From 82a5b67c44759755dfbec2266363e279ab0e4792 Mon Sep 17 00:00:00 2001
From: dopplershift <dopplershift@users.noreply.github.com>
Date: Fri, 14 Jun 2024 17:04:42 +0000
Subject: [PATCH] Delete preview for pull request \#3

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-<p><strong>:warning: THIS COOKBOOK IS UNDER CONSTRUCTION :warning:</strong></p>
-<section class="tex2jax_ignore mathjax_ignore" id="metpy-cookbook">
-<h1>MetPy Cookbook<a class="headerlink" href="#metpy-cookbook" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p><a class="reference external" href="https://github.com/ProjectPythia/metpy-cookbook/actions/workflows/nightly-build.yaml"><img alt="nightly-build" src="https://github.com/ProjectPythia/metpy-cookbook/actions/workflows/nightly-build.yaml/badge.svg" /></a>
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-<a class="reference external" href="https://zenodo.org/badge/latestdoi/620944317"><img alt="DOI" src="https://zenodo.org/badge/620944317.svg" /></a></p>
-<p>This Cookbook is the oversized recipe book for all your MetPy needs.
-We provide a gallery of real workflows centered around meteorological data,
-and the building blocks you need to recreate those workflows or cook up brand new ones yourself.
-Create the maps and analyses you’ve seen from class and professional institutions alike!</p>
-<section id="authors">
-<h2>Authors<a class="headerlink" href="#authors" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>MetPy Maintainers and the MetPy Community.</p>
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-<h3>Contributors<a class="headerlink" href="#contributors" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
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-<h2>Structure<a class="headerlink" href="#structure" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
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-<h3>The MetPy Example Gallery<a class="headerlink" href="#the-metpy-example-gallery" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
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-<h2>Running the Notebooks<a class="headerlink" href="#running-the-notebooks" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>You can either run the notebook using <a class="reference external" href="https://mybinder.org/">Binder</a> or on your local machine.</p>
-<section id="running-on-binder">
-<h3>Running on Binder<a class="headerlink" href="#running-on-binder" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
-<p>The simplest way to interact with a Jupyter Notebook is through
-<a class="reference external" href="https://mybinder.org/">Binder</a>, which enables the execution of a
-<a class="reference external" href="https://jupyterbook.org">Jupyter Book</a> in the cloud. The details of how this works are not
-important for now. All you need to know is how to launch a Pythia
-Cookbooks chapter via Binder. Simply navigate your mouse to
-the top right corner of the book chapter you are viewing and click
-on the rocket ship icon, (see figure below), and be sure to select
-“launch Binder”. After a moment you should be presented with a
-notebook that you can interact with. I.e. you’ll be able to execute
-and even change the example programs. You’ll see that the code cells
-have no output at first, until you execute them by pressing
-<kbd class="kbd docutils literal notranslate">Shift</kbd>+<kbd class="kbd docutils literal notranslate">Enter</kbd>. Complete details on how to interact with
-a live Jupyter notebook are described in <a class="reference external" href="https://foundations.projectpythia.org/foundations/getting-started-jupyter.html">Getting Started with
-Jupyter</a>.</p>
-</section>
-<section id="running-on-your-own-machine">
-<h3>Running on Your Own Machine<a class="headerlink" href="#running-on-your-own-machine" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
-<p>If you are interested in running this material locally on your computer, you will need to follow this workflow:</p>
-<ol class="arabic">
-<li><p>Clone the <code class="docutils literal notranslate"><span class="pre">https://github.com/ProjectPythia/metpy-cookbook</span></code> repository:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/ProjectPythia/metpy-cookbook.git
-</pre></div>
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-<li><p>Move into the <code class="docutils literal notranslate"><span class="pre">metpy-cookbook</span></code> directory</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>metpy-cookbook
-</pre></div>
-</div>
-</li>
-<li><p>Create and activate your conda environment from the <code class="docutils literal notranslate"><span class="pre">environment.yml</span></code> file</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>env<span class="w"> </span>create<span class="w"> </span>-f<span class="w"> </span>environment.yml
-conda<span class="w"> </span>activate<span class="w"> </span>metpy-cookbook
-</pre></div>
-</div>
-</li>
-<li><p>Move into the <code class="docutils literal notranslate"><span class="pre">notebooks</span></code> directory and start up Jupyter Lab</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>notebooks/
-jupyter<span class="w"> </span>lab
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diff --git a/_preview/3/_images/ProjectPythia_Logo_Final-01-Blue.svg b/_preview/3/_images/ProjectPythia_Logo_Final-01-Blue.svg
deleted file mode 100644
index 961efc2..0000000
--- a/_preview/3/_images/ProjectPythia_Logo_Final-01-Blue.svg
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diff --git a/_preview/3/_sources/README.md b/_preview/3/_sources/README.md
deleted file mode 100644
index 99dbc07..0000000
--- a/_preview/3/_sources/README.md
+++ /dev/null
@@ -1,81 +0,0 @@
-<div class="row">
-   <div class="column">
-      <img src="pythia.png" alt="Project Pythia Logo" width="35%"/>
-   </div>
-   <div class="column">
-      <img src="unidata.png" alt="Unidata Logo" width="35%"/>
-   </div>
-</div>
-
-**:warning: THIS COOKBOOK IS UNDER CONSTRUCTION :warning:**
-
-# MetPy Cookbook
-
-[![nightly-build](https://github.com/ProjectPythia/metpy-cookbook/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ProjectPythia/metpy-cookbook/actions/workflows/nightly-build.yaml)
-[![Binder](http://binder.projectpythia.org/badge_logo.svg)](http://binder.projectpythia.org/v2/gh/ProjectPythia/metpy-cookbook/main?labpath=notebooks)
-[![DOI](https://zenodo.org/badge/620944317.svg)](https://zenodo.org/badge/latestdoi/620944317)
-
-This Cookbook is the oversized recipe book for all your MetPy needs.
-We provide a gallery of real workflows centered around meteorological data,
-and the building blocks you need to recreate those workflows or cook up brand new ones yourself.
-Create the maps and analyses you've seen from class and professional institutions alike!
-
-## Authors
-
-MetPy Maintainers and the MetPy Community.
-
-### Contributors
-
-<a href="https://github.com/ProjectPythia/metpy-cookbook/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=ProjectPythia/metpy-cookbook" />
-</a>
-
-## Structure
-
-### The MetPy Example Gallery
-
-## Running the Notebooks
-
-You can either run the notebook using [Binder](https://mybinder.org/) or on your local machine.
-
-### Running on Binder
-
-The simplest way to interact with a Jupyter Notebook is through
-[Binder](https://mybinder.org/), which enables the execution of a
-[Jupyter Book](https://jupyterbook.org) in the cloud. The details of how this works are not
-important for now. All you need to know is how to launch a Pythia
-Cookbooks chapter via Binder. Simply navigate your mouse to
-the top right corner of the book chapter you are viewing and click
-on the rocket ship icon, (see figure below), and be sure to select
-“launch Binder”. After a moment you should be presented with a
-notebook that you can interact with. I.e. you’ll be able to execute
-and even change the example programs. You’ll see that the code cells
-have no output at first, until you execute them by pressing
-{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with
-a live Jupyter notebook are described in [Getting Started with
-Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html).
-
-### Running on Your Own Machine
-
-If you are interested in running this material locally on your computer, you will need to follow this workflow:
-
-1. Clone the `https://github.com/ProjectPythia/metpy-cookbook` repository:
-
-   ```bash
-    git clone https://github.com/ProjectPythia/metpy-cookbook.git
-   ```
-
-1. Move into the `metpy-cookbook` directory
-   ```bash
-   cd metpy-cookbook
-   ```
-1. Create and activate your conda environment from the `environment.yml` file
-   ```bash
-   conda env create -f environment.yml
-   conda activate metpy-cookbook
-   ```
-1. Move into the `notebooks` directory and start up Jupyter Lab
-   ```bash
-   cd notebooks/
-   jupyter lab
-   ```
diff --git a/_preview/3/_sources/notebooks/Advanced_Sounding.ipynb b/_preview/3/_sources/notebooks/Advanced_Sounding.ipynb
deleted file mode 100644
index f4d9131..0000000
--- a/_preview/3/_sources/notebooks/Advanced_Sounding.ipynb
+++ /dev/null
@@ -1,220 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Advanced Sounding\n",
-    "\n",
-    "Plot a sounding using MetPy with more advanced features. This will use the same formatting and another dataset from MetPy's sample data.<br>\n",
-    "> New additions\n",
-    "> - Lifted condensation level calculation (LCL)\n",
-    "> - Surface based parcel profile \n",
-    "> - Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) shaded\n",
-    "> - 0 degree isotherm distinguished\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.cbook import get_test_data\n",
-    "from metpy.plots import add_metpy_logo, SkewT\n",
-    "from metpy.units import units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Obtain Data and Format"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy's sample data.\n",
-    "`as_file_obj=False), skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)` is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n",
-    "\n",
-    "sounding_data = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),\n",
-    "                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)\n",
-    "\n",
-    "# Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds\n",
-    "sounding_data = sounding_data.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'\n",
-    "                       ), how='all').reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assign Units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detail in the Simple Sounding notebook and in the [Metpy documentation](https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pres = sounding_data['pressure'].values * units.hPa\n",
-    "temp = sounding_data['temperature'].values * units.degC\n",
-    "dewpoint = sounding_data['dewpoint'].values * units.degC\n",
-    "wind_speed = sounding_data['speed'].values * units.knots\n",
-    "wind_dir = sounding_data['direction'].values * units.degrees\n",
-    "u, v = mpcalc.wind_components(wind_speed, wind_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Sounding Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create figure and set size\n",
-    "fig = plt.figure(figsize=(9, 9))\n",
-    "skew = SkewT(fig, rotation=45)\n",
-    "\n",
-    "# Plot temperature, dewpoint and wind barbs\n",
-    "skew.plot(pres, temp, 'red')\n",
-    "skew.plot(pres, dewpoint, 'green')\n",
-    "\n",
-    "# Plot wind barbs\n",
-    "my_interval = np.arange(100, 1000, 50) * units('hPa') #set spacing interval\n",
-    "ix = mpcalc.resample_nn_1d(pres, my_interval) #find nearest indices for chosen interval\n",
-    "skew.plot_barbs(pres[ix], u[ix], v[ix], xloc=1) #plot values closest to chosen interval\n",
-    "\n",
-    "# Improve labels and set axis limits\n",
-    "skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "skew.ax.set_ylim(1000, 100)\n",
-    "skew.ax.set_xlim(-40, 59)\n",
-    "\n",
-    "# Calculate LCL height and plot as black dot.\n",
-    "lcl_pressure, lcl_temperature = mpcalc.lcl(pres[0], temp[0], dewpoint[0]) #index 0 is chosen to lift parcel from the surface\n",
-    "skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')\n",
-    "\n",
-    "# Calculate full parcel profile and add to plot as black line\n",
-    "prof = mpcalc.parcel_profile(pres, temp[0], dewpoint[0]).to('degC')\n",
-    "skew.plot(pres, prof, 'black', linewidth=2)\n",
-    "\n",
-    "# Shade areas of CAPE and CIN\n",
-    "skew.shade_cin(pres, temp, prof, dewpoint)\n",
-    "skew.shade_cape(pres, temp, prof)\n",
-    "\n",
-    "# Add emphasis to 0 degree isotherm with color change\n",
-    "skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\n",
-    "\n",
-    "# Add the relevant special lines throughout the figure\n",
-    "skew.plot_dry_adiabats(t0=np.arange(233, 533, 15) * units.K, alpha=0.25, color='orangered')\n",
-    "skew.plot_moist_adiabats(t0=np.arange(233, 400, 10) * units.K, alpha=0.25, color='tab:green')\n",
-    "skew.plot_mixing_lines(pressure=np.arange(1000, 99, -25) * units.hPa, linestyle='dotted', color='tab:blue')\n",
-    "\n",
-    "# Add the MetPy logo!\n",
-    "fig = plt.gcf()\n",
-    "add_metpy_logo(fig, 115, 100, size='small');\n",
-    "\n",
-    "# Add a title\n",
-    "plt.title('Advanced Sounding');"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {
-    "tags": []
-   },
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/Foundational_Sounding.ipynb b/_preview/3/_sources/notebooks/Foundational_Sounding.ipynb
deleted file mode 100644
index 49f8205..0000000
--- a/_preview/3/_sources/notebooks/Foundational_Sounding.ipynb
+++ /dev/null
@@ -1,693 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img src=\"images/ProjectPythia_Logo_Final-01-Blue.svg\" width=250 alt=\"Project Pythia Logo\"></img>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MetPy Skew-T Cookbook"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Overview\n",
-    "This Project Pythia Cookbook will cover creating Skew-T plots with MetPy. <br>\n",
-    "\n",
-    "In this Cookbook, you will learn:\n",
-    "1. The foundations of creating a basic Skew-T with MetPy.\n",
-    "1. Adding advanced features to a Skew-T such as Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) shading, Lifted condensation Level marker (LCL) and parcel profile.\n",
-    "1. Creating a Hodograph with multiple layout options.\n",
-    "1. Accessing remote Upper Air data with Siphon from University of Wyoming and Iowa State University.\n",
-    "1. Using MetPy to calculate several Skew-T parameters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "\n",
-    "| Concepts | Importance | Notes |\n",
-    "| --- | --- | --- |\n",
-    "| [Intermediate NumPy](https://foundations.projectpythia.org/core/numpy/intermediate-numpy.html)| Necessary | |\n",
-    "| [Matplotlib Basis](https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html) | Necessary |\n",
-    "\n",
-    "- **Time to learn**: 30 minutes\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.cbook import get_test_data\n",
-    "from metpy.plots import add_metpy_logo, SkewT\n",
-    "from metpy.units import units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Simple Sounding with MetPy Test Data\n",
-    "\n",
-    "Use MetPy to make a simple Skew-T plot. This will serve as a base for all Skew-T plots throughout this cookbook. <br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Obtain Data and Format"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy's sample data.\n",
-    "`as_file_obj=False), skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)` is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pressure</th>\n",
-       "      <th>height</th>\n",
-       "      <th>temperature</th>\n",
-       "      <th>dewpoint</th>\n",
-       "      <th>direction</th>\n",
-       "      <th>speed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>978.0</td>\n",
-       "      <td>345</td>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>325</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>971.0</td>\n",
-       "      <td>404</td>\n",
-       "      <td>7.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>327</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>946.7</td>\n",
-       "      <td>610</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>-1.8</td>\n",
-       "      <td>335</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>944.0</td>\n",
-       "      <td>634</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>336</td>\n",
-       "      <td>27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>925.0</td>\n",
-       "      <td>798</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>-2.6</td>\n",
-       "      <td>340</td>\n",
-       "      <td>32</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   pressure  height  temperature  dewpoint  direction  speed\n",
-       "0     978.0     345          7.8       0.8        325     14\n",
-       "1     971.0     404          7.2       0.2        327     17\n",
-       "2     946.7     610          5.2      -1.8        335     26\n",
-       "3     944.0     634          5.0      -2.0        336     27\n",
-       "4     925.0     798          3.4      -2.6        340     32"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n",
-    "\n",
-    "sounding_data= pd.read_fwf(get_test_data('jan20_sounding.txt', as_file_obj=False),\n",
-    "                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)\n",
-    "\n",
-    "# Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds\n",
-    "sounding_data = sounding_data.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'\n",
-    "                       ), how='all').reset_index(drop=True)\n",
-    "\n",
-    "sounding_data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assign Units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One of MetPy's powerful features is its unit syntax. MetPy typically requires units to be attached to arrays before being passed to MetPy functions. The MetPy documentation has a full tutorial section on [working with units](https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html). <br>\n",
-    "\n",
-    "In order to plot our sounding data, we will need to pass units to the individual variables in our dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pres = sounding_data['pressure'].values * units.hPa\n",
-    "temp = sounding_data['temperature'].values * units.degC\n",
-    "dewpoint = sounding_data['dewpoint'].values * units.degC\n",
-    "wind_speed = sounding_data['speed'].values * units.knots\n",
-    "wind_dir = sounding_data['direction'].values * units.degrees\n",
-    "u, v = mpcalc.wind_components(wind_speed, wind_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[978.0 971.0 946.7 944.0 925.0 911.8 906.0 877.9 850.0 841.0 823.0 813.6<br> 809.0 798.0 791.0 783.9 755.1 727.3 700.5 700.0 698.0 687.0 648.9 631.0<br> 600.7 592.0 577.6 555.3 536.0 533.8 500.0 472.3 453.0 400.0 399.7 387.0<br> 382.7 342.0 320.4 318.0 310.0 306.1 305.0 300.0 292.0 276.0 264.0 251.0<br> 250.0 247.0 244.0 243.3 220.0 212.0 210.0 200.0 194.0 183.0 164.0 152.0<br> 150.0 136.0 132.0 131.4 128.0 125.0 119.0 112.0 108.0 107.8 105.0 103.0<br> 100.0]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>hectopascal</td></tr></tbody></table>"
-      ],
-      "text/latex": [
-       "$\\begin{pmatrix} &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & \\end{pmatrix}\\ \\mathrm{hectopascal}$"
-      ],
-      "text/plain": [
-       "array([978. , 971. , 946.7, 944. , 925. , 911.8, 906. , 877.9, 850. ,\n",
-       "       841. , 823. , 813.6, 809. , 798. , 791. , 783.9, 755.1, 727.3,\n",
-       "       700.5, 700. , 698. , 687. , 648.9, 631. , 600.7, 592. , 577.6,\n",
-       "       555.3, 536. , 533.8, 500. , 472.3, 453. , 400. , 399.7, 387. ,\n",
-       "       382.7, 342. , 320.4, 318. , 310. , 306.1, 305. , 300. , 292. ,\n",
-       "       276. , 264. , 251. , 250. , 247. , 244. , 243.3, 220. , 212. ,\n",
-       "       210. , 200. , 194. , 183. , 164. , 152. , 150. , 136. , 132. ,\n",
-       "       131.4, 128. , 125. , 119. , 112. , 108. , 107.8, 105. , 103. ,\n",
-       "       100. ]) <Unit('hectopascal')>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pres"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Sounding Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create figure and set size\n",
-    "fig = plt.figure(figsize=(9, 9))\n",
-    "skew = SkewT(fig, rotation=45)\n",
-    "\n",
-    "# Plot temperature and dewpoint lines\n",
-    "skew.plot(pres, temp, 'red')\n",
-    "skew.plot(pres, dewpoint, 'green')\n",
-    "\n",
-    "# Plot wind barbs\n",
-    "skew.plot_barbs(pres, u, v)\n",
-    "\n",
-    "# Improve labels and set axis limits\n",
-    "skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "skew.ax.set_ylim(1000, 100)\n",
-    "\n",
-    "# Add the relevant special lines\n",
-    "skew.plot_dry_adiabats()\n",
-    "skew.plot_moist_adiabats()\n",
-    "skew.plot_mixing_lines()\n",
-    "\n",
-    "# Add the MetPy logo!\n",
-    "fig = plt.gcf()\n",
-    "add_metpy_logo(fig, 115, 100, size='small');\n",
-    "\n",
-    "# Add a title\n",
-    "plt.title('Simple Sounding');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's Unpack the the Previous Code Cell"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ">`fig = plt.figure(figsize=(6, 6))` <br>\n",
-    "> - This line creates the figure and sets the size. Equal proportions are typically recommended for a Skew-T. <br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " \n",
-    "\n",
-    ">`skew = SkewT(fig, rotation=45)` <br>\n",
-    "> - Calls the MetPy [Skew-T](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html) function to create a figure that we can easily plot upper air data on. Rotation is set at 45 to create the characteristic skewed appearence of a Skew-T."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ">`skew.plot(pres, temp, 'red')` <br>\n",
-    ">`skew.plot(pres, dewpoint, 'green')` <br>\n",
-    ">- These two lines plot the [temperature and dewpoint lines](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot). The lines are set to the standard colors, but can be adjusted for colorblind accessibility. Matplotlib has extensive [documentation](https://matplotlib.org/stable/tutorials/colors/colormaps.html) on choosing colormaps and color vision deficiencies resources."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> `skew.plot_barbs(pres, u, v)`<br>\n",
-    "> - This line plots the wind barbs. Customization options can be found [here](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot_barbs)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ">`skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')` <br>\n",
-    ">`skew.ax.set_ylabel('Pressure (hPa)')` <br>\n",
-    ">`skew.ax.set_ylim(1000, 100)` <br>\n",
-    "> - These lines improve the axes labels and set the y-axis limit to the standard values typically used in a Skew-T diagram. \n",
-    "<br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ">`skew.plot_dry_adiabats()`<br>\n",
-    ">`skew.plot_moist_adiabats()`<br>\n",
-    ">`skew.plot_mixing_lines()`<br>\n",
-    "> - These add each relevant special line if desired. The argument `colors='preferred color here'` can be passed if you would like to change the colors from the default. Further customization options can be found in the MetPy [documentation](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot_dry_adiabats) for each line."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ">`fig = plt.gcf()`<br>\n",
-    "> - Gcf stands for get current figure. This retrieves the current figure and allows us to modify it with the following line. <br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ">`add_metpy_logo(fig, 115, 100)`<br>\n",
-    ">- When `add_metpy_logo` is included in the imports cell, it allows use of the `add_metpy_logo()` function. This is greatly appreciated when utilizing MetPy for your plots. The [position and size](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.add_metpy_logo.html#metpy.plots.add_metpy_logo) of the logo can be adjusted. \n",
-    "<br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    ">`plt.title('Simple Sounding');`<br>\n",
-    ">- This adds the title to the figure. The ; removes text that populates above the figure. \n",
-    "<br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Now, Let's Improve the Wind Barbs\n",
-    "We will use the same code as above, but will add three additional lines of code. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create figure and set size\n",
-    "fig = plt.figure(figsize=(9, 9))\n",
-    "skew = SkewT(fig, rotation=45)\n",
-    "\n",
-    "# Plot temperature and dewpoint lines\n",
-    "skew.plot(pres, temp, 'red')\n",
-    "skew.plot(pres, dewpoint, 'green')\n",
-    "\n",
-    "# Set spacing interval--Every 50 hPa from 1000 to 100 hPa\n",
-    "my_interval = np.arange(100, 1000, 50) * units('hPa')\n",
-    "\n",
-    "# Get indexes of values closest to chosen interval\n",
-    "ix = mpcalc.resample_nn_1d(pres, my_interval)\n",
-    "\n",
-    "# Plot wind barbs with values nearest to chosen interval values\n",
-    "skew.plot_barbs(pres[ix], u[ix], v[ix])\n",
-    "\n",
-    "# Improve labels and set axis limits\n",
-    "skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "skew.ax.set_ylim(1000, 100)\n",
-    "\n",
-    "# Add the relevant special lines\n",
-    "skew.plot_dry_adiabats()\n",
-    "skew.plot_moist_adiabats()\n",
-    "skew.plot_mixing_lines()\n",
-    "\n",
-    "# Add the MetPy logo!\n",
-    "fig = plt.gcf()\n",
-    "add_metpy_logo(fig, 115, 100, size='small');\n",
-    "\n",
-    "# Add a title\n",
-    "plt.title('Simple Sounding');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The wind barbs on this plot are much more readable. Let's review the added code to understand why."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "> `my_interval = np.arange(100, 1000, 50) * units('hPa')`\n",
-    "> - This creates a custom spacing interval from the pressure levels the wind barbs are plotted on."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> `ix = mpcalc.resample_nn_1d(pres, my_interval)`\n",
-    "> - We defined where we want the wind barbs with `my_interval`. Since mb is not an index in our dataset, we have to use the function `mpcalc.resample_nn_1d()` to determine what index value each interval closest matches up with. Further information can be found in the MetPy [documentation](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.resample_nn_1d.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    ">`skew.plot_barbs(pres[ix], u[ix], v[ix])`\n",
-    "> - Now we just add the `ix` variable to each parameter in `.plot_barbs()` to produce the wind barbs at the interval we set."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Optional Step\n",
-    "We can extend the moist adiabat, dry adiabat, and mixing ratio lines throughout the figure by adding including optional parameters. See the [documentation](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot_dry_adiabats) for details on how to further adjust these for readability preferences. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create figure and set size\n",
-    "fig = plt.figure(figsize=(9, 9))\n",
-    "skew = SkewT(fig, rotation=45)\n",
-    "\n",
-    "# Plot temperature and dewpoint lines\n",
-    "skew.plot(pres, temp, 'red')\n",
-    "skew.plot(pres, dewpoint, 'green')\n",
-    "\n",
-    "# Set spacing interval--Every 50 hPa from 1000 to 100 hPa\n",
-    "my_interval = np.arange(100, 1000, 50) * units('hPa')\n",
-    "\n",
-    "# Get indexes of values closest to chosen interval\n",
-    "ix = mpcalc.resample_nn_1d(pres, my_interval)\n",
-    "\n",
-    "# Plot wind barbs with values nearest to chosen interval values\n",
-    "skew.plot_barbs(pres[ix], u[ix], v[ix])\n",
-    "\n",
-    "# Improve labels and set axis limits\n",
-    "skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "skew.ax.set_ylim(1000, 100)\n",
-    "\n",
-    "# Add the relevant special lines\n",
-    "skew.plot_dry_adiabats(t0=np.arange(233, 533, 10) * units.K, alpha=0.25, color='orangered')\n",
-    "skew.plot_moist_adiabats(t0=np.arange(233, 400, 5) * units.K, alpha=0.25, color='tab:green')\n",
-    "skew.plot_mixing_lines(pressure=np.arange(1000, 99, -25) * units.hPa, linestyle='dotted', color='tab:blue')\n",
-    "\n",
-    "# Add the MetPy logo!\n",
-    "fig = plt.gcf()\n",
-    "add_metpy_logo(fig, 115, 100, size='small');\n",
-    "\n",
-    "# Add a title\n",
-    "plt.title('Simple Sounding');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Resources and references\n",
-    "> [MetPy Documentation](https://unidata.github.io/MetPy/latest/index.html) <br>\n",
-    "> [Unidata Main Website](https://www.unidata.ucar.edu) <br>\n",
-    "> [Unidata GitHub](https://github.com/Unidata) <br>\n",
-    "> [Unidata YouTube Channel](https://www.youtube.com/user/unidatanews) <br>\n",
-    "> [Matplotlib Documentation](https://matplotlib.org) <br>\n",
-    "> [Unidata Python Training](https://unidata.github.io/python-training/) <br>\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  },
-  "nbdime-conflicts": {
-   "local_diff": [
-    {
-     "diff": [
-      {
-       "diff": [
-        {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "Python 3"
-         ]
-        },
-        {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
-        }
-       ],
-       "key": "display_name",
-       "op": "patch"
-      }
-     ],
-     "key": "kernelspec",
-     "op": "patch"
-    }
-   ],
-   "remote_diff": [
-    {
-     "diff": [
-      {
-       "diff": [
-        {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "Python3"
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-        },
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-         "key": 0,
-         "length": 1,
-         "op": "removerange"
-        }
-       ],
-       "key": "display_name",
-       "op": "patch"
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-     ],
-     "key": "kernelspec",
-     "op": "patch"
-    }
-   ]
-  },
-  "toc-autonumbering": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/Skew-T_Analysis.ipynb b/_preview/3/_sources/notebooks/Skew-T_Analysis.ipynb
deleted file mode 100644
index 589cbca..0000000
--- a/_preview/3/_sources/notebooks/Skew-T_Analysis.ipynb
+++ /dev/null
@@ -1,497 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Skew-T Analysis\n",
-    "===============\n",
-    "\n",
-    "Create a Skew-T plot using remote data from University of Wyoming.\n",
-    "\n",
-    "This example uses example data from the University of Wyoming sounding\n",
-    "archive for 12 UTC 31 October 2016 for Minneapolis, MN (MPX)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.cbook import get_test_data\n",
-    "from metpy.plots import add_metpy_logo, SkewT\n",
-    "from metpy.units import units\n",
-    "\n",
-    "from datetime import datetime\n",
-    "from siphon.simplewebservice.wyoming import WyomingUpperAir"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Obtain Data and Format\n",
-    "\n",
-    "This requires an internet connection to access the sounding data from a\n",
-    "remote server at the University of Wyoming. <br>\n",
-    "\n",
-    "First we will create variables for date, time and station. <br>\n",
-    "Format: `datetime(YYYY, MM, DD, HH)` <br>\n",
-    "\n",
-    "The `station` entry will need the be the three letter identifier of the station you want to look at. The National Center for Atmospheric Research (NCAR) [Research Applications Labratory](https://ral.ucar.edu) (RAL) continously updates a [detailed list of all stations](https://weather.rap.ucar.edu/surface/stations.txt). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create variables for preferred date, time and station\n",
-    "preferred_date = datetime(2016, 10, 26, 12)\n",
-    "station = 'MPX'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Read remote sounding data based on preferred_date and station\n",
-    "sounding_data = WyomingUpperAir.request_data(preferred_date, station)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pressure</th>\n",
-       "      <th>height</th>\n",
-       "      <th>temperature</th>\n",
-       "      <th>dewpoint</th>\n",
-       "      <th>direction</th>\n",
-       "      <th>speed</th>\n",
-       "      <th>u_wind</th>\n",
-       "      <th>v_wind</th>\n",
-       "      <th>station</th>\n",
-       "      <th>station_number</th>\n",
-       "      <th>time</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>elevation</th>\n",
-       "      <th>pw</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>984.0</td>\n",
-       "      <td>287</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>-2.897777</td>\n",
-       "      <td>0.776457</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>979.0</td>\n",
-       "      <td>329</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>5.4</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>-5.795555</td>\n",
-       "      <td>1.552914</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>946.3</td>\n",
-       "      <td>610</td>\n",
-       "      <td>4.6</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>105.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>-22.216294</td>\n",
-       "      <td>5.952838</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>925.0</td>\n",
-       "      <td>798</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>115.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>-22.657695</td>\n",
-       "      <td>10.565457</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>911.8</td>\n",
-       "      <td>914</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>-23.382686</td>\n",
-       "      <td>13.500000</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123</th>\n",
-       "      <td>20.0</td>\n",
-       "      <td>26230</td>\n",
-       "      <td>-55.9</td>\n",
-       "      <td>-69.9</td>\n",
-       "      <td>260.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>19.696155</td>\n",
-       "      <td>3.472964</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>19.5</td>\n",
-       "      <td>26391</td>\n",
-       "      <td>-55.1</td>\n",
-       "      <td>-70.1</td>\n",
-       "      <td>262.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>19.805361</td>\n",
-       "      <td>2.783462</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>125</th>\n",
-       "      <td>16.6</td>\n",
-       "      <td>27432</td>\n",
-       "      <td>-57.1</td>\n",
-       "      <td>-69.7</td>\n",
-       "      <td>275.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>22.912478</td>\n",
-       "      <td>-2.004582</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>15.0</td>\n",
-       "      <td>28042</td>\n",
-       "      <td>-58.3</td>\n",
-       "      <td>-69.5</td>\n",
-       "      <td>290.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>20.673238</td>\n",
-       "      <td>-7.524443</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>14.8</td>\n",
-       "      <td>28138</td>\n",
-       "      <td>-58.5</td>\n",
-       "      <td>-69.5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>MPX</td>\n",
-       "      <td>72649</td>\n",
-       "      <td>2016-10-26 12:00:00</td>\n",
-       "      <td>44.85</td>\n",
-       "      <td>-93.56</td>\n",
-       "      <td>287.0</td>\n",
-       "      <td>24.32</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>128 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     pressure  height  temperature  dewpoint  direction  speed     u_wind  \\\n",
-       "0       984.0     287          6.6       6.2      105.0    3.0  -2.897777   \n",
-       "1       979.0     329          6.4       5.4      105.0    6.0  -5.795555   \n",
-       "2       946.3     610          4.6       4.0      105.0   23.0 -22.216294   \n",
-       "3       925.0     798          3.4       3.0      115.0   25.0 -22.657695   \n",
-       "4       911.8     914          2.8       2.4      120.0   27.0 -23.382686   \n",
-       "..        ...     ...          ...       ...        ...    ...        ...   \n",
-       "123      20.0   26230        -55.9     -69.9      260.0   20.0  19.696155   \n",
-       "124      19.5   26391        -55.1     -70.1      262.0   20.0  19.805361   \n",
-       "125      16.6   27432        -57.1     -69.7      275.0   23.0  22.912478   \n",
-       "126      15.0   28042        -58.3     -69.5      290.0   22.0  20.673238   \n",
-       "127      14.8   28138        -58.5     -69.5        NaN    NaN        NaN   \n",
-       "\n",
-       "        v_wind station  station_number                time  latitude  \\\n",
-       "0     0.776457     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "1     1.552914     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "2     5.952838     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "3    10.565457     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "4    13.500000     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "..         ...     ...             ...                 ...       ...   \n",
-       "123   3.472964     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "124   2.783462     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "125  -2.004582     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "126  -7.524443     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "127        NaN     MPX           72649 2016-10-26 12:00:00     44.85   \n",
-       "\n",
-       "     longitude  elevation     pw  \n",
-       "0       -93.56      287.0  24.32  \n",
-       "1       -93.56      287.0  24.32  \n",
-       "2       -93.56      287.0  24.32  \n",
-       "3       -93.56      287.0  24.32  \n",
-       "4       -93.56      287.0  24.32  \n",
-       "..         ...        ...    ...  \n",
-       "123     -93.56      287.0  24.32  \n",
-       "124     -93.56      287.0  24.32  \n",
-       "125     -93.56      287.0  24.32  \n",
-       "126     -93.56      287.0  24.32  \n",
-       "127     -93.56      287.0  24.32  \n",
-       "\n",
-       "[128 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sounding_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assign Units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the [Metpy documentation](https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pres = sounding_data['pressure'].values * units.hPa\n",
-    "temp = sounding_data['temperature'].values * units.degC\n",
-    "dewpoint = sounding_data['dewpoint'].values * units.degC\n",
-    "wind_speed = sounding_data['speed'].values * units.knots\n",
-    "wind_dir = sounding_data['direction'].values * units.degrees\n",
-    "u = sounding_data['u_wind'].values * units.knots\n",
-    "v = sounding_data['v_wind'].values * units.knots"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Create Sounding Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create figure and set size\n",
-    "fig = plt.figure(figsize=(9, 9))\n",
-    "skew = SkewT(fig, rotation=45)\n",
-    "\n",
-    "# Plot temperature and dewpoint lines\n",
-    "skew.plot(pres, temp, 'red')\n",
-    "skew.plot(pres, dewpoint, 'green')\n",
-    "\n",
-    "# Plot wind barbs\n",
-    "my_interval = np.arange(100, 1000, 25) * units('hPa')\n",
-    "ix = mpcalc.resample_nn_1d(pres, my_interval)\n",
-    "skew.plot_barbs(pres[ix], u[ix], v[ix], y_clip_radius=0.001)\n",
-    "\n",
-    "# Improve labels and set axis limits\n",
-    "skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "skew.ax.set_ylim(1000, 100)\n",
-    "\n",
-    "# Add the relevant special lines\n",
-    "skew.plot_dry_adiabats(t0=np.arange(233, 533, 10) * units.K, alpha=0.25, color='orangered')\n",
-    "skew.plot_moist_adiabats(t0=np.arange(233, 400, 5) * units.K, alpha=0.25, color='tab:green')\n",
-    "skew.plot_mixing_lines(pressure=np.arange(1000, 99, -25) * units.hPa, linestyle='dotted', color='tab:blue')\n",
-    "\n",
-    "# Add the MetPy logo!\n",
-    "fig = plt.gcf()\n",
-    "add_metpy_logo(fig, 115, 100, size='small');\n",
-    "\n",
-    "# Add some descriptive titles\n",
-    "plt.title('{} Sounding'.format(station), loc='left')\n",
-    "plt.title('Valid Time: {}'.format(preferred_date), loc='right');"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/Skew-T_Hodograph_Inset.ipynb b/_preview/3/_sources/notebooks/Skew-T_Hodograph_Inset.ipynb
deleted file mode 100644
index 0c6ae62..0000000
--- a/_preview/3/_sources/notebooks/Skew-T_Hodograph_Inset.ipynb
+++ /dev/null
@@ -1,201 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Hodograph Inset\n",
-    "\n",
-    "Layout a Skew-T plot with a hodograph inset into the plot.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.cbook import get_test_data\n",
-    "from metpy.plots import add_metpy_logo, Hodograph, SkewT\n",
-    "from metpy.units import units\n",
-    "\n",
-    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Obtain Data and Format"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy's sample data.\n",
-    "`as_file_obj=False), skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)` is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n",
-    "\n",
-    "sounding_data = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),\n",
-    "                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)\n",
-    "\n",
-    "# Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds\n",
-    "sounding_data = sounding_data.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'\n",
-    "                       ), how='all').reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assign Units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the [Metpy documentation](https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pres = sounding_data['pressure'].values * units.hPa\n",
-    "temp = sounding_data['temperature'].values * units.degC\n",
-    "dewpoint = sounding_data['dewpoint'].values * units.degC\n",
-    "wind_speed = sounding_data['speed'].values * units.knots\n",
-    "wind_dir = sounding_data['direction'].values * units.degrees\n",
-    "u, v = mpcalc.wind_components(wind_speed, wind_dir)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x900 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create figure and grid for plots\n",
-    "fig = plt.figure(figsize=(9, 9))\n",
-    "skew = SkewT(fig, rotation=45)\n",
-    "\n",
-    "# Plot temperature and dewpoint lines\n",
-    "skew.plot(pres, temp, 'red')\n",
-    "skew.plot(pres, dewpoint, 'green')\n",
-    "\n",
-    "# Plot wind barbs\n",
-    "my_interval = np.arange(100, 1000, 50) * units('hPa')\n",
-    "ix = mpcalc.resample_nn_1d(pres, my_interval)\n",
-    "skew.plot_barbs(pres[ix], u[ix], v[ix])\n",
-    "\n",
-    "# Improve labels and set axis limits\n",
-    "skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "skew.ax.set_ylim(1000, 100)\n",
-    "skew.ax.set_xlim(-30, 40)\n",
-    "\n",
-    "# Add the relevant special lines\n",
-    "skew.plot_dry_adiabats(t0=np.arange(233, 533, 10) * units.K, alpha=0.25, color='orangered')\n",
-    "skew.plot_moist_adiabats(t0=np.arange(233, 400, 5) * units.K, alpha=0.25, color='tab:green')\n",
-    "skew.plot_mixing_lines(pressure=np.arange(1000, 99, -25) * units.hPa, linestyle='dotted', color='tab:blue')\n",
-    "\n",
-    "# Create a hodograph\n",
-    "ax_hod = inset_axes(skew.ax, '35%', '35%', loc=1)\n",
-    "h = Hodograph(ax_hod, component_range=80)\n",
-    "h.add_grid(increment=20)\n",
-    "h.plot_colormapped(u, v, pres)\n",
-    "\n",
-    "# Add the MetPy logo!\n",
-    "fig = plt.gcf()\n",
-    "add_metpy_logo(fig, 110, 650, size='small');\n",
-    "\n",
-    "# Add title\n",
-    "skew.ax.set_title('Advanced Sounding with Hodograph Inset');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/Skew-T_Layout_Hodograph.ipynb b/_preview/3/_sources/notebooks/Skew-T_Layout_Hodograph.ipynb
deleted file mode 100644
index 771b01a..0000000
--- a/_preview/3/_sources/notebooks/Skew-T_Layout_Hodograph.ipynb
+++ /dev/null
@@ -1,203 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Skew-T with Complex Layout\n",
-    "\n",
-    "Combine a Skew-T and a hodograph using Matplotlib's `GridSpec` layout capability.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.gridspec as gridspec\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.cbook import get_test_data\n",
-    "from metpy.plots import add_metpy_logo, Hodograph, SkewT\n",
-    "from metpy.units import units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Obtain Data and Format"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy's sample data.\n",
-    "`as_file_obj=False), skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)` is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n",
-    "\n",
-    "sounding_data = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),\n",
-    "                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)\n",
-    "\n",
-    "# Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds\n",
-    "sounding_data = sounding_data.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'\n",
-    "                       ), how='all').reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assign Units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the [Metpy documentation](https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pres = sounding_data['pressure'].values * units.hPa\n",
-    "temp = sounding_data['temperature'].values * units.degC\n",
-    "dewpoint = sounding_data['dewpoint'].values * units.degC\n",
-    "wind_speed = sounding_data['speed'].values * units.knots\n",
-    "wind_dir = sounding_data['direction'].values * units.degrees\n",
-    "u, v = mpcalc.wind_components(wind_speed, wind_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Sounding Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x900 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create figure and grid for plots\n",
-    "gs = gridspec.GridSpec(ncols=3, nrows=3)\n",
-    "fig = plt.figure(figsize=(9, 9))\n",
-    "skew = SkewT(fig, rotation=45, subplot=gs[:, :2])\n",
-    "\n",
-    "# Plot temperature and dewpoint lines\n",
-    "skew.plot(pres, temp, 'red')\n",
-    "skew.plot(pres, dewpoint, 'green')\n",
-    "\n",
-    "# Plot wind barbs\n",
-    "my_interval = np.arange(100, 1000, 50) * units('hPa')\n",
-    "ix = mpcalc.resample_nn_1d(pres, my_interval)\n",
-    "skew.plot_barbs(pres[ix], u[ix], v[ix])\n",
-    "\n",
-    "# Improve labels and set axis limits\n",
-    "skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "skew.ax.set_ylim(1000, 100)\n",
-    "skew.ax.set_xlim(-30, 40)\n",
-    "\n",
-    "# Add the relevant special lines\n",
-    "skew.plot_dry_adiabats(t0=np.arange(233, 533, 10) * units.K, alpha=0.25, color='orangered')\n",
-    "skew.plot_moist_adiabats(t0=np.arange(233, 400, 5) * units.K, alpha=0.25, color='tab:green')\n",
-    "skew.plot_mixing_lines(pressure=np.arange(1000, 99, -25) * units.hPa, linestyle='dotted', color='tab:blue')\n",
-    "\n",
-    "# Create a hodograph \n",
-    "ax = fig.add_subplot(gs[0, -1])\n",
-    "h = Hodograph(ax, component_range=80)\n",
-    "h.add_grid(increment=20)\n",
-    "h.plot_colormapped(u, v, pres)\n",
-    "ax.text(0.5, -0.15, 'knots', fontsize=8, ha='center', va='center', transform=ax.transAxes) #unit label\n",
-    "\n",
-    "# Add the MetPy logo!\n",
-    "fig = plt.gcf()\n",
-    "add_metpy_logo(fig, 600, 70, size='large');\n",
-    "\n",
-    "# Add titles\n",
-    "skew.ax.set_title('Advanced Sounding with Complex Layout', fontweight='bold', pad=10)\n",
-    "ax.set_title('Hodograph', fontsize=10);"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/Sounding_Calculations.ipynb b/_preview/3/_sources/notebooks/Sounding_Calculations.ipynb
deleted file mode 100644
index 4c548db..0000000
--- a/_preview/3/_sources/notebooks/Sounding_Calculations.ipynb
+++ /dev/null
@@ -1,558 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Sounding Calculation Examples\n",
-    "\n",
-    "Use functions from `metpy.calc` to perform a number of calculations using sounding data.\n",
-    "\n",
-    "The code below uses example data to perform many sounding calculations for a severe weather\n",
-    "event on May 22, 2011 from the Norman, OK sounding.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.cbook import get_test_data\n",
-    "from metpy.units import units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Effective Shear Algorithm for use in Supercell Composite Calculation\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def effective_layer(pressure, temperature, dewpoint, height, height_layer=False):\n",
-    "    \"\"\"A function that determines the effective inflow layer for a convective sounding.\n",
-    "\n",
-    "    Uses the default values of Thompason et al. (2004) for CAPE (100 J/kg) and CIN (-250 J/kg).\n",
-    "\n",
-    "    Input:\n",
-    "      - pressure: sounding pressure with units\n",
-    "      - temperature: sounding temperature with units\n",
-    "      - dewpoint: sounding dewpoint temperature with units\n",
-    "      - height: sounding heights with units\n",
-    "\n",
-    "    Returns:\n",
-    "      - pbot/hbot, ptop/htop: pressure/height of the bottom level,\n",
-    "                              pressure/height of the top level\n",
-    "    \"\"\"\n",
-    "    from metpy.calc import cape_cin, parcel_profile\n",
-    "    from metpy.units import units\n",
-    "\n",
-    "    pbot = None\n",
-    "\n",
-    "    for i in range(pressure.shape[0]):\n",
-    "        prof = parcel_profile(pressure[i:], temperature[i], dewpoint[i])\n",
-    "        sbcape, sbcin = cape_cin(pressure[i:], temperature[i:], dewpoint[i:], prof)\n",
-    "        if sbcape >= 100 * units('J/kg') and sbcin > -250 * units('J/kg'):\n",
-    "            pbot = pressure[i]\n",
-    "            hbot = height[i]\n",
-    "            bot_idx = i\n",
-    "            break\n",
-    "    if not pbot:\n",
-    "        return None, None\n",
-    "\n",
-    "    for i in range(bot_idx + 1, pressure.shape[0]):\n",
-    "        prof = parcel_profile(pressure[i:], temperature[i], dewpoint[i])\n",
-    "        sbcape, sbcin = cape_cin(pressure[i:], temperature[i:], dewpoint[i:], prof)\n",
-    "        if sbcape < 100 * units('J/kg') or sbcin < -250 * units('J/kg'):\n",
-    "            ptop = pressure[i]\n",
-    "            htop = height[i]\n",
-    "            break\n",
-    "\n",
-    "    if height_layer:\n",
-    "        return hbot, htop\n",
-    "    else:\n",
-    "        return pbot, ptop"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Obtain Data and Format"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy's sample data.\n",
-    "`as_file_obj=False), skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)` is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n",
-    "\n",
-    "sounding_data = pd.read_fwf(get_test_data('20110522_OUN_12Z.txt', as_file_obj=False),\n",
-    "                 skiprows=7, usecols=[0, 1, 2, 3, 6, 7], names=col_names)\n",
-    "\n",
-    "# Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds\n",
-    "sounding_data = sounding_data.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'),\n",
-    "               how='all').reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assign Units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the [Metpy documentation](https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pres = sounding_data['pressure'].values * units.hPa\n",
-    "temp = sounding_data['temperature'].values * units.degC\n",
-    "dewpoint = sounding_data['dewpoint'].values * units.degC\n",
-    "wind_speed = sounding_data['speed'].values * units.knots\n",
-    "wind_dir = sounding_data['direction'].values * units.degrees\n",
-    "u, v = mpcalc.wind_components(wind_speed, wind_dir)\n",
-    "height = sounding_data['height'].values * units.meter"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the wind components\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "u, v = mpcalc.wind_components(wind_speed, wind_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute common sounding index parameters\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ctotals = mpcalc.cross_totals(pres, temp, dewpoint)\n",
-    "kindex = mpcalc.k_index(pres, temp, dewpoint)\n",
-    "showalter = mpcalc.showalter_index(pres, temp, dewpoint)\n",
-    "total_totals = mpcalc.total_totals_index(pres, temp, dewpoint)\n",
-    "vert_totals = mpcalc.vertical_totals(pres, temp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compture the parcel profile for a surface-based parcel\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "prof = mpcalc.parcel_profile(pres, temp[0], dewpoint[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the corresponding Lifted Index (LI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN) values for a surface parcel\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "lift_index = mpcalc.lifted_index(pres, temp, prof)\n",
-    "cape, cin = mpcalc.cape_cin(pres, temp, dewpoint, prof)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Determine the Lifted Condensation Level (LCL), Level of Free Convection (LFC), and Equilibrium Level (EL) for our surface parcel\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "lclp, lclt = mpcalc.lcl(pres[0], temp[0], dewpoint[0])\n",
-    "lfcp, _ = mpcalc.lfc(pres, temp, dewpoint)\n",
-    "el_pressure, _ = mpcalc.el(pres, temp, dewpoint, prof)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the characteristics of a mean layer parcel (50-hPa depth)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ml_t, ml_td = mpcalc.mixed_layer(pres, temp, dewpoint, depth=50 * units.hPa)\n",
-    "ml_p, _, _ = mpcalc.mixed_parcel(pres, temp, dewpoint, depth=50 * units.hPa)\n",
-    "mlcape, mlcin = mpcalc.mixed_layer_cape_cin(pres, temp, prof, depth=50 * units.hPa)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the characteristics of the most unstable parcel (50-hPa depth)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(pres, temp, dewpoint, depth=50 * units.hPa)\n",
-    "mucape, mucin = mpcalc.most_unstable_cape_cin(pres, temp, dewpoint, depth=50 * units.hPa)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the Bunkers Storm Motion vector and use to calculate the critical angle\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "(u_storm, v_storm), *_ = mpcalc.bunkers_storm_motion(pres, u, v, height)\n",
-    "critical_angle = mpcalc.critical_angle(pres, u, v, height, u_storm, v_storm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Work on the calculations needed to compute the significant tornado parameter\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Estimate height of LCL in meters from hydrostatic thickness\n",
-    "new_p = np.append(pres[pres > lclp], lclp)\n",
-    "new_t = np.append(temp[pres > lclp], lclt)\n",
-    "lcl_height = mpcalc.thickness_hydrostatic(new_p, new_t)\n",
-    "\n",
-    "# Compute Surface-based CAPE\n",
-    "sbcape, _ = mpcalc.surface_based_cape_cin(pres, temp, dewpoint)\n",
-    "\n",
-    "# Compute SRH, given a motion vector toward the NE at 9.9 m/s\n",
-    "*_, total_helicity = mpcalc.storm_relative_helicity(height, u, v, depth=1 * units.km,\n",
-    "                                                    storm_u=u_storm, storm_v=v_storm)\n",
-    "\n",
-    "# Copmute Bulk Shear components and then magnitude\n",
-    "ubshr, vbshr = mpcalc.bulk_shear(pres, u, v, height=height, depth=6 * units.km)\n",
-    "bshear = mpcalc.wind_speed(ubshr, vbshr)\n",
-    "\n",
-    "# Use all computed pieces to calculate the Significant Tornado parameter\n",
-    "sig_tor = mpcalc.significant_tornado(sbcape, lcl_height,\n",
-    "                                     total_helicity, bshear).to_base_units()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compute the supercell composite parameter, if possible\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Determine the top and bottom of the effective layer using our own function\n",
-    "hbot, htop = effective_layer(pres, temp, dewpoint, height, height_layer=True)\n",
-    "\n",
-    "# Perform the calculation of supercell composite if an effective layer exists\n",
-    "if hbot:\n",
-    "    esrh = mpcalc.storm_relative_helicity(height, u, v, depth=htop - hbot, bottom=hbot)\n",
-    "    eubshr, evbshr = mpcalc.bulk_shear(pres, u, v, height=height, depth=htop - hbot, bottom=hbot)\n",
-    "    ebshear = mpcalc.wind_speed(eubshr, evbshr)\n",
-    "\n",
-    "    super_comp = mpcalc.supercell_composite(mucape, esrh[0], ebshear)\n",
-    "else:\n",
-    "    super_comp = np.nan"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Print Important Sounding Parameters\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Important Sounding Parameters for KOUN on 22 Mary 2011 12 UTC\n",
-      "\n",
-      "        CAPE: 3223.89 joule / kilogram\n",
-      "         CIN: -96.26 joule / kilogram\n",
-      "LCL Pressure: 949.09 hectopascal\n",
-      "LFC Pressure: 735.99 hectopascal\n",
-      " EL Pressure: 194.72 hectopascal\n",
-      "\n",
-      "   Lifted Index: [-6.96] delta_degree_Celsius\n",
-      "        K-Index: 22.10 degree_Celsius\n",
-      "Showalter Index: [-0.08] delta_degree_Celsius\n",
-      "   Cross Totals: 17.10 delta_degree_Celsius\n",
-      "   Total Totals: 50.20 delta_degree_Celsius\n",
-      "Vertical Totals: 33.10 delta_degree_Celsius\n",
-      "\n",
-      "Mixed Layer - Lowest 50-hPa\n",
-      "     ML Temp: 20.99 degree_Celsius\n",
-      "     ML Dewp: 20.55 degree_Celsius\n",
-      "     ML CAPE: 3254.17 joule / kilogram\n",
-      "      ML CIN: -138.20 joule / kilogram\n",
-      "\n",
-      "Most Unstable - Lowest 50-hPa\n",
-      "     MU Temp: 20.40 degree_Celsius\n",
-      "     MU Dewp: 20.40 degree_Celsius\n",
-      " MU Pressure: 925.00 hectopascal\n",
-      "     MU CAPE: 3693.64 joule / kilogram\n",
-      "      MU CIN: -60.50 joule / kilogram\n",
-      "\n",
-      "Bunkers Storm Motion Vector\n",
-      "  u_storm: 21.85 knot\n",
-      "  v_storm: 4.55 knot\n",
-      "Critical Angle: 67.33 degree\n",
-      "\n",
-      "Storm Relative Helicity: 279.49 meter ** 2 / second ** 2\n",
-      "Significant Tornado Parameter: [4.60] dimensionless\n",
-      "Supercell Composite Parameter: [9.07] dimensionless\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Important Sounding Parameters for KOUN on 22 Mary 2011 12 UTC')\n",
-    "print()\n",
-    "print(f'        CAPE: {cape:.2f}')\n",
-    "print(f'         CIN: {cin:.2f}')\n",
-    "print(f'LCL Pressure: {lclp:.2f}')\n",
-    "print(f'LFC Pressure: {lfcp:.2f}')\n",
-    "print(f' EL Pressure: {el_pressure:.2f}')\n",
-    "print()\n",
-    "print(f'   Lifted Index: {lift_index:.2f}')\n",
-    "print(f'        K-Index: {kindex:.2f}')\n",
-    "print(f'Showalter Index: {showalter:.2f}')\n",
-    "print(f'   Cross Totals: {ctotals:.2f}')\n",
-    "print(f'   Total Totals: {total_totals:.2f}')\n",
-    "print(f'Vertical Totals: {vert_totals:.2f}')\n",
-    "print()\n",
-    "print('Mixed Layer - Lowest 50-hPa')\n",
-    "print(f'     ML Temp: {ml_t:.2f}')\n",
-    "print(f'     ML Dewp: {ml_td:.2f}')\n",
-    "print(f'     ML CAPE: {mlcape:.2f}')\n",
-    "print(f'      ML CIN: {mlcin:.2f}')\n",
-    "print()\n",
-    "print('Most Unstable - Lowest 50-hPa')\n",
-    "print(f'     MU Temp: {mu_t:.2f}')\n",
-    "print(f'     MU Dewp: {mu_td:.2f}')\n",
-    "print(f' MU Pressure: {mu_p:.2f}')\n",
-    "print(f'     MU CAPE: {mucape:.2f}')\n",
-    "print(f'      MU CIN: {mucin:.2f}')\n",
-    "print()\n",
-    "print('Bunkers Storm Motion Vector')\n",
-    "print(f'  u_storm: {u_storm:.2f}')\n",
-    "print(f'  v_storm: {v_storm:.2f}')\n",
-    "print(f'Critical Angle: {critical_angle:.2f}')\n",
-    "print()\n",
-    "print(f'Storm Relative Helicity: {total_helicity:.2f}')\n",
-    "print(f'Significant Tornado Parameter: {sig_tor:.2f}')\n",
-    "print(f'Supercell Composite Parameter: {super_comp:.2f}')"
-   ]
-  }
- ],
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diff --git a/_preview/3/_sources/notebooks/Sounding_LCL_Dataset.ipynb b/_preview/3/_sources/notebooks/Sounding_LCL_Dataset.ipynb
deleted file mode 100644
index 8173281..0000000
--- a/_preview/3/_sources/notebooks/Sounding_LCL_Dataset.ipynb
+++ /dev/null
@@ -1,638 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Sounding as Dataset Example\n",
-    "\n",
-    "Use MetPy to make a Skew-T LogP plot from an xarray Dataset after computing LCL parcel profile.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.cbook import get_test_data\n",
-    "from metpy.plots import add_metpy_logo, SkewT\n",
-    "from metpy.units import units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Obtain Data and Format"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy's sample data.\n",
-    "`as_file_obj=False), skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)` is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']\n",
-    "\n",
-    "sounding_data = pd.read_fwf(get_test_data('20110522_OUN_12Z.txt', as_file_obj=False),\n",
-    "                 skiprows=7, usecols=[0, 1, 2, 3, 6, 7], names=col_names)\n",
-    "\n",
-    "# Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds\n",
-    "sounding_data = sounding_data.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'),\n",
-    "               how='all').reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Assign Units"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the [Metpy documentation](https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pres = sounding_data['pressure'].values * units.hPa\n",
-    "temp = sounding_data['temperature'].values * units.degC\n",
-    "dewpoint = sounding_data['dewpoint'].values * units.degC\n",
-    "wind_speed = sounding_data['speed'].values * units.knots\n",
-    "wind_dir = sounding_data['direction'].values * units.degrees\n",
-    "u, v = mpcalc.wind_components(wind_speed, wind_dir)\n",
-    "\n",
-    "lcl_ds = mpcalc.parcel_profile_with_lcl_as_dataset(pres, temp, dewpoint)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
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-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:              (isobaric: 71)\n",
-       "Coordinates:\n",
-       "  * isobaric             (isobaric) float64 966.0 953.0 949.1 ... 104.0 100.0\n",
-       "Data variables:\n",
-       "    ambient_temperature  (isobaric) float64 &lt;Quantity([ 22.2         21.4    ...\n",
-       "    ambient_dew_point    (isobaric) float64 &lt;Quantity([ 21.          20.7    ...\n",
-       "    parcel_temperature   (isobaric) float64 &lt;Quantity([295.35       294.20887...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0e4f83f9-7b65-4ad2-a645-07d969b426da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0e4f83f9-7b65-4ad2-a645-07d969b426da' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>isobaric</span>: 71</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-561c3774-1fc1-4ded-b885-626689bf7072' class='xr-section-summary-in' type='checkbox'  checked><label for='section-561c3774-1fc1-4ded-b885-626689bf7072' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaric</span></div><div class='xr-var-dims'>(isobaric)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>966.0 953.0 949.1 ... 104.0 100.0</div><input id='attrs-7f6f1466-507c-478a-b94a-43fb8515db17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f6f1466-507c-478a-b94a-43fb8515db17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-663b8e01-04bd-4573-809a-3d6e399edfb6' class='xr-var-data-in' type='checkbox'><label for='data-663b8e01-04bd-4573-809a-3d6e399edfb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hectopascal</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([966.      , 953.      , 949.085447, 936.9     , 925.      , 904.5     ,\n",
-       "       896.      , 890.      , 886.      , 873.3     , 873.      , 850.      ,\n",
-       "       846.      , 813.8     , 802.      , 785.      , 757.1     , 730.1     ,\n",
-       "       700.      , 653.3     , 639.      , 606.      , 605.6     , 584.      ,\n",
-       "       582.7     , 582.      , 577.      , 571.      , 561.      , 560.7     ,\n",
-       "       539.4     , 539.      , 500.      , 478.9     , 453.      , 443.      ,\n",
-       "       406.3     , 400.      , 389.3     , 327.3     , 313.4     , 300.      ,\n",
-       "       286.      , 250.      , 249.      , 220.      , 210.      , 200.      ,\n",
-       "       197.      , 196.5     , 190.      , 181.      , 173.      , 159.      ,\n",
-       "       155.      , 154.2     , 150.      , 148.      , 146.9     , 146.      ,\n",
-       "       142.      , 140.      , 137.      , 133.3     , 127.      , 126.      ,\n",
-       "       120.9     , 111.      , 109.      , 104.      , 100.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fda900ee-94bc-4e79-b7a5-d5f505d85ecb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fda900ee-94bc-4e79-b7a5-d5f505d85ecb' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ambient_temperature</span></div><div class='xr-var-dims'>(isobaric)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>&lt;Quantity([ 22.2         21.4   ...</div><input id='attrs-20879781-0b1c-43e9-9cd4-8dd4b4ea21bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20879781-0b1c-43e9-9cd4-8dd4b4ea21bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2517d216-bccf-4060-b382-a0140ae76228' class='xr-var-data-in' type='checkbox'><label for='data-2517d216-bccf-4060-b382-a0140ae76228' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[22.2 21.4 21.254116023095705 20.8 20.4 19.3 18.8 20.0 22.2 23.2 23.2 22.0<br> 21.8 19.2 18.2 16.5 13.7 10.9 7.6 2.3 0.6 -2.9 -2.9 -4.5 -4.4 -4.3 -3.7<br> -3.3 -3.9 -3.9 -6.3 -6.3 -11.1 -13.7 -17.1 -18.3 -23.9 -24.9 -26.6 -37.9<br> -40.7 -43.5 -46.3 -52.1 -52.3 -54.1 -55.9 -56.5 -56.5 -56.5 -56.5 -57.9<br> -56.9 -59.3 -58.9 -59.0 -59.5 -59.9 -59.2 -58.7 -57.9 -56.7 -56.5 -57.7<br> -59.8 -60.1 -61.0 -62.9 -64.3 -63.3 -64.3]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>degree_Celsius</td></tr></tbody></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ambient_dew_point</span></div><div class='xr-var-dims'>(isobaric)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>&lt;Quantity([ 21.          20.7   ...</div><input id='attrs-624b0afa-6190-4944-a0a1-666dafef7e04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-624b0afa-6190-4944-a0a1-666dafef7e04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fb18b3d-7ca6-480f-abf7-4bb7195502fc' class='xr-var-data-in' type='checkbox'><label for='data-4fb18b3d-7ca6-480f-abf7-4bb7195502fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd></dl></div><div class='xr-var-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[21.0 20.7 20.65137200769857 20.5 20.4 19.3 18.8 20.0 19.0 13.3 13.2 6.0<br> 3.8 -1.7 -3.8 -4.7 -6.2 -7.7 -9.4 -10.9 -11.4 -12.9 -12.9 -14.5 -14.4<br> -14.3 -24.7 -36.3 -31.9 -31.8 -27.4 -27.3 -29.1 -31.3 -34.1 -32.3 -37.0<br> -37.9 -39.3 -48.1 -50.3 -52.5 -55.3 -62.1 -62.3 -64.1 -65.9 -66.5 -66.5<br> -66.5 -66.5 -67.9 -66.9 -69.3 -68.9 -69.0 -69.5 -69.9 -69.2 -68.7 -67.9<br> -66.7 -66.5 -67.7 -69.8 -70.1 -71.0 -72.9 -73.3 -73.3 -74.3]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>degree_Celsius</td></tr></tbody></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>parcel_temperature</span></div><div class='xr-var-dims'>(isobaric)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>&lt;Quantity([295.35       294.2088...</div><input id='attrs-be155271-ca35-403b-b84e-df507532216b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be155271-ca35-403b-b84e-df507532216b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8657508c-1dcb-4b59-8a54-6184a469c23b' class='xr-var-data-in' type='checkbox'><label for='data-8657508c-1dcb-4b59-8a54-6184a469c23b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air_temperature_of_lifted_parcel</dd></dl></div><div class='xr-var-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[295.34999999999997 294.20887396170207 293.86308176800566<br> 293.40999598297236 292.9605366516025 292.1693871501292 291.83484364691674<br> 291.5963220045112 291.4361963323114 290.92178176440206 290.9095176897098<br> 289.95324507449203 289.78361661237284 288.3800144790221<br> 287.84779705003416 287.0630619897046 285.72612496874217 284.3692057775776<br> 282.77536756242387 280.10893536751206 279.23922058870653<br> 277.1232960053971 277.09664797744415 275.61892684782913 275.5274725480196<br> 275.47810502547424 275.1229494554376 274.69078901458516 273.9555257073228<br> 273.9331702389749 272.29917542768135 272.26757383165204 269.0055991481036<br> 267.0737915535153 264.5137445137853 263.4630088557104 259.2614990911962<br> 258.4793263502483 257.10515037181517 247.80090458906085<br> 245.34140461782874 242.81799102546762 240.01050451730532<br> 231.91012214705813 231.6657585024839 224.08711786721128<br> 221.24235897557074 218.27111704455257 217.35421917769239<br> 217.2002322728787 215.16743230137462 212.25494058759193 209.5659695215338<br> 204.6164100334584 203.1411807522514 202.84269696570666 201.25632033826017<br> 200.48922395508256 200.0640310151734 199.71438559354843<br> 198.14083284802103 197.341775309911 196.1273252033388 194.6024080711456<br> 191.9334291191558 191.50099464989884 189.25609420942234<br> 184.69553916489568 183.73898623782966 181.291232491021 179.27154995827192]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>kelvin</td></tr></tbody></table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6de4fffe-7020-4a9c-bba2-29c56ae4a635' class='xr-section-summary-in' type='checkbox'  ><label for='section-6de4fffe-7020-4a9c-bba2-29c56ae4a635' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>isobaric</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-904af60a-3b23-4882-b3c3-a0883218ec88' class='xr-index-data-in' type='checkbox'/><label for='index-904af60a-3b23-4882-b3c3-a0883218ec88' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            966.0,             953.0, 949.0854466197347,\n",
-       "                   936.9,             925.0,             904.5,\n",
-       "                   896.0,             890.0,             886.0,\n",
-       "                   873.3,             873.0,             850.0,\n",
-       "                   846.0,             813.8,             802.0,\n",
-       "                   785.0,             757.1,             730.1,\n",
-       "                   700.0,             653.3,             639.0,\n",
-       "                   606.0,             605.6,             584.0,\n",
-       "                   582.7,             582.0,             577.0,\n",
-       "                   571.0,             561.0,             560.7,\n",
-       "                   539.4,             539.0,             500.0,\n",
-       "                   478.9,             453.0,             443.0,\n",
-       "                   406.3,             400.0,             389.3,\n",
-       "                   327.3,             313.4,             300.0,\n",
-       "                   286.0,             250.0,             249.0,\n",
-       "                   220.0,             210.0,             200.0,\n",
-       "                   197.0,             196.5,             190.0,\n",
-       "                   181.0,             173.0,             159.0,\n",
-       "                   155.0,             154.2,             150.0,\n",
-       "                   148.0,             146.9,             146.0,\n",
-       "                   142.0,             140.0,             137.0,\n",
-       "                   133.3,             127.0,             126.0,\n",
-       "                   120.9,             111.0,             109.0,\n",
-       "                   104.0,             100.0],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;isobaric&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e241e768-cdeb-47ae-a40a-9ebce7867dde' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e241e768-cdeb-47ae-a40a-9ebce7867dde' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:              (isobaric: 71)\n",
-       "Coordinates:\n",
-       "  * isobaric             (isobaric) float64 966.0 953.0 949.1 ... 104.0 100.0\n",
-       "Data variables:\n",
-       "    ambient_temperature  (isobaric) float64 <Quantity([ 22.2         21.4    ...\n",
-       "    ambient_dew_point    (isobaric) float64 <Quantity([ 21.          20.7    ...\n",
-       "    parcel_temperature   (isobaric) float64 <Quantity([295.35       294.20887..."
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lcl_ds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Sounding Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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gMByECSoGg8FgMBgMBoPBcBCvF1Rbt27FJZdcgoSEBHAc18VLn+d5LFu2DAkJCfD398f555+PI0eOmC3T1taGe+65B1FRUQgMDMSll15qtUcUg8FgMBgMBoPBYIjF6wVVU1MTMjMz8eabb1r8+wsvvIBXXnkFb775Jvbs2YO4uDhceOGFaGho6Fhm4cKFWLduHb7++mts27YNjY2NmDVrltVu1AwGg8FgMBgMBoMhBp9y+eM4DuvWrcPs2bMBUHQqISEBCxcuxCOPPAKAolGxsbF4/vnncdttt6Gurg7R0dH4/PPPcdVVVwEAzpw5g+TkZPz0008dXaQ709bWhra2to7/GwwGVFdXIzIyUnTXawaDwWAwGAwGw5vR6/XIz89HZmYmZDKvj7V4JT7d2DcvLw9lZWWYNm1ax2tqtRrnnXcetm/fjttuuw379u2DVqs1WyYhIQGDBw/G9u3brQqqZ599FsuXL3f5MTAYDAaDwWAwGJ6mqKgISUlJnt4Nn8SnBVVZWRkAIDY21uz12NhYFBQUdCyjUqkQHh7eZRnh/ZZ47LHH8MADD3T8v66uDikpKfj9999RW1uLUaNGdXQQdwc8D+zZw0GjATIyeHQ6HNsUFIArKgIfEQEMGiT5/jU2NmLv3r1ITExEenq6e6N4hw6Bq68Hn5wMmHR99wZ4nsfRo0dRXV2N0aNHQ6v1w5kzQGIiIOr0aWsDSkqA0FAgMtLl+wsA69evx80334zQ0FBkZWUhODhY+o00NqJx2zacOnkSkRdeiJSBA6XfhqdpagJUKkCptOttBoMBhw4dQktLC0aOHAmVSuWiHXSAEyfAVVaiQqHBwG3zAADH7jiG6MBoi4tXVAAnT3LgOGDYMB6Bgc5tvrKyEgcPHkRGRgbi4+OdW5kpBgO4rCyguRl8eDiQkeHwquR33gnZmjXQ33MPDEuXSreP3aDT6bB//35wHIfhw4dDoTB5tDc3gzt4ENDrwScmAqmpbtknt1BZCe7ECQAAn54OxMV1WSS3MBdr96zFzefejOhoy+epV8LzwKlT4CorwWdkAGFhot6m1WvxcdbHGB43HGMSx1hdTqPRYN++ffD398fQoUPdF5XgeaCoCEhOBrwg2ycvDygp4RAXxyM9nV4rKChATk4ORo0ahdDQUPEra20Fd+gQoNHgxYr/4bfmbNw7diEu7nuxa3ZeInie77g2XPK8/5fg04JKoPPgned5mwN6W8uo1Wqo1eour9fW1mLatGkeOenS02lsrdc7MLZWq4H6erqZhYUBcrlk+9XQ0IBjx45h0KBBGDBggPtTIjMygKNHSXxERHjFTRqgc+zgwYPQaDS46KKL4O/vDwDo3duOleTn08Cc44B+/Vyxm1244YYb8Pzzz+PkyZP49ttv8dBDD0m+jWqOQ359PQb174+EiAi3iUW3UVxMT+q4OKB/f9FvMxgM2Lt3L+RyOaZNm+ZdYgoARo8G9uzBz0UbAT9gWNwwDEgZYHXxyEhApwPOngUqK50bQ5WXlyMnJwcTJkxwzQzqOecA+/cDBgPQ2kqzHvbC88Dff9Pvl13mlvNap9Nh586dCAkJwdixY83FFED74OcHHDkCNDbSAyQmxuX75RaEY8vPB6qqgIQEmM42FhUV4YVdLyDHkIP4mng8MOAB6+vyNngeKC+n59qZM0B0NBASIuqtj097vNu/azQabN++HTExMRg1apT7U7yiooy/8zyg0WBP1SHUtNZgWp9p1t/nAnieLgt/fzqdcnNzUVpaigsvvLDLRLwowsOhO7APmqoWcIZWDEkehEgvf769++67Hb+zkhbH8elEybj22ajOkaaKioqOqFVcXBw0Gg1qamqsLmMP48aN85iCF+5BVVV0E7CLoCC6YxgMtAKJaGhowD///INevXp5RkwB9MEolfTgOXvW/du3gCCmqqqqMGHChA4xZTeJiSR+GxuBTuewq5DL5R01ia+88opZLaEUVFdXY8eOHUg57zwknHOO24SiWwkOpou0tJQmMkQgiKnm5maMHz/e+8QUQNdZ3774rWYfAGBa0vk239KvH6BQAA0NpDMdoby8HHv27MGwYcNcl44SFASkpdHvubl0zdnLkSP0nfv7AxMmSLt/FhDEFMdxlsWUQHQ0kJJCv584QZM0PYXevYHYWLrejhzpOLbi4mJkZWVhyuApCPILcvtA3Wk4Dhg4kASiXg9kZwPNzRYXNfAGbC3YCjEl8YKYCggI8IyYMoXngRMnUL9rK5b88QQe/+Nx/HjyR7fuQng4MH48MHQoianjx49j/PjxjokpAAgIgGLkaLyU8QA+nfomhieOknaHXcAdd9zh6V3oEfi0oEpNTUVcXBx+++23jtc0Gg22bNmC8ePHAwBGjhwJpVJptkxpaSkOHz7csYw9eDIcGhYGxMc7Mf4UZiUrKyXZH68QUwAgkxlTPUpLPbMPJogRU83NNK4pKbGxMqWSvnQAKCyUfmetcO211yIpKQmlpaX49NNPJVuvIKYGDhyI1EGDKP3I2iDQlwkNNZ6Tp07ZnAHxCTHVDh8Vhd/qDgAALpSn0yRNN6jV6Eilycuj4I89uEVMCSQl0TQ1xwEtLfa/X3jOnHsuRU5ciGgxJZCaSqNHuZzChj2J/v3pmtPpgOxsFOfnIysrC2PHjsUdE+/Aj/N/xOCYwZ7eS/uRyYDBgykypdUCBw9avIA+zfoUD/z6AJ7a+lS3q/MqMQWQUGxsRIBehsvUmUgL6YWpaVPdugtyOWVmSyKmBPz8wI0ejUFDJkuzky6iqrkKr/39mqd3o8fg9YKqsbERWVlZyMrKAkBGFFlZWSgsLATHcVi4cCGeeeYZrFu3DocPH8aNN96IgIAAzJ8/HwAQGhqKm2++GQ8++CD++OMPHDhwANdeey2GDBmCqVPde+E6C8fRcyM62sG0GSF/vLra6Qeq14gpgYQE+vfsWftHbBIiNjJVV0far6hIRLQxKYm+8JoamuZ3AyqVCg8++CAAak2gk2AAZiqm0oRIgClardPb8CrS0oyhmW7qNX1JTAHA0cqjONNaAT+ZChNVfUVF4OLiaD6nb1/7dIZbxZTAgAHAyJHG+6U9CILqwgul3adO2C2mALqHDBpEx2ZPXYgvIAgPf3+U8jx2HdqHkaNGIqZ9EjHML8yz++cMcjkwZAgQEEBZGIcOdblXKuVKyGVyDIsbZnU1XiemALo/ZmZCERSC22MvxhfJ98FP637jaanE1C85v+DtPW9Dq9eal1XodEBWluhsBXfx7N/PYsWiFQDM0/4YjuEFV1T37N27F8OHD8fw4cMBAA888ACGDx+OJUuWAAAefvhhLFy4EHfeeSdGjRqFkpISbNq0ySyS9Oqrr2L27NmYN28eJkyYgICAAPzwww+QS1hH5BMEBdFN2c/PKdHhdWIKoBSb8HC6QXsoncWeNL/YWJoVa20VETD08zNGF90Ypfrvf/+LyMhI5Obm4ttvv3VqXd2KqZYWql3JynIgl9WLUamMBXOnT1sUjL4mpgDgt9MkGs5NmgC/sRNEF8sPGmQMtorBI2IKoKhwQID972trA/76i36f5rr0MofElIBSSSFDgZ40iaFUojguDnurqrAFW7B0z1JUNkmTjeFxlEogM5OeBS0tXSbWrh16LdZeuRaX9LvE4tu9UkwBaGhrAK9QAMOGAYGBUGkN9BxwJDrsILm5uThw4BRCQyehuNhxMVXfVo/ntj2Hjw98jA0nNpj/MS8PqK2lCGNtrVP7KyWXDbgMVVupBOSaa67x8N74Pt5xVXXD+eefD57nu/ysWrUKABXQLVu2DKWlpWhtbcWWLVsweLB5aN/Pzw9vvPEGzp49i+bmZvzwww9ITk72wNFIQ1OT0avAboYPB8aMEWkx1xWvFFMC/ftTYbkHCkDtrZmSyYxBtaIiERsQ6h8qK932sAkMDMS9994LgNoIONqyzmZkSqmkHMimJrKF60kkJgKBgTRwzcsz+5MviinAKKgu7D8Tjtr26XSkP6zhMTHVmbo6YO9ecRNQ27fTtRkXR9ESF+CUmOpMZSWwa5fX1J06S3FxMbKys5GUkYSss1k4WJaF8sKjnt4t6VCrqdBnyBAgIgI8z8PAG9NtU0JTLD6PvVVM6Q163PvLvXjg1wdQrWsgwRgYSDeGrCy3ZJoIkalzzhkLnS4ENTU2M5itEqIOweJzF2NC8gTMHjDb/I9pacZauEOHvOaa6831BoCOSC7DObzjymLYRV4eCSqHxp52Wjib4tViCqDZOw9EHR01oEhMJGHV0CBi0iowkKJUycluPca7774bQUFBOHToEH766Se7329TTAEUVRQEY16e4080b4TjKM9NoTATH74qptp0bfgr/y8AwIVpJmltNtIaTamvB/bsAY4dsxyQ9BoxBVBksbHR+s6aIqT7TZ3qEqdRScUUQDcdnY6OzY0RAVcgGFCMGTMGw9OG44vZn2FZ0nUYXM57RV2tZAQEkJMtgB9P/oi7frgdlY3WBwLeKqYA4EjlERyvOo4DZQeg0Wsoop+ZSceo0Vg14JAK0zS/xMRwKJX06HHEi0ZgStoUvDbjNchlnZ7RQtpmZCRt5PBhyWrZ7YXnebTpaDbrqquuAgD8+KN7jUB6Kt5zdTFEI6T2O2XWZzB0P0XcCa8XU51xU9qfM25+SqXRt0BUlGrQIKBPH3rwuImIiAjcdtttAOyPUokSUwKJicYcyJ40AAIoJW7cuA4bbl8VUwDVCDRrmxEbGIshsUPoxcZGStk8eVLUdadU0ji+trbrV+1VYgoglzWFgiJV7b0NLXL6NLBmDf3ugnQ/ycUUQPcSwcjh8GGaPfdBTMWUMNOeEt4bMwa2p7+dPOk2h1R30aZrwxs7VmJP7t/4Zdsqi8t4s5gCgKGxQ/HZ7M+wYvIKxAW1PwhVKkr/Gzq0Qzi6Aks1U0JZYV2dfeuqaKpAq05ENE2o84uJocmZo0dFT0JJyc85P2Pet/Owv3Q/du7cCQAYPXq02/ejJ+JdVxhDFIIJVVOTgxOLlZXAP//Qg0YEPiWm9Hqa/t6zx+UpA1JYowuZp2fPunxCzmEeeOABqFQq/PPPP9iyZYuo99glpgCawRPqjQoKfHZwZ5X2AbAvi6lthduw4H8LAABXDLwCMq798REURIMfgwE4ftxmJMff39hbNjfXeJl6nZgCKOot2KoWFHQNJWs0wDPPUC+83FxK65k+XdJdcImYAmiAl5FBg9imJrId9TEEMTV69Gh8W/At8mpM0mo726l76w3WAdQKNT449yXMizoPC9SjSNCb4O1iSqBvZF9MTJlo/qJKZdZLDC0tdk3+2sKaAYUjgkpv0OPh3x7G/O/mI6c6x/YbBCv8+Hg6L/Py3PqsM/AGfHHoC5TUl+Cbjd8AAC5sN9Bp8fEotTfgnVcZo1sUCmMduENR44AAuohFuP35lJgCjB6ogEsjHVL1mfL3p3tr7952ZGPW1tIAwU3WxwkJCbjlllsAAE8++aTN5e0WUwLx8fSBaDSONyzyYgwGAw788QdkBw9i/JgxPiempn8xHU3aJkxNm4qXpr1kvoBpsykR4dbERBrA6PU0r+OVYkogJoZCyTxP6XGCkcPff1NN6hNPkCqcMgXYudMxd0AruExMCahUJKo4jnLIRYXKvQPTyNSu2l34+MDH+M/6/6ChzcSwwdRO/dAhurf0EHqlDcfDU5fSxEZhYcd35+1i6vODn6O0QeSzuaWF6qmysiQRVd25+TkiqEoaSlDWWIbqlmoEq0S21OE4ul/26kUpjm5M4ZdxMrx/yfu4Y9QdePO+NwEAn3/+OVpaWrBjxw637UdPxbuuNIZonEr7CwykH57vdgU+J6YEBLeH0lKXuMZJ1rS3nf797RBUPE99jSorgTNnnNquPTzyyCNQKpXYvHkztm3bZnU5h8UUQA8aIXRRXe3E3nofBoMBe/fsAXJykNm7N1Ru/O6cpbOY2nD1BvgrO53zps2m8vNtRgOEFhAyGZCTU41Nmw55p5gS6NuXxH5bGxk53Hwz9Zo6epQE1xdfUA2VhE2qXS6mBEJDjd/d6dNeZ+1sic5pfhOSJ2BM4hjcPPxmBKtNBrYmdupobaXURh+u0dxfuh+FdSZOr/HxZs2oNYWFXi2mfj/9O17b9RoW/G8B6ttEnGccZ+wJd/CgU4LYljV6UBCdLlqt+GBmSmgK1ly5Bq9c9Apig2LF74zwrDN1E3VTBDVIFYSbht+E5vbthYSEYNu2bYiKinLL9nsy3nW1MUQjGNnV1zs4cSMoMishLp8VUwB9OCoV3XwldtORWkzZDccZ8wSLi902OEhJScGNN94IAHjqKcvNI50SUwLR0TQAGjbMsfd7IR1pfi0tGDJnDpRKJXV0dqb62U2IElMCcXF2pf4FBACBgZU4ceIEAgMzER3tpWIKoFnkgQOBTZuASy8FPv6YXr/1VjrWBQskNaJwm5gSSEyk7y8xEfBg83oxWKqZigyIxJsz38SCoQu6vkGpJEMAhYIG5j6a2lTVXIVHfn8EC/63ANnl2cY/pKQAycnQarU48r//IUSj8UoxBQADowZiaOxQzB00FyHqENtv8POjZ4GfHwmOrCyHRJWYPlMyGWUahofb91gNUYdgRPwIu/fJjJoachPNEZE26AAVTRXYVbyr4//vvPMOAGDhwoXYtm0boqOjMWTIEJds+9+E911xDFGo1dQ8XaFwcGJDsMm0kPbn02IKoDuj4PYgYSTAlWKK50n7iaoNj4mhE0CjAcrLJdsHWzz22GOQy+XYtGlTRzGrgCRiCqBBaVSUS1zSPEGXmqnYWBKNQqTRi7FLTAn070/io77epg1peXk5zpzZhczMNPTvHwMvHP8ZOXECmD0bePZZGvwMHgxs2wa89555vYcEuF1MCfTvT5EqL772TMVUdHQ08mvzO/4m42TGur7OBASQqBoxwmGrf2+gT3gfJIcko39Uf7PXNcnJOFBaCj+1GsPCwyHz0u8wMSQRH1zyAW4deav4N/n5UWqcWk2DHTsjVfY07R0yhDZlq6vM5wc/x74z+0Tvg01aWkjFFRfTvUbCzBqe5/H01qdx10934fODnwMA7rrrLgBUPxUdHY3MzEzfG+d5Id78CGPYYNAgYPx4B5/nND3cJe3P58WUgNBBtLpaEnMKd0SmTp2ir8KmRpLJjFGqwkK3NcNNTU3F9ddfD8A8SiWZmOqMweDTqX9WDSj69CHRUVfnVkFsDw6JKYAGPX370k83vU2Emqnhw4fhootikZHhVEcH19HaCixdSq5jmzdT6thzz5Gr4YQJkl97HhNTgLmQ4nmvOzc7R6a+P/495q2dhy8OfSFuBaGh9P0JuKkGVSqiAqLw9sVv482Zb0IlN9ZfCjVT6N8f/adNg2zYMK8TxY0aYzReLpNDIbPzvPb3p0iVWk0GKgcPimpKbY+YEsuh8kN4fffruH3j7Thdc9r2G8SQkAAMGEDfW2mpqAi/WPS8HokhiVDJVZiQMqEj1Y82m8DElIQwQeXD+PnBuVldYcDTPpPcY8QUQDdg4QbqZLNYd4gp00y+oiIR99L4eGMKi1P++fbx+OOPQyaT4aeffsK+fftcJ6a0WqpVyc72SXeubt38/PyMfbdyc71uYOewmBIQUses3D86G1B0rsn2mhKXP/4gIfXkkzQjPnMmmcE88ggd29GjkkYZPSqmTOF5GrAeO+bWOs3usJTml12RDQNvMGtuK5qzZ8lAxGYDQM+j1RuFg4yTIcLfaCduZkAxejRkffqYz0x4gVvqiaoTmPXVLKw5ssbh5vAA6JmemUnp/CLGJs6IKY3G+keXHpGOywdcjkv6XYK0cAmfeXFxlFbMcTSZceSIJDdDhUyBhyc8jPVXr0daeBruueceAMCKFSuYmJIYJqh6CA6NyWJiqDAyPb1niSmB1FS6AQtKxQHcWTMVF2fUSDZLv+Tyjr5GKCzsflkJSU9Px/z58wEAixcvdo2YAmhQEBxstJb1IURZoycnGx0NnRT8UuK0mOqMXm82aO3OzU+joTHEsWOOb04SKiqAa6+lBr2nTtHkxdq1wI8/Gk1TGhpouTNnJJnQ8BoxBdCATugBdOqUx00qLIkpAFh87mKsnL4S1w691v6VVlQY+2958YRNk6YJ13x3DT47+FkX4WjTza+khNqHSGg57gjrT6xHo6bRrIbHYQICKFKVmdltSNsZMZWdDWzfbr11WYAyAI9PehyLzl1k13pFERND6cQyGd1XsrMlm2GKDoxGS0sLPm6v/3z88cd7xjjPi2CCyseprqaJtuPHHXizvz/Qqxca9PqeJ6YAKjILD3c4/cHdBhSmGkmUe3FSEiV7Cz0t3MQTTzwBjuPw888/A4D0YkpAGLxWVtIA1gcQ3WdKJqOalcGDja6UHkZyMdXWRgO67GygpcWmNbpGQ2OIykoH20E4i8EAvP8+fS9ffkn3jbvvJoU3d675fSQ83DhRc/y4U4NWrxJTAikpxlq/I0c8ZjduTUwBAMdxmJgy0XrdVHf070/PB52Ozk8R6WOeYOOpjcivzceaI2vQrDUKP5tiSq+nepzWVrKL9+Dx/d/4/8Pjkx7H4vMWSzO2CAgwF1OVlWYzys6m+anV9G9n+/TOroQOnXdiiIykYi6hBYyDn9mGExuwdPPSjv1uaWnBd999BwCIi4vrOeM8L4IJKh9HpaJ7ZnW1Y9H9HhmZsoSdgsNTbn6JiTTWrqsTMTGsVAKjRtGA3I3fW0xMDCZNmgQAWLVqles2FBhIjTmBLo0rvRG7m/aGhZEBhxcguZgC6Obk5wfo9Ti7fTv27N7drTV6UJAxE/LUKTePAbOzgUmTgNtuo4ja8OGUcvrGG8YGNZ1JTaUoqk5HosuBSQ2vFFMCAwbQNdjWRqLKjZM2gGUxtf74ery8/WWzNDiHkMlo0OrvTykBXmqnfuWgK/HEpCfw9OSnEaQipwRRfabkcqORQ1MTnd8eSv/jOA5zBs5BmF+Y9CsvLaVz8+BBQKeTpGbKUj+qutY6zFs7D09tecpM2LqM8HAyUBHqquykWduMlTtXYuOpjdh4ciNaWlqwbds2vPDCCwCAH3/8Ueo9ZoAJKp8nKIjGLAaD9RC1NQQxlRYUhAF6PTgvq+WQjNxcYMcO0eYUnrRGV6mMpW3e2GNTqJl67LHHAADffvstDh8+7LoNpqbSA6Wmxv4T3I3YLaY6o9V6LLXKJWIK6Gg2VV1Xh5O7d2NEfLzNPlO9etEYXqNxmYOwOW1twKOP0uBl+3a6ob76KrB7NzB6dPfvlcnIGUguJxFmZ+qtV4spgI4rI4PykOvq3PSFEJbEVHVLNV7Y/gJWH16Nn3N+dn4jpnbqdXWSGgFIBcdxuHzg5ciMywRgZ9NePz+qAVQo6N4iUU2OGKpbqvHZwc+gN7hYxAUH0/fY0ICijRtx4sgRpw0oBEHV0GD8uLYXbcfZlrM4WH4Qcs5NjXgDA41iSkh9FxkpDlAGYOX0lZjZdyYu7XNphzV6djbZ7Y8cOdJVe/2vhgmqHoAwyW1PmoxpZKqvWg2uqsqt5gZupbGRbkSltruze7zPFCiTKDjY2CrMJgYDHZuLozimBhTTp0/HFVdcAYCKW12Gn58xJc5Lo1ROi6mGBoqGHDni9llkjV6DuWvmSi+m2imvr8f+mhqkp6cjoaXF5qSGkAkp1GVL3EauK++8Azz/PEWZpk6lSNPChTQIFYO/v7GZb35+1zwhK3i9mBIICKBZcgAoK3NLPY61NL8I/wg8O+VZzEifgVn9ZkmzsYAAEo0cR3VVXuBsqNVrsTp7dZconF1iSiAwkESVXE5pLG4QjTzPY8XWFXh91+t4+u+nXbotBAUBmZkoKS9H8dGjmBgaivAQEf2tusHPjwJ7PG+c45rRdwbem/UeVkxeAbVCLcGO20leHlBQABw4IHpieGjsUDw27jFs/2c7oqOjUdM+IXnRRRe5ck//1TBB1QMQBt5nz4q7V3ZJ8xPSqryoOF5ShAF5aWm3H5A3iCmAnoEjR3brOm1OczP1rigqclnTSktufkuWLAEArFmzBkeOHHHJdgFQ2EIup6ecl0VRnRZTAH3hCgUNVgsKpN/Jbth4ciPKm8qREJwgvZhqr5kaMGUKotPTSSyeOGHzfSEhVB4IACdPuvgrv/BC44zUkSOiJl26EBtLPyK/e58RUwJRUWSDP3KkscDERXRXMwUA5/Y6F09Nfkra+pXwcFLxCQnGFGMP8s7ed/Dyjpfx4KYHO15zSEwJhISYi0aXz1IAU9OmItw/HNcMvsbl28otL0e2TIaMYcMQwvNUM+bkxJSgyUznR0bEj8CAqAFOrddh4uNJ6bW0UHNjK8/5quYq1LSQcBLS/IQ+UzNnzgQAfPrpp+7a638dTFD1AEJC6Fmu09l2gbVYMyUostpary3OdYqoKPqAhKp3C3iLmHKIoCBy5eJ5l+QJWrNGHzp0KK644grwPI/ly5dLvt0OVCpg7FgycPCiwackYgqgsEx6Ov1eVORW17FPD9LD9doh17pETA0bNgxJyckU5ZDJKG1ThGhJTTW2DJKgjZx1MjIovS8jg/br3HOBr7+2fz39+lE9o7V6q3Z8TkwJJCZSNMeFWBNTe0r2oEnT5NJtIy6OvkMvqCEeGjsUIeoQXDGQMgCcElMCERFkyd2nj8vrNjmOw8y+M/HDNT+gb2Rfl25LqJkaO3UqQiZONKZvOumOFxpKY4Ivsj/rYkbhEfz9qa4zIIBuiAcOUG2cCTzPY/lfyzHv23nYenqrmZgCgNb2G2msF0wa9FSYoLIXD7kddQfHkTEM0H3an1UDCn9/o0W1R+y1XAzH0QMTsDiY81YxJRg1iWoFI1Tzl5VJeo7a6jO1dOlSAMDatWs78rNdgqNixUVIJqYEoqKMothNtSqVTZXYeGojAOD6zOslW69FNz9/f1JJUVHGm1U3CJ4Bo0fTfIFLSU2l+qmLL6bByjXXAIsX2zcgk8tt9v/xWTHVmdpah004rGFNTOVW52Lhrwtx7bprUdnkpmcTz1PdrYfs1M/vfT7WX70e5/U+TxoxJRATY95CROLUP61eC43e+OzxU/hJuv7OdDGgCA421oyFhTnVpDM8HDisX4e1ha/junXXOW+CIgVqNVnGBwXRMz4ry8z9tq6tDpXNlWhobUDBkYIOMcVxHN566y0AwMMPP+yZff+XwASVvXhpI8DY2O4zFmy6+QlRqp4oqABj2l91tVm43FvFFEDBtJwcKs2wObYLC6NQpcFA/UckQEzT3iFDhuDKK68EACxbtkyS7XZLayt9KB505JJcTAn07UuDgOpqt9Qzrj68GjqDDiPjRyIjJkOSdXZrjZ6URFFGkZ9XQIAbA5IhIcD69cBDD9H/V6wA7rjDsXWVlVFNnMmAvMeIKaF3U3m5ZP3hukvza9O3IdwvHEnBSYgMsC3EJeH0aYoUu9FO3cAb0KI1PpeC1cHSiqnO6HTkjCdhzdh7+97D9euuR251rmTrtIZVN7+QEJqF6d3bqfUHBgJTMgcgPao3rsq4Ckq59Z5XbkWlIvfGkBA6N03cG8P8wvD+9PdxY/SNGJI8xKxpr9DM96mnnvLYrv8bYILKXrxUUIWFUcaCpYwTUdbopml/XhiFcxo/P2OzyvYolTeLKYAmFNVq+jpEPfeEKFVJidM55GLElMDSpUvBcRz+97//Ye/evU5tt1t4ngYBosN20uMyMQVQFEeYQc7JcblBxWcHPwMA3JB5gyTrs9Vnqks6lR0GB27wXKEo04svGkXV1q32r4Pn6WLVaICjRwGDoeeIKYAUrmDCUVjo9AScrZqpQdGD8NUVX0lfN9UdycnGehU32al/lf0VFvxvAY5XUUNJl4opgC6o2loyqZCgpqpJ04QfTv6AnOocFNS5tg7UpjW6aZ2fXk/C34HvcFD0IHw550tcPfhqJ/bWBSiVJKqE2j85uQ62tLRg145dGNd7nJmYajJJDZT0ecXoAhNU9uKlgsoaovtMCWl/wcE9U1ABVAeQlATExXm9mAJo/CmMS4uKRGRoREbStL5O55TgsEdMAUBGRgYWLFgAgJr+ugyOM4rGggK3G1S4VEwJpKTQYC4y0qVuXMcqj2Ff6T4oZApJBgw2xZQpej2ljO3ZI0pUNTSQl0VhoZtuv8eO0b9z5tj/Xo6jWhWlEmhshO7EiZ4jpgRMU8eOH+9SyyGW7sSUqd12iDrENT2MrKFSGVPH6upEGak4g0avwdqja1FYV4gjFUdcL6YAerDExhobN4t0p7RGoCoQq69YjYcnPIzJqZMl2smu2N1n6uhRelbYYRkvpC3yPNDWrEZNtXcMkzmOM/4oFOCGDQMXFdXxWkBAAKZNm4apU6di8ODBmDx5MubPn4+g9pzpL7/80sNH0PPxjjPFl2hr81hutS14nu6LglGY3U17hw+nfiwuL1rwEJGRQHo6eH9/rxdTAvHx9FxvbqZMsG7hOBroREQYbYrsxF4xJbB8+XIoFAps2rQJf/31l0PbFkVcHIlGrZYiVW7CLWIKoNnG0aMp/c+Fg+/PD30OAJiRPgPRgWL9+S1jl5gCKK2xuZkE8cmTNhcPDjZm7J444eLAXUUF8Msv9Pt11zm2DpUKGDgQer0ex37/Hcr6+p4jpgTS0igtQq+nwaqdkxvdianTNadx+TeXY1fxLgl32E5M7dTLyynv2kWo5Cp8fvnnWDhuIWb1meV6MQXQcQ0YQM8Kg4FSxxwUxgIR/hGYlzFPoh3sikNNe5OT6X5z9qwoUXXq7Clc9vVl+OP0H6itBfbvpybj3oDQ+9EWZ8+exdGjR7F582asXr264/X58+e7atcY7TBBZS9yudcKKoOBMqLy8oCyMjvFFOBUEaev4AuRKVMUChJVgEgDv/h4ml214TZmCUfFFACkpaXh1ltvBUA3ft5V0RWOIxMBgD4QN0RT3SamBOSubRxp4A344tAXAIDrhjooGtqxW0wBxsGcMNARkc+alkaZPC0tkpXuWGb1ahIJo0cb+y85gC4kBAfa0+FGBgZC4eb+Yi6H46ipsVpNz0M7+hvZSvP7cP+HONNwBl9mf+m6+4gYwsPNe4y5sEdViDoE8wbMw44dO1wvpgQ4jkRjaKixpsrOthtb8rfgUPkhF+2gEYfEFECif8gQ473m6NFuz9PPD33eYdYTHMyD46hs1w3t12zyzDPPgOd5s5/6xno88PkDmPfmdOj++B385s3gz5wBz/NobGzE77//DgAIcbI3F0McPX8ELTXjxrncdtRR5HJ6BjQ3N+PXX/fZJ6ZM0em8VjQ6gyCmavPzMTEiAt4tpYwkJdGzr7bWzNRHUpwRUwKLFi2Cv78/du7ciR9++EHiPTQhOprCFno95YG5ELeLKVOam2mQI3HfmC35W1BUX4RQdSgu6X+Jw+txSEwJBAZSfzGApoBtCGOFgsoFAApMOpmhZJ3PKXLncHQKRgOKtsREDBw9Ggqel9wVzytQqchkRCajh4+I47MlpgBg6XlLcX3m9Vh2/jL7n11SEx9PabhyueQR4815m7GnZA8AN9RMWUMuJ8ERGGis+xNJaUMplvy1BLdsuAX7S/e7bBcdFlMC4eHG87SqqltRtfjcxbhj1B1YdO4iKBQcAgPpdZfdb5ygpaUFO7fvxHWDr8NXt/8AeVJ7Gu6JE0BxMQIDA/Hoo48CADZv3uzBPf330INyENyEl0dx/P0bcfjwUaSm9sKAAb3tfyBVVtINJzSULDp7CKaRqYkJCfCrr6fCXAfFgztRqyndXa+3I3jR1kYjz7AwmzbVUogpAIiPj8d9992H5557Dk888QQuvvhiyF0VbUlLI7FRUkKK0096i16PiimAzs+aGpoiDQ+X7N4jpPtdOehKh62NnRJTAikpdL9pbKTUv8GDu108IoIyPsvKaMwwapTEt+Njx4B9+2jgfLVjdWVmBhTjxkGh0dA6e+oMcXAwNfwVRp7dIEZMAYBaoca9Y++Vci+dIzWVhJWE2QxnGs5g6V9L0aJrwcoLV0Kbr3W/mBJQKMjk4MgRY0ROBMHqYJybci5KGkqQGZvpkl1zWkwJRETQ/eXwYbrnnDxpnKExQSlX4uYRN3f8PzSUbk/19VQ66An++OMPTJ061eZyCoUCsZGRiAsNRVxEBOJ69eowiRoxYoSrd5MBFqFyHC+cbWxoaMDx4/8gLi4WUVG90dbmwOye0I+qB7n9dU7z8xNSxsrKPGq/bQ/9+1N2hujemmfOUEqcUFBnBanElMDDDz+MsLAwHD582Cx/W3LCw0koJie7pNbI42IKIOtfIc9NoobNzdpmfHv0WwDAdZmORWEkEVOAMfWP42jmuKLC5lv69KHASEsLaU1JEaJT06cbXU/twKKbX0AAZTWkpXlF01iXYCqmeN6i1bgtMZVXk4efTv3kyr10HI4zF1MtLU7bqYf7heOiPhdhaPRQtOa1ek5MCahUVEMtQhgLBKmC8NTkp/DWzLcgl0k/cSaZmBKIiKCHqEpFBlXttGhb8GvOrxbTS4XMeU9GqDIzM5EgFJF2g06nQ0l5OfadPImNO3fio2++AQBcccUVrt5FRjtMUNlLeTmwe7dLi1QdQTCg6NMnBUOGpIDjOMda2fj5GWdTe0BPKos1U1FRdFPVaCRPp3IVdo/FEhNp+r6+3qo1mtRiCgDCw8M7mgcuWbIEGleK8iFDaKAqsaDyCjEFUDiyTx/6vaCAIlVOsuHEBjRoGtArtBcmpky0+/2SiSmBoCBK/VOrRX2PSiWZ6A0fLqo/sHgMBkBwwXIg3a9ba3TThr8Gg8vt8D2GTkdRjoMHzY7Rlphq1bXikd8fwZLNS7A624WTMFJQX09OBU7aqfsr/fF/4/4PV4dcjeDAYM+KKUt007zZtF8WQMciNZKLKYHISJrgMDHeen3X63jizyfw3LbnuiwuCKrGRs9dtlFRUSgpKQHP82hubsYn6z/B0NeG4pwPz0FBbUGXuiq9Xo/KrCwc/vhj7Fy1Cmu//tozO/4vxIuuYB+iudkF06OO09nNLyaGRt8O6yHhoefjgsqqAQXHGZ0ePNTPyFFaWqjkxKaplkpF+VGAxeiGK8SUwL333ovY2Fjk5eXho48+knTdrsZrxJRATAylbRoMQK7zDTOFdL/rhl5nd18fycWUQEoKmUAIfeJsEB7uggy6rVupHi80FLjEvroy0X2mWlqAAwdcbsPtMfR6EhxCCifEpfmp5CpMTZuK6MBoXJR+kTv32H6EWjEH7dRrW2sBGGumQoNCvU9MmTZv7mQ20qJtwYL/LcBL219Cm841Tg0uE1MCJp81X1uL+CYOKrkKU9KmdFlUraY5Zp6nU9uTtLS0YNu2bRiWMgzrr1uPJy94EimhKV2Wk8lkiMrMRMbFF2Ps/PngepK7qJfjRVexjxAWRv82NLiti3p3WLJGFzwzWlsdnEQzbfLrDfY2DmDTzU8QVDU1djsbeZLDh6lsqL03cfckJ5N4PHuWBjntuFJMAUBgYCAWL14MgDqzN7va4KSujgaqTj7xvE5MCfTtS99jZaVTEznljeX4NedXAPan+7lMTAE0wDF96NuRTt3cTJm7TiOk+115pV21MnY17dVq6TqsqJBop70MtZqc/9qtxkv37BFVMyXjZLh15K34bt53iPAXJ6o9RmCgw3bqNS01uOrbq7B883L8te0vz6f5WUOhoDCwcIwmEzl/F/6NwrpC/Jn3J9r00o8NXC6mTNHpwB0+jOvVY7Bh1MsYkzDa4mLp6RQRd8A4VzIEMRUdHY3MzEz0Du+NqWk26qpiYsyj42fP+kyJg6/iZVeyD6BWU168UGfkQaz1mVKrqWB73DgHi7bVauPdw6G8Qc8iyhrdz884Iy5KnXgHwli2uFjEvdHf3yiO26NUrhZTAv/973/Ru3dvlJaW4o033nDZdgDQ4LSuDjh92uFVeK2YAmgQJ+T8l5Q4vJqvD38NPa/HmMQx6BcpvvjcpWKqM2Vl1PBXxGRVczOwdy8FCkzmC+ynuRlYu5Z+tyPdzy4xBVBYTajfPHWqRzqpIiwM6NMHlZWVyPv9d4zt39+qmKppqYGBN97EApRiC0Q9THg4TXIAdtmp7yjegbNNZ7H52Gb4+/t7p5gSiIw0tg0oLu6oxZ3WZxpen/E6VkxegRC1tGFit4opALxcTiYcHIeomjaKqlqYzImKouGQp74qQUwVcoWITI10zPnyzBnqNWZHg2OG/Xjp1ezlCANxD6b92WraGxTkZA20MBAXUSjuTdjVZyohgcSxl/eiMiU2lrL52tpEZmQmt1upVlSg+swZt4gpAFCpVFi+fDkA4Pnnn0etKycfevemp11trYjux13xajEl0Ls3TZUOGuTwKj479BkA+3pPuVVMGQwk/JubgZwcm4sHBNCtmOftaoPUlQ0bKOOgVy9gori6MrvFlEByMg3I9XpyU+2Bg5tiAEeqqjBwwABEV1ZazHJo1bXi9o2344FfH0Bdqxd6UtsiIcF4bz1xQpRrwdReU3FL/C24Z+A9mDB2gveKKYHYWLrnANT8rT09fnzyeIyIl9Y1zt1i6vfTv+POjXeiLMBgNMYpLfWeLr7tCGKKD+KxqnAVrvnuGpw8a7sZehf8/Iy9uA4d6rl1nB7Gy69oL0W44B0YvEmBLTFlCs87ONCIjqZZOCcGcO7G7qa9kZHAmDHG9D8fQCYzBitEGb8FBwNRUagNCMDO3bvdIqYEFixYgIyMDNTU1ODFF1903YbUauOHcvq0XSe8T4gpgNJwkpIcniY9WnkU+0v3QyFT4OrB4izB3SqmADo2YXBTXi4qOt6vH300jY1OtCQT0v2uvVbU5+uwmAKMzoZKJe20E1FVb0SomRo0ezbCk5Ot9jY6WnkURXVFOFp5FDqDrYJQLyUtjZ6TBoPNk0+omRoRNwKXn3e594spgaQkGFKS8Xn5JjQeO+iSrBx3iymdQYeVO1diz5k92HBiAwlHIRp35oxFUVVVRS83Nbl89zowTfMbnDEYg6IHYWDUQKRHpNu/sogIYOhQqgGsrSXjGJuF2Ax78ZGr2ssIC0NHC20319/YI6YKCoDt2x30lhAGqWq14zvrRuwWU4DP2hgnJNB9sbFRXJC0OiEB/1RUYMDQoW4TUwAgl8uxYsUKAMDKlStR5sq6kZQU48ha5AnvM2KqMzxvtzvl5wdJNMxIn4GoANuNyd0upgSCg40z/ydP2kz9U6mM2VcFBQ4MeMrLgV+prkxMup9TYkpArTZPp/IRp1FbmBlQxMdTrVFQkNGp0oQR8SOwavYqPD/1eUQGSGnX6EYEcdyrl9WJxxZtC57d+iw2bd3kvTVTNljbvBevVWzAf/JXQh8k3lZdDO4WUwCgkCnw9sVvY/aA2fjPsP/Qi6aiqqSki1nVmTP0sruqPDrXTCWEJOCdWe/g5YtetttMqIOwMOotqlBQvXFWllf4APQkfOvK9hbkcir4c3Nkwx4xBVBUV6v1ebM+mzgkpkwxGGhgJYEttTtQKrs18DPDXTVT1rjsssswduxYNDc34+mnn3bdhpRK40A8L89mlMpnxZTBQIVD2dmin+4G3oAvs8kS/PrM620u7zExJdC7N+XzaTSinA1jYynYbDBQ9pVdEfmvv6Yb5ZgxFht9miKJmBKIjKSIY3CwHc3lvBeLbn7+/tT014olY7/IfhgeP9yNe+kC5HKqi7PSwPylf17Cqj2r8G7+uxg5cqTPiSkAGBA1AAlRabhy7E2QK5S23yAST4gpgZTQFCw6dxGUcpPjiYuje0BUlPEB2447+1GZiqmhQ4d2jPNknMz5urXgYBJVKhVNPh44wNL/JMT3rm5vYeBAuvjcVH9jr5gCjGVQ1dVOXDOlpRQe9lKx4bSYAqjfxrFjThX8u5ukJNIQQh9mS3QRU/X1dJxuDPVzHIdnnnkGAPDee+8hLy/PdRtLSjJ2fe1mFsFnxRRAKWmC0+ipU6JqcLbkb0FRfRFC1aGY1W9Wt8t6XEwBxtQ/gEwq7Ej9q68X7RFACOl+NqJTkoopgbQ0sg/zoRpOS3RrjW7ynGqrrsTyXx/DmQbfalVhF3l5Hf2bNBoNejX3QlJQEpbOWAq5FdHl7WTGZeLruV/jyox59ALPU6qqE04wnhBTZxrOoLDORl5wfDwweLAx9be9ZsJdgspUTA0ZOgQLf12Izw5+Zmbe4jRBQSSq1GqajfLR89IbYYLKB3BETAE02PbzIzHlsFlfeTnllXmhza8kYgowzkaVlvpMXrG/P3DOOTQ5aul06CKmhMr98nJKM3IjkydPxtSpU6HVajuMKlyCXE6D1P79jbMJnfBpMSXQuzep6aamDvet7hB6T83LmAc/hZ/V5bxCTAmEhBgjjiLc8NRqqp9PTaUxgiiOHQP27SMldrX1ujKXiCmABm2mEQt3FmhIhJg+UwCAujq89dMy/HBkHe754U5pB4jeglDIV14OzZEj2L59OwZEDcAvN/+C0YmWLbm9mVadcRI1QBlgHHcUFtLPwYMOiSpPiCkDb8DSzUtxzXfXYHPeZvFvzMkBTpxAUCDNWra1uS6g0znN7+/Cv7G9aDve3/c+yhvtmSUSQUAA9f7r1cv4msOuPgwBJqicQWjw55Rnb/c4KqYEBK3gsB4S0hpLS73qgpNMTAFUsBkYSGLKhyzUrWWPWEzz4zijZXNREaVTuREhSvX555/jqIUidcmIi6Nz1sJ10iPEFEBiql+77XlhITnUWaFZ24xvj34LoHt3P68SUwKpqRTBSenavNIScXE0PhB9ixSiUzNmoKN5XydcJqY6k59PdvF2hdc8i2gxBQBBQbgm9VIMDUzFI5GXQqbrgWlGQUHAwIFo0bRi26Z1CK+pwahRo6CUME3OXewu2Y3Lvr4M/xT+0/WPiYk04aHVUh1ON/efzngqza9J0wSlXAkZJxPfMqKxkYqnysqgOHEEco4mAVzRmrOzmOI4Duf1Og9LzluCh8Y/hPhgF5SXmN7L9Hpy/2M4BRNUzlBQQDmoDttLdY+zYgowztbW1DiYtRcdTQO4tjaPuRp2RlIxBdAITJgNLyryOSvj6mpjqUm3NVNRURS21Otdds5aY/To0ZgzZw4MBgMWLVrkno3q9R2mMT1GTAlER1MdpxB5tHLOrj++Hg2aBvQO640JKRMsLuOVYgqgGQMHu2kaDDai8gYD8MUX9LuVdD+3iSnAOFl14oRdA1RPYZeYAgC5HPGjL8BHQ5dgjDqNagB7YO2GJiwMi/O/w+OtX6AGBZC5ORtAKj4/+DnONp/F1oKtXf+oUJBjXEgITUIePCjqnPVkzVSwOhhvznwTn87+FIkhieLeFBRExioyGVBVBXVRDqDXSy6oLIkpgNLlL+1/KWYPmC3tBi1RUkL50gynYILKGYR+VC7oQC2FmAIoNUwouXBo8lMmM6oyL4jeSC6mBGJiKG9Io/GpWeK2NhqbFBUB+fk13RtQcBylxAE08+bmurgVK1ZAJpNh3bp12L17t2s3Vl8P7N4NHD0Kg17fs8SUQN++VDPWTeqfkO537ZBrLbpDea2Y6kxLC7B/vygjDr2eMvkOH+5mDmjLFrpoQkOBSy7p8me3iimA0jgFZ43Dh90eQbYHe8RUm64NudXtsz0qFbjMTGOxW3utUU9Bo9Hg73/+xnFZKXQhauigp1ojH6rNFXj5opdx5+g7cf8591tewE5R5UkxJSDjZEgLt9OYKSqqw27cr60OyMlBW6N0zniWxJRHWgkkJ5N9MMMpmKByhpAQGoTr9ZJGb6QSUwKJiXS9WCkrsY2Q9nf2rEcf9C4TUwAJR2FAWVTkMw96tZq+nvr6evzww2Hbbn7h4fRjMFCakRsZOHAgrr+eXOYee+wx8K78jNuLBw11dTj4yy89T0wBxtS/sLAurlQAUN5Yjk25mwAA12V2jcL4jJgCqO6vvp56Gtmw+pXLjZNIx49bWVxI95s3j84VE9wupgCa7Bg4kGbA2troOL3wHmRvZOrlHS/junXXYePJjfRCQAAwZEjHrL8YF0dfQOgzFRwYjO9u+A7Pz3wVU4fNoT/m5IiqA/QmVHIVbhp+U7c1l1AogMxMmpTQ6aw2jPWkmPpw/4f45MAn0BuciIa22433S9VifFoZYs8ckCTvz5KYKq4vxq0/3Iobv78RNS0ieqJIRG1bncX2Bgz7YILKWQSVIpE3udRiCqBd7NPHCXfewEC6afK8x6JULhVTAgkJ9JDw8/MZcwoACA6uxrFjRxAZ2QdhYSJm4ATBVVbm9kL4ZcuWQaVS4c8//8SvQv8fV6BSwdCrF06cOAGcPo3xo0f3LDElEBVFjk0Wrocvs7+EntdjbOLYLnUDPiWmADpnBSt1EZEN08VPnuz0x6YmYO1a+r1Tup9HxJSAQkFiQ2i+mZPjvm2LwF4xpTPoUNlUCa1Ba95rKjTU6OLY2uqVwtEeBDEl9JlSK9WYkjaFagCTksgkxwes8YvqivBrjp33ZLmcIjjh4XScnRzjPCmm8mvz8f6+9/HWnrewu8TJjIjgYPiNGwZVkApca4vTdfPW0vxK6kugkqsQqAxEqJ9j6c72cqj8EC7+6mJ8cfALt2yvJ8MElbMIgqqqyum0P1eIKcmIj6ebZlCQ2zftFjEF0MNg7Fh6QCh9o5C4uroa+/fvwOjRiUhISIAoV/LgYApb9uvn9gd9r169cM899wAAHnroIehcJFwNBgP2njmDZpkMgwcMgMpHaxnspj2Nk+d5fJL1CQDgxmE3mi3ic2IKoGtTqGeorrbpVCmXU8CH42iuy8yU57vvaEDUpw8wcWLHyx4VUwIBAbTjgFfVNdhdMwVqoPrKRa/gg0s+wLikceZ/jImh6EZGhs82WAeMYmpX/S4UBRV1fWanp1uMHnsbOoMOizcvxhN/PoFVWavse7NcTt+lqbELz3s8za9XaC8sPncx5gycg3OSz3F+hQEBZJIzcCCl5zqINTEFAGOTxmLtlWvx5AVPOt7A105+zfkVbbo2FNXbaGrJsAkTVM5imvZX43iI1tViiudpHHL8uIO1wHFxdNN04kbiCG4TUwI+IqQAcwOKiRNTIJdTGrsoi/y+fSki54HBzBNPPIHw8HAcOXIEq1atknz9HQYULS0YMmcOlEolRVa9ZHDqEnieIhq7dgH19dhfuh+HKw5DLVfj6sFGS3CfFFMCgYE0QAWoNsXG9xkcbDS2PHWqw58EEM65G2/sOP+9QkwJREVRiG3AAKtNcd2JvWLKNJWX4zgMixtmecHwcPP7jyvs01yIIKbquXqsr1yPZ7c9i22F27p7A9UBeuF9iAOHCckTEOoXiunp051bWWsrir//Hjn79nm0ZorjOFzS/xI8PulxSdbX2grkFPshtyHG/EU7mlN1J6YEIgMiERsktveD8zw0/iGsmLwCd425y23b7KkwQeUsHGecmXGwjsodkSmOo0FFWZlk2Ykux+1iyhSNxitMOKzR2c1PpaKgE0C9Je3KonFzyk14eDiWLFkCAFiyZAkaJWw70MXNLzraOEN86pTPpxdZheOoWKjd9W9Ve3Tq8oGXI8wvDICPiymBhATKCuB5qjOyEeFMTqYSCL2evn7k5QGbN9Pn1V7P51ViSiAlxSsiG45Epl7c/iLe2/ue+F5TBgPN9O3da6J6vRvTNL+ZE2fijlF3YHLqZExMmWj9Tfn5JKYOHXJpqxVHkMvk+O/I/2L91esRF+TceVe4dSuKjh/H+KAghFvr7eFCCusKodVLZxwhoNdTYLxjWKDVkhnHwYNUX24Da2LKwBvw9NancazymOT7LAaO4zA9fTpC1J6fvPF1mKCSgsRECgULs6d24M40P6d7UgEkNAoLXW5O4VExpdUCO3eShbGXPfgA69boKSk0oW3aq88mlZXkhifigSAld955J/r06YPS0lK8/PLLkqzTqjV6WhrVp/j790ir5g7S0wGVCm2Ntfjq4JcAgP8M+w+AHiKmBPr3pzpHpdKmoOI4CvRERra37vrsM/rDlClASop3iqnOaLUUfXRzOwdHxNTRyqNYc2QNPtj/AQ5XHBa3IZ6nujatlsSGDdMRT9O5ZkohV+C6zOvw/NTnu39+9+ljbuDgBUYVGr3GLKIYpHIupT83NxfZra3ImDgRwQEBdJwinDmloqGtAbf/eDtu+P4GnGk4I+m61Wr6V6drf4zI5ZQGKDhzVlRYfW93kanvjn6HdcfX4c6f7kSz1j3nhFavxZoja1wiPP/NMEElBQEBdKO0Uwy5u2ZKEFS1tU5MBB45Quk2LrQW96iYAmigJkQdi7wrr7i7PlMKBTBiBJUniD6V6uvpZDh92q3RG5VKhWeffRYA8MILL6DUyWhgt32mVCpgzBhg0CDzZoY9jXbXvx/O7kC1phZJQYmYkjqlZ4kpwOguNnx4F4c+S/j5kdeDn8pglu7nE2KK52kGvLjYrSYVjogpABgUPQgrJq/AHaPuwNDYoeLeJJe3f0F+dC86fNhrewGaiqmIPhGAyX3W5vNbOM6gIJqQPHjQ7a0rOvPM389g4S8LUd3ivEtxR83UpEkImTCBUjr1eurr4SZRVVBXgDZ9G5q1zQj3kzbVUKEwem60tYHqOQcPprYyQsTcgkW+rTS/C/tciGl9puHu0XcjQOmemuZVWavwwj8v4L5f7nOt2+6/DCaoPIQnDCjUarrHAU7oIUGVnZF29kfA42JKQGj0W1Hh8YeeQLdNex0lJYWeFE1N3c6wuYK5c+di3LhxaG5u7kgBdARRTXt7osOfJaKi8EnNnwCA62MvRFV5Rc8SUwL+/jSgERAzAN+6FcjPBx8cgraZl3i/mAK69o5z0X3XFEfFlMD09Om4ecTN9r1JpSKxoVBQTYoX9qgyFVOJ/RPx3x//izt+vAN1reJraDr6NwUE0Kj84EGPtSIpqS/BptxN2FG8AwW1lvvYiaWLAYVMRt9nRIRbRdXgmMFYM3cNXrzwRfgrpR87CFGqjnI/IQQu5NufOmXWE1BMzVSYXxiemfIM5gycI/n+WiM1PBVhfmGYPWC2d5mf+ThMUEmFTkf+vHv22HwQeNLNT2gpVVbm4PMqJoamaVpaJL9Beo2YAqiiPTycPiQviFLZI6YMBpoo279fxDhTqSRRBVB9iRtnhjmO60j3+/jjj5GdnW33OkSJKVOEHj92FBL7EqUNpfiljArjL5OPxJEff+x5YsoUnqfzdt8+2+mcn1Bd2ZlJ8/Ddz6e8X0wJREQYRdWpUy49dx0VU5tyN6FN56SpRGAgzfgL1oynTzu3PgnpnOaXX5cPnUGHNn0bAlWB9q1MpaIIqxCRO3HCNTttg8SQRHwx5ws8MuERDI8f7vB6rLr5CREcQVS56fuMDIhE38i+Lll3F0EF0Pnaty815wboflRcbFNMde4z5c5x4NS0qVh31TpcmHah27b5b4AJKqmQy+kh0NTUrdDwtDV6VBRNkrW2OqiH5HIKcQOSzpZ6lZgSEIRGaalH8/odiUwVFFA2n6hMusREesi3trrdiGP8+PGYO3cuDAYDHn74Ybvea7eYAuiDqajosQYVXxz6AgbegDFRI6AtbMKAUaN6rpgCaCKrtJTuu92lxDU0AN9+CwDY3v88nD2rQp8+PiCmBFJSjGYcR464xBHPUTH1Z96fePyPx/Gf9f+BRu9ktCUszNijqrjYO+qMOokpmUyGCSkT8OWcL/HMlGegkDlwDqnVJKrCwmgw7iHSwtNwxaArHH6/TWt0QVQlJdG/LoDneby641UcqTjikvWbYlFQCfTuTXWsAQFoCQ7uVkwV1RXh0q8vxas7XnVrHZNpel+wOphFpySGCSqp4DibTX49LaYAur/FxBhrKR1CCHNVVUkiNLxSTAEUoQoONoZ8PIAjYkomMxpTFBSI8GGQy42za6LeIC3PPvsslEolfvnlF2zatEnUexwSUwAdp0JBZiNuSJ9yJ6a9p8YEjkevq65CwnDHZ559AqWSauMAElbW0lbXrgWam9GcnAx+7AAMGjQIubkKT2VbOcaAARTF0WgkrzNyJs0vSBWECP8IjEsaB5VcgtTa2FgycBBS4zyIJTElkBKagoTgBMdX7u9PTblF1AFKye+nf5fEsEF0nymZrMMwpwMJL7xNuZvwZfaXuPXHW7tEfaRG+KqszmckJaElIwPbdu82iikLi23O34wWbQtOnj3pmCB3gKK6Itzw/Q3ILrc/E4QhDiaopMRUUHWa/fYGMSWQnk41+g63lAoONgoNJ80pvFZMCSQn0wPBA9EMZ2qm4uPp5q/RiNQN8fH0gNdo3O6rn56ejjvvvBMANfvV2xB0DospgB7qQnOivDyP1S+4gprWGhyrIuvd2ybdhiRBJANeW+QvCWFhxgmBEycsOu4Ytm4FAJw9/3zMvnwYQkPl0GrJrdtnkMtpll9wN5To3HW2ZmpM4hh8Pfdr3Dn6Tkn2BwDddz3Uv0igs5hq1DbigV8fcLreyCqVlZSO7MJnTU51DhZvXoyrv70ahXWFDq/Hqaa9paXkoutgm5nOnJN8DtXtDb8Z4f6uPWcSE4Hx460HFVtaWrBt+3ajmCovJ6fDTs+06zOvx8rpK7H4vMVuGwu+uftNHK08ivf3ve+W7f0bYYJKSsLC6GGn1ZrluXuTmALMa7kdJj6eVuREhMrrxRRAIvmcc4yDcDfhrAGFTGYcYxYWigg6cRz5SmdmeqT/zZIlSxAeHo7s7Gx8/PHHVpdzSkwJJCSQ05ZO51V1Gs7SXGNMj4qNM2kMefYsNfztoXVjACgkGxpKJ/rRo2YCUqfTobRdZCXGxkKlUmDgQLpGqqs9Fnx2DH9/ityMHClJZMMZMWWa3hfhH+G6mfbmZioIdaM5kKXI1Ou7XsfWgq149I9HpXdGE9R9RQX96yJRFaAMwKCoQRgZPxLJIckOrcMpMQXQ/UiwGpdAVIWoQ7Bi8oqOFhGuRKmkOTlLQ7guNVN6PaUh19QAWVldxkoTUyY6F+G0k8cnPY7ZA2bjsUmPuW2b/zaYoJIS0ya/7bP83iamTNHrnWg/FBtLUzUOCg2fEFMAfadKpVs3KZWbX2wsZcxotVSOYJPwcI/NCkdERHQ4/S1atAh1Fgb/kogpwCgeAXJn6QFCo7y8HFn7sxDtT1HynGqTeqLKSspROX685/bh4jhK/VMqqV6qXSgL1uia9vuyrP31wECjz0NurktKklxHcLC5/b+D36kzYmprwVZcseYKHCo/5NC27eLUKWNDXBt9x6TAWprfbSNvw/jk8Vg0aZH0z3EhdZXjKOvDRRb5CcEJ+ODSD7Bi8gqHjsFpMQXQcUZFGUWVg4MQT5o6dMaiAYXQ3qH9nmTYvw+rD3zmtl5TnQn1C8Wicxe5VcT922CCSmpM0v4a6uu9Wkzt2EFupg7V/crlDvf08Rkx1Zn6epdbv0ppjc5xxihVUZGd4y6Nxu3pcHfeeSf69++PiooKrFixwuxvkokpgZAQYy1goeOpL96AaZ+pCb0mAAC2FW4zLpCeTtXULS2U5thTUaupzkguB4KCzPpMJc+bR8ts29Yx+5+YSPWkAwYYi819jpISij7aGb1xRkzxPI+PDnyE0oZS/H76d7ve6xDCF9Tc7PIeVd3VTEUHRuP1Ga8jIybDNRuPjAQGDqTfS0okjZ7rDEYhKuNk9jsTQiIxBVBoOCODxkoOiqqS+hJc9vVleGXHK242dSCte+SI8XnarZtfcDD1y1Orsa7wV7z817O48bvroDe4Z2KL53mcruk5WRjeDhNUUhMWBgQHozEwEP/8/bdXiimAxhyhofR7WZmTK2tsFD1z6LNiqqyM0k5OnXLZJlzRZyo6mgaOw4YZmxLapLycBmluTodTqVR49dVXAQCvvfYaTrV/1pKLKYG0NEoVE0wNfJDOTXsnpUwCAGwt3GpcSKEA+ven34uLe0REziqRkcDYsdBFRZn3mRo3jgblFRXU3gLGoJYDZUPegcFA9yXBpELkjImzNVMcx+HtmW/jlhG34J4x99j9frtRqynNUaGgCS0XpcRZElNavRYnz56UfFtWiYkxRs8LCyWZ7Klvq8e8tfOwOns1DLxjYlQyMSUgXHyCc+Xhw2RyJZItBVvQrG3G8arjkMvEPtich+OoBKyyki47MX2mEBAADB+OlNAUxCvCMUeVCXlz1zpPV/Db6d9w1bdXYeXOlW7Z3r8dJqikRiZDQ79+2FZail5paV4ppgSEUpnycieeT8ePA3v3ijKn8FkxBVCKglxO9swO50laxyVNe2FskREUZMeb/P1pcFZeTsfrRmbMmIEZM2ZAq9XiwQcfdJ2YAigVIzXVDqXpXXQWUwBwbq9zAVCEymzwFBFhvOB7cuofAJ1MZhRTI0dCIZfToHzcOFpg61aL79NoaG7IZxBm+pVK2vGTtgf+zoopgUBVIG4fdTuUcjelQwcG0rFyHIliiSOt1iJTb+15C9etuw5rjqyRdHvdkpBgzEc9fZpqcJzg++Pfo7CuEGuPrnXI1l5yMSVgOqPB85SqK5L5Q+bjtemvYdn5yyDj3DuMFaLZNTUixJSAnx9Gn78A34x5AfPCJ7pkDGGJY5XHwPM8ApX2RyQZ9sMElcR4c81UZyIj6Vnc1ubEPTs4mP61YSXn02IKoNnRhPbcY4kb/bpKTFlCVCAxJIQEpNA01c288sorUCgU+OGHH/Dmm2+6Rkx1huddns4pJZbEFAAMixuGIFUQaltrcbjisPmb/gWpf6ZpfmMHDIDiwAEgP5/+OImid/j77y7va2ykeaHDh91SpiMdfn5GoVFe3u29yVkxtat4F7YWWBajbiE83BhpLSx02mFWwJqYMvAGVDRVQG/QIy7IzUY9KSkUPU9MpKwXJ7hu6HV4ZMIjePKCJ+GnsM/IxGViSoDjKM1x0CC767EnpEzwSD2QWg20tbXh7793ixJTHQYmKhUCRo6DrG8/Y18TF3PfuPvw/iXv48ZhN7ple/92mKCSEDMx1a8fuJoar2hMaA2hJxXgRNpfbCytqKmJaows4PNiSiApiR4AtbVWj9Ve3CWmDAbKVtyxQ2QBfloaHWtVlWTHKpYBAwbg7rvvBkDiavTo0a4VUwYDuTBlZflEOpw1MQUACpkC45PHA0DXwa9p6p9O1+MaG5uJqbFjodDp6GQvKKBr9lyK3lmKUPn7U6CytdWlWb2uISyMxDJgNaLhrJiqaq7CE38+gQd+fQB/5v3p5A47QVwcFYaGhlLU1Um6q5mScTI8PflpfHzZxx2RX7eSmkrpBU5OynIchyszrsTgGPsa67pcTAlwnHnercFgcXJLb9Dj4wMfe8zUQYDnW5GdnY3g4CibYqq8sRxXfXsVthdtpxcUChpHCOj1ktnHW2NE/Aj3RZL/5TBBJRFdIlM5OeRK5OXNQ4UsoKoqB2dmFQrjzdDCsfYYMQXQ1FRsux21BLnt7oxMCZpXr6fxpU0CAownh5trqQwGA2bNmoXQ0FAUFBTgo48+cu0GZTJjA9FTp7xaaHQnpgTOTaHBn8VoQkQEMGoUFfp7cfTcXrqIKYWCrlXBeOToUbIal8vpAuh0/crlNFEuBHqs9Qf2WhIT6XrleaqYNzGUkSLNL1Qdipl9Z6J/VH9MTJko1V47Ru/eRvc0J+hOTAlwHIehsUOd2o4kGAzAsWOiU0kMvAEbTmxw2PzAbWKqMwYDOWUdPNilH+KqrFV4e8/b+O8P/3W4FsxZWlpacOjQHoSFhaFPn0E2M5A+3P8hTtecxgf7P+hqtS9cqxKPE5s0TXhj1xto0rg3XZ/BBJUkWEzzE7rmurlJqr0EB1N6usHgRNqfMGiprDRTZT1KTAmkpNC/VVVORR/dKaYEhIyK0lKLvU+70rs3iY3aWpfPogkINVNyuRxPP/00AGDp0qWosqNg2SHS0mhyoLHRaydBxIgpAJjUi1Lb/i7823K/HLsK6rwfi2JKID2dxLJGQ4YcI0bQ6xbS/kJCjJk4J0+6te2RNPTrZzyI9oiuVDVTSrkSD5zzAD6+9GOo5C6MFovFVPyUldn9ZXUnpr45/A1W7lzpVvc4mxQXk9I/fFhUxsAXh77Ak1uexD0/32N3zyyPiSmAZjTUahIbR4+azWwMjx+OhOAEXDvkWrfXTQFGA4q4uHD06dMHGo3tCakHxz+IBUMWYMm5SyyLL2FMdPKkyJlO27y+63V8evBTPPDrA5KsjyEeJqicxGrNVEQETXu2tbk9Zcpe+vcHxo41Or7bTWgoqTK9vuMG2CPFFECDs6goql1wsHmNJ8QUYMyS4XmR9261mma+Oc4tqaudDShuv/12DB06FLW1tVi6dKlrN65UGgvB8/LcbhlvC7FiCgDGJI6BSq5CWWOZeT+qzgjucD5UO9aZbsUUQPfgjAxjF9/hw+l1K8YUvXqRJtHpXNpf1TXIZHR8ydSwVQoxVdlUaTYgVyu8zF++uJi+qOxs0SkW3YmpyqZKvLbrNXxx6Av3WMKLJSmJasj0eopo2HBPiQuKQ5AqCBf1uciuOm6PiimAnjX9+xujrceOddTKjYgfgTVXrsH09Olu3y1TN78RIzLAcZyoR4Sfwg/3n3M/UsMt1IcJjlFCb5O8PPJkd/Kmc2GfC5Ecmoz/jvyvU+th2A8TVE7QrQGFTOYzUaqQEONEicMIUaqqqp4rpgT69SMF6sADx1NiSkCIUpWXi9RIKSl0rDYG8c5iyc1PLpdj5cqVAIB3330X2dnZLt0HxMdTyFanc3uaY3fYI6YAeoiPTRwLwEran0BhIUVaT5zwSdc/m2JKIDDQWGMkhKCsCCqhRl4uJ53ppcFK67Q/g4qLi5G1bx/GpqU5LKaqW6px7bpr8cSfT3i8bsUq0dE08dPUROlTNnpU2Urziw6MxrNTnsWsfrM8MnC3ikwGDB5sVPuHDnWbZjCtzzT876r/4dL+l4rehMfFlIAgquLjAZ6H9ujhDlHlp/Bzu9FXZ2v0iAgO48cbg92d4XnevobXvXsb70/FxXQ/dkJUjUoYhbVXrsWohFEOr4PhGExQOYgoNz+TJr++gsM9E2NjgYwM8IMH92wxBVA6jQM3dU+LKYD0gl0GfkolReNcSHfW6BdccAGuuOIKGAwGLFy40O70FbsQZgwBSiPyAoMKe8WUgNCP6u/CrqltHfTubXT98yIBKQbRYkogIYHux+edR/8/ftxqoZS/P41v4uONZYS+RHFxMQ7u24fxfn6Irqhw2KL5QOkB1LTUIKc6xyMpVqJQq4EhQ0gB19TQYNQKYmqmAOC83udh2fnLvM+hVy6nflxBQRRdPniwS6qjaW1RhH+E6GPwGjElwHFAv37I9q/HZYefwD+71krm6mgPlvpMyWTdDwF+yfkFN62/Ccv/Wi7+eZWUZKxrLSsT1QKhM6bNmxUyG/dDhkvw0rukdyPaGl1I+2tttavHgicQsn927XJwckSpBB8VhYOHDvVsMWUKz9NNXkSqiTeIKQEhSlVdDWjtKRNoaJC8f4aYPlMvvvgi1Go1/vzzT3z//feSbr8LISE0kg4KMq/T8ACOiinA2I+q2wiVQkEPcQAoKfGZ1D+7xZTAwIHAhAk00w8A27ZZXTQ+nibJfa1FmZDmN/qccxAhXOjHjjmUsjslbQo+vPRDPDf1Obvttt1KUJC5dbyFmSJbYupo5VHvjcKZolCQqAoIoHHF4cMdD+xdxbsw/7v5djci9joxJcBx+KRmMyrQiF9r9hqNg9yEqKa9FihrLIOMkyE5NNk+UR4XZ+wrl2CfHfzRyqOY/fVsz7Y1YDBBZS929ZmSy43Wrk4253M1SiWVerW1OeY/YJbmN348/NVelmvvCrKzabBiIy/Im8QUQBlQAwdSn1PRRlnV1cC+fZKmh4lt2puamooHH3wQAPDAAw+gRZSjhhOkp5MjnNBjzQM4I6YAYHzyeMg5OfJq81BY140jZXi48eHtAw1/HRZTgFEgC/bpv4uvkfGCYKVNutRMpadT4aRO53CDraGxQ5EW7vl7lk0iIigVG6AC0dLSjj/ZElMVTRW4+6e7seB/C3CmwQdyPFUqcjkUUlk5DjzP443dbyCnOgffH/9e9Kq8Vky18+zU53DLuLvw0GXPu/V+bEtMFRZShqmlefL/DP8PvpjzBa7PvN7+DUdF0YPZzmNdlbUKZY1l+PnUz/ZvkyEZTFDZyc6dO+1r2tu7NzB6tNEdzkvhOKMjuL09qczEVGoq/A8d8kh43u0IH1hxsdVcSW8TUwKxsXa6DoeFUS6U4JbmJGLFlMDjjz+OpKQk5Ofn48UXX3R6+90il3vUUtxZMQUAwepgjEwYCQD4K/+v7hdOS6O0ztZWr079c0pMmSIIql9/pdqbbuB5Klc5cMBtRpcOYdGAQiajGW+1miJUIlw2altrsfjPxTjbLG0k2i3Exxtr5NrFo5g0v+qWavgr/RGkCkJ0gKPOTG5Grab2B+1NfzmOw+szXse8jHm4d+y9olbh7WIKIBOU20ffgZCwWOOL9fVONM60jZjIVE0NVXJYC/z2i+zneNqdaVi8vh7Yv9+mAdZTFzyFW0bcgv+b8H+ObZMhCUxQ2UlKSop4MQXQLFJgoGt3SiJMe1KJTQXrYkChUtHAzOequR0gJoYGohqNxRu8t4qpzojKRpXJjG5ERUV25gqaY6+YAoDAwEC8/PLLAIBnn30W+fn5Dm9fNAYDkJ/fbV2G1EghpgQu6H0BAGBz/ubuFzRt+Ftd7ZVRKsnEFABcQJ8LTp8G/vmn2+PlOKNhz/HjTp32LqNbNz+VyuhyWFVl097z2b+fxc85P+OxPx5z4R67kNTUDqdDsTVTA6IGYPUVq/HC1Bd8qwGq6RikqQkR+eV4+JyHRKVnerOYatY2W58Eam2lGY7jx82ikFIhNs1PSMARdE51SzWe3PIkqlsknHXheaqlqq+nGZ1uMjPUCjVuH3U7Ivydb3bNcBwmqOykX79+3lesKhGBgRRpFkqDbGHRzS8ujm70DQ02rV19Ho4zut8VFZnN/vqCmBJm3/ftE5mRGhND9Qo6ncONjR0RUwJXXnklLrjgArS2tuKBB9zQY6OpiQRVaalbcr6kFFOAiaDKsyGoAEr9GzSIZr29rHBIUjEF0HmckUG/79hBVsXdkJZmbGXlQK24SxFljR4SYkyHKy3tNvXvjtF3ICMmA/833odnukNDjWJKrcaoIUMsiilTw4AQdQjig+PduZeScKbhDA6eOUDp5+Xl1LvJRhTSm8UUALy641U8tOkhvLbzta5/9PMzzvyeOCHpxK09NVOCoBI8QV7e/jI2nNiAJ/54QrL9AcdRvae/P23owAGzMZWBN2B3yW7ptsdwGiao7MQhMdXWRje6ffuk3yGJEe5VtiLqVq3RlUqju+G/IUoVH0/H3NJCM8DwDTEFmM++iwr4cJyxV1NJid19uJwRU7R5Dm+88QbkcjnWrVuHTZs22fV+uwkONrYDOHnSpU2JpBZTADAhZQIUMgUK6gqQVyPC0jEmpueLKYHJk+nfAwdIZFhx/APoIxk4kE7/ykqXZhvZhV19puLiyMFy5EiKSFqhd1hvrLpsFfpG9pV4b92HIKYCFQqMksshs1A/1qRpwi0bbvHpAamBN2Dxn4vx3423YYPytDEK2U1qp7eLKZ7nERkQCYVM0dGgvAvp6caJzJMnJRln2GtA0TlCdcOwGzAoehDuG3ef0/tihp8fRVwFZ8esrI7JvbVH1uLOjXdi+V/Lpd0mw2GYoHIHcjnd6BoabObse5qYGLovNzZaDzDZ7DMlDEIrKrwyfUhS5HJqfgsAhYU+I6YEevWi77uuTqSBX0QE5e0L6XAicVZMCWRkZODee6lG4N5774XG1Q1409JIMDc1kYh0Aa4QUwAQpArCmMQxAESk/ZnC83SsHjbScZmYAoxpf0eO0L8nTnSbUhMcbHTHPHWq20XdgkNNexMTKQWwE/Vt9WbGJb6cgWGa5jdyxAjIdDqLPao+yfoEB8sPYsXWFdDqvTCPUwQavQZJIUnwU/hhVL/zKcIsOB1aiLp6u5gC6Ny7fdTt2HDNBoyIt9LoCSBR1d7AGidPOnVvdsTNr7Og6hfZD5/O/hQDogY4vB9WUamAYcOMBjMHDwJnz6JJ2wQZJ8Og6EHSb5PhEExQuQOFwtgEtpuZUG9AqaT7VN++ltsPiWraKxgY6HT/DnOKxERAJkNdUxN2btvmM2IKoHu1oAdPnxYZhBFERkiIqG1IJaYEli5ditjYWJw4caKj8a/LUCqNI+m8vC59X5zFVWJKID2CGkZuKdgi/k0lJaQaPFg05FIxBRgbaebk0EBbr7eZLpWcTLc2vZ4+Gk/hkJjqTEUFcOgQeL0eT255Etf+71psybfjHPFCutRMBQSY96gyidzcMuIWzB00Fysmr/CtuikT/BR+WH7Bcqy5cg0SghPIIc60DYKJqPIFMWWaghkTKOK87tPHKKpOnXKo36ej1uiCoKppru94zaUTEYJdfmQk3a/KynDT8JvwzdxvcMWgK1y3XYZdMEHlLkwt9FzZnFQCUlNpkN15DCNKTAE0SyaM0iVwhPN6lEpUp6djW0MDBgwe7DNiSiAlxRiEEVXnGxJC1q7xtmsOpBZTABAaGornn38eAPDkk0+i2NXnWHw8zQ7q9ZIaVLhSTGn0Gtzx4x347OBnAICkYDvWHx9PRUNtbR5RDi4XU1u2AFOm0O/R0VRPFRTUYUFtDY6j8WpwMI3lPIEkYkqrpVn96mq0HD2IhrYGaA1axAbF2n6vl2LVgCI42NijqqKiI3XXT+GHRyc+iqGxQz274w7QuVlsXJBJ9+nYWKPBTHExUFzsE2Lqj9N/4MFND9rvLtmnD6VZhIeT2LADR8UUQFkdWbVb8PCBS/HjyR/t22dHkcuppio1tUM4p4anem/T7X8h7JtwF1FRpFDa2jyeSuMIosWUQHw8/Qwc6J4d9CDV1dXYceCAT0WmTFEqjQZ+eXki29WIqLVxhZgSuP766zFx4kQ0NTXh/vvvl2y9FuE4GqTIZOS4JEHarivF1JmGMzhv1Xl4d9+74MDhqQuewlOTnxK/Armc0odkMsoDdeOkiEvFFM8DK1eSmKqspDSaXbtoEDpyJIlmG/j50aIig7OSIomYAuiCb08PCzhbj3cyHsYHl3zgmnQlN2DTzS8iAhg0CDpej+3HfwNycz2zoxLx0vaX8MI/L6BVZyVaHh9PA+6wMOQ2N3u9mNLqtXh5x8vYWrAV/zv2P/tXkJpK0Rvhe+d5m5PWzogpgBJw8v3WQ+7fiNM17ms1Ud1agyWnP0Jlq4mbYEmJ1bYtDPfCBJW7kMkcb/TkAQwGilZkZwMGg51iCqBBWf/+Hm2O6g661ExptWRN7OVRyM4kJJDLo0JhZ1ZbZSX1yeikwlwppgBKr3jrrbcgl8vx7bffut6gIiCAJgdGjXK6DYIrxdTfBX9jxHsjsLN4J8L8wvDj/B+x6NxF9s9iBgUZwzCnT4v01ncOl4qppibg2muB+++nSOO115JlupDO2cmCWuykV1OTQ/1y7UYyMdUOHx7e4fwnKyzCYL19s/veglhrdERH4z39btyb8wZe2fO6d/rfiyC/Nh9rjq7BmiNrcKTiiPUF4+KQGxSE46dOebWYAgClXInXZ7yOmX1n4sZhNzq2EtPr9/RpamRtpX7bWTElbO7laS/i0YmP4vZRtzu2zw7w0vaX8NOpn7B482J6oaCA0h2zssi0wgk6Rz4Z9sMElTsRUqTsafTkIXie7ktVVTy2bDlsn5j6l9BFTPE8OTnm5flc7RjHUbnB6NE0lhaFwUAnSX29WX8bV4spgaFDh+Kee+4BANx9991os9N10G6io422iA7iKjHF8zze2PUGJn82GeVN5RgSMwR7/rsHM/vOdHyliYkUWTcYqL7IhQYzLhVTubnA+PHAV1/RRM9rrwGffUYiuTMNDTRBcOSITeeJ8nK63F3dpkxyMcXzWPrXUnxR+Qd4oeF8ewqgLyFaTLWjCIuAzD8AQ4dPt7OruffQO6w3Xp/+Om4fdXtH425L5Obm4viJE0YxlZ8vqrGzp0iPSMeTFzzpfD1bSwtFbM6epZ4gnWY7pBBTAnKZHHMHzYVK7prnmyX+M+w/GBo7FA+c0942JDSUZkGFBsDWOg3bgOd5nPS2nhA+CMczWSqK+vp6hIaGoq6uDiHO5HscOUJRm4SEbu1rvYHcXB6bN+dCp6vCDTdkOiamWluNEZsBvplSYgmrbn6FhSQy/PyAMWOMaQg9lepqenBxHDB6NAx+fm4RUwJ1dXUYMGAAysrKsGLFCjzxhIR9QLqjtpYmRYQWASJwhZiqb6vH76d/x2cHP8P6E+sBAFcPvhofXvIhAlUSNBTXaoG9e2n2c8gQSp+SGJeIKZ4nN6yNG4GXXqLvKyYGWLsWOPdc6+8zGOh9dXUkuEaOtJreKmgvnqdgvIiSQruRWkwBwI6iHbjn53sgl8mx+orVSCtvb0wul1ME1gcmzewVUwIFtQXoFdbLdEUWnQ99mS41U83NwJ49dKLGxtJz2AucHCuaKmDgDeY1YFJQV0epNTodZRMMHQqo1ZKIqYa2BmzO34xZfS/ByZMceJ4Cve7sNsHzvPm+NzfT8ba00Jhy8GByz7FjfcePH8fRo0cxd+5c58e4/2KYoBKJZILKR+B5Hrt3H8K2bVoMHjwEEyeqHct0amykARnHAWPHWrYO9DG6tUbX66kuQ6Mhq0TBnMOHEFyzAwJEjp+zs4GzZ2EIDcVerdZtYkrgq6++woIFC+Dn54ejR48iVUjjchU1NTToVigopCdYPnWDVGKK53mcPHsSG09txMZTG/F3wd/QGijaLefkePHCF7Fw3EJpHafq6+n6dUH6rqRiqrER+P13ElE//WTen2bsWOC778RdjxoNhZ7a2ihCJ5gaWECYP5HLSXtZCno5iivEFEDn0HfHvoNWr8U1Q64hEZmdbfSG94LBdnfYI6aE4Y3F66GqiiKv/fsb0/G9lH1n9qFfZD8Eq7u/Bq0aUFRWGl0sY2KMjdU8BM/zuPunu3G48jBWXLDCes8pR2lspIk+jQbw80NLv37Ytm+f05GpJ7c8iQ0nNmD2gMsx0UCTdxMmuD7g2ahpRJCqm9QRrZauYeFePWCAqHNaEFMFBQUYMmQIkpKS/jVjXFfQw6fPGY4gGFA0NFTi/PMHQ61WO16XHhREDjw83yMc/2z2mZLLyXUIoMicD/bhKioix13BUdom6ekwADixcye0Z864VUwBwDXXXIMLLrgAra2tuOeee1yfCx4WRq4EOp2ofC9nxVSbrg2bcjfhvp/vQ983+mLAWwPw4KYH8Wfen9AatEiPSMd9Y+/Drlt24f5z7pfevjckxHvFVE4OpfBNm0YuX5dfDnz4IYmpgADgkkuA998nZz+xkxsqFYkooVGqSTprZ5KT6fam1wPHjklXG+4qMQWQuJg7aC6JKYCOc8gQaofQg8QUAKw7vg4PbnoQta21Xf9YW0tf2PHjHU3ZvZHi+mLc/+v9uOa7a3CmwXoT227d/AQ3S8Ht8OhRjxoZNGga0KJrgVavRUpoivQbCAqihrj+/mirq0P2Z58hNjDQKTHF8zz6RvSFv9Ifs/pd3HGpuPpj3F2yG7O+moV1x9ZZX0ipJJOd6Ggaax0/brMY2lRMTZgwAcE9vN7dHXh3zllPxWCgG7hOR6l/XkRnNz+Nxg8HDlC9gNB+yG6Sk2lWv7SU7OS8PNXRGqKb9sbHkyppbaVQT4oLHhguJCGBtG9zM41LbWkAg1qNQ7W1QFsbxkRGQunO/AcYDSoyMzOxceNGrF+/HrNnz3blBmkGcO9eSnksKwPiLKetOCqmyhrLsOHEBvx06if8fvp3NGmNzoJKmRLn9T4PF/e9GDP7zkS/yH5OH5JoGhspLDNggFPprA6LKb0e+OsvikJt3Ei1P6akpQEXXwzMnAmcf77jEfGQEMrlOX6c6k+Cgiha1QnTU6GhgRZ11ujTFWKK53lsyt2EKWlToJBZ+KxNv0uDge5fyclelbJsr5hq0jThjd1voKGtAZtyN2FexjzzBfr0oWdwWRkJjCFDjP0ivYgmTRPC/cMRGxhrNT1OlDV6VBSlgx05QhErnje6ebqZEHUIPrz0QxyvOm6egikl/v5oGTAAh774ApFBQeiTnu7UZBPHcbhmyDW4uN/FCFGH4O9TdDtytaD6/vj3aNQ04lT1qe4XlMno+8zLo8mkbu59lsRUfX291eUZ4mApfyKRNOXv7FkKzyqVwDnneM1Dy5o1+t69NI5KS3NCG+zZQ5ZYTq3Ec4gWUwJlZTQYUyioZ5OPicjSUgq+KBSULWVNSHcYUDQ0YIJKBaVeTzd1iWfVxfD444/j2WefRXJyMo4ePYog0e4aDiLke1lJ/XNETNW21uK5bc9h5c6VaNMbTTbig+Ixs+9MXNz3YkxNm2oz9cclGAyUztrWRtGevn0dWo1DYorngV9/BR5+mO6dAgoFMGkSiaiLL6b0LSmjLKdO0aSIMBi1QlUVGYsBwIgRjtuquyoyteHEBjy55UkMixuG92a9B7msm0mP9hRexMZ6TdsLR2umTp49iXXH1uH/JvyfZadLnicxVVlJ2QVDh4qyz3c3zdpmNGubERXQVdTb3WequppOVoOBRKSd/Zt8hY6aqfBwZPbqBc6OetfOdKlbApmEarVUKi1lqm9nDLwB646tw4y+MxCgdGBDQm1V+0PcWmTq31bW4gp8a5TXU4iIoLQSjYYeXE5c6FLRXZ+ppCTKEnDqGktOJoFRUkIr9BIRKQa7xRRAg5HiYno4++CcRVwcfVWNjTTh1c9CEMTMzW/SJCiF/kwemuVdtGgRVq9ejfz8fCxbtgwvvfSSazeYnEwj6fp6ipQMGdLxJ3vFlEavwTt73sFTW5/C2RZqbjkifgQuH3A5ZvadieFxw6VP5bMXmYxOhOxsOjnCwy1GbbrDITG1fz8JqT/+oP+HhgJz5lAU6sILXTsATk+n0ZKNTIKoKNKYarXj2ZGuTPMLVYciWB2M8cnjuxdTAB1IdTWlJfj5Ga3lPYSjYgoA+kX2wyMTH7G+AMeRaNTr6Zizs4HMTK9o92E6iA9QBlgcTDvUtDcigiYHWlrcLqbWHFkDAJg7aK5LG9J2a0DR1kbGFSKvsd0lu/HBvg+w+LzFZumJ7kr5k3EyXDHoCsferNFQvW97Wi/v58fS/FwIi1CJRHL1fvo0zXBHRpoNxDyB3U17HcFgAHbupAt84ECvLwIWcEhMCfC819ckdEddHXDgAB3CyJHmduruska3l59++gkXX3wx5HI59u3bh8zMTNdusKmJDAwMBhqIhYfbJaZ4nseaI2vw+J+PdzSIHBg1EC9c+AIu7nux50WUJXJzKSXMDlMOwAExVVAALFoEfPEF/V+lAu65B3j8cZe4DXoSV4opgYqmCkQFRIkbyAoRdoBEtIdS0x0RU1llWYgLirPPPU6vJxODujqa8EtPd2KvnadF24J7fr4H12dej3N7WXaldEhMWUOrpUG3C9O1S+pLcOXaK6HRa/DytJdxXu/zXLKdbsWUTkcPtaYmSvlMTu52XTzP45rvrkFOdQ6uyrgK/zfh/zr+tnMnZfU7E5G2hkavwe+nf8f09OnOCc/mZhJUbW3gFQqcVKuRd/asRTHFIlTO4zthgp6GUHNRXU0zJh7CLWIKoJt1r15UQ+WFeeqWcEpMAT4tpgCa+I+JIV2Ym2t8XZSYamtzSzPYzsycORNz586FXq/H7bffDoOrpw8DA+nBPHCg3WJqa8FWjP1wLK7+7mqcrjmNuKA4vD/rfRy64xBm9ZvlnWIKoIhFcDANTgTnMBvYJaZqaoD/+z8ayAtiav58GuC/9JLnxBTPUwpgXp7NRQ0GGpuLwZViSqM3NvuMCYwRPziLi6N7NUDHfPaspPslBkfEVFVzFR7a9BCu+e4aHKs8Jn5jcjlNbPbpY2xo7UG+zP4SWWVZeG7bc2jTdR0fSCqmdDoadHfTDFcK4oPjsXDcQkxOnWxVJDqLTWt0udx4/8jNpYntbuA4Dq9c9Apm9ZuFu8bcZfY34XR0xSNmVdYqLNm8BI/+/qhzKwoIAEaMAB8YiMLcXNT+9Rcm9u/PIlMuggkqTxEQYEwH81ATWHvFVFsb3X8aGx3cYGIiPaS9JJrRHU6LKVMaGuhh5UHh7ChpaRREFSZsRYmp2lpg926POUmtXLkSwcHB2LlzJ95//33XbzAxEYiNFS2mjlUew6WrL8V5q87DnjN7EKgMxPLzl+PUPafw35H/tWwa4E0Ixc9yOamG/PxuFxctptragFdeoQHtSy9RNPv886n+8ssvPZ56hpoaSnUsKKCaGysIrusHD9JEeHe4Ukz9kvMLrvnuGpw862DDzt69SVgJdUZunCBxNM1Pq9ciKSQJ8UHx6BNhpzBSKChiIQzCeZ4iNx7g+szrce3Qa7Hs/GVQK8wjwJKKKYBS/1pa6PzOznaZqJJxMszLmIfnpz7vkskiUX2mOI7uL8IzvbCQioW7mRRKCE7AsvOXdUm5HD4cmDjRNRnHkf6R8Ff648K0C51eF69S4bi/P4qamzEkIwNBBQWUYcCQHCaoPIkQpSorc/umHYlMCVmKPcD9vFskFVMAzYTZsF/2Vvz8aOI2MNCONL/gYBpst7R45GRJTEzEihUrAACPPvooytxwfXWIqYwMJFmZ/SttKMVtP9yGwe8Mxg8nf4Cck+P2kbcj594cLDlvSfd9RrwNf38ygABooG1lQCJKTBkMwOrVZJf34IM0sMvIIBe/P/+kZrPeQESEMUXo+HGrM0tKJWVBGgzdW6m7UkwZeAM+OvARCmoL8Ff+X46vqH9/yijgOLdNjjhTMxUfHI8PL/0Qr01/DSq5ExN3wpd34IBHRJVKrsLCcQsxJnGM2euSiymA7teZmSQoa2sp9VFCUVXZVAkDbzx3PCamTElJMRrYlJaS86HJ+d2ibUF+bX63q1Aq6SOT6nAMBgOef/55VFVV4YpBV2D91esxNW2qU+vsMKAoLkbmggUIEIyEcnN7/kDOAzBB5UliYmjgqVS69abtaJqf0MalooJmYR3m7FkgK8uJUJfrkFxMAcaZ9dJSEhk+iCCmGhpabNdMyeXGtJmCAo9E5u666y6MHDkSdXV1ePDBB126LUFMDe/fH0mlpTTLa3KBFNcXY9lfy9D3jb54f//7MPAGXNb/Mhy+8zDemfWOfbUe3kRMDKntIUMsjipsiimNhkTTmDGU0pefTy0HPvyQ7g8zZ3pf2mxamrHx1OHDFu/bgpW6Ukm3OEtZRa6umZJxMnxwyQe4efjNuGXELY6viONI3I4Y4Rb3O0fFlOmAXSFTIDrQSaMnrZair0INik7n3PpEwPM8dpfsttpHzyViSiAkhBwOFQo6bomOWaPX4O6f78YtG27ptoeWM9gtpgTi4837zeXkdPzprT1vYf538/G/Y/9zyT5b4vjx43j00UexdOlSAECEf4RT4rOLm19ICDmzCkY7PlLH7kswQeVJ5HKy1B4+3PWttttxpmYqJIR+DAbSBg5TVkYzYV42Q+ISMQXQQCQigmbxbaRHeSMGgwG7d+/FyZM8ZLLx4HkRM7+xsXTcer15AZabkMvleO+99yCTyfDVV19h06ZNLtmOaZpfYno6oFbDoNVg7651WLp5KUa8NwLJryZj+ZblaNI2YWziWGy9cSu+v/p7DIga4JJ9ciuRkfaJqaoq4LPPgCuvJGu8WbMoPy4oCHjqKarXuflm720zwHGU7ujvTxXpVmrIVCoSVQDd5qqrjX9zhwEFAIT5heGO0Xc476amUJj7Qjc3uyRa5aiYatY249r/XYtvj34rXVNvtZqiNioVqWIXpsIJfH/8e9y58U4s/Wtpl+NwqZgSCAkxRqrq6ylS5aSoyqnOQVljGYrqi+CvkL4+22ExJRAVRUIyKIhqvEHivKS+BBq9BvFB8VbfWlJC2YJStW/ac2QPAOC9995DvpPjhG6b9iYlUdTfdMzphgmDfwNMUHkaNwkpQBoDCqE0pKTEiWeqkDZTXu41dUUuE1MCQpSqvNx2YYUXIUSmWlub0bfvCHCcUkxNPtG3Lw1AKyoojcvNjBw5EnfffTcA4I477kCLxNFBUzGlDldj9eGv8Z+8V5G88yqM/vNqPLn1SRwoOwAOHM5JOgdr5q7Bjpt3YFKvSZLuh1eg15N1fFGRuZgaORKK3buB5cuBCRNIaN9wA/Dtt5QqGBsL3Hcfie5Fiyi31NtRKsl2Wi6n89pKYXtkpDGqf/w4BeRcLaa2FmzF3jN7JV9vBzU1JIBPnJB0tc6k+X1//HucPHsSHx/4GM3aZul2KiDAPGoj9G5yEY2aRsg4GfpF9jMTBm4RUwLBwcCwYXSOt7U5nTkzKHoQvpn7DZ6f+jzC/aXdd6fFlEBYGNnYtruVyjgZXpn6Ij669COck3yO1bedPUsTy80SnXKf/vUpABJ0Tz75pMPr6VZMCZheX2fOUJ2qF2YM+RrMNl0kLreU1GrpieuiAYVUbn6SuZ8fOEAPqZQUY4Goh3C5mBIQOtTbaBLqLXSumWprU2HfPvqbaKtYoTFqYCDNirk5hauhoQGDBg1CcXExHnvsMTzzzDOSrLegpACfb/kcZ/zOYGflThwoO2D290CZHy6KHItLxlyLmQMvRUyg+xsdu5XycuDYMej0emQXFiLkyBGk5uRAtnlz1ynczEzgkkvoZ9Qon+pJZ0ZlJQmLAQOs9uMyGEh/NDUBBkMZmpv3ukxMnWk4g2u+uwbN2ma8NfOtLvU3klBTQ5ELnpfs3u2MmAJoAPr14a8xIGoARsSPcHp/ulBfTylwej19zxkZLruP5VbnIjU8tSOq6FYxZUpjI00YuMr110kkE1OWKC+nSZKhQ7sdjx0+TAF3qboK3H7P7fjg3Q9g0Bkgk8lw7Ngx9LPUALIbRIkp8zfQDaqxEfWtrQidMYPZpjuBl+ZV/MuorKTUkdBQmh2SGCmt0WUymnXNy6NxssOCKjmZBNWZMxRqd2H/i+5wm5gCKEpVVUU/DQ1e0TzSGpYMKFQq8lEpK6N08xFixi69e9PxCimPbhZUwcHBePPNNzF79my8+OKLuPrqqzF06FCn1rnjxA5MWzsNjXrzGb3M2ExcmHYhLkybinPPBsGvRQv4RQE9XUwBQGwsdGVlqL/tNgzftcv8b+HhwNSp1IT3ootoIN4TiI6m2e1usgwEQ8Tt28vR2LgfEya4Ls0v3C8cU1KnIL823zXCAqDvsn9/CrkVFpJrjROjSWfFFEARhflD5ju8DzYJCaEJsOxsyt1sbHTZvdvUmdBjYgowbzoIkJAOChKdUfNV9lcYkzgG6RHS9/NylZjKLs/GjqLt+I8hE8q2Npr0HTrU6syh1Lbp5cXl4PV8+7plWLp0KVavXi36/XaLKYCex8OG0WSvl5Vg+CI+OjXYwxAu2NpayU0LXNFnKj6e7qvBwU7cTCIjafZLp3OyIMtx3CqmAEohSUqiVDgvTm3qzs0vNZW0b329SLd/pZJMB/r08Vgk4rLLLsOcOXOg0+lw6623Qu9ELUR5eTk+3/45GvWNCPMLww2ZN+CLy79A2YNlyLo9Cy9OexHT0i+CX0YmPayqqrq12O4p6HQ67KyuRuiB9kjdiBHA00+TfX5lJbBmDfDf//YcMSVgOsBsbbXo1lNTU4y2tj2YMGGUS2um/JX+WHLeErx98duutd6XqEeVM2JKb9Bjw4kNZmYULiU8nCJTQ4dKKqYOlR/Cwl8WorLJ/B7hUTHVmepqEpNZWaLcqPae2YtXdryC69Zdh9IGaZ/trhJTWr0Wy7csx/v7P8AHhj00JhP6c5kWQJoghaDieb7DrOPUqVMdtXM6nQ7ffPMNsrOzRa/HbjEloFDQeR3no+ZIXgQTVN6AWm1sdiuhxbOrmvaqVMA555AucHiMzHHGWqriYlHNQaXE7WJKoE8fCvF5aZqTLWt0tbqjdhenT4us0/ZQ9NGU119/HSEhIdi1axfefvtth9Yh1ExpgmlQcfPwm7Fq9iosGLoAsUGdQrVCkXNcnM80snYUoWZKXVcHuUZD5/bzzwP/+Q8werRXfP8up66OUmc6mVRYqpmqqZG2FOdss7mg8VP4SbdyazjZo8rZyNRHBz7Ck1uexEObHpLOiMIWkZEUkRRwsr7IwBvw9N9PY1vhNnyw/4OO171KTAEUhVQqKW9VhKhKDUvFub3OxSX9LkF8sHVTB3txZZqfQqbArSNvRZ+IPrh2+A2UlhwRQQ+47GyqA+6EabsyR/n99O+Y880cfHzgYxR0aqsil8uxaNEim+twSkwJcBwN6BhO4Z2jun8jpj2pJHhAuEpMCUiiB2JjabDp5oadHhNTnfGy8kWxfaaSkugZq9fbWcfa2GisnXMziYmJeO655wAAjz/+OAoLC+16v6kBRamGZl37RdrIb+/dm+prvNWxTgJMDShGCGlCKSl0zKdOSVex7e0olaSSams77Jctian8fJr0lsr4sqyxDFeuvRLP/P0MNHpnelk4gNCjSq+3K11IijS/pJAkBCgDcGHahS7pa2STpiYq5Heit6CMk+HZKc/i3F7n4p4x9wDwQjEFUGbFsGE0m9bcTKKqGzOpyIBIvDztZfzf+P+TbBdcWjMF6o01rc80rL5iNULUITQJNHgwtYYQJg1KSszeI0WEamfxTugMOtTW1KKx08NUp9Nhw4YN2LvXusmMJGIKQFFREV577TWH3sswwgSVtxAVZXTWcdIRzdViypT6eicymuRymgmKjXVbbY3XiKnKSnogO5guIzWim/aCHiQZGZTJZ1drmpISElOnTnlETN52220YP348Ghsbceedd4qe2TYVU4GRgfi74G8AQEZ0hn070Npq7y57NZ2t0eUbN9IfMjNpoC2Xe42Lp8sJCCCXHgAoKcGZ/fstuvkJ2d0lJdJc+ruKd6FB04DjVcfBwc3CQuhRJUwciEAKMQUAM/vOxPdXf48ZfWc49H6nqaujSE1enlO1J2nhaXjlolcQrA72TjEl4O9PosrPz6qoatUZ728cx0Epl8bB2JViSqvXmk1EmLUYkMnomhasOjsdrxSCatG5i/DytJcxIXiCxb/L5XI8/vjjFv/miJhqbm7G119/jUsuuQQcx3X8pKSkYMmSJY4fCAMAE1Teg0xGsyGAU2l/7hRT1dXA/v3kluxCN1nJ8BoxBVCKTHMzPZA9HKmyR0wJBAdT6qddpKZS5KKx0SN1czKZDB988AGUSiU2btyIb775xuZ7TMVUUlIS3tj9Bho0DRgSM6RbS10ztFpKG9m3z60NvF1Jlz5TNTXAe+/RH++4gwYio0b1+HRHM6KigN69UVlZifxNmzB24MAuNVMREcZMZ8FK3RkuG3AZ3pzxJp6d8qxkA1i7UChIUJkOcq3cz5wVUzzPQ2cw9suJ8I9wZI+lISHBmFmRk2PX/ay6pRol9ebRDq8WUwKmoqqlhbIN2k/g+rZ6zF0zF+/seQdavXT3OFdHpj4+8DEW/G8BjlQcsbyAkAo3eHAXR8uUFCp9EFLgHYHjOJzX+zwUF1oW5Xq9Hr/99hu2bdtm9rotMcXzPLZv34677roL4eHhHcIpMDAQ11xzDX788Uez5adPn47333/f8QNhAGCCyruIb883PnvWIYXiTjEF0FhJraYxooUUY/EIKSOnTkm2b53xKjEF0KhKEBceNC1wREx1prpaZHNDlco4CMnL84i4GDRoEJ544gkAwL333ouz3YQJOouphrYGrNy5EgDwxKQnxDdMlcspOqXVuvQcdxcWm/a++ioNskaNAqZNo++6va8LAI9PGriLYrkcRysqMLB/f0RXVFhUTKmpVGKn1QLHjjn/0YxNGovEkETnViIFPE/nt4WDkiIy9dOpn3D9uutxusZy3y+306uXUR2fOCHqIcjzPFZsXYFrvrsGf+X/BcBHxJSAnx+JKn9/Sk9oN2XZlLsJZY1l+O30b5IZhbhaTLXp2rDh5Abk1eR1GENYxbQtgsEAFBRAKTdArbY/o7tF24LPDn5mFhnLy8uzek3I5XI8+uijHRkVncVUTU0Nnn/++Y7PiOM4yGQyTJgwAW+//TZqa2s71pWRkYFnnnkGp0+fBs/zHT8///wzrrrqKvsOhNGFnpvc74sEBVHqRGSk3UVK7hZTAE3eJCaSOUFxsRMmMW1tHXUHSEyk9BkJ8ToxBdCDKDmZhEV+Plkwu7kWQAoxVVJCY6igIOqNaPMQEhLIKr+piY7dzj4bUvDYY49h7dq1OHLkCB544AF8+umnXZbpLKYA4N2976KmtQb9Ivth7qC54jcok9F1vX8/Dbqio+nHB7EopmpqgDffpAWeeKLrSVBVRUVDmZk0IOuhFBcXI+vgQYyZMwfhxcV0jufndznHBSv1vXvpoysuNo7LxVDVXIW397yNheMWUr2Ht9DYSNc2z9P33H6vlUJM6Qw6vL//fZTUl+Cv/L+QFu4l9/E+fWhC8MwZEpJyOT2/rdCsbUZDWwO0Bi0SgxN9S0wJ+PkBw4fTM6z9Wp87aC7C/MIQGxgLtUJtYwW2cbWYAgC1Qo3VV6zGz6d+xtS0qeLfePw43cfr6ijl1U7TnXf3vosvs7/EnpI9eGPmGwBIUMnlchgsTKTr9Xr8888/2LBhA5qamvDBBx/gr7/+6nYbwcHBWLBgAebPn48JEyY4nFrLsA/2KXsbcXGiez0IeEJMCcTH0wChsdEJr4GAAONDSOJeCF4ppgSSkui7bm6W1N1RDFKIKYCyVIVAm6hDMHUTOnPGbncwKVCpVPjwww/BcRw+++wz/Prrr2Z/tySmWrQteHnHywCAxyY+BrnMTue64GCjZfipUz6Z+mdRTAEkphoaKC3m0kvN38Tz1K+opaWLA15PwsyAIj6ePouEBBpwWyAgAEhvb9Fz+rR95XVPbnkSG05swNLNSyXYcwkJDiajCoC+8zNnJKuZUsgU+OjSj3BD5g24cdiN0u2zFPTtS3XAwrneDYGqQLx3yXv46NKPIKuV+Z6YElCpzG3ucnIwNX4ChsQOcXrV7hBTAiHqEFw1+Cr7thEXB8jlqCusQ86Px1FWZN+9fGjsUET4R+CaIdd0vJabmwutjWfC7NmzsWDBgi5iaubMmfjiiy/Q0NDQEXGqr6/HO++8g0mTJjEx5UbYJ+3NiBh8eFJMAaQHhOa+TmkhYYq2rEyywaZXiymAZraEQXZ+vtsK0aQSUwB9/0JLmrw8at1hk7AwY73gGRupFi5i3LhxuPfeewGQWYXgsGRJTAFk01zeVI5eob2wYMgCxzbaqxf1H9NofC71z6qYamwEVq6k3x9/vGtkneMoHKNQUF5oXp5b99sdWHLzg78/Raa6mb1OSKAJqUGD7Avc3THqDvSL7If7xt3n5J67gLi4jrRe7dGj2P3zz06LKYGogCjcM/Ye8am27oLjKALduzcwxLagkHEyqOvVviumTPjp1E9oyz1BD/8DB5x29XSHmCqoLcDBsoOOryAiAsjMRJPeD8V5WlRtO26X+c6UtClYf/V6jE8e3/HaqfbngVwuh9zknhEYGIixY8diypQp6N+/P2688UYcOnTILF1v48aNWLBgAYI6N2NmuB2Od1sTB9+mvr4eoaGhqKurQ4iVztmSUVNDA+zgYOM0pgU8LaYEBAdZjgPGjnUiq2ffPprp7t3bOEp3EK8XUwIGA7BrF92QBw0yCg2XbU46MWVcJ6UvNTeTLrYyKW9OWxulgiUkuD3VUaCxsRGDBw9GQUEB7rvvPjz22GMWxZRGr0H66+koqi/C2zPfxh2j73B8ow0NlPrH8xTFMM3N91KsiikAePll4KGHaJZeSHmyRGUlcKS98FtwAewBWBRTneF5stcOD7fTFtM6PM97xi5cJJrsbBz94w+o/P0x4OqrIXPwuA+VHwIHTpLIh9vR6ToKbD47+BkUMgWuHnw18k7n9Qgx9fvp3/Ho74+iT1gqvuj1AJQtbRS5ysx0qHG9O8SUgTfglg23ILsiG09MegKzB8x2eF2luc048UseIv2bMWSQnprjdlOu0N01O336dOTn52PEiBEYMmRIx09K+4SrFNbotnDrGLeHwmqovBGDgfLnmpspD93CzJ63iCmA7p3h4ZTV09rqhKBKTjb2e0hJcbjZlc+IKYCOsW9fGoi6+OHqCjEF0CH06UNGdsXFpJFsno5qtdGO1kMEBQXhvffew/Tp0/H6668jKSkJV199tZmYAoBPsz5FUX0R4oLi8J/h/3Fuo8HBdJ5XVtqd2usJuhVTdXXG6NQjj3RfSxAdbayfO3aMzCskOv88hSgxRQvSBNmZM1RoqLZcY6LR0C3ftHesQF1rHVp1rR0NpL1aTGk02F5VhcjoaAyIj4fsyBGaabOz1qS+rR6P/v4oqpqr8PK0lzGp1yQX7bELKCyk733YMBRoKvHWnregN+ihbFIi4GyAz4spAAhUBiIqIAoXpE2BMnMUNVhrbCRL9WHD7BJV7krz0+g16B3WG7k1uTgnSaRLqxVkQQFA374wVJwAWs8Chw9TI3ML+55fm4/H/3gc/zf+/zA8fniXv//yyy8WtyFVnymGe/Cy2DkDAIWUBfu8qqouf/YmMSUwcCA9My0NBkQTHU1qTKt12Fbbp8SUQFSUz4opgchIOm0Foy+70Ostnufu4KKLLsKVV14Jnufx/vvvI9rELKJF24L7f7kft/54KwDg3jH3wk8hgalC794kKCSKVriKbsXU5s00Iyu40SwQkQaZnm5MefTxeirRYgogIRkURMednU3neyeEKP+RI12zh3iex7K/luGa767BruJdEh6F9HTUTAUGImPePMjCw22mPlpDxskwLG4YEkMSMTJhpAv21kUYDGRaoNEAWVlIUUbhkQmPYEL0BPhX+fcIMQUA5ySfgzVXrsHNw2+myaHMTJow0mop/U+U9at7a6b8FH5Yct4SfDfvu47JCUeRyQCoVDD0G0ADnwEDrGZbvL3nbZw8exKfZH0iev3uFlM7ina4dP3/Bpig8kY4zmih3qk7tzeKKcC8RtVhOI4iU1FRDikznxRTndFonG9O0wlXiymB9HTKeLAr8KTXU77g4cNOuJo4Tnl5OS6//HJER0fj1KlTeOqppwAA+87sw8j3R2LlrpUAgNtG3oYHxz8ozUZlMvMBpoUBtqexKqZaWoAHHgAmT6ZZ+LQ0YP16cWFpoSO0XE7CykcFlV1iCqDjHTyYbpKNjRbFpL+/cS7pxAnztzdoGnC25Sxada0I9/fegXgXAwqlktzgHExrDVIF4enJT2PVZasQoJTW+dWlyGTGtDeNBtzBgxim64tpqmmYMGGCz4sp0yqREHWIsf+ZIKpCQijd0crkgSnuFFOmRAU4n2otlDzL1EqKyJmmyXVymVl07iLMHTQXj018TNS6PRGZ8ld6x1jSl2E1VCJxe36pRgPs2EEP3pEjgeBgrxVTpgiTczExDmfsOUSPEFMVFTSaioykeioJcJeYEuB5B4T1iRMUkfT3p5QJN504pgYUe/bswZw5cyCXy3Hru7fig9IPoDPoEBcUh48v/Rgz+s5wzU6UlFA62PDhkrcLcBSrYmr/fuC660gQAMCtt1INlb3F0G1tVtPevB27xZQp9fWUDmUwUNSqk516czPNLRgMlAVsOjGh1WtxpPIIhsUNc/oYXIEoN7/WVrI07N+/24iVRq+BSu7b6aAAUFiVi4TTlSg/lYuC0lIMvPpqhHdKJ/Y1Kpsq8cCmB7Bw7ELrUUO9nu4RCQndWsi7U0ytO7YOx6uO496x9yJQZX99lyVKS42PazMvksZGitDFx1MuvJ3H5ak0P1ZD5TwsQuWtqFRGg4KSEp8QUwBw6BC1aXAwY88heoSYAmhArdcbe1w4ibvFFGD+7BAdeOnTh873lhYSF26gs5vf5ZdfjhmXzYBer8c7i96BTqvDlYOuxOE7DrtOTPE8pTpqtTQAcZPLY3dYFFM6HbBiBeX0Hj1Ktp4//gi89579Ygro2vDXHs9wD+KUmAJoBluYKDlzBigqMvtzQIDR0CU319wwTSlX+raY4nmKQldUUOTCyrmu1Wtxy4Zb8MqOV8wan/oa9W31uO3Xu3Hl0Wexv/w0hvTvj/CCAhps+zDv7XsPxyqP4bVdr8HqXLxcTgrDVEx1cu51p5hq1DRi5a6V+O7Yd/g552fJ1tsRoep8utfXA3o9+KIi5Oz62a4MBFYz5dswQeXNtE9R8uXlOLR3r9eLKcCoAQsLnRwftrYCJ0/atNXuMWIKoMGpkOqZk+NUSpQnxJQpZ85QgFWULlQojLP1RUUuH3R0FlM8z+Ot3W9h88DNgD+AcmBuzVx8M/cbRAZYn2F1Go6j4kOlko759GnXbUsEFsXUyZPAxInA4sUkrObOpYHxxRc7v0GtlmZgsrK8vi+X02JKICqKcmNlMoumHAkJVE6p1/O4f/1yrDv2vfWBqxcgus8UxxkjU7W1VCxm4bi2F23H0cqj+PHkj6htrXXpvruS0zWnUdNQg4q6Koy6+gYEJyTQOe6BtGYpuX/c/Zg7aC6Wnb9MvAhqaaECwfbJMnen+QWpgvDKtFcwPX065gycI9l6Y2NpjqmLEXNCApCRgT/rsnD1P/fjhW/vBy/CVt0TYsrAG/Dwbw9jc95mr77P+ApMUHkzISHg4+NxWK9HZW2t14spgOrTVSrK6ikvd2JF1dU0Ki8osKrMepSYEkhNpUFHQ4PDH6CnxRRAu6/T0XhclLCOiiJTEp6nEKeLbu6dxVRpQyku+uIi3P3z3Wj1a8XgGwYDANa/tx5HhdQ2V6JSUTEzQAYP1dWu36YFLIqpd9+lVMRdu8hA4/PPgTVrpLV6b22ln2PHvLamSjIxJZCURKmtsV2L4oWWRkca/8ZfZ37Ak38+i6L6Igsr8Tx2N+0NDqbIhUwGnD1r8To/r/d5ePWiV7Hs/GWICXRtCwlXEtwUjHvi7sEbl76B+IQUqi0aONDjzqbOEqgKxKMTH0VauB3P2+pqKmHIz0drdja2/f2322umRiaMxIrJKyTtYaZQUJa6xezl6GiciDQAHIcQvQLcgQPkPGMFT0WmNp7ciD/z/sTSv5airs23xb43wGqoROKJ/FJfSfPrTFERpaz4+wNjxjhoVmHan6lzQQF6qJgSKCykaIVKZbfdsDeIKYAmY/fsoedoair1tLWJRgPs3k1KLC3N2PRYIiw17Z399WysP7Eefgo/vDD1Bdw5+k5cPvty/PDDDxg1ahR27Nhh7m7nKk6donoqlcrtluIWxdS2bcCkdpvqKVOATz4xNt+WEqHeQK+n79vLrmXJxZQlNBo6fpP7e1m5AasOfI6keCWuzZzvmu06gd1iypSz7RbTPG+xlszXyc3Ntd1nSqejiQQfaMbK8zwOVxx2rhdYSQnaDh9GdnY2AlNTMWD2bHAOOD/aQ21rLeScHMFqz6XNHSzYhYGlOqhataTAhg/vYifvyTS/Nl0bPjrwEWICYzAtaRqroXISFqHyUnxVTAH0jFQqKdJfWengSmQy4yi8U5SqR4spgGaw/f1poFVQIPpt3iKmAPr+hXqQggI6F2yiUlH+RGSkxdl7Z7Akpniex9aCrQCAnxf8jHvG3gO5TI53330XYWFh2Lt3L5577jlJ98MqffoYLcVdGKHrjFUDiq30uWDWLGDTJteIKYAGlP370++FhU7cMKTHLWKquZkamh86ZJb2GBcrw6PTb+h5Ygqg63vgQJppO3MGfGEh1hxZg2Zts+33ejEG3oAH1z+I37N+ty2mhFRXkdbinmTd8XX4z/r/4KXtLzm8jpaICOyoq0NoWBgGRESAO3LEpe6mPM/j6a1PY96387DvzD6XbOPsWSAvD6ipsb5MZq+xUI0cQ67FISFdjIc8XTOlVqhx5+g7MXfQXLdut6fCBJUX0kVMKZU0Kj1yxNO7Jgq5nDQBQLvt8NgwLo68hDWajlqqHi+mAGOnXDtCe94kpgRiY6k3lcFAqX+iiIujtCAJXeAsiSkAKKovQk1rDRQyhVmTx4SEBLz55psAgOXLlyMrK0uyfbGKTEaGBXK52/pTddtnSjjmc891vetiTIxRsB0/3m1qjLtwi5gCaNaa42jG4fBhbM37CwbePEeW57sftLkTp8WUQEwMRaaCg/F9/S688M8LuH7d9dDqvbuWrjve3PwmNpzcgC9rv4Q6qJv7F8fRjyCsvFxUlTWWAQASghMcer9QMxXWrx/SL78cnEJBaYAHD7qsdrK+rR65Nbmobql2WYSqpobGN7W1xtdada14a/dbaGhrML6oVFLPvowM4zOd58EbDB4TU75cn+jNMEHlZViMTBkMVNBZWUnFKT5AYiKNFVQqem44hExmTPsqLER1VVXPF1MCUVGU7ifiOL1RTAn07UtfY02NgyVhTvbksiamACCrLAsAMCh6ENQK8wHQ/Pnzcfnll0On0+GGG25Am4iiYqcJDATGjaPIrItrC7oVU4BRUA0b5tL96CAtTXBjoHoqD+I2MQXQDXLoUEChwE+nf8UDP9yNe3++p0NU8TyZ4h086LHe1x1IJqYE4uOBESOQGtUX8cHxuKz/ZcaeRj5Gbm4uYhticVH/i3D/+Pu775sll9N3HhZGD8eDB73arOLO0Xfis8s/w9WDr7b7vV0MKCIjqZ5MqaST20WTNaF+oVh9xWq8Nv019It0TUqpJZe/9/e9j0+yPsHdP99tbvLQqfcgf/IkTv/4Iwry8twuplp1rbh+3fV4aNNDqGnxkpmaHgITVF6E1TS/ThbqvoBCQfVTwr3TYdqjVPVVVTiwceO/Q0wJiGiW6s1iCqDMxd69jZPwotHrKVqxa5fDttrdiSnAKKgs2VFzHId3330XUVFROHToEJ588kmH9sFuTC8Wg8ElaTE2xVRjI7lMAnQBuwOOowhdeDilg3kIt4opgYAAYPBgcJwMfnoOQ2UJHcXzHGcsuThxQvKe36KRXEwJcByGxQ3D11d8jQXRk510MvIMQs3UtHOn4Y1L3sDsAbNtv0mwFg8Lo2v80CHzUIeXMSh6kN2GDlbd/EJCqJZo6FC76oPtRa1QY1zSOJet35KgmpgyESmhKbhl+C1WDTf4xkYU7tqFs8ePY1JwMILFNEWXkP2l+1HeVI5jVce6TCQynIMJKi/BZs2UYMpQUeG5p6qdSDK2l8lQExaGvZWVSB037t8jpkxpbKScuU65k94upgSSk6k3de/edrxJJiMFptfbkS9oxJaYAoCD5QcBAMNih1n8e0xMDN59910AwHPPPYddu3bZvR8O09REtTWCsJEIm2IKoJCIYBjgLlEBkJjMzOxStO0uPCKmBMLCMGPCDfh60BLcoj7HrF1Eaip9JFotiSp34yox1aYzRn0Dm7WQnTxFkyieDsXZQW5uLnZk7+iomeI4TrxznSCqhMhsdrbXiKrCukIs/2s56tscS0e0aY0eEGA+eVRcLEn2zY6iHfgz70+n1yMGS4JqRPwIrLlyDSb1mmTxPTzP43hREU4FBGDwsGEI1OupYXqz++oHxyePx5dzvsRTFzzVfSSVYTdMUHkBogwoQkLox2Bwb9dcCdBoHA+sVVdXY3tuLnpfcAHShOL1fxMGA6WEnDlj9r37ipgCaJbdbjMroW+NTEb59nbMXIsRU4AxQpUZZz0Kc8UVV2D+/PkwGAy44YYb0GJXmM0JtFoSVaWlkhk1iBJTgDHdz13RKWvU1bnNRt6TYqqjZio2Fkn9R1Mk4MyZjgkUmYyCdoLbuDtv/64SU7tLduOyry/DjqId9EJoKGUj8Dw1j/aWorFuyM3NxY/7fsSrZa/iy1NfOtbHRxBVERH0uxfcx3mex4qtK/DDyR/w3Db7TXns7jNVWUkTR1lZTgnKhrYGPLn1STz828PYeHKjw+sRi6mg0huMmQQKmeX7qqkBxdiLLkLAxImUwtHSQqLKjWI6PSIdI+JHuG17/xaYoPIwdrn5CVEqk4ett6PXkxP2qVP2p4lbNaDwkWOXBFO3w7w8QKfzKTHVmeZmmoQW1ZsqIMB47Dk5oiKzYsVUXWsdTtdQI93M2O6FwxtvvIH4+HicOHECixYtErHjEhAWZqwfPHHC4bRHAdFiCnB//ZQlamtpP44edfnsrSfF1J95f+K6ddehsK6QXujdmwxphg0zq6MLCqJIFUCXgjt0vcvS/AB8mvUpqpqr8Ff+X/SCMIESHU03h8OHvbquSEjz08RowMk4VLdUO95TSSYDBg+mNLgAz0cMOI7DfWPvQ0ZMBu4Zc49d73WoaW94uHnqo4MRSrVCjUv7XYq08DRMTZvq0DrsQXiGnWkqxOXfXI4/Tv9hVVRbdPMLCABGjKCJcqGWrqLCZfu7u2Q3Kpu8x0W1J8IElQex2xo9OtrYNdeL7IW74//ZO+/4pur1j39O0qR779LS3QJlb0RxorhAEUUcv+tA5bqFi+LECYqKioh7gAgoIohbUGTJLNDSXbr33k2bdX5/PD1N0qbNSXJOhvZ9X7zk0uSck/SM7+cZn0cqpcMGzHIANy6mWlpokeUkfWSCMWwY3XxVKmiLipxWTLEsPS+rq8khmxfDh9NqUqUyWf7GV0wBQHpNOgAgyicKgR6Bg742ICAAH3/8MQDgrbfewsGDB3kevJXExuoetlYMvjVLTAGOIah8fXWfPSNDNItle4opjVaD9cfXI7c+Fz/m/aj7QVQUNaFy9PzeIyN1686cHHGPTUwxBQBrr1iLJZOX4LEZj+n+kWEoFRcQoCuBa28XdL9CoD9navlFy7H2irVYNmOZdRuVSAzmkKGpyW5DvgEgJSQFX8z7AuHe4bzfY5GYAuhcHzuWjJi0WnIztiANK5fK8d8p/8Xm+Ztt0hvECapv8r5AZVsldmTvMPq6Qa3RZTK6z3KLJJF6yhoVjXhi7xO44ZsbkNdgfgn9EPwYElR2wqI5UxIJPVXDwuzWZ2AJw4fTs7KxkV+Z9ICZqc5OilyXloo6w8LhYBggIQFarRa5f/yBrsZGpxNTAH0M7tdZWsoz8cBFrhmGoncDRC/NEVOAXv+UEUMKY1x99dW46667wLIs7rjjDrTbYqHHLTClUgom8FahOswWU9xCFrCvoGIYshl2ddWlNQXGrj1TAM08u+YDLBq9CPdOutf4i8rLqZdOrQbDACNGkM5MSBDvuMQWUwBlExZPXAw3lz4N+RIJ/d59fXVRewfqGTY2tHdW9Cx4ygV8Hre30zWYkUE1njZCo9WgUaETceZk3CwWUxzc7z08nAIIubm873dqrdogMySX2ua5OGIEzWB/7pIVuHfSvXj6gqf7fW5ec6a4kRnjx9OMNhHoUHYg2jcaw32HI94/XpR9DDEkqOyCVUN7hw+nK9mJBJW7u6633VSWatA5U0bmUv1b0Pr5IbOqCt1dXZgREuJ0YoojJISeGWbNpvL2pqi9XG7UTtxcMQXo9U+ZKPfT56233sLw4cNRWFiIxx9/nPf7rMLdneb1ADQ6wYwyKLPFFEC1uQoFZUTj7fzglct1s1vq6iwSlANhbzHFEeIZgmXnLTPed6FS0Wdub6fSR5aFmxtVCYnlsiymmCpsKsRv534z/UKur8jbm7LzDnKv48RU8oRkbMzdiC61dWW4A+LhobtJZmbaTFRtStuEG7ffiH1F+8x6n9ViioMLnnGlzoWFvGZ0rT2yFv/7/X+o77StmYmbGxVPeLrJce+kezHMZ5jBz80a2sswhvMHFQr63Vs8c8aQKN8ofDbvM6y7ch2kEvGcFf/tDAkqG2OVmHJiuHtkff3AcztNDu1lGF1PTVnZvyZLxfVMNQUGImX0aMhaWx3GDcoSEhNpzdTcTOV/vIiJAaZM6RfBs0RMAYNbpg+Ej48PPvvsMwDA+++/jz179vB+r1WEhtIfX19eVvqAhWIK0JX7iWxpzBsfHzphAFpgCVAGZW8xdaLiBLLqsky/UCYjYSGV0uc2EoHo6BDuNiimmFJqlHjmz2fw9J9PY8vZLabf4OJCPUVmWYOKh35m6t20d7E5fTOe/+t5cXbGZSz0+8lEdj7Uslr8VfIXWrpa0KHiP1hbMDGlT1wcBXO4kudBqO2oxa6cXdhfsh8FjQXW79sM0qrTzOuZMofsbAoinTolWMOkhJEgwD1AkG0NYZwhQWVDBBVT7e32HUxiJp6eujJhY4Fmk2KK41+WpdI3oJh+ySWQJSTQAlM/muVkuLnp1kkFBRSIN4lE0m+gmaViSq1VI6M2A4B5ggoALr30UjzwwAMAgLvuugsttmqcT0oi1z1X070BFospwDH6p/oSEUGlQIDVvaP2FlN1HXV48o8ncdf3d+FExQnTb/D2psU1w1Bfid7Ns7KSqgELC60/LrHL/KSMFBdGX4hAj0BcEX8FvzfpH4NGQ9lTgSL25tC3zO+WMbcg0idy4DJNIeDmsoWEUAlcZqaofdMSRoJPrv0EL178Iq5OvJrXe0QRUxxRUbrgKUAPCSNORiGeIdh0/SY8NPUhTIucJtz+TfBn0Z+4fcfduP+7FejuNhRVVospgDJ1XLnzqVO8MnXG2HhmI3Zk7dA5iQ4hKkOCykYInpnKy6MHrBNZqA8fbnRdzF9MAf+qLJVRN7+YGCqDEfLhZQciI3VeE+XlZr65pgYNe/bgxNGjZospAMitz0W3phteci/E+seauXPgtddeQ3x8PMrLy/HYY4+ZfoMQSKWGv/MBAilWiSmAelYA+1um9yUxkUqdrRidYG8xBQBuLm6YFD4Jcf5xg9r1GxAYqGuaKizsdQJzc6M1ZkWFdYk7W/RMSSVS3Df5PuxcuNOkCYxRcnLog6an2/Seb6xnalrkNHy38DvE+Ys8E5HroQwN1dnJixjAkUlluCrxKl7CSFQx1ReurzM93aigTghIwH/G/0e8/Ruhpr0GGpULXLuioVLpPrsgYgqgCDRX26tSUaDLTEFd2lKK90++j9WHVuNk5UnLjmMIsxgSVDZAlDI/J7RQ9/YGzjvPsKHaLDHFERZGvSVKpVnziZwJXtboWq3TCkqGoaRLfLyZVT0aDRpPnkTeyZOYFBhotpgCDPunJIz5t0BPT0988cUXYBgGn3/+OX788UfTbxIKrZYi9ceO9XP1sFpMAY6ZoQIoEhMWZvHbHUFMAYC3qzdevexVfHDNB+Y1zw8bRlEIgMSFQoGAAN1jICeHZ6a3D2KLKYVKYVAWZfEg0ZgYKgNsbaUSOF5zF6xDX0x5+3qjrVvnqGTJfcMiOCeS0FBywTNRAmcuufW5+CH3B7NmaNlUTAF0n+vo0I1SUCpR2FSI2g7xLMZNsWjMIqxM2YKrwxf3JlIFE1Mcrq46owqun86MPtJIn0g8Nv0xXJlwJaZETLHuWIbgxZCgEhnReqac0EIdMHQDtkhMAfSQiY+nB004f1tXZ4GXmGpqAk6cEKbex074+FBlhznP45r6epxoaUFCQgLCNRp+tpF9sKR/qi/nn38+li5dCgC455570GArNy6G0TXOZGf3LiwFEVPV1fSHYah3x1HRaGhxwTO16QhiqkOp60thGAY+rhYsjOPj6b4fF9drsR0XRx4GSqUZJi89iC2mWJbFk388iaW/LUWTwspBvZ6eur6+pqZekw6x6JuZ+uz0Z7h5x81IrUwVbZ8DwokqrvRTIJQaJVb+tRIv7H8BX6Z/yes9NhdTAEViJ0yg9U57O1SpJ/Dk74/jxu038iubFYkw1zjIJHJIJCKIKQ6plGaUcZGT+nrewQQJI8HC0Qvx0iUv2eb3NMSQoBITUQ0oJBLqLQCcci5TSUkjfv75lPliiiMoiCLW/7AbhVlDexUKylAO5PLhRGi1pn02uJ6plFmzEDx6NC2oeE8J1nGm5gwA6wQVALz88ssYOXIkqqur8eCDD1q1Ld5wZUAyGYnJoiJhxBSgK/dLSnJsF9G6OvpTUGDypHEEMdWkaMKN22/EumProNZa0QPEWcnrZWWlUjodOCNEviYvtijzK2gqwPGK4zhWcUwYBzYfHxL6EgktLHNyRBFVfcWUUqPE7wW/o6a9Bg0K29mYG8Awumcdy5J65u3oYxwpI8WchDkI8QzBtUnXmny9XcQUh5cXiSo3N7S018OjVQE5pEgMTLTZIXSru/HqoVdR005VMdxjh2FEElMcDEMlz8nJJK5MXKsKlWKoZ8pODAkqkbCJm19EBF1sLS0WRertRUlJI7ZsyYWLyyhERgpQh67V2qQERGzMElP+/iQqWZYWlk6MSgWcPEnr+YG0YT8DioQEEhUdHdRLxxOWZZFWTcLBHMt0Y7i5uWHjxo2QSqXYtm0btm/fbtX2eOPq2ttLpCkuxok9e6wXU4Dj9k/1JSxM11eSmQl0GbevdgQxBQD7ivehtqMWB0oOQKWxoC5vINRqoKAA3p7a3rLZ/HwqWhgMW4gpgHpbNl2/CSsvXCncwtfPT2elX1MjeIbeWM+UXCrHpus34bkLn8Pl8ZcLuj+LqK2lQFpOjlWiSiqR4o7xd2Dnwp3wd/cf9LV2FVMc7u7AhAkI8ovApwnL8Mmw++GntN0S9tPTn+LbrG9x/8/3Q62h9QbLssjLE1FM6RMebjhCoLLS6L1v9aHVWLx7MYqaisQ7liGMMiSoRMBm1uhyuW7Ak5NkqRobG5GRcQSjRkUhLCzCfEOCvtTWUj+J1RuyL2aJKY74eIpWNTbadACk0MhkVLbEBV77Bp2NuvnJZDo77eJi3lm6qvYq1HXWQcJIMDpktNXHPmXKFDz55JMAgP/+97+osVVPX1AQ1CEhyMrKgmdJCaZNnGidmAIct3/KGMnJOleTzMx+ARVHEVMAMH/kfKyZvQarL10Nd5mAz4KzZymYkJ2N4VEs/PzIr2ewW4etxBRHQkACrkjg6erHl8BASsvpP/8EwJiY4vCQeWBu8lzB9mUVISGGzXNmGlOptWqDDIary+DOoQ4hpjh6+ookvn6I9ozo73AlInMS5mBs6Fg8NPUhgJWAZVmUlpairMwGYqovtbX0sDx1yiCYXt9Zj7+K/8LZ2rPoVHUOsoEhxGBIUAmMzedMcYMPPSxs9rUh+j1TM2fSwriiwkonXK2WQrJO7PhnkZgCKGLHCYxz55w6S8fNpmppMVwfDGqNzk0JZlneNmdc/9SIoBGCLW6fffZZjBs3Dg0NDViyZIlZDd6WolarcbSuDlp3d6QkJsLl3DnrN+pMgkoiofIXrvRRr4HIkcQUxyWxlwhfnhQbS99DXR2YokKMG0dOqgOtd20lpr7J/AaVbSKPtAgJAaZNE2zCsTExdbT8KPYX7xdk+4LClYBxoio316wRIu8dfw8P/vwgqtpMCzFHElNbz27FN5nfQOsipSz6+PG9vYS2IM4/Dp/M/QQXxVwEjYbEVE1NDS64wMZiCqDyV09Pap48c6Y3oBrkEYTtN27HMxc8g5SQFNse0xBDgkpI7DK018cHmDFDNznXQelrQBEYSPcDjcbK5FpoKN1UVSqnydLpY7GY4hg+nAS1QuGUn5/D1ZXWhwBV8ZCBI485U0lJ9GCNiuK1H66J2dr+KX3kcjk2btwImUyGXbt2YfPmzYJt2xi9PVMuLkhZsABSd3e6DqyhpYUWZoBzCCqAfMO5Rv3qaqCiwmHEVGt3K94++ra4UWI/P52NfFkZmErd9a/VGlYD2UpMHS49jDWH1+CWHbegUWH9EOZB0R883dpq8QgRY2KquasZz+17Dst+X4bfC34X4miFJzFRF1DLy+N1/29SNGFH9g4crziO/Mb8QV/rSGKqorUC7x5/F2sOr8Hh0sP0u9cPIjc2ivb8U2p0IyokDGWmzp3Lgbd3Hm65ZaTtxRRA974JE4CAAFpEZWT0fv5Qr1DMGzHP9sc0xJCgEgq7iCkOBzdmMObmpz9OqrzciuSSE8+lslpMAWSbyCkRJ+qjM8awYRRwVquBo0fr+Q3tdXWlhaUJmrua8cBPD+CF/S8AAKZGTBXoqIlx48Zh5cqVAICHHnoIpWbY25pDPwMKPz9g+nTrSp9276aIr1ZLCzQr7Mltjr8/Wd3J5ahoaXEIMQUAqw+uxub0zVixd4W4OwoN1V3/584BDQ3o7KSexLNn6VdqyzK/GL8YjA0di2uTrkWAe4Bo+zFAoaD+v9xcs3uKBirz85B54Jqka5AQkICLYi4S+IAFJCFBF0wqKDDZQOfv7o/N8zfj4WkPY1b0rAFf50hiCgDCvcPx6PRHcVncZTh/+PmGP1QoSFDk5wNFwvYNlbeW45ot11BmjNX2uvmVlZVg9uzpiIy0g5jicHEhk5bwcJQpalCUfoC+AycZo/NPhGFtUZ/yD6C1tRW+vr5oaWmBT59ZEHYVU7qDoLQvw1AZlIMwmDU6ywLHj9P9MDbWcDC6WbAsWYh3dtK8ErMGG9kHQcQUB8tShoGHsHB02tqAvXsbkZOTi6uuisa4cRH839zVReo8Pr43yMCyLL46+xWW/b6sd27JrWNuxSdzP4Gbi5ugx65Wq3HBBRfg6NGjuPDCC/HHH39Aqh9FF2D7Jt38urspesunn6qkBHj4YRJUAGU7N20CLrxQsGO2FeVFRTiTmekQYgoATlWdwvN/PY9XL3sVo4JHib/D3FzK0EgkUI0cixP5flAqgfBwJaqqbNczBQAarQYaVmPenC1ryc/XZSi4YbgmGKxnikOpUdr2c1hKYSFFo4KDrd6Uo4kpXhQX0x+AfvfJySbd8Pjw9tG3sTl9M6ZETMF7V72H3Nxc2xhQmAHLsrhv261IrzqDldH/wZWX3EuBJjMZbI07BD+GMlRW4hBiCqCHaUYGRakcRCObmjPFMCSkXF0pg20x3IYAylIplYO/3s4IKqYA+vz/ADEFAJ2dNWhoOI1Jk+KQnGyGmNJqgdOnSVD1uP7l1Ofg0k2X4vadt6O2oxbJgcn44//+wOb5mwUXUwDg4uKCL7/8Ep6enti/fz/Wrl0r2LZ5ianmZkpNmLKTViqB116jhefu3SS+nniCZvs4o5gqLzcUU+3tVjZmWs/E8In4buF3thFTAJW+BgUBMhlknnIkJgIqlQq//JIJwEt0MaU/Z0sqkdpehCQk6MaIZGebHPg+kJjqVHUa9EA6hZgCKEurL6aUSoN7QFZdFkpbTGfNHU1MtXa38rMAj4khEcW5P6anC3IPeHjaw3hi5hN46oKnDMSUVOqN0lLHGAGqUCvgFRAGqacXxo262CIxNYQwDAkqK3AYMQVQyY+LC2Vp6gWY+WElfIf2BgdTb7G1LSAIDqZ+Mo3GIT7/QAgupvqiUpGodkKDCq5n6oorkjF7dqh5Ilsi6RXVneey8fQvyzH2/bHYV7wPbi5ueOWSV5C2JA2XxF4izsH3kJCQgHfeeQcA8PTTTyONsyK3At5zpqRS3fk/kJX8/v1Ue79iBaWGZ82ipuZXX3Xs2VMD0K9nqqGBhHVGhs2vgU5Vp0HfkIvEStdFc2AY6iebOBHw8ICvrxLl5Wfg6uoGT8+JYBjxHvX5Dfm4esvV+CbzG5sYshiFM2rgIaoGElNaVotHfnkET+x9As1dzTY4aJHo7gZSU3sDK52qTqzYuwKLdiwadBCuo4kplmXxxJ4nsHj3Yl5iEOHhVAInlVJw6dSpAUcq8EXCSLBg1AK0V7YbZKba2ykp6Ahtyx4yD7x5+ZvYdvMORIzUK2VXKh16LfRPZEhQWYhDiSmAxBTn+iNS/wZf+IopgJ6DggVOExNpsRhhRmbDhogupgBaHJeVUTmXE6FvQBEVFWnQFqjiO7onLAw/qbOQcuIOrDr+BlRaFa5OvBpZ92fhqQueMmkPLBR33XUXrrvuOqhUKtx6663osuKhbtbQXm9vnZV8UZHh0NvaWuA//wEuuogyUcHBwMaNwF9/0VwfJ8SoAYVrz++4uZnKwGwIZ8ZwquqUTffbi0QCuLr29kyNDG9DSlIcOjokok6V+D73e7Qr23Gk7Ih4O+GDMVHV1GTwksHK/DJrM5Fem46j5Ued23K6rY1z9QEyMqDo7sAw72EIcA8Y0PnN0cQUAJS2lCKzLhO5DblgwPN4AgJoDeDqSsFlM9wP9fm77G+Dnqm+ZX7cM8mGru2DwjAMonz1jJm0WgoqZWRQKaSDVC390xnqoeKJfn2pt7e3Y4kpDpUKOHKELqZx4+yS+jVHTOnDsrTmUyicogXKbGwipgCqQcjMpMXFlClOYac/kJufRkPJtro6+iiDfWVlLWV45NdHsDNnJwAgyjUE66auxLyL/2uXxUFdXR3GjBmDmpoaPProo3jrrbfM3oZZYkofbuCnXE6Li02bgCefpMUlwwD33gusWkWLDydlUDe/hgZaSLAs9dPxdIC0hnZlO+78/k6UNJfgw2s+xITwCaLv0xicmPJRqTBeJkNNpy9y5WMgkbtgyhRxXKZZlsV32d/h4tiLbWdEMfgB6SYcp6T0Ruz49Exl12Wjqr1K9Ey26DQ06Oaz+flBmzIKdd1NCPXqXwriiGKKo6a9Btn12eYbg3R3U/l3XJzZpl2HSw/jkV8fwdjQsViasBTlZeX9eqZKSihmFR6uM9q0NWnVaThWcQx3jL+jf2kqy9LDk4ukBAcDI0YYOmP2YaiHynqGMlRm4nCZKX1kMrrCAbtkqSwVUwAF1bKz6UalUAhwMN3dvIe9io3NxBRAN05uNpPebB5HZTBrdIYhN2SVauCPotKo8Prh1zHyvZHYmbMTLhIXLJ/4ELKmfIHrJKPA9IlQ24rg4GB89tlnAIC3334be/bsMev9FospgKL0Xl60oJo6FViyhMTU+PEUcPngg3+umALo/I+Pp78XFNik7MVL7oVN123Cm5e/aXcx5eHhgfEzZ0Li6opwjxb41+XB10sjmhkswzC4YdQNjiGmAF2mykwxBQAjg0c6v5gC6BoYN44qV5qbIUlLR6i8/+d2ZDEFkAW4RS6Lrq4G5kRgWd4NT23KNnjIPBCKUKNiCrB/hkqj1WDVoVX4KPUjfJT6Uf8XMAz1FXJ9ZXV1VAptZQnkEIMzJKjM5NSps44ppjiiougCamqi1aiNsEZMAdT+FBBA9z3OrMdi6uuBY8dMN+fbAJuKKQ5uSm5zs9k2wrbE1JwpiYSCagxDv9K+LRGHSg9h4kcT8fjex9Gh6sAFwy/A6ftOY8216+A1PIFeJGatkwmuuuoq3H///QCAO+64A408hw9bJaYACiR8/jkJqYwMElfvvENOmNOmmfsxHArec6YiI3Ul0FlZNhkp4C5zxwXRF4i+H2P0s0b38ADGjgVkMqSE1GGcNANucuF6yhoVjdh4ZiM0WgcdU6FXS15QUIDSP//EzPh4o2Lqx7wfUdfhAO4CAvNm5ifYKMuExkVCRi1nzhjYqjuqmDpWfgwFjQXCbjQvjwJM586ZXBNcEX8FVo1fhemy6QO6+dlbUEkYCe6ZeA8SAxNx+9jbB35heDgF0uRyOgdSUw1LwYcQlCFBZSYFBe2OK6YAsssLDaV+ChuJCWvFFAf31poauvYtxseHHqhtbXa14bGLmALoHODqJgsKzGhCsh28hvaCtAD3UfLzqTWgrqMOd31/Fy74/AJk1GYgyCMIn8/7HPvv2I/RIaPpxXFx9Gf0aPE/zCC8/vrrSE5ORmVlJZYsWWKyad8qMcWywNdfkwrdsIHKfa68kgTFww/zs1J3YMwe2puQQFEardbiXgpTbDixAXsKzMs+Cs2Ac6Y8PYExY+Ail1CALTsbYFmrHwssy+KFv17Au8ffxepDq63/ACJSUFCAwmPHMDksDH7V1f0CTKerTuOF/S9g4bcLUd/5z2ngz67LxtaMrXg37WNkRvbY6DJMb8mXo4qp5q5mPLPvGdz63a1IrUwVbsPceq28nITVAPMquZ6p9pp2XHLBJQNao3MGgva6pTIMg8viLsOW+Vvg6+Y7+It9fYFJk2hNqFLxEpVDWIZzP2HtQETERLi5OaiY4uAyFDZAKDEF0OI5JIR6qYqKyLDHIuRymqlTVERWPEFBAjpf8MNuYoojMlKnTIuLdWYFDgBfMcUxfDhlqFpatXjl10+xPmdFr5vaPRPvwepLVyPQo8/sNamU3mhnPDw8sHnzZsyYMQPbt2/Htddei9tvNx5RtEpM5ecDDzwAcKWFiYnAe+8Bs2cL8Cnsj9liCtA531VWitJHdaLiBD47TWWdsf6xSAhIEHwfpjA5tNfHh4IKZ89CXV2PgtIiKCPjLL+3ghZzcxLmILs+G4tGL7LuA4hIb5nfnDnwrq+n8yAnh37YM7za390fI4JGIDEgEUEeQXY8WmEZETQCKy9ciZKWEoyNmQaEd9P14OLisGIKIKfFsSFjUd5WjrGhY4Xb8PDhJCpzcuhhkpZG10XPc1mtVePlAy9jusd0oBEm50zZK0PFsiw0rKbXQZT3787Vlfppz53TVTENIThDphQ84Rr20tJakJLiYyu94tAIKaY4OjupMoll6fr3NRF8GRCNhsr+lEqKVPNYuAuF3cUUR2sriarYWIfJTpgrpjjOlOXhtm/vQGYrOYmNCx2HD675ANMjp5t+M8tST2FoqJUDzyznlVdewTPPPANvb2+kp6cjpo/zisViimWBF18kkwmlkh6cTz0FPP644WdlWVpQhoXZLNgiFBaJqcFgWUEWFBqtBh+mfgi1Vo2Hpz1s/XGZiUkxpU9dHTpSc5BaFwVtZDRGjmKsHlXRre62mXOmufTrmeKMKrhM5YgRvaJKrVVDpVHBXebggVIB4MRUOMsiZcoUMA44s4hlWbQp2+DjKoIxQksLlUGrVHR/HDsW8PDAxjMb8fqB1+GmdcNvd/yGQL/AQTfT2UnVk15ethVVfxX/hfXH1+PJ85/EpIhJ1m+wtpZmWMrlQ6YUAjBU8mcmMTFOtB5Rq2khKUIjohhiCiBTup7nHIqKrNiQVKob9ltSYrNBnw4jpgCKTicmOr2YAoAH9tyJzNYjcJd64aWZb+HkvSf5iSmAonJFRb0lT/ZgxYoVmDlzJtra2nDbbbdBrXc+WpWZ2rULeP55ElNXXEGLheee6y8c8/JoQZmbK8jnsRWCiimtlj5/gTD9GVKJFPdPuR8PTX1IkO2Zg1liCgCCg+F53jhEXxANMAzOnTO/ElipUUKp0Q1NdxoxBfSzVFdnZ/aW/7lIXP4xYqq0pRQqjfFfLCemwuRypMhkYM6eJTdAB0A/rs8wjDhiCqAI7YQJVALY1QWkp4PVaBDPxmOE2wisuHiFSTEF0DrF39+2YoplWXx2+jMUNxfjWMUx6zfY2EjPxNRUm/SY/hsYElT/ZHJyqORtoCGfFiKWmOKIiaGgidX26WFh1EegUtnE9dChxJQx7Oh6aI2YUmlUvQMpD995BM9c9qh5Q1MjI0lgt7QIfi3wRSqV4ssvv4SPjw8OHz6MVatWARDAgOJIz+yf//s/4JdfKBtrjLAwWlTW1trVqMMcBM9MtbQAVVX0+a3oqcqozei3ALQlZospDh8fRA1n4OUFqJQs8k+Zt4haf3w9/m/n/wlvGCAgg7r59YiqxkAP3JD5HHYe+gSsIJayjkG7sh1LflyC/+z6DyrbDM9v/TK/0eefDyY4WDeraIAByLbkjb/fwPsn3jcQ7KLh4UEDsH19wSYmIicvD+017fh80ee4YewN4u/fQhiGwfqr1uOuCXfhrgl3Wb9BNzcSlt3d5ABox37zfwpDgspMWJaCOlVV9j4SHnAuV1VVFMEWALHFFEBVS+PHk6iyCoYhYwKJRPQeKocWUxoN1YynplKtgo2xRkwBQH5jPlRaFbzl3hgfoRtMyTvZ5O6uExpFRVY6nlhObGwsNmzYAAB48cUXceDAAevEFEALIgCYMWPwMjZfX0Mr8ZYW8/dlQwQXUwCFlLmsdX4+RWjN5HjFcdz5/Z1Yvmc51FrbZL31sVhM9SCRAMlJLJiSYtQeKUBDLj8jhtbuVvx67lecazyHqnbHfPjxskZnGGzvPo0KthXbuk5AJXeWchPTFDcXo1vTjU5VJ/zddJ+/X8+UVEqW8qGhdBPNzgYqKux23AWNBfg682t8evpTZNVl2WanMhnY8eNxprJUN7RXLucVHNFoKD4rks/NoPi4+uD+KffDzUWA0nVOWAYGkrjm+guHsJghQWUmzc3A2bO0JhnAKMZx8Pensi+tVpAbpi3ElDGsqtIKDASmTxd1WrBDiymAsjMMQ+eBjWdTWSumAOBszVkAwOiQ0b0PvLo64PhxAxfgwQkPJ3MSbgGhFc5C2hxuvfVW3HbbbdBoNLj55pvR0dFhuZgC6GYE8HNwiYwk1xeWJec/gYIsQiOKmOKIjqZsHfcdmJm1rW6vhovEBf5u/uZlSQXAWjHF4e3DICqSbqp5f5RBXWd6VpuPqw+2LdiGpy54CucPP9+i/YoJ3zlTAHDPpHvw6IVP4KUrX9cNRLXT/UBIRoeMxvYbt2PN7DW9JYwDGlAwDPWRcffk/HwB5pVYRnxAPNbMXoM7x9+J8WHjbbJPlmVxNO0o7t57N7I8s+DKaIGTJ6kk2MSCQ6mkwh+BKodNomW1yK7LFmfjLi5kzuEABk7/BIYElZn4+ZGwV6sdIlNuGu5Cqaiwqo/IHmJKo6GEwqlTVooqEQWOw4spjqQkCk/bcDaVEGIKAM7W6gQVQOdCWRkNgDarJSg5mc6Fjg56ItqJd955B+Hh4aiqqsLGjRstF1NNTbryvZSUwV/LkZxMN7DubhIUDuZJJKqY4khKooydWk2C1AxhOTd5LjZetxHLzlsmzrENgFBiiiPm4li4h/pAq2HRmZrNa2ZhgHsA5o+cb9V+xcAcMQXQDJ/bxt6GpMAk+ofublpMO/DMPr4EuAf0fi6Tbn7c8Fcu2FhcTPcUO3BJ7CV4YOoDNtkXZ42+K2MX4A6k1qVC2tZB94OqKiA9fdC1kq0d/nbl7MLtO2/Hm3+/Kc4OuEqe5GRxtv8vYkhQmQnD9Pa12jNLzp/AQOojUqstzlHbMzNVWUn9koKI15YWQcNKTiOmAJvPphJKTAE6QTUmhLIwXHBVIqGqLd7ltzKZ7qEhYBmsOajVamRlZWHlypWQSqXYunUrNm/ebNnGMjPpv1FR/OtjpVKKSEqltIh2oGZkm4gpgE6c0aN1jenc9zgI+j1TSYFJwpTc8ERoMQUAEimD0dfEYOoMKXzclCQsjZQD/3buN6TXpFu9P7HgK6a0rBa/nvsVWtZIJqq6mj57To5TiqqPUz9GZq3hOWyWNXpMDBl2REdTVYuNqGyrRLeab4mBMHBiqqSkBM/MewZvXvEmnp31LKThEbr7YlMT9RQNUP7APTpt5fVU1kJ9vxHeEaLup81RZ6s6EUOCygI45+GODicYOs0wuhks5eVmlzbYS0wBdMPiEmzFxVZWZSiV1EdUVmZR70RfnEpMcURF6Uw6RMzQCCmmAF3J35hQXVmbh4euHebcOTOMLAMDqZdo0iRRM5fG0DeguPvuu7Fy5UoAwP33349CS34f5pT76ePhQfOZJkygkmAHwGZiikMmo+/NzY0WkoNwtuYs/vvTf1HTbvuSBDHEFIentwSyCaN1Az/T0gwupKKmIrx44EUs3r249xp0JMzJTH2V/hWe+fMZLPttWf/h2tHRuiipk4mqv8v+xoepH+Lu3Xf3np8WzZkaNkx3QwUoACtiGaRKo8LS35Zi0Y5FNjM50RdT3Jypi2MvxoigEfSCwEBq3uaqGE6dMtpvyyWvbJWhemT6I9h0/SbclHKTaPtoa2vD0aNHRdv+v4UhQWUBLi7oneHhFFmq0FCKxvr7m1X2Z08xxTFsGJlUdHVZ2QQql+semoWFVpU6OaWYAkhcJ/WUuVRViRINEFpMtSvbUdRM/vlcyR9HZCRVbmk0Zpb+RUWRqLAhxtz8nnrqKVxwwQVoa2vDrbfeCpW5WUNOUI0ePfjrjBEYSAtpB8DmYorDwwOYOhUICBjwJSzLYtWhVThZeRIfn/rYdscGccVUL1IpMHYs6pS+KCmTGGSpgj2DcUnMJZg6bCpSQniWlNoIc8v8fN184S5zx4UxFxoXGElJTimqUoJTMCdhDhamLESoV6gwQ3s597/0dNEaxctby9Hc1YyW7hYEuA98/QmFvpiSxEggdx/gme3tTUYNnp4697s+WXx7DPUdFTwKUok4BiptbW04fPgwhg/1UVnNkKCyEM5Ar75elDFPwsIwwJQpwMiRvKPyjiCmAKrO4QLIJSVW3t+jo0kNt7dbXEPotGKKw9eXFg4eHoI7HwotpgD0lrKEeYUhyCPI4GcMQxV8EglVaVgkuFta6CIWkYGs0aVSKTZv3gxfX18cPXoUL730knkbtjRD1Zf2dlpA2cFlx25iikP/GlAo+p0LDMPg9dmv49LYS/Ho9Edtdlg2EVM9tHXJkIkUFHumoNVFt7j1knvhpUtewtor1kLCOM5SwVwxBVDv246bdmBe8ryBX+SEosrXzRcvX/IyHpn+iDBiCiBR3dZGAbczZ0QpD4/1j8X2G7fjrSvegr+7uGWG+mIqcEQgnjn4DBbtWIR25QBur25ulL3396fJvZ6eBj+2RcmfUqPEByc/QGu36d5Ga+DEVHR0NJK4YOsQFuM4d0knw9OTrjeugsrhMeOB7ChiiiM8nBJsKpWVI3RkMp06Kyoyu6TB6cUUR3w8MHmyoOVeYogpoH//VF88PKifFrDAEZ5bMOTkiBYVMTVnavjw4fjoo48AAK+88goOHjzIb8Msq7NMt0ZQcdupryenLxtidzGlj0JBJT5ZWf0s5SN9IvHa7NfgJfeyyaHYUkwBFJQPjZKD9fRCbi7Qomij66Hn/tjrhOcAmCum9Mv7QjxDTIsMfVFVWuqw7n+dKsObXXdXtzBiCiARMX48PS/b2gbtJ7IGb1dvjA0dK/h29elb5sfKWAR6BCIlOGXw69nFhe6rY8YYrp1Y1iYlf5vSNuGTU59gyY9L+peoCoS+mBoxYoTN5+n9ExkSVFaQkkLrUgepnOFHZyeJiQEuUkcTUwBlIrjybgvawAwZNowiUN3dZqmzf4yYAqjMR8BFmlhiCtDrnxpAUAH0K50wYeCZtgPi60sXr1pNokrgBxffob033XQT7rjjDmi1Wtx6661o4uO0VVFBglAqJYcOS+EcPhiGIvI2Gq7iUGIKoHuCn19vuVNhVRYKm2zvBGlrMcWRkEALxJqWFszbdANe/24ZlBlpDuUCaa6YOlN9BnftvgvFzcXm7SgpiR4448aJPr/QElq7W7HgmwVYe2QtutXdwmWm9PH2ppuqqyutGU6fFmSG4c/5PyO1MtX64+OBsZ6pqcOmYvuN27F85nLTG5BIDNNQRUVAdjaihmkxbpyu7UMMJoZPRIxfDP4z7j+iCJ0hMSUOjne3cCJs5fIiGFotRWFLSmg6cR8cUUxxBAdTcmnSJCufcRKJTp2VlvJKL/6jxJQ+LEvfAZfpsAAxxRTQ3zLdGAxD2shsGIbKYKVSEidWpT8N4SumONatW4eEhASUlZVhyRIeUUmu3C8piRY91uDnZ+jwIbLzn8OJKUAnLL29oehqxxO7H8Lt392Gv8v+ttkh2EtMASSmEhOBtOb9qGqtx+GGTGjr63jN5bEFlmSm3jzyJs7WnMVX6V+Zv8PoaMPryoHq+v8q/gu1HbU4XHZYHDHF4eFBosrDgz7/6dNWDUWvaK3AqoOrcN+P9+FU1SnhjtMIxsQUh5fcCz6uZlZnKBT0rKythVtuGvy9VKK24U4Mn4itN2zF5fGXC77tITElHkOCSgA0GqC21t5HwQOJxLCcQQ9HFlOALkvlJoRbcUgINeUnJJhUxf9YMQVQlq64mMq9LOgpE1tM/ZD7Aw6VHgIA3gMfu7tJHyoUPHfi7q5LbRUWWrVg4DBXTAGAt7c3tmzZAhcXF3zzzTf4/PPPB35xayvw8sv09/HjrT5eAGSnGRREQZfMTNHqmB1STHH0WMorZRKESnzgrWQwMtCK7J8Z2FNMcYSEAPNGzMVDSevxf7Gvw1XiSllLW00wHQBLeqYYhsGbl7+Ja5OuxSPTH7HuAGprgWPHHKanam7yXKy7ch2emvEUThw9IY6Y4nBzo3uMV095nBX78HXzxVWJV2FyxGRRB/j2FVNeXl54af9LOFl50vKNursDY8fSeqGlhQLTvB8y/NEPpMmkMsF/p0NiSlwYVqwCzX8Yra2t8PX1RUtLC3z0ek+0WuDIEVp/TJzoMC7EA6NUAkeP0oGPHw/4+Tm8mDJGd7f1gXlT/KPFFEdJCZUyyOVkXMKzMNwWYuqGb26ASqvCzaNvxpb5W3jd/Ll2ID8/qtjh/bzg3ujpaVUa1BIxpc9rr72GFStWwMPDA6mpqRjRt5yvtRW48krg77+piXP/futNKXQHD6Sm0kIhIIC2K+AD16HFlD7t7dCeSkW1og4RMWNEH3jpCGKKo7sbOHGCToWxYbUIqM6iH8TGmrSXFwNLxJTgnDuny16PGEFzU+yMqJkpY6jVlKXysr6PUKVRQSYVpwHJWGbq5/yf8dy+5+Dm4oYfb/kRfm5+lu+gowMVf+SAUXYjOBg0dkCgRV+7sh337roXMVUxeOnBlyCVkqsfwzD45JNPcPfdd1u1fVNiaqA17hD8GcpQWYlEQskOwEks1OVy3QOhpMTpxBQXQD96VMAAkZGmrH+FmAJ0s6mUSt6zqWwppm5KuQlfXv8l7wVDQoKugs+s6zEpSTd/xMJ0s7ViCgCWL1+Oyy67DJ2dnbj55pvRpV9q1FdM7d0rnJgCKPqakqITkwI25DuDmNJoe1wOvbwgSRmNCLdgOh9EdD90BDGl0qjw4ckP0a5sh6srXQrjxgEBI0J02duiIps/4CwRU02KJuTWmzNDgQcJCTpb35wcMyaJC8vu3N3oVHXaXkwBdG/QF1NNTbwzdr3XVQ+2FFMAMCt6FhaMWoD7Jt1nnZgCAE9PFHqNRV5DIFQKNZkaCVSetOXsFhzdcxSrH12NH3/+0eBnixcvtmrbQ5kp2zAkqASAu9fW1jpUqfXADB8OMAxaS0pwYu9epxFTAK31tFoq6xekEoUr59AzAvjXiCmAvlD92VQmhh7bWkx9Nf8ruEj4CxM3NzIxBMys4JPLKRORlGRRBFoIMQUAEokEmzZtQnBwMNLS0vDEE0/QD4yJqYkTLdrHoHh50XbHjCFlKgDOIKaUGiXu2n0XPj31KbSslqJkY8ZQFl+g76HfPh1ATAHAh6kf4uNTH+O/P/0XLMsiJIROMQA07C0mhv5eU2OzfipLxBTLsnjl4Cv4v13/h925u4U9oMRE3YM+N5cGxNuQfUX78OL+F7Hwm4X488CfthVTfenqoox+Tg5VOAwCy7JYsXcFXj7wMtq6xevNNNUzteL8Fbh93O1W70ejATQSGZCQAHmov6BBpzvG34G4DlqHffj+h73/PrpnzuDx48ct2u6QmLIdQ4JKALy96QHEsoL2tYuHmxua3d2RmZmJFA8PpxFTHPHxVIlUXy/AbNqWFqpzOXcOYNl/l5ji8PWlhRNAi4UBhj87upjiiIig9bBWC2Rnm/HMCwzU9RiagVBiiiM8PBwbN24EQGYVP3z9tW3EFIeXl2Gpn1Jp8aacQUwBwB+FfyCzNhNbMragSdETXAkIMCz9FDBa5ihiCqAIfoR3BO4af1e/xVZ3N9AeFEOBhrFjBS0BHQhLy/xUWhUkjAQMGIwIEqH3LTGRgpF0kNR/aiN83XwR6hGKGE0MhoUOs5+YAqjWnhOXRUVAXt6AQjuvIQ/7ivfhx7wfUdNh2exHUwwkpgacM2UF3K1QKpfCZfxouiYEuq/JpXK05NPIhl9//RXFPefXr7/+CgCYNm2a2dscElO2ZaiHiiem6ksbG2mwuFQKzJjh2A6AjY2NOHbgAMaqVBg2dWpvxsqZyM+nChQvL2p5sfjw1WrKUKlU0MbF4WR19b9LTHFoNMDJk7SCGj2aFpN6OIuY4lAq6eMolaQVzbZUV6spCh0TM+jJJbSY0mfZsmVYu3YtAl1ckKZWY5gtxJQ+Wi0tlpqa6CIz83pwFjEF0KLsp/yf4Ofmh/OHn9//BYWFdMMZN87qnglHElO9x6RR9ps31dREiQg3NxoPYnAZdHZCDJsza3umWJZFUXMR4vxFDBJyfadRUbp0uMgoFArs3b8XYSFhmDxhsmMsjCsqegORCAwERo0yms09XXUahU2FuGHUDYIfwkBiqlPViYXfLsSUiClYOmOpYDPkuNGF7u5AP33T3U0ByaQks9yzKtsqEe4VDqVSCS8vL6jVakilUjz++ONYtWoVAPT+vjs7O+Hu7s5ru+aKqaEeKuux/538H0JAAC3uNRrH7qXieqaSx47FsAULqNHYEW7OZhITQ6K1vd1K8yUXFyAuDlqtFjm//gpFS8u/T0wB9CAcNYoWzk4upgBdBR9AC0Oz2mBYFkhLo4XTIH1lYoopAFi1YgUmenqiQa3GbS4u0Pz2m+3EFECCqrVVZ51oRnmLM4kpgBYs1yRdY1xMsSzdaDQasqu3Yh6Po4gplmUNSrCMDe/18qIEXUdHn/FkZWXkXGGBM+hgCGFAwTCMuGIKoGfmuHE2EVMsy/b2TEWFRTmOmAIoS8X1XDY0kNIwks2eED7BpmIKAI6UHUFVWxVOVp6ElBGuZJf7eEYNsfLzKbKemtpvOPhA1HXU4eZvb8bS35bi8PHDUPdUh2g0Gnz44Yfo7hmo/OWXXwLg30s1lJmyD0OCSkCiokibWFEhIyr9DCic+CKTyXSl/UVFA1ap8UIbEoKssjIoOzsxIzT03yemOLy9yaBCD2cUUxxc0HTiRDPbYBhG52hWVmZ0ZpvYYgqtrXC97jps6+iAJ4C/1Gq8+vvvwu7DFC4u1Efk4kLCKi+P19ucRUxptBpsObsFSo2JGzbD0MLR25vsXNPTLbrJO4qYAoAf8ui6O1x6eMDXyGS68WRFRXpO+goFicycHKCuTpDjsUZMbTm7BeuOrTP9exQS/WPUamkMicDFPk2KJty8/WZ88PMH9u2ZGoygIOozlMlofl1Pz0NufS5au1tF2+1gYgoALo27FJ/O/RTPX/Q83GX8Mjp86NE3xgVVYqLuHnHmDC/zkjPVZ6DUKNHU1YS0k2kG94TGxkZ89913AIDbbrsNALBlyxaD94eFhSEnJ8fg34bElP0YElQCEhICTJ1K15WjMaibX0MDPRydjIgISr0zjOWOf1qtFidTU9EUFISUlBTIGxtp8fhvp7UVdSdOOK2Y4ggJsdBTICjI0NmLe5LCBmKqra23ZyrR3x8bXngBALBy5UocPjzwAlgU3N1JTDAMpYJNNOM7i5gCgE9Pf4q1R9biwZ8fND1IWSolcenuTr1U6elmRXEcSUyxLItdObvQqGjEucZzg742PJwyVWo1iSoA9IALCyMBkZVFzaxWYI2Yqu2oxbvH38WmtE34q/gvq47DYrKzKZOdmSmoScFHJz7C6dLT+LXxV4weM9pxF8Y+PhS1Cg8HYmPRoezA0t+XYsE3C4R3XIRpMcUxLmwcJoYLm9HnHgNGY66uriQuQ0Lo2sjN1ZVEDsDs+NnYesNWPHfhc73PFA6JRIL169f3/v8rr7wSAPDDDz8AoLVLTU0NRo4c2fuaITFlX4YElYAwDD1vHY1BxZRKRQ/F6mqrH4y2RiKhdp+pUykwZC76BhTTZs+GLCqKfmDC6e4fT3c3Gv/4A+f27sXE6GhRxNSPeT+KLqb0YVkKIpvleBwfr4s4ZmUBLGsbMTVnDhlQ+PkBe/fi/557Drfddhs0Gg0WLVqERlufn/7+uvKmggKjGTvAucQUAKQEp8DPzQ/zR87nt/CQy6kJXS6nEkCeZZCOJKYAKov74JoPsPy85SadzxhGFyCsrOxxzWQYqqcNDaULKzNzwHPCFNaW+YV4hmD1pasxN3kuZsfNtugYrCYsjB5G9fVUEiqAzb5CocBoxWjMi52Ht+a9BRepAzdlA7TwSU4GGAYNigZ4uHjAHS6I9hN2dpkpMXW49LCombGoKNJM4eEDvIArnedSu+XlJoMvsf6xiPOPw6FDh6DRO3e0Wi3+/vtvZGRkAAC2bdsGAJg7dy4AGNxHWlpahsSUA+DQgmr16tWYMmUKvL29ERISguuuuw65uYYRD5Zl8fzzzyMiIgLu7u646KKLkJmZafCa7u5uPPTQQwgKCoKnpyfmzp2LcpHt+BQKWhvZG5NzpmQyncNbUZHNbHGFwtPTsgyEUTe/uDi6W3K1hP9Sapqbcaa+HgkJCYhob7euntIIP+b9iPlfz4dKq8KNo24UXUwB5I5fWEgBQ94tMBIJPRxdXICWFqjz820uprieqQ0bNiAhIQFlZWW4++67TWdUhCYyUreKyM7ud044m5gCgJnDZ2Lnwp2YkzCH/5vc3UlUccPO9MYtGMPRxBSHXCrHwtELIWFMH4+vr87ILD+/5x8ZhobcctH4zEyzA1FCDe29KOYiPHfhc/ZbQAYG6s6Jpiazs5d94XqmhoUOw5r5azAqZJSABys+w32H46uxz+Pd4P/ArcYyoW0MU2KqrKUMy/csx4JvFqCyrXKArViHqyvdmk36sURHU2ZfKqXy4D7n5u8Fv6OmXdeDWFVVhcrK/sfs4uKC999/HwAMjCIaegIYu3fTeIAFCxYMiSkHwDHu7gOwf/9+PPDAAzh69Cj27NkDtVqNyy+/HB0dHb2vWbNmDdauXYv169fjxIkTCAsLw+zZs9Gmp2YeffRR7Ny5E9u2bcOhQ4fQ3t6Oa665xiAaICTcaCOeLQeiwXtob1QULRo7OgSribc1LEtJNj7VegNao8vldLf8F8P1TCVdcQWCo6N1lvIC0VdMbblhi+hiCkDvbB2NhvQAbz3SE3nVaDTI3LcPjFZrOzE1aVLvj729vfH1119DJpNh165d2LBhg7D750NiIpVCciKzB2cSU1pWi06VTlF7u1qQ2vbyotT4yJG6qe5GcDQxdbrqNH7J/8Wi98bH06/cx0cvKccw9B0EB9M/6j2XTWGtmCppLoFCJdRkdwHw8yOjip7gC86c0Ws6409nZyc2/bbJcXum+MCykEtkGO4WSgq8oMDqQC2fMj+FWoFIn0gkBCQg3GugFJINCQ4GJkyge4Ve1Le4uRjP7XsOC7YvQHkrBfaPHTtmdBNqtRpffPEF2nsGKu7duxcAcP311wMArr322t5/t1ZMiWEz/2/DqWzT6+rqEBISgv3792PWrFlgWRYRERF49NFHewdgdnd3IzQ0FK+99hruu+8+tLS0IDg4GF9++SUWLlwIAKisrERUVBR+/vlnXHHFFUb31d3d3euwApClZFRUFOrr601aSiqVwLFjDLRaYNw41i5r9KamJhw/fhzJycmI4ZNxKSkBU1wMeHiA7eeR6/iUlADFxQy8vVlMmDDw4Wu1Wpw+fRoKhQJTp04d2ICiq4si0BYMeXVWamtrcerUKYwdOxYRERFASwuYM2cAAOzo0YMuHvnwZ9GfuPbra6HSqnDDiBvw5XVf2kRMcXR3AydPMlCrgeHD2d6qDFOo1Wqk/fIL1P7+mDR9uvBiSqWC9PLLITl8GKyfH9S//jqgm9+7776LZcuWQS6X49ChQxg/frywx2ImlZWVSE9Px6RJkxAcHGzXY+HDlowt+Db7W7x80csYFSxg5J9lDW46SqUSx48fh7u7OyZMmGB3MdXW3Ybbv78d1e3VWDFzBa5Lvs7sbajVA4wDYVkq+QsK4rWdoqIi5OXlYdq0afCz4OHYqerEbbtug4SR4I3L3kCMX4zZ2xCN9nYwZ88CSiVYHx9aUPOkq6sL639Zjy8rv8T1o6/HylkrnUpQbUrfhHCvcFwWexkdd2kpmJ7GOzY4mDKaFlwHLMsiLy8PZWVlmD59Ory8BrZAV2qUaFe2I8A9YMDXWENxMWWpQkMt+ihAeTlK2iuwquAzeMm98MZlb4BhGDz11FN4++23e13+9GEYBuvXr8c999wDAL1rFqVSifb2dsyfPx9//fUXPv30U9x+O7/hxSqVCvn5+cjMzERGRgYyMjJw8MRBNFc3D9mmW4FTCapz584hMTERZ8+exejRo1FYWIj4+HicOnUKE/RuXPPmzYOfnx82btyIP//8E5deeikaGxsNImHjxo3Dddddhxd6Gr778vzzzxv92ZYtW+DBY/5GZaUnGhrc4O2tQkyME5gcaDQIyMsDo1ajPTIS3VaUYNgDlYpBXp4/tFoGUVHt8PPrNv2mAZCoVPDLywPDsmiOj4fGERvjbIRHVRXc6+uhlcnQnJgI1iKHB6BT04mHch5Cg6oB5/mdh2XRywS1s+VLS4scpaUU3YyLa4Gnp7DljJaQtH07Rn71FVQeHjj84otoGWRoFsuyWLVqFU6cOIGIiAi8+eabvOeSCI2kuxuura1QOIGQAgA1q8YbxW+gTlmHG0JvwHl+5wmyXUalgm9JCRQBAegOEGchZy0aVoO9DXtxuu00Hot+DK4SYzZlwsBoNJAqlVCLdF5WdVfh4/KPIWEkWBa9DO5Sx7o/S7u74V1SgvZhw6Du45pqin2N+/Bz/c+4LOAyXBFkPNjriFR0VeDt0rehZbV4aPhDiHGPAQC4NjfDq7wcYFmoPD3RFh1t8TPE3qjVDLKz6fpOSWkwW1BJFQr49VR7KPz90BAaAHcZnR9PPvkksrOzjb6PYRhERkZi3bp1YBgG7733Hvbs2YMlS5Zgzpw56O7u7k0WvPLKKygpKUFpaSlKSkpQUlKCTjPHPAwJKstxGkHFsizmzZuHpqYmHDx4EADw999/Y+bMmaioqKCIeg/33nsvSkpK8Ntvv2HLli248847DbJNAHD55ZcjNjYWH374odH9WZOhAqiH6sQJBiwLTJrEYpCgiqCYnZnSp6wMTGEh4OYGdsoUC0Mw9qO0FCgqYuDqCkyZwhr0VvHOTHFkZoKprwfr60t9Vf9g+mWm9NFqwaSmAp2dYGNjaQi0BTz060P48NSHiPOLw6l7TsFDJvxQUL7k5AA1NQzc3OjaHCjhpFarceLECTAMg8mTJ+syU9XVlO6KFqDhOjsbLlOmgFEqod64EeyiRSbf0tDQgClTpqC8vByLFi3CF198YfNIdmVJCcp27MCI+Hj4Tp06SJe2Y9HW3Yafzv2EhaMWCvedcZF4hkF3YiKOFxQ4TGaqL8YG+JpLWxtVcSUnGzFhUquBs2fBdHSAHTvWYAiytZkpg2PobkN1RzUSAxzQUhfol7Hs9//16OrqwpEjRxAUFITRo0cjvzEfsX6xkEllNjpY61FpVNiYvhHlreV4/sLnDX/Y1AQmKwtQq8F6e2PQEhI9+Gam3j72NlKCU3SZMZHo6KAKB5kMOO88C5fN5eW0xmJZymCmpEAtkcDf37/fGrUv06ZNQ3Nzcz8fAXORyWQYPXq0wZ+UlBRIXaWICo8aElRW4ODWMToefPBBpKen49ChQ/1+1vciYlnW5IVl6jWurq5wNTJsQCaTQSYzfaOTyWiNUVtL6y89Z0vRaGxsxIkTJzBq1KjBe6YGIjqanpYREdRP5ETlBgAZ69TVUbVedbXOW4ITU11dXZg5cya/OVMjRgDHj9NdtKlJ15X9D6OmpganT5/GhAkTBnbzGz2a7L36ii2eHCg5gA9PUeDik7mfwNfD19LDFYSRI8mYorubAh/GkgpqtRqpqamQSqWGPVOtrbSaBKgE0ppMrkYDLFlCNcJXXw2X22/ndc2FhYVh69atuOiii7B161bMnj0bd955p+XHYSbl5eU4m5OD6ZddhqC2NjKz8fFxiv7DAFkAbh/PryyGN/HxgEoFVVkZ8nbuhPeYMZg4dapDiKm27jZ4yb16n3V8nl2mKC+n22JpKd0aDJBI6NnR2UnNiuPGAd7eKCgoQH5+PmbOnGmVAQVHgCwAAV6OmQ3sR3s7mXaMGtXPjlahUODo0aMICQnp7ZlKCUux04Fajkwmw3+n/tf4uiokhFwcMjLoWuHx/OV6psrLy3H++ecPaI1+svIkvs76GgzDYGTISMT686zjtgCtlkpePT1pfWcu7x57F0EeQbhp3GWQ5uTSNXL2LDI0ml4xxTBM77NGrVYbmA8N1GcFUI+tUqnEAw88gDFjxmD06NEYOXIkPM3IkLYOjYuxGqcQVA899BB2796NAwcOGCz6wnr6W6qrqxGuFyGtra1FaGho72uUSiWampoMbuS1tbU47zxhSj4GIiqKBFVtLS323dzE2xdvA4rBkErpAeikSCR0v87MpId9eDggkw1gQGEKNzcSmEVFtIAODLRwoJHjwntor4+PQaTZHBQqBRbvpunu90y8BxfHXmzRdoTExYXWNgwDo5njQa3RfXxIWFZW0oJx8mReCwSjbNgAHDlCi6z33zcrgHH++efjxRdfxNNPP40HHngA06ZNw6hR4ruB6RtQBIWEkJ18bS1ddJMmiXuTs5DfC36Hl9wL50WJd79XxsYi7fBheMnlSJbJIOnq4mEFJi4arQaP/vooPOWeeP6i5wXrK4mPpxhTfT0Z+xkEJLh5XWfPUg9qWhoKfXyQU1ZmlZsfy7J488ibmBU9C1OHTRXkc9iMoiKK3KSl0XfjSwElzs1P7ivHzqadiFJEIdDDuj5VW9Op6oS7i3uviBowSO3lRfNN9IMMWq3RKhi+c6YAYGzoWCyZvASdqk5RxRRgYgaVCfIb8rEpfRNYlkXKvBSMnTiRrhGFAl6VlRg9ciQ8fXwQERGB0NBQhISEGPzXy8sLfn5+CAgIwJEjRwRz82vrbkNGbQZmRM2wajtDEA4tqFiWxUMPPYSdO3fir7/+QmyfLvLY2FiEhYVhz549vT1USqUS+/fvx2uvvQYAmDRpEmQyGfbs2YObbroJAFlUZmRkYM2aNaIev7c3BbDb2iiiJ9ZaQxAx9Q8hOJieVy0twLlzWnR0WCCmOKKiKNWlUJDrxT/ou+UtpvqiVtNKiqdZxwv7X0B+Yz4ivCOwZra415s5DPSM5jVnKiGBMlXt7SQoxo0zP5tbXAw8+ST9fc0aOtfMZMWKFfjrr796723Hjx/n1d9pKUbd/JKTdTMizp4lMw0HCjyUtpTixf0vokvdhfevfh9Thk0RfB9KpRJ/HzkCj1GjSEy1t5N19oQJ1MFuJ3IbcpFdnw0XiQu61F2CbdfTk2Zel5eTAeiUKX1Of05UpaejIisL1ZWVOO/mm63KTO0r3odtGdvwbda32L1oN0I8nahiYORIujZaWui8GD0aCjc3HDp0CMHBwfi6/mvsKdyDmo4afHDNB/Y+Wt5oWS0e/fVRuLu44+lZT5v+neiLp64u4PRpijTrPUvMEVMA2f8vnrjYmo/BG6WS/mvJJR0fEI8nz38S5xrPYWzoWPrHiROBrCwkAji7Z49ukPwAiDFn6qPUj7A1YytuGXMLFqfY5nv8J+PQguqBBx7Ali1b8P3338Pb2xvV1dUAAF9fX7i7U1Tk0UcfxapVq5CYmIjExESsWrUKHh4euOWWW3pfe/fdd2PZsmUIDAxEQEAA/ve//2HMmDG47LLLRP8MycmUHhZrjSGKmNJqKQJfXU2LAgdaIPEhIQHIzdWipuY0JBILxRRAD4CEBHoYlpXRjd/OUWchsFhMabVAaiotoF1cTLp6pVam4o2/3wAAvH/1+/Bz87PiqMWjrQ2oqADi49U4dozHnCluPlVqKkXhS0rMm13GssB991GUZdYs4N57LTpuiUSCL7/8EuPGjUNmZiYeeeQRfPzxxxZtyxQDWqNLpVT3lZpKnyc7m+avOEi5cJhXGOYmz0VxczEmRUwy/QYz6WeNrtHQQrGzE8jNpflEdmJU8Chsnr8ZFa0ViPC2rFx3IGJigJoa+pgVFbpRhr1IpSjw8EBlfT3GjxgB79JSSmWZadLAMSNyBhaMWoAgjyDnElMA3SvHju2d1dV98iSOtbQgOCEB48aNg0ejByrbK/HY9MfsfaRmkdeQh7O1Z+EicYFaa6a5T2UlpXxycnr7Uc0RU31LWW0Bl6GyRFBJGAnmj5xv+I8yGZ0XdXUmWwrEEFMsy0ImlUEqkWJm1EyrtzeEg5tSDHTSfP7557jjjjsA0Enxwgsv4MMPP0RTUxOmTZuG9957D6P1iru7urqwfPlybNmyBQqFApdeeik2bNiAKDOiwq2trfD19XWohj3RMlNaLfUPdXVRfYcF0XN7MuCcKUvJyKCFwPDhTicu+2KxmOIoLKR6SrmcQtMDFJOrNCpM+XgK0mrSsDBlIbYt2GblkYuDRkNVd93dGjQ3n0JYmJL/nKmaGhIQABmX8O0h2rgRuOMOSlmnpQFJSRYePfHHH39g9uzZYFkWX331VW8wSSh4zZlqbaXZO97elJ0Q2lreSlQaleBN/gPOmerqIjGVnOyQJZBCUVVFH9PFhaq59G+zvXOmpk6Ff2kprUbHjbNYUHHw6Y92WLRadJ0+jYx9++Dr54eEa64B05OdcdbPVdhUiKKmIlwad6l5b2RZKoUsLaX/GxaGHK0WJaWlJsWUltXizu/vhK+rL56Z9YzNBPbZszQdICmJfztxp6oTbi5uvAZoA6AKkLw8Wnf1KDcxxJQ+Ne01CPUKdcg1rrPh0ILKkRDiZGtspDWXEL3Kopf5VVdT9EgmA6ZPdxohYUxMDWKw9K/CajEFkNg+eZJC06GhA7qtrDq4Ck//+TQC3AOQ/UC2Q0eVy8vV2LkzGwwD3HrrSPj7myEGcnNpZck38FBdTdmtpibgtdeAxx+3/MD1eO655/DSSy/By8sLp06dQmKiMO5nZg3tbWkhQeUAZgy1HbWinnOONrRXny/TvsSlcZcKnpXqC8sCp05RhjcmRpek7Te0V6WiPxZk96vbqxHm9c+YBahQKHDo4EFEdnRghJ8fWn3k8J30L88MVFSAzc9HaUkJyjo7MW7RInibCExl1WXh7t13Qy6V49sbv0Wwp23GNnR10R93d/5Zqmf/fBalraV4dtazSAgYeBxGL1xPqlwOjB6NNoYRVUzpMySorMdxngL/cDIyqHy6p2rRKmzSMxUaSg9AlYqK5Z2AvmJKKpWjqAg4cYJ0gCCwrNVT3+2BIGIKoMXyiBGkUGtqqJ+qDzn1OXhhP81we2fOOw4tptRqNYqLj8LPT4WRI0chP98FGo0ZG0hIoOwU3yzuQw+RmJo4EVi61JJDNspzzz2HWbNmob29HTfddBO6uqzvmTFLTAHUvKgvLIwMqbQF1e3VWPjtQqzct1LQ3iEOs8VUXR01G9mAP4v+xDvH3sGt392K1m5xXbsYBkhMpIg9N0Ggn5gCKCinL6aam6ls2ASlLaVY8M0CvPDXC+hWWz5X0BHgDCiCQ0IwYt48/OFRjbnpT+KH3B/sfWhmc6rqFKraqgTZFhsRgXy5HNW1tZgYHQ3vggJds9IAjAoehW03bMNLF79kMzEFULLZz4+/mGrobMCB0gPIqsuCUjP4Z+olNpayuEolOv/+Gyd+/FFwMdWp6sSaw2tQ11EnyPaG0DEkqGwE92wpK7NuPW4zAwqG0YUcy8rstjjiy0BlftXVlEwpKxNgJ21tFJLtKVNwFgQTUxw+PjoBkZdHorsHLavF4t2LodQocWXClbh1zK3W708k9A0oFiwYCw8PKTo7da7ovJBK+Zf6ffcd8O23VCP12WeClsW5uLhgy5YtCAwMxJkzZ7B8+XKrtme2mNKHZak09MQJk4sjMThVdQodyg4UNRcJPjzabDHV1UVR5/Jy6rUTmRFBIzA+bDwWjFwAH1fxo8yc6SXDDCCm+tLcTJHFtDT6bgbhZOVJdGu6UdNR41QzmfrSK6aCg8kaXSLBz80n0KHqRHlrT7CysdG+B8mTlq4WPLH3Cdz07U3IqM2waltcz1RhaytG3XILPHrcD/lUw0T7RWNW9Cyr9i82gR6B2HHTDqy8cCVGBfN0YHV3ByZMQIebGzLS0xGvVGKEXA4h81KfnPoE32R+g0d+fQRDBWrCMlTyxxNr06EaDXD0KK09U1LIjc5cbO7mx7JU3tXRQSHIPi6LjsJgPVNcm4tUCkybZrnDNQBKxWdlURR+6lSn6I8QXExxcAYVHR20ourpA3rv+Ht48JcH4SX3Qub9mRjua9kgYLEx5ubX1ETrPIB8Fkx4bvRHoaCTLS6uv8hqaqJSv+pq4OmngZdfFuJj9OPnn3/G1VdfDQDYsWMH5s+fb+Id/bFKTAF0s+sZBg0fH8rg2bgk7nTVaQR7BiPSR7hz3uIyv4oKID+f/p6cLPoQZC2rhZbVwkViuz62goICZGXlYPz48xAVNYibn1JJph0KBd0/TTghnqk+g3CvcIR6hYpw1OLTT0z1ZBm0rBbf53yPa5Kugaysglw/IyMp4+3AVLVV4dl9z6K1uxWb52+2eEi0UQMKhYIe1AM8pM9Un0GoZyjCvW0/RFylosvY1VX8GeZtbW04fOgQEqRSxMtkdM4EBVF5vQCtF1l1WVhzeA0WT1yM84ef3/vvQyV/1jMkqHgixMlWXEx/vL1pZIs52M0ava6O3IlcXIAZMxyul8qUAQXL0vO7tZVuhMnJVu7wzBmKsgYHkzJ2YEQTUxxtbZT6S0gA5HJUt1cj8d1EtCvb8d5V7+H+KfcLv08BGMwavaCAPtIg7WEDk5dH7lWurjSfSt+w47//BT74gMolT58WVYw//vjjeP311+Hr64szZ84gxgwHQqvFFIdCQaJKrSZ3zBEjLN+WA2B1z1RREWWoGIbuG2ar9cFpV7bDS25kqJoNKCgowJkzefD2ngkvLx9Mm2biMdHdTfdRhYIi8uPH29VeXiwGElP90Bfc4eEUnHLgpl8tq0WjohFBHpadw7zd/MrK6D4ZHIx2ZTtu3H4j2rrb8N5V72FcmG3nZba2UnGKqystgwajqq0KHaoOfj1TfehnQFFbS326cjktGgUYzA0YN0EZElTWM1TyZ0OGDaNAbVubeRl+u86ZCgqi8q7x451OTAH0XOKCflVVdGO0ioQE2mhdnUOXaYgupgCKDIwa1RtRPFV1Cu3KdsT6xWLJ5CXi7NNKTM2Zio2ltb9F6//4eOoV6e6mTKZ+rGr/fvrvSy+Jntl85ZVXMH36dLS0tGDhwoVQ8iy7E0xMAbRQ5uzTq6tFL3drUjRh5b6VaFQIf00KYkARG0uLZZalc6OpSbDjy2vIw9VbrsbWs1ttXsLDlflddNF0+Pr6QKnkUV7t6kqOf25uJKrOnOkt/1NqlHjryFto7moW+9BFxZiYau5qxnfZ30HL9mnoHTZM15daVUUBTMGafoVHwkjEF1NNTRTdyswESkvRrmxHpHckQjxDMDLY3EiX9fC1TGdZFq8eehW37LgFu3J2mbUPo25+oaG09ho92moxpX9vcEZHSWdgSFDZEJlMZ7fJd31h96G9DEMLRS/7RD8HwhxrdB8fui8BFAi0as3h5aUbupKXB/McDGyDTcSUEUI0JBS6Nd38bWJtCJ+hvRIJJVQset5IpSQipFJaEBQW6n7GCRS9fjOxkMlk2LZtG/z8/HD8+HGsWLHC5HsEFVMc/v7kWgBQhqamRpjtGmH1odX4Kf8nPPPnM4JuV1A3v6QkClBpteRS1NkpyDH+kPsDOpQdOFl5UpDt8UW/Zyow0L+3Iry8nMdp7uZGC0V9UdXdjQ9OfoCvzn6F+3+632n7OwbKTK05vAarDq7Ca4de6/+msDAKTkkkZPSTluZQfcvvHX8PW85u6S8GzcCsob1+frpBt4WFCCtvxodXv48Pr/nQ4jJDa+Da/UzFwpQaJVxdXCFhJBgfNp739ge1RvfxMVx/VVXRAtKM66NL3YXbd96OHVk7rPodDjE4jrfq+YczfDjdM9Vq0w8du4spY3Tb323JkjlT8fFUtdjRAbS3W3kAMTF0Z+3qohpOB8JeYgq5uRhWQlH36vZq8wc9igwfMdUXjYaqLVpazNiRp6cuvVVWRplMQCfCKyrMO3ALiY6OxsaNGwEAb731Fr7//vsBXyuKmOKIiKCbHkBfpkj3j/sm3YeRwSOxdIZwzomCW6MzDC2aAwIoW+XuLshxLp2xFE9f8DSemfWMzSLPxgwogoPp9FereRrDcj1U7u6U7ZbLcUX8FYjzj8OSyUucMoo+kJhiWRbjQsfBU+6JeSPmGX9zcDANenVxoZvOmTMO4Sib15CHL9K+wNoja5Fek27RNswSU4DOQjIxsTfLLUk/i2C5n0X7txa+gsrVxRVrZq/BNzd+gxi/GF7bNmvOVFcXRYWLiih7x1N078rZhZz6HHx25jP+joNDmM1QDxVPhKwvbW+nB89g141Diqn8fOoRmTCBoiZ2wJqhvY2NVJElSMVVQwNN+vP1pUirAzz87SamAKCyEprcbLgdvAJqVoOyx8oENQSwBkvEFKDrp3Jzo5Yos0z5uAHIUilZpL/4IrBmDfDII8Dbb1v0OSxh2bJlWLt2Lfz8/HD69Ol+/VSiiikOliUxFRCgy9SJshvhhqOKOmdKq3WIWV2WMpibX309Jd+kUhpfyKtKSamkF/b87jRaDaQSxyov5wOfnilevW4dHeSEGBMjvgMCD7SsFrtydqGgsQDLZ5rvHmq2mNJjy9ktQHs7FqqTIdWwJL7HjLFoppk1WDLUlw8WDe2tqqK1mFZL38fo0SYHZmu0GuzI3oEwr7AB3RGHeqisx3nv6k6Ml5cTiimAQvYsa6avtHBYI6YAWs8J1r4SGEjRxCExRUREQBoUgnB5IACgokUIn3rrsVRMAWRs6e5OQcHcXDN3HBtLJW8eHrS65MpXbJSh4li9ejWmTp2K5uZm3HzzzQb9VDYRUwBdHyNGCC6mOpQdqGyr1NuNE4gpwFBMabVUOsxjLpM+jYpGfHHmC2i0ti05NmWNHhREySaNxozpEnI52lUd9HeWhbSomESFEzGYmNIvseJlHOLpCUyZYiim7Bj3HnnWNgABAABJREFUljASzB853+ZiqqqtCuuPr8fasx/hUEjPRF2FQoBGaPPhMlQDJZULGguw4cQGs+beWSSmADovOHdMhYLcMrhKiAGQSqS4KeUmh7ead3aGBJUd0Wj6txU4rJgCaJEokVA5gpGBrmJirZjqS0uLAPflgIAhMaVPcjKGudE8gIr8U/Y7jh6sEVMAZaRGjdJ5kJilhTg3twkTSMXbuOSPQy6X4+uvv4afnx+OHTuGJ598EoANxZQxuruBnByr+g9ZlsXqQ6uxaMciHCg5INihiS6m+lJQQFn/tDTe5ZAsy+Kl/S9h/fH1eOXgK+Ienx685kxBN12D72zpAyUHMHfrXPxe8DulhMvKqNzN6tps2zCYmPqz6E8s3r0YpS1mzi7Uv1epVOSY2dAg0BHzo1HRaLueKSOEeoVi+XnLcXn85ZiVdDll+pOTqd/MxgxW8sfdiz47/RneOvIWr+1ZLKY4vL2pbMLfn+6jmZmGPbs91HbUOm0vojMyJKjshFZLcy+zs3WGTw4tpgCKiHADXQsKbBY1E1pMVVeTc3VurkAfQaOhmxnfFYSAOIyYAgC5HMOC6LytqMq1qwuitWKKw9ub+u8AOuXNWuO5uOiyEXbKUAFATEwMvvjiCwDA2rVr8emnn9pPTAFUE1Zd3d8J0QwUagUq2yqhUCng5+YnyGHZXEwBhmnQtDReg5AZhsGchDkIcA/ALWNuEf8YwV9MARRnmjyZ/1SJndk70drdiqy6LKqn8vEhEXHmjF2yEeZgKjO1/vh6pNek48e8Hy3fSVkZ3Xi468YGaFktlv62FHfsusN8MQjrxRRAmbHrR16PVZeuou9VJjPM2qlU1MNsg3XI5Mk6Y0pj3Db2NsT6x+LOCXea3JbVYopDJqMqGW5N1seFWalR4t4f7sXi3YtR1VZl2T6GMIshQWUnJBKqGgPonuDwYopj+HCyyVYoKLIqMkKLKYC+d5mMqkp4NU+bIi+P6lu4WSI2wqHEVA+RPYKqvLvO6iyEpQglpjgiI+mc0WpJA1j0kbg3VVbaxRJ53rx5eOyxxwAAjzzyCMLDw+0jpgBqNJdIKOJ+7pxFm/CQeeDjaz/G+1e/j7GhY60+JLuIKYDupePGUbCqs5N6Z3g0ml+RcAV+WPSDRbNuzMUcMcVhjins65e/juXnLae5dS4utEj09aXvIS3NTFcY22GqZ0rCSLDh6g24YeQNWDxxseU7io2lrAzL0j1V5BEEAFDUVITi5mKUtJTAzcW8OnlrxVSnqtN0ZoxlSWAWF1ODk8iOiO7ulAwypn0YhsFFMRfh6wVfI8xr8OyZYGJKt3OK+I0frzP/AQCWRU59Duo761HRVgFfN1/r9jMEL4YElR3hHP/KylqxZ88JxxdTAEVBuMb24mJRb2RiiCmAxBT3NRcXC2A8Fh1NN7aGBqC21trD44UjiikAGOZNmZgKtlWwye7mILSY4hgxgta8KpXZ7S6En5/O3tMGCyJjPPjgg0hKSkJHRwceeOAB3vOpBMfHRzc1uaKCx+Ai40glUkyKMHNCuhHsJqY43NxIVMnllIlITzeq2lUaFbrVupuVq4v4w3AtEVP6dHebviW6SFywcPRCnR02J6r8/Oh7SE+nYeoOBN+hvWFeYXjygiets/rmehC5BXNRkQDzPwYnPiAe22/cjtcuew0hnvwDL0Jkpl499Cru2HUHzjUOEmxhGMrMSKVUCXHqlIU3ZuvQF36mRoUILqb08fPTqT2NBjh9GmPZEHy38DusvnQ1PGS2NfH4tzIkqOyIqyvg7t6MzMxMeHikOL6Y4ggPp2Z7lhWtzl0sMcURFkbrOo1GAI8NDw8SVQBF3EWOljmqmAKAYT49gsqlk0J6NkQsMQWQCB89mko/LBrJNmKELiV98KDNM3fl5eXIysrC5s2be/upnnrqKZsegwHBwYa1lCaaqjnePvo2vsn8RrC+ALuLKQ4PDxJVLi5U5paV1e8l7598H7fvvB35DbbJhFsrprq6gGPHqKy971q3pr0Gu3J2Dfx7lErJzY3rEcnIcJi5TKbEVEtXCwoaRTBuiovTTamvqKBzRMRsd7BnMKZHTuf9eiHEVKOiEQdLDyK7PtsgeGCUoCCdOUNnJ4kqEbKZDQ2kYftq+rM1Z7HgmwU4Wn7U5DZEFVN9qaqie0hODkIqmjEhdJx4+xrCgCFBZS4Clrk1NjaiouIIYmOj4e0d2dtL5fBw81SmTaPIiMCILaYA+ghJSfTf2lpY/90PH06LIqXSaHOoUDiymAJ0GaryVr1ayq4uwYaYDoSYYorD25ue3RYhkehEd2mpBbaBlqNvQDFlyhR8/vnnAIA333wTu3fvttlx9CMqStdblp1tsl8mvSYdm9M3Y83hNciuz7Z69w4jpjg8PXXlf/rlOyBHw5/zf0ZhUyEq2sTvw7NWTAGUePPzo7ibflJWy2rxwv4X8PKBl7H++PqBN8CJqqAgMiMQ4Zo2Fz6Zqdf/fh237bwNu3NFuLYiI3VOOe3tggdmDpUeQl5DntnvE0JMAUCAewC237gdKy9ciZQQHk14Xl5kVuHtreu7E7jPrKGBzt++a4SPT32M0pZS/Hru10Hfb1MxBSBL3oKS4J55BZWVvUOzhxCfIUFlLsXFPMbAm4brmRo7NhmTJtFgAztVAlmGlxfPISPmYQsxxeHlpVvPcWMdLEYiIYUG0E1MhEiZo4spQGddXd/Z4wLZ1ETuKyJGU20hpvpSV0ftHbw/EsvqDCpaW0nFC9LANzjG3Pyuu+46PProowCAO+64AyX2vPEkJFDmzt2dSt4GYUzIGCw/bznumnAXRgWPsmq3DiemOLy9KVDla9jz4Cn3xNYbtuLJ85/ERTEXiXoIQogpDq46vKbGMKYyM2omfFx9Bh5yyyGRUGo4OFj3b3boQQT4iSmlRolOVSc0Wg3i/ePFOZCQECqJHDtW0Gdwo6IRz+17DrfvvB2nqvi7tAolpjiCPIJwTdI1/N/g6ko9RMHBdJ8tLRX0HBnIMn31pavxf+P+D49Me2TA99paTKk0Kjz310osPLIMBwLbdRnv1FSHK5v9J+IgTxEnQqOx2qmrrwEF10slk9mlh996GhsFmRtiSzHFERNDwio6WoBZm35+OhcigWd1OYOYYlkWz/z5DABgTsIc+kdPT/pi29upbkJg7CGm1GryIWlqMiMZ+eOPwPHjJBrm9SwiCwpErfsfzBr9tddew5QpU9DU1ISFCxfar5+Ky3Zz9vKDvpTBwtELybzAChxWTHHoH097e6+jqr+7P24YdYOouxZSTAFUVh0YSOvc4mL6Nwkjwa1jb8WPt/yI4b7DB31/P7q6KEBjo15VDr49U3KpHG9e/ia+vP5LfhkWS/H3N1zh19UJUgUwOWIyEgISMI5nmZhQYqqwqdCizFgvUindR2JjSYALeE1zt+i+tydPuScenvYw/N2NXye2FlMA0KHqQKRPJLxdvTEh6UJg0iRa4CiVFAG08XXzb8PBniROQnm5xcrHmJufqytNlU9JsXkPv/WUlFDTsIVuXRz2EFMABXAmTwZCQwXaYHw8NWjx9QzmgTOIKQD4OvNrHCw9CHcXd6y5bA39o1xO5ToAmQ8IWNdqDzEF0DkzYgT9vbycx0i2ri6gJyOExx4DZs4ke+gRIwaeFGklpuZM9Z1PZdd+KqnUsJyrqckgwnyi4oRgA2wdXkzpo1bjz32f4Uz2Pqvvr3wQWkxxcHOpqmo0aGvX/V4tapSvrKQVbnZ2/yGOIsFXTHEwDIPkoGSbHBsAul6ysmgWSFubxZsJcA/Amtlr8NE1H0EqMb0QEUpMaVktXtz/Im777jb8nP+zRdsAQMGZ6Ggqveeoq7Oq3I1ldW/nBFVhk+komj3EFAD4ufnh7TlvY8v8LfB29abny4QJtMBxcemX+R5CWBz4aeKguLtTiNqCXqrBrNFtpB+EJzSUokFNTRbPHbKXmDKG1RlCbrVtcbONIc4ipjqUHfjf7/8DADx5/pOI8o3S/TAoiAQEQLa/ApTM2ktMcQQG6mb15uaaeGa/9RalssLDgaefpn9LShJQxRvCd2hvbGys4/RTcXBDbnNyAJbFycqTuP/n+3HvD/eablI3gVOJKQAVnTV4vnIL7s17E6dy9omS4eUQS0wBFCAPDgZ+qf4Md323BJVtVvQhx8bSdcSyJKqqxJ2vw1dMHSg5gPdPvA+Vxvp7m9l4etKXzPUQmfkc7msM4in35PUeocr8utRdCPMKg5uLGyZHTLZ4O/3ghOapUxYLTaWSYjsMQ4/0Y+XHcNP2m/DCXy8MaKhiLzGlT7CnXomsVEquqpMnG65LHMTk5Z+EYz9RHBFuFVVebladLt85U11dVALsNLi56RqRLBj260hiqraWXKkEHUhvxWBKZxFTAFndVrRVIMYvBv8773/9XxAfT5HD7m6rDRnsLaY44uJ0vdADzqitqABeeYX+/tpr9Ia+KJWC9VPxFVMcDtVPBdA5wjnFFBWhXdkOdxd3xPjFWGUT7mxiCqBo8yXJczAuYgLGeyVQNYAIDwcxxRRHUEQ7/qzdgpyWU0irTrd8Q5ybEBegyc0VbR4iXzHVqerEKwdfwaenP8W2jG2iHMugyOXUQ8Q5Ip49a1b2bs3hNXj32Lu8AxZC90x5yDzw6mWvYvuN282yaDeJm5vumXPmDG8nUX24/ilXVzr1MusywTAMvOReRs8He4mpM9Vn8HHqx1BqBind1hdT9fXA0aMWB8GHMA7DCuU/+w+ntbUVvr6+aGlqgk92Nl2kycmGk7sHgK+YUquBI0fonjh+vCgGeuKgVtPFqVbz/k4AxxJTACURSkvpPjxligDll3l59LA34zvhcCYxVdhUiFHvjUK3phs7btqB+SPnG39hWxtFC1mW6t0tGCzrKGKKQ6EATp6kazYmRteE38tttwFffQXMmAEcOtS/tl+joZ6Qri6LzhN9zBVTHEqlEhdccAGOHz+OadOm4cCBA/a9FmtqKPsAAElJqPBiEeAeAHeZZeWRziim9OlSd8GtslbXsJeYqAtiWYktxBRHSWMF9hT/grsn3C3MQvPcOV0gIiFBF+wUAHPL/PYW7sU3md9g/VXrrZs5ZQ1aLQlMTkzFx5OT5iAUNhXipu03AQA+mfsJxoeNH/T1Qosp0VGrKdrFCYe4uH4OmoPB3Yr8/cmAEwCy6rIQ7RvdL5NnLzGl0Wpw+87bkdeQhzvG34EHpz5o+k3p6brvJDYWGD4crW1ttMZtaYGPj4+4B/0PxbmeLI6AREI3KZ4eynzFFEDVYmE9g7a5Bl6nwMVFt5IsKuJVN+doYgqg8mtXVwGzhFx/TEEBZSF44kxiCgD+9/v/0K3pxiWxl+D6EdcP/EJvb3qgRUVRGaCZOJqYAuhXzLWI9augOHyYxBTDAOvWGW+Ulkp10fb8fIszmpaKKaB/P9WTTz5p0TEIRmgoWO5+kp+PYSq3f5WY6lQZmgu4ubjRIpCz3c/P59G4ZxpbiikAiA4YhsUTFwu30ExI0C2Oq6oEc3YzV0wBwGVxl+HDaz60n5gC6P4yYoROWBYUmMxAxPnH4c3L38TiiYttLqb2F+/HumPr0KXusmo7g+LiQtb7XACisJDKiXmeKyEhZLqZmKj7t1HBoxxGTAFk8nLH+DuQEJCA28bexu9No0frnjtFRQ41582ZcfyniyMybBi5pwQEDPoyc8QUx/DhtP5qbnYyl8uICErtKJVkPjAIjiimAFrbcnMTy8oEME2KjKTadrWad1O5s4mpvYV7sTNnJ6SMFO/Mecf0gyQqiiKnZi5sHVFMcYSEUHk6d+4AoKDCww/T3++6i14wEMOHk8DUaoHMTLPEN2CdmOKIiYnBF198AQBYu3Ytvv/+e4u2IwQ59Tm47dTTKPBSUjYzK8uiHghnFVO37LgFaw6v6b/QjI2le4qfn9VDs20lpjJrM1HcXGzwb62tAraDxcXRanfcOEGc3cwRU52qToMSK3v0yvSDYehGFB9Pz2QTaxQAuDDmQiyZvGTQ1wgtprrV3Xjt8GvYlLZJ/DJJhqFzJDGR/l5dzbv8j2EANzcWuwu3oaXL+CgUe/dMMQyDy+Mvx9YbtsLPzY/fm7gxL8nJ9PeGBiqLHMIqHP8J44jwuGAsEVMAZUi4qh+nylJJJPRw8/Aw3ifSg6OKKY7gYHoGabUUCLYKhqEbFtcTYiJa6GxiSqVR4ZFfaQbH/VPux+iQ0eZtgGV5uf45spji8PLS/Z1lAfazz6m80ccHWLXK9AZGjNDV+w/YkNUfIcQUx7x58/DYY48BoH6qYjvdgNYeWYvc+lx82rxP1xdiZq2/M4opgAarlreW40DJAeMGBwkJNH/IinpkW4mpTlUnnvzjSdyy4xYcrzgOgGIFp09TO5hgo/qGDTN0dbIwy2tuZur1w69j0Y5FyKrLsmh/ohIVpZuLCNA1pFc5Ut1ezTszJEaZn6uLK1acvwKTIyZj0ehFVm+PF8OGUbYqMtIsQ6C/iv/CG3+/gYXfLuzXZ2ZvMaXfsWPRvsPDdSMrRBzh8W/BOZ4yjopaTbVhfRaFloopjuhoJ81ShYRQ81FgoNEfO7qY4khM1BkXWj22wdtbV26QlzdgOaSziSkA2HBiA7LqshDoHogXLnrBvDdrNLSySksb9CR3BjGlT3c3cPZgM7Qresrmnn+eX6+YiwuVYUil9H3wmGMmpJjiePXVVzF16lQ0Nzfj5ptvtst8qlcvexXXJl2LFRc8SeMHRo7UlbvxwFnFFABcHn851l+1Hq9c8grZHhtD//MYef4Mhi3L/LrUXYj2jUaQR1BvsEUu1wUMRTEtrKqiQIaZcwDNFVMtXS04Un4EpS2l9nH2Mwcuy3vmDKBUQqPV4PE9j+Pmb29Gbv3gBkFi9kzNip6FD675wCqzGbMJCDAsJVCrB71+zp0DupqCEOeXgLnJcw2O1d5iKqc+B4t2LMKJihPWbcjbmyqunKZp33FxnieNI1JaSjW5ek8Ga8UUYJil4j041FEY4KbiLGIKoL4YrjTfirEeOmJjKQLU1WX0F+qMYqquow4r/1oJAFh16aoBhxsOiFRKdr8APeyNLNydTUwBlEjxf/cFSBvroUkeCTzIo0GYw8ODxAO3oUF6EcUQU0D/fqoVK1YItm2+BLgHYOVFK+Hj6kNCUz+arNUO2v/gzGKKY3rkdIwL4zFYtaaG7idnz/ISVbbumQpwD8C6K9fhk7mfGMyc0g8YCnJ/1Yc7N8rKyKCBR6bXkp4pXzdffHPjN3jlklf4/a7sSVcXZe16TIEqa86hvrMezV3NCPQwHvwExBFTGq3G6vEHgsFZ76elGW2a1mrJpNW9dQw2zv0Kiycu7v2ZvcUUAHyU+hHONZ7Drpxd1m9MJqOA3hBW4XxPG0ciMpIWhq2tQEODIGKKIzqaonkBAWY7kdsfliX3pbNnATiXmOIYPpycFuPjBdiYVEqlf3J5v94HZxRTAPD0n0+jpbsFE8Im4O4Jd1u2kYQEElVKJT3Y9E50ZxRTABDelIXIne8CAHKXvA0VZOZtICiIHBAnThywrEssMcWh30/11ltv2aSfqqylDGeqzwz+IrWa3KkyM43eFJ1VTGlZLTalbUJbt5nqIjiYqgG0WrrXDpLptaWY6luG1NcK29VVl7QVaFqAjmHDqHyWYShblZk5qAC3RExx+Lj64PL4y4U4anFxd6f7iYcH0NWFqII6bJ/9Ed6Z8w6CPIybA4mVmdqWsQ03fXtTbwmo3eGMxQoL6VzRC2J1ddFtRioF3N2kvYYjjiCmAOD5i57HLWNuwSPTHxFmg47QA+jkOMcTx1GRy3vLuVrS0gQTUwBd59Onk3me053nSiVl7RoaoK2qcjoxBVBVjaAZcH9/+oXquds5q5g6VXUKn5z6BACw7sp1kEos7OeQSkk8SKUUYe+JEjqrmALLAo88AkajQeOseagdfzk3n9Y8QkIoM6O/3R7EFlMc8+bNw9KlSwGI30+l0qjw1J9P4d4f7sXP+T8P/MLOzt7gVd9ZZs4qpgDg64yvse7YOtz5/Z3QaM2YLC6RUEkk1/Q5gKiydWZq5V8r8empTwf9LNztrrbWxFBsSwgLo+9FIiE3xLNnjWZ7LRFTp6pO4VTVKYEP2Aa4u1OvjK8voFbDM7sA41jj9w+xxJRGq8F3Od+horXCuuHOQsHNNEtKor/X1VG5aFcXWJbF038+haMNP8PNTXf/dRQxBZCgXzpjqbCzu4awCud56jgqUVFo7ehA1rFjSAkNFURMcTjRmsAQV1cgOhparRY5P/2EzrY2pxJTfenqGrT9iT96v9CaykqnFFMsy+LhXx4GCxaLRi/C+cPPt26Dnp46T9riYqjr651TTAHA998De/cCrq5wXb+21zzJhOnl4JSXA6mpgFptMzHFsXr1akybNg3Nzc1YuHChaP1Uaq0asX6x8JJ7YXLEIG6IPj60UOacunr6ZJxZTAHA6JDRiPSJxM2jbzY/OCGRUKlOQIBuqKueqLK1mDpVdQo/5/+Mj059hKLmgZukvL0pYMWyVFYlOEFBZEDABWvS0gwyVZaIqU5VJ57b9xzu/eFe7C3cK8JBi8u+8kM4HtRNJbQsS0GJPjcnMXumpBIpvrz+SyydsRRzk+cKtl2riYigchS5HOjoAFJTse/sbvxR/Ds2l76MLik5AjqKmKrrMH9A8RC2wbmePA5IY1sbTlZXIzo6GjGAKPV5zc0mKxccDm1EBLKLi6Fsb8d5w4Y5rZhiWaoyqqwkZyohqM3IwLnNmzEhJsapxBQAbM3YisNlh+Eh88Ca2WuE2WhYGBAWBo1ajYwdO8AAziemurqAnowOli2D55i4Xp1YVGSho5laTQue9nZU79uHM6dP20xMAYb9VMePHxetn8pd5o4XL34R2xZsMx1tDQzUDf4qK4OyoMCpxRQAjAkdg603bMUNI2+wbAPGRJVSaXMxBQATwibgpYtfwkNTH0JCQMKgr42MpMSJu2UjxkzDTWOVyejvPeeGNWV+50Wdh2E+w3Be1HkiHbQ4NCoa8dKBl3D/Lw/ioEc99RRIpQZlGLYY2ush88AtY26BhHGw69TXl4wZvL0BlQoXtAdiUdx/MS/ifkT6hziMmCppLsHcbXPxwl8vOL4Zyr8QBzurnQuuZyrm/PMRERUFtLcLMnBRH62W2kvq6mhR7wxotVqcPHUKTYGBSElJgbymRoChTvaBYcgNHqC1rbVN1DU1Ncj8+28kxcZiWHu7Uw3Ta1e2Y/me5QCAp85/CpE+wolBdWwszlRWQpGQgGnTpzuXmAKAN98k5TRsGNAzGDc8nILBHh60pjObHue/2vp6FJ08iRmhoTYTUxzR0dHYuHEjAOH7qdRaw3Ofd+lKWBgQHw+VSoWsH36Ar0LhlGJK37baXeZu3SJNX1TFx6OgrMzmYgqgnqkrE6/kNWA0MBCYOlVnwCQKPj40Ay42FoB1YspD5oGnLngKW+ZvMTDZcAbcXNwwJ2EORgSNwIyoGfR9TJ3aO+KEZVnkZGeLIqbale2O0zM1GK6uVBYZHg7ZmHG4atjdmB16GzSadocQUwBwpPwIVBoVmrqaIJNa8lAZQkyc6wnkQBgYUCQn09yHkBCdc5lASCTURwVQhsTR19/6BhRTr7wSMq68gOdgW0ckKIh+tVyVhKVJSK5nKvGKKxAcFTWg65+jsvrgalS2VSLWLxbLzlsm2HbVajWOnjiBrrg4TLnwQucTU+XlullTa9YYDKVKStL1g1u06ZYWnOnqwogRIxDY2UmN9jZm7ty5Bv1URQL4XWtZLR7+5WGsObzGYDgqX5ShoUitrYWrqyvGeXlB4mTOPccrjmPu1rk4UHJAuI1KJMCYMShQKGwupjJqM8z+PTKMjfqDe4wHFAoFDu3fj6jWVoyLj+e9MNayhqUhnnJhn/G2wEPmgcdnPo7P5n0GF0nP/bXne2FZFnknT6Jp717MnDhR8MzUhhMbcP9P92PDiQ2CblcM2tWdYJOSAG9vKBRAZ2cnsg79jpjwcLuLKQC4efTN+Hze5/jfef+z63EMYZwhQWUBRt38YmKowd7SldMghIXRZlUqEVyRBMSom19CAj01m5qoPtlJSUighEF7u2W/AwMDiuhocqICKO1o5tBSe1DQWIA3jrwBAFh7xVq4ubgJst0BDSicaQjb449TBnbmTGCR4ZBKqdTQrM+cBnyuZ2r85ZfDf8IE+se8PLt8L0L3U6VWpuJ4xXHszt2N6vZqs97L9UxJEhKQfNllkAziiOiobE7fjEZFIw6XHhZ0uwWFhTox5eVFs94Em6BrnIrWCvz3p//i9p23o6Gzwez3a7XUEifmac1lpoYplUgOCACTlsb7e3nz7zfxyoFX0K5sF+8ARaKvGOSc6ji4Mr+GY8cwPiEB3vn5gp4vLMvCReICCSPBlIgpgm1XDLggz0O/PISqtiqMGtUGTcsejGY6kNzeDsbCgdFCMyZ0jKDVIUMIx5CgMhMhrdH5wjC9FQsoKzM6ssfuDGiN7uFBYfrJkwXP3tkSThsCVNllzlBxo25+fn66gb+5uQ6felz6+1IoNUrMjpuNecnzBNnmgGKqoYGayAeYT+VQHDwIbN1KF+m77w4aci8tBY4d47dw7GdAER1NNtksSw2VNv5euH4qf39/nDhxAk888YRV25sybArWXbkOz856FsN9h/N+n4EBxZQpkCQmGt5XnCRT9cblb+CBKQ/gsRmPCbbNfj1TxcW0OE5PF1VU1XTUwN3FHb6uvubPowNVXuTkCNej2hf9Mr+RV14Jxs+P7rdpaSaDWRWtFdietR07c3Yiqy5LnAMUkTf/fhPP//U8Wrr6//71e6bGLFoEj9BQitqmpQkw0Z5gGAZLZyzFrpt3YcowxxZU+Q35yKnPQXpNOhSdCvz992EkjhiO2JHJYFQqGoxsp76Lo+VH0drtGIJuiIFhWNZJnkB2prW1Fb6+vvjmm28wadKkgcWUQkFPhoAA3bANgUhNpR6eYcN0xmiOgDPOmbKUtDRKtoWF6ZJMgzGoNbpGA5w8SedMeLiu2d7B+O3cb5jz1RxIGSnS/5uOUcGjrN7moNboGg3Z13Z0UDP52LGOOTtAo6FAwZkzwL33Ah9+OOjLc3IoEi+X09sGukwGdPPTaOgEDAnR+U7bmB9++AFz55JD186dO3HdddfZbN8m3fyamymDN3YsDdL+F2HUgEKjATIy6IYllZJBg4+PKPtvUjRBpVVZZOHc1UWBBpal60KvYtZqjPZMaTQUlGhspPvKyJGDPqtPVZ3CycqTuHfSvcIdmA2obq/G3K1zoWW12HD1BkwdNrX3Z0YNKDQaatjm+sDj4nQT7v8llLWU4WzFWUhKJbqeKa2Wbt51Pe56EREUXbVRz2Z1ezVu+OYGuLu4Y+N1GzHMZ5go++HWuC0tLfAR6T7xT2coQ2UmSUlJg2emamtp1VRcLHi0lNttZSU9hBwBs8VUR4cIg0dsR1ISJQuSkky/1uScKalUN4RSInHI6LpKo8Kjvz0KAHho6kPiiylgwPlUDscnn5CY8vMDXn7Z5Mu5ZIqROca9DGqNLpVS07QdnSGvvfZaLFtG/XN33nmnWf1ULMvi64yvLSqdMimmWJZs1Ds7SXQ6YGazorUCP+f/DKFjmAO6+UmlZFTh56cT4yKVLfm7+1s8D8fNjZKvgLAl7QMaUHDfC9cYm5U1aOZhYvhEpxNTABDmFYZP536Keyfda1pMAfS9pKTo7i+FhRY3DVe2VeKl/S+hSdEkxEexGX4SP0hKJfD0jAPLjkBDA6P7XvQXYDa8x7R0tSDCOwKxfrGI8I6wyT6HsIwhQWUmsVzt3UAMG0aWXp2dgqXNOfz9KZExcqRuwLc9MVtMVVRQRqZnfowz4u5O5ZemglO8h/b6+pLbUmKiQ2Zh1h9fj5z6HAR7BGPlRSut3h7vob195lM5XD9VUxPw9NP09xde0K0IB6GvTuxb4sRrzpT+OaJWU/DGxqxevRrTp083u5/q+9zv8frfr+P/dv6fWQYGvOZMMQwtkt3cKON79qxDldFqtBo8u+9ZPLfvOXx+5nPBtmvSGl0qpXlMnKhKTxdEVKk0KqzYuwLpNelWbwswHPQrxDrVpJufREIP0oieBWpREZW79VDYVPiPKLEaEzrGQAyatEZnGMq+cM8jlWXW3GsOr8H3ud9j1cFV1hy+TShsKkRlW6WBNXpwcCJqaxm068d+hg+na8nFhUpobXTvTQ5KxtYbtuLVy161uynGEIMzJKiExsWFHP8AUbJUyckUWLP3dWVRmZ+vL30ftbWOt0C2AJY17pLPW0xxiDaIxTpq2mvw/P7nAQCrLl0FPzc/q7bHW0xxhIVRBIGLIjtS1mHlSur1SkkB/vtf3m/z9NRlN4uLSVgBPMWUPlotGQ5wdYQ2RCaTYdu2bb39VI8//jiv9yUEJCDCOwLXjbiuX3P8QJg1tNfVlcr9ZDKqjXag4X0Mw+D84efD180XVyZcKcg2ec+Z0hdVarV1VqU9bDm7BXsL92LZ78sM7N8txceH/mi11g/65W2NzjB0McbE6M4bAN3qbvzv9//hxu03OmXfVGVbJZq7mvv9u1lzpoYNo+9k5EiLFht3T7gbo4JH4cGpD5r9XluiZbV4dt+zmL9tPj785cPeMj+Fgj5zP4+xwECybY2M1K3zbICLxAWBHoE2298QljEkqMSAy1IpFEBNjWi7sddaweKeKS8vXUTw3DmHLHHjC8tStVdGhqGoMltM6dPVRRttMN8pSwye/vNptHa3YlL4JNw5/k6rtmW2mOJISNDVydkhG2OUjAxgQ48F8DvvmD1kKjRUN3snOxsoKjJTTAEUYQ8Kor/n5oru5NYX/flU77zzDnbu3GnyPaNDRmPrDVt5zSgCzBRTHB4etBDk0oAD1VbaGAkjwV0T7sLum3cj3Nv6wUtmD+3lRFVoKGXyrIzIzR85H1clXoXHz3tcMMdPbn1aWWn5s82iOVMxMb3zmACgrr4EDBhIGInTualpWS2e+fMZ3Lj9RqRWpvb+u0VDe/39Dd0zCwp4D2IcEzoGG6/biGi/aHM/gk1p7W6FK1yhaFNgZtLMXmt0znTKaKzTw0PnXgzQyVpeLvh95qe8n/B7we+ClwgPIR5DgkoMpFJdM2dJiSgP9Koq4OhRm6+jrDegiI3V+Y87y6RiIzAMJdwAID+fAr9WiSmAQrNcY72dy5VOVp7EZ6c/AwCsu3IdpBLLbaktFlOArn49KckxGqRZFnjkESqfmj8fuPRSizaTmEhrOJmsEpmZZoopjpgYnfNfRobNGyv59lN1qnRDvT3lnpAwph87FokpDm9vEg0Sid0nois1SgPraiFmGJktpjikUso46K8SNRqLjsHb1RsvXvwiZsfPtuj9xggKoopNLy/LktHWDO3tpa0Nkfk12DLyGay/Yh18XJ2rOb9R0YgOVQe61d29YtAiMdWXigqyGDYR8OtW6/qjnaE8TaqS4kb3G/H2hW9j+rjpYBgGarXu/ONVPJKXRwHi9HSLSyT70qhoxJrDa/Dk70/ir+K/BNnmEOIzJKjEIiKCbLwUClEi621tdNHbci6sIG5+MpnOA75P3bqzERNDN9zubuD48XrrxFTfDdpxEDI3j4MFi9vG3obzos6zeFtWiSkODw9dZtPe7NwJ/PknlZe98YbFm5FIgJCQctTUnLJMTAGk6keMoBWoSkV9QxYukC2F66dqaWkx2k+1r2gf5m2bh7/L/ua9TavEFIe/P4kH/XSgHXjt0Gt4+JeHUd9ppDbYAiwWU8ZoaCB7PZ5ZBwAobRHPIIZhyOVv3DjzTRoFEVMA3XtZFq6NLYiv6LT59WQtQR5B2Hz9ZnxwzQcI9QoVRkwBdB35++ucI43UZabXpOOardfg5/yfrfwUtoHrmYqJicFFEy/qPWe47JRcTrFfkwQG6jLiqakwbLyyDE+ZJ7KezcKp+09hcvBkq7dnCv2g1xCWMySoxEIqpQUyF0UWmOhoWpS1tNimQkxQa/SICFoEqtUkqpwUiYR62hobG7F/fz5iYydYLqYAnesfQCLcTqV/X6V/hSPlR+Ap88Rrl71m8XYEEVP9N0olbvbop1IogKVL6e+PP64LDFhAeXk50tJ0mSm12qx1rQ6ulEsuJwfNrCyblrjJZDKD+VT6/VQsy+LrzK/RpGjCiYoTvLYniJjiCA4mUWUje+O+VLVV4Zdzv+BYxTFBhIigYgqgMiWlkhzLeJx8JytPYsE3C7Dm8Jp+A2OFwpJbhFBialPaJuxtPUPXE7dATktzuqCfTCrDqOBRwokpgH4xY8fqelrz8/uV7W89uxVNiiacrDwpwKcQl0MFh/DS9y9hWNSw3jI/jkHL/YwRHEx9Ve7uVCVw+rTOYt0M5HJ573G4urjizVVvAgBCQ0LN3pY5sCyLh395GI/v4dcLO8TADAkqMYmIIEElxEKyD66uhu6mYq6hBJ8zxTkJubgY6fp0Lrq7a1Bbm4aEhAR0dAyzvq/N11fXTGCHgb9t3W14Yi8NbX1m1jMW27SKIqYA6ompqrJPb8wbb1AJb1QUsGKFxZvpa0ChUFBgMz3dwokCrq66Erf2dpuLzeHDhxvtp2IYBuuuXIdHpj2C+6fcb3I7goqpvrAsLQAFdl4djHDvcHw1/ys8ft7jmBg+0aptCS6mADpnfH11Q25NiKoz1WegZbVQa9W8yjatgW/LpFBiKq8hD+uPr8eKvStwVllOaTIXF3JEPHPGsQxxjLC/eL+BJb+gYoqDYSiCyNmHl5eT8UtPFu+lS17Co9MfxSPTHrF+XyLS1NKEp35+Cj83/oy/FX/3O2fMKvfj8PQkUcVl8TIzKVg8yDOKYRiMGTOm9/9zM/3uvJP6lf/v//4PAJ3jP/30kxkHYx7nGs8hozYDp6tOi7aPfwtDg315IsjQM5YV1J5PraY+KrWaEhthYYJtuhdRh/aq1aKITVvB9UyNHj0elZWRUCopc2hF4oLQaslevrOTSi1GjhTkePmwYu8KvHb4NcT7xyPz/ky4upjvzy+amALoO0lNpYcWlwG2BaWldJEpFMC2bcDChRZtxpibn1ZLc4zb22l9O368hbeJxkbK/NppsPby5cvxxhtvwNfXF6dPnzY9YkIPUcUUQKvznBz6YseMocHrToIoYopDrdZZqbu4kJAYZPF9vOI4RoeMhodMvECYSgUcOULXxZQptFY1hmBlfqBet89Of4bKtkq8ePGL9I8dHbp5Q25uNAPOEeaV9KG1uxULvlmARkUjXrjoBVyVeJXwYqovtbV0PbEs3bC4hmIHp62tDQcPHUSRrAh/Nf6FT+d9arRPTqulR4yZfkP0fRQWUr+ZTEY1rD3nDMMwePLJJ7FqFVnJSyQSsCyLHTt2YP78+WBZtve+dyzzGKaOmoqGhgYE9ZgPaTQa4e+LPZQ0l+C7fd9h6fylQ4N9rWAoQ2ULWlpoxSTk1ELQ84/r0y8qEr7cW1QxBfwjxNT48eMRExOJpCRah1jSCtMPiUQ38Le93WZZqvyGfLx19C0AwFtXvOV4YgqgjKa+77ityiIff5zE1KxZwE03WbSJgazRJRLy3ZBK6VZhcRVsQIChmLJx/8eqVat6+6mumHsFunmm20QXUwAFJrhBrhkZoo5t2Fu4F+WtwtzrRRVTgK6Uy8dHl6kaZE7V1GFTRRVTAK1DOb07UJZKSDEFAHKpHEsmL8ELF72g+0cu6+DuTn+3U6DCFB4yD9w8+maMCBqBy+IuE19MAXQtjRsHNjkZR9uyncKJjuuZio2Jxf2X3o+vb/x6QNMRicQCMQXQMzs+noKgo0b1E+CrV6/uNe9p6pmZccMNN0CtVkPDajD9uekAgGkp0wAAgYGBvWXUs2bNsuCATMOyLJqKmxAniRNl+/8mhgSVLejspIdUaangi+PISAqedXdTgFooRBdT+jQ2UtbBwcsqOIy5+QUF0bN3oGiq2fj4UCR90iSbCc+n/nwKSo0Sl8VdhmuSrjH7/aKLKY7QUBpNAFDpH1f0LhbHjgFff01/X7fOovSRqTlT7u669rnSUuPzzcyitpbS1x0dVm6IPzKZDJ9s+gQyLxnyM/Kx4J4FJt9jEzEF6Aw8AgMp/Hz2rCADbvtyrvEcntv3HG7ZcQuKm4ut2pboYoqDy0xxc6r0Rn00dzXjjb/fQLvS+kZ7c+B8RGpq+ldNCSmmWrtbDcRAv21xmalRo+w//HEAXCQuuGvCXdh43UYU5heKL6Y4fH3xW/sZPPjzg1j2+zKw7e0W9Q7ZAk5MRQ2P6u2ZErVslTPx4GhowLGfyawjrqdk0tfXFy+8QAJ+6tSpcJG4YOtjW+EfTu97++23AQCvvUZ9zIcPH8bZs2cFO8SGzgZUt1UjLS0N9fX1mD59umDb/rcyJKhsQVgYRdZVKlotCQhnjDBpknDeFzYVU1yKvK2N5lw4OINZo+s/by3qhelLQIBNG+oLm8gycmrEVLMXKTYTUxzx8br+j4wMcbMxXMrIzY2i+WbCd2hvcLCuLzInxwoXdJYlq3CVyorGLMsYmTASS15aAgD48csfsWvXrgFfazMxxcGlAv386HxJTxfEkUsfL7kXUoJTMCFsAqJ9LZ/BYzMxxcGZm8THU39rD68deg3bMrbhqT+eEv8Y9OCSrUqlYRJaSDGl1qqx5MclWPb7MjQqBolGyuWG85jy86mP086otWqDnqm83DzbiakeWrtbIZfKMTpwJJjMTOodKi62yb75wompJs8mrM5Zjcy6zAFf291NxUR5eQIeQHc3kJ2Nqe7uSElMBIDesr/nnnsOAHD69GmkpqYixi8G1cWUln3sscfQ0RMQy87OBgCMteD5MxBvHX0Lc76Yg5/yfoKvry/+85//CLbtfytDgsoWMIxhI6fACxx//0HL3s3CpmIK0E2rBygc2ZMGd0T4zpkqKhJ4RhjLkhmCwCWjfVk0ehEAYHfebrNKOGwupgBaHI8aRYsdtVpc0XDVVbSfri6zRT9fMcURF6ervrLYOZ9hSDh4eND3YkM7dQkjwbpH1+HRxx4FANx9990oN3Le2lxM9R6ghIQD9yUL/N2EeYXhw2s/xKpLV1m82Le5mOKQSslwhTtulsXCmKsR4xfDy1RESBiGgvyAruxP6DK/szVnUdhUiLSaNP5vqq8ny/DcXLon25ENJzbg4V8eRkVrhW3K/IxwU8pN+ObGb3D7+P9Q9hcgQZWV5RCW85yYGj58OH6p/wU59Tn4veD3AV/f0UGJa0ErguXyXqOpM++/DwB4+umn0dqTIc/PzwcATJ48ueflcnzxxRcAgNCei2DEiBGYPZtmvj3yiPWmHxXVFdj22jakPZyGZxY+g6lTp+K3336zerv/doYEla0ICqKIulYr6o1YoaAKQ0uwuZji8PHRlXDl58N6qzzhMWdob88oE+TkCPRMqa8nlVZQIHhEXZ/FExfDU+aJjNoM7Cncw+s9dhFTHK6utDiePFlct0gfH9oHABw6xPtt5oopQKcTQ0Io82wxMhll0+RyOmcyM0V1RWxUNBpYab/26muYNGkSGhsbcdttt0GjdyHYTUxxSKW6vqHERMPsg4WoNDprbQkjsXiAr93EVF96bmDjKzT45qL3MCJohM0PgTNZamgAWluFFVMAMCF8ArbcsAUvX/wyAtx5mpQEBZHzEED35Lw827uNAmjpasH2rO04Un4Ef5750y5iiiPSJxIyFzllNpOTSQ3X1pI7og2z433hxFR0dDRGjhyJt+e8jYUpC3HfpPsGfA+3dhL0ccIwdM6MHQsXNzdsffZZAICfnx8AoMKlAgET6PxbvHgxAPRmizo6OvBzT6kgJ3jWrVuHWjPdSlUqFTZs2ABvb28wDIPI8EgU/K4LDs6ZMwcHDhyw/DMOAWBIUNkWLktVVWW56hmE2lrgxAkKnpmL3cQUR2wsLf46OwUvi7QWc8QUQM8VV1cSt4KM2QoOpgc5y1LPkEiC08/ND3dPuBsAsPbIWpOvt6uY4vD2NuweFkuMz5xJ/z18mNfLLRFTHG5uJKosaoruu6ExY0ilNTZSsEIEFCoF7v3hXjz666NoUlCGWS6XY+vWrfD09MT+/fvx6quvAnAAMcXh4kK9MT0OWtbQ2t2KG7ffiK/Sv7JqPpOjiCmWZdHa3cr9H0iycwz6qmyFpydd3kplN/bsOSaomOKI84/DjKgZ5r0pNpaEOECltXrW4bbC180Xm6/fjOuGXYfgzmCbiim1Vo1XD71q3HglPJz68WQyKuNPTbVwwJ516IsprmcqwD0Ay2cuHzTYwS3LBOuF1icgAJg0CTfPmweArrOdH36IPYV7EHNfDADg008/RUXP0OS6nn60q6++GlqtFgzD4PffKbvGZa4GY8+ePZgwYQIYhoFcLscDDzyA9p6AbEREBLZs2QKtVguWZfHLL79g3LhxQn/ifx1DgsqW+PrqFsYi1GBzzqUtLeb1htpdTAG0wOFq90tLRRGclmCumALoo3DZhfJygcoHkpN1w1sLCwXYoHEemf4IJIwEvxX8hozajAFf5xBiqi/V1cDx4+JERc8/n/7LI0NljZgyRnW1FZeDtzepM4AWf1a7XfQnuz4blW2VyGvIM1jsJiYmYv369QCAlStX4sCBA44hpjj0F+ZdXVT+Z4Exzu7c3ShvLceO7B3oVlt27jmKmAKAXTm7sGD7jTjgXqsb5MrNf7Mx0dEKaDQHER/vJ5iY+rPoT1S38xhyNRjDhulmv9XX23wAMMuy6KzqxGTpZJtnpr5K/wrfZn2LJT8ugUZrREj6+VFTt6cnXU+CRBX501dMVbZV8n6vKBkqfXpMTppO0KDz+UuW4PlZz2PVpavw2x+UgdIZXQXhf//7HwDg4osvBgDMnj0bgT2llR9//LHBpvPz83HTTTeBYRgwDIPLL78cZ86c6f356tWr8dehv/DctueQk5eDRYsWCRqcGGJIUNmeuDiy1OSyVQLi6qqbCVtYyC9Y7xBiiiMkhKI4Wq1NB3AOhCViiiMgQOdUlZsrQABTJtPZwJWXC2vpqEecfxyuH3E9AODto28bfY1DiimWpe+lq4sixkJnqs47j/6bkzOoKBFaTJWX0y6tCoIHBVFEffhwXZ+DgEwMn4jN8zfjtcteg5+bn8HP/vOf/+Dmm2+GRqPBwoULodVqHUNM9SU7m2rL0tPNXhjfOuZWPHXBU3jp4pfgLjNnGijhSGKKZVl8n/s9GhWNKGstp/5Wrhw7N5f6h2yEQqHA6dOHEBYWJJiYKm4uxjN/PoObtt+E0hYrKyGCgnQDgNvaRC3H5qhur7ZrzxQAXBx7MaZETMH9U+6HVDJAuSznjhgRYdM5in3F1OGyw7j+6+vx1pG3ePUFc6aoYlaQQyKB3+TJWLl8OQBg+vTpuCLhClx+yeWI6lnAvfPOOwCA119/HQBw4MABZGRQgLOsrAwAcO+99+KJJ57oFVBJSUnYvn17727uvvtulJaWgmVZaLVazJkzB98XfY8fmn7A0weeFvED/ntxsKfavwAPD+q2FSkyMHw4JTIUCtPPPocSUxxJSdRQb6uBrQNgjZjiiI/Xlf4JklQKCNAtbnJyRIuILp2xFACwOX0zatoNS30cUkwBOiMGFxfqKrbY0WEAgoJ0C4O//zb6EqHFFEAxBi4xaZXz1LBhFMQR6b4T5x+HcWH9S0YYhsG6desQFhaG6upqfPzxx44ZFR0xQtdvdvasWeMtGIbB/JHzkRKSYvZuHUlMAfRZPrr2Izw+83EsGrOIzpfERF2kLj+fhpaKjDEDCiFud1JGihFBIzA+bDyifKKs36CvLwmHkSMNbbJFgGVZvHTgJVy76VrsSt9lt56p4b7DseHqDbgy4crBX+jiQs9z/drl6mrRyiONlfmlVqZCy2ohlUhN3ndUKt0jVVRB1cOch+cDAFJTU3EqNRU4exb5PX1Mjz76KDp70mVZWVkAgDFjxuCLL75AjN7aaM2aNb1/nzVrFv7++2+wLAuWZfHJJ58gKioKLMv2WqPPGDsDAR4BuDbpWvE/4L+QIUFlTzQawUvbpFJd8qukZOA1t0OKKYAiW0L5v1uIEGIKoOfJiBH0OxGsJjs+nu72arVotekzImdg2rBp6NZ04/2T7/f+u8OKKQ53d8PyNqFLlLg+KiNlf2KIKYDW+NwInJoagT6SVkvZBivuPVwfRUXr4FEbpVKJzMxMvPzyy5BKpdi2bRu+/PJLi/crGu7uut6P1laTVvxaVoud2Tuh1lo+V9DRxBSHXCrHTSk3Gc7piY+nxnqJRKQGEx19xZRSySA1lap5rU08R/lG4ZO5n+CVS14RTth7ehpOdFcoRHGr7VB2oK6hDl3dXZh3/jybiyn9c53LiphFVRUFAk+fFrws25iYAqiEfcPVG7B44mKT21Aqafnh5iaIT82gnKmm+V3XridhM2nyZKChAa7Fxfj0lVcAAGFhYTh8+DDuueee3vfdeeedBoYUH3zwAdRqss/fv38/Zsww7AfUF1MzZ87EjWNvxK6bd+GyuMvE/YD/UoYElb1obaWBoSK4b4WGUuuEWm28fNlhxVRfVCqdZ66NEEpMcfj7A9OnU+WDIHCzdCZNooyVCDAM05ul2nBiAxQqheOLKY6AAGoaByiaLqToHKCPSiwxxeHnZ/iRrK4sKiigxU16usXDtD8//Tm+zfoW9/54r4HDnT76BhR33nknVq5cCQB44IEHcE7oDKIQeHqS+5+LCzU+DlI6+mXal3jl4Ct46OeHzBoxwOFoYqq4uRi/nvt18M8SGwtMmSLafQcwnpmSy2n9rVIZzqQyB/3PJWEk8HYVSYxws9/S0wU18mBZFmWFZbgz6E58deNXGDVslGDb5kO7sh0LvlmATWmbLA8ieHjossCpqYIN1h5ITHFMHTYVHjLTKSdPT3pWT50qyGENSqOiET6uPpg+djrmzp0LALjvo49QWlPT67bX1taG888/H4f1jJD+97//oaWlpTcLdd9990E6gPrrK6bc3akc2Uvu5ZhVAv8AhgSVvfDwoId1R4fgDkoMQwFFqZRKzvRxGjGlVlNIMidHwIFOgyO0mOIQ3ITO01P0KPH8kfMx3Hc46jrrsOnMJucQUxzDh1OJnlZLmQahSiM5QXXyJEWhIb6Y4oiKovYnrZbW+WZUpPUnJoYyMpwRgwUlONcmX4txoePw2PTHIJP2tyM05ub31FNPYdasWWhvb8eiRYugtFDMiYq3NzkjSqXUpzhAre4wn2HwdvXGlYlXmr04cTQxpdFq8Ny+5/DMn89gU9qmwV/srtcj1tlJETuBAoIDzZliGJ2FuiXxNS2rxWO/PYZtGduscmHkhVRK5xBn5CFAeSTLsr09U+effz4mDJ8gwIGaB2e8sitnl+Xfoa8vMHEi4OVFgZwzZ6xe+wwkpnbn7kanyrIMvC3aOy+JvQQ7F+7Eg1Mf7B1+/tHWrYi++WZs1JsHNf+aa3Du3LleAfX666/Dx8fH5Pb1xdR5552HdanrBjWZGkIYhgSVvXBxMZxnIXATvZ8fRVu4XQBOJKYA+n44S+PcXNFnU4klpvRpaiKNKKifRGsrDVEUOMvpInHBI9NogODqv1aDBescYgqgFdiIERS0CA+nc0kI4uIo/atSASdP2kxMAbqP5OZGWs6qc4ibUcVZG2dnm33+hHmF4eO5HxstHRnIGl0qlWLz5s3w9/fHyZMn8WzPPBaHw9eXHNx8fEicG+GyuMvw3U3fmd2L4GhiiuOC4RfAz80PVyaa6Ivh0GjI2a6kRJBZTKaG9nKCqrHR/KTqX8V/4VDpIaw/vh61HSKbHUkk1E/FPUMKCqif08Lvh2VZfH34a3x28jNMnzHdLj1TAA19X3nhSjwz6xnIpVasGzizCi7glZ1NQQsLvp+BxNSBkgN4cf+LWLRjEZQaBwza9ODr5gsfVx8wDIO/e/pyp0yZgj9++QXsyZNg9+3DjmXLEO9untFN38zUoapD2J61Hff/dD/aleIbp/ybGRJU9mTYMEohdXeL4p5kmBlxIjHFERenm00lYiO0LcQUQOUqXV2kD63KMHBoNJRhqK0VZVj0HWPvgIfUAyWdJWgJanEOMcXh4kJlkTExwhkxMExvlqrlp59sJqY4ZDKq9hw71rBlwyLc3Q1tn3mU4LEsa+CMZtBj04OpOVNRUVH45JNPAFBD9d69e638ICLh70/R9D73SX2baH93f7OyU44qpqQSKe6ZdA92L9qNEE+eJ5ZUSiWADKPrjbFQNJgSUwDFRnx8aBfmJjUuirkIT57/JJbOWIowrzCLjtEsGIZGgMTH0/8vL7dofiDLskjLTMPbqW/jj44/sLt4twgHyw+GYXBt8rWYGD7R+o1JpXQj44IVpaVml/8NVubn5uKGCO8IzI6bbZb4S02lpFlP8YEofJP5DbLrsvv9+4wZM8CyLI4fP45L5szROSRKpbp5ODwwVuY3IWwCrk26FneMvwNeci8hP84QfWBYMwvAi4uLcfDgQRQXF6OzsxPBwcGYMGECZsyYATc3N7GO0+60trbC19cXLS0tvFKuvKmupoeRiwullERYtDY2avH77xkIDW3EzJlOIqY4amroYSSRUP2+mdEa05u3jZgCSP9w1WJhYToXdKuoraUMFcMA48ebdfMdDK5nan3+enxd+jUui7sMe27fI8i27YJWS7X71l67b78NPPYYaiZPBvPTTzYTU6JRV0c1hAAtAge5Br7O+BpvH3sbS6cvxY0pN/b7uTlDe++77z589NFHCAsLQ3p6OoLtbERjkqoq/F18EG+Xf4uXLn4JyUHJZr3dEcWURquBhJFY109RW6vLcAYHU3bGjJopPmKKo7KSkmGenvQocAr0v5/ISN2sRRNwZX7FxcVoDW/F9wXf45O5n/DqBRKS7LpsJAUmDWyPbi01NZRyjOLvuGiqZwoAutXdYMHCzYXfmlSjAQ4epL/PnCnAUHUjFDQWYNGORWDBYvuN2xHjF2P6TV1dlNXjUKkGPLiBeqb0fz7Y9SXaGvdfBO8735YtWzB9+nTExcVh+fLl2LVrFw4ePIhPPvkEc+bMQWhoKO6//36UiBAp/0cTGkpPCLWaIjUCo1JpsWNHDqqrtYiJcTIxBdD34+9PC+L8fEE3bUsxBVCwiRNR1dWWN1gbEBJC3xFXsy+AJa2+AcUrc1+BhJFgb+FepNekC3DAdkClImeptDTdoBELqelZEAXn5yOEK0m1E11dArSIBQdTJF0mo96PAWBZFmeqz0ClURntoTBHTAHAW2+9hZEjR6K6uhp33nmnRcYONqOzE2xuLjac+RiFNdn4Me9Hs97uiGIKAN4/+T4e+fUR1HWYMQW+LyEhlG2QSHTinGcmxhwxxe1KIqFLmI/XzLnGc+L3TJkiJIRSyt7ehvX3g9C3Z2rh+IXYPH+zzcVUVVsV7vnhHty+83Y0KsSZe4jQUEMx1dU1aM80HzEFAK4urrzFFKAzPJXJxBFTAODj6oPL4y/HxTEX8xNTgKGYamkBjh41avVqTEz1vacOGVGIDy9BNXHiRKxduxa33XYbiouLUV1djdTUVBw6dAhZWVlobW3F999/3zu0UX+42BAmYBidz7nATdparRanT5+En18zUlJSUFkpF6bUzNYkJdGTtLFRsIG/thZTHL6+uudHbq5AfgmJiXTj7eqyWnT2dfOLD4zHglELAABvHX1LgIO1Ay4upGY1GqscHcrLy3FSrQbr7g5JSwtlBu1IVhZV61nQAmVIVBSF/AfJbjIMg1WXrsLaK9bippSbDH5mrpgCAA8PD2zduhVyuRw//fQT1q9fb8UHEBkPDzCJiVif+Ahu8b0AD4bP5f1WRxVTTYombMvYhr/L/kZmXaZ1GwsK0pWPNjQYt5btg7liCqDLODaWdmXKk6e2oxaLdy/GXd/fJZ4Y4Iu/P5Uf66/UB7jxc2KqsLjQoGfKWHmt2JS2lEIulcNT5gl/NxucuxoNRYjOnDHqPjKYmGJZFqsPrsah0v4jLfjACSoxvZ6CPYPx8iUvY9WlqyzbADfDKzfXoK98oMzUKwdfwZt/v4nWbmHcFIcwDa+r9KWXXsLJkyfx4IMPYriRJl1XV1dcdNFF+OCDD5CdnW0weGwIHgQG0oJGkBowQr9nau7cyfDzk0GlAoqLBduF7XB3p+heRIQglr32ElMcsbHUE6BUCpR0c3HRDZ2trrZYdA5kjb50Olmof5X+FaraBJ7tZAu4ob+urvTktECBcAYUU847Dww360PPztYeJCfr4gxWFwboZ67b243OqGIYBrOiZxksZCwRUxzjxo3D66+/DgBYvnw50tMdOAM6bBj8ksdhadRNcC2p4NXT6ahiCqD+r03Xb8J/J/8XF8VcZP0GAwIoE+PrazITY4mY4oiKIv1m6jQrbCoE2/M/Pzc/3tu3CVVVNDKlz6wq/cxUeUA5HvzjQeTU59jpIIFpkdPw7U3f4oWLX7BddsPdne7NOTkGZh6mMlMHSw9iR/YOLN+z3CLjEe52Z4uBvi4SC9s6kpIM+xZPnQLb2WlUTJW2lGJXzi5szdiKshbxB3EPQfB6+l199dW8NxgUFIQpTlPg7EAIGBrpa0Dh6irv7Y+tqLC66sk+REfTDcXKHjN7iymAFgMjRtB9kWUFMjDUX8hYYAE32JypaZHTcF7UeVBpVdhwYoMAB2sHZDKzo+gc/dz8BphHZWs8PemSAChQIsgs0dZWKo9MTwe6u7GnYA82nNhgYMbAYY2Y4njooYdw9dVXo7u7G4sWLUKnwIPOraW8tRynqk7R/4mKIpMTgNzbyssHfJ8jiymOOP843D3xbuE26OdHzfT69+g+JcjWiClzmB45Hdtv3I6XLn7JLtmdAeFcNdRqusZ6yrf0xdSU6VPw/bnvkVOfg7yGPLseboB7ACK8hRqiaAKplKaYc9dYeTmQloa2xkaTZX6TIybj/8b9H+4cfyd/YxU9uDWRGBmqL858gbePvm29wx7D0DO+x6GVbWvDuW3b0JKf369narjvcLx31Xu4d9K9SAlJsfITDMEXs00p9FEoFFD1SV3/U5vZbNaw19VFWQYLs3yDufllZFCJkJ8f+Rc4Nd3d/YdsmcARxJQ+7e00kkMwWJb6GMw0SuAztHdH1g4s2L4Age6BKH2s1Ob1/ILBmZwAlNULDR305Uat0X//HbjiCrpGzRBmYpGbS+symQyYPNnsy8IQrt+ssxMtchZzc1eiQ9WJJ89/EjeMuqH3ZUKIKY66ujqMHTsW1dXVWLJkCd5//30rPoBwaLQaLP5hMTJqM/DsrGcxN7mn1K+oSJcSnDChX6mkI4upzNpM+Lv722aRXF5OEbxx4wA3N8HEFPeIlEgGdLV3bLRaysD0VBKww4cjp6sLJaWlmDlzJry9vdGoaMSunF24c/ydNu992ZaxDeNCx2Fk8Eib7teAnlrmzrY2pOXmIuCCC5A0aZJo38WxY2QWNX48rY+EoknRhGu2XoNudTdevexVo2MmLIHt6kLe99+jvbwco0ePhuvUqVRaagVDphTWY/ZTsLOzEw8++CBCQkLg5eUFf39/gz9DWIFGQ96dxcW0MDYTU9boCQn0EGpuFngWki1RKslc4NQps3phHE1MAQKLKYAiWCKIKQC4bsR1iPWLRYOiAV+mfSnE0dqH0FDdKqywcND04IBzpqZPpwupuFiUcQfmkphI55JKJcBIMm5GlasrfJUMno64GTMjz8N1I67rfYmQYgoAgoODsWkTDZT94IMPsHPnTqu2JxQqrQqxfrHwlHli6rCpuh/ExlKkODraqcRUu7IdT+x9Ajd/ezNOV50Wd2caDQkqhQI4fRqK+nrBMlOdnXTplZf3P9c3nNiA/AZhzYsERyKhTEx0NI0iOHgQ9QcPYuaMGb09UwHuAbhrwl02F1M59TlYe2Qt/rPrPwYjEmxOUBDaEhORlpeHiIAAJEmMu1EKMWeKZanSUC4XvuTP390fr89+HXOT5+LS2EsF2SbLskjLyUFZQABSrroKrpGRvWJKqVGiS90lyH6GMB+zn4TLly/Hn3/+iQ0bNsDV1RWffPIJXnjhBURERPQ+FIewEKmUZlMBVFJiRi0YnzlTbm5k6DVypCCtSPZBKqUQZXc3LYh54IhiSh9uISyoyaNaTZmYQawE+YopgGbVPDr9UQDA2qNr7e+eZQ2xsVS+NWHCgM0Ygw7t9fGhqDtg9z4qgD5CSgpdGlqtAEYnbm4kqlxccLnrKLwduRjSnrIpocUUx+zZs7F8+XIAwOLFi1E+SDmdrXBzccNzFz6H7Tdu7z+/KDaW/nCwrEOLKQDoVHUi3Csc/u7+Ztu+m41USteXpye6W1tx9v/ZO++4qur/j7/OZe+9QRQQFBQk3DM1rRw5snJUavXVliMry6xfZWq2zbJtWpmZuTXLWe6NCAiCbGTvzZ3n98ebw73sO86590I8e/BI4NxzPvdyxuf1eb/fr/f27fCwsuIlzc/RkbIKJZKm7YtOpZ/Cjzd+xMIDC1FeX67TMfQB27Mn7piYIL+gAFF+fpDdjsHtVnoU6RN3G3dMDJyI+wLuQw8Hw4X/qqqqcD4mBo7jx6PHoEFgwlqmrbEsixVHV+CNk2+guLZY62MxDN3uhg9v0XaOF4b7Dcf/jfk/XsRxEwOKkSNh2bcvifMGtt/4CQ9vn4ozmWd0PlY3mqPx0/DQoUP46quvMGvWLJiammLUqFF48803sX79evz6669CjPG/hZ8f5ezU17ebo6+KJk17fXw6zHIybkxMqBofoMYk5eXtbm7sYgpQmhemp1MaIC/k5FB62+3bJD6boYmY4lg4YCEcLByQXJKMI3eO8DRQA8AwtLLQRt+8dsUUx4gR9H8D11FxWFlRukpkpO6TgtTSVNRbmAD9+wMiEZjSUiA5WTAxxbF27VpERUWhtLQUjz/+OOQ8tADQhuaLBW42HfTIksuR/eefSD93zmjFFECT5W+nfovvpnynn5RdCwvUhYTgRmoqnG1t0V+hANPB/VodRCLycQIoM4wjwiMCY3uOxePhjxufEUUzGt38amsROns2rO3t8WnuHjy5fz5+j//dYONytnLG2nFrsWbsGoONoYkBRb9+YEJDm+YxFxQAUinulN7BlZwr+DfjX6OLyihYBcSyls9dXWizz1SDUFOwCvx9cw8KSrJQl3GHp+LsbjRB4ydiaWkpejWszNnb26O0IXds5MiROHOmWxXrjImJ0kY9M7NDK3VNxFRzZLJW59rGj6MjOf4BVEDSxsSrM4gpgASuqyulHiQk8HQf9POj3iet5IFpI6YAwM7CDouiFgEAPr34KQ+DNBJKSqhjKNQUU4DSmMIIIlQcdnZNA27anEdldWV44cgLeHLfk8gV1dDqJ8NAWl2NC+fOCSamAMDc3By//fYbbGxscPr0aWzYsIH3Y6jDhnMb8P7Z91EnrVNr+4wrV3A3Lg5DnJ3hVKfeawyFiBHBw1Y/K2p1dXU4d/kyLIcMQeDAgWAUCiAurqkK0hKuBZzqrlysXfDhhA8b71HGiqoBxYgRI2DXowfkUfdAbk854P3c++mYt6s5zRcRtHai05EO+0xxfSKuX0ewhTd+mfELVo1cBV977Z/vQnzUexP34pE/HuEtUtRR014AEIHBL2M24k3/JzBREUBlEUZ+P+pqaPxUDAgIQEaD93ZoaCh27doFgCJXjnxW8/2XcXentCK5vN2id13EVFkZcOUKBTA6JQEBtGpVV9fqZ9RZxBRHSAhFFmpr1c5kbB8uT9/EhBoCNlyz2oopjiWDl8CEMcE/Gf8IX4ehD8Ri6k2Vm4u8y5fVE1OAMkIVE6Nel1E9wrJ0ScTEaC6q8qrzGu2mna2cAVdXSPr2xfmKCljb2gompjh69+7d2JPq7bffxsWLFwU7VmuklaVh3+192Ht7r1qW1ampqbhVWorQ+++n+pfkZIqcGxHv/PsOdsTt0GuabhMDinvuARMeTk2kFQpeJnnOznSLq6sDisuV0QmGYYzL1a8ZLcRUQ82UiZU11o9fj99n/Y4wlz5kDFNQoLdxrT65Gp9d/Ay1UsO5bKrVtNfKir7q64HoaIQonDA1ZKpOx42LAy5e5K+unGVZ7E3ci9yqXORXt+ynpc3+OhJTAACGgUVQH0wf9zwYc3NKd7l2jbfend10jMZ3noULF+LmzZsAgFWrVjXWUr300kuNOfDd6AjDkIMEQPZdreSB6SKmAMp2kslIWGnhf2F4TE2VqX937zZJpu9sYgogLwCuDdnduzzd3K2slJ9RZiZkRUU6iSkA8HPwa2zs2mkb/apiYQEEBaGoqAgZp05hSFBQx2IKAHx9yeVPoaDu9UaEREJz+spKKsXUhFC3UOx8eCc+nvAxLE0tKc0vIaGpmBK478L8+fMxe/ZsyOVyzJ07FxUVFYIeT5UApwBsnrQZLw56EZFeke1uq1oz5XDPPcqO3UYkqq7kXMHh5MPYeGkjMsoz9HLMVt38uAWefv2Un5MOmJiQqKqRVeLR3Q9j85XNvBgUCElbYkqVXk69lBdvYqJeGkcmFCXgeNpx/Bb/G3KrDHPeqiWmAMDGBrnBXii1bSgYTUigm5wOYaaaGlpX07EjSyMMw2DLQ1uwYtgKPNz34Y5f0A7qiCmWZRFboNLDz9mZ7F4dHGhRPiGB7kndKYCCo7Ggeumll7B06VIAwNixY3H79m389ttviI6OxrJly3gf4H8We3vAy4smbc0uIl3FFEC75J5rKSltZs0ZN87OgKcnWfM03IA7o5jicHZWepLcvs2DuQBA0U4vL8jlctzavRsimUxrMcWxYhg1+v0t/jfkVBre5U5X7ioUiC8rQ58+feBWVKS+YOCiVEaU9geQRuTEeU6O5guUTlZO8Hf0b1kzxTDUifraNUFtQhmGwTfffIOePXsiIyMDzz33HHTo7qExg30GY/6A+e1u06oBRWAgCW2AJjB5hm+CPch7EN4Y9QZeGPQCApwCBD9eu9boDKPM1QNoRU8Hl0xXVyCm8gSK6wtwMv2kXs8RTWlLTCUWJWLjpY1N00t9fZVOpBkZJKwEnAyHuoVi04ObsGzIMgQ5Bwl2nLZQW0yBPse1Fzbg4fj/wwXLhpXg7Gzq6aXFA1MqVZY98OnwZ2Vmhbn958JEZKL1PtSNTJ1MP4mnDjyF10+8rrwGLCyoqJbrTVlS0kkneZ0LtQWVQqHARx99hBEjRmDw4MF44403UF9fjx49emDmzJmI4FyvuuGPkBASVCbKi5IPMcXRowdFqsRinh3m9ElQEK3G2Nl1ajHFERCg1Id81bfJevZEXFoaGIbB4IgIncQUQE0UR/uPhkwhw+arm/kZpIHgaqbCpk2DU69e9NCJi1Pv4WwkDX5bw8VFOSdLSqJU0raok9bhhT9fUDawRTtuflIprQbfutXUYo1nHBwcsGPHDpiYmOC3337DL78Ia9V/JecKKsXqvZ923fyCgpSiKjWVp1UR7WEYBjP7zuxQIPKBRn2mWJauszt3SHxqIYbc3YE3ZszEpikf451734GFqS4N2ISjLTGlYBVYe3Yttsdub3ofZRh6EISE0L8LCqhViIDn0nC/4ZgXPk+w/beFJmIKACrFlagQV0AsF8M/dLjS3rSsTKvO5tx90dJS9wgVy7JIKErQbScq+1IrzQ9ATmUOTEQmCHAKaLmA0asXWRiGhlIaTDeConZj3/fffx9vvvkmxo8fDysrKxw9ehRPPvkkvvvuO6HHaBQYvOkZy5KYun6dFzHFUVxMDX9FImDQoBbBsE5Do5iKiIAvDyklhqS2luqp+EhB4GqmTCQSDBo6FKY8LcMduH0A03+fDidLJ2S/lA0bcwFazAtMCwMKqVRZyOvoSNbo7T3g4+PJCc/Ghtwm+coZ4QmWpXlYeTkN8Z57mqzNNPLV1a/w440f4WnriX2P7QMrZ9t28+OMBcrK6AEdGcl/8xYV1q5di7feegu2tra4ceMGgoL4X0HPqsjCnD1z4GDhgB+n/djSIl0Fta3R09MphNJKWpc+SC5JRqBToE4r5JqgVdPe3NxGMxi4u1NYVcD6PEPQUZrfuaxz+OrqV/hy0pdUs9icsjJavJDJ6OEcHs7bQ7qgugD2FvawMjPMQ19TMcWhYBW4XXwboW4NduE1NRSB0aLLM3cKurjQrVwXTqWfwsrjK/FA0AN4b+x7WtukayKmOLIqsuBm7abe37KggOqqucakDRh8jtsFUPvutW3bNnzxxRc4duwYDhw4gP379+Pnn3826jB7l6GyEopr1xB75AivYgqgZ76zM82T7hh5P8S2KCgowNUrVxDl7g7foqJOnytsbc2vmGIYBoNGj24qpnS8bqcET0GQcxDK6svw082fdByp/mnVzc/MjGo8TExIgXREaCgJr5oaUi5GBsPQEM3NaYhtXd8LBizA1OCpePfed9sXUwA9gPv1UzpIxsYKahW6atUqjBkzBtXV1ZgzZw4kHbieakO9rB7uNu7o6dgT7jZt189p1GeqV6+mYkqDJuS6kl+dj/8d+h8WHliI0jrhO7hrJaYAcmptcJFEYSEtUKiZlvRP+j9NrLLbi8AaCnVqpkb2GIlfZ/7aupgCqGFrZCSFUOTy9hd4NEDBKrDq5Co88scjiC+M52WfmqCtmALIqbJRTAF0r1YVUxIJOSSr8YzjsrvVud13RGZ5JkSMCL72vnoVUwDQw6GHemJKKlXWeHa7APKO2oIqMzMTU6ZMafz+/vvvB8uyyDWS4tuujKK6GknXroFNT8fwgQN5E1Mc3EKFuXnn0yKNkal+/eAllVIaUna2oYfFG3l5NGfVVP+06+ZXUEB1MDqkkJiITLB8yHIAZE4hV3Se/Ox2rdFtbChU27t3x5MXkYi6QQJGmfYH0DXNmT22ZcJqbWaNt+99G/1d+6vXZ8rERLlSXl9PJ6hAgsHExAS//PILnJyccO3aNbz11lu8HyPYJRg7Zu7A2nFr23SI06lpb2UlGZfk6+74pQ6Z5ZlgwMBUZCp4PyatxRSHuzuFBkxMqC5PjXMpOi8arx5/FXP3zEVVfS0uXybHWgG0tta0J6ZYlm3So6jDz4wLL4eHt9k7T1MKawpRVFuESnFlu4sIQqCNmMooz8DBpIMdO1VyvUfS09Wqq+L8vvgQVAsjF2LnrJ2YH6Fdeq2mYurP5D9RVKOho5iZGaVJmpl1uwAKgNqCSiKRNPkDMwwDc3NziDtlI6POg0KhwLXsbNSIROjXty/MBRCw1tbAkCGdL+OiSc1Ur140CQaokFdgJzJ9IJFQGUZpqWY1bu2KKYVC+fkkJek0vgUDFsDJ0gkppSk4nHxYp33pC7X6TKlOWli2/XPJiOuoOBwdgaFDyb+FQ66Q42rO1cbvNW7aa2ZGKZGc1z8PDVvbws/PD1u2bAEAfPjhhzhx4gQv+1XNrrAys2ozSqCTmALIRlUmI6cZPVhhD/Edgl2P7GpXIPKBzmKKw9mZziVTU0pFSmi/DkWmkMHNxg1RXlGws7RuLA3hob0VL3QUmTqaehSP/PEILmZr0BLA3BywtVV+X1xMURgt8bT1xK5Zu7DpwU16FVTaiCmWZfHh+Q+x5vQafHnly/Y3ZhiKfHJ1Vdevt+qSzOHgQF+qH60uBDgFaJVCqamYSilNwbun38XMXTNRWKOhIGrNBfBOdyNgPtDobvvWW29hxYoVjV8SiQTr1q1r8rNu+KPRgKKuDuEzZ8LMzKxNG3VdsTDOet42adWAwt2dkqFZliYvnTwd1dy8qUZUxwOgwz5TnH2xSEQP5bt3tR6fjbkNFkctBgB8esn4G/2q3bSXQyajVc4bN9pOjVB1+jPi8021HlkqBb659h2e+/M5fHH5C83FFIelJa2ah4U1dW8TgBkzZmDxYjrXnnjiCRTp2OuhVlqLpw4+hX8z/m13O53FFEAGA1wj8sREvYgqdxt3eNt5C7Z/3sQUh709pbbZ2JBbYjsM9hmMPx75A8uHLgdA7a0A42j/0ZGYYlkWO+J2ILcqV3sDA7FYGYW5fVvribCVmRUGeA7QbgxaoG2aHwsWw3yHwdHSETP7zuz4Be7uyjTJ+nq6f7cRhQkIUJ522rIvcR+Ka7VX89qk+TFg0M+9H4b5DtNOEHMugFyqZE6OUaatdzbUNqW49957O7wAGIbBqVOneBmYsaHvgr1W3fwSEujG4OhIF4MA1NdTY9levYzXoKJdNz+xGLh6lSbDgYG89DwxNNyf3cqKFpZaMxYANGzam5NDq1IiET1RtCyez63KRc+NPSFVSHH1f1cx0HugVvsRGo3FFEATlZgYUrLW1pR20/wzraujlT6plMKJAcJbU+tCZSXVuB8q3IRjBT/j3dHvwibfRnMx1RYymWDmHLW1tRg0aBASEhIwefJkHDp0SOuJ/PfXv8e317+Fp60n9jy6p1WHOF7EFAfL0vWWm0ur6H36AB4euu1TBQWrwPtn38cjYY8g2CWYt/22Bu9iShWWbZpqK5e3fcNrHA9w+TK9bPhww5mZqVMzBZCY/z3+dzwe/jjMTLQcbG4unU8sS/MBLo2rA27m30SNtAbD/YZrd1wt0aVmikMsE2vm5CiV0sOTc//r0YMmNjyer7EFsXjqwFOwMbfBwdkH4WDpoNHrta2Z4l5bJ6uDtZmOpkClpUBiIiptbeEwYEC3KYUOqC2o/uvoU1C1aY1eX0/J4goF3UC5pTkeiYsjwxxnZ1p8NjbUskbPy6N0NpEIiIriJ0HagMhkpBHFYmpNxvXqbbqNBmKKIz6eolSWlvQ5aTkTeXLfk/gl9hfM7T8Xv878Vat9CIlWYopDIqG0EbGYCsTDw1s+kIcPBy5eBH76CXjySf4GLgCVlbRgy7IA45wItrSAPzFVX0+rnC4uysbkPBMbG4vBgwdDLBZj06ZNWLJkiVb7kcgl+ObaNxjhNwJR3lEtfs+rmOJg2ab9qUJC6ILmgd/jf8dHFz6CvYU9/pz7p2DObYKKqeaUldGEOCwMEjtrvHb8NcztPxeDfAa12PTqVcrMDQ2lAIW+UVdM8UppKa2OyOW02ta/f7uOmxK5BHP2zEFmeSbeGv0WpvWZJvwYwY+Y0hqWpRXi7Gz6bKKiGgW6REL/7ECvt0tScRLWn1uPQKdA/N+Y/9NwaNqLKd4Ri1FZXw8HR8duQaUDnahi5r9Bu32mLC2VIVqBCgkDA0mHlJYaX62i2n2mvLxIEQKCpEfqG1NToG9f+ndeXsu/i1ZiCqBVci4tQocUyZeGvgSAJnVZFcbV0EwnMQVQ3iXn/FdWprR5VoWrozp7VrfBCgzLsrCzYxEQAEilUsT8Uw/Alh8xBZBaq6ujNNKMDN331wrh4eH48MMPAQCvvvoqYmNjtdqPuYk5lg5Zqj8xBZAQDw5Wpv/xWPQzIXACxvUahyWDl3QNMQXQedTgJLnzwnc4m3UWq0+tbuLux8Hd7rVoRaQz6ogpsUyMKzlX+D2ws7Myta2ujlzb2mm4rWAVGOk3Eu427hjXaxy/Y2kDXcRUVkUWlv61FGlladoPgGFoUhMWpuxZ1UBqKt2ydegtjRDXEGydthWvDn9Vo9dpK6YOJR3CH7f+4N8EysKC18jdfxWNI1RyuRzbtm3DyZMnUVhYCEWz/N3ulD/tUatpr0JByeLu7oJdABkZ9GVuDgwebBztdTRu2iuR0MO4k0enVElPpzrkXr2UDdC1FlMc1dX0IPb11SkdYvzP43Eq/RSmBE/BwdkH9bsK2QY6iylVSkoooseylNanatP711/ApEm0ShwTQ5NmI+SPW3/get51vDrkVRz4Iw1isS0GDAjBoEEi/q5xLpUUECzllmVZTJ06FX/++SciIiJw5coVtZxPWZbF5ZzLGOIzpM3zUzAx1ZzcXHIJ4dEFiHuUC3Ht6V1MAfSsS0wEiopQK6/HF/VnMLDveIwPGN9iU84g0NKSDFj0hbqRKa7X29z+c7FiGM+15hIJRaoqKuh666AGrU5ap5feU7pGplYcXYEzmWcw2n80Pr2f5xrdnBxcizFFtY0H+vUTvAS0CdqKqUpxJabvnI5KcSXWjF2DSb0n8Tqu7j5UuqPx3XzZsmVYtmwZ5HI5+vXrh4iIiCZf3WiHWmIKoAewh4egqwk9elB0XCKhaLmh0VhMAaQGu5CYAoCePamMhzcxBZC90ZAhJBJ0OKc2PbAJ5ibmOJx82Cj6UvEqpgBKY+MmKllZTe14H3gAmDCBVokXLFC7l44+qRJX4YsrX+B46nFs/nsz+vQBIiJCIBaLWg26aY2PDwlzgJaAufQ2HmEYBlu2bIGLiwtu3ryJNWvWqPW6vYl78eKRF/H2v2+32j9Rb2IKoCiVqpjSMrRSUlvS+G+GYbqOmAKUBjo+PrA2scRrNhMxHr1ajaQ7ONCaUHCw/rxhNEnzkylkEDEiRHpG8j8Qc3NySQwKUquGszOIKQB4ZfgrGNdrXKP5CG/U1ECRnIKaO7lARgZsrTS/X2+9sRV7E/d2bOPeDF3S/GzMbPDswGcR5RWF+wPv13TI3egBjSNUrq6u+PnnnzFpEr/q2NgRUr2rLaaaI5dT/hdPefiqlJfTYjtAk3hDLVhoJaaaU1VFoR3O3a4LIJPJcPHiJYhEOoip1lAo6LzSop7qg3Mf4PWTr8Pewh7xz8XDz8EwhiC8iylVMjNpObO5WM/OptTAykrggw+AlSv5PS4PxOTE4KuTX+HpkKcxaNAgVFeLGuup+vRpaquuM2lpSq9/gQpbdu/ejUceeQQikQgXLlzAkCFD2t3+19hfsenKJiwbsgxz+89t8ju9iqnmcJ9Vz570pSbJJclYsH8B5vSbgxcGvyCIRbrBxFQDeVV58LLzos+HW93z8KAT1oBRcG1qprIqstDDoUeH2/GCQkEpDT164FjWPzidcRorhq2Ai7WL4Ic2aM2UmtQk5+DqkSKYmrAYOVRG6YDt1J+pcrfyLmbtmgWZQoavJn+FwT6D1XodXzVTLMsK8pl2R6h0R+M7sLm5OYIEKjj+L6K1mFIoqClbUpIgfrGOjsoJliY9kPiEFzGlUCjNF4wh3MYDMpkMp09fQXKyLYKDeRRTYjGp6Lg4rax4Xxn+Cob6DkWluBJPH3y61SiA0AgqpgAKD7YW+fTzAzZupH+/9Ral4BgREokE5XfK8UyfZzBo0CCIRCLY29OCtpubACkvqlbhmZmChA1mzZqFuXPnQqFQYP78+ahry9q+gXnh87Dz4Z2Y3W92k58bVEwBypzqjAyaBKvJvxn/QiKXIKsiCwy6UGSqgfSydDy862G8+++7kPh4GlxEcWhrQKE3MQVQdDg7G9JrV/DZuY9xNPUoDiQdEPywfIipWmmtACNrSrWDDxAUBBs7EbmZXL+u9jzK09YTy4Ysw/2B92OQd0uDlNbQVUypPkuNUaB2Q2gsqF5++WV8/vnnBpksdTW0FlOAMvUPAFJSBEkzCgykBdPQUN533SG8iCmAPieupuXuXUEbkOoDLs2vqMgCPXv2x507ph01g1cfhYKatFZWaiU+TUQm2DZtGyxNLXE87Ti+vf4tTwNTD8HFVHMqKsgyTyaj7xcsAKZMoVzZ+fPB3x9Ge6LzopFTntNmnyk/P1qcFaROsndvEqAREYJNhL/44gt4eXkhKSkJq1ev7nD7Xk69mkRyDC6mAMqx5hYpMzNpMqwGi6IW4ZOJn2DVqFW8T7IMLaYA4GruVUgVUpTUlcBMZEYrfJGR5I7YynhYljIn09OFS/vTREwV1hTivdPvobSubaMIwfD2BiwtYSaW4hOPJzDRexQeD39c0EPyIaayKrLw4K8P4ttr32qcTqcJ1dUAbG1hO6gvrR7L5bQIpsZzz1Rkijn952Dd+HVqNybWRUxdy72GZw4+o33fsm70hlopfzNnNm2mdurUKTg7OyMsLIyazaqwd+9efkdoJPAdDtVJTCl3Qjbq9fX0UDbyHjjqwpuYUiU5mQrBLS2pmZMxOG1oiGrN1MCBQ3Dzpilqa6m8p39/ng5SUkIRKkDrVK2NlzbipaMvwcbMBrHPxSLASfjzUu9iimXp2qura2qnnpdHCqWsDFizhqJVBqKgugCzd89GRUUF3gx/E9PHTO/Qza+0VOmYJggC9Kk6cuQIJk+eDIZh8M8//2DMmDGNv4stiMUP0T/gzdFvtmiAaRRiShVVQw8fWkE3RETGGMQUx838m/C09YSHbSs9u1iWxKePD2BlBZal/toymTBp6ppGplYeX4lT6acwwm8EPn/wc34How5SKYmE8nKl2x1fz9Nm8JXm9/XVr7HlxhYM8x2GTQ9uEuzci42le11ICODlyZIKz8qia66Nz0jBKsBAszpFPtL8njrwFGILYvFo2KNYOUK4VPLulD/dUStC5eDg0ORrxowZGDNmDFxdXVv8rpuO4UVMARR94VY2s7MpuiAQLKufTvSCiCmAHiacRbiaK8DGRHMDCgsL08aSsJISCr7xgouL0sEuKUmrc2rpkKUY7T8aNdIaPHXgKUFXGgEDiCmAJiicDa+qnbqXF/Dll/TvNWuUhYgGoLq+GqI6ETytPTF55OQOxVRiIk00cnMFGlB+PnVg5bmVwaRJk/DMM8+AZVksXLgQVVVVAGgys/7selzIvoAfon9o8hqjE1MACQOuyZyquFJBLBPj++vft2odzgfGJKYAIMIzonUxBVA07+5dcimtrATDULAB4N8+XZs0vwUDFiDULRRLhyzldzBqIFfIUS6voYUeLy96gKek0D1di3Tu9uCzZurZgc9iw30b8OqIVwU999zcKOBpbw+6lwcEkApXnXM0+5x2xu/E4sOLkVyinosPXzVTG+7bgJl9Z2JR1CKtXt+N/uhu7KsmfKl33sSUKgJ342VZmhdWVAjbOFEwMcXBpWgBFNJxEb5Alw/ac/Pj5l0MQ88DXvpJsiw1aC0vb9EMUV3SytIQ/nU4aqQ12Hj/RiwbuoyHgbXEIGJKldbs1FkWmDUL2LuXrserV8mJS49IJBJcuHABZpZm6N2vN9xtO/5ssrNprUEkonPJ1pbHAaneRMzNgQED1C4CV4fKykqEh4cjMzMTixYtwrffUrppRnkGNl/ZjLfvfRu25vSGjFJMqVJQQH3hQkJaOIV8evFT7IjbgXu87sF3U7/j9bDGIKZYlsW2mG2Y2XcmHCw7WKCVSOjZV1VF96fQUOTUu+DOHRJWAwbwNyZtm/YKZSDQETviduCH6B/w6vBX8WDvB0l4pqbS5xQVRS0eeKAzGFBohVxOQt3TE/Dzg1QuxdTfpqK4thirR63GjL4z2n25UTXtVZPuCJXuGLXl2ddff43w8HDY29vD3t4ew4YNw19//dX4e5Zl8c4778Db2xtWVla49957catZMbhYLMaSJUvg6uoKGxsbPPTQQ7jL23K+ZggipgCKUnHdeAUIIzEMZTUBtMjFlYzwieBiCiBvXa4vjrF1LW6DjqzRfXzIUIBlKcLASykdw5BytrCgCFVKisa7CHAKwEcTPgIArDq5Su1VPU0wuJgCSJRzUeK0NLr+GAb4+mv6w8TGUqRKj9SL6xtrpoYNHqaWmAJocdbFhRZmb93i+TpnGFrEsLWlifDNmxQt5gl7e3ts27YNAPDdd9/h77//BgD0dOyJjyZ+1HnEFEC1sUOGtGq7ONxvONxt3DE/Yj6vhzQGMQUA+27vw+arm/HEvicglXdQg8gJc2dnuvHFx8NJnA+AykD5uBdqI6ZUo4eGEqX/pP+DSnEl6mQNRi2+vnT9hYYapZiKyY/p+O+tT/LzyawiNRW4dQtmLINt07dhfsR8TOszrd2X8iWmqiX8RvK7ER61BNUDDzyACxcudLhdVVUVPvjgA2zevFnngQGAr68vNmzYgGvXruHatWsYN24cpk2b1iiaPvzwQ3z66af48ssvcfXqVXh6emLChAmNKR8AsHz5cuzbtw87d+7EuXPnUF1djSlTpkCu514xgokpgG6Qfn4UOhJoZUHI3lR6EVMcvXqRW1SfPsIehwfU7TMVEkLax8SEx0mwuTk9fO3smjax1YBnBz6L+wLuQ52sDgv2L+C1u7tRiCkOHx9lqkhiIqW0ubsD33xDP9uwgaJUeqC4qhgPbHkA0dXRiIqK6jDNTxWGocvCwoJKw3jtTwVQ7VREBN1IxGISVRIJb7u/9957sWwZRUIXPLUAZc3yvjqFmOKwtFT+WyKhRQ2FAkN9h2LfY/swoscI3g5lLGIKAEJcQhDgFIDZ/WbDzESN1g0mJtSuwMMDYFlYZ92GZVkeFAoKhuqCNmIqtiAWU3ZMweHkw7odXAcYhsE3U77BmrFrML3PdOUvnJ2bFkiWl1OEXQv4FFPZFdl4/s/nMWfPHJTV8Zyr2Qq1taSV2s3N8vEhMyuGoUWy6Gh4MnZYMmRJu+0J+BJT+dX5mLxjMj69+KlxCc1u2kWtp+0jjzyCRx99FH379sVrr72GP/74A+fPn8f169dx4sQJbNq0CY8++ii8vLxw48YNPPTQQ7wMburUqZg0aRKCg4MRHByMdevWwdbWFpcuXQLLsti4cSNWr16NmTNnol+/fvjpp59QW1uLHTt2AAAqKiqwZcsWfPLJJ7jvvvsQGRmJ7du3Iy4uDidOnOBljOogqJji4Oz4LCz43zeamuXl5tIKIB/oVUwB9EY8PY3Cfrc9NGnaa2ZGC7WRkTz/+R0cKPdLy4cCwzDY8tAW2FvY4+Ldi/jk4ie8DMuoxBRHYCCFd1xdlalsDz8MzJlDS+Xz5/MakWkNiUSCDw5/gAJxAU5XnYaM1Vxdm5lRaRjDUBCX93oqMzMSVZaWpNpu3uTVDXHdunVw9HFEQV4BHn3q0cafdyoxpQrLAnFxkGSlU2qpQgELU/4ucmMSUwAQ5h6G7TO3t7C2bxeRCOjbt3Hhx6nmLiAW61RHpW2a3874nSivL8f13OvaH5wHTEQmmNR7UtuT//p6Op/i4ijXVwP4TvPLq86DrbktPG094WjpqNO+1CEzk9a3OmwH4+0NdsAA5KOaVFh0dLuZLXym+R1LPYYaSQ1uF9+GqajzGWj9V1G7hkoikWD37t34/fffcfbsWZQ32E8zDIPQ0FDcf//9+N///ocQrqiWZ+RyOf744w/Mnz8fN27cgKWlJQIDAxEdHY3ISGX38WnTpsHR0RE//fQTTp06hfHjx6O0tLTJQzQiIgLTp0/Hu+++2+bxxGIxxGJx4/eVlZXw8/NDcXGxRvmlCoUCN27cQF1dHQYPHiyMmGr9wII0sb19GygoYGBjA0RFsTrpksLCQkRHRyM8PBzeXM8afSKXk7uPn59gQlQbZDIZrl692uDmN1CrPlOC/PnLyqgHk4bn8Lab27Doz0UwNzHHjf/dQG/n3loPITc3F7GxsYiKioKbm5vW+xEEhYKUiOpFUVoK0wEDwOTnQ/7CC1B89pkgh5ZIJLhy5QosLC0QbxqPIT5DEOqmfb+D7GwgLY2BiQkwZAirTZ/n9qmrAxMTA0gkYHl0H6uV1uLJzU/i4GsHARbYtWsXIiIikJycjCFDhsCRcy3oRFxOPI73T6/Bat/HMchvKEVkNKxpbI36+npcvHgRrq6u6Nevn0HFlIJV8NOYOCcHhVVWSCxwgYMDq1UdFcuySE5ORnZ2NoYOHQpbDYoJZQoZ/kj8A5MCJ3VcA8YzddI6nMk6g4kBEzv+W7IscOcOmLw8+tbDg1ZMO3hoVFdX49KlS/Dz80NwcDBv50yVuAp1sroWTpxCcP06g+pqICyM7bD/3sn0k3jn37cx33U8/mc/DgDABgVRBEsFlmURHx+PkpISDB06FJaqEWYtuZxzGS5WLghy1k/f1y+++AIvv/xydw2VDmhtSlFRUYG6ujq4uLi0sE7nk7i4OAwbNgz19fWwtbXFjh07MGnSJFy4cAEjRoxATk5Ok8n4okWLkJmZiaNHj2LHjh1YuHBhE2EEABMnTkSvXr0aC5db45133mlVcO3YsQPWPBZT8w0jlcI2Px+MXI7Knj15379MxuDOHSfIZAw8PWvh5tZ+M01jxi47G+bl5ZDa2qKyVy9DD4cXWBYoKLBGTY0ZAgIqeAvEWZaWwiYnhz6rnj01ivCxLIt30t7BzaqbeMDlATzr9yw/gzJmWBYWZWUQOzrCIzoaQ9euBQDceOEFZE2YYODBdQzLArm5tnBwqIetrQBFkwBM6uthXlmJOp6jjApWga+3fY3jB47D0dERX375pUaTYmNjc/ZmpFen4D5pMOYxIyG1sUGlvz8vosoYqJJVYXP2Zkx0mYhIu0idJ+lyOQOJRARLSzlMJWIoTE3BdpHPqj3+LPoTp0pPIco+CnO95qr1GsuSEtjk5QEsC5m1NSp79AAr4HzO0CgUQEKCC1gWCAkpg7l5+46Hv+f/jisVVzDRZSJmyAfAoqwM5YGBXe4zWrt2La5duwYA3YJKB7SOJerLJj0kJAQxMTEoLy/Hnj17MH/+fJw+fbrx981vvuq46qizzapVq7BixYrG77kI1cSJE9U62QwWmaqrA3PtGqBQgA0NJX9QnsnPp3Y7vXtr5wRm8MgUR20tmOvX6bNqZdVJ3/ARmRKLgWvXGMhkgK8vi8BAngZXUwPmxg1ALgfr56dxzzPbTFtM+HUCTlecxtYntsLFWjOHRaOOTLVGUhKY/Hyw7u7AlCmQMwxM3nsPA777Dv1nzgQ7ahQvh5FIJDh/6TxiamKwZOISmJp04vQQzqaYh9DqfePvw4ABA5CWlobDhw/j559/1nmfhuJe6b3YHrcdT/ScButEcgVi7e0pUqXFxM6YIlMA8OXVLyEpliDJMgmrHlwFExFP4qe+nu5ZpqZg+/dvWpfWCtpGpliWxcW7FzHMd5hBP8uKWxW4df0WFt+7GKN6aHB/KSsDk5BABbgWFmDDwlrYxQoRmTqUfAg9HHogwiNC532pS3U1YGfHwNQUGDGi41jCg+yDuJRzCZGekbA0tWzRR4+trkZ8ejpvkam0sjT42PnwmtbbHnK5vFM4EHYWOp1t+n333YfAwEC89tprgqb8NUcTS0m91Ey1R0YGfZmbA4MHC9LElmW1K0PSe81UR3C+4yIR2cna2BhkGJrUTHVEURG5tAE8u8MXFgIJDd3aNfTPZ1kW93x3D2LyY7B+3HqsGrVK7dcaZc1UR5SWUn0Cy1JtR69eVE/1++/0B7lyRedG3Jw1+r6CfThfcR4PBD2AtePW8vQGmlJbS18dpchojUJBNR1cfy8NRdWPN36EmcgM88LnQcSIkJqain379mHlypVgWRZ///037r//foEGr0eqqsg5UiqlxbKwMI1ebmw1UwClyf0U8xNG9hiJEFceSwZqauizEovpWdivX5umTbpYox9OPox3/n0Ho3qMwqf3f2rQz7S0rhTOVlp05q6tpeuvthbw9lYWTEMYa/TcqlzM2jULErkEW6dtRX8PvjrTt09+PpUt8GGrzxYUIPXQIeRbWCDq4YdhpWPmkkQuwaxds6BgFfjs/s/Q20X71Hh1KCkpgWvDDd3R0REZGRlwdHTsjlDpgFHbprcGy7IQi8Xo1asXPD09cfz48cbfSSQSnD59GsOHDwcAREVFwczMrMk2eXl5iI+Pb9yGbwwupgBhLfkaUL2nqussZ3RiCqCoFOcVnZDAe9NDdeBTTAE0z+KCbbdv03yCF9zdlbbzSUkaNWhlGAYvDX0JAK1Iq+tc1CnFFEBuWtykJCuL3B22bgUGDiRnralTdXJ24cSUtbU1RvcfDUtTSzwQ9ABPg29KdTVw/ToZGArWO7ymRuk6lpDQgQVXUzLKM/DNtW/w+eXPcfnu5UYDiqeffhpLl1JT1UWLFjVxfzV2KsWVOJd1ruUv7OxoJmhvr7TrVxNjFFMAYCoyxdP3PM2rmKqrAxIybRBvEaW06o+JAYqLW2yri5gCqHbJ3MQckV66pyvqilZiCqD5wj330P1d5bwSqs+UtZk1JvWehEHeg9DPvR8v+1QH7pHVUfDxTOYZSORtO5CyLIvEK1dQUVGBQe7usEpK0vlBe7fyLmQKGRSsAr72ws6Prl+/3iimnnjiCZSVlRn83O0KGHWE6o033sCDDz4IPz8/VFVVYefOndiwYQP+/vtvTJgwAR988AHef/99bN26Fb1798b69evx77//IikpqfGm+Nxzz+Hw4cPYtm0bnJ2d8corr6CkpATXr1+HiQZ51epEqIxCTHGUl9MDBKAbpQArDgoFBcLy8mie2J6vg1GKKQ6JBLh2jf7v40O5jHqCbzHFoVBQD+OqKjLrGzCAJ2NDlqVV37IySqGJilI77UgsE8N/oz8Kagrw68xfMbd/+3n+nVZMqZKZScYnAK2QSyTAoEEksCZNAg4e1LgWRlVMDRw4ECKRSPuVaTVQ7fNsa0u3EwH8biiq1+BkBzc3ioKqcdKyLIsDSQdwq/AWZvvMbuLmV1NTg/79+yM9PR0vvPACvvzySwEGzj+rT67G0dSjWBS1CIuiFnX8Arm83fPIGMVUTH4MIjyEGUt9PXDpEp0+I4fJYZKUoLQIDwpqNEHRVUxx5Fblwt3GXe+ObJXiSqw7sw7PD3oe/o7+vO67qrIS0fv2wT0yEn369xfk7yRTyPT6mcXE0H2sT59W27wBAOIK4rDwwEL4Ofhhx8wdsDJrmhLXxM2vd29YZWXR9WdmRm6Tztrfh+tl9ciqyEKwS3DHG2vJli1b8MwzzzT++6mnngLQ3diXD4w6QlVQUIAnnngCISEhGD9+PC5fvtwopgBg5cqVWL58OZ5//nkMHDgQOTk5OHbsWJOb4meffYbp06fj0UcfxYgRI2BtbY1Dhw5pJKbUwajEFEAxbe6OkZSk0YqvujAM3Zyk0vZ71hi1mAIoHYTrS1VUxKuNc3sIJaYAmvCGhlK2Z0WFck6vM1zTX0tLmrVo0CDZwtQCLwx6AQDw2aXP0N5aTpcQUwDg708pNABFXmxsgAMHyIr+yBFg5UqNdseJKUsrS0RERjT2mRJKTAH0J+/bl+YL1dWCBb1pIsJ5thcVUXhVjfsWwzCY3md6CzEFADY2Nvj+++8BAJs3b8bZs2cFGjx/KFhF4+R8hJ8a/aaKioDLl2n1pBWMUUxdunsJzxx8Bsv/Xg4Fy39WgKUl3dZZFqiqbehVxV2HKSlAbi5vYgoAvO28DWJvvfnKZpxMP4k3/3mz3fupplRVVSF63z70BNCnpgZMTQ1v+1ZF35+Zvz9lX7dX/l8lqYKbjRsGeAxoX0yNGAErf39aTba1pXlDbCw9bLX8W1iaWgoqpp544olGMXXt2rVGMdUNP2gVoSovL8fu3buRmpqKV199Fc7OzoiOjoaHhwd8DFzYLxTtqXejE1McUinVaohEyoaaPFNTQ+lACgVNujw8mv7e6MWUKnl5lP6nh7+fkGJKFW5eGhzc8m+jE9XVlLKmoalIUU0R/D7zg1guxrmF51ptUNplxBQHy1LkpayMLhI3N2DXLuCxx+j3P/wAPP10h7tRjUwlmifiZPpJrBu3Dn4OfgK/AaKkhMrCAJ5r85pTXExFgCxLi0IhIa1GqtLL0uHn4AdTkWmHfaaeeeYZbNmyBb1798bNmzc7RSF2YU1hxzbSLEvL7hUVFKHq358W0xowRjEFAPsS9+GjCx9hZt+ZeGX4K4Ic49Ytuv8FBKj0Js/OBgoLwUZE4PadO1qLKbFMjA3nNuCpyKf0dv21Rk5lDj48/yEWRi7EAM8BvOyTS/Pr6eaGEKkUTH09nVshIRrVzbbGLzd/QU5VDp4f9DzsLYw3ClIrrYVMIWsyxnb7TCkUjUIdAKWEqNmiIbM8EwU1BRjsM5i/N9AMlmVhb2+P6oacx+LiYrg0u4F3R6h0R2NBFRsbi/vuuw8ODg7IyMhAUlISAgIC8NZbbyEzM7NTuym1R1snm9GKKY7KSloVF9A2lstqMjOjbCbuI+hUYkqP6EtMcUgketGIavPMwWew5cYWPNz3Yex+dHeT33U5McUhl9Pqg+qD6t13gXfeoQvnxAlg9Og2X64qpsIiwjBj1wyU1pXizdFvYnqf6YIPnyMlBbh7l4bcUZqvThQVUUSPM4tpthhUVleGx3Y/Bg9bDywJXoKijKJ2m/aWl5cjLCwMubm5eO2117BhwwaBBq496rjPtopMRoK9vJw+r7AwwMXFaMUUR2Z5Jtxs3GBtJkwbkuxsIDWVjFT6qZTpsHI5bicnK8WUlZXGxk1fXf0KP974EX4Oftjz6B5+emgZAS1qpmQyKp4sLaUNOIMdLc6lSnElJu+YjDppHdaMXYNJvSfxPHrhULtpb2EhFZpq0LJm2V/LcD77PF4Y9AIWRi7kZ8AqcHNXDplM1mqGVreg0h2N7wIrVqzAggULcOfOnSYWkQ8++CDOnDnD6+CMHaMXUwBN4ATuwdGjhzLinZJCP+v0YqqoSLnaxCP6FlNAUzEllQqQ/SmV0oROTZOK5UOXAwD23d6H9DJlLmKXFVMAXYOqDymxGHjjDeDRR+nzmzmzzVy65jVTNhY2+GXGL3h24LOYFjJNT2+ACAhQXuvZ2QIeiKuhaiOynlmRCZlChpLyEuSm5bYrpgBysfr6668BAB9//DGuX78u2NC15VjqMbx+4nWU15dr9kJTUyA8XGmuEx+PusxMoxZTAODv6C+YmAKUl5uq9wvLsk3FVFkZ1c9qmNI2NXgqBvsMxtLBSw0ipupl9bzvs1UDCjMzinpyIb6sLApTq+tEpYK9hT023r8RD4U8hAeDHuR59B1TXk7Bb0kbXhN7E/fiem7L+4LaYgqgCJ6qmKqvpxXnNh66coUcPRx6wNLUEuMDxqv/ZtTk1q1bjWLqoYceAsuyvJe7dKNE4wiVg4MDoqOjERgYCDs7O9y8eRMBAQHIzMxESEgI6uv5v9CNgebqvVOIKVVYljxDTU0F6U1VVQVER9Nh3N0LkZZ2pfOKqbIyqsIXiagCn6emoIYQU6qUl9Nio4eHzo7dTUlOJvGpgUnFxF8m4njacawYugKf3P9J1xZTzeHsnK2tgcBAYOxYmtSFhgIXLzYRXq0ZUBia2lqgoIDmDXqdp9fXN+kldOXWFcQlxWHm2JntiilV5syZg507dyI8PBxXr141mvu2WCbG1N+morSuFEsGL8H8AfM13wnLArdvQ5yVhbi4OFhGRCDsvvuMRkxJ5BJ8eP5DPBX5FLzthO8/qFAAZ8/SxzJkCGBp2axmysaGrrvaWnouhoUBap5HgA4RRR0pqC7AvL3zMKffHCwYsICXvl1qufkVFlL+OMDrc1FfxMVR2nLv3i1bTqrauP847UeEe4QD0FBMNYdlyRmqspLS/0JD20wVqRRX8p4CuXPnTsyZMwcA8Pnnnzc6nrZFd4RKdzR+OltaWqKyFbvfpKSkztFwkwc6nZgCaAaUlESTXwFMF+zsyHG1oqIU16/f7LxiCqCHKrfam5jIi5W6ocUUQH92sZgWGbkMDl4ICCCThfp6Ze1LB3AW6j/c+AGJaYn/HTEF0Pkkk5Fwz84G9u0DvLwoxW327MbV3+Zi6lz2Odwuvm3gwZMO1DLrR3uqqmjym5oKAEhNTUVRRpFGYgoANm3aBFdXV8TGxuKjjz4SarQaY2Fqgc8f+BwPBj2IeeHztNsJw6DO3x/XcnPh6OiIsJ49jUZMAcAP0T9g/+39eOHIC4IYUTRHJKK1CYqotmJAIRIBkZHkUCCT0SJHfn67+xTLlNbYhvpsDyYdRHl9OS5kX+BlDGpbo7u7k5Dq21cjMSVTyFArFarfgvq0Z5luY2aDaSHTMMRnCPq7U08sncQUQDdIHx/KTigvp/tXWVmLzdLT03kXUy+++GKjmDp37lyHYqobftA4QrVo0SIUFRVh165dcHZ2RmxsLExMTDB9+nSMHj0aGzduFGiohoVT72VlZUhOTu5cYgqgSRy3GuflRQWmPJOXV4CLF69j8ODwziumOHi0UjcGMcVx5w71Mua9BqamhkKUcjlZEnfQI0fBKhD2VRhuF9/G075PY/3U9f8NMcWh2vjXz48etKNHUwOdpUsh+eijJmIqvyYfc/bMgVgmxndTv2tcQTU0CgVpQh8fQfqHK8nPhyIxAe9l/oKRbmNQD7cO0/zaYseOHZg3bx4sLS0RHx+PwMBAAQasf5rUTHl7g3Fz07PqbZ+siiysPbMWc/vPxb0979XLMWl204Gbn0JBkRfOsZSzgmtGUU0R5u2dh7n95+KJ8Cd4iQxpA8uyOJZ6DEHOQQh01u3c1bnPVFUVhX38/ds813bE7cDPN3/GyhErMa7XOJ3Gqy1SKXD+PP175Mi271UKVgERI9JdTKlSW0sLjVxaac+eqPV2w1fXvsY9uAfjR1CqX0JCAvr27av9cUDnRs+ePZGVlQWA+q56tuUP34zuCJXuaByh+vjjj1FUVAR3d3fU1dVhzJgxCAoKgp2dHdatWyfEGI2K6OjoziemAFqN40RUXh65QvFIQUEBrl+/2jXEFNDUSj0nR9nDREOMSUwBlGFmZ0cPGA37p7aPjY3y87p7lyKi7SBiRFgYQgW4R8uPwtlVONtvo8TZWfl5ZWeTmx1n6LNpEzJWrmyS5mdjZoNB3oMQ5haGMLcww427GbdvkyFNUpLAB/L0xE5RAvbmn8Zr199DlJerVmIKoLS/++67D/X19Xj++ed5tZvWlMKaQhTVFOm8nxYGFO7uygmuQkEpuQZuOdnDoQe+nfKt3sQUoYY1ukhEURf/hj5OmZnK1DYVDiQdQGldKf7J+MegkT+GYXB/0P2GF1NyOdXOZmSQYGilroplWfyV8heKa4tRUc/vnEMTuOhUR/4jvIspgEL6UVG0kA0AGRn46ch67Iz7DT/k/dDYBig0NBQPPPBAq/cjhmHAMAwuXrzY5mFqa2shEokaxZREIlFbTHXDDxoLKnt7e5w7dw579uzBhg0b8OKLL+LIkSM4ffo0bGxshBijUVFXV9f5xBSHg4Pyok5K4iWVDWjdgKKsjNx85XJeDmEYnJ0bG0Di9u22q1nbwNjEFCBgfyqAavO4SUlSUpt9cQAyoAiqDYKLpQvuVt/F7oTdbW7bZVEtZktJAcaMgWzNGgBA7y+/xMCSksaaKQdLB3w04SNsenCTwVbGW8PPT9k2SgAPlyaEuo9EX4u+WBkwD56VdTTx1QKGYfD111/DwsICx44dw++//87zSNWDZVm8+++7eGz3Y7iY3fZEqSM6dPO7fZtSvdXs68U3qule+hQiqn2mhg0bAVvbdqzRGYaiUpxFfysr9E9HPo13730Xb4560yBGFNF50ZApNDeDaA2dxRRAqWy9etFDpbiYMhRqm6b2MQyDLQ9twRuj3sC0Pvo10FGlrXS/g0kHsfbMWpTWUQ4872KKg1vQ7tsX8ZmZOPdzNPq79cf/7vkfjh07hriGfhRHjx6FSCTCv//+2+TlL7xA/RuHDx+Ofv36QdZMvKakpDTOv0ePHg2WZWGmRi1zN/yi0V1BJpPB1NQU8fHxGDduHF555RWsXLkS9913n1DjMzqGDBnSOcUUR2AgRV9qa3mx6WpNTCkUNJ8uLxewCai+ULU1Ky5W+2XGKKY4rKyUwcqsrFbTurWnZ09lL682HtKcAcXIoSOxdCjldn9y8RODRgoMRo8ejRXS0tRUnBsxAsWTJoGRyyGaPRt1N5WuUwzDwMbcuBat7OyaakKB+n8iNTUVOWk5+Gr+L3hkNDWmRHo6ncBaEBQUhNWrVwMAli9fjvLycp5Gqj6V4kpUSipRL6vX2qRBLWt0Fxe6FgsKKJLA00KaOqSVpWHyjsnYHrtdL3VTHKpiyslpFGJi7NRLyvDyAgYPbrW/HsMwmBw8Gb1dtE//1pa0sjQ8/+fzmLtnLirFLWvYNYEXMcXh6Uk9lywsaE4RHd0im8PcxBwz+840qLV8a4JKLBPjyytfYv/t/TiWekw4MaWKhwc+O3UKO/b9jW0P/wRxkhhgWfQLCwPLsnj++ecBAGPHjoW/vz8kDYu4X375JTIyMgCQc5+ZmRn27NkDADh8+DB6N5QkrF27FqdPn+Z/3N2ohUZnuKmpKfz9/SHv1GEH3ejUYgqg0ARX35KZqZNBRVvW6KrZhTk5vGcX6hcuHaR/f7Wb2BqzmOJwc6N5vJsbTYp5g2Ho84qKarX6t7mb33MDn4OlqSWu5V7D2ayzPA6kExEUBImPDy7U1MDa1hbOu3cDo0YBlZWomDAKW45u4G1lWgh8fSmYq1BQGinf8/Xzcecbm/Y6OzuD4XrhAFSLpqUQX7lyJUJCQlBQUIA33niDxxGrh4OlA7ZO24pvp3wLf0d/jV+vdp8pDw9qxMRFEuLi9JY6cOD2AVSJq3A15yoY6Cc6pSqmRowYAVtbG7BsU/v0dlGdSEuluHLqF8jK+XTx0ZyC6gLYmtvC195XJwMDXsUUh7093e85c4+4OCgy0nE5+5LRLJJxyRKqjyQLUwt8NOEjTAyciIf7Piy8mGpgy08/4dVXXwVA7YZcXVxQf/UqIJFg8+bNyMnJAQBkZWXBwsICv/76KwDA398fLMvivffeAwDMmjULDMNg6tSpAIBjx441LhJ1Yxg0NqXYunUr/vjjD2zfvh3Ozv+duocuV7B35w49aLV8L+r0mUpKonItKysyQPgvtD/oDGKKg2X1VLNeVwdYWbVpjf7s4Wfx7fVv8VDIQzgw+4AeBmRctGqNXlCA6kERsM0uQGKIC3peS4GVraOhh9omqh4uaniSqM2ey3uw6swqPDXwKbx272tNJ4AFBdS1VYcbyz///INx48Y11icMGTKEh1ELj1ZNe8vLlWLKzo4WiQReIGRZFgeTDmKo71B42HoIeizueM1rprgGv25u5IyuCQlXj2DB2ZcRaOWNLVN/gLVvS7MKfVEproRELoGrtatWrxdETKmiUFCYOjcXu+ujsSFrBx4IegBrx63l9zhaUFtLUSonp5ZdPfQSmWo4zmsnXsMQnyGY3mc6SktKmzwHv3/tNTyzciWtTgH47LPPsGLFisbfl5WVwdHREQA1KletIc3MzEQPrleYlnS5Oa4B0DgGu2nTJpw9exbe3t4ICQnBPffc0+Srq1NXZ+gR8ETv3oKKKYCyCy0s6DPr9Kl/HGIxWam3UoDbmcQU0FJMCZL1lJ8PXLmCvKtX27RG5yzUDyYdRFKx0O4GxkWrYio7G8jIgO3hY5Da2aBvUgmsnlticFOB9lD1cMnL07jcsFVSU1NxMuEkbGxtwJgxLSeAHh5NxZQWJ/DYsWPx5JNPgmVZLF68uEVtghDsS9yH/bf3a716r5WYAqgXzoABNKOsqiJxJTAMw2Ban2kGE1OAMiqhZt/xJpS4WsPO0gGBlt6wTsmkNFMDXYf2FvbGK6YAioAGBwN9+6LazQGmIlOjcSO1tibXd05McY2R9SWmAOBc1jmcSj+FTy99ipK6Eri5uYFlWXz9+ecAgP998AEYFxeUXLsGKBR46aWXUFVV1diI18nJCWvXroVYLG4ipmpra3UWU93wg8YRqnfffbfd37/99ts6DchY4dT7hQsVGDasi6n32lq6Gao0zWwLdcUUB9cjFwAiIjTqm2icXL9Ok5Fmy52dTUypwrKUqlVURG+J13Zy2dkounQJd1JS0HvmTLi1Ydf/0G8P4VDyISyOWoxvpnzD4wCMl1bFlFwOXL1KPb1sbakeYfJk+vl77wFvvmnoYbfL3btUsqPrvCQ1NbUxzS+xOhGRnpGwMmtnp+nplMLcq5fSGEVNioqK0KdPH5SWluLjjz/Gyy+/rNvg2yG3KheP/PEIxDIxPpn4Ccb0HKPR67UWU6rU1pI7W0gIpWnxDMuy+Dvlb0wMnKg3A5W2xBTQ1DJ71CjNA5rldWVAZiYc88vpB+7u9NnpIeXip5ifcI/XPejv0V/rfehFTLVCdkU2vO28YXInhcS8h/CiWh0u3b2E//vn/7Bk8BL41fnpRUwB1I9rb+JeSOQSPB7+eJPf1dXUwMvLCxUNuYlvLFyIdZs3N95I9+7di4cffrjJa/r374/Y2FjextcdodIdjQXVfxXuZDt8uAJDh9rDxcXQI+IJrvu5gwMpnnbQVExxJCeTAxifqUAGo7KSup+zLK3GeXt3ajHFkZZG9f0mJpQOb23Nz37v3r2LO4cOYYCnJ5zc3KgxZCs7P5N5BmO2jYGlqSWylmfBzaZrNwlvVUyBCqV/uvo9npCFwUrO0HV58SLQ4PKE338HHn3UgCMXHlUxpbY1emam0rKyZ0/60oAtW7bgmWeegbW1NRITEwVb8VWwCuyI24HovGh8MvETjSa3vIgpjub5vnI5bwJh/+39WHtmLSI9I/Ht1G8FNyNoT0xxXLxIyQVcH1+tyM+nPHaW1UvKZFxBHBYeWAgRI8Lex/bC117zdiSGElONFBWREQpARbuBgbR4q0dyc+n0dnUlffLy0ZdxOvM0xjiPwYOOD+pFTKnLkd9+w+S5cxu/v3P7NoIaFiGPHTuG+++/HwDw3HPP4auvvuL12N2CSncMZ7vSiUlJ0atRkrBw+RBlZe12iddWTAF0Dw0L6wJiCqA0SRVbM1l5eacXUwAt7Ds60oPn1i1+ata5mqmwGTPg1LNnY8Fya0Yoo3qMwkDvgaiX1ePra1/rfnAjpi0xBQCfX/4c38Vtw/LiX8CamJCjy6hRwLJltMH8+cDlywYauWaUlSl7papLamoqLsZdxHXL67Cw1aDrtL+/8rrMyKAvDVi4cCFGjhyJ2tpaLFmyRKPXaoKIEeHx8McNK6aApmKquprOqSLd+2EBgJWpFewt7DGm5xijEFOA8jGnjgsly7L47OJnuFV4q+kvPD1p0dHMjO5lAosTX3tfTA2eiinBUzqFmFKwCnx19SvkV6vMI1xdlRHjnBxKVxGLBR1Hc3JzqYaO+9u/P/59zPKehdG2o/UipiRyidrpvZPmzIGsuhpD+vUDAPTu0wezZs3CBx980Cim9u/fz7uYAgBFl5nUGg6NI1QikajdC7OrOgBy6v3YsQqYmdkjIIAcj7sEXNWuqSkwaBAVPqmgi5jqssTFQV5YiLjUVNT06YMhw4d3WjHFoWos4OmprInRhhYGFFIppUvW15NyCw9vsVK5M34n5uyZAzdrN2Quz2w/xauT0p6YAoCrOVfx9r9v463Rb2GYfShNQBQKmpi8/jrw55+UcnT5ssZRGH1SXk596ExMyJBGnTlLamoqEhMTsU+yDzeLb2Ji4ESsH79eswNz9zKAJnKcG6Aa3Lp1CwMGDIBMJsOxY8caG27yQXl9Oewt7LUSGLyLqeZwKQQMQ7W1arqZtkdpXSkcLR0FFVTqiimAUlErKui+1lF2ycm0k3jtxGswNzHHkXlH4Gjp2HSDujqKUvEVxu8ABavQ+HM0RGTqUNIhvHv6XThbOePw3MMwN1GJ3pWUKGuPzc2pGWKDwYKQKBTA2bP05xo2DDA311/NFMenFz9FYlEiXh3xKoJdgtV7Ecsi+sYNREVFNflxcnJyo0U6n2RlZcG/Qfh2R6i0R+O73b59+7B3797Gr99//x2vv/46vLy88N133wkxRqOCez5nZup9oUU4fH0phUEmI/c/FfgWU1IpPb91cGs3CmRBQYi/cwcmYjGGODl1ejEFKJ9zDEPByrw87fbTqpufmRmlyJiY0Gy7oKDF62aFzkIPhx4oqi3C9tjt2r8RI6UjMQUAg3wGYd9j+zDMbxjlJoWF0R+kuBjYvJlWyAsLgUmTBHIR4QcHB2XEMzGx4zp+Ls1vxIgReHbIs+jl1AvPDnxW8wP7+VFIHGiaBqgGYWFhjQ00V61axduKrVwhx7K/l+HZw882Xb1XA8HFFEAiysuL/kjJyRpH91rD2crZaMQUQI+4sLCOxRQARHpFYlLvSVgwYEFLMQXQ6oCqmMrL07ofWms079fVGcQUAPRx7YNIz0g8Hv54UzEF0AcfFQXY2NCK3c2bwncCB0WlWJYeP1nVdxATE6NXMVUjqcGBpAO4kX+jsXmwWjAM7rnnHrAsi4Xz58PUxATVf/+N3gKM+fjx441iqhvd4K2GaseOHfj9999x4EDXtD1WzS9NTbVHZSW122lmWNZ5qamh8ATL0qza3V2QyFRMDM0DPTzo8+uMcDVTpjU1GGhmBlM7O0rO7wKiCqC5QVoaaZ+hQ1vazLZHW9bojZSUkKlHG9GVzy5+hhXHVqCPax/cev6WQZtB8kl7YkrBKlAjqYGdRRuTwoICWoHw9aWl9iFDaDIybhzw11+CW19rS3093VJksva9IlqrmdJmVb4Jd+9SbnZICIkFNSkqKkJgYCCqqqrw+++/41Ee6tVuF9/G/w79DyaMCXY9sgvuNuo9NPQiplThjD0AqncJClI7ra2ivgKrT63Gi4NfRB9XHULbaqCpmNLlOB1+5rW1ZCLDshT+Cg7WuUbojZNvwNHSEc8Peh625i17+bWHoWumWJYFC7bta1cuJ9FeWEiLQwJHqXJz6XC15ul47epD8Lfwxy/zfoGTnf7csQprCnEs9VgLIwq1kUqpdq+4mL53caH0EU0ezG2wbt06vNlgdLRp0yYsXbq0O0KlA7wJqtTUVISHh6NGnSTlToiqoDI1pZNNTxF//cHVHpiZocDfH1djYnhP81P1dOjXjzKZOhMtDCgqK+mhoOdCWyHhFqo9PTUr4O5QTKlBpbgSfp/5oVJciUNzDmFK8BSt9mNMdBSZ+vnmz9gZvxNrx63FPV5qtJ64cQMYPZpqXxYsAH78UU8NxTSnoIAiVAxDaw7Nn9OcmBo0ZBAs7Sx1alragpoaWhHXkDVr1uDtt99GUFAQEhISYMbDxCWnMgeZFZkY7jdcre31LqY4cnKUWQpubrTqpca9bcO5DdidsBvBLsH4deavgo1XFzHFsiTyzcxaX/uSyqUwM9Hib52TQ+KdZemG2a+f1pPdOyV3MGfPHIgYEbbP3K5+ihgML6Y0orq6aZddhUKQZ2hyMpCTw+JM7nfYVbAJ40PGY9PkTbwfRy/k5FA6s0JBZRl9++okSMeMGYMzZ84AAE6dOoWoqKhuUwod4eUMrqurwxdffPGfqa+xtu6CYgqgojA7OxRZWuLqjRuC1EzZ21NWDkCLLp0p9a9VNz9n5y4lpgCa/GrqqKyVmJLLyWGytrbxR/YW9lgctRgA8MnFTzQZtlHSkZiSKWT4886fKKwpRFaFGmlDXDH8d9/RebdtG7BunTCD5wEPD4risywJK9USW9XI1K60XZi9ezau5lzl7+CqYkoqpfoqNXjppZfg5uaGlJQUbNmyhZeh+Nj7GL+YAigyxeX9atCTa1HUIkwMnIhVI1cZpZgCKMvs8mWgtJXMK5lChvn75+OTC5+gVlrbcoP28PGhdGZTUyrUun5dPfeLVujt0htfTf4KSwYv6RRiimVZrDy+Er/F/Qa5QoP6eVUxVVvLqymKKpWVLFJTUxFm0QeHnjiE10e/zvsxWkPBKpBXpWXOfFv4+ChdcsViOqEzMjTuiyaVSsEwTKOYio+Px9ixY/kd638UjWeCTk5OcHZ2bvxycnKCnZ0dfvzxR3z00UdCjNGoqaoS5D5gGEQiFPj44HJeHgZERgomkHv2pLkOV0/VGejQGp1lKV0mqes1pq2pab+eSuvIVGoqFWs1c/5bOmQpTEWm+DfjX1zPva7DyA2LOjVTpiJTbJ22FatGrsK0kGkd7zQ3l3Jmvb2BDz+kn731FrBjB7+D55HgYGWDb871T1VM2djb4HTmaRTWFKJKUsX/ABQKmnykplIkoQPs7Ozw1ltvAaC+i9pmXRxOPozM8kyNXmNQMcXh7k4NgMPC1F4scrZyxvrx63Xql9QefKT5ceUnrf05z2edR3JJMv5K+QtSuRarfM7OFIK1sqIw2I0brSs3NRjsMxhPRDyh9vaGjExdvHsRp9JP4YsrX6CgpmVdrFpkZ5NAuHWLcs156uSjULCIjU1BRUUFxo0bjAC3AHjb6W64og7HU49jxu8z8M01nnsq2tpSLZqnJ31O1dUaZSfk5+fDvCFF3MPDAykpKQhT6afZjW5onPK3bdu2JhesSCSCm5sbhgwZon7PkE5Iax79nJOVqSmVNfCQGWJQWtRMKRR00QrQwLC6mhbyVEq2jBa1+kxVVQHR0fSG+vY1miaGulJXR3UwCkXrKe86pfk1d/6LiGh8ODyx7wlsj92OOf3mYMfDxisW2kIdMaUVXKinsJCuy127gC+/pDqqEyfIYt0IKS+nOZOHR+s1U/WyepxKP4VJvScJMwCumAKgld4OnLIkEgn69OmD9PR0rF+/HqtWrdLocMklyXhi3xMwYUywc9ZO9HDo2BLWKMRUW2RlUQpgs6L4guoCeNgKe6/jq2aKy2Z0caGAUnMu3b2EOmkdxvbSYbVeKqWmyRUVGjWZLq4thrWZNazNNEt9MXSan4JV4MDtA6iV1mJe+DztdsKyJKS4CLKjI00KdKgNZVly8/sn9TTGDpiMAYH67dmy9sxa7L+9H88NfA5P3/O0MAcpKqLPipt4Nu8t14zz589j5MiRAIApU6bg888/RwDXagLdfaj4oLuxr5q0drKxLM0Hq6up7rmh/1qnpIWYqqqidCw7O938s9uBK9niFl2Maf7AoVHTXu4N8d0d18AkJlItjLk5WWBzzzk+aqZQU0NCVC6nqEswpbnE5Mcg8ttImDAmSFuWptaE1FhQR0ydSj8FBox2kzeFgiZtpaV0rn38MXDwIK2SX7zY+BkaI1o17eWLvDxlBNnbm0RVOzedX3/9FY8//jgcHByQlpYGZ2dntQ9VWFOI906/ByszK3xw3wcdTnSNWkxxYtTcnNodNKRrxeTHYPHhxZjbby6WDlkqyJj5NKCoqKDAkYUFWWgLhkJBN0w1zVBYlsXSv5YitSxV/TpKGF5M8U5REc055HL6I4WFtSy6VANOTGXmZ+KzvM8gVojx3ZTvBIuetsXlu5cR4RkBS1NL/RwwIYHEVSvNkzdt2oRlDb0MV65cicWLFzcRU0C3oOIDjZdN//77b5w7d67x+82bN2PAgAGYO3cuysrKeB2cscO17QDoWV1ZadjxaEurbn4KBU128/O1Tl3oCH9/Sv8bMKALiCmA3hDf3XGNgOBgpdttQgItJPAipgDacWgo/Ts3l9zZAAzwHIBxvcZBzsrx+aXPeXgX+kEdMZVXlYd3T7+LV4+/ijOZZzQ/iEiknGzI5cDy5aR0S0vJTt1Ic5AbDSgGDUdVlRPOZ53HqfRT+huAl5dycYgTCe2sJ86ZMwfh4eGoqKjA+++/r9Gh3G3csenBTVgzdk3nFlMAOQfZ2tINICaGOjYDuJB9AXKFHJXiSqMXU4CypE4sVmYYJxYlal4z1REiUVMxJZdTaKyNguGy+jJkVWahvL4crtbquTQZWkxViata2LvrjJubciFSLCb1W1Gh0S44MVVcXIz+Uf0R6h6KHg49EOoWyu9Y1WCI7xCNxdRrr72Gf/75R/ODVVVR1kJOTosavmnTpjWKqc2bN7cqprrhB40jVP3798cHH3yASZMmIS4uDgMHDsTLL7+MU6dOoW/fvti6datQYzUo7al3bgXfzo5qBo3tedge7Vqjp6TQBNfCghr+dhFbcHXQWExx8Nkd14ioraX7tFwOmJrmobz8uu5iShXVhqwN9o9/3fkLk3ZMgp25HbJfyoaDpQYuGQZA3TQ/qVyKr699jdiCWHw75VuYiLRMqZVKaYJbU0P/XrSIIqTDhwMnTwKWeloZVQNOTA0dOhx37jihsKoUn2Q+hhpFGdaPX4+JgRP1N5j8fIpUsSwZ8bQzuThy5AgmT54MCwsL3LlzB36co04byBVyjf6eRi+mOGQyioqWl9MDrk8fwMMD57POo79Hf37dGSGcNfqlS5RhHBkJMJaVmLVrFsxMzLB50mb0dOzJyzFawE0QrKzo3taK86RYJkZcYRwGeg/scHeGFlMAsOLoChTXFuP/xvwfgpx5TqfjzIokEo1WW1XF1IgRI5CVZQWAhYNHJTwd9fPsuHz3MiK9Ilv24VIT7m/p4+OD1NRUWFhYqP/i0lLl5yYSQd6zJ0xVUk537dqFqKioNsVUd4RKdzSOUKWnpyO0YUV5z549mDp1KtavX4+vvvoKf/31F+8D7AwEBpLWqKrSS6863uiwz1SvXvQQEIvVKubWlby8JoZvBkNrMQXw1x3XyLC2pjlUUVERzp7NQEDAEP7EFEDWj15elLLQkFP4QNADCHULRZWkCt9Hf8/fsQRAk5opMxMzLB2yFN9M+UZ7MQXQZ8UVto0YARw5Qv++cAGYP5+izEaAapqfi4sTvL0BaxM7RNlMR5BTCO7tea9+B+TpSXWOVlaU+tcODz74IEaPHg2xWIx333233W0lcgme3P8kfrzxo1qOZ51GTAH0gAsPb2rZmJWFET1GdBoxBShLwGprgfzqfFiaWsLazFpYswI/P1rcqKuj9OaSkhabWJhadBoxVVBdgGu515BUkgRTkQCLrCYmFIHv318ppli23clBczFlaWmFggIgL4+Bg4V+xFRmeSaW/LUED+96GBX1mkXWOPIa5gs5OTmwtLTETz/9pP6LnZ0pU8HZGSVlZY1iys7ODocOHWpXTHXDDxoLKnNzc9Q2nNgnTpzAxIm0sujs7IzKzprzpiPm5spFzrQ0WiAwdtRq2mtiQhMPThxwjeUEICODFo0TE3kz+dEKncQUh6MjidEuZqcuFt9FRUUc+vTpA4XCjf8DBAfTA6FhdYxhGLw87GUAwOeXP9fOfUsPqCumimuLoZoQwMtkxNycVnGtrela3buXhNauXcDq1brvX0daq5ny8wOcHc3wkNcLeLX3Nq1Xc3XC3Z2i7h1E8RiGwQcffAAA2Lp1KxITE9vc9njqcSQVJ2Fn/M4O3Qo7lZjiEIkgDwnGz9JrqJOL6WFXXc3rIYRu2uvuTkFJW1sg2CUYux7ZhU8mfiLsOcgVCXPp4HFxQGYmUkpTNEr5NQYxBQAeth7Y99g+vDf2PeGiekDTjJjUVMr8yM9vsVlzMRVXGofvr/wMsUwCU1P9BeqLaovgau2K3s691c6myM7OBsMw+OYbcgP09PQEy7LYtIl6ZS1YsAAMw6hfTmNujmiZDK7TpwMAHrv3XhxZswahoaHdYkoPaJzy99BDD0EikWDEiBF47733kJ6eDh8fHxw7dgwvvvgikjuLD7aGdBQOZVm6T7q60kK7MT8f1RJTqqSlkcuTuTlNQgSwMxSLqeG8TEZ1VT178n6IDuFFTHGwLK1IdhFjCq5matCgwZDL3fVzjldXQ2zKwP/LQBTUFGD7jO3aO0kJhLpiqlJcidm7Z6Ofez+8Nfot2FnwO1FspKyMXP/+7//o++++A/73P2GO1QGtiakaSQ2szaxRV8c0ukeGhKhdvy8cxcWUXhAW1qqr6YwZM7B//37MmDEDe/fubXUXLMvi75S/YWtui1H+bbstdkox1cC2mG348sqX6Gvjj5/HfA6Gx9YaQospg8OylOmRkwMFq8AzWV8iVpyFZUOWdWiTbixiyiBwkyuultvTk4rXTUxaiClzC3PM3jMbyYXpmOj8LOb1fQYDBuhvqPWyetRIauBi7aLW9sXFxXBzUy5OFhYWNn5fU1MDV1dX1NfXAwDeeecdvP322+3u78cff8TTT5Or4MYPPkCUuTl8x4xBz8jIDsfSnfKnOxovoX/55ZcwNTXF7t278fXXX8PHxwcA8Ndff+GBBx7gfYCdBYahjAhv7y4mpgBl4ygbG8HSiCwslOZkmZn6N/jgVUwBdBKoiimZzLChNx1QNaDw8HDXzzleWgrcuAGL5FQsGfwiAGr0a0ympJqk+cXkx6CkrgQppSm6pfm1h0JBId5Ro6ieCgCeew44dkyY47VDa2JKrpBjyV9LsPL4SkhE5ejVi7ZNSaG6FoMhl1N4vLQUiI1ttaHtunXrIBKJsG/fPly6dKnV3TAMgwd7P9hlxRQAhLiEwNPWE7MHPdVUTNXX65SaoU8xtSdhD85lnet4Q77hXKyCgyEHi0gLf9ib2eL+oPvbfZmxiKmimiKklqbq/8AMQ+l/vXops2Wio8HW1DQRU1ZWVmAYBvMj5sPXOghj3R6DvjW5paml2mIKAFxdXVFbWwsXF3qNu7s7Vq5cCQCwsbFBXV0d9u/fD4AEFcMwyMjIaHVf8+fPbxRTJ0+eRFBYGLynTm0qpoqLeY8qd6Ok2zZdTTRV73I5XfvGlPWllZjikEgoMiXwzVy1fnfgQEFaYLWAdzHVnKoqcv3z9qZ8k05Ee25+CgVlYlhaUhoXr1RVkeGCXI4SZ0v4HRqHOlkdTj55EuN6jeP5YJqjTZ+phKIEmDAmCHEVsL8C97nJZMAnnwB//kluOefPt958RwDaskaPLYjFokOLYGFqgZ0P74SnrRdiYsjIy8ODMhYNRmWlUkzZ2dHqWLNI/FNPPYWtW7dizJgx+OeffxontuezzmOQz6AO08Y6u5jiqJPWwdLUUjl+mUzZgy88vEWvqo7Qp5jKrsjGo388hnqpBD9M+w5R3upZlPNORQXAMKixNIGNeUuTCg5jEVMAsPrkahxPO45Xhr+CR8MeNcwgysqAxESwYjFS0tORa2eHwVOmwKrZOXftGovqakYvPS7PZZ2DhYkFBvkMUmv7hQsXYtu2bXj++eexefPmxp///fffePDBBxu/T0pKQnDDKrNcLsfAgQMRExMDAJg7dy5+/fVXAHT9ODk5oaLBEfHOnTtISkpC3759m6b5cU0lWZbEqa9vk/lcd4RKdzSe7kdHRyMuLq7x+wMHDmD69Ol44403IOkMxUN6oLSU0teysgw9EiU6iSmA0v1Ub+YC6fDevZX1u3rwwRBeTAG0IlRfD6SnN1oOdwY6skYvLiaX1rQ0Mv/iFTu7Rjt1l9J6PBX0CACKUhkabZv2hrqFCiumAKUYMDEBli6lVYmqKmDyZL045rTXZyrcIxw/z/gZ7419D152Xo1mcV5eHfbZFR57e6pFMzNTitJmz7N3330XFhYWOH36NI4ePQqAIo/Ljy7Hk/uebNd+u7OLKdV1Vyszq6bjl0ppdYUzXdAgvUDfaX4uVq4YZPUYepkNR1/HjtOgBMPBAbC3V4qpoqIWBkbGJKZkChmkCqphDfcIN9g44OQENioKyYWFqCwrw1AnJ1g1PLO5c1ShAGpr6bMSOkJVL6vH+rPr8dyfz+F46nG1XvPqq68CAL766iswDINbt24BAB544AHI5XKMGDECABASEoJp06aBZVmYmJjgxo0buHz5MgBgx44dYBgGp06dgkgkahRThYWFrYspgGrSnJyUK6GxsVRr0Q1vaCyoFi9e3FgnlZaWhtmzZ8Pa2hp//PFHY6jyv45cTvPnrCzjcK3TWUypIpdT/5b4eH4G1wxTU6XTeH6+sJ+fXsQUQDNGT08SoQkJneImpk6fKXd3iiwI9rZcXIAgsuRdbj8RDBgcuXMECUUJPB9IfTQRUxK5BB+c+wCFNYV6HCFostavH61MvPUWrUZmZwNTpwqa7qFO095gl2CM9h/d+L2VFdVQGUVHBltbElXm5mRFHxPT5KT28/PDiy9S+unrr78OhUKBWmktHC0dEeoWCmuz1uslO7uYKqguwOw9s3Ex+2LrG1hZUb8QOzullb8aBkaGqJmyNrfCE8HL8GLgRtTV6f/vcDP/Jl4/8XrTe0JdHdldJyVRvyqWNSoxBZCBzocTPsSuR3ahj6vhWoGwLIubiYnIdnZG2KRJsIiIAMzMkFGegQUHFiA6LxoSCV3CpqYaB0s1Rq6Q496e98LX3hdjeo5R6zWhoaFQKBS4/35K9ezXrx/uu+8+sCwLkUiEc+fO4caNGwCAgwcPQiQS4fz58wCAwYMHQ6FQYPbs2QCA8ePHAwCmTJmCkpISXLlypXUxBdBiUb9+VFshEtHi7rVrgpqN/dfQWFAlJydjQEOV3x9//IHRo0djx44d2LZtG/bs2cP3+Dolbm7kYKlQ0P3RkPAqpgCaYOTnk/WrQJbgjo5kRc+ZlwmB3sQUR3AwTdikUmV3XCNFk6a93Nvimv7yXmLn6wv4+iLIygcz3Kg25dOLn/J8EPXQNDL1zbVv8EfCH3j+z+f5b4LZEc7OFOFzcADee4++j44G5s4VpOF0e2Jqb+JetUVlSYmBLw0bG2pSZGlJqznN7nGrVq2Cvb09bt68iZ07d2K433DsmrULK4ataHV3nV1MAcD30d8jtTQVP0T/0HYNI+c26eJCN4Fbtyh83Qb6FlOqNvbW1oCIEaGuTtBDtoBlWXx44UOcSDuBrTdU+nVaWSlTwXNyUHPhAi6cPm00YkoVQV39OqCJAcXIkbDs25cmWwC+vfYtbuXFYvvl72FpCQwdCgwbJvyYbMxtsHLESux6ZJdGTpEMw+Dvv/9ujE6dPHkSIpEIp05Ro/MBAwaAZVk89dRTAICRI0ciJCQEUqkUDMNgxowZjftasmQJfvrpJ1y8eLFtMaWKtzdlLnALIPHxtEjejc5oLKhYloWiYdZ04sQJTJo0CQCt3hV3K91GevdWLgIU6nmBmoN3MQXQ00gP1eR+fjQXFAK9iymAToawMFo2q6hQNrE1MjQRU4CyZQj3ttLSBBhUYCDg4oKXfSjt75fYX5Bf3dI+V0i0SfObFjINfd36YtmQZRAxBiimdHWlwqSoKODQIXJ+OXQIeOklXg/Tnpi6knMF68+ux+zds1FaV9rufhISyMzL4L38rKxIHPToAag0xgQAFxeXxkyMt956CxKJBE5WTrA1t22xm64gpgBgxbAVmNd/Ht4c/Wb778HEhFbAvbxIFd+506qoMkRkav3Z9Vh1YhWKa4sbF+n0nT3CMAzeHvM2RvYYiWcHPtv0l/7+QL9+qBWLcev8eQRXV6OPn5/Bz5ny+nJ8f/171En1rD6b0dzNr3nN1KuDlmGW/VAst7yXHkIsq5f6aw5tbfdDQ0PBsiyWLl0KgCJOvr6+EDdExrds2YKshtqR5ORkmJubIzg4GI899hgA4MyZM3jnnXfUF1Mc1ta0cMQJeaNIEej8aPyUHzhwINauXYtffvkFp0+fxuTJkwFQw18PDw/eB9hZsbJSPotTUlo1jhIUQcQUh68vqR2uo7nAS8q1tZRizgcGEVMcVlbKfMa7d40u1K6pmOKwslIaCty9K8ACAsMAoaEYHvkQhvoMhUQuweYrmzt+HU9oWzPl7+iPn6b/1K7rm+C4u9M5N3w48Msv9LMvvgA+/5yX3XeU5udp64lQt1BMDJwIZyvndvfFLaCkpUHv0YMWWFpSc0HVxqINg1r8wmJYO1kjLS0N33/fesPpriKmAMDazBovDXsJvZx6dbwxw1AOZ69e9BmqWEIDhhFTeVV5OHznMI6nHUdOZU6T5r76JtglGBsf2Nhqn6IqCwucr6uDW48e6OnhAebGDYM/I76++jW+vf4tXj/xusHG0JGYAgBnO3e8PuRl9LD0oFqLZum6fJNRnoG1Z9aiuJafv8/nn3/eZlNfPz8/sCyL999/HwCZTgBAbm4uwsLCNBdTHCIR3eMiIw3Tp6YLorGg2rhxI6Kjo/Hiiy9i9erVCGqocdi9ezeGDx/O+wA7M35+tBAgkZAfgb4QVEwBaKwmNzEhN4J2Ujt0pbqa0nwTE3V/ABpUTHG4utKqkIuLcCE4LdBWTHG4uNACgqmpQM6MJiZAjx54ZfgrAICvrn2FWkmNAAdqiqZiSsEqkFelTBMzSGSqLWbNAl6hzw8vvQQcOKDT7tSpmerh0ANbp23FS0M7jop5eyt7nyYlGVFWLMvSDSg6Gqiqwu/Jv8P1QVcAwJo1a1DdrC6tq4ippOIk7V/s7089C82VK/esQmGQPlNedl74efrPWDpkKSI8IxojVPoU7dWS9msXuZop35AQ9Jw5E4yzM10IDWYDhmKo71B423lj/oD5Bjl+R2Kq0QhGJAKCgqDoG4YLCY64eV0G2aVrgplAbbq8Cftv78cnF/gzSeKa+n7esNil2tS3trYWq1atatxWIpHAwsJCezGlioODcdlRd2I0/hTDw8MRFxeHioqKJk3GPvroo0ZF3Q0hEindqyQS/UwQBBdTHFZWlIoF0JKyQMt9NjZ0vXMtdrSt0TEKMcXRqxelxgjQIFkbdBVTHD17Umq2i/ptODRmep/pCHAKQGldKbYd/UC4A0G7yNTv8b/jkT8ewZ/Jfwo6Nq0oKQEmTQKmTKGb0dy5tFqhBR2JKYlc6ZBnIjKBhalFh/tsvk5j8NQ/Ds5lSCoFbt7EEz0fwpMLn4Svvy8KCwvx2WefNW7aVcTUuaxzmLd3Ht7+523te7+prKyw+fnI2L0b2XfuGKRpb4hrCJ6MeBKAsi5XLBaknLAFl+5ewpQdU7AnofUa8xYGFObm5NTZuzdFEAzI2F5jsfexvbjHS//28h2JqSpxFabvnI71Z9c3CqsaazdIAvqgmrGDKUvXKzIyeJ98LRiwAJGekS1TN3lg6dKlqK6uhoUF3TOdnZ1hY0NukCNHjgTbYFrCi5jqhle0kqXl5eX44YcfsGrVKpQ2dK9OSEhAoaGKhYwYJyeaZIaFCd8MVW9iisPbm4rdTUwEC69zkyzOzbiNnnbtYlRiCqA3pXoylLZfWyIkfIkpgN6SpaXyeyEWEUxEJnhpwHMAgM8Sf4Q8RRjXF23EFMuyuHT3Eupl9e1aaBsMV1eKHCxfTtGD2loSV5mZGu2mIzFVWFOIGb/PwN7EvRpPxi0tm67TGLThL4epKRARQeEzmQz2t9PxXuRyfLThIwC0mFhUVNRlxBQApJamQsSI4GzlrPP7YOVyZPz7L4oyMjDC2hr6klIF1QWt1u2ZmtLiD9dIXmgOJh1EtaQaGeUZLX7XppsfwwA+PsrnhEJB6fUGyIU1Fen/ealOmt+ZzDMorSvFjfwbjTVMVVUALC1he08w1fIB9JnxfC2Ge4Tj+4e+h7+jf8cba4GNjQ3q6+sbm/oCFA0/e/YsSktLu8WUkaJxY9/Y2FiMHz8ejo6OyMjIQFJSEgICAvDWW28hMzMTP//8s1BjNSjG3vRM72KKg5s1W3S8Aq0LxcVKp/aICBKq6mB0Yqo5ycm0FB8QoPemv3yKqeaUlZHJgK9vi7p+namR1MDvU1+UicuxN2wNZgx7iiYfPKFtzRRAKX+n0k9hfK/xxjuhTkmhnLqlS0m1hIVR4181UlDVSfP76upX+PHGj+jr1hdbp23VeELGsmhs+Oviord+xB2SVZaBHnerKNLHMFCEhGDgpEm4ceMGlixZgqlTp3YJMcWRUJSAAKcAWJpadrxxG3A1U1l37mCkgwNsWJZSN0JDSeALBMuyePHIi0goTsDasWsxoscIwY7VEQpWgf2392Ni4MQm5iUaWaOnpVFtkKkpFawKmAZQK63FS3+/hCcjnjTI56aOmOK4kXcDDMNggOcAAHRby8ujZ06vXqDia27Rl3auk7hiWVbv1zbLsqiuroadnZ2gYsrY57idAY0jVCtWrMDChQtx584dWKosRz/44IM4c+YMr4PrakgklLbG9yKTwcQUQDnyAospgJ693t7078REyr7pCKMXUwB5jgP0wNRjpEpIMQVQwFIqpdrBkhJ+921jboPnBj0PAPgkexe5ifFUvK2LmAKoZuq+gPuMe0IdFETL8++/TxfWrVtUX9XBRaWOmAKAZwc+ixXDVmDNvWu0Wt3mfA1sbakO1Ri4knMFD+9+BB9WHAHr5gawLES3b2NDQ5POb775BlKptMuIKYAaUfMhpjIzMzF87FjYjByp7CcSH0+90QSiSlKFCnEFxDIxejjod6GqOSJGhJl9Z2ovpgClEZRMRlaYAqSxcWyP3Y7redfx4YUPIZWr8aDlEU3EFABEekU2iilA2VO6MaPUza1pUW98fKMLoKZI5BIsOLAAexL2NLHhFxqGYQQXU93wg8aC6urVq1i8eHGLn/v4+CA/X79Wxp2NO3eAggJaReELg4qp5hQXA9evC2ZpGBhINVUSScfP4k4hpgBSiVxqQkKCXlI6hBZTAPUxVhXAfL+tFwe/CHMTc5yvjMelygT67HQs4NZWTJ3OOI2fb/6s/15TutC7N1mDr19P9ZAnTgDPPtvmRENdMQXQBHJu/7nqucK1gbU1pUo7Omq9C16JyY8By7JgATChoXRyi0QYOWYMIiIiIJVKsWvXrk4tpiRyCT67+FmH9vbq0Kqbn4kJhRu5aHJqqmD9b+wt7PHT9J/w/dTv4efQUpVLpVSnV1UlyOHBsizOZ51v9Z6gVdNec3NKzeA+u4wMWggR4Fk7r/88PBH+BF4Z9grMTPRX56uumMqqyGo1pVouV5Zyt1qiV1ZGq3tZWcCNGxrnEx+4fQC3Cm9hy40tkCr0KzS7xVTnQGNBZWlpiUpuGUCFpKQkuDWzSO2mKQEBlO1QXs5PT1yjElMKBT0gq6oE62ZsYkLZDj17KlthtUanEVMcvXsD9vb0cLx1S9BKaX2IKY6gIOXbio/n92152XlhXv95AIBPCvcrm4lqeRBtxVR5fTnePf0uNl3ehH2J+7Q6tsHo3RsYMwb44Qe6Mf34I0WtmqGOmJIr5DiUdEgwUalORFpIFkUtwteTv8aSIUsohBYcjLqwMJy/dQuvNDgn/vzzz4jn8pI7IT9E/4Bf437Fc38+p70RBTqwRmcYOu8a3IGFNOYxEZkgzD2s1d8VFFBaaUOLH945nXkay/5ehmcPP9vkmtBKTHFwLld9+tC/i4vJeZJnQygbcxssG7pMr+0e1BVTClaB10+8jhm/z8DN/JtNflddTetBbSbNODkpmyZWVpIhjwaZDdP7TMerw1/FimErdIrcakq3mOo8aCyopk2bhjVr1kDa8IRjGAZZWVl4/fXX8fDDD/M+wK6ElZVSCKSmUqRFW4xKTAF0g+/blx6YBQX0JQC2tiSo2noOdToxBSib/pqb01OBzxCmCvoUU0DTt1VTw//bWjFsBQBgb+5JpJnXKC3iNESXND8HCwcsG7IM93jdg4dCHtL42AalQRhg7lxg0yb62erVwM6djZuoG5n68caPePf0u3jl2Cu8DzM7G7h0STAHZLUZ5DMI1mZkEVdXV4dz0dFwc3PDvHnz8PC0aWBZFqtXrzbsIHVgbM+xCHYJxrNRz2odaVO7z5SvL3DPPbz3v4ktiMWB2wc6FPZctYJQpieV4kpYm1ljgOeAxtYJOokpVTw9qXeQhQVNIniKihrKSEeTNL+C6gLUSmshlolbNYRwcuqgvtrNjULfqit9KSlq2QebmZjhsX6P4b6A+9R5W7zQLaY6FxqbUlRWVmLSpEm4desWqqqq4O3tjfz8fAwbNgxHjhxptHfsavBVsMeyje1M4O5OtbmaYnRiSpWMDPoyNaUbl6VwKzkKBTWS9fGheXSnFFOqVFTQsilAD0weC0P1LaZU4d4Wy5Lm8fTkb98PbH8AR1OPYsngJdj04CaNX69rzRSHIYqVeWfpUmr6a2EBnDyJVE9PtdP8DiUdwkcXPsIbo97AA0EP8DoszrfFyorMCfXVMqVKXIUtN7bgmXueaVL/0sLNj2WRtGsXwubNg1yhwLkzZzBilAGbOeuAXCGHiUi7RnI6Ne1VKCht19+/jXytjpEpZJi3dx5SS1Px4uAXsWDAgja35fobmpkBIwTyXSisKYS9hT0sTS35E1OqSCSUS81DP0OJXILZu2cj1C0Urwx/BY6WjrqPTw00rZkCaKyppano69ZX+wMrFFTgy9UOuLnR6l8r1EprYWVqpff7u77FVLcphe5o/Giyt7fHuXPnsGfPHmzYsAEvvvgijhw5gtOnT3dZMcUnXME1wwCFhZoX7Bu1mALogcit/ty+LWjzrVu3qL40NbULiCmAHozBwVRn0EXEFEBvKzCQhBTfh+ca/f5440dl7UddHYXDOlh11EVM5VblNinY7vRiCgBeeAEYNQoQiyGfPBlZp06pJaYAYGrIVOyfvR/3B97P+7ACAkjj1dVp1zZBWz679Bm2x25vEnVr1RpdJELI+PF4atIkAMDry5aBFaiOVAhUz2ODiCmA/rDFxVTboqXBDAMGDwU/BG87b8zoM6PdbbmUMKlUuAxrdxt34cQUQKF/VTFVVkaGFVqce9dyryG7MhtXc6/CTKSfuiltxBQAmJuY6yamAFqVCQykZ625ebvuN2//8zYWH16MtLI03Y6pAd2Rqc6JRhEqmUwGS0tLxMTEoF+/fkKOy+jgW71zLqj29pT5oA5GL6Y46upo+U8uF9QOvKyM+vbJ5XLU1l6Fs7O884opgTC0mBIalmUx4NsBiC2Ixfpx67Fq5OvAlSt0Dnp4UBpqK+gipmqltZi7Zy7sLOzw4X0fwsvOi6+3Y1hYFrh+HZLHHoN5WhrkPXvC5Nq1di2aFayiMaVJSLi2CQwDREUpzTGF5EbeDbx35j3835j/wwDPAR32mcqJj0dQVBTqJRIc2rgRU154gSL1Rkx2RTYWH16M5wY+hynBU7Sa7OsspgASAQkJSqfToCBKC9QCmUKmlrvk2bP0iBo8WNnsVxdYlsWmy5swNWQqApxoEiyYmGqOQgFcvkz2qlZW1DhewwXuxKJElNWXYbjfcGHGqIKmYiq3KhcppSkY1WNUq5+hQkF/S61K8hSKpmHv8nKKkpqYoLCmENN3TodMIcPOWTsb/65CYigx1R2h0h2NnoSmpqbw9/eHXB/txbs4/v70FR6u3vadRkwBdEPnio4FavgLUK60t7cMCQkJyM62xoABXUxM1dXRJEPL681YxRTLUgqXGmnrHcIwDF4ZRhGEL658AbFcQoXbXC1fWstVRV3T/DLKM1AprkRZXRlszLtQVJ5hkOroiGvPPAOFuztMMjKo8W8bhSaX717G/P3z9bJy6+pKWTksS8FHAQPfjUR6ReKPR/5QS0wBgE+/flj6HDWdXvX555Bfv65boawe+DXuVxTWFOKvlL+0ej0vYgog4dm/v9IaNCWFzI3U+EOzLNvERENdq36+66j+Tvkbv8T+gqcOPIVaaa3+xBRAgqB/f3pTdXVUV1BYqNEu+rr1NUoxBQBfXP4CK46uwJdXvmz19xUV1EqPy5jXCNX7f3U1EBtLn19NDdxt3LHvsX14a/RbXVpMdcMPGi8tvvnmm1i1ahVK9dgzpytiYkIGFerM/zuVmOLw8qLQW+/egh1CJpMhP/8SrK3lCAkJw507pnqZaOkFlqX0jcJCrayFjVVMAZQJmpxMcyY+eKzfY/C280ZedR5+i/+Net2EhNAvs7KAnJzGbfmomQp1C8XOWTvx8cSPYW/RdVbyUlNTcTspCX0XLYLo229phfvSJeDxx1uoX5ZlsfHyRiQWJWJv4l69jC8oiO6XVVVN/qS8UydVevybiEzUElMcr7/9NhwdHRGfno4dBw60KuiNiVeGv4KlQ5bizdFvajzh501McXAmKYGB9H1OjlopbMfTjuPZw88iozxDo8PxLagiPCMwxn8M5kfMh7xerj8xxWFrS+FbJydahEtIoHz4dh6K6WXpKK8vF35sDWgjphSsAr72vrA0tcT9Qa2nFHP29+bmOg5QLqebTE0NtYDJz4eHrQemhkzVcccd0y2mOj8am1JERkYiJSUFUqkU/v7+LeqmoqOjeR2gsSB0OLSwkFZhm8/vOqWY0gOqNVPh4UMQE2MKuZxEqn9L85/OSXk55TSyLE0y1OxyasxiCqCsnthY+ndIiLINly58eP5DvHbiNfRz74fYZ2NpApOZSYXHABAWBomDAy8GFF2RFm5+CgWwZQvw3HM0yVi9Gli7tslrimqK8EP0D3hp2Et6sxHOzaXARa9ewmQSn808i7Vn12L1qNUY7T9aIzHF8cEHH+D111+Hv7c3kpKTYdEFa4t5F1PNKS4mQWBiQgtz7dhoz/h9BnIqc7A4ajH+F/U/jQ4hkVCvMz5S/jgqKitw8cJF/YopVViW7nucJ7yTE0Wvmt3vFKwCj+99HPnV+fhwwocY6D1Q4GFpVzPFUSmubHMB69YtoKhIo8dk20gkkNyKRXFhBrwtXCl1vHdvQdN3jUFMdaf86Y7GZ8i0adO6RgG2EZGQQIKqZ8+mLrJdRkyJxdTd1deXVKOOtGZA0bs3RT44178ukfnn6EhPiJQUWum2saHoSzsYu5gC6C306kXP/Dt36G3pev9eFLUI7515D/GF8TiWeoxWMv396dzLzYU0NhaX6+pg7e6ulZiSK+RYc3oNZvebrXtBtJHRqjW6SAQ8/TSp39dfB9ato3rIp55qfJ2bjRtWjVql17F6edH8UMO5mNr8Fv8bSmpLcCPvBga5D9JYTAHAkiVLsGnTJmTm5uKbH37AsmXL6Bf19YK6nqoLy7K4knMFg30GG65mqiNcXcnpVKFo948tYkT4evLX+Pnmz5g/YL7Gh+AD1TrCqqoqw4opgCJ9AQEUsUpKIgeOVu53xbXFYBv+C3QKFHRIuoopAO1mA3CtUXk5Fc3NsdskCZtSP8Ei5wl4Cg/SAfr25dUsisMYxFQ3/KBxhOq/ipDqvbCQRJVqwXWXEVMAzZwzM6lidODANrruqUd7bn6ZmbSYZARzFn65fRvIzyeVeM89bS6ndgYxxcGytKpYXEynQ1SU7ukaL/39EjZe3ogJARNw7IljjQeSxsTg5tWrYPv1Q9TQoVpFpn6++TM2Xd4ER0tHHJpzCFZmAs3o9YxafabeeouiU6amKNv1M/JGRiDUTYt+D50AiVyCnfE78VDAQ7h66arGYorju+++w+LFi+Hq6oq0tDTYlZfTfTAsrF2TD31wMu0kXjvxGob7DcfGBzZqZCqiFzHVFiUllP7n4aG/Y3aAglXg2cPPIsorCg8HPoxrl68ZVkw1p7aWbrBcf75mBgxyhRzp5ekIcg4SbAjaiql6WT02XtqIJyOehLedd5vbSSTAhQs0fxo5UqtWhC144+QbOJZ6DG9GvYTp0kBaDBEg/cWYxFR3hEp31L6T1tbW4oUXXoCPjw/c3d0xd+5cFGtpb9pNU9zdlQXXNHfuQmIKUPYWkUopUqWlhu/IGt3fvwuKKYDqClQbEbZSU9CZxBRAD78+fUgbisW0oKDr0s6yocsgYkQ4nnYcN/NvAgAkUinOl5VB0b+/1mIKAKb3mY5xvcbh1eGv/rfEFACsWUPNf2UyWM57Emu+eAQHkw7qb6BtUFVF5w0f5iYc5ibmeCT4EZ3EFAA89dRTCA4ORnFxMT75+GNK31Uo6PrNz+dvwFpQIa6AhakFQt1CO4+Y4gx6EhOB9HTI5TLcrbyr0y7lcvqzaNq6RJXzWecRnReNn2N+xolzJ4xLTAF0g+UUBreKlZjYaHRkIjIxSjEFANtjt2N3wm4s+WtJu42aueiU6lvVlXXj1uGbKd9gauRsWgQWIMfYmMRUN/yg9t307bffxrZt2zB58mTMnj0bx48fx3MNjkb/OQQI6vXuTQGcrKxSHDkS33XEFEArYqGhdLcrL1fmdmuApn2mSkup3qJLIBKRDa6FBUWpms0gO5uY4jA1pbdlYkIPRa6wWFt6OvbErNBZAIBPL32qNKCwtcXAIUOUYqqgQGP3NXsLe3xw3wdtFkV3NtQWUwCp3y1boIgcAKs6GTZ+eQeRVXrwLW8HTpsUFip7c2pLpbgSJ9NOgmVZrWqmWsPU1BTr1q0DAHzy6acodHenRmzcqpkW90C+mNl3Jn6f9Tueinyq440bMKiYAmiljHMAzMzEb0c/xiO7ZmFH3A6td1lfT65wiYnaD2tkj5F4Z8Q7mGAzARFBEcYlpppTVQWUliIt4waO/P0F2NpaQQ+na5rfaP/RGOQ9CIujFrcr/LnnBp+nJMMwGOg9kPqymZrSai33d1Uo6MTRQYl3i6muidqCau/evdiyZQu+++47bNq0CX/++Sf279//37NQz86mHkt8LouC69FXhKSkJNjZ9YeTUxcRUxxWVhRpAaiJY0WF2i/VVExVVpLpwZ07ytWrTo+5OTBgAH2p5MZ1VjHFYW1NWjsykp/0dM5C/be433DgnwMtDShycmgGFRvboXsYy7JIKVVaERrtRElDNBJTHJaWEB09BrZHD3iWSeH3+PNUsGgguL6cAKX66uLU9vGFj/Haidfw6flPeRFTHA8//DAGDhyI6upqrF23jkKyXMV8WhrVRhoo497X3hfmJurl2BpcTAE0mQ0MBPr0AQsgNi8G0upK2ED7PGHuNiqTaf84r66uhmm2KR4b8JhxiykAsLcHGx6Oj3L/wP/d/hpfH3xL6ybKHcFHzVSwSzC+mvwVJgRMaHc7BwfS2nxk0p7NPAuJvIPFtrt3aWE4Lo4mGRqePN1iquuitqDKzs7GqFGjGr8fPHgwTE1NkdtlwgBqkpdHlpoZGbzutqCgABkZlzFoUC+4ubkjKYnX3RsHHh70xbKUviGVdvgSTcUUQBNzLoUyIUGrxvHGiZVVk/z3uykpnVpMcbi48Le6OMhnEEb6jYRUIcXhwsMtDSicnWkmVV1ND8R2HoZH7hzBnD1z8NXVr/gZnBGglZjicHMDc/w4uUIkJQFz5gg2IVMHd3fybVEotLfgZ1kWvva+MGVMYVdsx5uYAkiAb9iwAQDwzTffIC0tjUQBpwTv3qVolR6ol9Xj7X/eRnaFZuE8oxBTqnh6gomMxAchL+KLXi9gaqWn1qFtMzPl7VTTdmH51fkoKS/RvzW6jrCODhgaMRmOlo6Y7jSMwrxpabwKez7EFAfDMB1+rs7OtFbr5qb1YQAACUUJeOnoS5i1axZqpe1E73x9lU2nc3Iae1apQ7eY6tqoLajkcjnMm1WNm5qaQtZlZqtqwj0Ms7NpUsYDqgYUo0d7wsFB2Re3yxEcTMLAxKRDpaONmOIICaEskfp6dD1xyrLIO38eWbt3Y0jfvp1aTDWnqoqyKdTQ2q0ikUjwgP0DAICDuQdRI232oLOyom7apqYUJb11q83JRHJJMliW1ZsluNBoK6Z+vPEjNl7aSCu3wcHAwYMkSs+dA55/nvJrDQTXw7m4WLthMAyDJ0KfwKs+r2Jwz8G8iSmO8ePHY8KECZBKpXj77bfph35+5BjGMIK4hrXG99e/x593/sSyv5e1W4+iitGJKQ4HBzADB2KY92CIpDJK4dUSbkqjiaBSsAos/XMpHvr5IbDObKcRUwC5Is4f+Az+XHQa3oED6IdZWbw9JHUVUyzL4pVjr2B77PaOI0U8U15fDncbd0R4RMDarB0ffZGIJmjh4aTKq6upZ1UHwYVuMdX1UdvlTyQS4cEHH4SFikPboUOHMG7cuCa9qPbu1U+TR33TxAElO5uaHtjZkeuaDjfTLuXmpy51dfQka6eCVBcxxVFZCdy4QfPl4GBlCn5n5252NlIPHkS4tzecPDzIIk8H50RjgWUpm7amhoIg4eGaXVpczZSllSXmX5mP5NJkbLx/I5YNXdZy44oK6vGlUFBtS58+re7zas5VRHlHaVS8b4xoK6byq/Mxbec0yBVyfDjhQ4zrNY5+sXMnRagA4J13AE4sGIDUVFrfsrICBg1q1SG6VViWRX19Pa9pfq0RHR2NqKgoMAyDmJgYhIeH0y/q6oTzf29GTmUO1p1dh7n952Jkj5Edbm+MYqpWWot9ifvwaNijMDMxowW5u3eb1rdoSHQ0PSf69VPfRj0hJwGP73ocZhZmOLLgCFysDevaqBOFhZS2Fh6uc5oAH5Gpi9kXseSvJTA3Mcfex/bC09az3e1ra+k0sLVV/7pvj3pZPepl9XC0dFTvBRIJRZm51ZzmvW8a6AxiqtvlT3fUFlQLFy5Ua4dbt27VaUDGSpOTzdISuHKFruSgIGX4V0PUEVN1dRRp6SQLYNrBsk3eIB9iiiM7myZcIhFpX1vD1tLrTGPNVFQU3O/eJfVhZ0e1VXxZHBmQmhqa5MjlZKqk7rOn0YCioWbq++jv8eyfz6KnY0/cWXIHpqJWzqGSEkp5YVmKGnDR5y6GTml+AM5knsHF7ItYOWJlU8Hx/vvAG2/QxXXwIDB5Mo+jVh+ZjG7HEon6Cyf/ZvyL3fG7MUY0Br29ewsmpjhmz56N33//HZMmTcKff/7ZcgOplKIEQUGCWZWyLKvWezRGMQUAn178FDvidmBkj5HY+MDGlhuwLN3wfX3Vnl3Hx1N0U93zpqqqCufPn4ertytEriLc432PZm/CQORX5+PD8x9iyeAl6OXUq+kv5fKmz46aGmoOqAF8pfkpWAUOJx9GpbgSj4c/3uH2KSmkqX19DZjVw7I0iOzsVhc3tRVTd+/exd27dzF06FC+R9wq3YJKd7r7UKlJi5MtNxdITqYb0aBBGj8E1RFTd+9SenOvXjx0/zZGuAdgYSG5EpiY8CqmOOLiaO7s70+fZWelhQFFfT2lGkilVFAS2jV6A3F92QB6Sx1lNDYXUyKRCHXSOvTY2APFtcX4deavmNt/busvzs+nFcaAAKBHD0TnRePInSNYMWxF+2kfnQRdxVS7sCywaBHwww+UunbpEs1KHRz4PY4aFBXRZeDl1fHik0whw0M7HkJKfgoeCXwEa6evFTxl686dO+jbty/kcjliYmIQERHRdAOuu7u5OUULeFr5qZXWanQeG6uYAoCjKUfxycVP8M6972C43/CWG3Czazs7oH9/tRrbJSfTo1ydZwMnpjpTzRTHm6fexN8pf2Owz2B8NbmdmtCqKkrrcHWlvHk1Fun4rJnSFC7C2Lev9u3JzmSegbWZNQZ6D9RtMM2FaUkJSgFcvHRJLTFVVFQEU1PTxvu0ra0tampqcPv2bYSEhOg2NjXoFlS607nzWAyJlxdNHBQKja3k1E3zMzGh3aenU6Sqy8GlbFRXA6mpgogpgLK5QkO7mJgCSMSHhdEMsrCQ7M66AO7uygWEpKT2631bE1MAYGVmheVDlgMA3j/3ftt1I56etCDSowekcin+75//w/7b+7EtZht/b8hA6CKmUktT2y/MBui827wZGDGC7oFTplCHTV0a+2iJmxtpOXXmuFKxFLMdZuNen3vxf1P/Ty8T4969e2PWrAZL/08/bblBYCBFBSQSKiLUwAW1LTLLMzF5x2RsvbFVrbopYxZTAHB/0P04MPtA62IKIBFgZkai4Pp1tWqc3d3VMzSoqqrCr8d/RY19TacTUwCwOGoxxviPwfKhy9vfsKaGFkoKC0mtdGCtzpeYEsvEatf2KY+t/BNrO/+vldZi3dl1ePbwsziVfkq7nXA0E1OV58/j1q+/IjQgoFUxVVdXB9V4hru7O5ydnVHfYFl64cIFAECfNtLRuzE+ugWVtnCdSQcO7HgJXQVNaqa8vKiWRKGgRfQuF0s0M6OlJQDy7Gxc++sv3sUUd5jO7NvQrjW6oyNV5gOkvAXuLaIvAgLo3JfL2+xl3KaY4nhh8Auwt7BHfGE8DicfbvtgDektZiZmeHfUWxjkGo4FAxbw+G70jy5iqkpchSV/LcGcPXOQUZ7R/sbm5sDu3XSzSksDPviAQsIGdP+Ty9u+DLg+U+F+4fh29rewMNVf7eHLL78MAPjtt99auuNaWFCU3sGBTvabN3X+DA8lH0KVuArRedFg0L4AMHYxxdFuU21HR8rr5rqF37hBoct2cHQkId5eQLCqqgpnzp3BgfIDeD/+ffx5p5WUTSPHz8EPn9z/CYJdgtvf0NNT2ZqjpoaEaRufIZ+Rqc8vf475++fjVuEttV9TU0NzI1NT7UsR5Qo5xvcaD39Hf7VqC9WlrKQE8bdvI9DVFb1KSkigNsPa2rrJM+ubb74BAPRsqMEKDw/H8OG0ePD666/zNrZuhKNTCar3338fDMNg+fLljT9jWRbvvPMOvL29YWVlhXvvvRe3bjW9KMViMZYsWQJXV1fY2NjgoYcewl0+eqhYWWmUa6yNAQUXda+oMGjbF+FwcoLM2xsJCQmwzs7GkPBwXsVUc6RSmqCr6XJqcNTqM+XtTSGd0FCaTHQBGIbejqVl62+pIzEFAI6Wjnhh0AsAgHVn16HD7GaZDFGFpvjadQGsq8V8vA2DoGuaX0FNAUSMCAwYuNuosRLh6Qns2UMrF6dPA7/9Ru6JrUwihKa6Grh6VVkap0p+eT4OnDogqAFFewwaNAijRo2CVCrFF1980XIDU1MgIoIiLVzn4pwcrY/3wqAX8O6972LVqFXtvldjFlPFtcV45uAzuJl/U70XWFmRqHJ2JmV96xa1ONFyNZJL8/Py9cKIoBFwsnLCqB6jOn6hkaCVU56DAy0UOzoqP8Nm/Zb4FFO10lr8nfI3EosSW7qytgOXGKTL6WpnYYeVI1bit4d/U7svW0eUlpbiwp078Jo6Fd59+tACSUICGIZBb27xE8CECdRfa9kyMk1avHgxAJonnjx5EgBw9uxZAMAHH3yAEgNE/rvRjE4jqK5evYrvvvtO6ZDUwIcffohPP/0UX375Ja5evQpPT09MmDABVSq9KZYvX459+/Zh586dOHfuHKqrqzFlyhR+mxJXVVGEoA20dfOztFQWW3ahAEQjMpkMlwoKILe1RVhICEyTk3lvmqxKaiot/N66Rc8KY0ajpr2BgZ07DNcKZma0aN+vH801OdQRUxzLhy6HpaklruRcaTOlI7cqFxX1FbRyYWmpnMx2wq7QfNRMBTkHYeesnfjs/s/Ur78ZNgzgRMIPP5CqSUykGjU9YmmpjFDl5Sl/XldXh5d2v4QP0z9EsV2xwVK2uCjVN998g+rWUtJEIkrj5RwSsrO1vlExDIPJwZPhbde224IxiykA+ObaN4jJj8Fnlz7reEGEw9SUaqi452x2NkWsWkEuB8rKWs9SVa2ZiuwXiTdHv4m9j+6Fg6X+awS1oaK+AtN3Tsf317/XXFiZm5O479GDvs/JabSn57tmytrMGrsf3Y2VI1ZisM9gtV/H3Z75KPfhU0xxBhS9+valB1jPno25yCkpKTjyxx8AgKNHjwIANm3ahIKGzza/4X553333gWVZiEQiHDp0CADgqq4NZTcGo1MIqurqasybNw/ff/99k0kCy7LYuHEjVq9ejZkzZ6Jfv3746aefUFtbix07dgAAKioqsGXLFnzyySe47777EBkZie3btyMuLg4nTpzgZ4ASCaUXZGa2mqahqzW6lxctuCkUNEfpKql/jTVTIhH6PfooTCwtSZimpgp2zMBAyq6prTXu/lQaianmiMWUI2rsilENLCya1sVUVKgvpgDA3cYd/7vnfwCA9efWt/i9RC7Bq8dfxew9s5FQnEgpqNzqdmwsb73m9AGfBhS25rYt3cA6YtEi4Omn6Ua1di1V+9++rVdRZWqqdC1OT6fF4bq6Opw8fRL1JvUwtTCFt73h+idMnToVvXv3Rnl5OX788cfWN2IYKuwJDCSDCg3cO1mWxbHUY5ApOu4PaexiCgCWDF6CaSHTsGpk+1G2FjAMrUT26UPXdBumUWIxZVcmJjb9eVsGFHYWxvcZtcWfd/5EYU0hTqafhAmjhQMsw1Dudf/+5Pjg6SmYAYWzlTMeDXtUo9foIqjSy9Kx/ux6FNfyl5rcqpsfw9ANKTISxQ0CavKjj0KhUIBhGBw7dgwA4OlJ9vAeHh544QXKqpjc4Jo6ZcqUxnZF27dv52283fBPp3D5mz9/PpydnfHZZ5/h3nvvxYABA7Bx40akpaUhMDAQ0dHRiIyMbNx+2rRpcHR0xE8//YRTp05h/PjxKC0tbTLJiIiIwPTp0/Huu++2ekyxWAyxyqpWZWUl/Pz8UFxc3LoDSloamOxswNwc7MCBtLwOoLCwENHR0QgPD4e3Do2QxGIgOpqBjw8LP7/Ob6Muk8lw9epVMAyDgQMHUppfaSmYW7fA9uqltRW9OlALIgYsCwQFsfDxEexQWpGbm4vY2FhERUXBTZv279HRYKqqwLq4KE0rOjkKBXDrlhRnz95GRIQMw4ZFdCimOLIqstDn6z6QKWQ4Me8ERvuPbvxdXnUelh1dhor6Cmyfvh1uNm6NYoqprKTrOSLC6FMp09PTkZycjCFDhsDR0VHj17Msi/fPv49xPcdhqK8ONr319TAZNw6ia9fAhoRA/tlnYIcO1Wu/AupnxqC2FnB3r0d+/gW4uroiNCwUt4pvIdw9vOOdCMi3336LJUuWoFevXkhISICJuoKptJRmj+2kRJ9MP4nV/6xGX9e++GHKDzARtb5vlmWRnJyM7OxsDB06FLadvZ+EulRU0M2kYS4glQIXLtD9cfRoFgxDC7iXLl2Cn58fKmwrcCrjFJ4f+DzsLTqX8xnLsjiZcRJu1m6I8Ijo+AVq7C8+Ph4lhYUY5u8Pi8BAnZ4tClaBrIos9HTsqdXry8tJVHl5NU631ObVE6/ibNZZjO81HuvGrtPq+KqUlZXhypUrCAkJaayBaoFcjlUvvYRPvvkGo0ePpgV9hQJePj4oKSnBV199hWeeeQYAYN7gUBkfH4/g4GBUV1fD2dkZAFBTUwMzTd9wO2RmZmLNmjX45ZdfAKDb5U8HjF5Q7dy5E+vWrcPVq1dhaWnZRFBduHABI0aMQE5OThOxsmjRImRmZuLo0aPYsWMHFi5c2EQcAcDEiRPRq1cvfPvtt60e95133mlVbO3YsQPWrU2uFAo4pqTARCyG2MkJ1QIIguaunF0RkVQKBY83i7YoKrJEfr4NRCIgIKACVlYdr+h2FkxrauCQkQEoFKhzdUWtl5ehh6QzLAukpzugpsYUFhZyBAZWwMRE/VvXN9nf4O+Sv+Ft4Y2NIRthLlKmeIgVYhRJiuBrqbxmGbkcDunpMKmrg8LMDBUBAVCoYcPcWbleeR078nbAlDHF6oDVsDfV/oFqWVSEMa+8AsuKCuQNHowrr7/OT9dNDaisNENmpj1EIqB37zKYmwuXRqwpYrEYzzzzDKqqqrBq1SoMGTKkw9eYVVfDPiMDcgsLVPbs2eY9MrYqFn8U/IGRjiNxv+v9fA9db1TKKnU6B1tDJJXCMSUFjFyOGi8v1Lu4gGWB+HhqzNunTynMzJT3FAWrwEcZH6FQUoixzmMxxW0Kr+PprNjevQuLsjLIbGxQ5een9fP6Uvkl7Cncg3ud7sVkN/32sEurTcNfxX9hlscseFho6beuJdOnTwcAfPHppwiXSlHm4IDpzz4LANi1axfMzc2RkZHR6BWwf//+xv9v27YN3t7e+OqrdqzvO6C+vh4HDx5szOJqTreg0h7hqv95IDs7G8uWLcOxY8dg2U6fp+apAOo0MOxom1WrVmHFihWN33MRqokTJ7Z9slVWgrlxAwBQ5OmJa+npOkem2oIrM9LzPEVnWo1Mtb0xvUGB3mR8PFBSwsDSEoiKYttb+NULOkemVCkoAHP7NgCADQ6mZbxOikQiwZUrVzBwoBUUikhIpSK4uLAaBd+G1w9HxHcRyK3OxTXba1h771p1Dgzm5k1AJgMbHq5xs0t9oGtkimOcbBysr1nDy9YLs/vN1nlcTHAw2AkT4HXlCqZcuQLFmjUUHSgvp6Y/AlNfX4/t2+OQIksFY8VixcTHIGKM52Z59epVfPzxx7h06VKbWRJNqK4GExdH6eUWFmD792/1fJyESVhctxg25jat1oV0hshUWlkaFhxcgCm9p2DF0BWtN+XWBoUCSEoC02CWwnp6Ar17w9HRBDIZ0KdPFeLiKDIVHBwMhmHgk++DH2N+xPqx6ztNul9WRRa87bx5+9waI1MlJRg6dCgsq6vBJCXR89nMDGyfPpQmrSFxF+JgV2+HkQNHYlLYJF7Gqgkv4kWd96FWZKoZN2/eREREBJasWAHp8eMAgE2vv46lGzZg2bJlyGxof/Lbb7/h8uXLuHDhAtauXYtJkyZh27ZtyM3NhZOTE4YNG6bW8RQKBXbv3o3Vq1c37luVQYMGYf369YiMjOyu09IRo45Q7d+/HzNmzGiSEiGXy8EwDEQiEZKSkhAUFCRIyl9z1G56lpqK0ps3kZCWhp6zZsFXzYtME6qqKOfb1ZVSnDsLGvWZqq4m5wgnJ6onEGQ8wLVrNCnv39+wWV061Uy1RUYGfTEM1WLw3dhVDzQ3oKiuFiEmhuZGvXppNjfff3s/Zvw+AyaMCZ655xk8EvoIxgeMb/9FYjEdTI/NKtVF0Ka9fPDzz8D8+fTvn36im5VMRoXuAt64OGt0WNjgjZh3UMuW4t0JqzAr9GHBjqkpWVlZCAgIgFwuR2xsLPr379/xi+rrqa6vtpbS/sLCNLqmO0PNFAD8cvMXfH75c4zqMQqf3v8p/wYi2dlk8c+ygIMDLtf0Q0mlFHV1FxAa6tMp+0xxiGVizPpjFixNLfHJxE/Qw6GHTvtrs2aqro6ez1yNqb9/E/MFdbmZfxOhbqEwM9EsypWXR9k6Tk6ap/vxRas1U2oyYcIEnDhxAsueegobn3wSYFkwY8cCAE6dOoWxY8dCLpc3zpFKSkrg7OyM7Oxs9GgwCuHqsFrj+vXreP3111v1CXB2dsYHH3yABQsWNJmDdTf21R3jWbJrhfHjxyMuLg4xMTGNXwMHDsS8efMQExODgIAAeHp64niDygdoAnb69OlG//6oqCiYmZk12SYvLw/x8fGN2/BJgZUVEtLTEeLvD1+B8vPEYnqmZmfz0v9RL2jctFcioZt2bq5g9sumpqQzoqK6oJgC6AHn7k4Th1u3Op1FZGtufvb2TdtulZaqv7/pfabjkdBHIGfl+CX2F6w+tRoF1QXtv8jCoqmYqqw0CrMPvsRUdkU2j6NqxpNPAitX0r8XLVJaqWVlASkpghySE1Nubm4YP3IoXhq1GIN79sdDIVMFOZ629OjRAzNmzAAAfPnll+q9yNKyaa+q2FigoAD1snq8fPTldnv4dBYxBQBPRDyBb6d8i5UjVgojbPz8aAXN1BSoqIAsLhoJ167By6tHo5gSyzpn24T08nTUSetQK61Vr+VBO7RrQMHZ03PZN5mZ5O4h0cxNMMIzQmMxBdC9PyFBs0eaVC7F/w7+D3sT90Ku0O0erouYAtBoRvH5jz+iqEcPwMYGeXv2AADGjRsHVi6HiYkJDh48CABwcaG0VD8/PzzxxBMAgDlz5jTuLy8vD8899xwYhmnM/lEVUytXrkRJSQlYlkVJSQmeeeYZQdvT/GdhOxljxoxhly1b1vj9hg0bWAcHB3bv3r1sXFwcO2fOHNbLy4utrKxs3ObZZ59lfX192RMnTrDR0dHsuHHj2IiICFYmk6l93IqKChYAW1FR0eY2+fn57KFDh9i7t26xbE4OyyoUWr1HdUhIYNl//mHZS5dYVoO3YRCkUil79uxZ9ty5c6xUKlX/hWlp9CbPnGHZ2lrBxqeKXK6XwzSSnZ3NHjp0iC0oKBDmAHI5y16/zrJXrrBsXZ0wxxAAsVjM/vPPP+zly5dZeSt/lKQkOjXOndPs/M+rymPt1tuxeAfsi3++qNmgSkpY9vRplo2J0f+JokJKSgp7+PBhtrS0VKf9JBcns0N/GMq+deotViwT8zS6ZshkLDt1KssCLOvmxrIXL9If7p9/WPb2bV7vkbW1teyxY8fYGzdusAqV/SoEvA/rwr///ssCYO3s7Njq6mr1XyiXs2x8fOPn+PWZT9mob6PYKTumsFJ5y/urQqFgExIS2L/++qvJc/E/T00NW33qFPvr8i3s9vUX2JwcOk/OZ51nJ/4ykT2actTAA9SOyvpKNqEwQad9KBQK9saNG+zx48fZ2o6evfn59Iw+d45l6+s73PfJtJNsjaRG67HV19Op/++/mt379yXuY6O+jWIn/DxBp+OXlJSwhw8fZlNTU7XeB8uy7NGjR1kALAC6ppOT2WcfeogFwE4eN65xOzMq7GN/+eWXxp9xr2vra8aMGWxCgmbngDpz3G7ax6gjVOqwcuVKLF++HM8//zwGDhyInJwcHDt2rMkK3GeffYbp06fj0UcfxYgRI2BtbY1Dhw6p766kBqrW6D6hobRyI2DaQO/etHheV0fZC8aKxpEpVXr2bNpcUMD+VAC12rh6lZyf9IFgkSlVRCJajY2MbNM62NhQp89UUBDg4kLNfzW5jD1tPfHaiNcAAIeSD6FeVq/+i01N6ZouK6PlUQNkS/OZ5nez4CbkCjmqJdUwEwmUN2NiAuzYAQwYABQVka06d2/MyyNbdR4+R9XIVO/Q3lCwynsFwzAQiykoJvAtRCNGjx6NoKAgVFVV4Y+G3jRqIRLRie/rC3h64rGBCzCp9yS8OvzVFnUzbCeKTB1NOYpqif7aFFTJ5ThXW4vew/wx9vFBcHSk5/XO+J0oqS1pN+JnzNhZ2KGvW1+tX89qao3u4UFpHmFhNClph4SiBKw8vhIP73pY6781Z5duY6PZvX9y78l4ZfgrWD50ufr99Zqha2RKlYkTJzbew7ds3Qr07o2vt24FAPx56hTu3LnTeEwAeOKJJxASEtJq1LZfv344cuQIFAoFWJbF3r170bev9udAN9ph1DVUxkR7+aXt9pmSyykvSVeTgVYoLaWsD4B68BlbGYVOYopDLKZCJ6mUJmIC1VMpFHSY2lqqr+3fX1i3cb2IqbaoqtKtvbyAaNK0V1NiC2LR370/6mR1CP4iGDlVOfjwvg/x6ohX1d9JeTlddAoFTST69NGbLb0QNVOxBbHwtfeFs5XmReUacfcuMGQIpfBOmABs3UoKh2Wp35Kfn9a7VhVTERERWH1qNXKqcvDuve82WjJfuULXdkCAslepMbBhwwasWrUKI0aMoLovTWFZ5fnHpaI2zDL/n73rjo+i3r5ndrOb3kgPhCSEJBAIAUIPRUXBgoqgNHuBn8/Kw4rliYrYedZnf4ooIiAoqAgIPnoJKaSS3jvpbfv8/riZ7G6ym2yZ2Www5/PJh5DszkxmZ75zz73nnjuYyFRqdSoe2PsA/F39seO2HXCTCmuWYWzOFFgWipJC7Gg8gVvG3QpXqf0Z0RhCVl0W1Bo1YgNM6MXrA2aTKWO4dInu9bFj9ZqckiqT8MqxVxAXEIdXrnzFok0XFFC7g4DhgEHwSaY4dHZ2drtGy2QyODo6IiUlBZMnTwYA3HTTTd2yP104OjrihRdewFNPPdU9o8paDPVQWY9BX6EaaPRJplQqKnlkZlJWm2cMG6aVMF+8SLuzF/BCpgDKeMXE0PcC9lOJRJRgE4uJqBoww+ENA0qmysuBpCRh/0ALYQ2Z6ujoe37smfIzuO+X+/D0oafhKHbEa1fR7JHXjr9m3nBHLy/tbK+aGiA31yaVKqEMKCYETBCeTAFUTdm3j5oVDx0CXn2V7msfH1gzCK4nmaptr8WpslPIrstGh1LbYMGZl5SW2q4CbQruvvtuiMVinDx5Etk9p8v2g8bORi2ZYllyKurqYxlMZAoAGDAY6TkSs0JmDRyZAoDSUkiLy3CHcixcVYPDmELDarDx2Ebc+8u92Jezz+Lt8EamNBpaFxsaKEvZ1NT9q/jgeOy8bSeenPWkxcdp7kBfhZruB2sgBJkCAGdn5+4eSn9/fzz55JPdZAqAHpm6/uqrUV1dDZZlIZPJ8MILL/BGpobAD4YIlRXok0wBJBHi7EQ5m1GeERFB/aEuLvYjZ+GNTHHw9ib5n7e3oGU4V1dtxqu42DzDA1MxoGQK0FrQFxURIbATWEOmZDIgOZluMWN5i6rWKjiIHODn6gexSIw7JtyBiYET0SxvxqtHXzXvYH18KPPKydYEMljgwCeZ6lB24M0Tb6KhU4CLuz9MnkzyP4YBPvsM2LqVSsG6n7UZhh89yRTDMAhwC8CO23bg5SteRoxfTPdr/f1ptrBKRdlte0FQUBAWLaL5Rl999ZXJ7ytrLsNN22/CO6fegVKtpJuguRloaQGblISc5ORBQ6YAMifYfut2/HPGPwXdjyEypVDQunGy4AJYb29K4nV0UOLpkhnJlgGCTCXDGN8x8HD0wOyRsy3aBm9kCqD7mRuGLpcTyS8u7k48ScVSi4cks6zWWNDUy/rNE2/iwV8fRH6DZeu0UGSKw8MPPwyAKkTvvvtu98/d3NyQdOIE2PPnwf71F357/nkE1NfbV0ZoCHoYIlQWol8yxSEignpXZDKqVfMMsZjaEyZMAOxh7ijvZIpDaCj9kQJ7pAYEaKt+WVn0sfGFASdTAP1xnLzq4kW97OFAwVqZn5MTjRBgWeOf2S1jb8HWW7bi8emPAwDEIjHeueYdAMB/zv8HefV55h20vz/J/QB6wAlUpeK7MvXB2Q+wM2sn1v6x1uqsrUW4+WaACxqefBL45Rft7/LzgdRUkwIGQ2SKg7+rP66LvE7v9QxDNvsAFWnldmTidv/99wMAtmzZAoWJLmlHS46iU9mJwsZC6pvqcl1jnZxQmpuLpr/+QkJMzKAgUxykYqmgEjtjlamGBmDr0eNY8+v9eOL0BmjiJ2t7dzMyKPlkx50RLhIX/Gvev/Dzip/h7Wz+GsErmeLg6kp9VYGBAMvi96TtOH7ka7BW3ngdHfSxODiY5szbJGvCgYIDSKpK0qtYmwqhyRSHS5cu4aqrrsLOnTu7+6BaW1sxOSGBElGcJX1tLVX9hMj2DsFqDBEqC2AymQKI8XCBV1WV1jqYRzg66rdxDFSlSjAyBdAfqPtHCiCh5DB6NGW/VCr+vDDsgkxxGDVKa6eekQG0tw/YofDVMxUVRZ+ZUmn8M4v0iYSjg1YiMX/UfFwfeT1UGhWePfys+TsNCKBsBlet4hlCyPyWjF2CSJ9IPDb9sYGbtbN2LfDgg3T9rVpF5UW5nCqmra1EqvoIvAyRqXMV55Bbn9vnbn18yHFco7Evxet1112HoKAgXLp0Cfv2mSbZumPCHfjo+o/w3Jznuj9H1skJF11cUN7aigkxMXAvKupbBzvAYFkWm45vwl9FfwlO7vuS+Tk4AJcUlRAzEoR5hUEkdaQKC/dsLymhddKeNPUGYEnVRxAyxaEr9mkeNRxvl+/AP1PfxPE/v7KqwuLqCsycCYwfb9qS6+XkhV3LduHphKcxIWCCWfuyFZkCyBb98OHDuPXWW3uvywxDhGryZG3VLy2N9MtDsCsMESozUVtbazqZ4uDlpa0K5OQIVrJVq2nzaWm2T6gJSqZ6Ii+PZAQCLShcP5VUSl4i1voi2BWZAmiBHjNGO88mPd3s+SF8gE8DCu4zk0goJs/NBQobC/HY/sdQ22687+6tq9+CiBFhd/ZunCi1wBTAy0u/j4WnzKFQPVNRPlH4fsn3mBI8hbdtmg2GAT74AFiwgFLON95IDoATJ1J2qL2dSJWBUqMhMtUsa8YLR17AnXvuRGJFYp+75qpUVVXkkGoPcHBwwL333gsA+PLLL01+34wRMzDCg55B3T1TlZWYcOedcAkLo+vx4kX70jjq4HjpcezO3o31h9ejpl04+XGfPVOguP8q/+V4beKPeGDyA/RDhqHM2tixtLg0NtrPBdOFwsZCvHPqHbTIWyx6v6BkSgeSoOFYEn87xnlGYnbkfKtVJo6OtOyaikC3QCwbt8ysfdiSTJkMd3eq+g0fTtenvbmQDWGIUJmL5ORk88gUh/Bwyi4oFCQhEAAKBVWEm5pI1mIr2JRMAZSmAug8CiRZc3ICpk2z3hHM7sgUB5GI0nzOzhS4CmT2YQxCuPk5OZHPAbU2sXjuj404VXYK7515z+h7xvmPwwOTKIh68uCT1mXKuWyGlTcf32SKZVk0yZq6/y9i7GDZl0iAHTvoA6usJFLFsmTv7+xMwWtKit7kTmMyPw2rwcTAiQj1DEVcYFyfu/XyInloSIjg6mGzcN999wEADhw4gNI+EkV7c/b2ki71MqDw9CQiMHIk3eeenoIeu6WYNnwa7pt0H+6bdB8C3QIF2Ud/ZAqgChUA+EpG9rbTDgigysCYMXbnjLr59GZsz9iOd069Y/Z7bUWmAJIkPjr7CXx9788QjY7U/kIu51dTrwMNq+l/aLsR2CWZ4iAW08yc6dP1r8fmZruWpf5dYAdP1sGF2NhY88kUQA+2MWOo5BEWxvtxARSHjB5N3xcVaZs3hYTNyRRAfUBd2mxkZQnWEKH7p6jV5ico7ZZMcZBIqC8tKkorb7EBhLRG9/am3AXDMLg7dANmhST06yj18pUvw1XiirMVZ7Ezy4x5QD3BBSX5+TTUzAIIUZk6UHAAi7cvxh/5f/CyPd7g6Qn89hvJT1NTgZUr6ZqcOJGSJnI5karW1j57prydvfHm1W/iy5u+hFTcfyPp+PGkerXFUmUqIiIicOWVV4JlWXzdNYumJ46VHMMrR1/Byp9WQq6iNc+omx/D0B85bZrpdmg2hpODEx6a+hDWxK8RZPv9kSmWZfF1yteo6yRZpFE/FDc3ukY5tLXZRV/VXXF3YazfWLPPny3JlC5EYget3INzpTx/3mTjD05MUVLS/6n/s/BPLP5xMT47/5lZx2jXZEoXujMl29po/UxO1ktAmQuBuO3fCkOEykwMt8LiFx4eWi2ZQAgKogysRkPrlZD9VANCpjhERtKDTqEgUiXgH8q5yKWlmS6jt3syxcHZWevCAQgeJAhJpjiMHAlERwMLZ4zEB9e9368tuO6w32f/fLY7WDUboaHakmZeHunKzIAQZIplWfye9zvaFG0oa7ZD6VdYGBlTODkBv/5KRhWOjkSqupriOhsbDZIp3WoiwzAWO4fZCx54gCql//3vf6E2EN27SlwR7B6Ma0ZdA0cHR9Os0XUDr9ZWcq4bYOmaJeYA5sKUytSxkmP4OPFj3PfbSnSq201b2zUa6qcqKaHofgD7qqYNn4ZvF3/bLfs0BbYkU0mVSXju8HOoajWwDqpUxGBVKjqfJkzdbm2lFvSqqv77p86Un4FSrew16LovDBoy1RMKBVWuWluJoJaXW/QcF+BR/LfD0GBfEyHI0LPmZkHkGAoF3VcKBUlbIiJ438XAkikOnZ0UIKhUVGHhynM8Q6mk3chk1NjeX0PsoCFTPcG5cAwbZtWgVWOwBZlq6GxAi7yle6CrqWhXtCPqoyhUtlbi3QXvYt3MdZYfRH6+VvY3dizJhvqBUD1TAKDWqLE3Zy9ujL7RrADDptixA1i+nL7/+GPgoYcAlQqd1dU4kZ1tsDL10l8vwd/VH6vjV5tUmeqJ5mYqNIwaZR9FHJlMhuDgYDQ2NuLAgQNYsGBBr9d0KjshYkSQiqXmz5lKTqYhPhIJ2dUPwB+tYTW475f74Ofih2dmPwNfF1/e92EKmQKoB+mNE28g1j8OsZ1kXT1nTvdcZOOoqSF5r0ZDCanx47UydBuAZVmLDGVsSaZYlsXdP9+NrLosLBu3DE8nPN37RRoN3YBcj5+7O0mAjRxXSQm93N9fO5qyr/2frTiLuIA4OEv6/zsHLZniIJfTNcn18Hp7kyLKjDlVQ4N9rccQJx0oXLxIchYBZgFJpZShB2it4tsQzy7IFEALL+egWF4umFudREKFRZGIMmR9eWEMWjIFkPSisZHs/evqeN20LcgUy7J4+X8v4/bdt+Nw4eGun9Gf09+oKFepKzZeuREA8OqxV62b0zR6tP7E7X7OpZBkCiCL+FvG3mK/ZAoAli0DXqNhy3jsMeCPP9CpVOqTKYWi2yU1qy4Lv+X9hi0XtqCwsdCiXVZVUQumQC2tZsPJyQl33HEHAOPmFM4SZ8vIFECLGGeFmZrK+z1uCrLqspB9KRtnK84K4uxnKpkCgFHeo/DZos/wj6n/h4gIUj6bxFO4vionJ0rqJSfb7FzWd9Rj+a7lOJB/wKzzZ2uZH8MweH7O85gbOherJ682/CKRiLK9sbFaN6Hz543285oz0JdhGMwYMePvQaYAIk4TJpByhzNRSUy0eW/03x1DhGqgwC1oeXmC9AD5+FBMJ5Hwq4azGzLFwdeXUsyxsYJmCd3daa0CaEahIZI6qMkUQH1pnKQ1O5tS+DzAFmQKADpVnVBpVGBZtrtC1dxMSYXy8v5zF3fF3YUJARPQJGvCxmMbrTuYyEg6n0CfUZpQZKq0uRS7snYNzKwpS7F+PXD33YBaDXbZMqRu3aolUyoVOXtmZABVVYjxi8E7C97BI9MewRjfMRbtLixMG3sIOIXBLHAzqX7++WfU1dVBoVbg0d8fxfGS4wD66JkyBZyU0seHHgqZmTa3Xh7vPx7fL/keG67YAD9XP163bQ6Z4sAwDBxEDggJoeelyUuTmxs5rnl7k3QtM5MyNwLfb1vTtqKwsRA/ZPwAFqbta6B6pqJ9o7F54eb+Z2P5+ABTppBaR62mUpSBoMUUQnWh+gIUatMday8LMqWL4cPpXHp4kOLExNiyvn5QzLC2ewxJ/kwE7+VQlqUKVUsLLcoTJvA+y0atpi++WrbsjkwNAHJyKLMtkdDzlGtRGPRkigPLUnBw6RJ17U+aZBVRtRWZ4qBhNcirz0O0b3T3z4qK6BktElFi2c3N+PsPFRzCgu8WQCKSIPvhbEQMs0Ivy7LUMGwk6BWKTKk1ajyw7wGk16Tjvkn34aGpD/G2bcGhUEB99dUQHz8ORWAgJMnJYIKC6Fzm5mr70iIieJGl5uWRf4inJ13q9oCpU6fi/PnzePfdd+F/tT/eO/MehjkPw57le1BaUGoZmdIFy+obpwQGUnlmEDdRmEqmWJbF80eex3j/8Vg+bjnEov70ff2AZYHCQsraDBtGiT0B57vJVXJ8n/49pg2fhvH+4004PNuTKbVGbdl5ZVlaqP38ej1zOjuBs2fp1M6ZY/hSrW2vxS0/3gIfZx98ddNX/RL2y45M6YJlaQZdYKD2elSpjDrxJCcDVVUtWLRoSPJnDQbvCjrYwc0CEospPWqhK1hfEIv1yZQ1lapBQ6ZkMkEzhZGRWtVMXh797LIhUwBdl2PHajNcaWkWV1BtTaYAsgTXJVMAVSKGDdMm5fvqI78m4hpcO/paKDVKrD+83rqDYRh9MiWTdZdChJT5MQyDhREL4ePigyVjl/C6baHRqVbj2OOPQzZyJKTV1WAWLybnKoYBoqOR56WGTKOge7zQMqmfLjhn8eZmwSYwmA3OnOKrr77C0rFLcVfcXXhq1lP8kCmAzmVkJH0xDC1mAg95zq3PRV27MLI4cypTiZWJOFhwEB+c/QCVrZXdP29ro1vT7BGRDEPkfvx4wQZ868LRwRH3TbrPbslUfUc9btp+E75P+x5qjTHbRCPgBtjqkqmyMqCiors65e5unPeXt5TDw9EDfi5+/fbmDRYylZmZiWXLzJuhBYDOZVCQ9nrUaIg1XbzY6wEok2mrf0OwDkOEaiDh4qJ1jCgsFKwHCKCCw9mzlrlqDhoypdFQ1a+sjDJdAoAbIOvjQ0ndy4pMcRCLKdPKTWXPzjZ7E7YkUx+c/QBfp3wNDWs4Y8BxRK7lITu7b7799jVvQ8SIsDNrJ06XnebnIDkL8PR0FCUlCdozJWJEWDF+Bfau2CvYfB8hwFmje0dEwPHQIWLB586RDFCjQYu8BY9kvIWVJZtRKqshuVpOjlXJE0dHijsAwZYMs7Fy5Uq4uLggKysLqedT8ei0RzFCMYIfMqWL4cOBuDjt8DaBoFArsP7weizdsRRJlUm8bttcmd+U4Cl4Ye4LeGjqQwjx1FY4L14kRWlrq4UH4uurP9gsN5fXJKm5PZ0DJfPblbULNW01OFh40Pp5d+3tFBfl5UGZlg2RRtWn3G9y0GTsWb4HG6/a2Od1YE9kasOGDThxQjtQftSoUXrH/s0332Dnzp344osvrNsRN5i6upp6q3QG0HNSP3d3AS2h/yYYIlQDjeBgraa9v0jPClRUUExnrsP4oCFTALEdboEsLhZMFOzkRHyjru4yJPAxVW8AAQAASURBVFMcuBlVus1jJsKWZOripYv49sK3+DjxY6TVpBl9nURCSWTOWKSv4Hm8/3jcN5EGrT5x8Al++pCkUsDdHRVlZaj+808kjB3LO5lSqpV6pNLRwXSHp4FGrzlTUVHAnj30we3aBbzwAipbKyFmxBC7uiEwdiY3wdnqSlVICG2qsZG3tkGr4OHhgUWLFwEAvvjiC8t7pkyBl5e+rV1+Pu8NZU2yJrhJ3eDk4IQonyjetmtJz5SIEWHxmMW4K+4uvZ9zp8DoLCpz0NhIw6rz8uiZbuVGO5QduH337Xh8/+MmEauBIlMAsDp+NV6c+yKemPmERU6EenB1JYMfkQgjJDWY43gOYZ59X5tODk4Icg8y+vuBJFOcO+Obb77Z/bOXX34Zc+bM6f7/5MmTAaB7Ft3GjdTLu2aNlbPafHy0Q9PlclKe5OYCajXq6gCNRoPq6nTr9jGEIUJlF4iOpsaOUaMEyxSOGUOxCTeT0BQMKjLFISBAO6Q2O9uqQXd9QbcyxTD+l2fJ3MmJGsXM6KGytcwv2icaG67YgHsm3oOJgRP7fK2bG1UVxWIqvvWFV658BS4SF5wuP42fsn+y/kAZBgWOjshvasL4mBh4lZfzHrh+cv4TrN67GuUt5bxuV2gYHdo7dy7Aud29/jrG7DuNHbftwDsL3oF0+EgqFbu6Wt1L5eRESqMxY+zDPr22vRYXR14EAPzwww/IysoShkz1RE0NubekpZk9Q60v+Lv64+ubv8ZXN30Fd0d+/gZzyVSLvMVoBRvQysh4MXDy9iblCcPQOU1JsWr2V2p1Kho6G1DUVAQ3aR8NoBhYMgUQYb15zM2YEDCBnw0OH06Nry4uYJQKOGReoASKTpIrqy4LyVXJ/W7KXipTzz77bPf3t9xyCwCqRAHA999/DwC47z5K6Dk6OiKyK6GpW8myCB4eZFjBxUeVlZCfPI/Gshbk5ORAIrG0PDsEDkOEyh4gldKFPqzvAaTWwNFR30q9oZ9E16AkUxwiIijzqlaTKxjPwxd1yRTL+iMzk3pzFKabCw1O9OMxPRA9UwzDYFHUIjwy7RGTXh8YCEyfTrNM+kKQexCenkWzU5758xmznKMMoaCgABdzczFhxQq4h4VR5JaezhupapG3YHf2blyouYCChgJetmkLGCVTHO66C3jhBfp+zRq4nUzUzhjz9aV1k4dG0dBQ/f7tgcT5yvOQhkvhFugGmUyGyspK4ckUQGYAAQEUrObk8NqLKmJEehI7a2AumWJZFi8ceQH3/HyP0XuDq1Dx5ogbEkJySqmUsphJSd2W/+ZiVsgs7Lh1B1658pU+560NJJm61HGpT8JqFThHRW4URWkp6TNZFizL4u1Tb2PNvjXYkbnD6CbsgUwxDNNNoL799lsAwLZt2wAA9957LwAiUBFdbSCnTp0CABw/Tg6fupUsiyEWU9Vv4kTAyQm1VUoUnz0LB4d2zJkzzfrt/80xRKjsETKZIBPYfX21rtgXLxonAIOaTAEUFcXEEIvs6KA/lqfAoGfPlK+vttUoK0tw19yBAycTKCnRDmLUga3JlLn2uLrQjb8VCsOKnJq2GgxzpgRHYWMhvkn9xqJ9AT0MKHx8tE14HKniQWfm4eiBbUu3Ye2MtZgXNs/q7dkC/ZKpLny8KAD1N19Da+KSJdqhyYA+A6qpoTk2MplVxzXQ9/B1o6/Dy3Ev4/qF1wPQZq0Fh0hEzYbh4fT/sjJKSFkoWTtafBQ/Zf3Ea6Bticyvqq0KF2ouILc+FxKxxOBruOWKF8kfBy8vIgKcwU96usU29aFeoX1W4AeSTGlYDR7+/WHc8/M9KG3m34a/vBw4lyRGhWsUrZ0ODkT+GQYKtQJjfMbA3dEd88PnG3y/PZApDhyBuvvuuwHQ7Lnwrvvt9Gnq1+UqUQkJCQCAAJ3B8CV8NXp6eUETH4/k6ja0eHlh/vwJkPJlB/03xhChsjdcukRBAWchxzMiIkglo1BQErInBj2Z4iCVaqfxyuW8PCkNGVCIxdSbIxZTAYcH4zH7hKOjNtAqKNAb6mRrMlXQUIB//PYP3PvLvWiWWU5Gmpu1txrLskivScem45sw48sZCHo3CI/98Vj3ay0dimvQzU/X2cTFpX/9oYkIdg/GHRPu4GVbQsNUMnW67DS+vvANbl7QAMWkOLrJXnqp9ws1Guqb7OggiZWFBj9VVWTe09Zm0dutBjdnStQowqYXN0EsFuPs2bPIE+h5YBChoZSQ4hoOU1LMJqkdyg68cfINvH7idezO3s3LYVlCpgC6L3Yv241N8zdhpOdIg6/hvULFgZv9xWUy+5rZ0AP5Dfmobe9/MOtAy/zyG/JR1VqF8pZyeDl58b79lha6rdVqEJGaNq37fDo6OOKZyY/i12W/wMfFp9d77YlMAUSgQkNDAQBnzpwBAJw8eRIAMGvWLABAYKDWSKisK3mZnEySxqlTp3b/rqGhATNmzECnBXJSjUaD8ykp8Ixzwr1r4hASMkSm+MAQobI3SKW0ctTUCDLlmktCikS01us+QC4bMsXBw4OMFSZNMjp/wVT05ebn4kLnFKCk7mU7nDwkRKu/vngRaGwcEJlffWc9nCXO8HH2gYej5U0vcpUCp2sO4bkTjyF08yhM+HQCnj/yPM5WnAULFvFB8Xj5ipeR8n8puG/SfWZvv09rdI5UxcXpu4OZieSqZBQ2Di4WbyqZAoC4wDjcFnMbbp18B6T/+ZR++M03pLHVhUhEgaurq9ZR0YLKX1MTcYfiYrPfajV2Z+/GmQtnug0oIiIiMH8+Zd1//PFH2x6Mvz+dT6mUyKmZQZuTgxPuibsHY3zH4Kbom6w+HEvJFAcfFx9cFX6V0d8LUqHS3XhkJDB1qr6sv4+dqTVqvPjXi1jy4xKcKjtl9HUDTaYAIMonCj+v+BlvXP2GVeuxMfQa6KtbSemq/LmmZfeyaLQ3MsWBk/LNnDkTABAUpDXR4AhUUhK5YXIEalLXkLy6ujq0dyWLcnJycPbsWdx8881m7V+j0eD8+fPo6OjArFmz4OkppUfQQGWRLiMMDfY1EbwP9u0L3CRSBwdahB35d+uSybRDaYHLkEwZg0Zj9gBLU63RCwtJ0SEWUx+tFfNw7RcsS2YftbVQajQ4I5fD0cfHZmSKQ117HcQicbcsz1TUd9Rjf/5+7M3ZiwMFB9Ai17qJOImdMH/UfNwYdSMWRS3CcI/hFh+fRXOmqqqInXt6mvTyZlkzlu1ahlZ5Kz647gNMCZ5i8fHaCuaQKV1wDllYuhTYvRu46Sbgl196v1CpJGlVSwvd5+PHm9Wb2t5OrsIALb22uocTKxJx70/3wkHtgF/v/BUjfClx8c033+Dee+9FTEwMMjIyrHdOMxfckBoLXUw1rMZq+2xLyVRFSwU6lB2I9OnfpbSxkeJJDw+Tbz/r0NlJpD8sTNsbpIOGzgY8dfApFDUVYc/yPfB06n1Q9kCmhIZcDpw+Tcre2bO1lcQOZQc+O/8Z7hy9BL75lfRChiFjrxEj0NDYaJdkigN3DZeVlWHEiBFISkrClClTEBQUhMrKSr3XtLW1wdXVFTt27MDy5cuxYMECHDhwQO81arXapOevLpmaMWMWnJy05NSmMe5liqEKlT0iLIzsqlUqwazUdcmUUqnCqVOXOZliWSKqSUlm9aeZM2cqPJwMntTq/k0/Bi26BlIrXV2RmZYGn4oKTBk/3qZkCgD8XP1MJlM5l3Lwzql3MO+befB/xx937rkTO7N2okXeAn9XfywJux+vjvsZv827hD23/Yr/m/J/tidTly6RBjctzeQJs2pWjTE+YxDsHoxY/1iLj9dWMIdM6RJdQBs4YNMmiqr27gW6pDJ6kEio6sdNck5Pp9krJsLVlVRFgMXtLmaDZVlcqriEQHEgFk9Y3E2mAHIBk0qlyMrKQkZGhm0OSBdOTvpkqqODkn1GnkkaVqPXMzVQZIplWbx58k3cvvt2/Hzx535f7+1NBXibkCmAbNUVCrKuvnixV7VqmPMwfHnTl9iyeIvdkqlLHZdQ1GiiZbCF4IpOLi767v7fpX2H79O/x2PH1oONj6cGcZYFCgrQfPw4zh47ZrdkCgDOnTsHAJgxYwYAID4+HgBQVVWFji5n4u3btwMAli5dCgDdA34PHjzYPcrjxRdfBKDvHGgMumRqypRZSEyUIiNDAJnr3xhDhMoewU0i5RpzDJgA8IX2dhW++y4dFRUuly+ZAohEVVdTCtpEkmru0F7OC2PcOKudnO0aCpUKp1paIPH2xpgZMyDSZecCoaGzAQ/sfQCZtZn9vxhAY2cjnjr4FKI/isaYj8fgqUNP4VjJMWhYDWL9Y/Hc7Odw5v4zqHqiCjvu+BILRt4MkdrVamMRi8gUQASAY+Pp6SaRqmHOw/Dete/h8xs/t/uZU+aQqTZFG1bsWoEN/9uAdkWPXqjoaOD+++n7Z54x/GFxjY2cY52ZoxNGdrXZ1NZS4ltIcD1TTAODXXfswrPz9AMjT09PXH89mVP88MMPwh5Mf9BoyKSiqIgceAxI1nZn78bqvauR35Bv9e6skfkp1Ao4OzhDLBJjctBkq4+Fd4wapR2TUl0NJCf3uk4ZhjHojGgPZAoAPj73MZbvWo7tGdsF2wcn9+tJdKcNn4bx/uNx78R7wUildL9HRaGlrQ1ZJ09igkKBUXZcZeGkfBUVFd09UJz5zK233goAWL58OQDgwIED3QTq+eefBwCsX78eAM2xAoC33367z/31lPm1tEihVtP6xuVCm0xM5A3BOIYIlb3CxUU7ULWoSBB9q0qlwrFjiWhrc4C/fxwaGi5TMgX0nuzazzAuc8mU7m64DDcw8I5hfIPrmXJ2d8fYlSsh4ow/BMbH5z5GanUqXjv+mkmuYa8dfw3vnH6HnL1EElwz6hp8cO0HKHq8CGn/SMNr81/D9BHTIWJEEIuJBIvFJP2pqLDsGC0mUwCdw9hYIlZqNVWqjFiq6/79DMOYLXu0NcyV+Z0pP4O6jjqkVqdCLBL3fsFLL9GAypMngU8/NbwRkYgGS8XEaId9mwh3dzJoY1l9Q0G+wbIssrOzu3umvDy94OTQOzmxcuVKANRHNaAKfZGI2KZIBNTV9TKrUGlU+G/Kf3Gh5gKSKpOs2pW1PVOODo5485o3seu2XUaNKHShUGjND2wChqFzyVmrt7cDSUkozj+PHZk7oNYY7q+yFzKlYTVoU7RBw2ow3n+8YPvh2iB7cqOJgRPx9c1f4+pRV3f/rMHJCSflcoTExGC4vz+vs9SEAEeguMrTqlWrAAD79+/vvs854vTcc88BAF599VUA6B4OzDAMFi2iIeDfffedwf1wZKq8vBxVVVVYt24d5s2LxZVXMpgyhQHD0BdnljEEyzHUQ2UiBkxfmpVFC+6oUbwGrro9U/7+01FR4QCxmMa6XIZSbC1qa+mcAlQF1LEk5WApmeoJhYJ650eM0CdZgxV9GlCwLDGR4GBBCFaLvAXvnHoHd8XdhdHDRvf7+h8zfsSKn1bA2cEZxWuL4e/a/+dYU0NcOzpaX15iCqwiU7rQaOiiqa832APEsiyeOPgExviOwX2T7rPYfdBWsLRnKq0mDQwYxAYYkTJu3gw88QT1l545QwYK/UGjIR0fRwr6QEMDcVqxGJg1y/zroT+wLIus7CysP7YeK6atwIpJK4zK49rb2+Hr6wuZTIYLFy5gwgSehqZaiuZm7eA9zk21q4RQ01aDHzN/xCPTHrFY7mctmbIE5eVAfj6pG2NiBN+dPhQKeiY1NeGx/A9wSlGIZXGr8HTC03ovsxcypYuixiKEe4cLtv28PMorjR/ftxmqngFFWBjd58HB+gYWdgju2tZoNGAYBs8++yzefPNNrF+/Hps2bQLLst3PWS5UX7RoEX777Td89913uP3229HZ2QmXrpOzZcsWHD16FEePHkVBgenzCBmGQUJCAk6cODHUQ2UFhgiViRgwQsWyvE+b7GlAIRY7IDWVnpPu7mSKZ+OWGNuCc48QieiP1RmayReZAsgprLj48jCp6NfNLzeX+gJ8fSnAGuAJqUq1EqM+GIXylnJ8ffPXuGfiPYLtizcyxaEnqZo6tTvLcab8DB75/RFIxVJsv3W7Sdn3gYKlZMoksCxw883Avn1UyT9/vncauyeysiih4uVFEVo/8ua8PAqw+e6r4WR+Oy/sxM9NP8PdyR17lu/ps9J48803Y+/evdiwYQNeMmQbb2vIZCT/a2ujazQqiiYjWwlryVR5Szn25uzFvRPvhbPEdMJRUUGft58fLV82B8uCLSzET9k/4cumw/j8xi/07m17JFMDgXdOvYNR3qNwc/TN3dVrk9z8cnJoDQ0JGfBnky6eeeYZvPXWW3jxxRfxyiuvGCRQ119/Pfbv34+XX34Z/v7++OOPP/CLIUOePuDi4oJ58+Zh3rx5iImZCyeneHh7SzFFx8doyJTCegwRKhNhFxcby5I0wIxZFj1hzM1PLqeYRKmkisro/osAgxcsS8FAfT1luKdNA8RiXskUtxtOueXsTDMeB2OLmknW6E1N9MdqNBRYjRlj/X7VCmTWZmJS0CSL3v/WybfwzJ/PYELABKT+X6rZwVl1NQXUfSUXeCdTHDQaIgCurtr5X104WHAQbYo2LBm7hL/98QxLyNTenL2YGzrX9Fk2DQ2UECktBZYvB374oe9gqbFRO6jWzY1GKtg4g82RqZKSEsyYOQO/l/4ON6lbv9biW7ZswT333IO4uDikpqba5mD7g1oNXLwIZU0VKh3lCJ11vVXBKh+VqX/+8U8cLz2O6yOvxytXvmLy+6qqKOb28SHl7YBBo4GSVdMAYpYFWlrAenjYDZlSa9TYmbUTN0ffbBZh5QPZddm4c8+dYBgG25duR8SwCNPIVHMzyVMBSqaMHSuIc7Il0Gg0EHeVv5OSknD06FGsW7fO7O24u7vDxcUF69evx7x58zBu3DikpKR090zpDu1NS6OlMzycRs5xsIsYd5BjiFCZiAG/2JRKCgba20mXZ4ERQH/W6Jcu0S4Aeqj49J6Td/lApQJSU4k9BgbyTqY4KJVkLCiTkXIrNtauEmT9wqw5U5cuUWWFZUlWZaXD0ubTm7EtfRv+MeUfuH/y/Wa/v7GzESP+PQIdyg4cvutwn3NoeiInh4KsoCCSABqCYGSKgwDVaVvAEjJ1ruIcHvrtIQxzHoZdy3aZPs/m9Glg7ly6nz/9FPi//+v79W1tFFEoFLSGTpjA22Dl/qBLphISEuCuUxnvDw0NDfD394darUZBQYH9uJexLP575F18XrgTD097BHfG3WnRZviS+R0tPor3z76PzQs3I8wrzOT3VVeT2d6wYXRJ2AWKisAWFyO7owOVUikSZs8e8MrU7uzd2HR8EyKGReCHpT9Y7eLYF+Ryyndwl4JCrcBPWT+hqq0K62auM2/OVHU1lSDVaspqRkfbjQ5/wYIFOHToUL+vmz17Nu68807MmzcPUVFRRu+RngYUumRKpaLWU5alPLLu0jfgMe5lgMtZ2HV5gSM/KhUssSIzZc6Ury/xCxcXi/ja4IKDA5WMBCRTgL4XRkNDv14YdgWzh/b6+mrZR2mpVe6ULMtCqVYCgElzZAzB29kb98TdAwB478x7Zr2Xuwyqqgy7bgtOpgA9MpVUfg6K1CQirXYMS2V+Ho4eGOU9CleFX2XecNCZM4HXX6fvH3+ckiR9wc2NqlrOzpTlSEnRWokZgFJJvTXJyaYfkiHokqnwCeFwdTNP/zts2DDMmzcPALBnzx7rDoZHsADy2HqoWDV8XLoycOXlZg0C5rNnal7YPOxatsssMgVoq9ADYSHd2NmIx/Y/hqy6LL2fs0olCgoK0HnxImZ7eMCZ70Y+C+Dn4odg92DcMuYWQckUQLfyiRPa21MqlmJl7ErzyRRAqokpU0gWrFJR4s+AXf1A4Ndff8WCBQtw//3349tvv0VxcTFYlu31dfz4caxZswbR0dEWkSkOERHUOm6jPNLfCkMVKhNhF+xdJiNdnkpFtdoeUiBjMGdor0ZDXM0O1m6boJtMTZwIf0dHwbJWNTXk1g6QRt9OkmNGYTaZ0kVpKfWpAST9s6K3Ir8h3yQTCmPIrc9F9EfRYMAg55Ecs8hZSQkRYJGIeuA4pa1NyJQO8hvycef2ZQiBBz6PehJecdPt8gKytmdKoVZArVGbLyXSaKif6tdfTe+nUijInr61leQ/06cb1HaqVFQEU6upcmHGjOBu6JKp2CmxuHf/vQjzCsPr81/XkhAT8NFHH+HRRx/tbh63F7Asi+SqZEwOmgymro4Sfg4OtND1c3/wRaa6Bz9biLo6irE9PYlv2xJvnXwLOzJ3INo3Gt/d8h0YhunumWrJy8N0b284Ojj0MgAZKCjUCogYkaCGOEqldsxcQgILBwetgYPZZEoXLEuNzaWl9P1AfOACwRQy1RfsIsYd5BiqUA0mODlR8y9AC4IJcwPMIVMAxRS6ZEroOSwDCT0yVVZGgYARq2prERBA/bCurla1wNkEVpEpgOR+ISF0MZnZNNYzv2MNmQKAKJ8oLIpaBBYs3j/7vlnvHTlSOx82M5OCa1uTKQBoljXD3d0Xw71C4Sl21Zor2BEsJVO6n7dULLWsL0MkAr75hq65vDyS/fWXJ5RKyRmQcyEwco07OJDsE7Bs0G9PmV+5rBwdyg40yhrh7mi65A8AFi9eDAA4deoUampqzD8YgcAwDOKD4+kz9/LSVgHS0vqcQcAXmapsrcSq3atwquyUhX+B9uMfiPTyvRPvxQ2RN+CJmU/okalLly5h6qJFcJw1i8oJCgWVbTgyMECQiqWCu4tyVSkXF+BQ8X7847d/IKsuyzoyBVDVPzyc7n0nJ/0mokEMa8nUEPjBUIXKRNgVe794kXRIjo5UxpZIDL7MXDLVE2VllKGPje030Tjo0Evmx51TBwcqRwhQD2dZCs7tufpnNZnSRXu72daGu7N3I70mHU8lPAUXCT+fwZGiI5j/7Xy4SFxQ/s9yeDubfjHr9sB1dpZCo0m3KZni0CRrgkajxrDSOrpOGYbklTw4q1kLS8lUp7ITD/72IFaOX4mFEQutdwE8dYr6qdRq4LPPgDVrzN9GZ2evuREyGXD2LN2/U6eafkkb65mqbK1Em6INUT5RZh/etGnTkJiYiM8++wxrLPn7eEKHsgO7snZhxfgVkIp7BG8aDTUhcqQvKIgqhzprCZ8yv1eOvoK9OXsxJXgKPrnhE4u21dlJh+voqCXQAwGjbn5qNTmp1tTQA2TqVJvq8r9I+gJj/cYiISTBJjb2RUWkEAgIYPFC+q0oaSrBPePuQXhTuOVkqic0Gv1ESkMD3fuDzEHRVDLV1ERrmY+P4ZDRrmLcQYqhCtVgRGQkBfxyOS2yBmAtmQJoyKFGQ1I1hcLag7YfGOyZioqi8r9KRVIgpZL3/TKMPplqabGvwb+8kilAP/KUyUhe1Qda5C1478x72Je7D3/k/2HdvnVwZdiVmBAwAR3KDnyR/IVZ75VIqIBRWVmBCxcqMGGC7ckUAHg5eWGYiw+RqKAgunAuXiSr+gGENTK/HZk7kFmbiQ/PfQiZStb/G/rDrFnafqrHHgMuXDDv/W1tJBfMydG7MZ2cqD0QMH3oc18GFMHuwRaRKQC45ZZbAAC7d++26P184YukL/DB2Q+w7oABRzKRiJzUIiJo0auqospK10OE7zlTT8x8AndOuBNPJzxt8bacnYGwMNuSKa5HlEOf1uhiMZ3TqCj6siGZyq3PxefJn2PtH2tR2Fhok31yA329vBh8dN1HWBS+CMMbhvNHpgB9MiWTUeX//PkBX1PNgTmVqYoKemRY0do8hH4wRKgGI8Rimj7o7m6wj4oPMgUQb3N11c4dtKfg31IYNaAQiShydnKidGVmpqAdyhUV1A9faJvnU7/gnUzporOT/ti0NKpaGYGHowfeu/Y93BR9ExaPWczb7hmGwdrpawEAH577sFcg0x9qawug0WRj5coxCAmxDZmSqWR4bP9jSK1O1f8Fw1BANXw4/b+oSBDybwqs7Zm6fcLt+MeUf+CFOS/wZ8H8xBPADTdQsum22/ol8Xpoa6N7vqqKkio6Devc6a6poZxLXzBEpvbm7EVxU7H5f08PcITqyJEjaOaizgHAOP9x8HXxxYrxK4y/KCSE5A0ODpQ9amkRZGivq9QVj894HKO87cT50AQo1Aqs+GkFNp/ejA5lh+lzpoKD9YfRNzdThCzgwznILQh3TrgTi6IWIWJYhGD74cCy2tvWwwNwVDpiqmIq4sbFCeduyTCkxecqgenpdp9FNodMqdVUgAPssv32ssGQ5M9EDJZyKF9kikNHB0me1GqzfDDsEia5+bW3k6WXWt23Z7aVqK0lkgpQ4lH3GWlrCEqmADqXFy5QUCWVal3WbAiZSobQ90JR216L7Uu3Y/n45Sa9byB6pgDg0/Of4svkLxHgFoA9y/f0llUB1Fzt46M3mNpWEHRor7Wor6ceifJyYOVK4PvvTbefr6+nG9PArKrERFoeRo8mN1RDMESm8urzcMeeOyBiRPjx1h+tHsYcExOD7OxsfP/991i1apVV27IGMpUMTg4mVEo6O4GGBrR6ePBKpmrba+Hvyo8rq1pNRQqGsY372aGCQ1h/eD38XP2w67ZdyMvKM3/OlEpFF6VcTuvAmDFG5f98wFrjD1PBFYpV6MSk2E6cOWNFz5Q5YFlaM4qKKLEikdDznytP2xHM7ZniTFecnIAZMwy/ZrDEuPaMoQrV5YLmZqhkMl7JFEAPF84Ho6REMM8GwWGyNbqrK1X/GIayf/2loy2Evz+ZHgCkMDInkc4nBCdTAFVUY2O15c4LF/TcTrLrstEka+J/vzpwcnDCQ1MeAgD8+8y/TXqPMTLV3k7eB0Kmou6YcAdujLoRL8x5wTCZAkijpEumZDzI5kyAtWQqqy6rl/kIr/DxAX78ka67H34AvjBD5unjQ2RMIqHILjmZskqg+3XkSOPxlTGZn7ujO6YPn47ZIbOtJlOAtko10PbpJpEpAHB21idT4eFgioutuoGqWquw5McleP7w8+hUmm7RbgytrcRNuDmMQuOaiGvw0fUfYX3CesvIFECVv7AwUlfU1xMLEbBqaaukiUQCeATVYX3mQjz78zOIiIqwzdw1hqGqanw8JVO42Z85OcLv2wxYYkDBTdsYqk4JiyFCdTmgshKqpCRc2LmTVzLFISBAqy3PyhowhZHFMHvOlI8Pyf8mTzbbpc4chIdrXeQyMmyvMLAJmeIgkQBxcdr5P2lpgFKJZlkz1h1ch+W7liOvPk+4/QN4cMqDkIqlOFtxFqfLTvf5WmNkSq2mdpCKCmG16G5SN7x0xUuYGTLTtDe0tFBEKLCG1FoylV6Tjrt/vhuP//E4FGoBL/hZs4BNm+h7c/up3N3p3ueu1S5SFRBAs6oNta/01TMV6BaI9699H69e9aqVfxSBI1T79+9HpxnznqxFs6wZq/euxvnK82a9T0/mFx0NJiuLsnNda4AlOFdxDnK1HHUddaYTuz7AXca21OtMHz4dHk0elpEpDkFBWhMluZxXF8BfLv6CF468gMpW2/YUOToCZ+t3oamjCk2OTYgeLYxKxChcXemchoTQ/+3IRcoSMqXRDBEqW2GIUF0GUDk5ISszE9KmJkwPCeGVTHGIjKQ4IzRUUFUB77B4aK+vrz6ZEqCfimGoGMY9C23Zp2ZTMsVBKiVS5ehIZZ60NDS318NN6gY3qRtGeBjRUfGEALcA3B57OwDg3dPvGn1dXzI/sZiCaoCUISZMLjALBQ0Flr2xrY3YXmmpYOUzPmR+xU3FkIgk8HbyNl554wtPPglcfz3dXCtW9Nm/1wvOzhRUeXjQVx/BrjEypVuFYxiGl8AfAOLj4zFixAi0t7fj6NGjvGzTFHyV8hVSqlPw9qm3oWFNWw979UyJRBSoisUkd0hKomvXTNw85mZsvWUrnpvzHC+VE1spVmvaaiBXyU3vmTIFbm5UVfH3p/u+sJAydFYMrVWqlfg06VP8kf8H/lf8P8uPzQI0NDQgpCEEm+ZswssLXx4YObFIRKYqkyZpF3yAsp4D1CVjqTV6YyNdCo6OA6IO/1thqIfKRNirvpTrmXKsrcVkT0+IJRJaXM20qzYFLGu7Bw8fsJhM9URFBWmrJ03q7qfgE7p9arbopxoQMqWLjg4yqXB0BOLiIGPUqO+ox3CP4YLvOrM2E+M/GW900K+pPVPZ2WRQIJXS5AI+LotjJcew7sA6LBu3DE/Nesr8QKKyUuv6GRREWl2eblg+e6aKm4oxzHkYPBxtsI7W1RGJr6oCVq8GPv/cvPf3nHTOsmhsYlBRQfGWk5NxMvXsn88i0icSd8fdDYmY3yzUmjVr8MUXX+Dxxx/He++9x+u2jaFF3oL/JP4HCyMWYlJQ/8NQ+zSgaG+noL+zk87tmDEDmj5vaaFCZF89JtaCZVms3rca1W3VuGv4XRimHGY9meqJqipKqHAqCytw8dJF/JD+A16Y+wLv168x1NY24M8/kzBpUgTGjrUzkxGWpQqgRkMPakFGqxjuU7NmzhQ3oH74cEqMG4O9xriDCUMVqkEMXQOKSTfeCLGfn3YKqRXZKWPQvc/V6oHr+zEFvJEptZrIlIDOfy4uVKmKjPwbkCkAcHEBGxfX3avi5OBkEzIFkDsZN+i3Z5XKHAOKqChtS1h2Nj9Jy4uXLgKg3hSLCEtwMAWmnFX1xYu8HBjfBhRhXmG2IVMABelbt9I5+eILYNcu897fc9J5Xh7KT5bgUh2L8nLjMr/kqmQcLjqML5O/REWriV7rZuDaa68FAPzxB3/jBfqDh6MHnp39rPVkCtDKqry9aY3NzKSor5/r9Uz5GUH6LW2RKKzrqENVaxXK68vBtDP8kymAEinx8fpmSlxSwEyM8R2Dl6982WZkqqGhAXsO/AkVG4nWVjsjUwDFAB0dFPicP2/6DAUT0dTUhICAABw6dEjv59YO7Q0NJQX0SOvbN4fQD4YI1SBFLzc/iYSyJo6OdNPnCdePIpPRepKWpuctYDfgjUwBWkMFBwdq+BWoQdXHR2vNLBTsgkwBUGlUeOToMzhYckT7w8ZGm0kpnpr1FADgm9RvUNNGw0fNdfPjJhdwyqWSEuuPa038Gnxx4xd4cMqDlm8kMJDWAYahElpWllVJAD7IlEKtwKbjm2zei9GN+fOBZ56h71evJlmkJWhpASorMVxTBraoCGdPF6GwsDeZAoDJQZOxaf4mPDrtUYR5hVl3/AYwf/58iMVi5OTkoKioiPft66Kx0zwnIpOt0SUSclHkLBPr6/u8Vmvba/HUoaew5MclKG228DPsB0IuQX4ufvjXmH/h/0L+D4uuXMQ/meLg6qovV8/ONssGXNDeRiNoaGjAqVOn8FvnL/is8TFUadJsfgz9wsWF5Ahc43NeXi+DJWuQnZ2Nuro6PPHEE9B03QfWkikOUimFhkMQFkOEahDCqDU6R6oYBqiu5r/BowtSKQWSSqXg45rMBq9kioOLC8knuCC1uJif7RqBSkXFBT7Jqr2QKQDYnb0bZyvO4o0Tb6BF3kIVwAsXeg1UFQpzRs7B9OHTIVfL8dG5jyy2Rnd11TpgNjfzc+iTgiZZ31vk70/Xq0hEN6eF6Xe+KlPfpH6D3dm78eCvD0Kt4b9ybhJeeQWYNo3WxNtvt8y908MDiImBl4caTUXZaDqfiujwab3IFEA9UwsiFuD2Cbdbf+wG4OnpiVmzZgEQtkpV116HxT8uxitHXzFp+LLZc6YYhnzox46la7YPA4BWeStCPEIQ5hUmeL8l3+B6plobW3HPwnuEI1M90d5ORLWhgbKg/dj0FjYW4oZtN2Bb+jZhnTh10NDQgNOnTyNgVADq1fVoVl5CmB9Pz26+4ehISYDISFpfGxvJDKimxupNc4mR9PR07NmzhxcyZU+x2d8BQ4RqkKHfOVNeXtREGRVF3wsAbgYuN6/RXobTCkKmOHh7a6Pn4mJeFlBjyMkhPpyRwc+CaE9kCgCWjF2CByY/gOfnPE/SL0dHbRIgP1/w/TMMg6cTngYAfHj2Q6Rkplg8ZyogABg/np6xlvCN2vZabDq+Ca1ynvWzvr4kqeQSAWaCT5nf9ZHXY3LQZDw+/XGIRQPkmCWRkIW6uztw4gTw2msWbYb180OeizOgqcDo4GC0JpbrmV1UtlaaPTjaUthC9nei9ATaFe0obCzsl+hbNbQ3IEDf+KOsjPrfdBAxLAJbb9mKt695GyKG3zVMKiVJVHAwr5sFANS21eK7v77jx4DCXLi6anuquZEVfUgrd2XtQmNnI1KrU21iBsGRqbFjx2LK2Kl4eewePBzxHkYHBgq+b6swfDhVqzw8KDnDw3DloqIiODg4QCQSYf369Th79qxVZIplgbNnqe3LHpVElyOGTClMhD007PE9tNdaXLqkndsxbtzAWnIKSqZ0UVhIkiGGoYy3AA9HmYxMKpRKijPGjrV8W/ZGpoyipoakKQC5gEVECLo7tUaNiPciUNJagtfnvo5nr3xW0P0Zw2P7H8OpslO4IuwKvLPgHWF3VlZGksB+bDqFGNprq6Gg/WLbNqpQiUTA//4HzJlj8lt13fymjJuMzN/qwcrkmDa+Ay7xY6HwcMWqn1ZBxIjw1jVvCSL100VKSgomT54MV1dXNDQ0WCwH6g9pNWlwkbhg9LDRRl9jFZnqieZmMq0BiOWEhw8uNyQdsCyLh358CIfLD+OhWQ9h7ay1A3MgajUlq6qq6P+enqRZ7qEDU2vU2Je7D5ODJvMyM60v6JKpUaNGdQ/0dXAAEhIGyUfOshQP+PpabQT2wAMPYMuWLVB1Vc+fe+45vPTSSxbf101NRKZMPZ/2EOMOdthpdEXYsGEDGIbR+wrUyVywLIsNGzYgODgYzs7OuOKKK5CZmam3DblcjkcffRS+vr5wdXXFTTfdhPLyclv/KVbDYjKlUpGkSgD4+mobHS9e7J5/aXPYjEwB9HD396eAX6BMo5OTvsLQ0nlH9kSmWJbFmfIzxmUkAQHaRuqyMsFllcVFxVg0bBEA4NMLn0KlsX6As0ZDJnvmfF4PTH4AkT6ReGTaI1bvv0+UlAAFBf2mK/kkU+0KbeXGLsgUAKxaBdx1F31Yt99u8qTyntboviH+GDZtNODqiqpaMaDRoKSpBC3yFjTJmjDMeZjAfwgQFxeHgIAAtLe34+TJk4LtZ0LABNuRKYCy/twMoNJS7D3wAX7N+tlkq3Z7AcuySE1NBeSAl4cXrhl9zcAdjFhM62tMjLYfOD2998tEYiwes9imZGpE6AhcqL6Alhb6nbv7ICFTAB1oaKg+mSoupoDITFlxfn5+N5liGAZbt2616l7iZk/5+g6i8znIYdeECgDGjRuHqqqq7q90nUXgrbfewubNm/HRRx8hMTERgYGBuOaaa9CqYz+3du1a7NmzB9u3b8eJEyfQ1taGRYsWQW2hC95A1PMsJlMaDXnB5ueTpbIACA8nZSGXALM1bEqmAO3wqBHCavi9vKi1AKCiWEODee+3JzIFAPvz9+OR3x/BM38+Y5xUBQVp/+jiYsvNA/oB1zP13A3Pwc/FDyXNJdiZudPq7V66RLdZYSG6g4P+MCFgArYt2SZ4NQN+ftr5X6mpVAbtAT7JVH5DPq7fdj22Xthqf4HwRx/RdVZWRiYV/SzqxuZMBYdK4DohAq6TowEfH0T6RGLXsl14d8G7NnExFIlEWLhwIQD+ZX+nyk5Rf2M/4J1MAbTGRkQAMTFo0LTj3Yv/xYYD6/G/7P3Wb9sINBoyceNLGsX1TNXX12Pzss3Yf8d+jPO3zsacF/j7kwTQw0PPQ7uqtcrmPVNcZWp39m7cv/d+vHl6IwAqng1ayOX03KquNqlnTRf5OgEUy7IoKyvDN998Y/GhcIrZoWG+toPdEyoHBwcEBgZ2f/l1XR0sy+K9997D888/jyVLlmD8+PHYsmULOjo6sG3bNgBAc3MzvvrqK7z77ru4+uqrMWnSJHz33XdIT0/Hn3/+adHxCOTzYBRWyfxEIgpSAWI7Avicc/zC358cm20Jm5MpQ1CpKIIWoPtz+HD6+FiWzNpMrQDaG5kCgA5lByRiCaJ9ovsOukaM0A5SFOCc6hpQBPsF47HpjwEA3jr1ltUBhb8/Pby4z8tYglLDavSsn21SvXFxoTlqzs4UOaak6F1QfMv89ubsRbuinXoxYGfpUXd3YPt2kj7+9BPw5ZdGX2qMTAHkzDl1ugiBY7y6f+ahkSC22dFm3eBC9FGVt5TjyYNP4tYdt6K6rdro6wQhU7rw94d7/CzcP3Ix4l1H44o6V8F6V9vaqN+EUxpaA0NDe31cfKzfMF/ghlZ3MRe5So4HfroLd++6XXAnzp5kCgAaOhsgYkSYOToGY8cOcgLQNVsRzs6UtLpwgWKvfhL4CoUCVZwcswsMw+Bf//oXZAaSX/2hpYW4nVhM7d9DsA0GtgnHBOTl5SE4OBiOjo6YPn06Nm3ahFGjRqGoqAjV1dVYsGBB92sdHR0xb948nDp1Cv/3f/+HpKQkKJVKvdcEBwdj/PjxOHXqVHd2zxDkcjnkOumqlq6Us5ubEkqlbZqOVSoVEhMTwTAM4uPjwbKs+fsODAQuXQJTXw9cuAA2Pl7fUpUHMIw22WWjU4PKykqkpaUhPj4e3t7eNvtMeiElBUxLC9jWVmKWPCMsjNQZcjkDuZztr/0FCoUC586dg7OzM+Li4qBWqy2uxvKJmyNvRpxfHEZ6juz/swoKIgLg6cnrBVVUVITc3FxMnz4dbm5uUCqVWD1xNV4/8TpSq1NxuvQ0pgZPtWofo0YBjY0M2tqAjAzW4GzNrelbsS19G56b/RzmjDS9h8dqiMXAuHFg0tOpUpWYCHbCBMgcHHD69Gn4+voiJiamW3ZiDR6JfwRhHmGYOWImL9vjHRMmQPTqqxA/+yzYxx+HKi6OCKcOWJZFbm4uysrKMGPGDDg5ORm8do8UHYGvqy8m+MWCSU0F2tvBNjWRW0l/N6yVuOKKK8AwDNLS0lBSUoJgHlwVGtsbEeQWBD8XPwyTDjP4N7e1teHMmTMICQlBRESEcJ+xixtWXvsMVmRlQdPUBLVcLshDRqkEVCoGKhWgVFqeWGFZFhkZGaiqq0KaYxpi5DED3uvcH9JLEtHcWAO2tRUeDZ1QOgnzLG1sbMS5c+cQHR2NkJCQ7utq9cTVWBi+EMPdh0Msop8N1OOcF7i4EKkqKABTVUVqi9pasNHRVBnsAY1Gg99//73bKp0Dy7KoqanBJ598gkceMU8SXlVF17O3Nwu12jCfUyqVOHToELZv347t27ebtf0hGIZdm1Ls378fHR0diIqKQk1NDTZu3IiLFy8iMzMTOTk5SEhIQEVFhd5DZM2aNSgpKcGBAwewbds23HvvvXrECAAWLFiA8PBwfPbZZ0b3vWHDBrz88su9fr5t2za4CDAhW0gwajW88vMhUiig8PBAa2iooPtranKEo6Mazs52GEjxDElbGzyKiwGWRae/PzoEmMyrVFLmVyKx21vVKHgxI1CrIenshNLNjZ+D0oFCo8BdGXdBppHhnah3MNrFeK+IqejsdEBBgSdYFggOboePjzbDqGE1+KD0A5TJyrAscBmme063en/mglGp4FFcDIfOTrAODmiMigLbh131ZQuNBjM2bkRAcjI6fH1x7O23ITczndusasabRW+hQ6nA/w1/EBPEAXAvLQWjVkPj6Ijm0FBoBB4As27dOhQWFuKJJ57AHDNMNvqCilWhU90Jd4felvC2gIbVgAGjXTtYFtLWVigEapbv6KB7VirVIDravLlbhnCo/hD+uPQHAhwD8FToU/bTQ2gAIqUS6pKLaOqoxmgmEHJvb7QFBfVpYT8E0yBpbYVbRQVESiVYsRiN0dEG19r09HS8+OKLBrfh7u6OL774Ak5OTibvNzfXG3K5CCNHtsLTU4Hi4mIcP34cx44dQ10P98yeGDKlsBx2nTq57rrrur+PjY3FzJkzERERgS1btmDGjBkAestlTAngTHnN+vXrsW7duu7/t7S0ICQkBAsWLIBE4oH6euHaaHQrU1OmTOEnw9XaStlTjQbsqFHapl+eUVkJ5OUxcHIC4uNZvothepUpP3vRBlRVgcnNBQDKQgls+SqX9x7Sp1uZmjRpkl3I/NJq0/B50ud4fs7zCHILsmwjajWQlgamtRWshXoQ3cqUV49RAr/n/w5Zmgwj3Efg0aWP8hb4lJcDBQUMRCJg0iQWulxwgXoBDhQcwKLIRQMXaKlUUCQnI7G8HCPCwjB+/Hirj0XDavBb3m+4fvT1A2ePbi5mzwY7ezZccnOx4NNPoT50CKyjo15lyq0PIt8ib8GpXwtQ2FCIxdMfRsQoEdDeDiYjgyQ/EgnYmBjBRlgAwF9//YX3338fbW1tuP766wXbD6BfmYqKihLs+t19cTf+Kv4L62asQ7hXeO8XKJVkMRsRYTDrby5aWoCUFAbOzsC0aeYnrrjKVH19PWbMmIHQ1lC0nm3F8pjluGbUAJpRmAr2JqC0FExJCWmWnZ1pvTUwY81c6FamwsLCun9+uOgwJgZMhI+LD2pq6Lnm60sFnssKSiUNAfb0JB1/FzQaDVJSUtDZ2QkfH+OS0La2NhQUFOCpp57qd1eXLl3Crl0/YceOt3D+/Kk+Xzty5EgsX74cK1asQGxsLFpaWuDr62v63zWEXrBrQtUTrq6uiI2NRV5eHhYvXgwAqK6uRlCQNlirra1FQFeVIDAwEAqFAo2NjXozZmpra7uHIhqDo6MjHA1kFllWggsXJFCraR3v4z6wCCqVCklJSRCLxfxaow8bRi4/eXnUrRgWRj1WPGP4cCo3y2QkHR4/nj+HmfLycqSnp2P69OkD1zNlCCNHUsNMaSn1U7m60vkWANXV5CQ3Zgz17ABEphITE+Hq6mo3PVMsy+LdM+8itz4XW9O34vm5z1u2IQcHutE6OujalUrpqWsiCgoKkJeXh4SEBINzpvbl7QMALB6zmFfb6fBw6svgBv7qKr8kEgmWjFvC274sQadKhVPt7fCLjtb2TGk0Vq0J36V9h/fOvIdDRYfwnxv+Y9dZ+W74+QG//gpMnw7R2bNgHnkEF595BuXl5Zg9e7bBob268JH44JWrXkZqhhxNjY5wcAAYLy8aqZCRQZF6VhatvQIlWq644gq8//77OHHiBCRWSAy/vfAtgtyCcPWoqw1+dq2trTh79izCwsKE6Znqgkqjwjdp36CuvQ6ptamI8ovq/aKiIloTMjLIYEQnULUEDg7aL3NPIdcz1dDQgNmzZ8PZ2RkT3Sfim8XfALAjh8seqGqtglwt1xrijB5N90NWFrGb9HQirFZkjhsaGpCYmIiYmJjunikAqGipwIZjGyARS7Dztp24dCkQTU1apfdlBYmEJIA60DQ0IPP4ccg8PJCQkIAjR47AwcHBoHSWZVm8+eabePjhh+HZdXKUSiUOHjyI77//Hj/88EO/h7Bq1SqsWrUK11xzjdHnnDVrxxAIAx95mQG5XI7s7GwEBQUhPDwcgYGBOHToUPfvFQoFjh492k2W4uPjIZFI9F5TVVWFjIyMfgmVMUil2uF/Fy/SrDy+IPicqeHDaYGcPFkQMgXQA2n8eNp8fT25NfMBuzCg6AucnTrLApmZFE0LgI4OinsvXiSPEXs0oAAoiHjrmrdw9air8fiMx63ZEAWjuue2vt6kt+oaUBgiU2qNGntz9gIgQsU3xowBpk4lbp1Vl4Xf8363mZNWX+g2oPD315IphQJITLTKDdTPxQ8ejh5YOHqh3QaRBhEZCezYAVYsBvPtt2A2b+5lQNETup+jry/gLHGETKbj7iiV0mBlzqWkslIwi9jZs2cDADIzM3GJ80o2E4WNhfg48WOsP7we6bW97bQFN6DQgYPIAV/e+CVWjl+JpWOXGn7RqFHac5uXRzPsrOgV5T4ac/8sQwYUHLhRL/aK9868h2U7l+GnrJ+0P/T0pIG13Lm1opnJkAEFhw5lB6J9ozExYCICXAO775vLjkwZgEalQvbPPwNFRUhwdoZUqURRUVGfz4aWlhZ4eXl1X1NSqRSLFi3qRaZmz56N//znP6irqwPLst1f33//PW644QbBZtUNgWDXFaonn3wSN954I0aOHIna2lps3LgRLS0tuPvuu8EwDNauXYtNmzYhMjISkZGR2LRpE1xcXLBq1SoAgKenJ+6//3488cQT8PHxwbBhw/Dkk08iNjYWV199tcXHFR5ObphtbRTYxsZaX4Wx2dBegaR+unBzA6Ki6NwUF1OBwZqCjd2TKYAugDFjKDDt7BRs8EN4OPkJ1NcDKSlKyOWn4elpX2SKwwiPEXjj6jes3xDD0HRjlqXqamYmsfY+Lqr+yBRA1tB1HXXwdvLG3NC51h9nD3AJP7lKjpf+9xKKGovQJGvCqthVvO/LVBh186uupus2N5eqrSPNn0OzcPRCTBs+DV5OXvwetA3Azp+PmvXrEbhxI6L/+18wixcDixYZff2/z/wbSrUSD097GG5SN/j50SmsrtYJCkUiMqrhBioLtCb4+vpi3LhxyMzMxIkTJ7rVG+ZghMcI3D/pfhQ3FWNCwAS939mSTHEY7jEcT8x6wvgLHBxoWF9ZGakCampoYRw3zqL5gJYQqp5kysnJCU8efBIzR8zE4jGL7Vr2qlQru2fvTQycqP9LiYTOY12dvhrAjAp2X2QKACJ9IvHNzd+gTdGGtjbatIODYKMd7QYajQbnk5Oh9PLCtPBwSGQyICkJeZmZvcyjRCIRRCKRwarVyJEjcfvtt2PlypWIjY3t/rlMRuM7eFBqDsEC2DWhKi8vx8qVK3Hp0iX4+flhxowZOHPmDEK7TBWefvppdHZ24qGHHkJjYyOmT5+OgwcP6mUW//3vf8PBwQHLli1DZ2cn5s+fj2+++QZiKxouRSKK75KSaD5QRYV1/VQ2I1M9UV1NJQ8DC561CAykbG1lJSkIpkyhgbXmYlCQKQ4iEQX6anXvJieewHGLs2eVSEzMxLBhPrjyyhi7IVM1bTXoUHYg3NtA34M14Pz5MzPpiZGRAUyYYLA3xRQyBQB7Lu4BACyKWgSJWDi5g4PIAXMCr0NV3W5cPfIGwfbTH/q0Rh85kq7bkhIKUJVKqmabCW/nwefR222NPmsWrrr/fki/+gpYuRI4fZru5x6obK3E9ozt0LAaXBl+JaYNn4bAQFpO6+qo4NV9OzJMb3JaWUlacR7XiLlz5yIzMxPHjh2ziFBJxVKsiV/TK0tuSzKlYTWoa69DgJsZxj4hIRQ9ZmVRhjMpiTKcZpY6HB1JwGFqAt9QZepo8VH8r/h/OFN+BnNC58Df1X6fVxKxBO8ufBelzaXGh/jq9qtqNOQp7+lJ8UIfz5v+yBQHhmHg7uiOiq6iqofH5T2AVqPR4Pz58+jo6MCsRYsgYVlKYNXXo1xnGjzDMAgNDcWkSZMQFxeH2NhYxMbGYtSoUf3GrbW1tHzX1/dSGQ7BBrBrlz97QktLCzw9PfUcUCor6X4QiUhFZ4kJ2YCRqbY2GjwHaAdJ8QyNhmaItrRQxcpcR99BRaaMobWVeqp4JDwKhQJ//XUGJSW+GD16DIKDRTafAWYIGlaDf/z6D6TXpmPjVRtxVfhVAuxEQ6SqpYWeGD1uOlPJFMuyiPggAkVNRdi9bDduGXsL/8favS+ar13fLIf/MEfExdk+cDB5zlR5uXZCd2AgyS37ONjK1kq8cOQFPDXrKYz1GyvAkQuLXnOmnJyABQuA//2PysHnzhns2UusSERiZSIemvpQ13ZojpFMRsl9o94pNTUkT3N0pMCfJ+fK7du3Y+XKlYiPj8d5bl03ASqNCmJGbLRnypaVqd9yf8Nrx1/Dg1MexF1xd5n3Zrlc2/sTHy+oXb0xmZ9Ko8Lu7N1QaVQDWoUWBFwSC6BrNibGoHtEf2QqrSYNhY2FuDHqxu4KXlYWEYHwcEBgA+IBgx6ZmjVLX3pXXY1fvv4aDU1NiI2IQMySJXCx0BwiKYlCDkviLUMx7hDMg32ktQcpgoPpWavRkPLAXAwYmQJoUeQypzk5pk+NNQMiEQUXsbF/UzJVV0dZvZwc3jbJ9Ux5ezthyZIxEItFqK6m591Ao0PZAalYCrFIjNHDrLcfNwjuojKQwTCVTAH0YC9qKoKTgxMWRCzo87WWQqFWdDmKUvzhLHFEUxN5l9gSZg3tHTGCpKsMQyWXzMw+h9S+f+Z9pNWk4cNzHwpw5MLC4NBeiQTYtYuy8EVFwK23GmyUnTp8ajeZAuh0cctUn+2THh4UiMrltDbwdONydukpKSndMxNNwbun3sW6A+t6DXQdCJnfqbJTUKgtbErmBqpOnKhPpniewddXz5SDyAHLxi2zazKl0qiwLX0bOpWd5r3R15ce5BKJNhnbYxBtf2SKZVlsPr0ZG49txJfJ2mHa3OV6ucbwfZIpAAgMxM3r1uHe22/HlJkzLSZTMhmRKcAs36Yh8IghQmUloqMps2JuhWBAyRSH8HCSTKnVlH0SYACso6P5ToiXBZkCaI4Hy1JWuqjI6s31NKDw9RUhIoIMG/l2m7QEblI3vH/d+9iyeItxGQkfEIn0xfbNzShMTTWZTAHAzxd/BgAsjFgIV6mrIIf5zql38Oj+R1HbXgtnZ+3w6+JiHfMCgWEWmeIQGEikVSSiREsf68Kzs5/F9ZHXY/3s9TwetfAwSKY4+PgA+/aRlOzoUeCRRwCWRV17HdoUxtnSiBHAzJm0rBqFszMlA7y9teuuJdm4Hhg+fDgiIiKg0Whw6lTfdskc6jvq8UvOLzheehxVrdrgeCDIFABsvGoj3l3wLlaOX2nZBkQifV15ZSUZrXBRZh/QaIg39zWf2BiZkqlkdmE2Ywp2Z+/G5tObcf/e+80/Zh8fctnx9qYTlpNDCReVyiSZn4bVYEHEAgS6BeLWmFsBkLJYoaCExOVIqPolUxwcHUnCHhOj/ZlKZZaZDZeb8fQ0Xbo6BH4xRKishERCZWpznjl2QaYAbV+KoyMFTjxWUgxBJqOkbF/Pt8uGTAFkmhDVZflbUtIro2cOjLn5jRhBhGogtee6D2YRI8Iob/578oyitRUV+/ej+uBBzIqNNYlMAdr+KSHc/QCgtr0Wv+X9hjPlZ1DaTCWpwECtWWFWVt/BGx+wiExx8PWlB/yECX3Kp7ydvfHKla8gxFN4sxu+0CeZ4hATA2zfTjfWF1+A/fBDbPjfBty28zYkVyUb3K5UamJblIMDnVeubF9QQGuvlUH53LlkrHLs2DGTXu/j4oPvl3yPx6Y/hvjgeAADR6YA6h2ZFzaPn35GlqXmZu6h0497ZX09cOoUOYUb3pzxytSbJ97E6n2rkd+Qb/1xC4wA1wAEuwdjydglln22UilduxERdG/U1aH53DmTeqbEIjFWxa7C3pV74eNCGUCJBJg9m1Sal9scYZPJlC50T0JeHvWUXLhA13E/4AiVvYzn/DtiiFDxCI2Gss9yufHX2A2Z4iCVUvDAMCRk5iFbagzFxTSXJyPDsN38ZUWmOAQFaYXhXQ2o5sJUa3SNhhpShQ7Ue+LtU2/jq+SvoGGNS8OEQkFVFQpraxE7diy8S0tNsqsvaizChZoLEDEi3Bh1oyDH5e/qj++XfI+nZj2FKcFTun8eFUVJdJmMnpdCwSoyxcHLSz/jX1sLyCgbX9hYyNux2hImkSkO118PvP02ff/Pf8LvZCqaZc3wdelfT9PvPcgwdDGM7pLGVlWRdawVMJdQAUC4d3h3v9JAkCmWZXEg/0C34xxvYBhg0iStJj83l2xnjVRb+3L564tMNXY24s+iP5FanQqZqv+gd6AxL2wedi3bZV0iiWHIDGTyZDRrNDhVXd0vmdKFiNF/folEvLUS2g0sIlM94eFBBKupiSqtFRVGky4KBb0MGJL7DSSGCBWPyM0l0nDxouHr3u7IFAdPT+2DvY9+CWsxerS2fSArS/8cXZZkikN4OJUnuDlKZui9zJkzlZVF/TnZ2YKNvOm9z7os7MjcgU/Of4LM2kzb7LQLBQUFuJiXh/GrVsF9+HCKYlNT+yVVnNxvbujc7kypEAjzCsPy8cv1fubgQC6NDEOBhBCfEy9kqifq6+nCSknBb+k/YcWuFfhP4n+s364NYRaZ4rBuHXDPPWA0Grz0n0x8HvlEn3JWlgXS0oCTJ01KKlOJOTaW1ggrh4FzhOrcuXPo7DTeI9PY2Yja9lq9nw1UZepw0WE8f+R53P3z3fwnZDhr9VGjtD2ByckG+4W5+7Dn8toXmQKoQvvTsp/w3JznMN6/tyOkPUIqlsJBZH3s0aBU4kRnJ6InTtSSqcpKGr+gA7lKjheOvICsuiyr9zkYwAuZAvD2tm2Y+eST2raMvDyqVhm4t9va6Np1d7fMTXkI/GCIUPGIkSMpodDY2Lvx3G7JFIfhw6nuLqDNDjf0l0u6FHYluS9rMsUhOpoCJo2GeqpMgLlDe0NDtQOVeWjZMgljfcfilStfwZr4NYgNiO3/DTxBz4CCk6d5eJhEqn7O+RkAcMsY/p39zleeR0VLRZ+v8fSkVoR+DPQsgiBkCqAUclc2JDv9CDQqJVwlwvSeCQGLyBRAH9CnnwKzZoFpbsb41c/1WUliGLrFuZFpJsHHR3/dlctpHoeZCA8Px/Dhw6FUKnH27Fmjr3v/7PtYumMp/sj/A8DAyvwAIiXzQuf1qlzwAs62Pi6O1Bjt7USqegys5fKIun96f2SKg7+rP5aMXcL/sfOIr5K/wsnSk7z1ehnsmWpspKxyD8OKnVk78Uf+H3j60NNQa7QVQo2GXOlycwVp3x4QWEOmXn/9dbz66qvd/9+3bx/OnDuHtw8epAZcLnA6f77X+jBsGDBrFiXrhjBwGCJUPMLFRb/xvLmZvrd7MsVBN8hQqwWpVrm4aA08ysqA1NTKy59MAfSkHjeOynRcNbAPmEumAPr4oqPp+9JSk3mbVWAYBtdHXo818WuE31kXDLr5cX0puqTKQCa6rr0OJ0pPAABujr6Z1+Nq6GzAs38+ixU/rUBGbUafr+3pOMxHnCMYmQKoOWjSJMDTE08NX4ZPQ/6BO4Kv5W/7AsJiMgUgpSoF/6s6DezeTYF5bi6wfHmfmj5uGautNfoS49BoSBOdnk4W9maAYZjuKtXRo0cNvkahVqC8pRwylQwjPEYMOJm6etTV2L1sN+6Ou1vYHXl50TBELy+Sq/XoC+xZoeqPTCnUCpQ1CyeP5xP5Dfn4LOkzPP7H4yhoLLB6e0YNKFxctNUUzrBCqcRV4Vfhhsgb8H/x/6c37Litjfqp6+ouj/4pc8nUe++9hyNHjnT//7nnnsO//vUvnDt3DgBw+PBhAMDTzzyDNk9P7fUrFhuc3OvgYNDJfgg2xBCh4hm6jefZ2YBMNkjIlC64Rt7cXEE27+dHsUldXR3++KMY48ZNv7zJFAexmCQ+XNDCsgZTc5aQKQ4BAfpu+CYYXFmElKoUyy2OrUCf1ui6pKpn/08X9uXug4bVYHLQZIR68VuNVaqVGOU9CiM8RiDaJ9qk98jlJBGr6Luo1S8EJVMcuPPr44MpblEQZ1+0ymjFFrCGTCnUCrx67FU8efBJ7G44CezdSxHLoUPAE08YfZ+fH93ira0G1Tn9w82N1ob8fLqJzUhs9ddHJRVL8fmNn+Orm75CqHPogJIpDu6O7nB0EGYQuh6kUqpU6Q5a7uwEZDK9CpUplakf0n/AbTtvwzep3wh/3FbC39Ufd0y4A9dHXm/1OIs+3fw463pOYllXB5w/j2CVM16+8mXcGK3fr8olnM2cwWyXMIVM/fDDD6jSWS//+c9/Yv78+WjuOhFFXbKS6dOnAwAkEgm2bt0KAPD19SWH0Lg4cgnVSQiwtXW20/gPoU8MESoBEBVF1357uxo7d6YNLjIF0EOmvZ0059ZGekYgkZSjujoLkyePQVDQ39CWRqMhxp2RoRcwWUOmOISHk4qIS3YbMgCxBoWNhXj494dx9893o0nWxO/G+4BJc6YcHOihExNjcJhyt7tf9GLejy/ALQCfLvoUH1//sclOZfX1pN4oLKRbzhLYgkw1djbiw7MfolOjIN0u1xOYk6ONjOwM1pApDlePuhpB7kE0qywuDvjuO/rFBx8An39u8D0SCfF5wAzZHweRiMrMo0dTUFpVRYy7h0TNGDhCdfr0aSiM3PgiRoRwl/ABJVMfnP0AaTVpNt0nADqn3N/K2dYnJYFtbOr6tWkyv5z6HKg0Kvi72n8i0MPRA49NfwwvX/GyVdsxxRq9W2I5ebK2YfrCBZLs9MDlMn/K1MrUqlWrEBwc3C273LVrFwDAq2uxCAsLw7JlywAAd99NFds77rgDACCXy7Fv3z46v7qJwpoaZP1WhKRtF9FUyf8s0SGYhyFCJQAcHIDISBWysjLR2irBhAmDiEwBNGeCWzDz83kPmMrLy3HhQiqWLh2L+fOH/T2bKDs7KZpubOx2MeGDTAG05o4dS88zjcbE5ngz0NDZAFepK/xd/OHpaJv0ojlDeyEW65OpwkKgrQ1tijYcKjgEALhlLH/9Uz1t44c5m24uEBysJb/Z2earbG1SmQI5OW65sAUvHHmBLrAxYyhwGjHCLlPMfJApqViKh6Y+hF237YKbtMuG7JZbgI0b6fuHH6Y5VQZglewP0JpVODhQ30RSkkmMe+zYsfD19UVnZyeSkpK6f17bXosdmTugYTUDLvM7WXoS3174Fmv2rUF9h/mup7xBpaJ1QqmEa1EGAtXlKC/L6JdMAcCm+Zvw+Y2f49rRg0P2CsCqz9kkMqULd3e8rz6J91sPoUXVrj83sAuXQ4XKHJnfPffcAwBYvpyMipYuXdr9u507dwIAtm/fDgD49ttvUd4l+a3vcga+6aaboOnxgFCrgYZ2R7TWySFOSyGt/1C1asAwRKgEgEqlQlbWGYSFyXDnnWPh7j6IyBSHkBCtdjEzs28veDOga0ARHOyvp522SB4zWOHqSpn+Lrt6RXY2L2SKg4MDxWPx8fxnAKcET8GPt/6Il654ySbBmFlkqifKyyErzseJo99i2falkKvliPCOwDi/cbwcG8uyeOLgE9h6YavFLmXR0VTVaGszz0zEVmQKAG6KvgnDPYbjgckPaH84apR+P6BAfZfmwloy1bNxv5cc7bnngBUrKCBfurS3AxHIuphh6DM10MpnGoYNo741Z2fKipgwJ5BhGMyZMweAvuzv36f/jbdOvoWX/nxpwGV+Y/3G4ubom7EqdpWgLpv9gusLDA6Gt7sSDkV/wL3oFBKmTeuTTHGYHDRZGCMNnnCw4CD+9de/9IY2WwKzyRSAmrYabMvcjq1VB5Az0oW06Bzkcshk2oG+FuQ67ALm9kx9/fXXAIg8cfK+pi6v82XLlkGlUoFhGJw8eRIAEBJCs/2GDRuG9etpcDp3b3NolAZAHTUWTr5ucHdSUvIwJcUiuYPSxCr4EIzDfleDQQpdA4rrrps8OMkUh+hoCvwVCiJVVgZLfbn5lZQA585ph9P9LeDtDYwZA6VSiayDB+HV2soLmeLg7KyvDuBzPtUw52FmVWIshSVkqrGzEb/l/ob1f67H7APL4XlyEeacfxj7iw4CAG6NuZW3QPJ46XEcKzmGT85/YnHgIpVqzUTKyrTzRPqCLckUAMwYMQO7l+3GWD8jNlK6ZgoDaNnFR2Vqz8U9eOrgU72sxbvBMMB//0vf19cD11zT6yUSCRXwoqPp87UYrq4kn/LzM9nCiwu6uMCMZVlMCZ4Cd4k7QttCB7xnapjzMLw470U8Ou3RAdm/HkQisJGRyNRo0NTaiklhYXDOzDSoymBZFr/n/T4gvaPmQsNq8HHix/g973fsz99v8XYsIVMA9W1tXrAZy8ctx9SoK7W/UCqB5GS0nMkClEq4uRlUZts9LHXz4wwnuHPp6emJV155BQAwZQrNK5w1axYiIiIAAO+++y4AYNOmTQCAU6dOIS1NK5WtqwMglcJ3xmhSDTg4kJYyKcmk/tbDhw8jPj4eDMNQn9YQrALD8uWjeZmjpaUFnp6eaG5uhoeRlH9fbn6NjfRlxppkH+jspJtTpSIZigkOdYbQnzV6fj6ZWonFFD+4Dh5HZqugUCiQ9PPP8KqvR3R0NEQxMdSfwjO4MUIxMZaNu2mWNePJg0/isemP2cwe3VQyVdpcihOlJ3Ci9ASOlx436LAX4OiD2e7jkOA9AQ8sXA93n2BejpFlWezN2Qs1q7baOjknh56BTk5kq27M+cqWZEqhVkAqNiFYaGujzKhaTaYKEyZYySTMBx9kSq6S44ZtN6BJ1oSnZj3Va46YHn78kSpVAPDrr8ANN1h45Bagvp4SMgai0VOnTiEhIQGBgYGorKwEwzBobW3FX8f/QmR45ICRKZZlB4zEGQNnQFFTcwmz4ibCraQQTGcHNcFNnKj32mMlx7DuwDqEeoXix1t/5GWWk5DIrsvG1rSteHHui3CW9F9x6wlLyVSfqK8HMjNRfckBJZdc4TMxBKOnCZ+Y4xPWzpmaMGEC0tPTsXHjRjz//PMAtHLMxMRETJkyBQqFAo6OVBlva2uDq6srcnJyMKbLIpllWbAszbtTqboNWElJlJtL53niRG0zZxfy8/Pxwgsv4McffzR6fH3FuEPoG0OEykT0R6j6IlMyGXD2LKnnYmK0+vpBg4YG0iKNG2fR1DhT5kyxLPWuNjVRZSU+npItlzP0eqa8vSGqqKAoesaMXra+1oIL1h0ctP3C5uCNE29gV9YuRAyLwA9LfxBc6mKMTGlYDTJrM7vJ04nSEyhr6W1fHO0TjdkjZ3d/RbiHgklPp+ydrhugHUGtBhITqQASG2v4M7IlmTpVdgqvHX8Nz895HrNCZvX/htZWqlApFLROTJhgMx9fPsgUh/yGfOzI3IFnZz/b/3X+6KPARx9R4JKcTI4wQqO+ns6zuzvJhh31JYkdHR3w8PCAWq1GWVkZPD09B1zmBwD/+utfcJO64cEpD8LDceDvPV03P3//2WhocEJYiBph6gKaC9bjvB4rOYY3TryBGyJvwMPTHh6go7YNrCFTao1azx69F9rbKbvX1gaWBZjgIErUDgLvdD6G9qpUKki6nu9cPFlYWNhdleJC8m+//RZ33303nJ2d0dGlG7722mtx4MABPPLII3jllQ9x4QLlrWbO7DHTsKUF8PBAS0sL3n77bWzk+j574L777sOGDRsQEhJiUtFgCH1jiFCZiL4uNlPmTBUVkaxNLKZxAiZItO0LLGvRFFJzhvYqlVQMk8moihIby//gU3uBQQOKvDyS9vTIKvEBjYYIa3MzxbiTJ5tHWFvlrXj71NtYFbsKY3zH8H58utAlU87uzjhfeb6bQJ0qO9XLWdBB5IDJQZMxO4TIU8LIBMPuW2o1OaY1N9MfP2OGxaw9rSYNMX4xvGepOzoojjMUW9ha5rdm3xokVyVjVewqrJu5zrQ3dXbSOe7spKRAbKzgxJVPMmU2FApg7lzKmE2eTCljnaSTXE4yZkdH6qviBU1N3TN+IJUSqepxjidOnIgLFy7gqqevwooZKzB7zOwBJVOFjYVYtnMZGIbBd7d8h2hf08YKCIWe1uglJc6oriYFia6rOgDqkfPyAjw8IFPJwLKsRRUfW0GlUVm1LllDplKqUrDh6AY8PPVhcsY0Bo2GgqKyrmSYszNJ1uzYoYIPMsVhx44d3eYUXAh+66234qeffsK9996L/3ZJirn7dd++fVi0aBFYlu1uCTh5shoKRQCCgrSScbVaje+//x7PPPMMqqure+13zsyZeP3tt5GQkNDrd0OEynoMESoTYexiM3VoL8vSrNHmZlLETJ48OLXDAChCcHPrt1plDpni0NZGiV6Nhh5sg04iaQL4cvMzf79EWOVy+yWsyVnJ2JuyF02eTThfex6JlYm9ehZcJa6YFTKru/o0ffh0uEpN1IhyVslBQRaXigsbC3HH7jsQMSwCH1//sU2y7bYmUwDQqezE1rStuCvuLjg5mFGZViiogtLaSovcuHFkZSgA+CJTHcoOtCnaLLPBLisjzU19PbB6tZ6denk5yZkNKMisg0xG57i9nc5xVJSeVHj16tX48ssv4X2VNxbeuxDbbt824HK785XnkVmbibsnCjzEtx8YmjOVlUWOjKNHk7K9Gw0NlCBgGHoYdRkF2Cuq26px3y/34Y4Jd2DF+BVmKwmslfk9efBJ/K/4f1gydgmem/Ocwdeo1XTJMgwoOZCdTQ+lgACT+wRtDT7JFAfufvzpp5+wZMkSPbJUVlaGESNGoL6+vru3Sa1WQyQS4fDhw7j66qsBAJmZLEpLT+G1157BiRMneu0jMDAQb770Em4fNw5irrd1xAiqpPfI2g0RKutxmYuqhIWpZAqgxSMmBjh/nkhDQQEQGWnDg+ULVVWkH3NzoyDCSJneEjIF0Gajo2mNLS2lgs1gdQEyBJPJVHs72albKLM0BC6ZnZKinX3UpTIwCJVGhYzaDEwMnMjL/g2B6386XnIcRwqOILep9zDpANcAzAmd012BiguMszwDKxbTPCFdmFl9rWmrgZODE7ydvOEuFebiZFkaAdfeDowcaXsyBQDOEmesiV9j/hulUmIPWVkUMPWQTvEFPitTn57/FL/k/IL1s9ebb4UdEgL88AOwcCHwxRekv7n3XgBUleImTyiVPCp5nZwoK5edTQmuixfpwRIRATAMYmOpz9GlzgUbr9844GQKIHfQKcFTBvQYjA3t5fyWei3HHh74rvMsopQemFbA0gfJ2XLaIXZk7kBtey2OFh/FyvErzXovHz1Tr175Kn7I+AGLohYZfU15OeUgQkKA0FAvahgtLgbCwizap9AQgkwB5PDn5eWFpUuXQqlUwsHBAceOHcPcuXMREhIClmXh4+ODZ555Bm+++Sbmzp2LEydOICoqqnsb48b1vq9ffPFFPPHEE/DUrfapVLQQVVfTB3DpEl3H5rrmDqFPDFWoTERP9m4OmdIFJ38HKFb2G2wzbeVyKnMoFHTw43rbT1tKpnRRVEQxQ1CQtQdsPzCrMpWaqm0omzSJ1wb/2lqKdQFqczFmUvHp+U/xZfKXeGDyA3hwyoNW75frf+J6n4z1P0X5RGHOyDna/ifvCOECQrmcMtCjR5v1cKlrryNnJBdhnJHa2jj5qxytracxerSXTchUh7IDqdWppvVM9QeWJQ2jAA4zfJIptUaN1ftWI60mDR9c94Hlf/vGjcCLL9LCdfp0d0mKS6KNGSOA3wzLkpacG5w6fjxaHR2xZcsWPProo/Dy8kJDQ8OAEapWeSvEIjFcJLbppesLxsgUQEtAQ0Pvz6iosQjLdy2HRiHHtoinEeU0nD7fmBi768EE6Frel7sP0T7Rxh05DUAQAwojSE+nOKhXNVAXWVkk/xs+XNBj6Q9CkSkOr7zyCl566SVMmjQJycnJAGjAb0lJCf79739j7dq16OjogGsfa+iKFSvw6quvYrQphmENDWRawQ2nDA6m6jaGKlR8YIhQmQjdi83FxcUiMsWhoIAyNMOHD9IqVXMzBfwsS6Xj0NDuX/FBpi5HmC3zk8uplCSTUdlu4kReXTqKiihpNXq04eIMy7J49/S72J6xHZvmb+pbD28EMpUM5yvP43jJcZwoO2Gw/0nMiDFu2DiMchiFJVOWYOHYhZbJrixFXh6VgkQiKt9ZYoEoELKyZPj99wz4+npg1apISKXCB8XvnHoH2zO24664u/DY9Mf43XhzM1BTQ4ueFQG+ED1TGlaDM+VnrCOSGg1w443A77+TPCwpCfDyQnEx8R1fX7rEBEFdHdDcjNaAABw8ehCRIyMxffp0yGQy5ObmInKAHjSbjm/CidITeGHuC/yQdAvRF5kCtPmrnqZRzbJmfJ70Oeo66vDWjBcp0O/spOs3MpIC0kEOPshUq7wV7o7934csC5w6RdXa+Hgj6hPdrPOwYcRybewYCghPpjhwyY7z588jPj4ecrkcTn2oUuLj4/HEE2/gppuutixXpVaTPKWigqqCXZXBIUJlPYYkf2bC0sqULkaNouTLoLX99/SkrEZODkXmrq6Ar69gZEqppCp1WJj99fyYAot6phwdSZqWnEzp7YwMKifx1G/VnxkZwzB4ctaTuCn6JkT5RPX94i40djbiVNmp7gqUsf6nmSEzuytQfgo/lOaXWja0lw9ERGidAzIyjPb7KNQKvPTXS7gr7i6zMr+WorOzE2VlJxAUNBLBwZHIy2MMFYN5BcuycBQ7QsSIMG34NH43zvWuKZWUJBg3ziJXL6EMKESMyPqAXyQCtm6lSLGwELj7bmDPHvj6ilBcTMlhtVogMzM/P7Q6OWHPkT14v/x93Ol9O+LGj8PZ80lITEwcEEIlU8mQWJmI2vZaODsMnIlDf2QKMC7583TyxFMJT9HQbkZEn21ODhFYO7KhbexshJeTl9mVSD7IlFKtxB177kCEdwTWz14PP1fjspvOTloCRCLKExrEsGGU6SsspJsmMZHiDRvKeWxFpgCyMh89enT3HKqe8PT0xJtvvon77rsPEokE7e10SpKSgIQEC9YTsZiSAQEB+h+CxRPIh8DBflaEQYLExES4ublZTKYAIgWDlkxxCAqiQL+iAsjORkVAAFJzc3knU5yZR3s7PfT66vmxR1hlQOHsTCSKS59mZVEgyjOrZFkirMOH9w4odMmUhtWgpq0GxU3FKGkuoX+bSlDcXIyChgLkNeT12naAawBmj5zdTaB0+58KCgpwMd+8ob28QySitHR2NgVJGRn0/x4P728vfItDhYeQWpOKX1b8YtpsJgvBGVAEBPhh5swopKQwqKujwxMypmAYBo9OfxRLY5Yi2J3nzLtYTJnmzEwKklJTyRXFjECFbzJV3VaNk6UnccvYW/gbAzBsGLBrF1m57t0LfP453B58EE5OxCMbG4VZ+1tbW3Hy5EmUOpSCkTAoKEzElBEhRKjOncOqVav432k/cHJwwo+3/oiTpScxKWiSzfcPmEamAFL7Ojoab1ftvj4cHGgNbmrSd2MVjCn3Dw2rwSP7H4FEJMGGKzYgzCvMpPfxJfO7UHMBVa1V6FR2wk1qjCURuHnJ7u59PMYYhrSA3t7d9urIzCQCEBkpOJG1JZkCgIiICCxfvlxvNtSTTz6JZ599Fj4Gknt1dfSvt7eVl5xuFYplqQ9zCFZhiFCZCWsqU4agVNJ1HBg4CPupRo8G2ttRl5eH7Lw8TFu0iHeZH8NQZSozk2SSbm60rg4G8OLm5+5OgWdaGlVRSkp4b97NyqJFuqVVjRTVD/iz8E/MGTkHVW1V3YSppKkEJc0lvSpOPRHlE4XZIbPJRKKP/idTh/baBLqkimswGzNG70K7NeZW5DfkY0HEApuQKV0DitBQkozl5lIMJ3Q/PO9kioOPD0lXOQfA5GSTZ1XxTaZYlsVbJ9/CsZJjKGwsxFMJT1m1PT3ExwPTp5OVemIi8OCD8PEBKispQ883ODIVGhqKhWMW4orSeQhtZPFX1n58DCDp+HHS9w5ARUUqluLK8Cttvl/AdDIF9K7Y78neg/TadDw09SHDfZK6ZIqzTx0xgr5sLKMobCxEaXMpRIwIno6m2Y7z2TM1JXgKdt62E5Wtlf3aybe00L8muaO7upLxSnExPfxrakhNwKtdpj5sTaY4bN++HV988YVJ69qlS/Qvr/Giou/n+hBMwxChMhNTpkzhjUwB9JCtr9faqQ+q+VQMg3JPT2TJZJgoAJni4OdHbVolJaS2cHGxf+c/Xq3RvbwoK1pe3kcXb/9QqpUobynXrzA1lyC/rhgF9SWolZdBzaoAAFsubDG4DREjwgiPEQj1DEWYV1j3v+He4RjvP96k/ie7IlMcGIYse0UickIqLaVmiq7gyMvJC29c/Yagh2DMGn3kSCK8HR2UGOc78ZJVl4Xv0r7Dk7OexDBngXvIPDwoSOJmVSUnU2NRH7PXhJL5TR8+HanVqVgas5SX7ekhPp4IVdf9GhpKUm++ixi6ZIqbMzU7dA4QCkxqUAFvvIHUixehOX8eIhsNWm5XtCO1OhUJI3vPurEVzCFTPaFQK/DJ+U/Q0NmAaJ9oLB+/vO83VFdToF9QQCXIsWNt6gI4etho/Lz8Z+TU58Dbuf/1VAgDilCvUIR6hfb7Oq5CZfK4KZGIbhxfX8o8Czg4e6DIFAdT1jWZjAp2DMPzJApHRzK/GoJVGCJUZoJPMgVQsNTQQAtNZubgmk9VXl6O1MxMTFu4UEumLBwA3B/Cwmghqa8nVVZ8/ID0qZoEQeZM+fj0u4LKVXKUNpf2IkycNK+itYJ6AfqAGA5wlbphcvBEhHuFa4mTF/073H04JGLLgwW7JFMcGIasZDl7SYZBZWulcBUbHfQ1Z0ok0o5nMdp3YCFYlsVrx19DzqUcuEhc8MLcF/jdgSE4O9NCl55OKeuqKqOESigyxTAMlo9fjpuibxJmSCvnotWlIRNirdIlU01uTZCr5XrzwsbMmQNHR0e0dnSgqKAAESoVXUgCzQTj8GXyl9iathXLxi3D0wlPC7ovQ7CETOnORpKKpXhnwTvYmbnTNLI9ciRV//Lz6WF+/jydZwEGtBuDj4sPZrn03wPIJ5lqU7RBoVaYlYTx96dYx2zPAw8PslfXjS1qa+n+4sFAYaDJlKngqlOengJw9sHYoG5nGCJUA4ye86ny87tdLO0aBg0olEpihb6+VlVSDIErICQnU6Y+I4Mq//ZGPm01tHf/qS04WnMOJZqGbsJU1VbV7/scxY6UTexRYQr1CoW6PgyqpiA4SsSIj+e/WmrXZIoDpzEFkFSZhH/89g/cMeY2PDr7ScGsp00Z2ss3keLAMAxenPsiPjj7AR6e+rAwOzEEiYRu4NJSCkgNQCgyxbJs9zkWhEwBWkJlYAaXRmP9uqVLpqQBUqzdvRYBrgHYsnhLd5VCIpFg/PjxSEpKQkplJSKGD6cHjLe3oAunVCyFg8gBCSG2r1BZWpk6cYJygTNn0kc2IWACJgRMMH3HwcEU5WZm0gPqwgUqS4aGChaotspb0dDZYFJlCOC/MvVN6jfYkbkDa2esxZKxS0x6j1Vqdd3z2NFBFSuWpfUjNNTia3qwkClA2z816HvwL1MMESo7gKMjkYW0NJIAenrad5+QUTe/ujrSJDU3k7SEZwtqBwdqJ0pKIu6mUPA285YX2IpMXarMx/WH7rHovXK1HCVNJahtr0V6bTqcHJzgIHKAi4MLXKWuUMmcIdI4wTnDCUF+znCROMHJwQnODs5wcuj6XqLzvYk/ryipQEFuARISEuyXTPXA2Yqz0CjlaC3MATOyVG88AF8whUz1RFsbJcKN8BCzMcZ3DP5zw3/42Zg5EIn0IyyWpT6JgACwgCBkKr0mHR+c/QDr56zHKG8B5u2kpACvv07GFIAeE25pIZd+sdi6NpCeMr+U6hT4uvhi9LDRvSRfkyZNQlJSElKbmnDriBHUrCtwFuofU/+BpTFLbTv+AJaTKZalLwBQsXI4wsJB1K6uJJ3IyyMZYHExnWu+btQe+Cb1G2xN24qHpz6Muyfe3edr+SZTLMsirSYNHcoOwWbx9QmplLTPNTXUC8ANETPTR3wwkSm1msy5gCFCZa8YIlR2gmHDtH1CubnUI2QDubvZ6NMaPTiYoobqamrsnzyZ9z+CM75zdravYfW2IlMA4BMUgdcmrsOFimR0ahSQuUghY9ToVHVCppJBppKhU6nzvapTT+onV8shV8v731ENv8fNgIFzmplkzEoix33POQuag4emPoQ4NgCTWl1pPIBGw6uG3xIyxc3VZllaIyzlpmqNGk2yJvi4CCv/MgtFRUBpKdjaWlwUiVBSXs4rmQKAf5/5N9Jq0vBd2nf417x/8bZdnDwJvPYasH+/9mc33wws1UrGpFLy4mAYyz0iDPVMTQ6ajF237UKnqrfjRVxcHADgAje8Whd1dYI17g4WMgVQoAoA+W2peG3n03h46kNYPGaxZQfCuVl6e2utUwUAy7LdEu7+EgNC9EwxDIPPFn2GcxXnTB6x0NxMlxovnMXBQStfzcujGyspidZnE41BBhOZAujSmjWLQix7SiQPQYuhwb4mwhZDz1iWlALcmBZ7M14wac6URkN/BLd6xscL7i41gI61AGxLpvSQk0P9JyIRle6MRNcsy0KlUXWTK13SVdZShndPvYsWRQvWzVgHsUiMxtZOyDUysKLer5epZJCpexM2Y0ROppLZ5lz0AweRQy/S5ejg2P0zR7HO9w6OcBLrfN+hgGNzO5xEUjgN84Nj4Ajj7+vj57qkzhIyxYGbRezkRG0Fllz73174Fl+nfo31s9dbNLRZENTVgc3KQmlxMSpaWhC7ahXcee73qWmrwceJH2PdzHXwcvKybmMVFcBPPwE//ACcOUM/E4mA5cuBZ5+lzE8PnDtHaqWeA2RNgSEy1R+OHz+OuXPnIiQkBKWlpdpftLRQNU0spoOxUk2gUCvw/pn3cVfcXQhws628whoyBVCS4vRp4Mui53BReRBLxi7Bc3Oe4+PA9AP7qiqqEPIoAcyuy8YYX+PXghBkyhKwLMkq1Wpg2jSe86wKBT0L6+vp/97edO/1cZ4HG5myBYYG+1qPoQqVHYHrp2IY+6q+ACaSKYACinHjqNmps5M05f0sbtagspKUFZMmDYxD4oCRKYCa7VQq7fykCRMM2icxDAOJWAKJWNJrmn1cYByuGXUNKlorjGY6LfUZKSgoQHZ2NqZMnwInd6c+SZe5PzflfboW7yqNCm2KNrQp2sz/Q3RRZPlbRYyISJbYCVBT/46Hiwccz5pI7Lp+LxU7oabWCYzaESdbnRA63HRi5yh2BAsWx0qOoVXeajeEFwBYX1/kOjuj8dIlTBw7Fi55ecQazZTx9IUAtwC8cuUrlm+gspJI1I4dFCFykEiAe+4Bnn66dyVIBz4+RKgaGswjVIbI1Hdp3yFyWCSmj5hu9H0TukhdWVkZGhsbtXJbR0fK2LW0kNY8PJykaRau09+lfYcfM3/E6fLT2LVsF39zvfqBtWQK0A71vX/UK6jznYirR13Nz8HpnsuyMnIBrKqiygpPD6u+howLRaaSq5IxMXCiWZ9xWxuRKQcHAZ7TUiklFKuqqEfQ0/OyI1MCeX0NgWcMVahMxECx9wEaH6IHk8mULtraKAOqVlMJvo8gw1KwLO2ipYVirkmTbHuuBpRMcdBoiEw1NNAfP20ar5Zizc00nmn8ePOMEezBgEKtUUOulhslXXIV/Y57jUwlw66sXUivSUeIZwgWRCzo/rlcJYestRGypjrINUrIHACZk0P3e7lt9dyeSqMakL+9L0jFUjg5OEHEiOAude8mWxm1GQCA1ZNX4z83/McimaSl0DOgmDwZ7kVFlJDhBqlacQ2xLIuqtirL3Rrr6oBvvwV+/pmkfbqPzIQE4LbbgGXLyBmyHzQ0EH9xdCQDBFNgiEzlN+Rj1U+roGE1+H7J94j2jTb6/tDQUJSWluLYsWOYM2eO9hcaDQWglZX0f19fkqtZsIjm1ufi7ZNvY2nMUlw7+lqz328J+CBTAD2qzp+nZXNW/0Z5luHSJTJR4B7oUVHmlyi7cCD/AOaFzdNzdOwJochUfkM+Vv60EuFe4diyeIvJpi4VFVRZHzbMYOGWP8hkdHNx7KOjg6qwXeYwg5FMAbQEFRaSgpRnv69u5OfnIzIycqhCZQWGKlR2jKoqSmrFxQ2c/M8iMgVQ9B0TQ39AsDC20wxDsVZSEjVrcoG/LTI5dkGmAG1FMD2dmnRNeECwLIuX/vcSEkISsHD0wj5fW1ZGzyjOqt6Uyqk9kCkAEIvEcBG5wEXiApgYa433H4+NxzZi41UbDbt81dbShTZiBBAR0e/2dEldU1sTTpw5ATcvN4SPDu9FxnSJmDGCJlfJIVPT9/VNcrR2yqBmZJA40c+NvU8XCrWiu3rXJGvqdcxfJH+BF+a+gJGewjTT94RBNz9vb7rompvp3xkzLC7bHyw4iJf+9xLWxK/BfZPuM+/NO3YAd91FujAOs2YRgVq61OzoxsuLblm5nGK9/qRPxmR+gW6BWD5uOeo66vokUwAQGxuL0tJSpKWl6RMqkYgCe3d3inYvXSJlwbhxZlcFo3yi8PmNn5v1HmvAF5kCgOLGUrBsCMRiAR8cvr7AlCm0djQ3U49xQwMQGWmWZjepMgnPH3keQe5B2HXbLjg69DbQEFLmV95SDnepO0Z5jzLLIdPs+VOWQre5SKOh8yyTAaNHQ+PvPyjJFECEqrNTuPm7NTU1SE5OFmbjfyMMESo7Rn09JbQyM00PZvmExWSKg48PpaQEZDiOjkSiUlPpfBUV0RxAIWE3ZIqDWEys28TzfLjoMH7P+x2HCg8hLjAOgW6BRl87ZgwRVk692d9u7IVMWYppw6dh9/Ldxqsz/v4UBZtYruNIHaNikJSahAkhE8zumTIGhYJ6clQq+pwCjXyMLMtCqVGiqLEIp8tP46qwq7RkTi1HfUc9rv1eW1U4cMeBgSVTAC12cXHUG+HjY9Xid7biLFQaVb8z2Azif//TkqkPPgAWLwZCQiw+FpGIgsrGRvrqi1D11TPlJnXDE7OeMOlvio2NxW+//YbMzEzDLwgKous5I4NYXn29yYRK14JeqLEChvbJF5mqa6/D6j9WYYRjDJ6b8g4AATPzTk5k71hcTO5T1dUkr4iJMXk9UagVCHYPRkJIgs3JFABcEXYFpgRPMVsq3NJC/9q08KFS0Q2nUkGTlYXMI0fQ6eeHWfPmDSoypdEQ9waEcferqalBYmIiYmNj+d/43wxDhMqOoRvMZmeTTNhWOlqryRQH3QNubKTAiOehOh4eNI81O5vG2ri6Cmc7b3dkioPueVYqySpy9GiDc3CuDLsSa+LXwF3q3ieZAvSt6puaSCEUGWn4tYOZTHUqO7szrv1K3XSvX42GWHxoqFGplDUGFH1BKqXPQqXq+3pnGAYSkQQfnP0AZyvOokXegsemP6b3momBE5FanYptS7bZzKSi3zlTuhONOXR20jVtxn334twXcUXYFZgxYob5B5mQAHzyCTmWPvqo+e83AD8/WgYtIVMaVqPXu2JKH0tMTAwAICsry/iL3N2pglJZaTJhVGvU+Mdv/8BV4VfhtpjbIBYJ7wzEJ5kCgMy6TDAiDdw8lZg41gYyEIahfjVvb3pgdXbqS0j7wcyQmdi1bBeUamWv39nKgMJN6gY3qenPcLmcikQMY2NCJZUCkyZBU1KCnD/+gFomw6zgYEiamiyWW9oSSqUSHR0dUKs9oVLRn8O3UokjUxMnThyS+fEAO4kGh2AIXPuASEQZCl2TJiHBG5nSRX09NQ+kp+vLZ3hCQIB23EdODj2n+IbdkqmeyM0ljcCFCwY1AmKRGGvi12Bl7EqTNufiQklUgLTwVQbmBw9mMnWw4CCW7liKM+VnzH9zbi7pIlNTicj2gFBkikNAAOnq+9ssCxazQmbBw9HDoCX0VWFXAQCOFB3h9fiMHo8lQ3vlcjrPFy4YPNfGwDAM5obOhVRsQVb6yivp35QUbZrYSgQH0/1k7DbpqzK1+fRm/Ouvf6Gh0/Rj4QhVdnZ23y+USPQH0arVJAU0cq4PFhxEclUyPk/6HK2KVpOPx1LwTaYAqrjsWb4HL8590WYVNgCk/ZwypbedrwnkSiqWwlWqX0EUmkxl1WUh51KORe/lqlOurrZ349WwLM7X1qIuNBTjpk+HBCAZYGam1i/fTqDRaKBraRAcHAwvLy/k59O97uvLb0Jdl0yNEKox628GO40ItaioqMAdd9wBHx8fuLi4YOLEiUhKSur+Pcuy2LBhA4KDg+Hs7Iwrrriil7RBLpfj0Ucfha+vL1xdXXHTTTehvLzc1n+KRXBzI5k7QInwxkZh9ycImQJI5+LsTEFRRoYgi1l4OKmDwsL4dxIaNGQKoMqUkxPJd9LSugOijNoMWOpB4+OjHcGUm6t9SAKDm0yxLIsfMn5AbXst0mrSzN/AiBGUOuRMWHSSBUKTqZ5Qq2kciyGIGBFun3A7fl31q0E53/xR8wGQHFRoWESmADq3ajU1ZCQn0/VtBEq1Ej+k/6Dn9GgRgoOpSsayJP8TGH2Rqdr2WuzM2onf835HfkO+ydscM2YMvb+2FpcuXTL9YPLzKYOSlGTwwlo4eiGem/Mc/jnjn9Zb0PcDIcgUB3/XAGGGPPcHiURfw9XWRhrepqZeLz1ceBinyk4ZXL+FJlMsy+L1E6/j9t2349fcX81+v6cnqW0Emm9sFLoGFDPmz4dkxgwKDhiGEo129gwXi8UQiUTdn/F///tfAMD06TQ6gk+53xCZEgb2dUX1QGNjIxISEiCRSLB//35kZWXh3XffhZeXV/dr3nrrLWzevBkfffQREhMTERgYiGuuuQatOg+AtWvXYs+ePdi+fTtOnDiBtrY2LFq0CGo7y1AYQ2Cg1kAqK0u4xkTByBSg1Y5JJPRwvnjRLKmDKWAY6qfie+EeVGQKIElUXJw20E9PR0p5Iu795V6s/WOtQbmIKQgNJbmSn5+2xWIwkymAqhef3PAJHp32qPmGBQBlPCZOpHPe0UGkqrPT5mSqsxNITNTjzwAoGNLts3GRGNaZzRk5Bw4iBxQ1FaGo0Qpv+H5gMZkCSC80eTIlCzo7iVQZCD4BYGvaVrx7+l089NtDFicRujGfyCaO8Fu9a2/XT0z0N2fK39Uf/73pv1g9ebXJw1QBwNXVFWFhYQBMqFLpYsQIykzJZHRd9yhNixgRloxdghujbzR9mxZACDKVUZuB8hZKqpaWAkePUjFuQFFcTNd1aiplT7uu2w5lB948+SYe2/8Y/ir+S+8ttpD5dao6EeYZBlepKxJCEsx+v1RKMYwtVXYG3fwYhgjV5MnE8HQrsWZUvPkCwzB669/DDz8MALjhhhsAADfeeGN3r9fhw99BJ+y1CkNkSjjYdWT45ptvIiQkBF9//TWmTZuGsLAwzJ8/HxFd7losy+K9997D888/jyVLlmD8+PHYsmULOjo6sG3bNgBAc3MzvvrqK7z77ru4+uqrMWnSJHz33XdIT0/Hn3/+OZB/nlmIjCRlwPDhwphTCEqmODg7E+MRibQ+oDxDNwZRqynBag0GHZni4OxMpEoiAVpaUJF+ChKRA3xcfCARW34BjR1LciWxePCTKQ5ODk64e+LdltuEu7hoB6HJZJCfOYPThw7ZjEwBxOfEYooLinT40KHCQ3jw1wdR1lzW5/vdHd27g3ShZH9WkSkOLi4UEHl4UPPYhQvU3N8DYV5h8HHxwdKxS60//1eRHBKH+aveVVcTAS4ooP+bOrR3nP84/N+U/zN7fyb1UfWEqyu5Ifn4UK9gTg6Qk4PmjkbLDD4sgBBkSqlW4l9//Qu37rgVJ0tPQq22kzk/Y8dqM6clJd3JGZZlcX3k9Rg9bDTmhs7tfrmteqZcJC549apXsXfFXng72/863681uru7voSlqIhuRm4wsEBgGAb33ntv9/99fX3R1taGL7/8EgDw0UcfAQD279+P3NxcAEBpKR3Txo13Qm1hIlQXQ2RKWNj1HKqYmBgsXLgQ5eXlOHr0KIYPH46HHnoIq1evBgAUFhYiIiICycnJmDRpUvf7br75Znh5eWHLli04cuQI5s+fj4aGBr2gLy4uDosXL8bLL79scN9yuRxyHflOS0sLQkJCcOnSpQFr3tNohKlSV1ZWIi0tDfHx8fDz8+N/Bz1RUwPm4kUAABsZKYitOjejqrWVQXg4a1HVSqFQ4Ny5c3B2dsakSZMGD5nSRWsrmLQ0QKVCgYscgRNnG61SmIOioiLk5uYiMnIGRo0S2guXf9S01eBi/UXMC53H30YVCijOn0fWuXNw9/fHqBUrwNjwmmlqAi5coKhw0iQWrm5q3PrTrahqrcLqSatx/6T7+3z/hqMbsOnkJiyPWY6ti7fyemwsyyI3NxdlZWWYMWMG3Kw1ptFogIsXwdTV0fajo3vZHHYoO+Ds4Gw9oWpshENQEBiNBsqiIspqWYnOTuDcOQYiETBhQisSE88gJCQEUVFRvY63TdEGtUYNTyfL77Nnn30WmzdvxiOPPILNmzeb92aWBUpLwRQXg2VZPFj6ERQernjxig0I9wq3+Jj63y2LjIwM1NfXY8aMGXDStcS2Ag2dDXjl2CvIbcjFzqU7UVniispKBqGhLLoKeQOLujowubmUMBCL6RkZEAC1Rt1t/NHY2Ihz584hOjq6u/poj2hvpzYFLy/evagMQqPRICUlBZ2dnZg2bVr/bn4aDZjkZDpQAKy/P2WvBRhoyR1LSkoKxo0bh87OTnh2+ci3tLTAyckJFy5cwNSpUwFQ/AEAmzdvxrPPPovRo0eblxDpgdraWiQnJ2PChAkINhBztbS0wNfXd2gOlRWwa0LFLaDr1q3DbbfdhnPnzmHt2rX47LPPcNddd+HUqVNISEhARUWF3gWyZs0alJSU4MCBA9i2bRvuvfdePXIEAAsWLEB4eDg+++wzg/vesGGDQbK1bds2uPQ3PMQG0GiAjg4HuLnZ3+BQU+BcWwuXmhrIhw1DGw8BiiHU1zuhspK0aaGhrfDwEEgraedw6OiAW0UFWkaOhMaA65+lKCtzR1OTFMHB7fDxMc9GdyDBsiw+r/gcue25uM73OlztczVv22bUariXlqIjIACqAVgnysrc0NTkCGdnFSIimtGoasCRhiNY7L8YDkzfQUJGWwZeyH8BXg5e+Hrc17Zt0rcELAuX2lpIW1rQPGoUWAE73uc++SS88/ORtHYtyq+4gpdtXrzoDaVShPDwFri5Gc8+767ZjZTWFNwWcBsmuFs2FfXw4cP48MMPERcXZzSJ2B8kbW1oL03DRuUOKJwc8eyo9fCSeFm0LXtAs6oZng6e3fdMYGA7/PzsYx0TKRRwLy+HQ1eg3xoaCoWNg1ylRomjjUcxy2sWXMSWrWV1dc6ornaBp6cCI0cKb1xiETQauNTUwLm+HmBZaCQStAUHQ8nz+S4rK8OjXU6hP//8MwDg999/x+effw5vb298/fXXAICnn34aubm5uPXWW3HHHXcAABYvXgwAeOONN7p7IvlGR0cHVq1aNUSorIBdEyqpVIopU6bg1KlT3T977LHHkJiYiNOnT3cTqsrKSgTpTKlfvXo1ysrK8McffxglVNdccw0iIiLw6aefGty3PVaoOCiVQFoag44OYOJE1mIrTZtXpnri0iVhBivoIDcXqKpiIBZ3Ze1NGK9yWVSmQFWYt0+/jXUz1iHYLYgXTQtXmZo+fTpaW71QWMiAYYAJE1jeNN5CQ6VR4bOkz/DTxZ/w9U1fI9Qz1OptymQynD59Gr6+vhg/frw+GVGpBMl4GoJCASQmMlCpgNGjWbOKKXKVHO5v0WKSujoVMX4xVh8P75UpQ1CrAbEYLfIWPHP4GayZcB8mjZjK6y7EDzwA0bffQnPddVD/8gsv20xK6sDRoxcRF+eFq64KN0hgFWoF7t93P/Ia8vDxdR8jPijeon2dO3cOs2fPRnBwMIqLiy0/aJkMl5qrkC0vx5yRc/p/vQUQqjJlDBkZQH09g6goFjphxICjtKkEW45/iNUB1yNw6pUAw9i0MrUjawc2n9mMcK9wbLtlm0UJlvR0oKGBQUQEa+4MbLNgdmXKEFpawOTkdJvdsAEBZPDE49p97bXX4siRI1i7di3eeustANrK1cGDB3HFFVdArVZ3S1vz8qoxcuQwlJWVYvTo0QAoNjXns+ivMsWyLH799Vc888wzyM/PHyJUVsCu51AFBQV1a785jB07Fj/99BMAILBL4lFdXa1HqGpraxHQNZglMDAQCoUCjY2NepK/2tpazJo1y+i+HR0d4Wggmy+RSCCx9YTdHnBwIHm7TEaEwZKhv+Xl5UhPT8f06dOF65nqD7pPL5alwJPncxsTQwS0qYl8MPo7VwqFAomJiXB1dR1cPVMGsPncZpwsPwnVaRU+vuFj7S8aGoCaGv3GXBNQUFCAvLw8JCQkwNvbG35+ZLxWU6O9DgWOfXiBBBKsnbUW90y+hxd3ss7OTpw5cwb+/v69e6ZaW6nPJyICtojWJBIgOKwNyRfr4VQeiuBgago3BUpoqyR8rHNcz1R5eTlmz55tWc+UKeg6zi3ntyC1/BzeqSrC9tv3QOTOY1AwbRrw7bcQ7d8PUVoaXexWoLW1FYWFyQgMHAV//5GQSg3fhxKJBN8t/Q6ny05jxkgL5mh1Yfz48QAoiSaTySz/LCQSBLm7Iwhd1rN1dWRWMXYsL2s31zPV0NCA2bNn8+rmV95SjsOFh7EydqWehT7D0DPVyUmY/mRL8eWFr3Do0km0u4rxrnQhGhoakHjmDGJ9fRE6erTgTV8RPhGI9InEsnHLLB6E29lJ59bK2dx9giNTMpkMCQkJlg/t9fEBZsygnqrycnITFYl4PfBDhw5BLBbjvffew3PPPQc/Pz9UVlYiODgYCxYsgEajgUQiwS+//IKbb74ZkZGBqKxkERERgTvuuAPfffcd7r77bvzwww8m7a+mpgYpKSmYNGmSXs9URkYG1q9fj19/Nd+1cQjGYdfRYkJCAnJy9Gcf5ObmIjSUMsrh4eEIDAzEoUOHun+vUChw9OjRbrIUHx8PiUSi95qqqipkZGT0SajsGQxDzy/OgCk72zzDPJsYUJgDtZrmQly4wLudOsPQqA/uXGVkkFzSEAatAYURPDnrScwcMRPPzH5G+0Olks51TY1ZTovGDCiiokgbz23W2Lm1B7Asq+f4xheZ6tPNr66OEgU5OTSvygb4seQ9vJ67Eskdv5i1LnC28QGuAVZXp3gxoDATqyfejyXDZuO5ESshSr1AFXDeNr4auLpLGnr99VYZ6nAGFGPHBmHkyJFoa2P6vG8cRA6YE2pdNcjT07NbhVDAOWGYgeq2ahQ29vibNRqyxmtoMGqtbg6EtEYHgA/OfoAPz32I14+/rvdz7pFjoyKyybgz7k5MGz4ND075R7cBxQRXV4QqFOQEKBNWnjhjxAxsW7oNN0ffbNH7OzrouSASCdc/1a8BhbkQiSj5NWkSJRx1k+o8xCYikQh//PEHAHTHXkFBQVizZg0A6v8HgIULb4JYTBfkwYPfAwC2bqW+1u3bt6OkpKTffekaUDg5OWHdunVgGAYMwyA2NlaPTD322GPIzzd9FMMQDMOuI8Z//vOfOHPmDDZt2oT8/Hxs27YNn3/+ebe9JMMwWLt2LTZt2oQ9e/YgIyMD99xzD1xcXLBq1SoA9CC5//778cQTT+Dw4cNISUnBHXfcgdjYWFx9NX+9E7ZGz6G/pqo47I5MAbTqNjeTxXdWFu926hIJObY7OFDGzNBc4cuNTAFAsHswPrz+Q/25QxIJsXGG0ZaW+jnffbn5icVk3Mi54ffIf9gVtmdsxzN/PmPWUNS+YJI1+qhRWh//ggJBnC11odKocKmjDg6OCsyODYE5LXMpVSkAgElBk/p5Zd8YCDIFAO7Onnjutg8wMWQqBT8ZGfyRWKkU+Okncs6srQWuvdYiwqbr5jdxYjScnIhM6dqnA0CLvAUHCw5ab/muA04ylGeBP/i7p97Fil0rsDt7t/aH5KjRp7W6qRCaTAHAvNB5CHQLxJ1xd+r93MODhixbG4vzjRi/GPznhv/Ah/HpdvMLGT+eFt3mZuD8eboWBYSIEXUbYZgL7pp2dxfGTIt3MqULDw/9doT6euDsWV6cABcuXNg9+uerr74CgO5e/n379iEvLw/19cC+fTR09J577oCyy9b9zBkaPt+f3LO8vBwvv/wyVqxYgZCQEPj5+eHf//539++vu+46pKSkdCcZ33///YFp+7jMYNdR49SpU7Fnzx788MMPGD9+PF599VW89957uP3227tf8/TTT2Pt2rV46KGHMGXKFFRUVODgwYN6D/F///vfWLx4MZYtW4aEhAS4uLhg3759ENt6bDfPcHMDoqPp+5KS/p/vdkmmANJaxMbSqltfL8hAEBcXCvzj43sP/b2cyJRcJe9/jpCvL2khGYYCoD7OtynW6E5ORO4Zhp7vXX3UdoU2RRs+TfoUR4qO4GjxUau3Z9acqVGj6AugoTcmkFhL4SBywHvXvoctt3yDyUGTzXpvSnUXoQq0nFANBJm61KGz8Dk4UJDP9QoUFBDL56N06uEB/P47EeS8PODGG/scLtwThqzRIyLocHuepk8SP8Fzh5/DpuObrD/uLnCEytxMtEKtAAu6XicGTtT/pZtbb2v1ixfNyubbgkwBwA1RN+CXFb/0GuIbGUk82RYudOailzV6QAAwZYp2bEBWFklUVPyZU+3I3IGDBQettsZvbqZ/PQUwgRWUTBlCeTk1qKan0/m2cm5VZWUlAOCBBx7o7tVPTk4GAERFReHSJcDZ2Q3r178JQCvZnT59OsaOHQuADCp08eeff2Ly5MlgGAYhISH45JNP0N71MB49ejR2794NjUYDlmXx+++/Y+LEiVb9DUMwAHYIJqG5uZkFwDY3Nw/0ofRCXh7L/vUXy545w7IajeHXlJWVsfv27WNramoEPRaNRtP9pdb50v25UdTW0h/y118sW1Ii6HGyLMuq1Swrl8vZv/76iz179iyrVqsF36fQeOvEW+zML2eyv1z8pf8XV1drz3dubq9f5+fns7/++ivb0NBg0r4rK1nWDm+PbuRcymHfOvFW39egCejo6GAPHjzIpqSkmLetykqW/d//6HxnZNAFKDDkcpbNzmbZ8vL+Xzvl8yksNoDdkbHDon1pNBo2KyuL3b9/P9vS0mLRNsxFaVMpO+urWeyrR19l5Sq5/i/LyrTnOzXV+OJoLrKyWNbbm2UBlr3pJpZVKvt9S0tLC7t//342KyvLpGvm65Sv2YSvEtjzFef5OGKWZVn2lVdeYQGw9913n0XvL2nqY03WaFi2uFh7vhMTWVal6nebGo2GTUlJYQ8dOsR2dHRYdFyXE9QaNfuPX//BbkvbxlbVVrG//vorW1BQ0PuFGg3LFhZqz/fp0yzb2mr1/i+1X2ITvkpg4z+LZ0+XnbZqW4mJdGh1dVYflh7UajV79uxZ9q+//mLlcnn/b+Bnpyybn6893ydPUrxiBT788EMWABsSEtL9s6lTp7IA2DvvfIH96y+WbW9nWQAsAPbUqVMsy7KsQqHo/tnNN9/c/X3PrzfeeINtb283+XjsOcYdLBi8qfghdCMigkajxMUZ7lMVqjLFsiw0GhZqtRpqNWU+OI0uwzAQ6Xzp/hygDBP3HpbL1vv5kasOQNIoAeUMdXXA8eNKHDly5rKoTAGAWqNGeUs5FGoFfF1McE8MCCCdOEATkGtqun9lydDeoCBKnNoronyi8FTCU1bZgZtVmeqJoCCqDIpEvPcKptWk4cvkL6HS6GeqL12iIbJFRX0nVZVqJdJr0gFYJvljB0jmd6rsFOQqOapaqyAR9WgeHzFCq/X19eWviX/sWGDvXuqv2LsXePTRPiuOpg7t1cU9E+/Bb6t+Q3ywdeYXurC0QsVBTzrcEwwDhIZSuU0qpcFD/ShAWBtUpmQqGf75xz+RWJHI+7aFwF9Ff+FcxTl8fPZjHD993PjQXoYBwsOBiRNJIqBW82Ke4Cxxxj0T78GMETMwffh0q7Y1aRJ98en+avPKFAfd3ipXV6pWZWbSl8KycSyPPPIIALJTP3qUVBOnT58GAGzduhEqVSNcXNDdLzVr1iw8//zzen/zLzqOo7fffju+/vprlJWVgWVZPPPMM3Yx4ufvBLu2TbcntLS0wNPTE80NDfAwMcC0BwhBptQaDfD/7J13eFRl2sZ/50xNJ40UCCT0EkoITZodFRVRrIio2F3dtax+uq676trb2lfXtStWimXtXXqAhBaSQEII6b1Ppp3z/fHOTHoyM5kJYeW+rlyQyTnvOXPK+z73U+5HVbtMmbTbFcxWK2arlcqaOlAkJI3E4IgwNBoNRr0OTRfExW5XkGUH4TpwQITYtVqhuuPjamFVhW3brGzYsJfQUB2XXjoeg+HoJlNOqKpKemm6Z+leJSUiP2PsWJAkr8hURzQ2ijTU8eP9kz/vLgrqCjBqjQwO6vuz3ycy1Rb19WJR9lHKsdVuZdmaZRysOcjKlJXcOONG199UVegFNDaKLLgxY7oeY0/5Hib9axIh+hBq76pFlty/aUeKTDmRXpJOdFA0Q0O70WU2m9sXl/uqQ/rq1XDBBeIiP/QQ/OUvnTZxh0xVVwsV0thYkZrsL6SlpTFz5kzi4uJcKUc9ocZUw+p9q1k+eTlGrQfynRaLmLOd19hqFb+3+e79QaYA3sx4kxe2vkBcSBxrLlyDTtOedFgssGmTOL05c/wunNcr7Iqd97a/R3Z2NlfNuaprMtURNptIPW3rzbJa+0SwnM7RgYQjRqY6n4hY3AoKxLufnOx1+5fi4mKGOHpbKIqCJEm88MJabr75PADefPNN7rzzTsq7cC5HRUWxevVqFixY0E6AYqiX+vQuG/eYbLrXGGC6NkcB9u8XEroDGJWVQnxBknxHplRVRVFVNLLsIkSqqlJQWkFGVi5ZeYfJKyyluKKaipo6TC1mrDY7IvosYTBoMasSJyaP5oZLzyMowMi+vHwmjEokMS4ajaZ1THXECGRFERaGH6SXrFYLdXWbCAmJIjFxPPv2yUyefGQNf19BkiSPa2eIi3NJeufm5pK1bx9zHNLo3kBRhB5AS4vgDH7qQ9grbIqNe368h8N1h3n81MeZOcT799ZnZAo6h/EOHRLRQi8157WylqtTrua19NdYPnl5u79JkqgRSU+H4mJBqrqqFXEKUkyJnXJUkSlwI6LWlkzZbOJixMXR58Y4S5fCs8/CH/8I99wjxluxwvVndyNThYWCVFmket7Oe5Trp1/fczTISzgjVCUlJTQ1NRHUS1O+l9JeYm3WWjIrMnn6tKfdP1BbQ1dVxWQAIjprMPQbmQJYMm4J5U3lpMSmdCJTIB4Hkbt55MkUQF1tHZHlke6TKRBrZNs5pbJS1PmMGuV1q4ZjZKoHyLKIDkZHd+6l6eGDFB8fzzXXXMOrr77Keeedxx133MGHH7a+a1dccUW77V9//XVWrFjRzpntCzJ1DL7BMULlKdzpDHsE0dAg1q+Kigpstr2cfHLfyJSqqtgVBa1Gg8YxUezMyuXHLbv4efsuDhQUY1VA1mhQFQXVEb1SUcEZ+5TAblJottrYnXuYd7/4EcVq5b+bdvDIjSs4dLiM79anc+3FpzIsPlZM5mPGCI+N2N1ncApQhIYGcuGF48nIkKmtFTzZKfBxtOGz7M8obSzlqpSrvFZkglYyNS8qirDaWiF95QVkWVzLXbtEullICB41mPUV6lrqkJCQJZmkQUlej+NTMtURhYUiH6+oSOTsejG/SJLEaaNO49SRp3ZJhsLCYPBgkUG7f7/IWukIbwQpjiSZ+mr/V8wdNpdQg4ee1LIyoZpy4ID4d/TovnlSbr5ZKAk+8QRcdZVwAi1c6FGaX1iYIFSvpr/ML5XfUlBXwDvnvuNzozY8PJyIiAiqq6vJzc1l8uTJPW5/XMJxbCnawoopK3rcrkc0N4vrbLPBtm2o48ezs6CgX8gUiNYId869s9u/O7UcjrRkutVupb62ns2bN3ef5ucuyspECmB2tniwxozpNVqVX5vPS2kvcf306zuJdniDnBzBK4YO7SwC5SkGFJlqi+Dg9t4pi0U4a4YPF/OAm/j3v//Nq6++yrp161i3bl27v911113cfffd3UaMjpGpgYVjhMpTJCQc6TPoEUKitJQDBw6SnDyTkBDv0xPtioJGltFqNJitVv778xY++PpX0rPykHU6B4GyI0sg2R1uPocelAQdmJColQrWa0nfm8WPW/YxffxQYmMHI8kanvjuVxqba7nxsgsIMuoZHBmGTivIgdrQgHTwoJCS60OaVFdqfhMmCOGekhJhyx5tc1JVcxVPbHwCk9VEfEg8Z405y6txnGl+cydMIKygQERNJAl6kWftDuHhQtguN1fYrsHB/lF76gmRgZG8cc4b5NfmEx3knSSsX8kUCC9nSYkwOtPTRc2PmxfKrtixq3ZXk9KeIksjRwoBzbo6Qaw6+lg8JVRHkkxllGZw70/3EhkYyScXfEKIwYNjDxki5qncXHHdTSYxr/Sl/uTRRwUhXrUKli6l6csv2dDU5HbNlPN2zxt0MdaAQq6YeoXfIgSjRo1i69atHDhwoFdCdVLSSSwYvgCt3AczIShIqADu3Yva0EDumjU0Go3MPe88v5IpRVXcirQOFEL1zG/P8O2eb7ntuNv6RqZARAIPHxaOmooKkWI8blyPDrJXtr3Cjwd/RFEVnlz4ZJ8Or6qtnM4ptuktBiyZ6gqFhWI+ycoSk+yYMW5nHezZs4fk5GQuueQSHnjgAVc0uSccI1MDD/8DSU5HCHZ7n6Uz/YHCwkJqa7cxY8ZYQkLC2bPH8/p3VW0lU6qq8tE3v3LWjX/n7hfeZef+Q4CKrNiRVDsSoCgqNrsdu6KgKKpLaMIlOKGq6CWJBkVl2TmnMSlxCLYQI5VNLbzx4TqKikp4etkiVCQUtNQ128jKK6K2oRlVUZD27hWetr17vZab7k4aPTJSGJsgbKyO/WAGOiIDI7ln/j0cP/x4Fo1e5NUYbWumBo0Y0SoMkp/vfoOzLpCQIAx3VRW3rqv+X/6GRtYwMmKkV/v6nUyBSEdLSRFWtc0mmlu72d/o3V3vsnzNcvaW73XrMM52WAUF7f+mqioZpRmAe4IURzrNTyNpSByUyPxh8z0jU04MHSrqHjQaUby0fXvftP5lGd54A046CRob0ZxzDqO0WrcFKEJChO8iXB7G4yc+53nKrgcYPXo04H4vqj6RKScCAlBTUsiqq6Ouro4Z0dEE7N/vdTG/O7j161t58NcHe+0551wbj2QHldKKUlbtWEWpWkpApA9IpiSJl33aNFGUZzaLeeXAgW7bB1yTeg0nJZ3E9dOv7/PhGxvFddVq+1YTeFSRKRBpgCNGtDYHTUsTedZu2CwTJ05EVVX+/OdVKMqoXk3LY2RqYOIYofIGJhPs2NEnA98fcApQzJ49k/nzI9DrhZ2QleX+GHZFQZJAI8uk7crm7Jvv5x+vfEhReRVBGgjUCNU+u93eXqGvB6jAnrom6reUgKJywTmLOH1CInkNzbz8yzZ+TdvB6BEjWHzKAoqKijh46BAWO2zdk0tmXjGNI0eLFa+6GjXL886xvfWZSkhoLak4AiUgfcbpo07nqdOe8qj2xYkuBSiGDm1lmX0kVWPHtooiZWb6ph1Qb3g9/XVWZ67uU1PUfiFTTjh7Jzl7+ezd22uDVIvdwseZH5NXk8fB2l76jjmQkNAqDNYW+bX51LbUopN1TIie0OMYR5pMAUyKmcSqpau47bjbvB8kMlIYnEajKPbbsUOQK2+h19Pw1lvUJyVhrKlh5E03IVW710BawerKHPK3Q6c3pT+T1cSN/72RjYc3+uyYqqqyc/duioKDmXDeeRgCAqCmxrOFyQNkV2az4fAGvsj5gmZrz33CnIarDwTyvEJ1dTXbt27n1dNf5c/z/8zxw4/33eAhISI66My3Lizs9hkfET6Cx099nFERvUdGeoPzGQ4N9b4u7agjU9BKZJ19wux2kfuYkeFWv7qGBnHtysp6JvjHyNTAxbGUP2+gqmIRbmoS8t4jvfOA+xJdqflNnCje5YoK4ZUe1kuds+KIStlsNm597m3e+/o3RkeGsa+qBq1Og7mqCWSJ+MhgQvRarIqK3GHGlACDRoMKWB3Ws9mucPOJM5lw3Uj+9eFnrDz3dGZPmUhZTT0l9U388+Pv2bAriyUnzmFT+m4+35XLDafM5N+bd/PIhYs47YS5hA9JYmjBAaSyUhSDHtnNtAh3m/aOGTMwipLdRWF9IYODBrvSvbxBj2p+ztTW3NxWQuVF+p9GI4IB27cLx52vBNa6Q251Li9vexlFVRg+aDjT46d7PEa/kiknnBcqO1sUnuXkCL3hbtKi9Bo97533Hp9lf8aZo8906xCyLNL7O8IZnUoenNzj8zQQyJQTeo2+T88+0C4dDZOpT+70hoYGNuzZw8jXXydkxQqk7GzR+PfLL3vUja4313PpmkuZE34uM/SX0dCg65SO6Uv0RqhW7V7F1qKtFNYXsvrC1V0KOXiCLgUoBg8WZMqNtCZvMDZqLK+f8zrZldndKz86cCRT/jo27Z2O53NVr9BoRJ1gZKQgUxER7f7sDzW/vjb0PSrJVFsEBoqsg+JiYR/W1Yn/9/K8OxMTIiK6XyOPkamBjWOEyhsEBoqc5L17Ra5ySEjnooR+RHfS6GFhYi7Nyek9u0JRFGRZ5lBRCYv/+iyDtVpuOftEoiLCOO7gYWx2hTHDh1Df2MS3WzLYWVpNYrARVVWRJZH2Z1dVmiw29lc2gATDBwWhohIVFMDEcaMZHBXJxsJypu7Zxw0rLkYvqeQWlREREsQTL3zK0pPmcMe1Kxj5xTc88963TBkfz91PvENjYyOXnn8Oe2OGM6qyCGNBAYpej9zLhOIumYL2ZEpRhDNv6NCBqfzXbG3mpi9vwqA18OSpT5IQ5nldn1vS6G1JVUGBUKLzou4hIECsL4GB/ietSeFJ3Dr7VvZX7z96yJQTkiTmFYNBRE56udZhxjAum3KZ14draBBT1/d53wP0eL2ONJlSVZX7f7mfecPmcXLSyb67LzqdEAMxmzur07l5jLYCFKPGjUP66iuYN0/occ+eDV980a0x9Xn255Q0lLDJ9jUzh67oU+ahOxjpcP7l5uZ2+feLki+i3lxPSlzXqnieoFs1PyeRbYuKCkE8fRQqmhwzmckxPdeIgXjVwsP7X2vKSabiRsT1vWbKHUREtCdTZjNqTg63F7zKuNhklk9eTqDON5r9fSFURz2ZckKSRGQwMlI4JJPaiCJ1M7c4CVV3CuzHyNTAxzFC5S2io0XIp6BAeJWDgo6IAmBvfabi48Vp9TS5qaqKLMvkFhQz6ep7uGz2ZC479wwMegOK3c70ycmoqoper0dVVebOmEZdYyPFZRX84bHXwWKDIAME6UkdHM4dy5dQWVXN6p+3oAI7DlWSk5tPQVEp2FVOmJ2KqtgZN3okkyaOZ+3XPzJ1zmhmTp1EUIARnUEP0cFYFJU50xLZsHMfEYPCWHjifHaHxjC6oYJBBw6gBgUhdUMGPCFTHbF3ryjgN5kGpvLf4brDNFubsSpWBhkHeby/R32mEhLE5B8Q0Ce5po6vhs3mH6+wLMlcMukSr/Y9omSqLdouviCMfZ0OZJmShhIqmivcMha7g6KIkoq6OpiSYuWjzI8AOH/C+V1uf6TJFMDP+T/zRc4XfJv7LZMGTyImOMZ3g0tS++Lx0lIhMpGc3F5yvQt0qeY3cSL89BMsXizWhpkzRc+qE0/stP+yScuICIggKiCW5EhtnxXRekOiI8pcVFSE1WpF14HABOuDufW4W/t8HI+k0WtrRT6wXi8EFbwMbZisJlRUj4hBTIz46U84yZQmTsONG27kvOrzuGNO3xqOe4wDB8jI38yv+79lU8FGzhl7jk8IldksfiTJ8/T5/xky1RZGY/u+Iaoq5G9DQ0W6gMMucSY8SVKnICJwjEwdLThGqPqCpCTh5q2pEVrlqan9mjvgbtPetuuTqgqDypmjq6gibW9XVh6ZuYW8etNlDI2PIdBoxGq1uSZ5SZKwOMJcIUGBhA8Ko7aunvtWLmZwxCCMgYH8umUHG7IPMXXieAIDjCyYPYNmUwstFgux0VGUV1Twd4OekYnDaTa1MHzoECRZYkzSMGZOTSYsJBir3S7K0hSV2hYLyUlDmT99Kn985UM+HBzF1OQJHAoIwG6uJzI01BVZa4u+kCkQjqXqalHGEhg48IQdx0aN5YPzP6CiqcLjonyvmvZ2nMD70DRSUURtdE2Nb1+X8qZyogKjvKojgwFEpjrCahXsR69HnTiRh397mM1Fm7lzzp1cMPECr4aU5Vae8N7m76hsrmRw0GBOSjqp07YDgUwBzB02l2tTr8WgMfiWTHWEogh1NLNZ5KkmJ3fuG+ZAj9LoU6fC1q2wZAls2QILF8JLL8E117QbQ5Ikzhh9hv++TwfExMRgMBgwm80UFhaS5CDvLbYWz5r39gCP+0xptcJZ09wsctRHjPBq0n0j4w3WZa3jzrl3csqIU7w7eT+jbZrfN1XfYFNsNFub+3++GTGCKeYWHrVdQ4mliphDlTAmos8RQmcPbb3eM6GP/0ky1RVqalp/KiqExzYszBWdCgvrfAuOkamjB8cIVV8gScKjtm2bCGfk5vZbSMNdMtUWVquIvmi1womqqoKMVFTXg9bIiGFDMRj0WK2WdmTKCefvdkXBbjYzccwoJo8fi0aWkWWZoXGxBH//C3a7DZvNRkhwIKEhwUgOEYshcbHExcRgtVqRJAmrTRzj5PnHodjtKKqCzWYnJ7+QiAA9gwx6Nmfns27PAeICdWzcnkFtfQODIyOwDYlHrTcRFR7cjlT1lUyB8BCNHCkM/9xcQaoiIz0exq+ICIggIqALV1YP8IpMdYTJJIyeuDivaqrsdkFWW1pE78nk5L6nAbbYWrj+i+uJCIjg4ZMfZnCQZ+m3A5ZMgbjeFgs0N2PdkUaUYRA6WdenJsUgbNbKSliX9x4AF0+8uJOi20AhUyBqpq5Nvdb/B5JlkZ+6e7dwGWdkiALLDn1l3OozFRsrIlVXXQXvvw/XXisiMU88QV5DAcPDhvepb5w3kGWZ4cOHk5OTQ35+PklJSdgVO5evu5yxkWO5dfathAd432rDq6a9wcHCu5KdLeSmc3NF1GrcOLcNfEVV+K3gN6pN1b5RJvQDOtZM3TDiBmYNncWQkCPQpC8gADllGqdERbfKq9fViWveVYjETYSGwnHHeaYs/LshUyCu7cSJsH8/lro69OnpMGQIlfUjAE2ndL9jZOrowgCsEDnKoNMJq3DQIK979ngKb8gUCCO2vl4YUvn5goRU1zVwuKwGc0sLoNLS0oLd3nOhqiRJSJKEqqpYrVZMLS00NTczKDSEy847iwCjqK1S7Co2mw2r1YqiCMU1jSy1i3qpqordZkOn1WKx2vjmlw18tucAccEB2BQFWYJQWSY8OIjTTphHWVU1P27ahtli5v4X3mTHnhzkoiLsJpNPyJQTQ4e29tDIzOybqrIvoKoqD//2MDtLd3q1v0/IFAjPmtks8sIPuqcu1xbO10WWRVrloUPen4oT2ZXZVDZXUtxQTIDWs5ypAU2mQFgoU6eCXo/eZOHvQWey5ux3GT6oC3UJD2A0QkRMExsq1wFwSfKydn8fKGSq3lzfJ7VGr2A0CgXAqCgRscrKEka+4zw8adpLQAC89x48+KD4/ZlnsJ15Brd+uJJla5ZR0tCq5lhXJxxeeXn+/HIw3KFMcsjx8m0v2U5eTR4bDm/oE8Hzikw5odEI5+SYMa2Tw/btbsseypLM20ve5uGTH/ZIKW/HDli/Xkxr/kRHMuXEtLhp/o24dgFFVVBUpVWVLjVVeA0tFpGOVl7e52O4G536XZEpJ6KjYeZMDAsXMv7yy6GoiJCDuzCa69oRqmNk6ujDMULlC4SECKOnl3x7X8BbMgXiNMeMAVA4dEjmcFELBwrKXb2jwEmW3B9TkiRkR4TK2XPKZQBJreTLOWZb00hVVfQ6HUgSeYcLsdlsBAcYmTUkmqJGE5UtFmaNSeS1u//AnSuWEhoczJknLeDSJWdgMBgI0GuZs+wuCnftRUlPZ8PPv/iETDkxerQoWLbbhcPaj21TesVn2Z+xZt8abvrqJupa6jza12dkCgTLdBbYHzrklfUXHOx8DgUvc1NdultMiZ3CB+d/wKOnPOpRCuSAJ1NOBAe39pRpaSE2p7i18rsP2GFaR4vSTLxxJMM0rRGvgUKmbIqNaz+/ltu/vZ3KZvd6c/kMGo3wJDtlEQ8fht27aaivd59MOSFJcM898PHHEBCA9tvvefYfaUSV1hMV2GpB2WwiUFBV5afv5ICzjirfod45c8hM3lryFn9b8DdCDV2nN/aGPpGptoiPF896QIDwADY0uL2rTqNj4ciFHr3HFou47v7sQ9WRTOXV5NFia/HfAXvBNwe+4ZLVl7ChYIP4IDhYSH0PHdqndAxP/R6/NzI1ePBgfvrpJ/GLI9c9q6CAf65dy8ioOmZH7sdoEBexP8lUbW0tF110EWHeyjIegwvHCJU/UFEhUnV8jL6QKSdiY2HoUBlJslNcWYrBYMDuaedfH0BVVTQaDRU1tXz0+Tfs2refQWFhjBiewLUXnMXpk8ZQU9dMTGQ4uzOz+frnDRSWlLiiWqgq5552EktOSeX45z9i+6bNRJeWMG3KVJ+QKWjN6AwIEIuuH26p2zh5xMksGr2Im2bcRJjR/YnPp2TKiaFDW0lVQYHw3nuI2NjWCOC+fcJ26gviQ+I9Emo4asgU8NPBn3h02zM0J48VEStnA+A+utXf3vUGAKfGLCc/X8JuHzhkCmBP+R7ya/PZWbbzyKRxSZKok504ETQaGmWZDRs3ekam2uL88+G33yA+nsTiZp77+xZ0Gza5/uzsRdXc7Hkzdk+Q4KhPKiwsdH02IXoCxyd61wPJZ2TKCWcK4KhRrT2Uejj29uLtXkcx/S2b3pFMWe1Wbvn6Fs798FyyKz3vqegLvL/nfXKrc8muanN8WRbXOzW1lV2qqpD7drN5YF0dbNggMjd7w++NTAFUVFRw0kknUeXwmDQ50l5ue+45siwWV++WsrIy0rZu9SuZstvtPPLII0iSRHh4OB999JFfjvN7wzFC5WsUF4u8jT17fLoq+oJMOTFyJASFVxASFkhLi7nf+y8pioLRYKC4rIKrH3mBp1f/QHZeAd//upG7Xnqb97/4jrpmE0MjQ1i7eSf3vvIJH6zfyZXPvMmuzGyCg4PIyTtIcXkFd1x9KRecOo8HMgqYMHQomuwsnzZb1ulg0iThND2SDpxgfTAPnPgAF0680O19/EKmnBg6VITwQHjvu+lr0xNGjRJRU6tVpFV6ettW7V5FXo3nEbKjiUyZbWYe2/AYn2R+wgfZq4XEd1SUiIb3QVX0UO0hfjz4IwCLh12OTgctLQOHTAFMjZ3K+0vf5x8n/sMrNUufITqahrFjWX/4cCuZ8naOSU2FtDSYPh1NdQ2ccgq8IYits5hfVf2bYhzv8GQcLDhIvblvnYR9Tqac0Grbi+HYbKKerUNk9reC37jui+v441d/9JhUqWorofJHY9+u0vyKGopQUVFUhWFhvTSG9BNeXPQi16Vex8XJF3f+Y9tQXVGR6LmyY4dbD2RdnZjLezN7fo9kCuC990S9apQjry8wMJANDofK+NNOwxYY6IpMpUZHM7Sqqu+exg744osvkCQJrVbLX/7yF9fnn376KXU+yHr4veMYofI1oqLEqtjU5LNO8L4iU4rD01RRXU9AsERDXUO/kylVVR1kqpyX319LbZOJlOHRfLtnP7c99ianTBnH4MhwvtqbS3iggZhAI2fNSWbOmAQmhAVx9YtvsyV9F0ajkZ8272Db7kxQ7Xx1uIrv84qQqquxZ2b69JwDA9v3/HQuwv2Bw3WH2/3urvHvVzLlxJAhrbl7tbUeOxBkWTj/AwJEZpUnz+LWoq08velplq9ZTnmT+zn/RxOZAjBoDTxw4gPMSZjD8snLW1PRUlLa903yEG/vfBsVlRMTT2TRnCRSU1UKCgYOmXIiKTyJOQlzjug5NDQ0sGHHDoYnJraSqfR0ka/qgRG/8fBGMisyRWj2l1/gwguFBbpyJdxxB9jtLqlpDzLdPMYQR9QnY38GSz5YwqbDm3rZo2v4jUx1hfx8McdkZIhUY8d1L20sRa/RMy7K84hh23nc1xGq7mqmEgclsvrC1by46EUCdH7WyO8GIYYQrkm9pneZdKNRMM3GRlHPVlDQ4/PuLHfrRhQT+P2SKYBly5ZxoqN1woIFCwAYN242F154OwBBQUEizS85mTibjSGnnIIUECAcln1wEmdnZzNx4kQkSeLss892ff7ggw9is9lQVZXFixf34ZsdgxPHCJWvode3SpdVVPS56t5XZMrZa8pms1NUUYPZbMZLhek+nYNWqyW/sJh7X3yLr7IPMSwkkPoWKzGBRsaPiSMjO5ct+/JIiY1AkmTGDIvj/65bwd//cAWP3X4dZ49L4uoL/sG/3ltDQ2MT17/6EZ9u2M6oYCMP/JxOndmCpqIC9YDnaWjuoKZGqCBXVPhl+HZIL0ln6UdLeXzD46KI2E30C5lyIj5eGPhTpnhViGA0ijY9nqbtJw1KYm7CXM4Ze47bqn5HG5lyYuaQmTx3xnPoNQ7jQ5Lak6nSUpE36WZqjqIqvLnzTQCunHolBoNKdvbAIVM7S3dS1lh2RM/BiS4FKCorBePJzxfZCG54WBotjdz3831cvu5yNh7eKDw0H3wAf/+72ODJJ2HJEkJocBzXf9/JSagaqxppsbWQOCjR4zH6lUyBSL2MiRGG5cGDQjzBYuHCiRey9qK1XDH1Co+HtFrFv1qtbxuOd0emnNBr9IyK6LrRsz/RZPEw7BkVBTNmiMlZUUS9bEZGl7nvqtpKqLrL5Pg9kyknfvxRZAX89ttvvPPOO1RWwg03PAkIheKvv/6aoYmJMG0aYxy1jqcvXSqihI2Nbh+nrq6OSy65BEmSGDduHJkOJ/P5559PdXU1qqpyzz33oPFn8eDvEMcIlT8QGtrquT940OsqY1+m+TnTIYrKqzEGBGGz9q/CgqiZkmlobOKR/7xPemklkyNCsCsqWo2ETVGwKQpFdU1IEpgsVnaVVaPXadHptEQOCiNiUBhXXXA2V/7fYvYWV/DB1t1MCNBhNpkxaGQqGk38O6cEJAkl0D8LfFWVWIj37fOv0QOQXpqOoiq02Frc7q/Ur2TKiejo9jkzNTUeedTaGjPOBoe9HjIommdOf4bbjrvNrWMcbWTKZDXRaHFjAbVYRFpOWZkwMt0w7n879Bt5NXmE6EM4b/x5rjS/446bS21tiK+zTDxCk6WJu364iws+voBdZbuO3InQg5rf4MFCXlqWBbnasaPXAku7Ymf20NkMDxvOjPgZ4kNJgvvuE5LqRiN88QVDL5qDsTTfE9vJYzgJlaXBwosLXyQuJM6j/fudTIFw1owfL667RiPmmG3boKaGmOAYgvSep7/6o36qOzJVY6ph0+FN/a9Y6YDJauK8j87jLz/8xbM0T71e5LyPHSuue12duO4dPIomk1gXZbm1FrAtjpGpVjhrqFasWEFmZiHV1dW8/fYXALz44oukpaVBYCA/btkCwDdpaXz8+eciSpiX163jTFEUHnvsMSRJYtCgQXzwwQcAjB49mr1796KqKh9//HH/2QW/QxwjVP5CXFz7qvvmZo929yWZAtF/xGJTqKproqmxAZziDv0IrUbLoaJithaUMTkihNoWC1m1jdS0WNDJMipg1LQ+khEBBmrrG7HbbNgVBbPFQkx0FDdfcQm3LD0Ni9WORpYICQqgwWoDm5V3f95CUeJINPHxKH74fiNHilYSiiLK5Mxmnx/ChZUpK3npzJf485w/u7X9ESFTHVFUJAQTsjyvZauvF2v1nj3d8wKLvdURIEkSOk3vxQ9HG5kCeCntJS78+EI2F27ueUNnRFyjESlR6em9PpRvZIianQsnXkhBboErMlVUFEJurgi8HCk0WBqIC44jIiCCsZH909Ovy/PoTRo9NrZV2bW5WRg7PTjOwoxhPHDiA7x97tudn9mLLxYpgLGxaPftYdoNMwnZtcGXpaDtEB4ejsGhSBtm9aww9IiQqbaIjYVp0/i2eTfFDcVirinzLpopy0LF1Ve1sT1Fpl5Lf42bv7qZpzY95ZuDeYjNhZupaq4isyKz91S/rhAXJ6JVgwaJxa/DfXeW34SGdo72HSNT7REREcGXX34JwLnnJpCTs4/jj5/Cnj17AJg5cyYmkwlJlil3SNhfeP/9lFZVQWFhp/n9yy+/RJIkNBoNd911l+vztWvXoqoqOTk5TJgwoZ++3e8bxwiVPzFqlJitbTaPtKF9Taac63LazkzsqoTVakWWZXT+qMTt7hxUQJIwmy1gVTBbbQwJD+HqBdOZOiyOfQ3NaGUJp+9FI0lUN1uYNnEMAQEBImXR0QxYliQGhYVBUT27Cyr548WLufzUOeSV1wEy736/0XFMVXjwfdhkxKn8FxQk5jUfa490wswhM91aAAcEmQIRqZIkYeTs2+cRqQoIELzAZOpaKarB3MD5H53Pv7f/G5viXiHb0UimzDYzGws3Ut5U7p7TIyKitaaqqanH9JAGcwMfZ34MwEnhJ7VL83NqAJSVeez/8Rlig2P5z+L/8O+z/41B6/82FF3B7T5ToaFCZMI5x+/eLdIve0C37/LMmUKsIiUFfW0FY284Cendd/r4TTqjrLGMrw987YpSFRcXu73vESdTDhQrddxXsoql+x8kX6n2uhFtcLDIVB4/vu/n1Fuan1FrRKfRsWD4gr4fzAucmHQiq5au4q8L/uq9YqbRKC5YSkr7MFRTU7f1U8fIVNc444wzOP30pQA8//xyhg0bysSJE3nssccAIVgBEB0dzbp16wCIW7oUdeRICAggJyeHSZMmIUkSZ555pmvcBx54wFUXtWTJkn79TsdwjFD5F86q+4kT2ysW9QCfkykVJKCqroFLH3qJOx59nnuefoXVX35HaXmFb5PHe4AkgaoqBAcHEToogH1ldVy66CRuuOwC/nbTVdxz9olkldcTqNOgAlZFJTEsgG83bifvUAFarbaVVFmtjByewPLz53LL0pNISR7P/FnTGR4bToPJxOe/bKG+qRmNzYa6Y4cwdGprffZdtFoRFNDpRNqfF8GYblHeVM7Dvz3sXrqXAwOGTIFIh5o4Udzw8nJRY+JmbY9O17prRYUIdrXFVwe+orihmK8PfO0WoToayRQIIYpV563i4ZMf5riE49zbydmrysn009O7dOJ8nPkxzdZmkkKSCG8Mb1czFRoqyiZU9chGqWRJdrsuztfwqGkvCBI7ZYrIRtDphAe/Db7N/ZanNz3t3vs8dKiQVT/vPOEIWrEC/vIXt98fd/Dslme596d7UUMcKeAdX7JuMFDIFIj0yZTYaUxNPI7h885qn27s7zzsLtAbmQK4aeZNfHHJF8wcMrPLv/cHxkSOYXr89L4NIkntWVNjI2zbRlBhNoOCrO0e/2NkqnuUlZVx6ql/AqC4OJ9nn30WgDvvvNOlwrly5UoAzjnnHBc5kocORZIkxo4d64ponXfmmVRVVaGqKvfee++xuqgjiGOEyt/Q60WNiRM9WN6+JlMAiiLCJ//9ZSuBikpeRTVZpZU8+sl3XPbIS7yz+vN+6UMlSRI2m53EofGMiQzj2tOOI3VKMjarFaNBxxknL+DslDHk1DURoNVgV1UCdVo25Jfy9qffiqiao0mwoqqEhQRz04qLuHjxGdhsNiLDB3HNmSdS0NBMfWML32zYATodSkCAMEh27/aoqLM3BAS01x4pKfHNuP/45R+s2beGB355wK3tBxSZciIqSuTdO2tM9uxx2ygMDRVplSCU2NvaRxdMuICHT36Y+064D6PW2OM4RyuZcsKgNbBw5ELPdjIahfd40CARNq3vXCvxRrpI9zth0AnMmzevkwCFow6a8nKfvi69Yu2+tXy450OPxFd8DY/JlBOyLGpmZ8wQ98ABc0sTT296mlW7V7F231r3xgoKEg2AnZLGjzwCS5f65GaoqsqoiFEE6gIZP0KEZdwhVAOJTAEkhCXwwqIXeOq0p5A6irNs3w779/uUhPYEd8iUE5GB3jXM7QsaLY0eOec8hmOCHqopYaplKxF2UVt1jEx1j7KyMjZs2E5Cwji++krcm1tuuYWcnBwADh8Wyr5vvPEG3333HU888YQrSuXEiBEj2L12LepPP7H6z38morDwyDbKPAbgGKHqX1gsQiWnvLPMsz/IFOBqcvvtxu3YVZVgnZYQvY7JcRHo7XZ+Td+LJEnI/VBTpSgKBr2eu1ZexDmnHo9Bp0NVVaxWGwEGAyvPPxMzEtXNZnSyMGb0ei16rQYRZ2uFzW5HkiQkCRRVRQJGJQ0HSUYrS3yzYRtIEnLypFYDc+dOn+YyhYWJWt3YWPHjC1yTeg2jIkZx08ybet12QJIpJyIiBKnSaESkZPdut8N4Q4cKH4SqthdRkySJhSMX9trA92glUweqD/Bz/s99G0SrhcmTxYPpZEcO5FTmsP7wemRk7jrjri7V/IKDRZAR+i9KVdlcyT83/5MnNj7B93nf989BO8BrMtUWbY3GqioM29K5L+VW5g2bx0XJF7k9TF2DzLZzH6LgoXdEjda6dTB/vpBP7gMkSWJlykq+uvQrtwnVQCNTTkiS1Dl90qmmUlQk0l57metzcmD9elGW4g3cIVPv737/iCpWvrbjNRa/v5gv93/pnwPExYm016AgoUqxdy/K3r1s27z5GJnqAs4+UzNnTuaMM6KZOjWIjRtFicLYsWOx2WzIsswrr7wCwMKFC7nzzjtd+7/00kuoqkpubi7JixcL9UtZFmtsWlqv0vbH4F8cI1T9iZISUb2ZldXO9e4vMqUoKpIkcbCojB37clFsNmyKgqKqmKw2Bhl11JitbN6xC0mW0ciyZ6TKaXQ49xF5fT1sLmG320kaOoSY6CgsVqsgc7KM1Wpl+JB4Xr3hUgaFBGG1K5htdiwKnLfwePR6bTuRibYGj7O2amhcDLefOZ/0jQfYmXWA0opqJI2MOjFZWIpWq1BC86GSRGxsq+CXLzA5ZjLvL32/16aPA5pMOREeLox7jUaQWg+M1LFjRRSwpQXe2fRNOzGKnnC0kim7YueBXx7gz9/+mfd2vde3wWRZGDquwe2o+fk89YOQ5z058WTGxI3pdvfERHGrnOrg/kZEQAR/mvUn5iTM4ZQRp/j/gB3gEzLVEcXFYLMxu8LAM+NuQS+7X6+q0YiAVMGC5fDTT4LhZmSICJhD+asvCNIHudKKeiJUA41MrS9Yz9s73+5+LkhMFPNN295JPdS0Wa3CWePN7XaHTKWXpPPUpqe44OML+txA2RsoqsK2km3Um+v92hi7WQ7GNiUVhg1DUVWyf/0VbXo6c8aNO0am2sBJpqZOncrQoUMJDhb2w3HHHcdttwm1Wp1OhyRJXHfdda79/v73v2O1WlFVlRtuuKF1QFkWDRxnzBBrrVPafvv2I5L6egzHCFX/Ytiw9hJxFovfyBS0SqWn7cnGjoRGbo1CqYBdVTHKMre++D5rvvoei83mkVCFZDaL1ciZs2uzCQ95F6TKuWjZFBVFsWOz2dBohLKf5NjAbLWSOnkij9x0OTqdFrNd4Z3bVzJ2ZBJWq61HQ0dVVTSyzNmnnsCD91xKVbOV77aLHGO7LImFNjBQWOg7d7Y2IfEhnLUnnspOW+wWqk2t9S69GXRHBZlyIixMFNwPH+7RblqtqKcq0PzIi/vuYcXaFb2SqqOVTIEwfmYPnc0g4yBOG3WaT8dW9+3j4M8/sTZHiFFcM/2aHrcPDBQtf6KjvWot5jFkSWbphKU8e/qzbrcH8BX8QaZUVcU8dpRofA0e9asCcf0lSWzeknIcbN0q5q+yMjj+eCGz7gEaLY3c++O9HKpt7YnYmyjFQCNTNsXG05ue5rktz/XscIiIaDUw7XbhvNy3r8tr71wCPNVmcjfNL0gfRGpcKqePOp1QQw/dbv0EWZJ5a8lb/PO0f3LcUDdrMb1ATg6s3yhTFpTIDlWlGZg0Zgx6N5/33wM6kqmOeOqpp9rVPi1ZsoTKykpUVeW+++5D25O2f0CAqOMcN67VmeBO75Fj8DmOEar+hFMiLjAQzGbKfvyRjB07/EKmxPHEPxlZeeCoP2oLJ+9Jjo/g4Y+/5an/vMfPG7f0HKVSVVSdDm3ufoxvvoLh5x/AZsPwwzcEvPUqckU5qlbXiVTZFZUfsqp5Y1s5hdUtBBm1bD5Qwy/ZNZjsKm9uKSWzsAGtLE680WonPNBIdFQkqht1Fc7oV4DBwJTk8SyZP4PaCkFSZFkW6TiTJ4sUGvBLjv3Bg8J22r3bM+W/f6X9i4s/uVg0/OwFRxWZcsLQRq3NbhcSfpbeI07BwTAq0UhEQATzh81vbWzbBY5mMgWg0+i4ccaNfHbJZ0QFRvlsXFVVyWlo4IvSjVQotUToQlk8vPfarLFjBaEN9EJh2V1Y7JZ2NVP9fc/8EpkCfjz4I+d9cj4/aQvFhWzbr8oNQ0eWRQYVODYfPhw2bIDFi0V0fdky+Nvf3J7DXtvxGl8d+Io7v7/TNbc7CVVXEaqBRqZAkIOVKSsZFzWu9/RJ51yflNSqONpFDZo3hMqTmqkxkWN4+ayX3W574Q/Iksz84fP99m45G/oqikJ29g4aZZlJV1yBbsKEVocC+FcKd4CjI5mqrBQcv6P4sM1mc5GotWvXEulpt/vYWOG8HDGifQ2CG2vtMfgGA55QJSYmOmpl2v/84Q9/AHAx+Pj4eAICAjjhhBPYu3dvuzHMZjM333wzUVFRBAUFsXjxYgq9TZzuKxwSceXV1eSlp3NcZKRfyJQzYqOoKnsOHEK127ss+FaBFrud5JhB/JaVx+33vcGB/AIMen3XfZwkCclqxT4skZbLrkKTm4N+VwYEBmFdcBL6HWnQppeUqoJOI1NQ3YLNaufEpBAyDjdQ0Wjlx/xG6k02Qo1aZgwJoqHZCqgYjQbGxESwe+dhikvL0cjuucllWcZitdLQ0MSys05lTspklzKgqqqtsq/O/jE+xpAhrcrVmZnupTJb7Ba2FG2h2lTdq3LdUUmmOiI7W6S+ZmS4lXo5J2EOH1/wMVdPu4bDh7sOLB7NZKqj88KrHjE9jJ2VlcXBujp+HSqKxZdFn4xh195eU0L64xK+su0VVn66kv1V+/1/sA7wF5kC+GDPB5Q1lrG/er9IvUxJae1XtWOHWyHsdoQKhHdhzRpw1lP84x9w0UVu1YSeO/5cFgxfwB9n/tH1PdsSqrbP4EAkUyCIwVljzuKdc99x7x2RJEFEp04VKjcd1BfBc0LlCZlqPQ2pR0eQv7CvYl+/9JlsaACbTSE3NwtFaRQ1UwEBYjF0vlN2u2gwmJPzuyNWXUWmyssFx++qm4vHJKojdDqRCeWEzdba3PFIdmz/nWDAE6q0tDRKSkpcP9999x0AF1xwAQCPP/44Tz/9NC+88AJpaWnExsZy6qmn0tDGYLjllltYu3YtH3zwAevXr6exsZGzzjqrX9TtukJhdTXpZjPjxo8n0mr1virWDVTVNVBQWoaqKj0a+C02OzFBAQwZP5hVn39HVW0d2h5qqpTAQPQ707HOPxFVq0ENDUWJiUVqbgZ7W68z2BSFuDADAUYtW/IbaLIqrN5ZQVSQlgqTDbNNYXCYgUC9BklViQgL40+XXcDoKcPQyL0bOqqqoigqiqIgyTLjRiViNOiwKgpWR9qBy2AKDGxfPF5b67MiToNBKP/JsujxmZfX+z56jZ43l7zJY6c81mOPkv8JMgWizsFpXKandzvJt33uwoxh5Ofqyc0VGTxtcTSTKYB1Weu449s7qGyu9Om4TjJ16NAhJqRO4Iv8rwG4coRDkjs9XURNekFLi7CDuhAM7BOarc2sy17HnvI9lDT6SCLTTfiTTAE8v+h5/jjrj1w+5XLxQUgITJ8u0tBiYtopAXaHToQKRP7lY4/BG28Iw+mTT2DBgs79BTpgWNgwnj7taeYOm+v6zFlDZTabqXbI6w9UMtUWHt+rsDBISGj93WRydWT3hFB5QqZ+yPuBD/d86Hbdp69RUFfA5esu57K1l9Fs9W9DudpahezsbDSapu4FKKqrxXUvLhbCCT5sYTKQ0RWZUtXWbhZ95U5uoa5OeA4qK8W1P3z4mGiFHzHgCVV0dDSxsbGuny+++IKRI0dy/PHHo6oqzzzzDPfccw/nnXceycnJvPXWWzQ3N7Nq1SoA6urqeO2113jqqac45ZRTSElJ4d1332X37t18/33/K0o5a6ZSTj6Z8OnThYHvhzfL+coUllbQbLK49RKZbXaC9To25JfQ2NSMrOni8VBVVK0O4/pf0L/0FFit2OOGoMneh/67r7AnjXAUXrT1eoJeIzM41IBFUUmMMLIgMZTYAA2VJjtmq0J6QQMbDjdRXGcG1U58TDRv3HsLY0YkusQruoIsSQQYjQQGGtFqtQQYDZjNVr7/dRN1dfWYzNZ216MdSktFPZWHDWh7QmioSGUGMXe5I6eu1+g5ecTJ3f79f4ZMgXjeU1JaFSfS0zt52atN1Vz12VVklGa4PhsypJWoOsXOjnYy1WJr4cW0F/kp/ye+y/3OZ+O2JVNz587lv/n/xWw3MzlmMinHX9xax+mGx/jQIWEH+VrxL1AXyAdLP+DW2bf2a7NTf5MpEE1cV0xZ0b4xsU4n0tBGjWr9zGrttpazS0LlxBVXwI8/ivYE27eLmqFt2zpt1pMEvcFgcHnDi4uLByyZKmko4apPr2J78XbfDJidDZWV2DZvQ62tA3onVJ6QKYvd4lKsXJe1zjfn7CEOVB/AqDUSHRjt04h3RyiKwsaNezGbzSxYMKV7AYroaJEZYjSKOT8jQ0jb/w9Hq7qrmaqtFUEjna5zE2S/IDJSOHPCwsT1zs09JlrhR3jZMvvIwGKx8O6773LbbbchSRJ5eXmUlpaycGFrTYDBYOD4449n48aNXHfddWzfvh2r1dpum/j4eJKTk9m4cSOnndZ1AbjZbMbcJiWp3uGitVqtWL0UNCguLmbXrl2kpqYSHh6OFcSiqNH4XCTBrihoZJmi0gqQZCTsvacASGBVFIYE6Gk2mVDULjyCkoRkt2FLGI5y9wOgqNijorHMnotcWYk1eQqSxQxdFJerqsrcxFDGxAQSoNMwNMLIbJMNo1ZieKSRuEEGlzKhzW5Hr9f1eM6yJNHYbGLX9p0oVguGoCD0Gg07M7NJy8nntvhYdmXnMXvyWBRFRe4i2iXZbOAwKBjTvfKZJwgPFwTg0CGJzEzQatVOGSc/HvwRs93M6SNP79GoO3jwIDk5OcyaNYvg4GCvn70BBY0GJk5E2r1bWIzbtqFOmiTSmoAXt7zIztKdPLb+Md5c/CayJGMwiAye/fslcnJAlk3s3buJqKgoJkyYgO0oLIDWoOG5057j48yPOW/seT65t6qqkpOTw+HDh5k9ezZGo5GP9woxissmXYZNVQXjz80V6nGK0mMtTlwcFBZKlJdDVZXqUyNgkH4QF46/sN+e6cbGRjZv3kxCQgIjR4706TOjqiqZlZlMjJ7Y+8Z2u3Dg7NqFZDKhTpwoolhtoNeDXi+h16tdLw2zZsGGDWjPPRcpMxN1/nzsL7+MumwZIEQcrv7iahYMW8ClyZe2J3cOxMfHU1VVRX5+PlarlaqqKmbPno1Wqx0w88zLaS+TUZrBK9te4YXTX+g7AU5KQtq3D0tNE6Fl+7DZBmO3DsHejUxrTU0NW7duZezYsSQkJPR6XRRFYcWkFXyW8xmLRiw6Itdx/tD5rD5/Nc3WZr8dX1EU0tPTqazUMXbsBMLCpJ6PFRws0i9zc5FKSoSnprwcdcyYLtMxj2aUl5ezY8cOJk+eTExMTLvrUloKNptEZKTaTielvr6eqKgofvrpJ+bOndvFqH2AXi8KYktLkfLyBKvbsgU1Pl44eCSJjz76iOXLl/v2uL9DSGp/JNr6CB999BHLli2joKCA+Ph4Nm7cyNy5cykqKnKlMABce+21HDp0iG+++YZVq1Zx5ZVXtiNHIPT9k5KSXHr/HXHfffdx//33d/p81apVBPqpUlvX2IjNaETtSdHFTSiKgizLbMk6xOoNmaDYuq6J6gBZkrCrKrnl9bx251VMTR6PtYsIkarVOrTCJTQ2K4qsQZVlQaYQBoaz3k1VFFTAoBO1UBabkG6XJaE8aFdU9FoZCaECaFNUels2VRU0Gpna+gb2Ze+nsrqGNZt3klnTgLKzmGcfv46FJx1PbuZObObuGxvqa2sJcYQ8TNHRNPuqoRRw+HAI9fV6hg5tICysNf2j1lrLE/lP0KK0sCJ+BVNCpvjsmEcTJJuNsPx8NCYTdoOB2tGjQZIw2U18XvE5cwfNZYhxSLt9Dh8OobZWj16vMGpULRrNUTN9HTHcn3s/6Q3p/CHhD5waeWqX22ibmrAZjV3K+hUWBlNTYyAkxEJiYt88m422RhrsDcQZ4nrf+CjCnsY9vFH0BlNDprI8bnmvhr9stRJ68CAasxlkmca4OMwRER4fV9vcTOrTTxPriFAdOOccMlesYEfTLt4teZdgTTB3Jd1FgKZzxOnvf/87O3fu5E9/+hMnnniix8fuDzTYGviu6jtmhs1kqLGzOppXUBSCysowOlJe7QEBNCQkYPdhXa1z/ftfhsUik50djiTB+PFVbiuC6hobCS4sRLZasYSE0NChX97/MrKzw7FYZIYPbyA0tNUmsFgsXHjhhQC88MILXSoB+gKS1UpQaSmG2lrMgwbxdloab7/9drtt6urqCO2X8Nn/Ho6qCNVrr73GGWec0Y48QecoijuTWW/b3H333a7eACA8CAkJCSxcuNDjh61tZCo6OrrrjUpLkXJyUENDRWpIHxsb2e0KGo1Mccs3SJuykJBQ7L0bn4qqYtDIhAYb+OTbnxk7Kgm9rnOkSLJaHf2frPzww3ccf+IpGAwGFFVFBbRaLYpdwWI2YzAakSSZFqsVCdGMV3Zce5tdRZLAbHV4yaWOLXy7hiQJ0jgoJJgT5s1mQ1o6eRU/Mz7YyN6hIbRYbdTXN5KSMpXoiNCe73dJCZKjS7mamOixxHd3UBSRzeYIvLhgV+y07Gphc9Fm7jzjzi5FN9pGpgb9j3nw2sFmEymXSUntLtRSlna5eWNjC++8k4nRGE5S0nEkJ/fXifoO9eZ6WmwtDA7ynRhNx8hUcJtr+f2335O+LR1DvIFFJy/qvHNDA9LOnRAQgJqc3EmwxWSCtDQJVYVp09SOwRSPcP+v9/Nt3rfcOutWzh9/vvcDeYC2kakxY8b4xdCt2l1FRHMEC5IXcOb0M93byWaDrCykqioA1NhYGD3a87n/vPOw33cfmkcfZdSnnzKisZHT33mHmfUzATh1RNck+sMPP2Tnzp00Nzdz0kknYXSjtutI4CLcb4rsEaqqkLKzRXaILKNOmeLKw2obmUo8Sgz+Q3WHUFWVxEGJfjuGMzJlMpmYNm0ms2bpMZs79RDvHTabkMUdNqx1vlHV/lHD8RPaRqY62qggkjGCgiRkGebMUQkI0HPBBRfw3nuiDcBnn33G4sWLuemmmygoKCDWh87dtrDb7dx0zTW89u67rs9OPvFE3vj3vxk2erRfjvl7wVFDqA4dOsT333/PmjVrXJ85H7jS0lLi2jSyLC8vJyYmxrWNxWKhpqamXf1JeXk5c+bM6fZ4BoMBQxceK51O51GvpsLCQnbv3s2sWbN6VvOLiGiViMvPF3K7fYAk2dFqNVht9i4l03uCxa6QEBLAVzmHuayohHGjkrBY2kepVEDSaMnN3sc9d/yR1979hMlTU1FUOzqdjrraWnbvTGf//ixQ4axzziN6cIwjzaZ1HOeQ3syjqgp2VaGpqZncgmLCA40kDI5k4czJ/OWlj1i0Yy+P3raS+JhIV8SuSwwbJk4gN1cIhBiN4CMPUdtHyGIRudM6Scd1M67jmunXdNl3Jzc3l/379zN37tyjv2aqN+h0MG0aqqqSXZXNuKhxrf3MOsBkMrFt22ZSU2Ow2UbT0CBhsbTWmxwteG79c/x86Gf+fvzfOSnppD6P56yZKiwsZN68eYR0YDwTBk8AYH/N/q7nLq1WPKgtLULzPzm5XQqaTgfx8UKZqqRETFXewK7YsSgWVFQmxU7yaB71Fg0NDWzZsoXExES/1UwBrJy2khOSTiA2ONb976XTiTSoggIx51dWCuXLiRPbCVfY7W70A3vkEUhNhSuuQP7hB/Rz57Jo3TrhnOsCqqq65sOQkJBOz8yRhsVu8b86XmyseJidfarCw0GWqa6uJi0tjQkTJrit5ldjquHen+7lqpSrSIlL8e95d4Pn055nU+Em7p53N+eOP9fn4zvJVEtLC3PnzkWv1xMW5uVgOp1oIdMWWVniQR8xon8a4PkQZWVlpKenk5KS0m10yWq1MmiQFoOh9fX++OOPSUxM5PHHH+fss8/m1Vdf5ZprrmHYsGE0NjYS5MPFrbm5mbPPPpsff/zR9dm1117LSy+9hGbfPur397/a6v8aBrwohRNvvPEGgwcP5swzW71/SUlJxMbGupT/QIROf/nlFxdZSk1NRafTtdumpKSEPXv29EiofAGPmvYGBYkJRpKE1eKsvO8j1E7/cQ+yJEGjmYaGRmRJbqfZoKoqWq2W6qoK3nj1X1x57R94+omHKDh0EIPBiLmlhYz0bUyeOo3AwCBefOZxnnrsQVpMJpEC6IMsUyGHLohvc4uZJaedwHv33cZ1y85lSHwcBOspqqyhuLzSsX0vAyYktLrZ/CAv2tAAP26qYf/+1hPpjkz9zwhQeICvDnzF8jXLefLHB2HzZqEt2wZtBSjmzJnEuHESqalHH5ky28wcqjtEs7WZ6MBuotUeoKMARVeG8dgo4ZzJqszq9DdAeOWnTRMX02wWReMdFACdSrwVFW4pdXcJjazh8VMf5/2l75M82P+hxf4QoGiLEeEjPBcBcMp7T54sjMyGBtFzAVFv8dtvQjvELZx/PnU/fY2alCS8/8cdBx9/3GkzpwCFU0Sgpiv95iMIm2Jj+ZrlPPTrQ9S11PnlGAUFsH495BU6elY5skKqq6vZtHEjE4YOdZtMAbyZ8SabCzfz9Oan+0WuvCMsdgtaWYskSUyPn+7z8RVFYdu2bTQ3N3ev5tcXNDaKB76o6KhTAuytaa8TgwfrmTFDYuxYUTzV7JhIn3jiCV566SUArr76au655x4AgoODfVLnWVZWRlJSEkFBQS4y9eijj6KqKq+88goaEA4FP/Tm/L3hqCBUiqLwxhtvcPnll7frGC1JErfccgsPP/wwa9euZc+ePVxxxRUEBgayzFGcGxYWxlVXXcXtt9/ODz/8QHp6OsuXL2fSpEmccsopfjtnj8iUE5GRomcGiGiJIxXEGziNh9DAACGZ7lYinWNfwK6qYNQRGBjoSJdrP7aiKAQEBjF12nSOm7uAc5dezLDhiSiKnZaWFmJi4oiMjkZRFKZMm0FVVSVlZSUEBAa6PKPeLjyqqqLRaLCrKr9s2srab36irqGJ/Xn5rPr0a/787qeMiwihwWyhsFbUT6nuMMrExM5KXD5CQ7OFx/fewF2//JE9uV3f198rmQLIr80HIMIsi8k9M1PIy9G1ml9MzNFHpgAMWgOvn/M6L5/5MpNiJvVpLHfIFCCifkBeTV73Us5Go1BfjIgQIZE9e9o5dYKChEN/2DDPGqF2hVERvn+/OqK/yNT7u9+noqmi7wOFh4sI06BBruwErVbcCncJrKqq/F/Zu6y8dzJNJ8wRO154IfzlLy5FtbZqftOnC8O7osIH5+9DbC3aSl5NHj/l/+R2D0JPYbHQKgogSaDTudT8JoWEkFRTI9ZgN43M5ZOXs3T8Um6eefMRqZ3Sa/Q8ddpTfHrxpySEJfS+gwfoikzZ7YL7dNEv2TsEB4u11xkpP0qUAN0lUwArVqwAwGAQJRQBAQGud+8Pf/gDn332GQAPPvggF10k0lx1XZRbuIusrCwkSSI2NpZ8h0zrBx98IOaJ//u/1g01GhEp72NW1DEcJYTq+++/p6CggJUrV3b625133sktt9zCjTfeyPTp0ykqKuLbb79tZ1z885//ZMmSJVx44YXMnTuXwMBAPv/8czRehJWdPQR6gldkyomhQ0V+DQij0usZS7yEYcGBoCgoqvsTkyRJ1LRYOH5kPAnxsZ3S9JzQarVUVJTx/rtvcPqZi5E1GqHSJ0s0NTWiKiqxcfG88sYqbr71TjL37ibvQA6NjQ1IkoTBYECj0bgImjsTh7NRb7PJxFsff8otV71AQ0MDq7/8jmv+8iKrd+1nXJARi6IgARGBIrbu9hLXNp9JUXzWgKdczaSOAg6bsjl4UOr0HP2eyRTAjTNu5PVzXufyhXe2Pv85ObTs39+rNHp9vXDIHy2QJZnU+NQ+jeEumQKIC44jRB+CXbWTW53b/aBaLUya1Hr9c3PbRQrHjRPZOJ4SqsL6Qv6V9i/Mtt4bOfsC/UWmNh7eyFObnuKiTy6iydKVvrmHMBqFYePwFAQGAnV1NNfb3OrqUNpYyv7q/WQr5dSteR/+/Gfxh0cegbPPRq2paSeN7mzuW94hGnykMSdhDq8tfo175t9DsD649x28QMceVG2l0Yc5n//Dh0UjZjcYbXRQNHfPv5uZQ2b65XzdRWywb+tuuotM1dcLvrNnjw8PFhEhWgA4r78zWjXAIqhOeEKmzGZ4/fW3mDFjBoCrXjEqKor9jlS7c845h7S0NEAQnylThFiVpzX7v/zyC5IkMX78eNdnv/32G6qquohal/DUVj2GTjgqCNXChQtRVZUxXchaS5LEfffdR0lJCS0tLfzyyy8kd6hUNxqNPP/881RVVdHc3Mznn39OQoJ3XpzsbFGk3R36RKacGD1aeCztdpFj4w0cRkRYaLBQ2fPAyWHQaigpr+eMOdMIDQnGrihd1jgpikJwcAiTp6ayM30bpuZmVFV8ZrPZSN++FbvNxoGcbKorK5l//MkoisrB3APsythBVuZeqqursNlsGAMC2kUfu/9aEgajgQ3b0nn5/R+Zee541uzYx+sbMpg8aShjQgKwA6qiotjtRA4Ka3c93IaiiNUiI8MnE/rU2Kl8fMm7/N/0hwnRRpCZ2bpO/97JlBOTYyaj0WiFfP2wYZjNZvZ8+ilDLJZuyZQzQ82hwjtgsbtsN59kftJjbyB34QmZAvHO9Jr217qxuP6jRomWDt2J6Hhwro+uf5TX0l/j0fWP9mksd9CfaX5RgVEkD07mrDFnEaT3fbg0wFKHlH8Q+74czJW9qyvGhcSx9qK1PLnwSeLDh8ETT8B77wmi9tVXmKdOxbR9u6vPlFMgaaBFqACmxE7hxCT/KQ+2JVSd+kyNHi3qCHU64dDcts0VLe+IIy2SrKoqn2V/5pcGvj2l+Tn9jD4Xg9M65v+2fav27Rtw6WiekCkQ5HPDBvj8860EBARgsVhITRVOtVGjRrF582YAZs6cSW6ucHplZGQgyzKNjY3Mnj2712OsWrUKSZI44YQTAJBlmezsbFRVZd68eV5+02PwBEcFoRpIsNmEnd1VJNonZAqEUTNxogjBJiV5OYQwJOKiIpElL2RcFRWNLKTMu8uWc9ZSnXfBJaz/7ScO5h1whainz5yNXm8gIjIKm83K3AUnEBoaRmLSCFJSZzB2/ETCBoVTWlzEnl0ZbN28gcqKni1iCVAUlX378zhQUMyIsbHUmMwMCTQwPiyIZqsNm6K6ttZpZeKiw9tdD7chSSIUriiiUL+u77n8I8JHcN7s6YSFiedo927Iyvr9kqnypnIe+e0RGsydjUVTXBxbKysZNGgQ4wMDkQ4c6LIQzmAQ5W/Qu7PjSMFit/DArw/w6PpHeSvjrT6N5SmZcsKZ9tcroXJi6FBhVDrfG0Vx1RbW1MCuXSJtyh0sHb+UoaFDuTLlSvd28BL9XTM1JnIMr5/zOjfNvMkv40s6LQEhWrBYaN6yu1ujvi1CDaHMSWhTG7xsGer69VhiYzEWFDD7llsI+OYbgAFHqOpa6vxCDLqC89ltaqrpumlvVJRoiBoe3toEe8+eTv0i//LDX3h8w+NUm9xIXfEDNhVu4oFfHuDCjy/Eavddz6neaqacy6HXohS9ITxcRKuGDBEEq4+qx76Ep2RKUcScqSgi6tzk6Na9Y8cOLr30UgBmzZrFunXrAEGwqhzlHs4aqi1btnDFFVd0GltVVR566CEkSXKNNWrUKMrLy7Hb7V0GIY7Bfxg4T+lRAqcQX1YHu8RnZMoJrVZ01XRCVd1QVmiF05RIHBJNWEgQUhcCCN3BbLOTEDeIT37aTHVdPVqtpp0nTkVFQsKoN7Lk3IswGozc9ue/MnbsBBR7a+re+ORJzJh1HFNSUtFqdXz1xTpee+VFzGYzRqORwTExTJoyjQnJk3n3rdc4/YRZvPPGq116/SRJwmq38+oHa7j4gRf5cNNOtJKEiuhdZW3nwRLkcVBIMLFR4e2uh/sXUILx40Vdm6IIC9KL9L/X01+nqL7I9bssCzvVaIQDB4r49dfDv0syBfDwbw+zet9q7v+lfb83Z81UyMSJjDzjDGEY92C9Jya2NoLft8+j16RfoJW1LB2/lCGhQ1g6oWs5eHfgLZkCGBspIlTZVdneHXz/fti+HWprycsTqc9u2PdIksSJSSey5qI1DAsb5t2x3UB/kyknZEn2nxpdUBCB08ZBWBjNJkkY9fv2dfLmVTZXklmR2eUQqqqyU6Nh0/PPY1+wAKmxEc49F+67j8FRUYCQlTcNAE/EM5ufYckHS/j10K9+P5bVKlqh7NixpTOZcsJgEHU9I0eK9aCmhrbdWPNq8vgu7zs+yfzEb+IZvUFCIiEsgZOTTkan8Y1qZm9kSlX9GKFqC41GRAsdzykgpEazstrdh/6Ep2QKhL6G3S4ep5AQhy3jIOarVq3ib3/7GyBS/p5//nlApAKaHCJeFsfa99Zbb/Hggw8CQvp85cqVyLLMX//6V0BkcjU3N7N///7u2/Mcg19xjFB5iHHjxNxaUSGUgsAPZKojnOEMLwpFAo1GRgyNBVl29X7qDYqqEmbUs2V/EVt27ETWtD4mKipaSUO9pZ4vi7/mMEUoKGyr3MZPZb9QYapAK2tRUbGYzbS0tNDS0gKolJeXsW3rJlBVVFXFZrNhsZiRJYmwUOHqGjchGb1e357AqSo6rZbd+3J4/YuNTIkNY1hIgBDO6AKyJCPJGkYNiyfQKHTLvTKwZFlECp3pl7t2CRUuN/Ft7re8lPYSl629rF0URqeDoKA86usPcN55U36XZApgZcpKRkeObufh7yRAMWSISP8YP77btE0n99VqxUJ/6FB/fQP3IEsyFydfzOoLVxNq8M4C6QuZgtYI1b7KfZ4f3G4XqU9WK+zcSYJBRJKLinquGW+b3tiVoqWv0N9k6rH1j7E6c7VP0jd7Q2CoFhITMQ0eLh70srJOdT3Pb3meFWtX8GbGm+32bStAMfPMM9F8/z388Y/ij/ffT+gVV7gk3o90lKrF1sLOsp1Um6qJDIj0+/EqK+vZu3cvycljelbzkyQRAk9NFYt/QGuD5BGDknj5rJe5aeZNJIV7l0nSVxyXcByfXPAJ10+/3ifjuaPm19wsTBKNpnOPRb/CbocDB4Qa4NatnZRI/Q1vyBS0nmZkm8daq9XS4LAl/vGPf/DGG28AcNNNN3H77bcDEBgYiKIo6HQ66h0M9t5770WSJLRarWufG264AbvdzjfffENAQOcG3sfQfzhGqDxEWJhwmgDk5cHu3UX+JVMgPGPV1YLBlZa6tYskSdgd1k7yqERkWeNxL6oR0SG89fWvVFTVoNU4olQqaGQt6dUZDDHGU9FUzq6a3VS2VCIBeQ15glA5UgxlWUaSJDQaLaGhoURGRaNtU9UuOaJMBQWHOP/iy5gx6zhsNpvrXJ39Uqrr6vnom1+IiwvDbFex2pVuo06SJCHJMpNGJwK4roNXcIaUnHl6u3aJEKUbSB6cTEpsChcnX0yIodUAzs3NpaBgHxdfPJmoqN8nmQJRM7XqvFWuRpRdqfkBrv4wgHCPHj7cyUNpNIrMEBCEygcZmn2GoirtjG6t7F3bv76SKYDxUaJAeV/FPs+JgFMFKjoaVJXoikyMlYVYLSplZV3vklmRyQUfX8CWwi0en6sn6G8ytatsFx9nfsyjGx4lrybPr8cC4dGOjJIIHhMvHAvOFAmHqo2iKsiSjCzJzIif4dqvLZly1kyh08Gzz8Ibb4DBgPTZZ0Q5nFKV/WycdoRRa+TD8z/k2dOfZeLgiX49VkVFNYcOZTB+fAJjx7opjR4c3L6esLYWtm9neug4VkxZ4ZfzdBcaWUOAru+GtLvS6M7olIi29Pmw7kOjEWtxYKDIWNizRwh3WX2X6tgdvCVT0CrWHNnBTxAcHExJSQkAK1eu5BtHKu6TTz7J2WefDeASTmtqauokovbEE0+gqiovvfRS9z02j6FfcewueIH4eJGNV1lZzs6de/1LpkBM5MOHi/9nZ7svkuCY7WZMGoNit3lkSCmqSpBBR/bhSrbt3IO2jXynVbHRaGtkcsQkYoyDqbXVIUsaSlvKCNYGo6gKEq2ESKPRUFNTxd49u8jOyqSstEQQrjbHM7e0cPFy4TFV2qTvOaNTZrOFrLIqQnRa7L0UqNoVBcVuZfrE0e2ug9fQaIT6WWioSP9zM90gPiSeV85+hatSrnJ91p0ARUGBewqSRztUVaXe3Jo66TSAuyVTHZGfL9TnMjI6pQEOHiykvVWVbg39/sQHez7gms+ucUnCewNfkCkQ9T56jZ4GS4N356PRiD55iYnCaa+KZrSH8+1dpli+uv1VDtUe4vOcz706X3dwJNL8JkZP5I45d7Bi8op+kX+PjhZTT1wcQlJ9+nSR4+ow6mRJ5u8n/J11F69zEZEuyVRbXHEF/PwzxMQQ4ZjLan77ze/fpTfoNDrmDpvr12NUV1ezdesmFi0aypIlQ70qzVFUBcuBbBG13bFDOHj6Oc94S+EWfj30q89EMTzpM+X3+qmeEBYm3oFhw8S6Xl4uolV+nPD7QqYaG4VwkiwLv2BHxMbGkunoO3f66aeTkZEBwGeffcYoR/sWSZKIi4tzOYafflr0OvuzU8XzGAYMjhEqLxEQUIiqbmfhwqn+JVNOJCUJi1FVYe9et6RcnSl+qeNHERpoQFU9S31rsSmMjA3j7W9+I7+g0CFzLqOVNEToI/i1fD3FLaVoJS021c6okJEUm4rbH0NV0ep05B3YT9KIUVx25TVs3bxRiFcgzsdiNrPiymtITBqBxWJpF53S6/U0NDeTdSCPYK2G3iih2FciMjSYaRNGt7sOfYJWK/LpU1J6XUkaLa1S97Iku3qpdEemSktFtLOt8t//KtZlreP8j87n5/yfXZ+5TaZAWJh6vVip0tM7NWEePVpouRzpWlyL3cKbGW+ys2wn6SXpXo3hKzIFwlidGC0M7l1lu7wbRJKEMT9hArFRNrSNtZgyD3aZefOPk/7B8snLue2427w+555wpGqmNLKGi5Iv4uZZN/fL8TpBr29tQA6uwsF4vXB/90qmnJg9G9LSCHdItFfffju8+aZ/z70LKKrClsIt/aKW10nNz0v8kPcDS7Lu40trpnCw5eaKzAVz/7QFUFSFJzc9yW3f3MaafWv6Pp6HTXtHjxbB0piYPh/aO8iy6N8wbZqIHFqtorbQzcwRT9AXMgWt0amIiO51NcaPH8+vv4q6wZSUFAoKCvjpp584cOBAu+02bNiAqqrceuutHp/HMfQPjhEqL1BYWMiuXRkcf3yqi0z1S43kuHHtU896CXVLkoSiqkQOCmX2lPFIGp1HjRJVVUUryzSYWvjbv97m3TVfUFxajiTDpPBktIrM8OBhzB88l4TAISg2OzOiZmBXbC4VCEmWsVmtjBk3noCAQFRFJWXadGgTwTIajZxw8kIURWlnGOn1erLzDnLfc69x28sfYrfbkehWdBAQBo+k0bJg+iTCggMdku8+Mra02vZJ442NnQz6A9UHOHPVmazavapdRLAnafTBg2mn/NcPGQxHBKqq8nnO51Sbqilu6L5pb48IDhak1mgUkn7p6e0WUo2mvZbLkYJeo+ftc99mZcpKloxb4vH+viRTTkyOmQzAztKdfRto8GA0qVMZEq/C4MFt+/+6EKwP5pbZtxARENH5j33EkSBTLbaWIyqRbbG0r1czWU38K+1f1O/LEN75bdtQq6rcI1NOJCQQ4ZBYrrHb4corRe+qfmym+kXOF/zhyz9wx3d3+PU4viJTAGuz1lLeXEFhhEPiW6MRWSNpad63OfEAFruF+cPmExMcw2mjTuvTWJ6SKRBfNzzc0SftSCIkRJCqpCRR5+bjTu99JVMgSOfIkb2vSfPnz+fDDz8EYPjw4Zx00kmAqLXKyspCVVXmzJnT0xDHMABwjFB5iKKizjVTDQ1iLnWKVPgNznqegABhyGd2rezUFk4j4JwTjkNV7R41+AWwqSoheh0tLWaefvdrnnr7E6rq6wgPGMQpw09hbNhYVFUlJSqFeXHzCNOHYVftrpQ/ALvNRljYIPQGPbt3ppMwPBGzWRARVVGRNRq0Wq0r1c9ZN5WWsZtlT77G9oJSJsWFu+qteoKi2lEVO4vmzehlyz6ioUGknWVktPNMfp79OU2WJtKK0lzXoLc+U22V/0wmEYAcaEp1voAkSbx81svcNe8uLk6+2HMy5URAgCBVQUHi2qend6nAaLcLYbQOnLffEBscy40zbvTY4PcHmQKYEiMaRe4q9zJC1RahoQw5cyohQ0KJi3M8rxaL3+uKjlRk6okNT3D1Z1f33BjZT0hPh40bRdmOE2/vfJvX0l/jpqynUYOCUC0WcteupXHPHubOmeN2cXq4Q0Gt2mHA8dRTsHixzxqa94Z6cz16jd71bPoDHclUURGsXy+EK73BM6c/wx1z7mD55OUi/z81VRj3NpuYvNveKD/AqDXyx1l/ZN1F6/rU+NgbMjXgIMuiHGLkyNbPTCbhmeyDcqUvyBSINT0hoXP9VFe48MILefLJJwEYM2YM5eXlWK1Wxo4d6/Xxj6F/cYxQeYjdu3d3qply5snm5bWGeP0GnU4k1QcFtZ9EuoEz3W3BjGSGDY5ERUaW3TdCJMCuqqiSxIwJQ/l1UzZ5BwuorKlh6+4MrKoVVVUx28yYbCbsqp1OIuWShM1mZ8bMOVx25dXo9Xr0eh0ajQZJltBoNC5SJQQsNDQ1m3jug8+I1coMDQnAZOudCMqyhCrJjE6IZV7qxHbf3+fQ68W9aGlpR6pumX0Lf13wV+49XqjxuNu013lbNRqxHnu72A906DV6zp9wPuYWs3dkygmDQQglhIa2Rmw7hIlzcoS0d39KqVebqvtEKvxFpsCHESoH9EaZ1FThfZVaTGz87nUufPccHvj5fr9Ec44Umaox1fBd3nfsLNtJk9X3aUW9wWnntrUPU+JSGBE+gsunrYRp08iqr6euro4Z0dEE7N/vdpOwiAgRQaxOTYUPPhAW4JdfwnHHiQXNz1g+eTlrLlrDRckX+WX8riJTFkvfMkr0Gj0XJV9EoM4RogkMFJGS4cNFSvKgQX0/cTfQF5l0b8lUcbHIcPRA7Lb/ceCAMMTS0ryqb/MVmfIGt99+O6qqkp2dfUz6/CjEMULlIVJTUzvVTMXFCUcVCOPN73UwgYGiMNMNzVIVsNntGHQ6Ll50ApJG45WEsaqqNFqsEBFAdl4+eYeLuPKR12lpsaB1qM/IktwuMuWEJEnYbFbi4+OJjR/K3pwDZB3Ip76xCZvdzg8bNrPmy+8orah09WdoMZupM9sI0WuxuEGmXMeXtVx0xvFoZFmkCPrL6HIa9M6w0s6d4Kj/WjJuCREBEW6TKSeCgkTdP4iFq6io5+2PFhQ3FPPV/q9cRrbXkamO0OlEMn9kpEi90bZX0UtKEgS1ro4u09L8gcfWP8ay1cv4LPszj/f1J5kCmBIrogC5Nbnt6vx8gqoqMutzkaxWgqsbkXoRjvEUR4pMAYQHhPPxBR9z97y7XaS0P+EMNrUlVDOHzOT9pe9zYuKJ7Ny9m6KgICYsXYohMFCkn23b5pbV6yRUNTU1cNFF8NtvYjHLzISZM4V4hZ8RGxzrl15e3aX5ObmmzkM+Ut7UQ+N5SRITjnMCB5G7nZ8v6qx8AKvdylMbn6KwvrBP4/QlMlVWJuZSP5Qr+Q6jRglS66xv27HD7RP2JZlyijIfoZZZx3AEcIxQeYjuvAajRrXWwezZ0w8vUVuDoq4OHPKbHSFLkovwXH3eaSRFh2NT6TZKJUkSg4w6wgP0RAToCdJrXWl2VrvC+MFhPPX5L+zJPsBfl53GVz+vR5JlUdvUgyNIlmVy8g7yr3c+4pLHXuHSJ1/l/hde59GX3+LxD//Lo5//xEMvvclfn3yJVWv/S2REONcuWsD++mYCdL3LTcuyhF2FoVGhnH/qPNcx/QoHqWrQ2nk7by22Hdtcq7WnZMqJyMjWPpL9KknrJyiqwgO/PMC9P93Lqzte9R2ZcsKpwNjWyeF4+YzG1hYHBw/636tqsVuw2C0oquJqpOsu/E2mAKICo4gPEZ6f3WW7fTau3Q5F0lDOGH8nb46/m+vDThZGjI9yLY8kmXIiJjimT02Z+wKjUfzb8XLKksyuXbtaa6YSE0X6mbOWxGDodWzn3FTtlBidPl149mfMEF7+U0+Ff//bR9+kFV/kfOGqo/QHeqqZchIqTzLcGi2NXPzJxdz43xupNvUgx9r22dy/XxAqDwz6nvBp9qe8v+d9rv/ieq97oPWFTClK6xzq14a+fUVAgHB2jh0rnGwNDcLBcPBgj+TWl2RKUUT7jqysI5dyfgz9j2OEykdw9oA1GESEqt/SjJqaRHQkJ6dTvqHdbiczM5O9e/eye/duDh06xLw5qeg1GtSuIkkAqsq2z/NIW53F1tWZ7NxXiq5N7ZKiqMQEG8k8eJhZ0yZzuKSML3/4FYPRCKhdpvpIkoTNbufFDz/n7e/TmDQoiPGhARwsr+azvblUbS5H3VLFpo/28tOq7fzz3n+xcfsudHoj7KikyWwlRK/t0ZCSJQ2SrOHGi84k0Gjwb3SqLYxGHm/+judK1vG3zBdh505ys7K8IlNOJCQIu8YZ9TzaMSN+BiGGEE5MONG3ZKormM1i8XSkK8XGutonsW+fz5zFXUKv0fP0aU+zaukqxka5T6j6g0w54Ur7K/NN2h+IgMj+/ZDfFM242ecSGBAq5qXt2/tcT3IkyVSDuYFDtUe+S7QzQtXUrHDHt3fwbe63KIrStQCFM/3M2bPKiW6EJtpFqJyIj4dffoFLLhHOieuuEw2BfeQlPFR7iAd+eYClHy31C6nqTYDCKfrjCaHaWbqTZmsz5U3lhBnc1AsfPFiEwRobxbvQR3n15MHJzEmYw4opK7zKMulrzVRjo5g/dboBIEjhDuLihGMgKkpc90OHROpHF/B1ml9trXjlDIZ+bn7cB7z33ntH+hSOenjXafIYuoReL8QF0tMFtyksFMaxXxEUJKRkSkpEmsbUqSjBwciSRHZ2Nt9v2M60SeOxmM2UVTdAYz36iEFQXYsdm6unkySBza4SZNTz6Wd3ERhgQKvRkH3wMNe//hnjgw3YFBW7qhJu1LPtUAmP/vs9brjkHF54by1RkYOYlTIFm9WK4mjq64Lj9+GDIxkUUYJNVVEAmwqnj07gT3+/Aa3W0TjYoeMnSRK1jc387aGL+G7LbjZtzWPMuMGiYXGHHlZajRZFkkgZM5ylp8539b7qL8wfeRKbirdycdxpHC4tJaukhDnz53tFppxoK1jktGO0R+HbKksyV027isUjF5OxNcO/ZApEM6+WFpFvYbXCmDGMGSNRVyccHbm5rVErf0CSJI/6E/UnmQIhTPH1ga+9l07vAFVV+bLoXeI1i8EaRpkplPjUVBGmb2gQzp4pU7yqKznSkalXtr/CJ5mfcMvsW7g4+eJ+PXZbOLnSr8Xf8FPFT2wp2kJAdQDmOnPXan4aTfsJpLxcPPgTJnRq+TDIcV9qOvY2DAiA994TXsK//hWef154JD76qOuGOh5ievx0DBqDK2LqK7ij5udNhGrusLmsuWgNNaYa95Vyo6JEKCc7WxgEubni33HjWsOOHmBc1DieO+M5r+oTfSFA4dQpGdDRqY4wGIRRVlEhyFQXXkp/1Ew5W0m4I0YxEHDBBRfwySefHOnTOOpxFJpoAxshISLSXF7ej/LNY8YII7KmRqjbpKRAQACqqjJt0njmzZ7u2tQQGMjBjEJyg00U1dqEcp6qEqzTkp5Twr/+djmLTz/JtX10TAw88zG6UCM2VQUVrIrKsJBANuw6yFkLSli26CRueOwNHrz2PE6ZfxyyLLsk0J3kprqmjsz8QkJ1WuyKilaWKDFZWBg5iNNPXtDtV1uiKPyxvoHvft3CNS+8z2CthFaWURyLioh+Ccbx1+suAURTYk0/Gl8LRy5k/rD5lOTksSs3t89kqi2am8UtNRpFG6yjJQ3Q2dxZkiRMJlP/kClofelycoSTwWpFN2EC48bJ7Nol1tXERM/rJ3rCjpIdpBWlcWXKlR7VgvQ3mYJWpb+M0gyfjPf1ga95bsuzhGk+4K6ktRQV6YmPd9QXZmWJiKEXFtiRJlOKqlDSUIJNsTEivG8y232FwSDe+6lhJxESX0hDZU33ZKojVFVERsxmIZ4zYoRoCuy4nmEOglXflaqfJME99wgidtll8P33oq7q888FKfASwwcN58VFL9Ji820ulLvS6N4QKhCN2j0mgHq9SEl2qjnU1ooIenKy1+IVnr4LvlLzczb0PaoIlRPR0eLHCVWFPXso12pJy831uQCFM1looBMqRVEwGAzYjhV6+QTHUv78gJgYMYf2W0RBkoQnMThYrBaOHlWSJNHiWD1azGbsip0506cyae4Y5iRPJdCgR5YkNJJEbYuFCclDWHL6CaiqismhWmdqaQGNjKo66rFkCZ0sYbHbSR4xmKc/+YrftmVwxemz+etf3uDz7352NextPT0Ji9VCVaOJQK2mlezIIhWwydSC1WbDbLFitduxKUprA19VISIslIsXL2T1vdeTV1iHUStqtrSyhE4joTcYuf3ypSSPGo6iKMiO/ltqmx9fQ1EVLPZWJa3igmL25ee3J1PFxX1uKqUo4pbW1Ij1+GjBJ5mf8Kev/8ShykP+T/PriLg48T7IsnAV7txJRIiVsWNFKqUvyZTFbuHBXx/k1R2v8kb6G27vdyTIFMDU2KmAaO7rbR1GWwwJHcLIiJFcMvV8jDo9TU2OLD+NRtyDyZNbO1qqqltpY0eaTIGIrD512lO8fe7bzBwys9+P3xaSJFJXkxL0pEozmWaY5h6Zcu48dWprU/jcXBE9dMxLoQ7ruM5pLXeFc88Vuu3DhwsFtVmz4Kuv+vidJAJ07km7uwN3yZSiCB4THOweoapqrupZjMJdxMeLySc0VLwbHuSBrdm3hnd2vuMVAfWlNLqTc/fS1/7oQHEx1fv3k7tuHdMDAxnaQWisL3CqPsuyT4K5fkNNTQ0ajQabzYbRaOwcpT4Gj3GMUPUDiovdVrH1HlqtYHHOIq49e8BBLgA0Gg12u4JGglPmzyRCK3P23BRUjY5BQQYO7irnb1eeS2xUJFab3ZUyJ0myI2UP6i028pstFDZbONxs4WBjCy0KfLwjm6/T9zH5+JE89Mo6dmdmozcYRLq4Q11BljXU2OwcbDZTZDJjtQtjTpYkZFlGkiQMeh1FJeW89PYnvPXhp6TvyQJJxma3Y7PbOXHODG6/+AR2ldSikSUOmCwcMtlZMHMy11+4CEURqYKSJCE7/nX++Bof7vmQ5WuWk1OV07UARUGBiJJ0IeftCYKDW53BhYXdao8MKJisJl7Z/grrD63nP9/9p3/JlBNRUSLVTKsVrtWMDOIizB57pXuDTtZx44wbGRs1lksnX+rWPkeKTAGMjhyNUWukydrkk55Kk2Mm895573FFymUuXZB2ZQptvUoHD4pakh4K9AcCmWqLCdETet/Iz7DYLYwZo9LUtJP6+gr3yZQTGo2IMo0ZI6y8qioRJamraxeh6tHxNHkybN0K8+cLy/qss+Dppz2qCfr10K+8u+vddo4oX8CTpr2yLL7K9OmtPL8nvJj2Iks+WMK6rHV9P1Fn/7ypU9u/Fz30/Gq2NvNS2ks8u+VZfsj7waPD+ZJMWa3iVkuSyMI52lEmSWRUVTFy1ChiZVkIsZSW+mRsZ3QqPNy9Z+xIICMjw1U/eeGFF2Iymfwv4vU7wLGUPz8jP1/8lJUJ+86vz6zBIFaLHTuE+63NYqciVO/sdjvJo5P4JmwTly2YRfahIn7JLuD0RZM4dcFxjvon2jfQlYSC3tioUIINelf0p+3fVVWluqkFbXwYaTv3UFtfj9URJZMlGavNxryEKCxWGxpZpqS2kYSQIAYPHoyqKoAgcOVVNfzpyudhfATsa2Lzjn8yK2USLRYLRr2ehXNTeeqez4ldFseYmHAUVSVMAzuyDjJtXBIAmQcLWf3VT8RHhCIBpXWNXLH0DOKjIlA71nd5AYvdwvt73qe4oZhvdn7DCNOIzgIUkZEi1aZtHYmXIcvoaJGmlp8vOFpg4MD2EgboAnhx4Yv88+t/cu64c/ufTDkRFiYMmF2OeqE2dXXl5eLXvqZkSJLEKSNO4eSkk936jkeSTAFoZS2TBk8irTiNnWU7GR3Z94IyrSye6yFDBOGvqBAOpHb2m80mLnpLi5ifJkzodPEHAplqtjazOnM1FyVf5Bcpb29wzw/3UFJWwlkRZ3HOSed4RqbaIj5eREgyM4XTLSODsPHjAbBarZjNZow91fYMHizS/v7wB/jPf+D220U+8ssv96ooaLVbeXrT0xTWF6KoCiumrPDuO3SAJ2TKU9gVO0X1RVjsFkaG997z0S1IUmtRHIgXJjtbRNVHjWo3RwEYNAb+OOuPfJv7LaePOt3tw/i6aa9OB3PmiMhLP5Yn+wVlZWWk7djB1DPOYHBYmLj+jY0iRbmsTDgevH3HcLWkHLDpfm+//TaXX345AP/617+4/vrrAY6l/fkAxwiVnzF4sIgs1NUJJSy/N70OChIqT4GBoojYCYfeg5MGnX/mKbz5zve8+eidjJ60ktv/ei4RYSHY7Xa0Gg1Wx8slAWhl9lY3s/mBPzF90nhMZjMaWcYpIAGg0Wp57f11bF3xIlf+8y5SJoyhxWx21VFJksStV13g2valtz6ioKyO6IhBKG2k17RaDcwfzglDI/jZWMT3G7YxfcpEV6QtPHwQcoKR6IgwXnrgNsLDQmi2KDz80ltMG3c1AK/892fuWb6EwYNCaLbaufuxl4iPivDZJdZr9Ly15C1e+fUVkpqTmDO3CzW/oCDhiczI8AmpGj5cOPYrKkTwMTXVq7rmfoHJZOLw7sPcMPWGI0emnAgKEqRKklzXvqxMvBp6vRCB8iYF0K7Ysat2l9F9NJApJ6bETCGtOI2M0gzOn3C+x/vbFTu3fnMrZ4w6g9NHne767sHBgsPqdF2Iymm14qHdu1fkBO7eLfr2DB8ODAwyBfBG+hu8kfEGmwo38dKZLx2Rc2iLovoivt73NVablduPvwtJ6mOaXHCwuA/Z2WAwEBwV5Zqj6+rqeiZUIF6af/9bZEPceiu8+abw8qxZI3Ldu4FG1rAyZSUfZ37s1TPXFfxJpkCc88tnvcy+yn3+i1Q6re+SEpHXPX58O2+ZRtaweOxiFo9d7PaQviZTbeGGEv+ARpcCFKmpwvmZn98qWTrZ+35zY8aIqW0g1jtfffXVvPbaawBs2bKFmTNFOrPNZiMtLe1Intr/BI7F+PyMwEDhjJUkMWcW9q0nn/sH7fA2SxLY7QomswW7ojA8fjBRsToOHTzIn/9yDsfPES9WdV0DVbX17VXyJEBR0ev1aDQajAYDGo0WWSMjazRIsuxK2wMbWp0OvV6P3mAkMDCQkJAQgoKC0Gi0omeVJBESHMQpc2ag2O2dBdztKla7HYINNLdYsCutUSVZllGSwvlqYzZpGZloZA0hRh0zJo5i/fr1mM1mEo0aosJEjvqmrTs4ZUqiGNau+MxQqy6uZpJtEnPnzu1egMJJqnS6VlLlpRdIkkTqX3CwSL84cMD7c/cXihuKOVB+oP9rpnqD0djOEoi2FBFkrcViEbagN3hv93ssW72MnaXuyY8PFDIFrXVU3kqnf5HzBRsPb+TxjY9Tb26frjR1qqi379LB62zEPGSI+P3gQcjMpKG2dkCQKYCxUWMZHDSYiyZedMTOwQlVVanIq+D/Rv4ff5r+d+ryksnO9sHAzhTAESOQZdn1LNa720lckoSM+tdfi4KkjRuFZyI9vdtdZElm8djFvL3kbQJ1fdfc9pZMFRbC+vXuz5+SJPk37TMxsbVBfEuLuIa5uV73d/AnmTra0a2anyTBsGHiGY6IEJHCPkKnG1iqvKqqEh0d7SJT5eXl7cjU5s2bB8ZafZRjAN3y/11ERAhxpdxc8RMYKD7zO1TVlfYnITzLX/y0iSWnzCXAYOCc005gVuqNfPLZX9E5Gv1++dMGpk+eQFR4h5wyCVckyWK18tCLb5N18DCBRgOKoiJLEodKK4k7dST3P/8W0WEh5JRV88SfVjA7ZRJmq42XV63jt+17MWglVFXixsvGYlfa5+Db7QpsrWSbVoXf0jnub9ej12owO4rQmptNjC6BH39+lhc+/JK5s6YRGmjklPnH8e8PPiUr611OWXASsiRhslhZv203f//TSgA0mr75D3aX7cam2AhtDnW/z1RXkapp07xyX2k0wljNzRVesIEERVX46/d/Je1gGrdMvoVTppwyMCfo2lrk3P2Mt2nYXj+eCqIoL2/fF7g32BQbq/etpqi+iEN1h5gSO6XH7QcSmYJWQuWt0t+i0YuoMlURFRhFmLH9PNHrLZckoVsfFAT799Ocn8+uH39k+MknH3EyBXDKiFOYN2weBs2RdcWrqurqM7X05KWYzQFkZPi4SajjWoeGhlJfX09dWprIUxo2zL356dRTYcsWWLxYRLzmzYO33oLzu49A+eL+9iUyZbG459P6Jf8X5iTMQafxoYJNdxg0SBR15eYKr+vhw9SXFXB3+fssT72S2UNnu3Xd/EWm7HZRPhcSInj40Vhq45Y0ekBA58iUUwkqMdGtXEdFGXjXp7Gxsd2aY7PZXA7ztmRq+vTp3Q1xDG5igN36/10kJAilJlVtTWH3O9pMwqqqotPp+GlzOrv37QcgNjqCD9b9lUnjhXVeXFbB/e99SWCAsX0NVUeokJNfxJpf97B2RzartmfxblomeZW1hOi1/Lb/MN+mZ7H+rS2YTK0WwOHiMtZ8vZ1vdh3g+8xcvvl1oyBKbc4zNCSYm/92JnedexJf/vgqJ8+fhd1ud6X8bd+9jy+/vJeh8bGcOGEIP67fIvYLNDA4NJDPdh5mVKKYMPdm5xEXIFZPezeNLd1Fk6WJe368h8s/uZwPt37oWdPetpGquLg+5QIYjUI8zZdKdb5ARV0FpWWl6HQ6Tp95+hE3jLtFWBjExREcYGe49QCUlLB/v2eiMVpZyzvnvsMts2/h7DFn97jtQCNT0Nrct7C+kKrmql627gydRsfKlJU9piGZTL0IqMTH0zByJLuysoieOnVAkCknjFrjET0XVVXZvH0zB0oOuAQonJl4zgwxX8IpTFHX2Ciihrt2uf9CjBkDmzfDaaeJRe2CC0TfKsd8W95UzjWfXcP24u0+Ode+pvm5I5m+o2QHt397OxevvhirvW8qrW5DqxX1AMnJoNfzfv7nbClJ45ktz6D2vBoD/o1MNTSI566hYeCRBXfgdZ8pk0mENA8fFqIV1dU9bq4osGmT8J271ghxAACFB0lEQVR6K0LmFNHyleJeVlaWa8057bTT2vXobEumZs2ahXYghdSOUhyFr8fRizFjRE2w3S4mp36BwzCQEEp/wwZHsvrrX1AQL+/8GVMw6IV1/uuWHUyyR4g8+p7UmyQICTAwNDaUCeHBTIsIJiUyhCCdFruqEhtoID40EJIGtVOO0eu0EBnI8LAgog06HvrPpxwsOIwsaxz9pOyMSIjn8f+7gbv/cAVnnDgPg06HJMvodDqyDhZSUmph1KhRWK12TjvtNDbu3k91g1ANO/64VG699GwC9DrsKvy4YSvLli0D6LOCjSRJJBmTCLAHsOLkFZ73mQoKEnLDXTQW7AtKS1ubCB4pmEwmdqXt4sFZD/Lhsg8ZEjrkyJ5QT5AkYbgkJjJscAvB9cVYcwvYn+OZtH6oIZTlk5f3aHgPRDIFEGIIcTUf3lGyw+39qk3VbrUgsFiERzs7u/uISkNDAxv27CHs1FMZPXdu63X0uxxqZ1jsFm775ja2Fm3t92N3hDMy9V7me/yz+J98nf810EoAFKXPnRg6wSmd3hgZKbzwNTVCBdBdo27QIPjiC1FTBfDQQ3DmmVBdzX92/If00nRe3vZyn9tX+KJmyklIe+Ib9eZ6IgMjmRk/s38iVG0RFQUzZnDB3GtZNvUy/jDjD8iS3EVRYiv8nebnVNQfyEJI3aFPTXsDAgTBdaZj7tolvOHdzFF1deLdbG723uE5ypFuGBERwf333+/dIA6sXr2a8Q7RmSeeeIKvv/7a9bdjZMo/GNCEymaz8de//pWkpCQCAgIYMWIEDzzwQDsRA1VVue+++4iPjycgIIATTjiBvXv3thvHbDZz8803ExUVRVBQEIsXL6awX4qZ2kOWxfs5eXKP9bv+gSQhAyMiQhiia2H7rn1oZJkWswWdVktFbSNph6uYdMIQtLo2E7IEEpIrQuREeaOJwr1VpBXVsqOghvTCWqyKggzYFBW7ooC5fR64qqqgKEIyXZIIjAlFon0UTavVYNTrMOjECy7JMla7wm/bdvGf1V/xjzuWOT4X+yydN4kvv/sFgCGxg5k3YyoAuYcKsVVLBAcH+6R2qqSghIX6hXx08UcMHexlA8C2k5bVKpQR+mAdVVYKYaJ9+3pUovYrTCaTq2YqZWqKT1Tj+gWJicjjxzJuWDNSdRUVW/JoqO05ilnVXOU2ARmoZMqJ6fEivWN7iXuRA7PNzMpPV/LHr/5IRVNFj9vq9a09S7uKUrUToEhObk+mtm8XD7WXNSTe4MM9H/LroV+596d7Mdv8EAJyE04yVV5RjinUhNluJjJQSIXJcisJ8GnaHxDs6InU4BQOCQoS92LnTsjLc08aXasVMurvvisM0W++genT+YN2DhdOvJA/zf5Tn+ZgXwlQuBOhOiHxBNZdtI4bZtzg9XH6BJ2OiOHjuO2421gwfIGw0DdvFpGSDveiP2qmnKruR1tD3z6RKSciI0VtlbMZdnm58BZ1MbE55dIjIjxLQilu02di//79rFu3DoD77rsPSZLIz8/3+LRvv/12znek3v7888/8+c9/dv3tGJnyHwY0oXrsscd4+eWXeeGFF9i3bx+PP/44TzzxBM8//7xrm8cff5ynn36aF154gbS0NGJjYzn11FNpaBMCuuWWW1i7di0ffPAB69evp7GxkbPOOqvPaWDeQK9v3+ytH+0GVKCx2cw1M2bw7a9bsCkqep0WFVi15nNOGBaCUW/oJC2uSqA4okgglIceu/VKtn3zJNtfu4/0dx5k86t/Y9igYOqt9tbGvd2dhAPNttbrL8iUlpKyCr79ZSObtu9i9X+/45X31vLkq++Rv28XT/75GvR6PYqioNVoUBSVWbNmkZZXTF2TCa1G41pwvvpxPVdeeTLQt+iUXbG36zM1PHa412O1w759Qm5u506vSVVkpHiW7HYhmuZrz3VvMJlMPLzuYba2bGXS5EkDJmXLbcTFETxzAqOGWZgcXUKIuedQ3+MbHufaz6/lnZ3v9LjdQCdTAKlxqQBsK97m1vZ7yvdQ1lRGbk0uQfqgXrd3BmJLStrbgD2q+dXWCou3tFTkzfgjv60LnDPuHC6aeBG3zLoFg/bI1E61rZmaP28+/znnP7y46EXmD5vv2sZfaX9OQtXY2CgKfFNTW29gdbVHvaa49FJh/I8YAQcPEnbSGdyZG0vy4GSvz8+Xan5OQtWbWl2ALoBQQ/8ziC6jeKWlYnLPzRXrhckE9A+ZUtWjM0LlEzLlhEYjhCqmTROqUDabuBcdFlwnofJELj09PZ0hQ4YgSRL794tSjHPOOQebzcbUqVMBSEpK4tJL3etxCDB+/HiefvppAAoLCzn++ONdfztGpvyLAU2oNm3axDnnnMOZZ55JYmIi559/PgsXLmTbNmEEqKrKM888wz333MN5551HcnIyb731Fs3NzaxatQoQHeBfe+01nnrqKU455RRSUlJ499132b17N99///2R/HqYTCKzol+atUoSKmBRFQKA4VqV9WkZyLJMQUkFP2/LISYmBn1YRLtJXavV8pfzFnHJnGloHXFsWZaYOHoEqcnjmDZxLFMnjGFa8lhCjHrq7Yp73hnVWaCstv4O5BeW8OoXG8gtaKa+PI4z5p3BPTes4LLLlovNVLWVIDmOc8XJqaz98ntUQKfVUlhWxaFmO3FxcY5mv95dMlVVufrjq3nwpwdJmZnieZpfTxg1SrDrxkavk64lSRQJBwQIr/XevZ7ZPn2ByWTi8x8/5/PKz1lbspaf8n/qnwP7GpGRDDllPBFTEnoMG9sVO6GGULSylllDZ3W73dFApsDzCFVqfCofnv8hD570oFsqbZGR4vG2WFoNjV6l0QcPFuF7rVa4xbdvb7Xm/IhQQyh3zL2DM0af4fdjdYW2ZMpZMyVJErOGzmp3jZwkwF8RqsbGRvGBLIv89IkTvVMhmDwZ29bNsGiRONnLL4ebbvJqjvMlmWqbLtkV99hatNVt1U5/4cW0F3nw1wcpbWzTZHbECJGmrNEIp8O2bSiFhf2i5mcyCf4gy4JLHA3wKZlqi5AQ4WwYOVKs323y+kzNKiaTWJM9MRNSUlKYP184TcaMGcOSJUtcdU7p6els2SJqxFetWoUkST1Km5tMJiRJIisrCxCZWUOGtKbfHyNT/seAvqLz5s3j5ZdfJicnhzFjxrBz507Wr1/PM888A8DBgwcpLS1l4cKFrn0MBgPHH388Gzdu5LrrrmP79u1YrdZ228THx5OcnMzGjRs57bTTujy22WzG3MYVWO+Ie1utVqw+CgUUF0N9vURmJmi1qitNpq9QVKG61/Y8nSRJNhppHjmSi447jvuefYP5s1L47OvvWX5yMlOmTOGn7OJ222u0OmaNGsZxY4ajkbWoqorNrvDc2x9SVF6DXiOj2G3Y7HYK65pICNCDJKGRZZH73QaSJKHVyGg1MoqqoCI5YuOCTamqSmhoKHNnT2HGhFQOH5bIzYWgICvBwUqXKn12IDk5mW+2ZmJTVLQSVNbUMjEgBKvVil1RHD2zPMdXO75ifcF6BoUMokFt8Nl9B8RkPHEi0s6dwmjcvh118uSec1G6wdixkJ4uOVIAVV+ovvaIlpYWNm3axMjYkdwz/B42F25mwdAFvr0+/YmAAPFjtWI2Q0ujjTBdc6cclzuPu5PLJl1GXHBcl99VVVVycnI4fPgws2fPxmg0DthrkhwpIgb5tfm8tOUlloxdQnRQdI/7xAXGERfY9XfvCqK3tURBgYpG08jmzZtJSEhg5MiR3TeRDA6GyZOR9u4VeazbtqGOGuXz2kMQtVNHunmvqqrs2bOHqqoqZs6ayQ+HfuCUpFPQyJ0VxUJDxXRpMPg2Gh0YKAhybW1t+3vrXJCcn+Xni/+PHNkjyVJUhat+vpWxt8/ktskTCHr0SXjxRZT0dOzr1uHuQldTU8PWrVsZO3YsCQkJfX6XbDZxDa1W4XhqO5xNsfHQrw9RWF/I/cffz2kju7YL/Il6cz2rdq/CbDOzIGEBkYY2oY6oKPFuZGWh1tRw4LPPQK9nxnnnIXVY632Jykqw2SRCQ1VvO370K8rLy9mxYweTJ08mJibGP9clNlb86xy7qoqyzYXYGMWgoUGdnq2O2LdvH2+//TYPP/wwkiTxww8/kJGRwcyZM/n000+RZZmff/6ZOXPmkJKSgtls5rLLLuOjjz5i5syZTJ06lU2bNrVrbZOfn88Yh/Tv9OnT2bhxo+MUxYk4+0xJkkRqaiqqqna6NmVlZT66QL9fDGhC9X//93/U1dUxbtw4NBoNdrudhx56iEsuuQSA0lLhxYnp4FmOiYnh0KFDrm30en2n6EJMTIxr/67wyCOPdFkU+O2337oWIF/g8OEQamv1pKWpjBhRi8HQ9xxAJ6E6fPgw4yalAK0EqbG5mS82bMBgMCBLdu596lUkazPDIhL5+uuvyT1wADi5NUqlqmzYsBVVVUkcl+DynmQdyKcmJ49dZjhUWYdFhVEx4Wh0BhRFQZE0KJrOoSGbRocia0GyYtTK6DQaJElGVVXXT94vW9inMXGoIIj6OgNbt9LrtWlpanQ0qBQEKj19N1FRqutaeItrh1xLra2WrE1ZZJHl9TjdQTabCTt4ENlqxb5+PfVJSSheVLTW1+s5dEhEQ4YObSQ83P/pUoWFhejRs4AFfPXVV34/nr/R3KzlYF4IIWXFTIo+iGn4ECxd5Lmk032/HSd+/fVXf5yiT5EUkMRB00H+8PUfuPnrm5kQPIHZYbOZHTabKH0UALnNuYTrwonQed7nwWzWkJMzyKEDUoNOp5Cbm0uuU4q4J9jthBQVoa+rg02baBoyhBYf9ppQVIVnCp4h3hDPWVFnEaw98u73R9c+ysdlHzMiYAQ3JtzYbQqtIzPIZ3AaUrt37+bLL7/schvZYiE8JwdUFXtAAA0JCdi7yZs70HyAzYc3kyFnMGL63Yy85x5S//lPdBs3Ujt/Ppvuuw9bl43KukZmZiaZmZmef7Ee0HG6MtlNhNSHQBM072vmy+yur4O/cVHQRWQ0ZFC9s5ovd3VxDqqKsaqKoNpaUBQ2rF6NyZOeDx6irk5PVVUAQUEWSkpMfjuOr5GRkUFGRka/HCssN5f83GAaWvYRPUKh+FAwag/Rn+eee44ff/yRp556igceeIDJDqn2devW8fzzz/PDDz9wwgknMGTIEJ599lm0Wi3Lli3jpJNO4vrrrycjI4OAgAD+8pe/MHPmTHbs2MEDDzwAwIUXXsiyZcu6fY9B2LAdoaoq5557bh+vxDEMaEL14Ycf8u6777Jq1SomTpxIRkYGt9xyC/Hx8Vx++eWu7TouPB1rgLpCb9vcfffd3Hbbba7f6+vrSUhIYOHChS5VJF9AUZwtiiSHurba54ZwThKRmZlJVb3QZ3d+1+CQEM46S8hnDtL/Sl1aGscvOZeAcWORJIns+v+22x4gNXWa6/+SJKHRyMyeOoEV94vr09DUzMHDxWTlF3GgsJSqmnpkxY6SJ2qkRJqeRIDRyB8WHMdxx41lSEwUI+MjWfvjFvR6nWjw6zjmxIlTOPPMM7BYFfbukamvlwgIgJQUtVv1nEPvfQqOe6oCkydPYtGiM7yKUB08eJCcnBxmzZrFokGLPNrXK5hMSLt2QUsL6qBBovmpF8jPh0OHJBITVYb7qNSrLZyRKV2ojtlTZ6PVDOjpw2MoCmzfqtC87xBR2mGMi29mxyAT3zRm8IfpfyDE0HX6XsfIVPBRkhszdf5U3t71Nmuz15Jems6exj3sadzDf4r+w4z4GSwauYjvK79HQeG5055jSoznz+XGjSbS0jKJixvFjBkjPa+zKyhAKi9HTUlxqw+Mu0grTqOxrJFSg8hwGGQc5LOx3UHbyJQzmqnuV9m8dTPLpy7nzIln9tu5bN++nXXr1hETE8OiRT3Md9XVSNnZInVPo0EdOVK0gegCC8oWUNpYyukjT4fFwLnnop56KhHZ2Zzx8svYP/1U1Gt1gbaRqcTExL5/QTexlKWYrCYCdO6Tvf6Eoiikp6djCgsjZfFi9GVlIjXzaKtd9QPaRqbi/RDN7hE2GyO2FFC1v5pR8SYCgh3vRjcp5IsWLeK2227jhRde4G9/+xtDhgwhKysLg8HAokWLOHz4MCNHjqSoqIjzzz+fVatWuQQmVq5cyX333cfDDz/Mww8/3G7czz77jNNPP73DqbVGpqZPn95tml/bOqtj8B4D2iK64447uOuuu7j44osBmDRpEocOHeKRRx7h8ssvJ9YRei0tLSWuzcReXl7uilrFxsZisVioqalpF6UqLy9nzpw53R7bYDBg6MIDp9Pp0Pm4CdDUqbBjhyg23r8fJk3q2xzpJFRtz9NpyIgojkpaWhqhqsr8efOQq6uwl5ahGZbgCiO3NXy0Wk27cRRF5WBJJSZHcWyg0cC05HGkThrf7jy+n/09kiQjSRKyRsPgyDD+dvMV7baR5a2ucZ3H1Go16HQ6ZI3C1KkyO3aIVPycHFFi0RU/kjWt3w+ERLxOp0P2kFDl5uayevtqLj3pUqKje06B8hl0OtHYMSsLxo3zWnN11ChRhuKP4mGTycTmzZuJio7ihYIXeP2r13nwxAcZPsgPzO0IYuJkSLeNprIwgLrmgzyW/QCHlBpCDMHcctytnbZ31kwVFhYyb968AVsz1RWGRwzn3hPu5d4T7iW/Np+1+9ayJmsNGwo2kFacRlqxyNcPNYTyac6nGHQGkgcnu02KGhoaqK3dzOmnJzBxopd9pkaOhKSk9i99c3O3xri7mDN8Dm8ueZOK5gqiQ/rpPXfAWTNVXV3NvHnzCHBEa5ZMWMJJI08iQBvQrVy31Sr4TFDvuiBuw+kgbGlp6Xlti4kRBSL79glJ9bw8UQM6ZgwdvYAzhs5ov29qKnz7LZx8MvIvvyBfdBF8+mkndYjq6mrS0tKYMGFCn2umOkJVe19Xfb22uwtFVTqlyLf7u4NMtbS0MHfuXFEz1XZ9UhQh6x0XdwQkhI8sysrKSE9PJyUlxbc1U+5Cp2PYCWMZllInjJSmJjhwQBSPjhkj0sk74Pnnn+eee+4hLi6OoqIiQkJCeOutt1ixYgUjRoxAVVUeffRR7r77bpYtW8ayZcuor68nJCSEhx56iNtvv53INuoXeXl5JCUltTuGzWZj+/btaDSaHmum1q9fz6ZNm3x7TX6nGNCiFM3NzZ0U2jQajUs2PSkpidjYWL777jvX3y0WC7/88ouLLKWmpqLT6dptU1JSwp49e3okVP0Jg0HIqcuyEFVyZCv6BKoiUvcURUFVQVUUNm3aRGBgIFNOOw0cLy8H9kN5OZIEqqq4xPiUNioHzjRAq8XMxKQhfPjfH1j9zS+8u+4b3l7zJfc8/hJ79uxxbW+321FVxXUeqoqLhDnvoQooiur6ve3xQJQUTZok1uva2u5766lt9lW9lE7Mzc3lm/Rv+KTuE67//nqqTT038vMpjEbBrJ1SXuCxBKQktSdTiuIbFcm20ujhieHk1+ZzqPbQEa8/8QfCwmDIUAkSEtivjOGuoctJNSRyVcDcThfzaBGgcAeJgxK59bhb+e3K3yi+vZh/nfkvTh1xKlpZS725ngd/e5DJL09m7Atj+csPf2FHyY4e+wo5BSjGjOkDmXKi7RrgbLLZRmrYW0yKmcRJSSf1eRxP0JUARVuEGkK7JVMmE2zYILQ6fIkgBztrcqf3gl4vvFojRrTKSKeng6rSbG2m2dpDx/rp0+HLLwUZ/uYbWLasXX8lXwpQdIX8fPjtN/GvE83WZp7b8lz/zvUdUNZYxpIPlvDBng9Q1M4TtltqfiUlYoHctw/27PFJPzerlQFfN+U3AQpvEBYmHAcjRog5q6ZGOH+6QWxsLKqq8uyzzwJw+eWXt2vqe9ddd1FbW+vaPjQ0lCeffBKr1dqOTDU0NHRJptwRoFAUxSWK0dZ2OwbvMKAJ1dlnn81DDz3Ef//7X/Lz81m7di1PP/20K9dTkiRuueUWHn74YdauXcuePXu44oorCAwMdDV0DQsL46qrruL222/nhx9+ID09neXLlzNp0iROOeWUI/n12iEkBMaPF++kLyPWRoNYnPU6HRoJzGYLgYGBTJ8+HVmWkRMTkZwT0b590GJGr9O5ukPpdVpX9Ejv6A0VaDRyyeKFXHPJuVx5wdlcffE5XL70TM44fmYnw0nn2EenkdFptK4X20mUNbKMVpbQOT4Xx2v/HYKChNjUhAmiNrcraGQZvUO0Qq/TeRzhc0qjp0xNYVj4MKbGTiUiwHc1Gx6jslL0u+hhQu4JLS3CzulrvUVbMjVlyhSSwpP46IKPePSUR4kL6Trd52jHiBGOXo6DYgmPOY9Xxv6ZkNpmEUF04H+JTHVEbHAs10+/nm8v+5ayP5fx1pK3WDx2MQaNgf3V+3lk/SOk/juVkc+N5M7v7mRL4ZZ25Ko7NT+fqNM1NIhQQ06OV/2qihuKaTD3V1f19uiOTL26/VX2VezrdX9nMMfXzX2d59Hs7lwjSTBsGKSkiBdlyBCQJP6z4z+c++G5/Hjwx+73nTsXHIIKrFkDN9wAqup3MgUiA8Rubx+lWrV7FW/vfJubv7q5z42HvcXHmR9T3FDMjwd/bNeXETyQRo+PF5FcSRJrR1qaILt9QGGhIPAHD/ZpGL9hIJApVRW+HdfcJsvi3ZgxQywkbTXUu3lp//jHP9LY2OjKiIqIiHDVRIWFhaGqKu+//z4gsracz8CYMWNQFKVTirknan4pKSmucRMSEjz+/sfQHgM65e/555/n3nvv5cYbb6S8vJz4+Hiuu+46/va3v7m2ufPOOzGZTNx4443U1NQwa9Ysvv3223YGzj//+U+0Wi0XXnghJpOJk08+mTfffLOdSspAQHS0IAy+SolWFIXMvMMMjh9Cc3MzWr2B4ooqUpefiyzLrQvIqFFiRqiqwl5cTGZuAWGBejQaDbmHy7AaBVHJyS9GURQsVlu7aV9RFAIDA8g8cIg5U8a6PrfbZXIPlxEZUYjFrpBf0llFpry6jr25h7FZLej1OnLyi7EEOHh+m/Wtt3r08uo6dh8oQCup5Bwq6XKM7tC2z1R4eDjvDX+vS09hv0FVhRu1pUUU2E2Z4nGOj8kksnEaGgRZ94akdyRTTsM4KjCKqMBumO3/ACRZIS6xiYNZIRSZo4gZMYWQoiychWn/y2QK4LPsz2gwN3Bx8sVEBESwYsoKVkxZQYO5gf/u/y+r963mvzn/5WDtQZ7Y+ARPbHyChNAElo5fyqLERVgPWklKTHKRKbNZtM8xm2HOnD6WQk2YINTODh4U/XmamoS8d9vIbjdQVZW///R3DtYe5JGTH2HGkBm97uMrdEemdpTs4JXtr/Cf9P/w+SWfMzioe4EBWRbZwM60P19lpznPxZk94DZCQ4XhqNFgV+ysL1hPVWMFBqWXBezkk+H99+GCC+DVVzEFB7Pp5JP9Sqag6x5UU2OnMnHwRC6ddOkR66N3Xep1xIfEMypiVLtz8KjPlCSJ+Sny/9s77/imqv//v27SNN27dFFa6KAte1M2slQUEAFZihMcqCh+RRQFRcHx+4AoiqgIispQhqgooMgsZZQCLS10L9rSke6mmef3x+GmSZu0aZo2KT3PxyOPNvfe3Jx7csd5nffypBMN1dVAUhIVVeHhJmWPraigjyIjLq12xxrEFEC7OSWF3tNGjdIau9nbU2HFI5fTCVIvL+rG3ODidXR0RF1dHQ4ePIiHHnoIq1evxurVq5GZmYng4GDMnTsX3t7eGiPAU089hW+//bZRe1oipv755x9cu3YNAK3nymeyZpgORyw1LdPBqKyshKurKyoqKsyalKIpbt+mg+GWhgvwCTdqaqXYeyQGstuVyMjIhFAowqB7h+HhMUMaPTzUShUE1xNx/UY6TtwshkNgVxACyMRCTJ9EBx6/HbsIsUylt9YRIWoIPR0wZ8oIODrQB3RxqQS//XcZgkoZlEoCrwgfPDR6sM53/3vhGlIvZ8PWlgogub0A940diCB/H4M+7zIZfVb06FHv4sbvRywWQGbX/D54eDEVHR0NDzNmEWs1CgUdhVZX05tvv34tLgSSk0PDHDiOehO2JLaqoZg6mn4UQW5BiPCKaNlxdEB+uvYTfrj2AxZ2ewsju45BUBAgFNATiRdTORkZGDFmzF0npsqkZXhoz0Oollfj3XHvYmq4/uQINfIa/J32N35N/hV/pPyBanm1Zp23nTdm95mN2VGzMbrbaAg4Ic6fp/MDUVE0zq/1DS2jNwGFgl4fUVHNFoAprS3Fs38+i4KqAuybsw8+Tu0Ta9KUm19RTRE+P/85HG0d8caoN5rd18WLVEP269eyejdNcejQIUyfPh3Dhg1DbGysyftRyKQ4+c83mOA6EFxEhGF3Ap5vvgEWLwYAlKxaBa+1a03+bmPg+65vX90JOn4IZE2FyVtVtJcQGjeQnU3/9/KiMQUtgBDgzBlq0RsyxLwxe63FWsQUQOc9s7LoZHivXk1sWFhY7+EgEtHBi6+v3sGJUqnEoEGDNGJnwYIFGDhwIJYvXw4A+OWXXzRJKhp+zlgxpVQqNfGC+fn58PPzs8gY926DCSojae+TLT+fznzY29MC3S2djeRFlVwuR0xMjI6bn8HPKFXgMjOo60ArUw0ak2nR1M+npND+EYlo3xjKwNtcG3gx1XtQb7xx9g0sGbQE44LHmdxms6NUUlFVVUV/j759G9VGag5+ktLWlrp3G8h0rENDMZVbmYt5++ZBoVJgx4wdiPKOMvGArB81UeOxA4/hRskNrBqzCjMiZmjW8WLq1vXrGOHiAocBA3QDw+8C1ESN32/+jmMZx/DZfZ81GSjPU6esw2+Jv+Gbs9/gfMV5VCvqxVUXxy54KOIhjHSfBT/ZWHTxEuFOluDWw1ezrqqi5pvhw5udiVeqlbhRcgO9u7RsgGkqzcVM8TSXlIDn6lWqJSMjzZd74NixY5g8eTL69u2Lq1dbUdi2ro7G7/AFggMCmqxZJZFIUPTKK4j44Qe6YOdOYOFC07+/Gc6epfrbWgSCXCWHSCBqPLnZGjGlTXU19fmOiDD8kGzio5cu0cfOyJHWk0jQmsQUQOMZq6poLUgDCS/rqaykgxf++nB1pdZDAyfj+fPnMXz4cJ1lSUlJiIyMbLRtS4v2hoSEICMjA2vWrMHq1avvNI8JqtZi1TFUnRkvL2pql0rpmKGlspe62shw5sxZiO3sMGDgwDsJIAy7snE2QqhDQ6ESCKBSq+lLpdLUh9IsM/RSqXVm+wghUKnq1+v7bv37aHq2MCSEWu4UCprYSKFAi/eh7eb3W/ZvSC1NxRcXv4BSbUVRuDY2dCra1bVeXFVUtGgXPXtSw5ZcTs+j5kJO9Ln5Ods6Y0y3MRjkNwiRXo1v5ncTAk6A76Z/h7dGv4XpPadrlqvVBImJ1M0vunt3ONja0g7NzbVga82PgBNgesR0bL5/s1EDfABQSBVwLXTF5/d8juL/K8af8//EE/2fgLudO4pqirA1bise+2cSHj7ni5XnnsKh5L8gV7U+aB52djSOx9eX3hSMGHjaCGysTkwBMLqv+UPUqjnfauzu+HS12OXvDtduX6P3fTs7OsPFx2LcukVHnHqSXfAxU7bvvAO8/DJd+MQTNGlFG6Add2ZrC2y7vA2Hbh6CSq1q+oNtyIdnPsQzvz+jEz9nNjEF0Bv/gAG6Yiory6jYKv4xwxeTtgasTUzJ5VRMAbqhUgZxcaGzmiEh1EewooKqVgNBasOGDYNarcYjjzwCgAoec4ipP/74AxkZGQCgEVMM88AsVEZiCfVeU0PTqatUdPajZ8/mP8PTEsuUQfLy6M23Xz+z1oAxB3I5NOnU3dwMp1PXR8OYKblKjq2XtmJSyCTrdGlTqYCEBJrFKSAACAtr0celUjquUSqBrl1pyJz+7fTHTPHUKetgZ2OFDvVtTE0Nwd9/Z6Go6BYWLuwHZycnmhb31i26gb8//U2sZeRhAkq1EgJOYPTAnsdQAgoAUKgUOJF1Ar8m/Yr9N/ajpLZEs85V7IrpEdPxcOTDmBwy2bznVU0N/S3u+ErnVeYhuTgZE3tMbDe3rubE1NdxX6NGXoOnBz5tsL6ZPjIyqCtvU9dxS7l06RKGDBmCrl27IreFEwTn887jhcMvYHjX4boWTYmEujjJ5fTGHBJC713Qk81PrQYWLQJ+/JEO/o8do2YRM1JXB8TG0qaEDsjHzD0zoVQr8e20b9Hft79Zv8sYqmRVmPrzVNQqarF9+nb08eljXjGlj8pK+tAEqGU9LMzgJERyMg05CA6mL0tjbWIKqPfic3amOqlF1NXRZ0hJSasu5paKKblcrkl+UVRUpFMahlmoWg+zUFkxfHY7jqNZUY191plFTCkU1Ae7spIO5s2Rf9uMNEynfvOmcZ9rKKYAwFZoixeHvWidYgqgYrZvX3rTNeHGa29P/budnem9Wx/6xFTD2du7WUyVSctwPPN4o0xftGjvTWRkFCEoaCCkUmd6QYaF1f8W+fn0GrH2HMNNsPPqTiw6uMiobHM8TYkpABAJRZgUMglbH9yKguUF2Dv1OKb7Pw9PsS8qZBX44eoPmL57Orw/8cb8ffOxP3l/02m3jUGppG5ncXFAcTEAYMO5DVj570p8cfGL1u3bSJoTU6W1pdhxZQd+SvgJlwsut2jfrq70GnZzM197+fbVmZCGMaciB7ZCWwS7BeuKcQ8Pmibdw4M+O+6kgtabzU8gAL77DrjvPjr788AD9Dc0I4RQK4K7O+Bp74mlQ5diSsgUi4gpAHAWO+PXOb9ixcgV7SOmAGqxCg6m96/iYhpUdrtxoihA10JlaaxRTAG0zBTQfMIsvdjZ0bi2Pn10FatUanQ61JaKKQCaTH4ff/xx+9XZ7EQwC5WRWFK95+XRyQyAXoNNxfqaRUzxVFXRLHMqFf3SXr2sbhZeIqFjWULoJGhTmT+1xZSrmyticmMwMnCkVQUjGw0h9KnXgpGVoQQd+sSUmqjx3B/PIco7Cs8OfhZiGyOCrzowq46vwt9pf+Oxfo/hpWEvAdDN5te16yiUlDjB1hYYOlQrxLCkhAaqqdV0wNK/f6vjD9sbuUqO6buno7imGO+Nfw/3h93f7GeaE1P6UCiAmBhAqVaBBJzDHxm/4tekX3Gr6pZmGweRA6aGTcXDkQ9javhUONm2LBELFArqinmnfos6sCu+LfsXPyX+jO9nfI9gt+CW7a+FGOvmF5Mbg/8y/8Obo9+0+P0nIyMDISEhcHR0RHV1dfMfaEBhdSHsbezhaqcn6w0hdDbQ2xuSqioqpiIi0CMkpPG2NTXApEnAuXPU6nv2rHWYR9qYdhFT2lRX12cCBKjSDA/XBNjyqcArK+m8kSVvZ9Yqpgih9zKFgnq5mm1IeOUKHXcFB9OZEwP3BlPE1K+//orZs2ffaX/jYT+zULUeJqiMxNInG5+IoUcP3Wyc2phVTPGUl9NAJbWaxir07Gl1oio/n3pf9eljOMVrQ8vUzqs7sen8JjwY/iBWj+tgfsSEUJ+M4mL6e/j6tngXEgkd/6tU+t38YvNisfTwUtiL7LFn1h74O5uxOJqVQQjB13Ff4/ur3+Pbad8iyjuqUWp0R0dnXLxIJxAbeV1WVVFV7+5OswV0QCRSCQ7eOIgn+j/R7ADfFDHFk5dHPfHc3eltRE3UuHDrAn5NouIqu6K+qrmdjR0mh0xGT8+e8HbwhrejN7wdvOHl4KX538nWqfH3E0J943iTvpsbasOC4eDoZnQ7TaElMVPWREFBAfz9/SEQCKBUKttE4OlYpmQy6mLQvXtjP22JBBgzhori0FDqp2dUgErHoVJWCRcxHUO0u5jiIYT6jmZl1edGHzbMqp7t1iqmeJRKOjzy9DRTtymV9DnCmwcdHanQbZCe1xQxVVdXp7kfSSQSjXeONpYe494NMEFlJJY+2QihF6+hVLltIqZ4SkrqM2OY03nfjKjVhmOo9Ln5/XD1B3xx8QusHLVSJ5Nbh4AvbFpQQN+HhWniE4yBzyApFtehquo0unTRHzN1KvsUquXVRlks7gYkUgk87D0M1pkqK6N5QTiOzkrqZEyXyWjaSXNed1ZIa8RUcxBCcLngMhVXyb8iTZLW7GfEQrGOwNIWXd04N4RX2CBc7A8Ppzt5jdvo3m2MmFITNZRqJWyFpg+aCaGz4gqF+TLVlZWVaUpGyGQyowb1MbkxCHYLNmqiRUdMeXnVx/E4OVGf9oZ1QW7dojFU2dnAJ58Ar73W4mNqCCHAzdIb+PLil3hu8HOI9LbMxEd2eTbm7ZuHaT2n4bXo13A57nL7iyltamqotSogwKSJubbC2sVUm0EIdcNMT6/PosIn3RGJTBJTAODg4ACpVIrPP/8cS5cu1buNpce4dwNMUBmJtZ1sqjvhLUJhG4spntu3qVUEMG8RlDagpIQONuzt9YspnpyKHAS6BFrc5cZk0tLolD/QvL+jFrW1wLlzMsTHJyAszAHTpkV23D4wM80V7eXT0Ds7U1Glt9sIoRvyAS9WilQhRW5lLsI9w43avi3FVEMIIbh2+xqOph9FQXUBimuLUVxTjJLaEs3/UqXxWek8bVwQ7tId4YH90dOzJ8I9wxHuGY5Qj1DYi1pnSTLWMnU49TC+uvQVXo1+1eTyDAoF9YQDgLFjzTMzLpVK4XBH1BjzfKuSVWH67umQKqX45sFvmsyaqDdmqqSEBr0qFHeyRIQ2rjz+0UfAG28A06cDBw+25vAA0Mvx3dhXcUN6CtMi78Pae9q25pUhvov/Dl9e/BKju43GPI95lhVTPA18wUtSJLAXyuHQw9ciBqtOK6a0USiolZ2fNBWJoOzdG7EJCS0WUzt37sRjjz0GQL+rH0/v3r1x/fp1qxnjdkQ6lrM/AwCdDE9MpC7PYWFynDvXxmIKoEVPlEpqCrJiMVVURB+e9vaAq2s60tP1iykA6OZqwHeyoxAaShV1djad0VIqqRtNM3CcFOXl5+Hm1hUuLiEoKuI0NW0uF1xGpFdkqweZHYHEokTsuLIDr498HV0cuzQrpgDa5RIJ/V+hMJAkq6SEumMWF1MfwdBQq3Kl4fn28rfYeW0nnh/yPB7v/3iT25pTTEml9XXkDLkvcxyHfr790M+3n8H91MhrUFx7R2TVFOuIriPpR5AmSYOaqFGjqEGpshLnJFdxTtK4zlI3125UYHlQkdXTiwquINcgCAVNZzc1VkwRQrD3+l7kV+Ujuzxb7zbGYGNDTyVCaAI9Y+rKNYf2YF7Bz4o3QZW8ChFeESipLWmyjIJeMQXQeFwXFzpBV1ZGzeWlpbReEl9wccIE+vfff+mBtlJwyOXArIDluKB0weJBT7VqX63hyQFPom+XvshNzbUOMQXo3JvUciWS/smHWq7E0OFFcOgX1uIaVq2hI4gpvvydlxcQFNRGXyIS1bvzp6ZCqVAg9to1cAJBi8RUTU2NRkxVVlYa3C4uLg7Xr183S9M7M0xQdUBkMmqpl0gUuHr1MsLC2lhM8TR0KzOU5cCCuLlRd/C0tFuQSPIxf369mLpccBk7ruzAqjGr0MWxi2Ubai66d6eiKiODCitCaKCdAfgEFCEh3nBxCUFODoebN6lFr1ydh5f+egmeDp745sFv7p4+0gMhBB+d/QjJxclwt3PHm6PfbFZMAXRcN2AA9VIyeOp7e1OLYXo6dV+SSqlrkxUlqyCEoLi2GGqiRg93w+cLYH7LVE0NDW8Si6lR1dTdOdo6wtHWUW+iicWDFmNj7EYsHrQY3d26I02ShpTSFNwsvYmU0hSkFCTiZmUmymXlyKnIQU5FDv7J+EdnH7ZCW4S4h2isWdovH0c6A2FszBTHcdgydQt+TfoVj/R+xLQDBu0rkYgKBIXCPIJKKBRCKBRCpVJBZkSBK39nf3xx/xeoklcZFJwGxRSPrS3NXJqXR+vwlJbSWF0+//TAgUCXLnSG7OxZYPz41hwiZDLAWxyAVcPXNAxJaVfUajXUt9Rwh7t1iKkGVEuFUHv7QFR0Cw51ElonqXt3+uxv42d9RxBTAD1V+VribSaoQAXQxk8/xeOLFiEvIwOcWEzFlEBAY9+6dm32meLkRJP6bNu2zeAzDQAGDx5szqZ3WqznCc8wGhcXoEcPOX79NQlisSsCAnq2vZhqCJ+eOCCADiCtBFtbwNExA7duFSIysj+Kipzh6UnjF9afWY/MskyNVeKuoVs3KqrS05u0HjbM5gdwqK6mFpfr1wHbwDK42rnCz8kPXg5NpJK8C+A4Du+OexebL2zG80OeN0pM8RgVuxIYSGd2k5NpB8fHN501pZ3hOA7vjX8P83rPazKepC3c/Dw86DhAJqOZxNpigNvdvTs+u+8zzXsda9cdMzYRCFAa3AUpnISKLC3BlVqaCplKhuSSZCSXNE4l7yJ2QVf7rvC18UV0z2gUpxUj3DMcYZ5hmoQDDbEX2ePRfo+2+ti0BZW5sLW1hVQqhVxuXMFljuMMHmezYqp+J/Q6cXen14n2tgIBMGUKsHMn8NdfrRJUhBDIZPS8tZR+KZOWwd7GHtfir1mPZUoPlVUc4O0Nl2BnQHSDBm6npdFrJiKicbybmegoYgqo91Bo61wp+/btw5o1a7BmzRoAQEhICL766itMDAujgio/n07c8e4lDfj66681/z/55JMGv2fz5s0AgL59++LatWtma39nhMVQGYk1xVDxMVPl5W5wcekLoVCAvn3b2RMvJ4daRTiODhRNKsZgfviYqcjIkcjNdQMhdIItKAjIKs/Clxe/xJpxa+AgapsHg0WRyQxOWRsq2qtQ0JI9dXV3Egp51aBWUQtvR+sRyW2JMW5+hlCp6GXg6dlEroOqKjrxwCet6NPHOoq7GEFbxkzduEELY5o7x41KrWrWTQ9yOfULvpNaHV270oGJtusTUSO3IlcjtFJKU5AiScHNkpvIKs8CgeHHpq+TL3UdvBOr5SJ2wZigMejh3qNVCSl4rlyhTY+MNDiWajHu7u4oLy/HzZs3ER6uP6YuXZKO2LxYzO412+BxGC2mGtLQ26GkBPj9d+DJJ2mtkISElhyO1m4Jnjm0BPZVvXGf7xO49x5ni+SNWfnPSpxMOYn5AfOx+L7FVimmADqxVlx8J5tw4J2U9+np9GYnENCaEWaeFOpIYkqtBs6coX+HDDFfYhh9KJVKbNmyBf/73/+Qnd3YTXj9M8/glVmzIO7ShSam0moMP14FqNufgwEhrFKpNO6DxcXF8Pb2tooxbkeFWag6GNoJKMaM6YubNwW4fZveCAcMaNsLXIfAQFrHoqiIDhj79jVvtUkT0E1A4QY7O+qen5lJjQXBXYLx8aSPLdrGNkVbTNXWUhfA8HBI5XK9YgqgY/xevagbFk3yRN2o7lbURA2JVAIvB69WiSmAThLm5tIZS4MJKvjsFQkJ9DexMFWyKuxK3IWFfRc2OanQ1gkovL2poCoqaqRlTKZaXo2F+xdiRsQMLOizACKhSP+GtrY0sU5mJlXEeXlU+EZFaa4hASdAkFsQgtyCMClkkuajhBBcuHwBibcS4RTshOyqbB3RdbvmNgqrC1FYXYhT2ad0vlbACdDdrbvGbVA7OUaAS4BuYdwm4MOMzG2hAtCkhWrzhc04nXMaORU5WDl6ZaP1JospQPcEkEqpxcrLiw7iExPphWZk0h1tLuVfwuWCy1BKk3F/14UWEVPVsmqcTjmNUmkpxg0dZ7ViCqAWY+DOnA/H0WQhnp70QWpj06nFFEAnMtRqeptoazEVGxuLgQMHIi0tDTY2NigoKMCKFSuwc+dOAMDKb77Bym++AQA8NHo0Nn7wAYJGjACEQo2Y+vnnnw2KKQB44IEHAABr1qyx6vOyo8AsVEZiDRYqfdn81Gqayrmigt4EBw5sxwYRQh92paXU5axfP4vNvhvK5heXJEFGbi0GhnaFvlqSdyWEABcvArW1qBOLcaaiAl6+vnpTo6uJGmtOrMF9ofchOjDaQg1uP/Yn78ensZ/ileGvIAIRJospgBo6Llyg3q/h4Y0TlemgUtFBu4UnHT4++zH2Xt+L4V2HY/P9m/Vu0x7Z/NTqO0V+lbQWsjm65eeEn7Hh3AZ0c+2GPbP2GBZU2pSUUHOZUkmFVlSUwcYYk4CivK4cqaWpGoH1T+Y/uFJ4BUq1EnKVYbFib2OPUI9QdHXpCj8nP/g6+cLP2Q9+Tn46f+1s7JCaSkPzgoKMykFjFIGBgcjLy8OlS5cwiI9janDsh24ewrfx3+KL+79olNCnVWKqIQoF/U1iYoBnn6XLNm8GXnihxbsihOCv62dxKbkI93efifYOFeHrTFVUV0AULMK4HuPatwEtoK6Olv3iOGDUKPpI10G7NolMRq1X3bqZXCqio4kpAJprz9+f3vPbAj7bXlRUFLZv346hQ4c22kapVOLLL7/Eyy+/bHA/Tk5OqKqqMrj+9u3b8L2TKp8QYhVj3I4Os1B1EAylRhcIqEfEjRsNio22BxxHzRvXrtUXAO7fn9YXaUcMiSlCCHbmrMX5nEtYF/4eQtC6wOYOA8cBPXtCdukSEs+eRaCfH8LHj9c7MP4z5U8cTj2M45nH8cf8P+AodENqKrUamCPg3ZoghOBk1knUKmqRlp0Ge9ibLKYAOv4ODqYhBpmZNIbeYIywUKg7UK+uppaR8PB2rV0V3TUaZ3LOGMzq116p0QUCaoAoLKSaxhyCam7vuXCzc4OXg5dxYgqgjRg0iJr4q6vr61E0wNhsfm52bhgSMARDAoYAAN4d/y4yyzLhKHKEiqjqk2JovdLL0iFVSpFQlICEoqZd29zs3NDF3g8etr4IKPBDcGpj0eXn5AcXsUuLfjvRHbOXIQsVx3GYHjEdD/Z8sJElzWxiihAaa/jLL8DevdSlnIeP4+nSskQ5HMdhZOAoBJJ2TVYHQLdo79hRY63eAsBbp5yc9IgpQPc+lZpKL9yiIpqNroWBkB1RTAH18VNtFeGgVCo1giopKQnDhg0DAAgEAmzduhVPPvkkBAIBbGxs8NJLL+Gll14CAJz58088v2wZEtLqa/cVFxc3+V28mDp+/HjbHEwnhFmojMSS6r1d6ky1BpWKmskqK6kdfPDgdsv+11SdqRp5DV458goSihLw40M/IsQjBITQ5lpRwrU2QSqV4tyxY+gqkSAsKAicoyN1y2zgsiFVSPHlxS8R6BqIOb3mIDGRPiddXKg2trZTrbWo1Cp8d/I7BNQFYPSo0SaLKR5CaCKsmpoWxAOp1dS0VVdHO7p373aNlleoFHoFR3vWmQKoYfvmTTrbGxzcpl/VPGo1HS15NU7GYqyYMhWlWonMskykl6XjVuUtFFYXoqC6gL6q6v/KVM1n4OOxt7HXtXI1EF2+Tr7wc/KDt6M3BJwAPXv2REpKCk6ePIkxY8YY/T2tFlOE0GfH3r30lZ6udRD2wH33AcOHU9ErENCgsYiIZp8vKrUKBAQ2Asvc6HkxlVaahlkTZlm9mAKokbaykv4kzSZcKCmhboC8AA8IoIFXepWYLh1VTKlU9H5VXg4MG2bUobaIhkV709PT8fLLL+PIkSONtl2yZAk++OADeGr/UISAu/PA3rVrF+YOGkQbGRTUqLEnTpzA+DuJXngJwCxUrYcJKiOx1Mlmipji03q26yBFqazP1NROgVxNiSkeNVHjRskNRHlHQamkE9GEUG1xt4kFHp0EFGFh4BIS6OBdLKZumU34VNfVUYGgVNJnZLtbPduQ1sZMGaKsjI4JOY7OJRh1+peX05ORz33dp0+bWnYJIU0KpPYWU7RN9G9rvyq/Kh++Tr5GxyAZRV0dkJgIEhaGq5mZLRZTMbkx6OnZE54O5kkFRghBeV25RlxpRBcvuLT+r5QZrjfTECEnhI+TDyT/k6Auvw73r70fg0cOrhdjzn749fqvmNdnHgb66fqTt1hMKZX0vC8ro5aNv/6iIio1tX4be3tg6lRgzhzg/vvpxUQIjQfNzq5PINIMv934DTuu7sBLQ1/C+O7t65nAi6nU0lRsyNmAoQFD8dl9nxlvNe0oKJVUAPPFZ8Viaq1qwnzTUcWUNm1RLUapVGLt2rXo1asXZs6c2ajOVE1NDdavX48PPvig0WcHDhyILVu24Pjx41i5ciV8fX1RkJ5OJ+0A+ruEhupkY+bv74WFhfC5k9mGCarWwwSVkVjiZDNFTNXW0vAZQui9zc+vHRpqiDasU2WMmGpITQ1w+TKdafL1pROddxt6s/nJZHTEX1tLZ+B790ZRTZHBOlOlpfVJtaKiWuxlY3VUy6txJO0IIhCB3Jxcs4opHt6y5+lJtZFRSKX1ySqEQpq2TY+FpLVIpBK8/PfLeHbQsxjZbWSj9ZYQU+ZCrpJjzi9zYC+yx0cTPzJfse6kJJDbt5GWno5CJycMnj7daDElkUowY/cMAMD26dsR4mHe4E1CqA5XqfS7sdUqaqngqiowKLoKqwtRXFNcn63wKwCFABYA0DOJ4ihyxAPhD2CQ3yD084hEeJk98mKvIsTDA352dlQk8S+JRPc9/zIUz2FnR8XTnDlUTBmaWKiqogKLfw7K5dTVoMFzkRCCRQcXIak4Ca8MfwXzei9ot8kzbTe/Uu9SfHLuE4wLHocPJ37YPg2wBGVl1HRTV0ffG0g/eTeIqbZAqVTixIkTmDSpPvmNr68vtm7digcffLDR/ZgQgkOHDmHJkiW4fft2o/3J5XLqwltSQl1l+d/FwwMIDcV7/+//YfXq1Zg8ebKO9YsJqtZj9YKqqqoKb7/9Ng4cOICioiIMGDAAmzZtwpAh1EedEIJ3330XX3/9NcrKyjBs2DB88cUX6NWrl2YfMpkMr732Gnbt2gWpVIoJEybgyy+/bNFF3d4nW2vc/LKy6Ivj0P7p1HnKyqgPfJ8+Zndnak5M7by6ExzHYX6f+Xr8/ekYVjud+t2CodToAOgILD0dCA1FgbQYj/z6CMYFj8PKUSthL2o8KsvMpBPCQiH1tmmj8iPtwvrT6/FT/E/oa98XW+duNbuYAqg2ysykBtoWJcLizaZlZfR9jx400NuM/C/mf9iVuAsRXhH44aEfdK4JaxBThNCxsim31RslN/D8n8/DVmiL/Y/sN1s5BKJQ4OahQ6jJykLv3r0h7tqVzlAZ4SucXZ6N1SdWQ03U2DFjh3ktZ6B9FRdHJ56jW5FHRqFSoKimCAXVBZh/73ykJqRi3rp5cOnjgoLqAqSXpOJ6aeMaXADgVQNc/xLoUmPCFzs704fSwIHAI49QEdXSa5KPt1Kp6AC+gQirVdRif/J+zI6ajWvxYtTV0WdhWxb11RZTfJ2pwupCqIka/s5NZayxPHzCXnd3E8cLKhUddJSUUDN9Axezji6m1GqqS8z9HNR28xMIBHjhhRcQHx/faLtVq1Zh5cqVejP2paenI/SOr/m2bdt0a07xtT1ycwG1GnKlEuI7wk2pVEKo9TsxQdV6rD6S5Omnn0ZiYiJ27twJf39//Pjjj5g4cSKSkpIQEBCAjz/+GBs2bMCOHTsQHh6O999/H5MmTcLNmzc1A6dly5bh999/x+7du+Hp6Ynly5fjgQceQFxcnM4JZS20NmYqOJgO8CySTh2gD7vUVDrzfvUqDcYRmcfdoTkxlVORg80XN0OlViHYLRijuo3SWX9nkgapqXQA7OBgVXWJTaZJMQXQ/r9jkruYfxF1yjrcKsuG2EZ/5ongYOpPX1ZGz6GBA83vM94eEELgUOcATsnh2fHPtomYAqilICrKhA/a2NCRXmoqLdRYUWF2y+5zQ56DjcAGE3pMsEoxdf48HayYUtclwisC+x/Zj9yKXPOJKUJw9fp1lLi6YuS0aRDn59PiPNXVNAlPM66ZQW5B+G76dyivKze7mALMlzZdJBQhwCUAAS4B8HLyQipSURt3ARVFlUgSliHLWWnws761HGx9/QFHTzoC9/CoH41rvxoud3MzTwCrVEpfCgV1O+jenboD3jmHHUQOWNh3IQBqoFepzPYI0os+MQXQmmQdAYmEjrulUhMFlVBIXTGDg+sfFIQAmZm4LRTiYkJChxVTAH0OJiTQ07d/f/Pss2HMlI2NDS5fvgwAKCkpwVtvvaUpzvv+++/j/fffBwBMmTIFn332maZe3I4dOwAAoaGhjQv4CoX02vD1BVJTMWL2bADAZxs3WuXYt6Nj1RYqqVQKZ2dn/Pbbb5g6dapmef/+/fHAAw9g7dq18Pf3x7Jly7BixQoA1Brl4+ODjz76CEuWLEFFRQW8vb2xc+dOPPLIIwCA/Px8BAYG4vDhw5gyZYpRbWkv9W6uBBTa6dTt7OiAuF3jYqVSOoMol9PZx379Wv0gNcbNjxCCgzcO4urtq1g9drXBQWJaGk2yJhDQG2RHnpBpVkzpIfHaP3ApkKBbvzF8AapGyOU0nkoopGP+9s6S1Vq0Y6b6DekHP8/2839VKEwYwBUWUpe/dsiYYg1iiichgbqZWoPFWG8CispKWgi4ro7eMCxcc0+ppMVFAWDMGPPEgo4ZMwanT58GZgOod+6Ady2HKJkLosRdEeISAjHxwrAxD2PwqHs1AfAWQy6nrmalpfS9mxvKgn3g7lZ/natUwOnT9P/Ro9tmUqihmCqRlUAkEHWo4ui8y3JIiEnlvvRz6xYk58/jRmoqgu+5B/7tmKzK3KSk0Pkuc8UV6xNThlCr1fjuu++wZMkSqNVqnXXu7u4ou+PdoFAomtxPVlYWut+ps6Az7K+rA+zsEBUVheTkZGahagVWbaFSKpVQqVSwa+BDY29vjzNnziAzMxOFhYWYPHmyZp1YLMbYsWMRExODJUuWIC4uDgqFQmcbf39/9O7dGzExMQYFlUwmg0xWn1Wp8k5OUYVCAYU5KypqIZfLceHCBdjb26Nfv35QqVRQGUjjaww9ewLx8Ryqq4H4eIJ+/drRymBjA0RFgbt6FSgrA7l8mQ5ETGxAZmYmUlJSMGzYMDg5OTX5GzwQ+gAeCH0ASqXhGdZu3eg4SSLhkJAADBlCOuS9vq6uDufOnYOXlxeioqKaPGZtegp8wQkBZWIiSG2t3qcox1FvGjs7+nO20WnfJhBCkJKSgtzcXAwfPrzZc8ZcKJXU2CSRcBg6lLRMVHl61gfIANSE6u1tcrIKQ3Fy1dXViI2NRWBgIEJCQow+Z9oKV1fg9m0OhYWk6VpeWqSUpkBN1IjwMl8gJCEEiYmJKC0txfDhw2FjY0PPGXt7eu+6cQOcXA5ib6/3Yrhw6wJuVd3C9J7T28QypY1KxYEQQColZpko00zcEcC/RoCn/B+EMLAbFk9cCS8HL5SVleHChQvo2bMngoODoVSpDKaYbzc4jlrdCwrApaej5nYuHj75JPoGDMKqKevhaueK2lpAqeRgYwOo1QQNxqOtRq1WIz4+HlKpFEOHDgXHcfhfzP8QkxuDFSNWYGrY1OZ3YgVIJByUSsDenpjtPl8slSI1Jwc9g4LgVVEBxcWLtExEu7rLmIeiIto/zs6t7x+lUomLFy+C4zgMGjQIhJBmn02LFi3CokWLAACXL1/Giy++iIsXL2rE1JEjR5rdDy+mYmNj67crLgaXnIzE2lokJ+t372UYj1ULKmdnZ0RHR2Pt2rWIjIyEj48Pdu3ahfPnzyMsLAyFhYUAoMlSwuPj44Ps7GwANIuJra1tI4uGj4+P5vP6WL9+Pd59991Gy48ePdpk5WlzUFlZib///tss+5LJBMjIcIOrqwwFBTXtLhqEUilcMzPBqVRQnD2LSj0pPFtCTEyM3uXF8mJ4iDwg5Izft0oF5OU5w8enFiUlFh4ctJLc3Fzk5uYaXE8IwXHJcQx1HQpnG+r25lBQAPuSEiA2FlJvb9QasFTp7qdjTDIWygqxr2gfZnaZCfkpwwVVzQ0hQHq6G6RSIZKT6+Dvb0qQCSCWSOB06xaIQIDqwEDIWzhjWK4ox0dZHyHKMQpzfOdALGjs2pmeno507TTVFkKh4HDjBs0MlptbBpGo6VGvmqjxWc5nyKvLw1zfuRjsav5qrXprsxACTqUC4QPBCYFALodaLIaKqPBJ1icolhcj5lIMxnu0bWa55GQPKJUcSkvLYWfX+ntXeXk5AECoAPId1TiXEYNMeSIc5d0Q5lA/JZ+UlISkpKRWf5+5EchkSM8+icLaIqiqzuO08BQEAiGqq0XIzHSBnZ0KFRXlbdqGf/75Bwq1Agm3ElBSW4LCxEIcTj3cpt9pDmQyAVJS3MFxQFVVqXkTeAQH42pZGRwuXACnVgPHjtFnjbd3h0mzW1cnRGqqGwQCoLKy1KyT0kePHjXpc2+99RYA6mlQWloKqVSKw4cNn2sX7mT8s7OzQ2FhoWZbx/x82JWWYvTbb5vUDoYuVi2oAGDnzp148sknERAQAKFQiIEDB2L+/PkaX1MAerOgNOfC0tw2K1euxKuvvqp5X1lZicDAQEyePNns5lBty9SAAQPMXmdKJrNwkdaqKnDXrgFKJUhwcIv8erQtU24G3GyqZFVYcHABPAWe+PCeD+Hj1DjD0N2GtmWqd+/ezZ7vv6f8jrNnziJNmoZfZv0CW+Gdae2cHHCZmQAA4utLZxAN7OvWLaCggEP//sRq63jxlqnvznyHYmExUl1S8dSEp9q1DeXlwNWr3J006sS0QOY7ySq4OwPdll43v6f8DkeJI5y7OGP6/dRiom2ZCg8Pt6psftRizCE0lCAgoOltaxW1uHD2AmLyYrB0xlJ42LeuymZDy1RDjwiDZGWBy80FCQ2F0scbNck12H9jP1Y/uBpOtm1b3NzLi4NUCvTvT8ySaOHLL78EAIyW++EECuDICfDCqBewIHSBjmXKqiEzMDV5DiodbdA3aCgA6kXr48PB3Z2gb1/zfVVDy5R2nalpZBoyyjMQ4m7e7I5txe3bgIcHBxcXggEDWr+/oqIiXL58GX379oU/b3KWyYDUVHB33DOJt7eJQaftT24u4OnZ+nNI2zI1ePDgJt3zWsq5c+fg7u6OCAOpi2fMmAEAyMnJaTSO+u2nnwAAXdzdUcQnR2KYhJUOi+oJCQnByZMnUVNTg8rKSvj5+eGRRx5B9+7dNZWeCwsL4aeVH7yoqEhjtfL19YVcLkdZWZmOlaqoqAgjRoww+L1isRhiPSpEJBJpqsqbA7lcjosXL8LR0bHNivZqN5cQOuBr18x/Hh40iCs/nzppGzmQS09PR2pqKkaOHNlkavQ8SR5kKhmqFdXwdPI0+fcpL6fBuabUp2xPpFIpYmNj0aVLF6NjpqJ8ohDuFY77Qu+Do52Wy0VICM3MkZJCneg5jgbeN0ClooMTmYx6olnjs5CPmcrLy8PGhzbiu+vf4cWhL5r1ejUGb28allZSQh/GvXubsBORiKZXTEujSjYvj8aM9OxplIV3Zq+ZiPKJgq3QFmJbMaqqqnD+/HkEBwdbPGZKH76+NIdNZWXz9fNcRa5YP2k9JFKJWcTU1atXIZFIMGrUKOOL9hJSH1OVkQFRTQ0e7TMfC/otaHN3P4B6ISoU9HI1x+nNB6iP7zsdJ/AVDrvexkcOQ3Dx4kVERUWZVrTXAoT1G6vzXp2dC5t8Aic/f4hE5hnu8GKqrq4OI0eO1Fu0N6JLx6nJIZVSl24Pj9afS7dv30Z8fDwGDBigm4BCJKLZsYqL6T2tR4+2zRJiRqqqaP/4+JjeZKVSqUmC1lzMlCmMHat73vfv3x/Lly/HnDlzsHz5cgDAggUL4K0nA9fsJ54AAFw6fBjdWpM2lIGOYXMF4OjoCD8/P5SVleHIkSOYPn26RlQdO3ZMs51cLsfJkyc1YmnQoEEQiUQ62xQUFCAxMbFJQdUemCsBhbGo1cC1a/TFx/G2Gy4ujSvcN5EPpSV1pvr69MXuWbvx8aSP9aYANwaZjPZLTg4du1orpiSgAGg2tB9m/IAFfRY0XunnR0WUQGAwr7BQSEUUx9H0uvn5rTkK89OwaG8Pnx54/573LWat7NGD9lVJCRXqJsFxNAKatxoWFQFXrtCT1QgivCLQw72HVSWgMITnnfq35eXGh+aYS0y1tGgvAPp79O5d/0Pfvg3ExUFQK21Vm4zF25smtTOX5wEvqHxDB2BYuSMUQuDr7S8bX7TXglTKKlFeV954hUwGu+JceCpvwznjaisuxHoMZfMDgNi8WChUHSjQ9A41d7ySW2vpNCo1urc3MGyYbpr8vDwqtKwQlar+tGmiXnGTtCQBhT4kEkmzsfSXL1/Ggw8+qHl/5coVPProoxCLxdi8eTMA6u3VED6sZfr06XC1xlnSDobVC6ojR47g77//RmZmJo4dO4bx48ejZ8+eeOKJJ8BxHJYtW4Z169bhwIEDSExMxOOPPw4HBwfMnz8fAODq6oqnnnoKy5cvx7///ov4+HgsXLgQffr0wcSJEy12XO0tpgA6XrazozomKclwncU2hxBqEbl+Xa+oMqVobxfHLgj3DDe5SWIxzTJGv98CgtMITBFTKnX9jVgkFEEoMGDd8PIChg6lozQDuLjUW+/S0mgWaWuAF1NZWVno0a9Hm6VGbwkODvVFtdPTm5w7aB5/f5olUySinV5ba3DTU9mnUCatd9voCGIKoHHqPXrQwzR0K8wuz8am2E2okZsWl6ZNq8QUD8chxxV4XrITyYoC+rvExQEFBa1uX3MEBtLyDybmK2kE//zZdG4TJrgMAgActLmG4CDz1kRrC76J+wbTdk3DoZuHdFeIxfAZH4U+USr4O1fRyYj0dJiamaIpMZUmScPSw0vx8N6HUaswfH1aI/3701t/a7xWWlRnSvsCr62lv8n16zTVoJGTRe0Fx9FSmkFBptWgaq2YUigU8PT0hI2NDTiOg1gsxsqVK5HfYEZzwIABOHToEAghIISgqKgI7733nmb9+++/3+jer1KpsGbNGgDA/v37W35wjEZYvaCqqKjACy+8gIiICDz22GMYNWoUjh49qnHjef3117Fs2TI8//zzGDx4MG7duoWjR4/qDKo2btyIGTNmYM6cORg5ciQcHBzw+++/WywPvyXEFE94OJ1pUaloumK+iHa7UlND/cdKSoDkZJ3RprFiihCCT85+guRi82WmCQykg2BecFqLYABME1PFNcWYuXcm/kz5E0ZVR9COHVEq6QNOqjvj3rUrtSao1fQZaOlEX9qWKVU3FR7981F8Hfe1ZRt1B74ki0xmhuvMzY26AEZEGBz55FXm4Y1/3sDDex/GrcpbHUZM8XTrRmfJDTXzf+f+h53XduKjsx+16nvMIqbusOXiFlwovoIt8rP0xqpW0zSPFrmxmg7/LKyUVqKoSyBc64AMFyX+2feJhVvWNGqixvXi66hV1Oqv+eTmRguc8bMbublU9LZwNrEpMQXQjJqeDp7o5d3LbPXQ2hMHB9OrNbSqaK+dHb3weXP+hQvUxdlKqvkIBPSy5idbW0JrxRRAQ0w2b96sGe/K5XJ8+OGHCAgIAMdx4DgOkyZNwt9//63zjPf29sbDDz+sec8nsdCGt2itW7euXcegdzNWXYfKmjBXHSpLiikepZKWiKqpoTPDAwa0S+kbXUpL6YCdEOqcHBGB9IwMoy1Tv934DWtPrYWDyAF/zv8TzmLzWCUIoa5/ZWXUajVoUDvX79KDqW5+m2I3Yee1nYjyjsL26dsNW6f0ceMGFb22tnSKTmuCQqGg9alkMqBLF8vFUzV089t8ZTP2Je/DkwOexPNDnrdMoxpQXk67rk3mbqRS+hsFBwMchzRJGlafWA1XsSs+HP0hYmJiOoyYMoaY3Bh8dv4zfDzpY3RzNc1yYk4xBQCF1YX44sIXeGLAE+jh1p0O2EWi+gF8G6FW18dQmeP+NGvWLOzbtw+THpuEd//vXTy2+16kiSqxWjESa94/0/ovaEMIIbiUfwmD/Qc3Os+VSq1nW2kprVsll9OF0dFGXZjNiSkemVKGGkVNq11ROxKtElPa1NRQr5WKCvrexYXO/prLBNvOmENM6UOlUuHgwYPYsGGDwYzHrq6uWL58Od555x0AQHJycqNkFeXl5ZoxFi8B2qvW6t0ME1RGYo6TzRrEFI9MRifq5HI64d2njwWymJaUaNz+cmQyJCgUGNFMAgqe8rpyrDu9Dv19+2N+n/lmbZZSCVy+TL0RXFyoS4SlfipTxRRA3f1+uPoDxgSNQYhHCzNOyeVUWVZX00FH7946lpGKClo4OjiYTjC2Nw3FFG+RPpNzBkMDhtZnMbxbIQS4eJGepO7uVNWKRFATNfJL85F4KbFDiimJhN4WAgL0l6sxJoOrIcwtpoyispJeQ8YW2DKS7GyaHMbPj+YpaS0zZszAb7/9htWrVyOjXwZ2XtsJn2rgstdb8F/xfuu/wALwRX2FQmDEiDvaSaGgA3d3d6N+E2PFVEclJYV2SbduumFNxmA2McVDCA3OzcysV8LDh1tgtpcilVLvXU/PlsWXmUNMyWQyzJ07F6NGjcLTTz8N1yYakJmZiU2bNmHTpk2N1jk6OqJaj6uNp6cnJBIJ/vjjD0ydSmulMUHVepidr52wJjEFUOsLX2e3srLJsIy2w8sLiIrCrfx83Lp8GaO8veFuIDV6Q9zs3PDRxI8wr/c8szfLxoYKTJFI1wuuvWmNmAIAoUCIJwY80XIxBdBp7/796cBDpaLiqqhIs9rVlT7rrElMAcCobqOsUkwRQuOuzeYiyXHUD0UopObUy5eBmhrUVNd0WDEF0PFUfj4VVTzac37WIqaMSj6gVFLfYT5e1IwFlPkxmjl2eTPvJiQSCQAguSYZO6/thJBw2PML4N81svVf0EZcu30NamI4Hor3uuQ4LUOUSEQT8GiLqbIyallsMLdsjJjKqcjBtdvXWnsoFqO4mL5aOq1udjEF0B8qIIC6aHp708AlC9bnKCmhSarulDQ1CnNZpjIyMnDw4EG89tprcHNz07j3zZs3D5cuXdLZtnv37vj000818VMyrTi0Aj3xnMnJyZrrnRdTDPPABFU7YG1iisfJiRoeBgywnGU9vbISSQB69+kD17q6ZgOXimvqswHxN5m2wN6euvtFRVnGOmWqmCKE4FzuOeNippqDV5ZdutQHlmmlQNQeX6hUJsd6t4iGYkrsIMaP136EXNV+xXtNITmZjqmbqL3ccry9gQED8H3pvziU+w+qT5/ChcOHO6yYAuozad153qO0thTz98/H8czjJp/T5hZTUoUUD+99GJtiN0GqaCKrn40NDTrkODpqvXTJbJmA+HFaawV67u1czN07F9lKOmrcl7QPALD+qjfGZqPNXRdNJas8C08fehqP/PqIwSQQ/LiyyUyIKhV1A0xPp0kr7sSMGmuZ2nJxC5787Ulsu7ytFUdjGaRSap0SCFr2/G8TMaWNWExFb2Bg/bLycnoTlbfffZ6/Bxmb3c+cbn6RkZGora3F1q1bER5en2xr9+7dGDJkiGbsExoaii1btqBWa0b8scceAwAsW7ZMb4KmqDs++snJ5os/Z1CsY2R/F2OtYorH3V33Ztoeg2IePgHF4Pvug/PQoVS9NOF3kFycjOm7p+Oz8581OTNpLrStU4S0X5KK1limjmUcw4t/vYhlfy8zTx8JBEBkZH32v9zcRtPiNTXUQJKW1vqvawp9lqkdV3bg09hP8cKfL5hHRLYRfPmP3FzzjgmylaX4UnIEa3J2Yt+VvxBaV4cIe3t0PClF4QcvlZX0NPv+6vdILU3Fjis7QNDy37ct3Pz+zfwX+VX5+Dfz3+bjErt2pTNWdnbUZHL5sl5rSEvhLS6tsVBJJBLsObEHnB0HNUfvFSqVCveG3ovXkt3oRlZawTunIgdOtk4IdAk0mASCF1RNehkIhdQSIhRSP+ZLl6DOzcWlixebFVNqooaz2BkioQhjg8fq3caaqaykf52cjJ80bHMxpQ9CaKKX27dp0oqCgjZPWqFS1YdzGSOo2iJmyt7eHosXL8bNmzc11qdLly5pMlgDdAz1/PPPw9HRUSOy9uzZA4AmY2vIH3/8AYC6/BkqAswwHesa3d9lWLuYakhFBRAbSz0g2ppG2fy6dKkfdQJ06qzBTfNMzhnIVXLcqrwFrh2HjGo1Nc5cvlx/k20rWuvmVyOvga3QFr269DJfgVGOozmaQ0Opn2iDh4VMRkVVfn7blRMx5ObX07MnvBy8MKfXHKu2yHh707kClYq6kZiLAJcAPNX3aYQ79cfIfvehW7du4MrKrCZLVkuxs6OxU4TQGeLnBj+Hpwc+jf8b8X8tPp/bKmZqathUfHbfZ1g5aqVx7qUuLsDgwfQkIIRaQxITW2Veaq3Ln0Qiwblz5zBzyEwcfuwwnGzprJrYRoxvHvwGHN/XVnoejQkag0PzDuGNUW8Y3IZ3+Wu2VpefH3Uzc3ODWqHAzT/+AJeQgBEDBzYZMyXgBHhz9Jv4a8FfCPUINeEoLIt2/gdjsIiYAujzJyKCKj+lkloUr1ypL6DVBpSX0+e+nV3z6dLNKaZOnTqlEUYvvvgi0tPTddYPGjQIP/30k0ZgVVRUYOPGjY1+j99++03v/vnMfmltPfvZSbHuEX4HpqOJKYAOiOVy6prUhveq5lOj19XRjBmpqToP9GcGPYMNUzZg5eiV7Tp45r9KrabjoLbKiNxaMQUAD0U+hD2z9uCJ/k+Yv4Fdu+pmCigrA5RKeHjUx1LdvGn+/mkqZmps8Fjsf2Q/JvawXE05Y+FreOXnm6+PpDVSdCvrhtWjVyPk3nvBRUXVF2nuoPAzwmVlgL3IHs8OfhZ9fPq0aB9tmYCC4ziMCByB6MBo4z9kY0N/l/Bw+tvoBPa0HP6jpmgyXkzxRXsr6iqQUpYCAAjMCwRXxdWfP+3pstBCnGyd0MWxi8H1Rrn88djZQd2nD67W1qJOLkf/oCDYXr1q1IXqZudmXIOtDN5CZUzCBYuJKR5nZ+qDHxKiY01EZmabnKPGuvuZ2zLlphVDvnnzZoSGhmoEVnR0NH755RcotWZRXFxcsGzZMuTm5uL8+fOa5dOmTWu073Xr1gGgcVNuRsaqM1pGx33qWjEdUUwBNFuUqyudBEpIaBt3ZaPqTFVV0adhfn4jUTUmaEy7P8D4CTJnZ2o4S0gwa3w5APOIKZ5A10CIhCIztk4P5eW0I65cAeRydO9O+0epbFRarFUYElPa7n0OIgertk7xuLvTsjhqdcsCnfWhUClQWVnZuM6Uj49ucFtWVv3ooIPg7g5UK8tNbnZbianimuLWx+r5+9OBoXZqPpWqxReMqYKKF1Pewd6w96b9svzocqh7qyG0FSLtahr69u2L/fyMWmpqy76gjcksy0RKaYpR2xrl8ncHtVqNS3FxqHByQq/HH4fIy4uOpg18+HDqYZTWWmH1dyNRqeonTZuzUFlcTPFwHI2rGjKEpt4jhN5IS83/O/D3Hk9Pw9u0hZtf3759QQiBWq3G0aNHMWXKFM262NhYzJkzByKRCBzHwcbGBitWrEDenbjmYcOGAQCy9TxcVCqVphaVIesV7yrIMJ2OMdLvQHRUMQXQScnevWlChro6Ol42Z+FWY4v2wtubKhgAVTlp2PznatTK29BkZgR85nBbW/ogSkoyn2horZgqryvH68deR15lXvMbmwuhkM66V1cDly+Dk9YiKqp+8rC1ggEwLKaKaoqw6OAiXMq/1MwerA++QGRhYaOayS3i/53+f5j30zzYeNkYTkBRWkoFVUKCmbNhtC22jrVYmzwPX6T8H4oqW+Z/3FZiihCCVcdXYdbeWa3P6uboSLPN8fAuTFrZuZpDJAJ8fenLWLQtUwcLD2Lm3pnYfmU7/kr7CwgD/jj5BwYNGgSJRIKHs7PxNIDqt97Sye5paTbGbsT8ffOx9/reZrd1c6MD4uZctholoHB1pRlOtWNMFAoawwOaEGP1idWYvns6JNKONVnBI5dTDzo7u6YteFYjprSxs6PJknr3pheAdqiAGR7KCgUd93AcPYf00VZ1pngaFuzVFx+sUqnw8ccfIzAwUHP/Hzp0KLrpSb07c+ZMAMB7772nKeLdkNdee82MR9A56Tij/Q5ARxZTPCIRDZMRiaihyFzCwWgxxePjA0RG4v/l7cGOm3uw4tdnLe7PLxbX1+uSSGgoRGsxh2Xqs/Of4Xjmcbz575vtl5jB2ZkG2/PqOz4e9opK8AmJsrNbF2/WlJvf13FfI6k4CZsvbLbqRBT6cHWlgzw+HMAU8kry8PPln5GtyIajj6Phc8bdncaH8HE7SUnmnSFpI+IL46AWl6JKnAI3fcWoDNCWbn7FtcXIqcxBSW1Jk25mLaaujgrfigpaV0w7X3wTiER0vB9qZOiOtpgKDApEmbQMCpUCN4pvQE3UGBE4AvcOvxcxMTF44403wHEctgEYUFKCi488YvF7L0Ctss62zrAR2CC6a/PulkFBjWqSN8JgNr+GLpmpqdT0npgImbQKvbv0xvCuwztsEV8+i+0do4ZerFJMaePlpSt6lUp6DeXnt+p8FYlozechQ/R75ba1mDKEWq1GTEwMFi5caHCbCxcuwM/PD++9954mZXpFRQUOHToEAHj77bcNfraS9wFlmAwr7GskzRU9uxvElDaVlXTSVK2mbsvaGUxbSovFlBYXrv2F90+9j/eDn0TfsFE0BsHC7l3FxTTOTCCgN11Tx23mcvPLr8rHB6c+wPNDnkevLr1Ma4ypyOXUAlJVRTskKgrJxV6QyeizzpQ6Xk2JKQCollfjiwtfYGbkTIR5hpnpQNoPvmalKVRVVeHs2bOw87ZDtjAbi/ovav5Dt27RFIyE1NdKsGSBNSNIl6SjUlaJAX4DjNq+PYr2ShVSJBQlYGjAUDPvWErFLp9SPSCA3nTN9AxpGDMF0P5Kk6Rh1i/U4vbV1K+wZPASzWdOnDiBx+bNQ25hIWwArJ09G2/sbd4q1B6U1JbAy8Gr1ftpUdHenBwar0MIYGMDEhoKqaeLwQyDHR2rF1P64KtdA9SPMTzc7PVgLCWmDKFWq3HkyBFs2bIFv//+e5PbHjhwADNmzDC4nh9/sMK+psMElZE0JajuNjHFU1JCvT0iIkx/trdGTPEoC27BJiWN+m4MGGAVqXzz8ug929T7jjljpiyOSkUHhHd82dX9BoBzczVJ9zYnpjozvJgyqc5UeTn9jeRyOgUbFUUtWFYO/3Rq6lDbQ0y1OWo1HQzyrpmOjvQ3asJCp1LRl0hkuH/0iSme3Ipc9NzcEyqiQuHyQrjb654PZWVleG7sWOxJSMDKXr2wLjGxVYfYnqjV9GXoUdEiMcVTUwPcuFEvfL286KDdmM9aGSqV4ZwoHVJMAfRmcesWvY54n72uXYHgYKMTwBBi+FqylJiaNWsW9u3bh3vuuQfPP/88pk2bBpHIcIy0XC7H/v37sWXLFpw6dUqzvLmhPhNUrefuGPlbkLtVTAH0edGawramiilCCGq0YqZs/AJohqx+/axCTAH0Pm1pMWU1QdF8gJmfH+DjA4Gbi85DydjkJs2JqdyKjhMHZAwqFZ1UNSajZlVVFY6ePAqxt9i0or1ubtTHh8+sYu6sKmYgXZKuc07fvAmcPVufjUwfbS2mKmWViMuPM+s+9SIQUKtU3771gZqJiU26Lp0/D8TEGD5/GoopQggOpx7WJNYIdA3E7ddu468FfzUSUwDg7u6OXZcv48Drr2NNXDv0gQEIIdiXtA+VMuNdkiQS4MwZ6mXREJPEFIBcpQSHHPOgDAqko+6SEupi1sFcpaRS2jfx8Y3XdVgxBdQLqKFD60sU5ObS2lVGutKWldFrKiNDd7klLVO8697x48cxa9Ys2NraajL/Pfzww/jnn390xJKtrS3mzp2LkydPapYldqDJkI7M3TP6twB3s5hqCCF04sfYuJjWWKYO3jiI2b/MxoVbF+oXennpzgSWl1uFXz9A8zJcu2bcGNVcYupMzhk8uOtB/HjtR5M+b3Y4jmYui4jQTPGplWqk3lTjwoXmsw83J6au3b6Gh/c+jPdOvtcuRZ3bg9RUek1lZTW9HW+ZOiM/g7euvIWDNw6a9oViMbXw9uqlG8htBajUKrx1/C08tOchzXWvUtFrylBdvPawTH17+Vss+WMJPo391Oz71ouHB61Z5eHRrHtzU5n+9Fmm/s38F+/89w4eO/CY5hpyFjvjnu73GPwOzsYGMz76CLZG5R5vG2LzYrH+zHrM/mW20VkW+ftNw4l8U8UUAGyL34b3Tq3F+pyf6eQEXxG3uawXVkZlJX10Nnx8dmgxpY1YTO9xffpQ12aZzGhBJZHQCUCFon6Zpd38zp49C0IIsrOzsWrVKnho5XLfv38/Jk2aBIFAoBFZTz31FOLi4nDkyBEAgLOzM3r1audQgE7K3asA2pjOJKYAaknPzqbhMrW1TW/bGjGlJmrsS96Hopoiw+lxCwvp1OONGxYXVYRQTyqJpPkEHuZ08zueeRxylRxFNdaTgQtA/QCQEHA3klEdnwqlTNVkKnVj3PziC+KhJmoQQsxXsNjC8HGJxcVUlOuDF1NdA7tCYaeAXCVHN9fGWZyMRiDQFVMyGZ0NaE3KQTNQVlcGW6EtbAQ2iPCigeb8rUOfoGpPNz8BJ8CwgCai982NrS21VGnfO4uLG81mGRJUhtz8OHDwcvDCPd3v6VDXkK3QFiEeIZgSMsW4QsrQnzK9NWIKAMI9w+Fh74GHox6mYmrQoMZeE4bUvxXBG9S0PSzuGjGljacnDXIODqbWXx6FwuDDqGH9KUuLKW26deuGtWvXorS0VJP5LzExEUuXLtXJ3Pfdd99h8ODBuPfeewEAWc3N2DHMBouhMhLtGCo7O7tOJaYA+tC+epXejO3sgIED9buOmyNmSqqQ4sCNA5jbe67+B39xcb166dIFiIy0aKKK6mrqPqFSUY8DfZm3zB0zRQjBsYxjGBM0BnY2VphgoLYWiItDnZTgYpY3VMEh6BFhi4YZXVsSM3W18CqC3YLhamdEJcoOQlISjVP09qaTqto0jJkCgMSixBYXuW2SxEQ6e2tjQ6+jpgqvtDFqokZuRS6C3IIAUCtDbCy9tEeNqhcQ7R0zVVBVAD9nvzb9jiaRSmkRU7Wapq4LCgI4DvHxVGNpGxybipkCgDolNd1Y5T2jCdREDblKbnS7r1+nj4nQUHpPbq2Y4pGr5IZFXUEB9VO18tiquDgaBhYVRR+fd6WYaoqEBHpzCQ/XqWqsfb8ZORIALCumtMcIoaGheO655/D444/rWKgaQghBbGwsRowYAQBwcHBAjTE+5WAxVObAokrg1KlTePDBB+Hv7w+O43Dw4EGd9YQQrFmzBv7+/rC3t8e4ceNw/fp1nW1kMhlefPFFeHl5wdHREdOmTdMUOuMpKyvDo48+CldXV7i6uuLRRx9FeXm5SW3ubJYpHqGQWtCbqlFlDjEFAPYie8zvM9/wLCo/+uQ4OhpNSmqTaunG4uRUn701L48+V7VpiwQUHMdhcshk6x0Y3UkgYucsQphXGZCSgszrtTqWmJYmoOjn2++uElMAHRsDdPCn/dzTl4CC4zjziikACAujU9V8Ne/sbItZfQWcQCOmADpxY2dHm8MbZyyRgMKiYgqgA3M+JiQri1rn6+o0sa38ra85MQVQIWW194wmEHCCFrVb20JlLjEFoGkLmVJZH1t14YKmbpU1oVLVW8NdXTuhmJLJ6KxwTQ2dBb1xQ+Pfx1unqJawvGVqw4YNmv/T0tKwfPlyeHp6ap4FgwcPxrZt21Cr5TLEt5enwsgYjbIOYFntCFhUDdTU1KBfv37YvHmz3vUff/wxNmzYgM2bN+PixYvw9fXFpEmTUMVn2QGwbNkyHDhwALt378aZM2dQXV2NBx54ACqt0f78+fNx5coV/P333/j7779x5coVPProoya1+fz5851OTPGIRFRU8TWqtN24WiumUkpT8F/mf8Z/wMuLJkIQCOrzmFtQVHl71xdtTUmhIV6AecVUraIWP137CUq19SUU0IuTEzBwIHwDRfB2rAVJTUPSuQqo1caJKUIIfrj6Q4uC0Tsajo711gW+GLK2mPIJ8sFPCT9BoVIY3klrEItpEVN/f/o+M5NardopaUVGWQYOJB8wGBfHF9akIZPtI6bURI3v4r+znqKtQiGdsYmMpJbEigrg0iUIy2kCD5WqaTH1d9rfuFxw2RItbxWxebE4ln7MpJjJ+hiq1oupX67/gutF15vfMDCQxr85O9PrJzmZTlK0oGBzW1NVRZ/ZYjFQXt7JxBRAD3zo0Pr7XWEhze6Sn48yCR3MuLhYXkwBwCuvvKJx7auqqsLXX3+NAQPqy0jExcXh6aefhqOjo0ZkTZgwAV260Dp5q1atMrrt6eYoqsmwHpc/juN08uQTQuDv749ly5ZhxYoVAKg1ysfHBx999BGWLFmCiooKeHt7Y+fOnXjkkUcAAPn5+QgMDMThw4cxZcoUJCcnIyoqCrGxsRrlHhsbi+joaNy4cQM9e/Y0qn28y9+///6LcePGdToxpU1FBXX/U6tpyRSBoHViSq6S49EDjyJdko7XR76OOb3mGP9hiYQOANVq6vjcp49F3f94Fy6RCIiMlCIuznyWqQ/PfIhfk37F+ODx+GTyJ2ZqcTugVEJx5TouxqogVwoQMNQfSvfSZi1Th24ewnsn34O/sz/2P7IfNgLryPBobqqrqUcXAERGViE+vt4y9f6p9/Hbzd8wscdEfDjxw7ZtSGEhnQ1Qq6kpum9f04usGQEhBEsPL8X5W+exsO9CLBu+TG+TbtwAnJ0JhML2sUwdTj2Md/57B96O3jg09xBEQsMpitudujp6k6msRHK2A25zvvAc4IycnBi9YqqirgLTd09HtbwaX0790vw1tNoINVFj7q9zkVGWgeXRyzGvzzzjP6sGTp2ilik7uzjI5TUmi6n8qnw8tOchqNQq7Jm1ByEeIc1/iBBatyo7uz53e8+eVpEIJieHZrDjuBJUVcV2LjHVkMpKer+rrgYhwNkMP8j8u0ElvAJHR7XFxFRSUhKqqqowdOjQJscMJSUl2LFjB7Zs2YKMhmkJ0XyadG12796NefPoNcZc/kzHakcomZmZKCwsxOTJkzXLxGIxxo4di5iYGCxZsgRxcXFQKBQ62/j7+6N3796IiYnBlClTcO7cObi6uuqYQYcPHw5XV1fExMQYFFQymQwyrZkl3nQaHBzMzKMAfHyAlBQOt28XIz//KgYPHgy1Wo3S0pan8laoFBjoNhDFpcUY6Dqw5fsICACXlATi7Fxvt7cQXl50EKhUAidPxsDT0xVdu3aFxAztCrEPgaPaEVP8p5jUzxYlyB9dSjORclWKPIkMkoI8DB48GHK53OCxuBJX+Iv8cW/AvagoMzK9ZAfFxgYgRIX//juL4GA/eHt7QyKRIMwhDM7EGfcG3Nv2v7lIBHTrBu7mTUCpBKmqaj4DTStQEzX6uvZFUl4SJvhO0Ht8KhUdn+bl5YCQbAwZMgS1tbU6bi7mxlnljBCHEIwMGInKciu0jnbrBuTkQK0ugtDeBhcvxmHAgGC4uro26sOKugqM6jIKKaUp6C7u3mHuG3KVHMM9h6O8vBzRntEtardSST2Ob97Mgrv7bQwaNEjHq6UlVNZUYpzPOEikErgRN+Pb4eREkyGkpYErLQWprjZboebWIJMBKpUUqakXMWpUGOzt7TvMOdEmBAcD+flQZebCXliI5JxSdO1WidDQAUa7y5mbhhn5hEIhnnrqKSxatEhnvMpxHJ544gk88cQTmmV+fn5QKBTYuHFji37Xa9euaf63EhtLx4RYCQDIgQMHNO/Pnj1LAJBbt27pbPfMM8+QyZMnE0II+emnn4itrW2jfU2aNIksXryYEELIBx98QMLCwhptExYWRtatW2ewPatXryYA2Iu92Iu92Iu92Iu92Iu97vpXbm6uKUN4BiHEai1UPA1NnoSQZl2nGm6jb/vm9rNy5Uq8+uqrmvfl5eUICgpCTk4OXLUyw3R2KisrERgYiNzcXGYmbgDrG/2wfjEM6xv9sH4xDOsb/bB+0Q/rF8NYY9+o1WqcOHEC27Ztwx9//NHs9jExMS2uO3Xvvffi3LlzSEtLgz8fX8ZoMVYrqHx9fQEAhYWF8POrz7JUVFQEHx8fzTZyuRxlZWU6sTtFRUWatJG+vr64rSfbTnFxsWY/+hCLxRDrKWbo6upqNReaNeHi4sL6xQCsb/TD+sUwrG/0w/rFMKxv9MP6RT+sXwxjbX0zY8YMTX4BAFAoFDh48CC2bNmC//77T7O8S5cuiI6ObvH++VpVISFGxAgyDGJ5p14DdO/eHb6+vjh27JhmmVwux8mTJzViadCgQRCJRDrbFBQUIDExUbNNdHQ0KioqcOHCBc0258+fR0VFhWYbBoPBYDAYDAbD2hGJRJg9ezaOHz+uyQSoUqn0Gg+MoaBhrReGSVjUQlVdXY20tDTN+8zMTFy5cgUeHh7o1q0bli1bhnXr1iEsLAxhYWFYt24dHBwcMH/+fADUWvTUU09p8vN7eHjgtddeQ58+fTBx4kQAQGRkJO69914888wz2Lp1KwBg8eLFeOCBB4zO8MdgMBgMBoPBYFgjnTnztLVgUUF16dIljB8/XvOej1latGgRduzYgddffx1SqRTPP/88ysrKMGzYMBw9elQnzfLGjRthY2ODOXPmQCqVYsKECdixYweEQqFmm59++gkvvfSSJhvgtGnTDNa+MoRYLMbq1av1ugF2Zli/GIb1jX5YvxiG9Y1+WL8YhvWNfli/6If1i2FY3zBag9XUoWIwGAwGg8FgMBjtB5+gjcmB1sFshAwGg8FgMBgMBoNhIkxQMRgMBoPBYDAYDIaJMEHFYDAYDAaDwWB0UpoqI8QwDiaoGAwGg8FgMBiMToZUKgUAhIaGWrglHR8mqFqATCZD//79wXEcrly5orMuJycHDz74IBwdHeHl5YWXXnoJcrncMg1tJ6ZNm4Zu3brBzs4Ofn5+ePTRR5Gfn6+zTWfrl6ysLDz11FPo3r077O3tERISgtWrVzc65s7WLzwffPABRowYAQcHB7i5uendprP2zZdffonu3bvDzs4OgwYNwunTpy3dpHbn1KlTePDBB+Hv7w+O43Dw4EGd9YQQrFmzBv7+/rC3t8e4ceNw/fp1yzS2HVm/fj2GDBkCZ2dndOnSBTNmzMDNmzd1tumMfbNlyxb07dtXU4g1Ojoaf/31l2Z9Z+wTfaxfvx4cx2HZsmWaZZ21b9asWQOO43Revr6+mvWdrV/4or5MULUeJqhawOuvvw5/f/9Gy1UqFaZOnYqamhqcOXMGu3fvxr59+7B8+XILtLL9GD9+PPbu3YubN29i3759SE9Px6xZszTrO2O/3LhxA2q1Glu3bsX169exceNGfPXVV3jzzTc123TGfuGRy+WYPXs2nnvuOb3rO2vf7NmzB8uWLcNbb72F+Ph4jB49Gvfddx9ycnIs3bR2paamBv369TNY1uLjjz/Ghg0bsHnzZly8eBG+vr6YNGkSqqqq2rml7cvJkyfxwgsvIDY2FseOHYNSqcTkyZNRU1Oj2aYz9k3Xrl3x4Ycf4tKlS7h06RLuueceTJ8+XTMA7ox90pCLFy/i66+/Rt++fXWWd+a+6dWrFwoKCjSvhIQEzbrO1i/p6ekAmKAyC4RhFIcPHyYRERHk+vXrBACJj4/XWScQCMitW7c0y3bt2kXEYjGpqKiwQGstw2+//UY4jiNyuZwQwvqF5+OPPybdu3fXvGf9Qsj27duJq6tro+WdtW+GDh1Knn32WZ1lERER5I033rBQiywPAHLgwAHNe7VaTXx9fcmHH36oWVZXV0dcXV3JV199ZYEWWo6ioiICgJw8eZIQwvpGG3d3d/Ltt9+yPiGEVFVVkbCwMHLs2DEyduxY8vLLLxNCOvf5snr1atKvXz+96zpjv2zcuJEAILt27bJ0Uzo8zEJlBLdv38YzzzyDnTt3wsHBodH6c+fOoXfv3jrWqylTpkAmkyEuLq49m2oxJBIJfvrpJ4wYMQIikQgA6xeeiooKeHh4aN6zfjFMZ+wbuVyOuLg4TeFxnsmTJyMmJsZCrbI+MjMzUVhYqNNPYrEYY8eO7XT9VFFRAQCa+wrrG2rd3r17N2pqahAdHc36BMALL7yAqVOnYuLEiTrLO3vfpKamwt/fH927d8fcuXORkZEBoHP2S1paGgAgJCTEwi3p+DBB1QyEEDz++ON49tlnMXjwYL3bFBYWNsqQ4u7uDltbWxQWFrZHMy3GihUr4OjoCE9PT+Tk5OC3337TrOvM/cKTnp6Ozz//HM8++6xmGesXw3TGvikpKYFKpWp03D4+PnftMZsC3xedvZ8IIXj11VcxatQo9O7dG0Dn7puEhAQ4OTlBLBbj2WefxYEDBxAVFdWp+wQAdu/ejcuXL2P9+vWN1nXmvhk2bBh++OEHHDlyBN988w0KCwsxYsQIlJaWdsp+YYLKfHRaQaUvMLHh69KlS/j8889RWVmJlStXNrk/vtK0NoQQvcutGWP7hef//u//EB8fj6NHj0IoFOKxxx7TqbbdWfsFAPLz83Hvvfdi9uzZePrpp3XW3S39ApjWN01xN/VNS2h4fJ3hmE2hs/fT0qVLce3aNezatavRus7YNz179sSVK1cQGxuL5557DosWLUJSUpJmfWfsk9zcXLz88sv48ccfYWdnZ3C7ztg39913Hx5++GH06dMHEydOxJ9//gkA+P777zXbdKZ+4ZMfaXvRMEzDxtINsBRLly7F3Llzm9wmODgY77//PmJjYyEWi3XWDR48GAsWLMD3338PX19fnD9/Xmd9WVkZFApFh8vtb2y/8Hh5ecHLywvh4eGIjIxEYGAgYmNjER0d3an7JT8/H+PHj0d0dDS+/vprne3upn4BWt43TXG39Y0xeHl5QSgUNpoBLSoqumuP2RT4TFyFhYXw8/PTLO9M/fTiiy/i0KFDOHXqFLp27apZ3pn7xtbWVhNQP3jwYFy8eBGbNm3CihUrAHTOPomLi0NRUREGDRqkWaZSqXDq1Cls3rxZkyGyM/ZNQxwdHdGnTx+kpqZixowZAO7efiGE4Pjx41i/fj3+/fdfSzfn7sMSgVsdiezsbJKQkKB5HTlyhAAgv/76K8nNzSWE1AfS5+fnaz63e/fuuz6QviE5OTkEAPnvv/8IIZ23X/Ly8khYWBiZO3cuUSqVjdZ31n7RprmkFJ2tb4YOHUqee+45nWWRkZEsKYWepBQfffSRZplMJrurA8Z51Go1eeGFF4i/vz9JSUnRu76z9k1D7rnnHrJo0aJO3SeVlZU645aEhAQyePBgsnDhQpKQkNCp+6YhdXV1JCAggLz77rt3bb+cO3eOAGjyxWg9rBdbSGZmZqMsf0qlkvTu3ZtMmDCBXL58mfzzzz+ka9euZOnSpZZraBtz/vx58vnnn5P4+HiSlZVFjh8/TkaNGkVCQkJIXV0dIaRz9sutW7dIaGgoueeee0heXh4pKCjQvHg6Y7/wZGdnk/j4ePLuu+8SJycnEh8fT+Lj40lVVRUhpPP2ze7du4lIJCLbtm0jSUlJZNmyZcTR0ZFkZWVZumntSlVVleacAEA2bNhA4uPjSXZ2NiGEkA8//JC4urqS/fv3k4SEBDJv3jzi5+dHKisrLdzytuW5554jrq6u5MSJEzr3lNraWs02nbFvVq5cSU6dOkUyMzPJtWvXyJtvvkkEAgE5evQoIaRz9okhtLP8EdJ5+2b58uXkxIkTJCMjg8TGxpIHHniAODs7a+61d2O/fPPNNxrhtHjxYpKamqpZxwSV+WC92EL0CSpC6EBx6tSpxN7ennh4eJClS5dqhMXdyLVr18j48eOJh4cHEYvFJDg4mDz77LMkLy9PZ7vO1i/bt283agaos/ULz6JFi/T2DW/VJKTz9s0XX3xBgoKCiK2tLRk4cKAmJXZn4r///tN7fixatIgQQi0xq1evJr6+vkQsFpMxY8aQhIQEyza6HTB0T9m+fbtmm87YN08++aTmmvH29iYTJkzQiClCOmefGKKhoOqsffPII48QPz8/IhKJiL+/P5k5cya5fv26Zn1n6xcmqMwHR4hWBgEGg8FgMBgMBoNx1yGXy7Fz506sW7dOky4eAJgUaD2dNikFg8FgMBgMBoNxt1JWVobNmzdj3bp1qKur07vN0aNH27lVdydMUDEYDAaDwWAwGB2cjIwMfPLJJ/jqq6/0rndxccHKlSvx3HPPwdXVtZ1bd3fDBBWDwWAwGAwGg9FBefrpp7Ft27ZGy3v27ImVK1di/vz5EIlEFmhZ54EJKgaDwWAwGAwGo4Nia2sLABg/fjxWrlyJiRMn3rXFiK0VlpSCwWAwGAwGg8FgMExEYOkGMBgMBoPBYDAYDEZHhQkqBoPBYDAYDAaDwTARJqgYDAaDwWAwGAwGw0SYoGIwGAwGoxm2bduGyZMnN1peWlpqsChmXV0dCgsLoVKpdJbLZDJ069YNcXFxbdJWBoPBYLQvTFAxGAxGB4HjuCZfjz/+uKWbaHbGjRuHZcuWWbQNMpkM77zzDt5++23NspqaGowdOxaTJ09GVFQUrl+/rll37tw5jB8/Hk5OTvDz84ODgwPGjh2rKaApFovx2muvYcWKFe1+LAwGg8EwP0xQMRgMRgehoKBA8/r000/h4uKis2zTpk2WbqLRKBSKDvN9+/btg5OTE0aPHq1ZtnPnTowZMwZxcXH47LPP8P777wMA/v33X4wdOxaBgYFISEiAXC5HdnY2FixYgC+++ELz+QULFuD06dNITk42/aAYDAaDYRUwQcVgMBgdBF9fX83L1dUVHMfpLDt16hQGDRoEOzs79OjRA++++y6USqXm8xzHYevWrXjggQfg4OCAyMhInDt3DmlpaRg3bhwcHR0RHR2N9PR0zWfWrFmD/v37Y+vWrQgMDISDgwNmz56N8vJynbZt374dkZGRsLOzQ0REBL788kvNuqysLHAch71792LcuHGws7PDjz/+iNLSUsybNw9du3aFg4MD+vTpg127dmk+9/jjj+PkyZPYtGmTxgqXlZWFHTt2wM3NTef7Dx48qFN3hW/3d999hx49ekAsFoMQgoqKCixevBhdunSBi4sL7rnnHly9erXJft+9ezemTZums8zW1hYVFRUAgIqKCtjZ2UGtVmPJkiUYPXo0fvjhB0RGRkIkEsHX1xeLFy/GL7/8ovm8p6cnRowYoXO8DAaDweiYMEHFYDAYdwFHjhzBwoUL8dJLLyEpKQlbt27Fjh078MEHH+hst3btWjz22GO4cuUKIiIiMH/+fCxZsgQrV67EpUuXAABLly7V+UxaWhr27t2L33//HX///TeuXLmCF154QbP+m2++wVtvvYUPPvgAycnJWLduHd5++218//33OvtZsWIFXnrpJSQnJ2PKlCmoq6vDoEGD8McffyAxMRGLFy/Go48+ivPnzwMANm3ahOjoaDzzzDMaK1xgYKDRfcK3e9++fbhy5QoAYOrUqSgsLMThw4cRFxeHgQMHYsKECZBIJAb3c/r0aQwePFhn2cKFC1FYWIiQkBB89tlnWLt2LeLj45Geno5XX31V73744ps8Q4cOxenTp40+HgaDwWBYKYTBYDAYHY7t27cTV1dXzfvRo0eTdevW6Wyzc+dO4ufnp3kPgKxatUrz/ty5cwQA2bZtm2bZrl27iJ2dneb96tWriVAoJLm5uZplf/31FxEIBKSgoIAQQkhgYCD5+eefdb577dq1JDo6mhBCSGZmJgFAPv3002aP6/777yfLly/XvB87dix5+eWXmzx2Qgg5cOAA0X6krV69mohEIlJUVKRZ9u+//xIXFxdSV1en89mQkBCydetWve0pKysjAMipU6eabfvu3bsJAJKSktLstoQQsmnTJhIcHGzUtgwGg8GwXmwsKeYYDAaDYR7i4uJw8eJFHYuUSqVCXV0damtr4eDgAADo27evZr2Pjw8AoE+fPjrL6urqUFlZCRcXFwBAt27d0LVrV8020dHRUKvVuHnzJoRCIXJzc/HUU0/hmWee0WyjVCrh6uqq08aGVh6VSoUPP/wQe/bswa1btyCTySCTyeDo6Nja7gAABAUFwdvbW/M+Li4O1dXV8PT01NlOKpXquDk2XAcAdnZ2Rn+vtuthU9jb26O2ttbo/TIYDAbDOmGCisFgMO4C1Go13n33XcycObPROm0xIBKJNP/zA399y9RqtcHv4rfhOE6z3TfffINhw4bpbCcUCnXeNxRK//vf/7Bx40Z8+umn6NOnDxwdHbFs2TLI5XLDBwpAIBA0SlWuL+lEw+9Tq9Xw8/PDiRMnGm3bMCaLx9PTExzHoaysrMk2AUBoaCgA4ObNm5r/m0IikegIPgaDwWB0TJigYjAYjLuAgQMHGj2Qbyk5OTnIz8+Hv78/AJoWXCAQIDw8HD4+PggICEBGRgYWLFjQov2ePn0a06dPx8KFCwFQwZOamorIyEjNNra2to3qOHl7e6Oqqgo1NTUa0cTHSDXFwIEDUVhYCBsbGwQHBxvVRltbW0RFRSEpKUlvHSptBgwYgJCQEGzYsAFTp05ttF67vQCQmJiIAQMGGNUOBoPBYFgvLCkFg8Fg3AW88847+OGHH7BmzRpcv34dycnJ2LNnD1atWtXqfdvZ2WHRokW4evUqTp8+jZdeeglz5syBr68vAJpRb/369di0aRNSUlKQkJCA7du3Y8OGDU3uNzQ0FMeOHUNMTAySk5OxZMkSFBYW6mwTHByM8+fPIysrCyUlJVCr1Rg2bBgcHBzw5ptvIi0tDT///DN27NjR7HFMnDgR0dHRmDFjBo4cOYKsrCzExMRg1apVmoQc+pgyZQrOnDnT7P4FAgG2bt2K06dPY9GiRbhx4wYUCgUKCgrwxRdfYNasWTrbnz59ulmRxmAwGAzrhwkqBoPBuAuYMmUK/vjjDxw7dgxDhgzB8OHDsWHDBgQFBbV636GhoZg5cybuv/9+TJ48Gb1799ZJi/7000/j22+/xY4dO9CnTx+MHTsWO3bsQPfu3Zvc79tvv42BAwdiypQpGDduHHx9fTFjxgydbV577TUIhUJERUXB29sbOTk58PDwwI8//ojDhw9rUq2vWbOm2ePgOA6HDx/GmDFj8OSTTyI8PBxz585FVlaWJp5MH8888wwOHz6sSZPeFBMmTMDJkyeRk5OD3r17w9bWFt26dcOuXbvwyiuvaLY7d+4cKioqGoksBoPBYHQ8ONLQEZ3BYDAYjDusWbMGBw8eNMql7m5mzpw5GDBgAFauXGn0Z+rq6lBWVoYuXbo0iiebPXs2BgwYgDfffNPcTWUwGAxGO8MsVAwGg8FgNMMnn3wCJyenFn3Gzs4Ofn5+jcSUTCZDv379dCxWDAaDwei4MAsVg8FgMAzCLFQMBoPBYDQNE1QMBoPBYDAYDAaDYSLM5Y/BYDAYDAaDwWAwTIQJKgaDwWAwGAwGg8EwESaoGAwGg8FgMBgMBsNEmKBiMBgMBoPBYDAYDBNhgorBYDAYDAaDwWAwTIQJKgaDwWAwGAwGg8EwESaoGAwGg8FgMBgMBsNEmKBiMBgMBoPBYDAYDBNhgorBYDAYDAaDwWAwTIQJKgaDwWAwGAwGg8Ewkf8PgCHE/ylvcTMAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create figure and set size\n",
-    "fig = plt.figure(figsize=(9, 9))\n",
-    "skew = SkewT(fig, rotation=45)\n",
-    "\n",
-    "# Plot the data using the data from the xarray Dataset including the parcel temperature with\n",
-    "# the LCL level included\n",
-    "skew.plot(lcl_ds.isobaric, lcl_ds.ambient_temperature, 'red')\n",
-    "skew.plot(lcl_ds.isobaric, lcl_ds.ambient_dew_point, 'green')\n",
-    "skew.plot(lcl_ds.isobaric, lcl_ds.parcel_temperature.metpy.convert_units('degC'), 'black')\n",
-    "\n",
-    "# Plot wind barbs\n",
-    "my_interval = np.arange(100, 1000, 50) * units('hPa') #set spacing interval\n",
-    "ix = mpcalc.resample_nn_1d(pres, my_interval) #find nearest indices for chosen interval\n",
-    "skew.plot_barbs(pres[ix], u[ix], v[ix], xloc=1) #plot values closest to chosen interval\n",
-    "\n",
-    "# Improve labels and set axis limits\n",
-    "skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "skew.ax.set_ylim(1000, 100)\n",
-    "skew.ax.set_xlim(-40, 59)\n",
-    "\n",
-    "# Add the relevant special lines throughout the figure\n",
-    "skew.plot_dry_adiabats(t0=np.arange(233, 533, 15) * units.K, alpha=0.25, color='orangered')\n",
-    "skew.plot_moist_adiabats(t0=np.arange(233, 400, 10) * units.K, alpha=0.25, color='tab:green')\n",
-    "skew.plot_mixing_lines(pressure=np.arange(1000, 99, -25) * units.hPa, linestyle='dotted', color='tab:blue')\n",
-    "\n",
-    "# Add the MetPy logo!\n",
-    "fig = plt.gcf()\n",
-    "add_metpy_logo(fig, 115, 100, size='small');\n",
-    "\n",
-    "# Add titles\n",
-    "plt.title('KOUN', loc='left')\n",
-    "plt.title('2011-05-22 12 UTC', loc='right');"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/Sounding_Plotter.ipynb b/_preview/3/_sources/notebooks/Sounding_Plotter.ipynb
deleted file mode 100644
index 52b0680..0000000
--- a/_preview/3/_sources/notebooks/Sounding_Plotter.ipynb
+++ /dev/null
@@ -1,309 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Sounding Plotter\n",
-    "================\n",
-    "\n",
-    "This can be run as a script from the command line or as a notebook.\n",
-    "\n",
-    "Download and plot the most recent sounding data for a specified site.\n",
-    "\n",
-    "Provides a simple command line interface to specify a site. Using the current\n",
-    "UTC time, the script calculates what the most recent sounding should be and requests\n",
-    "it from the Wyoming archive using Siphon."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import posixpath\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.plots import add_metpy_logo, add_timestamp, SkewT\n",
-    "from metpy.units import units\n",
-    "\n",
-    "from siphon.simplewebservice.wyoming import WyomingUpperAir\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "This class encapsulates the code needed to upload an image to Google Drive"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "class DriveUploader:\n",
-    "    def __init__(self, credsfile='mycreds.txt'):\n",
-    "        from pydrive.drive import GoogleDrive\n",
-    "        self.gdrive = GoogleDrive(self._get_auth(credsfile))\n",
-    "\n",
-    "    def _get_auth(self, credsfile):\n",
-    "        from pydrive.auth import GoogleAuth\n",
-    "        gauth = GoogleAuth()\n",
-    "\n",
-    "        # Try to load saved client credentials\n",
-    "        gauth.LoadCredentialsFile(credsfile)\n",
-    "        if gauth.credentials is None:\n",
-    "            # Authenticate if they're not there\n",
-    "            gauth.LocalWebserverAuth()\n",
-    "        elif gauth.access_token_expired:\n",
-    "            # Refresh them if expired\n",
-    "            gauth.Refresh()\n",
-    "        else:\n",
-    "            # Initialize the saved creds\n",
-    "            gauth.Authorize()\n",
-    "        # Save the current credentials to a file\n",
-    "        gauth.SaveCredentialsFile(credsfile)\n",
-    "        return gauth\n",
-    "\n",
-    "    def _get_first_file_id(self, title, parent, **kwargs):\n",
-    "        query = \"title='{}' and '{}' in parents\".format(title, parent)\n",
-    "        for k, v in kwargs.items():\n",
-    "            query += \" and {}='{}'\".format(k, v)\n",
-    "        res = next(self.gdrive.ListFile({'q': query}))\n",
-    "        if res:\n",
-    "            return res[0]['id']\n",
-    "        return None\n",
-    "\n",
-    "    def get_folder(self, path):\n",
-    "        parent = 'root'\n",
-    "        for part in path.split('/'):\n",
-    "            if not part:\n",
-    "                continue\n",
-    "            parent = self._get_first_file_id(part, parent,\n",
-    "                                             mimeType='application/vnd.google-apps.folder')\n",
-    "        return parent\n",
-    "\n",
-    "    def create_or_get_file(self, path):\n",
-    "        pathname, filename = posixpath.split(path)\n",
-    "        folder = self.get_folder(pathname)\n",
-    "        create_file_args = {'parents': [{'kind': 'drive#fileLink', 'id': folder}]}\n",
-    "\n",
-    "        file_id = self._get_first_file_id(filename, folder)\n",
-    "        if file_id is not None:\n",
-    "            create_file_args['id'] = file_id\n",
-    "        return self.gdrive.CreateFile(create_file_args)\n",
-    "\n",
-    "    def upload_to(self, local_path, remote_path):\n",
-    "        f = self.create_or_get_file(remote_path)\n",
-    "        f.SetContentFile(local_path)\n",
-    "        f['title'] = posixpath.basename(remote_path)\n",
-    "        f.Upload()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "This function takes care of actually generating a skewT from the `DataFrame`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "def plot_skewt(df):\n",
-    "    # We will pull the data out of the example dataset into individual variables\n",
-    "    # and assign units.\n",
-    "    pres = df['pressure'].values * units.hPa\n",
-    "    temp = df['temperature'].values * units.degC\n",
-    "    dewpoint = df['dewpoint'].values * units.degC\n",
-    "    wind_speed = df['speed'].values * units.knots\n",
-    "    wind_dir = df['direction'].values * units.degrees\n",
-    "    u, v = mpcalc.wind_components(wind_speed, wind_dir)\n",
-    "\n",
-    "    # Create a new figure. The dimensions here give a good aspect ratio.\n",
-    "    fig = plt.figure(figsize=(9, 9))\n",
-    "    skew = SkewT(fig, rotation=45)\n",
-    "\n",
-    "    # Plot temperature, dewpoint and wind barbs\n",
-    "    skew.plot(pres, temp, 'red')\n",
-    "    skew.plot(pres, dewpoint, 'green')\n",
-    "    \n",
-    "    # Plot wind barbs\n",
-    "    my_interval = np.arange(100, 1000, 50) * units('hPa') #set spacing interval\n",
-    "    ix = mpcalc.resample_nn_1d(pres, my_interval) #find nearest indices for chosen interval\n",
-    "    skew.plot_barbs(pres[ix], u[ix], v[ix], xloc=1) #plot values closest to chosen interval\n",
-    "    \n",
-    "    # Improve labels and set axis limits\n",
-    "    skew.ax.set_xlabel('Temperature (\\N{DEGREE CELSIUS})')\n",
-    "    skew.ax.set_ylabel('Pressure (hPa)')\n",
-    "    skew.ax.set_ylim(1000, 100)\n",
-    "    skew.ax.set_xlim(-40, 59)\n",
-    "\n",
-    "    # Calculate LCL height and plot as black dot.\n",
-    "    lcl_pressure, lcl_temperature = mpcalc.lcl(pres[0], temp[0], dewpoint[0]) #index 0 is chosen to lift parcel from the surface\n",
-    "    skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')\n",
-    "\n",
-    "    # Calculate full parcel profile and add to plot as black line\n",
-    "    prof = mpcalc.parcel_profile(pres, temp[0], dewpoint[0]).to('degC')\n",
-    "    skew.plot(pres, prof, 'black', linewidth=2)\n",
-    "\n",
-    "    # Shade areas of CAPE and CIN\n",
-    "    skew.shade_cin(pres, temp, prof, dewpoint)\n",
-    "    skew.shade_cape(pres, temp, prof)\n",
-    "\n",
-    "    # Add emphasis to 0 degree isotherm with color change\n",
-    "    skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\n",
-    "\n",
-    "    # Add the relevant special lines throughout the figure\n",
-    "    skew.plot_dry_adiabats(t0=np.arange(233, 533, 15) * units.K, alpha=0.25, color='orangered')\n",
-    "    skew.plot_moist_adiabats(t0=np.arange(233, 400, 10) * units.K, alpha=0.25, color='tab:green')\n",
-    "    skew.plot_mixing_lines(pressure=np.arange(1000, 99, -25) * units.hPa, linestyle='dotted', color='tab:blue')\n",
-    "\n",
-    "    # Add the MetPy logo!\n",
-    "    fig = plt.gcf()\n",
-    "    add_metpy_logo(fig, 115, 100, size='small');\n",
-    "    \n",
-    "    return skew\n",
-    "\n",
-    "\n",
-    "def make_name(site, time):\n",
-    "    return '{site}_{dt:%Y%m%d_%H%M}.png'.format(site=site, dt=time)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "This is where the command line script will actually enter, and handles parsing\n",
-    "command line arguments and driving everything else."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "if __name__ == '__main__':\n",
-    "    import argparse\n",
-    "    from datetime import datetime, timedelta\n",
-    "    import tempfile\n",
-    "\n",
-    "    # Set up argument parsing for the script. Provides one argument for the site, and another\n",
-    "    # that controls whether the plot should be shown or saved as an image.\n",
-    "    parser = argparse.ArgumentParser(description='Download sounding data and plot.')\n",
-    "    parser.add_argument('-s', '--site', help='Site to obtain data for', type=str,\n",
-    "                        default='DDC')\n",
-    "    parser.add_argument('--show', help='Whether to show the plot rather than save to disk',\n",
-    "                        action='store_true')\n",
-    "    parser.add_argument('-d', '--date', help='Date and time to request data for in YYYYMMDDHH.'\n",
-    "                        ' Defaults to most recent 00/12 hour.', type=str)\n",
-    "    parser.add_argument('-g', '--gdrive', help='Google Drive upload path', type=str)\n",
-    "    parser.add_argument('-f', '--filename', help='Image filename', type=str)\n",
-    "    args = parser.parse_args()\n",
-    "\n",
-    "    if args.date:\n",
-    "        request_time = datetime.strptime(args.date, '%Y%m%d%H')\n",
-    "    else:\n",
-    "        # Figure out the most recent sounding, 00 or 12. Subtracting two hours\n",
-    "        # helps ensure that we choose a time with data available.\n",
-    "        now = datetime.utcnow() - timedelta(hours=2)\n",
-    "        request_time = now.replace(hour=(now.hour // 12) * 12, minute=0, second=0)\n",
-    "\n",
-    "    # Request the data and plot\n",
-    "    df = WyomingUpperAir.request_data(request_time, args.site)\n",
-    "    skewt = plot_skewt(df)\n",
-    "\n",
-    "    # Add the timestamp for the data to the plot\n",
-    "    add_timestamp(skewt.ax, request_time, y=1.02, x=0, ha='left', fontsize='large')\n",
-    "    skewt.ax.set_title(args.site)\n",
-    "\n",
-    "    if args.show:\n",
-    "        plt.show()\n",
-    "    else:\n",
-    "        fname = args.filename if args.filename else make_name(args.site, request_time)\n",
-    "        if args.gdrive:\n",
-    "            uploader = DriveUploader()\n",
-    "            with tempfile.NamedTemporaryFile(suffix='.png') as f:\n",
-    "                skewt.ax.figure.savefig(f.name)\n",
-    "                uploader.upload_to(f.name, posixpath.join(args.gdrive, fname))\n",
-    "        else:\n",
-    "            skewt.ax.figure.savefig(make_name(args.site, request_time))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/250hPa_Hemispheric_Plot.ipynb b/_preview/3/_sources/notebooks/gallery/250hPa_Hemispheric_Plot.ipynb
deleted file mode 100644
index 924fc69..0000000
--- a/_preview/3/_sources/notebooks/gallery/250hPa_Hemispheric_Plot.ipynb
+++ /dev/null
@@ -1,210 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\"",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "A 250-hPa Hemispheric Map using Python\n",
-    "======================================\n",
-    "\n",
-    "This example plots a hemispheric plot of GFS 250-hPa Geopotential Heights and\n",
-    "wind speed in knots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import cartopy.util as cutil\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "import numpy as np\n",
-    "from siphon.catalog import TDSCatalog\n",
-    "from siphon.ncss import NCSS\n",
-    "from xarray.backends import NetCDF4DataStore\n",
-    "import xarray as xr\n",
-    "\n",
-    "# Latest GFS Dataset\n",
-    "cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'\n",
-    "                 'NCEP/GFS/Global_0p5deg/latest.xml')\n",
-    "best_ds = list(cat.datasets.values())[0]\n",
-    "ncss = NCSS(best_ds.access_urls['NetcdfSubset'])\n",
-    "\n",
-    "# Set the time to current\n",
-    "now = datetime.utcnow()\n",
-    "\n",
-    "# Query for Latest GFS Run\n",
-    "gfsdata_hght = ncss.query().time(now).accept('netcdf4')\n",
-    "gfsdata_hght.variables('Geopotential_height_isobaric')\n",
-    "\n",
-    "# Set the lat/lon box for the data you want to pull in.\n",
-    "# lonlat_box(north_lat,south_lat,east_lon,west_lon)\n",
-    "gfsdata_hght.lonlat_box(0, 359.9, 0, 90)\n",
-    "\n",
-    "# Set desired level 50000 = 50000 Pa = 500 hPa\n",
-    "gfsdata_hght.vertical_level(25000)\n",
-    "\n",
-    "# Actually getting the data\n",
-    "data_hght = ncss.get_data(gfsdata_hght)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds_hght = xr.open_dataset(NetCDF4DataStore(data_hght))\n",
-    "\n",
-    "# Query for Latest GFS Run\n",
-    "gfsdata_wind = ncss.query().time(now).accept('netcdf4')\n",
-    "gfsdata_wind.variables('u-component_of_wind_isobaric',\n",
-    "                       'v-component_of_wind_isobaric')\n",
-    "\n",
-    "# Set the lat/lon box for the data you want to pull in.\n",
-    "# lonlat_box(north_lat,south_lat,east_lon,west_lon)\n",
-    "gfsdata_wind.lonlat_box(0, 359.9, 0, 90)\n",
-    "\n",
-    "# Set desired level 50000 = 50000 Pa = 500 hPa\n",
-    "gfsdata_wind.vertical_level(25000)\n",
-    "\n",
-    "# Actually getting the data\n",
-    "data_wind = ncss.get_data(gfsdata_wind)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds_wind = xr.open_dataset(NetCDF4DataStore(data_wind))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#################################################"
-   },
-   "source": [
-    "The next cell will take the downloaded data and parse it to different variables\n",
-    "for use later on. Add a cyclic point using the cartopy utility add_cyclic_point\n",
-    "to the longitudes (the cyclic dimension) as well as any data that is being\n",
-    "contoured or filled."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lat = ds_hght.latitude.values\n",
-    "lon = ds_hght.longitude.values\n",
-    "\n",
-    "# Converting times using the num2date function available through netCDF4\n",
-    "vtimes = ds_hght.Geopotential_height_isobaric.metpy.time.values.astype('datetime64[ms]').astype('O')\n",
-    "\n",
-    "# Smooth the 250-hPa heights using a gaussian filter from scipy.ndimage\n",
-    "hgt = ds_hght.Geopotential_height_isobaric.squeeze()\n",
-    "hgt_250, lon = cutil.add_cyclic_point(hgt, coord=lon)\n",
-    "\n",
-    "Z_250 = mpcalc.smooth_n_point(hgt_250, 9, 2)\n",
-    "\n",
-    "# Calculate wind speed from u and v components, add cyclic point,\n",
-    "# and smooth slightly\n",
-    "u250 = ds_wind['u-component_of_wind_isobaric'].squeeze()\n",
-    "v250 = ds_wind['v-component_of_wind_isobaric'].squeeze()\n",
-    "\n",
-    "wspd250 = mpcalc.wind_speed(u250, v250).metpy.convert_units('knots')\n",
-    "wspd250 = cutil.add_cyclic_point(wspd250)\n",
-    "\n",
-    "smooth_wspd250 = mpcalc.smooth_n_point(wspd250, 9, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#################################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "The next cell sets up the geographic details for the plot that we are going to do later.\n",
-    "This is done using the Cartopy package. We will also bring in some geographic data to\n",
-    "geo-reference the image for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datacrs = ccrs.PlateCarree()\n",
-    "plotcrs = ccrs.NorthPolarStereo(central_longitude=-100.0)\n",
-    "\n",
-    "# Make a grid of lat/lon values to use for plotting.\n",
-    "lons, lats = np.meshgrid(lon, lat)\n",
-    "\n",
-    "fig = plt.figure(1, figsize=(12., 15.))\n",
-    "ax = plt.subplot(111, projection=plotcrs)\n",
-    "\n",
-    "# Set some titles for the plots\n",
-    "ax.set_title('250-hPa Geopotential Heights (m)', loc='left')\n",
-    "ax.set_title(f'VALID: {vtimes[0]}', loc='right')\n",
-    "\n",
-    "# Set the extent of the image for the NH and add\n",
-    "#   ax.set_extent([west long, east long, south lat, north lat])\n",
-    "ax.set_extent([-180, 180, 10, 90], ccrs.PlateCarree())\n",
-    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'), edgecolor='black',\n",
-    "               linewidth=0.5)\n",
-    "ax.add_feature(cfeature.STATES.with_scale('50m'), linewidth=0.5)\n",
-    "\n",
-    "# Add geopotential height contours every 120 m\n",
-    "clev250 = np.arange(9000, 12000, 120)\n",
-    "cs = ax.contour(lons, lats, Z_250, clev250, colors='k',\n",
-    "                linewidths=1.0, linestyles='solid', transform=datacrs)\n",
-    "plt.clabel(cs, fontsize=8, inline=1, inline_spacing=10, fmt='%i',\n",
-    "           rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Add colorfilled wind speed in knots every 20 kts\n",
-    "clevsped250 = np.arange(50, 200, 20)\n",
-    "cmap = plt.cm.get_cmap('BuPu')\n",
-    "cf = ax.contourf(lons, lats, smooth_wspd250, clevsped250, cmap=cmap,\n",
-    "                 extend='max', transform=datacrs)\n",
-    "cbar = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
-    "                    extendrect=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/500hPa_Absolute_Vorticity_winds.ipynb b/_preview/3/_sources/notebooks/gallery/500hPa_Absolute_Vorticity_winds.ipynb
deleted file mode 100644
index 127d9a7..0000000
--- a/_preview/3/_sources/notebooks/gallery/500hPa_Absolute_Vorticity_winds.ipynb
+++ /dev/null
@@ -1,325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "500 hPa Geopotential Heights, Absolute Vorticity, and Winds\n",
-    "===========================================================\n",
-    "\n",
-    "Classic 500-hPa absolute vorticity plot using NAM analysis file.\n",
-    "\n",
-    "This example uses example data from the NAM analysis for 12 UTC 31\n",
-    "October 2016 and uses xarray as the main read source with using MetPy to\n",
-    "calculate absolute vorticity and wind speed with geographic plotting\n",
-    "using Cartopy for a CONUS view of the 500-hPa geopotential heights,\n",
-    "absolute vorticity, and wind barbs.\n",
-    "\n",
-    "Note: NAM winds are grid relative and this example introduces a simple\n",
-    "function to convert the grid-relative wind components to an\n",
-    "earth-relative (north/east oriented winds) to enable easier plotting of\n",
-    "wind barbs and calculation of absolute vorticity.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Import the needed modules\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Function to Compute Earth-Relative Winds\n",
-    "----------------------------------------\n",
-    "\n",
-    "This function takes a data array with relevant information about the\n",
-    "projection of the wind component data, along with the grid-relative\n",
-    "components of the wind. It outputs the earth-relative components of the\n",
-    "wind.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "def earth_relative_wind_components(ugrd, vgrd):\n",
-    "    \"\"\"Calculate the north-relative components of the\n",
-    "    wind from the grid-relative components using Cartopy\n",
-    "    transform_vectors.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "        ugrd : Xarray DataArray (M, N)\n",
-    "            grid relative u-component of the wind\n",
-    "        vgrd : Xarray DataArray (M, N)\n",
-    "            grid relative v-component of the wind\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "        unr, vnr : tuple of array-like Quantity\n",
-    "            The north-relative wind components in the X (East-West)\n",
-    "            and Y (North-South) directions, respectively.\n",
-    "    \"\"\"\n",
-    "    if 'metpy_crs' not in ugrd.coords:\n",
-    "        raise ValueError('No CRS in coordinate, be sure to'\n",
-    "                         'use the MetPy accessor parse_cf()')\n",
-    "\n",
-    "    data_crs = ugrd.metpy.cartopy_crs\n",
-    "\n",
-    "    x = ugrd.x.values\n",
-    "    y = ugrd.y.values\n",
-    "\n",
-    "    xx, yy = np.meshgrid(x, y)\n",
-    "\n",
-    "    ut, vt = ccrs.PlateCarree().transform_vectors(data_crs, xx, yy,\n",
-    "                                                  ugrd.values, vgrd.values)\n",
-    "\n",
-    "    # Make a copy of u and v component DataArrays\n",
-    "    uer = ugrd.copy()\n",
-    "    ver = vgrd.copy()\n",
-    "\n",
-    "    # Update values with transformed winds\n",
-    "    uer.values = ut\n",
-    "    ver.values = vt\n",
-    "\n",
-    "    return uer, ver\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "The following code reads the example data using the xarray open_dataset\n",
-    "function and prints the coordinate values that are associated with the\n",
-    "various variables contained within the file\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies/'\n",
-    "                     'python-gallery/NAM_20161031_1200.nc').metpy.parse_cf()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Data Retrieval\n",
-    "--------------\n",
-    "\n",
-    "This code retrieves the necessary data from the file and completes some\n",
-    "smoothing of the geopotential height and wind fields using the SciPy\n",
-    "function gaussian_filter. A nicely formated valid time (vtime) variable\n",
-    "is also created\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Grab lat/lon values (NAM will be 2D)\n",
-    "lats = ds.lat.data\n",
-    "lons = ds.lon.data\n",
-    "\n",
-    "# Grab x, y data and make 2D for wind component plotting because\n",
-    "# u- and v-components are grid relative\n",
-    "x = ds['u-component_of_wind_isobaric'].x\n",
-    "y = ds['u-component_of_wind_isobaric'].y\n",
-    "\n",
-    "xx, yy = np.meshgrid(x, y)\n",
-    "\n",
-    "# Grab Cartopy CRS from metadata for plotting wind barbs\n",
-    "datacrs = ds['u-component_of_wind_isobaric'].metpy.cartopy_crs\n",
-    "\n",
-    "# Select and grab 500-hPa geopotential heights and smooth with n-point smoother\n",
-    "level = 500 * units.hPa\n",
-    "hght_500 = mpcalc.smooth_n_point(ds.Geopotential_height_isobaric.metpy.sel(\n",
-    "    vertical=level).squeeze(), 9, 50)\n",
-    "\n",
-    "# Select and grab 500-hPa wind components\n",
-    "uwnd_500 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=level).squeeze().metpy.assign_latitude_longitude()\n",
-    "vwnd_500 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=level).squeeze().metpy.assign_latitude_longitude()\n",
