u0561-\u0587\u05D0-\u05EA]|[\u05F0-\u05F2\u0620-\u064A\u066E\u066F\u0671-\u06D3\u06D5\u06E5\u06E6\u06EE]|[\u06EF\u06FA-\u06FC\u06FF\u0710\u0712-\u072F\u074D-\u07A5\u07B1\u07CA-\u07EA]|[\u07F4\u07F5\u07FA\u0800-\u0815\u081A\u0824\u0828\u0840-\u0858\u08A0]|[\u08A2-\u08AC\u0904-\u0939\u093D\u0950\u0958-\u0961\u0971-\u0977]|[\u0979-\u097F\u0985-\u098C\u098F\u0990\u0993-\u09A8\u09AA-\u09B0\u09B2]|[\u09B6-\u09B9\u09BD\u09CE\u09DC\u09DD\u09DF-\u09E1\u09F0\u09F1\u0A05-\u0A0A]|[\u0A0F\u0A10\u0A13-\u0A28\u0A2A-\u0A30\u0A32\u0A33\u0A35\u0A36\u0A38\u0A39]|[\u0A59-\u0A5C\u0A5E\u0A72-\u0A74\u0A85-\u0A8D\u0A8F-\u0A91\u0A93-\u0AA8]|[\u0AAA-\u0AB0\u0AB2\u0AB3\u0AB5-\u0AB9\u0ABD\u0AD0\u0AE0\u0AE1\u0B05-\u0B0C]|[\u0B0F\u0B10\u0B13-\u0B28\u0B2A-\u0B30\u0B32\u0B33\u0B35-\u0B39\u0B3D\u0B5C]|[\u0B5D\u0B5F-\u0B61\u0B71\u0B83\u0B85-\u0B8A\u0B8E-\u0B90\u0B92-\u0B95\u0B99]|[\u0B9A\u0B9C\u0B9E\u0B9F\u0BA3\u0BA4\u0BA8-\u0BAA\u0BAE-\u0BB9\u0BD0]|[\u0C05-\u0C0C\u0C0E-\u0C10\u0C12-\u0C28\u0C2A-\u0C33\u0C35-\u0C39\u0C3D]|[\u0C58\u0C59\u0C60\u0C61\u0C85-\u0C8C\u0C8E-\u0C90\u0C92-\u0CA8\u0CAA-\u0CB3]|[\u0CB5-\u0CB9\u0CBD\u0CDE\u0CE0\u0CE1\u0CF1\u0CF2\u0D05-\u0D0C\u0D0E-\u0D10]|[\u0D12-\u0D3A\u0D3D\u0D4E\u0D60\u0D61\u0D7A-\u0D7F\u0D85-\u0D96\u0D9A-\u0DB1]|[\u0DB3-\u0DBB\u0DBD\u0DC0-\u0DC6\u0E01-\u0E30\u0E32\u0E33\u0E40-\u0E46\u0E81]|[\u0E82\u0E84\u0E87\u0E88\u0E8A\u0E8D\u0E94-\u0E97\u0E99-\u0E9F\u0EA1-\u0EA3]|[\u0EA5\u0EA7\u0EAA\u0EAB\u0EAD-\u0EB0\u0EB2\u0EB3\u0EBD\u0EC0-\u0EC4\u0EC6]|[\u0EDC-\u0EDF\u0F00\u0F40-\u0F47\u0F49-\u0F6C\u0F88-\u0F8C\u1000-\u102A]|[\u103F\u1050-\u1055\u105A-\u105D\u1061\u1065\u1066\u106E-\u1070\u1075-\u1081]|[\u108E\u10A0-\u10C5\u10C7\u10CD\u10D0-\u10FA\u10FC-\u1248\u124A-\u124D]|[\u1250-\u1256\u1258\u125A-\u125D\u1260-\u1288\u128A-\u128D\u1290-\u12B0]|[\u12B2-\u12B5\u12B8-\u12BE\u12C0\u12C2-\u12C5\u12C8-\u12D6\u12D8-\u1310]|[\u1312-\u1315\u1318-\u135A\u1380-\u138F\u13A0-\u13F4\u1401-\u166C]|[\u166F-\u167F\u1681-\u169A\u16A0-\u16EA\u1700-\u170C\u170E-\u1711]|[\u1720-\u1731\u1740-\u1751\u1760-\u176C\u176E-\u1770\u1780-\u17B3\u17D7]|[\u17DC\u1820-\u1877\u1880-\u18A8\u18AA\u18B0-\u18F5\u1900-\u191C]|[\u1950-\u196D\u1970-\u1974\u1980-\u19AB\u19C1-\u19C7\u1A00-\u1A16]|[\u1A20-\u1A54\u1AA7\u1B05-\u1B33\u1B45-\u1B4B\u1B83-\u1BA0\u1BAE\u1BAF]|[\u1BBA-\u1BE5\u1C00-\u1C23\u1C4D-\u1C4F\u1C5A-\u1C7D\u1CE9-\u1CEC]|[\u1CEE-\u1CF1\u1CF5\u1CF6\u1D00-\u1DBF\u1E00-\u1F15\u1F18-\u1F1D]|[\u1F20-\u1F45\u1F48-\u1F4D\u1F50-\u1F57\u1F59\u1F5B\u1F5D\u1F5F-\u1F7D]|[\u1F80-\u1FB4\u1FB6-\u1FBC\u1FBE\u1FC2-\u1FC4\u1FC6-\u1FCC\u1FD0-\u1FD3]|[\u1FD6-\u1FDB\u1FE0-\u1FEC\u1FF2-\u1FF4\u1FF6-\u1FFC\u2071\u207F]|[\u2090-\u209C\u2102\u2107\u210A-\u2113\u2115\u2119-\u211D\u2124\u2126\u2128]|[\u212A-\u212D\u212F-\u2139\u213C-\u213F\u2145-\u2149\u214E\u2183\u2184]|[\u2C00-\u2C2E\u2C30-\u2C5E\u2C60-\u2CE4\u2CEB-\u2CEE\u2CF2\u2CF3]|[\u2D00-\u2D25\u2D27\u2D2D\u2D30-\u2D67\u2D6F\u2D80-\u2D96\u2DA0-\u2DA6]|[\u2DA8-\u2DAE\u2DB0-\u2DB6\u2DB8-\u2DBE\u2DC0-\u2DC6\u2DC8-\u2DCE]|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- ${n}
- To fix this refactor your grammar to remove the left recursion.
-see: https://en.wikipedia.org/wiki/LL_parser#Left_factoring.`},buildInvalidRuleNameError(t){return"deprecated"},buildDuplicateRuleNameError(t){let e;return t.topLevelRule instanceof as?e=t.topLevelRule.name:e=t.topLevelRule,`Duplicate definition, rule: ->${e}<- is already defined in the grammar: ->${t.grammarName}<-`}}});function fse(t,e){let r=new RN(t,e);return r.resolveRefs(),r.errors}var RN,dse=N(()=>{"use strict";Fs();qt();os();o(fse,"resolveGrammar");RN=class extends ss{static{o(this,"GastRefResolverVisitor")}constructor(e,r){super(),this.nameToTopRule=e,this.errMsgProvider=r,this.errors=[]}resolveRefs(){Ae(br(this.nameToTopRule),e=>{this.currTopLevel=e,e.accept(this)})}visitNonTerminal(e){let r=this.nameToTopRule[e.nonTerminalName];if(r)e.referencedRule=r;else{let n=this.errMsgProvider.buildRuleNotFoundError(this.currTopLevel,e);this.errors.push({message:n,type:zi.UNRESOLVED_SUBRULE_REF,ruleName:this.currTopLevel.name,unresolvedRefName:e.nonTerminalName})}}}});function Fk(t,e,r=[]){r=an(r);let n=[],i=0;function a(l){return l.concat(gi(t,i+1))}o(a,"remainingPathWith");function s(l){let u=Fk(a(l),e,r);return n.concat(u)}for(o(s,"getAlternativesForProd");r.length<e&&i<t.length;){let l=t[i];if(l instanceof Dn)return s(l.definition);if(l instanceof on)return s(l.definition);if(l instanceof ln)n=s(l.definition);else if(l instanceof Ln){let u=l.definition.concat([new Or({definition:l.definition})]);return s(u)}else if(l instanceof Rn){let u=[new Dn({definition:l.definition}),new Or({definition:[new kr({terminalType:l.separator})].concat(l.definition)})];return s(u)}else if(l instanceof wn){let u=l.definition.concat([new Or({definition:[new kr({terminalType:l.separator})].concat(l.definition)})]);n=s(u)}else if(l instanceof Or){let u=l.definition.concat([new Or({definition:l.definition})]);n=s(u)}else{if(l instanceof Tn)return Ae(l.definition,u=>{ur(u.definition)===!1&&(n=s(u.definition))}),n;if(l instanceof kr)r.push(l.terminalType);else throw Error("non exhaustive match")}i++}return n.push({partialPath:r,suffixDef:gi(t,i)}),n}function $k(t,e,r,n){let i="EXIT_NONE_TERMINAL",a=[i],s="EXIT_ALTERNATIVE",l=!1,u=e.length,h=u-n-1,f=[],d=[];for(d.push({idx:-1,def:t,ruleStack:[],occurrenceStack:[]});!ur(d);){let p=d.pop();if(p===s){l&&ga(d).idx<=h&&d.pop();continue}let m=p.def,g=p.idx,y=p.ruleStack,v=p.occurrenceStack;if(ur(m))continue;let x=m[0];if(x===i){let b={idx:g,def:gi(m),ruleStack:Nu(y),occurrenceStack:Nu(v)};d.push(b)}else if(x instanceof kr)if(g<u-1){let b=g+1,w=e[b];if(r(w,x.terminalType)){let C={idx:b,def:gi(m),ruleStack:y,occurrenceStack:v};d.push(C)}}else if(g===u-1)f.push({nextTokenType:x.terminalType,nextTokenOccurrence:x.idx,ruleStack:y,occurrenceStack:v}),l=!0;else throw Error("non exhaustive match");else if(x instanceof on){let b=an(y);b.push(x.nonTerminalName);let w=an(v);w.push(x.idx);let C={idx:g,def:x.definition.concat(a,gi(m)),ruleStack:b,occurrenceStack:w};d.push(C)}else if(x instanceof ln){let b={idx:g,def:gi(m),ruleStack:y,occurrenceStack:v};d.push(b),d.push(s);let w={idx:g,def:x.definition.concat(gi(m)),ruleStack:y,occurrenceStack:v};d.push(w)}else if(x instanceof Ln){let b=new Or({definition:x.definition,idx:x.idx}),w=x.definition.concat([b],gi(m)),C={idx:g,def:w,ruleStack:y,occurrenceStack:v};d.push(C)}else if(x instanceof Rn){let b=new kr({terminalType:x.separator}),w=new Or({definition:[b].concat(x.definition),idx:x.idx}),C=x.definition.concat([w],gi(m)),T={idx:g,def:C,ruleStack:y,occurrenceStack:v};d.push(T)}else if(x instanceof wn){let b={idx:g,def:gi(m),ruleStack:y,occurrenceStack:v};d.push(b),d.push(s);let w=new kr({terminalType:x.separator}),C=new Or({definition:[w].concat(x.definition),idx:x.idx}),T=x.definition.concat([C],gi(m)),E={idx:g,def:T,ruleStack:y,occurrenceStack:v};d.push(E)}else if(x instanceof Or){let b={idx:g,def:gi(m),ruleStack:y,occurrenceStack:v};d.push(b),d.push(s);let w=new Or({definition:x.definition,idx:x.idx}),C=x.definition.concat([w],gi(m)),T={idx:g,def:C,ruleStack:y,occurrenceStack:v};d.push(T)}else if(x instanceof Tn)for(let b=x.definition.length-1;b>=0;b--){let w=x.definition[b],C={idx:g,def:w.definition.concat(gi(m)),ruleStack:y,occurrenceStack:v};d.push(C),d.push(s)}else if(x instanceof Dn)d.push({idx:g,def:x.definition.concat(gi(m)),ruleStack:y,occurrenceStack:v});else if(x instanceof as)d.push(KPe(x,g,y,v));else throw Error("non exhaustive match")}return f}function KPe(t,e,r,n){let i=an(r);i.push(t.name);let a=an(n);return a.push(1),{idx:e,def:t.definition,ruleStack:i,occurrenceStack:a}}var NN,Ok,Ug,Pk,ox,Bk,lx,cx=N(()=>{"use strict";qt();xN();Ak();os();NN=class extends Ou{static{o(this,"AbstractNextPossibleTokensWalker")}constructor(e,r){super(),this.topProd=e,this.path=r,this.possibleTokTypes=[],this.nextProductionName="",this.nextProductionOccurrence=0,this.found=!1,this.isAtEndOfPath=!1}startWalking(){if(this.found=!1,this.path.ruleStack[0]!==this.topProd.name)throw Error("The path does not start with the walker's top Rule!");return this.ruleStack=an(this.path.ruleStack).reverse(),this.occurrenceStack=an(this.path.occurrenceStack).reverse(),this.ruleStack.pop(),this.occurrenceStack.pop(),this.updateExpectedNext(),this.walk(this.topProd),this.possibleTokTypes}walk(e,r=[]){this.found||super.walk(e,r)}walkProdRef(e,r,n){if(e.referencedRule.name===this.nextProductionName&&e.idx===this.nextProductionOccurrence){let i=r.concat(n);this.updateExpectedNext(),this.walk(e.referencedRule,i)}}updateExpectedNext(){ur(this.ruleStack)?(this.nextProductionName="",this.nextProductionOccurrence=0,this.isAtEndOfPath=!0):(this.nextProductionName=this.ruleStack.pop(),this.nextProductionOccurrence=this.occurrenceStack.pop())}},Ok=class extends NN{static{o(this,"NextAfterTokenWalker")}constructor(e,r){super(e,r),this.path=r,this.nextTerminalName="",this.nextTerminalOccurrence=0,this.nextTerminalName=this.path.lastTok.name,this.nextTerminalOccurrence=this.path.lastTokOccurrence}walkTerminal(e,r,n){if(this.isAtEndOfPath&&e.terminalType.name===this.nextTerminalName&&e.idx===this.nextTerminalOccurrence&&!this.found){let i=r.concat(n),a=new Dn({definition:i});this.possibleTokTypes=op(a),this.found=!0}}},Ug=class extends Ou{static{o(this,"AbstractNextTerminalAfterProductionWalker")}constructor(e,r){super(),this.topRule=e,this.occurrence=r,this.result={token:void 0,occurrence:void 0,isEndOfRule:void 0}}startWalking(){return this.walk(this.topRule),this.result}},Pk=class extends Ug{static{o(this,"NextTerminalAfterManyWalker")}walkMany(e,r,n){if(e.idx===this.occurrence){let i=ia(r.concat(n));this.result.isEndOfRule=i===void 0,i instanceof kr&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else super.walkMany(e,r,n)}},ox=class extends Ug{static{o(this,"NextTerminalAfterManySepWalker")}walkManySep(e,r,n){if(e.idx===this.occurrence){let i=ia(r.concat(n));this.result.isEndOfRule=i===void 0,i instanceof kr&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else super.walkManySep(e,r,n)}},Bk=class extends Ug{static{o(this,"NextTerminalAfterAtLeastOneWalker")}walkAtLeastOne(e,r,n){if(e.idx===this.occurrence){let i=ia(r.concat(n));this.result.isEndOfRule=i===void 0,i instanceof kr&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else super.walkAtLeastOne(e,r,n)}},lx=class extends Ug{static{o(this,"NextTerminalAfterAtLeastOneSepWalker")}walkAtLeastOneSep(e,r,n){if(e.idx===this.occurrence){let i=ia(r.concat(n));this.result.isEndOfRule=i===void 0,i instanceof kr&&(this.result.token=i.terminalType,this.result.occurrence=i.idx)}else super.walkAtLeastOneSep(e,r,n)}};o(Fk,"possiblePathsFrom");o($k,"nextPossibleTokensAfter");o(KPe,"expandTopLevelRule")});function ux(t){if(t instanceof ln||t==="Option")return jn.OPTION;if(t instanceof Or||t==="Repetition")return jn.REPETITION;if(t instanceof Ln||t==="RepetitionMandatory")return jn.REPETITION_MANDATORY;if(t instanceof Rn||t==="RepetitionMandatoryWithSeparator")return jn.REPETITION_MANDATORY_WITH_SEPARATOR;if(t instanceof wn||t==="RepetitionWithSeparator")return jn.REPETITION_WITH_SEPARATOR;if(t instanceof Tn||t==="Alternation")return jn.ALTERNATION;throw Error("non exhaustive match")}function Gk(t){let{occurrence:e,rule:r,prodType:n,maxLookahead:i}=t,a=ux(n);return a===jn.ALTERNATION?Hg(e,r,i):Wg(e,r,a,i)}function mse(t,e,r,n,i,a){let s=Hg(t,e,r),l=wse(s)?zg:Pu;return a(s,n,l,i)}function gse(t,e,r,n,i,a){let s=Wg(t,e,i,r),l=wse(s)?zg:Pu;return a(s[0],l,n)}function yse(t,e,r,n){let i=t.length,a=Ma(t,s=>Ma(s,l=>l.length===1));if(e)return function(s){let l=Je(s,u=>u.GATE);for(let u=0;u<i;u++){let h=t[u],f=h.length,d=l[u];if(!(d!==void 0&&d.call(this)===!1))e:for(let p=0;p<f;p++){let m=h[p],g=m.length;for(let y=0;y<g;y++){let v=this.LA(y+1);if(r(v,m[y])===!1)continue e}return u}}};if(a&&!n){let s=Je(t,u=>qr(u)),l=Xr(s,(u,h,f)=>(Ae(h,d=>{Bt(u,d.tokenTypeIdx)||(u[d.tokenTypeIdx]=f),Ae(d.categoryMatches,p=>{Bt(u,p)||(u[p]=f)})}),u),{});return function(){let u=this.LA(1);return l[u.tokenTypeIdx]}}else return function(){for(let s=0;s<i;s++){let l=t[s],u=l.length;e:for(let h=0;h<u;h++){let f=l[h],d=f.length;for(let p=0;p<d;p++){let m=this.LA(p+1);if(r(m,f[p])===!1)continue e}return s}}}}function vse(t,e,r){let n=Ma(t,a=>a.length===1),i=t.length;if(n&&!r){let a=qr(t);if(a.length===1&&ur(a[0].categoryMatches)){let l=a[0].tokenTypeIdx;return function(){return this.LA(1).tokenTypeIdx===l}}else{let s=Xr(a,(l,u,h)=>(l[u.tokenTypeIdx]=!0,Ae(u.categoryMatches,f=>{l[f]=!0}),l),[]);return function(){let l=this.LA(1);return s[l.tokenTypeIdx]===!0}}}else return function(){e:for(let a=0;a<i;a++){let s=t[a],l=s.length;for(let u=0;u<l;u++){let h=this.LA(u+1);if(e(h,s[u])===!1)continue e}return!0}return!1}}function pse(t){let e=new Array(t);for(let r=0;r<t;r++)e[r]=[];return e}function MN(t){let e=[""];for(let r=0;r<t.length;r++){let n=t[r],i=[];for(let a=0;a<e.length;a++){let s=e[a];i.push(s+"_"+n.tokenTypeIdx);for(let l=0;l<n.categoryMatches.length;l++){let u="_"+n.categoryMatches[l];i.push(s+u)}}e=i}return e}function QPe(t,e,r){for(let n=0;n<t.length;n++){if(n===r)continue;let i=t[n];for(let a=0;a<e.length;a++){let s=e[a];if(i[s]===!0)return!1}}return!0}function xse(t,e){let r=Je(t,s=>Fk([s],1)),n=pse(r.length),i=Je(r,s=>{let l={};return Ae(s,u=>{let h=MN(u.partialPath);Ae(h,f=>{l[f]=!0})}),l}),a=r;for(let s=1;s<=e;s++){let l=a;a=pse(l.length);for(let u=0;u<l.length;u++){let h=l[u];for(let f=0;f<h.length;f++){let d=h[f].partialPath,p=h[f].suffixDef,m=MN(d);if(QPe(i,m,u)||ur(p)||d.length===e){let y=n[u];if(Vk(y,d)===!1){y.push(d);for(let v=0;v<m.length;v++){let x=m[v];i[u][x]=!0}}}else{let y=Fk(p,s+1,d);a[u]=a[u].concat(y),Ae(y,v=>{let x=MN(v.partialPath);Ae(x,b=>{i[u][b]=!0})})}}}}return n}function Hg(t,e,r,n){let i=new zk(t,jn.ALTERNATION,n);return e.accept(i),xse(i.result,r)}function Wg(t,e,r,n){let i=new zk(t,r);e.accept(i);let a=i.result,l=new IN(e,t,r).startWalking(),u=new Dn({definition:a}),h=new Dn({definition:l});return xse([u,h],n)}function Vk(t,e){e:for(let r=0;r<t.length;r++){let n=t[r];if(n.length===e.length){for(let i=0;i<n.length;i++){let a=e[i],s=n[i];if((a===s||s.categoryMatchesMap[a.tokenTypeIdx]!==void 0)===!1)continue e}return!0}}return!1}function bse(t,e){return t.length<e.length&&Ma(t,(r,n)=>{let i=e[n];return r===i||i.categoryMatchesMap[r.tokenTypeIdx]})}function wse(t){return Ma(t,e=>Ma(e,r=>Ma(r,n=>ur(n.categoryMatches))))}var jn,IN,zk,qg=N(()=>{"use strict";qt();cx();Ak();cp();os();(function(t){t[t.OPTION=0]="OPTION",t[t.REPETITION=1]="REPETITION",t[t.REPETITION_MANDATORY=2]="REPETITION_MANDATORY",t[t.REPETITION_MANDATORY_WITH_SEPARATOR=3]="REPETITION_MANDATORY_WITH_SEPARATOR",t[t.REPETITION_WITH_SEPARATOR=4]="REPETITION_WITH_SEPARATOR",t[t.ALTERNATION=5]="ALTERNATION"})(jn||(jn={}));o(ux,"getProdType");o(Gk,"getLookaheadPaths");o(mse,"buildLookaheadFuncForOr");o(gse,"buildLookaheadFuncForOptionalProd");o(yse,"buildAlternativesLookAheadFunc");o(vse,"buildSingleAlternativeLookaheadFunction");IN=class extends Ou{static{o(this,"RestDefinitionFinderWalker")}constructor(e,r,n){super(),this.topProd=e,this.targetOccurrence=r,this.targetProdType=n}startWalking(){return this.walk(this.topProd),this.restDef}checkIsTarget(e,r,n,i){return 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i=ya(t,u=>ZPe(u,r)),a=iBe(t,e,r),s=ya(t,u=>tBe(u,r)),l=ya(t,u=>eBe(u,t,n,r));return i.concat(a,s,l)}function ZPe(t,e){let r=new ON;t.accept(r);let n=r.allProductions,i=IL(n,JPe),a=Os(i,l=>l.length>1);return Je(br(a),l=>{let u=ia(l),h=e.buildDuplicateFoundError(t,l),f=Bs(u),d={message:h,type:zi.DUPLICATE_PRODUCTIONS,ruleName:t.name,dslName:f,occurrence:u.idx},p=Ese(u);return p&&(d.parameter=p),d})}function JPe(t){return`${Bs(t)}_#_${t.idx}_#_${Ese(t)}`}function Ese(t){return t instanceof kr?t.terminalType.name:t instanceof on?t.nonTerminalName:""}function eBe(t,e,r,n){let i=[];if(Xr(e,(s,l)=>l.name===t.name?s+1:s,0)>1){let s=n.buildDuplicateRuleNameError({topLevelRule:t,grammarName:r});i.push({message:s,type:zi.DUPLICATE_RULE_NAME,ruleName:t.name})}return i}function Sse(t,e,r){let n=[],i;return qn(e,t)||(i=`Invalid rule override, rule: ->${t}<- cannot be overridden in the grammar: ->${r}<-as it is not defined in any of the super grammars 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ur(h)?[{message:e.buildEmptyAlternationError({topLevelRule:t,alternation:a,emptyChoiceIdx:u}),type:zi.NONE_LAST_EMPTY_ALT,ruleName:t.name,occurrence:a.idx,alternative:u+1}]:[]})})}function Ase(t,e,r){let n=new hx;t.accept(n);let i=n.alternations;return i=Jh(i,s=>s.ignoreAmbiguities===!0),ya(i,s=>{let l=s.idx,u=s.maxLookahead||e,h=Hg(l,t,u,s),f=rBe(h,s,t,r),d=nBe(h,s,t,r);return f.concat(d)})}function tBe(t,e){let r=new hx;t.accept(r);let n=r.alternations;return ya(n,a=>a.definition.length>255?[{message:e.buildTooManyAlternativesError({topLevelRule:t,alternation:a}),type:zi.TOO_MANY_ALTS,ruleName:t.name,occurrence:a.idx}]:[])}function _se(t,e,r){let n=[];return Ae(t,i=>{let a=new PN;i.accept(a);let s=a.allProductions;Ae(s,l=>{let u=ux(l),h=l.maxLookahead||e,f=l.idx,p=Wg(f,i,u,h)[0];if(ur(qr(p))){let m=r.buildEmptyRepetitionError({topLevelRule:i,repetition:l});n.push({message:m,type:zi.NO_NON_EMPTY_LOOKAHEAD,ruleName:i.name})}})}),n}function rBe(t,e,r,n){let 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this.subruleInternal(e,2,r)}SUBRULE3(e,r){return this.subruleInternal(e,3,r)}SUBRULE4(e,r){return this.subruleInternal(e,4,r)}SUBRULE5(e,r){return this.subruleInternal(e,5,r)}SUBRULE6(e,r){return this.subruleInternal(e,6,r)}SUBRULE7(e,r){return this.subruleInternal(e,7,r)}SUBRULE8(e,r){return this.subruleInternal(e,8,r)}SUBRULE9(e,r){return this.subruleInternal(e,9,r)}OPTION(e){return this.optionInternal(e,0)}OPTION1(e){return this.optionInternal(e,1)}OPTION2(e){return this.optionInternal(e,2)}OPTION3(e){return this.optionInternal(e,3)}OPTION4(e){return this.optionInternal(e,4)}OPTION5(e){return this.optionInternal(e,5)}OPTION6(e){return this.optionInternal(e,6)}OPTION7(e){return this.optionInternal(e,7)}OPTION8(e){return this.optionInternal(e,8)}OPTION9(e){return this.optionInternal(e,9)}OR(e){return this.orInternal(e,0)}OR1(e){return this.orInternal(e,1)}OR2(e){return this.orInternal(e,2)}OR3(e){return this.orInternal(e,3)}OR4(e){return this.orInternal(e,4)}OR5(e){return this.orInternal(e,5)}OR6(e){return this.orInternal(e,6)}OR7(e){return this.orInternal(e,7)}OR8(e){return this.orInternal(e,8)}OR9(e){return this.orInternal(e,9)}MANY(e){this.manyInternal(0,e)}MANY1(e){this.manyInternal(1,e)}MANY2(e){this.manyInternal(2,e)}MANY3(e){this.manyInternal(3,e)}MANY4(e){this.manyInternal(4,e)}MANY5(e){this.manyInternal(5,e)}MANY6(e){this.manyInternal(6,e)}MANY7(e){this.manyInternal(7,e)}MANY8(e){this.manyInternal(8,e)}MANY9(e){this.manyInternal(9,e)}MANY_SEP(e){this.manySepFirstInternal(0,e)}MANY_SEP1(e){this.manySepFirstInternal(1,e)}MANY_SEP2(e){this.manySepFirstInternal(2,e)}MANY_SEP3(e){this.manySepFirstInternal(3,e)}MANY_SEP4(e){this.manySepFirstInternal(4,e)}MANY_SEP5(e){this.manySepFirstInternal(5,e)}MANY_SEP6(e){this.manySepFirstInternal(6,e)}MANY_SEP7(e){this.manySepFirstInternal(7,e)}MANY_SEP8(e){this.manySepFirstInternal(8,e)}MANY_SEP9(e){this.manySepFirstInternal(9,e)}AT_LEAST_ONE(e){this.atLeastOneInternal(0,e)}AT_LEAST_ONE1(e){return this.atLeastOneInternal(1,e)}AT_LEAST_ONE2(e){this.atLeastOneInternal(2,e)}AT_LEAST_ONE3(e){this.atLeastOneInternal(3,e)}AT_LEAST_ONE4(e){this.atLeastOneInternal(4,e)}AT_LEAST_ONE5(e){this.atLeastOneInternal(5,e)}AT_LEAST_ONE6(e){this.atLeastOneInternal(6,e)}AT_LEAST_ONE7(e){this.atLeastOneInternal(7,e)}AT_LEAST_ONE8(e){this.atLeastOneInternal(8,e)}AT_LEAST_ONE9(e){this.atLeastOneInternal(9,e)}AT_LEAST_ONE_SEP(e){this.atLeastOneSepFirstInternal(0,e)}AT_LEAST_ONE_SEP1(e){this.atLeastOneSepFirstInternal(1,e)}AT_LEAST_ONE_SEP2(e){this.atLeastOneSepFirstInternal(2,e)}AT_LEAST_ONE_SEP3(e){this.atLeastOneSepFirstInternal(3,e)}AT_LEAST_ONE_SEP4(e){this.atLeastOneSepFirstInternal(4,e)}AT_LEAST_ONE_SEP5(e){this.atLeastOneSepFirstInternal(5,e)}AT_LEAST_ONE_SEP6(e){this.atLeastOneSepFirstInternal(6,e)}AT_LEAST_ONE_SEP7(e){this.atLeastOneSepFirstInternal(7,e)}AT_LEAST_ONE_SEP8(e){this.atLeastOneSepFirstInternal(8,e)}AT_LEAST_ONE_SEP9(e){this.atLeastOneSepFirstInternal(9,e)}RULE(e,r,n=Kg){if(qn(this.definedRulesNames,e)){let s={message:Pl.buildDuplicateRuleNameError({topLevelRule:e,grammarName:this.className}),type:zi.DUPLICATE_RULE_NAME,ruleName:e};this.definitionErrors.push(s)}this.definedRulesNames.push(e);let i=this.defineRule(e,r,n);return this[e]=i,i}OVERRIDE_RULE(e,r,n=Kg){let i=Sse(e,this.definedRulesNames,this.className);this.definitionErrors=this.definitionErrors.concat(i);let a=this.defineRule(e,r,n);return this[e]=a,a}BACKTRACK(e,r){return function(){this.isBackTrackingStack.push(1);let n=this.saveRecogState();try{return e.apply(this,r),!0}catch(i){if(lf(i))return!1;throw i}finally{this.reloadRecogState(n),this.isBackTrackingStack.pop()}}}getGAstProductions(){return this.gastProductionsCache}getSerializedGastProductions(){return Sk(br(this.gastProductionsCache))}}});var tE,Xse=N(()=>{"use strict";qt();qk();Xg();qg();cx();Fs();GN();up();cp();tE=class{static{o(this,"RecognizerEngine")}initRecognizerEngine(e,r){if(this.className=this.constructor.name,this.shortRuleNameToFull={},this.fullRuleNameToShort={},this.ruleShortNameIdx=256,this.tokenMatcher=zg,this.subruleIdx=0,this.definedRulesNames=[],this.tokensMap={},this.isBackTrackingStack=[],this.RULE_STACK=[],this.RULE_OCCURRENCE_STACK=[],this.gastProductionsCache={},Bt(r,"serializedGrammar"))throw Error(`The Parser's configuration can no longer contain a <serializedGrammar> property.
-	See: https://chevrotain.io/docs/changes/BREAKING_CHANGES.html#_6-0-0
-	For Further details.`);if(Pt(e)){if(ur(e))throw Error(`A Token Vocabulary cannot be empty.
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-	For Further details.`)}if(Pt(e))this.tokensMap=Xr(e,(a,s)=>(a[s.name]=s,a),{});else if(Bt(e,"modes")&&Ma(qr(br(e.modes)),rse)){let a=qr(br(e.modes)),s=Bm(a);this.tokensMap=Xr(s,(l,u)=>(l[u.name]=u,l),{})}else if(bn(e))this.tokensMap=an(e);else throw new Error("<tokensDictionary> argument must be An Array of Token constructors, A dictionary of Token constructors or an IMultiModeLexerDefinition");this.tokensMap.EOF=lo;let n=Bt(e,"modes")?qr(br(e.modes)):br(e),i=Ma(n,a=>ur(a.categoryMatches));this.tokenMatcher=i?zg:Pu,Bu(br(this.tokensMap))}defineRule(e,r,n){if(this.selfAnalysisDone)throw Error(`Grammar rule <${e}> may not be defined after the 'performSelfAnalysis' method has been called'
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i=this.RULE_STACK.length===1,a=r&&!this.isBackTracking()&&this.recoveryEnabled;if(lf(e)){let s=e;if(a){let l=this.findReSyncTokenType();if(this.isInCurrentRuleReSyncSet(l))if(s.resyncedTokens=this.reSyncTo(l),this.outputCst){let u=this.CST_STACK[this.CST_STACK.length-1];return u.recoveredNode=!0,u}else return n(e);else{if(this.outputCst){let u=this.CST_STACK[this.CST_STACK.length-1];u.recoveredNode=!0,s.partialCstResult=u}throw s}}else{if(i)return this.moveToTerminatedState(),n(e);throw s}}else throw e}optionInternal(e,r){let n=this.getKeyForAutomaticLookahead(512,r);return this.optionInternalLogic(e,r,n)}optionInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof e!="function"){a=e.DEF;let s=e.GATE;if(s!==void 0){let l=i;i=o(()=>s.call(this)&&l.call(this),"lookAheadFunc")}}else a=e;if(i.call(this)===!0)return a.call(this)}atLeastOneInternal(e,r){let n=this.getKeyForAutomaticLookahead(1024,e);return this.atLeastOneInternalLogic(e,r,n)}atLeastOneInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof r!="function"){a=r.DEF;let s=r.GATE;if(s!==void 0){let l=i;i=o(()=>s.call(this)&&l.call(this),"lookAheadFunc")}}else a=r;if(i.call(this)===!0){let s=this.doSingleRepetition(a);for(;i.call(this)===!0&&s===!0;)s=this.doSingleRepetition(a)}else throw this.raiseEarlyExitException(e,jn.REPETITION_MANDATORY,r.ERR_MSG);this.attemptInRepetitionRecovery(this.atLeastOneInternal,[e,r],i,1024,e,Bk)}atLeastOneSepFirstInternal(e,r){let n=this.getKeyForAutomaticLookahead(1536,e);this.atLeastOneSepFirstInternalLogic(e,r,n)}atLeastOneSepFirstInternalLogic(e,r,n){let i=r.DEF,a=r.SEP;if(this.getLaFuncFromCache(n).call(this)===!0){i.call(this);let l=o(()=>this.tokenMatcher(this.LA(1),a),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),a)===!0;)this.CONSUME(a),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,a,l,i,lx],l,1536,e,lx)}else throw this.raiseEarlyExitException(e,jn.REPETITION_MANDATORY_WITH_SEPARATOR,r.ERR_MSG)}manyInternal(e,r){let n=this.getKeyForAutomaticLookahead(768,e);return this.manyInternalLogic(e,r,n)}manyInternalLogic(e,r,n){let i=this.getLaFuncFromCache(n),a;if(typeof r!="function"){a=r.DEF;let l=r.GATE;if(l!==void 0){let u=i;i=o(()=>l.call(this)&&u.call(this),"lookaheadFunction")}}else a=r;let s=!0;for(;i.call(this)===!0&&s===!0;)s=this.doSingleRepetition(a);this.attemptInRepetitionRecovery(this.manyInternal,[e,r],i,768,e,Pk,s)}manySepFirstInternal(e,r){let n=this.getKeyForAutomaticLookahead(1280,e);this.manySepFirstInternalLogic(e,r,n)}manySepFirstInternalLogic(e,r,n){let i=r.DEF,a=r.SEP;if(this.getLaFuncFromCache(n).call(this)===!0){i.call(this);let l=o(()=>this.tokenMatcher(this.LA(1),a),"separatorLookAheadFunc");for(;this.tokenMatcher(this.LA(1),a)===!0;)this.CONSUME(a),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,a,l,i,ox],l,1280,e,ox)}}repetitionSepSecondInternal(e,r,n,i,a){for(;n();)this.CONSUME(r),i.call(this);this.attemptInRepetitionRecovery(this.repetitionSepSecondInternal,[e,r,n,i,a],n,1536,e,a)}doSingleRepetition(e){let r=this.getLexerPosition();return e.call(this),this.getLexerPosition()>r}orInternal(e,r){let n=this.getKeyForAutomaticLookahead(256,r),i=Pt(e)?e:e.DEF,s=this.getLaFuncFromCache(n).call(this,i);if(s!==void 0)return i[s].ALT.call(this);this.raiseNoAltException(r,e.ERR_MSG)}ruleFinallyStateUpdate(){if(this.RULE_STACK.pop(),this.RULE_OCCURRENCE_STACK.pop(),this.cstFinallyStateUpdate(),this.RULE_STACK.length===0&&this.isAtEndOfInput()===!1){let e=this.LA(1),r=this.errorMessageProvider.buildNotAllInputParsedMessage({firstRedundant:e,ruleName:this.getCurrRuleFullName()});this.SAVE_ERROR(new px(r,e))}}subruleInternal(e,r,n){let i;try{let a=n!==void 0?n.ARGS:void 0;return this.subruleIdx=r,i=e.apply(this,a),this.cstPostNonTerminal(i,n!==void 0&&n.LABEL!==void 0?n.LABEL:e.ruleName),i}catch(a){throw this.subruleInternalError(a,n,e.ruleName)}}subruleInternalError(e,r,n){throw lf(e)&&e.partialCstResult!==void 0&&(this.cstPostNonTerminal(e.partialCstResult,r!==void 0&&r.LABEL!==void 0?r.LABEL:n),delete e.partialCstResult),e}consumeInternal(e,r,n){let i;try{let a=this.LA(1);this.tokenMatcher(a,e)===!0?(this.consumeToken(),i=a):this.consumeInternalError(e,a,n)}catch(a){i=this.consumeInternalRecovery(e,r,a)}return this.cstPostTerminal(n!==void 0&&n.LABEL!==void 0?n.LABEL:e.name,i),i}consumeInternalError(e,r,n){let i,a=this.LA(0);throw n!==void 0&&n.ERR_MSG?i=n.ERR_MSG:i=this.errorMessageProvider.buildMismatchTokenMessage({expected:e,actual:r,previous:a,ruleName:this.getCurrRuleFullName()}),this.SAVE_ERROR(new hp(i,r,a))}consumeInternalRecovery(e,r,n){if(this.recoveryEnabled&&n.name==="MismatchedTokenException"&&!this.isBackTracking()){let i=this.getFollowsForInRuleRecovery(e,r);try{return this.tryInRuleRecovery(e,i)}catch(a){throw a.name===zN?n:a}}else throw n}saveRecogState(){let e=this.errors,r=an(this.RULE_STACK);return{errors:e,lexerState:this.exportLexerState(),RULE_STACK:r,CST_STACK:this.CST_STACK}}reloadRecogState(e){this.errors=e.errors,this.importLexerState(e.lexerState),this.RULE_STACK=e.RULE_STACK}ruleInvocationStateUpdate(e,r,n){this.RULE_OCCURRENCE_STACK.push(n),this.RULE_STACK.push(e),this.cstInvocationStateUpdate(r)}isBackTracking(){return this.isBackTrackingStack.length!==0}getCurrRuleFullName(){let e=this.getLastExplicitRuleShortName();return this.shortRuleNameToFull[e]}shortRuleNameToFullName(e){return this.shortRuleNameToFull[e]}isAtEndOfInput(){return this.tokenMatcher(this.LA(1),lo)}reset(){this.resetLexerState(),this.subruleIdx=0,this.isBackTrackingStack=[],this.errors=[],this.RULE_STACK=[],this.CST_STACK=[],this.RULE_OCCURRENCE_STACK=[]}}});var rE,jse=N(()=>{"use strict";Xg();qt();qg();Fs();rE=class{static{o(this,"ErrorHandler")}initErrorHandler(e){this._errors=[],this.errorMessageProvider=Bt(e,"errorMessageProvider")?e.errorMessageProvider:ls.errorMessageProvider}SAVE_ERROR(e){if(lf(e))return e.context={ruleStack:this.getHumanReadableRuleStack(),ruleOccurrenceStack:an(this.RULE_OCCURRENCE_STACK)},this._errors.push(e),e;throw Error("Trying to save an Error which is not a RecognitionException")}get errors(){return an(this._errors)}set errors(e){this._errors=e}raiseEarlyExitException(e,r,n){let i=this.getCurrRuleFullName(),a=this.getGAstProductions()[i],l=Wg(e,a,r,this.maxLookahead)[0],u=[];for(let f=1;f<=this.maxLookahead;f++)u.push(this.LA(f));let h=this.errorMessageProvider.buildEarlyExitMessage({expectedIterationPaths:l,actual:u,previous:this.LA(0),customUserDescription:n,ruleName:i});throw this.SAVE_ERROR(new mx(h,this.LA(1),this.LA(0)))}raiseNoAltException(e,r){let n=this.getCurrRuleFullName(),i=this.getGAstProductions()[n],a=Hg(e,i,this.maxLookahead),s=[];for(let h=1;h<=this.maxLookahead;h++)s.push(this.LA(h));let l=this.LA(0),u=this.errorMessageProvider.buildNoViableAltMessage({expectedPathsPerAlt:a,actual:s,previous:l,customUserDescription:r,ruleName:this.getCurrRuleFullName()});throw this.SAVE_ERROR(new dx(u,this.LA(1),l))}}});var nE,Kse=N(()=>{"use strict";cx();qt();nE=class{static{o(this,"ContentAssist")}initContentAssist(){}computeContentAssist(e,r){let n=this.gastProductionsCache[e];if(pr(n))throw Error(`Rule ->${e}<- does not exist in this grammar.`);return $k([n],r,this.tokenMatcher,this.maxLookahead)}getNextPossibleTokenTypes(e){let r=ia(e.ruleStack),i=this.getGAstProductions()[r];return new Ok(i,e).startWalking()}}});function yx(t,e,r,n=!1){aE(r);let i=ga(this.recordingProdStack),a=Si(e)?e:e.DEF,s=new t({definition:[],idx:r});return n&&(s.separator=e.SEP),Bt(e,"MAX_LOOKAHEAD")&&(s.maxLookahead=e.MAX_LOOKAHEAD),this.recordingProdStack.push(s),a.call(this),i.definition.push(s),this.recordingProdStack.pop(),sE}function dBe(t,e){aE(e);let r=ga(this.recordingProdStack),n=Pt(t)===!1,i=n===!1?t:t.DEF,a=new Tn({definition:[],idx:e,ignoreAmbiguities:n&&t.IGNORE_AMBIGUITIES===!0});Bt(t,"MAX_LOOKAHEAD")&&(a.maxLookahead=t.MAX_LOOKAHEAD);let s=A2(i,l=>Si(l.GATE));return a.hasPredicates=s,r.definition.push(a),Ae(i,l=>{let u=new Dn({definition:[]});a.definition.push(u),Bt(l,"IGNORE_AMBIGUITIES")?u.ignoreAmbiguities=l.IGNORE_AMBIGUITIES:Bt(l,"GATE")&&(u.ignoreAmbiguities=!0),this.recordingProdStack.push(u),l.ALT.call(this),this.recordingProdStack.pop()}),sE}function Jse(t){return t===0?"":`${t}`}function aE(t){if(t<0||t>Zse){let e=new Error(`Invalid DSL Method idx value: <${t}>
-	Idx value must be a none negative value smaller than ${Zse+1}`);throw e.KNOWN_RECORDER_ERROR=!0,e}}var sE,Qse,Zse,eoe,toe,fBe,iE,roe=N(()=>{"use strict";qt();os();ix();cp();up();Fs();qk();sE={description:"This Object indicates the Parser is during Recording Phase"};Object.freeze(sE);Qse=!0,Zse=Math.pow(2,8)-1,eoe=of({name:"RECORDING_PHASE_TOKEN",pattern:Xn.NA});Bu([eoe]);toe=$u(eoe,`This IToken indicates the Parser is in Recording Phase
-	See: https://chevrotain.io/docs/guide/internals.html#grammar-recording for details`,-1,-1,-1,-1,-1,-1);Object.freeze(toe);fBe={name:`This CSTNode indicates the Parser is in Recording Phase
-	See: https://chevrotain.io/docs/guide/internals.html#grammar-recording for details`,children:{}},iE=class{static{o(this,"GastRecorder")}initGastRecorder(e){this.recordingProdStack=[],this.RECORDING_PHASE=!1}enableRecording(){this.RECORDING_PHASE=!0,this.TRACE_INIT("Enable Recording",()=>{for(let e=0;e<10;e++){let r=e>0?e:"";this[`CONSUME${r}`]=function(n,i){return this.consumeInternalRecord(n,e,i)},this[`SUBRULE${r}`]=function(n,i){return this.subruleInternalRecord(n,e,i)},this[`OPTION${r}`]=function(n){return this.optionInternalRecord(n,e)},this[`OR${r}`]=function(n){return this.orInternalRecord(n,e)},this[`MANY${r}`]=function(n){this.manyInternalRecord(e,n)},this[`MANY_SEP${r}`]=function(n){this.manySepFirstInternalRecord(e,n)},this[`AT_LEAST_ONE${r}`]=function(n){this.atLeastOneInternalRecord(e,n)},this[`AT_LEAST_ONE_SEP${r}`]=function(n){this.atLeastOneSepFirstInternalRecord(e,n)}}this.consume=function(e,r,n){return this.consumeInternalRecord(r,e,n)},this.subrule=function(e,r,n){return this.subruleInternalRecord(r,e,n)},this.option=function(e,r){return this.optionInternalRecord(r,e)},this.or=function(e,r){return this.orInternalRecord(r,e)},this.many=function(e,r){this.manyInternalRecord(e,r)},this.atLeastOne=function(e,r){this.atLeastOneInternalRecord(e,r)},this.ACTION=this.ACTION_RECORD,this.BACKTRACK=this.BACKTRACK_RECORD,this.LA=this.LA_RECORD})}disableRecording(){this.RECORDING_PHASE=!1,this.TRACE_INIT("Deleting Recording methods",()=>{let e=this;for(let r=0;r<10;r++){let n=r>0?r:"";delete e[`CONSUME${n}`],delete e[`SUBRULE${n}`],delete e[`OPTION${n}`],delete e[`OR${n}`],delete e[`MANY${n}`],delete e[`MANY_SEP${n}`],delete e[`AT_LEAST_ONE${n}`],delete e[`AT_LEAST_ONE_SEP${n}`]}delete e.consume,delete e.subrule,delete e.option,delete e.or,delete e.many,delete e.atLeastOne,delete e.ACTION,delete e.BACKTRACK,delete e.LA})}ACTION_RECORD(e){}BACKTRACK_RECORD(e,r){return()=>!0}LA_RECORD(e){return jg}topLevelRuleRecord(e,r){try{let n=new as({definition:[],name:e});return n.name=e,this.recordingProdStack.push(n),r.call(this),this.recordingProdStack.pop(),n}catch(n){if(n.KNOWN_RECORDER_ERROR!==!0)try{n.message=n.message+`
-	 This error was thrown during the "grammar recording phase" For more info see:
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u=this.getRefNode(n,i.$ref,a.uriConverter);if(ii(u))return s||(s=this.nameProvider.getName(u)),{$refText:s??"",ref:u};l=u}if(l){let u={$refText:s??""};return u.error={container:e,property:r,message:l,reference:u},u}else return}getRefNode(e,r,n){try{let i=r.indexOf("#");if(i===0){let u=this.astNodeLocator.getAstNode(e,r.substring(1));return u||"Could not resolve path: "+r}if(i<0){let u=n?n(r):us.parse(r),h=this.langiumDocuments.getDocument(u);return h?h.parseResult.value:"Could not find document for URI: "+r}let a=n?n(r.substring(0,i)):us.parse(r.substring(0,i)),s=this.langiumDocuments.getDocument(a);if(!s)return"Could not find document for URI: "+r;if(i===r.length-1)return s.parseResult.value;let l=this.astNodeLocator.getAstNode(s.parseResult.value,r.substring(i+1));return l||"Could not resolve URI: "+r}catch(i){return String(i)}}}});var Vx,HM=N(()=>{"use strict";Fc();Vx=class{static{o(this,"DefaultServiceRegistry")}get map(){return this.fileExtensionMap}constructor(e){this.languageIdMap=new Map,this.fileExtensionMap=new Map,this.textDocuments=e?.workspace.TextDocuments}register(e){let r=e.LanguageMetaData;for(let n of r.fileExtensions)this.fileExtensionMap.has(n)&&console.warn(`The file extension ${n} is used by multiple languages. It is now assigned to '${r.languageId}'.`),this.fileExtensionMap.set(n,e);this.languageIdMap.set(r.languageId,e),this.languageIdMap.size===1?this.singleton=e:this.singleton=void 0}getServices(e){var r,n;if(this.singleton!==void 0)return this.singleton;if(this.languageIdMap.size===0)throw new Error("The service registry is empty. 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Error("The 'built-in' category is reserved for lexer, parser, and linker errors.");for(let[i,a]of Object.entries(e)){let s=a;if(Array.isArray(s))for(let l of s){let u={check:this.wrapValidationException(l,r),category:n};this.addEntry(i,u)}else if(typeof s=="function"){let l={check:this.wrapValidationException(s,r),category:n};this.addEntry(i,l)}else Lc(s)}}wrapValidationException(e,r){return async(n,i,a)=>{await this.handleException(()=>e.call(r,n,i,a),"An error occurred during validation",i,n)}}async handleException(e,r,n,i){try{await e()}catch(a){if(Bc(a))throw a;console.error(`${r}:`,a),a instanceof Error&&a.stack&&console.error(a.stack);let s=a instanceof Error?a.message:String(a);n("error",`${r}: ${s}`,{node:i})}}addEntry(e,r){if(e==="AstNode"){this.entries.add("AstNode",r);return}for(let n of this.reflection.getAllSubTypes(e))this.entries.add(n,r)}getChecks(e,r){let n=en(this.entries.get(e)).concat(this.entries.get("AstNode"));return r&&(n=n.filter(i=>r.includes(i.category))),n.map(i=>i.check)}registerBeforeDocument(e,r=this){this.entriesBefore.push(this.wrapPreparationException(e,"An error occurred during set-up of the validation",r))}registerAfterDocument(e,r=this){this.entriesAfter.push(this.wrapPreparationException(e,"An error occurred during tear-down of the validation",r))}wrapPreparationException(e,r,n){return async(i,a,s,l)=>{await this.handleException(()=>e.call(n,i,a,s,l),r,a,i)}}get checksBefore(){return this.entriesBefore}get checksAfter(){return this.entriesAfter}}});function Yle(t){if(t.range)return t.range;let e;return typeof t.property=="string"?e=Q2(t.node.$cstNode,t.property,t.index):typeof t.keyword=="string"&&(e=cN(t.node.$cstNode,t.keyword,t.index)),e??(e=t.node.$cstNode),e?e.range:{start:{line:0,character:0},end:{line:0,character:0}}}function RE(t){switch(t){case"error":return 1;case"warning":return 2;case"info":return 3;case"hint":return 4;default:throw new Error("Invalid diagnostic severity: 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l;return((l=s.data)===null||l===void 0?void 0:l.code)===jo.LinkingError}))))return a;try{a.push(...await this.validateAst(i.value,r,n))}catch(s){if(Bc(s))throw s;console.error("An error occurred during validation:",s)}return await xi(n),a}processLexingErrors(e,r,n){var i,a,s;let l=[...e.lexerErrors,...(a=(i=e.lexerReport)===null||i===void 0?void 0:i.diagnostics)!==null&&a!==void 0?a:[]];for(let u of l){let h=(s=u.severity)!==null&&s!==void 0?s:"error",f={severity:RE(h),range:{start:{line:u.line-1,character:u.column-1},end:{line:u.line-1,character:u.column+u.length-1}},message:u.message,data:Xle(h),source:this.getSource()};r.push(f)}}processParsingErrors(e,r,n){for(let i of e.parserErrors){let a;if(isNaN(i.token.startOffset)){if("previousToken"in i){let s=i.previousToken;if(isNaN(s.startOffset)){let l={line:0,character:0};a={start:l,end:l}}else{let l={line:s.endLine-1,character:s.endColumn};a={start:l,end:l}}}}else a=Gm(i.token);if(a){let s={severity:RE("error"),range:a,message:i.message,data:bp(jo.ParsingError),source:this.getSource()};r.push(s)}}}processLinkingErrors(e,r,n){for(let i of e.references){let a=i.error;if(a){let s={node:a.container,property:a.property,index:a.index,data:{code:jo.LinkingError,containerType:a.container.$type,property:a.property,refText:a.reference.$refText}};r.push(this.toDiagnostic("error",a.message,s))}}}async validateAst(e,r,n=yr.CancellationToken.None){let i=[],a=o((s,l,u)=>{i.push(this.toDiagnostic(s,l,u))},"acceptor");return await this.validateAstBefore(e,r,a,n),await this.validateAstNodes(e,r,a,n),await this.validateAstAfter(e,r,a,n),i}async validateAstBefore(e,r,n,i=yr.CancellationToken.None){var a;let s=this.validationRegistry.checksBefore;for(let l of s)await xi(i),await l(e,n,(a=r.categories)!==null&&a!==void 0?a:[],i)}async validateAstNodes(e,r,n,i=yr.CancellationToken.None){await Promise.all(Wo(e).map(async a=>{await xi(i);let s=this.validationRegistry.getChecks(a.$type,r.categories);for(let l of s)await l(a,n,i)}))}async validateAstAfter(e,r,n,i=yr.CancellationToken.None){var a;let s=this.validationRegistry.checksAfter;for(let l of s)await xi(i),await l(e,n,(a=r.categories)!==null&&a!==void 0?a:[],i)}toDiagnostic(e,r,n){return{message:r,range:Yle(n),severity:RE(e),code:n.code,codeDescription:n.codeDescription,tags:n.tags,relatedInformation:n.relatedInformation,data:n.data,source:this.getSource()}}getSource(){return this.metadata.languageId}};o(Yle,"getDiagnosticRange");o(RE,"toDiagnosticSeverity");o(Xle,"toDiagnosticData");(function(t){t.LexingError="lexing-error",t.LexingWarning="lexing-warning",t.LexingInfo="lexing-info",t.LexingHint="lexing-hint",t.ParsingError="parsing-error",t.LinkingError="linking-error"})(jo||(jo={}))});var qx,Yx,qM=N(()=>{"use strict";qo();Rl();is();Nl();Yo();Fc();qx=class{static{o(this,"DefaultAstNodeDescriptionProvider")}constructor(e){this.astNodeLocator=e.workspace.AstNodeLocator,this.nameProvider=e.references.NameProvider}createDescription(e,r,n){let i=n??Pa(e);r??(r=this.nameProvider.getName(e));let a=this.astNodeLocator.getAstNodePath(e);if(!r)throw new Error(`Node at path ${a} has no name.`);let s,l=o(()=>{var u;return s??(s=Qd((u=this.nameProvider.getNameNode(e))!==null&&u!==void 0?u:e.$cstNode))},"nameSegmentGetter");return{node:e,name:r,get nameSegment(){return l()},selectionSegment:Qd(e.$cstNode),type:e.$type,documentUri:i.uri,path:a}}},Yx=class{static{o(this,"DefaultReferenceDescriptionProvider")}constructor(e){this.nodeLocator=e.workspace.AstNodeLocator}async createDescriptions(e,r=yr.CancellationToken.None){let n=[],i=e.parseResult.value;for(let a of Wo(i))await xi(r),Ag(a).filter(s=>!jd(s)).forEach(s=>{let l=this.createDescription(s);l&&n.push(l)});return n}createDescription(e){let r=e.reference.$nodeDescription,n=e.reference.$refNode;if(!r||!n)return;let i=Pa(e.container).uri;return{sourceUri:i,sourcePath:this.nodeLocator.getAstNodePath(e.container),targetUri:r.documentUri,targetPath:r.path,segment:Qd(n),local:hs.equals(r.documentUri,i)}}}});var Xx,YM=N(()=>{"use strict";Xx=class{static{o(this,"DefaultAstNodeLocator")}constructor(){this.segmentSeparator="/",this.indexSeparator="@"}getAstNodePath(e){if(e.$container){let r=this.getAstNodePath(e.$container),n=this.getPathSegment(e);return r+this.segmentSeparator+n}return""}getPathSegment({$containerProperty:e,$containerIndex:r}){if(!e)throw new Error("Missing '$containerProperty' in AST node.");return r!==void 0?e+this.indexSeparator+r:e}getAstNode(e,r){return r.split(this.segmentSeparator).reduce((i,a)=>{if(!i||a.length===0)return i;let s=a.indexOf(this.indexSeparator);if(s>0){let l=a.substring(0,s),u=parseInt(a.substring(s+1)),h=i[l];return h?.[u]}return i[a]},e)}}});var Kn={};var NE=N(()=>{"use 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n=e.settings[r];this.updateSectionConfiguration(r,n),this.onConfigurationSectionUpdateEmitter.fire({section:r,configuration:n})})}updateSectionConfiguration(e,r){this.settings[e]=r}async getConfiguration(e,r){await this.ready;let n=this.toSectionName(e);if(this.settings[n])return this.settings[n][r]}toSectionName(e){return`${e}`}get onConfigurationSectionUpdate(){return this.onConfigurationSectionUpdateEmitter.event}}});var ff,jM=N(()=>{"use strict";(function(t){function e(r){return{dispose:o(async()=>await r(),"dispose")}}o(e,"create"),t.create=e})(ff||(ff={}))});var Kx,KM=N(()=>{"use strict";qo();jM();f1();Yo();Ps();Hx();h1();Kx=class{static{o(this,"DefaultDocumentBuilder")}constructor(e){this.updateBuildOptions={validation:{categories:["built-in","fast"]}},this.updateListeners=[],this.buildPhaseListeners=new Bl,this.documentPhaseListeners=new Bl,this.buildState=new Map,this.documentBuildWaiters=new Map,this.currentState=kn.Changed,this.langiumDocuments=e.workspace.LangiumDocuments,this.langiumDocumentFactory=e.workspace.LangiumDocumentFactory,this.textDocuments=e.workspace.TextDocuments,this.indexManager=e.workspace.IndexManager,this.serviceRegistry=e.ServiceRegistry}async build(e,r={},n=yr.CancellationToken.None){var i,a;for(let s of e){let l=s.uri.toString();if(s.state===kn.Validated){if(typeof r.validation=="boolean"&&r.validation)s.state=kn.IndexedReferences,s.diagnostics=void 0,this.buildState.delete(l);else if(typeof r.validation=="object"){let u=this.buildState.get(l),h=(i=u?.result)===null||i===void 0?void 0:i.validationChecks;if(h){let d=((a=r.validation.categories)!==null&&a!==void 0?a:g1.all).filter(p=>!h.includes(p));d.length>0&&(this.buildState.set(l,{completed:!1,options:{validation:Object.assign(Object.assign({},r.validation),{categories:d})},result:u.result}),s.state=kn.IndexedReferences)}}}else this.buildState.delete(l)}this.currentState=kn.Changed,await this.emitUpdate(e.map(s=>s.uri),[]),await this.buildDocuments(e,r,n)}async update(e,r,n=yr.CancellationToken.None){this.currentState=kn.Changed;for(let s of r)this.langiumDocuments.deleteDocument(s),this.buildState.delete(s.toString()),this.indexManager.remove(s);for(let s of e){if(!this.langiumDocuments.invalidateDocument(s)){let u=this.langiumDocumentFactory.fromModel({$type:"INVALID"},s);u.state=kn.Changed,this.langiumDocuments.addDocument(u)}this.buildState.delete(s.toString())}let i=en(e).concat(r).map(s=>s.toString()).toSet();this.langiumDocuments.all.filter(s=>!i.has(s.uri.toString())&&this.shouldRelink(s,i)).forEach(s=>{this.serviceRegistry.getServices(s.uri).references.Linker.unlink(s),s.state=Math.min(s.state,kn.ComputedScopes),s.diagnostics=void 0}),await this.emitUpdate(e,r),await xi(n);let a=this.sortDocuments(this.langiumDocuments.all.filter(s=>{var l;return s.state<kn.Linked||!(!((l=this.buildState.get(s.uri.toString()))===null||l===void 0)&&l.completed)}).toArray());await this.buildDocuments(a,this.updateBuildOptions,n)}async emitUpdate(e,r){await Promise.all(this.updateListeners.map(n=>n(e,r)))}sortDocuments(e){let r=0,n=e.length-1;for(;r<n;){for(;r<e.length&&this.hasTextDocument(e[r]);)r++;for(;n>=0&&!this.hasTextDocument(e[n]);)n--;r<n&&([e[r],e[n]]=[e[n],e[r]])}return e}hasTextDocument(e){var r;return!!(!((r=this.textDocuments)===null||r===void 0)&&r.get(e.uri))}shouldRelink(e,r){return e.references.some(n=>n.error!==void 0)?!0:this.indexManager.isAffected(e,r)}onUpdate(e){return this.updateListeners.push(e),ff.create(()=>{let r=this.updateListeners.indexOf(e);r>=0&&this.updateListeners.splice(r,1)})}async buildDocuments(e,r,n){this.prepareBuild(e,r),await this.runCancelable(e,kn.Parsed,n,a=>this.langiumDocumentFactory.update(a,n)),await this.runCancelable(e,kn.IndexedContent,n,a=>this.indexManager.updateContent(a,n)),await this.runCancelable(e,kn.ComputedScopes,n,async a=>{let s=this.serviceRegistry.getServices(a.uri).references.ScopeComputation;a.precomputedScopes=await s.computeLocalScopes(a,n)}),await this.runCancelable(e,kn.Linked,n,a=>this.serviceRegistry.getServices(a.uri).references.Linker.link(a,n)),await this.runCancelable(e,kn.IndexedReferences,n,a=>this.indexManager.updateReferences(a,n));let i=e.filter(a=>this.shouldValidate(a));await this.runCancelable(i,kn.Validated,n,a=>this.validate(a,n));for(let a of e){let s=this.buildState.get(a.uri.toString());s&&(s.completed=!0)}}prepareBuild(e,r){for(let n of e){let i=n.uri.toString(),a=this.buildState.get(i);(!a||a.completed)&&this.buildState.set(i,{completed:!1,options:r,result:a?.result})}}async runCancelable(e,r,n,i){let a=e.filter(l=>l.state<r);for(let l of a)await xi(n),await i(l),l.state=r,await this.notifyDocumentPhase(l,r,n);let s=e.filter(l=>l.state===r);await this.notifyBuildPhase(s,r,n),this.currentState=r}onBuildPhase(e,r){return this.buildPhaseListeners.add(e,r),ff.create(()=>{this.buildPhaseListeners.delete(e,r)})}onDocumentPhase(e,r){return this.documentPhaseListeners.add(e,r),ff.create(()=>{this.documentPhaseListeners.delete(e,r)})}waitUntil(e,r,n){let i;if(r&&"path"in r?i=r:n=r,n??(n=yr.CancellationToken.None),i){let a=this.langiumDocuments.getDocument(i);if(a&&a.state>e)return Promise.resolve(i)}return this.currentState>=e?Promise.resolve(void 0):n.isCancellationRequested?Promise.reject(Pc):new Promise((a,s)=>{let l=this.onBuildPhase(e,()=>{if(l.dispose(),u.dispose(),i){let h=this.langiumDocuments.getDocument(i);a(h?.uri)}else a(void 0)}),u=n.onCancellationRequested(()=>{l.dispose(),u.dispose(),s(Pc)})})}async notifyDocumentPhase(e,r,n){let a=this.documentPhaseListeners.get(r).slice();for(let s of a)try{await s(e,n)}catch(l){if(!Bc(l))throw l}}async notifyBuildPhase(e,r,n){if(e.length===0)return;let a=this.buildPhaseListeners.get(r).slice();for(let s of a)await xi(n),await 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this.begin("acc_title"),31;break;case 2:return this.popState(),"acc_title_value";break;case 3:return this.begin("acc_descr"),33;break;case 4:return this.popState(),"acc_descr_value";break;case 5:this.begin("acc_descr_multiline");break;case 6:this.popState();break;case 7:return"acc_descr_multiline_value";case 8:break;case 9:break;case 10:break;case 11:return 10;case 12:break;case 13:break;case 14:this.begin("href");break;case 15:this.popState();break;case 16:return 43;case 17:this.begin("callbackname");break;case 18:this.popState();break;case 19:this.popState(),this.begin("callbackargs");break;case 20:return 41;case 21:this.popState();break;case 22:return 42;case 23:this.begin("click");break;case 24:this.popState();break;case 25:return 40;case 26:return 4;case 27:return 22;case 28:return 23;case 29:return 24;case 30:return 25;case 31:return 26;case 32:return 28;case 33:return 27;case 34:return 29;case 35:return 12;case 36:return 13;case 37:return 14;case 38:return 15;case 39:return 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-Expecting `+tn.join(", ")+", got '"+(this.terminals_[Mt]||Mt)+"'":_r="Parse error on line "+(we+1)+": Unexpected "+(Mt==at?"end of input":"'"+(this.terminals_[Mt]||Mt)+"'"),this.parseError(_r,{text:st.match,token:this.terminals_[Mt]||Mt,line:st.yylineno,loc:We,expected:tn})}if(Et[0]instanceof Array&&Et.length>1)throw new Error("Parse Error: multiple actions possible at state: "+ut+", token: "+Mt);switch(Et[0]){case 1:xe.push(Mt),pe.push(st.yytext),ve.push(st.yylloc),xe.push(Et[1]),Mt=null,xt?(Mt=xt,xt=null):(Ve=st.yyleng,_e=st.yytext,we=st.yylineno,We=st.yylloc,De>0&&De--);break;case 2:if(Tt=this.productions_[Et[1]][1],nt.$=pe[pe.length-Tt],nt._$={first_line:ve[ve.length-(Tt||1)].first_line,last_line:ve[ve.length-1].last_line,first_column:ve[ve.length-(Tt||1)].first_column,last_column:ve[ve.length-1].last_column},ot&&(nt._$.range=[ve[ve.length-(Tt||1)].range[0],ve[ve.length-1].range[1]]),yt=this.performAction.apply(nt,[_e,Ve,we,Ue.yy,Et[1],pe,ve].concat(Rt)),typeof yt<"u")return yt;Tt&&(xe=xe.slice(0,-1*Tt*2),pe=pe.slice(0,-1*Tt),ve=ve.slice(0,-1*Tt)),xe.push(this.productions_[Et[1]][0]),pe.push(nt.$),ve.push(nt._$),On=Pe[xe[xe.length-2]][xe[xe.length-1]],xe.push(On);break;case 3:return!0}}return!0},"parse")},W=function(){var re={EOF:1,parseError:o(function(V,xe){if(this.yy.parser)this.yy.parser.parseError(V,xe);else throw new Error(V)},"parseError"),setInput:o(function(oe,V){return this.yy=V||this.yy||{},this._input=oe,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var oe=this._input[0];this.yytext+=oe,this.yyleng++,this.offset++,this.match+=oe,this.matched+=oe;var V=oe.match(/(?:\r\n?|\n).*/g);return V?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),oe},"input"),unput:o(function(oe){var V=oe.length,xe=oe.split(/(?:\r\n?|\n)/g);this._input=oe+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-V),this.offset-=V;var q=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),xe.length-1&&(this.yylineno-=xe.length-1);var pe=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:xe?(xe.length===q.length?this.yylloc.first_column:0)+q[q.length-xe.length].length-xe[0].length:this.yylloc.first_column-V},this.options.ranges&&(this.yylloc.range=[pe[0],pe[0]+this.yyleng-V]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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-`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var V=this.next();return V||this.lex()},"lex"),begin:o(function(V){this.conditionStack.push(V)},"begin"),popState:o(function(){var V=this.conditionStack.length-1;return V>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(V){return V=this.conditionStack.length-1-Math.abs(V||0),V>=0?this.conditionStack[V]:"INITIAL"},"topState"),pushState:o(function(V){this.begin(V)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{"case-insensitive":!0},performAction:o(function(V,xe,q,pe){var ve=pe;switch(q){case 0:return"title";case 1:return this.begin("acc_title"),9;break;case 2:return this.popState(),"acc_title_value";break;case 3:return this.begin("acc_descr"),11;break;case 4:return this.popState(),"acc_descr_value";break;case 5:this.begin("acc_descr_multiline");break;case 6:this.popState();break;case 7:return"acc_descr_multiline_value";case 8:return 21;case 9:return 22;case 10:return 23;case 11:return 24;case 12:return 5;case 13:break;case 14:break;case 15:break;case 16:return 8;case 17:return 6;case 18:return 27;case 19:return 40;case 20:return 29;case 21:return 32;case 22:return 31;case 23:return 34;case 24:return 36;case 25:return 38;case 26:return 41;case 27:return 42;case 28:return 43;case 29:return 44;case 30:return 45;case 31:return 46;case 32:return 47;case 33:return 48;case 34:return 49;case 35:return 50;case 36:return 51;case 37:return 52;case 38:return 53;case 39:return 54;case 40:return 65;case 41:return 66;case 42:return 67;case 43:return 68;case 44:return 69;case 45:return 70;case 46:return 71;case 47:return 57;case 48:return 59;case 49:return this.begin("style"),77;break;case 50:return 75;case 51:return 81;case 52:return 88;case 53:return"PERCENT";case 54:return 86;case 55:return 84;case 56:break;case 57:this.begin("string");break;case 58:this.popState();break;case 59:return this.begin("style"),72;break;case 60:return this.begin("style"),74;break;case 61:return 61;case 62:return 64;case 63:return 63;case 64:this.begin("string");break;case 65:this.popState();break;case 66:return"qString";case 67:return xe.yytext=xe.yytext.trim(),89;break;case 68:return 75;case 69:return 80;case 70:return 76}},"anonymous"),rules:[/^(?:title\s[^#\n;]+)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:.*direction\s+TB[^\n]*)/i,/^(?:.*direction\s+BT[^\n]*)/i,/^(?:.*direction\s+RL[^\n]*)/i,/^(?:.*direction\s+LR[^\n]*)/i,/^(?:(\r?\n)+)/i,/^(?:\s+)/i,/^(?:#[^\n]*)/i,/^(?:%[^\n]*)/i,/^(?:$)/i,/^(?:requirementDiagram\b)/i,/^(?:\{)/i,/^(?:\})/i,/^(?::{3})/i,/^(?::)/i,/^(?:id\b)/i,/^(?:text\b)/i,/^(?:risk\b)/i,/^(?:verifyMethod\b)/i,/^(?:requirement\b)/i,/^(?:functionalRequirement\b)/i,/^(?:interfaceRequirement\b)/i,/^(?:performanceRequirement\b)/i,/^(?:physicalRequirement\b)/i,/^(?:designConstraint\b)/i,/^(?:low\b)/i,/^(?:medium\b)/i,/^(?:high\b)/i,/^(?:analysis\b)/i,/^(?:demonstration\b)/i,/^(?:inspection\b)/i,/^(?:test\b)/i,/^(?:element\b)/i,/^(?:contains\b)/i,/^(?:copies\b)/i,/^(?:derives\b)/i,/^(?:satisfies\b)/i,/^(?:verifies\b)/i,/^(?:refines\b)/i,/^(?:traces\b)/i,/^(?:type\b)/i,/^(?:docref\b)/i,/^(?:style\b)/i,/^(?:\w+)/i,/^(?::)/i,/^(?:;)/i,/^(?:%)/i,/^(?:-)/i,/^(?:#)/i,/^(?: )/i,/^(?:["])/i,/^(?:\n)/i,/^(?:classDef\b)/i,/^(?:class\b)/i,/^(?:<-)/i,/^(?:->)/i,/^(?:-)/i,/^(?:["])/i,/^(?:["])/i,/^(?:[^"]*)/i,/^(?:[\w][^:,\r\n\{\<\>\-\=]*)/i,/^(?:\w+)/i,/^(?:[0-9]+)/i,/^(?:,)/i],conditions:{acc_descr_multiline:{rules:[6,7,68,69,70],inclusive:!1},acc_descr:{rules:[4,68,69,70],inclusive:!1},acc_title:{rules:[2,68,69,70],inclusive:!1},style:{rules:[50,51,52,53,54,55,56,57,58,68,69,70],inclusive:!1},unqString:{rules:[68,69,70],inclusive:!1},token:{rules:[68,69,70],inclusive:!1},string:{rules:[65,66,68,69,70],inclusive:!1},INITIAL:{rules:[0,1,3,5,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,59,60,61,62,63,64,67,68,69,70],inclusive:!0}}};return re}();be.lexer=W;function de(){this.yy={}}return o(de,"Parser"),de.prototype=be,be.Parser=de,new de}();EO.parser=EO;Zhe=EO});var A6,efe=N(()=>{"use strict";zt();vt();mi();A6=class{constructor(){this.relations=[];this.latestRequirement=this.getInitialRequirement();this.requirements=new Map;this.latestElement=this.getInitialElement();this.elements=new Map;this.classes=new Map;this.direction="TB";this.RequirementType={REQUIREMENT:"Requirement",FUNCTIONAL_REQUIREMENT:"Functional Requirement",INTERFACE_REQUIREMENT:"Interface Requirement",PERFORMANCE_REQUIREMENT:"Performance Requirement",PHYSICAL_REQUIREMENT:"Physical Requirement",DESIGN_CONSTRAINT:"Design Constraint"};this.RiskLevel={LOW_RISK:"Low",MED_RISK:"Medium",HIGH_RISK:"High"};this.VerifyType={VERIFY_ANALYSIS:"Analysis",VERIFY_DEMONSTRATION:"Demonstration",VERIFY_INSPECTION:"Inspection",VERIFY_TEST:"Test"};this.Relationships={CONTAINS:"contains",COPIES:"copies",DERIVES:"derives",SATISFIES:"satisfies",VERIFIES:"verifies",REFINES:"refines",TRACES:"traces"};this.setAccTitle=Lr;this.getAccTitle=Rr;this.setAccDescription=Nr;this.getAccDescription=Mr;this.setDiagramTitle=$r;this.getDiagramTitle=Ir;this.getConfig=o(()=>me().requirement,"getConfig");this.clear(),this.setDirection=this.setDirection.bind(this),this.addRequirement=this.addRequirement.bind(this),this.setNewReqId=this.setNewReqId.bind(this),this.setNewReqRisk=this.setNewReqRisk.bind(this),this.setNewReqText=this.setNewReqText.bind(this),this.setNewReqVerifyMethod=this.setNewReqVerifyMethod.bind(this),this.addElement=this.addElement.bind(this),this.setNewElementType=this.setNewElementType.bind(this),this.setNewElementDocRef=this.setNewElementDocRef.bind(this),this.addRelationship=this.addRelationship.bind(this),this.setCssStyle=this.setCssStyle.bind(this),this.setClass=this.setClass.bind(this),this.defineClass=this.defineClass.bind(this),this.setAccTitle=this.setAccTitle.bind(this),this.setAccDescription=this.setAccDescription.bind(this)}static{o(this,"RequirementDB")}getDirection(){return this.direction}setDirection(e){this.direction=e}resetLatestRequirement(){this.latestRequirement=this.getInitialRequirement()}resetLatestElement(){this.latestElement=this.getInitialElement()}getInitialRequirement(){return{requirementId:"",text:"",risk:"",verifyMethod:"",name:"",type:"",cssStyles:[],classes:["default"]}}getInitialElement(){return{name:"",type:"",docRef:"",cssStyles:[],classes:["default"]}}addRequirement(e,r){return this.requirements.has(e)||this.requirements.set(e,{name:e,type:r,requirementId:this.latestRequirement.requirementId,text:this.latestRequirement.text,risk:this.latestRequirement.risk,verifyMethod:this.latestRequirement.verifyMethod,cssStyles:[],classes:["default"]}),this.resetLatestRequirement(),this.requirements.get(e)}getRequirements(){return this.requirements}setNewReqId(e){this.latestRequirement!==void 0&&(this.latestRequirement.requirementId=e)}setNewReqText(e){this.latestRequirement!==void 0&&(this.latestRequirement.text=e)}setNewReqRisk(e){this.latestRequirement!==void 0&&(this.latestRequirement.risk=e)}setNewReqVerifyMethod(e){this.latestRequirement!==void 0&&(this.latestRequirement.verifyMethod=e)}addElement(e){return this.elements.has(e)||(this.elements.set(e,{name:e,type:this.latestElement.type,docRef:this.latestElement.docRef,cssStyles:[],classes:["default"]}),Y.info("Added new element: ",e)),this.resetLatestElement(),this.elements.get(e)}getElements(){return this.elements}setNewElementType(e){this.latestElement!==void 0&&(this.latestElement.type=e)}setNewElementDocRef(e){this.latestElement!==void 0&&(this.latestElement.docRef=e)}addRelationship(e,r,n){this.relations.push({type:e,src:r,dst:n})}getRelationships(){return this.relations}clear(){this.relations=[],this.resetLatestRequirement(),this.requirements=new Map,this.resetLatestElement(),this.elements=new Map,this.classes=new Map,Ar()}setCssStyle(e,r){for(let n of e){let i=this.requirements.get(n)??this.elements.get(n);if(!r||!i)return;for(let a of r)a.includes(",")?i.cssStyles.push(...a.split(",")):i.cssStyles.push(a)}}setClass(e,r){for(let n of e){let i=this.requirements.get(n)??this.elements.get(n);if(i)for(let a of r){i.classes.push(a);let s=this.classes.get(a)?.styles;s&&i.cssStyles.push(...s)}}}defineClass(e,r){for(let n of e){let i=this.classes.get(n);i===void 0&&(i={id:n,styles:[],textStyles:[]},this.classes.set(n,i)),r&&r.forEach(function(a){if(/color/.exec(a)){let s=a.replace("fill","bgFill");i.textStyles.push(s)}i.styles.push(a)}),this.requirements.forEach(a=>{a.classes.includes(n)&&a.cssStyles.push(...r.flatMap(s=>s.split(",")))}),this.elements.forEach(a=>{a.classes.includes(n)&&a.cssStyles.push(...r.flatMap(s=>s.split(",")))})}}getClasses(){return this.classes}getData(){let e=me(),r=[],n=[];for(let i of this.requirements.values()){let a=i;a.id=i.name,a.cssStyles=i.cssStyles,a.cssClasses=i.classes.join(" "),a.shape="requirementBox",a.look=e.look,r.push(a)}for(let i of this.elements.values()){let a=i;a.shape="requirementBox",a.look=e.look,a.id=i.name,a.cssStyles=i.cssStyles,a.cssClasses=i.classes.join(" "),r.push(a)}for(let i of this.relations){let a=0,s=i.type===this.Relationships.CONTAINS,l={id:`${i.src}-${i.dst}-${a}`,start:this.requirements.get(i.src)?.name??this.elements.get(i.src)?.name,end:this.requirements.get(i.dst)?.name??this.elements.get(i.dst)?.name,label:`&lt;&lt;${i.type}&gt;&gt;`,classes:"relationshipLine",style:["fill:none",s?"":"stroke-dasharray: 10,7"],labelpos:"c",thickness:"normal",type:"normal",pattern:s?"normal":"dashed",arrowTypeStart:s?"requirement_contains":"",arrowTypeEnd:s?"":"requirement_arrow",look:e.look};n.push(l),a++}return{nodes:r,edges:n,other:{},config:e,direction:this.getDirection()}}}});var ZGe,tfe,rfe=N(()=>{"use strict";ZGe=o(t=>`
-
-  marker {
-    fill: ${t.relationColor};
-    stroke: ${t.relationColor};
-  }
-
-  marker.cross {
-    stroke: ${t.lineColor};
-  }
-
-  svg {
-    font-family: ${t.fontFamily};
-    font-size: ${t.fontSize};
-  }
-
-  .reqBox {
-    fill: ${t.requirementBackground};
-    fill-opacity: 1.0;
-    stroke: ${t.requirementBorderColor};
-    stroke-width: ${t.requirementBorderSize};
-  }
-  
-  .reqTitle, .reqLabel{
-    fill:  ${t.requirementTextColor};
-  }
-  .reqLabelBox {
-    fill: ${t.relationLabelBackground};
-    fill-opacity: 1.0;
-  }
-
-  .req-title-line {
-    stroke: ${t.requirementBorderColor};
-    stroke-width: ${t.requirementBorderSize};
-  }
-  .relationshipLine {
-    stroke: ${t.relationColor};
-    stroke-width: 1;
-  }
-  .relationshipLabel {
-    fill: ${t.relationLabelColor};
-  }
-  .divider {
-    stroke: ${t.nodeBorder};
-    stroke-width: 1;
-  }
-  .label {
-    font-family: ${t.fontFamily};
-    color: ${t.nodeTextColor||t.textColor};
-  }
-  .label text,span {
-    fill: ${t.nodeTextColor||t.textColor};
-    color: ${t.nodeTextColor||t.textColor};
-  }
-  .labelBkg {
-    background-color: ${t.edgeLabelBackground};
-  }
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74:this.$=se.LINETYPE.BIDIRECTIONAL_DOTTED;break;case 75:this.$=se.LINETYPE.SOLID_CROSS;break;case 76:this.$=se.LINETYPE.DOTTED_CROSS;break;case 77:this.$=se.LINETYPE.SOLID_POINT;break;case 78:this.$=se.LINETYPE.DOTTED_POINT;break;case 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J=new Error(X);throw J.hash=te,J}},"parseError"),parse:o(function(X){var te=this,J=[0],se=[],ue=[null],Z=[],Se=this.table,ce="",ae=0,Oe=0,ge=0,ze=2,He=1,$e=Z.slice.call(arguments,1),Re=Object.create(this.lexer),Ie={yy:{}};for(var be in this.yy)Object.prototype.hasOwnProperty.call(this.yy,be)&&(Ie.yy[be]=this.yy[be]);Re.setInput(X,Ie.yy),Ie.yy.lexer=Re,Ie.yy.parser=this,typeof Re.yylloc>"u"&&(Re.yylloc={});var W=Re.yylloc;Z.push(W);var de=Re.options&&Re.options.ranges;typeof Ie.yy.parseError=="function"?this.parseError=Ie.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function re(Rt){J.length=J.length-2*Rt,ue.length=ue.length-Rt,Z.length=Z.length-Rt}o(re,"popStack");function oe(){var Rt;return Rt=se.pop()||Re.lex()||He,typeof Rt!="number"&&(Rt instanceof Array&&(se=Rt,Rt=se.pop()),Rt=te.symbols_[Rt]||Rt),Rt}o(oe,"lex");for(var V,xe,q,pe,ve,Pe,_e={},we,Ve,De,qe;;){if(q=J[J.length-1],this.defaultActions[q]?pe=this.defaultActions[q]:((V===null||typeof V>"u")&&(V=oe()),pe=Se[q]&&Se[q][V]),typeof pe>"u"||!pe.length||!pe[0]){var at="";qe=[];for(we in Se[q])this.terminals_[we]&&we>ze&&qe.push("'"+this.terminals_[we]+"'");Re.showPosition?at="Parse error on line "+(ae+1)+`:
-`+Re.showPosition()+`
-Expecting `+qe.join(", ")+", got '"+(this.terminals_[V]||V)+"'":at="Parse error on line "+(ae+1)+": Unexpected "+(V==He?"end of input":"'"+(this.terminals_[V]||V)+"'"),this.parseError(at,{text:Re.match,token:this.terminals_[V]||V,line:Re.yylineno,loc:W,expected:qe})}if(pe[0]instanceof Array&&pe.length>1)throw new Error("Parse Error: multiple actions possible at state: "+q+", token: "+V);switch(pe[0]){case 1:J.push(V),ue.push(Re.yytext),Z.push(Re.yylloc),J.push(pe[1]),V=null,xe?(V=xe,xe=null):(Oe=Re.yyleng,ce=Re.yytext,ae=Re.yylineno,W=Re.yylloc,ge>0&&ge--);break;case 2:if(Ve=this.productions_[pe[1]][1],_e.$=ue[ue.length-Ve],_e._$={first_line:Z[Z.length-(Ve||1)].first_line,last_line:Z[Z.length-1].last_line,first_column:Z[Z.length-(Ve||1)].first_column,last_column:Z[Z.length-1].last_column},de&&(_e._$.range=[Z[Z.length-(Ve||1)].range[0],Z[Z.length-1].range[1]]),Pe=this.performAction.apply(_e,[ce,Oe,ae,Ie.yy,pe[1],ue,Z].concat($e)),typeof Pe<"u")return Pe;Ve&&(J=J.slice(0,-1*Ve*2),ue=ue.slice(0,-1*Ve),Z=Z.slice(0,-1*Ve)),J.push(this.productions_[pe[1]][0]),ue.push(_e.$),Z.push(_e._$),De=Se[J[J.length-2]][J[J.length-1]],J.push(De);break;case 3:return!0}}return!0},"parse")},le=function(){var K={EOF:1,parseError:o(function(te,J){if(this.yy.parser)this.yy.parser.parseError(te,J);else throw new Error(te)},"parseError"),setInput:o(function(X,te){return this.yy=te||this.yy||{},this._input=X,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var X=this._input[0];this.yytext+=X,this.yyleng++,this.offset++,this.match+=X,this.matched+=X;var te=X.match(/(?:\r\n?|\n).*/g);return te?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),X},"input"),unput:o(function(X){var te=X.length,J=X.split(/(?:\r\n?|\n)/g);this._input=X+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-te),this.offset-=te;var se=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),J.length-1&&(this.yylineno-=J.length-1);var ue=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:J?(J.length===se.length?this.yylloc.first_column:0)+se[se.length-J.length].length-J[0].length:this.yylloc.first_column-te},this.options.ranges&&(this.yylloc.range=[ue[0],ue[0]+this.yyleng-te]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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5:break;case 6:return 19;case 7:return this.begin("LINE"),14;break;case 8:return this.begin("ID"),50;break;case 9:return this.begin("ID"),52;break;case 10:return 13;case 11:return this.begin("ID"),53;break;case 12:return J.yytext=J.yytext.trim(),this.begin("ALIAS"),70;break;case 13:return this.popState(),this.popState(),this.begin("LINE"),51;break;case 14:return this.popState(),this.popState(),5;break;case 15:return this.begin("LINE"),36;break;case 16:return this.begin("LINE"),37;break;case 17:return this.begin("LINE"),38;break;case 18:return this.begin("LINE"),39;break;case 19:return this.begin("LINE"),49;break;case 20:return this.begin("LINE"),41;break;case 21:return this.begin("LINE"),43;break;case 22:return this.begin("LINE"),48;break;case 23:return this.begin("LINE"),44;break;case 24:return this.begin("LINE"),47;break;case 25:return this.begin("LINE"),46;break;case 26:return this.popState(),15;break;case 27:return 16;case 28:return 65;case 29:return 66;case 30:return 59;case 31:return 60;case 32:return 61;case 33:return 62;case 34:return 57;case 35:return 54;case 36:return this.begin("ID"),21;break;case 37:return this.begin("ID"),23;break;case 38:return 29;case 39:return 30;case 40:return this.begin("acc_title"),31;break;case 41:return this.popState(),"acc_title_value";break;case 42:return this.begin("acc_descr"),33;break;case 43:return this.popState(),"acc_descr_value";break;case 44:this.begin("acc_descr_multiline");break;case 45:this.popState();break;case 46:return"acc_descr_multiline_value";case 47:return 6;case 48:return 18;case 49:return 20;case 50:return 64;case 51:return 5;case 52:return J.yytext=J.yytext.trim(),70;break;case 53:return 73;case 54:return 74;case 55:return 75;case 56:return 76;case 57:return 71;case 58:return 72;case 59:return 77;case 60:return 78;case 61:return 79;case 62:return 80;case 63:return 81;case 64:return 68;case 65:return 69;case 66:return 5;case 67:return"INVALID"}},"anonymous"),rules:[/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:((?!\n)\s)+)/i,/^(?:#[^\n]*)/i,/^(?:%(?!\{)[^\n]*)/i,/^(?:[^\}]%%[^\n]*)/i,/^(?:[0-9]+(?=[ \n]+))/i,/^(?:box\b)/i,/^(?:participant\b)/i,/^(?:actor\b)/i,/^(?:create\b)/i,/^(?:destroy\b)/i,/^(?:[^\<->\->:\n,;]+?([\-]*[^\<->\->:\n,;]+?)*?(?=((?!\n)\s)+as(?!\n)\s|[#\n;]|$))/i,/^(?:as\b)/i,/^(?:(?:))/i,/^(?:loop\b)/i,/^(?:rect\b)/i,/^(?:opt\b)/i,/^(?:alt\b)/i,/^(?:else\b)/i,/^(?:par\b)/i,/^(?:par_over\b)/i,/^(?:and\b)/i,/^(?:critical\b)/i,/^(?:option\b)/i,/^(?:break\b)/i,/^(?:(?:[:]?(?:no)?wrap)?[^#\n;]*)/i,/^(?:end\b)/i,/^(?:left of\b)/i,/^(?:right of\b)/i,/^(?:links\b)/i,/^(?:link\b)/i,/^(?:properties\b)/i,/^(?:details\b)/i,/^(?:over\b)/i,/^(?:note\b)/i,/^(?:activate\b)/i,/^(?:deactivate\b)/i,/^(?:title\s[^#\n;]+)/i,/^(?:title:\s[^#\n;]+)/i,/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:sequenceDiagram\b)/i,/^(?:autonumber\b)/i,/^(?:off\b)/i,/^(?:,)/i,/^(?:;)/i,/^(?:[^\+\<->\->:\n,;]+((?!(-x|--x|-\)|--\)))[\-]*[^\+\<->\->:\n,;]+)*)/i,/^(?:->>)/i,/^(?:<<->>)/i,/^(?:-->>)/i,/^(?:<<-->>)/i,/^(?:->)/i,/^(?:-->)/i,/^(?:-[x])/i,/^(?:--[x])/i,/^(?:-[\)])/i,/^(?:--[\)])/i,/^(?::(?:(?:no)?wrap)?[^#\n;]+)/i,/^(?:\+)/i,/^(?:-)/i,/^(?:$)/i,/^(?:.)/i],conditions:{acc_descr_multiline:{rules:[45,46],inclusive:!1},acc_descr:{rules:[43],inclusive:!1},acc_title:{rules:[41],inclusive:!1},ID:{rules:[2,3,12],inclusive:!1},ALIAS:{rules:[2,3,13,14],inclusive:!1},LINE:{rules:[2,3,26],inclusive:!1},INITIAL:{rules:[0,1,3,4,5,6,7,8,9,10,11,15,16,17,18,19,20,21,22,23,24,25,27,28,29,30,31,32,33,34,35,36,37,38,39,40,42,44,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67],inclusive:!0}}};return K}();ne.lexer=le;function he(){this.yy={}}return o(he,"Parser"),he.prototype=ne,ne.Parser=he,new he}();CO.parser=CO;lfe=CO});var iVe,aVe,sVe,_6,ufe=N(()=>{"use strict";zt();vt();s6();gr();mi();iVe={SOLID:0,DOTTED:1,NOTE:2,SOLID_CROSS:3,DOTTED_CROSS:4,SOLID_OPEN:5,DOTTED_OPEN:6,LOOP_START:10,LOOP_END:11,ALT_START:12,ALT_ELSE:13,ALT_END:14,OPT_START:15,OPT_END:16,ACTIVE_START:17,ACTIVE_END:18,PAR_START:19,PAR_AND:20,PAR_END:21,RECT_START:22,RECT_END:23,SOLID_POINT:24,DOTTED_POINT:25,AUTONUMBER:26,CRITICAL_START:27,CRITICAL_OPTION:28,CRITICAL_END:29,BREAK_START:30,BREAK_END:31,PAR_OVER_START:32,BIDIRECTIONAL_SOLID:33,BIDIRECTIONAL_DOTTED:34},aVe={FILLED:0,OPEN:1},sVe={LEFTOF:0,RIGHTOF:1,OVER:2},_6=class{constructor(){this.state=new pf(()=>({prevActor:void 0,actors:new Map,createdActors:new Map,destroyedActors:new Map,boxes:[],messages:[],notes:[],sequenceNumbersEnabled:!1,wrapEnabled:void 0,currentBox:void 0,lastCreated:void 0,lastDestroyed:void 0}));this.setAccTitle=Lr;this.setAccDescription=Nr;this.setDiagramTitle=$r;this.getAccTitle=Rr;this.getAccDescription=Mr;this.getDiagramTitle=Ir;this.apply=this.apply.bind(this),this.parseBoxData=this.parseBoxData.bind(this),this.parseMessage=this.parseMessage.bind(this),this.clear(),this.setWrap(me().wrap),this.LINETYPE=iVe,this.ARROWTYPE=aVe,this.PLACEMENT=sVe}static{o(this,"SequenceDB")}addBox(e){this.state.records.boxes.push({name:e.text,wrap:e.wrap??this.autoWrap(),fill:e.color,actorKeys:[]}),this.state.records.currentBox=this.state.records.boxes.slice(-1)[0]}addActor(e,r,n,i){let a=this.state.records.currentBox,s=this.state.records.actors.get(e);if(s){if(this.state.records.currentBox&&s.box&&this.state.records.currentBox!==s.box)throw new Error(`A same participant should only be defined in one Box: ${s.name} can't be in '${s.box.name}' and in '${this.state.records.currentBox.name}' at the same time.`);if(a=s.box?s.box:this.state.records.currentBox,s.box=a,s&&r===s.name&&n==null)return}if(n?.text==null&&(n={text:r,type:i}),(i==null||n.text==null)&&(n={text:r,type:i}),this.state.records.actors.set(e,{box:a,name:r,description:n.text,wrap:n.wrap??this.autoWrap(),prevActor:this.state.records.prevActor,links:{},properties:{},actorCnt:null,rectData:null,type:i??"participant"}),this.state.records.prevActor){let l=this.state.records.actors.get(this.state.records.prevActor);l&&(l.nextActor=e)}this.state.records.currentBox&&this.state.records.currentBox.actorKeys.push(e),this.state.records.prevActor=e}activationCount(e){let r,n=0;if(!e)return 0;for(r=0;r<this.state.records.messages.length;r++)this.state.records.messages[r].type===this.LINETYPE.ACTIVE_START&&this.state.records.messages[r].from===e&&n++,this.state.records.messages[r].type===this.LINETYPE.ACTIVE_END&&this.state.records.messages[r].from===e&&n--;return n}addMessage(e,r,n,i){this.state.records.messages.push({id:this.state.records.messages.length.toString(),from:e,to:r,message:n.text,wrap:n.wrap??this.autoWrap(),answer:i})}addSignal(e,r,n,i,a=!1){if(i===this.LINETYPE.ACTIVE_END&&this.activationCount(e??"")<1){let l=new Error("Trying to inactivate an inactive participant ("+e+")");throw l.hash={text:"->>-",token:"->>-",line:"1",loc:{first_line:1,last_line:1,first_column:1,last_column:1},expected:["'ACTIVE_PARTICIPANT'"]},l}return this.state.records.messages.push({id:this.state.records.messages.length.toString(),from:e,to:r,message:n?.text??"",wrap:n?.wrap??this.autoWrap(),type:i,activate:a}),!0}hasAtLeastOneBox(){return this.state.records.boxes.length>0}hasAtLeastOneBoxWithTitle(){return this.state.records.boxes.some(e=>e.name)}getMessages(){return this.state.records.messages}getBoxes(){return this.state.records.boxes}getActors(){return this.state.records.actors}getCreatedActors(){return this.state.records.createdActors}getDestroyedActors(){return this.state.records.destroyedActors}getActor(e){return this.state.records.actors.get(e)}getActorKeys(){return[...this.state.records.actors.keys()]}enableSequenceNumbers(){this.state.records.sequenceNumbersEnabled=!0}disableSequenceNumbers(){this.state.records.sequenceNumbersEnabled=!1}showSequenceNumbers(){return this.state.records.sequenceNumbersEnabled}setWrap(e){this.state.records.wrapEnabled=e}extractWrap(e){if(e===void 0)return{};e=e.trim();let r=/^:?wrap:/.exec(e)!==null?!0:/^:?nowrap:/.exec(e)!==null?!1:void 0;return{cleanedText:(r===void 0?e:e.replace(/^:?(?:no)?wrap:/,"")).trim(),wrap:r}}autoWrap(){return this.state.records.wrapEnabled!==void 0?this.state.records.wrapEnabled:me().sequence?.wrap??!1}clear(){this.state.reset(),Ar()}parseMessage(e){let r=e.trim(),{wrap:n,cleanedText:i}=this.extractWrap(r),a={text:i,wrap:n};return Y.debug(`parseMessage: ${JSON.stringify(a)}`),a}parseBoxData(e){let r=/^((?:rgba?|hsla?)\s*\(.*\)|\w*)(.*)$/.exec(e),n=r?.[1]?r[1].trim():"transparent",i=r?.[2]?r[2].trim():void 0;if(window?.CSS)window.CSS.supports("color",n)||(n="transparent",i=e.trim());else{let l=new Option().style;l.color=n,l.color!==n&&(n="transparent",i=e.trim())}let{wrap:a,cleanedText:s}=this.extractWrap(i);return{text:s?Tr(s,me()):void 0,color:n,wrap:a}}addNote(e,r,n){let i={actor:e,placement:r,message:n.text,wrap:n.wrap??this.autoWrap()},a=[].concat(e,e);this.state.records.notes.push(i),this.state.records.messages.push({id:this.state.records.messages.length.toString(),from:a[0],to:a[1],message:n.text,wrap:n.wrap??this.autoWrap(),type:this.LINETYPE.NOTE,placement:r})}addLinks(e,r){let n=this.getActor(e);try{let i=Tr(r.text,me());i=i.replace(/&equals;/g,"="),i=i.replace(/&amp;/g,"&");let a=JSON.parse(i);this.insertLinks(n,a)}catch(i){Y.error("error while parsing actor link text",i)}}addALink(e,r){let n=this.getActor(e);try{let i={},a=Tr(r.text,me()),s=a.indexOf("@");a=a.replace(/&equals;/g,"="),a=a.replace(/&amp;/g,"&");let l=a.slice(0,s-1).trim(),u=a.slice(s+1).trim();i[l]=u,this.insertLinks(n,i)}catch(i){Y.error("error while parsing actor link text",i)}}insertLinks(e,r){if(e.links==null)e.links=r;else for(let n in r)e.links[n]=r[n]}addProperties(e,r){let n=this.getActor(e);try{let i=Tr(r.text,me()),a=JSON.parse(i);this.insertProperties(n,a)}catch(i){Y.error("error while parsing actor properties text",i)}}insertProperties(e,r){if(e.properties==null)e.properties=r;else for(let n in r)e.properties[n]=r[n]}boxEnd(){this.state.records.currentBox=void 0}addDetails(e,r){let n=this.getActor(e),i=document.getElementById(r.text);try{let a=i.innerHTML,s=JSON.parse(a);s.properties&&this.insertProperties(n,s.properties),s.links&&this.insertLinks(n,s.links)}catch(a){Y.error("error while parsing actor details text",a)}}getActorProperty(e,r){if(e?.properties!==void 0)return e.properties[r]}apply(e){if(Array.isArray(e))e.forEach(r=>{this.apply(r)});else switch(e.type){case"sequenceIndex":this.state.records.messages.push({id:this.state.records.messages.length.toString(),from:void 0,to:void 0,message:{start:e.sequenceIndex,step:e.sequenceIndexStep,visible:e.sequenceVisible},wrap:!1,type:e.signalType});break;case"addParticipant":this.addActor(e.actor,e.actor,e.description,e.draw);break;case"createParticipant":if(this.state.records.actors.has(e.actor))throw new Error("It is not possible to have actors with the same id, even if one is destroyed before the next is created. Use 'AS' aliases to simulate the behavior");this.state.records.lastCreated=e.actor,this.addActor(e.actor,e.actor,e.description,e.draw),this.state.records.createdActors.set(e.actor,this.state.records.messages.length);break;case"destroyParticipant":this.state.records.lastDestroyed=e.actor,this.state.records.destroyedActors.set(e.actor,this.state.records.messages.length);break;case"activeStart":this.addSignal(e.actor,void 0,void 0,e.signalType);break;case"activeEnd":this.addSignal(e.actor,void 0,void 0,e.signalType);break;case"addNote":this.addNote(e.actor,e.placement,e.text);break;case"addLinks":this.addLinks(e.actor,e.text);break;case"addALink":this.addALink(e.actor,e.text);break;case"addProperties":this.addProperties(e.actor,e.text);break;case"addDetails":this.addDetails(e.actor,e.text);break;case"addMessage":if(this.state.records.lastCreated){if(e.to!==this.state.records.lastCreated)throw new Error("The created participant "+this.state.records.lastCreated.name+" does not have an associated creating message after its declaration. Please check the sequence diagram.");this.state.records.lastCreated=void 0}else if(this.state.records.lastDestroyed){if(e.to!==this.state.records.lastDestroyed&&e.from!==this.state.records.lastDestroyed)throw new Error("The destroyed participant "+this.state.records.lastDestroyed.name+" does not have an associated destroying message after its declaration. Please check the sequence diagram.");this.state.records.lastDestroyed=void 0}this.addSignal(e.from,e.to,e.msg,e.signalType,e.activate);break;case"boxStart":this.addBox(e.boxData);break;case"boxEnd":this.boxEnd();break;case"loopStart":this.addSignal(void 0,void 0,e.loopText,e.signalType);break;case"loopEnd":this.addSignal(void 0,void 0,void 0,e.signalType);break;case"rectStart":this.addSignal(void 0,void 0,e.color,e.signalType);break;case"rectEnd":this.addSignal(void 0,void 0,void 0,e.signalType);break;case"optStart":this.addSignal(void 0,void 0,e.optText,e.signalType);break;case"optEnd":this.addSignal(void 0,void 0,void 0,e.signalType);break;case"altStart":this.addSignal(void 0,void 0,e.altText,e.signalType);break;case"else":this.addSignal(void 0,void 0,e.altText,e.signalType);break;case"altEnd":this.addSignal(void 0,void 0,void 0,e.signalType);break;case"setAccTitle":Lr(e.text);break;case"parStart":this.addSignal(void 0,void 0,e.parText,e.signalType);break;case"and":this.addSignal(void 0,void 0,e.parText,e.signalType);break;case"parEnd":this.addSignal(void 0,void 0,void 0,e.signalType);break;case"criticalStart":this.addSignal(void 0,void 0,e.criticalText,e.signalType);break;case"option":this.addSignal(void 0,void 0,e.optionText,e.signalType);break;case"criticalEnd":this.addSignal(void 0,void 0,void 0,e.signalType);break;case"breakStart":this.addSignal(void 0,void 0,e.breakText,e.signalType);break;case"breakEnd":this.addSignal(void 0,void 0,void 0,e.signalType);break}}getConfig(){return me().sequence}}});var oVe,hfe,ffe=N(()=>{"use strict";oVe=o(t=>`.actor {