-    "\n",
-    "# Compute north-relative wind components for plotting purposes\n",
-    "uwnd_er, vwnd_er = earth_relative_wind_components(uwnd_500, vwnd_500)\n",
-    "\n",
-    "# Smooth wind components as desired\n",
-    "uwnd_er = mpcalc.smooth_n_point(uwnd_er, 9, 50)\n",
-    "vwnd_er = mpcalc.smooth_n_point(vwnd_er, 9, 50)\n",
-    "\n",
-    "# Create a clean datetime object for plotting based on time\n",
-    "# of Geopotential heights\n",
-    "vtime = ds.time.data[0].astype('datetime64[ms]').astype('O')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "MetPy Absolute Vorticity Calculation\n",
-    "------------------------------------\n",
-    "\n",
-    "This code calculates ``absolute_vorticity()``\n",
-    "using the wind components and makes use of the\n",
-    "xarray data object features to complete the necessary\n",
-    "grid-aware calculation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate absolute vorticity from MetPy function\n",
-    "avor_500 = mpcalc.absolute_vorticity(uwnd_er, vwnd_er)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Map Creation\n",
-    "------------\n",
-    "\n",
-    "This next set of code creates the plot and draws contours on a Lambert\n",
-    "Conformal map centered on -100 E longitude. The main view is over the\n",
-    "CONUS with geopotential heights contoured every 60 m and absolute\n",
-    "vorticity colorshaded (`*10^5`).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up the projection that will be used for plotting\n",
-    "mapcrs = ccrs.LambertConformal(central_longitude=-100, central_latitude=35,\n",
-    "                               standard_parallels=(30, 60))\n",
-    "\n",
-    "# Set up the projection of the data;\n",
-    "# if lat/lon then PlateCarree is what you want\n",
-    "datacrs = ccrs.PlateCarree()\n",
-    "\n",
-    "# Start the figure and create plot axes with proper projection\n",
-    "fig = plt.figure(1, figsize=(14, 12))\n",
-    "ax = plt.subplot(111, projection=mapcrs)\n",
-    "ax.set_extent([-130, -72, 20, 55], ccrs.PlateCarree())\n",
-    "\n",
-    "# Add geopolitical boundaries for map reference\n",
-    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax.add_feature(cfeature.STATES.with_scale('50m'))\n",
-    "\n",
-    "# Absolute Vorticity colors\n",
-    "# Use two different colormaps from matplotlib and combine into one color set\n",
-    "clevs_500_avor = list(range(-8, 1, 1))+list(range(8, 46, 1))\n",
-    "colors1 = plt.cm.YlOrRd(np.linspace(0, 1, 48))\n",
-    "colors2 = plt.cm.BuPu(np.linspace(0.5, 0.75, 8))\n",
-    "colors = np.vstack((colors2, (1, 1, 1, 1), colors1))\n",
-    "\n",
-    "# Plot absolute vorticity values (multiplying by 10^5 to scale appropriately)\n",
-    "cf = ax.contourf(lons, lats, avor_500*1e5, clevs_500_avor, colors=colors,\n",
-    "                 extend='max', transform=datacrs)\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
-    "                  extendrect=True)\n",
-    "cb.set_label('Abs. Vorticity ($s^{-1}$)')\n",
-    "\n",
-    "# Plot 500-hPa Geopotential Heights in meters\n",
-    "clevs_500_hght = np.arange(0, 8000, 60)\n",
-    "cs = ax.contour(lons, lats, hght_500, clevs_500_hght, colors='black',\n",
-    "                transform=datacrs)\n",
-    "plt.clabel(cs, fmt='%d')\n",
-    "\n",
-    "# Set up a 2D slice to reduce the number of wind barbs plotted (every 20th)\n",
-    "wind_slice = (slice(None, None, 20), slice(None, None, 20))\n",
-    "ax.barbs(lons[wind_slice], lats[wind_slice],\n",
-    "         uwnd_er.metpy.convert_units('kt')[wind_slice].values,\n",
-    "         vwnd_er[wind_slice].metpy.convert_units('kt').values,\n",
-    "         pivot='middle', color='black', transform=datacrs)\n",
-    "\n",
-    "# Plot two titles, one on right and left side\n",
-    "plt.title('500-hPa NAM Geopotential Heights (m)'\n",
-    "          ' and Wind Barbs (kt)', loc='left')\n",
-    "plt.title(f'Valid Time: {vtime}', loc='right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/500hPa_Vorticity_Advection.ipynb b/_preview/3/_sources/notebooks/gallery/500hPa_Vorticity_Advection.ipynb
deleted file mode 100644
index 3b7312c..0000000
--- a/_preview/3/_sources/notebooks/gallery/500hPa_Vorticity_Advection.ipynb
+++ /dev/null
@@ -1,349 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "500 hPa Vorticity Advection\n",
-    "===========================\n",
-    "\n",
-    "Plot an 500-hPa map with calculating vorticity advection using MetPy calculations.\n",
-    "\n",
-    "Beyond just plotting 500-hPa level data, this uses calculations from `metpy.calc` to find\n",
-    "the vorticity and vorticity advection. Currently, this needs an extra helper function to\n",
-    "calculate the distance between lat/lon grid points."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "from siphon.catalog import TDSCatalog\n",
-    "from xarray.backends import NetCDF4DataStore\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Function to Compute Earth-Relative Winds\n",
-    "----------------------------------------\n",
-    "\n",
-    "This function takes a data array with relevant information about the\n",
-    "projection of the wind component data, along with the grid-relative\n",
-    "components of the wind. It outputs the earth-relative components of the\n",
-    "wind.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "def earth_relative_wind_components(ugrd, vgrd):\n",
-    "    \"\"\"Calculate the north-relative components of the\n",
-    "    wind from the grid-relative components using Cartopy\n",
-    "    transform_vectors.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "        ugrd : Xarray DataArray (M, N)\n",
-    "            grid relative u-component of the wind\n",
-    "        vgrd : Xarray DataArray (M, N)\n",
-    "            grid relative v-component of the wind\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "        unr, vnr : tuple of array-like Quantity\n",
-    "            The north-relative wind components in the X (East-West)\n",
-    "            and Y (North-South) directions, respectively.\n",
-    "    \"\"\"\n",
-    "    if 'metpy_crs' not in ugrd.coords:\n",
-    "        raise ValueError('No CRS in coordinate, be sure to use'\n",
-    "                         'the MetPy accessor parse_cf()')\n",
-    "\n",
-    "    data_crs = ugrd.metpy.cartopy_crs\n",
-    "\n",
-    "    x = ugrd.x.values\n",
-    "    y = ugrd.y.values\n",
-    "\n",
-    "    xx, yy = np.meshgrid(x, y)\n",
-    "\n",
-    "    ut, vt = ccrs.PlateCarree().transform_vectors(data_crs, xx, yy,\n",
-    "                                                  ugrd.values, vgrd.values)\n",
-    "\n",
-    "    # Make a copy of u and v component DataArrays\n",
-    "    uer = ugrd.copy()\n",
-    "    ver = vgrd.copy()\n",
-    "\n",
-    "    # Update values with transformed winds\n",
-    "    uer.values = ut\n",
-    "    ver.values = vt\n",
-    "\n",
-    "    return uer, ver\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#######################################"
-   },
-   "source": [
-    "Data Aquisition\n",
-    "---------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = datetime(2016, 4, 16, 18)\n",
-    "\n",
-    "# Assemble our URL to the THREDDS Data Server catalog,\n",
-    "# and access our desired dataset within via NCSS\n",
-    "base_url = 'https://www.ncei.noaa.gov/thredds/catalog/model-namanl-old/'\n",
-    "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n",
-    "ncss = cat.datasets[f'namanl_218_{dt:%Y%m%d}_{dt:%H}00_000.grb'].subset()\n",
-    "\n",
-    "# Query for Latest GFS Run\n",
-    "query = ncss.query()\n",
-    "\n",
-    "query.time(dt)\n",
-    "query.accept('netcdf')\n",
-    "query.variables('Geopotential_height_isobaric',\n",
-    "                'u-component_of_wind_isobaric',\n",
-    "                'v-component_of_wind_isobaric')\n",
-    "\n",
-    "# Obtain our queried data\n",
-    "data = ncss.get_data(query)\n",
-    "ds_data = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
-    "ds = ds_data.metpy.assign_latitude_longitude()\n",
-    "\n",
-    "times = ds.Geopotential_height_isobaric.metpy.time\n",
-    "vtime = times.values.squeeze().astype('datetime64[ms]').astype('O')\n",
-    "\n",
-    "lev_500 = 500 * units.hPa\n",
-    "\n",
-    "hght_500 = ds.Geopotential_height_isobaric.metpy.sel(\n",
-    "    vertical=lev_500).squeeze()\n",
-    "hght_500 = mpcalc.smooth_gaussian(hght_500, 4)\n",
-    "\n",
-    "uwnd_500 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=lev_500).squeeze()\n",
-    "vwnd_500 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=lev_500).squeeze()\n",
-    "\n",
-    "# Compute north-relative wind components for calculation purposes\n",
-    "uwnd_500er, vwnd_500er = earth_relative_wind_components(uwnd_500, vwnd_500)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#######################################"
-   },
-   "source": [
-    "Begin Data Calculations\n",
-    "-----------------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/fp/_l45s1m93s5009vb713pyg580000gn/T/ipykernel_51141/3729036084.py:5: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.\n",
-      "  vort_adv = mpcalc.advection(avor, uwnd_500er, vwnd_500er) * 1e4\n"
-     ]
-    }
-   ],
-   "source": [
-    "avor = mpcalc.vorticity(uwnd_500er, vwnd_500er)\n",
-    "\n",
-    "avor = mpcalc.smooth_n_point(avor, 9, 10) * 1e5\n",
-    "\n",
-    "vort_adv = mpcalc.advection(avor, uwnd_500er, vwnd_500er) * 1e4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#######################################"
-   },
-   "source": [
-    "Map Creation\n",
-    "------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.quiver.Barbs at 0x156fd2810>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
-      "  return lib.buffer(\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
-      "  return lib.intersects(a, b, **kwargs)\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:688: RuntimeWarning: invalid value encountered in covers\n",
-      "  return lib.covers(a, b, **kwargs)\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:730: RuntimeWarning: invalid value encountered in disjoint\n",
-      "  return lib.disjoint(a, b, **kwargs)\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
-      "  return lib.buffer(\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
-      "  return lib.intersects(a, b, **kwargs)\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n",
-      "  return lib.buffer(\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
-      "  return lib.intersects(a, b, **kwargs)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set up Coordinate System for Plot and Transforms\n",
-    "datacrs = ds.Geopotential_height_isobaric.metpy.cartopy_crs\n",
-    "plotcrs = ccrs.LambertConformal(central_latitude=45., central_longitude=-100.,\n",
-    "                                standard_parallels=[30, 60])\n",
-    "\n",
-    "fig = plt.figure(1, figsize=(12., 14.))\n",
-    "ax = plt.subplot(111, projection=plotcrs)\n",
-    "\n",
-    "# Plot Titles\n",
-    "plt.title(r'500-hPa Heights (m), AVOR$*10^5$ ($s^{-1}$),'\n",
-    "          'AVOR Adv$*10^9$ ($s^{-2}$)', loc='left')\n",
-    "plt.title(f'VALID: {vtime}', loc='right')\n",
-    "\n",
-    "# Plot Background\n",
-    "ax.set_extent([235., 290., 20., 58.], ccrs.PlateCarree())\n",
-    "ax.coastlines('50m', edgecolor='black', linewidth=0.75)\n",
-    "ax.add_feature(cfeature.STATES, linewidth=.5)\n",
-    "\n",
-    "# Plot Height Contours\n",
-    "clev500 = np.arange(5100, 6061, 60)\n",
-    "cs = ax.contour(hght_500.longitude, hght_500.latitude,\n",
-    "                hght_500, clev500,\n",
-    "                colors='black', linewidths=1.0,\n",
-    "                linestyles='solid', transform=ccrs.PlateCarree())\n",
-    "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n",
-    "           rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Plot Absolute Vorticity Contours\n",
-    "clevvort500 = np.arange(-9, 50, 5)\n",
-    "cs2 = ax.contour(avor.longitude, avor.latitude,\n",
-    "                 avor, clevvort500,\n",
-    "                 colors='grey', linewidths=1.25, linestyles='dashed',\n",
-    "                 transform=ccrs.PlateCarree())\n",
-    "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n",
-    "           rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Plot Colorfill of Vorticity Advection\n",
-    "clev_avoradv = np.arange(-30, 31, 5)\n",
-    "cf = ax.contourf(vort_adv.longitude, vort_adv.latitude, vort_adv,\n",
-    "                 clev_avoradv[clev_avoradv != 0], extend='both',\n",
-    "                 cmap='bwr', transform=ccrs.PlateCarree())\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
-    "                  extendrect='True', ticks=clev_avoradv)\n",
-    "cb.set_label(r'$1/s^2$', size='large')\n",
-    "\n",
-    "# Plot Wind Barbs\n",
-    "# Transform Vectors and plot wind barbs.\n",
-    "wind_slice = (slice(None, None, 20), slice(None, None, 20))\n",
-    "xx, yy = np.meshgrid(uwnd_500.x.values[wind_slice[0]],\n",
-    "                     uwnd_500.y.values[wind_slice[0]])\n",
-    "ax.barbs(xx, yy, uwnd_500.values[wind_slice], vwnd_500.values[wind_slice],\n",
-    "         length=6, pivot='middle', transform=datacrs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/850hPa_Frontogenesis.ipynb b/_preview/3/_sources/notebooks/gallery/850hPa_Frontogenesis.ipynb
deleted file mode 100644
index 613f29a..0000000
--- a/_preview/3/_sources/notebooks/gallery/850hPa_Frontogenesis.ipynb
+++ /dev/null
@@ -1,243 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "850-hPa Geopotential Heights, Temperature, Frontogenesis, and Winds\n",
-    "===================================================================\n",
-    "\n",
-    "Frontogenesis at 850-hPa with Geopotential Heights, Temperature, and\n",
-    "Winds\n",
-    "\n",
-    "This example uses example data from the GFS analysis for 12 UTC 26\n",
-    "October 2010 and uses xarray as the main read source with using MetPy to\n",
-    "calculate frontogenesis and wind speed with geographic plotting using\n",
-    "Cartopy for a CONUS view.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Import the needed modules.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Use Xarray to access GFS data from THREDDS resource and uses\n",
-    "metpy accessor to parse file to make it easy to pull data using\n",
-    "common coordinate names (e.g., vertical) and attach units.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies/'\n",
-    "                     'python-gallery/GFS_20101026_1200.nc').metpy.parse_cf()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Subset data based on latitude and longitude values, calculate potential\n",
-    "temperature for frontogenesis calculation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set subset slice for the geographic extent of data to limit download\n",
-    "lon_slice = slice(200, 350)\n",
-    "lat_slice = slice(85, 10)\n",
-    "\n",
-    "# Grab lat/lon values (GFS will be 1D)\n",
-    "lats = ds.lat.sel(lat=lat_slice).values\n",
-    "lons = ds.lon.sel(lon=lon_slice).values\n",
-    "\n",
-    "level = 850 * units.hPa\n",
-    "hght_850 = ds.Geopotential_height_isobaric.metpy.sel(\n",
-    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze()\n",
-    "tmpk_850 = ds.Temperature_isobaric.metpy.sel(\n",
-    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze()\n",
-    "uwnd_850 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze()\n",
-    "vwnd_850 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze()\n",
-    "\n",
-    "# Convert temperatures to degree Celsius for plotting purposes\n",
-    "tmpc_850 = tmpk_850.metpy.convert_units('degC')\n",
-    "\n",
-    "# Calculate potential temperature for frontogenesis calculation\n",
-    "thta_850 = mpcalc.potential_temperature(level, tmpk_850)\n",
-    "\n",
-    "# Get a sensible datetime format\n",
-    "vtime = ds.time.data[0].astype('datetime64[ms]').astype('O')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Calculate frontogenesis\n",
-    "-----------------------\n",
-    "\n",
-    "Frontogenesis calculation in MetPy requires temperature, wind\n",
-    "components, and grid spacings. First compute the grid deltas using MetPy\n",
-    "functionality, then put it all together in the frontogenesis function.\n",
-    "\n",
-    "Note: MetPy will give the output with SI units, but typically\n",
-    "frontogenesis (read: GEMPAK) output this variable with units of K per\n",
-    "100 km per 3 h; a conversion factor is included here to use at plot time\n",
-    "to reflect those units.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fronto_850 = mpcalc.frontogenesis(thta_850, uwnd_850, vwnd_850)\n",
-    "\n",
-    "# A conversion factor to get frontogensis units of K per 100 km per 3 h\n",
-    "convert_to_per_100km_3h = 1000*100*3600*3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Plotting Frontogenesis\n",
-    "----------------------\n",
-    "\n",
-    "Using a Lambert Conformal projection from Cartopy to plot 850-hPa\n",
-    "variables including frontogenesis.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set map projection\n",
-    "mapcrs = ccrs.LambertConformal(central_longitude=-100, central_latitude=35,\n",
-    "                               standard_parallels=(30, 60))\n",
-    "\n",
-    "# Set projection of the data (GFS is lat/lon)\n",
-    "datacrs = ccrs.PlateCarree()\n",
-    "\n",
-    "# Start figure and limit the graphical area extent\n",
-    "fig = plt.figure(1, figsize=(14, 12))\n",
-    "ax = plt.subplot(111, projection=mapcrs)\n",
-    "ax.set_extent([-130, -72, 20, 55], ccrs.PlateCarree())\n",
-    "\n",
-    "# Add map features of Coastlines and States\n",
-    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax.add_feature(cfeature.STATES.with_scale('50m'))\n",
-    "\n",
-    "# Plot 850-hPa Frontogenesis\n",
-    "clevs_tmpc = np.arange(-40, 41, 2)\n",
-    "cf = ax.contourf(lons, lats, fronto_850*convert_to_per_100km_3h,\n",
-    "                 np.arange(-8, 8.5, 0.5), cmap=plt.cm.bwr, extend='both',\n",
-    "                 transform=datacrs)\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
-    "                  extendrect=True)\n",
-    "cb.set_label('Frontogenesis K / 100 km / 3 h')\n",
-    "\n",
-    "# Plot 850-hPa Temperature in Celsius\n",
-    "csf = ax.contour(lons, lats, tmpc_850, clevs_tmpc, colors='grey',\n",
-    "                 linestyles='dashed', transform=datacrs)\n",
-    "plt.clabel(csf, fmt='%d')\n",
-    "\n",
-    "# Plot 850-hPa Geopotential Heights\n",
-    "clevs_850_hght = np.arange(0, 8000, 30)\n",
-    "cs = ax.contour(lons, lats, hght_850, clevs_850_hght, colors='black',\n",
-    "                transform=datacrs)\n",
-    "plt.clabel(cs, fmt='%d')\n",
-    "\n",
-    "# Plot 850-hPa Wind Barbs only plotting every fifth barb\n",
-    "wind_slice = (slice(None, None, 5), slice(None, None, 5))\n",
-    "ax.barbs(lons[wind_slice[0]], lats[wind_slice[1]],\n",
-    "         uwnd_850[wind_slice].metpy.convert_units('kt').values,\n",
-    "         vwnd_850[wind_slice].metpy.convert_units('kt').values,\n",
-    "         color='black', transform=datacrs)\n",
-    "\n",
-    "# Plot some titles\n",
-    "plt.title('GFS 850-hPa Geopotential Heights (m), Temp (C), and Winds',\n",
-    "          loc='left')\n",
-    "plt.title(f'Valid Time: {vtime}', loc='right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/850hPa_QVectors.ipynb b/_preview/3/_sources/notebooks/gallery/850hPa_QVectors.ipynb
deleted file mode 100644
index 60aa1a7..0000000
--- a/_preview/3/_sources/notebooks/gallery/850hPa_QVectors.ipynb
+++ /dev/null
@@ -1,240 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "QVector Example\n",
-    "===============\n",
-    "\n",
-    "Computing Q-vectors and Q-vector divergence for a real case.\n",
-    "\n",
-    "By: Kevin Goebbert\n",
-    "\n",
-    "This example uses GFS output to compute the 850-hPa Q-vectors and\n",
-    "Q-vector divergence for 12 UTC 26 October 2010."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Import needed modules\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.plots import named_areas\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Use Xarray to access GFS data from THREDDS resource and uses\n",
-    "metpy accessor to parse file to make it easy to pull data using\n",
-    "common coordinate names (e.g., vertical) and attach units.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies/'\n",
-    "                     'python-gallery/GFS_20101026_1200.nc')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Subset data based on latitude and longitude values and select only data\n",
-    "from 850 hPa\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set subset slice for the geographic extent of data to limit download\n",
-    "lon_slice = slice(200, 350)\n",
-    "lat_slice = slice(85, 10)\n",
-    "\n",
-    "# Grab data and smooth using a nine-point filter applied 50 times to grab\n",
-    "# the synoptic signal\n",
-    "level = 850 * units.hPa\n",
-    "hght_850 = mpcalc.smooth_n_point(ds.Geopotential_height_isobaric.metpy.sel(\n",
-    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze(), 9, 50)\n",
-    "tmpk_850 = mpcalc.smooth_n_point(ds.Temperature_isobaric.metpy.sel(\n",
-    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze(), 9, 25)\n",
-    "uwnd_850 = mpcalc.smooth_n_point(ds['u-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze(), 9, 50)\n",
-    "vwnd_850 = mpcalc.smooth_n_point(ds['v-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=level, lat=lat_slice, lon=lon_slice).squeeze(), 9, 50)\n",
-    "\n",
-    "# Grab lat/lon values (GFS will be 1D) from a subset variable\n",
-    "lons, lats = hght_850.lon, hght_850.lat\n",
-    "\n",
-    "# Convert temperatures to degree Celsius for plotting purposes\n",
-    "tmpc_850 = tmpk_850.metpy.convert_units('degC')\n",
-    "\n",
-    "# Get a sensible datetime format\n",
-    "vtime = ds.time.data[0].astype('datetime64[ms]').astype('O')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Compute Q-vectors\n",
-    "-----------------\n",
-    "\n",
-    "Use the MetPy module to compute Q-vectors from requisite data and\n",
-    "additionally compute the Q-vector divergence (and multiply by -2) to\n",
-    "calculate the right hand side forcing of the Q-G Omega equation.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compute grid spacings for data\n",
-    "#dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)\n",
-    "\n",
-    "# Compute the Q-vector components\n",
-    "uqvect, vqvect = mpcalc.q_vector(uwnd_850, vwnd_850, tmpk_850, 850*units.hPa)\n",
-    "\n",
-    "# Compute the divergence of the Q-vectors calculated above\n",
-    "q_div = -2*mpcalc.divergence(uqvect, vqvect)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Plot Data\n",
-    "---------\n",
-    "\n",
-    "Use Cartopy to plot data on a map using a Lambert Conformal projection.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set the map projection (how the data will be displayed)\n",
-    "mapcrs = named_areas['uslcc'].projection\n",
-    "\n",
-    "# Set the data project (GFS is lat/lon format)\n",
-    "datacrs = ccrs.PlateCarree()\n",
-    "\n",
-    "# Start the figure and set an extent to only display a smaller graphics area\n",
-    "fig = plt.figure(1, figsize=(14, 12))\n",
-    "ax = plt.subplot(111, projection=mapcrs)\n",
-    "ax.set_extent(named_areas['uslcc'].bounds, ccrs.PlateCarree())\n",
-    "\n",
-    "# Add map features to plot coastlines and state boundaries\n",
-    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax.add_feature(cfeature.STATES.with_scale('50m'))\n",
-    "\n",
-    "# Plot 850-hPa Q-Vector Divergence and scale\n",
-    "clevs_850_tmpc = np.arange(-40, 41, 2)\n",
-    "clevs_qdiv = list(range(-35, -4, 5))+list(range(5, 36, 5))\n",
-    "cf = ax.contourf(lons, lats, q_div*1e18, clevs_qdiv, cmap=plt.cm.bwr,\n",
-    "                 extend='both', transform=datacrs)\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
-    "                  extendrect=True, ticks=clevs_qdiv)\n",
-    "cb.set_label('Q-Vector Div. (*10$^{18}$ m s$^{-1}$ kg$^{-1}$)')\n",
-    "\n",
-    "# Plot 850-hPa Temperatures\n",
-    "csf = ax.contour(lons, lats, tmpc_850, clevs_850_tmpc, colors='grey',\n",
-    "                 linestyles='dashed', transform=datacrs)\n",
-    "plt.clabel(csf, fmt='%d')\n",
-    "\n",
-    "# Plot 850-hPa Geopotential Heights\n",
-    "clevs_850_hght = np.arange(0, 8000, 30)\n",
-    "cs = ax.contour(lons, lats, hght_850, clevs_850_hght, colors='black',\n",
-    "                transform=datacrs)\n",
-    "plt.clabel(cs, fmt='%d')\n",
-    "\n",
-    "# Plot 850-hPa Q-vectors, scale to get nice sized arrows\n",
-    "wind_slice = (slice(None, None, 5), slice(None, None, 5))\n",
-    "ax.quiver(lons[wind_slice[0]], lats[wind_slice[1]],\n",
-    "          uqvect[wind_slice].values,\n",
-    "          vqvect[wind_slice].values,\n",
-    "          pivot='mid', color='black',\n",
-    "          scale=5e-12, scale_units='inches',\n",
-    "          transform=datacrs)\n",
-    "\n",
-    "# Add some titles\n",
-    "plt.title('850-hPa GFS Geo. Heights (m), Temp (C),'\n",
-    "          ' and Q-Vectors (m$^2$ kg$^{-1}$ s$^{-1}$)', loc='left')\n",
-    "plt.title(f'Valid Time: {vtime}', loc='right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/850hPa_Temperature_Advection.ipynb b/_preview/3/_sources/notebooks/gallery/850hPa_Temperature_Advection.ipynb
deleted file mode 100644
index b044ea6..0000000
--- a/_preview/3/_sources/notebooks/gallery/850hPa_Temperature_Advection.ipynb
+++ /dev/null
@@ -1,238 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "850 hPa Temperature Advection\n",
-    "=============================\n",
-    "\n",
-    "Plot an 850 hPa map with calculating advection using MetPy.\n",
-    "\n",
-    "Beyond just plotting 850-hPa level data, this uses calculations from `metpy.calc` to find\n",
-    "the temperature advection. Currently, this needs an extra helper function to calculate\n",
-    "the distance between lat/lon grid points."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "###############################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "import numpy as np\n",
-    "from metpy.units import units\n",
-    "from siphon.catalog import TDSCatalog\n",
-    "from xarray.backends import NetCDF4DataStore\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "###############################################"
-   },
-   "source": [
-    "Create NCSS object to access the NetcdfSubset\n",
-    "---------------------------------------------\n",
-    "Data from NCEI GFS 0.5 deg Analysis Archive"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = datetime(2017, 4, 5, 12)\n",
-    "\n",
-    "# Assemble our URL to the THREDDS Data Server catalog,\n",
-    "# and access our desired dataset within via NCSS\n",
-    "base_url = 'https://www.ncei.noaa.gov/thredds/model-gfs-g4-anl-files-old/'\n",
-    "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n",
-    "ncss = cat.datasets[f'gfsanl_4_{dt:%Y%m%d}_{dt:%H}00_000.grb2'].subset()\n",
-    "\n",
-    "# Create NCSS query for our desired time, region, and data variables\n",
-    "query = ncss.query()\n",
-    "\n",
-    "query.time(dt)\n",
-    "query.lonlat_box(north=65, south=15, east=310, west=220)\n",
-    "query.accept('netcdf')\n",
-    "query.variables('Geopotential_height_isobaric',\n",
-    "                'Temperature_isobaric',\n",
-    "                'u-component_of_wind_isobaric',\n",
-    "                'v-component_of_wind_isobaric')\n",
-    "\n",
-    "# Obtain the queried data\n",
-    "data = ncss.get_data(query)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
-    "\n",
-    "# Pull out variables you want to use\n",
-    "level = 850 * units.hPa\n",
-    "hght_850 = ds.Geopotential_height_isobaric.metpy.sel(\n",
-    "    vertical=level).squeeze()\n",
-    "temp_850 = ds.Temperature_isobaric.metpy.sel(\n",
-    "    vertical=level).squeeze()\n",
-    "u_wind_850 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=level).squeeze()\n",
-    "v_wind_850 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=level).squeeze()\n",
-    "time = hght_850.metpy.time\n",
-    "lat = ds.lat.values\n",
-    "lon = ds.lon.values\n",
-    "\n",
-    "# Convert number of hours since the reference time into an actual date\n",
-    "vtime = time.values.astype('datetime64[ms]').astype('O')\n",
-    "\n",
-    "# Combine 1D latitude and longitudes into a 2D grid of locations\n",
-    "lon_2d, lat_2d = np.meshgrid(lon, lat)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "###############################################"
-   },
-   "source": [
-    "Begin data calculations\n",
-    "-----------------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate temperature advection using metpy function\n",
-    "adv = mpcalc.advection(temp_850, u_wind_850, v_wind_850)\n",
-    "\n",
-    "# Smooth heights and advection a little\n",
-    "# Be sure to only put in a 2D lat/lon or Y/X array for smoothing\n",
-    "Z_850 = mpcalc.smooth_gaussian(hght_850, 2)\n",
-    "adv = mpcalc.smooth_gaussian(adv, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "###############################################"
-   },
-   "source": [
-    "Begin map creation\n",
-    "------------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set Projection of Data\n",
-    "datacrs = ccrs.PlateCarree()\n",
-    "\n",
-    "# Set Projection of Plot\n",
-    "plotcrs = ccrs.LambertConformal(central_latitude=45,\n",
-    "                                central_longitude=-100, standard_parallels=[30, 60])\n",
-    "\n",
-    "# Create new figure\n",
-    "fig = plt.figure(figsize=(14, 12))\n",
-    "\n",
-    "# Add the map and set the extent\n",
-    "ax = plt.subplot(111, projection=plotcrs)\n",
-    "plt.title(f'850mb Temperature Advection for {vtime:%d %B %Y %H:%MZ}',\n",
-    "          fontsize=16)\n",
-    "ax.set_extent([235., 290., 20., 55.])\n",
-    "\n",
-    "# Add state/country boundaries to plot\n",
-    "ax.add_feature(cfeature.STATES)\n",
-    "ax.add_feature(cfeature.BORDERS)\n",
-    "\n",
-    "# Plot Height Contours\n",
-    "clev850 = np.arange(900, 3000, 30)\n",
-    "cs = ax.contour(lon_2d, lat_2d, Z_850, clev850, colors='black', linewidths=1.5,\n",
-    "                linestyles='solid', transform=datacrs)\n",
-    "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n",
-    "           rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Plot Temperature Contours\n",
-    "clevtemp850 = np.arange(-20, 20, 2)\n",
-    "cs2 = ax.contour(lon_2d, lat_2d, temp_850.metpy.convert_units('degC'),\n",
-    "                 clevtemp850, colors='grey', linewidths=1.25,\n",
-    "                 linestyles='dashed', transform=datacrs)\n",
-    "plt.clabel(cs2, fontsize=10, inline=1, inline_spacing=10, fmt='%i',\n",
-    "           rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Plot Colorfill of Temperature Advection\n",
-    "cint = np.arange(-8, 9)\n",
-    "cf = ax.contourf(lon_2d, lat_2d, 3*adv.metpy.convert_units('delta_degC/hour'),\n",
-    "                 cint[cint != 0],\n",
-    "                 extend='both', cmap='bwr', transform=datacrs)\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
-    "                  extendrect=True, ticks=cint)\n",
-    "cb.set_label(r'$^{o}C/3h$', size='large')\n",
-    "\n",
-    "wind_slice = (slice(None, None, 10), slice(None, None, 10))\n",
-    "# Plot Wind Barbs\n",
-    "ax.barbs(lon_2d[wind_slice], lat_2d[wind_slice],\n",
-    "         u_wind_850.metpy.convert_units('kt').values[wind_slice],\n",
-    "         v_wind_850.metpy.convert_units('kt').values[wind_slice],\n",
-    "         length=6, pivot='middle', transform=datacrs);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Ageostrophic_Wind_Example.ipynb b/_preview/3/_sources/notebooks/gallery/Ageostrophic_Wind_Example.ipynb
deleted file mode 100644
index 05efaa7..0000000
--- a/_preview/3/_sources/notebooks/gallery/Ageostrophic_Wind_Example.ipynb
+++ /dev/null
@@ -1,216 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Geostrophic and Ageostrophic Wind\n",
-    "=================================\n",
-    "\n",
-    "Plot a 1000-hPa map calculating the geostrophic from MetPy and finding the\n",
-    "ageostrophic wind from the total wind and the geostrophic wind.\n",
-    "\n",
-    "This uses the geostrophic wind calculation from `metpy.calc` to find\n",
-    "the geostrophic wind, then performs the simple subtraction to find the ageostrophic\n",
-    "wind. Currently, this needs an extra helper function to calculate\n",
-    "the distance between lat/lon grid points.\n",
-    "\n",
-    "Additionally, we utilize the `ndimage.zoom` method for smoothing the 1000-hPa\n",
-    "height contours without smoothing the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "import numpy as np\n",
-    "from scipy import ndimage\n",
-    "from siphon.catalog import TDSCatalog\n",
-    "from xarray.backends import NetCDF4DataStore\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################"
-   },
-   "source": [
-    "Set up access to the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = datetime(2016, 8, 22, 18)\n",
-    "forecast_hour = 3\n",
-    "h = timedelta(hours=forecast_hour)\n",
-    "\n",
-    "# Assemble our URL to the THREDDS Data Server catalog,\n",
-    "# and access our desired dataset within via NCSS\n",
-    "base_url = 'https://www.ncei.noaa.gov/thredds/model-gfs-g4-anl-files-old/'\n",
-    "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n",
-    "ncss = cat.datasets[f'gfsanl_4_{dt:%Y%m%d}_{dt:%H}'\n",
-    "                    f'00_00{forecast_hour}.grb2'].subset()\n",
-    "\n",
-    "# Create NCSS query for our desired time, region, and data variables\n",
-    "query = ncss.query()\n",
-    "\n",
-    "query.lonlat_box(north=50, south=30, east=-80, west=-115)\n",
-    "query.time(dt + h)\n",
-    "query.variables('Geopotential_height_isobaric',\n",
-    "                'u-component_of_wind_isobaric',\n",
-    "                'v-component_of_wind_isobaric')\n",
-    "query.vertical_level(100000)\n",
-    "\n",
-    "data = ncss.get_data(query)\n",
-    "\n",
-    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Pull out variables you want to use\n",
-    "height = ds.Geopotential_height_isobaric.squeeze()\n",
-    "u_wind = ds['u-component_of_wind_isobaric'].squeeze().metpy.quantify()\n",
-    "v_wind = ds['v-component_of_wind_isobaric'].squeeze().metpy.quantify()\n",
-    "\n",
-    "vtime = height.time.values.squeeze().astype('datetime64[ms]').astype('O')\n",
-    "\n",
-    "lat = ds.lat\n",
-    "lon = ds.lon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Combine 1D latitude and longitudes into a 2D grid of locations\n",
-    "lon_2d, lat_2d = np.meshgrid(lon, lat)\n",
-    "\n",
-    "# Smooth height data\n",
-    "height = mpcalc.smooth_n_point(height, 9, 3)\n",
-    "\n",
-    "# Compute the geostrophic wind\n",
-    "geo_wind_u, geo_wind_v = mpcalc.geostrophic_wind(height)\n",
-    "\n",
-    "# Calculate ageostrophic wind components\n",
-    "ageo_wind_u = u_wind - geo_wind_u\n",
-    "ageo_wind_v = v_wind - geo_wind_v"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create new figure\n",
-    "fig = plt.figure(figsize=(15, 10), facecolor='black')\n",
-    "\n",
-    "# Add the map and set the extent\n",
-    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
-    "ax.set_extent([-105., -93., 35., 43.])\n",
-    "ax.patch.set_fill(False)\n",
-    "\n",
-    "# Add state boundaries to plot\n",
-    "ax.add_feature(cfeature.STATES, edgecolor='white', linewidth=2)\n",
-    "\n",
-    "# Contour the heights every 10 m\n",
-    "contours = np.arange(10, 200, 10)\n",
-    "# Because we have a very local graphics area, the contours have joints\n",
-    "# to smooth those out we can use `ndimage.zoom`\n",
-    "zoom_500 = mpcalc.zoom_xarray(height, 5)\n",
-    "c = ax.contour(zoom_500.lon, zoom_500.lat, zoom_500, levels=contours,\n",
-    "               colors='red', linewidths=4)\n",
-    "ax.clabel(c, fontsize=12, inline=1, inline_spacing=3, fmt='%i')\n",
-    "\n",
-    "# Set up parameters for quiver plot. The slices below are used to\n",
-    "# subset the data (here taking every 4th point in x and y). The\n",
-    "# quiver_kwargs are parameters to control the appearance of the\n",
-    "# quiver so that they stay consistent between the calls.\n",
-    "quiver_slices = (slice(None, None, 2), slice(None, None, 2))\n",
-    "quiver_kwargs = {'headlength': 4, 'headwidth': 3, 'angles': 'uv',\n",
-    "                 'scale_units': 'xy', 'scale': 20}\n",
-    "\n",
-    "# Plot the wind vectors\n",
-    "wind = ax.quiver(lon_2d[quiver_slices], lat_2d[quiver_slices],\n",
-    "                 u_wind[quiver_slices], v_wind[quiver_slices],\n",
-    "                 color='blue', **quiver_kwargs)\n",
-    "geo = ax.quiver(lon_2d[quiver_slices], lat_2d[quiver_slices],\n",
-    "                geo_wind_u[quiver_slices], geo_wind_v[quiver_slices],\n",
-    "                color='darkorchid', **quiver_kwargs)\n",
-    "ageo = ax.quiver(lon_2d[quiver_slices], lat_2d[quiver_slices],\n",
-    "                 ageo_wind_u[quiver_slices], ageo_wind_v[quiver_slices],\n",
-    "                 color='lime', **quiver_kwargs)\n",
-    "\n",
-    "# Add a title to the plot\n",
-    "plt.title('1000mb Geopotential Heights(m), Wind(blue), '\n",
-    "          'Geostrophic Wind(purple), and \\n Ageostrophic Wind(green) '\n",
-    "          f'for {vtime:%d %B %Y %H:%MZ}', color='white', size=14)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Analytic_300hPa_Trough.ipynb b/_preview/3/_sources/notebooks/gallery/Analytic_300hPa_Trough.ipynb
deleted file mode 100644
index a563ab1..0000000
--- a/_preview/3/_sources/notebooks/gallery/Analytic_300hPa_Trough.ipynb
+++ /dev/null
@@ -1,275 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Analytic 300-hPa Trough\n",
-    "======================="
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Below are three definitions to create an analytic 300-hPa trough roughly\n",
-    "based on the Sanders Analytic Model with modified coefficients to create\n",
-    "different style waves.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "def single_300hPa_trough(parameter='hght'):\n",
-    "    \"\"\" Single trough with heights and Temperatures based on Sanders Analytic Model\n",
-    "    \"\"\"\n",
-    "    X = np.linspace(.25, .75, 101)\n",
-    "    Y = np.linspace(.25, .75, 101)\n",
-    "\n",
-    "    x, y = np.meshgrid(X, Y)\n",
-    "\n",
-    "    p = 4\n",
-    "    q = 2\n",
-    "\n",
-    "    if parameter == 'hght':\n",
-    "        return (9240 + 100 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
-    "                + 200 * np.cos(y * np.pi) + 300 * y * np.cos(x * np.pi + np.pi / 2))\n",
-    "    elif parameter == 'temp':\n",
-    "        return (-50 + 2 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
-    "                + 2 * np.cos(y * np.pi) + 0.5 * y * np.cos(x * np.pi + np.pi / 2))\n",
-    "\n",
-    "\n",
-    "def lifting_300hPa_trough(parameter='hght'):\n",
-    "    \"\"\" Lifting trough with heights and Temperatures based on Sanders Analytic Model\n",
-    "    \"\"\"\n",
-    "    X = np.linspace(.25, .75, 101)\n",
-    "    Y = np.linspace(.25, .75, 101)\n",
-    "\n",
-    "    x, y = np.meshgrid(X, Y)\n",
-    "\n",
-    "    p = 4\n",
-    "    q = 2\n",
-    "\n",
-    "    if parameter == 'hght':\n",
-    "        return (9240 + 150 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
-    "                + 200 * np.cos(y * np.pi) + 400 * y * np.cos(x * np.pi + np.pi))\n",
-    "    elif parameter == 'temp':\n",
-    "        return (-50 + 2 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
-    "                + 2 * np.cos(y * np.pi) + 5 * y * np.cos(x * np.pi + np.pi))\n",
-    "\n",
-    "\n",
-    "def digging_300hPa_trough(parameter='hght'):\n",
-    "    \"\"\" Digging trough with heights and Temperatures based on Sanders Analytic Model\n",
-    "    \"\"\"\n",
-    "    X = np.linspace(.25, .75, 101)\n",
-    "    Y = np.linspace(.25, .75, 101)\n",
-    "\n",
-    "    x, y = np.meshgrid(X, Y)\n",
-    "\n",
-    "    p = 4\n",
-    "    q = 2\n",
-    "\n",
-    "    if parameter == 'hght':\n",
-    "        return (9240 + 150 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
-    "                + 200 * np.cos(y * np.pi) + 400 * y * np.sin(x * np.pi + 5 * np.pi / 2))\n",
-    "    elif parameter == 'temp':\n",
-    "        return (-50 + 2 * np.cos(p * x * np.pi) * np.cos(q * y * np.pi)\n",
-    "                + 2 * np.cos(y * np.pi) + 5 * y * np.sin(x * np.pi + np.pi / 2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Call the appropriate definition to develop the desired wave.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Single Trough\n",
-    "Z = single_300hPa_trough(parameter='hght')\n",
-    "T = single_300hPa_trough(parameter='temp')\n",
-    "\n",
-    "# Lifting Trough\n",
-    "# Z = lifting_300hPa_trough(parameter='hght')\n",
-    "# T = lifting_300hPa_trough(parameter='temp')\n",
-    "\n",
-    "# Digging Trough\n",
-    "# Z = digging_300hPa_trough(parameter='hght')\n",
-    "# T = digging_300hPa_trough(parameter='temp')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Set geographic parameters for analytic grid to then\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lats = np.linspace(35, 50, 101)\n",
-    "lons = np.linspace(260, 290, 101)\n",
-    "lon, lat = np.meshgrid(lons, lats)\n",
-    "\n",
-    "# Coriolis Parameter\n",
-    "f = mpcalc.coriolis_parameter(lat * units.degrees)\n",
-    "\n",
-    "# Calculate Geostrophic Wind from Analytic Heights\n",
-    "dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)\n",
-    "ugeo, vgeo = mpcalc.geostrophic_wind(Z*units.meter, dx, dy, latitude=lat * units.degrees)\n",
-    "\n",
-    "# Get the wind direction for each point\n",
-    "wdir = mpcalc.wind_direction(ugeo, vgeo)\n",
-    "\n",
-    "# Compute the Gradient Wind via an approximation\n",
-    "dydx = mpcalc.first_derivative(Z, delta=dx, axis=1)\n",
-    "d2ydx2 = mpcalc.first_derivative(dydx, delta=dx, axis=1)\n",
-    "R = ((1 + dydx.m**2)**(3. / 2.)) / d2ydx2.m\n",
-    "\n",
-    "geo_mag = mpcalc.wind_speed(ugeo, vgeo)\n",
-    "grad_mag = geo_mag.m - (geo_mag.m**2) / (f.magnitude * R)\n",
-    "\n",
-    "ugrad, vgrad = mpcalc.wind_components(grad_mag * units('m/s'), wdir)\n",
-    "\n",
-    "# Calculate Ageostrophic wind\n",
-    "uageo = ugrad - ugeo\n",
-    "vageo = vgrad - vgeo\n",
-    "\n",
-    "# Compute QVectors\n",
-    "uqvect, vqvect = mpcalc.q_vector(ugeo, vgeo, T * units.degC, 500 * units.hPa, dx, dy)\n",
-    "\n",
-    "# Calculate divergence of the ageostrophic wind\n",
-    "div = mpcalc.divergence(uageo, vageo, dx=dx, dy=dy)\n",
-    "\n",
-    "# Calculate Relative Vorticity Advection\n",
-    "relvor = mpcalc.vorticity(ugeo, vgeo, dx=dx, dy=dy)\n",
-    "adv = mpcalc.advection(relvor, ugeo, vgeo, dx=dx, dy=dy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Create figure containing Geopotential Heights, Temperature, Divergence\n",
-    "of the Ageostrophic Wind, Relative Vorticity Advection (shaded),\n",
-    "geostrphic wind barbs, and Q-vectors.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(10, 10))\n",
-    "ax = plt.subplot(111)\n",
-    "\n",
-    "# Plot Geopotential Height Contours\n",
-    "cs = ax.contour(lons, lats, Z, range(0, 12000, 120), colors='k')\n",
-    "plt.clabel(cs, fmt='%d')\n",
-    "\n",
-    "# Plot Temperature Contours\n",
-    "cs2 = ax.contour(lons, lats, T, range(-50, 50, 2), colors='r', linestyles='dashed')\n",
-    "plt.clabel(cs2, fmt='%d')\n",
-    "\n",
-    "# Plot Divergence of Ageo Wind Contours\n",
-    "cs3 = ax.contour(lons, lats, div*10**9, np.arange(-25, 26, 3), colors='grey',\n",
-    "                 linestyles='dotted')\n",
-    "plt.clabel(cs3, fmt='%d')\n",
-    "\n",
-    "# Plot Rel. Vor. Adv. colorfilled\n",
-    "cf = ax.contourf(lons, lats, adv*10**9, np.arange(-20, 21, 1), cmap=plt.cm.bwr)\n",
-    "cbar = plt.colorbar(cf, orientation='horizontal', pad=0.05, aspect=50)\n",
-    "cbar.set_label('Rel. Vor. Adv.')\n",
-    "\n",
-    "# Plot Geostrophic Wind Barbs\n",
-    "wind_slice = slice(5, None, 10)\n",
-    "ax.barbs(lons[wind_slice], lats[wind_slice],\n",
-    "         ugeo[wind_slice, wind_slice].to('kt').m, vgeo[wind_slice, wind_slice].to('kt').m)\n",
-    "\n",
-    "# Plot Ageostrophic Wind Vectors\n",
-    "# ageo_slice = slice(None, None, 10)\n",
-    "# ax.quiver(lons[ageo_slice], lats[ageo_slice],\n",
-    "#           uageo[ageo_slice, ageo_slice].m, vageo[ageo_slice, ageo_slice].m,\n",
-    "#           color='blue', pivot='mid')\n",
-    "\n",
-    "# Plot QVectors\n",
-    "qvec_slice = slice(None, None, 10)\n",
-    "ax.quiver(lons[qvec_slice], lats[qvec_slice],\n",
-    "          uqvect[qvec_slice, qvec_slice].m, vqvect[qvec_slice, qvec_slice].m,\n",
-    "          color='darkcyan', pivot='mid')\n",
-    "\n",
-    "plt.title('300-hPa Geo Heights (black), Q-Vector (dark cyan), Divergence (grey; dashed)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Declarative_300hPa.ipynb b/_preview/3/_sources/notebooks/gallery/Declarative_300hPa.ipynb
deleted file mode 100644
index 3ba6bcd..0000000
--- a/_preview/3/_sources/notebooks/gallery/Declarative_300hPa.ipynb
+++ /dev/null
@@ -1,193 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "MetPy Declarative - 300 hPa\n",
-    "===========================\n",
-    "\n",
-    "By: Kevin Goebbert\n",
-    "\n",
-    "This example uses the declarative syntax available through the MetPy\n",
-    "package to allow a more convenient method for creating simple maps of\n",
-    "atmospheric data. The key thing the declarative language does is to\n",
-    "reduce the number of packages that users will need to know in detail and\n",
-    "instead allow them to set key parameters to get the map they desire. One\n",
-    "key element is the use of xarray as the data object, which allows\n",
-    "coordinate information to be associated with atmospheric variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.plots.declarative import *\n",
-    "from metpy.units import units\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Open dataset using xarray module and subset global GFS to be over the\n",
-    "CONUS.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies'\n",
-    "                     '/python-gallery/GFS_20101026_1200.nc').sel(\n",
-    "    lon=slice(360-150, 360-50, 2), lat=slice(65, 20, 2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Calculate Variable and Add to Dataset\n",
-    "-------------------------------------\n",
-    "\n",
-    "Here it is demonstrated how you can calculate a new variable and add it\n",
-    "to the xarray dataset (ds) so that it can be plotted with the\n",
-    "declarative syntax. The key to adding a variable to an xarray dataset\n",
-    "for use in the declarative syntax is the need to add a ``grid_mapping``\n",
-    "and ``units`` attribute.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate New Variables and place into Xarray Dataset\n",
-    "uwnd = ds['u-component_of_wind_isobaric']\n",
-    "vwnd = ds['v-component_of_wind_isobaric']\n",
-    "\n",
-    "# Compute wind speed using MetPy\n",
-    "wspd = mpcalc.wind_speed(uwnd, vwnd)\n",
-    "\n",
-    "# Place wind speed (wspd) into xarray dataset and attach needed attributes\n",
-    "ds['wind_speed'] = wspd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Declarative Plot\n",
-    "----------------\n",
-    "\n",
-    "The following settings create a single panel map plot of 300 hPa\n",
-    "geopotential heights, wind speed, and wind barbs.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Countour Plot of Geopotential Heights\n",
-    "contour = ContourPlot()\n",
-    "contour.data = ds\n",
-    "contour.time = datetime(2010, 10, 31, 12)\n",
-    "contour.field = 'Geopotential_height_isobaric'\n",
-    "contour.level = 300 * units.hPa\n",
-    "contour.linecolor = 'black'\n",
-    "contour.linestyle = '-'\n",
-    "contour.linewidth = 2\n",
-    "contour.clabels = True\n",
-    "contour.contours = list(range(0, 20000, 120))\n",
-    "\n",
-    "# Colorfilled Plot of Wind Speed\n",
-    "cfill = FilledContourPlot()\n",
-    "cfill.data = ds\n",
-    "cfill.field = 'wind_speed'\n",
-    "cfill.level = 300 * units.hPa\n",
-    "cfill.colormap = 'BuPu'\n",
-    "cfill.contours = list(range(50, 171, 20))\n",
-    "cfill.colorbar = 'vertical'\n",
-    "cfill.plot_units = 'kt'\n",
-    "\n",
-    "# Plot wind barbs\n",
-    "barb = BarbPlot()\n",
-    "barb.data = ds\n",
-    "barb.level = 300 * units.hPa\n",
-    "barb.field = ['u-component_of_wind_isobaric', 'v-component_of_wind_isobaric']\n",
-    "barb.skip = (3, 3)\n",
-    "barb.color = 'black'\n",
-    "barb.barblength = 6.5\n",
-    "barb.earth_relative = False\n",
-    "barb.plot_units = 'kt'\n",
-    "\n",
-    "# Panel for plot with Map features\n",
-    "panel = MapPanel()\n",
-    "panel.layout = (1, 1, 1)\n",
-    "panel.area = (-124, -72, 20, 53)\n",
-    "panel.projection = 'lcc'\n",
-    "panel.layers = ['coastline', 'borders', 'states', 'land']\n",
-    "panel.plots = [cfill, contour, barb]\n",
-    "\n",
-    "# Bringing it all together\n",
-    "pc = PanelContainer()\n",
-    "pc.size = (15, 9)\n",
-    "pc.panels = [panel]\n",
-    "\n",
-    "pc.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Differential_Temperature_Advection.ipynb b/_preview/3/_sources/notebooks/gallery/Differential_Temperature_Advection.ipynb
deleted file mode 100644
index cea2fdb..0000000
--- a/_preview/3/_sources/notebooks/gallery/Differential_Temperature_Advection.ipynb
+++ /dev/null
@@ -1,325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Differential Temperature Advection with NARR Data\n",
-    "=================================================\n",
-    "\n",
-    "By: Kevin Goebbert\n",
-    "\n",
-    "This example creates a four-panel plot to illustrate the difference\n",
-    "between single level temperature advection and a computed differential\n",
-    "temperature advection between two layers. This example makes use of NARR\n",
-    "output."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.gridspec as gridspec\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Data Input\n",
-    "----------\n",
-    "\n",
-    "Use Xarray to access GFS data from THREDDS resource and uses\n",
-    "metpy accessor to parse file to make it easy to pull data using\n",
-    "common coordinate names (e.g., vertical) and attach units.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies/'\n",
-    "                     'python-gallery/NARR_19930313_1800.nc').metpy.parse_cf()\n",
-    "ds = ds.metpy.assign_latitude_longitude()\n",
-    "\n",
-    "# Get lat/lon data from file\n",
-    "lats = ds.lat\n",
-    "lons = ds.lon\n",
-    "\n",
-    "# Get 700-hPa data and smooth\n",
-    "level = 700 * units.hPa\n",
-    "hght_700 = mpcalc.smooth_n_point(ds['Geopotential_height_isobaric'].metpy.sel(\n",
-    "    vertical=level).squeeze(), 9)\n",
-    "tmpk_700 = mpcalc.smooth_n_point(ds['Temperature_isobaric'].metpy.sel(\n",
-    "    vertical=level).squeeze(), 9)\n",
-    "uwnd_700 = mpcalc.smooth_n_point(\n",
-    "    ds['u-component_of_wind_isobaric'].metpy.sel(vertical=level).squeeze(), 9)\n",
-    "vwnd_700 = mpcalc.smooth_n_point(\n",
-    "    ds['v-component_of_wind_isobaric'].metpy.sel(vertical=level).squeeze(), 9)\n",
-    "\n",
-    "# Get 300-hPa data and\n",
-    "level = 300 * units.hPa\n",
-    "hght_300 = mpcalc.smooth_n_point(ds['Geopotential_height_isobaric'].metpy.sel(\n",
-    "    vertical=level).squeeze(), 9)\n",
-    "tmpk_300 = mpcalc.smooth_n_point(ds['Temperature_isobaric'].metpy.sel(\n",
-    "    vertical=level).squeeze(), 9)\n",
-    "uwnd_300 = mpcalc.smooth_n_point(\n",
-    "    ds['u-component_of_wind_isobaric'].metpy.sel(vertical=level).squeeze(), 9)\n",
-    "vwnd_300 = mpcalc.smooth_n_point(\n",
-    "    ds['v-component_of_wind_isobaric'].metpy.sel(vertical=level).squeeze(), 9)\n",
-    "\n",
-    "# Convert Temperatures to degC\n",
-    "tmpc_700 = tmpk_700.metpy.convert_units('degC')\n",
-    "tmpc_300 = tmpk_300.metpy.convert_units('degC')\n",
-    "\n",
-    "# Get time in a nice datetime object format\n",
-    "vtime = ds.time.values.astype('datetime64[ms]').astype('O')[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Differential Temperature Advection Calculation\n",
-    "----------------------------------------------\n",
-    "\n",
-    "Use MetPy advection funtion to calculate temperature advection at 700\n",
-    "and 300 hPa, then manually compute the differential between those two\n",
-    "layers. The differential temperature advection is then valid at 500 hPa\n",
-    "(due to centered differencing) and is the same level that height changes\n",
-    "due to absolute vorticity advection is commonly assessed.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Use MetPy advection function to calculate temperature advection at two levels\n",
-    "tadv_700 = mpcalc.advection(tmpk_700, uwnd_700, vwnd_700)\n",
-    "tadv_300 = mpcalc.advection(tmpk_300, uwnd_300, vwnd_300)\n",
-    "\n",
-    "# Centered finite difference to calculate differential temperature advection\n",
-    "diff_tadv = ((tadv_700.data - tadv_300.data)/(400 * units.hPa)).to_base_units()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Make Four Panel Plot\n",
-    "--------------------\n",
-    "\n",
-    "A four panel plot is produced to illustrate the temperature fields at\n",
-    "two levels (700 and 300 hPa), which are common fields to be plotted.\n",
-    "Then a panel containing the evaluated temperature advection at 700 hPa\n",
-    "and differential temperature advection between 700 and 300 hPa. Of\n",
-    "meteorological significance is the difference between these two\n",
-    "advection plots. For the QG Height Tendency equation, the forcing term\n",
-    "is proportional to the differential temperature advection, which paints\n",
-    "a slightly different picture than just the 700-hPa temperature advection\n",
-    "alone.\n",
-    "\n",
-    "To create the four panel plot it takes a bit of code at this point. The\n",
-    "following code is segmented into Upper-left, Lower-left, Upper-right,\n",
-    "Lower-right panels using matplotlib’s gridspec to help with spacing.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up plot crs (mapcrs) and the data crs, will need to\n",
-    "# transform all variables\n",
-    "mapcrs = ccrs.LambertConformal(central_longitude=-100,\n",
-    "                               central_latitude=35,\n",
-    "                               standard_parallels=(30, 60))\n",
-    "datacrs = ccrs.PlateCarree()\n",
-    "\n",
-    "# Set some common contour interval levels\n",
-    "clevs_700_tmpc = np.arange(-40, 41, 2)\n",
-    "clevs_700_hght = np.arange(0, 8000, 30)\n",
-    "clevs_300_hght = np.arange(0, 10000, 120)\n",
-    "\n",
-    "# Create slice to reduce number of wind barbs at plot time\n",
-    "wind_slice = (slice(None, None, 10), slice(None, None, 10))\n",
-    "\n",
-    "# Start figure\n",
-    "fig = plt.figure(1, figsize=(22, 15))\n",
-    "\n",
-    "# Use gridspec to help size elements of plot; small top plot\n",
-    "# and big bottom plot\n",
-    "gs = gridspec.GridSpec(nrows=2, ncols=2, height_ratios=[1, 1],\n",
-    "                       hspace=0.03, wspace=0.03)\n",
-    "\n",
-    "\n",
-    "# Upper-left panel (700-hPa TMPC)\n",
-    "ax1 = plt.subplot(gs[0, 0], projection=mapcrs)\n",
-    "ax1.set_extent([-130, -72, 25, 49], ccrs.PlateCarree())\n",
-    "ax1.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax1.add_feature(cfeature.STATES.with_scale('50m'))\n",
-    "\n",
-    "cf = ax1.contourf(lons, lats, tmpc_700, clevs_700_tmpc,\n",
-    "                  cmap=plt.cm.coolwarm, transform=datacrs)\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50)\n",
-    "cb.set_label(r'$^{\\circ}$C')\n",
-    "\n",
-    "csf = ax1.contour(lons, lats, tmpc_700, clevs_700_tmpc, colors='grey',\n",
-    "                  linestyles='dashed', transform=datacrs)\n",
-    "plt.clabel(csf, fmt='%d')\n",
-    "\n",
-    "cs = ax1.contour(lons, lats, hght_700, clevs_700_hght, colors='black',\n",
-    "                 transform=datacrs)\n",
-    "plt.clabel(cs, fmt='%d')\n",
-    "\n",
-    "ax1.barbs(lons[wind_slice].values, lats[wind_slice].values,\n",
-    "          uwnd_700.metpy.convert_units('kt')[wind_slice].values,\n",
-    "          vwnd_700[wind_slice].metpy.convert_units('kt').values,\n",
-    "          pivot='middle', color='black', transform=datacrs)\n",
-    "\n",
-    "plt.title('700-hPa NARR HGHT (m), TMPC, and Wind (kt)', loc='left')\n",
-    "plt.title(f'Valid: {vtime}', loc='right')\n",
-    "\n",
-    "\n",
-    "# Lower-left panel (300-hPa TMPC)\n",
-    "ax2 = plt.subplot(gs[1, 0], projection=mapcrs)\n",
-    "ax2.set_extent([-130, -72, 25, 49], ccrs.PlateCarree())\n",
-    "ax2.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax2.add_feature(cfeature.STATES.with_scale('50m'))\n",
-    "\n",
-    "cf = ax2.contourf(lons, lats, tmpc_300, range(-60, -24, 2),\n",
-    "                  cmap=plt.cm.BuPu_r, transform=datacrs)\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50)\n",
-    "cb.set_label(r'$^{\\circ}$C')\n",
-    "\n",
-    "csf = ax2.contour(lons, lats, tmpc_300, range(-60, 0, 2),\n",
-    "                  colors='grey', linestyles='dashed', transform=datacrs)\n",
-    "plt.clabel(csf, fmt='%d')\n",
-    "\n",
-    "cs = ax2.contour(lons, lats, hght_300, clevs_300_hght, colors='black',\n",
-    "                 transform=datacrs)\n",
-    "plt.clabel(cs, fmt='%d')\n",
-    "\n",
-    "ax2.barbs(lons[wind_slice].values, lats[wind_slice].values,\n",
-    "          uwnd_300.metpy.convert_units('kt')[wind_slice].values,\n",
-    "          vwnd_300[wind_slice].metpy.convert_units('kt').values,\n",
-    "          pivot='middle', color='black', transform=datacrs)\n",
-    "\n",
-    "plt.title('300-hPa NARR HGHT (m), TMPC, and Wind (kt)', loc='left')\n",
-    "plt.title(f'Valid: {vtime}', loc='right')\n",
-    "\n",
-    "\n",
-    "# Upper-right panel (700-hPa TMPC Adv)\n",
-    "ax3 = plt.subplot(gs[0, 1], projection=mapcrs)\n",
-    "ax3.set_extent([-130, -72, 25, 49], ccrs.PlateCarree())\n",
-    "ax3.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax3.add_feature(cfeature.STATES.with_scale('50m'))\n",
-    "\n",
-    "cf = ax3.contourf(lons, lats, tadv_700*3600, range(-8, 9, 1),\n",
-    "                  cmap=plt.cm.coolwarm, transform=datacrs)\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50)\n",
-    "cb.set_label(r'TMPC ADV ($^{\\circ}$C h$^{-1}$)')\n",
-    "\n",
-    "csf = ax3.contour(lons, lats, tmpc_700, clevs_700_tmpc, colors='grey',\n",
-    "                  linestyles='dashed', transform=datacrs)\n",
-    "plt.clabel(csf, fmt='%d')\n",
-    "\n",
-    "cs = ax3.contour(lons, lats, hght_700, clevs_700_hght, colors='black',\n",
-    "                 transform=datacrs)\n",
-    "plt.clabel(cs, fmt='%d')\n",
-    "\n",
-    "ax3.barbs(lons[wind_slice].values, lats[wind_slice].values,\n",
-    "          uwnd_700.metpy.convert_units('kt')[wind_slice].values,\n",
-    "          vwnd_700[wind_slice].metpy.convert_units('kt').values,\n",
-    "          pivot='middle', color='black', transform=datacrs)\n",
-    "\n",
-    "plt.title('700-hPa NARR HGHT (m), TMP ADV (C/h), and Wind (kt)', loc='left')\n",
-    "plt.title(f'Valid: {vtime}', loc='right')\n",
-    "\n",
-    "\n",
-    "# Lower-right panel (diff TMPC)\n",
-    "ax4 = plt.subplot(gs[1, 1], projection=mapcrs)\n",
-    "ax4.set_extent([-130, -72, 25, 49], ccrs.PlateCarree())\n",
-    "ax4.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax4.add_feature(cfeature.STATES.with_scale('50m'))\n",
-    "\n",
-    "cf = ax4.contourf(lons, lats, diff_tadv*1e9, clevs_700_tmpc,\n",
-    "                  cmap=plt.cm.coolwarm, extend='both', transform=datacrs)\n",
-    "cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,\n",
-    "                  extendrect=True)\n",
-    "cb.set_label(r'dTMPC ($10^9$ $^{\\circ}$C s$^{-1}$ Pa$^{-1}$)')\n",
-    "\n",
-    "csf = ax4.contour(lons, lats, tmpc_700, clevs_700_tmpc, colors='grey',\n",
-    "                  linestyles='dashed', transform=datacrs)\n",
-    "plt.clabel(csf, fmt='%d')\n",
-    "\n",
-    "cs = ax4.contour(lons, lats, hght_700, clevs_700_hght, colors='black',\n",
-    "                 transform=datacrs)\n",
-    "plt.clabel(cs, fmt='%d')\n",
-    "\n",
-    "ax4.barbs(lons[wind_slice].values, lats[wind_slice].values,\n",
-    "          uwnd_700.metpy.convert_units('kt')[wind_slice].values,\n",
-    "          vwnd_700[wind_slice].metpy.convert_units('kt').values,\n",
-    "          pivot='middle', color='black', transform=datacrs)\n",
-    "\n",
-    "plt.title('700-hPa NARR HGHT (m), Diff. TMP ADV (C/s/Pa), and Wind (kt)', loc='left')\n",
-    "plt.title(f'Valid: {vtime}', loc='right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Hovmoller_Diagram.ipynb b/_preview/3/_sources/notebooks/gallery/Hovmoller_Diagram.ipynb
deleted file mode 100644
index ae4ed55..0000000
--- a/_preview/3/_sources/notebooks/gallery/Hovmoller_Diagram.ipynb
+++ /dev/null
@@ -1,200 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Hovmoller Diagram Example\n",
-    "=========================\n",
-    "\n",
-    "By: Kevin Goebbert\n",
-    "\n",
-    "Northern Hemispheric v-wind component over the mid-latitudes in a\n",
-    "Hovmoller diagram. This diagram can be used to illustrate upper-level\n",
-    "wave and energy propogation (e.g., downstream baroclinic development)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.gridspec as gridspec\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "import numpy as np\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Get the data\n",
-    "------------\n",
-    "\n",
-    "Using NCEP/NCAR reanalysis data via xarray remote access using the\n",
-    "OPeNDAP protocol.\n",
-    "\n",
-    "Set the time range, parameter, and level to desired values\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create time slice from dates\n",
-    "start_time = '2011-01-20'\n",
-    "end_time = '2011-02-06'\n",
-    "\n",
-    "# Select NCEP/NCAR parameter and level\n",
-    "param = 'vwnd'\n",
-    "level = 250\n",
-    "\n",
-    "# Remote get dataset using OPeNDAP method via xarray\n",
-    "ds = xr.open_dataset('http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/'\n",
-    "                     f'ncep.reanalysis/pressure/{param}.{start_time[:4]}.nc')\n",
-    "\n",
-    "# Create slice variables subset domain\n",
-    "time_slice = slice(start_time, end_time)\n",
-    "lat_slice = slice(60, 40)\n",
-    "lon_slice = slice(0, 360)\n",
-    "\n",
-    "# Get data, selecting time, level, lat/lon slice\n",
-    "data = ds[param].sel(time=time_slice,\n",
-    "                     level=level,\n",
-    "                     lat=lat_slice,\n",
-    "                     lon=lon_slice)\n",
-    "\n",
-    "# Compute weights and take weighted average over latitude dimension\n",
-    "weights = np.cos(np.deg2rad(data.lat.values))\n",
-    "avg_data = (data * weights[None, :, None]).sum(dim='lat') / np.sum(weights)\n",
-    "\n",
-    "# Get times and make array of datetime objects\n",
-    "vtimes = data.time.values.astype('datetime64[ms]').astype('O')\n",
-    "\n",
-    "# Specify longitude values for chosen domain\n",
-    "lons = data.lon.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Make the Hovmoller Plot\n",
-    "-----------------------\n",
-    "\n",
-    "Pretty simple to use common matplotlib/cartopy to create the diagram.\n",
-    "Cartopy is used to create a geographic reference map to highlight the\n",
-    "area being averaged as well as the visual reference for longitude.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Start figure\n",
-    "fig = plt.figure(figsize=(10, 13))\n",
-    "\n",
-    "# Use gridspec to help size elements of plot; small top plot\n",
-    "# and big bottom plot\n",
-    "gs = gridspec.GridSpec(nrows=2, ncols=1, height_ratios=[1, 6], hspace=0.03)\n",
-    "\n",
-    "# Tick labels\n",
-    "x_tick_labels = [u'0\\N{DEGREE SIGN}E', u'90\\N{DEGREE SIGN}E',\n",
-    "                 u'180\\N{DEGREE SIGN}E', u'90\\N{DEGREE SIGN}W',\n",
-    "                 u'0\\N{DEGREE SIGN}E']\n",
-    "\n",
-    "# Top plot for geographic reference (makes small map)\n",
-    "ax1 = fig.add_subplot(gs[0, 0],\n",
-    "                      projection=ccrs.PlateCarree(central_longitude=180))\n",
-    "ax1.set_extent([0, 357.5, 35, 65], ccrs.PlateCarree(central_longitude=180))\n",
-    "ax1.set_yticks([40, 60])\n",
-    "ax1.set_yticklabels([u'40\\N{DEGREE SIGN}N', u'60\\N{DEGREE SIGN}N'])\n",
-    "ax1.set_xticks([-180, -90, 0, 90, 180])\n",
-    "ax1.set_xticklabels(x_tick_labels)\n",
-    "ax1.grid(linestyle='dotted', linewidth=2)\n",
-    "\n",
-    "# Add geopolitical boundaries for map reference\n",
-    "ax1.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax1.add_feature(cfeature.LAKES.with_scale('50m'), color='black',\n",
-    "                linewidths=0.5)\n",
-    "\n",
-    "# Set some titles\n",
-    "plt.title('Hovmoller Diagram', loc='left')\n",
-    "plt.title('NCEP/NCAR Reanalysis', loc='right')\n",
-    "\n",
-    "# Bottom plot for Hovmoller diagram\n",
-    "ax2 = fig.add_subplot(gs[1, 0])\n",
-    "ax2.invert_yaxis()  # Reverse the time order to do oldest first\n",
-    "\n",
-    "# Plot of chosen variable averaged over latitude and slightly smoothed\n",
-    "clevs = np.arange(-50, 51, 5)\n",
-    "cf = ax2.contourf(lons, vtimes, mpcalc.smooth_n_point(\n",
-    "    avg_data, 9, 2), clevs, cmap=plt.cm.bwr, extend='both')\n",
-    "cs = ax2.contour(lons, vtimes, mpcalc.smooth_n_point(\n",
-    "    avg_data, 9, 2), clevs, colors='k', linewidths=1)\n",
-    "cbar = plt.colorbar(cf, orientation='horizontal', pad=0.04, aspect=50,\n",
-    "                    extendrect=True)\n",
-    "cbar.set_label('m $s^{-1}$')\n",
-    "\n",
-    "# Make some ticks and tick labels\n",
-    "ax2.set_xticks([0, 90, 180, 270, 357.5])\n",
-    "ax2.set_xticklabels(x_tick_labels)\n",
-    "ax2.set_yticks(vtimes[4::8])\n",
-    "ax2.set_yticklabels(vtimes[4::8])\n",
-    "\n",
-    "# Set some titles\n",
-    "plt.title('250-hPa V-wind', loc='left', fontsize=10)\n",
-    "plt.title(f'Time Range: {vtimes[0]:%Y%m%d %HZ} - {vtimes[-1]:%Y%m%d %HZ}',\n",
-    "          loc='right', fontsize=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Isentropic_Interpolation.ipynb b/_preview/3/_sources/notebooks/gallery/Isentropic_Interpolation.ipynb
deleted file mode 100644
index 815e4fe..0000000
--- a/_preview/3/_sources/notebooks/gallery/Isentropic_Interpolation.ipynb
+++ /dev/null
@@ -1,306 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Isentropic Analysis\n",
-    "===================\n",
-    "\n",
-    "The MetPy function `metpy.calc.isentropic_interpolation` allows for isentropic analysis from\n",
-    "model analysis data in isobaric coordinates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "from siphon.catalog import TDSCatalog\n",
-    "from xarray.backends import NetCDF4DataStore\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "###########################"
-   },
-   "source": [
-    "Getting the data\n",
-    "\n",
-    "In this example, the latest GFS forecasts data from the National Centers for\n",
-    "Environmental Information (https://nomads.ncdc.noaa.gov) will be used, courtesy of the\n",
-    "Univeristy Corporation for Atmospheric Research Thredds Data Server."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Latest GFS Dataset\n",
-    "cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'\n",
-    "                 'NCEP/GFS/Global_0p5deg/catalog.xml')\n",
-    "ncss = cat.latest.subset()\n",
-    "\n",
-    "# Find the start of the model run and define time range\n",
-    "start_time = ncss.metadata.time_span['begin']\n",
-    "start = datetime.strptime(start_time, '%Y-%m-%dT%H:%M:%Sz')\n",
-    "end = start + timedelta(hours=9)\n",
-    "\n",
-    "# Query for Latest GFS Run\n",
-    "gfsdata = ncss.query().time_range(start, end).accept('netcdf4')\n",
-    "\n",
-    "gfsdata.variables('Temperature_isobaric',\n",
-    "                  'u-component_of_wind_isobaric',\n",
-    "                  'v-component_of_wind_isobaric',\n",
-    "                  'Relative_humidity_isobaric').add_lonlat()\n",
-    "\n",
-    "# Set the lat/lon box for the data you want to pull in.\n",
-    "# lonlat_box(north_lat,south_lat,east_lon,west_lon)\n",
-    "gfsdata.lonlat_box(-150, -50, 15, 65)\n",
-    "\n",
-    "# Actually getting the data\n",
-    "data = ncss.get_data(gfsdata)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
-    "\n",
-    "dlev_hght = ds.Temperature_isobaric.dims[1]\n",
-    "dlev_uwnd = ds['u-component_of_wind_isobaric'].dims[1]\n",
-    "lat = ds.latitude\n",
-    "lon = ds.longitude\n",
-    "\n",
-    "lev_hght = ds[dlev_hght]\n",
-    "lev_uwnd = ds[dlev_uwnd]\n",
-    "\n",
-    "# Due to a different number of vertical levels find where they are common\n",
-    "_, _, common_ind = np.intersect1d(lev_uwnd, lev_hght, return_indices=True)\n",
-    "\n",
-    "common_levels = lev_hght[common_ind].metpy.unit_array\n",
-    "times = ds.Temperature_isobaric.metpy.time\n",
-    "vtimes = times.values.astype('datetime64[ms]').astype('O')\n",
-    "temps = ds.Temperature_isobaric.metpy.unit_array\n",
-    "uwnd = ds['u-component_of_wind_isobaric'].metpy.unit_array\n",
-    "vwnd = ds['v-component_of_wind_isobaric'].metpy.unit_array\n",
-    "relh = ds.Relative_humidity_isobaric.metpy.unit_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################"
-   },
-   "source": [
-    "To properly interpolate to isentropic coordinates, the function must know the desired output\n",
-    "isentropic levels. An array with these levels will be created below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isentlevs = np.arange(310, 316, 5) * units.kelvin"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################"
-   },
-   "source": [
-    "Conversion to Isentropic Coordinates\n",
-    "\n",
-    "Once model data in isobaric coordinates has been pulled and the desired isentropic levels\n",
-    "created, the conversion to isentropic coordinates can begin. Data will be passed to the\n",
-    "function as below. The function requires that isentropic levels, isobaric levels, and\n",
-    "temperature be input. Any additional inputs (in this case relative humidity, u, and v wind\n",
-    "components) will be linearly interpolated to isentropic space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isent_anal = mpcalc.isentropic_interpolation(isentlevs,\n",
-    "                                             common_levels,\n",
-    "                                             temps[:, common_ind, :, :],\n",
-    "                                             relh,\n",
-    "                                             uwnd,\n",
-    "                                             vwnd,\n",
-    "                                             vertical_dim=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "####################"
-   },
-   "source": [
-    "The output is a list, so now we will separate the variables to different names before\n",
-    "plotting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isentprs, isentrh, isentu, isentv = isent_anal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##########################"
-   },
-   "source": [
-    "A quick look at the shape of these variables will show that the data is now in isentropic\n",
-    "coordinates, with the number of vertical levels as specified above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(isentprs.shape)\n",
-    "print(isentrh.shape)\n",
-    "print(isentu.shape)\n",
-    "print(isentv.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "####################"
-   },
-   "source": [
-    "**Plotting the Isentropic Analysis**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#####################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Set up our projection"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)\n",
-    "\n",
-    "# Set up our array of latitude and longitude values and transform to\n",
-    "# the desired projection.\n",
-    "clons, clats = np.meshgrid(lon, lat)\n",
-    "\n",
-    "# Get data to plot state and province boundaries\n",
-    "states_provinces = cfeature.NaturalEarthFeature(\n",
-    "    category='cultural',\n",
-    "    name='admin_1_states_provinces_lakes',\n",
-    "    scale='50m',\n",
-    "    facecolor='none')\n",
-    "\n",
-    "# Choose Isentropic Level index value\n",
-    "level = 0 # [310, 315]\n",
-    "\n",
-    "# Choose forecast hour index value\n",
-    "FH = 0 # [0, 3, 6, 9]\n",
-    "\n",
-    "# Start Figure\n",
-    "fig = plt.figure(1, figsize=(14., 12.))\n",
-    "ax = plt.subplot(111, projection=crs)\n",
-    "\n",
-    "# Set plot extent\n",
-    "ax.set_extent((-121., -74., 25., 50.), crs=ccrs.PlateCarree())\n",
-    "ax.coastlines('50m', edgecolor='black', linewidth=0.75)\n",
-    "ax.add_feature(states_provinces, edgecolor='black', linewidth=0.5)\n",
-    "\n",
-    "# Plot the 300K surface\n",
-    "clevisent = np.arange(0, 1000, 25)\n",
-    "cs = ax.contour(clons, clats,\n",
-    "                mpcalc.smooth_n_point(isentprs[FH, level, :, :], 9, 4),\n",
-    "                clevisent,\n",
-    "                transform=ccrs.PlateCarree(),\n",
-    "                colors='k', linewidths=1.5, linestyles='solid')\n",
-    "plt.clabel(cs, fontsize=10, inline=1, inline_spacing=7,\n",
-    "           fmt='%i', rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "cf = ax.contourf(clons, clats,\n",
-    "                 mpcalc.smooth_n_point(isentrh[FH, level, :, :], 9, 4),\n",
-    "                 np.arange(50, 101, 1),\n",
-    "                 transform=ccrs.PlateCarree(),\n",
-    "                 cmap=plt.cm.YlGn)\n",
-    "plt.colorbar(cf, orientation='horizontal', aspect=65, pad=0, extendrect='True')\n",
-    "\n",
-    "wind_slice = (FH, level, slice(None, None, 5), slice(None, None, 5))\n",
-    "ax.barbs(clons[wind_slice[2:]], clats[wind_slice[2:]],\n",
-    "         isentu[wind_slice].m, isentv[wind_slice].m, length=6,\n",
-    "         transform=ccrs.PlateCarree())\n",
-    "\n",
-    "# Make some titles\n",
-    "plt.title(f'{isentlevs[level].m:.0f} K Isentropic Level', loc='left')\n",
-    "plt.title(f'VALID: {vtimes[FH]} UTC', loc='right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Observational_Data_Cross_Section.ipynb b/_preview/3/_sources/notebooks/gallery/Observational_Data_Cross_Section.ipynb
deleted file mode 100644
index dacd349..0000000
--- a/_preview/3/_sources/notebooks/gallery/Observational_Data_Cross_Section.ipynb
+++ /dev/null
@@ -1,430 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Real Data Cross-Section Example\n",
-    "===============================\n",
-    "\n",
-    "Cross-section using real data from soundings.\n",
-    "\n",
-    "This example uses actual soundings to create a cross-section. There are\n",
-    "two functions defined to help interpolate radiosonde observations, which\n",
-    "won’t all be at the same level, to a standard grid. The vertical\n",
-    "interpolation assumes a log-linear relationship. Each radisosonde\n",
-    "vertical profile is interpolated first, then the\n",
-    "``scipy.interpolate.griddata`` function is used to generate a full 2D\n",
-    "(x, p) grid between each station. Pyproj is used to calculate the\n",
-    "distance between each station and the standard atmosphere is used to\n",
-    "convert the elevation of each station to a pressure value for plotting\n",
-    "purposes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "from siphon.simplewebservice.wyoming import WyomingUpperAir"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Vertical Interpolation Function\n",
-    "-------------------------------\n",
-    "\n",
-    "Function interpolates to given pressure level data to set grid."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "def vertical_interpolate(vcoord_data, interp_var, interp_levels):\n",
-    "    \"\"\"A function to interpolate sounding data from each station to\n",
-    "    every millibar. Assumes a log-linear relationship.\n",
-    "\n",
-    "    Input\n",
-    "    -----\n",
-    "    vcoord_data : A 1D array of vertical level values (e.g., pressure from a radiosonde)\n",
-    "    interp_var : A 1D array of the variable to be interpolated to all pressure levels\n",
-    "    vcoord_interp_levels : A 1D array containing veritcal levels to interpolate to\n",
-    "\n",
-    "    Return\n",
-    "    ------\n",
-    "    interp_data : A 1D array that contains the interpolated variable on the interp_levels\n",
-    "    \"\"\"\n",
-    "    # Make veritcal coordinate data and grid level log variables\n",
-    "    lnp = np.log(vcoord_data)\n",
-    "    lnp_intervals = np.log(interp_levels)\n",
-    "\n",
-    "    # Use numpy to interpolate from observed levels to grid levels\n",
-    "    interp_data = np.interp(lnp_intervals[::-1], lnp[::-1], interp_var[::-1])[::-1]\n",
-    "\n",
-    "    # Mask for missing data (generally only near the surface)\n",
-    "    mask_low = interp_levels > vcoord_data[0]\n",
-    "    mask_high = interp_levels < vcoord_data[-1]\n",
-    "    interp_data[mask_low] = interp_var[0]\n",
-    "    interp_data[mask_high] = interp_var[-1]\n",
-    "\n",
-    "    return interp_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Radiosonde Observation Interpolation Function\n",
-    "---------------------------------------------\n",
-    "\n",
-    "This function interpolates given radiosonde data into a 2D array for all\n",
-    "meteorological variables given in dataframe. Returns a dictionary that\n",
-    "will have requesite data for plotting a cross section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "def radiosonde_cross_section(stns, data, start=1000, end=100, step=10):\n",
-    "    \"\"\"This function takes a list of radiosonde observation sites with a\n",
-    "    dictionary of Pandas Dataframes with the requesite data for each station.\n",
-    "\n",
-    "    Input\n",
-    "    -----\n",
-    "    stns : List of statition three-letter identifiers\n",
-    "    data : A dictionary of Pandas Dataframes containing the radiosonde observations\n",
-    "    for the stations\n",
-    "    start : interpolation start value, optional (default = 1000 hPa)\n",
-    "    end : Interpolation end value, optional (default = 100 hPa)\n",
-    "    step : Interpolation interval, option (default = 10 hPa)\n",
-    "\n",
-    "    Return\n",
-    "    ------\n",
-    "    cross_section : A dictionary that contains the following variables\n",
-    "\n",
-    "        grid_data : An interpolated grid with 100 points between the first and last station,\n",
-    "        with the corresponding number of vertical points based on start, end, and interval\n",
-    "        (default is 90)\n",
-    "        obs_distance : An array of distances between each radiosonde observation location\n",
-    "        x_grid : A 2D array of horizontal direction grid points\n",
-    "        p_grid : A 2D array of vertical pressure levels\n",
-    "        ground_elevation : A representation of the terrain between radiosonde observation sites\n",
-    "        based on the elevation of each station converted to pressure using the standard\n",
-    "        atmosphere\n",
-    "\n",
-    "    \"\"\"\n",
-    "    from pyproj import Geod\n",
-    "    from scipy.interpolate import griddata\n",
-    "    \n",
-    "    data_units = {'pressure': 'hPa',\n",
-    "                  'height': 'meter',\n",
-    "                  'temperature': 'degC',\n",
-    "                  'dewpoint': 'degC',\n",
-    "                  'direction': 'degrees',\n",
-    "                  'speed': 'knot',\n",
-    "                  'u_wind': 'knot',\n",
-    "                  'v_wind': 'knot',\n",
-    "                  'station': None,\n",
-    "                  'station_number': None,\n",
-    "                  'time': None,\n",
-    "                  'latitude': 'degrees',\n",
-    "                  'longitude': 'degrees',\n",
-    "                  'elevation': 'meter',\n",
-    "                  'pw': 'millimeter'}\n",
-    "    # Set up vertical grid, largest value first (high pressure nearest surface)\n",
-    "    vertical_levels = np.arange(start, end-1, -step) * units(data_units['pressure'])\n",
-    "\n",
-    "    # Number of vertical levels and stations\n",
-    "    plevs = len(vertical_levels)\n",
-    "    nstns = len(stns)\n",
-    "\n",
-    "    # Create dictionary of interpolated values and include neccsary attribute data\n",
-    "    # including lat, lon, and elevation of each station\n",
-    "    lats = []\n",
-    "    lons = []\n",
-    "    elev = []\n",
-    "    keys = data[stns[0]].keys()[:8]\n",
-    "    tmp_grid = dict.fromkeys(keys)\n",
-    "\n",
-    "    # Interpolate all variables for each radiosonde observation\n",
-    "    # Temperature, Dewpoint, U-wind, V-wind\n",
-    "    for key in tmp_grid.keys():\n",
-    "        tmp_grid[key] = np.empty((nstns, plevs))\n",
-    "        for station, loc in zip(stns, range(nstns)):\n",
-    "            if key == 'pressure':\n",
-    "                lats.append(data[station].latitude[0])\n",
-    "                lons.append(data[station].longitude[0])\n",
-    "                elev.append(data[station].elevation[0])\n",
-    "                tmp_grid[key][loc, :] = vertical_levels\n",
-    "            else:\n",
-    "                tmp_grid[key][loc, :] = vertical_interpolate(\n",
-    "                    data[station]['pressure'].values, data[station][key].values,\n",
-    "                    vertical_levels.m)\n",
-    "\n",
-    "    # Compute distance between each station using Pyproj\n",
-    "    g = Geod(ellps='sphere')\n",
-    "    _, _, dist = g.inv(nstns*[lons[0]], nstns*[lats[0]], lons[:], lats[:])\n",
-    "\n",
-    "    # Compute sudo ground elevation in pressure from standard atmsophere and the elevation\n",
-    "    # of each station\n",
-    "    ground_elevation = mpcalc.height_to_pressure_std(np.array(elev) * units('meters'))\n",
-    "\n",
-    "    # Set up grid for 2D interpolation\n",
-    "    grid = dict.fromkeys(keys)\n",
-    "    x = np.linspace(dist[0], dist[-1], 100)\n",
-    "    nx = len(x)\n",
-    "\n",
-    "    pp, xx = np.meshgrid(vertical_levels.m, x)\n",
-    "    pdist, ddist = np.meshgrid(vertical_levels.m, dist)\n",
-    "\n",
-    "    # Interpolate to 2D grid using scipy.interpolate.griddata\n",
-    "    for key in grid.keys():\n",
-    "        grid[key] = np.empty((nx, plevs)) * units(data_units[key])\n",
-    "        grid[key][:] = griddata((ddist.flatten(), pdist.flatten()),\n",
-    "                                tmp_grid[key][:].flatten(),\n",
-    "                                (xx, pp),\n",
-    "                                method='cubic') * units(data_units[key])\n",
-    "\n",
-    "    # Gather needed data in dictionary for return\n",
-    "    cross_section = {'grid_data': grid, 'obs_distance': dist * units.meter,\n",
-    "                     'x_grid': xx * units.meter, 'p_grid': pp * units.hPa, 'elevation': ground_elevation}\n",
-    "    return cross_section"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Stations and Time\n",
-    "-----------------\n",
-    "\n",
-    "Select cross section stations by creating a list of three-letter\n",
-    "identifiers and choose a date by creating a datetime object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# A roughly east-west cross section\n",
-    "stn_list = ['DNR', 'LBF', 'OAX', 'DVN', 'DTX', 'BUF']\n",
-    "\n",
-    "# Set a date and hour of your choosing\n",
-    "date = datetime(2019, 6, 1, 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Get Radiosonde Data\n",
-    "-------------------\n",
-    "\n",
-    "This example is built around the data from the University of Wyoming\n",
-    "sounding archive and using the Siphon package to remotely access that\n",
-    "data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up empty dictionary to fill with Wyoming Sounding data\n",
-    "df = {}\n",
-    "\n",
-    "# Loop over stations to get data and put into dictionary\n",
-    "for station in stn_list:\n",
-    "    df[station] = WyomingUpperAir.request_data(date, station)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Create Interpolated fields\n",
-    "--------------------------\n",
-    "\n",
-    "Use the function ``radisonde_cross_section`` to generate the 2D grid (x,\n",
-    "p) for all radiosonde variables including, Temperature, Dewpoint,\n",
-    "u-component of the wind, and v-component of the wind."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xsect = radiosonde_cross_section(stn_list, df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Calculate Variables for Plotting\n",
-    "--------------------------------\n",
-    "\n",
-    "Use MetPy to calculate common variables for plotting a cross section,\n",
-    "specifically potential temperature and mixing ratio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "potemp = mpcalc.potential_temperature(\n",
-    "    xsect['p_grid'], xsect['grid_data']['temperature'])\n",
-    "\n",
-    "relhum = mpcalc.relative_humidity_from_dewpoint(\n",
-    "    xsect['grid_data']['temperature'],\n",
-    "    xsect['grid_data']['dewpoint'])\n",
-    "\n",
-    "mixrat = mpcalc.mixing_ratio_from_relative_humidity(xsect['p_grid'],\n",
-    "                                                    xsect['grid_data']['temperature'],\n",
-    "                                                    relhum)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Plot Cross Section\n",
-    "------------------\n",
-    "\n",
-    "Use standard Matplotlib to plot the now 2D cross section grid using the\n",
-    "data from xsect and those calculated above. Additionally, the actualy\n",
-    "radiosonde wind observations are plotted as barbs on this plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Start Figure, set big size for cross section\n",
-    "fig = plt.figure(figsize=(17, 11))\n",
-    "\n",
-    "# Specify plotting axis (single panel)\n",
-    "ax = plt.subplot(111)\n",
-    "\n",
-    "# Set y-scale to be log since pressure decreases exponentially with height\n",
-    "ax.set_yscale('log')\n",
-    "\n",
-    "# Set limits, tickmarks, and ticklabels for y-axis\n",
-    "ax.set_ylim([1030, 101])\n",
-    "ax.set_yticks(range(1000, 101, -100))\n",
-    "ax.set_yticklabels(range(1000, 101, -100))\n",
-    "\n",
-    "# Invert the y-axis since pressure decreases with increasing height\n",
-    "ax.yaxis_inverted()\n",
-    "\n",
-    "# Plot the sudo elevation on the cross section\n",
-    "ax.fill_between(xsect['obs_distance'], xsect['elevation'].m, 1030,\n",
-    "                where=xsect['elevation'].m <= 1030, facecolor='lightgrey',\n",
-    "                interpolate=True, zorder=10)\n",
-    "# Don't plot xticks\n",
-    "plt.xticks([], [])\n",
-    "\n",
-    "# Plot wind barbs for each sounding location\n",
-    "for stn, stn_name in zip(range(len(stn_list)), stn_list):\n",
-    "    ax.axvline(xsect['obs_distance'][stn], ymin=0, ymax=1,\n",
-    "               linewidth=2, color='blue', zorder=11)\n",
-    "    ax.text(xsect['obs_distance'][stn], 1100, stn_name, ha='center', color='blue')\n",
-    "    ax.barbs(xsect['obs_distance'][stn], df[stn_name]['pressure'][::2],\n",
-    "             df[stn_name]['u_wind'].values[::2, None],\n",
-    "             df[stn_name]['v_wind'].values[::2, None], zorder=15)\n",
-    "\n",
-    "# Plot smoothed potential temperature grid (K)\n",
-    "cs = ax.contour(xsect['x_grid'], xsect['p_grid'], mpcalc.smooth_gaussian(potemp, 1),\n",
-    "                range(0, 500, 5), colors='red')\n",
-    "ax.clabel(cs, fmt='%i')\n",
-    "\n",
-    "# Plot smoothed mixing ratio grid (g/kg)\n",
-    "cs = ax.contour(xsect['x_grid'], xsect['p_grid'], mpcalc.smooth_gaussian(mixrat*1000, 2),\n",
-    "                range(0, 41, 2), colors='tab:green', linestyles='dotted')\n",
-    "ax.clabel(cs, fmt='%i')\n",
-    "\n",
-    "# Add some informative titles\n",
-    "plt.title('Cross-Section from DNR to BUF Potential Temp. '\n",
-    "          '(K; red) and Mix. Rat. (g/kg; green)', loc='left')\n",
-    "plt.title(date, loc='right');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/PV_baroclinic_isobaric.ipynb b/_preview/3/_sources/notebooks/gallery/PV_baroclinic_isobaric.ipynb
deleted file mode 100644
index 83a7d74..0000000
--- a/_preview/3/_sources/notebooks/gallery/PV_baroclinic_isobaric.ipynb
+++ /dev/null
@@ -1,243 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Baroclinic Potential Vorticity Analysis, Divergence, and Winds\n",
-    "==============================================================\n",
-    "\n",
-    "Classic baroclinic potential vorticity plot at 250 hPa using GFS\n",
-    "analysis file.\n",
-    "\n",
-    "This example uses example data from the GFS analysis for 12 UTC 31\n",
-    "October 2016 and uses xarray as the main read source with using MetPy to\n",
-    "calculate the baroclinic potential vorticity, divergence and wind speed\n",
-    "with geographic plotting using Cartopy for a CONUS view of the 250-hPa\n",
-    "surface with divergence and wind barbs.\n",
-    "\n",
-    "By: Kevin Goebbert"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "The following code reads the example data using the xarray open_dataset\n",
-    "function and prints the coordinate values that are associated with the\n",
-    "various variables contained within the file.\n",
-    "\n",
-    "This code uses the metpy accessor to parse file to make it easy to pull\n",
-    "data using common coordinate names (e.g., vertical) and attach units.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/casestudies'\n",
-    "                     '/python-gallery/GFS_20101026_1200.nc').metpy.parse_cf()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Data Retrieval\n",
-    "--------------\n",
-    "\n",
-    "This code retrieves the necessary data from the file and completes some\n",
-    "smoothing of the temperature, and wind fields using the MetPy function\n",
-    "smooth_n_point. A nicely formated valid time (vtime) variable is also\n",
-    "created.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set subset slice for the geographic extent of data to limit download\n",
-    "lon_slice = slice(200, 350)\n",
-    "lat_slice = slice(85, 10)\n",
-    "\n",
-    "# Grab lat/lon values (GFS will be 1D)\n",
-    "lats = ds.lat.sel(lat=lat_slice).values\n",
-    "lons = ds.lon.sel(lon=lon_slice).values\n",
-    "\n",
-    "\n",
-    "# Grab the pressure levels and select the data to be imported\n",
-    "# Need all pressure levels for Temperatures, U and V Wind,\n",
-    "# and Rel. Humidity\n",
-    "# Smooth with the gaussian filter from scipy\n",
-    "pres = ds.Temperature_isobaric.metpy.vertical\n",
-    "\n",
-    "tmpk_var = ds['Temperature_isobaric'].metpy.sel(\n",
-    "    lat=lat_slice, lon=lon_slice).squeeze()\n",
-    "tmpk = mpcalc.smooth_n_point(tmpk_var, 9, 2)\n",
-    "thta = mpcalc.potential_temperature(pres, tmpk)\n",
-    "\n",
-    "uwnd_var = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
-    "    lat=lat_slice, lon=lon_slice).squeeze()\n",
-    "vwnd_var = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
-    "    lat=lat_slice, lon=lon_slice).squeeze()\n",
-    "uwnd = mpcalc.smooth_n_point(uwnd_var, 9, 2)\n",
-    "vwnd = mpcalc.smooth_n_point(vwnd_var, 9, 2)\n",
-    "\n",
-    "# Create a clean datetime object for plotting based on time of\n",
-    "# Geopotential heights\n",
-    "vtime = ds.time.data[0].astype('datetime64[ms]').astype('O')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Use MetPy to compute the baroclinic potential vorticity on all isobaric\n",
-    "levels and other variables\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Comput the PV on all isobaric surfaces\n",
-    "pv = mpcalc.potential_vorticity_baroclinic(thta, pres, uwnd, vwnd)\n",
-    "\n",
-    "# Use MetPy to compute the divergence on the pressure surfaces\n",
-    "div = mpcalc.divergence(uwnd, vwnd)\n",
-    "\n",
-    "# Select the 250-hPa surface\n",
-    "pv_250 = pv.metpy.sel(vertical=250*units.hPa)\n",
-    "div_250 = div.metpy.sel(vertical=250*units.hPa)\n",
-    "uwnd_250 = uwnd.metpy.sel(vertical=250*units.hPa)\n",
-    "vwnd_250 = vwnd.metpy.sel(vertical=250*units.hPa)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Map Creation\n",
-    "------------\n",
-    "\n",
-    "This next set of code creates the plot and draws contours on a Lambert\n",
-    "Conformal map centered on -100 E longitude. The main view is over the\n",
-    "CONUS with isobaric PV map with PV contoured every 1 PVU and divergence\n",
-    "colorshaded.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up the projection that will be used for plotting\n",
-    "mapcrs = ccrs.LambertConformal(central_longitude=-100,\n",
-    "                               central_latitude=35,\n",
-    "                               standard_parallels=(30, 60))\n",
-    "\n",
-    "# Set up the projection of the data;\n",
-    "# if lat/lon then PlateCarree is what you want\n",
-    "datacrs = ccrs.PlateCarree()\n",
-    "\n",
-    "# Start the figure and create plot axes with proper projection\n",
-    "fig = plt.figure(1, figsize=(14, 12))\n",
-    "ax = plt.subplot(111, projection=mapcrs)\n",
-    "ax.set_extent([-130, -72, 20, 55], ccrs.PlateCarree())\n",
-    "\n",
-    "# Add geopolitical boundaries for map reference\n",
-    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\n",
-    "ax.add_feature(cfeature.STATES.with_scale('50m'))\n",
-    "\n",
-    "# Plot the contours of PV at 250 hPa, scaling 10^6 every 1 PVU\n",
-    "clevs_pv = np.arange(0, 25, 1)\n",
-    "cs1 = ax.contour(lons, lats, pv_250*1e6, clevs_pv, colors='black',\n",
-    "                 transform=datacrs)\n",
-    "plt.clabel(cs1, fmt='%d', fontsize='large')\n",
-    "\n",
-    "# Plot the colorfill of divergence, scaled 10^5 every 1 s^1\n",
-    "clevs_div = np.arange(-15, 16, 1)\n",
-    "cs1 = ax.contourf(lons, lats, div_250*1e5, clevs_div, cmap=plt.cm.PuOr,\n",
-    "                  extend='both', transform=datacrs)\n",
-    "plt.colorbar(cs1, orientation='horizontal', pad=0, aspect=50, extendrect=True)\n",
-    "\n",
-    "# Plot the wind barbs at 250 hPa\n",
-    "wind_slice = slice(None, None, 6)\n",
-    "ax.barbs(lons[wind_slice], lats[wind_slice],\n",
-    "         uwnd_250[wind_slice, wind_slice].metpy.convert_units('kt').values,\n",
-    "         vwnd_250[wind_slice, wind_slice].metpy.convert_units('kt').values,\n",
-    "         pivot='middle', color='black', length=6.5, transform=datacrs)\n",
-    "\n",
-    "# Plot some titles to tell people what is on the map\n",
-    "plt.title('250-hPa GFS PV (PVU), Divergence ($10^5$ s$^{-1}$),'\n",
-    "          ' and Wind Barbs (kt)', loc='left')\n",
-    "plt.title(f'Valid Time: {vtime}', loc='right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Smoothing_Contours.ipynb b/_preview/3/_sources/notebooks/gallery/Smoothing_Contours.ipynb
deleted file mode 100644
index 7b995ae..0000000
--- a/_preview/3/_sources/notebooks/gallery/Smoothing_Contours.ipynb
+++ /dev/null
@@ -1,258 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "Smoothing Contours\n",
-    "==================\n",
-    "\n",
-    "Demonstrate how to smooth contour values from a higher resolution\n",
-    "model field.\n",
-    "\n",
-    "By: Kevin Goebbert\n",
-    "\n",
-    "Date: 13 April 2017"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Do the needed imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "import numpy as np\n",
-    "from metpy.units import units\n",
-    "from siphon.catalog import TDSCatalog\n",
-    "from xarray.backends import NetCDF4DataStore\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Set up netCDF Subset Service link"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify our date/time of product desired\n",
-    "dt = datetime.utcnow()\n",
-    "\n",
-    "# Construct the URL for our THREDDS Data Server Catalog,\n",
-    "# and access our desired dataset within via NCSS\n",
-    "cat = TDSCatalog('https://thredds.ucar.edu/thredds/catalog/grib/'\n",
-    "                 'NCEP/NAM/CONUS_12km/latest.xml')\n",
-    "ncss = cat.datasets[0].subset()\n",
-    "\n",
-    "# Create our NCSS query with desired specifications\n",
-    "query = ncss.query()\n",
-    "query.time(dt)\n",
-    "query.add_lonlat()\n",
-    "query.variables('Geopotential_height_isobaric',\n",
-    "                'u-component_of_wind_isobaric',\n",
-    "                'v-component_of_wind_isobaric')\n",
-    "\n",
-    "# Obtain the data we've queried for\n",
-    "data = ncss.get_data(query)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################"
-   },
-   "source": [
-    "Pull apart the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get dimension names to pull appropriate variables\n",
-    "# dtime = ds.Geopotential_height_isobaric.dims[0]\n",
-    "# dlev = ds.Geopotential_height_isobaric.dims[1]\n",
-    "# dlat = ds.Geopotential_height_isobaric.dims[2]\n",
-    "# dlon = ds.Geopotential_height_isobaric.dims[3]\n",
-    "\n",
-    "# Get lat and lon data, as well as time data and metadata\n",
-    "lats = ds.lat\n",
-    "lons = ds.lon\n",
-    "\n",
-    "# Need 2D lat/lons for plotting, do so if necessary\n",
-    "if lats.ndim < 2:\n",
-    "    lons, lats = np.meshgrid(lons, lats)\n",
-    "\n",
-    "# Determine the level of 500 hPa\n",
-    "lev_500 = 500 * units.hPa\n",
-    "\n",
-    "# Create more useable times for output\n",
-    "times = ds.Geopotential_height_isobaric.metpy.time.squeeze()\n",
-    "vtimes = times.values.astype('datetime64[ms]').astype('O')\n",
-    "\n",
-    "# Pull out the 500 hPa Heights\n",
-    "hght_500 = ds.Geopotential_height_isobaric.metpy.sel(\n",
-    "    vertical=lev_500).squeeze()\n",
-    "uwnd_500 = ds['u-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=lev_500).squeeze()\n",
-    "vwnd_500 = ds['v-component_of_wind_isobaric'].metpy.sel(\n",
-    "    vertical=lev_500).squeeze()\n",
-    "\n",
-    "# Calculate the magnitude of the wind speed in kts\n",
-    "sped = mpcalc.wind_speed(uwnd_500, vwnd_500).metpy.convert_units('knots')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Set up the projection for LCC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotcrs = ccrs.LambertConformal(central_longitude=-100.0,\n",
-    "                                central_latitude=45.0)\n",
-    "datacrs = ccrs.PlateCarree(central_longitude=0.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################"
-   },
-   "source": [
-    "Subset and smooth"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Smooth the 500-hPa geopotential height field\n",
-    "# Be sure to only smooth the 2D field\n",
-    "Z_500 = mpcalc.smooth_gaussian(hght_500, 50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "##################################"
-   },
-   "source": [
-    "Plot the contours"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Start plot with new figure and axis\n",
-    "fig = plt.figure(figsize=(17., 11.))\n",
-    "ax = plt.subplot(1, 1, 1, projection=plotcrs)\n",
-    "\n",
-    "# Add some titles to make the plot readable by someone else\n",
-    "plt.title('500-hPa Geo Heights (m; black), Smoothed 500-hPa Geo. '\n",
-    "          'Heights (m; red)', loc='left')\n",
-    "plt.title(f'VALID: {vtimes}', loc='right')\n",
-    "\n",
-    "# Set GAREA and add map features\n",
-    "ax.set_extent([-125., -67., 22., 52.], ccrs.PlateCarree())\n",
-    "ax.coastlines('50m', edgecolor='black', linewidth=0.75)\n",
-    "ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
-    "\n",
-    "# Set the CINT\n",
-    "clev500 = np.arange(5100, 6000, 60)\n",
-    "\n",
-    "# Plot smoothed 500-hPa contours\n",
-    "cs2 = ax.contour(lons, lats, Z_500, clev500, colors='black',\n",
-    "                 linewidths=3, linestyles='solid', transform=datacrs)\n",
-    "c2 = plt.clabel(cs2, fontsize=12, colors='black', inline=1, inline_spacing=8,\n",
-    "                fmt='%i', rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Contour the 500 hPa heights with labels\n",
-    "cs = ax.contour(lons, lats, hght_500, clev500, colors='red',\n",
-    "                linewidths=2.5, linestyles='solid', alpha=0.6,\n",
-    "                transform=datacrs)\n",
-    "cl = plt.clabel(cs, fontsize=12, colors='red', inline=1, inline_spacing=8,\n",
-    "                fmt='%i', rightside_up=True, use_clabeltext=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Upperair_Obs.ipynb b/_preview/3/_sources/notebooks/gallery/Upperair_Obs.ipynb
deleted file mode 100644
index b7147a6..0000000
--- a/_preview/3/_sources/notebooks/gallery/Upperair_Obs.ipynb
+++ /dev/null
@@ -1,507 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\""
-   },
-   "source": [
-    "DIFAX Replication\n",
-    "=================\n",
-    "\n",
-    "This example replicates the traditional DIFAX images for upper-level\n",
-    "observations.\n",
-    "\n",
-    "By: Kevin Goebbert\n",
-    "\n",
-    "Observation data comes from Iowa State Archive, accessed through the\n",
-    "Siphon package. Contour data comes from the GFS 0.5 degree analysis.\n",
-    "Classic upper-level data of Geopotential Height and Temperature are\n",
-    "plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import urllib.request\n",
-    "\n",
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.ticker import FixedLocator\n",
-    "import metpy.calc as mpcalc\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "\n",
-    "from metpy.plots import StationPlot\n",
-    "from metpy.units import units\n",
-    "from siphon.simplewebservice.iastate import IAStateUpperAir"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Plotting High/Low Symbols\n",
-    "-------------------------\n",
-    "\n",
-    "A helper function to plot a text symbol (e.g., H, L) for relative\n",
-    "maximum/minimum for a given field (e.g., geopotential height)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_maxmin_points(lon, lat, data, extrema, nsize, symbol, color='k',\n",
-    "                       plotValue=True, transform=None):\n",
-    "    \"\"\"\n",
-    "    This function will find and plot relative maximum and minimum for a 2D grid. The function\n",
-    "    can be used to plot an H for maximum values (e.g., High pressure) and an L for minimum\n",
-    "    values (e.g., low pressue). It is best to used filetered data to obtain  a synoptic scale\n",
-    "    max/min value. The symbol text can be set to a string value and optionally the color of the\n",
-    "    symbol and any plotted value can be set with the parameter color.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "        lon : 2D array\n",
-    "            Plotting longitude values\n",
-    "        lat : 2D array\n",
-    "            Plotting latitude values\n",
-    "        data : 2D array\n",
-    "            Data that you wish to plot the max/min symbol placement\n",
-    "        extrema : str\n",
-    "            Either a value of max for Maximum Values or min for Minimum Values\n",
-    "        nsize : int\n",
-    "            Size of the grid box to filter the max and min values to plot a reasonable number\n",
-    "        symbol : str\n",
-    "            Text to be placed at location of max/min value\n",
-    "        color : str\n",
-    "            Name of matplotlib colorname to plot the symbol (and numerical value, if plotted)\n",
-    "        plot_value : Boolean (True/False)\n",
-    "            Whether to plot the numeric value of max/min point\n",
-    "\n",
-    "    Return\n",
-    "    ------\n",
-    "        The max/min symbol will be plotted on the current axes within the bounding frame\n",
-    "        (e.g., clip_on=True)\n",
-    "    \"\"\"\n",
-    "    from scipy.ndimage import maximum_filter, minimum_filter\n",
-    "\n",
-    "    if (extrema == 'max'):\n",
-    "        data_ext = maximum_filter(data, nsize, mode='nearest')\n",
-    "    elif (extrema == 'min'):\n",
-    "        data_ext = minimum_filter(data, nsize, mode='nearest')\n",
-    "    else:\n",
-    "        raise ValueError('Value for hilo must be either max or min')\n",
-    "\n",
-    "    if lon.ndim == 1:\n",
-    "        lon, lat = np.meshgrid(lon, lat)\n",
-    "\n",
-    "    mxx, mxy = np.where(data_ext == data)\n",
-    "\n",
-    "    for i in range(len(mxy)):\n",
-    "        ax.text(lon[mxx[i], mxy[i]], lat[mxx[i], mxy[i]], symbol, color=color, size=36,\n",
-    "                clip_on=True, horizontalalignment='center', verticalalignment='center',\n",
-    "                transform=transform)\n",
-    "        ax.text(lon[mxx[i], mxy[i]], lat[mxx[i], mxy[i]],\n",
-    "                '\\n' + str(np.int64(data[mxx[i], mxy[i]])),\n",
-    "                color=color, size=12, clip_on=True, fontweight='bold',\n",
-    "                horizontalalignment='center', verticalalignment='top', transform=transform)\n",
-    "        ax.plot(lon[mxx[i], mxy[i]], lat[mxx[i], mxy[i]], marker='o', markeredgecolor='black',\n",
-    "                markerfacecolor='white', transform=transform)\n",
-    "        ax.plot(lon[mxx[i], mxy[i]], lat[mxx[i], mxy[i]],\n",
-    "                marker='x', color='black', transform=transform)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Station Information\n",
-    "-------------------\n",
-    "\n",
-    "A helper function for obtaining radiosonde station information (e.g.,\n",
-    "latitude/longitude) requried to plot data obtained from each station.\n",
-    "Original code by github user sgdecker."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
-   "outputs": [],
-   "source": [
-    "def station_info(stid):\n",
-    "    r\"\"\"Provide information about weather stations.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    stid: str or iterable object containing strs\n",
-    "         The ICAO or IATA code(s) for which station information is requested.\n",
-    "    with_units: bool\n",
-    "         Whether to include units for values that have them. Default True.\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    info: dict\n",
-    "         Information about the station(s) within a dictionary with these keys:\n",
-    "             'state': Two-character ID of the state/province where the station is located,\n",
-    "                       if applicable\n",
-    "             'name': The name of the station\n",
-    "             'lat': The latitude of the station [deg]\n",
-    "             'lon': The longitude of the station [deg]\n",
-    "             'elevation': The elevation of the station [m]\n",
-    "             'country': Two-character ID of the country where the station is located\n",
-    "\n",
-    "    Modified code from Steven Decker, Rutgers University\n",
-    "\n",
-    "    \"\"\"\n",
-    "    # Provide a helper function for later usage\n",
-    "    def str2latlon(s):\n",
-    "        deg = float(s[:3])\n",
-    "        mn = float(s[-3:-1])\n",
-    "        if s[-1] == 'S' or s[-1] == 'W':\n",
-    "            deg = -deg\n",
-    "            mn = -mn\n",
-    "        return deg + mn / 60.\n",
-    "\n",
-    "    # Various constants describing the underlying data\n",
-    "    url = 'https://www.aviationweather.gov/docs/metar/stations.txt'\n",
-    "    # file = 'stations.txt'\n",
-    "    state_bnds = slice(0, 2)\n",
-    "    name_bnds = slice(3, 19)\n",
-    "    icao_bnds = slice(20, 24)\n",
-    "    iata_bnds = slice(26, 29)\n",
-    "    lat_bnds = slice(39, 45)\n",
-    "    lon_bnds = slice(47, 54)\n",
-    "    z_bnds = slice(55, 59)\n",
-    "    cntry_bnds = slice(81, 83)\n",
-    "\n",
-    "    # Generalize to any number of IDs\n",
-    "    if isinstance(stid, str):\n",
-    "        stid = [stid]\n",
-    "\n",
-    "    # Get the station dataset\n",
-    "    infile = urllib.request.urlopen(url)\n",
-    "    data = infile.readlines()\n",
-    "\n",
-    "    state = []\n",
-    "    name = []\n",
-    "    lat = []\n",
-    "    lon = []\n",
-    "    z = []\n",
-    "    cntry = []\n",
-    "\n",
-    "    for s in stid:\n",
-    "        s = s.upper()\n",
-    "        for line_bytes in data:\n",
-    "            line = line_bytes.decode('UTF-8')\n",
-    "            icao = line[icao_bnds]\n",
-    "            iata = line[iata_bnds]\n",
-    "            if len(s) == 3 and s in iata or len(s) == 4 and s in icao:\n",
-    "                state.append(line[state_bnds].strip())\n",
-    "                name.append(line[name_bnds].strip())\n",
-    "                lat.append(str2latlon(line[lat_bnds]))\n",
-    "                lon.append(str2latlon(line[lon_bnds]))\n",
-    "                z.append(float(line[z_bnds]))\n",
-    "                cntry.append(line[cntry_bnds])\n",
-    "\n",
-    "                break\n",
-    "        else:\n",
-    "            state.append('NA')\n",
-    "            name.append('NA')\n",
-    "            lat.append(np.nan)\n",
-    "            lon.append(np.nan)\n",
-    "            z.append(np.nan)\n",
-    "            cntry.append('NA')\n",
-    "\n",
-    "    infile.close()\n",
-    "\n",
-    "    return {'state': np.array(state), 'name': np.array(name), 'lat': np.array(lat),\n",
-    "            'lon': np.array(lon), 'elevation': np.array(z), 'country': np.array(cntry),\n",
-    "            'units': {'lat': 'deg', 'lon': 'deg', 'z': 'm'}}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Observation Data\n",
-    "----------------\n",
-    "\n",
-    "Set a date and time for upper-air observations (should only be 00 or 12\n",
-    "UTC for the hour).\n",
-    "\n",
-    "Request all data from Iowa State using the Siphon package. The result is\n",
-    "a pandas DataFrame containing all of the sounding data from all\n",
-    "available stations.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set date for desired UPA data\n",
-    "today = datetime.utcnow()\n",
-    "\n",
-    "# Go back one day to ensure data availability\n",
-    "date = datetime(today.year, today.month, today.day, 0) - timedelta(days=1)\n",
-    "\n",
-    "# Request data using Siphon request for data from Iowa State Archive\n",
-    "data = IAStateUpperAir.request_all_data(date)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Subset Observational Data\n",
-    "-------------------------\n",
-    "\n",
-    "From the request above will give all levels from all radisonde sites\n",
-    "available through the service. For plotting a pressure surface map there\n",
-    "is only need to have the data from that level. Below the data is subset\n",
-    "and a few parameters set based on the level chosen. Additionally, the\n",
-    "station information is obtained and latitude and longitude data is added\n",
-    "to the DataFrame.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "level = 500\n",
-    "\n",
-    "if (level == 925) | (level == 850) | (level == 700):\n",
-    "    cint = 30\n",
-    "    def hght_format(v): return format(v, '.0f')[1:]\n",
-    "elif level == 500:\n",
-    "    cint = 60\n",
-    "    def hght_format(v): return format(v, '.0f')[:3]\n",
-    "elif level == 300:\n",
-    "    cint = 120\n",
-    "    def hght_format(v): return format(v, '.0f')[:3]\n",
-    "elif level < 300:\n",
-    "    cint = 120\n",
-    "    def hght_format(v): return format(v, '.0f')[1:4]\n",
-    "\n",
-    "# Create subset of all data for a given level\n",
-    "data_subset = data.pressure == level\n",
-    "df = data[data_subset]\n",
-    "\n",
-    "# Get station lat/lon from look-up file; add to Dataframe\n",
-    "stn_info = station_info(list(df.station.values))\n",
-    "df.insert(10, 'latitude', stn_info['lat'])\n",
-    "df.insert(11, 'longitude', stn_info['lon'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Gridded Data\n",
-    "------------\n",
-    "\n",
-    "Obtain GFS gridded output for contour plotting. Specifically,\n",
-    "geopotential height and temperature data for the given level and subset\n",
-    "for over North America. Data are smoothed for aesthetic reasons.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get GFS data and subset to North America for Geopotential Height and Temperature\n",
-    "ds = xr.open_dataset('https://thredds.ucar.edu/thredds/dodsC/grib/NCEP/GFS/Global_0p5deg_ana/'\n",
-    "                     'GFS_Global_0p5deg_ana_{0:%Y%m%d}_{0:%H}00.grib2'.format(\n",
-    "                         date)).metpy.parse_cf()\n",
-    "\n",
-    "# Geopotential height and smooth\n",
-    "hght = ds.Geopotential_height_isobaric.metpy.sel(\n",
-    "    vertical=level*units.hPa, time=date, lat=slice(70, 15), lon=slice(360-145, 360-50))\n",
-    "smooth_hght = mpcalc.smooth_n_point(hght, 9, 10)\n",
-    "\n",
-    "# Temperature, smooth, and convert to Celsius\n",
-    "tmpk = ds.Temperature_isobaric.metpy.sel(\n",
-    "    vertical=level*units.hPa, time=date, lat=slice(70, 15), lon=slice(360-145, 360-50))\n",
-    "smooth_tmpc = (mpcalc.smooth_n_point(tmpk, 9, 10)).metpy.convert_units('degC')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################################################"
-   },
-   "source": [
-    "Create DIFAX Replication\n",
-    "------------------------\n",
-    "\n",
-    "Plot the observational data and contours on a Lambert Conformal map and\n",
-    "add features that resemble the historic DIFAX maps.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up map coordinate reference system\n",
-    "mapcrs = ccrs.LambertConformal(\n",
-    "    central_latitude=45, central_longitude=-100, standard_parallels=(30, 60))\n",
-    "\n",
-    "# Set up station locations for plotting observations\n",
-    "point_locs = mapcrs.transform_points(\n",
-    "    ccrs.PlateCarree(), df['longitude'].values, df['latitude'].values)\n",
-    "\n",
-    "# Start figure and set graphics extent\n",
-    "fig = plt.figure(1, figsize=(17, 15))\n",
-    "ax = plt.subplot(111, projection=mapcrs)\n",
-    "ax.set_extent([-125, -70, 20, 55])\n",
-    "\n",
-    "# Add map features for geographic reference\n",
-    "ax.add_feature(cfeature.COASTLINE.with_scale('50m'), edgecolor='grey')\n",
-    "ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='white')\n",
-    "ax.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='grey')\n",
-    "\n",
-    "# Plot plus signs every degree lat/lon\n",
-    "plus_lat = []\n",
-    "plus_lon = []\n",
-    "other_lat = []\n",
-    "other_lon = []\n",
-    "\n",
-    "for x in hght.lon.values[::2]:\n",
-    "    for y in hght.lat.values[::2]:\n",
-    "        if (x % 5 == 0) | (y % 5 == 0):\n",
-    "            plus_lon.append(x)\n",
-    "            plus_lat.append(y)\n",
-    "        else:\n",
-    "            other_lon.append(x)\n",
-    "            other_lat.append(y)\n",
-    "ax.scatter(other_lon, other_lat, s=2, marker='o',\n",
-    "           transform=ccrs.PlateCarree(), color='lightgrey', zorder=-1)\n",
-    "ax.scatter(plus_lon, plus_lat, s=30, marker='+', transform=ccrs.PlateCarree(),\n",
-    "           color='lightgrey')\n",
-    "\n",
-    "# Add gridlines for every 5 degree lat/lon\n",
-    "ax.gridlines(linestyle='solid', ylocs=range(15, 71, 5), xlocs=range(-150, -49, 5))\n",
-    "\n",
-    "# Start the station plot by specifying the axes to draw on, as well as the\n",
-    "# lon/lat of the stations (with transform). We also the fontsize to 10 pt.\n",
-    "stationplot = StationPlot(ax, df['longitude'].values, df['latitude'].values, clip_on=True,\n",
-    "                          transform=ccrs.PlateCarree(), fontsize=10)\n",
-    "\n",
-    "# Plot the temperature and dew point to the upper and lower left, respectively, of\n",
-    "# the center point.\n",
-    "stationplot.plot_parameter('NW', df['temperature'], color='black')\n",
-    "stationplot.plot_parameter('SW', df['dewpoint'], color='black')\n",
-    "\n",
-    "# A more complex example uses a custom formatter to control how the geopotential height\n",
-    "# values are plotted. This is set in an earlier if-statement to work appropriate for\n",
-    "# different levels.\n",
-    "stationplot.plot_parameter('NE', df['height'], formatter=hght_format)\n",
-    "\n",
-    "# Add wind barbs\n",
-    "stationplot.plot_barb(df['u_wind'], df['v_wind'], length=7, pivot='tip')\n",
-    "\n",
-    "# Plot Solid Contours of Geopotential Height\n",
-    "cs = ax.contour(hght.lon, hght.lat, smooth_hght,\n",
-    "                range(0, 20000, cint), colors='black', transform=ccrs.PlateCarree())\n",
-    "clabels = plt.clabel(cs, fmt='%d', colors='white', inline_spacing=5, use_clabeltext=True)\n",
-    "\n",
-    "# Contour labels with black boxes and white text\n",
-    "for t in cs.labelTexts:\n",
-    "    t.set_bbox({'facecolor': 'black', 'pad': 4})\n",
-    "    t.set_fontweight('heavy')\n",
-    "\n",
-    "# Plot Dashed Contours of Temperature\n",
-    "cs2 = ax.contour(hght.lon, hght.lat, smooth_tmpc, range(-60, 51, 5),\n",
-    "                 colors='black', transform=ccrs.PlateCarree())\n",
-    "clabels = plt.clabel(cs2, fmt='%d', colors='black', inline_spacing=5, use_clabeltext=True)\n",
-    "\n",
-    "# Set longer dashes than default\n",
-    "for c in cs2.collections:\n",
-    "    c.set_dashes([(0, (5.0, 3.0))])\n",
-    "\n",
-    "# Contour labels with black boxes and white text\n",
-    "for t in cs.labelTexts:\n",
-    "    t.set_bbox({'facecolor': 'black', 'pad': 4})\n",
-    "    t.set_fontweight('heavy')\n",
-    "\n",
-    "# Plot filled circles for Radiosonde Obs\n",
-    "ax.scatter(df['longitude'].values, df['latitude'].values, s=10,\n",
-    "           marker='o', color='black', transform=ccrs.PlateCarree())\n",
-    "\n",
-    "# Use definition to plot H/L symbols\n",
-    "plot_maxmin_points(hght.lon, hght.lat, smooth_hght.values, 'max', 50,\n",
-    "                   symbol='H', color='black', transform=ccrs.PlateCarree())\n",
-    "plot_maxmin_points(hght.lon, hght.lat, smooth_hght.values, 'min', 25,\n",
-    "                   symbol='L', color='black', transform=ccrs.PlateCarree())\n",
-    "\n",
-    "# Add titles\n",
-    "plt.title(f'Upper-air Observations at {level}-hPa Analysis Heights/Temperature',\n",
-    "          loc='left')\n",
-    "plt.title(f'Valid: {date}', loc='right');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/Wind_Shear_Vectors_Example.ipynb b/_preview/3/_sources/notebooks/gallery/Wind_Shear_Vectors_Example.ipynb
deleted file mode 100644
index 92d133c..0000000
--- a/_preview/3/_sources/notebooks/gallery/Wind_Shear_Vectors_Example.ipynb
+++ /dev/null
@@ -1,333 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\"",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Calculating Wind Shear\n",
-    "======================\n",
-    "\n",
-    "This example plots calculates the 850-500 hPa Bulk Wind Shear\n",
-    "\n",
-    "Plotting over a regional domain, accessing the Best Collection of GFS from the\n",
-    "Unidata Thredds server, plots MSLP (hPa), 850-hPa Wind Vector (m/s), 500-hPa\n",
-    "Wind Vector (m/s), and the Wind Shear between the two layers (m/s)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#####################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "## Import necessary packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "import scipy.ndimage as ndimage\n",
-    "from siphon.catalog import TDSCatalog\n",
-    "from xarray.backends import NetCDF4DataStore\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#####################################"
-   },
-   "source": [
-    "## Obtain data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Construct a TDSCatalog instance pointing to the gfs dataset\n",
-    "best_gfs = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/grib/'\n",
-    "                      'NCEP/GFS/Global_0p5deg/catalog.xml')\n",
-    "\n",
-    "# Interface with the Best GFS Half Degree Forecast Time Series dataset\n",
-    "# via NCSS directly\n",
-    "ncss = best_gfs.datasets['Best GFS Half Degree Forecast Time Series'].subset()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#####################################"
-   },
-   "source": [
-    "## First Query for MSLP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create our NCSS query with desired specifications\n",
-    "query = ncss.query()\n",
-    "query.lonlat_box(north=50, south=30, east=-80, west=-115)\n",
-    "query.time(datetime.utcnow())\n",
-    "query.accept('netcdf4')\n",
-    "query.variables('MSLP_Eta_model_reduction_msl')\n",
-    "\n",
-    "# Obtain the data we've queried for as a netcdf4-python dataset\n",
-    "data = ncss.get_data(query)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
-    "\n",
-    "# Pull out the variables you want to use\n",
-    "mslp_var = ds.MSLP_Eta_model_reduction_msl\n",
-    "time_var = ds.MSLP_Eta_model_reduction_msl.metpy.time\n",
-    "lat_var = ds.latitude\n",
-    "lon_var = ds.longitude"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#####################################"
-   },
-   "source": [
-    "## Second Query for 850-hPa data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Request data for 850-hPa winds\n",
-    "# First clear the query's variables from previous query for MSLP\n",
-    "query.var = set()\n",
-    "query.vertical_level(85000)\n",
-    "query.variables('u-component_of_wind_isobaric',\n",
-    "                'v-component_of_wind_isobaric')\n",
-    "\n",
-    "data = ncss.get_data(query)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds_wind = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
-    "\n",
-    "u_wind_var850 = ds_wind['u-component_of_wind_isobaric']\n",
-    "v_wind_var850 = ds_wind['v-component_of_wind_isobaric']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#####################################"
-   },
-   "source": [
-    "Third Query for 500-hPa data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Request data for 500-hPa winds\n",
-    "# First clear the query's variables from previous query for 850-hPa data\n",
-    "query.var = set()\n",
-    "query.vertical_level(50000)\n",
-    "query.variables('u-component_of_wind_isobaric',\n",
-    "                'v-component_of_wind_isobaric')\n",
-    "\n",
-    "data = ncss.get_data(query)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds_wind = xr.open_dataset(NetCDF4DataStore(data)).metpy.parse_cf()\n",
-    "\n",
-    "u_wind_var500 = ds_wind['u-component_of_wind_isobaric']\n",
-    "v_wind_var500 = ds_wind['v-component_of_wind_isobaric']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#####################################"
-   },
-   "source": [
-    "## Data Manipulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get actual data values and remove any size 1 dimensions\n",
-    "lat = lat_var.values.squeeze()\n",
-    "lon = lon_var.values.squeeze()\n",
-    "mslp = mslp_var.metpy.convert_units('hPa').squeeze()\n",
-    "u_wind850 = u_wind_var850.squeeze()\n",
-    "v_wind850 = v_wind_var850.squeeze()\n",
-    "u_wind500 = u_wind_var500.squeeze()\n",
-    "v_wind500 = v_wind_var500.squeeze()\n",
-    "\n",
-    "# Convert number of hours since the reference time into an actual date\n",
-    "time = time_var.values.astype('datetime64[ms]').astype('O').squeeze()\n",
-    "\n",
-    "# Combine 1D latitude and longitudes into a 2D grid of locations\n",
-    "lon_2d, lat_2d = np.meshgrid(lon, lat)\n",
-    "\n",
-    "# Smooth mslp data\n",
-    "mslp = ndimage.gaussian_filter(mslp, sigma=3, order=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#####################################"
-   },
-   "source": [
-    "Begin making figure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/fp/_l45s1m93s5009vb713pyg580000gn/T/ipykernel_52398/1356670571.py:43: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n",
-      "  legend.legendHandles[0].set_color('gold')\n",
-      "/var/folders/fp/_l45s1m93s5009vb713pyg580000gn/T/ipykernel_52398/1356670571.py:44: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n",
-      "  legend.legendHandles[1].set_color('cornflowerblue')\n",
-      "/var/folders/fp/_l45s1m93s5009vb713pyg580000gn/T/ipykernel_52398/1356670571.py:45: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.\n",
-      "  legend.legendHandles[2].set_color('deeppink')\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
-      "  return lib.intersects(a, b, **kwargs)\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
-      "  return lib.intersects(a, b, **kwargs)\n",
-      "/Users/kgoebber/miniconda3/envs/main/lib/python3.11/site-packages/shapely/predicates.py:798: RuntimeWarning: invalid value encountered in intersects\n",
-      "  return lib.intersects(a, b, **kwargs)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1500x1200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create new figure\n",
-    "fig = plt.figure(figsize=(15, 12), facecolor='black')\n",
-    "\n",
-    "# Add the map and set the extent\n",
-    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
-    "ax.set_extent([-108., -91., 33., 45.])\n",
-    "ax.patch.set_fill(False)\n",
-    "\n",
-    "# Add state boundaries to plot\n",
-    "ax.add_feature(cfeature.STATES, edgecolor='white', linewidth=2)\n",
-    "\n",
-    "# Contour the MSLP\n",
-    "c = ax.contour(lon_2d, lat_2d, mslp, colors='lime', linewidths=6)\n",
-    "ax.clabel(c, fontsize=12, inline=1, inline_spacing=4, fmt='%i')\n",
-    "\n",
-    "wslice = slice(1, None, 4)\n",
-    "# Plot 850-hPa wind vectors\n",
-    "vectors850 = ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],\n",
-    "                       u_wind850[wslice, wslice], v_wind850[wslice, wslice],\n",
-    "                       headlength=4, headwidth=3, angles='xy',\n",
-    "                       scale_units='xy', scale=12, color='gold',\n",
-    "                       label='850mb wind')\n",
-    "\n",
-    "# Plot 500-hPa wind vectors\n",
-    "vectors500 = ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],\n",
-    "                       u_wind500[wslice, wslice], v_wind500[wslice, wslice],\n",
-    "                       headlength=4, headwidth=3, angles='xy',\n",
-    "                       scale_units='xy', scale=12, color='cornflowerblue',\n",
-    "                       label='500mb wind')\n",
-    "\n",
-    "# Plot 500-850 shear\n",
-    "shear = ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],\n",
-    "                  u_wind500[wslice, wslice] - u_wind850[wslice, wslice],\n",
-    "                  v_wind500[wslice, wslice] - v_wind850[wslice, wslice],\n",
-    "                  headlength=4, headwidth=3, angles='xy', scale_units='xy',\n",
-    "                  scale=12, color='deeppink', label='500-850mb shear')\n",
-    "\n",
-    "# Add a legend\n",
-    "ax.legend(('850mb wind', '500mb wind', '500-850mb shear'), loc=4)\n",
-    "\n",
-    "# Manually set colors for legend\n",
-    "legend = ax.get_legend()\n",
-    "legend.legendHandles[0].set_color('gold')\n",
-    "legend.legendHandles[1].set_color('cornflowerblue')\n",
-    "legend.legendHandles[2].set_color('deeppink')\n",
-    "\n",
-    "# Add a title to the plot\n",
-    "plt.title('MSLP, 850mb Wind, 500mb Wind, and 500-850mb Vertical Wind Shear \\n'\n",
-    "          f' for {time:%d %B %Y %H:%MZ}', color='white', size=14);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/declarative_500_hPa.ipynb b/_preview/3/_sources/notebooks/gallery/declarative_500_hPa.ipynb
deleted file mode 100644
index 6523755..0000000
--- a/_preview/3/_sources/notebooks/gallery/declarative_500_hPa.ipynb
+++ /dev/null
@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Declarative 500-hPa Absolute Vorticity\n",
-    "\n",
-    "By: Kevin Goebbert\n",
-    "\n",
-    "This example uses the declarative syntax available through the MetPy package to allow a more convenient method for creating simple maps of atmospheric data. To plot aboslute vorticity, the data is scaled and reassigned to the xarray object for use in the declarative plotting interface."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import xarray as xr\n",
-    "\n",
-    "from metpy.plots import declarative\n",
-    "from metpy.units import units"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set date for desired dataset\n",
-    "dt = datetime(2012, 10, 31, 12)\n",
-    "\n",
-    "# Open dataset from NCEI\n",
-    "ds = xr.open_dataset('https://www.ncei.noaa.gov/thredds/dodsC/'\n",
-    "                     f'model-gfs-g4-anl-files-old/{dt:%Y%m}/{dt:%Y%m%d}/'\n",
-    "                     f'gfsanl_4_{dt:%Y%m%d}_{dt:%H}00_000.grb2'\n",
-    "                     ).metpy.parse_cf()\n",
-    "\n",
-    "# Subset Data to be just over CONUS\n",
-    "ds_us = ds.sel(lon=slice(360-160, 360-40), lat=slice(65, 10))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Contour Intervals\n",
-    "\n",
-    "Since absolute vorticity rarely goes below zero in the Northern Hemisphere, we can set up a list of contour levels that doesn't include values near but greater than zero. The following code yields a list containing: `[-8, -7, -6, -5, -4, -3, -2, -1, 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]`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Absolute Vorticity colors\n",
-    "# Use two different colormaps from matplotlib and combine into one color set\n",
-    "clevs_500_avor = list(range(-8, 1, 1))+list(range(8, 46, 1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The Plot\n",
-    "\n",
-    "Using the declarative interface in MetPy to plot the 500-hPa Geopotential Heights and Absolute Vorticity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set Contour Plot Parameters\n",
-    "contour = declarative.ContourPlot()\n",
-    "contour.data = ds_us\n",
-    "contour.time = dt\n",
-    "contour.field = 'Geopotential_height_isobaric'\n",
-    "contour.level = 500 * units.hPa\n",
-    "contour.linecolor = 'black'\n",
-    "contour.linestyle = '-'\n",
-    "contour.linewidth = 2\n",
-    "contour.clabels = True\n",
-    "contour.contours = list(range(0, 20000, 60))\n",
-    "\n",
-    "# Set Color-filled Contour Parameters\n",
-    "cfill = declarative.FilledContourPlot()\n",
-    "cfill.data = ds_us\n",
-    "cfill.time = dt\n",
-    "cfill.field = 'Absolute_vorticity_isobaric'\n",
-    "cfill.level = 500 * units.hPa\n",
-    "cfill.contours = clevs_500_avor\n",
-    "cfill.colormap = 'PuOr_r'\n",
-    "cfill.image_range = (-45, 45)\n",
-    "cfill.colorbar = 'horizontal'\n",
-    "cfill.scale = 1e5\n",
-    "\n",
-    "# Panel for plot with Map features\n",
-    "panel = declarative.MapPanel()\n",
-    "panel.layout = (1, 1, 1)\n",
-    "panel.area = 'uslcc'\n",
-    "panel.projection = 'area'\n",
-    "panel.layers = ['coastline', 'states', 'borders']\n",
-    "panel.layers_edgecolor = ['black', 'grey', 'black']\n",
-    "panel.layers_linewidth = [1.25, .75, 1]\n",
-    "panel.title = (f'{cfill.level} GFS Geopotential Heights'\n",
-    "               f'and Absolute Vorticity at {dt}')\n",
-    "panel.plots = [cfill, contour]\n",
-    "\n",
-    "# Bringing it all together\n",
-    "pc = declarative.PanelContainer()\n",
-    "pc.size = (15, 14)\n",
-    "pc.panels = [panel]\n",
-    "\n",
-    "pc.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/index1.md b/_preview/3/_sources/notebooks/gallery/index1.md
deleted file mode 100644
index d77b797..0000000
--- a/_preview/3/_sources/notebooks/gallery/index1.md
+++ /dev/null
@@ -1 +0,0 @@
-# a potential entry?