-    stroke: ${t.actorBorder};
-    fill: ${t.actorBkg};
-  }
-
-  text.actor > tspan {
-    fill: ${t.actorTextColor};
-    stroke: none;
-  }
-
-  .actor-line {
-    stroke: ${t.actorLineColor};
-  }
-
-  .messageLine0 {
-    stroke-width: 1.5;
-    stroke-dasharray: none;
-    stroke: ${t.signalColor};
-  }
-
-  .messageLine1 {
-    stroke-width: 1.5;
-    stroke-dasharray: 2, 2;
-    stroke: ${t.signalColor};
-  }
-
-  #arrowhead path {
-    fill: ${t.signalColor};
-    stroke: ${t.signalColor};
-  }
-
-  .sequenceNumber {
-    fill: ${t.sequenceNumberColor};
-  }
-
-  #sequencenumber {
-    fill: ${t.signalColor};
-  }
-
-  #crosshead path {
-    fill: ${t.signalColor};
-    stroke: ${t.signalColor};
-  }
-
-  .messageText {
-    fill: ${t.signalTextColor};
-    stroke: none;
-  }
-
-  .labelBox {
-    stroke: ${t.labelBoxBorderColor};
-    fill: ${t.labelBoxBkgColor};
-  }
-
-  .labelText, .labelText > tspan {
-    fill: ${t.labelTextColor};
-    stroke: none;
-  }
-
-  .loopText, .loopText > tspan {
-    fill: ${t.loopTextColor};
-    stroke: none;
-  }
-
-  .loopLine {
-    stroke-width: 2px;
-    stroke-dasharray: 2, 2;
-    stroke: ${t.labelBoxBorderColor};
-    fill: ${t.labelBoxBorderColor};
-  }
-
-  .note {
-    //stroke: #decc93;
-    stroke: ${t.noteBorderColor};
-    fill: ${t.noteBkgColor};
-  }
-
-  .noteText, .noteText > tspan {
-    fill: ${t.noteTextColor};
-    stroke: none;
-  }
-
-  .activation0 {
-    fill: ${t.activationBkgColor};
-    stroke: ${t.activationBorderColor};
-  }
-
-  .activation1 {
-    fill: ${t.activationBkgColor};
-    stroke: ${t.activationBorderColor};
-  }
-
-  .activation2 {
-    fill: ${t.activationBkgColor};
-    stroke: ${t.activationBorderColor};
-  }
-
-  .actorPopupMenu {
-    position: absolute;
-  }
-
-  .actorPopupMenuPanel {
-    position: absolute;
-    fill: ${t.actorBkg};
-    box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
-    filter: drop-shadow(3px 5px 2px rgb(0 0 0 / 0.4));
-}
-  .actor-man line {
-    stroke: ${t.actorBorder};
-    fill: ${t.actorBkg};
-  }
-  .actor-man circle, line {
-    stroke: ${t.actorBorder};
-    fill: ${t.actorBkg};
-    stroke-width: 2px;
-  }
-`,"getStyles"),hfe=oVe});var AO,vf,pfe,mfe,lVe,dfe,_O,cVe,uVe,Tb,_p,gfe,Uc,DO,hVe,fVe,dVe,pVe,mVe,gVe,yVe,yfe,vVe,xVe,bVe,wVe,TVe,kVe,EVe,vfe,SVe,LO,CVe,hi,xfe=N(()=>{"use strict";gr();Wv();ir();AO=Sa(z0(),1);ji();vf=18*2,pfe="actor-top",mfe="actor-bottom",lVe="actor-box",dfe="actor-man",_O=o(function(t,e){return kd(t,e)},"drawRect"),cVe=o(function(t,e,r,n,i){if(e.links===void 0||e.links===null||Object.keys(e.links).length===0)return{height:0,width:0};let a=e.links,s=e.actorCnt,l=e.rectData;var u="none";i&&(u="block !important");let h=t.append("g");h.attr("id","actor"+s+"_popup"),h.attr("class","actorPopupMenu"),h.attr("display",u);var f="";l.class!==void 0&&(f=" "+l.class);let d=l.width>r?l.width:r,p=h.append("rect");if(p.attr("class","actorPopupMenuPanel"+f),p.attr("x",l.x),p.attr("y",l.height),p.attr("fill",l.fill),p.attr("stroke",l.stroke),p.attr("width",d),p.attr("height",l.height),p.attr("rx",l.rx),p.attr("ry",l.ry),a!=null){var m=20;for(let v in a){var g=h.append("a"),y=(0,AO.sanitizeUrl)(a[v]);g.attr("xlink:href",y),g.attr("target","_blank"),CVe(n)(v,g,l.x+10,l.height+m,d,20,{class:"actor"},n),m+=30}}return p.attr("height",m),{height:l.height+m,width:d}},"drawPopup"),uVe=o(function(t){return"var pu = document.getElementById('"+t+"'); if (pu != null) { pu.style.display = pu.style.display == 'block' ? 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-Expecting `+nt.join(", ")+", got '"+(this.terminals_[We]||We)+"'":dn="Parse error on line "+(pe+1)+": Unexpected "+(We==we?"end of input":"'"+(this.terminals_[We]||We)+"'"),this.parseError(dn,{text:De.match,token:this.terminals_[We]||We,line:De.yylineno,loc:Rt,expected:nt})}if(bt[0]instanceof Array&&bt.length>1)throw new Error("Parse Error: multiple actions possible at state: "+Yt+", token: "+We);switch(bt[0]){case 1:de.push(We),oe.push(De.yytext),V.push(De.yylloc),de.push(bt[1]),We=null,ot?(We=ot,ot=null):(ve=De.yyleng,q=De.yytext,pe=De.yylineno,Rt=De.yylloc,Pe>0&&Pe--);break;case 2:if(ft=this.productions_[bt[1]][1],ut.$=oe[oe.length-ft],ut._$={first_line:V[V.length-(ft||1)].first_line,last_line:V[V.length-1].last_line,first_column:V[V.length-(ft||1)].first_column,last_column:V[V.length-1].last_column},st&&(ut._$.range=[V[V.length-(ft||1)].range[0],V[V.length-1].range[1]]),xt=this.performAction.apply(ut,[q,ve,pe,qe.yy,bt[1],oe,V].concat(Ve)),typeof xt<"u")return xt;ft&&(de=de.slice(0,-1*ft*2),oe=oe.slice(0,-1*ft),V=V.slice(0,-1*ft)),de.push(this.productions_[bt[1]][0]),oe.push(ut.$),V.push(ut._$),yt=xe[de[de.length-2]][de[de.length-1]],de.push(yt);break;case 3:return!0}}return!0},"parse")},$e=function(){var Ie={EOF:1,parseError:o(function(W,de){if(this.yy.parser)this.yy.parser.parseError(W,de);else throw new Error(W)},"parseError"),setInput:o(function(be,W){return this.yy=W||this.yy||{},this._input=be,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},"setInput"),input:o(function(){var be=this._input[0];this.yytext+=be,this.yyleng++,this.offset++,this.match+=be,this.matched+=be;var W=be.match(/(?:\r\n?|\n).*/g);return W?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),be},"input"),unput:o(function(be){var W=be.length,de=be.split(/(?:\r\n?|\n)/g);this._input=be+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-W),this.offset-=W;var re=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),de.length-1&&(this.yylineno-=de.length-1);var oe=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:de?(de.length===re.length?this.yylloc.first_column:0)+re[re.length-de.length].length-de[0].length:this.yylloc.first_column-W},this.options.ranges&&(this.yylloc.range=[oe[0],oe[0]+this.yyleng-W]),this.yyleng=this.yytext.length,this},"unput"),more:o(function(){return this._more=!0,this},"more"),reject:o(function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
-`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},"reject"),less:o(function(be){this.unput(this.match.slice(be))},"less"),pastInput:o(function(){var be=this.matched.substr(0,this.matched.length-this.match.length);return(be.length>20?"...":"")+be.substr(-20).replace(/\n/g,"")},"pastInput"),upcomingInput:o(function(){var be=this.match;return be.length<20&&(be+=this._input.substr(0,20-be.length)),(be.substr(0,20)+(be.length>20?"...":"")).replace(/\n/g,"")},"upcomingInput"),showPosition:o(function(){var be=this.pastInput(),W=new Array(be.length+1).join("-");return be+this.upcomingInput()+`
-`+W+"^"},"showPosition"),test_match:o(function(be,W){var de,re,oe;if(this.options.backtrack_lexer&&(oe={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(oe.yylloc.range=this.yylloc.range.slice(0))),re=be[0].match(/(?:\r\n?|\n).*/g),re&&(this.yylineno+=re.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:re?re[re.length-1].length-re[re.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+be[0].length},this.yytext+=be[0],this.match+=be[0],this.matches=be,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(be[0].length),this.matched+=be[0],de=this.performAction.call(this,this.yy,this,W,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),de)return de;if(this._backtrack){for(var V in oe)this[V]=oe[V];return!1}return!1},"test_match"),next:o(function(){if(this.done)return this.EOF;this._input||(this.done=!0);var be,W,de,re;this._more||(this.yytext="",this.match="");for(var oe=this._currentRules(),V=0;V<oe.length;V++)if(de=this._input.match(this.rules[oe[V]]),de&&(!W||de[0].length>W[0].length)){if(W=de,re=V,this.options.backtrack_lexer){if(be=this.test_match(de,oe[V]),be!==!1)return be;if(this._backtrack){W=!1;continue}else return!1}else if(!this.options.flex)break}return W?(be=this.test_match(W,oe[re]),be!==!1?be:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
-`+this.showPosition(),{text:"",token:null,line:this.yylineno})},"next"),lex:o(function(){var W=this.next();return W||this.lex()},"lex"),begin:o(function(W){this.conditionStack.push(W)},"begin"),popState:o(function(){var W=this.conditionStack.length-1;return W>0?this.conditionStack.pop():this.conditionStack[0]},"popState"),_currentRules:o(function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},"_currentRules"),topState:o(function(W){return W=this.conditionStack.length-1-Math.abs(W||0),W>=0?this.conditionStack[W]:"INITIAL"},"topState"),pushState:o(function(W){this.begin(W)},"pushState"),stateStackSize:o(function(){return this.conditionStack.length},"stateStackSize"),options:{},performAction:o(function(W,de,re,oe){var V=oe;switch(re){case 0:return 60;case 1:return 61;case 2:return 62;case 3:return 63;case 4:break;case 5:break;case 6:return this.begin("acc_title"),33;break;case 7:return this.popState(),"acc_title_value";break;case 8:return this.begin("acc_descr"),35;break;case 9:return this.popState(),"acc_descr_value";break;case 10:this.begin("acc_descr_multiline");break;case 11:this.popState();break;case 12:return"acc_descr_multiline_value";case 13:return 8;case 14:break;case 15:return 7;case 16:return 7;case 17:return"EDGE_STATE";case 18:this.begin("callback_name");break;case 19:this.popState();break;case 20:this.popState(),this.begin("callback_args");break;case 21:return 77;case 22:this.popState();break;case 23:return 78;case 24:this.popState();break;case 25:return"STR";case 26:this.begin("string");break;case 27:return 80;case 28:return 55;case 29:return this.begin("namespace"),42;break;case 30:return this.popState(),8;break;case 31:break;case 32:return this.begin("namespace-body"),39;break;case 33:return this.popState(),41;break;case 34:return"EOF_IN_STRUCT";case 35:return 8;case 36:break;case 37:return"EDGE_STATE";case 38:return this.begin("class"),46;break;case 39:return this.popState(),8;break;case 40:break;case 41:return this.popState(),this.popState(),41;break;case 42:return this.begin("class-body"),39;break;case 43:return this.popState(),41;break;case 44:return"EOF_IN_STRUCT";case 45:return"EDGE_STATE";case 46:return"OPEN_IN_STRUCT";case 47:break;case 48:return"MEMBER";case 49:return 81;case 50:return 73;case 51:return 74;case 52:return 76;case 53:return 52;case 54:return 54;case 55:return 47;case 56:return 48;case 57:return 79;case 58:this.popState();break;case 59:return"GENERICTYPE";case 60:this.begin("generic");break;case 61:this.popState();break;case 62:return"BQUOTE_STR";case 63:this.begin("bqstring");break;case 64:return 75;case 65:return 75;case 66:return 75;case 67:return 75;case 68:return 67;case 69:return 67;case 70:return 69;case 71:return 69;case 72:return 68;case 73:return 66;case 74:return 70;case 75:return 71;case 76:return 72;case 77:return 22;case 78:return 44;case 79:return 99;case 80:return 17;case 81:return"PLUS";case 82:return 85;case 83:return 59;case 84:return 88;case 85:return 88;case 86:return 89;case 87:return"EQUALS";case 88:return"EQUALS";case 89:return 58;case 90:return 12;case 91:return 14;case 92:return"PUNCTUATION";case 93:return 84;case 94:return 101;case 95:return 87;case 96:return 87;case 97:return 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-    color: ${t.textColor};
-  }
-  .mouth {
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-  }
-
-  line {
-    stroke: ${t.textColor}
-  }
-
-  .legend {
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-    font-family: ${t.fontFamily};
-  }
-
-  .label text {
-    fill: #333;
-  }
-  .label {
-    color: ${t.textColor}
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-
-  .face {
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-    stroke: #999;
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-
-  .node rect,
-  .node circle,
-  .node ellipse,
-  .node polygon,
-  .node path {
-    fill: ${t.mainBkg};
-    stroke: ${t.nodeBorder};
-    stroke-width: 1px;
-  }
-
-  .node .label {
-    text-align: center;
-  }
-  .node.clickable {
-    cursor: pointer;
-  }
-
-  .arrowheadPath {
-    fill: ${t.arrowheadColor};
-  }
-
-  .edgePath .path {
-    stroke: ${t.lineColor};
-    stroke-width: 1.5px;
-  }
-
-  .flowchart-link {
-    stroke: ${t.lineColor};
-    fill: none;
-  }
-
-  .edgeLabel {
-    background-color: ${t.edgeLabelBackground};
-    rect {
-      opacity: 0.5;
-    }
-    text-align: center;
-  }
-
-  .cluster rect {
-  }
-
-  .cluster text {
-    fill: ${t.titleColor};
-  }
-
-  div.mermaidTooltip {
-    position: absolute;
-    text-align: center;
-    max-width: 200px;
-    padding: 2px;
-    font-family: ${t.fontFamily};
-    font-size: 12px;
-    background: ${t.tertiaryColor};
-    border: 1px solid ${t.border2};
-    border-radius: 2px;
-    pointer-events: none;
-    z-index: 100;
-  }
-
-  .task-type-0, .section-type-0  {
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-  .task-type-3, .section-type-3  {
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-  .task-type-4, .section-type-4  {
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-  }
-  .task-type-5, .section-type-5  {
-    ${t.fillType0?`fill: ${t.fillType5}`:""};
-  }
-  .task-type-6, .section-type-6  {
-    ${t.fillType0?`fill: ${t.fillType6}`:""};
-  }
-  .task-type-7, .section-type-7  {
-    ${t.fillType0?`fill: ${t.fillType7}`:""};
-  }
-
-  .actor-0 {
-    ${t.actor0?`fill: ${t.actor0}`:""};
-  }
-  .actor-1 {
-    ${t.actor1?`fill: ${t.actor1}`:""};
-  }
-  .actor-2 {
-    ${t.actor2?`fill: ${t.actor2}`:""};
-  }
-  .actor-3 {
-    ${t.actor3?`fill: ${t.actor3}`:""};
-  }
-  .actor-4 {
-    ${t.actor4?`fill: ${t.actor4}`:""};
-  }
-  .actor-5 {
-    ${t.actor5?`fill: ${t.actor5}`:""};
-  }
-`,"getStyles"),Ide=WUe});var cP,qUe,Bde,Fde,YUe,XUe,Pde,jUe,KUe,$de,QUe,I1,zde=N(()=>{"use strict";dr();Wv();cP=o(function(t,e){return kd(t,e)},"drawRect"),qUe=o(function(t,e){let n=t.append("circle").attr("cx",e.cx).attr("cy",e.cy).attr("class","face").attr("r",15).attr("stroke-width",2).attr("overflow","visible"),i=t.append("g");i.append("circle").attr("cx",e.cx-15/3).attr("cy",e.cy-15/3).attr("r",1.5).attr("stroke-width",2).attr("fill","#666").attr("stroke","#666"),i.append("circle").attr("cx",e.cx+15/3).attr("cy",e.cy-15/3).attr("r",1.5).attr("stroke-width",2).attr("fill","#666").attr("stroke","#666");function a(u){let h=bl().startAngle(Math.PI/2).endAngle(3*(Math.PI/2)).innerRadius(7.5).outerRadius(6.8181818181818175);u.append("path").attr("class","mouth").attr("d",h).attr("transform","translate("+e.cx+","+(e.cy+2)+")")}o(a,"smile");function s(u){let h=bl().startAngle(3*Math.PI/2).endAngle(5*(Math.PI/2)).innerRadius(7.5).outerRadius(6.8181818181818175);u.append("path").attr("class","mouth").attr("d",h).attr("transform","translate("+e.cx+","+(e.cy+7)+")")}o(s,"sad");function l(u){u.append("line").attr("class","mouth").attr("stroke",2).attr("x1",e.cx-5).attr("y1",e.cy+7).attr("x2",e.cx+5).attr("y2",e.cy+7).attr("class","mouth").attr("stroke-width","1px").attr("stroke","#666")}return o(l,"ambivalent"),e.score>3?a(i):e.score<3?s(i):l(i),n},"drawFace"),Bde=o(function(t,e){let r=t.append("circle");return r.attr("cx",e.cx),r.attr("cy",e.cy),r.attr("class","actor-"+e.pos),r.attr("fill",e.fill),r.attr("stroke",e.stroke),r.attr("r",e.r),r.class!==void 0&&r.attr("class",r.class),e.title!==void 0&&r.append("title").text(e.title),r},"drawCircle"),Fde=o(function(t,e){return Nq(t,e)},"drawText"),YUe=o(function(t,e){function r(i,a,s,l,u){return i+","+a+" "+(i+s)+","+a+" "+(i+s)+","+(a+l-u)+" "+(i+s-u*1.2)+","+(a+l)+" 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l=e.x+14;e.people.forEach(u=>{let h=e.actors[u].color,f={cx:l,cy:e.y,r:7,fill:h,stroke:"#000",title:u,pos:e.actors[u].position};Bde(i,f),l+=10}),$de(r)(e.task,i,s.x,s.y,s.width,s.height,{class:"task"},r,e.colour)},"drawTask"),KUe=o(function(t,e){q5(t,e)},"drawBackgroundRect"),$de=function(){function t(i,a,s,l,u,h,f,d){let p=a.append("text").attr("x",s+u/2).attr("y",l+h/2+5).style("font-color",d).style("text-anchor","middle").text(i);n(p,f)}o(t,"byText");function e(i,a,s,l,u,h,f,d,p){let{taskFontSize:m,taskFontFamily:g}=d,y=i.split(/<br\s*\/?>/gi);for(let v=0;v<y.length;v++){let x=v*m-m*(y.length-1)/2,b=a.append("text").attr("x",s+u/2).attr("y",l).attr("fill",p).style("text-anchor","middle").style("font-size",m).style("font-family",g);b.append("tspan").attr("x",s+u/2).attr("dy",x).text(y[v]),b.attr("y",l+h/2).attr("dominant-baseline","central").attr("alignment-baseline","central"),n(b,f)}}o(e,"byTspan");function r(i,a,s,l,u,h,f,d){let 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r=this,n=r._private,i=n.options;return!vS(e)&&vS(e[0])&&(e=e[0]),r.stopAnimationLoop(),r.destroyRenderer(),n.container=e,n.styleEnabled=!0,r.invalidateSize(),r.initRenderer(rr({},i,i.renderer,{name:i.renderer.name==="null"?"canvas":i.renderer.name})),r.startAnimationLoop(),r.style(i.style),r.emit("mount"),r}},"mount"),unmount:o(function(){var e=this;return e.stopAnimationLoop(),e.destroyRenderer(),e.initRenderer({name:"null"}),e.emit("unmount"),e},"unmount"),options:o(function(){return Yc(this._private.options)},"options"),json:o(function(e){var r=this,n=r._private,i=r.mutableElements(),a=o(function(C){return r.getElementById(C.id())},"getFreshRef");if(Ur(e)){if(r.startBatch(),e.elements){var s={},l=o(function(C,T){for(var E=[],A=[],S=0;S<C.length;S++){var _=C[S];if(!_.data.id){un("cy.json() cannot handle elements without an ID attribute");continue}var I=""+_.data.id,D=r.getElementById(I);s[I]=!0,D.length!==0?A.push({ele:D,json:_}):(T&&(_.group=T),E.push(_))}r.add(E);for(var 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C=w.group();x.elements[C]||(x.elements[C]=[]),x.elements[C].push(w.json())})),this._private.styleEnabled&&(x.style=r.style().json()),x.data=Yc(r.data());var b=n.options;return x.zoomingEnabled=n.zoomingEnabled,x.userZoomingEnabled=n.userZoomingEnabled,x.zoom=n.zoom,x.minZoom=n.minZoom,x.maxZoom=n.maxZoom,x.panningEnabled=n.panningEnabled,x.userPanningEnabled=n.userPanningEnabled,x.pan=Yc(n.pan),x.boxSelectionEnabled=n.boxSelectionEnabled,x.renderer=Yc(b.renderer),x.hideEdgesOnViewport=b.hideEdgesOnViewport,x.textureOnViewport=b.textureOnViewport,x.wheelSensitivity=b.wheelSensitivity,x.motionBlur=b.motionBlur,x.multiClickDebounceTime=b.multiClickDebounceTime,x}},"json")});SS.$id=SS.getElementById;[VKe,YKe,ege,VP,pS,jKe,UP,mS,ZKe,Hp,Zb].forEach(function(t){rr(SS,t)});eQe={fit:!0,directed:!1,padding:30,circle:!1,grid:!1,spacingFactor:1.75,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,roots:void 0,depthSort:void 0,animate:!1,animationDuration:500,animationEasing:void 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Bailing maximal adjustment due to cycle.  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se={x:h.x1+h.w/2,y:h.y1+h.h/2},ue=n.reduce(function(ae,Oe){return function(ge){return{w:ae.w===-1?ge.w:(ae.w+ge.w)/2,h:ae.h===-1?ge.h:(ae.h+ge.h)/2}}(Oe.boundingBox({includeLabels:t.nodeDimensionsIncludeLabels}))},{w:-1,h:-1}),Z=Math.max(ne===1?0:l?(h.h-t.padding*2-ue.h)/(ne-1):(h.h-t.padding*2-ue.h)/(ne+1),B),Se=b.reduce(function(ae,Oe){return Math.max(ae,Oe.length)},0),ce=o(function(Oe){var ge=z1(Oe),ze=ge.depth,He=ge.index;if(t.circle){var $e=Math.min(h.w/2/ne,h.h/2/ne);$e=Math.max($e,B);var Re=$e*ze+$e-(ne>0&&b[0].length<=3?$e/2:0),Ie=2*Math.PI/b[ze].length*He;return ze===0&&b[0].length===1&&(Re=1),{x:se.x+Re*Math.cos(Ie),y:se.y+Re*Math.sin(Ie)}}else{var be=b[ze].length,W=Math.max(be===1?0:l?(h.w-t.padding*2-ue.w)/((t.grid?Se:be)-1):(h.w-t.padding*2-ue.w)/((t.grid?Se:be)+1),B),de={x:se.x+(He+1-(be+1)/2)*W,y:se.y+(ze+1-(ne+1)/2)*Z};return de}},"getPosition");return r.nodes().layoutPositions(this,t,ce),this};rQe={fit:!0,padding:30,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,spacingFactor:void 0,radius:void 0,startAngle:3/2*Math.PI,sweep:void 0,clockwise:!0,sort:void 0,animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(e,r){return r},"transform")};o(rge,"CircleLayout");rge.prototype.run=function(){var t=this.options,e=t,r=t.cy,n=e.eles,i=e.counterclockwise!==void 0?!e.counterclockwise:e.clockwise,a=n.nodes().not(":parent");e.sort&&(a=a.sort(e.sort));for(var s=Hs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:r.width(),h:r.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=e.sweep===void 0?2*Math.PI-2*Math.PI/a.length:e.sweep,h=u/Math.max(1,a.length-1),f,d=0,p=0;p<a.length;p++){var m=a[p],g=m.layoutDimensions(e),y=g.w,v=g.h;d=Math.max(d,y,v)}if(Ct(e.radius)?f=e.radius:a.length<=1?f=0:f=Math.min(s.h,s.w)/2-d,a.length>1&&e.avoidOverlap){d*=1.75;var x=Math.cos(h)-Math.cos(0),b=Math.sin(h)-Math.sin(0),w=Math.sqrt(d*d/(x*x+b*b));f=Math.max(w,f)}var C=o(function(E,A){var S=e.startAngle+A*h*(i?1:-1),_=f*Math.cos(S),I=f*Math.sin(S),D={x:l.x+_,y:l.y+I};return D},"getPos");return n.nodes().layoutPositions(this,e,C),this};nQe={fit:!0,padding:30,startAngle:3/2*Math.PI,sweep:void 0,clockwise:!0,equidistant:!1,minNodeSpacing:10,boundingBox:void 0,avoidOverlap:!0,nodeDimensionsIncludeLabels:!1,height:void 0,width:void 0,spacingFactor:void 0,concentric:o(function(e){return e.degree()},"concentric"),levelWidth:o(function(e){return e.maxDegree()/4},"levelWidth"),animate:!1,animationDuration:500,animationEasing:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),ready:void 0,stop:void 0,transform:o(function(e,r){return r},"transform")};o(nge,"ConcentricLayout");nge.prototype.run=function(){for(var t=this.options,e=t,r=e.counterclockwise!==void 0?!e.counterclockwise:e.clockwise,n=t.cy,i=e.eles,a=i.nodes().not(":parent"),s=Hs(e.boundingBox?e.boundingBox:{x1:0,y1:0,w:n.width(),h:n.height()}),l={x:s.x1+s.w/2,y:s.y1+s.h/2},u=[],h=0,f=0;f<a.length;f++){var d=a[f],p=void 0;p=e.concentric(d),u.push({value:p,node:d}),d._private.scratch.concentric=p}a.updateStyle();for(var m=0;m<a.length;m++){var g=a[m],y=g.layoutDimensions(e);h=Math.max(h,y.w,y.h)}u.sort(function(ue,Z){return Z.value-ue.value});for(var v=e.levelWidth(a),x=[[]],b=x[0],w=0;w<u.length;w++){var C=u[w];if(b.length>0){var T=Math.abs(b[0].value-C.value);T>=v&&(b=[],x.push(b))}b.push(C)}var E=h+e.minNodeSpacing;if(!e.avoidOverlap){var A=x.length>0&&x[0].length>1,S=Math.min(s.w,s.h)/2-E,_=S/(x.length+A?1:0);E=Math.min(E,_)}for(var I=0,D=0;D<x.length;D++){var k=x[D],L=e.sweep===void 0?2*Math.PI-2*Math.PI/k.length:e.sweep,R=k.dTheta=L/Math.max(1,k.length-1);if(k.length>1&&e.avoidOverlap){var O=Math.cos(R)-Math.cos(0),M=Math.sin(R)-Math.sin(0),B=Math.sqrt(E*E/(O*O+M*M));I=Math.max(B,I)}k.r=I,I+=E}if(e.equidistant){for(var F=0,P=0,z=0;z<x.length;z++){var $=x[z],H=$.r-P;F=Math.max(F,H)}P=0;for(var Q=0;Q<x.length;Q++){var j=x[Q];Q===0&&(P=j.r),j.r=P,P+=F}}for(var ie={},ne=0;ne<x.length;ne++)for(var le=x[ne],he=le.dTheta,K=le.r,X=0;X<le.length;X++){var te=le[X],J=e.startAngle+(r?1:-1)*he*X,se={x:l.x+K*Math.cos(J),y:l.y+K*Math.sin(J)};ie[te.node.id()]=se}return i.nodes().layoutPositions(this,e,function(ue){var Z=ue.id();return ie[Z]}),this};iQe={ready:o(function(){},"ready"),stop:o(function(){},"stop"),animate:!0,animationEasing:void 0,animationDuration:void 0,animateFilter:o(function(e,r){return!0},"animateFilter"),animationThreshold:250,refresh:20,fit:!0,padding:30,boundingBox:void 0,nodeDimensionsIncludeLabels:!1,randomize:!1,componentSpacing:40,nodeRepulsion:o(function(e){return 2048},"nodeRepulsion"),nodeOverlap:4,idealEdgeLength:o(function(e){return 32},"idealEdgeLength"),edgeElasticity:o(function(e){return 32},"edgeElasticity"),nestingFactor:1.2,gravity:1,numIter:1e3,initialTemp:1e3,coolingFactor:.99,minTemp:1};o(HS,"CoseLayout");HS.prototype.run=function(){var t=this.options,e=t.cy,r=this;r.stopped=!1,(t.animate===!0||t.animate===!1)&&r.emit({type:"layoutstart",layout:r}),t.debug===!0?_P=!0:_P=!1;var n=aQe(e,r,t);_P&&lQe(n),t.randomize&&cQe(n);var i=Qu(),a=o(function(){uQe(n,e,t),t.fit===!0&&e.fit(t.padding)},"refresh"),s=o(function(p){return!(r.stopped||p>=t.numIter||(hQe(n,t),n.temperature=n.temperature*t.coolingFactor,n.temperature<t.minTemp))},"mainLoop"),l=o(function(){if(t.animate===!0||t.animate===!1)a(),r.one("layoutstop",t.stop),r.emit({type:"layoutstop",layout:r});else{var p=t.eles.nodes(),m=ige(n,t,p);p.layoutPositions(r,t,m)}},"done"),u=0,h=!0;if(t.animate===!0){var f=o(function d(){for(var p=0;h&&p<t.refresh;)h=s(u),u++,p++;if(!h)E0e(n,t),l();else{var 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g=a[f],y=g.layoutDimensions(n),v={};v.isLocked=g.locked(),v.id=g.data("id"),v.parentId=g.data("parent"),v.cmptId=h[g.id()],v.children=[],v.positionX=g.position("x"),v.positionY=g.position("y"),v.offsetX=0,v.offsetY=0,v.height=y.w,v.width=y.h,v.maxX=v.positionX+v.width/2,v.minX=v.positionX-v.width/2,v.maxY=v.positionY+v.height/2,v.minY=v.positionY-v.height/2,v.padLeft=parseFloat(g.style("padding")),v.padRight=parseFloat(g.style("padding")),v.padTop=parseFloat(g.style("padding")),v.padBottom=parseFloat(g.style("padding")),v.nodeRepulsion=si(n.nodeRepulsion)?n.nodeRepulsion(g):n.nodeRepulsion,l.layoutNodes.push(v),l.idToIndex[v.id]=f}for(var x=[],b=0,w=-1,C=[],f=0;f<l.nodeSize;f++){var g=l.layoutNodes[f],T=g.parentId;T!=null?l.layoutNodes[l.idToIndex[T]].children.push(g.id):(x[++w]=g.id,C.push(g.id))}for(l.graphSet.push(C);b<=w;){var E=x[b++],A=l.idToIndex[E],m=l.layoutNodes[A],S=m.children;if(S.length>0){l.graphSet.push(S);for(var f=0;f<S.length;f++)x[++w]=S[f]}}for(var f=0;f<l.graphSet.length;f++)for(var _=l.graphSet[f],p=0;p<_.length;p++){var I=l.idToIndex[_[p]];l.indexToGraph[I]=f}for(var f=0;f<l.edgeSize;f++){var D=i[f],k={};k.id=D.data("id"),k.sourceId=D.data("source"),k.targetId=D.data("target");var L=si(n.idealEdgeLength)?n.idealEdgeLength(D):n.idealEdgeLength,R=si(n.edgeElasticity)?n.edgeElasticity(D):n.edgeElasticity,O=l.idToIndex[k.sourceId],M=l.idToIndex[k.targetId],B=l.indexToGraph[O],F=l.indexToGraph[M];if(B!=F){for(var P=sQe(k.sourceId,k.targetId,l),z=l.graphSet[P],$=0,v=l.layoutNodes[O];z.indexOf(v.id)===-1;)v=l.layoutNodes[l.idToIndex[v.parentId]],$++;for(v=l.layoutNodes[M];z.indexOf(v.id)===-1;)v=l.layoutNodes[l.idToIndex[v.parentId]],$++;L*=$*n.nestingFactor}k.idealLength=L,k.elasticity=R,l.layoutEdges.push(k)}return l},"createLayoutInfo"),sQe=o(function(e,r,n){var i=oQe(e,r,0,n);return 2>i.count?0:i.graph},"findLCA"),oQe=o(function t(e,r,n,i){var a=i.graphSet[n];if(-1<a.indexOf(e)&&-1<a.indexOf(r))return{count:2,graph:n};for(var s=0,l=0;l<a.length;l++){var u=a[l],h=i.idToIndex[u],f=i.layoutNodes[h].children;if(f.length!==0){var d=i.indexToGraph[i.idToIndex[f[0]]],p=t(e,r,d,i);if(p.count!==0)if(p.count===1){if(s++,s===2)break}else return p}}return{count:s,graph:n}},"findLCA_aux"),cQe=o(function(e,r){for(var n=e.clientWidth,i=e.clientHeight,a=0;a<e.nodeSize;a++){var s=e.layoutNodes[a];s.children.length===0&&!s.isLocked&&(s.positionX=Math.random()*n,s.positionY=Math.random()*i)}},"randomizePositions"),ige=o(function(e,r,n){var i=e.boundingBox,a={x1:1/0,x2:-1/0,y1:1/0,y2:-1/0};return r.boundingBox&&(n.forEach(function(s){var l=e.layoutNodes[e.idToIndex[s.data("id")]];a.x1=Math.min(a.x1,l.positionX),a.x2=Math.max(a.x2,l.positionX),a.y1=Math.min(a.y1,l.positionY),a.y2=Math.max(a.y2,l.positionY)}),a.w=a.x2-a.x1,a.h=a.y2-a.y1),function(s,l){var u=e.layoutNodes[e.idToIndex[s.data("id")]];if(r.boundingBox){var h=(u.positionX-a.x1)/a.w,f=(u.positionY-a.y1)/a.h;return{x:i.x1+h*i.w,y:i.y1+f*i.h}}else 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y=CS(e,l,u),v=CS(r,-1*l,-1*u),x=v.x-y.x,b=v.y-y.y,w=x*x+b*b,p=Math.sqrt(w),d=(e.nodeRepulsion+r.nodeRepulsion)/w,m=d*x/p,g=d*b/p;e.isLocked||(e.offsetX-=m,e.offsetY-=g),r.isLocked||(r.offsetX+=m,r.offsetY+=g)}},"nodeRepulsion"),pQe=o(function(e,r,n,i){if(n>0)var a=e.maxX-r.minX;else var a=r.maxX-e.minX;if(i>0)var s=e.maxY-r.minY;else var s=r.maxY-e.minY;return a>=0&&s>=0?Math.sqrt(a*a+s*s):0},"nodesOverlap"),CS=o(function(e,r,n){var i=e.positionX,a=e.positionY,s=e.height||1,l=e.width||1,u=n/r,h=s/l,f={};return r===0&&0<n||r===0&&0>n?(f.x=i,f.y=a+s/2,f):0<r&&-1*h<=u&&u<=h?(f.x=i+l/2,f.y=a+l*n/2/r,f):0>r&&-1*h<=u&&u<=h?(f.x=i-l/2,f.y=a-l*n/2/r,f):0<n&&(u<=-1*h||u>=h)?(f.x=i+s*r/2/n,f.y=a+s/2,f):(0>n&&(u<=-1*h||u>=h)&&(f.x=i-s*r/2/n,f.y=a-s/2),f)},"findClippingPoint"),mQe=o(function(e,r){for(var n=0;n<e.edgeSize;n++){var 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T=i[C],E=T._private.position;(E.x==null||E.y==null)&&(E.x=0,E.y=0);var A=T.layoutDimensions(e),S=e.avoidOverlapPadding,_=A.w+S,I=A.h+S;b=Math.max(b,_),w=Math.max(w,I)}for(var D={},k=o(function(j,ie){return!!D["c-"+j+"-"+ie]},"used"),L=o(function(j,ie){D["c-"+j+"-"+ie]=!0},"use"),R=0,O=0,M=o(function(){O++,O>=h&&(O=0,R++)},"moveToNextCell"),B={},F=0;F<i.length;F++){var P=i[F],z=e.position(P);if(z&&(z.row!==void 0||z.col!==void 0)){var $={row:z.row,col:z.col};if($.col===void 0)for($.col=0;k($.row,$.col);)$.col++;else if($.row===void 0)for($.row=0;k($.row,$.col);)$.row++;B[P.id()]=$,L($.row,$.col)}}var H=o(function(j,ie){var ne,le;if(j.locked()||j.isParent())return!1;var he=B[j.id()];if(he)ne=he.col*b+b/2+a.x1,le=he.row*w+w/2+a.y1;else{for(;k(R,O);)M();ne=O*b+b/2+a.x1,le=R*w+w/2+a.y1,L(R,O),M()}return{x:ne,y:le}},"getPos");i.layoutPositions(this,e,H)}return this};TQe={ready:o(function(){},"ready"),stop:o(function(){},"stop")};o(gB,"NullLayout");gB.prototype.run=function(){var 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parseFloat(r.getPropertyValue(x))},"styleValue"),i={left:n("padding-left"),right:n("padding-right"),top:n("padding-top"),bottom:n("padding-bottom")},a={left:n("border-left-width"),right:n("border-right-width"),top:n("border-top-width"),bottom:n("border-bottom-width")},s=t.clientWidth,l=t.clientHeight,u=i.left+i.right,h=i.top+i.bottom,f=a.left+a.right,d=e.width/(s+f),p=s-u,m=l-h,g=e.left+i.left+a.left,y=e.top+i.top+a.top;return this.containerBB=[g,y,p,m,d]};qp.invalidateContainerClientCoordsCache=function(){this.containerBB=null};qp.findNearestElement=function(t,e,r,n){return this.findNearestElements(t,e,r,n)[0]};qp.findNearestElements=function(t,e,r,n){var i=this,a=this,s=a.getCachedZSortedEles(),l=[],u=a.cy.zoom(),h=a.cy.hasCompoundNodes(),f=(n?24:8)/u,d=(n?8:2)/u,p=(n?8:2)/u,m=1/0,g,y;r&&(s=s.interactive);function v(A,S){if(A.isNode()){if(y)return;y=A,l.push(A)}if(A.isEdge()&&(S==null||S<m))if(g){if(g.pstyle("z-compound-depth").value===A.pstyle("z-compound-depth").value&&g.pstyle("z-compound-depth").value===A.pstyle("z-compound-depth").value){for(var _=0;_<l.length;_++)if(l[_].isEdge()){l[_]=A,g=A,m=S??m;break}}}else l.push(A),g=A,m=S??m}o(v,"addEle");function x(A){var S=A.outerWidth()+2*d,_=A.outerHeight()+2*d,I=S/2,D=_/2,k=A.position(),L=A.pstyle("corner-radius").value==="auto"?"auto":A.pstyle("corner-radius").pfValue,R=A._private.rscratch;if(k.x-I<=t&&t<=k.x+I&&k.y-D<=e&&e<=k.y+D){var O=a.nodeShapes[i.getNodeShape(A)];if(O.checkPoint(t,e,0,S,_,k.x,k.y,L,R))return v(A,0),!0}}o(x,"checkNode");function b(A){var S=A._private,_=S.rscratch,I=A.pstyle("width").pfValue,D=A.pstyle("arrow-scale").value,k=I/2+f,L=k*k,R=k*2,F=S.source,P=S.target,O;if(_.edgeType==="segments"||_.edgeType==="straight"||_.edgeType==="haystack"){for(var M=_.allpts,B=0;B+3<M.length;B+=2)if(zqe(t,e,M[B],M[B+1],M[B+2],M[B+3],R)&&L>(O=Wqe(t,e,M[B],M[B+1],M[B+2],M[B+3])))return v(A,O),!0}else if(_.edgeType==="bezier"||_.edgeType==="multibezier"||_.edgeType==="self"||_.edgeType==="compound"){for(var M=_.allpts,B=0;B+5<_.allpts.length;B+=4)if(Gqe(t,e,M[B],M[B+1],M[B+2],M[B+3],M[B+4],M[B+5],R)&&L>(O=Hqe(t,e,M[B],M[B+1],M[B+2],M[B+3],M[B+4],M[B+5])))return v(A,O),!0}for(var F=F||S.source,P=P||S.target,z=i.getArrowWidth(I,D),$=[{name:"source",x:_.arrowStartX,y:_.arrowStartY,angle:_.srcArrowAngle},{name:"target",x:_.arrowEndX,y:_.arrowEndY,angle:_.tgtArrowAngle},{name:"mid-source",x:_.midX,y:_.midY,angle:_.midsrcArrowAngle},{name:"mid-target",x:_.midX,y:_.midY,angle:_.midtgtArrowAngle}],B=0;B<$.length;B++){var H=$[B],Q=a.arrowShapes[A.pstyle(H.name+"-arrow-shape").value],j=A.pstyle("width").pfValue;if(Q.roughCollide(t,e,z,H.angle,{x:H.x,y:H.y},j,f)&&Q.collide(t,e,z,H.angle,{x:H.x,y:H.y},j,f))return v(A),!0}h&&l.length>0&&(x(F),x(P))}o(b,"checkEdge");function w(A,S,_){return Gl(A,S,_)}o(w,"preprop");function C(A,S){var _=A._private,I=p,D;S?D=S+"-":D="",A.boundingBox();var k=_.labelBounds[S||"main"],L=A.pstyle(D+"label").value,R=A.pstyle("text-events").strValue==="yes";if(!(!R||!L)){var O=w(_.rscratch,"labelX",S),M=w(_.rscratch,"labelY",S),B=w(_.rscratch,"labelAngle",S),F=A.pstyle(D+"text-margin-x").pfValue,P=A.pstyle(D+"text-margin-y").pfValue,z=k.x1-I-F,$=k.x2+I-F,H=k.y1-I-P,Q=k.y2+I-P;if(B){var j=Math.cos(B),ie=Math.sin(B),ne=o(function(se,ue){return se=se-O,ue=ue-M,{x:se*j-ue*ie+O,y:se*ie+ue*j+M}},"rotate"),le=ne(z,H),he=ne(z,Q),K=ne($,H),X=ne($,Q),te=[le.x+F,le.y+P,K.x+F,K.y+P,X.x+F,X.y+P,he.x+F,he.y+P];if(Us(t,e,te))return v(A),!0}else if(K1(k,t,e))return v(A),!0}}o(C,"checkLabel");for(var T=s.length-1;T>=0;T--){var E=s[T];E.isNode()?x(E)||C(E):b(E)||C(E)||C(E,"source")||C(E,"target")}return l};qp.getAllInBox=function(t,e,r,n){var i=this.getCachedZSortedEles().interactive,a=[],s=Math.min(t,r),l=Math.max(t,r),u=Math.min(e,n),h=Math.max(e,n);t=s,r=l,e=u,n=h;for(var f=Hs({x1:t,y1:e,x2:r,y2:n}),d=0;d<i.length;d++){var p=i[d];if(p.isNode()){var m=p,g=m.boundingBox({includeNodes:!0,includeEdges:!1,includeLabels:!1});aB(f,g)&&!yme(g,f)&&a.push(m)}else{var y=p,v=y._private,x=v.rscratch;if(x.startX!=null&&x.startY!=null&&!K1(f,x.startX,x.startY)||x.endX!=null&&x.endY!=null&&!K1(f,x.endX,x.endY))continue;if(x.edgeType==="bezier"||x.edgeType==="multibezier"||x.edgeType==="self"||x.edgeType==="compound"||x.edgeType==="segments"||x.edgeType==="haystack"){for(var b=v.rstyle.bezierPts||v.rstyle.linePts||v.rstyle.haystackPts,w=!0,C=0;C<b.length;C++)if(!$qe(f,b[C])){w=!1;break}w&&a.push(y)}else(x.edgeType==="haystack"||x.edgeType==="straight")&&a.push(y)}}return a};AS={};AS.calculateArrowAngles=function(t){var e=t._private.rscratch,r=e.edgeType==="haystack",n=e.edgeType==="bezier",i=e.edgeType==="multibezier",a=e.edgeType==="segments",s=e.edgeType==="compound",l=e.edgeType==="self",u,h,f,d,p,m,x,b;if(r?(f=e.haystackPts[0],d=e.haystackPts[1],p=e.haystackPts[2],m=e.haystackPts[3]):(f=e.arrowStartX,d=e.arrowStartY,p=e.arrowEndX,m=e.arrowEndY),x=e.midX,b=e.midY,a)u=f-e.segpts[0],h=d-e.segpts[1];else if(i||s||l||n){var g=e.allpts,y=oa(g[0],g[2],g[4],.1),v=oa(g[1],g[3],g[5],.1);u=f-y,h=d-v}else u=f-x,h=d-b;e.srcArrowAngle=K6(u,h);var x=e.midX,b=e.midY;if(r&&(x=(f+p)/2,b=(d+m)/2),u=p-f,h=m-d,a){var g=e.allpts;if(g.length/2%2===0){var w=g.length/2,C=w-2;u=g[w]-g[C],h=g[w+1]-g[C+1]}else if(e.isRound)u=e.midVector[1],h=-e.midVector[0];else{var w=g.length/2-1,C=w-2;u=g[w]-g[C],h=g[w+1]-g[C+1]}}else if(i||s||l){var g=e.allpts,T=e.ctrlpts,E,A,S,_;if(T.length/2%2===0){var I=g.length/2-1,D=I+2,k=D+2;E=oa(g[I],g[D],g[k],0),A=oa(g[I+1],g[D+1],g[k+1],0),S=oa(g[I],g[D],g[k],1e-4),_=oa(g[I+1],g[D+1],g[k+1],1e-4)}else{var D=g.length/2-1,I=D-2,k=D+2;E=oa(g[I],g[D],g[k],.4999),A=oa(g[I+1],g[D+1],g[k+1],.4999),S=oa(g[I],g[D],g[k],.5),_=oa(g[I+1],g[D+1],g[k+1],.5)}u=S-E,h=_-A}if(e.midtgtArrowAngle=K6(u,h),e.midDispX=u,e.midDispY=h,u*=-1,h*=-1,a){var g=e.allpts;if(g.length/2%2!==0){if(!e.isRound){var w=g.length/2-1,L=w+2;u=-(g[L]-g[w]),h=-(g[L+1]-g[w+1])}}}if(e.midsrcArrowAngle=K6(u,h),a)u=p-e.segpts[e.segpts.length-2],h=m-e.segpts[e.segpts.length-1];else if(i||s||l||n){var g=e.allpts,R=g.length,y=oa(g[R-6],g[R-4],g[R-2],.9),v=oa(g[R-5],g[R-3],g[R-1],.9);u=p-y,h=m-v}else u=p-x,h=m-b;e.tgtArrowAngle=K6(u,h)};AS.getArrowWidth=AS.getArrowHeight=function(t,e){var r=this.arrowWidthCache=this.arrowWidthCache||{},n=r[t+", "+e];return n||(n=Math.max(Math.pow(t*13.37,.9),29)*e,r[t+", "+e]=n,n)};qc={},Jo={},L0e=o(function(e,r,n){n.x=r.x-e.x,n.y=r.y-e.y,n.len=Math.sqrt(n.x*n.x+n.y*n.y),n.nx=n.x/n.len,n.ny=n.y/n.len,n.ang=Math.atan2(n.ny,n.nx)},"asVec"),CQe=o(function(e,r){r.x=e.x*-1,r.y=e.y*-1,r.nx=e.nx*-1,r.ny=e.ny*-1,r.ang=e.ang>0?-(Math.PI-e.ang):Math.PI+e.ang},"invertVec"),AQe=o(function(e,r,n,i,a){if(e!==D0e?L0e(r,e,qc):CQe(Jo,qc),L0e(r,n,Jo),A0e=qc.nx*Jo.ny-qc.ny*Jo.nx,_0e=qc.nx*Jo.nx-qc.ny*-Jo.ny,Ku=Math.asin(Math.max(-1,Math.min(1,A0e))),Math.abs(Ku)<1e-6){HP=r.x,WP=r.y,Bp=G1=0;return}Fp=1,gS=!1,_0e<0?Ku<0?Ku=Math.PI+Ku:(Ku=Math.PI-Ku,Fp=-1,gS=!0):Ku>0&&(Fp=-1,gS=!0),r.radius!==void 0?G1=r.radius:G1=i,Mp=Ku/2,aS=Math.min(qc.len/2,Jo.len/2),a?(Wc=Math.abs(Math.cos(Mp)*G1/Math.sin(Mp)),Wc>aS?(Wc=aS,Bp=Math.abs(Wc*Math.sin(Mp)/Math.cos(Mp))):Bp=G1):(Wc=Math.min(aS,G1),Bp=Math.abs(Wc*Math.sin(Mp)/Math.cos(Mp))),qP=r.x+Jo.nx*Wc,YP=r.y+Jo.ny*Wc,HP=qP-Jo.ny*Bp*Fp,WP=YP+Jo.nx*Bp*Fp,cge=r.x+qc.nx*Wc,uge=r.y+qc.ny*Wc,D0e=r},"calcCornerArc");o(hge,"drawPreparedRoundCorner");o(vB,"getRoundCorner");Va={};Va.findMidptPtsEtc=function(t,e){var r=e.posPts,n=e.intersectionPts,i=e.vectorNormInverse,a,s=t.pstyle("source-endpoint"),l=t.pstyle("target-endpoint"),u=s.units!=null&&l.units!=null,h=o(function(T,E,A,S){var _=S-E,I=A-T,D=Math.sqrt(I*I+_*_);return{x:-_/D,y:I/D}},"recalcVectorNormInverse"),f=t.pstyle("edge-distances").value;switch(f){case"node-position":a=r;break;case"intersection":a=n;break;case"endpoints":{if(u){var d=this.manualEndptToPx(t.source()[0],s),p=_i(d,2),m=p[0],g=p[1],y=this.manualEndptToPx(t.target()[0],l),v=_i(y,2),x=v[0],b=v[1],w={x1:m,y1:g,x2:x,y2:b};i=h(m,g,x,b),a=w}else un("Edge ".concat(t.id()," has edge-distances:endpoints specified without manual endpoints specified via source-endpoint and target-endpoint.  Falling back on edge-distances:intersection (default).")),a=n;break}}return{midptPts:a,vectorNormInverse:i}};Va.findHaystackPoints=function(t){for(var e=0;e<t.length;e++){var r=t[e],n=r._private,i=n.rscratch;if(!i.haystack){var a=Math.random()*2*Math.PI;i.source={x:Math.cos(a),y:Math.sin(a)},a=Math.random()*2*Math.PI,i.target={x:Math.cos(a),y:Math.sin(a)}}var s=n.source,l=n.target,u=s.position(),h=l.position(),f=s.width(),d=l.width(),p=s.height(),m=l.height(),g=r.pstyle("haystack-radius").value,y=g/2;i.haystackPts=i.allpts=[i.source.x*f*y+u.x,i.source.y*p*y+u.y,i.target.x*d*y+h.x,i.target.y*m*y+h.y],i.midX=(i.allpts[0]+i.allpts[2])/2,i.midY=(i.allpts[1]+i.allpts[3])/2,i.edgeType="haystack",i.haystack=!0,this.storeEdgeProjections(r),this.calculateArrowAngles(r),this.recalculateEdgeLabelProjections(r),this.calculateLabelAngles(r)}};Va.findSegmentsPoints=function(t,e){var r=t._private.rscratch,n=t.pstyle("segment-weights"),i=t.pstyle("segment-distances"),a=t.pstyle("segment-radii"),s=t.pstyle("radius-type"),l=Math.min(n.pfValue.length,i.pfValue.length),u=a.pfValue[a.pfValue.length-1],h=s.pfValue[s.pfValue.length-1];r.edgeType="segments",r.segpts=[],r.radii=[],r.isArcRadius=[];for(var f=0;f<l;f++){var d=n.pfValue[f],p=i.pfValue[f],m=1-d,g=d,y=this.findMidptPtsEtc(t,e),v=y.midptPts,x=y.vectorNormInverse,b={x:v.x1*m+v.x2*g,y:v.y1*m+v.y2*g};r.segpts.push(b.x+x.x*p,b.y+x.y*p),r.radii.push(a.pfValue[f]!==void 0?a.pfValue[f]:u),r.isArcRadius.push((s.pfValue[f]!==void 0?s.pfValue[f]:h)==="arc-radius")}};Va.findLoopPoints=function(t,e,r,n){var i=t._private.rscratch,a=e.dirCounts,s=e.srcPos,l=t.pstyle("control-point-distances"),u=l?l.pfValue[0]:void 0,h=t.pstyle("loop-direction").pfValue,f=t.pstyle("loop-sweep").pfValue,d=t.pstyle("control-point-step-size").pfValue;i.edgeType="self";var p=r,m=d;n&&(p=0,m=u);var g=h-Math.PI/2,y=g-f/2,v=g+f/2,x=h+"_"+f;p=a[x]===void 0?a[x]=0:++a[x],i.ctrlpts=[s.x+Math.cos(y)*1.4*m*(p/3+1),s.y+Math.sin(y)*1.4*m*(p/3+1),s.x+Math.cos(v)*1.4*m*(p/3+1),s.y+Math.sin(v)*1.4*m*(p/3+1)]};Va.findCompoundLoopPoints=function(t,e,r,n){var i=t._private.rscratch;i.edgeType="compound";var a=e.srcPos,s=e.tgtPos,l=e.srcW,u=e.srcH,h=e.tgtW,f=e.tgtH,d=t.pstyle("control-point-step-size").pfValue,p=t.pstyle("control-point-distances"),m=p?p.pfValue[0]:void 0,g=r,y=d;n&&(g=0,y=m);var v=50,x={x:a.x-l/2,y:a.y-u/2},b={x:s.x-h/2,y:s.y-f/2},w={x:Math.min(x.x,b.x),y:Math.min(x.y,b.y)},C=.5,T=Math.max(C,Math.log(l*.01)),E=Math.max(C,Math.log(h*.01));i.ctrlpts=[w.x,w.y-(1+Math.pow(v,1.12)/100)*y*(g/3+1)*T,w.x-(1+Math.pow(v,1.12)/100)*y*(g/3+1)*E,w.y]};Va.findStraightEdgePoints=function(t){t._private.rscratch.edgeType="straight"};Va.findBezierPoints=function(t,e,r,n,i){var a=t._private.rscratch,s=t.pstyle("control-point-step-size").pfValue,l=t.pstyle("control-point-distances"),u=t.pstyle("control-point-weights"),h=l&&u?Math.min(l.value.length,u.value.length):1,f=l?l.pfValue[0]:void 0,d=u.value[0],p=n;a.edgeType=p?"multibezier":"bezier",a.ctrlpts=[];for(var m=0;m<h;m++){var g=(.5-e.eles.length/2+r)*s*(i?-1:1),y=void 0,v=mme(g);p&&(f=l?l.pfValue[m]:s,d=u.value[m]),n?y=f:y=f!==void 0?v*f:void 0;var x=y!==void 0?y:g,b=1-d,w=d,C=this.findMidptPtsEtc(t,e),T=C.midptPts,E=C.vectorNormInverse,A={x:T.x1*b+T.x2*w,y:T.y1*b+T.y2*w};a.ctrlpts.push(A.x+E.x*x,A.y+E.y*x)}};Va.findTaxiPoints=function(t,e){var r=t._private.rscratch;r.edgeType="segments";var n="vertical",i="horizontal",a="leftward",s="rightward",l="downward",u="upward",h="auto",f=e.posPts,d=e.srcW,p=e.srcH,m=e.tgtW,g=e.tgtH,y=t.pstyle("edge-distances").value,v=y!=="node-position",x=t.pstyle("taxi-direction").value,b=x,w=t.pstyle("taxi-turn"),C=w.units==="%",T=w.pfValue,E=T<0,A=t.pstyle("taxi-turn-min-distance").pfValue,S=v?(d+m)/2:0,_=v?(p+g)/2:0,I=f.x2-f.x1,D=f.y2-f.y1,k=o(function(q,pe){return q>0?Math.max(q-pe,0):Math.min(q+pe,0)},"subDWH"),L=k(I,S),R=k(D,_),O=!1;b===h?x=Math.abs(L)>Math.abs(R)?i:n:b===u||b===l?(x=n,O=!0):(b===a||b===s)&&(x=i,O=!0);var M=x===n,B=M?R:L,F=M?D:I,P=mme(F),z=!1;!(O&&(C||E))&&(b===l&&F<0||b===u&&F>0||b===a&&F>0||b===s&&F<0)&&(P*=-1,B=P*Math.abs(B),z=!0);var $;if(C){var H=T<0?1+T:T;$=H*B}else{var Q=T<0?B:0;$=Q+T*P}var j=o(function(q){return Math.abs(q)<A||Math.abs(q)>=Math.abs(B)},"getIsTooClose"),ie=j($),ne=j(Math.abs(B)-Math.abs($)),le=ie||ne;if(le&&!z)if(M){var he=Math.abs(F)<=p/2,K=Math.abs(I)<=m/2;if(he){var X=(f.x1+f.x2)/2,te=f.y1,J=f.y2;r.segpts=[X,te,X,J]}else if(K){var se=(f.y1+f.y2)/2,ue=f.x1,Z=f.x2;r.segpts=[ue,se,Z,se]}else r.segpts=[f.x1,f.y2]}else{var Se=Math.abs(F)<=d/2,ce=Math.abs(D)<=g/2;if(Se){var ae=(f.y1+f.y2)/2,Oe=f.x1,ge=f.x2;r.segpts=[Oe,ae,ge,ae]}else if(ce){var ze=(f.x1+f.x2)/2,He=f.y1,$e=f.y2;r.segpts=[ze,He,ze,$e]}else r.segpts=[f.x2,f.y1]}else if(M){var Re=f.y1+$+(v?p/2*P:0),Ie=f.x1,be=f.x2;r.segpts=[Ie,Re,be,Re]}else{var W=f.x1+$+(v?d/2*P:0),de=f.y1,re=f.y2;r.segpts=[W,de,W,re]}if(r.isRound){var oe=t.pstyle("taxi-radius").value,V=t.pstyle("radius-type").value[0]==="arc-radius";r.radii=new Array(r.segpts.length/2).fill(oe),r.isArcRadius=new Array(r.segpts.length/2).fill(V)}};Va.tryToCorrectInvalidPoints=function(t,e){var r=t._private.rscratch;if(r.edgeType==="bezier"){var n=e.srcPos,i=e.tgtPos,a=e.srcW,s=e.srcH,l=e.tgtW,u=e.tgtH,h=e.srcShape,f=e.tgtShape,d=e.srcCornerRadius,p=e.tgtCornerRadius,m=e.srcRs,g=e.tgtRs,y=!Ct(r.startX)||!Ct(r.startY),v=!Ct(r.arrowStartX)||!Ct(r.arrowStartY),x=!Ct(r.endX)||!Ct(r.endY),b=!Ct(r.arrowEndX)||!Ct(r.arrowEndY),w=3,C=this.getArrowWidth(t.pstyle("width").pfValue,t.pstyle("arrow-scale").value)*this.arrowShapeWidth,T=w*C,E=Gp({x:r.ctrlpts[0],y:r.ctrlpts[1]},{x:r.startX,y:r.startY}),A=E<T,S=Gp({x:r.ctrlpts[0],y:r.ctrlpts[1]},{x:r.endX,y:r.endY}),_=S<T,I=!1;if(y||v||A){I=!0;var D={x:r.ctrlpts[0]-n.x,y:r.ctrlpts[1]-n.y},k=Math.sqrt(D.x*D.x+D.y*D.y),L={x:D.x/k,y:D.y/k},R=Math.max(a,s),O={x:r.ctrlpts[0]+L.x*2*R,y:r.ctrlpts[1]+L.y*2*R},M=h.intersectLine(n.x,n.y,a,s,O.x,O.y,0,d,m);A?(r.ctrlpts[0]=r.ctrlpts[0]+L.x*(T-E),r.ctrlpts[1]=r.ctrlpts[1]+L.y*(T-E)):(r.ctrlpts[0]=M[0]+L.x*T,r.ctrlpts[1]=M[1]+L.y*T)}if(x||b||_){I=!0;var B={x:r.ctrlpts[0]-i.x,y:r.ctrlpts[1]-i.y},F=Math.sqrt(B.x*B.x+B.y*B.y),P={x:B.x/F,y:B.y/F},z=Math.max(a,s),$={x:r.ctrlpts[0]+P.x*2*z,y:r.ctrlpts[1]+P.y*2*z},H=f.intersectLine(i.x,i.y,l,u,$.x,$.y,0,p,g);_?(r.ctrlpts[0]=r.ctrlpts[0]+P.x*(T-S),r.ctrlpts[1]=r.ctrlpts[1]+P.y*(T-S)):(r.ctrlpts[0]=H[0]+P.x*T,r.ctrlpts[1]=H[1]+P.y*T)}I&&this.findEndpoints(t)}};Va.storeAllpts=function(t){var e=t._private.rscratch;if(e.edgeType==="multibezier"||e.edgeType==="bezier"||e.edgeType==="self"||e.edgeType==="compound"){e.allpts=[],e.allpts.push(e.startX,e.startY);for(var r=0;r+1<e.ctrlpts.length;r+=2)e.allpts.push(e.ctrlpts[r],e.ctrlpts[r+1]),r+3<e.ctrlpts.length&&e.allpts.push((e.ctrlpts[r]+e.ctrlpts[r+2])/2,(e.ctrlpts[r+1]+e.ctrlpts[r+3])/2);e.allpts.push(e.endX,e.endY);var n,i;e.ctrlpts.length/2%2===0?(n=e.allpts.length/2-1,e.midX=e.allpts[n],e.midY=e.allpts[n+1]):(n=e.allpts.length/2-3,i=.5,e.midX=oa(e.allpts[n],e.allpts[n+2],e.allpts[n+4],i),e.midY=oa(e.allpts[n+1],e.allpts[n+3],e.allpts[n+5],i))}else if(e.edgeType==="straight")e.allpts=[e.startX,e.startY,e.endX,e.endY],e.midX=(e.startX+e.endX+e.arrowStartX+e.arrowEndX)/4,e.midY=(e.startY+e.endY+e.arrowStartY+e.arrowEndY)/4;else if(e.edgeType==="segments"){if(e.allpts=[],e.allpts.push(e.startX,e.startY),e.allpts.push.apply(e.allpts,e.segpts),e.allpts.push(e.endX,e.endY),e.isRound){e.roundCorners=[];for(var a=2;a+3<e.allpts.length;a+=2){var s=e.radii[a/2-1],l=e.isArcRadius[a/2-1];e.roundCorners.push(vB({x:e.allpts[a-2],y:e.allpts[a-1]},{x:e.allpts[a],y:e.allpts[a+1],radius:s},{x:e.allpts[a+2],y:e.allpts[a+3]},s,l))}}if(e.segpts.length%4===0){var u=e.segpts.length/2,h=u-2;e.midX=(e.segpts[h]+e.segpts[u])/2,e.midY=(e.segpts[h+1]+e.segpts[u+1])/2}else{var f=e.segpts.length/2-1;if(!e.isRound)e.midX=e.segpts[f],e.midY=e.segpts[f+1];else{var d={x:e.segpts[f],y:e.segpts[f+1]},p=e.roundCorners[f/2],m=[d.x-p.cx,d.y-p.cy],g=p.radius/Math.sqrt(Math.pow(m[0],2)+Math.pow(m[1],2));m=m.map(function(y){return y*g}),e.midX=p.cx+m[0],e.midY=p.cy+m[1],e.midVector=m}}}};Va.checkForInvalidEdgeWarning=function(t){var e=t[0]._private.rscratch;e.nodesOverlap||Ct(e.startX)&&Ct(e.startY)&&Ct(e.endX)&&Ct(e.endY)?e.loggedErr=!1:e.loggedErr||(e.loggedErr=!0,un("Edge `"+t.id()+"` has invalid endpoints and so it is impossible to draw.  Adjust your edge style (e.g. control points) accordingly or use an alternative edge type.  This is expected behaviour when the source node and the target node overlap."))};Va.findEdgeControlPoints=function(t){var e=this;if(!(!t||t.length===0)){for(var r=this,n=r.cy,i=n.hasCompoundNodes(),a={map:new Xc,get:o(function(A){var S=this.map.get(A[0]);return S!=null?S.get(A[1]):null},"get"),set:o(function(A,S){var _=this.map.get(A[0]);_==null&&(_=new Xc,this.map.set(A[0],_)),_.set(A[1],S)},"set")},s=[],l=[],u=0;u<t.length;u++){var h=t[u],f=h._private,d=h.pstyle("curve-style").value;if(!(h.removed()||!h.takesUpSpace())){if(d==="haystack"){l.push(h);continue}var p=d==="unbundled-bezier"||d.endsWith("segments")||d==="straight"||d==="straight-triangle"||d.endsWith("taxi"),m=d==="unbundled-bezier"||d==="bezier",g=f.source,y=f.target,v=g.poolIndex(),x=y.poolIndex(),b=[v,x].sort(),w=a.get(b);w==null&&(w={eles:[]},a.set(b,w),s.push(b)),w.eles.push(h),p&&(w.hasUnbundled=!0),m&&(w.hasBezier=!0)}}for(var C=o(function(A){var S=s[A],_=a.get(S),I=void 0;if(!_.hasUnbundled){var D=_.eles[0].parallelEdges().filter(function(W){return W.isBundledBezier()});nB(_.eles),D.forEach(function(W){return _.eles.push(W)}),_.eles.sort(function(W,de){return W.poolIndex()-de.poolIndex()})}var k=_.eles[0],L=k.source(),R=k.target();if(L.poolIndex()>R.poolIndex()){var O=L;L=R,R=O}var M=_.srcPos=L.position(),B=_.tgtPos=R.position(),F=_.srcW=L.outerWidth(),P=_.srcH=L.outerHeight(),z=_.tgtW=R.outerWidth(),$=_.tgtH=R.outerHeight(),H=_.srcShape=r.nodeShapes[e.getNodeShape(L)],Q=_.tgtShape=r.nodeShapes[e.getNodeShape(R)],j=_.srcCornerRadius=L.pstyle("corner-radius").value==="auto"?"auto":L.pstyle("corner-radius").pfValue,ie=_.tgtCornerRadius=R.pstyle("corner-radius").value==="auto"?"auto":R.pstyle("corner-radius").pfValue,ne=_.tgtRs=R._private.rscratch,le=_.srcRs=L._private.rscratch;_.dirCounts={north:0,west:0,south:0,east:0,northwest:0,southwest:0,northeast:0,southeast:0};for(var he=0;he<_.eles.length;he++){var K=_.eles[he],X=K[0]._private.rscratch,te=K.pstyle("curve-style").value,J=te==="unbundled-bezier"||te.endsWith("segments")||te.endsWith("taxi"),se=!L.same(K.source());if(!_.calculatedIntersection&&L!==R&&(_.hasBezier||_.hasUnbundled)){_.calculatedIntersection=!0;var ue=H.intersectLine(M.x,M.y,F,P,B.x,B.y,0,j,le),Z=_.srcIntn=ue,Se=Q.intersectLine(B.x,B.y,z,$,M.x,M.y,0,ie,ne),ce=_.tgtIntn=Se,ae=_.intersectionPts={x1:ue[0],x2:Se[0],y1:ue[1],y2:Se[1]},Oe=_.posPts={x1:M.x,x2:B.x,y1:M.y,y2:B.y},ge=Se[1]-ue[1],ze=Se[0]-ue[0],He=Math.sqrt(ze*ze+ge*ge),$e=_.vector={x:ze,y:ge},Re=_.vectorNorm={x:$e.x/He,y:$e.y/He},Ie={x:-Re.y,y:Re.x};_.nodesOverlap=!Ct(He)||Q.checkPoint(ue[0],ue[1],0,z,$,B.x,B.y,ie,ne)||H.checkPoint(Se[0],Se[1],0,F,P,M.x,M.y,j,le),_.vectorNormInverse=Ie,I={nodesOverlap:_.nodesOverlap,dirCounts:_.dirCounts,calculatedIntersection:!0,hasBezier:_.hasBezier,hasUnbundled:_.hasUnbundled,eles:_.eles,srcPos:B,srcRs:ne,tgtPos:M,tgtRs:le,srcW:z,srcH:$,tgtW:F,tgtH:P,srcIntn:ce,tgtIntn:Z,srcShape:Q,tgtShape:H,posPts:{x1:Oe.x2,y1:Oe.y2,x2:Oe.x1,y2:Oe.y1},intersectionPts:{x1:ae.x2,y1:ae.y2,x2:ae.x1,y2:ae.y1},vector:{x:-$e.x,y:-$e.y},vectorNorm:{x:-Re.x,y:-Re.y},vectorNormInverse:{x:-Ie.x,y:-Ie.y}}}var be=se?I:_;X.nodesOverlap=be.nodesOverlap,X.srcIntn=be.srcIntn,X.tgtIntn=be.tgtIntn,X.isRound=te.startsWith("round"),i&&(L.isParent()||L.isChild()||R.isParent()||R.isChild())&&(L.parents().anySame(R)||R.parents().anySame(L)||L.same(R)&&L.isParent())?e.findCompoundLoopPoints(K,be,he,J):L===R?e.findLoopPoints(K,be,he,J):te.endsWith("segments")?e.findSegmentsPoints(K,be):te.endsWith("taxi")?e.findTaxiPoints(K,be):te==="straight"||!J&&_.eles.length%2===1&&he===Math.floor(_.eles.length/2)?e.findStraightEdgePoints(K):e.findBezierPoints(K,be,he,J,se),e.findEndpoints(K),e.tryToCorrectInvalidPoints(K,be),e.checkForInvalidEdgeWarning(K),e.storeAllpts(K),e.storeEdgeProjections(K),e.calculateArrowAngles(K),e.recalculateEdgeLabelProjections(K),e.calculateLabelAngles(K)}},"_loop"),T=0;T<s.length;T++)C(T);this.findHaystackPoints(l)}};o(fge,"getPts");Va.getSegmentPoints=function(t){var e=t[0]._private.rscratch;this.recalculateRenderedStyle(t);var r=e.edgeType;if(r==="segments")return fge(e.segpts)};Va.getControlPoints=function(t){var e=t[0]._private.rscratch;this.recalculateRenderedStyle(t);var r=e.edgeType;if(r==="bezier"||r==="multibezier"||r==="self"||r==="compound")return fge(e.ctrlpts)};Va.getEdgeMidpoint=function(t){var e=t[0]._private.rscratch;return this.recalculateRenderedStyle(t),{x:e.midX,y:e.midY}};o4={};o4.manualEndptToPx=function(t,e){var r=this,n=t.position(),i=t.outerWidth(),a=t.outerHeight(),s=t._private.rscratch;if(e.value.length===2){var l=[e.pfValue[0],e.pfValue[1]];return e.units[0]==="%"&&(l[0]=l[0]*i),e.units[1]==="%"&&(l[1]=l[1]*a),l[0]+=n.x,l[1]+=n.y,l}else{var u=e.pfValue[0];u=-Math.PI/2+u;var h=2*Math.max(i,a),f=[n.x+Math.cos(u)*h,n.y+Math.sin(u)*h];return r.nodeShapes[this.getNodeShape(t)].intersectLine(n.x,n.y,i,a,f[0],f[1],0,t.pstyle("corner-radius").value==="auto"?"auto":t.pstyle("corner-radius").pfValue,s)}};o4.findEndpoints=function(t){var e=this,r,n=t.source()[0],i=t.target()[0],a=n.position(),s=i.position(),l=t.pstyle("target-arrow-shape").value,u=t.pstyle("source-arrow-shape").value,h=t.pstyle("target-distance-from-node").pfValue,f=t.pstyle("source-distance-from-node").pfValue,d=n._private.rscratch,p=i._private.rscratch,m=t.pstyle("curve-style").value,g=t._private.rscratch,y=g.edgeType,v=m==="taxi",x=y==="self"||y==="compound",b=y==="bezier"||y==="multibezier"||x,w=y!=="bezier",C=y==="straight"||y==="segments",T=y==="segments",E=b||w||C,A=x||v,S=t.pstyle("source-endpoint"),_=A?"outside-to-node":S.value,I=n.pstyle("corner-radius").value==="auto"?"auto":n.pstyle("corner-radius").pfValue,D=t.pstyle("target-endpoint"),k=A?"outside-to-node":D.value,L=i.pstyle("corner-radius").value==="auto"?"auto":i.pstyle("corner-radius").pfValue;g.srcManEndpt=S,g.tgtManEndpt=D;var R,O,M,B;if(b){var F=[g.ctrlpts[0],g.ctrlpts[1]],P=w?[g.ctrlpts[g.ctrlpts.length-2],g.ctrlpts[g.ctrlpts.length-1]]:F;R=P,O=F}else if(C){var z=T?g.segpts.slice(0,2):[s.x,s.y],$=T?g.segpts.slice(g.segpts.length-2):[a.x,a.y];R=$,O=z}if(k==="inside-to-node")r=[s.x,s.y];else if(D.units)r=this.manualEndptToPx(i,D);else if(k==="outside-to-line")r=g.tgtIntn;else if(k==="outside-to-node"||k==="outside-to-node-or-label"?M=R:(k==="outside-to-line"||k==="outside-to-line-or-label")&&(M=[a.x,a.y]),r=e.nodeShapes[this.getNodeShape(i)].intersectLine(s.x,s.y,i.outerWidth(),i.outerHeight(),M[0],M[1],0,L,p),k==="outside-to-node-or-label"||k==="outside-to-line-or-label"){var H=i._private.rscratch,Q=H.labelWidth,j=H.labelHeight,ie=H.labelX,ne=H.labelY,le=Q/2,he=j/2,K=i.pstyle("text-valign").value;K==="top"?ne-=he:K==="bottom"&&(ne+=he);var X=i.pstyle("text-halign").value;X==="left"?ie-=le:X==="right"&&(ie+=le);var te=Xb(M[0],M[1],[ie-le,ne-he,ie+le,ne-he,ie+le,ne+he,ie-le,ne+he],s.x,s.y);if(te.length>0){var J=a,se=Op(J,U1(r)),ue=Op(J,U1(te)),Z=se;if(ue<se&&(r=te,Z=ue),te.length>2){var Se=Op(J,{x:te[2],y:te[3]});Se<Z&&(r=[te[2],te[3]])}}}var ce=Q6(r,R,e.arrowShapes[l].spacing(t)+h),ae=Q6(r,R,e.arrowShapes[l].gap(t)+h);if(g.endX=ae[0],g.endY=ae[1],g.arrowEndX=ce[0],g.arrowEndY=ce[1],_==="inside-to-node")r=[a.x,a.y];else if(S.units)r=this.manualEndptToPx(n,S);else if(_==="outside-to-line")r=g.srcIntn;else if(_==="outside-to-node"||_==="outside-to-node-or-label"?B=O:(_==="outside-to-line"||_==="outside-to-line-or-label")&&(B=[s.x,s.y]),r=e.nodeShapes[this.getNodeShape(n)].intersectLine(a.x,a.y,n.outerWidth(),n.outerHeight(),B[0],B[1],0,I,d),_==="outside-to-node-or-label"||_==="outside-to-line-or-label"){var Oe=n._private.rscratch,ge=Oe.labelWidth,ze=Oe.labelHeight,He=Oe.labelX,$e=Oe.labelY,Re=ge/2,Ie=ze/2,be=n.pstyle("text-valign").value;be==="top"?$e-=Ie:be==="bottom"&&($e+=Ie);var W=n.pstyle("text-halign").value;W==="left"?He-=Re:W==="right"&&(He+=Re);var de=Xb(B[0],B[1],[He-Re,$e-Ie,He+Re,$e-Ie,He+Re,$e+Ie,He-Re,$e+Ie],a.x,a.y);if(de.length>0){var re=s,oe=Op(re,U1(r)),V=Op(re,U1(de)),xe=oe;if(V<oe&&(r=[de[0],de[1]],xe=V),de.length>2){var q=Op(re,{x:de[2],y:de[3]});q<xe&&(r=[de[2],de[3]])}}}var pe=Q6(r,O,e.arrowShapes[u].spacing(t)+f),ve=Q6(r,O,e.arrowShapes[u].gap(t)+f);g.startX=ve[0],g.startY=ve[1],g.arrowStartX=pe[0],g.arrowStartY=pe[1],E&&(!Ct(g.startX)||!Ct(g.startY)||!Ct(g.endX)||!Ct(g.endY)?g.badLine=!0:g.badLine=!1)};o4.getSourceEndpoint=function(t){var e=t[0]._private.rscratch;switch(this.recalculateRenderedStyle(t),e.edgeType){case"haystack":return{x:e.haystackPts[0],y:e.haystackPts[1]};default:return{x:e.arrowStartX,y:e.arrowStartY}}};o4.getTargetEndpoint=function(t){var e=t[0]._private.rscratch;switch(this.recalculateRenderedStyle(t),e.edgeType){case"haystack":return{x:e.haystackPts[2],y:e.haystackPts[3]};default:return{x:e.arrowEndX,y:e.arrowEndY}}};xB={};o(_Qe,"pushBezierPts");xB.storeEdgeProjections=function(t){var e=t._private,r=e.rscratch,n=r.edgeType;if(e.rstyle.bezierPts=null,e.rstyle.linePts=null,e.rstyle.haystackPts=null,n==="multibezier"||n==="bezier"||n==="self"||n==="compound"){e.rstyle.bezierPts=[];for(var i=0;i+5<r.allpts.length;i+=4)_Qe(this,t,r.allpts.slice(i,i+6))}else if(n==="segments")for(var a=e.rstyle.linePts=[],i=0;i+1<r.allpts.length;i+=2)a.push({x:r.allpts[i],y:r.allpts[i+1]});else if(n==="haystack"){var s=r.haystackPts;e.rstyle.haystackPts=[{x:s[0],y:s[1]},{x:s[2],y:s[3]}]}e.rstyle.arrowWidth=this.getArrowWidth(t.pstyle("width").pfValue,t.pstyle("arrow-scale").value)*this.arrowShapeWidth};xB.recalculateEdgeProjections=function(t){this.findEdgeControlPoints(t)};Kc={};Kc.recalculateNodeLabelProjection=function(t){var e=t.pstyle("label").strValue;if(!Af(e)){var r,n,i=t._private,a=t.width(),s=t.height(),l=t.padding(),u=t.position(),h=t.pstyle("text-halign").strValue,f=t.pstyle("text-valign").strValue,d=i.rscratch,p=i.rstyle;switch(h){case"left":r=u.x-a/2-l;break;case"right":r=u.x+a/2+l;break;default:r=u.x}switch(f){case"top":n=u.y-s/2-l;break;case"bottom":n=u.y+s/2+l;break;default:n=u.y}d.labelX=r,d.labelY=n,p.labelX=r,p.labelY=n,this.calculateLabelAngles(t),this.applyLabelDimensions(t)}};dge=o(function(e,r){var n=Math.atan(r/e);return e===0&&n<0&&(n=n*-1),n},"lineAngleFromDelta"),pge=o(function(e,r){var n=r.x-e.x,i=r.y-e.y;return dge(n,i)},"lineAngle"),DQe=o(function(e,r,n,i){var a=Yb(0,i-.001,1),s=Yb(0,i+.001,1),l=W1(e,r,n,a),u=W1(e,r,n,s);return pge(l,u)},"bezierAngle");Kc.recalculateEdgeLabelProjections=function(t){var e,r=t._private,n=r.rscratch,i=this,a={mid:t.pstyle("label").strValue,source:t.pstyle("source-label").strValue,target:t.pstyle("target-label").strValue};if(a.mid||a.source||a.target){e={x:n.midX,y:n.midY};var s=o(function(d,p,m){kf(r.rscratch,d,p,m),kf(r.rstyle,d,p,m)},"setRs");s("labelX",null,e.x),s("labelY",null,e.y);var l=dge(n.midDispX,n.midDispY);s("labelAutoAngle",null,l);var u=o(function f(){if(f.cache)return f.cache;for(var d=[],p=0;p+5<n.allpts.length;p+=4){var m={x:n.allpts[p],y:n.allpts[p+1]},g={x:n.allpts[p+2],y:n.allpts[p+3]},y={x:n.allpts[p+4],y:n.allpts[p+5]};d.push({p0:m,p1:g,p2:y,startDist:0,length:0,segments:[]})}var v=r.rstyle.bezierPts,x=i.bezierProjPcts.length;function b(A,S,_,I,D){var k=Gp(S,_),L=A.segments[A.segments.length-1],R={p0:S,p1:_,t0:I,t1:D,startDist:L?L.startDist+L.length:0,length:k};A.segments.push(R),A.length+=k}o(b,"addSegment");for(var w=0;w<d.length;w++){var C=d[w],T=d[w-1];T&&(C.startDist=T.startDist+T.length),b(C,C.p0,v[w*x],0,i.bezierProjPcts[0]);for(var E=0;E<x-1;E++)b(C,v[w*x+E],v[w*x+E+1],i.bezierProjPcts[E],i.bezierProjPcts[E+1]);b(C,v[w*x+x-1],C.p2,i.bezierProjPcts[x-1],1)}return f.cache=d},"createControlPointInfo"),h=o(function(d){var 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re=!0;if(t.cy.hasCompoundNodes()&&W&&W.pannable())for(var oe=0;de&&oe<de.length;oe++){var W=de[oe];if(W.isNode()&&W.isParent()&&!W.pannable()){re=!1;break}}else re=!0;return re},"allowPanningPassthrough"),l=o(function(W){W[0]._private.grabbed=!0},"setGrabbed"),u=o(function(W){W[0]._private.grabbed=!1},"setFreed"),h=o(function(W){W[0]._private.rscratch.inDragLayer=!0},"setInDragLayer"),f=o(function(W){W[0]._private.rscratch.inDragLayer=!1},"setOutDragLayer"),d=o(function(W){W[0]._private.rscratch.isGrabTarget=!0},"setGrabTarget"),p=o(function(W){W[0]._private.rscratch.isGrabTarget=!1},"removeGrabTarget"),m=o(function(W,de){var re=de.addToList,oe=re.has(W);!oe&&W.grabbable()&&!W.locked()&&(re.merge(W),l(W))},"addToDragList"),g=o(function(W,de){if(W.cy().hasCompoundNodes()&&!(de.inDragLayer==null&&de.addToList==null)){var re=W.descendants();de.inDragLayer&&(re.forEach(h),re.connectedEdges().forEach(h)),de.addToList&&m(re,de)}},"addDescendantsToDrag"),y=o(function(W,de){de=de||{};var re=W.cy().hasCompoundNodes();de.inDragLayer&&(W.forEach(h),W.neighborhood().stdFilter(function(oe){return!re||oe.isEdge()}).forEach(h)),de.addToList&&W.forEach(function(oe){m(oe,de)}),g(W,de),b(W,{inDragLayer:de.inDragLayer}),t.updateCachedGrabbedEles()},"addNodesToDrag"),v=y,x=o(function(W){W&&(t.getCachedZSortedEles().forEach(function(de){u(de),f(de),p(de)}),t.updateCachedGrabbedEles())},"freeDraggedElements"),b=o(function(W,de){if(!(de.inDragLayer==null&&de.addToList==null)&&W.cy().hasCompoundNodes()){var re=W.ancestors().orphans();if(!re.same(W)){var oe=re.descendants().spawnSelf().merge(re).unmerge(W).unmerge(W.descendants()),V=oe.connectedEdges();de.inDragLayer&&(V.forEach(h),oe.forEach(h)),de.addToList&&oe.forEach(function(xe){m(xe,de)})}}},"updateAncestorsInDragLayer"),w=o(function(){document.activeElement!=null&&document.activeElement.blur!=null&&document.activeElement.blur()},"blurActiveDomElement"),C=typeof MutationObserver<"u",T=typeof 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Pe={originalEvent:W,type:"cxttapstart",position:{x:oe[0],y:oe[1]}};q?(q.activate(),q.emit(Pe),t.hoverData.down=q):de.emit(Pe),t.hoverData.downTime=new Date().getTime(),t.hoverData.cxtDragged=!1}else if(W.which==1){q&&q.activate();{if(q!=null&&t.nodeIsGrabbable(q)){var _e=o(function(qe){return{originalEvent:W,type:qe,position:{x:oe[0],y:oe[1]}}},"makeEvent"),we=o(function(qe){qe.emit(_e("grab"))},"triggerGrab");if(d(q),!q.selected())pe=t.dragData.possibleDragElements=de.collection(),v(q,{addToList:pe}),q.emit(_e("grabon")).emit(_e("grab"));else{pe=t.dragData.possibleDragElements=de.collection();var Ve=de.$(function(De){return De.isNode()&&De.selected()&&t.nodeIsGrabbable(De)});y(Ve,{addToList:pe}),q.emit(_e("grabon")),Ve.forEach(we)}t.redrawHint("eles",!0),t.redrawHint("drag",!0)}t.hoverData.down=q,t.hoverData.downs=xe,t.hoverData.downTime=new 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Tt=t.hoverData.dragDelta=t.hoverData.dragDelta||[];Tt.length===0?(Tt.push(De[0]),Tt.push(De[1])):(Tt[0]+=De[0],Tt[1]+=De[1])},"updateDragDelta");re=!0,i(_e,["mousemove","vmousemove","tapdrag"],W,{x:q[0],y:q[1]});var bt=o(function(){t.data.bgActivePosistion=void 0,t.hoverData.selecting||oe.emit({originalEvent:W,type:"boxstart",position:{x:q[0],y:q[1]}}),Pe[4]=1,t.hoverData.selecting=!0,t.redrawHint("select",!0),t.redraw()},"goIntoBoxMode");if(t.hoverData.which===3){if(at){var Mt={originalEvent:W,type:"cxtdrag",position:{x:q[0],y:q[1]}};Ve?Ve.emit(Mt):oe.emit(Mt),t.hoverData.cxtDragged=!0,(!t.hoverData.cxtOver||_e!==t.hoverData.cxtOver)&&(t.hoverData.cxtOver&&t.hoverData.cxtOver.emit({originalEvent:W,type:"cxtdragout",position:{x:q[0],y:q[1]}}),t.hoverData.cxtOver=_e,_e&&_e.emit({originalEvent:W,type:"cxtdragover",position:{x:q[0],y:q[1]}}))}}else if(t.hoverData.dragging){if(re=!0,oe.panningEnabled()&&oe.userPanningEnabled()){var xt;if(t.hoverData.justStartedPan){var ut=t.hoverData.mdownPos;xt={x:(q[0]-ut[0])*V,y:(q[1]-ut[1])*V},t.hoverData.justStartedPan=!1}else xt={x:De[0]*V,y:De[1]*V};oe.panBy(xt),oe.emit("dragpan"),t.hoverData.dragged=!0}q=t.projectIntoViewport(W.clientX,W.clientY)}else if(Pe[4]==1&&(Ve==null||Ve.pannable())){if(at){if(!t.hoverData.dragging&&oe.boxSelectionEnabled()&&(ot||!oe.panningEnabled()||!oe.userPanningEnabled()))bt();else if(!t.hoverData.selecting&&oe.panningEnabled()&&oe.userPanningEnabled()){var Et=s(Ve,t.hoverData.downs);Et&&(t.hoverData.dragging=!0,t.hoverData.justStartedPan=!0,Pe[4]=0,t.data.bgActivePosistion=U1(pe),t.redrawHint("select",!0),t.redraw())}Ve&&Ve.pannable()&&Ve.active()&&Ve.unactivate()}}else{if(Ve&&Ve.pannable()&&Ve.active()&&Ve.unactivate(),(!Ve||!Ve.grabbed())&&_e!=we&&(we&&i(we,["mouseout","tapdragout"],W,{x:q[0],y:q[1]}),_e&&i(_e,["mouseover","tapdragover"],W,{x:q[0],y:q[1]}),t.hoverData.last=_e),Ve)if(at){if(oe.boxSelectionEnabled()&&ot)Ve&&Ve.grabbed()&&(x(qe),Ve.emit("freeon"),qe.emit("free"),t.dragData.didDrag&&(Ve.emit("dragfreeon"),qe.emit("dragfree"))),bt();else if(Ve&&Ve.grabbed()&&t.nodeIsDraggable(Ve)){var ft=!t.dragData.didDrag;ft&&t.redrawHint("eles",!0),t.dragData.didDrag=!0,t.hoverData.draggingEles||y(qe,{inDragLayer:!0});var yt={x:0,y:0};if(Ct(De[0])&&Ct(De[1])&&(yt.x+=De[0],yt.y+=De[1],ft)){var nt=t.hoverData.dragDelta;nt&&Ct(nt[0])&&Ct(nt[1])&&(yt.x+=nt[0],yt.y+=nt[1])}t.hoverData.draggingEles=!0,qe.silentShift(yt).emit("position drag"),t.redrawHint("drag",!0),t.redraw()}}else Yt();re=!0}if(Pe[2]=q[0],Pe[3]=q[1],re)return W.stopPropagation&&W.stopPropagation(),W.preventDefault&&W.preventDefault(),!1}},"mousemoveHandler"),!1);var k,L,R;t.registerBinding(e,"mouseup",o(function(W){if(!(t.hoverData.which===1&&W.which!==1&&t.hoverData.capture)){var de=t.hoverData.capture;if(de){t.hoverData.capture=!1;var re=t.cy,oe=t.projectIntoViewport(W.clientX,W.clientY),V=t.selection,xe=t.findNearestElement(oe[0],oe[1],!0,!1),q=t.dragData.possibleDragElements,pe=t.hoverData.down,ve=a(W);if(t.data.bgActivePosistion&&(t.redrawHint("select",!0),t.redraw()),t.hoverData.tapholdCancelled=!0,t.data.bgActivePosistion=void 0,pe&&pe.unactivate(),t.hoverData.which===3){var Pe={originalEvent:W,type:"cxttapend",position:{x:oe[0],y:oe[1]}};if(pe?pe.emit(Pe):re.emit(Pe),!t.hoverData.cxtDragged){var _e={originalEvent:W,type:"cxttap",position:{x:oe[0],y:oe[1]}};pe?pe.emit(_e):re.emit(_e)}t.hoverData.cxtDragged=!1,t.hoverData.which=null}else if(t.hoverData.which===1){if(i(xe,["mouseup","tapend","vmouseup"],W,{x:oe[0],y:oe[1]}),!t.dragData.didDrag&&!t.hoverData.dragged&&!t.hoverData.selecting&&!t.hoverData.isOverThresholdDrag&&(i(pe,["click","tap","vclick"],W,{x:oe[0],y:oe[1]}),L=!1,W.timeStamp-R<=re.multiClickDebounceTime()?(k&&clearTimeout(k),L=!0,R=null,i(pe,["dblclick","dbltap","vdblclick"],W,{x:oe[0],y:oe[1]})):(k=setTimeout(function(){L||i(pe,["oneclick","onetap","voneclick"],W,{x:oe[0],y:oe[1]})},re.multiClickDebounceTime()),R=W.timeStamp)),pe==null&&!t.dragData.didDrag&&!t.hoverData.selecting&&!t.hoverData.dragged&&!a(W)&&(re.$(r).unselect(["tapunselect"]),q.length>0&&t.redrawHint("eles",!0),t.dragData.possibleDragElements=q=re.collection()),xe==pe&&!t.dragData.didDrag&&!t.hoverData.selecting&&xe!=null&&xe._private.selectable&&(t.hoverData.dragging||(re.selectionType()==="additive"||ve?xe.selected()?xe.unselect(["tapunselect"]):xe.select(["tapselect"]):ve||(re.$(r).unmerge(xe).unselect(["tapunselect"]),xe.select(["tapselect"]))),t.redrawHint("eles",!0)),t.hoverData.selecting){var we=re.collection(t.getAllInBox(V[0],V[1],V[2],V[3]));t.redrawHint("select",!0),we.length>0&&t.redrawHint("eles",!0),re.emit({type:"boxend",originalEvent:W,position:{x:oe[0],y:oe[1]}});var Ve=o(function(at){return at.selectable()&&!at.selected()},"eleWouldBeSelected");re.selectionType()==="additive"||ve||re.$(r).unmerge(we).unselect(),we.emit("box").stdFilter(Ve).select().emit("boxselect"),t.redraw()}if(t.hoverData.dragging&&(t.hoverData.dragging=!1,t.redrawHint("select",!0),t.redrawHint("eles",!0),t.redraw()),!V[4]){t.redrawHint("drag",!0),t.redrawHint("eles",!0);var De=pe&&pe.grabbed();x(q),De&&(pe.emit("freeon"),q.emit("free"),t.dragData.didDrag&&(pe.emit("dragfreeon"),q.emit("dragfree")))}}V[4]=0,t.hoverData.down=null,t.hoverData.cxtStarted=!1,t.hoverData.draggingEles=!1,t.hoverData.selecting=!1,t.hoverData.isOverThresholdDrag=!1,t.dragData.didDrag=!1,t.hoverData.dragged=!1,t.hoverData.dragDelta=[],t.hoverData.mdownPos=null,t.hoverData.mdownGPos=null,t.hoverData.which=null}}},"mouseupHandler"),!1);var O=o(function(W){if(!t.scrollingPage){var de=t.cy,re=de.zoom(),oe=de.pan(),V=t.projectIntoViewport(W.clientX,W.clientY),xe=[V[0]*re+oe.x,V[1]*re+oe.y];if(t.hoverData.draggingEles||t.hoverData.dragging||t.hoverData.cxtStarted||_()){W.preventDefault();return}if(de.panningEnabled()&&de.userPanningEnabled()&&de.zoomingEnabled()&&de.userZoomingEnabled()){W.preventDefault(),t.data.wheelZooming=!0,clearTimeout(t.data.wheelTimeout),t.data.wheelTimeout=setTimeout(function(){t.data.wheelZooming=!1,t.redrawHint("eles",!0),t.redraw()},150);var q;W.deltaY!=null?q=W.deltaY/-250:W.wheelDeltaY!=null?q=W.wheelDeltaY/1e3:q=W.wheelDelta/1e3,q=q*t.wheelSensitivity;var pe=W.deltaMode===1;pe&&(q*=33);var ve=de.zoom()*Math.pow(10,q);W.type==="gesturechange"&&(ve=t.gestureStartZoom*W.scale),de.zoom({level:ve,renderedPosition:{x:xe[0],y:xe[1]}}),de.emit(W.type==="gesturechange"?"pinchzoom":"scrollzoom")}}},"wheelHandler");t.registerBinding(t.container,"wheel",O,!0),t.registerBinding(e,"scroll",o(function(W){t.scrollingPage=!0,clearTimeout(t.scrollingPageTimeout),t.scrollingPageTimeout=setTimeout(function(){t.scrollingPage=!1},250)},"scrollHandler"),!0),t.registerBinding(t.container,"gesturestart",o(function(W){t.gestureStartZoom=t.cy.zoom(),t.hasTouchStarted||W.preventDefault()},"gestureStartHandler"),!0),t.registerBinding(t.container,"gesturechange",function(be){t.hasTouchStarted||O(be)},!0),t.registerBinding(t.container,"mouseout",o(function(W){var de=t.projectIntoViewport(W.clientX,W.clientY);t.cy.emit({originalEvent:W,type:"mouseout",position:{x:de[0],y:de[1]}})},"mouseOutHandler"),!1),t.registerBinding(t.container,"mouseover",o(function(W){var de=t.projectIntoViewport(W.clientX,W.clientY);t.cy.emit({originalEvent:W,type:"mouseover",position:{x:de[0],y:de[1]}})},"mouseOverHandler"),!1);var M,B,F,P,z,$,H,Q,j,ie,ne,le,he,K=o(function(W,de,re,oe){return Math.sqrt((re-W)*(re-W)+(oe-de)*(oe-de))},"distance"),X=o(function(W,de,re,oe){return(re-W)*(re-W)+(oe-de)*(oe-de)},"distanceSq"),te;t.registerBinding(t.container,"touchstart",te=o(function(W){if(t.hasTouchStarted=!0,!!I(W)){w(),t.touchData.capture=!0,t.data.bgActivePosistion=void 0;var de=t.cy,re=t.touchData.now,oe=t.touchData.earlier;if(W.touches[0]){var V=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);re[0]=V[0],re[1]=V[1]}if(W.touches[1]){var V=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);re[2]=V[0],re[3]=V[1]}if(W.touches[2]){var V=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);re[4]=V[0],re[5]=V[1]}if(W.touches[1]){t.touchData.singleTouchMoved=!0,x(t.dragData.touchDragEles);var xe=t.findContainerClientCoords();j=xe[0],ie=xe[1],ne=xe[2],le=xe[3],M=W.touches[0].clientX-j,B=W.touches[0].clientY-ie,F=W.touches[1].clientX-j,P=W.touches[1].clientY-ie,he=0<=M&&M<=ne&&0<=F&&F<=ne&&0<=B&&B<=le&&0<=P&&P<=le;var q=de.pan(),pe=de.zoom();z=K(M,B,F,P),$=X(M,B,F,P),H=[(M+F)/2,(B+P)/2],Q=[(H[0]-q.x)/pe,(H[1]-q.y)/pe];var ve=200,Pe=ve*ve;if($<Pe&&!W.touches[2]){var _e=t.findNearestElement(re[0],re[1],!0,!0),we=t.findNearestElement(re[2],re[3],!0,!0);_e&&_e.isNode()?(_e.activate().emit({originalEvent:W,type:"cxttapstart",position:{x:re[0],y:re[1]}}),t.touchData.start=_e):we&&we.isNode()?(we.activate().emit({originalEvent:W,type:"cxttapstart",position:{x:re[0],y:re[1]}}),t.touchData.start=we):de.emit({originalEvent:W,type:"cxttapstart",position:{x:re[0],y:re[1]}}),t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxt=!0,t.touchData.cxtDragged=!1,t.data.bgActivePosistion=void 0,t.redraw();return}}if(W.touches[2])de.boxSelectionEnabled()&&W.preventDefault();else if(!W.touches[1]){if(W.touches[0]){var Ve=t.findNearestElements(re[0],re[1],!0,!0),De=Ve[0];if(De!=null&&(De.activate(),t.touchData.start=De,t.touchData.starts=Ve,t.nodeIsGrabbable(De))){var qe=t.dragData.touchDragEles=de.collection(),at=null;t.redrawHint("eles",!0),t.redrawHint("drag",!0),De.selected()?(at=de.$(function(We){return We.selected()&&t.nodeIsGrabbable(We)}),y(at,{addToList:qe})):v(De,{addToList:qe}),d(De);var Rt=o(function(ot){return{originalEvent:W,type:ot,position:{x:re[0],y:re[1]}}},"makeEvent");De.emit(Rt("grabon")),at?at.forEach(function(We){We.emit(Rt("grab"))}):De.emit(Rt("grab"))}i(De,["touchstart","tapstart","vmousedown"],W,{x:re[0],y:re[1]}),De==null&&(t.data.bgActivePosistion={x:V[0],y:V[1]},t.redrawHint("select",!0),t.redraw()),t.touchData.singleTouchMoved=!1,t.touchData.singleTouchStartTime=+new Date,clearTimeout(t.touchData.tapholdTimeout),t.touchData.tapholdTimeout=setTimeout(function(){t.touchData.singleTouchMoved===!1&&!t.pinching&&!t.touchData.selecting&&i(t.touchData.start,["taphold"],W,{x:re[0],y:re[1]})},t.tapholdDuration)}}if(W.touches.length>=1){for(var st=t.touchData.startPosition=[null,null,null,null,null,null],Ue=0;Ue<re.length;Ue++)st[Ue]=oe[Ue]=re[Ue];var ct=W.touches[0];t.touchData.startGPosition=[ct.clientX,ct.clientY]}}},"touchstartHandler"),!1);var J;t.registerBinding(e,"touchmove",J=o(function(W){var de=t.touchData.capture;if(!(!de&&!I(W))){var re=t.selection,oe=t.cy,V=t.touchData.now,xe=t.touchData.earlier,q=oe.zoom();if(W.touches[0]){var pe=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);V[0]=pe[0],V[1]=pe[1]}if(W.touches[1]){var pe=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);V[2]=pe[0],V[3]=pe[1]}if(W.touches[2]){var pe=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);V[4]=pe[0],V[5]=pe[1]}var ve=t.touchData.startGPosition,Pe;if(de&&W.touches[0]&&ve){for(var _e=[],we=0;we<V.length;we++)_e[we]=V[we]-xe[we];var Ve=W.touches[0].clientX-ve[0],De=Ve*Ve,qe=W.touches[0].clientY-ve[1],at=qe*qe,Rt=De+at;Pe=Rt>=t.touchTapThreshold2}if(de&&t.touchData.cxt){W.preventDefault();var st=W.touches[0].clientX-j,Ue=W.touches[0].clientY-ie,ct=W.touches[1].clientX-j,We=W.touches[1].clientY-ie,ot=X(st,Ue,ct,We),Yt=ot/$,bt=150,Mt=bt*bt,xt=1.5,ut=xt*xt;if(Yt>=ut||ot>=Mt){t.touchData.cxt=!1,t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var Et={originalEvent:W,type:"cxttapend",position:{x:V[0],y:V[1]}};t.touchData.start?(t.touchData.start.unactivate().emit(Et),t.touchData.start=null):oe.emit(Et)}}if(de&&t.touchData.cxt){var Et={originalEvent:W,type:"cxtdrag",position:{x:V[0],y:V[1]}};t.data.bgActivePosistion=void 0,t.redrawHint("select",!0),t.touchData.start?t.touchData.start.emit(Et):oe.emit(Et),t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxtDragged=!0;var ft=t.findNearestElement(V[0],V[1],!0,!0);(!t.touchData.cxtOver||ft!==t.touchData.cxtOver)&&(t.touchData.cxtOver&&t.touchData.cxtOver.emit({originalEvent:W,type:"cxtdragout",position:{x:V[0],y:V[1]}}),t.touchData.cxtOver=ft,ft&&ft.emit({originalEvent:W,type:"cxtdragover",position:{x:V[0],y:V[1]}}))}else if(de&&W.touches[2]&&oe.boxSelectionEnabled())W.preventDefault(),t.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,t.touchData.selecting||oe.emit({originalEvent:W,type:"boxstart",position:{x:V[0],y:V[1]}}),t.touchData.selecting=!0,t.touchData.didSelect=!0,re[4]=1,!re||re.length===0||re[0]===void 0?(re[0]=(V[0]+V[2]+V[4])/3,re[1]=(V[1]+V[3]+V[5])/3,re[2]=(V[0]+V[2]+V[4])/3+1,re[3]=(V[1]+V[3]+V[5])/3+1):(re[2]=(V[0]+V[2]+V[4])/3,re[3]=(V[1]+V[3]+V[5])/3),t.redrawHint("select",!0),t.redraw();else if(de&&W.touches[1]&&!t.touchData.didSelect&&oe.zoomingEnabled()&&oe.panningEnabled()&&oe.userZoomingEnabled()&&oe.userPanningEnabled()){W.preventDefault(),t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var yt=t.dragData.touchDragEles;if(yt){t.redrawHint("drag",!0);for(var nt=0;nt<yt.length;nt++){var dn=yt[nt]._private;dn.grabbed=!1,dn.rscratch.inDragLayer=!1}}var Tt=t.touchData.start,st=W.touches[0].clientX-j,Ue=W.touches[0].clientY-ie,ct=W.touches[1].clientX-j,We=W.touches[1].clientY-ie,On=K(st,Ue,ct,We),tn=On/z;if(he){var _r=st-M,Dr=Ue-B,Pn=ct-F,At=We-P,Ce=(_r+Pn)/2,tt=(Dr+At)/2,St=oe.zoom(),mr=St*tn,rn=oe.pan(),gn=Q[0]*St+rn.x,Zr=Q[1]*St+rn.y,Ni={x:-mr/St*(gn-rn.x-Ce)+gn,y:-mr/St*(Zr-rn.y-tt)+Zr};if(Tt&&Tt.active()){var yt=t.dragData.touchDragEles;x(yt),t.redrawHint("drag",!0),t.redrawHint("eles",!0),Tt.unactivate().emit("freeon"),yt.emit("free"),t.dragData.didDrag&&(Tt.emit("dragfreeon"),yt.emit("dragfree"))}oe.viewport({zoom:mr,pan:Ni,cancelOnFailedZoom:!0}),oe.emit("pinchzoom"),z=On,M=st,B=Ue,F=ct,P=We,t.pinching=!0}if(W.touches[0]){var pe=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);V[0]=pe[0],V[1]=pe[1]}if(W.touches[1]){var pe=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);V[2]=pe[0],V[3]=pe[1]}if(W.touches[2]){var pe=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);V[4]=pe[0],V[5]=pe[1]}}else if(W.touches[0]&&!t.touchData.didSelect){var Zn=t.touchData.start,Sn=t.touchData.last,ft;if(!t.hoverData.draggingEles&&!t.swipePanning&&(ft=t.findNearestElement(V[0],V[1],!0,!0)),de&&Zn!=null&&W.preventDefault(),de&&Zn!=null&&t.nodeIsDraggable(Zn))if(Pe){var yt=t.dragData.touchDragEles,Hr=!t.dragData.didDrag;Hr&&y(yt,{inDragLayer:!0}),t.dragData.didDrag=!0;var et={x:0,y:0};if(Ct(_e[0])&&Ct(_e[1])&&(et.x+=_e[0],et.y+=_e[1],Hr)){t.redrawHint("eles",!0);var mt=t.touchData.dragDelta;mt&&Ct(mt[0])&&Ct(mt[1])&&(et.x+=mt[0],et.y+=mt[1])}t.hoverData.draggingEles=!0,yt.silentShift(et).emit("position drag"),t.redrawHint("drag",!0),t.touchData.startPosition[0]==xe[0]&&t.touchData.startPosition[1]==xe[1]&&t.redrawHint("eles",!0),t.redraw()}else{var mt=t.touchData.dragDelta=t.touchData.dragDelta||[];mt.length===0?(mt.push(_e[0]),mt.push(_e[1])):(mt[0]+=_e[0],mt[1]+=_e[1])}if(i(Zn||ft,["touchmove","tapdrag","vmousemove"],W,{x:V[0],y:V[1]}),(!Zn||!Zn.grabbed())&&ft!=Sn&&(Sn&&Sn.emit({originalEvent:W,type:"tapdragout",position:{x:V[0],y:V[1]}}),ft&&ft.emit({originalEvent:W,type:"tapdragover",position:{x:V[0],y:V[1]}})),t.touchData.last=ft,de)for(var nt=0;nt<V.length;nt++)V[nt]&&t.touchData.startPosition[nt]&&Pe&&(t.touchData.singleTouchMoved=!0);if(de&&(Zn==null||Zn.pannable())&&oe.panningEnabled()&&oe.userPanningEnabled()){var Kt=s(Zn,t.touchData.starts);Kt&&(W.preventDefault(),t.data.bgActivePosistion||(t.data.bgActivePosistion=U1(t.touchData.startPosition)),t.swipePanning?(oe.panBy({x:_e[0]*q,y:_e[1]*q}),oe.emit("dragpan")):Pe&&(t.swipePanning=!0,oe.panBy({x:Ve*q,y:qe*q}),oe.emit("dragpan"),Zn&&(Zn.unactivate(),t.redrawHint("select",!0),t.touchData.start=null)));var pe=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);V[0]=pe[0],V[1]=pe[1]}}for(var we=0;we<V.length;we++)xe[we]=V[we];de&&W.touches.length>0&&!t.hoverData.draggingEles&&!t.swipePanning&&t.data.bgActivePosistion!=null&&(t.data.bgActivePosistion=void 0,t.redrawHint("select",!0),t.redraw())}},"touchmoveHandler"),!1);var se;t.registerBinding(e,"touchcancel",se=o(function(W){var de=t.touchData.start;t.touchData.capture=!1,de&&de.unactivate()},"touchcancelHandler"));var ue,Z,Se,ce;if(t.registerBinding(e,"touchend",ue=o(function(W){var de=t.touchData.start,re=t.touchData.capture;if(re)W.touches.length===0&&(t.touchData.capture=!1),W.preventDefault();else return;var oe=t.selection;t.swipePanning=!1,t.hoverData.draggingEles=!1;var V=t.cy,xe=V.zoom(),q=t.touchData.now,pe=t.touchData.earlier;if(W.touches[0]){var ve=t.projectIntoViewport(W.touches[0].clientX,W.touches[0].clientY);q[0]=ve[0],q[1]=ve[1]}if(W.touches[1]){var ve=t.projectIntoViewport(W.touches[1].clientX,W.touches[1].clientY);q[2]=ve[0],q[3]=ve[1]}if(W.touches[2]){var ve=t.projectIntoViewport(W.touches[2].clientX,W.touches[2].clientY);q[4]=ve[0],q[5]=ve[1]}de&&de.unactivate();var Pe;if(t.touchData.cxt){if(Pe={originalEvent:W,type:"cxttapend",position:{x:q[0],y:q[1]}},de?de.emit(Pe):V.emit(Pe),!t.touchData.cxtDragged){var _e={originalEvent:W,type:"cxttap",position:{x:q[0],y:q[1]}};de?de.emit(_e):V.emit(_e)}t.touchData.start&&(t.touchData.start._private.grabbed=!1),t.touchData.cxt=!1,t.touchData.start=null,t.redraw();return}if(!W.touches[2]&&V.boxSelectionEnabled()&&t.touchData.selecting){t.touchData.selecting=!1;var we=V.collection(t.getAllInBox(oe[0],oe[1],oe[2],oe[3]));oe[0]=void 0,oe[1]=void 0,oe[2]=void 0,oe[3]=void 0,oe[4]=0,t.redrawHint("select",!0),V.emit({type:"boxend",originalEvent:W,position:{x:q[0],y:q[1]}});var Ve=o(function(Mt){return Mt.selectable()&&!Mt.selected()},"eleWouldBeSelected");we.emit("box").stdFilter(Ve).select().emit("boxselect"),we.nonempty()&&t.redrawHint("eles",!0),t.redraw()}if(de?.unactivate(),W.touches[2])t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);else if(!W.touches[1]){if(!W.touches[0]){if(!W.touches[0]){t.data.bgActivePosistion=void 0,t.redrawHint("select",!0);var De=t.dragData.touchDragEles;if(de!=null){var qe=de._private.grabbed;x(De),t.redrawHint("drag",!0),t.redrawHint("eles",!0),qe&&(de.emit("freeon"),De.emit("free"),t.dragData.didDrag&&(de.emit("dragfreeon"),De.emit("dragfree"))),i(de,["touchend","tapend","vmouseup","tapdragout"],W,{x:q[0],y:q[1]}),de.unactivate(),t.touchData.start=null}else{var at=t.findNearestElement(q[0],q[1],!0,!0);i(at,["touchend","tapend","vmouseup","tapdragout"],W,{x:q[0],y:q[1]})}var Rt=t.touchData.startPosition[0]-q[0],st=Rt*Rt,Ue=t.touchData.startPosition[1]-q[1],ct=Ue*Ue,We=st+ct,ot=We*xe*xe;t.touchData.singleTouchMoved||(de||V.$(":selected").unselect(["tapunselect"]),i(de,["tap","vclick"],W,{x:q[0],y:q[1]}),Z=!1,W.timeStamp-ce<=V.multiClickDebounceTime()?(Se&&clearTimeout(Se),Z=!0,ce=null,i(de,["dbltap","vdblclick"],W,{x:q[0],y:q[1]})):(Se=setTimeout(function(){Z||i(de,["onetap","voneclick"],W,{x:q[0],y:q[1]})},V.multiClickDebounceTime()),ce=W.timeStamp)),de!=null&&!t.dragData.didDrag&&de._private.selectable&&ot<t.touchTapThreshold2&&!t.pinching&&(V.selectionType()==="single"?(V.$(r).unmerge(de).unselect(["tapunselect"]),de.select(["tapselect"])):de.selected()?de.unselect(["tapunselect"]):de.select(["tapselect"]),t.redrawHint("eles",!0)),t.touchData.singleTouchMoved=!0}}}for(var Yt=0;Yt<q.length;Yt++)pe[Yt]=q[Yt];t.dragData.didDrag=!1,W.touches.length===0&&(t.touchData.dragDelta=[],t.touchData.startPosition=[null,null,null,null,null,null],t.touchData.startGPosition=null,t.touchData.didSelect=!1),W.touches.length<2&&(W.touches.length===1&&(t.touchData.startGPosition=[W.touches[0].clientX,W.touches[0].clientY]),t.pinching=!1,t.redrawHint("eles",!0),t.redraw())},"touchendHandler"),!1),typeof TouchEvent>"u"){var ae=[],Oe=o(function(W){return{clientX:W.clientX,clientY:W.clientY,force:1,identifier:W.pointerId,pageX:W.pageX,pageY:W.pageY,radiusX:W.width/2,radiusY:W.height/2,screenX:W.screenX,screenY:W.screenY,target:W.target}},"makeTouch"),ge=o(function(W){return{event:W,touch:Oe(W)}},"makePointer"),ze=o(function(W){ae.push(ge(W))},"addPointer"),He=o(function(W){for(var de=0;de<ae.length;de++){var re=ae[de];if(re.event.pointerId===W.pointerId){ae.splice(de,1);return}}},"removePointer"),$e=o(function(W){var de=ae.filter(function(re){return re.event.pointerId===W.pointerId})[0];de.event=W,de.touch=Oe(W)},"updatePointer"),Re=o(function(W){W.touches=ae.map(function(de){return de.touch})},"addTouchesToEvent"),Ie=o(function(W){return W.pointerType==="mouse"||W.pointerType===4},"pointerIsMouse");t.registerBinding(t.container,"pointerdown",function(be){Ie(be)||(be.preventDefault(),ze(be),Re(be),te(be))}),t.registerBinding(t.container,"pointerup",function(be){Ie(be)||(He(be),Re(be),ue(be))}),t.registerBinding(t.container,"pointercancel",function(be){Ie(be)||(He(be),Re(be),se(be))}),t.registerBinding(t.container,"pointermove",function(be){Ie(be)||(be.preventDefault(),$e(be),Re(be),J(be))})}};eh={};eh.generatePolygon=function(t,e){return this.nodeShapes[t]={renderer:this,name:t,points:e,draw:o(function(n,i,a,s,l,u){this.renderer.nodeShapeImpl("polygon",n,i,a,s,l,this.points)},"draw"),intersectLine:o(function(n,i,a,s,l,u,h,f){return Xb(l,u,this.points,n,i,a/2,s/2,h)},"intersectLine"),checkPoint:o(function(n,i,a,s,l,u,h,f){return Zu(n,i,this.points,u,h,s,l,[0,-1],a)},"checkPoint")}};eh.generateEllipse=function(){return this.nodeShapes.ellipse={renderer:this,name:"ellipse",draw:o(function(e,r,n,i,a,s){this.renderer.nodeShapeImpl(this.name,e,r,n,i,a)},"draw"),intersectLine:o(function(e,r,n,i,a,s,l,u){return Yqe(a,s,e,r,n/2+l,i/2+l)},"intersectLine"),checkPoint:o(function(e,r,n,i,a,s,l,u){return $p(e,r,i,a,s,l,n)},"checkPoint")}};eh.generateRoundPolygon=function(t,e){return this.nodeShapes[t]={renderer:this,name:t,points:e,getOrCreateCorners:o(function(n,i,a,s,l,u,h){if(u[h]!==void 0&&u[h+"-cx"]===n&&u[h+"-cy"]===i)return u[h];u[h]=new Array(e.length/2),u[h+"-cx"]=n,u[h+"-cy"]=i;var f=a/2,d=s/2;l=l==="auto"?bme(a,s):l;for(var p=new Array(e.length/2),m=0;m<e.length/2;m++)p[m]={x:n+f*e[m*2],y:i+d*e[m*2+1]};var g,y,v,x,b=p.length;for(y=p[b-1],g=0;g<b;g++)v=p[g%b],x=p[(g+1)%b],u[h][g]=vB(y,v,x,l),y=v,v=x;return u[h]},"getOrCreateCorners"),draw:o(function(n,i,a,s,l,u,h){this.renderer.nodeShapeImpl("round-polygon",n,i,a,s,l,this.points,this.getOrCreateCorners(i,a,s,l,u,h,"drawCorners"))},"draw"),intersectLine:o(function(n,i,a,s,l,u,h,f,d){return Xqe(l,u,this.points,n,i,a,s,h,this.getOrCreateCorners(n,i,a,s,f,d,"corners"))},"intersectLine"),checkPoint:o(function(n,i,a,s,l,u,h,f,d){return qqe(n,i,this.points,u,h,s,l,this.getOrCreateCorners(u,h,s,l,f,d,"corners"))},"checkPoint")}};eh.generateRoundRectangle=function(){return this.nodeShapes["round-rectangle"]=this.nodeShapes.roundrectangle={renderer:this,name:"round-rectangle",points:gs(4,0),draw:o(function(e,r,n,i,a,s){this.renderer.nodeShapeImpl(this.name,e,r,n,i,a,this.points,s)},"draw"),intersectLine:o(function(e,r,n,i,a,s,l,u){return vme(a,s,e,r,n,i,l,u)},"intersectLine"),checkPoint:o(function(e,r,n,i,a,s,l,u){var h=i/2,f=a/2;u=u==="auto"?Vp(i,a):u,u=Math.min(h,f,u);var d=u*2;return!!(Zu(e,r,this.points,s,l,i,a-d,[0,-1],n)||Zu(e,r,this.points,s,l,i-d,a,[0,-1],n)||$p(e,r,d,d,s-h+u,l-f+u,n)||$p(e,r,d,d,s+h-u,l-f+u,n)||$p(e,r,d,d,s+h-u,l+f-u,n)||$p(e,r,d,d,s-h+u,l+f-u,n))},"checkPoint")}};eh.generateCutRectangle=function(){return this.nodeShapes["cut-rectangle"]=this.nodeShapes.cutrectangle={renderer:this,name:"cut-rectangle",cornerLength:sB(),points:gs(4,0),draw:o(function(e,r,n,i,a,s){this.renderer.nodeShapeImpl(this.name,e,r,n,i,a,null,s)},"draw"),generateCutTrianglePts:o(function(e,r,n,i,a){var s=a==="auto"?this.cornerLength:a,l=r/2,u=e/2,h=n-u,f=n+u,d=i-l,p=i+l;return{topLeft:[h,d+s,h+s,d,h+s,d+s],topRight:[f-s,d,f,d+s,f-s,d+s],bottomRight:[f,p-s,f-s,p,f-s,p-s],bottomLeft:[h+s,p,h,p-s,h+s,p-s]}},"generateCutTrianglePts"),intersectLine:o(function(e,r,n,i,a,s,l,u){var h=this.generateCutTrianglePts(n+2*l,i+2*l,e,r,u),f=[].concat.apply([],[h.topLeft.splice(0,4),h.topRight.splice(0,4),h.bottomRight.splice(0,4),h.bottomLeft.splice(0,4)]);return Xb(a,s,f,e,r)},"intersectLine"),checkPoint:o(function(e,r,n,i,a,s,l,u){var h=u==="auto"?this.cornerLength:u;if(Zu(e,r,this.points,s,l,i,a-2*h,[0,-1],n)||Zu(e,r,this.points,s,l,i-2*h,a,[0,-1],n))return!0;var f=this.generateCutTrianglePts(i,a,s,l);return Us(e,r,f.topLeft)||Us(e,r,f.topRight)||Us(e,r,f.bottomRight)||Us(e,r,f.bottomLeft)},"checkPoint")}};eh.generateBarrel=function(){return this.nodeShapes.barrel={renderer:this,name:"barrel",points:gs(4,0),draw:o(function(e,r,n,i,a,s){this.renderer.nodeShapeImpl(this.name,e,r,n,i,a)},"draw"),intersectLine:o(function(e,r,n,i,a,s,l,u){var h=.15,f=.5,d=.85,p=this.generateBarrelBezierPts(n+2*l,i+2*l,e,r),m=o(function(v){var x=W1({x:v[0],y:v[1]},{x:v[2],y:v[3]},{x:v[4],y:v[5]},h),b=W1({x:v[0],y:v[1]},{x:v[2],y:v[3]},{x:v[4],y:v[5]},f),w=W1({x:v[0],y:v[1]},{x:v[2],y:v[3]},{x:v[4],y:v[5]},d);return[v[0],v[1],x.x,x.y,b.x,b.y,w.x,w.y,v[4],v[5]]},"approximateBarrelCurvePts"),g=[].concat(m(p.topLeft),m(p.topRight),m(p.bottomRight),m(p.bottomLeft));return Xb(a,s,g,e,r)},"intersectLine"),generateBarrelBezierPts:o(function(e,r,n,i){var a=r/2,s=e/2,l=n-s,u=n+s,h=i-a,f=i+a,d=BP(e,r),p=d.heightOffset,m=d.widthOffset,g=d.ctrlPtOffsetPct*e,y={topLeft:[l,h+p,l+g,h,l+m,h],topRight:[u-m,h,u-g,h,u,h+p],bottomRight:[u,f-p,u-g,f,u-m,f],bottomLeft:[l+m,f,l+g,f,l,f-p]};return y.topLeft.isTop=!0,y.topRight.isTop=!0,y.bottomLeft.isBottom=!0,y.bottomRight.isBottom=!0,y},"generateBarrelBezierPts"),checkPoint:o(function(e,r,n,i,a,s,l,u){var h=BP(i,a),f=h.heightOffset,d=h.widthOffset;if(Zu(e,r,this.points,s,l,i,a-2*f,[0,-1],n)||Zu(e,r,this.points,s,l,i-2*d,a,[0,-1],n))return!0;for(var p=this.generateBarrelBezierPts(i,a,s,l),m=o(function(S,_,I){var D=I[4],k=I[2],L=I[0],R=I[5],O=I[1],M=Math.min(D,L),B=Math.max(D,L),F=Math.min(R,O),P=Math.max(R,O);if(M<=S&&S<=B&&F<=_&&_<=P){var z=jqe(D,k,L),$=Vqe(z[0],z[1],z[2],S),H=$.filter(function(Q){return 0<=Q&&Q<=1});if(H.length>0)return H[0]}return null},"getCurveT"),g=Object.keys(p),y=0;y<g.length;y++){var v=g[y],x=p[v],b=m(e,r,x);if(b!=null){var w=x[5],C=x[3],T=x[1],E=oa(w,C,T,b);if(x.isTop&&E<=r||x.isBottom&&r<=E)return!0}}return!1},"checkPoint")}};eh.generateBottomRoundrectangle=function(){return this.nodeShapes["bottom-round-rectangle"]=this.nodeShapes.bottomroundrectangle={renderer:this,name:"bottom-round-rectangle",points:gs(4,0),draw:o(function(e,r,n,i,a,s){this.renderer.nodeShapeImpl(this.name,e,r,n,i,a,this.points,s)},"draw"),intersectLine:o(function(e,r,n,i,a,s,l,u){var h=e-(n/2+l),f=r-(i/2+l),d=f,p=e+(n/2+l),m=Ef(a,s,e,r,h,f,p,d,!1);return m.length>0?m:vme(a,s,e,r,n,i,l,u)},"intersectLine"),checkPoint:o(function(e,r,n,i,a,s,l,u){u=u==="auto"?Vp(i,a):u;var h=2*u;if(Zu(e,r,this.points,s,l,i,a-h,[0,-1],n)||Zu(e,r,this.points,s,l,i-h,a,[0,-1],n))return!0;var f=i/2+2*n,d=a/2+2*n,p=[s-f,l-d,s-f,l,s+f,l,s+f,l-d];return!!(Us(e,r,p)||$p(e,r,h,h,s+i/2-u,l+a/2-u,n)||$p(e,r,h,h,s-i/2+u,l+a/2-u,n))},"checkPoint")}};eh.registerNodeShapes=function(){var t=this.nodeShapes={},e=this;this.generateEllipse(),this.generatePolygon("triangle",gs(3,0)),this.generateRoundPolygon("round-triangle",gs(3,0)),this.generatePolygon("rectangle",gs(4,0)),t.square=t.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();{var r=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",r),this.generateRoundPolygon("round-diamond",r)}this.generatePolygon("pentagon",gs(5,0)),this.generateRoundPolygon("round-pentagon",gs(5,0)),this.generatePolygon("hexagon",gs(6,0)),this.generateRoundPolygon("round-hexagon",gs(6,0)),this.generatePolygon("heptagon",gs(7,0)),this.generateRoundPolygon("round-heptagon",gs(7,0)),this.generatePolygon("octagon",gs(8,0)),this.generateRoundPolygon("round-octagon",gs(8,0));var n=new Array(20);{var i=PP(5,0),a=PP(5,Math.PI/5),s=.5*(3-Math.sqrt(5));s*=1.57;for(var l=0;l<a.length/2;l++)a[l*2]*=s,a[l*2+1]*=s;for(var l=0;l<20/4;l++)n[l*4]=i[l*2],n[l*4+1]=i[l*2+1],n[l*4+2]=a[l*2],n[l*4+3]=a[l*2+1]}n=xme(n),this.generatePolygon("star",n),this.generatePolygon("vee",[-1,-1,0,-.333,1,-1,0,1]),this.generatePolygon("rhomboid",[-1,-1,.333,-1,1,1,-.333,1]),this.generatePolygon("right-rhomboid",[-.333,-1,1,-1,.333,1,-1,1]),this.nodeShapes.concavehexagon=this.generatePolygon("concave-hexagon",[-1,-.95,-.75,0,-1,.95,1,.95,.75,0,1,-.95]);{var u=[-1,-1,.25,-1,1,0,.25,1,-1,1];this.generatePolygon("tag",u),this.generateRoundPolygon("round-tag",u)}t.makePolygon=function(h){var f=h.join("$"),d="polygon-"+f,p;return(p=this[d])?p:e.generatePolygon(d,h)}};l4={};l4.timeToRender=function(){return this.redrawTotalTime/this.redrawCount};l4.redraw=function(t){t=t||dme();var e=this;e.averageRedrawTime===void 0&&(e.averageRedrawTime=0),e.lastRedrawTime===void 0&&(e.lastRedrawTime=0),e.lastDrawTime===void 0&&(e.lastDrawTime=0),e.requestedFrame=!0,e.renderOptions=t};l4.beforeRender=function(t,e){if(!this.destroyed){e==null&&ai("Priority is not optional for beforeRender");var r=this.beforeRenderCallbacks;r.push({fn:t,priority:e}),r.sort(function(n,i){return i.priority-n.priority})}};M0e=o(function(e,r,n){for(var i=e.beforeRenderCallbacks,a=0;a<i.length;a++)i[a].fn(r,n)},"beforeRenderCallbacks");l4.startRenderLoop=function(){var t=this,e=t.cy;if(!t.renderLoopStarted){t.renderLoopStarted=!0;var r=o(function n(i){if(!t.destroyed){if(!e.batching())if(t.requestedFrame&&!t.skipFrame){M0e(t,!0,i);var a=Qu();t.render(t.renderOptions);var s=t.lastDrawTime=Qu();t.averageRedrawTime===void 0&&(t.averageRedrawTime=s-a),t.redrawCount===void 0&&(t.redrawCount=0),t.redrawCount++,t.redrawTotalTime===void 0&&(t.redrawTotalTime=0);var l=s-a;t.redrawTotalTime+=l,t.lastRedrawTime=l,t.averageRedrawTime=t.averageRedrawTime/2+l/2,t.requestedFrame=!1}else M0e(t,!1,i);t.skipFrame=!1,xS(n)}},"renderFn");xS(r)}};LQe=o(function(e){this.init(e)},"BaseRenderer"),vge=LQe,oy=vge.prototype;oy.clientFunctions=["redrawHint","render","renderTo","matchCanvasSize","nodeShapeImpl","arrowShapeImpl"];oy.init=function(t){var e=this;e.options=t,e.cy=t.cy;var r=e.container=t.cy.container(),n=e.cy.window();if(n){var i=n.document,a=i.head,s="__________cytoscape_stylesheet",l="__________cytoscape_container",u=i.getElementById(s)!=null;if(r.className.indexOf(l)<0&&(r.className=(r.className||"")+" "+l),!u){var h=i.createElement("style");h.id=s,h.textContent="."+l+" { position: relative; }",a.insertBefore(h,a.children[0])}var f=n.getComputedStyle(r),d=f.getPropertyValue("position");d==="static"&&un("A Cytoscape container has style position:static and so can not use UI extensions properly")}e.selection=[void 0,void 0,void 0,void 0,0],e.bezierProjPcts=[.05,.225,.4,.5,.6,.775,.95],e.hoverData={down:null,last:null,downTime:null,triggerMode:null,dragging:!1,initialPan:[null,null],capture:!1},e.dragData={possibleDragElements:[]},e.touchData={start:null,capture:!1,startPosition:[null,null,null,null,null,null],singleTouchStartTime:null,singleTouchMoved:!0,now:[null,null,null,null,null,null],earlier:[null,null,null,null,null,null]},e.redraws=0,e.showFps=t.showFps,e.debug=t.debug,e.webgl=t.webgl,e.hideEdgesOnViewport=t.hideEdgesOnViewport,e.textureOnViewport=t.textureOnViewport,e.wheelSensitivity=t.wheelSensitivity,e.motionBlurEnabled=t.motionBlur,e.forcedPixelRatio=Ct(t.pixelRatio)?t.pixelRatio:null,e.motionBlur=t.motionBlur,e.motionBlurOpacity=t.motionBlurOpacity,e.motionBlurTransparency=1-e.motionBlurOpacity,e.motionBlurPxRatio=1,e.mbPxRBlurry=1,e.minMbLowQualFrames=4,e.fullQualityMb=!1,e.clearedForMotionBlur=[],e.desktopTapThreshold=t.desktopTapThreshold,e.desktopTapThreshold2=t.desktopTapThreshold*t.desktopTapThreshold,e.touchTapThreshold=t.touchTapThreshold,e.touchTapThreshold2=t.touchTapThreshold*t.touchTapThreshold,e.tapholdDuration=500,e.bindings=[],e.beforeRenderCallbacks=[],e.beforeRenderPriorities={animations:400,eleCalcs:300,eleTxrDeq:200,lyrTxrDeq:150,lyrTxrSkip:100},e.registerNodeShapes(),e.registerArrowShapes(),e.registerCalculationListeners()};oy.notify=function(t,e){var r=this,n=r.cy;if(!this.destroyed){if(t==="init"){r.load();return}if(t==="destroy"){r.destroy();return}(t==="add"||t==="remove"||t==="move"&&n.hasCompoundNodes()||t==="load"||t==="zorder"||t==="mount")&&r.invalidateCachedZSortedEles(),t==="viewport"&&r.redrawHint("select",!0),t==="gc"&&r.redrawHint("gc",!0),(t==="load"||t==="resize"||t==="mount")&&(r.invalidateContainerClientCoordsCache(),r.matchCanvasSize(r.container)),r.redrawHint("eles",!0),r.redrawHint("drag",!0),this.startRenderLoop(),this.redraw()}};oy.destroy=function(){var t=this;t.destroyed=!0,t.cy.stopAnimationLoop();for(var e=0;e<t.bindings.length;e++){var r=t.bindings[e],n=r,i=n.target;(i.off||i.removeEventListener).apply(i,n.args)}if(t.bindings=[],t.beforeRenderCallbacks=[],t.onUpdateEleCalcsFns=[],t.removeObserver&&t.removeObserver.disconnect(),t.styleObserver&&t.styleObserver.disconnect(),t.resizeObserver&&t.resizeObserver.disconnect(),t.labelCalcDiv)try{document.body.removeChild(t.labelCalcDiv)}catch{}};oy.isHeadless=function(){return!1};[yB,gge,yge,sy,eh,l4].forEach(function(t){rr(oy,t)});DP=1e3/60,xge={setupDequeueing:o(function(e){return o(function(){var n=this,i=this.renderer;if(!n.dequeueingSetup){n.dequeueingSetup=!0;var a=n4(function(){i.redrawHint("eles",!0),i.redrawHint("drag",!0),i.redraw()},e.deqRedrawThreshold),s=o(function(h,f){var d=Qu(),p=i.averageRedrawTime,m=i.lastRedrawTime,g=[],y=i.cy.extent(),v=i.getPixelRatio();for(h||i.flushRenderedStyleQueue();;){var x=Qu(),b=x-d,w=x-f;if(m<DP){var C=DP-(h?p:0);if(w>=e.deqFastCost*C)break}else if(h){if(b>=e.deqCost*m||b>=e.deqAvgCost*p)break}else if(w>=e.deqNoDrawCost*DP)break;var T=e.deq(n,v,y);if(T.length>0)for(var E=0;E<T.length;E++)g.push(T[E]);else break}g.length>0&&(e.onDeqd(n,g),!h&&e.shouldRedraw(n,g,v,y)&&a())},"dequeue"),l=e.priority||rB;i.beforeRender(s,l(n))}},"setupDequeueingImpl")},"setupDequeueing")},RQe=function(){function t(e){var r=arguments.length>1&&arguments[1]!==void 0?arguments[1]:bS;Mf(this,t),this.idsByKey=new Xc,this.keyForId=new Xc,this.cachesByLvl=new Xc,this.lvls=[],this.getKey=e,this.doesEleInvalidateKey=r}return o(t,"ElementTextureCacheLookup"),If(t,[{key:"getIdsFor",value:o(function(r){r==null&&ai("Can not get id list for null key");var n=this.idsByKey,i=this.idsByKey.get(r);return i||(i=new J1,n.set(r,i)),i},"getIdsFor")},{key:"addIdForKey",value:o(function(r,n){r!=null&&this.getIdsFor(r).add(n)},"addIdForKey")},{key:"deleteIdForKey",value:o(function(r,n){r!=null&&this.getIdsFor(r).delete(n)},"deleteIdForKey")},{key:"getNumberOfIdsForKey",value:o(function(r){return r==null?0:this.getIdsFor(r).size},"getNumberOfIdsForKey")},{key:"updateKeyMappingFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n),a=this.getKey(r);this.deleteIdForKey(i,n),this.addIdForKey(a,n),this.keyForId.set(n,a)},"updateKeyMappingFor")},{key:"deleteKeyMappingFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n);this.deleteIdForKey(i,n),this.keyForId.delete(n)},"deleteKeyMappingFor")},{key:"keyHasChangedFor",value:o(function(r){var n=r.id(),i=this.keyForId.get(n),a=this.getKey(r);return i!==a},"keyHasChangedFor")},{key:"isInvalid",value:o(function(r){return this.keyHasChangedFor(r)||this.doesEleInvalidateKey(r)},"isInvalid")},{key:"getCachesAt",value:o(function(r){var n=this.cachesByLvl,i=this.lvls,a=n.get(r);return a||(a=new Xc,n.set(r,a),i.push(r)),a},"getCachesAt")},{key:"getCache",value:o(function(r,n){return this.getCachesAt(n).get(r)},"getCache")},{key:"get",value:o(function(r,n){var i=this.getKey(r),a=this.getCache(i,n);return a!=null&&this.updateKeyMappingFor(r),a},"get")},{key:"getForCachedKey",value:o(function(r,n){var i=this.keyForId.get(r.id()),a=this.getCache(i,n);return a},"getForCachedKey")},{key:"hasCache",value:o(function(r,n){return this.getCachesAt(n).has(r)},"hasCache")},{key:"has",value:o(function(r,n){var i=this.getKey(r);return this.hasCache(i,n)},"has")},{key:"setCache",value:o(function(r,n,i){i.key=r,this.getCachesAt(n).set(r,i)},"setCache")},{key:"set",value:o(function(r,n,i){var a=this.getKey(r);this.setCache(a,n,i),this.updateKeyMappingFor(r)},"set")},{key:"deleteCache",value:o(function(r,n){this.getCachesAt(n).delete(r)},"deleteCache")},{key:"delete",value:o(function(r,n){var i=this.getKey(r);this.deleteCache(i,n)},"_delete")},{key:"invalidateKey",value:o(function(r){var n=this;this.lvls.forEach(function(i){return n.deleteCache(r,i)})},"invalidateKey")},{key:"invalidate",value:o(function(r){var n=r.id(),i=this.keyForId.get(n);this.deleteKeyMappingFor(r);var a=this.doesEleInvalidateKey(r);return a&&this.invalidateKey(i),a||this.getNumberOfIdsForKey(i)===0},"invalidate")}]),t}(),I0e=25,sS=50,yS=-4,XP=3,bge=7.99,NQe=8,MQe=1024,IQe=1024,OQe=1024,PQe=.2,BQe=.8,FQe=10,$Qe=.15,zQe=.1,GQe=.9,VQe=.9,UQe=100,HQe=1,H1={dequeue:"dequeue",downscale:"downscale",highQuality:"highQuality"},WQe=la({getKey:null,doesEleInvalidateKey:bS,drawElement:null,getBoundingBox:null,getRotationPoint:null,getRotationOffset:null,isVisible:ume,allowEdgeTxrCaching:!0,allowParentTxrCaching:!0}),Fb=o(function(e,r){var n=this;n.renderer=e,n.onDequeues=[];var i=WQe(r);rr(n,i),n.lookup=new RQe(i.getKey,i.doesEleInvalidateKey),n.setupDequeueing()},"ElementTextureCache"),qi=Fb.prototype;qi.reasons=H1;qi.getTextureQueue=function(t){var e=this;return e.eleImgCaches=e.eleImgCaches||{},e.eleImgCaches[t]=e.eleImgCaches[t]||[]};qi.getRetiredTextureQueue=function(t){var e=this,r=e.eleImgCaches.retired=e.eleImgCaches.retired||{},n=r[t]=r[t]||[];return n};qi.getElementQueue=function(){var t=this,e=t.eleCacheQueue=t.eleCacheQueue||new i4(function(r,n){return n.reqs-r.reqs});return e};qi.getElementKeyToQueue=function(){var t=this,e=t.eleKeyToCacheQueue=t.eleKeyToCacheQueue||{};return e};qi.getElement=function(t,e,r,n,i){var a=this,s=this.renderer,l=s.cy.zoom(),u=this.lookup;if(!e||e.w===0||e.h===0||isNaN(e.w)||isNaN(e.h)||!t.visible()||t.removed()||!a.allowEdgeTxrCaching&&t.isEdge()||!a.allowParentTxrCaching&&t.isParent())return null;if(n==null&&(n=Math.ceil(iB(l*r))),n<yS)n=yS;else if(l>=bge||n>XP)return null;var h=Math.pow(2,n),f=e.h*h,d=e.w*h,p=s.eleTextBiggerThanMin(t,h);if(!this.isVisible(t,p))return null;var m=u.get(t,n);if(m&&m.invalidated&&(m.invalidated=!1,m.texture.invalidatedWidth-=m.width),m)return m;var g;if(f<=I0e?g=I0e:f<=sS?g=sS:g=Math.ceil(f/sS)*sS,f>OQe||d>IQe)return null;var y=a.getTextureQueue(g),v=y[y.length-2],x=o(function(){return a.recycleTexture(g,d)||a.addTexture(g,d)},"addNewTxr");v||(v=y[y.length-1]),v||(v=x()),v.width-v.usedWidth<d&&(v=x());for(var b=o(function(M){return M&&M.scaledLabelShown===p},"scalableFrom"),w=i&&i===H1.dequeue,C=i&&i===H1.highQuality,T=i&&i===H1.downscale,E,A=n+1;A<=XP;A++){var S=u.get(t,A);if(S){E=S;break}}var _=E&&E.level===n+1?E:null,I=o(function(){v.context.drawImage(_.texture.canvas,_.x,0,_.width,_.height,v.usedWidth,0,d,f)},"downscale");if(v.context.setTransform(1,0,0,1,0,0),v.context.clearRect(v.usedWidth,0,d,g),b(_))I();else if(b(E))if(C){for(var D=E.level;D>n;D--)_=a.getElement(t,e,r,D,H1.downscale);I()}else return a.queueElement(t,E.level-1),E;else{var k;if(!w&&!C&&!T)for(var L=n-1;L>=yS;L--){var R=u.get(t,L);if(R){k=R;break}}if(b(k))return a.queueElement(t,n),k;v.context.translate(v.usedWidth,0),v.context.scale(h,h),this.drawElement(v.context,t,e,p,!1),v.context.scale(1/h,1/h),v.context.translate(-v.usedWidth,0)}return m={x:v.usedWidth,texture:v,level:n,scale:h,width:d,height:f,scaledLabelShown:p},v.usedWidth+=Math.ceil(d+NQe),v.eleCaches.push(m),u.set(t,n,m),a.checkTextureFullness(v),m};qi.invalidateElements=function(t){for(var e=0;e<t.length;e++)this.invalidateElement(t[e])};qi.invalidateElement=function(t){var e=this,r=e.lookup,n=[],i=r.isInvalid(t);if(i){for(var a=yS;a<=XP;a++){var s=r.getForCachedKey(t,a);s&&n.push(s)}var l=r.invalidate(t);if(l)for(var u=0;u<n.length;u++){var h=n[u],f=h.texture;f.invalidatedWidth+=h.width,h.invalidated=!0,e.checkTextureUtility(f)}e.removeFromQueue(t)}};qi.checkTextureUtility=function(t){t.invalidatedWidth>=PQe*t.width&&this.retireTexture(t)};qi.checkTextureFullness=function(t){var e=this,r=e.getTextureQueue(t.height);t.usedWidth/t.width>BQe&&t.fullnessChecks>=FQe?Df(r,t):t.fullnessChecks++};qi.retireTexture=function(t){var e=this,r=t.height,n=e.getTextureQueue(r),i=this.lookup;Df(n,t),t.retired=!0;for(var a=t.eleCaches,s=0;s<a.length;s++){var l=a[s];i.deleteCache(l.key,l.level)}nB(a);var u=e.getRetiredTextureQueue(r);u.push(t)};qi.addTexture=function(t,e){var r=this,n=r.getTextureQueue(t),i={};return n.push(i),i.eleCaches=[],i.height=t,i.width=Math.max(MQe,e),i.usedWidth=0,i.invalidatedWidth=0,i.fullnessChecks=0,i.canvas=r.renderer.makeOffscreenCanvas(i.width,i.height),i.context=i.canvas.getContext("2d"),i};qi.recycleTexture=function(t,e){for(var r=this,n=r.getTextureQueue(t),i=r.getRetiredTextureQueue(t),a=0;a<i.length;a++){var s=i[a];if(s.width>=e)return s.retired=!1,s.usedWidth=0,s.invalidatedWidth=0,s.fullnessChecks=0,nB(s.eleCaches),s.context.setTransform(1,0,0,1,0,0),s.context.clearRect(0,0,s.width,s.height),Df(i,s),n.push(s),s}};qi.queueElement=function(t,e){var r=this,n=r.getElementQueue(),i=r.getElementKeyToQueue(),a=this.getKey(t),s=i[a];if(s)s.level=Math.max(s.level,e),s.eles.merge(t),s.reqs++,n.updateItem(s);else{var l={eles:t.spawn().merge(t),level:e,reqs:1,key:a};n.push(l),i[a]=l}};qi.dequeue=function(t){for(var e=this,r=e.getElementQueue(),n=e.getElementKeyToQueue(),i=[],a=e.lookup,s=0;s<HQe&&r.size()>0;s++){var l=r.pop(),u=l.key,h=l.eles[0],f=a.hasCache(h,l.level);if(n[u]=null,f)continue;i.push(l);var d=e.getBoundingBox(h);e.getElement(h,d,t,l.level,H1.dequeue)}return i};qi.removeFromQueue=function(t){var e=this,r=e.getElementQueue(),n=e.getElementKeyToQueue(),i=this.getKey(t),a=n[i];a!=null&&(a.eles.length===1?(a.reqs=tB,r.updateItem(a),r.pop(),n[i]=null):a.eles.unmerge(t))};qi.onDequeue=function(t){this.onDequeues.push(t)};qi.offDequeue=function(t){Df(this.onDequeues,t)};qi.setupDequeueing=xge.setupDequeueing({deqRedrawThreshold:UQe,deqCost:$Qe,deqAvgCost:zQe,deqNoDrawCost:GQe,deqFastCost:VQe,deq:o(function(e,r,n){return e.dequeue(r,n)},"deq"),onDeqd:o(function(e,r){for(var n=0;n<e.onDequeues.length;n++){var i=e.onDequeues[n];i(r)}},"onDeqd"),shouldRedraw:o(function(e,r,n,i){for(var a=0;a<r.length;a++)for(var s=r[a].eles,l=0;l<s.length;l++){var u=s[l].boundingBox();if(aB(u,i))return!0}return!1},"shouldRedraw"),priority:o(function(e){return e.renderer.beforeRenderPriorities.eleTxrDeq},"priority")});qQe=1,zb=-4,_S=2,YQe=3.99,XQe=50,jQe=50,KQe=.15,QQe=.1,ZQe=.9,JQe=.9,eZe=1,O0e=250,tZe=4e3*4e3,P0e=32767,rZe=!0,wge=o(function(e){var r=this,n=r.renderer=e,i=n.cy;r.layersByLevel={},r.firstGet=!0,r.lastInvalidationTime=Qu()-2*O0e,r.skipping=!1,r.eleTxrDeqs=i.collection(),r.scheduleElementRefinement=n4(function(){r.refineElementTextures(r.eleTxrDeqs),r.eleTxrDeqs.unmerge(r.eleTxrDeqs)},jQe),n.beforeRender(function(s,l){l-r.lastInvalidationTime<=O0e?r.skipping=!0:r.skipping=!1},n.beforeRenderPriorities.lyrTxrSkip);var a=o(function(l,u){return u.reqs-l.reqs},"qSort");r.layersQueue=new i4(a),r.setupDequeueing()},"LayeredTextureCache"),Ea=wge.prototype,B0e=0,nZe=Math.pow(2,53)-1;Ea.makeLayer=function(t,e){var 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i=this.createGradientStyleFor(t,"background",e,r,n);if(!i)return null;t.fillStyle=i};ys.colorFillStyle=function(t,e,r,n,i){t.fillStyle="rgba("+e+","+r+","+n+","+i+")"};ys.eleFillStyle=function(t,e,r){var n=e.pstyle("background-fill").value;if(n==="linear-gradient"||n==="radial-gradient")this.gradientFillStyle(t,e,n,r);else{var i=e.pstyle("background-color").value;this.colorFillStyle(t,i[0],i[1],i[2],r)}};ys.gradientStrokeStyle=function(t,e,r,n){var i=this.createGradientStyleFor(t,"line",e,r,n);if(!i)return null;t.strokeStyle=i};ys.colorStrokeStyle=function(t,e,r,n,i){t.strokeStyle="rgba("+e+","+r+","+n+","+i+")"};ys.eleStrokeStyle=function(t,e,r){var n=e.pstyle("line-fill").value;if(n==="linear-gradient"||n==="radial-gradient")this.gradientStrokeStyle(t,e,n,r);else{var i=e.pstyle("line-color").value;this.colorStrokeStyle(t,i[0],i[1],i[2],r)}};ys.matchCanvasSize=function(t){var e=this,r=e.data,n=e.findContainerClientCoords(),i=n[2],a=n[3],s=e.getPixelRatio(),l=e.motionBlurPxRatio;(t===e.data.bufferCanvases[e.MOTIONBLUR_BUFFER_NODE]||t===e.data.bufferCanvases[e.MOTIONBLUR_BUFFER_DRAG])&&(s=l);var u=i*s,h=a*s,f;if(!(u===e.canvasWidth&&h===e.canvasHeight)){e.fontCaches=null;var d=r.canvasContainer;d.style.width=i+"px",d.style.height=a+"px";for(var p=0;p<e.CANVAS_LAYERS;p++)f=r.canvases[p],f.width=u,f.height=h,f.style.width=i+"px",f.style.height=a+"px";for(var p=0;p<e.BUFFER_COUNT;p++)f=r.bufferCanvases[p],f.width=u,f.height=h,f.style.width=i+"px",f.style.height=a+"px";e.textureMult=1,s<=1&&(f=r.bufferCanvases[e.TEXTURE_BUFFER],e.textureMult=2,f.width=u*e.textureMult,f.height=h*e.textureMult),e.canvasWidth=u,e.canvasHeight=h,e.pixelRatio=s}};ys.renderTo=function(t,e,r,n){this.render({forcedContext:t,forcedZoom:e,forcedPan:r,drawAllLayers:!0,forcedPxRatio:n})};ys.clearCanvas=function(){var t=this,e=t.data;function r(n){n.clearRect(0,0,t.canvasWidth,t.canvasHeight)}o(r,"clear"),r(e.contexts[t.NODE]),r(e.contexts[t.DRAG])};ys.render=function(t){var e=this;t=t||dme();var r=e.cy,n=t.forcedContext,i=t.drawAllLayers,a=t.drawOnlyNodeLayer,s=t.forcedZoom,l=t.forcedPan,u=t.forcedPxRatio===void 0?this.getPixelRatio():t.forcedPxRatio,h=e.data,f=h.canvasNeedsRedraw,d=e.textureOnViewport&&!n&&(e.pinching||e.hoverData.dragging||e.swipePanning||e.data.wheelZooming),p=t.motionBlur!==void 0?t.motionBlur:e.motionBlur,m=e.motionBlurPxRatio,g=r.hasCompoundNodes(),y=e.hoverData.draggingEles,v=!!(e.hoverData.selecting||e.touchData.selecting);p=p&&!n&&e.motionBlurEnabled&&!v;var x=p;n||(e.prevPxRatio!==u&&(e.invalidateContainerClientCoordsCache(),e.matchCanvasSize(e.container),e.redrawHint("eles",!0),e.redrawHint("drag",!0)),e.prevPxRatio=u),!n&&e.motionBlurTimeout&&clearTimeout(e.motionBlurTimeout),p&&(e.mbFrames==null&&(e.mbFrames=0),e.mbFrames++,e.mbFrames<3&&(x=!1),e.mbFrames>e.minMbLowQualFrames&&(e.motionBlurPxRatio=e.mbPxRBlurry)),e.clearingMotionBlur&&(e.motionBlurPxRatio=1),e.textureDrawLastFrame&&!d&&(f[e.NODE]=!0,f[e.SELECT_BOX]=!0);var b=r.style(),w=r.zoom(),C=s!==void 0?s:w,T=r.pan(),E={x:T.x,y:T.y},A={zoom:w,pan:{x:T.x,y:T.y}},S=e.prevViewport,_=S===void 0||A.zoom!==S.zoom||A.pan.x!==S.pan.x||A.pan.y!==S.pan.y;!_&&!(y&&!g)&&(e.motionBlurPxRatio=1),l&&(E=l),C*=u,E.x*=u,E.y*=u;var I=e.getCachedZSortedEles();function D(K,X,te,J,se){var ue=K.globalCompositeOperation;K.globalCompositeOperation="destination-out",e.colorFillStyle(K,255,255,255,e.motionBlurTransparency),K.fillRect(X,te,J,se),K.globalCompositeOperation=ue}o(D,"mbclear");function k(K,X){var te,J,se,ue;!e.clearingMotionBlur&&(K===h.bufferContexts[e.MOTIONBLUR_BUFFER_NODE]||K===h.bufferContexts[e.MOTIONBLUR_BUFFER_DRAG])?(te={x:T.x*m,y:T.y*m},J=w*m,se=e.canvasWidth*m,ue=e.canvasHeight*m):(te=E,J=C,se=e.canvasWidth,ue=e.canvasHeight),K.setTransform(1,0,0,1,0,0),X==="motionBlur"?D(K,0,0,se,ue):!n&&(X===void 0||X)&&K.clearRect(0,0,se,ue),i||(K.translate(te.x,te.y),K.scale(J,J)),l&&K.translate(l.x,l.y),s&&K.scale(s,s)}if(o(k,"setContextTransform"),d||(e.textureDrawLastFrame=!1),d){if(e.textureDrawLastFrame=!0,!e.textureCache){e.textureCache={},e.textureCache.bb=r.mutableElements().boundingBox(),e.textureCache.texture=e.data.bufferCanvases[e.TEXTURE_BUFFER];var L=e.data.bufferContexts[e.TEXTURE_BUFFER];L.setTransform(1,0,0,1,0,0),L.clearRect(0,0,e.canvasWidth*e.textureMult,e.canvasHeight*e.textureMult),e.render({forcedContext:L,drawOnlyNodeLayer:!0,forcedPxRatio:u*e.textureMult});var A=e.textureCache.viewport={zoom:r.zoom(),pan:r.pan(),width:e.canvasWidth,height:e.canvasHeight};A.mpan={x:(0-A.pan.x)/A.zoom,y:(0-A.pan.y)/A.zoom}}f[e.DRAG]=!1,f[e.NODE]=!1;var R=h.contexts[e.NODE],O=e.textureCache.texture,A=e.textureCache.viewport;R.setTransform(1,0,0,1,0,0),p?D(R,0,0,A.width,A.height):R.clearRect(0,0,A.width,A.height);var M=b.core("outside-texture-bg-color").value,B=b.core("outside-texture-bg-opacity").value;e.colorFillStyle(R,M[0],M[1],M[2],B),R.fillRect(0,0,A.width,A.height);var w=r.zoom();k(R,!1),R.clearRect(A.mpan.x,A.mpan.y,A.width/A.zoom/u,A.height/A.zoom/u),R.drawImage(O,A.mpan.x,A.mpan.y,A.width/A.zoom/u,A.height/A.zoom/u)}else e.textureOnViewport&&!n&&(e.textureCache=null);var F=r.extent(),P=e.pinching||e.hoverData.dragging||e.swipePanning||e.data.wheelZooming||e.hoverData.draggingEles||e.cy.animated(),z=e.hideEdgesOnViewport&&P,$=[];if($[e.NODE]=!f[e.NODE]&&p&&!e.clearedForMotionBlur[e.NODE]||e.clearingMotionBlur,$[e.NODE]&&(e.clearedForMotionBlur[e.NODE]=!0),$[e.DRAG]=!f[e.DRAG]&&p&&!e.clearedForMotionBlur[e.DRAG]||e.clearingMotionBlur,$[e.DRAG]&&(e.clearedForMotionBlur[e.DRAG]=!0),f[e.NODE]||i||a||$[e.NODE]){var H=p&&!$[e.NODE]&&m!==1,R=n||(H?e.data.bufferContexts[e.MOTIONBLUR_BUFFER_NODE]:h.contexts[e.NODE]),Q=p&&!H?"motionBlur":void 0;k(R,Q),z?e.drawCachedNodes(R,I.nondrag,u,F):e.drawLayeredElements(R,I.nondrag,u,F),e.debug&&e.drawDebugPoints(R,I.nondrag),!i&&!p&&(f[e.NODE]=!1)}if(!a&&(f[e.DRAG]||i||$[e.DRAG])){var H=p&&!$[e.DRAG]&&m!==1,R=n||(H?e.data.bufferContexts[e.MOTIONBLUR_BUFFER_DRAG]:h.contexts[e.DRAG]);k(R,p&&!H?"motionBlur":void 0),z?e.drawCachedNodes(R,I.drag,u,F):e.drawCachedElements(R,I.drag,u,F),e.debug&&e.drawDebugPoints(R,I.drag),!i&&!p&&(f[e.DRAG]=!1)}if(this.drawSelectionRectangle(t,k),p&&m!==1){var j=h.contexts[e.NODE],ie=e.data.bufferCanvases[e.MOTIONBLUR_BUFFER_NODE],ne=h.contexts[e.DRAG],le=e.data.bufferCanvases[e.MOTIONBLUR_BUFFER_DRAG],he=o(function(X,te,J){X.setTransform(1,0,0,1,0,0),J||!x?X.clearRect(0,0,e.canvasWidth,e.canvasHeight):D(X,0,0,e.canvasWidth,e.canvasHeight);var se=m;X.drawImage(te,0,0,e.canvasWidth*se,e.canvasHeight*se,0,0,e.canvasWidth,e.canvasHeight)},"drawMotionBlur");(f[e.NODE]||$[e.NODE])&&(he(j,ie,$[e.NODE]),f[e.NODE]=!1),(f[e.DRAG]||$[e.DRAG])&&(he(ne,le,$[e.DRAG]),f[e.DRAG]=!1)}e.prevViewport=A,e.clearingMotionBlur&&(e.clearingMotionBlur=!1,e.motionBlurCleared=!0,e.motionBlur=!0),p&&(e.motionBlurTimeout=setTimeout(function(){e.motionBlurTimeout=null,e.clearedForMotionBlur[e.NODE]=!1,e.clearedForMotionBlur[e.DRAG]=!1,e.motionBlur=!1,e.clearingMotionBlur=!d,e.mbFrames=0,f[e.NODE]=!0,f[e.DRAG]=!0,e.redraw()},dZe)),n||r.emit("render")};ys.drawSelectionRectangle=function(t,e){var r=this,n=r.cy,i=r.data,a=n.style(),s=t.drawOnlyNodeLayer,l=t.drawAllLayers,u=i.canvasNeedsRedraw,h=t.forcedContext;if(r.showFps||!s&&u[r.SELECT_BOX]&&!l){var f=h||i.contexts[r.SELECT_BOX];if(e(f),r.selection[4]==1&&(r.hoverData.selecting||r.touchData.selecting)){var d=r.cy.zoom(),p=a.core("selection-box-border-width").value/d;f.lineWidth=p,f.fillStyle="rgba("+a.core("selection-box-color").value[0]+","+a.core("selection-box-color").value[1]+","+a.core("selection-box-color").value[2]+","+a.core("selection-box-opacity").value+")",f.fillRect(r.selection[0],r.selection[1],r.selection[2]-r.selection[0],r.selection[3]-r.selection[1]),p>0&&(f.strokeStyle="rgba("+a.core("selection-box-border-color").value[0]+","+a.core("selection-box-border-color").value[1]+","+a.core("selection-box-border-color").value[2]+","+a.core("selection-box-opacity").value+")",f.strokeRect(r.selection[0],r.selection[1],r.selection[2]-r.selection[0],r.selection[3]-r.selection[1]))}if(i.bgActivePosistion&&!r.hoverData.selecting){var d=r.cy.zoom(),m=i.bgActivePosistion;f.fillStyle="rgba("+a.core("active-bg-color").value[0]+","+a.core("active-bg-color").value[1]+","+a.core("active-bg-color").value[2]+","+a.core("active-bg-opacity").value+")",f.beginPath(),f.arc(m.x,m.y,a.core("active-bg-size").pfValue/d,0,2*Math.PI),f.fill()}var g=r.lastRedrawTime;if(r.showFps&&g){g=Math.round(g);var y=Math.round(1e3/g),v="1 frame = "+g+" ms = "+y+" fps";if(f.setTransform(1,0,0,1,0,0),f.fillStyle="rgba(255, 0, 0, 0.75)",f.strokeStyle="rgba(255, 0, 0, 0.75)",f.font="30px Arial",!Nb){var x=f.measureText(v);Nb=x.actualBoundingBoxAscent}f.fillText(v,0,Nb);var b=60;f.strokeRect(0,Nb+10,250,20),f.fillRect(0,Nb+10,250*Math.min(y/b,1),20)}l||(u[r.SELECT_BOX]=!1)}};o(z0e,"compileShader");o(pZe,"createProgram");o(mZe,"createTextureCanvas");o(wB,"getEffectivePanZoom");o(NP,"modelToRenderedPosition");o(oS,"toWebGLColor");o(lS,"indexToVec4");o(gZe,"vec4ToIndex");o(yZe,"createTexture");o(Sge,"getTypeInfo");o(Cge,"createTypedArray");o(vZe,"createTypedArrayView");o(xZe,"createBufferStaticDraw");o(po,"createBufferDynamicDraw");o(bZe,"createPickingFrameBuffer");G0e=typeof Float32Array<"u"?Float32Array:Array;Math.hypot||(Math.hypot=function(){for(var t=0,e=arguments.length;e--;)t+=arguments[e]*arguments[e];return Math.sqrt(t)});o(Gb,"create");o(Age,"identity");o(wZe,"multiply");o(DS,"translate");o(_ge,"rotate");o(TB,"scale");o(TZe,"projection");Vb={SCREEN:{name:"screen",screen:!0},PICKING:{name:"picking",picking:!0}},Mb=la({getKey:null,drawElement:null,getBoundingBox:null,getRotation:null,getRotationPoint:null,getRotationOffset:null,isVisible:null,getPadding:null}),kZe=function(){function t(e,r){Mf(this,t),this.debugID=Math.floor(Math.random()*1e4),this.r=e,this.atlasSize=r.webglTexSize,this.rows=r.webglTexRows,this.enableWrapping=r.enableWrapping,this.texHeight=Math.floor(this.atlasSize/this.rows),this.maxTexWidth=this.atlasSize,this.texture=null,this.canvas=null,this.needsBuffer=!0,this.freePointer={x:0,row:0},this.keyToLocation=new Map,this.canvas=r.createTextureCanvas(e,this.atlasSize,this.atlasSize),this.scratch=r.createTextureCanvas(e,this.atlasSize,this.texHeight,"scratch")}return o(t,"Atlas"),If(t,[{key:"getKeys",value:o(function(){return new Set(this.keyToLocation.keys())},"getKeys")},{key:"getScale",value:o(function(r){var n=r.w,i=r.h,a=this.texHeight,s=this.maxTexWidth,l=a/i,u=n*l,h=i*l;return u>s&&(l=s/n,u=n*l,h=i*l),{scale:l,texW:u,texH:h}},"getScale")},{key:"draw",value:o(function(r,n,i){var a=this,s=this.atlasSize,l=this.rows,u=this.texHeight,h=this.getScale(n),f=h.scale,d=h.texW,p=h.texH,m=[null,null],g=o(function(w,C){if(i&&C){var T=C.context,E=w.x,A=w.row,S=E,_=u*A;T.save(),T.translate(S,_),T.scale(f,f),i(T,n),T.restore()}},"drawAt"),y=o(function(){g(a.freePointer,a.canvas),m[0]={x:a.freePointer.x,y:a.freePointer.row*u,w:d,h:p},m[1]={x:a.freePointer.x+d,y:a.freePointer.row*u,w:0,h:p},a.freePointer.x+=d,a.freePointer.x==s&&(a.freePointer.x=0,a.freePointer.row++)},"drawNormal"),v=o(function(){var w=a.scratch,C=a.canvas;w.clear(),g({x:0,row:0},w);var T=s-a.freePointer.x,E=d-T,A=u;{var S=a.freePointer.x,_=a.freePointer.row*u,I=T;C.context.drawImage(w,0,0,I,A,S,_,I,A),m[0]={x:S,y:_,w:I,h:p}}{var D=T,k=(a.freePointer.row+1)*u,L=E;C&&C.context.drawImage(w,D,0,L,A,0,k,L,A),m[1]={x:0,y:k,w:L,h:p}}a.freePointer.x=E,a.freePointer.row++},"drawWrapped"),x=o(function(){a.freePointer.x=0,a.freePointer.row++},"moveToStartOfNextRow");if(this.freePointer.x+d<=s)y();else{if(this.freePointer.row>=l-1)return!1;this.freePointer.x===s?(x(),y()):this.enableWrapping?v():(x(),y())}return this.keyToLocation.set(r,m),this.needsBuffer=!0,m},"draw")},{key:"getOffsets",value:o(function(r){return this.keyToLocation.get(r)},"getOffsets")},{key:"isEmpty",value:o(function(){return this.freePointer.x===0&&this.freePointer.row===0},"isEmpty")},{key:"canFit",value:o(function(r){var n=this.atlasSize,i=this.rows,a=this.getScale(r),s=a.texW;return this.freePointer.x+s>n?this.freePointer.row<i-1:!0},"canFit")},{key:"bufferIfNeeded",value:o(function(r){this.texture||(this.texture=yZe(r,this.debugID)),this.needsBuffer&&(this.texture.buffer(this.canvas),this.needsBuffer=!1)},"bufferIfNeeded")},{key:"dispose",value:o(function(){this.texture&&(this.texture.deleteTexture(),this.texture=null,this.needsBuffer=!0)},"dispose")}]),t}(),EZe=function(){function t(e,r){Mf(this,t),this.r=e,this.opts=r,this.keyToIds=new Map,this.idToKey=new Map,this.atlases=[],this.styleKeyToAtlas=new Map,this.styleKeyNeedsRedraw=new Set,this.forceGC=!1}return o(t,"AtlasCollection"),If(t,[{key:"getKeys",value:o(function(){return new Set(this.styleKeyToAtlas.keys())},"getKeys")},{key:"getIdsFor",value:o(function(r){var n=this.keyToIds.get(r);return n||(n=new Set,this.keyToIds.set(r,n)),n},"getIdsFor")},{key:"_createAtlas",value:o(function(){var r=this.r,n=this.opts;return new kZe(r,n)},"_createAtlas")},{key:"_getScratchCanvas",value:o(function(){if(!this.scratch){var r=this.r,n=this.opts,i=n.webglTexSize,a=Math.floor(i/n.webglTexRows);this.scratch=n.createTextureCanvas(r,i,a,"scratch")}return this.scratch},"_getScratchCanvas")},{key:"draw",value:o(function(r,n,i,a){if(this.styleKeyNeedsRedraw.has(n)){this.styleKeyNeedsRedraw.delete(n),this.deleteKey(r,n);var s=this.styleKeyToAtlas.get(n);s&&(s.forceGC=!0),this.styleKeyToAtlas.delete(n)}var l=this.styleKeyToAtlas.get(n);return l||(l=this.atlases[this.atlases.length-1],(!l||!l.canFit(i))&&(l=this._createAtlas(),this.atlases.push(l)),l.draw(n,i,a),this.styleKeyToAtlas.set(n,l),this.getIdsFor(n).add(r),this.idToKey.set(r,n)),l},"draw")},{key:"getAtlas",value:o(function(r){return this.styleKeyToAtlas.get(r)},"getAtlas")},{key:"hasAtlas",value:o(function(r){return this.styleKeyToAtlas.has(r)},"hasAtlas")},{key:"deleteKey",value:o(function(r,n){this.idToKey.delete(r),this.getIdsFor(n).delete(r)},"deleteKey")},{key:"checkKeyIsInvalid",value:o(function(r,n){if(!this.idToKey.has(r))return!1;var i=this.idToKey.get(r);return i!=n?(this.deleteKey(r,i),!0):!1},"checkKeyIsInvalid")},{key:"_getKeysToCollect",value:o(function(){var r=new Set,n=mo(this.styleKeyToAtlas.keys()),i;try{for(n.s();!(i=n.n()).done;){var a=i.value;this.getIdsFor(a).size==0&&r.add(a)}}catch(s){n.e(s)}finally{n.f()}return r},"_getKeysToCollect")},{key:"gc",value:o(function(){var r=this,n=this.atlases.some(function(p){return p.forceGC}),i=this._getKeysToCollect();if(i.size===0&&!n){console.log("nothing to garbage collect");return}var a=[],s=new Map,l=null,u=mo(this.atlases),h;try{var f=o(function(){var m=h.value,g=m.getKeys(),y=SZe(i,g);if(y.size===0&&!m.forceGC)return a.push(m),g.forEach(function(A){return s.set(A,m)}),"continue";l||(l=r._createAtlas(),a.push(l));var v=mo(g),x;try{for(v.s();!(x=v.n()).done;){var b=x.value;if(!y.has(b)){var w=m.getOffsets(b),C=_i(w,2),T=C[0],E=C[1];l.canFit({w:T.w+E.w,h:T.h})||(l=r._createAtlas(),a.push(l)),r._copyTextureToNewAtlas(b,m,l),s.set(b,l)}}}catch(A){v.e(A)}finally{v.f()}},"_loop");for(u.s();!(h=u.n()).done;)var d=f()}catch(p){u.e(p)}finally{u.f()}this.atlases=a,this.styleKeyToAtlas=s},"gc")},{key:"_copyTextureToNewAtlas",value:o(function(r,n,i){var a=n.getOffsets(r),s=_i(a,2),l=s[0],u=s[1];if(u.w===0)i.draw(r,l,function(p){p.drawImage(n.canvas,l.x,l.y,l.w,l.h,0,0,l.w,l.h)});else{var h=this._getScratchCanvas();h.clear(),h.context.drawImage(n.canvas,l.x,l.y,l.w,l.h,0,0,l.w,l.h),h.context.drawImage(n.canvas,u.x,u.y,u.w,u.h,l.w,0,u.w,u.h);var f=l.w+u.w,d=l.h;i.draw(r,{w:f,h:d},function(p){p.drawImage(h,0,0,f,d,0,0,f,d)})}},"_copyTextureToNewAtlas")},{key:"getCounts",value:o(function(){return{keyCount:this.styleKeyToAtlas.size,atlasCount:new Set(this.styleKeyToAtlas.values()).size}},"getCounts")}]),t}();o(SZe,"intersection");CZe=function(){function t(e,r){Mf(this,t),this.r=e;var n=r;this.globalOptions=n,this.maxAtlases=n.webglTexPerBatch,this.atlasSize=n.webglTexSize,this.renderTypes=new Map,this.maxAtlasesPerBatch=r.webglTexPerBatch,this.batchAtlases=[],this._cacheScratchCanvas(n)}return o(t,"AtlasManager"),If(t,[{key:"_cacheScratchCanvas",value:o(function(r){var n=-1,i=-1,a=null,s=r.createTextureCanvas;r.createTextureCanvas=function(l,u,h,f){return f?((!a||u!=n||h!=i)&&(n=u,i=h,a=s(l,u,h)),a):s(l,u,h)}},"_cacheScratchCanvas")},{key:"addRenderType",value:o(function(r,n){var i=new EZe(this.r,this.globalOptions),a=n;this.renderTypes.set(r,rr({type:r,atlasCollection:i},a))},"addRenderType")},{key:"getRenderTypes",value:o(function(){return j0e(this.renderTypes.values())},"getRenderTypes")},{key:"getRenderTypeOpts",value:o(function(r){return this.renderTypes.get(r)},"getRenderTypeOpts")},{key:"invalidate",value:o(function(r){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},i=n.forceRedraw,a=i===void 0?!1:i,s=n.filterEle,l=s===void 0?function(){return!0}:s,u=n.filterType,h=u===void 0?function(){return!0}:u,f=!1,d=mo(r),p;try{for(d.s();!(p=d.n()).done;){var m=p.value;if(l(m)){var g=m.id(),y=mo(this.getRenderTypes()),v;try{for(y.s();!(v=y.n()).done;){var x=v.value;if(h(x.type)){var b=x.getKey(m);a?(x.atlasCollection.deleteKey(g,b),x.atlasCollection.styleKeyNeedsRedraw.add(b),f=!0):f|=x.atlasCollection.checkKeyIsInvalid(g,b)}}}catch(w){y.e(w)}finally{y.f()}}}}catch(w){d.e(w)}finally{d.f()}return f},"invalidate")},{key:"gc",value:o(function(){var r=mo(this.getRenderTypes()),n;try{for(r.s();!(n=r.n()).done;){var i=n.value;i.atlasCollection.gc()}}catch(a){r.e(a)}finally{r.f()}},"gc")},{key:"isRenderable",value:o(function(r,n){var i=this.getRenderTypeOpts(n);return i&&i.isVisible(r)},"isRenderable")},{key:"startBatch",value:o(function(){this.batchAtlases=[]},"startBatch")},{key:"getAtlasCount",value:o(function(){return this.batchAtlases.length},"getAtlasCount")},{key:"getAtlases",value:o(function(){return this.batchAtlases},"getAtlases")},{key:"getOrCreateAtlas",value:o(function(r,n,i){var a=this.renderTypes.get(i),s=a.getKey(r),l=r.id();return a.atlasCollection.draw(l,s,n,function(u){a.drawElement(u,r,n,!0,!0)})},"getOrCreateAtlas")},{key:"getAtlasIndexForBatch",value:o(function(r){var n=this.batchAtlases.indexOf(r);if(n<0){if(this.batchAtlases.length===this.maxAtlasesPerBatch)return;this.batchAtlases.push(r),n=this.batchAtlases.length-1}return n},"getAtlasIndexForBatch")},{key:"getIndexArray",value:o(function(){return Array.from({length:this.maxAtlases},function(r,n){return n})},"getIndexArray")},{key:"getAtlasInfo",value:o(function(r,n){var i=this.renderTypes.get(n),a=i.getBoundingBox(r),s=this.getOrCreateAtlas(r,a,n),l=this.getAtlasIndexForBatch(s);if(l!==void 0){var u=i.getKey(r),h=s.getOffsets(u),f=_i(h,2),d=f[0],p=f[1];return{atlasID:l,tex:d,tex1:d,tex2:p,bb:a,type:n,styleKey:u}}},"getAtlasInfo")},{key:"canAddToCurrentBatch",value:o(function(r,n){if(this.batchAtlases.length===this.maxAtlasesPerBatch){var i=this.renderTypes.get(n),a=i.getKey(r),s=i.atlasCollection.getAtlas(a);return s&&this.batchAtlases.includes(s)}return!0},"canAddToCurrentBatch")},{key:"setTransformMatrix",value:o(function(r,n,i){var a=arguments.length>3&&arguments[3]!==void 0?arguments[3]:!0,s=n.bb,l=n.type,u=n.tex1,h=n.tex2,f=this.getRenderTypeOpts(l),d=f.getPadding?f.getPadding(i):0,p=u.w/(u.w+h.w);a||(p=1-p);var m=this.getAdjustedBB(s,d,a,p),g,y;Age(r);var v=f.getRotation?f.getRotation(i):0;if(v!==0){var x=f.getRotationPoint(i),b=x.x,w=x.y;DS(r,r,[b,w]),_ge(r,r,v);var C=f.getRotationOffset(i);g=C.x+m.xOffset,y=C.y}else g=m.x1,y=m.y1;DS(r,r,[g,y]),TB(r,r,[m.w,m.h])},"setTransformMatrix")},{key:"getTransformMatrix",value:o(function(r,n){var i=arguments.length>2&&arguments[2]!==void 0?arguments[2]:!0,a=Gb();return this.setTransformMatrix(a,r,n,i),a},"getTransformMatrix")},{key:"getAdjustedBB",value:o(function(r,n,i,a){var s=r.x1,l=r.y1,u=r.w,h=r.h;n&&(s-=n,l-=n,u+=2*n,h+=2*n);var f=0,d=u*a;return i&&a<1?u=d:!i&&a<1&&(f=u-d,s+=f,u=d),{x1:s,y1:l,w:u,h,xOffset:f}},"getAdjustedBB")},{key:"getDebugInfo",value:o(function(){var r=[],n=mo(this.renderTypes),i;try{for(n.s();!(i=n.n()).done;){var a=_i(i.value,2),s=a[0],l=a[1],u=l.atlasCollection.getCounts(),h=u.keyCount,f=u.atlasCount;r.push({type:s,keyCount:h,atlasCount:f})}}catch(d){n.e(d)}finally{n.f()}return r},"getDebugInfo")}]),t}(),MP=0,V0e=1,U0e=2,IP=3,AZe=function(){function t(e,r,n){Mf(this,t),this.r=e,this.gl=r,this.maxInstances=n.webglBatchSize,this.maxAtlases=n.webglTexPerBatch,this.atlasSize=n.webglTexSize,this.bgColor=n.bgColor,n.enableWrapping=!0,n.createTextureCanvas=mZe,this.atlasManager=new CZe(e,n),this.program=this.createShaderProgram(Vb.SCREEN),this.pickingProgram=this.createShaderProgram(Vb.PICKING),this.vao=this.createVAO(),this.debugInfo=[]}return o(t,"ElementDrawingWebGL"),If(t,[{key:"addTextureRenderType",value:o(function(r,n){this.atlasManager.addRenderType(r,n)},"addTextureRenderType")},{key:"invalidate",value:o(function(r){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},i=n.type,a=this.atlasManager;return i?a.invalidate(r,{filterType:o(function(l){return l===i},"filterType"),forceRedraw:!0}):a.invalidate(r)},"invalidate")},{key:"gc",value:o(function(){this.atlasManager.gc()},"gc")},{key:"createShaderProgram",value:o(function(r){var n=this.gl,i=`#version 300 es
-      precision highp float;
-
-      uniform mat3 uPanZoomMatrix;
-      uniform int  uAtlasSize;
-      
-      // instanced
-      in vec2 aPosition; 
-
-      // what are we rendering?
-      in int aVertType;
-
-      // for picking
-      in vec4 aIndex;
-      
-      // For textures
-      in int aAtlasId; // which shader unit/atlas to use
-      in vec4 aTex1; // x/y/w/h of texture in atlas
-      in vec4 aTex2; 
-
-      // for any transforms that are needed
-      in vec4 aScaleRotate1;  // vectors use fewer attributes than matrices
-      in vec2 aTranslate1;
-      in vec4 aScaleRotate2;
-      in vec2 aTranslate2;
-
-      // for edges
-      in vec4 aPointAPointB;
-      in vec4 aPointCPointD;
-      in float aLineWidth;
-      in vec4 aEdgeColor;
-
-      out vec2 vTexCoord;
-      out vec4 vEdgeColor;
-      flat out int vAtlasId;
-      flat out vec4 vIndex;
-      flat out int vVertType;
-
-      void main(void) {
-        int vid = gl_VertexID;
-        vec2 position = aPosition;
-
-        if(aVertType == `.concat(MP,`) {
-          float texX;
-          float texY;
-          float texW;
-          float texH;
-          mat3  texMatrix;
-
-          int vid = gl_VertexID;
-          if(vid <= 5) {
-            texX = aTex1.x;
-            texY = aTex1.y;
-            texW = aTex1.z;
-            texH = aTex1.w;
-            texMatrix = mat3(
-              vec3(aScaleRotate1.xy, 0.0),
-              vec3(aScaleRotate2.zw, 0.0),
-              vec3(aTranslate1,      1.0)
-            );
-          } else {
-            texX = aTex2.x;
-            texY = aTex2.y;
-            texW = aTex2.z;
-            texH = aTex2.w;
-            texMatrix = mat3(
-              vec3(aScaleRotate2.xy, 0.0),
-              vec3(aScaleRotate2.zw, 0.0),
-              vec3(aTranslate2,      1.0)
-            );
-          }
-
-          if(vid == 1 || vid == 2 || vid == 4 || vid == 7 || vid == 8 || vid == 10) {
-            texX += texW;
-          }
-          if(vid == 2 || vid == 4 || vid == 5 || vid == 8 || vid == 10 || vid == 11) {
-            texY += texH;
-          }
-
-          float d = float(uAtlasSize);
-          vTexCoord = vec2(texX / d, texY / d); // tex coords must be between 0 and 1
-
-          gl_Position = vec4(uPanZoomMatrix * texMatrix * vec3(position, 1.0), 1.0);
-        } 
-        else if(aVertType == `).concat(V0e,` && vid < 6) {
-          vec2 source = aPointAPointB.xy;
-          vec2 target = aPointAPointB.zw;
-
-          // adjust the geometry so that the line is centered on the edge
-          position.y = position.y - 0.5;
-
-          vec2 xBasis = target - source;
-          vec2 yBasis = normalize(vec2(-xBasis.y, xBasis.x));
-          vec2 point = source + xBasis * position.x + yBasis * aLineWidth * position.y;
-
-          gl_Position = vec4(uPanZoomMatrix * vec3(point, 1.0), 1.0);
-          vEdgeColor = aEdgeColor;
-        } 
-        else if(aVertType == `).concat(U0e,` && vid < 6) {
-          vec2 pointA = aPointAPointB.xy;
-          vec2 pointB = aPointAPointB.zw;
-          vec2 pointC = aPointCPointD.xy;
-          vec2 pointD = aPointCPointD.zw;
-
-          // adjust the geometry so that the line is centered on the edge
-          position.y = position.y - 0.5;
-
-          vec2 p0 = pointA;
-          vec2 p1 = pointB;
-          vec2 p2 = pointC;
-          vec2 pos = position;
-          if(position.x == 1.0) {
-            p0 = pointD;
-            p1 = pointC;
-            p2 = pointB;
-            pos = vec2(0.0, -position.y);
-          }
-
-          vec2 p01 = p1 - p0;
-          vec2 p12 = p2 - p1;
-          vec2 p21 = p1 - p2;
-
-          // Find the normal vector.
-          vec2 tangent = normalize(normalize(p12) + normalize(p01));
-          vec2 normal = vec2(-tangent.y, tangent.x);
-
-          // Find the vector perpendicular to p0 -> p1.
-          vec2 p01Norm = normalize(vec2(-p01.y, p01.x));
-
-          // Determine the bend direction.
-          float sigma = sign(dot(p01 + p21, normal));
-          float width = aLineWidth;
-
-          if(sign(pos.y) == -sigma) {
-            // This is an intersecting vertex. Adjust the position so that there's no overlap.
-            vec2 point = 0.5 * width * normal * -sigma / dot(normal, p01Norm);
-            gl_Position = vec4(uPanZoomMatrix * vec3(p1 + point, 1.0), 1.0);
-          } else {
-            // This is a non-intersecting vertex. Treat it like a mitre join.
-            vec2 point = 0.5 * width * normal * sigma * dot(normal, p01Norm);
-            gl_Position = vec4(uPanZoomMatrix * vec3(p1 + point, 1.0), 1.0);
-          }
-
-          vEdgeColor = aEdgeColor;
-        } 
-        else if(aVertType == `).concat(IP,` && vid < 3) {
-          // massage the first triangle into an edge arrow
-          if(vid == 0)
-            position = vec2(-0.15, -0.3);
-          if(vid == 1)
-            position = vec2( 0.0,   0.0);
-          if(vid == 2)
-            position = vec2( 0.15, -0.3);
-
-          mat3 transform = mat3(
-            vec3(aScaleRotate1.xy, 0.0),
-            vec3(aScaleRotate1.zw, 0.0),
-            vec3(aTranslate1,      1.0)
-          );
-          gl_Position = vec4(uPanZoomMatrix * transform * vec3(position, 1.0), 1.0);
-          vEdgeColor = aEdgeColor;
-        } else {
-          gl_Position = vec4(2.0, 0.0, 0.0, 1.0); // discard vertex by putting it outside webgl clip space
-        }
-
-        vAtlasId = aAtlasId;
-        vIndex = aIndex;
-        vVertType = aVertType;
-      }
-    `),a=this.atlasManager.getIndexArray(),s=`#version 300 es
-      precision highp float;
-
-      // define texture unit for each node in the batch
-      `.concat(a.map(function(h){return"uniform sampler2D uTexture".concat(h,";")}).join(`
-	`),`
-
-      uniform vec4 uBGColor;
-
-      in vec2 vTexCoord;
-      in vec4 vEdgeColor;
-      flat in int vAtlasId;
-      flat in vec4 vIndex;
-      flat in int vVertType;
-
-      out vec4 outColor;
-
-      void main(void) {
-        if(vVertType == `).concat(MP,`) {
-          `).concat(a.map(function(h){return"if(vAtlasId == ".concat(h,") outColor = texture(uTexture").concat(h,", vTexCoord);")}).join(`
-	else `),`
-        } else if(vVertType == `).concat(IP,`) {
-          // blend arrow color with background (using premultiplied alpha)
-          outColor.rgb = vEdgeColor.rgb + (uBGColor.rgb * (1.0 - vEdgeColor.a)); 
-          outColor.a = 1.0; // make opaque, masks out line under arrow
-        } else {
-          outColor = vEdgeColor;
-        }
-
-        `).concat(r.picking?`if(outColor.a == 0.0) discard;
-             else outColor = vIndex;`:"",`
-      }
-    `),l=pZe(n,i,s);l.aPosition=n.getAttribLocation(l,"aPosition"),l.aIndex=n.getAttribLocation(l,"aIndex"),l.aVertType=n.getAttribLocation(l,"aVertType"),l.aAtlasId=n.getAttribLocation(l,"aAtlasId"),l.aTex1=n.getAttribLocation(l,"aTex1"),l.aTex2=n.getAttribLocation(l,"aTex2"),l.aScaleRotate1=n.getAttribLocation(l,"aScaleRotate1"),l.aTranslate1=n.getAttribLocation(l,"aTranslate1"),l.aScaleRotate2=n.getAttribLocation(l,"aScaleRotate2"),l.aTranslate2=n.getAttribLocation(l,"aTranslate2"),l.aPointAPointB=n.getAttribLocation(l,"aPointAPointB"),l.aPointCPointD=n.getAttribLocation(l,"aPointCPointD"),l.aLineWidth=n.getAttribLocation(l,"aLineWidth"),l.aEdgeColor=n.getAttribLocation(l,"aEdgeColor"),l.uPanZoomMatrix=n.getUniformLocation(l,"uPanZoomMatrix"),l.uAtlasSize=n.getUniformLocation(l,"uAtlasSize"),l.uBGColor=n.getUniformLocation(l,"uBGColor"),l.uTextures=[];for(var u=0;u<this.atlasManager.maxAtlases;u++)l.uTextures.push(n.getUniformLocation(l,"uTexture".concat(u)));return l},"createShaderProgram")},{key:"createVAO",value:o(function(){var r=[0,0,1,0,1,1,0,0,1,1,0,1],n=[].concat(r,r);this.vertexCount=n.length/2;var i=this.maxInstances,a=this.gl,s=this.program,l=a.createVertexArray();return a.bindVertexArray(l),xZe(a,"vec2",s.aPosition,n),this.indexBuffer=po(a,i,"vec4",s.aIndex),this.vertTypeBuffer=po(a,i,"int",s.aVertType),this.atlasIdBuffer=po(a,i,"int",s.aAtlasId),this.tex1Buffer=po(a,i,"vec4",s.aTex1),this.tex2Buffer=po(a,i,"vec4",s.aTex2),this.scaleRotate1Buffer=po(a,i,"vec4",s.aScaleRotate1),this.translate1Buffer=po(a,i,"vec2",s.aTranslate1),this.scaleRotate2Buffer=po(a,i,"vec4",s.aScaleRotate2),this.translate2Buffer=po(a,i,"vec2",s.aTranslate2),this.pointAPointBBuffer=po(a,i,"vec4",s.aPointAPointB),this.pointCPointDBuffer=po(a,i,"vec4",s.aPointCPointD),this.lineWidthBuffer=po(a,i,"float",s.aLineWidth),this.edgeColorBuffer=po(a,i,"vec4",s.aEdgeColor),a.bindVertexArray(null),l},"createVAO")},{key:"buffers",get:o(function(){var r=this;return this._buffers||(this._buffers=Object.keys(this).filter(function(n){return n.endsWith("Buffer")}).map(function(n){return r[n]})),this._buffers},"get")},{key:"startFrame",value:o(function(r,n){var i=arguments.length>2&&arguments[2]!==void 0?arguments[2]:Vb.SCREEN;this.panZoomMatrix=r,this.debugInfo=n,this.renderTarget=i,this.startBatch()},"startFrame")},{key:"startBatch",value:o(function(){this.instanceCount=0,this.atlasManager.startBatch()},"startBatch")},{key:"endFrame",value:o(function(){this.endBatch()},"endFrame")},{key:"getTempMatrix",value:o(function(){return this.tempMatrix=this.tempMatrix||Gb()},"getTempMatrix")},{key:"drawTexture",value:o(function(r,n,i){var a=this.atlasManager;if(a.isRenderable(r,i)){a.canAddToCurrentBatch(r,i)||this.endBatch();var s=this.instanceCount;this.vertTypeBuffer.getView(s)[0]=MP;var l=this.indexBuffer.getView(s);lS(n,l);var u=a.getAtlasInfo(r,i,u),h=u.atlasID,f=u.tex1,d=u.tex2,p=this.atlasIdBuffer.getView(s);p[0]=h;var m=this.tex1Buffer.getView(s);m[0]=f.x,m[1]=f.y,m[2]=f.w,m[3]=f.h;var g=this.tex2Buffer.getView(s);g[0]=d.x,g[1]=d.y,g[2]=d.w,g[3]=d.h;for(var y=this.getTempMatrix(),v=0,x=[1,2];v<x.length;v++){var b=x[v];a.setTransformMatrix(y,u,r,b===1);var w=this["scaleRotate".concat(b,"Buffer")].getView(s);w[0]=y[0],w[1]=y[1],w[2]=y[3],w[3]=y[4];var C=this["translate".concat(b,"Buffer")].getView(s);C[0]=y[6],C[1]=y[7]}this.instanceCount++,this.instanceCount>=this.maxInstances&&this.endBatch()}},"drawTexture")},{key:"drawEdgeArrow",value:o(function(r,n,i){var a=r._private.rscratch,s,l,u;if(i==="source"?(s=a.arrowStartX,l=a.arrowStartY,u=a.srcArrowAngle):(s=a.arrowEndX,l=a.arrowEndY,u=a.tgtArrowAngle),!(isNaN(s)||s==null||isNaN(l)||l==null||isNaN(u)||u==null)){var h=r.pstyle(i+"-arrow-shape").value;if(h!=="none"){var f=r.pstyle(i+"-arrow-color").value,d=r.pstyle("opacity").value,p=r.pstyle("line-opacity").value,m=d*p,g=r.pstyle("width").pfValue,y=r.pstyle("arrow-scale").value,v=this.r.getArrowWidth(g,y),x=this.getTempMatrix();Age(x),DS(x,x,[s,l]),TB(x,x,[v,v]),_ge(x,x,u);var b=this.instanceCount;this.vertTypeBuffer.getView(b)[0]=IP;var w=this.indexBuffer.getView(b);lS(n,w);var C=this.edgeColorBuffer.getView(b);oS(f,m,C);var T=this.scaleRotate1Buffer.getView(b);T[0]=x[0],T[1]=x[1],T[2]=x[3],T[3]=x[4];var E=this.translate1Buffer.getView(b);E[0]=x[6],E[1]=x[7],this.instanceCount++,this.instanceCount>=this.maxInstances&&this.endBatch()}}},"drawEdgeArrow")},{key:"drawEdgeLine",value:o(function(r,n){var i=r.pstyle("opacity").value,a=r.pstyle("line-opacity").value,s=r.pstyle("width").pfValue,l=r.pstyle("line-color").value,u=i*a,h=this.getEdgePoints(r);if(h.length/2+this.instanceCount>this.maxInstances&&this.endBatch(),h.length==4){var f=this.instanceCount;this.vertTypeBuffer.getView(f)[0]=V0e;var 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f=this.getOtherEnd(u),d=h.getGraphManager().getRoot();;){if(f.getOwner()==h)return f;if(f.getOwner()==d)break;f=f.getOwner().getParent()}return null},s.prototype.updateLength=function(){var u=new Array(4);this.isOverlapingSourceAndTarget=i.getIntersection(this.target.getRect(),this.source.getRect(),u),this.isOverlapingSourceAndTarget||(this.lengthX=u[0]-u[2],this.lengthY=u[1]-u[3],Math.abs(this.lengthX)<1&&(this.lengthX=a.sign(this.lengthX)),Math.abs(this.lengthY)<1&&(this.lengthY=a.sign(this.lengthY)),this.length=Math.sqrt(this.lengthX*this.lengthX+this.lengthY*this.lengthY))},s.prototype.updateLengthSimple=function(){this.lengthX=this.target.getCenterX()-this.source.getCenterX(),this.lengthY=this.target.getCenterY()-this.source.getCenterY(),Math.abs(this.lengthX)<1&&(this.lengthX=a.sign(this.lengthX)),Math.abs(this.lengthY)<1&&(this.lengthY=a.sign(this.lengthY)),this.length=Math.sqrt(this.lengthX*this.lengthX+this.lengthY*this.lengthY)},t.exports=s},function(t,e,r){"use strict";function n(i){this.vGraphObject=i}o(n,"LGraphObject"),t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(13),s=r(0),l=r(16),u=r(4);function h(d,p,m,g){m==null&&g==null&&(g=p),n.call(this,g),d.graphManager!=null&&(d=d.graphManager),this.estimatedSize=i.MIN_VALUE,this.inclusionTreeDepth=i.MAX_VALUE,this.vGraphObject=g,this.edges=[],this.graphManager=d,m!=null&&p!=null?this.rect=new a(p.x,p.y,m.width,m.height):this.rect=new a}o(h,"LNode"),h.prototype=Object.create(n.prototype);for(var f in n)h[f]=n[f];h.prototype.getEdges=function(){return this.edges},h.prototype.getChild=function(){return this.child},h.prototype.getOwner=function(){return this.owner},h.prototype.getWidth=function(){return this.rect.width},h.prototype.setWidth=function(d){this.rect.width=d},h.prototype.getHeight=function(){return this.rect.height},h.prototype.setHeight=function(d){this.rect.height=d},h.prototype.getCenterX=function(){return this.rect.x+this.rect.width/2},h.prototype.getCenterY=function(){return this.rect.y+this.rect.height/2},h.prototype.getCenter=function(){return new u(this.rect.x+this.rect.width/2,this.rect.y+this.rect.height/2)},h.prototype.getLocation=function(){return new u(this.rect.x,this.rect.y)},h.prototype.getRect=function(){return this.rect},h.prototype.getDiagonal=function(){return Math.sqrt(this.rect.width*this.rect.width+this.rect.height*this.rect.height)},h.prototype.getHalfTheDiagonal=function(){return Math.sqrt(this.rect.height*this.rect.height+this.rect.width*this.rect.width)/2},h.prototype.setRect=function(d,p){this.rect.x=d.x,this.rect.y=d.y,this.rect.width=p.width,this.rect.height=p.height},h.prototype.setCenter=function(d,p){this.rect.x=d-this.rect.width/2,this.rect.y=p-this.rect.height/2},h.prototype.setLocation=function(d,p){this.rect.x=d,this.rect.y=p},h.prototype.moveBy=function(d,p){this.rect.x+=d,this.rect.y+=p},h.prototype.getEdgeListToNode=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(y.target==d){if(y.source!=g)throw"Incorrect edge source!";p.push(y)}}),p},h.prototype.getEdgesBetween=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(!(y.source==g||y.target==g))throw"Incorrect edge source and/or target";(y.target==d||y.source==d)&&p.push(y)}),p},h.prototype.getNeighborsList=function(){var d=new Set,p=this;return p.edges.forEach(function(m){if(m.source==p)d.add(m.target);else{if(m.target!=p)throw"Incorrect incidency!";d.add(m.source)}}),d},h.prototype.withChildren=function(){var d=new Set,p,m;if(d.add(this),this.child!=null)for(var g=this.child.getNodes(),y=0;y<g.length;y++)p=g[y],m=p.withChildren(),m.forEach(function(v){d.add(v)});return d},h.prototype.getNoOfChildren=function(){var d=0,p;if(this.child==null)d=1;else for(var m=this.child.getNodes(),g=0;g<m.length;g++)p=m[g],d+=p.getNoOfChildren();return d==0&&(d=1),d},h.prototype.getEstimatedSize=function(){if(this.estimatedSize==i.MIN_VALUE)throw"assert failed";return this.estimatedSize},h.prototype.calcEstimatedSize=function(){return this.child==null?this.estimatedSize=(this.rect.width+this.rect.height)/2:(this.estimatedSize=this.child.calcEstimatedSize(),this.rect.width=this.estimatedSize,this.rect.height=this.estimatedSize,this.estimatedSize)},h.prototype.scatter=function(){var d,p,m=-s.INITIAL_WORLD_BOUNDARY,g=s.INITIAL_WORLD_BOUNDARY;d=s.WORLD_CENTER_X+l.nextDouble()*(g-m)+m;var y=-s.INITIAL_WORLD_BOUNDARY,v=s.INITIAL_WORLD_BOUNDARY;p=s.WORLD_CENTER_Y+l.nextDouble()*(v-y)+y,this.rect.x=d,this.rect.y=p},h.prototype.updateBounds=function(){if(this.getChild()==null)throw"assert failed";if(this.getChild().getNodes().length!=0){var d=this.getChild();if(d.updateBounds(!0),this.rect.x=d.getLeft(),this.rect.y=d.getTop(),this.setWidth(d.getRight()-d.getLeft()),this.setHeight(d.getBottom()-d.getTop()),s.NODE_DIMENSIONS_INCLUDE_LABELS){var p=d.getRight()-d.getLeft(),m=d.getBottom()-d.getTop();this.labelWidth>p&&(this.rect.x-=(this.labelWidth-p)/2,this.setWidth(this.labelWidth)),this.labelHeight>m&&(this.labelPos=="center"?this.rect.y-=(this.labelHeight-m)/2:this.labelPos=="top"&&(this.rect.y-=this.labelHeight-m),this.setHeight(this.labelHeight))}}},h.prototype.getInclusionTreeDepth=function(){if(this.inclusionTreeDepth==i.MAX_VALUE)throw"assert failed";return this.inclusionTreeDepth},h.prototype.transform=function(d){var p=this.rect.x;p>s.WORLD_BOUNDARY?p=s.WORLD_BOUNDARY:p<-s.WORLD_BOUNDARY&&(p=-s.WORLD_BOUNDARY);var m=this.rect.y;m>s.WORLD_BOUNDARY?m=s.WORLD_BOUNDARY:m<-s.WORLD_BOUNDARY&&(m=-s.WORLD_BOUNDARY);var g=new u(p,m),y=d.inverseTransformPoint(g);this.setLocation(y.x,y.y)},h.prototype.getLeft=function(){return this.rect.x},h.prototype.getRight=function(){return this.rect.x+this.rect.width},h.prototype.getTop=function(){return this.rect.y},h.prototype.getBottom=function(){return this.rect.y+this.rect.height},h.prototype.getParent=function(){return this.owner==null?null:this.owner.getParent()},t.exports=h},function(t,e,r){"use strict";function n(i,a){i==null&&a==null?(this.x=0,this.y=0):(this.x=i,this.y=a)}o(n,"PointD"),n.prototype.getX=function(){return this.x},n.prototype.getY=function(){return this.y},n.prototype.setX=function(i){this.x=i},n.prototype.setY=function(i){this.y=i},n.prototype.getDifference=function(i){return new DimensionD(this.x-i.x,this.y-i.y)},n.prototype.getCopy=function(){return new n(this.x,this.y)},n.prototype.translate=function(i){return this.x+=i.width,this.y+=i.height,this},t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(0),s=r(6),l=r(3),u=r(1),h=r(13),f=r(12),d=r(11);function p(g,y,v){n.call(this,v),this.estimatedSize=i.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(n.prototype);for(var m in n)p[m]=n[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,w=0;w<b;w++)x=v[w],x.isInterGraph?this.graphManager.remove(x):x.source.owner.remove(x);var C=this.nodes.indexOf(y);if(C==-1)throw"Node not in owner node list!";this.nodes.splice(C,1)}else if(g instanceof u){var x=g;if(x==null)throw"Edge is null!";if(!(x.source!=null&&x.target!=null))throw"Source and/or target is null!";if(!(x.source.owner!=null&&x.target.owner!=null&&x.source.owner==this&&x.target.owner==this))throw"Source and/or target owner is invalid!";var T=x.source.edges.indexOf(x),E=x.target.edges.indexOf(x);if(!(T>-1&&E>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(T,1),x.target!=x.source&&x.target.edges.splice(E,1);var C=x.source.owner.getEdges().indexOf(x);if(C==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(C,1)}},p.prototype.updateLeftTop=function(){for(var g=i.MAX_VALUE,y=i.MAX_VALUE,v,x,b,w=this.getNodes(),C=w.length,T=0;T<C;T++){var E=w[T];v=E.getTop(),x=E.getLeft(),g>v&&(g=v),y>x&&(y=x)}return g==i.MAX_VALUE?null:(w[0].getParent().paddingLeft!=null?b=w[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new f(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,C,T,E,A,S=this.nodes,_=S.length,I=0;I<_;I++){var D=S[I];g&&D.child!=null&&D.updateBounds(),w=D.getLeft(),C=D.getRight(),T=D.getTop(),E=D.getBottom(),y>w&&(y=w),v<C&&(v=C),x>T&&(x=T),b<E&&(b=E)}var k=new h(y,x,v-y,b-x);y==i.MAX_VALUE&&(this.left=this.parent.getLeft(),this.right=this.parent.getRight(),this.top=this.parent.getTop(),this.bottom=this.parent.getBottom()),S[0].getParent().paddingLeft!=null?A=S[0].getParent().paddingLeft:A=this.margin,this.left=k.x-A,this.right=k.x+k.width+A,this.top=k.y-A,this.bottom=k.y+k.height+A},p.calculateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,C,T,E,A=g.length,S=0;S<A;S++){var _=g[S];w=_.getLeft(),C=_.getRight(),T=_.getTop(),E=_.getBottom(),y>w&&(y=w),v<C&&(v=C),x>T&&(x=T),b<E&&(b=E)}var I=new h(y,x,v-y,b-x);return I},p.prototype.getInclusionTreeDepth=function(){return this==this.graphManager.getRoot()?1:this.parent.getInclusionTreeDepth()},p.prototype.getEstimatedSize=function(){if(this.estimatedSize==i.MIN_VALUE)throw"assert failed";return this.estimatedSize},p.prototype.calcEstimatedSize=function(){for(var g=0,y=this.nodes,v=y.length,x=0;x<v;x++){var b=y[x];g+=b.calcEstimatedSize()}return g==0?this.estimatedSize=a.EMPTY_COMPOUND_NODE_SIZE:this.estimatedSize=g/Math.sqrt(this.nodes.length),this.estimatedSize},p.prototype.updateConnected=function(){var g=this;if(this.nodes.length==0){this.isConnected=!0;return}var y=new d,v=new Set,x=this.nodes[0],b,w,C=x.withChildren();for(C.forEach(function(I){y.push(I),v.add(I)});y.length!==0;){x=y.shift(),b=x.getEdges();for(var T=b.length,E=0;E<T;E++){var A=b[E];if(w=A.getOtherEndInGraph(x,this),w!=null&&!v.has(w)){var S=w.withChildren();S.forEach(function(I){y.push(I),v.add(I)})}}}if(this.isConnected=!1,v.size>=this.nodes.length){var _=0;v.forEach(function(I){I.owner==g&&_++}),_==this.nodes.length&&(this.isConnected=!0)}},t.exports=p},function(t,e,r){"use strict";var n,i=r(1);function a(s){n=r(5),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,f){if(u==null&&h==null&&f==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{f=u,h=l,u=s;var d=h.getOwner(),p=f.getOwner();if(!(d!=null&&d.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(d==p)return u.isInterGraph=!1,d.add(u,h,f);if(u.isInterGraph=!0,u.source=h,u.target=f,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof n){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,f=u.length,d=0;d<f;d++)h=u[d],l.remove(h);var p=[];p=p.concat(l.getNodes());var m;f=p.length;for(var d=0;d<f;d++)m=p[d],l.remove(m);l==this.rootGraph&&this.setRootGraph(null);var g=this.graphs.indexOf(l);this.graphs.splice(g,1),l.parent=null}else if(s instanceof i){if(h=s,h==null)throw"Edge is null!";if(!h.isInterGraph)throw"Not an inter-graph edge!";if(!