\ No newline at end of file
diff --git a/_preview/3/_sources/notebooks/gallery/index2.md b/_preview/3/_sources/notebooks/gallery/index2.md
deleted file mode 100644
index ca23be9..0000000
--- a/_preview/3/_sources/notebooks/gallery/index2.md
+++ /dev/null
@@ -1 +0,0 @@
-# a different category, perhaps?
\ No newline at end of file
diff --git a/_preview/3/_sources/notebooks/gallery/miller_composite.ipynb b/_preview/3/_sources/notebooks/gallery/miller_composite.ipynb
deleted file mode 100644
index 266f7c3..0000000
--- a/_preview/3/_sources/notebooks/gallery/miller_composite.ipynb
+++ /dev/null
@@ -1,470 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\"",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Miller Composite Chart\n",
-    "======================\n",
-    "Create a Miller Composite chart based on Miller 1972 in Python with MetPy and\n",
-    "Matplotlib."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.lines as lines\n",
-    "import matplotlib.patches as mpatches\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "import numpy as np\n",
-    "import numpy.ma as ma\n",
-    "\n",
-    "from metpy.units import units\n",
-    "from netCDF4 import num2date\n",
-    "from scipy.ndimage import gaussian_filter\n",
-    "from siphon.catalog import TDSCatalog\n",
-    "from xarray.backends import NetCDF4DataStore\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "###########################"
-   },
-   "source": [
-    "**Get the data**\n",
-    "\n",
-    "This example will use data from the North American Mesoscale Model Analysis for 18 UTC 27 April 2011."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify our date/time of product desired\n",
-    "dt = datetime(2011, 4, 27, 18)\n",
-    "\n",
-    "# Construct the URL for our THREDDS Data Server Catalog,\n",
-    "# and access our desired dataset within via NCSS\n",
-    "base_url = 'https://www.ncei.noaa.gov/thredds/model-namanl-old/'\n",
-    "cat = TDSCatalog(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/catalog.xml')\n",
-    "ncss = cat.datasets[f'namanl_218_{dt:%Y%m%d}_{dt:%H}00_000.grb'].subset()\n",
-    "\n",
-    "# Create our NCSS query with desired specifications\n",
-    "query = ncss.query()\n",
-    "query.all_times()\n",
-    "query.add_lonlat()\n",
-    "query.lonlat_box(-135, -60, 15, 65)\n",
-    "query.variables('Geopotential_height_isobaric',\n",
-    "                'u-component_of_wind_isobaric',\n",
-    "                'v-component_of_wind_isobaric',\n",
-    "                'Temperature_isobaric',\n",
-    "                'Relative_humidity_isobaric',\n",
-    "                'Best_4_layer_lifted_index_layer_between_two_pressure_'\n",
-    "                'difference_from_ground_layer',\n",
-    "                'Absolute_vorticity_isobaric',\n",
-    "                'Pressure_reduced_to_MSL_msl',\n",
-    "                'Dew_point_temperature_height_above_ground')\n",
-    "\n",
-    "# Obtain the data we've queried for\n",
-    "data_18z = ncss.get_data(query)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds_18z = xr.open_dataset(NetCDF4DataStore(data_18z)).metpy.parse_cf()\n",
-    "\n",
-    "# Assign variable names to collected data\n",
-    "lat = ds_18z.lat\n",
-    "lon = ds_18z.lon\n",
-    "\n",
-    "# Create more useable times for output\n",
-    "times = ds_18z.Geopotential_height_isobaric.metpy.time.squeeze()\n",
-    "vtimes = times.values.astype('datetime64[ms]').astype('O')\n",
-    "\n",
-    "temps = ds_18z.Temperature_isobaric.squeeze()\n",
-    "uwnd = ds_18z['u-component_of_wind_isobaric'].squeeze()\n",
-    "vwnd = ds_18z['v-component_of_wind_isobaric'].squeeze()\n",
-    "hgt = ds_18z.Geopotential_height_isobaric.squeeze()\n",
-    "relh = ds_18z.Relative_humidity_isobaric.squeeze()\n",
-    "lifted_index = ds_18z['Best_4_layer_lifted_index_layer_between_two_'\n",
-    "                      'pressure_difference_from_ground_layer'].squeeze()\n",
-    "Td_sfc = ds_18z.Dew_point_temperature_height_above_ground.squeeze()\n",
-    "avor = ds_18z.Absolute_vorticity_isobaric.squeeze()\n",
-    "pmsl = ds_18z.Pressure_reduced_to_MSL_msl.squeeze()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################"
-   },
-   "source": [
-    "Repeat the above process to query for the analysis from 12 hours earlier (06 UTC) to calculate pressure falls and height change."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "td = timedelta(hours=12)\n",
-    "\n",
-    "ncss_06z = cat.datasets[f'namanl_218_{dt:%Y%m%d}_{dt-td:%H}00_000.grb'].subset()\n",
-    "\n",
-    "query = ncss_06z.query()\n",
-    "query.all_times()\n",
-    "query.add_lonlat()\n",
-    "query.lonlat_box(-135, -60, 15, 65)\n",
-    "query.variables('Geopotential_height_isobaric',\n",
-    "                'Pressure_reduced_to_MSL_msl')\n",
-    "\n",
-    "# Actually getting the data\n",
-    "data_06z = ncss_06z.get_data(query)\n",
-    "\n",
-    "# Make into an xarray Dataset object\n",
-    "ds_06z = xr.open_dataset(NetCDF4DataStore(data_06z)).metpy.parse_cf()\n",
-    "\n",
-    "hgt_06z = ds_06z.Geopotential_height_isobaric.squeeze()\n",
-    "pmsl_06z = ds_06z.Pressure_reduced_to_MSL_msl.squeeze()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "###########################"
-   },
-   "source": [
-    "**Subset the Data**\n",
-    "\n",
-    "With the data pulled in, we will now subset to the specific levels desired"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 300 hPa\n",
-    "u_300 = uwnd.metpy.sel(vertical=300*units.hPa).metpy.convert_units('kt').squeeze()\n",
-    "v_300 = vwnd.metpy.sel(vertical=300*units.hPa).metpy.convert_units('kt').squeeze()\n",
-    "\n",
-    "# 500 hPa\n",
-    "avor_500 = avor.metpy.sel(vertical=500*units.hPa)\n",
-    "u_500 = uwnd.metpy.sel(vertical=500*units.hPa).metpy.convert_units('kt').squeeze()\n",
-    "v_500 = vwnd.metpy.sel(vertical=500*units.hPa).metpy.convert_units('kt').squeeze()\n",
-    "hgt_500 = hgt.metpy.sel(vertical=500*units.hPa).squeeze()\n",
-    "hgt_500_06z = hgt_06z.metpy.sel(vertical=500*units.hPa).squeeze()\n",
-    "\n",
-    "# 700 hPa\n",
-    "tmp_700 = temps.metpy.sel(vertical=700*units.hPa).metpy.convert_units('degC').squeeze()\n",
-    "rh_700 = relh.metpy.sel(vertical=700*units.hPa).squeeze()\n",
-    "\n",
-    "# 850 hPa\n",
-    "u_850 = uwnd.metpy.sel(vertical=850*units.hPa).metpy.convert_units('kt').squeeze()\n",
-    "v_850 = vwnd.metpy.sel(vertical=850*units.hPa).metpy.convert_units('kt').squeeze()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################"
-   },
-   "source": [
-    "**Prepare Variables for Plotting**\n",
-    "\n",
-    "With the data queried and subset, we will make any needed calculations in preparation for\n",
-    "plotting.\n",
-    "\n",
-    "**The following fields should be plotted:**\n",
-    "\n",
-    "  500-hPa cyclonic vorticity advection\n",
-    "\n",
-    "  Surface-based Lifted Index\n",
-    "\n",
-    "  The axis of the 300-hPa, 500-hPa, and 850-hPa jets\n",
-    "\n",
-    "  Surface dewpoint\n",
-    "\n",
-    "  700-hPa dewpoint depression\n",
-    "\n",
-    "  12-hr surface pressure falls and 500-hPa height changes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 500 hPa CVA\n",
-    "vort_adv_500 = mpcalc.advection(avor_500, u_500, v_500,) * 1e9\n",
-    "vort_adv_500_smooth = gaussian_filter(vort_adv_500, 4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "####################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "For the jet axes, we will calculate the windspeed at each level, and plot the highest values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wspd_300 = gaussian_filter(mpcalc.wind_speed(u_300, v_300), 5)\n",
-    "wspd_500 = gaussian_filter(mpcalc.wind_speed(u_500, v_500), 5)\n",
-    "wspd_850 = gaussian_filter(mpcalc.wind_speed(u_850, v_850), 5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "700-hPa dewpoint depression will be calculated from Temperature_isobaric and RH"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Td_dep_700 = tmp_700 - mpcalc.dewpoint_from_relative_humidity(tmp_700, rh_700)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "12-hr surface pressure falls and 500-hPa height changes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pmsl_change = pmsl.metpy.quantify() - pmsl_06z.metpy.quantify()\n",
-    "hgt_500_change = hgt_500.metpy.quantify() - hgt_500_06z.metpy.quantify()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "######################################"
-   },
-   "source": [
-    "To plot the jet axes, we will mask the wind fields below the upper 1/3 of windspeed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 500 hPa\n",
-    "u_500_masked = u_500.where(wspd_500 > 0.66 * wspd_500.max(), np.nan)\n",
-    "v_500_masked = v_500.where(wspd_500 > 0.66 * wspd_500.max(), np.nan)\n",
-    "\n",
-    "# 300 hPa\n",
-    "u_300_masked = u_300.where(wspd_300 > 0.66 * wspd_300.max(), np.nan)\n",
-    "v_300_masked = v_300.where(wspd_300 > 0.66 * wspd_300.max(), np.nan)\n",
-    "\n",
-    "# 850 hPa\n",
-    "u_850_masked = u_850.where(wspd_850 > 0.66 * wspd_850.max(), np.nan)\n",
-    "v_850_masked = v_850.where(wspd_850 > 0.66 * wspd_850.max(), np.nan)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "################################"
-   },
-   "source": [
-    "**Create the Plot**\n",
-    "\n",
-    "With the data now ready, we will create the plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set up our projection\n",
-    "crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)\n",
-    "\n",
-    "# Coordinates to limit map area\n",
-    "bounds = [-122., -75., 25., 50.]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "#########################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Plot the composite"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(1, figsize=(17, 12))\n",
-    "ax = fig.add_subplot(1, 1, 1, projection=crs)\n",
-    "ax.set_extent(bounds, crs=ccrs.PlateCarree())\n",
-    "ax.coastlines('50m', edgecolor='black', linewidth=0.75)\n",
-    "ax.add_feature(cfeature.STATES, linewidth=0.25)\n",
-    "\n",
-    "# Plot Lifted Index\n",
-    "cs1 = ax.contour(lon, lat, lifted_index, range(-8, -2, 2), transform=ccrs.PlateCarree(),\n",
-    "                 colors='red', linewidths=0.75, linestyles='solid', zorder=7)\n",
-    "cs1.clabel(fontsize=10, inline=1, inline_spacing=7,\n",
-    "           fmt='%i', rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Plot Surface pressure falls\n",
-    "cs2 = ax.contour(lon, lat, pmsl_change.metpy.convert_units('hPa'), range(-10, -1, 4),\n",
-    "                 transform=ccrs.PlateCarree(),\n",
-    "                 colors='k', linewidths=0.75, linestyles='dashed', zorder=6)\n",
-    "cs2.clabel(fontsize=10, inline=1, inline_spacing=7,\n",
-    "           fmt='%i', rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Plot 500-hPa height falls\n",
-    "cs3 = ax.contour(lon, lat, hgt_500_change, range(-60, -29, 15),\n",
-    "                 transform=ccrs.PlateCarree(), colors='k', linewidths=0.75,\n",
-    "                 linestyles='solid', zorder=5)\n",
-    "cs3.clabel(fontsize=10, inline=1, inline_spacing=7,\n",
-    "           fmt='%i', rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Plot surface pressure\n",
-    "ax.contourf(lon, lat, pmsl.metpy.convert_units('hPa'), range(990, 1011, 20), alpha=0.5,\n",
-    "            transform=ccrs.PlateCarree(),\n",
-    "            colors='yellow', zorder=1)\n",
-    "\n",
-    "# Plot surface dewpoint\n",
-    "ax.contourf(lon, lat, Td_sfc.metpy.convert_units('degF'), range(65, 76, 10), alpha=0.4,\n",
-    "            transform=ccrs.PlateCarree(),\n",
-    "            colors=['green'], zorder=2)\n",
-    "\n",
-    "# Plot 700-hPa dewpoint depression\n",
-    "ax.contourf(lon, lat, Td_dep_700, range(15, 46, 30), alpha=0.5, transform=ccrs.PlateCarree(),\n",
-    "            colors='tan', zorder=3)\n",
-    "\n",
-    "# Plot Vorticity Advection\n",
-    "purple = ax.contourf(lon, lat, vort_adv_500_smooth, range(5, 106, 100), alpha=0.5,\n",
-    "            transform=ccrs.PlateCarree(),\n",
-    "            colors='BlueViolet', zorder=4)\n",
-    "\n",
-    "# Define a skip to reduce the barb point density\n",
-    "skip_300 = (slice(None, None, 12), slice(None, None, 12))\n",
-    "skip_500 = (slice(None, None, 10), slice(None, None, 10))\n",
-    "skip_850 = (slice(None, None, 8), slice(None, None, 8))\n",
-    "\n",
-    "# 300-hPa wind barbs\n",
-    "jet300 = ax.barbs(lon[skip_300].values, lat[skip_300].values,\n",
-    "                  u_300_masked[skip_300].values, v_300_masked[skip_300].values,\n",
-    "                  length=6,\n",
-    "                  transform=ccrs.PlateCarree(),\n",
-    "                  color='green', zorder=10, label='300-hPa Jet Core Winds (kt)')\n",
-    "\n",
-    "\n",
-    "# 500-hPa wind barbs\n",
-    "jet500 = ax.barbs(lon[skip_500].values, lat[skip_500].values,\n",
-    "                  u_500_masked[skip_500].values, v_500_masked[skip_500].values,\n",
-    "                  length=6,\n",
-    "                  transform=ccrs.PlateCarree(),\n",
-    "                  color='blue', zorder=9, label='500-hPa Jet Core Winds (kt)')\n",
-    "\n",
-    "# 850-hPa wind barbs\n",
-    "jet850 = ax.barbs(lon[skip_850].values, lat[skip_850].values,\n",
-    "                  u_850_masked[skip_850].values, v_850_masked[skip_850].values,\n",
-    "                  length=6,\n",
-    "                  transform=ccrs.PlateCarree(),\n",
-    "                  color='k', zorder=8, label='850-hPa Jet Core Winds (kt)')\n",
-    "\n",
-    "# Legend\n",
-    "purple = mpatches.Patch(color='BlueViolet', alpha=0.5, label='Cyclonic Absolute Vorticity Advection')\n",
-    "yellow = mpatches.Patch(color='yellow', alpha=0.5, label='Surface MSLP < 1010 hPa')\n",
-    "green = mpatches.Patch(color='green', alpha=0.5, label='Surface Td > 65 F')\n",
-    "tan = mpatches.Patch(color='tan', alpha=0.5, label='700 hPa Dewpoint Depression > 15 C')\n",
-    "red_line = lines.Line2D([], [], color='red', label='Best Lifted Index (C)')\n",
-    "dashed_black_line = lines.Line2D([], [], linestyle='dashed', color='k',\n",
-    "                                 label='12-hr Surface Pressure Falls (hPa)')\n",
-    "black_line = lines.Line2D([], [], linestyle='solid', color='k',\n",
-    "                          label='12-hr 500-hPa Height Falls (m)')\n",
-    "leg = plt.legend(handles=[jet300, jet500, jet850, dashed_black_line, black_line, red_line,\n",
-    "                          purple, tan, green, yellow], loc=3,\n",
-    "                 title=f'Composite Analysis Valid: {vtimes}',\n",
-    "                 framealpha=1)\n",
-    "leg.set_zorder(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/gallery/xarray_500hPa_map.ipynb b/_preview/3/_sources/notebooks/gallery/xarray_500hPa_map.ipynb
deleted file mode 100644
index 657fc6e..0000000
--- a/_preview/3/_sources/notebooks/gallery/xarray_500hPa_map.ipynb
+++ /dev/null
@@ -1,227 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "\"\"\"",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Using Xarray for Data read and selection\n",
-    "========================================\n",
-    "\n",
-    "Use Xarray module to read in model data from nomads server.\n",
-    "\n",
-    "This example uses the xarray module to access data from the nomads server for\n",
-    "archive NAM analysis data via OPeNDAP. Xarray makes it easier to select times\n",
-    "and levels, although you still have to know the coordinate variable name. A\n",
-    "simple 500 hPa plot is created after selecting with xarray."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################",
-    "lines_to_next_cell": 0
-   },
-   "source": [
-    "Import all of our needed modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import matplotlib.pyplot as plt\n",
-    "import metpy.calc as mpcalc\n",
-    "from metpy.units import units\n",
-    "import numpy as np\n",
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################"
-   },
-   "source": [
-    "Accessing data using Xarray\n",
-    "---------------------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify our date/time of product desired\n",
-    "dt = datetime(2016, 4, 16, 18)\n",
-    "\n",
-    "# Construct our OPeNDAP access URL\n",
-    "base_url = 'https://www.ncei.noaa.gov/thredds/dodsC/model-namanl-old/'\n",
-    "data = xr.open_dataset(f'{base_url}{dt:%Y%m}/{dt:%Y%m%d}/'\n",
-    "                       f'namanl_218_{dt:%Y%m%d}_{dt:%H}00_000.grb').metpy.parse_cf()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################"
-   },
-   "source": [
-    "NAM data is in a projected coordinate and you get back the projection\n",
-    "X and Y values in km"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a 2-d meshgrid of our x, y coordinates\n",
-    "# manually converted to meters (km * 1000)\n",
-    "#x, y = np.meshgrid(data['x'].values * 1000, data['y'].values * 1000)\n",
-    "x = data.Geopotential_height_isobaric.metpy.x.metpy.convert_units('meter').values\n",
-    "y = data.Geopotential_height_isobaric.metpy.y.metpy.convert_units('meter').values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################"
-   },
-   "source": [
-    "Getting the valid times in a more useable format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the valid times from the file\n",
-    "vtimes = data.Geopotential_height_isobaric.metpy.time.data.astype('datetime64[ms]').astype('O')\n",
-    "print(vtimes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################"
-   },
-   "source": [
-    "Xarray has some nice functionality to choose the time and level that\n",
-    "you specifically want to use. In this example the time variable is 'time' and\n",
-    "the level variable is 'isobaric1'. Unfortunately, these can be different with\n",
-    "each file you use, so you'll always need to check what they are by listing\n",
-    "the coordinate variable names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# print(data.Geopotential_height.coords)\n",
-    "hght_500 = data.Geopotential_height_isobaric.metpy.sel(time1=vtimes[0], vertical=500*units.hPa)\n",
-    "uwnd_500 = data['u-component_of_wind_isobaric'].metpy.sel(time1=vtimes[0], vertical=500*units.hPa)\n",
-    "vwnd_500 = data['v-component_of_wind_isobaric'].metpy.sel(time1=vtimes[0], vertical=500*units.hPa)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "cell_marker": "########################################"
-   },
-   "source": [
-    "Now make the 500-hPa map\n",
-    "------------------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Must set data projection, NAM is LCC projection\n",
-    "datacrs = data.Geopotential_height_isobaric.metpy.cartopy_crs\n",
-    "\n",
-    "# A different LCC projection for the plot.\n",
-    "plotcrs = ccrs.LambertConformal(central_latitude=45., central_longitude=-100.,\n",
-    "                                standard_parallels=[30, 60])\n",
-    "\n",
-    "fig = plt.figure(figsize=(17., 11.))\n",
-    "ax = plt.axes(projection=plotcrs)\n",
-    "ax.coastlines('50m', edgecolor='black')\n",
-    "ax.add_feature(cfeature.STATES, linewidth=0.5)\n",
-    "ax.set_extent([-130, -67, 20, 50], ccrs.PlateCarree())\n",
-    "\n",
-    "clev500 = np.arange(5100, 6000, 60)\n",
-    "cs = ax.contour(x, y, mpcalc.smooth_n_point(hght_500, 9, 5), clev500,\n",
-    "                colors='k', linewidths=2.5, linestyles='solid', transform=datacrs)\n",
-    "ax.clabel(cs, fontsize=12, colors='k', inline=1, inline_spacing=8,\n",
-    "          fmt='%i', rightside_up=True, use_clabeltext=True)\n",
-    "\n",
-    "# Here we put boxes around the clabels with a black boarder white facecolor\n",
-    "# `labelTexts` necessary as ~cartopy.mpl.contour.GeoContourSet.clabel\n",
-    "# does not return list of texts as of 0.18\n",
-    "for t in cs.labelTexts:\n",
-    "    t.set_bbox({'fc': 'w'})\n",
-    "\n",
-    "# Transform Vectors before plotting, then plot wind barbs.\n",
-    "wind_slice = slice(None, None, 16)\n",
-    "ax.barbs(x[wind_slice], y[wind_slice],\n",
-    "         uwnd_500.data[wind_slice, wind_slice], vwnd_500.data[wind_slice, wind_slice],\n",
-    "         length=7, transform=datacrs)\n",
-    "\n",
-    "# Add some titles to make the plot readable by someone else\n",
-    "plt.title('500-hPa Geopotential Heights (m)', loc='left')\n",
-    "plt.title(f'VALID: {vtimes[0]}', loc='right');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sources/notebooks/notebook-template.ipynb b/_preview/3/_sources/notebooks/notebook-template.ipynb
deleted file mode 100644
index 4266bda..0000000
--- a/_preview/3/_sources/notebooks/notebook-template.ipynb
+++ /dev/null
@@ -1,358 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start here! If you can directly link to an image relevant to your notebook, such as [canonical logos](https://github.com/numpy/numpy/blob/main/doc/source/_static/numpylogo.svg), do so here at the top of your notebook. You can do this with Markdown syntax,\n",
-    "\n",
-    "> `![<image title>](http://link.com/to/image.png \"image alt text\")`\n",
-    "\n",
-    "or edit this cell to see raw HTML `img` demonstration. This is preferred if you need to shrink your embedded image. **Either way be sure to include `alt` text for any embedded images to make your content more accessible.**\n",
-    "\n",
-    "<img src=\"images/ProjectPythia_Logo_Final-01-Blue.svg\" width=250 alt=\"Project Pythia Logo\"></img>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Project Pythia Notebook Template\n",
-    "\n",
-    "Next, title your notebook appropriately with a top-level Markdown header, `#`. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a `---` cell to visually distinguish the transition to the prerequisites section."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Overview\n",
-    "If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,\n",
-    "\n",
-    "1. This is a numbered list of the specific topics\n",
-    "1. These should map approximately to your main sections of content\n",
-    "1. Or each second-level, `##`, header in your notebook\n",
-    "1. Keep the size and scope of your notebook in check\n",
-    "1. And be sure to let the reader know up front the important concepts they'll be leaving with"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prerequisites\n",
-    "This section was inspired by [this template](https://github.com/alan-turing-institute/the-turing-way/blob/master/book/templates/chapter-template/chapter-landing-page.md) of the wonderful [The Turing Way](https://the-turing-way.netlify.app) Jupyter Book.\n",
-    "\n",
-    "Following your overview, tell your reader what concepts, packages, or other background information they'll **need** before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with `|` vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.\n",
-    "\n",
-    "Label the importance of each concept explicitly as **helpful/necessary**.\n",
-    "\n",
-    "| Concepts | Importance | Notes |\n",
-    "| --- | --- | --- |\n",
-    "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
-    "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
-    "| Project management | Helpful | |\n",
-    "\n",
-    "- **Time to learn**: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.\n",
-    "- **System requirements**:\n",
-    "    - Populate with any system, version, or non-Python software requirements if necessary\n",
-    "    - Otherwise use the concepts table above and the Imports section below to describe required packages as necessary\n",
-    "    - If no extra requirements, remove the **System requirements** point altogether"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports\n",
-    "Begin your body of content with another `---` divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports **up-front**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Your first content section"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# as well as any and all of your code cells\n",
-    "print(\"Hello world!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### A content subsection\n",
-    "Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# some subsection code\n",
-    "new = \"helpful information\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Another content subsection\n",
-    "Keep up the good work! A note, *try to avoid using code comments as narrative*, and instead let them only exist as brief clarifications where necessary."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Your second content section\n",
-    "Here we can move on to our second objective, and we can demonstrate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Subsection to the second section\n",
-    "\n",
-    "#### a quick demonstration\n",
-    "\n",
-    "##### of further and further\n",
-    "\n",
-    "###### header levels"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "as well $m = a * t / h$ text! Similarly, you have access to other $\\LaTeX$ equation [**functionality**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html) via MathJax (demo below from link),\n",
-    "\n",
-    "\\begin{align}\n",
-    "\\dot{x} & = \\sigma(y-x) \\\\\n",
-    "\\dot{y} & = \\rho x - y - xz \\\\\n",
-    "\\dot{z} & = -\\beta z + xy\n",
-    "\\end{align}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check out [**any number of helpful Markdown resources**](https://www.markdownguide.org/basic-syntax/) for further customizing your notebooks and the [**Jupyter docs**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html) for Jupyter-specific formatting information. Don't hesitate to ask questions if you have problems getting it to look *just right*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Last Section\n",
-    "\n",
-    "If you're comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-info\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Info</p>\n",
-    "    Your relevant information here!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Feel free to copy this around and edit or play around with yourself. Some other `admonitions` you can put in:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-success\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Success</p>\n",
-    "    We got this done after all!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-warning\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Warning</p>\n",
-    "    Be careful!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-danger\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Danger</p>\n",
-    "    Scary stuff be here.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We also suggest checking out Jupyter Book's [brief demonstration](https://jupyterbook.org/content/metadata.html#jupyter-cell-tags) on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. Using these cell tags can allow you to [customize](https://jupyterbook.org/interactive/hiding.html) how your code content is displayed and even [demonstrate errors](https://jupyterbook.org/content/execute.html#dealing-with-code-that-raises-errors) without altogether crashing our loyal army of machines!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "Add one final `---` marking the end of your body of content, and then conclude with a brief single paragraph summarizing at a high level the key pieces that were learned and how they tied to your objectives. Look to reiterate what the most important takeaways were.\n",
-    "\n",
-    "### What's next?\n",
-    "Let Jupyter book tie this to the next (sequential) piece of content that people could move on to down below and in the sidebar. However, if this page uniquely enables your reader to tackle other nonsequential concepts throughout this book, or even external content, link to it here!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Resources and references\n",
-    "Finally, be rigorous in your citations and references as necessary. Give credit where credit is due. Also, feel free to link to relevant external material, further reading, documentation, etc. Then you're done! Give yourself a quick review, a high five, and send us a pull request. A few final notes:\n",
-    " - `Kernel > Restart Kernel and Run All Cells...` to confirm that your notebook will cleanly run from start to finish\n",
-    " - `Kernel > Restart Kernel and Clear All Outputs...` before committing your notebook, our machines will do the heavy lifting\n",
-    " - Take credit! Provide author contact information if you'd like; if so, consider adding information here at the bottom of your notebook\n",
-    " - Give credit! Attribute appropriate authorship for referenced code, information, images, etc.\n",
-    " - Only include what you're legally allowed: **no copyright infringement or plagiarism**\n",
-    " \n",
-    "Thank you for your contribution!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.0"
-  },
-  "nbdime-conflicts": {
-   "local_diff": [
-    {
-     "diff": [
-      {
-       "diff": [
-        {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "Python 3"
-         ]
-        },
-        {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
-        }
-       ],
-       "key": "display_name",
-       "op": "patch"
-      }
-     ],
-     "key": "kernelspec",
-     "op": "patch"
-    }
-   ],
-   "remote_diff": [
-    {
-     "diff": [
-      {
-       "diff": [
-        {
-         "key": 0,
-         "op": "addrange",
-         "valuelist": [
-          "Python3"
-         ]
-        },
-        {
-         "key": 0,
-         "length": 1,
-         "op": "removerange"
-        }
-       ],
-       "key": "display_name",
-       "op": "patch"
-      }
-     ],
-     "key": "kernelspec",
-     "op": "patch"
-    }
-   ]
-  },
-  "toc-autonumbering": false
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_preview/3/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_preview/3/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
deleted file mode 100644
index eb19f69..0000000
--- a/_preview/3/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/_preview/3/_sphinx_design_static/design-tabs.js b/_preview/3/_sphinx_design_static/design-tabs.js
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index 36b38cf..0000000
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e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/_preview/3/_static/copy-button.svg b/_preview/3/_static/copy-button.svg
deleted file mode 100644
index 9c074da..0000000
--- a/_preview/3/_static/copy-button.svg
+++ /dev/null
@@ -1,5 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <rect x="8" y="8" width="12" height="12" rx="2" />
-  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
-</svg>
diff --git a/_preview/3/_static/copybutton.css b/_preview/3/_static/copybutton.css
deleted file mode 100644
index f1916ec..0000000
--- a/_preview/3/_static/copybutton.css
+++ /dev/null
@@ -1,94 +0,0 @@
-/* Copy buttons */
-button.copybtn {
-    position: absolute;
-    display: flex;
-    top: .3em;
-    right: .3em;
-    width: 1.7em;
-    height: 1.7em;
-	opacity: 0;
-    transition: opacity 0.3s, border .3s, background-color .3s;
-    user-select: none;
-    padding: 0;
-    border: none;
-    outline: none;
-    border-radius: 0.4em;
-    /* The colors that GitHub uses */
-    border: #1b1f2426 1px solid;
-    background-color: #f6f8fa;
-    color: #57606a;
-}
-
-button.copybtn.success {
-    border-color: #22863a;
-    color: #22863a;
-}
-
-button.copybtn svg {
-    stroke: currentColor;
-    width: 1.5em;
-    height: 1.5em;
-    padding: 0.1em;
-}
-
-div.highlight  {
-    position: relative;
-}
-
-/* Show the copybutton */
-.highlight:hover button.copybtn, button.copybtn.success {
-	opacity: 1;
-}
-
-.highlight button.copybtn:hover {
-    background-color: rgb(235, 235, 235);
-}
-
-.highlight button.copybtn:active {
-    background-color: rgb(187, 187, 187);
-}
-
-/**
- * A minimal CSS-only tooltip copied from:
- *   https://codepen.io/mildrenben/pen/rVBrpK
- *
- * To use, write HTML like the following:
- *
- * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
- */
- .o-tooltip--left {
-  position: relative;
- }
-
- .o-tooltip--left:after {
-    opacity: 0;
-    visibility: hidden;
-    position: absolute;
-    content: attr(data-tooltip);
-    padding: .2em;
-    font-size: .8em;
-    left: -.2em;
-    background: grey;
-    color: white;
-    white-space: nowrap;
-    z-index: 2;
-    border-radius: 2px;
-    transform: translateX(-102%) translateY(0);
-    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
-}
-
-.o-tooltip--left:hover:after {
-    display: block;
-    opacity: 1;
-    visibility: visible;
-    transform: translateX(-100%) translateY(0);
-    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
-    transition-delay: .5s;
-}
-
-/* By default the copy button shouldn't show up when printing a page */
-@media print {
-    button.copybtn {
-        display: none;
-    }
-}
diff --git a/_preview/3/_static/copybutton.js b/_preview/3/_static/copybutton.js
deleted file mode 100644
index 2ea7ff3..0000000
--- a/_preview/3/_static/copybutton.js
+++ /dev/null
@@ -1,248 +0,0 @@
-// Localization support
-const messages = {
-  'en': {
-    'copy': 'Copy',
-    'copy_to_clipboard': 'Copy to clipboard',
-    'copy_success': 'Copied!',
-    'copy_failure': 'Failed to copy',
-  },
-  'es' : {
-    'copy': 'Copiar',
-    'copy_to_clipboard': 'Copiar al portapapeles',
-    'copy_success': '¡Copiado!',
-    'copy_failure': 'Error al copiar',
-  },
-  'de' : {
-    'copy': 'Kopieren',
-    'copy_to_clipboard': 'In die Zwischenablage kopieren',
-    'copy_success': 'Kopiert!',
-    'copy_failure': 'Fehler beim Kopieren',
-  },
-  'fr' : {
-    'copy': 'Copier',
-    'copy_to_clipboard': 'Copier dans le presse-papier',
-    'copy_success': 'Copié !',
-    'copy_failure': 'Échec de la copie',
-  },
-  'ru': {
-    'copy': 'Скопировать',
-    'copy_to_clipboard': 'Скопировать в буфер',
-    'copy_success': 'Скопировано!',
-    'copy_failure': 'Не удалось скопировать',
-  },
-  'zh-CN': {
-    'copy': '复制',
-    'copy_to_clipboard': '复制到剪贴板',
-    'copy_success': '复制成功!',
-    'copy_failure': '复制失败',
-  },
-  'it' : {
-    'copy': 'Copiare',
-    'copy_to_clipboard': 'Copiato negli appunti',
-    'copy_success': 'Copiato!',
-    'copy_failure': 'Errore durante la copia',
-  }
-}
-
-let locale = 'en'
-if( document.documentElement.lang !== undefined
-    && messages[document.documentElement.lang] !== undefined ) {
-  locale = document.documentElement.lang
-}
-
-let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
-if (doc_url_root == '#') {
-    doc_url_root = '';
-}
-
-/**
- * SVG files for our copy buttons
- */
-let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <title>${messages[locale]['copy_success']}</title>
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <path d="M5 12l5 5l10 -10" />
-</svg>`
-
-// If the user specified their own SVG use that, otherwise use the default
-let iconCopy = ``;
-if (!iconCopy) {
-  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
-  <title>${messages[locale]['copy_to_clipboard']}</title>
-  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-  <rect x="8" y="8" width="12" height="12" rx="2" />
-  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
-</svg>`
-}
-
-/**
- * Set up copy/paste for code blocks
- */
-
-const runWhenDOMLoaded = cb => {
-  if (document.readyState != 'loading') {
-    cb()
-  } else if (document.addEventListener) {
-    document.addEventListener('DOMContentLoaded', cb)
-  } else {
-    document.attachEvent('onreadystatechange', function() {
-      if (document.readyState == 'complete') cb()
-    })
-  }
-}
-
-const codeCellId = index => `codecell${index}`
-
-// Clears selected text since ClipboardJS will select the text when copying
-const clearSelection = () => {
-  if (window.getSelection) {
-    window.getSelection().removeAllRanges()
-  } else if (document.selection) {
-    document.selection.empty()
-  }
-}
-
-// Changes tooltip text for a moment, then changes it back
-// We want the timeout of our `success` class to be a bit shorter than the
-// tooltip and icon change, so that we can hide the icon before changing back.
-var timeoutIcon = 2000;
-var timeoutSuccessClass = 1500;
-
-const temporarilyChangeTooltip = (el, oldText, newText) => {
-  el.setAttribute('data-tooltip', newText)
-  el.classList.add('success')
-  // Remove success a little bit sooner than we change the tooltip
-  // So that we can use CSS to hide the copybutton first
-  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
-  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
-}
-
-// Changes the copy button icon for two seconds, then changes it back
-const temporarilyChangeIcon = (el) => {
-  el.innerHTML = iconCheck;
-  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
-}
-
-const addCopyButtonToCodeCells = () => {
-  // If ClipboardJS hasn't loaded, wait a bit and try again. This
-  // happens because we load ClipboardJS asynchronously.
-  if (window.ClipboardJS === undefined) {
-    setTimeout(addCopyButtonToCodeCells, 250)
-    return
-  }
-
-  // Add copybuttons to all of our code cells
-  const COPYBUTTON_SELECTOR = 'div.highlight pre';
-  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
-  codeCells.forEach((codeCell, index) => {
-    const id = codeCellId(index)
-    codeCell.setAttribute('id', id)
-
-    const clipboardButton = id =>
-    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
-      ${iconCopy}
-    </button>`
-    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
-  })
-
-function escapeRegExp(string) {
-    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-    var regexp;
-    var match;
-
-    // Do we check for line continuation characters and "HERE-documents"?
-    var useLineCont = !!lineContinuationChar
-    var useHereDoc = !!hereDocDelim
-
-    // create regexp to capture prompt and remaining line
-    if (isRegexp) {
-        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
-    } else {
-        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
-    }
-
-    const outputLines = [];
-    var promptFound = false;
-    var gotLineCont = false;
-    var gotHereDoc = false;
-    const lineGotPrompt = [];
-    for (const line of textContent.split('\n')) {
-        match = line.match(regexp)
-        if (match || gotLineCont || gotHereDoc) {
-            promptFound = regexp.test(line)
-            lineGotPrompt.push(promptFound)
-            if (removePrompts && promptFound) {
-                outputLines.push(match[2])
-            } else {
-                outputLines.push(line)
-            }
-            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
-            if (line.includes(hereDocDelim) & useHereDoc)
-                gotHereDoc = !gotHereDoc
-        } else if (!onlyCopyPromptLines) {
-            outputLines.push(line)
-        } else if (copyEmptyLines && line.trim() === '') {
-            outputLines.push(line)
-        }
-    }
-
-    // If no lines with the prompt were found then just use original lines
-    if (lineGotPrompt.some(v => v === true)) {
-        textContent = outputLines.join('\n');
-    }
-
-    // Remove a trailing newline to avoid auto-running when pasting
-    if (textContent.endsWith("\n")) {
-        textContent = textContent.slice(0, -1)
-    }
-    return textContent
-}
-
-
-var copyTargetText = (trigger) => {
-  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
-
-  // get filtered text
-  let exclude = '.linenos';
-
-  let text = filterText(target, exclude);
-  return formatCopyText(text, '', false, true, true, true, '', '')
-}
-
-  // Initialize with a callback so we can modify the text before copy
-  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
-
-  // Update UI with error/success messages
-  clipboard.on('success', event => {
-    clearSelection()
-    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
-    temporarilyChangeIcon(event.trigger)
-  })
-
-  clipboard.on('error', event => {
-    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
-  })
-}
-
-runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/_preview/3/_static/copybutton_funcs.js b/_preview/3/_static/copybutton_funcs.js
deleted file mode 100644
index dbe1aaa..0000000
--- a/_preview/3/_static/copybutton_funcs.js
+++ /dev/null
@@ -1,73 +0,0 @@
-function escapeRegExp(string) {
-    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-}
-
-/**
- * Removes excluded text from a Node.
- *
- * @param {Node} target Node to filter.
- * @param {string} exclude CSS selector of nodes to exclude.
- * @returns {DOMString} Text from `target` with text removed.
- */
-export function filterText(target, exclude) {
-    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
-    if (exclude) {
-        // remove excluded nodes
-        clone.querySelectorAll(exclude).forEach(node => node.remove());
-    }
-    return clone.innerText;
-}
-
-// Callback when a copy button is clicked. Will be passed the node that was clicked
-// should then grab the text and replace pieces of text that shouldn't be used in output
-export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-    var regexp;
-    var match;
-
-    // Do we check for line continuation characters and "HERE-documents"?
-    var useLineCont = !!lineContinuationChar
-    var useHereDoc = !!hereDocDelim
-
-    // create regexp to capture prompt and remaining line
-    if (isRegexp) {
-        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
-    } else {
-        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
-    }
-
-    const outputLines = [];
-    var promptFound = false;
-    var gotLineCont = false;
-    var gotHereDoc = false;
-    const lineGotPrompt = [];
-    for (const line of textContent.split('\n')) {
-        match = line.match(regexp)
-        if (match || gotLineCont || gotHereDoc) {
-            promptFound = regexp.test(line)
-            lineGotPrompt.push(promptFound)
-            if (removePrompts && promptFound) {
-                outputLines.push(match[2])
-            } else {
-                outputLines.push(line)
-            }
-            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
-            if (line.includes(hereDocDelim) & useHereDoc)
-                gotHereDoc = !gotHereDoc
-        } else if (!onlyCopyPromptLines) {
-            outputLines.push(line)
-        } else if (copyEmptyLines && line.trim() === '') {
-            outputLines.push(line)
-        }
-    }
-
-    // If no lines with the prompt were found then just use original lines
-    if (lineGotPrompt.some(v => v === true)) {
-        textContent = outputLines.join('\n');
-    }
-
-    // Remove a trailing newline to avoid auto-running when pasting
-    if (textContent.endsWith("\n")) {
-        textContent = textContent.slice(0, -1)
-    }
-    return textContent
-}
diff --git a/_preview/3/_static/custom.css b/_preview/3/_static/custom.css
deleted file mode 100644
index 0c9948b..0000000
--- a/_preview/3/_static/custom.css
+++ /dev/null
@@ -1,6 +0,0 @@
-.bd-main .bd-content .bd-article-container {
-    max-width: 100%; /* default is 60em */
-  }
-  .bd-page-width {
-    max-width: 100%; /* default is 88rem */
-  }
diff --git a/_preview/3/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_preview/3/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
deleted file mode 100644
index eb19f69..0000000
--- a/_preview/3/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/_preview/3/_static/design-tabs.js b/_preview/3/_static/design-tabs.js
deleted file mode 100644
index 36b38cf..0000000
--- a/_preview/3/_static/design-tabs.js
+++ /dev/null
@@ -1,27 +0,0 @@
-var sd_labels_by_text = {};
-
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-  const li = document.getElementsByClassName("sd-tab-label");
-  for (const label of li) {
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-      label.onclick = onLabelClick;
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-        sd_labels_by_text[syncId] = [];
-      }
-      sd_labels_by_text[syncId].push(label);
-    }
-  }
-}
-
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-  // Activate other inputs with the same sync id.
-  syncId = this.getAttribute("data-sync-id");
-  for (label of sd_labels_by_text[syncId]) {
-    if (label === this) continue;
-    label.previousElementSibling.checked = true;
-  }
-  window.localStorage.setItem("sphinx-design-last-tab", syncId);
-}
-
-document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/_preview/3/_static/doctools.js b/_preview/3/_static/doctools.js
deleted file mode 100644
index 4d67807..0000000
--- a/_preview/3/_static/doctools.js
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
-  "TEXTAREA",
-  "INPUT",
-  "SELECT",
-  "BUTTON",
-]);
-
-const _ready = (callback) => {
-  if (document.readyState !== "loading") {
-    callback();
-  } else {
-    document.addEventListener("DOMContentLoaded", callback);
-  }
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const Documentation = {
-  init: () => {
-    Documentation.initDomainIndexTable();
-    Documentation.initOnKeyListeners();
-  },
-
-  /**
-   * i18n support
-   */
-  TRANSLATIONS: {},
-  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
-  LOCALE: "unknown",
-
-  // gettext and ngettext don't access this so that the functions
-  // can safely bound to a different name (_ = Documentation.gettext)
-  gettext: (string) => {
-    const translated = Documentation.TRANSLATIONS[string];
-    switch (typeof translated) {
-      case "undefined":
-        return string; // no translation
-      case "string":
-        return translated; // translation exists
-      default:
-        return translated[0]; // (singular, plural) translation tuple exists
-    }
-  },
-
-  ngettext: (singular, plural, n) => {
-    const translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated !== "undefined")
-      return translated[Documentation.PLURAL_EXPR(n)];
-    return n === 1 ? singular : plural;
-  },
-
-  addTranslations: (catalog) => {
-    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
-    Documentation.PLURAL_EXPR = new Function(
-      "n",
-      `return (${catalog.plural_expr})`
-    );
-    Documentation.LOCALE = catalog.locale;
-  },
-
-  /**
-   * helper function to focus on search bar
-   */
-  focusSearchBar: () => {
-    document.querySelectorAll("input[name=q]")[0]?.focus();
-  },
-
-  /**
-   * Initialise the domain index toggle buttons
-   */
-  initDomainIndexTable: () => {
-    const toggler = (el) => {
-      const idNumber = el.id.substr(7);
-      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
-      if (el.src.substr(-9) === "minus.png") {
-        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
-        toggledRows.forEach((el) => (el.style.display = "none"));
-      } else {
-        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
-        toggledRows.forEach((el) => (el.style.display = ""));
-      }
-    };
-
-    const togglerElements = document.querySelectorAll("img.toggler");
-    togglerElements.forEach((el) =>
-      el.addEventListener("click", (event) => toggler(event.currentTarget))
-    );
-    togglerElements.forEach((el) => (el.style.display = ""));
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
-  },
-
-  initOnKeyListeners: () => {
-    // only install a listener if it is really needed
-    if (
-      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
-      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
-    )
-      return;
-
-    document.addEventListener("keydown", (event) => {
-      // bail for input elements
-      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
-      // bail with special keys
-      if (event.altKey || event.ctrlKey || event.metaKey) return;
-
-      if (!event.shiftKey) {
-        switch (event.key) {
-          case "ArrowLeft":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const prevLink = document.querySelector('link[rel="prev"]');
-            if (prevLink && prevLink.href) {
-              window.location.href = prevLink.href;
-              event.preventDefault();
-            }
-            break;
-          case "ArrowRight":
-            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
-            const nextLink = document.querySelector('link[rel="next"]');
-            if (nextLink && nextLink.href) {
-              window.location.href = nextLink.href;
-              event.preventDefault();
-            }
-            break;
-        }
-      }
-
-      // some keyboard layouts may need Shift to get /
-      switch (event.key) {
-        case "/":
-          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-          Documentation.focusSearchBar();
-          event.preventDefault();
-      }
-    });
-  },
-};
-
-// quick alias for translations
-const _ = Documentation.gettext;
-
-_ready(Documentation.init);
diff --git a/_preview/3/_static/documentation_options.js b/_preview/3/_static/documentation_options.js
deleted file mode 100644
index dab586c..0000000
--- a/_preview/3/_static/documentation_options.js
+++ /dev/null
@@ -1,13 +0,0 @@
-const DOCUMENTATION_OPTIONS = {
-    VERSION: '',
-    LANGUAGE: 'en',
-    COLLAPSE_INDEX: false,
-    BUILDER: 'html',
-    FILE_SUFFIX: '.html',
-    LINK_SUFFIX: '.html',
-    HAS_SOURCE: true,
-    SOURCELINK_SUFFIX: '',
-    NAVIGATION_WITH_KEYS: false,
-    SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: true,
-};
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- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, if available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
-  var step2list = {
-    ational: 'ate',
-    tional: 'tion',
-    enci: 'ence',
-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
-    ousli: 'ous',
-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
diff --git a/_preview/3/_static/locales/ar/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ar/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 34d404c..0000000
--- a/_preview/3/_static/locales/ar/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ar\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "طباعة إلى PDF"
-
-msgid "Theme by the"
-msgstr "موضوع بواسطة"
-
-msgid "Download source file"
-msgstr "تنزيل ملف المصدر"
-
-msgid "open issue"
-msgstr "قضية مفتوحة"
-
-msgid "Contents"
-msgstr "محتويات"
-
-msgid "previous page"
-msgstr "الصفحة السابقة"
-
-msgid "Download notebook file"
-msgstr "تنزيل ملف دفتر الملاحظات"
-
-msgid "Copyright"
-msgstr "حقوق النشر"
-
-msgid "Download this page"
-msgstr "قم بتنزيل هذه الصفحة"
-
-msgid "Source repository"
-msgstr "مستودع المصدر"
-
-msgid "By"
-msgstr "بواسطة"
-
-msgid "repository"
-msgstr "مخزن"
-
-msgid "Last updated on"
-msgstr "آخر تحديث في"
-
-msgid "Toggle navigation"
-msgstr "تبديل التنقل"
-
-msgid "Sphinx Book Theme"
-msgstr "موضوع كتاب أبو الهول"
-
-msgid "suggest edit"
-msgstr "أقترح تحرير"
-
-msgid "Open an issue"
-msgstr "افتح قضية"
-
-msgid "Launch"
-msgstr "إطلاق"
-
-msgid "Fullscreen mode"
-msgstr "وضع ملء الشاشة"
-
-msgid "Edit this page"
-msgstr "قم بتحرير هذه الصفحة"
-
-msgid "By the"
-msgstr "بواسطة"
-
-msgid "next page"
-msgstr "الصفحة التالية"
diff --git a/_preview/3/_static/locales/bg/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/bg/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 7420c19..0000000
--- a/_preview/3/_static/locales/bg/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: bg\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Печат в PDF"
-
-msgid "Theme by the"
-msgstr "Тема от"
-
-msgid "Download source file"
-msgstr "Изтеглете изходния файл"
-
-msgid "open issue"
-msgstr "отворен брой"
-
-msgid "Contents"
-msgstr "Съдържание"
-
-msgid "previous page"
-msgstr "предишна страница"
-
-msgid "Download notebook file"
-msgstr "Изтеглете файла на бележника"
-
-msgid "Copyright"
-msgstr "Авторско право"
-
-msgid "Download this page"
-msgstr "Изтеглете тази страница"
-
-msgid "Source repository"
-msgstr "Хранилище на източника"
-
-msgid "By"
-msgstr "От"
-
-msgid "repository"
-msgstr "хранилище"
-
-msgid "Last updated on"
-msgstr "Последна актуализация на"
-
-msgid "Toggle navigation"
-msgstr "Превключване на навигацията"
-
-msgid "Sphinx Book Theme"
-msgstr "Тема на книгата Sphinx"
-
-msgid "suggest edit"
-msgstr "предложи редактиране"
-
-msgid "Open an issue"
-msgstr "Отворете проблем"
-
-msgid "Launch"
-msgstr "Стартиране"
-
-msgid "Fullscreen mode"
-msgstr "Режим на цял екран"
-
-msgid "Edit this page"
-msgstr "Редактирайте тази страница"
-
-msgid "By the"
-msgstr "По"
-
-msgid "next page"
-msgstr "Следваща страница"
diff --git a/_preview/3/_static/locales/bn/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/bn/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 63a07c3..0000000
--- a/_preview/3/_static/locales/bn/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,63 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: bn\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "পিডিএফ প্রিন্ট করুন"
-
-msgid "Theme by the"
-msgstr "থিম দ্বারা"
-
-msgid "Download source file"
-msgstr "উত্স ফাইল ডাউনলোড করুন"
-
-msgid "open issue"
-msgstr "খোলা সমস্যা"
-
-msgid "previous page"
-msgstr "আগের পৃষ্ঠা"
-
-msgid "Download notebook file"
-msgstr "নোটবুক ফাইল ডাউনলোড করুন"
-
-msgid "Copyright"
-msgstr "কপিরাইট"
-
-msgid "Download this page"
-msgstr "এই পৃষ্ঠাটি ডাউনলোড করুন"
-
-msgid "Source repository"
-msgstr "উত্স সংগ্রহস্থল"
-
-msgid "By"
-msgstr "দ্বারা"
-
-msgid "Last updated on"
-msgstr "সর্বশেষ আপডেট"
-
-msgid "Toggle navigation"
-msgstr "নেভিগেশন টগল করুন"
-
-msgid "Sphinx Book Theme"
-msgstr "স্পিনিক্স বুক থিম"
-
-msgid "Open an issue"
-msgstr "একটি সমস্যা খুলুন"
-
-msgid "Launch"
-msgstr "শুরু করা"
-
-msgid "Edit this page"
-msgstr "এই পৃষ্ঠাটি সম্পাদনা করুন"
-
-msgid "By the"
-msgstr "দ্বারা"
-
-msgid "next page"
-msgstr "পরবর্তী পৃষ্ঠা"
diff --git a/_preview/3/_static/locales/ca/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ca/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 8fb358b..0000000
--- a/_preview/3/_static/locales/ca/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ca\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Imprimeix a PDF"
-
-msgid "Theme by the"
-msgstr "Tema del"
-
-msgid "Download source file"
-msgstr "Baixeu el fitxer font"
-
-msgid "open issue"
-msgstr "número obert"
-
-msgid "previous page"
-msgstr "Pàgina anterior"
-
-msgid "Download notebook file"
-msgstr "Descarregar fitxer de quadern"
-
-msgid "Copyright"
-msgstr "Copyright"
-
-msgid "Download this page"
-msgstr "Descarregueu aquesta pàgina"
-
-msgid "Source repository"
-msgstr "Dipòsit de fonts"
-
-msgid "By"
-msgstr "Per"
-
-msgid "Last updated on"
-msgstr "Darrera actualització el"
-
-msgid "Toggle navigation"
-msgstr "Commuta la navegació"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema del llibre Esfinx"
-
-msgid "suggest edit"
-msgstr "suggerir edició"
-
-msgid "Open an issue"
-msgstr "Obriu un número"
-
-msgid "Launch"
-msgstr "Llançament"
-
-msgid "Edit this page"
-msgstr "Editeu aquesta pàgina"
-
-msgid "By the"
-msgstr "Per la"
-
-msgid "next page"
-msgstr "pàgina següent"
diff --git a/_preview/3/_static/locales/cs/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/cs/LC_MESSAGES/booktheme.po
deleted file mode 100644
index c6ef469..0000000
--- a/_preview/3/_static/locales/cs/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: cs\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Tisk do PDF"
-
-msgid "Theme by the"
-msgstr "Téma od"
-
-msgid "Download source file"
-msgstr "Stáhněte si zdrojový soubor"
-
-msgid "open issue"
-msgstr "otevřené číslo"
-
-msgid "Contents"
-msgstr "Obsah"
-
-msgid "previous page"
-msgstr "předchozí stránka"
-
-msgid "Download notebook file"
-msgstr "Stáhnout soubor poznámkového bloku"
-
-msgid "Copyright"
-msgstr "autorská práva"
-
-msgid "Download this page"
-msgstr "Stáhněte si tuto stránku"
-
-msgid "Source repository"
-msgstr "Zdrojové úložiště"
-
-msgid "By"
-msgstr "Podle"
-
-msgid "repository"
-msgstr "úložiště"
-
-msgid "Last updated on"
-msgstr "Naposledy aktualizováno"
-
-msgid "Toggle navigation"
-msgstr "Přepnout navigaci"
-
-msgid "Sphinx Book Theme"
-msgstr "Téma knihy Sfinga"
-
-msgid "suggest edit"
-msgstr "navrhnout úpravy"
-
-msgid "Open an issue"
-msgstr "Otevřete problém"
-
-msgid "Launch"
-msgstr "Zahájení"
-
-msgid "Fullscreen mode"
-msgstr "Režim celé obrazovky"
-
-msgid "Edit this page"
-msgstr "Upravit tuto stránku"
-
-msgid "By the"
-msgstr "Podle"
-
-msgid "next page"
-msgstr "další strana"
diff --git a/_preview/3/_static/locales/da/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/da/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 306a38e..0000000
--- a/_preview/3/_static/locales/da/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: da\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Udskriv til PDF"
-
-msgid "Theme by the"
-msgstr "Tema af"
-
-msgid "Download source file"
-msgstr "Download kildefil"
-
-msgid "open issue"
-msgstr "åbent nummer"
-
-msgid "Contents"
-msgstr "Indhold"
-
-msgid "previous page"
-msgstr "forrige side"
-
-msgid "Download notebook file"
-msgstr "Download notesbog-fil"
-
-msgid "Copyright"
-msgstr "ophavsret"
-
-msgid "Download this page"
-msgstr "Download denne side"
-
-msgid "Source repository"
-msgstr "Kildelager"
-
-msgid "By"
-msgstr "Ved"
-
-msgid "repository"
-msgstr "lager"
-
-msgid "Last updated on"
-msgstr "Sidst opdateret den"
-
-msgid "Toggle navigation"
-msgstr "Skift navigation"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx bogtema"
-
-msgid "suggest edit"
-msgstr "foreslå redigering"
-
-msgid "Open an issue"
-msgstr "Åbn et problem"
-
-msgid "Launch"
-msgstr "Start"
-
-msgid "Fullscreen mode"
-msgstr "Fuldskærmstilstand"
-
-msgid "Edit this page"
-msgstr "Rediger denne side"
-
-msgid "By the"
-msgstr "Ved"
-
-msgid "next page"
-msgstr "Næste side"
diff --git a/_preview/3/_static/locales/de/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/de/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 4925360..0000000
--- a/_preview/3/_static/locales/de/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: de\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "In PDF drucken"
-
-msgid "Theme by the"
-msgstr "Thema von der"
-
-msgid "Download source file"
-msgstr "Quelldatei herunterladen"
-
-msgid "open issue"
-msgstr "offenes Thema"
-
-msgid "Contents"
-msgstr "Inhalt"
-
-msgid "previous page"
-msgstr "vorherige Seite"
-
-msgid "Download notebook file"
-msgstr "Notebook-Datei herunterladen"
-
-msgid "Copyright"
-msgstr "Urheberrechte ©"
-
-msgid "Download this page"
-msgstr "Laden Sie diese Seite herunter"
-
-msgid "Source repository"
-msgstr "Quell-Repository"
-
-msgid "By"
-msgstr "Durch"
-
-msgid "repository"
-msgstr "Repository"
-
-msgid "Last updated on"
-msgstr "Zuletzt aktualisiert am"
-
-msgid "Toggle navigation"
-msgstr "Navigation umschalten"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx-Buch-Thema"
-
-msgid "suggest edit"
-msgstr "vorschlagen zu bearbeiten"
-
-msgid "Open an issue"
-msgstr "Öffnen Sie ein Problem"
-
-msgid "Launch"
-msgstr "Starten"
-
-msgid "Fullscreen mode"
-msgstr "Vollbildmodus"
-
-msgid "Edit this page"
-msgstr "Bearbeite diese Seite"
-
-msgid "By the"
-msgstr "Bis zum"
-
-msgid "next page"
-msgstr "Nächste Seite"
diff --git a/_preview/3/_static/locales/el/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/el/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 3e01acb..0000000
--- a/_preview/3/_static/locales/el/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: el\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Εκτύπωση σε PDF"
-
-msgid "Theme by the"
-msgstr "Θέμα από το"
-
-msgid "Download source file"
-msgstr "Λήψη αρχείου προέλευσης"
-
-msgid "open issue"
-msgstr "ανοιχτό ζήτημα"
-
-msgid "Contents"
-msgstr "Περιεχόμενα"
-
-msgid "previous page"
-msgstr "προηγούμενη σελίδα"
-
-msgid "Download notebook file"
-msgstr "Λήψη αρχείου σημειωματάριου"
-
-msgid "Copyright"
-msgstr "Πνευματική ιδιοκτησία"
-
-msgid "Download this page"
-msgstr "Λήψη αυτής της σελίδας"
-
-msgid "Source repository"
-msgstr "Αποθήκη πηγής"
-
-msgid "By"
-msgstr "Με"
-
-msgid "repository"
-msgstr "αποθήκη"
-
-msgid "Last updated on"
-msgstr "Τελευταία ενημέρωση στις"
-
-msgid "Toggle navigation"
-msgstr "Εναλλαγή πλοήγησης"
-
-msgid "Sphinx Book Theme"
-msgstr "Θέμα βιβλίου Sphinx"
-
-msgid "suggest edit"
-msgstr "προτείνω επεξεργασία"
-
-msgid "Open an issue"
-msgstr "Ανοίξτε ένα ζήτημα"
-
-msgid "Launch"
-msgstr "Εκτόξευση"
-
-msgid "Fullscreen mode"
-msgstr "ΛΕΙΤΟΥΡΓΙΑ ΠΛΗΡΟΥΣ ΟΘΟΝΗΣ"
-
-msgid "Edit this page"
-msgstr "Επεξεργαστείτε αυτήν τη σελίδα"
-
-msgid "By the"
-msgstr "Από το"
-
-msgid "next page"
-msgstr "επόμενη σελίδα"
diff --git a/_preview/3/_static/locales/eo/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/eo/LC_MESSAGES/booktheme.po
deleted file mode 100644
index f7ed226..0000000
--- a/_preview/3/_static/locales/eo/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: eo\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Presi al PDF"
-
-msgid "Theme by the"
-msgstr "Temo de la"
-
-msgid "Download source file"
-msgstr "Elŝutu fontodosieron"
-
-msgid "open issue"
-msgstr "malferma numero"
-
-msgid "Contents"
-msgstr "Enhavo"
-
-msgid "previous page"
-msgstr "antaŭa paĝo"
-
-msgid "Download notebook file"
-msgstr "Elŝutu kajeran dosieron"
-
-msgid "Copyright"
-msgstr "Kopirajto"
-
-msgid "Download this page"
-msgstr "Elŝutu ĉi tiun paĝon"
-
-msgid "Source repository"
-msgstr "Fonto-deponejo"
-
-msgid "By"
-msgstr "De"
-
-msgid "repository"
-msgstr "deponejo"
-
-msgid "Last updated on"
-msgstr "Laste ĝisdatigita la"
-
-msgid "Toggle navigation"
-msgstr "Ŝalti navigadon"
-
-msgid "Sphinx Book Theme"
-msgstr "Sfinksa Libro-Temo"
-
-msgid "suggest edit"
-msgstr "sugesti redaktadon"
-
-msgid "Open an issue"
-msgstr "Malfermu numeron"
-
-msgid "Launch"
-msgstr "Lanĉo"
-
-msgid "Fullscreen mode"
-msgstr "Plenekrana reĝimo"
-
-msgid "Edit this page"
-msgstr "Redaktu ĉi tiun paĝon"
-
-msgid "By the"
-msgstr "Per la"
-
-msgid "next page"
-msgstr "sekva paĝo"
diff --git a/_preview/3/_static/locales/es/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/es/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 5e0029e..0000000
--- a/_preview/3/_static/locales/es/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: es\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Imprimir en PDF"
-
-msgid "Theme by the"
-msgstr "Tema por el"
-
-msgid "Download source file"
-msgstr "Descargar archivo fuente"
-
-msgid "open issue"
-msgstr "Tema abierto"
-
-msgid "Contents"
-msgstr "Contenido"
-
-msgid "previous page"
-msgstr "pagina anterior"
-
-msgid "Download notebook file"
-msgstr "Descargar archivo de cuaderno"
-
-msgid "Copyright"
-msgstr "Derechos de autor"
-
-msgid "Download this page"
-msgstr "Descarga esta pagina"
-
-msgid "Source repository"
-msgstr "Repositorio de origen"
-
-msgid "By"
-msgstr "Por"
-
-msgid "repository"
-msgstr "repositorio"
-
-msgid "Last updated on"
-msgstr "Ultima actualización en"
-
-msgid "Toggle navigation"
-msgstr "Navegación de palanca"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema del libro de la esfinge"
-
-msgid "suggest edit"
-msgstr "sugerir editar"
-
-msgid "Open an issue"
-msgstr "Abrir un problema"
-
-msgid "Launch"
-msgstr "Lanzamiento"
-
-msgid "Fullscreen mode"
-msgstr "Modo de pantalla completa"
-
-msgid "Edit this page"
-msgstr "Edita esta página"
-
-msgid "By the"
-msgstr "Por el"
-
-msgid "next page"
-msgstr "siguiente página"
diff --git a/_preview/3/_static/locales/et/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/et/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 8680982..0000000
--- a/_preview/3/_static/locales/et/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: et\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Prindi PDF-i"
-
-msgid "Theme by the"
-msgstr "Teema"
-
-msgid "Download source file"
-msgstr "Laadige alla lähtefail"
-
-msgid "open issue"
-msgstr "avatud küsimus"
-
-msgid "Contents"
-msgstr "Sisu"
-
-msgid "previous page"
-msgstr "eelmine leht"
-
-msgid "Download notebook file"
-msgstr "Laadige sülearvuti fail alla"
-
-msgid "Copyright"
-msgstr "Autoriõigus"
-
-msgid "Download this page"
-msgstr "Laadige see leht alla"
-
-msgid "Source repository"
-msgstr "Allikahoidla"
-
-msgid "By"
-msgstr "Kõrval"
-
-msgid "repository"
-msgstr "hoidla"
-
-msgid "Last updated on"
-msgstr "Viimati uuendatud"
-
-msgid "Toggle navigation"
-msgstr "Lülita navigeerimine sisse"
-
-msgid "Sphinx Book Theme"
-msgstr "Sfinksiraamatu teema"
-
-msgid "suggest edit"
-msgstr "soovita muuta"
-
-msgid "Open an issue"
-msgstr "Avage probleem"
-
-msgid "Launch"
-msgstr "Käivitage"
-
-msgid "Fullscreen mode"
-msgstr "Täisekraanirežiim"
-
-msgid "Edit this page"
-msgstr "Muutke seda lehte"
-
-msgid "By the"
-msgstr "Autor"
-
-msgid "next page"
-msgstr "järgmine leht"
diff --git a/_preview/3/_static/locales/fi/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/fi/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 34dac21..0000000
--- a/_preview/3/_static/locales/fi/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: fi\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Tulosta PDF-tiedostoon"
-
-msgid "Theme by the"
-msgstr "Teeman tekijä"
-
-msgid "Download source file"
-msgstr "Lataa lähdetiedosto"
-
-msgid "open issue"
-msgstr "avoin ongelma"
-
-msgid "Contents"
-msgstr "Sisällys"
-
-msgid "previous page"
-msgstr "Edellinen sivu"
-
-msgid "Download notebook file"
-msgstr "Lataa muistikirjatiedosto"
-
-msgid "Copyright"
-msgstr "Tekijänoikeus"
-
-msgid "Download this page"
-msgstr "Lataa tämä sivu"
-
-msgid "Source repository"
-msgstr "Lähteen arkisto"
-
-msgid "By"
-msgstr "Tekijä"
-
-msgid "repository"
-msgstr "arkisto"
-
-msgid "Last updated on"
-msgstr "Viimeksi päivitetty"
-
-msgid "Toggle navigation"
-msgstr "Vaihda navigointia"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx-kirjan teema"
-
-msgid "suggest edit"
-msgstr "ehdottaa muokkausta"
-
-msgid "Open an issue"
-msgstr "Avaa ongelma"
-
-msgid "Launch"
-msgstr "Tuoda markkinoille"
-
-msgid "Fullscreen mode"
-msgstr "Koko näytön tila"
-
-msgid "Edit this page"
-msgstr "Muokkaa tätä sivua"
-
-msgid "By the"
-msgstr "Mukaan"
-
-msgid "next page"
-msgstr "seuraava sivu"
diff --git a/_preview/3/_static/locales/fr/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/fr/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 8991a1b..0000000
--- a/_preview/3/_static/locales/fr/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: fr\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Imprimer au format PDF"
-
-msgid "Theme by the"
-msgstr "Thème par le"
-
-msgid "Download source file"
-msgstr "Télécharger le fichier source"
-
-msgid "open issue"
-msgstr "signaler un problème"
-
-msgid "Contents"
-msgstr "Contenu"
-
-msgid "previous page"
-msgstr "page précédente"
-
-msgid "Download notebook file"
-msgstr "Télécharger le fichier notebook"
-
-msgid "Copyright"
-msgstr "droits d'auteur"
-
-msgid "Download this page"
-msgstr "Téléchargez cette page"
-
-msgid "Source repository"
-msgstr "Dépôt source"
-
-msgid "By"
-msgstr "Par"
-
-msgid "repository"
-msgstr "dépôt"
-
-msgid "Last updated on"
-msgstr "Dernière mise à jour le"
-
-msgid "Toggle navigation"
-msgstr "Basculer la navigation"
-
-msgid "Sphinx Book Theme"
-msgstr "Thème du livre Sphinx"
-
-msgid "suggest edit"
-msgstr "suggestion de modification"
-
-msgid "Open an issue"
-msgstr "Ouvrez un problème"
-
-msgid "Launch"
-msgstr "lancement"
-
-msgid "Fullscreen mode"
-msgstr "Mode plein écran"
-
-msgid "Edit this page"
-msgstr "Modifier cette page"
-
-msgid "By the"
-msgstr "Par le"
-
-msgid "next page"
-msgstr "page suivante"
diff --git a/_preview/3/_static/locales/hr/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/hr/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 42c4233..0000000
--- a/_preview/3/_static/locales/hr/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: hr\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Ispis u PDF"
-
-msgid "Theme by the"
-msgstr "Tema autora"
-
-msgid "Download source file"
-msgstr "Preuzmi izvornu datoteku"
-
-msgid "open issue"
-msgstr "otvoreno izdanje"
-
-msgid "Contents"
-msgstr "Sadržaj"
-
-msgid "previous page"
-msgstr "Prethodna stranica"
-
-msgid "Download notebook file"
-msgstr "Preuzmi datoteku bilježnice"
-
-msgid "Copyright"
-msgstr "Autorska prava"
-
-msgid "Download this page"
-msgstr "Preuzmite ovu stranicu"
-
-msgid "Source repository"
-msgstr "Izvorno spremište"
-
-msgid "By"
-msgstr "Po"
-
-msgid "repository"
-msgstr "spremište"
-
-msgid "Last updated on"
-msgstr "Posljednje ažuriranje:"
-
-msgid "Toggle navigation"
-msgstr "Uključi / isključi navigaciju"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema knjige Sphinx"
-
-msgid "suggest edit"
-msgstr "predloži uređivanje"
-
-msgid "Open an issue"
-msgstr "Otvorite izdanje"
-
-msgid "Launch"
-msgstr "Pokrenite"
-
-msgid "Fullscreen mode"
-msgstr "Način preko cijelog zaslona"
-
-msgid "Edit this page"
-msgstr "Uredite ovu stranicu"
-
-msgid "By the"
-msgstr "Od strane"
-
-msgid "next page"
-msgstr "sljedeća stranica"
diff --git a/_preview/3/_static/locales/id/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/id/LC_MESSAGES/booktheme.po
deleted file mode 100644
index b8d8d89..0000000
--- a/_preview/3/_static/locales/id/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: id\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Cetak ke PDF"
-
-msgid "Theme by the"
-msgstr "Tema oleh"
-
-msgid "Download source file"
-msgstr "Unduh file sumber"
-
-msgid "open issue"
-msgstr "masalah terbuka"
-
-msgid "Contents"
-msgstr "Isi"
-
-msgid "previous page"
-msgstr "halaman sebelumnya"
-
-msgid "Download notebook file"
-msgstr "Unduh file notebook"
-
-msgid "Copyright"
-msgstr "hak cipta"
-
-msgid "Download this page"
-msgstr "Unduh halaman ini"
-
-msgid "Source repository"
-msgstr "Repositori sumber"
-
-msgid "By"
-msgstr "Oleh"
-
-msgid "repository"
-msgstr "gudang"
-
-msgid "Last updated on"
-msgstr "Terakhir diperbarui saat"
-
-msgid "Toggle navigation"
-msgstr "Alihkan navigasi"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema Buku Sphinx"
-
-msgid "suggest edit"
-msgstr "menyarankan edit"
-
-msgid "Open an issue"
-msgstr "Buka masalah"
-
-msgid "Launch"
-msgstr "Meluncurkan"
-
-msgid "Fullscreen mode"
-msgstr "Mode layar penuh"
-
-msgid "Edit this page"
-msgstr "Edit halaman ini"
-
-msgid "By the"
-msgstr "Oleh"
-
-msgid "next page"
-msgstr "halaman selanjutnya"
diff --git a/_preview/3/_static/locales/it/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/it/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 36fca59..0000000
--- a/_preview/3/_static/locales/it/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: it\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Stampa in PDF"
-
-msgid "Theme by the"
-msgstr "Tema di"
-
-msgid "Download source file"
-msgstr "Scarica il file sorgente"
-
-msgid "open issue"
-msgstr "questione aperta"
-
-msgid "Contents"
-msgstr "Contenuti"
-
-msgid "previous page"
-msgstr "pagina precedente"
-
-msgid "Download notebook file"
-msgstr "Scarica il file del taccuino"
-
-msgid "Copyright"
-msgstr "Diritto d'autore"
-
-msgid "Download this page"
-msgstr "Scarica questa pagina"
-
-msgid "Source repository"
-msgstr "Repository di origine"
-
-msgid "By"
-msgstr "Di"
-
-msgid "repository"
-msgstr "repository"
-
-msgid "Last updated on"
-msgstr "Ultimo aggiornamento il"
-
-msgid "Toggle navigation"
-msgstr "Attiva / disattiva la navigazione"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema del libro della Sfinge"
-
-msgid "suggest edit"
-msgstr "suggerisci modifica"
-
-msgid "Open an issue"
-msgstr "Apri un problema"
-
-msgid "Launch"
-msgstr "Lanciare"
-
-msgid "Fullscreen mode"
-msgstr "Modalità schermo intero"
-
-msgid "Edit this page"
-msgstr "Modifica questa pagina"
-
-msgid "By the"
-msgstr "Dal"
-
-msgid "next page"
-msgstr "pagina successiva"
diff --git a/_preview/3/_static/locales/iw/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/iw/LC_MESSAGES/booktheme.po
deleted file mode 100644
index dede9cb..0000000
--- a/_preview/3/_static/locales/iw/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: iw\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "הדפס לקובץ PDF"
-
-msgid "Theme by the"
-msgstr "נושא מאת"
-
-msgid "Download source file"
-msgstr "הורד את קובץ המקור"
-
-msgid "open issue"
-msgstr "בעיה פתוחה"
-
-msgid "Contents"
-msgstr "תוכן"
-
-msgid "previous page"
-msgstr "עמוד קודם"
-
-msgid "Download notebook file"
-msgstr "הורד קובץ מחברת"
-
-msgid "Copyright"
-msgstr "זכויות יוצרים"
-
-msgid "Download this page"
-msgstr "הורד דף זה"
-
-msgid "Source repository"
-msgstr "מאגר המקורות"
-
-msgid "By"
-msgstr "על ידי"
-
-msgid "repository"
-msgstr "מאגר"
-
-msgid "Last updated on"
-msgstr "עודכן לאחרונה ב"
-
-msgid "Toggle navigation"
-msgstr "החלף ניווט"
-
-msgid "Sphinx Book Theme"
-msgstr "נושא ספר ספינקס"
-
-msgid "suggest edit"
-msgstr "מציע לערוך"
-
-msgid "Open an issue"
-msgstr "פתח גיליון"
-
-msgid "Launch"
-msgstr "לְהַשִׁיק"
-
-msgid "Fullscreen mode"
-msgstr "מצב מסך מלא"
-
-msgid "Edit this page"
-msgstr "ערוך דף זה"
-
-msgid "By the"
-msgstr "דרך"
-
-msgid "next page"
-msgstr "עמוד הבא"
diff --git a/_preview/3/_static/locales/ja/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ja/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 2615f0d..0000000
--- a/_preview/3/_static/locales/ja/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ja\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "PDFに印刷"
-
-msgid "Theme by the"
-msgstr "のテーマ"
-
-msgid "Download source file"
-msgstr "ソースファイルをダウンロード"
-
-msgid "open issue"
-msgstr "未解決の問題"
-
-msgid "Contents"
-msgstr "目次"
-
-msgid "previous page"
-msgstr "前のページ"
-
-msgid "Download notebook file"
-msgstr "ノートブックファイルをダウンロード"
-
-msgid "Copyright"
-msgstr "Copyright"
-
-msgid "Download this page"
-msgstr "このページをダウンロード"
-
-msgid "Source repository"
-msgstr "ソースリポジトリ"
-
-msgid "By"
-msgstr "著者"
-
-msgid "repository"
-msgstr "リポジトリ"
-
-msgid "Last updated on"
-msgstr "最終更新日"
-
-msgid "Toggle navigation"
-msgstr "ナビゲーションを切り替え"
-
-msgid "Sphinx Book Theme"
-msgstr "スフィンクスの本のテーマ"
-
-msgid "suggest edit"
-msgstr "編集を提案する"
-
-msgid "Open an issue"
-msgstr "問題を報告"
-
-msgid "Launch"
-msgstr "起動"
-
-msgid "Fullscreen mode"
-msgstr "全画面モード"
-
-msgid "Edit this page"
-msgstr "このページを編集"
-
-msgid "By the"
-msgstr "によって"
-
-msgid "next page"
-msgstr "次のページ"
diff --git a/_preview/3/_static/locales/ko/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ko/LC_MESSAGES/booktheme.po
deleted file mode 100644
index c9e13a4..0000000
--- a/_preview/3/_static/locales/ko/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ko\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "PDF로 인쇄"
-
-msgid "Theme by the"
-msgstr "테마별"
-
-msgid "Download source file"
-msgstr "소스 파일 다운로드"
-
-msgid "open issue"
-msgstr "열린 문제"
-
-msgid "Contents"
-msgstr "내용"
-
-msgid "previous page"
-msgstr "이전 페이지"
-
-msgid "Download notebook file"
-msgstr "노트북 파일 다운로드"
-
-msgid "Copyright"
-msgstr "저작권"
-
-msgid "Download this page"
-msgstr "이 페이지 다운로드"
-
-msgid "Source repository"
-msgstr "소스 저장소"
-
-msgid "By"
-msgstr "으로"
-
-msgid "repository"
-msgstr "저장소"
-
-msgid "Last updated on"
-msgstr "마지막 업데이트"
-
-msgid "Toggle navigation"
-msgstr "탐색 전환"
-
-msgid "Sphinx Book Theme"
-msgstr "스핑크스 도서 테마"
-
-msgid "suggest edit"
-msgstr "편집 제안"
-
-msgid "Open an issue"
-msgstr "이슈 열기"
-
-msgid "Launch"
-msgstr "시작하다"
-
-msgid "Fullscreen mode"
-msgstr "전체 화면으로보기"
-
-msgid "Edit this page"
-msgstr "이 페이지 편집"
-
-msgid "By the"
-msgstr "에 의해"
-
-msgid "next page"
-msgstr "다음 페이지"
diff --git a/_preview/3/_static/locales/lt/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/lt/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 35eabd9..0000000
--- a/_preview/3/_static/locales/lt/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: lt\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Spausdinti į PDF"
-
-msgid "Theme by the"
-msgstr "Tema"
-
-msgid "Download source file"
-msgstr "Atsisiųsti šaltinio failą"
-
-msgid "open issue"
-msgstr "atviras klausimas"
-
-msgid "Contents"
-msgstr "Turinys"
-
-msgid "previous page"
-msgstr "Ankstesnis puslapis"
-
-msgid "Download notebook file"
-msgstr "Atsisiųsti nešiojamojo kompiuterio failą"
-
-msgid "Copyright"
-msgstr "Autorių teisės"
-
-msgid "Download this page"
-msgstr "Atsisiųskite šį puslapį"
-
-msgid "Source repository"
-msgstr "Šaltinio saugykla"
-
-msgid "By"
-msgstr "Iki"
-
-msgid "repository"
-msgstr "saugykla"
-
-msgid "Last updated on"
-msgstr "Paskutinį kartą atnaujinta"
-
-msgid "Toggle navigation"
-msgstr "Perjungti naršymą"
-
-msgid "Sphinx Book Theme"
-msgstr "Sfinkso knygos tema"
-
-msgid "suggest edit"
-msgstr "pasiūlyti redaguoti"
-
-msgid "Open an issue"
-msgstr "Atidarykite problemą"
-
-msgid "Launch"
-msgstr "Paleiskite"
-
-msgid "Fullscreen mode"
-msgstr "Pilno ekrano režimas"
-
-msgid "Edit this page"
-msgstr "Redaguoti šį puslapį"
-
-msgid "By the"
-msgstr "Prie"
-
-msgid "next page"
-msgstr "Kitas puslapis"
diff --git a/_preview/3/_static/locales/lv/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/lv/LC_MESSAGES/booktheme.po
deleted file mode 100644
index ee1bd08..0000000
--- a/_preview/3/_static/locales/lv/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: lv\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Drukāt PDF formātā"
-
-msgid "Theme by the"
-msgstr "Autora tēma"
-
-msgid "Download source file"
-msgstr "Lejupielādēt avota failu"
-
-msgid "open issue"
-msgstr "atklāts jautājums"
-
-msgid "Contents"
-msgstr "Saturs"
-
-msgid "previous page"
-msgstr "iepriekšējā lapa"
-
-msgid "Download notebook file"
-msgstr "Lejupielādēt piezīmju grāmatiņu"
-
-msgid "Copyright"
-msgstr "Autortiesības"
-
-msgid "Download this page"
-msgstr "Lejupielādējiet šo lapu"
-
-msgid "Source repository"
-msgstr "Avota krātuve"
-
-msgid "By"
-msgstr "Autors"
-
-msgid "repository"
-msgstr "krātuve"
-
-msgid "Last updated on"
-msgstr "Pēdējoreiz atjaunināts"
-
-msgid "Toggle navigation"
-msgstr "Pārslēgt navigāciju"
-
-msgid "Sphinx Book Theme"
-msgstr "Sfinksa grāmatas tēma"
-
-msgid "suggest edit"
-msgstr "ieteikt rediģēt"
-
-msgid "Open an issue"
-msgstr "Atveriet problēmu"
-
-msgid "Launch"
-msgstr "Uzsākt"
-
-msgid "Fullscreen mode"
-msgstr "Pilnekrāna režīms"
-
-msgid "Edit this page"
-msgstr "Rediģēt šo lapu"
-
-msgid "By the"
-msgstr "Ar"
-
-msgid "next page"
-msgstr "nākamā lapaspuse"
diff --git a/_preview/3/_static/locales/ml/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ml/LC_MESSAGES/booktheme.po
deleted file mode 100644
index d471277..0000000
--- a/_preview/3/_static/locales/ml/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ml\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "PDF- ലേക്ക് പ്രിന്റുചെയ്യുക"
-
-msgid "Theme by the"
-msgstr "പ്രമേയം"
-
-msgid "Download source file"
-msgstr "ഉറവിട ഫയൽ ഡൗൺലോഡുചെയ്യുക"
-
-msgid "open issue"
-msgstr "തുറന്ന പ്രശ്നം"
-
-msgid "previous page"
-msgstr "മുൻപത്തെ താൾ"
-
-msgid "Download notebook file"
-msgstr "നോട്ട്ബുക്ക് ഫയൽ ഡൺലോഡ് ചെയ്യുക"
-
-msgid "Copyright"
-msgstr "പകർപ്പവകാശം"
-
-msgid "Download this page"
-msgstr "ഈ പേജ് ഡൗൺലോഡുചെയ്യുക"
-
-msgid "Source repository"
-msgstr "ഉറവിട ശേഖരം"
-
-msgid "By"
-msgstr "എഴുതിയത്"
-
-msgid "Last updated on"
-msgstr "അവസാനം അപ്‌ഡേറ്റുചെയ്‌തത്"
-
-msgid "Toggle navigation"
-msgstr "നാവിഗേഷൻ ടോഗിൾ ചെയ്യുക"
-
-msgid "Sphinx Book Theme"
-msgstr "സ്ഫിങ്ക്സ് പുസ്തക തീം"
-
-msgid "suggest edit"
-msgstr "എഡിറ്റുചെയ്യാൻ നിർദ്ദേശിക്കുക"
-
-msgid "Open an issue"
-msgstr "ഒരു പ്രശ്നം തുറക്കുക"
-
-msgid "Launch"
-msgstr "സമാരംഭിക്കുക"
-
-msgid "Edit this page"
-msgstr "ഈ പേജ് എഡിറ്റുചെയ്യുക"
-
-msgid "By the"
-msgstr "എഴുതിയത്"
-
-msgid "next page"
-msgstr "അടുത്ത പേജ്"
diff --git a/_preview/3/_static/locales/mr/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/mr/LC_MESSAGES/booktheme.po
deleted file mode 100644
index f3694ac..0000000
--- a/_preview/3/_static/locales/mr/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: mr\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "पीडीएफवर मुद्रित करा"
-
-msgid "Theme by the"
-msgstr "द्वारा थीम"
-
-msgid "Download source file"
-msgstr "स्त्रोत फाइल डाउनलोड करा"
-
-msgid "open issue"
-msgstr "खुला मुद्दा"
-
-msgid "previous page"
-msgstr "मागील पान"
-
-msgid "Download notebook file"
-msgstr "नोटबुक फाईल डाउनलोड करा"
-
-msgid "Copyright"
-msgstr "कॉपीराइट"
-
-msgid "Download this page"
-msgstr "हे पृष्ठ डाउनलोड करा"
-
-msgid "Source repository"
-msgstr "स्त्रोत भांडार"
-
-msgid "By"
-msgstr "द्वारा"
-
-msgid "Last updated on"
-msgstr "अखेरचे अद्यतनित"
-
-msgid "Toggle navigation"
-msgstr "नेव्हिगेशन टॉगल करा"
-
-msgid "Sphinx Book Theme"
-msgstr "स्फिंक्स बुक थीम"
-
-msgid "suggest edit"
-msgstr "संपादन सुचवा"
-
-msgid "Open an issue"
-msgstr "एक मुद्दा उघडा"
-
-msgid "Launch"
-msgstr "लाँच करा"
-
-msgid "Edit this page"
-msgstr "हे पृष्ठ संपादित करा"
-
-msgid "By the"
-msgstr "द्वारा"
-
-msgid "next page"
-msgstr "पुढील पृष्ठ"
diff --git a/_preview/3/_static/locales/ms/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ms/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 65b7c60..0000000
--- a/_preview/3/_static/locales/ms/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ms\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Cetak ke PDF"
-
-msgid "Theme by the"
-msgstr "Tema oleh"
-
-msgid "Download source file"
-msgstr "Muat turun fail sumber"
-
-msgid "open issue"
-msgstr "isu terbuka"
-
-msgid "previous page"
-msgstr "halaman sebelumnya"
-
-msgid "Download notebook file"
-msgstr "Muat turun fail buku nota"
-
-msgid "Copyright"
-msgstr "hak cipta"
-
-msgid "Download this page"
-msgstr "Muat turun halaman ini"
-
-msgid "Source repository"
-msgstr "Repositori sumber"
-
-msgid "By"
-msgstr "Oleh"
-
-msgid "Last updated on"
-msgstr "Terakhir dikemas kini pada"
-
-msgid "Toggle navigation"
-msgstr "Togol navigasi"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema Buku Sphinx"
-
-msgid "suggest edit"
-msgstr "cadangkan edit"
-
-msgid "Open an issue"
-msgstr "Buka masalah"
-
-msgid "Launch"
-msgstr "Lancarkan"
-
-msgid "Edit this page"
-msgstr "Edit halaman ini"
-
-msgid "By the"
-msgstr "Oleh"
-
-msgid "next page"
-msgstr "muka surat seterusnya"
diff --git a/_preview/3/_static/locales/nl/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/nl/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 71bd1cd..0000000
--- a/_preview/3/_static/locales/nl/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: nl\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Afdrukken naar pdf"
-
-msgid "Theme by the"
-msgstr "Thema door de"
-
-msgid "Download source file"
-msgstr "Download het bronbestand"
-
-msgid "open issue"
-msgstr "open probleem"
-
-msgid "Contents"
-msgstr "Inhoud"
-
-msgid "previous page"
-msgstr "vorige pagina"
-
-msgid "Download notebook file"
-msgstr "Download notebookbestand"
-
-msgid "Copyright"
-msgstr "auteursrechten"
-
-msgid "Download this page"
-msgstr "Download deze pagina"
-
-msgid "Source repository"
-msgstr "Bronopslagplaats"
-
-msgid "By"
-msgstr "Door"
-
-msgid "repository"
-msgstr "repository"
-
-msgid "Last updated on"
-msgstr "Laatst geupdate op"
-
-msgid "Toggle navigation"
-msgstr "Schakel navigatie"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx-boekthema"
-
-msgid "suggest edit"
-msgstr "suggereren bewerken"
-
-msgid "Open an issue"
-msgstr "Open een probleem"
-
-msgid "Launch"
-msgstr "Lancering"
-
-msgid "Fullscreen mode"
-msgstr "Volledig scherm"
-
-msgid "Edit this page"
-msgstr "bewerk deze pagina"
-
-msgid "By the"
-msgstr "Door de"
-
-msgid "next page"
-msgstr "volgende bladzijde"
diff --git a/_preview/3/_static/locales/no/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/no/LC_MESSAGES/booktheme.po
deleted file mode 100644
index b21346a..0000000
--- a/_preview/3/_static/locales/no/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: no\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Skriv ut til PDF"
-
-msgid "Theme by the"
-msgstr "Tema av"
-
-msgid "Download source file"
-msgstr "Last ned kildefilen"
-
-msgid "open issue"
-msgstr "åpent nummer"
-
-msgid "Contents"
-msgstr "Innhold"
-
-msgid "previous page"
-msgstr "forrige side"
-
-msgid "Download notebook file"
-msgstr "Last ned notatbokfilen"
-
-msgid "Copyright"
-msgstr "opphavsrett"
-
-msgid "Download this page"
-msgstr "Last ned denne siden"
-
-msgid "Source repository"
-msgstr "Kildedepot"
-
-msgid "By"
-msgstr "Av"
-
-msgid "repository"
-msgstr "oppbevaringssted"
-
-msgid "Last updated on"
-msgstr "Sist oppdatert den"
-
-msgid "Toggle navigation"
-msgstr "Bytt navigasjon"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx boktema"
-
-msgid "suggest edit"
-msgstr "foreslå redigering"
-
-msgid "Open an issue"
-msgstr "Åpne et problem"
-
-msgid "Launch"
-msgstr "Start"
-
-msgid "Fullscreen mode"
-msgstr "Fullskjerm-modus"
-
-msgid "Edit this page"
-msgstr "Rediger denne siden"
-
-msgid "By the"
-msgstr "Ved"
-
-msgid "next page"
-msgstr "neste side"
diff --git a/_preview/3/_static/locales/pl/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/pl/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 1b7233f..0000000
--- a/_preview/3/_static/locales/pl/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: pl\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Drukuj do PDF"
-
-msgid "Theme by the"
-msgstr "Motyw autorstwa"
-
-msgid "Download source file"
-msgstr "Pobierz plik źródłowy"
-
-msgid "open issue"
-msgstr "otwarty problem"
-
-msgid "Contents"
-msgstr "Zawartość"
-
-msgid "previous page"
-msgstr "Poprzednia strona"
-
-msgid "Download notebook file"
-msgstr "Pobierz plik notatnika"
-
-msgid "Copyright"
-msgstr "prawa autorskie"
-
-msgid "Download this page"
-msgstr "Pobierz tę stronę"
-
-msgid "Source repository"
-msgstr "Repozytorium źródłowe"
-
-msgid "By"
-msgstr "Przez"
-
-msgid "repository"
-msgstr "magazyn"
-
-msgid "Last updated on"
-msgstr "Ostatnia aktualizacja"
-
-msgid "Toggle navigation"
-msgstr "Przełącz nawigację"
-
-msgid "Sphinx Book Theme"
-msgstr "Motyw książki Sphinx"
-
-msgid "suggest edit"
-msgstr "zaproponuj edycję"
-
-msgid "Open an issue"
-msgstr "Otwórz problem"
-
-msgid "Launch"
-msgstr "Uruchomić"
-
-msgid "Fullscreen mode"
-msgstr "Pełny ekran"
-
-msgid "Edit this page"
-msgstr "Edytuj tę strone"
-
-msgid "By the"
-msgstr "Przez"
-
-msgid "next page"
-msgstr "Następna strona"
diff --git a/_preview/3/_static/locales/pt/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/pt/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 1b27314..0000000
--- a/_preview/3/_static/locales/pt/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: pt\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Imprimir em PDF"
-
-msgid "Theme by the"
-msgstr "Tema por"
-
-msgid "Download source file"
-msgstr "Baixar arquivo fonte"
-
-msgid "open issue"
-msgstr "questão aberta"
-
-msgid "Contents"
-msgstr "Conteúdo"
-
-msgid "previous page"
-msgstr "página anterior"
-
-msgid "Download notebook file"
-msgstr "Baixar arquivo de notebook"
-
-msgid "Copyright"
-msgstr "direito autoral"
-
-msgid "Download this page"
-msgstr "Baixe esta página"
-
-msgid "Source repository"
-msgstr "Repositório fonte"
-
-msgid "By"
-msgstr "De"
-
-msgid "repository"
-msgstr "repositório"
-
-msgid "Last updated on"
-msgstr "Última atualização em"
-
-msgid "Toggle navigation"
-msgstr "Alternar de navegação"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema do livro Sphinx"
-
-msgid "suggest edit"
-msgstr "sugerir edição"
-
-msgid "Open an issue"
-msgstr "Abra um problema"
-
-msgid "Launch"
-msgstr "Lançamento"
-
-msgid "Fullscreen mode"
-msgstr "Modo tela cheia"
-
-msgid "Edit this page"
-msgstr "Edite essa página"
-
-msgid "By the"
-msgstr "Pelo"
-
-msgid "next page"
-msgstr "próxima página"
diff --git a/_preview/3/_static/locales/ro/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ro/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 1783ad2..0000000
--- a/_preview/3/_static/locales/ro/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ro\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Imprimați în PDF"
-
-msgid "Theme by the"
-msgstr "Tema de"
-
-msgid "Download source file"
-msgstr "Descărcați fișierul sursă"
-
-msgid "open issue"
-msgstr "problema deschisă"
-
-msgid "Contents"
-msgstr "Cuprins"
-
-msgid "previous page"
-msgstr "pagina anterioară"
-
-msgid "Download notebook file"
-msgstr "Descărcați fișierul notebook"
-
-msgid "Copyright"
-msgstr "Drepturi de autor"
-
-msgid "Download this page"
-msgstr "Descarcă această pagină"
-
-msgid "Source repository"
-msgstr "Depozit sursă"
-
-msgid "By"
-msgstr "De"
-
-msgid "repository"
-msgstr "repertoriu"
-
-msgid "Last updated on"
-msgstr "Ultima actualizare la"
-
-msgid "Toggle navigation"
-msgstr "Comutare navigare"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema Sphinx Book"
-
-msgid "suggest edit"
-msgstr "sugerează editare"
-
-msgid "Open an issue"
-msgstr "Deschideți o problemă"
-
-msgid "Launch"
-msgstr "Lansa"
-
-msgid "Fullscreen mode"
-msgstr "Modul ecran întreg"
-
-msgid "Edit this page"
-msgstr "Editați această pagină"
-
-msgid "By the"
-msgstr "Langa"
-
-msgid "next page"
-msgstr "pagina următoare"
diff --git a/_preview/3/_static/locales/ru/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ru/LC_MESSAGES/booktheme.po
deleted file mode 100644
index b1176b7..0000000
--- a/_preview/3/_static/locales/ru/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ru\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Распечатать в PDF"
-
-msgid "Theme by the"
-msgstr "Тема от"
-
-msgid "Download source file"
-msgstr "Скачать исходный файл"
-
-msgid "open issue"
-msgstr "открытый вопрос"
-
-msgid "Contents"
-msgstr "Содержание"
-
-msgid "previous page"
-msgstr "Предыдущая страница"
-
-msgid "Download notebook file"
-msgstr "Скачать файл записной книжки"
-
-msgid "Copyright"
-msgstr "авторское право"
-
-msgid "Download this page"
-msgstr "Загрузите эту страницу"
-
-msgid "Source repository"
-msgstr "Исходный репозиторий"
-
-msgid "By"
-msgstr "По"
-
-msgid "repository"
-msgstr "хранилище"
-
-msgid "Last updated on"
-msgstr "Последнее обновление"
-
-msgid "Toggle navigation"
-msgstr "Переключить навигацию"
-
-msgid "Sphinx Book Theme"
-msgstr "Тема книги Сфинкс"
-
-msgid "suggest edit"
-msgstr "предложить редактировать"
-
-msgid "Open an issue"
-msgstr "Открыть вопрос"
-
-msgid "Launch"
-msgstr "Запуск"
-
-msgid "Fullscreen mode"
-msgstr "Полноэкранный режим"
-
-msgid "Edit this page"
-msgstr "Редактировать эту страницу"
-
-msgid "By the"
-msgstr "Посредством"
-
-msgid "next page"
-msgstr "Следующая страница"
diff --git a/_preview/3/_static/locales/sk/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/sk/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 6501288..0000000
--- a/_preview/3/_static/locales/sk/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: sk\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Tlač do PDF"
-
-msgid "Theme by the"
-msgstr "Téma od"
-
-msgid "Download source file"
-msgstr "Stiahnite si zdrojový súbor"
-
-msgid "open issue"
-msgstr "otvorené vydanie"
-
-msgid "Contents"
-msgstr "Obsah"
-
-msgid "previous page"
-msgstr "predchádzajúca strana"
-
-msgid "Download notebook file"
-msgstr "Stiahnite si zošit"
-
-msgid "Copyright"
-msgstr "Autorské práva"
-
-msgid "Download this page"
-msgstr "Stiahnite si túto stránku"
-
-msgid "Source repository"
-msgstr "Zdrojové úložisko"
-
-msgid "By"
-msgstr "Autor:"
-
-msgid "repository"
-msgstr "Úložisko"
-
-msgid "Last updated on"
-msgstr "Posledná aktualizácia dňa"
-
-msgid "Toggle navigation"
-msgstr "Prepnúť navigáciu"
-
-msgid "Sphinx Book Theme"
-msgstr "Téma knihy Sfinga"
-
-msgid "suggest edit"
-msgstr "navrhnúť úpravu"
-
-msgid "Open an issue"
-msgstr "Otvorte problém"
-
-msgid "Launch"
-msgstr "Spustiť"
-
-msgid "Fullscreen mode"
-msgstr "Režim celej obrazovky"
-
-msgid "Edit this page"
-msgstr "Upraviť túto stránku"
-
-msgid "By the"
-msgstr "Podľa"
-
-msgid "next page"
-msgstr "ďalšia strana"
diff --git a/_preview/3/_static/locales/sl/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/sl/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 3c7e3a8..0000000
--- a/_preview/3/_static/locales/sl/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: sl\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Natisni v PDF"
-
-msgid "Theme by the"
-msgstr "Tema avtorja"
-
-msgid "Download source file"
-msgstr "Prenesite izvorno datoteko"
-
-msgid "open issue"
-msgstr "odprto vprašanje"
-
-msgid "Contents"
-msgstr "Vsebina"
-
-msgid "previous page"
-msgstr "Prejšnja stran"
-
-msgid "Download notebook file"
-msgstr "Prenesite datoteko zvezka"
-
-msgid "Copyright"
-msgstr "avtorske pravice"
-
-msgid "Download this page"
-msgstr "Prenesite to stran"
-
-msgid "Source repository"
-msgstr "Izvorno skladišče"
-
-msgid "By"
-msgstr "Avtor"
-
-msgid "repository"
-msgstr "odlagališče"
-
-msgid "Last updated on"
-msgstr "Nazadnje posodobljeno dne"
-
-msgid "Toggle navigation"
-msgstr "Preklopi navigacijo"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema knjige Sphinx"
-
-msgid "suggest edit"
-msgstr "predlagajte urejanje"
-
-msgid "Open an issue"
-msgstr "Odprite številko"
-
-msgid "Launch"
-msgstr "Kosilo"
-
-msgid "Fullscreen mode"
-msgstr "Celozaslonski način"
-
-msgid "Edit this page"
-msgstr "Uredite to stran"
-
-msgid "By the"
-msgstr "Avtor"
-
-msgid "next page"
-msgstr "Naslednja stran"
diff --git a/_preview/3/_static/locales/sr/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/sr/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 773b8ad..0000000
--- a/_preview/3/_static/locales/sr/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: sr\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Испис у ПДФ"
-
-msgid "Theme by the"
-msgstr "Тхеме би"
-
-msgid "Download source file"
-msgstr "Преузми изворну датотеку"
-
-msgid "open issue"
-msgstr "отворено издање"
-
-msgid "Contents"
-msgstr "Садржај"
-
-msgid "previous page"
-msgstr "Претходна страница"
-
-msgid "Download notebook file"
-msgstr "Преузмите датотеку бележнице"
-
-msgid "Copyright"
-msgstr "Ауторско право"
-
-msgid "Download this page"
-msgstr "Преузмите ову страницу"
-
-msgid "Source repository"
-msgstr "Изворно спремиште"
-
-msgid "By"
-msgstr "Од стране"
-
-msgid "repository"
-msgstr "спремиште"
-
-msgid "Last updated on"
-msgstr "Последње ажурирање"
-
-msgid "Toggle navigation"
-msgstr "Укључи / искључи навигацију"
-
-msgid "Sphinx Book Theme"
-msgstr "Тема књиге Спхинк"
-
-msgid "suggest edit"
-msgstr "предложи уређивање"
-
-msgid "Open an issue"
-msgstr "Отворите издање"
-
-msgid "Launch"
-msgstr "Лансирање"
-
-msgid "Fullscreen mode"
-msgstr "Режим целог екрана"
-
-msgid "Edit this page"
-msgstr "Уредите ову страницу"
-
-msgid "By the"
-msgstr "Од"
-
-msgid "next page"
-msgstr "Следећа страна"
diff --git a/_preview/3/_static/locales/sv/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/sv/LC_MESSAGES/booktheme.po
deleted file mode 100644
index bcac54c..0000000
--- a/_preview/3/_static/locales/sv/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: sv\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Skriv ut till PDF"
-
-msgid "Theme by the"
-msgstr "Tema av"
-
-msgid "Download source file"
-msgstr "Ladda ner källfil"
-
-msgid "open issue"
-msgstr "öppna problemrapport"
-
-msgid "Contents"
-msgstr "Innehåll"
-
-msgid "previous page"
-msgstr "föregående sida"
-
-msgid "Download notebook file"
-msgstr "Ladda ner notebook-fil"
-
-msgid "Copyright"
-msgstr "Upphovsrätt"
-
-msgid "Download this page"
-msgstr "Ladda ner den här sidan"
-
-msgid "Source repository"
-msgstr "Källkodsrepositorium"
-
-msgid "By"
-msgstr "Av"
-
-msgid "repository"
-msgstr "repositorium"
-
-msgid "Last updated on"
-msgstr "Senast uppdaterad den"
-
-msgid "Toggle navigation"
-msgstr "Växla navigering"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx Boktema"
-
-msgid "suggest edit"
-msgstr "föreslå ändring"
-
-msgid "Open an issue"
-msgstr "Öppna en problemrapport"
-
-msgid "Launch"
-msgstr "Öppna"
-
-msgid "Fullscreen mode"
-msgstr "Fullskärmsläge"
-
-msgid "Edit this page"
-msgstr "Redigera den här sidan"
-
-msgid "By the"
-msgstr "Av den"
-
-msgid "next page"
-msgstr "nästa sida"
diff --git a/_preview/3/_static/locales/ta/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ta/LC_MESSAGES/booktheme.po
deleted file mode 100644
index b48bdfa..0000000
--- a/_preview/3/_static/locales/ta/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ta\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "PDF இல் அச்சிடுக"
-
-msgid "Theme by the"
-msgstr "வழங்கிய தீம்"
-
-msgid "Download source file"
-msgstr "மூல கோப்பைப் பதிவிறக்குக"
-
-msgid "open issue"
-msgstr "திறந்த பிரச்சினை"
-
-msgid "previous page"
-msgstr "முந்தைய பக்கம்"
-
-msgid "Download notebook file"
-msgstr "நோட்புக் கோப்பைப் பதிவிறக்கவும்"
-
-msgid "Copyright"
-msgstr "பதிப்புரிமை"
-
-msgid "Download this page"
-msgstr "இந்தப் பக்கத்தைப் பதிவிறக்கவும்"
-
-msgid "Source repository"
-msgstr "மூல களஞ்சியம்"
-
-msgid "By"
-msgstr "வழங்கியவர்"
-
-msgid "Last updated on"
-msgstr "கடைசியாக புதுப்பிக்கப்பட்டது"
-
-msgid "Toggle navigation"
-msgstr "வழிசெலுத்தலை நிலைமாற்று"
-
-msgid "Sphinx Book Theme"
-msgstr "ஸ்பிங்க்ஸ் புத்தக தீம்"
-
-msgid "suggest edit"
-msgstr "திருத்த பரிந்துரைக்கவும்"
-
-msgid "Open an issue"
-msgstr "சிக்கலைத் திறக்கவும்"
-
-msgid "Launch"
-msgstr "தொடங்க"
-
-msgid "Edit this page"
-msgstr "இந்தப் பக்கத்தைத் திருத்தவும்"
-
-msgid "By the"
-msgstr "மூலம்"
-
-msgid "next page"
-msgstr "அடுத்த பக்கம்"
diff --git a/_preview/3/_static/locales/te/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/te/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 952278f..0000000
--- a/_preview/3/_static/locales/te/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: te\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "PDF కి ముద్రించండి"
-
-msgid "Theme by the"
-msgstr "ద్వారా థీమ్"
-
-msgid "Download source file"
-msgstr "మూల ఫైల్‌ను డౌన్‌లోడ్ చేయండి"
-
-msgid "open issue"
-msgstr "ఓపెన్ ఇష్యూ"
-
-msgid "previous page"
-msgstr "ముందు పేజి"
-
-msgid "Download notebook file"
-msgstr "నోట్బుక్ ఫైల్ను డౌన్లోడ్ చేయండి"
-
-msgid "Copyright"
-msgstr "కాపీరైట్"
-
-msgid "Download this page"
-msgstr "ఈ పేజీని డౌన్‌లోడ్ చేయండి"
-
-msgid "Source repository"
-msgstr "మూల రిపోజిటరీ"
-
-msgid "By"
-msgstr "ద్వారా"
-
-msgid "Last updated on"
-msgstr "చివరిగా నవీకరించబడింది"
-
-msgid "Toggle navigation"
-msgstr "నావిగేషన్‌ను టోగుల్ చేయండి"
-
-msgid "Sphinx Book Theme"
-msgstr "సింహిక పుస్తక థీమ్"
-
-msgid "suggest edit"
-msgstr "సవరించమని సూచించండి"
-
-msgid "Open an issue"
-msgstr "సమస్యను తెరవండి"
-
-msgid "Launch"
-msgstr "ప్రారంభించండి"
-
-msgid "Edit this page"
-msgstr "ఈ పేజీని సవరించండి"
-
-msgid "By the"
-msgstr "ద్వారా"
-
-msgid "next page"
-msgstr "తరువాతి పేజీ"
diff --git a/_preview/3/_static/locales/tg/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/tg/LC_MESSAGES/booktheme.po
deleted file mode 100644
index c33dc42..0000000
--- a/_preview/3/_static/locales/tg/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: tg\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Чоп ба PDF"
-
-msgid "Theme by the"
-msgstr "Мавзӯъи аз"
-
-msgid "Download source file"
-msgstr "Файли манбаъро зеркашӣ кунед"
-
-msgid "open issue"
-msgstr "барориши кушод"
-
-msgid "Contents"
-msgstr "Мундариҷа"
-
-msgid "previous page"
-msgstr "саҳифаи қаблӣ"
-
-msgid "Download notebook file"
-msgstr "Файли дафтарро зеркашӣ кунед"
-
-msgid "Copyright"
-msgstr "Ҳуқуқи муаллиф"
-
-msgid "Download this page"
-msgstr "Ин саҳифаро зеркашӣ кунед"
-
-msgid "Source repository"
-msgstr "Анбори манбаъ"
-
-msgid "By"
-msgstr "Бо"
-
-msgid "repository"
-msgstr "анбор"
-
-msgid "Last updated on"
-msgstr "Last навсозӣ дар"
-
-msgid "Toggle navigation"
-msgstr "Гузаришро иваз кунед"
-
-msgid "Sphinx Book Theme"
-msgstr "Сфинкс Мавзӯи китоб"
-
-msgid "suggest edit"
-msgstr "пешниҳод вироиш"
-
-msgid "Open an issue"
-msgstr "Масъаларо кушоед"
-
-msgid "Launch"
-msgstr "Оғоз"
-
-msgid "Fullscreen mode"
-msgstr "Ҳолати экрани пурра"
-
-msgid "Edit this page"
-msgstr "Ин саҳифаро таҳрир кунед"
-
-msgid "By the"
-msgstr "Бо"
-
-msgid "next page"
-msgstr "саҳифаи оянда"
diff --git a/_preview/3/_static/locales/th/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/th/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 9d24294..0000000
--- a/_preview/3/_static/locales/th/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: th\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "พิมพ์เป็น PDF"
-
-msgid "Theme by the"
-msgstr "ธีมโดย"
-
-msgid "Download source file"
-msgstr "ดาวน์โหลดไฟล์ต้นฉบับ"
-
-msgid "open issue"
-msgstr "เปิดปัญหา"
-
-msgid "Contents"
-msgstr "สารบัญ"
-
-msgid "previous page"
-msgstr "หน้าที่แล้ว"
-
-msgid "Download notebook file"
-msgstr "ดาวน์โหลดไฟล์สมุดบันทึก"
-
-msgid "Copyright"
-msgstr "ลิขสิทธิ์"
-
-msgid "Download this page"
-msgstr "ดาวน์โหลดหน้านี้"
-
-msgid "Source repository"
-msgstr "ที่เก็บซอร์ส"
-
-msgid "By"
-msgstr "โดย"
-
-msgid "repository"
-msgstr "ที่เก็บ"
-
-msgid "Last updated on"
-msgstr "ปรับปรุงล่าสุดเมื่อ"
-
-msgid "Toggle navigation"
-msgstr "ไม่ต้องสลับช่องทาง"
-
-msgid "Sphinx Book Theme"
-msgstr "ธีมหนังสือสฟิงซ์"
-
-msgid "suggest edit"
-msgstr "แนะนำแก้ไข"
-
-msgid "Open an issue"
-msgstr "เปิดปัญหา"
-
-msgid "Launch"
-msgstr "เปิด"
-
-msgid "Fullscreen mode"
-msgstr "โหมดเต็มหน้าจอ"
-
-msgid "Edit this page"
-msgstr "แก้ไขหน้านี้"
-
-msgid "By the"
-msgstr "โดย"
-
-msgid "next page"
-msgstr "หน้าต่อไป"
diff --git a/_preview/3/_static/locales/tl/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/tl/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 20e0d07..0000000
--- a/_preview/3/_static/locales/tl/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: tl\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "I-print sa PDF"
-
-msgid "Theme by the"
-msgstr "Tema ng"
-
-msgid "Download source file"
-msgstr "Mag-download ng file ng pinagmulan"
-
-msgid "open issue"
-msgstr "bukas na isyu"
-
-msgid "previous page"
-msgstr "Nakaraang pahina"
-
-msgid "Download notebook file"
-msgstr "Mag-download ng file ng notebook"
-
-msgid "Copyright"
-msgstr "Copyright"
-
-msgid "Download this page"
-msgstr "I-download ang pahinang ito"
-
-msgid "Source repository"
-msgstr "Pinagmulan ng imbakan"
-
-msgid "By"
-msgstr "Ni"
-
-msgid "Last updated on"
-msgstr "Huling na-update noong"
-
-msgid "Toggle navigation"
-msgstr "I-toggle ang pag-navigate"
-
-msgid "Sphinx Book Theme"
-msgstr "Tema ng Sphinx Book"
-
-msgid "suggest edit"
-msgstr "iminumungkahi i-edit"
-
-msgid "Open an issue"
-msgstr "Magbukas ng isyu"
-
-msgid "Launch"
-msgstr "Ilunsad"
-
-msgid "Edit this page"
-msgstr "I-edit ang pahinang ito"
-
-msgid "By the"
-msgstr "Sa pamamagitan ng"
-
-msgid "next page"
-msgstr "Susunod na pahina"
diff --git a/_preview/3/_static/locales/tr/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/tr/LC_MESSAGES/booktheme.po
deleted file mode 100644
index a77eb02..0000000
--- a/_preview/3/_static/locales/tr/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: tr\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "PDF olarak yazdır"
-
-msgid "Theme by the"
-msgstr "Tarafından tema"
-
-msgid "Download source file"
-msgstr "Kaynak dosyayı indirin"
-
-msgid "open issue"
-msgstr "Açık konu"
-
-msgid "Contents"
-msgstr "İçindekiler"
-
-msgid "previous page"
-msgstr "önceki sayfa"
-
-msgid "Download notebook file"
-msgstr "Defter dosyasını indirin"
-
-msgid "Copyright"
-msgstr "Telif hakkı"
-
-msgid "Download this page"
-msgstr "Bu sayfayı indirin"
-
-msgid "Source repository"
-msgstr "Kaynak kod deposu"
-
-msgid "By"
-msgstr "Tarafından"
-
-msgid "repository"
-msgstr "depo"
-
-msgid "Last updated on"
-msgstr "Son güncelleme tarihi"
-
-msgid "Toggle navigation"
-msgstr "Gezinmeyi değiştir"
-
-msgid "Sphinx Book Theme"
-msgstr "Sfenks Kitap Teması"
-
-msgid "suggest edit"
-msgstr "düzenleme öner"
-
-msgid "Open an issue"
-msgstr "Bir sorunu açın"
-
-msgid "Launch"
-msgstr "Başlatmak"
-
-msgid "Fullscreen mode"
-msgstr "Tam ekran modu"
-
-msgid "Edit this page"
-msgstr "Bu sayfayı düzenle"
-
-msgid "By the"
-msgstr "Tarafından"
-
-msgid "next page"
-msgstr "sonraki Sayfa"
diff --git a/_preview/3/_static/locales/uk/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/uk/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 993dd07..0000000
--- a/_preview/3/_static/locales/uk/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: uk\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "Друк у форматі PDF"
-
-msgid "Theme by the"
-msgstr "Тема від"
-
-msgid "Download source file"
-msgstr "Завантажити вихідний файл"
-
-msgid "open issue"
-msgstr "відкритий випуск"
-
-msgid "Contents"
-msgstr "Зміст"
-
-msgid "previous page"
-msgstr "Попередня сторінка"
-
-msgid "Download notebook file"
-msgstr "Завантажте файл блокнота"
-
-msgid "Copyright"
-msgstr "Авторське право"
-
-msgid "Download this page"
-msgstr "Завантажте цю сторінку"
-
-msgid "Source repository"
-msgstr "Джерело сховища"
-
-msgid "By"
-msgstr "Автор"
-
-msgid "repository"
-msgstr "сховище"
-
-msgid "Last updated on"
-msgstr "Останнє оновлення:"
-
-msgid "Toggle navigation"
-msgstr "Переключити навігацію"
-
-msgid "Sphinx Book Theme"
-msgstr "Тема книги \"Сфінкс\""
-
-msgid "suggest edit"
-msgstr "запропонувати редагувати"
-
-msgid "Open an issue"
-msgstr "Відкрийте випуск"
-
-msgid "Launch"
-msgstr "Запуск"
-
-msgid "Fullscreen mode"
-msgstr "Повноекранний режим"
-
-msgid "Edit this page"
-msgstr "Редагувати цю сторінку"
-
-msgid "By the"
-msgstr "По"
-
-msgid "next page"
-msgstr "Наступна сторінка"
diff --git a/_preview/3/_static/locales/ur/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/ur/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 2f77426..0000000
--- a/_preview/3/_static/locales/ur/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,66 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: ur\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "پی ڈی ایف پرنٹ کریں"
-
-msgid "Theme by the"
-msgstr "کے ذریعہ تھیم"
-
-msgid "Download source file"
-msgstr "سورس فائل ڈاؤن لوڈ کریں"
-
-msgid "open issue"
-msgstr "کھلا مسئلہ"
-
-msgid "previous page"
-msgstr "سابقہ ​​صفحہ"
-
-msgid "Download notebook file"
-msgstr "نوٹ بک فائل ڈاؤن لوڈ کریں"
-
-msgid "Copyright"
-msgstr "کاپی رائٹ"
-
-msgid "Download this page"
-msgstr "اس صفحے کو ڈاؤن لوڈ کریں"
-
-msgid "Source repository"
-msgstr "ماخذ ذخیرہ"
-
-msgid "By"
-msgstr "بذریعہ"
-
-msgid "Last updated on"
-msgstr "آخری بار تازہ کاری ہوئی"
-
-msgid "Toggle navigation"
-msgstr "نیویگیشن ٹوگل کریں"
-
-msgid "Sphinx Book Theme"
-msgstr "سپنکس بک تھیم"
-
-msgid "suggest edit"
-msgstr "ترمیم کی تجویز کریں"
-
-msgid "Open an issue"
-msgstr "ایک مسئلہ کھولیں"
-
-msgid "Launch"
-msgstr "لانچ کریں"
-
-msgid "Edit this page"
-msgstr "اس صفحے میں ترمیم کریں"
-
-msgid "By the"
-msgstr "کی طرف"
-
-msgid "next page"
-msgstr "اگلا صفحہ"
diff --git a/_preview/3/_static/locales/vi/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/vi/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 33159f3..0000000
--- a/_preview/3/_static/locales/vi/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: vi\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "In sang PDF"
-
-msgid "Theme by the"
-msgstr "Chủ đề của"
-
-msgid "Download source file"
-msgstr "Tải xuống tệp nguồn"
-
-msgid "open issue"
-msgstr "vấn đề mở"
-
-msgid "Contents"
-msgstr "Nội dung"
-
-msgid "previous page"
-msgstr "trang trước"
-
-msgid "Download notebook file"
-msgstr "Tải xuống tệp sổ tay"
-
-msgid "Copyright"
-msgstr "Bản quyền"
-
-msgid "Download this page"
-msgstr "Tải xuống trang này"
-
-msgid "Source repository"
-msgstr "Kho nguồn"
-
-msgid "By"
-msgstr "Bởi"
-
-msgid "repository"
-msgstr "kho"
-
-msgid "Last updated on"
-msgstr "Cập nhật lần cuối vào"
-
-msgid "Toggle navigation"
-msgstr "Chuyển đổi điều hướng thành"
-
-msgid "Sphinx Book Theme"
-msgstr "Chủ đề sách nhân sư"
-
-msgid "suggest edit"
-msgstr "đề nghị chỉnh sửa"
-
-msgid "Open an issue"
-msgstr "Mở một vấn đề"
-
-msgid "Launch"
-msgstr "Phóng"
-
-msgid "Fullscreen mode"
-msgstr "Chế độ toàn màn hình"
-
-msgid "Edit this page"
-msgstr "chỉnh sửa trang này"
-
-msgid "By the"
-msgstr "Bằng"
-
-msgid "next page"
-msgstr "Trang tiếp theo"
diff --git a/_preview/3/_static/locales/zh_CN/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/zh_CN/LC_MESSAGES/booktheme.po
deleted file mode 100644
index 2e519ef..0000000
--- a/_preview/3/_static/locales/zh_CN/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: zh_CN\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "列印成 PDF"
-
-msgid "Theme by the"
-msgstr "主题作者："
-
-msgid "Download source file"
-msgstr "下载源文件"
-
-msgid "open issue"
-msgstr "创建议题"
-
-msgid "Contents"
-msgstr "目录"
-
-msgid "previous page"
-msgstr "上一页"
-
-msgid "Download notebook file"
-msgstr "下载笔记本文件"
-
-msgid "Copyright"
-msgstr "版权"
-
-msgid "Download this page"
-msgstr "下载此页面"
-
-msgid "Source repository"
-msgstr "源码库"
-
-msgid "By"
-msgstr "作者："
-
-msgid "repository"
-msgstr "仓库"
-
-msgid "Last updated on"
-msgstr "上次更新时间："
-
-msgid "Toggle navigation"
-msgstr "显示或隐藏导航栏"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx Book 主题"
-
-msgid "suggest edit"
-msgstr "提出修改建议"
-
-msgid "Open an issue"
-msgstr "创建议题"
-
-msgid "Launch"
-msgstr "启动"
-
-msgid "Fullscreen mode"
-msgstr "全屏模式"
-
-msgid "Edit this page"
-msgstr "编辑此页面"
-
-msgid "By the"
-msgstr "作者："
-
-msgid "next page"
-msgstr "下一页"
diff --git a/_preview/3/_static/locales/zh_TW/LC_MESSAGES/booktheme.po b/_preview/3/_static/locales/zh_TW/LC_MESSAGES/booktheme.po
deleted file mode 100644
index beecb07..0000000
--- a/_preview/3/_static/locales/zh_TW/LC_MESSAGES/booktheme.po
+++ /dev/null
@@ -1,75 +0,0 @@
-
-msgid ""
-msgstr ""
-"Project-Id-Version: Sphinx-Book-Theme\n"
-"MIME-Version: 1.0\n"
-"Content-Type: text/plain; charset=UTF-8\n"
-"Content-Transfer-Encoding: 8bit\n"
-"Language: zh_TW\n"
-"Plural-Forms: nplurals=2; plural=(n != 1);\n"
-
-msgid "Print to PDF"
-msgstr "列印成 PDF"
-
-msgid "Theme by the"
-msgstr "佈景主題作者："
-
-msgid "Download source file"
-msgstr "下載原始檔"
-
-msgid "open issue"
-msgstr "公開的問題"
-
-msgid "Contents"
-msgstr "目錄"
-
-msgid "previous page"
-msgstr "上一頁"
-
-msgid "Download notebook file"
-msgstr "下載 Notebook 檔案"
-
-msgid "Copyright"
-msgstr "Copyright"
-
-msgid "Download this page"
-msgstr "下載此頁面"
-
-msgid "Source repository"
-msgstr "來源儲存庫"
-
-msgid "By"
-msgstr "作者："
-
-msgid "repository"
-msgstr "儲存庫"
-
-msgid "Last updated on"
-msgstr "最後更新時間："
-
-msgid "Toggle navigation"
-msgstr "顯示或隱藏導覽列"
-
-msgid "Sphinx Book Theme"
-msgstr "Sphinx Book 佈景主題"
-
-msgid "suggest edit"
-msgstr "提出修改建議"
-
-msgid "Open an issue"
-msgstr "開啟議題"
-
-msgid "Launch"
-msgstr "啟動"
-
-msgid "Fullscreen mode"
-msgstr "全螢幕模式"
-
-msgid "Edit this page"
-msgstr "編輯此頁面"
-
-msgid "By the"
-msgstr "作者："
-
-msgid "next page"
-msgstr "下一頁"
diff --git a/_preview/3/_static/minus.png b/_preview/3/_static/minus.png
deleted file mode 100644
index d96755fdaf8bb2214971e0db9c1fd3077d7c419d..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 90
zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj
kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK

diff --git a/_preview/3/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css b/_preview/3/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css
deleted file mode 100644
index 3356631..0000000
--- a/_preview/3/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css
+++ /dev/null
@@ -1,2342 +0,0 @@
-/* Variables */
-:root {
-  --mystnb-source-bg-color: #f7f7f7;
-  --mystnb-stdout-bg-color: #fcfcfc;
-  --mystnb-stderr-bg-color: #fdd;
-  --mystnb-traceback-bg-color: #fcfcfc;
-  --mystnb-source-border-color: #ccc;
-  --mystnb-source-margin-color: green;
-  --mystnb-stdout-border-color: #f7f7f7;
-  --mystnb-stderr-border-color: #f7f7f7;
-  --mystnb-traceback-border-color: #ffd6d6;
-  --mystnb-hide-prompt-opacity: 70%;
-  --mystnb-source-border-radius: .4em;
-  --mystnb-source-border-width: 1px;
-}
-
-/* Whole cell */
-div.container.cell {
-  padding-left: 0;
-  margin-bottom: 1em;
-}
-
-/* Removing all background formatting so we can control at the div level */
-.cell_input div.highlight,
-.cell_output pre,
-.cell_input pre,
-.cell_output .output {
-  border: none;
-  box-shadow: none;
-}
-
-.cell_output .output pre,
-.cell_input pre {
-  margin: 0px;
-}
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-/* Input cells */
-div.cell div.cell_input,
-div.cell details.above-input>summary {
-  padding-left: 0em;
-  padding-right: 0em;
-  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
-  background-color: var(--mystnb-source-bg-color);
-  border-left-color: var(--mystnb-source-margin-color);
-  border-left-width: medium;
-  border-radius: var(--mystnb-source-border-radius);
-}
-
-div.cell_input>div,
-div.cell_output div.output>div.highlight {
-  margin: 0em !important;
-  border: none !important;
-}
-
-/* All cell outputs */
-.cell_output {
-  padding-left: 1em;
-  padding-right: 0em;
-  margin-top: 1em;
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-/* Text outputs from cells */
-.cell_output .output.text_plain,
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-.cell_output .output.stream,
-.cell_output .output.stderr {
-  margin-top: 1em;
-  margin-bottom: 0em;
-  box-shadow: none;
-}
-
-.cell_output .output.text_plain,
-.cell_output .output.stream {
-  background: var(--mystnb-stdout-bg-color);
-  border: 1px solid var(--mystnb-stdout-border-color);
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-
-.cell_output .output.stderr {
-  background: var(--mystnb-stderr-bg-color);
-  border: 1px solid var(--mystnb-stderr-border-color);
-}
-
-.cell_output .output.traceback {
-  background: var(--mystnb-traceback-bg-color);
-  border: 1px solid var(--mystnb-traceback-border-color);
-}
-
-/* Collapsible cell content */
-div.cell details.above-input div.cell_input {
-  border-top-left-radius: 0;
-  border-top-right-radius: 0;
-  border-top: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed;
-}
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-div.cell div.cell_input.above-output-prompt {
-  border-bottom-left-radius: 0;
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-}
-
-div.cell details.above-input>summary {
-  border-bottom-left-radius: 0;
-  border-bottom-right-radius: 0;
-  border-bottom: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed;
-  padding-left: 1em;
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-}
-
-div.cell details.above-output>summary {
-  background-color: var(--mystnb-source-bg-color);
-  padding-left: 1em;
-  padding-right: 0em;
-  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
-  border-radius: var(--mystnb-source-border-radius);
-  border-left-color: var(--mystnb-source-margin-color);
-  border-left-width: medium;
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-div.cell details.below-input>summary {
-  background-color: var(--mystnb-source-bg-color);
-  padding-left: 1em;
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-  border-top: none;
-  border-bottom-left-radius: var(--mystnb-source-border-radius);
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-/* Math align to the left */
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-/* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */
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-  border-spacing: 0;
-  color: black;
-  font-size: 1em;
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-div.cell_output tr,
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-  padding: 0.5em 0.5em;
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-  white-space: normal;
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-div.cell_output th {
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-div.cell_output tbody tr:nth-child(odd) {
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-
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-  background: rgba(66, 165, 245, 0.2);
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-
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-}
-
-div.highlight .-Color[class*=-Magenta] {
-  color: #D160C4
-}
-
-div.highlight .-Color[class*=-Cyan] {
-  color: #60C6C8
-}
-
-div.highlight .-Color[class*=-White] {
-  color: #C5C1B4
-}
-
-div.highlight .-Color[class*=-BGBlack] {
-  background-color: #3E424D
-}
-
-div.highlight .-Color[class*=-BGRed] {
-  background-color: #E75C58
-}
-
-div.highlight .-Color[class*=-BGGreen] {
-  background-color: #00A250
-}
-
-div.highlight .-Color[class*=-BGYellow] {
-  background-color: #DDB62B
-}
-
-div.highlight .-Color[class*=-BGBlue] {
-  background-color: #208FFB
-}
-
-div.highlight .-Color[class*=-BGMagenta] {
-  background-color: #D160C4
-}
-
-div.highlight .-Color[class*=-BGCyan] {
-  background-color: #60C6C8
-}
-
-div.highlight .-Color[class*=-BGWhite] {
-  background-color: #C5C1B4
-}
-
-/* Font colors for 8-bit ANSI */
-
-div.highlight .-Color[class*=-C0] {
-  color: #000000
-}
-
-div.highlight .-Color[class*=-BGC0] {
-  background-color: #000000
-}
-
-div.highlight .-Color[class*=-C1] {
-  color: #800000
-}
-
-div.highlight .-Color[class*=-BGC1] {
-  background-color: #800000
-}
-
-div.highlight .-Color[class*=-C2] {
-  color: #008000
-}
-
-div.highlight .-Color[class*=-BGC2] {
-  background-color: #008000
-}
-
-div.highlight .-Color[class*=-C3] {
-  color: #808000
-}
-
-div.highlight .-Color[class*=-BGC3] {
-  background-color: #808000
-}
-
-div.highlight .-Color[class*=-C4] {
-  color: #000080
-}
-
-div.highlight .-Color[class*=-BGC4] {
-  background-color: #000080
-}
-
-div.highlight .-Color[class*=-C5] {
-  color: #800080
-}
-
-div.highlight .-Color[class*=-BGC5] {
-  background-color: #800080
-}
-
-div.highlight .-Color[class*=-C6] {
-  color: #008080
-}
-
-div.highlight .-Color[class*=-BGC6] {
-  background-color: #008080
-}
-
-div.highlight .-Color[class*=-C7] {
-  color: #C0C0C0
-}
-
-div.highlight .-Color[class*=-BGC7] {
-  background-color: #C0C0C0
-}
-
-div.highlight .-Color[class*=-C8] {
-  color: #808080
-}
-
-div.highlight .-Color[class*=-BGC8] {
-  background-color: #808080
-}
-
-div.highlight .-Color[class*=-C9] {
-  color: #FF0000
-}
-
-div.highlight .-Color[class*=-BGC9] {
-  background-color: #FF0000
-}
-
-div.highlight .-Color[class*=-C10] {
-  color: #00FF00
-}
-
-div.highlight .-Color[class*=-BGC10] {
-  background-color: #00FF00
-}
-
-div.highlight .-Color[class*=-C11] {
-  color: #FFFF00
-}
-
-div.highlight .-Color[class*=-BGC11] {
-  background-color: #FFFF00
-}
-
-div.highlight .-Color[class*=-C12] {
-  color: #0000FF
-}
-
-div.highlight .-Color[class*=-BGC12] {
-  background-color: #0000FF
-}
-
-div.highlight .-Color[class*=-C13] {
-  color: #FF00FF
-}
-
-div.highlight .-Color[class*=-BGC13] {
-  background-color: #FF00FF
-}
-
-div.highlight .-Color[class*=-C14] {
-  color: #00FFFF
-}
-
-div.highlight .-Color[class*=-BGC14] {
-  background-color: #00FFFF
-}
-
-div.highlight .-Color[class*=-C15] {
-  color: #FFFFFF
-}
-
-div.highlight .-Color[class*=-BGC15] {
-  background-color: #FFFFFF
-}
-
-div.highlight .-Color[class*=-C16] {
-  color: #000000
-}
-
-div.highlight .-Color[class*=-BGC16] {
-  background-color: #000000
-}
-
-div.highlight .-Color[class*=-C17] {
-  color: #00005F
-}
-
-div.highlight .-Color[class*=-BGC17] {
-  background-color: #00005F
-}
-
-div.highlight .-Color[class*=-C18] {
-  color: #000087
-}
-
-div.highlight .-Color[class*=-BGC18] {
-  background-color: #000087
-}
-
-div.highlight .-Color[class*=-C19] {
-  color: #0000AF
-}
-
-div.highlight .-Color[class*=-BGC19] {
-  background-color: #0000AF
-}
-
-div.highlight .-Color[class*=-C20] {
-  color: #0000D7
-}
-
-div.highlight .-Color[class*=-BGC20] {
-  background-color: #0000D7
-}
-
-div.highlight .-Color[class*=-C21] {
-  color: #0000FF
-}
-
-div.highlight .-Color[class*=-BGC21] {
-  background-color: #0000FF
-}
-
-div.highlight .-Color[class*=-C22] {
-  color: #005F00
-}
-
-div.highlight .-Color[class*=-BGC22] {
-  background-color: #005F00
-}
-
-div.highlight .-Color[class*=-C23] {
-  color: #005F5F
-}
-
-div.highlight .-Color[class*=-BGC23] {
-  background-color: #005F5F
-}
-
-div.highlight .-Color[class*=-C24] {
-  color: #005F87
-}
-
-div.highlight .-Color[class*=-BGC24] {
-  background-color: #005F87
-}
-
-div.highlight .-Color[class*=-C25] {
-  color: #005FAF
-}
-
-div.highlight .-Color[class*=-BGC25] {
-  background-color: #005FAF
-}
-
-div.highlight .-Color[class*=-C26] {
-  color: #005FD7
-}
-
-div.highlight .-Color[class*=-BGC26] {
-  background-color: #005FD7
-}
-
-div.highlight .-Color[class*=-C27] {
-  color: #005FFF
-}
-
-div.highlight .-Color[class*=-BGC27] {
-  background-color: #005FFF
-}
-
-div.highlight .-Color[class*=-C28] {
-  color: #008700
-}
-
-div.highlight .-Color[class*=-BGC28] {
-  background-color: #008700
-}
-
-div.highlight .-Color[class*=-C29] {
-  color: #00875F
-}
-
-div.highlight .-Color[class*=-BGC29] {
-  background-color: #00875F
-}
-
-div.highlight .-Color[class*=-C30] {
-  color: #008787
-}
-
-div.highlight .-Color[class*=-BGC30] {
-  background-color: #008787
-}
-
-div.highlight .-Color[class*=-C31] {
-  color: #0087AF
-}
-
-div.highlight .-Color[class*=-BGC31] {
-  background-color: #0087AF
-}
-
-div.highlight .-Color[class*=-C32] {
-  color: #0087D7
-}
-
-div.highlight .-Color[class*=-BGC32] {
-  background-color: #0087D7
-}
-
-div.highlight .-Color[class*=-C33] {
-  color: #0087FF
-}
-
-div.highlight .-Color[class*=-BGC33] {
-  background-color: #0087FF
-}
-
-div.highlight .-Color[class*=-C34] {
-  color: #00AF00
-}
-
-div.highlight .-Color[class*=-BGC34] {
-  background-color: #00AF00
-}
-
-div.highlight .-Color[class*=-C35] {
-  color: #00AF5F
-}
-
-div.highlight .-Color[class*=-BGC35] {
-  background-color: #00AF5F
-}
-
-div.highlight .-Color[class*=-C36] {
-  color: #00AF87
-}
-
-div.highlight .-Color[class*=-BGC36] {
-  background-color: #00AF87
-}
-
-div.highlight .-Color[class*=-C37] {
-  color: #00AFAF
-}
-
-div.highlight .-Color[class*=-BGC37] {
-  background-color: #00AFAF
-}
-
-div.highlight .-Color[class*=-C38] {
-  color: #00AFD7
-}
-
-div.highlight .-Color[class*=-BGC38] {
-  background-color: #00AFD7
-}
-
-div.highlight .-Color[class*=-C39] {
-  color: #00AFFF
-}
-
-div.highlight .-Color[class*=-BGC39] {
-  background-color: #00AFFF
-}
-
-div.highlight .-Color[class*=-C40] {
-  color: #00D700
-}
-
-div.highlight .-Color[class*=-BGC40] {
-  background-color: #00D700
-}
-
-div.highlight .-Color[class*=-C41] {
-  color: #00D75F
-}
-
-div.highlight .-Color[class*=-BGC41] {
-  background-color: #00D75F
-}
-
-div.highlight .-Color[class*=-C42] {
-  color: #00D787
-}
-
-div.highlight .-Color[class*=-BGC42] {
-  background-color: #00D787
-}
-
-div.highlight .-Color[class*=-C43] {
-  color: #00D7AF
-}
-
-div.highlight .-Color[class*=-BGC43] {
-  background-color: #00D7AF
-}
-
-div.highlight .-Color[class*=-C44] {
-  color: #00D7D7
-}
-
-div.highlight .-Color[class*=-BGC44] {
-  background-color: #00D7D7
-}
-
-div.highlight .-Color[class*=-C45] {
-  color: #00D7FF
-}
-
-div.highlight .-Color[class*=-BGC45] {
-  background-color: #00D7FF
-}
-
-div.highlight .-Color[class*=-C46] {
-  color: #00FF00
-}
-
-div.highlight .-Color[class*=-BGC46] {
-  background-color: #00FF00
-}
-
-div.highlight .-Color[class*=-C47] {
-  color: #00FF5F
-}
-
-div.highlight .-Color[class*=-BGC47] {
-  background-color: #00FF5F
-}
-
-div.highlight .-Color[class*=-C48] {
-  color: #00FF87
-}
-
-div.highlight .-Color[class*=-BGC48] {
-  background-color: #00FF87
-}
-
-div.highlight .-Color[class*=-C49] {
-  color: #00FFAF
-}
-
-div.highlight .-Color[class*=-BGC49] {
-  background-color: #00FFAF
-}
-
-div.highlight .-Color[class*=-C50] {
-  color: #00FFD7
-}
-
-div.highlight .-Color[class*=-BGC50] {
-  background-color: #00FFD7
-}
-
-div.highlight .-Color[class*=-C51] {
-  color: #00FFFF
-}
-
-div.highlight .-Color[class*=-BGC51] {
-  background-color: #00FFFF
-}
-
-div.highlight .-Color[class*=-C52] {
-  color: #5F0000
-}
-
-div.highlight .-Color[class*=-BGC52] {
-  background-color: #5F0000
-}
-
-div.highlight .-Color[class*=-C53] {
-  color: #5F005F
-}
-
-div.highlight .-Color[class*=-BGC53] {
-  background-color: #5F005F
-}
-
-div.highlight .-Color[class*=-C54] {
-  color: #5F0087
-}
-
-div.highlight .-Color[class*=-BGC54] {
-  background-color: #5F0087
-}
-
-div.highlight .-Color[class*=-C55] {
-  color: #5F00AF
-}
-
-div.highlight .-Color[class*=-BGC55] {
-  background-color: #5F00AF
-}
-
-div.highlight .-Color[class*=-C56] {
-  color: #5F00D7
-}
-
-div.highlight .-Color[class*=-BGC56] {
-  background-color: #5F00D7
-}
-
-div.highlight .-Color[class*=-C57] {
-  color: #5F00FF
-}
-
-div.highlight .-Color[class*=-BGC57] {
-  background-color: #5F00FF
-}
-
-div.highlight .-Color[class*=-C58] {
-  color: #5F5F00
-}
-
-div.highlight .-Color[class*=-BGC58] {
-  background-color: #5F5F00
-}
-
-div.highlight .-Color[class*=-C59] {
-  color: #5F5F5F
-}
-
-div.highlight .-Color[class*=-BGC59] {
-  background-color: #5F5F5F
-}
-
-div.highlight .-Color[class*=-C60] {
-  color: #5F5F87
-}
-
-div.highlight .-Color[class*=-BGC60] {
-  background-color: #5F5F87
-}
-
-div.highlight .-Color[class*=-C61] {
-  color: #5F5FAF
-}
-
-div.highlight .-Color[class*=-BGC61] {
-  background-color: #5F5FAF
-}
-
-div.highlight .-Color[class*=-C62] {
-  color: #5F5FD7
-}
-
-div.highlight .-Color[class*=-BGC62] {
-  background-color: #5F5FD7
-}
-
-div.highlight .-Color[class*=-C63] {
-  color: #5F5FFF
-}
-
-div.highlight .-Color[class*=-BGC63] {
-  background-color: #5F5FFF
-}
-
-div.highlight .-Color[class*=-C64] {
-  color: #5F8700
-}
-
-div.highlight .-Color[class*=-BGC64] {
-  background-color: #5F8700
-}
-
-div.highlight .-Color[class*=-C65] {
-  color: #5F875F
-}
-
-div.highlight .-Color[class*=-BGC65] {
-  background-color: #5F875F
-}
-
-div.highlight .-Color[class*=-C66] {
-  color: #5F8787
-}
-
-div.highlight .-Color[class*=-BGC66] {
-  background-color: #5F8787
-}
-
-div.highlight .-Color[class*=-C67] {
-  color: #5F87AF
-}
-
-div.highlight .-Color[class*=-BGC67] {
-  background-color: #5F87AF
-}
-
-div.highlight .-Color[class*=-C68] {
-  color: #5F87D7
-}
-
-div.highlight .-Color[class*=-BGC68] {
-  background-color: #5F87D7
-}
-
-div.highlight .-Color[class*=-C69] {
-  color: #5F87FF
-}
-
-div.highlight .-Color[class*=-BGC69] {
-  background-color: #5F87FF
-}
-
-div.highlight .-Color[class*=-C70] {
-  color: #5FAF00
-}
-
-div.highlight .-Color[class*=-BGC70] {
-  background-color: #5FAF00
-}
-
-div.highlight .-Color[class*=-C71] {
-  color: #5FAF5F
-}
-
-div.highlight .-Color[class*=-BGC71] {
-  background-color: #5FAF5F
-}
-
-div.highlight .-Color[class*=-C72] {
-  color: #5FAF87
-}
-
-div.highlight .-Color[class*=-BGC72] {
-  background-color: #5FAF87
-}
-
-div.highlight .-Color[class*=-C73] {
-  color: #5FAFAF
-}
-
-div.highlight .-Color[class*=-BGC73] {
-  background-color: #5FAFAF
-}
-
-div.highlight .-Color[class*=-C74] {
-  color: #5FAFD7
-}
-
-div.highlight .-Color[class*=-BGC74] {
-  background-color: #5FAFD7
-}
-
-div.highlight .-Color[class*=-C75] {
-  color: #5FAFFF
-}
-
-div.highlight .-Color[class*=-BGC75] {
-  background-color: #5FAFFF
-}
-
-div.highlight .-Color[class*=-C76] {
-  color: #5FD700
-}
-
-div.highlight .-Color[class*=-BGC76] {
-  background-color: #5FD700
-}
-
-div.highlight .-Color[class*=-C77] {
-  color: #5FD75F
-}
-
-div.highlight .-Color[class*=-BGC77] {
-  background-color: #5FD75F
-}
-
-div.highlight .-Color[class*=-C78] {
-  color: #5FD787
-}
-
-div.highlight .-Color[class*=-BGC78] {
-  background-color: #5FD787
-}
-
-div.highlight .-Color[class*=-C79] {
-  color: #5FD7AF
-}
-
-div.highlight .-Color[class*=-BGC79] {
-  background-color: #5FD7AF
-}
-
-div.highlight .-Color[class*=-C80] {
-  color: #5FD7D7
-}
-
-div.highlight .-Color[class*=-BGC80] {
-  background-color: #5FD7D7
-}
-
-div.highlight .-Color[class*=-C81] {
-  color: #5FD7FF
-}
-
-div.highlight .-Color[class*=-BGC81] {
-  background-color: #5FD7FF
-}
-
-div.highlight .-Color[class*=-C82] {
-  color: #5FFF00
-}
-
-div.highlight .-Color[class*=-BGC82] {
-  background-color: #5FFF00
-}
-
-div.highlight .-Color[class*=-C83] {
-  color: #5FFF5F
-}
-
-div.highlight .-Color[class*=-BGC83] {
-  background-color: #5FFF5F
-}
-
-div.highlight .-Color[class*=-C84] {
-  color: #5FFF87
-}
-
-div.highlight .-Color[class*=-BGC84] {
-  background-color: #5FFF87
-}
-
-div.highlight .-Color[class*=-C85] {
-  color: #5FFFAF
-}
-
-div.highlight .-Color[class*=-BGC85] {
-  background-color: #5FFFAF
-}
-
-div.highlight .-Color[class*=-C86] {
-  color: #5FFFD7
-}
-
-div.highlight .-Color[class*=-BGC86] {
-  background-color: #5FFFD7
-}
-
-div.highlight .-Color[class*=-C87] {
-  color: #5FFFFF
-}
-
-div.highlight .-Color[class*=-BGC87] {
-  background-color: #5FFFFF
-}
-
-div.highlight .-Color[class*=-C88] {
-  color: #870000
-}
-
-div.highlight .-Color[class*=-BGC88] {
-  background-color: #870000
-}
-
-div.highlight .-Color[class*=-C89] {
-  color: #87005F
-}
-
-div.highlight .-Color[class*=-BGC89] {
-  background-color: #87005F
-}
-
-div.highlight .-Color[class*=-C90] {
-  color: #870087
-}
-
-div.highlight .-Color[class*=-BGC90] {
-  background-color: #870087
-}
-
-div.highlight .-Color[class*=-C91] {
-  color: #8700AF
-}
-
-div.highlight .-Color[class*=-BGC91] {
-  background-color: #8700AF
-}
-
-div.highlight .-Color[class*=-C92] {
-  color: #8700D7
-}
-
-div.highlight .-Color[class*=-BGC92] {
-  background-color: #8700D7
-}
-
-div.highlight .-Color[class*=-C93] {
-  color: #8700FF
-}
-
-div.highlight .-Color[class*=-BGC93] {
-  background-color: #8700FF
-}
-
-div.highlight .-Color[class*=-C94] {
-  color: #875F00
-}
-
-div.highlight .-Color[class*=-BGC94] {
-  background-color: #875F00
-}
-
-div.highlight .-Color[class*=-C95] {
-  color: #875F5F
-}
-
-div.highlight .-Color[class*=-BGC95] {
-  background-color: #875F5F
-}
-
-div.highlight .-Color[class*=-C96] {
-  color: #875F87
-}
-
-div.highlight .-Color[class*=-BGC96] {
-  background-color: #875F87
-}
-
-div.highlight .-Color[class*=-C97] {
-  color: #875FAF
-}
-
-div.highlight .-Color[class*=-BGC97] {
-  background-color: #875FAF
-}
-
-div.highlight .-Color[class*=-C98] {
-  color: #875FD7
-}
-
-div.highlight .-Color[class*=-BGC98] {
-  background-color: #875FD7
-}
-
-div.highlight .-Color[class*=-C99] {
-  color: #875FFF
-}
-
-div.highlight .-Color[class*=-BGC99] {
-  background-color: #875FFF
-}
-
-div.highlight .-Color[class*=-C100] {
-  color: #878700
-}
-
-div.highlight .-Color[class*=-BGC100] {
-  background-color: #878700
-}
-
-div.highlight .-Color[class*=-C101] {
-  color: #87875F
-}
-
-div.highlight .-Color[class*=-BGC101] {
-  background-color: #87875F
-}
-
-div.highlight .-Color[class*=-C102] {
-  color: #878787
-}
-
-div.highlight .-Color[class*=-BGC102] {
-  background-color: #878787
-}
-
-div.highlight .-Color[class*=-C103] {
-  color: #8787AF
-}
-
-div.highlight .-Color[class*=-BGC103] {
-  background-color: #8787AF
-}
-
-div.highlight .-Color[class*=-C104] {
-  color: #8787D7
-}
-
-div.highlight .-Color[class*=-BGC104] {
-  background-color: #8787D7
-}
-
-div.highlight .-Color[class*=-C105] {
-  color: #8787FF
-}
-
-div.highlight .-Color[class*=-BGC105] {
-  background-color: #8787FF
-}
-
-div.highlight .-Color[class*=-C106] {
-  color: #87AF00
-}
-
-div.highlight .-Color[class*=-BGC106] {
-  background-color: #87AF00
-}
-
-div.highlight .-Color[class*=-C107] {
-  color: #87AF5F
-}
-
-div.highlight .-Color[class*=-BGC107] {
-  background-color: #87AF5F
-}
-
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diff --git a/_preview/3/_static/sbt-webpack-macros.html b/_preview/3/_static/sbt-webpack-macros.html
deleted file mode 100644
index 6cbf559..0000000
--- a/_preview/3/_static/sbt-webpack-macros.html
+++ /dev/null
@@ -1,11 +0,0 @@
-<!--
-  All these macros are auto-generated and must **NOT** be edited by hand.
-  See the webpack.config.js file, to learn more about how this is generated.
--->
-{% macro head_pre_bootstrap() %}
-  <link href="{{ pathto('_static/styles/sphinx-book-theme.css', 1) }}" rel="stylesheet">
-{% endmacro %}
-
-{% macro body_post() %}
-  <script src="{{ pathto('_static/scripts/sphinx-book-theme.js', 1) }}"></script>
-{% endmacro %}
diff --git a/_preview/3/_static/scripts/bootstrap.js b/_preview/3/_static/scripts/bootstrap.js
deleted file mode 100644
index c8178de..0000000
--- a/_preview/3/_static/scripts/bootstrap.js
+++ /dev/null
@@ -1,3 +0,0 @@
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t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(_i),this._element.classList.remove(gi,mi);for(const t of this._triggerArray){const e=we.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(_i),this._element.classList.add(gi),fe.trigger(this._element,fi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(mi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(vi);for(const e of t){const t=we.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=we.find(bi,this._config.parent);return we.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=Ei.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,pi,vi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of we.getMultipleElementsFromSelector(this))Ei.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(Ei);const Ai="dropdown",Ti=".bs.dropdown",Ci=".data-api",Oi="ArrowUp",xi="ArrowDown",ki=`hide${Ti}`,Li=`hidden${Ti}`,Si=`show${Ti}`,Di=`shown${Ti}`,$i=`click${Ti}${Ci}`,Ii=`keydown${Ti}${Ci}`,Ni=`keyup${Ti}${Ci}`,Pi="show",Mi='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',ji=`${Mi}.${Pi}`,Fi=".dropdown-menu",Hi=Kt()?"top-end":"top-start",Bi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",zi=Kt()?"bottom-start":"bottom-end",Ri=Kt()?"left-start":"right-start",qi=Kt()?"right-start":"left-start",Vi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Yi={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Ki extends ve{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=we.next(this._element,Fi)[0]||we.prev(this._element,Fi)[0]||we.findOne(Fi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return Vi}static get DefaultType(){return Yi}static get NAME(){return Ai}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,Si,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Pi),this._element.classList.add(Pi),fe.trigger(this._element,Di,t)}}hide(){if(Wt(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(!fe.trigger(this._element,ki,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Pi),this._element.classList.remove(Pi),this._element.setAttribute("aria-expanded","false"),_e.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,Li,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Ft(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ai.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Ft(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Pi)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Ri;if(t.classList.contains("dropstart"))return qi;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?Bi:Hi:e?zi:Wi}_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)&&(_e.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Xt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=we.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Gt(i,e,t===xi,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Ki.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=we.find(ji);for(const i of e){const e=Ki.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Oi,xi].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Mi)?this:we.prev(this,Mi)[0]||we.next(this,Mi)[0]||we.findOne(Mi,t.delegateTarget.parentNode),o=Ki.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,Ii,Mi,Ki.dataApiKeydownHandler),fe.on(document,Ii,Fi,Ki.dataApiKeydownHandler),fe.on(document,$i,Ki.clearMenus),fe.on(document,Ni,Ki.clearMenus),fe.on(document,$i,Mi,(function(t){t.preventDefault(),Ki.getOrCreateInstance(this).toggle()})),Qt(Ki);const Qi="backdrop",Xi="show",Ui=`mousedown.bs.${Qi}`,Gi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ji={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Zi extends be{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Gi}static get DefaultType(){return Ji}static get NAME(){return Qi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Xi),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Xi),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(fe.off(this._element,Ui),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=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,Ui,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const tn=".bs.focustrap",en=`focusin${tn}`,nn=`keydown.tab${tn}`,sn="backward",on={autofocus:!0,trapElement:null},rn={autofocus:"boolean",trapElement:"element"};class an extends be{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return on}static get DefaultType(){return rn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,tn),fe.on(document,en,(t=>this._handleFocusin(t))),fe.on(document,nn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,tn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=we.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===sn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?sn:"forward")}}const ln=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",cn=".sticky-top",hn="padding-right",dn="margin-right";class un{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,hn,(e=>e+t)),this._setElementAttributes(ln,hn,(e=>e+t)),this._setElementAttributes(cn,dn,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,hn),this._resetElementAttributes(ln,hn),this._resetElementAttributes(cn,dn)}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,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&_e.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=_e.getDataAttribute(t,e);null!==i?(_e.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Ft(t))e(t);else for(const i of we.find(t,this._element))e(i)}}const fn=".bs.modal",pn=`hide${fn}`,mn=`hidePrevented${fn}`,gn=`hidden${fn}`,_n=`show${fn}`,bn=`shown${fn}`,vn=`resize${fn}`,yn=`click.dismiss${fn}`,wn=`mousedown.dismiss${fn}`,En=`keydown.dismiss${fn}`,An=`click${fn}.data-api`,Tn="modal-open",Cn="show",On="modal-static",xn={backdrop:!0,focus:!0,keyboard:!0},kn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class Ln extends ve{constructor(t,e){super(t,e),this._dialog=we.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new un,this._addEventListeners()}static get Default(){return xn}static get DefaultType(){return kn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||fe.trigger(this._element,_n,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(Tn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(fe.trigger(this._element,pn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(Cn),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){fe.off(window,fn),fe.off(this._dialog,fn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Zi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new an({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=we.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(Cn),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,bn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,En,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),fe.on(window,vn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),fe.on(this._element,wn,(t=>{fe.one(this._element,yn,(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(Tn),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,gn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,mn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(On)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(On),this._queueCallback((()=>{this._element.classList.remove(On),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=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Ln.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,An,'[data-bs-toggle="modal"]',(function(t){const e=we.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,_n,(t=>{t.defaultPrevented||fe.one(e,gn,(()=>{Bt(this)&&this.focus()}))}));const i=we.findOne(".modal.show");i&&Ln.getInstance(i).hide(),Ln.getOrCreateInstance(e).toggle(this)})),Ee(Ln),Qt(Ln);const Sn=".bs.offcanvas",Dn=".data-api",$n=`load${Sn}${Dn}`,In="show",Nn="showing",Pn="hiding",Mn=".offcanvas.show",jn=`show${Sn}`,Fn=`shown${Sn}`,Hn=`hide${Sn}`,Bn=`hidePrevented${Sn}`,Wn=`hidden${Sn}`,zn=`resize${Sn}`,Rn=`click${Sn}${Dn}`,qn=`keydown.dismiss${Sn}`,Vn={backdrop:!0,keyboard:!0,scroll:!1},Yn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Kn extends ve{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return Vn}static get DefaultType(){return Yn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||fe.trigger(this._element,jn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new un).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Nn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(In),this._element.classList.remove(Nn),fe.trigger(this._element,Fn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(fe.trigger(this._element,Hn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(Pn),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(In,Pn),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new un).reset(),fe.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Zi({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,Bn)}:null})}_initializeFocusTrap(){return new an({trapElement:this._element})}_addEventListeners(){fe.on(this._element,qn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,Bn))}))}static jQueryInterface(t){return this.each((function(){const e=Kn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,Rn,'[data-bs-toggle="offcanvas"]',(function(t){const e=we.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;fe.one(e,Wn,(()=>{Bt(this)&&this.focus()}));const i=we.findOne(Mn);i&&i!==e&&Kn.getInstance(i).hide(),Kn.getOrCreateInstance(e).toggle(this)})),fe.on(window,$n,(()=>{for(const t of we.find(Mn))Kn.getOrCreateInstance(t).show()})),fe.on(window,zn,(()=>{for(const t of we.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Kn.getOrCreateInstance(t).hide()})),Ee(Kn),Qt(Kn);const Qn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],dd:[],div:[],dl:[],dt:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Xn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Un=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Gn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Xn.has(i)||Boolean(Un.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Jn={allowList:Qn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Zn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},ts={entry:"(string|element|function|null)",selector:"(string|element)"};class es extends be{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Jn}static get DefaultType(){return Zn}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},ts)}_setContent(t,e,i){const n=we.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Ft(e)?this._putElementInTemplate(Ht(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)Gn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Xt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const is=new Set(["sanitize","allowList","sanitizeFn"]),ns="fade",ss="show",os=".tooltip-inner",rs=".modal",as="hide.bs.modal",ls="hover",cs="focus",hs={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},ds={allowList:Qn,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"},us={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 fs extends ve{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),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 ds}static get DefaultType(){return us}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),fe.off(this._element.closest(rs),as,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=fe.trigger(this._element,this.constructor.eventName("show")),e=(zt(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),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback((()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ss),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger[cs]=!1,this._activeTrigger[ls]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),fe.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(ns,ss),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ns),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new es({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{[os]:this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ns)}_isShown(){return this.tip&&this.tip.classList.contains(ss)}_createPopper(t){const e=Xt(this._config.placement,[this,t,this._element]),i=hs[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Xt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Xt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)fe.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ls?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ls?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");fe.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?cs:ls]=!0,e._enter()})),fe.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?cs:ls]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},fe.on(this._element.closest(rs),as,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=_e.getDataAttributes(this._element);for(const t of Object.keys(e))is.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:Ht(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=fs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(fs);const ps=".popover-header",ms=".popover-body",gs={...fs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},_s={...fs.DefaultType,content:"(null|string|element|function)"};class bs extends fs{static get Default(){return gs}static get DefaultType(){return _s}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{[ps]:this._getTitle(),[ms]:this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=bs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(bs);const vs=".bs.scrollspy",ys=`activate${vs}`,ws=`click${vs}`,Es=`load${vs}.data-api`,As="active",Ts="[href]",Cs=".nav-link",Os=`${Cs}, .nav-item > ${Cs}, .list-group-item`,xs={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ks={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ls extends ve{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return xs}static get DefaultType(){return ks}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,ws),fe.on(this._config.target,ws,Ts,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=we.find(Ts,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=we.findOne(decodeURI(e.hash),this._element);Bt(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(As),this._activateParents(t),fe.trigger(this._element,ys,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))we.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(As);else for(const e of we.parents(t,".nav, .list-group"))for(const t of we.prev(e,Os))t.classList.add(As)}_clearActiveClass(t){t.classList.remove(As);const e=we.find(`${Ts}.${As}`,t);for(const t of e)t.classList.remove(As)}static jQueryInterface(t){return this.each((function(){const e=Ls.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,Es,(()=>{for(const t of we.find('[data-bs-spy="scroll"]'))Ls.getOrCreateInstance(t)})),Qt(Ls);const Ss=".bs.tab",Ds=`hide${Ss}`,$s=`hidden${Ss}`,Is=`show${Ss}`,Ns=`shown${Ss}`,Ps=`click${Ss}`,Ms=`keydown${Ss}`,js=`load${Ss}`,Fs="ArrowLeft",Hs="ArrowRight",Bs="ArrowUp",Ws="ArrowDown",zs="Home",Rs="End",qs="active",Vs="fade",Ys="show",Ks=".dropdown-toggle",Qs=`:not(${Ks})`,Xs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Us=`.nav-link${Qs}, .list-group-item${Qs}, [role="tab"]${Qs}, ${Xs}`,Gs=`.${qs}[data-bs-toggle="tab"], .${qs}[data-bs-toggle="pill"], .${qs}[data-bs-toggle="list"]`;class Js extends ve{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),fe.on(this._element,Ms,(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?fe.trigger(e,Ds,{relatedTarget:t}):null;fe.trigger(t,Is,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(qs),this._activate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,Ns,{relatedTarget:e})):t.classList.add(Ys)}),t,t.classList.contains(Vs)))}_deactivate(t,e){t&&(t.classList.remove(qs),t.blur(),this._deactivate(we.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,$s,{relatedTarget:e})):t.classList.remove(Ys)}),t,t.classList.contains(Vs)))}_keydown(t){if(![Fs,Hs,Bs,Ws,zs,Rs].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Wt(t)));let i;if([zs,Rs].includes(t.key))i=e[t.key===zs?0:e.length-1];else{const n=[Hs,Ws].includes(t.key);i=Gt(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Js.getOrCreateInstance(i).show())}_getChildren(){return we.find(Us,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=we.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=we.findOne(t,i);s&&s.classList.toggle(n,e)};n(Ks,qs),n(".dropdown-menu",Ys),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(qs)}_getInnerElement(t){return t.matches(Us)?t:we.findOne(Us,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Js.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(document,Ps,Xs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this)||Js.getOrCreateInstance(this).show()})),fe.on(window,js,(()=>{for(const t of we.find(Gs))Js.getOrCreateInstance(t)})),Qt(Js);const Zs=".bs.toast",to=`mouseover${Zs}`,eo=`mouseout${Zs}`,io=`focusin${Zs}`,no=`focusout${Zs}`,so=`hide${Zs}`,oo=`hidden${Zs}`,ro=`show${Zs}`,ao=`shown${Zs}`,lo="hide",co="show",ho="showing",uo={animation:"boolean",autohide:"boolean",delay:"number"},fo={animation:!0,autohide:!0,delay:5e3};class po extends ve{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return fo}static get DefaultType(){return uo}static get NAME(){return"toast"}show(){fe.trigger(this._element,ro).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(lo),qt(this._element),this._element.classList.add(co,ho),this._queueCallback((()=>{this._element.classList.remove(ho),fe.trigger(this._element,ao),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(fe.trigger(this._element,so).defaultPrevented||(this._element.classList.add(ho),this._queueCallback((()=>{this._element.classList.add(lo),this._element.classList.remove(ho,co),fe.trigger(this._element,oo)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(co),super.dispose()}isShown(){return this._element.classList.contains(co)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){fe.on(this._element,to,(t=>this._onInteraction(t,!0))),fe.on(this._element,eo,(t=>this._onInteraction(t,!1))),fe.on(this._element,io,(t=>this._onInteraction(t,!0))),fe.on(this._element,no,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=po.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}function mo(t){"loading"!=document.readyState?t():document.addEventListener("DOMContentLoaded",t)}Ee(po),Qt(po),mo((function(){[].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')).map((function(t){return new fs(t,{delay:{show:500,hide:100}})}))})),mo((function(){document.getElementById("pst-back-to-top").addEventListener("click",(function(){document.body.scrollTop=0,document.documentElement.scrollTop=0}))})),mo((function(){var t=document.getElementById("pst-back-to-top"),e=document.getElementsByClassName("bd-header")[0].getBoundingClientRect();window.addEventListener("scroll",(function(){this.oldScroll>this.scrollY&&this.scrollY>e.bottom?t.style.display="block":t.style.display="none",this.oldScroll=this.scrollY}))})),window.bootstrap=i})();
-//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/_preview/3/_static/scripts/bootstrap.js.LICENSE.txt b/_preview/3/_static/scripts/bootstrap.js.LICENSE.txt
deleted file mode 100644
index 28755c2..0000000
--- a/_preview/3/_static/scripts/bootstrap.js.LICENSE.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-/*!
-  * Bootstrap v5.3.3 (https://getbootstrap.com/)
-  * Copyright 2011-2024 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
-  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
-  */
diff --git a/_preview/3/_static/scripts/bootstrap.js.map b/_preview/3/_static/scripts/bootstrap.js.map
deleted file mode 100644
index e9e8158..0000000
--- a/_preview/3/_static/scripts/bootstrap.js.map
+++ /dev/null
@@ -1 +0,0 @@
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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.3.3 (https://getbootstrap.com/)\n  * Copyright 2011-2024 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n    const instanceMap = elementMap.get(element);\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n    instanceMap.set(key, instance);\n  },\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n    return null;\n  },\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key);\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n  }\n  return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n  return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  }\n\n  // Get transition-duration of the element\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n  return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object));\n  }\n  return null;\n};\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])');\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n    if (summary === null) {\n      return false;\n    }\n  }\n  return elementIsVisible;\n};\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n  if (element instanceof ShadowRoot) {\n    return element;\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null;\n  }\n  return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n  return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement);\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n  index += shouldGetNext ? 1 : -1;\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n    return fn.apply(element, [event]);\n  };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string';\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n  return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n    callable = wrapFunction(callable);\n  }\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n  if (!fn) {\n    return;\n  }\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n    const evt = hydrateObj(new Event(event, {\n      bubbles,\n      cancelable: true\n    }), args);\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n    return evt;\n  }\n};\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value;\n        }\n      });\n    }\n  }\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n  if (value === 'false') {\n    return false;\n  }\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n  if (value === '' || value === 'null') {\n    return null;\n  }\n  if (typeof value !== 'string') {\n    return value;\n  }\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n    return attributes;\n  },\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n  static get DefaultType() {\n    return {};\n  }\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    return config;\n  }\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.3';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n    if (!element) {\n      return;\n    }\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n  static get VERSION() {\n    return VERSION;\n  }\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href');\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n  }\n  return selector ? selector.split(',').map(sel => parseSelector(sel)).join(',') : null;\n};\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n    return parents;\n  },\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n      previous = previous.previousElementSibling;\n    }\n    return [];\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n      next = next.nextElementSibling;\n    }\n    return [];\n  },\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  },\n  getSelectorFromElement(element) {\n    const selector = getSelector(element);\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null;\n    }\n    return null;\n  },\n  getElementFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.findOne(selector) : null;\n  },\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.find(selector) : [];\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n    if (isDisabled(this)) {\n      return;\n    }\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target);\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove();\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n    this._initEvents();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$c;\n  }\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n  static get NAME() {\n    return NAME$d;\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n    this._handleSwipe();\n    execute(this._config.endCallback);\n  }\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n    if (!direction) {\n      return;\n    }\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n    this._addEventListeners();\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$b;\n  }\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n  static get NAME() {\n    return NAME$c;\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n    this._clearInterval();\n  }\n  cycle() {\n    this._clearInterval();\n    this._updateInterval();\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n    this.cycle();\n  }\n  to(index) {\n    const items = this._getItems();\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n    const activeIndex = this._getItemIndex(this._getActive());\n    if (activeIndex === index) {\n      return;\n    }\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n    this._slide(order, items[index]);\n  }\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause();\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n    const direction = KEY_TO_DIRECTION[event.key];\n    if (direction) {\n      event.preventDefault();\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n    if (!element) {\n      return;\n    }\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n    const activeElement = this._getActive();\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n    if (nextElement === activeElement) {\n      return;\n    }\n    const nextElementIndex = this._getItemIndex(nextElement);\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return;\n    }\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n    this._setActiveIndicatorElement(nextElementIndex);\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n  if (slideIndex) {\n    carousel.to(slideIndex);\n    carousel._maybeEnableCycle();\n    return;\n  }\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n    carousel._maybeEnableCycle();\n    return;\n  }\n  carousel.prev();\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n    this._initializeChildren();\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$a;\n  }\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n  static get NAME() {\n    return NAME$b;\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n    let activeChildren = [];\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n    const dimension = this._getDimension();\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.style[dimension] = 0;\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n    this._queueCallback(complete, this._element, true);\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    const dimension = this._getDimension();\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger);\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n    this._element.style[dimension] = '';\n    this._queueCallback(complete, this._element, true);\n  }\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n    config.parent = getElement(config.parent);\n    return config;\n  }\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element);\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {};\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$9;\n  }\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n  static get NAME() {\n    return NAME$a;\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._createPopper();\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    this._element.focus();\n    this._element.setAttribute('aria-expanded', true);\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    this._completeHide(relatedTarget);\n  }\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    super.dispose();\n  }\n  update() {\n    this._inNavbar = this._detectNavbar();\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.setAttribute('aria-expanded', 'false');\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n  _getConfig(config) {\n    config = super._getConfig(config);\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n    return config;\n  }\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n    let referenceElement = this._element;\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n    const popperConfig = this._getPopperConfig();\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n  _getPlacement() {\n    const parentDropdown = this._parent;\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    };\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n    if (!items.length) {\n      return;\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n      context._completeHide(relatedTarget);\n    }\n  }\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n    event.preventDefault();\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n      instance._selectMenuItem(event);\n      return;\n    }\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n};\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$8;\n  }\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n  static get NAME() {\n    return NAME$9;\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._append();\n    const element = this._getElement();\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n    element.classList.add(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n    this._element.remove();\n    this._isAppended = false;\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n      this._element = backdrop;\n    }\n    return this._element;\n  }\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n    const element = this._getElement();\n    this._config.rootElement.append(element);\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n};\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$7;\n  }\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n  static get NAME() {\n    return NAME$8;\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n    const elements = SelectorEngine.focusableChildren(trapElement);\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n  hide() {\n    const width = this.getWidth();\n    this._disableOverFlow();\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n  isOverflowing() {\n    return this.getWidth() > 0;\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n    this._element.style.overflow = 'hidden';\n  }\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n      this._saveInitialAttribute(element, styleProperty);\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty);\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$6;\n  }\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n  static get NAME() {\n    return NAME$7;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._isTransitioning = true;\n    this._scrollBar.hide();\n    document.body.classList.add(CLASS_NAME_OPEN);\n    this._adjustDialog();\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = false;\n    this._isTransitioning = true;\n    this._focustrap.deactivate();\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n  dispose() {\n    EventHandler.off(window, EVENT_KEY$4);\n    EventHandler.off(this._dialog, EVENT_KEY$4);\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n  handleUpdate() {\n    this._adjustDialog();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n    this._element.style.display = 'block';\n    this._element.removeAttribute('aria-hidden');\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n          return;\n        }\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n  _hideModal() {\n    this._element.style.display = 'none';\n    this._element.setAttribute('aria-hidden', true);\n    this._element.removeAttribute('aria-modal');\n    this._element.removeAttribute('role');\n    this._isTransitioning = false;\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n      this._resetAdjustments();\n      this._scrollBar.reset();\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY;\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n    this._element.classList.add(CLASS_NAME_STATIC);\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n    this._element.focus();\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const scrollbarWidth = this._scrollBar.getWidth();\n    const isBodyOverflowing = scrollbarWidth > 0;\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](relatedTarget);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  });\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$5;\n  }\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n  static get NAME() {\n    return NAME$6;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._backdrop.show();\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._focustrap.deactivate();\n    this._element.blur();\n    this._isShown = false;\n    this._element.classList.add(CLASS_NAME_HIDING);\n    this._backdrop.hide();\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n      this._element.removeAttribute('aria-modal');\n      this._element.removeAttribute('role');\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n    this._queueCallback(completeCallback, this._element, true);\n  }\n  dispose() {\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n      this.hide();\n    };\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n    });\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  });\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  dd: [],\n  div: [],\n  dl: [],\n  dt: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n    }\n    return true;\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  }\n\n  // Getters\n  static get Default() {\n    return Default$4;\n  }\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n  static get NAME() {\n    return NAME$5;\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n  changeContent(content) {\n    this._checkContent(content);\n    this._config.content = {\n      ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n    const template = templateWrapper.children[0];\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n    return template;\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n    this._checkContent(config.content);\n  }\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n    if (!templateElement) {\n      return;\n    }\n    content = this._resolvePossibleFunction(content);\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n      return;\n    }\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n    templateElement.textContent = content;\n  }\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this]);\n  }\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n    templateElement.textContent = element.textContent;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' + '<div class=\"tooltip-arrow\"></div>' + '<div class=\"tooltip-inner\"></div>' + '</div>',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n    super(element, config);\n\n    // Private\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null;\n\n    // Protected\n    this.tip = null;\n    this._setListeners();\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$3;\n  }\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n  static get NAME() {\n    return NAME$4;\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true;\n  }\n  disable() {\n    this._isEnabled = false;\n  }\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n    this._activeTrigger.click = !this._activeTrigger.click;\n    if (this._isShown()) {\n      this._leave();\n      return;\n    }\n    this._enter();\n  }\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n    this._disposePopper();\n    super.dispose();\n  }\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper();\n    const tip = this._getTipElement();\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n    const {\n      container\n    } = this._config;\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n      if (this._isHovered === false) {\n        this._leave();\n      }\n      this._isHovered = false;\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const tip = this._getTipElement();\n    tip.classList.remove(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n      this._element.removeAttribute('aria-describedby');\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n    return this.tip;\n  }\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml();\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null;\n    }\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n    return tip;\n  }\n  setContent(content) {\n    this._newContent = content;\n    if (this._isShown()) {\n      this._disposePopper();\n      this.show();\n    }\n  }\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n    return this._templateFactory;\n  }\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element]);\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element]);\n  }\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n          context._leave();\n        });\n      }\n    }\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n    if (!title) {\n      return;\n    }\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title');\n  }\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n    this._isHovered = true;\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n    this._isHovered = false;\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n    return config;\n  }\n  _getDelegateConfig() {\n    const config = {};\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value;\n      }\n    }\n    config.selector = false;\n    config.trigger = 'manual';\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config;\n  }\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n      this._popper = null;\n    }\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' + '<div class=\"popover-arrow\"></div>' + '<h3 class=\"popover-header\"></h3>' + '<div class=\"popover-body\"></div>' + '</div>',\n  trigger: 'click'\n};\nconst DefaultType$2 = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n  static get NAME() {\n    return NAME$3;\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default$1;\n  }\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n  static get NAME() {\n    return NAME$2;\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables();\n    this._maybeEnableSmoothScroll();\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n  dispose() {\n    this._observer.disconnect();\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body;\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n    return config;\n  }\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height;\n      }\n    });\n  }\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n      this._process(targetElement(entry));\n    };\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n        this._clearActiveClass(targetElement(entry));\n        continue;\n      }\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry);\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return;\n        }\n        continue;\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor);\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n    this._clearActiveClass(this._config.target);\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n    this._activateParents(target);\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE$1);\n      }\n    }\n  }\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE$1);\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE$1}`, parent);\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE$1);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API$1, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$1 = 'tab';\nconst DATA_KEY$1 = 'bs.tab';\nconst EVENT_KEY$1 = `.${DATA_KEY$1}`;\nconst EVENT_HIDE$1 = `hide${EVENT_KEY$1}`;\nconst EVENT_HIDDEN$1 = `hidden${EVENT_KEY$1}`;\nconst EVENT_SHOW$1 = `show${EVENT_KEY$1}`;\nconst EVENT_SHOWN$1 = `shown${EVENT_KEY$1}`;\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY$1}`;\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY$1}`;\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY$1}`;\nconst ARROW_LEFT_KEY = 'ArrowLeft';\nconst ARROW_RIGHT_KEY = 'ArrowRight';\nconst ARROW_UP_KEY = 'ArrowUp';\nconst ARROW_DOWN_KEY = 'ArrowDown';\nconst HOME_KEY = 'Home';\nconst END_KEY = 'End';\nconst CLASS_NAME_ACTIVE = 'active';\nconst CLASS_NAME_FADE$1 = 'fade';\nconst CLASS_NAME_SHOW$1 = 'show';\nconst CLASS_DROPDOWN = 'dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle';\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu';\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = `:not(${SELECTOR_DROPDOWN_TOGGLE})`;\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]';\nconst SELECTOR_OUTER = '.nav-item, .list-group-item';\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'; // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`;\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element);\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL);\n    if (!this._parent) {\n      return;\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren());\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event));\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME$1;\n  }\n\n  // Public\n  show() {\n    // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element;\n    if (this._elemIsActive(innerElem)) {\n      return;\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem();\n    const hideEvent = active ? EventHandler.trigger(active, EVENT_HIDE$1, {\n      relatedTarget: innerElem\n    }) : null;\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW$1, {\n      relatedTarget: active\n    });\n    if (showEvent.defaultPrevented || hideEvent && hideEvent.defaultPrevented) {\n      return;\n    }\n    this._deactivate(active, innerElem);\n    this._activate(innerElem, active);\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.add(CLASS_NAME_ACTIVE);\n    this._activate(SelectorEngine.getElementFromSelector(element)); // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.removeAttribute('tabindex');\n      element.setAttribute('aria-selected', true);\n      this._toggleDropDown(element, true);\n      EventHandler.trigger(element, EVENT_SHOWN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.remove(CLASS_NAME_ACTIVE);\n    element.blur();\n    this._deactivate(SelectorEngine.getElementFromSelector(element)); // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.setAttribute('aria-selected', false);\n      element.setAttribute('tabindex', '-1');\n      this._toggleDropDown(element, false);\n      EventHandler.trigger(element, EVENT_HIDDEN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _keydown(event) {\n    if (![ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key)) {\n      return;\n    }\n    event.stopPropagation(); // stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault();\n    const children = this._getChildren().filter(element => !isDisabled(element));\n    let nextActiveElement;\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1];\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key);\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true);\n    }\n    if (nextActiveElement) {\n      nextActiveElement.focus({\n        preventScroll: true\n      });\n      Tab.getOrCreateInstance(nextActiveElement).show();\n    }\n  }\n  _getChildren() {\n    // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent);\n  }\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null;\n  }\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist');\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child);\n    }\n  }\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child);\n    const isActive = this._elemIsActive(child);\n    const outerElem = this._getOuterElement(child);\n    child.setAttribute('aria-selected', isActive);\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation');\n    }\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1');\n    }\n    this._setAttributeIfNotExists(child, 'role', 'tab');\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child);\n  }\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child);\n    if (!target) {\n      return;\n    }\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel');\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`);\n    }\n  }\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element);\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return;\n    }\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem);\n      if (element) {\n        element.classList.toggle(className, open);\n      }\n    };\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE);\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW$1);\n    outerElem.setAttribute('aria-expanded', open);\n  }\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value);\n    }\n  }\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE);\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem);\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  Tab.getOrCreateInstance(this).show();\n});\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME = 'toast';\nconst DATA_KEY = 'bs.toast';\nconst EVENT_KEY = `.${DATA_KEY}`;\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`;\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`;\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`;\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`;\nconst EVENT_HIDE = `hide${EVENT_KEY}`;\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`;\nconst EVENT_SHOW = `show${EVENT_KEY}`;\nconst EVENT_SHOWN = `shown${EVENT_KEY}`;\nconst CLASS_NAME_FADE = 'fade';\nconst CLASS_NAME_HIDE = 'hide'; // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show';\nconst CLASS_NAME_SHOWING = 'showing';\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n};\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n};\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._timeout = null;\n    this._hasMouseInteraction = false;\n    this._hasKeyboardInteraction = false;\n    this._setListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default;\n  }\n  static get DefaultType() {\n    return DefaultType;\n  }\n  static get NAME() {\n    return NAME;\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._clearTimeout();\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE);\n    }\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING);\n      EventHandler.trigger(this._element, EVENT_SHOWN);\n      this._maybeScheduleHide();\n    };\n    this._element.classList.remove(CLASS_NAME_HIDE); // @deprecated\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  hide() {\n    if (!this.isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE); // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW);\n      EventHandler.trigger(this._element, EVENT_HIDDEN);\n    };\n    this._element.classList.add(CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  dispose() {\n    this._clearTimeout();\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW);\n    }\n    super.dispose();\n  }\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW);\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return;\n    }\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return;\n    }\n    this._timeout = setTimeout(() => {\n      this.hide();\n    }, this._config.delay);\n  }\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        {\n          this._hasMouseInteraction = isInteracting;\n          break;\n        }\n      case 'focusin':\n      case 'focusout':\n        {\n          this._hasKeyboardInteraction = isInteracting;\n          break;\n        }\n    }\n    if (isInteracting) {\n      this._clearTimeout();\n      return;\n    }\n    const nextElement = event.relatedTarget;\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return;\n    }\n    this._maybeScheduleHide();\n  }\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false));\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false));\n  }\n  _clearTimeout() {\n    clearTimeout(this._timeout);\n    this._timeout = null;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config](this);\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast);\n\nexport { Alert, Button, Carousel, Collapse, Dropdown, Modal, Offcanvas, Popover, ScrollSpy, Tab, Toast, Tooltip };\n//# sourceMappingURL=bootstrap.esm.js.map\n","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if 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define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","/**\n * Compare [semver](https://semver.org/) version strings to find greater, equal or lesser.\n * This library supports the full semver specification, including comparing versions with different number of digits like `1.0.0`, `1.0`, `1`, and pre-release versions like `1.0.0-alpha`.\n * @param v1 - First version to compare\n * @param v2 - Second version to compare\n * @returns Numeric value compatible with the [Array.sort(fn) interface](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/sort#Parameters).\n */\nexport const compareVersions = (v1, v2) => {\n    // validate input and split into segments\n    const n1 = validateAndParse(v1);\n    const n2 = validateAndParse(v2);\n    // pop off the patch\n    const p1 = n1.pop();\n    const p2 = n2.pop();\n    // validate numbers\n    const r = compareSegments(n1, n2);\n    if (r !== 0)\n        return r;\n    // validate pre-release\n    if (p1 && p2) {\n        return compareSegments(p1.split('.'), p2.split('.'));\n    }\n    else if (p1 || p2) {\n        return p1 ? -1 : 1;\n    }\n    return 0;\n};\n/**\n * Validate [semver](https://semver.org/) version strings.\n *\n * @param version Version number to validate\n * @returns `true` if the version number is a valid semver version number, `false` otherwise.\n *\n * @example\n * ```\n * validate('1.0.0-rc.1'); // return true\n * validate('1.0-rc.1'); // return false\n * validate('foo'); // return false\n * ```\n */\nexport const validate = (version) => typeof version === 'string' && /^[v\\d]/.test(version) && semver.test(version);\n/**\n * Compare [semver](https://semver.org/) version strings using the specified operator.\n *\n * @param v1 First version to compare\n * @param v2 Second version to compare\n * @param operator Allowed arithmetic operator to use\n * @returns `true` if the comparison between the firstVersion and the secondVersion satisfies the operator, `false` otherwise.\n *\n * @example\n * ```\n * compare('10.1.8', '10.0.4', '>'); // return true\n * compare('10.0.1', '10.0.1', '='); // return true\n * compare('10.1.1', '10.2.2', '<'); // return true\n * compare('10.1.1', '10.2.2', '<='); // return true\n * compare('10.1.1', '10.2.2', '>='); // return false\n * ```\n */\nexport const compare = (v1, v2, operator) => {\n    // validate input operator\n    assertValidOperator(operator);\n    // since result of compareVersions can only be -1 or 0 or 1\n    // a simple map can be used to replace switch\n    const res = compareVersions(v1, v2);\n    return operatorResMap[operator].includes(res);\n};\n/**\n * Match [npm semver](https://docs.npmjs.com/cli/v6/using-npm/semver) version range.\n *\n * @param version Version number to match\n * @param range Range pattern for version\n * @returns `true` if the version number is within the range, `false` otherwise.\n *\n * @example\n * ```\n * satisfies('1.1.0', '^1.0.0'); // return true\n * satisfies('1.1.0', '~1.0.0'); // return false\n * ```\n */\nexport const satisfies = (version, range) => {\n    // if no range operator then \"=\"\n    const m = range.match(/^([<>=~^]+)/);\n    const op = m ? m[1] : '=';\n    // if gt/lt/eq then operator compare\n    if (op !== '^' && op !== '~')\n        return compare(version, range, op);\n    // else range of either \"~\" or \"^\" is assumed\n    const [v1, v2, v3, , vp] = validateAndParse(version);\n    const [r1, r2, r3, , rp] = validateAndParse(range);\n    const v = [v1, v2, v3];\n    const r = [r1, r2 !== null && r2 !== void 0 ? r2 : 'x', r3 !== null && r3 !== void 0 ? r3 : 'x'];\n    // validate pre-release\n    if (rp) {\n        if (!vp)\n            return false;\n        if (compareSegments(v, r) !== 0)\n            return false;\n        if (compareSegments(vp.split('.'), rp.split('.')) === -1)\n            return false;\n    }\n    // first non-zero number\n    const nonZero = r.findIndex((v) => v !== '0') + 1;\n    // pointer to where segments can be >=\n    const i = op === '~' ? 2 : nonZero > 1 ? nonZero : 1;\n    // before pointer must be equal\n    if (compareSegments(v.slice(0, i), r.slice(0, i)) !== 0)\n        return false;\n    // after pointer must be >=\n    if (compareSegments(v.slice(i), r.slice(i)) === -1)\n        return false;\n    return true;\n};\nconst semver = /^[v^~<>=]*?(\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+))?(?:-([\\da-z\\-]+(?:\\.[\\da-z\\-]+)*))?(?:\\+[\\da-z\\-]+(?:\\.[\\da-z\\-]+)*)?)?)?$/i;\nconst validateAndParse = (version) => {\n    if (typeof version !== 'string') {\n        throw new TypeError('Invalid argument expected string');\n    }\n    const match = version.match(semver);\n    if (!match) {\n        throw new Error(`Invalid argument not valid semver ('${version}' received)`);\n    }\n    match.shift();\n    return match;\n};\nconst isWildcard = (s) => s === '*' || s === 'x' || s === 'X';\nconst tryParse = (v) => {\n    const n = parseInt(v, 10);\n    return isNaN(n) ? v : n;\n};\nconst forceType = (a, b) => typeof a !== typeof b ? [String(a), String(b)] : [a, b];\nconst compareStrings = (a, b) => {\n    if (isWildcard(a) || isWildcard(b))\n        return 0;\n    const [ap, bp] = forceType(tryParse(a), tryParse(b));\n    if (ap > bp)\n        return 1;\n    if (ap < bp)\n        return -1;\n    return 0;\n};\nconst compareSegments = (a, b) => {\n    for (let i = 0; i < Math.max(a.length, b.length); i++) {\n        const r = compareStrings(a[i] || '0', b[i] || '0');\n        if (r !== 0)\n            return r;\n    }\n    return 0;\n};\nconst operatorResMap = {\n    '>': [1],\n    '>=': [0, 1],\n    '=': [0],\n    '<=': [-1, 0],\n    '<': [-1],\n};\nconst allowedOperators = Object.keys(operatorResMap);\nconst assertValidOperator = (op) => {\n    if (typeof op !== 'string') {\n        throw new TypeError(`Invalid operator type, expected string but got ${typeof op}`);\n    }\n    if (allowedOperators.indexOf(op) === -1) {\n        throw new Error(`Invalid operator, expected one of ${allowedOperators.join('|')}`);\n    }\n};\n//# sourceMappingURL=index.js.map","// Define the custom behavior of the page\nimport { documentReady } from \"./mixin\";\nimport { compare, validate } from \"compare-versions\";\n\nimport \"../styles/pydata-sphinx-theme.scss\";\n\n/*******************************************************************************\n * Theme interaction\n */\n\nvar prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\");\n\n/**\n * set the the body theme to the one specified by the user browser\n *\n * @param {event} e\n */\nfunction autoTheme(e) {\n  document.documentElement.dataset.theme = prefersDark.matches\n    ? \"dark\"\n    : \"light\";\n}\n\n/**\n * Set the theme using the specified mode.\n * It can be one of [\"auto\", \"dark\", \"light\"]\n *\n * @param {str} mode\n */\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  // get the theme\n  var colorScheme = prefersDark.matches ? \"dark\" : \"light\";\n  document.documentElement.dataset.mode = mode;\n  var theme = mode == \"auto\" ? colorScheme : mode;\n  document.documentElement.dataset.theme = theme;\n  // TODO: remove this line after Bootstrap upgrade\n  // v5.3 has a colors mode: https://getbootstrap.com/docs/5.3/customize/color-modes/\n  document.querySelectorAll(\".dropdown-menu\").forEach((el) => {\n    if (theme === \"dark\") {\n      el.classList.add(\"dropdown-menu-dark\");\n    } else {\n      el.classList.remove(\"dropdown-menu-dark\");\n    }\n  });\n\n  // save mode and theme\n  localStorage.setItem(\"mode\", mode);\n  localStorage.setItem(\"theme\", theme);\n  console.log(`[PST]: Changed to ${mode} mode using the ${theme} theme.`);\n\n  // add a listener if set on auto\n  prefersDark.onchange = mode == \"auto\" ? autoTheme : \"\";\n}\n\n/**\n * Change the theme option order so that clicking on the btn is always a change\n * from \"auto\"\n */\nfunction cycleMode() {\n  const defaultMode = document.documentElement.dataset.defaultMode || \"auto\";\n  const currentMode = localStorage.getItem(\"mode\") || defaultMode;\n\n  var loopArray = (arr, current) => {\n    var nextPosition = arr.indexOf(current) + 1;\n    if (nextPosition === arr.length) {\n      nextPosition = 0;\n    }\n    return arr[nextPosition];\n  };\n\n  // make sure the next theme after auto is always a change\n  var modeList = prefersDark.matches\n    ? [\"auto\", \"light\", \"dark\"]\n    : [\"auto\", \"dark\", \"light\"];\n  var newMode = loopArray(modeList, currentMode);\n  setTheme(newMode);\n}\n\n/**\n * add the theme listener on the btns of the navbar\n */\nfunction addModeListener() {\n  // the theme was set a first time using the initial mini-script\n  // running setMode will ensure the use of the dark mode if auto is selected\n  setTheme(document.documentElement.dataset.mode);\n\n  // Attach event handlers for toggling themes colors\n  document.querySelectorAll(\".theme-switch-button\").forEach((el) => {\n    el.addEventListener(\"click\", cycleMode);\n  });\n}\n\n/*******************************************************************************\n * TOC interactivity\n */\n\n/**\n * TOC sidebar - add \"active\" class to parent list\n *\n * Bootstrap's scrollspy adds the active class to the <a> link,\n * but for the automatic collapsing we need this on the parent list item.\n *\n * The event is triggered on \"window\" (and not the nav item as documented),\n * see https://github.com/twbs/bootstrap/issues/20086\n */\nfunction addTOCInteractivity() {\n  window.addEventListener(\"activate.bs.scrollspy\", function () {\n    const navLinks = document.querySelectorAll(\".bd-toc-nav a\");\n\n    navLinks.forEach((navLink) => {\n      navLink.parentElement.classList.remove(\"active\");\n    });\n\n    const activeNavLinks = document.querySelectorAll(\".bd-toc-nav a.active\");\n    activeNavLinks.forEach((navLink) => {\n      navLink.parentElement.classList.add(\"active\");\n    });\n  });\n}\n\n/*******************************************************************************\n * Scroll\n */\n\n/**\n * Navigation sidebar scrolling to active page\n */\nfunction scrollToActive() {\n  // If the docs nav doesn't exist, do nothing (e.g., on search page)\n  if (!document.querySelector(\".bd-docs-nav\")) {\n    return;\n  }\n\n  var sidebar = document.querySelector(\"div.bd-sidebar\");\n\n  // Remember the sidebar scroll position between page loads\n  // Inspired on source of revealjs.com\n  let storedScrollTop = parseInt(\n    sessionStorage.getItem(\"sidebar-scroll-top\"),\n    10,\n  );\n\n  if (!isNaN(storedScrollTop)) {\n    // If we've got a saved scroll position, just use that\n    sidebar.scrollTop = storedScrollTop;\n    console.log(\"[PST]: Scrolled sidebar using stored browser position...\");\n  } else {\n    // Otherwise, calculate a position to scroll to based on the lowest `active` link\n    var sidebarNav = document.querySelector(\".bd-docs-nav\");\n    var active_pages = sidebarNav.querySelectorAll(\".active\");\n    if (active_pages.length > 0) {\n      // Use the last active page as the offset since it's the page we're on\n      var latest_active = active_pages[active_pages.length - 1];\n      var offset =\n        latest_active.getBoundingClientRect().y -\n        sidebar.getBoundingClientRect().y;\n      // Only scroll the navbar if the active link is lower than 50% of the page\n      if (latest_active.getBoundingClientRect().y > window.innerHeight * 0.5) {\n        let buffer = 0.25; // Buffer so we have some space above the scrolled item\n        sidebar.scrollTop = offset - sidebar.clientHeight * buffer;\n        console.log(\"[PST]: Scrolled sidebar using last active link...\");\n      }\n    }\n  }\n\n  // Store the sidebar scroll position\n  window.addEventListener(\"beforeunload\", () => {\n    sessionStorage.setItem(\"sidebar-scroll-top\", sidebar.scrollTop);\n  });\n}\n\n/*******************************************************************************\n * Search\n */\n\n/**\n * Find any search forms on the page and return their input element\n */\nvar findSearchInput = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  if (!forms.length) {\n    // no search form found\n    return;\n  } else {\n    var form;\n    if (forms.length == 1) {\n      // there is exactly one search form (persistent or hidden)\n      form = forms[0];\n    } else {\n      // must be at least one persistent form, use the first persistent one\n      form = document.querySelector(\n        \"div:not(.search-button__search-container) > form.bd-search\",\n      );\n    }\n    return form.querySelector(\"input\");\n  }\n};\n\n/**\n * Activate the search field on the page.\n * - If there is a search field already visible it will be activated.\n * - If not, then a search field will pop up.\n */\nvar toggleSearchField = () => {\n  // Find the search input to highlight\n  let input = findSearchInput();\n\n  // if the input field is the hidden one (the one associated with the\n  // search button) then toggle the button state (to show/hide the field)\n  let searchPopupWrapper = document.querySelector(\".search-button__wrapper\");\n  let hiddenInput = searchPopupWrapper.querySelector(\"input\");\n  if (input === hiddenInput) {\n    searchPopupWrapper.classList.toggle(\"show\");\n  }\n  // when toggling off the search field, remove its focus\n  if (document.activeElement === input) {\n    input.blur();\n  } else {\n    input.focus();\n    input.select();\n    input.scrollIntoView({ block: \"center\" });\n  }\n};\n\n/**\n * Add an event listener for toggleSearchField() for Ctrl/Cmd + K\n */\nvar addEventListenerForSearchKeyboard = () => {\n  window.addEventListener(\n    \"keydown\",\n    (event) => {\n      let input = findSearchInput();\n      // toggle on Ctrl+k or ⌘+k\n      if (\n        // Ignore if shift or alt are pressed\n        !event.shiftKey &&\n        !event.altKey &&\n        // On Mac use ⌘, all other OS use Ctrl\n        (useCommandKey\n          ? event.metaKey && !event.ctrlKey\n          : !event.metaKey && event.ctrlKey) &&\n        // Case-insensitive so the shortcut still works with caps lock\n        /^k$/i.test(event.key)\n      ) {\n        event.preventDefault();\n        toggleSearchField();\n      }\n      // also allow Escape key to hide (but not show) the dynamic search field\n      else if (document.activeElement === input && /Escape/i.test(event.key)) {\n        toggleSearchField();\n      }\n    },\n    true,\n  );\n};\n\n/**\n * If the user is on a Mac, use command (⌘) instead of control (ctrl) key\n *\n * Note: `navigator.platform` is deprecated; however MDN still recommends using\n * it for the one specific use case of detecting whether a keyboard shortcut\n * should use control or command:\n * https://developer.mozilla.org/en-US/docs/Web/API/Navigator/platform#examples\n */\nvar useCommandKey =\n  navigator.platform.indexOf(\"Mac\") === 0 || navigator.platform === \"iPhone\";\n\n/**\n * Change the search hint to `meta key` if we are a Mac\n */\n\nvar changeSearchShortcutKey = () => {\n  let shortcuts = document.querySelectorAll(\".search-button__kbd-shortcut\");\n  if (useCommandKey) {\n    shortcuts.forEach(\n      (f) => (f.querySelector(\"kbd.kbd-shortcut__modifier\").innerText = \"⌘\"),\n    );\n  }\n};\n\n/**\n * Activate callbacks for search button popup\n */\nvar setupSearchButtons = () => {\n  changeSearchShortcutKey();\n  addEventListenerForSearchKeyboard();\n\n  // Add the search button trigger event callback\n  document.querySelectorAll(\".search-button__button\").forEach((btn) => {\n    btn.onclick = toggleSearchField;\n  });\n\n  // Add the search button overlay event callback\n  let overlay = document.querySelector(\".search-button__overlay\");\n  if (overlay) {\n    overlay.onclick = toggleSearchField;\n  }\n};\n\n/*******************************************************************************\n * Version Switcher\n * Note that this depends on two variables existing that are defined in\n * and `html-page-context` hook:\n *\n * - DOCUMENTATION_OPTIONS.pagename\n * - DOCUMENTATION_OPTIONS.theme_switcher_url\n */\n\n/**\n * path component of URL\n */\nvar getCurrentUrlPath = () => {\n  if (DOCUMENTATION_OPTIONS.BUILDER == \"dirhtml\") {\n    return DOCUMENTATION_OPTIONS.pagename == \"index\"\n      ? `/`\n      : `${DOCUMENTATION_OPTIONS.pagename}/`;\n  }\n  return `${DOCUMENTATION_OPTIONS.pagename}.html`;\n};\n\n/**\n * Allow user to dismiss the warning banner about the docs version being dev / old.\n * We store the dismissal date and version, to give us flexibility about making the\n * dismissal last for longer than one browser session, if we decide to do that.\n *\n * @param {event} event the event that trigger the check\n */\nasync function DismissBannerAndStorePref(event) {\n  const banner = document.querySelector(\"#bd-header-version-warning\");\n  banner.remove();\n  const version = DOCUMENTATION_OPTIONS.VERSION;\n  const now = new Date();\n  const banner_pref = JSON.parse(\n    localStorage.getItem(\"pst_banner_pref\") || \"{}\",\n  );\n  console.debug(\n    `[PST] Dismissing the version warning banner on ${version} starting ${now}.`,\n  );\n  banner_pref[version] = now;\n  localStorage.setItem(\"pst_banner_pref\", JSON.stringify(banner_pref));\n}\n\n/**\n * Check if corresponding page path exists in other version of docs\n * and, if so, go there instead of the homepage of the other docs version\n *\n * @param {event} event the event that trigger the check\n */\nasync function checkPageExistsAndRedirect(event) {\n  // ensure we don't follow the initial link\n  event.preventDefault();\n  const currentFilePath = getCurrentUrlPath();\n  let tryUrl = event.currentTarget.getAttribute(\"href\");\n  let otherDocsHomepage = tryUrl.replace(currentFilePath, \"\");\n  try {\n    let head = await fetch(tryUrl, { method: \"HEAD\" });\n    if (head.ok) {\n      location.href = tryUrl; // the page exists, go there\n    } else {\n      location.href = otherDocsHomepage;\n    }\n  } catch (err) {\n    // something went wrong, probably CORS restriction, fallback to other docs homepage\n    location.href = otherDocsHomepage;\n  }\n}\n\n/**\n * Load and parse the version switcher JSON file from an absolute or relative URL.\n *\n * @param {string} url The URL to load version switcher entries from.\n */\nasync function fetchVersionSwitcherJSON(url) {\n  // first check if it's a valid URL\n  try {\n    var result = new URL(url);\n  } catch (err) {\n    if (err instanceof TypeError) {\n      if (!window.location.origin) {\n        // window.location.origin is null for local static sites\n        // (ie. window.location.protocol == 'file:')\n        //\n        // TODO: Fix this to return the static version switcher by working out\n        // how to get the correct path to the switcher JSON file on local static builds\n        return null;\n      }\n      // assume we got a relative path, and fix accordingly. But first, we need to\n      // use `fetch()` to follow redirects so we get the correct final base URL\n      const origin = await fetch(window.location.origin, { method: \"HEAD\" });\n      result = new URL(url, origin.url);\n    } else {\n      // something unexpected happened\n      throw err;\n    }\n  }\n  // load and return the JSON\n  const response = await fetch(result);\n  const data = await response.json();\n  return data;\n}\n\n// Populate the version switcher from the JSON data\nfunction populateVersionSwitcher(data, versionSwitcherBtns) {\n  const currentFilePath = getCurrentUrlPath();\n  versionSwitcherBtns.forEach((btn) => {\n    // Set empty strings by default so that these attributes exist and can be used in CSS selectors\n    btn.dataset[\"activeVersionName\"] = \"\";\n    btn.dataset[\"activeVersion\"] = \"\";\n  });\n  // in case there are multiple entries with the same version string, this helps us\n  // decide which entry's `name` to put on the button itself. Without this, it would\n  // always be the *last* version-matching entry; now it will be either the\n  // version-matching entry that is also marked as `\"preferred\": true`, or if that\n  // doesn't exist: the *first* version-matching entry.\n  data = data.map((entry) => {\n    // does this entry match the version that we're currently building/viewing?\n    entry.match =\n      entry.version == DOCUMENTATION_OPTIONS.theme_switcher_version_match;\n    entry.preferred = entry.preferred || false;\n    // if no custom name specified (e.g., \"latest\"), use version string\n    if (!(\"name\" in entry)) {\n      entry.name = entry.version;\n    }\n    return entry;\n  });\n  const hasMatchingPreferredEntry = data\n    .map((entry) => entry.preferred && entry.match)\n    .some(Boolean);\n  var foundMatch = false;\n  // create links to the corresponding page in the other docs versions\n  data.forEach((entry) => {\n    // create the node\n    const anchor = document.createElement(\"a\");\n    anchor.setAttribute(\n      \"class\",\n      \"dropdown-item list-group-item list-group-item-action py-1\",\n    );\n    anchor.setAttribute(\"href\", `${entry.url}${currentFilePath}`);\n    anchor.setAttribute(\"role\", \"option\");\n    const span = document.createElement(\"span\");\n    span.textContent = `${entry.name}`;\n    anchor.appendChild(span);\n    // Add dataset values for the version and name in case people want\n    // to apply CSS styling based on this information.\n    anchor.dataset[\"versionName\"] = entry.name;\n    anchor.dataset[\"version\"] = entry.version;\n    // replace dropdown button text with the preferred display name of the\n    // currently-viewed version, rather than using sphinx's {{ version }} variable.\n    // also highlight the dropdown entry for the currently-viewed version's entry\n    let matchesAndIsPreferred = hasMatchingPreferredEntry && entry.preferred;\n    let matchesAndIsFirst =\n      !hasMatchingPreferredEntry && !foundMatch && entry.match;\n    if (matchesAndIsPreferred || matchesAndIsFirst) {\n      anchor.classList.add(\"active\");\n      versionSwitcherBtns.forEach((btn) => {\n        btn.innerText = entry.name;\n        btn.dataset[\"activeVersionName\"] = entry.name;\n        btn.dataset[\"activeVersion\"] = entry.version;\n      });\n      foundMatch = true;\n    }\n    // There may be multiple version-switcher elements, e.g. one\n    // in a slide-over panel displayed on smaller screens.\n    document.querySelectorAll(\".version-switcher__menu\").forEach((menu) => {\n      // we need to clone the node for each menu, but onclick attributes are not\n      // preserved by `.cloneNode()` so we add onclick here after cloning.\n      let node = anchor.cloneNode(true);\n      node.onclick = checkPageExistsAndRedirect;\n      // on click, AJAX calls will check if the linked page exists before\n      // trying to redirect, and if not, will redirect to the homepage\n      // for that version of the docs.\n      menu.append(node);\n    });\n  });\n}\n\n/*******************************************************************************\n * Warning banner when viewing non-stable version of the docs.\n */\n\n/**\n * Show a warning banner when viewing a non-stable version of the docs.\n *\n * adapted 2023-06 from https://mne.tools/versionwarning.js, which was\n * originally adapted 2020-05 from https://scikit-learn.org/versionwarning.js\n *\n * @param {Array} data The version data used to populate the switcher menu.\n */\nfunction showVersionWarningBanner(data) {\n  var version = DOCUMENTATION_OPTIONS.VERSION;\n  // figure out what latest stable version is\n  var preferredEntries = data.filter((entry) => entry.preferred);\n  if (preferredEntries.length !== 1) {\n    const howMany = preferredEntries.length == 0 ? \"No\" : \"Multiple\";\n    console.log(\n      `[PST] ${howMany} versions marked \"preferred\" found in versions JSON, ignoring.`,\n    );\n    return;\n  }\n  const preferredVersion = preferredEntries[0].version;\n  const preferredURL = preferredEntries[0].url;\n  // if already on preferred version, nothing to do\n  const versionsAreComparable = validate(version) && validate(preferredVersion);\n  if (versionsAreComparable && compare(version, preferredVersion, \"=\")) {\n    console.log(\n      \"This is the prefered version of the docs, not showing the warning banner.\",\n    );\n    return;\n  }\n  // check if banner has been dismissed recently\n  const dismiss_date_str = JSON.parse(\n    localStorage.getItem(\"pst_banner_pref\") || \"{}\",\n  )[version];\n  if (dismiss_date_str != null) {\n    const dismiss_date = new Date(dismiss_date_str);\n    const now = new Date();\n    const milliseconds_in_a_day = 24 * 60 * 60 * 1000;\n    const days_passed = (now - dismiss_date) / milliseconds_in_a_day;\n    const timeout_in_days = 14;\n    if (days_passed < timeout_in_days) {\n      console.info(\n        `[PST] Suppressing version warning banner; was dismissed ${Math.floor(days_passed)} day(s) ago`,\n      );\n      return;\n    }\n  }\n\n  // now construct the warning banner\n  const banner = document.querySelector(\"#bd-header-version-warning\");\n  const middle = document.createElement(\"div\");\n  const inner = document.createElement(\"div\");\n  const bold = document.createElement(\"strong\");\n  const button = document.createElement(\"a\");\n  const close_btn = document.createElement(\"a\");\n  // these classes exist since pydata-sphinx-theme v0.10.0\n  // the init class is used for animation\n  middle.classList = \"bd-header-announcement__content  ms-auto me-auto\";\n  inner.classList = \"sidebar-message\";\n  button.classList =\n    \"btn text-wrap font-weight-bold ms-3 my-1 align-baseline pst-button-link-to-stable-version\";\n  button.href = `${preferredURL}${getCurrentUrlPath()}`;\n  button.innerText = \"Switch to stable version\";\n  button.onclick = checkPageExistsAndRedirect;\n  close_btn.classList = \"ms-3 my-1 align-baseline\";\n  const close_x = document.createElement(\"i\");\n  close_btn.append(close_x);\n  close_x.classList = \"fa-solid fa-xmark\";\n  close_btn.onclick = DismissBannerAndStorePref;\n  // add the version-dependent text\n  inner.innerText = \"This is documentation for \";\n  const isDev =\n    version.includes(\"dev\") ||\n    version.includes(\"rc\") ||\n    version.includes(\"pre\");\n  const newerThanPreferred =\n    versionsAreComparable && compare(version, preferredVersion, \">\");\n  if (isDev || newerThanPreferred) {\n    bold.innerText = \"an unstable development version\";\n  } else if (versionsAreComparable && compare(version, preferredVersion, \"<\")) {\n    bold.innerText = `an old version (${version})`;\n  } else if (!version) {\n    bold.innerText = \"an unknown version\"; // e.g., an empty string\n  } else {\n    bold.innerText = `version ${version}`;\n  }\n  banner.appendChild(middle);\n  banner.append(close_btn);\n  middle.appendChild(inner);\n  inner.appendChild(bold);\n  inner.appendChild(document.createTextNode(\".\"));\n  inner.appendChild(button);\n  banner.classList.remove(\"d-none\");\n}\n\n/*******************************************************************************\n * MutationObserver to move the ReadTheDocs button\n */\n\n/**\n * intercept the RTD flyout and place it in the rtd-footer-container if existing\n * if not it stays where on top of the page\n */\nfunction initRTDObserver() {\n  const mutatedCallback = (mutationList, observer) => {\n    mutationList.forEach((mutation) => {\n      // Check whether the mutation is for RTD, which will have a specific structure\n      if (mutation.addedNodes.length === 0) {\n        return;\n      }\n      if (mutation.addedNodes[0].data === undefined) {\n        return;\n      }\n      if (mutation.addedNodes[0].data.search(\"Inserted RTD Footer\") != -1) {\n        mutation.addedNodes.forEach((node) => {\n          document.getElementById(\"rtd-footer-container\").append(node);\n        });\n      }\n    });\n  };\n\n  const observer = new MutationObserver(mutatedCallback);\n  const config = { childList: true };\n  observer.observe(document.body, config);\n}\n\nasync function fetchAndUseVersions() {\n  // fetch the JSON version data (only once), then use it to populate the version\n  // switcher and maybe show the version warning bar\n  var versionSwitcherBtns = document.querySelectorAll(\n    \".version-switcher__button\",\n  );\n  const hasSwitcherMenu = versionSwitcherBtns.length > 0;\n  const hasVersionsJSON = DOCUMENTATION_OPTIONS.hasOwnProperty(\n    \"theme_switcher_json_url\",\n  );\n  const wantsWarningBanner = DOCUMENTATION_OPTIONS.show_version_warning_banner;\n\n  if (hasVersionsJSON && (hasSwitcherMenu || wantsWarningBanner)) {\n    const data = await fetchVersionSwitcherJSON(\n      DOCUMENTATION_OPTIONS.theme_switcher_json_url,\n    );\n    // TODO: remove the `if(data)` once the `return null` is fixed within fetchVersionSwitcherJSON.\n    // We don't really want the switcher and warning bar to silently not work.\n    if (data) {\n      populateVersionSwitcher(data, versionSwitcherBtns);\n      if (wantsWarningBanner) {\n        showVersionWarningBanner(data);\n      }\n    }\n  }\n}\n\n/*******************************************************************************\n * Add keyboard functionality to mobile sidebars.\n *\n * Wire up the hamburger-style buttons using the click event which (on buttons)\n * handles both mouse clicks and the space and enter keys.\n */\nfunction setupMobileSidebarKeyboardHandlers() {\n  // These are hidden checkboxes at the top of the page whose :checked property\n  // allows the mobile sidebars to be hidden or revealed via CSS.\n  const primaryToggle = document.getElementById(\"pst-primary-sidebar-checkbox\");\n  const secondaryToggle = document.getElementById(\n    \"pst-secondary-sidebar-checkbox\",\n  );\n  const primarySidebar = document.querySelector(\".bd-sidebar-primary\");\n  const secondarySidebar = document.querySelector(\".bd-sidebar-secondary\");\n\n  // Toggle buttons -\n  //\n  // These are the hamburger-style buttons in the header nav bar. When the user\n  // clicks, the button transmits the click to the hidden checkboxes used by the\n  // CSS to control whether the sidebar is open or closed.\n  const primaryClickTransmitter = document.querySelector(\".primary-toggle\");\n  const secondaryClickTransmitter = document.querySelector(\".secondary-toggle\");\n  [\n    [primaryClickTransmitter, primaryToggle, primarySidebar],\n    [secondaryClickTransmitter, secondaryToggle, secondarySidebar],\n  ].forEach(([clickTransmitter, toggle, sidebar]) => {\n    if (!clickTransmitter) {\n      return;\n    }\n    clickTransmitter.addEventListener(\"click\", (event) => {\n      event.preventDefault();\n      event.stopPropagation();\n      toggle.checked = !toggle.checked;\n\n      // If we are opening the sidebar, move focus to the first focusable item\n      // in the sidebar\n      if (toggle.checked) {\n        // Note: this selector is not exhaustive, and we may need to update it\n        // in the future\n        const tabStop = sidebar.querySelector(\"a, button\");\n        // use setTimeout because you cannot move focus synchronously during a\n        // click in the handler for the click event\n        setTimeout(() => tabStop.focus(), 100);\n      }\n    });\n  });\n\n  // Escape key -\n  //\n  // When sidebar is open, user should be able to press escape key to close the\n  // sidebar.\n  [\n    [primarySidebar, primaryToggle, primaryClickTransmitter],\n    [secondarySidebar, secondaryToggle, secondaryClickTransmitter],\n  ].forEach(([sidebar, toggle, transmitter]) => {\n    if (!sidebar) {\n      return;\n    }\n    sidebar.addEventListener(\"keydown\", (event) => {\n      if (event.key === \"Escape\") {\n        event.preventDefault();\n        event.stopPropagation();\n        toggle.checked = false;\n        transmitter.focus();\n      }\n    });\n  });\n\n  // When the <label> overlay is clicked to close the sidebar, return focus to\n  // the opener button in the nav bar.\n  [\n    [primaryToggle, primaryClickTransmitter],\n    [secondaryToggle, secondaryClickTransmitter],\n  ].forEach(([toggle, transmitter]) => {\n    toggle.addEventListener(\"change\", (event) => {\n      if (!event.currentTarget.checked) {\n        transmitter.focus();\n      }\n    });\n  });\n}\n\n/**\n * When the page loads, or the window resizes, or descendant nodes are added or\n * removed from the main element, check all code blocks and Jupyter notebook\n * outputs, and for each one that has scrollable overflow, set tabIndex = 0.\n */\nfunction addTabStopsToScrollableElements() {\n  const updateTabStops = () => {\n    document\n      .querySelectorAll(\n        \"pre, \" + // code blocks\n          \".nboutput > .output_area, \" + // NBSphinx notebook output\n          \".cell_output > .output, \" + // Myst-NB\n          \".jp-RenderedHTMLCommon\", // ipywidgets\n      )\n      .forEach((el) => {\n        el.tabIndex =\n          el.scrollWidth > el.clientWidth || el.scrollHeight > el.clientHeight\n            ? 0\n            : -1;\n      });\n  };\n  const debouncedUpdateTabStops = debounce(updateTabStops, 300);\n\n  // On window resize\n  window.addEventListener(\"resize\", debouncedUpdateTabStops);\n\n  // The following MutationObserver is for ipywidgets, which take some time to\n  // finish loading and rendering on the page (so even after the \"load\" event is\n  // fired, they still have not finished rendering). Would be nice to replace\n  // the MutationObserver if there is a way to hook into the ipywidgets code to\n  // know when it is done.\n  const mainObserver = new MutationObserver(debouncedUpdateTabStops);\n\n  // On descendant nodes added/removed from main element\n  mainObserver.observe(document.getElementById(\"main-content\"), {\n    subtree: true,\n    childList: true,\n  });\n\n  // On page load (when this function gets called)\n  updateTabStops();\n}\nfunction debounce(callback, wait) {\n  let timeoutId = null;\n  return (...args) => {\n    clearTimeout(timeoutId);\n    timeoutId = setTimeout(() => {\n      callback(...args);\n    }, wait);\n  };\n}\n\n/*******************************************************************************\n * Announcement banner - fetch and load remote HTML\n */\nasync function setupAnnouncementBanner() {\n  const banner = document.querySelector(\".bd-header-announcement\");\n  const { pstAnnouncementUrl } = banner.dataset;\n\n  if (!pstAnnouncementUrl) {\n    return;\n  }\n\n  try {\n    const response = await fetch(pstAnnouncementUrl);\n    if (!response.ok) {\n      throw new Error(\n        `[PST]: HTTP response status not ok: ${response.status} ${response.statusText}`,\n      );\n    }\n    const data = await response.text();\n    if (data.length === 0) {\n      console.log(`[PST]: Empty announcement at: ${pstAnnouncementUrl}`);\n      return;\n    }\n    banner.innerHTML = `<div class=\"bd-header-announcement__content\">${data}</div>`;\n    banner.classList.remove(\"d-none\");\n  } catch (_error) {\n    console.log(`[PST]: Failed to load announcement at: ${pstAnnouncementUrl}`);\n    console.error(_error);\n  }\n}\n\n/*******************************************************************************\n * Reveal (and animate) the banners (version warning, announcement) together\n */\nasync function fetchRevealBannersTogether() {\n  // Wait until finished fetching and loading banners\n  await Promise.allSettled([fetchAndUseVersions(), setupAnnouncementBanner()]);\n\n  // The revealer element should have CSS rules that set height to 0, overflow\n  // to hidden, and an animation transition on the height (unless the user has\n  // turned off animations)\n  const revealer = document.querySelector(\".pst-async-banner-revealer\");\n\n  // Remove the d-none (display-none) class to calculate the children heights.\n  revealer.classList.remove(\"d-none\");\n\n  // Add together the heights of the element's children\n  const height = Array.from(revealer.children).reduce(\n    (height, el) => height + el.offsetHeight,\n    0,\n  );\n\n  // Use the calculated height to give the revealer a non-zero height (if\n  // animations allowed, the height change will animate)\n  revealer.style.setProperty(\"height\", `${height}px`);\n\n  // Wait for a bit more than 300ms (the transition duration), then set height\n  // to auto so the banner can resize if the window is resized.\n  setTimeout(() => {\n    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\ No newline at end of file
diff --git a/_preview/3/_static/scripts/sphinx-book-theme.js b/_preview/3/_static/scripts/sphinx-book-theme.js
deleted file mode 100644
index 481ba0c..0000000
--- a/_preview/3/_static/scripts/sphinx-book-theme.js
+++ /dev/null
@@ -1,2 +0,0 @@
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\ No newline at end of file
diff --git a/_preview/3/_static/scripts/sphinx-book-theme.js.map b/_preview/3/_static/scripts/sphinx-book-theme.js.map
deleted file mode 100644
index 2cc145a..0000000
--- a/_preview/3/_static/scripts/sphinx-book-theme.js.map
+++ /dev/null
@@ -1 +0,0 @@
-{"version":3,"file":"scripts/sphinx-book-theme.js","mappings":"mBASA,IAAIA,EAAsBC,IACG,WAAvBC,SAASC,WACXF,IACSC,SAASE,iBAClBF,SAASE,iBAAiB,mBAAoBH,GAE9CC,SAASG,YAAY,sBAAsB,WACd,YAAvBH,SAASC,YAA0BF,GACzC,GACF,EAkKFK,OAAOC,aAtCY,KACjB,IAAIC,EAAQN,SAASO,cAAc,cACrBD,EAAME,mBAGPC,UAAUC,SAAS,wBAC9BJ,EAAMK,mBACJ,WACA,iDAIJC,WAAW,EA2BbR,OAAOS,iBAzJgB,KACrB,IAAIC,EACDd,SAASe,mBAAoD,OAA/Bf,SAASe,mBACvCf,SAASgB,yBAC6B,OAArChB,SAASgB,wBACb,IAAIC,EAASjB,SAASkB,gBACjBJ,GAQHK,QAAQC,IAAI,8BACRpB,SAASqB,eACXrB,SAASqB,iBACArB,SAASsB,sBAClBtB,SAASsB,yBAXXH,QAAQC,IAAI,+BACRH,EAAOM,kBACTN,EAAOM,oBACEN,EAAOO,yBAChBP,EAAOO,0BASX,EA0IF1B,GAzHkB,KAChB,IAAI2B,EAAgB,GACpB,IAwCIC,EAAc,IAAIC,sBAxCA,CAACC,EAASC,KAE9BD,EAAQE,SAASC,IACf,GAAIA,EAAMC,eAERP,EAAcQ,KAAKF,EAAMG,aAGzB,IAAK,IAAIC,EAAK,EAAGA,EAAKV,EAAcW,OAAQD,IAC1C,GAAIV,EAAcU,KAAQJ,EAAMG,OAAQ,CACtCT,EAAcY,OAAOF,EAAI,GACzB,KACF,CAEJ,IAIEV,EAAcW,OAAS,EACzBpC,SAASO,cAAc,4BAA4BE,UAAU6B,IAAI,QAEjEtC,SACGO,cAAc,4BACdE,UAAU8B,OAAO,OACtB,GAYY,CAEZC,WAAY,qBAaVC,EAAiB,GATG,CACtB,aACA,WACA,SACA,iBACA,aACA,UACA,UAGcX,SAASK,IAEvBM,EAAeR,KAEX,IAAIE,IACJ,QAAQA,IACR,IAAIA,EAAGO,QAAQ,IAAK,OACpB,QAAQP,EAAGO,QAAQ,IAAK,OAE3B,IAEH1C,SAAS2C,iBAAiBF,EAAeG,KAAK,OAAOd,SAASK,IAC5DT,EAAYmB,QAAQV,EAAG,IAIJ,IAAIR,sBA1CO,CAACC,EAASC,KAEpCD,EAAQ,GAAGkB,mBAAmBC,EAAI,EACpC/C,SAASgD,KAAKvC,UAAU6B,IAAI,YAE5BtC,SAASgD,KAAKvC,UAAU8B,OAAO,WACjC,IAqCaM,QAAQ7C,SAASO,cAAc,4BAA4B,IAmD5ET,GA1BA,WACE,IAAImD,EAAkB,CACpB,0BACA,aACA,qBACA,sBACA,wBACA,qBACA,qBACA,cACAL,KAAK,KACP5C,SAAS2C,iBAAiBM,GAAiBnB,SAASK,IAClDA,EAAG1B,UAAU6B,IAAI,UAAU,GAE/B,G","sources":["webpack://sphinx_book_theme/./src/sphinx_book_theme/assets/scripts/index.js"],"sourcesContent":["// Import CSS variables\n// ref: https://css-tricks.com/getting-javascript-to-talk-to-css-and-sass/\nimport \"../styles/index.scss\";\n\n/**\n * A helper function to load scripts when the DOM is loaded.\n * This waits for everything to be on the page first before running, since\n * some functionality doesn't behave properly until everything is ready.\n */\nvar sbRunWhenDOMLoaded = (cb) => {\n  if (document.readyState != \"loading\") {\n    cb();\n  } else if (document.addEventListener) {\n    document.addEventListener(\"DOMContentLoaded\", cb);\n  } else {\n    document.attachEvent(\"onreadystatechange\", function () {\n      if (document.readyState == \"complete\") cb();\n    });\n  }\n};\n\n/**\n * Toggle full-screen with button\n *\n * There are some browser-specific hacks in here:\n * - Safari requires a `webkit` prefix, so this uses conditionals to check for that\n *   ref: https://developer.mozilla.org/en-US/docs/Web/API/Fullscreen_API\n */\nvar toggleFullScreen = () => {\n  var isInFullScreen =\n    (document.fullscreenElement && document.fullscreenElement !== null) ||\n    (document.webkitFullscreenElement &&\n      document.webkitFullscreenElement !== null);\n  let docElm = document.documentElement;\n  if (!isInFullScreen) {\n    console.log(\"[SBT]: Entering full screen\");\n    if (docElm.requestFullscreen) {\n      docElm.requestFullscreen();\n    } else if (docElm.webkitRequestFullscreen) {\n      docElm.webkitRequestFullscreen();\n    }\n  } else {\n    console.log(\"[SBT]: Exiting full screen\");\n    if (document.exitFullscreen) {\n      document.exitFullscreen();\n    } else if (document.webkitExitFullscreen) {\n      document.webkitExitFullscreen();\n    }\n  }\n};\n\n/**\n * Manage scrolling behavior. This is primarily two things:\n *\n * 1. Hide the Table of Contents any time sidebar content is on the screen.\n *\n * This will be triggered any time a sidebar item enters or exits the screen.\n * It adds/removes items from an array if they have entered the screen, and\n * removes them when they exit the screen. It hides the TOC if anything is\n * on-screen.\n *\n * ref: https://developer.mozilla.org/en-US/docs/Web/API/Intersection_Observer_API\n *\n * 2. Add a `scrolled` class to <body> to trigger CSS changes.\n */\nvar initTocHide = () => {\n  var onScreenItems = [];\n  let hideTocCallback = (entries, observer) => {\n    // Check whether any sidebar item is displayed\n    entries.forEach((entry) => {\n      if (entry.isIntersecting) {\n        // If an element just came on screen, add it our list\n        onScreenItems.push(entry.target);\n      } else {\n        // Otherwise, if it's in our list then remove it\n        for (let ii = 0; ii < onScreenItems.length; ii++) {\n          if (onScreenItems[ii] === entry.target) {\n            onScreenItems.splice(ii, 1);\n            break;\n          }\n        }\n      }\n    });\n\n    // Hide the TOC if any margin content is displayed on the screen\n    if (onScreenItems.length > 0) {\n      document.querySelector(\"div.bd-sidebar-secondary\").classList.add(\"hide\");\n    } else {\n      document\n        .querySelector(\"div.bd-sidebar-secondary\")\n        .classList.remove(\"hide\");\n    }\n  };\n  let manageScrolledClassOnBody = (entries, observer) => {\n    // The pixel is at the top, so if we're < 0 that it means we've scrolled\n    if (entries[0].boundingClientRect.y < 0) {\n      document.body.classList.add(\"scrolled\");\n    } else {\n      document.body.classList.remove(\"scrolled\");\n    }\n  };\n\n  // Set up the intersection observer to watch all margin content\n  let options = {\n    // Trigger callback when the top of a margin item is 1/3 up the screen\n    rootMargin: \"0px 0px -33% 0px\",\n  };\n  let tocObserver = new IntersectionObserver(hideTocCallback, options);\n  // TODO: deprecate popout after v0.5.0\n  const selectorClasses = [\n    \"marginnote\",\n    \"sidenote\",\n    \"margin\",\n    \"margin-caption\",\n    \"full-width\",\n    \"sidebar\",\n    \"popout\",\n  ];\n  let marginSelector = [];\n  selectorClasses.forEach((ii) => {\n    // Use three permutations of each class name because `tag_` and `_` used to be supported\n    marginSelector.push(\n      ...[\n        `.${ii}`,\n        `.tag_${ii}`,\n        `.${ii.replace(\"-\", \"_\")}`,\n        `.tag_${ii.replace(\"-\", \"_\")}`,\n      ],\n    );\n  });\n  document.querySelectorAll(marginSelector.join(\", \")).forEach((ii) => {\n    tocObserver.observe(ii);\n  });\n\n  // Set up the observer to check if we've scrolled from top of page\n  let scrollObserver = new IntersectionObserver(manageScrolledClassOnBody);\n  scrollObserver.observe(document.querySelector(\".sbt-scroll-pixel-helper\"));\n};\n\n/**\n * Activate Thebe with a custom button click.\n */\nvar initThebeSBT = () => {\n  var title = document.querySelector(\"section h1\");\n  var sibling = title.nextElementSibling;\n  // If the next element after the title isn't a thebe button, add one now.\n  // That way it is initiatlized when thebe is first-clicked and isn't re-added after.\n  if (!sibling.classList.contains(\"thebe-launch-button\")) {\n    title.insertAdjacentHTML(\n      \"afterend\",\n      \"<button class='thebe-launch-button'></button>\",\n    );\n  }\n  // This function is provided by sphinx-thebe\n  initThebe();\n};\n\n/**\n * Add no print class to certain DOM elements\n */\n\nfunction addNoPrint() {\n  var noPrintSelector = [\n    \".bd-header-announcement\",\n    \".bd-header\",\n    \".bd-header-article\",\n    \".bd-sidebar-primary\",\n    \".bd-sidebar-secondary\",\n    \".bd-footer-article\",\n    \".bd-footer-content\",\n    \".bd-footer\",\n  ].join(\",\");\n  document.querySelectorAll(noPrintSelector).forEach((ii) => {\n    ii.classList.add(\"noprint\");\n  });\n}\n\n/**\n * Set up callback functions for UI click actions\n */\nwindow.initThebeSBT = initThebeSBT;\nwindow.toggleFullScreen = toggleFullScreen;\n\n/**\n * Set up functions to load when the DOM is ready\n */\nsbRunWhenDOMLoaded(initTocHide);\nsbRunWhenDOMLoaded(addNoPrint);\n"],"names":["sbRunWhenDOMLoaded","cb","document","readyState","addEventListener","attachEvent","window","initThebeSBT","title","querySelector","nextElementSibling","classList","contains","insertAdjacentHTML","initThebe","toggleFullScreen","isInFullScreen","fullscreenElement","webkitFullscreenElement","docElm","documentElement","console","log","exitFullscreen","webkitExitFullscreen","requestFullscreen","webkitRequestFullscreen","onScreenItems","tocObserver","IntersectionObserver","entries","observer","forEach","entry","isIntersecting","push","target","ii","length","splice","add","remove","rootMargin","marginSelector","replace","querySelectorAll","join","observe","boundingClientRect","y","body","noPrintSelector"],"sourceRoot":""}
\ No newline at end of file
diff --git a/_preview/3/_static/searchtools.js b/_preview/3/_static/searchtools.js
deleted file mode 100644
index 92da3f8..0000000
--- a/_preview/3/_static/searchtools.js
+++ /dev/null
@@ -1,619 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
-  var Scorer = {
-    // Implement the following function to further tweak the score for each result
-    // The function takes a result array [docname, title, anchor, descr, score, filename]
-    // and returns the new score.