(h.source!=null&&h.target!=null))throw"Source and/or target is null!";if(!(h.source.edges.indexOf(h)!=-1&&h.target.edges.indexOf(h)!=-1))throw"Source and/or target doesn't know this edge!";var g=h.source.edges.indexOf(h);if(h.source.edges.splice(g,1),g=h.target.edges.indexOf(h),h.target.edges.splice(g,1),!(h.source.owner!=null&&h.source.owner.getGraphManager()!=null))throw"Edge owner graph or owner graph manager is null!";if(h.source.owner.getGraphManager().edges.indexOf(h)==-1)throw"Not in owner graph manager's edge list!";var g=h.source.owner.getGraphManager().edges.indexOf(h);h.source.owner.getGraphManager().edges.splice(g,1)}},a.prototype.updateBounds=function(){this.rootGraph.updateBounds(!0)},a.prototype.getGraphs=function(){return this.graphs},a.prototype.getAllNodes=function(){if(this.allNodes==null){for(var s=[],l=this.getGraphs(),u=l.length,h=0;h<u;h++)s=s.concat(l[h].getNodes());this.allNodes=s}return this.allNodes},a.prototype.resetAllNodes=function(){this.allNodes=null},a.prototype.resetAllEdges=function(){this.allEdges=null},a.prototype.resetAllNodesToApplyGravitation=function(){this.allNodesToApplyGravitation=null},a.prototype.getAllEdges=function(){if(this.allEdges==null){for(var s=[],l=this.getGraphs(),u=l.length,h=0;h<l.length;h++)s=s.concat(l[h].getEdges());s=s.concat(this.edges),this.allEdges=s}return this.allEdges},a.prototype.getAllNodesToApplyGravitation=function(){return this.allNodesToApplyGravitation},a.prototype.setAllNodesToApplyGravitation=function(s){if(this.allNodesToApplyGravitation!=null)throw"assert failed";this.allNodesToApplyGravitation=s},a.prototype.getRoot=function(){return this.rootGraph},a.prototype.setRootGraph=function(s){if(s.getGraphManager()!=this)throw"Root not in this graph mgr!";this.rootGraph=s,s.parent==null&&(s.parent=this.layout.newNode("Root node"))},a.prototype.getLayout=function(){return this.layout},a.prototype.isOneAncestorOfOther=function(s,l){if(!(s!=null&&l!=null))throw"assert failed";if(s==l)return!0;var u=s.getOwner(),h;do{if(h=u.getParent(),h==null)break;if(h==l)return!0;if(u=h.getOwner(),u==null)break}while(!0);u=l.getOwner();do{if(h=u.getParent(),h==null)break;if(h==s)return!0;if(u=h.getOwner(),u==null)break}while(!0);return!1},a.prototype.calcLowestCommonAncestors=function(){for(var s,l,u,h,f,d=this.getAllEdges(),p=d.length,m=0;m<p;m++){if(s=d[m],l=s.source,u=s.target,s.lca=null,s.sourceInLca=l,s.targetInLca=u,l==u){s.lca=l.getOwner();continue}for(h=l.getOwner();s.lca==null;){for(s.targetInLca=u,f=u.getOwner();s.lca==null;){if(f==h){s.lca=f;break}if(f==this.rootGraph)break;if(s.lca!=null)throw"assert failed";s.targetInLca=f.getParent(),f=s.targetInLca.getOwner()}if(h==this.rootGraph)break;s.lca==null&&(s.sourceInLca=h.getParent(),h=s.sourceInLca.getOwner())}if(s.lca==null)throw"assert failed"}},a.prototype.calcLowestCommonAncestor=function(s,l){if(s==l)return s.getOwner();var u=s.getOwner();do{if(u==null)break;var h=l.getOwner();do{if(h==null)break;if(h==u)return h;h=h.getParent().getOwner()}while(!0);u=u.getParent().getOwner()}while(!0);return u},a.prototype.calcInclusionTreeDepths=function(s,l){s==null&&l==null&&(s=this.rootGraph,l=1);for(var u,h=s.getNodes(),f=h.length,d=0;d<f;d++)u=h[d],u.inclusionTreeDepth=l,u.child!=null&&this.calcInclusionTreeDepths(u.child,l+1)},a.prototype.includesInvalidEdge=function(){for(var s,l=this.edges.length,u=0;u<l;u++)if(s=this.edges[u],this.isOneAncestorOfOther(s.source,s.target))return!0;return!1},t.exports=a},function(t,e,r){"use strict";var n=r(0);function i(){}o(i,"FDLayoutConstants");for(var a in n)i[a]=n[a];i.MAX_ITERATIONS=2500,i.DEFAULT_EDGE_LENGTH=50,i.DEFAULT_SPRING_STRENGTH=.45,i.DEFAULT_REPULSION_STRENGTH=4500,i.DEFAULT_GRAVITY_STRENGTH=.4,i.DEFAULT_COMPOUND_GRAVITY_STRENGTH=1,i.DEFAULT_GRAVITY_RANGE_FACTOR=3.8,i.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=1.5,i.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION=!0,i.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION=!0,i.DEFAULT_COOLING_FACTOR_INCREMENTAL=.3,i.COOLING_ADAPTATION_FACTOR=.33,i.ADAPTATION_LOWER_NODE_LIMIT=1e3,i.ADAPTATION_UPPER_NODE_LIMIT=5e3,i.MAX_NODE_DISPLACEMENT_INCREMENTAL=100,i.MAX_NODE_DISPLACEMENT=i.MAX_NODE_DISPLACEMENT_INCREMENTAL*3,i.MIN_REPULSION_DIST=i.DEFAULT_EDGE_LENGTH/10,i.CONVERGENCE_CHECK_PERIOD=100,i.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=.1,i.MIN_EDGE_LENGTH=1,i.GRID_CALCULATION_CHECK_PERIOD=10,t.exports=i},function(t,e,r){"use strict";var n=r(12);function i(){}o(i,"IGeometry"),i.calcSeparationAmount=function(a,s,l,u){if(!a.intersects(s))throw"assert failed";var h=new 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w=this.newEdge(null);this.graphManager.add(w,y,b),g.add(b),y=b}var w=this.newEdge(null);return this.graphManager.add(w,y,m.target),this.edgeToDummyNodes.set(m,g),m.isInterGraph()?this.graphManager.remove(m):v.remove(m),g},p.prototype.createBendpointsFromDummyNodes=function(){var m=[];m=m.concat(this.graphManager.getAllEdges()),m=[].concat(n(this.edgeToDummyNodes.keys())).concat(m);for(var g=0;g<m.length;g++){var y=m[g];if(y.bendpoints.length>0){for(var v=this.edgeToDummyNodes.get(y),x=0;x<v.length;x++){var b=v[x],w=new h(b.getCenterX(),b.getCenterY()),C=y.bendpoints.get(x);C.x=w.x,C.y=w.y,b.getOwner().remove(b)}this.graphManager.add(y,y.source,y.target)}}},p.transform=function(m,g,y,v){if(y!=null&&v!=null){var x=g;if(m<=50){var b=g/y;x-=(g-b)/50*(50-m)}else{var w=g*v;x+=(w-g)/50*(m-50)}return x}else{var C,T;return m<=50?(C=9*g/500,T=g/10):(C=9*g/50,T=-8*g),C*m+T}},p.findCenterOfTree=function(m){var g=[];g=g.concat(m);var y=[],v=new Map,x=!1,b=null;(g.length==1||g.length==2)&&(x=!0,b=g[0]);for(var w=0;w<g.length;w++){var C=g[w],T=C.getNeighborsList().size;v.set(C,C.getNeighborsList().size),T==1&&y.push(C)}var E=[];for(E=E.concat(y);!x;){var A=[];A=A.concat(E),E=[];for(var w=0;w<g.length;w++){var C=g[w],S=g.indexOf(C);S>=0&&g.splice(S,1);var _=C.getNeighborsList();_.forEach(function(k){if(y.indexOf(k)<0){var L=v.get(k),R=L-1;R==1&&E.push(k),v.set(k,R)}})}y=y.concat(E),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},t.exports=p},function(t,e,r){"use strict";function n(){}o(n,"RandomSeed"),n.seed=1,n.x=0,n.nextDouble=function(){return n.x=Math.sin(n.seed++)*1e4,n.x-Math.floor(n.x)},t.exports=n},function(t,e,r){"use strict";var n=r(4);function i(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(i,"Transform"),i.prototype.getWorldOrgX=function(){return this.lworldOrgX},i.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},i.prototype.getWorldOrgY=function(){return this.lworldOrgY},i.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},i.prototype.getWorldExtX=function(){return this.lworldExtX},i.prototype.setWorldExtX=function(a){this.lworldExtX=a},i.prototype.getWorldExtY=function(){return this.lworldExtY},i.prototype.setWorldExtY=function(a){this.lworldExtY=a},i.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},i.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},i.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},i.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},i.prototype.getDeviceExtX=function(){return this.ldeviceExtX},i.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},i.prototype.getDeviceExtY=function(){return this.ldeviceExtY},i.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},i.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},i.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},i.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},i.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},i.prototype.inverseTransformPoint=function(a){var s=new n(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},t.exports=i},function(t,e,r){"use strict";function n(d){if(Array.isArray(d)){for(var p=0,m=Array(d.length);p<d.length;p++)m[p]=d[p];return m}else return Array.from(d)}o(n,"_toConsumableArray");var i=r(15),a=r(7),s=r(0),l=r(8),u=r(9);function h(){i.call(this),this.useSmartIdealEdgeLengthCalculation=a.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.idealEdgeLength=a.DEFAULT_EDGE_LENGTH,this.springConstant=a.DEFAULT_SPRING_STRENGTH,this.repulsionConstant=a.DEFAULT_REPULSION_STRENGTH,this.gravityConstant=a.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=a.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=a.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=a.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.displacementThresholdPerNode=3*a.DEFAULT_EDGE_LENGTH/100,this.coolingFactor=a.DEFAULT_COOLING_FACTOR_INCREMENTAL,this.initialCoolingFactor=a.DEFAULT_COOLING_FACTOR_INCREMENTAL,this.totalDisplacement=0,this.oldTotalDisplacement=0,this.maxIterations=a.MAX_ITERATIONS}o(h,"FDLayout"),h.prototype=Object.create(i.prototype);for(var f in i)h[f]=i[f];h.prototype.initParameters=function(){i.prototype.initParameters.call(this,arguments),this.totalIterations=0,this.notAnimatedIterations=0,this.useFRGridVariant=a.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION,this.grid=[]},h.prototype.calcIdealEdgeLengths=function(){for(var d,p,m,g,y,v,x=this.getGraphManager().getAllEdges(),b=0;b<x.length;b++)d=x[b],d.idealLength=this.idealEdgeLength,d.isInterGraph&&(m=d.getSource(),g=d.getTarget(),y=d.getSourceInLca().getEstimatedSize(),v=d.getTargetInLca().getEstimatedSize(),this.useSmartIdealEdgeLengthCalculation&&(d.idealLength+=y+v-2*s.SIMPLE_NODE_SIZE),p=d.getLca().getInclusionTreeDepth(),d.idealLength+=a.DEFAULT_EDGE_LENGTH*a.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR*(m.getInclusionTreeDepth()+g.getInclusionTreeDepth()-2*p))},h.prototype.initSpringEmbedder=function(){var d=this.getAllNodes().length;this.incremental?(d>a.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(d>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var d=this.getAllEdges(),p,m=0;m<d.length;m++)p=d[m],this.calcSpringForce(p,p.idealLength)},h.prototype.calcRepulsionForces=function(){var d=arguments.length>0&&arguments[0]!==void 0?arguments[0]:!0,p=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,m,g,y,v,x=this.getAllNodes(),b;if(this.useFRGridVariant)for(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&d&&this.updateGrid(),b=new Set,m=0;m<x.length;m++)y=x[m],this.calculateRepulsionForceOfANode(y,b,d,p),b.add(y);else for(m=0;m<x.length;m++)for(y=x[m],g=m+1;g<x.length;g++)v=x[g],y.getOwner()==v.getOwner()&&this.calcRepulsionForce(y,v)},h.prototype.calcGravitationalForces=function(){for(var d,p=this.getAllNodesToApplyGravitation(),m=0;m<p.length;m++)d=p[m],this.calcGravitationalForce(d)},h.prototype.moveNodes=function(){for(var d=this.getAllNodes(),p,m=0;m<d.length;m++)p=d[m],p.move()},h.prototype.calcSpringForce=function(d,p){var m=d.getSource(),g=d.getTarget(),y,v,x,b;if(this.uniformLeafNodeSizes&&m.getChild()==null&&g.getChild()==null)d.updateLengthSimple();else if(d.updateLength(),d.isOverlapingSourceAndTarget)return;y=d.getLength(),y!=0&&(v=this.springConstant*(y-p),x=v*(d.lengthX/y),b=v*(d.lengthY/y),m.springForceX+=x,m.springForceY+=b,g.springForceX-=x,g.springForceY-=b)},h.prototype.calcRepulsionForce=function(d,p){var m=d.getRect(),g=p.getRect(),y=new Array(2),v=new Array(4),x,b,w,C,T,E,A;if(m.intersects(g)){l.calcSeparationAmount(m,g,y,a.DEFAULT_EDGE_LENGTH/2),E=2*y[0],A=2*y[1];var S=d.noOfChildren*p.noOfChildren/(d.noOfChildren+p.noOfChildren);d.repulsionForceX-=S*E,d.repulsionForceY-=S*A,p.repulsionForceX+=S*E,p.repulsionForceY+=S*A}else this.uniformLeafNodeSizes&&d.getChild()==null&&p.getChild()==null?(x=g.getCenterX()-m.getCenterX(),b=g.getCenterY()-m.getCenterY()):(l.getIntersection(m,g,v),x=v[2]-v[0],b=v[3]-v[1]),Math.abs(x)<a.MIN_REPULSION_DIST&&(x=u.sign(x)*a.MIN_REPULSION_DIST),Math.abs(b)<a.MIN_REPULSION_DIST&&(b=u.sign(b)*a.MIN_REPULSION_DIST),w=x*x+b*b,C=Math.sqrt(w),T=this.repulsionConstant*d.noOfChildren*p.noOfChildren/w,E=T*x/C,A=T*b/C,d.repulsionForceX-=E,d.repulsionForceY-=A,p.repulsionForceX+=E,p.repulsionForceY+=A},h.prototype.calcGravitationalForce=function(d){var p,m,g,y,v,x,b,w;p=d.getOwner(),m=(p.getRight()+p.getLeft())/2,g=(p.getTop()+p.getBottom())/2,y=d.getCenterX()-m,v=d.getCenterY()-g,x=Math.abs(y)+d.getWidth()/2,b=Math.abs(v)+d.getHeight()/2,d.getOwner()==this.graphManager.getRoot()?(w=p.getEstimatedSize()*this.gravityRangeFactor,(x>w||b>w)&&(d.gravitationForceX=-this.gravityConstant*y,d.gravitationForceY=-this.gravityConstant*v)):(w=p.getEstimatedSize()*this.compoundGravityRangeFactor,(x>w||b>w)&&(d.gravitationForceX=-this.gravityConstant*y*this.compoundGravityConstant,d.gravitationForceY=-this.gravityConstant*v*this.compoundGravityConstant))},h.prototype.isConverged=function(){var d,p=!1;return this.totalIterations>this.maxIterations/3&&(p=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),d=this.totalDisplacement<this.totalDisplacementThreshold,this.oldTotalDisplacement=this.totalDisplacement,d||p},h.prototype.animate=function(){this.animationDuringLayout&&!this.isSubLayout&&(this.notAnimatedIterations==this.animationPeriod?(this.update(),this.notAnimatedIterations=0):this.notAnimatedIterations++)},h.prototype.calcNoOfChildrenForAllNodes=function(){for(var d,p=this.graphManager.getAllNodes(),m=0;m<p.length;m++)d=p[m],d.noOfChildren=d.getNoOfChildren()},h.prototype.calcGrid=function(d){var p=0,m=0;p=parseInt(Math.ceil((d.getRight()-d.getLeft())/this.repulsionRange)),m=parseInt(Math.ceil((d.getBottom()-d.getTop())/this.repulsionRange));for(var g=new Array(p),y=0;y<p;y++)g[y]=new Array(m);for(var y=0;y<p;y++)for(var v=0;v<m;v++)g[y][v]=new Array;return g},h.prototype.addNodeToGrid=function(d,p,m){var g=0,y=0,v=0,x=0;g=parseInt(Math.floor((d.getRect().x-p)/this.repulsionRange)),y=parseInt(Math.floor((d.getRect().width+d.getRect().x-p)/this.repulsionRange)),v=parseInt(Math.floor((d.getRect().y-m)/this.repulsionRange)),x=parseInt(Math.floor((d.getRect().height+d.getRect().y-m)/this.repulsionRange));for(var b=g;b<=y;b++)for(var w=v;w<=x;w++)this.grid[b][w].push(d),d.setGridCoordinates(g,y,v,x)},h.prototype.updateGrid=function(){var d,p,m=this.getAllNodes();for(this.grid=this.calcGrid(this.graphManager.getRoot()),d=0;d<m.length;d++)p=m[d],this.addNodeToGrid(p,this.graphManager.getRoot().getLeft(),this.graphManager.getRoot().getTop())},h.prototype.calculateRepulsionForceOfANode=function(d,p,m,g){if(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&m||g){var y=new Set;d.surrounding=new Array;for(var v,x=this.grid,b=d.startX-1;b<d.finishX+2;b++)for(var w=d.startY-1;w<d.finishY+2;w++)if(!(b<0||w<0||b>=x.length||w>=x[0].length)){for(var C=0;C<x[b][w].length;C++)if(v=x[b][w][C],!(d.getOwner()!=v.getOwner()||d==v)&&!p.has(v)&&!y.has(v)){var T=Math.abs(d.getCenterX()-v.getCenterX())-(d.getWidth()/2+v.getWidth()/2),E=Math.abs(d.getCenterY()-v.getCenterY())-(d.getHeight()/2+v.getHeight()/2);T<=this.repulsionRange&&E<=this.repulsionRange&&y.add(v)}}d.surrounding=[].concat(n(y))}for(b=0;b<d.surrounding.length;b++)this.calcRepulsionForce(d,d.surrounding[b])},h.prototype.calcRepulsionRange=function(){return 0},t.exports=h},function(t,e,r){"use strict";var n=r(1),i=r(7);function a(l,u,h){n.call(this,l,u,h),this.idealLength=i.DEFAULT_EDGE_LENGTH}o(a,"FDLayoutEdge"),a.prototype=Object.create(n.prototype);for(var s in n)a[s]=n[s];t.exports=a},function(t,e,r){"use strict";var n=r(3);function i(s,l,u,h){n.call(this,s,l,u,h),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0,this.startX=0,this.finishX=0,this.startY=0,this.finishY=0,this.surrounding=[]}o(i,"FDLayoutNode"),i.prototype=Object.create(n.prototype);for(var a in n)i[a]=n[a];i.prototype.setGridCoordinates=function(s,l,u,h){this.startX=s,this.finishX=l,this.startY=u,this.finishY=h},t.exports=i},function(t,e,r){"use strict";function n(i,a){this.width=0,this.height=0,i!==null&&a!==null&&(this.height=a,this.width=i)}o(n,"DimensionD"),n.prototype.getWidth=function(){return this.width},n.prototype.setWidth=function(i){this.width=i},n.prototype.getHeight=function(){return this.height},n.prototype.setHeight=function(i){this.height=i},t.exports=n},function(t,e,r){"use strict";var n=r(14);function i(){this.map={},this.keys=[]}o(i,"HashMap"),i.prototype.put=function(a,s){var l=n.createID(a);this.contains(l)||(this.map[l]=s,this.keys.push(a))},i.prototype.contains=function(a){var s=n.createID(a);return this.map[a]!=null},i.prototype.get=function(a){var s=n.createID(a);return this.map[s]},i.prototype.keySet=function(){return this.keys},t.exports=i},function(t,e,r){"use strict";var n=r(14);function i(){this.set={}}o(i,"HashSet"),i.prototype.add=function(a){var s=n.createID(a);this.contains(s)||(this.set[s]=a)},i.prototype.remove=function(a){delete this.set[n.createID(a)]},i.prototype.clear=function(){this.set={}},i.prototype.contains=function(a){return this.set[n.createID(a)]==a},i.prototype.isEmpty=function(){return this.size()===0},i.prototype.size=function(){return Object.keys(this.set).length},i.prototype.addAllTo=function(a){for(var s=Object.keys(this.set),l=s.length,u=0;u<l;u++)a.push(this.set[s[u]])},i.prototype.size=function(){return Object.keys(this.set).length},i.prototype.addAll=function(a){for(var s=a.length,l=0;l<s;l++){var u=a[l];this.add(u)}},t.exports=i},function(t,e,r){"use strict";var n=function(){function l(u,h){for(var f=0;f<h.length;f++){var d=h[f];d.enumerable=d.enumerable||!1,d.configurable=!0,"value"in d&&(d.writable=!0),Object.defineProperty(u,d.key,d)}}return o(l,"defineProperties"),function(u,h,f){return h&&l(u.prototype,h),f&&l(u,f),u}}();function i(l,u){if(!(l instanceof u))throw new TypeError("Cannot call a class as a function")}o(i,"_classCallCheck");var a=r(11),s=function(){function l(u,h){i(this,l),(h!==null||h!==void 0)&&(this.compareFunction=this._defaultCompareFunction);var f=void 0;u instanceof a?f=u.size():f=u.length,this._quicksort(u,0,f-1)}return o(l,"Quicksort"),n(l,[{key:"_quicksort",value:o(function(h,f,d){if(f<d){var p=this._partition(h,f,d);this._quicksort(h,f,p),this._quicksort(h,p+1,d)}},"_quicksort")},{key:"_partition",value:o(function(h,f,d){for(var p=this._get(h,f),m=f,g=d;;){for(;this.compareFunction(p,this._get(h,g));)g--;for(;this.compareFunction(this._get(h,m),p);)m++;if(m<g)this._swap(h,m,g),m++,g--;else return g}},"_partition")},{key:"_get",value:o(function(h,f){return h instanceof a?h.get_object_at(f):h[f]},"_get")},{key:"_set",value:o(function(h,f,d){h instanceof a?h.set_object_at(f,d):h[f]=d},"_set")},{key:"_swap",value:o(function(h,f,d){var p=this._get(h,f);this._set(h,f,this._get(h,d)),this._set(h,d,p)},"_swap")},{key:"_defaultCompareFunction",value:o(function(h,f){return f>h},"_defaultCompareFunction")}]),l}();t.exports=s},function(t,e,r){"use strict";var n=function(){function s(l,u){for(var h=0;h<u.length;h++){var f=u[h];f.enumerable=f.enumerable||!1,f.configurable=!0,"value"in f&&(f.writable=!0),Object.defineProperty(l,f.key,f)}}return o(s,"defineProperties"),function(l,u,h){return u&&s(l.prototype,u),h&&s(l,h),l}}();function i(s,l){if(!(s instanceof l))throw new TypeError("Cannot call a class as a function")}o(i,"_classCallCheck");var a=function(){function s(l,u){var h=arguments.length>2&&arguments[2]!==void 0?arguments[2]:1,f=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,d=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;i(this,s),this.sequence1=l,this.sequence2=u,this.match_score=h,this.mismatch_penalty=f,this.gap_penalty=d,this.iMax=l.length+1,this.jMax=u.length+1,this.grid=new Array(this.iMax);for(var p=0;p<this.iMax;p++){this.grid[p]=new Array(this.jMax);for(var m=0;m<this.jMax;m++)this.grid[p][m]=0}this.tracebackGrid=new Array(this.iMax);for(var g=0;g<this.iMax;g++){this.tracebackGrid[g]=new Array(this.jMax);for(var y=0;y<this.jMax;y++)this.tracebackGrid[g][y]=[null,null,null]}this.alignments=[],this.score=-1,this.computeGrids()}return o(s,"NeedlemanWunsch"),n(s,[{key:"getScore",value:o(function(){return this.score},"getScore")},{key:"getAlignments",value:o(function(){return this.alignments},"getAlignments")},{key:"computeGrids",value:o(function(){for(var u=1;u<this.jMax;u++)this.grid[0][u]=this.grid[0][u-1]+this.gap_penalty,this.tracebackGrid[0][u]=[!1,!1,!0];for(var h=1;h<this.iMax;h++)this.grid[h][0]=this.grid[h-1][0]+this.gap_penalty,this.tracebackGrid[h][0]=[!1,!0,!1];for(var f=1;f<this.iMax;f++)for(var d=1;d<this.jMax;d++){var p=void 0;this.sequence1[f-1]===this.sequence2[d-1]?p=this.grid[f-1][d-1]+this.match_score:p=this.grid[f-1][d-1]+this.mismatch_penalty;var m=this.grid[f-1][d]+this.gap_penalty,g=this.grid[f][d-1]+this.gap_penalty,y=[p,m,g],v=this.arrayAllMaxIndexes(y);this.grid[f][d]=y[v[0]],this.tracebackGrid[f][d]=[v.includes(0),v.includes(1),v.includes(2)]}this.score=this.grid[this.iMax-1][this.jMax-1]},"computeGrids")},{key:"alignmentTraceback",value:o(function(){var u=[];for(u.push({pos:[this.sequence1.length,this.sequence2.length],seq1:"",seq2:""});u[0];){var h=u[0],f=this.tracebackGrid[h.pos[0]][h.pos[1]];f[0]&&u.push({pos:[h.pos[0]-1,h.pos[1]-1],seq1:this.sequence1[h.pos[0]-1]+h.seq1,seq2:this.sequence2[h.pos[1]-1]+h.seq2}),f[1]&&u.push({pos:[h.pos[0]-1,h.pos[1]],seq1:this.sequence1[h.pos[0]-1]+h.seq1,seq2:"-"+h.seq2}),f[2]&&u.push({pos:[h.pos[0],h.pos[1]-1],seq1:"-"+h.seq1,seq2:this.sequence2[h.pos[1]-1]+h.seq2}),h.pos[0]===0&&h.pos[1]===0&&this.alignments.push({sequence1:h.seq1,sequence2:h.seq2}),u.shift()}return this.alignments},"alignmentTraceback")},{key:"getAllIndexes",value:o(function(u,h){for(var f=[],d=-1;(d=u.indexOf(h,d+1))!==-1;)f.push(d);return f},"getAllIndexes")},{key:"arrayAllMaxIndexes",value:o(function(u){return this.getAllIndexes(u,Math.max.apply(null,u))},"arrayAllMaxIndexes")}]),s}();t.exports=a},function(t,e,r){"use strict";var n=o(function(){},"layoutBase");n.FDLayout=r(18),n.FDLayoutConstants=r(7),n.FDLayoutEdge=r(19),n.FDLayoutNode=r(20),n.DimensionD=r(21),n.HashMap=r(22),n.HashSet=r(23),n.IGeometry=r(8),n.IMath=r(9),n.Integer=r(10),n.Point=r(12),n.PointD=r(4),n.RandomSeed=r(16),n.RectangleD=r(13),n.Transform=r(17),n.UniqueIDGeneretor=r(14),n.Quicksort=r(24),n.LinkedList=r(11),n.LGraphObject=r(2),n.LGraph=r(5),n.LEdge=r(1),n.LGraphManager=r(6),n.LNode=r(3),n.Layout=r(15),n.LayoutConstants=r(0),n.NeedlemanWunsch=r(25),t.exports=n},function(t,e,r){"use strict";function n(){this.listeners=[]}o(n,"Emitter");var i=n.prototype;i.addListener=function(a,s){this.listeners.push({event:a,callback:s})},i.removeListener=function(a,s){for(var l=this.listeners.length;l>=0;l--){var u=this.listeners[l];u.event===a&&u.callback===s&&this.listeners.splice(l,1)}},i.emit=function(a,s){for(var l=0;l<this.listeners.length;l++){var u=this.listeners[l];a===u.event&&u.callback(s)}},t.exports=n}])})});var AB=Mi((h4,CB)=>{"use 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i)a[s]=i[s];a.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,a.DEFAULT_RADIAL_SEPARATION=i.DEFAULT_EDGE_LENGTH,a.DEFAULT_COMPONENT_SEPERATION=60,a.TILE=!0,a.TILING_PADDING_VERTICAL=10,a.TILING_PADDING_HORIZONTAL=10,a.TREE_REDUCTION_ON_INCREMENTAL=!1,e.exports=a},function(e,r,n){"use strict";var i=n(0).FDLayoutEdge;function a(l,u,h){i.call(this,l,u,h)}o(a,"CoSEEdge"),a.prototype=Object.create(i.prototype);for(var s in i)a[s]=i[s];e.exports=a},function(e,r,n){"use strict";var i=n(0).LGraph;function a(l,u,h){i.call(this,l,u,h)}o(a,"CoSEGraph"),a.prototype=Object.create(i.prototype);for(var s in i)a[s]=i[s];e.exports=a},function(e,r,n){"use strict";var i=n(0).LGraphManager;function a(l){i.call(this,l)}o(a,"CoSEGraphManager"),a.prototype=Object.create(i.prototype);for(var s in i)a[s]=i[s];e.exports=a},function(e,r,n){"use strict";var i=n(0).FDLayoutNode,a=n(0).IMath;function s(u,h,f,d){i.call(this,u,h,f,d)}o(s,"CoSENode"),s.prototype=Object.create(i.prototype);for(var l in i)s[l]=i[l];s.prototype.move=function(){var u=this.graphManager.getLayout();this.displacementX=u.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY=u.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren,Math.abs(this.displacementX)>u.coolingFactor*u.maxNodeDisplacement&&(this.displacementX=u.coolingFactor*u.maxNodeDisplacement*a.sign(this.displacementX)),Math.abs(this.displacementY)>u.coolingFactor*u.maxNodeDisplacement&&(this.displacementY=u.coolingFactor*u.maxNodeDisplacement*a.sign(this.displacementY)),this.child==null?this.moveBy(this.displacementX,this.displacementY):this.child.getNodes().length==0?this.moveBy(this.displacementX,this.displacementY):this.propogateDisplacementToChildren(this.displacementX,this.displacementY),u.totalDisplacement+=Math.abs(this.displacementX)+Math.abs(this.displacementY),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0},s.prototype.propogateDisplacementToChildren=function(u,h){for(var f=this.getChild().getNodes(),d,p=0;p<f.length;p++)d=f[p],d.getChild()==null?(d.moveBy(u,h),d.displacementX+=u,d.displacementY+=h):d.propogateDisplacementToChildren(u,h)},s.prototype.setPred1=function(u){this.pred1=u},s.prototype.getPred1=function(){return pred1},s.prototype.getPred2=function(){return pred2},s.prototype.setNext=function(u){this.next=u},s.prototype.getNext=function(){return next},s.prototype.setProcessed=function(u){this.processed=u},s.prototype.isProcessed=function(){return processed},e.exports=s},function(e,r,n){"use strict";var i=n(0).FDLayout,a=n(4),s=n(3),l=n(5),u=n(2),h=n(1),f=n(0).FDLayoutConstants,d=n(0).LayoutConstants,p=n(0).Point,m=n(0).PointD,g=n(0).Layout,y=n(0).Integer,v=n(0).IGeometry,x=n(0).LGraph,b=n(0).Transform;function w(){i.call(this),this.toBeTiled={}}o(w,"CoSELayout"),w.prototype=Object.create(i.prototype);for(var C in i)w[C]=i[C];w.prototype.newGraphManager=function(){var T=new a(this);return this.graphManager=T,T},w.prototype.newGraph=function(T){return new s(null,this.graphManager,T)},w.prototype.newNode=function(T){return new l(this.graphManager,T)},w.prototype.newEdge=function(T){return new u(null,null,T)},w.prototype.initParameters=function(){i.prototype.initParameters.call(this,arguments),this.isSubLayout||(h.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=h.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=h.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.springConstant=f.DEFAULT_SPRING_STRENGTH,this.repulsionConstant=f.DEFAULT_REPULSION_STRENGTH,this.gravityConstant=f.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=f.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=f.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=f.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1,this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/f.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=f.CONVERGENCE_CHECK_PERIOD/this.maxIterations,this.coolingAdjuster=1)},w.prototype.layout=function(){var T=d.DEFAULT_CREATE_BENDS_AS_NEEDED;return T&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},w.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(h.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var E=new Set(this.getAllNodes()),A=this.nodesWithGravity.filter(function(I){return E.has(I)});this.graphManager.setAllNodesToApplyGravitation(A)}}else{var T=this.getFlatForest();if(T.length>0)this.positionNodesRadially(T);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var E=new Set(this.getAllNodes()),A=this.nodesWithGravity.filter(function(S){return E.has(S)});this.graphManager.setAllNodesToApplyGravitation(A),this.positionNodesRandomly()}}return this.initSpringEmbedder(),this.runSpringEmbedder(),!0},w.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%f.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var T=new Set(this.getAllNodes()),E=this.nodesWithGravity.filter(function(_){return T.has(_)});this.graphManager.setAllNodesToApplyGravitation(E),this.graphManager.updateBounds(),this.updateGrid(),this.coolingFactor=f.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),this.coolingFactor=f.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var A=!this.isTreeGrowing&&!this.isGrowthFinished,S=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(A,S),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},w.prototype.getPositionsData=function(){for(var T=this.graphManager.getAllNodes(),E={},A=0;A<T.length;A++){var S=T[A].rect,_=T[A].id;E[_]={id:_,x:S.getCenterX(),y:S.getCenterY(),w:S.width,h:S.height}}return E},w.prototype.runSpringEmbedder=function(){this.initialAnimationPeriod=25,this.animationPeriod=this.initialAnimationPeriod;var T=!1;if(f.ANIMATE==="during")this.emit("layoutstarted");else{for(;!T;)T=this.tick();this.graphManager.updateBounds()}},w.prototype.calculateNodesToApplyGravitationTo=function(){var T=[],E,A=this.graphManager.getGraphs(),S=A.length,_;for(_=0;_<S;_++)E=A[_],E.updateConnected(),E.isConnected||(T=T.concat(E.getNodes()));return T},w.prototype.createBendpoints=function(){var T=[];T=T.concat(this.graphManager.getAllEdges());var E=new Set,A;for(A=0;A<T.length;A++){var S=T[A];if(!E.has(S)){var _=S.getSource(),I=S.getTarget();if(_==I)S.getBendpoints().push(new m),S.getBendpoints().push(new m),this.createDummyNodesForBendpoints(S),E.add(S);else{var D=[];if(D=D.concat(_.getEdgeListToNode(I)),D=D.concat(I.getEdgeListToNode(_)),!E.has(D[0])){if(D.length>1){var k;for(k=0;k<D.length;k++){var L=D[k];L.getBendpoints().push(new m),this.createDummyNodesForBendpoints(L)}}D.forEach(function(R){E.add(R)})}}}if(E.size==T.length)break}},w.prototype.positionNodesRadially=function(T){for(var E=new p(0,0),A=Math.ceil(Math.sqrt(T.length)),S=0,_=0,I=0,D=new m(0,0),k=0;k<T.length;k++){k%A==0&&(I=0,_=S,k!=0&&(_+=h.DEFAULT_COMPONENT_SEPERATION),S=0);var L=T[k],R=g.findCenterOfTree(L);E.x=I,E.y=_,D=w.radialLayout(L,R,E),D.y>S&&(S=Math.floor(D.y)),I=Math.floor(D.x+h.DEFAULT_COMPONENT_SEPERATION)}this.transform(new m(d.WORLD_CENTER_X-D.x/2,d.WORLD_CENTER_Y-D.y/2))},w.radialLayout=function(T,E,A){var S=Math.max(this.maxDiagonalInTree(T),h.DEFAULT_RADIAL_SEPARATION);w.branchRadialLayout(E,null,0,359,0,S);var _=x.calculateBounds(T),I=new b;I.setDeviceOrgX(_.getMinX()),I.setDeviceOrgY(_.getMinY()),I.setWorldOrgX(A.x),I.setWorldOrgY(A.y);for(var D=0;D<T.length;D++){var k=T[D];k.transform(I)}var L=new m(_.getMaxX(),_.getMaxY());return I.inverseTransformPoint(L)},w.branchRadialLayout=function(T,E,A,S,_,I){var D=(S-A+1)/2;D<0&&(D+=180);var k=(D+A)%360,L=k*v.TWO_PI/360,R=Math.cos(L),O=_*Math.cos(L),M=_*Math.sin(L);T.setCenter(O,M);var B=[];B=B.concat(T.getEdges());var F=B.length;E!=null&&F--;for(var P=0,z=B.length,$,H=T.getEdgesBetween(E);H.length>1;){var Q=H[0];H.splice(0,1);var j=B.indexOf(Q);j>=0&&B.splice(j,1),z--,F--}E!=null?$=(B.indexOf(H[0])+1)%z:$=0;for(var ie=Math.abs(S-A)/F,ne=$;P!=F;ne=++ne%z){var le=B[ne].getOtherEnd(T);if(le!=E){var he=(A+P*ie)%360,K=(he+ie)%360;w.branchRadialLayout(le,T,he,K,_+I,I),P++}}},w.maxDiagonalInTree=function(T){for(var E=y.MIN_VALUE,A=0;A<T.length;A++){var S=T[A],_=S.getDiagonal();_>E&&(E=_)}return E},w.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},w.prototype.groupZeroDegreeMembers=function(){var T=this,E={};this.memberGroups={},this.idToDummyNode={};for(var A=[],S=this.graphManager.getAllNodes(),_=0;_<S.length;_++){var I=S[_],D=I.getParent();this.getNodeDegreeWithChildren(I)===0&&(D.id==null||!this.getToBeTiled(D))&&A.push(I)}for(var _=0;_<A.length;_++){var I=A[_],k=I.getParent().id;typeof E[k]>"u"&&(E[k]=[]),E[k]=E[k].concat(I)}Object.keys(E).forEach(function(L){if(E[L].length>1){var R="DummyCompound_"+L;T.memberGroups[R]=E[L];var O=E[L][0].getParent(),M=new l(T.graphManager);M.id=R,M.paddingLeft=O.paddingLeft||0,M.paddingRight=O.paddingRight||0,M.paddingBottom=O.paddingBottom||0,M.paddingTop=O.paddingTop||0,T.idToDummyNode[R]=M;var B=T.getGraphManager().add(T.newGraph(),M),F=O.getChild();F.add(M);for(var P=0;P<E[L].length;P++){var z=E[L][P];F.remove(z),B.add(z)}}})},w.prototype.clearCompounds=function(){var T={},E={};this.performDFSOnCompounds();for(var A=0;A<this.compoundOrder.length;A++)E[this.compoundOrder[A].id]=this.compoundOrder[A],T[this.compoundOrder[A].id]=[].concat(this.compoundOrder[A].getChild().getNodes()),this.graphManager.remove(this.compoundOrder[A].getChild()),this.compoundOrder[A].child=null;this.graphManager.resetAllNodes(),this.tileCompoundMembers(T,E)},w.prototype.clearZeroDegreeMembers=function(){var T=this,E=this.tiledZeroDegreePack=[];Object.keys(this.memberGroups).forEach(function(A){var S=T.idToDummyNode[A];E[A]=T.tileNodes(T.memberGroups[A],S.paddingLeft+S.paddingRight),S.rect.width=E[A].width,S.rect.height=E[A].height})},w.prototype.repopulateCompounds=function(){for(var T=this.compoundOrder.length-1;T>=0;T--){var E=this.compoundOrder[T],A=E.id,S=E.paddingLeft,_=E.paddingTop;this.adjustLocations(this.tiledMemberPack[A],E.rect.x,E.rect.y,S,_)}},w.prototype.repopulateZeroDegreeMembers=function(){var T=this,E=this.tiledZeroDegreePack;Object.keys(E).forEach(function(A){var S=T.idToDummyNode[A],_=S.paddingLeft,I=S.paddingTop;T.adjustLocations(E[A],S.rect.x,S.rect.y,_,I)})},w.prototype.getToBeTiled=function(T){var E=T.id;if(this.toBeTiled[E]!=null)return this.toBeTiled[E];var A=T.getChild();if(A==null)return this.toBeTiled[E]=!1,!1;for(var S=A.getNodes(),_=0;_<S.length;_++){var I=S[_];if(this.getNodeDegree(I)>0)return this.toBeTiled[E]=!1,!1;if(I.getChild()==null){this.toBeTiled[I.id]=!1;continue}if(!this.getToBeTiled(I))return this.toBeTiled[E]=!1,!1}return this.toBeTiled[E]=!0,!0},w.prototype.getNodeDegree=function(T){for(var E=T.id,A=T.getEdges(),S=0,_=0;_<A.length;_++){var I=A[_];I.getSource().id!==I.getTarget().id&&(S=S+1)}return S},w.prototype.getNodeDegreeWithChildren=function(T){var E=this.getNodeDegree(T);if(T.getChild()==null)return E;for(var A=T.getChild().getNodes(),S=0;S<A.length;S++){var _=A[S];E+=this.getNodeDegreeWithChildren(_)}return 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Please create the service/group before declaring an edge to it.`);let h=vr.records.nodes[t].in,f=vr.records.nodes[e].in;if(s&&h&&f&&h==f)throw new Error(`The left-hand id [${t}] is modified to traverse the group boundary, but the edge does not pass through two groups.`);if(l&&h&&f&&h==f)throw new Error(`The right-hand id [${e}] is modified to traverse the group boundary, but the edge does not pass through two groups.`);let d={lhsId:t,lhsDir:r,lhsInto:i,lhsGroup:s,rhsId:e,rhsDir:n,rhsInto:a,rhsGroup:l,title:u};vr.records.edges.push(d),vr.records.nodes[t]&&vr.records.nodes[e]&&(vr.records.nodes[t].edges.push(vr.records.edges[vr.records.edges.length-1]),vr.records.nodes[e].edges.push(vr.records.edges[vr.records.edges.length-1]))},"addEdge"),xrt=o(()=>vr.records.edges,"getEdges"),brt=o(()=>{if(vr.records.dataStructures===void 0){let t={},e=Object.entries(vr.records.nodes).reduce((l,[u,h])=>(l[u]=h.edges.reduce((f,d)=>{let p=hF(d.lhsId)?.in,m=hF(d.rhsId)?.in;if(p&&m&&p!==m){let g=_ve(d.lhsDir,d.rhsDir);g!=="bend"&&(t[p]??={},t[p][m]=g,t[m]??={},t[m][p]=g)}if(d.lhsId===u){let g=b4(d.lhsDir,d.rhsDir);g&&(f[g]=d.rhsId)}else{let g=b4(d.rhsDir,d.lhsDir);g&&(f[g]=d.lhsId)}return f},{}),l),{}),r=Object.keys(e)[0],n={[r]:1},i=Object.keys(e).reduce((l,u)=>u===r?l:{...l,[u]:1},{}),a=o(l=>{let u={[l]:[0,0]},h=[l];for(;h.length>0;){let f=h.shift();if(f){n[f]=1,delete i[f];let d=e[f],[p,m]=u[f];Object.entries(d).forEach(([g,y])=>{n[y]||(u[y]=Cve([p,m],g),h.push(y))})}}return u},"BFS"),s=[a(r)];for(;Object.keys(i).length>0;)s.push(a(Object.keys(i)[0]));vr.records.dataStructures={adjList:e,spatialMaps:s,groupAlignments:t}}return vr.records.dataStructures},"getDataStructures"),wrt=o((t,e)=>{vr.records.elements[t]=e},"setElementForId"),Trt=o(t=>vr.records.elements[t],"getElementById"),Qp={clear:urt,setDiagramTitle:$r,getDiagramTitle:Ir,setAccTitle:Lr,getAccTitle:Rr,setAccDescription:Nr,getAccDescription:Mr,addService:hrt,getServices:frt,addJunction:drt,getJunctions:prt,getNodes:mrt,getNode:hF,addGroup:grt,getGroups:yrt,addEdge:vrt,getEdges:xrt,setElementForId:wrt,getElementById:Trt,getDataStructures:brt};o(Li,"getConfigField")});var krt,Nve,Mve=N(()=>{"use strict";kp();vt();T1();w4();krt=o((t,e)=>{$c(t,e),t.groups.map(e.addGroup),t.services.map(r=>e.addService({...r,type:"service"})),t.junctions.map(r=>e.addJunction({...r,type:"junction"})),t.edges.map(e.addEdge)},"populateDb"),Nve={parse:o(async t=>{let e=await uo("architecture",t);Y.debug(e),krt(e,Qp)},"parse")}});var Ert,Ive,Ove=N(()=>{"use strict";Ert=o(t=>`
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-
-  .arrow {
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-  }
-
-  .node-bkg {
-    fill: none;
-    stroke: ${t.archGroupBorderColor};
-    stroke-width: ${t.archGroupBorderWidth};
-    stroke-dasharray: 8;
-  }
-  .node-icon-text {
-    display: flex; 
-    align-items: center;
-  }
-  
-  .node-icon-text > div {
-    color: #fff;
-    margin: 1px;
-    height: fit-content;
-    text-align: center;
-    overflow: hidden;
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-    -webkit-box-orient: vertical;
-  }
-`,"getStyles"),Ive=Ert});var dF=Mi((T4,fF)=>{"use strict";o(function(e,r){typeof T4=="object"&&typeof fF=="object"?fF.exports=r():typeof define=="function"&&define.amd?define([],r):typeof T4=="object"?T4.layoutBase=r():e.layoutBase=r()},"webpackUniversalModuleDefinition")(T4,function(){return function(t){var e={};function r(n){if(e[n])return e[n].exports;var i=e[n]={i:n,l:!1,exports:{}};return t[n].call(i.exports,i,i.exports,r),i.l=!0,i.exports}return o(r,"__webpack_require__"),r.m=t,r.c=e,r.i=function(n){return n},r.d=function(n,i,a){r.o(n,i)||Object.defineProperty(n,i,{configurable:!1,enumerable:!0,get:a})},r.n=function(n){var i=n&&n.__esModule?o(function(){return n.default},"getDefault"):o(function(){return n},"getModuleExports");return r.d(i,"a",i),i},r.o=function(n,i){return Object.prototype.hasOwnProperty.call(n,i)},r.p="",r(r.s=28)}([function(t,e,r){"use strict";function n(){}o(n,"LayoutConstants"),n.QUALITY=1,n.DEFAULT_CREATE_BENDS_AS_NEEDED=!1,n.DEFAULT_INCREMENTAL=!1,n.DEFAULT_ANIMATION_ON_LAYOUT=!0,n.DEFAULT_ANIMATION_DURING_LAYOUT=!1,n.DEFAULT_ANIMATION_PERIOD=50,n.DEFAULT_UNIFORM_LEAF_NODE_SIZES=!1,n.DEFAULT_GRAPH_MARGIN=15,n.NODE_DIMENSIONS_INCLUDE_LABELS=!1,n.SIMPLE_NODE_SIZE=40,n.SIMPLE_NODE_HALF_SIZE=n.SIMPLE_NODE_SIZE/2,n.EMPTY_COMPOUND_NODE_SIZE=40,n.MIN_EDGE_LENGTH=1,n.WORLD_BOUNDARY=1e6,n.INITIAL_WORLD_BOUNDARY=n.WORLD_BOUNDARY/1e3,n.WORLD_CENTER_X=1200,n.WORLD_CENTER_Y=900,t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(8),a=r(9);function s(u,h,f){n.call(this,f),this.isOverlapingSourceAndTarget=!1,this.vGraphObject=f,this.bendpoints=[],this.source=u,this.target=h}o(s,"LEdge"),s.prototype=Object.create(n.prototype);for(var l in n)s[l]=n[l];s.prototype.getSource=function(){return this.source},s.prototype.getTarget=function(){return this.target},s.prototype.isInterGraph=function(){return this.isInterGraph},s.prototype.getLength=function(){return this.length},s.prototype.isOverlapingSourceAndTarget=function(){return this.isOverlapingSourceAndTarget},s.prototype.getBendpoints=function(){return this.bendpoints},s.prototype.getLca=function(){return this.lca},s.prototype.getSourceInLca=function(){return this.sourceInLca},s.prototype.getTargetInLca=function(){return this.targetInLca},s.prototype.getOtherEnd=function(u){if(this.source===u)return this.target;if(this.target===u)return this.source;throw"Node is not incident with this edge"},s.prototype.getOtherEndInGraph=function(u,h){for(var f=this.getOtherEnd(u),d=h.getGraphManager().getRoot();;){if(f.getOwner()==h)return f;if(f.getOwner()==d)break;f=f.getOwner().getParent()}return null},s.prototype.updateLength=function(){var u=new Array(4);this.isOverlapingSourceAndTarget=i.getIntersection(this.target.getRect(),this.source.getRect(),u),this.isOverlapingSourceAndTarget||(this.lengthX=u[0]-u[2],this.lengthY=u[1]-u[3],Math.abs(this.lengthX)<1&&(this.lengthX=a.sign(this.lengthX)),Math.abs(this.lengthY)<1&&(this.lengthY=a.sign(this.lengthY)),this.length=Math.sqrt(this.lengthX*this.lengthX+this.lengthY*this.lengthY))},s.prototype.updateLengthSimple=function(){this.lengthX=this.target.getCenterX()-this.source.getCenterX(),this.lengthY=this.target.getCenterY()-this.source.getCenterY(),Math.abs(this.lengthX)<1&&(this.lengthX=a.sign(this.lengthX)),Math.abs(this.lengthY)<1&&(this.lengthY=a.sign(this.lengthY)),this.length=Math.sqrt(this.lengthX*this.lengthX+this.lengthY*this.lengthY)},t.exports=s},function(t,e,r){"use strict";function n(i){this.vGraphObject=i}o(n,"LGraphObject"),t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(13),s=r(0),l=r(16),u=r(5);function h(d,p,m,g){m==null&&g==null&&(g=p),n.call(this,g),d.graphManager!=null&&(d=d.graphManager),this.estimatedSize=i.MIN_VALUE,this.inclusionTreeDepth=i.MAX_VALUE,this.vGraphObject=g,this.edges=[],this.graphManager=d,m!=null&&p!=null?this.rect=new a(p.x,p.y,m.width,m.height):this.rect=new a}o(h,"LNode"),h.prototype=Object.create(n.prototype);for(var f in n)h[f]=n[f];h.prototype.getEdges=function(){return this.edges},h.prototype.getChild=function(){return this.child},h.prototype.getOwner=function(){return this.owner},h.prototype.getWidth=function(){return this.rect.width},h.prototype.setWidth=function(d){this.rect.width=d},h.prototype.getHeight=function(){return this.rect.height},h.prototype.setHeight=function(d){this.rect.height=d},h.prototype.getCenterX=function(){return this.rect.x+this.rect.width/2},h.prototype.getCenterY=function(){return this.rect.y+this.rect.height/2},h.prototype.getCenter=function(){return new u(this.rect.x+this.rect.width/2,this.rect.y+this.rect.height/2)},h.prototype.getLocation=function(){return new u(this.rect.x,this.rect.y)},h.prototype.getRect=function(){return this.rect},h.prototype.getDiagonal=function(){return Math.sqrt(this.rect.width*this.rect.width+this.rect.height*this.rect.height)},h.prototype.getHalfTheDiagonal=function(){return Math.sqrt(this.rect.height*this.rect.height+this.rect.width*this.rect.width)/2},h.prototype.setRect=function(d,p){this.rect.x=d.x,this.rect.y=d.y,this.rect.width=p.width,this.rect.height=p.height},h.prototype.setCenter=function(d,p){this.rect.x=d-this.rect.width/2,this.rect.y=p-this.rect.height/2},h.prototype.setLocation=function(d,p){this.rect.x=d,this.rect.y=p},h.prototype.moveBy=function(d,p){this.rect.x+=d,this.rect.y+=p},h.prototype.getEdgeListToNode=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(y.target==d){if(y.source!=g)throw"Incorrect edge source!";p.push(y)}}),p},h.prototype.getEdgesBetween=function(d){var p=[],m,g=this;return g.edges.forEach(function(y){if(!(y.source==g||y.target==g))throw"Incorrect edge source and/or target";(y.target==d||y.source==d)&&p.push(y)}),p},h.prototype.getNeighborsList=function(){var d=new Set,p=this;return p.edges.forEach(function(m){if(m.source==p)d.add(m.target);else{if(m.target!=p)throw"Incorrect incidency!";d.add(m.source)}}),d},h.prototype.withChildren=function(){var d=new Set,p,m;if(d.add(this),this.child!=null)for(var g=this.child.getNodes(),y=0;y<g.length;y++)p=g[y],m=p.withChildren(),m.forEach(function(v){d.add(v)});return d},h.prototype.getNoOfChildren=function(){var d=0,p;if(this.child==null)d=1;else for(var m=this.child.getNodes(),g=0;g<m.length;g++)p=m[g],d+=p.getNoOfChildren();return d==0&&(d=1),d},h.prototype.getEstimatedSize=function(){if(this.estimatedSize==i.MIN_VALUE)throw"assert failed";return this.estimatedSize},h.prototype.calcEstimatedSize=function(){return this.child==null?this.estimatedSize=(this.rect.width+this.rect.height)/2:(this.estimatedSize=this.child.calcEstimatedSize(),this.rect.width=this.estimatedSize,this.rect.height=this.estimatedSize,this.estimatedSize)},h.prototype.scatter=function(){var d,p,m=-s.INITIAL_WORLD_BOUNDARY,g=s.INITIAL_WORLD_BOUNDARY;d=s.WORLD_CENTER_X+l.nextDouble()*(g-m)+m;var y=-s.INITIAL_WORLD_BOUNDARY,v=s.INITIAL_WORLD_BOUNDARY;p=s.WORLD_CENTER_Y+l.nextDouble()*(v-y)+y,this.rect.x=d,this.rect.y=p},h.prototype.updateBounds=function(){if(this.getChild()==null)throw"assert failed";if(this.getChild().getNodes().length!=0){var d=this.getChild();if(d.updateBounds(!0),this.rect.x=d.getLeft(),this.rect.y=d.getTop(),this.setWidth(d.getRight()-d.getLeft()),this.setHeight(d.getBottom()-d.getTop()),s.NODE_DIMENSIONS_INCLUDE_LABELS){var 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this.y},n.prototype.setX=function(i){this.x=i},n.prototype.setY=function(i){this.y=i},n.prototype.getDifference=function(i){return new DimensionD(this.x-i.x,this.y-i.y)},n.prototype.getCopy=function(){return new n(this.x,this.y)},n.prototype.translate=function(i){return this.x+=i.width,this.y+=i.height,this},t.exports=n},function(t,e,r){"use strict";var n=r(2),i=r(10),a=r(0),s=r(7),l=r(3),u=r(1),h=r(13),f=r(12),d=r(11);function p(g,y,v){n.call(this,v),this.estimatedSize=i.MIN_VALUE,this.margin=a.DEFAULT_GRAPH_MARGIN,this.edges=[],this.nodes=[],this.isConnected=!1,this.parent=g,y!=null&&y instanceof s?this.graphManager=y:y!=null&&y instanceof Layout&&(this.graphManager=y.graphManager)}o(p,"LGraph"),p.prototype=Object.create(n.prototype);for(var m in n)p[m]=n[m];p.prototype.getNodes=function(){return this.nodes},p.prototype.getEdges=function(){return this.edges},p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getParent=function(){return this.parent},p.prototype.getLeft=function(){return this.left},p.prototype.getRight=function(){return this.right},p.prototype.getTop=function(){return this.top},p.prototype.getBottom=function(){return this.bottom},p.prototype.isConnected=function(){return this.isConnected},p.prototype.add=function(g,y,v){if(y==null&&v==null){var x=g;if(this.graphManager==null)throw"Graph has no graph mgr!";if(this.getNodes().indexOf(x)>-1)throw"Node already in graph!";return x.owner=this,this.getNodes().push(x),x}else{var b=g;if(!(this.getNodes().indexOf(y)>-1&&this.getNodes().indexOf(v)>-1))throw"Source or target not in graph!";if(!(y.owner==v.owner&&y.owner==this))throw"Both owners must be this graph!";return y.owner!=v.owner?null:(b.source=y,b.target=v,b.isInterGraph=!1,this.getEdges().push(b),y.edges.push(b),v!=y&&v.edges.push(b),b)}},p.prototype.remove=function(g){var y=g;if(g instanceof l){if(y==null)throw"Node is null!";if(!(y.owner!=null&&y.owner==this))throw"Owner graph is invalid!";if(this.graphManager==null)throw"Owner graph manager is invalid!";for(var v=y.edges.slice(),x,b=v.length,w=0;w<b;w++)x=v[w],x.isInterGraph?this.graphManager.remove(x):x.source.owner.remove(x);var C=this.nodes.indexOf(y);if(C==-1)throw"Node not in owner node list!";this.nodes.splice(C,1)}else if(g instanceof u){var x=g;if(x==null)throw"Edge is null!";if(!(x.source!=null&&x.target!=null))throw"Source and/or target is null!";if(!(x.source.owner!=null&&x.target.owner!=null&&x.source.owner==this&&x.target.owner==this))throw"Source and/or target owner is invalid!";var T=x.source.edges.indexOf(x),E=x.target.edges.indexOf(x);if(!(T>-1&&E>-1))throw"Source and/or target doesn't know this edge!";x.source.edges.splice(T,1),x.target!=x.source&&x.target.edges.splice(E,1);var C=x.source.owner.getEdges().indexOf(x);if(C==-1)throw"Not in owner's edge list!";x.source.owner.getEdges().splice(C,1)}},p.prototype.updateLeftTop=function(){for(var g=i.MAX_VALUE,y=i.MAX_VALUE,v,x,b,w=this.getNodes(),C=w.length,T=0;T<C;T++){var E=w[T];v=E.getTop(),x=E.getLeft(),g>v&&(g=v),y>x&&(y=x)}return g==i.MAX_VALUE?null:(w[0].getParent().paddingLeft!=null?b=w[0].getParent().paddingLeft:b=this.margin,this.left=y-b,this.top=g-b,new f(this.left,this.top))},p.prototype.updateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,C,T,E,A,S=this.nodes,_=S.length,I=0;I<_;I++){var D=S[I];g&&D.child!=null&&D.updateBounds(),w=D.getLeft(),C=D.getRight(),T=D.getTop(),E=D.getBottom(),y>w&&(y=w),v<C&&(v=C),x>T&&(x=T),b<E&&(b=E)}var k=new h(y,x,v-y,b-x);y==i.MAX_VALUE&&(this.left=this.parent.getLeft(),this.right=this.parent.getRight(),this.top=this.parent.getTop(),this.bottom=this.parent.getBottom()),S[0].getParent().paddingLeft!=null?A=S[0].getParent().paddingLeft:A=this.margin,this.left=k.x-A,this.right=k.x+k.width+A,this.top=k.y-A,this.bottom=k.y+k.height+A},p.calculateBounds=function(g){for(var y=i.MAX_VALUE,v=-i.MAX_VALUE,x=i.MAX_VALUE,b=-i.MAX_VALUE,w,C,T,E,A=g.length,S=0;S<A;S++){var _=g[S];w=_.getLeft(),C=_.getRight(),T=_.getTop(),E=_.getBottom(),y>w&&(y=w),v<C&&(v=C),x>T&&(x=T),b<E&&(b=E)}var I=new h(y,x,v-y,b-x);return I},p.prototype.getInclusionTreeDepth=function(){return this==this.graphManager.getRoot()?1:this.parent.getInclusionTreeDepth()},p.prototype.getEstimatedSize=function(){if(this.estimatedSize==i.MIN_VALUE)throw"assert failed";return this.estimatedSize},p.prototype.calcEstimatedSize=function(){for(var g=0,y=this.nodes,v=y.length,x=0;x<v;x++){var b=y[x];g+=b.calcEstimatedSize()}return g==0?this.estimatedSize=a.EMPTY_COMPOUND_NODE_SIZE:this.estimatedSize=g/Math.sqrt(this.nodes.length),this.estimatedSize},p.prototype.updateConnected=function(){var g=this;if(this.nodes.length==0){this.isConnected=!0;return}var y=new d,v=new Set,x=this.nodes[0],b,w,C=x.withChildren();for(C.forEach(function(I){y.push(I),v.add(I)});y.length!==0;){x=y.shift(),b=x.getEdges();for(var T=b.length,E=0;E<T;E++){var A=b[E];if(w=A.getOtherEndInGraph(x,this),w!=null&&!v.has(w)){var S=w.withChildren();S.forEach(function(I){y.push(I),v.add(I)})}}}if(this.isConnected=!1,v.size>=this.nodes.length){var _=0;v.forEach(function(I){I.owner==g&&_++}),_==this.nodes.length&&(this.isConnected=!0)}},t.exports=p},function(t,e,r){"use strict";var n,i=r(1);function a(s){n=r(6),this.layout=s,this.graphs=[],this.edges=[]}o(a,"LGraphManager"),a.prototype.addRoot=function(){var s=this.layout.newGraph(),l=this.layout.newNode(null),u=this.add(s,l);return this.setRootGraph(u),this.rootGraph},a.prototype.add=function(s,l,u,h,f){if(u==null&&h==null&&f==null){if(s==null)throw"Graph is null!";if(l==null)throw"Parent node is null!";if(this.graphs.indexOf(s)>-1)throw"Graph already in this graph mgr!";if(this.graphs.push(s),s.parent!=null)throw"Already has a parent!";if(l.child!=null)throw"Already has a child!";return s.parent=l,l.child=s,s}else{f=u,h=l,u=s;var d=h.getOwner(),p=f.getOwner();if(!(d!=null&&d.getGraphManager()==this))throw"Source not in this graph mgr!";if(!(p!=null&&p.getGraphManager()==this))throw"Target not in this graph mgr!";if(d==p)return u.isInterGraph=!1,d.add(u,h,f);if(u.isInterGraph=!0,u.source=h,u.target=f,this.edges.indexOf(u)>-1)throw"Edge already in inter-graph edge list!";if(this.edges.push(u),!(u.source!=null&&u.target!=null))throw"Edge source and/or target is null!";if(!(u.source.edges.indexOf(u)==-1&&u.target.edges.indexOf(u)==-1))throw"Edge already in source and/or target incidency list!";return u.source.edges.push(u),u.target.edges.push(u),u}},a.prototype.remove=function(s){if(s instanceof n){var l=s;if(l.getGraphManager()!=this)throw"Graph not in this graph mgr";if(!(l==this.rootGraph||l.parent!=null&&l.parent.graphManager==this))throw"Invalid parent node!";var u=[];u=u.concat(l.getEdges());for(var h,f=u.length,d=0;d<f;d++)h=u[d],l.remove(h);var p=[];p=p.concat(l.getNodes());var m;f=p.length;for(var d=0;d<f;d++)m=p[d],l.remove(m);l==this.rootGraph&&this.setRootGraph(null);var g=this.graphs.indexOf(l);this.graphs.splice(g,1),l.parent=null}else if(s instanceof i){if(h=s,h==null)throw"Edge is null!";if(!h.isInterGraph)throw"Not an inter-graph edge!";if(!(h.source!=null&&h.target!=null))throw"Source and/or target is null!";if(!(h.source.edges.indexOf(h)!=-1&&h.target.edges.indexOf(h)!=-1))throw"Source and/or target doesn't know this edge!";var g=h.source.edges.indexOf(h);if(h.source.edges.splice(g,1),g=h.target.edges.indexOf(h),h.target.edges.splice(g,1),!(h.source.owner!=null&&h.source.owner.getGraphManager()!=null))throw"Edge owner graph or owner graph manager is null!";if(h.source.owner.getGraphManager().edges.indexOf(h)==-1)throw"Not in owner graph manager's edge list!";var g=h.source.owner.getGraphManager().edges.indexOf(h);h.source.owner.getGraphManager().edges.splice(g,1)}},a.prototype.updateBounds=function(){this.rootGraph.updateBounds(!0)},a.prototype.getGraphs=function(){return this.graphs},a.prototype.getAllNodes=function(){if(this.allNodes==null){for(var s=[],l=this.getGraphs(),u=l.length,h=0;h<u;h++)s=s.concat(l[h].getNodes());this.allNodes=s}return this.allNodes},a.prototype.resetAllNodes=function(){this.allNodes=null},a.prototype.resetAllEdges=function(){this.allEdges=null},a.prototype.resetAllNodesToApplyGravitation=function(){this.allNodesToApplyGravitation=null},a.prototype.getAllEdges=function(){if(this.allEdges==null){for(var s=[],l=this.getGraphs(),u=l.length,h=0;h<l.length;h++)s=s.concat(l[h].getEdges());s=s.concat(this.edges),this.allEdges=s}return this.allEdges},a.prototype.getAllNodesToApplyGravitation=function(){return this.allNodesToApplyGravitation},a.prototype.setAllNodesToApplyGravitation=function(s){if(this.allNodesToApplyGravitation!=null)throw"assert failed";this.allNodesToApplyGravitation=s},a.prototype.getRoot=function(){return this.rootGraph},a.prototype.setRootGraph=function(s){if(s.getGraphManager()!=this)throw"Root not in this graph mgr!";this.rootGraph=s,s.parent==null&&(s.parent=this.layout.newNode("Root node"))},a.prototype.getLayout=function(){return this.layout},a.prototype.isOneAncestorOfOther=function(s,l){if(!(s!=null&&l!=null))throw"assert failed";if(s==l)return!0;var u=s.getOwner(),h;do{if(h=u.getParent(),h==null)break;if(h==l)return!0;if(u=h.getOwner(),u==null)break}while(!0);u=l.getOwner();do{if(h=u.getParent(),h==null)break;if(h==s)return!0;if(u=h.getOwner(),u==null)break}while(!0);return!1},a.prototype.calcLowestCommonAncestors=function(){for(var s,l,u,h,f,d=this.getAllEdges(),p=d.length,m=0;m<p;m++){if(s=d[m],l=s.source,u=s.target,s.lca=null,s.sourceInLca=l,s.targetInLca=u,l==u){s.lca=l.getOwner();continue}for(h=l.getOwner();s.lca==null;){for(s.targetInLca=u,f=u.getOwner();s.lca==null;){if(f==h){s.lca=f;break}if(f==this.rootGraph)break;if(s.lca!=null)throw"assert failed";s.targetInLca=f.getParent(),f=s.targetInLca.getOwner()}if(h==this.rootGraph)break;s.lca==null&&(s.sourceInLca=h.getParent(),h=s.sourceInLca.getOwner())}if(s.lca==null)throw"assert failed"}},a.prototype.calcLowestCommonAncestor=function(s,l){if(s==l)return s.getOwner();var u=s.getOwner();do{if(u==null)break;var h=l.getOwner();do{if(h==null)break;if(h==u)return h;h=h.getParent().getOwner()}while(!0);u=u.getParent().getOwner()}while(!0);return u},a.prototype.calcInclusionTreeDepths=function(s,l){s==null&&l==null&&(s=this.rootGraph,l=1);for(var u,h=s.getNodes(),f=h.length,d=0;d<f;d++)u=h[d],u.inclusionTreeDepth=l,u.child!=null&&this.calcInclusionTreeDepths(u.child,l+1)},a.prototype.includesInvalidEdge=function(){for(var s,l=[],u=this.edges.length,h=0;h<u;h++)s=this.edges[h],this.isOneAncestorOfOther(s.source,s.target)&&l.push(s);for(var h=0;h<l.length;h++)this.remove(l[h]);return!1},t.exports=a},function(t,e,r){"use strict";var n=r(12);function i(){}o(i,"IGeometry"),i.calcSeparationAmount=function(a,s,l,u){if(!a.intersects(s))throw"assert failed";var h=new 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n(i,a,s,l){this.x=0,this.y=0,this.width=0,this.height=0,i!=null&&a!=null&&s!=null&&l!=null&&(this.x=i,this.y=a,this.width=s,this.height=l)}o(n,"RectangleD"),n.prototype.getX=function(){return this.x},n.prototype.setX=function(i){this.x=i},n.prototype.getY=function(){return this.y},n.prototype.setY=function(i){this.y=i},n.prototype.getWidth=function(){return this.width},n.prototype.setWidth=function(i){this.width=i},n.prototype.getHeight=function(){return this.height},n.prototype.setHeight=function(i){this.height=i},n.prototype.getRight=function(){return this.x+this.width},n.prototype.getBottom=function(){return this.y+this.height},n.prototype.intersects=function(i){return!(this.getRight()<i.x||this.getBottom()<i.y||i.getRight()<this.x||i.getBottom()<this.y)},n.prototype.getCenterX=function(){return this.x+this.width/2},n.prototype.getMinX=function(){return this.getX()},n.prototype.getMaxX=function(){return this.getX()+this.width},n.prototype.getCenterY=function(){return this.y+this.height/2},n.prototype.getMinY=function(){return this.getY()},n.prototype.getMaxY=function(){return this.getY()+this.height},n.prototype.getWidthHalf=function(){return this.width/2},n.prototype.getHeightHalf=function(){return this.height/2},t.exports=n},function(t,e,r){"use strict";var n=typeof Symbol=="function"&&typeof Symbol.iterator=="symbol"?function(a){return typeof a}:function(a){return a&&typeof Symbol=="function"&&a.constructor===Symbol&&a!==Symbol.prototype?"symbol":typeof a};function i(){}o(i,"UniqueIDGeneretor"),i.lastID=0,i.createID=function(a){return i.isPrimitive(a)?a:(a.uniqueID!=null||(a.uniqueID=i.getString(),i.lastID++),a.uniqueID)},i.getString=function(a){return a==null&&(a=i.lastID),"Object#"+a},i.isPrimitive=function(a){var s=typeof a>"u"?"undefined":n(a);return a==null||s!="object"&&s!="function"},t.exports=i},function(t,e,r){"use strict";function n(m){if(Array.isArray(m)){for(var g=0,y=Array(m.length);g<m.length;g++)y[g]=m[g];return y}else return Array.from(m)}o(n,"_toConsumableArray");var i=r(0),a=r(7),s=r(3),l=r(1),u=r(6),h=r(5),f=r(17),d=r(29);function p(m){d.call(this),this.layoutQuality=i.QUALITY,this.createBendsAsNeeded=i.DEFAULT_CREATE_BENDS_AS_NEEDED,this.incremental=i.DEFAULT_INCREMENTAL,this.animationOnLayout=i.DEFAULT_ANIMATION_ON_LAYOUT,this.animationDuringLayout=i.DEFAULT_ANIMATION_DURING_LAYOUT,this.animationPeriod=i.DEFAULT_ANIMATION_PERIOD,this.uniformLeafNodeSizes=i.DEFAULT_UNIFORM_LEAF_NODE_SIZES,this.edgeToDummyNodes=new Map,this.graphManager=new a(this),this.isLayoutFinished=!1,this.isSubLayout=!1,this.isRemoteUse=!1,m!=null&&(this.isRemoteUse=m)}o(p,"Layout"),p.RANDOM_SEED=1,p.prototype=Object.create(d.prototype),p.prototype.getGraphManager=function(){return this.graphManager},p.prototype.getAllNodes=function(){return this.graphManager.getAllNodes()},p.prototype.getAllEdges=function(){return this.graphManager.getAllEdges()},p.prototype.getAllNodesToApplyGravitation=function(){return this.graphManager.getAllNodesToApplyGravitation()},p.prototype.newGraphManager=function(){var m=new a(this);return this.graphManager=m,m},p.prototype.newGraph=function(m){return new u(null,this.graphManager,m)},p.prototype.newNode=function(m){return new s(this.graphManager,m)},p.prototype.newEdge=function(m){return new l(null,null,m)},p.prototype.checkLayoutSuccess=function(){return this.graphManager.getRoot()==null||this.graphManager.getRoot().getNodes().length==0||this.graphManager.includesInvalidEdge()},p.prototype.runLayout=function(){this.isLayoutFinished=!1,this.tilingPreLayout&&this.tilingPreLayout(),this.initParameters();var m;return this.checkLayoutSuccess()?m=!1:m=this.layout(),i.ANIMATE==="during"?!1:(m&&(this.isSubLayout||this.doPostLayout()),this.tilingPostLayout&&this.tilingPostLayout(),this.isLayoutFinished=!0,m)},p.prototype.doPostLayout=function(){this.incremental||this.transform(),this.update()},p.prototype.update2=function(){if(this.createBendsAsNeeded&&(this.createBendpointsFromDummyNodes(),this.graphManager.resetAllEdges()),!this.isRemoteUse){for(var m,g=this.graphManager.getAllEdges(),y=0;y<g.length;y++)m=g[y];for(var v,x=this.graphManager.getRoot().getNodes(),y=0;y<x.length;y++)v=x[y];this.update(this.graphManager.getRoot())}},p.prototype.update=function(m){if(m==null)this.update2();else if(m instanceof s){var g=m;if(g.getChild()!=null)for(var y=g.getChild().getNodes(),v=0;v<y.length;v++)update(y[v]);if(g.vGraphObject!=null){var x=g.vGraphObject;x.update(g)}}else if(m instanceof l){var b=m;if(b.vGraphObject!=null){var w=b.vGraphObject;w.update(b)}}else if(m instanceof u){var C=m;if(C.vGraphObject!=null){var T=C.vGraphObject;T.update(C)}}},p.prototype.initParameters=function(){this.isSubLayout||(this.layoutQuality=i.QUALITY,this.animationDuringLayout=i.DEFAULT_ANIMATION_DURING_LAYOUT,this.animationPeriod=i.DEFAULT_ANIMATION_PERIOD,this.animationOnLayout=i.DEFAULT_ANIMATION_ON_LAYOUT,this.incremental=i.DEFAULT_INCREMENTAL,this.createBendsAsNeeded=i.DEFAULT_CREATE_BENDS_AS_NEEDED,this.uniformLeafNodeSizes=i.DEFAULT_UNIFORM_LEAF_NODE_SIZES),this.animationDuringLayout&&(this.animationOnLayout=!1)},p.prototype.transform=function(m){if(m==null)this.transform(new h(0,0));else{var g=new f,y=this.graphManager.getRoot().updateLeftTop();if(y!=null){g.setWorldOrgX(m.x),g.setWorldOrgY(m.y),g.setDeviceOrgX(y.x),g.setDeviceOrgY(y.y);for(var v=this.getAllNodes(),x,b=0;b<v.length;b++)x=v[b],x.transform(g)}}},p.prototype.positionNodesRandomly=function(m){if(m==null)this.positionNodesRandomly(this.getGraphManager().getRoot()),this.getGraphManager().getRoot().updateBounds(!0);else for(var g,y,v=m.getNodes(),x=0;x<v.length;x++)g=v[x],y=g.getChild(),y==null||y.getNodes().length==0?g.scatter():(this.positionNodesRandomly(y),g.updateBounds())},p.prototype.getFlatForest=function(){for(var m=[],g=!0,y=this.graphManager.getRoot().getNodes(),v=!0,x=0;x<y.length;x++)y[x].getChild()!=null&&(v=!1);if(!v)return m;var b=new Set,w=[],C=new Map,T=[];for(T=T.concat(y);T.length>0&&g;){for(w.push(T[0]);w.length>0&&g;){var E=w[0];w.splice(0,1),b.add(E);for(var A=E.getEdges(),x=0;x<A.length;x++){var S=A[x].getOtherEnd(E);if(C.get(E)!=S)if(!b.has(S))w.push(S),C.set(S,E);else{g=!1;break}}}if(!g)m=[];else{var _=[].concat(n(b));m.push(_);for(var x=0;x<_.length;x++){var I=_[x],D=T.indexOf(I);D>-1&&T.splice(D,1)}b=new Set,C=new Map}}return m},p.prototype.createDummyNodesForBendpoints=function(m){for(var g=[],y=m.source,v=this.graphManager.calcLowestCommonAncestor(m.source,m.target),x=0;x<m.bendpoints.length;x++){var b=this.newNode(null);b.setRect(new Point(0,0),new Dimension(1,1)),v.add(b);var w=this.newEdge(null);this.graphManager.add(w,y,b),g.add(b),y=b}var w=this.newEdge(null);return this.graphManager.add(w,y,m.target),this.edgeToDummyNodes.set(m,g),m.isInterGraph()?this.graphManager.remove(m):v.remove(m),g},p.prototype.createBendpointsFromDummyNodes=function(){var m=[];m=m.concat(this.graphManager.getAllEdges()),m=[].concat(n(this.edgeToDummyNodes.keys())).concat(m);for(var g=0;g<m.length;g++){var y=m[g];if(y.bendpoints.length>0){for(var v=this.edgeToDummyNodes.get(y),x=0;x<v.length;x++){var b=v[x],w=new h(b.getCenterX(),b.getCenterY()),C=y.bendpoints.get(x);C.x=w.x,C.y=w.y,b.getOwner().remove(b)}this.graphManager.add(y,y.source,y.target)}}},p.transform=function(m,g,y,v){if(y!=null&&v!=null){var x=g;if(m<=50){var b=g/y;x-=(g-b)/50*(50-m)}else{var w=g*v;x+=(w-g)/50*(m-50)}return x}else{var C,T;return m<=50?(C=9*g/500,T=g/10):(C=9*g/50,T=-8*g),C*m+T}},p.findCenterOfTree=function(m){var g=[];g=g.concat(m);var y=[],v=new Map,x=!1,b=null;(g.length==1||g.length==2)&&(x=!0,b=g[0]);for(var w=0;w<g.length;w++){var C=g[w],T=C.getNeighborsList().size;v.set(C,C.getNeighborsList().size),T==1&&y.push(C)}var E=[];for(E=E.concat(y);!x;){var A=[];A=A.concat(E),E=[];for(var w=0;w<g.length;w++){var C=g[w],S=g.indexOf(C);S>=0&&g.splice(S,1);var _=C.getNeighborsList();_.forEach(function(k){if(y.indexOf(k)<0){var L=v.get(k),R=L-1;R==1&&E.push(k),v.set(k,R)}})}y=y.concat(E),(g.length==1||g.length==2)&&(x=!0,b=g[0])}return b},p.prototype.setGraphManager=function(m){this.graphManager=m},t.exports=p},function(t,e,r){"use strict";function n(){}o(n,"RandomSeed"),n.seed=1,n.x=0,n.nextDouble=function(){return n.x=Math.sin(n.seed++)*1e4,n.x-Math.floor(n.x)},t.exports=n},function(t,e,r){"use strict";var n=r(5);function i(a,s){this.lworldOrgX=0,this.lworldOrgY=0,this.ldeviceOrgX=0,this.ldeviceOrgY=0,this.lworldExtX=1,this.lworldExtY=1,this.ldeviceExtX=1,this.ldeviceExtY=1}o(i,"Transform"),i.prototype.getWorldOrgX=function(){return this.lworldOrgX},i.prototype.setWorldOrgX=function(a){this.lworldOrgX=a},i.prototype.getWorldOrgY=function(){return this.lworldOrgY},i.prototype.setWorldOrgY=function(a){this.lworldOrgY=a},i.prototype.getWorldExtX=function(){return this.lworldExtX},i.prototype.setWorldExtX=function(a){this.lworldExtX=a},i.prototype.getWorldExtY=function(){return this.lworldExtY},i.prototype.setWorldExtY=function(a){this.lworldExtY=a},i.prototype.getDeviceOrgX=function(){return this.ldeviceOrgX},i.prototype.setDeviceOrgX=function(a){this.ldeviceOrgX=a},i.prototype.getDeviceOrgY=function(){return this.ldeviceOrgY},i.prototype.setDeviceOrgY=function(a){this.ldeviceOrgY=a},i.prototype.getDeviceExtX=function(){return this.ldeviceExtX},i.prototype.setDeviceExtX=function(a){this.ldeviceExtX=a},i.prototype.getDeviceExtY=function(){return this.ldeviceExtY},i.prototype.setDeviceExtY=function(a){this.ldeviceExtY=a},i.prototype.transformX=function(a){var s=0,l=this.lworldExtX;return l!=0&&(s=this.ldeviceOrgX+(a-this.lworldOrgX)*this.ldeviceExtX/l),s},i.prototype.transformY=function(a){var s=0,l=this.lworldExtY;return l!=0&&(s=this.ldeviceOrgY+(a-this.lworldOrgY)*this.ldeviceExtY/l),s},i.prototype.inverseTransformX=function(a){var s=0,l=this.ldeviceExtX;return l!=0&&(s=this.lworldOrgX+(a-this.ldeviceOrgX)*this.lworldExtX/l),s},i.prototype.inverseTransformY=function(a){var s=0,l=this.ldeviceExtY;return l!=0&&(s=this.lworldOrgY+(a-this.ldeviceOrgY)*this.lworldExtY/l),s},i.prototype.inverseTransformPoint=function(a){var s=new n(this.inverseTransformX(a.x),this.inverseTransformY(a.y));return s},t.exports=i},function(t,e,r){"use strict";function n(d){if(Array.isArray(d)){for(var p=0,m=Array(d.length);p<d.length;p++)m[p]=d[p];return m}else return Array.from(d)}o(n,"_toConsumableArray");var i=r(15),a=r(4),s=r(0),l=r(8),u=r(9);function h(){i.call(this),this.useSmartIdealEdgeLengthCalculation=a.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.gravityConstant=a.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=a.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=a.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=a.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.displacementThresholdPerNode=3*a.DEFAULT_EDGE_LENGTH/100,this.coolingFactor=a.DEFAULT_COOLING_FACTOR_INCREMENTAL,this.initialCoolingFactor=a.DEFAULT_COOLING_FACTOR_INCREMENTAL,this.totalDisplacement=0,this.oldTotalDisplacement=0,this.maxIterations=a.MAX_ITERATIONS}o(h,"FDLayout"),h.prototype=Object.create(i.prototype);for(var f in i)h[f]=i[f];h.prototype.initParameters=function(){i.prototype.initParameters.call(this,arguments),this.totalIterations=0,this.notAnimatedIterations=0,this.useFRGridVariant=a.DEFAULT_USE_SMART_REPULSION_RANGE_CALCULATION,this.grid=[]},h.prototype.calcIdealEdgeLengths=function(){for(var d,p,m,g,y,v,x,b=this.getGraphManager().getAllEdges(),w=0;w<b.length;w++)d=b[w],p=d.idealLength,d.isInterGraph&&(g=d.getSource(),y=d.getTarget(),v=d.getSourceInLca().getEstimatedSize(),x=d.getTargetInLca().getEstimatedSize(),this.useSmartIdealEdgeLengthCalculation&&(d.idealLength+=v+x-2*s.SIMPLE_NODE_SIZE),m=d.getLca().getInclusionTreeDepth(),d.idealLength+=p*a.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR*(g.getInclusionTreeDepth()+y.getInclusionTreeDepth()-2*m))},h.prototype.initSpringEmbedder=function(){var d=this.getAllNodes().length;this.incremental?(d>a.ADAPTATION_LOWER_NODE_LIMIT&&(this.coolingFactor=Math.max(this.coolingFactor*a.COOLING_ADAPTATION_FACTOR,this.coolingFactor-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*this.coolingFactor*(1-a.COOLING_ADAPTATION_FACTOR))),this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT_INCREMENTAL):(d>a.ADAPTATION_LOWER_NODE_LIMIT?this.coolingFactor=Math.max(a.COOLING_ADAPTATION_FACTOR,1-(d-a.ADAPTATION_LOWER_NODE_LIMIT)/(a.ADAPTATION_UPPER_NODE_LIMIT-a.ADAPTATION_LOWER_NODE_LIMIT)*(1-a.COOLING_ADAPTATION_FACTOR)):this.coolingFactor=1,this.initialCoolingFactor=this.coolingFactor,this.maxNodeDisplacement=a.MAX_NODE_DISPLACEMENT),this.maxIterations=Math.max(this.getAllNodes().length*5,this.maxIterations),this.displacementThresholdPerNode=3*a.DEFAULT_EDGE_LENGTH/100,this.totalDisplacementThreshold=this.displacementThresholdPerNode*this.getAllNodes().length,this.repulsionRange=this.calcRepulsionRange()},h.prototype.calcSpringForces=function(){for(var d=this.getAllEdges(),p,m=0;m<d.length;m++)p=d[m],this.calcSpringForce(p,p.idealLength)},h.prototype.calcRepulsionForces=function(){var d=arguments.length>0&&arguments[0]!==void 0?arguments[0]:!0,p=arguments.length>1&&arguments[1]!==void 0?arguments[1]:!1,m,g,y,v,x=this.getAllNodes(),b;if(this.useFRGridVariant)for(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&d&&this.updateGrid(),b=new Set,m=0;m<x.length;m++)y=x[m],this.calculateRepulsionForceOfANode(y,b,d,p),b.add(y);else for(m=0;m<x.length;m++)for(y=x[m],g=m+1;g<x.length;g++)v=x[g],y.getOwner()==v.getOwner()&&this.calcRepulsionForce(y,v)},h.prototype.calcGravitationalForces=function(){for(var d,p=this.getAllNodesToApplyGravitation(),m=0;m<p.length;m++)d=p[m],this.calcGravitationalForce(d)},h.prototype.moveNodes=function(){for(var d=this.getAllNodes(),p,m=0;m<d.length;m++)p=d[m],p.move()},h.prototype.calcSpringForce=function(d,p){var m=d.getSource(),g=d.getTarget(),y,v,x,b;if(this.uniformLeafNodeSizes&&m.getChild()==null&&g.getChild()==null)d.updateLengthSimple();else if(d.updateLength(),d.isOverlapingSourceAndTarget)return;y=d.getLength(),y!=0&&(v=d.edgeElasticity*(y-p),x=v*(d.lengthX/y),b=v*(d.lengthY/y),m.springForceX+=x,m.springForceY+=b,g.springForceX-=x,g.springForceY-=b)},h.prototype.calcRepulsionForce=function(d,p){var m=d.getRect(),g=p.getRect(),y=new Array(2),v=new Array(4),x,b,w,C,T,E,A;if(m.intersects(g)){l.calcSeparationAmount(m,g,y,a.DEFAULT_EDGE_LENGTH/2),E=2*y[0],A=2*y[1];var S=d.noOfChildren*p.noOfChildren/(d.noOfChildren+p.noOfChildren);d.repulsionForceX-=S*E,d.repulsionForceY-=S*A,p.repulsionForceX+=S*E,p.repulsionForceY+=S*A}else this.uniformLeafNodeSizes&&d.getChild()==null&&p.getChild()==null?(x=g.getCenterX()-m.getCenterX(),b=g.getCenterY()-m.getCenterY()):(l.getIntersection(m,g,v),x=v[2]-v[0],b=v[3]-v[1]),Math.abs(x)<a.MIN_REPULSION_DIST&&(x=u.sign(x)*a.MIN_REPULSION_DIST),Math.abs(b)<a.MIN_REPULSION_DIST&&(b=u.sign(b)*a.MIN_REPULSION_DIST),w=x*x+b*b,C=Math.sqrt(w),T=(d.nodeRepulsion/2+p.nodeRepulsion/2)*d.noOfChildren*p.noOfChildren/w,E=T*x/C,A=T*b/C,d.repulsionForceX-=E,d.repulsionForceY-=A,p.repulsionForceX+=E,p.repulsionForceY+=A},h.prototype.calcGravitationalForce=function(d){var p,m,g,y,v,x,b,w;p=d.getOwner(),m=(p.getRight()+p.getLeft())/2,g=(p.getTop()+p.getBottom())/2,y=d.getCenterX()-m,v=d.getCenterY()-g,x=Math.abs(y)+d.getWidth()/2,b=Math.abs(v)+d.getHeight()/2,d.getOwner()==this.graphManager.getRoot()?(w=p.getEstimatedSize()*this.gravityRangeFactor,(x>w||b>w)&&(d.gravitationForceX=-this.gravityConstant*y,d.gravitationForceY=-this.gravityConstant*v)):(w=p.getEstimatedSize()*this.compoundGravityRangeFactor,(x>w||b>w)&&(d.gravitationForceX=-this.gravityConstant*y*this.compoundGravityConstant,d.gravitationForceY=-this.gravityConstant*v*this.compoundGravityConstant))},h.prototype.isConverged=function(){var d,p=!1;return this.totalIterations>this.maxIterations/3&&(p=Math.abs(this.totalDisplacement-this.oldTotalDisplacement)<2),d=this.totalDisplacement<this.totalDisplacementThreshold,this.oldTotalDisplacement=this.totalDisplacement,d||p},h.prototype.animate=function(){this.animationDuringLayout&&!this.isSubLayout&&(this.notAnimatedIterations==this.animationPeriod?(this.update(),this.notAnimatedIterations=0):this.notAnimatedIterations++)},h.prototype.calcNoOfChildrenForAllNodes=function(){for(var d,p=this.graphManager.getAllNodes(),m=0;m<p.length;m++)d=p[m],d.noOfChildren=d.getNoOfChildren()},h.prototype.calcGrid=function(d){var p=0,m=0;p=parseInt(Math.ceil((d.getRight()-d.getLeft())/this.repulsionRange)),m=parseInt(Math.ceil((d.getBottom()-d.getTop())/this.repulsionRange));for(var g=new Array(p),y=0;y<p;y++)g[y]=new Array(m);for(var y=0;y<p;y++)for(var v=0;v<m;v++)g[y][v]=new Array;return g},h.prototype.addNodeToGrid=function(d,p,m){var g=0,y=0,v=0,x=0;g=parseInt(Math.floor((d.getRect().x-p)/this.repulsionRange)),y=parseInt(Math.floor((d.getRect().width+d.getRect().x-p)/this.repulsionRange)),v=parseInt(Math.floor((d.getRect().y-m)/this.repulsionRange)),x=parseInt(Math.floor((d.getRect().height+d.getRect().y-m)/this.repulsionRange));for(var b=g;b<=y;b++)for(var w=v;w<=x;w++)this.grid[b][w].push(d),d.setGridCoordinates(g,y,v,x)},h.prototype.updateGrid=function(){var d,p,m=this.getAllNodes();for(this.grid=this.calcGrid(this.graphManager.getRoot()),d=0;d<m.length;d++)p=m[d],this.addNodeToGrid(p,this.graphManager.getRoot().getLeft(),this.graphManager.getRoot().getTop())},h.prototype.calculateRepulsionForceOfANode=function(d,p,m,g){if(this.totalIterations%a.GRID_CALCULATION_CHECK_PERIOD==1&&m||g){var y=new Set;d.surrounding=new Array;for(var v,x=this.grid,b=d.startX-1;b<d.finishX+2;b++)for(var w=d.startY-1;w<d.finishY+2;w++)if(!(b<0||w<0||b>=x.length||w>=x[0].length)){for(var C=0;C<x[b][w].length;C++)if(v=x[b][w][C],!(d.getOwner()!=v.getOwner()||d==v)&&!p.has(v)&&!y.has(v)){var T=Math.abs(d.getCenterX()-v.getCenterX())-(d.getWidth()/2+v.getWidth()/2),E=Math.abs(d.getCenterY()-v.getCenterY())-(d.getHeight()/2+v.getHeight()/2);T<=this.repulsionRange&&E<=this.repulsionRange&&y.add(v)}}d.surrounding=[].concat(n(y))}for(b=0;b<d.surrounding.length;b++)this.calcRepulsionForce(d,d.surrounding[b])},h.prototype.calcRepulsionRange=function(){return 0},t.exports=h},function(t,e,r){"use strict";var n=r(1),i=r(4);function a(l,u,h){n.call(this,l,u,h),this.idealLength=i.DEFAULT_EDGE_LENGTH,this.edgeElasticity=i.DEFAULT_SPRING_STRENGTH}o(a,"FDLayoutEdge"),a.prototype=Object.create(n.prototype);for(var s in n)a[s]=n[s];t.exports=a},function(t,e,r){"use strict";var n=r(3),i=r(4);function a(l,u,h,f){n.call(this,l,u,h,f),this.nodeRepulsion=i.DEFAULT_REPULSION_STRENGTH,this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0,this.startX=0,this.finishX=0,this.startY=0,this.finishY=0,this.surrounding=[]}o(a,"FDLayoutNode"),a.prototype=Object.create(n.prototype);for(var s in n)a[s]=n[s];a.prototype.setGridCoordinates=function(l,u,h,f){this.startX=l,this.finishX=u,this.startY=h,this.finishY=f},t.exports=a},function(t,e,r){"use strict";function n(i,a){this.width=0,this.height=0,i!==null&&a!==null&&(this.height=a,this.width=i)}o(n,"DimensionD"),n.prototype.getWidth=function(){return this.width},n.prototype.setWidth=function(i){this.width=i},n.prototype.getHeight=function(){return this.height},n.prototype.setHeight=function(i){this.height=i},t.exports=n},function(t,e,r){"use strict";var n=r(14);function i(){this.map={},this.keys=[]}o(i,"HashMap"),i.prototype.put=function(a,s){var l=n.createID(a);this.contains(l)||(this.map[l]=s,this.keys.push(a))},i.prototype.contains=function(a){var s=n.createID(a);return this.map[a]!=null},i.prototype.get=function(a){var s=n.createID(a);return this.map[s]},i.prototype.keySet=function(){return this.keys},t.exports=i},function(t,e,r){"use strict";var n=r(14);function i(){this.set={}}o(i,"HashSet"),i.prototype.add=function(a){var s=n.createID(a);this.contains(s)||(this.set[s]=a)},i.prototype.remove=function(a){delete this.set[n.createID(a)]},i.prototype.clear=function(){this.set={}},i.prototype.contains=function(a){return this.set[n.createID(a)]==a},i.prototype.isEmpty=function(){return this.size()===0},i.prototype.size=function(){return Object.keys(this.set).length},i.prototype.addAllTo=function(a){for(var s=Object.keys(this.set),l=s.length,u=0;u<l;u++)a.push(this.set[s[u]])},i.prototype.size=function(){return 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a=Math.min(this.m,this.n);this.s=function(xt){for(var ut=[];xt-- >0;)ut.push(0);return ut}(Math.min(this.m+1,this.n)),this.U=function(xt){var ut=o(function Et(ft){if(ft.length==0)return 0;for(var yt=[],nt=0;nt<ft[0];nt++)yt.push(Et(ft.slice(1)));return yt},"allocate");return ut(xt)}([this.m,a]),this.V=function(xt){var ut=o(function Et(ft){if(ft.length==0)return 0;for(var yt=[],nt=0;nt<ft[0];nt++)yt.push(Et(ft.slice(1)));return yt},"allocate");return ut(xt)}([this.n,this.n]);for(var s=function(xt){for(var ut=[];xt-- >0;)ut.push(0);return ut}(this.n),l=function(xt){for(var ut=[];xt-- >0;)ut.push(0);return ut}(this.m),u=!0,h=!0,f=Math.min(this.m-1,this.n),d=Math.max(0,Math.min(this.n-2,this.m)),p=0;p<Math.max(f,d);p++){if(p<f){this.s[p]=0;for(var m=p;m<this.m;m++)this.s[p]=n.hypot(this.s[p],i[m][p]);if(this.s[p]!==0){i[p][p]<0&&(this.s[p]=-this.s[p]);for(var g=p;g<this.m;g++)i[g][p]/=this.s[p];i[p][p]+=1}this.s[p]=-this.s[p]}for(var y=p+1;y<this.n;y++){if(function(xt,ut){return xt&&ut}(p<f,this.s[p]!==0)){for(var v=0,x=p;x<this.m;x++)v+=i[x][p]*i[x][y];v=-v/i[p][p];for(var b=p;b<this.m;b++)i[b][y]+=v*i[b][p]}s[y]=i[p][y]}if(function(xt,ut){return xt&&ut}(u,p<f))for(var w=p;w<this.m;w++)this.U[w][p]=i[w][p];if(p<d){s[p]=0;for(var C=p+1;C<this.n;C++)s[p]=n.hypot(s[p],s[C]);if(s[p]!==0){s[p+1]<0&&(s[p]=-s[p]);for(var T=p+1;T<this.n;T++)s[T]/=s[p];s[p+1]+=1}if(s[p]=-s[p],function(xt,ut){return xt&&ut}(p+1<this.m,s[p]!==0)){for(var E=p+1;E<this.m;E++)l[E]=0;for(var A=p+1;A<this.n;A++)for(var S=p+1;S<this.m;S++)l[S]+=s[A]*i[S][A];for(var _=p+1;_<this.n;_++)for(var I=-s[_]/s[p+1],D=p+1;D<this.m;D++)i[D][_]+=I*l[D]}if(h)for(var k=p+1;k<this.n;k++)this.V[k][p]=s[k]}}var L=Math.min(this.n,this.m+1);if(f<this.n&&(this.s[f]=i[f][f]),this.m<L&&(this.s[L-1]=0),d+1<L&&(s[d]=i[d][L-1]),s[L-1]=0,u){for(var R=f;R<a;R++){for(var O=0;O<this.m;O++)this.U[O][R]=0;this.U[R][R]=1}for(var M=f-1;M>=0;M--)if(this.s[M]!==0){for(var B=M+1;B<a;B++){for(var F=0,P=M;P<this.m;P++)F+=this.U[P][M]*this.U[P][B];F=-F/this.U[M][M];for(var z=M;z<this.m;z++)this.U[z][B]+=F*this.U[z][M]}for(var $=M;$<this.m;$++)this.U[$][M]=-this.U[$][M];this.U[M][M]=1+this.U[M][M];for(var H=0;H<M-1;H++)this.U[H][M]=0}else{for(var Q=0;Q<this.m;Q++)this.U[Q][M]=0;this.U[M][M]=1}}if(h)for(var j=this.n-1;j>=0;j--){if(function(xt,ut){return xt&&ut}(j<d,s[j]!==0))for(var ie=j+1;ie<a;ie++){for(var ne=0,le=j+1;le<this.n;le++)ne+=this.V[le][j]*this.V[le][ie];ne=-ne/this.V[j+1][j];for(var he=j+1;he<this.n;he++)this.V[he][ie]+=ne*this.V[he][j]}for(var K=0;K<this.n;K++)this.V[K][j]=0;this.V[j][j]=1}for(var X=L-1,te=0,J=Math.pow(2,-52),se=Math.pow(2,-966);L>0;){var ue=void 0,Z=void 0;for(ue=L-2;ue>=-1&&ue!==-1;ue--)if(Math.abs(s[ue])<=se+J*(Math.abs(this.s[ue])+Math.abs(this.s[ue+1]))){s[ue]=0;break}if(ue===L-2)Z=4;else{var Se=void 0;for(Se=L-1;Se>=ue&&Se!==ue;Se--){var ce=(Se!==L?Math.abs(s[Se]):0)+(Se!==ue+1?Math.abs(s[Se-1]):0);if(Math.abs(this.s[Se])<=se+J*ce){this.s[Se]=0;break}}Se===ue?Z=3:Se===L-1?Z=1:(Z=2,ue=Se)}switch(ue++,Z){case 1:{var ae=s[L-2];s[L-2]=0;for(var Oe=L-2;Oe>=ue;Oe--){var ge=n.hypot(this.s[Oe],ae),ze=this.s[Oe]/ge,He=ae/ge;if(this.s[Oe]=ge,Oe!==ue&&(ae=-He*s[Oe-1],s[Oe-1]=ze*s[Oe-1]),h)for(var $e=0;$e<this.n;$e++)ge=ze*this.V[$e][Oe]+He*this.V[$e][L-1],this.V[$e][L-1]=-He*this.V[$e][Oe]+ze*this.V[$e][L-1],this.V[$e][Oe]=ge}}break;case 2:{var Re=s[ue-1];s[ue-1]=0;for(var Ie=ue;Ie<L;Ie++){var be=n.hypot(this.s[Ie],Re),W=this.s[Ie]/be,de=Re/be;if(this.s[Ie]=be,Re=-de*s[Ie],s[Ie]=W*s[Ie],u)for(var re=0;re<this.m;re++)be=W*this.U[re][Ie]+de*this.U[re][ue-1],this.U[re][ue-1]=-de*this.U[re][Ie]+W*this.U[re][ue-1],this.U[re][Ie]=be}}break;case 3:{var oe=Math.max(Math.max(Math.max(Math.max(Math.abs(this.s[L-1]),Math.abs(this.s[L-2])),Math.abs(s[L-2])),Math.abs(this.s[ue])),Math.abs(s[ue])),V=this.s[L-1]/oe,xe=this.s[L-2]/oe,q=s[L-2]/oe,pe=this.s[ue]/oe,ve=s[ue]/oe,Pe=((xe+V)*(xe-V)+q*q)/2,_e=V*q*(V*q),we=0;(function(xt,ut){return xt||ut})(Pe!==0,_e!==0)&&(we=Math.sqrt(Pe*Pe+_e),Pe<0&&(we=-we),we=_e/(Pe+we));for(var Ve=(pe+V)*(pe-V)+we,De=pe*ve,qe=ue;qe<L-1;qe++){var at=n.hypot(Ve,De),Rt=Ve/at,st=De/at;if(qe!==ue&&(s[qe-1]=at),Ve=Rt*this.s[qe]+st*s[qe],s[qe]=Rt*s[qe]-st*this.s[qe],De=st*this.s[qe+1],this.s[qe+1]=Rt*this.s[qe+1],h)for(var Ue=0;Ue<this.n;Ue++)at=Rt*this.V[Ue][qe]+st*this.V[Ue][qe+1],this.V[Ue][qe+1]=-st*this.V[Ue][qe]+Rt*this.V[Ue][qe+1],this.V[Ue][qe]=at;if(at=n.hypot(Ve,De),Rt=Ve/at,st=De/at,this.s[qe]=at,Ve=Rt*s[qe]+st*this.s[qe+1],this.s[qe+1]=-st*s[qe]+Rt*this.s[qe+1],De=st*s[qe+1],s[qe+1]=Rt*s[qe+1],u&&qe<this.m-1)for(var ct=0;ct<this.m;ct++)at=Rt*this.U[ct][qe]+st*this.U[ct][qe+1],this.U[ct][qe+1]=-st*this.U[ct][qe]+Rt*this.U[ct][qe+1],this.U[ct][qe]=at}s[L-2]=Ve,te=te+1}break;case 4:{if(this.s[ue]<=0&&(this.s[ue]=this.s[ue]<0?-this.s[ue]:0,h))for(var We=0;We<=X;We++)this.V[We][ue]=-this.V[We][ue];for(;ue<X&&!(this.s[ue]>=this.s[ue+1]);){var ot=this.s[ue];if(this.s[ue]=this.s[ue+1],this.s[ue+1]=ot,h&&ue<this.n-1)for(var Yt=0;Yt<this.n;Yt++)ot=this.V[Yt][ue+1],this.V[Yt][ue+1]=this.V[Yt][ue],this.V[Yt][ue]=ot;if(u&&ue<this.m-1)for(var bt=0;bt<this.m;bt++)ot=this.U[bt][ue+1],this.U[bt][ue+1]=this.U[bt][ue],this.U[bt][ue]=ot;ue++}te=0,L--}break}}var Mt={U:this.U,V:this.V,S:this.s};return Mt},n.hypot=function(i,a){var s=void 0;return Math.abs(i)>Math.abs(a)?(s=a/i,s=Math.abs(i)*Math.sqrt(1+s*s)):a!=0?(s=i/a,s=Math.abs(a)*Math.sqrt(1+s*s)):s=0,s},t.exports=n},function(t,e,r){"use strict";var n=function(){function s(l,u){for(var h=0;h<u.length;h++){var f=u[h];f.enumerable=f.enumerable||!1,f.configurable=!0,"value"in f&&(f.writable=!0),Object.defineProperty(l,f.key,f)}}return o(s,"defineProperties"),function(l,u,h){return u&&s(l.prototype,u),h&&s(l,h),l}}();function i(s,l){if(!(s instanceof l))throw new TypeError("Cannot call a class as a function")}o(i,"_classCallCheck");var a=function(){function s(l,u){var h=arguments.length>2&&arguments[2]!==void 0?arguments[2]:1,f=arguments.length>3&&arguments[3]!==void 0?arguments[3]:-1,d=arguments.length>4&&arguments[4]!==void 0?arguments[4]:-1;i(this,s),this.sequence1=l,this.sequence2=u,this.match_score=h,this.mismatch_penalty=f,this.gap_penalty=d,this.iMax=l.length+1,this.jMax=u.length+1,this.grid=new Array(this.iMax);for(var p=0;p<this.iMax;p++){this.grid[p]=new Array(this.jMax);for(var m=0;m<this.jMax;m++)this.grid[p][m]=0}this.tracebackGrid=new Array(this.iMax);for(var g=0;g<this.iMax;g++){this.tracebackGrid[g]=new Array(this.jMax);for(var y=0;y<this.jMax;y++)this.tracebackGrid[g][y]=[null,null,null]}this.alignments=[],this.score=-1,this.computeGrids()}return o(s,"NeedlemanWunsch"),n(s,[{key:"getScore",value:o(function(){return this.score},"getScore")},{key:"getAlignments",value:o(function(){return this.alignments},"getAlignments")},{key:"computeGrids",value:o(function(){for(var u=1;u<this.jMax;u++)this.grid[0][u]=this.grid[0][u-1]+this.gap_penalty,this.tracebackGrid[0][u]=[!1,!1,!0];for(var h=1;h<this.iMax;h++)this.grid[h][0]=this.grid[h-1][0]+this.gap_penalty,this.tracebackGrid[h][0]=[!1,!0,!1];for(var f=1;f<this.iMax;f++)for(var d=1;d<this.jMax;d++){var p=void 0;this.sequence1[f-1]===this.sequence2[d-1]?p=this.grid[f-1][d-1]+this.match_score:p=this.grid[f-1][d-1]+this.mismatch_penalty;var m=this.grid[f-1][d]+this.gap_penalty,g=this.grid[f][d-1]+this.gap_penalty,y=[p,m,g],v=this.arrayAllMaxIndexes(y);this.grid[f][d]=y[v[0]],this.tracebackGrid[f][d]=[v.includes(0),v.includes(1),v.includes(2)]}this.score=this.grid[this.iMax-1][this.jMax-1]},"computeGrids")},{key:"alignmentTraceback",value:o(function(){var u=[];for(u.push({pos:[this.sequence1.length,this.sequence2.length],seq1:"",seq2:""});u[0];){var h=u[0],f=this.tracebackGrid[h.pos[0]][h.pos[1]];f[0]&&u.push({pos:[h.pos[0]-1,h.pos[1]-1],seq1:this.sequence1[h.pos[0]-1]+h.seq1,seq2:this.sequence2[h.pos[1]-1]+h.seq2}),f[1]&&u.push({pos:[h.pos[0]-1,h.pos[1]],seq1:this.sequence1[h.pos[0]-1]+h.seq1,seq2:"-"+h.seq2}),f[2]&&u.push({pos:[h.pos[0],h.pos[1]-1],seq1:"-"+h.seq1,seq2:this.sequence2[h.pos[1]-1]+h.seq2}),h.pos[0]===0&&h.pos[1]===0&&this.alignments.push({sequence1:h.seq1,sequence2:h.seq2}),u.shift()}return this.alignments},"alignmentTraceback")},{key:"getAllIndexes",value:o(function(u,h){for(var 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l=this.listeners.length;l>=0;l--){var u=this.listeners[l];u.event===a&&u.callback===s&&this.listeners.splice(l,1)}},i.emit=function(a,s){for(var l=0;l<this.listeners.length;l++){var u=this.listeners[l];a===u.event&&u.callback(s)}},t.exports=n}])})});var mF=Mi((k4,pF)=>{"use strict";o(function(e,r){typeof k4=="object"&&typeof pF=="object"?pF.exports=r(dF()):typeof define=="function"&&define.amd?define(["layout-base"],r):typeof k4=="object"?k4.coseBase=r(dF()):e.coseBase=r(e.layoutBase)},"webpackUniversalModuleDefinition")(k4,function(t){return(()=>{"use strict";var e={45:(a,s,l)=>{var u={};u.layoutBase=l(551),u.CoSEConstants=l(806),u.CoSEEdge=l(767),u.CoSEGraph=l(880),u.CoSEGraphManager=l(578),u.CoSELayout=l(765),u.CoSENode=l(991),u.ConstraintHandler=l(902),a.exports=u},806:(a,s,l)=>{var u=l(551).FDLayoutConstants;function h(){}o(h,"CoSEConstants");for(var f in u)h[f]=u[f];h.DEFAULT_USE_MULTI_LEVEL_SCALING=!1,h.DEFAULT_RADIAL_SEPARATION=u.DEFAULT_EDGE_LENGTH,h.DEFAULT_COMPONENT_SEPERATION=60,h.TILE=!0,h.TILING_PADDING_VERTICAL=10,h.TILING_PADDING_HORIZONTAL=10,h.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,h.ENFORCE_CONSTRAINTS=!0,h.APPLY_LAYOUT=!0,h.RELAX_MOVEMENT_ON_CONSTRAINTS=!0,h.TREE_REDUCTION_ON_INCREMENTAL=!0,h.PURE_INCREMENTAL=h.DEFAULT_INCREMENTAL,a.exports=h},767:(a,s,l)=>{var u=l(551).FDLayoutEdge;function h(d,p,m){u.call(this,d,p,m)}o(h,"CoSEEdge"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},880:(a,s,l)=>{var u=l(551).LGraph;function h(d,p,m){u.call(this,d,p,m)}o(h,"CoSEGraph"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},578:(a,s,l)=>{var u=l(551).LGraphManager;function h(d){u.call(this,d)}o(h,"CoSEGraphManager"),h.prototype=Object.create(u.prototype);for(var f in u)h[f]=u[f];a.exports=h},765:(a,s,l)=>{var u=l(551).FDLayout,h=l(578),f=l(880),d=l(991),p=l(767),m=l(806),g=l(902),y=l(551).FDLayoutConstants,v=l(551).LayoutConstants,x=l(551).Point,b=l(551).PointD,w=l(551).DimensionD,C=l(551).Layout,T=l(551).Integer,E=l(551).IGeometry,A=l(551).LGraph,S=l(551).Transform,_=l(551).LinkedList;function I(){u.call(this),this.toBeTiled={},this.constraints={}}o(I,"CoSELayout"),I.prototype=Object.create(u.prototype);for(var D in u)I[D]=u[D];I.prototype.newGraphManager=function(){var k=new h(this);return this.graphManager=k,k},I.prototype.newGraph=function(k){return new f(null,this.graphManager,k)},I.prototype.newNode=function(k){return new d(this.graphManager,k)},I.prototype.newEdge=function(k){return new p(null,null,k)},I.prototype.initParameters=function(){u.prototype.initParameters.call(this,arguments),this.isSubLayout||(m.DEFAULT_EDGE_LENGTH<10?this.idealEdgeLength=10:this.idealEdgeLength=m.DEFAULT_EDGE_LENGTH,this.useSmartIdealEdgeLengthCalculation=m.DEFAULT_USE_SMART_IDEAL_EDGE_LENGTH_CALCULATION,this.gravityConstant=y.DEFAULT_GRAVITY_STRENGTH,this.compoundGravityConstant=y.DEFAULT_COMPOUND_GRAVITY_STRENGTH,this.gravityRangeFactor=y.DEFAULT_GRAVITY_RANGE_FACTOR,this.compoundGravityRangeFactor=y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR,this.prunedNodesAll=[],this.growTreeIterations=0,this.afterGrowthIterations=0,this.isTreeGrowing=!1,this.isGrowthFinished=!1)},I.prototype.initSpringEmbedder=function(){u.prototype.initSpringEmbedder.call(this),this.coolingCycle=0,this.maxCoolingCycle=this.maxIterations/y.CONVERGENCE_CHECK_PERIOD,this.finalTemperature=.04,this.coolingAdjuster=1},I.prototype.layout=function(){var k=v.DEFAULT_CREATE_BENDS_AS_NEEDED;return k&&(this.createBendpoints(),this.graphManager.resetAllEdges()),this.level=0,this.classicLayout()},I.prototype.classicLayout=function(){if(this.nodesWithGravity=this.calculateNodesToApplyGravitationTo(),this.graphManager.setAllNodesToApplyGravitation(this.nodesWithGravity),this.calcNoOfChildrenForAllNodes(),this.graphManager.calcLowestCommonAncestors(),this.graphManager.calcInclusionTreeDepths(),this.graphManager.getRoot().calcEstimatedSize(),this.calcIdealEdgeLengths(),this.incremental){if(m.TREE_REDUCTION_ON_INCREMENTAL){this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var L=new Set(this.getAllNodes()),R=this.nodesWithGravity.filter(function(B){return L.has(B)});this.graphManager.setAllNodesToApplyGravitation(R)}}else{var k=this.getFlatForest();if(k.length>0)this.positionNodesRadially(k);else{this.reduceTrees(),this.graphManager.resetAllNodesToApplyGravitation();var L=new Set(this.getAllNodes()),R=this.nodesWithGravity.filter(function(O){return L.has(O)});this.graphManager.setAllNodesToApplyGravitation(R),this.positionNodesRandomly()}}return Object.keys(this.constraints).length>0&&(g.handleConstraints(this),this.initConstraintVariables()),this.initSpringEmbedder(),m.APPLY_LAYOUT&&this.runSpringEmbedder(),!0},I.prototype.tick=function(){if(this.totalIterations++,this.totalIterations===this.maxIterations&&!this.isTreeGrowing&&!this.isGrowthFinished)if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;if(this.totalIterations%y.CONVERGENCE_CHECK_PERIOD==0&&!this.isTreeGrowing&&!this.isGrowthFinished){if(this.isConverged())if(this.prunedNodesAll.length>0)this.isTreeGrowing=!0;else return!0;this.coolingCycle++,this.layoutQuality==0?this.coolingAdjuster=this.coolingCycle:this.layoutQuality==1&&(this.coolingAdjuster=this.coolingCycle/3),this.coolingFactor=Math.max(this.initialCoolingFactor-Math.pow(this.coolingCycle,Math.log(100*(this.initialCoolingFactor-this.finalTemperature))/Math.log(this.maxCoolingCycle))/100*this.coolingAdjuster,this.finalTemperature),this.animationPeriod=Math.ceil(this.initialAnimationPeriod*Math.sqrt(this.coolingFactor))}if(this.isTreeGrowing){if(this.growTreeIterations%10==0)if(this.prunedNodesAll.length>0){this.graphManager.updateBounds(),this.updateGrid(),this.growTree(this.prunedNodesAll),this.graphManager.resetAllNodesToApplyGravitation();var k=new Set(this.getAllNodes()),L=this.nodesWithGravity.filter(function(M){return k.has(M)});this.graphManager.setAllNodesToApplyGravitation(L),this.graphManager.updateBounds(),this.updateGrid(),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2:this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL}else this.isTreeGrowing=!1,this.isGrowthFinished=!0;this.growTreeIterations++}if(this.isGrowthFinished){if(this.isConverged())return!0;this.afterGrowthIterations%10==0&&(this.graphManager.updateBounds(),this.updateGrid()),m.PURE_INCREMENTAL?this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL/2*((100-this.afterGrowthIterations)/100):this.coolingFactor=y.DEFAULT_COOLING_FACTOR_INCREMENTAL*((100-this.afterGrowthIterations)/100),this.afterGrowthIterations++}var R=!this.isTreeGrowing&&!this.isGrowthFinished,O=this.growTreeIterations%10==1&&this.isTreeGrowing||this.afterGrowthIterations%10==1&&this.isGrowthFinished;return this.totalDisplacement=0,this.graphManager.updateBounds(),this.calcSpringForces(),this.calcRepulsionForces(R,O),this.calcGravitationalForces(),this.moveNodes(),this.animate(),!1},I.prototype.getPositionsData=function(){for(var k=this.graphManager.getAllNodes(),L={},R=0;R<k.length;R++){var O=k[R].rect,M=k[R].id;L[M]={id:M,x:O.getCenterX(),y:O.getCenterY(),w:O.width,h:O.height}}return L},I.prototype.runSpringEmbedder=function(){this.initialAnimationPeriod=25,this.animationPeriod=this.initialAnimationPeriod;var k=!1;if(y.ANIMATE==="during")this.emit("layoutstarted");else{for(;!k;)k=this.tick();this.graphManager.updateBounds()}},I.prototype.moveNodes=function(){for(var k=this.getAllNodes(),L,R=0;R<k.length;R++)L=k[R],L.calculateDisplacement();Object.keys(this.constraints).length>0&&this.updateDisplacements();for(var R=0;R<k.length;R++)L=k[R],L.move()},I.prototype.initConstraintVariables=function(){var k=this;this.idToNodeMap=new Map,this.fixedNodeSet=new Set;for(var L=this.graphManager.getAllNodes(),R=0;R<L.length;R++){var O=L[R];this.idToNodeMap.set(O.id,O)}var M=o(function le(he){for(var K=he.getChild().getNodes(),X,te=0,J=0;J<K.length;J++)X=K[J],X.getChild()==null?k.fixedNodeSet.has(X.id)&&(te+=100):te+=le(X);return te},"calculateCompoundWeight");if(this.constraints.fixedNodeConstraint){this.constraints.fixedNodeConstraint.forEach(function(K){k.fixedNodeSet.add(K.nodeId)});for(var L=this.graphManager.getAllNodes(),O,R=0;R<L.length;R++)if(O=L[R],O.getChild()!=null){var B=M(O);B>0&&(O.fixedNodeWeight=B)}}if(this.constraints.relativePlacementConstraint){var F=new Map,P=new Map;if(this.dummyToNodeForVerticalAlignment=new Map,this.dummyToNodeForHorizontalAlignment=new Map,this.fixedNodesOnHorizontal=new Set,this.fixedNodesOnVertical=new Set,this.fixedNodeSet.forEach(function(le){k.fixedNodesOnHorizontal.add(le),k.fixedNodesOnVertical.add(le)}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var z=this.constraints.alignmentConstraint.vertical,R=0;R<z.length;R++)this.dummyToNodeForVerticalAlignment.set("dummy"+R,[]),z[R].forEach(function(he){F.set(he,"dummy"+R),k.dummyToNodeForVerticalAlignment.get("dummy"+R).push(he),k.fixedNodeSet.has(he)&&k.fixedNodesOnHorizontal.add("dummy"+R)});if(this.constraints.alignmentConstraint.horizontal)for(var $=this.constraints.alignmentConstraint.horizontal,R=0;R<$.length;R++)this.dummyToNodeForHorizontalAlignment.set("dummy"+R,[]),$[R].forEach(function(he){P.set(he,"dummy"+R),k.dummyToNodeForHorizontalAlignment.get("dummy"+R).push(he),k.fixedNodeSet.has(he)&&k.fixedNodesOnVertical.add("dummy"+R)})}if(m.RELAX_MOVEMENT_ON_CONSTRAINTS)this.shuffle=function(le){var he,K,X;for(X=le.length-1;X>=2*le.length/3;X--)he=Math.floor(Math.random()*(X+1)),K=le[X],le[X]=le[he],le[he]=K;return le},this.nodesInRelativeHorizontal=[],this.nodesInRelativeVertical=[],this.nodeToRelativeConstraintMapHorizontal=new Map,this.nodeToRelativeConstraintMapVertical=new Map,this.nodeToTempPositionMapHorizontal=new Map,this.nodeToTempPositionMapVertical=new Map,this.constraints.relativePlacementConstraint.forEach(function(le){if(le.left){var he=F.has(le.left)?F.get(le.left):le.left,K=F.has(le.right)?F.get(le.right):le.right;k.nodesInRelativeHorizontal.includes(he)||(k.nodesInRelativeHorizontal.push(he),k.nodeToRelativeConstraintMapHorizontal.set(he,[]),k.dummyToNodeForVerticalAlignment.has(he)?k.nodeToTempPositionMapHorizontal.set(he,k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(he)[0]).getCenterX()):k.nodeToTempPositionMapHorizontal.set(he,k.idToNodeMap.get(he).getCenterX())),k.nodesInRelativeHorizontal.includes(K)||(k.nodesInRelativeHorizontal.push(K),k.nodeToRelativeConstraintMapHorizontal.set(K,[]),k.dummyToNodeForVerticalAlignment.has(K)?k.nodeToTempPositionMapHorizontal.set(K,k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(K)[0]).getCenterX()):k.nodeToTempPositionMapHorizontal.set(K,k.idToNodeMap.get(K).getCenterX())),k.nodeToRelativeConstraintMapHorizontal.get(he).push({right:K,gap:le.gap}),k.nodeToRelativeConstraintMapHorizontal.get(K).push({left:he,gap:le.gap})}else{var X=P.has(le.top)?P.get(le.top):le.top,te=P.has(le.bottom)?P.get(le.bottom):le.bottom;k.nodesInRelativeVertical.includes(X)||(k.nodesInRelativeVertical.push(X),k.nodeToRelativeConstraintMapVertical.set(X,[]),k.dummyToNodeForHorizontalAlignment.has(X)?k.nodeToTempPositionMapVertical.set(X,k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(X)[0]).getCenterY()):k.nodeToTempPositionMapVertical.set(X,k.idToNodeMap.get(X).getCenterY())),k.nodesInRelativeVertical.includes(te)||(k.nodesInRelativeVertical.push(te),k.nodeToRelativeConstraintMapVertical.set(te,[]),k.dummyToNodeForHorizontalAlignment.has(te)?k.nodeToTempPositionMapVertical.set(te,k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(te)[0]).getCenterY()):k.nodeToTempPositionMapVertical.set(te,k.idToNodeMap.get(te).getCenterY())),k.nodeToRelativeConstraintMapVertical.get(X).push({bottom:te,gap:le.gap}),k.nodeToRelativeConstraintMapVertical.get(te).push({top:X,gap:le.gap})}});else{var H=new Map,Q=new Map;this.constraints.relativePlacementConstraint.forEach(function(le){if(le.left){var he=F.has(le.left)?F.get(le.left):le.left,K=F.has(le.right)?F.get(le.right):le.right;H.has(he)?H.get(he).push(K):H.set(he,[K]),H.has(K)?H.get(K).push(he):H.set(K,[he])}else{var X=P.has(le.top)?P.get(le.top):le.top,te=P.has(le.bottom)?P.get(le.bottom):le.bottom;Q.has(X)?Q.get(X).push(te):Q.set(X,[te]),Q.has(te)?Q.get(te).push(X):Q.set(te,[X])}});var j=o(function(he,K){var X=[],te=[],J=new _,se=new Set,ue=0;return he.forEach(function(Z,Se){if(!se.has(Se)){X[ue]=[],te[ue]=!1;var ce=Se;for(J.push(ce),se.add(ce),X[ue].push(ce);J.length!=0;){ce=J.shift(),K.has(ce)&&(te[ue]=!0);var ae=he.get(ce);ae.forEach(function(Oe){se.has(Oe)||(J.push(Oe),se.add(Oe),X[ue].push(Oe))})}ue++}}),{components:X,isFixed:te}},"constructComponents"),ie=j(H,k.fixedNodesOnHorizontal);this.componentsOnHorizontal=ie.components,this.fixedComponentsOnHorizontal=ie.isFixed;var ne=j(Q,k.fixedNodesOnVertical);this.componentsOnVertical=ne.components,this.fixedComponentsOnVertical=ne.isFixed}}},I.prototype.updateDisplacements=function(){var k=this;if(this.constraints.fixedNodeConstraint&&this.constraints.fixedNodeConstraint.forEach(function(ne){var le=k.idToNodeMap.get(ne.nodeId);le.displacementX=0,le.displacementY=0}),this.constraints.alignmentConstraint){if(this.constraints.alignmentConstraint.vertical)for(var L=this.constraints.alignmentConstraint.vertical,R=0;R<L.length;R++){for(var O=0,M=0;M<L[R].length;M++){if(this.fixedNodeSet.has(L[R][M])){O=0;break}O+=this.idToNodeMap.get(L[R][M]).displacementX}for(var B=O/L[R].length,M=0;M<L[R].length;M++)this.idToNodeMap.get(L[R][M]).displacementX=B}if(this.constraints.alignmentConstraint.horizontal)for(var F=this.constraints.alignmentConstraint.horizontal,R=0;R<F.length;R++){for(var P=0,M=0;M<F[R].length;M++){if(this.fixedNodeSet.has(F[R][M])){P=0;break}P+=this.idToNodeMap.get(F[R][M]).displacementY}for(var z=P/F[R].length,M=0;M<F[R].length;M++)this.idToNodeMap.get(F[R][M]).displacementY=z}}if(this.constraints.relativePlacementConstraint)if(m.RELAX_MOVEMENT_ON_CONSTRAINTS)this.totalIterations%10==0&&(this.shuffle(this.nodesInRelativeHorizontal),this.shuffle(this.nodesInRelativeVertical)),this.nodesInRelativeHorizontal.forEach(function(ne){if(!k.fixedNodesOnHorizontal.has(ne)){var le=0;k.dummyToNodeForVerticalAlignment.has(ne)?le=k.idToNodeMap.get(k.dummyToNodeForVerticalAlignment.get(ne)[0]).displacementX:le=k.idToNodeMap.get(ne).displacementX,k.nodeToRelativeConstraintMapHorizontal.get(ne).forEach(function(he){if(he.right){var K=k.nodeToTempPositionMapHorizontal.get(he.right)-k.nodeToTempPositionMapHorizontal.get(ne)-le;K<he.gap&&(le-=he.gap-K)}else{var K=k.nodeToTempPositionMapHorizontal.get(ne)-k.nodeToTempPositionMapHorizontal.get(he.left)+le;K<he.gap&&(le+=he.gap-K)}}),k.nodeToTempPositionMapHorizontal.set(ne,k.nodeToTempPositionMapHorizontal.get(ne)+le),k.dummyToNodeForVerticalAlignment.has(ne)?k.dummyToNodeForVerticalAlignment.get(ne).forEach(function(he){k.idToNodeMap.get(he).displacementX=le}):k.idToNodeMap.get(ne).displacementX=le}}),this.nodesInRelativeVertical.forEach(function(ne){if(!k.fixedNodesOnHorizontal.has(ne)){var le=0;k.dummyToNodeForHorizontalAlignment.has(ne)?le=k.idToNodeMap.get(k.dummyToNodeForHorizontalAlignment.get(ne)[0]).displacementY:le=k.idToNodeMap.get(ne).displacementY,k.nodeToRelativeConstraintMapVertical.get(ne).forEach(function(he){if(he.bottom){var K=k.nodeToTempPositionMapVertical.get(he.bottom)-k.nodeToTempPositionMapVertical.get(ne)-le;K<he.gap&&(le-=he.gap-K)}else{var K=k.nodeToTempPositionMapVertical.get(ne)-k.nodeToTempPositionMapVertical.get(he.top)+le;K<he.gap&&(le+=he.gap-K)}}),k.nodeToTempPositionMapVertical.set(ne,k.nodeToTempPositionMapVertical.get(ne)+le),k.dummyToNodeForHorizontalAlignment.has(ne)?k.dummyToNodeForHorizontalAlignment.get(ne).forEach(function(he){k.idToNodeMap.get(he).displacementY=le}):k.idToNodeMap.get(ne).displacementY=le}});else{for(var R=0;R<this.componentsOnHorizontal.length;R++){var $=this.componentsOnHorizontal[R];if(this.fixedComponentsOnHorizontal[R])for(var M=0;M<$.length;M++)this.dummyToNodeForVerticalAlignment.has($[M])?this.dummyToNodeForVerticalAlignment.get($[M]).forEach(function(he){k.idToNodeMap.get(he).displacementX=0}):this.idToNodeMap.get($[M]).displacementX=0;else{for(var H=0,Q=0,M=0;M<$.length;M++)if(this.dummyToNodeForVerticalAlignment.has($[M])){var j=this.dummyToNodeForVerticalAlignment.get($[M]);H+=j.length*this.idToNodeMap.get(j[0]).displacementX,Q+=j.length}else H+=this.idToNodeMap.get($[M]).displacementX,Q++;for(var ie=H/Q,M=0;M<$.length;M++)this.dummyToNodeForVerticalAlignment.has($[M])?this.dummyToNodeForVerticalAlignment.get($[M]).forEach(function(he){k.idToNodeMap.get(he).displacementX=ie}):this.idToNodeMap.get($[M]).displacementX=ie}}for(var R=0;R<this.componentsOnVertical.length;R++){var $=this.componentsOnVertical[R];if(this.fixedComponentsOnVertical[R])for(var M=0;M<$.length;M++)this.dummyToNodeForHorizontalAlignment.has($[M])?this.dummyToNodeForHorizontalAlignment.get($[M]).forEach(function(K){k.idToNodeMap.get(K).displacementY=0}):this.idToNodeMap.get($[M]).displacementY=0;else{for(var H=0,Q=0,M=0;M<$.length;M++)if(this.dummyToNodeForHorizontalAlignment.has($[M])){var j=this.dummyToNodeForHorizontalAlignment.get($[M]);H+=j.length*this.idToNodeMap.get(j[0]).displacementY,Q+=j.length}else H+=this.idToNodeMap.get($[M]).displacementY,Q++;for(var ie=H/Q,M=0;M<$.length;M++)this.dummyToNodeForHorizontalAlignment.has($[M])?this.dummyToNodeForHorizontalAlignment.get($[M]).forEach(function(J){k.idToNodeMap.get(J).displacementY=ie}):this.idToNodeMap.get($[M]).displacementY=ie}}}},I.prototype.calculateNodesToApplyGravitationTo=function(){var k=[],L,R=this.graphManager.getGraphs(),O=R.length,M;for(M=0;M<O;M++)L=R[M],L.updateConnected(),L.isConnected||(k=k.concat(L.getNodes()));return k},I.prototype.createBendpoints=function(){var k=[];k=k.concat(this.graphManager.getAllEdges());var L=new Set,R;for(R=0;R<k.length;R++){var O=k[R];if(!L.has(O)){var M=O.getSource(),B=O.getTarget();if(M==B)O.getBendpoints().push(new b),O.getBendpoints().push(new b),this.createDummyNodesForBendpoints(O),L.add(O);else{var F=[];if(F=F.concat(M.getEdgeListToNode(B)),F=F.concat(B.getEdgeListToNode(M)),!L.has(F[0])){if(F.length>1){var P;for(P=0;P<F.length;P++){var z=F[P];z.getBendpoints().push(new b),this.createDummyNodesForBendpoints(z)}}F.forEach(function($){L.add($)})}}}if(L.size==k.length)break}},I.prototype.positionNodesRadially=function(k){for(var L=new x(0,0),R=Math.ceil(Math.sqrt(k.length)),O=0,M=0,B=0,F=new b(0,0),P=0;P<k.length;P++){P%R==0&&(B=0,M=O,P!=0&&(M+=m.DEFAULT_COMPONENT_SEPERATION),O=0);var z=k[P],$=C.findCenterOfTree(z);L.x=B,L.y=M,F=I.radialLayout(z,$,L),F.y>O&&(O=Math.floor(F.y)),B=Math.floor(F.x+m.DEFAULT_COMPONENT_SEPERATION)}this.transform(new b(v.WORLD_CENTER_X-F.x/2,v.WORLD_CENTER_Y-F.y/2))},I.radialLayout=function(k,L,R){var O=Math.max(this.maxDiagonalInTree(k),m.DEFAULT_RADIAL_SEPARATION);I.branchRadialLayout(L,null,0,359,0,O);var M=A.calculateBounds(k),B=new S;B.setDeviceOrgX(M.getMinX()),B.setDeviceOrgY(M.getMinY()),B.setWorldOrgX(R.x),B.setWorldOrgY(R.y);for(var F=0;F<k.length;F++){var P=k[F];P.transform(B)}var z=new b(M.getMaxX(),M.getMaxY());return B.inverseTransformPoint(z)},I.branchRadialLayout=function(k,L,R,O,M,B){var F=(O-R+1)/2;F<0&&(F+=180);var P=(F+R)%360,z=P*E.TWO_PI/360,$=Math.cos(z),H=M*Math.cos(z),Q=M*Math.sin(z);k.setCenter(H,Q);var j=[];j=j.concat(k.getEdges());var ie=j.length;L!=null&&ie--;for(var ne=0,le=j.length,he,K=k.getEdgesBetween(L);K.length>1;){var X=K[0];K.splice(0,1);var te=j.indexOf(X);te>=0&&j.splice(te,1),le--,ie--}L!=null?he=(j.indexOf(K[0])+1)%le:he=0;for(var J=Math.abs(O-R)/ie,se=he;ne!=ie;se=++se%le){var ue=j[se].getOtherEnd(k);if(ue!=L){var Z=(R+ne*J)%360,Se=(Z+J)%360;I.branchRadialLayout(ue,k,Z,Se,M+B,B),ne++}}},I.maxDiagonalInTree=function(k){for(var L=T.MIN_VALUE,R=0;R<k.length;R++){var O=k[R],M=O.getDiagonal();M>L&&(L=M)}return L},I.prototype.calcRepulsionRange=function(){return 2*(this.level+1)*this.idealEdgeLength},I.prototype.groupZeroDegreeMembers=function(){var k=this,L={};this.memberGroups={},this.idToDummyNode={};for(var R=[],O=this.graphManager.getAllNodes(),M=0;M<O.length;M++){var B=O[M],F=B.getParent();this.getNodeDegreeWithChildren(B)===0&&(F.id==null||!this.getToBeTiled(F))&&R.push(B)}for(var M=0;M<R.length;M++){var B=R[M],P=B.getParent().id;typeof L[P]>"u"&&(L[P]=[]),L[P]=L[P].concat(B)}Object.keys(L).forEach(function(z){if(L[z].length>1){var $="DummyCompound_"+z;k.memberGroups[$]=L[z];var H=L[z][0].getParent(),Q=new d(k.graphManager);Q.id=$,Q.paddingLeft=H.paddingLeft||0,Q.paddingRight=H.paddingRight||0,Q.paddingBottom=H.paddingBottom||0,Q.paddingTop=H.paddingTop||0,k.idToDummyNode[$]=Q;var j=k.getGraphManager().add(k.newGraph(),Q),ie=H.getChild();ie.add(Q);for(var ne=0;ne<L[z].length;ne++){var le=L[z][ne];ie.remove(le),j.add(le)}}})},I.prototype.clearCompounds=function(){var k={},L={};this.performDFSOnCompounds();for(var R=0;R<this.compoundOrder.length;R++)L[this.compoundOrder[R].id]=this.compoundOrder[R],k[this.compoundOrder[R].id]=[].concat(this.compoundOrder[R].getChild().getNodes()),this.graphManager.remove(this.compoundOrder[R].getChild()),this.compoundOrder[R].child=null;this.graphManager.resetAllNodes(),this.tileCompoundMembers(k,L)},I.prototype.clearZeroDegreeMembers=function(){var k=this,L=this.tiledZeroDegreePack=[];Object.keys(this.memberGroups).forEach(function(R){var O=k.idToDummyNode[R];if(L[R]=k.tileNodes(k.memberGroups[R],O.paddingLeft+O.paddingRight),O.rect.width=L[R].width,O.rect.height=L[R].height,O.setCenter(L[R].centerX,L[R].centerY),O.labelMarginLeft=0,O.labelMarginTop=0,m.NODE_DIMENSIONS_INCLUDE_LABELS){var M=O.rect.width,B=O.rect.height;O.labelWidth&&(O.labelPosHorizontal=="left"?(O.rect.x-=O.labelWidth,O.setWidth(M+O.labelWidth),O.labelMarginLeft=O.labelWidth):O.labelPosHorizontal=="center"&&O.labelWidth>M?(O.rect.x-=(O.labelWidth-M)/2,O.setWidth(O.labelWidth),O.labelMarginLeft=(O.labelWidth-M)/2):O.labelPosHorizontal=="right"&&O.setWidth(M+O.labelWidth)),O.labelHeight&&(O.labelPosVertical=="top"?(O.rect.y-=O.labelHeight,O.setHeight(B+O.labelHeight),O.labelMarginTop=O.labelHeight):O.labelPosVertical=="center"&&O.labelHeight>B?(O.rect.y-=(O.labelHeight-B)/2,O.setHeight(O.labelHeight),O.labelMarginTop=(O.labelHeight-B)/2):O.labelPosVertical=="bottom"&&O.setHeight(B+O.labelHeight))}})},I.prototype.repopulateCompounds=function(){for(var k=this.compoundOrder.length-1;k>=0;k--){var L=this.compoundOrder[k],R=L.id,O=L.paddingLeft,M=L.paddingTop,B=L.labelMarginLeft,F=L.labelMarginTop;this.adjustLocations(this.tiledMemberPack[R],L.rect.x,L.rect.y,O,M,B,F)}},I.prototype.repopulateZeroDegreeMembers=function(){var k=this,L=this.tiledZeroDegreePack;Object.keys(L).forEach(function(R){var O=k.idToDummyNode[R],M=O.paddingLeft,B=O.paddingTop,F=O.labelMarginLeft,P=O.labelMarginTop;k.adjustLocations(L[R],O.rect.x,O.rect.y,M,B,F,P)})},I.prototype.getToBeTiled=function(k){var L=k.id;if(this.toBeTiled[L]!=null)return this.toBeTiled[L];var R=k.getChild();if(R==null)return this.toBeTiled[L]=!1,!1;for(var O=R.getNodes(),M=0;M<O.length;M++){var B=O[M];if(this.getNodeDegree(B)>0)return this.toBeTiled[L]=!1,!1;if(B.getChild()==null){this.toBeTiled[B.id]=!1;continue}if(!this.getToBeTiled(B))return this.toBeTiled[L]=!1,!1}return this.toBeTiled[L]=!0,!0},I.prototype.getNodeDegree=function(k){for(var L=k.id,R=k.getEdges(),O=0,M=0;M<R.length;M++){var B=R[M];B.getSource().id!==B.getTarget().id&&(O=O+1)}return O},I.prototype.getNodeDegreeWithChildren=function(k){var L=this.getNodeDegree(k);if(k.getChild()==null)return L;for(var R=k.getChild().getNodes(),O=0;O<R.length;O++){var M=R[O];L+=this.getNodeDegreeWithChildren(M)}return L},I.prototype.performDFSOnCompounds=function(){this.compoundOrder=[],this.fillCompexOrderByDFS(this.graphManager.getRoot().getNodes())},I.prototype.fillCompexOrderByDFS=function(k){for(var L=0;L<k.length;L++){var R=k[L];R.getChild()!=null&&this.fillCompexOrderByDFS(R.getChild().getNodes()),this.getToBeTiled(R)&&this.compoundOrder.push(R)}},I.prototype.adjustLocations=function(k,L,R,O,M,B,F){L+=O+B,R+=M+F;for(var P=L,z=0;z<k.rows.length;z++){var $=k.rows[z];L=P;for(var H=0,Q=0;Q<$.length;Q++){var j=$[Q];j.rect.x=L,j.rect.y=R,L+=j.rect.width+k.horizontalPadding,j.rect.height>H&&(H=j.rect.height)}R+=H+k.verticalPadding}},I.prototype.tileCompoundMembers=function(k,L){var R=this;this.tiledMemberPack=[],Object.keys(k).forEach(function(O){var M=L[O];if(R.tiledMemberPack[O]=R.tileNodes(k[O],M.paddingLeft+M.paddingRight),M.rect.width=R.tiledMemberPack[O].width,M.rect.height=R.tiledMemberPack[O].height,M.setCenter(R.tiledMemberPack[O].centerX,R.tiledMemberPack[O].centerY),M.labelMarginLeft=0,M.labelMarginTop=0,m.NODE_DIMENSIONS_INCLUDE_LABELS){var B=M.rect.width,F=M.rect.height;M.labelWidth&&(M.labelPosHorizontal=="left"?(M.rect.x-=M.labelWidth,M.setWidth(B+M.labelWidth),M.labelMarginLeft=M.labelWidth):M.labelPosHorizontal=="center"&&M.labelWidth>B?(M.rect.x-=(M.labelWidth-B)/2,M.setWidth(M.labelWidth),M.labelMarginLeft=(M.labelWidth-B)/2):M.labelPosHorizontal=="right"&&M.setWidth(B+M.labelWidth)),M.labelHeight&&(M.labelPosVertical=="top"?(M.rect.y-=M.labelHeight,M.setHeight(F+M.labelHeight),M.labelMarginTop=M.labelHeight):M.labelPosVertical=="center"&&M.labelHeight>F?(M.rect.y-=(M.labelHeight-F)/2,M.setHeight(M.labelHeight),M.labelMarginTop=(M.labelHeight-F)/2):M.labelPosVertical=="bottom"&&M.setHeight(F+M.labelHeight))}})},I.prototype.tileNodes=function(k,L){var R=this.tileNodesByFavoringDim(k,L,!0),O=this.tileNodesByFavoringDim(k,L,!1),M=this.getOrgRatio(R),B=this.getOrgRatio(O),F;return B<M?F=O:F=R,F},I.prototype.getOrgRatio=function(k){var L=k.width,R=k.height,O=L/R;return O<1&&(O=1/O),O},I.prototype.calcIdealRowWidth=function(k,L){var R=m.TILING_PADDING_VERTICAL,O=m.TILING_PADDING_HORIZONTAL,M=k.length,B=0,F=0,P=0;k.forEach(function(ne){B+=ne.getWidth(),F+=ne.getHeight(),ne.getWidth()>P&&(P=ne.getWidth())});var z=B/M,$=F/M,H=Math.pow(R-O,2)+4*(z+O)*($+R)*M,Q=(O-R+Math.sqrt(H))/(2*(z+O)),j;L?(j=Math.ceil(Q),j==Q&&j++):j=Math.floor(Q);var ie=j*(z+O)-O;return P>ie&&(ie=P),ie+=O*2,ie},I.prototype.tileNodesByFavoringDim=function(k,L,R){var O=m.TILING_PADDING_VERTICAL,M=m.TILING_PADDING_HORIZONTAL,B=m.TILING_COMPARE_BY,F={rows:[],rowWidth:[],rowHeight:[],width:0,height:L,verticalPadding:O,horizontalPadding:M,centerX:0,centerY:0};B&&(F.idealRowWidth=this.calcIdealRowWidth(k,R));var P=o(function(le){return le.rect.width*le.rect.height},"getNodeArea"),z=o(function(le,he){return P(he)-P(le)},"areaCompareFcn");k.sort(function(ne,le){var he=z;return F.idealRowWidth?(he=B,he(ne.id,le.id)):he(ne,le)});for(var $=0,H=0,Q=0;Q<k.length;Q++){var j=k[Q];$+=j.getCenterX(),H+=j.getCenterY()}F.centerX=$/k.length,F.centerY=H/k.length;for(var Q=0;Q<k.length;Q++){var j=k[Q];if(F.rows.length==0)this.insertNodeToRow(F,j,0,L);else if(this.canAddHorizontal(F,j.rect.width,j.rect.height)){var ie=F.rows.length-1;F.idealRowWidth||(ie=this.getShortestRowIndex(F)),this.insertNodeToRow(F,j,ie,L)}else this.insertNodeToRow(F,j,F.rows.length,L);this.shiftToLastRow(F)}return F},I.prototype.insertNodeToRow=function(k,L,R,O){var M=O;if(R==k.rows.length){var B=[];k.rows.push(B),k.rowWidth.push(M),k.rowHeight.push(0)}var F=k.rowWidth[R]+L.rect.width;k.rows[R].length>0&&(F+=k.horizontalPadding),k.rowWidth[R]=F,k.width<F&&(k.width=F);var P=L.rect.height;R>0&&(P+=k.verticalPadding);var z=0;P>k.rowHeight[R]&&(z=k.rowHeight[R],k.rowHeight[R]=P,z=k.rowHeight[R]-z),k.height+=z,k.rows[R].push(L)},I.prototype.getShortestRowIndex=function(k){for(var L=-1,R=Number.MAX_VALUE,O=0;O<k.rows.length;O++)k.rowWidth[O]<R&&(L=O,R=k.rowWidth[O]);return L},I.prototype.getLongestRowIndex=function(k){for(var L=-1,R=Number.MIN_VALUE,O=0;O<k.rows.length;O++)k.rowWidth[O]>R&&(L=O,R=k.rowWidth[O]);return L},I.prototype.canAddHorizontal=function(k,L,R){if(k.idealRowWidth){var O=k.rows.length-1,M=k.rowWidth[O];return M+L+k.horizontalPadding<=k.idealRowWidth}var B=this.getShortestRowIndex(k);if(B<0)return!0;var F=k.rowWidth[B];if(F+k.horizontalPadding+L<=k.width)return!0;var P=0;k.rowHeight[B]<R&&B>0&&(P=R+k.verticalPadding-k.rowHeight[B]);var z;k.width-F>=L+k.horizontalPadding?z=(k.height+P)/(F+L+k.horizontalPadding):z=(k.height+P)/k.width,P=R+k.verticalPadding;var $;return k.width<L?$=(k.height+P)/L:$=(k.height+P)/k.width,$<1&&($=1/$),z<1&&(z=1/z),z<$},I.prototype.shiftToLastRow=function(k){var L=this.getLongestRowIndex(k),R=k.rowWidth.length-1,O=k.rows[L],M=O[O.length-1],B=M.width+k.horizontalPadding;if(k.width-k.rowWidth[R]>B&&L!=R){O.splice(-1,1),k.rows[R].push(M),k.rowWidth[L]=k.rowWidth[L]-B,k.rowWidth[R]=k.rowWidth[R]+B,k.width=k.rowWidth[instance.getLongestRowIndex(k)];for(var F=Number.MIN_VALUE,P=0;P<O.length;P++)O[P].height>F&&(F=O[P].height);L>0&&(F+=k.verticalPadding);var z=k.rowHeight[L]+k.rowHeight[R];k.rowHeight[L]=F,k.rowHeight[R]<M.height+k.verticalPadding&&(k.rowHeight[R]=M.height+k.verticalPadding);var $=k.rowHeight[L]+k.rowHeight[R];k.height+=$-z,this.shiftToLastRow(k)}},I.prototype.tilingPreLayout=function(){m.TILE&&(this.groupZeroDegreeMembers(),this.clearCompounds(),this.clearZeroDegreeMembers())},I.prototype.tilingPostLayout=function(){m.TILE&&(this.repopulateZeroDegreeMembers(),this.repopulateCompounds())},I.prototype.reduceTrees=function(){for(var k=[],L=!0,R;L;){var O=this.graphManager.getAllNodes(),M=[];L=!1;for(var B=0;B<O.length;B++)if(R=O[B],R.getEdges().length==1&&!R.getEdges()[0].isInterGraph&&R.getChild()==null){if(m.PURE_INCREMENTAL){var F=R.getEdges()[0].getOtherEnd(R),P=new w(R.getCenterX()-F.getCenterX(),R.getCenterY()-F.getCenterY());M.push([R,R.getEdges()[0],R.getOwner(),P])}else M.push([R,R.getEdges()[0],R.getOwner()]);L=!0}if(L==!0){for(var z=[],$=0;$<M.length;$++)M[$][0].getEdges().length==1&&(z.push(M[$]),M[$][0].getOwner().remove(M[$][0]));k.push(z),this.graphManager.resetAllNodes(),this.graphManager.resetAllEdges()}}this.prunedNodesAll=k},I.prototype.growTree=function(k){for(var L=k.length,R=k[L-1],O,M=0;M<R.length;M++)O=R[M],this.findPlaceforPrunedNode(O),O[2].add(O[0]),O[2].add(O[1],O[1].source,O[1].target);k.splice(k.length-1,1),this.graphManager.resetAllNodes(),this.graphManager.resetAllEdges()},I.prototype.findPlaceforPrunedNode=function(k){var L,R,O=k[0];if(O==k[1].source?R=k[1].target:R=k[1].source,m.PURE_INCREMENTAL)O.setCenter(R.getCenterX()+k[3].getWidth(),R.getCenterY()+k[3].getHeight());else{var M=R.startX,B=R.finishX,F=R.startY,P=R.finishY,z=0,$=0,H=0,Q=0,j=[z,H,$,Q];if(F>0)for(var ie=M;ie<=B;ie++)j[0]+=this.grid[ie][F-1].length+this.grid[ie][F].length-1;if(B<this.grid.length-1)for(var ie=F;ie<=P;ie++)j[1]+=this.grid[B+1][ie].length+this.grid[B][ie].length-1;if(P<this.grid[0].length-1)for(var ie=M;ie<=B;ie++)j[2]+=this.grid[ie][P+1].length+this.grid[ie][P].length-1;if(M>0)for(var ie=F;ie<=P;ie++)j[3]+=this.grid[M-1][ie].length+this.grid[M][ie].length-1;for(var ne=T.MAX_VALUE,le,he,K=0;K<j.length;K++)j[K]<ne?(ne=j[K],le=1,he=K):j[K]==ne&&le++;if(le==3&&ne==0)j[0]==0&&j[1]==0&&j[2]==0?L=1:j[0]==0&&j[1]==0&&j[3]==0?L=0:j[0]==0&&j[2]==0&&j[3]==0?L=3:j[1]==0&&j[2]==0&&j[3]==0&&(L=2);else if(le==2&&ne==0){var X=Math.floor(Math.random()*2);j[0]==0&&j[1]==0?X==0?L=0:L=1:j[0]==0&&j[2]==0?X==0?L=0:L=2:j[0]==0&&j[3]==0?X==0?L=0:L=3:j[1]==0&&j[2]==0?X==0?L=1:L=2:j[1]==0&&j[3]==0?X==0?L=1:L=3:X==0?L=2:L=3}else if(le==4&&ne==0){var X=Math.floor(Math.random()*4);L=X}else L=he;L==0?O.setCenter(R.getCenterX(),R.getCenterY()-R.getHeight()/2-y.DEFAULT_EDGE_LENGTH-O.getHeight()/2):L==1?O.setCenter(R.getCenterX()+R.getWidth()/2+y.DEFAULT_EDGE_LENGTH+O.getWidth()/2,R.getCenterY()):L==2?O.setCenter(R.getCenterX(),R.getCenterY()+R.getHeight()/2+y.DEFAULT_EDGE_LENGTH+O.getHeight()/2):O.setCenter(R.getCenterX()-R.getWidth()/2-y.DEFAULT_EDGE_LENGTH-O.getWidth()/2,R.getCenterY())}},a.exports=I},991:(a,s,l)=>{var u=l(551).FDLayoutNode,h=l(551).IMath;function f(p,m,g,y){u.call(this,p,m,g,y)}o(f,"CoSENode"),f.prototype=Object.create(u.prototype);for(var d in u)f[d]=u[d];f.prototype.calculateDisplacement=function(){var p=this.graphManager.getLayout();this.getChild()!=null&&this.fixedNodeWeight?(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.fixedNodeWeight,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.fixedNodeWeight):(this.displacementX+=p.coolingFactor*(this.springForceX+this.repulsionForceX+this.gravitationForceX)/this.noOfChildren,this.displacementY+=p.coolingFactor*(this.springForceY+this.repulsionForceY+this.gravitationForceY)/this.noOfChildren),Math.abs(this.displacementX)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementX=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementX)),Math.abs(this.displacementY)>p.coolingFactor*p.maxNodeDisplacement&&(this.displacementY=p.coolingFactor*p.maxNodeDisplacement*h.sign(this.displacementY)),this.child&&this.child.getNodes().length>0&&this.propogateDisplacementToChildren(this.displacementX,this.displacementY)},f.prototype.propogateDisplacementToChildren=function(p,m){for(var g=this.getChild().getNodes(),y,v=0;v<g.length;v++)y=g[v],y.getChild()==null?(y.displacementX+=p,y.displacementY+=m):y.propogateDisplacementToChildren(p,m)},f.prototype.move=function(){var p=this.graphManager.getLayout();(this.child==null||this.child.getNodes().length==0)&&(this.moveBy(this.displacementX,this.displacementY),p.totalDisplacement+=Math.abs(this.displacementX)+Math.abs(this.displacementY)),this.springForceX=0,this.springForceY=0,this.repulsionForceX=0,this.repulsionForceY=0,this.gravitationForceX=0,this.gravitationForceY=0,this.displacementX=0,this.displacementY=0},f.prototype.setPred1=function(p){this.pred1=p},f.prototype.getPred1=function(){return pred1},f.prototype.getPred2=function(){return pred2},f.prototype.setNext=function(p){this.next=p},f.prototype.getNext=function(){return next},f.prototype.setProcessed=function(p){this.processed=p},f.prototype.isProcessed=function(){return processed},a.exports=f},902:(a,s,l)=>{function u(g){if(Array.isArray(g)){for(var y=0,v=Array(g.length);y<g.length;y++)v[y]=g[y];return v}else return Array.from(g)}o(u,"_toConsumableArray");var h=l(806),f=l(551).LinkedList,d=l(551).Matrix,p=l(551).SVD;function m(){}o(m,"ConstraintHandler"),m.handleConstraints=function(g){var y={};y.fixedNodeConstraint=g.constraints.fixedNodeConstraint,y.alignmentConstraint=g.constraints.alignmentConstraint,y.relativePlacementConstraint=g.constraints.relativePlacementConstraint;for(var v=new Map,x=new Map,b=[],w=[],C=g.getAllNodes(),T=0,E=0;E<C.length;E++){var A=C[E];A.getChild()==null&&(x.set(A.id,T++),b.push(A.getCenterX()),w.push(A.getCenterY()),v.set(A.id,A))}y.relativePlacementConstraint&&y.relativePlacementConstraint.forEach(function(oe){!oe.gap&&oe.gap!=0&&(oe.left?oe.gap=h.DEFAULT_EDGE_LENGTH+v.get(oe.left).getWidth()/2+v.get(oe.right).getWidth()/2:oe.gap=h.DEFAULT_EDGE_LENGTH+v.get(oe.top).getHeight()/2+v.get(oe.bottom).getHeight()/2)});var 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tn=I(st,"horizontal",Pe,pe,Tt),_r=I(Ue,"vertical",_e,ve,On),Dr=o(function(et){xe.get(et)?xe.get(et).forEach(function(mt){b[x.get(mt)]=tn.get(et)}):b[x.get(et)]=tn.get(et)},"_loop9"),Pn=!0,At=!1,Ce=void 0;try{for(var tt=tn.keys()[Symbol.iterator](),St;!(Pn=(St=tt.next()).done);Pn=!0){var mr=St.value;Dr(mr)}}catch(Hr){At=!0,Ce=Hr}finally{try{!Pn&&tt.return&&tt.return()}finally{if(At)throw Ce}}var rn=o(function(et){q.get(et)?q.get(et).forEach(function(mt){w[x.get(mt)]=_r.get(et)}):w[x.get(et)]=_r.get(et)},"_loop10"),gn=!0,Zr=!1,Ni=void 0;try{for(var Zn=_r.keys()[Symbol.iterator](),Sn;!(gn=(Sn=Zn.next()).done);gn=!0){var mr=Sn.value;rn(mr)}}catch(Hr){Zr=!0,Ni=Hr}finally{try{!gn&&Zn.return&&Zn.return()}finally{if(Zr)throw Ni}}}()}for(var de=0;de<C.length;de++){var re=C[de];re.getChild()==null&&re.setCenter(b[x.get(re.id)],w[x.get(re.id)])}},a.exports=m},551:a=>{a.exports=t}},r={};function n(a){var s=r[a];if(s!==void 0)return s.exports;var l=r[a]={exports:{}};return 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k=D.boundingBox(),L=p.xCoords[I]-k.w/2,R=p.xCoords[I]+k.w/2,O=p.yCoords[I]-k.h/2,M=p.yCoords[I]+k.h/2;L<g&&(g=L),R>y&&(y=R),O<v&&(v=O),M>x&&(x=M)}}}catch($){w=!0,C=$}finally{try{!b&&T.return&&T.return()}finally{if(w)throw C}}var B=d.x-(y+g)/2,F=d.y-(x+v)/2;p.xCoords=p.xCoords.map(function($){return $+B}),p.yCoords=p.yCoords.map(function($){return $+F})}else{Object.keys(p).forEach(function($){var H=p[$],Q=H.getRect().x,j=H.getRect().x+H.getRect().width,ie=H.getRect().y,ne=H.getRect().y+H.getRect().height;Q<g&&(g=Q),j>y&&(y=j),ie<v&&(v=ie),ne>x&&(x=ne)});var P=d.x-(y+g)/2,z=d.y-(x+v)/2;Object.keys(p).forEach(function($){var H=p[$];H.setCenter(H.getCenterX()+P,H.getCenterY()+z)})}}},f.calcBoundingBox=function(d,p,m,g){for(var y=Number.MAX_SAFE_INTEGER,v=Number.MIN_SAFE_INTEGER,x=Number.MAX_SAFE_INTEGER,b=Number.MIN_SAFE_INTEGER,w=void 0,C=void 0,T=void 0,E=void 0,A=d.descendants().not(":parent"),S=A.length,_=0;_<S;_++){var I=A[_];w=p[g.get(I.id())]-I.width()/2,C=p[g.get(I.id())]+I.width()/2,T=m[g.get(I.id())]-I.height()/2,E=m[g.get(I.id())]+I.height()/2,y>w&&(y=w),v<C&&(v=C),x>T&&(x=T),b<E&&(b=E)}var D={};return D.topLeftX=y,D.topLeftY=x,D.width=v-y,D.height=b-x,D},f.calcParentsWithoutChildren=function(d,p){var m=d.collection();return p.nodes(":parent").forEach(function(g){var y=!1;g.children().forEach(function(v){v.css("display")!="none"&&(y=!0)}),y||m.merge(g)}),m},a.exports=f},816:(a,s,l)=>{var u=l(548),h=l(140).CoSELayout,f=l(140).CoSENode,d=l(140).layoutBase.PointD,p=l(140).layoutBase.DimensionD,m=l(140).layoutBase.LayoutConstants,g=l(140).layoutBase.FDLayoutConstants,y=l(140).CoSEConstants,v=o(function(b,w){var C=b.cy,T=b.eles,E=T.nodes(),A=T.edges(),S=void 0,_=void 0,I=void 0,D={};b.randomize&&(S=w.nodeIndexes,_=w.xCoords,I=w.yCoords);var k=o(function($){return typeof $=="function"},"isFn"),L=o(function($,H){return k($)?$(H):$},"optFn"),R=u.calcParentsWithoutChildren(C,T),O=o(function z($,H,Q,j){for(var ie=H.length,ne=0;ne<ie;ne++){var le=H[ne],he=null;le.intersection(R).length==0&&(he=le.children());var K=void 0,X=le.layoutDimensions({nodeDimensionsIncludeLabels:j.nodeDimensionsIncludeLabels});if(le.outerWidth()!=null&&le.outerHeight()!=null)if(j.randomize)if(!le.isParent())K=$.add(new f(Q.graphManager,new d(_[S.get(le.id())]-X.w/2,I[S.get(le.id())]-X.h/2),new p(parseFloat(X.w),parseFloat(X.h))));else{var te=u.calcBoundingBox(le,_,I,S);le.intersection(R).length==0?K=$.add(new f(Q.graphManager,new d(te.topLeftX,te.topLeftY),new p(te.width,te.height))):K=$.add(new f(Q.graphManager,new d(te.topLeftX,te.topLeftY),new p(parseFloat(X.w),parseFloat(X.h))))}else K=$.add(new f(Q.graphManager,new d(le.position("x")-X.w/2,le.position("y")-X.h/2),new p(parseFloat(X.w),parseFloat(X.h))));else K=$.add(new f(this.graphManager));if(K.id=le.data("id"),K.nodeRepulsion=L(j.nodeRepulsion,le),K.paddingLeft=parseInt(le.css("padding")),K.paddingTop=parseInt(le.css("padding")),K.paddingRight=parseInt(le.css("padding")),K.paddingBottom=parseInt(le.css("padding")),j.nodeDimensionsIncludeLabels&&(K.labelWidth=le.boundingBox({includeLabels:!0,includeNodes:!1,includeOverlays:!1}).w,K.labelHeight=le.boundingBox({includeLabels:!0,includeNodes:!1,includeOverlays:!1}).h,K.labelPosVertical=le.css("text-valign"),K.labelPosHorizontal=le.css("text-halign")),D[le.data("id")]=K,isNaN(K.rect.x)&&(K.rect.x=0),isNaN(K.rect.y)&&(K.rect.y=0),he!=null&&he.length>0){var J=void 0;J=Q.getGraphManager().add(Q.newGraph(),K),z(J,he,Q,j)}}},"processChildrenList"),M=o(function($,H,Q){for(var j=0,ie=0,ne=0;ne<Q.length;ne++){var le=Q[ne],he=D[le.data("source")],K=D[le.data("target")];if(he&&K&&he!==K&&he.getEdgesBetween(K).length==0){var X=H.add($.newEdge(),he,K);X.id=le.id(),X.idealLength=L(b.idealEdgeLength,le),X.edgeElasticity=L(b.edgeElasticity,le),j+=X.idealLength,ie++}}b.idealEdgeLength!=null&&(ie>0?y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=j/ie:k(b.idealEdgeLength)?y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=50:y.DEFAULT_EDGE_LENGTH=g.DEFAULT_EDGE_LENGTH=b.idealEdgeLength,y.MIN_REPULSION_DIST=g.MIN_REPULSION_DIST=g.DEFAULT_EDGE_LENGTH/10,y.DEFAULT_RADIAL_SEPARATION=g.DEFAULT_EDGE_LENGTH)},"processEdges"),B=o(function($,H){H.fixedNodeConstraint&&($.constraints.fixedNodeConstraint=H.fixedNodeConstraint),H.alignmentConstraint&&($.constraints.alignmentConstraint=H.alignmentConstraint),H.relativePlacementConstraint&&($.constraints.relativePlacementConstraint=H.relativePlacementConstraint)},"processConstraints");b.nestingFactor!=null&&(y.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=g.PER_LEVEL_IDEAL_EDGE_LENGTH_FACTOR=b.nestingFactor),b.gravity!=null&&(y.DEFAULT_GRAVITY_STRENGTH=g.DEFAULT_GRAVITY_STRENGTH=b.gravity),b.numIter!=null&&(y.MAX_ITERATIONS=g.MAX_ITERATIONS=b.numIter),b.gravityRange!=null&&(y.DEFAULT_GRAVITY_RANGE_FACTOR=g.DEFAULT_GRAVITY_RANGE_FACTOR=b.gravityRange),b.gravityCompound!=null&&(y.DEFAULT_COMPOUND_GRAVITY_STRENGTH=g.DEFAULT_COMPOUND_GRAVITY_STRENGTH=b.gravityCompound),b.gravityRangeCompound!=null&&(y.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=g.DEFAULT_COMPOUND_GRAVITY_RANGE_FACTOR=b.gravityRangeCompound),b.initialEnergyOnIncremental!=null&&(y.DEFAULT_COOLING_FACTOR_INCREMENTAL=g.DEFAULT_COOLING_FACTOR_INCREMENTAL=b.initialEnergyOnIncremental),b.tilingCompareBy!=null&&(y.TILING_COMPARE_BY=b.tilingCompareBy),b.quality=="proof"?m.QUALITY=2:m.QUALITY=0,y.NODE_DIMENSIONS_INCLUDE_LABELS=g.NODE_DIMENSIONS_INCLUDE_LABELS=m.NODE_DIMENSIONS_INCLUDE_LABELS=b.nodeDimensionsIncludeLabels,y.DEFAULT_INCREMENTAL=g.DEFAULT_INCREMENTAL=m.DEFAULT_INCREMENTAL=!b.randomize,y.ANIMATE=g.ANIMATE=m.ANIMATE=b.animate,y.TILE=b.tile,y.TILING_PADDING_VERTICAL=typeof b.tilingPaddingVertical=="function"?b.tilingPaddingVertical.call():b.tilingPaddingVertical,y.TILING_PADDING_HORIZONTAL=typeof b.tilingPaddingHorizontal=="function"?b.tilingPaddingHorizontal.call():b.tilingPaddingHorizontal,y.DEFAULT_INCREMENTAL=g.DEFAULT_INCREMENTAL=m.DEFAULT_INCREMENTAL=!0,y.PURE_INCREMENTAL=!b.randomize,m.DEFAULT_UNIFORM_LEAF_NODE_SIZES=b.uniformNodeDimensions,b.step=="transformed"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!0,y.ENFORCE_CONSTRAINTS=!1,y.APPLY_LAYOUT=!1),b.step=="enforced"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!0,y.APPLY_LAYOUT=!1),b.step=="cose"&&(y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!1,y.APPLY_LAYOUT=!0),b.step=="all"&&(b.randomize?y.TRANSFORM_ON_CONSTRAINT_HANDLING=!0:y.TRANSFORM_ON_CONSTRAINT_HANDLING=!1,y.ENFORCE_CONSTRAINTS=!0,y.APPLY_LAYOUT=!0),b.fixedNodeConstraint||b.alignmentConstraint||b.relativePlacementConstraint?y.TREE_REDUCTION_ON_INCREMENTAL=!1:y.TREE_REDUCTION_ON_INCREMENTAL=!0;var F=new h,P=F.newGraphManager();return O(P.addRoot(),u.getTopMostNodes(E),F,b),M(F,P,A),B(F,b),F.runLayout(),D},"coseLayout");a.exports={coseLayout:v}},212:(a,s,l)=>{var u=function(){function b(w,C){for(var T=0;T<C.length;T++){var E=C[T];E.enumerable=E.enumerable||!1,E.configurable=!0,"value"in E&&(E.writable=!0),Object.defineProperty(w,E.key,E)}}return o(b,"defineProperties"),function(w,C,T){return C&&b(w.prototype,C),T&&b(w,T),w}}();function h(b,w){if(!(b instanceof w))throw new TypeError("Cannot call a class as a function")}o(h,"_classCallCheck");var f=l(658),d=l(548),p=l(657),m=p.spectralLayout,g=l(816),y=g.coseLayout,v=Object.freeze({quality:"default",randomize:!0,animate:!0,animationDuration:1e3,animationEasing:void 0,fit:!0,padding:30,nodeDimensionsIncludeLabels:!1,uniformNodeDimensions:!1,packComponents:!0,step:"all",samplingType:!0,sampleSize:25,nodeSeparation:75,piTol:1e-7,nodeRepulsion:o(function(w){return 4500},"nodeRepulsion"),idealEdgeLength:o(function(w){return 50},"idealEdgeLength"),edgeElasticity:o(function(w){return .45},"edgeElasticity"),nestingFactor:.1,gravity:.25,numIter:2500,tile:!0,tilingCompareBy:void 0,tilingPaddingVertical:10,tilingPaddingHorizontal:10,gravityRangeCompound:1.5,gravityCompound:1,gravityRange:3.8,initialEnergyOnIncremental:.3,fixedNodeConstraint:void 0,alignmentConstraint:void 0,relativePlacementConstraint:void 0,ready:o(function(){},"ready"),stop:o(function(){},"stop")}),x=function(){function b(w){h(this,b),this.options=f({},v,w)}return o(b,"Layout"),u(b,[{key:"run",value:o(function(){var C=this,T=this.options,E=T.cy,A=T.eles,S=[],_=void 0,I=void 0,D=[],k=void 0,L=[];T.fixedNodeConstraint&&(!Array.isArray(T.fixedNodeConstraint)||T.fixedNodeConstraint.length==0)&&(T.fixedNodeConstraint=void 0),T.alignmentConstraint&&(T.alignmentConstraint.vertical&&(!Array.isArray(T.alignmentConstraint.vertical)||T.alignmentConstraint.vertical.length==0)&&(T.alignmentConstraint.vertical=void 0),T.alignmentConstraint.horizontal&&(!Array.isArray(T.alignmentConstraint.horizontal)||T.alignmentConstraint.horizontal.length==0)&&(T.alignmentConstraint.horizontal=void 0)),T.relativePlacementConstraint&&(!Array.isArray(T.relativePlacementConstraint)||T.relativePlacementConstraint.length==0)&&(T.relativePlacementConstraint=void 0);var R=T.fixedNodeConstraint||T.alignmentConstraint||T.relativePlacementConstraint;R&&(T.tile=!1,T.packComponents=!1);var O=void 0,M=!1;if(E.layoutUtilities&&T.packComponents&&(O=E.layoutUtilities("get"),O||(O=E.layoutUtilities()),M=!0),A.nodes().length>0)if(M){var P=d.getTopMostNodes(T.eles.nodes());if(k=d.connectComponents(E,T.eles,P),k.forEach(function(ce){var ae=ce.boundingBox();L.push({x:ae.x1+ae.w/2,y:ae.y1+ae.h/2})}),T.randomize&&k.forEach(function(ce){T.eles=ce,S.push(m(T))}),T.quality=="default"||T.quality=="proof"){var z=E.collection();if(T.tile){var $=new Map,H=[],Q=[],j=0,ie={nodeIndexes:$,xCoords:H,yCoords:Q},ne=[];if(k.forEach(function(ce,ae){ce.edges().length==0&&(ce.nodes().forEach(function(Oe,ge){z.merge(ce.nodes()[ge]),Oe.isParent()||(ie.nodeIndexes.set(ce.nodes()[ge].id(),j++),ie.xCoords.push(ce.nodes()[0].position().x),ie.yCoords.push(ce.nodes()[0].position().y))}),ne.push(ae))}),z.length>1){var le=z.boundingBox();L.push({x:le.x1+le.w/2,y:le.y1+le.h/2}),k.push(z),S.push(ie);for(var he=ne.length-1;he>=0;he--)k.splice(ne[he],1),S.splice(ne[he],1),L.splice(ne[he],1)}}k.forEach(function(ce,ae){T.eles=ce,D.push(y(T,S[ae])),d.relocateComponent(L[ae],D[ae],T)})}else k.forEach(function(ce,ae){d.relocateComponent(L[ae],S[ae],T)});var K=new Set;if(k.length>1){var X=[],te=A.filter(function(ce){return ce.css("display")=="none"});k.forEach(function(ce,ae){var Oe=void 0;if(T.quality=="draft"&&(Oe=S[ae].nodeIndexes),ce.nodes().not(te).length>0){var ge={};ge.edges=[],ge.nodes=[];var ze=void 0;ce.nodes().not(te).forEach(function(He){if(T.quality=="draft")if(!He.isParent())ze=Oe.get(He.id()),ge.nodes.push({x:S[ae].xCoords[ze]-He.boundingbox().w/2,y:S[ae].yCoords[ze]-He.boundingbox().h/2,width:He.boundingbox().w,height:He.boundingbox().h});else{var $e=d.calcBoundingBox(He,S[ae].xCoords,S[ae].yCoords,Oe);ge.nodes.push({x:$e.topLeftX,y:$e.topLeftY,width:$e.width,height:$e.height})}else D[ae][He.id()]&&ge.nodes.push({x:D[ae][He.id()].getLeft(),y:D[ae][He.id()].getTop(),width:D[ae][He.id()].getWidth(),height:D[ae][He.id()].getHeight()})}),ce.edges().forEach(function(He){var $e=He.source(),Re=He.target();if($e.css("display")!="none"&&Re.css("display")!="none")if(T.quality=="draft"){var Ie=Oe.get($e.id()),be=Oe.get(Re.id()),W=[],de=[];if($e.isParent()){var re=d.calcBoundingBox($e,S[ae].xCoords,S[ae].yCoords,Oe);W.push(re.topLeftX+re.width/2),W.push(re.topLeftY+re.height/2)}else W.push(S[ae].xCoords[Ie]),W.push(S[ae].yCoords[Ie]);if(Re.isParent()){var oe=d.calcBoundingBox(Re,S[ae].xCoords,S[ae].yCoords,Oe);de.push(oe.topLeftX+oe.width/2),de.push(oe.topLeftY+oe.height/2)}else de.push(S[ae].xCoords[be]),de.push(S[ae].yCoords[be]);ge.edges.push({startX:W[0],startY:W[1],endX:de[0],endY:de[1]})}else D[ae][$e.id()]&&D[ae][Re.id()]&&ge.edges.push({startX:D[ae][$e.id()].getCenterX(),startY:D[ae][$e.id()].getCenterY(),endX:D[ae][Re.id()].getCenterX(),endY:D[ae][Re.id()].getCenterY()})}),ge.nodes.length>0&&(X.push(ge),K.add(ae))}});var J=O.packComponents(X,T.randomize).shifts;if(T.quality=="draft")S.forEach(function(ce,ae){var Oe=ce.xCoords.map(function(ze){return ze+J[ae].dx}),ge=ce.yCoords.map(function(ze){return ze+J[ae].dy});ce.xCoords=Oe,ce.yCoords=ge});else{var se=0;K.forEach(function(ce){Object.keys(D[ce]).forEach(function(ae){var Oe=D[ce][ae];Oe.setCenter(Oe.getCenterX()+J[se].dx,Oe.getCenterY()+J[se].dy)}),se++})}}}else{var B=T.eles.boundingBox();if(L.push({x:B.x1+B.w/2,y:B.y1+B.h/2}),T.randomize){var F=m(T);S.push(F)}T.quality=="default"||T.quality=="proof"?(D.push(y(T,S[0])),d.relocateComponent(L[0],D[0],T)):d.relocateComponent(L[0],S[0],T)}var ue=o(function(ae,Oe){if(T.quality=="default"||T.quality=="proof"){typeof ae=="number"&&(ae=Oe);var ge=void 0,ze=void 0,He=ae.data("id");return D.forEach(function(Re){He in Re&&(ge={x:Re[He].getRect().getCenterX(),y:Re[He].getRect().getCenterY()},ze=Re[He])}),T.nodeDimensionsIncludeLabels&&(ze.labelWidth&&(ze.labelPosHorizontal=="left"?ge.x+=ze.labelWidth/2:ze.labelPosHorizontal=="right"&&(ge.x-=ze.labelWidth/2)),ze.labelHeight&&(ze.labelPosVertical=="top"?ge.y+=ze.labelHeight/2:ze.labelPosVertical=="bottom"&&(ge.y-=ze.labelHeight/2))),ge==null&&(ge={x:ae.position("x"),y:ae.position("y")}),{x:ge.x,y:ge.y}}else{var $e=void 0;return S.forEach(function(Re){var Ie=Re.nodeIndexes.get(ae.id());Ie!=null&&($e={x:Re.xCoords[Ie],y:Re.yCoords[Ie]})}),$e==null&&($e={x:ae.position("x"),y:ae.position("y")}),{x:$e.x,y:$e.y}}},"getPositions");if(T.quality=="default"||T.quality=="proof"||T.randomize){var Z=d.calcParentsWithoutChildren(E,A),Se=A.filter(function(ce){return ce.css("display")=="none"});T.eles=A.not(Se),A.nodes().not(":parent").not(Se).layoutPositions(C,T,ue),Z.length>0&&Z.forEach(function(ce){ce.position(ue(ce))})}else console.log("If randomize option is set to false, then quality option must be 'default' or 'proof'.")},"run")}]),b}();a.exports=x},657:(a,s,l)=>{var u=l(548),h=l(140).layoutBase.Matrix,f=l(140).layoutBase.SVD,d=o(function(m){var g=m.cy,y=m.eles,v=y.nodes(),x=y.nodes(":parent"),b=new Map,w=new Map,C=new Map,T=[],E=[],A=[],S=[],_=[],I=[],D=[],k=[],L=void 0,R=void 0,O=1e8,M=1e-9,B=m.piTol,F=m.samplingType,P=m.nodeSeparation,z=void 0,$=o(function(){for(var xe=0,q=0,pe=!1;q<z;){xe=Math.floor(Math.random()*R),pe=!1;for(var ve=0;ve<q;ve++)if(S[ve]==xe){pe=!0;break}if(!pe)S[q]=xe,q++;else continue}},"randomSampleCR"),H=o(function(xe,q,pe){for(var ve=[],Pe=0,_e=0,we=0,Ve=void 0,De=[],qe=0,at=1,Rt=0;Rt<R;Rt++)De[Rt]=O;for(ve[_e]=xe,De[xe]=0;_e>=Pe;){we=ve[Pe++];for(var st=T[we],Ue=0;Ue<st.length;Ue++)Ve=w.get(st[Ue]),De[Ve]==O&&(De[Ve]=De[we]+1,ve[++_e]=Ve);I[we][q]=De[we]*P}if(pe){for(var ct=0;ct<R;ct++)I[ct][q]<_[ct]&&(_[ct]=I[ct][q]);for(var We=0;We<R;We++)_[We]>qe&&(qe=_[We],at=We)}return at},"BFS"),Q=o(function(xe){var q=void 0;if(xe){q=Math.floor(Math.random()*R),L=q;for(var ve=0;ve<R;ve++)_[ve]=O;for(var Pe=0;Pe<z;Pe++)S[Pe]=q,q=H(q,Pe,xe)}else{$();for(var pe=0;pe<z;pe++)H(S[pe],pe,xe,!1)}for(var _e=0;_e<R;_e++)for(var we=0;we<z;we++)I[_e][we]*=I[_e][we];for(var Ve=0;Ve<z;Ve++)D[Ve]=[];for(var De=0;De<z;De++)for(var qe=0;qe<z;qe++)D[De][qe]=I[S[qe]][De]},"allBFS"),j=o(function(){for(var xe=f.svd(D),q=xe.S,pe=xe.U,ve=xe.V,Pe=q[0]*q[0]*q[0],_e=[],we=0;we<z;we++){_e[we]=[];for(var 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diff --git a/book/latest/site_libs/quarto-html/anchor.min.js b/book/latest/site_libs/quarto-html/anchor.min.js
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0===(h=document.head.querySelector('[rel="stylesheet"],style'))?document.head.appendChild(A):document.head.insertBefore(A,h),A.sheet.insertRule(".anchorjs-link{opacity:0;text-decoration:none;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}",A.sheet.cssRules.length),A.sheet.insertRule(":hover>.anchorjs-link,.anchorjs-link:focus{opacity:1}",A.sheet.cssRules.length),A.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",A.sheet.cssRules.length),A.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,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) format("truetype")}',A.sheet.cssRules.length)),h=document.querySelectorAll("[id]"),t=[].map.call(h,function(A){return A.id}),i=0;i<e.length;i++)if(this.hasAnchorJSLink(e[i]))p.push(i);else{if(e[i].hasAttribute("id"))o=e[i].getAttribute("id");else if(e[i].hasAttribute("data-anchor-id"))o=e[i].getAttribute("data-anchor-id");else{for(r=a=this.urlify(e[i].textContent),s=0;n=t.indexOf(r=void 0!==n?a+"-"+s:r),s+=1,-1!==n;);n=void 0,t.push(r),e[i].setAttribute("id",r),o=r}(l=document.createElement("a")).className="anchorjs-link "+this.options.class,l.setAttribute("aria-label",this.options.ariaLabel),l.setAttribute("data-anchorjs-icon",this.options.icon),this.options.titleText&&(l.title=this.options.titleText),c=document.querySelector("base")?window.location.pathname+window.location.search:"",c=this.options.base||c,l.href=c+"#"+o,"always"===this.options.visible&&(l.style.opacity="1"),""===this.options.icon&&(l.style.font="1em/1 anchorjs-icons","left"===this.options.placement)&&(l.style.lineHeight="inherit"),"left"===this.options.placement?(l.style.position="absolute",l.style.marginLeft="-1.25em",l.style.paddingRight=".25em",l.style.paddingLeft=".25em",e[i].insertBefore(l,e[i].firstChild)):(l.style.marginLeft=".1875em",l.style.paddingRight=".1875em",l.style.paddingLeft=".1875em",e[i].appendChild(l))}for(i=0;i<p.length;i++)e.splice(p[i]-i,1);this.elements=this.elements.concat(e)}return this},this.remove=function(A){for(var e,t,o=d(A),i=0;i<o.length;i++)(t=o[i].querySelector(".anchorjs-link"))&&(-1!==(e=this.elements.indexOf(o[i]))&&this.elements.splice(e,1),o[i].removeChild(t));return this},this.removeAll=function(){this.remove(this.elements)},this.urlify=function(A){var e=document.createElement("textarea");return e.innerHTML=A,A=e.value,this.options.truncate||u(this.options),A.trim().replace(/'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),A=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||A||!1}}});
-// @license-end
\ No newline at end of file
diff --git a/book/latest/site_libs/quarto-html/axe/axe-check.js b/book/latest/site_libs/quarto-html/axe/axe-check.js
deleted file mode 100644
index 8808085..0000000
--- a/book/latest/site_libs/quarto-html/axe/axe-check.js
+++ /dev/null
@@ -1,145 +0,0 @@
-class QuartoAxeReporter {
-  constructor(axeResult, options) {
-    this.axeResult = axeResult;
-    this.options = options;
-  }
-
-  report() {
-    throw new Error("report() is an abstract method");
-  }
-}
-
-class QuartoAxeJsonReporter extends QuartoAxeReporter {
-  constructor(axeResult, options) {
-    super(axeResult, options);
-  }
-
-  report() {
-    console.log(JSON.stringify(this.axeResult, null, 2));
-  }
-}
-
-class QuartoAxeConsoleReporter extends QuartoAxeReporter {
-  constructor(axeResult, options) {
-    super(axeResult, options);
-  }
-
-  report() {
-    for (const violation of this.axeResult.violations) {
-      console.log(violation.description);
-      for (const node of violation.nodes) {
-        for (const target of node.target) {
-          console.log(target);
-          console.log(document.querySelector(target));
-        }
-      }
-    }
-  }
-}
-
-class QuartoAxeDocumentReporter extends QuartoAxeReporter {
-  constructor(axeResult, options) {
-    super(axeResult, options);
-  }
-
-  createViolationElement(violation) {
-    const violationElement = document.createElement("div");
-
-    const descriptionElement = document.createElement("div");
-    descriptionElement.className = "quarto-axe-violation-description";
-    descriptionElement.innerText = `${violation.impact.replace(/^[a-z]/, match => match.toLocaleUpperCase())}: ${violation.description}`;
-    violationElement.appendChild(descriptionElement);
-
-    const helpElement = document.createElement("div");
-    helpElement.className = "quarto-axe-violation-help";
-    helpElement.innerText = violation.help;
-    violationElement.appendChild(helpElement);
-
-    const nodesElement = document.createElement("div");
-    nodesElement.className = "quarto-axe-violation-nodes";
-    violationElement.appendChild(nodesElement);
-    const nodeElement = document.createElement("div");
-    nodeElement.className = "quarto-axe-violation-selector";
-    for (const node of violation.nodes) {
-      for (const target of node.target) {
-        const targetElement = document.createElement("span");
-        targetElement.className = "quarto-axe-violation-target";
-        targetElement.innerText = target;
-        nodeElement.appendChild(targetElement);
-        nodeElement.addEventListener("mouseenter", () => {
-          const element = document.querySelector(target);
-          if (element) {
-            element.scrollIntoView({ behavior: "smooth", block: "center" });
-            element.classList.add("quarto-axe-hover-highlight");
-            setTimeout(() => {
-              element.style.border = "";
-            }, 2000);
-          }
-        });
-        nodeElement.addEventListener("mouseleave", () => {
-          const element = document.querySelector(target);
-          if (element) {
-            element.classList.remove("quarto-axe-hover-highlight");
-          }
-        });
-        nodeElement.addEventListener("click", () => {
-          console.log(document.querySelector(target));
-        });
-        nodeElement.appendChild(targetElement);
-      }
-      nodesElement.appendChild(nodeElement);
-    }
-    return violationElement;
-  }
-
-  report() {
-    const violations = this.axeResult.violations;
-    const reportElement = document.createElement("div");
-    reportElement.className = "quarto-axe-report";
-    if (violations.length === 0) {
-      const noViolationsElement = document.createElement("div");
-      noViolationsElement.className = "quarto-axe-no-violations";
-      noViolationsElement.innerText = "No axe-core violations found.";
-      reportElement.appendChild(noViolationsElement);
-    }
-    violations.forEach((violation) => {
-      reportElement.appendChild(this.createViolationElement(violation));
-    });
-    document.querySelector("main").appendChild(reportElement);
-  }
-}
-
-const reporters = {
-  json: QuartoAxeJsonReporter,
-  console: QuartoAxeConsoleReporter,
-  document: QuartoAxeDocumentReporter,
-};
-
-class QuartoAxeChecker {
-  constructor(opts) {
-    this.options = opts;
-  }
-  async init() {
-    const axe = (await import("https://cdn.skypack.dev/pin/axe-core@v4.10.3-aVOFXWsJaCpVrtv89pCa/mode=imports,min/optimized/axe-core.js")).default;
-    const result = await axe.run({
-      exclude: [
-       // https://github.com/microsoft/tabster/issues/288
-       // MS has claimed they won't fix this, so we need to add an exclusion to
-       // all tabster elements
-       "[data-tabster-dummy]"
-      ],
-      preload: { assets: ['cssom'], timeout: 50000 }    
-    });
-    const reporter = this.options === true ? new QuartoAxeConsoleReporter(result) : new reporters[this.options.output](result, this.options);
-    reporter.report();
-  }
-}
-
-export async function init() {
-  const opts = document.querySelector("#quarto-axe-checker-options");
-  if (opts) {
-    const jsonOptions = JSON.parse(atob(opts.textContent));
-    const checker = new QuartoAxeChecker(jsonOptions);
-    await checker.init();
-  }
-}
\ No newline at end of file
diff --git a/book/latest/site_libs/quarto-html/popper.min.js b/book/latest/site_libs/quarto-html/popper.min.js
deleted file mode 100644
index e3726d7..0000000
--- a/book/latest/site_libs/quarto-html/popper.min.js
+++ /dev/null
@@ -1,6 +0,0 @@
-/**
- * @popperjs/core v2.11.7 - MIT License
- */
-
-!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(){var e=navigator.userAgentData;return null!=e&&e.brands&&Array.isArray(e.brands)?e.brands.map((function(e){return e.brand+"/"+e.version})).join(" "):navigator.userAgent}function c(){return!/^((?!chrome|android).)*safari/i.test(f())}function p(e,o,i){void 0===o&&(o=!1),void 0===i&&(i=!1);var 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1!==n||1!==r}(n),m=d(n),y=p(e,c,o),g={scrollLeft:0,scrollTop:0},b={x:0,y:0};return(f||!f&&!o)&&(("body"!==l(n)||v(m))&&(g=(i=n)!==t(i)&&r(i)?{scrollLeft:(a=i).scrollLeft,scrollTop:a.scrollTop}:u(i)),r(n)?((b=p(n,!0)).x+=n.clientLeft,b.y+=n.clientTop):m&&(b.x=h(m))),{x:y.left+g.scrollLeft-b.x,y:y.top+g.scrollTop-b.y,width:y.width,height:y.height}}function g(e){var t=p(e),n=e.offsetWidth,r=e.offsetHeight;return Math.abs(t.width-n)<=1&&(n=t.width),Math.abs(t.height-r)<=1&&(r=t.height),{x:e.offsetLeft,y:e.offsetTop,width:n,height:r}}function b(e){return"html"===l(e)?e:e.assignedSlot||e.parentNode||(o(e)?e.host:null)||d(e)}function x(e){return["html","body","#document"].indexOf(l(e))>=0?e.ownerDocument.body:r(e)&&v(e)?e:x(b(e))}function w(e,n){var r;void 0===n&&(n=[]);var o=x(e),i=o===(null==(r=e.ownerDocument)?void 0:r.body),a=t(o),s=i?[a].concat(a.visualViewport||[],v(o)?o:[]):o,f=n.concat(s);return i?f:f.concat(w(b(s)))}function O(e){return["table","td","th"].indexOf(l(e))>=0}function j(e){return r(e)&&"fixed"!==m(e).position?e.offsetParent:null}function E(e){for(var n=t(e),i=j(e);i&&O(i)&&"static"===m(i).position;)i=j(i);return i&&("html"===l(i)||"body"===l(i)&&"static"===m(i).position)?n:i||function(e){var t=/firefox/i.test(f());if(/Trident/i.test(f())&&r(e)&&"fixed"===m(e).position)return null;var n=b(e);for(o(n)&&(n=n.host);r(n)&&["html","body"].indexOf(l(n))<0;){var i=m(n);if("none"!==i.transform||"none"!==i.perspective||"paint"===i.contain||-1!==["transform","perspective"].indexOf(i.willChange)||t&&"filter"===i.willChange||t&&i.filter&&"none"!==i.filter)return n;n=n.parentNode}return null}(e)||n}var D="top",A="bottom",L="right",P="left",M="auto",k=[D,A,L,P],W="start",B="end",H="viewport",T="popper",R=k.reduce((function(e,t){return e.concat([t+"-"+W,t+"-"+B])}),[]),S=[].concat(k,[M]).reduce((function(e,t){return e.concat([t,t+"-"+W,t+"-"+B])}),[]),V=["beforeRead","read","afterRead","beforeMain","main","afterMain","beforeWrite","write","afterWrite"];function q(e){var t=new Map,n=new Set,r=[];function o(e){n.add(e.name),[].concat(e.requires||[],e.requiresIfExists||[]).forEach((function(e){if(!n.has(e)){var r=t.get(e);r&&o(r)}})),r.push(e)}return e.forEach((function(e){t.set(e.name,e)})),e.forEach((function(e){n.has(e.name)||o(e)})),r}function C(e){return e.split("-")[0]}function N(e,t){var n=t.getRootNode&&t.getRootNode();if(e.contains(t))return!0;if(n&&o(n)){var r=t;do{if(r&&e.isSameNode(r))return!0;r=r.parentNode||r.host}while(r)}return!1}function I(e){return Object.assign({},e,{left:e.x,top:e.y,right:e.x+e.width,bottom:e.y+e.height})}function _(e,r,o){return r===H?I(function(e,n){var r=t(e),o=d(e),i=r.visualViewport,a=o.clientWidth,s=o.clientHeight,f=0,p=0;if(i){a=i.width,s=i.height;var u=c();(u||!u&&"fixed"===n)&&(f=i.offsetLeft,p=i.offsetTop)}return{width:a,height:s,x:f+h(e),y:p}}(e,o)):n(r)?function(e,t){var n=p(e,!1,"fixed"===t);return n.top=n.top+e.clientTop,n.left=n.left+e.clientLeft,n.bottom=n.top+e.clientHeight,n.right=n.left+e.clientWidth,n.width=e.clientWidth,n.height=e.clientHeight,n.x=n.left,n.y=n.top,n}(r,o):I(function(e){var t,n=d(e),r=u(e),o=null==(t=e.ownerDocument)?void 0:t.body,a=i(n.scrollWidth,n.clientWidth,o?o.scrollWidth:0,o?o.clientWidth:0),s=i(n.scrollHeight,n.clientHeight,o?o.scrollHeight:0,o?o.clientHeight:0),f=-r.scrollLeft+h(e),c=-r.scrollTop;return"rtl"===m(o||n).direction&&(f+=i(n.clientWidth,o?o.clientWidth:0)-a),{width:a,height:s,x:f,y:c}}(d(e)))}function F(e,t,o,s){var f="clippingParents"===t?function(e){var t=w(b(e)),o=["absolute","fixed"].indexOf(m(e).position)>=0&&r(e)?E(e):e;return n(o)?t.filter((function(e){return n(e)&&N(e,o)&&"body"!==l(e)})):[]}(e):[].concat(t),c=[].concat(f,[o]),p=c[0],u=c.reduce((function(t,n){var r=_(e,n,s);return t.top=i(r.top,t.top),t.right=a(r.right,t.right),t.bottom=a(r.bottom,t.bottom),t.left=i(r.left,t.left),t}),_(e,p,s));return 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diff --git a/book/latest/site_libs/quarto-html/quarto-syntax-highlighting-7b89279ff1a6dce999919e0e67d4d9ec.css b/book/latest/site_libs/quarto-html/quarto-syntax-highlighting-7b89279ff1a6dce999919e0e67d4d9ec.css
deleted file mode 100644
index 7fc5b67..0000000
--- a/book/latest/site_libs/quarto-html/quarto-syntax-highlighting-7b89279ff1a6dce999919e0e67d4d9ec.css
+++ /dev/null
@@ -1,236 +0,0 @@
-/* quarto syntax highlight colors */
-:root {
-  --quarto-hl-ot-color: #003B4F;
-  --quarto-hl-at-color: #657422;
-  --quarto-hl-ss-color: #20794D;
-  --quarto-hl-an-color: #5E5E5E;
-  --quarto-hl-fu-color: #4758AB;
-  --quarto-hl-st-color: #20794D;
-  --quarto-hl-cf-color: #003B4F;
-  --quarto-hl-op-color: #5E5E5E;
-  --quarto-hl-er-color: #AD0000;
-  --quarto-hl-bn-color: #AD0000;
-  --quarto-hl-al-color: #AD0000;
-  --quarto-hl-va-color: #111111;
-  --quarto-hl-bu-color: inherit;
-  --quarto-hl-ex-color: inherit;
-  --quarto-hl-pp-color: #AD0000;
-  --quarto-hl-in-color: #5E5E5E;
-  --quarto-hl-vs-color: #20794D;
-  --quarto-hl-wa-color: #5E5E5E;
-  --quarto-hl-do-color: #5E5E5E;
-  --quarto-hl-im-color: #00769E;
-  --quarto-hl-ch-color: #20794D;
-  --quarto-hl-dt-color: #AD0000;
-  --quarto-hl-fl-color: #AD0000;
-  --quarto-hl-co-color: #5E5E5E;
-  --quarto-hl-cv-color: #5E5E5E;
-  --quarto-hl-cn-color: #8f5902;
-  --quarto-hl-sc-color: #5E5E5E;
-  --quarto-hl-dv-color: #AD0000;
-  --quarto-hl-kw-color: #003B4F;
-}
-
-/* other quarto variables */
-:root {
-  --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
-}
-
-/* syntax highlight based on Pandoc's rules */
-pre > code.sourceCode > span {
-  color: #003B4F;
-}
-
-code.sourceCode > span {
-  color: #003B4F;
-}
-
-div.sourceCode,
-div.sourceCode pre.sourceCode {
-  color: #003B4F;
-}
-
-/* Normal */
-code span {
-  color: #003B4F;
-}
-
-/* Alert */
-code span.al {
-  color: #AD0000;
-  font-style: inherit;
-}
-
-/* Annotation */
-code span.an {
-  color: #5E5E5E;
-  font-style: inherit;
-}
-
-/* Attribute */
-code span.at {
-  color: #657422;
-  font-style: inherit;
-}
-
-/* BaseN */
-code span.bn {
-  color: #AD0000;
-  font-style: inherit;
-}
-
-/* BuiltIn */
-code span.bu {
-  font-style: inherit;
-}
-
-/* ControlFlow */
-code span.cf {
-  color: #003B4F;
-  font-weight: bold;
-  font-style: inherit;
-}
-
-/* Char */
-code span.ch {
-  color: #20794D;
-  font-style: inherit;
-}
-
-/* Constant */
-code span.cn {
-  color: #8f5902;
-  font-style: inherit;
-}
-
-/* Comment */
-code span.co {
-  color: #5E5E5E;
-  font-style: inherit;
-}
-
-/* CommentVar */
-code span.cv {
-  color: #5E5E5E;
-  font-style: italic;
-}
-
-/* Documentation */
-code span.do {
-  color: #5E5E5E;
-  font-style: italic;
-}
-
-/* DataType */
-code span.dt {
-  color: #AD0000;
-  font-style: inherit;
-}
-
-/* DecVal */
-code span.dv {
-  color: #AD0000;
-  font-style: inherit;
-}
-
-/* Error */
-code span.er {
-  color: #AD0000;
-  font-style: inherit;
-}
-
-/* Extension */
-code span.ex {
-  font-style: inherit;
-}
-
-/* Float */
-code span.fl {
-  color: #AD0000;
-  font-style: inherit;
-}
-
-/* Function */
-code span.fu {
-  color: #4758AB;
-  font-style: inherit;
-}
-
-/* Import */
-code span.im {
-  color: #00769E;
-  font-style: inherit;
-}
-
-/* Information */
-code span.in {
-  color: #5E5E5E;
-  font-style: inherit;
-}
-
-/* Keyword */
-code span.kw {
-  color: #003B4F;
-  font-weight: bold;
-  font-style: inherit;
-}
-
-/* Operator */
-code span.op {
-  color: #5E5E5E;
-  font-style: inherit;
-}
-
-/* Other */
-code span.ot {
-  color: #003B4F;
-  font-style: inherit;
-}
-
-/* Preprocessor */
-code span.pp {
-  color: #AD0000;
-  font-style: inherit;
-}
-
-/* SpecialChar */
-code span.sc {
-  color: #5E5E5E;
-  font-style: inherit;
-}
-
-/* SpecialString */
-code span.ss {
-  color: #20794D;
-  font-style: inherit;
-}
-
-/* String */