-    /*
-    score: result => {
-      const [docname, title, anchor, descr, score, filename] = result
-      return score
-    },
-    */
-
-    // query matches the full name of an object
-    objNameMatch: 11,
-    // or matches in the last dotted part of the object name
-    objPartialMatch: 6,
-    // Additive scores depending on the priority of the object
-    objPrio: {
-      0: 15, // used to be importantResults
-      1: 5, // used to be objectResults
-      2: -5, // used to be unimportantResults
-    },
-    //  Used when the priority is not in the mapping.
-    objPrioDefault: 0,
-
-    // query found in title
-    title: 15,
-    partialTitle: 7,
-    // query found in terms
-    term: 5,
-    partialTerm: 2,
-  };
-}
-
-const _removeChildren = (element) => {
-  while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
-  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms, highlightTerms) => {
-  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
-  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
-  const contentRoot = document.documentElement.dataset.content_root;
-
-  const [docName, title, anchor, descr, score, _filename] = item;
-
-  let listItem = document.createElement("li");
-  let requestUrl;
-  let linkUrl;
-  if (docBuilder === "dirhtml") {
-    // dirhtml builder
-    let dirname = docName + "/";
-    if (dirname.match(/\/index\/$/))
-      dirname = dirname.substring(0, dirname.length - 6);
-    else if (dirname === "index/") dirname = "";
-    requestUrl = contentRoot + dirname;
-    linkUrl = requestUrl;
-  } else {
-    // normal html builders
-    requestUrl = contentRoot + docName + docFileSuffix;
-    linkUrl = docName + docLinkSuffix;
-  }
-  let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + anchor;
-  linkEl.dataset.score = score;
-  linkEl.innerHTML = title;
-  if (descr) {
-    listItem.appendChild(document.createElement("span")).innerHTML =
-      " (" + descr + ")";
-    // highlight search terms in the description
-    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
-      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
-  }
-  else if (showSearchSummary)
-    fetch(requestUrl)
-      .then((responseData) => responseData.text())
-      .then((data) => {
-        if (data)
-          listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, anchor)
-          );
-        // highlight search terms in the summary
-        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
-          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
-      });
-  Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
-  Search.stopPulse();
-  Search.title.innerText = _("Search Results");
-  if (!resultCount)
-    Search.status.innerText = Documentation.gettext(
-      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
-    );
-  else
-    Search.status.innerText = _(
-      "Search finished, found ${resultCount} page(s) matching the search query."
-    ).replace('${resultCount}', resultCount);
-};
-const _displayNextItem = (
-  results,
-  resultCount,
-  searchTerms,
-  highlightTerms,
-) => {
-  // results left, load the summary and display it
-  // this is intended to be dynamic (don't sub resultsCount)
-  if (results.length) {
-    _displayItem(results.pop(), searchTerms, highlightTerms);
-    setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
-      5
-    );
-  }
-  // search finished, update title and status message
-  else _finishSearch(resultCount);
-};
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
-  const leftScore = a[4];
-  const rightScore = b[4];
-  if (leftScore === rightScore) {
-    // same score: sort alphabetically
-    const leftTitle = a[1].toLowerCase();
-    const rightTitle = b[1].toLowerCase();
-    if (leftTitle === rightTitle) return 0;
-    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-  }
-  return leftScore > rightScore ? 1 : -1;
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
-  var splitQuery = (query) => query
-      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
-      .filter(term => term)  // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
-  _index: null,
-  _queued_query: null,
-  _pulse_status: -1,
-
-  htmlToText: (htmlString, anchor) => {
-    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    for (const removalQuery of [".headerlinks", "script", "style"]) {
-      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
-    }
-    if (anchor) {
-      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
-      if (anchorContent) return anchorContent.textContent;
-
-      console.warn(
-        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
-      );
-    }
-
-    // if anchor not specified or not found, fall back to main content
-    const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent) return docContent.textContent;
-
-    console.warn(
-      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
-    );
-    return "";
-  },
-
-  init: () => {
-    const query = new URLSearchParams(window.location.search).get("q");
-    document
-      .querySelectorAll('input[name="q"]')
-      .forEach((el) => (el.value = query));
-    if (query) Search.performSearch(query);
-  },
-
-  loadIndex: (url) =>
-    (document.body.appendChild(document.createElement("script")).src = url),
-
-  setIndex: (index) => {
-    Search._index = index;
-    if (Search._queued_query !== null) {
-      const query = Search._queued_query;
-      Search._queued_query = null;
-      Search.query(query);
-    }
-  },
-
-  hasIndex: () => Search._index !== null,
-
-  deferQuery: (query) => (Search._queued_query = query),
-
-  stopPulse: () => (Search._pulse_status = -1),
-
-  startPulse: () => {
-    if (Search._pulse_status >= 0) return;
-
-    const pulse = () => {
-      Search._pulse_status = (Search._pulse_status + 1) % 4;
-      Search.dots.innerText = ".".repeat(Search._pulse_status);
-      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
-    };
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch: (query) => {
-    // create the required interface elements
-    const searchText = document.createElement("h2");
-    searchText.textContent = _("Searching");
-    const searchSummary = document.createElement("p");
-    searchSummary.classList.add("search-summary");
-    searchSummary.innerText = "";
-    const searchList = document.createElement("ul");
-    searchList.classList.add("search");
-
-    const out = document.getElementById("search-results");
-    Search.title = out.appendChild(searchText);
-    Search.dots = Search.title.appendChild(document.createElement("span"));
-    Search.status = out.appendChild(searchSummary);
-    Search.output = out.appendChild(searchList);
-
-    const searchProgress = document.getElementById("search-progress");
-    // Some themes don't use the search progress node
-    if (searchProgress) {
-      searchProgress.innerText = _("Preparing search...");
-    }
-    Search.startPulse();
-
-    // index already loaded, the browser was quick!
-    if (Search.hasIndex()) Search.query(query);
-    else Search.deferQuery(query);
-  },
-
-  _parseQuery: (query) => {
-    // stem the search terms and add them to the correct list
-    const stemmer = new Stemmer();
-    const searchTerms = new Set();
-    const excludedTerms = new Set();
-    const highlightTerms = new Set();
-    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
-    splitQuery(query.trim()).forEach((queryTerm) => {
-      const queryTermLower = queryTerm.toLowerCase();
-
-      // maybe skip this "word"
-      // stopwords array is from language_data.js
-      if (
-        stopwords.indexOf(queryTermLower) !== -1 ||
-        queryTerm.match(/^\d+$/)
-      )
-        return;
-
-      // stem the word
-      let word = stemmer.stemWord(queryTermLower);
-      // select the correct list
-      if (word[0] === "-") excludedTerms.add(word.substr(1));
-      else {
-        searchTerms.add(word);
-        highlightTerms.add(queryTermLower);
-      }
-    });
-
-    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
-      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
-    }
-
-    // console.debug("SEARCH: searching for:");
-    // console.info("required: ", [...searchTerms]);
-    // console.info("excluded: ", [...excludedTerms]);
-
-    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
-    // Collect multiple result groups to be sorted separately and then ordered.
-    // Each is an array of [docname, title, anchor, descr, score, filename].
-    const normalResults = [];
-    const nonMainIndexResults = [];
-
-    _removeChildren(document.getElementById("search-progress"));
-
-    const queryLower = query.toLowerCase().trim();
-    for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
-        for (const [file, id] of foundTitles) {
-          let score = Math.round(100 * queryLower.length / title.length)
-          normalResults.push([
-            docNames[file],
-            titles[file] !== title ? `${titles[file]} > ${title}` : title,
-            id !== null ? "#" + id : "",
-            null,
-            score,
-            filenames[file],
-          ]);
-        }
-      }
-    }
-
-    // search for explicit entries in index directives
-    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
-      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id, isMain] of foundEntries) {
-          const score = Math.round(100 * queryLower.length / entry.length);
-          const result = [
-            docNames[file],
-            titles[file],
-            id ? "#" + id : "",
-            null,
-            score,
-            filenames[file],
-          ];
-          if (isMain) {
-            normalResults.push(result);
-          } else {
-            nonMainIndexResults.push(result);
-          }
-        }
-      }
-    }
-
-    // lookup as object
-    objectTerms.forEach((term) =>
-      normalResults.push(...Search.performObjectSearch(term, objectTerms))
-    );
-
-    // lookup as search terms in fulltext
-    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
-      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
-    }
-
-    // Sort each group of results by score and then alphabetically by name.
-    normalResults.sort(_orderResultsByScoreThenName);
-    nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
-    // Combine the result groups in (reverse) order.
-    // Non-main index entries are typically arbitrary cross-references,
-    // so display them after other results.
-    let results = [...nonMainIndexResults, ...normalResults];
-
-    // remove duplicate search results
-    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
-    let seen = new Set();
-    results = results.reverse().reduce((acc, result) => {
-      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
-      if (!seen.has(resultStr)) {
-        acc.push(result);
-        seen.add(resultStr);
-      }
-      return acc;
-    }, []);
-
-    return results.reverse();
-  },
-
-  query: (query) => {
-    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
-    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    // console.info("search results:", Search.lastresults);
-
-    // print the results
-    _displayNextItem(results, results.length, searchTerms, highlightTerms);
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch: (object, objectTerms) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const objects = Search._index.objects;
-    const objNames = Search._index.objnames;
-    const titles = Search._index.titles;
-
-    const results = [];
-
-    const objectSearchCallback = (prefix, match) => {
-      const name = match[4]
-      const fullname = (prefix ? prefix + "." : "") + name;
-      const fullnameLower = fullname.toLowerCase();
-      if (fullnameLower.indexOf(object) < 0) return;
-
-      let score = 0;
-      const parts = fullnameLower.split(".");
-
-      // check for different match types: exact matches of full name or
-      // "last name" (i.e. last dotted part)
-      if (fullnameLower === object || parts.slice(-1)[0] === object)
-        score += Scorer.objNameMatch;
-      else if (parts.slice(-1)[0].indexOf(object) > -1)
-        score += Scorer.objPartialMatch; // matches in last name
-
-      const objName = objNames[match[1]][2];
-      const title = titles[match[0]];
-
-      // If more than one term searched for, we require other words to be
-      // found in the name/title/description
-      const otherTerms = new Set(objectTerms);
-      otherTerms.delete(object);
-      if (otherTerms.size > 0) {
-        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
-        if (
-          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
-        )
-          return;
-      }
-
-      let anchor = match[3];
-      if (anchor === "") anchor = fullname;
-      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
-      const descr = objName + _(", in ") + title;
-
-      // add custom score for some objects according to scorer
-      if (Scorer.objPrio.hasOwnProperty(match[2]))
-        score += Scorer.objPrio[match[2]];
-      else score += Scorer.objPrioDefault;
-
-      results.push([
-        docNames[match[0]],
-        fullname,
-        "#" + anchor,
-        descr,
-        score,
-        filenames[match[0]],
-      ]);
-    };
-    Object.keys(objects).forEach((prefix) =>
-      objects[prefix].forEach((array) =>
-        objectSearchCallback(prefix, array)
-      )
-    );
-    return results;
-  },
-
-  /**
-   * search for full-text terms in the index
-   */
-  performTermsSearch: (searchTerms, excludedTerms) => {
-    // prepare search
-    const terms = Search._index.terms;
-    const titleTerms = Search._index.titleterms;
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-
-    const scoreMap = new Map();
-    const fileMap = new Map();
-
-    // perform the search on the required terms
-    searchTerms.forEach((word) => {
-      const files = [];
-      const arr = [
-        { files: terms[word], score: Scorer.term },
-        { files: titleTerms[word], score: Scorer.title },
-      ];
-      // add support for partial matches
-      if (word.length > 2) {
-        const escapedWord = _escapeRegExp(word);
-        if (!terms.hasOwnProperty(word)) {
-          Object.keys(terms).forEach((term) => {
-            if (term.match(escapedWord))
-              arr.push({ files: terms[term], score: Scorer.partialTerm });
-          });
-        }
-        if (!titleTerms.hasOwnProperty(word)) {
-          Object.keys(titleTerms).forEach((term) => {
-            if (term.match(escapedWord))
-              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
-          });
-        }
-      }
-
-      // no match but word was a required one
-      if (arr.every((record) => record.files === undefined)) return;
-
-      // found search word in contents
-      arr.forEach((record) => {
-        if (record.files === undefined) return;
-
-        let recordFiles = record.files;
-        if (recordFiles.length === undefined) recordFiles = [recordFiles];
-        files.push(...recordFiles);
-
-        // set score for the word in each file
-        recordFiles.forEach((file) => {
-          if (!scoreMap.has(file)) scoreMap.set(file, {});
-          scoreMap.get(file)[word] = record.score;
-        });
-      });
-
-      // create the mapping
-      files.forEach((file) => {
-        if (!fileMap.has(file)) fileMap.set(file, [word]);
-        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
-      });
-    });
-
-    // now check if the files don't contain excluded terms
-    const results = [];
-    for (const [file, wordList] of fileMap) {
-      // check if all requirements are matched
-
-      // as search terms with length < 3 are discarded
-      const filteredTermCount = [...searchTerms].filter(
-        (term) => term.length > 2
-      ).length;
-      if (
-        wordList.length !== searchTerms.size &&
-        wordList.length !== filteredTermCount
-      )
-        continue;
-
-      // ensure that none of the excluded terms is in the search result
-      if (
-        [...excludedTerms].some(
-          (term) =>
-            terms[term] === file ||
-            titleTerms[term] === file ||
-            (terms[term] || []).includes(file) ||
-            (titleTerms[term] || []).includes(file)
-        )
-      )
-        break;
-
-      // select one (max) score for the file.
-      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
-      // add result to the result list
-      results.push([
-        docNames[file],
-        titles[file],
-        "",
-        null,
-        score,
-        filenames[file],
-      ]);
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words.
-   */
-  makeSearchSummary: (htmlText, keywords, anchor) => {
-    const text = Search.htmlToText(htmlText, anchor);
-    if (text === "") return null;
-
-    const textLower = text.toLowerCase();
-    const actualStartPosition = [...keywords]
-      .map((k) => textLower.indexOf(k.toLowerCase()))
-      .filter((i) => i > -1)
-      .slice(-1)[0];
-    const startWithContext = Math.max(actualStartPosition - 120, 0);
-
-    const top = startWithContext === 0 ? "" : "...";
-    const tail = startWithContext + 240 < text.length ? "..." : "";
-
-    let summary = document.createElement("p");
-    summary.classList.add("context");
-    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
-    return summary;
-  },
-};
-
-_ready(Search.init);
diff --git a/_preview/3/_static/sphinx-pythia-theme.min.css b/_preview/3/_static/sphinx-pythia-theme.min.css
deleted file mode 100644
index 0609200..0000000
--- a/_preview/3/_static/sphinx-pythia-theme.min.css
+++ /dev/null
@@ -1,2 +0,0 @@
-@import url(sphinx-book-theme.css);:root{--spt-font-body: 'Poppins';--spt-font-headings: 'Poppins';--spt-font-footnote: 'Cormorant';--spt-color-gray-100: 248, 249, 250;--spt-color-gray-200: 233, 236, 239;--spt-color-gray-300: 222, 226, 230;--spt-color-gray-400: 206, 212, 218;--spt-color-gray-500: 173, 181, 189;--spt-color-gray-600: 108, 117, 125;--spt-color-gray-700: 73, 80, 87;--spt-color-gray-800: 52, 58, 64;--spt-color-gray-900: 33, 37, 41;--spt-color-light: var(--spt-color-gray-100);--spt-color-dark: var(--spt-color-gray-800);--spt-color-green: 0, 121, 124;--spt-color-green-lighter: 20, 141, 144;--spt-color-green-lightest: 130, 231, 234;--spt-color-blue: 26, 101, 143;--spt-color-blue-lighter: 46, 121, 163;--spt-color-blue-lightest: 156, 191, 213;--spt-color-deep-blue: 1, 33, 105;--spt-color-green-highlight: 168, 199, 0;--pst-color-primary: var(--spt-color-blue) !important;--pst-color-primary-hover: var(--spt-color-blue-lighter) !important;--pst-color-secondary: var(--spt-color-blue-lightest) !important;--pst-color-link: #1a648f !important;--pst-color-link-hover: #9cbfd5 !important;--pst-color-headerlink: var(--spt-color-gray-300) !important;--pst-color-headerlink-hover: var(--spt-color-gray-100)!important;--pst-color-text-muted: #6c757d !important;--tabs-size-label: 0.8rem !important;--tabs-color-label-active: rgba(var(--pst-color-primary), 1) !important;--tabs-color-label-inactive: rgba(var(--spt-color-blue-lightest), 1) !important
-}body{font-family:var(--spt-font-body),sans-serif}html{scroll-behavior:smooth;scroll-padding-top:128px}.admonition.alert-info,div.admonition.alert-info{border-color:#0c5460 !important}.admonition.alert-info .admonition-title::before,div.admonition.alert-info .admonition-title::before{color:#0c5460 !important}.admonition.alert-success,div.admonition.alert-success{border-color:#155724 !important}.admonition.alert-success .admonition-title::before,div.admonition.alert-success .admonition-title::before{color:#155724 !important}.admonition.alert-warning,div.admonition.alert-warning{border-color:#856404 !important}.admonition.alert-warning .admonition-title::before,div.admonition.alert-warning .admonition-title::before{color:#856404 !important}.admonition.alert-danger,div.admonition.alert-danger{border-color:#721c24 !important}.admonition.alert-danger .admonition-title::before,div.admonition.alert-danger .admonition-title::before{color:#721c24 !important}.bg-primary{background-color:rgba(var(--pst-color-primary), 1) !important;color:white !important}.btn-primary{background-color:rgba(var(--pst-color-primary), 1) !important;border-color:rgba(var(--pst-color-primary), 1) !important;color:white !important}.btn-primary:hover{background-color:rgba(var(--pst-color-primary-hover), 1) !important;color:white !important}.btn-outline-primary{border-color:rgba(var(--pst-color-primary), 1) !important;color:rgba(var(--pst-color-primary), 1) !important}.btn-outline-primary:hover,.show>.btn-outline-primary.dropdown-toggle{background-color:rgba(var(--pst-color-primary), 1) !important;color:white !important}.btn-link{color:rgba(var(--pst-color-primary), 1) !important}.display-1{font-size:5rem !important}.display-2{font-size:3rem !important}.display-3{font-size:2rem !important}.display-4{font-size:1.6667rem !important}.display-5{font-size:1.3333rem !important}.display-6{font-size:1rem !important}.form-control:focus{outline:none !important;box-shadow:none !important}.navbar-brand{padding:.75rem 0 !important}.row{margin-left:0 !important;margin-right:0 !important}a.badge{text-decoration:none !important}a.navbar-brand img{vertical-align:middle !important}body>div.container-fluid,body>div.container-xl{padding-right:0;padding-left:0}i.fab,i.far,i.fas{font-size:70%}a.headerlink i.fab,a.headerlink i.far,a.headerlink i.fas{position:absolute;top:50%;transform:translateY(-50%)}a.headerlink{position:relative;text-decoration:none;font-size:unset !important;padding:0 0.7rem !important}a.headerlink:hover{color:rgba(var(--spt-color-gray-500), 1);background-color:transparent}div.highlight pre{border:1px solid rgba(var(--spt-color-gray-300), 1) !important;border-radius:4px !important;background-color:rgba(var(--spt-color-gray-200), 1) !important}.bd-sidebar .nav li>a{padding-top:0.1rem;padding-bottom:0.1rem}.bd-toc nav>.nav{border-left:1px solid #eee;border-bottom:1px solid #eee;border-radius:0 0 0 6px;padding-bottom:0.5em}.prev-next-area{margin:2rem auto 0 auto;border-top:1px solid rgba(var(--spt-color-gray-300), 1);max-width:calc(0.5 * (var(--bs-breakpoint-md) + var(--bs-breakpoint-lg)))}.prev-next-area i{font-size:1.1rem;color:rgba(var(--spt-color-gray-600), 1)}.prev-next-area a p.prev-next-title{font-size:1rem;font-weight:600;color:rgba(var(--spt-color-gray-600), 1)}#banner{margin-top:var(--pst-header-height) !important}#site-navigation{top:var(--pst-header-height) !important;height:calc(100vh - var(--pst-header-height)) !important;padding-top:0}#main-content{min-height:calc(100vh - var(--pst-header-height))}.topbar{top:var(--pst-header-height) !important;padding:0 !important}.topbar-main button:focus{outline:none !important}.cell .cell_input,.cell .cell_output{padding:0 !important}.cell .cell_input{border:1px solid rgba(var(--spt-color-gray-300), 1) !important;border-radius:4px !important;border-left-color:rgba(var(--pst-color-primary), 1) !important;border-left-width:medium !important;background-color:rgba(var(--spt-color-gray-200), 1) !important}.cell .cell_input pre{border:none !important;background-color:rgba(var(--spt-color-gray-200), 1) !important}.cell .cell_output{border:none !important}.cell .cell_output pre{border:1px solid rgba(var(--spt-color-gray-300), 1) !important;border-radius:4px !important;background-color:rgba(var(--spt-color-gray-200), 1) !important}.tabbed-content{box-shadow:0 -0.0625rem var(--tabs-color-overline) !important;padding-bottom:0 !important}.tabbed-set>label{padding:0 1rem !important;margin:0 !important}.thebe-launch-button{height:2.2em !important;font-size:.8em !important;border:1px solid rgba(var(--spt-color-gray-600), 1) !important;border-radius:4px !important;color:rgba(var(--spt-color-gray-600), 1)}#shim{padding:0;margin-top:var(--pst-header-height)}#navbar-main{z-index:5000;padding-left:15px;padding-right:15px}#navbar-main .container-xl{padding:0}.bd-header{background:#343a40 !important}#navbar-icon-links i:before{color:var(--spt-color-light) !important}main.standalone-main{padding:0 !important;background-color:white}main.standalone-main div.sectionwrapper-1,main.standalone-main div.sectionwrapper-2{color:rgba(var(--spt-color-dark), 1)}main.standalone-main div.section-1,main.standalone-main div.section-2{position:relative;padding-top:1rem;padding-bottom:1rem;max-width:calc(0.5 * (var(--bs-breakpoint-md) + var(--bs-breakpoint-lg)))}main.standalone-main div.section-1 h6,main.standalone-main div.section-1 .h6,main.standalone-main div.section-1 h5,main.standalone-main div.section-1 .h5,main.standalone-main div.section-1 h4,main.standalone-main div.section-1 .h4,main.standalone-main div.section-1 h3,main.standalone-main div.section-1 .h3,main.standalone-main div.section-1 h2,main.standalone-main div.section-1 .h2,main.standalone-main div.section-1 h1,main.standalone-main div.section-1 .h1,main.standalone-main div.section-2 h6,main.standalone-main div.section-2 .h6,main.standalone-main div.section-2 h5,main.standalone-main div.section-2 .h5,main.standalone-main div.section-2 h4,main.standalone-main div.section-2 .h4,main.standalone-main div.section-2 h3,main.standalone-main div.section-2 .h3,main.standalone-main div.section-2 h2,main.standalone-main div.section-2 .h2,main.standalone-main div.section-2 h1,main.standalone-main div.section-2 .h1{margin-top:0;margin-bottom:0.5rem}main.standalone-main div.section-1 h3,main.standalone-main div.section-1 .h3,main.standalone-main div.section-1 h4,main.standalone-main div.section-1 .h4,main.standalone-main div.section-1 h5,main.standalone-main div.section-1 .h5,main.standalone-main div.section-1 h6,main.standalone-main div.section-1 .h6,main.standalone-main div.section-2 h3,main.standalone-main div.section-2 .h3,main.standalone-main div.section-2 h4,main.standalone-main div.section-2 .h4,main.standalone-main div.section-2 h5,main.standalone-main div.section-2 .h5,main.standalone-main div.section-2 h6,main.standalone-main div.section-2 .h6{padding:1rem 0}main.standalone-main div.section-title-wrapper{display:flex;justify-content:flex-start}main.standalone-main div.section-title{padding:0 4rem 0.1rem 0;margin:0 0 1.6rem 0;border-bottom:2px solid rgba(var(--spt-color-deep-blue), 1)}main.banner-main{padding:0 !important}main.banner-main #main-content{padding:0 !important}main.banner-main div.sectionwrapper-1{position:relative;background-size:cover;background-position:center center;color:rgba(var(--spt-color-light), 1);background-color:rgba(var(--spt-color-gray-700), 1)}main.banner-main div.sectionwrapper-1 div.section-banner-caption{position:absolute;right:0;bottom:0;font-size:0.8rem;opacity:0.6;padding-right:0.5rem}main.banner-main div.sectionwrapper-2{color:rgba(var(--spt-color-dark), 1);background-color:rgba(var(--spt-color-light), 1)}main.banner-main div.sectionwrapper-2:nth-child(even){background-color:rgba(var(--spt-color-gray-300), 1)}main.banner-main div.section-1,main.banner-main div.section-2{position:relative;padding-top:7rem;padding-bottom:7rem;max-width:calc(0.5 * (var(--bs-breakpoint-md) + var(--bs-breakpoint-lg)))}main.banner-main div.section-1 h3,main.banner-main div.section-1 h4,main.banner-main div.section-1 h5,main.banner-main div.section-1 h6,main.banner-main div.section-2 h3,main.banner-main div.section-2 h4,main.banner-main div.section-2 h5,main.banner-main div.section-2 h6{padding:2rem 0 1rem 0;text-align:center}main.banner-main div.section-title-wrapper{display:flex;justify-content:center}main.banner-main div.section-title{padding:0 4rem 1rem 4rem;margin-bottom:3rem;border-bottom:2px solid rgba(var(--spt-color-deep-blue), 1)}main.banner-main p{font-size:1.25rem;font-weight:300}main.banner-main div.section.dark-banner h1,main.banner-main div.section.dark-banner h2,main.banner-main div.section.dark-banner h3,main.banner-main div.section.dark-banner h4,main.banner-main div.section.dark-banner h5,main.banner-main div.section.dark-banner h6{color:var(--spt-color-light) !important}main.banner-main div.section.dark-banner p{color:var(--spt-color-light) !important}.bd-main .bd-content .bd-article-container{max-width:100%}.bd-page-width{max-width:85%}.bd-footer{color:rgba(var(--spt-color-light), 1);font-size:0.8rem;font-family:var(--spt-font-footnote);padding:0}.bd-footer h1,.bd-footer h2,.bd-footer h3,.bd-footer h4,.bd-footer h5,.bd-footer h6,.bd-footer p,.bd-footer a{color:rgba(var(--spt-color-light), 1);font-family:var(--spt-font-footnote)}.bd-footer h1,.bd-footer h2,.bd-footer h3,.bd-footer h4,.bd-footer h5,.bd-footer h6{font-size:1rem;margin-bottom:5px}.bd-footer p{font-size:0.8rem}.bd-footer .footer-logos{background-color:rgba(var(--spt-color-gray-200), 1)}.bd-footer .footer-info,.bd-footer .footer-menu{color:rgba(var(--spt-color-gray-400), 1);background-color:rgba(var(--spt-color-dark), 1);padding-top:0.4rem;padding-bottom:0.4rem}.bd-footer .footer-info li,.bd-footer .footer-menu li{line-height:1rem;margin-bottom:5px}.bd-footer .footer-extra{background-color:rgba(var(--spt-color-gray-700), 1);padding-top:1rem;padding-bottom:1rem}.bd-footer .footer-extra img{height:60px}.iframe,.iframe-4x3,.iframe-16x9{position:relative;height:0;width:75%;margin-bottom:1rem;margin-left:auto;margin-right:auto}.iframe iframe,.iframe-4x3 iframe,.iframe-16x9 iframe{border:0;position:absolute;top:0;left:0;bottom:0;right:0;width:100%;height:100%}.iframe-16x9{padding-bottom:calc(0.75 * 56.25%)}.iframe-4x3{padding-bottom:calc(0.75 * 75%)}.sd-btn-primary,.sd-btn-outline-primary:hover,.sd-btn-outline-primary:focus{color:#0a7d91 !important}@media (prefers-color-scheme: dark){p{color:black}}html[data-theme="dark"] p{color:white}html[data-theme="dark"] .section-title{color:white}html[data-theme="dark"] h1.display-1{color:white}html[data-theme="dark"] h2.display-2{color:white}html[data-theme="dark"] h3.display-3{color:white}html[data-theme="dark"] main.banner-main div.sectionwrapper-2:nth-child(even){background-color:#495057}html[data-theme="dark"] main.banner-main div.sectionwrapper-2{background-color:#14181e}html[data-theme="dark"] .container-fluid{background-color:#14181e}html[data-theme="dark"] .sd-btn-primary,.sd-btn-outline-primary:hover,.sd-btn-outline-primary:focus{color:#0a7d91 !important}html[data-theme="dark"] .btn{--bs-btn-color: white}html[data-theme="dark"] .search-button{color:white}html[data-theme="dark"] .nav-link{color:white}html[data-theme="dark"] .highlight .c{color:#797129}html[data-theme="dark"] .highlight .c1{color:#797129}html[data-theme="dark"] .highlight .cp{color:#797129}html[data-theme="dark"] .highlight .k{color:#7928a1}html[data-theme="dark"] .highlight .kc{color:#7928a1}html[data-theme="dark"] .highlight .kn{color:#7928a1}html[data-theme="dark"] .highlight .l{color:#797129}html[data-theme="dark"] .highlight .m{color:#797129}html[data-theme="dark"] .highlight .mf{color:#797129}html[data-theme="dark"] .highlight .mi{color:#797129}html[data-theme="dark"] .highlight .n{color:#545454}html[data-theme="dark"] .highlight .nb{color:#797129}html[data-theme=dark] .highlight .nc{color:#0078a1 !important}html[data-theme="dark"] .highlight .nd{color:#797129}html[data-theme=dark] .highlight .nf{color:#0078a1 !important}html[data-theme="dark"] .highlight .nn{color:#545454}html[data-theme="dark"] .highlight .nt{color:#007faa}html[data-theme="dark"] .highlight .o{color:#008000}html[data-theme="dark"] .highlight .ow{color:#7928a1}html[data-theme="dark"] .highlight .p{color:#545454}html[data-theme="dark"] .highlight .s{color:#008000}html[data-theme="dark"] .highlight .sa{color:#008000}html[data-theme="dark"] .highlight .sd{color:#008000}html[data-theme="dark"] .highlight .se{color:#008000}html[data-theme="dark"] .highlight .si{color:#008000}html[data-theme="dark"] .highlight .s1{color:#008000}html[data-theme="dark"] .highlight .s2{color:#008000}html[data-theme="dark"] .highlight pre{color:black}html[data-theme="dark"] summary{color:black}html[data-theme="dark"] svg.tb-icon.toggle-chevron-right{color:white}html[data-theme="dark"] span.toggle-details__summary-text{color:white}html[data-theme="dark"] .output.text_html table thead tr{background-color:white}html[data-theme="dark"] .output.text_html table tbody tr:nth-child(2n){background-color:white}html[data-theme="dark"] .output.text_html table.dataframe thead tr{background-color:#29313d}html[data-theme="dark"] .output.text_html table.dataframe tbody tr:nth-child(2n){background-color:#222832}
diff --git a/_preview/3/_static/sphinx-pythia-theme.min.js b/_preview/3/_static/sphinx-pythia-theme.min.js
deleted file mode 100644
index 8b13789..0000000
--- a/_preview/3/_static/sphinx-pythia-theme.min.js
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/_preview/3/_static/sphinx-thebe.css b/_preview/3/_static/sphinx-thebe.css
deleted file mode 100644
index 1da2793..0000000
--- a/_preview/3/_static/sphinx-thebe.css
+++ /dev/null
@@ -1,129 +0,0 @@
-/* Thebelab Buttons */
-.thebelab-button {
-  z-index: 999;
-  display: inline-block;
-  padding: 0.35em 1.2em;
-  margin: 0px 1px;
-  border-radius: 0.12em;
-  box-sizing: border-box;
-  text-decoration: none;
-  font-family: "Roboto", sans-serif;
-  font-weight: 300;
-  text-align: center;
-  transition: all 0.2s;
-  background-color: #dddddd;
-  border: 0.05em solid white;
-  color: #000000;
-}
-
-.thebelab-button:hover {
-  border: 0.05em solid black;
-  background-color: #fcfcfc;
-}
-
-.thebe-launch-button {
-  height: 2.2em;
-  font-size: 0.8em;
-  border: 1px black solid;
-}
-
-/* Thebelab Cell */
-.thebelab-cell pre {
-  background: none;
-}
-
-.thebelab-cell .thebelab-input {
-  padding-left: 1em;
-  margin-bottom: 0.5em;
-  margin-top: 0.5em;
-}
-
-.thebelab-cell .jp-OutputArea {
-  margin-top: 0.5em;
-  margin-left: 1em;
-}
-
-button.thebelab-button.thebelab-run-button {
-  margin-left: 1.5em;
-  margin-bottom: 0.5em;
-}
-
-/* Loading button */
-button.thebe-launch-button div.spinner {
-  float: left;
-  margin-right: 1em;
-}
-
-/* Remove the spinner when thebelab is ready */
-.thebe-launch-button.thebe-status-ready .spinner {
-  display: none;
-}
-
-.thebe-launch-button span.status {
-  font-family: monospace;
-  font-weight: bold;
-}
-
-.thebe-launch-button.thebe-status-ready span.status {
-  color: green;
-}
-
-.spinner {
-  height: 2em;
-  text-align: center;
-  font-size: 0.7em;
-}
-
-.spinner > div {
-  background-color: #f37726;
-  height: 100%;
-  width: 6px;
-  display: inline-block;
-
-  -webkit-animation: sk-stretchdelay 1.2s infinite ease-in-out;
-  animation: sk-stretchdelay 1.2s infinite ease-in-out;
-}
-
-.spinner .rect2 {
-  -webkit-animation-delay: -1.1s;
-  animation-delay: -1.1s;
-}
-
-.spinner .rect3 {
-  -webkit-animation-delay: -1s;
-  animation-delay: -1s;
-}
-
-.spinner .rect4 {
-  -webkit-animation-delay: -0.9s;
-  animation-delay: -0.9s;
-}
-
-.spinner .rect5 {
-  -webkit-animation-delay: -0.8s;
-  animation-delay: -0.8s;
-}
-
-@-webkit-keyframes sk-stretchdelay {
-  0%,
-  40%,
-  100% {
-    -webkit-transform: scaleY(0.4);
-  }
-  20% {
-    -webkit-transform: scaleY(1);
-  }
-}
-
-@keyframes sk-stretchdelay {
-  0%,
-  40%,
-  100% {
-    transform: scaleY(0.4);
-    -webkit-transform: scaleY(0.4);
-  }
-  20% {
-    transform: scaleY(1);
-    -webkit-transform: scaleY(1);
-  }
-}
diff --git a/_preview/3/_static/sphinx-thebe.js b/_preview/3/_static/sphinx-thebe.js
deleted file mode 100644
index d92b1d3..0000000
--- a/_preview/3/_static/sphinx-thebe.js
+++ /dev/null
@@ -1,127 +0,0 @@
-/**
- * Add attributes to Thebe blocks to initialize thebe properly
- */
-var configureThebe = () => {
-  // Load thebe config in case we want to update it as some point
-  console.log("[sphinx-thebe]: Loading thebe config...");
-  thebe_config = document.querySelector("script[type=\"text/x-thebe-config\"]");
-
-  // If we already detect a Thebe cell, don't re-run
-  if (document.querySelectorAll("div.thebe-cell").length > 0) {
-    return;
-  }
-
-  // Update thebe buttons with loading message
-  document.querySelectorAll(".thebe-launch-button").forEach((button) => {
-    button.innerHTML = `
-        <div class="spinner">
-            <div class="rect1"></div>
-            <div class="rect2"></div>
-            <div class="rect3"></div>
-            <div class="rect4"></div>
-        </div>
-        <span class="loading-text"></span>`;
-  });
-
-  // Set thebe event hooks
-  var thebeStatus;
-  thebelab.on("status", function (evt, data) {
-    console.log("Status changed:", data.status, data.message);
-
-    const button = document.querySelector(".thebe-launch-button ");
-    button.classList.replace(
-	    `thebe-status-${thebeStatus}`, 
-	    `thebe-status-${data.status}`
-    )
-    button.querySelector(".loading-text")
-      .innerHTML = (
-        `<span class='launch_msg'>Launching from mybinder.org: </span>
-	 <span class='status'>${data.status}</span>`
-      );
-
-    // Now update our thebe status
-    thebeStatus = data.status;
-
-    // Find any cells with an initialization tag and ask thebe to run them when ready
-    if (data.status === "ready") {
-      var thebeInitCells = document.querySelectorAll(
-        ".thebe-init, .tag_thebe-init"
-      );
-      thebeInitCells.forEach((cell) => {
-        console.log("Initializing Thebe with cell: " + cell.id);
-        cell.querySelector(".thebelab-run-button").click();
-      });
-    }
-  });
-};
-
-/**
- * Update the page DOM to use Thebe elements
- */
-var modifyDOMForThebe = () => {
-  // Find all code cells, replace with Thebe interactive code cells
-  const codeCells = document.querySelectorAll(thebe_selector);
-  codeCells.forEach((codeCell, index) => {
-    const codeCellId = (index) => `codecell${index}`;
-    codeCell.id = codeCellId(index);
-    codeCellText = codeCell.querySelector(thebe_selector_input);
-    codeCellOutput = codeCell.querySelector(thebe_selector_output);
-
-    // Clean up the language to make it work w/ CodeMirror and add it to the cell
-    dataLanguage = detectLanguage(kernelName);
-
-    // Re-arrange the cell and add metadata
-    if (codeCellText) {
-      codeCellText.setAttribute("data-language", dataLanguage);
-      codeCellText.setAttribute("data-executable", "true");
-
-      // If we had an output, insert it just after the `pre` cell
-      if (codeCellOutput) {
-        codeCellOutput.setAttribute("data-output", "");
-        codeCellText.insertAdjacentElement('afterend', codeCellOutput);
-      }
-    }
-
-    // Remove sphinx-copybutton blocks, which are common in Sphinx
-    codeCell.querySelectorAll("button.copybtn").forEach((el) => {
-      el.remove();
-    });
-  });
-};
-
-var initThebe = () => {
-  // Load thebe dynamically if it's not already loaded
-  if (typeof thebelab === "undefined") {
-    console.log("[sphinx-thebe]: Loading thebe from CDN...");
-    document.querySelector(".thebe-launch-button ").innerText = "Loading thebe from CDN...";
-
-    const script = document.createElement("script");
-    script.src = `${THEBE_JS_URL}`;
-    document.head.appendChild(script);
-
-    // Runs once the script has finished loading
-    script.addEventListener("load", () => {
-      console.log("[sphinx-thebe]: Finished loading thebe from CDN...");
-      configureThebe();
-      modifyDOMForThebe();
-      thebelab.bootstrap();
-    });
-  } else {
-    console.log(
-      "[sphinx-thebe]: thebe already loaded, not loading from CDN..."
-    );
-    configureThebe();
-    modifyDOMForThebe();
-    thebelab.bootstrap();
-  }
-};
-
-// Helper function to munge the language name
-var detectLanguage = (language) => {
-  if (language.indexOf("python") > -1) {
-    language = "python";
-  } else if (language === "ir") {
-    language = "r";
-  }
-  return language;
-};
diff --git a/_preview/3/_static/sphinx_highlight.js b/_preview/3/_static/sphinx_highlight.js
deleted file mode 100644
index 8a96c69..0000000
--- a/_preview/3/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,154 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      const rest = document.createTextNode(val.substr(pos + text.length));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
-          rest,
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-      /* There may be more occurrences of search term in this node. So call this
-       * function recursively on the remaining fragment.
-       */
-      _highlight(rest, addItems, text, className);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
-  /**
-   * highlight the search words provided in localstorage in the text
-   */
-  highlightSearchWords: () => {
-    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
-
-    // get and clear terms from localstorage
-    const url = new URL(window.location);
-    const highlight =
-        localStorage.getItem("sphinx_highlight_terms")
-        || url.searchParams.get("highlight")
-        || "";
-    localStorage.removeItem("sphinx_highlight_terms")
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-
-    // get individual terms from highlight string
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
-            _("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    localStorage.removeItem("sphinx_highlight_terms")
-  },
-
-  initEscapeListener: () => {
-    // only install a listener if it is really needed
-    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
-    document.addEventListener("keydown", (event) => {
-      // bail for input elements
-      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
-      // bail with special keys
-      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
-      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
-        SphinxHighlight.hideSearchWords();
-        event.preventDefault();
-      }
-    });
-  },
-};
-
-_ready(() => {
-  /* Do not call highlightSearchWords() when we are on the search page.
-   * It will highlight words from the *previous* search query.
-   */
-  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
-  SphinxHighlight.initEscapeListener();
-});
diff --git a/_preview/3/_static/styles/bootstrap.css b/_preview/3/_static/styles/bootstrap.css
deleted file mode 100644
index 5ac709f..0000000
--- a/_preview/3/_static/styles/bootstrap.css
+++ /dev/null
@@ -1,6 +0,0 @@
-/*!
- * Bootstrap  v5.3.3 (https://getbootstrap.com/)
- * Copyright 2011-2024 The Bootstrap Authors
- * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
- */:root,[data-bs-theme=light]{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-black:#000;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-primary-text-emphasis:#052c65;--bs-secondary-text-emphasis:#2b2f32;--bs-success-text-emphasis:#0a3622;--bs-info-text-emphasis:#055160;--bs-warning-text-emphasis:#664d03;--bs-danger-text-emphasis:#58151c;--bs-light-text-emphasis:#495057;--bs-dark-text-emphasis:#495057;--bs-primary-bg-subtle:#cfe2ff;--bs-secondary-bg-subtle:#e2e3e5;--bs-success-bg-subtle:#d1e7dd;--bs-info-bg-subtle:#cff4fc;--bs-warning-bg-subtle:#fff3cd;--bs-danger-bg-subtle:#f8d7da;--bs-light-bg-subtle:#fcfcfd;--bs-dark-bg-subtle:#ced4da;--bs-primary-border-subtle:#9ec5fe;--bs-secondary-border-subtle:#c4c8cb;--bs-success-border-subtle:#a3cfbb;--bs-info-border-subtle:#9eeaf9;--bs-warning-border-subtle:#ffe69c;--bs-danger-border-subtle:#f1aeb5;--bs-light-border-subtle:#e9ecef;--bs-dark-border-subtle:#adb5bd;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-font-sans-serif:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--bs-font-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;--bs-gradient:linear-gradient(180deg,hsla(0,0%,100%,.15),hsla(0,0%,100%,0));--bs-body-font-family:var(--bs-font-sans-serif);--bs-body-font-size:1rem;--bs-body-font-weight:400;--bs-body-line-height:1.5;--bs-body-color:#212529;--bs-body-color-rgb:33,37,41;--bs-body-bg:#fff;--bs-body-bg-rgb:255,255,255;--bs-emphasis-color:#000;--bs-emphasis-color-rgb:0,0,0;--bs-secondary-color:rgba(33,37,41,.75);--bs-secondary-color-rgb:33,37,41;--bs-secondary-bg:#e9ecef;--bs-secondary-bg-rgb:233,236,239;--bs-tertiary-color:rgba(33,37,41,.5);--bs-tertiary-color-rgb:33,37,41;--bs-tertiary-bg:#f8f9fa;--bs-tertiary-bg-rgb:248,249,250;--bs-heading-color:inherit;--bs-link-color:#0d6efd;--bs-link-color-rgb:13,110,253;--bs-link-decoration:underline;--bs-link-hover-color:#0a58ca;--bs-link-hover-color-rgb:10,88,202;--bs-code-color:#d63384;--bs-highlight-color:#212529;--bs-highlight-bg:#fff3cd;--bs-border-width:1px;--bs-border-style:solid;--bs-border-color:#dee2e6;--bs-border-color-translucent:rgba(0,0,0,.175);--bs-border-radius:0.375rem;--bs-border-radius-sm:0.25rem;--bs-border-radius-lg:0.5rem;--bs-border-radius-xl:1rem;--bs-border-radius-xxl:2rem;--bs-border-radius-2xl:var(--bs-border-radius-xxl);--bs-border-radius-pill:50rem;--bs-box-shadow:0 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.offcanvas{background-color:transparent!important;border:0!important;flex-grow:1;height:auto!important;position:static;transform:none!important;transition:none;visibility:visible!important;width:auto!important;z-index:auto}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;flex-grow:0;overflow-y:visible;padding:0}}@media(min-width:960px){.navbar-expand-lg{flex-wrap:nowrap;justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-left:var(--bs-navbar-nav-link-padding-x);padding-right:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex!important;flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{background-color:transparent!important;border:0!important;flex-grow:1;height:auto!important;position:static;transform:none!important;transition:none;visibility:visible!important;width:auto!important;z-index:auto}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;flex-grow:0;overflow-y:visible;padding:0}}@media(min-width:1200px){.navbar-expand-xl{flex-wrap:nowrap;justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-left:var(--bs-navbar-nav-link-padding-x);padding-right:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex!important;flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl 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.offcanvas{background-color:transparent!important;border:0!important;flex-grow:1;height:auto!important;position:static;transform:none!important;transition:none;visibility:visible!important;width:auto!important;z-index:auto}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;flex-grow:0;overflow-y:visible;padding:0}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color:hsla(0,0%,100%,.55);--bs-navbar-hover-color:hsla(0,0%,100%,.75);--bs-navbar-disabled-color:hsla(0,0%,100%,.25);--bs-navbar-active-color:#fff;--bs-navbar-brand-color:#fff;--bs-navbar-brand-hover-color:#fff;--bs-navbar-toggler-border-color:hsla(0,0%,100%,.1)}.navbar-dark,.navbar[data-bs-theme=dark],[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3E%3Cpath stroke='rgba(255, 255, 255, 0.55)' stroke-linecap='round' stroke-miterlimit='10' 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var(--bs-accordion-border-color);color:var(--bs-accordion-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type>.accordion-header .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-left-radius:var(--bs-accordion-border-radius);border-bottom-right-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type>.accordion-header 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16 16' fill='%236ea8fe'%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;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%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:var(--bs-secondary-color);--bs-breadcrumb-item-padding-x:0.5rem;--bs-breadcrumb-item-active-color:var(--bs-secondary-color);background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius);display:flex;flex-wrap:wrap;font-size:var(--bs-breadcrumb-font-size);list-style:none;margin-bottom:var(--bs-breadcrumb-margin-bottom);padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.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:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.1875rem var(--pst-color-accent);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;list-style:none;padding-left:0}.page-link{background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);color:var(--bs-pagination-color);display:block;font-size:var(--bs-pagination-font-size);padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);position:relative;text-decoration:none;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{background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color);color:var(--bs-pagination-hover-color);z-index:2}.page-link:focus{background-color:var(--bs-pagination-focus-bg);box-shadow:var(--bs-pagination-focus-box-shadow);color:var(--bs-pagination-focus-color);outline:0;z-index:3}.active>.page-link,.page-link.active{background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color);color:var(--bs-pagination-active-color);z-index:3}.disabled>.page-link,.page-link.disabled{background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color);color:var(--bs-pagination-disabled-color);pointer-events:none}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width)*-1)}.page-item:first-child .page-link{border-bottom-left-radius:var(--bs-pagination-border-radius);border-top-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-bottom-right-radius:var(--bs-pagination-border-radius);border-top-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.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:var(--bs-border-radius);border-radius:var(--bs-badge-border-radius);color:var(--bs-badge-color);display:inline-block;font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);text-align:center;vertical-align:baseline;white-space:nowrap}.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:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius);color:var(--bs-alert-color);margin-bottom:var(--bs-alert-margin-bottom);padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);position:relative}.alert-heading{color:inherit}.alert-link{color:var(--bs-alert-link-color);font-weight:700}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{padding:1.25rem 1rem;position:absolute;right:0;top:0;z-index:2}.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:1rem}}.progress,.progress-stacked{--bs-progress-height:1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg:var(--bs-secondary-bg);--bs-progress-border-radius:var(--bs-border-radius);--bs-progress-box-shadow:var(--bs-box-shadow-inset);--bs-progress-bar-color:#fff;--bs-progress-bar-bg:#0d6efd;--bs-progress-bar-transition:width 0.6s ease;background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius);display:flex;font-size:var(--bs-progress-font-size);height:var(--bs-progress-height);overflow:hidden}.progress-bar{background-color:var(--bs-progress-bar-bg);color:var(--bs-progress-bar-color);display:flex;flex-direction:column;justify-content:center;overflow:hidden;text-align:center;transition:var(--bs-progress-bar-transition);white-space:nowrap}@media(prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,hsla(0,0%,100%,.15) 25%,transparent 0,transparent 50%,hsla(0,0%,100%,.15) 0,hsla(0,0%,100%,.15) 75%,transparent 0,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:progress-bar-stripes 1s linear infinite}@media(prefers-reduced-motion:reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color:var(--bs-body-color);--bs-list-group-bg:var(--bs-body-bg);--bs-list-group-border-color:var(--bs-border-color);--bs-list-group-border-width:var(--bs-border-width);--bs-list-group-border-radius:var(--bs-border-radius);--bs-list-group-item-padding-x:1rem;--bs-list-group-item-padding-y:0.5rem;--bs-list-group-action-color:var(--bs-secondary-color);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-tertiary-bg);--bs-list-group-action-active-color:var(--bs-body-color);--bs-list-group-action-active-bg:var(--bs-secondary-bg);--bs-list-group-disabled-color:var(--bs-secondary-color);--bs-list-group-disabled-bg:var(--bs-body-bg);--bs-list-group-active-color:#fff;--bs-list-group-active-bg:#0d6efd;--bs-list-group-active-border-color:#0d6efd;border-radius:var(--bs-list-group-border-radius);display:flex;flex-direction:column;margin-bottom:0;padding-left:0}.list-group-numbered{counter-reset:section;list-style-type:none}.list-group-numbered>.list-group-item:before{content:counters(section,".") ". ";counter-increment:section}.list-group-item-action{color:var(--bs-list-group-action-color);text-align:inherit;width:100%}.list-group-item-action:focus,.list-group-item-action:hover{background-color:var(--bs-list-group-action-hover-bg);color:var(--bs-list-group-action-hover-color);text-decoration:none;z-index:1}.list-group-item-action:active{background-color:var(--bs-list-group-action-active-bg);color:var(--bs-list-group-action-active-color)}.list-group-item{background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color);color:var(--bs-list-group-color);display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);position:relative;text-decoration:none}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-left-radius:inherit;border-bottom-right-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{background-color:var(--bs-list-group-disabled-bg);color:var(--bs-list-group-disabled-color);pointer-events:none}.list-group-item.active{background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color);color:var(--bs-list-group-active-color);z-index:2}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{border-top-width:var(--bs-list-group-border-width);margin-top:calc(var(--bs-list-group-border-width)*-1)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}@media(min-width:540px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:720px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-md>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:960px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}.list-group-flush{border-radius:0}.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-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;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%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 0.1875rem var(--pst-color-accent);--bs-btn-close-focus-opacity:1;--bs-btn-close-disabled-opacity:0.25;--bs-btn-close-white-filter:invert(1) grayscale(100%) brightness(200%);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;box-sizing:content-box;height:1em;opacity:var(--bs-btn-close-opacity);padding:.25em;width:1em}.btn-close,.btn-close:hover{color:var(--bs-btn-close-color)}.btn-close:hover{opacity:var(--bs-btn-close-hover-opacity);text-decoration:none}.btn-close:focus{box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity);outline:0}.btn-close.disabled,.btn-close:disabled{opacity:var(--bs-btn-close-disabled-opacity);pointer-events:none;user-select:none}.btn-close-white,[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(var(--bs-body-bg-rgb),0.85);--bs-toast-border-width:var(--bs-border-width);--bs-toast-border-color:var(--bs-border-color-translucent);--bs-toast-border-radius:var(--bs-border-radius);--bs-toast-box-shadow:var(--bs-box-shadow);--bs-toast-header-color:var(--bs-secondary-color);--bs-toast-header-bg:rgba(var(--bs-body-bg-rgb),0.85);--bs-toast-header-border-color:var(--bs-border-color-translucent);background-clip:padding-box;background-color:var(--bs-toast-bg);border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);border-radius:var(--bs-toast-border-radius);box-shadow:var(--bs-toast-box-shadow);color:var(--bs-toast-color);font-size:var(--bs-toast-font-size);max-width:100%;pointer-events:auto;width:var(--bs-toast-max-width)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex:1090;max-width:100%;pointer-events:none;position:absolute;width:max-content;z-index:var(--bs-toast-zindex)}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{align-items:center;background-clip:padding-box;background-color:var(--bs-toast-header-bg);border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));color:var(--bs-toast-header-color);display:flex;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x)}.toast-header .btn-close{margin-left:var(--bs-toast-padding-x);margin-right:calc(var(--bs-toast-padding-x)*-.5)}.toast-body{word-wrap:break-word;padding:var(--bs-toast-padding-x)}.modal{--bs-modal-zindex:1055;--bs-modal-width:500px;--bs-modal-padding:1rem;--bs-modal-margin:0.5rem;--bs-modal-color: ;--bs-modal-bg:var(--bs-body-bg);--bs-modal-border-color:var(--bs-border-color-translucent);--bs-modal-border-width:var(--bs-border-width);--bs-modal-border-radius:var(--bs-border-radius-lg);--bs-modal-box-shadow:var(--bs-box-shadow-sm);--bs-modal-inner-border-radius:calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-modal-header-padding-x:1rem;--bs-modal-header-padding-y:1rem;--bs-modal-header-padding:1rem 1rem;--bs-modal-header-border-color:var(--bs-border-color);--bs-modal-header-border-width:var(--bs-border-width);--bs-modal-title-line-height:1.5;--bs-modal-footer-gap:0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color:var(--bs-border-color);--bs-modal-footer-border-width:var(--bs-border-width);display:none;height:100%;left:0;outline:0;overflow-x:hidden;overflow-y:auto;position:fixed;top:0;width:100%;z-index:var(--bs-modal-zindex)}.modal-dialog{margin:var(--bs-modal-margin);pointer-events:none;position:relative;width:auto}.modal.fade .modal-dialog{transform:translateY(-50px);transition:transform .3s ease-out}@media(prefers-reduced-motion:reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{align-items:center;display:flex;min-height:calc(100% - 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0)}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:rgba(0,0,0,0)}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: var(--pst-color-surface);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: var(--pst-color-surface);--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;flex-wrap:wrap;justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width)*-1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;align-items:flex-start;justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width)*-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: 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1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25rem;--bs-navbar-toggler-padding-x: 0.75rem;--bs-navbar-toggler-font-size: 1.25rem;--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='rgba%2833, 37, 41, 0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: 0.1875rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.offcanvas-body{display:flex;flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;justify-content:flex-start}.navbar-expand .navbar-nav{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;flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;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;flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: rgba(255, 255, 255, 0.55);--bs-navbar-hover-color: rgba(255, 255, 255, 0.75);--bs-navbar-disabled-color: rgba(255, 255, 255, 0.25);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(255, 255, 255, 0.1);--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='rgba%28255, 255, 255, 0.55%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='rgba%28255, 255, 255, 0.55%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: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;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);border-radius:var(--bs-card-border-radius)}.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;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{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-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.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-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.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);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 540px){.card-group{display:flex;flex-flow:row wrap}.card-group>.card{flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='%23212529' stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='%23052c65' stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");--bs-accordion-btn-focus-box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;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;border-radius: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;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;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:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type>.accordion-header .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type>.accordion-header .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type>.accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush>.accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush>.accordion-item:first-child{border-top:0}.accordion-flush>.accordion-item:last-child{border-bottom:0}.accordion-flush>.accordion-item>.accordion-header .accordion-button,.accordion-flush>.accordion-item>.accordion-header .accordion-button.collapsed{border-radius:0}.accordion-flush>.accordion-item>.accordion-collapse{border-radius: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='%236ea8fe'%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='%236ea8fe'%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: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;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);border-radius:var(--bs-breadcrumb-border-radius)}.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: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 0.1875rem var(--pst-color-accent);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display: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;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(var(--bs-border-width)*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.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: var(--bs-border-radius);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;border-radius:var(--bs-badge-border-radius)}.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: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--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);border-radius:var(--bs-alert-border-radius)}.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-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:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;flex-direction:column;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: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: 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var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.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}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.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: 540px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.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: 720px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.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: 960px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.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}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.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)}}.list-group-flush{border-radius:0}.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-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 0.1875rem var(--pst-color-accent);--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;border-radius:.375rem;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;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(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);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);border-radius:var(--bs-toast-border-radius)}.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;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;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);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.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: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: var(--bs-box-shadow-sm);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;flex-shrink:0;align-items:center;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;flex-shrink:0;flex-wrap:wrap;align-items:center;justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 540px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: var(--bs-box-shadow)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 960px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen 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!important}.p-lg-5{padding:3rem !important}.px-lg-0{padding-right:0 !important;padding-left:0 !important}.px-lg-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-lg-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-lg-3{padding-right:1rem !important;padding-left:1rem !important}.px-lg-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-lg-5{padding-right:3rem !important;padding-left:3rem !important}.py-lg-0{padding-top:0 !important;padding-bottom:0 !important}.py-lg-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-lg-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-lg-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-lg-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-lg-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-lg-0{padding-top:0 !important}.pt-lg-1{padding-top:.25rem !important}.pt-lg-2{padding-top:.5rem !important}.pt-lg-3{padding-top:1rem !important}.pt-lg-4{padding-top:1.5rem !important}.pt-lg-5{padding-top:3rem !important}.pe-lg-0{padding-right:0 !important}.pe-lg-1{padding-right:.25rem !important}.pe-lg-2{padding-right:.5rem !important}.pe-lg-3{padding-right:1rem !important}.pe-lg-4{padding-right:1.5rem !important}.pe-lg-5{padding-right:3rem !important}.pb-lg-0{padding-bottom:0 !important}.pb-lg-1{padding-bottom:.25rem !important}.pb-lg-2{padding-bottom:.5rem !important}.pb-lg-3{padding-bottom:1rem !important}.pb-lg-4{padding-bottom:1.5rem !important}.pb-lg-5{padding-bottom:3rem !important}.ps-lg-0{padding-left:0 !important}.ps-lg-1{padding-left:.25rem !important}.ps-lg-2{padding-left:.5rem !important}.ps-lg-3{padding-left:1rem !important}.ps-lg-4{padding-left:1.5rem !important}.ps-lg-5{padding-left:3rem !important}.gap-lg-0{gap:0 !important}.gap-lg-1{gap:.25rem !important}.gap-lg-2{gap:.5rem !important}.gap-lg-3{gap:1rem !important}.gap-lg-4{gap:1.5rem !important}.gap-lg-5{gap:3rem !important}.row-gap-lg-0{row-gap:0 !important}.row-gap-lg-1{row-gap:.25rem !important}.row-gap-lg-2{row-gap:.5rem !important}.row-gap-lg-3{row-gap:1rem !important}.row-gap-lg-4{row-gap:1.5rem !important}.row-gap-lg-5{row-gap:3rem !important}.column-gap-lg-0{column-gap:0 !important}.column-gap-lg-1{column-gap:.25rem !important}.column-gap-lg-2{column-gap:.5rem !important}.column-gap-lg-3{column-gap:1rem !important}.column-gap-lg-4{column-gap:1.5rem !important}.column-gap-lg-5{column-gap:3rem !important}.text-lg-start{text-align:left !important}.text-lg-end{text-align:right !important}.text-lg-center{text-align:center !important}}@media(min-width: 1200px){.float-xl-start{float:left !important}.float-xl-end{float:right !important}.float-xl-none{float:none !important}.object-fit-xl-contain{object-fit:contain !important}.object-fit-xl-cover{object-fit:cover !important}.object-fit-xl-fill{object-fit:fill !important}.object-fit-xl-scale{object-fit:scale-down !important}.object-fit-xl-none{object-fit:none !important}.d-xl-inline{display:inline !important}.d-xl-inline-block{display:inline-block !important}.d-xl-block{display:block !important}.d-xl-grid{display:grid !important}.d-xl-inline-grid{display:inline-grid !important}.d-xl-table{display:table !important}.d-xl-table-row{display:table-row !important}.d-xl-table-cell{display:table-cell !important}.d-xl-flex{display:flex !important}.d-xl-inline-flex{display:inline-flex !important}.d-xl-none{display:none !important}.flex-xl-fill{flex:1 1 auto !important}.flex-xl-row{flex-direction:row !important}.flex-xl-column{flex-direction:column !important}.flex-xl-row-reverse{flex-direction:row-reverse !important}.flex-xl-column-reverse{flex-direction:column-reverse !important}.flex-xl-grow-0{flex-grow:0 !important}.flex-xl-grow-1{flex-grow:1 !important}.flex-xl-shrink-0{flex-shrink:0 !important}.flex-xl-shrink-1{flex-shrink:1 !important}.flex-xl-wrap{flex-wrap:wrap !important}.flex-xl-nowrap{flex-wrap:nowrap !important}.flex-xl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xl-start{justify-content:flex-start !important}.justify-content-xl-end{justify-content:flex-end !important}.justify-content-xl-center{justify-content:center !important}.justify-content-xl-between{justify-content:space-between !important}.justify-content-xl-around{justify-content:space-around !important}.justify-content-xl-evenly{justify-content:space-evenly !important}.align-items-xl-start{align-items:flex-start !important}.align-items-xl-end{align-items:flex-end !important}.align-items-xl-center{align-items:center !important}.align-items-xl-baseline{align-items:baseline !important}.align-items-xl-stretch{align-items:stretch !important}.align-content-xl-start{align-content:flex-start !important}.align-content-xl-end{align-content:flex-end !important}.align-content-xl-center{align-content:center !important}.align-content-xl-between{align-content:space-between !important}.align-content-xl-around{align-content:space-around !important}.align-content-xl-stretch{align-content:stretch !important}.align-self-xl-auto{align-self:auto !important}.align-self-xl-start{align-self:flex-start !important}.align-self-xl-end{align-self:flex-end !important}.align-self-xl-center{align-self:center !important}.align-self-xl-baseline{align-self:baseline !important}.align-self-xl-stretch{align-self:stretch !important}.order-xl-first{order:-1 !important}.order-xl-0{order:0 !important}.order-xl-1{order:1 !important}.order-xl-2{order:2 !important}.order-xl-3{order:3 !important}.order-xl-4{order:4 !important}.order-xl-5{order:5 !important}.order-xl-last{order:6 !important}.m-xl-0{margin:0 !important}.m-xl-1{margin:.25rem !important}.m-xl-2{margin:.5rem !important}.m-xl-3{margin:1rem !important}.m-xl-4{margin:1.5rem !important}.m-xl-5{margin:3rem !important}.m-xl-auto{margin:auto !important}.mx-xl-0{margin-right:0 !important;margin-left:0 !important}.mx-xl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xl-auto{margin-right:auto !important;margin-left:auto !important}.my-xl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xl-0{margin-top:0 !important}.mt-xl-1{margin-top:.25rem !important}.mt-xl-2{margin-top:.5rem !important}.mt-xl-3{margin-top:1rem !important}.mt-xl-4{margin-top:1.5rem !important}.mt-xl-5{margin-top:3rem !important}.mt-xl-auto{margin-top:auto !important}.me-xl-0{margin-right:0 !important}.me-xl-1{margin-right:.25rem !important}.me-xl-2{margin-right:.5rem !important}.me-xl-3{margin-right:1rem !important}.me-xl-4{margin-right:1.5rem !important}.me-xl-5{margin-right:3rem !important}.me-xl-auto{margin-right:auto !important}.mb-xl-0{margin-bottom:0 !important}.mb-xl-1{margin-bottom:.25rem !important}.mb-xl-2{margin-bottom:.5rem !important}.mb-xl-3{margin-bottom:1rem !important}.mb-xl-4{margin-bottom:1.5rem !important}.mb-xl-5{margin-bottom:3rem !important}.mb-xl-auto{margin-bottom:auto !important}.ms-xl-0{margin-left:0 !important}.ms-xl-1{margin-left:.25rem !important}.ms-xl-2{margin-left:.5rem !important}.ms-xl-3{margin-left:1rem !important}.ms-xl-4{margin-left:1.5rem !important}.ms-xl-5{margin-left:3rem !important}.ms-xl-auto{margin-left:auto !important}.p-xl-0{padding:0 !important}.p-xl-1{padding:.25rem !important}.p-xl-2{padding:.5rem !important}.p-xl-3{padding:1rem !important}.p-xl-4{padding:1.5rem !important}.p-xl-5{padding:3rem !important}.px-xl-0{padding-right:0 !important;padding-left:0 !important}.px-xl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xl-0{padding-top:0 !important}.pt-xl-1{padding-top:.25rem !important}.pt-xl-2{padding-top:.5rem !important}.pt-xl-3{padding-top:1rem !important}.pt-xl-4{padding-top:1.5rem !important}.pt-xl-5{padding-top:3rem !important}.pe-xl-0{padding-right:0 !important}.pe-xl-1{padding-right:.25rem !important}.pe-xl-2{padding-right:.5rem !important}.pe-xl-3{padding-right:1rem !important}.pe-xl-4{padding-right:1.5rem !important}.pe-xl-5{padding-right:3rem !important}.pb-xl-0{padding-bottom:0 !important}.pb-xl-1{padding-bottom:.25rem !important}.pb-xl-2{padding-bottom:.5rem !important}.pb-xl-3{padding-bottom:1rem !important}.pb-xl-4{padding-bottom:1.5rem !important}.pb-xl-5{padding-bottom:3rem !important}.ps-xl-0{padding-left:0 !important}.ps-xl-1{padding-left:.25rem !important}.ps-xl-2{padding-left:.5rem !important}.ps-xl-3{padding-left:1rem !important}.ps-xl-4{padding-left:1.5rem !important}.ps-xl-5{padding-left:3rem !important}.gap-xl-0{gap:0 !important}.gap-xl-1{gap:.25rem !important}.gap-xl-2{gap:.5rem !important}.gap-xl-3{gap:1rem !important}.gap-xl-4{gap:1.5rem !important}.gap-xl-5{gap:3rem !important}.row-gap-xl-0{row-gap:0 !important}.row-gap-xl-1{row-gap:.25rem !important}.row-gap-xl-2{row-gap:.5rem !important}.row-gap-xl-3{row-gap:1rem !important}.row-gap-xl-4{row-gap:1.5rem !important}.row-gap-xl-5{row-gap:3rem !important}.column-gap-xl-0{column-gap:0 !important}.column-gap-xl-1{column-gap:.25rem !important}.column-gap-xl-2{column-gap:.5rem !important}.column-gap-xl-3{column-gap:1rem !important}.column-gap-xl-4{column-gap:1.5rem !important}.column-gap-xl-5{column-gap:3rem !important}.text-xl-start{text-align:left !important}.text-xl-end{text-align:right !important}.text-xl-center{text-align:center !important}}@media(min-width: 1200px){.fs-1{font-size:2.5rem !important}.fs-2{font-size:2rem !important}.fs-3{font-size:1.75rem !important}.fs-4{font-size:1.5rem !important}}@media print{.d-print-inline{display:inline !important}.d-print-inline-block{display:inline-block !important}.d-print-block{display:block !important}.d-print-grid{display:grid !important}.d-print-inline-grid{display:inline-grid !important}.d-print-table{display:table !important}.d-print-table-row{display:table-row !important}.d-print-table-cell{display:table-cell !important}.d-print-flex{display:flex !important}.d-print-inline-flex{display:inline-flex !important}.d-print-none{display:none !important}}","@mixin bsBanner($file) {\n  /*!\n   * Bootstrap #{$file} v5.3.3 (https://getbootstrap.com/)\n   * Copyright 2011-2024 The Bootstrap Authors\n   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n   */\n}\n",":root,\n[data-bs-theme=\"light\"] {\n  // Note: Custom variable values only support SassScript inside `#{}`.\n\n  // Colors\n  //\n  // Generate palettes for full colors, grays, and theme colors.\n\n  @each $color, $value in $colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $grays {\n    --#{$prefix}gray-#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-rgb {\n    --#{$prefix}#{$color}-rgb: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-text {\n    --#{$prefix}#{$color}-text-emphasis: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-bg-subtle {\n    --#{$prefix}#{$color}-bg-subtle: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-border-subtle {\n    --#{$prefix}#{$color}-border-subtle: #{$value};\n  }\n\n  --#{$prefix}white-rgb: #{to-rgb($white)};\n  --#{$prefix}black-rgb: #{to-rgb($black)};\n\n  // Fonts\n\n  // Note: Use `inspect` for lists so that quoted items keep the quotes.\n  // See https://github.com/sass/sass/issues/2383#issuecomment-336349172\n  --#{$prefix}font-sans-serif: #{inspect($font-family-sans-serif)};\n  --#{$prefix}font-monospace: #{inspect($font-family-monospace)};\n  --#{$prefix}gradient: #{$gradient};\n\n  // Root and body\n  // scss-docs-start root-body-variables\n  @if $font-size-root != null {\n    --#{$prefix}root-font-size: #{$font-size-root};\n  }\n  --#{$prefix}body-font-family: #{inspect($font-family-base)};\n  @include rfs($font-size-base, --#{$prefix}body-font-size);\n  --#{$prefix}body-font-weight: #{$font-weight-base};\n  --#{$prefix}body-line-height: #{$line-height-base};\n  @if $body-text-align != null {\n    --#{$prefix}body-text-align: #{$body-text-align};\n  }\n\n  --#{$prefix}body-color: #{$body-color};\n  --#{$prefix}body-color-rgb: #{to-rgb($body-color)};\n  --#{$prefix}body-bg: #{$body-bg};\n  --#{$prefix}body-bg-rgb: #{to-rgb($body-bg)};\n\n  --#{$prefix}emphasis-color: #{$body-emphasis-color};\n  --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color)};\n\n  --#{$prefix}secondary-color: #{$body-secondary-color};\n  --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color)};\n  --#{$prefix}secondary-bg: #{$body-secondary-bg};\n  --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg)};\n\n  --#{$prefix}tertiary-color: #{$body-tertiary-color};\n  --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color)};\n  --#{$prefix}tertiary-bg: #{$body-tertiary-bg};\n  --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg)};\n  // scss-docs-end root-body-variables\n\n  --#{$prefix}heading-color: #{$headings-color};\n\n  --#{$prefix}link-color: #{$link-color};\n  --#{$prefix}link-color-rgb: #{to-rgb($link-color)};\n  --#{$prefix}link-decoration: #{$link-decoration};\n\n  --#{$prefix}link-hover-color: #{$link-hover-color};\n  --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color)};\n\n  @if $link-hover-decoration != null {\n    --#{$prefix}link-hover-decoration: #{$link-hover-decoration};\n  }\n\n  --#{$prefix}code-color: #{$code-color};\n  --#{$prefix}highlight-color: #{$mark-color};\n  --#{$prefix}highlight-bg: #{$mark-bg};\n\n  // scss-docs-start root-border-var\n  --#{$prefix}border-width: #{$border-width};\n  --#{$prefix}border-style: #{$border-style};\n  --#{$prefix}border-color: #{$border-color};\n  --#{$prefix}border-color-translucent: #{$border-color-translucent};\n\n  --#{$prefix}border-radius: #{$border-radius};\n  --#{$prefix}border-radius-sm: #{$border-radius-sm};\n  --#{$prefix}border-radius-lg: #{$border-radius-lg};\n  --#{$prefix}border-radius-xl: #{$border-radius-xl};\n  --#{$prefix}border-radius-xxl: #{$border-radius-xxl};\n  --#{$prefix}border-radius-2xl: var(--#{$prefix}border-radius-xxl); // Deprecated in v5.3.0 for consistency\n  --#{$prefix}border-radius-pill: #{$border-radius-pill};\n  // scss-docs-end root-border-var\n\n  --#{$prefix}box-shadow: #{$box-shadow};\n  --#{$prefix}box-shadow-sm: #{$box-shadow-sm};\n  --#{$prefix}box-shadow-lg: #{$box-shadow-lg};\n  --#{$prefix}box-shadow-inset: #{$box-shadow-inset};\n\n  // Focus styles\n  // scss-docs-start root-focus-variables\n  --#{$prefix}focus-ring-width: #{$focus-ring-width};\n  --#{$prefix}focus-ring-opacity: #{$focus-ring-opacity};\n  --#{$prefix}focus-ring-color: #{$focus-ring-color};\n  // scss-docs-end root-focus-variables\n\n  // scss-docs-start root-form-validation-variables\n  --#{$prefix}form-valid-color: #{$form-valid-color};\n  --#{$prefix}form-valid-border-color: #{$form-valid-border-color};\n  --#{$prefix}form-invalid-color: #{$form-invalid-color};\n  --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color};\n  // scss-docs-end root-form-validation-variables\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark, true) {\n    color-scheme: dark;\n\n    // scss-docs-start root-dark-mode-vars\n    --#{$prefix}body-color: #{$body-color-dark};\n    --#{$prefix}body-color-rgb: #{to-rgb($body-color-dark)};\n    --#{$prefix}body-bg: #{$body-bg-dark};\n    --#{$prefix}body-bg-rgb: #{to-rgb($body-bg-dark)};\n\n    --#{$prefix}emphasis-color: #{$body-emphasis-color-dark};\n    --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color-dark)};\n\n    --#{$prefix}secondary-color: #{$body-secondary-color-dark};\n    --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color-dark)};\n    --#{$prefix}secondary-bg: #{$body-secondary-bg-dark};\n    --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg-dark)};\n\n    --#{$prefix}tertiary-color: #{$body-tertiary-color-dark};\n    --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color-dark)};\n    --#{$prefix}tertiary-bg: #{$body-tertiary-bg-dark};\n    --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg-dark)};\n\n    @each $color, $value in $theme-colors-text-dark {\n      --#{$prefix}#{$color}-text-emphasis: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-bg-subtle-dark {\n      --#{$prefix}#{$color}-bg-subtle: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-border-subtle-dark {\n      --#{$prefix}#{$color}-border-subtle: #{$value};\n    }\n\n    --#{$prefix}heading-color: #{$headings-color-dark};\n\n    --#{$prefix}link-color: #{$link-color-dark};\n    --#{$prefix}link-hover-color: #{$link-hover-color-dark};\n    --#{$prefix}link-color-rgb: #{to-rgb($link-color-dark)};\n    --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color-dark)};\n\n    --#{$prefix}code-color: #{$code-color-dark};\n    --#{$prefix}highlight-color: #{$mark-color-dark};\n    --#{$prefix}highlight-bg: #{$mark-bg-dark};\n\n    --#{$prefix}border-color: #{$border-color-dark};\n    --#{$prefix}border-color-translucent: #{$border-color-translucent-dark};\n\n    --#{$prefix}form-valid-color: #{$form-valid-color-dark};\n    --#{$prefix}form-valid-border-color: #{$form-valid-border-color-dark};\n    --#{$prefix}form-invalid-color: #{$form-invalid-color-dark};\n    --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color-dark};\n    // scss-docs-end root-dark-mode-vars\n  }\n}\n","// stylelint-disable scss/dimension-no-non-numeric-values\n\n// SCSS RFS mixin\n//\n// Automated responsive values for font sizes, paddings, margins and much more\n//\n// Licensed under MIT (https://github.com/twbs/rfs/blob/main/LICENSE)\n\n// Configuration\n\n// Base value\n$rfs-base-value: 1.25rem !default;\n$rfs-unit: rem !default;\n\n@if $rfs-unit != rem and $rfs-unit != px {\n  @error \"`#{$rfs-unit}` is not a valid unit for $rfs-unit. Use `px` or `rem`.\";\n}\n\n// Breakpoint at where values start decreasing if screen width is smaller\n$rfs-breakpoint: 1200px !default;\n$rfs-breakpoint-unit: px !default;\n\n@if $rfs-breakpoint-unit != px and $rfs-breakpoint-unit != em and $rfs-breakpoint-unit != rem {\n  @error \"`#{$rfs-breakpoint-unit}` is not a valid unit for $rfs-breakpoint-unit. Use `px`, `em` or `rem`.\";\n}\n\n// Resize values based on screen height and width\n$rfs-two-dimensional: false !default;\n\n// Factor of decrease\n$rfs-factor: 10 !default;\n\n@if type-of($rfs-factor) != number or $rfs-factor <= 1 {\n  @error \"`#{$rfs-factor}` is not a valid  $rfs-factor, it must be greater than 1.\";\n}\n\n// Mode. Possibilities: \"min-media-query\", \"max-media-query\"\n$rfs-mode: min-media-query !default;\n\n// Generate enable or disable classes. Possibilities: false, \"enable\" or \"disable\"\n$rfs-class: false !default;\n\n// 1 rem = $rfs-rem-value px\n$rfs-rem-value: 16 !default;\n\n// Safari iframe resize bug: https://github.com/twbs/rfs/issues/14\n$rfs-safari-iframe-resize-bug-fix: false !default;\n\n// Disable RFS by setting $enable-rfs to false\n$enable-rfs: true !default;\n\n// Cache $rfs-base-value unit\n$rfs-base-value-unit: unit($rfs-base-value);\n\n@function divide($dividend, $divisor, $precision: 10) {\n  $sign: if($dividend > 0 and $divisor > 0 or $dividend < 0 and $divisor < 0, 1, -1);\n  $dividend: abs($dividend);\n  $divisor: abs($divisor);\n  @if $dividend == 0 {\n    @return 0;\n  }\n  @if $divisor == 0 {\n    @error \"Cannot divide by 0\";\n  }\n  $remainder: $dividend;\n  $result: 0;\n  $factor: 10;\n  @while ($remainder > 0 and $precision >= 0) {\n    $quotient: 0;\n    @while ($remainder >= $divisor) {\n      $remainder: $remainder - $divisor;\n      $quotient: $quotient + 1;\n    }\n    $result: $result * 10 + $quotient;\n    $factor: $factor * .1;\n    $remainder: $remainder * 10;\n    $precision: $precision - 1;\n    @if ($precision < 0 and $remainder >= $divisor * 5) {\n      $result: $result + 1;\n    }\n  }\n  $result: $result * $factor * $sign;\n  $dividend-unit: unit($dividend);\n  $divisor-unit: unit($divisor);\n  $unit-map: (\n    \"px\": 1px,\n    \"rem\": 1rem,\n    \"em\": 1em,\n    \"%\": 1%\n  );\n  @if ($dividend-unit != $divisor-unit and map-has-key($unit-map, $dividend-unit)) {\n    $result: $result * map-get($unit-map, $dividend-unit);\n  }\n  @return $result;\n}\n\n// Remove px-unit from $rfs-base-value for calculations\n@if $rfs-base-value-unit == px {\n  $rfs-base-value: divide($rfs-base-value, $rfs-base-value * 0 + 1);\n}\n@else if $rfs-base-value-unit == rem {\n  $rfs-base-value: divide($rfs-base-value, divide($rfs-base-value * 0 + 1, $rfs-rem-value));\n}\n\n// Cache $rfs-breakpoint unit to prevent multiple calls\n$rfs-breakpoint-unit-cache: unit($rfs-breakpoint);\n\n// Remove unit from $rfs-breakpoint for calculations\n@if $rfs-breakpoint-unit-cache == px {\n  $rfs-breakpoint: divide($rfs-breakpoint, $rfs-breakpoint * 0 + 1);\n}\n@else if $rfs-breakpoint-unit-cache == rem or $rfs-breakpoint-unit-cache == \"em\" {\n  $rfs-breakpoint: divide($rfs-breakpoint, divide($rfs-breakpoint * 0 + 1, $rfs-rem-value));\n}\n\n// Calculate the media query value\n$rfs-mq-value: if($rfs-breakpoint-unit == px, #{$rfs-breakpoint}px, #{divide($rfs-breakpoint, $rfs-rem-value)}#{$rfs-breakpoint-unit});\n$rfs-mq-property-width: if($rfs-mode == max-media-query, max-width, min-width);\n$rfs-mq-property-height: if($rfs-mode == max-media-query, max-height, min-height);\n\n// Internal mixin used to determine which media query needs to be used\n@mixin _rfs-media-query {\n  @if $rfs-two-dimensional {\n    @if $rfs-mode == max-media-query {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}), (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n    @else {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) and (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n  }\n  @else {\n    @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) {\n      @content;\n    }\n  }\n}\n\n// Internal mixin that adds disable classes to the selector if needed.\n@mixin _rfs-rule {\n  @if $rfs-class == disable and $rfs-mode == max-media-query {\n    // Adding an extra class increases specificity, which prevents the media query to override the property\n    &,\n    .disable-rfs &,\n    &.disable-rfs {\n      @content;\n    }\n  }\n  @else if $rfs-class == enable and $rfs-mode == min-media-query {\n    .enable-rfs &,\n    &.enable-rfs {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Internal mixin that adds enable classes to the selector if needed.\n@mixin _rfs-media-query-rule {\n\n  @if $rfs-class == enable {\n    @if $rfs-mode == min-media-query {\n      @content;\n    }\n\n    @include _rfs-media-query () {\n      .enable-rfs &,\n      &.enable-rfs {\n        @content;\n      }\n    }\n  }\n  @else {\n    @if $rfs-class == disable and $rfs-mode == min-media-query {\n      .disable-rfs &,\n      &.disable-rfs {\n        @content;\n      }\n    }\n    @include _rfs-media-query () {\n      @content;\n    }\n  }\n}\n\n// Helper function to get the formatted non-responsive value\n@function rfs-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    }\n    @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      @if $unit == px {\n        // Convert to rem if needed\n        $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $value * 0 + $rfs-rem-value)}rem, $value);\n      }\n      @else if $unit == rem {\n        // Convert to px if needed\n        $val: $val + \" \" + if($rfs-unit == px, #{divide($value, $value * 0 + 1) * $rfs-rem-value}px, $value);\n      } @else {\n        // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n        $val: $val + \" \" + $value;\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// Helper function to get the responsive value calculated by RFS\n@function rfs-fluid-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    } @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n      @if not $unit or $unit != px and $unit != rem {\n        $val: $val + \" \" + $value;\n      } @else {\n        // Remove unit from $value for calculations\n        $value: divide($value, $value * 0 + if($unit == px, 1, divide(1, $rfs-rem-value)));\n\n        // Only add the media query if the value is greater than the minimum value\n        @if abs($value) <= $rfs-base-value or not $enable-rfs {\n          $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $rfs-rem-value)}rem, #{$value}px);\n        }\n        @else {\n          // Calculate the minimum value\n          $value-min: $rfs-base-value + divide(abs($value) - $rfs-base-value, $rfs-factor);\n\n          // Calculate difference between $value and the minimum value\n          $value-diff: abs($value) - $value-min;\n\n          // Base value formatting\n          $min-width: if($rfs-unit == rem, #{divide($value-min, $rfs-rem-value)}rem, #{$value-min}px);\n\n          // Use negative value if needed\n          $min-width: if($value < 0, -$min-width, $min-width);\n\n          // Use `vmin` if two-dimensional is enabled\n          $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n          // Calculate the variable width between 0 and $rfs-breakpoint\n          $variable-width: #{divide($value-diff * 100, $rfs-breakpoint)}#{$variable-unit};\n\n          // Return the calculated value\n          $val: $val + \" calc(\" + $min-width + if($value < 0, \" - \", \" + \") + $variable-width + \")\";\n        }\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// RFS mixin\n@mixin rfs($values, $property: font-size) {\n  @if $values != null {\n    $val: rfs-value($values);\n    $fluid-val: rfs-fluid-value($values);\n\n    // Do not print the media query if responsive & non-responsive values are the same\n    @if $val == $fluid-val {\n      #{$property}: $val;\n    }\n    @else {\n      @include _rfs-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $val, $fluid-val);\n\n        // Include safari iframe resize fix if needed\n        min-width: if($rfs-safari-iframe-resize-bug-fix, (0 * 1vw), null);\n      }\n\n      @include _rfs-media-query-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $fluid-val, $val);\n      }\n    }\n  }\n}\n\n// Shorthand helper mixins\n@mixin font-size($value) {\n  @include rfs($value);\n}\n\n@mixin padding($value) {\n  @include rfs($value, padding);\n}\n\n@mixin padding-top($value) {\n  @include rfs($value, padding-top);\n}\n\n@mixin padding-right($value) {\n  @include rfs($value, padding-right);\n}\n\n@mixin padding-bottom($value) {\n  @include rfs($value, padding-bottom);\n}\n\n@mixin padding-left($value) {\n  @include rfs($value, padding-left);\n}\n\n@mixin margin($value) {\n  @include rfs($value, margin);\n}\n\n@mixin margin-top($value) {\n  @include rfs($value, margin-top);\n}\n\n@mixin margin-right($value) {\n  @include rfs($value, margin-right);\n}\n\n@mixin margin-bottom($value) {\n  @include rfs($value, margin-bottom);\n}\n\n@mixin margin-left($value) {\n  @include rfs($value, margin-left);\n}\n","// scss-docs-start color-mode-mixin\n@mixin color-mode($mode: light, $root: false) {\n  @if $color-mode-type == \"media-query\" {\n    @if $root == true {\n      @media (prefers-color-scheme: $mode) {\n        :root {\n          @content;\n        }\n      }\n    } @else {\n      @media (prefers-color-scheme: $mode) {\n        @content;\n      }\n    }\n  } @else {\n    [data-bs-theme=\"#{$mode}\"] {\n      @content;\n    }\n  }\n}\n// scss-docs-end color-mode-mixin\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type, property-no-vendor-prefix\n\n\n// Reboot\n//\n// Normalization of HTML elements, manually forked from Normalize.css to remove\n// styles targeting irrelevant browsers while applying new styles.\n//\n// Normalize is licensed MIT. https://github.com/necolas/normalize.css\n\n\n// Document\n//\n// Change from `box-sizing: content-box` so that `width` is not affected by `padding` or `border`.\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n}\n\n\n// Root\n//\n// Ability to the value of the root font sizes, affecting the value of `rem`.\n// null by default, thus nothing is generated.\n\n:root {\n  @if $font-size-root != null {\n    @include font-size(var(--#{$prefix}root-font-size));\n  }\n\n  @if $enable-smooth-scroll {\n    @media (prefers-reduced-motion: no-preference) {\n      scroll-behavior: smooth;\n    }\n  }\n}\n\n\n// Body\n//\n// 1. Remove the margin in all browsers.\n// 2. As a best practice, apply a default `background-color`.\n// 3. Prevent adjustments of font size after orientation changes in iOS.\n// 4. Change the default tap highlight to be completely transparent in iOS.\n\n// scss-docs-start reboot-body-rules\nbody {\n  margin: 0; // 1\n  font-family: var(--#{$prefix}body-font-family);\n  @include font-size(var(--#{$prefix}body-font-size));\n  font-weight: var(--#{$prefix}body-font-weight);\n  line-height: var(--#{$prefix}body-line-height);\n  color: var(--#{$prefix}body-color);\n  text-align: var(--#{$prefix}body-text-align);\n  background-color: var(--#{$prefix}body-bg); // 2\n  -webkit-text-size-adjust: 100%; // 3\n  -webkit-tap-highlight-color: rgba($black, 0); // 4\n}\n// scss-docs-end reboot-body-rules\n\n\n// Content grouping\n//\n// 1. Reset Firefox's gray color\n\nhr {\n  margin: $hr-margin-y 0;\n  color: $hr-color; // 1\n  border: 0;\n  border-top: $hr-border-width solid $hr-border-color;\n  opacity: $hr-opacity;\n}\n\n\n// Typography\n//\n// 1. Remove top margins from headings\n//    By default, `<h1>`-`<h6>` all receive top and bottom margins. We nuke the top\n//    margin for easier control within type scales as it avoids margin collapsing.\n\n%heading {\n  margin-top: 0; // 1\n  margin-bottom: $headings-margin-bottom;\n  font-family: $headings-font-family;\n  font-style: $headings-font-style;\n  font-weight: $headings-font-weight;\n  line-height: $headings-line-height;\n  color: var(--#{$prefix}heading-color);\n}\n\nh1 {\n  @extend %heading;\n  @include font-size($h1-font-size);\n}\n\nh2 {\n  @extend %heading;\n  @include font-size($h2-font-size);\n}\n\nh3 {\n  @extend %heading;\n  @include font-size($h3-font-size);\n}\n\nh4 {\n  @extend %heading;\n  @include font-size($h4-font-size);\n}\n\nh5 {\n  @extend %heading;\n  @include font-size($h5-font-size);\n}\n\nh6 {\n  @extend %heading;\n  @include font-size($h6-font-size);\n}\n\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on `<p>`s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\n\np {\n  margin-top: 0;\n  margin-bottom: $paragraph-margin-bottom;\n}\n\n\n// Abbreviations\n//\n// 1. Add the correct text decoration in Chrome, Edge, Opera, and Safari.\n// 2. Add explicit cursor to indicate changed behavior.\n// 3. Prevent the text-decoration to be skipped.\n\nabbr[title] {\n  text-decoration: underline dotted; // 1\n  cursor: help; // 2\n  text-decoration-skip-ink: none; // 3\n}\n\n\n// Address\n\naddress {\n  margin-bottom: 1rem;\n  font-style: normal;\n  line-height: inherit;\n}\n\n\n// Lists\n\nol,\nul {\n  padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n  margin-bottom: 0;\n}\n\ndt {\n  font-weight: $dt-font-weight;\n}\n\n// 1. Undo browser default\n\ndd {\n  margin-bottom: .5rem;\n  margin-left: 0; // 1\n}\n\n\n// Blockquote\n\nblockquote {\n  margin: 0 0 1rem;\n}\n\n\n// Strong\n//\n// Add the correct font weight in Chrome, Edge, and Safari\n\nb,\nstrong {\n  font-weight: $font-weight-bolder;\n}\n\n\n// Small\n//\n// Add the correct font size in all browsers\n\nsmall {\n  @include font-size($small-font-size);\n}\n\n\n// Mark\n\nmark {\n  padding: $mark-padding;\n  color: var(--#{$prefix}highlight-color);\n  background-color: var(--#{$prefix}highlight-bg);\n}\n\n\n// Sub and Sup\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n\nsub,\nsup {\n  position: relative;\n  @include font-size($sub-sup-font-size);\n  line-height: 0;\n  vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n// Links\n\na {\n  color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, 1));\n  text-decoration: $link-decoration;\n\n  &:hover {\n    --#{$prefix}link-color-rgb: var(--#{$prefix}link-hover-color-rgb);\n    text-decoration: $link-hover-decoration;\n  }\n}\n\n// And undo these styles for placeholder links/named anchors (without href).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([class]) {\n  &,\n  &:hover {\n    color: inherit;\n    text-decoration: none;\n  }\n}\n\n\n// Code\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: $font-family-code;\n  @include font-size(1em); // Correct the odd `em` font sizing in all browsers.\n}\n\n// 1. Remove browser default top margin\n// 2. Reset browser default of `1em` to use `rem`s\n// 3. Don't allow content to break outside\n\npre {\n  display: block;\n  margin-top: 0; // 1\n  margin-bottom: 1rem; // 2\n  overflow: auto; // 3\n  @include font-size($code-font-size);\n  color: $pre-color;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    @include font-size(inherit);\n    color: inherit;\n    word-break: normal;\n  }\n}\n\ncode {\n  @include font-size($code-font-size);\n  color: var(--#{$prefix}code-color);\n  word-wrap: break-word;\n\n  // Streamline the style when inside anchors to avoid broken underline and more\n  a > & {\n    color: inherit;\n  }\n}\n\nkbd {\n  padding: $kbd-padding-y $kbd-padding-x;\n  @include font-size($kbd-font-size);\n  color: $kbd-color;\n  background-color: $kbd-bg;\n  @include border-radius($border-radius-sm);\n\n  kbd {\n    padding: 0;\n    @include font-size(1em);\n    font-weight: $nested-kbd-font-weight;\n  }\n}\n\n\n// Figures\n//\n// Apply a consistent margin strategy (matches our type styles).\n\nfigure {\n  margin: 0 0 1rem;\n}\n\n\n// Images and content\n\nimg,\nsvg {\n  vertical-align: middle;\n}\n\n\n// Tables\n//\n// Prevent double borders\n\ntable {\n  caption-side: bottom;\n  border-collapse: collapse;\n}\n\ncaption {\n  padding-top: $table-cell-padding-y;\n  padding-bottom: $table-cell-padding-y;\n  color: $table-caption-color;\n  text-align: left;\n}\n\n// 1. Removes font-weight bold by inheriting\n// 2. Matches default `<td>` alignment by inheriting `text-align`.\n// 3. Fix alignment for Safari\n\nth {\n  font-weight: $table-th-font-weight; // 1\n  text-align: inherit; // 2\n  text-align: -webkit-match-parent; // 3\n}\n\nthead,\ntbody,\ntfoot,\ntr,\ntd,\nth {\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n}\n\n\n// Forms\n//\n// 1. Allow labels to use `margin` for spacing.\n\nlabel {\n  display: inline-block; // 1\n}\n\n// Remove the default `border-radius` that macOS Chrome adds.\n// See https://github.com/twbs/bootstrap/issues/24093\n\nbutton {\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 0;\n}\n\n// Explicitly remove focus outline in Chromium when it shouldn't be\n// visible (e.g. as result of mouse click or touch tap). It already\n// should be doing this automatically, but seems to currently be\n// confused and applies its very visible two-tone outline anyway.\n\nbutton:focus:not(:focus-visible) {\n  outline: 0;\n}\n\n// 1. Remove the margin in Firefox and Safari\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n  margin: 0; // 1\n  font-family: inherit;\n  @include font-size(inherit);\n  line-height: inherit;\n}\n\n// Remove the inheritance of text transform in Firefox\nbutton,\nselect {\n  text-transform: none;\n}\n// Set the cursor for non-`<button>` buttons\n//\n// Details at https://github.com/twbs/bootstrap/pull/30562\n[role=\"button\"] {\n  cursor: pointer;\n}\n\nselect {\n  // Remove the inheritance of word-wrap in Safari.\n  // See https://github.com/twbs/bootstrap/issues/24990\n  word-wrap: normal;\n\n  // Undo the opacity change from Chrome\n  &:disabled {\n    opacity: 1;\n  }\n}\n\n// Remove the dropdown arrow only from text type inputs built with datalists in Chrome.\n// See https://stackoverflow.com/a/54997118\n\n[list]:not([type=\"date\"]):not([type=\"datetime-local\"]):not([type=\"month\"]):not([type=\"week\"]):not([type=\"time\"])::-webkit-calendar-picker-indicator {\n  display: none !important;\n}\n\n// 1. Prevent a WebKit bug where (2) destroys native `audio` and `video`\n//    controls in Android 4.\n// 2. Correct the inability to style clickable types in iOS and Safari.\n// 3. Opinionated: add \"hand\" cursor to non-disabled button elements.\n\nbutton,\n[type=\"button\"], // 1\n[type=\"reset\"],\n[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n\n  @if $enable-button-pointers {\n    &:not(:disabled) {\n      cursor: pointer; // 3\n    }\n  }\n}\n\n// Remove inner border and padding from Firefox, but don't restore the outline like Normalize.\n\n::-moz-focus-inner {\n  padding: 0;\n  border-style: none;\n}\n\n// 1. Textareas should really only resize vertically so they don't break their (horizontal) containers.\n\ntextarea {\n  resize: vertical; // 1\n}\n\n// 1. Browsers set a default `min-width: min-content;` on fieldsets,\n//    unlike e.g. `<div>`s, which have `min-width: 0;` by default.\n//    So we reset that to ensure fieldsets behave more like a standard block element.\n//    See https://github.com/twbs/bootstrap/issues/12359\n//    and https://html.spec.whatwg.org/multipage/#the-fieldset-and-legend-elements\n// 2. Reset the default outline behavior of fieldsets so they don't affect page layout.\n\nfieldset {\n  min-width: 0; // 1\n  padding: 0; // 2\n  margin: 0; // 2\n  border: 0; // 2\n}\n\n// 1. By using `float: left`, the legend will behave like a block element.\n//    This way the border of a fieldset wraps around the legend if present.\n// 2. Fix wrapping bug.\n//    See https://github.com/twbs/bootstrap/issues/29712\n\nlegend {\n  float: left; // 1\n  width: 100%;\n  padding: 0;\n  margin-bottom: $legend-margin-bottom;\n  @include font-size($legend-font-size);\n  font-weight: $legend-font-weight;\n  line-height: inherit;\n\n  + * {\n    clear: left; // 2\n  }\n}\n\n// Fix height of inputs with a type of datetime-local, date, month, week, or time\n// See https://github.com/twbs/bootstrap/issues/18842\n\n::-webkit-datetime-edit-fields-wrapper,\n::-webkit-datetime-edit-text,\n::-webkit-datetime-edit-minute,\n::-webkit-datetime-edit-hour-field,\n::-webkit-datetime-edit-day-field,\n::-webkit-datetime-edit-month-field,\n::-webkit-datetime-edit-year-field {\n  padding: 0;\n}\n\n::-webkit-inner-spin-button {\n  height: auto;\n}\n\n// 1. This overrides the extra rounded corners on search inputs in iOS so that our\n//    `.form-control` class can properly style them. Note that this cannot simply\n//    be added to `.form-control` as it's not specific enough. For details, see\n//    https://github.com/twbs/bootstrap/issues/11586.\n// 2. Correct the outline style in Safari.\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  outline-offset: -2px; // 2\n}\n\n// 1. A few input types should stay LTR\n// See https://rtlstyling.com/posts/rtl-styling#form-inputs\n// 2. RTL only output\n// See https://rtlcss.com/learn/usage-guide/control-directives/#raw\n\n/* rtl:raw:\n[type=\"tel\"],\n[type=\"url\"],\n[type=\"email\"],\n[type=\"number\"] {\n  direction: ltr;\n}\n*/\n\n// Remove the inner padding in Chrome and Safari on macOS.\n\n::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n// Remove padding around color pickers in webkit browsers\n\n::-webkit-color-swatch-wrapper {\n  padding: 0;\n}\n\n\n// 1. Inherit font family and line height for file input buttons\n// 2. Correct the inability to style clickable types in iOS and Safari.\n\n::file-selector-button {\n  font: inherit; // 1\n  -webkit-appearance: button; // 2\n}\n\n// Correct element displays\n\noutput {\n  display: inline-block;\n}\n\n// Remove border from iframe\n\niframe {\n  border: 0;\n}\n\n// Summary\n//\n// 1. Add the correct display in all browsers\n\nsummary {\n  display: list-item; // 1\n  cursor: pointer;\n}\n\n\n// Progress\n//\n// Add the correct vertical alignment in Chrome, Firefox, and Opera.\n\nprogress {\n  vertical-align: baseline;\n}\n\n\n// Hidden attribute\n//\n// Always hide an element with the `hidden` HTML attribute.\n\n[hidden] {\n  display: none !important;\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n// fusv-disable\n$accordion-button-focus-border-color:     $input-focus-border-color !default; // Deprecated in v5.3.3\n// fusv-enable\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-active-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n\n@import \"variables-dark\"; // TODO: can be removed safely in v6, only here to avoid breaking changes in v5.3\n","// stylelint-disable property-disallowed-list\n// Single side border-radius\n\n// Helper function to replace negative values with 0\n@function valid-radius($radius) {\n  $return: ();\n  @each $value in $radius {\n    @if type-of($value) == number {\n      $return: append($return, max($value, 0));\n    } @else {\n      $return: append($return, $value);\n    }\n  }\n  @return $return;\n}\n\n// scss-docs-start border-radius-mixins\n@mixin border-radius($radius: $border-radius, $fallback-border-radius: false) {\n  @if $enable-rounded {\n    border-radius: valid-radius($radius);\n  }\n  @else if $fallback-border-radius != false {\n    border-radius: $fallback-border-radius;\n  }\n}\n\n@mixin border-top-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n// scss-docs-end border-radius-mixins\n","//\n// Headings\n//\n.h1 {\n  @extend h1;\n}\n\n.h2 {\n  @extend h2;\n}\n\n.h3 {\n  @extend h3;\n}\n\n.h4 {\n  @extend h4;\n}\n\n.h5 {\n  @extend h5;\n}\n\n.h6 {\n  @extend h6;\n}\n\n\n.lead {\n  @include font-size($lead-font-size);\n  font-weight: $lead-font-weight;\n}\n\n// Type display classes\n@each $display, $font-size in $display-font-sizes {\n  .display-#{$display} {\n    @include font-size($font-size);\n    font-family: $display-font-family;\n    font-style: $display-font-style;\n    font-weight: $display-font-weight;\n    line-height: $display-line-height;\n  }\n}\n\n//\n// Emphasis\n//\n.small {\n  @extend small;\n}\n\n.mark {\n  @extend mark;\n}\n\n//\n// Lists\n//\n\n.list-unstyled {\n  @include list-unstyled();\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n  @include list-unstyled();\n}\n.list-inline-item {\n  display: inline-block;\n\n  &:not(:last-child) {\n    margin-right: $list-inline-padding;\n  }\n}\n\n\n//\n// Misc\n//\n\n// Builds on `abbr`\n.initialism {\n  @include font-size($initialism-font-size);\n  text-transform: uppercase;\n}\n\n// Blockquotes\n.blockquote {\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-font-size);\n\n  > :last-child {\n    margin-bottom: 0;\n  }\n}\n\n.blockquote-footer {\n  margin-top: -$blockquote-margin-y;\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-footer-font-size);\n  color: $blockquote-footer-color;\n\n  &::before {\n    content: \"\\2014\\00A0\"; // em dash, nbsp\n  }\n}\n","// Lists\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n@mixin list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n","// Override bootstrap variables\n$spacer: 1rem;\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1400px,\n);\n$grid-breakpoints: (\n  xs: 0,\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1200px,\n);\n$dropdown-link-hover-bg: var(--pst-color-surface);\n\n// --pst-color-surface can also be assigned to the dark variant because it is\n// scoped to different values depending on light/dark theme\n$dropdown-dark-link-hover-bg: var(--pst-color-surface);\n$dropdown-link-active-bg: var(--pst-color-surface);\n$dropdown-dark-link-active-bg: var(--pst-color-surface);\n$focus-ring-width: 0.1875rem; // 3px at 100% zoom (0.1875 * 16px base font = 3px)\n$focus-ring-opacity: 1;\n$focus-ring-color: var(--pst-color-accent);\n$focus-ring-blur: 0;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color;\n\n// outline creates the same style of focus ring, it just uses CSS outline instead of box shadow\n$focus-ring-outline: $focus-ring-color solid $focus-ring-width;\n$btn-focus-box-shadow: $focus-ring-box-shadow;\n","// Responsive images (ensure images don't scale beyond their parents)\n//\n// This is purposefully opt-in via an explicit class rather than being the default for all `<img>`s.\n// We previously tried the \"images are responsive by default\" approach in Bootstrap v2,\n// and abandoned it in Bootstrap v3 because it breaks lots of third-party widgets (including Google Maps)\n// which weren't expecting the images within themselves to be involuntarily resized.\n// See also https://github.com/twbs/bootstrap/issues/18178\n.img-fluid {\n  @include img-fluid();\n}\n\n\n// Image thumbnails\n.img-thumbnail {\n  padding: $thumbnail-padding;\n  background-color: $thumbnail-bg;\n  border: $thumbnail-border-width solid $thumbnail-border-color;\n  @include border-radius($thumbnail-border-radius);\n  @include box-shadow($thumbnail-box-shadow);\n\n  // Keep them at most 100% wide\n  @include img-fluid();\n}\n\n//\n// Figures\n//\n\n.figure {\n  // Ensures the caption's text aligns with the image.\n  display: inline-block;\n}\n\n.figure-img {\n  margin-bottom: $spacer * .5;\n  line-height: 1;\n}\n\n.figure-caption {\n  @include font-size($figure-caption-font-size);\n  color: $figure-caption-color;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n\n@mixin img-fluid {\n  // Part 1: Set a maximum relative to the parent\n  max-width: 100%;\n  // Part 2: Override the height to auto, otherwise images will be stretched\n  // when setting a width and height attribute on the img element.\n  height: auto;\n}\n","// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n@if $enable-container-classes {\n  // Single container class with breakpoint max-widths\n  .container,\n  // 100% wide container at all breakpoints\n  .container-fluid {\n    @include make-container();\n  }\n\n  // Responsive containers that are 100% wide until a breakpoint\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    .container-#{$breakpoint} {\n      @extend .container-fluid;\n    }\n\n    @include media-breakpoint-up($breakpoint, $grid-breakpoints) {\n      %responsive-container-#{$breakpoint} {\n        max-width: $container-max-width;\n      }\n\n      // Extend each breakpoint which is smaller or equal to the current breakpoint\n      $extend-breakpoint: true;\n\n      @each $name, $width in $grid-breakpoints {\n        @if ($extend-breakpoint) {\n          .container#{breakpoint-infix($name, $grid-breakpoints)} {\n            @extend %responsive-container-#{$breakpoint};\n          }\n\n          // Once the current breakpoint is reached, stop extending\n          @if ($breakpoint == $name) {\n            $extend-breakpoint: false;\n          }\n        }\n      }\n    }\n  }\n}\n","// Container mixins\n\n@mixin make-container($gutter: $container-padding-x) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  width: 100%;\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-right: auto;\n  margin-left: auto;\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n","// Row\n//\n// Rows contain your columns.\n\n:root {\n  @each $name, $value in $grid-breakpoints {\n    --#{$prefix}breakpoint-#{$name}: #{$value};\n  }\n}\n\n@if $enable-grid-classes {\n  .row {\n    @include make-row();\n\n    > * {\n      @include make-col-ready();\n    }\n  }\n}\n\n@if $enable-cssgrid {\n  .grid {\n    display: grid;\n    grid-template-rows: repeat(var(--#{$prefix}rows, 1), 1fr);\n    grid-template-columns: repeat(var(--#{$prefix}columns, #{$grid-columns}), 1fr);\n    gap: var(--#{$prefix}gap, #{$grid-gutter-width});\n\n    @include make-cssgrid();\n  }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n@if $enable-grid-classes {\n  @include make-grid-columns();\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is an invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Basic Bootstrap table\n//\n\n.table {\n  // Reset needed for nesting tables\n  --#{$prefix}table-color-type: initial;\n  --#{$prefix}table-bg-type: initial;\n  --#{$prefix}table-color-state: initial;\n  --#{$prefix}table-bg-state: initial;\n  // End of reset\n  --#{$prefix}table-color: #{$table-color};\n  --#{$prefix}table-bg: #{$table-bg};\n  --#{$prefix}table-border-color: #{$table-border-color};\n  --#{$prefix}table-accent-bg: #{$table-accent-bg};\n  --#{$prefix}table-striped-color: #{$table-striped-color};\n  --#{$prefix}table-striped-bg: #{$table-striped-bg};\n  --#{$prefix}table-active-color: #{$table-active-color};\n  --#{$prefix}table-active-bg: #{$table-active-bg};\n  --#{$prefix}table-hover-color: #{$table-hover-color};\n  --#{$prefix}table-hover-bg: #{$table-hover-bg};\n\n  width: 100%;\n  margin-bottom: $spacer;\n  vertical-align: $table-cell-vertical-align;\n  border-color: var(--#{$prefix}table-border-color);\n\n  // Target th & td\n  // We need the child combinator to prevent styles leaking to nested tables which doesn't have a `.table` class.\n  // We use the universal selectors here to simplify the selector (else we would need 6 different selectors).\n  // Another advantage is that this generates less code and makes the selector less specific making it easier to override.\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y $table-cell-padding-x;\n    // Following the precept of cascades: https://codepen.io/miriamsuzanne/full/vYNgodb\n    color: var(--#{$prefix}table-color-state, var(--#{$prefix}table-color-type, var(--#{$prefix}table-color)));\n    background-color: var(--#{$prefix}table-bg);\n    border-bottom-width: $table-border-width;\n    box-shadow: inset 0 0 0 9999px var(--#{$prefix}table-bg-state, var(--#{$prefix}table-bg-type, var(--#{$prefix}table-accent-bg)));\n  }\n\n  > tbody {\n    vertical-align: inherit;\n  }\n\n  > thead {\n    vertical-align: bottom;\n  }\n}\n\n.table-group-divider {\n  border-top: calc(#{$table-border-width} * 2) solid $table-group-separator-color; // stylelint-disable-line function-disallowed-list\n}\n\n//\n// Change placement of captions with a class\n//\n\n.caption-top {\n  caption-side: top;\n}\n\n\n//\n// Condensed table w/ half padding\n//\n\n.table-sm {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y-sm $table-cell-padding-x-sm;\n  }\n}\n\n\n// Border versions\n//\n// Add or remove borders all around the table and between all the columns.\n//\n// When borders are added on all sides of the cells, the corners can render odd when\n// these borders do not have the same color or if they are semi-transparent.\n// Therefore we add top and border bottoms to the `tr`s and left and right borders\n// to the `td`s or `th`s\n\n.table-bordered {\n  > :not(caption) > * {\n    border-width: $table-border-width 0;\n\n    // stylelint-disable-next-line selector-max-universal\n    > * {\n      border-width: 0 $table-border-width;\n    }\n  }\n}\n\n.table-borderless {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    border-bottom-width: 0;\n  }\n\n  > :not(:first-child) {\n    border-top-width: 0;\n  }\n}\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n// For rows\n.table-striped {\n  > tbody > tr:nth-of-type(#{$table-striped-order}) > * {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// For columns\n.table-striped-columns {\n  > :not(caption) > tr > :nth-child(#{$table-striped-columns-order}) {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// Active table\n//\n// The `.table-active` class can be added to highlight rows or cells\n\n.table-active {\n  --#{$prefix}table-color-state: var(--#{$prefix}table-active-color);\n  --#{$prefix}table-bg-state: var(--#{$prefix}table-active-bg);\n}\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover > * {\n    --#{$prefix}table-color-state: var(--#{$prefix}table-hover-color);\n    --#{$prefix}table-bg-state: var(--#{$prefix}table-hover-bg);\n  }\n}\n\n\n// Table variants\n//\n// Table variants set the table cell backgrounds, border colors\n// and the colors of the striped, hovered & active tables\n\n@each $color, $value in $table-variants {\n  @include table-variant($color, $value);\n}\n\n// Responsive tables\n//\n// Generate series of `.table-responsive-*` classes for configuring the screen\n// size of where your table will overflow.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n  @include media-breakpoint-down($breakpoint) {\n    .table-responsive#{$infix} {\n      overflow-x: auto;\n      -webkit-overflow-scrolling: touch;\n    }\n  }\n}\n","// scss-docs-start table-variant\n@mixin table-variant($state, $background) {\n  .table-#{$state} {\n    $color: color-contrast(opaque($body-bg, $background));\n    $hover-bg: mix($color, $background, percentage($table-hover-bg-factor));\n    $striped-bg: mix($color, $background, percentage($table-striped-bg-factor));\n    $active-bg: mix($color, $background, percentage($table-active-bg-factor));\n    $table-border-color: mix($color, $background, percentage($table-border-factor));\n\n    --#{$prefix}table-color: #{$color};\n    --#{$prefix}table-bg: #{$background};\n    --#{$prefix}table-border-color: #{$table-border-color};\n    --#{$prefix}table-striped-bg: #{$striped-bg};\n    --#{$prefix}table-striped-color: #{color-contrast($striped-bg)};\n    --#{$prefix}table-active-bg: #{$active-bg};\n    --#{$prefix}table-active-color: #{color-contrast($active-bg)};\n    --#{$prefix}table-hover-bg: #{$hover-bg};\n    --#{$prefix}table-hover-color: #{color-contrast($hover-bg)};\n\n    color: var(--#{$prefix}table-color);\n    border-color: var(--#{$prefix}table-border-color);\n  }\n}\n// scss-docs-end table-variant\n","//\n// Labels\n//\n\n.form-label {\n  margin-bottom: $form-label-margin-bottom;\n  @include font-size($form-label-font-size);\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  color: $form-label-color;\n}\n\n// For use with horizontal and inline forms, when you need the label (or legend)\n// text to align with the form controls.\n.col-form-label {\n  padding-top: add($input-padding-y, $input-border-width);\n  padding-bottom: add($input-padding-y, $input-border-width);\n  margin-bottom: 0; // Override the `<legend>` default\n  @include font-size(inherit); // Override the `<legend>` default\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  line-height: $input-line-height;\n  color: $form-label-color;\n}\n\n.col-form-label-lg {\n  padding-top: add($input-padding-y-lg, $input-border-width);\n  padding-bottom: add($input-padding-y-lg, $input-border-width);\n  @include font-size($input-font-size-lg);\n}\n\n.col-form-label-sm {\n  padding-top: add($input-padding-y-sm, $input-border-width);\n  padding-bottom: add($input-padding-y-sm, $input-border-width);\n  @include font-size($input-font-size-sm);\n}\n","//\n// Form text\n//\n\n.form-text {\n  margin-top: $form-text-margin-top;\n  @include font-size($form-text-font-size);\n  font-style: $form-text-font-style;\n  font-weight: $form-text-font-weight;\n  color: $form-text-color;\n}\n","//\n// General form controls (plus a few specific high-level interventions)\n//\n\n.form-control {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y $input-padding-x;\n  font-family: $input-font-family;\n  @include font-size($input-font-size);\n  font-weight: $input-font-weight;\n  line-height: $input-line-height;\n  color: $input-color;\n  appearance: none; // Fix appearance for date inputs in Safari\n  background-color: $input-bg;\n  background-clip: padding-box;\n  border: $input-border-width solid $input-border-color;\n\n  // Note: This has no effect on <select>s in some browsers, due to the limited stylability of `<select>`s in CSS.\n  @include border-radius($input-border-radius, 0);\n\n  @include box-shadow($input-box-shadow);\n  @include transition($input-transition);\n\n  &[type=\"file\"] {\n    overflow: hidden; // prevent pseudo element button overlap\n\n    &:not(:disabled):not([readonly]) {\n      cursor: pointer;\n    }\n  }\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  &:focus {\n    color: $input-focus-color;\n    background-color: $input-focus-bg;\n    border-color: $input-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($input-box-shadow, $input-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $input-focus-box-shadow;\n    }\n  }\n\n  &::-webkit-date-and-time-value {\n    // On Android Chrome, form-control's \"width: 100%\" makes the input width too small\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    //\n    // On iOS Safari, form-control's \"appearance: none\" + \"width: 100%\" makes the input width too small\n    // Tested under iOS 16.2 / Safari 16.2\n    min-width: 85px; // Seems to be a good minimum safe width\n\n    // Add some height to date inputs on iOS\n    // https://github.com/twbs/bootstrap/issues/23307\n    // TODO: we can remove this workaround once https://bugs.webkit.org/show_bug.cgi?id=198959 is resolved\n    // Multiply line-height by 1em if it has no unit\n    height: if(unit($input-line-height) == \"\", $input-line-height * 1em, $input-line-height);\n\n    // Android Chrome type=\"date\" is taller than the other inputs\n    // because of \"margin: 1px 24px 1px 4px\" inside the shadow DOM\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    margin: 0;\n  }\n\n  // Prevent excessive date input height in Webkit\n  // https://github.com/twbs/bootstrap/issues/34433\n  &::-webkit-datetime-edit {\n    display: block;\n    padding: 0;\n  }\n\n  // Placeholder\n  &::placeholder {\n    color: $input-placeholder-color;\n    // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526.\n    opacity: 1;\n  }\n\n  // Disabled inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &:disabled {\n    color: $input-disabled-color;\n    background-color: $input-disabled-bg;\n    border-color: $input-disabled-border-color;\n    // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655.\n    opacity: 1;\n  }\n\n  // File input buttons theming\n  &::file-selector-button {\n    padding: $input-padding-y $input-padding-x;\n    margin: (-$input-padding-y) (-$input-padding-x);\n    margin-inline-end: $input-padding-x;\n    color: $form-file-button-color;\n    @include gradient-bg($form-file-button-bg);\n    pointer-events: none;\n    border-color: inherit;\n    border-style: solid;\n    border-width: 0;\n    border-inline-end-width: $input-border-width;\n    border-radius: 0; // stylelint-disable-line property-disallowed-list\n    @include transition($btn-transition);\n  }\n\n  &:hover:not(:disabled):not([readonly])::file-selector-button {\n    background-color: $form-file-button-hover-bg;\n  }\n}\n\n// Readonly controls as plain text\n//\n// Apply class to a readonly input to make it appear like regular plain\n// text (without any border, background color, focus indicator)\n\n.form-control-plaintext {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y 0;\n  margin-bottom: 0; // match inputs if this class comes on inputs with default margins\n  line-height: $input-line-height;\n  color: $input-plaintext-color;\n  background-color: transparent;\n  border: solid transparent;\n  border-width: $input-border-width 0;\n\n  &:focus {\n    outline: 0;\n  }\n\n  &.form-control-sm,\n  &.form-control-lg {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// Repeated in `_input_group.scss` to avoid Sass extend issues.\n\n.form-control-sm {\n  min-height: $input-height-sm;\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n\n  &::file-selector-button {\n    padding: $input-padding-y-sm $input-padding-x-sm;\n    margin: (-$input-padding-y-sm) (-$input-padding-x-sm);\n    margin-inline-end: $input-padding-x-sm;\n  }\n}\n\n.form-control-lg {\n  min-height: $input-height-lg;\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n\n  &::file-selector-button {\n    padding: $input-padding-y-lg $input-padding-x-lg;\n    margin: (-$input-padding-y-lg) (-$input-padding-x-lg);\n    margin-inline-end: $input-padding-x-lg;\n  }\n}\n\n// Make sure textareas don't shrink too much when resized\n// https://github.com/twbs/bootstrap/pull/29124\n// stylelint-disable selector-no-qualifying-type\ntextarea {\n  &.form-control {\n    min-height: $input-height;\n  }\n\n  &.form-control-sm {\n    min-height: $input-height-sm;\n  }\n\n  &.form-control-lg {\n    min-height: $input-height-lg;\n  }\n}\n// stylelint-enable selector-no-qualifying-type\n\n.form-control-color {\n  width: $form-color-width;\n  height: $input-height;\n  padding: $input-padding-y;\n\n  &:not(:disabled):not([readonly]) {\n    cursor: pointer;\n  }\n\n  &::-moz-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &::-webkit-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &.form-control-sm { height: $input-height-sm; }\n  &.form-control-lg { height: $input-height-lg; }\n}\n","// stylelint-disable property-disallowed-list\n@mixin transition($transition...) {\n  @if length($transition) == 0 {\n    $transition: $transition-base;\n  }\n\n  @if length($transition) > 1 {\n    @each $value in $transition {\n      @if $value == null or $value == none {\n        @warn \"The keyword 'none' or 'null' must be used as a single argument.\";\n      }\n    }\n  }\n\n  @if $enable-transitions {\n    @if nth($transition, 1) != null {\n      transition: $transition;\n    }\n\n    @if $enable-reduced-motion and nth($transition, 1) != null and nth($transition, 1) != none {\n      @media (prefers-reduced-motion: reduce) {\n        transition: none;\n      }\n    }\n  }\n}\n","// Gradients\n\n// scss-docs-start gradient-bg-mixin\n@mixin gradient-bg($color: null) {\n  background-color: $color;\n\n  @if $enable-gradients {\n    background-image: var(--#{$prefix}gradient);\n  }\n}\n// scss-docs-end gradient-bg-mixin\n\n// scss-docs-start gradient-mixins\n// Horizontal gradient, from left to right\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-x($start-color: $gray-700, $end-color: $gray-800, $start-percent: 0%, $end-percent: 100%) {\n  background-image: linear-gradient(to right, $start-color $start-percent, $end-color $end-percent);\n}\n\n// Vertical gradient, from top to bottom\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-y($start-color: $gray-700, $end-color: $gray-800, $start-percent: null, $end-percent: null) {\n  background-image: linear-gradient(to bottom, $start-color $start-percent, $end-color $end-percent);\n}\n\n@mixin gradient-directional($start-color: $gray-700, $end-color: $gray-800, $deg: 45deg) {\n  background-image: linear-gradient($deg, $start-color, $end-color);\n}\n\n@mixin gradient-x-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient(to right, $start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-y-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient($start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-radial($inner-color: $gray-700, $outer-color: $gray-800) {\n  background-image: radial-gradient(circle, $inner-color, $outer-color);\n}\n\n@mixin gradient-striped($color: rgba($white, .15), $angle: 45deg) {\n  background-image: linear-gradient($angle, $color 25%, transparent 25%, transparent 50%, $color 50%, $color 75%, transparent 75%, transparent);\n}\n// scss-docs-end gradient-mixins\n","// Select\n//\n// Replaces the browser default select with a custom one, mostly pulled from\n// https://primer.github.io/.\n\n.form-select {\n  --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator)};\n\n  display: block;\n  width: 100%;\n  padding: $form-select-padding-y $form-select-indicator-padding $form-select-padding-y $form-select-padding-x;\n  font-family: $form-select-font-family;\n  @include font-size($form-select-font-size);\n  font-weight: $form-select-font-weight;\n  line-height: $form-select-line-height;\n  color: $form-select-color;\n  appearance: none;\n  background-color: $form-select-bg;\n  background-image: var(--#{$prefix}form-select-bg-img), var(--#{$prefix}form-select-bg-icon, none);\n  background-repeat: no-repeat;\n  background-position: $form-select-bg-position;\n  background-size: $form-select-bg-size;\n  border: $form-select-border-width solid $form-select-border-color;\n  @include border-radius($form-select-border-radius, 0);\n  @include box-shadow($form-select-box-shadow);\n  @include transition($form-select-transition);\n\n  &:focus {\n    border-color: $form-select-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($form-select-box-shadow, $form-select-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $form-select-focus-box-shadow;\n    }\n  }\n\n  &[multiple],\n  &[size]:not([size=\"1\"]) {\n    padding-right: $form-select-padding-x;\n    background-image: none;\n  }\n\n  &:disabled {\n    color: $form-select-disabled-color;\n    background-color: $form-select-disabled-bg;\n    border-color: $form-select-disabled-border-color;\n  }\n\n  // Remove outline from select box in FF\n  &:-moz-focusring {\n    color: transparent;\n    text-shadow: 0 0 0 $form-select-color;\n  }\n}\n\n.form-select-sm {\n  padding-top: $form-select-padding-y-sm;\n  padding-bottom: $form-select-padding-y-sm;\n  padding-left: $form-select-padding-x-sm;\n  @include font-size($form-select-font-size-sm);\n  @include border-radius($form-select-border-radius-sm);\n}\n\n.form-select-lg {\n  padding-top: $form-select-padding-y-lg;\n  padding-bottom: $form-select-padding-y-lg;\n  padding-left: $form-select-padding-x-lg;\n  @include font-size($form-select-font-size-lg);\n  @include border-radius($form-select-border-radius-lg);\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-select {\n      --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator-dark)};\n    }\n  }\n}\n","//\n// Check/radio\n//\n\n.form-check {\n  display: block;\n  min-height: $form-check-min-height;\n  padding-left: $form-check-padding-start;\n  margin-bottom: $form-check-margin-bottom;\n\n  .form-check-input {\n    float: left;\n    margin-left: $form-check-padding-start * -1;\n  }\n}\n\n.form-check-reverse {\n  padding-right: $form-check-padding-start;\n  padding-left: 0;\n  text-align: right;\n\n  .form-check-input {\n    float: right;\n    margin-right: $form-check-padding-start * -1;\n    margin-left: 0;\n  }\n}\n\n.form-check-input {\n  --#{$prefix}form-check-bg: #{$form-check-input-bg};\n\n  flex-shrink: 0;\n  width: $form-check-input-width;\n  height: $form-check-input-width;\n  margin-top: ($line-height-base - $form-check-input-width) * .5; // line-height minus check height\n  vertical-align: top;\n  appearance: none;\n  background-color: var(--#{$prefix}form-check-bg);\n  background-image: var(--#{$prefix}form-check-bg-image);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: contain;\n  border: $form-check-input-border;\n  print-color-adjust: exact; // Keep themed appearance for print\n  @include transition($form-check-transition);\n\n  &[type=\"checkbox\"] {\n    @include border-radius($form-check-input-border-radius);\n  }\n\n  &[type=\"radio\"] {\n    // stylelint-disable-next-line property-disallowed-list\n    border-radius: $form-check-radio-border-radius;\n  }\n\n  &:active {\n    filter: $form-check-input-active-filter;\n  }\n\n  &:focus {\n    border-color: $form-check-input-focus-border;\n    outline: 0;\n    box-shadow: $form-check-input-focus-box-shadow;\n  }\n\n  &:checked {\n    background-color: $form-check-input-checked-bg-color;\n    border-color: $form-check-input-checked-border-color;\n\n    &[type=\"checkbox\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)};\n      }\n    }\n\n    &[type=\"radio\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)};\n      }\n    }\n  }\n\n  &[type=\"checkbox\"]:indeterminate {\n    background-color: $form-check-input-indeterminate-bg-color;\n    border-color: $form-check-input-indeterminate-border-color;\n\n    @if $enable-gradients {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)}, var(--#{$prefix}gradient);\n    } @else {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)};\n    }\n  }\n\n  &:disabled {\n    pointer-events: none;\n    filter: none;\n    opacity: $form-check-input-disabled-opacity;\n  }\n\n  // Use disabled attribute in addition of :disabled pseudo-class\n  // See: https://github.com/twbs/bootstrap/issues/28247\n  &[disabled],\n  &:disabled {\n    ~ .form-check-label {\n      cursor: default;\n      opacity: $form-check-label-disabled-opacity;\n    }\n  }\n}\n\n.form-check-label {\n  color: $form-check-label-color;\n  cursor: $form-check-label-cursor;\n}\n\n//\n// Switch\n//\n\n.form-switch {\n  padding-left: $form-switch-padding-start;\n\n  .form-check-input {\n    --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image)};\n\n    width: $form-switch-width;\n    margin-left: $form-switch-padding-start * -1;\n    background-image: var(--#{$prefix}form-switch-bg);\n    background-position: left center;\n    @include border-radius($form-switch-border-radius, 0);\n    @include transition($form-switch-transition);\n\n    &:focus {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-focus-bg-image)};\n    }\n\n    &:checked {\n      background-position: $form-switch-checked-bg-position;\n\n      @if $enable-gradients {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)};\n      }\n    }\n  }\n\n  &.form-check-reverse {\n    padding-right: $form-switch-padding-start;\n    padding-left: 0;\n\n    .form-check-input {\n      margin-right: $form-switch-padding-start * -1;\n      margin-left: 0;\n    }\n  }\n}\n\n.form-check-inline {\n  display: inline-block;\n  margin-right: $form-check-inline-margin-end;\n}\n\n.btn-check {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n\n  &[disabled],\n  &:disabled {\n    + .btn {\n      pointer-events: none;\n      filter: none;\n      opacity: $form-check-btn-check-disabled-opacity;\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-switch .form-check-input:not(:checked):not(:focus) {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image-dark)};\n    }\n  }\n}\n","// Range\n//\n// Style range inputs the same across browsers. Vendor-specific rules for pseudo\n// elements cannot be mixed. As such, there are no shared styles for focus or\n// active states on prefixed selectors.\n\n.form-range {\n  width: 100%;\n  height: add($form-range-thumb-height, $form-range-thumb-focus-box-shadow-width * 2);\n  padding: 0; // Need to reset padding\n  appearance: none;\n  background-color: transparent;\n\n  &:focus {\n    outline: 0;\n\n    // Pseudo-elements must be split across multiple rulesets to have an effect.\n    // No box-shadow() mixin for focus accessibility.\n    &::-webkit-slider-thumb { box-shadow: $form-range-thumb-focus-box-shadow; }\n    &::-moz-range-thumb     { box-shadow: $form-range-thumb-focus-box-shadow; }\n  }\n\n  &::-moz-focus-outer {\n    border: 0;\n  }\n\n  &::-webkit-slider-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    margin-top: ($form-range-track-height - $form-range-thumb-height) * .5; // Webkit specific\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-webkit-slider-runnable-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent; // Why?\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent;\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &::-moz-range-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-moz-range-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent;\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent; // Firefox specific?\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &:disabled {\n    pointer-events: none;\n\n    &::-webkit-slider-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n\n    &::-moz-range-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n  }\n}\n",".form-floating {\n  position: relative;\n\n  > .form-control,\n  > .form-control-plaintext,\n  > .form-select {\n    height: $form-floating-height;\n    min-height: $form-floating-height;\n    line-height: $form-floating-line-height;\n  }\n\n  > label {\n    position: absolute;\n    top: 0;\n    left: 0;\n    z-index: 2;\n    height: 100%; // allow textareas\n    padding: $form-floating-padding-y $form-floating-padding-x;\n    overflow: hidden;\n    text-align: start;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n    pointer-events: none;\n    border: $input-border-width solid transparent; // Required for aligning label's text with the input as it affects inner box model\n    transform-origin: 0 0;\n    @include transition($form-floating-transition);\n  }\n\n  > .form-control,\n  > .form-control-plaintext {\n    padding: $form-floating-padding-y $form-floating-padding-x;\n\n    &::placeholder {\n      color: transparent;\n    }\n\n    &:focus,\n    &:not(:placeholder-shown) {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n    // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n    &:-webkit-autofill {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n  }\n\n  > .form-select {\n    padding-top: $form-floating-input-padding-t;\n    padding-bottom: $form-floating-input-padding-b;\n  }\n\n  > .form-control:focus,\n  > .form-control:not(:placeholder-shown),\n  > .form-control-plaintext,\n  > .form-select {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n\n      &::after {\n        position: absolute;\n        inset: $form-floating-padding-y ($form-floating-padding-x * .5);\n        z-index: -1;\n        height: $form-floating-label-height;\n        content: \"\";\n        background-color: $input-bg;\n        @include border-radius($input-border-radius);\n      }\n    }\n  }\n  // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n  > .form-control:-webkit-autofill {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n    }\n  }\n\n  > .form-control-plaintext {\n    ~ label {\n      border-width: $input-border-width 0; // Required to properly position label text - as explained above\n    }\n  }\n\n  > :disabled ~ label,\n  > .form-control:disabled ~ label { // Required for `.form-control`s because of specificity\n    color: $form-floating-label-disabled-color;\n\n    &::after {\n      background-color: $input-disabled-bg;\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.input-group {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // For form validation feedback\n  align-items: stretch;\n  width: 100%;\n\n  > .form-control,\n  > .form-select,\n  > .form-floating {\n    position: relative; // For focus state's z-index\n    flex: 1 1 auto;\n    width: 1%;\n    min-width: 0; // https://stackoverflow.com/questions/36247140/why-dont-flex-items-shrink-past-content-size\n  }\n\n  // Bring the \"active\" form control to the top of surrounding elements\n  > .form-control:focus,\n  > .form-select:focus,\n  > .form-floating:focus-within {\n    z-index: 5;\n  }\n\n  // Ensure buttons are always above inputs for more visually pleasing borders.\n  // This isn't needed for `.input-group-text` since it shares the same border-color\n  // as our inputs.\n  .btn {\n    position: relative;\n    z-index: 2;\n\n    &:focus {\n      z-index: 5;\n    }\n  }\n}\n\n\n// Textual addons\n//\n// Serves as a catch-all element for any text or radio/checkbox input you wish\n// to prepend or append to an input.\n\n.input-group-text {\n  display: flex;\n  align-items: center;\n  padding: $input-group-addon-padding-y $input-group-addon-padding-x;\n  @include font-size($input-font-size); // Match inputs\n  font-weight: $input-group-addon-font-weight;\n  line-height: $input-line-height;\n  color: $input-group-addon-color;\n  text-align: center;\n  white-space: nowrap;\n  background-color: $input-group-addon-bg;\n  border: $input-border-width solid $input-group-addon-border-color;\n  @include border-radius($input-border-radius);\n}\n\n\n// Sizing\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .form-select,\n.input-group-lg > .input-group-text,\n.input-group-lg > .btn {\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n}\n\n.input-group-sm > .form-control,\n.input-group-sm > .form-select,\n.input-group-sm > .input-group-text,\n.input-group-sm > .btn {\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n}\n\n.input-group-lg > .form-select,\n.input-group-sm > .form-select {\n  padding-right: $form-select-padding-x + $form-select-indicator-padding;\n}\n\n\n// Rounded corners\n//\n// These rulesets must come after the sizing ones to properly override sm and lg\n// border-radius values when extending. They're more specific than we'd like\n// with the `.input-group >` part, but without it, we cannot override the sizing.\n\n// stylelint-disable-next-line no-duplicate-selectors\n.input-group {\n  &:not(.has-validation) {\n    > :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 3),\n    > .form-floating:not(:last-child) > .form-control,\n    > .form-floating:not(:last-child) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  &.has-validation {\n    > :nth-last-child(n + 3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 4),\n    > .form-floating:nth-last-child(n + 3) > .form-control,\n    > .form-floating:nth-last-child(n + 3) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  $validation-messages: \"\";\n  @each $state in map-keys($form-validation-states) {\n    $validation-messages: $validation-messages + \":not(.\" + unquote($state) + \"-tooltip)\" + \":not(.\" + unquote($state) + \"-feedback)\";\n  }\n\n  > :not(:first-child):not(.dropdown-menu)#{$validation-messages} {\n    margin-left: calc(#{$input-border-width} * -1); // stylelint-disable-line function-disallowed-list\n    @include border-start-radius(0);\n  }\n\n  > .form-floating:not(:first-child) > .form-control,\n  > .form-floating:not(:first-child) > .form-select {\n    @include border-start-radius(0);\n  }\n}\n","// This mixin uses an `if()` technique to be compatible with Dart Sass\n// See https://github.com/sass/sass/issues/1873#issuecomment-152293725 for more details\n\n// scss-docs-start form-validation-mixins\n@mixin form-validation-state-selector($state) {\n  @if ($state == \"valid\" or $state == \"invalid\") {\n    .was-validated #{if(&, \"&\", \"\")}:#{$state},\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  } @else {\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  }\n}\n\n@mixin form-validation-state(\n  $state,\n  $color,\n  $icon,\n  $tooltip-color: color-contrast($color),\n  $tooltip-bg-color: rgba($color, $form-feedback-tooltip-opacity),\n  $focus-box-shadow: 0 0 $input-btn-focus-blur $input-focus-width rgba($color, $input-btn-focus-color-opacity),\n  $border-color: $color\n) {\n  .#{$state}-feedback {\n    display: none;\n    width: 100%;\n    margin-top: $form-feedback-margin-top;\n    @include font-size($form-feedback-font-size);\n    font-style: $form-feedback-font-style;\n    color: $color;\n  }\n\n  .#{$state}-tooltip {\n    position: absolute;\n    top: 100%;\n    z-index: 5;\n    display: none;\n    max-width: 100%; // Contain to parent when possible\n    padding: $form-feedback-tooltip-padding-y $form-feedback-tooltip-padding-x;\n    margin-top: .1rem;\n    @include font-size($form-feedback-tooltip-font-size);\n    line-height: $form-feedback-tooltip-line-height;\n    color: $tooltip-color;\n    background-color: $tooltip-bg-color;\n    @include border-radius($form-feedback-tooltip-border-radius);\n  }\n\n  @include form-validation-state-selector($state) {\n    ~ .#{$state}-feedback,\n    ~ .#{$state}-tooltip {\n      display: block;\n    }\n  }\n\n  .form-control {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-image: escape-svg($icon);\n        background-repeat: no-repeat;\n        background-position: right $input-height-inner-quarter center;\n        background-size: $input-height-inner-half $input-height-inner-half;\n      }\n\n      &:focus {\n        border-color: $border-color;\n        @if $enable-shadows {\n          @include box-shadow($input-box-shadow, $focus-box-shadow);\n        } @else {\n          // Avoid using mixin so we can pass custom focus shadow properly\n          box-shadow: $focus-box-shadow;\n        }\n      }\n    }\n  }\n\n  // stylelint-disable-next-line selector-no-qualifying-type\n  textarea.form-control {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-position: top $input-height-inner-quarter right $input-height-inner-quarter;\n      }\n    }\n  }\n\n  .form-select {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        &:not([multiple]):not([size]),\n        &:not([multiple])[size=\"1\"] {\n          --#{$prefix}form-select-bg-icon: #{escape-svg($icon)};\n          padding-right: $form-select-feedback-icon-padding-end;\n          background-position: $form-select-bg-position, $form-select-feedback-icon-position;\n          background-size: $form-select-bg-size, $form-select-feedback-icon-size;\n        }\n      }\n\n      &:focus {\n        border-color: $border-color;\n        @if $enable-shadows {\n          @include box-shadow($form-select-box-shadow, $focus-box-shadow);\n        } @else {\n          // Avoid using mixin so we can pass custom focus shadow properly\n          box-shadow: $focus-box-shadow;\n        }\n      }\n    }\n  }\n\n  .form-control-color {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        width: add($form-color-width, $input-height-inner);\n      }\n    }\n  }\n\n  .form-check-input {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      &:checked {\n        background-color: $color;\n      }\n\n      &:focus {\n        box-shadow: $focus-box-shadow;\n      }\n\n      ~ .form-check-label {\n        color: $color;\n      }\n    }\n  }\n  .form-check-inline .form-check-input {\n    ~ .#{$state}-feedback {\n      margin-left: .5em;\n    }\n  }\n\n  .input-group {\n    > .form-control:not(:focus),\n    > .form-select:not(:focus),\n    > .form-floating:not(:focus-within) {\n      @include form-validation-state-selector($state) {\n        @if $state == \"valid\" {\n          z-index: 3;\n        } @else if $state == \"invalid\" {\n          z-index: 4;\n        }\n      }\n    }\n  }\n}\n// scss-docs-end form-validation-mixins\n","//\n// Base styles\n//\n\n.btn {\n  // scss-docs-start btn-css-vars\n  --#{$prefix}btn-padding-x: #{$btn-padding-x};\n  --#{$prefix}btn-padding-y: #{$btn-padding-y};\n  --#{$prefix}btn-font-family: #{$btn-font-family};\n  @include rfs($btn-font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-font-weight: #{$btn-font-weight};\n  --#{$prefix}btn-line-height: #{$btn-line-height};\n  --#{$prefix}btn-color: #{$btn-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-width: #{$btn-border-width};\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-border-radius: #{$btn-border-radius};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-box-shadow: #{$btn-box-shadow};\n  --#{$prefix}btn-disabled-opacity: #{$btn-disabled-opacity};\n  --#{$prefix}btn-focus-box-shadow: 0 0 0 #{$btn-focus-width} rgba(var(--#{$prefix}btn-focus-shadow-rgb), .5);\n  // scss-docs-end btn-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}btn-padding-y) var(--#{$prefix}btn-padding-x);\n  font-family: var(--#{$prefix}btn-font-family);\n  @include font-size(var(--#{$prefix}btn-font-size));\n  font-weight: var(--#{$prefix}btn-font-weight);\n  line-height: var(--#{$prefix}btn-line-height);\n  color: var(--#{$prefix}btn-color);\n  text-align: center;\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: $btn-white-space;\n  vertical-align: middle;\n  cursor: if($enable-button-pointers, pointer, null);\n  user-select: none;\n  border: var(--#{$prefix}btn-border-width) solid var(--#{$prefix}btn-border-color);\n  @include border-radius(var(--#{$prefix}btn-border-radius));\n  @include gradient-bg(var(--#{$prefix}btn-bg));\n  @include box-shadow(var(--#{$prefix}btn-box-shadow));\n  @include transition($btn-transition);\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}btn-hover-bg);\n    border-color: var(--#{$prefix}btn-hover-border-color);\n  }\n\n  .btn-check + &:hover {\n    // override for the checkbox/radio buttons\n    color: var(--#{$prefix}btn-color);\n    background-color: var(--#{$prefix}btn-bg);\n    border-color: var(--#{$prefix}btn-border-color);\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-hover-color);\n    @include gradient-bg(var(--#{$prefix}btn-hover-bg));\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:focus-visible + & {\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:checked + &,\n  :not(.btn-check) + &:active,\n  &:first-child:active,\n  &.active,\n  &.show {\n    color: var(--#{$prefix}btn-active-color);\n    background-color: var(--#{$prefix}btn-active-bg);\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-active-border-color);\n    @include box-shadow(var(--#{$prefix}btn-active-shadow));\n\n    &:focus-visible {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      @if $enable-shadows {\n        box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n      } @else {\n        box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n      }\n    }\n  }\n\n  .btn-check:checked:focus-visible + & {\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  &:disabled,\n  &.disabled,\n  fieldset:disabled & {\n    color: var(--#{$prefix}btn-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}btn-disabled-bg);\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-disabled-border-color);\n    opacity: var(--#{$prefix}btn-disabled-opacity);\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Alternate buttons\n//\n\n// scss-docs-start btn-variant-loops\n@each $color, $value in $theme-colors {\n  .btn-#{$color} {\n    @if $color == \"light\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: shade-color($value, $btn-hover-bg-shade-amount),\n        $hover-border: shade-color($value, $btn-hover-border-shade-amount),\n        $active-background: shade-color($value, $btn-active-bg-shade-amount),\n        $active-border: shade-color($value, $btn-active-border-shade-amount)\n      );\n    } @else if $color == \"dark\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: tint-color($value, $btn-hover-bg-tint-amount),\n        $hover-border: tint-color($value, $btn-hover-border-tint-amount),\n        $active-background: tint-color($value, $btn-active-bg-tint-amount),\n        $active-border: tint-color($value, $btn-active-border-tint-amount)\n      );\n    } @else {\n      @include button-variant($value, $value);\n    }\n  }\n}\n\n@each $color, $value in $theme-colors {\n  .btn-outline-#{$color} {\n    @include button-outline-variant($value);\n  }\n}\n// scss-docs-end btn-variant-loops\n\n\n//\n// Link buttons\n//\n\n// Make a button look and behave like a link\n.btn-link {\n  --#{$prefix}btn-font-weight: #{$font-weight-normal};\n  --#{$prefix}btn-color: #{$btn-link-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-hover-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-active-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-active-border-color: transparent;\n  --#{$prefix}btn-disabled-color: #{$btn-link-disabled-color};\n  --#{$prefix}btn-disabled-border-color: transparent;\n  --#{$prefix}btn-box-shadow: 0 0 0 #000; // Can't use `none` as keyword negates all values when used with multiple shadows\n  --#{$prefix}btn-focus-shadow-rgb: #{$btn-link-focus-shadow-rgb};\n\n  text-decoration: $link-decoration;\n  @if $enable-gradients {\n    background-image: none;\n  }\n\n  &:hover,\n  &:focus-visible {\n    text-decoration: $link-hover-decoration;\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-color);\n  }\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n  }\n\n  // No need for an active state here\n}\n\n\n//\n// Button Sizes\n//\n\n.btn-lg {\n  @include button-size($btn-padding-y-lg, $btn-padding-x-lg, $btn-font-size-lg, $btn-border-radius-lg);\n}\n\n.btn-sm {\n  @include button-size($btn-padding-y-sm, $btn-padding-x-sm, $btn-font-size-sm, $btn-border-radius-sm);\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n// scss-docs-start btn-variant-mixin\n@mixin button-variant(\n  $background,\n  $border,\n  $color: color-contrast($background),\n  $hover-background: if($color == $color-contrast-light, shade-color($background, $btn-hover-bg-shade-amount), tint-color($background, $btn-hover-bg-tint-amount)),\n  $hover-border: if($color == $color-contrast-light, shade-color($border, $btn-hover-border-shade-amount), tint-color($border, $btn-hover-border-tint-amount)),\n  $hover-color: color-contrast($hover-background),\n  $active-background: if($color == $color-contrast-light, shade-color($background, $btn-active-bg-shade-amount), tint-color($background, $btn-active-bg-tint-amount)),\n  $active-border: if($color == $color-contrast-light, shade-color($border, $btn-active-border-shade-amount), tint-color($border, $btn-active-border-tint-amount)),\n  $active-color: color-contrast($active-background),\n  $disabled-background: $background,\n  $disabled-border: $border,\n  $disabled-color: color-contrast($disabled-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-bg: #{$background};\n  --#{$prefix}btn-border-color: #{$border};\n  --#{$prefix}btn-hover-color: #{$hover-color};\n  --#{$prefix}btn-hover-bg: #{$hover-background};\n  --#{$prefix}btn-hover-border-color: #{$hover-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb(mix($color, $border, 15%))};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$disabled-color};\n  --#{$prefix}btn-disabled-bg: #{$disabled-background};\n  --#{$prefix}btn-disabled-border-color: #{$disabled-border};\n}\n// scss-docs-end btn-variant-mixin\n\n// scss-docs-start btn-outline-variant-mixin\n@mixin button-outline-variant(\n  $color,\n  $color-hover: color-contrast($color),\n  $active-background: $color,\n  $active-border: $color,\n  $active-color: color-contrast($active-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-border-color: #{$color};\n  --#{$prefix}btn-hover-color: #{$color-hover};\n  --#{$prefix}btn-hover-bg: #{$active-background};\n  --#{$prefix}btn-hover-border-color: #{$active-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb($color)};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$color};\n  --#{$prefix}btn-disabled-bg: transparent;\n  --#{$prefix}btn-disabled-border-color: #{$color};\n  --#{$prefix}gradient: none;\n}\n// scss-docs-end btn-outline-variant-mixin\n\n// scss-docs-start btn-size-mixin\n@mixin button-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}btn-padding-y: #{$padding-y};\n  --#{$prefix}btn-padding-x: #{$padding-x};\n  @include rfs($font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-border-radius: #{$border-radius};\n}\n// scss-docs-end btn-size-mixin\n",".fade {\n  @include transition($transition-fade);\n\n  &:not(.show) {\n    opacity: 0;\n  }\n}\n\n// scss-docs-start collapse-classes\n.collapse {\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.collapsing {\n  height: 0;\n  overflow: hidden;\n  @include transition($transition-collapse);\n\n  &.collapse-horizontal {\n    width: 0;\n    height: auto;\n    @include transition($transition-collapse-width);\n  }\n}\n// scss-docs-end collapse-classes\n","// The dropdown wrapper (`<div>`)\n.dropup,\n.dropend,\n.dropdown,\n.dropstart,\n.dropup-center,\n.dropdown-center {\n  position: relative;\n}\n\n.dropdown-toggle {\n  white-space: nowrap;\n\n  // Generate the caret automatically\n  @include caret();\n}\n\n// The dropdown menu\n.dropdown-menu {\n  // scss-docs-start dropdown-css-vars\n  --#{$prefix}dropdown-zindex: #{$zindex-dropdown};\n  --#{$prefix}dropdown-min-width: #{$dropdown-min-width};\n  --#{$prefix}dropdown-padding-x: #{$dropdown-padding-x};\n  --#{$prefix}dropdown-padding-y: #{$dropdown-padding-y};\n  --#{$prefix}dropdown-spacer: #{$dropdown-spacer};\n  @include rfs($dropdown-font-size, --#{$prefix}dropdown-font-size);\n  --#{$prefix}dropdown-color: #{$dropdown-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-border-color};\n  --#{$prefix}dropdown-border-radius: #{$dropdown-border-radius};\n  --#{$prefix}dropdown-border-width: #{$dropdown-border-width};\n  --#{$prefix}dropdown-inner-border-radius: #{$dropdown-inner-border-radius};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-divider-bg};\n  --#{$prefix}dropdown-divider-margin-y: #{$dropdown-divider-margin-y};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-link-hover-color};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-link-disabled-color};\n  --#{$prefix}dropdown-item-padding-x: #{$dropdown-item-padding-x};\n  --#{$prefix}dropdown-item-padding-y: #{$dropdown-item-padding-y};\n  --#{$prefix}dropdown-header-color: #{$dropdown-header-color};\n  --#{$prefix}dropdown-header-padding-x: #{$dropdown-header-padding-x};\n  --#{$prefix}dropdown-header-padding-y: #{$dropdown-header-padding-y};\n  // scss-docs-end dropdown-css-vars\n\n  position: absolute;\n  z-index: var(--#{$prefix}dropdown-zindex);\n  display: none; // none by default, but block on \"open\" of the menu\n  min-width: var(--#{$prefix}dropdown-min-width);\n  padding: var(--#{$prefix}dropdown-padding-y) var(--#{$prefix}dropdown-padding-x);\n  margin: 0; // Override default margin of ul\n  @include font-size(var(--#{$prefix}dropdown-font-size));\n  color: var(--#{$prefix}dropdown-color);\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  list-style: none;\n  background-color: var(--#{$prefix}dropdown-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}dropdown-border-width) solid var(--#{$prefix}dropdown-border-color);\n  @include border-radius(var(--#{$prefix}dropdown-border-radius));\n  @include box-shadow(var(--#{$prefix}dropdown-box-shadow));\n\n  &[data-bs-popper] {\n    top: 100%;\n    left: 0;\n    margin-top: var(--#{$prefix}dropdown-spacer);\n  }\n\n  @if $dropdown-padding-y == 0 {\n    > .dropdown-item:first-child,\n    > li:first-child .dropdown-item {\n      @include border-top-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n    > .dropdown-item:last-child,\n    > li:last-child .dropdown-item {\n      @include border-bottom-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n\n  }\n}\n\n// scss-docs-start responsive-breakpoints\n// We deliberately hardcode the `bs-` prefix because we check\n// this custom property in JS to determine Popper's positioning\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .dropdown-menu#{$infix}-start {\n      --bs-position: start;\n\n      &[data-bs-popper] {\n        right: auto;\n        left: 0;\n      }\n    }\n\n    .dropdown-menu#{$infix}-end {\n      --bs-position: end;\n\n      &[data-bs-popper] {\n        right: 0;\n        left: auto;\n      }\n    }\n  }\n}\n// scss-docs-end responsive-breakpoints\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n// Just add .dropup after the standard .dropdown class and you're set.\n.dropup {\n  .dropdown-menu[data-bs-popper] {\n    top: auto;\n    bottom: 100%;\n    margin-top: 0;\n    margin-bottom: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(up);\n  }\n}\n\n.dropend {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: auto;\n    left: 100%;\n    margin-top: 0;\n    margin-left: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(end);\n    &::after {\n      vertical-align: 0;\n    }\n  }\n}\n\n.dropstart {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: 100%;\n    left: auto;\n    margin-top: 0;\n    margin-right: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(start);\n    &::before {\n      vertical-align: 0;\n    }\n  }\n}\n\n\n// Dividers (basically an `<hr>`) within the dropdown\n.dropdown-divider {\n  height: 0;\n  margin: var(--#{$prefix}dropdown-divider-margin-y) 0;\n  overflow: hidden;\n  border-top: 1px solid var(--#{$prefix}dropdown-divider-bg);\n  opacity: 1; // Revisit in v6 to de-dupe styles that conflict with <hr> element\n}\n\n// Links, buttons, and more within the dropdown menu\n//\n// `<button>`-specific styles are denoted with `// For <button>s`\n.dropdown-item {\n  display: block;\n  width: 100%; // For `<button>`s\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  clear: both;\n  font-weight: $font-weight-normal;\n  color: var(--#{$prefix}dropdown-link-color);\n  text-align: inherit; // For `<button>`s\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap; // prevent links from randomly breaking onto new lines\n  background-color: transparent; // For `<button>`s\n  border: 0; // For `<button>`s\n  @include border-radius(var(--#{$prefix}dropdown-item-border-radius, 0));\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}dropdown-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    @include gradient-bg(var(--#{$prefix}dropdown-link-hover-bg));\n  }\n\n  &.active,\n  &:active {\n    color: var(--#{$prefix}dropdown-link-active-color);\n    text-decoration: none;\n    @include gradient-bg(var(--#{$prefix}dropdown-link-active-bg));\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}dropdown-link-disabled-color);\n    pointer-events: none;\n    background-color: transparent;\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n  }\n}\n\n.dropdown-menu.show {\n  display: block;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: var(--#{$prefix}dropdown-header-padding-y) var(--#{$prefix}dropdown-header-padding-x);\n  margin-bottom: 0; // for use with heading elements\n  @include font-size($font-size-sm);\n  color: var(--#{$prefix}dropdown-header-color);\n  white-space: nowrap; // as with > li > a\n}\n\n// Dropdown text\n.dropdown-item-text {\n  display: block;\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  color: var(--#{$prefix}dropdown-link-color);\n}\n\n// Dark dropdowns\n.dropdown-menu-dark {\n  // scss-docs-start dropdown-dark-css-vars\n  --#{$prefix}dropdown-color: #{$dropdown-dark-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-dark-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-dark-border-color};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-dark-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-dark-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-dark-link-hover-color};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-dark-divider-bg};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-dark-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-dark-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-dark-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-dark-link-disabled-color};\n  --#{$prefix}dropdown-header-color: #{$dropdown-dark-header-color};\n  // scss-docs-end dropdown-dark-css-vars\n}\n","// scss-docs-start caret-mixins\n@mixin caret-down($width: $caret-width) {\n  border-top: $width solid;\n  border-right: $width solid transparent;\n  border-bottom: 0;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-up($width: $caret-width) {\n  border-top: 0;\n  border-right: $width solid transparent;\n  border-bottom: $width solid;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-end($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: 0;\n  border-bottom: $width solid transparent;\n  border-left: $width solid;\n}\n\n@mixin caret-start($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: $width solid;\n  border-bottom: $width solid transparent;\n}\n\n@mixin caret(\n  $direction: down,\n  $width: $caret-width,\n  $spacing: $caret-spacing,\n  $vertical-align: $caret-vertical-align\n) {\n  @if $enable-caret {\n    &::after {\n      display: inline-block;\n      margin-left: $spacing;\n      vertical-align: $vertical-align;\n      content: \"\";\n      @if $direction == down {\n        @include caret-down($width);\n      } @else if $direction == up {\n        @include caret-up($width);\n      } @else if $direction == end {\n        @include caret-end($width);\n      }\n    }\n\n    @if $direction == start {\n      &::after {\n        display: none;\n      }\n\n      &::before {\n        display: inline-block;\n        margin-right: $spacing;\n        vertical-align: $vertical-align;\n        content: \"\";\n        @include caret-start($width);\n      }\n    }\n\n    &:empty::after {\n      margin-left: 0;\n    }\n  }\n}\n// scss-docs-end caret-mixins\n","// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-flex;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n\n  > .btn {\n    position: relative;\n    flex: 1 1 auto;\n  }\n\n  // Bring the hover, focused, and \"active\" buttons to the front to overlay\n  // the borders properly\n  > .btn-check:checked + .btn,\n  > .btn-check:focus + .btn,\n  > .btn:hover,\n  > .btn:focus,\n  > .btn:active,\n  > .btn.active {\n    z-index: 1;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  display: flex;\n  flex-wrap: wrap;\n  justify-content: flex-start;\n\n  .input-group {\n    width: auto;\n  }\n}\n\n.btn-group {\n  @include border-radius($btn-border-radius);\n\n  // Prevent double borders when buttons are next to each other\n  > :not(.btn-check:first-child) + .btn,\n  > .btn-group:not(:first-child) {\n    margin-left: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn.dropdown-toggle-split:first-child,\n  > .btn-group:not(:last-child) > .btn {\n    @include border-end-radius(0);\n  }\n\n  // The left radius should be 0 if the button is:\n  // - the \"third or more\" child\n  // - the second child and the previous element isn't `.btn-check` (making it the first child visually)\n  // - part of a btn-group which isn't the first child\n  > .btn:nth-child(n + 3),\n  > :not(.btn-check) + .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-start-radius(0);\n  }\n}\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-sm > .btn { @extend .btn-sm; }\n.btn-group-lg > .btn { @extend .btn-lg; }\n\n\n//\n// Split button dropdowns\n//\n\n.dropdown-toggle-split {\n  padding-right: $btn-padding-x * .75;\n  padding-left: $btn-padding-x * .75;\n\n  &::after,\n  .dropup &::after,\n  .dropend &::after {\n    margin-left: 0;\n  }\n\n  .dropstart &::before {\n    margin-right: 0;\n  }\n}\n\n.btn-sm + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-sm * .75;\n  padding-left: $btn-padding-x-sm * .75;\n}\n\n.btn-lg + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-lg * .75;\n  padding-left: $btn-padding-x-lg * .75;\n}\n\n\n// The clickable button for toggling the menu\n// Set the same inset shadow as the :active state\n.btn-group.show .dropdown-toggle {\n  @include box-shadow($btn-active-box-shadow);\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Vertical button groups\n//\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n\n  > .btn,\n  > .btn-group {\n    width: 100%;\n  }\n\n  > .btn:not(:first-child),\n  > .btn-group:not(:first-child) {\n    margin-top: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn-group:not(:last-child) > .btn {\n    @include border-bottom-radius(0);\n  }\n\n  > .btn ~ .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-top-radius(0);\n  }\n}\n","// Base class\n//\n// Kickstart any navigation component with a set of style resets. Works with\n// `<nav>`s, `<ul>`s or `<ol>`s.\n\n.nav {\n  // scss-docs-start nav-css-vars\n  --#{$prefix}nav-link-padding-x: #{$nav-link-padding-x};\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: #{$nav-link-color};\n  --#{$prefix}nav-link-hover-color: #{$nav-link-hover-color};\n  --#{$prefix}nav-link-disabled-color: #{$nav-link-disabled-color};\n  // scss-docs-end nav-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n\n.nav-link {\n  display: block;\n  padding: var(--#{$prefix}nav-link-padding-y) var(--#{$prefix}nav-link-padding-x);\n  @include font-size(var(--#{$prefix}nav-link-font-size));\n  font-weight: var(--#{$prefix}nav-link-font-weight);\n  color: var(--#{$prefix}nav-link-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background: none;\n  border: 0;\n  @include transition($nav-link-transition);\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}nav-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  &:focus-visible {\n    outline: 0;\n    box-shadow: $nav-link-focus-box-shadow;\n  }\n\n  // Disabled state lightens text\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}nav-link-disabled-color);\n    pointer-events: none;\n    cursor: default;\n  }\n}\n\n//\n// Tabs\n//\n\n.nav-tabs {\n  // scss-docs-start nav-tabs-css-vars\n  --#{$prefix}nav-tabs-border-width: #{$nav-tabs-border-width};\n  --#{$prefix}nav-tabs-border-color: #{$nav-tabs-border-color};\n  --#{$prefix}nav-tabs-border-radius: #{$nav-tabs-border-radius};\n  --#{$prefix}nav-tabs-link-hover-border-color: #{$nav-tabs-link-hover-border-color};\n  --#{$prefix}nav-tabs-link-active-color: #{$nav-tabs-link-active-color};\n  --#{$prefix}nav-tabs-link-active-bg: #{$nav-tabs-link-active-bg};\n  --#{$prefix}nav-tabs-link-active-border-color: #{$nav-tabs-link-active-border-color};\n  // scss-docs-end nav-tabs-css-vars\n\n  border-bottom: var(--#{$prefix}nav-tabs-border-width) solid var(--#{$prefix}nav-tabs-border-color);\n\n  .nav-link {\n    margin-bottom: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    border: var(--#{$prefix}nav-tabs-border-width) solid transparent;\n    @include border-top-radius(var(--#{$prefix}nav-tabs-border-radius));\n\n    &:hover,\n    &:focus {\n      // Prevents active .nav-link tab overlapping focus outline of previous/next .nav-link\n      isolation: isolate;\n      border-color: var(--#{$prefix}nav-tabs-link-hover-border-color);\n    }\n  }\n\n  .nav-link.active,\n  .nav-item.show .nav-link {\n    color: var(--#{$prefix}nav-tabs-link-active-color);\n    background-color: var(--#{$prefix}nav-tabs-link-active-bg);\n    border-color: var(--#{$prefix}nav-tabs-link-active-border-color);\n  }\n\n  .dropdown-menu {\n    // Make dropdown border overlap tab border\n    margin-top: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    // Remove the top rounded corners here since there is a hard edge above the menu\n    @include border-top-radius(0);\n  }\n}\n\n\n//\n// Pills\n//\n\n.nav-pills {\n  // scss-docs-start nav-pills-css-vars\n  --#{$prefix}nav-pills-border-radius: #{$nav-pills-border-radius};\n  --#{$prefix}nav-pills-link-active-color: #{$nav-pills-link-active-color};\n  --#{$prefix}nav-pills-link-active-bg: #{$nav-pills-link-active-bg};\n  // scss-docs-end nav-pills-css-vars\n\n  .nav-link {\n    @include border-radius(var(--#{$prefix}nav-pills-border-radius));\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    color: var(--#{$prefix}nav-pills-link-active-color);\n    @include gradient-bg(var(--#{$prefix}nav-pills-link-active-bg));\n  }\n}\n\n\n//\n// Underline\n//\n\n.nav-underline {\n  // scss-docs-start nav-underline-css-vars\n  --#{$prefix}nav-underline-gap: #{$nav-underline-gap};\n  --#{$prefix}nav-underline-border-width: #{$nav-underline-border-width};\n  --#{$prefix}nav-underline-link-active-color: #{$nav-underline-link-active-color};\n  // scss-docs-end nav-underline-css-vars\n\n  gap: var(--#{$prefix}nav-underline-gap);\n\n  .nav-link {\n    padding-right: 0;\n    padding-left: 0;\n    border-bottom: var(--#{$prefix}nav-underline-border-width) solid transparent;\n\n    &:hover,\n    &:focus {\n      border-bottom-color: currentcolor;\n    }\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    font-weight: $font-weight-bold;\n    color: var(--#{$prefix}nav-underline-link-active-color);\n    border-bottom-color: currentcolor;\n  }\n}\n\n\n//\n// Justified variants\n//\n\n.nav-fill {\n  > .nav-link,\n  .nav-item {\n    flex: 1 1 auto;\n    text-align: center;\n  }\n}\n\n.nav-justified {\n  > .nav-link,\n  .nav-item {\n    flex-basis: 0;\n    flex-grow: 1;\n    text-align: center;\n  }\n}\n\n.nav-fill,\n.nav-justified {\n  .nav-item .nav-link {\n    width: 100%; // Make sure button will grow\n  }\n}\n\n\n// Tabbable tabs\n//\n// Hide tabbable panes to start, show them when `.active`\n\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n","// Navbar\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  // scss-docs-start navbar-css-vars\n  --#{$prefix}navbar-padding-x: #{if($navbar-padding-x == null, 0, $navbar-padding-x)};\n  --#{$prefix}navbar-padding-y: #{$navbar-padding-y};\n  --#{$prefix}navbar-color: #{$navbar-light-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-light-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-light-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-light-active-color};\n  --#{$prefix}navbar-brand-padding-y: #{$navbar-brand-padding-y};\n  --#{$prefix}navbar-brand-margin-end: #{$navbar-brand-margin-end};\n  --#{$prefix}navbar-brand-font-size: #{$navbar-brand-font-size};\n  --#{$prefix}navbar-brand-color: #{$navbar-light-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-light-brand-hover-color};\n  --#{$prefix}navbar-nav-link-padding-x: #{$navbar-nav-link-padding-x};\n  --#{$prefix}navbar-toggler-padding-y: #{$navbar-toggler-padding-y};\n  --#{$prefix}navbar-toggler-padding-x: #{$navbar-toggler-padding-x};\n  --#{$prefix}navbar-toggler-font-size: #{$navbar-toggler-font-size};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-light-toggler-icon-bg)};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-light-toggler-border-color};\n  --#{$prefix}navbar-toggler-border-radius: #{$navbar-toggler-border-radius};\n  --#{$prefix}navbar-toggler-focus-width: #{$navbar-toggler-focus-width};\n  --#{$prefix}navbar-toggler-transition: #{$navbar-toggler-transition};\n  // scss-docs-end navbar-css-vars\n\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // allow us to do the line break for collapsing content\n  align-items: center;\n  justify-content: space-between; // space out brand from logo\n  padding: var(--#{$prefix}navbar-padding-y) var(--#{$prefix}navbar-padding-x);\n  @include gradient-bg();\n\n  // Because flex properties aren't inherited, we need to redeclare these first\n  // few properties so that content nested within behave properly.\n  // The `flex-wrap` property is inherited to simplify the expanded navbars\n  %container-flex-properties {\n    display: flex;\n    flex-wrap: inherit;\n    align-items: center;\n    justify-content: space-between;\n  }\n\n  > .container,\n  > .container-fluid {\n    @extend %container-flex-properties;\n  }\n\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    > .container#{breakpoint-infix($breakpoint, $container-max-widths)} {\n      @extend %container-flex-properties;\n    }\n  }\n}\n\n\n// Navbar brand\n//\n// Used for brand, project, or site names.\n\n.navbar-brand {\n  padding-top: var(--#{$prefix}navbar-brand-padding-y);\n  padding-bottom: var(--#{$prefix}navbar-brand-padding-y);\n  margin-right: var(--#{$prefix}navbar-brand-margin-end);\n  @include font-size(var(--#{$prefix}navbar-brand-font-size));\n  color: var(--#{$prefix}navbar-brand-color);\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap;\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}navbar-brand-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n}\n\n\n// Navbar nav\n//\n// Custom navbar navigation (doesn't require `.nav`, but does make use of `.nav-link`).\n\n.navbar-nav {\n  // scss-docs-start navbar-nav-css-vars\n  --#{$prefix}nav-link-padding-x: 0;\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: var(--#{$prefix}navbar-color);\n  --#{$prefix}nav-link-hover-color: var(--#{$prefix}navbar-hover-color);\n  --#{$prefix}nav-link-disabled-color: var(--#{$prefix}navbar-disabled-color);\n  // scss-docs-end navbar-nav-css-vars\n\n  display: flex;\n  flex-direction: column; // cannot use `inherit` to get the `.navbar`s value\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n\n  .nav-link {\n    &.active,\n    &.show {\n      color: var(--#{$prefix}navbar-active-color);\n    }\n  }\n\n  .dropdown-menu {\n    position: static;\n  }\n}\n\n\n// Navbar text\n//\n//\n\n.navbar-text {\n  padding-top: $nav-link-padding-y;\n  padding-bottom: $nav-link-padding-y;\n  color: var(--#{$prefix}navbar-color);\n\n  a,\n  a:hover,\n  a:focus  {\n    color: var(--#{$prefix}navbar-active-color);\n  }\n}\n\n\n// Responsive navbar\n//\n// Custom styles for responsive collapsing and toggling of navbar contents.\n// Powered by the collapse Bootstrap JavaScript plugin.\n\n// When collapsed, prevent the toggleable navbar contents from appearing in\n// the default flexbox row orientation. Requires the use of `flex-wrap: wrap`\n// on the `.navbar` parent.\n.navbar-collapse {\n  flex-basis: 100%;\n  flex-grow: 1;\n  // For always expanded or extra full navbars, ensure content aligns itself\n  // properly vertically. Can be easily overridden with flex utilities.\n  align-items: center;\n}\n\n// Button for toggling the navbar when in its collapsed state\n.navbar-toggler {\n  padding: var(--#{$prefix}navbar-toggler-padding-y) var(--#{$prefix}navbar-toggler-padding-x);\n  @include font-size(var(--#{$prefix}navbar-toggler-font-size));\n  line-height: 1;\n  color: var(--#{$prefix}navbar-color);\n  background-color: transparent; // remove default button style\n  border: var(--#{$prefix}border-width) solid var(--#{$prefix}navbar-toggler-border-color); // remove default button style\n  @include border-radius(var(--#{$prefix}navbar-toggler-border-radius));\n  @include transition(var(--#{$prefix}navbar-toggler-transition));\n\n  &:hover {\n    text-decoration: none;\n  }\n\n  &:focus {\n    text-decoration: none;\n    outline: 0;\n    box-shadow: 0 0 0 var(--#{$prefix}navbar-toggler-focus-width);\n  }\n}\n\n// Keep as a separate element so folks can easily override it with another icon\n// or image file as needed.\n.navbar-toggler-icon {\n  display: inline-block;\n  width: 1.5em;\n  height: 1.5em;\n  vertical-align: middle;\n  background-image: var(--#{$prefix}navbar-toggler-icon-bg);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: 100%;\n}\n\n.navbar-nav-scroll {\n  max-height: var(--#{$prefix}scroll-height, 75vh);\n  overflow-y: auto;\n}\n\n// scss-docs-start navbar-expand-loop\n// Generate series of `.navbar-expand-*` responsive classes for configuring\n// where your navbar collapses.\n.navbar-expand {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $next: breakpoint-next($breakpoint, $grid-breakpoints);\n    $infix: breakpoint-infix($next, $grid-breakpoints);\n\n    // stylelint-disable-next-line scss/selector-no-union-class-name\n    &#{$infix} {\n      @include media-breakpoint-up($next) {\n        flex-wrap: nowrap;\n        justify-content: flex-start;\n\n        .navbar-nav {\n          flex-direction: row;\n\n          .dropdown-menu {\n            position: absolute;\n          }\n\n          .nav-link {\n            padding-right: var(--#{$prefix}navbar-nav-link-padding-x);\n            padding-left: var(--#{$prefix}navbar-nav-link-padding-x);\n          }\n        }\n\n        .navbar-nav-scroll {\n          overflow: visible;\n        }\n\n        .navbar-collapse {\n          display: flex !important; // stylelint-disable-line declaration-no-important\n          flex-basis: auto;\n        }\n\n        .navbar-toggler {\n          display: none;\n        }\n\n        .offcanvas {\n          // stylelint-disable declaration-no-important\n          position: static;\n          z-index: auto;\n          flex-grow: 1;\n          width: auto !important;\n          height: auto !important;\n          visibility: visible !important;\n          background-color: transparent !important;\n          border: 0 !important;\n          transform: none !important;\n          @include box-shadow(none);\n          @include transition(none);\n          // stylelint-enable declaration-no-important\n\n          .offcanvas-header {\n            display: none;\n          }\n\n          .offcanvas-body {\n            display: flex;\n            flex-grow: 0;\n            padding: 0;\n            overflow-y: visible;\n          }\n        }\n      }\n    }\n  }\n}\n// scss-docs-end navbar-expand-loop\n\n// Navbar themes\n//\n// Styles for switching between navbars with light or dark background.\n\n.navbar-light {\n  @include deprecate(\"`.navbar-light`\", \"v5.2.0\", \"v6.0.0\", true);\n}\n\n.navbar-dark,\n.navbar[data-bs-theme=\"dark\"] {\n  // scss-docs-start navbar-dark-css-vars\n  --#{$prefix}navbar-color: #{$navbar-dark-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-dark-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-dark-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-dark-active-color};\n  --#{$prefix}navbar-brand-color: #{$navbar-dark-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-dark-brand-hover-color};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-dark-toggler-border-color};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n  // scss-docs-end navbar-dark-css-vars\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .navbar-toggler-icon {\n      --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.card {\n  // scss-docs-start card-css-vars\n  --#{$prefix}card-spacer-y: #{$card-spacer-y};\n  --#{$prefix}card-spacer-x: #{$card-spacer-x};\n  --#{$prefix}card-title-spacer-y: #{$card-title-spacer-y};\n  --#{$prefix}card-title-color: #{$card-title-color};\n  --#{$prefix}card-subtitle-color: #{$card-subtitle-color};\n  --#{$prefix}card-border-width: #{$card-border-width};\n  --#{$prefix}card-border-color: #{$card-border-color};\n  --#{$prefix}card-border-radius: #{$card-border-radius};\n  --#{$prefix}card-box-shadow: #{$card-box-shadow};\n  --#{$prefix}card-inner-border-radius: #{$card-inner-border-radius};\n  --#{$prefix}card-cap-padding-y: #{$card-cap-padding-y};\n  --#{$prefix}card-cap-padding-x: #{$card-cap-padding-x};\n  --#{$prefix}card-cap-bg: #{$card-cap-bg};\n  --#{$prefix}card-cap-color: #{$card-cap-color};\n  --#{$prefix}card-height: #{$card-height};\n  --#{$prefix}card-color: #{$card-color};\n  --#{$prefix}card-bg: #{$card-bg};\n  --#{$prefix}card-img-overlay-padding: #{$card-img-overlay-padding};\n  --#{$prefix}card-group-margin: #{$card-group-margin};\n  // scss-docs-end card-css-vars\n\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  min-width: 0; // See https://github.com/twbs/bootstrap/pull/22740#issuecomment-305868106\n  height: var(--#{$prefix}card-height);\n  color: var(--#{$prefix}body-color);\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}card-bg);\n  background-clip: border-box;\n  border: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n  @include border-radius(var(--#{$prefix}card-border-radius));\n  @include box-shadow(var(--#{$prefix}card-box-shadow));\n\n  > hr {\n    margin-right: 0;\n    margin-left: 0;\n  }\n\n  > .list-group {\n    border-top: inherit;\n    border-bottom: inherit;\n\n    &:first-child {\n      border-top-width: 0;\n      @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n\n    &:last-child  {\n      border-bottom-width: 0;\n      @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n  }\n\n  // Due to specificity of the above selector (`.card > .list-group`), we must\n  // use a child selector here to prevent double borders.\n  > .card-header + .list-group,\n  > .list-group + .card-footer {\n    border-top: 0;\n  }\n}\n\n.card-body {\n  // Enable `flex-grow: 1` for decks and groups so that card blocks take up\n  // as much space as possible, ensuring footers are aligned to the bottom.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}card-spacer-y) var(--#{$prefix}card-spacer-x);\n  color: var(--#{$prefix}card-color);\n}\n\n.card-title {\n  margin-bottom: var(--#{$prefix}card-title-spacer-y);\n  color: var(--#{$prefix}card-title-color);\n}\n\n.card-subtitle {\n  margin-top: calc(-.5 * var(--#{$prefix}card-title-spacer-y)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: 0;\n  color: var(--#{$prefix}card-subtitle-color);\n}\n\n.card-text:last-child {\n  margin-bottom: 0;\n}\n\n.card-link {\n  &:hover {\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  + .card-link {\n    margin-left: var(--#{$prefix}card-spacer-x);\n  }\n}\n\n//\n// Optional textual caps\n//\n\n.card-header {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  margin-bottom: 0; // Removes the default margin-bottom of <hN>\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-bottom: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:first-child {\n    @include border-radius(var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius) 0 0);\n  }\n}\n\n.card-footer {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-top: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:last-child {\n    @include border-radius(0 0 var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius));\n  }\n}\n\n\n//\n// Header navs\n//\n\n.card-header-tabs {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: calc(-1 * var(--#{$prefix}card-cap-padding-y)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  border-bottom: 0;\n\n  .nav-link.active {\n    background-color: var(--#{$prefix}card-bg);\n    border-bottom-color: var(--#{$prefix}card-bg);\n  }\n}\n\n.card-header-pills {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n}\n\n// Card image\n.card-img-overlay {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  padding: var(--#{$prefix}card-img-overlay-padding);\n  @include border-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-top,\n.card-img-bottom {\n  width: 100%; // Required because we use flexbox and this inherently applies align-self: stretch\n}\n\n.card-img,\n.card-img-top {\n  @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-bottom {\n  @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n\n//\n// Card groups\n//\n\n.card-group {\n  // The child selector allows nested `.card` within `.card-group`\n  // to display properly.\n  > .card {\n    margin-bottom: var(--#{$prefix}card-group-margin);\n  }\n\n  @include media-breakpoint-up(sm) {\n    display: flex;\n    flex-flow: row wrap;\n    // The child selector allows nested `.card` within `.card-group`\n    // to display properly.\n    > .card {\n      // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      flex: 1 0 0%;\n      margin-bottom: 0;\n\n      + .card {\n        margin-left: 0;\n        border-left: 0;\n      }\n\n      // Handle rounded corners\n      @if $enable-rounded {\n        &:not(:last-child) {\n          @include border-end-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-right-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-right-radius: 0;\n          }\n        }\n\n        &:not(:first-child) {\n          @include border-start-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-left-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-left-radius: 0;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.accordion {\n  // scss-docs-start accordion-css-vars\n  --#{$prefix}accordion-color: #{$accordion-color};\n  --#{$prefix}accordion-bg: #{$accordion-bg};\n  --#{$prefix}accordion-transition: #{$accordion-transition};\n  --#{$prefix}accordion-border-color: #{$accordion-border-color};\n  --#{$prefix}accordion-border-width: #{$accordion-border-width};\n  --#{$prefix}accordion-border-radius: #{$accordion-border-radius};\n  --#{$prefix}accordion-inner-border-radius: #{$accordion-inner-border-radius};\n  --#{$prefix}accordion-btn-padding-x: #{$accordion-button-padding-x};\n  --#{$prefix}accordion-btn-padding-y: #{$accordion-button-padding-y};\n  --#{$prefix}accordion-btn-color: #{$accordion-button-color};\n  --#{$prefix}accordion-btn-bg: #{$accordion-button-bg};\n  --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon)};\n  --#{$prefix}accordion-btn-icon-width: #{$accordion-icon-width};\n  --#{$prefix}accordion-btn-icon-transform: #{$accordion-icon-transform};\n  --#{$prefix}accordion-btn-icon-transition: #{$accordion-icon-transition};\n  --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon)};\n  --#{$prefix}accordion-btn-focus-box-shadow: #{$accordion-button-focus-box-shadow};\n  --#{$prefix}accordion-body-padding-x: #{$accordion-body-padding-x};\n  --#{$prefix}accordion-body-padding-y: #{$accordion-body-padding-y};\n  --#{$prefix}accordion-active-color: #{$accordion-button-active-color};\n  --#{$prefix}accordion-active-bg: #{$accordion-button-active-bg};\n  // scss-docs-end accordion-css-vars\n}\n\n.accordion-button {\n  position: relative;\n  display: flex;\n  align-items: center;\n  width: 100%;\n  padding: var(--#{$prefix}accordion-btn-padding-y) var(--#{$prefix}accordion-btn-padding-x);\n  @include font-size($font-size-base);\n  color: var(--#{$prefix}accordion-btn-color);\n  text-align: left; // Reset button style\n  background-color: var(--#{$prefix}accordion-btn-bg);\n  border: 0;\n  @include border-radius(0);\n  overflow-anchor: none;\n  @include transition(var(--#{$prefix}accordion-transition));\n\n  &:not(.collapsed) {\n    color: var(--#{$prefix}accordion-active-color);\n    background-color: var(--#{$prefix}accordion-active-bg);\n    box-shadow: inset 0 calc(-1 * var(--#{$prefix}accordion-border-width)) 0 var(--#{$prefix}accordion-border-color); // stylelint-disable-line function-disallowed-list\n\n    &::after {\n      background-image: var(--#{$prefix}accordion-btn-active-icon);\n      transform: var(--#{$prefix}accordion-btn-icon-transform);\n    }\n  }\n\n  // Accordion icon\n  &::after {\n    flex-shrink: 0;\n    width: var(--#{$prefix}accordion-btn-icon-width);\n    height: var(--#{$prefix}accordion-btn-icon-width);\n    margin-left: auto;\n    content: \"\";\n    background-image: var(--#{$prefix}accordion-btn-icon);\n    background-repeat: no-repeat;\n    background-size: var(--#{$prefix}accordion-btn-icon-width);\n    @include transition(var(--#{$prefix}accordion-btn-icon-transition));\n  }\n\n  &:hover {\n    z-index: 2;\n  }\n\n  &:focus {\n    z-index: 3;\n    outline: 0;\n    box-shadow: var(--#{$prefix}accordion-btn-focus-box-shadow);\n  }\n}\n\n.accordion-header {\n  margin-bottom: 0;\n}\n\n.accordion-item {\n  color: var(--#{$prefix}accordion-color);\n  background-color: var(--#{$prefix}accordion-bg);\n  border: var(--#{$prefix}accordion-border-width) solid var(--#{$prefix}accordion-border-color);\n\n  &:first-of-type {\n    @include border-top-radius(var(--#{$prefix}accordion-border-radius));\n\n    > .accordion-header .accordion-button {\n      @include border-top-radius(var(--#{$prefix}accordion-inner-border-radius));\n    }\n  }\n\n  &:not(:first-of-type) {\n    border-top: 0;\n  }\n\n  // Only set a border-radius on the last item if the accordion is collapsed\n  &:last-of-type {\n    @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n\n    > .accordion-header .accordion-button {\n      &.collapsed {\n        @include border-bottom-radius(var(--#{$prefix}accordion-inner-border-radius));\n      }\n    }\n\n    > .accordion-collapse {\n      @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n    }\n  }\n}\n\n.accordion-body {\n  padding: var(--#{$prefix}accordion-body-padding-y) var(--#{$prefix}accordion-body-padding-x);\n}\n\n\n// Flush accordion items\n//\n// Remove borders and border-radius to keep accordion items edge-to-edge.\n\n.accordion-flush {\n  > .accordion-item {\n    border-right: 0;\n    border-left: 0;\n    @include border-radius(0);\n\n    &:first-child { border-top: 0; }\n    &:last-child { border-bottom: 0; }\n\n    // stylelint-disable selector-max-class\n    > .accordion-header .accordion-button {\n      &,\n      &.collapsed {\n        @include border-radius(0);\n      }\n    }\n    // stylelint-enable selector-max-class\n\n    > .accordion-collapse {\n      @include border-radius(0);\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .accordion-button::after {\n      --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon-dark)};\n      --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon-dark)};\n    }\n  }\n}\n",".breadcrumb {\n  // scss-docs-start breadcrumb-css-vars\n  --#{$prefix}breadcrumb-padding-x: #{$breadcrumb-padding-x};\n  --#{$prefix}breadcrumb-padding-y: #{$breadcrumb-padding-y};\n  --#{$prefix}breadcrumb-margin-bottom: #{$breadcrumb-margin-bottom};\n  @include rfs($breadcrumb-font-size, --#{$prefix}breadcrumb-font-size);\n  --#{$prefix}breadcrumb-bg: #{$breadcrumb-bg};\n  --#{$prefix}breadcrumb-border-radius: #{$breadcrumb-border-radius};\n  --#{$prefix}breadcrumb-divider-color: #{$breadcrumb-divider-color};\n  --#{$prefix}breadcrumb-item-padding-x: #{$breadcrumb-item-padding-x};\n  --#{$prefix}breadcrumb-item-active-color: #{$breadcrumb-active-color};\n  // scss-docs-end breadcrumb-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding: var(--#{$prefix}breadcrumb-padding-y) var(--#{$prefix}breadcrumb-padding-x);\n  margin-bottom: var(--#{$prefix}breadcrumb-margin-bottom);\n  @include font-size(var(--#{$prefix}breadcrumb-font-size));\n  list-style: none;\n  background-color: var(--#{$prefix}breadcrumb-bg);\n  @include border-radius(var(--#{$prefix}breadcrumb-border-radius));\n}\n\n.breadcrumb-item {\n  // The separator between breadcrumbs (by default, a forward-slash: \"/\")\n  + .breadcrumb-item {\n    padding-left: var(--#{$prefix}breadcrumb-item-padding-x);\n\n    &::before {\n      float: left; // Suppress inline spacings and underlining of the separator\n      padding-right: var(--#{$prefix}breadcrumb-item-padding-x);\n      color: var(--#{$prefix}breadcrumb-divider-color);\n      content: var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider)) #{\"/* rtl:\"} var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider-flipped)) #{\"*/\"};\n    }\n  }\n\n  &.active {\n    color: var(--#{$prefix}breadcrumb-item-active-color);\n  }\n}\n",".pagination {\n  // scss-docs-start pagination-css-vars\n  --#{$prefix}pagination-padding-x: #{$pagination-padding-x};\n  --#{$prefix}pagination-padding-y: #{$pagination-padding-y};\n  @include rfs($pagination-font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-color: #{$pagination-color};\n  --#{$prefix}pagination-bg: #{$pagination-bg};\n  --#{$prefix}pagination-border-width: #{$pagination-border-width};\n  --#{$prefix}pagination-border-color: #{$pagination-border-color};\n  --#{$prefix}pagination-border-radius: #{$pagination-border-radius};\n  --#{$prefix}pagination-hover-color: #{$pagination-hover-color};\n  --#{$prefix}pagination-hover-bg: #{$pagination-hover-bg};\n  --#{$prefix}pagination-hover-border-color: #{$pagination-hover-border-color};\n  --#{$prefix}pagination-focus-color: #{$pagination-focus-color};\n  --#{$prefix}pagination-focus-bg: #{$pagination-focus-bg};\n  --#{$prefix}pagination-focus-box-shadow: #{$pagination-focus-box-shadow};\n  --#{$prefix}pagination-active-color: #{$pagination-active-color};\n  --#{$prefix}pagination-active-bg: #{$pagination-active-bg};\n  --#{$prefix}pagination-active-border-color: #{$pagination-active-border-color};\n  --#{$prefix}pagination-disabled-color: #{$pagination-disabled-color};\n  --#{$prefix}pagination-disabled-bg: #{$pagination-disabled-bg};\n  --#{$prefix}pagination-disabled-border-color: #{$pagination-disabled-border-color};\n  // scss-docs-end pagination-css-vars\n\n  display: flex;\n  @include list-unstyled();\n}\n\n.page-link {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}pagination-padding-y) var(--#{$prefix}pagination-padding-x);\n  @include font-size(var(--#{$prefix}pagination-font-size));\n  color: var(--#{$prefix}pagination-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}pagination-bg);\n  border: var(--#{$prefix}pagination-border-width) solid var(--#{$prefix}pagination-border-color);\n  @include transition($pagination-transition);\n\n  &:hover {\n    z-index: 2;\n    color: var(--#{$prefix}pagination-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}pagination-hover-bg);\n    border-color: var(--#{$prefix}pagination-hover-border-color);\n  }\n\n  &:focus {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-focus-color);\n    background-color: var(--#{$prefix}pagination-focus-bg);\n    outline: $pagination-focus-outline;\n    box-shadow: var(--#{$prefix}pagination-focus-box-shadow);\n  }\n\n  &.active,\n  .active > & {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-active-color);\n    @include gradient-bg(var(--#{$prefix}pagination-active-bg));\n    border-color: var(--#{$prefix}pagination-active-border-color);\n  }\n\n  &.disabled,\n  .disabled > & {\n    color: var(--#{$prefix}pagination-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}pagination-disabled-bg);\n    border-color: var(--#{$prefix}pagination-disabled-border-color);\n  }\n}\n\n.page-item {\n  &:not(:first-child) .page-link {\n    margin-left: $pagination-margin-start;\n  }\n\n  @if $pagination-margin-start == calc(#{$pagination-border-width} * -1) {\n    &:first-child {\n      .page-link {\n        @include border-start-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n\n    &:last-child {\n      .page-link {\n        @include border-end-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n  } @else {\n    // Add border-radius to all pageLinks in case they have left margin\n    .page-link {\n      @include border-radius(var(--#{$prefix}pagination-border-radius));\n    }\n  }\n}\n\n\n//\n// Sizing\n//\n\n.pagination-lg {\n  @include pagination-size($pagination-padding-y-lg, $pagination-padding-x-lg, $font-size-lg, $pagination-border-radius-lg);\n}\n\n.pagination-sm {\n  @include pagination-size($pagination-padding-y-sm, $pagination-padding-x-sm, $font-size-sm, $pagination-border-radius-sm);\n}\n","// Pagination\n\n// scss-docs-start pagination-mixin\n@mixin pagination-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}pagination-padding-x: #{$padding-x};\n  --#{$prefix}pagination-padding-y: #{$padding-y};\n  @include rfs($font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-border-radius: #{$border-radius};\n}\n// scss-docs-end pagination-mixin\n","// Base class\n//\n// Requires one of the contextual, color modifier classes for `color` and\n// `background-color`.\n\n.badge {\n  // scss-docs-start badge-css-vars\n  --#{$prefix}badge-padding-x: #{$badge-padding-x};\n  --#{$prefix}badge-padding-y: #{$badge-padding-y};\n  @include rfs($badge-font-size, --#{$prefix}badge-font-size);\n  --#{$prefix}badge-font-weight: #{$badge-font-weight};\n  --#{$prefix}badge-color: #{$badge-color};\n  --#{$prefix}badge-border-radius: #{$badge-border-radius};\n  // scss-docs-end badge-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}badge-padding-y) var(--#{$prefix}badge-padding-x);\n  @include font-size(var(--#{$prefix}badge-font-size));\n  font-weight: var(--#{$prefix}badge-font-weight);\n  line-height: 1;\n  color: var(--#{$prefix}badge-color);\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  @include border-radius(var(--#{$prefix}badge-border-radius));\n  @include gradient-bg();\n\n  // Empty badges collapse automatically\n  &:empty {\n    display: none;\n  }\n}\n\n// Quick fix for badges in buttons\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n","//\n// Base styles\n//\n\n.alert {\n  // scss-docs-start alert-css-vars\n  --#{$prefix}alert-bg: transparent;\n  --#{$prefix}alert-padding-x: #{$alert-padding-x};\n  --#{$prefix}alert-padding-y: #{$alert-padding-y};\n  --#{$prefix}alert-margin-bottom: #{$alert-margin-bottom};\n  --#{$prefix}alert-color: inherit;\n  --#{$prefix}alert-border-color: transparent;\n  --#{$prefix}alert-border: #{$alert-border-width} solid var(--#{$prefix}alert-border-color);\n  --#{$prefix}alert-border-radius: #{$alert-border-radius};\n  --#{$prefix}alert-link-color: inherit;\n  // scss-docs-end alert-css-vars\n\n  position: relative;\n  padding: var(--#{$prefix}alert-padding-y) var(--#{$prefix}alert-padding-x);\n  margin-bottom: var(--#{$prefix}alert-margin-bottom);\n  color: var(--#{$prefix}alert-color);\n  background-color: var(--#{$prefix}alert-bg);\n  border: var(--#{$prefix}alert-border);\n  @include border-radius(var(--#{$prefix}alert-border-radius));\n}\n\n// Headings for larger alerts\n.alert-heading {\n  // Specified to prevent conflicts of changing $headings-color\n  color: inherit;\n}\n\n// Provide class for links that match alerts\n.alert-link {\n  font-weight: $alert-link-font-weight;\n  color: var(--#{$prefix}alert-link-color);\n}\n\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissible {\n  padding-right: $alert-dismissible-padding-r;\n\n  // Adjust close link position\n  .btn-close {\n    position: absolute;\n    top: 0;\n    right: 0;\n    z-index: $stretched-link-z-index + 1;\n    padding: $alert-padding-y * 1.25 $alert-padding-x;\n  }\n}\n\n\n// scss-docs-start alert-modifiers\n// Generate contextual modifier classes for colorizing the alert\n@each $state in map-keys($theme-colors) {\n  .alert-#{$state} {\n    --#{$prefix}alert-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}alert-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}alert-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}alert-link-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end alert-modifiers\n","// Disable animation if transitions are disabled\n\n// scss-docs-start progress-keyframes\n@if $enable-transitions {\n  @keyframes progress-bar-stripes {\n    0% { background-position-x: $progress-height; }\n  }\n}\n// scss-docs-end progress-keyframes\n\n.progress,\n.progress-stacked {\n  // scss-docs-start progress-css-vars\n  --#{$prefix}progress-height: #{$progress-height};\n  @include rfs($progress-font-size, --#{$prefix}progress-font-size);\n  --#{$prefix}progress-bg: #{$progress-bg};\n  --#{$prefix}progress-border-radius: #{$progress-border-radius};\n  --#{$prefix}progress-box-shadow: #{$progress-box-shadow};\n  --#{$prefix}progress-bar-color: #{$progress-bar-color};\n  --#{$prefix}progress-bar-bg: #{$progress-bar-bg};\n  --#{$prefix}progress-bar-transition: #{$progress-bar-transition};\n  // scss-docs-end progress-css-vars\n\n  display: flex;\n  height: var(--#{$prefix}progress-height);\n  overflow: hidden; // force rounded corners by cropping it\n  @include font-size(var(--#{$prefix}progress-font-size));\n  background-color: var(--#{$prefix}progress-bg);\n  @include border-radius(var(--#{$prefix}progress-border-radius));\n  @include box-shadow(var(--#{$prefix}progress-box-shadow));\n}\n\n.progress-bar {\n  display: flex;\n  flex-direction: column;\n  justify-content: center;\n  overflow: hidden;\n  color: var(--#{$prefix}progress-bar-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--#{$prefix}progress-bar-bg);\n  @include transition(var(--#{$prefix}progress-bar-transition));\n}\n\n.progress-bar-striped {\n  @include gradient-striped();\n  background-size: var(--#{$prefix}progress-height) var(--#{$prefix}progress-height);\n}\n\n.progress-stacked > .progress {\n  overflow: visible;\n}\n\n.progress-stacked > .progress > .progress-bar {\n  width: 100%;\n}\n\n@if $enable-transitions {\n  .progress-bar-animated {\n    animation: $progress-bar-animation-timing progress-bar-stripes;\n\n    @if $enable-reduced-motion {\n      @media (prefers-reduced-motion: reduce) {\n        animation: none;\n      }\n    }\n  }\n}\n","// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // scss-docs-start list-group-css-vars\n  --#{$prefix}list-group-color: #{$list-group-color};\n  --#{$prefix}list-group-bg: #{$list-group-bg};\n  --#{$prefix}list-group-border-color: #{$list-group-border-color};\n  --#{$prefix}list-group-border-width: #{$list-group-border-width};\n  --#{$prefix}list-group-border-radius: #{$list-group-border-radius};\n  --#{$prefix}list-group-item-padding-x: #{$list-group-item-padding-x};\n  --#{$prefix}list-group-item-padding-y: #{$list-group-item-padding-y};\n  --#{$prefix}list-group-action-color: #{$list-group-action-color};\n  --#{$prefix}list-group-action-hover-color: #{$list-group-action-hover-color};\n  --#{$prefix}list-group-action-hover-bg: #{$list-group-hover-bg};\n  --#{$prefix}list-group-action-active-color: #{$list-group-action-active-color};\n  --#{$prefix}list-group-action-active-bg: #{$list-group-action-active-bg};\n  --#{$prefix}list-group-disabled-color: #{$list-group-disabled-color};\n  --#{$prefix}list-group-disabled-bg: #{$list-group-disabled-bg};\n  --#{$prefix}list-group-active-color: #{$list-group-active-color};\n  --#{$prefix}list-group-active-bg: #{$list-group-active-bg};\n  --#{$prefix}list-group-active-border-color: #{$list-group-active-border-color};\n  // scss-docs-end list-group-css-vars\n\n  display: flex;\n  flex-direction: column;\n\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 0;\n  @include border-radius(var(--#{$prefix}list-group-border-radius));\n}\n\n.list-group-numbered {\n  list-style-type: none;\n  counter-reset: section;\n\n  > .list-group-item::before {\n    // Increments only this instance of the section counter\n    content: counters(section, \".\") \". \";\n    counter-increment: section;\n  }\n}\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive\n// list items. Includes an extra `.active` modifier class for selected items.\n\n.list-group-item-action {\n  width: 100%; // For `<button>`s (anchors become 100% by default though)\n  color: var(--#{$prefix}list-group-action-color);\n  text-align: inherit; // For `<button>`s (anchors inherit)\n\n  // Hover state\n  &:hover,\n  &:focus {\n    z-index: 1; // Place hover/focus items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-action-hover-color);\n    text-decoration: none;\n    background-color: var(--#{$prefix}list-group-action-hover-bg);\n  }\n\n  &:active {\n    color: var(--#{$prefix}list-group-action-active-color);\n    background-color: var(--#{$prefix}list-group-action-active-bg);\n  }\n}\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}list-group-item-padding-y) var(--#{$prefix}list-group-item-padding-x);\n  color: var(--#{$prefix}list-group-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}list-group-bg);\n  border: var(--#{$prefix}list-group-border-width) solid var(--#{$prefix}list-group-border-color);\n\n  &:first-child {\n    @include border-top-radius(inherit);\n  }\n\n  &:last-child {\n    @include border-bottom-radius(inherit);\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}list-group-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}list-group-disabled-bg);\n  }\n\n  // Include both here for `<a>`s and `<button>`s\n  &.active {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-active-color);\n    background-color: var(--#{$prefix}list-group-active-bg);\n    border-color: var(--#{$prefix}list-group-active-border-color);\n  }\n\n  // stylelint-disable-next-line scss/selector-no-redundant-nesting-selector\n  & + .list-group-item {\n    border-top-width: 0;\n\n    &.active {\n      margin-top: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n      border-top-width: var(--#{$prefix}list-group-border-width);\n    }\n  }\n}\n\n// Horizontal\n//\n// Change the layout of list group items from vertical (default) to horizontal.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .list-group-horizontal#{$infix} {\n      flex-direction: row;\n\n      > .list-group-item {\n        &:first-child:not(:last-child) {\n          @include border-bottom-start-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-top-end-radius(0);\n        }\n\n        &:last-child:not(:first-child) {\n          @include border-top-end-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-bottom-start-radius(0);\n        }\n\n        &.active {\n          margin-top: 0;\n        }\n\n        + .list-group-item {\n          border-top-width: var(--#{$prefix}list-group-border-width);\n          border-left-width: 0;\n\n          &.active {\n            margin-left: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n            border-left-width: var(--#{$prefix}list-group-border-width);\n          }\n        }\n      }\n    }\n  }\n}\n\n\n// Flush list items\n//\n// Remove borders and border-radius to keep list group items edge-to-edge. Most\n// useful within other components (e.g., cards).\n\n.list-group-flush {\n  @include border-radius(0);\n\n  > .list-group-item {\n    border-width: 0 0 var(--#{$prefix}list-group-border-width);\n\n    &:last-child {\n      border-bottom-width: 0;\n    }\n  }\n}\n\n\n// scss-docs-start list-group-modifiers\n// List group contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n@each $state in map-keys($theme-colors) {\n  .list-group-item-#{$state} {\n    --#{$prefix}list-group-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-hover-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-hover-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-active-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-active-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-active-color: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-active-bg: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-active-border-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end list-group-modifiers\n","// Transparent background and border properties included for button version.\n// iOS requires the button element instead of an anchor tag.\n// If you want the anchor version, it requires `href=\"#\"`.\n// See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n.btn-close {\n  // scss-docs-start close-css-vars\n  --#{$prefix}btn-close-color: #{$btn-close-color};\n  --#{$prefix}btn-close-bg: #{ escape-svg($btn-close-bg) };\n  --#{$prefix}btn-close-opacity: #{$btn-close-opacity};\n  --#{$prefix}btn-close-hover-opacity: #{$btn-close-hover-opacity};\n  --#{$prefix}btn-close-focus-shadow: #{$btn-close-focus-shadow};\n  --#{$prefix}btn-close-focus-opacity: #{$btn-close-focus-opacity};\n  --#{$prefix}btn-close-disabled-opacity: #{$btn-close-disabled-opacity};\n  --#{$prefix}btn-close-white-filter: #{$btn-close-white-filter};\n  // scss-docs-end close-css-vars\n\n  box-sizing: content-box;\n  width: $btn-close-width;\n  height: $btn-close-height;\n  padding: $btn-close-padding-y $btn-close-padding-x;\n  color: var(--#{$prefix}btn-close-color);\n  background: transparent var(--#{$prefix}btn-close-bg) center / $btn-close-width auto no-repeat; // include transparent for button elements\n  border: 0; // for button elements\n  @include border-radius();\n  opacity: var(--#{$prefix}btn-close-opacity);\n\n  // Override <a>'s hover style\n  &:hover {\n    color: var(--#{$prefix}btn-close-color);\n    text-decoration: none;\n    opacity: var(--#{$prefix}btn-close-hover-opacity);\n  }\n\n  &:focus {\n    outline: 0;\n    box-shadow: var(--#{$prefix}btn-close-focus-shadow);\n    opacity: var(--#{$prefix}btn-close-focus-opacity);\n  }\n\n  &:disabled,\n  &.disabled {\n    pointer-events: none;\n    user-select: none;\n    opacity: var(--#{$prefix}btn-close-disabled-opacity);\n  }\n}\n\n@mixin btn-close-white() {\n  filter: var(--#{$prefix}btn-close-white-filter);\n}\n\n.btn-close-white {\n  @include btn-close-white();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .btn-close {\n      @include btn-close-white();\n    }\n  }\n}\n",".toast {\n  // scss-docs-start toast-css-vars\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n  --#{$prefix}toast-padding-x: #{$toast-padding-x};\n  --#{$prefix}toast-padding-y: #{$toast-padding-y};\n  --#{$prefix}toast-spacing: #{$toast-spacing};\n  --#{$prefix}toast-max-width: #{$toast-max-width};\n  @include rfs($toast-font-size, --#{$prefix}toast-font-size);\n  --#{$prefix}toast-color: #{$toast-color};\n  --#{$prefix}toast-bg: #{$toast-background-color};\n  --#{$prefix}toast-border-width: #{$toast-border-width};\n  --#{$prefix}toast-border-color: #{$toast-border-color};\n  --#{$prefix}toast-border-radius: #{$toast-border-radius};\n  --#{$prefix}toast-box-shadow: #{$toast-box-shadow};\n  --#{$prefix}toast-header-color: #{$toast-header-color};\n  --#{$prefix}toast-header-bg: #{$toast-header-background-color};\n  --#{$prefix}toast-header-border-color: #{$toast-header-border-color};\n  // scss-docs-end toast-css-vars\n\n  width: var(--#{$prefix}toast-max-width);\n  max-width: 100%;\n  @include font-size(var(--#{$prefix}toast-font-size));\n  color: var(--#{$prefix}toast-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}toast-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-border-color);\n  box-shadow: var(--#{$prefix}toast-box-shadow);\n  @include border-radius(var(--#{$prefix}toast-border-radius));\n\n  &.showing {\n    opacity: 0;\n  }\n\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.toast-container {\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n\n  position: absolute;\n  z-index: var(--#{$prefix}toast-zindex);\n  width: max-content;\n  max-width: 100%;\n  pointer-events: none;\n\n  > :not(:last-child) {\n    margin-bottom: var(--#{$prefix}toast-spacing);\n  }\n}\n\n.toast-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}toast-padding-y) var(--#{$prefix}toast-padding-x);\n  color: var(--#{$prefix}toast-header-color);\n  background-color: var(--#{$prefix}toast-header-bg);\n  background-clip: padding-box;\n  border-bottom: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-header-border-color);\n  @include border-top-radius(calc(var(--#{$prefix}toast-border-radius) - var(--#{$prefix}toast-border-width)));\n\n  .btn-close {\n    margin-right: calc(-.5 * var(--#{$prefix}toast-padding-x)); // stylelint-disable-line function-disallowed-list\n    margin-left: var(--#{$prefix}toast-padding-x);\n  }\n}\n\n.toast-body {\n  padding: var(--#{$prefix}toast-padding-x);\n  word-wrap: break-word;\n}\n","// stylelint-disable function-disallowed-list\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and stuff\n\n\n// Container that the modal scrolls within\n.modal {\n  // scss-docs-start modal-css-vars\n  --#{$prefix}modal-zindex: #{$zindex-modal};\n  --#{$prefix}modal-width: #{$modal-md};\n  --#{$prefix}modal-padding: #{$modal-inner-padding};\n  --#{$prefix}modal-margin: #{$modal-dialog-margin};\n  --#{$prefix}modal-color: #{$modal-content-color};\n  --#{$prefix}modal-bg: #{$modal-content-bg};\n  --#{$prefix}modal-border-color: #{$modal-content-border-color};\n  --#{$prefix}modal-border-width: #{$modal-content-border-width};\n  --#{$prefix}modal-border-radius: #{$modal-content-border-radius};\n  --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-xs};\n  --#{$prefix}modal-inner-border-radius: #{$modal-content-inner-border-radius};\n  --#{$prefix}modal-header-padding-x: #{$modal-header-padding-x};\n  --#{$prefix}modal-header-padding-y: #{$modal-header-padding-y};\n  --#{$prefix}modal-header-padding: #{$modal-header-padding}; // Todo in v6: Split this padding into x and y\n  --#{$prefix}modal-header-border-color: #{$modal-header-border-color};\n  --#{$prefix}modal-header-border-width: #{$modal-header-border-width};\n  --#{$prefix}modal-title-line-height: #{$modal-title-line-height};\n  --#{$prefix}modal-footer-gap: #{$modal-footer-margin-between};\n  --#{$prefix}modal-footer-bg: #{$modal-footer-bg};\n  --#{$prefix}modal-footer-border-color: #{$modal-footer-border-color};\n  --#{$prefix}modal-footer-border-width: #{$modal-footer-border-width};\n  // scss-docs-end modal-css-vars\n\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--#{$prefix}modal-zindex);\n  display: none;\n  width: 100%;\n  height: 100%;\n  overflow-x: hidden;\n  overflow-y: auto;\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n  // We deliberately don't use `-webkit-overflow-scrolling: touch;` due to a\n  // gnarly iOS Safari bug: https://bugs.webkit.org/show_bug.cgi?id=158342\n  // See also https://github.com/twbs/bootstrap/issues/17695\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: var(--#{$prefix}modal-margin);\n  // allow clicks to pass through for custom click handling to close modal\n  pointer-events: none;\n\n  // When fading in the modal, animate it to slide down\n  .modal.fade & {\n    @include transition($modal-transition);\n    transform: $modal-fade-transform;\n  }\n  .modal.show & {\n    transform: $modal-show-transform;\n  }\n\n  // When trying to close, animate focus to scale\n  .modal.modal-static & {\n    transform: $modal-scale-transform;\n  }\n}\n\n.modal-dialog-scrollable {\n  height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n\n  .modal-content {\n    max-height: 100%;\n    overflow: hidden;\n  }\n\n  .modal-body {\n    overflow-y: auto;\n  }\n}\n\n.modal-dialog-centered {\n  display: flex;\n  align-items: center;\n  min-height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  width: 100%; // Ensure `.modal-content` extends the full width of the parent `.modal-dialog`\n  // counteract the pointer-events: none; in the .modal-dialog\n  color: var(--#{$prefix}modal-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}modal-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}modal-border-width) solid var(--#{$prefix}modal-border-color);\n  @include border-radius(var(--#{$prefix}modal-border-radius));\n  @include box-shadow(var(--#{$prefix}modal-box-shadow));\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  // scss-docs-start modal-backdrop-css-vars\n  --#{$prefix}backdrop-zindex: #{$zindex-modal-backdrop};\n  --#{$prefix}backdrop-bg: #{$modal-backdrop-bg};\n  --#{$prefix}backdrop-opacity: #{$modal-backdrop-opacity};\n  // scss-docs-end modal-backdrop-css-vars\n\n  @include overlay-backdrop(var(--#{$prefix}backdrop-zindex), var(--#{$prefix}backdrop-bg), var(--#{$prefix}backdrop-opacity));\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  padding: var(--#{$prefix}modal-header-padding);\n  border-bottom: var(--#{$prefix}modal-header-border-width) solid var(--#{$prefix}modal-header-border-color);\n  @include border-top-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}modal-header-padding-y) * .5) calc(var(--#{$prefix}modal-header-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}modal-header-padding-y)) calc(-.5 * var(--#{$prefix}modal-header-padding-x)) calc(-.5 * var(--#{$prefix}modal-header-padding-y)) auto;\n  }\n}\n\n// Title text within header\n.modal-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}modal-title-line-height);\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  // Enable `flex-grow: 1` so that the body take up as much space as possible\n  // when there should be a fixed height on `.modal-dialog`.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}modal-padding);\n}\n\n// Footer (for actions)\n.modal-footer {\n  display: flex;\n  flex-shrink: 0;\n  flex-wrap: wrap;\n  align-items: center; // vertically center\n  justify-content: flex-end; // Right align buttons with flex property because text-align doesn't work on flex items\n  padding: calc(var(--#{$prefix}modal-padding) - var(--#{$prefix}modal-footer-gap) * .5);\n  background-color: var(--#{$prefix}modal-footer-bg);\n  border-top: var(--#{$prefix}modal-footer-border-width) solid var(--#{$prefix}modal-footer-border-color);\n  @include border-bottom-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  // Place margin between footer elements\n  // This solution is far from ideal because of the universal selector usage,\n  // but is needed to fix https://github.com/twbs/bootstrap/issues/24800\n  > * {\n    margin: calc(var(--#{$prefix}modal-footer-gap) * .5); // Todo in v6: replace with gap on parent class\n  }\n}\n\n// Scale up the modal\n@include media-breakpoint-up(sm) {\n  .modal {\n    --#{$prefix}modal-margin: #{$modal-dialog-margin-y-sm-up};\n    --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-sm-up};\n  }\n\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    max-width: var(--#{$prefix}modal-width);\n    margin-right: auto;\n    margin-left: auto;\n  }\n\n  .modal-sm {\n    --#{$prefix}modal-width: #{$modal-sm};\n  }\n}\n\n@include media-breakpoint-up(lg) {\n  .modal-lg,\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-lg};\n  }\n}\n\n@include media-breakpoint-up(xl) {\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-xl};\n  }\n}\n\n// scss-docs-start modal-fullscreen-loop\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n  $postfix: if($infix != \"\", $infix + \"-down\", \"\");\n\n  @include media-breakpoint-down($breakpoint) {\n    .modal-fullscreen#{$postfix} {\n      width: 100vw;\n      max-width: none;\n      height: 100%;\n      margin: 0;\n\n      .modal-content {\n        height: 100%;\n        border: 0;\n        @include border-radius(0);\n      }\n\n      .modal-header,\n      .modal-footer {\n        @include border-radius(0);\n      }\n\n      .modal-body {\n        overflow-y: auto;\n      }\n    }\n  }\n}\n// scss-docs-end modal-fullscreen-loop\n","// Shared between modals and offcanvases\n@mixin overlay-backdrop($zindex, $backdrop-bg, $backdrop-opacity) {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: $zindex;\n  width: 100vw;\n  height: 100vh;\n  background-color: $backdrop-bg;\n\n  // Fade for backdrop\n  &.fade { opacity: 0; }\n  &.show { opacity: $backdrop-opacity; }\n}\n","// Base class\n.tooltip {\n  // scss-docs-start tooltip-css-vars\n  --#{$prefix}tooltip-zindex: #{$zindex-tooltip};\n  --#{$prefix}tooltip-max-width: #{$tooltip-max-width};\n  --#{$prefix}tooltip-padding-x: #{$tooltip-padding-x};\n  --#{$prefix}tooltip-padding-y: #{$tooltip-padding-y};\n  --#{$prefix}tooltip-margin: #{$tooltip-margin};\n  @include rfs($tooltip-font-size, --#{$prefix}tooltip-font-size);\n  --#{$prefix}tooltip-color: #{$tooltip-color};\n  --#{$prefix}tooltip-bg: #{$tooltip-bg};\n  --#{$prefix}tooltip-border-radius: #{$tooltip-border-radius};\n  --#{$prefix}tooltip-opacity: #{$tooltip-opacity};\n  --#{$prefix}tooltip-arrow-width: #{$tooltip-arrow-width};\n  --#{$prefix}tooltip-arrow-height: #{$tooltip-arrow-height};\n  // scss-docs-end tooltip-css-vars\n\n  z-index: var(--#{$prefix}tooltip-zindex);\n  display: block;\n  margin: var(--#{$prefix}tooltip-margin);\n  @include deprecate(\"`$tooltip-margin`\", \"v5\", \"v5.x\", true);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}tooltip-font-size));\n  // Allow breaking very long words so they don't overflow the tooltip's bounds\n  word-wrap: break-word;\n  opacity: 0;\n\n  &.show { opacity: var(--#{$prefix}tooltip-opacity); }\n\n  .tooltip-arrow {\n    display: block;\n    width: var(--#{$prefix}tooltip-arrow-width);\n    height: var(--#{$prefix}tooltip-arrow-height);\n\n    &::before {\n      position: absolute;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n    }\n  }\n}\n\n.bs-tooltip-top .tooltip-arrow {\n  bottom: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    top: -1px;\n    border-width: var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-top-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-end .tooltip-arrow {\n  left: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    right: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-right-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-bottom .tooltip-arrow {\n  top: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    bottom: -1px;\n    border-width: 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-bottom-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-start .tooltip-arrow {\n  right: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    left: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-left-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-tooltip-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-tooltip-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-tooltip-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-tooltip-start;\n  }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: var(--#{$prefix}tooltip-max-width);\n  padding: var(--#{$prefix}tooltip-padding-y) var(--#{$prefix}tooltip-padding-x);\n  color: var(--#{$prefix}tooltip-color);\n  text-align: center;\n  background-color: var(--#{$prefix}tooltip-bg);\n  @include border-radius(var(--#{$prefix}tooltip-border-radius));\n}\n","@mixin reset-text {\n  font-family: $font-family-base;\n  // We deliberately do NOT reset font-size or overflow-wrap / word-wrap.\n  font-style: normal;\n  font-weight: $font-weight-normal;\n  line-height: $line-height-base;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n}\n",".popover {\n  // scss-docs-start popover-css-vars\n  --#{$prefix}popover-zindex: #{$zindex-popover};\n  --#{$prefix}popover-max-width: #{$popover-max-width};\n  @include rfs($popover-font-size, --#{$prefix}popover-font-size);\n  --#{$prefix}popover-bg: #{$popover-bg};\n  --#{$prefix}popover-border-width: #{$popover-border-width};\n  --#{$prefix}popover-border-color: #{$popover-border-color};\n  --#{$prefix}popover-border-radius: #{$popover-border-radius};\n  --#{$prefix}popover-inner-border-radius: #{$popover-inner-border-radius};\n  --#{$prefix}popover-box-shadow: #{$popover-box-shadow};\n  --#{$prefix}popover-header-padding-x: #{$popover-header-padding-x};\n  --#{$prefix}popover-header-padding-y: #{$popover-header-padding-y};\n  @include rfs($popover-header-font-size, --#{$prefix}popover-header-font-size);\n  --#{$prefix}popover-header-color: #{$popover-header-color};\n  --#{$prefix}popover-header-bg: #{$popover-header-bg};\n  --#{$prefix}popover-body-padding-x: #{$popover-body-padding-x};\n  --#{$prefix}popover-body-padding-y: #{$popover-body-padding-y};\n  --#{$prefix}popover-body-color: #{$popover-body-color};\n  --#{$prefix}popover-arrow-width: #{$popover-arrow-width};\n  --#{$prefix}popover-arrow-height: #{$popover-arrow-height};\n  --#{$prefix}popover-arrow-border: var(--#{$prefix}popover-border-color);\n  // scss-docs-end popover-css-vars\n\n  z-index: var(--#{$prefix}popover-zindex);\n  display: block;\n  max-width: var(--#{$prefix}popover-max-width);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}popover-font-size));\n  // Allow breaking very long words so they don't overflow the popover's bounds\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}popover-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-radius(var(--#{$prefix}popover-border-radius));\n  @include box-shadow(var(--#{$prefix}popover-box-shadow));\n\n  .popover-arrow {\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    height: var(--#{$prefix}popover-arrow-height);\n\n    &::before,\n    &::after {\n      position: absolute;\n      display: block;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n      border-width: 0;\n    }\n  }\n}\n\n.bs-popover-top {\n  > .popover-arrow {\n    bottom: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      bottom: 0;\n      border-top-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      bottom: var(--#{$prefix}popover-border-width);\n      border-top-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-end {\n  > .popover-arrow {\n    left: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      left: 0;\n      border-right-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      left: var(--#{$prefix}popover-border-width);\n      border-right-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-bottom {\n  > .popover-arrow {\n    top: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      top: 0;\n      border-bottom-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      top: var(--#{$prefix}popover-border-width);\n      border-bottom-color: var(--#{$prefix}popover-bg);\n    }\n  }\n\n  // This will remove the popover-header's border just below the arrow\n  .popover-header::before {\n    position: absolute;\n    top: 0;\n    left: 50%;\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    margin-left: calc(-.5 * var(--#{$prefix}popover-arrow-width)); // stylelint-disable-line function-disallowed-list\n    content: \"\";\n    border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-header-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-start {\n  > .popover-arrow {\n    right: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      right: 0;\n      border-left-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      right: var(--#{$prefix}popover-border-width);\n      border-left-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-popover-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-popover-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-popover-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-popover-start;\n  }\n}\n\n// Offset the popover to account for the popover arrow\n.popover-header {\n  padding: var(--#{$prefix}popover-header-padding-y) var(--#{$prefix}popover-header-padding-x);\n  margin-bottom: 0; // Reset the default from Reboot\n  @include font-size(var(--#{$prefix}popover-header-font-size));\n  color: var(--#{$prefix}popover-header-color);\n  background-color: var(--#{$prefix}popover-header-bg);\n  border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-top-radius(var(--#{$prefix}popover-inner-border-radius));\n\n  &:empty {\n    display: none;\n  }\n}\n\n.popover-body {\n  padding: var(--#{$prefix}popover-body-padding-y) var(--#{$prefix}popover-body-padding-x);\n  color: var(--#{$prefix}popover-body-color);\n}\n","// Notes on the classes:\n//\n// 1. .carousel.pointer-event should ideally be pan-y (to allow for users to scroll vertically)\n//    even when their scroll action started on a carousel, but for compatibility (with Firefox)\n//    we're preventing all actions instead\n// 2. The .carousel-item-start and .carousel-item-end is used to indicate where\n//    the active slide is heading.\n// 3. .active.carousel-item is the current slide.\n// 4. .active.carousel-item-start and .active.carousel-item-end is the current\n//    slide in its in-transition state. Only one of these occurs at a time.\n// 5. .carousel-item-next.carousel-item-start and .carousel-item-prev.carousel-item-end\n//    is the upcoming slide in transition.\n\n.carousel {\n  position: relative;\n}\n\n.carousel.pointer-event {\n  touch-action: pan-y;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n  @include clearfix();\n}\n\n.carousel-item {\n  position: relative;\n  display: none;\n  float: left;\n  width: 100%;\n  margin-right: -100%;\n  backface-visibility: hidden;\n  @include transition($carousel-transition);\n}\n\n.carousel-item.active,\n.carousel-item-next,\n.carousel-item-prev {\n  display: block;\n}\n\n.carousel-item-next:not(.carousel-item-start),\n.active.carousel-item-end {\n  transform: translateX(100%);\n}\n\n.carousel-item-prev:not(.carousel-item-end),\n.active.carousel-item-start {\n  transform: translateX(-100%);\n}\n\n\n//\n// Alternate transitions\n//\n\n.carousel-fade {\n  .carousel-item {\n    opacity: 0;\n    transition-property: opacity;\n    transform: none;\n  }\n\n  .carousel-item.active,\n  .carousel-item-next.carousel-item-start,\n  .carousel-item-prev.carousel-item-end {\n    z-index: 1;\n    opacity: 1;\n  }\n\n  .active.carousel-item-start,\n  .active.carousel-item-end {\n    z-index: 0;\n    opacity: 0;\n    @include transition(opacity 0s $carousel-transition-duration);\n  }\n}\n\n\n//\n// Left/right controls for nav\n//\n\n.carousel-control-prev,\n.carousel-control-next {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  z-index: 1;\n  // Use flex for alignment (1-3)\n  display: flex; // 1. allow flex styles\n  align-items: center; // 2. vertically center contents\n  justify-content: center; // 3. horizontally center contents\n  width: $carousel-control-width;\n  padding: 0;\n  color: $carousel-control-color;\n  text-align: center;\n  background: none;\n  border: 0;\n  opacity: $carousel-control-opacity;\n  @include transition($carousel-control-transition);\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    color: $carousel-control-color;\n    text-decoration: none;\n    outline: 0;\n    opacity: $carousel-control-hover-opacity;\n  }\n}\n.carousel-control-prev {\n  left: 0;\n  background-image: if($enable-gradients, linear-gradient(90deg, rgba($black, .25), rgba($black, .001)), null);\n}\n.carousel-control-next {\n  right: 0;\n  background-image: if($enable-gradients, linear-gradient(270deg, rgba($black, .25), rgba($black, .001)), null);\n}\n\n// Icons for within\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: $carousel-control-icon-width;\n  height: $carousel-control-icon-width;\n  background-repeat: no-repeat;\n  background-position: 50%;\n  background-size: 100% 100%;\n}\n\n.carousel-control-prev-icon {\n  background-image: escape-svg($carousel-control-prev-icon-bg) #{\"/*rtl:\" + escape-svg($carousel-control-next-icon-bg) + \"*/\"};\n}\n.carousel-control-next-icon {\n  background-image: escape-svg($carousel-control-next-icon-bg) #{\"/*rtl:\" + escape-svg($carousel-control-prev-icon-bg) + \"*/\"};\n}\n\n// Optional indicator pips/controls\n//\n// Add a container (such as a list) with the following class and add an item (ideally a focusable control,\n// like a button) with data-bs-target for each slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 2;\n  display: flex;\n  justify-content: center;\n  padding: 0;\n  // Use the .carousel-control's width as margin so we don't overlay those\n  margin-right: $carousel-control-width;\n  margin-bottom: 1rem;\n  margin-left: $carousel-control-width;\n\n  [data-bs-target] {\n    box-sizing: content-box;\n    flex: 0 1 auto;\n    width: $carousel-indicator-width;\n    height: $carousel-indicator-height;\n    padding: 0;\n    margin-right: $carousel-indicator-spacer;\n    margin-left: $carousel-indicator-spacer;\n    text-indent: -999px;\n    cursor: pointer;\n    background-color: $carousel-indicator-active-bg;\n    background-clip: padding-box;\n    border: 0;\n    // Use transparent borders to increase the hit area by 10px on top and bottom.\n    border-top: $carousel-indicator-hit-area-height solid transparent;\n    border-bottom: $carousel-indicator-hit-area-height solid transparent;\n    opacity: $carousel-indicator-opacity;\n    @include transition($carousel-indicator-transition);\n  }\n\n  .active {\n    opacity: $carousel-indicator-active-opacity;\n  }\n}\n\n\n// Optional captions\n//\n//\n\n.carousel-caption {\n  position: absolute;\n  right: (100% - $carousel-caption-width) * .5;\n  bottom: $carousel-caption-spacer;\n  left: (100% - $carousel-caption-width) * .5;\n  padding-top: $carousel-caption-padding-y;\n  padding-bottom: $carousel-caption-padding-y;\n  color: $carousel-caption-color;\n  text-align: center;\n}\n\n// Dark mode carousel\n\n@mixin carousel-dark() {\n  .carousel-control-prev-icon,\n  .carousel-control-next-icon {\n    filter: $carousel-dark-control-icon-filter;\n  }\n\n  .carousel-indicators [data-bs-target] {\n    background-color: $carousel-dark-indicator-active-bg;\n  }\n\n  .carousel-caption {\n    color: $carousel-dark-caption-color;\n  }\n}\n\n.carousel-dark {\n  @include carousel-dark();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    @if $color-mode-type == \"media-query\" {\n      .carousel {\n        @include carousel-dark();\n      }\n    } @else {\n      .carousel,\n      &.carousel {\n        @include carousel-dark();\n      }\n    }\n  }\n}\n","// scss-docs-start clearfix\n@mixin clearfix() {\n  &::after {\n    display: block;\n    clear: both;\n    content: \"\";\n  }\n}\n// scss-docs-end clearfix\n","//\n// Rotating border\n//\n\n.spinner-grow,\n.spinner-border {\n  display: inline-block;\n  width: var(--#{$prefix}spinner-width);\n  height: var(--#{$prefix}spinner-height);\n  vertical-align: var(--#{$prefix}spinner-vertical-align);\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 50%;\n  animation: var(--#{$prefix}spinner-animation-speed) linear infinite var(--#{$prefix}spinner-animation-name);\n}\n\n// scss-docs-start spinner-border-keyframes\n@keyframes spinner-border {\n  to { transform: rotate(360deg) #{\"/* rtl:ignore */\"}; }\n}\n// scss-docs-end spinner-border-keyframes\n\n.spinner-border {\n  // scss-docs-start spinner-border-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-border;\n  // scss-docs-end spinner-border-css-vars\n\n  border: var(--#{$prefix}spinner-border-width) solid currentcolor;\n  border-right-color: transparent;\n}\n\n.spinner-border-sm {\n  // scss-docs-start spinner-border-sm-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width-sm};\n  // scss-docs-end spinner-border-sm-css-vars\n}\n\n//\n// Growing circle\n//\n\n// scss-docs-start spinner-grow-keyframes\n@keyframes spinner-grow {\n  0% {\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n    transform: none;\n  }\n}\n// scss-docs-end spinner-grow-keyframes\n\n.spinner-grow {\n  // scss-docs-start spinner-grow-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-grow;\n  // scss-docs-end spinner-grow-css-vars\n\n  background-color: currentcolor;\n  opacity: 0;\n}\n\n.spinner-grow-sm {\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n}\n\n@if $enable-reduced-motion {\n  @media (prefers-reduced-motion: reduce) {\n    .spinner-border,\n    .spinner-grow {\n      --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed * 2};\n    }\n  }\n}\n","// stylelint-disable function-disallowed-list\n\n%offcanvas-css-vars {\n  // scss-docs-start offcanvas-css-vars\n  --#{$prefix}offcanvas-zindex: #{$zindex-offcanvas};\n  --#{$prefix}offcanvas-width: #{$offcanvas-horizontal-width};\n  --#{$prefix}offcanvas-height: #{$offcanvas-vertical-height};\n  --#{$prefix}offcanvas-padding-x: #{$offcanvas-padding-x};\n  --#{$prefix}offcanvas-padding-y: #{$offcanvas-padding-y};\n  --#{$prefix}offcanvas-color: #{$offcanvas-color};\n  --#{$prefix}offcanvas-bg: #{$offcanvas-bg-color};\n  --#{$prefix}offcanvas-border-width: #{$offcanvas-border-width};\n  --#{$prefix}offcanvas-border-color: #{$offcanvas-border-color};\n  --#{$prefix}offcanvas-box-shadow: #{$offcanvas-box-shadow};\n  --#{$prefix}offcanvas-transition: #{transform $offcanvas-transition-duration ease-in-out};\n  --#{$prefix}offcanvas-title-line-height: #{$offcanvas-title-line-height};\n  // scss-docs-end offcanvas-css-vars\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @extend %offcanvas-css-vars;\n  }\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @include media-breakpoint-down($next) {\n      position: fixed;\n      bottom: 0;\n      z-index: var(--#{$prefix}offcanvas-zindex);\n      display: flex;\n      flex-direction: column;\n      max-width: 100%;\n      color: var(--#{$prefix}offcanvas-color);\n      visibility: hidden;\n      background-color: var(--#{$prefix}offcanvas-bg);\n      background-clip: padding-box;\n      outline: 0;\n      @include box-shadow(var(--#{$prefix}offcanvas-box-shadow));\n      @include transition(var(--#{$prefix}offcanvas-transition));\n\n      &.offcanvas-start {\n        top: 0;\n        left: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-right: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(-100%);\n      }\n\n      &.offcanvas-end {\n        top: 0;\n        right: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-left: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(100%);\n      }\n\n      &.offcanvas-top {\n        top: 0;\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-bottom: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(-100%);\n      }\n\n      &.offcanvas-bottom {\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-top: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(100%);\n      }\n\n      &.showing,\n      &.show:not(.hiding) {\n        transform: none;\n      }\n\n      &.showing,\n      &.hiding,\n      &.show {\n        visibility: visible;\n      }\n    }\n\n    @if not ($infix == \"\") {\n      @include media-breakpoint-up($next) {\n        --#{$prefix}offcanvas-height: auto;\n        --#{$prefix}offcanvas-border-width: 0;\n        background-color: transparent !important; // stylelint-disable-line declaration-no-important\n\n        .offcanvas-header {\n          display: none;\n        }\n\n        .offcanvas-body {\n          display: flex;\n          flex-grow: 0;\n          padding: 0;\n          overflow-y: visible;\n          // Reset `background-color` in case `.bg-*` classes are used in offcanvas\n          background-color: transparent !important; // stylelint-disable-line declaration-no-important\n        }\n      }\n    }\n  }\n}\n\n.offcanvas-backdrop {\n  @include overlay-backdrop($zindex-offcanvas-backdrop, $offcanvas-backdrop-bg, $offcanvas-backdrop-opacity);\n}\n\n.offcanvas-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}offcanvas-padding-y) * .5) calc(var(--#{$prefix}offcanvas-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}offcanvas-padding-y)) calc(-.5 * var(--#{$prefix}offcanvas-padding-x)) calc(-.5 * var(--#{$prefix}offcanvas-padding-y)) auto;\n  }\n}\n\n.offcanvas-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}offcanvas-title-line-height);\n}\n\n.offcanvas-body {\n  flex-grow: 1;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n  overflow-y: auto;\n}\n",".placeholder {\n  display: inline-block;\n  min-height: 1em;\n  vertical-align: middle;\n  cursor: wait;\n  background-color: currentcolor;\n  opacity: $placeholder-opacity-max;\n\n  &.btn::before {\n    display: inline-block;\n    content: \"\";\n  }\n}\n\n// Sizing\n.placeholder-xs {\n  min-height: .6em;\n}\n\n.placeholder-sm {\n  min-height: .8em;\n}\n\n.placeholder-lg {\n  min-height: 1.2em;\n}\n\n// Animation\n.placeholder-glow {\n  .placeholder {\n    animation: placeholder-glow 2s ease-in-out infinite;\n  }\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: $placeholder-opacity-min;\n  }\n}\n\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, $black 55%, rgba(0, 0, 0, (1 - $placeholder-opacity-min)) 75%, $black 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .text-bg-#{$color} {\n    color: color-contrast($value) if($enable-important-utilities, !important, null);\n    background-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}bg-opacity, 1)) if($enable-important-utilities, !important, null);\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .link-#{$color} {\n    color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n    text-decoration-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n    @if $link-shade-percentage != 0 {\n      &:hover,\n      &:focus {\n        $hover-color: if(color-contrast($value) == $color-contrast-light, shade-color($value, $link-shade-percentage), tint-color($value, $link-shade-percentage));\n        color: RGBA(#{to-rgb($hover-color)}, var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n        text-decoration-color: RGBA(to-rgb($hover-color), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n      }\n    }\n  }\n}\n\n// One-off special link helper as a bridge until v6\n.link-body-emphasis {\n  color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n  text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n  @if $link-shade-percentage != 0 {\n    &:hover,\n    &:focus {\n      color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, .75)) if($enable-important-utilities, !important, null);\n      text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, .75)) if($enable-important-utilities, !important, null);\n    }\n  }\n}\n",".focus-ring:focus {\n  outline: 0;\n  // By default, there is no `--bs-focus-ring-x`, `--bs-focus-ring-y`, or `--bs-focus-ring-blur`, but we provide CSS variables with fallbacks to initial `0` values\n  box-shadow: var(--#{$prefix}focus-ring-x, 0) var(--#{$prefix}focus-ring-y, 0) var(--#{$prefix}focus-ring-blur, 0) var(--#{$prefix}focus-ring-width) var(--#{$prefix}focus-ring-color);\n}\n",".icon-link {\n  display: inline-flex;\n  gap: $icon-link-gap;\n  align-items: center;\n  text-decoration-color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, .5));\n  text-underline-offset: $icon-link-underline-offset;\n  backface-visibility: hidden;\n\n  > .bi {\n    flex-shrink: 0;\n    width: $icon-link-icon-size;\n    height: $icon-link-icon-size;\n    fill: currentcolor;\n    @include transition($icon-link-icon-transition);\n  }\n}\n\n.icon-link-hover {\n  &:hover,\n  &:focus-visible {\n    > .bi {\n      transform: var(--#{$prefix}icon-link-transform, $icon-link-icon-transform);\n    }\n  }\n}\n","// Credit: Nicolas Gallagher and SUIT CSS.\n\n.ratio {\n  position: relative;\n  width: 100%;\n\n  &::before {\n    display: block;\n    padding-top: var(--#{$prefix}aspect-ratio);\n    content: \"\";\n  }\n\n  > * {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n  }\n}\n\n@each $key, $ratio in $aspect-ratios {\n  .ratio-#{$key} {\n    --#{$prefix}aspect-ratio: #{$ratio};\n  }\n}\n","// Shorthand\n\n.fixed-top {\n  position: fixed;\n  top: 0;\n  right: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n.fixed-bottom {\n  position: fixed;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n// Responsive sticky top and bottom\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .sticky#{$infix}-top {\n      position: sticky;\n      top: 0;\n      z-index: $zindex-sticky;\n    }\n\n    .sticky#{$infix}-bottom {\n      position: sticky;\n      bottom: 0;\n      z-index: $zindex-sticky;\n    }\n  }\n}\n","// scss-docs-start stacks\n.hstack {\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  align-self: stretch;\n}\n\n.vstack {\n  display: flex;\n  flex: 1 1 auto;\n  flex-direction: column;\n  align-self: stretch;\n}\n// scss-docs-end stacks\n","//\n// Visually hidden\n//\n\n.visually-hidden,\n.visually-hidden-focusable:not(:focus):not(:focus-within) {\n  @include visually-hidden();\n}\n","// stylelint-disable declaration-no-important\n\n// Hide content visually while keeping it accessible to assistive technologies\n//\n// See: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// See: https://kittygiraudel.com/2016/10/13/css-hide-and-seek/\n\n@mixin visually-hidden() {\n  width: 1px !important;\n  height: 1px !important;\n  padding: 0 !important;\n  margin: -1px !important; // Fix for https://github.com/twbs/bootstrap/issues/25686\n  overflow: hidden !important;\n  clip: rect(0, 0, 0, 0) !important;\n  white-space: nowrap !important;\n  border: 0 !important;\n\n  // Fix for positioned table caption that could become anonymous cells\n  &:not(caption) {\n    position: absolute !important;\n  }\n}\n\n// Use to only display content when it's focused, or one of its child elements is focused\n// (i.e. when focus is within the element/container that the class was applied to)\n//\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n\n@mixin visually-hidden-focusable() {\n  &:not(:focus):not(:focus-within) {\n    @include visually-hidden();\n  }\n}\n","//\n// Stretched link\n//\n\n.stretched-link {\n  &::#{$stretched-link-pseudo-element} {\n    position: absolute;\n    top: 0;\n    right: 0;\n    bottom: 0;\n    left: 0;\n    z-index: $stretched-link-z-index;\n    content: \"\";\n  }\n}\n","//\n// Text truncation\n//\n\n.text-truncate {\n  @include text-truncate();\n}\n","// Text truncate\n// Requires inline-block or block for proper styling\n\n@mixin text-truncate() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n",".vr {\n  display: inline-block;\n  align-self: stretch;\n  width: $vr-border-width;\n  min-height: 1em;\n  background-color: currentcolor;\n  opacity: $hr-opacity;\n}\n","// Utility generator\n// Used to generate utilities & print utilities\n@mixin generate-utility($utility, $infix: \"\", $is-rfs-media-query: false) {\n  $values: map-get($utility, values);\n\n  // If the values are a list or string, convert it into a map\n  @if type-of($values) == \"string\" or type-of(nth($values, 1)) != \"list\" {\n    $values: zip($values, $values);\n  }\n\n  @each $key, $value in $values {\n    $properties: map-get($utility, property);\n\n    // Multiple properties are possible, for example with vertical or horizontal margins or paddings\n    @if type-of($properties) == \"string\" {\n      $properties: append((), $properties);\n    }\n\n    // Use custom class if present\n    $property-class: if(map-has-key($utility, class), map-get($utility, class), nth($properties, 1));\n    $property-class: if($property-class == null, \"\", $property-class);\n\n    // Use custom CSS variable name if present, otherwise default to `class`\n    $css-variable-name: if(map-has-key($utility, css-variable-name), map-get($utility, css-variable-name), map-get($utility, class));\n\n    // State params to generate pseudo-classes\n    $state: if(map-has-key($utility, state), map-get($utility, state), ());\n\n    $infix: if($property-class == \"\" and str-slice($infix, 1, 1) == \"-\", str-slice($infix, 2), $infix);\n\n    // Don't prefix if value key is null (e.g. with shadow class)\n    $property-class-modifier: if($key, if($property-class == \"\" and $infix == \"\", \"\", \"-\") + $key, \"\");\n\n    @if map-get($utility, rfs) {\n      // Inside the media query\n      @if $is-rfs-media-query {\n        $val: rfs-value($value);\n\n        // Do not render anything if fluid and non fluid values are the same\n        $value: if($val == rfs-fluid-value($value), null, $val);\n      }\n      @else {\n        $value: rfs-fluid-value($value);\n      }\n    }\n\n    $is-css-var: map-get($utility, css-var);\n    $is-local-vars: map-get($utility, local-vars);\n    $is-rtl: map-get($utility, rtl);\n\n    @if $value != null {\n      @if $is-rtl == false {\n        /* rtl:begin:remove */\n      }\n\n      @if $is-css-var {\n        .#{$property-class + $infix + $property-class-modifier} {\n          --#{$prefix}#{$css-variable-name}: #{$value};\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            --#{$prefix}#{$css-variable-name}: #{$value};\n          }\n        }\n      } @else {\n        .#{$property-class + $infix + $property-class-modifier} {\n          @each $property in $properties {\n            @if $is-local-vars {\n              @each $local-var, $variable in $is-local-vars {\n                --#{$prefix}#{$local-var}: #{$variable};\n              }\n            }\n            #{$property}: $value if($enable-important-utilities, !important, null);\n          }\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            @each $property in $properties {\n              @if $is-local-vars {\n                @each $local-var, $variable in $is-local-vars {\n                  --#{$prefix}#{$local-var}: #{$variable};\n                }\n              }\n              #{$property}: $value if($enable-important-utilities, !important, null);\n            }\n          }\n        }\n      }\n\n      @if $is-rtl == false {\n        /* rtl:end:remove */\n      }\n    }\n  }\n}\n","// Loop over each breakpoint\n@each $breakpoint in map-keys($grid-breakpoints) {\n\n  // Generate media query if needed\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    // Loop over each utility property\n    @each $key, $utility in $utilities {\n      // The utility can be disabled with `false`, thus check if the utility is a map first\n      // Only proceed if responsive media queries are enabled or if it's the base media query\n      @if type-of($utility) == \"map\" and (map-get($utility, responsive) or $infix == \"\") {\n        @include generate-utility($utility, $infix);\n      }\n    }\n  }\n}\n\n// RFS rescaling\n@media (min-width: $rfs-mq-value) {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    @if (map-get($grid-breakpoints, $breakpoint) < $rfs-breakpoint) {\n      // Loop over each utility property\n      @each $key, $utility in $utilities {\n        // The utility can be disabled with `false`, thus check if the utility is a map first\n        // Only proceed if responsive media queries are enabled or if it's the base media query\n        @if type-of($utility) == \"map\" and map-get($utility, rfs) and (map-get($utility, responsive) or $infix == \"\") {\n          @include generate-utility($utility, $infix, true);\n        }\n      }\n    }\n  }\n}\n\n\n// Print utilities\n@media print {\n  @each $key, $utility in $utilities {\n    // The utility can be disabled with `false`, thus check if the utility is a map first\n    // Then check if the utility needs print styles\n    @if type-of($utility) == \"map\" and map-get($utility, print) == true {\n      @include generate-utility($utility, \"-print\");\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/_preview/3/_static/styles/pydata-sphinx-theme.css b/_preview/3/_static/styles/pydata-sphinx-theme.css
deleted file mode 100644
index 90ba101..0000000
--- a/_preview/3/_static/styles/pydata-sphinx-theme.css
+++ /dev/null
@@ -1,2 +0,0 @@
-html{--pst-header-height:4rem;--pst-header-article-height:calc(var(--pst-header-height)*2/3);--pst-sidebar-secondary:17rem;--pst-font-size-base:1rem;--pst-font-size-h1:2.5rem;--pst-font-size-h2:2rem;--pst-font-size-h3:1.75rem;--pst-font-size-h4:1.5rem;--pst-font-size-h5:1.25rem;--pst-font-size-h6:1.1rem;--pst-font-size-milli:0.9rem;--pst-sidebar-font-size:0.9rem;--pst-sidebar-font-size-mobile:1.1rem;--pst-sidebar-header-font-size:1.2rem;--pst-sidebar-header-font-weight:600;--pst-admonition-font-weight-heading:600;--pst-font-weight-caption:300;--pst-font-weight-heading:400;--pst-font-family-base-system:-apple-system,"BlinkMacSystemFont","Segoe UI","Helvetica Neue","Arial",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";--pst-font-family-monospace-system:"SFMono-Regular","Menlo","Consolas","Monaco","Liberation Mono","Lucida Console",monospace;--pst-font-family-base:var(--pst-font-family-base-system);--pst-font-family-heading:var(--pst-font-family-base-system);--pst-font-family-monospace:var(--pst-font-family-monospace-system);--pst-font-size-icon:1.5rem;--pst-icon-check-circle:"";--pst-icon-info-circle:"";--pst-icon-exclamation-triangle:"";--pst-icon-exclamation-circle:"";--pst-icon-times-circle:"";--pst-icon-lightbulb:"";--pst-icon-download:"";--pst-icon-angle-left:"";--pst-icon-angle-right:"";--pst-icon-external-link:"";--pst-icon-search-minus:"";--pst-icon-github:"";--pst-icon-gitlab:"";--pst-icon-share:"";--pst-icon-bell:"";--pst-icon-pencil:"";--pst-breadcrumb-divider:"";--pst-icon-admonition-default:var(--pst-icon-bell);--pst-icon-admonition-note:var(--pst-icon-info-circle);--pst-icon-admonition-attention:var(--pst-icon-exclamation-circle);--pst-icon-admonition-caution:var(--pst-icon-exclamation-triangle);--pst-icon-admonition-warning:var(--pst-icon-exclamation-triangle);--pst-icon-admonition-danger:var(--pst-icon-exclamation-triangle);--pst-icon-admonition-error:var(--pst-icon-times-circle);--pst-icon-admonition-hint:var(--pst-icon-lightbulb);--pst-icon-admonition-tip:var(--pst-icon-lightbulb);--pst-icon-admonition-important:var(--pst-icon-exclamation-circle);--pst-icon-admonition-seealso:var(--pst-icon-share);--pst-icon-admonition-todo:var(--pst-icon-pencil);--pst-icon-versionmodified-default:var(--pst-icon-exclamation-circle);--pst-icon-versionmodified-added:var(--pst-icon-exclamation-circle);--pst-icon-versionmodified-changed:var(--pst-icon-exclamation-circle);--pst-icon-versionmodified-deprecated:var(--pst-icon-exclamation-circle);font-size:var(--pst-font-size-base);scroll-padding-top:calc(var(--pst-header-height) + 1rem)}body{background-color:var(--pst-color-background);color:var(--pst-color-text-base);display:flex;flex-direction:column;font-family:var(--pst-font-family-base);font-weight:400;line-height:1.65;min-height:100vh}body::-webkit-scrollbar-track{background-color:var(--pst-color-background)}p{font-size:1em;margin-bottom:1.15rem}p.rubric{border-bottom:1px solid var(--pst-color-border)}p.centered{text-align:center}a{word-wrap:break-word;color:var(--pst-color-link);text-decoration:underline;text-decoration-thickness:max(1px,.0625rem);text-underline-offset:.1578em}a:hover{color:var(--pst-color-link-hover);text-decoration-skip-ink:none;text-decoration-thickness:max(3px,.1875rem,.12em)}a:active,a:visited{color:var(--pst-color-link)}a:visited:hover{color:var(--pst-color-link-hover)}a.headerlink{color:var(--pst-color-secondary);font-size:.8em;margin-left:.2em;opacity:.7;padding:0 4px;text-decoration:none;transition:all .2s ease-out;user-select:none}a.headerlink:hover{opacity:1}a.github:before,a.gitlab:before{color:var(--pst-color-text-muted);font:var(--fa-font-brands);margin-right:.25rem}a.github:before{content:var(--pst-icon-github)}a.gitlab:before{content:var(--pst-icon-gitlab)}h1,h2,h3,h4,h5,h6{font-family:var(--pst-font-family-heading);font-weight:var(--pst-font-weight-heading);line-height:1.15;margin:2.75rem 0 1.05rem}h1{font-size:var(--pst-font-size-h1);margin-top:0}h1,h2{color:var(--pst-heading-color)}h2{font-size:var(--pst-font-size-h2)}h3{font-size:var(--pst-font-size-h3)}h3,h4{color:var(--pst-heading-color)}h4{font-size:var(--pst-font-size-h4)}h5{font-size:var(--pst-font-size-h5)}h5,h6{color:var(--pst-color-text-base)}h6{font-size:var(--pst-font-size-h6)}.text_small,small{font-size:var(--pst-font-size-milli)}hr{border:0;border-top:1px solid var(--pst-color-border)}code,kbd,pre,samp{font-family:var(--pst-font-family-monospace)}kbd{background-color:var(--pst-color-on-background);color:var(--pst-color-text-muted)}kbd:not(.compound){border:1px solid var(--pst-color-border);box-shadow:1px 1px 1px var(--pst-color-shadow);margin:0 .1rem;padding:.1rem .4rem}code{color:var(--pst-color-inline-code)}pre{background-color:var(--pst-color-surface);border:1px solid var(--pst-color-border);border-radius:.25rem;color:var(--pst-color-text-base);line-height:1.2em;margin:1.5em 0;padding:1rem}pre .linenos{opacity:.8;padding-right:10px}#pst-back-to-top{background-color:var(--pst-color-secondary);border:none;color:var(--pst-color-secondary-text);display:none;left:50vw;position:fixed;top:90vh;transform:translate(-50%);z-index:1080}#pst-back-to-top 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table.dataframe tbody tr:hover{background-color:var(--pst-color-table-row-hover-bg)}html[data-theme=dark] .bd-content .nboutput .output_area.rendered_html:not(:has(table.dataframe)),html[data-theme=dark] .bd-content .nboutput .output_area .widget-subarea{color:var(--pst-color-on-background);background-color:var(--pst-color-text-base);border-radius:.25rem;padding:.5rem}html[data-theme=dark] .bd-content .nboutput .output_area.stderr{background-color:var(--pst-color-danger)}div.nblast.container{margin-bottom:1rem}div.cell_output .output{max-width:100%;overflow-x:auto}.bd-content div.cell_output table.dataframe{color:var(--pst-color-table);border:1px solid var(--pst-color-table-outer-border)}.bd-content div.cell_output table.dataframe th~th,.bd-content div.cell_output table.dataframe th~td,.bd-content div.cell_output table.dataframe td~th,.bd-content div.cell_output table.dataframe td~td{border-left:1px solid var(--pst-color-table-inner-border)}.bd-content div.cell_output table.dataframe thead tr{background-color:var(--pst-color-table-heading-bg);border-bottom:2px solid var(--pst-color-primary)}.bd-content div.cell_output table.dataframe tbody tr:nth-child(odd){background-color:var(--pst-color-table-row-zebra-low-bg)}.bd-content div.cell_output table.dataframe tbody tr:nth-child(even){background-color:var(--pst-color-table-row-zebra-high-bg)}.bd-content div.cell_output table.dataframe tbody tr:hover{background-color:var(--pst-color-table-row-hover-bg)}html[data-theme=dark] .bd-content div.cell_output img,html[data-theme=dark] .bd-content div.cell_output .text_html:not(:has(table.dataframe)),html[data-theme=dark] .bd-content div.cell_output .widget-subarea{color:var(--pst-color-on-background);background-color:var(--pst-color-text-base);border-radius:.25rem;padding:.5rem}.bd-content div.cell_input{display:flex;flex-direction:column;justify-content:stretch}.bd-content div.cell_input,.bd-content div.output{border-radius:.25rem}.bd-content div.output table{table-layout:auto}html[data-theme=dark] .bd-content img.leaflet-tile.leaflet-tile-loaded{border-radius:0;padding:0}.bd-search-container div#search-results>h2{font-size:var(--pst-font-size-icon);margin-top:1rem}.bd-search-container div#search-results p.search-summary{color:var(--pst-color-text-muted)}.bd-search-container ul.search{margin:0;list-style:none}.bd-search-container ul.search li{background-image:none;padding:1rem 0;margin:1rem 0;border-top:1px solid var(--pst-color-text-muted)}.bd-search-container ul.search li>a{font-size:1.2em}.bd-search-container ul.search li div.context,.bd-search-container ul.search li p.context{color:var(--pst-color-text-base);margin:.5em 0 0}.bd-search-container ul.search li div.context a::before,.bd-search-container ul.search li p.context a::before{content:\"#\";padding-right:.2em;color:var(--pst-color-text-muted)}","html {\n  /*****************************************************************************\n  * Overall Layout Variables\n  */\n\n  // Header height will impact the top offset for many sections\n  // Article header is 66% of Header\n  --pst-header-height: 4rem;\n  --pst-header-article-height: calc(var(--pst-header-height) * 2 / 3);\n  --pst-sidebar-secondary: 17rem;\n}\n\n/*******************************************************************************\n* Breakpoints that trigger UI changes\n*\n* Note that media-breakpoint-down begins at the next highest level!\n* So we should choose a media-breakpoint-down one *lower* than when we want to start\n* example: media-breakpoint-up(md) and media-breakpoint-down(sm) trigger at the same time\n* ref: https://github.com/twbs/bootstrap/issues/31214\n*/\n$breakpoint-sidebar-primary: lg; // When we collapse the primary sidebar\n$breakpoint-sidebar-secondary: xl; // When we collapse the secondary sidebar\n$breakpoint-page-width: 88rem; // taken from sphinx-basic-ng, which we are ultimately going to inherit\n\n/*******************************************************************************\n* Define the animation behaviour\n*/\n$animation-time: 200ms;\n\n/*******************************************************************************\n* UI shaping and padding\n*/\n$admonition-border-radius: 0.25rem;\n\n// In this theme, some focus rings have rounded corners while others do not.\n// This variable sets the border radius for the rounded focus rings.\n$focus-ring-radius: 0.125rem; // 2px at 100% zoom and 16px base font.\n","html {\n  /*****************************************************************************\n  * Font features used in this theme\n  */\n\n  // base font size - applied at body/html level\n  --pst-font-size-base: 1rem;\n\n  // heading font sizes based on bootstrap sizing\n  --pst-font-size-h1: 2.5rem;\n  --pst-font-size-h2: 2rem;\n  --pst-font-size-h3: 1.75rem;\n  --pst-font-size-h4: 1.5rem;\n  --pst-font-size-h5: 1.25rem;\n  --pst-font-size-h6: 1.1rem;\n\n  // smaller than heading font sizes\n  --pst-font-size-milli: 0.9rem;\n\n  // Sidebar styles\n  --pst-sidebar-font-size: 0.9rem;\n  --pst-sidebar-font-size-mobile: 1.1rem;\n  --pst-sidebar-header-font-size: 1.2rem;\n  --pst-sidebar-header-font-weight: 600;\n\n  // Admonition styles\n  --pst-admonition-font-weight-heading: 600;\n\n  // Font weights\n  --pst-font-weight-caption: 300;\n  --pst-font-weight-heading: 400;\n\n  // Font family\n  // These are adapted from https://systemfontstack.com/ */\n  --pst-font-family-base-system: -apple-system, \"BlinkMacSystemFont\", \"Segoe UI\",\n    \"Helvetica Neue\", \"Arial\", sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\",\n    \"Segoe UI Symbol\";\n  --pst-font-family-monospace-system: \"SFMono-Regular\", \"Menlo\", \"Consolas\",\n    \"Monaco\", \"Liberation Mono\", \"Lucida Console\", monospace;\n  --pst-font-family-base: var(--pst-font-family-base-system);\n  --pst-font-family-heading: var(--pst-font-family-base-system);\n  --pst-font-family-monospace: var(--pst-font-family-monospace-system);\n}\n","html {\n  /*****************************************************************************\n  * Icon\n  */\n\n  // Font size across all icons\n  --pst-font-size-icon: 1.5rem;\n\n  // Font Awesome default icons\n  --pst-icon-check-circle: \"\\f058\"; // fa-solid fa-circle-check\n  --pst-icon-info-circle: \"\\f05a\"; // fa-solid fa-circle-info\n  --pst-icon-exclamation-triangle: \"\\f071\"; // fa-solid fa-triangle-exclamation\n  --pst-icon-exclamation-circle: \"\\f06a\"; // fa-solid fa-circle-exclamation\n  --pst-icon-times-circle: \"\\f057\"; // fa-solid fa-circle-xmark\n  --pst-icon-lightbulb: \"\\f0eb\"; // fa-solid fa-lightbulb\n  --pst-icon-download: \"\\f019\"; // fa-solid fa-download\n  --pst-icon-angle-left: \"\\f104\"; // fa-solid fa-angle-left\n  --pst-icon-angle-right: \"\\f105\"; // fa-solid fa-angle-right\n  --pst-icon-external-link: \"\\f35d\"; // fa-solid fa-up-right-from-square\n  --pst-icon-search-minus: \"\\f010\"; // fa-solid fa-magnifying-glass-minus\n  --pst-icon-github: \"\\f09b\"; // fa-brands fa-github\n  --pst-icon-gitlab: \"\\f296\"; // fa-brands fa-gitlab\n  --pst-icon-share: \"\\f064\"; // fa-solid fa-share\n  --pst-icon-bell: \"\\f0f3\"; // fa-solid fa-bell\n  --pst-icon-pencil: \"\\f303\"; // fa-solid fa-pencil\n\n  // Bootstrap icons\n  --pst-breadcrumb-divider: \"\\f105\";\n}\n","html {\n  /*****************************************************************************\n  * Admonitions\n  **/\n\n  --pst-icon-admonition-default: var(--pst-icon-bell);\n  --pst-icon-admonition-note: var(--pst-icon-info-circle);\n  --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-error: var(--pst-icon-times-circle);\n  --pst-icon-admonition-hint: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-tip: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-seealso: var(--pst-icon-share);\n  --pst-icon-admonition-todo: var(--pst-icon-pencil);\n}\n","html {\n  /*****************************************************************************\n  * versionmodified\n  **/\n\n  --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle);\n}\n","html {\n  font-size: var(--pst-font-size-base);\n  scroll-padding-top: calc(var(--pst-header-height) + 1rem);\n}\n\nbody {\n  background-color: var(--pst-color-background);\n  font-family: var(--pst-font-family-base);\n  font-weight: 400;\n  line-height: 1.65;\n  color: var(--pst-color-text-base);\n  min-height: 100vh;\n  display: flex;\n  flex-direction: column;\n\n  // hack to avoid the black background on some browser including Safari\n  &::-webkit-scrollbar-track {\n    background-color: var(--pst-color-background);\n  }\n}\n\np {\n  margin-bottom: 1.15rem;\n  font-size: 1em;\n\n  /* section header in docstring pages */\n  &.rubric {\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  &.centered {\n    text-align: center;\n  }\n}\n\na {\n  @include link-style-default;\n\n  // The # anchor that appears on hover over headings\n  &.headerlink {\n    color: var(--pst-color-secondary);\n    opacity: 0.7;\n    font-size: 0.8em;\n    padding: 0 4px;\n    margin-left: 0.2em;\n    text-decoration: none;\n    transition: all 0.2s ease-out;\n    user-select: none;\n\n    &:hover {\n      opacity: 1;\n    }\n  }\n\n  // set up a icon next to the shorten links from github and gitlab\n  &.github,\n  &.gitlab {\n    &::before {\n      color: var(--pst-color-text-muted);\n      font: var(--fa-font-brands);\n      margin-right: 0.25rem;\n    }\n  }\n\n  &.github::before {\n    content: var(--pst-icon-github);\n  }\n\n  &.gitlab::before {\n    content: var(--pst-icon-gitlab);\n  }\n}\n\n%heading-style {\n  margin: 2.75rem 0 1.05rem;\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-heading);\n  line-height: 1.15;\n}\n\nh1 {\n  @extend %heading-style;\n\n  margin-top: 0;\n  font-size: var(--pst-font-size-h1);\n  color: var(--pst-heading-color);\n}\n\nh2 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h2);\n  color: var(--pst-heading-color);\n}\n\nh3 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h3);\n  color: var(--pst-heading-color);\n}\n\nh4 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h4);\n  color: var(--pst-heading-color);\n}\n\nh5 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h5);\n  color: var(--pst-color-text-base);\n}\n\nh6 {\n  @extend %heading-style;\n\n  font-size: var(--pst-font-size-h6);\n  color: var(--pst-color-text-base);\n}\n\nsmall,\n.text_small {\n  font-size: var(--pst-font-size-milli);\n}\n\nhr {\n  border: 0;\n  border-top: 1px solid var(--pst-color-border);\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--pst-font-family-monospace);\n}\n\nkbd {\n  // use theme negative\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-muted);\n\n  // Compound keyboard elements will have nested kbd in them, so this prevents double lines\n  &:not(.compound) {\n    border: 1px solid var(--pst-color-border);\n    margin: 0 0.1rem;\n    padding: 0.1rem 0.4rem;\n    box-shadow: 1px 1px 1px var(--pst-color-shadow);\n  }\n}\n\ncode {\n  color: var(--pst-color-inline-code);\n}\n\npre {\n  margin: 1.5em 0;\n  padding: 1rem;\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n  line-height: 1.2em;\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  .linenos {\n    // minimum opacity to make the line numbers WCAG AA conformant\n    opacity: 0.8;\n    padding-right: 10px;\n  }\n}\n\n// the back to top btn\n#pst-back-to-top {\n  z-index: $zindex-tooltip;\n  position: fixed;\n  display: none;\n  top: 90vh;\n  left: 50vw;\n  transform: translate(-50%);\n  color: var(--pst-color-secondary-text);\n  background-color: var(--pst-color-secondary);\n  border: none;\n\n  .fa-arrow-up {\n    // Using margin instead of a space character prevents the space between the\n    // icon and the text from being underlined when the button is hovered.\n    margin-inline-end: 0.5em;\n  }\n\n  &:hover {\n    @include link-style-hover;\n\n    text-decoration-thickness: 1px;\n    background-color: var(--pst-violet-600);\n    color: var(--pst-color-secondary-text);\n  }\n\n  &:focus-visible {\n    box-shadow: none;\n    outline: $focus-ring-outline;\n    outline-color: var(--pst-color-secondary);\n    outline-offset: $focus-ring-width;\n  }\n}\n\n// Focus ring\n// ----------\n// Note: The Bootstrap stylesheet provides the focus ring (customized by this\n// theme via Sass variables in _bootstrap.scss) in some cases. This rule covers\n// all other cases.\n:focus-visible {\n  outline: $focus-ring-outline;\n  box-shadow: none; // override Bootstrap\n}\n","// Consistent styling for links\n// ============================\n\n@use \"sass:string\";\n\n// Define some useful variables for links styling consistency\n\n// The default thickness of the underline for links will be either:\n//  - 1px\n//  - 0.0625rem if it's thicker than 1px because the user has changed the text\n//    size in their browser\n$link-underline-thickness: string.unquote(\"max(1px, .0625rem)\") !default;\n\n// Offset of link underlines from text baseline\n// The default is 3px expressed as ems, as calculated against the default body\n// font size (on desktop).\n$link-underline-offset: 0.1578em !default;\n\n// Thickness of link underlines in hover state\n// The default for each link will be the thickest of the following:\n//  - 3px\n//  - 0.1875rem, if it's thicker than 3px because the user has changed the text\n//    size in their browser\n//  - 0.12em (relative to the link's text size)\n$link-hover-decoration-thickness: string.unquote(\n  \"max(3px, .1875rem, .12em)\"\n) !default;\n\n// Ensures links have an underline decoration by default - needed to meet\n// WCAG SC 1.4.1\n@mixin link-decoration {\n  text-decoration: underline;\n\n  @if $link-underline-thickness {\n    text-decoration-thickness: $link-underline-thickness;\n  }\n\n  @if $link-underline-offset {\n    text-underline-offset: $link-underline-offset;\n  }\n}\n\n// Ensures links have an underline decoration on hover - distinct from the\n// default behaviour\n@mixin link-decoration-hover {\n  @if $link-hover-decoration-thickness {\n    text-decoration-thickness: $link-hover-decoration-thickness;\n\n    // Disable ink skipping on underlines on hover.\n    text-decoration-skip-ink: none;\n  }\n}\n\n// Simple hover style - can be used alone or in conjunction with other mixins\n// Add the text underline and change in thickness on hover.\n// Intended for use with the `:hover` pseudo-class.\n@mixin link-style-hover {\n  @include link-decoration;\n  @include link-decoration-hover;\n\n  color: var(--pst-color-link-hover);\n}\n\n// Default link styles\n// -------------------\n// Defines: default unvisited, visited, hover, and active.\n// TODO: @trallard to improve focus styles in subsequent PR\n@mixin link-style-default {\n  // So that really long links don't spill out of their container\n  word-wrap: break-word;\n  color: var(--pst-color-link);\n\n  @include link-decoration;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to add active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link);\n  }\n\n  // Visited should still be hoverable\n  &:visited {\n    color: var(--pst-color-link);\n\n    &:hover {\n      color: var(--pst-color-link-hover);\n    }\n  }\n}\n\n// Text link styles\n// ----------------\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation bar links.\n@mixin link-style-text {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration;\n    @include link-decoration-hover;\n  }\n}\n\n// Sidebar and TOC links\n// ---------------------\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation the primary sidebar and table of contents.\n// Active and hover should work together rather than one overriding the other.\n@mixin link-sidebar {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    text-decoration: underline;\n    background-color: transparent;\n    color: var(--pst-color-link-hover);\n\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to update active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link-hover);\n  }\n\n  &:focus-visible {\n    box-shadow: $focus-ring-box-shadow;\n    outline: none;\n    z-index: 10; // keep focus ring on top (prevent the link-sidebar-current notch from lying on top of the ring)\n  }\n}\n\n// Sidebar current page link styles\n// --------------------------------\n// Adds a vertical line on the left hand side of the link to indicate that\n// it's the current page. Note this is distinct from an active state.\n// Used on the primary sidebar and the TOC.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-sidebar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n\n  @if $link-hover-decoration-thickness {\n    $notch-shadow: inset\n      $link-hover-decoration-thickness\n      0\n      0\n      var(--pst-color-primary);\n\n    box-shadow: $notch-shadow;\n\n    &:focus-visible {\n      box-shadow: $notch-shadow, $focus-ring-box-shadow;\n      outline: none;\n    }\n  }\n}\n\n// Navigation bar current page link styles\n// ---------------------------------------\n// Adds a bottom underline, this leaves enough space for the hover state without\n// cluttering the navbar.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-navbar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n\n  @if $link-hover-decoration-thickness {\n    border-bottom: $link-hover-decoration-thickness\n      solid\n      var(--pst-color-primary);\n  }\n}\n\n// Navigation bar icon links hover styles\n// --------------------------------------\n// Adds a bottom box-shadow - since there is no text we cannot use text-decoration\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin icon-navbar-hover {\n  &:hover {\n    color: var(--pst-color-link-hover);\n\n    @if $link-hover-decoration-thickness {\n      box-shadow: 0\n        $link-hover-decoration-thickness\n        0\n        var(--pst-color-link-hover);\n    }\n  }\n}\n\n// Mixin for links in the header (and the More dropdown toggle).\n\n// The mixin assumes it will be applied to some element X with a markup structure\n// like: X > .nav-link, or X > .dropdown-toggle.\n\n// It also assumes X.current is how the app annotates which item in the header nav\n// corresponds to the section in the docs that the user is currently reading.\n@mixin header-link {\n  // Target the child and not the parent because we want the underline in the\n  // mobile sidebar to only span the width of the text not the entire row/line.\n  > .nav-link,\n  > .dropdown-toggle {\n    border-radius: 2px;\n    color: var(--pst-color-text-muted);\n  }\n\n  > .nav-link {\n    // Set up pseudo-element for hover and current states below.\n    position: relative;\n\n    &::before {\n      content: \"\";\n      display: block;\n      position: absolute;\n      inset: 0;\n      background-color: transparent;\n    }\n\n    // Underline on hover.\n    // - Don't use text-decoration because it will wrap across two lines if\n    //   the link text also wraps across two lines.\n    // - Use pseudo-element in order to avoid the border-radius values\n    //   rounding the edges of the underline. (And since a header link can be\n    //   both focused and hovered at the same time and we want the focus ring\n    //   but not the underline to be rounded, we cannot use a box shadow or\n    //   bottom border link element to create the underline, or else it will\n    //   be rounded and if we apply border-radius 0 then the hovered focus\n    //   ring would go from rounded to sharp. So we have to use the\n    //   pseudo-element.)\n    &:hover {\n      color: var(--pst-color-secondary);\n      text-decoration: none; // override the link-style-hover mixin\n      &::before {\n        border-bottom: 3px solid var(--pst-color-secondary);\n      }\n    }\n\n    &:focus-visible {\n      box-shadow: none; // override Bootstrap\n      outline: 3px solid var(--pst-color-accent);\n      outline-offset: 3px;\n    }\n  }\n\n  > .dropdown-toggle {\n    &:focus-visible {\n      box-shadow: $focus-ring-box-shadow;\n    }\n\n    &:hover {\n      text-decoration: none;\n      box-shadow: 0 0 0 $focus-ring-width var(--pst-color-link-hover); // purple focus ring\n      // Brighten the text on hover (muted -> base)\n      color: var(--pst-color-text-base);\n    }\n  }\n\n  &.current {\n    > .nav-link {\n      color: var(--pst-color-primary);\n\n      // Underline the current navbar item\n      &::before {\n        border-bottom: 3px solid var(--pst-color-primary);\n      }\n    }\n  }\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n// fusv-disable\n$accordion-button-focus-border-color:     $input-focus-border-color !default; // Deprecated in v5.3.3\n// fusv-enable\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='none' stroke='#{$accordion-icon-active-color}' stroke-linecap='round' stroke-linejoin='round'><path d='M2 5L8 11L14 5'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n\n@import \"variables-dark\"; // TODO: can be removed safely in v6, only here to avoid breaking changes in v5.3\n","// Override bootstrap variables\n$spacer: 1rem;\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1400px,\n);\n$grid-breakpoints: (\n  xs: 0,\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1200px,\n);\n$dropdown-link-hover-bg: var(--pst-color-surface);\n\n// --pst-color-surface can also be assigned to the dark variant because it is\n// scoped to different values depending on light/dark theme\n$dropdown-dark-link-hover-bg: var(--pst-color-surface);\n$dropdown-link-active-bg: var(--pst-color-surface);\n$dropdown-dark-link-active-bg: var(--pst-color-surface);\n$focus-ring-width: 0.1875rem; // 3px at 100% zoom (0.1875 * 16px base font = 3px)\n$focus-ring-opacity: 1;\n$focus-ring-color: var(--pst-color-accent);\n$focus-ring-blur: 0;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color;\n\n// outline creates the same style of focus ring, it just uses CSS outline instead of box shadow\n$focus-ring-outline: $focus-ring-color solid $focus-ring-width;\n$btn-focus-box-shadow: $focus-ring-box-shadow;\n","/***\n * Rules for the UX/UI of skip navigation link btn.\n * It's only visible to people\n * navigating with keyboard for accessibility purposes\n * ref: https://www.youtube.com/watch?v=VUR0I5mqq7I\n ***/\n\n.skip-link {\n  position: fixed;\n  top: 0;\n  left: 0;\n  right: 0;\n  text-align: center;\n  background-color: var(--pst-color-warning);\n  padding: 0.5rem;\n  z-index: $zindex-modal;\n  border-bottom: 1px solid var(--pst-color-border);\n\n  // This shows / hides the button\n  transform: translateY(-100%);\n  transition: transform 150ms ease-in-out;\n\n  &:focus-within {\n    transform: translateY(0%);\n  }\n\n  a {\n    // Ensure we are using a WCAG conformant colour\n    color: var(--pst-color-warning-text) !important;\n\n    &:focus-visible {\n      // use color with sufficient contrast\n      outline-color: $foundation-black;\n    }\n  }\n}\n","/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:string\";\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetch\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map.get($map, $key);\n  }\n\n  @return $map;\n}\n\n/* Assign base colors for the PyData theme */\n$color-palette: (\n  // Primary color\n  \"teal\":\n    (\n      \"50\": #f4fbfc,\n      \"100\": #e9f6f8,\n      \"200\": #d0ecf1,\n      \"300\": #abdde6,\n      \"400\": #3fb1c5,\n      \"500\": #0a7d91,\n      \"600\": #085d6c,\n      \"700\": #064752,\n      \"800\": #042c33,\n      \"900\": #021b1f,\n    ),\n  // Secondary color\n  \"violet\":\n    (\n      \"50\": #f4eefb,\n      \"100\": #e0c7ff,\n      \"200\": #d5b4fd,\n      \"300\": #b780ff,\n      \"400\": #9c5ffd,\n      \"500\": #8045e5,\n      \"600\": #6432bd,\n      \"700\": #4b258f,\n      \"800\": #341a61,\n      \"900\": #1e0e39,\n    ),\n  // Neutrals\n  \"gray\":\n    (\n      \"50\": #f9f9fa,\n      \"100\": #f3f4f5,\n      \"200\": #e5e7ea,\n      \"300\": #d1d5da,\n      \"400\": #9ca4af,\n      \"500\": #677384,\n      \"600\": #48566b,\n      \"700\": #29313d,\n      \"800\": #222832,\n      \"900\": #14181e,\n    ),\n  // Accent color\n  \"pink\":\n    (\n      \"50\": #fcf8fd,\n      \"100\": #fcf0fa,\n      \"200\": #f8dff5,\n      \"300\": #f3c7ee,\n      \"400\": #e47fd7,\n      \"500\": #c132af,\n      \"600\": #912583,\n      \"700\": #6e1c64,\n      \"800\": #46123f,\n      \"900\": #2b0b27,\n    ),\n  \"foundation\": (\n    \"white\": #ffffff,\n    // gray-900\n    \"black\": #14181e,\n  )\n);\n\n:root {\n  // Add theme colours to the html root element\n  @each $group-color, $color in $color-palette {\n    @each $color-name, $definition in $color {\n      --pst-#{$group-color}-#{$color-name}: #{$definition};\n    }\n  }\n}\n\n// Static SCSS variables used thoroughout the theme\n// Minimum contrast ratio used for the theme.\n// Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n// 4.5 - is for text that is 14pt or less\n$min-contrast-ratio-4: 4.5;\n\n// 3 is for text that is 18pt or bold, or for non-text elements\n$min-contrast-ratio-3: 3;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$foundation-black: #14181e;\n$foundation-white: #fff;\n\n// This is a custom - calculated  color between gray 100 and 200 - to reduce\n// the contrast ratio (avoid a jarring effect)\n$base-light-text: #ced6dd;\n\n// used in sphinx_design - gray 100\n$foundation-light-gray: #f3f4f5;\n\n// used in sphinx_design - gray 700\n$foundation-muted-gray: #29313d;\n\n// used in sphinx_design - gray 800\n$foundation-dark-gray: #222832;\n$pst-semantic-colors: (\n  \"primary\": (\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"teal\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"teal\", \"800\")},\n  ),\n  \"secondary\": (\n    \"light\": #{map-deep-get($color-palette, \"violet\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"violet\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"800\")},\n  ),\n  \"accent\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"pink\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"pink\", \"800\")},\n  ),\n  \"info\": (\n    \"light\": #276be9,\n    \"bg-light\": #dce7fc,\n    \"dark\": #79a3f2,\n    \"bg-dark\": #06245d,\n  ),\n  \"warning\": (\n    \"light\": #f66a0a,\n    \"bg-light\": #f8e3d0,\n    \"dark\": #ff9245,\n    \"bg-dark\": #652a02,\n  ),\n  \"success\": (\n    \"light\": #00843f,\n    \"bg-light\": #d6ece1,\n    \"dark\": #5fb488,\n    \"bg-dark\": #002f17,\n  ),\n  // This is based on the warning color\n  \"attention\":\n    (\n      \"light\": var(--pst-color-warning),\n      \"bg-light\": var(--pst-color-warning-bg),\n      \"dark\": var(--pst-color-warning),\n      \"bg-dark\": var(--pst-color-warning-bg),\n    ),\n  \"danger\": (\n    \"light\": #d72d47,\n    \"bg-light\": #f9e1e4,\n    \"dark\": #e78894,\n    \"bg-dark\": #4e111b,\n  ),\n  \"text-base\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    \"dark\": $base-light-text,\n  ),\n  \"text-muted\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"heading-color\": (\n    \"light\": #{$foundation-white},\n    \"dark\": #{$foundation-black},\n  ),\n  \"shadow\": (\n    \"light\": rgba(0, 0, 0, 0.1),\n    \"dark\": rgba(0, 0, 0, 0.2),\n  ),\n  \"border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"300\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n  ),\n  \"border-muted\": (\n    \"light\": rgba(23, 23, 26, 0.2),\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  \"blockquote-notch\": (\n    // These colors have a contrast ratio > 3.0 against both the background and\n    // surface colors that the notch is sandwiched between\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"500\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"inline-code\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"300\")},\n  ),\n  \"inline-code-links\": (\n    // need to make sure there is enough contrast against the code bg\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"600\")},\n    // keep primary color for dark mode\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n  ),\n  \"target\": (\n    \"light\": #f3cf95,\n    \"dark\": #675c04,\n  ),\n  \"table\": (\n    \"light\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    \"dark\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n  ),\n  \"table-row-hover\": (\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"300\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"600\")},\n  ),\n  \"table-inner-border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"200\")},\n    \"dark\": #364150,\n  ),\n  // DEPTH COLORS - you can see the elevation colours and shades\n  // in the Figma file https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1492%3A922&t=sQeQZehkOzposYEg-1\n  // background: color of the canvas / the furthest back layer\n  \"background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    ),\n  // on-background: provides slight contrast against background\n  // (by use of shadows in light theme)\n  \"on-background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    ),\n  \"surface\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  // on_surface: object on top of surface object (without shadows)\n  \"on-surface\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n      \"dark\": $foundation-light-gray,\n    ),\n);\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * #{something} inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n@mixin theme-colors($mode) {\n  // check if this color is defined differently for light/dark\n  @each $col-name, $definition in $pst-semantic-colors {\n    @if meta.type-of($definition) == map {\n      @each $key, $val in $definition {\n        @if string.index($key, $mode) {\n          // since now we define the bg colours in the semantic colours and not\n          // by changing opacity, we need to check if the key contains bg and the\n          // correct mode (light/dark)\n          @if string.index($key, \"bg\") {\n            --pst-color-#{$col-name}-bg: #{$val};\n          } @else {\n            --pst-color-#{$col-name}: #{$val};\n          }\n        }\n      }\n    } @else {\n      --pst-color-#{$col-name}: #{$definition};\n    }\n  }\n\n  // assign the \"duplicate\" colors (ones that just reference other variables)\n  & {\n    --pst-color-link: var(--pst-color-primary);\n    --pst-color-link-hover: var(--pst-color-secondary);\n    --pst-color-table-outer-border: var(--pst-color-surface);\n    --pst-color-table-heading-bg: var(--pst-color-surface);\n    --pst-color-table-row-zebra-high-bg: var(--pst-color-on-background);\n    --pst-color-table-row-zebra-low-bg: var(--pst-color-surface);\n  }\n\n  // adapt to light/dark-specific content\n  @if $mode == \"light\" {\n    .only-dark,\n    .only-dark ~ figcaption {\n      display: none !important;\n    }\n  } @else {\n    .only-light,\n    .only-light ~ figcaption {\n      display: none !important;\n    }\n\n    /* Adjust images in dark mode (unless they have class .only-dark or\n     * .dark-light, in which case assume they're already optimized for dark\n     * mode).\n     */\n    img:not(.only-dark, .dark-light) {\n      filter: brightness(0.8) contrast(1.2);\n    }\n\n    /* Give images a light background in dark mode in case they have\n    *  transparency and black text (unless they have class .only-dark or .dark-light, in\n    *  which case assume they're already optimized for dark mode).\n    */\n    .bd-content img:not(.only-dark, .dark-light) {\n      background-color: rgb(255 255 255);\n      border-radius: 0.25rem;\n    }\n\n    // MathJax SVG outputs should be filled to same color as text.\n    .MathJax_SVG * {\n      fill: var(--pst-color-text-base);\n    }\n  }\n}\n\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  @include theme-colors(\"light\");\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    @include theme-colors($mode);\n\n    color-scheme: $mode;\n  }\n}\n\n// assign classes too, for runtime use of theme colors\n@each $col-name, $definition in $pst-semantic-colors {\n  .pst-color-#{$col-name} {\n    color: var(--pst-color-#{$col-name});\n  }\n}\n",".bd-container {\n  flex-grow: 1;\n  display: flex;\n  justify-content: center;\n\n  .bd-container__inner {\n    display: flex;\n  }\n}\n\n.bd-page-width {\n  width: 100%;\n\n  @include media-breakpoint-up(lg) {\n    max-width: $breakpoint-page-width;\n  }\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n",".pst-async-banner-revealer {\n  // Setting height to 0 and overflow to hidden allows us to add up the heights\n  // of this element's children before revealing them.\n  height: 0;\n  overflow: hidden;\n\n  // Height to be set by JavaScript, which should trigger the following\n  // transition rule (unless the user has set their system to reduce motion).\n  transition: height 300ms ease-in-out;\n\n  @media (prefers-reduced-motion) {\n    transition: none;\n  }\n}\n\n#bd-header-version-warning,\n.bd-header-announcement {\n  min-height: 3rem;\n  width: 100%;\n  display: flex;\n  position: relative;\n  align-items: center;\n  justify-content: center;\n  text-align: center;\n  padding: 0.5rem 12.5%; // Horizontal padding so the width is 75%\n  // One breakpoint less than $breakpoint-sidebar-primary. See variables/_layout.scss for more info.\n  @include media-breakpoint-down(lg) {\n    // Announcements can take a bit more width on mobile\n    padding: 0.5rem 2%;\n  }\n\n  p {\n    font-weight: bold;\n    margin: 0;\n  }\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n\n  // The \"Switch to stable version\" link (styled like a button)\n  .pst-button-link-to-stable-version {\n    @include box-shadow;\n\n    $background-color: var(--pst-color-danger);\n    $hover-background-color: var(--pst-color-danger-highlight);\n    $color: var(--pst-color-danger-text);\n\n    background-color: $background-color;\n    border-color: $background-color;\n    color: $color;\n    border-radius: 0.25rem;\n\n    &:hover {\n      background-color: $hover-background-color;\n      border-color: $hover-background-color;\n      color: $color;\n    }\n\n    &:focus-visible {\n      outline: $focus-ring-width solid $background-color;\n      outline-offset: $focus-ring-width;\n    }\n  }\n}\n\n// Bg color is now defined in the theme color palette - using our secondary color\n.bd-header-announcement {\n  background-color: var(--pst-color-secondary-bg);\n}\n\n#bd-header-version-warning {\n  background-color: var(--pst-color-danger-bg);\n}\n","/*********************************************\n* SASS Mixins\n*********************************************/\n\n/**\n * A consistent box shadow style we apply across elements.\n */\n@mixin box-shadow() {\n  box-shadow:\n    0 0.2rem 0.5rem var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n\n/**\n   * Set background of some cell outputs to white-ish to make sure colors work\n   * This is because many libraries make output that only looks good on white\n   */\n@mixin cell-output-background {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\n\n@mixin table-colors {\n  color: var(--pst-color-table);\n  border: 1px solid var(--pst-color-table-outer-border);\n\n  th,\n  td {\n    ~ th,\n    ~ td {\n      border-left: 1px solid var(--pst-color-table-inner-border);\n    }\n  }\n\n  thead {\n    tr {\n      background-color: var(--pst-color-table-heading-bg);\n      border-bottom: 2px solid var(--pst-color-primary);\n    }\n  }\n\n  tbody {\n    tr {\n      &:nth-child(odd) {\n        background-color: var(--pst-color-table-row-zebra-low-bg);\n      }\n\n      &:nth-child(even) {\n        background-color: var(--pst-color-table-row-zebra-high-bg);\n      }\n\n      &:hover {\n        background-color: var(--pst-color-table-row-hover-bg);\n      }\n    }\n  }\n}\n","/**\n * Main content area\n */\n.bd-main {\n  flex-grow: 1;\n  flex-direction: column;\n  display: flex;\n  min-width: 0;\n\n  .bd-content {\n    display: flex;\n    justify-content: center;\n    height: 100%;\n\n    .bd-article-container {\n      justify-content: start;\n      display: flex;\n      flex-direction: column;\n\n      // Max-width is slightly more than the W3 since our docs often have images.\n      // We shoot for about 100 characters per line instead of 80.\n      // ref: https://www.w3.org/WAI/tutorials/page-structure/styling/#line-length\n      width: 100%;\n      max-width: 60em;\n      overflow-x: auto; // Prevent wide content from pushing off the secondary sidebar\n      padding: 1rem;\n\n      .bd-article {\n        // Give a bit more verticle spacing on wide screens\n        @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n          padding-top: 1.5rem;\n          padding-left: 2rem;\n        }\n      }\n    }\n  }\n}\n",".bd-footer {\n  width: 100%;\n  border-top: 1px solid var(--pst-color-border);\n\n  .bd-footer__inner {\n    display: flex;\n    flex-grow: 1;\n    padding: 1rem;\n    margin: auto;\n  }\n\n  .footer-items__start,\n  .footer-items__center,\n  .footer-items__end {\n    display: flex;\n    flex-direction: column;\n    gap: 0.5rem;\n    justify-content: center;\n    flex-grow: 1;\n  }\n\n  .footer-items__center {\n    text-align: center;\n  }\n\n  .footer-items__end {\n    text-align: end;\n  }\n\n  // So that paragraphs don't take up extra room\n  .footer-item p {\n    margin-bottom: 0;\n  }\n}\n",".bd-footer-article {\n  margin-top: auto;\n\n  .footer-article-items {\n    display: flex;\n    flex-direction: column;\n  }\n}\n",".bd-footer-content {\n  .footer-content-items {\n    display: flex;\n    flex-direction: column;\n    margin-top: auto;\n  }\n}\n","/**\n * Header at the top of the page\n * It includes the announcement bar and the navigation bar.\n */\n\n// Styling for the Icon links can be found in components/_icon-links.scss\n\n// If we want the shadow to only point downward in the future, set\n// box-shadow to: 0 0.125rem 0.25rem -0.125rem rgba(0, 0, 0, 0.11);\n.bd-header {\n  position: sticky;\n  top: 0;\n  z-index: $zindex-fixed;\n\n  // Overrides bootstrap\n  background-color: var(--pst-color-on-background) !important;\n  box-shadow: 0 0.125rem 0.25rem 0 var(--pst-color-shadow);\n  width: 100%;\n  padding: 0;\n  max-width: 100vw;\n  justify-content: center;\n\n  .bd-header__inner {\n    display: flex;\n    align-items: center;\n    height: fit-content;\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n\n  :focus-visible {\n    border-radius: $focus-ring-radius;\n  }\n\n  // These items will define the height of the header\n  .navbar-item {\n    height: var(--pst-header-height);\n    max-height: var(--pst-header-height);\n    display: flex;\n    align-items: center;\n  }\n\n  // Hide the navbar header items on mobile because they're in the sidebar\n  .navbar-header-items {\n    display: none;\n    flex-shrink: 1;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: inherit;\n      flex-grow: 1;\n      padding: 0 0 0 0.5rem;\n    }\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center,\n  .navbar-header-items__start {\n    display: flex;\n    align-items: center;\n    flex-flow: wrap;\n\n    // In case we wrap our items to multiple rows on small screens\n    row-gap: 0;\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center {\n    column-gap: 1rem;\n  }\n\n  // A little smaller because this is displayed by default on mobile\n  .navbar-header-items__start {\n    flex-shrink: 0;\n    margin-right: auto;\n    gap: 0.5rem;\n  }\n\n  .navbar-header-items__end {\n    // End navbar items should snap to the right\n    justify-content: end;\n  }\n\n  // Contains the navigation links within the navbar\n  .navbar-nav {\n    display: flex;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      // Center align on wide screens so the dropdown button is centered properly\n      align-items: baseline;\n    }\n\n    li.pst-header-nav-item {\n      margin-inline: 2px; // breathing room so hover and focus styles do not overlap\n      &.dropdown {\n        margin-inline: 4px;\n\n        button {\n          padding-inline: 8px;\n        }\n      }\n\n      a {\n        padding-inline: 6px;\n      }\n\n      @include header-link;\n    }\n\n    li a.nav-link.dropdown-item {\n      @include link-style-text;\n    }\n\n    // Dropdowns for the extra links\n    .dropdown {\n      button {\n        display: unset;\n        border: none;\n\n        &:hover {\n          @include link-style-hover;\n        }\n      }\n\n      .dropdown-menu {\n        z-index: $zindex-popover;\n        border: 1px solid var(--pst-color-border);\n        box-shadow: 0 0 0.3rem 0.1rem var(--pst-color-shadow);\n        background-color: var(--pst-color-on-background);\n        padding: 0.5rem 0;\n        margin: 0.5rem 0;\n        min-width: 20rem;\n\n        .dropdown-item {\n          // Give the items in the dropdown some breathing room but let the hit\n          // and hover area of the items extend to the edges of the menu\n          padding: 0.25rem 1.5rem;\n\n          // Override Bootstrap\n          &:focus:not(:hover, :active) {\n            background-color: inherit;\n          }\n\n          &:focus-visible {\n            z-index: 10; // keep focus ring on top (prevent the hover background of the next dropdown item from covering the ring)\n          }\n        }\n\n        // Hide the menu unless show has been clicked\n        &:not(.show) {\n          display: none;\n        }\n      }\n    }\n  }\n\n  // **************************************************************\n  // Showing and hiding the sidebar toggle buttons and header items\n  // **************************************************************\n\n  // Toggle buttons\n  button.sidebar-toggle {\n    display: flex;\n    cursor: pointer;\n    font-size: var(--pst-font-size-icon);\n    align-items: center;\n    color: var(--pst-color-muted);\n    margin-bottom: 0;\n    padding-bottom: 0.25rem;\n    background-color: inherit;\n    border: none;\n\n    @include icon-navbar-hover;\n  }\n\n  button.primary-toggle {\n    margin-right: 1rem;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  button.secondary-toggle {\n    margin-left: 1rem;\n\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      display: none;\n    }\n  }\n}\n\n// inline the element in the navbar as long as they fit and use display block when collapsing\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  .navbar-center-items .navbar-item {\n    display: inline-block;\n  }\n}\n\n.nav-link {\n  &:hover {\n    @include link-style-hover;\n  }\n\n  // Override Bootstrap\n  transition: none;\n\n  &.nav-external::after {\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-external-link);\n    font-size: 0.75em;\n    margin-left: 0.3em;\n  }\n}\n\n.bd-navbar-elements li.nav-item i {\n  font-size: 0.7rem;\n  padding-left: 2px;\n  vertical-align: middle;\n}\n\n// THe elements next to the hamburger menu only show on narrow screens\n.navbar-persistent--mobile {\n  margin-left: auto;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: none;\n  }\n}\n\n// The navbar-persistent content should only show on wide screens\n.navbar-persistent--container {\n  display: none;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: flex;\n  }\n}\n",".header-article__inner {\n  display: flex;\n  padding: 0 0.5rem;\n\n  // The items define the height so that it disappears if there are no items\n  .header-article-item {\n    min-height: var(--pst-header-article-height);\n    height: var(--pst-header-article-height);\n  }\n\n  .header-article-items__start,\n  .header-article-items__end {\n    display: flex;\n    align-items: start;\n    gap: 0.5rem;\n  }\n\n  .header-article-items__end {\n    margin-left: auto;\n  }\n}\n","/**\n * The primary sidebar on the left.\n * e.g., between-pages navigation.\n */\n\n$sidebar-padding-right: 1rem;\n\n.bd-sidebar-primary {\n  display: flex;\n  flex-direction: column;\n  gap: 1rem;\n  max-height: calc(100vh - var(--pst-header-height));\n  position: sticky;\n  top: var(--pst-header-height);\n\n  @include make-col(3);\n\n  // Borders padding and whitespace\n  padding: 2rem $sidebar-padding-right 1rem 1rem;\n  border-right: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n  font-size: var(--pst-sidebar-font-size-mobile);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  :focus-visible {\n    border-radius: $focus-ring-radius;\n  }\n\n  // override bootstrap when navlink are displayed in the sidebar\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n  }\n\n  &.no-sidebar {\n    border-right: 0;\n  }\n\n  &.hide-on-wide {\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  // Headers shouldn't be colored in the sidebars and some extensions add headers\n  h1,\n  h2,\n  h3,\n  h4 {\n    color: var(--pst-color-text-base);\n  }\n\n  .sidebar-primary-items__start,\n  .sidebar-primary-items__end {\n    .sidebar-primary-item {\n      padding: 0.5rem 0;\n    }\n  }\n\n  // Hide the sidebar header items on widescreen since they are visible in the header\n  .sidebar-header-items {\n    display: flex;\n    flex-direction: column;\n\n    .sidebar-header-items__title {\n      font-weight: var(--pst-sidebar-header-font-weight);\n      font-size: var(--pst-sidebar-header-font-size);\n      color: var(--pst-color-text-base);\n      margin-bottom: 0.5rem;\n    }\n\n    // The dropdown toggle for extra links just shows them all instead.\n    .nav-item.dropdown {\n      // On mobile, the dropdown behaves like any other link, no hiding\n      button {\n        display: none;\n      }\n\n      .dropdown-menu {\n        display: flex;\n        flex-direction: column;\n        padding: 0;\n        margin: 0;\n        border: none;\n        background-color: inherit;\n        font-size: inherit;\n\n        .dropdown-item {\n          &:hover,\n          &:focus {\n            // In the mobile sidebar, the dropdown menu is inlined with the\n            // other links, which do not have background-color changes on hover\n            // and focus\n            background-color: unset;\n          }\n        }\n      }\n    }\n\n    .bd-navbar-elements {\n      .nav-link {\n        &:focus-visible {\n          box-shadow: none; // Override Bootstrap\n          outline: $focus-ring-outline;\n          outline-offset: $focus-ring-width;\n        }\n      }\n    }\n\n    // Center header items get displayed vertically, end items are displayed horizontally\n    .sidebar-header-items__center {\n      display: flex;\n      flex-direction: column;\n    }\n\n    // Positioning end items\n    .sidebar-header-items__end {\n      display: flex;\n      align-items: center;\n      gap: 1rem;\n    }\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  .sidebar-primary-items__start {\n    // Add a border on mobile to separate it from the header sidebar area\n    border-top: 1px solid var(--pst-color-border);\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      border-top: none;\n    }\n  }\n\n  .sidebar-primary-items__end {\n    margin-top: auto;\n    margin-bottom: 1em;\n  }\n\n  .list-caption {\n    list-style: none;\n    padding-left: 0;\n\n    // Level 0 TOC heading is put inside the <summary> tag\n    // so let the <summary> tag take up more space\n    li.toctree-l0.has-children {\n      > details {\n        > summary {\n          position: relative;\n          height: auto;\n          width: auto;\n          display: flex;\n          justify-content: space-between;\n          align-items: baseline;\n\n          .toctree-toggle {\n            // Prevent toggle icon from getting squished by summary being a\n            // flexbox\n            flex: 0 0 auto;\n\n            // Make the level 0 chevron icon slightly bigger than descendant\n            // levels\n            .fa-chevron-down {\n              font-size: 1rem;\n            }\n          }\n        }\n      }\n    }\n  }\n\n  li.has-children {\n    $toctree-toggle-width: 30px;\n\n    position: relative;\n\n    > .reference,\n    .caption {\n      margin-right: calc(\n        $toctree-toggle-width + $focus-ring-width\n      ); // keep clear of the toggle icon\n\n      padding-top: 0.25rem; // align caption text with toggle chevron\n    }\n\n    > details {\n      > summary {\n        // Remove browser default toggle icon\n        list-style: none;\n\n        &::-webkit-details-marker {\n          display: none;\n        }\n\n        // The summary element is natively focusable, but delegate the focus state to the toggle icon\n        &:focus-visible {\n          outline: none;\n\n          > .toctree-toggle {\n            outline: $focus-ring-outline;\n            outline-offset: -$focus-ring-width; // Prevent right side of focus ring from disappearing underneath the sidebar's right edge\n          }\n        }\n\n        // Container for expand/collapse chevron icon\n        .toctree-toggle {\n          cursor: pointer;\n\n          // Position it so that it's aligned with the top right corner of the\n          // last positioned element, in this case the li.has-children\n          position: absolute;\n          top: 0;\n          right: 0;\n\n          // Give it dimensions\n          width: $toctree-toggle-width;\n          height: $toctree-toggle-width; // make it square\n\n          // Vertically and horizontally center the icon within the container\n          display: inline-flex;\n          justify-content: center;\n          align-items: center;\n\n          .fa-chevron-down {\n            font-size: 0.75rem;\n          }\n        }\n      }\n\n      // The section is open/expanded, rotate the toggle icon (chevron) so it\n      // points up instead of down\n      &[open] {\n        > summary {\n          .fa-chevron-down {\n            transform: rotate(180deg);\n          }\n        }\n      }\n    }\n  }\n}\n\n/* Between-page links and captions */\nnav.bd-links {\n  margin-right: -$sidebar-padding-right; // align toctree toggle chevrons with right edge of sidebar and allow text to flow closer to the right edge\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: block;\n  }\n\n  ul {\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  li > a {\n    display: block;\n    padding: 0.25rem 0.65rem;\n\n    @include link-sidebar;\n\n    box-shadow: none;\n    margin-right: $focus-ring-width; // prevent the right side focus ring from disappearing under the sidebar right edge\n\n    &.reference.external {\n      &::after {\n        font: var(--fa-font-solid);\n        content: var(--pst-icon-external-link);\n        font-size: 0.75em;\n        margin-left: 0.3em;\n      }\n    }\n  }\n\n  .current > a {\n    @include link-sidebar-current;\n\n    background-color: transparent;\n  }\n\n  // Title\n  p.bd-links__title {\n    font-size: var(--pst-sidebar-header-font-size);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n\n  // Toctree captions\n  p.caption {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    position: relative;\n    margin-top: 1.25rem;\n    margin-bottom: 0.5rem;\n    color: var(--pst-color-text-base);\n\n    &:first-child {\n      margin-top: 0;\n    }\n\n    font-size: var(--pst-sidebar-font-size-mobile);\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is an invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","/**\n * Secondary sidebar on the right.\n * e.g., in-page table of contents.\n */\n.bd-sidebar-secondary {\n  display: flex;\n  order: 2;\n  flex-shrink: 0;\n  flex-direction: column;\n  position: sticky;\n  top: var(--pst-header-height);\n  max-height: calc(100vh - var(--pst-header-height));\n  padding: 2rem 1rem 1rem;\n  width: var(--pst-sidebar-secondary);\n  font-size: var(--pst-sidebar-font-size-mobile);\n\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  // Color and border\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n}\n\n.sidebar-secondary-item {\n  padding: 0.5rem;\n\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    border-left: 1px solid var(--pst-color-border);\n    padding-left: 1rem;\n  }\n\n  i {\n    padding-right: 0.5rem;\n  }\n}\n","/*******************************************************************************\n* Rules for the UX/UI of sidebar sliding drawers on mobile\n* Note that this sheet controls styles across many parts of the theme\n* It is aggregated into this one sheet instead of being split across\n* components in order to keep it easier to debug in one place.\n* It is broken up into major sections below.\n*/\n\n/*******************************************************************************\n* Buttons and overlays\n*/\ninput.sidebar-toggle {\n  display: none;\n}\n\n// Background overlays\nlabel.overlay {\n  background-color: black;\n  opacity: 0.5;\n  height: 0;\n  width: 0;\n  position: fixed;\n  top: 0;\n  left: 0;\n  transition: opacity $animation-time ease-out;\n  z-index: $zindex-modal-backdrop;\n}\n\ninput {\n  // Show the correct overlay when its input is checked\n  &#pst-primary-sidebar-checkbox:checked + label.overlay.overlay-primary,\n  &#pst-secondary-sidebar-checkbox:checked + label.overlay.overlay-secondary {\n    height: 100vh;\n    width: 100vw;\n  }\n\n  // Primary sidebar slides in from the left\n  &#pst-primary-sidebar-checkbox:checked ~ .bd-container .bd-sidebar-primary {\n    visibility: visible;\n    margin-left: 0;\n  }\n\n  // Secondary sidebar slides in from the right\n  &#pst-secondary-sidebar-checkbox:checked\n    ~ .bd-container\n    .bd-sidebar-secondary {\n    visibility: visible;\n    margin-right: 0;\n  }\n}\n\n/*******************************************************************************\n* Sidebar drawer behavior\n*/\n\n/**\n * Behavior for sliding drawer elements that will be toggled with an input\n *\n * NOTE: We use this mixin to define the toggle behavior on narrow screens,\n * And the wide-screen behavior of the sections is defined in their own section\n * .scss files.\n */\n@mixin sliding-drawer($side: \"left\") {\n  position: fixed;\n  top: 0;\n  z-index: $zindex-modal;\n  height: 100vh;\n  max-height: 100vh;\n  width: 75%;\n  flex-grow: 0.75;\n  max-width: 350px;\n  transition:\n    visibility $animation-time ease-out,\n    margin $animation-time ease-out;\n  visibility: hidden;\n\n  @if $side == \"right\" {\n    margin-right: -75%;\n    right: 0;\n  } @else {\n    margin-left: -75%;\n    left: 0;\n  }\n}\n\n// Primary sidebar hides/shows at earlier widths\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  .sidebar-toggle.primary-toggle {\n    display: none;\n  }\n\n  input#pst-primary-sidebar-checkbox {\n    &:checked + label.overlay.overlay-primary {\n      height: 0;\n      width: 0;\n    }\n  }\n\n  .bd-sidebar-primary {\n    margin-left: 0;\n    visibility: visible;\n  }\n}\n\n.bd-sidebar-primary {\n  @include media-breakpoint-down($breakpoint-sidebar-primary) {\n    @include sliding-drawer(\"left\");\n  }\n}\n\n.bd-sidebar-secondary {\n  @include media-breakpoint-down($breakpoint-sidebar-secondary) {\n    @include sliding-drawer(\"right\");\n  }\n}\n","/**\n * Breadcrumbs for parent pages meant for the article header\n */\nul.bd-breadcrumbs {\n  list-style: none;\n  padding-left: 0;\n  display: flex;\n  flex-wrap: wrap;\n\n  // Font size slightly smaller to avoid cluttering up space too much\n  font-size: 0.8rem;\n\n  li.breadcrumb-item {\n    display: flex;\n    align-items: center;\n\n    // Style should look like heavier in-page links\n    // keeping visited in the default link colour\n    font-weight: bold;\n\n    a {\n      @include link-style-text;\n    }\n\n    // Items that aren't the home have a caret to the left\n    &:not(.breadcrumb-home)::before {\n      font: var(--fa-font-solid);\n      font-size: 0.8rem;\n      content: var(--pst-breadcrumb-divider);\n      color: var(--pst-color-text-muted);\n      padding: 0 0.5rem;\n    }\n  }\n}\n","/**\n * Icon links in the navbar\n */\n\n.navbar-icon-links {\n  display: flex;\n  flex-flow: row wrap;\n  column-gap: 1rem;\n  justify-content: space-evenly;\n\n  // Remove the padding so that we can define it with flexbox gap above\n  li.nav-item a.nav-link {\n    padding-left: 0;\n    padding-right: 0;\n\n    @include icon-navbar-hover;\n\n    &:focus {\n      color: inherit;\n    }\n  }\n\n  // Spacing and centering\n  a span {\n    display: flex;\n    align-items: center;\n  }\n\n  // Icons styling\n  i {\n    &.fa-brands,\n    &.fa-regular,\n    &.fa-solid {\n      vertical-align: middle;\n      font-style: normal;\n      font-size: var(--pst-font-size-icon);\n    }\n\n    /* Social media buttons hard-code the brand color */\n    &.fa-square-twitter::before {\n      color: #55acee;\n    }\n\n    &.fa-square-gitlab::before {\n      color: #548;\n    }\n\n    &.fa-bitbucket::before {\n      color: #0052cc;\n    }\n  }\n\n  // Force images to be icon-sized\n  img.icon-link-image {\n    height: 1.5em;\n    border-radius: 0.2rem;\n  }\n\n  .fa-pydata {\n    stroke: var(--pst-color-background);\n    stroke-linejoin: round;\n    stroke-width: 0.35;\n  }\n}\n","/**\n * Logo in the navbar\n */\n\n.navbar-brand {\n  position: relative;\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  padding: 0.5rem 0;\n  width: auto;\n  margin: 0;\n  display: flex;\n\n  // Ensure that the logo stays the same length while other content shrinks\n  flex-shrink: 0;\n  align-items: center;\n  gap: 0.5rem;\n\n  // If there's no logo image, we use a p element w/ the site title\n  p {\n    color: var(--pst-color-text-base);\n    margin-bottom: 0;\n  }\n\n  // If there's a logo, it'll be in an img block\n  img {\n    max-width: 100%;\n    height: 100%;\n    width: auto;\n  }\n\n  &:hover,\n  &:visited:hover {\n    @include link-style-hover;\n\n    color: var(--pst-color-text-base);\n  }\n}\n","/**\n * Navigation text links in the navbar\n */\n.navbar-nav {\n  ul {\n    display: block;\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  // Navbar links - do not have an underline by default\n  li {\n    display: flex;\n    flex-direction: column;\n\n    a {\n      display: flex;\n      align-items: center;\n      height: 100%;\n      padding-top: 0.25rem;\n      padding-bottom: 0.25rem;\n\n      @include link-style-text;\n    }\n  }\n}\n","/**\n * The list of in-page TOC links\n */\n.page-toc {\n  .section-nav {\n    padding-left: 0;\n    border-bottom: none;\n\n    ul {\n      padding-left: 1rem;\n    }\n  }\n\n  // override bootstrap settings\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n\n  .onthispage {\n    color: var(--pst-color-text-base);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n}\n","/**\n* Previous / Next navigation buttons\n**/\n.prev-next-area {\n  width: 100%;\n\n  p {\n    color: var(--pst-color-text-muted);\n    margin: 0 0.3em;\n    line-height: 1.3em;\n  }\n\n  i {\n    font-size: 1.2em;\n  }\n\n  a {\n    // So that buttons align with icons\n    display: flex;\n    align-items: center;\n    border: none;\n    padding: 10px;\n    max-width: 45%;\n    overflow-x: hidden;\n    color: var(--pst-color-text-muted);\n    text-decoration: none;\n\n    p.prev-next-title {\n      @include link-style-default;\n\n      font-weight: var(--pst-admonition-font-weight-heading);\n      font-size: 1.1em;\n    }\n\n    &:hover,\n    &:visited:hover {\n      p.prev-next-title {\n        @include link-style-hover;\n      }\n    }\n\n    .prev-next-info {\n      flex-direction: column;\n      margin: 0 0.5em;\n\n      .prev-next-subtitle {\n        text-transform: capitalize;\n      }\n    }\n\n    &.left-prev {\n      float: left;\n    }\n\n    &.right-next {\n      float: right;\n\n      div.prev-next-info {\n        text-align: right;\n      }\n    }\n  }\n}\n","/**\n * Search field\n **/\n.bd-search {\n  position: relative;\n  padding-left: 0.5rem;\n  gap: 0.5rem;\n  background-color: var(--pst-color-background);\n  border-radius: $admonition-border-radius;\n  border: 1px solid var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Background should always be same color regardless of active or not\n  &:active {\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n\n  .icon {\n    position: absolute;\n    color: var(--pst-color-border);\n    left: 25px;\n  }\n\n  .fa-solid.fa-magnifying-glass {\n    position: absolute;\n    left: calc((2.5rem - 0.7em) / 2);\n    color: var(--pst-color-text-muted);\n  }\n\n  input {\n    // Inner-text of the search bar\n    &::placeholder {\n      color: var(--pst-color-text-muted);\n    }\n\n    // Remove the little \"x\" that pops up when you start typing\n    &::-webkit-search-cancel-button,\n    &::-webkit-search-decoration {\n      appearance: none;\n    }\n  }\n\n  // Shows off the keyboard shortcuts for the button\n  .search-button__kbd-shortcut {\n    display: flex;\n    position: absolute;\n    right: 0.5rem;\n    color: var(--pst-color-border);\n  }\n}\n\n.form-control {\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-text-base);\n\n  &:focus,\n  &:focus-visible {\n    border: none;\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n}\n\n/**\n * Search button - located in the navbar\n */\n\n// Search link icon should be a bit bigger since it is separate from icon links\n.search-button {\n  display: flex;\n  align-items: center;\n  align-content: center;\n  color: var(--pst-color-text-muted);\n  padding: 0;\n  border-radius: 0;\n  border: none; // Override Bootstrap button border\n  font-size: 1rem; // Override Bootstrap button font size\n\n  // Override Bootstrap button padding-x. Whitespace in nav bar is controlled\n  // via column gap rule on the container.\n  padding-left: 0;\n  padding-right: 0;\n\n  @include icon-navbar-hover;\n\n  i {\n    font-size: 1.3rem;\n  }\n}\n\n// __search-container will only show up when we use the search pop-up bar\n.search-button__search-container,\n.search-button__overlay {\n  display: none;\n}\n\n.search-button__wrapper.show {\n  .search-button__search-container {\n    display: flex;\n\n    // Center in middle of screen just underneath header\n    position: fixed;\n    z-index: $zindex-modal;\n    top: 30%;\n    left: 50%;\n    transform: translate(-50%, -50%);\n    right: 1rem;\n    margin-top: 0.5rem;\n    width: 90%;\n    max-width: 800px;\n  }\n\n  .search-button__overlay {\n    display: flex;\n    position: fixed;\n    z-index: $zindex-modal-backdrop;\n    background-color: black;\n    opacity: 0.5;\n    width: 100%;\n    height: 100%;\n    top: 0;\n    left: 0;\n  }\n\n  form.bd-search {\n    flex-grow: 1;\n    padding-top: 0;\n    padding-bottom: 0;\n  }\n\n  // Font and input text a bit bigger\n  svg,\n  input {\n    font-size: var(--pst-font-size-icon);\n  }\n}\n\n/**\n * The search button component that looks like a field.\n * Lives at components/search-button-field.html\n */\n.search-button-field {\n  $search-button-border-radius: 1.5em;\n\n  display: inline-flex;\n  align-items: center;\n  border: var(--pst-color-border) solid 1px;\n  border-radius: $search-button-border-radius;\n  color: var(--pst-color-text-muted);\n  padding: 0.5em;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    box-shadow: 0 0 0 $focus-ring-width var(--pst-color-link-hover);\n  }\n\n  &:focus-visible {\n    border-radius: $search-button-border-radius;\n  }\n\n  // The keyboard shotcut text\n  .search-button__default-text {\n    font-size: var(--bs-nav-link-font-size);\n    font-weight: var(--bs-nav-link-font-weight);\n    margin-right: 0.5em;\n    margin-left: 0.5em;\n  }\n\n  .kbd-shortcut__modifier {\n    font-size: 0.75em;\n  }\n\n  // Ensures that all the text lines up in the middle\n  > * {\n    align-items: center;\n  }\n\n  // Only the icon should be visible on narrow screens\n  > :not(svg) {\n    display: none;\n\n    @include media-breakpoint-up(lg) {\n      display: flex;\n    }\n  }\n}\n","/**\n * The 'Hide Search Matches' button.\n * This only shows up when a person lands on a page after clicking a search result.\n * Clicking it removes the highlighting of the search term from the page.\n * We want it to behave like a button.\n */\ndiv#searchbox {\n  // Leave `#searchbox` rules empty so that it doesn't show at all when it is empty\n  p.highlight-link {\n    margin: 1rem 0;\n    width: fit-content;\n\n    // A bit more margin on wide screens to mimic article behavior\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      margin-left: 2rem;\n    }\n\n    // Put outer shadow on this one so that we can darken the link w/ an inner shadow\n    @include box-shadow;\n\n    // Style the button to look like a Sphinx Design button\n    a {\n      border-radius: 0.25rem;\n      font-size: 1.25rem;\n      padding: 0.75rem;\n      background-color: var(--pst-color-primary);\n      color: var(--pst-color-primary-text);\n      text-decoration: none;\n\n      // The box shadow is inset so that it darkens the button on hover\n      transition: box-shadow 0.25s ease-out;\n\n      &:hover {\n        box-shadow: inset 0 0 50px 50px rgb(0 0 0 / 25%);\n      }\n\n      &::before {\n        content: var(--pst-icon-search-minus);\n        color: unset;\n        font: var(--fa-font-solid);\n        margin-right: 0.5rem;\n      }\n    }\n  }\n}\n","/**\n * Light/dark theme switcher\n */\n\n.theme-switch-button {\n  color: var(--pst-color-text-muted);\n  border-radius: 0;\n  border: none; // Override Bootstrap button border\n  font-size: 1rem; // Override Bootstrap's button font size\n\n  // Override Bootstrap button padding-x. Whitespace in nav bar is controlled\n  // via column gap rule on the container.\n  padding-left: 0;\n  padding-right: 0;\n\n  &:hover {\n    @include icon-navbar-hover;\n  }\n\n  span {\n    display: none;\n\n    &:active {\n      text-decoration: none;\n      color: var(--pst-color-link-hover);\n    }\n\n    .fa-lg {\n      aspect-ratio: 1 / 1;\n    }\n  }\n}\n\nhtml[data-mode=\"auto\"] .theme-switch-button span[data-mode=\"auto\"] {\n  display: flex;\n}\n\nhtml[data-mode=\"light\"] .theme-switch-button span[data-mode=\"light\"] {\n  display: flex;\n}\n\nhtml[data-mode=\"dark\"] .theme-switch-button span[data-mode=\"dark\"] {\n  display: flex;\n}\n","button.btn.version-switcher__button {\n  border-color: var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Add a margin on narrow screens to avoid feeling cramped\n  margin-bottom: 1em;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    margin-bottom: unset;\n  }\n\n  &:hover {\n    box-shadow: 0 0 0 $focus-ring-width var(--pst-color-secondary);\n    border-color: transparent;\n  }\n\n  &:active {\n    color: var(--pst-color-text-base);\n    border-color: var(--pst-color-border);\n  }\n\n  &:focus-visible {\n    border-color: transparent;\n  }\n}\n\n.version-switcher__menu {\n  border-color: var(--pst-color-border);\n  border-radius: var(--bs-dropdown-border-radius);\n\n  a.list-group-item {\n    background-color: var(--pst-color-on-background);\n    color: var(--pst-color-text-base);\n    padding: 0.75rem 1.25rem;\n\n    &:not(:last-child) {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n\n    &:hover {\n      @include link-style-hover;\n\n      background-color: var(--pst-color-surface);\n    }\n\n    &.active {\n      @include link-sidebar-current;\n\n      position: relative;\n      z-index: 1;\n\n      span::before {\n        content: \"\";\n        width: 100%;\n        height: 100%;\n        position: absolute;\n        z-index: -1;\n        left: 0;\n        top: 0;\n      }\n    }\n\n    &:focus-visible {\n      z-index: 10; // keep focus ring on top (prevent the hover background of the next dropdown item from covering the ring)\n    }\n  }\n}\n\n// Font behavior on mobile\nbutton.version-switcher__button,\n.version-switcher__menu {\n  font-size: 1.1em; // A bit smaller than other menu font\n  z-index: $zindex-modal; // higher than the sidebars\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: unset;\n  }\n}\n","/* Collapsing of the TOC sidebar while scrolling */\n\n/* Nav: hide second level (shown on .active) */\n\nnav.page-toc {\n  // A little extra space before the buttons\n  margin-bottom: 1rem;\n}\n\n.bd-toc .nav {\n  .nav {\n    display: none;\n\n    // So we can manually specify a level as visible in the config\n    &.visible {\n      display: block;\n    }\n  }\n\n  > .active > ul {\n    display: block;\n  }\n}\n\n// Each entry of the in-page TOC\n.toc-entry {\n  display: block;\n\n  a > code {\n    color: var(--pst-color-text-muted);\n  }\n\n  a.nav-link {\n    display: block;\n    padding: 0.125rem 0;\n\n    // Padding w/ negative margin so the top TOC item highlight overlaps w/ the TOC border\n    padding-left: 1rem;\n    margin-left: -1rem;\n\n    @include link-sidebar;\n\n    &.active {\n      @include link-sidebar-current;\n\n      background-color: transparent;\n\n      &:hover {\n        color: var(--pst-color-link-hover);\n      }\n    }\n\n    &:focus-visible {\n      border-radius: $focus-ring-radius;\n    }\n  }\n}\n","div.versionadded,\ndiv.versionchanged,\ndiv.deprecated {\n  vertical-align: middle;\n  margin: 1.5625em auto;\n  padding: 0 0.6rem;\n  overflow: hidden;\n\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n\n  @include box-shadow;\n\n  position: relative;\n\n  > p {\n    margin-bottom: 0.6rem;\n    margin-top: 0.6rem;\n  }\n}\n\ndiv.versionadded {\n  border-color: var(--pst-color-success);\n  background-color: var(--pst-color-success-bg);\n}\n\ndiv.versionchanged {\n  border-color: var(--pst-color-warning);\n  background-color: var(--pst-color-warning-bg);\n}\n\ndiv.deprecated {\n  border-color: var(--pst-color-danger);\n  background-color: var(--pst-color-danger-bg);\n}\n\nspan.versionmodified {\n  font-weight: 600;\n\n  &::before {\n    margin-right: 0.6rem;\n    color: var(--pst-color-info);\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-versionmodified-default);\n  }\n}\n\nspan.versionmodified.added {\n  &::before {\n    color: var(--pst-color-success);\n    content: var(--pst-icon-versionmodified-added);\n  }\n}\n\nspan.versionmodified.changed {\n  &::before {\n    color: var(--pst-color-warning);\n    content: var(--pst-icon-versionmodified-changed);\n  }\n}\n\nspan.versionmodified.deprecated {\n  &::before {\n    color: var(--pst-color-danger);\n    content: var(--pst-icon-versionmodified-deprecated);\n  }\n}\n",".sidebar-indices-items {\n  display: flex;\n  flex-direction: column;\n  border-top: 1px solid var(--pst-color-border);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    border-top: none;\n  }\n\n  .sidebar-indices-items__title {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    font-size: var(--pst-sidebar-header-font-size);\n    color: var(--pst-color-text-base);\n    margin-bottom: 0.5rem;\n  }\n\n  ul.indices-link {\n    margin-right: -1rem;\n    list-style: none;\n    padding: 0;\n\n    li > a {\n      display: block;\n      padding: 0.25rem 0;\n      color: var(--pst-color-text-muted);\n\n      &:hover {\n        color: var(--pst-color-primary);\n        text-decoration: none;\n        background-color: transparent;\n      }\n    }\n  }\n}\n",".bd-sidebar-primary div#rtd-footer-container {\n  position: sticky;\n  bottom: -1rem;\n  margin: -1rem; // ignore sidebar padding\n\n  .rst-versions.rst-badge {\n    position: unset;\n    font-size: 0.9em;\n    font-family: var(--pst-font-family-base);\n    max-width: unset;\n\n    .rst-current-version {\n      display: flex;\n      align-items: center;\n      gap: 0.2rem;\n      height: 2.5rem;\n      transition: background-color 0.2s ease-out;\n      background-color: var(--pst-color-background);\n      color: var(--pst-color-success);\n      border-top: 1px solid var(--pst-color-border);\n    }\n\n    .fa.fa-book {\n      color: var(--pst-color-text-muted);\n      margin-right: auto;\n\n      &::after {\n        color: var(--pst-color-text-base);\n        content: \"Read The Docs\";\n        font-family: var(--pst-font-family-base);\n        font-weight: var(--pst-admonition-font-weight-heading);\n      }\n    }\n\n    .fa.fa-caret-down {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  .rst-versions.rst-badge.shift-up {\n    .rst-current-version {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n  }\n\n  .rst-other-versions {\n    background-color: var(--pst-color-surface);\n    color: var(--pst-color-text-base);\n\n    dl {\n      dd a {\n        color: var(--pst-color-text-muted);\n      }\n    }\n\n    hr {\n      background-color: var(--pst-color-border);\n    }\n\n    small a {\n      color: var(--pst-color-link);\n    }\n\n    input {\n      padding-left: 0.5rem;\n      border: 1px solid var(--pst-color-border);\n      background-color: var(--pst-color-surface);\n    }\n  }\n}\n","/**\n * Admonitions and blocks of styled content.\n * Admonitions CSS originally inspired by https://squidfunk.github.io/mkdocs-material/getting-started/\n */\n$admonition-border-radius: 0.25rem;\n$admonition-left-border-width: 0.2rem;\n\ndiv.admonition,\n.admonition {\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0.8rem;\n  overflow: hidden;\n\n  /* break-inside has replaced page-break-inside and is widely usable since 2019 */\n  page-break-inside: avoid;\n  break-inside: avoid;\n  border-left: $admonition-left-border-width solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n\n  @include box-shadow;\n\n  // Last item should have no spacing since we'll control that w/ padding\n  *:last-child {\n    margin-bottom: 0;\n  }\n\n  // Items after the title should be indented\n  p.admonition-title ~ * {\n    margin-left: 1.4rem;\n    margin-right: 1.4rem;\n  }\n\n  // Lists need to have left margin so they don't spill into it\n  > ol,\n  > ul {\n    margin-left: 1em;\n  }\n\n  // Defaults for all admonitions\n  > .admonition-title {\n    margin: 0 -0.6rem;\n    padding: 0.4rem 0.6rem 0.4rem 2rem;\n    font-weight: var(--pst-admonition-font-weight-heading);\n    position: relative;\n\n    @include legacy-backdrop-placeholder;\n\n    background-color: var(--pst-color-info-bg);\n\n    // now that we use solid colors we want the title on top\n    z-index: 1;\n\n    &::after {\n      position: absolute;\n      left: 0.5rem;\n      width: 1rem;\n      height: 1rem;\n      color: var(--pst-color-info);\n      font: var(--fa-font-solid);\n      line-height: inherit;\n      content: var(--pst-icon-admonition-default);\n      opacity: 1;\n    }\n\n    // Next element after title needs some extra upper-space\n    + * {\n      margin-top: 0.4em;\n    }\n  }\n\n  &.attention {\n    border-color: var(--pst-color-attention);\n\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &::after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-attention);\n      }\n    }\n  }\n\n  &.caution {\n    border-color: var(--pst-color-warning);\n\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &::after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-caution);\n      }\n    }\n  }\n\n  &.warning {\n    border-color: var(--pst-color-warning);\n\n    > .admonition-title {\n      background-color: var(--pst-color-warning-bg);\n\n      &::after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-warning);\n      }\n    }\n  }\n\n  &.danger {\n    border-color: var(--pst-color-danger);\n\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &::after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-danger);\n      }\n    }\n  }\n\n  &.error {\n    border-color: var(--pst-color-danger);\n\n    > .admonition-title {\n      background-color: var(--pst-color-danger-bg);\n\n      &::after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-error);\n      }\n    }\n  }\n\n  &.hint {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-hint);\n      }\n    }\n  }\n\n  &.tip {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-tip);\n      }\n    }\n  }\n\n  &.important {\n    border-color: var(--pst-color-attention);\n\n    > .admonition-title {\n      background-color: var(--pst-color-attention-bg);\n\n      &::after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-important);\n      }\n    }\n  }\n\n  &.note {\n    border-color: var(--pst-color-info);\n\n    > .admonition-title {\n      background-color: var(--pst-color-info-bg);\n\n      &::after {\n        color: var(--pst-color-info);\n        content: var(--pst-icon-admonition-note);\n      }\n    }\n  }\n\n  &.seealso {\n    border-color: var(--pst-color-success);\n\n    > .admonition-title {\n      background-color: var(--pst-color-success-bg);\n\n      &::after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-seealso);\n      }\n    }\n  }\n\n  &.admonition-todo {\n    border-color: var(--pst-color-secondary);\n\n    > .admonition-title {\n      background-color: var(--pst-color-secondary-bg);\n\n      &::after {\n        color: var(--pst-color-secondary);\n        content: var(--pst-icon-admonition-todo);\n      }\n    }\n  }\n\n  /**\n   * Special-case for a `sidebar` class that makes the admonition float to\n   * the right like the {sidebar} directive.\n   */\n  &.sidebar {\n    max-width: 40%;\n    float: right;\n    clear: both;\n    margin-left: 0.5rem;\n    margin-top: 0;\n\n    // Undo the .sidebar directive border\n    border-width: 0 0 0 $admonition-left-border-width;\n\n    // TODO: these semantic-color-names border-color rules might no longer be\n    //       needed when we drop support for Sphinx 4 / docutils 0.17\n    &.attention,\n    &.important {\n      border-color: var(--pst-color-attention);\n    }\n\n    &.caution,\n    &.warning {\n      border-color: var(--pst-color-warning);\n    }\n\n    &.danger,\n    &.error {\n      border-color: var(--pst-color-danger);\n    }\n\n    &.hint,\n    &.tip,\n    &.seealso {\n      border-color: var(--pst-color-success);\n    }\n\n    &.note,\n    &.todo {\n      border-color: var(--pst-color-info);\n    }\n\n    // No inner margin since we have less horizontal space w/ the sidebar\n    p.admonition-title ~ * {\n      margin-left: 0;\n      margin-right: 0;\n    }\n  }\n}\n\n/**************************************************************\n * Similar content blocks that are not technically admonitions.\n */\n\n/**\n * Topics and the {contents} directive\n */\n// Docutils <= 0.17\ndiv.topic,\ndiv.topic.contents,\n// Docutils >= 0.18\nnav.contents,\naside.topic {\n  display: flex;\n  flex-direction: column;\n  background-color: var(--pst-color-surface);\n  border-color: var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n  padding: 1rem 1.25rem;\n\n  @include box-shadow;\n\n  .topic-title {\n    margin: 0 0 0.5rem;\n  }\n\n  // Over-ride text color to ensure enough contrast\n  p {\n    color: var(--pst-color-on-surface) !important;\n  }\n\n  // Over-ride large default padding\n  ul.simple {\n    padding-left: 1rem;\n\n    ul {\n      // So that sub-lists will have a bit less padding\n      padding-left: 2em;\n    }\n  }\n}\n\n/**\n * Sidebar directive\n */\naside.sidebar {\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n  border-radius: $admonition-border-radius;\n\n  // to match the admonition-styled sidebars:\n  margin-left: 0.5rem;\n  padding: 0;\n\n  > *:last-child {\n    padding-bottom: 1rem;\n  }\n\n  p.sidebar-title {\n    position: relative;\n    margin-bottom: 0;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n    border-bottom: 1px solid var(--pst-color-border);\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-admonition-font-weight-heading);\n  }\n\n  // Add margin to the first non-`.sidebar-title` item\n  > *:not(.sidebar-title):first-child,\n  > p.sidebar-title + * {\n    margin-top: 1rem;\n  }\n\n  > * {\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n}\n\n/**\n * Rubrics look kind of like section headers\n */\np.rubric {\n  display: flex;\n  flex-direction: column;\n}\n\n/**\n * Seealso is kind of like a vertically-collapsed admonition\n */\n.seealso dd {\n  margin-top: 0;\n  margin-bottom: 0;\n}\n","/**\n * Miscellaneous color functions and mixins\n**/\n\n@use \"sass:list\";\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:math\";\n@use \"sass:string\";\n\n// We must add ::before pseudo-element to some theme components (such as admonitions)\n// because users were instructed to customize the background color this way.\n@mixin legacy-backdrop-placeholder {\n  &::before {\n    content: \"\";\n    width: 100%;\n    height: 100%;\n    position: absolute;\n    left: 0;\n    top: 0;\n    z-index: -1;\n\n    // So that hovering over the text within background is still possible.\n    // Otherwise the background overlays the text and you cannot click or select easily.\n    // ref: https://developer.mozilla.org/en-US/docs/Web/CSS/pointer-events\n    pointer-events: none;\n  }\n}\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetc\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map.get($map, $key);\n  }\n\n  @return $map;\n}\n\n/**\n  * Function to check if the color needs converting to a \"color\" type\n  * if it is a string we cannot use other color manipulation functions\n  * It is used to create the sphinx-design colours as these are often interpolated\n  */\n// @param {String/Color} $color - Color definition from map\n// @return {Color} - Color type (in hex)\n@function check-color($color) {\n  @if meta.type-of($color) == string {\n    $color: from-hex($color);\n  }\n\n  @return $color;\n}\n\n/**\n  * Function to convert the string representation of a color to a color type (hex)\n  */\n// @param {String} $string - String representation of a color\n@function from-hex($string) {\n  $string-lower: string.to-lower-case($string);\n  $r: \"\";\n  $g: \"\";\n  $b: \"\";\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $length: string.length($string);\n  $max: if($length == 4, 1, 2);\n\n  // Check for length accuracy\n  @if $length != 4 and $length != 7 {\n    @return $string;\n  }\n\n  // Loop from the second character (omitting #)\n  @for $i from 2 through $length {\n    $c: string.slice($string-lower, $i, $i);\n\n    // If wrong character, return\n    @if not list.index($hex, $c) {\n      @return $string;\n    }\n\n    @if string.length($r) < $max {\n      $r: $r + $c;\n    } @else if string.length($g) < $max {\n      $g: $g + $c;\n    } @else if string.length($b) < $max {\n      $b: $b + $c;\n    }\n  }\n\n  @if $length == 4 {\n    $r: $r + $r;\n    $g: $g + $g;\n    $b: $b + $b;\n  }\n\n  @return rgb(hex-to-dec($r), hex-to-dec($g), hex-to-dec($b));\n}\n\n@function hex-to-dec($string) {\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $string: string.to-lower-case($string);\n  $length: string.length($string);\n  $dec: 0;\n\n  @for $i from 1 through $length {\n    $factor: 1 + (15 * ($length - $i));\n    $index: list.index($hex, string.slice($string, $i, $i));\n    $dec: $dec + $factor * ($index - 1);\n  }\n\n  @return $dec;\n}\n","// Style API docs from sphinx' autodoc / autosummary\n\n/*******************************************************************************\n* Styling for field lists\n*/\n\n/* grey highlighting of 'parameter' and 'returns' field */\ntable.field-list {\n  border-collapse: separate;\n  border-spacing: 10px;\n  margin-left: 1px;\n\n  th.field-name {\n    padding: 1px 8px 1px 5px;\n    white-space: nowrap;\n    background-color: var(--pst-color-surface);\n  }\n\n  /* italic font for parameter types */\n  td.field-body {\n    p {\n      font-style: italic;\n\n      > strong {\n        font-style: normal;\n      }\n    }\n\n    /* reduced space around parameter description */\n    blockquote {\n      border-left: none;\n      margin: 0 0 0.3em;\n      padding-left: 30px;\n    }\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary tables\n*/\n\n.table.autosummary {\n  // The first column (with the signature) should not wrap\n  td:first-child {\n    white-space: nowrap;\n  }\n}\n\n/* overriding basic.css to use our own monospace font */\n.sig {\n  font-family: var(--pst-font-family-monospace);\n}\n\n/* C++ specific styling - overriding the basic.css to avoid custom colors */\n\n.sig-inline.c-texpr,\n.sig-inline.cpp-texpr {\n  font-family: unset;\n}\n\n.sig.c .k,\n.sig.c .kt,\n.sig.cpp .k,\n.sig.cpp .kt {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .m,\n.sig.cpp .m {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .s,\n.sig.c .sc,\n.sig.cpp .s,\n.sig.cpp .sc {\n  color: var(--pst-color-text-base);\n}\n\n// addition\n\n// .sig.c .sig-name .n,\n// .sig.cpp .sig-name .n {\n//   color: var(--pst-color-inline-code);\n// }\n\n.sig-name {\n  color: var(--pst-color-inline-code);\n}\n\n.sig-param .o,\n.sig-param .default_value {\n  color: var(--pst-color-text-muted);\n  font-weight: normal;\n}\n\n// change target color for dark theme\ndt:target,\nspan.highlighted {\n  background-color: var(--pst-color-target);\n}\n\n.viewcode-back {\n  font-family: var(--pst-font-family-base);\n}\n\n.viewcode-block:target {\n  border-top: 1px solid var(--pst-color-border);\n  border-bottom: 1px solid var(--pst-color-border);\n  position: relative;\n  background-color: var(--pst-color-target);\n}\n\ndl > dt > a:has(.viewcode-link) {\n  // Sphinx applies a `float:right` rule to the .viewcode-line span, which\n  // exposes a browser glitch in the focus ring. It seems the browser creates\n  // two separate boxes, an empty box where the anchor element gets laid out and\n  // then another box around the anchor's contents that have been floated right.\n  // Firefox draws the focus ring around the empty anchor element box. Chrome\n  // draws two focus rings: one around the empty anchor and one around the\n  // floated-right element. To fix the glitch, we apply the float rule on the\n  // parent rather than the child.\n  float: right;\n\n  .viewcode-link {\n    float: none;\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary titles like \"parameters\" and \"returns\"\n*/\n\n// the API selector\n// from https://github.com/pradyunsg/furo/blob/main/src/furo/assets/styles/content/_api.sass#L6)\ndl[class]:not(.option-list, .field-list, .footnote, .glossary, .simple) {\n  // increase margin bottom after the dl elements\n  margin-bottom: 3rem;\n\n  dd {\n    margin-left: 2rem;\n\n    // Fix until this will be solved to Sphinx https://github.com/sphinx-doc/sphinx/issues/10815\n    & > dl.simple > dt {\n      display: flex;\n    }\n  }\n\n  dl.field-list {\n    display: grid;\n    grid-template-columns: unset;\n  }\n\n  dt.field-odd,\n  dt.field-even {\n    margin-top: 0.2rem;\n    margin-bottom: 0.1rem;\n    background-color: var(--pst-color-surface);\n  }\n}\n","/**\n * Code block styling\n * Note that we inherit a lot of styling from Bootstrap so not many rules here.\n */\n\n// General code block behavior\n// Unset bootstrap behavior\ndiv[class*=\"highlight-\"],\ndiv.highlight,\ndiv.literal-block-wrapper {\n  display: flex;\n  flex-direction: column;\n  width: unset;\n  border-radius: $admonition-border-radius;\n  break-inside: avoid;\n}\n\n// Code blocks with captions\n// There's a wrapper when the code block has a title\ndiv.literal-block-wrapper {\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  // This is where the title goes\n  div.code-block-caption {\n    margin: 0;\n    border-bottom: 1px solid var(--pst-color-border);\n    padding: 0.5rem;\n    font-size: 1rem;\n    font-weight: var(--pst-font-weight-caption);\n\n    a.headerlink {\n      font-size: inherit;\n    }\n  }\n\n  // Remove the upper border radius since we want it to connect with the title\n  // Remove the box shadow so the wrapper gets the shadow instead\n  div[class*=\"highlight-\"] {\n    margin: 0;\n    border-radius: 0;\n\n    pre {\n      border: none;\n      box-shadow: none;\n    }\n  }\n}\n\n/**\n * In-line code\n */\ncode.literal {\n  padding: 0.1rem 0.25rem;\n  background-color: var(--pst-color-surface);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\n\na > code {\n  color: var(--pst-color-inline-code-links);\n}\n\n// Fix for Sphinx's \"highlight\" directive - this is an issue with our accessible pygments theme\n// and the colour we are using for the background of the code blocks.\nhtml[data-theme=\"light\"] .highlight .nf {\n  color: #0078a1 !important;\n}\n\n// Minimum opacity needed for linenos to be WCAG AA conformant\nspan.linenos {\n  opacity: 0.8 !important;\n}\n","figure > a,\nfigure > a > img,\nfigure > img,\nfigure > video {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n\nfigure {\n  a.headerlink {\n    // So that header link doesn't push caption to be off-center.\n    position: absolute;\n    font-size: inherit;\n  }\n\n  // Default headerlink hover doesn't trigger on figures\n  &:hover a.headerlink {\n    visibility: visible;\n  }\n\n  figcaption {\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-font-weight-caption);\n    color: var(--pst-color-text-muted);\n    margin-left: auto;\n    margin-right: auto;\n    margin-top: 0.3rem;\n    text-align: center;\n\n    & > p:last-child {\n      // Don't add extra margin to already existing figure bottom margin\n      margin-bottom: 0;\n    }\n\n    p {\n      text-align: start;\n      display: inline-block;\n    }\n\n    table.table {\n      width: fit-content;\n      margin-left: auto;\n      margin-right: auto;\n    }\n  }\n}\n","// For consistency, add bracket around footnotes/citations which are\n// cited more than once. E.g. [Newton](1,2) instead of Newton(1,2)\ndt.label > span.brackets:not(:only-child)::before {\n  content: \"[\";\n}\n\ndt.label > span.brackets:not(:only-child)::after {\n  content: \"]\";\n}\n\n// Make footnote as a superscript\na.footnote-reference {\n  vertical-align: super;\n  font-size: small;\n}\n\n// Docutils 0.18 uses an `aside.footnote` container with different internal structure\naside.footnote {\n  margin-bottom: 0.5rem;\n\n  &:last-child {\n    margin-bottom: 1rem;\n  }\n\n  span.label,\n  span.backrefs {\n    font-weight: bold;\n  }\n\n  &:target {\n    background-color: var(--pst-color-target);\n  }\n}\n","/**\n * Hacky fixes that don't fit cleanly into other sections\n */\n\n// Ensure user highlighting/selecting behaves properly\n// From https://stackoverflow.com/a/34372191\ntable.highlighttable td.linenos,\nspan.linenos,\ndiv.doctest > div.highlight span.gp {\n  /* gp: Generic.Prompt */\n  user-select: none;\n}\n","// Override bootstrap by restoring the basic theme default.\ndd {\n  margin-top: 3px;\n  margin-bottom: 10px;\n  margin-left: 30px;\n}\n\nol,\nul {\n  padding-inline-start: 2rem;\n\n  li > p:first-child {\n    margin-bottom: 0.25rem;\n    margin-top: 0.25rem;\n  }\n}\n","// GitHub blockquote style\nblockquote {\n  padding: 1em;\n  color: var(--pst-color-text-muted);\n  border-left: 0.25em solid var(--pst-color-blockquote-notch);\n  border-radius: $admonition-border-radius;\n  position: relative;\n\n  p {\n    color: var(--pst-color-text-base);\n  }\n\n  // remove padding from included line-block to avoid duplication\n  .line-block {\n    margin: 0;\n  }\n\n  // remove margin bottom for the last p\n  p:last-child {\n    margin-bottom: 0;\n  }\n\n  @include legacy-backdrop-placeholder;\n\n  background-color: var(--pst-color-surface);\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n\n  // hack to make the text in the blockquote selectable\n  &::before {\n    z-index: -1;\n  }\n}\n","/**\n * Span-level styling within content\n */\n\nspan.guilabel {\n  border: 1px solid var(--pst-color-info);\n  font-size: 80%;\n  font-weight: 700;\n  border-radius: 4px;\n  padding: 2.4px 6px;\n  margin: auto 2px;\n  position: relative;\n\n  @include legacy-backdrop-placeholder;\n\n  background-color: var(--pst-color-info-bg);\n}\n\na.reference.download::before {\n  content: var(--pst-icon-download);\n  font: var(--fa-font-solid);\n  font-size: 0.8em;\n  padding: 0 0.25em;\n  color: var(--pst-color-text-muted);\n}\n","/**\n * Tables\n */\n\n// ensure table will fit in the article width and make them y-scrollable\ntable {\n  @include table-colors;\n\n  // default to table-center\n  margin-left: auto;\n  margin-right: auto;\n\n  &.table-right {\n    margin-right: 0;\n  }\n\n  &.table-left {\n    margin-left: 0;\n  }\n}\n\n// customize table caption from bootstrap\n// to display them on top and centered\ntable caption {\n  text-align: center;\n  caption-side: top;\n  color: var(--pst-color-text-muted);\n}\n\n// MyST Markdown tables use these classes to control alignment\nth,\ntd {\n  &.text-left {\n    text-align: left;\n  }\n\n  &.text-right {\n    text-align: right;\n  }\n\n  &.text-center {\n    text-align: center;\n  }\n}\n\n// override bootstrap table colors\n.table {\n  @include table-colors;\n\n  --bs-table-bg: transparent; // background\n  --bs-table-color: var(\n    --pst-color-text-base\n  ); // ensure text and bullets are visible\n}\n\n.pst-scrollable-table-container {\n  // Put a horizontal scrollbar just below tables that are too wide to fit\n  // within the main column\n  overflow-x: auto;\n}\n","/**\n * Style the toctree component in pages (avoid modifying the navbars)\n */\n.toctree-wrapper {\n  p.caption {\n    font-size: 1.5em;\n    margin-bottom: 0;\n  }\n\n  & > ul {\n    padding-left: 0;\n  }\n\n  li[class^=\"toctree-l\"] {\n    list-style: none;\n    margin-bottom: 0.2em;\n\n    & > a {\n      list-style: none;\n      font-size: 1.1em;\n    }\n\n    & > ul {\n      list-style: none;\n      padding-inline-start: 1.5em;\n    }\n  }\n\n  // slightly bigger font for l1\n  .toctree-l1 > a {\n    font-size: 1.3em;\n  }\n}\n\ndiv.topic.contents,  // Docutils <= 0.17\nnav.contents  // Docutils >= 0.18\n{\n  // Style similarly to toctree\n  ul.simple {\n    list-style: none;\n    padding-left: 0;\n  }\n}\n","/**\n * Mathematics via MathJax.\n *\n * This is designed for MathJax v3\n * ref: https://www.sphinx-doc.org/en/master/usage/extensions/math.html#module-sphinx.ext.mathjax\n */\n\n// Applies to all math elements\nspan.math,\ndiv.math {\n  align-items: center;\n  display: flex;\n  max-width: 100%;\n\n  // This will be over-ridden for the y-direction and divs\n  overflow: hidden;\n}\n\n// Inline-only\nspan.math {\n  display: inline-flex;\n}\n\n// Block-level only\ndiv.math {\n  gap: 0.5em;\n\n  // So that the eqno shows up after the equation\n  flex-direction: row-reverse;\n\n  // The equation number / link\n  span.eqno a.headerlink {\n    position: relative;\n    font-size: 1em;\n  }\n\n  // The math container\n  mjx-container {\n    flex-grow: 1;\n    padding-bottom: 0.2rem;\n    overflow: auto;\n\n    // Set height to 0 so that it does not cause scrollbars to appear\n    // ref: https://github.com/mathjax/MathJax/issues/2521\n    mjx-assistive-mml {\n      height: 0;\n    }\n  }\n}\n","/**\n * ABlog\n * ref: https://ablog.readthedocs.io/\n */\n\n/**\n * Sidebar template components\n */\n.ablog-sidebar-item {\n  h2,\n  h3 {\n    font-size: var(--pst-sidebar-header-font-size);\n\n    // Remove unnecessary vertical whitespace\n    margin-top: 0.5rem;\n\n    // The headers are all links, but this makes them hard to parse\n    // So we change the colors to make them look like headers\n    a {\n      color: var(--pst-color-text-base);\n    }\n  }\n\n  ul {\n    // No bullet points for the primary sidebar items\n    list-style: none;\n    padding-left: 0;\n\n    // Otherwise a scrollbar randomly shows up\n    overflow-y: hidden;\n\n    // List of recent post items\n    display: flex;\n    flex-direction: column;\n    gap: 0.5em;\n    margin-bottom: 0;\n\n    // The ablog cloud should move horizontally\n    &.ablog-cloud {\n      flex-flow: row wrap;\n      gap: 0.5rem;\n\n      // Vertical-align tag clouds\n      li {\n        // Center the tag cloud items\n        display: flex;\n        align-items: center;\n      }\n    }\n  }\n}\n\n/**\n * Previous / next buttons at the bottom\n */\n.ablog__prev-next {\n  font-size: 1.2em;\n  display: flex;\n  padding: 1rem 0;\n\n  // The bottom previous / next arrows\n  > span {\n    // To ensure that the whole thing fits on one line even if there are long titles\n    display: flex;\n    max-width: 45%;\n\n    // Links within each span have the collection of icon + text\n    a {\n      display: flex;\n      align-items: center;\n      margin-left: auto;\n      gap: 1rem;\n      line-height: 1.5rem;\n\n      i::before {\n        color: var(--pst-color-text-base);\n      }\n    }\n  }\n\n  // The first span is for the previous page and aligns to the left\n  span.ablog__prev {\n    i.fa-arrow-circle-left::before {\n      content: var(--pst-icon-angle-left);\n    }\n  }\n\n  // The second span is just an empty space so we remove it because we're\n  // positioning with flex\n  span.ablog__spacer {\n    display: none;\n  }\n\n  // The third span is aligned to the right\n  span.ablog__next {\n    margin-left: auto;\n    text-align: right;\n\n    i.fa-arrow-circle-right::before {\n      content: var(--pst-icon-angle-right);\n    }\n  }\n}\n\n/**\n * {postlist} directive and posts page\n */\n.ablog__collection,\n.postlist {\n  padding-left: 0;\n\n  .ablog-post {\n    list-style: none;\n\n    // Post metadata tags (author, links ,etc) should be a bit smaller\n    .ablog-archive {\n      display: flex;\n      flex-flow: row wrap;\n      gap: 1rem;\n      list-style: none;\n      font-size: 0.75rem;\n      padding-left: 0;\n    }\n\n    // Title line should be a bit bigger and bold to stand out\n    .ablog-post-title {\n      margin-top: 0;\n      font-size: 1.25rem;\n\n      a {\n        font-weight: bold;\n      }\n    }\n\n    // Read more button should be a bit bigger\n    .ablog-post-expand {\n      margin-bottom: 0.5rem;\n    }\n  }\n}\n","/**\n * Special cases for Bootstrap functionality\n */\n\n// Bootstrap adds margin to their general container class. However, sphinx/docutils\n// can also generate output with the container class, but in those cases we should\n// not add the margin from bootstrap. Same for max-width.\n.docutils.container {\n  padding-left: unset;\n  padding-right: unset;\n  margin-left: unset;\n  margin-right: unset;\n  max-width: unset;\n  width: unset;\n}\n\n.btn {\n  --bs-btn-focus-box-shadow: #{$btn-focus-box-shadow};\n}\n","/**\n * Sphinx Copybutton\n * ref: https://sphinx-copybutton.readthedocs.io/\n */\n\ndiv.highlight button.copybtn {\n  // Nicer spacing\n  display: flex;\n  align-items: center;\n  justify-content: center;\n\n  // Don't over-ride the success color\n  &:not(.success) {\n    color: var(--pst-color-muted);\n  }\n\n  border: none;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    &:not(.success) {\n      color: var(--pst-color-text);\n      background-color: var(--pst-color-shadow);\n    }\n  }\n\n  // Tooltip styling\n  &.o-tooltip--left::after {\n    color: var(--pst-color-text);\n    background-color: var(--pst-color-surface);\n  }\n\n  &:focus {\n    // For keyboard users, make the copy button visible when focussed.\n    opacity: 1;\n  }\n\n  &:focus-visible {\n    outline: $focus-ring-outline;\n  }\n}\n\ndiv.highlight:has(button.copybtn) {\n  // Make sure the code block has enough height for the copy button.\n  // Sphinx-copybutton sets 0.3em top offset plus 1.7em height:\n  // https://github.com/executablebooks/sphinx-copybutton/blob/master/sphinx_copybutton/_static/copybutton.css\n  min-height: 2em;\n}\n","// adapt ethical ad to the theme\n#ethical-ad-placement {\n  .ethical-sidebar a,\n  .ethical-sidebar a:visited,\n  .ethical-sidebar a:hover,\n  .ethical-sidebar a:active,\n  .ethical-footer a,\n  .ethical-footer a:visited,\n  .ethical-footer a:hover,\n  .ethical-footer a:active {\n    color: var(--pst-color-text-base);\n  }\n\n  .ethical-sidebar,\n  .ethical-footer {\n    background-color: var(--pst-color-background);\n    border: 1px solid var(--pst-color-border);\n    border-radius: 5px;\n    color: var(--pst-color-text-base);\n    font-size: 14px;\n    line-height: 20px;\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/******************************************************************************\n * Jupyter Sphinx\n */\n\n.bd-content div.jupyter_container {\n  // We don't want borders around the whole container, just around code cells\n  border: none;\n  background-color: unset;\n  box-shadow: none;\n\n  // Code cells should have the same style as our other code objects\n  div.output,\n  div.highlight {\n    border-radius: 0.25rem;\n  }\n\n  div.highlight {\n    background-color: var(--pst-color-surface);\n  }\n\n  // Ensure the style is the same as our code cells. Jupyter Sphinx makes it tiny.\n  .cell_input,\n  .cell_output {\n    border-radius: 0.25rem;\n\n    pre {\n      padding: 1rem;\n    }\n  }\n}\n","/**\n * Special-cases for packages in the PyData ecosystem\n */\n\n// xarray output display in bootstrap\n.xr-wrap[hidden] {\n  display: block !important;\n}\n\n// ipywidgets\n.jp-OutputArea-output.lm-Widget {\n  // override overflow:hidden rule from Lumino (.lm-Widget) to allow scrolling\n  overflow: auto;\n}\n","/*******************************************************************************\n * Special-cases for the sphinx-design library, mainly to make it compatible\n * with the dark/light themes of pydata-sphinx-theme.\n *\n * NOTE: sphinx-design uses !important quite liberally, so here we must do the\n * same for our overrides to have any effect.\n */\n@use \"../variables/color\" as pst-color;\n@use \"sass:color\";\n@use \"sass:map\";\n@use \"sass:meta\";\n@use \"sass:string\";\n\n/*******************************************************************************\n  * Color and variables\n  *\n  * This is a list of the semantic color names from sphinx-design (we only\n  * need to override variables that sphinx-design has actually defined).\n  * https://github.com/executablebooks/sphinx-design/blob/9226a12a/style/_colors.scss#L31-L43\n  */\n$sd-semantic-color-names: (\n  \"primary\",\n  \"secondary\",\n  \"success\",\n  \"info\",\n  \"warning\",\n  \"danger\",\n  \"light\",\n  \"muted\",\n  \"dark\",\n  \"black\",\n  \"white\"\n);\n\n/**\n  * Here we create some extra --pst-color-* variables and use\n  * them to override the value of the corresponding sphinx-design variables.\n  * This is easier than re-writing the sphinx-design rules. Even easier would be\n  * directly assigning our values to the --sd-color-* variables, but then our\n  * downstream users couldn't override *our* colors and have it affect buttons\n  * and badges.\n  *\n  * First, define the extra keys needed to cover the full range of semantic\n  * color names used in sphinx-design, then merge them with the names we\n  * already define for our own needs.\n  * see https://sphinx-design.readthedocs.io/en/latest/css_variables.html\n  */\n$extra-semantic-colors: (\n  \"white\": $foundation-white,\n  \"light\": (\n    light: $foundation-light-gray,\n    bg-light: color.scale($foundation-light-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-light-gray, $lightness: -30%),\n  ),\n  \"muted\": (\n    light: $foundation-muted-gray,\n    bg-light: color.scale($foundation-muted-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-muted-gray, $lightness: -30%),\n  ),\n  \"dark\": $foundation-dark-gray,\n  \"black\": $foundation-black,\n);\n$all-colors: map.merge($pst-semantic-colors, $extra-semantic-colors);\n\n@mixin create-sd-colors($value, $name) {\n  // define the pst variables, so that downstream user overrides will work\n  --pst-color-#{$name}: #{$value};\n\n  // we are now using a11y-combination to calculate the text color - this is based\n  // on the WCAG color contrast guidelines\n  --pst-color-#{$name}-text: #{a11y-combination($value)};\n\n  // TODO: highlight seems to be used for buttons @trallard to fix on a11y follow-up work\n  --pst-color-#{$name}-highlight: #{color.adjust($value, $lightness: -15%)};\n\n  // override the sphinx-design variables\n  --sd-color-#{$name}: var(--pst-color-#{$name});\n  --sd-color-#{$name}-text: var(--pst-color-#{$name}-text);\n\n  // TODO: highlight seems to be used for buttons @trallard to fix on a11y follow-up work\n  --sd-color-#{$name}-highlight: var(--pst-color-#{$name}-highlight);\n}\n\n// Now we override the --sd-color-* variables.\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    // check if this color is defined differently for light/dark\n    @each $name in $sd-semantic-color-names {\n      $definition: map.get($all-colors, $name);\n\n      @if meta.type-of($definition) == map {\n        @each $key, $value in $definition {\n          @if string.index($key, $mode) {\n            // since now we define the bg colours in the semantic colours and not\n            // by changing opacity, we need to check if the key contains bg and the\n            // correct mode (light/dark)\n            @if string.index($key, \"bg\") {\n              --sd-color-#{$name}-bg: #{$value};\n\n              // create local variable\n              $value: check-color($value);\n\n              --sd-color-#{$name}-bg-text: #{a11y-combination($value)};\n            } @else {\n              $value: check-color($value);\n\n              @include create-sd-colors($value, $name);\n            }\n          }\n        }\n      } @else {\n        $value: map.get($all-colors, $name);\n\n        @include create-sd-colors($value, $name);\n      }\n    }\n  }\n}\n\n// Make sure the color border variables are set using our variables\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    --sd-color-card-border: var(--pst-color-border);\n  }\n}\n\n/*******************************************************************************\n  * shadows\n  */\nhtml[data-theme=\"light\"] {\n  .sd-shadow-xs,\n  .sd-shadow-sm,\n  .sd-shadow-md,\n  .sd-shadow-lg {\n    @include box-shadow;\n  }\n}\n\n/*******************************************************************************\n  * cards\n  */\n\n.bd-content .sd-card {\n  border: 1px solid var(--pst-color-border);\n\n  // TODO - --pst-color-panel-background is not defined... where is this coming from?\n  .sd-card-header {\n    background-color: var(--pst-color-panel-background);\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-footer {\n    background-color: var(--pst-color-panel-background);\n    border-top: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-body {\n    background-color: var(--pst-color-panel-background);\n  }\n\n  // Focus ring for link-cards\n  .sd-stretched-link:focus-visible {\n    // Don't put the focus ring on the <a> element (it has zero height in Sphinx Design cards)\n    outline: none;\n\n    // Put the focus ring on the <a> element's ::after pseudo-element\n    &::after {\n      outline: $focus-ring-outline;\n      border-radius: 0.25rem; // copied from Sphinx Design CSS for .sd-card\n    }\n  }\n\n  &.sd-card-hover:hover {\n    border-color: var(--pst-color-link-hover);\n  }\n}\n\n/*******************************************************************************\n  * tabs\n  */\n\n.bd-content .sd-tab-set {\n  > input {\n    // Active tab label\n    &:checked + label {\n      border-style: solid solid none;\n      border-color: var(--pst-color-primary) var(--pst-color-primary)\n        transparent; // top LR bottom\n\n      border-width: 0.125rem 0.125rem 0;\n      border-radius: 0.125rem 0.125rem 0 0;\n      background-color: var(--pst-color-on-background);\n      transform: translateY(0.125rem);\n      color: var(--pst-color-primary);\n    }\n\n    &:focus-visible + label {\n      border: 0.125rem solid var(--pst-color-accent);\n      border-radius: 0.125rem;\n      background-color: var(--pst-color-accent-bg);\n      color: var(--pst-color-on-surface);\n    }\n\n    // Hover label\n    &:not(:checked, :focus-visible) + label:hover {\n      border-color: transparent;\n      color: var(--pst-color-secondary);\n    }\n  }\n\n  // Tab label\n  > label {\n    color: var(--pst-color-on-surface);\n    border: 0.125rem solid transparent;\n    border-radius: 0.125rem 0.125rem 0 0;\n    background-color: var(--pst-color-surface);\n    padding: 0 0.75em;\n    margin-inline-end: 0.25rem;\n    line-height: 1.95;\n\n    html[data-theme=\"dark\"] & {\n      background-color: var(--pst-color-on-background);\n    }\n  }\n\n  // panel\n  .sd-tab-content {\n    border: 0.125rem solid var(--pst-color-primary);\n    border-radius: 0.1875rem;\n    box-shadow: unset;\n    padding: 0.625rem;\n    background-color: var(--pst-color-on-background);\n  }\n}\n\n/*******************************************************************************\n* Dropdowns\n*/\n\ndetails.sd-dropdown {\n  // Remove all borders to over-ride SD behavior, and we'll add our own later\n  border: 0 !important;\n\n  // Drop shadow should behave same as admonitions\n  @include box-shadow;\n\n  // Header is where the \"clickable\" box goes\n  summary.sd-card-header {\n    border: 0 !important;\n    display: flex;\n    align-items: center;\n    position: relative; // So background color works\n    font-weight: 600;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n\n    // Set a variable that we can re-use for colors later\n    // We must set this in the current and content sibling container\n    // so that it is defined in both places\n    --pst-sd-dropdown-color: var(--pst-gray-500);\n    --pst-sd-dropdown-bg-color: var(--pst-color-surface);\n\n    & + div.sd-summary-content {\n      border: 0;\n\n      --pst-sd-dropdown-color: var(--sd-color-card-border);\n    }\n\n    @each $name in $sd-semantic-color-names {\n      &.sd-bg-#{$name} {\n        --pst-sd-dropdown-color: var(--sd-color-#{$name});\n        --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n\n        // Otherwise it won't be defined in the sibling element\n        & + div.sd-summary-content {\n          --pst-sd-dropdown-color: var(--sd-color-#{$name});\n          --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n        }\n      }\n      &.sd-bg-text-#{$name} {\n        // Use the WCAG conformant text color\n        color: var(--sd-color-#{$name}-bg-text) !important;\n      }\n    }\n\n    @include legacy-backdrop-placeholder;\n\n    background-color: var(--pst-sd-dropdown-bg-color) !important;\n\n    // Add a left border with the same structure as our admonitions\n    border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n\n    // stylelint-disable-next-line no-duplicate-selectors\n    & + div.sd-summary-content {\n      border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n      border-bottom-left-radius: calc(0.25rem - 1px);\n      background-color: var(--pst-color-on-background);\n    }\n\n    span.sd-summary-icon {\n      display: inline-flex;\n      align-items: center;\n      color: var(--pst-sd-dropdown-color) !important;\n\n      svg {\n        opacity: 1;\n      }\n    }\n\n    // Positioning of the caret\n    .sd-summary-up,\n    .sd-summary-down {\n      top: 0.7rem;\n    }\n\n    // Focus ring\n    &:focus-visible {\n      outline: $focus-ring-outline;\n      outline-offset: -$focus-ring-width;\n    }\n  }\n}\n\n/*******************************************************************************\n* Buttons (which in Sphinx Design are actually links that look like buttons)\n* ref: https://sphinx-design.readthedocs.io/en/pydata-theme/badges_buttons.html#buttons\n*/\nhtml {\n  .sd-btn {\n    min-width: 2.25rem;\n    padding: 0.3125rem 0.75rem 0.4375rem; // 5px 12px 7px\n\n    &:hover {\n      @include link-style-hover; // override Sphinx Design\n\n      text-decoration-thickness: 1px;\n    }\n  }\n\n  @each $name in $sd-semantic-color-names {\n    .sd-btn-#{$name},\n    .sd-btn-outline-#{$name} {\n      &:focus-visible {\n        // Override Sphinx Design's use of -highlight colors. The -highlight\n        // colors are 15% darker, so this would create the effect of darkening\n        // the button when focused but we just want the button to have a focus\n        // ring of the same (non-highlight) color.\n        background-color: var(--sd-color-#{$name}) !important;\n        border-color: var(--sd-color-#{$name}) !important;\n        outline: var(--sd-color-#{$name}) solid $focus-ring-width;\n        outline-offset: $focus-ring-width;\n      }\n    }\n  }\n}\n","/**\n * Sphinx togglebutton\n */\n\n.bd-content {\n  @mixin chevron-down {\n    .toggle-chevron-right {\n      transform: rotate(90deg); // point chevron down\n      transition: none; // match non-animated behavior of other chevrons on site\n    }\n  }\n\n  @mixin chevron-up {\n    .toggle-chevron-right {\n      transform: rotate(-90deg); // point chevron up\n      transition: none; // match non-animated behavior of other chevrons on site\n    }\n  }\n\n  // Admonition toggles\n  .admonition {\n    button.toggle-button {\n      color: inherit;\n\n      // When disclosure widget is closed\n      &.toggle-button-hidden {\n        @include chevron-down;\n      }\n\n      // When open\n      @include chevron-up;\n    }\n\n    // Focus ring\n    // ----------\n    // Sphinx-togglebutton makes the entire admonition header clickable, but\n    // only the button within the header is focusable. We want the entire\n    // clickable area to be surrounded with a focus ring, so that's why we use\n    // the :focus-within selector, rather than a :focus-visible selector on the\n    // button.\n    &:focus-within {\n      overflow: visible;\n\n      // The complicated focus ring styles here are a consequence of the markup\n      // and border styles for this particular admonition class. (For the other\n      // type of admonition on this site, the focus ring style is achieved with\n      // simple `outline` and `outline-offset` rules on the admonition's\n      // header.) The problem is that Sphinx-togglebutton puts the admonition's\n      // left border on the outermost container (rather than separately setting\n      // the left border on the container's children). This makes it complicated\n      // to get the focus ring to simultaneously cover the left border in the\n      // header and align perfectly on the right with the body.\n      .admonition-title:focus-within::before {\n        content: \"\";\n        transform: translateX(\n          -$admonition-left-border-width\n        ); // align left edges of admonition and ring\n\n        width: calc(100% + $admonition-left-border-width); // align right edges\n        height: 100%;\n        border: $focus-ring-outline;\n        border-radius: $focus-ring-width;\n      }\n\n      // When expanded, sharpen the bottom left and right corners of the focus ring\n      &:not(.toggle-hidden) .admonition-title:focus-within::before {\n        border-bottom-left-radius: 0;\n        border-bottom-right-radius: 0;\n      }\n    }\n  }\n\n  // Details buttons\n  details.toggle-details {\n    // Over-ride border color to re-use our primary color\n    summary {\n      border-left: 3px solid var(--pst-color-primary);\n\n      @include chevron-down;\n    }\n\n    // When expanded, sharpen the bottom left and right corners of the focus ring\n    &[open] {\n      @include chevron-up;\n\n      :focus-visible {\n        border-bottom-left-radius: 0;\n        border-bottom-right-radius: 0;\n      }\n    }\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries to display pre-executed notebooks.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/*******************************************************************************\n * nbsphinx\n */\nhtml div.rendered_html,\n// NBsphinx ipywidgets output selector\nhtml .jp-RenderedHTMLCommon {\n  // Add some margin around the element box for the focus ring. Otherwise the\n  // focus ring gets clipped because the containing elements have `overflow:\n  // hidden` applied to them (via the `.lm-Widget` selector)\n  margin: $focus-ring-width;\n\n  table {\n    table-layout: auto;\n  }\n}\n\n.bd-content .nboutput {\n  .output_area {\n    &.rendered_html,\n    .jp-RenderedHTMLCommon {\n      // pandas\n      table.dataframe {\n        @include table-colors;\n      }\n    }\n\n    // Dark theme special-cases\n    html[data-theme=\"dark\"] & {\n      &.rendered_html:not(:has(table.dataframe)),\n      // ipywidgets\n      .widget-subarea {\n        @include cell-output-background;\n      }\n\n      &.stderr {\n        background-color: var(--pst-color-danger);\n      }\n    }\n  }\n}\n\n// Add extra padding to the final item in an nbsphinx container\ndiv.nblast.container {\n  margin-bottom: 1rem;\n}\n\n/*******************************************************************************\n * myst NB\n */\n\ndiv.cell_output .output {\n  max-width: 100%;\n  overflow-x: auto;\n}\n\n.bd-content div.cell_output {\n  // pandas\n  table.dataframe {\n    @include table-colors;\n  }\n\n  html[data-theme=\"dark\"] & {\n    img,\n    .text_html:not(:has(table.dataframe)),\n    // ipywidgets\n    .widget-subarea {\n      @include cell-output-background;\n    }\n  }\n}\n\n// Prevent tables from scrunching together\n.bd-content {\n  div.cell_input {\n    display: flex;\n    flex-direction: column;\n    justify-content: stretch;\n  }\n\n  div.cell_input,\n  div.output {\n    border-radius: $admonition-border-radius;\n  }\n\n  div.output {\n    table {\n      table-layout: auto;\n    }\n  }\n}\n","/**\n * style for the various mapping libs based on leaflet (folium, geemap, ipyleaflet)\n * mainly ensure the good display of the maps in both themes and avoid the customization\n * of the tiles\n */\n\n/**\n * avoid border override from pydata-sphinx-theme\n * minimal selctor to get the priority\n */\nhtml[data-theme=\"dark\"] .bd-content img.leaflet-tile.leaflet-tile-loaded {\n  border-radius: 0;\n  padding: 0;\n}\n","/**\n * /search.html page special-cases\n */\n\n.bd-search-container {\n  /*******************************************\n  * Search results\n  */\n  // Whitespace\n  div#search-results {\n    > h2 {\n      font-size: var(--pst-font-size-icon);\n      margin-top: 1rem;\n    }\n\n    p.search-summary {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  ul.search {\n    margin: 0;\n    list-style: none;\n\n    li {\n      background-image: none;\n      padding: 1rem 0;\n      margin: 1rem 0;\n      border-top: 1px solid var(--pst-color-text-muted);\n\n      // First link is the page title, it should be a bit bigger\n      > a {\n        font-size: 1.2em;\n      }\n\n      div.context,\n      p.context {\n        color: var(--pst-color-text-base);\n        margin: 0.5em 0 0;\n\n        // Add a # before page section titles to make it clear they are sections\n        a::before {\n          content: \"#\";\n          padding-right: 0.2em;\n          color: var(--pst-color-text-muted);\n        }\n      }\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/_preview/3/_static/styles/sphinx-book-theme.css b/_preview/3/_static/styles/sphinx-book-theme.css
deleted file mode 100644
index 5197540..0000000
--- a/_preview/3/_static/styles/sphinx-book-theme.css
+++ /dev/null
@@ -1,9 +0,0 @@
-/*! sphinx-book-theme CSS
- * BSD 3-Clause License
- * Copyright (c) 2020, EBP
- * All rights reserved.
- *
- * This follows the 7-1 pattern described here:
- * https://sass-guidelin.es/#architecture
- */html[data-theme=dark],html[data-theme=light]{--pst-font-size-base:none;--pst-color-secondary:#e89217}html[data-theme=light]{--sbt-color-announcement:#616161;--pst-color-primary:#176de8}html[data-theme=dark]{--pst-color-primary:#528fe4;--sbt-color-announcement:#616161;--pst-color-background:#121212}html{scroll-padding-top:4rem}.sbt-scroll-pixel-helper{height:0;left:0;position:absolute;top:0;width:0}.d-n,.onlyprint{display:none}@media print{.onlyprint{display:block!important}.noprint{display:none!important}}.bd-article-container h1,.bd-article-container h2,.bd-article-container h3,.bd-article-container h4,.bd-article-container h5,.bd-article-container p.caption{color:var(--pst-color-muted)}.bd-article-container h1,.bd-article-container h2{font-weight:500}a.brackets:before{color:inherit;font-family:inherit;margin-right:0}table{position:relative}@media print{.bd-main .bd-content{height:auto;margin-left:2rem}.bd-main .bd-content #jb-print-docs-body{margin-left:0}.bd-main .bd-content 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sphinx-book-theme CSS\n * BSD 3-Clause License\n * Copyright (c) 2020, EBP\n * All rights reserved.\n *\n * This follows the 7-1 pattern described here:\n * https://sass-guidelin.es/#architecture\n */\n// Variables and re-usable SCSS functions\n@import \"abstracts/mixins\";\n@import \"abstracts/variables\";\n\n// Basic styling applied throughout site\n@import \"base/base\";\n@import \"base/typography\";\n@import \"base/print\";\n\n// Major theme layout, skeleton, and whitespace\n@import \"sections/announcement\";\n@import \"sections/article\";\n@import \"sections/article-container\";\n@import \"sections/header-article\";\n@import \"sections/header-primary\";\n@import \"sections/sidebar-primary\";\n@import \"sections/sidebar-secondary\";\n@import \"sections/footer-content\";\n@import \"sections/footer-article\";\n\n// Re-usable components across the theme\n@import \"components/back-to-top\";\n@import \"components/icon-links\";\n@import \"components/logo\";\n@import 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 details.above-input,\n  details.below-input {\n    summary {\n      border-left: 3px solid var(--pst-color-primary);\n    }\n  }\n}\n","/*********************************************\n* Top Bar *\n*********************************************/\n/**\n * Mixin:\n * Header behavior on mobile\n */\n@mixin header-height-mobile {\n  @media (max-width: $breakpoint-md) {\n    height: $header-article-height + 0.5rem;\n  }\n}\n\n/**\n * Sidebar toggle over-rides for PST\n */\n// Primary toggle is always visible\nbutton.sidebar-toggle.primary-toggle {\n  @media (min-width: $breakpoint-md) {\n    display: inline-block;\n  }\n  @media (max-width: $breakpoint-md) {\n    margin-bottom: 0px;\n  }\n}\n\n// Secondary toggle mimics behavior of \"persistent header\" div of PST\nbutton.sidebar-toggle.secondary-toggle {\n  @media (min-width: $breakpoint-xl) {\n    display: none;\n  }\n  @media (max-width: $breakpoint-md) {\n    margin-bottom: 0px;\n  }\n}\n\n// Wrapper container\n.bd-header-article {\n  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var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n",".bd-header {\n  // Turn off sticky positioning so that it scrolls\n  position: inherit;\n\n  // Turn these off because we'll show it in the article header\n  // The primary sidebar toggle\n  button.sidebar-toggle {\n    display: none;\n  }\n}\n","/*********************************************\n* Left Nav Bar *\n*********************************************/\n\n.bd-sidebar-primary {\n  // So that it sticks to the top of the page instead of the header height\n  top: 0;\n  max-height: 100vh;\n\n  // Tighter padding so the spacing is more equal\n  padding: 1rem;\n\n  // Draw open/close animation\n  transition:\n    margin-left $animation-time ease 0s,\n    opacity $animation-time ease 0s,\n    visibility $animation-time ease 0s;\n\n  @media (max-width: $breakpoint-md) {\n    // So that tooltips don't overlap\n    z-index: $zindex-tooltip + 1;\n  }\n\n  // Remove the top border since there's often no header items above\n  .sidebar-primary-items__start {\n    border-top: none;\n  }\n\n  // On wide screens we have a smaller sidebar width than PST\n  @media (min-width: $breakpoint-lg) {\n    flex-basis: $sidebar-primary-width-widescreen;\n  }\n}\n\n// Default visibility for left sidebar is the reverse of what it is on mobile.\n// It is shown by default, and hidden with a click.\n// Mobile behavior is defined in the pydata sphinx theme\n@media (min-width: $breakpoint-lg) {\n  input#pst-primary-sidebar-checkbox:checked\n    ~ .bd-container\n    .bd-sidebar-primary {\n    margin-left: -$sidebar-primary-width-widescreen;\n    visibility: hidden;\n    opacity: 0;\n  }\n}\n","/*********************************************\n* Table of Contents (right nav bar)          *\n*********************************************/\n\n.bd-sidebar-secondary {\n  // So that it sticks to the top of the page instead of the header height\n  top: 0;\n\n  @media (max-width: $breakpoint-xl) {\n    // So that tooltips don't overlap\n    z-index: $zindex-tooltip + 1;\n  }\n\n  // So that we can center the TOC button with the article header buttons\n  .sidebar-secondary-items {\n    padding: 0;\n    display: flex;\n    gap: 0.5rem;\n\n    // Remove the manual padding since we'll use `gap`\n    .sidebar-secondary-item {\n      padding-top: 0;\n      padding-bottom: 0;\n    }\n  }\n\n  // The 'on this page' title div should take up the full header\n  .onthispage {\n    height: $header-article-height;\n    min-height: $header-article-height;\n    display: flex;\n    gap: 0.5rem;\n    align-items: center;\n    margin: 0;\n    color: var(--pst-color-muted);\n  }\n\n  // Wide screen behavior\n  @media (min-width: $breakpoint-xl) {\n    background: var(--pst-color-background);\n    height: fit-content;\n    transition: max-height 0.4s ease;\n\n    // To make sure it shows above the page content\n    z-index: $zindex-middle;\n\n    // Remove padding so that it's flush on the top and left\n    padding: 0;\n\n    // This is the container div for the secondary sidebar nav\n    .toc-item {\n      // Border is a bit less prominent\n      border-left-color: var(--pst-color-surface);\n      // Flush with the top so that we can fix the height of the toc header div\n      padding-top: 0;\n\n      // The table of contents <ul> after the title div\n      nav.page-toc {\n        // Remove extra margin the PST adds for other sidebar items\n        margin-bottom: 0;\n\n        // Items after \"on this page' should fade in and out\n        transition: opacity 0.4s ease;\n      }\n    }\n\n    // Rules for when sidebar is hidden\n    // the `not:hover` means that hovering over the sidebar will show it again.\n    &.hide:not(:hover) {\n      // Cap the height, and remove the scrollbar\n      max-height: $header-article-height;\n      overflow-y: hidden;\n\n      // Shadow matches what we have in the article header\n      .scrolled & {\n        box-shadow: 0 6px 6px -6px rgba(0, 0, 0, 0.3);\n      }\n\n      // Show a down carot\n      .onthispage:after {\n        opacity: 1;\n        content: \"\\f107\";\n        font-family: \"Font Awesome 5 Free\";\n        font-weight: 900;\n        padding-left: 0.5em;\n        transition: opacity 0.3s ease;\n      }\n\n      // The page-toc list is hidden\n      nav.page-toc {\n        opacity: 0;\n      }\n    }\n  }\n}\n","/*********************************************\n* Footer - content *\n*********************************************/\nfooter {\n  font-size: var(--sbt-font-size-small-1);\n  &.bd-footer-content {\n    display: flex;\n    flex-wrap: wrap;\n    padding: 15px;\n    border-top: 1px solid #ccc;\n    font-size: $sbt-font-size-small-1;\n    .bd-footer-content__inner {\n      padding-left: 0px;\n      p {\n        margin-bottom: 0px;\n      }\n    }\n  }\n}\n",".bd-footer-article {\n  padding: 0rem 1rem;\n  @media (max-width: $breakpoint-md) {\n    // More room for text on mobile\n    padding: 0rem 0.5rem;\n  }\n}\n","#pst-back-to-top {\n  // We should only display the back-to-top button when the TOC is not available.\n  // AKA, on narrow-ish screens.\n  // This over-rides a PST default\n  @media (min-width: $breakpoint-xl) {\n    display: none !important;\n  }\n\n  // Reduce size to be slightly less intrusive on the page\n  font-size: 0.8rem;\n}\n","/**\n * Icon links design for the primary sidebar, where it defaults in this theme.\n */\n.bd-sidebar-primary {\n  .navbar-icon-links {\n    column-gap: 0.5rem;\n\n    .nav-link {\n      // There are few kinds of elements that can be icon links and each is different\n      i,\n      span {\n        font-size: 1.2rem;\n      }\n\n      // Images usually fill more vertical space so we make them a bit smaller\n      img {\n        font-size: 0.8rem;\n      }\n    }\n  }\n}\n",".navbar-brand {\n  height: unset;\n  max-height: unset;\n\n  // Auto-center the image and title and make items vertical\n  flex-direction: column;\n  justify-content: center;\n  gap: 0.25rem;\n\n  // To prevent site text from having underlines\n  &:hover {\n    text-decoration: none;\n  }\n  .logo__title {\n    font-size: 1.25rem;\n    white-space: normal;\n    text-align: center;\n  }\n  // Prevent images from stretching vertically\n  .logo__image {\n    height: unset;\n  }\n}\n","// Clean up the search page so that it has less unnecessary text\n.bd-search-container {\n  margin: 2em;\n\n  #search-results {\n    h2:first-child {\n      display: none;\n    }\n  }\n}\n\n// Sidebar search button field\n.search-button-field {\n  width: 100%;\n  font-size: 0.9rem;\n  // Hide by default and we'll add a rule to show it on wide sreen below\n  display: none;\n\n  .search-button__kbd-shortcut {\n    margin-left: auto;\n  }\n}\n\n// Rules to switch off visibility of the field button and the header button\n@media (min-width: $breakpoint-lg) {\n  .search-button {\n    display: none !important;\n  }\n\n  .search-button-field {\n    display: flex;\n  }\n}\n\n// The \"clear search results\" button\n// Make it line up with our content better\ndiv#searchbox {\n  padding-right: 2rem;\n  padding-left: 2rem;\n  @media (max-width: $breakpoint-md) {\n    padding-right: 1rem;\n    padding-left: 1rem;\n  }\n\n  p.highlight-link {\n    // Remove the extra margin that is added\n    @media (min-width: $breakpoint-md) {\n      margin-left: 0;\n      a {\n        font-size: 1rem;\n      }\n    }\n  }\n}\n","// Images\nimg {\n  max-width: 100%;\n\n  &.align-center {\n    margin-left: auto;\n    margin-right: auto;\n    display: block;\n  }\n\n  &.align-left {\n    clear: left;\n    float: left;\n    margin-right: 1em;\n  }\n\n  &.align-right {\n    clear: right;\n    float: right;\n    margin-left: 1em;\n  }\n}\n\n// Figures\ndiv.figure {\n  width: 100%;\n  margin-bottom: 1em;\n  text-align: center;\n\n  &.align-left {\n    text-align: left;\n    p.caption {\n      margin-left: 0;\n    }\n  }\n  &.align-right {\n    text-align: right;\n    p.caption {\n      margin-right: 0;\n    }\n  }\n\n  p.caption {\n    margin: 0.5em 10%;\n  }\n\n  &.margin,\n  &.margin-caption {\n    p.caption {\n      margin: 0.5em 0;\n    }\n  }\n\n  &.margin-caption p.caption {\n    text-align: left;\n  }\n\n  span.caption-number {\n    font-weight: bold;\n  }\n\n  span {\n    font-size: 0.9rem;\n  }\n}\n","/**\n * Margin content\n * This is the area to the right of the main content\n * normally covered by the in-page table of contents\n * but if `.margin` classes are present, it shows up to the right in the margin\n * and the in-page TOC is hidden.\n * On narrow screens, margin content behaves like a sidebar\n */\n\n/**\n * Variables for calculating margin widths\n */\n// We want our margin content to be 1/3 the width of the main page content\n// after factoring in a small margin\n$margin-width-relative-to-content: 36%;\n$margin-gutter: 3%;\n$margin-width-width-gutter: $margin-width-relative-to-content - $margin-gutter;\n\n// re-subtract the gutter width because we want it to leave a white-space\n// This is how much to offset the margin content.\n$margin-offset: -$margin-width-relative-to-content;\n\n/**\n * This mixin will cause something to \"pop out\" to the margin on wide screens\n */\n@mixin margin-content() {\n  // On narrow screens this is the width of margin content\n  width: 40%;\n  float: right;\n  background-color: unset;\n  font-size: 0.9em;\n  margin-left: 0.5rem;\n\n  // Wide screens and printing should force margin behavior\n  @media (min-width: $breakpoint-lg), print {\n    width: $margin-width-width-gutter;\n    margin: 0 $margin-offset 0 0;\n\n    // Prevent sequential margin content from overlapping\n    clear: right;\n\n    p.sidebar-title {\n      margin-bottom: -1rem;\n      border-bottom: none;\n      padding-left: 0px;\n\n      // Content of admonitions has fewer horizontal white space to save space\n      ~ * {\n        padding-left: 0px;\n        padding-right: 0px;\n      }\n    }\n  }\n}\n\n/**\n * Sidenotes and marginnotes\n * over-rides the \"footnote\" behavior to pop out to the margin instead.\n */\nlabel.margin-toggle {\n  margin-bottom: 0em;\n  &.marginnote-label {\n    display: none;\n  }\n  sup {\n    user-select: none;\n  }\n  @media (max-width: $breakpoint-lg) {\n    cursor: pointer;\n    color: rgb(0, 113, 188);\n    &.marginnote-label {\n      display: inline;\n      &:after {\n        content: \"\\2295\";\n      }\n    }\n  }\n}\n\ninput.margin-toggle {\n  display: none;\n  @media (max-width: $breakpoint-lg) {\n    &:checked + .sidenote,\n    &:checked + .marginnote {\n      display: block;\n      float: left;\n      left: 1rem;\n      clear: both;\n      width: 95%;\n      margin: 1rem 2.5%;\n      position: relative;\n    }\n  }\n}\n\nspan.sidenote,\nspan.marginnote {\n  z-index: $zindex-middle;\n  position: relative;\n  sup {\n    user-select: none;\n  }\n  @include margin-content();\n  border-left: none;\n  @media (max-width: $breakpoint-lg) {\n    display: none;\n  }\n}\n\naside.sidebar {\n  .note {\n    // styling for notes inside sidebars\n    margin: 1rem;\n    padding: 0rem 0rem 1rem 0rem;\n  }\n  // The titles sticking\n  .admonition-title {\n    margin: 0rem -1rem 0rem 0rem;\n  }\n}\n\n/**\n * Sidebar content in the margin.\n * This will be added by the `margin` directive or the `sidebar` directive\n */\naside.sidebar.margin {\n  // Margin content can have an empty title so this prevents an empty title block\n  // from showing up on mobile\n  .sidebar-title:empty {\n    display: none;\n  }\n\n  // On narrow screens padding is added to all sidebar content\n  // This replaces padding w/ margin for admonitions because padding is used\n  // for the title background color\n  .admonition {\n    margin: 0.5rem;\n    padding-left: 0;\n    padding-right: 0;\n\n    // Remove the title margin so it\n    .admonition-title {\n      margin-left: 0;\n      margin-right: 0;\n    }\n  }\n\n  @media (min-width: $breakpoint-lg) {\n    // No border for margin sidebars on wide screen\n    border: none;\n\n    // Admonitions don't need the extra whitespace in the margin\n    .admonition {\n      margin: 1rem 0rem;\n      padding: 0rem 0rem 1rem 0rem;\n    }\n  }\n}\n\ndiv.margin,\nfigure.margin,\naside.margin,\n.margin.docutils.container,\n.cell.tag_popout,\n.cell.tag_margin {\n  z-index: $zindex-middle;\n  position: relative;\n  @include margin-content();\n\n  // Make cell outputs take up more space if they're in the margin\n  div.cell.tag_margin .cell_output {\n    padding-left: 0;\n  }\n}\n\n// A few permutations because docutils 0.18 switched to semantic tags\ndiv.figure.margin-caption p.caption,\ndiv.figure.margin-caption figcaption,\nfigure.margin-caption figcaption {\n  z-index: $zindex-middle;\n  position: relative;\n  @include margin-content();\n}\n\n// Margin captions\n.margin-caption figcaption {\n  text-align: left;\n}\n\n/**\n * Full width content\n */\n\n// Grow 100% by the ratio of the margin to the content width.\n$content-fullwidth-width: 100% + $margin-width-relative-to-content;\n\ndiv.cell.tag_full-width,\ndiv.cell.tag_full_width,\ndiv.full_width,\ndiv.full-width {\n  z-index: $zindex-middle;\n  position: relative;\n  @media (min-width: $breakpoint-lg) {\n    max-width: $content-fullwidth-width;\n    width: $content-fullwidth-width;\n  }\n}\n","/**\n * Epigraphs and other \"special\" quote blocks\n */\n\nblockquote {\n  &.pull-quote,\n  &.epigraph,\n  &.highlights {\n    font-size: 1.25em;\n    border-left: none;\n    background-color: var(--pst-color-background);\n  }\n\n  div > p + p.attribution {\n    font-style: normal;\n    font-size: 0.9em;\n    text-align: right;\n    color: #6c757d;\n    padding-right: 2em;\n  }\n}\n","// Over-ride Sphinx default so that content fills whitespace w/ margin content\npre,\ndiv[class*=\"highlight-\"] {\n  clear: none;\n}\n","// MyST-NB and Jupyter Notebooks\ndiv.cell.tag_output_scroll div.cell_output,\ndiv.cell.tag_scroll-output div.cell_output {\n  max-height: 24em;\n  overflow-y: auto;\n}\n\ndiv.cell.tag_scroll-input div.cell_input {\n  max-height: 24em;\n  overflow-y: auto;\n}\n","/**\n * Comment JavaScript libraries\n  * Libraries supported here are:\n  * - Hypothes.is : https://web.hypothes.is/\n  * - Utteranc.es : https://utteranc.es/\n  */\n// In general we want to hide any comment libraries on print\n@media only print {\n  hypothesis-sidebar,\n  div.utterances {\n    display: none;\n  }\n}\n","/*********************************************\n* Thebe *\n* ref: https://github.com/executablebooks/thebe\n*********************************************/\n.thebelab-cell {\n  border: none !important;\n  margin-right: 0.5em !important;\n}\n\n.thebelab-cell .thebelab-input {\n  padding-left: 10px !important;\n}\n\n.cell.docutils.container {\n  padding-right: 0px !important;\n}\n\nbutton.thebe-launch-button {\n  height: 2.5em;\n  font-size: 1em;\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/_preview/3/_static/styles/theme.css b/_preview/3/_static/styles/theme.css
deleted file mode 100644
index 4519dd9..0000000
--- a/_preview/3/_static/styles/theme.css
+++ /dev/null
@@ -1,2 +0,0 @@
-/* Provided by Sphinx's 'basic' theme, and included in the final set of assets */
-@import "../basic.css";
diff --git a/_preview/3/_static/togglebutton.css b/_preview/3/_static/togglebutton.css
deleted file mode 100644
index 54a6787..0000000
--- a/_preview/3/_static/togglebutton.css
+++ /dev/null
@@ -1,160 +0,0 @@
-/**
- * Admonition-based toggles
- */
-
-/* Visibility of the target */
-.admonition.toggle .admonition-title ~ * {
-    transition: opacity .3s, height .3s;
-}
-
-/* Toggle buttons inside admonitions so we see the title */
-.admonition.toggle {
-    position: relative;
-}
-
-/* Titles should cut off earlier to avoid overlapping w/ button */
-.admonition.toggle .admonition-title {
-    padding-right: 25%;
-    cursor: pointer;
-}
-
-/* Hovering will cause a slight shift in color to make it feel interactive */
-.admonition.toggle .admonition-title:hover {
-    box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 1%);
-}
-
-/* Hovering will cause a slight shift in color to make it feel interactive */
-.admonition.toggle .admonition-title:active {
-    box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 3%);
-}
-
-/* Remove extra whitespace below the admonition title when hidden */
-.admonition.toggle-hidden {
-    padding-bottom: 0;
-}
-
-.admonition.toggle-hidden .admonition-title {
-    margin-bottom: 0;
-}
-
-/* hides all the content of a page until de-toggled */
-.admonition.toggle-hidden .admonition-title ~ * {
-    height: 0;
-    margin: 0;
-    opacity: 0;
-    visibility: hidden;
-}
-
-/* General button style and position*/
-button.toggle-button {
-    /**
-     * Background and shape. By default there's no background
-     * but users can style as they wish
-     */  
-    background: none;
-    border: none;
-    outline: none;
-
-    /* Positioning just inside the admonition title */
-    position: absolute;
-    right: 0.5em;
-    padding: 0px;
-    border: none;
-    outline: none;
-}
-
-/* Display the toggle hint on wide screens */
-@media (min-width: 768px) {
-    button.toggle-button.toggle-button-hidden:before {
-        content: attr(data-toggle-hint);  /* This will be filled in by JS */
-        font-size: .8em;
-        align-self: center;
-    }
-}
-
-/* Icon behavior */
-.tb-icon {
-    transition: transform .2s ease-out;
-    height: 1.5em;
-    width: 1.5em;
-    stroke: currentColor;  /* So that we inherit the color of other text */
-}
-
-/* The icon should point right when closed, down when open. */
-/* Open */
-.admonition.toggle button .tb-icon {
-    transform: rotate(90deg);
-}
-
-/* Closed */
-.admonition.toggle button.toggle-button-hidden .tb-icon {
-    transform: rotate(0deg);
-}
-
-/* With details toggles, we don't rotate the icon so it points right */
-details.toggle-details .tb-icon {
-    height: 1.4em;
-    width: 1.4em;
-    margin-top: 0.1em;  /* To center the button vertically */
-}
-
-
-/**
- * Details-based toggles.
- * In this case, we wrap elements with `.toggle` in a details block.
- */
-
-/* Details blocks */
-details.toggle-details {
-    margin: 1em 0;
-}
-
-
-details.toggle-details summary {
-    display: flex;
-    align-items: center;
-    cursor: pointer;
-    list-style: none;
-    border-radius: .2em;
-    border-left: 3px solid #1976d2;
-    background-color: rgb(204 204 204 / 10%);
-    padding: 0.2em 0.7em 0.3em 0.5em; /* Less padding on left because the SVG has left margin */
-    font-size: 0.9em;
-}
-
-details.toggle-details summary:hover {
-    background-color: rgb(204 204 204 / 20%);
-}
-
-details.toggle-details summary:active {
-    background: rgb(204 204 204 / 28%);
-}
-
-.toggle-details__summary-text {
-    margin-left: 0.2em;
-}
-
-details.toggle-details[open] summary {
-    margin-bottom: .5em;
-}
-
-details.toggle-details[open] summary .tb-icon {
-    transform: rotate(90deg);
-}
-
-details.toggle-details[open] summary ~ * {
-    animation: toggle-fade-in .3s ease-out;
-}
-
-@keyframes toggle-fade-in {
-  from {opacity: 0%;}
-  to {opacity: 100%;}
-}
-
-/* Print rules - we hide all toggle button elements at print */
-@media print {
-    /* Always hide the summary so the button doesn't show up */
-    details.toggle-details summary {
-        display: none;
-    }
-}
\ No newline at end of file
diff --git a/_preview/3/_static/togglebutton.js b/_preview/3/_static/togglebutton.js
deleted file mode 100644
index 215a7ee..0000000
--- a/_preview/3/_static/togglebutton.js
+++ /dev/null
@@ -1,187 +0,0 @@
-/**
- * Add Toggle Buttons to elements
- */
-
-let toggleChevron = `
-<svg xmlns="http://www.w3.org/2000/svg" class="tb-icon toggle-chevron-right" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
-<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-<polyline points="9 6 15 12 9 18" />
-</svg>`;
-
-var initToggleItems = () => {
-  var itemsToToggle = document.querySelectorAll(togglebuttonSelector);
-  console.log(`[togglebutton]: Adding toggle buttons to ${itemsToToggle.length} items`)
-  // Add the button to each admonition and hook up a callback to toggle visibility
-  itemsToToggle.forEach((item, index) => {
-    if (item.classList.contains("admonition")) {
-      // If it's an admonition block, then we'll add a button inside
-      // Generate unique IDs for this item
-      var toggleID = `toggle-${index}`;
-      var buttonID = `button-${toggleID}`;
-
-      item.setAttribute('id', toggleID);
-      if (!item.classList.contains("toggle")){
-        item.classList.add("toggle");
-      }
-      // This is the button that will be added to each item to trigger the toggle
-      var collapseButton = `
-        <button type="button" id="${buttonID}" class="toggle-button" data-target="${toggleID}" data-button="${buttonID}" data-toggle-hint="${toggleHintShow}" aria-label="Toggle hidden content">
-            ${toggleChevron}
-        </button>`;
-
-      title = item.querySelector(".admonition-title")
-      title.insertAdjacentHTML("beforeend", collapseButton);
-      thisButton = document.getElementById(buttonID);
-
-      // Add click handlers for the button + admonition title (if admonition)
-      admonitionTitle = document.querySelector(`#${toggleID} > .admonition-title`)
-      if (admonitionTitle) {
-        // If an admonition, then make the whole title block clickable
-        admonitionTitle.addEventListener('click', toggleClickHandler);
-        admonitionTitle.dataset.target = toggleID
-        admonitionTitle.dataset.button = buttonID
-      } else {
-        // If not an admonition then we'll listen for the button click
-        thisButton.addEventListener('click', toggleClickHandler);
-      }
-
-      // Now hide the item for this toggle button unless explicitly noted to show
-      if (!item.classList.contains("toggle-shown")) {
-        toggleHidden(thisButton);
-      }
-    } else {
-      // If not an admonition, wrap the block in a <details> block
-      // Define the structure of the details block and insert it as a sibling
-      var detailsBlock = `
-        <details class="toggle-details">
-            <summary class="toggle-details__summary">
-              ${toggleChevron}
-              <span class="toggle-details__summary-text">${toggleHintShow}</span>
-            </summary>
-        </details>`;
-      item.insertAdjacentHTML("beforebegin", detailsBlock);
-
-      // Now move the toggle-able content inside of the details block
-      details = item.previousElementSibling
-      details.appendChild(item)
-      item.classList.add("toggle-details__container")
-
-      // Set up a click trigger to change the text as needed
-      details.addEventListener('click', (click) => {
-        let parent = click.target.parentElement;
-        if (parent.tagName.toLowerCase() == "details") {
-          summary = parent.querySelector("summary");
-          details = parent;
-        } else {
-          summary = parent;
-          details = parent.parentElement;
-        }
-        // Update the inner text for the proper hint
-        if (details.open) {
-          summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintShow;
-        } else {
-          summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintHide;
-        }
-        
-      });
-
-      // If we have a toggle-shown class, open details block should be open
-      if (item.classList.contains("toggle-shown")) {
-        details.click();
-      }
-    }
-  })
-};
-
-// This should simply add / remove the collapsed class and change the button text
-var toggleHidden = (button) => {
-  target = button.dataset['target']
-  var itemToToggle = document.getElementById(target);
-  if (itemToToggle.classList.contains("toggle-hidden")) {
-    itemToToggle.classList.remove("toggle-hidden");
-    button.classList.remove("toggle-button-hidden");
-  } else {
-    itemToToggle.classList.add("toggle-hidden");
-    button.classList.add("toggle-button-hidden");
-  }
-}
-
-var toggleClickHandler = (click) => {
-  // Be cause the admonition title is clickable and extends to the whole admonition
-  // We only look for a click event on this title to trigger the toggle.
-
-  if (click.target.classList.contains("admonition-title")) {
-    button = click.target.querySelector(".toggle-button");
-  } else if (click.target.classList.contains("tb-icon")) {
-    // We've clicked the icon and need to search up one parent for the button
-    button = click.target.parentElement;
-  } else if (click.target.tagName == "polyline") {
-    // We've clicked the SVG elements inside the button, need to up 2 layers
-    button = click.target.parentElement.parentElement;
-  } else if (click.target.classList.contains("toggle-button")) {
-    // We've clicked the button itself and so don't need to do anything
-    button = click.target;
-  } else {
-    console.log(`[togglebutton]: Couldn't find button for ${click.target}`)
-  }
-  target = document.getElementById(button.dataset['button']);
-  toggleHidden(target);
-}
-
-// If we want to blanket-add toggle classes to certain cells
-var addToggleToSelector = () => {
-  const selector = "";
-  if (selector.length > 0) {
-    document.querySelectorAll(selector).forEach((item) => {
-      item.classList.add("toggle");
-    })
-  }
-}
-
-// Helper function to run when the DOM is finished
-const sphinxToggleRunWhenDOMLoaded = cb => {
-  if (document.readyState != 'loading') {
-    cb()
-  } else if (document.addEventListener) {
-    document.addEventListener('DOMContentLoaded', cb)
-  } else {
-    document.attachEvent('onreadystatechange', function() {
-      if (document.readyState == 'complete') cb()
-    })
-  }
-}
-sphinxToggleRunWhenDOMLoaded(addToggleToSelector)
-sphinxToggleRunWhenDOMLoaded(initToggleItems)
-
-/** Toggle details blocks to be open when printing */
-if (toggleOpenOnPrint == "true") {
-  window.addEventListener("beforeprint", () => {
-    // Open the details
-    document.querySelectorAll("details.toggle-details").forEach((el) => {
-      el.dataset["togglestatus"] = el.open;
-      el.open = true;
-    });
-  
-    // Open the admonitions
-    document.querySelectorAll(".admonition.toggle.toggle-hidden").forEach((el) => {
-      console.log(el);
-      el.querySelector("button.toggle-button").click();
-      el.dataset["toggle_after_print"] = "true";
-    });
-  });
-  window.addEventListener("afterprint", () => {
-    // Re-close the details that were closed
-    document.querySelectorAll("details.toggle-details").forEach((el) => {
-      el.open = el.dataset["togglestatus"] == "true";
-      delete el.dataset["togglestatus"];
-    });
-  
-    // Re-close the admonition toggle buttons
-    document.querySelectorAll(".admonition.toggle").forEach((el) => {
-      if (el.dataset["toggle_after_print"] == "true") {
-        el.querySelector("button.toggle-button").click();
-        delete el.dataset["toggle_after_print"];
-      }
-    });
-  });
-}
diff --git a/_preview/3/_static/vendor/fontawesome/6.5.2/LICENSE.txt b/_preview/3/_static/vendor/fontawesome/6.5.2/LICENSE.txt
deleted file mode 100644
index e69c5e3..0000000
--- a/_preview/3/_static/vendor/fontawesome/6.5.2/LICENSE.txt
+++ /dev/null
@@ -1,165 +0,0 @@
-Fonticons, Inc. (https://fontawesome.com)
-
---------------------------------------------------------------------------------
-
-Font Awesome Free License
-
-Font Awesome Free is free, open source, and GPL friendly. You can use it for
-commercial projects, open source projects, or really almost whatever you want.
-Full Font Awesome Free license: https://fontawesome.com/license/free.
-
---------------------------------------------------------------------------------
-
-# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
-
-The Font Awesome Free download is licensed under a Creative Commons
-Attribution 4.0 International License and applies to all icons packaged
-as SVG and JS file types.
-
---------------------------------------------------------------------------------
-
-# Fonts: SIL OFL 1.1 License
-
-In the Font Awesome Free download, the SIL OFL license applies to all icons
-packaged as web and desktop font files.
-
-Copyright (c) 2024 Fonticons, Inc. (https://fontawesome.com)
-with Reserved Font Name: "Font Awesome".
-
-This Font Software is licensed under the SIL Open Font License, Version 1.1.
-This license is copied below, and is also available with a FAQ at:
-http://scripts.sil.org/OFL
-
-SIL OPEN FONT LICENSE
-Version 1.1 - 26 February 2007
-
-PREAMBLE
-The goals of the Open Font License (OFL) are to stimulate worldwide
-development of collaborative font projects, to support the font creation
-efforts of academic and linguistic communities, and to provide a free and
-open framework in which fonts may be shared and improved in partnership
-with others.
-
-The OFL allows the licensed fonts to be used, studied, modified and
-redistributed freely as long as they are not sold by themselves. The
-fonts, including any derivative works, can be bundled, embedded,
-redistributed and/or sold with any software provided that any reserved
-names are not used by derivative works. The fonts and derivatives,
-however, cannot be released under any other type of license. The
-requirement for fonts to remain under this license does not apply
-to any document created using the fonts or their derivatives.
-
-DEFINITIONS
-"Font Software" refers to the set of files released by the Copyright
-Holder(s) under this license and clearly marked as such. This may
-include source files, build scripts and documentation.
-
-"Reserved Font Name" refers to any names specified as such after the
-copyright statement(s).
-
-"Original Version" refers to the collection of Font Software components as
-distributed by the Copyright Holder(s).
-
-"Modified Version" refers to any derivative made by adding to, deleting,
-or substituting — in part or in whole — any of the components of the
-Original Version, by changing formats or by porting the Font Software to a
-new environment.
-
-"Author" refers to any designer, engineer, programmer, technical
-writer or other person who contributed to the Font Software.
-
-PERMISSION & CONDITIONS
-Permission is hereby granted, free of charge, to any person obtaining
-a copy of the Font Software, to use, study, copy, merge, embed, modify,
-redistribute, and sell modified and unmodified copies of the Font
-Software, subject to the following conditions:
-
-1) Neither the Font Software nor any of its individual components,
-in Original or Modified Versions, may be sold by itself.
-
-2) Original or Modified Versions of the Font Software may be bundled,
-redistributed and/or sold with any software, provided that each copy
-contains the above copyright notice and this license. These can be
-included either as stand-alone text files, human-readable headers or
-in the appropriate machine-readable metadata fields within text or
-binary files as long as those fields can be easily viewed by the user.
-
-3) No Modified Version of the Font Software may use the Reserved Font
-Name(s) unless explicit written permission is granted by the corresponding
-Copyright Holder. This restriction only applies to the primary font name as
-presented to the users.
-
-4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
-Software shall not be used to promote, endorse or advertise any
-Modified Version, except to acknowledge the contribution(s) of the
-Copyright Holder(s) and the Author(s) or with their explicit written
-permission.
-
-5) The Font Software, modified or unmodified, in part or in whole,
-must be distributed entirely under this license, and must not be
-distributed under any other license. The requirement for fonts to
-remain under this license does not apply to any document created
-using the Font Software.
-
-TERMINATION
-This license becomes null and void if any of the above conditions are
-not met.
-
-DISCLAIMER
-THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
-OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
-COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
-INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
-DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
-FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
-OTHER DEALINGS IN THE FONT SOFTWARE.
-
---------------------------------------------------------------------------------
-
-# Code: MIT License (https://opensource.org/licenses/MIT)
-
-In the Font Awesome Free download, the MIT license applies to all non-font and
-non-icon files.
-
-Copyright 2024 Fonticons, Inc.
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in the
-Software without restriction, including without limitation the rights to use, copy,
-modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
-and to permit persons to whom the Software is furnished to do so, subject to the
-following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
-INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
-PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
-HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
-OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
---------------------------------------------------------------------------------
-
-# Attribution
-
-Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font
-Awesome Free files already contain embedded comments with sufficient
-attribution, so you shouldn't need to do anything additional when using these
-files normally.
-
-We've kept attribution comments terse, so we ask that you do not actively work
-to remove them from files, especially code. They're a great way for folks to
-learn about Font Awesome.
-
---------------------------------------------------------------------------------
-
-# Brand Icons
-
-All brand icons are trademarks of their respective owners. The use of these
-trademarks does not indicate endorsement of the trademark holder by Font
-Awesome, nor vice versa. **Please do not use brand logos for any purpose except
-to represent the company, product, or service to which they refer.**
diff --git a/_preview/3/_static/vendor/fontawesome/6.5.2/css/all.min.css b/_preview/3/_static/vendor/fontawesome/6.5.2/css/all.min.css
deleted file mode 100644
index 628b2cc..0000000
--- a/_preview/3/_static/vendor/fontawesome/6.5.2/css/all.min.css
+++ /dev/null
@@ -1,5 +0,0 @@
-/*!
- * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
- * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
- * Copyright 2024 Fonticons, Inc.
- */.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:Font Awesome\ 6 Free}.fa-brands,.fab{font-family:Font Awesome\ 6 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(prefers-reduced-motion:reduce){.fa-beat,.fa-beat-fade,.fa-bounce,.fa-fade,.fa-flip,.fa-pulse,.fa-shake,.fa-spin,.fa-spin-pulse{-webkit-animation-delay:-1ms;animation-delay:-1ms;-webkit-animation-duration:1ms;animation-duration:1ms;-webkit-animation-iteration-count:1;animation-iteration-count:1;-webkit-transition-delay:0s;transition-delay:0s;-webkit-transition-duration:0s;transition-duration:0s}}@-webkit-keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@-webkit-keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) 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translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@-webkit-keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@-webkit-keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@-webkit-keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@keyframes 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diff --git a/_preview/3/_static/webpack-macros.html b/_preview/3/_static/webpack-macros.html
deleted file mode 100644
index c051fa9..0000000
--- a/_preview/3/_static/webpack-macros.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!--
-  AUTO-GENERATED from webpack.config.js, do **NOT** edit by hand.
-  These are re-used in layout.html
--->
-{# Load FontAwesome icons #}
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-<li class="toctree-l2"><a class="reference internal" href="gallery/Upperair_Obs.html">DIFAX Replication</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="gallery/declarative_500_hPa.html">Declarative 500-hPa Absolute Vorticity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/index1.html">a potential entry?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/index2.html">a different category, perhaps?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/miller_composite.html">Miller Composite Chart</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/xarray_500hPa_map.html">Using Xarray for Data read and selection</a></li>
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-</details></li>
-<li class="toctree-l1"><a class="reference internal" href="gallery/index1.html">do we want</a></li>
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-  <section class="tex2jax_ignore mathjax_ignore" id="advanced-sounding">
-<h1>Advanced Sounding<a class="headerlink" href="#advanced-sounding" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Plot a sounding using MetPy with more advanced features. This will use the same formatting and another dataset from MetPy’s sample data.<br></p>
-<blockquote>
-<div><p>New additions</p>
-<ul class="simple">
-<li><p>Lifted condensation level calculation (LCL)</p></li>
-<li><p>Surface based parcel profile</p></li>
-<li><p>Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) shaded</p></li>
-<li><p>0 degree isotherm distinguished</p></li>
-</ul>
-</div></blockquote>
-<section id="imports">
-<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.cbook</span> <span class="kn">import</span> <span class="n">get_test_data</span>
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">add_metpy_logo</span><span class="p">,</span> <span class="n">SkewT</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="obtain-data-and-format">
-<h2>Obtain Data and Format<a class="headerlink" href="#obtain-data-and-format" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy’s sample data.
-<code class="docutils literal notranslate"><span class="pre">as_file_obj=False),</span> <span class="pre">skiprows=5,</span> <span class="pre">usecols=[0,</span> <span class="pre">1,</span> <span class="pre">2,</span> <span class="pre">3,</span> <span class="pre">6,</span> <span class="pre">7],</span> <span class="pre">names=col_names)</span></code> is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="s1">&#39;height&#39;</span><span class="p">,</span> <span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span><span class="p">]</span>
-
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">get_test_data</span><span class="p">(</span><span class="s1">&#39;may4_sounding.txt&#39;</span><span class="p">,</span> <span class="n">as_file_obj</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-                 <span class="n">skiprows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">col_names</span><span class="p">)</span>
-
-<span class="c1"># Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds</span>
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span>
-                       <span class="p">),</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Downloading file &#39;may4_sounding.txt&#39; from &#39;https://github.com/Unidata/MetPy/raw/v1.6.2/staticdata/may4_sounding.txt&#39; to &#39;/home/runner/.cache/metpy/v1.6.2&#39;.
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="assign-units">
-<h2>Assign Units<a class="headerlink" href="#assign-units" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detail in the Simple Sounding notebook and in the <a class="reference external" href="https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html">Metpy documentation</a>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pres</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">temp</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">dewpoint</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">wind_speed</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;speed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-<span class="n">wind_dir</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span>
-<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">wind_speed</span><span class="p">,</span> <span class="n">wind_dir</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="create-sounding-plot">
-<h2>Create Sounding Plot<a class="headerlink" href="#create-sounding-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure and set size</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-<span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-<span class="c1"># Plot temperature, dewpoint and wind barbs</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot wind barbs</span>
-<span class="n">my_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span> <span class="c1">#set spacing interval</span>
-<span class="n">ix</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">resample_nn_1d</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">my_interval</span><span class="p">)</span> <span class="c1">#find nearest indices for chosen interval</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">xloc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1">#plot values closest to chosen interval</span>
-
-<span class="c1"># Improve labels and set axis limits</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">59</span><span class="p">)</span>
-
-<span class="c1"># Calculate LCL height and plot as black dot.</span>
-<span class="n">lcl_pressure</span><span class="p">,</span> <span class="n">lcl_temperature</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">lcl</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="c1">#index 0 is chosen to lift parcel from the surface</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lcl_pressure</span><span class="p">,</span> <span class="n">lcl_temperature</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-
-<span class="c1"># Calculate full parcel profile and add to plot as black line</span>
-<span class="n">prof</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">parcel_profile</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">prof</span><span class="p">,</span> <span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Shade areas of CAPE and CIN</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">shade_cin</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">prof</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">shade_cape</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">prof</span><span class="p">)</span>
-
-<span class="c1"># Add emphasis to 0 degree isotherm with color change</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Add the relevant special lines throughout the figure</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">533</span><span class="p">,</span> <span class="mi">15</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orangered&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">)</span>
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-<section class="tex2jax_ignore mathjax_ignore" id="metpy-skew-t-cookbook">
-<h1>MetPy Skew-T Cookbook<a class="headerlink" href="#metpy-skew-t-cookbook" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<hr class="docutils" />
-<section id="overview">
-<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This Project Pythia Cookbook will cover creating Skew-T plots with MetPy. <br></p>
-<p>In this Cookbook, you will learn:</p>
-<ol class="arabic simple">
-<li><p>The foundations of creating a basic Skew-T with MetPy.</p></li>
-<li><p>Adding advanced features to a Skew-T such as Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) shading, Lifted condensation Level marker (LCL) and parcel profile.</p></li>
-<li><p>Creating a Hodograph with multiple layout options.</p></li>
-<li><p>Accessing remote Upper Air data with Siphon from University of Wyoming and Iowa State University.</p></li>
-<li><p>Using MetPy to calculate several Skew-T parameters.</p></li>
-</ol>
-</section>
-<section id="prerequisites">
-<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="pst-scrollable-table-container"><table class="table">
-<thead>
-<tr class="row-odd"><th class="head"><p>Concepts</p></th>
-<th class="head"><p>Importance</p></th>
-<th class="head"><p>Notes</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p><a class="reference external" href="https://foundations.projectpythia.org/core/numpy/intermediate-numpy.html">Intermediate NumPy</a></p></td>
-<td><p>Necessary</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference external" href="https://foundations.projectpythia.org/core/matplotlib/matplotlib-basics.html">Matplotlib Basis</a></p></td>
-<td><p>Necessary</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<ul class="simple">
-<li><p><strong>Time to learn</strong>: 30 minutes</p></li>
-</ul>
-</section>
-<hr class="docutils" />
-<section id="imports">
-<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.cbook</span> <span class="kn">import</span> <span class="n">get_test_data</span>
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">add_metpy_logo</span><span class="p">,</span> <span class="n">SkewT</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
-<section class="tex2jax_ignore mathjax_ignore" id="simple-sounding-with-metpy-test-data">
-<h1>Simple Sounding with MetPy Test Data<a class="headerlink" href="#simple-sounding-with-metpy-test-data" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Use MetPy to make a simple Skew-T plot. This will serve as a base for all Skew-T plots throughout this cookbook. <br></p>
-<section id="obtain-data-and-format">
-<h2>Obtain Data and Format<a class="headerlink" href="#obtain-data-and-format" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy’s sample data.
-<code class="docutils literal notranslate"><span class="pre">as_file_obj=False),</span> <span class="pre">skiprows=5,</span> <span class="pre">usecols=[0,</span> <span class="pre">1,</span> <span class="pre">2,</span> <span class="pre">3,</span> <span class="pre">6,</span> <span class="pre">7],</span> <span class="pre">names=col_names)</span></code> is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="s1">&#39;height&#39;</span><span class="p">,</span> <span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span><span class="p">]</span>
-
-<span class="n">sounding_data</span><span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">get_test_data</span><span class="p">(</span><span class="s1">&#39;jan20_sounding.txt&#39;</span><span class="p">,</span> <span class="n">as_file_obj</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-                 <span class="n">skiprows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">col_names</span><span class="p">)</span>
-
-<span class="c1"># Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds</span>
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span>
-                       <span class="p">),</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">sounding_data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Downloading file &#39;jan20_sounding.txt&#39; from &#39;https://github.com/Unidata/MetPy/raw/v1.6.2/staticdata/jan20_sounding.txt&#39; to &#39;/home/runner/.cache/metpy/v1.6.2&#39;.
-</pre></div>
-</div>
-<div class="output text_html"><div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>pressure</th>
-      <th>height</th>
-      <th>temperature</th>
-      <th>dewpoint</th>
-      <th>direction</th>
-      <th>speed</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>978.0</td>
-      <td>345</td>
-      <td>7.8</td>
-      <td>0.8</td>
-      <td>325</td>
-      <td>14</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>971.0</td>
-      <td>404</td>
-      <td>7.2</td>
-      <td>0.2</td>
-      <td>327</td>
-      <td>17</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>946.7</td>
-      <td>610</td>
-      <td>5.2</td>
-      <td>-1.8</td>
-      <td>335</td>
-      <td>26</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>944.0</td>
-      <td>634</td>
-      <td>5.0</td>
-      <td>-2.0</td>
-      <td>336</td>
-      <td>27</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>925.0</td>
-      <td>798</td>
-      <td>3.4</td>
-      <td>-2.6</td>
-      <td>340</td>
-      <td>32</td>
-    </tr>
-  </tbody>
-</table>
-</div></div></div>
-</div>
-</section>
-<section id="assign-units">
-<h2>Assign Units<a class="headerlink" href="#assign-units" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>One of MetPy’s powerful features is its unit syntax. MetPy typically requires units to be attached to arrays before being passed to MetPy functions. The MetPy documentation has a full tutorial section on <a class="reference external" href="https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html">working with units</a>. <br></p>
-<p>In order to plot our sounding data, we will need to pass units to the individual variables in our dataset.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pres</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">temp</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">dewpoint</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">wind_speed</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;speed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-<span class="n">wind_dir</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span>
-<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">wind_speed</span><span class="p">,</span> <span class="n">wind_dir</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pres</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_html"><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[978.0 971.0 946.7 944.0 925.0 911.8 906.0 877.9 850.0 841.0 823.0 813.6 809.0 798.0 791.0 783.9 755.1 727.3 700.5 700.0 698.0 687.0 648.9 631.0 600.7 592.0 577.6 555.3 536.0 533.8 500.0 472.3 453.0 400.0 399.7 387.0 382.7 342.0 320.4 318.0 310.0 306.1 305.0 300.0 292.0 276.0 264.0 251.0 250.0 247.0 244.0 243.3 220.0 212.0 210.0 200.0 194.0 183.0 164.0 152.0 150.0 136.0 132.0 131.4 128.0 125.0 119.0 112.0 108.0 107.8 105.0 103.0 100.0]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>hectopascal</td></tr></tbody></table></div></div>
-</div>
-</section>
-<section id="create-sounding-plot">
-<h2>Create Sounding Plot<a class="headerlink" href="#create-sounding-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure and set size</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-<span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-<span class="c1"># Plot temperature and dewpoint lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot wind barbs</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
-
-<span class="c1"># Improve labels and set axis limits</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-
-<span class="c1"># Add the relevant special lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">()</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">()</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_mixing_lines</span><span class="p">()</span>
-
-<span class="c1"># Add the MetPy logo!</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">add_metpy_logo</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="mi">115</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
-
-<span class="c1"># Add a title</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Simple Sounding&#39;</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<img alt="../_images/9ed8a7da1ae1f900513904e62372cac672fdd225b4ef8a539280b7724131efe1.png" src="../_images/9ed8a7da1ae1f900513904e62372cac672fdd225b4ef8a539280b7724131efe1.png" />
-</div>
-</div>
-<section id="lets-unpack-the-the-previous-code-cell">
-<h3>Let’s Unpack the the Previous Code Cell<a class="headerlink" href="#lets-unpack-the-the-previous-code-cell" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">fig</span> <span class="pre">=</span> <span class="pre">plt.figure(figsize=(6,</span> <span class="pre">6))</span></code> <br></p>
-<ul class="simple">
-<li><p>This line creates the figure and sets the size. Equal proportions are typically recommended for a Skew-T. <br></p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">skew</span> <span class="pre">=</span> <span class="pre">SkewT(fig,</span> <span class="pre">rotation=45)</span></code> <br></p>
-<ul class="simple">
-<li><p>Calls the MetPy <a class="reference external" href="https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html">Skew-T</a> function to create a figure that we can easily plot upper air data on. Rotation is set at 45 to create the characteristic skewed appearence of a Skew-T.</p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">skew.plot(pres,</span> <span class="pre">temp,</span> <span class="pre">'red')</span></code> <br>
-<code class="docutils literal notranslate"><span class="pre">skew.plot(pres,</span> <span class="pre">dewpoint,</span> <span class="pre">'green')</span></code> <br></p>
-<ul class="simple">
-<li><p>These two lines plot the <a class="reference external" href="https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot">temperature and dewpoint lines</a>. The lines are set to the standard colors, but can be adjusted for colorblind accessibility. Matplotlib has extensive <a class="reference external" href="https://matplotlib.org/stable/tutorials/colors/colormaps.html">documentation</a> on choosing colormaps and color vision deficiencies resources.</p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">skew.plot_barbs(pres,</span> <span class="pre">u,</span> <span class="pre">v)</span></code><br></p>
-<ul class="simple">
-<li><p>This line plots the wind barbs. Customization options can be found <a class="reference external" href="https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot_barbs">here</a>.</p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">skew.ax.set_xlabel('Temperature</span> <span class="pre">(\N{DEGREE</span> <span class="pre">CELSIUS})')</span></code> <br>
-<code class="docutils literal notranslate"><span class="pre">skew.ax.set_ylabel('Pressure</span> <span class="pre">(hPa)')</span></code> <br>
-<code class="docutils literal notranslate"><span class="pre">skew.ax.set_ylim(1000,</span> <span class="pre">100)</span></code> <br></p>
-<ul class="simple">
-<li><p>These lines improve the axes labels and set the y-axis limit to the standard values typically used in a Skew-T diagram.
-<br></p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">skew.plot_dry_adiabats()</span></code><br>
-<code class="docutils literal notranslate"><span class="pre">skew.plot_moist_adiabats()</span></code><br>
-<code class="docutils literal notranslate"><span class="pre">skew.plot_mixing_lines()</span></code><br></p>
-<ul class="simple">
-<li><p>These add each relevant special line if desired. The argument <code class="docutils literal notranslate"><span class="pre">colors='preferred</span> <span class="pre">color</span> <span class="pre">here'</span></code> can be passed if you would like to change the colors from the default. Further customization options can be found in the MetPy <a class="reference external" href="https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot_dry_adiabats">documentation</a> for each line.</p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">fig</span> <span class="pre">=</span> <span class="pre">plt.gcf()</span></code><br></p>
-<ul class="simple">
-<li><p>Gcf stands for get current figure. This retrieves the current figure and allows us to modify it with the following line. <br></p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">add_metpy_logo(fig,</span> <span class="pre">115,</span> <span class="pre">100)</span></code><br></p>
-<ul class="simple">
-<li><p>When <code class="docutils literal notranslate"><span class="pre">add_metpy_logo</span></code> is included in the imports cell, it allows use of the <code class="docutils literal notranslate"><span class="pre">add_metpy_logo()</span></code> function. This is greatly appreciated when utilizing MetPy for your plots. The <a class="reference external" href="https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.add_metpy_logo.html#metpy.plots.add_metpy_logo">position and size</a> of the logo can be adjusted.
-<br></p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">plt.title('Simple</span> <span class="pre">Sounding');</span></code><br></p>
-<ul class="simple">
-<li><p>This adds the title to the figure. The ; removes text that populates above the figure.
-<br></p></li>
-</ul>
-</div></blockquote>
-</section>
-</section>
-<section id="now-lets-improve-the-wind-barbs">
-<h2>Now, Let’s Improve the Wind Barbs<a class="headerlink" href="#now-lets-improve-the-wind-barbs" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>We will use the same code as above, but will add three additional lines of code.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure and set size</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-<span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-<span class="c1"># Plot temperature and dewpoint lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set spacing interval--Every 50 hPa from 1000 to 100 hPa</span>
-<span class="n">my_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
-
-<span class="c1"># Get indexes of values closest to chosen interval</span>
-<span class="n">ix</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">resample_nn_1d</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">my_interval</span><span class="p">)</span>
-
-<span class="c1"># Plot wind barbs with values nearest to chosen interval values</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
-
-<span class="c1"># Improve labels and set axis limits</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-
-<span class="c1"># Add the relevant special lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">()</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">()</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_mixing_lines</span><span class="p">()</span>
-
-<span class="c1"># Add the MetPy logo!</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">add_metpy_logo</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="mi">115</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
-
-<span class="c1"># Add a title</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Simple Sounding&#39;</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<img alt="../_images/bd7e59c922c4651450502a76823feb97178695fcd6f5ae499a8fbc18c5f6b528.png" src="../_images/bd7e59c922c4651450502a76823feb97178695fcd6f5ae499a8fbc18c5f6b528.png" />
-</div>
-</div>
-<p>The wind barbs on this plot are much more readable. Let’s review the added code to understand why.</p>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">my_interval</span> <span class="pre">=</span> <span class="pre">np.arange(100,</span> <span class="pre">1000,</span> <span class="pre">50)</span> <span class="pre">*</span> <span class="pre">units('hPa')</span></code></p>
-<ul class="simple">
-<li><p>This creates a custom spacing interval from the pressure levels the wind barbs are plotted on.</p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">ix</span> <span class="pre">=</span> <span class="pre">mpcalc.resample_nn_1d(pres,</span> <span class="pre">my_interval)</span></code></p>
-<ul class="simple">
-<li><p>We defined where we want the wind barbs with <code class="docutils literal notranslate"><span class="pre">my_interval</span></code>. Since mb is not an index in our dataset, we have to use the function <code class="docutils literal notranslate"><span class="pre">mpcalc.resample_nn_1d()</span></code> to determine what index value each interval closest matches up with. Further information can be found in the MetPy <a class="reference external" href="https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.resample_nn_1d.html">documentation</a>.</p></li>
-</ul>
-</div></blockquote>
-<blockquote>
-<div><p><code class="docutils literal notranslate"><span class="pre">skew.plot_barbs(pres[ix],</span> <span class="pre">u[ix],</span> <span class="pre">v[ix])</span></code></p>
-<ul class="simple">
-<li><p>Now we just add the <code class="docutils literal notranslate"><span class="pre">ix</span></code> variable to each parameter in <code class="docutils literal notranslate"><span class="pre">.plot_barbs()</span></code> to produce the wind barbs at the interval we set.</p></li>
-</ul>
-</div></blockquote>
-</section>
-<section id="optional-step">
-<h2>Optional Step<a class="headerlink" href="#optional-step" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>We can extend the moist adiabat, dry adiabat, and mixing ratio lines throughout the figure by adding including optional parameters. See the <a class="reference external" href="https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html#metpy.plots.SkewT.plot_dry_adiabats">documentation</a> for details on how to further adjust these for readability preferences.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure and set size</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-<span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-<span class="c1"># Plot temperature and dewpoint lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set spacing interval--Every 50 hPa from 1000 to 100 hPa</span>
-<span class="n">my_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
-
-<span class="c1"># Get indexes of values closest to chosen interval</span>
-<span class="n">ix</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">resample_nn_1d</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">my_interval</span><span class="p">)</span>
-
-<span class="c1"># Plot wind barbs with values nearest to chosen interval values</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
-
-<span class="c1"># Improve labels and set axis limits</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-
-<span class="c1"># Add the relevant special lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">533</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orangered&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_mixing_lines</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="o">-</span><span class="mi">25</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">)</span>
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-<span class="n">add_metpy_logo</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="mi">115</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
-
-<span class="c1"># Add a title</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Simple Sounding&#39;</span><span class="p">);</span>
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-  <section class="tex2jax_ignore mathjax_ignore" id="skew-t-analysis">
-<h1>Skew-T Analysis<a class="headerlink" href="#skew-t-analysis" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Create a Skew-T plot using remote data from University of Wyoming.</p>
-<p>This example uses example data from the University of Wyoming sounding
-archive for 12 UTC 31 October 2016 for Minneapolis, MN (MPX).</p>
-<section id="imports">
-<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.cbook</span> <span class="kn">import</span> <span class="n">get_test_data</span>
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">add_metpy_logo</span><span class="p">,</span> <span class="n">SkewT</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-
-<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-<span class="kn">from</span> <span class="nn">siphon.simplewebservice.wyoming</span> <span class="kn">import</span> <span class="n">WyomingUpperAir</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="obtain-data-and-format">
-<h2>Obtain Data and Format<a class="headerlink" href="#obtain-data-and-format" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This requires an internet connection to access the sounding data from a
-remote server at the University of Wyoming. <br></p>
-<p>First we will create variables for date, time and station. <br>
-Format: <code class="docutils literal notranslate"><span class="pre">datetime(YYYY,</span> <span class="pre">MM,</span> <span class="pre">DD,</span> <span class="pre">HH)</span></code> <br></p>
-<p>The <code class="docutils literal notranslate"><span class="pre">station</span></code> entry will need the be the three letter identifier of the station you want to look at. The National Center for Atmospheric Research (NCAR) <a class="reference external" href="https://ral.ucar.edu">Research Applications Labratory</a> (RAL) continously updates a <a class="reference external" href="https://weather.rap.ucar.edu/surface/stations.txt">detailed list of all stations</a>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create variables for preferred date, time and station</span>
-<span class="n">preferred_date</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
-<span class="n">station</span> <span class="o">=</span> <span class="s1">&#39;MPX&#39;</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Read remote sounding data based on preferred_date and station</span>
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">WyomingUpperAir</span><span class="o">.</span><span class="n">request_data</span><span class="p">(</span><span class="n">preferred_date</span><span class="p">,</span> <span class="n">station</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">HTTPError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
-<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="c1"># Read remote sounding data based on preferred_date and station</span>
-<span class="ne">----&gt; </span><span class="mi">2</span> <span class="n">sounding_data</span> <span class="o">=</span> <span class="n">WyomingUpperAir</span><span class="o">.</span><span class="n">request_data</span><span class="p">(</span><span class="n">preferred_date</span><span class="p">,</span> <span class="n">station</span><span class="p">)</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:50,</span> in <span class="ni">WyomingUpperAir.request_data</span><span class="nt">(cls, time, site_id, **kwargs)</span>
-<span class="g g-Whitespace">     </span><span class="mi">30</span><span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Retrieve upper air observations from the Wyoming archive.</span>
-<span class="g g-Whitespace">     </span><span class="mi">31</span><span class="sd"> </span>
-<span class="g g-Whitespace">     </span><span class="mi">32</span><span class="sd"> Parameters</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">     </span><span class="mi">47</span><span class="sd"> </span>
-<span class="g g-Whitespace">     </span><span class="mi">48</span><span class="sd"> &quot;&quot;&quot;</span>
-<span class="g g-Whitespace">     </span><span class="mi">49</span> <span class="n">endpoint</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">()</span>
-<span class="ne">---&gt; </span><span class="mi">50</span> <span class="n">df</span> <span class="o">=</span> <span class="n">endpoint</span><span class="o">.</span><span class="n">_get_data</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">)</span>
-<span class="g g-Whitespace">     </span><span class="mi">51</span> <span class="k">return</span> <span class="n">df</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:70,</span> in <span class="ni">WyomingUpperAir._get_data</span><span class="nt">(self, time, site_id)</span>
-<span class="g g-Whitespace">     </span><span class="mi">53</span> <span class="k">def</span> <span class="nf">_get_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">):</span>
-<span class="g g-Whitespace">     </span><span class="mi">54</span><span class="w">     </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Download and parse upper air observations from an online archive.</span>
-<span class="g g-Whitespace">     </span><span class="mi">55</span><span class="sd"> </span>
-<span class="g g-Whitespace">     </span><span class="mi">56</span><span class="sd">     Parameters</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">     </span><span class="mi">68</span><span class="sd"> </span>
-<span class="g g-Whitespace">     </span><span class="mi">69</span><span class="sd">     &quot;&quot;&quot;</span>
-<span class="ne">---&gt; </span><span class="mi">70</span>     <span class="n">raw_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_data_raw</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">)</span>
-<span class="g g-Whitespace">     </span><span class="mi">71</span>     <span class="n">soup</span> <span class="o">=</span> <span class="n">BeautifulSoup</span><span class="p">(</span><span class="n">raw_data</span><span class="p">,</span> <span class="s1">&#39;html.parser&#39;</span><span class="p">)</span>
-<span class="g g-Whitespace">     </span><span class="mi">72</span>     <span class="n">tabular_data</span> <span class="o">=</span> <span class="n">StringIO</span><span class="p">(</span><span class="n">soup</span><span class="o">.</span><span class="n">find_all</span><span class="p">(</span><span class="s1">&#39;pre&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">contents</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:144,</span> in <span class="ni">WyomingUpperAir._get_data_raw</span><span class="nt">(self, time, site_id)</span>
-<span class="g g-Whitespace">    </span><span class="mi">126</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;Download data from the University of Wyoming&#39;s upper air archive.</span>
-<span class="g g-Whitespace">    </span><span class="mi">127</span><span class="sd"> </span>
-<span class="g g-Whitespace">    </span><span class="mi">128</span><span class="sd"> Parameters</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">    </span><span class="mi">138</span><span class="sd"> </span>
-<span class="g g-Whitespace">    </span><span class="mi">139</span><span class="sd"> &quot;&quot;&quot;</span>
-<span class="g g-Whitespace">    </span><span class="mi">140</span> <span class="n">path</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;?region=naconf&amp;TYPE=TEXT%3ALIST&#39;</span>
-<span class="g g-Whitespace">    </span><span class="mi">141</span>         <span class="s1">&#39;&amp;YEAR={time:%Y}&amp;MONTH={time:%m}&amp;FROM={time:</span><span class="si">%d</span><span class="s1">%H}&amp;TO={time:</span><span class="si">%d</span><span class="s1">%H}&#39;</span>
-<span class="g g-Whitespace">    </span><span class="mi">142</span>         <span class="s1">&#39;&amp;STNM=</span><span class="si">{stid}</span><span class="s1">&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="n">time</span><span class="p">,</span> <span class="n">stid</span><span class="o">=</span><span class="n">site_id</span><span class="p">)</span>
-<span class="ne">--&gt; </span><span class="mi">144</span> <span class="n">resp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">145</span> <span class="c1"># See if the return is valid, but has no data</span>
-<span class="g g-Whitespace">    </span><span class="mi">146</span> <span class="k">if</span> <span class="n">resp</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;Can&#39;t&quot;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/http_util.py:457,</span> in <span class="ni">HTTPEndPoint.get_path</span><span class="nt">(self, path, query)</span>
-<span class="g g-Whitespace">    </span><span class="mi">434</span> <span class="k">def</span> <span class="nf">get_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">query</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="g g-Whitespace">    </span><span class="mi">435</span><span class="w">     </span><span class="sd">&quot;&quot;&quot;Make a GET request, optionally including a query, to a relative path.</span>
-<span class="g g-Whitespace">    </span><span class="mi">436</span><span class="sd"> </span>
-<span class="g g-Whitespace">    </span><span class="mi">437</span><span class="sd">     The path of the request includes a path on top of the base URL</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">    </span><span class="mi">455</span><span class="sd"> </span>
-<span class="g g-Whitespace">    </span><span class="mi">456</span><span class="sd">     &quot;&quot;&quot;</span>
-<span class="ne">--&gt; </span><span class="mi">457</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">url_path</span><span class="p">(</span><span class="n">path</span><span class="p">),</span> <span class="n">query</span><span class="p">)</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/http_util.py:492,</span> in <span class="ni">HTTPEndPoint.get</span><span class="nt">(self, path, params)</span>
-<span class="g g-Whitespace">    </span><span class="mi">490</span>     <span class="k">else</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">491</span>         <span class="n">text</span> <span class="o">=</span> <span class="n">resp</span><span class="o">.</span><span class="n">text</span>
-<span class="ne">--&gt; </span><span class="mi">492</span>     <span class="k">raise</span> <span class="n">requests</span><span class="o">.</span><span class="n">HTTPError</span><span class="p">(</span><span class="s1">&#39;Error accessing </span><span class="si">{0}</span><span class="se">\n</span><span class="s1">&#39;</span>
-<span class="g g-Whitespace">    </span><span class="mi">493</span>                              <span class="s1">&#39;Server Error (</span><span class="si">{1:d}</span><span class="s1">: </span><span class="si">{2}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">resp</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">url</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">494</span>                                                                 <span class="n">resp</span><span class="o">.</span><span class="n">status_code</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">495</span>                                                                 <span class="n">text</span><span class="p">))</span>
-<span class="g g-Whitespace">    </span><span class="mi">496</span> <span class="k">return</span> <span class="n">resp</span>
-
-<span class="ne">HTTPError</span>: Error accessing http://weather.uwyo.edu/cgi-bin/sounding/?region=naconf&amp;TYPE=TEXT%3ALIST&amp;YEAR=2016&amp;MONTH=10&amp;FROM=2612&amp;TO=2612&amp;STNM=MPX
-<span class="n">Server</span> <span class="n">Error</span> <span class="p">(</span><span class="mi">503</span><span class="p">:</span> <span class="n">Sorry</span><span class="p">,</span> <span class="n">the</span> <span class="n">server</span> <span class="ow">is</span> <span class="n">too</span> <span class="n">busy</span> <span class="n">to</span> <span class="n">process</span> <span class="n">your</span> <span class="n">request</span><span class="o">.</span>
-<span class="n">Please</span> <span class="k">try</span> <span class="n">again</span> <span class="n">later</span><span class="o">.</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sounding_data</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">sounding_data</span>
-
-<span class="ne">NameError</span>: name &#39;sounding_data&#39; is not defined
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="assign-units">
-<h2>Assign Units<a class="headerlink" href="#assign-units" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the <a class="reference external" href="https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html">Metpy documentation</a>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pres</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">temp</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">dewpoint</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">wind_speed</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;speed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-<span class="n">wind_dir</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span>
-<span class="n">u</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;u_wind&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-<span class="n">v</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;v_wind&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">pres</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="g g-Whitespace">      </span><span class="mi">2</span> <span class="n">temp</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="g g-Whitespace">      </span><span class="mi">3</span> <span class="n">dewpoint</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-
-<span class="ne">NameError</span>: name &#39;sounding_data&#39; is not defined
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="create-sounding-plot">
-<h2>Create Sounding Plot<a class="headerlink" href="#create-sounding-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure and set size</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-<span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-<span class="c1"># Plot temperature and dewpoint lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot wind barbs</span>
-<span class="n">my_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
-<span class="n">ix</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">resample_nn_1d</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">my_interval</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">y_clip_radius</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
-
-<span class="c1"># Improve labels and set axis limits</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-
-<span class="c1"># Add the relevant special lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">533</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orangered&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_mixing_lines</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="o">-</span><span class="mi">25</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">)</span>
-
-<span class="c1"># Add the MetPy logo!</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">add_metpy_logo</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="mi">115</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
-
-<span class="c1"># Add some descriptive titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> Sounding&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">station</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Valid Time: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">preferred_date</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">line</span> <span class="mi">6</span>
-<span class="g g-Whitespace">      </span><span class="mi">3</span> <span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-<span class="g g-Whitespace">      </span><span class="mi">5</span> <span class="c1"># Plot temperature and dewpoint lines</span>
-<span class="ne">----&gt; </span><span class="mi">6</span> <span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="g g-Whitespace">      </span><span class="mi">7</span> <span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-<span class="g g-Whitespace">      </span><span class="mi">9</span> <span class="c1"># Plot wind barbs</span>
-
-<span class="ne">NameError</span>: name &#39;pres&#39; is not defined
-</pre></div>
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diff --git a/_preview/3/notebooks/Skew-T_Hodograph_Inset.html b/_preview/3/notebooks/Skew-T_Hodograph_Inset.html
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-<h1>Hodograph Inset<a class="headerlink" href="#hodograph-inset" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Layout a Skew-T plot with a hodograph inset into the plot.</p>
-<section id="imports">
-<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.cbook</span> <span class="kn">import</span> <span class="n">get_test_data</span>
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">add_metpy_logo</span><span class="p">,</span> <span class="n">Hodograph</span><span class="p">,</span> <span class="n">SkewT</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-
-<span class="kn">from</span> <span class="nn">mpl_toolkits.axes_grid1.inset_locator</span> <span class="kn">import</span> <span class="n">inset_axes</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="obtain-data-and-format">
-<h2>Obtain Data and Format<a class="headerlink" href="#obtain-data-and-format" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy’s sample data.
-<code class="docutils literal notranslate"><span class="pre">as_file_obj=False),</span> <span class="pre">skiprows=5,</span> <span class="pre">usecols=[0,</span> <span class="pre">1,</span> <span class="pre">2,</span> <span class="pre">3,</span> <span class="pre">6,</span> <span class="pre">7],</span> <span class="pre">names=col_names)</span></code> is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="s1">&#39;height&#39;</span><span class="p">,</span> <span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span><span class="p">]</span>
-
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">get_test_data</span><span class="p">(</span><span class="s1">&#39;may4_sounding.txt&#39;</span><span class="p">,</span> <span class="n">as_file_obj</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-                 <span class="n">skiprows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">col_names</span><span class="p">)</span>
-
-<span class="c1"># Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds</span>
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span>
-                       <span class="p">),</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="assign-units">
-<h2>Assign Units<a class="headerlink" href="#assign-units" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the <a class="reference external" href="https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html">Metpy documentation</a>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pres</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">temp</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">dewpoint</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">wind_speed</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;speed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-<span class="n">wind_dir</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span>
-<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">wind_speed</span><span class="p">,</span> <span class="n">wind_dir</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure and grid for plots</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-<span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-<span class="c1"># Plot temperature and dewpoint lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot wind barbs</span>
-<span class="n">my_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
-<span class="n">ix</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">resample_nn_1d</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">my_interval</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
-
-<span class="c1"># Improve labels and set axis limits</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">)</span>
-
-<span class="c1"># Add the relevant special lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">533</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orangered&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_mixing_lines</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="o">-</span><span class="mi">25</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">)</span>
-
-<span class="c1"># Create a hodograph</span>
-<span class="n">ax_hod</span> <span class="o">=</span> <span class="n">inset_axes</span><span class="p">(</span><span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="p">,</span> <span class="s1">&#39;35%&#39;</span><span class="p">,</span> <span class="s1">&#39;35%&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">h</span> <span class="o">=</span> <span class="n">Hodograph</span><span class="p">(</span><span class="n">ax_hod</span><span class="p">,</span> <span class="n">component_range</span><span class="o">=</span><span class="mi">80</span><span class="p">)</span>
-<span class="n">h</span><span class="o">.</span><span class="n">add_grid</span><span class="p">(</span><span class="n">increment</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-<span class="n">h</span><span class="o">.</span><span class="n">plot_colormapped</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">pres</span><span class="p">)</span>
-
-<span class="c1"># Add the MetPy logo!</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">add_metpy_logo</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">650</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
-
-<span class="c1"># Add title</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Advanced Sounding with Hodograph Inset&#39;</span><span class="p">);</span>
-</pre></div>
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-<h1>Skew-T with Complex Layout<a class="headerlink" href="#skew-t-with-complex-layout" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Combine a Skew-T and a hodograph using Matplotlib’s <code class="docutils literal notranslate"><span class="pre">GridSpec</span></code> layout capability.</p>
-<section id="imports">
-<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
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-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.cbook</span> <span class="kn">import</span> <span class="n">get_test_data</span>
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">add_metpy_logo</span><span class="p">,</span> <span class="n">Hodograph</span><span class="p">,</span> <span class="n">SkewT</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="obtain-data-and-format">
-<h2>Obtain Data and Format<a class="headerlink" href="#obtain-data-and-format" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy’s sample data.
-<code class="docutils literal notranslate"><span class="pre">as_file_obj=False),</span> <span class="pre">skiprows=5,</span> <span class="pre">usecols=[0,</span> <span class="pre">1,</span> <span class="pre">2,</span> <span class="pre">3,</span> <span class="pre">6,</span> <span class="pre">7],</span> <span class="pre">names=col_names)</span></code> is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="s1">&#39;height&#39;</span><span class="p">,</span> <span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span><span class="p">]</span>
-
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">get_test_data</span><span class="p">(</span><span class="s1">&#39;may4_sounding.txt&#39;</span><span class="p">,</span> <span class="n">as_file_obj</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-                 <span class="n">skiprows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">col_names</span><span class="p">)</span>
-
-<span class="c1"># Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds</span>
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span>
-                       <span class="p">),</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="assign-units">
-<h2>Assign Units<a class="headerlink" href="#assign-units" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the <a class="reference external" href="https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html">Metpy documentation</a>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pres</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">temp</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">dewpoint</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">wind_speed</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;speed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-<span class="n">wind_dir</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span>
-<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">wind_speed</span><span class="p">,</span> <span class="n">wind_dir</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="create-sounding-plot">
-<h2>Create Sounding Plot<a class="headerlink" href="#create-sounding-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure and grid for plots</span>
-<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-<span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="n">gs</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">])</span>
-
-<span class="c1"># Plot temperature and dewpoint lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot wind barbs</span>
-<span class="n">my_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span>
-<span class="n">ix</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">resample_nn_1d</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">my_interval</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
-
-<span class="c1"># Improve labels and set axis limits</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="mi">40</span><span class="p">)</span>
-
-<span class="c1"># Add the relevant special lines</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">533</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orangered&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_mixing_lines</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="o">-</span><span class="mi">25</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">)</span>
-
-<span class="c1"># Create a hodograph </span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-<span class="n">h</span> <span class="o">=</span> <span class="n">Hodograph</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">component_range</span><span class="o">=</span><span class="mi">80</span><span class="p">)</span>
-<span class="n">h</span><span class="o">.</span><span class="n">add_grid</span><span class="p">(</span><span class="n">increment</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-<span class="n">h</span><span class="o">.</span><span class="n">plot_colormapped</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">pres</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.15</span><span class="p">,</span> <span class="s1">&#39;knots&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span> <span class="c1">#unit label</span>
-
-<span class="c1"># Add the MetPy logo!</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">add_metpy_logo</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="mi">600</span><span class="p">,</span> <span class="mi">70</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">);</span>
-
-<span class="c1"># Add titles</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Advanced Sounding with Complex Layout&#39;</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">&#39;bold&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Hodograph&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">);</span>
-</pre></div>
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-<li class="toctree-l2"><a class="reference internal" href="gallery/Upperair_Obs.html">DIFAX Replication</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/Wind_Shear_Vectors_Example.html">Calculating Wind Shear</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/declarative_500_hPa.html">Declarative 500-hPa Absolute Vorticity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/index1.html">a potential entry?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/index2.html">a different category, perhaps?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/miller_composite.html">Miller Composite Chart</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/xarray_500hPa_map.html">Using Xarray for Data read and selection</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1"><a class="reference internal" href="gallery/index1.html">do we want</a></li>
-<li class="toctree-l1"><a class="reference internal" href="gallery/index2.html">for these "chapters"?</a></li>
-</ul>
-<p aria-level="2" class="caption" role="heading"><span class="caption-text">Skew-T Notebooks</span></p>
-<ul class="current nav bd-sidenav">
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-<li class="toctree-l1 current active"><a class="current reference internal" href="#">Sounding Calculation Examples</a></li>
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-  <section class="tex2jax_ignore mathjax_ignore" id="sounding-calculation-examples">
-<h1>Sounding Calculation Examples<a class="headerlink" href="#sounding-calculation-examples" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Use functions from <code class="docutils literal notranslate"><span class="pre">metpy.calc</span></code> to perform a number of calculations using sounding data.</p>
-<p>The code below uses example data to perform many sounding calculations for a severe weather
-event on May 22, 2011 from the Norman, OK sounding.</p>
-<section id="imports">
-<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.cbook</span> <span class="kn">import</span> <span class="n">get_test_data</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Effective Shear Algorithm for use in Supercell Composite Calculation</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">effective_layer</span><span class="p">(</span><span class="n">pressure</span><span class="p">,</span> <span class="n">temperature</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">height_layer</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;A function that determines the effective inflow layer for a convective sounding.</span>
-
-<span class="sd">    Uses the default values of Thompason et al. (2004) for CAPE (100 J/kg) and CIN (-250 J/kg).</span>
-
-<span class="sd">    Input:</span>
-<span class="sd">      - pressure: sounding pressure with units</span>
-<span class="sd">      - temperature: sounding temperature with units</span>
-<span class="sd">      - dewpoint: sounding dewpoint temperature with units</span>
-<span class="sd">      - height: sounding heights with units</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">      - pbot/hbot, ptop/htop: pressure/height of the bottom level,</span>
-<span class="sd">                              pressure/height of the top level</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">metpy.calc</span> <span class="kn">import</span> <span class="n">cape_cin</span><span class="p">,</span> <span class="n">parcel_profile</span>
-    <span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-
-    <span class="n">pbot</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">pressure</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-        <span class="n">prof</span> <span class="o">=</span> <span class="n">parcel_profile</span><span class="p">(</span><span class="n">pressure</span><span class="p">[</span><span class="n">i</span><span class="p">:],</span> <span class="n">temperature</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="n">sbcape</span><span class="p">,</span> <span class="n">sbcin</span> <span class="o">=</span> <span class="n">cape_cin</span><span class="p">(</span><span class="n">pressure</span><span class="p">[</span><span class="n">i</span><span class="p">:],</span> <span class="n">temperature</span><span class="p">[</span><span class="n">i</span><span class="p">:],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="n">i</span><span class="p">:],</span> <span class="n">prof</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">sbcape</span> <span class="o">&gt;=</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;J/kg&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sbcin</span> <span class="o">&gt;</span> <span class="o">-</span><span class="mi">250</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;J/kg&#39;</span><span class="p">):</span>
-            <span class="n">pbot</span> <span class="o">=</span> <span class="n">pressure</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">hbot</span> <span class="o">=</span> <span class="n">height</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">bot_idx</span> <span class="o">=</span> <span class="n">i</span>
-            <span class="k">break</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">pbot</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">bot_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">pressure</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-        <span class="n">prof</span> <span class="o">=</span> <span class="n">parcel_profile</span><span class="p">(</span><span class="n">pressure</span><span class="p">[</span><span class="n">i</span><span class="p">:],</span> <span class="n">temperature</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        <span class="n">sbcape</span><span class="p">,</span> <span class="n">sbcin</span> <span class="o">=</span> <span class="n">cape_cin</span><span class="p">(</span><span class="n">pressure</span><span class="p">[</span><span class="n">i</span><span class="p">:],</span> <span class="n">temperature</span><span class="p">[</span><span class="n">i</span><span class="p">:],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="n">i</span><span class="p">:],</span> <span class="n">prof</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">sbcape</span> <span class="o">&lt;</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;J/kg&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="n">sbcin</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">250</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;J/kg&#39;</span><span class="p">):</span>
-            <span class="n">ptop</span> <span class="o">=</span> <span class="n">pressure</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="n">htop</span> <span class="o">=</span> <span class="n">height</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">break</span>
-
-    <span class="k">if</span> <span class="n">height_layer</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">hbot</span><span class="p">,</span> <span class="n">htop</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">pbot</span><span class="p">,</span> <span class="n">ptop</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="obtain-data-and-format">
-<h2>Obtain Data and Format<a class="headerlink" href="#obtain-data-and-format" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy’s sample data.
-<code class="docutils literal notranslate"><span class="pre">as_file_obj=False),</span> <span class="pre">skiprows=5,</span> <span class="pre">usecols=[0,</span> <span class="pre">1,</span> <span class="pre">2,</span> <span class="pre">3,</span> <span class="pre">6,</span> <span class="pre">7],</span> <span class="pre">names=col_names)</span></code> is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="s1">&#39;height&#39;</span><span class="p">,</span> <span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span><span class="p">]</span>
-
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">get_test_data</span><span class="p">(</span><span class="s1">&#39;20110522_OUN_12Z.txt&#39;</span><span class="p">,</span> <span class="n">as_file_obj</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-                 <span class="n">skiprows</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">col_names</span><span class="p">)</span>
-
-<span class="c1"># Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds</span>
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span><span class="p">),</span>
-               <span class="n">how</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Downloading file &#39;20110522_OUN_12Z.txt&#39; from &#39;https://github.com/Unidata/MetPy/raw/v1.6.2/staticdata/20110522_OUN_12Z.txt&#39; to &#39;/home/runner/.cache/metpy/v1.6.2&#39;.