-code span.st {
-  color: #20794D;
-  font-style: inherit;
-}
-
-/* Variable */
-code span.va {
-  color: #111111;
-  font-style: inherit;
-}
-
-/* VerbatimString */
-code span.vs {
-  color: #20794D;
-  font-style: inherit;
-}
-
-/* Warning */
-code span.wa {
-  color: #5E5E5E;
-  font-style: italic;
-}
-
-.prevent-inlining {
-  content: "</";
-}
-
-/*# sourceMappingURL=3944bc705cfaf4c80e56f77c5205168a.css.map */
diff --git a/book/latest/site_libs/quarto-html/quarto.js b/book/latest/site_libs/quarto-html/quarto.js
deleted file mode 100644
index 84574ca..0000000
--- a/book/latest/site_libs/quarto-html/quarto.js
+++ /dev/null
@@ -1,847 +0,0 @@
-import * as tabsets from "./tabsets/tabsets.js";
-import * as axe from "./axe/axe-check.js";
-
-const sectionChanged = new CustomEvent("quarto-sectionChanged", {
-  detail: {},
-  bubbles: true,
-  cancelable: false,
-  composed: false,
-});
-
-const layoutMarginEls = () => {
-  // Find any conflicting margin elements and add margins to the
-  // top to prevent overlap
-  const marginChildren = window.document.querySelectorAll(
-    ".column-margin.column-container > *, .margin-caption, .aside"
-  );
-
-  let lastBottom = 0;
-  for (const marginChild of marginChildren) {
-    if (marginChild.offsetParent !== null) {
-      // clear the top margin so we recompute it
-      marginChild.style.marginTop = null;
-      const top = marginChild.getBoundingClientRect().top + window.scrollY;
-      if (top < lastBottom) {
-        const marginChildStyle = window.getComputedStyle(marginChild);
-        const marginBottom = parseFloat(marginChildStyle["marginBottom"]);
-        const margin = lastBottom - top + marginBottom;
-        marginChild.style.marginTop = `${margin}px`;
-      }
-      const styles = window.getComputedStyle(marginChild);
-      const marginTop = parseFloat(styles["marginTop"]);
-      lastBottom = top + marginChild.getBoundingClientRect().height + marginTop;
-    }
-  }
-};
-
-window.document.addEventListener("DOMContentLoaded", function (_event) {
-  // Recompute the position of margin elements anytime the body size changes
-  if (window.ResizeObserver) {
-    const resizeObserver = new window.ResizeObserver(
-      throttle(() => {
-        layoutMarginEls();
-        if (
-          window.document.body.getBoundingClientRect().width < 990 &&
-          isReaderMode()
-        ) {
-          quartoToggleReader();
-        }
-      }, 50)
-    );
-    resizeObserver.observe(window.document.body);
-  }
-
-  const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
-  const sidebarEl = window.document.getElementById("quarto-sidebar");
-  const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
-  const marginSidebarEl = window.document.getElementById(
-    "quarto-margin-sidebar"
-  );
-  // function to determine whether the element has a previous sibling that is active
-  const prevSiblingIsActiveLink = (el) => {
-    const sibling = el.previousElementSibling;
-    if (sibling && sibling.tagName === "A") {
-      return sibling.classList.contains("active");
-    } else {
-      return false;
-    }
-  };
-
-  // dispatch for htmlwidgets
-  // they use slideenter event to trigger resize
-  function fireSlideEnter() {
-    const event = window.document.createEvent("Event");
-    event.initEvent("slideenter", true, true);
-    window.document.dispatchEvent(event);
-  }
-
-  const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
-  tabs.forEach((tab) => {
-    tab.addEventListener("shown.bs.tab", fireSlideEnter);
-  });
-
-  // dispatch for shiny
-  // they use BS shown and hidden events to trigger rendering
-  function distpatchShinyEvents(previous, current) {
-    if (window.jQuery) {
-      if (previous) {
-        window.jQuery(previous).trigger("hidden");
-      }
-      if (current) {
-        window.jQuery(current).trigger("shown");
-      }
-    }
-  }
-
-  // tabby.js listener: Trigger event for htmlwidget and shiny
-  document.addEventListener(
-    "tabby",
-    function (event) {
-      fireSlideEnter();
-      distpatchShinyEvents(event.detail.previousTab, event.detail.tab);
-    },
-    false
-  );
-
-  // Track scrolling and mark TOC links as active
-  // get table of contents and sidebar (bail if we don't have at least one)
-  const tocLinks = tocEl
-    ? [...tocEl.querySelectorAll("a[data-scroll-target]")]
-    : [];
-  const makeActive = (link) => tocLinks[link].classList.add("active");
-  const removeActive = (link) => tocLinks[link].classList.remove("active");
-  const removeAllActive = () =>
-    [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));
-
-  // activate the anchor for a section associated with this TOC entry
-  tocLinks.forEach((link) => {
-    link.addEventListener("click", () => {
-      if (link.href.indexOf("#") !== -1) {
-        const anchor = link.href.split("#")[1];
-        const heading = window.document.querySelector(
-          `[data-anchor-id="${anchor}"]`
-        );
-        if (heading) {
-          // Add the class
-          heading.classList.add("reveal-anchorjs-link");
-
-          // function to show the anchor
-          const handleMouseout = () => {
-            heading.classList.remove("reveal-anchorjs-link");
-            heading.removeEventListener("mouseout", handleMouseout);
-          };
-
-          // add a function to clear the anchor when the user mouses out of it
-          heading.addEventListener("mouseout", handleMouseout);
-        }
-      }
-    });
-  });
-
-  const sections = tocLinks.map((link) => {
-    const target = link.getAttribute("data-scroll-target");
-    if (target.startsWith("#")) {
-      return window.document.getElementById(decodeURI(`${target.slice(1)}`));
-    } else {
-      return window.document.querySelector(decodeURI(`${target}`));
-    }
-  });
-
-  const sectionMargin = 200;
-  let currentActive = 0;
-  // track whether we've initialized state the first time
-  let init = false;
-
-  const updateActiveLink = () => {
-    // The index from bottom to top (e.g. reversed list)
-    let sectionIndex = -1;
-    if (
-      window.innerHeight + window.pageYOffset >=
-      window.document.body.offsetHeight
-    ) {
-      // This is the no-scroll case where last section should be the active one
-      sectionIndex = 0;
-    } else {
-      // This finds the last section visible on screen that should be made active
-      sectionIndex = [...sections].reverse().findIndex((section) => {
-        if (section) {
-          return window.pageYOffset >= section.offsetTop - sectionMargin;
-        } else {
-          return false;
-        }
-      });
-    }
-    if (sectionIndex > -1) {
-      const current = sections.length - sectionIndex - 1;
-      if (current !== currentActive) {
-        removeAllActive();
-        currentActive = current;
-        makeActive(current);
-        if (init) {
-          window.dispatchEvent(sectionChanged);
-        }
-        init = true;
-      }
-    }
-  };
-
-  const inHiddenRegion = (top, bottom, hiddenRegions) => {
-    for (const region of hiddenRegions) {
-      if (top <= region.bottom && bottom >= region.top) {
-        return true;
-      }
-    }
-    return false;
-  };
-
-  const categorySelector = "header.quarto-title-block .quarto-category";
-  const activateCategories = (href) => {
-    // Find any categories
-    // Surround them with a link pointing back to:
-    // #category=Authoring
-    try {
-      const categoryEls = window.document.querySelectorAll(categorySelector);
-      for (const categoryEl of categoryEls) {
-        const categoryText = categoryEl.textContent;
-        if (categoryText) {
-          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
-          const linkEl = window.document.createElement("a");
-          linkEl.setAttribute("href", link);
-          for (const child of categoryEl.childNodes) {
-            linkEl.append(child);
-          }
-          categoryEl.appendChild(linkEl);
-        }
-      }
-    } catch {
-      // Ignore errors
-    }
-  };
-  function hasTitleCategories() {
-    return window.document.querySelector(categorySelector) !== null;
-  }
-
-  function offsetRelativeUrl(url) {
-    const offset = getMeta("quarto:offset");
-    return offset ? offset + url : url;
-  }
-
-  function offsetAbsoluteUrl(url) {
-    const offset = getMeta("quarto:offset");
-    const baseUrl = new URL(offset, window.location);
-
-    const projRelativeUrl = url.replace(baseUrl, "");
-    if (projRelativeUrl.startsWith("/")) {
-      return projRelativeUrl;
-    } else {
-      return "/" + projRelativeUrl;
-    }
-  }
-
-  // read a meta tag value
-  function getMeta(metaName) {
-    const metas = window.document.getElementsByTagName("meta");
-    for (let i = 0; i < metas.length; i++) {
-      if (metas[i].getAttribute("name") === metaName) {
-        return metas[i].getAttribute("content");
-      }
-    }
-    return "";
-  }
-
-  async function findAndActivateCategories() {
-    // Categories search with listing only use path without query
-    const currentPagePath = offsetAbsoluteUrl(
-      window.location.origin + window.location.pathname
-    );
-    const response = await fetch(offsetRelativeUrl("listings.json"));
-    if (response.status == 200) {
-      return response.json().then(function (listingPaths) {
-        const listingHrefs = [];
-        for (const listingPath of listingPaths) {
-          const pathWithoutLeadingSlash = listingPath.listing.substring(1);
-          for (const item of listingPath.items) {
-            const encodedItem = encodeURI(item);
-            if (
-              encodedItem === currentPagePath ||
-              encodedItem === currentPagePath + "index.html"
-            ) {
-              // Resolve this path against the offset to be sure
-              // we already are using the correct path to the listing
-              // (this adjusts the listing urls to be rooted against
-              // whatever root the page is actually running against)
-              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
-              const baseUrl = window.location;
-              const resolvedPath = new URL(relative, baseUrl);
-              listingHrefs.push(resolvedPath.pathname);
-              break;
-            }
-          }
-        }
-
-        // Look up the tree for a nearby linting and use that if we find one
-        const nearestListing = findNearestParentListing(
-          offsetAbsoluteUrl(window.location.pathname),
-          listingHrefs
-        );
-        if (nearestListing) {
-          activateCategories(nearestListing);
-        } else {
-          // See if the referrer is a listing page for this item
-          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
-          const referrerListing = listingHrefs.find((listingHref) => {
-            const isListingReferrer =
-              listingHref === referredRelativePath ||
-              listingHref === referredRelativePath + "index.html";
-            return isListingReferrer;
-          });
-
-          if (referrerListing) {
-            // Try to use the referrer if possible
-            activateCategories(referrerListing);
-          } else if (listingHrefs.length > 0) {
-            // Otherwise, just fall back to the first listing
-            activateCategories(listingHrefs[0]);
-          }
-        }
-      });
-    }
-  }
-  if (hasTitleCategories()) {
-    findAndActivateCategories();
-  }
-
-  const findNearestParentListing = (href, listingHrefs) => {
-    if (!href || !listingHrefs) {
-      return undefined;
-    }
-    // Look up the tree for a nearby linting and use that if we find one
-    const relativeParts = href.substring(1).split("/");
-    while (relativeParts.length > 0) {
-      const path = relativeParts.join("/");
-      for (const listingHref of listingHrefs) {
-        if (listingHref.startsWith(path)) {
-          return listingHref;
-        }
-      }
-      relativeParts.pop();
-    }
-
-    return undefined;
-  };
-
-  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
-    let isVisible = true;
-    let elRect;
-
-    return (hiddenRegions) => {
-      if (el === null) {
-        return;
-      }
-
-      // Find the last element of the TOC
-      const lastChildEl = el.lastElementChild;
-
-      if (lastChildEl) {
-        // Converts the sidebar to a menu
-        const convertToMenu = () => {
-          for (const child of el.children) {
-            child.style.opacity = 0;
-            child.style.overflow = "hidden";
-            child.style.pointerEvents = "none";
-          }
-
-          nexttick(() => {
-            const toggleContainer = window.document.createElement("div");
-            toggleContainer.style.width = "100%";
-            toggleContainer.classList.add("zindex-over-content");
-            toggleContainer.classList.add("quarto-sidebar-toggle");
-            toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
-            toggleContainer.id = placeholderDescriptor.id;
-            toggleContainer.style.position = "fixed";
-
-            const toggleIcon = window.document.createElement("i");
-            toggleIcon.classList.add("quarto-sidebar-toggle-icon");
-            toggleIcon.classList.add("bi");
-            toggleIcon.classList.add("bi-caret-down-fill");
-
-            const toggleTitle = window.document.createElement("div");
-            const titleEl = window.document.body.querySelector(
-              placeholderDescriptor.titleSelector
-            );
-            if (titleEl) {
-              toggleTitle.append(
-                titleEl.textContent || titleEl.innerText,
-                toggleIcon
-              );
-            }
-            toggleTitle.classList.add("zindex-over-content");
-            toggleTitle.classList.add("quarto-sidebar-toggle-title");
-            toggleContainer.append(toggleTitle);
-
-            const toggleContents = window.document.createElement("div");
-            toggleContents.classList = el.classList;
-            toggleContents.classList.add("zindex-over-content");
-            toggleContents.classList.add("quarto-sidebar-toggle-contents");
-            for (const child of el.children) {
-              if (child.id === "toc-title") {
-                continue;
-              }
-
-              const clone = child.cloneNode(true);
-              clone.style.opacity = 1;
-              clone.style.pointerEvents = null;
-              clone.style.display = null;
-              toggleContents.append(clone);
-            }
-            toggleContents.style.height = "0px";
-            const positionToggle = () => {
-              // position the element (top left of parent, same width as parent)
-              if (!elRect) {
-                elRect = el.getBoundingClientRect();
-              }
-              toggleContainer.style.left = `${elRect.left}px`;
-              toggleContainer.style.top = `${elRect.top}px`;
-              toggleContainer.style.width = `${elRect.width}px`;
-            };
-            positionToggle();
-
-            toggleContainer.append(toggleContents);
-            el.parentElement.prepend(toggleContainer);
-
-            // Process clicks
-            let tocShowing = false;
-            // Allow the caller to control whether this is dismissed
-            // when it is clicked (e.g. sidebar navigation supports
-            // opening and closing the nav tree, so don't dismiss on click)
-            const clickEl = placeholderDescriptor.dismissOnClick
-              ? toggleContainer
-              : toggleTitle;
-
-            const closeToggle = () => {
-              if (tocShowing) {
-                toggleContainer.classList.remove("expanded");
-                toggleContents.style.height = "0px";
-                tocShowing = false;
-              }
-            };
-
-            // Get rid of any expanded toggle if the user scrolls
-            window.document.addEventListener(
-              "scroll",
-              throttle(() => {
-                closeToggle();
-              }, 50)
-            );
-
-            // Handle positioning of the toggle
-            window.addEventListener(
-              "resize",
-              throttle(() => {
-                elRect = undefined;
-                positionToggle();
-              }, 50)
-            );
-
-            window.addEventListener("quarto-hrChanged", () => {
-              elRect = undefined;
-            });
-
-            // Process the click
-            clickEl.onclick = () => {
-              if (!tocShowing) {
-                toggleContainer.classList.add("expanded");
-                toggleContents.style.height = null;
-                tocShowing = true;
-              } else {
-                closeToggle();
-              }
-            };
-          });
-        };
-
-        // Converts a sidebar from a menu back to a sidebar
-        const convertToSidebar = () => {
-          for (const child of el.children) {
-            child.style.opacity = 1;
-            child.style.overflow = null;
-            child.style.pointerEvents = null;
-          }
-
-          const placeholderEl = window.document.getElementById(
-            placeholderDescriptor.id
-          );
-          if (placeholderEl) {
-            placeholderEl.remove();
-          }
-
-          el.classList.remove("rollup");
-        };
-
-        if (isReaderMode()) {
-          convertToMenu();
-          isVisible = false;
-        } else {
-          // Find the top and bottom o the element that is being managed
-          const elTop = el.offsetTop;
-          const elBottom =
-            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
-
-          if (!isVisible) {
-            // If the element is current not visible reveal if there are
-            // no conflicts with overlay regions
-            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
-              convertToSidebar();
-              isVisible = true;
-            }
-          } else {
-            // If the element is visible, hide it if it conflicts with overlay regions
-            // and insert a placeholder toggle (or if we're in reader mode)
-            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
-              convertToMenu();
-              isVisible = false;
-            }
-          }
-        }
-      }
-    };
-  };
-
-  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
-  for (const tabEl of tabEls) {
-    const id = tabEl.getAttribute("data-bs-target");
-    if (id) {
-      const columnEl = document.querySelector(
-        `${id} .column-margin, .tabset-margin-content`
-      );
-      if (columnEl)
-        tabEl.addEventListener("shown.bs.tab", function (event) {
-          const el = event.srcElement;
-          if (el) {
-            const visibleCls = `${el.id}-margin-content`;
-            // walk up until we find a parent tabset
-            let panelTabsetEl = el.parentElement;
-            while (panelTabsetEl) {
-              if (panelTabsetEl.classList.contains("panel-tabset")) {
-                break;
-              }
-              panelTabsetEl = panelTabsetEl.parentElement;
-            }
-
-            if (panelTabsetEl) {
-              const prevSib = panelTabsetEl.previousElementSibling;
-              if (
-                prevSib &&
-                prevSib.classList.contains("tabset-margin-container")
-              ) {
-                const childNodes = prevSib.querySelectorAll(
-                  ".tabset-margin-content"
-                );
-                for (const childEl of childNodes) {
-                  if (childEl.classList.contains(visibleCls)) {
-                    childEl.classList.remove("collapse");
-                  } else {
-                    childEl.classList.add("collapse");
-                  }
-                }
-              }
-            }
-          }
-
-          layoutMarginEls();
-        });
-    }
-  }
-
-  // Manage the visibility of the toc and the sidebar
-  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
-    id: "quarto-toc-toggle",
-    titleSelector: "#toc-title",
-    dismissOnClick: true,
-  });
-  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
-    id: "quarto-sidebarnav-toggle",
-    titleSelector: ".title",
-    dismissOnClick: false,
-  });
-  let tocLeftScrollVisibility;
-  if (leftTocEl) {
-    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
-      id: "quarto-lefttoc-toggle",
-      titleSelector: "#toc-title",
-      dismissOnClick: true,
-    });
-  }
-
-  // Find the first element that uses formatting in special columns
-  const conflictingEls = window.document.body.querySelectorAll(
-    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
-  );
-
-  // Filter all the possibly conflicting elements into ones
-  // the do conflict on the left or ride side
-  const arrConflictingEls = Array.from(conflictingEls);
-  const leftSideConflictEls = arrConflictingEls.filter((el) => {
-    if (el.tagName === "ASIDE") {
-      return false;
-    }
-    return Array.from(el.classList).find((className) => {
-      return (
-        className !== "column-body" &&
-        className.startsWith("column-") &&
-        !className.endsWith("right") &&
-        !className.endsWith("container") &&
-        className !== "column-margin"
-      );
-    });
-  });
-  const rightSideConflictEls = arrConflictingEls.filter((el) => {
-    if (el.tagName === "ASIDE") {
-      return true;
-    }
-
-    const hasMarginCaption = Array.from(el.classList).find((className) => {
-      return className == "margin-caption";
-    });
-    if (hasMarginCaption) {
-      return true;
-    }
-
-    return Array.from(el.classList).find((className) => {
-      return (
-        className !== "column-body" &&
-        !className.endsWith("container") &&
-        className.startsWith("column-") &&
-        !className.endsWith("left")
-      );
-    });
-  });
-
-  const kOverlapPaddingSize = 10;
-  function toRegions(els) {
-    return els.map((el) => {
-      const boundRect = el.getBoundingClientRect();
-      const top =
-        boundRect.top +
-        document.documentElement.scrollTop -
-        kOverlapPaddingSize;
-      return {
-        top,
-        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
-      };
-    });
-  }
-
-  let hasObserved = false;
-  const visibleItemObserver = (els) => {
-    let visibleElements = [...els];
-    const intersectionObserver = new IntersectionObserver(
-      (entries, _observer) => {
-        entries.forEach((entry) => {
-          if (entry.isIntersecting) {
-            if (visibleElements.indexOf(entry.target) === -1) {
-              visibleElements.push(entry.target);
-            }
-          } else {
-            visibleElements = visibleElements.filter((visibleEntry) => {
-              return visibleEntry !== entry;
-            });
-          }
-        });
-
-        if (!hasObserved) {
-          hideOverlappedSidebars();
-        }
-        hasObserved = true;
-      },
-      {}
-    );
-    els.forEach((el) => {
-      intersectionObserver.observe(el);
-    });
-
-    return {
-      getVisibleEntries: () => {
-        return visibleElements;
-      },
-    };
-  };
-
-  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
-  const leftElementObserver = visibleItemObserver(leftSideConflictEls);
-
-  const hideOverlappedSidebars = () => {
-    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
-    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
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-      );
-    }
-  };
-
-  window.quartoToggleReader = () => {
-    // Applies a slow class (or removes it)
-    // to update the transition speed
-    const slowTransition = (slow) => {
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-        const el = document.getElementById(id);
-        if (el) {
-          if (slow) {
-            el.classList.add("slow");
-          } else {
-            el.classList.remove("slow");
-          }
-        }
-      };
-
-      manageTransition("TOC", slow);
-      manageTransition("quarto-sidebar", slow);
-    };
-    const readerMode = !isReaderMode();
-    setReaderModeValue(readerMode);
-
-    // If we're entering reader mode, slow the transition
-    if (readerMode) {
-      slowTransition(readerMode);
-    }
-    highlightReaderToggle(readerMode);
-    hideOverlappedSidebars();
-
-    // If we're exiting reader mode, restore the non-slow transition
-    if (!readerMode) {
-      slowTransition(!readerMode);
-    }
-  };
-
-  const highlightReaderToggle = (readerMode) => {
-    const els = document.querySelectorAll(".quarto-reader-toggle");
-    if (els) {
-      els.forEach((el) => {
-        if (readerMode) {
-          el.classList.add("reader");
-        } else {
-          el.classList.remove("reader");
-        }
-      });
-    }
-  };
-
-  const setReaderModeValue = (val) => {
-    if (window.location.protocol !== "file:") {
-      window.localStorage.setItem("quarto-reader-mode", val);
-    } else {
-      localReaderMode = val;
-    }
-  };
-
-  const isReaderMode = () => {
-    if (window.location.protocol !== "file:") {
-      return window.localStorage.getItem("quarto-reader-mode") === "true";
-    } else {
-      return localReaderMode;
-    }
-  };
-  let localReaderMode = null;
-
-  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
-  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
-
-  // Walk the TOC and collapse/expand nodes
-  // Nodes are expanded if:
-  // - they are top level
-  // - they have children that are 'active' links
-  // - they are directly below an link that is 'active'
-  const walk = (el, depth) => {
-    // Tick depth when we enter a UL
-    if (el.tagName === "UL") {
-      depth = depth + 1;
-    }
-
-    // It this is active link
-    let isActiveNode = false;
-    if (el.tagName === "A" && el.classList.contains("active")) {
-      isActiveNode = true;
-    }
-
-    // See if there is an active child to this element
-    let hasActiveChild = false;
-    for (const child of el.children) {
-      hasActiveChild = walk(child, depth) || hasActiveChild;
-    }
-
-    // Process the collapse state if this is an UL
-    if (el.tagName === "UL") {
-      if (tocOpenDepth === -1 && depth > 1) {
-        // toc-expand: false
-        el.classList.add("collapse");
-      } else if (
-        depth <= tocOpenDepth ||
-        hasActiveChild ||
-        prevSiblingIsActiveLink(el)
-      ) {
-        el.classList.remove("collapse");
-      } else {
-        el.classList.add("collapse");
-      }
-
-      // untick depth when we leave a UL
-      depth = depth - 1;
-    }
-    return hasActiveChild || isActiveNode;
-  };
-
-  // walk the TOC and expand / collapse any items that should be shown
-  if (tocEl) {
-    updateActiveLink();
-    walk(tocEl, 0);
-  }
-
-  // Throttle the scroll event and walk peridiocally
-  window.document.addEventListener(
-    "scroll",
-    throttle(() => {
-      if (tocEl) {
-        updateActiveLink();
-        walk(tocEl, 0);
-      }
-      if (!isReaderMode()) {
-        hideOverlappedSidebars();
-      }
-    }, 5)
-  );
-  window.addEventListener(
-    "resize",
-    throttle(() => {
-      if (tocEl) {
-        updateActiveLink();
-        walk(tocEl, 0);
-      }
-      if (!isReaderMode()) {
-        hideOverlappedSidebars();
-      }
-    }, 10)
-  );
-  hideOverlappedSidebars();
-  highlightReaderToggle(isReaderMode());
-});
-
-tabsets.init();
-axe.init();
-
-function throttle(func, wait) {
-  let waiting = false;
-  return function () {
-    if (!waiting) {
-      func.apply(this, arguments);
-      waiting = true;
-      setTimeout(function () {
-        waiting = false;
-      }, wait);
-    }
-  };
-}
-
-function nexttick(func) {
-  return setTimeout(func, 0);
-}
diff --git a/book/latest/site_libs/quarto-html/tabsets/tabsets.js b/book/latest/site_libs/quarto-html/tabsets/tabsets.js
deleted file mode 100644
index 51345d0..0000000
--- a/book/latest/site_libs/quarto-html/tabsets/tabsets.js
+++ /dev/null
@@ -1,95 +0,0 @@
-// grouped tabsets
-
-export function init() {
-  window.addEventListener("pageshow", (_event) => {
-    function getTabSettings() {
-      const data = localStorage.getItem("quarto-persistent-tabsets-data");
-      if (!data) {
-        localStorage.setItem("quarto-persistent-tabsets-data", "{}");
-        return {};
-      }
-      if (data) {
-        return JSON.parse(data);
-      }
-    }
-
-    function setTabSettings(data) {
-      localStorage.setItem(
-        "quarto-persistent-tabsets-data",
-        JSON.stringify(data)
-      );
-    }
-
-    function setTabState(groupName, groupValue) {
-      const data = getTabSettings();
-      data[groupName] = groupValue;
-      setTabSettings(data);
-    }
-
-    function toggleTab(tab, active) {
-      const tabPanelId = tab.getAttribute("aria-controls");
-      const tabPanel = document.getElementById(tabPanelId);
-      if (active) {
-        tab.classList.add("active");
-        tabPanel.classList.add("active");
-      } else {
-        tab.classList.remove("active");
-        tabPanel.classList.remove("active");
-      }
-    }
-
-    function toggleAll(selectedGroup, selectorsToSync) {
-      for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
-        const active = selectedGroup === thisGroup;
-        for (const tab of tabs) {
-          toggleTab(tab, active);
-        }
-      }
-    }
-
-    function findSelectorsToSyncByLanguage() {
-      const result = {};
-      const tabs = Array.from(
-        document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
-      );
-      for (const item of tabs) {
-        const div = item.parentElement.parentElement.parentElement;
-        const group = div.getAttribute("data-group");
-        if (!result[group]) {
-          result[group] = {};
-        }
-        const selectorsToSync = result[group];
-        const value = item.innerHTML;
-        if (!selectorsToSync[value]) {
-          selectorsToSync[value] = [];
-        }
-        selectorsToSync[value].push(item);
-      }
-      return result;
-    }
-
-    function setupSelectorSync() {
-      const selectorsToSync = findSelectorsToSyncByLanguage();
-      Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
-        Object.entries(tabSetsByValue).forEach(([value, items]) => {
-          items.forEach((item) => {
-            item.addEventListener("click", (_event) => {
-              setTabState(group, value);
-              toggleAll(value, selectorsToSync[group]);
-            });
-          });
-        });
-      });
-      return selectorsToSync;
-    }
-
-    const selectorsToSync = setupSelectorSync();
-    for (const [group, selectedName] of Object.entries(getTabSettings())) {
-      const selectors = selectorsToSync[group];
-      // it's possible that stale state gives us empty selections, so we explicitly check here.
-      if (selectors) {
-        toggleAll(selectedName, selectors);
-      }
-    }
-  });
-}
diff --git a/book/latest/site_libs/quarto-html/tippy.css b/book/latest/site_libs/quarto-html/tippy.css
deleted file mode 100644
index e6ae635..0000000
--- a/book/latest/site_libs/quarto-html/tippy.css
+++ /dev/null
@@ -1 +0,0 @@
-.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}
\ No newline at end of file
diff --git a/book/latest/site_libs/quarto-html/tippy.umd.min.js b/book/latest/site_libs/quarto-html/tippy.umd.min.js
deleted file mode 100644
index ca292be..0000000
--- a/book/latest/site_libs/quarto-html/tippy.umd.min.js
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/book/latest/site_libs/quarto-nav/headroom.min.js b/book/latest/site_libs/quarto-nav/headroom.min.js
deleted file mode 100644
index b08f1df..0000000
--- a/book/latest/site_libs/quarto-nav/headroom.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
- * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it
- * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
- * License: MIT
- */
-
-!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset[a.direction],a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t){return t===Object(t)?t:{down:t,up:t}}function s(t,n){n=n||{},Object.assign(this,s.options,n),this.classes=Object.assign({},s.options.classes,n.classes),this.elem=t,this.tolerance=o(this.tolerance),this.offset=o(this.offset),this.initialised=!1,this.frozen=!1}return s.prototype={constructor:s,init:function(){return s.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},s.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},s.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),s});
diff --git a/book/latest/site_libs/quarto-nav/quarto-nav.js b/book/latest/site_libs/quarto-nav/quarto-nav.js
deleted file mode 100644
index 38cc430..0000000
--- a/book/latest/site_libs/quarto-nav/quarto-nav.js
+++ /dev/null
@@ -1,325 +0,0 @@
-const headroomChanged = new CustomEvent("quarto-hrChanged", {
-  detail: {},
-  bubbles: true,
-  cancelable: false,
-  composed: false,
-});
-
-const announceDismiss = () => {
-  const annEl = window.document.getElementById("quarto-announcement");
-  if (annEl) {
-    annEl.remove();
-
-    const annId = annEl.getAttribute("data-announcement-id");
-    window.localStorage.setItem(`quarto-announce-${annId}`, "true");
-  }
-};
-
-const announceRegister = () => {
-  const annEl = window.document.getElementById("quarto-announcement");
-  if (annEl) {
-    const annId = annEl.getAttribute("data-announcement-id");
-    const isDismissed =
-      window.localStorage.getItem(`quarto-announce-${annId}`) || false;
-    if (isDismissed) {
-      announceDismiss();
-      return;
-    } else {
-      annEl.classList.remove("hidden");
-    }
-
-    const actionEl = annEl.querySelector(".quarto-announcement-action");
-    if (actionEl) {
-      actionEl.addEventListener("click", function (e) {
-        e.preventDefault();
-        // Hide the bar immediately
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diff --git a/book/latest/site_libs/quarto-search/autocomplete.umd.js b/book/latest/site_libs/quarto-search/autocomplete.umd.js
deleted file mode 100644
index 6090a55..0000000
--- a/book/latest/site_libs/quarto-search/autocomplete.umd.js
+++ /dev/null
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diff --git a/book/latest/site_libs/quarto-search/fuse.min.js b/book/latest/site_libs/quarto-search/fuse.min.js
deleted file mode 100644
index adc2835..0000000
--- a/book/latest/site_libs/quarto-search/fuse.min.js
+++ /dev/null
@@ -1,9 +0,0 @@
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- *
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- * All Rights Reserved. Apache Software License 2.0
- *
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- */
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ve(e,t){t.score=e.score}function ge(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.includeMatches,i=void 0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n<r;n+=1){var i=this._docs[n];e(i,n)&&(this.removeAt(n),n-=1,r-=1,t.push(i))}return t}},{key:"removeAt",value:function(e){this._docs.splice(e,1),this._myIndex.removeAt(e)}},{key:"getIndex",value:function(){return this._myIndex}},{key:"search",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u<h;u+=1){var l=e(n.children[u],r,i);if(l.length)s.push.apply(s,f(l));else if(n.operator===ie)return[]}return s},i=this._myIndex.records,o={},c=[];return i.forEach((function(e){var t=e.$,i=e.i;if(k(t)){var a=r(n,t,i);a.length&&(o[i]||(o[i]={idx:i,item:t,matches:[]},c.push(o[i])),a.forEach((function(e){var t,n=e.matches;(t=o[i].matches).push.apply(t,f(n))})))}})),c}},{key:"_searchObjectList",value:function(e){var t=this,n=re(e,this.options),r=this._myIndex,i=r.keys,o=r.records,c=[];return o.forEach((function(e){var r=e.$,o=e.i;if(k(r)){var a=[];i.forEach((function(e,i){a.push.apply(a,f(t._findMatches({key:e,value:r[i],searcher:n})))})),a.length&&c.push({idx:o,item:r,matches:a})}})),c}},{key:"_findMatches",value:function(e){var t=e.key,n=e.value,r=e.searcher;if(!k(n))return[];var i=[];if(v(n))n.forEach((function(e){var n=e.v,o=e.i,c=e.n;if(k(n)){var a=r.searchIn(n),s=a.isMatch,u=a.score,h=a.indices;s&&i.push({score:u,key:t,value:n,idx:o,norm:c,indices:h})}}));else{var o=n.v,c=n.n,a=r.searchIn(o),s=a.isMatch,u=a.score,h=a.indices;s&&i.push({score:u,key:t,value:o,norm:c,indices:h})}return i}}]),e}();return ye.version="6.6.2",ye.createIndex=F,ye.parseIndex=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t();
\ No newline at end of file
diff --git a/book/latest/site_libs/quarto-search/quarto-search.js b/book/latest/site_libs/quarto-search/quarto-search.js
deleted file mode 100644
index d788a95..0000000
--- a/book/latest/site_libs/quarto-search/quarto-search.js
+++ /dev/null
@@ -1,1290 +0,0 @@
-const kQueryArg = "q";
-const kResultsArg = "show-results";
-
-// If items don't provide a URL, then both the navigator and the onSelect
-// function aren't called (and therefore, the default implementation is used)
-//
-// We're using this sentinel URL to signal to those handlers that this
-// item is a more item (along with the type) and can be handled appropriately
-const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05";
-
-window.document.addEventListener("DOMContentLoaded", function (_event) {
-  // Ensure that search is available on this page. If it isn't,
-  // should return early and not do anything
-  var searchEl = window.document.getElementById("quarto-search");
-  if (!searchEl) return;
-
-  const { autocomplete } = window["@algolia/autocomplete-js"];
-
-  let quartoSearchOptions = {};
-  let language = {};
-  const searchOptionEl = window.document.getElementById(
-    "quarto-search-options"
-  );
-  if (searchOptionEl) {
-    const jsonStr = searchOptionEl.textContent;
-    quartoSearchOptions = JSON.parse(jsonStr);
-    language = quartoSearchOptions.language;
-  }
-
-  // note the search mode
-  if (quartoSearchOptions.type === "overlay") {
-    searchEl.classList.add("type-overlay");
-  } else {
-    searchEl.classList.add("type-textbox");
-  }
-
-  // Used to determine highlighting behavior for this page
-  // A `q` query param is expected when the user follows a search
-  // to this page
-  const currentUrl = new URL(window.location);
-  const query = currentUrl.searchParams.get(kQueryArg);
-  const showSearchResults = currentUrl.searchParams.get(kResultsArg);
-  const mainEl = window.document.querySelector("main");
-
-  // highlight matches on the page
-  if (query && mainEl) {
-    // perform any highlighting
-    highlight(escapeRegExp(query), mainEl);
-
-    // fix up the URL to remove the q query param
-    const replacementUrl = new URL(window.location);
-    replacementUrl.searchParams.delete(kQueryArg);
-    window.history.replaceState({}, "", replacementUrl);
-  }
-
-  // function to clear highlighting on the page when the search query changes
-  // (e.g. if the user edits the query or clears it)
-  let highlighting = true;
-  const resetHighlighting = (searchTerm) => {
-    if (mainEl && highlighting && query && searchTerm !== query) {
-      clearHighlight(query, mainEl);
-      highlighting = false;
-    }
-  };
-
-  // Clear search highlighting when the user scrolls sufficiently
-  const resetFn = () => {
-    resetHighlighting("");
-    window.removeEventListener("quarto-hrChanged", resetFn);
-    window.removeEventListener("quarto-sectionChanged", resetFn);
-  };
-
-  // Register this event after the initial scrolling and settling of events
-  // on the page
-  window.addEventListener("quarto-hrChanged", resetFn);
-  window.addEventListener("quarto-sectionChanged", resetFn);
-
-  // Responsively switch to overlay mode if the search is present on the navbar
-  // Note that switching the sidebar to overlay mode requires more coordinate (not just
-  // the media query since we generate different HTML for sidebar overlays than we do
-  // for sidebar input UI)
-  const detachedMediaQuery =
-    quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)";
-
-  // If configured, include the analytics client to send insights
-  const plugins = configurePlugins(quartoSearchOptions);
-
-  let lastState = null;
-  const { setIsOpen, setQuery, setCollections } = autocomplete({
-    container: searchEl,
-    detachedMediaQuery: detachedMediaQuery,
-    defaultActiveItemId: 0,
-    panelContainer: "#quarto-search-results",
-    panelPlacement: quartoSearchOptions["panel-placement"],
-    debug: false,
-    openOnFocus: true,
-    plugins,
-    classNames: {
-      form: "d-flex",
-    },
-    placeholder: language["search-text-placeholder"],
-    translations: {
-      clearButtonTitle: language["search-clear-button-title"],
-      detachedCancelButtonText: language["search-detached-cancel-button-title"],
-      submitButtonTitle: language["search-submit-button-title"],
-    },
-    initialState: {
-      query,
-    },
-    getItemUrl({ item }) {
-      return item.href;
-    },
-    onStateChange({ state }) {
-      // If this is a file URL, note that
-
-      // Perhaps reset highlighting
-      resetHighlighting(state.query);
-
-      // If the panel just opened, ensure the panel is positioned properly
-      if (state.isOpen) {
-        if (lastState && !lastState.isOpen) {
-          setTimeout(() => {
-            positionPanel(quartoSearchOptions["panel-placement"]);
-          }, 150);
-        }
-      }
-
-      // Perhaps show the copy link
-      showCopyLink(state.query, quartoSearchOptions);
-
-      lastState = state;
-    },
-    reshape({ sources, state }) {
-      return sources.map((source) => {
-        try {
-          const items = source.getItems();
-
-          // Validate the items
-          validateItems(items);
-
-          // group the items by document
-          const groupedItems = new Map();
-          items.forEach((item) => {
-            const hrefParts = item.href.split("#");
-            const baseHref = hrefParts[0];
-            const isDocumentItem = hrefParts.length === 1;
-
-            const items = groupedItems.get(baseHref);
-            if (!items) {
-              groupedItems.set(baseHref, [item]);
-            } else {
-              // If the href for this item matches the document
-              // exactly, place this item first as it is the item that represents
-              // the document itself
-              if (isDocumentItem) {
-                items.unshift(item);
-              } else {
-                items.push(item);
-              }
-              groupedItems.set(baseHref, items);
-            }
-          });
-
-          const reshapedItems = [];
-          let count = 1;
-          for (const [_key, value] of groupedItems) {
-            const firstItem = value[0];
-            reshapedItems.push({
-              ...firstItem,
-              type: kItemTypeDoc,
-            });
-
-            const collapseMatches = quartoSearchOptions["collapse-after"];
-            const collapseCount =
-              typeof collapseMatches === "number" ? collapseMatches : 1;
-
-            if (value.length > 1) {
-              const target = `search-more-${count}`;
-              const isExpanded =
-                state.context.expanded &&
-                state.context.expanded.includes(target);
-
-              const remainingCount = value.length - collapseCount;
-
-              for (let i = 1; i < value.length; i++) {
-                if (collapseMatches && i === collapseCount) {
-                  reshapedItems.push({
-                    target,
-                    title: isExpanded
-                      ? language["search-hide-matches-text"]
-                      : remainingCount === 1
-                      ? `${remainingCount} ${language["search-more-match-text"]}`
-                      : `${remainingCount} ${language["search-more-matches-text"]}`,
-                    type: kItemTypeMore,
-                    href: kItemTypeMoreHref,
-                  });
-                }
-
-                if (isExpanded || !collapseMatches || i < collapseCount) {
-                  reshapedItems.push({
-                    ...value[i],
-                    type: kItemTypeItem,
-                    target,
-                  });
-                }
-              }
-            }
-            count += 1;
-          }
-
-          return {
-            ...source,
-            getItems() {
-              return reshapedItems;
-            },
-          };
-        } catch (error) {
-          // Some form of error occurred
-          return {
-            ...source,
-            getItems() {
-              return [
-                {
-                  title: error.name || "An Error Occurred While Searching",
-                  text:
-                    error.message ||
-                    "An unknown error occurred while attempting to perform the requested search.",
-                  type: kItemTypeError,
-                },
-              ];
-            },
-          };
-        }
-      });
-    },
-    navigator: {
-      navigate({ itemUrl }) {
-        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
-          window.location.assign(itemUrl);
-        }
-      },
-      navigateNewTab({ itemUrl }) {
-        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
-          const windowReference = window.open(itemUrl, "_blank", "noopener");
-          if (windowReference) {
-            windowReference.focus();
-          }
-        }
-      },
-      navigateNewWindow({ itemUrl }) {
-        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
-          window.open(itemUrl, "_blank", "noopener");
-        }
-      },
-    },
-    getSources({ state, setContext, setActiveItemId, refresh }) {
-      return [
-        {
-          sourceId: "documents",
-          getItemUrl({ item }) {
-            if (item.href) {
-              return offsetURL(item.href);
-            } else {
-              return undefined;
-            }
-          },
-          onSelect({
-            item,
-            state,
-            setContext,
-            setIsOpen,
-            setActiveItemId,
-            refresh,
-          }) {
-            if (item.type === kItemTypeMore) {
-              toggleExpanded(item, state, setContext, setActiveItemId, refresh);
-
-              // Toggle more
-              setIsOpen(true);
-            }
-          },
-          getItems({ query }) {
-            if (query === null || query === "") {
-              return [];
-            }
-
-            const limit = quartoSearchOptions.limit;
-            if (quartoSearchOptions.algolia) {
-              return algoliaSearch(query, limit, quartoSearchOptions.algolia);
-            } else {
-              // Fuse search options
-              const fuseSearchOptions = {
-                isCaseSensitive: false,
-                shouldSort: true,
-                minMatchCharLength: 2,
-                limit: limit,
-              };
-
-              return readSearchData().then(function (fuse) {
-                return fuseSearch(query, fuse, fuseSearchOptions);
-              });
-            }
-          },
-          templates: {
-            noResults({ createElement }) {
-              const hasQuery = lastState.query;
-
-              return createElement(
-                "div",
-                {
-                  class: `quarto-search-no-results${
-                    hasQuery ? "" : " no-query"
-                  }`,
-                },
-                language["search-no-results-text"]
-              );
-            },
-            header({ items, createElement }) {
-              // count the documents
-              const count = items.filter((item) => {
-                return item.type === kItemTypeDoc;
-              }).length;
-
-              if (count > 0) {
-                return createElement(
-                  "div",
-                  { class: "search-result-header" },
-                  `${count} ${language["search-matching-documents-text"]}`
-                );
-              } else {
-                return createElement(
-                  "div",
-                  { class: "search-result-header-no-results" },
-                  ``
-                );
-              }
-            },
-            footer({ _items, createElement }) {
-              if (
-                quartoSearchOptions.algolia &&
-                quartoSearchOptions.algolia["show-logo"]
-              ) {
-                const libDir = quartoSearchOptions.algolia["libDir"];
-                const logo = createElement("img", {
-                  src: offsetURL(
-                    `${libDir}/quarto-search/search-by-algolia.svg`
-                  ),
-                  class: "algolia-search-logo",
-                });
-                return createElement(
-                  "a",
-                  { href: "http://www.algolia.com/" },
-                  logo
-                );
-              }
-            },
-
-            item({ item, createElement }) {
-              return renderItem(
-                item,
-                createElement,
-                state,
-                setActiveItemId,
-                setContext,
-                refresh,
-                quartoSearchOptions
-              );
-            },
-          },
-        },
-      ];
-    },
-  });
-
-  window.quartoOpenSearch = () => {
-    setIsOpen(false);
-    setIsOpen(true);
-    focusSearchInput();
-  };
-
-  document.addEventListener("keyup", (event) => {
-    const { key } = event;
-    const kbds = quartoSearchOptions["keyboard-shortcut"];
-    const focusedEl = document.activeElement;
-
-    const isFormElFocused = [
-      "input",
-      "select",
-      "textarea",
-      "button",
-      "option",
-    ].find((tag) => {
-      return focusedEl.tagName.toLowerCase() === tag;
-    });
-
-    if (
-      kbds &&
-      kbds.includes(key) &&
-      !isFormElFocused &&
-      !document.activeElement.isContentEditable
-    ) {
-      event.preventDefault();
-      window.quartoOpenSearch();
-    }
-  });
-
-  // Remove the labeleledby attribute since it is pointing
-  // to a non-existent label
-  if (quartoSearchOptions.type === "overlay") {
-    const inputEl = window.document.querySelector(
-      "#quarto-search .aa-Autocomplete"
-    );
-    if (inputEl) {
-      inputEl.removeAttribute("aria-labelledby");
-    }
-  }
-
-  function throttle(func, wait) {
-    let waiting = false;
-    return function () {
-      if (!waiting) {
-        func.apply(this, arguments);
-        waiting = true;
-        setTimeout(function () {
-          waiting = false;
-        }, wait);
-      }
-    };
-  }
-
-  // If the main document scrolls dismiss the search results
-  // (otherwise, since they're floating in the document they can scroll with the document)
-  window.document.body.onscroll = throttle(() => {
-    // Only do this if we're not detached
-    // Bug #7117
-    // This will happen when the keyboard is shown on ios (resulting in a scroll)
-    // which then closed the search UI
-    if (!window.matchMedia(detachedMediaQuery).matches) {
-      setIsOpen(false);
-    }
-  }, 50);
-
-  if (showSearchResults) {
-    setIsOpen(true);
-    focusSearchInput();
-  }
-});
-
-function configurePlugins(quartoSearchOptions) {
-  const autocompletePlugins = [];
-  const algoliaOptions = quartoSearchOptions.algolia;
-  if (
-    algoliaOptions &&
-    algoliaOptions["analytics-events"] &&
-    algoliaOptions["search-only-api-key"] &&
-    algoliaOptions["application-id"]
-  ) {
-    const apiKey = algoliaOptions["search-only-api-key"];
-    const appId = algoliaOptions["application-id"];
-
-    // Aloglia insights may not be loaded because they require cookie consent
-    // Use deferred loading so events will start being recorded when/if consent
-    // is granted.
-    const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => {
-      if (
-        window.aa &&
-        window["@algolia/autocomplete-plugin-algolia-insights"]
-      ) {
-        window.aa("init", {
-          appId,
-          apiKey,
-          useCookie: true,
-        });
-
-        const { createAlgoliaInsightsPlugin } =
-          window["@algolia/autocomplete-plugin-algolia-insights"];
-        // Register the insights client
-        const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({
-          insightsClient: window.aa,
-          onItemsChange({ insights, insightsEvents }) {
-            const events = insightsEvents.flatMap((event) => {
-              // This API limits the number of items per event to 20
-              const chunkSize = 20;
-              const itemChunks = [];
-              const eventItems = event.items;
-              for (let i = 0; i < eventItems.length; i += chunkSize) {
-                itemChunks.push(eventItems.slice(i, i + chunkSize));
-              }
-              // Split the items into multiple events that can be sent
-              const events = itemChunks.map((items) => {
-                return {
-                  ...event,
-                  items,
-                };
-              });
-              return events;
-            });
-
-            for (const event of events) {
-              insights.viewedObjectIDs(event);
-            }
-          },
-        });
-        return algoliaInsightsPlugin;
-      }
-    });
-
-    // Add the plugin
-    autocompletePlugins.push(algoliaInsightsDeferredPlugin);
-    return autocompletePlugins;
-  }
-}
-
-// For plugins that may not load immediately, create a wrapper
-// plugin and forward events and plugin data once the plugin
-// is initialized. This is useful for cases like cookie consent
-// which may prevent the analytics insights event plugin from initializing
-// immediately.
-function deferredLoadPlugin(createPlugin) {
-  let plugin = undefined;
-  let subscribeObj = undefined;
-  const wrappedPlugin = () => {
-    if (!plugin && subscribeObj) {
-      plugin = createPlugin();
-      if (plugin && plugin.subscribe) {
-        plugin.subscribe(subscribeObj);
-      }
-    }
-    return plugin;
-  };
-
-  return {
-    subscribe: (obj) => {
-      subscribeObj = obj;
-    },
-    onStateChange: (obj) => {
-      const plugin = wrappedPlugin();
-      if (plugin && plugin.onStateChange) {
-        plugin.onStateChange(obj);
-      }
-    },
-    onSubmit: (obj) => {
-      const plugin = wrappedPlugin();
-      if (plugin && plugin.onSubmit) {
-        plugin.onSubmit(obj);
-      }
-    },
-    onReset: (obj) => {
-      const plugin = wrappedPlugin();
-      if (plugin && plugin.onReset) {
-        plugin.onReset(obj);
-      }
-    },
-    getSources: (obj) => {
-      const plugin = wrappedPlugin();
-      if (plugin && plugin.getSources) {
-        return plugin.getSources(obj);
-      } else {
-        return Promise.resolve([]);
-      }
-    },
-    data: (obj) => {
-      const plugin = wrappedPlugin();
-      if (plugin && plugin.data) {
-        plugin.data(obj);
-      }
-    },
-  };
-}
-
-function validateItems(items) {
-  // Validate the first item
-  if (items.length > 0) {
-    const item = items[0];
-    const missingFields = [];
-    if (item.href == undefined) {
-      missingFields.push("href");
-    }
-    if (!item.title == undefined) {
-      missingFields.push("title");
-    }
-    if (!item.text == undefined) {
-      missingFields.push("text");
-    }
-
-    if (missingFields.length === 1) {
-      throw {
-        name: `Error: Search index is missing the <code>${missingFields[0]}</code> field.`,
-        message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the <code>${missingFields[0]}</code> field or use <code>index-fields</code> in your <code>_quarto.yml</code> file to specify the field names.`,
-      };
-    } else if (missingFields.length > 1) {
-      const missingFieldList = missingFields
-        .map((field) => {
-          return `<code>${field}</code>`;
-        })
-        .join(", ");
-
-      throw {
-        name: `Error: Search index is missing the following fields: ${missingFieldList}.`,
-        message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use <code>index-fields</code> in your <code>_quarto.yml</code> file to specify the field names.`,
-      };
-    }
-  }
-}
-
-let lastQuery = null;
-function showCopyLink(query, options) {
-  const language = options.language;
-  lastQuery = query;
-  // Insert share icon
-  const inputSuffixEl = window.document.body.querySelector(
-    ".aa-Form .aa-InputWrapperSuffix"
-  );
-
-  if (inputSuffixEl) {
-    let copyButtonEl = window.document.body.querySelector(
-      ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton"
-    );
-
-    if (copyButtonEl === null) {
-      copyButtonEl = window.document.createElement("button");
-      copyButtonEl.setAttribute("class", "aa-CopyButton");
-      copyButtonEl.setAttribute("type", "button");
-      copyButtonEl.setAttribute("title", language["search-copy-link-title"]);
-      copyButtonEl.onmousedown = (e) => {
-        e.preventDefault();
-        e.stopPropagation();
-      };
-
-      const linkIcon = "bi-clipboard";
-      const checkIcon = "bi-check2";
-
-      const shareIconEl = window.document.createElement("i");
-      shareIconEl.setAttribute("class", `bi ${linkIcon}`);
-      copyButtonEl.appendChild(shareIconEl);
-      inputSuffixEl.prepend(copyButtonEl);
-
-      const clipboard = new window.ClipboardJS(".aa-CopyButton", {
-        text: function (_trigger) {
-          const copyUrl = new URL(window.location);
-          copyUrl.searchParams.set(kQueryArg, lastQuery);
-          copyUrl.searchParams.set(kResultsArg, "1");
-          return copyUrl.toString();
-        },
-      });
-      clipboard.on("success", function (e) {
-        // Focus the input
-
-        // button target
-        const button = e.trigger;
-        const icon = button.querySelector("i.bi");
-
-        // flash "checked"
-        icon.classList.add(checkIcon);
-        icon.classList.remove(linkIcon);
-        setTimeout(function () {
-          icon.classList.remove(checkIcon);
-          icon.classList.add(linkIcon);
-        }, 1000);
-      });
-    }
-
-    // If there is a query, show the link icon
-    if (copyButtonEl) {
-      if (lastQuery && options["copy-button"]) {
-        copyButtonEl.style.display = "flex";
-      } else {
-        copyButtonEl.style.display = "none";
-      }
-    }
-  }
-}
-
-/* Search Index Handling */
-// create the index
-var fuseIndex = undefined;
-var shownWarning = false;
-
-// fuse index options
-const kFuseIndexOptions = {
-  keys: [
-    { name: "title", weight: 20 },
-    { name: "section", weight: 20 },
-    { name: "text", weight: 10 },
-  ],
-  ignoreLocation: true,
-  threshold: 0.1,
-};
-
-async function readSearchData() {
-  // Initialize the search index on demand
-  if (fuseIndex === undefined) {
-    if (window.location.protocol === "file:" && !shownWarning) {
-      window.alert(
-        "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server."
-      );
-      shownWarning = true;
-      return;
-    }
-    const fuse = new window.Fuse([], kFuseIndexOptions);
-
-    // fetch the main search.json
-    const response = await fetch(offsetURL("search.json"));
-    if (response.status == 200) {
-      return response.json().then(function (searchDocs) {
-        searchDocs.forEach(function (searchDoc) {
-          fuse.add(searchDoc);
-        });
-        fuseIndex = fuse;
-        return fuseIndex;
-      });
-    } else {
-      return Promise.reject(
-        new Error(
-          "Unexpected status from search index request: " + response.status
-        )
-      );
-    }
-  }
-
-  return fuseIndex;
-}
-
-function inputElement() {
-  return window.document.body.querySelector(".aa-Form .aa-Input");
-}
-
-function focusSearchInput() {
-  setTimeout(() => {
-    const inputEl = inputElement();
-    if (inputEl) {
-      inputEl.focus();
-    }
-  }, 50);
-}
-
-/* Panels */
-const kItemTypeDoc = "document";
-const kItemTypeMore = "document-more";
-const kItemTypeItem = "document-item";
-const kItemTypeError = "error";
-
-function renderItem(
-  item,
-  createElement,
-  state,
-  setActiveItemId,
-  setContext,
-  refresh,
-  quartoSearchOptions
-) {
-  switch (item.type) {
-    case kItemTypeDoc:
-      return createDocumentCard(
-        createElement,
-        "file-richtext",
-        item.title,
-        item.section,
-        item.text,
-        item.href,
-        item.crumbs,
-        quartoSearchOptions
-      );
-    case kItemTypeMore:
-      return createMoreCard(
-        createElement,
-        item,
-        state,
-        setActiveItemId,
-        setContext,
-        refresh
-      );
-    case kItemTypeItem:
-      return createSectionCard(
-        createElement,
-        item.section,
-        item.text,
-        item.href
-      );
-    case kItemTypeError:
-      return createErrorCard(createElement, item.title, item.text);
-    default:
-      return undefined;
-  }
-}
-
-function createDocumentCard(
-  createElement,
-  icon,
-  title,
-  section,
-  text,
-  href,
-  crumbs,
-  quartoSearchOptions
-) {
-  const iconEl = createElement("i", {
-    class: `bi bi-${icon} search-result-icon`,
-  });
-  const titleEl = createElement("p", { class: "search-result-title" }, title);
-  const titleContents = [iconEl, titleEl];
-  const showParent = quartoSearchOptions["show-item-context"];
-  if (crumbs && showParent) {
-    let crumbsOut = undefined;
-    const crumbClz = ["search-result-crumbs"];
-    if (showParent === "root") {
-      crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined;
-    } else if (showParent === "parent") {
-      crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined;
-    } else {
-      crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined;
-      crumbClz.push("search-result-crumbs-wrap");
-    }
-
-    const crumbEl = createElement(
-      "p",
-      { class: crumbClz.join(" ") },
-      crumbsOut
-    );
-    titleContents.push(crumbEl);
-  }
-
-  const titleContainerEl = createElement(
-    "div",
-    { class: "search-result-title-container" },
-    titleContents
-  );
-
-  const textEls = [];
-  if (section) {
-    const sectionEl = createElement(
-      "p",
-      { class: "search-result-section" },
-      section
-    );
-    textEls.push(sectionEl);
-  }
-  const descEl = createElement("p", {
-    class: "search-result-text",
-    dangerouslySetInnerHTML: {
-      __html: text,
-    },
-  });
-  textEls.push(descEl);
-
-  const textContainerEl = createElement(
-    "div",
-    { class: "search-result-text-container" },
-    textEls
-  );
-
-  const containerEl = createElement(
-    "div",
-    {
-      class: "search-result-container",
-    },
-    [titleContainerEl, textContainerEl]
-  );
-
-  const linkEl = createElement(
-    "a",
-    {
-      href: offsetURL(href),
-      class: "search-result-link",
-    },
-    containerEl
-  );
-
-  const classes = ["search-result-doc", "search-item"];
-  if (!section) {
-    classes.push("document-selectable");
-  }
-
-  return createElement(
-    "div",
-    {
-      class: classes.join(" "),
-    },
-    linkEl
-  );
-}
-
-function createMoreCard(
-  createElement,
-  item,
-  state,
-  setActiveItemId,
-  setContext,
-  refresh
-) {
-  const moreCardEl = createElement(
-    "div",
-    {
-      class: "search-result-more search-item",
-      onClick: (e) => {
-        // Handle expanding the sections by adding the expanded
-        // section to the list of expanded sections
-        toggleExpanded(item, state, setContext, setActiveItemId, refresh);
-        e.stopPropagation();
-      },
-    },
-    item.title
-  );
-
-  return moreCardEl;
-}
-
-function toggleExpanded(item, state, setContext, setActiveItemId, refresh) {
-  const expanded = state.context.expanded || [];
-  if (expanded.includes(item.target)) {
-    setContext({
-      expanded: expanded.filter((target) => target !== item.target),
-    });
-  } else {
-    setContext({ expanded: [...expanded, item.target] });
-  }
-
-  refresh();
-  setActiveItemId(item.__autocomplete_id);
-}
-
-function createSectionCard(createElement, section, text, href) {
-  const sectionEl = createSection(createElement, section, text, href);
-  return createElement(
-    "div",
-    {
-      class: "search-result-doc-section search-item",
-    },
-    sectionEl
-  );
-}
-
-function createSection(createElement, title, text, href) {
-  const descEl = createElement("p", {
-    class: "search-result-text",
-    dangerouslySetInnerHTML: {
-      __html: text,
-    },
-  });
-
-  const titleEl = createElement("p", { class: "search-result-section" }, title);
-  const linkEl = createElement(
-    "a",
-    {
-      href: offsetURL(href),
-      class: "search-result-link",
-    },
-    [titleEl, descEl]
-  );
-  return linkEl;
-}
-
-function createErrorCard(createElement, title, text) {
-  const descEl = createElement("p", {
-    class: "search-error-text",
-    dangerouslySetInnerHTML: {
-      __html: text,
-    },
-  });
-
-  const titleEl = createElement("p", {
-    class: "search-error-title",
-    dangerouslySetInnerHTML: {
-      __html: `<i class="bi bi-exclamation-circle search-error-icon"></i> ${title}`,
-    },
-  });
-  const errorEl = createElement("div", { class: "search-error" }, [
-    titleEl,
-    descEl,
-  ]);
-  return errorEl;
-}
-
-function positionPanel(pos) {
-  const panelEl = window.document.querySelector(
-    "#quarto-search-results .aa-Panel"
-  );
-  const inputEl = window.document.querySelector(
-    "#quarto-search .aa-Autocomplete"
-  );
-
-  if (panelEl && inputEl) {
-    panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`;
-    if (pos === "start") {
-      panelEl.style.left = `${Math.round(inputEl.left)}px`;
-    } else {
-      panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`;
-    }
-  }
-}
-
-/* Highlighting */
-// highlighting functions
-function highlightMatch(query, text) {
-  if (text) {
-    const start = text.toLowerCase().indexOf(query.toLowerCase());
-    if (start !== -1) {
-      const startMark = "<mark class='search-match'>";
-      const endMark = "</mark>";
-
-      const end = start + query.length;
-      text =
-        text.slice(0, start) +
-        startMark +
-        text.slice(start, end) +
-        endMark +
-        text.slice(end);
-      const startInfo = clipStart(text, start);
-      const endInfo = clipEnd(
-        text,
-        startInfo.position + startMark.length + endMark.length
-      );
-      text =
-        startInfo.prefix +
-        text.slice(startInfo.position, endInfo.position) +
-        endInfo.suffix;
-
-      return text;
-    } else {
-      return text;
-    }
-  } else {
-    return text;
-  }
-}
-
-function clipStart(text, pos) {
-  const clipStart = pos - 50;
-  if (clipStart < 0) {
-    // This will just return the start of the string
-    return {
-      position: 0,
-      prefix: "",
-    };
-  } else {
-    // We're clipping before the start of the string, walk backwards to the first space.
-    const spacePos = findSpace(text, pos, -1);
-    return {
-      position: spacePos.position,
-      prefix: "",
-    };
-  }
-}
-
-function clipEnd(text, pos) {
-  const clipEnd = pos + 200;
-  if (clipEnd > text.length) {
-    return {
-      position: text.length,
-      suffix: "",
-    };
-  } else {
-    const spacePos = findSpace(text, clipEnd, 1);
-    return {
-      position: spacePos.position,
-      suffix: spacePos.clipped ? "…" : "",
-    };
-  }
-}
-
-function findSpace(text, start, step) {
-  let stepPos = start;
-  while (stepPos > -1 && stepPos < text.length) {
-    const char = text[stepPos];
-    if (char === " " || char === "," || char === ":") {
-      return {
-        position: step === 1 ? stepPos : stepPos - step,
-        clipped: stepPos > 1 && stepPos < text.length,
-      };
-    }
-    stepPos = stepPos + step;
-  }
-
-  return {
-    position: stepPos - step,
-    clipped: false,
-  };
-}
-
-// removes highlighting as implemented by the mark tag
-function clearHighlight(searchterm, el) {
-  const childNodes = el.childNodes;
-  for (let i = childNodes.length - 1; i >= 0; i--) {
-    const node = childNodes[i];
-    if (node.nodeType === Node.ELEMENT_NODE) {
-      if (
-        node.tagName === "MARK" &&
-        node.innerText.toLowerCase() === searchterm.toLowerCase()
-      ) {
-        el.replaceChild(document.createTextNode(node.innerText), node);
-      } else {
-        clearHighlight(searchterm, node);
-      }
-    }
-  }
-}
-
-function escapeRegExp(string) {
-  return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-}
-
-// highlight matches
-function highlight(term, el) {
-  const termRegex = new RegExp(term, "ig");
-  const childNodes = el.childNodes;
-
-  // walk back to front avoid mutating elements in front of us
-  for (let i = childNodes.length - 1; i >= 0; i--) {
-    const node = childNodes[i];
-
-    if (node.nodeType === Node.TEXT_NODE) {
-      // Search text nodes for text to highlight
-      const text = node.nodeValue;
-
-      let startIndex = 0;
-      let matchIndex = text.search(termRegex);
-      if (matchIndex > -1) {
-        const markFragment = document.createDocumentFragment();
-        while (matchIndex > -1) {
-          const prefix = text.slice(startIndex, matchIndex);
-          markFragment.appendChild(document.createTextNode(prefix));
-
-          const mark = document.createElement("mark");
-          mark.appendChild(
-            document.createTextNode(
-              text.slice(matchIndex, matchIndex + term.length)
-            )
-          );
-          markFragment.appendChild(mark);
-
-          startIndex = matchIndex + term.length;
-          matchIndex = text.slice(startIndex).search(new RegExp(term, "ig"));
-          if (matchIndex > -1) {
-            matchIndex = startIndex + matchIndex;
-          }
-        }
-        if (startIndex < text.length) {
-          markFragment.appendChild(
-            document.createTextNode(text.slice(startIndex, text.length))
-          );
-        }
-
-        el.replaceChild(markFragment, node);
-      }
-    } else if (node.nodeType === Node.ELEMENT_NODE) {
-      // recurse through elements
-      highlight(term, node);
-    }
-  }
-}
-
-/* Link Handling */
-// get the offset from this page for a given site root relative url
-function offsetURL(url) {
-  var offset = getMeta("quarto:offset");
-  return offset ? offset + url : url;
-}
-
-// read a meta tag value
-function getMeta(metaName) {
-  var metas = window.document.getElementsByTagName("meta");
-  for (let i = 0; i < metas.length; i++) {
-    if (metas[i].getAttribute("name") === metaName) {
-      return metas[i].getAttribute("content");
-    }
-  }
-  return "";
-}
-
-function algoliaSearch(query, limit, algoliaOptions) {
-  const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"];
-
-  const applicationId = algoliaOptions["application-id"];
-  const searchOnlyApiKey = algoliaOptions["search-only-api-key"];
-  const indexName = algoliaOptions["index-name"];
-  const indexFields = algoliaOptions["index-fields"];
-  const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey);
-  const searchParams = algoliaOptions["params"];
-  const searchAnalytics = !!algoliaOptions["analytics-events"];
-
-  return getAlgoliaResults({
-    searchClient,
-    queries: [
-      {
-        indexName: indexName,
-        query,
-        params: {
-          hitsPerPage: limit,
-          clickAnalytics: searchAnalytics,
-          ...searchParams,
-        },
-      },
-    ],
-    transformResponse: (response) => {
-      if (!indexFields) {
-        return response.hits.map((hit) => {
-          return hit.map((item) => {
-            return {
-              ...item,
-              text: highlightMatch(query, item.text),
-            };
-          });
-        });
-      } else {
-        const remappedHits = response.hits.map((hit) => {
-          return hit.map((item) => {
-            const newItem = { ...item };
-            ["href", "section", "title", "text", "crumbs"].forEach(
-              (keyName) => {
-                const mappedName = indexFields[keyName];
-                if (
-                  mappedName &&
-                  item[mappedName] !== undefined &&
-                  mappedName !== keyName
-                ) {
-                  newItem[keyName] = item[mappedName];
-                  delete newItem[mappedName];
-                }
-              }
-            );
-            newItem.text = highlightMatch(query, newItem.text);
-            return newItem;
-          });
-        });
-        return remappedHits;
-      }
-    },
-  });
-}
-
-let subSearchTerm = undefined;
-let subSearchFuse = undefined;
-const kFuseMaxWait = 125;
-
-async function fuseSearch(query, fuse, fuseOptions) {
-  let index = fuse;
-  // Fuse.js using the Bitap algorithm for text matching which runs in
-  // O(nm) time (no matter the structure of the text). In our case this
-  // means that long search terms mixed with large index gets very slow
-  //
-  // This injects a subIndex that will be used once the terms get long enough
-  // Usually making this subindex is cheap since there will typically be
-  // a subset of results matching the existing query
-  if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) {
-    // Use the existing subSearchFuse
-    index = subSearchFuse;
-  } else if (subSearchFuse !== undefined) {
-    // The term changed, discard the existing fuse
-    subSearchFuse = undefined;
-    subSearchTerm = undefined;
-  }
-
-  // Search using the active fuse
-  const then = performance.now();
-  const resultsRaw = await index.search(query, fuseOptions);
-  const now = performance.now();
-
-  const results = resultsRaw.map((result) => {
-    const addParam = (url, name, value) => {
-      const anchorParts = url.split("#");
-      const baseUrl = anchorParts[0];
-      const sep = baseUrl.search("\\?") > 0 ? "&" : "?";
-      anchorParts[0] = baseUrl + sep + name + "=" + value;
-      return anchorParts.join("#");
-    };
-
-    return {
-      title: result.item.title,
-      section: result.item.section,
-      href: addParam(result.item.href, kQueryArg, query),
-      text: highlightMatch(query, result.item.text),
-      crumbs: result.item.crumbs,
-    };
-  });
-
-  // If we don't have a subfuse and the query is long enough, go ahead
-  // and create a subfuse to use for subsequent queries
-  if (
-    now - then > kFuseMaxWait &&
-    subSearchFuse === undefined &&
-    resultsRaw.length < fuseOptions.limit
-  ) {
-    subSearchTerm = query;
-    subSearchFuse = new window.Fuse([], kFuseIndexOptions);
-    resultsRaw.forEach((rr) => {
-      subSearchFuse.add(rr.item);
-    });
-  }
-  return results;
-}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..356f8ae
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,11 @@
+# Requires Python >=3.11,<3.14
+sktime>=0.39.0,<0.40.0
+matplotlib>=3.10.7,<4.0.0
+seaborn>=0.13.2,<0.14.0
+jupyter>=1.1.1,<2.0.0
+statsmodels>=0.14.5,<0.15.0
+scikit-learn>=1.7.2,<2.0.0
+lightgbm>=4.6.0,<5.0.0
+prophetverse>=0.10.0,<0.11.0
+pytorch-forecasting>=1.5.0,<2.0.0
+pytest>=8.4.2