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="assign-units">
-<h2>Assign Units<a class="headerlink" href="#assign-units" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the <a class="reference external" href="https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html">Metpy documentation</a>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pres</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">temp</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">dewpoint</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">wind_speed</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;speed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-<span class="n">wind_dir</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span>
-<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">wind_speed</span><span class="p">,</span> <span class="n">wind_dir</span><span class="p">)</span>
-<span class="n">height</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;height&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">meter</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Compute the wind components</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">wind_speed</span><span class="p">,</span> <span class="n">wind_dir</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Compute common sounding index parameters</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ctotals</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">cross_totals</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
-<span class="n">kindex</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">k_index</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
-<span class="n">showalter</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">showalter_index</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
-<span class="n">total_totals</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">total_totals_index</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
-<span class="n">vert_totals</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">vertical_totals</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Compture the parcel profile for a surface-based parcel</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">prof</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">parcel_profile</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Compute the corresponding Lifted Index (LI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN) values for a surface parcel</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lift_index</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">lifted_index</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">prof</span><span class="p">)</span>
-<span class="n">cape</span><span class="p">,</span> <span class="n">cin</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">cape_cin</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="n">prof</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Determine the Lifted Condensation Level (LCL), Level of Free Convection (LFC), and Equilibrium Level (EL) for our surface parcel</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lclp</span><span class="p">,</span> <span class="n">lclt</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">lcl</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">lfcp</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">lfc</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
-<span class="n">el_pressure</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">el</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="n">prof</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Compute the characteristics of a mean layer parcel (50-hPa depth)</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ml_t</span><span class="p">,</span> <span class="n">ml_td</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">mixed_layer</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="mi">50</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">ml_p</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">mixed_parcel</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="mi">50</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">mlcape</span><span class="p">,</span> <span class="n">mlcin</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">mixed_layer_cape_cin</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">prof</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="mi">50</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Compute the characteristics of the most unstable parcel (50-hPa depth)</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mu_p</span><span class="p">,</span> <span class="n">mu_t</span><span class="p">,</span> <span class="n">mu_td</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">most_unstable_parcel</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="mi">50</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">mucape</span><span class="p">,</span> <span class="n">mucin</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">most_unstable_cape_cin</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="mi">50</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Compute the Bunkers Storm Motion vector and use to calculate the critical angle</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="n">u_storm</span><span class="p">,</span> <span class="n">v_storm</span><span class="p">),</span> <span class="o">*</span><span class="n">_</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">bunkers_storm_motion</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">height</span><span class="p">)</span>
-<span class="n">critical_angle</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">critical_angle</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">u_storm</span><span class="p">,</span> <span class="n">v_storm</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Work on the calculations needed to compute the significant tornado parameter</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Estimate height of LCL in meters from hydrostatic thickness</span>
-<span class="n">new_p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">pres</span> <span class="o">&gt;</span> <span class="n">lclp</span><span class="p">],</span> <span class="n">lclp</span><span class="p">)</span>
-<span class="n">new_t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">temp</span><span class="p">[</span><span class="n">pres</span> <span class="o">&gt;</span> <span class="n">lclp</span><span class="p">],</span> <span class="n">lclt</span><span class="p">)</span>
-<span class="n">lcl_height</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">thickness_hydrostatic</span><span class="p">(</span><span class="n">new_p</span><span class="p">,</span> <span class="n">new_t</span><span class="p">)</span>
-
-<span class="c1"># Compute Surface-based CAPE</span>
-<span class="n">sbcape</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">surface_based_cape_cin</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
-
-<span class="c1"># Compute SRH, given a motion vector toward the NE at 9.9 m/s</span>
-<span class="o">*</span><span class="n">_</span><span class="p">,</span> <span class="n">total_helicity</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">storm_relative_helicity</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="mi">1</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">km</span><span class="p">,</span>
-                                                    <span class="n">storm_u</span><span class="o">=</span><span class="n">u_storm</span><span class="p">,</span> <span class="n">storm_v</span><span class="o">=</span><span class="n">v_storm</span><span class="p">)</span>
-
-<span class="c1"># Copmute Bulk Shear components and then magnitude</span>
-<span class="n">ubshr</span><span class="p">,</span> <span class="n">vbshr</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">bulk_shear</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="n">height</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="mi">6</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">km</span><span class="p">)</span>
-<span class="n">bshear</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">ubshr</span><span class="p">,</span> <span class="n">vbshr</span><span class="p">)</span>
-
-<span class="c1"># Use all computed pieces to calculate the Significant Tornado parameter</span>
-<span class="n">sig_tor</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">significant_tornado</span><span class="p">(</span><span class="n">sbcape</span><span class="p">,</span> <span class="n">lcl_height</span><span class="p">,</span>
-                                     <span class="n">total_helicity</span><span class="p">,</span> <span class="n">bshear</span><span class="p">)</span><span class="o">.</span><span class="n">to_base_units</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Compute the supercell composite parameter, if possible</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Determine the top and bottom of the effective layer using our own function</span>
-<span class="n">hbot</span><span class="p">,</span> <span class="n">htop</span> <span class="o">=</span> <span class="n">effective_layer</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">height_layer</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Perform the calculation of supercell composite if an effective layer exists</span>
-<span class="k">if</span> <span class="n">hbot</span><span class="p">:</span>
-    <span class="n">esrh</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">storm_relative_helicity</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="n">htop</span> <span class="o">-</span> <span class="n">hbot</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="n">hbot</span><span class="p">)</span>
-    <span class="n">eubshr</span><span class="p">,</span> <span class="n">evbshr</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">bulk_shear</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="n">height</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="n">htop</span> <span class="o">-</span> <span class="n">hbot</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="n">hbot</span><span class="p">)</span>
-    <span class="n">ebshear</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">eubshr</span><span class="p">,</span> <span class="n">evbshr</span><span class="p">)</span>
-
-    <span class="n">super_comp</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">supercell_composite</span><span class="p">(</span><span class="n">mucape</span><span class="p">,</span> <span class="n">esrh</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ebshear</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="n">super_comp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Print Important Sounding Parameters</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Important Sounding Parameters for KOUN on 22 Mary 2011 12 UTC&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;        CAPE: </span><span class="si">{</span><span class="n">cape</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;         CIN: </span><span class="si">{</span><span class="n">cin</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;LCL Pressure: </span><span class="si">{</span><span class="n">lclp</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;LFC Pressure: </span><span class="si">{</span><span class="n">lfcp</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39; EL Pressure: </span><span class="si">{</span><span class="n">el_pressure</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;   Lifted Index: </span><span class="si">{</span><span class="n">lift_index</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;        K-Index: </span><span class="si">{</span><span class="n">kindex</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Showalter Index: </span><span class="si">{</span><span class="n">showalter</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;   Cross Totals: </span><span class="si">{</span><span class="n">ctotals</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;   Total Totals: </span><span class="si">{</span><span class="n">total_totals</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Vertical Totals: </span><span class="si">{</span><span class="n">vert_totals</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Mixed Layer - Lowest 50-hPa&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;     ML Temp: </span><span class="si">{</span><span class="n">ml_t</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;     ML Dewp: </span><span class="si">{</span><span class="n">ml_td</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;     ML CAPE: </span><span class="si">{</span><span class="n">mlcape</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;      ML CIN: </span><span class="si">{</span><span class="n">mlcin</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Most Unstable - Lowest 50-hPa&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;     MU Temp: </span><span class="si">{</span><span class="n">mu_t</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;     MU Dewp: </span><span class="si">{</span><span class="n">mu_td</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39; MU Pressure: </span><span class="si">{</span><span class="n">mu_p</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;     MU CAPE: </span><span class="si">{</span><span class="n">mucape</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;      MU CIN: </span><span class="si">{</span><span class="n">mucin</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Bunkers Storm Motion Vector&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;  u_storm: </span><span class="si">{</span><span class="n">u_storm</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;  v_storm: </span><span class="si">{</span><span class="n">v_storm</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Critical Angle: </span><span class="si">{</span><span class="n">critical_angle</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Storm Relative Helicity: </span><span class="si">{</span><span class="n">total_helicity</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Significant Tornado Parameter: </span><span class="si">{</span><span class="n">sig_tor</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Supercell Composite Parameter: </span><span class="si">{</span><span class="n">super_comp</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Important Sounding Parameters for KOUN on 22 Mary 2011 12 UTC
-
-        CAPE: 3223.89 joule / kilogram
-         CIN: -96.26 joule / kilogram
-LCL Pressure: 949.09 hectopascal
-LFC Pressure: 735.99 hectopascal
- EL Pressure: 194.72 hectopascal
-
-   Lifted Index: [-6.96] delta_degree_Celsius
-        K-Index: 22.10 degree_Celsius
-Showalter Index: [-0.08] delta_degree_Celsius
-   Cross Totals: 17.10 delta_degree_Celsius
-   Total Totals: 50.20 delta_degree_Celsius
-Vertical Totals: 33.10 delta_degree_Celsius
-
-Mixed Layer - Lowest 50-hPa
-     ML Temp: 20.99 degree_Celsius
-     ML Dewp: 20.55 degree_Celsius
-     ML CAPE: 3254.17 joule / kilogram
-      ML CIN: -138.20 joule / kilogram
-
-Most Unstable - Lowest 50-hPa
-     MU Temp: 20.40 degree_Celsius
-     MU Dewp: 20.40 degree_Celsius
- MU Pressure: 925.00 hectopascal
-     MU CAPE: 3693.64 joule / kilogram
-      MU CIN: -60.50 joule / kilogram
-
-Bunkers Storm Motion Vector
-  u_storm: 21.85 knot
-  v_storm: 4.55 knot
-Critical Angle: 67.33 degree
-
-Storm Relative Helicity: 279.49 meter ** 2 / second ** 2
-Significant Tornado Parameter: [4.60] 
-Supercell Composite Parameter: [9.07] 
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-<h1>Sounding as Dataset Example<a class="headerlink" href="#sounding-as-dataset-example" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Use MetPy to make a Skew-T LogP plot from an xarray Dataset after computing LCL parcel profile.</p>
-<section id="imports">
-<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.cbook</span> <span class="kn">import</span> <span class="n">get_test_data</span>
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">add_metpy_logo</span><span class="p">,</span> <span class="n">SkewT</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="obtain-data-and-format">
-<h2>Obtain Data and Format<a class="headerlink" href="#obtain-data-and-format" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy’s sample data.
-<code class="docutils literal notranslate"><span class="pre">as_file_obj=False),</span> <span class="pre">skiprows=5,</span> <span class="pre">usecols=[0,</span> <span class="pre">1,</span> <span class="pre">2,</span> <span class="pre">3,</span> <span class="pre">6,</span> <span class="pre">7],</span> <span class="pre">names=col_names)</span></code> is necessary due to the formatting of the MetPy sample data. This formatting is not needed when using upper air data obtained via Siphon. Obtaining data with Siphon will be covered in a later notebook.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">col_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="s1">&#39;height&#39;</span><span class="p">,</span> <span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span><span class="p">]</span>
-
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_fwf</span><span class="p">(</span><span class="n">get_test_data</span><span class="p">(</span><span class="s1">&#39;20110522_OUN_12Z.txt&#39;</span><span class="p">,</span> <span class="n">as_file_obj</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-                 <span class="n">skiprows</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">usecols</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="n">col_names</span><span class="p">)</span>
-
-<span class="c1"># Drop any rows with all not a number (NaN) values for temperature, dewpoint, and winds</span>
-<span class="n">sounding_data</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;temperature&#39;</span><span class="p">,</span> <span class="s1">&#39;dewpoint&#39;</span><span class="p">,</span> <span class="s1">&#39;direction&#39;</span><span class="p">,</span> <span class="s1">&#39;speed&#39;</span><span class="p">),</span>
-               <span class="n">how</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="assign-units">
-<h2>Assign Units<a class="headerlink" href="#assign-units" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>We will pull the data out of the example dataset into individual variables and assign units. This is explained in further detain in the Simple Sounding notebook and in the <a class="reference external" href="https://unidata.github.io/MetPy/latest/tutorials/unit_tutorial.html">Metpy documentation</a>.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pres</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">temp</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">dewpoint</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-<span class="n">wind_speed</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;speed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-<span class="n">wind_dir</span> <span class="o">=</span> <span class="n">sounding_data</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span>
-<span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">wind_speed</span><span class="p">,</span> <span class="n">wind_dir</span><span class="p">)</span>
-
-<span class="n">lcl_ds</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">parcel_profile_with_lcl_as_dataset</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
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-</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB
-Dimensions:              (isobaric: 71)
-Coordinates:
-  * isobaric             (isobaric) float64 568B 966.0 953.0 ... 104.0 100.0
-Data variables:
-    ambient_temperature  (isobaric) float64 568B &lt;Quantity([ 22.2         21....
-    ambient_dew_point    (isobaric) float64 568B &lt;Quantity([ 21.          20....
-    parcel_temperature   (isobaric) float64 568B &lt;Quantity([295.35       294....</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-852fa064-0c03-4e7f-af97-39a4bda39476' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-852fa064-0c03-4e7f-af97-39a4bda39476' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>isobaric</span>: 71</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-43fefce4-84eb-41cc-892f-59362d01ce2f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43fefce4-84eb-41cc-892f-59362d01ce2f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isobaric</span></div><div class='xr-var-dims'>(isobaric)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>966.0 953.0 949.1 ... 104.0 100.0</div><input id='attrs-d4a905f7-7014-4376-9f0e-59f7e32a3b13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4a905f7-7014-4376-9f0e-59f7e32a3b13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b43f88a4-cd5e-4b97-a21f-31dab293af8f' class='xr-var-data-in' type='checkbox'><label for='data-b43f88a4-cd5e-4b97-a21f-31dab293af8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>hectopascal</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([966.      , 953.      , 949.085447, 936.9     , 925.      , 904.5     ,
-       896.      , 890.      , 886.      , 873.3     , 873.      , 850.      ,
-       846.      , 813.8     , 802.      , 785.      , 757.1     , 730.1     ,
-       700.      , 653.3     , 639.      , 606.      , 605.6     , 584.      ,
-       582.7     , 582.      , 577.      , 571.      , 561.      , 560.7     ,
-       539.4     , 539.      , 500.      , 478.9     , 453.      , 443.      ,
-       406.3     , 400.      , 389.3     , 327.3     , 313.4     , 300.      ,
-       286.      , 250.      , 249.      , 220.      , 210.      , 200.      ,
-       197.      , 196.5     , 190.      , 181.      , 173.      , 159.      ,
-       155.      , 154.2     , 150.      , 148.      , 146.9     , 146.      ,
-       142.      , 140.      , 137.      , 133.3     , 127.      , 126.      ,
-       120.9     , 111.      , 109.      , 104.      , 100.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6ab26041-58d5-40a5-b40e-8d765ac690cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6ab26041-58d5-40a5-b40e-8d765ac690cc' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ambient_temperature</span></div><div class='xr-var-dims'>(isobaric)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>&lt;Quantity([ 22.2         21.4   ...</div><input id='attrs-e8ff2349-809d-4c07-aae3-7900f040fabd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8ff2349-809d-4c07-aae3-7900f040fabd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eec37dd-9717-4aa6-9e02-b4a28a0fa1ee' class='xr-var-data-in' type='checkbox'><label for='data-7eec37dd-9717-4aa6-9e02-b4a28a0fa1ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd></dl></div><div class='xr-var-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[22.2 21.4 21.254116023095705 20.8 20.4 19.3 18.8 20.0 22.2 23.2 23.2 22.0 21.8 19.2 18.2 16.5 13.7 10.9 7.6 2.3 0.6 -2.9 -2.9 -4.5 -4.4 -4.3 -3.7 -3.3 -3.9 -3.9 -6.3 -6.3 -11.1 -13.7 -17.1 -18.3 -23.9 -24.9 -26.6 -37.9 -40.7 -43.5 -46.3 -52.1 -52.3 -54.1 -55.9 -56.5 -56.5 -56.5 -56.5 -57.9 -56.9 -59.3 -58.9 -59.0 -59.5 -59.9 -59.2 -58.7 -57.9 -56.7 -56.5 -57.7 -59.8 -60.1 -61.0 -62.9 -64.3 -63.3 -64.3]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>degree_Celsius</td></tr></tbody></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ambient_dew_point</span></div><div class='xr-var-dims'>(isobaric)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>&lt;Quantity([ 21.          20.7   ...</div><input id='attrs-b6347286-22e2-4e0b-ac78-e4d84323a029' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6347286-22e2-4e0b-ac78-e4d84323a029' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8112d119-cdd0-40b2-b458-ebec04eb2f3a' class='xr-var-data-in' type='checkbox'><label for='data-8112d119-cdd0-40b2-b458-ebec04eb2f3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd></dl></div><div class='xr-var-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[21.0 20.7 20.65137200769857 20.5 20.4 19.3 18.8 20.0 19.0 13.3 13.2 6.0 3.8 -1.7 -3.8 -4.7 -6.2 -7.7 -9.4 -10.9 -11.4 -12.9 -12.9 -14.5 -14.4 -14.3 -24.7 -36.3 -31.9 -31.8 -27.4 -27.3 -29.1 -31.3 -34.1 -32.3 -37.0 -37.9 -39.3 -48.1 -50.3 -52.5 -55.3 -62.1 -62.3 -64.1 -65.9 -66.5 -66.5 -66.5 -66.5 -67.9 -66.9 -69.3 -68.9 -69.0 -69.5 -69.9 -69.2 -68.7 -67.9 -66.7 -66.5 -67.7 -69.8 -70.1 -71.0 -72.9 -73.3 -73.3 -74.3]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>degree_Celsius</td></tr></tbody></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>parcel_temperature</span></div><div class='xr-var-dims'>(isobaric)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>&lt;Quantity([295.35       294.2088...</div><input id='attrs-7fc9dd53-040b-49fc-8f7e-1bd4d5ac20a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fc9dd53-040b-49fc-8f7e-1bd4d5ac20a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81c927bd-63f4-4aa7-bc6d-5bec1d887d63' class='xr-var-data-in' type='checkbox'><label for='data-81c927bd-63f4-4aa7-bc6d-5bec1d887d63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air_temperature_of_lifted_parcel</dd></dl></div><div class='xr-var-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[295.34999999999997 294.20887396170207 293.86308176800566 293.4099943495534 292.9605366516025 292.1693871501292 291.83484364691674 291.5963220045112 291.4361974572428 290.9217817645201 290.90951768975583 289.95324507449203 289.78361661237284 288.3800144790221 287.84779705003416 287.0630619897046 285.72612496785456 284.3692057775776 282.77536756242387 280.10893536751206 279.23922058870653 277.1232960053971 277.09664797744415 275.61892684782913 275.5274725480196 275.47810502547424 275.1229494554376 274.69078901458516 273.9555257073228 273.9331702389749 272.29917542768135 272.26757383165204 269.0055991481036 267.0737915535153 264.5137445137853 263.4630088557104 259.2614990911962 258.4793263502483 257.10515037181517 247.80090458906085 245.34140461782874 242.81799102546762 240.01050451730532 231.91012214705813 231.6657585024839 224.0871178675781 221.24235897557074 218.27111704455257 217.35421917769239 217.2002322728787 215.16743230137462 212.25494058759193 209.5659695215338 204.6164100334584 203.1411807522514 202.84269696570666 201.25632033826017 200.48922395508256 200.0640310151734 199.71438559354843 198.14083284802103 197.341775309911 196.1273252033388 194.6024080711456 191.9334291191558 191.50099464989884 189.25609420942234 184.69553916489568 183.73898623782966 181.291232491021 179.27154995827192]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>kelvin</td></tr></tbody></table></div></li></ul></div></li><li class='xr-section-item'><input id='section-38fd82f2-1047-4d61-a368-76effb60384f' class='xr-section-summary-in' type='checkbox'  ><label for='section-38fd82f2-1047-4d61-a368-76effb60384f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>isobaric</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7d82731c-d162-487f-976a-af8347c2007a' class='xr-index-data-in' type='checkbox'/><label for='index-7d82731c-d162-487f-976a-af8347c2007a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            966.0,             953.0, 949.0854466197347,
-                   936.9,             925.0,             904.5,
-                   896.0,             890.0,             886.0,
-                   873.3,             873.0,             850.0,
-                   846.0,             813.8,             802.0,
-                   785.0,             757.1,             730.1,
-                   700.0,             653.3,             639.0,
-                   606.0,             605.6,             584.0,
-                   582.7,             582.0,             577.0,
-                   571.0,             561.0,             560.7,
-                   539.4,             539.0,             500.0,
-                   478.9,             453.0,             443.0,
-                   406.3,             400.0,             389.3,
-                   327.3,             313.4,             300.0,
-                   286.0,             250.0,             249.0,
-                   220.0,             210.0,             200.0,
-                   197.0,             196.5,             190.0,
-                   181.0,             173.0,             159.0,
-                   155.0,             154.2,             150.0,
-                   148.0,             146.9,             146.0,
-                   142.0,             140.0,             137.0,
-                   133.3,             127.0,             126.0,
-                   120.9,             111.0,             109.0,
-                   104.0,             100.0],
-      dtype=&#x27;float64&#x27;, name=&#x27;isobaric&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3708c434-d62e-4494-b4ba-61c3adec7a96' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3708c434-d62e-4494-b4ba-61c3adec7a96' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div></div></div>
-</div>
-</section>
-<section id="create-sounding-plot">
-<h2>Create Sounding Plot<a class="headerlink" href="#create-sounding-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure and set size</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-<span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-<span class="c1"># Plot the data using the data from the xarray Dataset including the parcel temperature with</span>
-<span class="c1"># the LCL level included</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lcl_ds</span><span class="o">.</span><span class="n">isobaric</span><span class="p">,</span> <span class="n">lcl_ds</span><span class="o">.</span><span class="n">ambient_temperature</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lcl_ds</span><span class="o">.</span><span class="n">isobaric</span><span class="p">,</span> <span class="n">lcl_ds</span><span class="o">.</span><span class="n">ambient_dew_point</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lcl_ds</span><span class="o">.</span><span class="n">isobaric</span><span class="p">,</span> <span class="n">lcl_ds</span><span class="o">.</span><span class="n">parcel_temperature</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">),</span> <span class="s1">&#39;black&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot wind barbs</span>
-<span class="n">my_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span> <span class="c1">#set spacing interval</span>
-<span class="n">ix</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">resample_nn_1d</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">my_interval</span><span class="p">)</span> <span class="c1">#find nearest indices for chosen interval</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">xloc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1">#plot values closest to chosen interval</span>
-
-<span class="c1"># Improve labels and set axis limits</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">59</span><span class="p">)</span>
-
-<span class="c1"># Add the relevant special lines throughout the figure</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">533</span><span class="p">,</span> <span class="mi">15</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orangered&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">)</span>
-<span class="n">skew</span><span class="o">.</span><span class="n">plot_mixing_lines</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="o">-</span><span class="mi">25</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">)</span>
-
-<span class="c1"># Add the MetPy logo!</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">add_metpy_logo</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="mi">115</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
-
-<span class="c1"># Add titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;KOUN&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;2011-05-22 12 UTC&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">);</span>
-</pre></div>
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-<li class="toctree-l1 has-children"><a class="reference internal" href="notebook-template.html">What organization</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="gallery/250hPa_Hemispheric_Plot.html">A 250-hPa Hemispheric Map using Python</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/500hPa_Absolute_Vorticity_winds.html">500 hPa Geopotential Heights, Absolute Vorticity, and Winds</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="gallery/Upperair_Obs.html">DIFAX Replication</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/Wind_Shear_Vectors_Example.html">Calculating Wind Shear</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/declarative_500_hPa.html">Declarative 500-hPa Absolute Vorticity</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/index1.html">a potential entry?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/index2.html">a different category, perhaps?</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/miller_composite.html">Miller Composite Chart</a></li>
-<li class="toctree-l2"><a class="reference internal" href="gallery/xarray_500hPa_map.html">Using Xarray for Data read and selection</a></li>
-</ul>
-</details></li>
-<li class="toctree-l1"><a class="reference internal" href="gallery/index1.html">do we want</a></li>
-<li class="toctree-l1"><a class="reference internal" href="gallery/index2.html">for these "chapters"?</a></li>
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-<li class="toctree-l1 current active"><a class="current reference internal" href="#">Sounding Plotter</a></li>
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-  <section class="tex2jax_ignore mathjax_ignore" id="sounding-plotter">
-<h1>Sounding Plotter<a class="headerlink" href="#sounding-plotter" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>This can be run as a script from the command line or as a notebook.</p>
-<p>Download and plot the most recent sounding data for a specified site.</p>
-<p>Provides a simple command line interface to specify a site. Using the current
-UTC time, the script calculates what the most recent sounding should be and requests
-it from the Wyoming archive using Siphon.</p>
-<section id="imports">
-<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">posixpath</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">add_metpy_logo</span><span class="p">,</span> <span class="n">add_timestamp</span><span class="p">,</span> <span class="n">SkewT</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-
-<span class="kn">from</span> <span class="nn">siphon.simplewebservice.wyoming</span> <span class="kn">import</span> <span class="n">WyomingUpperAir</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>This class encapsulates the code needed to upload an image to Google Drive</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DriveUploader</span><span class="p">:</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">credsfile</span><span class="o">=</span><span class="s1">&#39;mycreds.txt&#39;</span><span class="p">):</span>
-        <span class="kn">from</span> <span class="nn">pydrive.drive</span> <span class="kn">import</span> <span class="n">GoogleDrive</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">gdrive</span> <span class="o">=</span> <span class="n">GoogleDrive</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_get_auth</span><span class="p">(</span><span class="n">credsfile</span><span class="p">))</span>
-
-    <span class="k">def</span> <span class="nf">_get_auth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">credsfile</span><span class="p">):</span>
-        <span class="kn">from</span> <span class="nn">pydrive.auth</span> <span class="kn">import</span> <span class="n">GoogleAuth</span>
-        <span class="n">gauth</span> <span class="o">=</span> <span class="n">GoogleAuth</span><span class="p">()</span>
-
-        <span class="c1"># Try to load saved client credentials</span>
-        <span class="n">gauth</span><span class="o">.</span><span class="n">LoadCredentialsFile</span><span class="p">(</span><span class="n">credsfile</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">gauth</span><span class="o">.</span><span class="n">credentials</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Authenticate if they&#39;re not there</span>
-            <span class="n">gauth</span><span class="o">.</span><span class="n">LocalWebserverAuth</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="n">gauth</span><span class="o">.</span><span class="n">access_token_expired</span><span class="p">:</span>
-            <span class="c1"># Refresh them if expired</span>
-            <span class="n">gauth</span><span class="o">.</span><span class="n">Refresh</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># Initialize the saved creds</span>
-            <span class="n">gauth</span><span class="o">.</span><span class="n">Authorize</span><span class="p">()</span>
-        <span class="c1"># Save the current credentials to a file</span>
-        <span class="n">gauth</span><span class="o">.</span><span class="n">SaveCredentialsFile</span><span class="p">(</span><span class="n">credsfile</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">gauth</span>
-
-    <span class="k">def</span> <span class="nf">_get_first_file_id</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">parent</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">query</span> <span class="o">=</span> <span class="s2">&quot;title=&#39;</span><span class="si">{}</span><span class="s2">&#39; and &#39;</span><span class="si">{}</span><span class="s2">&#39; in parents&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">parent</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="n">query</span> <span class="o">+=</span> <span class="s2">&quot; and </span><span class="si">{}</span><span class="s2">=&#39;</span><span class="si">{}</span><span class="s2">&#39;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">gdrive</span><span class="o">.</span><span class="n">ListFile</span><span class="p">({</span><span class="s1">&#39;q&#39;</span><span class="p">:</span> <span class="n">query</span><span class="p">}))</span>
-        <span class="k">if</span> <span class="n">res</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">res</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;id&#39;</span><span class="p">]</span>
-        <span class="k">return</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="nf">get_folder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
-        <span class="n">parent</span> <span class="o">=</span> <span class="s1">&#39;root&#39;</span>
-        <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">):</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">part</span><span class="p">:</span>
-                <span class="k">continue</span>
-            <span class="n">parent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_first_file_id</span><span class="p">(</span><span class="n">part</span><span class="p">,</span> <span class="n">parent</span><span class="p">,</span>
-                                             <span class="n">mimeType</span><span class="o">=</span><span class="s1">&#39;application/vnd.google-apps.folder&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">parent</span>
-
-    <span class="k">def</span> <span class="nf">create_or_get_file</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
-        <span class="n">pathname</span><span class="p">,</span> <span class="n">filename</span> <span class="o">=</span> <span class="n">posixpath</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-        <span class="n">folder</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_folder</span><span class="p">(</span><span class="n">pathname</span><span class="p">)</span>
-        <span class="n">create_file_args</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;parents&#39;</span><span class="p">:</span> <span class="p">[{</span><span class="s1">&#39;kind&#39;</span><span class="p">:</span> <span class="s1">&#39;drive#fileLink&#39;</span><span class="p">,</span> <span class="s1">&#39;id&#39;</span><span class="p">:</span> <span class="n">folder</span><span class="p">}]}</span>
-
-        <span class="n">file_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_first_file_id</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">folder</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">file_id</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">create_file_args</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">file_id</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gdrive</span><span class="o">.</span><span class="n">CreateFile</span><span class="p">(</span><span class="n">create_file_args</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">upload_to</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">local_path</span><span class="p">,</span> <span class="n">remote_path</span><span class="p">):</span>
-        <span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_or_get_file</span><span class="p">(</span><span class="n">remote_path</span><span class="p">)</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">SetContentFile</span><span class="p">(</span><span class="n">local_path</span><span class="p">)</span>
-        <span class="n">f</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">posixpath</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">remote_path</span><span class="p">)</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">Upload</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>This function takes care of actually generating a skewT from the <code class="docutils literal notranslate"><span class="pre">DataFrame</span></code></p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_skewt</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
-    <span class="c1"># We will pull the data out of the example dataset into individual variables</span>
-    <span class="c1"># and assign units.</span>
-    <span class="n">pres</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-    <span class="n">temp</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-    <span class="n">dewpoint</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span>
-    <span class="n">wind_speed</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;speed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">knots</span>
-    <span class="n">wind_dir</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span>
-    <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">wind_speed</span><span class="p">,</span> <span class="n">wind_dir</span><span class="p">)</span>
-
-    <span class="c1"># Create a new figure. The dimensions here give a good aspect ratio.</span>
-    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-    <span class="n">skew</span> <span class="o">=</span> <span class="n">SkewT</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-    <span class="c1"># Plot temperature, dewpoint and wind barbs</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">)</span>
-    
-    <span class="c1"># Plot wind barbs</span>
-    <span class="n">my_interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span> <span class="c1">#set spacing interval</span>
-    <span class="n">ix</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">resample_nn_1d</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">my_interval</span><span class="p">)</span> <span class="c1">#find nearest indices for chosen interval</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">plot_barbs</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">u</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">xloc</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1">#plot values closest to chosen interval</span>
-    
-    <span class="c1"># Improve labels and set axis limits</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (</span><span class="se">\N{DEGREE CELSIUS}</span><span class="s1">)&#39;</span><span class="p">)</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (hPa)&#39;</span><span class="p">)</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">59</span><span class="p">)</span>
-
-    <span class="c1"># Calculate LCL height and plot as black dot.</span>
-    <span class="n">lcl_pressure</span><span class="p">,</span> <span class="n">lcl_temperature</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">lcl</span><span class="p">(</span><span class="n">pres</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="c1">#index 0 is chosen to lift parcel from the surface</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lcl_pressure</span><span class="p">,</span> <span class="n">lcl_temperature</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-
-    <span class="c1"># Calculate full parcel profile and add to plot as black line</span>
-    <span class="n">prof</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">parcel_profile</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dewpoint</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">prof</span><span class="p">,</span> <span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-    <span class="c1"># Shade areas of CAPE and CIN</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">shade_cin</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">prof</span><span class="p">,</span> <span class="n">dewpoint</span><span class="p">)</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">shade_cape</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">temp</span><span class="p">,</span> <span class="n">prof</span><span class="p">)</span>
-
-    <span class="c1"># Add emphasis to 0 degree isotherm with color change</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-    <span class="c1"># Add the relevant special lines throughout the figure</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">plot_dry_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">533</span><span class="p">,</span> <span class="mi">15</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orangered&#39;</span><span class="p">)</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">plot_moist_adiabats</span><span class="p">(</span><span class="n">t0</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">233</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">)</span>
-    <span class="n">skew</span><span class="o">.</span><span class="n">plot_mixing_lines</span><span class="p">(</span><span class="n">pressure</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">99</span><span class="p">,</span> <span class="o">-</span><span class="mi">25</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">)</span>
-
-    <span class="c1"># Add the MetPy logo!</span>
-    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-    <span class="n">add_metpy_logo</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="mi">115</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;small&#39;</span><span class="p">);</span>
-    
-    <span class="k">return</span> <span class="n">skew</span>
-
-
-<span class="k">def</span> <span class="nf">make_name</span><span class="p">(</span><span class="n">site</span><span class="p">,</span> <span class="n">time</span><span class="p">):</span>
-    <span class="k">return</span> <span class="s1">&#39;</span><span class="si">{site}</span><span class="s1">_{dt:%Y%m</span><span class="si">%d</span><span class="s1">_%H%M}.png&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">site</span><span class="o">=</span><span class="n">site</span><span class="p">,</span> <span class="n">dt</span><span class="o">=</span><span class="n">time</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>This is where the command line script will actually enter, and handles parsing
-command line arguments and driving everything else.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="kn">import</span> <span class="nn">argparse</span>
-    <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
-    <span class="kn">import</span> <span class="nn">tempfile</span>
-
-    <span class="c1"># Set up argument parsing for the script. Provides one argument for the site, and another</span>
-    <span class="c1"># that controls whether the plot should be shown or saved as an image.</span>
-    <span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s1">&#39;Download sounding data and plot.&#39;</span><span class="p">)</span>
-    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;-s&#39;</span><span class="p">,</span> <span class="s1">&#39;--site&#39;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;Site to obtain data for&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span>
-                        <span class="n">default</span><span class="o">=</span><span class="s1">&#39;DDC&#39;</span><span class="p">)</span>
-    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--show&#39;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;Whether to show the plot rather than save to disk&#39;</span><span class="p">,</span>
-                        <span class="n">action</span><span class="o">=</span><span class="s1">&#39;store_true&#39;</span><span class="p">)</span>
-    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;-d&#39;</span><span class="p">,</span> <span class="s1">&#39;--date&#39;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;Date and time to request data for in YYYYMMDDHH.&#39;</span>
-                        <span class="s1">&#39; Defaults to most recent 00/12 hour.&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
-    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;-g&#39;</span><span class="p">,</span> <span class="s1">&#39;--gdrive&#39;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;Google Drive upload path&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
-    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;-f&#39;</span><span class="p">,</span> <span class="s1">&#39;--filename&#39;</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;Image filename&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
-    <span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
-
-    <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">date</span><span class="p">:</span>
-        <span class="n">request_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">date</span><span class="p">,</span> <span class="s1">&#39;%Y%m</span><span class="si">%d</span><span class="s1">%H&#39;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="c1"># Figure out the most recent sounding, 00 or 12. Subtracting two hours</span>
-        <span class="c1"># helps ensure that we choose a time with data available.</span>
-        <span class="n">now</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span> <span class="o">-</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-        <span class="n">request_time</span> <span class="o">=</span> <span class="n">now</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">hour</span><span class="o">=</span><span class="p">(</span><span class="n">now</span><span class="o">.</span><span class="n">hour</span> <span class="o">//</span> <span class="mi">12</span><span class="p">)</span> <span class="o">*</span> <span class="mi">12</span><span class="p">,</span> <span class="n">minute</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">second</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="c1"># Request the data and plot</span>
-    <span class="n">df</span> <span class="o">=</span> <span class="n">WyomingUpperAir</span><span class="o">.</span><span class="n">request_data</span><span class="p">(</span><span class="n">request_time</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">site</span><span class="p">)</span>
-    <span class="n">skewt</span> <span class="o">=</span> <span class="n">plot_skewt</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-
-    <span class="c1"># Add the timestamp for the data to the plot</span>
-    <span class="n">add_timestamp</span><span class="p">(</span><span class="n">skewt</span><span class="o">.</span><span class="n">ax</span><span class="p">,</span> <span class="n">request_time</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.02</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
-    <span class="n">skewt</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">site</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">show</span><span class="p">:</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fname</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">filename</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">filename</span> <span class="k">else</span> <span class="n">make_name</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">site</span><span class="p">,</span> <span class="n">request_time</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">gdrive</span><span class="p">:</span>
-            <span class="n">uploader</span> <span class="o">=</span> <span class="n">DriveUploader</span><span class="p">()</span>
-            <span class="k">with</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">NamedTemporaryFile</span><span class="p">(</span><span class="n">suffix</span><span class="o">=</span><span class="s1">&#39;.png&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-                <span class="n">skewt</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
-                <span class="n">uploader</span><span class="o">.</span><span class="n">upload_to</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">posixpath</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">gdrive</span><span class="p">,</span> <span class="n">fname</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">skewt</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">make_name</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">site</span><span class="p">,</span> <span class="n">request_time</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>usage: ipykernel_launcher.py [-h] [-s SITE] [--show] [-d DATE] [-g GDRIVE]
-                             [-f FILENAME]
-ipykernel_launcher.py: error: unrecognized arguments: --HistoryManager.hist_file=:memory:
-</pre></div>
-</div>
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="n">An</span> <span class="n">exception</span> <span class="n">has</span> <span class="n">occurred</span><span class="p">,</span> <span class="n">use</span> <span class="o">%</span><span class="k">tb</span> to see the full traceback.
-
-<span class="ne">SystemExit</span>: 2
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/IPython/core/interactiveshell.py:3585: UserWarning: To exit: use &#39;exit&#39;, &#39;quit&#39;, or Ctrl-D.
-  warn(&quot;To exit: use &#39;exit&#39;, &#39;quit&#39;, or Ctrl-D.&quot;, stacklevel=1)
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
-
-    <script type="text/x-thebe-config">
-    {
-        requestKernel: true,
-        binderOptions: {
-            repo: "binder-examples/jupyter-stacks-datascience",
-            ref: "master",
-        },
-        codeMirrorConfig: {
-            theme: "abcdef",
-            mode: "python"
-        },
-        kernelOptions: {
-            name: "python3",
-            path: "./notebooks"
-        },
-        predefinedOutput: true
-    }
-    </script>
-    <script>kernelName = 'python3'</script>
-
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-  <section class="tex2jax_ignore mathjax_ignore" id="a-250-hpa-hemispheric-map-using-python">
-<h1>A 250-hPa Hemispheric Map using Python<a class="headerlink" href="#a-250-hpa-hemispheric-map-using-python" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>This example plots a hemispheric plot of GFS 250-hPa Geopotential Heights and
-wind speed in knots.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">cartopy.util</span> <span class="k">as</span> <span class="nn">cutil</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
-<span class="kn">from</span> <span class="nn">siphon.ncss</span> <span class="kn">import</span> <span class="n">NCSS</span>
-<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-
-<span class="c1"># Latest GFS Dataset</span>
-<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="s1">&#39;http://thredds.ucar.edu/thredds/catalog/grib/&#39;</span>
-                 <span class="s1">&#39;NCEP/GFS/Global_0p5deg/latest.xml&#39;</span><span class="p">)</span>
-<span class="n">best_ds</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">values</span><span class="p">())[</span><span class="mi">0</span><span class="p">]</span>
-<span class="n">ncss</span> <span class="o">=</span> <span class="n">NCSS</span><span class="p">(</span><span class="n">best_ds</span><span class="o">.</span><span class="n">access_urls</span><span class="p">[</span><span class="s1">&#39;NetcdfSubset&#39;</span><span class="p">])</span>
-
-<span class="c1"># Set the time to current</span>
-<span class="n">now</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span>
-
-<span class="c1"># Query for Latest GFS Run</span>
-<span class="n">gfsdata_hght</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">now</span><span class="p">)</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-<span class="n">gfsdata_hght</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set the lat/lon box for the data you want to pull in.</span>
-<span class="c1"># lonlat_box(north_lat,south_lat,east_lon,west_lon)</span>
-<span class="n">gfsdata_hght</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">359.9</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">)</span>
-
-<span class="c1"># Set desired level 50000 = 50000 Pa = 500 hPa</span>
-<span class="n">gfsdata_hght</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">25000</span><span class="p">)</span>
-
-<span class="c1"># Actually getting the data</span>
-<span class="n">data_hght</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">gfsdata_hght</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds_hght</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data_hght</span><span class="p">))</span>
-
-<span class="c1"># Query for Latest GFS Run</span>
-<span class="n">gfsdata_wind</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">now</span><span class="p">)</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-<span class="n">gfsdata_wind</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                       <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set the lat/lon box for the data you want to pull in.</span>
-<span class="c1"># lonlat_box(north_lat,south_lat,east_lon,west_lon)</span>
-<span class="n">gfsdata_wind</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">359.9</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">)</span>
-
-<span class="c1"># Set desired level 50000 = 50000 Pa = 500 hPa</span>
-<span class="n">gfsdata_wind</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">25000</span><span class="p">)</span>
-
-<span class="c1"># Actually getting the data</span>
-<span class="n">data_wind</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">gfsdata_wind</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds_wind</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data_wind</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>The next cell will take the downloaded data and parse it to different variables
-for use later on. Add a cyclic point using the cartopy utility add_cyclic_point
-to the longitudes (the cyclic dimension) as well as any data that is being
-contoured or filled.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lat</span> <span class="o">=</span> <span class="n">ds_hght</span><span class="o">.</span><span class="n">latitude</span><span class="o">.</span><span class="n">values</span>
-<span class="n">lon</span> <span class="o">=</span> <span class="n">ds_hght</span><span class="o">.</span><span class="n">longitude</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Converting times using the num2date function available through netCDF4</span>
-<span class="n">vtimes</span> <span class="o">=</span> <span class="n">ds_hght</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-
-<span class="c1"># Smooth the 250-hPa heights using a gaussian filter from scipy.ndimage</span>
-<span class="n">hgt</span> <span class="o">=</span> <span class="n">ds_hght</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">hgt_250</span><span class="p">,</span> <span class="n">lon</span> <span class="o">=</span> <span class="n">cutil</span><span class="o">.</span><span class="n">add_cyclic_point</span><span class="p">(</span><span class="n">hgt</span><span class="p">,</span> <span class="n">coord</span><span class="o">=</span><span class="n">lon</span><span class="p">)</span>
-
-<span class="n">Z_250</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">hgt_250</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Calculate wind speed from u and v components, add cyclic point,</span>
-<span class="c1"># and smooth slightly</span>
-<span class="n">u250</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">v250</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="n">wspd250</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">u250</span><span class="p">,</span> <span class="n">v250</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;knots&#39;</span><span class="p">)</span>
-<span class="n">wspd250</span> <span class="o">=</span> <span class="n">cutil</span><span class="o">.</span><span class="n">add_cyclic_point</span><span class="p">(</span><span class="n">wspd250</span><span class="p">)</span>
-
-<span class="n">smooth_wspd250</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">wspd250</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>The next cell sets up the geographic details for the plot that we are going to do later.
-This is done using the Cartopy package. We will also bring in some geographic data to
-geo-reference the image for us.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
-<span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">NorthPolarStereo</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.0</span><span class="p">)</span>
-
-<span class="c1"># Make a grid of lat/lon values to use for plotting.</span>
-<span class="n">lons</span><span class="p">,</span> <span class="n">lats</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">12.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
-
-<span class="c1"># Set some titles for the plots</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;250-hPa Geopotential Heights (m)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set the extent of the image for the NH and add</span>
-<span class="c1">#   ax.set_extent([west long, east long, south lat, north lat])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="mi">180</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">90</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-               <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-
-<span class="c1"># Add geopotential height contours every 120 m</span>
-<span class="n">clev250</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">9000</span><span class="p">,</span> <span class="mi">12000</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">Z_250</span><span class="p">,</span> <span class="n">clev250</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span>
-                <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
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-<span class="c1"># Add colorfilled wind speed in knots every 20 kts</span>
-<span class="n">clevsped250</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
-<span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;BuPu&#39;</span><span class="p">)</span>
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">smooth_wspd250</span><span class="p">,</span> <span class="n">clevsped250</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span>
-                 <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;max&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
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-</pre></div>
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-  <section class="tex2jax_ignore mathjax_ignore" id="hpa-geopotential-heights-absolute-vorticity-and-winds">
-<h1>500 hPa Geopotential Heights, Absolute Vorticity, and Winds<a class="headerlink" href="#hpa-geopotential-heights-absolute-vorticity-and-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Classic 500-hPa absolute vorticity plot using NAM analysis file.</p>
-<p>This example uses example data from the NAM analysis for 12 UTC 31
-October 2016 and uses xarray as the main read source with using MetPy to
-calculate absolute vorticity and wind speed with geographic plotting
-using Cartopy for a CONUS view of the 500-hPa geopotential heights,
-absolute vorticity, and wind barbs.</p>
-<p>Note: NAM winds are grid relative and this example introduces a simple
-function to convert the grid-relative wind components to an
-earth-relative (north/east oriented winds) to enable easier plotting of
-wind barbs and calculation of absolute vorticity.</p>
-<p>Import the needed modules</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="function-to-compute-earth-relative-winds">
-<h2>Function to Compute Earth-Relative Winds<a class="headerlink" href="#function-to-compute-earth-relative-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This function takes a data array with relevant information about the
-projection of the wind component data, along with the grid-relative
-components of the wind. It outputs the earth-relative components of the
-wind.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">earth_relative_wind_components</span><span class="p">(</span><span class="n">ugrd</span><span class="p">,</span> <span class="n">vgrd</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate the north-relative components of the</span>
-<span class="sd">    wind from the grid-relative components using Cartopy</span>
-<span class="sd">    transform_vectors.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">        ugrd : Xarray DataArray (M, N)</span>
-<span class="sd">            grid relative u-component of the wind</span>
-<span class="sd">        vgrd : Xarray DataArray (M, N)</span>
-<span class="sd">            grid relative v-component of the wind</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">        unr, vnr : tuple of array-like Quantity</span>
-<span class="sd">            The north-relative wind components in the X (East-West)</span>
-<span class="sd">            and Y (North-South) directions, respectively.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="s1">&#39;metpy_crs&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No CRS in coordinate, be sure to&#39;</span>
-                         <span class="s1">&#39;use the MetPy accessor parse_cf()&#39;</span><span class="p">)</span>
-
-    <span class="n">data_crs</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
-
-    <span class="n">x</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span>
-
-    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-    <span class="n">ut</span><span class="p">,</span> <span class="n">vt</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span><span class="o">.</span><span class="n">transform_vectors</span><span class="p">(</span><span class="n">data_crs</span><span class="p">,</span> <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span>
-                                                  <span class="n">ugrd</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">vgrd</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-
-    <span class="c1"># Make a copy of u and v component DataArrays</span>
-    <span class="n">uer</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">ver</span> <span class="o">=</span> <span class="n">vgrd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="c1"># Update values with transformed winds</span>
-    <span class="n">uer</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">ut</span>
-    <span class="n">ver</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">vt</span>
-
-    <span class="k">return</span> <span class="n">uer</span><span class="p">,</span> <span class="n">ver</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>The following code reads the example data using the xarray open_dataset
-function and prints the coordinate values that are associated with the
-various variables contained within the file</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies/&#39;</span>
-                     <span class="s1">&#39;python-gallery/NAM_20161031_1200.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="data-retrieval">
-<h2>Data Retrieval<a class="headerlink" href="#data-retrieval" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This code retrieves the necessary data from the file and completes some
-smoothing of the geopotential height and wind fields using the SciPy
-function gaussian_filter. A nicely formated valid time (vtime) variable
-is also created</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Grab lat/lon values (NAM will be 2D)</span>
-<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">data</span>
-<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">data</span>
-
-<span class="c1"># Grab x, y data and make 2D for wind component plotting because</span>
-<span class="c1"># u- and v-components are grid relative</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">x</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">y</span>
-
-<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-<span class="c1"># Grab Cartopy CRS from metadata for plotting wind barbs</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
-
-<span class="c1"># Select and grab 500-hPa geopotential heights and smooth with n-point smoother</span>
-<span class="n">level</span> <span class="o">=</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">hght_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-
-<span class="c1"># Select and grab 500-hPa wind components</span>
-<span class="n">uwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">assign_latitude_longitude</span><span class="p">()</span>
-<span class="n">vwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">assign_latitude_longitude</span><span class="p">()</span>
-
-<span class="c1"># Compute north-relative wind components for plotting purposes</span>
-<span class="n">uwnd_er</span><span class="p">,</span> <span class="n">vwnd_er</span> <span class="o">=</span> <span class="n">earth_relative_wind_components</span><span class="p">(</span><span class="n">uwnd_500</span><span class="p">,</span> <span class="n">vwnd_500</span><span class="p">)</span>
-
-<span class="c1"># Smooth wind components as desired</span>
-<span class="n">uwnd_er</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">uwnd_er</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-<span class="n">vwnd_er</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">vwnd_er</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-
-<span class="c1"># Create a clean datetime object for plotting based on time</span>
-<span class="c1"># of Geopotential heights</span>
-<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="metpy-absolute-vorticity-calculation">
-<h2>MetPy Absolute Vorticity Calculation<a class="headerlink" href="#metpy-absolute-vorticity-calculation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This code calculates <code class="docutils literal notranslate"><span class="pre">absolute_vorticity()</span></code>
-using the wind components and makes use of the
-xarray data object features to complete the necessary
-grid-aware calculation.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate absolute vorticity from MetPy function</span>
-<span class="n">avor_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">absolute_vorticity</span><span class="p">(</span><span class="n">uwnd_er</span><span class="p">,</span> <span class="n">vwnd_er</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="map-creation">
-<h2>Map Creation<a class="headerlink" href="#map-creation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This next set of code creates the plot and draws contours on a Lambert
-Conformal map centered on -100 E longitude. The main view is over the
-CONUS with geopotential heights contoured every 60 m and absolute
-vorticity colorshaded (<code class="docutils literal notranslate"><span class="pre">*10^5</span></code>).</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up the projection that will be used for plotting</span>
-<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">central_latitude</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
-                               <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
-
-<span class="c1"># Set up the projection of the data;</span>
-<span class="c1"># if lat/lon then PlateCarree is what you want</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
-
-<span class="c1"># Start the figure and create plot axes with proper projection</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">55</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add geopolitical boundaries for map reference</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-
-<span class="c1"># Absolute Vorticity colors</span>
-<span class="c1"># Use two different colormaps from matplotlib and combine into one color set</span>
-<span class="n">clevs_500_avor</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">+</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">46</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-<span class="n">colors1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">YlOrRd</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">48</span><span class="p">))</span>
-<span class="n">colors2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">BuPu</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
-<span class="n">colors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">colors2</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">colors1</span><span class="p">))</span>
-
-<span class="c1"># Plot absolute vorticity values (multiplying by 10^5 to scale appropriately)</span>
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">avor_500</span><span class="o">*</span><span class="mf">1e5</span><span class="p">,</span> <span class="n">clevs_500_avor</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span>
-                 <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;max&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Abs. Vorticity ($s^{-1}$)&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 500-hPa Geopotential Heights in meters</span>
-<span class="n">clevs_500_hght</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8000</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_500</span><span class="p">,</span> <span class="n">clevs_500_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set up a 2D slice to reduce the number of wind barbs plotted (every 20th)</span>
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
-         <span class="n">uwnd_er</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-         <span class="n">vwnd_er</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-         <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-
-<span class="c1"># Plot two titles, one on right and left side</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;500-hPa NAM Geopotential Heights (m)&#39;</span>
-          <span class="s1">&#39; and Wind Barbs (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid Time: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Valid Time: 2016-10-31 12:00:00&#39;)
-</pre></div>
-</div>
-<img alt="../../_images/bc8bba4565a06490278c04e332a46216117fbcbcc2109fb150e142e6233168eb.png" src="../../_images/bc8bba4565a06490278c04e332a46216117fbcbcc2109fb150e142e6233168eb.png" />
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-</section>
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-  <section class="tex2jax_ignore mathjax_ignore" id="hpa-vorticity-advection">
-<h1>500 hPa Vorticity Advection<a class="headerlink" href="#hpa-vorticity-advection" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Plot an 500-hPa map with calculating vorticity advection using MetPy calculations.</p>
-<p>Beyond just plotting 500-hPa level data, this uses calculations from <code class="docutils literal notranslate"><span class="pre">metpy.calc</span></code> to find
-the vorticity and vorticity advection. Currently, this needs an extra helper function to
-calculate the distance between lat/lon grid points.</p>
-<p>Imports</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
-<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="function-to-compute-earth-relative-winds">
-<h2>Function to Compute Earth-Relative Winds<a class="headerlink" href="#function-to-compute-earth-relative-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This function takes a data array with relevant information about the
-projection of the wind component data, along with the grid-relative
-components of the wind. It outputs the earth-relative components of the
-wind.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">earth_relative_wind_components</span><span class="p">(</span><span class="n">ugrd</span><span class="p">,</span> <span class="n">vgrd</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate the north-relative components of the</span>
-<span class="sd">    wind from the grid-relative components using Cartopy</span>
-<span class="sd">    transform_vectors.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">        ugrd : Xarray DataArray (M, N)</span>
-<span class="sd">            grid relative u-component of the wind</span>
-<span class="sd">        vgrd : Xarray DataArray (M, N)</span>
-<span class="sd">            grid relative v-component of the wind</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">        unr, vnr : tuple of array-like Quantity</span>
-<span class="sd">            The north-relative wind components in the X (East-West)</span>
-<span class="sd">            and Y (North-South) directions, respectively.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="s1">&#39;metpy_crs&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">coords</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No CRS in coordinate, be sure to use&#39;</span>
-                         <span class="s1">&#39;the MetPy accessor parse_cf()&#39;</span><span class="p">)</span>
-
-    <span class="n">data_crs</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
-
-    <span class="n">x</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span>
-
-    <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-    <span class="n">ut</span><span class="p">,</span> <span class="n">vt</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span><span class="o">.</span><span class="n">transform_vectors</span><span class="p">(</span><span class="n">data_crs</span><span class="p">,</span> <span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span>
-                                                  <span class="n">ugrd</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">vgrd</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-
-    <span class="c1"># Make a copy of u and v component DataArrays</span>
-    <span class="n">uer</span> <span class="o">=</span> <span class="n">ugrd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">ver</span> <span class="o">=</span> <span class="n">vgrd</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="c1"># Update values with transformed winds</span>
-    <span class="n">uer</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">ut</span>
-    <span class="n">ver</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">vt</span>
-
-    <span class="k">return</span> <span class="n">uer</span><span class="p">,</span> <span class="n">ver</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="data-aquisition">
-<h2>Data Aquisition<a class="headerlink" href="#data-aquisition" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
-
-<span class="c1"># Assemble our URL to the THREDDS Data Server catalog,</span>
-<span class="c1"># and access our desired dataset within via NCSS</span>
-<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/catalog/model-namanl-old/&#39;</span>
-<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/catalog.xml&#39;</span><span class="p">)</span>
-<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;namanl_218_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
-
-<span class="c1"># Query for Latest GFS Run</span>
-<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
-
-<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf&#39;</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
-
-<span class="c1"># Obtain our queried data</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-<span class="n">ds_data</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">ds_data</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">assign_latitude_longitude</span><span class="p">()</span>
-
-<span class="n">times</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span>
-<span class="n">vtime</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-
-<span class="n">lev_500</span> <span class="o">=</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-
-<span class="n">hght_500</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">hght_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">hght_500</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-
-<span class="n">uwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">vwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="c1"># Compute north-relative wind components for calculation purposes</span>
-<span class="n">uwnd_500er</span><span class="p">,</span> <span class="n">vwnd_500er</span> <span class="o">=</span> <span class="n">earth_relative_wind_components</span><span class="p">(</span><span class="n">uwnd_500</span><span class="p">,</span> <span class="n">vwnd_500</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="begin-data-calculations">
-<h2>Begin Data Calculations<a class="headerlink" href="#begin-data-calculations" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">avor</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">vorticity</span><span class="p">(</span><span class="n">uwnd_500er</span><span class="p">,</span> <span class="n">vwnd_500er</span><span class="p">)</span>
-
-<span class="n">avor</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">avor</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1e5</span>
-
-<span class="n">vort_adv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">avor</span><span class="p">,</span> <span class="n">uwnd_500er</span><span class="p">,</span> <span class="n">vwnd_500er</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1e4</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2648/3729036084.py:5: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
-  vort_adv = mpcalc.advection(avor, uwnd_500er, vwnd_500er) * 1e4
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="map-creation">
-<h2>Map Creation<a class="headerlink" href="#map-creation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up Coordinate System for Plot and Transforms</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
-<span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.</span><span class="p">,</span> <span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.</span><span class="p">,</span>
-                                <span class="n">standard_parallels</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">12.</span><span class="p">,</span> <span class="mf">14.</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
-
-<span class="c1"># Plot Titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;500-hPa Heights (m), AVOR$*10^5$ ($s^{-1}$),&#39;</span>
-          <span class="s1">&#39;AVOR Adv$*10^9$ ($s^{-2}$)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot Background</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="mf">235.</span><span class="p">,</span> <span class="mf">290.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">,</span> <span class="mf">58.</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">.5</span><span class="p">)</span>
-
-<span class="c1"># Plot Height Contours</span>
-<span class="n">clev500</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5100</span><span class="p">,</span> <span class="mi">6061</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">hght_500</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">hght_500</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span>
-                <span class="n">hght_500</span><span class="p">,</span> <span class="n">clev500</span><span class="p">,</span>
-                <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
-                <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
-           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Plot Absolute Vorticity Contours</span>
-<span class="n">clevvort500</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">avor</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">avor</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span>
-                 <span class="n">avor</span><span class="p">,</span> <span class="n">clevvort500</span><span class="p">,</span>
-                 <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.25</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
-           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Plot Colorfill of Vorticity Advection</span>
-<span class="n">clev_avoradv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">vort_adv</span><span class="o">.</span><span class="n">longitude</span><span class="p">,</span> <span class="n">vort_adv</span><span class="o">.</span><span class="n">latitude</span><span class="p">,</span> <span class="n">vort_adv</span><span class="p">,</span>
-                 <span class="n">clev_avoradv</span><span class="p">[</span><span class="n">clev_avoradv</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">],</span> <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span>
-                 <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;bwr&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                  <span class="n">extendrect</span><span class="o">=</span><span class="s1">&#39;True&#39;</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="n">clev_avoradv</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$1/s^2$&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot Wind Barbs</span>
-<span class="c1"># Transform Vectors and plot wind barbs.</span>
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
-<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">uwnd_500</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span>
-                     <span class="n">uwnd_500</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">uwnd_500</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">vwnd_500</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
-         <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.quiver.Barbs at 0x7f57f878d780&gt;
-</pre></div>
-</div>
-<img alt="../../_images/b53d0ec6077dcd0cef13e5c86c2fe66c173a67bd7f3409cd077b0881dd62644d.png" src="../../_images/b53d0ec6077dcd0cef13e5c86c2fe66c173a67bd7f3409cd077b0881dd62644d.png" />
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-  <section class="tex2jax_ignore mathjax_ignore" id="hpa-geopotential-heights-temperature-frontogenesis-and-winds">
-<h1>850-hPa Geopotential Heights, Temperature, Frontogenesis, and Winds<a class="headerlink" href="#hpa-geopotential-heights-temperature-frontogenesis-and-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Frontogenesis at 850-hPa with Geopotential Heights, Temperature, and
-Winds</p>
-<p>This example uses example data from the GFS analysis for 12 UTC 26
-October 2010 and uses xarray as the main read source with using MetPy to
-calculate frontogenesis and wind speed with geographic plotting using
-Cartopy for a CONUS view.</p>
-<p>Import the needed modules.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Use Xarray to access GFS data from THREDDS resource and uses
-metpy accessor to parse file to make it easy to pull data using
-common coordinate names (e.g., vertical) and attach units.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies/&#39;</span>
-                     <span class="s1">&#39;python-gallery/GFS_20101026_1200.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-</div>
-</div>
-<p>Subset data based on latitude and longitude values, calculate potential
-temperature for frontogenesis calculation.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set subset slice for the geographic extent of data to limit download</span>
-<span class="n">lon_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">350</span><span class="p">)</span>
-<span class="n">lat_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">85</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-
-<span class="c1"># Grab lat/lon values (GFS will be 1D)</span>
-<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-
-<span class="n">level</span> <span class="o">=</span> <span class="mi">850</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">hght_850</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">tmpk_850</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">uwnd_850</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">vwnd_850</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="c1"># Convert temperatures to degree Celsius for plotting purposes</span>
-<span class="n">tmpc_850</span> <span class="o">=</span> <span class="n">tmpk_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
-
-<span class="c1"># Calculate potential temperature for frontogenesis calculation</span>
-<span class="n">thta_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_temperature</span><span class="p">(</span><span class="n">level</span><span class="p">,</span> <span class="n">tmpk_850</span><span class="p">)</span>
-
-<span class="c1"># Get a sensible datetime format</span>
-<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="calculate-frontogenesis">
-<h2>Calculate frontogenesis<a class="headerlink" href="#calculate-frontogenesis" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Frontogenesis calculation in MetPy requires temperature, wind
-components, and grid spacings. First compute the grid deltas using MetPy
-functionality, then put it all together in the frontogenesis function.</p>
-<p>Note: MetPy will give the output with SI units, but typically
-frontogenesis (read: GEMPAK) output this variable with units of K per
-100 km per 3 h; a conversion factor is included here to use at plot time
-to reflect those units.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fronto_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">frontogenesis</span><span class="p">(</span><span class="n">thta_850</span><span class="p">,</span> <span class="n">uwnd_850</span><span class="p">,</span> <span class="n">vwnd_850</span><span class="p">)</span>
-
-<span class="c1"># A conversion factor to get frontogensis units of K per 100 km per 3 h</span>
-<span class="n">convert_to_per_100km_3h</span> <span class="o">=</span> <span class="mi">1000</span><span class="o">*</span><span class="mi">100</span><span class="o">*</span><span class="mi">3600</span><span class="o">*</span><span class="mi">3</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pint/facets/plain/quantity.py:1006: RuntimeWarning: invalid value encountered in divide
-  magnitude = magnitude_op(new_self._magnitude, other._magnitude)
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="plotting-frontogenesis">
-<h2>Plotting Frontogenesis<a class="headerlink" href="#plotting-frontogenesis" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Using a Lambert Conformal projection from Cartopy to plot 850-hPa
-variables including frontogenesis.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set map projection</span>
-<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">central_latitude</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
-                               <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
-
-<span class="c1"># Set projection of the data (GFS is lat/lon)</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
-
-<span class="c1"># Start figure and limit the graphical area extent</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">55</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add map features of Coastlines and States</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-
-<span class="c1"># Plot 850-hPa Frontogenesis</span>
-<span class="n">clevs_tmpc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">fronto_850</span><span class="o">*</span><span class="n">convert_to_per_100km_3h</span><span class="p">,</span>
-                 <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mf">8.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">,</span> <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Frontogenesis K / 100 km / 3 h&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 850-hPa Temperature in Celsius</span>
-<span class="n">csf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_850</span><span class="p">,</span> <span class="n">clevs_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
-                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 850-hPa Geopotential Heights</span>
-<span class="n">clevs_850_hght</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8000</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_850</span><span class="p">,</span> <span class="n">clevs_850_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 850-hPa Wind Barbs only plotting every fifth barb</span>
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span>
-         <span class="n">uwnd_850</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-         <span class="n">vwnd_850</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-         <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-
-<span class="c1"># Plot some titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;GFS 850-hPa Geopotential Heights (m), Temp (C), and Winds&#39;</span><span class="p">,</span>
-          <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid Time: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-</pre></div>
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-<h1>QVector Example<a class="headerlink" href="#qvector-example" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Computing Q-vectors and Q-vector divergence for a real case.</p>
-<p>By: Kevin Goebbert</p>
-<p>This example uses GFS output to compute the 850-hPa Q-vectors and
-Q-vector divergence for 12 UTC 26 October 2010.</p>
-<p>Import needed modules</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">named_areas</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Use Xarray to access GFS data from THREDDS resource and uses
-metpy accessor to parse file to make it easy to pull data using
-common coordinate names (e.g., vertical) and attach units.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies/&#39;</span>
-                     <span class="s1">&#39;python-gallery/GFS_20101026_1200.nc&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Subset data based on latitude and longitude values and select only data
-from 850 hPa</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set subset slice for the geographic extent of data to limit download</span>
-<span class="n">lon_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">350</span><span class="p">)</span>
-<span class="n">lat_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">85</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-
-<span class="c1"># Grab data and smooth using a nine-point filter applied 50 times to grab</span>
-<span class="c1"># the synoptic signal</span>
-<span class="n">level</span> <span class="o">=</span> <span class="mi">850</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">hght_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-<span class="n">tmpk_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="n">uwnd_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-<span class="n">vwnd_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-
-<span class="c1"># Grab lat/lon values (GFS will be 1D) from a subset variable</span>
-<span class="n">lons</span><span class="p">,</span> <span class="n">lats</span> <span class="o">=</span> <span class="n">hght_850</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght_850</span><span class="o">.</span><span class="n">lat</span>
-
-<span class="c1"># Convert temperatures to degree Celsius for plotting purposes</span>
-<span class="n">tmpc_850</span> <span class="o">=</span> <span class="n">tmpk_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
-
-<span class="c1"># Get a sensible datetime format</span>
-<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="compute-q-vectors">
-<h2>Compute Q-vectors<a class="headerlink" href="#compute-q-vectors" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Use the MetPy module to compute Q-vectors from requisite data and
-additionally compute the Q-vector divergence (and multiply by -2) to
-calculate the right hand side forcing of the Q-G Omega equation.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compute grid spacings for data</span>
-<span class="c1">#dx, dy = mpcalc.lat_lon_grid_deltas(lons, lats)</span>
-
-<span class="c1"># Compute the Q-vector components</span>
-<span class="n">uqvect</span><span class="p">,</span> <span class="n">vqvect</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">q_vector</span><span class="p">(</span><span class="n">uwnd_850</span><span class="p">,</span> <span class="n">vwnd_850</span><span class="p">,</span> <span class="n">tmpk_850</span><span class="p">,</span> <span class="mi">850</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-
-<span class="c1"># Compute the divergence of the Q-vectors calculated above</span>
-<span class="n">q_div</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">divergence</span><span class="p">(</span><span class="n">uqvect</span><span class="p">,</span> <span class="n">vqvect</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="plot-data">
-<h2>Plot Data<a class="headerlink" href="#plot-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Use Cartopy to plot data on a map using a Lambert Conformal projection.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set the map projection (how the data will be displayed)</span>
-<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">named_areas</span><span class="p">[</span><span class="s1">&#39;uslcc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">projection</span>
-
-<span class="c1"># Set the data project (GFS is lat/lon format)</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
-
-<span class="c1"># Start the figure and set an extent to only display a smaller graphics area</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">(</span><span class="n">named_areas</span><span class="p">[</span><span class="s1">&#39;uslcc&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">bounds</span><span class="p">,</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add map features to plot coastlines and state boundaries</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-
-<span class="c1"># Plot 850-hPa Q-Vector Divergence and scale</span>
-<span class="n">clevs_850_tmpc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">clevs_qdiv</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">35</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span><span class="o">+</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">q_div</span><span class="o">*</span><span class="mf">1e18</span><span class="p">,</span> <span class="n">clevs_qdiv</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">,</span>
-                 <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="n">clevs_qdiv</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Q-Vector Div. (*10$^</span><span class="si">{18}</span><span class="s1">$ m s$^{-1}$ kg$^{-1}$)&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 850-hPa Temperatures</span>
-<span class="n">csf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_850</span><span class="p">,</span> <span class="n">clevs_850_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
-                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 850-hPa Geopotential Heights</span>
-<span class="n">clevs_850_hght</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8000</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_850</span><span class="p">,</span> <span class="n">clevs_850_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 850-hPa Q-vectors, scale to get nice sized arrows</span>
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span>
-          <span class="n">uqvect</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">vqvect</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;mid&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-          <span class="n">scale</span><span class="o">=</span><span class="mf">5e-12</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;inches&#39;</span><span class="p">,</span>
-          <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-
-<span class="c1"># Add some titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;850-hPa GFS Geo. Heights (m), Temp (C),&#39;</span>
-          <span class="s1">&#39; and Q-Vectors (m$^2$ kg$^{-1}$ s$^{-1}$)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid Time: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Valid Time: 2010-10-26 12:00:00&#39;)
-</pre></div>
-</div>
-<img alt="../../_images/61b814adbe3517c41496130b38e00b4819e8c3100898c1be06f84579e5406661.png" src="../../_images/61b814adbe3517c41496130b38e00b4819e8c3100898c1be06f84579e5406661.png" />
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-  <section class="tex2jax_ignore mathjax_ignore" id="hpa-temperature-advection">
-<h1>850 hPa Temperature Advection<a class="headerlink" href="#hpa-temperature-advection" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Plot an 850 hPa map with calculating advection using MetPy.</p>
-<p>Beyond just plotting 850-hPa level data, this uses calculations from <code class="docutils literal notranslate"><span class="pre">metpy.calc</span></code> to find
-the temperature advection. Currently, this needs an extra helper function to calculate
-the distance between lat/lon grid points.</p>
-<p>Imports</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
-<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="create-ncss-object-to-access-the-netcdfsubset">
-<h2>Create NCSS object to access the NetcdfSubset<a class="headerlink" href="#create-ncss-object-to-access-the-netcdfsubset" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Data from NCEI GFS 0.5 deg Analysis Archive</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2017</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
-
-<span class="c1"># Assemble our URL to the THREDDS Data Server catalog,</span>
-<span class="c1"># and access our desired dataset within via NCSS</span>
-<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/model-gfs-g4-anl-files-old/&#39;</span>
-<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/catalog.xml&#39;</span><span class="p">)</span>
-<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;gfsanl_4_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
-
-<span class="c1"># Create NCSS query for our desired time, region, and data variables</span>
-<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
-
-<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="n">north</span><span class="o">=</span><span class="mi">65</span><span class="p">,</span> <span class="n">south</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">east</span><span class="o">=</span><span class="mi">310</span><span class="p">,</span> <span class="n">west</span><span class="o">=</span><span class="mi">220</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf&#39;</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
-
-<span class="c1"># Obtain the queried data</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="c1"># Pull out variables you want to use</span>
-<span class="n">level</span> <span class="o">=</span> <span class="mi">850</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">hght_850</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">temp_850</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">u_wind_850</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">v_wind_850</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">time</span> <span class="o">=</span> <span class="n">hght_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span>
-<span class="n">lat</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">values</span>
-<span class="n">lon</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">values</span>
-
-<span class="c1"># Convert number of hours since the reference time into an actual date</span>
-<span class="n">vtime</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-
-<span class="c1"># Combine 1D latitude and longitudes into a 2D grid of locations</span>
-<span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="begin-data-calculations">
-<h2>Begin data calculations<a class="headerlink" href="#begin-data-calculations" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate temperature advection using metpy function</span>
-<span class="n">adv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">temp_850</span><span class="p">,</span> <span class="n">u_wind_850</span><span class="p">,</span> <span class="n">v_wind_850</span><span class="p">)</span>
-
-<span class="c1"># Smooth heights and advection a little</span>
-<span class="c1"># Be sure to only put in a 2D lat/lon or Y/X array for smoothing</span>
-<span class="n">Z_850</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">hght_850</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">adv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">adv</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2755/72829660.py:2: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
-  adv = mpcalc.advection(temp_850, u_wind_850, v_wind_850)
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="begin-map-creation">
-<h2>Begin map creation<a class="headerlink" href="#begin-map-creation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set Projection of Data</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
-
-<span class="c1"># Set Projection of Plot</span>
-<span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_latitude</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span>
-                                <span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">standard_parallels</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
-
-<span class="c1"># Create new figure</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
-
-<span class="c1"># Add the map and set the extent</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;850mb Temperature Advection for </span><span class="si">{</span><span class="n">vtime</span><span class="si">:</span><span class="s1">%d %B %Y %H:%MZ</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
-          <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="mf">235.</span><span class="p">,</span> <span class="mf">290.</span><span class="p">,</span> <span class="mf">20.</span><span class="p">,</span> <span class="mf">55.</span><span class="p">])</span>
-
-<span class="c1"># Add state/country boundaries to plot</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">BORDERS</span><span class="p">)</span>
-
-<span class="c1"># Plot Height Contours</span>
-<span class="n">clev850</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">900</span><span class="p">,</span> <span class="mi">3000</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span><span class="p">,</span> <span class="n">Z_850</span><span class="p">,</span> <span class="n">clev850</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span>
-                <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
-           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Plot Temperature Contours</span>
-<span class="n">clevtemp850</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span><span class="p">,</span> <span class="n">temp_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">),</span>
-                 <span class="n">clevtemp850</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.25</span><span class="p">,</span>
-                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span>
-           <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Plot Colorfill of Temperature Advection</span>
-<span class="n">cint</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">)</span>
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="n">adv</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;delta_degC/hour&#39;</span><span class="p">),</span>
-                 <span class="n">cint</span><span class="p">[</span><span class="n">cint</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">],</span>
-                 <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;bwr&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ticks</span><span class="o">=</span><span class="n">cint</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$^</span><span class="si">{o}</span><span class="s1">C/3h$&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
-
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="c1"># Plot Wind Barbs</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
-         <span class="n">u_wind_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
-         <span class="n">v_wind_850</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
-         <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">);</span>
-</pre></div>
-</div>
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-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_0_boundary_lines_land.zip
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-  <section class="tex2jax_ignore mathjax_ignore" id="geostrophic-and-ageostrophic-wind">
-<h1>Geostrophic and Ageostrophic Wind<a class="headerlink" href="#geostrophic-and-ageostrophic-wind" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Plot a 1000-hPa map calculating the geostrophic from MetPy and finding the
-ageostrophic wind from the total wind and the geostrophic wind.</p>
-<p>This uses the geostrophic wind calculation from <code class="docutils literal notranslate"><span class="pre">metpy.calc</span></code> to find
-the geostrophic wind, then performs the simple subtraction to find the ageostrophic
-wind. Currently, this needs an extra helper function to calculate
-the distance between lat/lon grid points.</p>
-<p>Additionally, we utilize the <code class="docutils literal notranslate"><span class="pre">ndimage.zoom</span></code> method for smoothing the 1000-hPa
-height contours without smoothing the data.</p>
-<p>Imports</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">ndimage</span>
-<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
-<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Set up access to the data</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">22</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
-<span class="n">forecast_hour</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="n">h</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="n">forecast_hour</span><span class="p">)</span>
-
-<span class="c1"># Assemble our URL to the THREDDS Data Server catalog,</span>
-<span class="c1"># and access our desired dataset within via NCSS</span>
-<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/model-gfs-g4-anl-files-old/&#39;</span>
-<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/catalog.xml&#39;</span><span class="p">)</span>
-<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;gfsanl_4_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">&#39;</span>
-                    <span class="sa">f</span><span class="s1">&#39;00_00</span><span class="si">{</span><span class="n">forecast_hour</span><span class="si">}</span><span class="s1">.grb2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
-
-<span class="c1"># Create NCSS query for our desired time, region, and data variables</span>
-<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
-
-<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="n">north</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">south</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">east</span><span class="o">=-</span><span class="mi">80</span><span class="p">,</span> <span class="n">west</span><span class="o">=-</span><span class="mi">115</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">dt</span> <span class="o">+</span> <span class="n">h</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">100000</span><span class="p">)</span>
-
-<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
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-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Pull out variables you want to use</span>
-<span class="n">height</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">u_wind</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span>
-<span class="n">v_wind</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span>
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-<span class="n">vtime</span> <span class="o">=</span> <span class="n">height</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-
-<span class="n">lat</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span>
-<span class="n">lon</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span>
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-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Combine 1D latitude and longitudes into a 2D grid of locations</span>
-<span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
-
-<span class="c1"># Smooth height data</span>
-<span class="n">height</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-
-<span class="c1"># Compute the geostrophic wind</span>
-<span class="n">geo_wind_u</span><span class="p">,</span> <span class="n">geo_wind_v</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">geostrophic_wind</span><span class="p">(</span><span class="n">height</span><span class="p">)</span>
-
-<span class="c1"># Calculate ageostrophic wind components</span>
-<span class="n">ageo_wind_u</span> <span class="o">=</span> <span class="n">u_wind</span> <span class="o">-</span> <span class="n">geo_wind_u</span>
-<span class="n">ageo_wind_v</span> <span class="o">=</span> <span class="n">v_wind</span> <span class="o">-</span> <span class="n">geo_wind_v</span>
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-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create new figure</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-
-<span class="c1"># Add the map and set the extent</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mf">105.</span><span class="p">,</span> <span class="o">-</span><span class="mf">93.</span><span class="p">,</span> <span class="mf">35.</span><span class="p">,</span> <span class="mf">43.</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">patch</span><span class="o">.</span><span class="n">set_fill</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="c1"># Add state boundaries to plot</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Contour the heights every 10 m</span>
-<span class="n">contours</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-<span class="c1"># Because we have a very local graphics area, the contours have joints</span>
-<span class="c1"># to smooth those out we can use `ndimage.zoom`</span>
-<span class="n">zoom_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">zoom_xarray</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="n">c</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">zoom_500</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">zoom_500</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">zoom_500</span><span class="p">,</span> <span class="n">levels</span><span class="o">=</span><span class="n">contours</span><span class="p">,</span>
-               <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set up parameters for quiver plot. The slices below are used to</span>
-<span class="c1"># subset the data (here taking every 4th point in x and y). The</span>
-<span class="c1"># quiver_kwargs are parameters to control the appearance of the</span>
-<span class="c1"># quiver so that they stay consistent between the calls.</span>
-<span class="n">quiver_slices</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-<span class="n">quiver_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;headlength&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span> <span class="s1">&#39;headwidth&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">&#39;angles&#39;</span><span class="p">:</span> <span class="s1">&#39;uv&#39;</span><span class="p">,</span>
-                 <span class="s1">&#39;scale_units&#39;</span><span class="p">:</span> <span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="s1">&#39;scale&#39;</span><span class="p">:</span> <span class="mi">20</span><span class="p">}</span>
-
-<span class="c1"># Plot the wind vectors</span>
-<span class="n">wind</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
-                 <span class="n">u_wind</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">v_wind</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
-                 <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">quiver_kwargs</span><span class="p">)</span>
-<span class="n">geo</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
-                <span class="n">geo_wind_u</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">geo_wind_v</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
-                <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkorchid&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">quiver_kwargs</span><span class="p">)</span>
-<span class="n">ageo</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
-                 <span class="n">ageo_wind_u</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span> <span class="n">ageo_wind_v</span><span class="p">[</span><span class="n">quiver_slices</span><span class="p">],</span>
-                 <span class="n">color</span><span class="o">=</span><span class="s1">&#39;lime&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">quiver_kwargs</span><span class="p">)</span>
-
-<span class="c1"># Add a title to the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;1000mb Geopotential Heights(m), Wind(blue), &#39;</span>
-          <span class="s1">&#39;Geostrophic Wind(purple), and </span><span class="se">\n</span><span class="s1"> Ageostrophic Wind(green) &#39;</span>
-          <span class="sa">f</span><span class="s1">&#39;for </span><span class="si">{</span><span class="n">vtime</span><span class="si">:</span><span class="s1">%d %B %Y %H:%MZ</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-</pre></div>
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-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;1000mb Geopotential Heights(m), Wind(blue), Geostrophic Wind(purple), and \n Ageostrophic Wind(green) for 22 August 2016 21:00Z&#39;)
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_cultural/ne_10m_admin_1_states_provinces_lakes.zip
-  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
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-  <section class="tex2jax_ignore mathjax_ignore" id="analytic-300-hpa-trough">
-<h1>Analytic 300-hPa Trough<a class="headerlink" href="#analytic-300-hpa-trough" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Below are three definitions to create an analytic 300-hPa trough roughly
-based on the Sanders Analytic Model with modified coefficients to create
-different style waves.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">single_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;hght&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot; Single trough with heights and Temperatures based on Sanders Analytic Model</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
-    <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
-
-    <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-    <span class="n">p</span> <span class="o">=</span> <span class="mi">4</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="mi">2</span>
-
-    <span class="k">if</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;hght&#39;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">(</span><span class="mi">9240</span> <span class="o">+</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">q</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-                <span class="o">+</span> <span class="mi">200</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mi">300</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="k">elif</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;temp&#39;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">50</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">q</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-                <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
-
-
-<span class="k">def</span> <span class="nf">lifting_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;hght&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot; Lifting trough with heights and Temperatures based on Sanders Analytic Model</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
-    <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
-
-    <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-    <span class="n">p</span> <span class="o">=</span> <span class="mi">4</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="mi">2</span>
-
-    <span class="k">if</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;hght&#39;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">(</span><span class="mi">9240</span> <span class="o">+</span> <span class="mi">150</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">q</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-                <span class="o">+</span> <span class="mi">200</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mi">400</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
-    <span class="k">elif</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;temp&#39;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">50</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">q</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-                <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
-
-
-<span class="k">def</span> <span class="nf">digging_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;hght&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot; Digging trough with heights and Temperatures based on Sanders Analytic Model</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
-    <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
-
-    <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">)</span>
-
-    <span class="n">p</span> <span class="o">=</span> <span class="mi">4</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="mi">2</span>
-
-    <span class="k">if</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;hght&#39;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">(</span><span class="mi">9240</span> <span class="o">+</span> <span class="mi">150</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">q</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-                <span class="o">+</span> <span class="mi">200</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mi">400</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="k">elif</span> <span class="n">parameter</span> <span class="o">==</span> <span class="s1">&#39;temp&#39;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">50</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">q</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-                <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Call the appropriate definition to develop the desired wave.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Single Trough</span>
-<span class="n">Z</span> <span class="o">=</span> <span class="n">single_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;hght&#39;</span><span class="p">)</span>
-<span class="n">T</span> <span class="o">=</span> <span class="n">single_300hPa_trough</span><span class="p">(</span><span class="n">parameter</span><span class="o">=</span><span class="s1">&#39;temp&#39;</span><span class="p">)</span>
-
-<span class="c1"># Lifting Trough</span>
-<span class="c1"># Z = lifting_300hPa_trough(parameter=&#39;hght&#39;)</span>
-<span class="c1"># T = lifting_300hPa_trough(parameter=&#39;temp&#39;)</span>
-
-<span class="c1"># Digging Trough</span>
-<span class="c1"># Z = digging_300hPa_trough(parameter=&#39;hght&#39;)</span>
-<span class="c1"># T = digging_300hPa_trough(parameter=&#39;temp&#39;)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Set geographic parameters for analytic grid to then</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lats</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">35</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
-<span class="n">lons</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">260</span><span class="p">,</span> <span class="mi">290</span><span class="p">,</span> <span class="mi">101</span><span class="p">)</span>
-<span class="n">lon</span><span class="p">,</span> <span class="n">lat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">)</span>
-
-<span class="c1"># Coriolis Parameter</span>
-<span class="n">f</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">coriolis_parameter</span><span class="p">(</span><span class="n">lat</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span><span class="p">)</span>
-
-<span class="c1"># Calculate Geostrophic Wind from Analytic Heights</span>
-<span class="n">dx</span><span class="p">,</span> <span class="n">dy</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">lat_lon_grid_deltas</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">)</span>
-<span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">geostrophic_wind</span><span class="p">(</span><span class="n">Z</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">meter</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span> <span class="n">latitude</span><span class="o">=</span><span class="n">lat</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degrees</span><span class="p">)</span>
-
-<span class="c1"># Get the wind direction for each point</span>
-<span class="n">wdir</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_direction</span><span class="p">(</span><span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">)</span>
-
-<span class="c1"># Compute the Gradient Wind via an approximation</span>
-<span class="n">dydx</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">first_derivative</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="n">dx</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">d2ydx2</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">first_derivative</span><span class="p">(</span><span class="n">dydx</span><span class="p">,</span> <span class="n">delta</span><span class="o">=</span><span class="n">dx</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">R</span> <span class="o">=</span> <span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">dydx</span><span class="o">.</span><span class="n">m</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mf">3.</span> <span class="o">/</span> <span class="mf">2.</span><span class="p">))</span> <span class="o">/</span> <span class="n">d2ydx2</span><span class="o">.</span><span class="n">m</span>
-
-<span class="n">geo_mag</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">)</span>
-<span class="n">grad_mag</span> <span class="o">=</span> <span class="n">geo_mag</span><span class="o">.</span><span class="n">m</span> <span class="o">-</span> <span class="p">(</span><span class="n">geo_mag</span><span class="o">.</span><span class="n">m</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">magnitude</span> <span class="o">*</span> <span class="n">R</span><span class="p">)</span>
-
-<span class="n">ugrad</span><span class="p">,</span> <span class="n">vgrad</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_components</span><span class="p">(</span><span class="n">grad_mag</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;m/s&#39;</span><span class="p">),</span> <span class="n">wdir</span><span class="p">)</span>
-
-<span class="c1"># Calculate Ageostrophic wind</span>
-<span class="n">uageo</span> <span class="o">=</span> <span class="n">ugrad</span> <span class="o">-</span> <span class="n">ugeo</span>
-<span class="n">vageo</span> <span class="o">=</span> <span class="n">vgrad</span> <span class="o">-</span> <span class="n">vgeo</span>
-
-<span class="c1"># Compute QVectors</span>
-<span class="n">uqvect</span><span class="p">,</span> <span class="n">vqvect</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">q_vector</span><span class="p">(</span><span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">,</span> <span class="n">T</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">degC</span><span class="p">,</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">)</span>
-
-<span class="c1"># Calculate divergence of the ageostrophic wind</span>
-<span class="n">div</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">divergence</span><span class="p">(</span><span class="n">uageo</span><span class="p">,</span> <span class="n">vageo</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="n">dy</span><span class="p">)</span>
-
-<span class="c1"># Calculate Relative Vorticity Advection</span>
-<span class="n">relvor</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">vorticity</span><span class="p">(</span><span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="n">dy</span><span class="p">)</span>
-<span class="n">adv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">relvor</span><span class="p">,</span> <span class="n">ugeo</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">,</span> <span class="n">dx</span><span class="o">=</span><span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="o">=</span><span class="n">dy</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Create figure containing Geopotential Heights, Temperature, Divergence
-of the Ageostrophic Wind, Relative Vorticity Advection (shaded),
-geostrphic wind barbs, and Q-vectors.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
-
-<span class="c1"># Plot Geopotential Height Contours</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">12000</span><span class="p">,</span> <span class="mi">120</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot Temperature Contours</span>
-<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot Divergence of Ageo Wind Contours</span>
-<span class="n">cs3</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">div</span><span class="o">*</span><span class="mi">10</span><span class="o">**</span><span class="mi">9</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">25</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
-                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs3</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot Rel. Vor. Adv. colorfilled</span>
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">adv</span><span class="o">*</span><span class="mi">10</span><span class="o">**</span><span class="mi">9</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">)</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Rel. Vor. Adv.&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot Geostrophic Wind Barbs</span>
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
-         <span class="n">ugeo</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">vgeo</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">m</span><span class="p">)</span>
-
-<span class="c1"># Plot Ageostrophic Wind Vectors</span>
-<span class="c1"># ageo_slice = slice(None, None, 10)</span>
-<span class="c1"># ax.quiver(lons[ageo_slice], lats[ageo_slice],</span>
-<span class="c1">#           uageo[ageo_slice, ageo_slice].m, vageo[ageo_slice, ageo_slice].m,</span>
-<span class="c1">#           color=&#39;blue&#39;, pivot=&#39;mid&#39;)</span>
-
-<span class="c1"># Plot QVectors</span>
-<span class="n">qvec_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">qvec_slice</span><span class="p">],</span> <span class="n">lats</span><span class="p">[</span><span class="n">qvec_slice</span><span class="p">],</span>
-          <span class="n">uqvect</span><span class="p">[</span><span class="n">qvec_slice</span><span class="p">,</span> <span class="n">qvec_slice</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">vqvect</span><span class="p">[</span><span class="n">qvec_slice</span><span class="p">,</span> <span class="n">qvec_slice</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span>
-          <span class="n">color</span><span class="o">=</span><span class="s1">&#39;darkcyan&#39;</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;mid&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;300-hPa Geo Heights (black), Q-Vector (dark cyan), Divergence (grey; dashed)&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(0.5, 1.0, &#39;300-hPa Geo Heights (black), Q-Vector (dark cyan), Divergence (grey; dashed)&#39;)
-</pre></div>
-</div>
-<img alt="../../_images/67cc747389a2e46fb6c9eb3a4f270e7fc3d82f365f981c079fae47d6a330ba62.png" src="../../_images/67cc747389a2e46fb6c9eb3a4f270e7fc3d82f365f981c079fae47d6a330ba62.png" />
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-  <section class="tex2jax_ignore mathjax_ignore" id="metpy-declarative-300-hpa">
-<h1>MetPy Declarative - 300 hPa<a class="headerlink" href="#metpy-declarative-300-hpa" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>By: Kevin Goebbert</p>
-<p>This example uses the declarative syntax available through the MetPy
-package to allow a more convenient method for creating simple maps of
-atmospheric data. The key thing the declarative language does is to
-reduce the number of packages that users will need to know in detail and
-instead allow them to set key parameters to get the map they desire. One
-key element is the use of xarray as the data object, which allows
-coordinate information to be associated with atmospheric variables.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.plots.declarative</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Open dataset using xarray module and subset global GFS to be over the
-CONUS.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies&#39;</span>
-                     <span class="s1">&#39;/python-gallery/GFS_20101026_1200.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">lon</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">360</span><span class="o">-</span><span class="mi">150</span><span class="p">,</span> <span class="mi">360</span><span class="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">lat</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">65</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="calculate-variable-and-add-to-dataset">
-<h2>Calculate Variable and Add to Dataset<a class="headerlink" href="#calculate-variable-and-add-to-dataset" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Here it is demonstrated how you can calculate a new variable and add it
-to the xarray dataset (ds) so that it can be plotted with the
-declarative syntax. The key to adding a variable to an xarray dataset
-for use in the declarative syntax is the need to add a <code class="docutils literal notranslate"><span class="pre">grid_mapping</span></code>
-and <code class="docutils literal notranslate"><span class="pre">units</span></code> attribute.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate New Variables and place into Xarray Dataset</span>
-<span class="n">uwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span>
-<span class="n">vwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span>
-
-<span class="c1"># Compute wind speed using MetPy</span>
-<span class="n">wspd</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">uwnd</span><span class="p">,</span> <span class="n">vwnd</span><span class="p">)</span>
-
-<span class="c1"># Place wind speed (wspd) into xarray dataset and attach needed attributes</span>
-<span class="n">ds</span><span class="p">[</span><span class="s1">&#39;wind_speed&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">wspd</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="declarative-plot">
-<h2>Declarative Plot<a class="headerlink" href="#declarative-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>The following settings create a single panel map plot of 300 hPa
-geopotential heights, wind speed, and wind barbs.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Countour Plot of Geopotential Heights</span>
-<span class="n">contour</span> <span class="o">=</span> <span class="n">ContourPlot</span><span class="p">()</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">ds</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">time</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2010</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">field</span> <span class="o">=</span> <span class="s1">&#39;Geopotential_height_isobaric&#39;</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">level</span> <span class="o">=</span> <span class="mi">300</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">linecolor</span> <span class="o">=</span> <span class="s1">&#39;black&#39;</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">linewidth</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">clabels</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">contours</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20000</span><span class="p">,</span> <span class="mi">120</span><span class="p">))</span>
-
-<span class="c1"># Colorfilled Plot of Wind Speed</span>
-<span class="n">cfill</span> <span class="o">=</span> <span class="n">FilledContourPlot</span><span class="p">()</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">ds</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">field</span> <span class="o">=</span> <span class="s1">&#39;wind_speed&#39;</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">level</span> <span class="o">=</span> <span class="mi">300</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">colormap</span> <span class="o">=</span> <span class="s1">&#39;BuPu&#39;</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">contours</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">171</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">colorbar</span> <span class="o">=</span> <span class="s1">&#39;vertical&#39;</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">plot_units</span> <span class="o">=</span> <span class="s1">&#39;kt&#39;</span>
-
-<span class="c1"># Plot wind barbs</span>
-<span class="n">barb</span> <span class="o">=</span> <span class="n">BarbPlot</span><span class="p">()</span>
-<span class="n">barb</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">ds</span>
-<span class="n">barb</span><span class="o">.</span><span class="n">level</span> <span class="o">=</span> <span class="mi">300</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">barb</span><span class="o">.</span><span class="n">field</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span> <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span>
-<span class="n">barb</span><span class="o">.</span><span class="n">skip</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="n">barb</span><span class="o">.</span><span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;black&#39;</span>
-<span class="n">barb</span><span class="o">.</span><span class="n">barblength</span> <span class="o">=</span> <span class="mf">6.5</span>
-<span class="n">barb</span><span class="o">.</span><span class="n">earth_relative</span> <span class="o">=</span> <span class="kc">False</span>
-<span class="n">barb</span><span class="o">.</span><span class="n">plot_units</span> <span class="o">=</span> <span class="s1">&#39;kt&#39;</span>
-
-<span class="c1"># Panel for plot with Map features</span>
-<span class="n">panel</span> <span class="o">=</span> <span class="n">MapPanel</span><span class="p">()</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">layout</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">area</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">124</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">53</span><span class="p">)</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">projection</span> <span class="o">=</span> <span class="s1">&#39;lcc&#39;</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;coastline&#39;</span><span class="p">,</span> <span class="s1">&#39;borders&#39;</span><span class="p">,</span> <span class="s1">&#39;states&#39;</span><span class="p">,</span> <span class="s1">&#39;land&#39;</span><span class="p">]</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">plots</span> <span class="o">=</span> <span class="p">[</span><span class="n">cfill</span><span class="p">,</span> <span class="n">contour</span><span class="p">,</span> <span class="n">barb</span><span class="p">]</span>
-
-<span class="c1"># Bringing it all together</span>
-<span class="n">pc</span> <span class="o">=</span> <span class="n">PanelContainer</span><span class="p">()</span>
-<span class="n">pc</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">9</span><span class="p">)</span>
-<span class="n">pc</span><span class="o">.</span><span class="n">panels</span> <span class="o">=</span> <span class="p">[</span><span class="n">panel</span><span class="p">]</span>
-
-<span class="n">pc</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_land.zip
-  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
-</pre></div>
-</div>
-<img alt="../../_images/d333531f29bb5443a5f6a77e5542a2c9ee5aa54bfa8159b49bde434026705a3b.png" src="../../_images/d333531f29bb5443a5f6a77e5542a2c9ee5aa54bfa8159b49bde434026705a3b.png" />
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-  <section class="tex2jax_ignore mathjax_ignore" id="differential-temperature-advection-with-narr-data">
-<h1>Differential Temperature Advection with NARR Data<a class="headerlink" href="#differential-temperature-advection-with-narr-data" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>By: Kevin Goebbert</p>
-<p>This example creates a four-panel plot to illustrate the difference
-between single level temperature advection and a computed differential
-temperature advection between two layers. This example makes use of NARR
-output.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="data-input">
-<h2>Data Input<a class="headerlink" href="#data-input" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Use Xarray to access GFS data from THREDDS resource and uses
-metpy accessor to parse file to make it easy to pull data using
-common coordinate names (e.g., vertical) and attach units.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies/&#39;</span>
-                     <span class="s1">&#39;python-gallery/NARR_19930313_1800.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">assign_latitude_longitude</span><span class="p">()</span>
-
-<span class="c1"># Get lat/lon data from file</span>
-<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span>
-<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span>
-
-<span class="c1"># Get 700-hPa data and smooth</span>
-<span class="n">level</span> <span class="o">=</span> <span class="mi">700</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">hght_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
-<span class="n">tmpk_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
-<span class="n">uwnd_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
-    <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
-<span class="n">vwnd_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
-    <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
-
-<span class="c1"># Get 300-hPa data and</span>
-<span class="n">level</span> <span class="o">=</span> <span class="mi">300</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">hght_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
-<span class="n">tmpk_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
-<span class="n">uwnd_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
-    <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
-<span class="n">vwnd_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
-    <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="mi">9</span><span class="p">)</span>
-
-<span class="c1"># Convert Temperatures to degC</span>
-<span class="n">tmpc_700</span> <span class="o">=</span> <span class="n">tmpk_700</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
-<span class="n">tmpc_300</span> <span class="o">=</span> <span class="n">tmpk_300</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
-
-<span class="c1"># Get time in a nice datetime object format</span>
-<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="differential-temperature-advection-calculation">
-<h2>Differential Temperature Advection Calculation<a class="headerlink" href="#differential-temperature-advection-calculation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Use MetPy advection funtion to calculate temperature advection at 700
-and 300 hPa, then manually compute the differential between those two
-layers. The differential temperature advection is then valid at 500 hPa
-(due to centered differencing) and is the same level that height changes
-due to absolute vorticity advection is commonly assessed.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Use MetPy advection function to calculate temperature advection at two levels</span>
-<span class="n">tadv_700</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">tmpk_700</span><span class="p">,</span> <span class="n">uwnd_700</span><span class="p">,</span> <span class="n">vwnd_700</span><span class="p">)</span>
-<span class="n">tadv_300</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">tmpk_300</span><span class="p">,</span> <span class="n">uwnd_300</span><span class="p">,</span> <span class="n">vwnd_300</span><span class="p">)</span>
-
-<span class="c1"># Centered finite difference to calculate differential temperature advection</span>
-<span class="n">diff_tadv</span> <span class="o">=</span> <span class="p">((</span><span class="n">tadv_700</span><span class="o">.</span><span class="n">data</span> <span class="o">-</span> <span class="n">tadv_300</span><span class="o">.</span><span class="n">data</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">400</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">))</span><span class="o">.</span><span class="n">to_base_units</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2861/3635212219.py:2: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
-  tadv_700 = mpcalc.advection(tmpk_700, uwnd_700, vwnd_700)
-/tmp/ipykernel_2861/3635212219.py:3: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
-  tadv_300 = mpcalc.advection(tmpk_300, uwnd_300, vwnd_300)
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="make-four-panel-plot">
-<h2>Make Four Panel Plot<a class="headerlink" href="#make-four-panel-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>A four panel plot is produced to illustrate the temperature fields at
-two levels (700 and 300 hPa), which are common fields to be plotted.
-Then a panel containing the evaluated temperature advection at 700 hPa
-and differential temperature advection between 700 and 300 hPa. Of
-meteorological significance is the difference between these two
-advection plots. For the QG Height Tendency equation, the forcing term
-is proportional to the differential temperature advection, which paints
-a slightly different picture than just the 700-hPa temperature advection
-alone.</p>
-<p>To create the four panel plot it takes a bit of code at this point. The
-following code is segmented into Upper-left, Lower-left, Upper-right,
-Lower-right panels using matplotlib’s gridspec to help with spacing.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up plot crs (mapcrs) and the data crs, will need to</span>
-<span class="c1"># transform all variables</span>
-<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span>
-                               <span class="n">central_latitude</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
-                               <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
-
-<span class="c1"># Set some common contour interval levels</span>
-<span class="n">clevs_700_tmpc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">40</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">clevs_700_hght</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8000</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
-<span class="n">clevs_300_hght</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
-
-<span class="c1"># Create slice to reduce number of wind barbs at plot time</span>
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-
-<span class="c1"># Start figure</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">22</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
-
-<span class="c1"># Use gridspec to help size elements of plot; small top plot</span>
-<span class="c1"># and big bottom plot</span>
-<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">height_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
-                       <span class="n">hspace</span><span class="o">=</span><span class="mf">0.03</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
-
-
-<span class="c1"># Upper-left panel (700-hPa TMPC)</span>
-<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">49</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_700</span><span class="p">,</span> <span class="n">clevs_700_tmpc</span><span class="p">,</span>
-                  <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$^{\circ}$C&#39;</span><span class="p">)</span>
-
-<span class="n">csf</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_700</span><span class="p">,</span> <span class="n">clevs_700_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
-                  <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_700</span><span class="p">,</span> <span class="n">clevs_700_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">ax1</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">uwnd_700</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">vwnd_700</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;700-hPa NARR HGHT (m), TMPC, and Wind (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-
-
-<span class="c1"># Lower-left panel (300-hPa TMPC)</span>
-<span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">49</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_300</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="o">-</span><span class="mi">24</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
-                  <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">BuPu_r</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$^{\circ}$C&#39;</span><span class="p">)</span>
-
-<span class="n">csf</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_300</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
-                  <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_300</span><span class="p">,</span> <span class="n">clevs_300_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">ax2</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">uwnd_300</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">vwnd_300</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;300-hPa NARR HGHT (m), TMPC, and Wind (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-
-
-<span class="c1"># Upper-right panel (700-hPa TMPC Adv)</span>
-<span class="n">ax3</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax3</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">49</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax3</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax3</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax3</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tadv_700</span><span class="o">*</span><span class="mi">3600</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                  <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;TMPC ADV ($^{\circ}$C h$^{-1}$)&#39;</span><span class="p">)</span>
-
-<span class="n">csf</span> <span class="o">=</span> <span class="n">ax3</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_700</span><span class="p">,</span> <span class="n">clevs_700_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
-                  <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax3</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_700</span><span class="p">,</span> <span class="n">clevs_700_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">ax3</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">uwnd_700</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">vwnd_700</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;700-hPa NARR HGHT (m), TMP ADV (C/h), and Wind (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-
-
-<span class="c1"># Lower-right panel (diff TMPC)</span>
-<span class="n">ax4</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax4</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">49</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax4</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax4</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax4</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">diff_tadv</span><span class="o">*</span><span class="mf">1e9</span><span class="p">,</span> <span class="n">clevs_700_tmpc</span><span class="p">,</span>
-                  <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span><span class="p">,</span> <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cb</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                  <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;dTMPC ($10^9$ $^{\circ}$C s$^{-1}$ Pa$^{-1}$)&#39;</span><span class="p">)</span>
-
-<span class="n">csf</span> <span class="o">=</span> <span class="n">ax4</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">tmpc_700</span><span class="p">,</span> <span class="n">clevs_700_tmpc</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span>
-                  <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">csf</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax4</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_700</span><span class="p">,</span> <span class="n">clevs_700_hght</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">ax4</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">uwnd_700</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">vwnd_700</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-          <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;700-hPa NARR HGHT (m), Diff. TMP ADV (C/s/Pa), and Wind (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
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-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Valid: 1993-03-13 18:00:00&#39;)
-</pre></div>
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-<h1>Hovmoller Diagram Example<a class="headerlink" href="#hovmoller-diagram-example" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>By: Kevin Goebbert</p>
-<p>Northern Hemispheric v-wind component over the mid-latitudes in a
-Hovmoller diagram. This diagram can be used to illustrate upper-level
-wave and energy propogation (e.g., downstream baroclinic development)</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.gridspec</span> <span class="k">as</span> <span class="nn">gridspec</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="get-the-data">
-<h2>Get the data<a class="headerlink" href="#get-the-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Using NCEP/NCAR reanalysis data via xarray remote access using the
-OPeNDAP protocol.</p>
-<p>Set the time range, parameter, and level to desired values</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create time slice from dates</span>
-<span class="n">start_time</span> <span class="o">=</span> <span class="s1">&#39;2011-01-20&#39;</span>
-<span class="n">end_time</span> <span class="o">=</span> <span class="s1">&#39;2011-02-06&#39;</span>
-
-<span class="c1"># Select NCEP/NCAR parameter and level</span>
-<span class="n">param</span> <span class="o">=</span> <span class="s1">&#39;vwnd&#39;</span>
-<span class="n">level</span> <span class="o">=</span> <span class="mi">250</span>
-
-<span class="c1"># Remote get dataset using OPeNDAP method via xarray</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/&#39;</span>
-                     <span class="sa">f</span><span class="s1">&#39;ncep.reanalysis/pressure/</span><span class="si">{</span><span class="n">param</span><span class="si">}</span><span class="s1">.</span><span class="si">{</span><span class="n">start_time</span><span class="p">[:</span><span class="mi">4</span><span class="p">]</span><span class="si">}</span><span class="s1">.nc&#39;</span><span class="p">)</span>
-
-<span class="c1"># Create slice variables subset domain</span>
-<span class="n">time_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">)</span>
-<span class="n">lat_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">60</span><span class="p">,</span> <span class="mi">40</span><span class="p">)</span>
-<span class="n">lon_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">360</span><span class="p">)</span>
-
-<span class="c1"># Get data, selecting time, level, lat/lon slice</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">param</span><span class="p">]</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="n">time_slice</span><span class="p">,</span>
-                     <span class="n">level</span><span class="o">=</span><span class="n">level</span><span class="p">,</span>
-                     <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span>
-                     <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span>
-
-<span class="c1"># Compute weights and take weighted average over latitude dimension</span>
-<span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
-<span class="n">avg_data</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span> <span class="o">*</span> <span class="n">weights</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="s1">&#39;lat&#39;</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
-
-<span class="c1"># Get times and make array of datetime objects</span>
-<span class="n">vtimes</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-
-<span class="c1"># Specify longitude values for chosen domain</span>
-<span class="n">lons</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">values</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="make-the-hovmoller-plot">
-<h2>Make the Hovmoller Plot<a class="headerlink" href="#make-the-hovmoller-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Pretty simple to use common matplotlib/cartopy to create the diagram.
-Cartopy is used to create a geographic reference map to highlight the
-area being averaged as well as the visual reference for longitude.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Start figure</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">13</span><span class="p">))</span>
-
-<span class="c1"># Use gridspec to help size elements of plot; small top plot</span>
-<span class="c1"># and big bottom plot</span>
-<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">height_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.03</span><span class="p">)</span>
-
-<span class="c1"># Tick labels</span>
-<span class="n">x_tick_labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">u</span><span class="s1">&#39;0</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">E&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;90</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">E&#39;</span><span class="p">,</span>
-                 <span class="sa">u</span><span class="s1">&#39;180</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">E&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;90</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">W&#39;</span><span class="p">,</span>
-                 <span class="sa">u</span><span class="s1">&#39;0</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">E&#39;</span><span class="p">]</span>
-
-<span class="c1"># Top plot for geographic reference (makes small map)</span>
-<span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
-                      <span class="n">projection</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=</span><span class="mi">180</span><span class="p">))</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mf">357.5</span><span class="p">,</span> <span class="mi">35</span><span class="p">,</span> <span class="mi">65</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=</span><span class="mi">180</span><span class="p">))</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="sa">u</span><span class="s1">&#39;40</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">N&#39;</span><span class="p">,</span> <span class="sa">u</span><span class="s1">&#39;60</span><span class="se">\N{DEGREE SIGN}</span><span class="s1">N&#39;</span><span class="p">])</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="o">-</span><span class="mi">90</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">,</span> <span class="mi">180</span><span class="p">])</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">x_tick_labels</span><span class="p">)</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Add geopolitical boundaries for map reference</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax1</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">LAKES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-
-<span class="c1"># Set some titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Hovmoller Diagram&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;NCEP/NCAR Reanalysis&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-
-<span class="c1"># Bottom plot for Hovmoller diagram</span>
-<span class="n">ax2</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>  <span class="c1"># Reverse the time order to do oldest first</span>
-
-<span class="c1"># Plot of chosen variable averaged over latitude and slightly smoothed</span>
-<span class="n">clevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="mi">51</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">vtimes</span><span class="p">,</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
-    <span class="n">avg_data</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">clevs</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">,</span> <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax2</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">vtimes</span><span class="p">,</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span>
-    <span class="n">avg_data</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">clevs</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.04</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-                    <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;m $s^{-1}$&#39;</span><span class="p">)</span>
-
-<span class="c1"># Make some ticks and tick labels</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">90</span><span class="p">,</span> <span class="mi">180</span><span class="p">,</span> <span class="mi">270</span><span class="p">,</span> <span class="mf">357.5</span><span class="p">])</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">x_tick_labels</span><span class="p">)</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">4</span><span class="p">::</span><span class="mi">8</span><span class="p">])</span>
-<span class="n">ax2</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">4</span><span class="p">::</span><span class="mi">8</span><span class="p">])</span>
-
-<span class="c1"># Set some titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;250-hPa V-wind&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Time Range: </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s1">%Y%m%d %HZ</span><span class="si">}</span><span class="s1"> - </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s1">%Y%m%d %HZ</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span>
-          <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;Time Range: 20110120 00Z - 20110206 18Z&#39;)
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_lakes.zip
-  warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
-</pre></div>
-</div>
-<img alt="../../_images/e0aaa611a5538ab5c1db36d423e044d63f4d43a86f82d2eebed9c7ec50781bef.png" src="../../_images/e0aaa611a5538ab5c1db36d423e044d63f4d43a86f82d2eebed9c7ec50781bef.png" />
-</div>
-</div>
-</section>
-</section>
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-  <section class="tex2jax_ignore mathjax_ignore" id="isentropic-analysis">
-<h1>Isentropic Analysis<a class="headerlink" href="#isentropic-analysis" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>The MetPy function <code class="docutils literal notranslate"><span class="pre">metpy.calc.isentropic_interpolation</span></code> allows for isentropic analysis from
-model analysis data in isobaric coordinates.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
-<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Getting the data</p>
-<p>In this example, the latest GFS forecasts data from the National Centers for
-Environmental Information (https://nomads.ncdc.noaa.gov) will be used, courtesy of the
-Univeristy Corporation for Atmospheric Research Thredds Data Server.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Latest GFS Dataset</span>
-<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="s1">&#39;http://thredds.ucar.edu/thredds/catalog/grib/&#39;</span>
-                 <span class="s1">&#39;NCEP/GFS/Global_0p5deg/catalog.xml&#39;</span><span class="p">)</span>
-<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">latest</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
-
-<span class="c1"># Find the start of the model run and define time range</span>
-<span class="n">start_time</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">time_span</span><span class="p">[</span><span class="s1">&#39;begin&#39;</span><span class="p">]</span>
-<span class="n">start</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">start_time</span><span class="p">,</span> <span class="s1">&#39;%Y-%m-</span><span class="si">%d</span><span class="s1">T%H:%M:%Sz&#39;</span><span class="p">)</span>
-<span class="n">end</span> <span class="o">=</span> <span class="n">start</span> <span class="o">+</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
-
-<span class="c1"># Query for Latest GFS Run</span>
-<span class="n">gfsdata</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span><span class="o">.</span><span class="n">time_range</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-
-<span class="n">gfsdata</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;Relative_humidity_isobaric&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">add_lonlat</span><span class="p">()</span>
-
-<span class="c1"># Set the lat/lon box for the data you want to pull in.</span>
-<span class="c1"># lonlat_box(north_lat,south_lat,east_lon,west_lon)</span>
-<span class="n">gfsdata</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="o">-</span><span class="mi">150</span><span class="p">,</span> <span class="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">65</span><span class="p">)</span>
-
-<span class="c1"># Actually getting the data</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">gfsdata</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="n">dlev_hght</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">dims</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="n">dlev_uwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dims</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="n">lat</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">latitude</span>
-<span class="n">lon</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">longitude</span>
-
-<span class="n">lev_hght</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">dlev_hght</span><span class="p">]</span>
-<span class="n">lev_uwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">dlev_uwnd</span><span class="p">]</span>
-
-<span class="c1"># Due to a different number of vertical levels find where they are common</span>
-<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">common_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intersect1d</span><span class="p">(</span><span class="n">lev_uwnd</span><span class="p">,</span> <span class="n">lev_hght</span><span class="p">,</span> <span class="n">return_indices</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">common_levels</span> <span class="o">=</span> <span class="n">lev_hght</span><span class="p">[</span><span class="n">common_ind</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
-<span class="n">times</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span>
-<span class="n">vtimes</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-<span class="n">temps</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
-<span class="n">uwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
-<span class="n">vwnd</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
-<span class="n">relh</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Relative_humidity_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">unit_array</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>To properly interpolate to isentropic coordinates, the function must know the desired output
-isentropic levels. An array with these levels will be created below.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">isentlevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">310</span><span class="p">,</span> <span class="mi">316</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">kelvin</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Conversion to Isentropic Coordinates</p>
-<p>Once model data in isobaric coordinates has been pulled and the desired isentropic levels
-created, the conversion to isentropic coordinates can begin. Data will be passed to the
-function as below. The function requires that isentropic levels, isobaric levels, and
-temperature be input. Any additional inputs (in this case relative humidity, u, and v wind
-components) will be linearly interpolated to isentropic space.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">isent_anal</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">isentropic_interpolation</span><span class="p">(</span><span class="n">isentlevs</span><span class="p">,</span>
-                                             <span class="n">common_levels</span><span class="p">,</span>
-                                             <span class="n">temps</span><span class="p">[:,</span> <span class="n">common_ind</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span>
-                                             <span class="n">relh</span><span class="p">,</span>
-                                             <span class="n">uwnd</span><span class="p">,</span>
-                                             <span class="n">vwnd</span><span class="p">,</span>
-                                             <span class="n">vertical_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_2916/414936893.py:1: UserWarning: Interpolation point out of data bounds encountered
-  isent_anal = mpcalc.isentropic_interpolation(isentlevs,
-</pre></div>
-</div>
-</div>
-</div>
-<p>The output is a list, so now we will separate the variables to different names before
-plotting.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">isentprs</span><span class="p">,</span> <span class="n">isentrh</span><span class="p">,</span> <span class="n">isentu</span><span class="p">,</span> <span class="n">isentv</span> <span class="o">=</span> <span class="n">isent_anal</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>A quick look at the shape of these variables will show that the data is now in isentropic
-coordinates, with the number of vertical levels as specified above.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">isentprs</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">isentrh</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">isentu</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">isentv</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(4, 2, 101, 201)
-(4, 2, 101, 201)
-(4, 2, 101, 201)
-(4, 2, 101, 201)
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>Plotting the Isentropic Analysis</strong></p>
-<p>Set up our projection</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">crs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.0</span><span class="p">)</span>
-
-<span class="c1"># Set up our array of latitude and longitude values and transform to</span>
-<span class="c1"># the desired projection.</span>
-<span class="n">clons</span><span class="p">,</span> <span class="n">clats</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
-
-<span class="c1"># Get data to plot state and province boundaries</span>
-<span class="n">states_provinces</span> <span class="o">=</span> <span class="n">cfeature</span><span class="o">.</span><span class="n">NaturalEarthFeature</span><span class="p">(</span>
-    <span class="n">category</span><span class="o">=</span><span class="s1">&#39;cultural&#39;</span><span class="p">,</span>
-    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;admin_1_states_provinces_lakes&#39;</span><span class="p">,</span>
-    <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span>
-    <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
-
-<span class="c1"># Choose Isentropic Level index value</span>
-<span class="n">level</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># [310, 315]</span>
-
-<span class="c1"># Choose forecast hour index value</span>
-<span class="n">FH</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># [0, 3, 6, 9]</span>
-
-<span class="c1"># Start Figure</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">14.</span><span class="p">,</span> <span class="mf">12.</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">crs</span><span class="p">)</span>
-
-<span class="c1"># Set plot extent</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">((</span><span class="o">-</span><span class="mf">121.</span><span class="p">,</span> <span class="o">-</span><span class="mf">74.</span><span class="p">,</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">50.</span><span class="p">),</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">states_provinces</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-
-<span class="c1"># Plot the 300K surface</span>
-<span class="n">clevisent</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">25</span><span class="p">)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">clons</span><span class="p">,</span> <span class="n">clats</span><span class="p">,</span>
-                <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">isentprs</span><span class="p">[</span><span class="n">FH</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                <span class="n">clevisent</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-                <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
-           <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">cf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">clons</span><span class="p">,</span> <span class="n">clats</span><span class="p">,</span>
-                 <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">isentrh</span><span class="p">[</span><span class="n">FH</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                 <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-                 <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">YlGn</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cf</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">65</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">extendrect</span><span class="o">=</span><span class="s1">&#39;True&#39;</span><span class="p">)</span>
-
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="p">(</span><span class="n">FH</span><span class="p">,</span> <span class="n">level</span><span class="p">,</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">clons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">2</span><span class="p">:]],</span> <span class="n">clats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">[</span><span class="mi">2</span><span class="p">:]],</span>
-         <span class="n">isentu</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">isentv</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-         <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Make some titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">isentlevs</span><span class="p">[</span><span class="n">level</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="si">:</span><span class="s1">.0f</span><span class="si">}</span><span class="s1"> K Isentropic Level&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="n">FH</span><span class="p">]</span><span class="si">}</span><span class="s1"> UTC&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Text(1.0, 1.0, &#39;VALID: 2024-06-14 06:00:00 UTC&#39;)
-</pre></div>
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-  <section class="tex2jax_ignore mathjax_ignore" id="real-data-cross-section-example">
-<h1>Real Data Cross-Section Example<a class="headerlink" href="#real-data-cross-section-example" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Cross-section using real data from soundings.</p>
-<p>This example uses actual soundings to create a cross-section. There are
-two functions defined to help interpolate radiosonde observations, which
-won’t all be at the same level, to a standard grid. The vertical
-interpolation assumes a log-linear relationship. Each radisosonde
-vertical profile is interpolated first, then the
-<code class="docutils literal notranslate"><span class="pre">scipy.interpolate.griddata</span></code> function is used to generate a full 2D
-(x, p) grid between each station. Pyproj is used to calculate the
-distance between each station and the standard atmosphere is used to
-convert the elevation of each station to a pressure value for plotting
-purposes.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">siphon.simplewebservice.wyoming</span> <span class="kn">import</span> <span class="n">WyomingUpperAir</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="vertical-interpolation-function">
-<h2>Vertical Interpolation Function<a class="headerlink" href="#vertical-interpolation-function" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Function interpolates to given pressure level data to set grid.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">vertical_interpolate</span><span class="p">(</span><span class="n">vcoord_data</span><span class="p">,</span> <span class="n">interp_var</span><span class="p">,</span> <span class="n">interp_levels</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;A function to interpolate sounding data from each station to</span>
-<span class="sd">    every millibar. Assumes a log-linear relationship.</span>
-
-<span class="sd">    Input</span>
-<span class="sd">    -----</span>
-<span class="sd">    vcoord_data : A 1D array of vertical level values (e.g., pressure from a radiosonde)</span>
-<span class="sd">    interp_var : A 1D array of the variable to be interpolated to all pressure levels</span>
-<span class="sd">    vcoord_interp_levels : A 1D array containing veritcal levels to interpolate to</span>
-
-<span class="sd">    Return</span>
-<span class="sd">    ------</span>
-<span class="sd">    interp_data : A 1D array that contains the interpolated variable on the interp_levels</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Make veritcal coordinate data and grid level log variables</span>
-    <span class="n">lnp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">vcoord_data</span><span class="p">)</span>
-    <span class="n">lnp_intervals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">interp_levels</span><span class="p">)</span>
-
-    <span class="c1"># Use numpy to interpolate from observed levels to grid levels</span>
-    <span class="n">interp_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">lnp_intervals</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">lnp</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">interp_var</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="c1"># Mask for missing data (generally only near the surface)</span>
-    <span class="n">mask_low</span> <span class="o">=</span> <span class="n">interp_levels</span> <span class="o">&gt;</span> <span class="n">vcoord_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">mask_high</span> <span class="o">=</span> <span class="n">interp_levels</span> <span class="o">&lt;</span> <span class="n">vcoord_data</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">interp_data</span><span class="p">[</span><span class="n">mask_low</span><span class="p">]</span> <span class="o">=</span> <span class="n">interp_var</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">interp_data</span><span class="p">[</span><span class="n">mask_high</span><span class="p">]</span> <span class="o">=</span> <span class="n">interp_var</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="k">return</span> <span class="n">interp_data</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="radiosonde-observation-interpolation-function">
-<h2>Radiosonde Observation Interpolation Function<a class="headerlink" href="#radiosonde-observation-interpolation-function" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This function interpolates given radiosonde data into a 2D array for all
-meteorological variables given in dataframe. Returns a dictionary that
-will have requesite data for plotting a cross section.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">radiosonde_cross_section</span><span class="p">(</span><span class="n">stns</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;This function takes a list of radiosonde observation sites with a</span>
-<span class="sd">    dictionary of Pandas Dataframes with the requesite data for each station.</span>
-
-<span class="sd">    Input</span>
-<span class="sd">    -----</span>
-<span class="sd">    stns : List of statition three-letter identifiers</span>
-<span class="sd">    data : A dictionary of Pandas Dataframes containing the radiosonde observations</span>
-<span class="sd">    for the stations</span>
-<span class="sd">    start : interpolation start value, optional (default = 1000 hPa)</span>
-<span class="sd">    end : Interpolation end value, optional (default = 100 hPa)</span>
-<span class="sd">    step : Interpolation interval, option (default = 10 hPa)</span>
-
-<span class="sd">    Return</span>
-<span class="sd">    ------</span>
-<span class="sd">    cross_section : A dictionary that contains the following variables</span>
-
-<span class="sd">        grid_data : An interpolated grid with 100 points between the first and last station,</span>
-<span class="sd">        with the corresponding number of vertical points based on start, end, and interval</span>
-<span class="sd">        (default is 90)</span>
-<span class="sd">        obs_distance : An array of distances between each radiosonde observation location</span>
-<span class="sd">        x_grid : A 2D array of horizontal direction grid points</span>
-<span class="sd">        p_grid : A 2D array of vertical pressure levels</span>
-<span class="sd">        ground_elevation : A representation of the terrain between radiosonde observation sites</span>
-<span class="sd">        based on the elevation of each station converted to pressure using the standard</span>
-<span class="sd">        atmosphere</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">pyproj</span> <span class="kn">import</span> <span class="n">Geod</span>
-    <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">griddata</span>
-    
-    <span class="n">data_units</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;pressure&#39;</span><span class="p">:</span> <span class="s1">&#39;hPa&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;height&#39;</span><span class="p">:</span> <span class="s1">&#39;meter&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;temperature&#39;</span><span class="p">:</span> <span class="s1">&#39;degC&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;dewpoint&#39;</span><span class="p">:</span> <span class="s1">&#39;degC&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;direction&#39;</span><span class="p">:</span> <span class="s1">&#39;degrees&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;speed&#39;</span><span class="p">:</span> <span class="s1">&#39;knot&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;u_wind&#39;</span><span class="p">:</span> <span class="s1">&#39;knot&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;v_wind&#39;</span><span class="p">:</span> <span class="s1">&#39;knot&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;station&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
-                  <span class="s1">&#39;station_number&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
-                  <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
-                  <span class="s1">&#39;latitude&#39;</span><span class="p">:</span> <span class="s1">&#39;degrees&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;longitude&#39;</span><span class="p">:</span> <span class="s1">&#39;degrees&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;elevation&#39;</span><span class="p">:</span> <span class="s1">&#39;meter&#39;</span><span class="p">,</span>
-                  <span class="s1">&#39;pw&#39;</span><span class="p">:</span> <span class="s1">&#39;millimeter&#39;</span><span class="p">}</span>
-    <span class="c1"># Set up vertical grid, largest value first (high pressure nearest surface)</span>
-    <span class="n">vertical_levels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="n">step</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="n">data_units</span><span class="p">[</span><span class="s1">&#39;pressure&#39;</span><span class="p">])</span>
-
-    <span class="c1"># Number of vertical levels and stations</span>
-    <span class="n">plevs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertical_levels</span><span class="p">)</span>
-    <span class="n">nstns</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">stns</span><span class="p">)</span>
-
-    <span class="c1"># Create dictionary of interpolated values and include neccsary attribute data</span>
-    <span class="c1"># including lat, lon, and elevation of each station</span>
-    <span class="n">lats</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">lons</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">elev</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">keys</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">stns</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">keys</span><span class="p">()[:</span><span class="mi">8</span><span class="p">]</span>
-    <span class="n">tmp_grid</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span>
-
-    <span class="c1"># Interpolate all variables for each radiosonde observation</span>
-    <span class="c1"># Temperature, Dewpoint, U-wind, V-wind</span>
-    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">tmp_grid</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-        <span class="n">tmp_grid</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">nstns</span><span class="p">,</span> <span class="n">plevs</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">station</span><span class="p">,</span> <span class="n">loc</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">stns</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="n">nstns</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;pressure&#39;</span><span class="p">:</span>
-                <span class="n">lats</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="n">lons</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="n">elev</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">elevation</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="n">tmp_grid</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">loc</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">vertical_levels</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">tmp_grid</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">loc</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">vertical_interpolate</span><span class="p">(</span>
-                    <span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">][</span><span class="s1">&#39;pressure&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">][</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                    <span class="n">vertical_levels</span><span class="o">.</span><span class="n">m</span><span class="p">)</span>
-
-    <span class="c1"># Compute distance between each station using Pyproj</span>
-    <span class="n">g</span> <span class="o">=</span> <span class="n">Geod</span><span class="p">(</span><span class="n">ellps</span><span class="o">=</span><span class="s1">&#39;sphere&#39;</span><span class="p">)</span>
-    <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">dist</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">nstns</span><span class="o">*</span><span class="p">[</span><span class="n">lons</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">nstns</span><span class="o">*</span><span class="p">[</span><span class="n">lats</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">lons</span><span class="p">[:],</span> <span class="n">lats</span><span class="p">[:])</span>
-
-    <span class="c1"># Compute sudo ground elevation in pressure from standard atmsophere and the elevation</span>
-    <span class="c1"># of each station</span>
-    <span class="n">ground_elevation</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">height_to_pressure_std</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">elev</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="s1">&#39;meters&#39;</span><span class="p">))</span>
-
-    <span class="c1"># Set up grid for 2D interpolation</span>
-    <span class="n">grid</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span>
-    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">dist</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dist</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">100</span><span class="p">)</span>
-    <span class="n">nx</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-    <span class="n">pp</span><span class="p">,</span> <span class="n">xx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">vertical_levels</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-    <span class="n">pdist</span><span class="p">,</span> <span class="n">ddist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">vertical_levels</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="n">dist</span><span class="p">)</span>
-
-    <span class="c1"># Interpolate to 2D grid using scipy.interpolate.griddata</span>
-    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">grid</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-        <span class="n">grid</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">nx</span><span class="p">,</span> <span class="n">plevs</span><span class="p">))</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="n">data_units</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-        <span class="n">grid</span><span class="p">[</span><span class="n">key</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">griddata</span><span class="p">((</span><span class="n">ddist</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">pdist</span><span class="o">.</span><span class="n">flatten</span><span class="p">()),</span>
-                                <span class="n">tmp_grid</span><span class="p">[</span><span class="n">key</span><span class="p">][:]</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
-                                <span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">pp</span><span class="p">),</span>
-                                <span class="n">method</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">)</span> <span class="o">*</span> <span class="n">units</span><span class="p">(</span><span class="n">data_units</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-
-    <span class="c1"># Gather needed data in dictionary for return</span>
-    <span class="n">cross_section</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;grid_data&#39;</span><span class="p">:</span> <span class="n">grid</span><span class="p">,</span> <span class="s1">&#39;obs_distance&#39;</span><span class="p">:</span> <span class="n">dist</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">meter</span><span class="p">,</span>
-                     <span class="s1">&#39;x_grid&#39;</span><span class="p">:</span> <span class="n">xx</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">meter</span><span class="p">,</span> <span class="s1">&#39;p_grid&#39;</span><span class="p">:</span> <span class="n">pp</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="s1">&#39;elevation&#39;</span><span class="p">:</span> <span class="n">ground_elevation</span><span class="p">}</span>
-    <span class="k">return</span> <span class="n">cross_section</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="stations-and-time">
-<h2>Stations and Time<a class="headerlink" href="#stations-and-time" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Select cross section stations by creating a list of three-letter
-identifiers and choose a date by creating a datetime object</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># A roughly east-west cross section</span>
-<span class="n">stn_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;DNR&#39;</span><span class="p">,</span> <span class="s1">&#39;LBF&#39;</span><span class="p">,</span> <span class="s1">&#39;OAX&#39;</span><span class="p">,</span> <span class="s1">&#39;DVN&#39;</span><span class="p">,</span> <span class="s1">&#39;DTX&#39;</span><span class="p">,</span> <span class="s1">&#39;BUF&#39;</span><span class="p">]</span>
-
-<span class="c1"># Set a date and hour of your choosing</span>
-<span class="n">date</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2019</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="get-radiosonde-data">
-<h2>Get Radiosonde Data<a class="headerlink" href="#get-radiosonde-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This example is built around the data from the University of Wyoming
-sounding archive and using the Siphon package to remotely access that
-data.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up empty dictionary to fill with Wyoming Sounding data</span>
-<span class="n">df</span> <span class="o">=</span> <span class="p">{}</span>
-
-<span class="c1"># Loop over stations to get data and put into dictionary</span>
-<span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">stn_list</span><span class="p">:</span>
-    <span class="n">df</span><span class="p">[</span><span class="n">station</span><span class="p">]</span> <span class="o">=</span> <span class="n">WyomingUpperAir</span><span class="o">.</span><span class="n">request_data</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">station</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">HTTPError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">6</span>
-<span class="g g-Whitespace">      </span><span class="mi">4</span> <span class="c1"># Loop over stations to get data and put into dictionary</span>
-<span class="g g-Whitespace">      </span><span class="mi">5</span> <span class="k">for</span> <span class="n">station</span> <span class="ow">in</span> <span class="n">stn_list</span><span class="p">:</span>
-<span class="ne">----&gt; </span><span class="mi">6</span>     <span class="n">df</span><span class="p">[</span><span class="n">station</span><span class="p">]</span> <span class="o">=</span> <span class="n">WyomingUpperAir</span><span class="o">.</span><span class="n">request_data</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">station</span><span class="p">)</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:50,</span> in <span class="ni">WyomingUpperAir.request_data</span><span class="nt">(cls, time, site_id, **kwargs)</span>
-<span class="g g-Whitespace">     </span><span class="mi">30</span><span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Retrieve upper air observations from the Wyoming archive.</span>
-<span class="g g-Whitespace">     </span><span class="mi">31</span><span class="sd"> </span>
-<span class="g g-Whitespace">     </span><span class="mi">32</span><span class="sd"> Parameters</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">     </span><span class="mi">47</span><span class="sd"> </span>
-<span class="g g-Whitespace">     </span><span class="mi">48</span><span class="sd"> &quot;&quot;&quot;</span>
-<span class="g g-Whitespace">     </span><span class="mi">49</span> <span class="n">endpoint</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">()</span>
-<span class="ne">---&gt; </span><span class="mi">50</span> <span class="n">df</span> <span class="o">=</span> <span class="n">endpoint</span><span class="o">.</span><span class="n">_get_data</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">)</span>
-<span class="g g-Whitespace">     </span><span class="mi">51</span> <span class="k">return</span> <span class="n">df</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:70,</span> in <span class="ni">WyomingUpperAir._get_data</span><span class="nt">(self, time, site_id)</span>
-<span class="g g-Whitespace">     </span><span class="mi">53</span> <span class="k">def</span> <span class="nf">_get_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">):</span>
-<span class="g g-Whitespace">     </span><span class="mi">54</span><span class="w">     </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Download and parse upper air observations from an online archive.</span>
-<span class="g g-Whitespace">     </span><span class="mi">55</span><span class="sd"> </span>
-<span class="g g-Whitespace">     </span><span class="mi">56</span><span class="sd">     Parameters</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">     </span><span class="mi">68</span><span class="sd"> </span>
-<span class="g g-Whitespace">     </span><span class="mi">69</span><span class="sd">     &quot;&quot;&quot;</span>
-<span class="ne">---&gt; </span><span class="mi">70</span>     <span class="n">raw_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_data_raw</span><span class="p">(</span><span class="n">time</span><span class="p">,</span> <span class="n">site_id</span><span class="p">)</span>
-<span class="g g-Whitespace">     </span><span class="mi">71</span>     <span class="n">soup</span> <span class="o">=</span> <span class="n">BeautifulSoup</span><span class="p">(</span><span class="n">raw_data</span><span class="p">,</span> <span class="s1">&#39;html.parser&#39;</span><span class="p">)</span>
-<span class="g g-Whitespace">     </span><span class="mi">72</span>     <span class="n">tabular_data</span> <span class="o">=</span> <span class="n">StringIO</span><span class="p">(</span><span class="n">soup</span><span class="o">.</span><span class="n">find_all</span><span class="p">(</span><span class="s1">&#39;pre&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">contents</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/simplewebservice/wyoming.py:144,</span> in <span class="ni">WyomingUpperAir._get_data_raw</span><span class="nt">(self, time, site_id)</span>
-<span class="g g-Whitespace">    </span><span class="mi">126</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;Download data from the University of Wyoming&#39;s upper air archive.</span>
-<span class="g g-Whitespace">    </span><span class="mi">127</span><span class="sd"> </span>
-<span class="g g-Whitespace">    </span><span class="mi">128</span><span class="sd"> Parameters</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">    </span><span class="mi">138</span><span class="sd"> </span>
-<span class="g g-Whitespace">    </span><span class="mi">139</span><span class="sd"> &quot;&quot;&quot;</span>
-<span class="g g-Whitespace">    </span><span class="mi">140</span> <span class="n">path</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;?region=naconf&amp;TYPE=TEXT%3ALIST&#39;</span>
-<span class="g g-Whitespace">    </span><span class="mi">141</span>         <span class="s1">&#39;&amp;YEAR={time:%Y}&amp;MONTH={time:%m}&amp;FROM={time:</span><span class="si">%d</span><span class="s1">%H}&amp;TO={time:</span><span class="si">%d</span><span class="s1">%H}&#39;</span>
-<span class="g g-Whitespace">    </span><span class="mi">142</span>         <span class="s1">&#39;&amp;STNM=</span><span class="si">{stid}</span><span class="s1">&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">=</span><span class="n">time</span><span class="p">,</span> <span class="n">stid</span><span class="o">=</span><span class="n">site_id</span><span class="p">)</span>
-<span class="ne">--&gt; </span><span class="mi">144</span> <span class="n">resp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">145</span> <span class="c1"># See if the return is valid, but has no data</span>
-<span class="g g-Whitespace">    </span><span class="mi">146</span> <span class="k">if</span> <span class="n">resp</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s2">&quot;Can&#39;t&quot;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/http_util.py:457,</span> in <span class="ni">HTTPEndPoint.get_path</span><span class="nt">(self, path, query)</span>
-<span class="g g-Whitespace">    </span><span class="mi">434</span> <span class="k">def</span> <span class="nf">get_path</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">query</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="g g-Whitespace">    </span><span class="mi">435</span><span class="w">     </span><span class="sd">&quot;&quot;&quot;Make a GET request, optionally including a query, to a relative path.</span>
-<span class="g g-Whitespace">    </span><span class="mi">436</span><span class="sd"> </span>
-<span class="g g-Whitespace">    </span><span class="mi">437</span><span class="sd">     The path of the request includes a path on top of the base URL</span>
-<span class="sd">   (...)</span>
-<span class="g g-Whitespace">    </span><span class="mi">455</span><span class="sd"> </span>
-<span class="g g-Whitespace">    </span><span class="mi">456</span><span class="sd">     &quot;&quot;&quot;</span>
-<span class="ne">--&gt; </span><span class="mi">457</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">url_path</span><span class="p">(</span><span class="n">path</span><span class="p">),</span> <span class="n">query</span><span class="p">)</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/siphon/http_util.py:492,</span> in <span class="ni">HTTPEndPoint.get</span><span class="nt">(self, path, params)</span>
-<span class="g g-Whitespace">    </span><span class="mi">490</span>     <span class="k">else</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">491</span>         <span class="n">text</span> <span class="o">=</span> <span class="n">resp</span><span class="o">.</span><span class="n">text</span>
-<span class="ne">--&gt; </span><span class="mi">492</span>     <span class="k">raise</span> <span class="n">requests</span><span class="o">.</span><span class="n">HTTPError</span><span class="p">(</span><span class="s1">&#39;Error accessing </span><span class="si">{0}</span><span class="se">\n</span><span class="s1">&#39;</span>
-<span class="g g-Whitespace">    </span><span class="mi">493</span>                              <span class="s1">&#39;Server Error (</span><span class="si">{1:d}</span><span class="s1">: </span><span class="si">{2}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">resp</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">url</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">494</span>                                                                 <span class="n">resp</span><span class="o">.</span><span class="n">status_code</span><span class="p">,</span>
-<span class="g g-Whitespace">    </span><span class="mi">495</span>                                                                 <span class="n">text</span><span class="p">))</span>
-<span class="g g-Whitespace">    </span><span class="mi">496</span> <span class="k">return</span> <span class="n">resp</span>
-
-<span class="ne">HTTPError</span>: Error accessing http://weather.uwyo.edu/cgi-bin/sounding/?region=naconf&amp;TYPE=TEXT%3ALIST&amp;YEAR=2019&amp;MONTH=06&amp;FROM=0100&amp;TO=0100&amp;STNM=OAX
-<span class="n">Server</span> <span class="n">Error</span> <span class="p">(</span><span class="mi">503</span><span class="p">:</span> <span class="n">Sorry</span><span class="p">,</span> <span class="n">the</span> <span class="n">server</span> <span class="ow">is</span> <span class="n">too</span> <span class="n">busy</span> <span class="n">to</span> <span class="n">process</span> <span class="n">your</span> <span class="n">request</span><span class="o">.</span>
-<span class="n">Please</span> <span class="k">try</span> <span class="n">again</span> <span class="n">later</span><span class="o">.</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="create-interpolated-fields">
-<h2>Create Interpolated fields<a class="headerlink" href="#create-interpolated-fields" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Use the function <code class="docutils literal notranslate"><span class="pre">radisonde_cross_section</span></code> to generate the 2D grid (x,
-p) for all radiosonde variables including, Temperature, Dewpoint,
-u-component of the wind, and v-component of the wind.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">xsect</span> <span class="o">=</span> <span class="n">radiosonde_cross_section</span><span class="p">(</span><span class="n">stn_list</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
-<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">xsect</span> <span class="o">=</span> <span class="n">radiosonde_cross_section</span><span class="p">(</span><span class="n">stn_list</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span>
-
-<span class="nn">Cell In[3], line 68,</span> in <span class="ni">radiosonde_cross_section</span><span class="nt">(stns, data, start, end, step)</span>
-<span class="g g-Whitespace">     </span><span class="mi">66</span> <span class="k">for</span> <span class="n">station</span><span class="p">,</span> <span class="n">loc</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">stns</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="n">nstns</span><span class="p">)):</span>
-<span class="g g-Whitespace">     </span><span class="mi">67</span>     <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;pressure&#39;</span><span class="p">:</span>
-<span class="ne">---&gt; </span><span class="mi">68</span>         <span class="n">lats</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">latitude</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="g g-Whitespace">     </span><span class="mi">69</span>         <span class="n">lons</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">longitude</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-<span class="g g-Whitespace">     </span><span class="mi">70</span>         <span class="n">elev</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">station</span><span class="p">]</span><span class="o">.</span><span class="n">elevation</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-<span class="ne">KeyError</span>: &#39;OAX&#39;
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="calculate-variables-for-plotting">
-<h2>Calculate Variables for Plotting<a class="headerlink" href="#calculate-variables-for-plotting" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Use MetPy to calculate common variables for plotting a cross section,
-specifically potential temperature and mixing ratio</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">potemp</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_temperature</span><span class="p">(</span>
-    <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">])</span>
-
-<span class="n">relhum</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">relative_humidity_from_dewpoint</span><span class="p">(</span>
-    <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span>
-    <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">])</span>
-
-<span class="n">mixrat</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">mixing_ratio_from_relative_humidity</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span>
-                                                    <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span>
-                                                    <span class="n">relhum</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">line</span> <span class="mi">2</span>
-<span class="g g-Whitespace">      </span><span class="mi">1</span> <span class="n">potemp</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_temperature</span><span class="p">(</span>
-<span class="ne">----&gt; </span><span class="mi">2</span>     <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">])</span>
-<span class="g g-Whitespace">      </span><span class="mi">4</span> <span class="n">relhum</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">relative_humidity_from_dewpoint</span><span class="p">(</span>
-<span class="g g-Whitespace">      </span><span class="mi">5</span>     <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span>
-<span class="g g-Whitespace">      </span><span class="mi">6</span>     <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">])</span>
-<span class="g g-Whitespace">      </span><span class="mi">8</span> <span class="n">mixrat</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">mixing_ratio_from_relative_humidity</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span>
-<span class="g g-Whitespace">      </span><span class="mi">9</span>                                                     <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;grid_data&#39;</span><span class="p">][</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span>
-<span class="g g-Whitespace">     </span><span class="mi">10</span>                                                     <span class="n">relhum</span><span class="p">)</span>
-
-<span class="ne">NameError</span>: name &#39;xsect&#39; is not defined
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="plot-cross-section">
-<h2>Plot Cross Section<a class="headerlink" href="#plot-cross-section" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Use standard Matplotlib to plot the now 2D cross section grid using the
-data from xsect and those calculated above. Additionally, the actualy
-radiosonde wind observations are plotted as barbs on this plot.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Start Figure, set big size for cross section</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
-
-<span class="c1"># Specify plotting axis (single panel)</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
-
-<span class="c1"># Set y-scale to be log since pressure decreases exponentially with height</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set limits, tickmarks, and ticklabels for y-axis</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mi">1030</span><span class="p">,</span> <span class="mi">101</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="o">-</span><span class="mi">100</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="o">-</span><span class="mi">100</span><span class="p">))</span>
-
-<span class="c1"># Invert the y-axis since pressure decreases with increasing height</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">yaxis_inverted</span><span class="p">()</span>
-
-<span class="c1"># Plot the sudo elevation on the cross section</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;elevation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="mi">1030</span><span class="p">,</span>
-                <span class="n">where</span><span class="o">=</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;elevation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">m</span> <span class="o">&lt;=</span> <span class="mi">1030</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
-                <span class="n">interpolate</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="c1"># Don&#39;t plot xticks</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([],</span> <span class="p">[])</span>
-
-<span class="c1"># Plot wind barbs for each sounding location</span>
-<span class="k">for</span> <span class="n">stn</span><span class="p">,</span> <span class="n">stn_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">stn_list</span><span class="p">)),</span> <span class="n">stn_list</span><span class="p">):</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">][</span><span class="n">stn</span><span class="p">],</span> <span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ymax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-               <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">][</span><span class="n">stn</span><span class="p">],</span> <span class="mi">1100</span><span class="p">,</span> <span class="n">stn_name</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">][</span><span class="n">stn</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="n">stn_name</span><span class="p">][</span><span class="s1">&#39;pressure&#39;</span><span class="p">][::</span><span class="mi">2</span><span class="p">],</span>
-             <span class="n">df</span><span class="p">[</span><span class="n">stn_name</span><span class="p">][</span><span class="s1">&#39;u_wind&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[::</span><span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
-             <span class="n">df</span><span class="p">[</span><span class="n">stn_name</span><span class="p">][</span><span class="s1">&#39;v_wind&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[::</span><span class="mi">2</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
-
-<span class="c1"># Plot smoothed potential temperature grid (K)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;x_grid&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">potemp</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot smoothed mixing ratio grid (g/kg)</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;x_grid&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;p_grid&#39;</span><span class="p">],</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">mixrat</span><span class="o">*</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
-                <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;tab:green&#39;</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Add some informative titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Cross-Section from DNR to BUF Potential Temp. &#39;</span>
-          <span class="s1">&#39;(K; red) and Mix. Rat. (g/kg; green)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">NameError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="n">line</span> <span class="mi">19</span>
-<span class="g g-Whitespace">     </span><span class="mi">16</span> <span class="n">ax</span><span class="o">.</span><span class="n">yaxis_inverted</span><span class="p">()</span>
-<span class="g g-Whitespace">     </span><span class="mi">18</span> <span class="c1"># Plot the sudo elevation on the cross section</span>
-<span class="ne">---&gt; </span><span class="mi">19</span> <span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;obs_distance&#39;</span><span class="p">],</span> <span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;elevation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">m</span><span class="p">,</span> <span class="mi">1030</span><span class="p">,</span>
-<span class="g g-Whitespace">     </span><span class="mi">20</span>                 <span class="n">where</span><span class="o">=</span><span class="n">xsect</span><span class="p">[</span><span class="s1">&#39;elevation&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">m</span> <span class="o">&lt;=</span> <span class="mi">1030</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span>
-<span class="g g-Whitespace">     </span><span class="mi">21</span>                 <span class="n">interpolate</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-<span class="g g-Whitespace">     </span><span class="mi">22</span> <span class="c1"># Don&#39;t plot xticks</span>
-<span class="g g-Whitespace">     </span><span class="mi">23</span> <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([],</span> <span class="p">[])</span>
-
-<span class="ne">NameError</span>: name &#39;xsect&#39; is not defined
-</pre></div>
-</div>
-<img alt="../../_images/6f0321e2d35497901ee209dbaf8d3a1712be4edc195bb182e05f0ab96cf5d35f.png" src="../../_images/6f0321e2d35497901ee209dbaf8d3a1712be4edc195bb182e05f0ab96cf5d35f.png" />
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-  <section class="tex2jax_ignore mathjax_ignore" id="baroclinic-potential-vorticity-analysis-divergence-and-winds">
-<h1>Baroclinic Potential Vorticity Analysis, Divergence, and Winds<a class="headerlink" href="#baroclinic-potential-vorticity-analysis-divergence-and-winds" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Classic baroclinic potential vorticity plot at 250 hPa using GFS
-analysis file.</p>
-<p>This example uses example data from the GFS analysis for 12 UTC 31
-October 2016 and uses xarray as the main read source with using MetPy to
-calculate the baroclinic potential vorticity, divergence and wind speed
-with geographic plotting using Cartopy for a CONUS view of the 250-hPa
-surface with divergence and wind barbs.</p>
-<p>By: Kevin Goebbert</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>The following code reads the example data using the xarray open_dataset
-function and prints the coordinate values that are associated with the
-various variables contained within the file.</p>
-<p>This code uses the metpy accessor to parse file to make it easy to pull
-data using common coordinate names (e.g., vertical) and attach units.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/casestudies&#39;</span>
-                     <span class="s1">&#39;/python-gallery/GFS_20101026_1200.nc&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Could not find variable corresponding to the value of grid_mapping: LatLon_Projection
-</pre></div>
-</div>
-</div>
-</div>
-<section id="data-retrieval">
-<h2>Data Retrieval<a class="headerlink" href="#data-retrieval" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This code retrieves the necessary data from the file and completes some
-smoothing of the temperature, and wind fields using the MetPy function
-smooth_n_point. A nicely formated valid time (vtime) variable is also
-created.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set subset slice for the geographic extent of data to limit download</span>
-<span class="n">lon_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">350</span><span class="p">)</span>
-<span class="n">lat_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">85</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-
-<span class="c1"># Grab lat/lon values (GFS will be 1D)</span>
-<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-
-
-<span class="c1"># Grab the pressure levels and select the data to be imported</span>
-<span class="c1"># Need all pressure levels for Temperatures, U and V Wind,</span>
-<span class="c1"># and Rel. Humidity</span>
-<span class="c1"># Smooth with the gaussian filter from scipy</span>
-<span class="n">pres</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">vertical</span>
-
-<span class="n">tmpk_var</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">tmpk</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">tmpk_var</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">thta</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_temperature</span><span class="p">(</span><span class="n">pres</span><span class="p">,</span> <span class="n">tmpk</span><span class="p">)</span>
-
-<span class="n">uwnd_var</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">vwnd_var</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">lat</span><span class="o">=</span><span class="n">lat_slice</span><span class="p">,</span> <span class="n">lon</span><span class="o">=</span><span class="n">lon_slice</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">uwnd</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">uwnd_var</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">vwnd</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">vwnd_var</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Create a clean datetime object for plotting based on time of</span>
-<span class="c1"># Geopotential heights</span>
-<span class="n">vtime</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Use MetPy to compute the baroclinic potential vorticity on all isobaric
-levels and other variables</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Comput the PV on all isobaric surfaces</span>
-<span class="n">pv</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">potential_vorticity_baroclinic</span><span class="p">(</span><span class="n">thta</span><span class="p">,</span> <span class="n">pres</span><span class="p">,</span> <span class="n">uwnd</span><span class="p">,</span> <span class="n">vwnd</span><span class="p">)</span>
-
-<span class="c1"># Use MetPy to compute the divergence on the pressure surfaces</span>
-<span class="n">div</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">divergence</span><span class="p">(</span><span class="n">uwnd</span><span class="p">,</span> <span class="n">vwnd</span><span class="p">)</span>
-
-<span class="c1"># Select the 250-hPa surface</span>
-<span class="n">pv_250</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">250</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">div_250</span> <span class="o">=</span> <span class="n">div</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">250</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">uwnd_250</span> <span class="o">=</span> <span class="n">uwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">250</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">vwnd_250</span> <span class="o">=</span> <span class="n">vwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">250</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="map-creation">
-<h2>Map Creation<a class="headerlink" href="#map-creation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This next set of code creates the plot and draws contours on a Lambert
-Conformal map centered on -100 E longitude. The main view is over the
-CONUS with isobaric PV map with PV contoured every 1 PVU and divergence
-colorshaded.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up the projection that will be used for plotting</span>
-<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span>
-                               <span class="n">central_latitude</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
-                               <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
-
-<span class="c1"># Set up the projection of the data;</span>
-<span class="c1"># if lat/lon then PlateCarree is what you want</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">()</span>
-
-<span class="c1"># Start the figure and create plot axes with proper projection</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">130</span><span class="p">,</span> <span class="o">-</span><span class="mi">72</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">55</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add geopolitical boundaries for map reference</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">))</span>
-
-<span class="c1"># Plot the contours of PV at 250 hPa, scaling 10^6 every 1 PVU</span>
-<span class="n">clevs_pv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">cs1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">pv_250</span><span class="o">*</span><span class="mf">1e6</span><span class="p">,</span> <span class="n">clevs_pv</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs1</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot the colorfill of divergence, scaled 10^5 every 1 s^1</span>
-<span class="n">clevs_div</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">15</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">cs1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">div_250</span><span class="o">*</span><span class="mf">1e5</span><span class="p">,</span> <span class="n">clevs_div</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">PuOr</span><span class="p">,</span>
-                  <span class="n">extend</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">cs1</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">aspect</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">extendrect</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Plot the wind barbs at 250 hPa</span>
-<span class="n">wind_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lons</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span> <span class="n">lats</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">],</span>
-         <span class="n">uwnd_250</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-         <span class="n">vwnd_250</span><span class="p">[</span><span class="n">wind_slice</span><span class="p">,</span> <span class="n">wind_slice</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-         <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;middle&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mf">6.5</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-
-<span class="c1"># Plot some titles to tell people what is on the map</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;250-hPa GFS PV (PVU), Divergence ($10^5$ s$^{-1}$),&#39;</span>
-          <span class="s1">&#39; and Wind Barbs (kt)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid Time: </span><span class="si">{</span><span class="n">vtime</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-</pre></div>
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-<h1>Smoothing Contours<a class="headerlink" href="#smoothing-contours" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Demonstrate how to smooth contour values from a higher resolution
-model field.</p>
-<p>By: Kevin Goebbert</p>
-<p>Date: 13 April 2017</p>
-<p>Do the needed imports</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
-<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Set up netCDF Subset Service link</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify our date/time of product desired</span>
-<span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span>
-
-<span class="c1"># Construct the URL for our THREDDS Data Server Catalog,</span>
-<span class="c1"># and access our desired dataset within via NCSS</span>
-<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/catalog/grib/&#39;</span>
-                 <span class="s1">&#39;NCEP/NAM/CONUS_12km/latest.xml&#39;</span><span class="p">)</span>
-<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
-
-<span class="c1"># Create our NCSS query with desired specifications</span>
-<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">add_lonlat</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
-
-<span class="c1"># Obtain the data we&#39;ve queried for</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Pull apart the data</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get dimension names to pull appropriate variables</span>
-<span class="c1"># dtime = ds.Geopotential_height_isobaric.dims[0]</span>
-<span class="c1"># dlev = ds.Geopotential_height_isobaric.dims[1]</span>
-<span class="c1"># dlat = ds.Geopotential_height_isobaric.dims[2]</span>
-<span class="c1"># dlon = ds.Geopotential_height_isobaric.dims[3]</span>
-
-<span class="c1"># Get lat and lon data, as well as time data and metadata</span>
-<span class="n">lats</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lat</span>
-<span class="n">lons</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">lon</span>
-
-<span class="c1"># Need 2D lat/lons for plotting, do so if necessary</span>
-<span class="k">if</span> <span class="n">lats</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
-    <span class="n">lons</span><span class="p">,</span> <span class="n">lats</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">)</span>
-
-<span class="c1"># Determine the level of 500 hPa</span>
-<span class="n">lev_500</span> <span class="o">=</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-
-<span class="c1"># Create more useable times for output</span>
-<span class="n">times</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">vtimes</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-
-<span class="c1"># Pull out the 500 hPa Heights</span>
-<span class="n">hght_500</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">uwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">vwnd_500</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">lev_500</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="c1"># Calculate the magnitude of the wind speed in kts</span>
-<span class="n">sped</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">uwnd_500</span><span class="p">,</span> <span class="n">vwnd_500</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;knots&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Set up the projection for LCC</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.0</span><span class="p">,</span>
-                                <span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.0</span><span class="p">)</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=</span><span class="mf">0.</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Subset and smooth</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Smooth the 500-hPa geopotential height field</span>
-<span class="c1"># Be sure to only smooth the 2D field</span>
-<span class="n">Z_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_gaussian</span><span class="p">(</span><span class="n">hght_500</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Plot the contours</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Start plot with new figure and axis</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">17.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
-
-<span class="c1"># Add some titles to make the plot readable by someone else</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;500-hPa Geo Heights (m; black), Smoothed 500-hPa Geo. &#39;</span>
-          <span class="s1">&#39;Heights (m; red)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtimes</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-
-<span class="c1"># Set GAREA and add map features</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mf">125.</span><span class="p">,</span> <span class="o">-</span><span class="mf">67.</span><span class="p">,</span> <span class="mf">22.</span><span class="p">,</span> <span class="mf">52.</span><span class="p">],</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-
-<span class="c1"># Set the CINT</span>
-<span class="n">clev500</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5100</span><span class="p">,</span> <span class="mi">6000</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
-
-<span class="c1"># Plot smoothed 500-hPa contours</span>
-<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">Z_500</span><span class="p">,</span> <span class="n">clev500</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                 <span class="n">linewidths</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">c2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-                <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Contour the 500 hPa heights with labels</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lons</span><span class="p">,</span> <span class="n">lats</span><span class="p">,</span> <span class="n">hght_500</span><span class="p">,</span> <span class="n">clev500</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span>
-                <span class="n">linewidths</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">datacrs</span><span class="p">)</span>
-<span class="n">cl</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-                <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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-  <section class="tex2jax_ignore mathjax_ignore" id="difax-replication">
-<h1>DIFAX Replication<a class="headerlink" href="#difax-replication" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>This example replicates the traditional DIFAX images for upper-level
-observations.</p>
-<p>By: Kevin Goebbert</p>
-<p>Observation data comes from Iowa State Archive, accessed through the
-Siphon package. Contour data comes from the GFS 0.5 degree analysis.
-Classic upper-level data of Geopotential Height and Temperature are
-plotted.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">urllib.request</span>
-
-<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">FixedLocator</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">StationPlot</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">from</span> <span class="nn">siphon.simplewebservice.iastate</span> <span class="kn">import</span> <span class="n">IAStateUpperAir</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="plotting-high-low-symbols">
-<h2>Plotting High/Low Symbols<a class="headerlink" href="#plotting-high-low-symbols" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>A helper function to plot a text symbol (e.g., H, L) for relative
-maximum/minimum for a given field (e.g., geopotential height).</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_maxmin_points</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">extrema</span><span class="p">,</span> <span class="n">nsize</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span>
-                       <span class="n">plotValue</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This function will find and plot relative maximum and minimum for a 2D grid. The function</span>
-<span class="sd">    can be used to plot an H for maximum values (e.g., High pressure) and an L for minimum</span>
-<span class="sd">    values (e.g., low pressue). It is best to used filetered data to obtain  a synoptic scale</span>
-<span class="sd">    max/min value. The symbol text can be set to a string value and optionally the color of the</span>
-<span class="sd">    symbol and any plotted value can be set with the parameter color.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">        lon : 2D array</span>
-<span class="sd">            Plotting longitude values</span>
-<span class="sd">        lat : 2D array</span>
-<span class="sd">            Plotting latitude values</span>
-<span class="sd">        data : 2D array</span>
-<span class="sd">            Data that you wish to plot the max/min symbol placement</span>
-<span class="sd">        extrema : str</span>
-<span class="sd">            Either a value of max for Maximum Values or min for Minimum Values</span>
-<span class="sd">        nsize : int</span>
-<span class="sd">            Size of the grid box to filter the max and min values to plot a reasonable number</span>
-<span class="sd">        symbol : str</span>
-<span class="sd">            Text to be placed at location of max/min value</span>
-<span class="sd">        color : str</span>
-<span class="sd">            Name of matplotlib colorname to plot the symbol (and numerical value, if plotted)</span>
-<span class="sd">        plot_value : Boolean (True/False)</span>
-<span class="sd">            Whether to plot the numeric value of max/min point</span>
-
-<span class="sd">    Return</span>
-<span class="sd">    ------</span>
-<span class="sd">        The max/min symbol will be plotted on the current axes within the bounding frame</span>
-<span class="sd">        (e.g., clip_on=True)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">maximum_filter</span><span class="p">,</span> <span class="n">minimum_filter</span>
-
-    <span class="k">if</span> <span class="p">(</span><span class="n">extrema</span> <span class="o">==</span> <span class="s1">&#39;max&#39;</span><span class="p">):</span>
-        <span class="n">data_ext</span> <span class="o">=</span> <span class="n">maximum_filter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">nsize</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="p">(</span><span class="n">extrema</span> <span class="o">==</span> <span class="s1">&#39;min&#39;</span><span class="p">):</span>
-        <span class="n">data_ext</span> <span class="o">=</span> <span class="n">minimum_filter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">nsize</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Value for hilo must be either max or min&#39;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">lon</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">lon</span><span class="p">,</span> <span class="n">lat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
-
-    <span class="n">mxx</span><span class="p">,</span> <span class="n">mxy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">data_ext</span> <span class="o">==</span> <span class="n">data</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mxy</span><span class="p">)):</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">lat</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">36</span><span class="p">,</span>
-                <span class="n">clip_on</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-                <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">lat</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span>
-                <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]])),</span>
-                <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">clip_on</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">&#39;bold&#39;</span><span class="p">,</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">lat</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">markeredgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span>
-                <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">lat</span><span class="p">[</span><span class="n">mxx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">mxy</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span>
-                <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="station-information">
-<h2>Station Information<a class="headerlink" href="#station-information" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>A helper function for obtaining radiosonde station information (e.g.,
-latitude/longitude) requried to plot data obtained from each station.
-Original code by github user sgdecker.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">station_info</span><span class="p">(</span><span class="n">stid</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Provide information about weather stations.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    stid: str or iterable object containing strs</span>
-<span class="sd">         The ICAO or IATA code(s) for which station information is requested.</span>
-<span class="sd">    with_units: bool</span>
-<span class="sd">         Whether to include units for values that have them. Default True.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    info: dict</span>
-<span class="sd">         Information about the station(s) within a dictionary with these keys:</span>
-<span class="sd">             &#39;state&#39;: Two-character ID of the state/province where the station is located,</span>
-<span class="sd">                       if applicable</span>
-<span class="sd">             &#39;name&#39;: The name of the station</span>
-<span class="sd">             &#39;lat&#39;: The latitude of the station [deg]</span>
-<span class="sd">             &#39;lon&#39;: The longitude of the station [deg]</span>
-<span class="sd">             &#39;elevation&#39;: The elevation of the station [m]</span>
-<span class="sd">             &#39;country&#39;: Two-character ID of the country where the station is located</span>
-
-<span class="sd">    Modified code from Steven Decker, Rutgers University</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Provide a helper function for later usage</span>
-    <span class="k">def</span> <span class="nf">str2latlon</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
-        <span class="n">deg</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
-        <span class="n">mn</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">if</span> <span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;S&#39;</span> <span class="ow">or</span> <span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;W&#39;</span><span class="p">:</span>
-            <span class="n">deg</span> <span class="o">=</span> <span class="o">-</span><span class="n">deg</span>
-            <span class="n">mn</span> <span class="o">=</span> <span class="o">-</span><span class="n">mn</span>
-        <span class="k">return</span> <span class="n">deg</span> <span class="o">+</span> <span class="n">mn</span> <span class="o">/</span> <span class="mf">60.</span>
-
-    <span class="c1"># Various constants describing the underlying data</span>
-    <span class="n">url</span> <span class="o">=</span> <span class="s1">&#39;https://www.aviationweather.gov/docs/metar/stations.txt&#39;</span>
-    <span class="c1"># file = &#39;stations.txt&#39;</span>
-    <span class="n">state_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">name_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">19</span><span class="p">)</span>
-    <span class="n">icao_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">24</span><span class="p">)</span>
-    <span class="n">iata_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">26</span><span class="p">,</span> <span class="mi">29</span><span class="p">)</span>
-    <span class="n">lat_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">39</span><span class="p">,</span> <span class="mi">45</span><span class="p">)</span>
-    <span class="n">lon_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">47</span><span class="p">,</span> <span class="mi">54</span><span class="p">)</span>
-    <span class="n">z_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">55</span><span class="p">,</span> <span class="mi">59</span><span class="p">)</span>
-    <span class="n">cntry_bnds</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">81</span><span class="p">,</span> <span class="mi">83</span><span class="p">)</span>
-
-    <span class="c1"># Generalize to any number of IDs</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">stid</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-        <span class="n">stid</span> <span class="o">=</span> <span class="p">[</span><span class="n">stid</span><span class="p">]</span>
-
-    <span class="c1"># Get the station dataset</span>
-    <span class="n">infile</span> <span class="o">=</span> <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlopen</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">infile</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
-
-    <span class="n">state</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">name</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">lat</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">lon</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">cntry</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">stid</span><span class="p">:</span>
-        <span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">line_bytes</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
-            <span class="n">line</span> <span class="o">=</span> <span class="n">line_bytes</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;UTF-8&#39;</span><span class="p">)</span>
-            <span class="n">icao</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="n">icao_bnds</span><span class="p">]</span>
-            <span class="n">iata</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="n">iata_bnds</span><span class="p">]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">iata</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span> <span class="ow">and</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">icao</span><span class="p">:</span>
-                <span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">state_bnds</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
-                <span class="n">name</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">name_bnds</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
-                <span class="n">lat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">str2latlon</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">lat_bnds</span><span class="p">]))</span>
-                <span class="n">lon</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">str2latlon</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">lon_bnds</span><span class="p">]))</span>
-                <span class="n">z</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">z_bnds</span><span class="p">]))</span>
-                <span class="n">cntry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">cntry_bnds</span><span class="p">])</span>
-
-                <span class="k">break</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">state</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;NA&#39;</span><span class="p">)</span>
-            <span class="n">name</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;NA&#39;</span><span class="p">)</span>
-            <span class="n">lat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-            <span class="n">lon</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-            <span class="n">z</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-            <span class="n">cntry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;NA&#39;</span><span class="p">)</span>
-
-    <span class="n">infile</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">state</span><span class="p">),</span> <span class="s1">&#39;name&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">name</span><span class="p">),</span> <span class="s1">&#39;lat&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">lat</span><span class="p">),</span>
-            <span class="s1">&#39;lon&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">lon</span><span class="p">),</span> <span class="s1">&#39;elevation&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">z</span><span class="p">),</span> <span class="s1">&#39;country&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cntry</span><span class="p">),</span>
-            <span class="s1">&#39;units&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;lat&#39;</span><span class="p">:</span> <span class="s1">&#39;deg&#39;</span><span class="p">,</span> <span class="s1">&#39;lon&#39;</span><span class="p">:</span> <span class="s1">&#39;deg&#39;</span><span class="p">,</span> <span class="s1">&#39;z&#39;</span><span class="p">:</span> <span class="s1">&#39;m&#39;</span><span class="p">}}</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="observation-data">
-<h2>Observation Data<a class="headerlink" href="#observation-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Set a date and time for upper-air observations (should only be 00 or 12
-UTC for the hour).</p>
-<p>Request all data from Iowa State using the Siphon package. The result is
-a pandas DataFrame containing all of the sounding data from all
-available stations.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set date for desired UPA data</span>
-<span class="n">today</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">()</span>
-
-<span class="c1"># Go back one day to ensure data availability</span>
-<span class="n">date</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="n">today</span><span class="o">.</span><span class="n">year</span><span class="p">,</span> <span class="n">today</span><span class="o">.</span><span class="n">month</span><span class="p">,</span> <span class="n">today</span><span class="o">.</span><span class="n">day</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="c1"># Request data using Siphon request for data from Iowa State Archive</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">IAStateUpperAir</span><span class="o">.</span><span class="n">request_all_data</span><span class="p">(</span><span class="n">date</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="subset-observational-data">
-<h2>Subset Observational Data<a class="headerlink" href="#subset-observational-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>From the request above will give all levels from all radisonde sites
-available through the service. For plotting a pressure surface map there
-is only need to have the data from that level. Below the data is subset
-and a few parameters set based on the level chosen. Additionally, the
-station information is obtained and latitude and longitude data is added
-to the DataFrame.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">level</span> <span class="o">=</span> <span class="mi">500</span>
-
-<span class="k">if</span> <span class="p">(</span><span class="n">level</span> <span class="o">==</span> <span class="mi">925</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">level</span> <span class="o">==</span> <span class="mi">850</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">level</span> <span class="o">==</span> <span class="mi">700</span><span class="p">):</span>
-    <span class="n">cint</span> <span class="o">=</span> <span class="mi">30</span>
-    <span class="k">def</span> <span class="nf">hght_format</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> <span class="k">return</span> <span class="nb">format</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;.0f&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span>
-<span class="k">elif</span> <span class="n">level</span> <span class="o">==</span> <span class="mi">500</span><span class="p">:</span>
-    <span class="n">cint</span> <span class="o">=</span> <span class="mi">60</span>
-    <span class="k">def</span> <span class="nf">hght_format</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> <span class="k">return</span> <span class="nb">format</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;.0f&#39;</span><span class="p">)[:</span><span class="mi">3</span><span class="p">]</span>
-<span class="k">elif</span> <span class="n">level</span> <span class="o">==</span> <span class="mi">300</span><span class="p">:</span>
-    <span class="n">cint</span> <span class="o">=</span> <span class="mi">120</span>
-    <span class="k">def</span> <span class="nf">hght_format</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> <span class="k">return</span> <span class="nb">format</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;.0f&#39;</span><span class="p">)[:</span><span class="mi">3</span><span class="p">]</span>
-<span class="k">elif</span> <span class="n">level</span> <span class="o">&lt;</span> <span class="mi">300</span><span class="p">:</span>
-    <span class="n">cint</span> <span class="o">=</span> <span class="mi">120</span>
-    <span class="k">def</span> <span class="nf">hght_format</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> <span class="k">return</span> <span class="nb">format</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="s1">&#39;.0f&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
-
-<span class="c1"># Create subset of all data for a given level</span>
-<span class="n">data_subset</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">pressure</span> <span class="o">==</span> <span class="n">level</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data_subset</span><span class="p">]</span>
-
-<span class="c1"># Get station lat/lon from look-up file; add to Dataframe</span>
-<span class="n">stn_info</span> <span class="o">=</span> <span class="n">station_info</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">station</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
-<span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="s1">&#39;latitude&#39;</span><span class="p">,</span> <span class="n">stn_info</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">])</span>
-<span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="s1">&#39;longitude&#39;</span><span class="p">,</span> <span class="n">stn_info</span><span class="p">[</span><span class="s1">&#39;lon&#39;</span><span class="p">])</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">HTTPError</span><span class="g g-Whitespace">                                 </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="n">line</span> <span class="mi">21</span>
-<span class="g g-Whitespace">     </span><span class="mi">18</span> <span class="n">df</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">data_subset</span><span class="p">]</span>
-<span class="g g-Whitespace">     </span><span class="mi">20</span> <span class="c1"># Get station lat/lon from look-up file; add to Dataframe</span>
-<span class="ne">---&gt; </span><span class="mi">21</span> <span class="n">stn_info</span> <span class="o">=</span> <span class="n">station_info</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">station</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
-<span class="g g-Whitespace">     </span><span class="mi">22</span> <span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="s1">&#39;latitude&#39;</span><span class="p">,</span> <span class="n">stn_info</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">])</span>
-<span class="g g-Whitespace">     </span><span class="mi">23</span> <span class="n">df</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">11</span><span class="p">,</span> <span class="s1">&#39;longitude&#39;</span><span class="p">,</span> <span class="n">stn_info</span><span class="p">[</span><span class="s1">&#39;lon&#39;</span><span class="p">])</span>
-
-<span class="nn">Cell In[3], line 52,</span> in <span class="ni">station_info</span><span class="nt">(stid)</span>
-<span class="g g-Whitespace">     </span><span class="mi">49</span>     <span class="n">stid</span> <span class="o">=</span> <span class="p">[</span><span class="n">stid</span><span class="p">]</span>
-<span class="g g-Whitespace">     </span><span class="mi">51</span> <span class="c1"># Get the station dataset</span>
-<span class="ne">---&gt; </span><span class="mi">52</span> <span class="n">infile</span> <span class="o">=</span> <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlopen</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
-<span class="g g-Whitespace">     </span><span class="mi">53</span> <span class="n">data</span> <span class="o">=</span> <span class="n">infile</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
-<span class="g g-Whitespace">     </span><span class="mi">55</span> <span class="n">state</span> <span class="o">=</span> <span class="p">[]</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:216,</span> in <span class="ni">urlopen</span><span class="nt">(url, data, timeout, cafile, capath, cadefault, context)</span>
-<span class="g g-Whitespace">    </span><span class="mi">214</span> <span class="k">else</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">215</span>     <span class="n">opener</span> <span class="o">=</span> <span class="n">_opener</span>
-<span class="ne">--&gt; </span><span class="mi">216</span> <span class="k">return</span> <span class="n">opener</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">timeout</span><span class="p">)</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:525,</span> in <span class="ni">OpenerDirector.open</span><span class="nt">(self, fullurl, data, timeout)</span>
-<span class="g g-Whitespace">    </span><span class="mi">523</span> <span class="k">for</span> <span class="n">processor</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">process_response</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="p">[]):</span>
-<span class="g g-Whitespace">    </span><span class="mi">524</span>     <span class="n">meth</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">processor</span><span class="p">,</span> <span class="n">meth_name</span><span class="p">)</span>
-<span class="ne">--&gt; </span><span class="mi">525</span>     <span class="n">response</span> <span class="o">=</span> <span class="n">meth</span><span class="p">(</span><span class="n">req</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">527</span> <span class="k">return</span> <span class="n">response</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:634,</span> in <span class="ni">HTTPErrorProcessor.http_response</span><span class="nt">(self, request, response)</span>
-<span class="g g-Whitespace">    </span><span class="mi">631</span> <span class="c1"># According to RFC 2616, &quot;2xx&quot; code indicates that the client&#39;s</span>
-<span class="g g-Whitespace">    </span><span class="mi">632</span> <span class="c1"># request was successfully received, understood, and accepted.</span>
-<span class="g g-Whitespace">    </span><span class="mi">633</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="mi">200</span> <span class="o">&lt;=</span> <span class="n">code</span> <span class="o">&lt;</span> <span class="mi">300</span><span class="p">):</span>
-<span class="ne">--&gt; </span><span class="mi">634</span>     <span class="n">response</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
-<span class="g g-Whitespace">    </span><span class="mi">635</span>         <span class="s1">&#39;http&#39;</span><span class="p">,</span> <span class="n">request</span><span class="p">,</span> <span class="n">response</span><span class="p">,</span> <span class="n">code</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="n">hdrs</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">637</span> <span class="k">return</span> <span class="n">response</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:563,</span> in <span class="ni">OpenerDirector.error</span><span class="nt">(self, proto, *args)</span>
-<span class="g g-Whitespace">    </span><span class="mi">561</span> <span class="k">if</span> <span class="n">http_err</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">562</span>     <span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="nb">dict</span><span class="p">,</span> <span class="s1">&#39;default&#39;</span><span class="p">,</span> <span class="s1">&#39;http_error_default&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">orig_args</span>
-<span class="ne">--&gt; </span><span class="mi">563</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_chain</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:496,</span> in <span class="ni">OpenerDirector._call_chain</span><span class="nt">(self, chain, kind, meth_name, *args)</span>
-<span class="g g-Whitespace">    </span><span class="mi">494</span> <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">handlers</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">495</span>     <span class="n">func</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">meth_name</span><span class="p">)</span>
-<span class="ne">--&gt; </span><span class="mi">496</span>     <span class="n">result</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">497</span>     <span class="k">if</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-<span class="g g-Whitespace">    </span><span class="mi">498</span>         <span class="k">return</span> <span class="n">result</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/urllib/request.py:643,</span> in <span class="ni">HTTPDefaultErrorHandler.http_error_default</span><span class="nt">(self, req, fp, code, msg, hdrs)</span>
-<span class="g g-Whitespace">    </span><span class="mi">642</span> <span class="k">def</span> <span class="nf">http_error_default</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">code</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="n">hdrs</span><span class="p">):</span>
-<span class="ne">--&gt; </span><span class="mi">643</span>     <span class="k">raise</span> <span class="n">HTTPError</span><span class="p">(</span><span class="n">req</span><span class="o">.</span><span class="n">full_url</span><span class="p">,</span> <span class="n">code</span><span class="p">,</span> <span class="n">msg</span><span class="p">,</span> <span class="n">hdrs</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span>
-
-<span class="ne">HTTPError</span>: HTTP Error 308: Permanent Redirect
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="gridded-data">
-<h2>Gridded Data<a class="headerlink" href="#gridded-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Obtain GFS gridded output for contour plotting. Specifically,
-geopotential height and temperature data for the given level and subset
-for over North America. Data are smoothed for aesthetic reasons.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get GFS data and subset to North America for Geopotential Height and Temperature</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://thredds.ucar.edu/thredds/dodsC/grib/NCEP/GFS/Global_0p5deg_ana/&#39;</span>
-                     <span class="s1">&#39;GFS_Global_0p5deg_ana_{0:%Y%m</span><span class="si">%d</span><span class="s1">}_{0:%H}00.grib2&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                         <span class="n">date</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="c1"># Geopotential height and smooth</span>
-<span class="n">hght</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="n">date</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">70</span><span class="p">,</span> <span class="mi">15</span><span class="p">),</span> <span class="n">lon</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">360</span><span class="o">-</span><span class="mi">145</span><span class="p">,</span> <span class="mi">360</span><span class="o">-</span><span class="mi">50</span><span class="p">))</span>
-<span class="n">smooth_hght</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">hght</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-
-<span class="c1"># Temperature, smooth, and convert to Celsius</span>
-<span class="n">tmpk</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span>
-    <span class="n">vertical</span><span class="o">=</span><span class="n">level</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">,</span> <span class="n">time</span><span class="o">=</span><span class="n">date</span><span class="p">,</span> <span class="n">lat</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">70</span><span class="p">,</span> <span class="mi">15</span><span class="p">),</span> <span class="n">lon</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">360</span><span class="o">-</span><span class="mi">145</span><span class="p">,</span> <span class="mi">360</span><span class="o">-</span><span class="mi">50</span><span class="p">))</span>
-<span class="n">smooth_tmpc</span> <span class="o">=</span> <span class="p">(</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">smooth_n_point</span><span class="p">(</span><span class="n">tmpk</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="create-difax-replication">
-<h2>Create DIFAX Replication<a class="headerlink" href="#create-difax-replication" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Plot the observational data and contours on a Lambert Conformal map and
-add features that resemble the historic DIFAX maps.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up map coordinate reference system</span>
-<span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span>
-    <span class="n">central_latitude</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
-
-<span class="c1"># Set up station locations for plotting observations</span>
-<span class="n">point_locs</span> <span class="o">=</span> <span class="n">mapcrs</span><span class="o">.</span><span class="n">transform_points</span><span class="p">(</span>
-    <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-
-<span class="c1"># Start figure and set graphics extent</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">mapcrs</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mi">125</span><span class="p">,</span> <span class="o">-</span><span class="mi">70</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">55</span><span class="p">])</span>
-
-<span class="c1"># Add map features for geographic reference</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">COASTLINE</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">LAND</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="o">.</span><span class="n">with_scale</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">),</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot plus signs every degree lat/lon</span>
-<span class="n">plus_lat</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">plus_lon</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">other_lat</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="n">other_lon</span> <span class="o">=</span> <span class="p">[]</span>
-
-<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="o">.</span><span class="n">values</span><span class="p">[::</span><span class="mi">2</span><span class="p">]:</span>
-    <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="o">.</span><span class="n">values</span><span class="p">[::</span><span class="mi">2</span><span class="p">]:</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">x</span> <span class="o">%</span> <span class="mi">5</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">y</span> <span class="o">%</span> <span class="mi">5</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="n">plus_lon</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-            <span class="n">plus_lat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">other_lon</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-            <span class="n">other_lat</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">other_lon</span><span class="p">,</span> <span class="n">other_lat</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span>
-           <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">plus_lon</span><span class="p">,</span> <span class="n">plus_lat</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-           <span class="n">color</span><span class="o">=</span><span class="s1">&#39;lightgrey&#39;</span><span class="p">)</span>
-
-<span class="c1"># Add gridlines for every 5 degree lat/lon</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">gridlines</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">ylocs</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">71</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">xlocs</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">150</span><span class="p">,</span> <span class="o">-</span><span class="mi">49</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-
-<span class="c1"># Start the station plot by specifying the axes to draw on, as well as the</span>
-<span class="c1"># lon/lat of the stations (with transform). We also the fontsize to 10 pt.</span>
-<span class="n">stationplot</span> <span class="o">=</span> <span class="n">StationPlot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">clip_on</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                          <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-
-<span class="c1"># Plot the temperature and dew point to the upper and lower left, respectively, of</span>
-<span class="c1"># the center point.</span>
-<span class="n">stationplot</span><span class="o">.</span><span class="n">plot_parameter</span><span class="p">(</span><span class="s1">&#39;NW&#39;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;temperature&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-<span class="n">stationplot</span><span class="o">.</span><span class="n">plot_parameter</span><span class="p">(</span><span class="s1">&#39;SW&#39;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;dewpoint&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-
-<span class="c1"># A more complex example uses a custom formatter to control how the geopotential height</span>
-<span class="c1"># values are plotted. This is set in an earlier if-statement to work appropriate for</span>
-<span class="c1"># different levels.</span>
-<span class="n">stationplot</span><span class="o">.</span><span class="n">plot_parameter</span><span class="p">(</span><span class="s1">&#39;NE&#39;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;height&#39;</span><span class="p">],</span> <span class="n">formatter</span><span class="o">=</span><span class="n">hght_format</span><span class="p">)</span>
-
-<span class="c1"># Add wind barbs</span>
-<span class="n">stationplot</span><span class="o">.</span><span class="n">plot_barb</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;u_wind&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;v_wind&#39;</span><span class="p">],</span> <span class="n">length</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">pivot</span><span class="o">=</span><span class="s1">&#39;tip&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot Solid Contours of Geopotential Height</span>
-<span class="n">cs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">smooth_hght</span><span class="p">,</span>
-                <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20000</span><span class="p">,</span> <span class="n">cint</span><span class="p">),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">clabels</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Contour labels with black boxes and white text</span>
-<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">cs</span><span class="o">.</span><span class="n">labelTexts</span><span class="p">:</span>
-    <span class="n">t</span><span class="o">.</span><span class="n">set_bbox</span><span class="p">({</span><span class="s1">&#39;facecolor&#39;</span><span class="p">:</span> <span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="s1">&#39;pad&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">})</span>
-    <span class="n">t</span><span class="o">.</span><span class="n">set_fontweight</span><span class="p">(</span><span class="s1">&#39;heavy&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot Dashed Contours of Temperature</span>
-<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">smooth_tmpc</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="mi">51</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span>
-                 <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">clabels</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">cs2</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Set longer dashes than default</span>
-<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cs2</span><span class="o">.</span><span class="n">collections</span><span class="p">:</span>
-    <span class="n">c</span><span class="o">.</span><span class="n">set_dashes</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">))])</span>
-
-<span class="c1"># Contour labels with black boxes and white text</span>
-<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">cs</span><span class="o">.</span><span class="n">labelTexts</span><span class="p">:</span>
-    <span class="n">t</span><span class="o">.</span><span class="n">set_bbox</span><span class="p">({</span><span class="s1">&#39;facecolor&#39;</span><span class="p">:</span> <span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="s1">&#39;pad&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">})</span>
-    <span class="n">t</span><span class="o">.</span><span class="n">set_fontweight</span><span class="p">(</span><span class="s1">&#39;heavy&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot filled circles for Radiosonde Obs</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-           <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Use definition to plot H/L symbols</span>
-<span class="n">plot_maxmin_points</span><span class="p">(</span><span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">smooth_hght</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="s1">&#39;max&#39;</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span>
-                   <span class="n">symbol</span><span class="o">=</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">plot_maxmin_points</span><span class="p">(</span><span class="n">hght</span><span class="o">.</span><span class="n">lon</span><span class="p">,</span> <span class="n">hght</span><span class="o">.</span><span class="n">lat</span><span class="p">,</span> <span class="n">smooth_hght</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span>
-                   <span class="n">symbol</span><span class="o">=</span><span class="s1">&#39;L&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-
-<span class="c1"># Add titles</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Upper-air Observations at </span><span class="si">{</span><span class="n">level</span><span class="si">}</span><span class="s1">-hPa Analysis Heights/Temperature&#39;</span><span class="p">,</span>
-          <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid: </span><span class="si">{</span><span class="n">date</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
-<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805,</span> in <span class="ni">Index.get_loc</span><span class="nt">(self, key)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3804</span> <span class="k">try</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">3805</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_engine</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">casted_key</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3806</span> <span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-
-<span class="nn">File index.pyx:167,</span> in <span class="ni">pandas._libs.index.IndexEngine.get_loc</span><span class="nt">()</span>
-
-<span class="nn">File index.pyx:196,</span> in <span class="ni">pandas._libs.index.IndexEngine.get_loc</span><span class="nt">()</span>
-
-<span class="nn">File pandas/_libs/hashtable_class_helper.pxi:7081,</span> in <span class="ni">pandas._libs.hashtable.PyObjectHashTable.get_item</span><span class="nt">()</span>
-
-<span class="nn">File pandas/_libs/hashtable_class_helper.pxi:7089,</span> in <span class="ni">pandas._libs.hashtable.PyObjectHashTable.get_item</span><span class="nt">()</span>
-
-<span class="ne">KeyError</span>: &#39;longitude&#39;
-
-<span class="n">The</span> <span class="n">above</span> <span class="n">exception</span> <span class="n">was</span> <span class="n">the</span> <span class="n">direct</span> <span class="n">cause</span> <span class="n">of</span> <span class="n">the</span> <span class="n">following</span> <span class="n">exception</span><span class="p">:</span>
-
-<span class="ne">KeyError</span><span class="g g-Whitespace">                                  </span>Traceback (most recent call last)
-<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">line</span> <span class="mi">7</span>
-<span class="g g-Whitespace">      </span><span class="mi">2</span> <span class="n">mapcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span>
-<span class="g g-Whitespace">      </span><span class="mi">3</span>     <span class="n">central_latitude</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">central_longitude</span><span class="o">=-</span><span class="mi">100</span><span class="p">,</span> <span class="n">standard_parallels</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
-<span class="g g-Whitespace">      </span><span class="mi">5</span> <span class="c1"># Set up station locations for plotting observations</span>
-<span class="g g-Whitespace">      </span><span class="mi">6</span> <span class="n">point_locs</span> <span class="o">=</span> <span class="n">mapcrs</span><span class="o">.</span><span class="n">transform_points</span><span class="p">(</span>
-<span class="ne">----&gt; </span><span class="mi">7</span>     <span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;longitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;latitude&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-<span class="g g-Whitespace">      </span><span class="mi">9</span> <span class="c1"># Start figure and set graphics extent</span>
-<span class="g g-Whitespace">     </span><span class="mi">10</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pandas/core/frame.py:4102,</span> in <span class="ni">DataFrame.__getitem__</span><span class="nt">(self, key)</span>
-<span class="g g-Whitespace">   </span><span class="mi">4100</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">nlevels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">4101</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_getitem_multilevel</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">4102</span> <span class="n">indexer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">4103</span> <span class="k">if</span> <span class="n">is_integer</span><span class="p">(</span><span class="n">indexer</span><span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">4104</span>     <span class="n">indexer</span> <span class="o">=</span> <span class="p">[</span><span class="n">indexer</span><span class="p">]</span>
-
-<span class="nn">File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812,</span> in <span class="ni">Index.get_loc</span><span class="nt">(self, key)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3807</span>     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">casted_key</span><span class="p">,</span> <span class="nb">slice</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span>
-<span class="g g-Whitespace">   </span><span class="mi">3808</span>         <span class="nb">isinstance</span><span class="p">(</span><span class="n">casted_key</span><span class="p">,</span> <span class="n">abc</span><span class="o">.</span><span class="n">Iterable</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3809</span>         <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">slice</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">casted_key</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">3810</span>     <span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">3811</span>         <span class="k">raise</span> <span class="n">InvalidIndexError</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">3812</span>     <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">err</span>
-<span class="g g-Whitespace">   </span><span class="mi">3813</span> <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">3814</span>     <span class="c1"># If we have a listlike key, _check_indexing_error will raise</span>
-<span class="g g-Whitespace">   </span><span class="mi">3815</span>     <span class="c1">#  InvalidIndexError. Otherwise we fall through and re-raise</span>
-<span class="g g-Whitespace">   </span><span class="mi">3816</span>     <span class="c1">#  the TypeError.</span>
-<span class="g g-Whitespace">   </span><span class="mi">3817</span>     <span class="bp">self</span><span class="o">.</span><span class="n">_check_indexing_error</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-
-<span class="ne">KeyError</span>: &#39;longitude&#39;
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-</section>
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-<h1>Calculating Wind Shear<a class="headerlink" href="#calculating-wind-shear" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>This example plots calculates the 850-500 hPa Bulk Wind Shear</p>
-<p>Plotting over a regional domain, accessing the Best Collection of GFS from the
-Unidata Thredds server, plots MSLP (hPa), 850-hPa Wind Vector (m/s), 500-hPa
-Wind Vector (m/s), and the Wind Shear between the two layers (m/s)</p>
-<section id="import-necessary-packages">
-<h2>Import necessary packages<a class="headerlink" href="#import-necessary-packages" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">scipy.ndimage</span> <span class="k">as</span> <span class="nn">ndimage</span>
-<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
-<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="obtain-data">
-<h2>Obtain data<a class="headerlink" href="#obtain-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Construct a TDSCatalog instance pointing to the gfs dataset</span>
-<span class="n">best_gfs</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="s1">&#39;http://thredds.ucar.edu/thredds/catalog/grib/&#39;</span>
-                      <span class="s1">&#39;NCEP/GFS/Global_0p5deg/catalog.xml&#39;</span><span class="p">)</span>
-
-<span class="c1"># Interface with the Best GFS Half Degree Forecast Time Series dataset</span>
-<span class="c1"># via NCSS directly</span>
-<span class="n">ncss</span> <span class="o">=</span> <span class="n">best_gfs</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="s1">&#39;Best GFS Half Degree Forecast Time Series&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="first-query-for-mslp">
-<h2>First Query for MSLP<a class="headerlink" href="#first-query-for-mslp" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create our NCSS query with desired specifications</span>
-<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="n">north</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">south</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">east</span><span class="o">=-</span><span class="mi">80</span><span class="p">,</span> <span class="n">west</span><span class="o">=-</span><span class="mi">115</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">utcnow</span><span class="p">())</span>
-<span class="n">query</span><span class="o">.</span><span class="n">accept</span><span class="p">(</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;MSLP_Eta_model_reduction_msl&#39;</span><span class="p">)</span>
-
-<span class="c1"># Obtain the data we&#39;ve queried for as a netcdf4-python dataset</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="c1"># Pull out the variables you want to use</span>
-<span class="n">mslp_var</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">MSLP_Eta_model_reduction_msl</span>
-<span class="n">time_var</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">MSLP_Eta_model_reduction_msl</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span>
-<span class="n">lat_var</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">latitude</span>
-<span class="n">lon_var</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">longitude</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="second-query-for-850-hpa-data">
-<h2>Second Query for 850-hPa data<a class="headerlink" href="#second-query-for-850-hpa-data" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Request data for 850-hPa winds</span>
-<span class="c1"># First clear the query&#39;s variables from previous query for MSLP</span>
-<span class="n">query</span><span class="o">.</span><span class="n">var</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">85000</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
-
-<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds_wind</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="n">u_wind_var850</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span>
-<span class="n">v_wind_var850</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Third Query for 500-hPa data</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Request data for 500-hPa winds</span>
-<span class="c1"># First clear the query&#39;s variables from previous query for 850-hPa data</span>
-<span class="n">query</span><span class="o">.</span><span class="n">var</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">vertical_level</span><span class="p">(</span><span class="mi">50000</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">)</span>
-
-<span class="n">data</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds_wind</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="n">u_wind_var500</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span>
-<span class="n">v_wind_var500</span> <span class="o">=</span> <span class="n">ds_wind</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="data-manipulation">
-<h2>Data Manipulation<a class="headerlink" href="#data-manipulation" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get actual data values and remove any size 1 dimensions</span>
-<span class="n">lat</span> <span class="o">=</span> <span class="n">lat_var</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">lon</span> <span class="o">=</span> <span class="n">lon_var</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">mslp</span> <span class="o">=</span> <span class="n">mslp_var</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">u_wind850</span> <span class="o">=</span> <span class="n">u_wind_var850</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">v_wind850</span> <span class="o">=</span> <span class="n">v_wind_var850</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">u_wind500</span> <span class="o">=</span> <span class="n">u_wind_var500</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">v_wind500</span> <span class="o">=</span> <span class="n">v_wind_var500</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="c1"># Convert number of hours since the reference time into an actual date</span>
-<span class="n">time</span> <span class="o">=</span> <span class="n">time_var</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="c1"># Combine 1D latitude and longitudes into a 2D grid of locations</span>
-<span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">)</span>
-
-<span class="c1"># Smooth mslp data</span>
-<span class="n">mslp</span> <span class="o">=</span> <span class="n">ndimage</span><span class="o">.</span><span class="n">gaussian_filter</span><span class="p">(</span><span class="n">mslp</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Begin making figure</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create new figure</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-
-<span class="c1"># Add the map and set the extent</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">([</span><span class="o">-</span><span class="mf">108.</span><span class="p">,</span> <span class="o">-</span><span class="mf">91.</span><span class="p">,</span> <span class="mf">33.</span><span class="p">,</span> <span class="mf">45.</span><span class="p">])</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">patch</span><span class="o">.</span><span class="n">set_fill</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="c1"># Add state boundaries to plot</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Contour the MSLP</span>
-<span class="n">c</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">,</span> <span class="n">lat_2d</span><span class="p">,</span> <span class="n">mslp</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;lime&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="n">wslice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="c1"># Plot 850-hPa wind vectors</span>
-<span class="n">vectors850</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
-                       <span class="n">u_wind850</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">v_wind850</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
-                       <span class="n">headlength</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span>
-                       <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;gold&#39;</span><span class="p">,</span>
-                       <span class="n">label</span><span class="o">=</span><span class="s1">&#39;850mb wind&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 500-hPa wind vectors</span>
-<span class="n">vectors500</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
-                       <span class="n">u_wind500</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">v_wind500</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
-                       <span class="n">headlength</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span>
-                       <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;cornflowerblue&#39;</span><span class="p">,</span>
-                       <span class="n">label</span><span class="o">=</span><span class="s1">&#39;500mb wind&#39;</span><span class="p">)</span>
-
-<span class="c1"># Plot 500-850 shear</span>
-<span class="n">shear</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="n">lon_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span> <span class="n">lat_2d</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
-                  <span class="n">u_wind500</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">]</span> <span class="o">-</span> <span class="n">u_wind850</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
-                  <span class="n">v_wind500</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">]</span> <span class="o">-</span> <span class="n">v_wind850</span><span class="p">[</span><span class="n">wslice</span><span class="p">,</span> <span class="n">wslice</span><span class="p">],</span>
-                  <span class="n">headlength</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">headwidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span>
-                  <span class="n">scale</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;deeppink&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;500-850mb shear&#39;</span><span class="p">)</span>
-
-<span class="c1"># Add a legend</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;850mb wind&#39;</span><span class="p">,</span> <span class="s1">&#39;500mb wind&#39;</span><span class="p">,</span> <span class="s1">&#39;500-850mb shear&#39;</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-
-<span class="c1"># Manually set colors for legend</span>
-<span class="n">legend</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span>
-<span class="n">legend</span><span class="o">.</span><span class="n">legendHandles</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;gold&#39;</span><span class="p">)</span>
-<span class="n">legend</span><span class="o">.</span><span class="n">legendHandles</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;cornflowerblue&#39;</span><span class="p">)</span>
-<span class="n">legend</span><span class="o">.</span><span class="n">legendHandles</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;deeppink&#39;</span><span class="p">)</span>
-
-<span class="c1"># Add a title to the plot</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;MSLP, 850mb Wind, 500mb Wind, and 500-850mb Vertical Wind Shear </span><span class="se">\n</span><span class="s1">&#39;</span>
-          <span class="sa">f</span><span class="s1">&#39; for </span><span class="si">{</span><span class="n">time</span><span class="si">:</span><span class="s1">%d %B %Y %H:%MZ</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">);</span>
-</pre></div>
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-  legend.legendHandles[0].set_color(&#39;gold&#39;)
-/tmp/ipykernel_3050/1356670571.py:44: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
-  legend.legendHandles[1].set_color(&#39;cornflowerblue&#39;)
-/tmp/ipykernel_3050/1356670571.py:45: MatplotlibDeprecationWarning: The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
-  legend.legendHandles[2].set_color(&#39;deeppink&#39;)
-</pre></div>
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-  <section class="tex2jax_ignore mathjax_ignore" id="declarative-500-hpa-absolute-vorticity">
-<h1>Declarative 500-hPa Absolute Vorticity<a class="headerlink" href="#declarative-500-hpa-absolute-vorticity" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>By: Kevin Goebbert</p>
-<p>This example uses the declarative syntax available through the MetPy package to allow a more convenient method for creating simple maps of atmospheric data. To plot aboslute vorticity, the data is scaled and reassigned to the xarray object for use in the declarative plotting interface.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-
-<span class="kn">from</span> <span class="nn">metpy.plots</span> <span class="kn">import</span> <span class="n">declarative</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set date for desired dataset</span>
-<span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2012</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
-
-<span class="c1"># Open dataset from NCEI</span>
-<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="s1">&#39;https://www.ncei.noaa.gov/thredds/dodsC/&#39;</span>
-                     <span class="sa">f</span><span class="s1">&#39;model-gfs-g4-anl-files-old/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/&#39;</span>
-                     <span class="sa">f</span><span class="s1">&#39;gfsanl_4_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb2&#39;</span>
-                     <span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="c1"># Subset Data to be just over CONUS</span>
-<span class="n">ds_us</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">lon</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">360</span><span class="o">-</span><span class="mi">160</span><span class="p">,</span> <span class="mi">360</span><span class="o">-</span><span class="mi">40</span><span class="p">),</span> <span class="n">lat</span><span class="o">=</span><span class="nb">slice</span><span class="p">(</span><span class="mi">65</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="contour-intervals">
-<h2>Contour Intervals<a class="headerlink" href="#contour-intervals" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Since absolute vorticity rarely goes below zero in the Northern Hemisphere, we can set up a list of contour levels that doesn’t include values near but greater than zero. The following code yields a list containing: <code class="docutils literal notranslate"><span class="pre">[-8,</span> <span class="pre">-7,</span> <span class="pre">-6,</span> <span class="pre">-5,</span> <span class="pre">-4,</span> <span class="pre">-3,</span> <span class="pre">-2,</span> <span class="pre">-1,</span> <span class="pre">8,</span> <span class="pre">9,</span> <span class="pre">10,</span> <span class="pre">11,</span> <span class="pre">12,</span> <span class="pre">13,</span> <span class="pre">14,</span> <span class="pre">15,</span> <span class="pre">16,</span> <span class="pre">17,</span> <span class="pre">18,</span> <span class="pre">19,</span> <span class="pre">20,</span> <span class="pre">21,</span> <span class="pre">22,</span> <span class="pre">23,</span> <span class="pre">24,</span> <span class="pre">25,</span> <span class="pre">26,</span> <span class="pre">27,</span> <span class="pre">28,</span> <span class="pre">29,</span> <span class="pre">30,</span> <span class="pre">31,</span> <span class="pre">32,</span> <span class="pre">33,</span> <span class="pre">34,</span> <span class="pre">35,</span> <span class="pre">36,</span> <span class="pre">37,</span> <span class="pre">38,</span> <span class="pre">39,</span> <span class="pre">40,</span> <span class="pre">41,</span> <span class="pre">42,</span> <span class="pre">43,</span> <span class="pre">44,</span> <span class="pre">45]</span></code></p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Absolute Vorticity colors</span>
-<span class="c1"># Use two different colormaps from matplotlib and combine into one color set</span>
-<span class="n">clevs_500_avor</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span><span class="o">+</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">46</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="the-plot">
-<h2>The Plot<a class="headerlink" href="#the-plot" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Using the declarative interface in MetPy to plot the 500-hPa Geopotential Heights and Absolute Vorticity.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set Contour Plot Parameters</span>
-<span class="n">contour</span> <span class="o">=</span> <span class="n">declarative</span><span class="o">.</span><span class="n">ContourPlot</span><span class="p">()</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">ds_us</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">time</span> <span class="o">=</span> <span class="n">dt</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">field</span> <span class="o">=</span> <span class="s1">&#39;Geopotential_height_isobaric&#39;</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">level</span> <span class="o">=</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">linecolor</span> <span class="o">=</span> <span class="s1">&#39;black&#39;</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">linewidth</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">clabels</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="n">contour</span><span class="o">.</span><span class="n">contours</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20000</span><span class="p">,</span> <span class="mi">60</span><span class="p">))</span>
-
-<span class="c1"># Set Color-filled Contour Parameters</span>
-<span class="n">cfill</span> <span class="o">=</span> <span class="n">declarative</span><span class="o">.</span><span class="n">FilledContourPlot</span><span class="p">()</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">ds_us</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">time</span> <span class="o">=</span> <span class="n">dt</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">field</span> <span class="o">=</span> <span class="s1">&#39;Absolute_vorticity_isobaric&#39;</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">level</span> <span class="o">=</span> <span class="mi">500</span> <span class="o">*</span> <span class="n">units</span><span class="o">.</span><span class="n">hPa</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">contours</span> <span class="o">=</span> <span class="n">clevs_500_avor</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">colormap</span> <span class="o">=</span> <span class="s1">&#39;PuOr_r&#39;</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">image_range</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">45</span><span class="p">,</span> <span class="mi">45</span><span class="p">)</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">colorbar</span> <span class="o">=</span> <span class="s1">&#39;horizontal&#39;</span>
-<span class="n">cfill</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="mf">1e5</span>
-
-<span class="c1"># Panel for plot with Map features</span>
-<span class="n">panel</span> <span class="o">=</span> <span class="n">declarative</span><span class="o">.</span><span class="n">MapPanel</span><span class="p">()</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">layout</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">area</span> <span class="o">=</span> <span class="s1">&#39;uslcc&#39;</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">projection</span> <span class="o">=</span> <span class="s1">&#39;area&#39;</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;coastline&#39;</span><span class="p">,</span> <span class="s1">&#39;states&#39;</span><span class="p">,</span> <span class="s1">&#39;borders&#39;</span><span class="p">]</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">layers_edgecolor</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="s1">&#39;black&#39;</span><span class="p">]</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">layers_linewidth</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.25</span><span class="p">,</span> <span class="mf">.75</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">title</span> <span class="o">=</span> <span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">cfill</span><span class="o">.</span><span class="n">level</span><span class="si">}</span><span class="s1"> GFS Geopotential Heights&#39;</span>
-               <span class="sa">f</span><span class="s1">&#39;and Absolute Vorticity at </span><span class="si">{</span><span class="n">dt</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<span class="n">panel</span><span class="o">.</span><span class="n">plots</span> <span class="o">=</span> <span class="p">[</span><span class="n">cfill</span><span class="p">,</span> <span class="n">contour</span><span class="p">]</span>
-
-<span class="c1"># Bringing it all together</span>
-<span class="n">pc</span> <span class="o">=</span> <span class="n">declarative</span><span class="o">.</span><span class="n">PanelContainer</span><span class="p">()</span>
-<span class="n">pc</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">14</span><span class="p">)</span>
-<span class="n">pc</span><span class="o">.</span><span class="n">panels</span> <span class="o">=</span> <span class="p">[</span><span class="n">panel</span><span class="p">]</span>
-
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-  <section class="tex2jax_ignore mathjax_ignore" id="miller-composite-chart">
-<h1>Miller Composite Chart<a class="headerlink" href="#miller-composite-chart" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Create a Miller Composite chart based on Miller 1972 in Python with MetPy and
-Matplotlib.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.lines</span> <span class="k">as</span> <span class="nn">lines</span>
-<span class="kn">import</span> <span class="nn">matplotlib.patches</span> <span class="k">as</span> <span class="nn">mpatches</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">numpy.ma</span> <span class="k">as</span> <span class="nn">ma</span>
-
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">from</span> <span class="nn">netCDF4</span> <span class="kn">import</span> <span class="n">num2date</span>
-<span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">gaussian_filter</span>
-<span class="kn">from</span> <span class="nn">siphon.catalog</span> <span class="kn">import</span> <span class="n">TDSCatalog</span>
-<span class="kn">from</span> <span class="nn">xarray.backends</span> <span class="kn">import</span> <span class="n">NetCDF4DataStore</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>Get the data</strong></p>
-<p>This example will use data from the North American Mesoscale Model Analysis for 18 UTC 27 April 2011.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify our date/time of product desired</span>
-<span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2011</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
-
-<span class="c1"># Construct the URL for our THREDDS Data Server Catalog,</span>
-<span class="c1"># and access our desired dataset within via NCSS</span>
-<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/model-namanl-old/&#39;</span>
-<span class="n">cat</span> <span class="o">=</span> <span class="n">TDSCatalog</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/catalog.xml&#39;</span><span class="p">)</span>
-<span class="n">ncss</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;namanl_218_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
-
-<span class="c1"># Create our NCSS query with desired specifications</span>
-<span class="n">query</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">all_times</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">add_lonlat</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="o">-</span><span class="mi">135</span><span class="p">,</span> <span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">65</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;Temperature_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;Relative_humidity_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;Best_4_layer_lifted_index_layer_between_two_pressure_&#39;</span>
-                <span class="s1">&#39;difference_from_ground_layer&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;Absolute_vorticity_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;Pressure_reduced_to_MSL_msl&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;Dew_point_temperature_height_above_ground&#39;</span><span class="p">)</span>
-
-<span class="c1"># Obtain the data we&#39;ve queried for</span>
-<span class="n">data_18z</span> <span class="o">=</span> <span class="n">ncss</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds_18z</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data_18z</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="c1"># Assign variable names to collected data</span>
-<span class="n">lat</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">lat</span>
-<span class="n">lon</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">lon</span>
-
-<span class="c1"># Create more useable times for output</span>
-<span class="n">times</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">vtimes</span> <span class="o">=</span> <span class="n">times</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-
-<span class="n">temps</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Temperature_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">uwnd</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">vwnd</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">hgt</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">relh</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Relative_humidity_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">lifted_index</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="p">[</span><span class="s1">&#39;Best_4_layer_lifted_index_layer_between_two_&#39;</span>
-                      <span class="s1">&#39;pressure_difference_from_ground_layer&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">Td_sfc</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Dew_point_temperature_height_above_ground</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">avor</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Absolute_vorticity_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">pmsl</span> <span class="o">=</span> <span class="n">ds_18z</span><span class="o">.</span><span class="n">Pressure_reduced_to_MSL_msl</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Repeat the above process to query for the analysis from 12 hours earlier (06 UTC) to calculate pressure falls and height change.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">td</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
-
-<span class="n">ncss_06z</span> <span class="o">=</span> <span class="n">cat</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;namanl_218_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="o">-</span><span class="n">td</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">subset</span><span class="p">()</span>
-
-<span class="n">query</span> <span class="o">=</span> <span class="n">ncss_06z</span><span class="o">.</span><span class="n">query</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">all_times</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">add_lonlat</span><span class="p">()</span>
-<span class="n">query</span><span class="o">.</span><span class="n">lonlat_box</span><span class="p">(</span><span class="o">-</span><span class="mi">135</span><span class="p">,</span> <span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">65</span><span class="p">)</span>
-<span class="n">query</span><span class="o">.</span><span class="n">variables</span><span class="p">(</span><span class="s1">&#39;Geopotential_height_isobaric&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;Pressure_reduced_to_MSL_msl&#39;</span><span class="p">)</span>
-
-<span class="c1"># Actually getting the data</span>
-<span class="n">data_06z</span> <span class="o">=</span> <span class="n">ncss_06z</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
-
-<span class="c1"># Make into an xarray Dataset object</span>
-<span class="n">ds_06z</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">NetCDF4DataStore</span><span class="p">(</span><span class="n">data_06z</span><span class="p">))</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-
-<span class="n">hgt_06z</span> <span class="o">=</span> <span class="n">ds_06z</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">pmsl_06z</span> <span class="o">=</span> <span class="n">ds_06z</span><span class="o">.</span><span class="n">Pressure_reduced_to_MSL_msl</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>Subset the Data</strong></p>
-<p>With the data pulled in, we will now subset to the specific levels desired</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 300 hPa</span>
-<span class="n">u_300</span> <span class="o">=</span> <span class="n">uwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">300</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">v_300</span> <span class="o">=</span> <span class="n">vwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">300</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="c1"># 500 hPa</span>
-<span class="n">avor_500</span> <span class="o">=</span> <span class="n">avor</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">u_500</span> <span class="o">=</span> <span class="n">uwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">v_500</span> <span class="o">=</span> <span class="n">vwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">hgt_500</span> <span class="o">=</span> <span class="n">hgt</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">hgt_500_06z</span> <span class="o">=</span> <span class="n">hgt_06z</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="c1"># 700 hPa</span>
-<span class="n">tmp_700</span> <span class="o">=</span> <span class="n">temps</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">700</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degC&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">rh_700</span> <span class="o">=</span> <span class="n">relh</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">700</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-
-<span class="c1"># 850 hPa</span>
-<span class="n">u_850</span> <span class="o">=</span> <span class="n">uwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">850</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="n">v_850</span> <span class="o">=</span> <span class="n">vwnd</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">vertical</span><span class="o">=</span><span class="mi">850</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;kt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>Prepare Variables for Plotting</strong></p>
-<p>With the data queried and subset, we will make any needed calculations in preparation for
-plotting.</p>
-<p><strong>The following fields should be plotted:</strong></p>
-<p>500-hPa cyclonic vorticity advection</p>
-<p>Surface-based Lifted Index</p>
-<p>The axis of the 300-hPa, 500-hPa, and 850-hPa jets</p>
-<p>Surface dewpoint</p>
-<p>700-hPa dewpoint depression</p>
-<p>12-hr surface pressure falls and 500-hPa height changes</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 500 hPa CVA</span>
-<span class="n">vort_adv_500</span> <span class="o">=</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">advection</span><span class="p">(</span><span class="n">avor_500</span><span class="p">,</span> <span class="n">u_500</span><span class="p">,</span> <span class="n">v_500</span><span class="p">,)</span> <span class="o">*</span> <span class="mf">1e9</span>
-<span class="n">vort_adv_500_smooth</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">vort_adv_500</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/tmp/ipykernel_3106/1703524492.py:2: UserWarning: Vertical dimension number not found. Defaulting to (..., Z, Y, X) order.
-  vort_adv_500 = mpcalc.advection(avor_500, u_500, v_500,) * 1e9
-/tmp/ipykernel_3106/1703524492.py:2: UserWarning: Latitude and longitude computed on-demand, which may be an expensive operation. To avoid repeating this computation, assign these coordinates ahead of time with .metpy.assign_latitude_longitude().
-  vort_adv_500 = mpcalc.advection(avor_500, u_500, v_500,) * 1e9
-</pre></div>
-</div>
-</div>
-</div>
-<p>For the jet axes, we will calculate the windspeed at each level, and plot the highest values</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">wspd_300</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">u_300</span><span class="p">,</span> <span class="n">v_300</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span>
-<span class="n">wspd_500</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">u_500</span><span class="p">,</span> <span class="n">v_500</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span>
-<span class="n">wspd_850</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">mpcalc</span><span class="o">.</span><span class="n">wind_speed</span><span class="p">(</span><span class="n">u_850</span><span class="p">,</span> <span class="n">v_850</span><span class="p">),</span> <span class="mi">5</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>700-hPa dewpoint depression will be calculated from Temperature_isobaric and RH</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Td_dep_700</span> <span class="o">=</span> <span class="n">tmp_700</span> <span class="o">-</span> <span class="n">mpcalc</span><span class="o">.</span><span class="n">dewpoint_from_relative_humidity</span><span class="p">(</span><span class="n">tmp_700</span><span class="p">,</span> <span class="n">rh_700</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>12-hr surface pressure falls and 500-hPa height changes</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pmsl_change</span> <span class="o">=</span> <span class="n">pmsl</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span> <span class="o">-</span> <span class="n">pmsl_06z</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span>
-<span class="n">hgt_500_change</span> <span class="o">=</span> <span class="n">hgt_500</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span> <span class="o">-</span> <span class="n">hgt_500_06z</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">quantify</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>To plot the jet axes, we will mask the wind fields below the upper 1/3 of windspeed.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 500 hPa</span>
-<span class="n">u_500_masked</span> <span class="o">=</span> <span class="n">u_500</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_500</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_500</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-<span class="n">v_500_masked</span> <span class="o">=</span> <span class="n">v_500</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_500</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_500</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-
-<span class="c1"># 300 hPa</span>
-<span class="n">u_300_masked</span> <span class="o">=</span> <span class="n">u_300</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_300</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_300</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-<span class="n">v_300_masked</span> <span class="o">=</span> <span class="n">v_300</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_300</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_300</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-
-<span class="c1"># 850 hPa</span>
-<span class="n">u_850_masked</span> <span class="o">=</span> <span class="n">u_850</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_850</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_850</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-<span class="n">v_850_masked</span> <span class="o">=</span> <span class="n">v_850</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wspd_850</span> <span class="o">&gt;</span> <span class="mf">0.66</span> <span class="o">*</span> <span class="n">wspd_850</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p><strong>Create the Plot</strong></p>
-<p>With the data now ready, we will create the plot</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Set up our projection</span>
-<span class="n">crs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.0</span><span class="p">,</span> <span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.0</span><span class="p">)</span>
-
-<span class="c1"># Coordinates to limit map area</span>
-<span class="n">bounds</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mf">122.</span><span class="p">,</span> <span class="o">-</span><span class="mf">75.</span><span class="p">,</span> <span class="mf">25.</span><span class="p">,</span> <span class="mf">50.</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Plot the composite</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="n">crs</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_extent</span><span class="p">(</span><span class="n">bounds</span><span class="p">,</span> <span class="n">crs</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">())</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.75</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
-
-<span class="c1"># Plot Lifted Index</span>
-<span class="n">cs1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">lifted_index</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-                 <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">7</span><span class="p">)</span>
-<span class="n">cs1</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
-           <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Plot Surface pressure falls</span>
-<span class="n">cs2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">pmsl_change</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-                 <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
-<span class="n">cs2</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
-           <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Plot 500-hPa height falls</span>
-<span class="n">cs3</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contour</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">hgt_500_change</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">60</span><span class="p">,</span> <span class="o">-</span><span class="mi">29</span><span class="p">,</span> <span class="mi">15</span><span class="p">),</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span>
-                 <span class="n">linestyles</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-<span class="n">cs3</span><span class="o">.</span><span class="n">clabel</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">inline</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inline_spacing</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
-           <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%i</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">rightside_up</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_clabeltext</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="c1"># Plot surface pressure</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">pmsl</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;hPa&#39;</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">990</span><span class="p">,</span> <span class="mi">1011</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-            <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;yellow&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-<span class="c1"># Plot surface dewpoint</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">Td_sfc</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;degF&#39;</span><span class="p">),</span> <span class="nb">range</span><span class="p">(</span><span class="mi">65</span><span class="p">,</span> <span class="mi">76</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
-            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-            <span class="n">colors</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;green&#39;</span><span class="p">],</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># Plot 700-hPa dewpoint depression</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">Td_dep_700</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">46</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-            <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;tan&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-
-<span class="c1"># Plot Vorticity Advection</span>
-<span class="n">purple</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">lon</span><span class="p">,</span> <span class="n">lat</span><span class="p">,</span> <span class="n">vort_adv_500_smooth</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">106</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-            <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-            <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;BlueViolet&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-
-<span class="c1"># Define a skip to reduce the barb point density</span>
-<span class="n">skip_300</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">12</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
-<span class="n">skip_500</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
-<span class="n">skip_850</span> <span class="o">=</span> <span class="p">(</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
-
-<span class="c1"># 300-hPa wind barbs</span>
-<span class="n">jet300</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">skip_300</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lat</span><span class="p">[</span><span class="n">skip_300</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                  <span class="n">u_300_masked</span><span class="p">[</span><span class="n">skip_300</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">v_300_masked</span><span class="p">[</span><span class="n">skip_300</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                  <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-                  <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-                  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;300-hPa Jet Core Winds (kt)&#39;</span><span class="p">)</span>
-
-
-<span class="c1"># 500-hPa wind barbs</span>
-<span class="n">jet500</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">skip_500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lat</span><span class="p">[</span><span class="n">skip_500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                  <span class="n">u_500_masked</span><span class="p">[</span><span class="n">skip_500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">v_500_masked</span><span class="p">[</span><span class="n">skip_500</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                  <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-                  <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-                  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;500-hPa Jet Core Winds (kt)&#39;</span><span class="p">)</span>
-
-<span class="c1"># 850-hPa wind barbs</span>
-<span class="n">jet850</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barbs</span><span class="p">(</span><span class="n">lon</span><span class="p">[</span><span class="n">skip_850</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">lat</span><span class="p">[</span><span class="n">skip_850</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                  <span class="n">u_850_masked</span><span class="p">[</span><span class="n">skip_850</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">v_850_masked</span><span class="p">[</span><span class="n">skip_850</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
-                  <span class="n">length</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
-                  <span class="n">transform</span><span class="o">=</span><span class="n">ccrs</span><span class="o">.</span><span class="n">PlateCarree</span><span class="p">(),</span>
-                  <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;850-hPa Jet Core Winds (kt)&#39;</span><span class="p">)</span>
-
-<span class="c1"># Legend</span>
-<span class="n">purple</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;BlueViolet&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Cyclonic Absolute Vorticity Advection&#39;</span><span class="p">)</span>
-<span class="n">yellow</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;yellow&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Surface MSLP &lt; 1010 hPa&#39;</span><span class="p">)</span>
-<span class="n">green</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Surface Td &gt; 65 F&#39;</span><span class="p">)</span>
-<span class="n">tan</span> <span class="o">=</span> <span class="n">mpatches</span><span class="o">.</span><span class="n">Patch</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;tan&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;700 hPa Dewpoint Depression &gt; 15 C&#39;</span><span class="p">)</span>
-<span class="n">red_line</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Best Lifted Index (C)&#39;</span><span class="p">)</span>
-<span class="n">dashed_black_line</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span>
-                                 <span class="n">label</span><span class="o">=</span><span class="s1">&#39;12-hr Surface Pressure Falls (hPa)&#39;</span><span class="p">)</span>
-<span class="n">black_line</span> <span class="o">=</span> <span class="n">lines</span><span class="o">.</span><span class="n">Line2D</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;solid&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span>
-                          <span class="n">label</span><span class="o">=</span><span class="s1">&#39;12-hr 500-hPa Height Falls (m)&#39;</span><span class="p">)</span>
-<span class="n">leg</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">jet300</span><span class="p">,</span> <span class="n">jet500</span><span class="p">,</span> <span class="n">jet850</span><span class="p">,</span> <span class="n">dashed_black_line</span><span class="p">,</span> <span class="n">black_line</span><span class="p">,</span> <span class="n">red_line</span><span class="p">,</span>
-                          <span class="n">purple</span><span class="p">,</span> <span class="n">tan</span><span class="p">,</span> <span class="n">green</span><span class="p">,</span> <span class="n">yellow</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
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-  <section class="tex2jax_ignore mathjax_ignore" id="using-xarray-for-data-read-and-selection">
-<h1>Using Xarray for Data read and selection<a class="headerlink" href="#using-xarray-for-data-read-and-selection" title="Link to this heading"><i class="fas fa-link"></i></a></h1>
-<p>Use Xarray module to read in model data from nomads server.</p>
-<p>This example uses the xarray module to access data from the nomads server for
-archive NAM analysis data via OPeNDAP. Xarray makes it easier to select times
-and levels, although you still have to know the coordinate variable name. A
-simple 500 hPa plot is created after selecting with xarray.</p>
-<p>Import all of our needed modules</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
-
-<span class="kn">import</span> <span class="nn">cartopy.crs</span> <span class="k">as</span> <span class="nn">ccrs</span>
-<span class="kn">import</span> <span class="nn">cartopy.feature</span> <span class="k">as</span> <span class="nn">cfeature</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">metpy.calc</span> <span class="k">as</span> <span class="nn">mpcalc</span>
-<span class="kn">from</span> <span class="nn">metpy.units</span> <span class="kn">import</span> <span class="n">units</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-</pre></div>
-</div>
-</div>
-</div>
-<section id="accessing-data-using-xarray">
-<h2>Accessing data using Xarray<a class="headerlink" href="#accessing-data-using-xarray" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Specify our date/time of product desired</span>
-<span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
-
-<span class="c1"># Construct our OPeNDAP access URL</span>
-<span class="n">base_url</span> <span class="o">=</span> <span class="s1">&#39;https://www.ncei.noaa.gov/thredds/dodsC/model-namanl-old/&#39;</span>
-<span class="n">data</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">base_url</span><span class="si">}{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">/&#39;</span>
-                       <span class="sa">f</span><span class="s1">&#39;namanl_218_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%Y%m%d</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">%H</span><span class="si">}</span><span class="s1">00_000.grb&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">parse_cf</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>NAM data is in a projected coordinate and you get back the projection
-X and Y values in km</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create a 2-d meshgrid of our x, y coordinates</span>
-<span class="c1"># manually converted to meters (km * 1000)</span>
-<span class="c1">#x, y = np.meshgrid(data[&#39;x&#39;].values * 1000, data[&#39;y&#39;].values * 1000)</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;meter&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">convert_units</span><span class="p">(</span><span class="s1">&#39;meter&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>Getting the valid times in a more useable format</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get the valid times from the file</span>
-<span class="n">vtimes</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">time</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;datetime64[ms]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">vtimes</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[datetime.datetime(2016, 4, 16, 18, 0)]
-</pre></div>
-</div>
-</div>
-</div>
-<p>Xarray has some nice functionality to choose the time and level that
-you specifically want to use. In this example the time variable is ‘time’ and
-the level variable is ‘isobaric1’. Unfortunately, these can be different with
-each file you use, so you’ll always need to check what they are by listing
-the coordinate variable names</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># print(data.Geopotential_height.coords)</span>
-<span class="n">hght_500</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time1</span><span class="o">=</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">uwnd_500</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;u-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time1</span><span class="o">=</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-<span class="n">vwnd_500</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;v-component_of_wind_isobaric&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">time1</span><span class="o">=</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">vertical</span><span class="o">=</span><span class="mi">500</span><span class="o">*</span><span class="n">units</span><span class="o">.</span><span class="n">hPa</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-</div>
-</section>
-<section id="now-make-the-500-hpa-map">
-<h2>Now make the 500-hPa map<a class="headerlink" href="#now-make-the-500-hpa-map" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Must set data projection, NAM is LCC projection</span>
-<span class="n">datacrs</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Geopotential_height_isobaric</span><span class="o">.</span><span class="n">metpy</span><span class="o">.</span><span class="n">cartopy_crs</span>
-
-<span class="c1"># A different LCC projection for the plot.</span>
-<span class="n">plotcrs</span> <span class="o">=</span> <span class="n">ccrs</span><span class="o">.</span><span class="n">LambertConformal</span><span class="p">(</span><span class="n">central_latitude</span><span class="o">=</span><span class="mf">45.</span><span class="p">,</span> <span class="n">central_longitude</span><span class="o">=-</span><span class="mf">100.</span><span class="p">,</span>
-                                <span class="n">standard_parallels</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">60</span><span class="p">])</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">17.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">projection</span><span class="o">=</span><span class="n">plotcrs</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">coastlines</span><span class="p">(</span><span class="s1">&#39;50m&#39;</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">add_feature</span><span class="p">(</span><span class="n">cfeature</span><span class="o">.</span><span class="n">STATES</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
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-
-<span class="n">clev500</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5100</span><span class="p">,</span> <span class="mi">6000</span><span class="p">,</span> <span class="mi">60</span><span class="p">)</span>
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-
-<span class="c1"># Here we put boxes around the clabels with a black boarder white facecolor</span>
-<span class="c1"># `labelTexts` necessary as ~cartopy.mpl.contour.GeoContourSet.clabel</span>
-<span class="c1"># does not return list of texts as of 0.18</span>
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-    <span class="n">t</span><span class="o">.</span><span class="n">set_bbox</span><span class="p">({</span><span class="s1">&#39;fc&#39;</span><span class="p">:</span> <span class="s1">&#39;w&#39;</span><span class="p">})</span>
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-<span class="c1"># Transform Vectors before plotting, then plot wind barbs.</span>
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-
-<span class="c1"># Add some titles to make the plot readable by someone else</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;500-hPa Geopotential Heights (m)&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;VALID: </span><span class="si">{</span><span class="n">vtimes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">);</span>
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-<p>Next, title your notebook appropriately with a top-level Markdown header, <code class="docutils literal notranslate"><span class="pre">#</span></code>. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a <code class="docutils literal notranslate"><span class="pre">---</span></code> cell to visually distinguish the transition to the prerequisites section.</p>
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-<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,</p>
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-<li><p>These should map approximately to your main sections of content</p></li>
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-<p>This section was inspired by <a class="reference external" href="https://github.com/alan-turing-institute/the-turing-way/blob/master/book/templates/chapter-template/chapter-landing-page.md">this template</a> of the wonderful <a class="reference external" href="https://the-turing-way.netlify.app">The Turing Way</a> Jupyter Book.</p>
-<p>Following your overview, tell your reader what concepts, packages, or other background information they’ll <strong>need</strong> before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with <code class="docutils literal notranslate"><span class="pre">|</span></code> vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.</p>
-<p>Label the importance of each concept explicitly as <strong>helpful/necessary</strong>.</p>
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-<td><p>Helpful</p></td>
-<td><p>Familiarity with metadata structure</p></td>
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-<td><p>Helpful</p></td>
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-<ul class="simple">
-<li><p><strong>Time to learn</strong>: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.</p></li>
-<li><p><strong>System requirements</strong>:</p>
-<ul>
-<li><p>Populate with any system, version, or non-Python software requirements if necessary</p></li>
-<li><p>Otherwise use the concepts table above and the Imports section below to describe required packages as necessary</p></li>
-<li><p>If no extra requirements, remove the <strong>System requirements</strong> point altogether</p></li>
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-<p>Begin your body of content with another <code class="docutils literal notranslate"><span class="pre">---</span></code> divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports <strong>up-front</strong>:</p>
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-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span>
-</pre></div>
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-</div>
-</div>
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-<section id="your-first-content-section">
-<h2>Your first content section<a class="headerlink" href="#your-first-content-section" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary,</p>
-<div class="cell docutils container">
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-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># as well as any and all of your code cells</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hello world!&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Hello world!
-</pre></div>
-</div>
-</div>
-</div>
-<section id="a-content-subsection">
-<h3>A content subsection<a class="headerlink" href="#a-content-subsection" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
-<p>Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># some subsection code</span>
-<span class="n">new</span> <span class="o">=</span> <span class="s2">&quot;helpful information&quot;</span>
-</pre></div>
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-<section id="another-content-subsection">
-<h3>Another content subsection<a class="headerlink" href="#another-content-subsection" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
-<p>Keep up the good work! A note, <em>try to avoid using code comments as narrative</em>, and instead let them only exist as brief clarifications where necessary.</p>
-</section>
-</section>
-<section id="your-second-content-section">
-<h2>Your second content section<a class="headerlink" href="#your-second-content-section" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Here we can move on to our second objective, and we can demonstrate</p>
-<section id="subsection-to-the-second-section">
-<h3>Subsection to the second section<a class="headerlink" href="#subsection-to-the-second-section" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
-<section id="a-quick-demonstration">
-<h4>a quick demonstration<a class="headerlink" href="#a-quick-demonstration" title="Link to this heading"><i class="fas fa-link"></i></a></h4>
-<section id="of-further-and-further">
-<h5>of further and further<a class="headerlink" href="#of-further-and-further" title="Link to this heading"><i class="fas fa-link"></i></a></h5>
-<section id="header-levels">
-<h6>header levels<a class="headerlink" href="#header-levels" title="Link to this heading"><i class="fas fa-link"></i></a></h6>
-<p>as well <span class="math notranslate nohighlight">\(m = a * t / h\)</span> text! Similarly, you have access to other <span class="math notranslate nohighlight">\(\LaTeX\)</span> equation <a class="reference external" href="https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html"><strong>functionality</strong></a> via MathJax (demo below from link),</p>
-<div class="amsmath math notranslate nohighlight" id="equation-13197f33-8ca1-4326-b866-0e4845022eb6">
-<span class="eqno">(1)<a class="headerlink" href="#equation-13197f33-8ca1-4326-b866-0e4845022eb6" title="Permalink to this equation"><i class="fas fa-link"></i></a></span>\[\begin{align}
-\dot{x} &amp; = \sigma(y-x) \\
-\dot{y} &amp; = \rho x - y - xz \\
-\dot{z} &amp; = -\beta z + xy
-\end{align}\]</div>
-<p>Check out <a class="reference external" href="https://www.markdownguide.org/basic-syntax/"><strong>any number of helpful Markdown resources</strong></a> for further customizing your notebooks and the <a class="reference external" href="https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html"><strong>Jupyter docs</strong></a> for Jupyter-specific formatting information. Don’t hesitate to ask questions if you have problems getting it to look <em>just right</em>.</p>
-</section>
-</section>
-</section>
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-<section id="last-section">
-<h2>Last Section<a class="headerlink" href="#last-section" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>If you’re comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):</p>
-<div class="admonition alert alert-info">
-<p class="admonition-title">Info</p>
-<p>Your relevant information here!</p>
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-<p>Feel free to copy this around and edit or play around with yourself. Some other <code class="docutils literal notranslate"><span class="pre">admonitions</span></code> you can put in:</p>
-<div class="admonition alert alert-success">
-<p class="admonition-title">Success</p>
-<p>We got this done after all!</p>
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-<p>Be careful!</p>
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-<div class="admonition alert alert-danger">
-<p class="admonition-title">Danger</p>
-<p>Scary stuff be here.</p>
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-<h2>Summary<a class="headerlink" href="#summary" title="Link to this heading"><i class="fas fa-link"></i></a></h2>
-<p>Add one final <code class="docutils literal notranslate"><span class="pre">---</span></code> marking the end of your body of content, and then conclude with a brief single paragraph summarizing at a high level the key pieces that were learned and how they tied to your objectives. Look to reiterate what the most important takeaways were.</p>
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-<h3>What’s next?<a class="headerlink" href="#whats-next" title="Link to this heading"><i class="fas fa-link"></i></a></h3>
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-<li><p><code class="docutils literal notranslate"><span class="pre">Kernel</span> <span class="pre">&gt;</span> <span class="pre">Restart</span> <span class="pre">Kernel</span> <span class="pre">and</span> <span class="pre">Clear</span> <span class="pre">All</span> <span class="pre">Outputs...</span></code> before committing your notebook, our machines will do the heavy lifting</p></li>
-<li><p>Take credit! Provide author contact information if you’d like; if so, consider adding information here at the bottom of your notebook</p></li>
-<li><p>Give credit! Attribute appropriate authorship for referenced code, information, images, etc.</p></li>
-<li><p>Only include what you’re legally allowed: <strong>no copyright infringement or plagiarism</strong></p></li>
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diff --git a/_preview/3/searchindex.js b/_preview/3/searchindex.js
deleted file mode 100644
index 5ec9f66..0000000
--- a/_preview/3/searchindex.js
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@@ -1 +0,0 @@
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