From a9ef1933b1559457c6b0d0c82fa61d7a1b0bfd38 Mon Sep 17 00:00:00 2001
From: felipeangelimvieira <felipeangelim@gmail.com>
Date: Tue, 21 Oct 2025 19:40:33 -0300
Subject: [PATCH 2/6] lgbm

---
 book/content/pt/part2/ml_models.qmd | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/book/content/pt/part2/ml_models.qmd b/book/content/pt/part2/ml_models.qmd
index feb8193..edef8fe 100644
--- a/book/content/pt/part2/ml_models.qmd
+++ b/book/content/pt/part2/ml_models.qmd
@@ -109,10 +109,10 @@ Primeiro, vamos import `ReductionForecaster`, que é a classe que implementa a a
 
 ```{python}
 from tsbook.forecasting.reduction import ReductionForecaster
-from sklearn.ensemble import RandomForestRegressor
+from lightgbm import LGBMRegressor
 
 model = ReductionForecaster(
-    RandomForestRegressor(n_estimators=100, random_state=42),
+    LGBMRegressor(n_estimators=100, random_state=42),
     window_length=30,
 )
 
@@ -135,7 +135,7 @@ Uma solução é usar a diferenciação para remover a tendência da série.
 ```{python}
 from sktime.transformations.series.difference import Differencer
 
-regressor = RandomForestRegressor(n_estimators=100, random_state=42)
+regressor = LGBMRegressor(n_estimators=100, random_state=42)
 
 model = Differencer() * ReductionForecaster(
     regressor,

From 7d5b5a4183c1e552a08cfac1b2f2ec28de0e1b7c Mon Sep 17 00:00:00 2001
From: felipeangelimvieira <felipeangelim@gmail.com>
Date: Tue, 21 Oct 2025 19:50:46 -0300
Subject: [PATCH 3/6] Add seed

---
 book/content/pt/part2/panel_data.qmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/book/content/pt/part2/panel_data.qmd b/book/content/pt/part2/panel_data.qmd
index 9846994..8fbce22 100644
--- a/book/content/pt/part2/panel_data.qmd
+++ b/book/content/pt/part2/panel_data.qmd
@@ -165,7 +165,7 @@ from tsbook.forecasting.reduction import ReductionForecaster
 from lightgbm import LGBMRegressor
 
 global_forecaster1 = ReductionForecaster(
-    LGBMRegressor(n_estimators=100, verbose=-1, objective="tweedie", random_state=42),
+    LGBMRegressor(n_estimators=100, verbose=-1, random_state=42),
     window_length=30,
 )
 

From b981c5e4cf172ee05234b8c593e13b4b050dcde2 Mon Sep 17 00:00:00 2001
From: felipeangelimvieira <felipeangelim@gmail.com>
Date: Tue, 21 Oct 2025 20:10:22 -0300
Subject: [PATCH 4/6] Minmax

---
 README.md                            | 25 +++++++++++++++++++++++++
 book/content/pt/part2/panel_data.qmd |  4 ++--
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 0bddf07..fcf0f58 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,27 @@
 # littlebooktimeseries
+
 Timeseries in 3 hours
+
+## Environment setup
+
+Choose one of the options below to get the workshop notebooks ready.
+
+### Poetry
+- Install Poetry: `curl -sSL https://install.python-poetry.org | python3 -`
+- Install dependencies: `poetry install`
+- Start the shell: `poetry shell`
+- Launch Jupyter: `jupyter lab`
+
+### pip
+- Create a virtual environment: `python3 -m venv .venv`
+- Activate it: `source .venv/bin/activate`
+- Install dependencies: `pip install -r requirements.txt`
+- Launch Jupyter: `jupyter lab`
+
+### uv
+- Install uv: `pip install uv`
+- Sync dependencies: `uv sync`
+- Activate the environment: `source .venv/bin/activate`
+- Launch Jupyter: `jupyter lab`
+
+> All options require Python 3.11 or 3.12. The notebooks live under `notebooks/`; open them in Jupyter Lab during the session.
diff --git a/book/content/pt/part2/panel_data.qmd b/book/content/pt/part2/panel_data.qmd
index 8fbce22..a046841 100644
--- a/book/content/pt/part2/panel_data.qmd
+++ b/book/content/pt/part2/panel_data.qmd
@@ -218,9 +218,9 @@ Sabemos como preprocessar séries temporais univariadas para melhorar o desempen
 
 ```{python}
 from sktime.transformations.series.difference import Differencer
+from sklearn.preprocessing import MinMaxScaler
 
-
-global_forecaster2 = Differencer() * global_forecaster1
+global_forecaster2 = Differencer() * MinMaxScaler() * global_forecaster1
 global_forecaster2.fit(y_train, X_train)
 ```
 

From d2bca62d7104f371716961bd7082fe15f83d1266 Mon Sep 17 00:00:00 2001
From: felipeangelimvieira <felipeangelim@gmail.com>
Date: Tue, 21 Oct 2025 20:12:07 -0300
Subject: [PATCH 5/6] Update hier

---
 book/content/pt/part2/hierarchical_forecasting.qmd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/book/content/pt/part2/hierarchical_forecasting.qmd b/book/content/pt/part2/hierarchical_forecasting.qmd
index 0694118..a029c31 100644
--- a/book/content/pt/part2/hierarchical_forecasting.qmd
+++ b/book/content/pt/part2/hierarchical_forecasting.qmd
@@ -105,7 +105,7 @@ from tsbook.forecasting.reduction import ReductionForecaster
 from lightgbm import LGBMRegressor
 
 forecaster = ReductionForecaster(
-    LGBMRegressor(n_estimators=50, verbose=-1, objective="tweedie"),
+    LGBMRegressor(n_estimators=100, verbose=-1, objective="tweedie", random_state=42),
     window_length=30,
     normalization_strategy="divide_mean",
 )
@@ -296,7 +296,7 @@ pd.DataFrame(
         "OptimalReconciler (ols)": metric(y_test, y_pred_optimal, y_train=y_train),
         "Mint Reconciler": metric(y_test, y_pred_mint, y_train=y_train),
     },
-    index=["Mean Absolute Scaled Error"],
+    index=["Mean Absolute Squared Error"],
 )
 ```
 

From b20810e898e0ba5598a5d161feb918228bf79e90 Mon Sep 17 00:00:00 2001
From: felipeangelimvieira <felipeangelim@gmail.com>
Date: Tue, 21 Oct 2025 20:13:10 -0300
Subject: [PATCH 6/6] update

---
 book/content/pt/part2/panel_data.qmd | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/book/content/pt/part2/panel_data.qmd b/book/content/pt/part2/panel_data.qmd
index a046841..548e691 100644
--- a/book/content/pt/part2/panel_data.qmd
+++ b/book/content/pt/part2/panel_data.qmd
@@ -218,9 +218,8 @@ Sabemos como preprocessar séries temporais univariadas para melhorar o desempen
 
 ```{python}
 from sktime.transformations.series.difference import Differencer
-from sklearn.preprocessing import MinMaxScaler
 
-global_forecaster2 = Differencer() * MinMaxScaler() * global_forecaster1
+global_forecaster2 = Differencer() * global_forecaster1
 global_forecaster2.fit(y_train, X_train)
 ```
 
@@ -249,7 +248,7 @@ errors["Local (2)"] = metric_local2
 errors
 ```
 
-Note como já superamos o modelo global incial, e o modelo local. Isso é para
+Note como já superamos o modelo global incial. Isso é para
 destacar que é **essencial** realizar um bom preprocessamento e engenharia de features para que modelos de Machine Learning tenham bom desempenho em dados em painel.
 
 ### Normalização por janela